Fruit and vegetable consumption and metabolic syndrome in Chinese adults: a cross-sectional study

Abstract

Background:

Evidence suggests inverse associations between fruit and vegetable intakes and metabolic syndrome (MetS), but data from Chinese populations remain limited, particularly regarding component-specific associations and whether associations vary according to concurrent intake of other major food groups.

Objective:

To examine associations of fruit and vegetable intakes with MetS and its individual components, and to explore whether these associations vary across co-consumption patterns of other major food groups.

Materials and methods:

This community-based cross-sectional study included 5,107 adults from Suzhou, China. Dietary intake was assessed using a food frequency questionnaire. Multivariable logistic regression models were used to estimate adjusted odds ratios (ORs) and 95% confidence intervals (CIs). Joint analyses evaluated combinations of fruit or vegetable intake with other major food groups.

Results:

Higher fruit and vegetable intakes were associated with lower odds of MetS. Comparing the highest with the lowest quartile, adjusted ORs were 0.82 (95% CI 0.69–0.97; p-trend = 0.02) for fruit and 0.84 (95% CI 0.70–0.99; p-trend = 0.03) for vegetables. In continuous analyses, ORs were 0.90 (95% CI 0.85–0.95) per 100 g/day increase in fruit intake and 0.91 (95% CI 0.85–0.98) per 200 g/day increase in vegetable intake. Fruit intake was inversely associated with multiple MetS components, whereas vegetable intake showed more selective associations. Joint analyses suggested that higher fruit or vegetable intake combined with lower red meat intake was associated with lower odds of MetS and several of its components.

Conclusion:

In this community-based sample of Chinese adults, higher fruit and vegetable intakes were associated with lower odds of MetS, with fruit showing broader inverse associations across individual components than vegetables. Exploratory joint analyses suggested that these associations varied according to co-consumption patterns of other major food groups, particularly red meat intake. Prospective studies are needed to clarify temporality and causality.

Introduction

Metabolic syndrome (MetS), characterized by a clustering of central obesity, dyslipidemia, elevated blood pressure, and impaired glucose metabolism, represents a major public health challenge worldwide. Individuals with MetS are at substantially increased risk of type 2 diabetes, cardiovascular disease, and premature mortality (1). The global prevalence of MetS has increased steadily over recent decades, underscoring its growing public health impact across diverse populations (2–4). In China, the prevalence of MetS rose markedly from 8.8% in 1991–1995 to 29.3% in 2011–2015, with a more pronounced increase among women (from 7.9 to 30.7%) than among men (from 9.4 to 27.2%) (4). Given this rapid rise, identifying modifiable dietary factors associated with MetS and its individual components may help inform population-level prevention and intervention strategies.

Adequate consumption of fruits and vegetables has been associated with favorable cardiometabolic outcomes, potentially owing to their high content of dietary fiber and phytochemicals (5–8). Consistent with this evidence, the Chinese Dietary Guidelines recommend that adults consume 200–350 g of fruit and 300–500 g of vegetables per day to promote optimal health (9). However, adherence to these recommendations remains suboptimal, particularly for fruit intake (10). At the same time, dietary patterns in China have undergone substantial transitions in recent decades, characterized by reduced consumption of traditional plant-based foods and increased intake of animal-source foods, particularly red meat, alongside increasingly energy-dense diets in urban populations. These dietary changes have coincided with a rising burden of obesity and other cardiometabolic disorders in China (11). Since fruits and vegetables are consumed within broader dietary patterns rather than in isolation, such shifts in overall dietary composition may complicate the interpretation of associations between fruit and vegetable intakes and metabolic health outcomes.

In habitual Chinese diets, higher intake of fruits and vegetables often co-occurs with varying levels of consumption of other major food groups, including red meat, poultry, fish, soy products, dairy, and nuts. Prior analyses conducted in this same community-based population have demonstrated that these food groups exhibit distinct—and sometimes opposing—associations with MetS and its individual components (12–14). Such dietary interrelationships raise the possibility that observed associations between fruit and vegetable intake and MetS may depend, at least in part, on the broader dietary context in which they are consumed.

