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Association between fat-soluble vitamins and metabolic syndromes in US adults: a cross-section study from NHANES database

BMC Endocrine Disorders volume 24, Article number: 178 (2024) Cite this article

Abstract

Background

Previous studies have shown significant associations between individual fat-soluble vitamins (FSVs) and metabolic syndromes (MetS). However, evidence on the multiple FSVs co-exposure and MetS odds is limited. Given that individuals are typically exposed to different levels of FSVs simultaneously, and FSVs can interact with each other. It’s necessary to explore the association between multiple FSVs co-exposure and MetS odds. This study aims to address this gap in general U.S. adults aged ≥ 20 years.

Methods

We conducted a cross-sectional study utilizing data from the National Health and Nutrition Examination Surveys (NHANESs) 2003–2006 and 2017–2018. Three FSV, including vitamin A (VA), vitamin E (VE), and vitamin D (VD), and MetS diagnosed according to the ATP III guidelines were selected as exposure and outcome, respectively. Multivariable-adjusted logistic model was used to explore the associations of individual FSV exposure with MetS odds and MetS components. Restricted cubic splines were performed to explore the dose–response relationships among them. The quantile g-computation method was adopted to explore the associations of multiple FSVs co-exposure with MetS odds and MetS components.

Results

The presented study included a total of 13,975 individuals, with 2400 (17.17%) were diagnosed with MetS. After adjusting for various confounders, a positive linear pattern was observed for serum VA and VE and MetS associations. Serum VD was found to be negatively associated with MetS in a linear dose–response way. For each component of MetS, higher serum VA and VE were associated with higher triglyceride and high-density lipoprotein; higher serum VD was negatively associated with triglyceride, blood pressure, and fasting plasma glucose. MetS odds increased by 15% and 13%, respectively, in response to one quartile increase in FSVs co-exposure index (qgcomp) in the conditional model (OR = 1.15, 95%CI: 1.06, 1.24) and the marginal structural model (OR = 1.13, 95%CI: 1.06, 1.20). Besides, co-exposure to VA, VE, and VD was positively associated with triglyceride, high-density lipoprotein, and blood pressure levels.

Conclusion

Findings in the present study revealed that high serum VA and VE levels were associated with elevated MetS odds, while serum VD was inversely associated with MetS odds. FSVs co-exposure was positively associated with MetS odds.

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Introduction

Metabolic syndrome (MetS) represents a complex cluster of interconnected metabolic abnormalities, including central obesity, impaired glucose metabolism, dyslipidemia, and hypertension, which collectively pose a significant threat to human health [1]. The diagnostic criteria for MetS was firstly established in 1998 [2]. The NCEP: ATP III made several revisions, formulating the definition of MetS that is most widely adopted in clinical practices [3]. MetS has emerged as a major public health challenge globally, affecting both developed and developing nations [4]. It is estimated that approximately one in four to five people worldwide experience MetS [4]. The prevalence of MetS exhibits considerable regional variation. A cross-sectional analysis of the National Health and Nutrition Examination Surveys (NHANESs) 2011–2016 data revealed a weighted MetS prevalence of 34.7% among American adults aged > 20 years [5]. In contrast, a study reported an estimated MetS prevalence of 15.5% in China in 2017 [6]. The rapid urbanization and economic growth in many countries have precipitated a nutritional transition characterized by increased intake of fast foods and ultra-processed foods [6, 7]. This dietary shift, marked by high intake of saturated fats and added sugars, and low dietary fiber content, coupled with increasingly sedentary lifestyle and reduced physical activity levels, will significantly increase the MetS risk. Moreover, the global demographic transition towards ageing population further exacerbates this trend, as the aging process shares common biochemical alterations with MetS [8]. The implications of this escalating MetS prevalence are substantial, not only for individual well-being but also for societal health and economic prosperity. MetS not only diminishes individual health outcomes but also imposes a substantial societal burden through the loss of a productive workforce and escalating healthcare expenditures.

