METHOD FOR THE ANALYSIS OF METABOLIC DIFT IN A PERSON

Description

technical field

[0001] The present invention relates to methods for analyzing biological parameter(s) of a subject. State of the art

[0002] Analyzing one or more biological parameters in a subject is an essential tool for diagnosing a pathology. Indeed, there are more or less direct links between certain biological parameters and given pathologies, for example, between blood glucose and diabetes, iron levels and anemia, blood pressure and hypertension, the presence of a pathogen and an infection related to that pathogen, and erythrocyte sedimentation rate and infection or inflammation. etc..Measuring certain biological parameters can help guide or confirm a diagnosis. It also allows monitoring a patient's response to treatment by tracking changes in parameters related to the disease in question. In this regard, document EP2645105A1 describes the use of specific biomarkers, such as lipid profiles, to predict the risk of age-related low-grade inflammation by comparing measured values ​​to reference levels.

[0003] The analysis of a biological parameter generally involves measuring its value and then determining whether the obtained value falls within a reference range defined by a minimum reference threshold and a maximum reference threshold. Thus, the results of a biological analysis are generally classified into three categories: (i) outside the minimum reference threshold, (ii) between the minimum and maximum reference thresholds, or (iii) outside the maximum reference threshold. A value within a reference range is considered "normal." The "normality" of a value outside a reference range is left to the practitioner's judgment, particularly based on the deviation of that value from the reference thresholds.

[0004] Reference values ​​may vary depending on a subject's geographic origin, sex, and / or age, and possibly on the assay method used. Generally, reference values ​​correspond to results obtained in a reference population whose members are free from pathologies and / or treatments that could alter the measured values. In practice, the reference threshold values ​​defined by the medical community are not necessarily based on statistical analyses of the population.

[0005] Reference values ​​are subject to international recommendations, in particular from the IFCC-LM ( International Federation of Clinical Chemistry and Laboratory Medicine ) and CLSI ( Clinical and Laboratory Standards Institute ) .In this context, document US9639667B2 proposes a reference interval testing engine designed to establish normality ranges from clinical databases. However, cohort optimization in the document relies heavily on the practitioner's expertise or known diagnostic codes.

[0006] There is still a need to provide more precise biological analysis methods and / or methods that allow for earlier detection of one or more biological parameters whose values ​​correspond to an abnormal or potentially abnormal state of health. Description

[0007] Surprisingly, the Inventor has demonstrated that a biological parameter can be drifting or potentially drifting, i.e. corresponding to an abnormal or potentially abnormal health state for that biological parameter, even though the value of that biological parameter is within the range of reference values ​​for that biological parameter.

[0008] In particular, within the scope of the present invention, a biological parameter that does not vary over time around a mean reference value is considered to be drifting or potentially drifting, even if its value falls within the range of reference values. Thus, the present invention makes it possible to detect biological parameters corresponding to an abnormal or potentially abnormal clinical condition, but which would not have been considered as such by comparing their values ​​to a range of reference values.

[0009] The analysis of the drift of a biological parameter according to the invention can advantageously be used as an aid to the early diagnosis of a disease, for example by guiding the diagnosis of a disease during its asymptomatic phase.

[0010] The analysis of the drift of a biological parameter according to the invention also advantageously makes it possible to prevent or at least delay the establishment of a disease, by allowing for example the implementation of early treatment to stop or slow down the drift of said biological parameter, for example even before the appearance of the first symptoms.

[0011] Advantageously, the present invention also allows medical analysis to be directed towards the measurement of at least one other biological parameter statistically linked to a biological parameter detected as being in drift or potentially in drift. This other biological parameter may, for example, be directly correlated to a pathology that the practitioner would not initially consider, based on the clinical examination and / or a biological analysis not specifically addressing metabolic drift.

[0012] A first object of the invention relates to a method for analyzing the metabolic drift of at least one quantitative biological parameter in a subject, according to claim 1.

[0013] The quantitative biological parameter measured in step a) is selected from the group consisting of a serum parameter, an infectious parameter, a clinical parameter and a multi-parameter indicator, according to claim 1.

[0014] The above method of analysis is preferably characterized in that: said quantitative biological parameter is in drift if: ∘ the absolute value of the difference between (i) the mean of said values ​​and (ii) the mean reference value is greater than or equal to 1 standard deviation of reference, or ∘ the absolute value of the difference between (i) the mean of said values ​​and (ii) the mean reference value is greater than or equal to 0.5 standard deviations of reference and less than 1 standard deviation of reference and the absolute value of the difference between (i) at least one of said values ​​and (ii) the mean reference value is greater than or equal to 2 standard deviations of reference, or ∘ at least one of said values ​​is greater than or equal to a maximum threshold reference value and / or at least one of said values ​​is less than or equal to a minimum threshold reference value,and / or said quantitative biological parameter is potentially drifting if: ∘ the absolute value of the difference between (i) at least one of said values ​​or the mean of said values ​​and (ii) the mean reference value is greater than or equal to 0.5 standard deviations of reference and less than 1 standard deviation of reference, and ∘ if the absolute value of the difference between (i) each of said values ​​and (ii) the mean reference value is less than 2 standard deviations of reference.

[0015] When said quantitative biological parameter is potentially drifting or drifting, the method as defined above preferably includes a subsequent step of measuring the value of said quantitative biological parameter at another time ti.

[0016] When the quantitative biological parameter is drifting or potentially drifting, the method as defined above preferably includes the following steps: measuring the slope of a line of fit passing through the values ​​provided in step a) and deducing whether the drifting or potentially drifting parameter is stable, worsening or improving.

[0017] When the said quantitative biological parameter is potentially drifting or drifting, the method as defined above includes at least one of the following subsequent steps: provide the value of at least one other biological parameter statistically related to the biological parameter in potential drift or drift, and / or implement a method for diagnosing a disease or the risk of suffering from a disease, said disease being associated with said biological parameter in potential drift or drift.

[0018] The reference mean value and / or reference standard deviation is defined according to the method for defining an optimized reference mean value and reference standard deviation of a biological parameter X as defined below.

[0019] Another object of the invention relates to a method for defining an optimized reference mean value and reference standard deviation of a biological parameter X, according to claim 4.

[0020] Also described is a method for assessing the drift state of a subject and / or for aiding in the diagnosis of a disease or in assessing the risk of suffering from a disease in a subject, including the implementation of the method for analyzing the metabolic drift of a subject as defined above.

[0021] Another object of the invention relates to a method for optimizing a cohort of subjects for the study of a biological parameter X, according to claim 6.

[0022] Also described is a method for assessing a subject's state of drift and / or for aiding in the diagnosis of a disease or the assessment of a subject's risk of developing a disease, comprising: a) measure the value of at least one biological parameter at at least one time t1 in a biological sample of the subject, to obtain at least one value v1, b) compare the value(s) obtained in step a) to a reference threshold value and / or determine whether the values ​​obtained in step a) fluctuate around a mean reference value, and c) if the value or one of the values ​​of said biological parameter is greater than a maximum reference threshold value or less than a minimum reference threshold value and / or if said values ​​do not fluctuate around a mean reference value, provide the value of at least one other biological parameter statistically linked to said biological parameter and / or deduce whether the subject suffers or is likely to suffer from the disease.

[0023] The said biological parameter may, for example, be the mean platelet volume, the said statistically linked biological parameter free thyroxine, and the said disease hypothyroidism and / or Graves' disease.

[0024] Another object of the invention relates to a computer program comprising program code instructions for the execution of the steps of the methods as defined above, when said program is executed on a computer. Biological parameter

[0025] By "biological parameter", we mean here any parameter, quantitative or qualitative, which allows, directly or indirectly, the assessment of a subject's state of health.

[0026] The biological parameter is selected from the group consisting of a serum parameter, an infectious parameter, a genetic parameter, a non-serum parameter (such as a clinical parameter or a lifestyle-related parameter) and a multi-parameter indicator.

[0027] The qualitative parameter can be a qualitative parameter that is fixed over time (for example, sex or the presence of a given gene) or variable over time.

[0028] Genetic and lifestyle-related parameters are preferably qualitative parameters.

[0029] Serum, clinical, non-serum and infectious parameters can be quantitative or qualitative parameters.

[0030] The biological parameter, in particular the serum, infectious, genetic and certain non-serum parameters, can be measured in a biological sample from a subject.

[0031] The biological sample can be a blood sample, urine sample, cerebrospinal fluid sample, stool sample, or a biopsy.

[0032] The biological sample may have undergone one or more preliminary processing steps prior to analysis (such as purification, centrifugation, filtration, precipitation, PCR and / or RT-PCT).

[0033] The term “serum parameter” here refers to a biological parameter measured in a blood sample or a sample obtained from a blood sample (e.g., in plasma).