Evidence from observational studies, largely conducted in Western populations, suggests that higher fruit and vegetable intakes are associated with lower odds of MetS (15, 16). However, data from Chinese populations remain relatively limited (17–20). Existing studies from mainland China and Taiwan have typically examined fruit and/or vegetable intake as secondary exposures within broader lifestyle or risk factor analyses, rather than as primary exposures of interest. Most prior investigations have focused exclusively on MetS as a composite outcome and have not examined its individual components separately. This distinction is important because associations with MetS may be driven by specific components—such as central obesity, hypertriglyceridemia, hypertension, or impaired glucose regulation—that differ in their relationships with fruit and vegetable intake. Furthermore, limited adjustment for other dietary factors and the use of broad intake categories have constrained efforts to clarify independent and component-specific associations in Chinese adults.

Therefore, using data from a community-based sample of Chinese adults, the present study aimed to: (1) examine associations of fruit and vegetable intakes—modeled both categorically and continuously—with MetS and its individual components; (2) evaluate potential heterogeneity of these associations across key population subgroups; and (3) explore how these associations vary across co-consumption patterns of other major food groups. By addressing both component-specific outcomes and dietary co-consumption patterns, this study seeks to provide a more comprehensive understanding of the relationship between fruit and vegetable intakes and metabolic health in Chinese adults.

Materials and methods

Study design and population

This cross-sectional analysis was conducted using data from a community-based health survey carried out between July 2013 and November 2014 in four residential communities in the Suzhou Industrial Park, Suzhou City, Jiangsu Province, China. All individuals aged 18 years or older who were registered residents of the selected communities were invited to participate through public announcements and outreach at local community health service centers. Among 7,866 participants who completed the baseline survey and provided written informed consent, individuals were included in the present analysis if they had complete information on fruit and vegetable intakes and all components of MetS. No additional inclusion or exclusion criteria were applied. After exclusions for missing exposure or outcome data, the final analytic sample comprised 5,107 participants. All data collection procedures, including interviews, physical examinations, and biological sample collection, were conducted by trained personnel using standardized protocols. The study protocol was approved by the Ethics Committee of Soochow University (approval number: ECSU-2010-002) and was conducted in accordance with the principles of the Declaration of Helsinki.

Dietary assessments

Habitual dietary intake over the previous year was assessed using a structured, interviewer-administered food frequency questionnaire (FFQ). Participants reported the frequency and usual portion size of major food groups, including fruits, vegetables, soy, red meat, poultry, fish, dairy, nuts, and salted vegetables. Salted vegetables were assessed as a separate food group and were not included in the primary vegetable intake exposure. Participants selected one of five frequency categories (never, yearly, monthly, weekly, or daily) and reported usual portion sizes using standard household measures [斤 (jin), 两 (liang), or mL]. Average daily intake (g/day) was calculated by multiplying the reported frequency by the portion size and converting the result to grams using standard food composition tables. The FFQ was designed primarily to rank habitual intake of major food groups rather than estimate nutrient or total energy intake; therefore, detailed information on total energy intake and cooking methods was not available. In addition, the FFQ has not been formally validated against repeated dietary recalls or biomarkers in this population.

Anthropometric and biochemical measurements

Fasting venous blood samples were collected after an overnight fast of 10–12 h. Plasma concentrations of fasting glucose, total cholesterol, triglycerides, high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) were measured using an automated biochemical analyzer (Olympus AU640, Kobe, Japan). Blood pressure was measured with participants seated using a calibrated mercury sphygmomanometer (Shanghai Zhangdong Med-Tech Ltd., Shanghai, China). Two measurements were taken after at least 5 min of rest, and the average was used for analysis. Anthropometric measurements were obtained by trained staff following standardized procedures. Body weight was measured to the nearest 0.1 kg, and height and waist circumference were measured to the nearest 0.1 cm. Body mass index (BMI) was calculated as weight (kg) divided by height squared (m²).

