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The associations between the energy and timing of sugar-sweetened beverage intake and phenotypic age acceleration in U.S. adults: a cross-sectional survey of NHANES 2007鈥�2010

樱花视频 volume听25, Article听number:听88 (2025) Cite this article

Abstract

Objectives

The relationship between sugar-sweetened beverage (SSB) intake and phenotypic age acceleration (PhenoAgeAccel) is unclear. The aim of this study was to explore the associations between the energy and timing of SSB intake and PhenoAgeAccel in adults.

Methods

A cross-sectional analysis was conducted using data from the National Health and Nutrition Examination Survey (NHANES) 2007鈥�2010, which involved U.S. adults aged 20 to 79 years. The assessment and estimation of SSB intake were conducted through 24-hour dietary recall interviews, categorizing participants into three groups: non-intake, low moderate-intake, and moderate鈥揾igh-intake. Furthermore, SSB consumers were divided into three time intervals based on intake timing: dawn-to-forenoon (5:00 a.m. to 11:59 a.m.), noon-to-afternoon (12:00 p.m. to 17:59 p.m.), and dusk-to-night (18:00 p.m. to 4:59 a.m.). Multivariable linear regression models were employed to evaluate the associations between SSB intake (energy and timing) and PhenoAgeAccel. Additionally, stratified analyses and interaction analyses were conducted. Furthermore, obesity was assessed via two distinct metrics: the body roundness index (BRI) and the body mass index (BMI). Mediation analysis was conducted to investigate the mediating effect of obesity on the relationship between the energy of SSB intake and PhenoAgeAccel.

Results

After controlling for covariates, SSB intake (per 100听kcal/day) was positively correlated with PhenoAgeAccel (尾鈥�=鈥�0.179, 95% confidence interval [CI]: 0.086鈥�0.271). The moderate鈥揾igh-intake group presented a significantly greater PhenoAgeAccel than the non-intake group (尾鈥�=鈥�1.023, 95% CI: 0.414鈥�1.632). This relationship remained stable across stratified analyses. Compared with those who abstained from SSB, those who consumed SSB during the dusk-to-night period exhibited notably elevated PhenoAgeAccel (尾鈥�=鈥�0.915, 95% CI: 0.316鈥�1.514). A significant interactive effect of smoking on the SSB intake timing鈥揚henoAgeAccel association was observed (P for interaction鈥�=鈥�0.002). Mediation analysis revealed that both BRI and BMI significantly mediated the relationship between energy intake from SSB and PhenoAgeAccel, with mediation proportions of 16.29% and 16.21%, respectively.

Conclusion

Our study revealed a positive correlation between SSB energy intake and PhenoAgeAccel, which may be partially mediated by obesity. Moreover, consuming SSB during the dusk-to-night period may increase PhenoAgeAccel.

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Introduction

The world is currently facing the challenge of a continuously aging population. People aged 60 and over will represent 22% of the world鈥檚 population by 2050 [1]. This demographic shift is primarily attributed to declining fertility rates and increased life expectancy. While advancements in living standards and healthcare have contributed to prolonged lifespans, they have also been accompanied by a rising prevalence of chronic diseases and disabilities among older adults. Consequently, the increase in healthy life expectancy has lagged significantly behind the overall growth in lifespan [2, 3]. Therefore, investigating the factors contributing to accelerated aging is necessary, as it is closely associated with increased susceptibility to chronic diseases and an increased mortality risk [4].

Phenotypic age (PhenoAge) and PhenoAgeAccel are emerging biomarkers of biological aging that integrate multiple physiological and biochemical indicators to effectively assess an individual鈥檚 biological aging status. These metrics not only reflect an individual鈥檚 relative aging level compared with their peers and provide an evaluation of current health status, but also serve as predictive tools for future disease risk and mortality. As such, they offer a valuable foundation for developing personalized prevention strategies [4, 5].

