Abstract
Background
Obesity among older adults presents a serious public health challenge worldwide. The gut microbiota plays a key role in metabolic health and obesity, however its specific alterations in older adults with obesity remain unclear. This study aimed to assess the differences in the abundance of certain indicator gut bacterial genera between older adults with obesity and those with normal weight.
Methods
We conducted a case-control study in 2025 involving 104 individuals with obesity and 185 with normal weight, selected from the participants in phase III of the Bushehr Elderly Health (BEH) Program. The obesity group included individuals with a body mass index (BMI) ≥ 30 kg/m² along with waist circumference > 88 cm for women or > 102 cm for men, while the normal weight group had BMI between 18.5 and 24.9 kg/m² and waist circumference below these thresholds. Quantitative real-time PCR was used to measure the abundance of six indicator gut bacterial genera. Statistical analyses, including multiple logistic regression, were employed to evaluate associations between bacterial genera abundance and obesity status, with confounders identified by Directed Acyclic Graph.
Results
The participants’ mean age was 72.78 ± 3.08 years. In the obesity and normal weight groups, 38.5% and 67.6% of participants were male, respectively. The abundance of Bacteroides (odds ratio [OR]: 0.681, 95% CI: 0.535–0.866), Blautia (OR: 0.450, 95% CI: 0.301–0.673) and Faecalibacterium (OR: 0.750, 95% CI: 0.583–0.965) were negatively associated with obesity in older adults, after adjusting for age, sex, socioeconomic status, physical activity, and nutritional status.
Conclusions
This study highlights the association between specific gut microbiota and obesity in older adults. Understanding this association and interventions targeting the indicator bacteria may contribute to healthy aging by mitigating obesity and its related metabolic disorders.
Graphical abstract
Keywords: Gut microbiota, Aged, Obesity, Case-Control study
Introduction
Obesity and its associated health risk factors are a growing public health concern and are highly prevalent among older population [1, 2]. According to a meta-analysis published in 2025, the prevalence of obesity (defined as a body mass index (BMI) ≥ 30 kg/m²) in older adults is 27.3% in North America, 33.6% in Europe, and 14.6% in Asia [3]. In addition, the prevalence of obesity (BMI ≥ 30 kg/m²) and abdominal obesity (based on international waist circumference (WC) cut-points 88 cm for women and < 102 cm for men) among adults aged over 60 years in Iran in 2021 was approximately 28% and 57%, respectively [4]. The demographic shift toward an older adult population highlights the need for special attention to manage obesity through targeted interventions to prevent related health issues and improve quality of life [5, 6].
The human gut microbiota consists of trillions of microbes, including bacteria, that inhabit the human gastrointestinal tract, playing a crucial role in various physiological processes and overall health [7]. The association of gut microbiota composition with obesity and metabolic disorders has been consistently demonstrated in several studies [8], including those involving older adults [9].
The gut microbiota composition is implicated in obesity through several mechanisms, such as influencing nutrient absorption, energy metabolism, inflammation, and the production of microbial metabolites like short-chain fatty acids, branched-chain amino acids (BCAAs), aromatic amino acids, and secondary bile acids [10, 11]. Moreover, changes in the diversity and composition of gut microbiota have been observed during the natural process of aging, which may lead to low-grade inflammation and metabolic disorders [12], highlighting the importance of understanding the association between gut microbiota and the development of obesity. Understanding the association between gut microbiota composition and the development of obesity in older adults provides valuable insights that can help the exploration of novel therapeutic approaches. Various interventions, including the administration of probiotics, prebiotics, and modifications in dietary patterns, which may promote gut microbiome hemostasis, demonstrate promising potential in the management of obesity and its associated metabolic disorders [10].
However, most studies comparing gut microbiota composition in obese and non-obese individuals have focused on young and middle-aged adults; yet, the gut microbiota undergoes changes with aging, which may impact obesity in older adults [13]. Moreover, the composition of gut microbiota can be vary based on other factors such as ethnicity, geographical region, dietary patterns, and lifestyle [14]. Notably, understanding alterations in gut microbiota among older adults with obesity in diverse populations may provide an insight into targeted interventions for these vulnerable individuals. According to our previous systematic review study, the genera Akkermansia, Bacteroides, Blautia, Faecalibacterium, Prevotella, and Ruminococcus showed significant alterations in older adults with obesity [9]. However, there remains a significant lack of data on the gut microbiota characteristics and their specific association with obesity in older adult populations in the Iranian context.
