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Article

Relationship between Circulating 25-Hydroxyvitamin D and Metabolic Syndrome in Chinese Adults: A Large Nationwide Longitudinal Study

by
Mi Shu
1,2,
Yue Xi
1,
Jie Wu
2,
Lai-Bao Zhuo
1,
Yan Yan
1,
Yi-Duo Yang
2,
Yue-Yue Feng
2,
Hua-Qiao Tan
1,
Hui-Fang Yang
2,* and
Yu-Ming Chen
1,*
1
Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
2
Yibicom Health Management Center, Guangzhou 510530, China
*
Authors to whom correspondence should be addressed.
Nutrients 2024, 16(10), 1480; https://doi.org/10.3390/nu16101480
Submission received: 2 April 2024 / Revised: 9 May 2024 / Accepted: 10 May 2024 / Published: 14 May 2024
(This article belongs to the Section Micronutrients and Human Health)
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Abstract

:
Objective: This study investigated the association of circulating levels of 25-hydroxyvitamin D (25[OH]D) with the risk of metabolic syndrome (MetS) and its components in adults. Methods: This nationwide cohort involved 23,810 Chinese adults attending annual health evaluations. Serum 25(OH)D levels, MetS status, and covariates were determined at each examination. Among them, 8146, 3310, and 1971 completed two, three, and more than three evaluations, respectively. A hybrid mixed-effects and Cox regression model was employed to determine the cross-sectional and longitudinal relationships. Results: The odds ratios (ORs) and 95% confidence intervals (CIs) of MetS were significantly lower in individuals within quartile 4 (vs. 1) of serum 25(OH)D for both between-individual (0.43 [0.35, 0.52]) and within-individual comparisons (0.60 [0.50, 0.73]), respectively (all p-trends < 0.001). Among the MetS components, the corresponding ORs (95% CI) in between- and within-individual comparisons were 0.40 (0.29, 0.54) and 0.26 (0.19, 0.36) for abdominal obesity, 0.49 (0.41, 0.58) and 0.78 (0.66, 0.93) for high triglycerides, 0.70 (0.59, 0.82) and 0.75 (0.64, 0.87) for hypertriglyceridemia, 0.48 (0.39, 0.59) and 0.87 (0.71, 1.07) for low HDL cholesterol, and 0.92 (0.76, 1.12) and 0.49 (0.41, 0.59) for hypertension, respectively. Decreased hazard ratios (95% CIs) in quartile 4 (vs. 1) of 25(OH)D were found for MetS (0.80 [0.65, 1.00]), high triglycerides (0.76 [0.62, 0.92]), abdominal obesity (0.77 [0.63, 0.96]), and low HDL cholesterol (0.64 [0.50, 0.81]). Conclusions: Decreased concentrations of serum 25(OH)D correlate significantly to a heightened MetS risk and specific components. Our findings underscore the potential preventive function of circulating vitamin D concerning metabolic disorders.

1. Introduction

Metabolic syndrome (MetS) is a medical condition distinguished by a collection of metabolic disorders, typically including abdominal obesity, dyslipidemia, hypertension, hyperglycemia, and insulin resistance [1]. Several studies have proposed potential mechanisms regarding the effect of vitamin D (VD) on MetS, such as stimulating renin gene expression [2,3], reducing insulin sensitivity [4], and influencing inflammatory responses [5]. The effect of VD on MetS may be related to the following factors: (1) In certain instances, a deficiency in VD is linked to endothelial dysfunction [6]. VD enhances nitric oxide levels, facilitating blood vessel dilation. VD deficiency can impact endothelial cells, leading to vascular dysfunction and disrupting vascular homeostasis [7]. (2) Reducing insulin resistance attributed to VD may involve phosphorylation of IRS1 at Ser307, enhanced expression of peroxisome proliferator-activated receptor gamma (PPAR-γ), and decreased phosphorylation of nuclear factor kappa B (NF-κB) p65 at Ser536 [8]. (3) Interaction between vitamin D and the VD receptor (VDR) reduces the levels of the proinflammatory cytokines, interleukin (IL)-2 and IL-12 [9]. (4) VD exerts a significant impact on lipid profiles through the suppressing the enzymatic activity of 3-Hydroxy-3-methylglutaryl-coenzyme A (HMG-CoA) reductase (HMGCR), which may enhance lipid metabolism in MetS [10,11]. Additionally, VD can serve as a fatty acid binding protein 4 (FABP4) inhibitor, offering therapeutic benefits for MetS by reducing bile secretion and regulating the PPAR signaling pathway [12].
The global prevalence of MetS is a significant health concern, affecting approximately 25% of the world’s population [13]. In the USA, this figure rises to 34.7% [14]. The situation in China is equally alarming, with a prevalence of 13.7% in 2001 [15] that surged to 33.9% in 2010 [14,16]. Particularly concerning is the annual growth rate of over 8% among individuals over 60 years [14]. This escalating trend, coupled with the high incidence rate, has significantly increased the health burden of MetS [13,17,18]. Therefore, the prevention and treatment of MetS is not just important but essential.
Several factors contribute to the development of MetS, including sex, age, ethnicity, and lifestyle changes, particularly unhealthy dietary habits leading to excessive energy intake and micronutrient deficiencies [19]. The relationship among micronutrients and VD has acquired significant attention due to its potential crucial position in metabolic diseases [20,21,22,23,24,25]. Negative associations have been discovered between VD and MetS. For instance, Lu et al. [26] reported a negative association through a cross-sectional investigation of 3289 participants aged 50–70. Similarly, Pham et al. [27] observed a beneficial association between serum 25(OH)D and MetS in a longitudinal analysis following 6682 participants for 7 years. Qi et al. [28] demonstrated in a meta-analysis that VD intervention reduced insulin resistance and hypertension but not lipids and HbA1c in adults with MetS. The results for the benefits of VD supplementation in MetS adults are inconclusive. However, this meta-analysis included randomized clinical trials (RCT) with petite sample sizes and most studies were conducted in Iran. Despite several studies investigating VD’s impact on MetS, the findings could be more consistent.
The investigation of the VD-MetS relationship has primarily relied on cross-sectional studies [21,22,23,24]. However, inconsistencies persist in the VD-MetS association [29]. Notably, a significant increase in MetS and VD deficiency among young and middle-aged adults has been observed. Yet, previous studies have predominantly focused on children [30,31], postmenopausal women [32,33], and older populations [24,34,35]. Therefore, our study’s focus on investigating the association of circulating 25(OH)D and MetS risk in young and middle-aged individuals is a novel and crucial contribution to the field.
Exploring the linkage between circulating 25(OH)D concentrations and the risks of MetS and its constituents in young and middle-aged Chinese individuals would help to provide new ideas for Mets prevention and management. The current study aims to understand the prevalence of MetS and VD deficiency in young and middle-aged Chinese people and explore the association of circulating 25(OH)D concentrations with the odds and incidence of MetS and its components. We hypothesized that people with decreased circulating 25(OH)D concentrations are more likely to be involved in MetS.

2. Study Participants and Methods

2.1. Study Participants

This cohort study encompassed participants who underwent annual health evalua-tions at the Yibicom Health Management Center in Guangzhou, China, covering five years from 2019 to 2024. Eligible participants are also required to be Chinese residents born in China, with an age of 18 or above. A total of 24,548 participants attended the health evaluations from 30 different cities across the country. Among them, we excluded those with missing data of MetS status or serum 25(OH)D (n = 560), 109 cases who did not meet the requirement of residence or the age range, and 69 cases with a malignant tumor. A total of 23,810 participants were included in the cross-sectional investigation. The longitudinal analyses contained 8146, 3310, and 1971 individuals who had completed two, three, and more than three annual health evaluations (Figure 1 and Figure 2).
The study protocol adhered to the ethical principles delineated in the 1975 Declaration of Helsinki and acquired permission from the Research Ethics Committee of Sun Yat-Sen University’s School of Public Health (No. 2024-026).