[0034] The serum parameter can, for example, be selected from the group consisting of: The number, concentration and / or proportion of given blood cells or blood cell populations: for example, the number, concentration and / or proportion of erythrocytes (also called red blood cells), leukocytes (also called white blood cells), granulocytes, neutrophils (also called polymorphonuclear neutrophils or neutrophil granulocytes), eosinophils (also called polymorphonuclear eosinophils), basophils (also called polymorphonuclear basophils), monocytes, lymphocytes or platelets, the complete blood count (CBC), the hemoglobin concentration, the hematocrit, i.e. the volume occupied by erythrocytes in a given volume of whole blood, the thrombocrit, the erythrocyte sedimentation rate, VGM ( Mean Corpuscular Volume ), CGMH ( Mean Corpuscular Hemoglobin Concentration ), TCMH ( Mean Corpuscular Hemoglobin Content), the quantity, concentration and / or proportion of a given enzyme, for example lipase, the quantity, concentration and / or proportion of Alkaline Phosphatase, Gamma GT, TgO transaminase or TgP transaminase, the quantity, concentration and / or proportion of lipids, in particular total cholesterol, HDL cholesterol, LDL cholesterol or triglycerides, blood glucose, the quantity, concentration and / or proportion of chloride, uric acid, urea, or creatinine, protein electrophoresis, in particular the quantity, concentration and / or proportion of total protein, albumin, alpha 1 globulin, alpha 2 globulin, beta 1 globulin, beta 2 globulin and / or gamma globulin, the protein profile, in particular the quantity, concentration and / or proportion of serum proteins, such as IgA, IgG, IgM and / or IgE, an ionogram, in particular the quantity, concentration and / or proportion of an or several ions, such as sodium, potassium, calcium, magnesium, chlorine, and / or bicarbonate,For example, in blood plasma, the quantity, concentration and / or proportion of one or more hormones, the quantity, concentration and / or proportion of an antigen, for example free PSA (prostate-specific antigen) and / or conjugated PSA, or the blood group (for example, the ABO blood group).

[0035] The infectious parameter is linked to the presence or absence of a pathogen in a subject.

[0036] The quantity, concentration and / or proportion of the pathogen may be important in determining whether the subject is suffering from an infection by said pathogen.

[0037] When the infectious parameter is qualitative, it takes, for example, the value "infection", "absence of infection" or "possible infection" by a given pathogen.

[0038] The pathogen can be selected from the group consisting of a virus, a bacterium, a fungus, a protozoan, and a parasite.

[0039] The virus is, for example, the mononucleosis virus, a cytomegalovirus, a herpes virus, or HIV.

[0040] The bacterium is, for example Helicobacter (preferably) Helicobacter pylori ) .

[0041] The mushroom is, for example, Candida (preferably, Candida albicans, Candida dubliniensis, Candida glabrata, Candida guilliermondii, Candida kefyr, Candida krusei, Candida lusitaniae, Candida parapsilosis and / or Candida tropicalis ).

[0042] By "genetic parameter" we mean here a qualitative parameter related to the subject's genome, for example related to the presence of a given gene or group of genes, a mutation, a variant and / or a given polymorphism.

[0043] The genetic parameter is, for example, selected from the group consisting of the HLA group, the mutation of the HFE gene resulting in hemochromatosis and the mutation of the CFTR gene resulting in cystic fibrosis.

[0044] The non-serum parameter here refers to any parameter that is not a serum, infectious or genetic parameter.

[0045] The non-serum parameter is preferably a clinical parameter or a lifestyle-related parameter.

[0046] The term "clinical parameter" here refers in particular to a quantitative or qualitative parameter that is not measured in a biological sample.

[0047] The clinical parameter is, for example, selected from the group consisting of blood pressure (e.g., systolic and / or diastolic blood pressure), height, weight, age, waist circumference, sex (in particular, male or female), season of birth, pathology (e.g., cancer, diabetes, anemia, depression).

[0048] The lifestyle parameter is, for example, selected from the group consisting of cigarette consumption, diet (e.g., gluten-free diet, dairy-free diet, vegetarian and / or vegan diet), sedentary lifestyle, place of residence (e.g., northern or southern France), sunshine, and day or night work.

[0049] A multiparameter indicator is the result of a mathematical equation that includes at least one quantitative biological parameter as a variable, for example, at least two quantitative biological parameters. The multiparameter indicator may, for example, include or consist of a product, ratio, sum, subtraction, average, and / or an equation involving cos, sin, tan, and / or exponential functions, and specifically involving at least one quantitative biological parameter.

[0050] The multi-parameter indicator is therefore obtained from one or more values ​​of at least one quantitative biological parameter which can notably be measured in a biological sample from a subject.

[0051] Non-exhaustive examples of multi-parameter markers include: the total white blood cell count divided by the red blood cell count; the amount or concentration of HDL cholesterol divided by the amount or concentration of LDL cholesterol; the amount or concentration of free PSA (prostate-specific antigen) divided by the amount or concentration of conjugated PSA; or the number of lymphocytes divided by the number of neutrophils.

[0052] By "biological parameter X statistically linked to a biological parameter Y", we mean here two biological parameters that are statistically significantly linked.

[0053] For example, two quantitative variables are statistically related if they tend to vary in relation to each other.

[0054] The statistical significance between two biological parameters can be shown by any statistical method well known to a person skilled in the art, such as linear, polynomial, logarithmic or non-linear regression, analysis of variance (ANOVA), Student's t-test, chi-square test, cross-sorting, general linear model and / or partial least squares.

[0055] To assess whether two quantitative biological parameters are statistically related, the statistical method can, for example, be linear, polynomial, logarithmic or non-linear regression.

[0056] To assess whether a quantitative biological parameter and a qualitative biological parameter are statistically related, the statistical method can, for example, be an ANOVA or a general linear model.

[0057] To assess whether two qualitative biological parameters are statistically related, the statistical method can, for example, be a three-way crossover or a Chi-square test.

[0058] Preferably, two biological parameters are considered statistically linked if the level of statistical significance is greater than or equal to 90%, preferably greater than or equal to 95%, and even more preferably greater than or equal to 99.5%, for example, greater than or equal to 99.9%. Statistical significance corresponds to the probability that the result obtained is not due to chance. Subject

[0059] The term "subject" here refers to a human subject or a non-human animal subject.

[0060] The non-human animal subject is preferably a mammal, for example a cat, dog, rodent, primate, equine, bovine, ovine, caprine.

[0061] Alternatively, the non-human animal subject is not a mammal.

[0062] Preferably, the subject is a human subject, also called an "individual".

[0063] The human subject can be of any sex, for example a man or a woman, of any age, for example an infant, toddler, child, adolescent, adult or elderly person.

[0064] The subject may be a healthy subject, appear to be a healthy subject, present at least one symptom of a disease, have at least one biological parameter not within a reference range for that parameter and / or be likely to suffer from a disease. Average value, standard deviation, threshold value and reference range

[0065] The term "average reference value" preferably refers to the average of the values ​​of a given biological parameter in a reference population.

[0066] Alternatively, the average reference value may be a fixed value, for example by an administration, this value not necessarily corresponding to the average of the values ​​of a given biological parameter in a reference population.

[0067] The term "reference standard deviation" here refers to the square root of the variance of the values ​​of a given biological parameter in a reference population.

[0068] The term "reference population", also called "reference group", refers to a population of individuals in which the biological parameter of interest is measured.

[0069] In one particular embodiment, the reference population may, for example, consist of healthy individuals, that is to say, individuals in good general health.

[0070] The reference population preferably includes at least 50 subjects, preferably at least 60 subjects, more preferably at least 70 subjects, at least 80 subjects, at least 90 subjects, more preferably still at least 100 subjects, at least 110 subjects, for example at least 120 subjects.

[0071] The term "reference threshold value" refers to a reference value corresponding to a tolerance level. Thus, a biological parameter whose value is greater than or equal to a maximum reference threshold value, or which is less than a minimum reference threshold value, is considered to be outside of tolerance. Reference threshold values ​​define limits beyond which medical intervention should normally be implemented.

[0072] Reference threshold values ​​are generally defined relative to the mean reference value. In an advantageous embodiment, the minimum reference threshold value may be equal to - [n x the reference standard deviation] and / or the maximum reference threshold value may be equal to + [n x the reference standard deviation]. For example, the minimum reference threshold value may be equal to -2 reference standard deviations and / or the maximum reference threshold value may be equal to +2 reference standard deviations. These values ​​may also be more or less arbitrary, for example, defined by an administration, for instance, as a result of strong industry agreements.

[0073] In general, a reference threshold value equal to + or - 2 standard deviations corresponds to an alert threshold corresponding to a probably pathological state, that is, beyond which a value is considered to correspond to a probably pathological state.

[0074] The term "reference range", also called "reference interval", refers to the range between the minimum reference threshold value and the maximum reference threshold value, with these threshold values ​​being included in the range. Method for analyzing the metabolic drift of at least one biological parameter in a subject

[0075] A first object of the invention is therefore a method for analyzing the metabolic drift of at least one biological parameter in a subject.

