Introduction

Vitamin D deficiency is common among adults1. The causes for vitamin D deficiency include reduced ability to synthesize vitamin D in the skin due to reduced sun exposure or skin pigmentation, decreased vitamin D dietary intake and or intestinal absorption, and increased adiposity2,3,4. The mechanisms associating increased adiposity (obesity) and vitamin D insufficiency are poorly understood; the main hypothesis is the sequestration of vitamin D by the adipose tissue, as suggested by the well reported negative association between serum vitamin D levels and fat mass in obesity5. In the Swiss Salt study, a one-unit increase in BMI was associated with an 8% decreased likelihood of being in the highest tertile of vitamin D3. Several systematic reviews and meta-analyses found inverse associations between vitamin D levels and waist circumference6 or fat mass7. However, most studies assessing the association between vitamin D and obesity focused on a single anthropometric marker. Several obesity markers such as waist circumference, body composition, the Valdez conicity index, the body roundness index (BRI) and a body shape index (ABSI)8,9,10 exist, but their joint analysis has seldom been conducted.

Some studies suggest that vitamin D modulates the secretions of many of these adipokines11.The Leptin has been identified as a marker that can be influenced by vitamin D levels, but the mechanisms that explain this association are still controversial11,12.

In this study, we aimed at assessing the associations between vitamin D levels and a large panel of anthropometric markers and adipokines, in a cross-sectional population-based study.

Participants and methods

Study design

The CoLaus (Cohorte Lausannoise) study is a population-based prospective study assessing the clinical, biological, and genetic determinants of cardiovascular disease aged 35 to 75 years at baseline, living in the city of Lausanne, Switzerland)13. In each survey, participants answered questionnaires, underwent a clinical examination and blood samples were drawn for analyses13. Recruitment began in June 2003 and ended in May 2006. For this cross-sectional analysis, all participants at were eligible and no control group was created.

Anthropometry

Anthropometric measurements were conducted using a standard methodology. Body weight and height were measured with participants barefoot and in light indoor clothes. Body weight was measured in kilograms to the nearest 100 g using a Seca® scale (Hamburg, Germany). Height was measured to the nearest 5 mm using a Seca® (Hamburg, Germany) height gauge14. Body mass index (BMI) was computed and categorized into underweight (< 18.5 kg/m2), normal (18.5–24.9 kg/m2), overweight (25–29.9 kg/m2) and obesity (≥ 30 kg/m2)15.

Waist circumference (WC) was measured mid-way between the lowest rib and the iliac crest, and hip was measured at the largest location, using a non-stretchable tape; the average of two measurements was taken14. Abdominal obesity was defined as a waist circumference > 102 cm (men) or > 88 cm (women). A high waist to height ratio (WHtR) was defined as > 0.516; the WHtR is considered as a good indicator of abdominal obesity17.

Fat and fat-free mass (in percent of total body weight) were assessed by electrical bioimpedance in the lying position after a 5-min rest using the Bodystat® 1500 body mass analyzer (Bodystat Ltd, Isle of Man, England)18. This device has been shown to correlate well (r = 0.968) with measurements from dual energy X-ray absorptiometry (DEXA)18.

The conicity index (CI) was calculated according to Valdez9. It is based upon the idea that people accumulate fat around the waist and, the shape of their bodies seems to change from that of a cylinder to that of a “double cone” (two cones with a common base). The CI is determined by the formula

$$CI=\frac{Waist}{0.109\times \sqrt{\frac{Body\, weight}{Height}}}$$

The CI was further categorized as normal if < 1.25 and < 1.18 for men and women, respectively, and as high if ≥ 1.25 and ≥ 1.18 for men and women, respectively19.

Body roundness index (BRI)20 was computed according to and is based on waist circumference and height.

$$BRI=364.2-365.5\times \sqrt{1-\left[\frac{{\left(\frac{Waist}{2\pi }\right)}^{2}}{{\left(0.5\times Height\right)}^{2}}\right]}$$

A Body shape index (ABSI)21 was computed according to and is based on waist, BMI and height. Since there are no clinical cutoff values for BRI or ABSI, high BRI and ABSI were defined as those within the highest quartile group (Q4).

$$ABSI=\frac{Waist}{{BMI}^{2/3} \times {Height}^{1/2}}$$

Vitamin D levels

Vitamin D was assessed at baseline through an ultra-HPLC tandem-MS system. The calibrators, 3Plus1 Multilevel Serum Calibrator Set 25-OH-Vitamin D3/D2 (ChromoSystems), were standardized against the National Institute of Standards and Technology 972 reference material. Serum 25(OH)D3 and 3-epi-25(OH)D3 were expressed in nanomoles per liter (conversion factor: 1 nmol/L = 0.4006 μg/L). The interday CV% was 4.6% at 40 nmol/L22. Vitamin D levels were further categorized as normal (≥ 30 ng/mL or ≥ 75 nmol/l), insufficiency (21 to 29 ng/mL or 50–75 nmol/l) and deficiency (< 20 ng/mL or < 50 nmol/l)23. Hypovitaminosis D was defined for vitamin D levels < 30 ng/mL or < 75 nmol/l, encompassing insufficiency plus deficiency24.

