Methods for determining a dog's health status

Abstract

This invention provides a method for determining the biological age, risk of death, and / or probability of healthy lifespan of a dog; the method comprising: a) determining the biological age, risk of death, and / or probability of healthy lifespan of a dog using the levels of one or more biomarkers selected from one or more samples obtained from the dog, wherein the one or more biomarkers are selected from white blood cell count, serum albumin, serum alkaline phosphatase, serum creatine kinase, hemoglobin, hematocrit, mean corpuscular hemoglobin, serum glucose, mean corpuscular volume, serum globulin, serum calcium, platelet count, and / or red blood cell count; and b) determining the biological age, risk of death, and / or probability of healthy lifespan of a dog using DNA methylation profiles from the dog.

Description

Technical Field

[0001] This invention relates to a method for determining the health status of dogs using a combination of biomarkers and DNA methylation mapping. In particular, this invention relates to determining a dog's biological age, risk of death, and / or probability of healthy lifespan using a combination of biomarkers and DNA methylation mapping. Background Technology

[0002] The ability to determine information about a dog's health is what is expected in informing a dog about its overall health and well-being.

[0003] It is well known that chronological age is a primary indicator of overall health status, with increasing chronological age associated with declining health. However, depending on genetics, nutrition, and lifestyle, an individual may age at a rate that is slower or faster than their chronological age. Therefore, chronological age may not always reflect an individual's rate of aging or risk of declining health. On the other hand, an individual's biological age (based on, for example, clinical biochemistry and cell biology measures) may differ from that of other individuals of the same chronological age. Methods used to determine biological age or phenotypic age may help identify individuals at risk of age-related conditions earlier than expected based on their chronological age (see, for example, WO2019 / 046725).

[0004] However, there is a need for other methods to determine the biological age of dogs and to use measurements of biological age to improve canine health outcomes. Summary of the Invention

[0005] This invention relates to a method for quantifying the health status of dogs by combining information based on biomarkers, particularly blood biomarkers, and DNA methylation profiles. This method enables the determination of a dog's biological age, risk of death, and / or probability of healthy lifespan by evaluating both biomarkers (i.e., blood biomarkers) and DNA methylation profiles.

[0006] Calculating an animal's biological age may involve determining a biomarker profile (i.e., a blood biomarker profile) or a DNA methylation profile compared to a predicted biomarker profile or DNA methylation profile at a given chronological age. Therefore, this approach is based on using chronological age as the primary indicator of overall health.

[0007] The present invention may also consider direct predictive values ​​of the described biomarkers and / or DNA methylation profiles for mortality risk and / or the probability of healthy lifespan. For example, a given biomarker and / or DNA methylation profile may not be directly related to chronological age, but may indicate a specific pathological condition, and thus indicate an increased risk of death and / or a reduced probability of healthy lifespan. In some embodiments, the method may be described as identifying the mortality risk and / or the probability of healthy lifespan in dogs. Therefore, the biomarkers, DNA methylation profiles, and DNA methylation profiles described in this invention may not necessarily be related to chronological age, but may be related to the difference between the dog's phenotypic age and chronological age.

[0008] In a first aspect, the present invention provides a method for determining the biological age, risk of death, and / or probability of healthy lifespan of a dog; the method comprising using (i) the levels of one or more biomarkers from one or more samples obtained from the dog, and (ii) a DNA methylation profile from the dog to determine the biological age, risk of death, and / or probability of healthy lifespan of the dog; wherein one or more biomarkers are selected from white blood cell count, serum albumin, serum alkaline phosphatase, serum creatine kinase, hemoglobin, hematocrit, mean corpuscular hemoglobin, serum glucose, mean corpuscular volume, serum globulin, serum calcium, platelet count, and / or red blood cell count.

[0009] The method may include:

[0010] a) Use the levels of one or more biomarkers from one or more samples obtained from the dog to determine the dog's biological age, risk of death, and / or probability of healthy lifespan, wherein the one or more biomarkers are selected from white blood cell count, serum albumin, serum alkaline phosphatase, serum creatine kinase, hemoglobin, hematocrit, mean corpuscular hemoglobin, serum glucose, mean corpuscular volume, serum globulin, serum calcium, platelet count, and / or red blood cell count; and

[0011] b) Use DNA methylation profiles from dogs to determine the dog’s biological age, risk of death, and / or probability of healthy lifespan.

[0012] Therefore, in some embodiments, the present invention relates to combining biological age, mortality risk, and / or healthy lifespan probability determined using one or more biomarkers described herein with biological age, mortality risk, and / or healthy lifespan probability determined using DNA methylation mapping. Not wishing to be bound by theory, the inventors believe that the combination of these aspects (i.e., determination using one or more biomarkers and determination using DNA methylation mapping – each as described herein) provides a differentiated method for determining the biological age, mortality risk, and / or healthy lifespan probability of a dog. For example, this combined method can provide improved determination of biological age, mortality risk, and / or healthy lifespan probability, for example, by enabling more accurate calculation of biological age, mortality risk, and / or healthy lifespan probability.

[0013] Suitable, the dog's biological age, risk of death, and / or probability of healthy lifespan determined in step a) can be combined with the dog's biological age, risk of death, and / or probability of healthy lifespan determined in step b) to provide a comprehensive biological age, risk of death, and / or probability of healthy lifespan for the dog.

[0014] Suitablely, biological age, mortality risk, and / or healthy lifespan probability can be provided as a combined score derived from the dog's biological age, mortality risk, and / or healthy lifespan probability determined in step a) and the dog's biological age, mortality risk, and / or healthy lifespan probability determined in step b).

[0015] Suitable, the score may be provided as biological age, Δ or a fraction derived from biological age (e.g., Δ_diff: the difference between biological age and actual age; or Δ_res: the residual of a linear model that explains biological age by actual age); mortality risk or healthy life probability.

[0016] A dog's biological age can be expressed in years, months, days, etc.

[0017] Determining mortality risk can refer to determining the likelihood that a dog will live a longer or shorter lifespan compared to, for example, dogs of the same age, sex, and breed. Therefore, this method can determine the probability of a dog's lifespan, health span, and / or longevity compared to, for example, dogs of the same age, sex, and breed. Furthermore, methods for improving a dog's biological age, mortality risk, and / or probability of healthy lifespan can improve the dog's probable lifespan, health span, and / or longevity.

[0018] As used in this article, "lifespan" can refer to the length of time a subject has lived (e.g., several years). "Health span" can refer to the length of time a subject has lived without disease (e.g., several years). "Longevity" can refer to the length of time a subject has lived beyond their expected lifespan (e.g., several years).

[0019] Suitablely, the risk of death can be equated to the probability of a dog's healthy lifespan; wherein a decrease in the risk of death is equivalent to an increase in the probability of a dog's longer healthy lifespan, or an increase in the risk of death is equivalent to a decrease in the probability of a dog's longer healthy lifespan. The risk of death can be expressed as the difference between a dog's determined age (i.e., biological age) and its chronological age. For example, an increase in the difference between the biological age and chronological age determined by this method may indicate an increased risk of death in the dog. A decrease in the difference between the biological age and chronological age determined by this method may indicate a decreased risk of death in the dog. Suitablely, the risk of death and / or the probability of healthy lifespan can be described as the dog's biological age. Suitablely, the risk of death and / or the probability of healthy lifespan determined using this biomarker may be described as the dog's phenotypic age (or phenoage). Suitablely, the biological age, risk of death, and / or the probability of healthy lifespan determined using DNA methylation mapping may be described as the dog's epigenetic age. Suitablely, the current biological clock determined using DNA methylation mapping may be referred to as the epigenetic clock.

[0020] Appropriately, determining that a dog's biological age is greater than its actual age indicates a higher risk of death. Appropriately, determining that a dog's biological age is less than its actual age indicates a reduced risk of death. Appropriately, determining that a dog's biological age is greater than its actual age indicates a reduced probability of a longer healthy lifespan. Appropriately, determining that a dog's biological age is less than its actual age indicates an increased probability of a longer healthy lifespan.

[0021] Suitablely, this method can be used to determine a dog's biological age based on its biological age, risk of death, and / or probability of a healthy lifespan.

[0022] The present invention further provides a method for selecting a lifestyle program, dietary program or therapeutic intervention for a dog, the method comprising: i) determining the dog's biological age, mortality risk and / or healthy lifespan probability according to the method of the present invention; and ii) selecting a suitable lifestyle program, dietary program or therapeutic intervention for the dog based on the biological age, mortality risk and / or healthy lifespan probability determined in step i).

[0023] As used in this article, “choosing a suitable lifestyle program, dietary program or therapeutic intervention for a dog” can also encompass “recommending a lifestyle program, dietary program or therapeutic intervention for a dog” or “providing a recommended lifestyle program, dietary program or therapeutic intervention for a dog”.

[0024] Suitablely, improving a dog's biological age, risk of death, and / or probability of healthy life can refer to a reduction in the difference between a dog's biological age and its chronological age, wherein the dog's biological age is greater than its chronological age. Alternatively, improving a dog's biological age, risk of death, and / or probability of healthy life can refer to maintaining or further increasing the difference between a dog's biological age and its chronological age, wherein the dog's biological age is less than its chronological age. Alternatively, deterioration in a dog's biological age, risk of death, and / or probability of healthy life can refer to an increase in the difference between a dog's biological age and its chronological age, wherein the dog's biological age is greater than its chronological age. Deterioration in a dog's biological age, risk of death, and / or probability of healthy life can also refer to a decrease in the difference between a dog's biological age and its chronological age, wherein the dog's biological age is less than its chronological age.

[0025] The present invention further provides a method for determining the efficacy of a lifestyle program, dietary program, or therapeutic intervention in improving the biological age, mortality risk, and / or healthy lifespan probability of a dog, the method comprising: ii) applying the lifestyle program, dietary program, or therapeutic intervention to the dog; ii) after applying the lifestyle program, dietary program, or therapeutic intervention to the dog for a period of time; determining the dog's biological age, mortality risk, and / or healthy lifespan probability according to the method of the present invention; iii) after following the lifestyle program, dietary program, or therapeutic intervention for the period of time, determining whether there is a change in the dog's biological age, mortality risk, and / or healthy lifespan probability.

[0026] Appropriately, improvements in a dog's biological age, mortality risk, and / or probability of healthy lifespan can refer to a reduction in the rate of change between a dog's biological age and its chronological age, where the dog's biological age is greater than its chronological age. For example, for every 1-year increase in chronological age, a dog's biological age may have increased by 1.5 years. Following lifestyle and dietary interventions, a reduction in the rate of change means that for every 1-year increase in chronological age, the dog's biological age subsequently increases by 1.25 years, which could provide improvements in the dog's biological age, mortality risk, and / or probability of healthy lifespan.

[0027] Improving biological age, mortality risk, and / or the probability of healthy lifespan can also refer to maintaining or increasing the rate of change between a dog's biological age and its chronological age, where the dog's biological age is less than its chronological age. For example, for every 1-year increase in chronological age, the dog's biological age may have increased by less than 1 year (e.g., 0.9 years). The rate of change can alter after lifestyle, dietary, or therapeutic interventions, such that for every 1-year increase in chronological age, the dog's biological age subsequently increases by, for example, 0.8 years or less, which can provide an improvement in the dog's biological age.

[0028] Therefore, this method enables the selection of appropriate lifestyle programs, dietary programs, or therapeutic interventions for dogs based on their biological age, mortality risk, and / or probability of healthy lifespan, as determined by DNA methylation profiling. For example, a highly digestible and high-quality protein diet is typically recommended based on a dog's chronological age. For instance, it might be recommended to switch a dog to a senior diet around 7 or 8 years of age. However, in the context of this invention, determining an increased mortality risk and / or decreased probability of healthy lifespan (i.e., increased biological age) compared to a dog's chronological age allows for the determination of switching the dog to a senior diet at an earlier age. In contrast, dogs with a decreased mortality risk and / or increased probability of healthy lifespan (i.e., decreased biological age) compared to their chronological age may be able to continue on an adult diet for a longer period.

[0029] Suitablely, this method may include selecting and / or applying lifestyle programs, dietary programs or therapeutic interventions to dogs after determining that the dogs have an increased risk of death and / or a reduced probability of healthy lifespan compared to their chronological age.

[0030] Appropriately, the disease is an age-related disease. For example, age-related diseases include osteoarthritis, dementia, cognitive impairment, prediabetes, diabetes, cancer, heart disease, obesity, gastrointestinal disorders, incontinence, kidney disease, sarcopenia, vision loss, hearing loss, osteoporosis, cataracts, cerebrovascular disease, and / or liver disease.

[0031] This method may also optionally include applying a lifestyle program, dietary program, or therapeutic intervention to the dog. Suitablely, the lifestyle program may be a dietary intervention or a therapeutic approach.

[0032] Appropriately, while anti-aging lifestyle programs, dietary programs, or therapeutic interventions may be effective for dogs based on chronological age, they may be particularly effective when applied to dogs with an increased risk of death and / or a reduced probability of healthy lifespan compared to their chronological age. Therefore, this approach advantageously allows for the selection of dogs for whom an anti-aging lifestyle program, dietary program, or therapeutic intervention has an increased likelihood of response or an improved amount of response.

[0033] Lifestyle programs, dietary programs, or therapeutic interventions can be selected based on the determination that the dog has an increased risk of death and / or a reduced probability of healthy lifespan (i.e., an increased biological age) compared to its chronological age.

[0034] Lifestyle programs, dietary programs, or therapeutic interventions can be dietary interventions. Dietary interventions can include calorie-restricted diets, diets for the elderly, or low-protein diets.

[0035] DNA methylation profiles may be associated with increased biological age in the following areas: (i) tissues; (ii) organs; or (iii) physiological systems such as the immune system, gastrointestinal system, urinary system, muscular system, cardiovascular system, and / or nervous system.

[0036] Advantageously, the present invention allows for the determination of biological age, mortality risk, and / or probability of healthy lifespan based on biomarkers of multiple organ systems and functions. Therefore, this method can advantageously cover a range of potential organ dysfunctions.

[0037] Evaluating a dog's biological age, mortality risk, and / or probability of healthy lifespan allows for the testing of several aspects of animal well-being. First, it can predict whether the animal is more likely to require dietary or supplement-based interventions. It can also be used to test the efficacy of dietary or supplement-based interventions on aging. Attached Figure Description

[0038] Figure 1 —Identification of blood biomarkers for predicting mortality risk. The Cox proportional hazards model was adapted for each of the 28 biomarkers assessed, including sex and breed category (small or medium). Adjusted values ​​were used for the p-values ​​of each parameter to account for multiple comparisons (by false discovery rate (fdr)). Parameters shown are those with an adjusted fdr below 0.05.

[0039] Figure 2 — Demonstrating the predictive power of biomarkers in multiparameter models used to determine phenotypic age.

[0040] Figure 3 —Performed age progression (the difference between manifested age and actual age) is associated with a significant increase in mortality risk.

[0041] Figure 4 —Survival differences in dogs stratified early based on high or low median phenotypic age.

[0042] Figure 5 —Early phenotyped age (Δ to actual age) is altered in middle age through calorie-restricted diets. This change occurs earlier in females compared to males.

[0043] Figure 6 —Validation of the illustrative epigenetic clock (second representative epigenetic clock) of this invention trained on a dataset including mortality in a calorie restriction study. Δ corresponds to the residual of the regression model of chronological age relative to the predicted phenoDNAmAge. Calorie-restricted dogs had significantly lower biological ages (lower Δ) compared to diet-controlled dogs.

[0044] Figure 7—Validation of the illustrative epigenetic clock (second representative epigenetic clock) of this invention trained on a dataset including mortality in a 6-year calorie restriction study. Δ corresponds to the residual of the regression model of chronological age relative to the predicted phenoDNAmAge. Calorie-restricted dogs had a lower biological age (lower Δ) compared to diet-controlled dogs.

[0045] Figure 8 —Adjusted linear mixed-effects model (DogID as a random effect). Calorically restricted dogs had significantly lower biological ages compared to diet-controlled dogs.

[0046] Figure 9 —Survival rate between biologically young and biologically old dogs determined by the illustrative epigenetic clock of the present invention.

[0047] Figure 10 —The Cox proportional hazards model is fitted using sex and ΔphenoDNAmAge (the residual from actual age to predicted phenoDNAmAge), stratified by breed type (small or medium) on the training set. An increase in Δ is associated with a higher risk of death.

[0048] Figure 11 —Illustrative epigenetic clocks containing the first 3, first 5, first 10, and first 20 methylation sites from the complete epigenetic clock are associated with actual age.

[0049] Figure 12 —The biological age predicted using the exemplary epigenetic clock (first-generation biological clock) trained at chronological age according to the present invention is highly correlated with chronological age.

[0050] Figure 13 —Validation of the exemplary first-generation observable clock of the present invention in a calorie restriction study. Δ corresponds to the residual of the regression model using the first-generation clock relative to the predicted biological age. Calorie-restricted dogs had significantly lower biological ages (lower Δ) compared to diet-controlled dogs.

[0051] Figure 14 —Illustrative first-generation observable clocks containing the first representative observable clocks (A) the first 3, B) the first 5, C) the first 10, D) the first 20, and E) the first 50 methylation sites are associated with actual age. Detailed Implementation

[0052] Preferred features and embodiments of the invention will now be described by way of non-limiting examples. Those skilled in the art will understand that they can combine all the features of the invention disclosed herein without departing from the scope of the invention as disclosed.

[0053] It must be noted that, as used herein and in the appended claims, the singular forms “a,” “an,” and “the” include plural references, unless the context clearly specifies otherwise.

[0054] As used herein, the terms “comprising” and “consisting of” are synonymous with “including” or “containing”, and are inclusive or open-ended, and do not exclude additional unlisted members, elements or method steps. The terms “comprising” and “consisting of” also include the term “composed of”.

[0055] The range of numbers includes the numbers that define that range.

[0056] The publications discussed herein are provided only for their disclosure prior to the filing date of this patent application. Nothing herein should be construed as an admission that such publications constitute prior art to the claims appended herein.

[0057] The methods and systems disclosed in this article can be used by veterinarians, healthcare professionals, laboratory technicians, pet care providers, and others.

[0058] method

[0059] This invention provides a method for determining the biological age, mortality risk, and / or probability of healthy lifespan of a dog; the method includes:

[0060] a) Use the levels of one or more biomarkers from one or more samples obtained from the dog to determine the dog's biological age, risk of death, and / or probability of healthy lifespan, wherein one or more biomarkers are selected from white blood cell count, serum albumin, serum alkaline phosphatase, serum creatine kinase, hemoglobin, hematocrit, mean corpuscular hemoglobin, serum glucose, mean corpuscular volume, serum globulin, serum calcium, platelet count, and / or red blood cell count; and

[0061] b) Use DNA methylation profiles from dogs to determine the dog’s biological age, risk of death, and / or probability of healthy lifespan.

[0062] The dog's biological age, risk of death, and / or probability of healthy lifespan determined in step a) can be combined with the dog's biological age, risk of death, and / or probability of healthy lifespan determined in step b) to provide a comprehensive biological age, risk of death, and / or probability of healthy lifespan for the dog.