[0076] The expression "metabolic drift" or the term "drift" associated with a biological parameter refers here to the fact that the values ​​of this biological parameter measured over time do not fluctuate around a reference average.

[0077] Indeed, for a biological parameter considered "normal," the values ​​fluctuate over time around a mean reference value. This means that the average of the values ​​of this biological parameter measured over time is equal to or equivalent to the mean reference value, preferably an optimized mean reference value, particularly one obtained using the method defined below. Once the values ​​of the biological parameter measured over time no longer fluctuate around a mean reference value, the fluctuations are no longer random but reflect a drift or potential drift of the biological parameter.

[0078] An average of values ​​considered equivalent to the reference average value is, for example, strictly greater than ["the reference average value" - 0.5 x "the reference standard deviation"] and strictly less than ["the reference average value" + 0.5 x "the reference standard deviation"].

[0079] For example, if the reference mean is equal to 10 and the reference standard deviation is 0.2, then a value other than 10 that is between 9.9 and 10.1 is equivalent to the reference mean.

[0080] The terms "biological parameter" and "subject" are specifically as defined above in the sections of the same name.

[0081] The present invention relates particularly to a method for analyzing the metabolic drift of at least one quantitative biological parameter in a subject, as defined in claim 1.

[0082] The method for analyzing metabolic drift is preferably implemented by a computer or at least partially implemented by a computer.

[0083] The method for analyzing metabolic drift is preferably a method in vitro and / or ex vivo.

[0084] Step a) consists of providing the values ​​of at least one quantitative biological parameter measured at at least two times t1 and t2 spread out over time, to obtain at least two values ​​v1 and v2.

[0085] The method for analyzing metabolic drift as defined above may or may not include a preliminary step of measuring said quantitative biological parameter in a biological sample, in particular at at least two times t1 and t2. The measurements taken over time are therefore carried out on biological samples from the same subject taken at different times.

[0086] The biological samples in which the biological parameter is measured over time are preferably of the same nature and / or are preferably collected and / or measured under the same conditions.

[0087] In an advantageous embodiment, step a) consists of providing the values ​​of at least one quantitative biological parameter measured at at least three times t1, t2 and t3 spread over time, to obtain at least three values ​​v1, v2 and v3, for example at at least 4 times t1, t2, t3 and t4, to obtain at least four values ​​v1, v2, v3 and v4.

[0088] More generally, step a) may consist of providing the values ​​of at least one quantitative biological parameter measured at i times t1 to ti spread over time, to obtain i values ​​v1 to vi, i being a positive integer greater than or equal to 2.

[0089] By "spread out over time" or "separated in time," we mean that the measurements of the biological parameter are spaced out over time, preferably at least 24 hours apart. The frequency of measurements depends in particular on the biological parameter in question.

[0090] The biological parameter can, for example, be measured on average every week, every two weeks, every three weeks, every month, every two months, every three months, every six months or even once a year.

[0091] The quantitative biological parameter measured in step a) is selected from the group consisting of a serum parameter, an infectious parameter, a clinical parameter and a multi-parameter indicator, as defined in claim 1. These different parameters are in particular as defined above in the section "biological parameter".

[0092] Step b) consists of determining whether said values ​​fluctuate around a mean reference value for said quantitative biological parameter.

[0093] The average reference value is specifically as defined above.

[0094] In a preferred embodiment, the mean reference value is obtained by the method as defined below to define an optimized mean reference value and an optimized standard deviation reference of a biological parameter X suitable for the analysis of metabolic drift.

[0095] The fluctuation of values ​​around a reference mean can be assessed visually, for example, by means of a graph representing the measured values ​​for the biological parameter over time. If the values ​​obtained have a random or substantially random distribution around the reference mean, particularly with values ​​alternately above and below the reference mean, these values ​​are fluctuating around the reference mean.

[0096] On the contrary, if said values ​​are systematically higher or mostly higher than the average reference value or, conversely, systematically lower or mostly lower than the average reference value, said values ​​do not fluctuate around the average reference value.

[0097] Alternatively, the fluctuation of values ​​around a reference average value can be assessed by comparing: the absolute value of the difference between (i) the average of said values ​​and (ii) the average reference value, and / or the absolute value of the difference between (i) at least one of said values, preferably each of said values, and (ii) the average reference value, at least one value equal to [n x the reference standard deviation], n being a positive number greater than or equal to 0.25, preferably greater than or equal to 0.5, for example equal to 0.5 or 1 or 1.5 or 2.

[0098] To refine the drift analysis, the fluctuation of values ​​around a reference average value can be assessed by comparing: the difference between (i) the average of said values ​​and (ii) the average reference value, and / or the difference between (i) at least one of said values, preferably each of said values, and (ii) the average reference value, at least one value equal to [n x the reference standard deviation] and / or at least one value equal to -[n x the reference standard deviation], n being a positive number greater than or equal to 0.25, preferably greater than or equal to 0.5, for example equal to 0.5 or 1 or 1.5 or 2.

[0099] A value equal to [nx the reference standard deviation] preferred according to the invention is equal to 0.5 reference standard deviation, 1 reference standard deviation, 1.5 reference standard deviation or 2 reference standard deviation.

[0100] The mean reference value and the standard deviation reference are, in particular, as defined above in the section " Average value, standard deviation, threshold value and reference range » .

[0101] In a preferred embodiment of the invention, the mean reference value and the standard deviation reference are therefore values ​​optimized for drift analysis, for example obtained by the method as defined below to define a mean reference value and a standard deviation reference of a biological parameter X suitable for metabolic drift analysis.

[0102] In step c), the biological parameter is considered to be drifting or potentially drifting if the values ​​do not fluctuate around the reference mean value.

[0103] When measured values ​​fluctuate around the reference mean value for one or more periods, and when they do not fluctuate around the reference mean value for one or more periods, the drift status of the biological parameter is given as a function of time. In other words, it is indicated whether the biological parameter is considered to be drifting or potentially drifting for each time period during which the fluctuation differs.

[0104] The state of drift is notably as defined below.

[0105] As explained below, it is also possible to distinguish drift substates and therefore to indicate in step c) the different drift states and / or drift substates of the successive biological parameter over time.

[0106] In one embodiment of the invention, if the absolute value of the difference between (i) the mean of said values ​​and (ii) the reference mean value is greater than or equal to [n x the reference standard deviation] and / or if the absolute value of the difference between (i) at least one of said values, preferably each of said values, and (ii) the reference mean value is greater than or equal to [n x the reference standard deviation], deduce that the said values ​​do not fluctuate around the said average reference value, in particular if n is greater than or equal to 0.5.

[0107] In an advantageous embodiment of the invention, if the difference between (i) the mean of said values ​​and (ii) the mean reference value is greater than or equal to [n x the reference standard deviation] or is less than or equal to -[n x the reference standard deviation], and / or if the difference between (i) at least one of said values, preferably each of said values, and (ii) the mean reference value is greater than or equal to [n x the reference standard deviation] or is less than or equal to -[n x the reference standard deviation], deduce that the said values ​​do not fluctuate around the said average reference value, in particular if n is greater than or equal to 0.5.

[0108] In a preferred embodiment, the method as defined above is characterized in that: said quantitative biological parameter is in drift if: ∘ the absolute value of the difference between (i) the mean of said values ​​and (ii) the mean reference value is greater than or equal to 1 standard deviation of reference, or ∘ the absolute value of the difference between (i) the mean of said values ​​and (ii) the mean reference value is greater than or equal to 0.5 standard deviations of reference and less than 1 standard deviation of reference and the absolute value of the difference between (i) at least one of said values ​​and (ii) the mean reference value is greater than or equal to 2 standard deviations of reference, or ∘ at least one of said values ​​is greater than or equal to a maximum threshold reference value and / or at least one of said values ​​is less than or equal to a minimum threshold reference value,and / or said quantitative biological parameter is potentially drifting if: ∘ the absolute value of the difference between (i) at least one of said values ​​or the mean of said values ​​and (ii) the mean reference value is greater than or equal to 0.5 standard deviations of reference and less than 1 standard deviation of reference, and ∘ if the absolute value of the difference between (i) each of said values ​​and (ii) the mean reference value is less than 2 standard deviations of reference.

[0109] The minimum or maximum reference threshold value is, in particular, as defined above in the section " Average value, standard deviation, threshold value and reference range » .

[0110] In an advantageous embodiment of the invention, the method as defined above is characterized in that, when said quantitative biological parameter is in potential drift or drift, the method as defined above includes a subsequent step of measuring the value of said quantitative biological parameter at another time ti.

[0111] This step can, for example, confirm a potential drift and / or monitor the evolution of a drifting or potentially drifting parameter, particularly after the implementation of a treatment and / or during the treatment aimed at correcting said biological parameter, i.e. aimed at stopping the drift or potential drift of said biological parameter.

[0112] The other measurement time ti is preferably located at least one week, preferably at least two weeks, and even more preferably at least three weeks, for example, three or four weeks after the last measured value. The other measurement time ti can also be taken one month, two months, three months, or six months later.

