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. 2019 May 23;2019(5):CD011629. doi: 10.1002/14651858.CD011629.pub2

Vitamin D supplementation for overweight or obese adults

Nur Syimah Izzah Abdullah Thani 1,✉, Roslaili Khairudin 2, Jacqueline J Ho 3, Nor Asiah Muhamad 4, Hirman Ismail 5
PMCID: PMC6532760

Abstract

This is a protocol for a Cochrane Review (Intervention). The objectives are as follows:

To assess the effects of vitamin D supplementation for overweight or obese adults.

Background

The conditions of overweight and obesity are at epidemic levels worldwide. Every year, 2.8 million adults die due to overweight and obesity (WHO 2013). Obesity appears to be slightly more common in women than in men, with the World Health Organization (WHO) estimating that of 1.4 billion overweight people, 200 million men and 300 million women were obese. Obesity worldwide almost doubled in the decades between 1980 and 2008 (Finucane 2011). This epidemic not only involves high‐income countries but also middle‐ and low‐income countries, with prevalence as high as 60% to 80% (Gill 2006).

Description of the condition

Overweight and obesity have been defined as an abnormal or excessive fat accumulation that may impair health (Mokdad 2003). Body mass index (BMI) is the standard tool to measure body fat. BMI is calculated as weight divided by the square of height (weight (kg)/height (m)²). The WHO classified weight into four categories (underweight as < 18.5 kg/m², normal as 18.5 to 24.99 kg/m², overweight as ≥ 25 kg/m², and obese as ≥ 30 kg/m²). The purpose of a BMI cut‐off point is to identify, within each population, the proportion of people with a high risk of an undesirable health state that warrants a public health or clinical intervention. In Asian populations, the WHO has suggested the cut‐off points of 23 kg/m² for overweight and 27.5 kg/m² for obesity should be added as amended trigger points for public health action (WHO 2004). More recently, interest in BMI measurements has been superseded by waist circumference and waist‐to‐hip ratio because they have been found to be better predictors of cardiovascular disease and diabetes (Wang 2004; Wei 1997).

Besides cardiovascular disease and diabetes, obesity is associated with an increased risk of other non‐communicable diseases such as musculoskeletal disorders and even cancer. It is a major social and psychosocial issue affecting all ages and socioeconomic groups. Preventing and treating obesity has proven to be a difficult problem. Strategies tested include pharmacotherapy, herbal medicines, diet, exercise and surgery (Colquitt 2014; Jull 2008; Jurgens 2012; Padwal 2003; Thomas 2006). The results of these interventions have generally had little or modest impact on weight loss. However governments and industry have invested heavily in prevention and control measures such as weight loss medication, weight loss programs, dietary supplements and replacement diets, as well as bariatric and cosmetic surgery. Exercise programmes also focus on obesity prevention. Modern culture places a great emphasis on personal appearance and health.

Description of the intervention

Vitamin D is known as the 'sunshine vitamin'. It is the only vitamin that can be synthesised in the body but its synthesis is dependent on sufficient exposure to sunlight. Between 10 am and 3 pm, 20 minutes of exposure to summer sun producing shortwave ultraviolet B (UVB) rays, of exposed arms and legs without any sunscreen, produces up to 20,000 international units (IU) of vitamin D (Holick 2007). Vitamin D is also cheap and readily available over the counter in many parts of the world as a dietary supplement in forms such as cholecalciferol (vitamin D₃) , ergocalciferol (vitamin D₂), calcitriol (the biologically active form of vitamin D) and cod liver oil. Natural sources of vitamin D can be obtained from oily fish such as mackerel, cod, salmon and tuna, as well as some types of mushrooms. It is also available from fortified dairy products and eggs. The recommended dietary allowance of supplemented vitamin D is provided in the 'Dietary Reference Intakes' developed by the Food and Nutrition Board of the Institute of Medicine (IOM 2010). The recommended dose for adults ranges from 600 to 800 IU (15 to 20 mcg) daily. These levels are adequate to maintain bone health and a normal calcium metabolism in healthy adults (Ross 2011).

Vitamin D from food and supplements is absorbed in the intestine to the liver and converted to calcidiol or 25‐hydroxyvitamin D (25‐OH‐D). 25‐OH‐D is converted in the kidney to its active form, 1,25 dihydroxyvitamin D also known as calcitriol, which is involved in regulating blood calcium and phosphate to maintain bone health. Adults with low levels of vitamin D will develop soft bone, also known as osteomalacia. In children, the disease is known as rickets. Other than that, vitamin D deficiency is associated with a number of adverse health outcomes including osteoporosis, certain cancers, autoimmune conditions, and more recently cardiovascular disease (CVD) and poor blood glucose control in diabetes (Raiten 2004).

The level of vitamin D can be estimated by measuring circulating 25‐OH‐D concentration in the blood. The optimum level of serum 25‐OH‐D has been suggested to be 75 to 100 nmol/L (Bischoff‐Ferrari 2006), deficient as < 30 nmol/L and insufficient as < 50 nmol/L. Unfortunately, there has not been a consistent definition of vitamin D deficiency and insufficiency. Vitamin D deficiency occurs in 4% to 55% and insufficiency occurs in 26% to 79% of the adult population (Ben‐Shoshan 2012; Lucas 2005). People with darker skin and of Asian ethnicity have low levels of circulating vitamin D, and sunscreen protection, obesity, season and latitude also influence vitamin D levels (Ginde 2009, Matsuoka 1987; Mitchell 2012; Webb 1988; Yin 2012; Zhang 2013). In general, vitamin D insufficiency and deficiency have the same effects on bone health, muscle strength, the immune system and the metabolic system, but with different severity (Holick 2007; Rosen 2011). As an example, vitamin D insufficiency results in osteoporosis while vitamin D deficiency leads to more severe bone loss causing bone pain and osteomalacia (Gloth 1991).

Vitamin D status is closely related to calcium status in weight loss. Vitamin D makes fat cells more metabolically active by increasing the release of calcium that is stored in fat via a specific membrane vitamin D receptor (Zemel 2004). Calcium increases fat oxidation, hence increasing burning of fat. Often when there is a low level of vitamin D, calcium is also low. This will lead to increased production of fatty acid synthase, an enzyme that converts calories into fat. Calcium deficiency can cause synthase production to increase, explaining the correlation between low levels of vitamin D and calcium with obesity (Zemel 2000). With oral supplementation, changes in circulating 25‐OH‐D blood level can be detected within 30 days to 3 months (Kuwabara 2009; Nimitphong 2013). However, it is not known how long supplementation is needed before a clinical change in body fat can be detected.