Thus, to address this gap in evidence, the aim of this study is to assess the main gut microbiota profile of older adults with obesity and normal weight in phase III of Bushehr Elderly Health (BEH) Program.
Methods
Study design and participant selection
This case-control study was conducted within the context of phase III of the BEH Program, a comprehensive, population-based, prospective cohort study aimed at investigating various health issues among older adults aged 60 years and older in Bushehr, Iran. The phase III was a remeasurement of the first recruitment of the BEH Program, including 1345 participants [15–17].
This study included 104 older adult participants with obesity and 185 with normal weight, whose stool samples were available. The inclusion criteria for this study were as follows: BMI ≥ 30 kg/m² for the obesity (case) group and BMI between 18.5 and 24.9 kg/m² for the normal weight (control) group, along with a waist circumference (WC) > 88 cm for women and > 102 cm for men in the obesity (case) group, and < 88 cm for women and < 102 cm for men in the normal weight (control) group. The exclusion criteria were: (1) cancer; (2) acute gastrointestinal diseases, inflammatory bowel disease (IBD), irritable bowel syndrome (IBS), or acute infections; (3) history of gastrointestinal surgery; (4) use of antibiotics within two months before and during the study; and (5) regular use of anti-inflammatory drugs within one month before and during the study. The study was conducted in accordance with the Declaration of Helsinki ethical principles for medical research involving human subjects and was approved by the Ethics Committee of Tehran University of Medical Sciences. Written informed consent was obtained from all participants.
General assessments and data source
During the implementation of the BEH Program, a comprehensive set of assessments was conducted, including anthropometric and biochemical measurements by trained physicians and health professionals, ensuring a thorough evaluation of each participant’s health status and lifestyle factors. The essential demographic, anthropometric, biochemical and clinical/health status information was obtained from the project administrator [17].
To determine the socioeconomic status (SES) index, factors such as assets, employment status, income, expenses, and housing conditions were considered. The data were analyzed using principal component analysis (PCA) and classified into five SES levels: very poor, poor, moderate, good, and very good (Q1–Q5). The International Mini Nutritional Assessment (MNA) [18], the Cognitive Impairment Test (6CIT) [19], and the International Physical Activity Questionnaire (IPAQ) [20] were used to evaluate nutritional status, cognitive function, and physical activity in the participants. Current smoking status indicates whether an individual actively smokes cigarettes at the time of assessment.
Anthropometric and biochemical measurements
In brief, height and weight were recorded with participants in light clothing and without shoes. Height was measured using a fixed stadiometer, and weight was obtained with a calibrated digital scale. WC was measured at the midpoint between the lowest ribs and the iliac crest, while hip circumference (HC) was measured at the widest part of the buttocks, using a flexible tape. BMI (kg/m²) was calculated as weight in kilograms (kg) divided by height squared in meters (m²). Fat mass, body fat percentage, and fat mass ratio were assessed through whole-body Dual-energy X-ray Absorptiometry (DXA) scans using the Horizon® DXA System, Hologic, USA.
Blood pressure measurements, including systolic (SBP) and diastolic (DBP), were taken using a mercury sphygmomanometer, with two separate readings recorded.
Laboratory analyses for fasting blood sugar (FBS), hemoglobin A1C (HbA1C), triglycerides (TG), high-density lipoprotein (HDL), and low-density lipoprotein (LDL) were performed using a BS-200 chemical analyzer (Shenzhen Mindray Bio-Medical Electronics Co, Ltd., Shenzhen, China).
Clinical definitions
Participants were diagnosed with Type 2 diabetes mellitus if they met any of the following criteria: (1) a mean fasting blood sugar (FBS) level ≥ 126 mg/dL based on repeated measurements; (2) hemoglobin A1C (HbA1C) ≥ 6.5%; or (3) current use of glucose-lowering pharmacotherapy, including oral antihyperglycemic agents or insulin. Hypertension was defined by the presence of any of the following: (1) a mean systolic blood pressure (SBP) ≥ 145 mmHg across two measurements; (2) a mean diastolic blood pressure (DBP) ≥ 90 mmHg across two measurements; or (3) current use of antihypertensive medication at the time of data collection. Dyslipidemia was diagnosed in accordance with established clinical guidelines if any one of the following lipid abnormalities was observed: (1) total cholesterol (TC) > 240 mg/dL; (2) low-density lipoprotein cholesterol (LDL-C) > 160 mg/dL; (3) High-density lipoprotein cholesterol (HDL-C) < 40 mg/dL for men or < 50 mg/dL for women; or (4) Triglycerides (TG) > 200 mg/dL.