[0113] Preferably, at least while the biological parameter is drifting or potentially drifting, the biological parameter is measured regularly over time, for example on average every week, every two weeks, every three weeks, every month, every two months, every three months, every 6 months or even once a year.

[0114] In an advantageous embodiment of the invention, the method as defined above is characterized in that, when the quantitative biological parameter is drifting or potentially drifting, the method as defined above comprises the following steps: measuring the slope of a line of fit passing through the values ​​provided in step a) and deducing whether the drifting or potentially drifting parameter is stable, worsening or improving.

[0115] The parameter in drift or potential drift is, for example, worsening if the slope of the fitted line is such that the values ​​obtained over time deviate from the average reference value.

[0116] The parameter in drift or potential drift is, for example, stable if the slope of the line of best fit is zero.

[0117] The parameter in drift or potential drift is, for example, improving if the slope of the line of adjustment is such that the values ​​obtained over time approach the average reference value.

[0118] The regression line is obtained by any appropriate method well known to a person skilled in the art. Preferably, the regression line is obtained by a linear regression model, for example affine regression, least squares, maximum likelihood, and / or Bayesian inference.

[0119] It is possible to define different states or substates of drift depending on the values ​​obtained. It is clear to those skilled in the art that the invention is by no means limited to the states and / or substates of drift or potential drift given here as examples.

[0120] As an example, it is possible to distinguish the following states: absence of drift, drift and potential drift.

[0121] The state corresponding to the absence of drift can, for example, include the sub-states "excellent" or "good".

[0122] The state of drift can, for example, include the sub-states "drift" and "significant drift".

[0123] The state or sub-state of drift or potential drift can be described as "low" if the average of the values ​​is less than the average reference value and as "high" if the average of the values ​​is greater than the average reference value.

[0124] A biological parameter can, for example, be classified as being in excellent condition when the absolute value of the difference between (i) the mean of the values ​​and (ii) the mean reference value is strictly less than 0.5 standard deviations and the absolute value of the difference between (i) the most recent value and (ii) the mean reference value is strictly less than 0.5 standard deviations.

[0125] A biological parameter can, for example, be classified as being in good condition when: the absolute value of the difference between (i) the mean of the values ​​and (ii) the reference mean value is strictly less than 0.5 standard deviations and the difference between (i) the most recent value and (ii) the reference mean value is greater than or equal to 0.5 standard deviations, the absolute value of the difference between (i) the mean of the values ​​and (ii) the reference mean value is strictly less than 0.5 standard deviations and the difference between (i) the most recent value and (ii) the reference mean value is less than or equal to -0.5 standard deviations, the difference between (i) the mean of the values ​​and (ii) the reference mean value is between 0.5 standard deviations and 1 standard deviation and the difference between (i) the most recent value and (ii) the reference mean value is strictly less than 0.5 standard deviations, the difference between (i) the mean of the values ​​and (ii) the reference mean value is between -1 standard deviation and -0,5 standard deviations and the difference between (i) the most recent value and (ii) the reference mean value is strictly greater than -0.5 standard deviations, the difference between (i) the mean of the values ​​and (ii) the reference mean value is between 0.5 standard deviations and 1 standard deviation and the difference between (i) at least one of the values ​​and (ii) the reference mean value is strictly greater than 0.5 standard deviations, or the absolute value of the difference between (i) the mean of the values ​​and (ii) the reference mean value is between -1 standard deviation and -0.5 standard deviations and the difference between (i) at least one of the values ​​and (ii) the reference mean value is strictly less than -0.5 standard deviations.

[0126] A biological parameter is, for example, classified as potentially drifting when: the difference between (i) the mean of the values ​​and (ii) the mean reference value is greater than or equal to 0.5 reference standard deviations and less than 1 reference standard deviation, and the difference between (i) each of the values ​​and (ii) the mean reference value is greater than or equal to 0.5 reference standard deviations and less than 1 reference standard deviation, the difference between (i) the mean of the values ​​and (ii) the mean reference value is greater than or equal to -1 reference standard deviation and less than -0.5 reference standard deviation, and the difference between (i) each of the values ​​and (ii) the mean reference value is greater than or equal to -1 reference standard deviation and less than -0.5 reference standard deviation,the difference between (i) the mean of the values ​​and (ii) the reference mean value is greater than or equal to 1 reference standard deviation and is less than 2 reference standard deviations and the difference between (i) the most recent value and (ii) the reference mean value is less than 1 reference standard deviation, or the difference between (i) the mean of the values ​​and (ii) the reference mean value is greater than or equal to -2 reference standard deviations and is less than -1 reference standard deviation and the difference between (i) the most recent value and (ii) the reference mean value is greater than -1 reference standard deviation.

[0127] A biological parameter is, for example, classified as drifting when: the difference between (i) the mean of the values ​​and (ii) the mean reference value is greater than or equal to 1 reference standard deviation and less than 2 reference standard deviations, the difference between (i) the mean of the values ​​and (ii) the mean reference value is greater than or equal to -2 reference standard deviations and less than -1 reference standard deviation, the difference between (i) the mean of the values ​​and (ii) the mean reference value is greater than or equal to 1 reference standard deviation and less than 2 reference standard deviations, and the difference between (i) each of the values ​​and (ii) the mean reference value is greater than or equal to 1 reference standard deviation and less than 2 reference standard deviations, the difference between (i) the mean of the values ​​and (ii) the mean reference value is greater than or equal to -2 reference standard deviations and is less than -1 reference standard deviation and the difference between (i) each of the values ​​and (ii) the mean reference value is greater than or equal to -2 reference standard deviations and is less than -1 reference standard deviation, the difference between (i) the mean of the values ​​and (ii) the mean reference value is greater than or equal to 1 reference standard deviation and is less than 2 reference standard deviations and the difference between (i) the most recent value and (ii) the mean reference value is greater than 2 reference standard deviations,the difference between (i) the mean of the values ​​and (ii) the reference mean value is greater than or equal to -2 reference standard deviations and is less than -1 reference standard deviation and the difference between (i) the most recent value and (ii) the reference mean value is less than -2 reference standard deviations, the difference between (i) the mean of the values ​​and (ii) the reference mean value is greater than or equal to 2 reference standard deviations and is less than 3 reference standard deviations and the difference between (i) the most recent value and (ii) the reference mean value is less than 2 reference standard deviations, or the difference between (i) the mean of the values ​​and (ii) the reference mean value is greater than or equal to -3 reference standard deviations and is less than -2 reference standard deviations and the difference between (i) the most recent value and (ii) the reference mean value is greater than -2 reference standard deviations.

[0128] A biological parameter is, for example, classified as being in significant drift when: the difference between (i) the mean of the values ​​and (ii) the mean reference value is greater than or equal to 2 reference standard deviations, for example greater than or equal to 3 reference standard deviations, the difference between (i) the mean of the values ​​and (ii) the mean reference value is less than or equal to -2 reference standard deviations, for example less than or equal to -3 reference standard deviations, or the most recent value is less than a minimum reference threshold value or greater than a maximum reference threshold value.

[0129] When at least 3 values ​​are provided in step a), step b) of the method as defined above preferably includes the analysis of the drift of the values ​​taken two by two, starting from the oldest measured value or alternatively from the most recent measured value.

[0130] The method as defined above can then be characterized in that: in step b), it is determined whether said values ​​fluctuate around a mean reference value for said quantitative biological parameter for each time period between two instants tx and tz such that the state of drift between each of the successive values ​​taken two at a time between instant tx and tz is identical ( that is to say that the state of drift between vx and vx+1, that between vx+1 and vx+2, that between vx+2 and vx+3, etc. up to that between vx+y-1 and vz, with x+y=z, are identical), in step c), for each period of time defined in step b), conclude that said biological parameter is drifting or potentially drifting if said values ​​do not fluctuate around said mean reference value.

[0131] Step b) can therefore advantageously include comparing the drift state and / or the drift substate of the values ​​vi and vi+1 with the drift state and / or the drift substate of the values ​​vi+1 and vi+2: if the drift states and / or drift substates are identical, step b) may then include comparing the drift state and / or drift substate of the values ​​vi, vi+1 and vi+2 with the drift state and / or drift substate of the values ​​vi+2 and vi+3, and so on; if the drift states and / or drift substates are different, step b) may then include comparing the drift state and / or drift substate of the values ​​vi+2 and vi+3 with the drift state and / or drift substate of the values ​​vi+3 and vi+4, and so on; and step c) shall include at least two different drift states or substates of the different biological parameter, one of which is a drift state or drift substate defined between times vi and vi+1.

[0132] In step c), several different drift states and / or drift substates over time of the biological parameter can therefore be determined.

