ABSTRACT
Although substantial evidence links sweetened beverage (SB) intake to chronic diseases, its association with biological aging and underlying mechanisms remains unclear. This population‐based study included 9104 adults aged 18–80 years from the WELL‐China cohort. SB consumption was assessed using a validated food frequency questionnaire. Biological age and biological age acceleration (BAacc) were estimated using the Klemera and Doubal method (KDM). After multivariable adjustment, frequent SB consumption was significantly associated with accelerated KDM‐derived BAacc, compared with non‐consumers (β = 0.29 years, 95% CI: 0.06–0.52). This association was stronger among participants younger than 55 years (P for interaction < 0.05). Gut microbiota was profiled using 16S rRNA gene sequencing. We identified 10 genera significantly associated with the frequent SB consumption, and 56 genera significantly associated with BAacc. Notably, five genera (Allisonella, Lactobacillus, Weissella, Lactococcus, and Bacilli_unclassified) were shared between the two analyses and enriched in both frequent SB consumers and individuals with higher BAacc. Frequent SB consumption is associated with accelerated biological aging, especially among adults younger than 55 years. Shared gut microbial signatures associated with both SB intake and BAacc suggest a potential microbiota‐related pathway. These findings support reducing SB consumption as a potential strategy for promoting healthy aging.
Keywords: aging, biological age acceleration, gut microbiota, sweetened beverage
Frequent sweetened beverage consumption is associated with accelerated biological aging in a large Chinese population. Five gut microbial genera are consistently enriched in both frequent sweetened beverage consumers and individuals with accelerated aging and show significant mediating effects, suggesting a potential microbiota‐related pathway linking sweetened beverage intake to biological aging.
1. Introduction
Human biological aging i driven by a complex interplay of genetic, dietary, and environmental factors, making chronological age an imperfect proxy for the true aging process [1, 2]. This has led to growing interest in the concept of biological age (BA), which reflects the functional decline across multiple physiological systems [3, 4]. Among various approaches to BA estimation, the Klemera‐Doubal Method (KDM) identifies a panel of age‐related biomarkers and integrates their values into an algorithm capable of capturing multisystem physiological dysregulation [5, 6, 7]. KDM‐derived BA is cost‐effective and well suited for large‐scale population studies [8].
In recent years, the consumption of sweetened beverages (SB) in China has been increasing, particularly among younger populations [9]. Previous studies have shown that frequent intake of SB, including both sugar‐sweetened and artificially sweetened types, is associated with a range of aging‐related diseases, such as metabolic syndrome, type 2 diabetes, and chronic kidney disease [10, 11, 12, 13]. Mechanistically, sugars in these beverages, especially fructose, may accelerate the biological aging process by promoting inflammation, inducing oxidative stress, and altering the composition of the gut microbiota [14]. However, despite the widespread consumption of SB, evidence directly linking their intake to biological aging remains limited. Moreover, the potential mediating role of the gut microbiota in this relationship has not yet been elucidated.
In this study, we utilized the KDM method to calculate BA and estimate biological aging status based on composite clinical biomarkers and examined the association between SB consumption and accelerated biological aging. Given that emerging evidence suggests that SB intake may be associated with alterations in gut microbiota composition, and that gut microbiota profiles have been linked to aging‐related processes [15, 16, 17], we further explored whether shared microbial signatures might underlie the relationship between SB consumption and biological age acceleration (BAacc).
2. Methods
2.1. Study Population
The Wellness Living Laboratory China (WELL‐China) was a prospective population‐based cohort study that enrolled a total of 10 268 participants aged 18–80 years between 2016 and 2019 [18]. Participants with missing data on clinical biomarkers, SB intake, or covariates, as well as those who self‐reported cancer or had implausible energy intake (<800 or >4000 kcal/day for males, <500 or >3500 kcal/day for females), were excluded. A total of 9104 participants were included in the final analysis. For gut microbiota analyses, participants without fecal samples or information on antibiotic use were further excluded, resulting in a final subsample of 6375 individuals. The detailed flow of participant selection is presented in Figure 1. Ethical approval was obtained from the Institutional Review Boards of Zhejiang University and Stanford University, and all the participants provided their written informed consent.
FIGURE 1.
Flowchart of participant selection in the study.