Sample collection
During this phase, fresh stool samples were collected from a sub-sample of the participants and immediately allocated into sterile containers. The samples were then stored in microbiota biobank at − 80 °C until further analysis for fecal microbial quantification [17].
Bacterial DNA extraction and quantitative Real-time PCR analysis
The bacterial genomic DNA was extracted using the FavorPrep™ Stool DNA Isolation Mini Kit (Favorgen, Taiwan) according to the manufacturer’s instructions. Quantitative real-time PCR for six bacterial genera including Akkermansia, Bacteroides, Blautia, Faecalibacterium, Prevotella and Ruminococcus, was carried out using SYBR Green qPCR master mix 2× (YektaTajhiz, Iran) with the Roche LightCycler® 96 System (Switzerland), in 20 µl under the standard thermal cycling conditions [21]. Genus-specific sequences of primers targeted genes encoding 16 S rRNA (NedayeFan Co. Iran) used in the current study that are detailed in Table 1. The optimal melting temperature (Tm) for each primer pair was specifically determined using the PCR electrophoresis method. The quantification of bacterial concentration within each sample was conducted by comparing the acquired threshold cycle values with the standard curve established through the utilization of serial 10-fold dilutions of bacterial genomic DNA derived from the standard E. coli (ATCC 25922) with a known concentration. Subsequently, the bacterial copy numbers for these six bacterial genera present in 1 g of fecal matter were determined separately.
Table 1.
Genus-specific sequences of primers targeted genes encoding 16 s rRNA
| Target bacteria | Oligonucleotide sequence sequences 5’→3’ | Amplicon size (bp) | Melting temperature (Tm) | References |
|---|---|---|---|---|
| Akkermansia |
F: CAGCACGTGAAGGTGGGGAC R: CCTTGCGGTTGGCTTCAGAT |
329 | 63 | [22] |
| Bacteroides |
F: GGTGTCGGCTTAAGTGCCAT R: CGGAYGTAAGGGCCGTGC |
140 | 60 | [23] |
| Blautia |
F: CTTGACATCCCTCTGACCGT R: GTGCCCACCATCATATGCTG |
159 | 58 | This study |
| Faecalibacterium |
F: GGAGGAAGAAGGTCTTCGG R: AATTCCGCCTACCTCTGCACT |
248 | 61 | [24] |
| Prevotella |
F: CACCAAGGCGACGATCA R: GGATAACGCCYGGACCT |
283 | 58 | [25] |
| Ruminococcus |
F: AGCAACGCGAAGAACCTTAC R: GTTTTGTCAACGGCAGTCCT |
204 | 59 | This study |
F: Forward. R: Reverse
Statistical analysis
Statistical analyses were performed using IBM SPSS Statistics version 27 (IBM Corp.) and STATA version 14 (StataCorp LP). Normally distributed continuous variables were described via mean and standard deviation and then compared between study groups using the independent samples t-test. Categorical variables were described via absolute frequency and percentage, and compared using the chi-square (χ²) test. SES index was estimated using principal component analysis. Logistic regression analyses were conducted to calculate odds ratios (OR) with 95% confidence intervals (CI) to explore potential associations between microbiota and obesity. The crude model did not adjust for any covariates. For the adjusted model, confounding variables were identified through a directed acyclic graph (DAG) created using DAGitty. A p-value less than 0.05 was considered statistically significant.
Results
Basic characteristics
The study included a total of 289 participants with a mean age of 72.78 ± 3.08 years. The obesity group (n = 104) had a mean age of 72.20 ± 2.51 years, while the normal weight group (n = 185) had a mean age of 73.08 ± 3.29 years (p < 0.001). The study population consisted of 57.1% male participants, with 38.5% in the obesity group and 67.6% in the normal weight group (p < 0.001).
Basic demographic characteristics, clinical and health status, anthropometric measurements, and biochemical data of the study population based on obesity status are presented in Table 2.
Table 2.