2.2. Research Method

2.2.1. Data Collection

Trained medical technicians collected data through a standardized electronic questionnaire and physical examination system during the physical examination. This questionnaire captured detailed information on demographics (age and gender), lifestyle factors (smoking and drinking history), and medical history (dyslipidemia, hypertension, coronary heart disease, stroke, and diabetes mellitus). Blood collection dates were automatically logged and categorized by season: spring, summer, autumn, and winter. Smoking status was defined as having smoked for six months or more, while alcohol consumption was considered as drinking daily for at least six consecutive months.

2.2.2. Physical Examination

Physical measurements were performed using an Omron electronic scale (model HNH-219, Tokyo, Japan) and an Omron electronic sphygmomanometer (model HBP-9021, Tokyo, Japan), adhering to international standards. Participants were required to wear a medical gown, stand barefoot and upright, and remove their shoes for precise height and weight assessments to the nearest 0.1 cm and 0.1 kg. Their body mass index (BMI) was then obtained by dividing their body weight (kg) by height2 (m). We determined the blood pressure for the participants after a 5 min rest, with no prior intake of coffee, alcohol, smoking, or emotional disturbance. Two readings were taken on the right arm, 1–2 min apart, and their average was recorded. We measured their waist circumference (WC) at the center between the iliac crest and the lower rib margin. Each measurement was taken twice, averaged, and rounded to the closest 0.1 cm.

2.2.3. Laboratory Examination

Laboratory tests were performed at the Yibicom Health Management Center, accredited under ISO15189 [36] (registration number: CNASMT0691). Subjects provided fasting venous blood samples after an 8–12 h fast and these were processed within 2 h. Blood samples were collected using the Abbott i2000SR chemiluminescent immunoassay analyzer (Abbott, Green Oaks, IL, USA) for serum 25(OH)D concentrations with a coefficient of variation (CV) of 6.25%. The hexokinase method measured fasting blood glucose (FBG) levels on an Abbott C16000 automated biochemistry analyzer. Fasting insulin levels were collected via a chemiluminescent microparticle immunoassay (CMIA). The Homeostatic Model Assessment for Insulin Resistance (HOMA-IR), calculated as fasting insulin (μU/mL) × fasting glucose (mmol/L)/22.5, was used to evaluate insulin resistance. Lipid profiles, including total triglycerides (TG) and cholesterol (TC) and HDL and LDL cholesterol (HDL-c and LDL-c), were assessed utilizing enzymatic methods. At the same time, glycated hemoglobin was analyzed through the high-performance liquid chromatography (HPLC) method on an analyzer (Bio-Rad D-10, Schiltigheim, France).

2.3. Relevant Definitions

MetS status was classified according to the National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATP III) criteria adapted for Asian Chinese individuals. Diagnosis required over two of the following five conditions: (1) abdominal obesity, with a WC ≥ 90/80 cm for men/women; (2) high TG (≥1.7 mmol/L) or ongoing treatment for dyslipidemia; (3) low HDL-c (<1.0/1.3 mmol/L for men/women); (4) hypertension, defined as diastolic blood pressure ≥ 85 mmHg or systolic blood pressure ≥ 130 mmHg, or the utilization of antihypertensive medication; and (5) hyperglycemia, represented by FBG ≥ 5.6 mmol/L or ongoing antidiabetic medication usage [37]. Incident Mets or its components were defined as detecting the condition at either follow-up in those participants without the condition at baseline. We defined VD deficiency according to the cutoffs of the Institute of Medicine of the National Academy of Sciences (IOM) [38] and the China Health Industry Standard [39] (VD deficiency < 12 ng/mL, insufficiency: 12–20 ng/mL, and sufficiency: ≥20 ng/mL for serum 25[OH]D). The VD distribution among the study participants was categorized into age- and sex-adjusted quartiles, with the following breakpoints: Q1 group (11.9 [3.1] ng/mL, 6663 cases), Q2 group (16.8 [3.6] ng/mL, 6172 cases), Q3 group (20.8 [4.2] ng/mL, 5865 cases), and Q4 group (28.0 [6.6] ng/mL, 5110 cases).

2.4. Statistical Methods

For this study, baseline data from the initial health evaluation were utilized to outline the participants’ essential characteristics. Participants were categorized into sex- and age-stratified quartiles (Q1–Q4) based on baseline serum 25(OH)D levels, with Q1 serving as the reference group. To examine distinctions in characteristics among groups, we employed an analysis of variance and Chi-square test. Survival analysis was conducted to explore the risk of MetS in quartiles 2–4 (vs. 1) of average 25(OH)D values before MetS onset in participants without MetS at baseline, and hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated by employing Cox regression in R studio (2022.07.1 Build 554) (R Studio, Boston, MA, USA). A logistic hybrid mix-effect model was employed to capture both the intra- and inter-subject relationships between VD and MetS [40]. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated to describe the estimated effect sizes for the associations between the quartiles of intra-subject ( X i j t − X ¯ i) and inter-subject variations ( X ¯ i) of 25(OH)D with the status of MetS (MetSt) at various time points (t), using the “xtreg” program of Stata/SE 17.0 software (StataCorp LLC, College Station, TX, USA), which indicate cross-sectional and longitudinal relationships between VD and the presence of MetS (Equation (1)) [40]. p-trend values were calculated by treating the quartiles as a continuous variable. We defined the statistical significance at a p-value < 0.05 (two-sided).
Y i t = b 0 + ∑ j = 1 J b B j X ¯ i + ∑ j = 1 J b W j X i j t − X ¯ i + … …
In this model [40], Yit represents the dependent (outcome) variable for participant i at time point t; b0 denotes the intercept; bWj is the regression coefficient indicating the within-individual component of the association; and bBj signifies the regression coefficient capturing the between-individual component of the association. Furthermore, Xijt is defined as the independent (exposure) variable j for individual i at time point t, while X ¯ i represents the mean of the exposure variable X across all observed time points for each participant.

3. Results

3.1. Participant Characteristics

The investigation involved 23,810 participants, comprising 12,596 males and 11,214 females. The mean (SD) values of age and serum 25(OH)D were 43.6 (13.3) years and 18.8 (7.3) ng/mL. A total of 17.9% (n = 4260), 42.9% (n = 10,213), and 39.2% (n = 9337) of participants had VD deficiency, insufficiency, and sufficiency based on serum 25(OH)D, respectively. VD deficiency and insufficiency were more frequent in women than men (64.0% vs. 57.9%). The VD deficiency prevalence was highest in summer and fall and decreased with age, and was most prevalent in the 18–44 age group. (Table S1).
Individuals in quartile 4 (vs. 1) of serum 25(OH)D had lower levels of diastolic blood pressure, pulse, BMI, WC, FBG, FBI, HOMA-IR, HbA1c, PBG, and TG (p-all < 0.05). The study also showed that MetS prevalence was significantly higher in quartile 1 (vs. 4) of serum 25(OH)D levels (30.1% vs. 23.8%) (Table 1).

3.2. Relationships between Serum 25(OH)D Levels and MetS Risk—Hybrid Mixed-Effect Model

The mixed-effects regression model revealed a significant beneficial association of inter-individual serum VD variations with the odds and risks of MetS and its components, including high WC, high TG, low HDL-C, and hyperglycemia. Specifically, participants in quartile 4 of serum 25(OH)D showed a notably lower likelihood of detecting MetS, high WC, high TG, low HDL-C, and hyperglycemia than those in quartile 1 (the lowest).
After adjusting for covariates, such as sex, age, season, smoking, and drinking habits, the results from the cross-sectional comparisons (between-subject relationship) showed that the highest (vs. lowest) quartile of VD exhibited a 57%, 60%, 51%, 52%, and 30% decrease in the likelihood of detecting MetS, high WC, high TG, low HDL-C, and hyperglycemia, respectively (all p-trends < 0.001). Longitudinal analyses (within-subject relationship) revealed that participants in quartile 4 (vs. 1) of VD had 40%, 74%, 22%, 51%, and 25% lower risks of developing MetS, high WC, HTG, hypertension, and hyperglycemia, respectively (all p-trends < 0.05). There is no significant between-subject relationship for hypertension and within-subject relationship for low HDL-c (Table 2).

