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Journal of Animal Science logoLink to Journal of Animal Science
. 2023 Aug 26;101:skad283. doi: 10.1093/jas/skad283

Changes in gut microbiota and short-chain fatty acids are involved in the process of canine obesity after neutering

Kang Yang 1, Xinye Lin 2, Shiyan Jian 3, Jiawei Wen 4, Xiaoying Jian 5, Shansong He 6, Chaoyu Wen 7, Tingting Liu 8, Xin Qi 9, Yulong Yin 10, Baichuan Deng 11,✉
PMCID: PMC10558198  PMID: 37632755

Abstract

Neutering is a significant risk factor for obesity in dogs. Changes in gut microbiota and its metabolites have been identified as a key player during obesity progression. However, the mechanisms that promote neuter-associated weight gain are not well understood. Therefore, in this study, sixteen clinically healthy Beagle dogs (6 male and 10 female, mean age = 8.22 ± 0.25 mo old) were neutered. Body weight (BW) and body condition score (BCS) were recorded at 1 d before neutering, 3, 6, 10, 16, and 21 mo after neutering. Dogs were grouped based on their BCS as ideal weight group (IW, n = 4, mean BW = 13.22 ± 1.30 kg, mean BCS = 5.00 ± 0.41) and obese group (OB, n = 12, mean BW = 18.57 ± 1.08 kg, mean BCS = 7.92 ± 0.82) at 21 mo after neutering. Serum lipid profile, glucose, and hormones and fecal microbiota and short-chain fatty acids (SCFAs) were measured. Our results showed that OB dogs had greater (P < 0.0001) BW (18.57 vs. 13.22 kg), BCS (7.92 vs. 5.00), and average daily gain (12.27 vs. 5.69 g/d) than IW dogs at 21 mo after neutering, and the obesity rate was up to 60%. In addition, significant increases (P < 0.05) in serum triglyceride (TG, 1.10 vs. 0.56 mmol/L) and high-density lipoprotein cholesterol (HDL-C, 6.96 vs. 5.40 mmol/L) levels and a significant decrease (P < 0.05) in serum adiponectin (APN, 54.06 vs. 58.39 μg/L) level were observed in OB dogs; serum total cholesterol (4.83 vs. 3.75 mmol/L) (P = 0.075) and leptin (LEP, 2.82 vs. 2.53 μg/L) (P = 0.065) levels tended to be greater in OB dogs; there was a trend towards a lower (P = 0.092) APN/LEP (19.32 vs. 21.81) in OB dogs. Results of fecal microbial alpha-diversity showed that Observed_species and Chao1 indices tended to be lower (P = 0.069) in OB dogs. The STAMP and LEfSe analyses revealed that OB dogs had a greater (P < 0.05 and LDA > 2) reduction in relative abundances of Bacteroides, Prevotella_9, and Megamonas than IW dogs. In addition, OB dogs also had greater (P < 0.05) reduction in fecal acetate, propionate, and butyrate concentrations than IW dogs. Moreover, clear negative correlations (|r| > 0.5 and P < 0.05) were found between SCFAs-producing bacteria and BW, TG, and HDL-C. The functional predictions of microbial communities based on PICRUSt2 analysis revealed that lipid metabolism and endocrine system were significantly disturbed in obese dogs after neutering. Thus, intervention with SCFAs-producing bacteria might represent a new target for the prevention or treatment of canine obesity after neutering. Moreover, weight control before neutering may also contribute to the prevention of canine obesity after neutering.

Keywords: gonadectomy, intestinal microbiota, pet obesity, spay

1)A significant decrease in SCFAs-producing bacteria is involved in the process of canine obesity after neutering by perturbing lipid metabolism and endocrine system.

2)Weight control before neutering may also contribute to the prevention of canine obesity after neutering.

Introduction

Dog overpopulation and stray dog menace remain important problems worldwide (Abdulkarim et al., 2021). In 2022, the Terrestrial Animal Health Code issued by the World Organization for Animal Health (OiE) declares in the chapter Dog Population Management that isolated activities of collection and euthanasia are ineffective methods implemented to control animal populations. Gonadectomy, known as neutering, is a very common elective surgery in many countries as a means of controlling reproduction and consequently reducing the stray dog population (Looney et al., 2008). Commonly, pet owners are advised to neuter their dogs to prevent undesired reproduction and improve canine health, such as preventing diseases of the reproductive system (Root Kustritz, 2005; McKenzie, 2010), modifying undesirable behavior (Sherman et al., 1996; Casey et al., 2014), reducing the incidence of cancers (Wilson and Hayes, 1979; Beauvais et al., 2012), and prolonging lifespan (Hoffman et al., 2013). Nonetheless, neutering also contributes to pet obesity risk (Robertson, 2003; Colliard et al., 2006; German et al., 2017). Numerous studies have demonstrated that neutered dogs may be more predisposed to obesity (Lefebvre et al., 2013; Mao et al., 2013; Simpson et al., 2019; Vendramini et al., 2020).

Obesity in humans and pets is a growing health concern, and the risk for several metabolic disorders such as hyperlipidemia and type 2 diabetes increased (Kealy et al., 2002; German, 2006; Caballero, 2019; Salt et al., 2019). Canine obesity is a multicausal disease, with a prevalence of over 40% in developed countries (Montoya-Alonso et al., 2017; Forster et al., 2018). Neutering increases obesity risk not solely due to increased energy intake, but primarily due to decreased metabolic rates and energy expenditure (Schauf et al., 2016). As part of a complex ecosystem, the gut microbiota has been identified as a key player during obesity progression (Villanueva-Millán et al., 2015). Gut microbiota influences the onset and progression of obesity and obesity-related diseases through changes in its composition and metabolites (Geng et al., 2022; Tokarek et al., 2022). Several studies have also proved that microbial metabolites short-chain fatty acids (SCFAs), including acetate, propionate, and butyrate, can affect obesity characteristics by regulating energy intake, insulin secretion, adipose tissue function, and chronic systemic inflammation (Chávez-Talavera et al., 2017; Kim et al., 2019; Lin et al., 2022). In addition, lack of estrogen production by gonadectomy has been demonstrated to cause changes in host physiology and microbial community structure and composition (Reichler, 2009; Zhao et al., 2019).

Therefore, developing successful preventive strategies seems to be essential to reduce the risk of canine obesity after neutering. To do this in an evidence-based manner, we designed a rigorous trial to learn more about the mechanism of canine obesity after neutering. Sixteen 8-mo-old Beagle dogs were neutered, and then this study lasted for 21 mo after neutering. Body weight (BW) and body condition score (BCS) were recorded over the next 21 mo, and serum lipid profile, glucose, hormones, fecal microbiota, and SCFAs were measured. Our main objective is to search for potential targets for the prevention or treatment of canine obesity after neutering from the perspective of regulating gut microbiota.

Materials and Methods

Approval of the experimental protocol (protocol code 2021E028) was granted by the Experimental Animal Ethics Committee of South China Agricultural University.