Adverse effects of the intervention

The advantage of obtaining vitamin D from sunlight is that the body is able to self‐regulate synthesis according to need,and vitamin D toxicity does not occur (Jones 2008). This self‐regulation is limited when vitamin D is taken orally, either in foods or supplements, because absorption from the gastrointestinal tract is independent of the blood concentration (Blank 1995). Vitamin D toxicity can lead to high blood calcium levels (hypercalcaemia) and high concentrations of calcium in urine (hypercalciuria) which leads to calcium deposition in the kidney (nephrocalcinosis) and renal stone formation (nephrolithiasis). People with excessive vitamin D present with non‐specific symptoms such as anorexia, weight loss, polyuria, and heart arrhythmias (IOM 2010). Research suggests that clinical toxicity occurs at blood levels greater than 80 ng/mL to 150 ng/mL (200 to 375 nmol/L) (Bischoff‐Ferrari 2006; Misra 2008). In one study, vitamin D toxicity was convincingly associated with hypercalcaemia occurring at serum 25‐OH‐D concentrations above 200 nmol/L (Adams 1997). To achieve blood levels this high would require a daily intake of 1000 μg (40.000 IU) of vitamin D or more (Vieth 1999). However, the United States (US) Institute of Medicine has set the safe upper limit for circulating 25‐OH‐D at 125 nmol/L and a daily intake upper limit of 100 μg/d (4000 IU) for adults (Ross 2011).

How the intervention might work

Besides vitamin D playing an important role in bone health and calcium homoeostasis, it also may have an ‘anti‐obesity’ effect. Vitamin D levels correlate inversely with BMI, body weight, abdominal fat and skin fold thickness (Kremer 2009; Vimaleswaran 2013). Vitamin D is stored in the adipose tissue (Ding 2012). In vitro studies have demonstrated that 10% to 20% of a supplemented dose of vitamin D is rapidly deposited in adipose tissue. At the same time, the release of vitamin D from the fat tissue is extremely slow and proportional to the concentration of vitamin D in the adipose tissue (Rosenstreich 1971). The accumulation of vitamin D in the adipose tissue might explain why obese people have low vitamin D blood levels (Holick 2007; Wortsman 2000). The mechanism of vitamin D for weight loss is still poorly understood. However, it has a role in lipid metabolism (Ding 2012). One of the possible mechanisms might be related to an important hormone, adiponectin, which is involved in lipid metabolism. Adiponectin is exclusively secreted from adipose tissue and is involved in glucose regulation and fatty acid breakdown. Low levels of adiponectin are reported as an independent risk factor for the 'metabolic syndrome' (Vaidya 2012). The levels of 25‐OH‐D and adiponectin are positively correlated as vitamin D may be involved in adiponectin synthesis (Cantorna 2005; Ruan 2003; Sun 2007), hence supplementing obese people with vitamin D could improve fat breakdown.

Vitamin D might work differently in some groups of people because of differences in lifestyle, ethnicity, age and seasonal variation, as well as pre‐ and post‐menopausal status in women (Dawson‐Hughes 2004; Nimitphong 2013). It has been reported that post‐menopausal women had an increased risk of obesity related to low levels of circulating oestrogen via a complex mechanism (Carr 2003). A close association between oestrogen and vitamin D has also been described in post‐menopausal women (Riggs 1998). Age‐related changes in body composition, metabolic factors and hormone levels may provide the underlying mechanism for the propensity towards menopausal gains in fat mass and replacement of lean tissue with adipose tissue.

To achieve optimum status, the dose of vitamin D might best be tailored taking into account all of the above‐mentioned factors.

Why it is important to do this review

A Cochrane review suggests that vitamin D might prevent mortality in the elderly (Bjelakovic 2014) and in addition there is some evidence that it might prevent fractures in frail people (Avenell 2009). Obesity is a major problem worldwide and programmes designed to prevent and treat obesity have been found to have little or no effect. Vitamin D supplementation is cheap and easily available. If found to be effective it could be used as an adjunct to existing or new programmes. Implementation would be easy and there would be very little additional cost. In addition there may be populations in which it could be used as the main management strategy. The potential harms need to be well quantified, especially if vitamin D supplementation is used on a wide scale.

Objectives

To assess the effects of vitamin D supplementation for overweight or obese adults.

Methods

Criteria for considering studies for this review

Types of studies

We will include randomised controlled clinical trials (RCTs).

Types of participants

Overweight or obese adults (aged 18 years and above).

Diagnostic criteria for overweight and obesity

Overweight: a body mass index (BMI) ≥ 25 kg/m² (WHO 2004).

Obesity: a BMI ≥ 30 kg/m² (WHO 2004).

Types of interventions

We plan to investigate the following comparisons of intervention versus control/comparator.

Intervention

(a) Vitamin D supplementation in any form.

(b) Vitamin D supplementation with other vitamin and mineral supplements.

(c) Vitamin D supplementation with other weight reduction strategies.

Comparator

  • Placebo compared with (a).

  • Other weight and mineral supplements compared with (b).

  • Other weight reduction strategies compared with (c).

Concomitant interventions will have to be the same in the intervention and comparator groups to establish fair comparisons.

Types of outcome measures

Primary outcomes

  • Weight.

  • Health‐related quality of life.

  • Adverse events.

Secondary outcomes

  • All‐cause mortality.

  • Morbidity.

  • Anthropometric measures other than weight.

  • Blood pressure.

  • Glucose levels.

  • Lipid levels.

  • Socioeconomic effects.

Method and timing of outcome measurement

  • Weight: measured in kg and analysed up to three months, above three months to six months, above six months to one year and at various other intervals as specified by the authors.

  • Health‐related quality of life: measured with a validated instrument such as the Impact of Weight on Quality of Life (IWQOL) or Short‐Form survey (SF‐36) and measured up to three months, above three months to six months, above six months to one year and at various other intervals as specified by the authors.

  • Adverse events: for example hypercalcaemia, hypercalciuria, nephrocalcinosis, nephrolithiasis and self‐reported adverse events, and measured up to three months, above three months to six months, above six months to one year and at various other intervals as specified by the authors.

  • All‐cause mortality: defined as the total number of deaths from any cause, measured as in‐hospital mortality, as 30‐day all‐cause mortality and at various other intervals as specified by the authors.

  • Morbidity: such as cardiovascular morbidity and renal failure, measured up to three months, above three months to six months, above six months to one year and at various other intervals as specified by the authors.

  • Anthropometric measures other than weight: changes in BMI, weight circumference (WC), waist‐to‐hip ratio (WHR) and measured up to three months, above three months to six months, above six months to one year and at various other intervals as specified by the authors.