Fat-soluble vitamins (FSVs) comprise a group of essential micronutrients that are soluble in lipids or fat solvents but not in water. These vitamins play crucial roles in various physiological processes, including metabolism, growth, and development. The FSV family includes vitamin A (VA), vitamin D (VD), vitamin E (VE), and vitamin K (VK), each with distinct biological functions. Deficiency in VA, VD, VE, and VK can lead to specific health issues, i.e., night blindness, bone disease, nerve damage, and spontaneous bleeding [9,10,11,12]. Accumulating evidence suggested a potential association between FSVs and MetS. A cross-sectional study in China demonstrated a dose-dependent positive association between serum VA levels and MetS [13], with similar findings reported in a Korean population study [14]. Waniek et al. conducted a study in Northern German, concluding that elevated α-tocopherol levels were associated with hypertriglyceridemia, low high-density lipoprotein (HDL), and increased odds of MetS [15]. Furthermore, a significant association between VD and MetS odds has been reported [16]. However, the existing evidence presented some inconsistencies regarding these associations [17,18,19,20]. Furthermore, dietary diversity inherently exposes individuals to multiple vitamins simultaneously, and interactions among FSVs can occur at the intestinal level, influencing absorption and metabolism [21]. Consequently, exploring the association between co-exposure to multiple FSVs and health outcomes is more valuable and may provide a more comprehensive understanding of their associations. While Pei et al. investigated the association between co-exposure to multiple water-soluble vitamins and MetS risk [22], evidence on the association between multiple FSVs co-exposure and MetS odds was still limited.

To address this knowledge gap, we conducted a cross-sectional study. Data from the NHANESs 2003–2006 and 2017–2018 was used. Our objective was to investigate the associations of exposure to serum FSVs (i.e., VA, VD, VE) with MetS odds and each MetS component in a representative sample of American adults. Additionally, we also explored potential dose–response relationships among them.

Participants and methods

Study design and participants

Data from three NHANES rounds, specifically the periods of 2003–2004, 2005–2006, and 2017–2018, was analyzed. Briefly, about 5,000 individuals were recruited from representative regions across America per year to assess the health and nutritional status of the civilian population. Details could be found in NHANES official website. The NHANES was approved by the National Center for Health Statistics Research Ethics Review Board, with written informed consent was obtained from all participants.

A total of 29,724 individuals were involved across the three NHANES rounds. There were 1427, 13,517, and 805 participants were excluded from our analysis, respectively, because they did not undergo a physical examination, under the age of 20, and lacked serum data for VA, VE, VD, and MetS. Consequently, the final study consisted of 13,975 U.S. adults aged > 20 years.

Exposure measurement

FSV family typically includes VA, VE, VD and VK (33549284). Unfortunately, data on VK was not available across all three NHANES rounds. Thus, the other three kinds of FSV, including VA (retinol), VE (α-tocopherols), and VD (25(OH)D) were selected as exposures in this study. Regarding VA and VE, they were measured using high performance liquid chromatography with multiwavelength photodiode-array absorbance detection in NHANES 2003–2004 (https://wwwn.cdc.gov/Nchs/Nhanes/2003-2004/L45VIT_C.htm), and using high performance liquid chromatography with photodiode array detection in NHANES 2005–2006 (https://wwwn.cdc.gov/Nchs/Nhanes/2005-2006/VITAEC_D.htm) and 2017–2018 (https://wwwn.cdc.gov/Nchs/Nhanes/2017-2018/VITAEC_J.htm). NHANES has made corresponding correction for measurements of different survey cycles to ensure that they can be combined for analysis. VD was measured primarily using the DiaSorin RIA kit (Stillwater MN) in NHANES 2003–2006 (https://wwwn.cdc.gov/nchs/nhanes/vitamind/analyticalnote.aspx?b=2003&e=2004&d=VID_C&x=htm), and using high performance liquid chromatography-tandem mass spectrometry (HPLC–MS/MS) method in NHANES 2017–2018 (https://wwwn.cdc.gov/Nchs/Nhanes/2017-2018/VID_J.htm). Meanwhile, NHANES officially reported the LC_MS/MS-equivalent data of VD and highly recommended that researchers use the equivalent data for all analyses.

Outcome measurement

MetS was assessed using by five variables, including waist circumference (WC), triglyceride (TG), high-density lipoprotein (HDL), blood pressure, and fasting glucose (FG). Based on ATP III guidelines, MetS is diagnosed when three or more of the following criteria are met: (1) abdominal obesity (WC > 40/ > 35 inches in men/women; (2) TG ≥ 150 mg/dL; (3) HDL < 40/ < 50 mg/dL in men/women; (4) systolic blood pressure ≥ 130 or diastolic blood pressure ≥ 85 mmHg; and (5) FG ≥ 110 mg/dL.

To comprehensively evaluate the associations between FSVs and MetS, each component of MetS was also categorized into two groups (low and high) based on the ATP III guidelines, which were used as secondary outcomes.

