Effects of overfeeding on the digestive efficiency, voluntary physical activity levels, and fecal characteristics and microbiota of adult cats

Abstract

The incidence of feline obesity continues to rise despite it being a preventable disease. There are many risks and health perturbations associated with obesity, with several of those impacting a pet’s quality of life, wellness, and longevity. Feline obesity is commonly studied, but most research has been focused on weight loss rather than weight gain. To our knowledge, feline studies have not examined the implications of overfeeding and weight gain on gastrointestinal transit time (GTT) nor the association it has with the fecal microbiota. Therefore, the objective of this study was to determine the effects of overfeeding and weight gain on apparent total tract digestibility (ATTD), GTT, blood hormones, serum metabolites, hematology, fecal microbiota populations, and voluntary physical activity of cats. Eleven lean adult spayed female cats [body weight (BW) = 4.11 ± 0.43 kg; body condition score = 5.41 ± 0.3; age = 5.22 ± 0.03 y] were used in a longitudinal weight gain study. After a 2-wk baseline phase, cats were allowed to overeat for 18 wk. A commercially available complete and balanced diet was fed during the baseline phase to identify the intake needed to maintain BW. Cats were then fed the same diet ad libitum to induce weight gain. Fecal samples, blood samples, and voluntary physical activity data were collected at baseline (week 0) and 6, 12, and 18 wk after weight gain. Fecal samples were collected for microbiota analysis, determination of ATTD, and GTT measurement while blood samples were collected for serum chemistry, hematology, and insulin and leptin measurements. Microbiota data were evaluated using QIIME2. All other measures were evaluated statistically using the mixed models procedure of SAS using repeated measures analysis, with time effects being the focus. A P < 0.05 was considered significant. The ATTD of dry matter (P = 0.0061), organic matter (P = 0.0130), crude protein (P < 0.0001), fat (P = 0.0002), and gross energy (P = 0.0002), and GTT (P = 0.0418) decreased with overfeeding and weight gain. Fecal bacterial alpha diversity measures were unchanged, but fecal bacterial beta diversity was impacted (P < 0.05) with overfeeding and weight gain. The relative abundances of 16 bacterial genera, including Bifidobacterium, Collinsella, Erysipelatoclostridium were affected (P < 0.05) by overfeeding and weight gain. In conclusion, overfeeding and subsequent weight gain reduced ATTD, reduced GTT, and caused changes to the fecal microbial community of adult cats.

This study was conducted to evaluate how overfeeding and weight gain affected apparent total tract digestibility, gastrointestinal transit time, blood hormones and metabolites, fecal microbiota populations, and voluntary physical activity of adult cats. Our results demonstrate that overfeeding and subsequent weight gain reduce nutrient digestibility, reduce transit time, and cause changes to the fecal microbial community.

Introduction

Feline obesity continues to increase worldwide, with approximately 60% of cats in the United States being reported as overweight or obese (APOP, 2022). Obesity develops as a result of a positive imbalance between energy intake and energy expenditure. This leads to a multitude of metabolic abnormalities that can include glucose intolerance, hyperlipidemia, exercise intolerance, diabetes mellitus, orthopedic stress, and chronic inflammation, all being detrimental to a pet’s well-being (German, 2006). While many of these are reversible with weight loss, an owner’s perception of their animal, compliance with feeding guidelines, and nutritional management miscues oftentimes inhibit success thereof. In general, on a 9-point scale, a cat that meets or exceeds a body condition score (BCS) of 6 is overweight by 10%, increasing by an additional 10% with every incremental BCS unit (Cline et al., 2021). Unfortunately, many pet owners underestimate their pet’s weight and fail to recognize an overweight condition (Gerstner and Liesegang, 2017; Lee et al., 2021; APOP, 2022). Overfeeding is another important factor. Even though 73% of pet owners “somewhat agree” or “strongly agree” that obesity is the result of overfeeding (APOP, 2022), many provide rations of food that are too large and/or too many treats and snacks. Despite the issues that exist with owner compliance, understanding the metabolic and gastrointestinal changes that companion animals face with the onset of weight gain and obesity may help with future prevention and treatment plans. Feline obesity is commonly studied, but most research is focused on weight loss rather than weight gain. While weight gain is expected to cause opposite effects than weight loss, definitive links and correlations have yet to be established.

Gastrointestinal transit time (GTT)—the time it takes for food to move from the mouth to the end of the intestine (anus)—is important because it impacts ingredient digestibility, nutrient absorption, and stool quality. Several factors are known to influence gastrointestinal physiology and corresponding GTT, including diet composition, exercise, disease state, body size, and host microbiota (Oswald et al., 2015; Pinna et al., 2016; Palerme et al., 2020; Badri et al., 2021; Telles et al., 2022; Tolbert et al., 2022). Dietary macronutrient composition, meal size, and caloric content may impact GTT by influencing gastric emptying time—the time it takes to release the chyme from the pylorus of the stomach into the small intestine (Goggin et al., 1998; Holst et al., 2016; Wei et al., 2021). Obesity is also known to influence gut motility and nutrient absorption, influencing appetite and satiety (Miron and Dumitrascu, 2019).

An overconsumption of food puts more demand on the digestive system, thereby decreasing its overall efficiency, leading to lower nutrient digestibility and absorption (Wei et al., 2021; Zhang et al., 2022). Reduced digestibility consequently leads to an increased passage of nutrients into the large intestine and potentially influences hindgut microbiota populations. While a higher substrate delivery to the large intestine may occur from overfeeding, changes to motility and GTT may also be impacted so the time available for microbial fermentation and the impact it has on microbiota populations is difficult to estimate. However, there have been a few studies conducted that suggest an increase in protein fermentation occurs in people or animals with faster transit (shorter transit time). In general, a decrease in colonic transit time is known to increase bacterial protein catabolism (Müller et al., 2018). In Crohn’s disease and irritable bowel patients, a shorter colonic transit time has been linked with increased concentrations of microbial protein catabolism end-products, increased abundance of Methanobrevibacter (methane producer), and reduced abundance of Faecalibacterium prausnitzii (anti-inflammatory microbe; Sokol et al., 2008; Ghoshal et al., 2016).

To our knowledge, the GTT and gut microbiota populations of cats during overfeeding and weight gain have not been evaluated but deserves study. Given the lack of research in the area, the objective of this study was to determine the effects of overfeeding and weight gain on apparent total tract digestibility (ATTD), GTT, blood hormones, serum metabolites, hematology, fecal microbiota populations, and voluntary physical activity of cats. We hypothesized that overfeeding and weight gain would decrease voluntary physical activity, increase blood lipid concentrations, increase leptin and insulin concentrations, and result in serum biochemistry parameters outside the reference ranges. Further, we hypothesized that weight gain and overconsumption of food would decrease GTT and negatively alter the fecal microbiota community.

