Method for predicting the tendency of horse weight loss

Abstract

The present invention provides a method for predicting the tendency of weight loss in Equus animals by evaluating multiple markers in a sample obtained from an Equus animal. Such markers include fermentation products and / or their metabolites, such as volatile fatty acids, bacterial population diversity indicators, and the abundance / relative abundance of specific bacterial taxa.

Description

Field of the Invention

[0001] The present invention belongs to the field of methods for predicting potential weight loss in horses. Background Art

[0002] Bacteria present in the gastrointestinal tract of all species play a fundamental role in the health and whole-body metabolism of the host, whereby disruption of energy balance - as observed in obesity and related metabolic diseases - is associated with dysregulation of the gut microbiota 1-4 . Associations between obesity and gut microbiome composition have been identified in humans 5,6 , dogs 7 and (more recently in) ponies 8 .

[0003] Horses and ponies have adapted to consuming a high-fiber diet, whereby microbial fermentation of dietary fiber in their mid- and hindgut produces volatile fatty acids (VFAs), including acetic, propionic, and butyric acids, which together contribute a large portion of the individual animal's daily energy requirements 9 . The combined increase in nutrient-dense energy supply and lack of exercise have led to a high prevalence of obesity in leisure horses and ponies in industrialized countries, and this is a major welfare issue 10-12 . It has been determined that certain breeds and types of horses and ponies (including native and Welsh breeds) are at higher risk of obesity than other breeds and types, indicating an important genetic component in the development of obesity in this species. As with humans, not all horses and ponies lose weight at the same rate when placed on a restricted-energy diet (e.g., 1.25% of body mass (BM) as daily dry matter (DM)), and some require further restriction (e.g., 1.0% BM as daily DM) to induce the desired reduction in BM 13 .

[0004] To date, studies evaluating the equine gut microbiome have generally focused on the effects of diet 14–16 . In addition, associations between fecal microbiome and chronic laminitis 17 and equine metabolic syndrome (EMS) 18 have been evaluated. Although no studies have described changes in fecal microbiome composition following weight loss in ponies, two studies have evaluated the stability of the fecal microbiome over time in multiple groups of horses and ponies. During a 6-week period following the end of a weight loss trial, it has been determined that 65% of the fecal bacterial communities in one group of horses remained unchanged between different time points 19Moreover, recently, the stability of the equine fecal microbiome was evaluated over a 52-week period in a small group of horses kept on pasture. Thus, we have some evidence that the equine gut microbiome is affected by diet and different disease states. Additionally, recently, the effects of age and obesity on the equine fecal microbiome were evaluated over a 2-year period, and changes mainly associated with the obese state were found. 8 .

[0005] Thus, there is evidence that the equine gut microbiome is affected by diet and different disease / metabolic states. 8 . However, there is still a need in the art for methods capable of predicting the propensity of horses to lose weight, thereby allowing horse owners to impose appropriate caloric restrictions. SUMMARY OF THE INVENTION

[0006] The inventors of the present invention have developed methods capable of predicting the propensity of Equus animals to lose weight. This is achieved by evaluating various markers in samples obtained from the animal (such as fecal, blood or urine samples). These include, for example, fermentation products and / or their metabolites, such as volatile fatty acids (VFAs), as well as indices of bacterial population diversity, and / or the abundance (and / or relative abundance) of specific bacterial taxa. The methods of the present invention are capable of predicting possible weight loss, as shown in the examples, and in certain embodiments, the methods of the present invention are capable of being used to quantitatively predict the initial weight loss after a period of caloric restriction.

[0007] Thus, in one aspect, the present invention provides a method for predicting weight loss in an Equus animal, comprising measuring the concentration of one or more fermentation products and / or their metabolites (such as one or more VFAs) in a sample obtained from the animal; wherein the concentration of the one or more fermentation products and / or their metabolites (such as one or more VFAs) is positively correlated with the predicted weight loss of the animal. For example, the inventors found that the concentrations of acetic, butyric, propionic and branched-chain VFAs before the caloric restriction period were higher in animals that subsequently lost more weight (see Example 1; Figure 9 ). For example, it was found that an acetic acid concentration of at least 12 mM is an accurate predictor of a good propensity to lose weight. For example, the inventors have found that such animals are able to lose at least 8% of their initial body weight after a 7-week caloric restriction period (see Figure 10 ). Thus, these markers provide a means of predicting the propensity of horses to lose weight.

[0008] On the other hand, the present invention provides a method for predicting weight loss in Equus animals, which includes detecting and / or quantifying multiple bacterial taxa in a sample obtained from the animal, and calculating a diversity measure of the bacterial population present in the sample; wherein, the diversity measure is negatively correlated with the predicted animal weight loss. The inventors found that specific diversity measures (Inverse-Simpson, Shannon-Weiner, and number of observed species) of the bacterial population before the calorie restriction period were higher in animals that subsequently lost less weight (see Example 1; Figure 8 ). Therefore, these markers provide a further means for predicting the tendency of horses to lose weight.

[0009] On the other hand, the present invention provides a method for predicting weight loss in Equus animals, which includes quantifying (i) bacteria of the genus Sphaerochaeta, and / or (ii) bacteria of the genus Treponema, and / or (iii) bacteria of the genus Anaerovorax and / or (iv) bacteria of the genus Mobilitalea, and / or (v) bacteria of the phylum Actinobacteria, and / or (vi) bacteria of the phylum Spirochaetes, and / or (vii) fiber-fermenting bacteria (such as bacteria of the genus Fibrobacter) in a specimen obtained from the animal, and the abundance and relative abundance thereof; wherein, the abundance and / or relative abundance of the bacteria is negatively correlated with the predicted animal weight loss. The inventors found that the abundance / relative abundance of such bacteria was higher in animals that subsequently lost less weight before the calorie restriction period (see Examples 1 and 2; Figures 12 - 14 ). Therefore, these further markers provide a further method for predicting the tendency of horses to lose weight.

[0010] The markers used in the above aspects of the present invention can be used alone or in combination as an index for predicting weight loss. The combination of these different markers can improve the predictive ability of the method of the present invention. Sequence Listing <110> Mas Company <120> Method for Predicting the Tendency of Horses to Lose Weight <130> P075185WO <141> 2020-05-29 <150> GB1907782.5 <151> 2019-05-31 <160> 2 <170> SIPOSequenceListing 1.0 <210> 1 <211> 20 <212> DNA <213> Artificial Sequence <400> 1 agagtttgat cctggctcag 20 <210> 2 <211> 25 <212> DNA <213> Artificial Sequence <400> 2 acgagtgcgt ctgctgccty ccgta 25 BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 The stacked bar graph shows the relative abundances of the dominant bacterial phyla (> 0.05% abundance) before dietary restriction (pre - defined diet) and 7 weeks after dietary restriction (post - defined diet; n = 15).

[0012] Figure 2 The stacked bar graph shows the relative abundances of the dominant bacterial genera (> 0.05% abundance) before dietary restriction (pre - defined diet) and 7 weeks after dietary restriction (post - defined diet; n = 15).

[0013] Figure 3 Distance - based redundancy analysis shows the structure of the initial bacterial community attributable to the weight - loss group (n = 15).

[0014] Figure 4 The resulting marginal means plot shows the predicted body - mass changes during the 7 - week defined dietary restriction in this study (n = 15). Shaded area = 95% confidence interval.

[0015] Figure 5 Predicted cumulative proportional weight loss of individual animals during the 7 - week defined dietary restriction in this study (adjusted to week 0; n = 15).

[0016] Figure 6 A phylogenetic tree depicting the fecal bacterial microbiome clustering within animals (n = 15).

[0017] Figure 7 Rarefaction curves

[0018] Figure 8The marginal means plot shows the change in predicted weight loss proportional to the following initial diversity indices: (A) observed species, (B) Shannon–Weiner, (C) inverse Simpson. The shaded area corresponds to the 95% CI. n = 15.

[0019] Figure 9 The marginal means plot shows the change in predicted weight loss proportional to the following initial VFA concentrations: (A) acetate, (B) propionate, (C) butyrate, (D) branched chained volatile fatty acid (BCVFA). The shaded area corresponds to the 95% CI. n = 15.

[0020] Figure 10 The receiver operating characteristic (ROC) curve of the initial acetate concentration as a predictor for animals with weight loss ≥8%. At the optimal cut-off of 12 mM acetate, the sensitivity was 88.9% while the specificity was 100%.

[0021] Figure 11 Initial abundances of Spharerochaeta in quartiles divided by initial acetate concentration according to ANCOM analysis (1 = low concentration; 4 = high concentration). n = 15.

[0022] Figure 12 Initial abundances of Actinobacteria between weight loss groups according to ANCOM analysis (1 = low concentration; 4 = high concentration). n = 15.

[0023] Figure 13 Initial abundances of the genus Mobilitalea between weight loss groups according to ANCOM analysis. n = 15.

