Abstract
Introduction:
Obesity is a multifactorial metabolic disorder increasingly associated with alterations in gut microbial composition and endocrine imbalance. In the present study, microbiological analyses were limited to culture-based characterization of facultative anaerobic Enterobacteriaceae. Members of the Enterobacteriaceae family, particularly Klebsiella pneumoniae (Kpn), have been implicated in metabolic endotoxemia and inflammation; however, integrated data combining microbial profiling, metabolic hormones, and dietary patterns in Indian populations remain limited.
Methods:
This case-control observational study involved 49 males (27 obese, 22 normal-weight controls). Anthropometric data were collected, and fasting serum leptin, ghrelin, and insulin were measured via ELISA. Stool samples were cultured for Enterobacteriaceae, identified biochemically, and genotyped using ERIC-PCR. Dietary patterns were evaluated with questionnaires.
Results:
Obese participants showed significantly higher BMI, leptin (5.43±0.67 ng/mL), and insulin levels (3.21±0.27 mIU/L) than controls (p<0.05), while ghrelin levels did not differ significantly. Leptin positively correlated with BMI in obese individuals (r=0.423, p=0.028). Microbiological analysis yielded 82 isolates, with Kpn more prevalent among obese (92.86%) than controls (7.14%), whereas Escherichia coli predominated among controls (51.22%). ERIC-PCR demonstrated distinct genetic clusters among isolates. Obese participants also reported higher intake of fast food, refined carbohydrates, and soft drinks, correlating with elevated insulin levels.
Conclusion:
These findings suggest possible associations between Enterobacteriaceae prevalence, metabolic hormones, and dietary patterns in obesity and support microbiota-targeted and dietary interventions.
Graphical Abstract
1 Introduction
Obesity has emerged as one of the most significant public health challenges of the 21st century, driven by rapid urbanization, sedentary lifestyles, and increased consumption of energy-dense, high-processed foods (). According to recent global estimates from the World Health Organization and the NCD Risk Factor Collaboration, more than one billion people worldwide are currently living with obesity, with prevalence rates nearly tripling in women and quadrupling in men since 1975 (). Projections suggest that by 2030, over 1.12 billion adults may be classified as obese, underscoring the urgency of understanding the complex biological mechanisms underlying this condition (; ). Obesity is not merely an excess accumulation of adipose tissue; it is a chronic, relapsing metabolic disorder strongly associated with type 2 diabetes mellitus, cardiovascular diseases, non-alcoholic fatty liver disease, certain cancers, and increased susceptibility to infections ().
Traditionally, obesity has been explained by an imbalance between caloric intake and energy expenditure (). However, emerging evidence indicates that this simplistic view does not fully capture the intricate network of hormonal, microbial, immunological, and metabolic factors involved. Among these, endocrine regulators such as leptin, ghrelin, and insulin play central roles in maintaining energy homeostasis (). Leptin, a 16 kDa adipokine secreted primarily by adipocytes, acts on the hypothalamus to suppress appetite and increase energy expenditure. In individuals with obesity, circulating leptin levels are typically elevated, yet appetite suppression fails due to a phenomenon known as leptin resistance. This impaired signaling contributes to persistent hyperphagia, reduced energy expenditure, and progressive weight gain (; ).
Ghrelin, predominantly secreted by the stomach, functions as an orexigenic hormone that stimulates appetite and promotes food intake, with levels rising before meals and declining afterward (; ; ). Insulin, secreted by pancreatic β-cells, regulates glucose uptake and metabolism and exerts inhibitory effects on ghrelin secretion. In obesity, chronic low-grade inflammation and excess adiposity contribute to insulin resistance and compensatory hyperinsulinemia (). Importantly, leptin and insulin signaling pathways interact closely within the hypothalamus, and their dysregulation creates a vicious cycle of metabolic imbalance (). Despite extensive research on hyperleptinemia and hyperinsulinemia in obesity, the upstream modulators of leptin resistance remain incompletely understood ().
In recent years, the gut microbiota has emerged as a critical determinant of host metabolism and endocrine regulation (). The human gastrointestinal tract harbors approximately 10¹4 microorganisms, representing more than 1,000 bacterial species. The dominant phyla Firmicutes, Bacteroidetes, Actinobacteria, and Proteobacteria collectively influence nutrient absorption, short-chain fatty acid production, immune maturation, and energy harvesting (). Dysbiosis, characterized by altered microbial composition and reduced diversity, has been consistently associated with obesity and metabolic syndrome. Microbial metabolites can modulate inflammatory pathways, intestinal permeability, and adipokine secretion, thereby influencing systemic metabolic homeostasis ().