Animals and diets

Beagle dogs were bought from National Canine Laboratory Animal Resource Bank, Guangzhou General Pharmaceutical Research Institute Co., Ltd (Guangzhou, China), license number: SCXK (Guangdong) 2018-0007. Sixteen clinically healthy Beagle dogs (6 male and 10 female; mean age = 8.22 ± 0.25 mo old; mean BW = 10.45 ± 0.84 kg; mean BCS = 5.19 ± 0.63; Supplementary Table S1) owned by the Laboratory Animal Center at the South China Agricultural University were used in this study. All dogs came from different litter groups. The serum biochemistry (Supplementary Table S2) and blood routine examination (Supplementary Table S3) in sixteen dogs 1 mo before neutering were normal. Dogs housed in the animal housing room had ad libitum access to fresh water and followed a 12-h light–dark cycle (light from 0700 to 1900 h). The animal housing room was kept at 21 to 23 °C with a relative humidity of 50% to 70%. Dogs were free to interact with people or each other at least once a day. In addition, all dogs lived in the same environment from birth to post-neutering and were fed the same commercial dog foods (Product name: Vittide) produced by Remical Animal Nutrition & Health Technology Co., Ltd (Foshan, China) for puppies or adult dogs throughout the study. The nutrient composition of the diet for puppy (3 to 12 mo) was 91.19% dry matter, 91.57% organic matter, 27.69% crude protein, 11.21% acid-hydrolyzed fat, 3.63% total dietary fiber, and 3,557.83 kcal/kg metabolizable energy, and the nutrient composition of the diet for adult dog (>12 mo) was 91.84% dry matter, 93.68% organic matter, 32.63% crude protein, 13.72% acid-hydrolyzed fat, 1.66% total dietary fiber, and 3,832.83 kcal/kg metabolizable energy. The dietary ingredient for puppy was corn, sweet potato flour, wheat flour, corn gluten meal, soybean meal, beet pulp, chicken meal, meat and bone meal, dicalcium phosphate, vitamins and minerals premix, blended oil and fat, and flavor enhancer. The dietary ingredient for adult dog was corn, sweet potato flour, wheat flour, corn gluten meal, soybean meal, beet pulp, chicken meal, duck meal, fish meal, meat and bone meal, dicalcium phosphate, vitamins and minerals premix, blended oil and fat, and flavor enhancer. Each puppy and adult dog was fed a restricted diet of 200 and 260 g/d, respectively, and 100 g (puppy) or 130 g (adult dog) per dog was consumed at each of the two daily meals at 0800 and 1700 hours. Both diets fulfilled the nutritional requirements of puppy and adult dog as defined by the Association of American Feed Control Officials (AAFCO, 2017).

Sedation, anesthesia, and surgical procedure

According to veterinary advice and recommendations found in the literature (Kustritz, 2007), dogs aged 7 to 10 mo are suitable for neutering. Thus, 16 Beagle dogs, approximately 8 mo old, were selected for neutering on 18 October 2020. The neutering procedure was performed by several veterinarians with veterinary certificates using standard techniques. In detail, anesthesia was induced using 5 mg/kg propofol and maintained with 3 to 4 μmol/L isoflurane. All dogs were fasted for 24 h and deprived of water for 4 h on the day before the neutering procedure. The postoperative course was uneventful with neutered dogs discharged 1 h later. Dogs were allowed to eat and drink ad libitum 6 h after surgery, and sodium penicillin (antibiotic) was diluted and the veterinarian gave each dog a subcutaneous injection of 0.5 mL for 3 d. Additionally, Elizabethan collars were put on the dog for the first 5 d post-operation.

Experiment design

After the neutering procedure, this study lasted for 21 mo. Each dog was kept in cages (1.3 × 1.1 × 1.2 m kennels) with outdoor exercise once a week during the trial. Body weight and BCS were recorded at 1 d before neutering, 3, 6, 10, 16, and 21 mo after neutering throughout the trial. Blood and fresh feces samples were collected on the last day for further analysis. Dogs were classified according to their BCS as ideal weight group (IW; BCS: 5.00 ± 0.41; n = 4) and the heavy/obese group (OB; BCS: 7.92 ± 0.82; n = 12) at 21 mo after neutering. The canine BCS was assessed according to the American Animal Hospital Association (AAHA) (Cline et al., 2021), where 1 was considered emaciated, 2 very thin, 3 thin, 4 underweight/low ideal, 5 ideal, 6 overweight, 7 heavy, 8 obese, and 9 grossly obese.

BW and BCS

Dogs were fed twice daily at 0830 and 1730 hours with a restricted amount of commercially extruded feed. Body weight and BCS were measured at 1 d before neutering, 3, 6, 10, 16, and 21 mo after neutering before the morning feeding.

Serum lipid profile, glucose, and hormones analysis

On the last day, fasted blood samples were collected via the forelimb vein and placed into 5-mL Vacutainer tubes (KWS, Shijiazhuang, China) for serum separation and stored at −80 °C for further analysis. Serum triglyceride (TG, A110-1-1), total cholesterol (TCHO, A111-1-1), glucose (GLU, F006-1-1), high-density lipoprotein cholesterol (HDL-C, A112-1-1), and low-density lipoprotein cholesterol (LDL-C, A113-1-1) were measured using commercial kits (Nanjing Jiancheng Bioengineering Institute, Nanjing, China) according to the manufacturer’s protocol. Serum ghrelin (GHR), insulin (INS), leptin (LEP), adiponectin (APN), free fatty acids (FFA), and cholecystokinin (CCK) were measured using commercial enzyme linked immunosorbent assay (ELISA) kits (MEIMIAN, Jiangsu Meimian Industrial Co., Ltd., Jiangsu, China).

Fecal microbiota analysis

On the last day, fresh fecal samples were collected immediately after defecation and then flash-frozen in liquid nitrogen and stored at −80 °C until processing. After thawed, the total DNA was extracted using the cetyltrimethylammonium bromide (CTAB). Nuclear-free water was used for blank. The variable region of bacterial 16S rRNA V3–V4 gene was amplified using the primers 341F (5ʹ-CCTACGGGNGGCWGCAG-3ʹ) and 805R (5ʹ-GACTACHVGGGTATCTAATCC-3ʹ), adding a barcode in the forward primer. PCR amplification reaction was run for 32 cycles. The PCR products were confirmed with 2% agarose gel electrophoresis. These products were purified by AMPure XT beads (A63880, Beckman, USA) and quantified by Qubit (Q32854, Invitrogen, USA). Illumina sequencing was performed on the NovaSeq PE250 platform at LC-Bio Technology Co., Ltd, Hang Zhou, Zhejiang Province, China.

Paired-end reads were merged using FLASH. Quality filtering was performed to obtain the high-quality clean tags using the fqtrim (v0.94). Chimeric sequences were filtered using Vsearch software (v2.3.4). After dereplication using DADA2, we obtained the feature table and feature sequence. Then feature abundance was normalized according to SILVA (release 138) classifier. Alpha-diversity indices (Observed_species, Chao1, Shannon, Simpson, and Goods_coverage) and beta-diversity were calculated using QIIME2. The principal component analysis (PCA) was displayed using the vegen (2.5.4) and ggplot2 (3.2.0) in R software (v3.4.4). Differential bacteria and functional predictions were analyzed and visualized using statistical analysis of taxonomic and functional profiles (STAMP v2.1.3) (nonparametric Wilcox test, P < 0.05). The linear discriminant analysis effect size (LEfSe) was applied to find bacterial biomarkers between the two groups based on Kruskal–Wallis test (P < 0.05) or pairwise Wilcoxon rank-sum test with linear discriminant analysis (LDA > 2). Finally, we used the feature table generated from QIIME2 to predict microbial community function with PICRUSt2 (https://github.com/picrust/picrust2).

Fecal short-chain fatty acids

On the last day, the SCFAs (acetate, propionate, butyrate, isobutyrate, isovalerate, and valerate) concentrations in fresh feces were determined by the GCMS-QP2020 system (Shimadzu, Kyoto, Japan) with a DB-FFAP capillary column (30 m × 0.25 mm × 0.25 μm, Onlysci, China) as described previously (Yang et al., 2022b). Helium (He, 99.999%) was the carrier gas with a flow rate of 3 mL/min. The ion source, interface, and injector temperatures were 230, 250, and 250 °C, respectively.

Statistical analysis

Data analysis was conducted with the SPSS 26.0 software. The significance of difference between OB and IW dogs was determined by unpaired, two-tailed Student’s t-test. P < 0.05 was considered statistically significant, whereas 0.05 ≤ P < 0.1 was considered a trend toward significance. Experimental data were presented as the mean ± SD. Correlation analysis among the different variables was assessed using Spearman’s test. Heatmap and clustering were performed using the OmicStudio tools at https://www.omicstudio.cn.