2.2. Calculation of Biological Age and Biological Age Acceleration
BA was estimated using the KDM, which integrates multiple clinical and anthropometric biomarkers reflecting diverse physiological systems [7]. Thirteen biomarkers were selected for BA estimation: alkaline phosphatase (ALP), albumin (ALB), blood urea nitrogen (BUN), creatinine (CR), systolic blood pressure (SBP), forced expiratory volume in one second (FEV1), fasting blood glucose (FBG), body mass index (BMI), platelet count (PLT), white blood cell count (WBC), glutamic oxaloacetic transaminase (GOT), gamma‐glutamyl transpeptidase (GGT), and lactate dehydrogenase (LDH), which were previously used and validated in a large Chinese cohort [19]. Detailed information on these biomarkers is provided in Table S1, and the correlations between these biomarkers are shown in Figure S1.
The KDM algorithm was implemented using the BioAge package in R software [20]. More details are presented in the Supporting Method. BAacc was defined as the residual of BA regressed on chronological age, representing the deviation of biological age from chronological expectations. A positive BAacc indicates accelerated biological aging, whereas a negative BAacc reflects decelerated aging. Given the physiological differences between sexes, BA and BAacc were estimated separately for men and women [21]. BAacc was analyzed both as a continuous variable and as a categorical variable reflecting three biological aging statuses: mitigated aging (BAacc < −1), normal aging (−1 ≤ BAacc ≤ 1), and accelerated aging (BAacc > 1), using cutoffs consistent with previous studies and approximately corresponding to ±1 standard deviation [21].
2.3. Assessment of Sweetened Beverage Consumption
Information on SB consumption was obtained through a validated food frequency questionnaire (FFQ) using the question: “How often have you consumed sweetened beverages (such as cola, Sprite, sports drinks, etc.) in the past year?” [22]. Participants were categorized into four groups based on consumption frequency: non‐consumers, rare consumers (less than once per week), occasional consumers (1–2 times per week), and frequent consumers (3 or more times per week). Additionally, dietary energy and carbohydrate intake were estimated based on the China Food Composition Table (Standard Edition) [23].
2.4. Fecal Samples Collection, Microbial DNA Extraction, and 16S rRNA Gene Profiling
Fecal samples were self‐collected by participants following a standardized study protocol, either on the morning of their physical examination day or the evening prior. Upon receiving the samples at the medical examination center, the staff immediately stored them on dry ice for cold storage. Subsequently, the samples were transported to our laboratory within 4 h and stored at −80°C until testing [24].
Following sequencing, raw FASTQ files of the 16S rRNA gene were processed using the Quantiative Insights Into Microbial Ecology 2 platform (QIIME 2, version 2022.2) [25]. Raw sequences were demultiplexed and imported into QIIME 2 using the q2‐demux plugin, followed by denoising with the DADA2 plugin. The DADA2 pipeline was applied to perform quality filtering, trimming of low‐quality regions, and removal of chimeric sequences, resulting in the generation of amplicon sequence variants (ASVs), which were summarized into a feature table. Taxonomic classification of ASVs was performed using a naïve Bayes classifier implemented in the q2‐feature‐classifier plugin (classify‐sklearn), trained on the SILVA 138 database (99% OTUs) [26].
Alpha diversity indices were calculated using the q2‐diversity plugin based on rarefied ASV counts (minimum sequencing depth of 10 000 reads). Genus‐level abundance tables were generated from the feature table and converted to relative abundance by normalizing to the total counts per sample for downstream comparative analyses of gut microbiota composition. Genera with a prevalence below 10% and a relative abundance below 0.0001% were excluded, leaving 188 genera for subsequent analyses. The relative abundances of genera were log‐transformed prior to analysis to improve normality.
2.5. Covariates
Demographic and lifestyle information was obtained through face‐to‐face interviews using a standardized questionnaire. Education level was classified as elementary (illiterate or elementary school), secondary (junior high or high/vocational school), and higher (college or above). Smoking and drinking status were categorized as current or non‐current. Tea and coffee consumption were grouped as never, occasional, or frequent. Physical activity was assessed using the short form of the International Physical Activity Questionnaire (IPAQ) and classified into low, moderate, or high levels [27]. Cardiovascular disease (CVD) was defined as self‐reported history of at least one of the following conditions: coronary heart disease, atherosclerosis, arrhythmia, heart failure, stroke, myocardial infarction, or other cardiovascular disease. Kidney disease was also defined based on self‐reported history. Diabetes mellitus was defined using both self‐reported history and objective clinical measurements, including fasting plasma glucose ≥ 7.0 mmol/L or glycosylated hemoglobin (HbA1c) ≥ 6.5% at baseline. Antibiotic use was assessed through face‐to‐face questionnaires, in which participants were asked whether they had used antibiotics within the past three months. Antibiotic use was included as a binary variable (yes/no).