Within the Proteobacteria phylum, the family Enterobacteriaceae occupies a unique and clinically significant position. This family includes several Gram-negative, facultatively anaerobic enteric bacteria such as Escherichia coli (E. coli), Klebsiella pneumoniae (Kpn), Enterobacter spp., and Citrobacter spp (). While many members of the human gut microbiota are commensals, certain strains possess pathogenic potential and can disrupt intestinal homeostasis (). A defining feature of Enterobacteriaceae is their ability to ferment glucose, producing acid and gas, and their oxidase-negative status, which aids in laboratory identification. Importantly, several species are lactose fermenters, allowing rapid detection in clinical microbiology ().
Emerging data suggest that an increased relative abundance of Enterobacteriaceae is associated with gut inflammation, endotoxemia, and metabolic dysregulation. These bacteria possess lipopolysaccharide (LPS) in their outer membrane, a potent endotoxin that can trigger Toll-like receptor 4 (TLR4)-mediated inflammatory responses. Chronic low-grade endotoxemia induced by LPS has been proposed as a mechanistic link between the altered prevalence of culturable Enterobacteriaceae and obesity-related insulin resistance. Inflammatory signaling can impair hypothalamic leptin sensitivity, thereby contributing to leptin resistance (). Furthermore, species such as adherent-invasive Kpn have been implicated in metabolic disturbances, suggesting a direct microbial contribution to adiposity and endocrine imbalance ().
The novelty of connecting Enterobacteriaceae with leptin dysregulation lies in integrating microbiological and hormonal paradigms of obesity. While hyperleptinemia is well documented, fewer studies have examined whether specific enteric pathogens or shifts within Enterobacteriaceae populations correlate with altered leptin levels in obese individuals. It is plausible that increased colonization or overgrowth of pro-inflammatory Enterobacteriaceae may enhance systemic inflammation () disrupt gut barrier integrity and modulate adipocyte-derived leptin secretion or signaling. Such interactions could create a bidirectional axis wherein obesity-related hormonal changes favor dysbiosis, and dysbiosis further exacerbates leptin resistance ().
Therefore, exploring the prevalence and characterization of Enterobacteriaceae (with a major focus on facultative anaerobes) in obese individuals, alongside detailed hormonal profiling of leptin, ghrelin, and insulin, may provide novel insights into the gut-endocrine axis in obesity. Understanding whether specific enteric bacterial patterns are associated with hyperleptinemia or leptin resistance could open new avenues for microbiota-targeted therapeutic strategies, including dietary modulation. By integrating microbial ecology with endocrine dysfunction, this study aims to contribute to a more comprehensive understanding of obesity pathophysiology, particularly regarding metabolic and inflammatory interactions mediated by Enterobacteriaceae.
2 Materials and methods
2.1 Study population, sampling procedures
A case-control observational study was conducted at the Department of Physiology, Institute of Medical Sciences, Banaras Hindu University, Varanasi, between January 2022 and May 2023. Participants were recruited from the outpatient department (OPD) by staff and students after obtaining written informed consent. The study protocol was reviewed and approved by the Institutional Ethics Committee of IMS-BHU (Ref. No. Dean/2022/EC/3333), ensuring adherence to established ethical standards.
Initially, 86 male participants aged 18–41 years were enrolled. After applying exclusion & inclusion criteria, 2 participants refused to enroll in this study following weight and height measurements, and 63 eligible subjects remained. Four additional individuals were excluded for refusing to provide blood samples, and 10 for refusing to provide stool samples. The final analysis, therefore, included 49 participants: 27 males with obesity (BMI >23 kg/m², according to WHO Asian-BMI criteria) and 22 age-matched normal-weight controls (BMI 18–22.9 kg/m²) (Supplementary Figure 1). Although the sample size was modest, it reflected the stringent eligibility criteria and participant compliance required for the simultaneous collection of clinical, hormonal, dietary, and microbiological data. Therefore, this investigation should be regarded as an exploratory study. While statistically significant associations were identified, the relatively small sample size may have limited statistical power, increased the possibility of type II errors, and restricted the generalizability of the findings. Larger, multicenter studies are warranted to confirm these observations ().