Results

BW and BCS

Body weight and BCS between the IW and OB groups at 1 d before neutering, 3, 6, 10, 16, and 21 mo after neutering are shown in Figure 1. At 21 mo after neutering, dogs were divided into the ideal weight group (BCS: 4 to 5; 1 male and 3 female) and the heavy/obese group (BCS: 7 to 9; 5 male and 7 female). Notably, dogs with a BCS between 5 and 7 were not considered (n = 4, Supplementary Table S4). From these results as a whole, OB dogs showed an obvious increase in BW and BCS than IW dogs from 1 d before neutering to 21 mo after neutering. In detail, we found that OB dogs had greater (P < 0.0001, Table 1) BW, BCS, and ADG than IW dogs at 21 mo after neutering. Subsequently, BW and BCS were compared between the two groups at 1 d before neutering. Interestingly, OB dogs also had greater (P < 0.05) BW and BCS at 1 d before neutering. In addition, the obesity rate (BCS: 7 to 9) was up to 60% (12/20) in this study. Among these, the obesity rate in female and male dogs were 58.3% (7/12) and 62.5% (5/8), respectively.

Figure 1.

Figure 1.

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BW and BCS between the IW and OB groups at 1 d before neutering, 3, 6, 10, 16, and 21 mo after neutering. IW, ideal weight; OB, heavy/obese; BW, body weight; BCS, body condition score; T1, 1 d before neutering; T2, 3 mo after neutering; T3, 6 mo after neutering; T4, 10 mo after neutering; T5, 16 mo after neutering; and T6, 21 mo after neutering.

Table 1.

BW and BCS between the IW and OB groups at 1 d before neutering and 21 mo after neutering

Item1 IW group (n = 4)2 OB group (n = 12)3 P-value
1 d before neutering
 BW, kg 9.59 ± 0.93 10.74 ± 0.60 0.011
 BCS 4.63 ± 0.75 5.38 ± 0.48 0.033
21 mo after neutering
 BW, kg 13.22 ± 1.30 18.57 ± 1.08 <0.0001
 BCS 5.00 ± 0.41 7.92 ± 0.82 <0.0001
 ADG, g/d 5.69 ± 1.55 12.27 ± 1.95 <0.0001
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1BW, body weight; BCS, body condition score; and ADG, average daily gain.

2IW, ideal weight.

3OB, heavy/obese.

Serum lipid profile, glucose, and hormones

Serum lipid profile, glucose, and hormones were measured in 16 dogs at 1 d before neutering and 21 mo after neutering (Table 2). There was no significant difference in serum TG, GLU, INS, LEP, and GHR concentrations between the two groups at 1 d before neutering (P > 0.05). However, OB dogs had greater (P < 0.05) serum TG and HDL-C and lower (P < 0.05) serum APN concentrations than IW dogs at 21 mo after neutering. Meanwhile, serum TCHO (P = 0.075) and LEP (P = 0.065) concentrations tended to be greater in OB dogs, and there was a trend towards a lower (P = 0.092) APN/LEP ratio in OB dogs. Serum GLU, INS, LDL-C, FFA, and CCK concentrations were not significantly different (P > 0.05).

Table 2.

Changes in serum lipid profile, glucose, and hormones between the IW and OB groups at 1 d before neutering and 21 mo after neutering

Item1 IW group (n = 4)2 OB group (n = 12)3 P-value
1 d before neutering
 TG, mmol/L 0.58 ± 0.12 0.58 ± 0.11 0.908
 GLU, mmol/L 5.65 ± 0.45 5.29 ± 0.46 0.198
 INS, mU/L 58.91 ± 9.16 61.38 ± 6.62 0.571
 LEP, μg/L 3.78 ± 0.67 4.05 ± 0.71 0.519
 GHR, ng/L 2,677.76 ± 474.72 2,897.28 ± 236.69 0.432
21 mo after neutering
 TG, mmol/L 0.56 ± 0.07 1.10 ± 0.45 0.002
 TCHO, mmol/L 3.75 ± 0.90 4.83 ± 0.99 0.075
 GLU, mmol/L 2.98 ± 0.85 2.77 ± 0.65 0.600
 INS, mU/L 28.96 ± 2.09 29.39 ± 2.85 0.786
 LEP, μg/L 2.53 ± 0.18 2.82 ± 0.27 0.065
 APN, μg/L 58.39 ± 2.68 54.06 ± 3.53 0.043
 APN/LEP 21.81 ± 2.37 19.32 ± 2.39 0.092
 HDL-C, mmol/L 5.40 ± 0.77 6.96 ± 0.95 0.011
 LDL-C, mmol/L 0.40 ± 0.22 0.59 ± 0.25 0.196
 FFA, μmol/L 732.23 ± 132.47 600.30 ± 47.75 0.139
 CCK, ng/L 48.13 ± 8.93 53.93 ± 22.15 0.624
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1TG, triglyceride; TCHO, total cholesterol; GLU, glucose; INS, insulin; LEP, leptin; APN, adiponectin; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; FFA, free fatty acid; CCK, cholecystokinin; and GHR, ghrelin.

2IW, ideal weight.

3OB, heavy/obese.

Fecal microbiota analysis

As shown in Table 3, fecal microbial alpha-diversity indices were impacted by canine obesity to some extent. Greater (P < 0.05) Shannon and Simpson indices were observed in the OB group at 1 d before neutering. Whereas both Observed_species and Chao1 indices tended to be lower (P = 0.069) in OB dogs at 21 mo after neutering. There were no significant differences (P > 0.05) in Shannon, Simpson, and Goods_coverage indices. Unsupervised PCA was used to observe the overall distribution among samples and the degree of dispersion among groups. As shown in Figure 2A and B, the overall distribution of 16 samples was more dispersed, and there was an obvious overlap between the IW and OB groups.

Table 3.

Fecal microbial alpha-diversity between the IW and OB groups at 1 d before neutering and 21 mo after neutering

Item IW group (n = 4)1 OB group (n = 12)2 P-value
1 d before neutering
 Observed_species 478.50 ± 164.09 487.67 ± 182.39 0.930
 Chao1 550.13 ± 201.15 554.55 ± 214.37 0.972
 Shannon 4.04 ± 0.10 4.81 ± 0.48 0.008
 Simpson 0.82 ± 0.06 0.91 ± 0.02 0.039
 Goods_coverage 0.998 ± 0.0013 0.998 ± 0.0012 0.905
21 mo after neutering
 Observed_species 353.50 ± 35.71 303.67 ± 45.79 0.069
 Chao1 354.26 ± 35.96 304.09 ± 46.07 0.069
 Shannon 5.68 ± 0.46 5.34 ± 0.47 0.221
 Simpson 0.94 ± 0.02 0.92 ± 0.04 0.334
 Goods_coverage 0.99 ± 0.00004 0.99 ± 0.00007 0.214
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1IW, ideal weight.

2OB, heavy/obese.

Figure 2.

Figure 2.

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Principal component analyses (PCA) plot of fecal microbial communities of all dogs at 1 d before neutering (A) and 21 mo after neutering (B). IW, ideal weight; OB, heavy/obese.

Analysis of the bacterial communities revealed that the dominant phyla were Firmicutes, Fusobacteria, Bacteroidetes, Actinobacteria, and Proteobacteria at 1 d before neutering and 21 mo after neutering (Figure 3A and C). The dominant bacterial genera within the fecal samples included Fusobacterium, Prevolella, Bacteroides, Alloprevotella, Escherichia-Shigella, Blautia, Peptoclostridium, Faecalibacterium, Streptococcus, and Lactobacillus at 1 d before neutering (Figure 3B). While there was a significant change in the dominant genus in dogs at 21 mo after neutering, and the predominant genera consisted of Fusobacterium, Faecalibacterium, Allobaculum, Phascolarctobacterium, Alloprevotella, Blautia, Collinsella, Holdemanella, [Ruminococcus]_gnavus_group, and [Ruminococcus]_torques_group (Figure 3D).

Figure 3.

Figure 3.

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Relative abundance of representative taxa in canine gut microbiota at the phylum and genus levels at 1 d before neutering (A, B) and 21 mo after neutering (C, D). IW, ideal weight; OB, heavy/obese.