Covariates

Referring previous studies, we selected age, sex, race, education background, marital status, family poverty income ratio (PIR), body mass index, smoking status, drinking status, physical activity, and dietary total energy as covariates (37251666). Age was categorized into three groups: 20–39, 40–59, and ≥ 60 years. Race included Mexican American, Hispanics, non-Hispanic White, non-Hispanic Black, and others. Education background was described as < high school, high school or equivalent, and > high school. Marital status was classified as married/living with a partner, widowed/separated/devoiced, and never married. BMI was calculated by using weight (Kg) divided by the square of height (m), and was categorized into four groups: underweight (< 18.5 kg/m2), normal (18.5–24.9 kg/m2), overweight (25–29.9 kg/m2), and obesity (≥ 30 kg/m2). Smoking and drinking status were described as never, ever, and current. We firstly calculated the metabolic equivalent (MET)-minutes per week based on the time and MET scores of vigorous and moderate leisure-time physical activities reported in the NHANES. Then physical activity was classified as sedentary (MET = 0), insufficient (0 < MET ≤ 500), moderate (500 < MET ≤ 1000), and high (MET > 1000).

Statistical analysis

All participants were grouped into MetS and non-MetS groups, and we then described the distribution of each variable separately. Prior to the statistical description, we used the Kolmogorov–Smirnov test to examine whether continuous variables followed a normal distribution. We also constructed quantile–quantile plots and bar charts to visualize their distributions. Mean ± standard deviation (SD) and median (interquartile range) were employed to describe continuous variables with and without a normal distribution, respectively. And we used the t-test or Wilcoxon rank-sum test for between-group comparisons. Categorical variables were described as n (%) and chi-square test was adopted for between-group differences.

Considering the complex design of the NHANES, we mainly employed weighted multivariable-adjusted logistic regression model to investigate the associations between exposure to single serum FSVs and the odds of MetS and each MetS component. To facilitate interpretation and comparison across different exposure levels, we categorized FSVs into four groups based on corresponding quartiles, with the first quartile serving as the reference. We conducted two models with adjustment for different covariates: model 1 adjusted for age, sex, race, education background, marital status, BMI, and family PIR, and model 2 further adjusted for physical activity, smoking and drinking status, and dietary total energy in addition to those in model 1. Furthermore, to test the robustness of the results, we also examined the aforementioned associations using continuous FSVs as exposures. Given the possibility of non-linear associations, we performed restricted cubic spline analysis with full covariate adjustment.

We used the quantile g-computation method to explore the associations between co-exposure to all studied FSVs and the odds of MetS and each MetS component. All FSVs were performed with log-transformation and standardization to ensure normal distributions and eliminate the influence of units. The “qgcomp.noboot” function of R “qgcomp” package was employed to obtain the contribution of each FSV in the association between co-exposure to multiple FSVs and MetS odds and MetS components, as well as conditional ORs and 95% confidence intervals (Cis) of the overall associations. The “qgcomp.boot” function of R “qgcomp” package was used to assess the dose–response relationship and get marginal ORs and 95% CIs.

The SAS 9.4 and R 4.3.1 software were used for analyses and figure production. All analyses were two-sided, and a P value < 0.05 indicated statistical significance.

Results

General characteristics

Of the 13,975 participants, 2,400 (17.2%) were diagnosed with MetS. Table 1 illustrated significant differences between the MetS and non-MetS groups regarding age, race, education background, marital status, BMI, physical activity, drinking and smoking status, family PIR and dietary total energy.

Table 1 General characteristics of participants in NHANES 2003–2006 and 2017–2018
Full size table

Serum VA and VE concentrations were significantly higher in the MetS group (VA: median = 1.99, IQR: 1.63, 2.41 μmol/L); VE: median = 29.49, IQR: 23.92, 38.08 μmol/L) compared to the non-MetS group (VA: median = 1.88, IQR: 1.54, 2.27 μmol/L; VE: median = 26.47, IQR: 21.50, 33.44 μmol/L). Conversely, serum VD concentration was significantly lower in MetS group (median = 55.90, IQR: 40.90, 72.90 nmol/L) than in the non-MetS group (median = 59.20, IQR: 43.40, 76.20 nmol/L) (all P < 0.05).

Association of individual FSV with MetS odds and each component

Table 2 presented the weighted multivariable-adjusted logistic regression analysis results. In the full adjustment model (model 2), the third (OR = 1.46, 95%CI: 1.09, 1.97) and highest quartiles (OR = 1.53, 95%CI: 1.10, 2.14) of VA, as well as the third (OR = 2.21, 95%CI: 1.63, 2.98) and the highest quartiles (OR = 2.79, 95%CI: 1.94, 4.03) of VE, were positively associated with the odds of MetS compared to their respective lowest quartiles. In contrast, the second (OR = 0.76, 95%CI: 0.60, 0.98), third (OR = 0.75, 95%CI: 0.56, 0.99) and highest quartiles (OR = 0.52, 95%CI: 0.37, 0.73) of VD were associated with 24%, 25% and 48% reductions in the odds of MetS, respectively. Analyses using continuous variables of VA, VE, and VD yielded similar trends.

