Metagenomic analysis revealing links between age, gut microbiota and bone loss in Chinese adults
Study design and participants
The study included 684 elderly adults from the Pinggu (PG) cohort, a perspective cohort established in the PG District, Beijing during 2013–2014. Detailed information on demographics, lifestyles (smoking status and intake of alcohol), dietary habits (consumption of red meat, poultry, tea, and dairy products), and medication use for metabolic disorders was collected via questionnaires, as described in the baseline study22. The inclusion criteria included (1) adults aged 50–76 years (men and menopausal women); (2) available lumbar vertebrae bone mass density data; (3) complete questionnaire information; (4) no use of antibiotics a month before stool collection; (5) no use of medication for dyslipidemia, hypertension and T2D and (6) without severe diseases (end-stage cancer, renal or liver disease), to minimize potential confounders in identifying gut microbial features related to BMD and osteoporosis. All participants provided signed informed consent. The study was approved by the ethics committee of Peking University Health Science Center and the institutional review board of BGI-Research.
Body mass index (BMI, kg/m2) and waist-to-hip ratio (WHR) were calculated based on height, weight, waist, and hip measurements. Smoking status was categorized into two groups: current smokers and non-smokers. Weekly alcohol intake was divided into three levels using sex-specific cutoffs: ‘0 g’, ‘>0 & < = 210 in men; >0 & <= 140 in women’, and ‘> 210 in men; >140 in women’22. Dietary information, including the frequency of red meat, poultry, tea, and dairy product consumption, was recorded and categorized into five levels: ‘never/rarely’, ‘monthly’, ‘1–3 d per week’, ‘4–6 d per week’, and ‘daily’. Summaries of all phenotypic variables are presented in Table 1 and Table S1.
Measurement of bone mass density (BMD)
BMD was measured by quantitative computed tomography (QCT). Trained medical staffs performed scans of the lumbar vertebrae L1–L3 and calculated the volumetric BMD in milligrams per cubic centimeter (mg/cm3) for each participant. Based on the mean BMD of the lumbar vertebrae L1-L3, all PG individuals were categorized into three groups: normal control (NC, Mean BMD > 120 mg/cm3), osteopenia (ON, 80 mg/cm3 ≥ Mean BMD ≥ 120 mg/cm3) and osteoporosis (OP, Mean BMD < 80 mg/cm3)41.
Methods for metagenomic data
Fecal shotgun metagenomic data from the 684 individuals were acquired from the PG baseline study22. After the removal of low-quality and human-derived reads, an average of 80.59 + 22.10 (SD) million high-quality non-human reads were retained per sample22. Taxonomic and functional profiles were determined using MetaPhlAn4 (v4.0.6)42 and HUMAnN3 (v3.0.0.alpha.3)43, respectively, with default parameters. Common microbial species (n = 242) and pathways (n = 323) with a relative abundance of at least 0.0001 in over 20% samples were included for analyses. Intra-individual alpha diversity was assessed using the Shannon index at the species level (the R function diversity in vegan package, version 2.6.2). Between-individual beta-diversity was estimated using Bray–Curtis dissimilarity at the species level (vegdist function in vegan).
Analyses of phenotypic variables among normal control (NC), osteopenia (ON) and osteoporosis (OP) groups
Analysis of Covariance (ANCOVA) was conducted to assess differences in continuous variables including age, BMI, WHR, and BMD parameters (L1-L3 BMD and the mean BMD values) among the three BMD groups with adjustments for other covariates (Table 1). All models were adjusted for sex, age (not for age model), BMI, WHR (not for obesity model), and environmental factors including lifestyles (smoke status and alcohol intake), and dietary habits (consumption frequency of red meat, poultry, tea, and dairy products). Pearson’s Chi-squared test was applied to evaluate differences in categorical variables between BMD groups. A BH-adjusted P-value less than 0.05 was considered statistically significant.
Partial Spearman’s correlation analysis was applied to assess correlations between continuous phenotypic variables (e.g., age, BMI, WHR and BMDs) with adjustments for other covariates. A BH-adjusted P-value less than 0.05 was considered statistically significant.
