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Cochrane Database of Systematic Reviews Protocol - Intervention

Vitamin D supplementation for prevention of cancer in adults

Authors' declarations of interest

Version published: 08 October 2008 Version history

https://doi.org/10.1002/14651858.CD007469

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Abstract

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

To assess the beneficial and harmful effects of vitamin D supplementation for prevention of cancer in adults.

Background

Description of the condition

Vitamin D is a fat‐soluble vitamin, which maintains calcium and phosphorus homeostasis (Holick 2007; Horst 2005; Lips 2006). Cutaneous synthesis during exposure to sunlight is the major source of vitamin D. Vitamin D3 (cholecalciferol) is synthesised in the skin from 7‐dehydrocholesterol. Alternatively, vitamin D, in the form of either vitamin D2 or D3, can be obtained from dietary sources. Vitamin D3 (cholecalciferol) is the predominant form of vitamin D in humans. Vitamin D2 (ergocalciferol) is derived mainly from irradiated plants. Vitamin D, as either D3 or D2, does not have biological activity. It must be metabolised within the liver (calcidiol) and the kidney to the biologically active form known as 1,25‐dihydroxycholecalciferol (calcitriol) (Holick 2007; Horst 2005; Lips 2006). The active form of vitamin D functions as a steroid like hormone (Horst 2005). The effects of vitamin D are mediated by its binding to vitamin D receptor (Norman 2006; Wesley Pike 2005). It has been recently recognised that the vitamin D receptor is present in most tissues and cells in our body (Holick 2006). Upon binding to its receptors, vitamin D may enhance cell differentiation, cell apoptosis, and inhibit cell proliferation in a variety of cell types (Flynn 2006).  Thus, suboptimal vitamin D status might contribute to cancer development (Garland 2006; Holick 2004; Holick 2007; Lips 2006; van Leeuwen 2005).

Vitamin D status

Vitamin D status can be defined as vitamin D deficiency, insufficiency, hypovitaminosis, adequacy, or toxicity (Anonymous 1997). The worldwide prevalence of suboptimal vitamin D status is estimated to be high (Holick 2007; Lamberg‐Allardt 2006; Zittermann 2003). The major causes of vitamin D deficiency are insufficient exposure to sunlight, decreased dietary intake, skin pigmentation, obesity, and advanced age (Lips 2006). Vitamin D deficiency in childhood results in rickets, while in adults precipitates or exacerbates osteopenia and osteoporosis, and induces osteomalacia (Holick 2007). Vitamin D hypovitaminosis is related to increased risk of cancer and cardiovascular diseases (Freedman 2007; Garland 2007; Giovannucci 2005; Gorham 2007; Michos 2008; Schwartz 2007; Zittermann 2005; Zittermann 2006), the leading causes of death in middle‐ and high‐income countries (Mathers 2006). Vitamin D hypovitaminosis might also be the consequence of a disease but not the cause (Marshall 2008).

How the intervention might work

Vitamin D supplementation prevents osteoporosis, osteomalacia, and fractures (Holick 2007; Lamberg‐Allardt 2006). It seems that vitamin D may have additional health effects beyond prevention of the bone diseases. Current interest in vitamin D is provoked by the hypothesis that it may prevent cancer and prolong life (Davis 2007; Giovannucci 2005; New Reference). The evidence on whether vitamin D is effective in decreasing cancers is contradictory. Most observational studies have associated increased vitamin D intake with decreased risk of cancer (Garland 2007; Gorham 2007; Schwartz 2007). Ecologic studies found that living at higher altitudes with lower exposure to sunlight is linked with increased cancer risk (Apperly 1941; Garland 1980). However, high vitamin D status was connected with increased pancreatic cancer risk (Stolzenberg 2006).

Adverse effects of the intervention

Vitamin D toxicity is the result of excessive vitamin D intake. Evidence that ingestion of high quantities of vitamin D is harmful is lacking. The majority of the trials that reported hypercalcaemia, hypercalciuria, or nephrocalcinosis were conducted in patients with renal failure (Cranney 2007).