The continuous increase in SSB consumption has made SSB intake a major global public health issue. In 2020, the global market for carbonated soft drinks alone was estimated to reach $392.6听billion [6, 7]. SSB is currently the primary source of added sugars for adults in the United States [8]. However, the data indicate a slight decline in SSB consumption among U.S. adults from 2003 to 2016, nearly half of the adults still consumed at least one serving of SSB daily [9, 10]. This situation is particularly alarming, as high SSB intake is closely related to various diseases, including metabolic disorders and cardiovascular disease (CVD) [7, 11,12,13]. Notably, previous studies investigating SSB intake have focused primarily on the number of servings, caloric content, or sugar content, often overlooking the critical factor of intake timing. However, consuming food at different times has varying effects on metabolic health [14].

Given the substantial evidence linking SSB intake to various chronic diseases, our study aimed to investigate the association between SSB intake and PhenoAgeAccel. Importantly, we focused not only on the energy of SSB intake but also on the underexplored yet valuable factor of consumption timing. By examining the unique role of SSB intake in influencing PhenoAgeAccel, we sought to provide novel insights into strategies for mitigating, halting, or even reversing the aging process.

Methods

Study population

The NHANES is a nationally representative cross-sectional survey designed to monitor the health and nutritional status of adults and children in the United States. For this study, we extracted data from two consecutive cycles of NHANES (2007鈥�2008 and 2009鈥�2010), which included 20,686 participants, of whom 11,164 were non-pregnant adults aged 20 to 79 years. Participants with incomplete information regarding SSB intake, PhenoAgeAccel, or covariates were excluded, and the sample size for final inclusion in the study was 3,925 individuals (Fig.听1).

Fig. 1
figure 1

Flowchart of the study design and participants

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NHANES: National Health and Nutrition Examination Survey; SSB: sugar-sweetened beverage; PhenoAgeAccel: phenotypic age acceleration.

SSB intake assessment

We assessed and estimated the energy and timing of SSB intake via data obtained from 24-hour dietary recall interviews. The participants were asked to report all the foods and beverages consumed in the past 24听h, which were subsequently matched with food and beverage entries from the food codes established by the Food and Nutrient Database for Dietary Studies (FNDDS). Additionally, participants were prompted to recall frequently forgotten food categories and detailed records were maintained regarding the timing and quantity of each food item consumed. The following beverages were classified as SSB in this study: soft drinks, sports drinks, energy drinks, fruit drinks, soda water, carbonated water, smoothies, and sweetened coffee and tea [15]. Those labeled as sugar-free/no sugar and unsweetened were excluded. The total daily intake of SSB was estimated from the sum of energy obtained from all drinks meeting the SSB criteria during the 24-hour dietary recall interview. In our study, the energy of SSB intake was also set as a categorical variable. The three groups for this purpose were as follows: Group 1: non-intake (0听kcal from SSB); Group 2: low鈥搈oderate intake (1鈥�299听kcal from SSB); and Group 3: moderate鈥揾igh intake (鈮モ��300听kcal from SSB). For reference, a 12-ounce serving of soda contains 140鈥�150听kcal, and a 13.7-ounce ready-to-drink Mocha Frappuccino coffee drink (Starbucks) contains 280听kcal. The participants who reported SSB intake were subsequently classified into three groups based on the timing of their consumption: dawn-to-forenoon period (5:00 a.m. to 11:59 a.m.), noon-to-afternoon period (12:00 p.m. to 5:59 p.m.), and dusk-to-night period (6:00 p.m. to 4:59 a.m.). The participants who consumed SSB during two or three different periods within a day were assigned to the group corresponding to the time period with the highest intake volume. In cases where the intake volume was identical across multiple time periods, participants were classified into the later time group. Additionally, we obtained participants鈥� total energy intake, protein intake, carbohydrate intake, total fat intake, caffeine intake, and moisture intake data from the 24-hour dietary recall interviews.