[0024] Figure 14 Distance-based redundancy analysis shows the association between the structure of the initial bacterial community of individual animals and the initial fecal VFA concentration in the low-weight loss group, medium-weight loss group, and high-weight loss group. n = 15.

[0025] Disclosure of the Invention

[0026] Horse

[0027] The method of the present invention can be used to predict weight loss in Equus animals.

[0028] This genus is part of the family Equidae, which includes many extant and extinct species. Species of the genus Equus include the horse (Equus caballus), mule (Equus mulus), hinny, wild horse (Equus ferus), mountain zebra (Equus zebra), African wild ass (Equus africanus), domestic donkey (Equus africanus asinus), Grevy's zebra (Equus grevyi), Asian wild ass (Equus hemionus), Tibetan wild ass (Equus kiang), and plains zebra (Equus quagga). Preferably, the animal is of the horse (Equus caballus) species, including horses and ponies, and more preferably, the animal is a pony. Animals of the horse (Equus caballus) species can be of any age, sex, or castration status, and thus the present invention can be used to evaluate (but is not limited to) foals, weanlings, yearlings, colts, fillies, mares, stallions, geldings, or rigs. Any and all breeds of animals of the genus Equus, preferably of the horse (Equus caballus) species, can be evaluated using the method of the present invention.

[0029] Preferably, the method of the present invention is performed on a sample from an Equus animal that is at least overweight before starting a calorie restriction period. The body weight status of a horse is preferably evaluated by body condition scoring (BCS), 20 which is a widely used method for estimating body fatness in horses and ponies that combines visual assessment of multiple body parts (such as the neck, withers, loin, base of the tail, ribs, shoulders) with subcutaneous body fat palpation, usually scoring each part from 1 - 9 and then calculating the overall average. As used herein, "overweight" is preferably defined as an animal with a BCS score greater than 6 / 9 (6 out of 9), and "obese" is preferably defined as an animal greater than 7 / 9 (7 out of 9). Although BCS scoring is the preferred method for evaluating body weight status / body fatness, other techniques available to those skilled in the art, such as deuterium oxide dilution and bioelectrical impedance analysis, can also be used.

[0030] Weight loss

[0031] As is well known in the art, "weight loss" refers to a reduction in body mass. In the context of the present invention, weight loss refers to a reduction in body mass of an Equus animal (e.g., in response to dietary changes such as calorie restriction and / or increased exercise). Preferably, the methods of the present invention are used to predict weight loss in response to calorie restriction (with or without increased exercise).

[0032] The rate of weight loss will depend on many factors, including but not limited to the animal's initial body fat content, the degree of calorie restriction, the duration of calorie restriction, and the season. As a guide, after being placed on a diet restriction of a low-calorie feed of 1.25% body mass of dry matter (DM), from the second week onwards, a good rate of weight loss should be ≥0.4% body mass / week. Under a diet restriction of a low-calorie feed of 1% DM, from the second week after being placed on the diet restriction, a good rate of weight loss can be ≥0.7% body mass / week.

[0033] At colder temperatures or during adverse weather (rain / wind), more weight loss can be expected as the animal expends more calories to maintain body temperature. Thus, during colder temperatures (e.g., in winter), under a diet restriction of a low-calorie feed of 1% DM, from the second week after being placed on the diet restriction, a good rate of weight loss should be ≥0.9% body mass / week.

[0034] Preferably, this weight loss is detected between the second and tenth weeks after the start of the low-calorie diet. This is to take into account the initial significant weight loss due to a potential reduction in gut fill. In addition, the inventors have found that around 10 - 12 weeks of feeding restriction, the rate of weight loss tends to decrease, at which point, if further weight loss is desired, it may be necessary to increase the amount of exercise (if possible) and / or (taking into account the physiological and behavioral requirements of the animal) further reduce calorie intake if possible.

[0035] Calorie restriction generally refers to a reduction in an animal's calorie intake compared to when the animal can eat ad libitum. As used herein, calorie restriction for weight management purposes can be defined as an Equus animal consuming less than 1.50%, 1.25%, 1.20%, 1.15%, 1.10%, 1.05%, or 1.00% of its body weight in dry matter per day on average. Preferably, calorie restriction is defined as an Equus animal consuming less than 1.50%, 1.25%, or 1.10% of its body weight in dry matter per day on average, more preferably less than 1.25% or 1.10%, such as approximately 1%. Preferably, as referred to herein, "dry matter" includes fibrous material (and more preferably consists essentially of, or consists of, it), such as preserved grass, such as hay or hay substitutes (preferably, for nutritional purposes, low-calorie vitamins / minerals and protein balancers are added). In these embodiments, calorie restriction is achieved by feeding approximately 1.0% of the body weight in dry matter (such as hay) per day and adding a balancer (see "Materials and Methods"). According to the embodiments, during calorie restriction, the supply of water should not be restricted. A basal level of exercise is also recommended during the calorie restriction period, which may be organized (such as riding, leading the horse by hand, or lunging, etc.) and unorganized (such as entering a large barn / feedlot / indoor or outdoor school or pasture, and appropriately wearing a muzzle to prevent or limit ingestion), including daily grazing, preferably, according to the embodiments, at least 30 minutes of free movement (see "Materials and Methods"). Due to veterinary or behavioral reasons, some animals may not be able to exercise organized or unorganized for all or part of the time while implementing the calorie restriction protocol.

[0036] To ensure sufficient weight loss, the calorie restriction period may need to be maintained for at least 4, 6, 8, 10, 12, 14, or 16 weeks. Preferably, the calorie restriction period will be maintained for at least 6 or 7 weeks, and more preferably, at least 7 weeks (according to the embodiments). Multiple calorie restriction periods can also be cycled, for example, by restricting calorie intake for a defined number of weeks, monitoring the resulting weight loss, and then restricting again when appropriate. The time period can refer to a continuous or cumulative time period, but preferably refers to a continuous time period.

[0037] Once a prediction of weight loss in an Equus animal is obtained by the method of the present invention, the horse owner can adjust the diet and / or exercise level (especially the diet) of the Equus animal according to the desired weight loss within a given time period. For example, an animal with a predicted reduced weight loss can be placed on a prescribed diet with a higher calorie restriction (e.g., about 1% of body mass per day of dry matter), or for a longer duration than originally planned, to ensure the desired weight loss is achieved. Conversely, an animal with a predicted excessive weight loss can be placed on a diet with a lower calorie restriction or for a shorter duration than originally planned (e.g., about 1.5% of body mass per day of dry matter, or about 1% but for a relatively short duration, e.g., <10 weeks). This helps to avoid unnecessary health and behavioral risks such as hyperlipidemia (special care is needed when imposing any calorie restriction on donkeys and pregnant mares), as well as foraging alternative behaviors such as cribbing / nibbling. Thus, the present invention is useful in ensuring that an Equus animal is placed on the most appropriate prescribed diet regime to achieve the desired weight loss level, taking into account the differences in weight loss propensity that exist between individuals. 13 。

[0038] Fermentation products and / or their metabolites as weight loss prediction indicators

[0039] According to one aspect of the present invention, the concentration of one or more fermentation products and / or their metabolites in a sample obtained from an Equus animal is used as a weight loss prediction indicator, preferably for weight loss in response to calorie restriction. As shown in the examples, the initial concentration of a variety of fermentation products (especially VFAs) and / or their metabolites is positively correlated with subsequent weight loss. "Positive correlation" means that there is a positive correlation between the concentration and subsequent weight loss (see Figure 9 for example), or in other words, a higher concentration of fermentation products and / or their metabolites in the sample predicts more subsequent weight loss (compared to a lower concentration), and vice versa. Generally, it can be expected that, under the same diet, an animal with a higher concentration of one or more fermentation products and / or their metabolites in the sample will lose weight faster than an animal with a lower concentration of one or more fermentation products and / or their metabolites. Additionally or alternatively, it can be expected that, under the same diet, an animal with a higher concentration of one or more fermentation products and / or their metabolites will lose more weight over the same time span than an animal with a lower concentration of one or more fermentation products and / or their metabolites.

[0040] "Fermentation products" refers to the decomposition products of all ingested feed fermented by symbiotic bacteria in the animal gastrointestinal tract. Such feed includes carbohydrates, such as cellulose, hemicellulose and pectin, but also includes starch and water-soluble carbohydrates. "Their metabolites" refers to the metabolites of such fermentation products, such as those produced in the animal gastrointestinal tract or liver. Fermentation products and / or their metabolites that can be measured according to the method of the present invention include, but are not limited to: volatile fatty acids (VFA), such as acetate, butyrate, propionate or branched-chain VFA (including, for example, isobutyrate, isovalerate or 2-methylbutyrate); precursors of VFA in bacterial fermentation, such as lactyl CoA, acrylyl CoA, propionyl CoA, pyruvate, acetyl CoA, acetyl phosphate, butyryl CoA, crotonyl CoA, β-OH butyryl CoA; and metabolites of VFA, such as acetoacetate, β-hydroxybutyrate, crotonyl CoA, L-β-hydroxybutyryl CoA and D-β-hydroxybutyryl CoA, lactate, propionyl CoA, acetyl CoA and malonyl CoA.