Materials and Methods

All procedures were approved by the University of Illinois Institutional Animal Care and Use Committee prior to experimentation (IACUC #22142) and were performed in accordance with the U.S. Public Health Service Policy on Humane Care and Use of Laboratory Animals.

Animals, diet, and experimental design

Eleven lean adult spayed female domestic shorthair cats (body weight [BW] = 4.11 ± 0.43 kg; body condition score [BCS] = 5.41 ± 0.3; age = 5.22 ± 0.03 y) were used in a longitudinal weight gain study. The experimental period was 20 wk, with a 2-wk baseline phase, followed by an 18-wk weight gain phase. All cats were housed individually in cages (1.02 m × 0.76 m × 0.71 m) during feeding time (7:00 to 9:00 am) and on fecal collection days, in a temperature- and light-controlled (14-h light: 10-h dark cycle) room in the Edward R. Madigan Animal Facility at the University of Illinois Urbana-Champaign. At other times, cats were group-housed and able to socialize and exercise outside their cages. Cats were allowed access to various toys and scratching poles for environmental enrichment and play time with human interaction at least two times per wk.

A commercial diet formulated to meet all Association of American Feed Control Officials (AAFCO, 2022) nutrient profiles for adult cats at maintenance was fed for the duration of the study (Supplementary Table S1). During a 2-wk baseline phase, cats were fed to maintain BW. After the baseline phase, cats had access to food ad libitum during their designated feeding period for 18 wk. Daily food intake was measured for each cat. Cats were weighed and BCS were assessed (9-point scale) once per wk prior to feeding. Body condition scores were determined by visual examination and physically touching the animal in various locations, including the ribs, vertebrae, hips, hind leg, etc. as instructed by the Laflamme (2006) and WSAVA guidelines (WSAVA, 2011).

Fecal collection, scoring, and handling

At weeks 0, 6, 12, and 18, fecal samples were collected from litter boxes (containing no litter) or the cage floor. During this time, a 5-d total fecal collection was conducted for measurement of ATTD, with fecal scores being noted. All fecal samples were scored using a 5-point scale to determine shape, consistency, and texture. The following scale was used: 1 = very hard and dry pellets; 2 = firm and formed, segmented in appearance, and holds shape; 3 = soft, formed, moist stool, retains shape, but little to no segmentation visible; 4 = soft and very moist, has some texture, but no defined shape; 5 = watery, liquid that is present in flat puddles with no texture. Total feces excreted during the collection phase were collected from each cat, weighed, and frozen at −20 °C until analyses.

One fresh fecal sample excreted per cat during each of the collection periods was collected and processed within 15 min of defecation for measurement of pH, dry matter (DM), and fecal microbiota. Fecal pH was measured using a pH meter (Accumet AP1001 Portable pH Meter; Fisher Scientific, Waltham, MA) equipped with an electrode (InLab Surface pH Electrodes 51343157; Mettler Toledo, Columbus, OH) immediately after collection. Two aliquots were collected for further analyses: (1) DM determination; an aliquot, in duplicate, was dried at 105 °C for 48 h according to procedures of the Association of Official Analytical Chemists (AOAC, 2006); (2) aliquots for microbial samples were immediately transferred to sterile cryogenic vials (Wheaton Science Products, Millville, NJ) and frozen on dry ice and stored at −80 °C until extraction.

To evaluate GTT, a fecal marker (Leaf Green Bakers’ Paste Food Coloring; Wilton Industries, Naperville, IL) was administered orally during feeding time the day before each fecal collection period. Bakers’ paste colorings are nontoxic, safe, free of harmful effects in cats, and are used as a fecal marker because they are not well absorbed from the gastrointestinal tract. The green color was chosen because it was not a color additive of the diet being fed and it documented consistent and readily identifiable color postexcretion in cats (Griffin, 2002). Each cat received 1 mL of bakers’ paste food coloring using a needle-free syringe (Cardinal Health, Dublin, OH), resulting in green-colored feces. Gastrointestinal transit time was recorded and measured from the moment the cat was orally administered the bakers’ paste until the first sight of excreted green feces. All cats were housed individually from the point of dye administration to ensure accurate measurement of GTT.

Blood collection and analysis

During weeks 0, 6, 12, and 18, overnight fasted (at least 12 h) blood samples were collected via jugular puncture under sedation. Just prior to blood collection, cats were sedated by an intramuscular (IM) injection of a mixture of dexmedetomidine (0.02 to 0.04 mg/kg IM) and butorphanol (0.4 to 0.8 mg/kg IM). Blood was immediately transferred to a vacutainer serum tube containing a clot activator and gel for serum separation (BD Vacutainer SST Tube—367988; Becton, Dickinson, and Co., Franklin Lakes, NJ) for serum chemistry profile, leptin, and insulin analyses. Serum tubes were centrifuged at 1,000 × g for 15 min at 4 °C to harvest serum. Whole blood tubes containing K₂EDTA (BD Microtainer Tubes-363706, Becton, Dickinson, and Co., Franklin Lakes, NJ) were used for hematology. Serum chemistry profile and hematology were analyzed at the University of Illinois Veterinary Medicine Diagnostics Laboratory using a Hitachi 911 clinical chemistry analyzer. Concentrations of leptin (MBS057075; MyBioSource, San Diego, CA) and insulin (MBS077484; MyBioSource, San Diego, CA) were measured using feline-specific commercial enzyme-linked immunosorbent assay kits. After blood was collected, an injection of the reversal agent for dexmedetomidine, atipamezole (0.2 to 0.4 mg/kg IM), was given.

Voluntary physical activity

Voluntary physical activity was measured using an Actical monitor (Actical device) and analyzed by computer software (Mini Mitter, Bend, OR). The device was attached to a collar and each cat wore the collar with the device around their neck for four consecutive days at weeks 0, and 6, 12, and 18. Mean activity was presented in activity counts per epoch (epoch length = 0.25 min), with periods of light (07:00 to 21:00 h) and dark (21:00 to 07:00 h) where activity counts were continuously measured. Human interference was controlled and limited to daily feeding and cleaning to avoid conflicts interfering with cats’ voluntary physical activity.