2.6. Statistical Analyses
Continuous variables were summarized as means (standard deviations, SD) and categorical variables as counts (percentages). Group differences were assessed using the chi‐square test for categorical variables, one‐way ANOVA for normally distributed continuous variables, and the Kruskal‐Wallis test for non‐normally distributed variables.
The association between SB consumption (main predictor) and BAacc (outcome, continuous variable) was evaluated using multivariable linear regression. The association between SB consumption (main predictor) and biological aging status (outcome; categorized as mitigated, normal, and accelerated aging) was further examined using multinomial logistic regression, with mitigated aging as the reference group. The covariates included age, sex, education level, physical activity, smoking status, drinking status, tea and coffee consumption, dietary energy intake, CVD, kidney disease, and diabetes mellitus. Analyses were conducted in the total population (n = 9104) and among SB consumers only (n = 3050) to minimize potential bias due to age differences between consumers and non‐consumers.
Interaction and stratified analyses were conducted to examine whether the associations between SB consumption and BAacc differed by age, sex, and education level. Multiplicative interaction terms between SB consumption and subgroup variables were included in the fully adjusted multivariable linear regression models in the total population and among SB consumers only. Age was dichotomized at 55 years, corresponding to the median age of the study population and a commonly used threshold to distinguish midlife from older adulthood [28]. Stratified analyses were subsequently performed in the total population and among SB consumers only.
In addition, sensitivity analyses were conducted by excluding participants with major chronic diseases at baseline and by using the normal aging group as the reference category to assess the robustness of the primary findings.
In gut microbiota analyses (n = 6375), associations of SB consumption and biological aging status (main predictor, analyzed separately) with gut microbial α‐diversity indices (outcomes: Shannon index, Pielou's evenness, and Faith's phylogenetic diversity) were assessed using multivariable linear regression, adjusting for the same covariates as above and antibiotic use. Differences in overall microbial composition (β‐diversity) were evaluated using permutational multivariate analysis of variance (PERMANOVA; 999 permutations, Bray‐Curtis distance), with SB consumption or biological aging status as the main predictor and the same covariates included. At the genus level, associations were assessed using MaAsLin2, with microbial genera as outcomes and SB consumption and biological aging status as the main predictors (analyzed separately), adjusting for the same covariates. To account for multiple testing, p values were corrected using the Benjamini‐Hochberg false discovery rate (FDR) method, with an FDR < 0.1 considered statistically significant [15, 29]. In mediation analyses, SB consumption levels (with non‐consumers as the reference group) were included as the independent variable; each overlapping genus was analyzed separately as a mediator, and continuous BAacc was treated as the outcome. Mediation effects were estimated using 1000 simulations. Genera with both significant average causal mediation effects (ACME) and average direct effects (ADE) (p < 0.05) were considered to exhibit statistically significant mediation.
Additional analyses were conducted to examine the associations between SB consumption and individual biomarkers included in the biological age model using multivariable linear regression. Furthermore, associations between the five overlapping genera and these biomarkers were assessed using multivariable linear regression models.
All statistical analyses above were performed with R version 4.2.3. A two‐sided p < 0.05 was considered statistically significant.
3. Results
3.1. Characteristics of Study Participants
A total of 9104 participants were included in the analysis, with a mean (±SD) age of 54.2 ± 13.5 years, and 61.6% were women. Baseline characteristics of participants across biological aging status are shown in Table 1. Among them, 33.7%, 37.6%, and 28.8% were categorized into the mitigated, normal, and accelerated aging groups, respectively. Participants with accelerated aging tended to be men, have lower education levels, and higher prevalence of cardiovascular diseases and diabetes compared with those with mitigated or normal aging. They were also more likely to be frequent consumers of SB, while differences in smoking, alcohol intake, and dietary factors were minimal across groups. Baseline characteristics of participants across SB consumption are shown in Table S2.
TABLE 1.