The STAMP software was used to perform the Wilcox test to analyze statistically significant differences of gut bacteria at the genus level between the IW and OB groups (Figure 4). The STAMP analysis revealed 5 genera that were differentially abundant between the two groups at 1 d before neutering (P < 0.05), and these genera showed a mean relative abundance below 0.1%. In contrast, 20 genera were significantly different between the two groups at 21 mo after neutering (P < 0.05). Among these, the mean relative abundances of 6 genera (Bacteroides, Prevotella_9, Megamonas, Amylibacter, Haemophilus, and Prevotella_7) were greater than 0.1%. The LEfSe was further used to identify bacterial biomarkers. The LEfSe analysis revealed a total of 33 bacterial biomarkers at 21 mo after ­neutering (Figure 5). Among these, Megamonas, Bacteroides, and Prevotella_9 genera were significantly enriched in IW dogs, and Clostridium_sp._AT4 and Streptococcus_peroris species were significantly enriched in OB dogs. As expected, no bacterial biomarker was found between the two groups at 1 d before neutering.

Figure 4.

Figure 4.

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Relative-abundance analysis of gut microbiota at the genus level between the IW and OB groups were performed by STAMP at 1 d before neutering (A) and 21 mo after neutering (B). Differentially abundant taxa as identified by STAMP analysis with 95% confidence intervals (Wilcox test, P < 0.05). IW, ideal weight; OB, heavy/obese.

Figure 5.

Figure 5.

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LEfSe analysis identifying taxonomic differences in the gut microbiota between the IW and OB groups at 21 mo after neutering. (A) IW-enriched taxa are indicated with a negative LDA score, and taxa enriched in the OB group are characterized by a positive score. (B) Taxonomic cladogram generated by LEfSe analysis of differential gut microbial taxa between the IW and OB groups. IW, ideal weight; OB, heavy/obese.

To predict microbial functions of different bacterial communities, we performed PICRUSt2 analysis based on the KEGG pathway at level III (Figure 6). Compared with the IW group, significant changes in lipid metabolism (ether lipid metabolism and steroid hormone biosynthesis), endocrine system (insulin secretion and oxytocin signaling pathway), signal transduction (apelin signaling pathway and calcium signaling pathway), circulatory system (adrenergic signaling in cardiomyocytes), immune system (RIG-I-like receptor signaling pathway), environmental adaptation (circadian entrainment), metabolism of terpenoids and polyketide (biosynthesis of ansamycins and carotenoid biosynthesis), and xenobiotics biodegradation and metabolism (bisphenol degradation) were found in the OB group (P < 0.05).

Figure 6.

Figure 6.

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Functional predictions analysis of gut microbiota between the IW and OB groups were performed by STAMP at 21 mo after neutering. Functional predictions were aligned in the KEGG database, and the functional parameters were assigned to tier 3. Differential functional predictions as identified by STAMP analysis with 95% confidence intervals (Wilcox test, P < 0.05). IW, ideal weight; OB, heavy/obese.

Fecal short-chain fatty acids

Significant changes in gut microbial composition could be responsible for the variation of microbial metabolites. As shown in Table 4, OB dogs also had greater (P < 0.05) reduction in fecal acetate, propionate, and total SCFAs concentrations than IW dogs; and fecal butyrate concentration tended to be decreased (P = 0.051) in OB dogs. Fecal isobutyrate, isovalerate, valerate, and total branched-chain fatty acids (BCFAs) concentrations were not significantly different (P > 0.05).

Table 4.

Changes in fecal SCFAs between the IW and OB groups at 21 mo after neutering

Item IW group (n = 4)1 OB group (n = 12)2 P-value
Acetate, ng/g 1,511.34 ± 159.07 1,067.78 ± 325.45 0.022
Propionate, ng/g 883.89 ± 153.50 553.63 ± 279.49 0.043
Butyrate, ng/g 358.18 ± 47.04 222.30 ± 121.78 0.051
Isobutyrate, ng/g 44.90 ± 10.33 33.18 ± 21.53 0.320
Isovalerate, ng/g 74.51 ± 22.73 50.62 ± 33.71 0.213
Valerate, ng/g 7.04 ± 0.41 5.24 ± 5.55 0.538
Total SCFAs3 2,753.40 ± 353.81 1,843.70 ± 699.42 0.028
Total BCFAs4 126.44 ± 33.29 89.04 ± 60.00 0.262
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1IW, ideal weight.

2OB, heavy/obese.

3Total SCFAs: total short-chain fatty acids = acetate + propionate + butyrate.

4Total BCFAs: total branched-chain fatty acids = isobutyrate + isovalerate + valerate.

Correlation analysis between differential fecal bacteria/SCFAs and obesity-related indices

To further clarify the association between gut microbiota and obesity, correlation analysis between the differential fecal bacteria/SCFAs and obesity-related indices was performed using Spearman’s test (Figure 7). The correlation is considered statistically significant with correlation coefficient |r| > 0.5 and P < 0.05. Fecal Prevotella_9 was negatively associated with BW. Fecal Megamonas, Bacteroides, acetate, propionate, butyrate, and total SCFAs were negatively associated with serum HDL-C. Fecal Megamonas, Bacteroides, Prevotella_9, acetate, propionate, and total SCFAs were negatively associated with serum TG.

Figure 7.

Figure 7.

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Heatmaps of Spearman’s correlation analysis between differential fecal bacteria/SCFAs and obesity-related indices at 21 mo after neutering. These boxes represent positive and negative correlations. The number in each box represents the association coefficients (rho). BCS, body condition score; BW, body weight; HDL-C, high-density lipoprotein cholesterol; TG, triglyceride; APN, adiponectin; LEP, leptin; and TCHO, total cholesterol. Total SCFAs: total short-chain fatty acids = acetate + propionate + butyrate. The symbol * indicates a significant correlation (* P < 0.05, ** P < 0.01, and *** P < 0.001).

Discussion

Obesity develops when energy intake consistently exceeds energy expenditure (Romieu et al., 2017). In addition to the increase in energy intake, neutering decreased energy ­expenditure and metabolic rate (Schauf et al., 2016). However, information about canine obesity after neutering is limited. In this study, sixteen 8-mo-old Beagle dogs were selected for neutering surgery on the same day using standard techniques, and the trial period lasted for 21 mo after neutering. On the last day, 16 dogs were divided into the ideal weight group (BCS: 4 to 5) and the heavy/obese group (BCS: 7 to 9). Consistent with previous studies (Bjørnvad et al., 2019; Chiang et al., 2022), significant increases in BW, BCS, and ADG were observed in obese dogs compared to ideal weight dogs after neutering. The obesity rate in neutered dogs was as high as 60% (female 58.3% and male 62.5%) in this study. These results agree with the findings of other studies, in which female dogs were reported to be more susceptible to obesity than male dogs (Sallander et al., 2010; Mao et al., 2013; Usui et al., 2016; German et al., 2017). Interestingly, obese dogs also had greater BW and BCS than ideal weight dogs at 1 d before neutering. This suggested to us that weight control before neutering may help alleviate canine obesity after neutering.

Many years of clinical studies have proved that obesity and hyperlipidemia are usually accompanied by high concentrations of LDL-C and low concentrations of HDL-C (Kühnast et al., 2015; Vekic et al., 2023). In this study, no significant difference in serum lipid profile, glucose, and hormones was found at 1 d before neutering. However, obese dogs after 21 mo of neutering had greater serum TG and HDL-C levels than ideal weight dogs. These results corroborate the findings of the previous work on canine obesity (Jeusette et al., 2005; Mori et al., 2011; Tvarijonaviciute et al., 2019). Moreover, adipose tissue is a source of major adipocytokines LEP and APN which are valuable quantitative markers of obesity in dogs (Bastard et al., 2006). The levels of LEP in blood circulation are proportionally increased with the increase in adipose tissue mass (Blum et al., 1997), and LEP plays a central role in regulating energy intake through the activation of anorexigenic pathways in the hypothalamus (Farooqi and O Rahilly, 2009; Yadav et al., 2013; Fischer et al., 2020). Under almost all physiological conditions, LEP and APN are regulated in an opposite manner (Ricci and Bevilacqua, 2012; Frühbeck et al., 2018). Similarly, at 21 mo after neutering, our results also showed that obese dogs with high circulating levels of LEP displayed lower levels of APN. Serum LEP levels are increased with adiposity, suggesting that obese dogs may develop LEP resistance and the inability of elevated LEP levels to suppress obesity (Rahmouni et al., 2008). Correspondingly, decrement in serum APN reduces insulin sensitivity and affects glucose homeostasis (Berg et al., 2001). A previous study also confirmed a significant decrease in serum APN level in obese dogs (Muñoz-Prieto et al., 2020).