Chemical analyses

Diet subsamples were collected at least once a month. Fecal samples were dried at 55 °C in a forced-air oven. A composite sample of all diet subsamples and the dried fecal samples from each cat were ground independently of one another through a 2-mm screen using a Wiley Mill (Model 4, Thomas Scientific, Swedesboro, NJ) and dry ice to minimize nutrient degradation. All ground samples (diet and fecal) were analyzed for DM and ash in accordance with methods established by the Association of Official Analytical Chemists (AOAC, 2006; DM: method 934.01; ash: method 942.05), with organic matter (OM) being calculated. Using ANKOM Technology (ANKOM Hydrolysis System, ANKOM XT15 Extractor, and ANKOM RD Dryer; Macedon, NY) acid hydrolysis and extraction methods were performed to measure total lipid content. Crude protein (CP) content was calculated from Leco total nitrogen values (TruMac N, Leco Corporation, St. Joseph, MI; AOAC, 2006). Gross energy was measured using an oxygen bomb calorimeter (Model 6200, Parr instruments, Moline, IL). The total dietary fiber of the experimental diet was determined according to Prosky et al. (1985). Dietary nitrogen-free extract (NFE) was calculated using equation (1). Metabolizable energy (ME) on an “as-is” basis was calculated using equation (2) from the National Research Council (NRC, 2006). Apparent total tract macronutrient digestibility values were calculated using equation (3).

$${\displaystyle \begin{array}{l}{\displaystyle \mathrm{N}\mathrm{F}\mathrm{E}\left\{\mathrm{\%}\right\}=100\text{-}(\mathrm{F}\mathrm{a}\mathrm{t}\left\{\mathrm{\%}\right\}+\mathrm{C}\mathrm{P}\left\{\mathrm{\%}\right\}+\mathrm{A}\mathrm{s}\mathrm{h}\left\{\mathrm{\%}\right\}+\mathrm{T}\mathrm{D}\mathrm{F}\{\mathrm{\%}\})}\end{array}}$$ (1)

$${\displaystyle \begin{array}{l}{\displaystyle \mathrm{M}\mathrm{E}\left\{\frac{\mathrm{k}\mathrm{c}\mathrm{a}\mathrm{l}}{100\mathrm{g}}\right\}=\mathrm{G}\mathrm{E}\left\{\frac{\mathrm{k}\mathrm{c}\mathrm{a}\mathrm{l}}{100\mathrm{g}}\right\}x\left(\frac{95.6-\left[0.89\mathrm{x}\mathrm{T}\mathrm{D}\mathrm{F}\left\{\mathrm{\%}\right\}\right]}{100}\right)-\left(0.77x\mathrm{C}\mathrm{P}\left\{\mathrm{\%}\right\}\right)}\end{array}}$$ (2)

$${\displaystyle \begin{array}{l}{\displaystyle \mathrm{A}\mathrm{p}\mathrm{p}\mathrm{a}\mathrm{r}\mathrm{e}\mathrm{n}\mathrm{t}\mathrm{t}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l}\mathrm{t}\mathrm{r}\mathrm{a}\mathrm{c}\mathrm{t}\mathrm{d}\mathrm{i}\mathrm{g}\mathrm{e}\mathrm{s}\mathrm{t}\mathrm{i}\mathrm{b}\mathrm{i}\mathrm{l}\mathrm{i}\mathrm{t}\mathrm{y}\left\{\mathrm{\%}\right\}=\left[\frac{\begin{array}{c}(\mathrm{n}\mathrm{u}\mathrm{t}\mathrm{r}\mathrm{i}\mathrm{e}\mathrm{n}\mathrm{t}\mathrm{i}\mathrm{n}\mathrm{t}\mathrm{a}\mathrm{k}\mathrm{e}\left(\frac{\mathrm{g}}{\mathrm{d}}\right)\text{-}fecal\mathrm{o}\mathrm{u}\mathrm{t}\mathrm{p}\mathrm{u}\mathrm{t}\left(\frac{\mathrm{g}}{\mathrm{d}}\right))\end{array}}{\mathrm{n}\mathrm{u}\mathrm{t}\mathrm{r}\mathrm{i}\mathrm{e}\mathrm{n}\mathrm{t}\mathrm{i}\mathrm{n}\mathrm{t}\mathrm{a}\mathrm{k}\mathrm{e}\left(\frac{\mathrm{g}}{\mathrm{d}}\right)}\right]\ast 100}\end{array}}$$ (3)

Fecal DNA extraction and MiSeq Illumina sequencing of 16S amplicons

DNA was extracted from fecal samples using a DNeasy PowerLyzer PowerSoil Kit (Qiagen, Carlsbad, CA). Using a Qubit 3.0 Fluorometer (Life Technologies, Grand Island, NY), concentration of extracted DNA samples was quantified. 16S rRNA gene amplicons were generated using a Fluidigm Access Array (Fluidigm Corporation, South San Francisco, CA) in combination with a Roche High Fidelity Fast Start Kit (Roche, Indianapolis, IN). Primers 515F (5ʹ-GTGCCAGCMGCCGCGGTAA-3ʹ) and 806R (5ʹ-GGACTACHVGGGTWTCTAAT-3ʹ) target a 252-bp fragment of the V4 region of the 16S rRNA gene, which was used for amplification (primers synthesized by IDT Corp., Coralville, IA) and in methods described by Caporaso et al. (2012). A CS1 ­forward tag and a CS2 reverse tag were added in accordance with the Fluidigm protocol. Amplicon quality was assessed using a Fragment Analyzer (Advanced Analytics, Ames, IA) to confirm amplicon regions and sizes. By combining equimolar amounts of the amplicons from each sample, a DNA pool was generated. Pooled DNA samples were then size selected on a 2% agarose E-gel (Life Technologies, Grand Island, NY) and extracted using a Qiagen gel purification kit (Qiagen). Cleaned and size-selected pooled products were run on an Agilent Bioanalyzer to confirm the appropriate profile and average size. Illumina sequencing was performed on a MiSeq using v3 reagents (Illumina Inc., San Diego, CA) at the Roy J. Carver Biotechnology Center at the University of Illinois.

Bioinformatics and statistical analysis for assessing fecal microbial communities

Illumina 16S rRNA gene amplicon sequencing produced a total of 2,730,760 sequences, with an average of 62,062 sequences per sample. Forward reads were trimmed using the FASTX-Toolkit (version 0.0.14), and sequences were analyzed using QIIME 2.0, version 2022.8.3 (Caporaso et al., 2010). Raw sequenced amplicons were imported into the QIIME2 package and analyzed by the DADA2 pipeline for quality control (QC value ≥ 20; Callahan et al., 2016). Samples were then rarefied to 30,553 reads. On average, 66.59% of features and 100% of the samples were retained after rarefication. A total of 2,018,730 reads were retained after rarefication, with an average of 45,880 reads (range = 30,553 to 56,901) per sample. Subsequent samples were assigned to taxonomic groups with the SILVA database [SILVA 138 99% amplicon sequence variant (ASV) from 515F/806R region of sequences, with the QIIME2 classifier trained on 515F/806R V4 region of 16S] (Bokulich et al., 2018; Robeson et al., 2021). The rarefied samples were used for analysis of alpha diversity (observed richness, Faith’s phylogenetic diversity, and Shannon Diversity Index) and beta diversity. Principal coordinate analysis was performed using weighted and unweighted unique fraction metric (UniFrac) distances (Lozupone and Knight, 2005).