  • Blood pressure: systolic and diastolic blood pressure measured up to three months, above three months to six months, above six months to one year and at various other intervals as specified by the authors.

  • Glucose levels: fasting and post‐prandial glucose levels measured up to six months, above six months to one year and at various other intervals as specified by the authors.

  • Lipid levels: as analysed by total cholesterol, LDL‐ and HDL‐cholesterol and triglycerides and measured up to three months, above three months to six months, above six months to one year and at various other intervals as specified by the authors.

  • Socioeconomic effects: such as costs, health impact on income and measured up to three months, above three months to six months, above six months to one year and at various other intervals as specified by the authors.

Summary of findings

We will present a 'Summary of findings table' reporting the following outcomes listed according to priority.

  1. Weight.

  2. Health‐related quality of life.

  3. Adverse events.

  4. All‐cause mortality.

  5. Morbidity.

  6. Socioeconomic effects.

Search methods for identification of studies

Electronic searches

We will search the following sources from inception of each database to the present and will place no restrictions on the language of publication.

  • Cochrane Library.

    • Cochrane Database of Systematic Reviews (CDSR)

    • Cochrane Central Register of Controlled Trials (CENTRAL)

    • Database of Abstracts of Reviews of Effects (DARE)

    • Health Technology Assessment (HTA) reports

  • MEDLINE.

  • EMBASE.

  • ClinicalTrials.gov

  • World Health Organization (WHO) International Clinical Trials Registry Platform (ICTRP) ‐ http://apps.who.int/trialsearch/, a meta‐register of studies with links to several trial registers.

    • Australian New Zealand Clinical Trials Registry

    • ClinicalTrials.gov

    • EU (European Union) Clinical Trials Register

    • ISRCTN (International Standard Randomised Controlled Trial Number) Register

    • Brazilian Clinical Trials Registry

    • Chinese Clinical Trial Registry

    • Clinical Trials Registry ‐ India

    • Clinical Research Information Service ‐ Republic of Korea

    • Cuban Public Registry of Clinical Trials

    • German Clinical Trials Register

    • Iranian Registry of Clinical Trials

    • Japan Primary Registries Network

    • Pan African Clinical Trial Registry

    • Sri Lanka Clinical Trials Registry

    • The Netherlands National Trial Register

    • Thai Clinical Trials Register

We will continuously apply a MEDLINE (via Ovid SP) email alert service established by the Cochrane Metabolic and Endocrine Disorders (CMED) Group to identify newly published studies using the same search strategy as described for MEDLINE (for details on search strategies see Appendix 1). After supplying the final review draft for editorial approval, the CMED will perform a complete updated search on all databases available at the editorial office and send the results to the review authors. Should we identify new studies for inclusion we will evaluate these, incorporate findings in our review and resubmit another review draft (Beller 2013).

If we detect additional relevant key words during any of the electronic or other searches, we will modify the electronic search strategies to incorporate these terms and document the changes.

Searching other resources

We will try to identify other potentially‐eligible trials or ancillary publications by searching the reference lists of retrieved included trials, (systematic) reviews, meta‐analyses and HTA reports. We will also contact study authors of included trials to identify any further studies that we may have missed.

Data collection and analysis

Selection of studies

Two review authors (NSI, RK) will independently scan the abstract, title, or both, of every record retrieved, to determine which studies should be assessed further. We will investigate all potentially relevant articles as full text. We will resolve any discrepancies through consensus or recourse to a third review author (NA, HI). If resolution of a disagreement is not possible, we will add the article to those 'awaiting assessment' and we will contact study authors for clarification. We will present an adapted PRISMA (Preferred Reporting Items for Systematic Reviews and Meta‐Analyses) flow diagram showing the process of study selection (Liberati 2009).

Data extraction and management

For studies that fulfil inclusion criteria, two review authors (NSI, RK) will independently abstract key participant and intervention characteristics and report data on efficacy outcomes and adverse events using standard data extraction templates as supplied by the CMED Group, with any disagreements to be resolved by discussion, or, if required, by consultation with a third review author (JJH).

We will provide information (including trial identifier) about potentially relevant ongoing studies in the table 'Characteristics of ongoing studies' and in a joint appendix. We will try to find the protocol of each included study and will report primary, secondary and other outcomes in comparison with data in publications in a joint appendix 'Matrix of study endpoint (publications and trial documents)'.

We will email all authors of included studies to enquire whether they are willing to answer questions regarding their trials. We will present the results of this survey in an appendix. Thereafter, we will seek relevant missing information on the trial from the primary author(s) of the article, if required.

Dealing with duplicate and companion publications

In the event of duplicate publications, companion documents or multiple reports of a primary study, we will maximise yield of information by collating all available data and use the most complete dataset aggregated across all known publications.

Assessment of risk of bias in included studies

Two review authors (NSI, RK) will assess the risk of bias of each included study independently. We will resolve disagreements by consensus, or by consultation with a third review author (NA, HI).

We will use the Cochrane Collaboration's tool for assessing risk of bias (Higgins 2011a; Higgins 2011b), and evaluate the following criteria.

  • Random sequence generation (selection bias).

  • Allocation concealment (selection bias).

  • Imbalances in baseline characteristics.

  • Blinding of participants and personnel (performance bias).

  • Blinding of outcome assessment (detection bias).

  • Incomplete outcome data (attrition bias).

  • Selective reporting (reporting bias).

  • Other potential sources of bias.

We will judge the above 'Risk of bias' criteria as 'low risk', 'high risk' or 'unclear risk' and will evaluate individual bias items as described in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011a). We will present a risk of bias graph and a risk of bias summary. We will assess the impact of individual bias domains on study results at the endpoint and study levels. In case of high risk of selection bias, all endpoints investigated in the associated study will be marked as high risk.

We will evaluate whether imbalances in baseline characteristics existed and how these were addressed (Egbewale 2014; Riley 2013).

For performance bias (blinding of participants and personnel) and detection bias (blinding of outcome assessors) we will evaluate the risk of bias separately for each outcome (Hróbjartsson 2013). We will note whether endpoints were self‐reported, investigator‐assessed or adjudicated outcome measures.

We will consider the implications of missing outcome data from individual participants per outcome such as high drop‐out rates (e.g. above 15%) or disparate attrition rates (e.g. difference of 10% or more between study arms).

We will assess outcome reporting bias by integrating the results of the appendix 'Matrix of study endpoints (publications and trial documents)' (Boutron 2014; Mathieu 2009) and the appendix 'Examination of outcome reporting bias' (Kirkham 2010). This analysis will form the basis of the judgement of selective reporting (reporting bias).