Analyses of gut microbial variables among BMD groups
Differences in species-level alpha diversity among BMD groups were assessed using ANCOVA with adjustments for sex, age, WHR, BMI, lifestyles, and dietary habits. Principal coordinates analysis (PCoA) and permutational multivariate analysis of variance (PERMANOVA) based on species-level Bray–Curtis dissimilarity were performed to assess the overall gut microbial compositional differences among the BMD groups (999 permutations, the R function adonis2 in vegan package). A P-value less than 0.05 was considered statistically significant.
MaAsLin2 was conducted to examine differences in the relative abundances of microbial species and pathways among the three BMD groups, adjusting for sex, age, WHR, BMI, lifestyles, and dietary habits (the R function Maaslin2 in Maaslin2 package, version 1.6.0). Prior to MaAsLin2, the relative abundance profiles were transformed using rank-based inverse normal transformations (INT). The Benjamini-Hochberg (BH) method was employed for multiple comparison corrections. A BH-adjusted P-value less than 0.05 was considered statistically significant, and a P-value less than 0.05 was considered of moderate significance.
Partial Spearman’s correlation analysis was applied to evaluate associations between BMD and relative abundances of gut species and pathways after adjusting for age, sex, WHR, BMI, lifestyles, and dietary habits (R function pcor.test in ppcor package, version 1.1). A BH-adjusted P-value less than 0.05 was considered statistically significant, and a P-value less than 0.05 was considered a moderate significance.
Identification of microbial genes encoding potential enzymes involved in L-arginine biosynthesis
Five key enzymes from the L-arginine biosynthesis pathways (PWY-5154, ARGSYNBSUB − PWY, ARGSYN − PWY, and PWY-7400) were extracted for functional annotation, including glutamine synthetase (EC:6.3.1.2), carbamoyl-phosphate synthase (EC:6.3.5.5), ornithine carbamoyl-transferase (EC:2.1.3.3), argininosuccinate synthase (EC:6.3.4.5) and argininosuccinate lyase (EC:4.3.2.1). A total of 4930 species-level genome bins (SGBs) were obtained from the SGB resource database (https://segatalab.cibio.unitn.it/data/Pasolli_et_al.html)44. Gene prediction for SGBs was conducted using the Prodigal software (version 2.6.3) with default parameters45, and functional annotation of the predicted genes was performed using eggnog-mapper (version 2.1.3, emapperdb-5.0.2) with default parameters46.
Enterotype analysis
Enterotype clustering analysis on the 684 samples was performed using the genus-level profiles based on Jensen-Shannon divergence (JSD) distance and the Partitioning Around Medoids (PAM) clustering algorithm47. In accordance with PG baseline study, two distinct enterotypes were identified, characterized by a high abundance of either Prevotella (ETP, n = 253) or Bacteroides (ETB, n = 431). The potential interaction between age and enterotypes on mean BMD was assessed using a generalized liner model (GLM) with adjustment for sex, BMI, WHR, lifestyles and dietary habits (R function glm in stats package, version 4.3.0).
For each enterotype, adjusted odds ratios (ORs) and 95% confidence intervals (95% CIs) were calculated to estimate the association between increased age and reduced BMD (patients with osteopenia and osteoporosis vs. normal controls) using a multi-variable regression model, adjusting for sex (not for sex-specific analysis), BMI, WHR, lifestyles and dietary habits (R function odds.ratio, package questionr, version 0.7.8). A P-value less than 0.05 was considered statistically significant. Considering the well-established sex differences in age-related BMD declines, we conducted a separate analysis for the interaction between age and enterotypes, as well as age-related ORs, within each sex group. Additionally, within each enterotype, associations between the relative abundances of the genus Bacteroides and microbial features (species and pathways) linked to higher BMDs were evaluated using Spearman’s correlation analysis (cor.test function in R stats package, version 4.1.0). A BH-adjusted P-value less than 0.05 was considered statistically significant.
To validate the enterotype-dependent association pattern between age and BMD, we utilized an independent dataset comprising shotgun metagenomic data and BMD measurements obtained via Dual-energy X-ray absorptiometry (DXA) from 302 postmenopausal Chinese women15. For validation purpose, the same analytical pipelines for taxonomic profiling and enterotyping were applied for this dataset as we used in the current cohort.
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