[0041] Equine (Equus) animals with a lower tendency to lose weight have a more diverse initial gut microbiome composition (as detailed below and demonstrated in the examples). Without wishing to be bound by theory, such animals may have higher metabolic efficiency, greater fermentative capacity, and thus greater absorption of fermentation products (such as VFAs) and / or their metabolites when entering the circulation from the gastrointestinal tract (thereby increasing their concentration in the blood from the gastrointestinal tract). Thus, or in addition, or alternatively, when fed a restricted-fiber defined diet, they may be able to better increase fiber digestibility, thereby (compared to those animals that lose more weight) improving metabolic efficiency in a state of negative energy balance. This may limit the breakdown of triglyceride reserves during negative energy balance (thereby limiting weight loss). Thus, in embodiments evaluating blood or urine samples, the concentration of one or more fermentation products and / or their metabolites (such as one or more VFAs) evaluated according to the present invention may be negatively correlated with predicted animal weight loss.

[0042] The concentration of fermentation products and / or their metabolites in a sample obtained from an animal can be measured by any suitable means known in the art, for example, the gas-liquid chromatography implemented in the examples. Preferably, the sample is a fecal, blood, or urine sample (more preferably a fecal sample), and an exemplary protocol for extracting fermentation products (such as VFAs) and / or their metabolites from fecal samples is provided in the examples. Determining the concentration of more than one different fermentation product and / or their metabolites (such as more than one different VFA) in each sample, for example at least 2, 3, 4, or 5, can improve the predictive ability of the method of the present invention.

[0043] Preferably, as analyzed in the examples, one or more fermentation products and / or their metabolites evaluated in the method of the present invention are one or more carbohydrate fermentation products, and more preferably one or more VFAs. More preferably, one or more VFAs are selected from the group consisting of acetic acid, butyric acid, propionic acid, and / or branched-chain VFAs. As shown in the examples, the initial concentration of these VFAs is highly positively correlated with weight loss (see Figure 9 ). More preferably, the VFA is acetic acid, which shows the strongest positive correlation with weight loss among all the VFAs analyzed in the examples.

[0044] In a particularly preferred embodiment, the acetic acid concentration in a sample obtained from an animal (preferably a fecal sample) is used to predict weight loss. An acetic acid concentration of at least 10 mM indicates that the animal has an acceptable tendency to lose weight. Preferably, an acetic acid concentration of at least 12 mM predicts a good tendency to lose weight, for example, between 12 mM and 20 mM, or between 12 mM and 15 mM. An acetic acid concentration above 20 mM predicts a very good tendency to lose weight. For example, the inventors have shown that an acetic acid concentration of at least 12 mM predicts a loss of at least 8% of the initial body mass after a calorie restriction period, preferably, where the animal consumes less than 1.10% of its body mass in dry matter per day on average for at least 7 weeks. This acetic acid concentration provides 88.9% test sensitivity and 100% specificity for predicting a good weight loss tendency as defined above. In another particularly preferred embodiment, an acetic acid concentration of at least 12 mM predicts a loss of at least 1.14% of the initial body mass per week during a calorie restriction period, preferably, where the animal consumes less than 1.10% of its body mass in dry matter per day on average.

[0045] In another preferred embodiment, an acetic acid concentration of at least 20 mM (for example acetic acid between 20 mM and 25 mM) predicts rapid weight loss, for example, a loss of at least 8.5% of the initial body mass after a calorie restriction period; preferably, where the animal consumes less than 1.10% of its body mass in dry matter per day on average for at least 7 weeks.

[0046] In other preferred embodiments, the propionate concentration in a sample obtained from an animal (preferably a fecal sample) is used to predict weight loss. For example, a propionate concentration of at least 4.0 mM (e.g., between 4.0 mM and 6.0 mM) indicates that the animal has a good tendency for weight loss. In such embodiments, (i) a propionate concentration of at least 4.0 mM (e.g., between 4.0 mM and 6.0 mM) predicts a weight loss of at least 7.5% of the initial body mass after a calorie restriction period, and / or (ii) a propionate concentration of at least 5.0 mM (e.g., between 5.0 mM and 6.0 mM) predicts a weight loss of at least 8.0% of the initial body mass after a calorie restriction period. In other preferred embodiments, the butyrate concentration in a sample obtained from an animal (preferably a fecal sample) is used to predict weight loss. For example, a butyrate concentration of at least 1.5 mM (e.g., between 1.5 mM and 3.0 mM) indicates that the animal has a good tendency for weight loss. In such embodiments, (i) a butyrate concentration of at least 1.5 mM (e.g., between 1.5 mM and 3.0 mM) predicts a weight loss of at least 8.0% of the initial body mass after a calorie restriction period, and / or (ii) a butyrate concentration of at least 2.0 mM (e.g., between 2.0 mM and 3.0 mM) predicts a weight loss of at least 8.5% of the initial body mass after a calorie restriction period, and / or (iii) a butyrate concentration of at least 2.5 mM (e.g., between 2.5 mM and 3.0 mM) predicts a weight loss of at least 9.0% of the initial body mass after a calorie restriction period. In other preferred embodiments, the branched-chain VFA concentration in a sample obtained from an animal (preferably a fecal sample) is used to predict weight loss. For example, a branched-chain VFA concentration of at least 1.0 mM (e.g., between 1.0 mM and 2.0 mM) indicates that the animal has a good tendency for weight loss. In such embodiments, (i) a branched-chain VFA concentration of at least 1.0 mM (e.g., between 1.0 mM and 2.0 mM) may predict a weight loss of at least 7.5% of the initial body mass after a calorie restriction period, and / or (ii) a branched-chain VFA concentration of at least 1.5 mM (e.g., between 1.5 mM and 2.0 mM) predicts a weight loss of at least 8.0% of the initial body mass after a calorie restriction period. In the above embodiments, the calorie restriction period is preferably at least 7 weeks, wherein the animal consumes less than 1.10% of its body mass of dry matter per day on average.

[0047] In other embodiments, the concentration ratio of different VFAs in a sample (preferably a fecal sample) can be used as an indicator for predicting weight loss. For example, in another preferred embodiment, the ratio of the sum of the concentrations of acetic acid plus butyric acid to the concentration of propionic acid in the sample is higher than 3.75:1, and preferably in the range of 3.79:1 - 4.95:1, indicating that the animal has a good tendency to lose weight. For example, after a calorie restriction period, it loses at least 8.0% or at least 8.7% of its initial body weight. Preferably, the animal consumes less than 1.10% of its body weight in dry matter per day on average for at least 7 weeks. In another preferred embodiment, the ratio of the sum of the concentrations of acetic acid plus butyric acid to the concentration of propionic acid in the sample is higher than 3.75:1, and preferably in the range of 3.79:1 - 4.95:1, indicating that it loses at least 1.25% of its initial body weight per week during the calorie restriction period. Preferably, the animal consumes less than 1.10% of its body weight in dry matter per day on average.

[0048] Those skilled in the art will understand that a lower degree of calorie restriction over a longer period of time is expected to result in a similar level of weight loss. Thus, in the above embodiments (where the concentration or ratio of acetic acid, butyric acid, propionic acid, or branched-chain VFAs is evaluated), the calorie restriction period can alternatively be defined as at least 8 weeks, 10 weeks, 12 weeks, 14 weeks, or 16 weeks or more, where, during the restriction period, the animal consumes less than 1.15%, 1.20%, 1.30%, 1.40%, or 1.50% of its body weight in dry matter per day, respectively.

[0049] The concentration of the fermentation product and / or its metabolite can be evaluated alone as detailed above, or in combination with other weight loss prediction indicators (bacterial population diversity, abundance / relative abundance of bacterial taxa) as detailed below.

[0050] Bacterial population diversity as an indicator for predicting weight loss

[0051] According to another aspect of the present invention, a measure of the diversity of the bacterial population in a sample obtained from an Equus animal is used as an indicator for predicting weight loss, preferably in response to calorie restriction-induced weight loss. As shown in the examples (see Example 1), multiple diversity measures are negatively correlated with subsequent weight loss. "Negatively correlated" means that there is a negative correlation between the diversity measure and subsequent weight loss (see Figure 8For example), or in other words, a lower diversity measure of the bacterial population in the sample predicts more subsequent weight loss (compared to a higher diversity measure), and vice versa. It is generally expected that, under the same diet, animals showing a higher diversity measure in the sample will lose weight more slowly compared to animals with a lower diversity measure (when the same measure is evaluated). Additionally or alternatively, it is expected that, under the same diet, animals with a higher diversity measure will lose less weight over the same time span compared to animals with a lower diversity measure (when the same measure is evaluated).