[0133] Thus, step b) could, for example, include: (i) determine the drift state and / or drift substate of the values ​​v1 and v2, (ii) determine the drift state of the values ​​v2 and v3, (iii) if the drift state and / or drift substate is the same for the values ​​v1 and v2 and for the values ​​v2 and v3, a. determine the drift state and / or drift substate of the values ​​v1, v2 and v3, b. determine the drift state and / or drift substate of the values ​​v3 and v4, c. if the drift state and / or drift substate is the same for the values ​​v1, v2 and v3 and for the values ​​v3 and v4, determine the drift state and / or drift substate of the values ​​v1 to v4, and compare it to the drift state and / or drift substate of the values ​​v4 and v5, and so on. d.if the drift state and / or drift substate for the values ​​v1, v2 and v3 is different from that for the values ​​v3 and v4, conclude the drift state and / or drift substate between times t1 and t3 in step c) and compare the drift state and / or drift substate of the values ​​v4 and v5 with that of the values ​​v5 and v6, etc., (iv) if the drift state and / or drift substate for the values ​​v1 and v2 is different from that for the values ​​v2 and v3, a. conclude the drift state and / or drift substate of the biological parameter between times t1 and t2 from the values ​​v1 and v2, b. compare the drift state and / or drift substate of the values ​​v3 and v4 and that of the values ​​v4 and v5, etc.

[0134] The values ​​analyzed taken two by two were preferably measured at less than 5 years intervals, for example at less than two years intervals.

[0135] Values ​​analyzed in pairs are preferably separated by less than 2 standard deviations.

[0136] If the values ​​analyzed two by two are separated by at least 2 standard deviations, each of its values ​​is analyzed individually or with another value.

[0137] When the quantitative biological parameter is potentially drifting or already drifting, the method as defined above may include a subsequent step of providing the value of at least one other biological parameter statistically related to the potentially drifting or already drifting biological parameter. Preferably, the method may then include the analysis of the metabolic drift of said at least one other biological parameter statistically related to the potentially drifting or already drifting biological parameter, for example, by the analytical method as defined above. Alternatively, the value of said other biological parameter is compared to a mean reference value and / or a threshold reference value.

[0138] This other biological parameter may be a quantitative parameter (e.g., a serum parameter, an infectious parameter, a clinical parameter, or a multi-parameter indicator) or a qualitative parameter (e.g., a genetic parameter, an infectious parameter, a non-serum parameter, or a lifestyle-related parameter).

[0139] A biological parameter statistically linked to a biological parameter is notably as defined above in the section " Biological parameter » .

[0140] Non-exhaustive examples are given below.

[0141] Vitamin D is an example of a biological parameter statistically linked to Vitamin B12. The Inventor has indeed shown that, when the concentration of vitamin B12 is in a low drift, more than 61% of patients have a vitamin D deficiency, whereas when the concentration of vitamin B12 is not in a low drift, only 44% of patients have a vitamin D deficiency.

[0142] Thus, if the vitamin B12 concentration is drifting low, particularly when the vitamin B12 concentration is less than 316pg / ml, the method as defined above preferably includes a subsequent step of measuring the vitamin D concentration.

[0143] If vitamin D levels are abnormal, particularly below 20 ng / ml (corresponding to vitamin D deficiency), the method preferably includes a subsequent step to determine HLA typing. If the patient is HLA-DQ02, HLA-DQ8, HLA-DR3, or HLA-A1, the method preferably includes a step to assess intestinal absorption or diagnose celiac disease. In Indeed, for example, a level below 21ng / ml is more specifically specific to the population carrying the HLA-DQ02, HLA-DQ8, HLA-DR3 or HLA-A1 genes, these same genes being known to cause intestinal malabsorption, in particular malabsorption of vitamin D.

[0144] For example, if the mean platelet volume is in low drift, the method includes a subsequent step of measuring free thyroxine.

[0145] When the quantitative biological parameter and / or a biological parameter statistically related to said biological parameter is potentially drifting or drifting, the method as defined above may include a subsequent step of implementing a method, preferably in vitro and / or ex vivo of diagnosis of a disease or of the risk of suffering from a disease, said disease being associated with the biological parameter in potential drift or drift and / or the biological parameter statistically linked to said biological parameter in potential drift or drift.

[0146] The method according to the invention has the advantage of allowing earlier diagnosis of a disease. Indeed, the diagnosis can be made as soon as a biological parameter associated with this disease is experiencing a drift or potential drift, instead of waiting until this biological parameter is outside the tolerance threshold.

[0147] When the said quantitative biological parameter is potentially drifting or drifting, the method as defined above includes at least one of the following subsequent steps: provide the value of at least one other biological parameter statistically related to the biological parameter in potential drift or drift, for example as defined above, and / or implement a method, preferably in vitro and / or ex vivo of diagnosis of a disease or of the risk of suffering from a disease, said disease being associated with the biological parameter in potential drift or drift or said biological parameter statistically linked to the biological parameter in potential drift or drift, for example as defined above.

[0148] If the biological parameter in drift or potential drift is the multi-parameter indicator defined by the total white blood cell count divided by the red blood cell count, the method includes a diagnostic step or an assessment of the risk of suffering from an infection by Candida albicans, for example, invasive candidiasis.

[0149] If the biological parameter in drift or potential drift is the multi-parameter indicator defined by the number of lymphocytes divided by the number of neutrophils, the method includes a diagnostic step or risk assessment of suffering from a Cytomegalovirus (CMV) infection. Method for defining an optimized reference mean value and an optimized reference standard deviation of a biological parameter X suitable for the analysis of the metabolic drift of said biological parameter

[0150] The present invention also makes it possible to provide a mean reference value and a standard deviation reference value of a biological parameter X optimized for the analysis of metabolic drift.

[0151] The method for defining a mean reference value and a standard deviation of a biological parameter X according to the invention takes into account the drift or the possible potential drift of biological parameter(s) statistically linked to the biological parameter X in order to define an optimized group, in order to obtain a mean reference value and standard deviation more representative of an optimum metabolic balance.

[0152] The present invention relates particularly to a method for defining an optimized reference mean value and reference standard deviation of a quantitative biological parameter X, according to claim 4.

[0153] The biological parameter is as defined above in the section of the same name.

[0154] The biological parameter is preferably selected from the group consisting of a serum parameter, an infectious parameter, a clinical parameter, a genetic parameter, a lifestyle parameter and a multi-parameter indicator, each of these parameters being in particular as defined above in the section "Biological parameter".

[0155] The biological parameter X is preferably a quantitative parameter.

[0156] The expression "biological parameter outside a reference threshold value" means that at least one of the values ​​and / or the average of the values ​​of said biological parameter is greater than or equal to a maximum reference threshold value or is less than or equal to a minimum reference threshold value.

[0157] The method for defining a mean reference value and a standard deviation reference of a biological parameter X is preferably implemented by a computer or at least partially implemented by a computer.

[0158] The method for defining a mean reference value and a standard deviation reference value for a biological parameter X is preferably a method in vitro and / or ex vivo.

[0159] Step a) consists of providing: ∘ the value or value of the biological parameter X measured at a given time and / or values ​​of the biological parameter X, for example at least two values, measured at different times separated in time, and ∘ the value of at least one other biological parameter measured at a given time and / or values ​​of at least one other biological parameter measured at different times separated in time, for example at least two values, for each subject in a group consisting of at least 50 subjects.

[0160] Step a) preferably includes providing at least two values ​​of at least one other quantitative biological parameter measured at different times separated in time and, optionally, the value of at least one other qualitative biological parameter measured at a given time and / or values ​​of at least one other qualitative biological parameter measured at different times separated in time.

[0161] The method for defining a mean reference value and a standard deviation reference of a biological parameter X as defined above may therefore include or not a prior step of measuring said biological parameter X and at least one other biological parameter, at a given time or at different times separated in time, for example in a biological sample.

[0162] The measurements taken over time are performed on biological samples from the same subject taken at different times.

[0163] Biological samples in which a given biological parameter is measured over time are preferably of the same nature and / or are preferably collected and / or measured under the same conditions.

[0164] By "spread out over time," we mean that the measurements of the biological parameter are spaced out over time, preferably at least 24 hours apart. The frequency of measurements depends in particular on the biological parameter in question.

[0165] The biological parameter can, for example, be measured on average every week, every two weeks, every three weeks, every month, every two months, every three months, every six months or even once a year.

[0166] Step b) consists of identifying the biological parameter(s) statistically linked to biological parameter X from said value and / or the average of said values ​​of biological parameter X and at least one other biological parameter.

[0167] In particular, step b) consists of identifying the quantitative biological parameter(s) and, optionally, the qualitative biological parameter(s) statistically linked to the quantitative biological parameter X, from one of said values ​​and / or the average of said values ​​of the quantitative biological parameter X, from one of said values ​​or the average of said values ​​of at least one other quantitative biological parameter and, optionally, from one of said values ​​of at least one other qualitative biological parameter.

[0168] A biological parameter statistically linked to a biological parameter is notably as defined above in the section " Biological parameter » .

[0169] In particular, the statistical significance between two biological parameters can be shown by any statistical method well known to a person skilled in the art, such as linear correlation, covariance, analysis of variance (ANOVA), Student's t-test, chi-square test, cross-sorting.

