Abstract
Purpose: To examine the associations between oral microbiome diversity and genus composition with the Zhejiang University Index (ZJU Index) and clinical biomarkers. Methods: We included 2,490 eligible participants from the U.S. National Health and Nutrition Examination Survey (NHANES). Oral microbiome diversity was assessed using alpha and beta diversity, and genus-level analyses were based on abundance transformed using the centered log-ratio (CLR) method to account for compositionality. Weighted logistic regression models were used to assess the corresponding associations. Beta diversity disparities were evaluated through Principal Coordinate Analysis (PCoA) and Permutational Multivariate Analysis of Variance (PERMANOVA). Results: Alpha diversity metrics were positively correlated with the ZJU Index in males aged 30–44 years (Faith’s Phylogenetic Diversity: unadjusted: β = 0.05, 95% CI: 0.006 to 0.094, p = 0.033; Model 1: β = 0.058, 95% CI: 0.011 to 0.104, p = 0.026; Model 2: β = 0.076, 95% CI: 0.032 to 0.120, p = 0.005; Model 3: β = 0.081, 95% CI: 0.035 to 0.128, p = 0.008) and in females aged 60–69 years (Observed ASVs: β = 1.242, 95% CI: 0.345 to 2.139, p = 0.042; Faith’s Phylogenetic Diversity: β = 0.097, 95% CI: 0.025 to 0.168, p = 0.045). Significant differences in beta diversity metrics were observed among ZJU Index-defined subgroups (p < 0.05), confirmed with age- and sex-stratified analyses. Genera including Bulleidia, Senegalimassilia, Fretibacterium, and Hungatella exhibited significant associations with the ZJU Index and with clinical biomarkers (triglycerides, low-density lipoproteins (LDL), high-density lipoproteins (HDL), insulin, and testosterone). Conclusions: Higher oral microbiome alpha diversity was associated with higher ZJU Index in certain populations. Beta diversity demonstrated that ZJU Index-defined subgroups differed in oral microbial composition. Specific genera were identified to be significantly associated with the ZJU Index and clinical biomarkers.
Keywords: Oral microbiome, Diversity, The ZJU index, NHANES, Cross-sectional study
Introduction
The oral cavity harbors a highly diverse microbial community and represents one of the most complex microbial ecosystems in the human body (Tuganbaev et al. 2022). Emerging evidence suggests that the oral microbiome exerts significant influences on systemic health, in addition to its local effects within the oral cavity (Akhi et al. 2025; Mondal et al. 2025; Xia et al. 2025; Hou et al. 2025. Dysbiosis of the oral microbiome contributes to a range of metabolic and inflammatory diseases through microbial metabolites and pro-inflammatory factors (Chen et al. 2023; Salman et al. 2025; Zhang et al. 2025). Notably, the oral microbiome also engages in direct crosstalk with the gut microbiome via bacterial translocation and colonization (Baker et al. 2024). Taken together, the oral microbiome is an important indicator of systemic health, as well as a potential target for therapeutic intervention. Therefore, identifying specific contributors to the oral microbiome community remains a critical area for further investigation.
The Zhejiang University Index (ZJU Index) is a simple and non-invasive metric that integrates multiple metabolic parameters, including body mass index (BMI), fasting plasma glucose (FPG), triglycerides (TG), alanine aminotransferase (ALT), and aspartate aminotransferase (AST) (Wang et al. 2015). Although first applied as a tool for screening non-alcoholic fatty liver disease (NAFLD), it has demonstrated the potential in other systemic pathologies, including type 2 diabetes, hypertension, sarcopenia, as well as female infertility (Wu et al. 2024; Chen et al. 2025; Gong et al. 2025; Tian et al. 2025; Xu et al. 2025). Notably, a recent study reported a non-linear positive association of periodontitis with the ZJU Index (Liu et al. 2025). These findings imply that the ZJU Index may serve as a practical non-invasive parameter for assessing metabolic status, with potential relevance for reproductive health and other systemic conditions.
Therefore, we aim to explore the relationship between the oral microbiome and the ZJU Index at both the community diversity and genus levels. This approach may further clarify the associations between the oral microbiome and host systemic status and may identify specific genera involved in the process.
Materials and methods
Study design and population
Data were obtained from the National Health and Nutrition Examination Survey (NHANES), a nationally representative survey conducted by the National Center for Health Statistics (NCHS) of the Centers for Disease Control and Prevention (CDC). The NHANES database covers a variety of physical assessments, biomarkers, and questionnaires, applying a stratified, multistage probability sampling design. The study procedure was approved by the National Center for Health Statistics (NCHS) Ethics Review Board. Data from NHANES 2009–2010 and 2011–2012 cycles were utilized for our analysis.