[0063] Appropriately, “comprehensive mortality risk and / or healthy life probability” may refer to the determination made in steps a) and b) of this method that provides a dog with a single biological age, mortality risk and / or healthy life probability.

[0064] In other words, this method may include (i) determining the biological age, risk of death, and / or probability of healthy life of a dog using the levels of one or more biomarkers as described herein; and (ii) providing a combination determination of the biological age, risk of death, and / or probability of healthy life of a dog using the biological age, risk of death, and / or probability of healthy life of a dog from a DNA methylation profile of the dog.

[0065] Appropriately, biological age, risk of death, and / or probability of healthy lifespan are provided as a combined score.

[0066] Suitablely, the method can be performed using a computer system or computer program product according to the invention, and optionally, a combined score can be provided.

[0067] The biological age, risk of death, and / or probability of healthy life of the dog determined in step a) can be combined with the biological age, risk of death, and / or probability of healthy life of the dog determined in step b) using any suitable method.

[0068] For example, the dog's biological age, risk of death, and / or probability of healthy life determined in step a) and the dog's biological age, risk of death, and / or probability of healthy life determined in step b) can be combined by calculating a combination of linear weighted combinations, averages, geometric mean (square root of the product), harmonic mean (reciprocal of the average of reciprocals), maximum difference, minimum difference, or Δ (e.g., difference relative to chronological age or residuals of a linear model based on chronological age).

[0069] Suitablely, the weights of the linearly weighted combination may not be equal between the determinations made for steps a) and b). The weights can be optimized through conventional practices in the art.

[0070] Suitablely, the combination can be calculated by calculating Δ (e.g., in years) for one or more biomarkers (step a) and DNA methylation profiles (step b) separately, where Δ is the residual from a linear model that interprets full age, and combining Δ using the method described above.

[0071] For example, the following can be used to combine the Δ values ​​of steps a) and b): average, geometric mean (square root of the product), harmonic mean (reciprocal of the average of the reciprocals), maximum difference, or minimum difference.

[0072] Suitablely, the Δ value calculated above can be transformed into categories such as -infinity; -2, -2; -1, -1; 0, 0; +1, +1; +2, +2; +infinity. From these categories, each category can be assigned a score, for example, from 1 to 6, and the sum of the scores from both parts a) and b) is considered. Suitablely, an average score or a weighted average score can be considered.

[0073] The number and size of the categories can be further modified, such as increasing the number of categories by 0.5 years, or having cut points at 0.5 and 1.5, for example.

[0074] Categories can be binary, meaning they are either below or above 0, or below / above 1 or 1.5. Categories can then be combined to obtain the final category.

[0075] In a further aspect, the invention may include combining one or more biomarkers as described herein and DNA methylation sites (suitably DNA methylation maps) as input to a linear model to interpret a dog's biological age, risk of death, and / or probability of healthy lifespan. A machine learning framework may be used to compute a suitable linear model. For example, the machine learning framework may include, for instance, using the glmnet R package to fit a penalized regression to a training dataset of dogs with known pedigree ages (and optionally breed and / or sex).

[0076] Machine learning frameworks may include, for example, using the glmnet R package to fit resilient network regressions to a training dataset of dogs with known ages (and optional breeds and / or sexes).

[0077] Suitable, machine learning frameworks may include penalized regressions, such as resilient network regressions, that fit the actual age as interpreted by one or more biomarkers and DNA methylation profiles (and optionally breed, age, and / or sex).

[0078] Appropriately, machine learning frameworks may include penalized regressions, such as resilient network regressions, that fit the true age as interpreted by one or more biomarkers and DNA methylation profiles, breed, age, and sex.

[0079] Subjects

[0080] This method is designed for canine subjects. Therefore, the subjects of this invention are dogs.

[0081] In an alternative aspect, the subject may be a feline subject. Therefore, in an alternative aspect of the invention, the subject is a cat. Unless otherwise stated, all disclosure herein applies equally to cats.

[0082] variety

[0083] This method can utilize information about dog breeds. For example, dogs can be classified as toy, small, medium, large, or giant breeds. Suitablely, dog breeds can be classified based on weight. Suitablely, dog breeds can be classified based on the average weight of dogs of a given breed.

[0084] Dogs may be classified as small or medium-sized breeds. Classification is determined by the average weight of adult dogs of that breed. Breeds with an average weight of less than 10 kg are classified as small breeds and / or breeds with an average weight of more than 10 kg are classified as medium breeds.

[0085] In the alternative of the subject being a cat, the cat may be a domestic cat. Suitablely, the cat may be a domestic shorthaired cat.

[0086] gender

[0087] Appropriately, dogs can be classified as male or female.

[0088] Actual age

[0089] Chronological age can be defined as the amount of time elapsed from a subject's birth to a given date. Chronological age can be expressed in years, months, days, etc.

[0090] Suitablely, this method can be applied to dogs of any actual age. In some implementations, the dog may be at least about 2 years old. Suitablely, the dog may be at least about 2 years old, at least about 3 years old, at least about 4 years old, at least about 5 years old, at least about 6 years old, at least about 7 years old, at least about 8 years old, at least about 9 years old, or at least about 10 years old.

[0091] Appropriately, the dog can be at least about 7 years old.

[0092] sample

[0093] The present invention includes the step of providing or determining a DNA methylation profile from one or more samples obtained from a subject.

[0094] Suitable samples used to provide or determine DNA methylation profiles are blood, hair follicle, oral swab, feces, saliva, or tissue samples.

[0095] Suitable samples for providing or determining DNA methylation profiles are hair follicle, oral swab, or saliva samples. Such sample types are particularly suitable if, for example, samples are provided outside a veterinary setting—for example, using a kit according to the invention.

[0096] Suitable of the sample used to determine the level of one or more biomarkers as described herein is a blood sample.

[0097] Suitablely, the sample is derived from blood. The sample may contain blood components or may be whole blood. The sample preferably includes whole blood. The sample may include peripheral blood mononuclear cell (PBMC) or lymphocyte samples. Techniques for collecting samples from subjects and extracting DNA (e.g., genomic DNA) from the samples are well known in the art.

[0098] Suitablely, parts (a) and (b) of this method are performed on the same sample type. Suitablely, parts (a) and (b) of this method are performed using blood samples. Suitablely, parts (a) and (b) of this method are performed using the same blood sample.

[0099] Preferably, at least part (a) of the method is performed on a blood sample.

[0100] This method can be performed on one or more samples obtained from the subject. For example, the method can be performed using a first sample obtained at a given time point and a second sample obtained after a time interval following the acquisition of the first sample. The method can be performed more than once on samples obtained from the same dog over a period of time. For example, samples can be obtained repeatedly monthly, annually, or every two years. Suitablely, samples can be obtained approximately once a year (e.g., during an annual veterinary health check). This may help determine the effects of specific treatments or lifestyle changes, such as dietary interventions or changes in exercise programs.

[0101] In one implementation, the method can be applied to samples obtained from subjects prior to lifestyle changes (e.g., dietary product interventions or exercise program changes). In another implementation, the method can be applied to samples obtained from subjects both before and after, for example, dietary product interventions or exercise program changes. The method can also be applied to samples obtained at predetermined times throughout, for example, the entire dietary product intervention or exercise program change process. These predetermined times throughout, for example, the entire dietary product intervention or exercise program change process can be periodic, such as daily or every three days, or may depend on the subjects being tested.

[0102] biomarkers

[0103] The biomarkers used in this invention can be determined using standard methods in the art and are typically measured as part of standard blood tests to determine the disease state of animals. For example, biomarkers are typically identified as part of standard clinical complete blood counts (CBCs) and standard clinical blood chemistry analyses.

[0104] A complete blood count provides information about blood cells and their properties, such as red blood cells, white blood cells, and platelets. Exemplary complete blood counts may include an automated process using flow cytometry or a Coulter counter to determine the number of cells in the blood. In addition to determining cell counts, such automated systems may also be able to determine other blood biomarker readings, depending on their complexity. Such systems can simultaneously measure blood cell counts as well as red blood cell volume, hemoglobin levels, mean corpuscular hemoglobin levels, and hematocrit. For example, IDEXX Laboratories offers a hematology analyzer (IDEXX Laboratories Inc.'s ProCyte Dx Hematology Analyzer) capable of determining white blood cell count (WBC), red blood cell count (RBC), platelet count (PLT), hemoglobin (HGB), hematocrit (HCT), mean corpuscular volume (MCV), and mean corpuscular hemoglobin (MCH).

[0105] The levels of other biomarkers unrelated to blood cells, such as serum protein / enzyme and / or molecular biomarker levels, can be measured using chemical tests, particularly automated chemical analyzer systems. These methods can be quantified using colorimetric-based approaches. For example, IDEXX Laboratories offers an automated chemical analyzer (IDEXX Laboratories Inc.'s Catalyst One Chemistry Analyzer) capable of quantifying serum albumin, serum alkaline phosphatase, serum creatine kinase, serum glucose, serum globulins, and serum calcium.

[0106] Therefore, methods for determining the levels of biomarkers used in this invention may include assays that result in spectrophotometric changes (e.g., chemical or antibody-related changes that result in detectable signals at certain wavelengths). Such tests can be highly automated and efficient, and are the basis of many routine veterinary health examinations.

[0107] Appropriately, biomarker levels can be determined after overnight fasting and measured using standard veterinary clinical practice.

[0108] The levels of individual biomarker species in a sample can be measured or determined by any suitable method known in the art. For example, mass spectrometry (MS), antibody detection methods (e.g., enzyme-linked immunosorbent assay (ELISA)), non-antibody protein scaffolds (e.g., fibronectin scaffolds), radioimmunoassay (RIA), or nucleic acid ligands can be used. Other spectrophotometric, chromatographic, labeling techniques, or quantitative chemical methods can also be used.

[0109] Suitable antibodies for the above methods are known in the art and / or can be generated using known techniques. Suitable assays for detecting antibody levels include, but are not limited to, immunoassays such as enzyme-linked immunosorbent assay (ELISA), radioimmunoassay, Western blotting, and immunoprecipitation.

[0110] White blood cell count

[0111] White blood cells, also known as leukocytes, are cells found in the blood. They perform various immune-related functions, depending on their subtypes: monocytes, lymphocytes, neutrophils, basophils, and eosinophils. White blood cells contain a nucleus and have variable cell shapes, which also depend on the subtype. The white blood cell count is the number of these cells in each volume of blood.

[0112] Methods for measuring white blood cell counts are known in the art, and white blood cell counts are typically expressed as kilocells per microliter (10^3 / uL). White blood cell count measurements can be performed manually on blood smears using staining and microscopy techniques, but can also be performed as part of an automated complete blood count (CBC). IDEXX Laboratories offers an automated hematology analyzer capable of performing white blood cell count measurements.

[0113] Appropriately, an increase in white blood cell count may be associated with a negative impact on reducing the risk of death. Therefore, an increase in white blood cell count may be associated with an increased risk of death.

[0114] serum albumin

[0115] Serum albumin is a globular protein found in the blood. It is a 65 kDa protein comprising three homologous domains. Albumin regulates osmotic pressure, preventing fluid loss from the blood to tissues, and acts as a transport protein for fatty acids, bilirubin, heme, heavy metals, hormones, and certain drugs. Albumin is present in high concentrations in the blood, accounting for 25-50% of total plasma protein weight, and is produced by the liver. Abnormally high or low levels of albumin in the blood may indicate liver or kidney disease.

[0116] Methods for measuring serum albumin levels are well known in the art, and serum albumin levels are typically expressed in grams per deciliter (g / dL); and include regional electrophoresis, dye binding assays involving bromocresol green (BCG) or bromocresol purple (BCP), and ELISA methods. For example, Eagle Biosciences provides an ELISA-based assay for canine serum albumin (EagleBiosciences Inc., product code SKU: CAE49-K01). Stokol et al. described an automated system for determining canine serum albumin levels using BCG binding assays in a clinical setting (Vet Clin Pathol. 2001; 30(4):170-176). Furthermore, IDEXX Laboratories provides an automated chemical analyzer that measures serum albumin levels as part of a combined blood test.

[0117] Appropriately, an increase in serum albumin levels may be associated with a positive effect on reducing the risk of death. Therefore, an increase in serum albumin levels may be associated with a reduction in the risk of death.

[0118] serum alkaline phosphatase

[0119] Alkaline phosphatase (ALP) is an enzyme that plays an important role in liver metabolism and bone development. It is an 86 kDa homodimeric protein. High levels of this protein in the blood may indicate liver damage or bone disease.

[0120] Methods for measuring serum alkaline phosphatase levels are well known in the art, and serum alkaline phosphatase levels are typically expressed in units per liter (U / L); and the most common methods consist of quantifying enzyme activity via colorimetric assay. Automated chemical analyzers capable of measuring serum alkaline phosphatase levels are available from IDEXX Laboratories.

[0121] Appropriately, an increase in serum alkaline phosphatase levels may be associated with a negative impact on reducing the risk of death. Therefore, an increase in serum alkaline phosphatase levels may be associated with an increased risk of death.

[0122] Serum creatine kinase

[0123] Creatine kinase (CK) is an enzyme primarily found in muscles. This enzyme converts creatine and ATP into phosphocreatine, used for rapid energy production during muscle contraction. High levels of this enzyme in the blood can indicate muscle damage.

[0124] Methods for determining creatine kinase levels in the blood are well known in the art, and creatine kinase levels are typically expressed in International Units per Liter (IU / L); examples of these methods use enzymatic assays to quantify creatine kinase levels. An automated chemical analyzer provided by IDEXX Laboratories is an example of a commercially available tool for measuring serum creatine kinase levels.

[0125] Appropriately, increased creatine kinase levels may be associated with a negative impact on reducing mortality risk. Therefore, increased creatine kinase levels may be associated with an increased risk of death.

[0126] hemoglobin

[0127] Hemoglobin is a transport protein in red blood cells. It consists of a tetramer of two α chains and two β chains. Each polypeptide chain is bound to a heme group, which is composed of a porphyrin ring bound to an iron ion. This group can reversibly bind oxygen, enabling hemoglobin to function as an oxygen transport carrier protein.

[0128] Methods for determining hemoglobin levels are well known in the art, and hemoglobin levels are typically expressed in grams per deciliter (g / dL). The International Committee for Standardization of Hematology describes a standardized method for determining hemoglobin cyanide using spectrophotometry (Br J Haematol. April 1967; 13:71-5), and this method can be used in commercially available automated chemical analyzers, such as those provided by IDEXX Laboratories.

[0129] Appropriately, an increase in hemoglobin levels may be associated with a positive effect on reducing the risk of death. Therefore, an increase in hemoglobin may be associated with a reduction in the risk of death.

[0130] Hematocrit

[0131] Hematocrit is the percentage of red blood cells in the blood. A hematocrit level above normal can indicate a disease or condition that causes the proportion of red blood cells in the blood to be higher or lower than normal. For example, a high hematocrit can indicate conditions such as dehydration; while a low hematocrit can indicate anemia, hemolysis, or reduced red blood cell production.

[0132] Methods for determining hematocrit are well known in the art, and hematocrit is typically expressed as a percentage (%) of blood volume. It can be measured manually using compacted cell volume by centrifuging blood in a microvolume tube. Alternatively, hematocrit can be calculated from mean corpuscular volume and red blood cell count, both of which can be measured directly in a standard complete blood count (CBC) using a modern hematology analyzer.

[0133] Appropriately, an increase in hematocrit levels may be associated with a positive effect on reducing the risk of death. Therefore, an increase in hematocrit may be associated with a reduction in the risk of death.

[0134] Mean corpuscular hemoglobin

[0135] Mean corpuscular hemoglobin (MCH) is the average mass of hemoglobin in each red blood cell. MCH values ​​outside the normal range can indicate certain conditions, such as macrocytic or hypochromic anemia.

[0136] Methods for measuring MCH are well known in the art. MCH is typically expressed in picograms (pg) and usually involves calculations from observations of hemoglobin levels and red blood cell counts, which can be measured during a complete blood count (CBC) using a hematology analyzer as described above.

[0137] Appropriately, an increase in MCH may be associated with a positive effect on reducing mortality risk. Therefore, an increase in MCH may be associated with a reduction in mortality risk.

[0138] serum glucose

[0139] Serum glucose is a measure of the amount of glucose present in the blood. Blood glucose levels are controlled by hormones such as insulin to keep glucose levels within a normal range. When blood glucose levels are outside the normal range, it can indicate a disease such as diabetes.

[0140] Methods for measuring serum glucose levels are well known in the art, and serum glucose levels are typically expressed in milligrams per deciliter (mg / dL). Most glucose assays are photometric, and many commercially available devices are available. For example, Acon Pharmaceuticals offers a veterinary glucose monitoring system (Acon Pharmaceuticals Inc.'s CentriVet...). ® GK), and Carradini et al. described the use of continuous glucose monitoring devices in dogs (Abbott Laboratories’ FreeStyle Libre).

[0141] Appropriately, an increase in serum glucose levels may be associated with a negative impact on reducing the risk of death. Therefore, an increase in serum glucose levels may be associated with an increased risk of death.

[0142] Mean corpuscular volume

[0143] Mean corpuscular volume (MCV) is a measure of the average volume of red blood cells in the blood. MCV is a diagnostic criterion that classifies possible anemias as microcytic, normocytic, or macrocytic, and can help identify underlying diseases or conditions. A high MCV may indicate conditions such as vitamin B12 deficiency, while a low MCV may indicate conditions such as iron deficiency.

[0144] Methods for determining mean corpuscular volume (MCV) are well known in the art, and MCV is typically expressed in nanopicoli (fL). While modern hematology analyzers (e.g., the ProCyte DxHematology Analyzer from IDEXX Laboratories Inc.) can directly measure MCV as part of a complete blood count (CBC), it can also be calculated from other measurements of hematocrit and red blood cell count.

[0145] Appropriately, an increase in MCV may be associated with a positive effect on reducing the risk of death. Therefore, an increase in MCV may be associated with a reduction in the risk of death.

[0146] serum globulin

[0147] Serum globulins are a measure of the concentration of globular proteins in the blood. These proteins are primarily secreted by the liver, with a small portion secreted by immune cells. Albumin is the most abundant serum globulin. The remaining serum globulins can be separated into various components based on their behavior in electrophoretic separation methods. Immunoglobulins are important components of the immune system and are secreted by immune cells. Examples of other serum globulins include immune system proteins such as complement, hormones, and carrier proteins such as ferritin. Changes in total serum globulin levels can indicate certain symptoms or diseases. An overall increase in serum globulin levels may indicate infection and inflammatory immune responses, while a decrease may indicate bleeding, gastrointestinal disorders, or severe malnutrition.

[0148] Methods for determining serum globulin levels are well known in the art, and serum globulin levels are typically expressed in grams per deciliter (g / dL). For example, chemical and physical methods are described in Tothova et al. (Veterinarni Medicina, 2016, 61:475-496), and automated chemical analyzers from Idexx are also capable of measuring serum globulin levels.

[0149] Appropriately, an increase in serum globulin levels may be associated with a negative impact on reducing the risk of death. Therefore, an increase in serum globulin levels may be associated with an increased risk of death.