We will distinguish between self‐reported, investigator‐assessed and adjudicated outcome measures.

We define the following endpoint as self‐reported outcomes.

  • Health‐related quality of life.

  • Adverse events, reported by participants.

  • Weight, measured by participants.

  • Measures of weight change, measured by participants.

  • Blood pressure, measured by participants

We define the following outcomes as investigator‐assessed outcomes.

  • Adverse events, reported by study personnel.

  • Weight, measured by study personnel.

  • Measures of weight change, measured by study personnel.

  • All‐cause mortality.

  • Morbidity.

  • Blood pressure, measured by study personnel.

  • Glucose levels.

  • Lipid levels.

  • Socioeconomic effects.

Measures of treatment effect

We will express dichotomous data as odds ratios (ORs) or risk ratios (RRs) with 95% confidence intervals (CIs). We will express continuous data as mean differences (MDs) with 95% CIs. We will express time‐to‐event data as hazard ratios (HRs) with 95% CIs.

Unit of analysis issues

We will take into account the level at which randomisation occurred, such as cross‐over trials, cluster‐randomised trials and multiple observations for the same outcome.

Dealing with missing data

We will obtain missing data from study authors, if feasible, and carefully evaluate important numerical data such as screened, randomised participants as well as intention‐to‐treat (ITT), and as‐treated and per‐protocol populations. We will investigate attrition rates, e.g. drop‐outs, losses to follow up and withdrawals, and critically appraise issues of missing data and imputation methods (e.g. last observation carried forward (LOCF)).

Where standard deviations for outcomes are not reported and we do not receive information from study authors, we will impute these values by assuming the standard deviation of the missing outcome to be the average of the standard deviations from those studies where this information was reported.

We will investigate the impact of imputation on meta‐analyses by means of sensitivity analysis.

Assessment of heterogeneity

In the event of substantial clinical, methodological or statistical heterogeneity, we will not report study results as the pooled effect estimate in a meta‐analysis.

We will identify heterogeneity (inconsistency) through visual inspection of the forest plots and by using a standard Chi² test with a significance level of α = 0.1. In view of the low power of this test, we will also consider the I² statistic, which quantifies inconsistency across studies to assess the impact of heterogeneity on the meta‐analysis (Higgins 2002; Higgins 2003); where an I² statistic of 75% or more indicates a considerable level of heterogeneity (Higgins 2011a).

When we find heterogeneity, we will attempt to determine possible reasons for it by examining individual study and subgroup characteristics.

Assessment of reporting biases

If we include 10 studies or more investigating a particular outcome, we will use funnel plots to assess small study effects. Several explanations can be offered for the asymmetry of a funnel plot, including true heterogeneity of effect with respect to trial size, poor methodological design (and hence bias of small trials) and publication bias. We will therefore interpret results carefully (Sterne 2011).

Data synthesis

Unless there is good evidence for homogeneous effects across studies, we will summarise primarily low risk of bias data using a random‐effects model (Wood 2008). We will interpret random‐effects meta‐analyses with due consideration of the whole distribution of effects, ideally by presenting a prediction interval (Higgins 2009). A prediction interval specifies a predicted range for the true treatment effect in an individual study (Riley 2011). In addition, we will perform statistical analyses according to the statistical guidelines contained in the latest version of the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011a).

Quality of evidence

We will present the overall quality of the evidence for each outcome according to the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach which takes into account issues not only related to internal validity (risk of bias, inconsistency, imprecision, publication bias) but also to external validity such as directness of results. Two review authors (HI, NSI) will independently rate the quality for each outcome. We will present a summary of the evidence in a 'Summary of findings' table, which provides key information about the best estimate of the magnitude of the effect, in relative terms and absolute differences for each relevant comparison of alternative management strategies, numbers of participants and studies addressing each important outcome and the rating of the overall confidence in effect estimates for each outcome. We will create the 'Summary of findings' table based on the methods described in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011a). We will present results for the outcomes as described in Types of outcome measures. If meta‐analysis is not possible, we will present results in a narrative 'Summary of findings' table.

In addition, we will establish an appendix 'Checklist to aid consistency and reproducibility of GRADE assessments' (Meader 2014) to help with standardisation of 'Summary of findings' tables.

Subgroup analysis and investigation of heterogeneity

We expect the following characteristics to introduce clinical heterogeneity, and plan to carry out subgroup analyses with investigation of interactions.

  • Participants: age, gender, ethnicity, baseline vitamin D status (normal, deficient, insufficient), pre‐ and post‐menopausal status in women.

  • Intervention: dose, duration, form of vitamin D, use of calcium as a co‐intervention.

  • Outcome: time point of measurement.

Sensitivity analysis

We plan to perform sensitivity analyses in order to explore the influence of the following factors (when applicable) on effect sizes.

  • Restricting the analysis to published studies.

  • Restricting the analysis by taking into account risk of bias, as specified in the section, Assessment of risk of bias in included studies.

  • Restricting the analysis to very long or large studies to establish the extent to which they dominate the results.

  • Restricting the analysis to studies using the following filters: diagnostic criteria, imputation, language of publication, source of funding (industry versus other), country.

We will also test the robustness of the results by repeating the analysis using different measures of effect size (RRs, ORs, etc.) and different statistical models (fixed‐effect and random‐effects models).

Acknowledgements

  • Director General of Health, Deputy Director General of Health (Research and Technical) and Director, Institute for Medical Research (Ministry of Health Malaysia) for permission to publish and for continuous support.

  • Dr Lai Nai Ming as group advisor for his valuable comments.

  • Ms Karla Bergerhoff (Trials Search Coordinator, Cochrane Metabolic and Endocrine Disorders Group).