20,293 participants were initially included in the study. We excluded participants missing oral microbiome data (n = 10,947), missing covariate data (n = 3,824), and missing associated laboratory test data used for calculating the ZJU Index (n = 3,032). A total of 2,490 participants were used for the final analysis, as shown in Fig. 1.
Fig. 1.
Flow diagram of the study population selection
Oral microbiome diversity
Oral rinse samples were collected for oral microbiome testing from participants in the 2009–2012 cycles. DNA extraction and PCR amplification, followed by raw sequencing and bioinformatics processing, were conducted to generate relative abundance and read count data from phylum to genus levels. Amplicon sequence variants (ASVs) were generated from raw sequences and used to compute alpha diversity metrics, including Observed ASVs, Faith’s Phylogenetic Diversity, Shannon-Weiner index, and inverse Simpson index. We employed data subsampled to 10,000 reads and measured the average values of the 10 subsamples to minimize the influence of uneven sequencing depth across samples (Xia et al. 2025).
Beta diversity was assessed by Aitchison Distance using the centered log-ratio (CLR) based on genus relative abundance levels to account for compositionality of the oral microbiome data and to reduce spurious correlations caused by relative abundance constraints using the compositions package. Prior to CLR transformation, a small pseudo-count was added to zero values to avoid undefined logarithmic transformations. We also employed Bray-Curtis Dissimilarity, unweighted UniFrac Distance, and weighted UniFrac Distance to evaluate dissimilarities of oral microbial communities across groups and subgroups. Bray–Curtis dissimilarity was used to evaluate differences in microbial community composition based on relative abundance. Unweighted UniFrac distance reflects phylogenetic differences based on the presence or absence of taxa, whereas weighted UniFrac distance additionally incorporates relative abundance information. The use of multiple complementary distance metrics provides a more comprehensive assessment of microbial community differences across groups and subgroups (Ramalho Guerra et al. 2025; Wei et al. 2025). As beta diversity measures overall community differences between samples, these metrics do not allow adjustment for covariates by nature. Therefore, potential confounding factors were not controlled in the beta diversity analysis.
ZJU Index
The ZJU Index is calculated as follows: ZJU Index = BMI (kg/m2) + TG (mmol/L) + FPG (mmol/L) + ALT (IU/L)/AST (IU/L) × 3 (+2 if females) (Wang et al. 2015). Fasting plasma glucose (FPG) was measured with an enzymatic method. The DcX800 used a kinetic rate method to measure alanine aminotransferase (ALT) activity, an enzymatic rate method to measure aspartate aminotransferase (AST) activity, as well as a time-endpoint method to measure triglycerides (TG) concentration.
Covariates
The covariates were chosen based on previous studies (Shen et al. 2024; Hou et al. 2025). The associations between the ZJU Index and oral microbiome diversity or genus abundance were adjusted for age, sex (male or female), race and ethnicity (Mexican American, non-Hispanic White, non-Hispanic Black, other Hispanic, or other races), education (<9th grade, 9–11th grade, high school, some college, or college or above), marital status (with or without partner), poverty income ratio (PIR; ≤1.3, or ≥1.3, <3.5, or >3.5), smoking status (current, former, or never) and drinking status (current or former, or never), total energy intake, as well as health conditions (hypertension, diabetes, cancer, periodontitis), applied with progressive adjustment in different models. Total energy intake was assessed using the first day 24-hour dietary recall data. Smoking and drinking status were obtained from household interviews. Self-reported questionnaire responses were used to determine the presence of hypertension, diabetes, and cancer. Periodontitis was defined by probing depth (PD) ≥5 mm or attachment loss (AL) ≥3 mm at two or more non-adjacent sites (Xia et al. 2025). The associations between genus abundance and clinical biomarkers were adjusted for age, sex, race and ethnicity, marital status, periodontitis, and total energy intake. However, oral hygiene–related variables such as brushing frequency, dental floss use, and recent dental visits were not included in the final models because these variables had substantial missing data in the NHANES oral microbiome subsample, which would have led to a considerable reduction in the analytical sample size and potentially affected the stability of the regression models.