3.3. Mean 25 Hydroxyvitamin D Values and the Incidence of MetS—Cox Regression Model

In quartile 4 (vs. 1) of 25(OH)D, the adjusted hazard ratios (95% CI) for the incidence of MetS, high WC, high TG, and low HDL-c are 0.80 (0.65, 1.00), 0.77 (0.63, 0.96), 0.76 (0.62, 0.92), and 0.64 (0.50, 0.81), respectively (all p-trends < 0.05). However, the beneficial association of 25(OH)D was not significant in relation to the risks of hypertension and hyperglycemia (Table 3).

4. Discussion

In this population-based study with a large sample size, we explored the associations between circulating 25(OH)D concentrations with both the odds and risk of MetS and its components among Chinese adults nationwide. Serum 25(OH)D deficiency and insufficiency were prevalent in China, with a proportion of 17.9% and 42.9%, respectively, and the MetS prevalence was 26.9%. Moreover, significant beneficial associations existed between circulating 25(OH)D concentrations and the odds and incidence of MetS and most of its components. These findings imply that VD deficiency and insufficiency may be essential in developing MetS among the Chinese population.

4.1. Prevalence of VD Deficiency and Insufficiency and VD Supplement Users in Chinese Adults

Many studies report prevalent VD deficiency and insufficiency in Chinese adults. Our findings showed 17.9% VD deficiency and 42.9% VD insufficiency among the middle-aged population. Notable variance occurred in the prevalence of VD deficiency and insufficiency among various Chinese populations, such as 28.6% deficiency and 37.6% insufficiency in urban middle-aged individuals aged 35–60 by Yin et al. [41], 78.3% deficiency (<20 ng/mL) in adolescents aged 14–28 [31], and 69.2% and 24.4% of deficiency and insufficiency among the elderly population aged 50–70 [26], respectively. This discrepancy may be due to the inconsistency in age and place of residence of the research participants.
VD insufficiency is prevalent in the Chinese population. However, domestic surveys on VD supplementation have focused on children [42] and pregnant women [43,44], with fewer surveys on young or middle-aged adults, with a proportion of VD supplement usage ranging between 4.8% to 50.9%. Our study did not collect data on supplement use, thereby limiting our ability to assess the impact of vitamin D supplements on serum levels and the prevalence of deficiency and insufficiency. Considering substantially high VD deficiency and insufficiency in this population, current VD usage is not enough to control this problem. Future studies should aim to fill this gap by examining the effects of supplementation across a broader demographic.

4.2. Serum 25(OH)D and Metabolic Syndrome

Our findings elucidate that individuals with serum 25(OH)D deficiency are more inclined to develop MetS in cross-sectional and longitudinal analyses. Importantly, this beneficial association persisted significantly even after the adjustments for sex, age, and season. This finding is in line with a comprehensive meta-analysis [45] that included 28 studies and 99,745 participants, indicating a significant reduction in the MetS prevalence with elevated serum 25(OH)D concentrations. Furthermore, large-scale, cross-sectional studies from the USA [46,47,48], the United Kingdom [49], Taiwan, and China [50] have reported similar findings among adults. However, there are differences in the results reported by Mansouri and Mehri in Iran [51,52]. These discrepancies may be because Iranian women typically cover their skin with clothing, limiting sunlight-induced VD synthesis. Intriguingly, our findings indicate that the beneficial association between VD and MetS is more pronounced in the ORs derived from hybrid logistic mixed models than in the HRs from Cox regression models. The ORs were calculated using hybrid logistic mixed-effect models to account for changes in VD and MetS status across all repeated measurements. Conversely, the Cox regression model defined incident cases based on the detection of MetS at any follow-up visit among initially MetS-free individuals, using average VD levels prior to the onset of MetS. This approach overlooked fluctuations in VD levels and subsequent changes in MetS status. Similar situations were also noted for some MeS components (e.g., hypertension and hyperglycemia). Therefore, the hybrid logistic mixed-effect models likely offer more robust evidence of the VD-MetS relationship than the Cox regression models.

4.3. Serum 25(OH)D and Abdominal Obesity and Dyslipidemia

Furthermore, our findings revealed a substantial beneficial correlation between serum 25(OH)D levels, abdominal obesity, and high TG. This finding is consistent with a prospective study on 3240 middle-aged and older adults [53]. In addition, Ford et al. [47] found a significant inverse correlation between 25(OH)D quintiles and the odds of abdominal obesity and hypertriglyceridemia in a cross-sectional analysis of 8421 20-year-old American males and nonpregnant females. Additionally, similar findings were reported in the studies conducted by Kayaniyil et al. [54] in Canada, which showed higher risks for type 2 diabetes, and Chacko et al. [33] in American postmenopausal women. Several possible factors may contribute to this correlation. Firstly, the resistance to insulin induced by free fatty acids could lead to the sequestration of VD within adipose tissue, which can be counteracted by serum 25(OH)D concentrations [55]. Alternatively, it suggests that the association may be related to obesity, often accompanied by unhealthy diet, sedentary lifestyles, reduced outdoor activity, and shorter sunlight exposure [56]. Perticone et al. [57] observed that patients on a very low-calorie ketogenic diet (VLCKD) experienced a significant increase in serum 25(OH)D levels from 18.4 (SD 5.9) to 29.3 (SD 6.8) ng/mL, attributed to a marked decrease in adiposity among the obese population. Vitamin D stored within the fat is released. The influence of vitamin D on lipid profiles involves a multifaceted mechanism [58], encompassing both the activation of its receptors and the reduction of lipid accumulation. When serum vitamin D concentrations surpass a specific limit, its beneficial impacts on lipid parameters become significantly more evident.

4.4. Serum 25(OH)D and Hyperglycemia

In the investigation of the association between VD and hyperglycemia, it was observed that FBG, FBI, HOMA-IR, HbA1c, and PBG levels were increased in participants with the highest quartile (Q4 vs. Q1) of circulating 25(OH)D. Both cross-sectional and longitudinal studies have documented a significant correlation between serum 25(OH)D and hyperglycemia. Cross-sectional studies in 3289 Chinese middle-aged individuals [26] and in 8421 American men and nonpregnant women [47] revealed a significant relationship between low 25(OH)D concentrations and increased insulin resistance risk. Cohort studies found lower concentrations of 25(OH)D were associated with increased blood glucose and insulin resistance in 524 non-diabetic subjects aged 40 to 69 years in the UK [59] and 4164 Australian adults [60]. Sciacqua et al. [61] also identified an inverse correlation between blood 25(OH)D levels and glucose tolerance. VD influences glucose and insulin homeostasis by regulating immune responses and β-cell function, enhancing peripheral insulin sensitivity, and ameliorating proinflammatory conditions [62]. Related studies suggest elevated PTH [35,61] and obesity [56] may also impact VD’s biological effects. The biological effects of VD are multifactorial and complex.

4.5. Serum 25(OH)D and Hypertension

Our longitudinal analysis found a fascinating negative correlation between serum VD and the risk of hypertension. Consistent findings were observed regarding the beneficial association between VD and hypertension [28,60,63,64]. A study on the removal of the VD receptor (VDR) gene in mice showed heightened activity in the renin–angiotensin–aldosterone system (RAAS) and elevated blood pressure levels [65]. A meta-analysis of randomized trials revealed that VD supplementation did not affect blood pressure in childhood populations [66]. A ten percent increase in serum 25(OH)D levels was not related to blood pressure (SBP) in 146,581 Europeans [67]. Therefore, large-sample, multicenter, randomized controlled trials are warranted to further understand the complex relationship between VD and blood pressure.