[0052] The diversity measure of the bacterial population can be calculated by methods including DNA sequencing, RNA sequencing, protein sequence homology, or using other biomarkers indicative of bacterial species. Preferably, DNA sequencing is used, more preferably 16srDNA sequencing, as performed in the examples. For example, genomic DNA can be extracted from the sample and amplified by quantitative PCR (qPCR) using 16s rDNA specific primers to create a DNA library, which can then be sequenced, as performed in the examples. The sequence data obtained from the library can be used to calculate the diversity measure. Exemplary 16s rDNA specific primers (as used in the examples) are listed below:

[0053] Forward primer: AGAGTTTGATCCTGGCTCAG (SEQ ID NO:1)

[0054] Reverse primer: ACGAGTGCGTCTGCTGCCTYCCGTA (SEQ ID NO:2)

[0055] In a preferred embodiment, the diversity measure of the bacterial population in the calculated sample (preferably a fecal sample) is the inverse Simpson diversity, the Shannon - Wiener diversity, or the number of different bacterial species in the sample (also known as the number of observed species, or S.obs). As shown in the examples, these diversity measures are negatively correlated with subsequent weight loss (see Figure 8) Combinations of different diversity measures (preferably those listed above) can also be used to improve the accuracy of predictions. For example, the following metrics all indicate that an animal has a good tendency to lose weight: the inverse Simpson diversity is less than 70, preferably less than 45 (e.g., between 10 and 70, preferably between 10 and 45), the Shannon-Wiener diversity is less than 6.0, preferably less than 5.3 (e.g., between 4.0 and 6.0, preferably between 4.0 and 5.3), and the number of observed species is less than 1900, preferably less than 1700 (e.g., between 1000 and 1900, preferably between 1000 and 1700). Calculating more than one different diversity measure for each sample, such as two or three such measures, can improve the predictive ability of the method of the present invention.

[0056] Calculate the inverse Simpson diversity according to formula (I):

[0057] (I) 1 / ∑pi 2 , where Pi is the proportion of individuals belonging to species i

[0058] In a preferred embodiment, an inverse Simpson diversity calculated to be less than (in order of increasing preference) 75, 70, 65, 60, 55, 50, 45, 40, 35, or 30 (e.g., between 10 and 70) indicates that after a calorie restriction period, at least 8% of the initial body mass is lost (or at least 1.14% of the body mass is lost per week during the calorie restriction period); preferably, less than 45, 40, 35, or 30 (e.g., between 10 and 45), more preferably less than 30 (e.g., between 10 and 30) indicates that after a calorie restriction period, at least 8.7% of the initial body mass is lost (or at least 1.25% of the body mass is lost per week during the calorie restriction period).

[0059] Calculate the Shannon-Wiener diversity according to formula (II):

[0060] (II) -∑Pi ln(Pi), where Pi is the proportion of individuals belonging to species i

[0061] In another preferred embodiment, a calculated Shannon - Wiener diversity less than (in order of increasing preference) 6.0, 5.9, 5.8, 5.7, 5.6, 5.5, 5.4, 5.3, 5.2, 5.1 or 5.0 (e.g., between 4.0 and 6.0) indicates that after a calorie restriction period, at least 8% of the initial body mass is lost (or at least 1.14% of the body mass is lost per week during the calorie restriction period); preferably, less than 5.3, 5.2, 5.1 or 5.0 (e.g., between 4.0 and 5.3), more preferably, less than 5.0 (e.g., between 4.0 and 5.0) indicates that after a calorie restriction period, at least 8.7% of the initial body mass is lost (or at least 1.25% of the body mass is lost per week during the calorie restriction period).

[0062] In another preferred embodiment, the number of different bacterial species present in a sample can be calculated, and the presence of fewer than (in order of increasing preference) 1900, 1850, 1800, 1750, 1700, 1650, 1600, 1550 (e.g., between 1000 and 1900) indicates that after a calorie restriction period, at least 8% of the initial body mass is lost (or at least 1.14% of the body mass is lost per week during the calorie restriction period); preferably, fewer than 1700, 1600, 1550 or 1500 (e.g., between 1000 and 1700), more preferably, fewer than 1500 (e.g., between 1000 and 1500) different bacterial species indicates that after a calorie restriction period, at least 8.7% of the initial body mass is lost (or at least 1.25% of the body mass is lost per week during the calorie restriction period).

[0063] In the above - mentioned embodiment, the calorie restriction period is preferably at least 7 weeks, wherein the animal consumes on average less than 1.10% of its body mass of dry matter per day.

[0064] In other preferred embodiments, a calculated inverse Simpson diversity (i) of less than 100 (e.g., between 10 and 100) predicts at least a 7.5% reduction in initial body mass after a calorie restriction period, and / or (ii) of less than 50 (e.g., between 10 and 50) predicts at least an 8.0% reduction in initial body mass after a calorie restriction period. In other preferred embodiments, a calculated Shannon-Wiener diversity (i) of less than 6.0 (e.g., between 4.0 and 6.0) predicts at least a 7.0% reduction in initial body mass after a calorie restriction period, and / or (ii) of less than 5.5 (e.g., between 4.0 and 5.5) predicts at least an 8.0% reduction in initial body mass after a calorie restriction period, and / or (iii) of less than 5.0 (e.g., between 4.0 and 5.0) predicts at least a 9.5% reduction in initial body mass after a calorie restriction period. In other preferred embodiments, a calculated number of different bacterial species (i) of less than 1800 (e.g., between 1000 - 1800) predicts at least an 8.0% reduction in initial body mass after a calorie restriction period, and / or (ii) of less than 1600 (e.g., between 1000 - 1600) predicts at least an 8.5% reduction in initial body mass after a calorie restriction period, and / or (iii) of less than 1400 (e.g., between 1000 - 1400) predicts at least a 9.0% reduction in initial body mass after a calorie restriction period. In the above embodiments, the calorie restriction period is preferably at least 7 weeks, wherein the animal consumes on average less than 1.10% of its body mass of dry matter per day.

[0065] In the above embodiments (where the inverse Simpson diversity, Shannon-Wiener diversity, or number of species observed is evaluated), the calorie restriction period can alternatively be defined as at least 8 weeks, 10 weeks, 12 weeks, 14 weeks, or 16 weeks or longer, wherein during that period, the animal consumes on average less than 1.10%, 1.20%, 1.30%, 1.40%, or 1.50% of its body mass of dry matter, respectively.

[0066] The bacterial population diversity metric can be used alone as detailed above, or in combination with other body weight loss prediction metrics (concentration of fermentation products or their metabolites and / or abundance / relative abundance of bacterial taxa) as detailed below.

[0067] Abundance / relative abundance of bacteria as a body weight loss prediction metric

[0068] According to another aspect of the present invention, the abundance / relative abundance of the following bacteria is used as a predictive indicator of weight loss (preferably weight loss in response to calorie restriction): (i) Sphaerochaeta bacteria, and / or (ii) Treponema bacteria, and / or (iii) Anaerovorax bacteria, and / or (iv) Mobilitalea bacteria, and / or (v) Actinobacteria bacteria, and / or (vi) Spirochaete bacteria, and / or (vi) fiber-fermenting bacteria (such as Fibrobacter bacteria). As shown in the examples, the abundance / relative abundance of such bacteria is negatively correlated with subsequent weight loss. "Negatively correlated" is used as defined above in relation to bacterial population diversity. The bacterial abundance and / or relative abundance can be calculated by the methods related to bacterial population diversity metrics detailed above. Calculating the abundance and / or relative abundance of more than one of the above-mentioned bacteria (as defined in (i) to (vii) above) for each sample and / or animal, such as 2, 3, 4, 5, 6 or 7 such bacteria, can improve the predictive ability of the method of the present invention. Preferably, the abundance and / or relative abundance of the following bacteria is used as a predictive indicator of weight loss: Sphaerochaeta bacteria, and / or (ii) Treponema bacteria, and / or (iii) Anaerovorax bacteria and / or (iv) Mobilitalea bacteria, and / or (v) Actinobacteria bacteria, and / or (vi) Spirochaete bacteria.

[0069] In a preferred embodiment, the abundance of Sphaerochaeta bacteria in the sample is less than log 10 2.5 counts (e.g., between log 10 1.0 and log 10 2.5 counts), and / or its relative abundance is less than 0.05%, indicating that the animal has a good tendency to lose weight; for example, after a calorie restriction period, at least 8% of the initial body mass is lost.