Baseline characteristics of the study population based on the status of the obesity
| Characteristics | Total (n = 289) Mean ± SD or N (%) | Obese (n = 104) Mean ± SD or N (%) | Normal (n = 185) Mean ± SD or N (%) | P-value | |
|---|---|---|---|---|---|
| Age (years) | 72.78 ± 3.08 | 72.20 ± 2.51 | 73.08 ± 3.29 | < 0.001 | |
| Gender | Male | 165 (57.10) | 40 (38.50) | 125 (67.60) | < 0.001 |
| Female | 124 (42.90) | 64 (61.50) | 60 (32.40) | ||
| Education | Illiterate | 54 (18.70) | 18 (17.30) | 36 (19.40) | 0.248 |
| Primary school and below | 96 (33.20) | 42 (40.40) | 54 (29.20) | ||
| High school | 103 (35.60) | 34 (32.70) | 69 (37.30) | ||
| Academic | 36 (12.50) | 10 (9.60) | 26 (14.10) | ||
| Type 2 Diabetes Mellitus | Yes | 122 (42.20) | 56(53.60) | 66 (35.70) | 0.002 |
| No | 167 (57.80) | 48 (46.20) | 119(64.30) | ||
| Hypertension | Yes | 219 (75.80) | 94(90.40) | 125(67.60) | < 0.001 |
| No | 70 (24.20) | 10 (9.60) | 60(32.40) | ||
| Dyslipidemia | Yes | 154 (53.50) | 72(69.20) | 82(44.60) | < 0.001 |
| No | 134(46.50) | 32 (30.80) | 102(55.40) | ||
| Social-Economic Status | Q1 | 58(20.10) | 17(16.30) | 41(22.20) | 0.032 |
| Q2 | 58(20.10) | 26(25.00) | 32(17.30) | ||
| Q3 | 58(20.10) | 22(21.20) | 36(19.50) | ||
| Q4 | 58(20.10) | 20(19.20) | 38(20.50) | ||
| Q5 | 57(19.70) | 19(18.30) | 38(20.50) | ||
| Nutritional Assessment | Malnourished | 46(15.90) | 10(10.00) | 36(19.50) | 0.028 |
| Normal | 243(84.10) | 94(90.00) | 149(80.50) | ||
| Cognitive Impairment | Yes | 52(18.00) | 23(22.10) | 29(15.70) | 0.171 |
| No | 237(82.00) | 81(77.90) | 156(84.30) | ||
| Physical Activity | Low | 122(42.21) | 39(37.50) | 83(44.90) | 0.429 |
| Moderate | 91(31.49) | 34(32.70) | 57(30.80) | ||
| High | 76(26.30) | 31(29.80) | 45(24.30) | ||
| Current Smoking Status | Yes | 35(12.10) | 9(8.70) | 26(14.10) | 0.177 |
| No | 254(87.90) | 95 (91.30) | 159(85.90) | ||
| Height (cm) | 161.66 ± 8.89 | 158.19 ± 8.68 | 163.61± 8.43 | < 0.001 | |
| Weight (kg) | 69.59 ± 14.02 | 83.95 ± 10.61 | 61.52 ± 7.91 | < 0.001 | |
| BMI (kg/ m²) | 26.74 ± 5.61 | 33.51 ± 3.01 | 22.94 ± 1.93 | < 0.001 | |
| Waist Circumference (cm) | 99.70 ± 12.04 | 112.53 ± 8.04 | 92.49 ± 6.74 | < 0.001 | |
| Hip Circumference (cm) | 104.23 ± 10.56 | 115.90 ± 7.37 | 97.66± 4.88 | < 0.001 | |
| WHR | 0.96 ± 0.06 | 0.97 ± 0.08 | 0.95 ± 0.05 | < 0.001 | |
| Fat Mass (kg) | 5.68 ± 0.04 | 8.34 ± 1.32 | 4.78 ± 0.99 | < 0.001 | |
| Body Fat Percentage | 36.26 ± 8.78 | 44.95 ± 5.89 | 33.30 ± 7.55 | < 0.001 | |
| Fat Mass Ratio | 1.05 ± 0.17 | 1.02 ± 0.10 | 1.06 ± 0.18 | 0.098 | |
| Fasting Blood Sugar (mg/dL) | 114.14 ± 43.54 | 117.37 ± 41.09 | 112.32 ± 44.87 | 0.345 | |
| HbA1C (%) | 6.29 ± 1.24 | 6.44 ± 1.16 | 6.20 ± 1.28 | < 0.001 | |
| Triglycerides (mg/dL) | 139.00± 66.26 | 165.77± 72.52 | 123.95± 57.41 | < 0.001 | |
| HDL (mg/dL) | 51.69 ± 15.73 | 49.95 ± 14.79 | 52.68 ± 16.18 | 0.157 | |
| LDL (mg/dL) | 103.92 ± 35.43 | 103.31 ± 39.22 | 104.26 ± 33.22 | 0.826 | |
| SBP (mmHg) | 138.94 ± 19.59 | 141.05 ± 19.15 | 137.75 ± 19.79 | 0.170 | |
| DBP (mmHg) | 80.39 ± 11.33 | 81.40 ± 11.35 | 79.83 ± 11.33 | 0.259 | |