4.6. Strengths and Weaknesses

To the authors’ knowledge, limited studies have evaluated the association between the distribution of circulating 25(OH)D concentrations and MetS risk in Chinese adults (≥18 years). Our study aims to contribute to understanding this relationship and providing approaches for preventing MetS and VD deficiency. Additionally, our study benefits from a large and diverse sample size, encompassing participants from different genders, age groups, seasons, and geographical regions across the nation. This enhances the representativeness of our findings and facilitates their generalizability. Notably, the repeated measures of serum 25(OH)D and MetS outcomes permitted our study to employ hybrid mixed-effects models to examine longitudinal outcomes, enabling us to observe dynamic relationships and individual variations between variables accurately. This approach enhances the reliability of inference.
However, our study has some limitations. Firstly, because of the retrospective nature of the data analyses, we could not exclude the potential effects of lifestyle habits and nutritional supplements on VD levels, such as outdoor activities, dietary patterns (particularly the consumption of fatty fish or fortified foods), and VD supplementation due to unavailable data. Additionally, as an observational study, we cannot establish causality, although we minimized categorization bias in exposure indicators by assessing changes in VD levels at multiple time points.

5. Conclusions

Our study has elucidated that lower serum 25(OH)D concentrations are linked to raised risks of MetS and several of its components in young and middle-aged adults in China. The robust evidence provided by our longitudinal study design, which utilized a large nationwide sample, supports these findings. However, further well-designed randomized controlled trials are necessary to establish a causal relationship. Additionally, greater focus on the adverse effects of VD deficiency is essential to effectively mitigate its negative impacts. Further in-depth studies examining the components of MetS and VD are also recommended.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/nu16101480/s1, Table S1: Baseline characteristics of participants based on the status of serum 25(OH)D.

Author Contributions

Y.-M.C. and H.-F.Y. conceived and designed the study; M.S., Y.X., L.-B.Z. and Y.Y. performed data analysis; M.S. and Y.X. wrote the manuscript. M.S., J.W., Y.-D.Y., Y.-Y.F. and H.-Q.T. performed the data collection and field organization. Y.-M.C. critically reviewed and revised the manuscript and was primarily responsible for the final content. All authors have read and agreed to the published version of the manuscript.

Funding

This study was jointly supported by the National Natural Science Foundation of China (Nos. 82073546 and 81773416). The funding sponsors had no role in the design of the study; in the collection, analysis, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Institutional Review Board Statement

The study protocol was approved by the Public Health Ethics Committee, Sun Yat-sen University (No. 2024-026), approval date: 3 April 2024. Patients or the public did not participate in the study design, conducting the study, reporting of findings, or dissemination plans of this research.

Informed Consent Statement

Patient consent was waived because the data analyzed in this article are routine health management data after removing personal identity information.

Data Availability Statement

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors.

Acknowledgments

We thank all study participants of this study and all other team members involved in the data collection and analyses in this study.

Conflicts of Interest

Authors Mi Shu, Jie Wu, Yi-Duo Yang, Yue-Yue Feng and Hui-Fang Yang were employed by the Yibicom Health Management Center. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