Baseline characteristics of study participants according to biological aging status.
| Mitigated Aging (Baacc < ‐1) | Normal (|BAacc ≤ 1) | Accelerated Aging (BAacc > 1) | p‐value | |
|---|---|---|---|---|
| Total participants | 3064 | 3422 | 2618 | |
| Age (years) | 54.75 (13.31) | 53.53 (13.57) | 54.31 (13.49) | <0.001 |
| Sex (%) | <0.001 | |||
| Male | 1191 (38.87) | 1220 (35.65) | 1084 (41.41) | |
| Female | 1873 (61.13) | 2202 (64.35) | 1534 (58.59) | |
| SB consumption (%) | 0.001 | |||
| Non‐consumer | 2047 (66.81) | 2224 (64.99) | 1783 (68.11) | |
| Rare consumer | 701 (22.88) | 830 (24.25) | 536 (20.47) | |
| Occasional consumer | 207 (6.76) | 205 (5.99) | 164 (6.26) | |
| Frequent consumer | 109 (3.56) | 163 (4.76) | 135 (5.16) | |
| Education level (%) | <0.001 | |||
| Elementary | 479 (15.63) | 692 (20.22) | 760 (29.03) | |
| Secondary | 1684 (54.96) | 1889 (55.20) | 1371 (52.37) | |
| Higher | 901 (29.41) | 841 (24.58) | 487 (18.60) | |
| Physical activity (%) | 0.005 | |||
| Light | 492 (16.06) | 590 (17.24) | 449 (17.15) | |
| Moderate | 1601 (52.25) | 1676 (48.98) | 1241 (47.40) | |
| Heavy | 971 (31.69) | 1156 (33.78) | 928 (35.45) | |
| Smoking status (%) | 0.377 | |||
| Non‐current | 2519 (82.21) | 2802 (81.88) | 2116 (80.83) | |
| Current | 545 (17.79) | 620 (18.12) | 502 (19.17) | |
| Drinking status (%) | 0.343 | |||
| Non‐current | 1672 (54.57) | 1929 (56.37) | 1457 (55.65) | |
| Current | 1392 (45.43) | 1493 (43.63) | 1161 (44.35) | |
| Tea consumption (%) | 0.124 | |||
| Never | 997 (32.54) | 1183 (34.57) | 922 (35.22) | |
| Occasional | 801 (26.14) | 915 (26.74) | 683 (26.09) | |
| Frequent | 1266 (41.32) | 1324 (38.69) | 1013 (38.69) | |
| Coffee consumption (%) | <0.001 | |||
| Never | 1936 (63.19) | 2287 (66.83) | 1859 (71.01) | |
| Occasional | 921 (30.06) | 932 (27.24) | 649 (24.79) | |
| Frequent | 207 (6.76) | 203 (5.93) | 110 (4.20) | |
| Dietary energy intake (kcal) | 1370.70 (520.24) | 1355.96 (528.63) | 1379.83 (533.64) | 0.204 |
| Cardiovascular disease (%) | 198 (6.46) | 195 (5.70) | 155 (5.92) | 0.421 |
| Diabetes mellitus (%) | 119 (3.89) | 172 (5.04) | 280 (10.72) | <0.001 |
| Kidney disease (%) | 72 (2.35) | 88 (2.57) | 85 (3.25) | 0.099 |
Continuous variables are presented as means ± standard deviations (SD), and categorical variables are presented as numbers (percentages). Differences across biological aging groups were assessed using the chi‐square test for categorical variables. For continuous variables, one‐way analysis of variance (ANOVA) was applied to normally distributed variables, whereas the Kruskal‐Wallis test was used for non‐normally distributed variables. Biological aging status was defined based on BAacc as follows: mitigated aging (BAacc < −1), normal aging (−1 ≤ BAacc ≤ 1), and accelerated aging (BAacc > 1).
3.2. Association of SB Consumption With Biological Aging
Multivariable linear regression analyses examining the association between SB consumption and BAacc are presented in Table 2. In the total population (n = 9,104), compared with non‐consumers, frequent SB consumers showed significantly higher BAacc in the fully adjusted model (β = 0.307, 95% CI: 0.072–0.542), whereas no significant associations were observed for rare or occasional consumers. Among SB consumers (n = 3050), using rare consumers as the reference group, frequent consumers remained significantly associated with higher BAacc (β = 0.333, 95% CI: 0.097–0.569). Results from multinomial logistic regression analyses showed that, compared with non‐consumers, frequent SB consumers had higher odds of accelerated aging (OR = 1.448, 95% CI: 1.289–1.627), while no significant associations were observed for rare or occasional consumers (Table 3). Among SB consumers, frequent consumers also had higher odds of accelerated aging compared with rare consumers (OR = 1.494, 95% CI: 1.271–1.756). Similar patterns were observed for normal aging status.