[0150] serum calcium

[0151] Serum calcium is a measure of the total concentration of calcium in the blood. Calcium in the blood can be ionized, complexed, or bound to proteins. Calcium is essential for a wide range of intracellular and extracellular functions, including muscle contraction and blood clotting, and is a major component of bone. Excessively high calcium levels can be a result of certain cancers or bone disorders, while excessively low calcium levels can be a result of kidney disease, pancreatitis, or decreased serum albumin.

[0152] Methods for determining serum calcium levels are well known in the art, and serum calcium levels are usually expressed in milligrams per deciliter (mg / dL). F. Gran described a colorimetric method for determining calcium in serum (Acta Physiologica Scandinavica;1960, 49: 192-197), and modern automated chemical analyzers are capable of measuring calcium levels in serum.

[0153] Appropriately, an increase in serum calcium levels may be associated with a positive effect on reducing the risk of death. Therefore, an increase in serum calcium levels may be associated with a reduction in the risk of death.

[0154] Platelet count

[0155] Platelets, also known as clotting cells, are small cells that make up blood. They are small, nucleus-less cells produced in the cytoplasm of bone marrow cells called megakaryocytes. Platelets help in the clotting process to stop bleeding at sites of injury in blood vessels. Platelet counts that are higher or lower than the normal range can indicate a condition or disease. In particular, a low platelet count may be the result of certain infections, cancers, immune system disorders, or pancreatitis.

[0156] Methods for measuring platelet counts are well known in the art, and platelet counts are typically expressed as thousands of cells per microliter (10^3 / uL). Platelet counts can be performed manually on blood smears using staining and microsurgical methods, but are usually performed as part of an automated complete blood count (CBC).

[0157] Appropriately, an increase in platelet count may be associated with a negative impact on reducing the risk of death. Therefore, an increase in platelet count may be associated with an increased risk of death.

[0158] Red blood cell count

[0159] Red blood cells, also known as erythrocytes, are the most abundant cells in the blood. These cells do not contain a nucleus and are primarily composed of hemoglobin within their cell membranes to maximize their oxygen-carrying potential. Red blood cell counts that are higher or lower than normal indicate symptoms or diseases. A low red blood cell count can indicate hemolysis, blood loss, or reduced red blood cell production, which can be caused by a variety of reasons. A high red blood cell count indicates a relative increase in the number of red blood cells per volume of blood compared to normal, due to dehydration or increased red blood cell production.

[0160] Methods for measuring red blood cell counts are well known in the art, and red blood cell counts are typically expressed as 10^3 / µL (thousand cells per microliter). Red blood cell counts can be measured manually on blood smears using microscopy, but are usually performed as part of an automated complete blood count (CBC).

[0161] Appropriately, an increase in red blood cell count may be associated with a positive effect on reducing the risk of death. Therefore, an increase in red blood cell count may be associated with a reduction in the risk of death.

[0162] Combination of biomarkers

[0163] While a single biomarker may have predictive value in the method of the present invention, the quality and / or predictive power of the method can be improved by combining values ​​from multiple biomarkers.

[0164] Therefore, this method may involve determining the levels of at least two of the biomarkers described herein. For example, the method may include determining the levels of two or more biomarkers selected from one or more samples, including white blood cell count, serum albumin, serum alkaline phosphatase, serum creatine kinase, hemoglobin, hematocrit, mean corpuscular hemoglobin, serum glucose, mean corpuscular volume, serum globulin, serum calcium, platelet count, and / or red blood cell count.

[0165] As used herein, the term "one or more biomarkers" may include at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, or at least thirteen biomarkers.

[0166] As used herein, the term "one or more biomarkers" may include one, two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, or thirteen biomarkers.

[0167] Suitablely, this method may include determining the levels of white blood cell count, serum albumin, and serum alkaline phosphatase in one or more samples. Advantageously, this combination of three biomarkers has been identified as significantly predicting biological age, risk of death, and / or probability of healthy lifespan. Predictive power can be further enhanced by combining one or more additional biomarkers selected from serum creatine kinase, hemoglobin, hematocrit, mean corpuscular hemoglobin, serum glucose, mean corpuscular volume, serum globulin, serum calcium, platelet count, and red blood cell count.

[0168] Suitablely, this method may include determining the level of each of the following in one or more samples: white blood cell count, serum albumin, serum alkaline phosphatase, serum creatine kinase, hemoglobin, hematocrit, mean corpuscular hemoglobin, serum glucose, mean corpuscular volume, serum globulin, serum calcium, platelet count, and red blood cell count.

[0169] Combine biomarker levels with other measures and / or characteristics

[0170] Suitablely, step (a) of this method further includes combining the levels of one or more biomarkers with one or more of the dog's actual age, breed, and / or sex. By combining this information, an improved model can be provided for the dog's biological age, risk of death, and / or probability of healthy lifespan.

[0171] In a preferred embodiment, the levels of one or more biomarkers defined herein are determined from samples from dogs, and these levels are combined with the dog's actual age, breed, and sex to determine the dog's biological age, risk of death, and / or probability of healthy lifespan.

[0172] Preferably, biological age, risk of death, and / or probability of healthy life are expressed as phenotypic age, which is given by the following formula:

[0173]

[0174] Where xb is the sum of the values ​​of each biomarker, sex, and species multiplied by their respective coefficients according to formula (2):

[0175]

[0176] In this context, sex is encoded as a numerical value, where 0 represents female and 1 represents male.

[0177] Furthermore, "breed" is coded as a numerical value, where 0 represents a small variety and 1 represents a medium variety.

[0178] The coefficient values ​​for each parameter typically depend on the units of measurement of all variables in the model. As those skilled in the art will understand, the exact value of each coefficient will therefore depend, for example, on the number and nature of the different parameters used in the model and the nature of the training data provided. Therefore, conventional statistical methods can be applied to the training dataset to obtain the coefficient values ​​used in the above formulas. This approach involves, for example, computing two gompertz functions on the training set (e.g., given the dog's state (survival or death)), one function modeling survival as a function of selected biomarkers, actual age, breed category (small or medium-sized dog), and sex (Model 1), while the second function considers only actual age, breed category, and sex (Model 2). These models can be fitted in the R software environment using the flexsurv package (v 2.1).

[0179] Appropriately, a negative coefficient for a given biomarker implies that a higher level of the biomarker has a positive effect on reducing the risk of death, and / or a lower level of the biomarker has a negative effect on reducing the risk of death. Similarly, a positive coefficient for a given biomarker implies that a higher level of the biomarker has a negative effect on reducing the risk of death, and / or a lower level of the biomarker has a positive effect on reducing the risk of death.

[0180] Phenotypic age can be defined as a time variable (“actual age”), under which the survival probability of animals given by Model 2 is equal to the survival probability given by Model 1 at their actual age.

[0181] A dog's phenotypic age can be expressed in years, months, days, etc.

[0182] Preferably, biological age, mortality risk, and / or probability of healthy lifespan are expressed as the difference between the dog's phenotypic age and its chronological age. This difference may be referred to as advanced phenotypic age in the dog.

[0183] For example, an increase in phenotypic age compared to chronological age indicates an increased risk of death in dogs. Conversely, a decrease in phenotypic age compared to chronological age indicates a decreased risk of death in dogs. As an illustration, the inventors determined that the difference between phenotypic age and chronological age (earlier phenotypic age) is associated with a significant increase in the risk of death, and the degree of influence was calculated as follows: the hazard ratio for a one-year increase in phenotypic age compared to chronological age is 1.75 (see Example 3). In other words, the inventors determined that a one-year increase in phenotypic age compared to chronological age is associated with a 75% increase in the risk of death at any given point in life.

[0184] Cat biomarkers

[0185] The method for determining the performance age of a cat (i.e., step a of this method) is described in PCT / EP2023 / 061059.

[0186] The method may include determining the white blood cell count in one or more samples obtained from a cat. The method may also include determining the levels of one or more biomarkers in one or more samples, these biomarkers being selected from hemoglobin, serum urea nitrogen, serum AST, serum chloride, serum total bilirubin, serum globulin, serum sodium, serum cholesterol, serum potassium, serum alkaline phosphatase, serum GGT, and / or red blood cell count.

[0187] In one embodiment, the method may include determining the level of hemoglobin in one or more samples obtained from a cat. The method may also include determining the level of one or more biomarkers in the one or more samples, these biomarkers being selected from white blood cell count, serum urea nitrogen, serum AST, serum chloride, serum total bilirubin, serum globulin, serum sodium, serum cholesterol, serum potassium, serum alkaline phosphatase, serum GGT, and / or red blood cell count.

[0188] In one embodiment, the method may include determining the level of serum urea nitrogen in one or more samples obtained from a cat. The method may also include determining the level of one or more biomarkers in the one or more samples, these biomarkers being selected from white blood cell count, hemoglobin, serum AST, serum chloride, serum total bilirubin, serum globulin, serum sodium, serum cholesterol, serum potassium, serum alkaline phosphatase, serum GGT, and / or red blood cell count.

[0189] In one embodiment, the method may include determining the level of serum AST in one or more samples obtained from a cat. The method may also include determining the level of one or more biomarkers in the one or more samples, these biomarkers being selected from white blood cell count, hemoglobin, serum urea nitrogen, serum chloride, serum total bilirubin, serum globulin, serum sodium, serum cholesterol, serum potassium, serum alkaline phosphatase, serum GGT, and / or red blood cell count.

[0190] In one embodiment, the method may include determining the level of serum chloride in one or more samples obtained from a cat. The method may also include determining the level of one or more biomarkers in the one or more samples, these biomarkers being selected from white blood cell count, hemoglobin, serum urea nitrogen, serum AST, serum total bilirubin, serum globulin, serum sodium, serum cholesterol, serum potassium, serum alkaline phosphatase, serum GGT, and / or red blood cell count.

[0191] In one embodiment, the method may include determining the level of serum total bilirubin in one or more samples obtained from a cat. The method may also include determining the level of one or more biomarkers in the one or more samples, these biomarkers being selected from white blood cell count, hemoglobin, serum urea nitrogen, serum AST, serum chloride, serum globulin, serum sodium, serum cholesterol, serum potassium, serum alkaline phosphatase, serum GGT, and / or red blood cell count.

[0192] In one embodiment, the method may include determining the level of serum globulin in one or more samples obtained from a cat. The method may also include determining the level of one or more biomarkers in the one or more samples, these biomarkers being selected from white blood cell count, hemoglobin, serum urea nitrogen, serum AST, serum chloride, serum total bilirubin, serum sodium, serum cholesterol, serum potassium, serum alkaline phosphatase, serum GGT, and / or red blood cell count.

[0193] In one embodiment, the method may include determining serum sodium levels in one or more samples obtained from a cat. The method may also include determining levels of one or more biomarkers in the one or more samples, these biomarkers being selected from white blood cell count, hemoglobin, serum urea nitrogen, serum AST, serum chloride, serum total bilirubin, serum globulin, serum cholesterol, serum potassium, serum alkaline phosphatase, serum GGT, and / or red blood cell count.

[0194] In one embodiment, the method may include determining serum cholesterol in one or more samples obtained from a cat. The method may also include determining the levels of one or more biomarkers in the one or more samples, these biomarkers being selected from white blood cell count, hemoglobin, serum urea nitrogen, serum AST, serum chloride, serum total bilirubin, serum globulin, serum cholesterol, serum potassium, serum alkaline phosphatase, serum GGT, and / or red blood cell count.

[0195] In one embodiment, the method may include determining the level of serum potassium in one or more samples obtained from a cat. The method may also include determining the level of one or more biomarkers in the one or more samples, these biomarkers being selected from white blood cell count, hemoglobin, serum urea nitrogen, serum AST, serum chloride, serum total bilirubin, serum globulin, serum cholesterol, serum alkaline phosphatase, serum GGT, and / or red blood cell count.

[0196] In one embodiment, the method may include determining the level of serum alkaline phosphatase in one or more samples obtained from a cat. The method may also include determining the level of one or more biomarkers in the one or more samples, these biomarkers being selected from white blood cell count, hemoglobin, serum urea nitrogen, serum AST, serum chloride, serum total bilirubin, serum globulin, serum cholesterol, serum potassium, serum GGT, and / or red blood cell count.

[0197] In one embodiment, the method may include determining the level of serum GGT in one or more samples obtained from a cat. The method may also include determining the level of one or more biomarkers in the one or more samples, these biomarkers being selected from white blood cell count, hemoglobin, serum urea nitrogen, serum AST, serum chloride, serum total bilirubin, serum globulin, serum cholesterol, serum potassium, serum alkaline phosphatase, and / or red blood cell count.

[0198] In one embodiment, the method may include determining the red blood cell count in one or more samples obtained from a cat. The method may also include determining the level of one or more biomarkers in the one or more samples, these biomarkers being selected from white blood cell count, hemoglobin, serum urea nitrogen, serum AST, serum chloride, serum total bilirubin, serum globulin, serum cholesterol, serum potassium, serum alkaline phosphatase, and / or serum GGT.

[0199] This method may include determining the level of each of the following: white blood cell count, serum hemoglobin, serum urea nitrogen, and serum AST.

[0200] The method may include determining the level of one or more additional biomarkers in one or more samples obtained from a cat, the additional one or more biomarkers being selected from hematocrit, red blood cell count, serum albumin and / or mean corpuscular hemoglobin concentration.

[0201] In one embodiment, the method includes determining the level of each of the following: white blood cell count, hemoglobin, serum urea nitrogen, serum AST, serum chloride, serum total bilirubin, serum globulin, hematocrit, red blood cell count, serum albumin, and mean corpuscular hemoglobin concentration.

[0202] In one implementation, the method may include:

[0203] a. Determine the levels of the following biomarkers: white blood cell count, hemoglobin, serum urea nitrogen, serum AST, serum chloride, serum total bilirubin, serum globulin, red blood cell count, serum sodium, serum cholesterol, serum potassium, serum alkaline phosphatase, and serum GGT in one or more samples obtained from the cat; and

[0204] b. Determine the cat's phenotypic age using formula (1):

[0205]

[0206] Where xb is the sum of the values ​​of each biomarker according to formula (2) multiplied by their corresponding coefficients:

[0207]

[0208] Furthermore, phenotypic age is used to determine the cat's risk of death and / or probability of a healthy lifespan.

[0209] Explanatory coefficients and The values ​​are provided below.

[0210]

[0211] DNA methylation

[0212] DNA methylation is the process of covalently adding a methyl group (CH3) to a cytosine base that is part of a DNA molecule. In vivo, this process is catalyzed by the DNA methyltransferase (Dnmt) family, which produces modified cytosine by transferring a methyl group from S-adenosylmethionine (SAM). The cytosine is modified at the 5th carbon atom, and the modified residue is called 5-methylcytosine (5mC). DNA methylation may also include 5-hydroxymethylcytosine (5hmc).

[0213] DNA methylation is an example of an epigenetic mechanism that can modify gene expression without altering the underlying DNA sequence. DNA methylation can suppress gene expression, for example, by acting as a recruitment signal for repressors or by directly blocking the recruitment of transcription factors. DNA methylation primarily occurs in the genome of mammalian somatic cells at sites where dinucleotides (CpGs) are adjacent to cytosine and guanine. While non-CpG methylation is observed in embryonic development, these modifications are significantly reduced in most cell types in adults. CpG islands are DNA segments with high CpG density but are typically unmethylated. These regions are associated with promoter regions, particularly those of housekeeping genes, and are thought to be kept in a permissive state that allows gene expression.

[0214] DNA methylation has been found to change with age in humans and other animals. Aging mammalian tissues show overall DNA hypomethylation, thought to be due to the gradual loss or mistargeting of DMNT1 methyltransferase activity, but with localized hypermethylation of CpG islands. Localized hypermethylation can lead to the repression of certain genes, and this can contribute to age-related diseases. The link between epigenetic changes in DNA methylation and age allows the use of “DNA methylation clocks” to estimate “biological age.” Typically, these clocks have been trained against chronological age using supervised machine learning methods, and the deviation of the “clock age” from an individual’s actual chronological age is considered an indicator of “biological” age. This is related to an individual’s chronological age, but deviations from this correlation can indicate a potential risk of age-related diseases or disorders in the individual.

[0215] Detection of specifically methylated DNA can be performed using a variety of methods (see, for example, Zuo et al., 2009; Epigenomics. 1(2): 331-345) and Rauluseviciute et al.; Clinical Epigenetics; 2019; 11(193)). Many methods can be used to detect differentially methylated DNA at specific loci in samples such as blood, urine, feces, or saliva. These methods are able to distinguish 5-methylcytosine or methylated DNA from unmethylated DNA and subsequently quantify the ratio of methylated to unmethylated DNA at specific genomic sites.

[0216] This method may include using any suitable method to determine the DNA methylation profile of a dog. Suitable methods include, but are not limited to, those described below.

[0217] Enzyme-mediated methylation sequencing (EM-seq)

[0218] Suitable for this purpose, enzymatic methods are used to detect 5mC and 5hmC. For example, enzymatic methylation sequencing (EM-seq) can be used.

[0219] In EM-seq, typically in the first enzymatic step, 5mC is oxidized to 5hmC, then to 5fC, and finally to 5caC by the activity of Tet methylcytosine dioxygenase 2 (TET2). Additionally, the use of T4-BGT enzyme glycosylates both the pre-existing 5hmC and the 5hmC generated by TET2 activity. In the second enzymatic step, after double-stranded DNA denaturation, the enzyme apolipoprotein B mRNA editing enzyme catalyzes peptide-like 3A (APOBEC3A) to deaminate cytosine, but not to the oxidized or glycosylated forms of 5mC and 5hmC. Only unmethylated cytosine is deaminated to form uracil bases. Prior to the first enzymatic step, DNA fragments can be generated by mechanical shearing and end repair, A-tailing, and ligation into sequencing adaptors, which can be achieved using, for example, NEBNext. ® DNA Ultra II reagent (NEB) is used. After the second enzymatic step, PCR can be performed using a polymerase that amplifies a template containing uracil (such as NEBNext). ® Q5U ™ EM-seq amplifies deamination-bound single-stranded DNA and can sequence or analyze the resulting library in the same manner as DNA samples generated by bisulfite sequencing. EM-seq output is typically the same as whole-genome bisulfite sequencing, but uses fewer DNA-damaging agents, thus reducing sample loss and offering superior coverage, sensitivity, and accuracy in cytosine methylation recall compared to bisulfite-converted samples. An illustrative EM-seq method is described by Vaisvila et al. (Genome Research; 2021;31:1-10).

[0220] Methods based on bisulfite conversion

[0221] When treated with sodium bisulfite, bisulfite conversion utilizes the selective conversion of unmethylated cytosine to uracil. Denatured DNA is treated with sodium bisulfite, which converts all unmodified cytosine to uracil, and subsequent PCR amplification converts these residues to thymine. Analysis of the resulting DNA sequences can be performed using a variety of methods, examples of which include, but are not limited to: denaturing gel electrophoresis, single-strand conformation polymorphism, melting curves, real-time fluorescence PCR (MethyLight), MALDI mass spectrometry, array hybridization, and sequencing (e.g., whole-genome bisulfite sequencing, WGBS). Recently developed techniques (such as SeqCap Epi) enrich the sequences of interest before sequencing, enabling deeper coverage of more concentrated regions. Comparing the sequence abundance in bisulfite-converted samples with that in untreated controls allows analysis of methylation at target sites, where the proportion of converted sequences indicates the level of methylation at the target site.