The gut microbiota creates a dynamic and complex ecosystem inhabiting the gastrointestinal tract and directly regulating the energy metabolism, immune function, and hormonal balance through direct contact and microbiota-derived metabolites (Pataky et al., 2010; Hansen and Sams, 2018; Amabebe et al., 2020). Obesity can influence gastrointestinal function and cause an imbalance of the gut microbiota (Shabana et al., 2018; Geng et al., 2022). In this study, microbial alpha-diversity indices Observed_species and Chao1 tended to be lower in obese dogs at 21 mo after neutering, indicating obesity might affect gut microbial diversity in neutered dogs (Park et al., 2015). Similarly, Firmicutes, Fusobacteria, Bacteroidetes, Actinobacteria, and Proteobacteria are the five predominant bacterial phyla in dogs (Pilla and Suchodolski, 2020; Yang et al., 2022a). However, there was a significant change in the dominant genus from 1 d before neutering to 21 mo after neutering. The STAMP and LEfSe analysis revealed that no bacterial biomarker was found between the two groups at 1 d before neutering, indicating dogs in the two groups had similar gut microbial composition and structure. Whereas the biomarkers Megamonas, Bacteroides, and Prevotella_9 were more abundant in ideal weight dogs at 21 mo after neutering. Previous studies have verified that Megamonas, Bacteroides, and Prevotella_9 in the gut possess the ability to produce SCFAs such as acetate, propionate, and butyrate (Salonen et al., 2014; Kovatcheva-Datchary et al., 2015; Shin et al., 2017). The SCFAs can promote insulin secretion and reduce fat accumulation, which can treat obesity as well as type II diabetes (He and Shi, 2017; Guo et al., 2020). Consistently, our results indicated that obese dogs had a significant decline in fecal acetate, propionate, and butyrate concentrations compared with ideal weight dogs. All of the above results proved that post-neutering obesity diminished the ability of canine gut microbiota to produce SCFAs, which was in line with the previous report (Kieler et al., 2017). Furthermore, PICRUSt2 analysis further predicted microbial functions, indicating that lipid metabolism and endocrine system were significantly disturbed in canine obesity (Osto and Lutz, 2015). Taken together, the results mentioned above suggested that gut bacteria were involved in changes in the systemic metabolic pathway in obesity.

In this study, correlation analysis further revealed a negative association between Prevotella_9 and BW. Murphy et al. (2010) also reported that Bacteroides and Prevotella are negatively associated with energy intake. Additionally, Bacteroides and Megamonas were negatively correlated with food addiction (Dong et al., 2020). However, Kieler et al. (2017) showed that Megamonas abundance was negatively correlated with weight loss rate, which differs from the findings presented here. We also found that Megamonas, Bacteroides, Prevotella_9, and its metabolites SCFAs were negatively associated with serum TG and HDL-C. Similar to the present results, previous studies have demonstrated that SCFA levels were associated with TG and TCHO (Joung et al., 2021; Amalia et al., 2022). Based on the results we have obtained, it was inferred that obesity altered the gut microbial diversity and diminished the ability of canine gut microbiota to produce SCFAs, thereby perturbing energy balance and causing later obesity. Hence, we recommend restricting dietary energy intake (i.e., feed intake) while increasing energy expenditure (i.e., exercise), and supplementing with natural functional additives, such as polyphenols, prebiotics, and probiotics (Lee et al., 2022; Lu et al., 2022; Yang et al., 2022a), that modulate gut microbiota, especially SCFAs-producing bacteria, to prevent or treat canine obesity after neutering. The main limitation of this work is the small sample size, thus, this finding should be confirmed in a larger dog study. In addition, due to the limited availability of experimental animals, this study lacked the control dogs that were not neutered. Our results should be interpreted with caution and further studies are needed to fully understand the relationship between neutering, gut microbiota changes, and obesity in dogs.

Conclusion

In summary, the 60% obesity rate indicated that neutering contributes to canine obesity risk in this study. Significant increases in BW, BCS, and ADG were observed in obese dogs at 21 mo after neutering, and there were significant changes in the serum TG, HDL-C, and APN in obese dogs. In addition, fecal microbial 16S rRNA amplicon sequencing found a decreasing microbial diversity in obese dogs. Significant decreases in the relative abundances of SCFAs-producing bacteria Megamonas, Bacteroides, and Prevotella_9 in obese dogs resulted in lower production of fecal acetate, propionate, and butyrate. Importantly, strong negative correlations between SCFAs-producing bacteria and BW, TG, and HDL-C revealed that reduced SCFAs-producing bacteria are involved in the process of canine obesity after neutering by perturbing lipid metabolism and endocrine system. Therefore, intervention with SCFAs-producing bacteria might represent a new target for the prevention or treatment of canine obesity after neutering. Moreover, weight control before neutering may also contribute to the prevention of canine obesity after neutering.

Supplementary Material

skad283_suppl_Supplementary_Material
Click here for additional data file. (47.6KB, docx)

Acknowledgments

Funding for this project was provided by National Natural Science Foundation of China (Grant 32002186), Natural Science Foundation of Guangdong Province (Grant 2020A1515010322), Basic and Applied Basic Research Foundation of Guangdong Province (2019B1515210002). We gratefully appreciate the Laboratory Animal Center at the South China Agricultural University (Guangzhou, China) for providing the experimental site.

Glossary

Abbreviations

AAFCO

Association of American Feed Control Officials

AAHA

American Animal Hospital Association

APN

adiponectin

ASVs

amplicon sequence variants

BCS

body condition score

BW

body weight

CCK

cholecystokinin

FFA

free fatty acids

GLU

glucose

HDL-C

high-density lipoprotein cholesterol

INS

insulin

LDA

linear discriminant analysis

LDL-C

low-density lipoprotein cholesterol

LEfSe

linear discriminant analysis effect size

LEP

leptin

PCA

principal component analysis

SCFAs

short-chain fatty acids

TCHO

total cholesterol

TG

triglyceride

Contributor Information

Kang Yang, School of Life and Health Science, Kaili University, Kaili 556011, China.

Xinye Lin, Guangdong Provincial Key Laboratory of Animal Nutrition Control, College of Animal Science, South China Agricultural University, Guangzhou 510642, China.

Shiyan Jian, Guangdong Provincial Key Laboratory of Animal Nutrition Control, College of Animal Science, South China Agricultural University, Guangzhou 510642, China.

Jiawei Wen, Guangdong Provincial Key Laboratory of Animal Nutrition Control, College of Animal Science, South China Agricultural University, Guangzhou 510642, China.

Xiaoying Jian, Guangdong Provincial Key Laboratory of Animal Nutrition Control, College of Animal Science, South China Agricultural University, Guangzhou 510642, China.

Shansong He, Guangdong Provincial Key Laboratory of Animal Nutrition Control, College of Animal Science, South China Agricultural University, Guangzhou 510642, China.

Chaoyu Wen, Guangdong Provincial Key Laboratory of Animal Nutrition Control, College of Animal Science, South China Agricultural University, Guangzhou 510642, China.

Tingting Liu, Guangdong Provincial Key Laboratory of Animal Nutrition Control, College of Animal Science, South China Agricultural University, Guangzhou 510642, China.

Xin Qi, Department of Technology, Beijing Veterinary Drug and Feed Monitoring Center, Beijing 101127, China.

Yulong Yin, Guangdong Provincial Key Laboratory of Animal Nutrition Control, College of Animal Science, South China Agricultural University, Guangzhou 510642, China.

Baichuan Deng, Guangdong Provincial Key Laboratory of Animal Nutrition Control, College of Animal Science, South China Agricultural University, Guangzhou 510642, China.

Conflict of interest statement

The authors declare no real or perceived conflicts of interest.