PhenoAge and PhenoAgeAccel

PhenoAge was calculated using nine conventional clinical chemical biomarkers in conjunction with chronological age. The final formula for calculating phenoAge is presented below [4]:

$$\:\text{P}\text{h}\text{e}\text{n}\text{o}\text{A}\text{g}\text{e}\:=\:141.5\:+\:\frac{\text{l}\text{n}[-0.00553\:\times\:\:\text{l}\text{n}\:(1\:-\:\text{m}\text{o}\text{r}\text{t}\text{a}\text{l}\text{i}\text{t}\text{y}\:\text{r}\text{i}\text{s}\text{k}\left)\right]}{0.09165}$$

where

\(\:\text{M}\text{o}\text{r}\text{t}\text{a}\text{l}\text{i}\text{t}\text{y}\:\text{r}\text{i}\text{s}\text{k}\:=\:1-\text{e}\text{x}\text{p}\:(\frac{-1.51714\:\times\:\:\text{e}\text{x}\text{p}\:\left(xb\right)}{0.0076927}\))

and

xb鈥�=鈥夆垝鈥�19.907鈥夆垝鈥�0.0336 脳 albumin鈥�+鈥�0.0095 脳 creatinine鈥�+鈥�0.1953 脳 glucose鈥�+鈥�0.0954 脳 ln (CRP)鈥夆垝鈥�0.0120 脳 lymphocyte percentage鈥�+鈥�0.0268 脳 mean cell volume鈥�+鈥�0.3306 脳 red cell distribution width鈥�+鈥�0.00188 脳 alkaline phosphatase鈥�+鈥�0.0554 脳 white blood cell count鈥�+鈥�0.0804 脳 chronological age.

Additionally, we computed PhenoAgeAccel, which was defined as the residual from the linear model obtained when PhenoAge was on regressed chronological age. Based on whether PhenoAgeAccel was greater than 0, it was defined as accelerated aging and delayed aging, respectively [16].

Anthropometric measures

Physical measurements were performed to exam height, weight, BMI and waist circumference (WC). The BRI was determined via a previously published formula [17].

Covariates

Race/ethnicity included non-Hispanic White, and other race. Education level was classified as <鈥塩ollege degree or 鈮モ�塩ollege degree. Marital status was categorized as married or living with partner and living alone. The poverty income ratio (PIR) was divided into two groups: < 1.3, 1.3鈥�3.5, and >鈥�3.5. Those who reported having smoked鈥�>鈥�100 cigarettes and smoked at the survey time were classified as current smokers. Those who had never smoked 100 cigarettes or had quit smoking were considered nonsmokers. Analogously, those who reported having鈥夆墺鈥�12 alcoholic drinks in the past year were defined as alcohol consumers, whereas those who did not were considered nonalcohol consumers. Recreational activity was assessed based on periods of moderate or vigorous recreational activities lasting鈥夆墺鈥�10 consecutive minutes per week, and those who performed鈥�<鈥�10 consecutive minutes of such activity per week were considered inactive. The definitions of diabetes, hypertension and stroke were based on participants鈥� responses to 鈥淗ave you ever been told that you have diabetes/high blood pressure/a stroke by a doctor or other health professional?鈥�. CVD in our study was defined as any reported diagnosis of coronary heart disease, angina pectoris, or heart attack.

Statistical analysis

All analyses and descriptive statistics appropriately utilized the weights recommended by the NHANES. Continuous variables were summarized as the means and standard deviations (SDs) and inter-group comparisons were performed via ANOVA. Categorical variables were presented as weighted percentages, and chi-square tests were uesd to assess differences among groups. Multivariate linear regression analyses assessed the relationships between the energy and timing of SSB intake and PhenoAgeAccel. Model 1 was unadjusted. Model 2 included adjustments for gender, age, race/ethnicity, education level, marital status, PIR, smoking status, drinking status, recreational activity, and medical history of diabetes, hypertension, CVD, and stroke. Model 3 included further adjustments for BRI and BMI. To evaluate potential variations in the estimates, stratified and interactive analyses of covariates were performed, and the results are presented using a forest plot. The mediation analysis was performed using the mediator package in R through EmpowerStats, applying the bootstrap method. This approach was employed to verify whether obesity mediates the association between energy intake from SSB and PhenoAgeAccel, with obesity measured via BMI and BRI. All the analyses were conducted using EmpowerStats version 4.2, STATA version 16.0, and SPSS version 22.0 software. A two-sided P-value less than 0.05 was considered statistically significant.