Assessment of covariates

Sociodemographic and lifestyle characteristics were collected through structured interviews. Covariates included age, sex, educational attainment (<high school, high school/vocational, ≥college), smoking status (never, former, current), alcohol consumption (0, 1–3, or >3 times/week), physical activity level (self-reported), sleep duration (hours/day), and television watching time (hours/day).

Definition of metabolic syndrome

The use of medications for diabetes, dyslipidemia, or hypertension was also considered when classifying the corresponding components.

Statistical analysis

Participants were categorized into quartiles according to their average daily intakes of fruits and vegetables. Differences in participant characteristics across quartiles were assessed using one-way analysis of variance (ANOVA) for continuous variables and chi-square tests for categorical variables.

Multivariable logistic regression models were used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for MetS and its individual components across quartiles of fruit and vegetable intakes, with the lowest quartile serving as the reference category. Given the cross-sectional design, ORs should be interpreted as prevalence ORs. Linear trends across quartiles were evaluated by modeling the median intake of each quartile as a continuous variable.

Fruit and vegetable intakes were analyzed both as categorical variables (quartiles) and as continuous variables, modeled per 100 g/day increase in fruit intake and per 200 g/day increase in vegetable intake to reflect meaningful differences in habitual consumption within the study population. Restricted cubic spline analyses were additionally performed to evaluate potential non-linear associations between fruit and vegetable intakes and MetS, using knots placed at the 10th, 50th, and 90th percentiles of the intake distributions. Tests for non-linearity were conducted by evaluating the statistical significance of the non-linear spline terms.

All multivariable models were adjusted for age, sex, educational level, smoking status, alcohol consumption, physical activity, body mass index (BMI), sleep duration, television watching time, and intake of other major food groups. Fruit and vegetable intakes were mutually adjusted in all relevant models.

Total energy intake was not included in the primary models because the FFQ was designed primarily to rank habitual intake of major food groups rather than estimate total energy intake, and detailed energy intake data were therefore unavailable.

Variance inflation factors were examined to assess potential multicollinearity among dietary variables included in the multivariable models, and no evidence of substantial multicollinearity was observed (all variance inflation factors <2.5).

Stratified analyses were conducted to examine whether associations differed by age (<55 vs. ≥55 years), sex, BMI (<25 vs. ≥25 kg/m²), and smoking status (never vs. former/current smokers). To preserve statistical power, subgroup analyses were performed with fruit and vegetable intakes modeled as continuous variables. Within each subgroup, ORs and 95% CIs were estimated for a 100 g/day increase in fruit intake and a 200 g/day increase in vegetable intake using multivariable logistic regression models, adjusted for the same covariates as in the primary analyses, with the stratifying variable excluded from the corresponding model. Statistical interaction was assessed by including multiplicative interaction terms between continuous fruit or vegetable intake and the stratifying variable in fully adjusted models, and p values for interaction were derived using Wald tests.

Joint analyses were conducted to examine whether associations of fruit and vegetable intake with metabolic outcomes varied across co-consumption patterns of other major food groups. These analyses were exploratory and do not represent formal tests of interaction or comprehensive dietary pattern analyses. Fruit, vegetables, red meat, poultry, fish, and soy were dichotomized into high- and low-intake categories based on the median intake in the study population. Because intakes of nuts and dairy were highly skewed, these food groups were categorized as non-consumers and consumers. Joint exposure categories were then created by combining fruit or vegetable intake with intake of red meat, poultry, fish, soy, nuts, or dairy. For each joint analysis, the reference category comprised participants with lower fruit or vegetable intake and lower intake of the corresponding food group; for nuts and dairy, the reference category comprised participants with lower fruit or vegetable intake and no consumption of the corresponding food group. Associations with metabolic outcomes were evaluated using multivariable logistic regression models. All joint models were adjusted for the same covariates as the primary analyses; when a food group was included in a joint exposure, it was excluded from the corresponding adjustment set to avoid over-adjustment from including the same exposure in both the model and the joint variable.