Statistical analyses

All data were analyzed using SAS (version 9.4; SAS Institute, Cary, NC) using the mixed models procedure with cat being the random effect. Data were analyzed using repeated measures analysis, with a difference in time being the primary focus. Fisher-protected least significant differences were determined with a Tukey adjustment to control for experiment-wise error. Using the univariate procedure and Shapiro–Wilk statistic, data normality was checked, with logarithmic transformation being applied when normal distribution was not observed. If normality was not achieved after the application of a logarithmic transformation, data were analyzed using the npar1way procedure and Wilcoxon statistic. Proc Mixed analysis (linear mixed model) was conducted on the microbiota taxonomic tables to detect differences between time points. Normality was verified for residuals by examining Gaussian distributions and applying the Shapiro–Wilk statistic using proc univariate. ­Additionally, ANOVA was executed on these models to determine significance. Data were reported as means, with P < 0.05 considered statistically significant and P < 0.10 considered trends.

Results

Once cats were allowed to overeat, all 11 of them immediately increased (P < 0.0001) their food and caloric intake by 129.77% ± 13.47% of baseline. Food intake was maintained between 126.85% ± 21.96% and 149.16% ± 18.96% of baseline for the entirety of the experiment (Figure 1a; Table 1). The greatest intake was observed during week 9 (309.51 ± 45.43 kcal/d), which was closely followed by intake during week 17 (308.32 ± 30.18 kcal/d). The lowest mean intake occurred during week 2 where cats consumed 262.32 ± 41.19 kcal/d, followed closely by the trends of week 5 where cats consumed 267.16 ± 30.77 kcal/d. Body weight increased (P < 0.0001) with overfeeding, starting at 4.11 ± 0.43 kg and ending at 4.75 ± 0.45 kg (Figure 1b). All cats started the study at baseline with an ideal BCS (5.41 ± 0.3), which increased (P < 0.0001) to a BCS of 8.27 ± 0.61 after 18 wk of overfeeding (Figure 1c).

Figure 1.

Weekly caloric intake (a), body weight (BW) (b), and body condition scores (BCS) (c) of cats during overfeeding and weight gain.

Table 1.

Food intake and fecal output of cats and dietary apparent total tract macronutrient digestibility during overfeeding and weight gain

Item	Week 0	Week 6	Week 12	Week 18	SEM1	P-value
Food intake
g food/d (as-is)	65.38c	88.08a	78.74b	79.50b	2.1309	<0.0001
g dry matter (DM)/d	60.67c	81.74a	73.07b	73.77b	1.9772	<0.0001
g organic matter/d	55.73c	75.08a	67.12b	67.77b	1.8166	<0.0001
g crude protein/d	20.79c	28.01a	25.04b	25.28b	0.6774	<0.0001
g fat/d	9.05c	12.19a	10.90b	11.00b	0.2948	<0.0001
kcal (gross energy)/d	309.41c	416.86a	372.63b	376.23b	10.0845	<0.0001
Fecal output
Fecal score	2.86	2.95	3.09	3.09	0.1313	0.4783
Fecal pH	6.45a	5.55b	5.56b	5.84ab	0.2003	0.0136
Fecal DM %	35.67	34.10	31.78	33.97	1.4082	0.1339
Fecal output, as-is (g/d)	29.94c	45.45b	53.55a	42.24b	3.4329	<0.0001
Fecal output, dry matter (g/d)	10.51b	15.73a	16.90a	14.11a	0.8852	<0.0001
As-is fecal output (g/d)/DM intake (g/d)	0.49b	0.56b	0.73a	0.57b	0.0466	<0.0001
Nutrient and energy digestibility, %
Dry matter	82.69a	80.72ab	76.94b	80.83ab	1.122	0.0061
Organic matter	87.11a	84.93ab	82.42b	84.85ab	0.9508	0.0130
Crude protein	90.13a	87.97a	81.18b	83.65b	0.8763	<0.0001
Fat	89.85a	86.09b	83.06c	85.88bc	1.1578	0.0002
Energy	87.33a	84.81a	81.17b	84.73a	0.8701	0.0002

¹SEM = pooled standard error of the means.

^a–cMean values within the same row with unlike superscript letters differ (P < 0.05).

Fecal output (as-is, DM basis) increased (P < 0.0001) and fecal pH decreased (P = 0.0136) with overfeeding and weight gain, while fecal scores and fecal DM % were unchanged (Table 1). Fecal pH at week 6 (5.55) and 12 (5.56) was lower than that baseline (6.45). In general, ATTD of DM, OM, CP, fat, and energy were decreased with overfeeding and weight gain (Table 1). While numerical reductions were present at all time points, statistical differences (P < 0.05) in ATTD were present at week 12 for DM, week 12 for OM, weeks 12 and 18 for CP, weeks 6, 12, and 18 for fat, and week 12 for energy. Gastrointestinal transit time decreased with overfeeding and weight gain, being lower (P = 0.0418) at week 12 (33.44 ± 6.76 h) and week 18 (33.26 ± 9.41 h) than at baseline (44.29 ± 13.16 h; Figure 2).

Figure 2.

Gastrointestinal transit time (GTT) of cats was decreased (P = 0.0418) by overfeeding and weight gain.

All serum metabolites were within the standard reference ranges for adult cats at baseline, except for cholesterol (171.64 mg/dL; reference range: 66 to 160 mg/dL) and Na/K ratio (37.91 U/L; reference range: 28 to 36 U/L; Table 2). Blood cholesterol was also outside the reference range at week 6 (165.00 mg/dL) and week 18 (174.64 mg/dL). The Na/K ratio remained outside the reference range at all time points. The albumin/globulin (A/G) ratios reported were outside the reference range on week 18 (1.21; reference range: 0.6-1.1 U/L). With overfeeding and weight gain, creatinine (P = 0.0172), BUN (P = 0.0003), calcium (P < 0.0001), phosphorus (P = 0.0012), cholesterol (P = 0.0032), protein (P < 0.0001), albumin (P < 0.0001), globulin (P < 0.0001), ALP (P < 0.0001), A/G ratio (P < 0.0001), and anion gap (P < 0.0001) were altered. Serum leptin (week 0 = 2.01 ± 0.59 ng/mL, week 6 = 2.15 ± 0.53 ng/mL, week 12 = 1.90 ± 0.47 ng/mL, week 18 = 1.99 ± 0.51 ng/mL) and insulin (week 0 = 48.56 ± 6.72 mIU/L, week 6 = 50.44 ± 5.07 mIU/L, week 12 = 45.04 ± 5.16 mIU/L, week 18 = 45.77 ± 8.32 mIU/L) concentrations were not impacted by overfeeding and weight gain (P > 0.10).

Table 2.