References

  1. Punthakee, Z.; Goldenberg, R.; Katz, P. Definition, Classification and Diagnosis of Diabetes, Prediabetes and Metabolic Syndrome. Can. J. Diabetes 2018, 42 (Suppl. S1), S10–S15. [Google Scholar] [CrossRef]
  2. Abbas, M.A. Physiological functions of Vitamin D in adipose tissue. J. Steroid Biochem. Mol. Biol. 2017, 165, 369–381. [Google Scholar] [CrossRef]
  3. Mosekilde, L. Vitamin D and the elderly. Clin. Endocrinol. 2005, 62, 265–281. [Google Scholar] [CrossRef]
  4. Pinelli, N.R.; Jaber, L.A.; Brown, M.B.; Herman, W.H. Serum 25-hydroxy vitamin d and insulin resistance, metabolic syndrome, and glucose intolerance among Arab Americans. Diabetes Care 2010, 33, 1373–1375. [Google Scholar] [CrossRef]
  5. Prietl, B.; Treiber, G.; Mader, J.K.; Hoeller, E.; Wolf, M.; Pilz, S.; Graninger, W.B.; Obermayer-Pietsch, B.M.; Pieber, T.R. High-dose cholecalciferol supplementation significantly increases peripheral CD4⁺ Tregs in healthy adults without negatively affecting the frequency of other immune cells. Eur. J. Nutr. 2014, 53, 751–759. [Google Scholar] [CrossRef]
  6. Tarcin, O.; Yavuz, D.G.; Ozben, B.; Telli, A.; Ogunc, A.V.; Yuksel, M.; Toprak, A.; Yazici, D.; Sancak, S.; Deyneli, O.; et al. Effect of vitamin D deficiency and replacement on endothelial function in asymptomatic subjects. J. Clin. Endocrinol. Metab. 2009, 94, 4023–4030. [Google Scholar] [CrossRef]
  7. Tran, V.; De Silva, T.M.; Sobey, C.G.; Lim, K.; Drummond, G.R.; Vinh, A.; Jelinic, M. The Vascular Consequences of Metabolic Syndrome: Rodent Models, Endothelial Dysfunction, and Current Therapies. Front. Pharmacol. 2020, 11, 148. [Google Scholar] [CrossRef]
  8. Krisnamurti, D.G.B.; Louisa, M.; Poerwaningsih, E.H.; Tarigan, T.J.E.; Soetikno, V.; Wibowo, H.; Nugroho, C.M.H. Vitamin D supplementation alleviates insulin resistance in prediabetic rats by modifying IRS-1 and PPARγ/NF-κB expressions. Front. Endocrinol. 2023, 14, 1089298. [Google Scholar] [CrossRef]
  9. Surdu, A.M.; Pînzariu, O.; Ciobanu, D.-M.; Negru, A.-G.; Căinap, S.-S.; Lazea, C.; Iacob, D.; Săraci, G.; Tirinescu, D.; Borda, I.M.; et al. Vitamin D and Its Role in the Lipid Metabolism and the Development of Atherosclerosis. Biomedicines 2021, 9, 172. [Google Scholar] [CrossRef] [PubMed]
  10. Faraji, S.; Alizadeh, M. Mechanistic Effects of Vitamin D Supplementation on Metabolic Syndrome Components in Patients with or without Vitamin D Deficiency. J. Obes. Metab. Syndr. 2020, 29, 270–280. [Google Scholar] [CrossRef] [PubMed]
  11. Gupta, A.K.; Sexton, R.C.; Rudney, H. Effect of vitamin D3 derivatives on cholesterol synthesis and HMG-CoA reductase activity in cultured cells. J. Lipid Res. 1989, 30, 379–386. [Google Scholar] [CrossRef] [PubMed]
  12. Xia, Y.; Yu, Y.; Zhao, Y.; Deng, Z.; Zhang, L.; Liang, G. Insight into the Interaction Mechanism of Vitamin D against Metabolic Syndrome: A Meta-Analysis and In Silico Study. Foods 2023, 12, 3973. [Google Scholar] [CrossRef] [PubMed]
  13. Nolan, P.B.; Carrick-Ranson, G.; Stinear, J.W.; Reading, S.A.; Dalleck, L.C. Prevalence of metabolic syndrome and metabolic syndrome components in young adults: A pooled analysis. Prev. Med. Rep. 2017, 7, 211–215. [Google Scholar] [CrossRef] [PubMed]
  14. Chen, Y.; Xu, W.; Zhang, W.; Tong, R.; Yuan, A.; Li, Z.; Jiang, H.; Hu, L.; Huang, L.; Xu, Y.; et al. Plasma metabolic fingerprints for large-scale screening and personalized risk stratification of metabolic syndrome. Cell Rep. Med. 2023, 4, 101109. [Google Scholar] [CrossRef]
  15. Gu, D.; Reynolds, K.; Wu, X.; Chen, J.; Duan, X.; Reynolds, R.F.; Whelton, P.K.; He, J. Prevalence of the metabolic syndrome and overweight among adults in China. Lancet 2005, 365, 1398–1405. [Google Scholar] [CrossRef] [PubMed]
  16. Lu, J.; Wang, L.; Li, M.; Xu, Y.; Jiang, Y.; Wang, W.; Li, J.; Mi, S.; Zhang, M.; Li, Y.; et al. Metabolic Syndrome Among Adults in China: The 2010 China Noncommunicable Disease Surveillance. J. Clin. Endocrinol. Metab. 2016, 102, 507–515. [Google Scholar] [CrossRef] [PubMed]
  17. Saklayen, M.G. The Global Epidemic of the Metabolic Syndrome. Curr. Hypertens. Rep. 2018, 20, 12. [Google Scholar] [CrossRef] [PubMed]
  18. Engin, A. The Definition and Prevalence of Obesity and Metabolic Syndrome. Adv. Exp. Med. Biol. 2017, 960, 1–17. [Google Scholar] [CrossRef] [PubMed]
  19. Cornier, M.A.; Dabelea, D.; Hernandez, T.L.; Lindstrom, R.C.; Steig, A.J.; Stob, N.R.; Van Pelt, R.E.; Wang, H.; Eckel, R.H. The metabolic syndrome. Endocr. Rev. 2008, 29, 777–822. [Google Scholar] [CrossRef]
  20. Lee, S.J.; Lee, E.Y.; Lee, J.H.; Kim, J.E.; Kim, K.J.; Rhee, Y.; Kim, H.C.; Youm, Y.; Kim, C.O. Associations of serum 25-hydroxyvitamin D with metabolic syndrome and its components in elderly men and women: The Korean Urban Rural Elderly cohort study. BMC Geriatr. 2019, 19, 102. [Google Scholar] [CrossRef]
  21. Schmitt, E.B.; Nahas-Neto, J.; Bueloni-Dias, F.; Poloni, P.F.; Orsatti, C.L.; Petri Nahás, E.A. Vitamin D deficiency is associated with metabolic syndrome in postmenopausal women. Maturitas 2018, 107, 97–102. [Google Scholar] [CrossRef]
  22. Huang, C.Y.; Chang, H.H.; Lu, C.W.; Tseng, F.Y.; Lee, L.T.; Huang, K.C. Vitamin D status and risk of metabolic syndrome among non-diabetic young adults. Clin. Nutr. 2015, 34, 484–489. [Google Scholar] [CrossRef]
  23. Zhu, W.; Heil, D.P. Associations of vitamin D status with markers of metabolic health: A community-based study in Shanghai, China. Diabetes Metab. Syndr. 2018, 12, 727–732. [Google Scholar] [CrossRef]
  24. Pott-Junior, H.; Nascimento, C.M.C.; Costa-Guarisco, L.P.; Gomes, G.A.O.; Gramani-Say, K.; Orlandi, F.S.; Gratão, A.C.M.; Orlandi, A.; Pavarini, S.C.I.; Vasilceac, F.A.; et al. Vitamin D Deficient Older Adults Are More Prone to Have Metabolic Syndrome, but Not to a Greater Number of Metabolic Syndrome Parameters. Nutrients 2020, 12, 748. [Google Scholar] [CrossRef]
  25. Liu, L.; Cao, Z.; Lu, F.; Liu, Y.; Lv, Y.; Qu, Y.; Gu, H.; Li, C.; Cai, J.; Ji, S.; et al. Vitamin D deficiency and metabolic syndrome in elderly Chinese individuals: Evidence from CLHLS. Nutr. Metab. 2020, 17, 58. [Google Scholar] [CrossRef]
  26. Lu, L.; Yu, Z.; Pan, A.; Hu, F.B.; Franco, O.H.; Li, H.; Li, X.; Yang, X.; Chen, Y.; Lin, X. Plasma 25-hydroxyvitamin D concentration and metabolic syndrome among middle-aged and elderly Chinese individuals. Diabetes Care 2009, 32, 1278–1283. [Google Scholar] [CrossRef]
  27. Pham, T.M.; Ekwaru, J.P.; Setayeshgar, S.; Veugelers, P.J. The Effect of Changing Serum 25-Hydroxyvitamin D Concentrations on Metabolic Syndrome: A Longitudinal Analysis of Participants of a Preventive Health Program. Nutrients 2015, 7, 7271–7284. [Google Scholar] [CrossRef]