All statistical analyses were performed using SPSS version 20.0 (SPSS Inc., Chicago, IL, USA). All tests were two-sided, and p values <0.05 were considered statistically significant.

Results

Participant characteristics

Participant characteristics according to quartiles of fruit and vegetable intakes are shown in Table 1. Higher intakes of fruits and vegetables were associated with younger age, higher educational attainment, lower waist circumference, lower systolic and diastolic blood pressure, lower fasting glucose concentrations, higher HDL-C levels, lower prevalence of current smoking, and slightly longer sleep duration (all p ≤ 0.031).

Physical activity did not differ across quartiles of fruit or vegetable intake. In contrast, alcohol consumption and television watching time varied significantly across quartiles (all p < 0.001), although no clear monotonic trends were observed. Body weight and BMI were similar across fruit intake quartiles but differed modestly across vegetable intake quartiles. Triglyceride concentrations did not vary significantly across quartiles of either exposure.

Higher fruit and vegetable intakes were associated with higher consumption of other food groups, including soy, red meat, poultry, fish, dairy, nuts, and salted vegetables, reflecting substantial co-consumption of dietary components.

Primary associations of fruit intake with metabolic syndrome and its components

Higher fruit intake was associated with lower prevalence and odds of MetS (Table 2). The prevalence decreased from 51.4% in the lowest quartile to 46.2% in the highest quartile. In multivariable-adjusted models, participants in the highest quartile had 18% lower odds of MetS compared with those in the lowest quartile (OR 0.82; 95% CI 0.69–0.97; p for trend = 0.02).

Higher fruit intake was also associated with lower odds of several MetS components, including elevated waist circumference, triglycerides, and fasting glucose, with a borderline inverse association observed for blood pressure. No statistically significant association was observed for reduced HDL-C in quartile-based analyses. Significant linear trends were observed across quartiles for these outcomes (p for trend ≤ 0.03).

When modeled continuously, each 100 g/day increase in fruit intake was associated with lower odds of MetS and multiple components, including waist circumference, triglycerides, blood pressure, and fasting glucose.

Primary associations of vegetable intake with metabolic syndrome and its components

Higher vegetable intake was similarly associated with lower odds of MetS (Table 3). The prevalence declined from 52.6% in the lowest quartile to 47.5% in the highest quartile. Participants in the highest quartile had 16% lower odds of MetS than those in the lowest quartile (OR 0.84; 95% CI 0.70–0.99; p for trend = 0.03).

In analyses of individual components, higher vegetable intake showed inverse but generally weaker associations. Borderline inverse associations were observed for waist circumference, blood pressure, and fasting glucose, whereas associations with triglycerides and HDL-C were not statistically significant.

Continuous analyses showed that each 200 g/day increase in vegetable intake was associated with lower odds of MetS, waist circumference, blood pressure, and fasting glucose, while associations with triglycerides and HDL-C remained non-significant.

Restricted cubic spline analyses

Restricted cubic spline analyses were conducted to examine potential non-linear associations between fruit and vegetable intakes and MetS (Figures 1A,B). The spline curves were generally consistent with inverse associations between fruit and vegetable intakes and MetS across the observed intake ranges. However, there was no statistically significant evidence of non-linearity for fruit intake (p for non-linearity = 0.64) or vegetable intake (p for non-linearity = 0.66).

Figure 1

Subgroup analyses

Inverse associations between fruit intake and MetS were consistently observed across subgroups defined by age, sex, BMI, and smoking status (Table 4). Associations were generally stronger among younger participants, women, individuals with BMI ≥ 25 kg/m², and never smokers, although no statistically significant interactions were detected (all p for interaction > 0.08). Similar patterns were observed for individual MetS components.