Serum metabolite concentrations of cats during overfeeding and weight gain

Item	Reference1	Week 0	Week 6	Week 12	Week 18	SEM2	P-value
Creatinine, mg/dL	0.4–1.6	1.26	1.34	1.37	1.23	0.0530	0.0172
BUN3, mg/dL	18–38	23.82b	24.00b	26.00a	22.91b	0.6055	0.0003
BUN:creatinine, mg/dL		19.15	18.19	19.32	19.09	0.9641	0.1554
Calcium, mg/dL	8.8–10.2	8.97b	9.42a	9.14b	9.16b	0.0768	<0.0001
Phosphorus, mg/dL	3.2–5.3	4.65b	4.28ab	4.09b	4.68a	0.1567	0.0012
Glucose, mg/dL	60–122	108.00	96.91	117.64	120.73	7.0882	0.0591
Cholesterol, mg/dL	66–160	171.64a	165.00ab	158.36b	174.64a	8.0017	0.0032
Triglycerides, mg/dL	21–166	40.36	32.00	37.45	42.00	2.9314	0.0505
Bicarbonate, mg/dL	12–21	18.00	18.27	18.00	18.18	0.3079	0.8661
Protein, g/dL	5.8–8.0	6.65a	6.72a	6.41b	5.93c	0.0637	<0.0001
Albumin, g/dL	2.8–4.1	3.01b	3.20a	3.19a	3.23a	0.0392	<0.0001
Globulin, g/dL	2.6–5.1	3.64a	3.51b	3.22c	2.70d	0.0576	<0.0001
Sodium, mmol/L	145–157	150.18	149.64	149.09	149.73	0.3208	0.0793
Potassium, mmol/L	3.6–5.3	4.02	3.92	4.05	4.17	0.1520	0.4706
Chloride, mmol/L	109–126	119.45a	116.73c	116.18c	118.27b	0.3118	<0.0001
ALP3, U/L	10–85	24.00c	26.82a	24.27bc	25.64ab	1.7742	<0.0001
ALT3, U/L	14–71	38.00	41.36	41.00	38.82	1.9531	0.0821
CPK3, U/L	10–250	114.55	112.45	128.27	95.55	16.9379	0.0577
A/G3 ratio	0.6–1.1	0.84c	0.92bc	0.99b	1.21a	0.0265	<0.0001
Na/K ratio	28–36	37.91	38.55	37.27	36.82	1.5982	0.7611
Anion gap	10–27	16.82c	18.64ab	18.91a	17.55bc	0.4796	0.0001

¹University of Illinois Veterinary Diagnostic Laboratory reference ranges.

²SEM, pooled standard error of the means.

³A/G, albumin/globulin; ALP, alkaline phosphatase; ALT, alanine aminotransferase; BUN, blood urea nitrogen; CPK, creatinine phosphokinase;.

^a–cMean values within the same row with unlike superscript letters differ (P < 0.05).

Most blood hematology measures were within the standard reference ranges for adult cats at baseline (Table 3). The exceptions were red blood cell numbers (9.44 × 10⁶/µL; reference range: 5.5 to 8.5 × 10⁶/µL), mean cell volume (36.95 fl; reference range: 58 to 76 fl), hemoglobin concentration (11.71 g/dL; reference range: 12.0 to 18.0 g/dL), monocyte numbers (0.19 × 10³/µL; reference range: 0.2 to 1.4 × 10³/µL) and eosinophil numbers (1.35 × 10³/µL; reference range: 0.1 to 1.0 × 10³/µl). With overfeeding and weight gain, red blood cells, mean cell volume, mean corpuscular ­hemoglobin, and hemoglobin remained outside the reference ranges. While monocyte (low, 0.19 × 10³/µL; reference range: 0.2 to 1.4 × 10³/µL) and eosinophil (high, 1.35 × 10³/µL; reference range: 0.1 to 1.0 × 10³/µL) numbers were outside the reference ranges at baseline, values returned to the reference ranges thereafter and for the entire duration of the study.

Table 3.

Hematology of cats during overfeeding and weight gain

Item	Reference1	Week 0	Week 6	Week 12	Week 18	SEM2	P-value
Red blood cells, 106/μL	5.5–8.5	9.44a	9.23ab	9.07ab	8.52b	0.2206	0.0190
Reticulocyte count, μL		0.07b	0.07b	0.06b	0.10a	0.0109	0.0054
Platelets, fl		334.54	412.18	346.00	391.82	26.8135	0.1831
Mean cell volume, fl	58–76	36.95b	38.06a	37.95a	38.14a	0.4129	0.0004
MCH3, pg	20–25	12.45b	12.95a	12.92a	12.97a	0.1805	<0.0001
MCHC3, g/dL	33–38.6	33.67	34.01	34.01	34.02	0.2685	0.0642
Hemoglobin, g/dL	12.0–18.0	11.71	11.94	11.69	11.05	0.2478	0.0938
Hematocrit, %		34.78	35.13	34.40	32.46	0.7636	0.0805
Lymphocytes, %		32.28	33.39	31.92	33.94	3.9878	0.9738
Monocytes, %		2.29b	4.25ab	4.49a	3.92a	0.7029	0.0390
Eosinophils, %		9.70	8.45	8.54	9.11	1.2501	0.7887
Basophils, %		0.29	0.51	0.35	0.26	0.1328	0.0772
Mean platelet volume, 103/μL		11.09d	11.92c	12.11b	12.41a	0.2799	0.0433
White blood cell count, 103/μL	6.0–17.0	7.40	8.28	8.23	7.80	0.8458	0.9930
Lymphocytes, 103/μL	1–4.8	2.68	2.72	2.72	2.76	0.4687	0.9970
Monocytes, 103/μL	0.2–1.4	0.19b	0.30a	0.36a	0.27ab	0.0504	0.0161
Eosinophils, 103/μL	0.1–1.0	1.35	0.63	0.67	0.68	0.2520	0.2638
Basophils, 103/μL	0–2	0.02	0.04	0.02	0.02	0.0100	0.7937

¹University of Illinois Veterinary Diagnostic Laboratory reference ranges.

²SEM, pooled standard error of the means.

³MCH, mean corpuscular hemoglobin; MCHC, mean corpuscular hemoglobin concentration.

^a–cMean values within the same row with unlike superscript letters differ (P < 0.05).

Voluntary activity during the dark period and the light activity:dark activity ratio were altered (P = 0.0010) with overfeeding and weight gain (Table 4). Activity during the dark period was lower at week 6 and 12 than week 18, where cats showed an increased interest in engage in voluntary physical activity at week 18. This impacted the light activity:dark activity ratio, which was greater at week 12 than at baseline and week 18.

Table 4.

Voluntary physical activity counts (AC) of cats during overfeeding and weight gain

Item	Week 0	Week 6	Week 12	Week 18	SEM1	P-value
Average AC/min	0.73	0.66	0.73	0.76	0.0514	0.2207
Average light AC/min	1.23	1.21	1.41	1.29	0.1022	0.3601
Average dark AC/min	1.80ab	1.44b	1.44b	1.87a	0.1932	0.0010
Light:dark ratio	0.71b	0.93ab	1.15a	0.75b	0.0952	0.0010

¹SEM, pooled standard error of the means.

^a,bMean values within the same row with unlike superscript letters differ (P < 0.05).