Other covariates

Educational level was categorized into university, high school, apprenticeship, and mandatory. Nationality as born in Switzerland or not. Smoking status was self-reported and categorized as never, former, and current. Physical activity was considered if the participant reported performing at least twice a week a minimum of 20 min of leisure-time physical activity13.

Adipokines (adiponectin and leptin) were assessed at baseline. Adiponectin was assessed by ELISA (R&D Systems, Inc, Minneapolis, USA), with a maximum inter-assay CV of 8.3% and a maximum intra-assay CV of 8.3%. Leptin was assessed by ELISA (American Laboratory Products Company, Windham, USA) with a maximum inter-assay CV of 12.8% and a maximum intra-assay CV of 5.8%. High sensitive C-reactive protein (CRP) was assessed by immunoassay and latex HS on a Modular P apparatus (Roche Diagnostics, Basel, Switzerland).

Exclusion criteria

Participants were excluded if they lacked any variable needed for the bivariate and the multivariate analyses. Hence, participants devoid of vitamin D data; without anthropometric measurements and any covariate needed for adjustment (education, smoking, BMI, or physical activity) were excluded. The exclusion procedure was conducted sequentially as follows: first, participants devoid of vitamin D data were excluded; of the remaining participants, those without anthropometric measurements were excluded; finally, of the remaining participants, those missing any covariate were excluded.

Statistical analysis

Statistical analyses were performed using Stata version 16.1 for Windows (Stata Corp, College Station, Texas, USA)25. Descriptive results were expressed as number of participants (percentage) for categorical variables and as average ± standard deviation or median [interquartile range] for continuous variables.

As adiposity measures differ between sexes, stratification on the latter was performed. The associations between vitamin D levels and anthropometric markers (BMI, waist, WHR, WHtR, %fat as assessed by bioimpedance, CI, BRI and AABSI) were assessed as follows. First, comparison of vitamin D levels according to categories of anthropometric markers was performed using one-way (bivariate) or multivariate analysis of variance. Multivariate analysis was adjusted for age (continuous), nationality (Swiss, other), month, smoking categories (never, former, current), vitamin D supplementation (yes, no) and physical activity (yes, no) and the results were expressed as adjusted mean ± standard error. Second, bivariate nonparametric Spearman correlations and their 95% CIs were calculated between vitamin D levels and anthropometric markers as continuous variables. A stepwise multivariate linear regression analysis with age (continuous), nationality (Swiss, other), month, smoking categories (never, former, current), vitamin D supplementation (yes, no) and physical activity (yes, no) as locked terms was conducted to identify the anthropometric marker most associated with vitamin D levels. For simplicity, all anthropometric markers were standardized (i.e., zero average and unit standard deviation) before the stepwise regression.

Third, the association between vitamin D deficiency and categories of anthropometric markers was assessed using chi-square (bivariate analysis) and multivariate logistic regression with vitamin D deficiency (yes, no) as the dependent variable and adjusting for age (continuous), nationality (Swiss, other), month, smoking categories (never, former, current), vitamin D supplementation (yes, no) and physical activity (yes, no).

Sensitivity analyses were conducted by excluding participants receiving medically prescribed vitamin D levels. Finally, the analysis of the associations between vitamin D levels and leptin and adiponectin levels as obesity markers was conducted using Spearman correlation and linear regression adjusting for the aforementioned covariates; results of the linear regression were expressed as standardized coefficients. Statistical significance was considered for a two-sided test with p < 0.05.

Ethical statement

The institutional Ethics Committee of the University of Lausanne, which afterwards became the Ethics Commission of Canton Vaud (https://www.cer-vd.ch) approved the baseline CoLaus study (reference 16/03, decisions of 13th January and 10th February 2003). The study was performed in agreement with the Helsinki declaration and its former amendments, and in accordance with the applicable Swiss legislation. All participants gave their signed informed consent before entering the study. Data analysis was conducted in Switzerland and no data was shared with outside groups.

Results

Characteristics of participants

Of the initial 6733 participants, 248 were excluded. The detailed reasons for exclusion are provided in supplementary Fig. 1 and the characteristics of excluded and eligible participants are provided in supplementary Table 1. Excluded participants were younger, less frequently born in Switzerland, had a higher educational level, were less frequently former smokers, less physically active and took vitamin D supplements less frequently than included participants.