  28. Qi, K.J.; Zhao, Z.T.; Zhang, W.; Yang, F. The impacts of vitamin D supplementation in adults with metabolic syndrome: A systematic review and meta-analysis of randomized controlled trials. Front. Pharmacol. 2022, 13, 1033026. [Google Scholar] [CrossRef]
  29. Melguizo-Rodríguez, L.; Costela-Ruiz, V.J.; García-Recio, E.; De Luna-Bertos, E.; Ruiz, C.; Illescas-Montes, R. Role of Vitamin D in the Metabolic Syndrome. Nutrients 2021, 13, 830. [Google Scholar] [CrossRef] [PubMed]
  30. Rouhani, P.; Hajhashemy, Z.; Saneei, P. Circulating serum vitamin D levels in relation to metabolic syndrome in children: A systematic review and dose-response meta-analysis of epidemiologic studies. Obes. Rev. 2021, 22, e13314. [Google Scholar] [CrossRef] [PubMed]
  31. Fu, J.; Han, L.; Zhao, Y.; Li, G.; Zhu, Y.; Li, Y.; Li, M.; Gao, S.; Willi, S.M. Vitamin D levels are associated with metabolic syndrome in adolescents and young adults: The BCAMS study. Clin. Nutr. 2019, 38, 2161–2167. [Google Scholar] [CrossRef] [PubMed]
  32. Abuhijleh, H.; Alkhatib, D.; Ganji, V. Hypovitaminosis D and Metabolic Syndrome in Postmenopausal Women. Healthcare 2022, 10, 2026. [Google Scholar] [CrossRef] [PubMed]
  33. Chacko, S.A.; Song, Y.; Manson, J.E.; Van Horn, L.; Eaton, C.; Martin, L.W.; McTiernan, A.; Curb, J.D.; Wylie-Rosett, J.; Phillips, L.S.; et al. Serum 25-hydroxyvitamin D concentrations in relation to cardiometabolic risk factors and metabolic syndrome in postmenopausal women. Am. J. Clin. Nutr. 2011, 94, 209–217. [Google Scholar] [CrossRef]
  34. Hjelmesaeth, J.; Røislien, J.; Hofsø, D.; Bollerslev, J. Plasma 25-hydroxyvitamin d concentration and metabolic syndrome among middle-aged and elderly chinese individuals: Response to Lu et al. Diabetes Care 2010, 33, e13. [Google Scholar] [CrossRef]
  35. Reis, J.P.; von Mühlen, D.; Kritz-Silverstein, D.; Wingard, D.L.; Barrett-Connor, E. Vitamin D, parathyroid hormone levels, and the prevalence of metabolic syndrome in community-dwelling older adults. Diabetes Care 2007, 30, 1549–1555. [Google Scholar] [CrossRef] [PubMed]
  36. ISO 15189:2022; Medical Laboratories—Requirements for Quality and Competence. ISO: Geneva, Switzerland, 2022.
  37. Alberti, K.G.; Eckel, R.H.; Grundy, S.M.; Zimmet, P.Z.; Cleeman, J.I.; Donato, K.A.; Fruchart, J.C.; James, W.P.; Loria, C.M.; Smith, S.C., Jr. Harmonizing the metabolic syndrome: A joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation 2009, 120, 1640–1645. [Google Scholar] [CrossRef] [PubMed]
  38. Holick, M.F.; Binkley, N.C.; Bischoff-Ferrari, H.A.; Gordon, C.M.; Hanley, D.A.; Heaney, R.P.; Murad, M.H.; Weaver, C.M. Evaluation, treatment, and prevention of vitamin D deficiency: An Endocrine Society clinical practice guideline. J. Clin. Endocrinol. Metab. 2011, 96, 1911–1930. [Google Scholar] [CrossRef]
  39. Method for Vitamin D Deficiency Screening. Biomed. Environ. Sci. 2023, 36, 785.
  40. Twisk, J.W.R.; de Vente, W. Hybrid models were found to be very elegant to disentangle longitudinal within- and between-subject relationships. J. Clin. Epidemiol. 2019, 107, 66–70. [Google Scholar] [CrossRef]
  41. Yin, X.; Sun, Q.; Zhang, X.; Lu, Y.; Sun, C.; Cui, Y.; Wang, S. Serum 25(OH)D is inversely associated with metabolic syndrome risk profile among urban middle-aged Chinese population. Nutr. J. 2012, 11, 68. [Google Scholar] [CrossRef]
  42. Ke, N.; Jin-Zi, W.; Huan, W. Investigation on the influencing factors of calcium and vitamin D supplementations in children from 9 areas of China. Matern. Child Health Care China 2014, 29, 5289–5293. [Google Scholar]
  43. Yin, W.J.; Tao, R.X.; Hu, H.L.; Zhang, Y.; Jiang, X.M.; Zhang, M.X.; Jin, D.; Yao, M.N.; Tao, F.B.; Zhu, P. The association of vitamin D status and supplementation during pregnancy with gestational diabetes mellitus: A Chinese prospective birth cohort study. Am. J. Clin. Nutr. 2020, 111, 122–130. [Google Scholar] [CrossRef] [PubMed]
  44. Hu, Y.; Wang, R.; Mao, D.; Chen, J.; Li, M.; Li, W.; Yang, Y.; Zhao, L.; Zhang, J.; Piao, J.; et al. Vitamin D Nutritional Status of Chinese Pregnant Women, Comparing the Chinese National Nutrition Surveillance (CNHS) 2015-2017 with CNHS 2010-2012. Nutrients 2021, 13, 2237. [Google Scholar] [CrossRef] [PubMed]
  45. Parker, J.; Hashmi, O.; Dutton, D.; Mavrodaris, A.; Stranges, S.; Kandala, N.B.; Clarke, A.; Franco, O.H. Levels of vitamin D and cardiometabolic disorders: Systematic review and meta-analysis. Maturitas 2010, 65, 225–236. [Google Scholar] [CrossRef] [PubMed]
  46. Reis, J.P.; von Mühlen, D.; Miller, E.R., 3rd. Relation of 25-hydroxyvitamin D and parathyroid hormone levels with metabolic syndrome among US adults. Eur. J. Endocrinol. 2008, 159, 41–48. [Google Scholar] [CrossRef] [PubMed]
  47. Ford, E.S.; Ajani, U.A.; McGuire, L.C.; Liu, S. Concentrations of serum vitamin D and the metabolic syndrome among U.S. adults. Diabetes Care 2005, 28, 1228–1230. [Google Scholar] [CrossRef] [PubMed]
  48. Ganji, V.; Tangpricha, V.; Zhang, X. Serum Vitamin D Concentration ≥75 nmol/L Is Related to Decreased Cardiometabolic and Inflammatory Biomarkers, Metabolic Syndrome, and Diabetes; and Increased Cardiorespiratory Fitness in US Adults. Nutrients 2020, 12, 730. [Google Scholar] [CrossRef] [PubMed]
  49. Hyppönen, E.; Boucher, B.J.; Berry, D.J.; Power, C. 25-hydroxyvitamin D, IGF-1, and metabolic syndrome at 45 years of age: A cross-sectional study in the 1958 British Birth Cohort. Diabetes 2008, 57, 298–305. [Google Scholar] [CrossRef] [PubMed]
  50. Wang, C.M.; Chang, C.S.; Chang, Y.F.; Wu, S.J.; Chiu, C.J.; Hou, M.T.; Chen, C.Y.; Liu, P.Y.; Wu, C.H. Inverse Relationship between Metabolic Syndrome and 25-Hydroxyvitamin D Concentration in Elderly People without Vitamin D deficiency. Sci. Rep. 2018, 8, 17052. [Google Scholar] [CrossRef]
  51. Mansouri, M.; Abasi, R.; Nasiri, M.; Sharifi, F.; Vesaly, S.; Sadeghi, O.; Rahimi, N.; Sharif, N.A. Association of vitamin D status with metabolic syndrome and its components: A cross-sectional study in a population of high educated Iranian adults. Diabetes Metab. Syndr. 2018, 12, 393–398. [Google Scholar] [CrossRef]
  52. Mehri, Z.; Salehi-Abargouei, A.; Shahvazi, S.; Samadi, M.; Zare, F.; Nadjarzadeh, A. The association between vitamin D status and metabolic syndrome and its components among female teachers residing in Yazd city. Endocrinol. Diabetes Nutr. 2019, 66, 628–638. [Google Scholar] [CrossRef]
  53. Vitezova, A.; Zillikens, M.C.; van Herpt, T.T.; Sijbrands, E.J.; Hofman, A.; Uitterlinden, A.G.; Franco, O.H.; Kiefte-de Jong, J.C. Vitamin D status and metabolic syndrome in the elderly: The Rotterdam Study. Eur. J. Endocrinol. 2015, 172, 327–335. [Google Scholar] [CrossRef]