Table 2 Associations of serum fat-soluble vitamins with metabolic syndromes in NHANES 2003–2006 and 2017–2018 participants
Full size table

Table S1 delineated the associations between individual FSV and MetS components. After controlling for all covariates (model 2), elevated serum VA and VE were associated with higher TG levels. The highest quartile of VA (OR = 0.67, 95% CI: 0.50, 0.89) and VD (OR = 0.54, 95% CI: 0.39, 0.73), and the second quartile of VE (OR = 0.74, 95% CI: 0.59, 0.93) were associated with higher HDL levels compared to their lowest quartiles. Additionally, higher VD levels were associated with lower blood pressure and lower FG.

Dose–response relationship between individual FSV and MetS odds

Figures 1, 2 and 3 illustrated the dose–response relationships between serum VA, VE and VE concentrations and MetS odds, as assessed using restricted cubic spline analyses. We found the odds of MetS increased with elevated VA and VE in a linear dose–response manner. Notably, the CIs for the association between VA levels below the reference point and MetS odds included the null value, while the CIs for VE levels below the reference point excluded the null. Conversely, a negative linear dose–response relationship was observed between VD concentration and MetS odds.

Fig. 1
figure 1

The dose–response relationship between serum Vitamin A and metabolic syndrome risk

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Fig. 2
figure 2

The dose–response relationship between serum Vitamin E and metabolic syndrome risk

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Fig. 3
figure 3

The dose–response relationship between serum Vitamin D and metabolic syndrome risk

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Associations of multiple FSVs co-exposure with MetS odds and each component

After adjusting for potential confounders, the FSV co-exposure index demonstrated a significant association with increased MetS odds. A one-quartile increase in the index corresponded to a 15% and 13% increase in MetS odds in the conditional model (OR = 1.15, 95%CI: 1.06, 1.24) and the marginal structural model (OR = 1.13, 95%CI: 1.06, 1.20), respectively (Table 3 and Fig. 4B). VE and VA contributed 61% and 39%, respectively, to this positive association (Fig. 4A and Table 4).

Table 3 Association of multiple fat-soluble vitamins co-exposure and metabolic syndromes and each component
Full size table
Fig. 4
figure 4

Quantile g-computation model regression index weighs (A) and joint effect (B) (95% confidence interval) of fat-soluble vitamins (i.e., Vitamin A, Vitamin E, and Vitamin D) on metabolic syndrome

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Table 4 Weights of each serum vitamin in the association of multiple fat-soluble vitamins co-exposure with metabolic syndromes and each component
Full size table

The relationships between multiple FSV co-exposure and individual MetS components were presented in Table 3 and Figures S1-S5 B. FSVs co-exposure was positively associated with TG, HDL, and blood pressure. The relative contribution of each FSV to these association were detailed in Table 4 and Figure S1-S5 A.

Discussion

In this cross-sectional study of a nationally representative U.S. adults, we investigated the associations between individual and combined FSVs (specifically VA, VD, VE) exposures and the odds of MetS, as well as its individual components. We also examined the dose–response relationships among them. After adjusting for various confounders, our findings revealed that serum VA and VE concentrations were positively associated with MetS odds in a linear dose–response manner, while serum VD levels exhibited an inverse relationship with MetS odds. Elevated serum VA and VE levels were positively associated with TG and HDL levels. Conversely, higher serum VD concentrations were associated with increased HDL and decreased TG, blood pressure, and FG levels. Furthermore, MetS odds increased by 15% and 13% in response to a one-quartile increase in the FSV co-exposure index in the conditional model and marginal structural model, respectively. Also, co-exposure to VA, VE, and VD was positively associated with TG, HDL, and blood pressure levels.