[0070] In another preferred embodiment, the abundance of Mobilitalea bacteria in the sample is less than log 10 2.5 counts, preferably less than log 10 1.6 counts (e.g., between log 10 0.5 and log 10 2.5 counts, preferably between log 10 0.5 and log10 1.6 counts, and / or its relative abundance is less than 0.03%, indicating that the animal has a good tendency to lose weight; for example, after a calorie restriction period, it loses at least 8.0% (preferably at least 8.7%) of its initial body weight; or it loses at least 1.14% (preferably at least 1.25%) of its body weight per week during the calorie restriction period.

[0071] In another preferred embodiment, the relative abundance of Treponema bacteria in the sample is less than 1.7%, indicating that the animal has a good tendency to lose weight; for example, after a calorie restriction period, it loses at least 8% of its initial body weight.

[0072] In another preferred embodiment, the relative abundance of Anaerovorax bacteria in the sample is less than 0.11%, indicating that the animal has a good tendency to lose weight; for example, after a calorie restriction period, it loses at least 8% of its initial body weight.

[0073] In another preferred embodiment, the abundance of Actinobacteria bacteria in the sample is less than log 10 3.7 counts, preferably less than log 10 2.5 counts (e.g., between log 10 2.0 and log 10 3.7 counts, preferably between log 10 2.5 and log 10 3.7 counts), indicating that the animal has a good tendency to lose weight; for example, after a calorie restriction period, it loses at least 8.0%, preferably at least 8.7% of its initial body weight; or it loses at least 1.14%, preferably at least 1.25% of its body weight per week during the calorie restriction period.

[0074] In another preferred embodiment, the relative abundance of Spirochaetes bacteria in the sample is less than 2.3%, indicating that the animal has a good tendency to lose weight; for example, after a calorie restriction period, it loses at least 8% of its body weight.

[0075] In some embodiments, calculating the abundance of fibrolytic fermenting bacteria (in addition to the above bacterial taxa) can further improve the predictive ability of the method of the present invention. Alternatively, in some embodiments, the calculation of the fibrolytic fermenting bacteria abundance (per se) is used as a weight loss prediction indicator. In embodiments where the abundance and / or relative abundance of fibrolytic fermenting bacteria is evaluated, the abundance of Fibrobacter bacteria in the sample can be calculated, wherein the abundance of the fibrolytic fermenting bacteria is negatively correlated with the predicted weight loss.

[0076] In the above embodiments (wherein the abundance and / or relative abundance of Sphaerochaeta, Mobilitalea, Treponema, Anaerovorax, Actinobacteria, Spirochaetes, and fibrolytic fermenting bacteria is evaluated), the calorie restriction period is preferably at least 7 weeks, wherein the animal ingests less than 1.10% of its body weight of dry matter per day on average. The calorie restriction period can alternatively be defined as at least 8 weeks, 10 weeks, 12 weeks, 14 weeks, or 16 weeks, wherein during said periods, the animal ingests less than 1.10%, 1.20%, 1.30%, 1.40%, or 1.50% of its body weight of dry matter per day, respectively.

[0077] The abundance and / or relative abundance of the above bacteria can be used alone, as detailed above, or in combination with other weight loss prediction indicators (fermentation product concentration and / or bacterial population diversity metrics), as detailed above.

[0078] Sample

[0079] The method of the present invention uses fecal samples, blood samples, urine samples, or samples from the gastrointestinal lumen of Equus animals. Fecal and urine samples are convenient, their collection is non-invasive, and it is easy to repeatedly sample an individual over a period of time, and they are the preferred samples used in the method of the present invention. As analyzed in the examples, fecal samples are particularly preferred samples used in the method of the present invention. The present invention can also be used for other samples, such as ileum, jejunum, duodenum samples, and colon samples. In some embodiments, the samples used in the method of the present invention are not blood or urine samples.

[0080] The sample can be a fresh sample. Before being used in the method of the present invention, the sample can also be frozen or stabilized in other ways, such as adding the sample to a preservation buffer or dehydrating it by methods such as freeze-drying.

[0081] Prior to the method of the present invention (except for the assessment of the concentration of the fermentation product and / or its metabolites), the sample is usually processed to extract DNA. Methods for isolating DNA are well known in the art and are described, for example, in reference 21 as described therein. Suitable methods include, for example, the Qiagen QIAamp Power Faecal DNA kit.

[0082] In the method of the present invention, the weight loss prediction index can be evaluated for a single sample, or for higher accuracy, the average value from multiple samples (e.g., 2, 3, 4, or 5 or more samples from the same animal) can be evaluated as the weight loss prediction index.

[0083] General description

[0084] Unless otherwise indicated, the practice of the present invention will employ conventional methods of chemistry, biochemistry, molecular biology, immunology, and pharmacology within the skill of the art. These techniques are explained in detail in the literature 22-29 .

[0085] The term "comprising" encompasses "including" as well as "consisting of", e.g., a composition "comprising" X can consist solely of X or can include additional components, e.g., X + Y.

[0086] The term "about" associated with a numerical value x is optional and means, for example, x + 10%.

[0087] The term "substantially" does not exclude "completely", e.g., a composition "substantially free of" Y can be completely free of Y. When necessary, the term "substantially" can be omitted from the definitions of the present invention.

[0088] Reference to the percent sequence identity between two nucleotide sequences means that when aligned, that percentage of nucleotides is identical when comparing the two sequences. Such alignment and percent homology or sequence identity can be determined using software programs known in the art, e.g., as described in Section 7.7.18 of reference 30. Preferred alignments are determined using the BLAST (Basic Local Alignment Search Tool) algorithm or the Smith-Waterman homology search algorithm 31 , which uses affine gap search with a gap opening penalty of 12 and a gap extension penalty of 2, and a BLOSUM matrix of 62. The Smith-Waterman homology search algorithm is disclosed in reference 31. The alignment can be performed over the entire reference sequence, i.e., it can be performed over 100% of the length of the sequences disclosed herein.

[0089] Unless otherwise specified, a process or method that includes multiple steps may include additional steps at the beginning or end of the method, or may include additional intermediate steps. Additionally, where appropriate, steps may be combined, omitted, or performed in an alternative order.

[0090] Various embodiments of the present invention are described herein. It should be understood that the features detailed in each embodiment may be combined with the features detailed in other embodiments to provide further embodiments. In particular, the suitable, typical, or preferred embodiments emphasized herein may be combined with each other (unless they are mutually exclusive).

[0091] Embodiment methods of the present invention

[0092] Materials and methods

[0093] Animals and animal husbandry

[0094] Over an 11-week period (December to February; Table S2), 16 obese (BCS > 7 / 9) Welsh Mountain (Section A) mares (n = 8 in year 1; n = 8 in year 2) were studied. These animals were lent to the study by local pony breeders within a 30-mile radius of Ness Heath Farm, University of Liverpool. All animals underwent a clinical examination at recruitment, showed no signs of obvious disease, and were considered to be in good general health. Routine foot care and vaccination programs were maintained throughout the study. The animals were individually housed in single stalls (3m x 5m) within the same barn, slept on wood shavings, and had free access to fresh water at all times. Where possible, the ponies had access to a paddock for 30 minutes each day for basic exercise and social contact. To ensure complete control of the nutrition supplied in the stable, a closed muzzle (Shires brand) was used during exercise to prevent forage intake.

[0095] All procedures were conducted in accordance with the requirements of the UK Home Office and were approved by the Animal Welfare Committee of the University of Liverpool. Written informed consent was obtained from all horse owners.

[0096] Study design

[0097] This 11-week study aimed to evaluate any association between weight loss response and gastrointestinal microbiome changes during a 7-week diet restriction period. These animals remained on a hay diet (the same batch in both years) throughout.

[0098] Before the onset of negative energy balance, the animals first underwent a 4-week adaptation period to the study's defined diet and housing conditions. During this 4-week "pre-defined diet" period, each individual animal was provided with hay equivalent to 2% of its initial BM as daily DM, a nutritional level expected to approximate maintenance in terms of digestible energy (DE) and crude protein (CP) content. To mimic standard husbandry practices, the daily hay ration for each animal was divided equally and provided as two meals per day (at 08.30 h and 16.30 h). To ensure adequate intake of vitamins and minerals, a moistened nutritionally balanced product (Spillers Lite) was fed daily (at 08.30 h, 0.1% BM as DM). The average composition of the major nutrients was: hay; gross energy (GE) 18.9 MJ / kg DM, ash 4.0%, crude protein (CP) 8.1%, acid detergent fibre (ADF) 41.2%, neutral detergent fibre (NDF) 64.7%, starch 0.6%, water-soluble carbohydrates (WSC) 15.6%. Vitamin / mineral balancer; GE 16.35 MJ / kg DM, DE 9.5 MJ / kg DM, ash 14.0%, CP 21.1%, ADF 14.3%, NDF 31.7%, starch 10.5, WSC 12.5%. Samples of the hay and balancer were analysed prior to the 4-week pre-defined diet phase in both years, and there were no differences in the major nutrient composition between years. During the remaining seven weeks of the study, hay intake was restricted to 1% BM as daily DM, and 0.1% BM as nutritionally balanced meals. This degree of negative energy balance was expected to result in a weight loss of 1% BM per week on average. Diets were provided as described previously. During this period, individual feed supplies were recalculated weekly to account for changes in body mass (BM).