TABLE 2.
Associations between sweetened beverage (SB) consumption and biological age acceleration (BAacc).
| Non‐consumer | Rare (<1 serving/week) | Occasional (1‐2 servings/week) | Frequent (≥3 servings/week) | P‐trend | |
|---|---|---|---|---|---|
| All participants (n = 9104) | 6054 | 2067 | 576 | 407 | |
| Fully adjusted model | Ref. | 0.027 (−0.092, 0.146) | 0.058 (−0.141, 0.258) | 0.307 (0.072, 0.542)* | 0.034 |
| SB consumers (n = 3050) | — | 2067 | 576 | 407 | |
| Fully adjusted model | — | Ref. | 0.061 (−0.138, 0.260) | 0.333 (0.097, 0.569)* | 0.010 |
TABLE 3.
Associations between sweetened beverage (SB) consumption and biological aging status.
| Non‐consumer | Rare (<1 serving/week) | Occasional (1–2 servings/week) | Frequent (≥3 servings/week) | P‐trend | |
|---|---|---|---|---|---|
| All participants (n = 9104) | 6054 | 2067 | 576 | 407 | |
| Normal | Ref. | 1.113 (0.990, 1.251) | 0.924 (0.817, 1.045) | 1.339 (1.178, 1.523)* | 0.104 |
| Accelerated | Ref. | 1.010 (0.887, 1.151) | 1.032 (0.928, 1.148) | 1.448 (1.289, 1.627)* | 0.047 |
| SB consumers (n = 3050) | — | 2067 | 576 | 407 | |
| Normal | — | Ref. | 0.827 (0.676, 1.013) | 1.232 (1.038, 1.463)* | 0.506 |
| Accelerated | — | Ref. | 1.049 (0.844, 1.303) | 1.494 (1.271, 1.756)* | 0.013 |
Multivariable linear regression models were applied to evaluate the associations between SB consumption and BAacc. Coefficients and 95% confidence intervals (CIs) are presented. Fully adjusted models included age, sex, education level, physical activity, smoking status, drinking status, tea and coffee consumption, dietary energy intake, cardiovascular disease, diabetes mellitus, and kidney disease. In the analysis of all participants, non‐consumers were used as the reference group; in the analysis of SB consumers, rare consumers (<1 serving/week) were used as the reference group. * p < 0.05
Multinomial logistic regression models were applied to evaluate the associations between SB consumption and biological aging status. Odds ratios (ORs) and 95% confidence intervals (CIs) are presented. The mitigated aging group was used as the reference category. Models were adjusted for age, sex, education level, physical activity, smoking status, drinking status, tea and coffee consumption, dietary energy intake, cardiovascular disease, diabetes mellitus, and kidney disease. * p < 0.05Significant interaction effects were observed between SB consumption and age in the total population (P for interaction = 0.013), whereas no significant interactions were observed for sex or education level (P for interaction > 0.05) (Figure 2). Subgroup analyses showed that the positive association between frequent SB consumption and BAacc was more evident among participants younger than 55 years (β = 0.414, 95% CI: 0.147–0.680), females (β = 0.487, 95% CI: 0.162–0.811), and individuals with higher education levels (β = 0.528, 95% CI: 0.159–0.897) in the total population. Similar patterns were observed among SB consumers only.
FIGURE 2.
Subgroup analyses of the association between sweetened beverage (SB) consumption and biological age acceleration (BAacc).
Significant interaction effects were observed between SB consumption and age in the total population (P for interaction = 0.013), whereas no significant interactions were observed for sex or education level (P for interaction > 0.05) (Figure 2). Subgroup analyses showed that the positive association between frequent SB consumption and BAacc was more evident among participants younger than 55 years (β = 0.414, 95% CI: 0.147–0.680), females (β = 0.487, 95% CI: 0.162–0.811), and individuals with higher education levels (β = 0.528, 95% CI: 0.159–0.897) in the total population. Similar patterns were observed among SB consumers only.
Subgroup analyses were conducted using multivariable linear regression models to examine the association between SB consumption and BAacc across different population subgroups. Models were adjusted for age, sex, physical activity, education level, smoking status, drinking status, tea and coffee consumption, dietary energy intake, cardiovascular disease, diabetes mellitus, and kidney disease. Interaction terms between SB consumption and subgroup variables (age, sex, and education) were included in the models to formally test effect modification, and corresponding P‐interaction values were reported. SB consumption categories were defined as follows: 0, non‐consumers; 1, <1 serving/week; 2, 1–2 servings/week; 3, ≥3 servings/week.