Appendices

Appendix 1. Search strategies

Cochrane Library
#1 MeSH descriptor Obesity explode all trees #2 MeSH descriptor Weight gain explode all trees #3 MeSH descriptor Weight loss explode all trees #4 MeSH descriptor Body mass index explode all trees #5 MeSH descriptor Skinfold thickness explode all trees #6 MeSH descriptor Waist‐hip ratio explode all trees #7 MeSH descriptor Abdominal fat explode all trees #8 MeSH descriptor Overweight explode all trees #9 (overweight* in All Text or (over in All Text and weight* in All Text)) #10 (fat in All Text and overload in All Text and syndrom* in All Text) #11 (overeat* in All Text or (over in All Text and eat* in All Text)) #12 (overfeed* in All Text or (over in All Text and feed* in All Text)) #13 (adipos* in All Text or obes* in All Text) #14 ((weight in All Text near/3 cyc* in All Text) or (weight in All Text near/3 reduc* in All Text) or (weight in All Text near/3 los* in All Text) or (weight in All Text near/3 maint* in All Text) or (weight in All Text near/3 decreas* in All Text) or (weight in All Text near/3 watch* in All Text) or (weight in All Text near/3 control* in All Text) or (weight in All Text near/3 gain* in All Text) or (weight in All Text near/3 chang* in All Text)) #15 ((body in All Text and mass in All Text and ind* in All Text) or (waist‐hip in All Text and ratio* in All Text)) #16 (skinfold in All Text and thickness* in All Text) #17 (abdominal in All Text and fat* in All Text) #18 (#1 or #2 or #3 or #4 or #5 or #6 or #7 or #8 or #9 or #10 or #11 or #12 or #13 or #14 or #15 or #16 or #17) #19 MeSH descriptor Vitamin D explode all trees #20 MeSH descriptor Dihydrotachysterol explode all trees #21 MeSH descriptor Calcitriol explode all trees #22 MeSH descriptor Cod liver oil explode all trees #23 ((vitamin in All Text and d? in All Text) or dihydrotachysterol in All Text or calcitriol in All Text or cholecalciferol in All Text or calcifediol in All Text) #24 (25‐hydroxycholecalciferol in All Text or ergocalciferol in All Text or alfacalcidol in All Text or alphacalcidol in All Text) #25 ((25‐hydroxyvitamin in All Text and d? in All Text) or colecalciferol in All Text or calciol in All Text or (dihydroxyvitamin in All Text and d? in All Text)) #26(cod in All Text and liver in All Text and oil* in All Text) #27(#19 or #20 or #21 or #22 or #23 or #24 or #25 or #26) #28(supplementation* in All Text or fortifi* in All Text) #29MeSH descriptor Food, Fortified explode all trees #30(#28 or #29) #31(#18 and #27 and #30) #32MeSH descriptor adult explode all trees #33adult* in All Text #34(#32 or #33) #35(#31 and #34)
MEDLINE
1 exp Vitamin D/ 2 exp Dihydrotachysterol/ 3 exp Calcitriol/ 4 (vitamin* d? or dihydrotachysterol or calcitriol or cholecalciferol or calcifediol).tw,ot. 5 (25‐hydroxycholecalciferol or ergocalciferol or alfacalcidol or alphacalcidol).tw,ot. 6 (25‐hydroxyvitamin* d? or colecalciferol or calciol or dihydroxyvitam* d?).tw,ot. 7 exp Cod Liver Oil/ 8 cod liver oil*.tw,ot. 9 or/1‐8 10 exp Food fortified/ 11 (fortifi* or supplementation*).tw,ot. 12 10 or 11 13 9 and 12 14 exp Obesity/ or exp Obesity hypoventilation syndrome/ or exp Obesity, abdominal/ or exp Obesity, morbid/ or exp Prader‐Willi Syndrome/ 15 exp Overweight/ 16 exp Adipose tissue/ 17 exp Weight gain/ or exp Weight loss/ 18 exp body fat distribution/ or exp body mass index/ or exp waist circumference/ or exp skinfold thickness/ or exp waist‐hip ratio/ 19 exp Body Composition/ 20 (overweight$ or over weight$).tw,ot. 21 fat overload syndrom$.tw,ot. 22 (overeat$ or over eat$).tw,ot. 23 (overfeed$ or over feed$).tw,ot. 24 (adipos$ or obes$).tw,ot. 25 (weight adj3 (cyc$ or reduc$ or los$ or maint$ or decreas$ or watch$ or control$ or gain$ or chang$ or regulat$)).tw,ot. 26 (body mass ind$ or BMI or quetelet ind$ or waist‐hip ratio$).tw,ot. 27 skinfold thickness$.tw,ot. 28 ((abdominal or subcutaneous or intra‐abdominal or visceral or retroperitoneal or retro peritoneal) adj3 fat*).tw,ot. 29 or/14‐28 30 13 and 29 31 randomized controlled trial.pt. 32 controlled clinical trial.pt. 33 randomi?ed.ab. 34 placebo.ab. 35 drug therapy.fs. 36 randomly.ab. 37 trial.ab. 38 groups.ab. 39 or/31‐38 40 Meta‐analysis.pt. 41 exp Technology Assessment, Biomedical/ 42 exp Meta‐analysis/ 43 exp Meta‐analysis as topic/ 44 hta.tw,ot. 45 (health technology adj6 assessment$).tw,ot. 46 (meta analy$ or metaanaly$ or meta?analy$).tw,ot. 47 (search* adj10 (medical databas*or medline or pubmed or embase or cochrane or cinahl or psycinfo or psyclit or healthstar or biosis or current content*)).tw,ot. 48 (systematic adj3 review*).tw,ot. 49 or/40‐48 50 39 or 49 51 (comment or editorial or historical‐article).pt. 52 50 not 51 53 30 and 52 54 limit 53 to humans 55 limit 54 to "all adult (19 plus years)"
EMBASE
1 exp calcitriol/ or exp vitamin D/ or exp colecalciferol/ 2 exp dihydrotachysterol/ 3 (vitamin* d? or dihydrotachysterol or calcitriol or cholecalciferol or colecalciferol or calcifediol).tw,ot. 4 (25‐hydroxycholecalciferol or 25‐hydroxycolecalciferol or ergocalciferol or alfacalcidol or alphacalcidol).tw,ot. 5 (25‐hydroxyvitamin* d? or colecalciferol or calciol or dihydroxyvitamin*).tw,ot. 6 exp cod liver oil/ 7 cod liver oil*.tw,ot. 8 or/1‐7 9 exp diet supplementation/ 10 (fortifi* or supplementation*).tw,ot. 11 9 or 10 12 8 and 11 13 exp Obesity/ 14 exp weight change/ or exp weight control/ or exp weight gain/ or exp weight reduction/ 15 exp body mass/ or exp waist circumference/ or exp waist hip ratio/ 16 exp abdominal fat/ or exp body fat distribution/ 17 exp skinfold thickness/ 18 (obes$ or adipos* or overweight or over weight).tw,ot. 19 (overeat or over eat or overfeed or over feed or fat overload syndrom$).tw,ot. 20 (weight adj6 (cyc$ or reduc$ or los$ or maint$ or decreas$ or watch$ or control or chang$ or gain or regulat$)).tw,ot. 21 (body mass ind$ or BMI or quetelet ind$ or waist hip ratio or waist circumferenc$).tw,ot. 22 (body fat adj3 distribution*).tw,ot. 23 (abdominal fat or skinfold thickness).tw,ot. 24 or/13‐23 25 12 and 24 26 exp Randomized Controlled Trial/ 27 exp Controlled Clinical Trial/ 28 exp Clinical Trial/ 29 exp Comparative Study/ 30 exp Drug comparison/ 31 exp Randomization/ 32 exp Crossover procedure/ 33 exp Double blind procedure/ 34 exp Single blind procedure/ 35 exp Placebo/ 36 exp Prospective Study/ 37 ((clinical or control$ or comparativ$ or placebo$ or prospectiv$ or randomi?ed) adj3 (trial$ or stud$)).ab,ti. 38 (random$ adj6 (allocat$ or assign$ or basis or order$)).ab,ti. 39 ((singl$ or doubl$ or trebl$ or tripl$) adj6 (blind$ or mask$)).ab,ti. 40 (cross over or crossover).ab,ti. 41 or/26‐40 42 exp meta analysis/ 43 (metaanaly$ or meta analy$ or meta?analy$).ab,ti,ot. 44 (search$ adj10 (medical database$ or medline or pubmed or embase or cochrane or cinahl or psycinfo or psyclit or healthstar or biosis or current content$ or systematic$)).ab,ti,ot. 45 exp Literature/ 46 exp Biomedical Technology Assessment/ 47 hta.tw,ot. 48 (health technology adj6 assessment$).tw,ot. 49 or/42‐48 50 41 or 49 51 (comment or editorial or historical‐article).pt. 52 50 not 51 53 25 and 52 54 limit 53 to human 55 exp pregnancy/ 56 child/ 57 adolescent/ 58 55 or 56 or 57 59 54 not 58
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Date Event Description
21 May 2019 Amended This protocol was withdrawn because finishing the review project within adequate deadlines could not be achieved.
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Contributions of authors