Fecal bacterial alpha diversity measures were not affected by overfeeding and weight gain (Supplementary Figure S1). Fecal bacterial beta diversity, however, was significantly impacted by overfeeding and weight gain ­(Figure 3). Weighted UniFrac distance measures at week 12 and week 18 were different (P < 0.05) than those at week 0 and week 6. Similarly, unweighted UniFrac distance measures at week 12 and week 18 were different (P < 0.05) than those at week 0. There were six predominant bacterial phyla detected, including Actinobacteriota, Bacteroidota, Campilobacterota, Firmicutes, Fusobacteriota, and Proteobacteria. Firmicutes, Actinobacteriota, and Bacteroidota were the most dominant phyla, accounting for over 97% of relative abundance at all time points. Lactobacillus and Blautia were the most predominant fecal bacterial genera found within the Firmicutes phylum, while Collinsella was most predominant within the Actinobacteriota phylum. The relative abundances of 1 fecal bacterial phylum and 16 fecal bacterial genera were affected (P < 0.05) and 10 bacterial genera tended to be affected (P < 0.10) by overfeeding and weight gain (Table 5; Supplementary Figure S2). At the phylum level, the relative abundance of fecal Proteobacteria decreased (P = 0.0211) with overfeeding and weight gain. The Firmicutes to Bacteroidetes ratio increased (P < 0.05) with overfeeding and weight gain (week 0: 9.6; week 6: 15.8; week 12: 22.4; week 18: 20.5). At the genus level, the relative abundances of fecal Bifidobacterium and Lactobacillus increased (P < 0.05) with overfeeding and weight gain. In contrast, the relative abundances of fecal Anaerostipes, Blautia, Collinsella, Escherichia-Shigella, Erysipelatoclostridium, Faecalicoccus, Holdemanella, Lachnoclostridium, Lachnospiraceae unclassified, Lachnospiraceae uncultured, Ruminococcus gnavus group, Sellimonas, and Turicibacter decreased with overfeeding and weight gain.

Figure 3.

Bacterial beta diversity measures of fecal samples collected from adult female cats. Principal coordinate analysis plots for weighted (a) and unweighted (b) UniFrac distances of fecal microbial communities were altered by weight gain and overfeeding. For weighted analyses, week 12 and week 18 were different (P < 0.05) than week 0 and week 6. For unweighted analyses, week 12 and week 18 were different (P < 0.05) than week 0.

Table 5.

Fecal bacterial phyla and genera (relative abundance, %) of cats during overfeeding and weight gain

Phyla	Genus	Week 0	Week 6	Week 12	Week 18	SEM1	P-value
Actinobacteriota		18.09	17.18	16.45	18.28	0.1715	0.6631
	Bifidobacterium	4.89b	3.56b	7.92ab	10.69a	1.3982	0.0009
	Collinsella	12.45a	12.88a	7.91b	6.88b	0.9528	0.0002
	Libanicoccus	0.31	0.29	0.30	0.31	0.0466	0.9899
	Olsenella	0.03	0.03	0.02	0.10	0.0352	0.3955
	Parvibacter	0.16	0.19	0.11	0.13	0.0383	0.0809
	Slackia	0.23	0.21	0.16	0.16	0.0328	0.0748
Bacteroidota		7.60	4.83	3.54	3.75	0.0138	0.6394
	Bacteroides	4.70	2.84	1.49	1.70	0.9308	0.1332
	Parabacteroides	0.55	0.38	0.49	0.52	0.1979	0.5527
	Prevotella	2.32	1.59	1.52	1.50	0.9824	0.6059
Campilobacterota		0.19	0.44	0.09	0.16	0.1539	0.2709
	Campylobacter	0.19	0.44	0.09	0.16	0.1543	0.4392
Firmicutes		72.67	76.16	79.20	76.97	0.0236	0.3546
	Acidaminococus	0.09	0.14	0.26	0.28	0.0876	0.1750
	Allisonella	0.33	0.44	0.32	0.41	0.0756	0.4068
	Anaerostipes	0.43a	0.23ab	0.13b	0.11b	0.0675	0.0101
	Blautia	10.81a	7.90b	5.72b	5.64b	0.6932	0.0024
	Butyricicoccus	0.48	0.91	0.53	0.51	0.1204	0.0601
	Catenibacterium	4.00	4.76	4.77	4.03	1.2357	0.9842
	Clostridia UCG-014	0.21	0.09	0.06	0.08	0.0532	0.2330
	Clostridium Sensu Stricto 1	1.54	1.50	1.93	2.80	0.4873	0.1435
	Colidextribacter	0.08	0.06	0.06	0.07	0.0241	0.7895
	Dialister	1.24	1.42	0.62	0.91	0.6826	0.4126
	Enterococcus	0.34	0.52	0.15	0.28	0.1347	0.2577
	Erysipelatoclostridium	0.38a	0.23ab	0.15b	0.12b	0.0535	0.0057
	Eubacterium hallii group	0.07	0.07	0.11	0.13	0.0337	0.2414
	Faecalibacterium	0.50	0.51	0.31	0.62	0.1252	0.0786
	Faecalicoccus	0.07a	0.02b	0.00b	0.01b	0.0136	0.0092
	Flavonifractor	0.06	0.03	0.01	0.03	0.0195	0.0950
	Holdemanella	6.66ab	7.04a	5.16a	3.52ac	1.1938	0.0282
	Incertae Sedis	0.09	0.07	0.06	0.05	0.0179	0.2396
	Lachnoclostridium	2.30a	1.90ab	1.45bc	1.29c	0.1760	<0.0001
	Lachnospiraceae NK4A136 group	0.13	0.09	0.07	0.09	0.0202	0.0926
	Lachnospiraceae unclassified	2.04a	1.83ab	1.21b	1.30b	0.1899	0.0053
	Lachnospiraceae uncultured	0.80a	0.64ab	0.41b	0.44b	0.0787	0.0064
	Lactobacillus	25.04b	32.50a	44.46a	39.11a	4.6045	0.0424
	Megamonas	0.29	0.36	0.50	0.35	0.2103	0.9082
	Megasphaera	0.71	1.12	1.02	2.55	0.4316	0.0808
	Negativibacillus	0.15	0.16	0.20	0.32	0.0572	0.1708
	Oscillospiraceae uncultured	0.22	0.15	0.15	0.14	0.0532	0.3693
	Peptoclostridium	2.37	1.76	3.44	3.04	0.6385	0.4704
	Peptococcus	0.89	1.14	0.82	0.95	0.2358	0.2049
	Peptostreptococcus	0.10	0.11	0.03	0.09	0.0592	0.4251
	Phascolarctobacterium	0.12	0.22	0.16	0.15	0.0633	0.3354
	Romboutsia	0.51	0.87	0.12	0.53	0.2758	0.2939
	Roseburia	0.09	0.10	0.04	0.05	0.0448	0.6526
	Ruminococcus gauvreauii group	1.06	0.79	0.83	0.83	0.1020	0.0567
	Ruminococcus gnavus group	1.65a	1.59b	1.09d	0.95c	0.2632	0.0194
	Ruminococcus torques group	0.88a	0.81a	0.45a	0.49b	0.1317	0.0227
	Sellimonas	1.60a	1.29ab	0.88b	0.81b	0.1817	0.0086
	Streptococcus	0.34	0.22	0.30	0.66	0.3087	0.4458
	Subdoligranulum	3.41	2.04	0.96	1.86	0.7086	0.0894
	Turicibacter	0.19a	0.24b	0.01d	0.13c	0.0956	0.0363
	Tyzzerella	0.08	0.07	0.05	0.15	0.0759	0.6603
Fusobacteriota		0.06	0.10	0.07	0.04	0.0225	0.4449
	Fusobacterium	0.06	0.10	0.07	0.04	0.0225	0.4448
Proteobacteria		1.38a	1.30ab	0.65b	0.80ab	0.2465	0.0211
	Escherichia-Shigella	0.65a	0.33ab	0.07b	0.32b	0.1786	0.0030
	Succinivibrio	0.35	0.61	0.20	0.18	0.1547	0.0643
	Sutterella	0.38	0.36	0.36	0.30	0.1179	0.9262