WC: Waist Circumference, HC: Hip Circumference, WHR: Waist to Hip Ratio, FBS: Fasting Blood Sugar, HbA1C: Hemoglobin A1C, TG: Triglycerides, HDL-C: High Density Lipoprotein Cholesterol, LDL-C: Low Density Lipoprotein Cholesterol, SBP: Systolic Blood Pressure, DBP: Diastolic Blood Pressure
Clinical and health status, and anthropometric measurements
According to the results, type 2 diabetes mellitus (P = 0.002), hypertension, and dyslipidemia (P < 0.001) were significantly more prevalent in obesity group. A slightly higher prevalence of malnutrition was observed in the control group. Additionally, the obesity group showed significantly higher values of weight, BMI, WC, HC, waist-to-hip ratio (WHR), fat mass and body fat percentage (P < 0.001). (Table 2)
Gut microbiota alteration in participants with obesity and normal weight
The results of the comparison of the abundances of six indicator genera are presented in Table 3. Bacteroides (p < 0.001), Blautia (p = 0.001), and Faecalibacterium (p = 0.030) were significantly reduced in the obesity group compared to the normal-weight participants, while other genera—Akkermansia, Prevotella, and Ruminococcus—showed no significant differences (Figs. 1 and 2). In this analysis, Prevotella/Bacteroides (P/B) ratio was not significantly different between obesity (0.78 ± 0.21) and normal-weight (0.74 ± 0.19) groups (P = 0.11).
Table 3.
Comparison of indicator gut microbiota abundance between older adults with obesity and normal weight
| Microbiota | Total (n = 289) Mean ± SD |
Obese (n = 104) Mean ± SD |
Normal (n = 185) Mean ± SD |
P-value |
|---|---|---|---|---|
| Akkermansia | 4.97 ± 1.81 | 4.93 ± 1.88 | 5.01 ± 1.78 | 0.772 |
| Bacteroides | 6.65 ± 1.13 | 6.34 ± 1.39 | 6.83 ± 0.92 | < 0.001 |
| Blautia | 2.84 ± 0.79 | 2.63 ± 0.71 | 2.94 ± 0.81 | 0.001 |
| Faecalibacterium | 6.10 ± 1.06 | 5.92 ± 1.32 | 6.21 ± 0.87 | 0.030 |
| Prevotella | 5.04 ± 1.61 | 4.87 ± 1.55 | 5.13 ± 1.65 | 0.183 |
| Ruminococcus | 5.19 ± 1.05 | 5.19 ± 1.29 | 5.18 ± 0.90 | 0.968 |
Fig. 1.
Gut microbiota composition in older adults with obesity and normal weight
Fig. 2.
Comparison of indicator gut microbiota abundance in older adults with obesity and normal weight. Relative Abundance calculated based on CFU/g stool. * = P ≤ 0.05, Error bar = 95% CI
In addition, the effects of specific gut microbiota genera (including Akkermansia, Bacteroides, Blautia, Faecalibacterium, Prevotella, and Ruminococcus) on obesity were assessed using a binary logistic regression model. According to the results of the multiple logistic regression analyses, negative association of Bacteroides (odds ratio [OR]: 0.681, 95% CI: 0.535–0.866), Blautia (OR: 0.450, 95% CI: 0.301–0.673) and Faecalibacterium (OR: 0.750, 95% CI: 0.583–0.965) with obesity remained significant after adjusting for confounding factors, including age, sex, socioeconomic status, physical activity, and nutritional status. A DAG illustrating the exposure, outcome, and potential confounders was constructed to identify the effects of the confounders (Fig. 3). Both crude and adjusted models are presented in Table 4.