Our findings demonstrated a positive, linear dose–response relationship between serum VA concentrations and MetS odds. And elevated serum VA levels were associated with increased TG and HDL. VA encompasses various compounds, including retinol, retinal, retinoic acid, and carotenoids, with retinol being the predominant retinoid found abundantly in animal-derived foods [23]. Our results aligned with previous research. A study of 606 adults from the European Health Examination Survey reported an association between increased VA levels and elevated MetS odds in women [24]. Similarly, Kim et al. using data from the Korea National Health and Nutrition Examination Survey, observed that participants with higher retinol levels exhibited increased MetS odds, as well as elevated FG, blood pressure, and TG [14]. Some other studies also drew the same conclusions [13, 25]. The proposed mechanism underlying this association is oxidative stress. Elevated retinol levels may enhance the activity of antioxidant enzymes such as catalase, superoxide dismutase, and glutathione peroxidase, potentially affecting metabolic processes [14]. Dietary patterns may also contribute to this relationship, as VA is primarily obtained from animal-based foods rich in fat, such as meat, dairy products, and oils. However, inconsistent evidence existed. A meta-analysis reported a significant inverse association between serum retinyl esters and MetS odds, while finding no significant association serum retinol and MetS odds [17]. Additionally, Park et al. observed that the interaction between total VA and vitamin C intake may reduce MetS risk in women in women [26]. This discrepancy could be attributed to differences in study population, study design and adjustments for confounders.

Our findings demonstrated that serum VE levels was positively associated with MetS odds, as well as TG and HDL. Epidemiological evidence regarding this association remained controversial. A large-scaled study of 5,885 Korean adults reported a dose-dependent positive association between α-tocopherol levels and MetS odds [14]. Similarly, another cross-sectional study suggested that elevated α-tocopherol levels were associated with increased visceral adipose tissue, TG and MetS odds [15]. Conversely, a comprehensive review of animal and human studies concluded that VE could potentially mitigate MetS symptom [27]. A meta-analysis conducted by Zhang et al. reported a weak inverse association between circulating VE and MetS [28]. Furthermore, some studies have found significant effect of VE supplementation on MetS [29]. Circulating VE levels reflect the net balance of replenishment, absorption, and excretion processes. Evidence suggested that MetS patients exhibit slower catabolism of α-tocopherol and reduced VE excretion compared to healthy individuals [30], potentially resulting in elevated serum VE levels. Additionally, the increased oxidative and inflammatory stressors associated with MetS patients might necessitate higher VE levels to counteract these deleterious effect [30]. Interestingly, our findings revealed that VE might be a protective factor against MetS within a specific dose range, highlighting its antioxidant properties. However, the optimal serum VE range for metabolic health requires further investigation.

Consistent with numerous previous studies, our findings demonstrated an inverse association between serum VD concentration and MetS odds. This association was further reflected in the observed relationships between higher VD levels and lower TG, blood pressure, and FG. VD, a unique FSV, can be endogenously synthesized in the skin upon ultraviolet radiation exposure [31]. A dose–response meta-analysis including 16 cross-sectional studies reported a 13% reduction in MetS odds for every 25 nmol/L increment in serum VD levels [32]. However, this study also showed no significant association between VD and MetS odds in pooled longitudinal studies. But this conclusion was limited due to the inclusion of only one cohort study and one nested case–control study. Buchmann et al. corroborated these findings, concluding that VD deficiency was associated with increased MetS odds, independent of obesity and insulin resistance [33]. Similar conclusions have been reported in other studies [34, 35]. Several mechanisms may explain these associations: (1) MetS patients may engage in less outdoor activity, resulting in reduced sunlight exposure and consequently lower VD synthesis; (2) MetS is often associated with increased body mass, potentially leading to a dilution effect of VD; (3) VD deficiency may influence fat metabolism by modulating insulin secretion and sensitivity [36].

A notable finding of our study is the significant positive association between co-exposure to VA, VD and VE and MetS odds, as well as TG, HDL, and blood pressure. Our analysis revealed that VE was the primary contributor to this association, followed by VA. The interplay between FSVs during intestinal absorption may explain these observations. Aurélie Goncalves et al. demonstrated that medium–high VE status significantly enhanced VA uptake by 40%, while medium–high VA and high VE status significantly reduced VD uptake [21]. These interactions may account for the concurrent elevated serum VA and VE levels and lower serum VD levels observed in the MetS group in our study. Furthermore, epidemiological evidence suggested that while VD was negatively associated with cardiovascular disease mortality, this inverse association may be attenuated by circulating VA levels [37]. The molecular structure of VA derivatives may allow them to form heterodimers with receptors, potentially increasing the catabolism of VD [38]. Despite so, each FSV possesses specific roles in human physiology and development. The positive association between FSV co-exposure and MetS odds need more studies to prove and find out an appropriate exposure dose.

We noted that the prevalence of MetS in our study differed significantly from that reported in the study by Hirode et al. [5], despite both studies utilizing NHANES data. This discrepancy could be attributed to two explanations. First, the prevalence of MetS increases over time due to socio-economic development and population aging. The study by Hirode et al. used NHANES data from 2011–2016, while our study included NHANES rounds from 2003–2006 and 2017–2018. The combination of earlier NHANES rounds (2003–2004 and 2005–2006) may have led to an underestimation of the overall MetS prevalence in our study. Second, our study specially focused on participants with available data on both serum vitamins A, E and D and MetS. This selection criteria may have introduced a bias in our study population, resulting in a lower prevalence of MetS compared to the overall population.