[0170] Preferably, two biological parameters are statistically linked if the level of statistical significance is greater than or equal to 90%, preferably greater than or equal to 95%, more preferably greater than or equal to 99%, for example greater than or equal to 99.5%.

[0171] In step c), for each biological parameter statistically linked to the biological parameter X identified in step b), the following are excluded from the group: (i) subjects in whom this biological parameter is outside a reference threshold value and / or in whom this biological parameter is drifting or potentially drifting, when it is a quantitative biological parameter, and / or (ii) subjects in whom this biological parameter is influential, when it is a qualitative biological parameter, in order to define a subgroup of subjects.

[0172] In particular, in step c), for each biological parameter identified in step b), the following are excluded from the group: (i) subjects in whom this quantitative biological parameter is drifting or potentially drifting and (ii) where applicable, subjects in whom this qualitative biological parameter is influential, in order to define a subgroup of subjects,

[0173] The assessment of the drift or potential drift of a biological parameter is notably carried out as described above in the method of analyzing the metabolic drift of at least one biological parameter in a subject.

[0174] Thus, the method according to the invention advantageously allows for the elimination of subjects from the group in whom a biological parameter correlated with the biological parameter X under study is drifting or potentially drifting (thanks to (i)). The method according to the invention thus makes it possible to optimize the reference group from which the mean reference value and the reference standard deviation of the biological parameter X are calculated.

[0175] By "subjects in which this biological parameter is influential", we mean subjects in which this qualitative biological parameter has a value that we do not wish to take into account in determining the optimized reference mean and reference standard deviation.

[0176] Non-exhaustive examples of parameters that may be influential include sex, weight, height and / or blood type.

[0177] When a qualitative biological parameter is influential, the method as defined above may include defining the reference mean value and the reference standard deviation in the other subgroup or one of the other subgroups corresponding to another value or another range of values ​​of the qualitative parameter, in particular among the subjects excluded in step c) ii). Optional step c') and step d) may then be carried out in each subgroup corresponding to another value or another range of values ​​of the qualitative parameter.

[0178] For example, if sex is an influential biological parameter, the reference mean and reference standard deviation can be calculated respectively in a subgroup consisting of male subjects and in a subgroup consisting of female subjects.

[0179] For example, blood type is a biological parameter that influences serum lipase levels. Individuals with blood type A generally have lower serum lipase levels than individuals with blood type O, and therefore significantly different mean values ​​and standard deviations. The optimized reference mean and standard deviation can then be calculated, respectively, for a subgroup of individuals with blood type A and a subgroup of individuals with blood type O.

[0180] The method as defined above may optionally include a step c') of providing the values ​​of at least one serum biological parameter measured at separate times, for example at at least two times t1 and t2 spread out over time, for each subject in the subgroup(s) defined in step c) and excluding from the subgroup(s) the subjects in whom this serum biological parameter is outside a reference threshold value and / or in whom this biological parameter is drifting or potentially drifting, in order to define one or more second subgroups of subjects.

[0181] Step c') can, for example, allow us to exclude from the subgroup one or more subjects who are developing an infection, a deficiency, or any other imbalances, without yet showing symptoms, the measured serum biological parameter preferably not being statistically linked to biological parameter X.

[0182] Step c') allows for further optimization of the reference group from which the mean reference value and the standard deviation of the biological parameter X are calculated.

[0183] Step d) consists of defining the mean and standard deviation of the biological parameter X in the subgroup of subjects defined in step c) or in the second subgroup(s) of subjects defined in step c', optionally taking into account at least one quantitative biological parameter.

[0184] Depending on the case, the optimized mean and reference standard deviation can indeed be defined according to one or more quantitative parameters statistically linked to the biological parameter X and / or according to one or more qualitative parameters statistically linked to the biological parameter X.

[0185] Non-limiting examples include the mean and standard deviation of ideal weight defined according to the subject's height, or the mean and standard deviation of hemoglobin levels according to the subject's height or weight. Method for optimizing a cohort of subjects

[0186] The present invention also makes it possible to provide a method for optimizing a cohort of subjects for the purpose of a study, for example of factors involved in and / or influencing a disease or a response to a treatment.

[0187] The method for optimizing a cohort of subjects according to the invention takes into account the drift or the possible drift of biological parameter(s) statistically linked to the biological parameter X under study in order to define an optimized cohort.

[0188] The present invention thus relates to a method for optimizing a cohort of subjects for the study of a biological parameter X, according to claim 6.

[0189] A method is also described for optimizing a cohort of subjects for the study of a biological parameter X, comprising the steps of: a) provide (i) at least two values ​​measured at different time points and / or an average of at least two values ​​measured at different time points of at least one quantitative biological parameter statistically related to biological parameter X and, optionally, (ii) a value measured at a given time or at least two values ​​measured at different time points of at least one qualitative biological parameter statistically related to biological parameter X, for each subject in a cohort of subjects, b) for each quantitative biological parameter, exclude from the cohort or place in a subgroup the subjects in whom this biological parameter is drifting or potentially drifting and, optionally, for each qualitative biological parameter, exclude from the cohort or place in a subgroup the subjects in whom this biological parameter is influential, in order to obtain an optimized cohort,in particular from the values ​​provided in step b), c) optionally, provide the value measured at a given time, at least two values ​​measured at different times separated in time and / or an average of at least two values ​​measured at different times separated in time of the biological parameter X in the optimized cohort defined in step b).

[0190] The different terms are as defined above in the different sections.

[0191] The optimized cohorts thus obtained may, for example, include a cohort of subjects of which at least one given parameter is not drifting or potentially drifting, a cohort of subjects in possible drift for said at least one biological parameter and / or a cohort of subjects in drift for said at least one biological parameter (for example in significant drift and / or drifting).

[0192] Within the cohort(s), it is also possible to take into account the various parameters influencing these parameters in drift or potential drift.

[0193] The reference mean and reference standard deviation are preferably defined according to the method for defining an optimized reference mean and reference standard deviation of an optimized biological parameter X, as defined above in the section of the same name.

[0194] The assessment of the drift or potential drift of a biological parameter is notably carried out as described above in the method of analyzing the metabolic drift of at least one biological parameter in a subject.

[0195] The method for optimizing a cohort of subjects as defined above is preferably implemented by a computer or at least partially implemented by a computer.

[0196] The method for optimizing a cohort of subjects as defined above is preferably a method in vitro and / or ex vivo.

[0197] The method for optimizing a cohort of subjects as defined above may therefore include a preliminary step of measuring said biological parameter X and at least one biological parameter statistically linked to said biological parameter in a biological sample,

[0198] Between steps a) and b), the method may include a step of analyzing the drift of at least one biological parameter and deducing whether this biological parameter is in one of the following three states: no drift, drifting or potential drifting.

[0199] It is also possible to use substates.

[0200] For example, a state in the absence of drift can be excellent or good.

[0201] For example, a drifting state can be drifting or significant drift.

[0202] These states and / or substates are, for example, as defined above.

[0203] It is possible to use subclasses, for example: excellent, good. In step b), (i), it is possible to define drift subclasses: for example, potential drift, drift, significant drift, and out of tolerance. Biological parameters whose values ​​are "normal" can be classified as excellent or good.

[0204] The method as defined above may include further steps of classifying the results of the cohort study according to the same method and of studying the evolution of drift levels. Method for assessing the state of drift of a subject and / or to aid in the diagnosis of a disease or in assessing the risk of developing a disease in a subject

[0205] A method for assessing the state of drift of a subject and / or for aiding in the diagnosis of a disease or in assessing the risk of suffering from a disease in a subject is also described.

[0206] The method is preferably implemented by a computer or at least partially implemented by a computer.

[0207] The method is, preferably, a method in vitro and / or ex vivo.

[0208] In a preferred embodiment, this method includes the implementation of a method for analyzing the metabolic drift of at least one biological parameter in a subject as defined above in the section of the same name.

[0209] In another embodiment, the method for assessing the drift state of a subject and / or aiding in the diagnosis of a disease or in assessing the risk of suffering from a disease in a subject, comprises the following steps: a) measure the value of at least one biological parameter at at least one time t1 in a biological sample of the subject, to obtain at least one value v1, b) compare the value(s) obtained in step a) to a reference threshold value and / or determine whether the values ​​obtained in step a) fluctuate around a mean reference value, and c) if the value or one of the values ​​of said biological parameter is greater than a maximum reference threshold value or less than a minimum reference threshold value and / or if said values ​​do not fluctuate around a mean reference value, provide the value of at least one other biological parameter statistically linked to said biological parameter and / or deduce whether the subject suffers or is likely to suffer from the disease.