The characteristics of the 6485 participants by sex are summarized in Table 1. Women were older, had a lower educational level, smoked less, and took vitamin D supplements more frequently. Women also had higher levels of vitamin D and presented less frequently with vitamin D deficiency. Regarding anthropometric markers, women had higher body fat percentage levels but lower levels for all other anthropometric markers than men (Table 1).

Table 1 Characteristics of the participants at baseline, by sex, CoLaus|PsyCoLaus study, Lausanne, 2003–2006.
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Associations between vitamin D levels and anthropometric markers

The levels of vitamin D according to categories of anthropometric markers are summarized in Table 2. On bivariate analysis, and regardless of the anthropometric markers considered, participants with high adiposity levels had lower vitamin D levels than participants with normal adiposity measures. Those findings were further confirmed by multivariate analysis, where a linear trend for a decrease in vitamin D levels with increasing adiposity was found, except for BMI in men (Table 2). Those findings were further confirmed after excluding participants taking vitamin D supplements (supplementary Table 2).

Table 2 Bivariate and multivariate comparisons of total vitamin D levels according to adiposity categories, overall and stratified by sex, CoLaus|PsyCoLaus study, Lausanne.
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The bivariate Spearman correlations between vitamin D levels and anthropometric markers are summarized in Fig. 1. Negative correlations were found for all anthropometric markers. With the exception of the correlations between vitamin D and ABSI (in women) and weight (in men), all correlations were statistically significant (p < 0.05). Similar findings were obtained after excluding participants taking vitamin D supplements (Fig. 1).

Figure 1
figure 1

Bivariate non-parametric Spearman correlations between vitamin D levels and anthropometric markers, stratified by gender, CoLaus|PsyCoLaus study, Lausanne. Top panel: all participants. Bottom panel: participants with prescribed supplemental vitamin D excluded. Within each panel, correlations are provided in the lower left for women and in the top right for men.

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The results of the stepwise linear regression analysis are summarized in Table 3. After adjusting for major confounders, % body fat was consistently and negatively associated with vitamin D levels overall and for both sexes. Associations were also found for WHtR (overall), BMI (women) and conicity index (men). Similar findings were obtained after excluding participants taking vitamin D supplements (supplementary Table 3) or adjusting for CRP levels (not shown).

Table 3 Results of the stepwise linear regression to assess the anthropometric markers most associated with vitamin D levels, overall and stratified by sex, CoLaus|PsyCoLaus study, Lausanne.
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Association between vitamin D deficiency levels and anthropometric markers

There were 1895 (55.7%) of women and 1854 (60.1%) men presenting with vitamin D deficiency. Table 4 presents the bivariate and multivariate analysis of the associations between vitamin D deficiency and the different measures of adiposity. The likelihood of vitamin D deficiency increased with increasing adiposity levels; the sole exceptions were no associations with ABSI (women) and BMI (men). Similar findings were obtained after excluding participants taking vitamin D supplements (supplementary Table 4).

Table 4 Bivariate and multivariate associations between vitamin D deficiency (i.e. defined as < 30 ng/mL or < 50 nmol/l) and adiposity measures, overall and stratified by sex, CoLaus|PsyCoLaus study, Lausanne.
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Association between vitamin D levels and adipokines

The associations between vitamin D, leptin and adiponectin levels are summarized in Fig. 1 (bivariate) and supplementary Table 5 (multivariate). In both univariate and multivariate analyses, leptin levels were negatively associated with vitamin D levels. Adipokine levels were positively associated with vitamin D levels in women, while no association was found in men. Similar findings were obtained after excluding participants taking vitamin D supplements (Fig. 1 and supplementary Table 5).

Discussion

To our knowledge, this is one of the most comprehensive study in a large population-based cohort of apparently healthy subjects associating vitamin D levels with a wide array of obesity markers. Our results show a negative association between various adiposity measurements and vitamin D levels, and that total body fatness is the adiposity marker most associated with vitamin D levels. Our results add further evidence to the hypothesis that low vitamin D levels might be explained by the sequestration of the vitamin by excess body fat stores.

Associations between vitamin D levels and anthropometric markers

Negative associations between vitamin D levels and all markers of adiposity were found, the sole exception being BMI in men, where underweight participants had vitamin D levels close to those of participants with obesity. A possible explanation is that underweight men might have reduced vitamin D dietary intake and/or be affected by pathophysiological conditions affecting vitamin D metabolism, but this hypothesis remains to be assessed. Still, our findings are consistent with the previous literature. Inverse associations have been reported between vitamin D levels and abdominal and visceral fat26; waist circumference and BRI27; the visceral adiposity index28, and with both total and regional adiposity29. Also, De Pergola et al., found that vitamin D circulating levels were progressively lower with the increase of fat mass in a cohort of healthy overweight and subjects with obesity5.