Statistical analysis
All analyses were conducted using NHANES sampling weights to account for the complex survey design and to produce estimates representative of the non-institutionalized U.S. civilian population. In this study, the fasting subsample weight (WTSAF2YR) was used because fasting laboratory measurements were included in the analysis. Since two NHANES cycles were combined, the 2-year fasting weights were divided by the number of combined cycles according to the NHANES analytic guidelines to obtain appropriate multi-cycle weights. Survey analyses were conducted using the survey package, specifying SDMVPSU as the cluster variable, SDMVSTRA as the stratification variable, and the recalculated sampling weight as the weight variable. All regression models were fitted using survey-weighted generalized linear models to account for the 29complex sampling design and to produce unbiased parameter estimates and valid standard errors. In baseline characteristics, continuous variables were summarized as weighted medians (Q1, Q3) and p-values. Significances of continuous variables were tested using weighted linear regression models. Categorical variables were summarized as unweighted frequencies, weighted percentages, and p-values. Significances of categorical variables were tested with weighted Chi-square tests. Participants were stratified into four subgroups defined by the ZJU Index, using cutoff values reported in clinical practice (Wang et al. 2015) and previous studies (Zheng et al. 2024; Liu et al. 2025): Z1 (<32), Z2 (≥32, <38), Z3 (≥38, <44), and Z4 (≥44). Differences of alpha diversity metrics across ZJU index subgroups were evaluated, and associations between alpha diversity metrics and the ZJU index were examined using weighted logistic regression models. We applied three models with progressive adjustment of covariates: an unadjusted model, Model 1 (adjusted for age, sex, race and ethnicity, education level, marital status, and PIR), Model 2 (adjusted for Model 1 covariates plus smoking and drinking status), and Model 3 (adjusted for Model 2 covariates plus hypertension, diabetes, cancer, total energy intake, and periodontitis). Subgroup analyses were conducted with further stratification by age and sex. Beta diversity differences were visualized and tested via principal coordinate analysis (PCoA) and permutational multivariate analysis of variance (PERMANOVA). Associations between the ZJU Index and specific genera, as well as between the genera and clinical biomarkers, were evaluated using weighted regression models. All genus abundances were centered log-ratio (CLR) transformed prior to analysis in this study. Statistical significance was defined as a two-sided p-value < 0.05, and no adjustment for multiple comparisons was applied. All statistical analyses were performed in R (version 4.5.0).
Results
Baseline characteristics
This study included 2,490 participants between the ages of 30–69, divided into four subgroups based on clinical cut-off values (Wang et al. 2015) and previous studies (Zheng et al. 2024; Liu et al. 2025): Z1 (<32), Z2 (≥32, <38), Z3 (≥38, <44), and Z4 (≥44). Participants in younger age groups tended to have lower ZJU Index values, while a greater proportion of participants shifted toward higher ZJU Index subgroups with increasing age. Additionally, participants in higher ZJU subgroups had a higher prevalence of comorbidities, including hypertension (p < 0.001), diabetes (p < 0.001), and periodontitis (p < 0.001). However, no significant differences were observed between ZJU subgroups in alpha diversity metrics, including Observed ASVs, Faith’s Phylogenetic Diversity, the Shannon-Weiner Index, and the Inverse Simpson Index, as detailed in Table 1.
Table 1.
Baseline characteristics of the study population, divided into four ZJU index-based subgroups
| ZJU Index | p-value | |||||
|---|---|---|---|---|---|---|
| Z1 (<32) | Z2 (≥32, <38) | Z3 (≥38, <44) | Z4 (≥44) | |||
| n | 338 | 752 | 716 | 684 | ||
| Age | 0.002 | |||||
