Does vitamin D status predict weight gain or increase in waist circumference? Results from the longitudinal Health 2000/2011 Survey

Associations between Vitamin D Levels and Insulin Resistance in Non-Diabetic Obesity: Results from NHANES 2001-2018

OBJECTIVE

Obesity is often accompanied by insulin resistance (IR) and diabetes. We explored the association between vitamin D levels and IR in non-diabetic obesity.

METHODS

We conducted a cross-sectional study based on the data of National Health and Nutrition Examination Survey (NHANES) from 2001 to 2018. Non-diabetic individuals (aged ≥20 years) with obesity (BMI ≥ 30kg/m2) were included in the study. And HOMA-IR ≥ 2.5 was defined as IR. The multivariable linear regression models were constructed to evaluate the associations between levels of 25(OH)D and HOMA-IR. We calculated the odds ratio (OR) and 95% confidential intervals (CIs) for associations between 25(OH)D deficiency and IR in obesity using multivariable logistic regression models.

RESULTS

Overall, a total of 3887 individuals were included in this study. Serum vitamin D level was significant lower in obesity participants with IR than that of non-IRs. The linear regression models showed that vitamin D level was inversely associated with HOMA-IR in obesity after adjusting for covariables (β=-0.15, 95%CI (-0.28, -0.02), p = 0.028). And the multivariable logistic regression models indicated an association between vitamin D deficiency and IR in obesity ((OR= 1.38, 95%CI (1.09-1.73), p = 0.007)). The further stratified regression analyses among different BMI demonstrated that vitamin D deficiency (OR = 1.4, 95%CI (1.05,1.86), p = 0.022) only contributed to developing IR in class I obesity.

CONCLUSION

This study suggested an association of vitamin D levels with IR in obesity. And vitamin D deficiency contributed to IR in class I obesity.

Vitamin D and Weight Change: A Mendelian Randomization, Prospective Study

The association between 25-hydroxyvitamin D and 5-, 10-, or 15-year weight change were assessed in a population-based, prospective study conducted in Lausanne, Switzerland. Data from the first (2009−2012, N = 3527, 51.3% women), second (2014−2017, N = 3237, 53.8% women), and third (2018−2021, N = 2567, 54.2% women) follow-ups were used. A weighted genetic risk score (GRS) of 115 SNPs associated with vitamin D levels was constructed. At baseline, the GRS correlated positively with 25-hydroxyvitamin D levels based on a Spearman rank correlation and 95% confidence interval: 0.198 (0.166; 0.231), p < 0.001; and with body mass index: 0.036 (0.004; 0.068), p = 0.028. No association was found between quartiles of GRS and weight changes at 5, 10, or 15 years: multivariate-adjusted weight changes ± SEM at 5-years follow-up were 1.39 ± 0.17, 1.13 ± 0.17, 1.24 ± 0.17, and 1.00 ± 0.17 kg for the first to the fourth quartile of the GRS, respectively (p = 0.401). Two-step linear regression showed a significant but clinically meaningless association between GRS-derived vitamin D and weight change at 5- and 15-years: slope and 95% confidence interval for a 5 nmol/L increase in GRS-derived 25-hydroxyvitamin D levels: 0.082 (0.013; 0.150) and 0.130 (0.018; 0.243) kg, respectively. We conclude that there is little association between genetically determined 25-hydroxyvitamin D levels and weight gain.

Serum vitamin D and cardiometabolic risk factors in the UK population

Low serum 25-hydroxyvitamin D (25(OH)D) concentrations have been associated with greater adiposity and an adverse cardiometabolic risk profile, yet findings are inconsistent and the role of vitamin D status in cardiovascular disease (CVD) remains uncertain. We aimed to examine the associations between serum 25(OH)D and CVD risk factors in the British population. We analysed data on 2842 subjects aged ≥40 years enrolled in the National Diet and Nutrition Survey (NDNS 2008-2018). Based on serum 25(OH)D concentrations, study subjects were grouped in three categories: vitamin D deficiency (<25 nmol/L), vitamin D insufficiency (25-49 nmol/L) and vitamin D sufficiency status (≥50 nmol/mL). Differences in CVD risk factors between vitamin D deficiency or insufficiency and vitamin D sufficiency status were expressed in standard deviation scores (SDSs) and estimated through weighted multiple linear regression models. We found that vitamin D deficiency was directly associated with BMI, waist circumference, triglycerides and inversely associated with high-density lipoprotein cholesterol (HDL) values. The strongest associations were found between vitamin D deficiency and triglycerides (0.50 SDS, 95% CI: 0.24, 0.77) among men, and vitamin D deficiency and waist circumference (0.70 SDS, 95% CI: 0.56, 0.94), BMI (0.63 SDS, 95% CI: 0.39, 0.88) and triglycerides (0.54 SDS, 95% CI: 0.30, 0.77) among women. When adjusting for BMI the association with triglyceride attenuated (from 0.50 SDS to 0.39 SDS among men and from 0.54 SDS to 0.30 SDS among women). Our data indicates a relationship between inadequate vitamin D status and an adverse CVD risk profile. However, interventional studies are needed to establish possible benefits of vitamin D supplementation on cardiovascular risk. This article is protected by copyright. All rights reserved.