Our study has several strengths. First, our study is the first to explore the associations between multiple FSVs co-exposure and MetS odds, as well as MetS components. Second, the quantile g-computation method is a new method that combined weighted quantile sum regression and Bayesian Kernel Machine regression, and it is very computationally efficient [39]. At the meantime, some limitations also should be noted. First, the present study is a cross-sectional study that cannot determine causality. Second, the single serum concentration measurement of FSVs could not represent the long-term exposure status. Third, we did not include Vitamin K due to the unavailable data. At last, we only included the U.S. population, thus the extrapolation of the conclusion would be limited.

Conclusion

Our study showed that serum VA and VE levels were positively associated with MetS odds, while high serum VD level was associated with decreased MetS odds. Co-exposure to FSVs (i.e. VA, VE, and VD) was positively associated with MetS odds. More prospective and experimental studies are needed to confirm our findings and elucidate the underlying mechanisms.

Availability of data and materials

All data used in this study came from the NHANES, a publicly accessed database. All data can be viewed online or downloaded for analysis through the following link: http://www.cdc.gov/nchs/nhanes/index.htm, without any accession number.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

BMI:

Body mass index

FG:

Fasting glucose

Family PIR:

Family poverty income ratio

FSV:

Fat-soluble vitamin

HDL:

High-density lipoprotein

MET:

Metabolic equivalent

MetS:

Metabolic syndrome

NHANES:

The National Health and Nutrition Examination Survey

TG:

Triglyceride

VA:

Vitamin A

VD:

Vitamin D

VE:

Vitamin E

WC:

Waist circumference

WHO:

The World Health Organization

References

  1. Duc Nguyen H, Ardeshir A, Fonseca VA, Kim WK. Cluster of differentiation molecules in the metabolic syndrome. Clin Chim Acta. 2024;561:119819.

    Article  CAS  PubMed  Google Scholar 

  2. Alberti KG, Zimmet PZ. Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation. Diabet Med. 1998;15(7):539–53.

    Article  CAS  PubMed  Google Scholar 

  3. Expert Panel on Detection E, Treatment of High Blood Cholesterol in A. Executive Summary of The Third Report of The National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, And Treatment of High Blood Cholesterol In Adults (Adult Treatment Panel III). JAMA. 2001;285(19):2486–97.

    Article  Google Scholar 

  4. Adjei NK, Samkange-Zeeb F, Boakye D, Saleem M, Christianson L, Kebede MM, et al. Ethnic differences in metabolic syndrome in high-income countries: a systematic review and meta-analysis. Rev Endocr Metab Disord. 2024;25(4):727–50.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Hirode G, Wong RJ. Trends in the prevalence of metabolic syndrome in the United States, 2011–2016. JAMA. 2020;323(24):2526–8.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Saklayen MG. The global epidemic of the metabolic syndrome. Curr Hypertens Rep. 2018;20(2):12.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Popkin BM, Adair LS, Ng SW. Global nutrition transition and the pandemic of obesity in developing countries. Nutr Rev. 2012;70(1):3–21.

    Article  PubMed  Google Scholar 

  8. Stout MB, Justice JN, Nicklas BJ, Kirkland JL. Physiological aging: links among adipose tissue dysfunction, diabetes, and frailty. Physiology (Bethesda). 2017;32(1):9–19.

    CAS  PubMed  Google Scholar 

  9. Dewett D, Lam-Kamath K, Poupault C, Khurana H, Rister J. Mechanisms of vitamin A metabolism and deficiency in the mammalian and fly visual system. Dev Biol. 2021;476:68–78.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Stevens CM, Jain SK. Vitamin D/bone mineral density and triglyceride paradoxes seen in african americans: a cross-sectional study and review of the literature. Int J Mol Sci. 2024;25(2):1305.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Traber MG. Human Vitamin E deficiency, and what is and is not Vitamin E? Free Radic Biol Med. 2024;213:285–92.