Results

Baseline characteristics

Table听1 presented the baseline characteristics of the participants. According to the diagnostic criteria, among the 3,925 participants, 923 were identified as having accelerated aging, with females accounting for 45.58% of this group. The participants with accelerated aging were generally older (49.62鈥壜扁��15.36 vs. 45.08鈥壜扁��15.28, P鈥�<鈥�0.0001), were current smokers (32.02% vs. 17.96%, P鈥�<鈥�0.0001), were inactive (61.14% vs. 41.88%, P鈥�<鈥�0.0001), had higher BRI and BMI values (6.58鈥壜扁��2.56 vs. 4.80鈥壜扁��1.89; 32.49鈥壜扁��7.60 vs. 27.66鈥壜扁��5.61, P鈥�<鈥�0.0001), and had higher SSB intake (174.29鈥壜扁��264.30 vs. 139.32鈥壜扁��225.75, P鈥�=鈥�0.0002). Significant differences were also observed in gender, race, education level, marital status, family income, drinking status, and history of diabetes, hypertension, CVD and stroke (P鈥�&濒迟;鈥�0.05).

Table 1 General characteristics of the study population
Full size table

Associations between SSB intake and PhenoAgeAccel

Table听2 presented the results of the multiple linear regression models examining the associations between SSB intake and PhenoAgeAccel. When SSB intake was treated as a continuous variable, a positive correlation with PhenoAgeAccel was observed in the crude model (尾鈥�=鈥�0.240, 95% CI: 0.141鈥�0.340). This association remained significant in both the partially adjusted model (尾鈥�=鈥�0.215, 95% CI: 0.119鈥�0.311) and the fully adjusted model (尾鈥�=鈥�0.179, 95% CI: 0.086鈥�0.271)., When the energy of SSB intake was subsequently analyzed as a categorical variable, the unadjusted model revealed a significant increase in PhenoAgeAccel for Group 3 vs. Group 1 (尾鈥�=鈥�1.318, 95% CI: 0.663鈥�1.974). Compared with Group 1, Group 3 consistently exhibited greater PhenoAgeAccel in both the partially adjusted model (尾鈥�=鈥�1.195, 95% CI: 0.564鈥�1.825) and the fully adjusted model (尾鈥�=鈥�1.023, 95% CI: 0.414鈥�1.632). However, regardless of whether the model was adjusted, SSB consumption during dusk-to-night period was significantly associated with increased PhenoAgeAccel. Notably, in the fully adjusted model, 尾 was 0.915 (95% CI: 0.316鈥�1.514).

Table 2 Association between SSB intake and PhenoAgeAccel
Full size table

Stratified analyses and interactive analyses

The results of the stratified analysis indicated that in all subgroups, the energy of SSB intake was positively correlated with PhenoAgeAccel, although a few associations were not statistically significant (Figs.听2 and 3). Taking into account the effects of various other lifestyle factors, we conducted interaction analyses on the SSB intake timing鈥揚henoAgeAccel association. The results demonstrated a significant interaction between smoking and the SSB intake timing鈥揚henoAgeAccel association (P for interaction鈥�=鈥�0.002). Unlike other groups, in the current smoking subgroup, individuals who consumed SSB during the dawn-to-forenoon period exhibited higher PhenoAgeAccel (尾鈥�=鈥�1.524, 95% CI: 0.039鈥�3.010). Conversely, no significant increase in PhenoAgeAccel was observed for those consuming SSB during the dusk-to-night period, and even a decrease was possible (Fig.听4) .