2.2 Inclusion and exclusion criteria
People between 18 and 41 years old with a BMI exceeding 23 kg/m² were included in the study based on WHO Asian-BMI criteria for obesity in the Asian population (; ; ). Exclusion criteria included people who had experienced significant weight loss in the past six months, were enrolled in any weight-loss program, had undergone weight reduction surgery, had chronic systemic illnesses, suffered from gastrointestinal disorders, or were taking hormonal supplements/antibiotics.
2.3 Data collection
All participants in the study completed a questionnaire to capture a detailed clinical history, followed by a physical examination and an assessment of their dietary habits and routine (). Dietary habits were assessed using a structured semi-quantitative questionnaire administered through direct interviews. Participants were asked about the habitual frequency of consumption of major food categories including fast foods, fried foods, refined carbohydrates, soft drinks, fruits, vegetables, home-cooked meals, meat products, and vitamin/mineral supplements over the preceding three months. Dietary variables were categorized according to frequency of intake and coded for correlation analysis with anthropometric and hormonal parameters. The questionnaire was designed to evaluate dietary patterns and meal behaviors rather than detailed caloric or macronutrient intake; therefore, precise energy consumption was not calculated. Participants’ heights were recorded barefoot, accurate to 0.1 cm, while their body weights were taken using a calibrated digital scale with a precision of 0.1 kg ().
2.4 Blood collection and serum assay
Morning at 09:00 am, blood samples (3 ml) were collected from participants via venipuncture using plain vacutainers. The serum was separated from the blood following centrifugation at 3000 rpm for 5 minutes at 4 °C. The obtained serum was stored at -20 °C and subsequently analyzed for hormone concentrations using commercially available assay kits ().
Commercially available ELISA kits (ImmunoTag, USA) were used to measure insulin, ghrelin, and leptin serum levels. The serum concentration of leptin was assessed by the Human Leptin ELISA Kit (Cat.No ITEH01559) of Geno Technology Inc. (USA). The serum concentration of Ghrelin was evaluated by the Human Ghrelin ELISA Kit (Cat.No ITEH03091) of Geno Technology Inc. (USA). The serum concentration of Insulin was assessed by the Human Insulin ELISA Kit (Cat.No ITEH00010) of Geno Technology Inc. (USA). This sandwich kit is for accurate quantitative detection of Human leptin/ghrelin/insulin in serum, plasma, cell culture supernatants, ascites, tissue homogenates, or other biological fluids. The given ImmunoTag ELISA Kit Protocol was strictly followed.
An in vitro sandwich Enzyme-Linked Immunosorbent Assay (ELISA) (Geno Technology Inc., USA) was employed to quantitatively measure human leptin, ghrelin, and insulin in serum. A monoclonal antibody against leptin, ghrelin, and insulin was coated to the microtiter wells. Test and control samples were incubated in the coated wells with a specific anti-leptin/ghrelin/insulin antibody. After incubation, the unbound material was washed off, and horseradish peroxidase (HRP) conjugate was added to detect the bound leptin/ghrelin/insulin. The amount of leptin, ghrelin, and insulin in the sample correlated with the color intensity that developed ().
2.5 Stool sample collection and processing for bacterial isolation
Stool samples were collected from participants in Sterile Clinicol (HIMEDIA PW015) clean, dry, and leakproof sterile bottles. The specimens were immediately transported to the Microbiology Enteric Laboratory in the Department of Microbiology, Institute of Medical Sciences, and refrigerated at 4 °C prior to bacterial culture and identification.
A sterile wire loop was dipped into the stool sample to obtain an inoculum, which was streaked onto the surface of the plate containing MacConkey agar and Deoxycholate Citrate agar using the standard method (; ). The procedure was applied to each sample, and the plates were incubated at 37 °C for 24 hours. Each isolate’s presumed colonies on agar plates were further subcultured to obtain a pure culture. Pure isolates were kept for further bacterial identification.
2.6 Genomic fingerprinting of clinical isolates
Clinical Isolates were collected from stool samples of obese individuals and normal weight controls, and were identified to the species level utilizing standard microbiological, biochemical tests, and molecular techniques (). The E. coli strain ATCC 25922 was used as a reference control to accurately identify the isolates. E. coli & Kpn strains underwent fingerprinting using ERIC-PCR with primers described by (). ERIC-PCR was used exclusively for molecular fingerprinting/strain typing (F: 5’ ATGTAAGCTCCTGGGGATTCAC-3’; R: 5’ AAGTAAGTGACTGGGGTGAGCG-3’), following established thermal cycling protocols. This involved an initial denaturation at 95 °C for 5 minutes, followed by 35 cycles of 10 seconds at 96 °C for denaturation, 30 seconds at 38.9 °C for annealing, and 1 minute at 72 °C for extension. The final extension step was completed at 72 °C for 10 minutes. The PCR products were analyzed using 1% agarose gel electrophoresis with a GeNei™ system (Sl. No-07/19/F/328, Peenya, Bangalore, India).