No Association between Vitamin D and Weight Gain: A Prospective, Population-Based Study

Background: The association between vitamin D and weight gain remains controversial due to important limitations in the studies. We investigated the relationship between vitamin D levels and 5 and 10 years of weight and waist circumference change in a population-based prospective cohort study. Methods: Prospective study including participants aged between 35 and 75 years living in the city of Lausanne, Switzerland. Weight and waist change at 5- and 10-year follow-up were assessed according to baseline vitamin D status (normal, insufficiency and deficiency). Results: A total of 3638 participants (47.9 % women, mean age 51.6 ± 10.4 years) were included for the 5-year follow-up. No association was found between vitamin D categories and weight change, multivariate-adjusted average ± standard error: 1.6 ± 0.3, 1.5 ± 0.2 and 1.2 ± 0.1 kg for normal, insufficiency and deficiency, respectively, p = 0.159. For waist change, the corresponding values were 3.3 ± 0.4, 3.3 ± 0.2 and 3.4 ± 0.2 cm, p = 0.792. For the 10-year follow-up, data from 2999 participants (45.8% women, mean age 50.8 ± 10.3 years) were used. No association was found for weight 2.3 ± 0.4, 2.3 ± 0.2 and 2.0 ± 0.2 kg, p = 0.588, or for waist 3.7 ± 0.4, 3.6 ± 0.3 and 4.2 ± 0.2 cm for normal, insufficiency and deficiency, respectively, p = 0.259. Conclusion: No association between vitamin D status and weight or waist gain at 5- and 10-year follow-up was found.

Predictive Ability of Machine-Learning Methods for Vitamin D Deficiency Prediction by Anthropometric Parameters

Background: Vitamin D deficiency affects the general population and is very common among elderly Europeans. This study compared different supervised learning algorithms in a cohort of Spanish individuals aged 35–75 years to predict which anthropometric parameter was most strongly associated with vitamin D deficiency. Methods: A total of 501 participants were recruited by simple random sampling with replacement (reference population: 43,946). The analyzed anthropometric parameters were waist circumference (WC), body mass index (BMI), waist-to-height ratio (WHtR), body roundness index (BRI), visceral adiposity index (VAI), and the Clinical University of Navarra body adiposity estimator (CUN-BAE) for body fat percentage. Results: All the anthropometric indices were associated, in males, with vitamin D deficiency (p < 0.01 for the entire sample) after controlling for possible confounding factors, except for CUN-BAE, which was the only parameter that showed a correlation in females. Conclusions: The capacity of anthropometric parameters to predict vitamin D deficiency differed according to sex; thus, WC, BMI, WHtR, VAI, and BRI were most useful for prediction in males, while CUN-BAE was more useful in females. The naïve Bayes approach for machine learning showed the best area under the curve with WC, BMI, WHtR, and BRI, while the logistic regression model did so in VAI and CUN-BAE.

Serum 25-hydroxyvitamin D level in relation to weight change and the risk of weight gain in adults of normal weight at baseline: the Norwegian HUNT cohort study

OBJECTIVE

We sought to investigate the relationship of serum 25-hydroxyvitamin D (25(OH)D) level with weight change and the risk of weight gain in an adult population who had normal weight at baseline and were followed up for 11 years.

DESIGN

A population-based prospective cohort study.

SETTING

Nord-Trøndelag, Norway.

PARTICIPANTS

The study included 1501 adults who participated in the second and third surveys of the Nord-Trøndelag Health Study (HUNT2 (1995-1997) and HUNT3 (2006-2008)) and had a normal body mass index ≥18.5 and <25.0 kg/m² at baseline.

PRIMARY AND SECONDARY OUTCOME MEASURES

Relative weight change (%) was calculated as ((HUNT3 weight-HUNT2 weight)/HUNT2 weight×100). Relative annual weight change (%) was calculated as (relative weight change/follow-up years×100). Clinical weight gain was defined as relative weight change ≥5% over the 11 years, while annual weight gain was defined as relative annual weight change >1.25%.

METHODS

Multiple regression models were used to estimate adjusted coefficients for the relative annual weight change and risk ratios (RRs) for the risk of clinical weight gain and of annual weight gain.

RESULTS

Each 25 nmol/L increase in season-standardised serum 25(OH)D level at baseline was associated with a reduction of 0.05% (95% CI -0.11 to 0.01) for relative annual weight change, a 10% (RR 0.90, 95% CI 0.82 to 0.97) reduced risk of clinical weight gain, and a 19% (RR 0.81, 95% CI 0.65 to 1.00) reduced risk of annual weight gain. A statistically significant trend was evident for the risk of clinical weight gain when 25(OH)D levels were treated as a categorical variable (p=0.006).

CONCLUSIONS

The findings suggested an inverse association of serum 25(OH)D level with the risk of clinical weight gain in adults who had normal weight at baseline over 11 years' follow-up.