Nur Syimah Izzah Abdullah Thani (NSIAT): protocol draft, search strategy development, acquiring trial reports, trial selection, data extraction, data analysis, data interpretation, review draft and update draft.

Roslaili Khairudin (RK): protocol draft, search strategy development, acquiring trial reports, trial selection, data extraction, data analysis, data interpretation, review draft and update draft.

Jacqueline J Ho (JJH): co‐drafting of protocol, search strategy development, acquiring trial reports, trial selection, data extraction, data analysis, data interpretation, review draft and update draft.

Nor Asiah Muhamad (NAM): acquiring trial reports, trial selection, data extraction, data analysis, data interpretation, review draft and update draft.

Hirman Ismail (HI): acquiring trial reports, trial selection, data extraction, data analysis, data interpretation, review draft and update draft.

Declarations of interest

NSIAT: none known.

RK: none known.

NAM: none known.

JJH: none known.

HI: none known.

Notes

This protocol was withdrawn because finishing the review project within adequate deadlines could not be achieved.

Withdrawn from publication for reasons stated in the review

References

Additional references

  1. Adams JS, Lee G. Gains in bone mineral density with resolution of vitamin D intoxication. Annals of Internal Medicine 1997;127(3):203‐6. [DOI] [PubMed] [Google Scholar]
  2. Avenell A, Gillespie WJ, Gillespie LD, O'Connell DL. Vitamin D and vitamin D analogues for preventing fractures associated with involutional and post‐menopausal osteoporosis. Cochrane Database of Systematic Reviews 2009, Issue 2. [DOI: 10.1002/14651858.CD000227.pub3] [DOI] [PubMed] [Google Scholar]
  3. Beller EM, Chen JK, Wang UL, Glasziou PP. Are systematic reviews up‐to‐date at the time of publication?. Systematic Reviews 2013;2(1):36. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Ben‐Shoshan M. Vitamin D deficiency/insufficiency and challenges in developing global vitamin D fortification and supplementation policy in adults. International Journal for Vitamin and Nutrition Research 2012;82(4):237‐59. [DOI] [PubMed] [Google Scholar]
  5. Bischoff‐Ferrari HA, Giovannucci E, Willett WC, Dietrich T, Dawson‐Hughes B. Estimation of optimal serum concentrations of 25‐hydroxyvitamin D for multiple health outcomes. American Journal of Clinical Nutrition 2006;84(1):18‐28. [DOI] [PubMed] [Google Scholar]
  6. Bjelakovic G, Gluud LL, Nikolova D, Whitfield K, Wetterslev J, Simonetti RG, et al. Vitamin D supplementation for prevention of mortality in adults. Cochrane Database of Systematic Reviews 2014, Issue 1. [DOI: 10.1002/14651858.CD007470.pub3] [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Blank S, Scanlon KS, Sinks TH, Lett S, Falk H. An outbreak of hypervitaminosis D associated with the overfortification of milk from a home‐delivery dairy. American Journal of Public Health 1995;85(5):656‐9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Boutron I, Altman DG, Hopewell S, Vera‐Badillo F, Tannock I, Ravaud P. Impact of spin in the abstracts of articles reporting results of randomized controlled trials in the field of cancer: the SPIIN randomized controlled t rial. Journal of Clinical Oncology 2014;32:4120‐6. [DOI] [PubMed] [Google Scholar]
  9. Cantorna MT, Mahon BD. D‐hormone and the immune system. Journal of Rheumatology 2005;76:11‐20. [PubMed] [Google Scholar]
  10. Carr MC. The emergence of the metabolic syndrome with menopause. Journal of Clinical Endocrinology and Metabolism 2003;88(6):2404‐11. [DOI] [PubMed] [Google Scholar]
  11. Colquitt JL, Pickett K, Loveman E, Frampton GK. Surgery for weight loss in adults. Cochrane Database of Systematic Reviews 2014, Issue 8. [DOI: 10.1002/14651858.CD003641.pub4] [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Dawson‐Hughes B. Racial/ethnic considerations in making recommendations for vitamin D for adult and elderly men and women. American Journal of Clinical Nutrition 2004;80:1763S‐6S. [DOI] [PubMed] [Google Scholar]
  13. Ding C, Gao D, Wilding J, Trayhurn P, Bing C. Vitamin D signalling in adipose tissue. British Journal of Nutrition 2012;108(11):1915‐23. [DOI] [PubMed] [Google Scholar]
  14. Finucane MM, Stevens GA, Cowan MJ, Danaei G, Lin JK, Paciorek CJ, et al. National, regional, and global trends in body‐mass index since 1980: systematic analysis of health examination surveys and epidemiological studies with 960 country‐years and 9.1 million participants. Lancet 2011;377(9765):557‐67. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Gill T. Epidemiology and health impact of obesity: an Asia Pacific perspective. Asia Pacific Journal of Clinical Nutrition 2006;15 Suppl:3‐14. [PubMed] [Google Scholar]
  16. Ginde AA, Liu MC, Camargo CA Jr. Demographic differences and trends of vitamin D insufficiency in the US population, 1988‐2004. Archives of Internal Medicine 2009;169(6):626‐32. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Gloth FM 3rd, Lindsay JM, Zelesnick LB, Greenough WB 3rd. Can vitamin D deficiency produce an unusual pain syndrome?. Archives of Internal Medicine 1991;151(8):1662‐4. [PubMed] [Google Scholar]
  18. Higgins JPT, Thompson SG. Quantifying heterogeneity in a meta‐analysis. Statistics in Medicine 2002;21:1539‐58. [DOI] [PubMed] [Google Scholar]