2.7 Scanning electron microscope
The morphology of E. coli & Kpn on GLA (glass cover slip) was assessed by SEM. Samples were fixed in 2.5% glutaraldehyde in PB buffer pH 7.2 for 10 min, dehydrated through an ethanol series (30, 50, 70, 90, and 100% v/v), and air-dried for 2 h. Surfaces underwent the same dehydration. All samples were dried for 1 day and sputter-coated with gold. Both surfaces and biofilm samples were viewed using a FESEM system (Zeiss GeminiSEM 500) at 7.00 kV. Cell morphology was measured using microscope software (xTMicroscope Control, FEI Company) ().
2.8 Statistical analysis
Data were analyzed using Excel and SPSS 25.0 and presented as mean ± standard deviation. The Mann-Whitney U test was used to assess statistical significance at a significance level of p < 0.05. Furthermore, Bonferroni multiple comparisons and correlations were conducted to examine the relationships among age, weight, height, BMI, dietary patterns, and leptin, ghrelin, and insulin levels in both groups.
3 Results
This study included 27 obese individuals and 22 age & height-matched controls (Supplementary Figure 1). The mean body weight of the obese was 81.04 ± 10.9 kg, which was considerably greater than that of the controls, 58.86 ± 5.962 kg. Similarly, the BMI in the study group was significantly higher at 28.644 ± 2.52 kg/m² than in the control group at 20.50 ± 1.997 kg/m² (Table 1).
Table 1
| Character | Controls (n=22) (Mean ± SD) | Obese (n=27) (Mean ± SD) | *p-value |
|---|---|---|---|
| Age (year) | 26.32 ± 6.129 | 27.81 ± 2.988 | 0.071 |
| Height (cm) | 169.93 ± 7.680 | 167.941 ± 6.736 | 0.247 |
| Weight (Kg) | 58.86 ± 5.962 | 81.04 ± 10.983 | 0.001 |
| BMI (Kg/m2) | 20.50 ± 1.997 | 28.644 ± 2.52 | 0.001 |
| Leptin (ng/mL) | 3.683 ± 0.399 | 5.433 ± 0.677 | 0.001 |
| Ghrelin (ng/mL) | 3.522 ± 0.350 | 3.455 ± 0.279 | 0.778 |
| Insulin (mIU/L) | 2.494 ± 0.738 | 3.211 ± 0.276 | 0.003 |
Comparison between characteristics of obese and controls.
*p < 0.05 statistically significant.
In obese individuals, the mean fasting serum leptin concentration was 5.433 ± 0.677 ng/ml, significantly higher than the mean of 3.683 ± 0.399 ng/ml observed in controls (p=0.001). Conversely, ghrelin levels were not significantly different between obese and controls (p=0.778). Insulin levels in obese were significantly higher at 3.211 ± 0.276 mUI/L, compared to 2.494 ± 0.738 mUI/L in the controls (p=0.003) (Table 1 and Figure 1).
Figure 1
3.1 Dietary data shows notable differences between obese and control people
Obese tend to consume more fast food (p= 0.002), fried foods (p= 0.009), white flour (p= 0.003), soft drinks (p= 0.008), and potato chips (p= 0.012), while intake of refined sugars was not significantly different from controls (p= 0.829). Obese gave a history of reduced intake of vegetables and freshly prepared home-cooked food. Additionally, they are more likely to eat a substantial breakfast, experience daytime hunger (p= 0.001), and have higher intake of meat (p= 0.003) compared to controls. However, they are less inclined to consume vitamins/minerals, fresh fruit juice, and fruits. Control group exhibit a higher intake of freshly prepared home-cooked food (p= 0.001), vegetables, fresh fruit juice (p= 0.008), fruits (p= 0.002), and vitamins/minerals (p= 0.003) and have history of lower consumption of fast food, fried foods, white flour, refined sugars, soft drinks along with a lower preponderance to experience day time hunger and meat consumption (Supplementary Table 1; Figure 2).