Fig. 3.
Directed acyclic graph of the exposure, outcome, and potential confounders
Table 4.
Exploring association between indicator gut microbiota and obesity using logistic regression analysis
| Microbiota | Crude | Adjusted | ||
|---|---|---|---|---|
| OR (95% confidence interval) | p-value | OR (95% confidence interval) | p-value | |
| Akkermansia | 0.980 (0.859–1.119) | 0.771 | 0.976 (0.843–1.132) | 0.753 |
| Bacteroides | 0.679 (0.540–0.852) | 0.001 | 0.681(0.535–0.866) | 0.002 |
| Blautia | 0.577 (0.408–0.816) | 0.002 | 0.450 (0.301–0.673) | < 0.001 |
| Faecalibacterium | 0.779 (0.619–0.980) | 0.033 | 0.750 (0.583–0.965) | 0.025 |
| Prevotella | 0.902 (0.775–1.049) | 0.182 | 0.933 (0.791–1.101) | 0.414 |
| Ruminococcus | 1.005 (0.798–1.264) | 0.968 | 0.947 (0.739–1.213) | 0.665 |
Crude and Adjusted Odds Ratios (ORs) with 95% Confidence Intervals and p-values for Various Microbial Genera. Adjusted for Age, Sex, Social-economic status, Physical activity and Nutritional status
Discussion
Based on the results of this study, the abundance of Bacteroides, Blautia, and Faecalibacterium had negative association with obesity in older adults, and this association remained significant after adjusting for confounding factors, suggesting a robust association between reduced levels of these genera and obesity. This finding highlights a potentially important role for these bacteria in body weight regulation and obesity prevention in older adult population. Therefore, it is essential to discuss the potential roles and impacts of our study in the management and control of obesity in older adults, as well as the directions for future research.
Experimental studies have shown that certain Bacteroides strains can significantly reduce obesity-related factors, including weight, fat accumulation, inflammation and biochemical markers. In addition, Bacteroides species can modulate branched-chain amino acid (BCAA) catabolism in brown adipose tissue and thereby reduce obesity [26, 27]. The abundance of Bacteroides has been found to decrease in high-fat diets (HFDs) that induce obesity and increased in protein-based diets [28]. Moreover, the Prevotella/Bacteroides (P/B) ratio, in combination with dietary factors, has been associated with obesity [29, 30]. Taken together, these factors may explain negative association of Bacteroides with obesity [31]. However, the co-abundance of Bacteroides with other key microbiota and its role at the species and strain level require further investigation.
The genus Blautia is an anaerobic, gram-positive bacterium with the potential to influence obesity through multiple pathways, including modifying energy, lipids and carbohydrate metabolism, producing short-chain fatty acids (SCFAs), providing antioxidative effects, reducing inflammation, altering the composition of the gut microbiota, improving glucose metabolism, and supporting intestinal barrier integrity. Studies have reported variable results regarding association of Blautia with obesity at the genus level, and these differences may be explained by variations at the species and strain levels [32, 33].
Faecalibacterium, mostly known as Faecalibacterium prausnitzii, is a gram-positive, butyrate-producing bacterium. Despite some inconsistency in different studies, its abundance is generally reduced in obese individuals. Faecalibacterium prausnitzii and its metabolites regulates the microbiota and mucus layer and prevents the activation of inflammatory pathways, enabling effective management of gut changes such as increased levels of free fatty acids (FFA) and lipopolysaccharides (LPS) associated with inflammation and insulin resistance that contribute to obesity and diabetes [34, 35].
In addition to Faecalibacterium, both Blautia and Bacteroides produce the short-chain fatty acids (SCFAs) acetate and propionate, which have beneficial effects on gut-mucus function and metabolism [36]. SCFAs may modulate obesity by decreasing appetite through central hypothalamic mechanisms; reducing food intake via Peptide YY (PYY) and glucagon-like peptide-1 (GLP-1) secretion through the activation of specific G-protein-coupled receptors (GPR41 and GPR43) and histone deacetylase (HDAC) inhibition; and regulating lipid metabolism and insulin release, and modulating gluconeogenesis, lipogenesis, and cholesterol metabolism [36, 37]. Although SCFAs are not the only gut microbiota-related metabolites associated with obesity, secondary bile acids and tryptophan metabolites also have beneficial effects on host metabolism [10, 38, 39].