Fig. 2
figure 2

Forest plot of stratified analysis of the association between the energy of SSB intake and PhenoAgeAccel. Note: SSB intake (per 100听kcal/d). Adjusted for Model 3 (adjusted covariates: gender, age, race/ethnicity, education level, marital status, PIR, smoking status, drinking status, recreational activity, BRI, BMI, and medical history of diabetes, hypertension, cardiovascular disease, and stroke)

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Fig. 3
figure 3

Forest plot of stratified analysis of the association between the energy of SSB intake and PhenoAgeAccel. Note: SSB intake (Group 3 vs. Group 1). Adjusted for Model 3 (adjusted covariates: gender, age, race/ethnicity, education level, marital status, PIR, smoking status, drinking status, recreational activity, BRI, BMI, and medical history of diabetes, hypertension, cardiovascular disease, and stroke)

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Fig. 4
figure 4

Forest plot of interactive analyses of the association between the timing of SSB intake and PhenoAgeAccel. Adjusted for Model 3 (adjusted covariates: gender, age, race/ethnicity, education level, marital status, PIR, smoking status, drinking status, recreational activity, BRI, BMI, and medical history of diabetes, hypertension, cardiovascular disease, and stroke)

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Mediation analysis

In this analysis, BRI and BMI were employed as measures of obesity. The results (Fig.听5) indicated that both BRI and BMI had significant indirect effects (mediation effects) on the relationship. Specifically, the mediation proportions were 16.29% and 16.21%, respectively (P鈥�&濒迟;鈥�0.05).

Fig. 5
figure 5

Mediation effects of obesity on the association between the energy of SSB intake and PhenoAgeAccel. Note: exposure: SSB intake, outcome: PhenoAgeAccel, mediator: BRI (A); BMI (B). Adjusted for Model 2 (adjusted covariates: gender, age, race/ethnicity, education level, marital status, PIR, smoking status, drinking status, recreational activity, and medical history of diabetes, hypertension, cardiovascular disease, and stroke)

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Discussion

Aging is a complex and heterogeneous process characterized by dysregulated nutrient sensing, chronic inflammation, dysbiosis, telomere attrition, among others [3, 18]. The evidence suggests that biological aging is modifiable through various interventions, including genetic manipulation, behavioral changes, and pharmacological treatments [19, 20].

Globally, particularly in developing countries, SSB consumption is on the rise, driven by increasing urbanization and aggressive beverage marketing [21]. For American adults, SSB is an important source of energy [8, 22].

The potential pathways linking SSB intake to PhenoAgeAccel may be numerous and complex. First, research has demonstrated that Western diets, characterized by high-sugar and high-fat contents, change the gut microbiota, negatively impacting metabolic health [23]. Notably, even noncaloric artificial sweeteners have been shown to affect the gut microbiota [24]. Recently, Kawano and colleagues revealed that the gut microbiota can prevent metabolic diseases by inducing a symbiotic response of Th17 cells. Conversely, a high-sugar, high-fat diet depletes the microbiota capable of inducing Th17 cells, thereby promoting the development of metabolic syndrome [25]. SSB intake may similarly shape the gut microbiota and the dynamic changes in the composition and structure of the gut microbiota play a critical role in the aging process. Age-related microbial dysbiosis has been linked to a decline in immune system function [26, 27]. Second, high sugar intake has long been demonstrated to induce oxidative stress and inflammatory responses [28,29,30]. Recent studies have also shown that SSB intake is associated with chronic inflammation [31, 32]. A randomized controlled trial further revealed that SSB intake disrupts glucose and lipid metabolism in young men, promoting inflammation [33]. Chronic inflammation is considered a core mechanism of aging, driven by age-related intrinsic dysregulation. Increasing evidence is shedding light on the intricate mechanisms and interactions between inflammation and oxidative stress during aging [34,35,36]. Excessive SSB intake may accelerate biological aging by inducing oxidative stress and inflammatory responses. Third, telomere length is considered a critical biological marker of aging [18, 37]. Studies have suggested a significant association between higher SSB intake and shorter telomere length [38,39,40], which may represent another important factor contributing to the increased PhenoAgeAccel observed with higher SSB intake.