[0210] The method for assessing a subject's state of drift and / or aiding in the diagnosis of a disease or the assessment of a subject's risk of developing a disease may, for example, include the following steps: a) provide the value of at least one biological parameter measured at at least two times t1 and t2 spread out over time in a biological sample of the subject, to obtain at least two values ​​v1 and v2, b) determine if the values ​​obtained in step a) fluctuate around a mean reference value, and c) if said values ​​do not fluctuate around a mean reference value, provide the value of at least one other biological parameter statistically linked to said biological parameter and / or deduce whether the subject suffers or is likely to suffer from the disease.

[0211] The different terms are as defined above.

[0212] In particular, the fluctuation of values ​​around a reference average value can be assessed as defined above in the section " Method for analyzing the metabolic drift of at least one biological parameter in a subject » .

[0213] The average reference value is preferably an optimized average reference value as defined above.

[0214] In the above methods, the quantitative biological parameter and / or said other statistically related biological parameter may be a direct or indirect marker of a disease or of the risk of suffering from a disease.

[0215] For example, the anti-thyroperoxidase biological marker is a marker of Hashimoto's thyroiditis, and can also be linked to Graves' disease or, in smaller quantities, to lupus, rheumatoid arthritis, hepatitis C, or breast cancer; the biological marker consisting of the presence of antibodies anti-candida albicansis a marker of Candida albicans infection; or the biological parameter consisting of the presence of anti- antibodies Helicobacter pylori are a marker of Helicobacter pylori infection.

[0216] In a preferred embodiment, the method as defined above is characterized in that said biological parameter is the mean platelet volume, said statistically related biological parameter is free thyroxine and said disease is hypothyroidism and / or Graves' disease.

[0217] For example, if the mean platelet volume is in a low drift, the method may include measuring free thyroxine (free T4). The probability of having an out-of-tolerance free thyroxine value is indeed greater than 30% in the population. If the mean platelet volume is in a low drift and / or if free T4 is indeed out-of-tolerance, infer that the subject has, or is likely to have, hypothyroidism and / or Hashimoto's thyroiditis and / or Graves' disease. The method may then include implementing a diagnostic method, preferably in vitro, hypothyroidism and / or Hashimoto's thyroiditis and / or Graves' disease.

[0218] In another embodiment, the method as defined above is characterized in that said biological parameter is a multi-parameter indicator defined by the total white blood cell count divided by the red blood cell count, and said disease is an infection by Candida albicans, for example, invasive candidiasis.

[0219] The total white blood cell count divided by the red blood cell count is indeed a much more significant indicator than the leukocyte or lymphocyte count for estimating the risk of a positive response to anti-Candida antibodies. albicans and therefore the risk of suffering from an infection-related illness Candida albicans.

[0220] In another embodiment, the method as defined above is characterized in that said biological parameter is a multi-parameter indicator defined by the number of lymphocytes divided by the number of neutrophils and said disease is a Cytomegalovirus (CMV) infection.

[0221] The number of lymphocytes divided by the number of neutrophils is indeed a more significant indicator for detecting a positive response to anti-cytomegalovirus IgG antibodies than, for example, the lymphocyte count alone or the neutrophil count alone. It is therefore possible to assess the drift of this indicator in order to further improve the accuracy of detecting cytomegalovirus infection. Computer program

[0222] The present invention also relates to a computer program comprising program code instructions for executing the steps of one of the methods as defined above, when said program is executed on a computer.

[0223] The present invention therefore relates to a computer program comprising program code instructions for the execution of the steps of the method for analyzing the metabolic drift of at least one quantitative biological parameter in a subject, as defined above, when said program is executed on a computer, in particular steps b) and c).

[0224] The present invention therefore relates to a computer program comprising program code instructions for executing the steps of the method for defining an optimized reference mean value and reference standard deviation of a biological parameter X, as defined above, when said program is executed on a computer, in particular steps b), c) and d) or steps b), c), c') and d).

[0225] The present invention therefore relates to a computer program comprising program code instructions for the execution of the steps of the method for optimizing a cohort of subjects for the study of a biological parameter X, as defined above, when said program is executed on a computer, in particular step b).

[0226] Also described is a computer program comprising program code instructions for the execution of one, several or steps of the method for assessing the drift state of a subject and / or for assisting in the diagnosis of a disease or in assessing the risk of suffering from a disease in a subject, as defined above, when said program is executed on a computer, in particular steps b) and c).

[0227] Also described is a computer-readable recording medium on which is recorded a computer program comprising program code instructions for the execution of one or more steps of one of the methods as defined above, the computer program preferably being as defined above.

[0228] In an advantageous embodiment, the computer program includes program code instructions for: record other biological parameters as they are recorded, refine the optimized mean and standard deviation of each biological parameter, dynamically search for at least one other influential biological parameter, and / or automatically produce a modifiable prescription.

[0229] Other features and advantages of the invention will become clearer from the following examples, which are given by way of illustration and not limitation. Figures

[0230] Figure 1 Analysis of leukocyte metabolic drift in a subject. Optimized mean: 5561.7 / mm3, Optimized standard deviation: 1405.8 / mm3, Upper reference threshold value: 10500 / mm3, Lower reference threshold value: 4000 / mm3. Figure 2 : Analysis of the metabolic drift of MCV in a subject. Optimized mean: 87.1, Optimized standard deviation: 4.9 g / dl, Upper reference threshold value: 97 g / dl, Lower reference threshold value: 82 g / dl. Figure 3Analysis of the metabolic drift of hematocrit in a subject. Optimized mean: 43.7%, Optimized standard deviation: 1.05%, Upper reference threshold value: 47%, Lower reference threshold value: 37%. Figure 4 Analysis of the metabolic drift of neutrophils in a subject. Optimized mean: 2931.9 mm3, Optimized standard deviation: 1016.8 mm3, Maximum reference threshold value: 7000 mm3, Minimum reference threshold value: 1500 mm3.

[0231] In the figures 1 to 4, “B” means a “Good” drift state, “E” a “Good” drift state, “DH” a high drift state, “DB” a low drift state, “DB P” a potential low drift state, “DH P” a potential high drift state; “DM imp” a significant low drift state; “ET” for “optimized reference standard deviation”; “Ref Threshold H”: maximum reference threshold defined by the HAS (Haute Autorité de Santé); “Ref Threshold B”: minimum reference threshold defined by the HAS; Ref Mean: optimized reference mean. EXAMPLES Example 1: Analysis of the metabolic drift of vitamin B12 Materials and Methods (i) Determination of the optimized mean reference value and the optimized reference standard deviation for vitamin B12

[0232] The initial group of subjects comprises 67 subjects.

[0233] The biological parameters studied include, among others, vitamin B12, vitamin D, red blood cell count, MCV, hemoglobin level, white blood cell count, neutrophil count, eosinophil count, basophil count, lymphocytes, monocytes, platelet count, mean platelet volume, fasting blood glucose, glycated hemoglobin, uric acid, creatinine, AST and ALT, Gamma GT, bilirubin, free bilirubin, conjugated bilirubin, HLA DQ, HLA A, HLA B, HLA C, HLA DR.

[0234] The values ​​of the biological parameters in the initial group are provided at a time t.

[0235] Biological parameters identified as being statistically linked to vitamin B12 concentration include vitamin D, HLA-DQ3, red blood cell count and hemoglobin level.

[0236] Vitamin D concentration is out of tolerance in more than 61 to 72% of patients with low vitamin B12 drift. 37 subjects with vitamin D concentration in drift or potential drift or out of tolerance are therefore excluded from the group for the calculation of the reference mean and reference standard deviation.

[0237] The mean and standard deviation are then calculated in the remaining group of 30 subjects. (ii) Metabolic drift analysis

[0238] The vitamin B12 values ​​measured over time in this patient are as follows: v1 = 250pg / ml; v2 = 300pg / ml; v3 = 278pg / ml (measurements taken 3 months apart).

[0239] The value v1 is equal to 1.36 optimized reference standard deviation of the optimized reference mean, the value v2 is equal to 1.09 and the value v3 is equal to 1.21.

[0240] The absolute value of the difference between the optimized reference mean and the mean v1 + v2 is equal to 225.5 pg / ml, i.e. equal to 1.22 x the optimized reference standard deviation.

[0241] The values ​​v1 and v2 are therefore drifting.

[0242] The absolute value of the difference between the optimized reference mean and the v2+v3 mean is equal to 211.5 pg / ml, i.e. equal to 1.15 x the optimized reference standard deviation.

[0243] The values ​​v2 and v3 are therefore drifting.

[0244] Therefore, it is also possible to look at the drift of the average of v1, v2 and v3.

[0245] The absolute value of the difference between the optimized reference mean and the mean v1 + v2 + v3 is equal to 224.5 pg / ml, i.e. equal to 1.22 x the optimized reference standard deviation.

[0246] Vitamin B12 levels are therefore deficient. Consequently, vitamin D levels are also likely deficient, and its levels are being measured to confirm this. Results (1) Optimized mean reference value and optimized reference standard deviation for vitamin B12

[0247] The mean value and reference standard deviation of vitamin B12, specifically calculated in the subgroup not including subjects with vitamin D concentration in drift or potential drift or out of tolerance, are 500.5pg / ml and 184.5pg / ml respectively. (2) Analysis of the metabolic drift of vitamin B12 in a subject

[0248] The optimized mean reference value and optimized reference standard deviation obtained previously are used to assess the metabolic drift of vitamin B12 in a subject.