  54. Kayaniyil, S.; Vieth, R.; Harris, S.B.; Retnakaran, R.; Knight, J.A.; Gerstein, H.C.; Perkins, B.A.; Zinman, B.; Hanley, A.J. Association of 25(OH)D and PTH with metabolic syndrome and its traditional and nontraditional components. J. Clin. Endocrinol. Metab. 2011, 96, 168–175. [Google Scholar] [CrossRef]
  55. Zhou, Q.G.; Hou, F.F.; Guo, Z.J.; Liang, M.; Wang, G.B.; Zhang, X. 1,25-Dihydroxyvitamin D improved the free fatty-acid-induced insulin resistance in cultured C2C12 cells. Diabetes Metab. Res. Rev. 2008, 24, 459–464. [Google Scholar] [CrossRef]
  56. Vimaleswaran, K.S.; Berry, D.J.; Lu, C.; Tikkanen, E.; Pilz, S.; Hiraki, L.T.; Cooper, J.D.; Dastani, Z.; Li, R.; Houston, D.K.; et al. Causal relationship between obesity and vitamin D status: Bi-directional Mendelian randomization analysis of multiple cohorts. PLoS Med. 2013, 10, e1001383. [Google Scholar] [CrossRef]
  57. Perticone, M.; Maio, R.; Sciacqua, A.; Suraci, E.; Pinto, A.; Pujia, R.; Zito, R.; Gigliotti, S.; Sesti, G.; Perticone, F. Ketogenic Diet-Induced Weight Loss is Associated with an Increase in Vitamin D Levels in Obese Adults. Molecules 2019, 24, 2499. [Google Scholar] [CrossRef]
  58. Wang, J.; Liu, Y. Research progress of mechanism of vitamin D in regulating obesity and insulin resistance on children. Chin. J. Appl. Clin. Pediatr. 2015, 30, 1598–1600. [Google Scholar] [CrossRef]
  59. Forouhi, N.G.; Luan, J.; Cooper, A.; Boucher, B.J.; Wareham, N.J. Baseline serum 25-hydroxy vitamin d is predictive of future glycemic status and insulin resistance: The Medical Research Council Ely Prospective Study 1990–2000. Diabetes 2008, 57, 2619–2625. [Google Scholar] [CrossRef]
  60. Gagnon, C.; Lu, Z.X.; Magliano, D.J.; Dunstan, D.W.; Shaw, J.E.; Zimmet, P.Z.; Sikaris, K.; Ebeling, P.R.; Daly, R.M. Low serum 25-hydroxyvitamin D is associated with increased risk of the development of the metabolic syndrome at five years: Results from a national, population-based prospective study (The Australian Diabetes, Obesity and Lifestyle Study: AusDiab). J. Clin. Endocrinol. Metab. 2012, 97, 1953–1961. [Google Scholar] [CrossRef] [PubMed]
  61. Sciacqua, A.; Perticone, M.; Grillo, N.; Falbo, T.; Bencardino, G.; Angotti, E.; Arturi, F.; Parlato, G.; Sesti, G.; Perticone, F. Vitamin D and 1-hour post-load plasma glucose in hypertensive patients. Cardiovasc. Diabetol. 2014, 13, 48. [Google Scholar] [CrossRef] [PubMed]
  62. Kabadi, S.M.; Lee, B.K.; Liu, L. Joint effects of obesity and vitamin D insufficiency on insulin resistance and type 2 diabetes: Results from the NHANES 2001-2006. Diabetes Care 2012, 35, 2048–2054. [Google Scholar] [CrossRef] [PubMed]
  63. Wang, T.J.; Pencina, M.J.; Booth, S.L.; Jacques, P.F.; Ingelsson, E.; Lanier, K.; Benjamin, E.J.; D’Agostino, R.B.; Wolf, M.; Vasan, R.S. Vitamin D deficiency and risk of cardiovascular disease. Circulation 2008, 117, 503–511. [Google Scholar] [CrossRef] [PubMed]
  64. Zhao, G.; Ford, E.S.; Li, C.; Kris-Etherton, P.M.; Etherton, T.D.; Balluz, L.S. Independent associations of serum concentrations of 25-hydroxyvitamin D and parathyroid hormone with blood pressure among US adults. J. Hypertens. 2010, 28, 1821–1828. [Google Scholar] [CrossRef]
  65. Jensen, N.S.; Wehland, M.; Wise, P.M.; Grimm, D. Latest Knowledge on the Role of Vitamin D in Hypertension. Int. J. Mol. Sci. 2023, 24, 4679. [Google Scholar] [CrossRef] [PubMed]
  66. Cai, B.; Luo, X.; Zhang, P.; Luan, Y.; Cai, X.; He, X. Effect of vitamin D supplementation on markers of cardiometabolic risk in children and adolescents: A meta-analysis of randomized clinical trials. Nutr. Metab. Cardiovasc. Dis. 2021, 31, 2800–2814. [Google Scholar] [CrossRef]
  67. Vimaleswaran, K.S.; Cavadino, A.; Berry, D.J.; Jorde, R.; Dieffenbach, A.K.; Lu, C.; Alves, A.C.; Heerspink, H.J.; Tikkanen, E.; Eriksson, J.; et al. Association of vitamin D status with arterial blood pressure and hypertension risk: A mendelian randomisation study. Lancet Diabetes Endocrinol. 2014, 2, 719–729. [Google Scholar] [CrossRef]
Nutrients 16 01480 g001
Figure 1. Flow chart of study participants.
Figure 1. Flow chart of study participants.
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Figure 2. Geographical distribution of participants.
Figure 2. Geographical distribution of participants.
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Table 1. Baseline characteristics of study participants based on quartiles (Q) of serum 25(OH)D levels.
Table 1. Baseline characteristics of study participants based on quartiles (Q) of serum 25(OH)D levels.
VariablesOverallQ1Q2 Q3 Q4 p-Value
n23,8106663617258655110
Sex, n (%) 0.490
   Male12,596 (52.9)3539 (53.1)3246 (52.6)3068 (52.3)2743 (53.7)
   Female11,214 (47.1)3124 (46.9)2926 (47.4)2797 (47.7)2367 (46.3)
Age, y43.6 (13.3)43.9 (13.3)43.7 (13.4)43.4 (13.3)43.3 (13.2)0.028
Age, n (%) 0.278
   18–4412,366 (51.9)3384 (50.8)3199 (51.8)3093 (52.7)2690 (52.6)
   45–598544 (35.9)2442 (36.7)2205 (35.7)2069 (35.3)1828 (35.8)
   ≥602900 (12.2)837 (12.6)768 (12.4)703 (12.0)592 (11.6)
Season, n (%) <0.001
   Spring3879 (16.3)1348 (20.2)1019 (16.5)862 (14.7)650 (12.7)
   Summer6131 (25.7)1127 (16.9)1510 (24.5)1786 (30.5)1708 (33.4)
   Fall6139 (25.8)1308 (19.6)1526 (24.7)1629 (27.8)1676 (32.8)
   Winter7661 (32.2)2880 (43.2)2117 (34.3)1588 (27.1)1076 (21.1)
Smoking, n (%)4852 (20.9)1366 (21.2)1195 (19.9)1164 (20.3)1127 (22.5)0.005
Drinking, n (%)6969 (30.1)1948 (30.3)1788 (29.8)1675 (29.2)1558 (31.1)0.186
Systolic blood pressure, mmHg122.3 (18.4)122.7 (18.6)122.3 (18.4)122.2 (18.4)122.1 (18.0)0.335
Diastolic blood pressure, mmHg71.8 (12.0)72.2 (12.1)71.8 (12.0)71.7 (12.0)71.5 (11.8)0.008
Body mass index, kg/m223.8 (3.4)23.9 (3.6)23.9 (3.4)23.8 (3.3)23.6 (3.2)<0.001
Waist circumference, cm83.0 (10.2)83.4 (10.8)83.2 (10.3)82.9 (10.0)82.5 (9.5)<0.001
25(OH)D, ng/mL18.8 (7.3)11.9 (3.1)16.8 (3.6)20.8 (4.2)28.0 (6.6)<0.001
Fasting glucose, mmol/L5.6 (1.1)5.7 (1.2)5.6 (1.1)5.6 (1.2)5.6 (1.0)<0.001
FBI, μU/mL9.0 (14.3)9.8 (21.7)9.0 (13.1)8.7 (9.6)8.2 (5.3)0.001
HOMA-IR2.4 (5.0)2.7 (7.4)2.5 (5.1)2.3 (3.0)2.2 (1.7)0.002
HbA1c, %5.7 (0.9)5.7 (0.9)5.7 (0.9)5.7 (0.9)5.6 (0.8)<0.001
Total cholesterol, mmol/L5.0 (1.0)5.0 (1.0)5.0 (1.0)5.0 (0.9)5.0 (1.0)0.006
LDL-C, mmol/L3.1 (0.9)3.0 (0.9)3.1 (0.9)3.1 (0.8)3.1 (0.9)0.028
HDL-C, mmol/L1.4 (0.3)1.3 (0.3)1.4 (0.3)1.4 (0.3)1.4 (0.3)<0.001
Triglycerides, mmol/L1.5 (1.4)1.6 (1.8)1.5 (1.3)1.5 (1.2)1.4 (1.0)<0.001
Metabolic syndrome, n (%)6231 (27.5)1907 (30.1)1680 (28.7)1485 (26.6)1159 (23.8)<0.001
Abdominal obesity, n (%)9307 (40.3)2651 (41.4)2501 (41.9)2248 (39.2)1907 (38.1)<0.001
High Triglycerides, n (%)8055 (34.4)2284 (35.2)2079 (34.3)1988 (34.3)1704 (33.7)0.340
Low HDL-C, n (%)6260 (26.7)1853 (28.6)1717 (28.3)1506 (26.0)1184 (23.3)<0.001