Literature Cited

  1. Association of American Feed Control Officials (AAFCO). 2017. Official publication 2017. Oxford (IN): AAFCO. [Google Scholar]
  2. Abdulkarim, A., Khan M. A. K. B. G., and Aklilu E... 2021. Stray animal population control: methods, public health concern, ethics, and animal welfare issues. World Vet. J. 11:319–326. doi: 10.54203/scil.2021.wvj44 [DOI] [Google Scholar]
  3. Amabebe, E., Robert F. O., Agbalalah T., and Orubu E. S. F... 2020. Microbial dysbiosis-induced obesity: role of gut microbiota in homoeostasis of energy metabolism. Brit. J. Nutr. 123:1127–1137. doi: 10.1017/S0007114520000380 [DOI] [PubMed] [Google Scholar]
  4. Amalia, R., Pramono A., Afifah D. N., Noer E. R., Muniroh M., and Kumoro A. C... 2022. Mangrove fruit (Bruguiera gymnorhiza) increases circulating GLP-1 and PYY, modulates lipid profiles, and reduces systemic inflammation by improving SCFA levels in obese wistar rats. Heliyon 8:e10887. doi: 10.1016/j.heliyon.2022.e10887 [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Bastard, J., Maachi M., Lagathu C., Kim M. J., Caron M., Vidal H., Capeau J., and Feve B... 2006. Recent advances in the relationship ­between obesity, inflammation, and insulin resistance. Eur. Cytokine Netw. 17:4–12 [PubMed] [Google Scholar]
  6. Beauvais, W., Cardwell J. M., and Brodbelt D. C... 2012. The effect of neutering on the risk of mammary tumours in dogs-a systematic review. J. Small Anim. Pract. 53:314–322. doi: 10.1111/j.1748-5827.2011.01220.x [DOI] [PubMed] [Google Scholar]
  7. Berg, A. H., Combs T. P., Du X., Brownlee M., and Scherer P. E... 2001. The adipocyte-secreted protein Acrp30 enhances hepatic insulin action. Nat. Med. 7:947–953. doi: 10.1038/90992 [DOI] [PubMed] [Google Scholar]
  8. Bjørnvad, C. R., Gloor S., Johansen S. S., Sandøe P., and Lund T. B... 2019. Neutering increases the risk of obesity in male dogs but not in bitches—A cross-sectional study of dog- and owner-related risk factors for obesity in Danish companion dogs. Prev. Vet. Med. 170:104730. doi: 10.1016/j.prevetmed.2019.104730 [DOI] [PubMed] [Google Scholar]
  9. Blum, W. F., Englaro P., Hanitsch S., Juul A., Hertel N. T., Müller J., Skakkebæk N. E., Heiman M. L., Birkett M., Attanasio A. M.,. et al. 1997. Plasma leptin levels in healthy children and adolescents: dependence on body mass index, body fat mass, gender, pubertal stage, and testosterone. J. Clin. Endocr. Metab. 82:2904–2910. doi: 10.1210/jcem.82.9.4251 [DOI] [PubMed] [Google Scholar]
  10. Caballero, B. 2019. Humans against obesity: who will win? Adv. Nutr. 10:S4–S9. doi: 10.1093/advances/nmy055 [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Casey, R. A., Loftus B., Bolster C., Richards G. J., and Blackwell E. J... 2014. Human directed aggression in domestic dogs (Canis familiaris): occurrence in different contexts and risk factors. Appl. Anim. Behav. Sci. 152:52–63. doi: 10.1016/j.applanim.2013.12.003 [DOI] [Google Scholar]
  12. Chávez-Talavera, O., Tailleux A., Lefebvre P., and Staels B... 2017. Bile acid control of metabolism and inflammation in obesity, type 2 diabetes, dyslipidemia, and nonalcoholic fatty liver disease. Gastroenterology 152:1679–1694.e3. doi: 10.1053/j.gastro.2017.01.055 [DOI] [PubMed] [Google Scholar]
  13. Chiang, C., Villaverde C., Chang W., Fascetti A. J., and Larsen J. A... 2022. Prevalence, risk factors, and disease associations of overweight and obesity in dogs that visited the veterinary medical teaching hospital at the University of California, Davis from January 2006 to December 2015. Top. Companion Anim. Med. 48:100640. doi: 10.1016/j.tcam.2022.100640 [DOI] [PubMed] [Google Scholar]
  14. Cline, M. G., Burns K. M., Coe J. B., Downing R., Durzi T., Murphy M., and Parker V... 2021. 2021 AAHA nutrition and weight management guidelines for dogs and cats. J. Am. Anim. Hosp. Assoc. 57:153–178. doi: 10.5326/JAAHA-MS-7232 [DOI] [PubMed] [Google Scholar]
  15. Colliard, L., Ancel J., Benet J. J., Paragon B. M., and Blanchard G... 2006. Risk factors for obesity in dogs in France. J. Nutr. 136:1951S–1954S. doi: 10.1093/jn/136.7.1951S [DOI] [PubMed] [Google Scholar]
  16. Dong, T. S., Mayer E. A., Osadchiy V., Chang C., Katzka W., Lagishetty V., Gonzalez K., Kalani A., Stains J., Jacobs J. P.,. et al. 2020. A distinct brain‐gut‐microbiome profile exists for females with obesity and food addiction. Obesity 28:1477–1486. doi: 10.1002/oby.22870 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Farooqi, I. S., and Rahilly S. O... 2009. Leptin: a pivotal regulator of human energy homeostasis. Am. J. Clin. Nutr. 89:980S–984S. doi: 10.3945/ajcn.2008.26788C [DOI] [PubMed] [Google Scholar]
  18. Fischer, A. W., Cannon B., and Nedergaard J... 2020. Leptin: is it thermogenic? Endocr Rev. 41:232–260. doi: 10.1210/endrev/bnz016 [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Forster, G. M., Stockman J., Noyes N., Heuberger A. L., Broeckling C. D., Bantle C. M., and Ryan E. P... 2018. A comparative study of serum biochemistry, metabolome and microbiome parameters of clinically healthy, normal weight, overweight, and obese companion dogs. Top. Companion Anim. Med. 33:126–135. doi: 10.1053/j.tcam.2018.08.003 [DOI] [PubMed] [Google Scholar]
  20. Frühbeck, G., Catalán V., Rodríguez A., and Gómez-Ambrosi J... 2018. Adiponectin-leptin ratio: a promising index to estimate adipose tissue dysfunction. Relation with obesity-associated cardiometabolic risk. Adipocyte. 7:57–62. doi: 10.1080/21623945.2017.1402151 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Geng, J., Ni Q., Sun W., Li L., and Feng X... 2022. The links between gut microbiota and obesity and obesity related diseases. Biomed. Pharmacother. 147:112678. doi: 10.1016/j.biopha.2022.112678 [DOI] [PubMed] [Google Scholar]
  22. German, A. J. 2006. The growing problem of obesity in dogs and cats. J. Nutr. 136:1940S–1946S. doi: 10.1093/jn/136.7.1940S [DOI] [PubMed] [Google Scholar]
  23. German, A. J., Blackwell E., Evans M., and Westgarth C... 2017. Overweight dogs exercise less frequently and for shorter periods: results of a large online survey of dog owners from the UK. J. Nutr. Sci. 6:e11. doi: 10.1017/jns.2017.6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Guo, C., Han L., Li M., and Yu L... 2020. Seabuckthorn (Hippophaë rhamnoides) freeze-dried powder protects against high-fat diet-induced obesity, lipid metabolism disorders by modulating the gut microbiota of mice. Nutrients 12:265. doi: 10.3390/nu12010265 [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Hansen, N., and Sams A... 2018. The microbiotic highway to health—new perspective on food structure, gut microbiota, and host inflammation. Nutrients 10:1590. doi: 10.3390/nu10111590 [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. He, M., and Shi B... 2017. Gut microbiota as a potential target of metabolic syndrome: the role of probiotics and prebiotics. Cell Biosci. 7:54. doi: 10.1186/s13578-017-0183-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Hoffman, J. M., Creevy K. E., and Promislow D. E... 2013. Reproductive capability is associated with lifespan and cause of death in companion dogs. PLoS One. 8:e61082. doi: 10.1371/journal.pone.0061082 [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Jeusette, I. C., Lhoest E. T., Istasse L. P., and Diez M. O... 2005. Influence of obesity on plasma lipid and lipoprotein concentrations in dogs. Am. J. Vet. Res. 66:81–86. doi: 10.2460/ajvr.2005.66.81 [DOI] [PubMed] [Google Scholar]