Obesity is another critical factor that warrants consideration. Our mediation analysis revealed that obesity partially mediated the relationship discussed above. Previous studies have consistently shown a strong associations between SSB intake and increased obesity and BMI in both adolescents and adults [13, 41,42,43]. Brunkwall et al., in a study involving 26,729 Swedish adults, reported a stronger correlation between SSB intake and BMI among individuals with obesity [44]. Similarly, McGlynn et al. reported that substituting SSB with low- and no-calorie sweetened beverage (LNCSB) may be associated with weight reduction [45]. Obesity further disrupts the metabolism, decreasing insulin sensitivity and increasing difficulty adapting to the demand for energy supply [46]. Obesity not only accelerates the progression of age-related diseases but also may directly impact the aging process [47, 48]. The deterioration of nutritional signaling pathways is one of the important metabolic effects of aging, with mechanistic target of rapamycin (mTOR) and sirtuins being the most classical examples [49]. The intricate interactions among obesity, metabolic dysregulation, and these nutrient-sensing pathways provide insights into how obesity accelerates aging [48]. Overall, these findings support our conclusion that SSB intake is significantly associated with PhenoAgeAccel, a relationship that may be partially mediated by obesity. Notably, both BRI and BMI were used as measures of obesity in this study. It has been reported that BRI offers a more accurate assessment of obesity [17].

Previous research has demonstrated that alterations in meal timing adversely affect metabolic health, with evidence suggesting that nighttime energy intake may impair glucose tolerance [50, 51]. The circadian rhythm of glucose tolerance is partially mediated by the diurnal rhythm of systemic insulin sensitivity [52]. In healthy individuals, time-dependent glucose tolerance is also strongly influenced by the rhythmic sensitivity of pancreatic 尾-cells to glucose [53]. Persistent late-night carbohydrate consumption may accelerate the progressive dysfunction and apoptosis of 尾-cells [54]. Furthermore, nighttime eating has been shown to increase plasma triglyceride concentrations [55]. Late eating is also associated with higher BMI, potentially increasing the risk of chronic obesity-related diseases [56]. The intake of SSB late at night may exacerbate metabolic dysregulation and lead to more severe obesity, contributing to related conditions such as accelerated aging, which aligns with our findings. On the other hand, sugar can enter the human body through the gastrointestinal tract. Intestinal epithelial cells throughout the gut contain a molecular clock synchronized by signals generated from food intake [57]. For example, brush border disaccharidases have been found to exhibit circadian rhythmic activity [58]. This may partially explain why the intake of SSB at different times of the day can lead to varying biological aging outcomes.

Our stratified and interaction analyses revealed two noteworthy findings. First, the association between the energy of SSB intake and PhenoAgeAccel appeared to be strongest in the 50鈥�79 age group across all subgroups. A potential explanation is that upstream health determinants, such as nationality, education, occupation, and financial status, exert influence throughout life, with some negative cumulative effects becoming apparent only in later life. These accumulations may accelerate the onset of age-related chronic diseases [59, 60]. Additionally, declines in attention, cognitive function, and memory capacity often begin in the middle-to-late stages of middle age [61]. In contrast to the upstream health determinants mentioned earlier, downstream health determinants include beliefs, stress, emotions, physical activity, smoking, alcohol consumption, and dietary patterns [62]. Positive psychological states can enhance mental resilience and improve emotional regulation [63], whereas subjective well-being has been shown to positively impact physical and mental health, particularly in older adults [64]. In the interaction analyses, we considered downstream factors, including smoking, alcohol consumption, and physical activity, that may have a moderating effect on the relationship between SSB intake timing and PhenoAgeAccel. Another interesting finding was that smoking is a moderating variable in this association. Recently, a cohort study based on the UK Biobank reported that a shorter time from waking up to smoking the first cigarette is associated with an increased risk of developing type 2 diabetes [65]. Similarly, Tang et al. reported that the duration from waking up to smoking the first cigarette is related to chronic kidney disease (CKD), and further research indicated that unhealthy dietary habits exacerbate this association [66]. Collectively, these health factors, along with biological age, PhenoAge, and age-related diseases, form a complex and interconnected network.