[0222] Other variations of the bisulfite conversion method are available that can distinguish 5mC from its oxidized form, 5-hydroxymethylcytosine (5hmC), which behaves identically to 5mC under standard bisulfite conversion, and can detect further modifications such as 5-formylcytosine (5fC). These methods, such as oxBS-Seq and redBS-Seq, utilize the oxidation and reduction of these markers to alter the sensitivity of each substance to bisulfite conversion and quantify the amount of each modification at the target locus through comparative analysis.

[0223] Selective restriction endonuclease digestion method

[0224] Existing methods for analyzing DNA methylation patterns may involve the use of restriction enzymes. These methods include, for example, Restriction Marker Genome Scan (RLGS) (Costello et al., 2000; Nat Genet.; 24(2):132-8), methylation sensitivity representative differential analysis (MS-RDA) (Ushijima et al., Proc Natl Acad Sci US A. 18 March 1997; 94(6):2284-9), and differential methylation hybridization (DMH) (Huang et al., Cancer Res. 15 March 1997; 57(6):1030-4). The digestive activity of restriction endonucleases can be methylation-dependent. This specificity can be used to distinguish between methylated and unmethylated sequences. Some restriction enzymes (e.g., BstUI, HpaII, and NotI) are sensitive to methylated recognition sequences. Other restriction enzymes (such as McrBC) are specific to methylated sequences.

[0225] For example, differential methylation hybridization (DMH) (Huang et al., as described above) requires initial fragmentation of the genome using a large number of genomic restriction enzymes (such as MseI) to fragment the genome into lengths less than 200 bp. Following this step, the genomic fragments are digested using methylation-sensitive restriction endonucleases (MREs) or, in some versions of the technique, a mixture of MREs to improve coverage. Depending on the specificity of one or more enzymes used, methylated or unmethylated sequences will be degraded. The digested sequences are not amplified in subsequent PCR steps. The resulting PCR products are suitable for further processing and analysis by sequencing or a combination of microarray hybridization and fluorescent dyes.

[0226] Suitablely, this method utilizes DNA methylation maps generated by methods including the use of one or more MREs.

[0227] Suitable comparators can be used to study methylation status between conditions. DNA from healthy subjects can be compared with that from older or diseased subjects to detect changes in methylation status (Huang et al., Hum Mol Genet. 1999 Mar; 8(3):459-70). Alternatively, methylation-insensitive forms of secondary digestive enzymes (such as HpaII isoschizase MspI) can be used to generate control samples, enabling intragenomic or intergenomic comparisons of DNA methylation (Khulan et al., Genome Res. 2006 Aug; 16(8):1046-55).

[0228] In some embodiments, methods for detecting methylation include randomly shearing or fragmenting genomic DNA, cutting the DNA with a methylation-dependent or methylation-sensitive restriction enzyme, and subsequently selectively identifying and / or analyzing the cut or uncut DNA. Selective identification may include, for example, separating the cut and uncut DNA (e.g., by size) and quantifying the cut or alternatively uncut sequences of interest. Alternatively, the method may encompass amplifying intact DNA after restriction enzyme digestion, thereby amplifying only the DNA that has not been cut by the restriction enzyme in the amplified region. In some embodiments, gene-specific primers may be used for amplification. Alternatively, an adaptor may be added to the end of the randomly fragmented DNA, the DNA may be digested with a methylation-dependent or methylation-sensitive restriction enzyme, and the intact DNA may be amplified using primers that hybridize to the adaptor sequence. In this case, a second step may be performed to determine the presence, absence, or amount of a specific gene in the DNA amplification pool. In some embodiments, real-time quantitative PCR is used to amplify the DNA.

[0229] Suitable methods for detecting nucleic acid digestion include selective hybridization of a probe or primer with undigested nucleic acids. Alternatively, the probe selectively hybridizes with both digested and undigested nucleic acids, but the distinction between the two forms is facilitated, for example, by electrophoresis. Suitable detection methods for achieving selective hybridization with the hybridization probe include, for example, Southern blotting or other nucleic acid hybridization.

[0230] Suitable hybridization conditions can be determined based on the melting temperature (Tm) of the nucleic acid duplex containing the probe. Those skilled in the art will recognize that optimal hybridization reaction conditions for each probe should be determined empirically, but some general principles can be applied. Preferably, hybridization with short oligonucleotide probes is performed with low to moderate stringency. High stringency hybridization and / or washing is preferred in the case of GC-rich probes or primers, or longer probes or primers. High stringency is defined herein as hybridization and / or washing performed in about 0.1×SSC buffer and / or about 0.1% (w / v) SDS or lower salt concentration and / or at a temperature of at least 65°C or equivalent conditions. Specific stringency levels mentioned herein cover equivalent conditions using wash / hybridization solutions other than SSC known to those skilled in the art.

[0231] Simplified representation of bisulfite sequencing (RRBS)

[0232] Simplified representation of bisulfite sequencing (RRBS) uses the MspI restriction enzyme to enrich CpG-rich genomic regions—which cuts DNA at all CpG sites regardless of their DNA methylation status at CG sites—and is able to measure 5% to 10% of DNA methylation levels at all CpG sites in the mammalian genome.

[0233] Therefore, this method involves digesting DNA with a methylation-insensitive MspI prior to bisulfite conversion and sequencing. Digesting genomic DNA with MspI produces fragments that always begin with C (if cytosine is methylated) or T (if cytosine is not methylated and is converted to uracil during the bisulfite conversion reaction). This results in a non-random base pair composition. Furthermore, the basic composition is skewed due to the skewed frequencies of C and T within the sample. Various software programs are available for alignment and analysis, such as Maq, BS Seeker, Bismark, or BSMAP. Alignment with a reference genome allows the procedure to identify methylated base pairs within the genome.

[0234] Affinity-based enrichment methods

[0235] Methylated DNA can be distinguished from unmethylated DNA by using antibodies containing a methyl-CpG-binding domain (MBD), such as anti-5mC and / or methylated CpG-binding proteins. Antibodies with MBD-domain proteins can specifically separate methylated DNA relative to unmethylated DNA. The antibody-based method is commonly referred to as MeDIP, while the method using methylated CpG-binding proteins is commonly referred to as the MBD or MIRA method.

[0236] These methods require initial fragmentation of the genome, which can be achieved through mass genome digestion with frequently cleaving enzymes such as MseI, followed by affinity purification of the methylated fragments. The input DNA can be compared with the purified methylated DNA via microarray hybridization or sequencing to obtain comparative analysis of methylation levels at specific sites.

[0237] Other variations of affinity-based enrichment methods are available, such as MethylCap-Seq or MBD-Seq. These methods reduce sample complexity by using a salt gradient to elute methylated DNA fragments in a methyl-CpG abundance-dependent manner, separating CpG islands and other highly methylated loci from loci with lower CpG density. These fragments can then be sequenced separately, thereby improving sequence coverage.

[0238] Based on single-molecule sequencing and de novo methylation sequencing methods

[0239] Modern sequencing methods can sequence individual molecules directly. Single-molecule real-time (SMRT) DNA sequencing is available, such as the Sequel system from Pacific Biosciences, and has been shown to identify modified bases (such as methylated cytosine) based on polymerase kinetics. Nanopore sequencing devices capable of sequencing long DNA chains individually (such as the MinION nanopore sequencer from Oxford Nanopore Technologies) can also detect de novo base modifications, including methylation.

[0240] DNA methylation sites

[0241] Appropriately, DNA methylation sites may refer to the presence or absence of 5mC at a single cytosine, or appropriately a single CpG dinucleotide.

[0242] Suitablely, a DNA methylation site can refer to the presence or absence of methylation at multiple CpG sites across a DNA region (i.e., the number or percentage of 5mC). Suitablely, a DNA methylation site can refer to the methylation level at multiple CpG sites across a DNA region (i.e., the number or percentage of 5mC). A “DNA region” can refer to a specific portion of genomic DNA. These DNA regions can be designated by reference gene names or a set of chromosomal coordinates. Both gene names and chromosomal coordinates are well known and understood by those skilled in the art.

[0243] Where appropriate, gene names and / or coordinates may be based on the “Tasha” canine reference genome (https: / / www.ncbi.nlm.nih.gov / assembly / GCF_000002285.5; Jagannathan et al.; Genes (Bsael); 2021; 12(6); 847).

[0244] For example, a DNA region can define the portion of DNA near the promoter of a gene. Promoter regions are known to be CpG-rich. For instance, a DNA region could refer to approximately 3 kb upstream and 3 kb downstream of the promoter; approximately 2 kb upstream and 2 kb downstream; approximately 2 kb upstream and 1 kb downstream; approximately 2 kb upstream and 0.5 kb downstream; approximately 1 kb upstream and 0.5 kb downstream; or approximately 0.5 kb upstream and 0.5 kb downstream. Suitablely, a DNA region could refer to approximately 1 kb upstream and 0.5 kb downstream of the promoter.

[0245] Suitable, the DNA region may contain or consist of CpG sites spaced less than about 5,000, 4,000, 3,000, 2,000, 1,000, 500, or 200 bases apart.

[0246] Suitable, the DNA region may contain or consist of CpG sites spaced about 200 to about 5,000, about 200 to about 4,000, about 200 to about 3,000, about 200 to about 2,000, or about 200 to about 1,000 bases apart.

[0247] Suitablely, a DNA region may contain one or more CpG islands. Suitablely, a DNA region may consist of CpG islands.

[0248] “CpG islands” can refer to DNA regions containing at least 200 bp, a GC percentage greater than 50%, and an observed CpG ratio greater than 60% of the expected CpG.

[0249] Suitable, the DNA methylation site does not contain X and / or Y chromosome CpG.

[0250] Suitable, the DNA methylation site does not contain CpGs that are known to contain SNPs at the CpG site.

[0251] References to each gene / DNA region detailed above should be understood as references to all forms of these molecules and their fragments or variants. As will be understood by those skilled in the art, some genes are known to exhibit allelic variations or single nucleotide polymorphisms among individuals. Variants include nucleic acid sequences from the same region sharing at least 90%, 95%, 98%, or 99% sequence identity, i.e., having one or more deletions, additions, substitutions, reverse sequences, etc., relative to the DNA region described herein. Therefore, the invention should be understood to extend to such variants, which, for the purposes of this application, can achieve the same results despite minor genetic variations in the actual nucleic acid sequences between individuals. Thus, the invention should be understood to extend to all forms of DNA resulting from any other mutation, polymorphism, or allelic variation.

[0252] Regarding the screening of methylation in these gene regions, it should be understood that the assay can be designed to screen specific DNA. Selecting which strand to analyze and targeting based on chromosome coordinates is entirely within the skill of those skilled in the art. In some cases, assays can be developed to screen two strands.

[0253] "Methylation state" can be understood as the presence, absence, and / or amount of methylation at one or more specific nucleotides within a DNA region. The methylation state of a particular DNA sequence (e.g., a DNA region as described herein) may indicate the methylation state of each base in the sequence, or the methylation state of a subset of base pairs within the sequence (e.g., the methylation state of cytosine or the methylation state of one or more specific restriction enzyme recognition sequences), or it may indicate information about the regional methylation density within the sequence without providing precise information about where methylation occurs within the sequence. Methylation state may optionally be represented or indicated by a "methylation value."

[0254] Suitablely, an EM-Seq strategy can be used to determine DNA methylation. In this method, the methylation level can be determined as the fraction of "C" bases in the total "C"+"U" bases at the target CpG site "i" after enzyme and APOBEC3A transformation treatment. In other embodiments, the methylation level can be determined as the fraction of "C" bases in the total "C"+"T" bases at site "i" after enzyme and APOBEC3A transformation treatment and subsequent nucleotide amplification. The average methylation level at each site can then be evaluated to determine whether one or more thresholds are met.

[0255] In some implementations, particularly when using bisulfite conversion and sequencing methods, the methylation level can be determined as the fraction of "C" bases in the total "C"+"U" bases at the target CpG site "i" after bisulfite treatment. In other implementations, the methylation level can be determined as the fraction of "C" bases in the total "C"+"T" bases at site "i" after bisulfite treatment and subsequent nucleotide amplification. The average methylation level at each site can then be evaluated to determine whether one or more thresholds are met.

[0256] Alternatively, methylation values ​​can be generated, for example, by quantifying the amount of intact DNA present after restriction digestion with a methylation-dependent restriction enzyme. In this example, if a specific sequence in the DNA is quantified using quantitative PCR, an amount of template DNA approximately equal to that of the simulated treatment control indicates that the sequence is not highly methylated, while a amount substantially less than that of the template present in the simulated treatment sample indicates the presence of methylated DNA at that sequence. Therefore, values ​​from the example above (i.e., methylation values) represent the methylation status and can thus be used as a quantitative indicator of methylation status. This is particularly useful when it is necessary to compare the methylation status of a sequence in a sample with a threshold.

[0257] This invention is not limited to the exact number of methylated residues considered to indicate biological age, as some variation will occur between samples. This invention is also not necessarily limited to the location of methylated residues (e.g., specific methylation sites).

[0258] In one implementation, a screening method may be employed that is specifically designed to assess the methylation status of one or more specific cytosine residues or the corresponding cytosine at position n+1 on the DNA strand.

[0259] Enrichment and Detection Methods

[0260] Determining a DNA methylation profile may include the step of enriching selected DNA regions in a DNA sample. For example, the method may include the step of enriching DNA regions in a DNA sample that contain DNA methylation sites, which constitute a DNA methylation profile.

[0261] Suitable enrichment methods are known in the art and include, for example, amplification-based or hybridization-based methods. Amplification enrichment typically refers to PCR-based enrichment, for example, using primers targeted at the DNA region to be enriched. Any suitable form of amplification can be used, such as polymerase chain reaction (PCR), rolling circle amplification (RCA), reverse polymerase chain reaction (iPCR), in situ PCR, strand displacement amplification, or cycling probe techniques.

[0262] Hybridization enrichment, or capture-based enrichment, typically refers to the use of hybridization probes (or capture probes) that hybridize with the DNA region to be enriched.

[0263] Hybridization probes can be directly attached to a solid vector, or they can contain a portion, such as biotin, to allow binding to a solid vector (e.g., beads coated with streptavidin) suitable for capturing the biotin portion. In either case, DNA containing a sequence complementary to the probe can be captured, allowing the separation of DNA containing the region of interest from DNA not containing that region. Therefore, such a capture step allows for the enrichment of the region of interest. For example, the DNA region could be a region adjacent to a gene promoter.

[0264] The arrays used in this article can vary depending on the probe composition and the intended use of the array. For example, the number of nucleic acids (or CpG sites) detected in the array can be at least 10, 100, 1,000, 10,000, 100,000, 1 million, 10 million, 100 million, or more. Alternatively or additionally, the number of nucleic acids (or CpG sites) detected can be selected to be no more than 100 million, 10 million, 1 million, 100,000, 10,000, 1,000, 100, or fewer. Similar ranges can be obtained using nucleic acid sequencing methods, such as those known in the art; for example, next-generation or massively parallel sequencing.

[0265] Appropriately, the enrichment step can be performed before or after the step of separating or differentiating methylated and unmethylated DNA.

[0266] As used herein, the term "enrichment" or "DNA" or "DNA region" refers to the process of increasing the (absolute) amount and / or proportion of DNA containing the desired sequence compared to the amount and / or proportion of DNA containing the desired sequence in the starting material. In this respect, enrichment by amplification increases the amount and proportion of the desired sequence. Enrichment by capture-based enrichment increases the proportion of DNA containing the desired sequence.

[0267] After processing the DNA to distinguish between methylated and unmethylated sites, this method may also include a step of identifying methylated or unmethylated sites (i.e., in the original sample).

[0268] The identification steps may include any suitable method known in the art, such as array detection or sequencing (e.g., next-generation sequencing).

[0269] The sequencing qualification step preferably includes next-generation sequencing (massively parallel or high-throughput sequencing). Next-generation sequencing methods are well known in the art, and in principle, any method can be considered for use in this invention. Next-generation sequencing technology can be performed according to the manufacturer's instructions (e.g., provided by Roche, Illumina, Applied Biosystems, PacBio, Oxford Nanopore, or MGI).

[0270] In a preferred embodiment, the sample is processed by using an enzymatic reaction to convert DNA methylation, preparing a whole-genome library, and measuring the methylation profile by sequencing (EM-Seq).

[0271] In a particularly preferred embodiment, the sample is processed by using an enzymatic reaction to convert DNA to methylation, preparing a whole-genome library, hybridizing the converted library with a capture probe (preferably a capture probe capable of capturing DNA regions near gene promoters), and measuring the methylation profile by sequencing (EM-Seq).

[0272] DNA methylation map

[0273] A “DNA methylation profile” or “methylation map” can refer to the presence, absence, amount, or level of 5mC at one or more DNA methylation sites. Preferably, a “methylation profile” refers to the presence, absence, amount, or level of 5mC at multiple DNA methylation sites. Therefore, the presence, absence, amount, or level of 5mC at each individual DNA methylation site within multiple sites can be assessed, and this helps determine the biological age, mortality risk, and / or probability of healthy lifespan in dogs. Therefore, the quality and / or efficacy of this method can be improved by combining values ​​from multiple DNA methylation markers.

[0274] Suitablely, the biological clock of the present invention includes methylation maps from multiple methylation sites.

[0275] Suitablely, the presence or absence of 5mC from at least 3, at least 5, at least 10, at least 20, at least 50, at least 100, at least 200, at least 500, at least 1000, at least 2000, at least 5000, at least 10000, at least 50000, at least 10000, at least 250000, or at least 500000 DNA methylation sites can be used to determine the dog’s biological age, risk of death, and / or probability of healthy lifespan.

[0276] Suitablely, a methylation profile can refer to the presence or absence of 5mC from at least 100, at least 200, at least 500, at least 1000, or at least 2000 DNA methylation sites.

[0277] Appropriately, a methylation profile can refer to the presence or absence of 5mC from approximately 100, 200, 500, 1000, or 2000 DNA methylation sites.

[0278] To generate epigenetic clocks (e.g., for determining biological age, risk of death, and / or probability of healthy lifespan), an initial methylation map can be processed or simplified to produce a restriction methylation map, which can then be used to generate a biological clock.

[0279] For example, the initial methylation map can be processed or simplified by, for instance, using DNA regions instead of individual cytosines, selecting a subset of methylation sites associated with specific physiological or biochemical pathways, performing correlation analysis and retaining one or more representative DNA methylation sites for each cluster, or performing differential analysis to pre-select DNA methylation sites or retain DNA methylation sites that vary more between young and older dogs.

[0280] For example, a DNA region can be any DNA region as defined herein.

[0281] Suitablely, a methylation map can point to DNA methylation sites of genes associated with specific physiological or biochemical pathways. Therefore, a methylation map can enable the determination of the biological age of a specific tissue, organ, or physiological system. Determining the biological age of a specific tissue, organ, or physiological system advantageously allows the method to be used in a manner focused on the pathology and disease of that tissue, organ, or physiological system. For example, if a particular breed of dog is known to be associated with muscular or cardiovascular disease, determining the biological age of that physiological system may be advantageous.