In comparison with other studies on older adults’ microbiota, Atzeni et al.. found Bacteroides enriched in the lowest tertile of BMI [40]. However, in the study by Zhong et al., Bacteroides and Blautia showed a similar direction of association with BMI, and Blautia and Faecalibacterium were positively associated with WC [41]. In Lippert et al.‘s study, Faecalibacterium was detected in non-obese subjects [42]. The inconsistent findings of the studies may be due to differences between study population characteristics such as genetic background, ethnicity, and lifestyle factors.
Despite detecting these associations between the gut bacterial genera and obesity, the interaction between gut microbiota and metabolic markers is also critical. For example, in the study by Zhong et al., for both metabolically healthy obese and metabolically unhealthy obese subjects, the changes in LDL, triglycerides and DBP, and BMI showed a similar pattern of association with Bacteroides and Blautia, and glucose levels, WC, and HDL were found to align with the genus Faecalibacterium [41].Therefore, the collective effect of gut microbiota on metabolic health indicators appear to be more important than the role of the association of any single bacterial genus with one of the obesity markers, and they may play a more significant role in determining metabolic health status and obesity. In addition, the co-abundance patterns and interactions among Bacteroides, Blautia, and Faecalibacterium within the gut microbiota community and their combined effects on host metabolism as well as variability at the species and strain levels within these genera, are important, since different species may have distinct effects on obesity and its related metabolic pathways.
In summary, reduced abundance of the bacterial genera Bacteroides, Blautia, and Faecalibacterium are associated with obesity in older adults, potentially through their involvement in producing SCFAs and regulating energy metabolism and inflammation. However, it should be noted that several factors, including age, host genetics, immune status, dietary patterns, medications, lifestyle and geographic differences, affect microbiota composition and function, impacting its association with obesity. This highlights the importance of personalized management of obesity through modulation of the gut microbiota in older adults [12, 31, 43].
Previous studies investigating gut microbiota composition in older adults have also observed significant alterations in Bacteroides, Blautia, and Faecalibacterium species, indicating their association with aging [12, 43, 44]. Older adults, compared with younger individuals, have lower energy expenditure, both for physical activity and basal metabolic. Additionally, age-related physiological changes affect their appetite and nutrient absorption. Therefore, considering the association between changes in dietary patterns and gut microbiota alterations can help develop suitable nutritional interventions to control obesity and metabolic-related disorders [45, 46].
In the study by Ramos et al., older adults with higher levels of physical activity had a greater relative abundance of Lactobacillus, Faecalibacterium prausnitzii, and Roseburia intestinalis [47].
Some recommendations, such as Mediterranean-style diets or fiber and polyphenol rich diets, are examples of nutritional interventions that can modulate gut microbiota to increase beneficial bacteria and their metabolites such as Faecalibacterium prausnitzii and SCFAs [48–50].
Moreover, studies on probiotics, prebiotics and postbiotics, particularly the development of next-generation biotics supplements, are promising for promoting healthy aging and managing obesity [50–52]. Bacteroides, Blautia, and Faecalibacterium genera are increasingly recognized for their potential beneficial role in metabolic health. Several Bacteroides species, including B. fragilis, B. uniformis, B. vulgatus, B. ovatus, B. acidifaciens, B. dorei, and B. thetaiotaomicron, as well as some Blautia species such as B. coccoides, B. wexlerae, B. hansenii, B. producta, and B. luti, along with Faecalibacterium prausnitzii, are examples of potential next-generation probiotics studied for their effects on obesity. However, it is important to consider that their effects may vary by species, strain and context, especially among older adults [32–34, 53–56]. Prebiotics stimulate the growth or activity of beneficial gut microbiota and can reduce obesity by modulating microbial composition and regulating appetite, inflammation, and lipid metabolism [57]. Postbiotics-such as pasteurized bacteria (e.g., Faecalibacterium prausnitzii), extracellular vesicles, and other bacterial cell components and metabolites (e.g., SCFAs)-are also being studied as bioactive compounds in obesity management [58, 59]. However, more research is needed to confirm their effectiveness specifically in older adult populations. Furthermore, fecal microbiota transplantation (FMT) shows potential as a therapeutic approach for obesity and metabolic disorders by increasing the abundance of associated beneficial bacteria and restoring gut microbiota diversity, but future researches considering all its aspects are necessary, particularly in older adults [60, 61].