Figure 2
Serum biomarker levels were measured in all participants using blood samples collected after an overnight fast. A significant positive correlation (r = 0.423, p = 0.028) was found between serum leptin levels and BMI in obese individuals, but not in controls, suggesting that leptin levels tend to rise with increasing BMI (Supplementary Figures 2A, B). In obese individuals, no correlation existed between BMI and ghrelin levels. Conversely, the control group showed a significant negative correlation (r = -0.635, p = 0.001), indicating that controls have higher ghrelin levels (Supplementary Figures 2C, D). The control group (r = 0.224, p = 0.317) and the obese (r = 0.219, p = 0.273) both showed a positive correlation between insulin and BMI. However, the correlation was less pronounced in the controls (Supplementary Figures 2E, F). In contrast, the controls only showed a strong positive correlation between insulin and ghrelin (r = 0.624, p = 0.002) (Supplementary Figures 2G, H) as shown in Table 2.
Table 2
| Correlation | Obese (n=27) | Controls (n=22) | ||
|---|---|---|---|---|
| r | p | r | p | |
| Leptin vs BMI | 0.423* | 0.028 | -0.029 | 0.898 |
| Ghrelin vs BMI | 0.254 | 0.200 | -0.635** | 0.001 |
| Insulin vs BMI | 0.219 | 0.273 | 0.224 | 0.317 |
| Leptin vs weight | 0.159 | 0.429 | -0.293 | 0.185 |
| Ghrelin vs weight | 0.064 | 0.752 | 0.644** | 0.001 |
| Insulin vs weight | 0.298 | 0.131 | 0.429* | 0.047 |
| Leptin vs ghrelin | 0.309 | 0.117 | -0.194 | 0.387 |
| Ghrelin vs insulin | 0.206 | 0.301 | 0.624** | 0.002 |
| Insulin vs leptin | 0.123 | 0.539 | -0.215 | 0.337 |
Correlation between serum leptin, ghrelin, and insulin concentration with BMI, weight, and leptin, ghrelin, and insulin.
*Correlation is significant at 0.05 (2-tailed).
**Correlation is significant at 0.01 level (2-tailed)
3.2 Dietary patterns correlate with hormones
Controls: Leptin shows no significant connections to dietary factors. Ghrelin shows a strong negative correlation with freshly home-cooked produce (r = -0.476, p < 0.05). This indicates that those who eat more home-cooked meals tend to have reduced ghrelin levels. Insulin demonstrates a significant positive correlation with refined sugars (r = 0.336, p < 0.05), suggesting that higher consumption of refined sugars is linked to increased insulin levels (Supplementary Table 2; Figure 3).
Figure 3
Obesity: Leptin showed a significant negative correlation with freshly prepared home-cooked meals (Supplementary Table 2). Ghrelin exhibited a significant negative correlation with vitamin and mineral intake (r = −0.428, p < 0.05), whereas its association with freshly prepared home-cooked meals was not statistically significant (r = −0.231, p = 0.246). Insulin showed a significant positive correlation with refined sugar intake (Supplementary Table 2), while no significant correlations were observed with lunch or other dietary variables. Results are fully consistent with the correlation coefficients and p-values shown in Supplementary Table 2; Figure 3.
3.3 Bacterial isolation and processing
The cultural characteristics of the recovered isolates on MacConkey and Deoxycholate citrate agar are shown in Figures 4A, E. The results demonstrated that the recovered isolate was identified based on colony morphology and staining features. All the isolates were identified as Gram-negative rods, appearing as small, pink-colored rods that were either single or paired under the microscope, as shown in supplementary Supplementary Figure 3A. SEM images show Kpn in Supplementary Figure 3B and E. coli in Supplementary Figure 3(C). The colorless colony with the jagged edge, clear colorless colony, mucoid pink colony, non-mucoid deeper pink colony, and pale-yellow colony are among the cultural characteristics. Figures 4B–D shows Kpn, while Figures 4F–H illustrates E. coli. Table 3 shows the biochemical tests of different bacterial isolates.
Figure 4
Table 3
| Organism name | Biochemical test | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| TSIA | SIM | Glu | Lac | Suc | Mann | Ureas | Citrate | Indole | H2S | Oxi | Cata | |
| Escherichia coli | A/A | +ve | +ve | +ve | v | +ve | -ve | -ve | +ve | -ve | -ve | +ve |
| Klebsiella pneumoniae | A/A | -ve | +ve | +ve | +ve | +ve | +ve | +ve | -ve | -ve | -ve | +ve |
| Citrobacter spp. | A/Ablack | +ve | +ve | +ve | +ve | +ve | v | +ve | -ve | +ve | -ve | +ve |
| Pseudomonas spp. | K/K | +ve | -ve | -ve | -ve | +ve | -ve | +ve | -ve | -ve | +ve | +ve |
Facultative anaerobic bacteria isolated from the stool of obese and control individuals.