¹SEM, pooled standard error of the means.

^a–dMean values within the same row with unlike superscript letters differ (P < 0.05).

Discussion

Pet obesity is an epidemic, with 61% of U.S. cats being classified as overweight or obese (APOP, 2022). Increased adiposity elicits a plethora of metabolic dysregulations and predisposes cats to a multitude of health perturbations, including chronic inflammation, skin disorders, orthopedic disease, metabolic and endocrine diseases, and decreased quality of life and longevity. With a high incidence of feline obesity within the cat population, it is no wonder that this is a focal point for many researchers. Numerous trials have been performed to analyze the effects of weight loss, but there is limited research in cats examining weight gain from a lean to an obese state, prompting our investigation.

As predicted, ad libitum feeding allowed all cats of the present study to dramatically increase food and caloric intake, proving to be a successful means for inducing weight gain. Previous literature has shown that obese cats cannot control or self-regulate their caloric intake, likely explained by their altered perception of macronutrients and dysregulation of other physiological mechanisms associated with intake, which has also been described in humans (Zoran and Buffington, 2011; Alegría-Morán et al., 2019). Because weight gain (a subsequent result to overfeeding) alone does not assess the degree of obesity, BCS coming from the palpation and inspection confirmed increased adiposity in this study. Body condition scoring can be used as a semi-quantitative tool to assess body fat percentage, and has been validated using dual-­energy X-ray absorptiometry in multiple canine and feline studies (Laflamme, 1997; Mawby et al., 2004; Santarossa et al., 2018). For every BCS unit increase, an equivalent 5% increase in body fat results. In the present study, this ­suggests that the resulting BCS of 8 after the 18-wk overfeeding period correlated with 35% to 39% body fat and cats being 30% overweight (Cline et al., 2021).

Obese cats are known to process nutrients differently than lean cats and have greater blood lipid concentrations, including triglycerides, non-esterified fatty acids, and cholesterol (Hoenig et al., 2003; Clark et al., 2013, Berman et al., 2022). Blood cholesterol concentrations of cats in the current study were outside (above) the normal reference range. While this has been demonstrated previously (Zini et al., 2021), blood cholesterol was above the reference range at baseline and during ad libitum feeding. Because blood cholesterol and triglyceride concentrations actually decreased during the weeks of greatest food intake, it appears that cats were able to adequately store excess calories in their adipose tissue and avoid spillover into the blood. In humans, overeating can rapidly impair metabolic health as it relates to insulin sensitivity (Brøns et al., 2009; Cornford et al., 2013; McLaughlin et al., 2016; Tam et al., 2017; Ludzki et al., 2022). This is known to occur in cats as well. Like blood lipids, however, cats in this study apparently were able to process and store excess calories without negative metabolic effects because blood glucose and insulin concentrations were not significantly impacted over the 18-wk overfeeding and weight gain period.

The impacts of overfeeding and weight gain on ATTD were one of the main focal areas of the current study. Nutrient digestibility is affected by many factors, including the sources by which nutrients are derived, methods of diet processing, and the physiological state of the animal. In the present study, nutrient and energy digestibilities were significantly reduced by overfeeding and weight gain. These decreased digestibilities demonstrate a reduced digestive efficiency. However, these reductions were quite low compared to the amount of overeating that occurred. Reduced nutrient digestibilities are unlikely to have any significant effects on pet health, but does lead to an increased passage of nutrients into the large intestine. The increased nutrient passage would be expected to influence the intestinal microbiota populations and increase the amount of feces excreted. Not surprisingly, as nutrient digestibility decreased, fecal output increased while maintaining adequate stool quality. Stool quality is a general indicator of gastrointestinal health and is an important marker of health that pet parents can identify. Fecal output is also influenced by a variety of factors, with food intake, nutrient digestibility, and water-holding capacity of dietary fibers having the greatest impact. Further, certain fecal characteristics, such as fecal scores and pH, may signify adequate digestibility and quality of the diet. Although a definitive link has not been defined in cats, humans having a low fecal pH suggests carbohydrate and/or fat malabsorption (Osuka et al., 2012; Calvo-Lerma et al., 2021). Such a response was observed in the current study, with reduced fecal pH aligning with times of highest food intakes and reduced digestibility.

Another key response of the current study was GTT, which decreased with overfeeding and weight gain as hypothesized. A previous cat study reported the transit time to be between 26 and 36 hours, depending on age (Peachey et al., 2000). The transit time of senior cats in that study (36 hours) was slightly lower than that measured in our cats at baseline. The GTT was reduced by about 25% with overeating and weight gain, putting them in the range reported in the previous study. Our study demonstrates that greater food intake speeds up the rate by which digesta travels through the gastrointestinal tract and was likely a primary reason for the reduced nutrient digestibility observed. In humans, the transit time has been measured to be higher in obese than lean individuals (Spiller et al., 1988), with gastrointestinal tract motility being diminished (Miron and Dumitrascu, 2019). These alterations in transit time and motility may have major impacts on several processes, including satiety and appetite regulation, glycemic control, and gut hormone signaling (Holst et al., 2016; Fändriks, 2017; Wei et al., 2021). Similar to the blood lipid data, it appears that the primary driver of transit time change in the current study was food intake. While this resulted in a reduced GTT, it would be of interest to measure transit time in obese vs. lean cats fed to maintain BW to determine whether this relationship is similar to that noted in humans.