WC, WHR and WHtR are traditional indicators of abdominal obesity. BRI and ABSI have recently been proposed as novel anthropometric measurements and are positively correlated with visceral adiposity10,21,27. Conversely, CI is rarely used as a measure to assess adiposity, despite being a good predictor of abdominal adiposity8. Notwithstanding, total body fat was the sole anthropometric marker consistently associated with low vitamin D levels, suggesting that it is the total amount of body fat, rather than localized or unevenly distributed body fat, that are associated with vitamin D levels. Our results thus add further evidence to the hypothesis that low levels of vitamin D in individuals with obesity can be explained by the sequestration of vitamin D by adipose tissue or by simple volumetric dilution in adipose tissue30.

Association between vitamin D deficiency levels and anthropometric markers

Over half of the sample presented with vitamin D deficiency, hypovitaminosis D (i.e. insufficiency or deficiency) being more frequent in men than in women. Our findings do not replicate recent studies showing that hypovitaminosis D is more prevalent in women than in men10,31,32. Possible explanations include the fact that the women in this study were less frequently were less frequently diagnosed with obesity than men, or an unhealthier dietary intake in men.

Overall, our results show that individuals with higher levels of adiposity are more predisposed to reduced serum concentrations of vitamin D. This reduced bioavailability would trigger a hypothalamic action, stimulating food intake and reducing energy expenditure33. Chronic inflammation linked to obesity could also explain hypovitaminosis D34 due to adipose tissue macrophage infiltration and production of proinflammatory adipokines35. It has also been postulated that the increase in adiposity would be promoted by resistance to stimulation of lipolysis by catecholamines and natriuretic peptide in people with obesity36. Furthermore, this imbalance may lead to reduced release of vitamin D from fat depots, given the fat-soluble nature of vitamin D36.

Importantly, it is unclear whether increasing vitamin D supplementation to correct low vitamin D levels in subjects with increased adiposity is the best option. One study demonstrated that adapting supplementation to both vitamin D deficit and adiposity levels would be more efficient37, but further studies are needed to confirm this finding. Besides, improvements in vitamin D levels have been associated with more weight loss, a result which can be partly attributed to the anti-inflammatory effects of vitamin D33.

Association between vitamin D levels and adipokines

Our study showed that vitamin D levels were inversely associated with leptin and positively associated with adiponectin. Our findings are in agreement with those of Gangloff et al.38, who reported a negative correlation vitamin D levels and leptin. The authors also reported that a decrease in visceral adipose tissue volume and corresponding decrease in leptin levels was associated with an increase in plasma vitamin D concentrations.

Studies have shown that vitamin D affects energy homeostasis by directly regulating leptin expression. However, the exact in vivo effect of vitamin D on leptin expression in humans is controversial11. Some studies have suggested that vitamin D can improve adipose tissue inflammation and suppress expression of leptin39. A recent study demonstrated that vitamin D acts through its receptor (VDR) to inhibit inflammatory pathways and the expression of adipokines in human adipocytes12. Overall, these findings suggest that improving vitamin D status in people with obesity may decrease adipose inflammation, which may contribute to reduce risks of obesity-associated pathophysiological processes. Although more recent studies point to CRP as a marker of inflammation associated with vitamin D levels, our results remained unchanged after adjusting for CRP.

Strengths and limitations

This study was carried out in a representative sample of the population and included a large panel of adiposity markers.

Some limitations should be acknowledged. First, the analysis was conducted in an urban Swiss population and the participants were mostly Caucasian. The results might not be generalizable to other countries. Still, our results are concordant with ones reported in other populations. Second, fat mass was measured by bioimpedance with the known limitations of this technique to accurately quantify adipose tissue particularly using a single time point measurement. Third, our cross-sectional study does not allow to draw any potential causality relationships between the investigated variables (i.e. whether it is obesity that causes vitamin D deficiency or vice versa). However, a prospective study is envisaged. Fourthly, we did not use different cutoff points for vitamin D according to seasonality as indicated in other studies40. Still, we adjusted the analysis considering the month. We chose this methodology as changing the threshold according to season would be difficult to apply in clinical practice.

Conclusions

Our data show a negative association between vitamin D levels and most adiposity markers and that people with excess adiposity are more likely exhibit to present with vitamin D deficiency as measured by total circulating 25-hydroxyvitamin D. Total fat mass is the adiposity marker most consistently associated with decreased vitamin D levels. Monitoring the levels of vitamin D in people with overweight/obesity is essential.