  29. Joung, H., Chu J., Kim B., Choi I., Kim W., and Park T... 2021. Probiotics ameliorate chronic low-grade inflammation and fat accumulation with gut microbiota composition change in diet-induced obese mice models. Appl. Microbiol. Biotechnol. 105:1203–1213. doi: 10.1007/s00253-020-11060-6 [DOI] [PubMed] [Google Scholar]
  30. Kealy, R. D., Lawler D. F., Ballam J. M., Mantz S. L., Biery D. N., Greeley E. H., Lust G., Segre M., Smith G. K., and Stowe H. D... 2002. Effects of diet restriction on life span and age-related changes in dogs. JAVMA-J. Am. Vet. Med. A. 220:1315–1320. doi: 10.2460/javma.2002.220.1315 [DOI] [PubMed] [Google Scholar]
  31. Kieler, I. N., Shamzir Kamal S., Vitger A. D., Nielsen D. S., Lauridsen C., and Bjornvad C. R... 2017. Gut microbiota composition may relate to weight loss rate in obese pet dogs. Vet. Med. Sci. 3:252–262. doi: 10.1002/vms3.80 [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Kim, K. N., Yao Y., and Ju S. Y... 2019. Short chain fatty acids and fecal microbiota abundance in humans with obesity: a systematic review and meta-analysis. Nutrients. 11:2512. doi: 10.3390/nu11102512 [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Kovatcheva-Datchary, P., Nilsson A., Akrami R., Lee Y. S., De Vadder F., Arora T., Hallen A., Martens E., Björck I., and Bäckhed F... 2015. Dietary fiber-induced improvement in glucose metabolism is associated with increased abundance of prevotella. Cell Metab. 22:971–982. doi: 10.1016/j.cmet.2015.10.001 [DOI] [PubMed] [Google Scholar]
  34. Kühnast, S., Fiocco M., van der Hoorn J. W. A., Princen H. M. G., and Jukema J. W... 2015. Innovative pharmaceutical interventions in cardiovascular disease: focusing on the contribution of non-HDL-C/LDL-C-lowering versus HDL-C-raising a systematic review and meta-analysis of relevant preclinical studies and clinical trials. Eur. J. Pharmacol. 763:48–63. doi: 10.1016/j.ejphar.2015.03.089 [DOI] [PubMed] [Google Scholar]
  35. Kustritz, M. V. R. 2007. Determining the optimal age for gonadectomy of dogs and cats. J. Am. Vet. Med. A. 231:1665–1675. doi: 10.2460/javma.231.11.1665 [DOI] [PubMed] [Google Scholar]
  36. Lee, H., Cho J. H., Cho W., Gang S., Park S., Jung B., Kim H. B., and Song K. H... 2022. Effects of synbiotic preparation containing Lactobacillus gasseri BNR17 on body fat in obese dogs: a pilot study. Animals. 12:642. doi: 10.3390/ani12050642 [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Lefebvre, S. L., Yang M., Wang M., Elliott D. A., Buff P. R., and Lund E. M... 2013. Effect of age at gonadectomy on the probability of dogs becoming overweight. J. Am. Vet. Med. A. 243:236–243. doi: 10.2460/javma.243.2.236 [DOI] [PubMed] [Google Scholar]
  38. Lin, K., Zhu L., and Yang L... 2022. Gut and obesity/metabolic disease: focus on microbiota metabolites. MedComm. 3:e171. doi: 10.1002/mco2.171 [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Looney, A. L., Bohling M. W., Bushby P. A., Howe L. M., Griffin B., Levy J. K., Eddlestone S. M., Weedon J. R., Appel L. D., Rigdon-Brestle Y. K.,. et al. ; Association of Shelter Veterinarians' Spay and Neuter Task Force. 2008. The Association of Shelter Veterinarians veterinary medical care guidelines for spay-neuter programs. J. Am. Vet. Med. A. 233:74–86. doi: 10.2460/javma.233.1.74 [DOI] [PubMed] [Google Scholar]
  40. Lu, J., Zhu D., Lu J., Liu J., Wu Z., and Liu L... 2022. Dietary supplementation with low and high polymerization inulin ameliorates adipose tissue inflammation via the TLR4/NF-κB pathway mediated by gut microbiota disturbance in obese dogs. Res. Vet. Sci. 152:624–632. doi: 10.1016/j.rvsc.2022.09.032 [DOI] [PubMed] [Google Scholar]
  41. Mao, J., Xia Z., Chen J., and Yu J... 2013. Prevalence and risk ­factors for canine obesity surveyed in veterinary practices in Beijing, China. Prev. Vet. Med. 112:438–442. doi: 10.1016/j.prevetmed.2013.08.012 [DOI] [PubMed] [Google Scholar]
  42. McKenzie, B. 2010. Evaluating the benefits and risks of neutering dogs and cats. CAB Rev 2010:1–18. doi: 10.1079/pavsnnr20105045 [DOI] [Google Scholar]
  43. Montoya-Alonso, J. A., Bautista-Castaño I., Peña C., Suárez L., Juste M. C., and Tvarijonaviciute A... 2017. Prevalence of canine obesity, obesity-related metabolic dysfunction, and relationship with owner obesity in an obesogenic region of Spain. Front. Vet. Sci. 4:59. doi: 10.3389/fvets.2017.00059 [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Mori, N., Lee P., Kondo K., Kido T., Saito T., and Arai T... 2011. Potential use of cholesterol lipoprotein profile to confirm obesity status in dogs. Vet. Res. Commun. 35:223–235. doi: 10.1007/s11259-011-9466-x [DOI] [PubMed] [Google Scholar]
  45. Muñoz-Prieto, A., Cerón J. J., Martínez-Subiela S., Mrljak V., and Tvarijonaviciute A... 2020. A systematic review and meta-analysis of serum adiponectin measurements in the framework of dog obesity. Animals. 10:1650. doi: 10.3390/ani10091650 [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Murphy, E. F., Cotter P. D., Healy S., Marques T. M., O’Sullivan O., Fouhy F., Clarke S. F., O’Toole P. W., Quigley E. M., Stanton C.,. et al. 2010. Composition and energy harvesting capacity of the gut microbiota: relationship to diet, obesity and time in mouse models. Gut. 59:1635–1642. doi: 10.1136/gut.2010.215665 [DOI] [PubMed] [Google Scholar]
  47. Osto, M., and Lutz T. A... 2015. Translational value of animal models of obesity—focus on dogs and cats. Eur. J. Pharmacol. 759:240–252. doi: 10.1016/j.ejphar.2015.03.036 [DOI] [PubMed] [Google Scholar]
  48. Park, H. J., Lee S. E., Kim H. B., Isaacson R. E., Seo K. W., and Song K. H... 2015. Association of obesity with serum leptin, adiponectin, and serotonin and gut microflora in beagle dogs. J. Vet. Intern. Med. 29:43–50. doi: 10.1111/jvim.12455 [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Pataky, Z., Bobbioni-Harsch E., and Golay A... 2010. Obesity: a complex growing challenge. Exp. Clin. Endocrinol. Diabetes. 118:427–433. doi: 10.1055/s-0029-1233448 [DOI] [PubMed] [Google Scholar]