Why it is important to do this review

The available evidence on vitamin D and cancer incidence is intriguing but inconclusive. Results of recently completed randomised clinical trials testing the influence of vitamin D supplementation for cancer prevention are inconsistent. Lappe et al found that vitamin D supplementation is associated with significantly decreased cancer incidence (Lappe 2007). On the contrary, another large randomised clinical trial found no effect of vitamin D and calcium supplementation on cancer incidence (Wactawski‐Wende 2006). A recent meta‐analysis by Autier and Gandini of 18 randomised clinical trials found significantly lower mortality in vitamin D supplemented participants (Autier 2007). We have been unable to identify any systematic reviews of randomised trials on vitamin D supplementation for cancer prevention.

Objectives

To assess the beneficial and harmful effects of vitamin D supplementation for prevention of cancer in adults.

Methods

Criteria for considering studies for this review

Types of studies

Randomised clinical trials, irrespective of blinding, publication status, or language.

Types of participants

Adult participants (aged 18 years or over) who are:

  • healthy or were recruited among the general population;

  • diagnosed with a specific disease in a stable phase;

  • diagnosed with vitamin D deficiency.

We will exclude trials including:

  • pregnant or lactating women (as they usually are in need of vitamin D);

  • patients with cancer.

Types of interventions

We will consider for inclusion randomised trials that compare vitamin D at any dose, duration, and route of administration versus placebo or no intervention.

The vitamin D can be administered:

  • as monotherapy;

  • in combination with calcium;

  • in combination with other vitamins or trace elements;

  • in combination with calcium and other vitamins and trace elements.

Concomitant interventions will be allowed if used equally in all intervention groups of the trial.

Types of outcome measures

Primary outcomes

  • cancer occurrence;

  • cancer mortality.

Secondary outcomes

  • adverse events (depending on the availability of data, we will attempt to classify adverse events as serious or non‐serious. Serious adverse events will be defined as any untoward medical occurrence that was life threatening, resulted in death, or persistent or significant disability, or any medical event, which might have jeopardised the patient, or required intervention to prevent it (ICH‐GCP 1997); all other adverse events (that is, any medical occurrence not necessarily having a causal relationship with the treatment, but did, however, cause a dose reduction or discontinuation of the treatment) will be considered as non‐serious).

  • health‐related quality‐of‐life;

  • health economics.

We will examine all‐cause mortality in another review (Bjelakovic 2008).

Covariates, effect modifiers, and confounders

We will note and record any possible covariates, effect modifiers and confounders (e.g., compliance, other medications).

Timing of outcome measurement

We will calculate outcomes at the end of follow‐up period. We will not apply any restrictions regarding the length of intervention or length of follow‐up.

Search methods for identification of studies

Electronic searches

We will use the following sources for the identification of trials:

  • The Cochrane Library (latest issue);

  • MEDLINE (until recent);

  • EMBASE (until recent);

  • LILACS (until recent);

  • Science Citation Index Expanded (until recent).

We will also search databases of ongoing trials: Current Controlled Trials (www.controlled‐trials.com ‐ with links to other databases of ongoing trials).

The described search strategy (see Appendix 1 for a detailed search strategy) will be used for MEDLINE. For use with EMBASE, The Cochrane Library and the other databases this strategy will be slightly adapted.

Additional key words of relevance may be detected during any of the electronic or other searches. If this is the case, electronic search strategies will be modified to incorporate these terms. Studies published in any language will be included.

Searching other resources

We will contact the main manufacturers of vitamin D to ask for unpublished randomised trials.

We will try to identify additional studies by searching the reference lists of included trials and (systematic) reviews, meta‐analyses and health technology assessment reports noticed.