| 30–44 years old | 237 (66.9) | 436 (55.9) | 348 (49.2) | 322 (47.3) | ||
| 45–59 years old | 74 (26.0) | 181 (28.7) | 217 (32.5) | 215 (35.8) | ||
| 60–69 years old | 27 (7.1) | 135 (15.4) | 151 (18.2) | 147 (16.9) | ||
| Sex | 0.072 | |||||
| Male | 172 (51.2) | 388 (49.5) | 358 (53.5) | 290 (43.5) | ||
| Female | 166 (48.8) | 364 (50.5) | 358 (46.5) | 394 (56.5) | ||
| Race | <0.001 | |||||
| Mexican American | 30 (4.7) | 106 (6.9) | 145 (9.9) | 125 (10.1) | ||
| Non-Hispanic White | 163 (70.1) | 326 (70.5) | 285 (68.6) | 271 (64.9) | ||
| Non-Hispanic Black | 55 (8.8) | 131 (9.0) | 164 (12.1) | 188 (16.8) | ||
| Other Hispanic | 23 (4.0) | 80 (5.3) | 72 (5.3) | 68 (5.2) | ||
| Other Races | 67 (12.4) | 109 (8.3) | 50 (4.1) | 32 (3.0) | ||
| Education | 0.003 | |||||
| <9th grade | 15 (2.7) | 47 (3.0) | 77 (4.8) | 55 (4.4) | ||
| 9-11th grade | 43 (11.9) | 85 (8.2) | 108 (10.9) | 104 (11.9) | ||
| High School | 55 (13.9) | 154 (17.9) | 151 (20.1) | 171 (24.8) | ||
| Some College | 104 (29.3) | 230 (30.7) | 196 (30.2) | 217 (33.9) | ||
| College or above | 121 (42.3) | 236 (40.2) | 184 (34.0) | 137 (25.1) | ||
| Marital Status | <0.001 | |||||
| With partner | 174 (58.5) | 440 (63.0) | 459 (68.8) | 416 (65.6) | ||
| Without partner | 164 (41.4) | 312 (36.9) | 257 (31.2) | 268 (34.5) | ||
| PIR | 0.010 | |||||
| ≤1.3 | 121 (26.8) | 222 (18.1) | 221 (19.9) | 270 (26.4) | ||
| 1.3–3.5 | 109 (30.8) | 253 (31.6) | 272 (38.6) | 225 (35.1) | ||
| >3.5 | 108 (42.5) | 277 (50.3) | 223 (41.4) | 189 (38.5) | ||
| Smoking status | 0.013 | |||||
| Current | 85 (24.4) | 148 (18.7) | 112 (14.2) | 117 (15.1) | ||
| Former | 40 (12.7) | 149 (22.5) | 158 (24.8) | 156 (24.5) | ||
| Never | 213 (63.0) | 455 (58.9) | 446 (61.0) | 411 (60.3) | ||
| Drinking status | <0.001 | |||||
| Current or former | 259 (82.1) | 591 (84.6) | 532 (82.2) | 448 (69.8) | ||
| Never | 79 (17.9) | 161 (15.4) | 184 (17.8) | 236 (30.2) | ||
| Total energy intake | 2173.00 (1565.50‚ 2736.25) | 1964.00 (1497.25‚ 2595.00) | 2006.50 (1548.75, 2631.50) | 2042.50 (1521.75‚ 2667.00) | 0.128 | |
| (kcal/day) | ||||||
| BMI | 20.64 (19.40‚ 21.80) | 24.90 (23.54‚ 26.31) | 29.40 (27.89‚ 30.79) | 36.00 (33.30‚ 40.36) | <0.001 | |
| Hypertension | 26 (7.1) | 135 (18.4) | 232 (31.4) | 326 (47.7) | <0.001 | |
| Diabetes | 1 (0.7) | 22 (1.9) | 56 (4.7) | 153 (18.8) | <0.001 | |
| Cancer | 19 (6.0) | 40 (6.9) | 26 (3.7) | 45 (8.4) | 0.131 | |
| Periodontitis | 124 (37.7) | 380 (47.7) | 447 (60.5) | 380 (52.2) | <0.001 | |
| Alpha diversity | ||||||
| Observed ASVs | 125.85 (101.22‚ 157.43) | 127.15 (101.85‚ 156.77) | 132.15 (105.55‚ 161.80) | 130.90 (103.52‚ 157.62) | 0.456 | |
| Faith’s Phylogenetic Diversity | 14.62 (12.51‚ 16.78) | 14.39 (12.45‚ 16.85) | 14.90 (12.64‚ 17.05) | 14.68 (12.65‚ 16.97) | 0.490 | |
| Shannon-Wiener Index | 4.82 (4.37‚ 5.15) | 4.78 (4.40‚ 5.14) | 4.82 (4.40‚ 5.18) | 4.79 (4.41‚ 5.21) | 0.754 | |
| Inverse Simpson Index | 0.93 (0.90‚ 0.94) | 0.93 (0.90‚ 0.94) | 0.93 (0.90‚ 0.94) | 0.93 (0.90‚ 0.94) | 0.420 | |
PIR: poverty income ratio; BMI: body mass index; ASVs: amplicon sequence variants. Values were expressed as median (Q1, Q3) or n (weighted %). Bold represents p-value 0.05
Association between alpha diversity and the ZJU Index
The associations between alpha diversity metrics and the ZJU Index are summarized in Table 2. In order to further explore the relationship, participants were further stratified by age and sex. In the male subgroup aged 30–44 years, Faith’s Phylogenetic Diversity showed positive correlation with the ZJU Index across all models (unadjusted: β = 0.05, 95% CI: 0.006 to 0.094, p = 0.033; Model 1: β = 0.058, 95% CI: 0.011 to 0.104, p = 0.026; Model 2: β = 0.076, 95% CI: 0.032 to 0.120, p = 0.005; Model 3: β = 0.081, 95% CI: 0.035 to 0.128, p = 0.008). Additionally, in females aged 60–69 years, Observed ASVs and Faith’s Phylogenetic Diversity were positively correlated with the ZJU Index in Model 3 (β = 1.242, 95% CI: 0.345 to 2.139, p = 0.042; β = 0.097, 95% CI: 0.025 to 0.168, p = 0.045).
Table 2.