[0282] Appropriately, the physiological system can be the inflammatory system, the muscular system, the cardiovascular system, and / or the nervous system.

[0283] The biological age of a specific tissue, organ, or physiological system can be determined using a DNA methylation map, which contains or is composed of methylation sites of genes preferentially or specifically expressed from that tissue, organ, or physiological system. Gene classification by specific tissue, organ, or physiological system is publicly available at, for example, Gene Ontology (http: / / geneontology.org / ), the KEGG pathway database (https: / / www.genome.jp / kegg / ), or MSIgDB (https: / / www.gsea-msigdb.org / gsea / msigdb / index.jsp).

[0284] In some implementations, the threshold is selected for those sites that have the highest average methylation values ​​for epigenetic age predictors. For example, the threshold could be those sites with the highest average methylation levels, which are the top 50%, top 40%, top 30%, top 20%, top 10%, top 5%, top 4%, top 3%, top 2%, or top 1% of the average methylation levels of all sites “i” tested for predictors such as biological clocks.

[0285] Alternatively, the threshold may be those sites where the average methylation level is at or above the percentile of 50, 60, 70, 80, 90, 95, 96, 97, 98, or 99. In other embodiments, the threshold may be based on the absolute value of the average methylation level. For example, the threshold may be those sites where the average methylation level is greater than 99%, greater than 98%, greater than 97%, greater than 96%, greater than 95%, greater than 90%, greater than 80%, greater than 70%, greater than 60%, greater than 50%, greater than 40%, greater than 30%, greater than 20%, greater than 10%, greater than 9%, greater than 8%, greater than 7%, greater than 6%, greater than 5%, greater than 4%, greater than 3%, or greater than 2%. Relative and absolute thresholds may be applied alone or in combination to the average methylation level at each site “i”. As an illustration of the combined threshold application, a subset of sites may be selected from the top 3% of all sites that pass the average methylation level test and also have an absolute average methylation level greater than 6%. The result of this selection process is a DNA methylation profile of a specific hypermethylation site (e.g., a CpG site), which is considered to be the most informative for determining biological age, risk of death, and / or probability of healthy lifespan.

[0286] Suitable, the DNA methylation profile may include at least one methylation site as listed in Table 3. Table 3 provides exemplary epigenetic clocks for determining a dog's biological age, risk of death, and / or probability of healthy lifespan. This may be referred to as a "second representative epigenetic clock".

[0287] Suitable, the DNA methylation map may include at least one methylation site as listed in Table 8. Table 8 provides exemplary epigenetic clocks for determining the biological age of dogs. This may be referred to as the “first representative epigenetic clock”.

[0288] Suitablely, a methylation site can be defined as a methylation marker present in any one or more of SEQ ID NO: 1-517. SEQ ID NO: 1-517 shows the sequence on either side of the methylation marker in the “Tasha” canine reference genome (https: / / www.ncbi.nlm.nih.gov / assembly / GCF_000002285.5; Jagannathan et al.; Genes (Bsael); 2021; 12(6); 847). The “CG” methylation marker is the 26th and 27th nucleotides in the sequence (i.e., 25 nucleotides before and 25 nucleotides after the methylation marker).

[0289] Suitablely, methylation sites can be defined as the insertion positions in the columns marked "Sites" in Table 3. For example, for sites chr1:3844418-3844420, the methylation marker is chr1:3844419. Suitablely, the DNA methylation map may contain at least 3, at least 5, at least 10, at least 20, at least 50, at least 100, at least 150, at least 200, or preferably each of the methylation sites listed in Table 3.

[0290] Suitable, a DNA methylation map may include methylation sites chr2.32494387.32494389; chr22.46374563.46374565; and chr6.45773846.45773848. These sites are shown in Table 4.

[0291] Suitable, the DNA methylation map may include methylation sites chr2.32494387.32494389; chr22.46374563.46374565; chr6.45773846.45773848; chr5.61645225.61645227; and chr3.70831746.70831748. These sites are shown in Table 5.

[0292] Suitablely, a DNA methylation map may include methylation sites chr2.32494387.32494389; chr22.46374563.46374565; chr6.45773846.45773848; chr5.61645225.61645227; chr3.70831746.70831748; chr15.10856498.10856500; chr16.8886545.8886547; chr3.46843750.46843752; chr20.44942213.44942215; and chr20.57347921.57347923. These sites are shown in Table 6.

[0293] Suitablely, a DNA methylation map may include methylation sites such as chr2.32494387.32494389; chr22.46374563.46374565; chr6.45773846.45773848; chr5.61645225.61645227; chr3.70831746.70831748; chr15.10856498.10856500; chr16.8886545.8886547; chr3.46843750.46843752; chr20.44942213.44942215; chr20.57347921.5 7347923; chr24.21051024.21051026; chr33.26512711.26512713; chr23.37987526.37987528; chr5.32347978.32347980; chr6.60675998.60676000; chr19.44679975.44679977; chr10.7411293.7411295; chr33.26512692.26512694; chr1.121182146.121182148; and chr17.62156690.62156692. These loci are shown in Table 7.

[0294] Suitablely, a DNA methylation map may include methylation sites: chr2.32494387.32494389; chr22.46374563.46374565; chr6.45773846.45773848; chr5.61645225.61645227; chr3.70831746.70831748; chr15.10856498.10856500; chr16.8886545.8886547; chr3.46843750.46843752; chr20.44942213.44942215; chr20.57347921.57347 923; chr24.21051024.21051026; chr33.26512711.26512713; chr23.3798 7526.37987528;chr5.32347978.32347980;chr6.60675998.60676000;chr 19.44679975.44679977; chr10.7411293.7411295; chr33.26512692.2651 2694; chr1.121182146.121182148; chr17.62156690.62156692; chr35.238 53863.23853865;chr17.49222883.49222885;chr14.5949013.5949015;c hr5.67427228.67427230; chr20.43366718.43366720; chr24.12518910.12 518912; chr29.19479611.19479613; chr4.36180013.36180015; chr35.23 852932.23852934;chr1.58324050.58324052;chr9.12522577.12522579;c hr5.32711703.32711705; chr33.23473772.23473774; chr2.51831832.51 831834; chr20.46098568.46098570; chr11.24476652.24476654; chr1.100 289525.100289527; chr17.33851462.33851464; chr6.12827818.1282782 0; chr20.54301453.54301455; chr1.19541665.19541667; chr5.55848493.55848495; chr10.44524681.44524683; chr9.50976682.50976684; chr33.26512695.26512697; chr24.24968751.24968753; chr10.18237423.18237425; chr35.23853763.23853765; chr9.1046331.1046333; and chr20.49940551.49940553.

[0295] Suitablely, methylation sites can be defined as insertion positions in the columns marked "Sites" in Table 8. For example, for sites chr1:32205238-32205240, the methylation marker is chr1:32205239. Suitablely, the DNA methylation map can contain at least 3, at least 5, at least 10, at least 20, at least 50, at least 100, at least 150, at least 200, or preferably each of the methylation sites listed in Table 8.

[0296] Suitable, a DNA methylation map may include methylation sites chr2.32494387.32494389; chr6.45773846.45773848; and chr16.8886545.8886547. These sites are shown in Table 9.

[0297] Suitable, a DNA methylation map may include methylation sites chr2.32494387.32494389; chr6.45773846.45773848; chr16.8886545.8886547; chr5.61645225.61645227; and chr15.10856498.10856500. These sites are shown in Table 10.

[0298] Suitablely, a DNA methylation map may include methylation sites chr2.32494387.32494389; chr6.45773846.45773848; chr16.8886545.8886547; chr5.61645225.61645227; chr15.10856498.10856500; chr33.26512711.26512713; chr20.57347921.57347923; chr12.12684190.12684192; chr24.30860619.30860621; and chr33.26512692.26512694. These sites are shown in Table 11.

[0299] Suitablely, a DNA methylation map may include methylation sites chr2.32494387.32494389; chr6.45773846.45773848; chr16.8886545.8886547; chr5.61645225.61645227; chr15.10856498.10856500; chr33.26512711.26512713; chr20.57347921.57347923; chr12.12684190.12684192; chr24.30860619.30860621; chr33.26512692 .26512694; chr10.7411293.7411295; chr1.22213697.22213699; chr5.32711703.32711705; chr24.21051024.21051026; chr5.21480151.21480153; chr17.33851462.33851464; chr3.46843750.46843752; chr13.37474592.37474594; chr33.19810904.19810906; and chr20.44942213.44942215. These loci are shown in Table 12.

[0300] Suitablely, a DNA methylation map may include methylation sites chr2.32494387.32494389; chr6.45773846.45773848; chr16.8886545.8886547; chr5.61645225.61645227; chr15.10856498.10856500; chr33.26512711.26512713; chr20.57347921.57347923; chr12.12684190.12684192; chr24.30860619.30860621; chr33.26512692.265 12694; chr10.7411293.7411295; chr1.22213697.22213699; chr5.327117 03.32711705; chr24.21051024.21051026; chr5.21480151.21480153; chr1 7.33851462.33851464; chr3.46843750.46843752; chr13.37474592.3747 4594; chr33.19810904.19810906; chr20.44942213.44942215; chr20.5430 1453.54301455;chr35.23852932.23852934;chr6.60675998.60676000;c hr22.46374563.46374565; chr24.12518910.12518912; chr16.8886356.88 86358; chr21.43490796.43490798; chr11.53576970.53576972; chr4.361 80013.36180015;chr4.43217432.43217434;chr9.35985983.35985985;ch r32.40409729.40409731; chr23.37987526.37987528; chr18.44679236.4 4679238; chr21.16233628.16233630; chr10.18237423.18237425; chr30.8 031060.8031062;chr1.121182146.121182148;chr9.50976682.50976684 ; chr14.58424689.58424691; chr20.49940551.49940553; chr2.31655107.31655109; chr18.46990963.46990965; chr20.46098568.46098570; chr10.68649436.68649438; chr9.25415871.25415873; chr14.39735273.39735275; chr11.33523800.33523802; chr5.32347978.32347980; and chr19.44679975.44679977.

[0301] DNA methylation sites / DNA methylation maps indicating biological age, risk of death, and / or probability of healthy lifespan The determination

[0302] This invention includes using DNA methylation mapping to determine the biological age, mortality risk, and / or probability of healthy lifespan in dogs. Therefore, this invention includes using DNA methylation mapping to generate a biological clock associated with biological age, mortality risk, and / or probability of healthy lifespan. The biological clock of this invention may also be referred to as an "epigenetic clock."

[0303] For example, DNA methylation sites or DNA methylation maps indicating biological age, risk of death, and / or probability of healthy lifespan can be provided through training datasets and machine learning methods. Suitablely, the machine learning method can be a supervised machine learning method.

[0304] For example, DNA methylation sites or DNA methylation maps can be trained on a dataset of dogs that have a known chronological age or mortality outcome (survival or death) and chronological age. Suitablely, DNA methylation sites or DNA methylation maps can be trained on a dataset of dogs that have a known (i) chronological age or (ii) mortality outcome and a combination of chronological age and known breed and / or sex.

[0305] For example, a model of DNA methylation sites or DNA methylation maps that indicates mortality risk and / or the probability of healthy lifespan can be provided by training a dataset of methylation states at multiple DNA methylation sites using a machine learning framework on a training dataset of dogs with known mortality outcomes (survival or death) and chronological age, and then testing the model against a retained cohort to validate its accuracy. This type of epigenetic clock can be referred to as a "second-representation epigenetic clock."

[0306] Machine learning frameworks may include, for example, using the glmnet R package to fit a penalty model to a training dataset of dogs with known mortality outcomes (survival or death) and actual age (and optional breed and / or sex).

[0307] Appropriately, the penalty model can be, for example, a penalty Cox regression, a minimum angle regression path (LARS) Cox regression, or a penalty survival model.

[0308] Machine learning frameworks may include, for example, using the glmnet R package to fit penalized Cox regression to a training dataset of dogs with known mortality outcomes (survival or death) and actual age (and optional breed and / or sex).

[0309] Suitable, the machine learning framework may include a penalized model, preferably penalized Cox regression, that fits known mortality outcomes (survival or death) / survival rates as interpreted by DNA methylation profiles and full age (and optionally breed and / or sex).

[0310] Suitable, the machine learning framework may include a penalized model, preferably penalized Cox regression, that fits known mortality outcomes (survival or death) / survival rates as interpreted by DNA methylation profiles, full age, breed, and sex.

[0311] As used in this article, "known mortality outcome (survival or death)" can also be referred to as "survival rate".

[0312] Appropriately, machine learning frameworks can be used to determine models that include a set of DNA methylation sites or DNA methylation maps that indicate biological age, risk of death, and / or probability of healthy lifespan.

[0313] The model may include the methylation state at multiple DNA methylation sites; where the methylation state at each site is considered by multiplying by a coefficient value in the model.

[0314] Appropriately, sex can be encoded as a numerical value, where 0 represents female and 1 represents male.

[0315] Appropriately, breed can be encoded as a numerical value, where 0 represents a small breed and 1 represents a medium breed.

[0316] A dog's biological age can be expressed in years, months, days, etc.

[0317] The coefficient value of each parameter typically depends on the unit of measurement of all variables in the model. As a technician will understand, the value of each coefficient will therefore depend on, for example, the number and nature of the different parameters used in the model, as well as the nature of the training data provided. Therefore, conventional statistical methods can be applied to the training dataset to obtain the coefficient values.

[0318] This approach involves, for example, computing two gompertz or weibull functions on the training set (e.g., given the dog's state (survival or death)). One function models survival as a function of methylation profile, actual age, breed type (small or medium-sized), and sex (Model 1), while the second function considers only actual age, breed type, and sex (Model 2). These models can be fitted using the flexsurv package (v 2.1) in the R software environment.

[0319] Biological age can be defined as a time variable (“actual age”), under which the survival probability of an animal given by Model 2 is equal to the survival probability given by Model 1 at its actual age.

[0320] A model for DNA methylation sites or DNA methylation maps that indicates biological age, mortality risk, and / or the probability of healthy life can be provided by training a dataset of methylation states at multiple DNA methylation sites against a dataset of phenotypic ages predicted at the age of DNA sample collection, and testing against a retained cohort to validate the model's accuracy.

[0321] This document describes a method for determining the phenotypic age of dogs. Specifically, phenotypic age is calculated in step (a) of this method. A method for determining the phenotypic age of cats is described in PCT / EP2023 / 061059. The calculation of phenotypic age takes into account direct predictive values ​​of blood biomarkers for mortality risk and / or the probability of healthy lifespan. For example, a given biomarker may not be directly related to chronological age but may indicate a specific pathological condition, thereby indicating an increased risk of mortality and / or a reduced probability of healthy lifespan.

[0322] Appropriately, models indicating DNA methylation sites or DNA methylation maps that indicate mortality risk and / or healthy lifespan probability trained against phenotypic age can be provided in a two-step process.

[0323] In the first step, the machine learning framework may include, for example, using the glmnet R package to fit a penalized model of phenotypic age (PhenoAge) explained by one or more blood biomarkers as described herein and chronological age (and optionally sex and / or breed). Preferably, the machine learning framework may include a penalized model of phenotypic age (PhenoAge) explained by one or more blood biomarkers as described herein, chronological age, sex, and breed.

[0324] Appropriately, the penalty model can be, for example, a penalty Cox regression, a minimum angle regression path (LARS) Cox regression, or a penalty survival model.

[0325] Machine learning frameworks may include penalized Cox regressions that fit phenotypic age (PhenoAge) as explained by one or more blood biomarkers, full-term age, sex, and breed as described herein.

[0326] In the second step, the machine learning framework may include a penalized regression that fits the phenotypic age interpreted from DNA methylation. Suitablely, the machine learning framework may include a penalized regression that fits the phenotypic age interpreted from DNA methylation patterns.

[0327] Penalized regression can be elastic network regression.

[0328] As used herein, the term "one or more biomarkers" may include at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, or at least thirteen biomarkers.

[0329] As used herein, the term "one or more biomarkers" may include one, two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, or thirteen biomarkers.

[0330] Suitablely, DNA methylation sites or DNA methylation profiles can be combined with the levels of one or more blood biomarkers described herein to generate models that indicate mortality risk and / or the probability of healthy lifespan. For example, a model that includes a combination of the DNA methylation profiles described herein and the levels of one or more blood biomarkers can be provided by training a dataset of methylation states at multiple DNA methylation sites and the levels of one or more blood biomarkers on a training dataset of dogs with known mortality outcomes (survival or death) and full-term ages, and testing the model against a retained cohort to validate its accuracy.

[0331] Machine learning frameworks may include, for example, using the glmnet R package to fit penalized regression to a training dataset of dogs with known mortality outcomes (survival or death) and actual age (and optional breed and / or sex).

[0332] Machine learning frameworks may include, for example, using the glmnet R package to fit penalized Cox regression to a training dataset of dogs with known mortality outcomes (survival or death) and actual age (and optional breed and / or sex).

[0333] A model indicating biological age, or a DNA methylation map, can be provided by training a dataset of methylation states at multiple DNA methylation sites using a machine learning framework on a training dataset of dogs with known chronological ages, and then testing the model against a retained cohort to validate its accuracy. This type of epigenetic clock can be referred to as a "first-representation epigenetic clock."

[0334] Machine learning frameworks may include, for example, using the glmnet R package to fit penalized regression to a training dataset of dogs with known paw ages (and optional breeds and / or sexes).

[0335] Machine learning frameworks may include, for example, using the glmnet R package to fit resilient network regressions to a training dataset of dogs with known ages (and optional breeds and / or sexes).

[0336] Suitable machine learning frameworks may include penalized regressions, such as resilient network regressions, that fit the actual age as interpreted by DNA methylation profiles (and optionally breed, age, and / or sex).

[0337] Appropriately, machine learning frameworks may include penalized regressions, such as resilient network regressions, that fit the actual age as interpreted by DNA methylation profiles, breed, age, and sex.

[0338] Appropriately, machine learning frameworks can be used to determine models that include a set of DNA methylation sites or DNA methylation maps that indicate biological age.

[0339] The model may include the methylation state at multiple DNA methylation sites; where the methylation state at each site is considered by multiplying by a coefficient value in the model.

[0340] The coefficient value of each parameter typically depends on the unit of measurement of all variables in the model. As a technician will understand, the value of each coefficient will therefore depend on, for example, the number and nature of the different parameters used in the model, as well as the nature of the training data provided. Therefore, conventional statistical methods can be applied to the training dataset to obtain the coefficient values.

[0341] Appropriately, sex can be encoded as a numerical value, where 0 represents female and 1 represents male.

[0342] Appropriately, breed can be encoded as a numerical value, where 0 represents a small breed and 1 represents a medium breed.