  50. Pilla, R., and Suchodolski J. S... 2020. The role of the canine gut microbiome and metabolome in health and gastrointestinal disease. Front. Vet. Sci. 6:498. doi: 10.3389/fvets.2019.00498 [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Rahmouni, K., Fath M. A., Seo S., Thedens D. R., Berry C. J., Weiss R., Nishimura D. Y., and Sheffield V. C... 2008. Leptin resistance contributes to obesity and hypertension in mouse models of Bardet–Biedl syndrome. J. Clin. Invest. 118:1458–1467. doi: 10.1172/JCI32357 [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Reichler, I. M. 2009. Gonadectomy in cats and dogs: a review of risks and benefits. Reprod. Domest. Anim. 44:29–35. doi: 10.1111/j.1439-0531.2009.01437.x [DOI] [PubMed] [Google Scholar]
  53. Ricci, R., and Bevilacqua F... 2012. The potential role of leptin and adiponectin in obesity: a comparative review. Vet. J. 191:292–298. doi: 10.1016/j.tvjl.2011.04.009 [DOI] [PubMed] [Google Scholar]
  54. Robertson, I. D. 2003. The association of exercise, diet and other factors with owner-perceived obesity in privately owned dogs from Metropolitan Perth, WA. Prev. Vet. Med. 58:75–83. doi: 10.1016/s0167-5877(03)00009-6 [DOI] [PubMed] [Google Scholar]
  55. Romieu, I., Dossus L., Barquera S., Blottiere H. M., Franks P. W., Gunter M., Hwalla N., Hursting S. D., Leitzmann M., Margetts B.,. et al. ; IARC working group on Energy Balance and Obesity. 2017. Energy balance and obesity: what are the main drivers? Cancer Cause. Control. 28:247–258. doi: 10.1007/s10552-017-0869-z [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Root Kustritz, M. V. 2005. Pregnancy diagnosis and abnormalities of pregnancy in the dog. Theriogenology. 64:755–765. doi: 10.1016/j.theriogenology.2005.05.024 [DOI] [PubMed] [Google Scholar]
  57. Sallander, M., Hagberg M., Hedhammar A., Rundgren M., Lindberg J. E., and Sveriges L... 2010. Energy-intake and activity risk factors for owner-perceived obesity in a defined population of Swedish dogs. Prev. Vet. Med. 96:132–141. doi: 10.1016/j.prevetmed.2010.05.004 [DOI] [PubMed] [Google Scholar]
  58. Salonen, A., Lahti L., Salojärvi J., Holtrop G., Korpela K., Duncan S. H., Date P., Farquharson F., Johnstone A. M., Lobley G. E.,. et al. 2014. Impact of diet and individual variation on intestinal microbiota composition and fermentation products in obese men. ISME J. 8:2218–2230. doi: 10.1038/ismej.2014.63 [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Salt, C., Morris P. J., Wilson D., Lund E. M., and German A. J... 2019. Association between life span and body condition in neutered client‐owned dogs. J. Vet. Intern. Med. 33:89–99. doi: 10.1111/jvim.15367 [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Schauf, S., Salas-Mani A., Torre C., Bosch G., Swarts H., and Castrillo C... 2016. Effect of sterilization and of dietary fat and carbohydrate content on food intake, activity level, and blood satiety-related hormones in female dogs. J. Anim. Sci. 94:4239–4250. doi: 10.2527/jas.2015-0109 [DOI] [PubMed] [Google Scholar]
  61. Shabana, S., Shahid U., and Irfan U... 2018. The gut microbiota and its potential role in obesity. Future Microbiol. 13:589–603. doi: 10.2217/fmb-2017-0179 [DOI] [PubMed] [Google Scholar]
  62. Sherman, C. K., Reisner I. R., Taliaferro L. A., and Houpt K. A... 1996. Characteristics, treatment, and outcome of 99 cases of aggression between dogs. Appl. Anim. Behav. Sci. 47:91–108. doi: 10.1016/0168-1591(95)01013-0 [DOI] [Google Scholar]
  63. Shin, Y., Park S., Paek J., Kim J., Rhee M., Kim H., Kook J., and Chang Y... 2017. Bacteroides koreensis sp. nov. and Bacteroides kribbi sp. nov., two new members of the genus Bacteroides. Int. J. Syst. Evol. Microbiol. 67:4352–4357. doi: 10.1099/ijsem.0.002226 [DOI] [PubMed] [Google Scholar]
  64. Simpson, M., Albright S., Wolfe B., Searfoss E., Street K., Diehl K., Page R., and Meyre D... 2019. Age at gonadectomy and risk of overweight/obesity and orthopedic injury in a cohort of Golden Retrievers. PLoS One. 14:e209131. doi: 10.1371/journal.pone.0209131 [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Tokarek, J., Gadzinowska J., Młynarska E., Franczyk B., and Rysz J... 2022. What is the role of gut microbiota in obesity prevalence? A few words about gut microbiota and its association with obesity and related diseases. Microorganisms. 10:52. doi: 10.3390/microorganisms10010052 [DOI] [PMC free article] [PubMed] [Google Scholar]
  66. Tvarijonaviciute, A., Barić-Rafaj R., Horvatic A., Muñoz-Prieto A., Guillemin N., Lamy E., Tumpa A., Ceron J. J., Martinez-Subiela S., and Mrljak V... 2019. Identification of changes in serum analytes and possible metabolic pathways associated with canine obesity-related metabolic dysfunction. Vet. J. 244:51–59. doi: 10.1016/j.tvjl.2018.12.006 [DOI] [PubMed] [Google Scholar]
  67. Usui, S., Yasuda H., and Koketsu Y... 2016. Characteristics of obese or overweight dogs visiting private Japanese veterinary clinics. Asian Pac. J. Trop. Biomed. 6:338–343. doi: 10.1016/j.apjtb.2016.01.011 [DOI] [Google Scholar]
  68. Vekic, J., Stefanovic A., and Zeljkovic A... 2023. Obesity and dyslipidemia: a review of current evidence. Curr. Obes. Rep. doi: 10.1007/s13679-023-00518-z [DOI] [PubMed] [Google Scholar]
  69. Vendramini, T. H. A., Amaral A. R., Pedrinelli V., Zafalon R. V. A., Rodrigues R. B. A., and Brunetto M. A... 2020. Neutering in dogs and cats: current scientific evidence and importance of adequate nutritional management. Nutr. Res. Rev. 33:134–144. doi: 10.1017/S0954422419000271 [DOI] [PubMed] [Google Scholar]
  70. Villanueva-Millán, M. J., Pérez-Matute P., and Oteo J. A... 2015. Gut microbiota: a key player in health and disease. A review focused on obesity. J. Physiol. Biochem. 71:509–525. doi: 10.1007/s13105-015-0390-3 [DOI] [PubMed] [Google Scholar]
  71. Wilson, G., and Hayes J. H... 1979. Castration for treatment of perianal gland neoplasms in the dog. J. Am. Vet. Med. Assoc. 174:1301–1303 [PubMed] [Google Scholar]
  72. Yadav, A., Kataria M. A., Saini V., and Yadav A... 2013. Role of leptin and adiponectin in insulin resistance. Clin. Chim. Acta. 417:80–84. doi: 10.1016/j.cca.2012.12.007 [DOI] [PubMed] [Google Scholar]
  73. Yang, K., Jian S., Guo D., Wen C., Xin Z., Zhang L., Kuang T., Wen J., Yin Y., and Deng B... 2022a. Fecal microbiota and metabolomics revealed the effect of long-term consumption of gallic acid on canine lipid metabolism and gut health. Food Chem. X.15:100377. doi: 10.1016/j.fochx.2022.100377 [DOI] [PMC free article] [PubMed] [Google Scholar]
  74. Yang, K., Deng X., Jian S., Zhang M., Wen C., Xin Z., Zhang L., Tong A., Ye S., Liao P.,. et al. 2022b. Gallic acid alleviates gut dysfunction and boosts immune and antioxidant activities in puppies under environmental stress based on microbiome–metabolomics analysis. Front. Immunol. 12:813890. doi: 10.3389/fimmu.2021.813890 [DOI] [PMC free article] [PubMed] [Google Scholar]
  75. Zhao, H., Chen J., Li X., Sun Q., Qin P., and Wang Q... 2019. Compositional and functional features of the female premenopausal and postmenopausal gut microbiota. FEBS Lett. 593:2655–2664. doi: 10.1002/1873-3468.13527 [DOI] [PubMed] [Google Scholar]

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