Association between alpha diversity and ZJU index in weighted linear regression models
| Sex | Age Group | Model | Observed ASVs | Faith’s Phylogenetic Diversity | Shannon-Wiener Index | Inverse Simpson Index | ||||
|---|---|---|---|---|---|---|---|---|---|---|
| β (95%CI) | p-value | β (95%CI) | p-value | β (95%CI) | p-value | β (95%CI) | p-value | |||
| Male | 30–44 | unadjusted | 0.424 (−0.062, 0.911) | 0.097 | 0.050 (0.006, 0.094) | 0.033 | 0.004 (−0.002, 0.011) | 0.198 | 0 (0, 0.001) | 0.096 |
| Model1 | 0.421 (−0.097, 0.939) | 0.131 | 0.058 (0.011, 0.104) | 0.026 | 0.004 (−0.002, 0.011) | 0.226 | 0 (0, 0.001) | 0.102 | ||
| Model2 | 0.550 (0.046, 1.054) | 0.052 | 0.076 (0.032, 0.120) | 0.005 | 0.005 (−0.001, 0.012) | 0.137 | 0 (0, 0.001) | 0.067 | ||
| Model3 | 0.632 (0.069, 1.196) | 0.059 | 0.081 (0.035, 0.128) | 0.008 | 0.006 (−0.001, 0.014) | 0.128 | 0 (0, 0.001) | 0.073 | ||
| 45–59 | unadjusted | −0.057 (−0.700, 0.586) | 0.864 | −0.015 (−0.066, 0.036) | 0.572 | 0.004 (−0.007, 0.015) | 0.498 | 0 (−0.001, 0.001) | 0.477 | |
| Model1 | 0.036 (−0.623, 0.696) | 0.915 | −0.006 (−0.059, 0.048) | 0.841 | 0.005 (−0.007, 0.016) | 0.434 | 0 (−0.001, 0.001) | 0.475 | ||
| Model2 | 0.064 (−0.580, 0.708) | 0.849 | −0.002 (−0.056, 0.051) | 0.93 | 0.003 (−0.008, 0.015) | 0.59 | 0 (−0.001, 0.001) | 0.625 | ||
| Model3 | −0.204 (−0.924, 0.516) | 0.593 | −0.016 (−0.073, 0.040) | 0.588 | −0.002 (−0.017, 0.012) | 0.764 | 0 (−0.002, 0.001) | 0.688 | ||
| 60–69 | unadjusted | 0.304 (−0.545, 1.152) | 0.488 | 0.022 (−0.046, 0.090) | 0.535 | 0.006 (−0.008, 0.02) | 0.391 | 0.001 (0, 0.001) | 0.26 | |
| Model1 | 0.175 (−0.601, 0.950) | 0.665 | 0.010 (−0.051, 0.071) | 0.753 | 0.004 (−0.009, 0.016) | 0.558 | 0 (0, 0.001) | 0.297 | ||
| Model2 | 0.174 (−0.580, 0.928) | 0.658 | 0.010 (−0.049, 0.069) | 0.751 | 0.003 (−0.008, 0.014) | 0.608 | 0 (0, 0.001) | 0.331 | ||
| Model3 | 0.411 (−0.437, 1.258) | 0.367 | 0.010 (−0.061, 0.081) | 0.786 | 0.006 (−0.006, 0.018) | 0.343 | 0.001 (0, 0.001) | 0.135 | ||
| Female | 30–44 | unadjusted | 0.238 (−0.074, 0.549) | 0.145 | 0.006 (−0.024, 0.035) | 0.717 | 0.002 (−0.003, 0.006) | 0.501 | 0 (0, 0) | 0.559 |
| Model1 | −0.044 (−0.356, 0.268) | 0.786 | −0.009 (−0.037, 0.020) | 0.558 | 0 (−0.005, 0.005) | 0.923 | 0 (0, 0) | 0.586 | ||
| Model2 | −0.068 (−0.379, 0.244) | 0.678 | −0.011 (−0.040, 0.017) | 0.455 | 0 (−0.005, 0.005) | 0.929 | 0 (0, 0) | 0.627 | ||
| Model3 | 0.004 (−0.336, 0.344) | 0.98 | −0.005 (−0.036, 0.025) | 0.742 | 0 (−0.005, 0.006) | 0.888 | 0 (0, 0.001) | 0.594 | ||
| 45–59 | unadjusted | 0.116 (−0.484, 0.715) | 0.708 | 0.018 (−0.030, 0.067) | 0.469 | 0.004 (−0.011, 0.019) | 0.639 | 0 (−0.001, 0.001) | 0.908 | |
| Model1 | −0.007 (−0.610, 0.595) | 0.981 | 0.004 (−0.043, 0.052) | 0.856 | 0.003 (−0.012, 0.019) | 0.673 | 0 (−0.001, 0.001) | 0.836 | ||
| Model2 | −0.015 (−0.606, 0.577) | 0.962 | 0.005 (−0.042, 0.052) | 0.85 | 0.002 (−0.013, 0.017) | 0.763 | 0 (−0.001, 0.001) | 0.879 | ||
| Model3 | 0.257 (−0.349, 0.864) | 0.429 | 0.021 (−0.027, 0.069) | 0.411 | 0.004 (−0.009, 0.017) | 0.56 | 0 (−0.001, 0.001) | 0.652 | ||
| 60–69 | unadjusted | 0.897 (−0.062, 1.856) | 0.078 | 0.057 (−0.028, 0.142) | 0.202 | 0.01 (−0.006, 0.026) | 0.229 | 0 (−0.001, 0.001) | 0.53 | |
| Model1 | 0.989 (0.052, 1.926) | 0.059 | 0.061 (−0.021, 0.144) | 0.167 | 0.011 (−0.006, 0.028) | 0.222 | 0 (−0.001, 0.001) | 0.721 | ||
| Model2 | 1.039 (0.107, 1.971) | 0.054 | 0.070 (−0.009, 0.148) | 0.113 | 0.011 (−0.006, 0.028) | 0.225 | 0 (−0.001, 0.001) | 0.731 | ||
| Model3 | 1.242 (0.345, 2.139) | 0.042 | 0.097 (0.025, 0.168) | 0.045 | 0.012 (−0.006, 0.030) | 0.252 | 0 (−0.001, 0.001) | 0.744 | ||
ASVs: amplicon sequence variants. Model 1: adjusted for age, sex, race and ethnicity, education level, marital status, and PIR. Model 2: adjusted for Model 1 covariates plus smoking and drinking status. Model 3: adjusted for Model 2 covariates plus hypertension, diabetes, cancer, total energy intake, and periodontitis. Bold represents p-value 0.05