[0343] Appropriately, machine learning platforms may include one or more deep neural networks. A neural network is a collection of neurons (also called units) connected in a non-cyclic graph. Neural network models are typically organized into different layers of neurons. For most neural networks, the most common layer type is the fully connected layer, where neurons in two adjacent layers are fully paired, but neurons within a single layer do not share connections. One of the main characteristics of deep neural networks is that neurons are controlled by non-linear activation functions. This non-linearity, combined with the deep architecture, enables more complex combinations of input features, ultimately leading to a broader understanding of the relationships between them, and thus a more reliable final output. Deep neural networks have been applied to many types of data, ranging from structural data to chemical descriptors or transcriptomics data.

[0344] Suitablely, the machine learning platform includes one or more generative adversarial networks. Suitablely, the machine learning platform includes an adversarial autoencoder architecture. Suitablely, the machine learning platform includes feature importance analysis for ranking DNA methylation sites by their importance in biological age determination.

[0345] A dog's biological age can be expressed in years, months, days, etc.

[0346] Preferably, biological age, mortality risk, and / or probability of healthy lifespan are expressed as the difference between the dog's biological age and its actual age.

[0347] Compare with reference or control

[0348] This method may also include the step of comparing differences in biomarkers and / or DNA methylation at one or more sites in the test sample with one or more references or controls. The levels of biomarkers and / or the presence or absence of DNA methylation at one or more sites in the reference or control may be associated with biological age or a predefined risk of death and / or probability of healthy lifespan. In some embodiments, the reference value is a value previously obtained for subjects or subject groups with a known biological age or risk of death and / or probability of healthy lifespan. The reference value may be based on known biomarker levels or known DNA methylation status at one or more sites associated with a given biological age or death status (survival or death), actual age, breed, and / or sex from subject groups with a known biological age or death status (survival or death), actual age, breed, and / or sex, such as average or median levels.

[0349] Combine biomarker levels with other measures and / or characteristics

[0350] Suitable, this method also includes combining the DNA methylation profile with one or more of the dog's actual age, breed, and / or sex.

[0351] Subject classification

[0352] The biological age determined by the method of the present invention can also be compared with one or more predetermined thresholds (e.g., the difference from chronological age). Using such thresholds, subjects can be stratified into categories indicating a defined risk (e.g., low, medium, or high defined risk). The degree of divergence from the thresholds can be used to determine which individuals will benefit most from certain interventions. In this way, dietary interventions and lifestyle changes can be optimized.

[0353] Methods for selecting / monitoring lifestyle programs, dietary plans, or therapeutic interventions for subjects.

[0354] In another aspect, the present invention provides methods for selecting lifestyle programs, dietary programs, or therapeutic interventions for subjects. Lifestyle changes can be any changes described herein, such as changes in dietary interventions and / or exercise programs. Lifestyle changes can also include the application of therapeutic methods.

[0355] Lifestyle programs, dietary programs, or therapeutic interventions can be applied to dogs at any suitable time period. After the stated time period, this method can be used to re-determine the dog's biological age, mortality risk, and / or healthy life expectancy probability to determine the efficacy of the lifestyle program, dietary program, or therapeutic intervention in reducing the dog's mortality risk and / or increasing its healthy life expectancy probability. For example, lifestyle programs, dietary programs, or therapeutic interventions can be applied for at least 2 weeks, at least 4 weeks, at least 8 weeks, at least 16 weeks, at least 32 weeks, or at least 64 weeks. Lifestyle programs, dietary programs, or therapeutic interventions can be applied for at least 3 months, at least 6 months, at least 12 months, at least 24 months, at least 36 months, at least 48 months, or at least 60 months.

[0356] Lifestyle programs, dietary programs, or therapeutic interventions can be referred to as anti-aging lifestyle programs, dietary programs, or therapeutic interventions.

[0357] Preferably, the change is a dietary intervention as described herein. The term "dietary intervention" refers to an external factor applied to a subject and causing a change in the subject's diet. More preferably, the dietary intervention includes the administration of at least one dietary product, dietary protocol, or nutritional supplement.

[0358] Dietary interventions can be diets, dietary plans, supplements, or supplement plans, or a combination of diets and supplements, or a combination of diets and multiple supplements.

[0359] The dietary interventions or dietary products described herein can be any suitable dietary program, such as calorie-restricted diets, diets for the elderly, low-protein diets, phosphorus diets, low-protein diets, potassium-supplemented diets, polyunsaturated fatty acid (PUFA) supplemented diets, antioxidant supplemented diets, vitamin B supplemented diets, liquid diets, selenium-supplemented diets, omega-3-6 ratio diets, or diets supplemented with carnitine, branched-chain amino acids or derivatives, nucleotides, nicotinamide precursors (such as nicotinamide mononucleotide (MNM) or nicotinamide nucleoside (NR)), or any combination thereof.

[0360] Appropriately, dietary interventions or dietary products may be calorie-restricted diets, diets for older adults, or low-protein diets. Appropriately, dietary interventions or dietary products may be calorie-restricted diets. Appropriately, dietary interventions or dietary products may be low-protein diets.

[0361] Dietary interventions can be determined based on the dog's baseline maintenance energy requirement (MER). Appropriately, MER can be the amount of food required to maintain the dog's stable weight (a change of less than 5% over three weeks).

[0362] For example, young, growing dogs are generally considered to benefit from a high-energy / high-protein diet; however, older dogs may have lower energy requirements, and therefore their diets can be adjusted accordingly. In particular, many manufacturers produce "senior" dog foods that are lower in calories and higher in fiber, but with protein and fat levels suitable for older dogs.

[0363] Appropriately, a calorie-restricted diet may comprise approximately 50%, 55%, 60%, 65%, 75%, 80%, 85%, or 90% of a dog's MER. Appropriately, a calorie-restricted diet may comprise approximately 60% or 75% of a dog's MER.

[0364] Appropriately, a low-protein diet may contain less than 20% protein (dry matter %). For example, a low-protein diet may contain less than 19% protein (dry matter %).

[0365] These diets are typically recommended based on the dog's chronological age. For example, it might be recommended to switch the dog to a senior diet around 7 or 8 years of age. However, in the context of this invention, determining an increased risk of death for the dog compared to what would be expected at a given chronological age allows for the decision to switch the dog to a senior diet at an earlier age. In contrast, dogs with a lower risk of death compared to their chronological age may be able to continue on an adult diet for a longer period.

[0366] Dietary interventions may include foods, supplements, and / or beverages that contain nutrients and / or bioactive agents that mimic the benefits of calorie restriction (CR) without restricting daily calorie intake. For example, foods, supplements, and / or beverages may contain functional ingredients with similar CR benefits. Suitably, foods, supplements, and / or beverages may contain autophagy inducers. Suitably, foods, supplements, and / or beverages may contain fruits and / or nuts (or extracts thereof).

[0367] Suitable examples include, but are not limited to, pomegranate, strawberry, blackberry, camu camu, walnut, chestnut, pistachio, and pecan. Appropriately, food products, supplements, and / or beverages may contain probiotics, with or without fruit or nut extracts.

[0368] Modifying a subject's lifestyle also includes instructing the subject to make lifestyle changes, such as prescribing more exercise. Similar to dietary interventions, determining an increased risk of death in dogs compared to what is expected at a given apex age can allow for the identification of appropriate exercise programs for the dogs.

[0369] Modifying a subject's lifestyle also includes selecting or recommending treatment methods or protocols. These treatment methods or protocols can be used to treat and / or prevent conditions such as arthritis, dental diseases, endocrine disorders, heart disease, diabetes, liver disease, kidney disease, prostate disorders, cancer, and behavioral or cognitive impairments. Suitablely, preventative treatment may be administered to dogs identified as being at risk for such conditions due to an increased risk of death and / or based on specific biomarkers known to be associated with disease-related pathways. In other implementations, dogs identified as being at risk for certain conditions (due to an increased risk of death and / or based on specific biomarkers known to be associated with disease-related pathways) may be monitored more regularly so that diagnosis and treatment can be initiated as early as possible.

[0370] This invention also relates to monitoring and / or determining the efficacy of anti-aging therapies or developing anti-aging therapies. Anti-aging therapies may include, for example, "rejuvenation" interventions. Rejuvenation interventions are designed to induce a reduction in the epigenetic or biological age of a subject. Suitably, a rejuvenation intervention may reprogram the epigenetic age to that of a young dog or a very young dog. Examples of such rejuvenation interventions include, but are not limited to, gene therapies that suitably reprogram the epigenetic age to that of a young dog or a very young dog. This method is particularly suitable for monitoring and / or determining the efficacy of lifestyle programs, dietary programs, or therapeutic interventions or developing lifestyle programs, dietary programs, or therapeutic interventions to reduce biological age.

[0371] Therefore, the present invention can advantageously identify dogs that are expected to respond particularly well to a given intervention (e.g., a lifestyle program, dietary program, or therapeutic intervention). Thus, the intervention can be applied in a more targeted manner to dogs that are expected to respond.

[0372] In one aspect, the present invention provides a method for determining the efficacy of a lifestyle program, dietary program, or therapeutic intervention in improving the biological age, mortality risk, and / or healthy lifespan probability of a dog, the method comprising: i) applying the lifestyle program, dietary program, or therapeutic intervention to a dog, optionally wherein the lifestyle program, dietary program, or therapeutic intervention has been selected according to the present invention; ii) after applying the lifestyle program, dietary program, or therapeutic intervention to the dog for a period of time; determining the dog's biological age, mortality risk, and / or healthy lifespan probability according to the method of the present invention; iii) after following the lifestyle program, dietary program, or therapeutic intervention for the period of time, determining whether there has been a change in the dog's biological age, mortality risk, and / or healthy lifespan probability.

[0373] The present invention further provides a method for determining the efficacy of a lifestyle program, dietary program, or therapeutic intervention in improving the biological age, mortality risk, and / or healthy lifespan probability of a dog, the method comprising: i) determining the dog's biological age, mortality risk, and / or healthy lifespan probability according to the method of the present invention; ii) applying a lifestyle program, dietary program, or therapeutic intervention selected based on the biological age, mortality risk, and / or healthy lifespan probability determined in step i) to the dog; iii) after applying the lifestyle program, dietary program, or therapeutic intervention to the dog for a period of time; determining the dog's biological age, mortality risk, and / or healthy lifespan probability according to the method of the present invention; iv) determining whether there is a change in the dog's biological age, mortality risk, and / or healthy lifespan probability between steps i) and iii).

[0374] Appropriately, a lifestyle program, dietary program, or therapeutic intervention may have been applied to a dog for a period of time before determining the initial biological age, risk of death, and / or probability of healthy life; however, the effectiveness of the lifestyle program, dietary program, or therapeutic intervention in improving the dog's biological age, risk of death, and / or probability of healthy life (i.e., reducing the risk of death and / or increasing the probability of healthy life) can still be monitored by determining the biological age, risk of death, and / or probability of healthy life at two or more times during the application of the lifestyle program, dietary program, or therapeutic intervention.

[0375] Suitablely, this method may include “ecosystems”; particularly digital ecosystems. Suitablely, this method may include providing a sample obtained from a dog, optionally using a kit according to the invention; and providing samples (e.g., by mail) for subsequent biomarker and DNA extraction to measure DNA methylation in the biomarkers as described herein and the extracted DNA from the sample, thereby obtaining a DNA methylation profile.

[0376] The biomarkers and DNA methylation maps can then be used according to any of the methods described herein; preferably, a computer system or computer program product according to the invention is used.

[0377] Then, the computer system or computer program can prepare and share reports detailing the results of the analysis / method or any other results of the method, for example, in the form of selecting or recommending suitable lifestyle programs, dietary programs or therapeutic interventions for dogs.

[0378] Suitable samples for determining the DNA methylation profile from dogs can be those readily available to the dog owner at home (e.g., not requiring a veterinarian or healthcare professional). Suitable samples can be hair follicles, oral swabs, or saliva samples. Use of dietary interventions.

[0379] In one aspect, the present invention provides dietary interventions for reducing the risk of death in dogs and / or increasing the probability of a healthy lifespan in dogs, wherein the dietary interventions are applied to dogs, and wherein biological age, risk of death, and / or probability of a healthy lifespan are determined by the method herein.

[0380] In another aspect, the present invention provides the use of dietary intervention in reducing the risk of death and / or increasing the probability of a healthy lifespan in dogs, wherein the dietary intervention is applied to the dogs, and wherein biological age, risk of death, and / or probability of healthy lifespan are determined by the method herein.

[0381] As described in this article, dietary interventions can be dietary products, dietary plans, or nutritional supplements.

[0382] Computer program products

[0383] This method can be executed using a computer. Therefore, this method can be executed on a computer.

[0384] Where appropriate, the computer can prepare and share a report detailing the results of this method.

[0385] The methods described herein can be implemented as a computer program running on general-purpose hardware such as one or more computer processors. In some embodiments, the functionality described herein can be implemented via a device such as a smartphone, tablet terminal, or personal computer.

[0386] In one aspect, the present invention provides a computer program product including computer-implementable instructions for causing a programmable computer to determine, as described herein, the biological age, risk of death, and / or probability of a healthy lifespan of a dog.

[0387] In one implementation, the user inputs the level of one or more of biomarkers and DNA methylation markers as defined herein, optionally along with the dog's age, breed, and sex. The device then processes this information and provides a determination of the dog's biological age, mortality risk, and / or probability of healthy lifespan. Alternatively, the device then processes this information and determines an appropriate lifestyle program, dietary program, or therapeutic intervention for the dog based on its biological age, mortality risk, and / or probability of healthy lifespan.

[0388] The device can typically be a server on a network. However, any device can be used as long as it can process biomarker data and / or additional parameters or characteristics using a processor, central processing unit (CPU), etc. For example, the device can be a smartphone, tablet terminal, or personal computer, and output information indicating the determined biological age of the dog or, based on the biological age of the dog, appropriate lifestyle programs, dietary programs, or therapeutic interventions.

[0389] Those skilled in the art will understand that they are free to combine all the features of the invention described herein without departing from the scope of the invention disclosed herein.

[0390] aspect

[0391] This invention provides the following aspects defined by numbered paragraphs:

[0392] 1. A method for determining the biological age, risk of death, and / or probability of healthy lifespan of a dog; the method comprising using (i) the levels of one or more biomarkers from one or more samples obtained from the dog, and (ii) a DNA methylation profile from the dog to determine the biological age, risk of death, and / or probability of healthy lifespan of the dog; wherein the one or more biomarkers are selected from white blood cell count, serum albumin, serum alkaline phosphatase, serum creatine kinase, hemoglobin, hematocrit, mean corpuscular hemoglobin, serum glucose, mean corpuscular volume, serum globulin, serum calcium, platelet count, and / or red blood cell count.

[0393] 2. The method according to paragraph 1; wherein the method includes:

[0394] a) Using the levels of one or more biomarkers from one or more samples obtained from the dog, the biological age, risk of death, and / or probability of healthy lifespan of the dog is determined, wherein the one or more biomarkers are selected from white blood cell count, serum albumin, serum alkaline phosphatase, serum creatine kinase, hemoglobin, hematocrit, mean corpuscular hemoglobin, serum glucose, mean corpuscular volume, serum globulin, serum calcium, platelet count, and / or red blood cell count; and

[0395] b) Use DNA methylation profiles from the dogs to determine the dogs’ biological age, risk of death, and / or probability of healthy lifespan.

[0396] 3. The method according to paragraph 2, wherein the biological age, mortality risk, and / or healthy lifespan probability of the dog determined in step a) is combined with the biological age, mortality risk, and / or healthy lifespan probability of the dog determined in step b) to provide a comprehensive biological age, mortality risk, and / or healthy lifespan probability of the dog.

[0397] 4. The method according to paragraph 2 or paragraph 3, wherein the biological age, mortality risk, and / or healthy life probability of the dog determined in step a) and the biological age, mortality risk, and / or healthy life probability of the dog determined in step b) are combined by calculating a combination of linear weighted combination, average, geometric mean (square root of product), harmonic mean (reciprocal of the average of reciprocals), maximum difference, minimum difference, or Δ (e.g., difference relative to chronological age or residual of a linear model based on chronological age).

[0398] 5. The method according to any of the preceding paragraphs, wherein the biological age, risk of death, and / or probability of healthy life are provided as a combined score.

[0399] 6. The method according to any of the preceding paragraphs, wherein the biomarker in step (a) is a white blood cell count.

[0400] 7. The method according to any of the preceding paragraphs, wherein the biomarker in step (a) is serum albumin.

[0401] 8. The method according to any of the preceding paragraphs, wherein the biomarker in step (a) is serum alkaline phosphatase.

[0402] 9. The method according to any of the preceding paragraphs, wherein the biomarkers in step (a) include white blood cell count, serum albumin, and serum alkaline phosphatase.

[0403] 10. The method according to any of the preceding paragraphs, wherein the biomarkers in step (a) include white blood cell count, serum albumin, serum alkaline phosphatase, serum creatine kinase, hemoglobin, hematocrit, mean corpuscular hemoglobin, serum glucose, mean corpuscular volume, and serum globulin.

[0404] 11. The method according to any of the preceding paragraphs, wherein the sample in step (a) is a blood sample.

[0405] 12. The method according to any of the preceding paragraphs, wherein step (a) further comprises combining the level of the one or more biomarkers with one or more of the dog’s actual age, breed and / or sex.

[0406] 13. According to the method described in paragraph 12, the variety is classified as small or medium-sized.

[0407] 14. The method according to paragraph 13, wherein step (a) comprises:

[0408] a. Determine the levels of the following biomarkers: white blood cell count, serum albumin, serum alkaline phosphatase, serum creatine kinase, hemoglobin, hematocrit, mean corpuscular hemoglobin, serum glucose, mean corpuscular volume, and serum globulin in one or more samples obtained from the dog; and

[0409] b. Determine the phenotypic age of the dog using formula (1):

[0410]

[0411] Where xb is the sum of the values ​​of each biomarker, sex, and species multiplied by their respective coefficients according to formula (2):

[0412]

[0413] In this context, sex is encoded as a numerical value, where 0 represents female and 1 represents male.

[0414] Breed is coded as a numerical value, where 0 represents a small breed and 1 represents a medium breed.

[0415] 15. The method according to paragraph 14, the method further comprising using the phenotypic age to determine the dog's risk of death and / or probability of healthy lifespan.

[0416] 16. The method according to paragraph 15, wherein determining that the phenotypic age of the dog is greater than its actual age indicates a higher risk of death and / or a reduced probability of healthy lifespan.

[0417] 17. The method according to paragraph 15, wherein determining that the phenotypic age of the dog is less than its actual age indicates a lower risk of death and / or an increased probability of healthy lifespan.

[0418] 18. The method according to any of the preceding paragraphs, wherein step (b) includes providing a DNA methylation map from a sample obtained from the dog.

[0419] 19. The method according to any of the preceding paragraphs, wherein step (b) further comprises determining a DNA methylation profile from a sample obtained from the dog.

[0420] 20. The method according to paragraph 19, wherein one or more of the following steps are used to determine DNA methylation: (a) treating sample DNA with APOBEC to deamination of cytosine; (b) enrichment based on capture; and / or (c) high-throughput sequencing.

[0421] 21. The method according to any of the preceding paragraphs, wherein the DNA methylation profile is determined from a blood, hair follicle, oral swab, feces, saliva, or tissue sample; optionally, wherein a blood sample is used for both steps (a) and (b).