Although our case-control study is not a case-cohort or nested case-control study, but, because it was conducted in the context of a cohort study, it has strengths such as reducing selection and measurement bias. Additionally, as another strength of our study, we report detailed phenotyping—demographic characteristics, anthropometric and biochemical measurements, and clinical/health status differences between groups—which helps ensure data reliability.
Our study also has some limitations. While we adjusted our results for nutritional status based on MNA, detailed quantitative dietary intake data (such as specific intake of fiber, fat, or protein from a Food Frequency Questionnaire (FFQ)) were not collected in the BEH program. This was not feasible because it was challenging and burdensome for older adult populations, potentially leading to unreliable data. In addition, data on specific medications were not included in our analysis, which represents another limitation. However, we excluded users of antibiotics and regular anti-inflammatory drugs to minimize the impact of major medications on the microbiota. Future studies incorporating these detailed measures are warranted. Due to budget and methodological constraints, microbiota profiling was limited to six bacterial genera and is not comprehensive; however, the result of this investigation can serve as a foundation for future studies employing more detailed methods such as 16 S rRNA gene sequencing. Thus, this study provides a valuable and necessary first step by identifying key bacterial targets and validating the association between obesity and the gut microbiota in older adults, particularly within this specific cohort. Moreover, studies on gut microbiota and obesity increasingly shifted toward using metagenomics, metabolomics, and other multi-omics approaches, which face challenges in interpreting large-scale data with conventional statistical methods; artificial intelligence and machine learning assist by enabling obesity prediction, identifying obesity-related biomarkers, and supporting personalized medicine and therapeutic strategies [62–64].
Finally, given the growing elderly population and their health concerns, further investigation into the associations between gut microbiota and metabolic diseases, including obesity, in older adults is essential and can provide effective preventive approaches, diagnostic tools, and therapeutic strategies that contribute to healthy aging.
Conclusion
This study found a significant negative association between the abundance of Bacteroides, Blautia, and Faecalibacterium and obesity in older adults. Understanding the alterations in gut microbiota composition with aging, along with the potential role of each bacterial genus and species in regulating body weight and preventing obesity in the elderly population, is crucial. Targeting these specific bacteria may provide a novel therapeutic approach to mitigate metabolic and inflammatory disorders associated with obesity, thereby improving overall health and quality of life in older adults. Future studies—including comprehensive microbiota profiling and mechanisms investigating—are necessary to better understand the causal relationships and to develop personalized microbiota-based interventions for obesity management in this vulnerable population.
Acknowledgements
We sincerely thank all members of the research team, personnel, and collaborators involved in the BEH Program for their invaluable contributions and dedication.
Author contributions
ZHT, HSE, KKH, IN, SDS, AO, SHR and BL: Conceptualization and study design. AO, BL and SHR: Supervision. ZHT, KKH, FEM, EM, NR and NAS: Project administration and investigation. ZHT, HSE, KKH, FEM and NAS: Drafting of the manuscript. EM, NR, IN, SDS, AO, SHR and BL: Critical review and intellectual revision. All authors have read and approved the final manuscript. Furthermore, all authors take responsibility for the integrity of the work presented and confirm the accuracy of the reported study.
Funding
This study is part of a project funded by the Endocrinology and Metabolism Research Institute, Tehran University of Medical Sciences, Tehran, Iran (Number: 1401-3-91-1213).
Data availability
The data presented in this study were used under license. Due to restrictions on their availability, they are not publicly accessible but can be obtained from the principal investigator upon reasonable request.
Declarations
Ethics approval and consent to participate
The participants provided written informed consent. The studies involving human participants were reviewed and approved by the Ethics Committee of the Endocrinology and Metabolism Research Institute, Tehran University of Medical Sciences (approval ID: IR.TUMS.EMRI.REC.1396.00223 and IR.TUMS.EMRI.REC.1401.031).
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Zahra Hoseini Tavassol and Hanieh-Sadat Ejtahed share equally the first authorship.
Contributor Information
Shirin Hasani-Ranjbar, Email: shirinhasanir@yahoo.com, Email: sh_hasani@tums.ac.ir.
Bagher Larijani, Email: larijanib1340@gmail.com, Email: Larijanib@tums.ac.ir.
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Associated Data
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Data Availability Statement
The data presented in this study were used under license. Due to restrictions on their availability, they are not publicly accessible but can be obtained from the principal investigator upon reasonable request.