[0099] Physical measurements

[0100] Weekly, BM was recorded (to the nearest 500 g) between 08.30 h and 09.00 h (regularly calibrated weighbridge: lightweight medium; weighing horses). Throughout the study, body condition score (BCS) of each animal was recorded by the same person using a system developed by Henneke et al. (1983) and modified by Kohnke (1992).

[0101] Faecal collection

[0102] During the entire 11-week study period, fecal samples were collected within 5 minutes of each animal's spontaneous defecation (first spontaneous bowel movement after 09:00) every week. In addition, fecal samples were also collected during the last 3 consecutive days of 2% hay feeding, the "before regulated diet" period, and the last 3 consecutive days of diet restriction. Fresh fecal samples were collected from the bedding / floor area with gloved hands, placed in clean steel bowls to minimize environmental contamination, manually mixed, and aliquoted into four 5-ml sterile vials (Scientific Laboratory Supplies, UK). Prior to DNA extraction, the vials were snap-frozen in liquid nitrogen and stored at -80 °C.

[0103] Estimation of total body composition

[0104] Total body water (TBW) and total body fat mass were calculated twice for each animal; once during the last week of the "before regulated diet" phase and once during the last week of the diet restriction phase using the deuterium oxide (D2O) dilution method, which has been described previously and validated for clinical use in ponies. 32 . A commercial laboratory (Iso-analytical, Cheshire, UK) analyzed the deuterium enrichment in plasma samples (in duplicate) by gas isotope ratio mass spectrometry.

[0105] Combined glucose-insulin tolerance test (CGIT)

[0106] Two dynamic CGIT tests were performed on each animal; once during the last week of the "before regulated diet" phase and once during the last week of diet restriction. 13 . Blood samples were immediately transferred to lithium heparin tubes (BD Vacutainer), mixed, and placed on ice until centrifugation (2000 g, 10 minutes). Prior to analysis, plasma was aliquoted into two parts and stored at -20 °C. Plasma glucose samples were analyzed using the hexokinase method. Plasma insulin concentration was measured using chemiluminescence immunoassay (Siemens Immulite 1000R, chemiluminescence immunoassay).

[0107] Apparent digestibility

[0108] Two digestibility trials were conducted on individual ponies by total fecal collection over 3 consecutive days (72 h), once during the last week of the "pre - prescribed diet" phase and once during the last week of dietary restriction. Any refused feed was recorded at the end of each 24 - h period. The total feces collected daily were weighed, thoroughly mixed and duplicate samples were collected for analysis. The dry matter (DM) content of feces was determined by drying (70 °C) duplicate samples (∼250 g) to a constant mass. Ash content was recorded after burning (Carbolite OAF:1; Carbolite furnace) duplicate DM samples at 550 °C. Three dried fecal samples collected over 72 h were ground, pooled and homogenized for assessment of GE content (MJ / kg DM) by bomb calorimetry in a commercial laboratory (Sciantec, UK). In addition, fiber content (ADF and NDF) of dried fecal samples was measured by wet chemistry in a commercial laboratory (Dairy One, Ithaca, USA).

[0109] DNA Extraction and Quantitative PCR

[0110] Genomic DNA was extracted from freeze - dried fecal samples (25 mg DM) by bead - milling in 4% SDS lysis buffer for 45 s. DNA was extracted using the CTAB / chloroform method (adapted from reference 33). The concentration and quality of genomic DNA were assessed by spectrophotometry (Nanodrop Nd - 100, Thermo Fisher Scientific, USA). Absolute DNA concentrations (10 1 to 10 5 ) from total bacteria, protozoa and fungi were determined by qPCR and serial dilutions of their respective standards, as described previously 34,35 . Quantitative polymerase chain reaction (qPCR) was performed in triplicate using a LightCycler 480 system (Roche, Mannheim, Germany).

[0111] Ion Torrent Next - Generation Sequencing

[0112] Next - generation sequencing (NGS) was used 36Study the bacterial community. For bacterial analysis, the V1-V2 hypervariable region of 16S rRNA was amplified using bacterial primers (27F and 357R) followed by Ion Torrent adaptors. The forward primer carried a 10-nucleotide barcode to allow sample identification. PCR was performed in a 25-μL reaction vessel containing DNA template (1 μL), 0.2 μL reverse primer, 1 μL forward primer, 5 μL buffer (PCR Biosystems Ltd., London, UK), 0.25 μL bio HiFi polymerase (PCR Biosystems), and 17.6 μL molecular-grade water. The amplification conditions for bacteria and methanogens were 95 °C for 1 min, followed by 22 cycles of 95 °C for 15 s, 55 °C for 15 s, and 72 °C for 30 s. The resulting amplicons were visualized on a 1% agarose gel to assess the quality of amplification. The PCR products were then purified using Agencourt AMpure XP magnetic beads (Beckman Coulter, Fullerton, USA), and the DNA concentration was determined using an Epoch microplate spectrophotometer equipped with a Take 3 Micro-Volume plate (BioTek, Wotton, UK) to pool equimolar amounts of samples with unique barcodes.

[0113] The library was further purified using an EGel system containing 2% agarose gel (Life Technologies Ltd., Paisley, UK). Quality assessment and quantification of the purified library were performed on an Agilent 2100 Bioanalyzer (Agilent Technologies Ltd., Stockport, UK) equipped with a high-sensitivity DNA chip. The library was prepared for NGS sequencing using an Ion Chef system (Life Technologies UK Ltd) and an Ion PGM HiQ Chef kit, and sequencing was performed using an Ion Torrent Personal Genome Machine (PGM) system on an Ion PGM Sequencing 316 Chips v2 BC.

[0114] After sequencing, the data were processed as previously described 36Briefly, sample identification numbers were assigned to multiplex reads using the MOTHUR software environment. The data were denoised by removing low-quality sequences, sequencing errors, and chimeras (quality parameters: a maximum of 10 homopolymers, qaverage 13, qwindow 25, for archaea, qwindow was set to 30, and erate = 1; chimera checking was performed using Uchime with de novo sequencing and databases). Sequences were clustered into OTUs with 97% identity using the Uparse pipeline. Bacterial taxonomic information for 16S rRNA sequences was obtained by comparison with the Ribosomal Database Project - I. 37 The number of reads per sample was normalized to the sample with the fewest sequences.

[0115] Measurement of volatile fatty acids

[0116] Fecal samples were thawed and diluted 1:5 w / v with distilled water (2 g sample / 8 mL water) to prepare fecal slurries, and the pH was recorded. Then, 20% orthophosphoric acid (containing 20 mM 2-ethylbutyric acid as an internal standard) was added in a ratio of 1:5 (1 mL acid / 4 mL fecal slurry) to deproteinize the samples. For VFA analysis, the slurries were left for 24 h to allow sediment to settle, then syringe-filtered through a glass fiber pre-filter (0.7 μm pore, Millipore) and a nitrocellulose membrane (0.45 μm pore; Millipore), injected into glass vials, and capped. VFAs were determined by gas-liquid chromatography using ethyl butyrate as an internal standard as described in reference 38.

[0117] Statistical analysis

[0118] Phenotypic data

[0119] All data were entered into Excel and exported to STATA 13.1 (StataCorp, Texas) for statistical analysis. The pattern of body weight change during the weight loss phase of this study was investigated by fitting a mixed effects regression model with body weight (kg) as the outcome variable. Initial body weight (week 0) was included to account for differences in initial BM among individual animals. Time (weeks) was added as an explanatory variable and was fitted if polynomial terms improved the model fit as judged by likelihood ratio tests. Pony identity was listed as a random intercept and time as a random slope. For the random effects, an unstructured covariance matrix was used. Body weight was predicted according to the model and proportional weight loss (adjusted to week 0) was calculated using the model and was used to rank the animals according to weight loss. These were then divided into tertile groups (high (8.77%, 9.17%, 11.32% and 11.59% initial body weight loss), medium (7.97%, 8.15%, 8.42%, 8.45% and 8.62% initial body weight loss) and low (7.11%, 7.22%, 7.53%, 7.94% and 7.94% initial body weight loss) weight loss) for further interrogation of the data. The Wilcoxon signed-rank test was used to evaluate differences in measurements of digestibility, body composition and glucose / insulin kinetics before and after the prescribed diet.