For vegetable intake, inverse associations with MetS were also generally consistent across subgroups, with somewhat stronger associations among younger participants and women. Associations with individual components were directionally similar but less consistent. No statistically significant interactions were detected (all p for interaction >0.09).

Joint associations of fruit or vegetable intake with other food groups

In joint analyses, higher fruit intake combined with lower red meat intake was consistently associated with lower odds of MetS and all of its individual components (Supplementary Table 1). Similar inverse associations were observed for combinations of higher fruit intake with lower intake of other food groups, although associations with waist circumference, blood pressure, and fasting glucose were generally weaker and often borderline statistically significant.

In the joint analyses, combinations involving higher vegetable intake showed more consistent inverse associations across outcomes (Supplementary Table 2). Higher vegetable intake, combined with lower red meat intake, was associated with lower odds of MetS and of all its individual components. Combinations of higher vegetable intake with lower intake of other food groups were also associated with lower odds of MetS and several components, particularly triglycerides, HDL-C, and waist circumference, while associations with blood pressure and fasting glucose were generally weaker. Conversely, combinations characterized by lower fruit or vegetable intake and higher red meat intake were generally associated with higher odds of MetS and several components, or showed no clear association for some outcomes.

Discussion

In this community-based cross-sectional study of Chinese adults, higher habitual intakes of fruits and vegetables were associated with lower odds of metabolic syndrome (MetS). Participants in the highest quartile of fruit and vegetable intakes had 18 and 16% lower odds of MetS, respectively, compared with those in the lowest quartile, with similar inverse associations observed in continuous analyses. Higher fruit intake was consistently associated with lower odds across multiple MetS components, including central adiposity, triglycerides, blood pressure, and fasting glucose, whereas associations for vegetable intake were generally weaker and more component-specific, particularly for central adiposity, blood pressure, and fasting glucose. These associations were broadly consistent across population subgroups, with no statistically significant interactions observed.

Our findings are consistent with prior observational studies and meta-analyses, largely conducted in Western populations, reporting inverse associations between fruit and vegetable intakes and MetS (15, 16). However, evidence from Chinese populations has been limited and inconsistent (17–20). Many previous studies examined fruit and vegetable intakes as secondary exposures and focused primarily on MetS as a composite outcome. In contrast, the present study treated fruit and vegetable intakes as primary exposures and examined both overall MetS and its individual components. Notably, the associations differed by component, with fruit intake showing more consistent inverse associations than vegetable intake, particularly for fasting glucose and triglycerides. Although the mechanisms underlying these differences cannot be established in the present cross-sectional study, they may partly reflect variation in consumption patterns, nutrient bioavailability, and preparation methods. Fruits and vegetables provide dietary fiber and phytochemicals that have been associated with favorable cardiometabolic effects in clinical trials, including improvements in fasting glucose, insulin resistance, blood pressure, and lipid metabolism (21, 22). Preparation practices may also partly influence these associations. Fruits are typically consumed raw, whereas vegetables in Chinese diets are often cooked or prepared with added oils, sauces, or salt. Thermal processing may reduce concentrations of heat-sensitive compounds such as glucosinolates and vitamin C (23), while added oils or sodium may introduce additional dietary exposures that could attenuate or obscure inverse associations. These culinary differences may help explain why vegetable intake showed weaker or more selective associations in the present study than fruit intake, and why these associations differed from findings in some Western populations, where raw vegetable consumption may be more common. Notably, restricted cubic spline analyses showed inverse associations between fruit and vegetable intakes and prevalent MetS, with no statistically significant evidence of non-linearity. The observed intake distributions encompassed the recommendations in the current Chinese Dietary Guidelines for fresh fruit (200–350 g/day) and vegetables (300–500 g/day) (9). Within these intake ranges, the spline analyses did not identify a clear threshold, inflection point, or plateau in the associations with prevalent MetS. Although these findings do not establish optimal intake levels or causal effects, they suggest that the observed associations were not confined to a specific intake level within the ranges studied.