In sensitivity analyses, after excluding participants with major chronic diseases, the associations remained materially unchanged. Frequent SB consumption was still significantly associated with higher BAacc in multivariable linear regression models (Table S3), both in the overall sample and among SB consumers. Consistently, in multinomial logistic regression models, frequent SB consumption was associated with higher odds of accelerated aging (vs. mitigated aging), both in the overall sample and among SB consumers (Table S4). In addition, sensitivity analyses using the normal aging group as the reference category yielded largely consistent results (Table S5). Among all participants, frequent SB consumption remained associated with lower odds of mitigated aging and higher odds of accelerated aging. Similar patterns were observed among SB consumers, with frequent consumers showing higher odds of accelerated aging.
3.3. Gut Microbial Signatures in Relation to SB Consumption and Biological Aging
We first evaluated the association between the gut microbiota and biological aging status. The accelerated aging group exhibited significantly reduced alpha‐diversity compared to the mitigated aging group, as evidenced by a lower Shannon index (p = 0.012), Pielou's evenness (p = 0.016), and Faith PD (p = 0.028; Figure 3A). Consistent with this, BAacc showed an inverse correlation with three alpha‐diversity indices (all p < 0.05; Figure S2). Although no significant differences in alpha‐diversity were observed across SB consumption levels (Figure 3B), Beta‐diversity analysis revealed distinct microbial structural shifts. Specifically, significant clustering was observed between the accelerated and mitigated aging groups (p < 0.001; Figure 3C), as well as between frequent SB consumers and non‐consumers (p < 0.001; Figure 3D).
FIGURE 3.
Associations of biological aging status and sweetened beverage (SB) consumption with gut microbial α‐ and β‐diversity (A, B) Multivariable linear regression models were applied to evaluate the associations of biological aging status (A) and SB consumption (B) with gut microbial α‐diversity indices. (C, D) Gut microbial β‐diversity was evaluated using principal coordinate analysis (PCoA) based on the Bray‐Curtis dissimilarity matrix at the genus level. Differences in microbial community structure across biological aging groups (accelerated aging vs. mitigated aging) (C) and SB consumption groups (frequent consumers vs. non‐consumers) (D) were assessed using permutational multivariate analysis of variance (PERMANOVA; 999 permutations). All models were adjusted for age, sex, physical activity, education level, smoking status, drinking status, tea and coffee consumption, dietary energy intake, cardiovascular disease, kidney disease, diabetes mellitus, and antibiotic use. * p < 0.05; ** p < 0.01; *** p < 0.001.
To further identify specific microbial contributors, MaAsLin2 analyses were performed. We identified 56 genera significantly associated with accelerated aging at FDR < 0.10, of which 39 remained significant at FDR < 0.05 (Figure 4A). We also identified 10 genera associated with frequent SB consumption at FDR < 0.10, of which eight remained significant at FDR < 0.05 (Figure 4B). Notably, five overlapping genera, Allisonella, Lactobacillus, Weissella, Lactococcus, and Bacilli_unclassified, were consistently enriched in both the accelerated aging and frequent SB consumption groups (Figure 4C). Mediation analysis was subsequently conducted to examine their potential role in the SB‐BAacc association. All five genera showed significant mediation effects, with Allisonella, Lactobacillus, Bacilli_unclassified, Weissella, and Lactococcus mediating 5.49%, 4.66%, 4.46%, 4.40%, and 4.13% of the total association, respectively (all p < 0.05; Figure 4D).
FIGURE 4.
Shared gut microbial signatures associated with accelerated biological aging and frequent sweetened beverage (SB) consumption. (A) 56 genera were significantly associated with biological aging status groups (accelerated aging vs. mitigated aging) at FDR < 0.1. (B) 10 genera were significantly associated with SB consumption (frequent consumers vs. non‐consumers) at FDR < 0.1. Genera marked with † in the upper‐left corner remained significant at FDR < 0.05; unmarked genera met the exploratory threshold of FDR < 0.1 only. (C) Five overlapping genera were identified in both the accelerated aging group and the frequent SB consumption group, representing descriptive overlap between the two analyses; no depleted genera were shared between the comparisons. (D) Mediation analyses of the five overlapping genera in the association between frequent SB consumption and BAacc. All models were adjusted for age, sex, physical activity, education level, smoking status, drinking status, tea and coffee consumption, dietary energy intake, cardiovascular disease, kidney disease, diabetes mellitus, and antibiotic use.