TSIA, Triple Sugar Iron Agar, SIM, Sulphur Indole Motility, Glu, Glucose, Lac, Lactose, Suc, Sucrose, Mann, Mannitol, Oxi, Oxidase, Cata, Catalase, A/A, Acidic butt/Acidic slant, K/K,Alkaline butt/Alkaline slant, (+ve = positive), (-ve = negative), (v= variable).
Table 4 presents the distribution of bacterial isolates obtained from stool samples of obese and control participants. A total of 84 isolates were identified, of which 54 (100%) were recovered from the obese group and 39 (100%) from the control group.
Table 4
| Isolates | Obese (n=27) | Control (n=22) | p value |
|---|---|---|---|
| Escherichia coli | 20 (48.78%) | 21 (51.22%) | >0.05 |
| Klebsiella pneumoniae | 26 (92.88%) | 2 (7.14%) | <0.001 |
| Pseudomonas sp | 4 (50%) | 4 (50%) | >0.05 |
| Citrobacter sp | 3 (60%) | 2 (40%) | >0.05 |
| Total (n=84) | 53 (64.63%) | 28 (35.37%) |
Clinical specimen isolates from stool samples from obese and control.
Gel electrophoresis analysis of Enterobacterial Repetitive Intergenic Consensus-Polymerase Chain Reaction (ERIC-PCR) fingerprinting of E. coli and Kpn strains isolated from control and obese stool samples. Phylogenetic tree based on the ERIC-PCR data showing the relationships among all 41 E. coli and 28 Kpn strains studied. Clusters were determined using the unweighted pair group method with arithmetic mean (UPGMA) method and the Dice similarity coefficient. The banding pattern revealed 4 distinct clusters and 11 singletons in Figures 5A, B. Cluster 2 had the highest prevalence (48%) and contained the majority of Kpn strains from obese individuals. This suggests that a specific strain of Kpn might be more closely linked to, or potentially associated with obesity. The banding pattern of E. coli revealed four distinct clusters and one singleton in Figures 6A, B. Clusters 2 and 3 had the highest prevalence.
Figure 5
Figure 6
4 Discussion
The present study explored the complex interplay between metabolic hormones, dietary habits, and enteric bacterial colonization in obese and normal-weight adult males. By integrating endocrine profiling with microbiological assessment, our findings provide a multidimensional perspective on obesity that extends beyond traditional energy imbalance models. In particular, the predominance of Enterobacteriaceae, especially Kpn in obese individuals highlights a potentially important microbial contribution to hormonal dysregulation and metabolic inflammation ().
4.1 Hormonal dysregulation in obesity
Consistent with established literature, obese participants in our study exhibited significantly elevated fasting serum leptin and insulin levels compared to controls (; ). Hyperleptinemia reflects increased adipose tissue mass, as leptin is secreted proportionally to fat stores. However, despite high circulating levels, appetite suppression fails in obesity due to leptin resistance a condition attributed to impaired hypothalamic signaling, reduced leptin receptor (LEPR/OB-R) sensitivity, and defective transport across the blood–brain barrier (; ). The significant positive correlation between leptin and BMI observed in obese participants further supports the notion that increasing adiposity drives greater leptin production without restoring energy balance ().
Similarly, fasting insulin levels were significantly higher in obese subjects, suggesting compensatory hyperinsulinemia caused by insulin resistance (; ). Chronic low-grade inflammation, often associated with obesity, contributes to impaired insulin receptor signaling and glucose uptake. The interaction between leptin and insulin is well recognized; hyperinsulinemia can enhance leptin secretion, while leptin resistance may further exacerbate insulin resistance, creating a self-perpetuating metabolic cycle ().
In contrast, fasting ghrelin levels did not differ significantly between the obese and control groups, consistent with previous finding (). However, the lack of a correlation between ghrelin and BMI in the obese group, unlike the strong positive correlation seen in controls, indicates a disruption in normal appetite signaling associated with obesity. A previous study reported that ghrelin concentrations in lean girls show diurnal variability, with increases before breakfast and dinner, consistent with our study, which collected blood samples at 9:00 am (). The preserved ghrelin-BMI association in controls likely reflects intact physiological regulation, whereas the breakdown of this relationship in obese individuals indicates endocrine dysregulation at higher BMI thresholds.