The joint analyses provide additional insight into how associations of fruit and vegetable intakes with MetS may vary across co-consumption patterns of other major food groups. Higher fruit or vegetable intake combined with lower red meat intake was consistently associated with lower odds of MetS and its individual components, whereas combinations characterized by lower fruit or vegetable intake and higher red meat intake were generally associated with higher odds of MetS and several of its components or showed no clear association. Although these analyses were exploratory and did not establish causal interaction, several biological mechanisms may plausibly contribute to these patterns. Lower red meat intake may reduce exposure to saturated fat and heme iron, which have been linked to elevated blood cholesterol, oxidative stress, lipid peroxidation, and insulin resistance (24). In contrast, dietary patterns characterized by higher fruit and vegetable intake may reflect broader dietary behaviors associated with more favorable metabolic profiles. Combinations of higher fruit or vegetable intake with greater consumption of fish, dairy, or soy were also associated with lower odds of MetS and several components, broadly consistent with previous analyses from this study population reporting associations of these food groups with cardiometabolic outcomes (12–14). Overall, these findings suggest that associations between fruit and vegetable intakes and metabolic health may depend, in part, on the broader dietary context in which these foods are consumed rather than on isolated food groups alone. This interpretation is consistent with findings from dietary pattern studies in Chinese populations, which have reported lower MetS prevalence with plant-forward or mixed plant–aquatic patterns and higher prevalence with meat-rich patterns (25, 26).

Several limitations should be considered. First, temporal direction cannot be established due to the cross-sectional design. Reverse causation is possible, whereby individuals diagnosed with hypertension, dyslipidemia, or impaired glucose regulation may have subsequently increased fruit or vegetable consumption following medical advice or health concerns. Such behavioral modification could attenuate or otherwise influence the observed associations, particularly for vegetables, where effect sizes were modest, and several associations were borderline statistically significant. Accordingly, the observed ORs should be interpreted as prevalence associations rather than estimates of causal effect. Second, dietary intake was assessed using an FFQ and is therefore subject to recall error and reporting bias. The lack of detailed information on total energy intake, food subtypes, and preparation methods may have introduced measurement error and limited the ability to account for overall diet quality. The relatively limited granularity of the five-category FFQ may therefore have introduced non-differential exposure misclassification, which would likely bias associations toward the null. Residual confounding by total energy intake, therefore, cannot be excluded. In addition, cooking methods may represent an important source of residual confounding in this population. In Chinese dietary practice, vegetables are frequently stir-fried or prepared with added oils, sauces, or salt, which may alter nutrient bioavailability and introduce additional metabolic exposures, including sodium and cooking fats. Since preparation methods were not captured in the FFQ, the observed associations may partly reflect the combined effects of vegetables and their preparation practices rather than vegetable intake alone. Third, residual confounding cannot be ruled out, as fruit and vegetable intakes are closely correlated with other health-related behaviors and aspects of diet that may not be fully captured by measured covariates. Although joint analyses accounted for co-consumption of major food groups, unmeasured dietary or nutrient-level factors may still contribute to the observed associations. Fourth, participants were recruited from urban communities in Suzhou Industrial Park, an economically developed region with relatively high healthcare access and health literacy. Dietary behaviors and cardiometabolic risk profiles in this population may differ from those in rural or lower-income regions of China, potentially limiting generalizability. Finally, the joint analyses were exploratory and descriptive and do not constitute formal tests of interaction or comprehensive dietary pattern analyses; therefore, they should be interpreted with caution and not as evidence of biological interaction or causal synergy.

Conclusion

In this community-based sample of Chinese adults, higher fruit and vegetable intakes were associated with lower odds of MetS, with fruit showing broader inverse associations across individual components than vegetables. Joint analyses suggested that these associations varied according to co-consumption patterns of other major food groups, particularly red meat intake, highlighting the importance of considering overall dietary context when interpreting associations between specific food groups and metabolic health.

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