  19. Higgins JPT, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta‐analysis. BMJ 2003;327:557‐60. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Higgins JPT, Thompson SG, Spiegelhalter DJ. A re‐evaluation of random‐effects meta‐analysis. Journal of the Royal Statistical Society: Series A (Statistics in Society) 2009;172(1):137‐59. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Higgins JPT, Green S (editors). Cochrane Handbook for Systematic Reviews of Interventions Version 5.1.0 [updated March 2011]. The Cochrane Collaboration, 2011. Available from www.cochrane‐handbook.org.
  22. Higgins JPT, Altman DG, Gøtzsche PC, Jüni P, Moher D, Oxman AD, et al. The Cochrane Collaboration's tool for assessing risk of bias in randomised trials. BMJ 2011;343:d5928. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Holick MF. Vitamin D deficiency. New England Journal of Medicine 2007;357(3):266‐81. [DOI] [PubMed] [Google Scholar]
  24. Hróbjartsson A, Thomsen AS, Emanuelsson F, Tendal B, Hilden J, Boutron I, et al. Observer bias in randomized clinical trials with measurement scale outcomes: a systematic review of trials with both blinded and nonblinded assessors. Canadian Medical Association Journal 2013;185(4):E201‐11. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Institute of Medicine, Food, Nutrition Board. Dietry reference intakes for calcium and vitamin D. http://www.ncbi.nlm.nih.gov/books/NBK56070/pdf/TOC.pdf (accessed 24 March 2015)2010.
  26. Jones G. Pharmacokinetics of vitamin D toxicity. American Journal of Clinical Nutrition 2008;88:582S‐6S. [DOI] [PubMed] [Google Scholar]
  27. Jull AB, Ni Mhurchu C, Bennett DA, Dunshea‐Mooij CA, Rodgers A. Chitosan for overweight or obesity. Cochrane Database of Systematic Reviews 2008, Issue 3. [DOI: 10.1002/14651858.CD003892.pub3] [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Jurgens TM, Whelan AM, Killian L, Doucette S, Kirk S, Foy E. Green tea for weight loss and weight maintenance in overweight or obese adults. Cochrane Database of Systematic Reviews 2012, Issue 12. [DOI: 10.1002/14651858.CD008650.pub2] [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Kirkham JJ, Dwan KM, Altman DG, Gamble C, Dodd S, Smyth R, et al. The impact of outcome reporting bias in randomised controlled trials on a cohort of systematic reviews. BMJ 2010;340:c365. [DOI: 10.1136/bmj.c365] [DOI] [PubMed] [Google Scholar]
  30. Kremer R, Campbell PP, Reinhardt T, Gilsanz V. Vitamin D status and its relationship to body fat, final height, and peak bone mass in young women. Journal of Clinical Endocrinology and Metabolism 2009;94:67‐73. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Kuwabara A, Tsugawa N, Tanaka K, Fujii M, Kawai N, Mukae S, et al. Improvement of vitamin D status in Japanese institutionalized elderly by supplementation with 800 IU of vitamin D(3). Journal of Nutritional Science and Vitaminology 2009;55:453‐8. [DOI] [PubMed] [Google Scholar]
  32. Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gøtzsche PC, Ioannidis JPA, et al. The PRISMA statement for reporting systematic and meta‐analyses of studies that evaluate interventions: explanation and elaboration. PLoS Medicine 2009;6(7):1‐28. [DOI: 10.1371/journal.pmed.1000100] [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Lucas JA, Bolland MJ, Grey AB, Ames RW, Mason BH, Horne AM, et al. Determinants of vitamin D status in older women living in a subtropical climate. Osteoporosis International 2005;16:1641‐8. [DOI] [PubMed] [Google Scholar]
  34. Mathieu S, Boutron I, Moher D, Altman DG, Ravaud P. Comparison of registered and published primary outcomes in randomized controlled trials. JAMA 2009;302:977‐84. [DOI] [PubMed] [Google Scholar]
  35. Matsuoka LY, Ide L, Wortsman J, MacLaughlin JA, Holick MF. Sunscreens suppress cutaneous vitamin D3 synthesis. Journal of Clinical Endocrinology and Metabolism 1987;64:1165‐8. [DOI] [PubMed] [Google Scholar]
  36. Meader N, King K, Llewellyn A, Norman G, Brown J, Rodgers M, et al. A checklist designed to aid consistency and reproducibility of GRADE assessments: development and pilot validation. Systematic Reviews 2014;3:82. [DOI: 10.1186/2046-4053-3-82] [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Misra M, Pacaud D, Petryk A, Collett‐Solberg PF, Kappy M, Drug and Therapeutics Committee of the Lawson Wilkins Pediatric Endocrine Society. Vitamin D deficiency in children and its management: review of current knowledge and recommendations. Pediatrics 2008;122(2):398‐417. [DOI] [PubMed] [Google Scholar]
  38. Mitchell DM, Henao MP, Finkelstein JS, Burnett‐Bowie SA. Prevalence and predictors of vitamin D deficiency in healthy adults. Endocrine Practice 2012;18:914‐23. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Mokdad AH, Ford ES, Bowman BA, Dietz WH, Vinicor F, Bales VS, et al. Prevalence of obesity, diabetes, and obesity‐related health risk factors, 2001. JAMA 2003;289:76‐9. [DOI] [PubMed] [Google Scholar]
  40. Nimitphong H, Saetung S, Chanprasertyotin S, Chailurkit LO, Ongphiphadhanakul B. Changes in circulating 25‐hydroxyvitamin D according to vitamin D binding protein genotypes after vitamin D(3) or D(2)supplementation. Nutrition Journal 2013;12:39. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Padwal R, Li SK, Lau DC. Long‐term pharmacotherapy for obesity and overweight. Cochrane Database of Systematic Reviews 2003, Issue 4. [DOI: 10.1002/14651858.CD004094.pub2] [DOI] [PubMed] [Google Scholar]
  42. Raiten DJ, Picciano MF. Vitamin D and health in the 21st century: bone and beyond. Executive summary. American Journal of Clinical Nutrition 2004;80:1673S‐7S. [DOI] [PubMed] [Google Scholar]