    Article  CAS  PubMed  Google Scholar 

  12. Araki S, Shirahata A. Vitamin K Deficiency Bleeding in Infancy. Nutrients. 2020;12(3):780.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Tian T, Wang Y, Xie W, Zhang J, Ni Y, Peng X, Sun G, Dai Y, Zhou Y. Associations between serum vitamin A and metabolic risk factors among Eastern Chinese children and adolescents. Nutrients. 2022;14(3):610.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Kim T, Kang J. Association between serum retinol and α-tocopherol levels and metabolic syndrome in Korean general population: analysis of population-based nationally representative data. Nutrients. 2020;12(6):1689.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Waniek S, di Giuseppe R, Plachta-Danielzik S, Ratjen I, Jacobs G, Koch M, Borggrefe J, Both M, Muller HP, Kassubek J, et al. Association of vitamin E levels with metabolic syndrome, and MRI-derived body fat volumes and liver fat content. Nutrients. 2017;9(10):1143.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Wimalawansa SJ. Associations of vitamin D with insulin resistance, obesity, type 2 diabetes, and metabolic syndrome. J Steroid Biochem Mol Biol. 2018;175:177–89.

    Article  CAS  PubMed  Google Scholar 

  17. Beydoun MA, Chen X, Jha K, Beydoun HA, Zonderman AB, Canas JA. Carotenoids, vitamin A, and their association with the metabolic syndrome: a systematic review and meta-analysis. Nutr Rev. 2019;77(1):32–45.

    Article  PubMed  Google Scholar 

  18. Peng Z, Wang Y, Huang X, Zhu Q, Zhao Y, Xie H, Wu J. Dietary vitamin intake and risk of metabolic syndrome among centenarians in China. Exp Ther Med. 2021;21(2):105.

    Article  CAS  PubMed  Google Scholar 

  19. Goncalves A, Amiot MJ. Fat-soluble micronutrients and metabolic syndrome. Curr Opin Clin Nutr Metab Care. 2017;20(6):492–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Hajhashemy Z, Shahdadian F, Moslemi E, Mirenayat FS, Saneei P. Serum vitamin D levels in relation to metabolic syndrome: a systematic review and dose-response meta-analysis of epidemiologic studies. Obes Rev. 2021;22(7):e13223.

    Article  CAS  PubMed  Google Scholar 

  21. Goncalves A, Roi S, Nowicki M, Dhaussy A, Huertas A, Amiot MJ, Reboul E. Fat-soluble vitamin intestinal absorption: absorption sites in the intestine and interactions for absorption. Food Chem. 2015;172:155–60.

    Article  CAS  PubMed  Google Scholar 

  22. Pei X, Yao J, Ran S, Lu H, Yang S, Zhang Y, Wang M, Shi H, Tan A. Association of serum water-soluble vitamin exposures with the risk of metabolic syndrome: results from NHANES 2003–2006. Front Endocrinol (Lausanne). 2023;14:1167317.

    Article  PubMed  Google Scholar 

  23. Olsen T, Lerner UH. Vitamin A - a scoping review for Nordic nutrition Recommendations 2023. Food Nutr Res. 2023;67:1.

    Article  Google Scholar 

  24. Ruiz-Castell M, Le Coroller G, Landrier JF, Kerkour D, Weber B, Fagherazzi G, Appenzeller BMR, Vaillant M, Bohn T. Micronutrients and markers of oxidative stress and inflammation related to cardiometabolic health: results from the EHES-LUX study. Nutrients. 2020;13(1):5.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Arranz B, Sanchez-Autet M, San L, Safont G, Fuente-Tomas L, Hernandez C, Bogas JL, Garcia-Portilla MP. Are plasma 25-hydroxyvitamin D and retinol levels and one-carbon metabolism related to metabolic syndrome in patients with a severe mental disorder? Psychiatry Res. 2019;273:22–9.

    Article  CAS  PubMed  Google Scholar 

  26. Park S, Ham JO, Lee BK. Effects of total vitamin A, vitamin C, and fruit intake on risk for metabolic syndrome in Korean women and men. Nutrition. 2015;31(1):111–8.

    Article  CAS  PubMed  Google Scholar 

  27. Wong SK, Chin KY, Suhaimi FH, Ahmad F, Ima-Nirwana S. Vitamin E as a potential interventional treatment for metabolic syndrome: evidence from animal and human studies. Front Pharmacol. 2017;8:444.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Zhang Y, Ding J, Guo H, Liu Z, Liu Q, Li Y, Zhang D, Liang J. Associations of dietary and circulating vitamin E level with metabolic syndrome. A meta-analysis of observational studies. Front Nutr. 2021;8:783990.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Mohammadi A, Sadeghnia HR, Saberi-Karimian M, Safarian H, Ferns GA, Ghayour-Mobarhan M, Sahebkar A. Effects of curcumin on serum vitamin E concentrations in individuals with metabolic syndrome. Phytother Res. 2017;31(4):657–62.