[0422] 22. The method according to any of the preceding paragraphs, wherein: (i) step (b) includes determining the biological age of the dog; optionally, wherein the DNA methylation map contains at least one methylation site as listed in Table 3 or Table 8; or (ii) step b includes determining the risk of death and / or the probability of healthy lifespan; optionally, wherein the DNA methylation map contains at least one methylation site as listed in Table 3 or Table 8.

[0423] 23. The method according to paragraph 22, wherein the DNA methylation map comprises at least 3 methylation sites, at least 5 methylation sites, at least 10 methylation sites, at least 20 methylation sites, at least 50 methylation sites, at least 100 methylation sites, at least 150 methylation sites, at least 200 methylation sites, or each methylation site, as listed in Table 3 or Table 8.

[0424] 24. The method according to paragraph 22, wherein the DNA methylation map includes methylation sites as listed in Table 4, Table 5, Table 6 or Table 7.

[0425] 25. The method according to paragraph 22, wherein the DNA methylation map includes methylation sites as listed in Table 9, Table 10, Table 11 or Table 12.

[0426] 26. The method according to any of the preceding paragraphs, wherein step (b) further comprises combining the DNA methylation profile with one or more of the dog's actual age, breed, and / or sex.

[0427] 27. A method for selecting a lifestyle program, dietary program, or therapeutic intervention for a dog, said method comprising:

[0428] i) Determine the dog's biological age, risk of death, and / or probability of healthy lifespan according to the methods described in any of the preceding paragraphs; and

[0429] ii) Select appropriate lifestyle programs, dietary programs or therapeutic interventions for the dog based on the biological age, risk of death and / or probability of healthy life determined in step i).

[0430] 28. A method for determining the efficacy of a lifestyle program, dietary program, or therapeutic intervention in improving a dog's biological age, risk of death, and / or probability of healthy lifespan, said method comprising:

[0431] i) Applying a lifestyle program, dietary program, or therapeutic intervention to the dog, optionally wherein the lifestyle program, dietary program, or therapeutic intervention has been selected in accordance with the method described in paragraph 27;

[0432] ii) After the lifestyle program, dietary program or therapeutic intervention has been applied to the dog for a period of time; determine the dog's biological age, risk of death and / or probability of healthy lifespan according to any one of paragraphs 1 to 26;

[0433] iii) After following the lifestyle program, dietary program, or treatment intervention for the specified period of time, determine whether there are any changes in the dog's biological age, risk of death, and / or probability of healthy lifespan.

[0434] 29. A method for determining the efficacy of a lifestyle program, dietary program, or therapeutic intervention in improving a dog's biological age, risk of death, and / or probability of healthy lifespan, said method comprising:

[0435] i) Determine the biological age, risk of death, and / or probability of healthy life of the dog according to the method described in any one of paragraphs 1 to 26;

[0436] ii) Apply the lifestyle program, dietary program or therapeutic intervention selected based on the biological age, risk of death and / or probability of healthy life determined in step i) to the dog;

[0437] iii) After applying a lifestyle program, dietary program, or therapeutic intervention to the dog for a period of time; determine the dog's biological age, risk of death, and / or probability of healthy lifespan according to any one of paragraphs 1 to 26;

[0438] iv) Determine whether there is a change in the dog’s biological age, risk of death, and / or probability of healthy life between steps i) and iii).

[0439] 30. A method for developing anti-aging lifestyle programs, dietary programs, or therapeutic interventions; said method includes;

[0440] i) Determine the dog’s first biological age, risk of death and / or probability of healthy lifespan according to the method described in any one of paragraphs 1 to 26;

[0441] ii) Apply lifestyle programs, dietary programs, or therapeutic interventions to the dogs;

[0442] iii) After the lifestyle program, dietary program or therapeutic intervention has been applied to the dog for a period of time; determine the dog's second biological age, risk of death and / or probability of healthy lifespan according to the method described in any one of paragraphs 1 to 26;

[0443] iv) After following the lifestyle program, dietary program or treatment intervention for the specified period of time, determine whether there is a change in the dog's first risk of death and second risk of death and / or probability of healthy lifespan;

[0444] If the lifestyle program, dietary program, or treatment intervention reduces the biological age, risk of death, and / or increases the probability of healthy lifespan; and / or reduces the rate of increase in the dog's risk of death and / or increases the rate of decrease in the dog's probability of healthy lifespan, then it is determined to be anti-aging.

[0445] 31. A method for preventing or reducing the risk of developing diseases in dogs; said method comprising:

[0446] i) Determining the mortality risk and / or healthy lifespan probability of the dog according to any one of paragraphs 1 to 26; wherein the mortality risk and / or healthy lifespan probability determined for the dog is associated with an increased likelihood of developing the disease; and

[0447] ii) Select a lifestyle program, dietary program or therapeutic intervention for the dog based on the biological age, risk of death and / or probability of healthy life determined in step i);

[0448] The lifestyle program, dietary program, or therapeutic intervention described herein prevents or reduces the risk of the dog developing the disease; preferably, the disease described herein is an age-related disease.

[0449] 32. A method for selecting dogs suitable for receiving anti-aging lifestyle programs, dietary programs, or therapeutic interventions; said method comprising:

[0450] i) Determine the biological age, risk of death, and / or probability of healthy life of the dog according to the method described in any one of paragraphs 1 to 26;

[0451] ii) If the dog has an increased biological age, risk of death, and / or a reduced probability of healthy lifespan compared to its actual age, the dog is selected as suitable for receiving an anti-aging lifestyle program, dietary program, or therapeutic intervention.

[0452] 33. The method according to any one of paragraphs 27 to 32, wherein a lifestyle program, dietary program or therapeutic intervention is selected based on the determination that the dog has an increased biological age, risk of death and / or a reduced probability of healthy lifespan compared to the dog's actual age.

[0453] 34. The method according to any one of paragraphs 27 to 33, wherein the lifestyle program, dietary program or therapeutic intervention is a dietary intervention.

[0454] 35. The method of claim 34, wherein the dietary intervention is a calorie-restricted diet, an old-age diet, or a low-protein diet.

[0455] 36. A dietary intervention or treatment modality for reducing a dog’s biological age, risk of death, and / or probability of a healthy lifespan, wherein the dietary intervention is applied to the dog, and wherein the biological age, risk of death, and / or probability of a healthy lifespan are determined by the method according to any one of paragraphs 1 to 26.

[0456] 37. Use of a dietary intervention therapy modality in reducing a dog’s biological age, risk of death, and / or probability of healthy lifespan, wherein the dietary intervention is applied to the dog, and wherein the biological age, risk of death, and / or probability of healthy lifespan are determined by the method according to any one of paragraphs 1 to 36.

[0457] 38. A computer-readable medium comprising instructions that, when executed, cause one or more processors to perform the method according to any one of claims 1 to 18 or 21 to 26.

[0458] 39. A computer system for determining the biological age, mortality risk, and / or probability of healthy lifespan of a dog; said computer system is programmed to use the following to determine the mortality risk of the dog:

[0459] (a) The levels of one or more biomarkers selected from one or more samples obtained from the dog, wherein the one or more biomarkers are selected from white blood cell count, serum albumin, serum alkaline phosphatase, serum creatine kinase, hemoglobin, hematocrit, mean corpuscular hemoglobin, serum glucose, mean corpuscular volume, serum globulin, serum calcium, platelet count, and / or red blood cell count; and

[0460] (b) DNA methylation profile of the dog.

[0461] 40. A computer system for selecting a suitable lifestyle program, dietary program, or therapeutic intervention for a dog, said computer system being programmed to perform one or more of the following steps:

[0462] i) Determine the biological age, mortality risk, and / or healthy lifespan probability of the dog according to the method described in any one of paragraphs 1 to 18 or 21 to 26; and

[0463] ii) Select appropriate lifestyle programs, dietary programs or therapeutic interventions for the dog based on the mortality risk and / or healthy lifespan probability determined in step i).

[0464] 41. A computer system for determining the efficacy of a lifestyle program, dietary program, or therapeutic intervention in improving a dog's biological age, risk of death, and / or probability of healthy lifespan, said computer system being programmed to perform one or more of the following steps:

[0465] i) Determine the biological age, risk of death, and / or probability of healthy life of the dog from samples obtained from the dog before and after the lifestyle program, dietary program, or treatment intervention, according to the method described in any one of paragraphs 1 to 18 or 21 to 26; and

[0466] ii) Determine whether there are any changes in the biological age, risk of death, and / or probability of healthy life of the dogs between the samples obtained from the dogs before and after the application of the lifestyle program, dietary program, or treatment intervention.

[0467] 42. A computer system for determining the likelihood that a dog will benefit from an anti-aging lifestyle program, dietary program, or therapeutic intervention; said computer system is programmed to perform one or more of the following steps:

[0468] a) Determine the biological age, risk of death, and / or probability of healthy life of the dog according to the method described in any one of paragraphs 1 to 18 or 21 to 26;

[0469] b) If the dog has an increased biological age, risk of death, and / or a reduced probability of healthy lifespan compared to its actual age, the dog is identified as potentially responsive to anti-aging lifestyle programs, dietary programs, or therapeutic interventions.

[0470] 43. A computer program product comprising computer-implementable instructions for causing a programmable computer to determine, according to any one of paragraphs 1 to 18 or 21 to 26, the biological age, mortality risk, and / or probability of healthy lifespan of a dog.

[0471] 44. A computer program product comprising computer-implementable instructions for causing a programmable computer to determine, according to any one of paragraphs 1 to 18 or 21 to 26, the biological age, mortality risk, and / or healthy lifespan probability of a dog; and to select, based on the determined biological age, mortality risk, and / or healthy lifespan probability, a suitable lifestyle program, dietary program, or therapeutic intervention for the dog.

[0472] 45. A computer program product comprising computer-implementable instructions for causing a programmable computer to: a) determine, according to any one of paragraphs 1 to 18 or 21 to 26, the biological age, risk of death, and / or probability of healthy life of a dog from samples obtained from the dog prior to and after the lifestyle program, dietary program, or treatment intervention; and b) determine whether there is a change in the biological age, risk of death, and / or probability of healthy life of the dog between the samples obtained from the dog before and after the application of the lifestyle program, dietary program, or treatment intervention.

[0473] 46. ​​A computer program product comprising computer-implementable instructions for causing a programmable computer to: a) determine the biological age, mortality risk, and / or healthy lifespan probability of a dog according to the method of any one of paragraphs 1 to 18 or 21 to 26; and b) identify the dog as potentially responsive to an anti-aging lifestyle program, dietary program, or therapeutic intervention if the dog has an increased biological age or mortality risk and / or a decreased healthy lifespan probability compared to its chronological age.

[0474] 47. A computer system or computer program product according to any one of paragraphs 39 to 46; wherein the computer system or computer program prepares and shares a report of the results of the detailed step (ii).

[0475] 48. A kit for determining the biological age, risk of death, and / or probability of healthy lifespan of a dog, said kit comprising apparatus and reagents for collecting and optionally stabilizing a sample from a subject; and instructions for collecting and optionally stabilizing said sample, wherein said sample is intended for mailing for subsequent biomarker and DNA extraction, for measuring one or more biomarkers and DNA methylation from said sample, thereby obtaining the levels of one or more biomarkers and a DNA methylation profile.

[0476] 49. A method for determining the biological age, risk of death, and / or probability of healthy lifespan of a dog using one or more biomarkers and a DNA methylation profile; the method comprising (i) providing a sample from the dog, optionally using a kit as described in paragraph 48; (ii) providing the sample for subsequent biomarker extraction and DNA extraction to measure one or more biomarkers from the sample and DNA methylation in the extracted DNA, thereby obtaining the levels of one or more biomarkers and a DNA methylation profile; and (iii) determining the biological age, risk of death, and / or probability of healthy lifespan of the dog using a computer system as described in paragraph 39 or a computer program product as described in paragraph 43; wherein the computer system prepares and shares a report detailing the results of step (c).

[0477] 50. A method for determining the biological age, risk of death, and / or probability of healthy lifespan of a dog using one or more biomarkers and a DNA methylation profile; and for selecting a suitable lifestyle program, dietary program, or therapeutic intervention for the dog based on the risk of death and / or probability of healthy lifespan determined using the one or more biomarkers and the DNA methylation profile; the method comprising (i) providing a sample from the dog, optionally using a kit as described in paragraph 48; (ii) providing the sample for subsequent biomarker extraction and DNA extraction to measure one or more biomarkers from the sample and DNA methylation in the extracted DNA, thereby obtaining the levels of one or more biomarkers and a DNA methylation profile; and (iii) using a computer system as described in paragraph 40 or a computer program product as described in paragraph 44 to determine the biological age, risk of death, and / or probability of healthy lifespan of the dog and to select a suitable lifestyle program, dietary program, or therapeutic intervention for the dog; wherein the computer system prepares and shares a report detailing the results of step (iii).

[0478] 51. A method for determining the efficacy of a lifestyle program, dietary program, or therapeutic intervention in improving the biological age, risk of death, and / or probability of healthy lifespan in a dog using one or more biomarkers and DNA methylation profiles, the method comprising: (i) providing samples obtained from the dog prior to and after the lifestyle program, dietary program, or therapeutic intervention, optionally using a kit as described in paragraph 48; (ii) providing the samples for subsequent biomarker extraction and DNA extraction to measure one or more biomarkers from the samples and DNA methylation in the extracted DNA, thereby obtaining levels of the one or more biomarkers and a DNA methylation profile; (iii) using a computer system as described in paragraph 41 or a computer program product as described in paragraph 45 to determine whether there is a change in the biological age, risk of death, and / or probability of healthy lifespan in the dog between the samples obtained from the dog before and after the application of the lifestyle program, dietary program, or therapeutic intervention; wherein the computer system prepares and shares a report detailing the results of step (iii).

[0479] 52. A method for identifying a dog as potentially responsive to an anti-aging lifestyle program, dietary program, or therapeutic intervention using one or more biomarkers and a DNA methylation profile; the method comprising: (i) providing a sample obtained from the dog; optionally using a kit as described in paragraph 48; (ii) providing the sample for subsequent biomarker extraction and DNA extraction to measure one or more biomarkers from the sample and DNA methylation in the extracted DNA, thereby obtaining levels of one or more biomarkers and a DNA methylation profile; (iii) using a computer system as described in paragraph 42 or a computer program product as described in paragraph 46, identifying the dog as potentially responsive to an anti-aging lifestyle or diet if, compared to the dog's chronological age, the dog has an increased biological age or risk of death and / or a reduced probability of healthy lifespan; wherein the computer system prepares and shares a report of the results of step (iii) detailed in paragraph 46.

[0480] Example

[0481] The present invention will now be further described by way of examples, which are intended to help those skilled in the art to implement the invention, without limiting the scope of the invention in any way.

[0482] Example 1 – Determination of Blood Biomarkers Associated with Mortality Risk in Dogs

[0483] Predictive blood biomarkers were identified from a panel of biomarkers consisting of standard clinical complete blood count (CBC) and standard clinical blood chemistry analysis. Serum samples were collected after overnight fasting and measured using standard veterinary clinical practice.

[0484] Table 1 – Clinical Complete Blood Count (CBC) and Clinical Blood Chemistry Analysis

[0485] *Numerical values ​​are logarithmically transformed using the natural logarithm.

[0486] We conducted a longitudinal study of the dogs, repeatedly measuring these parameters as well as the dogs' status (survival or death), sex, and breed. We first categorized the breed as small or medium based on the mean weight of adult dogs of that breed (less than 10 kg or more than 10 kg, respectively). We then organized the data using the R programming language. For each dog, we recorded the biomarkers as time-dependent covariates using left-open and right-closed time intervals (i.e., (tstart, tstop)), where the biomarker information corresponds to the start of the interval and the event (survival or death) is recorded as the last tstop value. For this, we used the tmerge function of the survival package in R (v. 3.2-13). We then fitted the Cox proportional hazards model to the data for each of the 28 biomarkers, including sex and breed category (small or medium). We then adjusted the p-value for each parameter to account for multiple comparisons (by false discovery rate (fdr)) and selected features with an adjusted fdr below 0.05. Figure 1 ).

[0487] Using this method, we identified 13 biomarkers that can individually predict the survival probability of dogs:

[0488] White blood cell count (10^3 / ul)

[0489] Serum albumin (g / dL)

[0490] Serum alkaline phosphatase (U / L, ln conversion)

[0491] Serum creatine kinase (IU / L, ln conversion)

[0492] Hemoglobin (g / dL)

[0493] Hematocrit (%)

[0494] Mean corpuscular hemoglobin (pg)

[0495] Serum sodium (mmol / L)

[0496] Mean corpuscular volume (fL)

[0497] Serum globulin (g / dL)

[0498] Serum calcium (mg / dL)

[0499] Serum platelet count (10^3 / uL)

[0500] Red blood cell count (10^3 / uL)

[0501] Example 2 – A Multi-Parameter Model for Predicting Mortality Risk

[0502] Next, we constructed an optimal model that considers multiple parameters simultaneously, as this is more likely to cover various organ dysfunctions that occur with age. However, selecting only a few potentially correlated features can lead to bias. To avoid this problem, we used a penalized regression method using the glmnet package (v4.1-3). We fitted the LASSO penalized Cox proportional hazards model onto the data and used 20-fold cross-validation to compare different values ​​of the penalized parameter λ. This method selected the 10 blood biomarkers that best predict survival, ranked by importance as follows:

[0503] • White blood cell count (10^3 / ul)

[0504] • Serum albumin (g / dL)

[0505] • Serum alkaline phosphatase (U / L, ln conversion)

[0506] • Serum creatine kinase (IU / L, ln conversion)

[0507] • Hemoglobin (g / dL)

[0508] • Hematocrit (%)

[0509] •Mean red blood cell hemoglobin (pg)

[0510] • Serum glucose (mg / dL)

[0511] • Mean corpuscular volume (fL)

[0512] • Serum globulin (g / dL)

[0513] We also found that the top 3 biomarkers on the list are the most predictive, and performance can be improved by combining each of the next 7 biomarkers.

[0514] To extract phenotypic age from animals, we computed two distinct gompertz functions on our training set: one modeling survival as a function of selected biomarkers, age, breed type (small or medium-sized dog), and sex (Model 1), and the second considering only age, breed type, and sex (Model 2). These models were fitted using the flexsurv package (v 2.1). Phenotypic age was defined as a time variable (“age”) under which Model 2 gives the animal’s survival probability equal to Model 1’s survival probability at its chronological age. This results in a mathematical function relating blood biomarkers to phenotypic age, and is given by the following formula:

[0515]

[0516] Where xb is the sum of the values ​​for each biomarker, sex, and breed multiplied by their corresponding coefficients. Sex and breed are encoded as numerical values, where 0 represents female and 1 represents male for sex, and 0 represents small breed and 1 represents medium breed for breed. The coefficients are given by two gompertz functions trained on our training set.

[0517] As an example, coefficients and and The values ​​have been measured against a complete list of biomarkers from our training set and are given in Table 2.

[0518]

[0519] Table 2 – Coefficients and Sum and The values ​​have been measured from the training set.