Hypertension, n (%)8829 (37.7)2588 (39.9)2284 (37.6)2158 (37.2)1799 (35.4)<0.0013
Hyperglycemia, n (%)4550 (21.9)1383 (23.5)1220 (22.6)1101 (21.7)846 (19.4)<0.001
Data are shown as mean (SD) or number (%). p-values were derived from one-way ANOVA or Pearson’s Chi-squared test. Q1–Q4:. Age-and sex-specific quartiles of serum 25 hydroxyvitamin D.
Table 2. Odds ratio of MetS and its components according to serum 25(OH)D quartiles *.
Table 2. Odds ratio of MetS and its components according to serum 25(OH)D quartiles *.
VariablesBetween-SubjectWithin-Subject
Model 1
OR (95% CI)		Model 2
OR (95% CI)		Model 1
OR (95% CI)		Model 2
OR (95% CI)
Metabolic syndrome
      Q11.00 (reference)1.00 (reference)1.00 (reference)1.00 (reference)
      Q20.77 (0.63, 0.94)0.78 (0.65, 0.95)0.21 (0.17, 0.25)0.76 (0.63, 0.90)
      Q30.58 (0.48, 0.72)0.61 (0.51, 0.75)0.30 (0.24, 0.36)0.63 (0.52, 0.75)
      Q40.38 (0.31, 0.47)0.43 (0.35, 0.52)0.28 (0.23, 0.34)0.60 (0.50, 0.73)
      p-trend<0.001<0.001<0.001<0.001
Abdominal obesity
      Q11.00 (reference)1.00 (reference)1.00 (reference)1.00 (reference)
      Q20.84 (0.63, 1.11)0.85 (0.64, 1.13)0.13 (0.10, 0.17)0.34 (0.26, 0.46)
      Q30.54 (0.40, 0.71)0.54 (0.40, 0.72)0.16 (0.12, 0.22)0.29 (0.21, 0.39)
      Q40.37 (0.28, 0.49)0.40 (0.29, 0.54)0.15 (0.11, 0.20)0.26 (0.19, 0.36)
      p-trend<0.001<0.001<0.001<0.001
HTG
      Q11.00 (reference)1.00 (reference)1.00 (reference)1.00 (reference)
      Q20.99 (0.84, 1.18)0.99 (0.84, 1.16)0.46 (0.40, 0.54)0.90 (0.77, 1.05)
      Q30.70 (0.59, 0.84)0.71 (0.60, 0.84)0.54 (0.46, 0.64)0.79 (0.67, 0.93)
      Q40.48 (0.40, 0.57)0.49 (0.41, 0.58)0.53 (0.44, 0.62)0.78 (0.66, 0.93)
      p-trend<0.001<0.001<0.001<0.001
Low HDL-C
      Q11.00 (reference)1.00 (reference)1.00 (reference)1.00 (reference)
      Q20.82 (0.67, 1.00)0.83 (0.68, 1.02)0.89 (0.74, 1.06)1.08 (0.89, 1.30)
      Q30.62 (0.51, 0.76)0.63 (0.51, 0.77)0.82 (0.67, 0.99)0.92 (0.75, 1.12)
      Q40.47 (0.38, 0.57)0.48 (0.39, 0.59)0.78 (0.64, 0.95)0.87 (0.71, 1.07)
      p-trend<0.001<0.001<0.0010.002
Hypertension
      Q11.00 (reference)1.00 (reference)1.00 (reference)1.00 (reference)
      Q20.88 (0.71, 1.09)0.89 (0.74, 1.08)0.08 (0.07, 0.10)0.53 (0.44, 0.63)
      Q30.86 (0.69, 1.07)0.92 (0.76, 1.12)0.20 (0.16, 0.25)0.60 (0.50, 0.72)
      Q40.81 (0.64, 1.01)0.92 (0.76, 1.12)0.16 (0.13, 0.20)0.49 (0.41, 0.59)
      p-trend0.0010.147<0.001<0.001
Hyperglycemia
      Q11.00 (reference)1.00 (reference)1.00 (reference)1.00 (reference)
      Q20.82 (0.70, 0.97)0.81 (0.69, 0.94)0.27 (0.23, 0.31)0.88 (0.76, 1.01)
      Q30.78 (0.66, 0.93)0.80 (0.68, 0.93)0.41 (0.35, 0.48)0.80 (0.69, 0.93)
      Q40.65 (0.54, 0.77)0.70 (0.59, 0.82)0.37 (0.31, 0.43)0.75 (0.64, 0.87)
      p-trend<0.001<0.001<0.001<0.001
*: Odds ratios (ORs) with 95% confidence intervals (CIs) for between-subjects and within-subject comparisons represent the between-subject (cross-sectional variations) and within-subject (longitudinal changes) components of the relationship calculated using hybrid logistic mixed models. The comparisons were made for age- and sex-specific quartiles (Q) 2 through 4 relative to quartile 1 (Q1) of 25-Hydroxyvitamin D levels. p-trend values were calculated by treating the quartiles as a continuous variable. Model 1: crude model; Model 2: adjustment for age, sex, collection season (spring, summer, fall, and winter), smoking (yes/no), and alcohol consumption (yes/no).
Table 3. Hazard ratios (HRs) and 95% confidence intervals (CIs) for MetS and its components by quartiles of average 25-hydroxyvitamin D levels across multiple time points.
Table 3. Hazard ratios (HRs) and 95% confidence intervals (CIs) for MetS and its components by quartiles of average 25-hydroxyvitamin D levels across multiple time points.
VariablesNModel 1Model 2
TotalNew CasesHR (95% CI) *p-TrendHR (95% CI) *p-Trend
Metabolic syndrome8146761 0.014 0.007
      Q117191401.00 1.00
      Q221252131.15 (0.93, 1.42) 1.15 (0.93, 1.42)
      Q321572181.07 (0.87, 1.32) 1.09 (0.88, 1.34)
      Q421451900.80 (0.64, 0.99) 0.80 (0.65, 1.00)
Abdominal obesity8146786 0.003 0.002
      Q117191481.00 1.00
      Q221252061.11 (0.90, 1.38) 1.11 (0.90, 1.37)
      Q321572250.98 (0.79, 1.20) 0.98 (0.80, 1.21)
      Q421452070.77 (0.63, 0.96) 0.77 (0.63, 0.96)
High triglycerides8146872 0.013 0.011
      Q117191771.00 1.00
      Q221252110.94 (0.77, 1.15) 0.94 (0.77, 1.14)
      Q321572631.04 (0.86, 1.25) 1.02 (0.85, 1.24)
      Q421452210.76 (0.63, 0.93) 0.76 (0.62, 0.92)
Low HDL-C8146575 <0.001 <0.001
      Q117191371.00 1.00
      Q221251430.80 (0.63, 1.01) 0.80 (0.64, 1.02)
      Q321571500.75 (0.60, 0.95) 0.76 (0.61, 0.96)
      Q421451450.63 (0.50, 0.80) 0.64 (0.50, 0.81)
Hypertension8146824 0.323 0.328
      Q117191541.00 1.00
      Q221252171.07 (0.87, 1.32) 1.06 (0.86, 1.31)
      Q321572391.15 (0.94, 1.40) 1.16 (0.95, 1.43)
      Q421452140.89 (0.72, 1.09) 0.89 (0.73, 1.10)
Hyperglycemia81461027 0.753 0.733
      Q117192041.00 1.00
      Q221252541.01 (0.84, 1.21) 1.00 (0.83, 1.21)
      Q321572841.01 (0.85, 1.21) 1.02 (0.85, 1.23)
      Q421452850.96 (0.80, 1.15) 0.97 (0.81, 1.16)
*: Cox regression analysis was used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) of metabolic syndrome incidence in quartiles 2–4 (vs. 1) of 25(OH)D. Quartile values were used as a continuous variable to calculate p-trend values. Q1–Q4: Age- and sex-specific quartiles 1–4 of average 25(OH)D levels across multiple time points before MetS onset among MetS-free individuals at baseline were used in the Cox regression model. Model 1: adjusted for sex and age. Model 2: adjusted for sex, age, season, smoking, and drinking.
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Shu, M.; Xi, Y.; Wu, J.; Zhuo, L.-B.; Yan, Y.; Yang, Y.-D.; Feng, Y.-Y.; Tan, H.-Q.; Yang, H.-F.; Chen, Y.-M. Relationship between Circulating 25-Hydroxyvitamin D and Metabolic Syndrome in Chinese Adults: A Large Nationwide Longitudinal Study. Nutrients 2024, 16, 1480. https://doi.org/10.3390/nu16101480

AMA Style

Shu M, Xi Y, Wu J, Zhuo L-B, Yan Y, Yang Y-D, Feng Y-Y, Tan H-Q, Yang H-F, Chen Y-M. Relationship between Circulating 25-Hydroxyvitamin D and Metabolic Syndrome in Chinese Adults: A Large Nationwide Longitudinal Study. Nutrients. 2024; 16(10):1480. https://doi.org/10.3390/nu16101480

Chicago/Turabian Style

Shu, Mi, Yue Xi, Jie Wu, Lai-Bao Zhuo, Yan Yan, Yi-Duo Yang, Yue-Yue Feng, Hua-Qiao Tan, Hui-Fang Yang, and Yu-Ming Chen. 2024. "Relationship between Circulating 25-Hydroxyvitamin D and Metabolic Syndrome in Chinese Adults: A Large Nationwide Longitudinal Study" Nutrients 16, no. 10: 1480. https://doi.org/10.3390/nu16101480

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