Comparison of beta diversity in ZJU index-defined subgroups
We further compared the differences in beta diversity metrics among ZJU Index-defined subgroups: Z1 (<32), Z2 (≥32, <38), Z3 (≥38, <44), and Z4 (≥44), as demonstrated in Fig. 2. Aitchison Distance was statistically significant among ZJU Index-defined subgroups in overall analysis (R2 = 0.002, p = 0.001), as well as in female subgroups (R2 = 0.005, p = 0.001) and in all age groups (R2 = 0.003, p = 0.033; R2 = 0.007, p = 0.001; R2 = 0.009, p = 0.022). Among-subgroup differences were consistently observed across Bray-Curtis Dissimilarity (R2 = 0.002, p = 0.001), Unweighted UniFrac Distance (R2 = 0.002, p = 0.002), and Weighted UniFrac Distance (R2 = 0.002, p = 0.034).
Fig. 2.
Comparison of beta diversity across ZJU index-defined groups. Aitchison distance is shown for (A) all participants, (B) males, (C) females, (D) the 30–44-year age group, (E) the 45–59-year age group, and (F) the 60–69-year age group. (G) bray-curtis dissimilarity. (H) unweighted UniFrac distance. (I) weighted UniFrac distance
Association between genus abundance and the ZJU Index
Given the observed associations between oral microbiome diversity and the ZJU Index, we aimed to identify the specific genera contributing to these relationships. To this end, weighted logistic regression analyses were performed using the models described above (Model 1, Model 2, Model 3). Results were further stratified by sex and age, as shown in Fig. 3. Genera including Bulleidia and Porphyromonas showed significant positive associations with the ZJU Index. In contrast, genera such as Clostridium sensu stricto 1 and Senegalimassilia, exhibited negative associations with the ZJU Index. Collectively, these correlations suggest that specific genera may contribute to the link between the oral microbiome and metabolic health.
Fig. 3.
Dot plot of genera significantly associated with the ZJU index (p < 0.05). Red and blue circles represent positive and negative associations, respectively. The sizes of circles represent statistical significance measured as -log10 (p-value). Model 1: adjusted for age, sex, race and ethnicity, education level, marital status, and PIR. Model 2: adjusted for Model 1 covariates plus smoking and drinking status. Model 3: adjusted for Model 2 covariates plus hypertension, diabetes, cancer, total energy intake, and periodontitis. Results are shown for (A) the top 20 genera in all participants, (B) males and females, (C) all age groups (30–44, 45–59, 60–69 years)
Association between genus abundance and clinical metabolic and reproductive biomarkers
To further elucidate the clinical relevance of the oral microbiome, we examined its associations with key metabolic biomarkers. Weighted logistic regression analyses were performed to evaluate the associations between genus abundance and parameters including triglycerides (TG), low-density lipoproteins (LDL), high-density lipoproteins (HDL), and insulin. The genera significantly associated with the parameters and the direction of their associations are summarized in Fig. 4. Notably, Fretibacterium and Parascardovia exhibited positive correlations with TG, LDL, and insulin, and showed negative associations with HDL. Lactobacillus and Bifidobacterium were positively correlated with TG and insulin, and negatively correlated with HDL. Since the ZJU Index is also related to female infertility (Tian et al. 2025), and prior studies have linked gut microbiota with reproductive disorders (Ahmad et al. 2025; Senthilkumar and Arumugam 2025), we further examined whether genus abundance is associated with serum testosterone. Separate analyses were performed in males and females, as shown in Fig. 4. Hungatella and Coriobacteriaceae UCG-003 exhibited negative and positive associations, respectively, with testosterone in both males and females.