[0249] The vitamin B12 values ​​measured over time in this patient are as follows: 250pg / ml; 300pg / ml; 278pg / ml (measurements taken 3 months apart).

[0250] The reference range for vitamin B12 used in France is from 180pg / ml to 914pg / ml.

[0251] Furthermore, using the optimized mean value ± 2 optimized standard deviations as the classical tolerance thresholds, we obtain an optimized minimum threshold value of 131.5 and an optimized maximum threshold value of 869.5.

[0252] In both cases, we therefore observe that the values ​​measured for this subject are within the range of reference values ​​according to the methods currently used.

[0253] However, the measured vitamin B12 levels do not fluctuate around the optimized average reference value. More precisely, the average level at these three points is within 1.2 standard deviations of the optimized average reference value. Therefore, vitamin B12 levels are drifting, which is not detected by currently used methods.

[0254] Because vitamin D is statistically linked to vitamin B12, vitamin D concentration in this subject is measured over time. Similarly, the values ​​obtained likely do not fluctuate around the reference mean and are probably drifting or potentially drifting.

[0255] The subject then receives treatment based on vitamin D, for example at a dose of 1 ampoule of 100,000 IU every 3 months and vitamin B12 for example by injection or orally.

[0256] Her vitamin B12 and vitamin D concentration are measured over time until sufficient correction is achieved. Example 2: Analysis of the metabolic drift of different biological parameters in the same subject

[0257] There figure 1 This shows the chronic drift of leukocytes over a period of one year, before a return to a stable state. However, the measured leukocyte values ​​begin to become low. This biological parameter therefore needs to be monitored.

[0258] There figure 2Regarding the analysis of the metabolic drift of VGM, no drift in this biological parameter has been observed for 12 years. This shows that a biological parameter can be very stable over time.

[0259] There figure 3 shows a significant chronic drift in hematocrit for 1.5 years, before returning to a stable state.

[0260] There figure 4 shows a chronic drift of Neutrophil Polymorphonuclear Cells for 1.5 years, before returning to an excellent state.

Claims

1. Method for analysing the metabolic drift of at least one quantitative biological parameter in a subject, characterized in that it comprises the following steps: a) providing the values of at least one quantitative biological parameter measured at least at two different spaced-apart times t1 and t2, to obtain at least two values v1 and v2; b) determining whether said values fluctuate around a mean reference value for said quantitative biological parameter; c) if said values do not fluctuate around said mean reference value, concluding therefrom that said biological parameter shows drift or potential drift, and when said quantitative biological parameter shows drift or potential drift, providing the value of at least one other biological parameter statistically related to the biological parameter showing drift or potential drift; and / or implementing a method to diagnose a disease or risk of suffering from a disease, said disease being associated with said biological parameter showing drift or potential drift or with a biological parameter statistically related to said biological parameter showing drift, characterized in that said values fluctuate around a mean reference value for said quantitative biological parameter if the mean of said values is equal or equivalent to said mean reference value, and characterized in that (i) said biological parameter is the mean platelet volume, said statistically related biological parameter is free thyroxine and said disease is hypothyroidism and / or a Graves-Basedow disease or (ii) said biological parameter is a multi-parameter indicator defined by the number of white blood cells divided by the number red blood cells and said disease is infection with Candida albicans, or (iii) said biological parameter is a multi-parameter indicator defined by the number of lymphocytes divided by the number of polymorphonuclear neutrophils and said disease is infection with Cytomegalovirus, characterized in that the mean reference value is defined according to the method of claim 4.

2. The analysis method according to claim 1, characterized in that: - said quantitative biological parameter shows drift if: o the absolute value of the difference between (i) the mean of said values and (ii) the mean reference value is higher than or equal to 1 reference standard deviation; or o the absolute value of the difference between (i) the mean of said values and (ii) the mean reference value is higher than or equal to 0.5 reference standard deviation and lower than 1 reference standard deviation, and the absolute value of the difference between (i) at least one of said values and (ii) the mean reference value is higher than or equal to 2 reference standard deviations; or o at least one of said values is higher than or equal to a maximum reference threshold value and / or at least one of said values is lower than or equal to a minimum reference threshold value; and / or - said quantitative biological parameter shows potential drift if: o the absolute value of the difference between (i) at least one of said values or the mean of said values and (ii) the mean reference value is higher than or equal to 0.5 reference standard deviation and lower than 1 reference standard deviation; and o if the absolute value of the difference between (i) each of said values and (ii) the mean reference value is lower than 2 reference standard deviations.

3. The analysis method according to one of claims 1 to 2, characterized in that when said quantitative biological parameter shows potential drift or drift, said method comprises a subsequent step to measure the value of said quantitative biological parameter at another time ti.

4. Method for defining an optimised mean reference value and optimised reference standard deviation of a quantitative biological parameter X, comprising: a) providing (i) a value of the quantitative biological parameter X measured at a given time or at least two values of the quantitative biological parameter X measured at different spaced-apart times and (ii) at least two values of at least one other quantitative biological parameter measured at different spaced-apart times, for each subject in a group of at least 50 subjects; b) identifying the quantitative biological parameter(s) statistically related to the quantitative biological parameter X from one of said values and / or from the mean of said values of the quantitative biological parameter X, and from one of said values or the mean of said values of at least one other quantitative biological parameter; c) for each biological parameter identified at step b), removing from the group those subjects in whom this biological parameter shows drift or potential drift, to define a sub-group of subjects; c') optionally, providing at least two values of at least one serum biological parameter measured at different spaced-apart times for each subject in the sub-group defined at step c) and removing from the sub-group those subjects in whom this serum biological parameter shows drift or potential drift, to define a second sub-group of subjects; and d) defining the mean and standard deviation of the biological parameter X in the sub-group of subjects defined at step c) or in the second sub-group of subjects defined at step c'), characterized in that the biological parameter shows drift or potential drift at step c), if the mean of the values for this biological parameter is not equal or equivalent to said mean reference value for this biological parameter, and characterized in that a biological parameter showing drift or potential drift corresponds to an abnormal or potentially abnormal clinical condition for this biological parameter, characterized in that (i) said biological parameter X is the mean platelet volume and said statistically related biological parameter is free thyroxine or (ii) said biological parameter X is a multi-parameter indicator defined by the number of white blood cells divided by the number red blood cells, or (iii) said biological parameter X is a multi-parameter indicator defined by the number of lymphocytes divided by the number of polymorphonuclear neutrophils.

5. The method according to claim 4, characterized in that step a) further comprises providing the value of at least one other qualitative biological parameter measured at a given time and / or values of at least one other qualitative biological parameter measured at different spaced-apart times, in that step b) further comprises the identification of qualitative biological parameter(s) statistically related to the quantitative biological parameter X from one of said values and / or from the mean of said values of the quantitative biological parameter X and from one or said value(s) of at least one other qualitative biological parameter, and in that step c) further comprises the removal from the group of those subjects in whom this qualitative biological parameter is influential.

6. Method for optimising a cohort of subjects for the purpose of studying a biological parameter X, comprising the steps of: a) providing at least two values measured at different spaced-apart times and / or a mean of at least two values measured at different spaced-apart times of at least one quantitative biological parameter statistically related to biological parameter X, for each subject in a cohort of subjects; b) for each quantitative biological parameter, removing from the cohort or placing in a sub-group those subjects in whom this biological parameter shows drift or potential drift, to obtain an optimised cohort; c) optionally, providing the value at a given time, at least two values measured at different spaced-apart times and / or a mean of at least two values measured at different spaced-apart times of the biological parameter X in the optimised cohort defined at step b), characterized in that the biological parameter shows drift or potential drift at step b), if the mean of the values for this biological parameter is not equal or equivalent to said mean reference value for this biological parameter, and characterized in that a biological parameter showing drift or potential drift corresponds to an abnormal or potentially abnormal clinical condition for this biological parameter, characterized in that (i) said biological parameter X is the mean platelet volume and said statistically related biological parameter is free thyroxine or (ii) said biological parameter X is a multi-parameter indicator defined by the number of white blood cells divided by the number red blood cells, or (iii) said biological parameter X is a multi-parameter indicator defined by the number of lymphocytes divided by the number of polymorphonuclear neutrophils.

7. The method according to claim 6, characterized in that step a) further comprises the providing of a value measured at a given time or of at least two values measured at different spaced-apart times of at least one qualitative biological parameter statistically related to biological parameter X, for each subject in the cohort of subjects, and in that step b) for each qualitative biological parameter further comprises removing from the cohort or placing in a sub-group those subjects in whom this qualitative biological parameter is influential.

8. Computer programme comprising programme code instructions to execute the steps of a method according to claims 1 to 7, when said programme is executed on a computer.