[0120] Diversity and faecal VFA analysis

[0121] Diversity indices (inverse Simpson and Shannon–Weiner), observed species (S.obs) and estimated species richness (S.Chao1) were calculated for all faecal samples at all time points using the recommended standardised data to reduce over-inflation of true diversity in the pyrosequencing dataset. 39 The “power ladder” method was used to visually assess the normality of the data. Transformations were made where appropriate. Student t tests were performed to evaluate the mean change in VFA concentration and diversity indices during the pre-prescribed diet period and the post-prescribed diet period (using the arithmetic mean of the last 3 days of pre-prescribed diet and post-prescribed diet samples). One-way ANOVA was used ( The 12th edition; VSN International Limited) evaluated the differences in initial diversity and fecal VFA concentrations among weight loss groups (high, medium, low), and corrected for multiple testing using P-values corrected by Bonferroni. If the P-value < 0.05, it was considered significant. The results were confirmed by univariate analysis (STATA 13.1, StatCorp, Texas), where initial diversity or fecal VFA concentration was the explanatory variable, and overall proportional body weight loss (logit-transformed) was the outcome variable. To further investigate the ability of initial acetate concentration to predict successful animal weight loss, a binary (yes / no) variable was generated, where successful weight loss was defined as a percentage of total weight loss ≥ 8%. This value was chosen based on the data collected in the current study. The receiver operating characteristic (ROC) curve of initial acetate concentration was generated as a predictor of weight loss (yes / no). This allowed investigation of the optimal cut-off score for initial acetate concentration and generation of sensitivity and specificity parameters. Additionally, analysis of microbiome composition (ANCOM) was performed using R-implementation version 1.1-367 to evaluate whether there were significant differences in the initial abundances of any individual genera among the initial acetate concentrations (divided into quartiles).

[0122] Differences in phylum and genus levels in the microbiome after weight loss were investigated by ANOVA( The 12th edition; VSN International Limited). Statistical analysis excluded those with recorded abundances less than 0.05%. The P-values for multiple testing were adjusted using the method proposed by Benjamini and Hochberg 40 to reduce the false discovery rate. When applying the Benjamini & Hochberg correction method (1995), results with P < 0.10 were considered statistically significant. ANCOM was used to evaluate differences in individual phylum / genus abundances among weight loss groups.

[0123] Differences in abundances at the OTU level were evaluated using the Bioconductor package DESeq21 in the statistical package R. This method is applicable to querying high-throughput, sequencing count data and allows construction of models using the negative binomial distribution to explain the read counts from each OTU 41 。

[0124] Permutational multivariate analysis of variance (PERMANOVA) was used to determine the overall significant differences in bacterial communities. Analyses were performed in PRIMER 6 and PERMANOVA+ (versions 6.1.18 and 1.0.8, respectively; Primer-E, Ivybridge, UK). Percentage abundance data were square root transformed, and a Bray-Curtis distance matrix was calculated. PERMANOVA was performed using the default settings with 9999 unrestricted permutations, and Monte Carlo P-values were calculated. Similarity analysis (ANOSIM) was performed in PRIMER 6 and PERMANOVA+ using the Bray-Curtis distance matrix calculated above. This analysis was used to provide a measure of the degree of difference between communities, as indicated by the R statistic.

[0125] To calculate the contribution of environmental data to the bacterial communities, distance-based linear models were used to calculate which environmental variables were significantly correlated with the community data. Significant variables were used in distance-based redundancy analysis (dbRDA) 42 , as performed in PRIMER 6 and PERMANOVA+.

[0126] Example 1 - Coverage, Diversity, and Volatile Fatty Acids

[0127] Quality filtering of the 16S rDNA amplicon sequences yielded 16,028,420 high-quality sequences (320 bp in length), which were clustered into 9,536 different OTUs. A phylogenetic tree was constructed (PRIMER 6 with Bray Curtis dissimilarity, Figure 6 ), which indicated that samples obtained from animals on each of the three consecutive sampling days before and after the defined diet tended to cluster together. This observation allowed the data generated from these series of samples to be pooled per animal. After standardization, a pooled analysis provided 14,500 sequences per animal for each period. The rarefaction curves ( Figure 7 ) indicated that the sample curves did not reach a plateau; indicating that complete sampling of these environments had not been achieved.

[0128] After weight loss, the alpha diversity metrics of inverse Simpson, Shannon-Weiner, and S.obs decreased significantly (p < 0.05), but no differences in Chao1 were observed (Table 1). Additionally, there were significant differences in the initial (3-day average before the defined diet) diversity metrics (inverse Simpson, Shannon-Weiner, and S.obs) among the three weight loss groups (Table 1). Univariate regression analysis confirmed this, where a significant negative correlation was found between the initial diversity metrics and subsequent weight loss (Tables 3 and Figure 8 ).

[0129] After weight loss, the concentrations of acetic, butyric, propionic, and branched-chain volatile fatty acids decreased significantly, but the fecal pH remained unchanged (Table 2). There was no difference in the ratio of acetic plus butyric acid to propionic acid between samples before and after the prescribed diet. However, the initial ratio (mean of 3 pre-prescribed diet samples) was greatest in the high weight loss group compared to the low weight loss group (4.37 ± 0.58 vs. 3.24 ± 0.50; p = 0.04). Additionally, a strong positive correlation was found between the initial VFA concentrations and subsequent weight loss (Acetate, R 2 = 0.52, p < 0.01; Butyrate, R 2 = 0.34, p = 0.02; Propionate, R 2 = 0.33, p = 0.03; Table 4 and Figure 9 ).

[0130] Due to the significant association between initial acetic acid and subsequent weight loss, a logistic regression model was fitted with a binary outcome of "weight loss >= 8% BM". This was used to construct a receiver operating characteristic (ROC) curve to determine the ability of the initial acetic acid concentration to predict subsequent weight loss success (using 8% of the initial body mass for weight loss), and resulted in an area under the curve of 0.91( Figure 10 ). The best test performance achieved using the current data provided a cut-off value of 12 mM acetic acid, with a test sensitivity of 88.9% (95% confidence interval: 51.8 - 99.7) and a specificity of 100% (95% confidence interval: 54.1 - 100.0) for predicting weight loss of more than 8%. Then ANCOM was used to investigate the association between the initial abundance of any particular genus and the initial acetic acid (divided into quartile groups). It was found that the genera Sphaerochaeta and Treponema (both belonging to the phylum Spirochaetes), Anaerovorax and Mobilitalea (both belonging to the phylum Firmicutes) were significantly associated with the initial acetic acid concentration( Figure 11 and Table 5), where the initial abundance of individual genera was greatest for those animals with the lowest quartile of initial acetic acid concentration.

[0131] Table 1. Diversity indices (mean ± SD) before dietary restriction (pre-prescribed diet) and 7 weeks after dietary restriction (post-prescribed diet; n = 15), and initial diversity indices (mean ± SD of 3 days before the prescribed diet) in high (n = 5), medium (n = 5), and low (n = 5) weight loss groups.

[0132]

[0133] Table 2. pH values and volatile fatty acid concentrations (mean ± SD) before dietary restriction (before the defined diet) and 7 weeks after dietary restriction (after the defined diet; n = 15), and the initial pH values and volatile fatty acid concentrations (3-day mean ± SD before the defined diet) in the high (n = 5), medium (n = 5), and low (n = 5) weight loss groups.

[0134]

[0135] Table 3: Association between weight loss and initial diversity metrics. The association between proportional total weight loss (corrected to week 0; logit-transformed) as the outcome variable and initial diversity metrics (3-day mean before the defined diet) as the explanatory variable was investigated using univariate regression analysis.

[0136]

[0137] Table 4: Association between weight loss and initial VFA concentration. Univariate regression analysis was used to investigate the association between proportional total weight loss (corrected to week 0; logit transformation).

[0138]

[0139] Table 5. Relative abundances of initial bacterial genera (3-day mean before the defined diet) among animals quartiled by initial acetate concentration (3-day mean before the defined diet). Animals were grouped into quartiles based on initial acetate concentration as follows: quartile 1, n = 4, 9.45 mM ± 0.27 (mean ± SD); quartile 2, n = 4, 11.66 mM ± 1.04, quartile 3, n = 3, 16.76 mM ± 1.13, quartile 4, n = 4, 22.63 mM ± 4.49. ANOVA analysis was used to evaluate differences in relative abundances of bacterial phyla between groups, and the resulting p-values were adjusted for multiple testing using the Benjamini-Hochberg correction.

[0140]

[0141]

[0142]

[0143] Example 2 - Changes in Bacterial Abundance after Weight Loss and Association with Weight Loss

[0144] In all samples / time points, Bacteriodetes was the most abundant phylum currently, followed by Firmicutes and Fibrobacteres. Although there was no significant change in the relative abundance of Bacteriodetes after weight loss, the relative abundances of Firmicutes and Tenericutes were significantly reduced after weight loss (Table 6 and Figure 1 ), and the ratio of Firmicutes:Bacteriodetes increased after weight loss (2.10 ± 0.54 before the defined diet compared to 2.54 ± 0.56 after the defined diet; p = 0.01). Similarly, the ratio of fibrolytic bacteria, Fibrobacteres:Firmicutes (ratio) increased after weight loss (0.74 ± 0.58 vs. 1.31 ± 0.88; p = 0.03). ANCOM and ANOVA analyses were used to explore the differences in the initial abundances of phyla (3-day mean before the defined diet) between the weight loss groups. It was found that the initial abundances of Actinobacteria and Spirochaetes were significantly higher in the low weight loss group compared to the high weight loss group ( Figure 12 and Table 7).