[0520]

[0521] Furthermore, by systematically removing one biomarker from the top of the list to reduce the set of 10 biomarkers, we observed a decrease in the strength of survival predictions (p-value). The decrease in the first parameter was the most pronounced, confirming their largest contribution, but we observed changes in prediction quality with each set reduction, indicating that each parameter contributes to the overall prediction. Figure 2 ).

[0522] Example 3 – Using phenotypic age to predict mortality risk

[0523] It was subsequently demonstrated that when the obtained phenotypic age was applied to a test set consisting only of dogs not used during algorithm training, the difference between phenotypic age and chronological age (early phenotypic age) was associated with a significantly increased risk of death. The magnitude of the effect was calculated as: a hazard ratio of 1.75 for a 1-year increase in phenotypic age compared to chronological age. Figure 3 ).

[0524] Furthermore, the survival rates of dogs stratified earlier according to high or low median phenotypic age showed a statistically significant difference between the upper 50% and lower 50%. Figure 4 ).

[0525] Phenotypic age advanced (Δ to chronological age) alters middle-aged life through a calorie-restricted diet (75% of baseline maintenance energy requirements (MER)). This change occurs earlier in females compared to males. Figure 5 ).

[0526] The stratification of dogs under 7 years old and over 7 years old revealed significant differences between older dogs and younger dogs. This was more pronounced in females than in males.

[0527] Example 4 – Low-protein diets reduce the premature phenotypic age

[0528] dog

[0529] Thirty dogs with a Body Condition Score (BCS) of 7 or higher were recruited for this weight loss study. The baseline maintenance energy requirement (MER) for each dog was determined as the amount of food required to maintain a stable dog weight (with a change of less than 5% over three weeks). The baseline body fat percentage for each dog was then determined by DEXA. Dogs were randomly assigned to two groups of 15 dogs each based on their baseline weight, MER, age, sex, and body fat percentage.

[0530] Test diet

[0531] The two diets have similar metabolizable energy (ME), but differ in protein, carbohydrates, fat, and fiber.

[0532]

[0533] Feeding instructions

[0534] Dogs in both groups were fed 75% of their baseline MER during the first 4 months of the study and 60% of their baseline MER during the last 2 months of the study.

[0535] Blood sample collection and analysis

[0536] Serum samples were collected at baseline, 2 months, 4 months, and 6 months of the study. At the end of the study, complete blood count (CBC) and hemochemistry analysis were performed on these serum samples.

[0537] result

[0538] Compared to baseline, all but one dog showed a reduction in phenotypic age, defined as the difference between the dog's phenotypic age and actual age after 6 months of weight-loss dieting. The mean difference in phenotypic age between baseline and intervention was 0.7 years, which was significant in a paired t-test (p = 0.00093). The dogs in the study ranged in age from 3 to 11 years. At the start of the study, we found no correlation between phenotypic age and age in the dogs, suggesting that the benefit extends across a wide range of canine life stages. Furthermore, despite the two diets having significantly different protein levels, the effects on phenotypic age were comparable, indicating that reduced calorie intake leads to a younger phenotypic age regardless of the canine macronutrient ratio.

[0539] Example 5 - An exemplary method for generating a second representative observable clock

[0540] Identification of DNA methylation sites

[0541] Whole blood samples from a cohort of canines were analyzed by DNA extraction, DNA methylation via enzymatic conversion, whole-genome library preparation, hybridization of the transformed library with capture probes targeting gene promoters, and measurement of methylation profiles by sequencing (EM-Seq).

[0542] The capture probe targets approximately 40,000 targets (promoter regions—approximately 1 kb upstream to 0.5 kb downstream of the transcription start site). These target regions contain potential methylation sites of interest (single cytosine residues that can be methylated).

[0543] The following bioinformatics steps were performed after sequencing and before further analysis:

[0544] Using FastQC for FastQ quality checks — https: / / www.bioinformatics.babraham.ac.uk / projects / fastqc /

[0545] Using trimGalore for weaver trimming — https: / / www.bioinformatics.babraham.ac.uk / projects / trim_galore /

[0546] Alignment with the canine genome was performed using bwa-meth (https: / / github.com / brentp / bwa-meth) or Bismark—https: / / www.bioinformatics.babraham.ac.uk / projects / bismark /

[0547] Marking duplicates using Picard — https: / / gatk.broadinstitute.org / hc / en-us / articles / 360037052812-MarkDuplicates-Picard-

[0548] Use Methyldackel to invoke methylation — https: / / github.com / dpryan79 / MethylDackel

[0549] Methods for filtering sites to generate restriction DNA methylation maps for training biological clocks

[0550] After determining the methylation status of methylation sites in each sample, the initial methylation map can be filtered / processed to generate a restricted methylation map containing fewer discrete methylation sites. The aim of this filtering is to provide a restricted methylation map containing, for example, 50,000 to 500,000 methylation sites that can be used to train a biological clock.

[0551] Methods for filtering initial methylation profiles include:

[0552] 1) Remove (un)methylated sites from all samples

[0553] 2) Remove sites that do not have at least 5 counts in at least 90% of the samples.

[0554] 3) Remove the X and / or Y loci on chromosomes.

[0555] Other potential filtering steps to reduce the number of discrete methylation sites include:

[0556] 1) Methylation sites are defined as DNA regions (CpG islands, sites less than, for example, 1000 bp apart are considered the same).

[0557] 2) Target specific sites, such as inflammatory sites, like genes associated with inflammation from the GO / KEGG pathway, and select sites on these genes.

[0558] 3) Correlation analysis and preservation of representative data for each cluster (e.g., weighted correlation network analysis (WGCNA) (Langfelder & Horvath; BMC Bioinformatics; 9(559); 2008 or EBModules (Zollinger et al.; Biostatistics, 19(2), 153-168; (2018)).

[0559] 4) Differential methylation analysis is used to pre-select DNA methylation sites (e.g., using logistic regression) or to identify sites that vary more between young and older dogs (e.g., using ANOVA). If differential methylation analysis is performed (using the preprocessQuantile function in R), normalization (quantile) is performed before the filtering step.

[0560] The second generation of biological clocks

[0561] The dataset, which includes information about the mortality status (living or dead), actual age, breed, and sex of the dog cohort, is divided into training and testing sets (e.g., 2 / 3 of the data is used for training and 1 / 3 for testing, thus ensuring a good partitioning of the metadata; for example, similar proportions for each breed / sex in the training and testing sets).

[0562] A penalized Cox model was fitted to survival rates using methylation sites, variety / variety, sex, and actual age as predictors. The glmnet package in R was used to estimate the model parameters and the penalty parameters.

[0563] Two gompertz or Weibull functions were used to calculate survival rate. One function modeled survival rate as a function of methylation profile, age at birth, breed type (small or medium-sized dog), and sex (Model 1), while the second function considered only age at birth, breed type, and sex (Model 2). The models were fitted using the flexsurv package (v 2.1) in the R software environment.

[0564] The probability of death and / or healthy lifespan at age can be defined as a time variable (“actual age”), under which the survival probability of animals given by Model 2 is equal to the survival probability given by Model 1 at their actual age.

[0565] Choose a penalty parameter to provide a reasonable number of DNA methylation sites (e.g., up to 1000) that have a good model fit for mortality risk and / or the probability of healthy lifespan.

[0566] The biological age model based on DNA methylation sites was then evaluated on a test set.

[0567] In this embodiment, the following steps are performed:

[0568] 1) Set all methylation sites with fewer than 15 count coverages to "missing";

[0569] 2) The “Boostme” algorithm was used for imputation (Zou et al.; BMC Genomics 19, 390 (2018)) and the training, test and validation sets were set to 200,000 randomly selected sites;

[0570] 3) Remove the ChrX site;

[0571] 4) Site filtering was performed using the EB module (Zollinger et al.; Biostatistics, 19(2), 153-168; (2018)); the dataset was divided into blocks of approximately 5000 sites by grouping sites for each target (1500 bp around the TSS) and each chromosome. The correlation matrix of the applied EB module was then calculated—as described by Zollinger et al. Each module was then represented by medoid sites. Some sites did not belong to any module and were called scattered sites (as defined by Zollinger et al.).

[0572] 5) Remove sites present on mammalian CpG arrays (Arneson et al.; Nature Communications; 13; 783; 2022).

[0573] 6) The elastic network regression was adjusted at 500K sites using phenoAge_pred (the predicted value of phenotypic age at DNA collection age) as the response variable. 261 sites were selected as the sites that form the biological clock (see Table 3).

[0574] A series of evaluations were used to verify the clock. Figure 6 The clock was validated under calorie restriction, where dogs under calorie restriction had a lower biological age (lower Δ) compared to dogs on a controlled diet. Figure 6 The Δ in the equation corresponds to the residual of the regression model of actual age relative to the predicted phenoDNAmAge.

[0575] Further verification is needed. Figure 7 The study showed a difference in Δ (phenoDNAmAge) between the two diets, with the restricted diet resulting in dogs being biologically younger at age 6.

[0576] Figure 8The study showed that there were significant differences between the two diets when the linear mixed-effects model was adjusted (DogID as a random effect).

[0577] Figure 9 This study demonstrates a significant difference in survival rates between biologically younger and biologically older dogs.

[0578] Figure 10 The study showed that when the Cox proportional hazards model was fitted with sex and ΔphenoDNAmAge (the residual from actual age to predicted phenoDNAmAge) and stratified on the training set by breed type (small or medium), an increase in ΔphenoDNAmAge was significantly associated with an increase in mortality risk.

[0579] Univariate analysis was performed to determine whether each of the 261 loci was statistically significant individually in relation to biological age (see Table 3).

[0580] Further biological clocks were generated using only the first 3, 5, 10, 20, and 50 sites from the complete list of sites shown in Table 3, and each site showed a correlation with biological age (see Table 3). Figure 11 These clocks were generated by selecting the first n sites based on the absolute values ​​of the coefficients of the full clock (in descending order, taking the largest coefficient first). The first n sites were used as predictors to fit linear models that account for full-age. Details of the first 3, 5, 10, and 20 clocks are shown in Tables 4–7.

[0581] Example 6 - An exemplary method for generating a first representative observable clock

[0582] The methylation sites were identified and filtered using the techniques described in Example 5.

[0583] To generate the first representative observable genetic clock (trained on full-age), the dataset, which includes information about the full-age, breed, and sex of the dog cohort, is divided into training and testing sets (e.g., 2 / 3 of the data is used for training and 1 / 3 for testing, thus ensuring a good partitioning of the metadata; e.g., similar proportions for each breed / sex in the training and testing sets).

[0584] Penalized regressions were fitted between actual age and restriction DNA methylation profiles, sex, and breed to identify DNA methylation sites associated with biological age (i.e., generating a DNA methylation biological clock).

[0585] Choose a penalty parameter to provide a reasonable number of DNA methylation sites (e.g., up to 1000) that have a good model fit for biological age.

[0586] The biological age model based on DNA methylation sites was then evaluated on a test set.

[0587] In this embodiment, the following steps are performed:

[0588] 1) Set all methylation sites with fewer than 15 count coverages to "missing";

[0589] 2) The “Boostme” algorithm was used for imputation (Zou et al.; BMC Genomics 19, 390 (2018)) and the training, test and validation sets were set to 200,000 randomly selected sites;

[0590] 3) Remove the ChrX site;

[0591] 4) Site filtering was performed using the EB module (Zollinger et al.; Biostatistics, 19(2), 153-168; (2018)); the dataset was divided into blocks of approximately 5000 sites by grouping sites for each target (1500 bp around the TSS) and each chromosome. The correlation matrix of the EB module was then calculated—as described by Zollinger et al. Each module was then represented by medoid sites. Some sites did not belong to any module and were referred to as scattered sites (as defined by Zollinger et al.).

[0592] 5) Remove sites present on mammalian CpG arrays (Arneson et al.; Nature Communications; 13; 783; 2022).

[0593] 6) The elastic network regression was adjusted at 500K sites using actual age (age at DNA collection) as the response variable. 255 sites were selected as the sites that form the biological clock (see Table 8).

[0594] Biological age predicted using the first-generation observatory clock is highly correlated with chronological age (see [link to observatory]). Figure 12 ).

[0595] The first-generation observable clock was also validated using calorie restriction studies (see [link]). Figure 13 Δ corresponds to the residual of the regression model using the biological clock to represent the actual age relative to the predicted biological age. Dogs on a calorie restriction diet were identified as having significantly lower biological ages (lower Δ) compared to dogs on a controlled diet. Figure 13 ).

[0596] Univariate analysis was performed to determine whether each of the 255 loci was statistically significant individually in relation to biological age (see Table 8).

[0597] Further biological clocks were generated using only the first 3, 5, 10, 20, and 50 sites from the complete list of sites shown in Table 8, and each site showed a correlation with biological age. Figure 14 These clocks were generated by selecting the first n sites based on the absolute values ​​of the coefficients of the full clock (in descending order, with the largest coefficients taken first). The first n sites were used as predictors to fit linear models that account for full-age, respectively. Details of the first 3, 5, 10, and 20 clocks are shown in Tables 9 through 12.

[0598] All publications mentioned in the foregoing description are incorporated herein by reference. Various modifications and variations of the methods, compositions, and uses disclosed herein will be apparent to those skilled in the art without departing from the scope and spirit of the invention. While the invention has been disclosed in conjunction with specific preferred embodiments, it should be understood that the invention protected by the claims should not be unduly limited to such specific embodiments. In fact, various modifications to the modes disclosed for practicing the invention that are apparent to those skilled in the art are intended to fall within the scope of the following claims.

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[0618]

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[0621]

Claims

1. A method for determining the biological age, risk of death, and / or probability of healthy lifespan of a dog; the method comprising using (i) the levels of one or more biomarkers from one or more samples obtained from the dog, and (ii) a DNA methylation profile from the dog to determine the biological age, risk of death, and / or probability of healthy lifespan of the dog; wherein the one or more biomarkers are selected from white blood cell count, serum albumin, serum alkaline phosphatase, serum creatine kinase, hemoglobin, hematocrit, mean corpuscular hemoglobin, serum glucose, mean corpuscular volume, serum globulin, serum calcium, platelet count, and / or red blood cell count.

2. The method according to claim 1; wherein the method comprises: a) Using the levels of one or more biomarkers from one or more samples obtained from the dog, the biological age, risk of death, and / or probability of healthy lifespan of the dog is determined, wherein the one or more biomarkers are selected from white blood cell count, serum albumin, serum alkaline phosphatase, serum creatine kinase, hemoglobin, hematocrit, mean corpuscular hemoglobin, serum glucose, mean corpuscular volume, serum globulin, serum calcium, platelet count, and / or red blood cell count; and b) Use DNA methylation profiles from the dogs to determine the dogs’ biological age, risk of death, and / or probability of healthy lifespan.

3. The method of claim 2, wherein the biological age, mortality risk, and / or healthy lifespan probability of the dog determined in step a) is combined with the biological age, mortality risk, and / or healthy lifespan probability of the dog determined in step b) to provide a comprehensive biological age, mortality risk, and / or healthy lifespan probability of the dog.

4. The method of claim 2 or claim 3, wherein the biological age, mortality risk, and / or healthy life probability of the dog determined in step a) and the biological age, mortality risk, and / or healthy life probability of the dog determined in step b) are combined by calculating a combination of linear weighted combination, average, geometric mean (square root of product), harmonic mean (reciprocal of the average of reciprocals), maximum difference, minimum difference, or Δ (delta) (e.g., difference relative to chronological age or residual of a linear model based on chronological age).

5. The method according to any of the preceding claims, wherein the biological age, risk of death, and / or probability of healthy life are provided as a combined score.

6. The method according to any of the preceding claims, wherein the biomarkers in step (a) include white blood cell count, serum albumin, serum alkaline phosphatase, serum creatine kinase, hemoglobin, hematocrit, mean corpuscular hemoglobin, serum glucose, mean corpuscular volume, and serum globulin.

7. The method according to any of the preceding claims, wherein the sample in step (a) is a blood sample.

8. The method according to any one of claims 2 to 7, wherein step (a) comprises: a. Determine the levels of the following biomarkers: white blood cell count, serum albumin, serum alkaline phosphatase, serum creatine kinase, hemoglobin, hematocrit, mean corpuscular hemoglobin, serum glucose, mean corpuscular volume, and serum globulin in one or more samples obtained from the dog; and b. Determine the phenotypic age of the dog using formula (1): Where xb is the sum of the values ​​of each biomarker, sex, and species multiplied by their respective coefficients according to formula (2): In this context, sex is encoded as a numerical value, where 0 represents female and 1 represents male. Breed is coded as a numerical value, where 0 represents a small breed and 1 represents a medium breed.

9. The method according to any of the preceding claims, wherein step (b) comprises providing a DNA methylation profile from a sample obtained from the dog.

10. The method according to any of the preceding claims, wherein the DNA methylation profile is determined from a blood, hair follicle, oral swab, feces, saliva, or tissue sample; optionally, wherein a blood sample is used for both steps (a) and (b).

11. The method according to any of the preceding claims, wherein: (i) Step (b) includes determining the biological age of the dog; optionally, the DNA methylation map contains at least one methylation site as listed in Table 3 or Table 8; or (ii) Step (b) includes determining the risk of death and / or the probability of healthy lifespan; optionally, the DNA methylation map contains at least one methylation site as listed in Table 3 or Table 8.

12. A method for selecting a lifestyle program, dietary program, or therapeutic intervention for a dog, said method comprising: i) Determine the biological age, mortality risk, and / or healthy lifespan probability of the dog according to the method of any of the preceding claims; as well as ii) Select appropriate lifestyle programs, dietary programs or therapeutic interventions for the dog based on the biological age, risk of death and / or probability of healthy life determined in step i).

13. A method for determining the efficacy of a lifestyle program, dietary program, or therapeutic intervention in improving a dog's biological age, risk of death, and / or probability of healthy lifespan, said method comprising: i) Applying a lifestyle program, dietary program, or therapeutic intervention to the dog, optionally wherein the lifestyle program, dietary program, or therapeutic intervention has been selected according to the method of claim 12; ii) After the lifestyle program, dietary program, or therapeutic intervention has been applied to the dog for a period of time; The method according to any one of claims 1 to 11 determines the biological age, mortality risk, and / or probability of healthy lifespan of the dog; iii) After following the lifestyle program, dietary program, or treatment intervention for the specified period of time, determine whether there are any changes in the dog's biological age, risk of death, and / or probability of healthy lifespan.

14. A computer-readable medium comprising instructions that, when executed, cause one or more processors to perform the method according to any one of claims 1 to 13.

15. A computer system for determining the biological age, mortality risk, and / or probability of healthy lifespan of a dog; said computer system is programmed to determine the mortality risk of the dog using the following: (a) The levels of one or more biomarkers selected from one or more samples obtained from the dog, wherein the one or more biomarkers are selected from white blood cell count, serum albumin, serum alkaline phosphatase, serum creatine kinase, hemoglobin, hematocrit, mean corpuscular hemoglobin, serum glucose, mean corpuscular volume, serum globulin, serum calcium, platelet count, and / or red blood cell count; and (b) DNA methylation profile of the dog.

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