4.2 Enterobacteriaceae and obesity: a microbial perspective
Beyond hormonal imbalance, our microbiological analysis revealed a notable predominance of Enterobacteriaceae in stool samples from obese individuals. This family, belonging to the phylum Proteobacteria (), comprises Gram-negative, facultatively anaerobic bacilli commonly residing in the human gut. While many are commensals, several species act as opportunistic pathogens and have been implicated in metabolic disorders ().
Among these, Kpn was predominantly isolated in obese individuals. This observation aligns with previous findings suggesting an association between Klebsiella overgrowth and weight gain in an animal model (). Kpn is known for its capacity to produce endotoxins (lipopolysaccharides, LPS), which can translocate across a compromised intestinal barrier, triggering systemic inflammation. Chronic endotoxemia has been proposed as a mechanistic link between altered prevalence of culturable Enterobacteriaceae and obesity (; ). LPS activates Toll-like receptor 4 (TLR4)-mediated pathways, leading to increased production of pro-inflammatory cytokines such as TNF-α and IL-6, both of which are implicated in insulin resistance and leptin signaling impairment (; ).
The predominance of Kpn in obese subjects in our cohort may therefore contribute to leptin resistance through inflammatory mechanisms. Elevated circulating LPS levels can disrupt hypothalamic leptin signaling pathways, reducing receptor sensitivity despite hyperleptinemia (; ). This possible associations between Enterobacteriaceae prevalence and metabolic hormone alterations interaction offers a plausible explanation for the persistent hunger and metabolic imbalance observed in obesity. Furthermore, Kpn species have demonstrated enhanced energy-harvesting capabilities from dietary substrates, potentially increasing caloric extraction and fat deposition (). Other Enterobacteriaceae members, including Escherichia coli, Salmonella, Shigella, Citrobacter, and Enterobacter cloacae, were also identified. Enterobacter cloacae has been experimentally associated with the development of obesity in both animal and human studies, potentially via endotoxin production and activation of inflammation ().
4.3 Correlation of Klebsiella with BMI and hormonal markers
A particularly noteworthy finding in our study was a positive association between Kpn and higher BMI. Obese individuals with Kpn showed tendencies toward higher leptin and insulin levels compared to control participants without predominant Kpn growth (). Although causality cannot be established in this cross-sectional design, the correlation suggests a possible synergistic effect between microbial dysbiosis and endocrine disruption.
The coexistence of hyperleptinemia, hyperinsulinemia, and Kpn dominance may suggest that microbial inflammation heightens metabolic resistance pathways (). Persistent exposure to endotoxins could maintain systemic inflammatory tone, exacerbating leptin receptor desensitization and impairing insulin signaling cascades (; ). Thus, the gut microbiota may not only reflect obesity-related changes but also actively contribute to metabolic deterioration.
4.4 Dietary patterns and microbial-hormonal interactions
Dietary assessment in our cohort revealed higher consumption of fast foods, fried foods, refined sugars, white flour products, and soft drinks among obese individuals. Such diets are known to favor the proliferation of Proteobacteria, including Enterobacteriaceae (). High-fat and high-sugar diets alter gut permeability and microbial diversity, potentially promoting Kpn overgrowth (). Significant correlations between refined sugar intake and insulin levels in both groups support the established link between dietary glycemic load and hyperinsulinemia. Additionally, the negative correlation between ghrelin levels and freshly home-cooked produce suggests that diets rich in fiber and micronutrients may beneficially modulate appetite-regulating hormones. The reduced intake of fruits and vegetables among obese participants may therefore not only influence endocrine balance but also promote microbial dysbiosis. The observed pattern indicates a triadic interaction: an unhealthy diet fosters Enterobacteriaceae overgrowth, which induces inflammatory responses and subsequently impairs leptin and insulin signaling (; ). This integrated model strengthens the hypothesis that obesity is not merely an endocrine disorder but a microbiota-associated inflammatory state.