Previous investigations have shown that replacing SSB with healthy beverages like plain water, low-fat milk, and unsweetened coffee can help reduce the incidence of diabetes and CKD [32, 67]. These substitutes are simple, affordable, safe and effective. Some have even suggested implementing taxes on SSB to encourage individuals to choose healthier beverage options [68, 69]. Our findings also support such substitution strategies as potential means to regulate the aging process. On the other hand, some researchers have proposed that nighttime diets low in sugar or restricting nighttime food intake may help mitigate the burden of metabolic diseases [70, 71]. In this context, we recommend strict control of SSB intake, particularly during the dusk-to-night period, as an intervention strategy to prevent accelerated biological aging.

This study demonstrates several advantages. First, we introduced the concept of the timing of SSB intake, providing a novel perspective on dietary behavior. Second, we established the associations between the energy and timing of SSB intake and PhenoAgeAccel, exploring their relationships from multiple dimensions. Third, the study is based on a nationally representative sample, and appropriate sample weights were applied. Finally, in the mediation analysis, BMI and BRI were used as indicators of obesity, which helped mitigate sensitivity issues to some extent. Several limitations should also be considered. First, the assessment of SSB intake was based on a 24-hour dietary recall interview, which is unique and detailed but it may not fully capture participants鈥� long-term SSB intake habits. Second, participants diagnosed with diabetes may have influenced the results, as they are often advised to control their weight or reduce sugar intake, potentially leading to an underestimation of the observed associations. Finally, although adolescents are a group with high SSB intake, given the lack of excessive information, they were not included in this study.

Conclusions

In adults, increased SSB intake is significantly associated with elevated PhenoAgeAccel. Obesity may partially mediate this relationship. Notably, consuming SSB during the dusk-to-night period may increase PhenoAgeAccel. These findings suggest that reducing SSB intake, particularly during dusk-to-night hours, may help slow or even reverse the biological aging process.

Data availability

This study utilized publicly available data from NHANES, which can be accessed through the following link: .

Abbreviations

SSB:

Sugar sweetened beverage

PhenoAgeAccel:

Phenotypic age acceleration

NHANES:

National Health and Nutrition Examination Survey

BRI:

Body roundness index

BMI:

Body mass index

CI:

Confidence interval

PhenoAge:

Phenotypic age

CVD:

Cardiovascular disease

FNDDS:

Food and Nutrient Database for Dietary Studies

WC:

Waist circumference

PIR:

Poverty income ratio

SD:

Standard deviation

LNCSB:

Low- and no-calorie sweetened beverage

mTOR:

mechanistic target of rapamycin

CKD:

Chronic kidney disease

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Author notes

  1. Teng Xia and Qian Yuan contributed equally to this work.

Authors and Affiliations

  1. Department of Thoracic Surgery, Nanjing Gaochun People鈥檚 Hospital (The Gaochun Affiliated Hospital of Jiangsu University), Nanjing, Jiangsu, 211300, China

    Teng Xia,听Qian Yuan听&听Yao Zhang

  2. Department of Clinical Nutrition, Nanjing Gaochun People鈥檚 Hospital (The Gaochun Affiliated Hospital of Jiangsu University), Nanjing, Jiangsu, 211300, China

    Guangmei Shan

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  1. Teng Xia

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  2. Qian Yuan

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  3. Yao Zhang

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Teng Xia and Qian Yuan wrote the main manuscript text and Yao Zhang and Guangmei Shan prepared all Tables and Figures. All authors reviewed the manuscript.

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Correspondence to Guangmei Shan.

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Xia, T., Yuan, Q., Zhang, Y. et al. The associations between the energy and timing of sugar-sweetened beverage intake and phenotypic age acceleration in U.S. adults: a cross-sectional survey of NHANES 2007鈥�2010. 樱花视频 25, 88 (2025). https://doi.org/10.1186/s12889-024-21249-3

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  • DOI: https://doi.org/10.1186/s12889-024-21249-3

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ISSN: 1471-2458

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