  43. Riggs BL, Khosla S, Melton LJ 3rd. A unitary model for involutional osteoporosis: estrogen deficiency causes both type I and type II osteoporosis in postmenopausal women and contributes to bone loss in aging men. Journal of Bone and Mineral Research 1998;13:763‐73. [DOI] [PubMed] [Google Scholar]
  44. Riley RD, Higgins JP, Deeks JJ. Interpretation of random effects meta‐analyses. BMJ 2011;342:d549. [DOI] [PubMed] [Google Scholar]
  45. Riley RD, Kauser I, Bland M, Thijs L, Staessen JA, Wang J, et al. Meta‐analysis of randomised trials with a continuous outcome according to baseline imbalance and availability of individual participant data. Statistics in Medicine 2013;32:2747‐66. [DOI] [PubMed] [Google Scholar]
  46. Rosen CJ. Clinical practice. Vitamin D insufficiency. New England Journal of Medicine 2011;364:248‐54. [DOI] [PubMed] [Google Scholar]
  47. Rosenstreich SJ, Rich C, Volwiler W. Deposition in and release of vitamin D3 from body fat: evidence for a storage site in the rat. Journal of Clinical Investigation 1971;50:679‐87. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Ross AC, Taylor CL, Yaktine AL, Valle HB (eds). Dietary Reference Intakes for Calcium and Vitamin D. Washington (DC): National Academies Press, 2011. [PUBMED: 21796828] [PubMed] [Google Scholar]
  49. Ruan H, Lodish HF. Insulin resistance in adipose tissue: direct and indirect effects of tumor necrosis factor‐alpha. Cytokine Growth Factor Reviews 2003;14(5):447‐55. [DOI] [PubMed] [Google Scholar]
  50. Sterne JA, Sutton AJ, Ioannidis JP, Terrin N, Jones DR, Lau J, et al. Recommendations for examining and interpreting funnel plot asymmetry in meta‐analyses of randomised controlled trials. BMJ 2011;343:d4002. [DOI] [PubMed] [Google Scholar]
  51. Sun X, Zemel MB. Calcium and 1,25‐dihydroxyvitamin D3 regulation of adipokine expression. Obesity (Silver Spring) 2007;15(2):340‐8. [DOI] [PubMed] [Google Scholar]
  52. Thomas DE, Elliott EJ, Naughton GA. Exercise for type 2 diabetes mellitus. Cochrane Database of Systematic Reviews 2006, Issue 3. [DOI: 10.1002/14651858.CD002968.pub2] [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Vaidya A, Williams JS, Forman JP. The independent association between 25‐hydroxyvitamin D and adiponectin and its relation with BMI in two large cohorts: the NHS and the HPFS. Obesity (Silver Spring) 2012;20:186‐91. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Vieth R. Vitamin D supplementation, 25‐hydroxyvitamin D concentrations, and safety. American Journal of Clinical Nutrition 1999;69:842‐56. [DOI] [PubMed] [Google Scholar]
  55. Vimaleswaran KS, Berry DJ, Lu C, Tikkanen E, Pilz S, Hiraki LT, et al. Causal relationship between obesity and vitamin D status: bi‐directional Mendelian randomization analysis of multiple cohorts. PLoS Medicine 2013;10:e1001383. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Wang Z, Hoy WE. Waist circumference, body mass index, hip circumference and waist‐to‐hip ratio as predictors of cardiovascular disease in Aboriginal people. European Journal of Clinical Nutrition 2004;58:888‐93. [DOI] [PubMed] [Google Scholar]
  57. Webb AR, Kline L, Holick MF. Influence of season and latitude on the cutaneous synthesis of vitamin D3: exposure to winter sunlight in Boston and Edmonton will not promote vitamin D3 synthesis in human skin. Journal of Clinical Endocrinology & Metabolism 1988;67:373‐8. [DOI] [PubMed] [Google Scholar]
  58. Wei M, Gaskill SP, Haffner SM, Stern MP. Waist circumference as the best predictor of noninsulin dependent diabetes mellitus (NIDDM) compared to body mass index, waist/hip ratio and other anthropometric measurements in Mexican Americans‐‐a 7‐year prospective study. Obesity Research 1997;5:16‐23. [DOI] [PubMed] [Google Scholar]
  59. WHO Expert Consultation. Appropriate body‐mass index for Asian populations and its implications for policy and intervention strategies. Lancet 2004;363(9403):157‐63. [DOI] [PubMed] [Google Scholar]
  60. World Health Organization. Fact sheet‐ Obesity. Available from http://www.who.int/mediacentre/factsheets/fs311/en/ issue Updated March 2013.
  61. Wood L, Egger M, Gluud LL, Schulz KF, Juni P, Altman DG, et al. Empirical evidence of bias in treatment effect estimates in controlled trials with different interventions and outcomes: meta‐epidemiological study. BMJ 2008;336(7644):601‐5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. Wortsman J, Matsuoka LY, Chen TC, Lu Z, Holick MF. Decreased bioavailability of vitamin D in obesity. American Journal of Clinical Nutrition 2000;72:690‐3. [DOI] [PubMed] [Google Scholar]
  63. Yin X, Sun Q, Zhang X, Lu Y, Sun C, Cui Y, et al. Serum 25(OH)D is inversely associated with metabolic syndrome risk profile among urban middle‐aged Chinese population. Nutrition Journal 2012;11:68. [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Zemel MB, Shi H, Greer B, Dirienzo D, Zemel PC. Regulation of adiposity by dietary calcium. Federation of American Society of Experimental Biology Journal (FASEB) 2000;14:1132‐8. [PubMed] [Google Scholar]
  65. Zemel MB, Thompson W, Milstead A, Morris K, Campbell P. Calcium and dairy acceleration of weight and fat loss during energy restriction in obese adults. Obesity Research 2004;12:582‐90. [DOI] [PubMed] [Google Scholar]
  66. Zhang W, Stoecklin E, Eggersdorfer M. A glimpse of vitamin D status in Mainland China. Nutrition 2013;29:953‐7. [DOI] [PubMed] [Google Scholar]

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