Fig. 4.
Dot plot of genera significantly associated with (A) metabolic biomarkers (triglycerides, LDL, HDL, and insulin) and (B) total testosterone. Red and blue dots represent positive and negative associations, respectively. The sizes of dots represent statistical significance measured as -log10 (p-value)
Discussion
This study systematically investigated the association between oral microbiome diversity and the ZJU Index, a novel indicator of metabolic health. Alpha and beta diversity analyses demonstrated the close relationship between oral microbiome and metabolic risk, and differences in composition across ZJU Index-defined subgroups. Additionally, specific genera were identified that correlated with the ZJU Index and with key metabolic and reproductive indicators, highlighting potential oral microbial signatures of systemic health.
The oral microbiome plays a significant role beyond oral health. Specific genera and species have been identified as key pathogens affecting multiple systems, some of which are able to translocate and colonize the intestine under pathological conditions. Recent studies show that Porphyromonas gingivalis, generally regarded as the main cause of periodontitis, causes gut microbial disturbances and aggravates cognitive impairment in T2DM mice (Liu et al. 2025). In a prospective study, intestinal colonization of Veillonella was found to be associated with the severity of Clostridioides difficile infection (CDI) in Crohn’s disease (Yang et al. 2025). These findings highlight the importance of the crosstalk between the oral and gut microbiome, offering an important aspect of how the oral microbiome affects systemic health.
The ZJU Index was primarily a composite indicator designed to reflect overall metabolic status. It was first applied clinically to predict nonalcoholic fatty liver disease (NAFLD) (Wang et al. 2015). A prospective cohort study indicated a high predictive capability of the ZJU Index for gestational diabetes mellitus (GDM) diagnosis (Xu et al. 2025). A cross-sectional study suggested higher risks of hypertension in participants with higher ZJU Index (Gong et al. 2025). Notably, recent studies reveal that the ZJU Index is not only a predictor of metabolic disorders. Another cross-sectional study indicated significant positive correlations between the ZJU Index and infertility in reproductive-aged women (Tian et al. 2025). Further studies are needed to fully elucidate the potential utility of the ZJU Index.
Our study found significant positive correlations between alpha diversity and the ZJU Index in male individuals aged 30–44 years and in females aged 60–69 years. Previous cross-sectional studies have associated higher oral microbiome diversity with favorable health outcomes, including reduced all-cause mortality, slower biological ageing, and improved cardiovascular health (Mondal et al. 2025; Hou et al. 2025). In this case, our findings present a paradox, given that the ZJU Index is also indicative of metabolic risk. One possible explanation is that these associations may reflect a stage-specific phenomenon during the early or transitional phases of metabolic dysregulation. Emerging evidence suggests that microbial communities may undergo adaptive restructuring in response to metabolic disturbances. During the initial stages of metabolic imbalance, ecological niches within the oral environment may become more permissive, allowing opportunistic or previously low-abundance taxa to proliferate. In males aged 30–44, the stabilization of hormones and lifestyle changes coincide with early metabolic dysregulation, shifting the systemic metabolic status (Pataky et al. 2021). In females aged 60–69, postmenopausal hormonal changes can substantially alter overall metabolic regulation (Zhao et al. 2025). Therefore, it is possible that higher alpha diversity during these stages may reflect a dysbiotic or adaptive response to metabolic perturbations.
Significant difference in beta diversity was identified across ZJU Index-defined subgroups, overall and within the female subgroup, as well as in all age groups. These findings are in line with previous studies showing the variation of gut microbial beta diversity with metabolic disturbances (Shi et al. 2025; Yanmin; Liu et al. 2025). Differences in beta diversity imply that the ZJU Index is capable of reflecting the differences in oral microbial composition. However, the proportion of variation in oral microbiome composition explained by the ZJU Index was relatively small (R2 = 0.002–0.009). This suggests that although the association reached statistical significance, the ZJU Index accounts for only a limited fraction of the overall microbial variation. A large proportion of variability in oral microbiome composition is likely influenced by other unmeasured factors, such as detailed dietary patterns, oral hygiene behaviors, environmental exposures, and host genetic background.