Connections between intestinal microbiota and gastrointestinal tract motility disorders have been observed in recent studies. While meal size and transit time were key indicators of decreased digestibility in the present study, the effects that these changes have on the large intestinal microbial populations also need to be considered. A host’s gut microbial populations are reliant upon the substrates available to them and play a significant role in many physiological and metabolic functions of the body, affecting cross-organ signaling and energy and substrate metabolism (Canfora et al., 2015), all of which likely play a role in the etiology of metabolic diseases such as obesity. A shift in the ­gastrointestinal ­microbiota as a result of chronic illness (e.g., obesity) tends to be gradual with time, whereas a shift or change in response to diet alterations is rather quick (David et al., 2014; Mori et al., 2019; Lin et al., 2022). In dogs, mice, and humans, an increase in the Firmicutes to Bacteroidetes ratio has been associated with obesity (Ley et al., 2005; Ley, 2010; Li et al., 2017; Coelho et al., 2018). Our study demonstrated a similar response, with overweight cats at the end of the study having a greater Firmicutes to Bacteriodetes ratio than at baseline. This has not always been the case, however, as Ma et al. (2022) reported an inverse relationship in obese cats, suggesting that more research is needed to study whether this outcome is truly changed with BCS or if there are other factors involved.

As a result of overfeeding and weight gain, several fecal microbiota taxa were impacted, with widespread differences observed in bacterial beta diversity, but not bacterial alpha diversity. The bacterial beta diversity composition, which measures the differences between samples from different groups, showed a significant shift between the lean cats at baseline and after 18 wk of weight gain. In another study using normal (lean, adult, intact, n = 8) and obese (adult, altered, n = 8) cats, bacterial beta diversity analysis also showed a distinct separation of microbiomes between the lean and obese groups (Ma et al., 2022). Therefore, our findings align with other researchers searching for associations between gut microbiota and weight status in cats. Due to the design of the current study, it was impossible to separate the effects of overfeeding from weight gain, but inferences may still be made.

In the current study, the six predominant bacterial phyla detected (Actinobacteriota, Bacteroidota, Campilobacterota, Firmicutes, Fusobacteriota, and Proteobacteria) were similar to those reported in previous cat studies (Lyu et al., 2020; Li et al., 2022; Ma et al., 2022). The most predominant phylum of cats in both the lean and obese state was Firmicutes, with the second most predominant (i.e., Actinobacteriota) having a much lower abundance. Within Firmicutes, the three most abundant taxa were Blautia, Lactobacillus, and Subdoligranulum. Blautia and Subdoligranulum decreased with overfeeding and weight gain. Blautia, a SCFA producer that inhabits the intestines and feces of many mammals, has been identified as having probiotic characteristics, antibacterial activity, and can contribute to anti-inflammatory responses of the host (Khattab et al., 2016; Chakravarthy et al., 2018; Liu et al., 2021). Lactobacillus, typically regarded as a beneficial microbe with probiotic functionality, increased over time with overfeeding and weight gain. These microbial changes were likely due to an increased substrate availability for fermentation or consequent effects on the gastrointestinal environment (e.g., pH).

Within the second most dominant phylum, Actinobacteriota, the relative abundance of Bifidobacterium, a SCFA producer that has antimicrobial activity, inhibits pathogens, and stimulates the immune system (Levine et al., 2013; LeBlanc et al., 2017) increased with overfeeding and weight gain. Previous cat studies have reported that this genus may be associated with BCS, as greater Bifidobacterium counts have been reported in obese/overweight cats than in lean cats (Kieler et al., 2019; Ma et al., 2022). Another genus within the Actinobacteriota phylum, Collinsella, a fiber degrader and H₂ consumer, was significantly reduced in cats as they overate and gained weight. In humans, Collinsella has been previously linked to pro-inflammatory diseases, such as obesity, type 2 diabetes, and atherosclerosis with a positive correlation between body mass index and circulating insulin concentrations (Karlsson et al., 2012; Candela et al., 2016; Gomez-Arango et al., 2016, 2018). The relative abundance of Collinsella in humans is observed to be highest in obese subjects, with decreases observed in those who lose weight (Frost et al., 2019; Martínez-Cuesta et al., 2021). Because the data from the current study are opposite to that measured in humans, it suggests that other factors beyond weight gain alone are involved.

In regard to physical activity, it is known that cats are crepuscular, making them most active during dawn and dusk, which is likely impacted by their housing (e.g., indoor only, access to outdoors), feeding schedule, and home environment. Previous literature has reported varied voluntary physical activity data between lean and obese cats. Some researchers have observed that lean cats are more active than obese cats, which true in both dark and light periods (de Godoy and Shoveller, 2017). Others, however, have not identified significant differences (Pallotto et al., 2018). This discrepancy across studies could be due to variations among individual cats, their housing and environment, or other factors. While voluntary activity is possible, pet parents are encouraged to engage in activities with their feline companions. To stimulate foraging behavior, pet parents can place small, size-appropriate portions of food in different locations within the home or toss food for a cat to chase or retrieve. Another option is to use food puzzles during mealtime to stimulate engagement and increase mental enrichment. Food puzzles have been recommended as a tool in the treatment of obesity as they thereby increase activity and encourage problem-solving (Meehan and Mench, 2007).

The present study has some limitations that need to be considered when applying these results to the general cat population. The present study included a small number (n = 11) of adult spayed female cats and was conducted in an extremely controlled environment. While the controlled environment served to limit variability, the application of these results to client-owned animals in dynamic environments may be difficult. Factors such as age, sex, spay/neuter status, accessibility to enrichment, diet, and overall living conditions can play significance in differing outcomes. Further, given the colony of cats utilized for this study was purpose-bred for research, their energy needs may differ from those of cats living outside a research environment. Those who live outside a research colony and controlled environment have more influencing factors to impact their day-to-day voluntary engagement in physical activity and everyday conditions (temperature, humidity, seasons, etc.).

In conclusion, overfeeding a commercial diet quickly led to voluntary overconsumption, weight gain, and increased BCS in adult female cats. Overfeeding led to decreased DM, OM, CP, fat, and energy digestibilities. Overfeeding also decreased GTT, which suggests that digesta traveled through the gastrointestinal tract at a faster rate and is the likely reason for the reduction in ATTD. Voluntary physical activity during the dark period was impacted by overfeeding and weight gain, but total activity was not influenced. Concentrations for all blood metabolites remained within reference ranges, except for mean cholesterol, A/G ratio, and Na/K ratio. Several fecal microbial shifts were noted with weight gain and overfeeding, but more studies are needed to determine if these shifts are merely due to faster digesta transit and differing substrate loads or if they are linked to metabolic processes, health perturbations, and/or the obese state.

Glossary

Abbreviations:

AC

activity count;

ALP

total alkaline phosphatase

ASV

amplicon sequence variant

BCS

body condition score

BW

body weight

CP

crude protein

DM

dry matter

GE

gross energy

GTT

gastrointestinal transit time

ME

metabolizable energy

NFE

nitrogen-free extract

OM

organic matter

TDF

total dietary fiber

Conflict of interest statement

All authors have no conflicts of interest.