3.4. SB Consumption, Identified Genera, and Biological Aging‐Related Biomarkers
Additional analyses showed that SB consumption was significantly associated with several biomarkers included in the biological age model (Table S6). In particular, frequent SB consumption was positively associated with BMI, WBC, and LDH (all p < 0.001), as well as ALP and BUN. Figure S3 shows the associations between the five overlapping genera and these biomarkers. Allisonella was positively associated with several metabolic and inflammatory markers, including BMI, WBC, SBP, and FBG, whereas Weissella was positively associated with BMI and LDH and inversely associated with PLT and ALB (all p < 0.05). Lactococcus also showed a positive association with BMI and inverse associations with FEV1 and ALB.
4. Discussion
In this large population‐based study, we found that frequent consumption of SB was significantly associated with BAacc, independent of chronological age. The association was particularly evident among younger adults. Moreover, we identified five overlapping microbial signatures between frequent SB consumers and individuals with biological age acceleration, with consistent directions of association. Together, these findings provide additional evidence that higher SB intake may be associated with accelerated biological aging, potentially in relation to gut microbial composition.
Evidence linking SB consumption to biological aging remains limited. A prior cross‐sectional analysis of US adults using NHANES data reported a positive association between sugar‐sweetened beverage intake and phenotypic age acceleration [30]. However, substantial differences exist in sweetened beverage consumption patterns and amounts between Chinese and U.S. populations, and the aging process itself may vary across ethnicities. This study is the first to demonstrate a positive association between SB consumption and accelerated biological aging in the Chinese population. Consistent associations have also been reported using alternative aging indicators, such as telomere length, similarly supporting the relevance of SB consumption to aging‐related physiological processes [31].
A significant age‐specific association was observed. Subgroup analyses showed that the positive association between SB consumption and BAacc was significant among younger (< 55 years) participants, whereas no significance was observed in older (≥ 55 years) populations. Consistent with this, previous studies have reported a 43% surge in SB‐related mortality among Chinese populations aged 15–49, alongside higher rates of type 2 diabetes‐related years of life with disability and disability‐adjusted life years in young and middle‐aged males compared to their older counterparts [32, 33]. The SB consumer population in China tends to be younger, and older people drink SB more infrequently due to their traditional dietary habits and higher awareness of health management [9, 34]. These findings suggest that reducing sugar‐sweetened beverage consumption during younger adulthood is a pivotal window of opportunity for slowing biological aging and mitigating the long‐term burden of age‐related diseases.
Biological aging was assessed using the KDM, a clinical biomarker‐based metric reflecting systemic physiological dysregulation [35]. Compared with other aging measures, such as DNA methylation‐based clocks, KDM captures systemic physiological dysregulation and has shown good predictive ability for mortality and health outcomes [36]. However, different aging metrics capture distinct aspects of the aging process, and KDM may be more closely linked to clinical and metabolic alterations rather than molecular aging signatures [37]. Therefore, integrating multiple measures offers a more comprehensive biological perspective.
The pervasive appeal of SB is largely driven by their high sugar content, particularly sucrose and fructose, which enhances palatability and may promote habitual overconsumption through reinforcement of sweet preference [14, 38, 39]. Although most existing studies linking sugars to gut microbiota are based on animal models, emerging human evidence also suggests that SB intake and dietary sugar intake may alter gut microbiota composition [15, 40, 41]. One recent population‐based study identified nine gut microbial species significantly associated with SB intake and further showed that these associations were largely driven by fructose and glucose intake from SB [15]. Another human study reported that high‐fructose corn syrup intake was associated with reduced abundance of Erysipelatoclostridium [40]. Consistently, we identified 10 genera significantly associated with SB consumption, including a reduced abundance of Erysipelotrichaceae_UCG‐003, a closely related genus within the same bacterial family. In addition, recent experimental evidence suggests that high‐dose fructose intake may overwhelm intestinal fructose absorption and clearance capacity, allowing fructose to reach both the liver and the colonic microbiota, which may partly explain why SB consumption could be associated with gut microbial alterations [42].