Our identification of specific genera associated with the ZJU Index provides further insight into potential oral microbial contributors to metabolic risk. Bulleidia, commonly found in the oral and gut microbiota, has been implicated in metabolic-associated liver disease and impaired fasting glucose (Wang et al. 2019, 2023). Porphyromonas is regarded as a primary pathogen in chronic periodontitis and contributes to multiple inflammatory diseases (29). Gut Clostridium sensu stricto 1 exhibits a negative association with fat accumulation (Ma et al. 2024), and gut Senegalimassilia has been reported to correlate with lower blood pressure in individuals with hypertension (Zhong et al. 2021). The known functions of the significant genera are summarized in Fig. 5. Our findings may expand the roles of these genera, suggesting that they may exert similar functions in the oral cavity.
Fig. 5.
The identified significant genera and their roles in physiological processes and diseases
Furthermore, the observed relationships between specific genera and clinical metabolic and reproductive biomarkers highlight the potential clinical significance of oral microbial signatures. Fretibacterium has been identified as a key contributor to periodontitis (Soueidan et al. 2024; Kageyama et al. 2025). Parascardovia has also been reported as an oral microbial signature in individuals with T2DM (Samodova et al. 2025). In our study, Lactobacillus and Bifidobacterium were positively correlated with triglycerides and insulin, and negatively correlated with HDL. Although generally considered beneficial (Xie et al. 2025), they may suggest a dysbiotic oral environment in the context of metabolic disorders (Yefei Liu et al. 2025). While direct evidence of the relationship between oral microbiome and testosterone is still limited, increased androgen levels have been shown to affect the gut microbial abundance of Hungatella in patients with polycystic ovary syndrome (PCOS) (Yin et al. 2022).
This study has several notable strengths. First, the NHANES database is a nationally representative dataset with demographic, clinical, and lifestyle information, enhancing the reliability and generalizability of our findings. Additionally, we assessed the oral microbiome diversity through both alpha and beta diversity, while also examining the contributions of specific genera. This approach enabled us to investigate the relationship between oral microbiome and the ZJU Index from multiple perspectives, offering deeper insights into functional roles of the oral microbiome and highlighting the clinical significance of the ZJU Index as an early indicator of metabolic health.
Several limitations should also be acknowledged. The cross-sectional design of this study restricts the determination of causal links between variables. Longitudinal or prospective studies are required to clarify temporal relationships in future studies. Additionally, the oral microbiome data are restricted to genus-level abundance due to limitations of the 16S rRNA gene sequencing data provided by the NHANES database (Balan et al. 2025; Wei et al. 2025), potentially overlooking important species- or strain-level alterations and deeper functional interpretations. We observed rather modest R2 values in beta diversity analyses, indicating that only a small proportion of oral microbial variation is explained by the ZJU Index. In addition, multiple genus-level associations and subgroup analyses were conducted without formal adjustment for multiple comparisons, which may increase the risk of type I error and should be considered when interpreting the findings. Furthermore, our assessment of reproductive associations was limited to total testosterone level. A comprehensive evaluation, including additional hormones, clinical and physiological traits, and reproductive outcomes, is necessary to fully establish the relationship between specific oral microbial genera and reproductive health. Moreover, although we adjusted for numerous demographic and clinical covariates, residual cofounding cannot be ruled out. Other important factors, including diet habits, physical activity, environmental exposures, medication usage, brushing frequency, dental floss use, and dental visits cannot be fully accounted for.
Conclusion
In this study, we found that oral microbiome diversity and genus composition are closely linked to systemic metabolic and reproductive health, as reflected by the ZJU Index and clinical metabolic and reproductive biomarkers. Specifically, alpha diversity was positively correlated with the ZJU Index in males aged 30–44 years and in females aged 60–69 years. Beta diversity differed significantly across ZJU Index-defined subgroups, confirmed with age- and sex-stratified analyses. At the genus level, several oral microbial genera, including Bulleidia, Senegalimassilia, Fretibacterium, and Hungatella were identified as significantly associated with the ZJU Index. and with clinical metabolic and reproductive biomarkers. These findings highlight the potential role of the oral microbiome in systemic metabolic and reproductive health, and that the ZJU Index may capture broader aspects of overall physiological status beyond metabolism alone.
Author contributions
K.W. conceived and designed the study, conducted data analysis and interpretation, and drafted the manuscript. L.Y. provided a critical revision of the manuscript. Z.C. and Y.D. made significant contributions to the conception and design of the study, and critically revised the manuscript. All authors read and approved the final version of the manuscript and agreed to be accountable for all aspects of the work.
Funding
This work was supported by grants from the National Key Research and Development Program of China (2024YFC2706703), National Natural Science Foundation of China (82571885) and Innovative research team of high-level local universities in Shanghai (SHSMU-ZLCX20210200).
Data availability
Declarations
Ethical approval
All study procedures were approved by the National Center for Health Statistics (NCHS) Ethics Review Board.
Informed consent
Not applicable.
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.
Contributor Information
Zi-Jiang Chen, Email: chenzijiang@hotmail.com.
Yanzhi Du, Email: duyz@sjtu.edu.cn.
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