    Article  CAS  PubMed  Google Scholar 

  30. Traber MG, Mah E, Leonard SW, Bobe G, Bruno RS. Metabolic syndrome increases dietary alpha-tocopherol requirements as assessed using urinary and plasma vitamin E catabolites: a double-blind, crossover clinical trial. Am J Clin Nutr. 2017;105(3):571–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Kift RC, Webb AR. Globally estimated UVB exposure times required to maintain sufficiency in vitamin D levels. Nutrients. 2024;16(10):1489.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Ju SY, Jeong HS, Kim DH. Blood vitamin D status and metabolic syndrome in the general adult population: a dose-response meta-analysis. J Clin Endocrinol Metab. 2014;99(3):1053–63.

    Article  CAS  PubMed  Google Scholar 

  33. Buchmann N, Eckstein N, Spira D, Demuth I, Steinhagen-Thiessen E, Norman K. Vitamin D insufficiency is associated with metabolic syndrome independent of insulin resistance and obesity in young adults - The Berlin Aging Study II. Diabetes Metab Res Rev. 2021;37(8):e3457.

    Article  CAS  PubMed  Google Scholar 

  34. Chew C, Reynolds JA, Lertratanakul A, Wu P, Urowitz M, Gladman DD, Fortin PR, Bae SC, Gordon C, Clarke AE, et al. Lower vitamin D is associated with metabolic syndrome and insulin resistance in systemic lupus: data from an international inception cohort. Rheumatology (Oxford). 2021;60(10):4737–47.

    Article  CAS  PubMed  Google Scholar 

  35. Schmitt EB, Nahas-Neto J, Bueloni-Dias F, Poloni PF, Orsatti CL, Petri Nahas EA. Vitamin D deficiency is associated with metabolic syndrome in postmenopausal women. Maturitas. 2018;107:97–102.

    Article  CAS  PubMed  Google Scholar 

  36. Melguizo-Rodriguez L, Costela-Ruiz VJ, Garcia-Recio E, De Luna-Bertos E, Ruiz C, Illescas-Montes R. Role of vitamin D in the metabolic syndrome. Nutrients. 2021;13(3):830.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Schmutz EA, Zimmermann MB, Rohrmann S. The inverse association between serum 25-hydroxyvitamin D and mortality may be modified by vitamin A status and use of vitamin A supplements. Eur J Nutr. 2016;55(1):393–402.

    Article  CAS  PubMed  Google Scholar 

  38. Hussein RS, Dayel SB, Abahussein O. Prospective study of the effects of isotretinoin and vitamin D levels on severe acne vulgaris. Turk J Med Sci. 2023;53(6):1732–7.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Keil AP, Buckley JP, O’Brien KM, Ferguson KK, Zhao S, White AJ. A quantile-based g-computation approach to addressing the effects of exposure mixtures. Environ Health Perspect. 2020;128(4):47004.

    Article  PubMed  Google Scholar 

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Acknowledgements

We thank the staff of the NHANES for their contributions.

Disclosure statement

The authors have nothing to disclose.

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The authors did not receive support from any organization for the submitted work.

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Authors and Affiliations

  1. College of Basic Medicine, Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, 150036, China

    Muxi Li & Deyou Jiang

  2. College of Traditional Chinese Medicine, Jinan University, Guangzhou, 510632, China

    Shan Jiang

  3. College of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun, 130117, China

    Chenxuan Dong

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  1. Muxi Li
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  2. Shan Jiang
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  4. Deyou Jiang
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Contributions

D.J clarified the research concepts and methods.M.L and S.J performed the data curation, formal analysis and wrote the manuscript. M.L, C.D and D.J reviewed and edited papers. All authors read, reviewed and approved the final manuscript.

Corresponding author

Correspondence to Deyou Jiang.

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Ethics approval and consent to participate

The National Center for Health Statistics Research Ethics Review Board approved the protocol of the NHANES (NCHS IRB/ERB Protocol Number: Protocol #98–12, Protocol #2005–06, Continuation of Protocol #2011–17, and Protocol #2018–01). Each participant signed the informed consent form. We conducted a secondary data analysis using NHANES data, which can be accessed by the public. No further ethical approval and informed consent are required.

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The authors declare no competing interests.

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Li, M., Jiang, S., Dong, C. et al. Association between fat-soluble vitamins and metabolic syndromes in US adults: a cross-section study from NHANES database. BMC Endocr Disord 24, 178 (2024). https://doi.org/10.1186/s12902-024-01711-4

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Keywords

BMC Endocrine Disorders

ISSN: 1472-6823

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