Notably, we identified five overlapping genera that were associated with both frequent SB consumption and biological age acceleration. These genera also showed significant mediation effects, suggesting that they may serve as candidate microbial features related to the link between SB consumption and biological aging. Among them, Allisonella, Lactobacillus, Weissella, and Lactococcus belong to the Firmicutes phylum, which has previously been associated with high‐fructose diets and metabolic disturbances, such as obesity [43, 44, 45]. Specifically, Allisonella has been associated with increased frailty risk, and emerging evidence suggests a potential association with sarcopenia and obesity, indicating its relevance to aging‐related decline [46, 47]. In our study, Allisonella also showed strong positive associations with BMI, FPG, and SBP. In addition, Weissella is a lactic acid bacterium with both probiotic potential and opportunistic pathogenic characteristics. In our data, Weissella was positively associated with BMI and LDH and inversely associated with PLT [48]. We also observed that SB consumption was significantly associated with several of these biomarkers, particularly BMI and LDH. Excessive SB intake may contribute to positive energy balance and metabolic dysfunction, including obesity and metabolic syndrome, which have been linked to accelerated biological aging [49, 50]. Taken together, these findings suggest that SB consumption may be linked to biological aging partly through microbiota‐related metabolic pathways involving biomarkers such as BMI, although further longitudinal and mechanistic studies are needed to clarify these associations.
Human gut‐derived bacterial genomes harbor abundant prophages [51]. Recent studies have further isolated and characterized inducible temperate phages from human gut bacteria, supporting their role in shaping microbial community structure [52]. Notably, sweeteners and other dietary compounds may influence the gut microbiome not only through direct effects on bacterial growth but also by modulating prophage induction and phage particle production in lysogenic bacteria, thereby indirectly altering bacterial communities [53]. This evidence hints at a possible phage‐mediated mechanism for how SB consumption may alter gut microbes, though our sequencing data cannot assess gut virome or phage activity.
There are several strengths in our study. To the best of our knowledge, this is the first population‐based study to explore the associations between SB consumption, gut microbiota, and BAacc. By employing a validated and modifiable aging metric, our study provides additional insights into potential nutrition‐oriented strategies for healthy aging. The observed gut microbial signatures may reflect microbiota‐related pathways associated with biological aging.
However, several limitations exist. First, our study was a cross‐sectional study and thus could not prove causality. Second, our FFQ did not distinguish sugar‐sweetened from non‐sugar‐sweetened beverages, limiting analyses by beverage type. This distinction is important because non‐sugar sweeteners may affect gut microbiota composition, although human evidence remains inconsistent [54, 55, 56]. However, in China, sugar‐sweetened beverages are still more commonly consumed than non‐sugar‐sweetened beverages, but the latter are increasingly popular [57]. Third, the information on SB consumption was self‐reported, potentially introducing the risk of recall bias and misclassification. Fourthly, despite our adjustment for a wide range of potential covariates, the inherent nature of observational studies means that residual confounding from unobserved factors cannot be entirely ruled out. Fifthly, our analyses were based on a complete‐case approach, which may introduce selection bias. However, the proportion of missing data was low, particularly for covariates. Sixthly, gut microbiota was profiled using 16S rRNA gene sequencing, which limits species‐level resolution and functional annotation.
5. Conclusions
In summary, high‐frequency consumption of SB was positively associated with BAacc, particularly among individuals under 55 years. Gut microbiota signatures were also associated with both SB consumption and BAacc, suggesting that these signatures may play a potential role in these associations. These findings highlight the importance of public health strategies aimed at reducing sweetened beverage consumption
Author Contributions
Yuwei Shi and Shankuan Zhu designed the study and were responsible for the manuscript. Yuwei Shi and Xinmei Li analyzed data. Yuwei Shi, Xinmei Li, Yufan Hao, Qiaoyu Wu, Ann W. Hsing, and Shankuan Zhu wrote and revised the manuscript. Yuwei Shi, Xinmei Li, Qiaoyu Wu, and Yuji Yu created the tables and figures. Shankuan Zhu supervised the whole study. All authors contributed to the critical revisions of the manuscript for intellectual content or data collection. All authors read and revised the manuscript and approved the final version.
Funding
This work was funded by the Nutrilite Health Institute Wellness Fund; the China Medical Board (CMB) [15‐216]; the Cyrus Tang Foundation [419600‐11102]; and the Hsun K. Chou Fund of Zhejiang University Education Foundation [419600‐11107].
Conflicts of Interest
The authors declare no conflicts of interest.
Supporting information
Supporting file: advs76460‐sup‐0001‐SuppMat.docx
Acknowledgements
The authors thank the staff and participants of the WELL‐China cohort for their important contributions.
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supporting file: advs76460‐sup‐0001‐SuppMat.docx
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.