[0145] Table 6. Relative abundances of bacterial phyla before dietary restriction (before the defined diet) and 7 weeks after dietary restriction (after the defined diet; n = 15).

[0146]

[0147] Table 7: Relative abundances of initial bacterial phyla (3-day mean before the defined diet) among three weight loss groups (n = 5 / group). ANOVA analysis was used to evaluate the between-group differences in the relative abundances of bacterial phyla, and the obtained p-values were adjusted for multiple testing using the Benjamini-Hochberg correction.

[0148]

[0149] At the genus level, although the largest proportion of samples was unclassified at this level, Fibrobacter was the second most abundant genus in all samples / time points. After weight loss, the abundances of many genera changed: the relative abundances of Rikenella and Anaerorhabdus (both belonging to Bacteroidetes) increased (Table 8 and Figure 2), while the abundances of Prevotella and Alloprevotella (belonging to Bacteroidetes), Clostridium XlVa, Phascolarctobacterium, and Pseudoflavonifractor (belonging to Firmicutes), as well as Anaeroplasma (belonging to Tenericutes) were decreased (Table 8 and Figure 1 ). It was found that after ANCOM and ANOVA, the initial abundance of the genus Mobilitalea (belonging to Firmicutes) in the low weight loss group was significantly higher than that in the high weight loss group ( Figure 13 and Table 9).

[0150] Table 8: Relative abundances of bacterial genera before dietary restriction (before the prescribed diet) and 7 weeks after dietary restriction (after the prescribed diet; n = 15).

[0151]

[0152]

[0153]

[0154] Table 9: Relative abundances of initial bacterial genera (3-day average before the prescribed diet) among three weight loss groups (n = 5 / group). ANOVA was used to evaluate the between-group differences in the relative abundances of bacterial phyla, and the obtained p-values were adjusted for multiple testing using the Benjamini-Hochberg correction.

[0155]

[0156]

[0157]

[0158] The study found that after weight loss, 61 OTUs belonging to 13 genera showed significant differences in abundance: after weight loss, the abundances of 7 of these OTUs decreased, while the abundances of the remaining 51 OTUs increased. Significant differences in the initial OTU abundances were also found between the weight loss groups: the largest number of differences was found between the high weight loss group and the low weight loss group (159 OTUs belonging to 18 genera were significantly different, and 101 of them had a greater initial abundance in the high weight loss group (compared to the low weight loss group)). The study found that 48 OTUs belonging to 9 genera showed significant differences in initial abundance between the low weight loss group and the medium weight loss group, while 28 OTUs belonging to 6 genera showed significant differences between the medium weight loss group and the high weight loss group.

[0159] Example 3 - Microbiome Structure as a Predictive Indicator of Weight Loss

[0160] To determine whether the initial bacterial community structure could predict subsequent weight loss, PERMANOVA and ANOSIM analyses were performed. There were significant differences in the initial bacterial community structure at the OTU level, with the largest differences found between the high weight loss group and the low weight loss group (R = 0.67, p < 0.01; Table 10). Distance-based redundancy analysis (dbRDA) was used to summarize the changes in the initial bacterial community structure attributable to the weight loss groups ( Figure 2 ). Although there was some overlap between the groups, the animals in the low weight loss group clustered together and were clearly distinguishable from the animals in the high weight loss group, which had a greater initial bacterial structure difference among themselves. A second dbRDA analysis was performed to evaluate the contribution of the initial VFA concentration and pH value to the initial bacterial community structure ( Figure 14 ). As Figure 14 shown, there was a close correlation between the differences in the initial acetate concentration and the initial bacterial community structure between the weight loss groups.

[0161] Table 10. Effects of weight loss groups on the bacterial community structure in horse feces. The significant P values of PERMANOVA are highlighted (P < 0.05). The Anosim R value represents the degree of separation between samples (0 = very similar; 1 = highly different), and the significant R values (P < 0.05) are shown in bold.

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[0163] References

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Claims

1. Use of one or more fermentation products in the preparation of a reagent for predicting weight loss in equine ( Equus ) animals, said use comprising measuring an initial concentration of one or more fermentation products in a sample obtained from said animal prior to caloric restriction; wherein, The sample is a fecal sample; wherein the concentration of one or more fermentation products is positively correlated with the predicted animal weight loss; wherein the one or more fermentation products are selected from the group consisting of acetic acid, butyric acid, propionic acid, and branched-chain volatile fatty acids, and wherein a concentration of at least 10 mM acetic acid in the sample, or a concentration of at least 4 mM propionic acid in the sample, or a concentration of at least 1.5 mM butyric acid in the sample, or a concentration of at least 1.0 mM branched-chain volatile fatty acids in the sample indicates that the animal has a good tendency for weight loss; And wherein, said equine ( Equus ) animal is a horse ( Equus caballus ) species.

2. The use according to claim 1, wherein The weight loss is in response to calorie restriction.

3. The use according to claim 1, wherein, The fermentation product is acetic acid.

4. Use according to claim 3, wherein, A concentration of at least 12 mM acetic acid in the sample indicates that the animal has a good tendency for weight loss.

5. The use according to claim 4, wherein, A concentration of at least 12 mM acetic acid in the sample indicates that after a calorie restriction period, at least 8.0% of the initial body mass is lost.

6. Use according to any one of claims 2 - 5, wherein, A concentration of at least 20 mM acetic acid in the sample indicates that the animal has a very good tendency for weight loss.

7. The use according to claim 6, wherein, A concentration of at least 20 mM acetic acid in the sample indicates that after a calorie restriction period, at least 8.5% of the initial body mass is lost.

8. Use according to any one of claims 1 - 5, wherein, In the sample, (i) a concentration of at least 4 mM propionic acid indicates that after a calorie restriction period, at least 7.5% of the initial body mass is lost, and / or (ii) a concentration of at least 5 mM propionic acid indicates that after a calorie restriction period, at least 8.0% of the initial body mass is lost.

9. Use according to any one of claims 1 - 5, wherein, In the sample, (i) a concentration of at least 1.5 mM butyric acid indicates that after a calorie restriction period, at least 8.0% of the initial body mass is lost, and / or (ii) a concentration of at least 2.0 mM butyric acid indicates that after a calorie restriction period, at least 8.5% of the initial body mass is lost, and / or (iii) a concentration of at least 2.5 mM butyric acid indicates that after a calorie restriction period, at least 9.0% of the initial body mass is lost.

10. The use according to claim 1, wherein, In the sample, (i) a concentration of at least 1.0 mM branched-chain volatile fatty acids indicates that after a calorie restriction period, at least 7.5% of the initial body mass is lost, and / or (ii) a concentration of at least 1.5 mM branched-chain VFA indicates that after a calorie restriction period, at least 8.0% of the initial body mass is lost.

11. According to the use according to claim 5, wherein, The calorie restriction period is selected from any one of at least 7 weeks, at least 8 weeks, at least 10 weeks, at least 12 weeks, at least 14 weeks, or at least 16 weeks.

12. The use according to claim 11, wherein, The animal consumes less than 1.10% of its body mass per day on average for at least 7 weeks, or less than 1.15% of its body mass per day on average for at least 8 weeks, or less than 1.20% of its body mass per day on average for at least 10 weeks, or less than 1.30% of its body mass per day on average for at least 12 weeks, or less than 1.40% of its body mass per day on average for at least 14 weeks, or less than 1.50% of its body mass per day on average for at least 16 weeks.

13. The use according to claim 12, wherein, The animal consumes less than 1.10% of its body mass per day on average for at least 7 weeks.

14. Use according to any one of claims 1-5, wherein, The animal is a pony.

15. The use according to claim 5, wherein, The animal is overweight before the calorie restriction.

16. The use according to claim 15, wherein Overweight is defined as a body composition score (BCS) greater than 6 / 9.

17. The use according to any one of claims 1 to 5, wherein, The use also includes detecting or quantifying bacteria in a sample obtained from the animal by: DNA sequencing, RNA sequencing, protein sequence homology, or other biomarkers indicative of bacterial species.

18. The use according to claim 17, wherein, The DNA sequencing is 16s rDNA sequencing.

19. The use according to claim 17, wherein, The bacteria are detected or quantified by 16s rDNA sequencing.

20. The use according to any one of claims 1-5, wherein the use further comprises adjusting the calorie intake and / or exercise level of the animal in response to the predicted weight loss.

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