4.5 Limitations
This study has several limitations that should be considered when interpreting the findings. First, the relatively small sample size reduces statistical power and limits the generalizability of the findings, precluding robust causal inference. Second, its cross-sectional design prevents determination of temporal relationships between Klebsiella pneumoniae predominance and leptin–insulin dysregulation, Unfortunately, fasting glucose measurements were not available; therefore, HOMA-IR calculation could not be performed. Third, an important limitation of the present study is that the microbiological analysis was restricted to selective culture-based identification of facultative anaerobic Enterobacteriaceae. This methodology does not comprehensively represent total gut microbial diversity, particularly anaerobic and unculturable taxa that constitute a major proportion of the intestinal microbiota. Therefore, the findings should not be interpreted as a complete characterization of gut dysbiosis. In addition, culture-based methods may introduce selection bias toward easily cultivable organisms. Future studies incorporating 16S rRNA sequencing, metagenomics, and metabolomic profiling are required for comprehensive microbiome characterization and validation of the observed microbial-hormonal associations. Finally, the absence of sequencing-based microbiome profiling and inflammatory biomarker measurements constrains mechanistic interpretation. Mechanistic pathways involving LPS-TLR4-TNF-α/IL-6 are now explicitly described as hypothetical and literature-supported rather than demonstrated by our data.
The study also has limited external generalizability because it included only adult Indian males recruited from a single center. Sex-specific hormonal variations, regional dietary habits, and ethnic variability may influence obesity-associated microbial and endocrine profiles. Furthermore, dietary assessment was based on self-reported frequency questionnaires without detailed caloric or macronutrient quantification, which may introduce recall bias and limit nutritional precision.
5 Conclusion
In summary, our findings suggest that obesity in this cohort of males is characterized not only by hyperleptinemia and hyperinsulinemia but also by a predominance of Enterobacteriaceae, particularly Klebsiella pneumoniae. The correlation between Klebsiella presence, higher BMI, and altered hormonal profiles supports a potential possible associations between Enterobacteriaceae prevalence and hormonal alterations contributing to metabolic dysregulation. These results underscore the importance of integrating microbial analysis with hormonal and dietary assessment to better understand obesity pathophysiology and to develop targeted therapeutic strategies addressing both endocrine imbalance and altered prevalence of culturable Enterobacteriaceae. These findings should be interpreted within the context of adult Indian males from a single-center cohort and require validation in larger, geographically and demographically diverse populations.
Statements
Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author.
Ethics statement
The studies involving humans were approved by Bhupendra Singh Yadav, MD Department of Physiology, Institute of Medical Sciences Banaras Hindu University, Varanasi, Uttar Pradesh, 221005. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.
Author contributions
RV: Investigation, Software, Data curation, Methodology, Writing – original draft. BY: Writing – review & editing, Supervision, Resources, Visualization, Conceptualization. PG: Writing – review & editing, Methodology, Data curation. MS: Writing – review & editing, Formal Analysis, Visualization. GN: Investigation, Supervision, Writing – review & editing, Data curation.
Funding
The author(s) declared that financial support was not received for this work and/or its publication.
Acknowledgments
Ranjeet Kumar Vishwakarma acknowledges the Department of Science and Technology (DST), India, for the DST-INSPIRE fellowship (No. DST/INSPIRE Fellowship/2020/IF200200). We also acknowledge the Department of Physiology, Institute of Medical Sciences, Banaras Hindu University, Varanasi, for the departmental facilities. We also thank the Viral Research and Diagnostic Laboratory, Department of Microbiology, Institute of Medical Sciences at Banaras Hindu University for supplying the infrastructure needed for this research.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/frmbi.2026.1867961/full#supplementary-material
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Summary
Keywords
body mass index, Enterobacteriaceae, ghrelin, insulin, leptin, obesity
Citation
Vishwakarma RK, Yadav BS, Gautam P, Sahu M and Nath G (2026) Integrated microbial and endocrine signatures of obesity: Enterobacteriaceae alterations, hormonal profiles, and dietary correlates in adult males. Front. Microbiomes 5:1867961. doi: 10.3389/frmbi.2026.1867961
Received
28 April 2026
Revised
18 July 2026
Accepted
21 July 2026
Published
09 September 2026
Volume
5 - 2026
Edited by
Saraid Mora Rochin, Universidad Autónoma de Sinaloa, Mexico
Reviewed by
Bidisha Barat, The University of Chicago, United States
Lorenzo Ulises Osuna Martínez, Universidad Autónoma de Sinaloa, Mexico
Updates
Copyright
© 2026 Vishwakarma, Yadav, Gautam, Sahu and Nath.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Bhupendra Singh Yadav, bsyadav@bhu.ac.in
†ORCID: Ranjeet Kumar Vishwakarma, orcid.org/0009-0001-8508-2740; Bhupendra Singh Yadav, orcid.org/0000-0003-4288-1080; Priyanka Gautam, orcid.org/0000-0002-4669-1527; Minakshi Sahu, orcid.org/0009-0002-4074-0225; Gopal Nath, orcid.org/0000-0003-2722-1308
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