Data collection and analysis

Selection of studies

One author (GB) will perform the electronic searches. Four authors (GB, LLG, DN, KW) will participate in the manual searches, identify trials eligible for inclusion from the search results, and extract data from included trials. Excluded trials and studies will be listed with the reason for exclusion. To determine the studies to be assessed further, GB and LLG or DN will independently scan the abstract, titles or both sections of every record retrieved. We will record the fulfilment of the inclusion criteria of the retrieved publications. We will also list the excluded studies with the reason for exclusion. GB and LLG or DN will independently apply the inclusion criteria and extract the data to all trials without blinding.

All potentially relevant articles will be investigated as full text. Interrater agreement for study selection will be measured using the kappa statistic (Cohen 1960). Differences will be marked and if these studies are later on included, the influence of the primary choice will be subjected to a sensitivity analysis. When a discrepancy occurs in the trial selection or data extraction, CG will be consulted in order to reach consensus. If resolving disagreement is not possible, the article will be added to those 'awaiting assessment' and authors will be contacted for clarification. An adapted QUOROM (quality of reporting of meta‐analyses) flow‐chart of study selection will be included in the review (Moher 1999).

Data extraction and management

For trials that fulfil inclusion criteria, two authors, GB and LLG or DN will independently abstract relevant population and intervention characteristics using standard data extraction templates (for details see 'Characteristics of includes studies', Table 1, Appendix 2 and Appendix 3) with any disagreements to be resolved by discussion, or if required by a third party. Any relevant missing information on the trial will be sought from the original author(s) of the article, if required.

Open in table viewer
Table 1. Study populations

study ID

intervention

[n] screened

[n] randomised

[n] safety

[n] ITT

[n] finishing study

comments

ID1

Intervention 1 (I1)

Intervention 2 (I2)

Control 1 (C1)

Control 2 (C2)

I1:

I2:

C1:

C2:

Total:

I1:

I2:

C1:

C2:

Total:

I1:

I2:

C1:

C2:

Total:

I1:

I2:

C1:

C2:

Total:

I1:

I2:

C1:

C2:

Total:

ID2

Intervention 1 (I1)

Intervention 2 (I2)

Control 1 (C1)

Control 2 (C2)

I1:

I2:

C1:

C2:

Total:

I1:

I2:

C1:

C2:

Total:

I1:

I2:

C1:

C2:

Total:

I1:

I2:

C1:

C2:

Total:

I1:

I2:

C1:

C2:

Total:

ID3

Intervention 1 (I1)

Intervention 2 (I2)

Control 1 (C1)

Control 2 (C2)

I1:

I2:

C1:

C2:

Total:

I1:

I2:

C1:

C2:

Total:

I1:

I2:

C1:

C2:

Total:

I1:

I2:

C1:

C2:

Total:

I1:

I2:

C1:

C2:

Total:

ID3

Intervention 1 (I1)

Intervention 2 (I2)

Control 1 (C1)

Control 2 (C2)

I1:

I2:

C1:

C2:

Total:

I1:

I2:

C1:

C2:

Total:

I1:

I2:

C1:

C2:

Total:

I1:

I2:

C1:

C2:

Total:

I1:

I2:

C1:

C2:

Total:

ID4

Intervention 1 (I1)

Intervention 2 (I2)

Control 1 (C1)

Control 2 (C2)

I1:

I2:

C1:

C2:

Total:

I1:

I2:

C1:

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Total:

I1:

I2:

C1:

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Total:

I1:

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C1:

C2:

Total:

I1:

I2:

C1:

C2:

Total:

ID5

Intervention 1 (I1)

Intervention 2 (I2)

Control 1 (C1)

Control 2 (C2)

I1:

I2:

C1:

C2:

Total:

I1:

I2:

C1:

C2:

Total:

I1:

I2:

C1:

C2:

Total:

I1:

I2:

C1:

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Total:

I1:

I2:

C1:

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Total:

ID6

Intervention 1 (I1)

Intervention 2 (I2)

Control 1 (C1)

Control 2 (C2)

I1:

I2:

C1:

C2:

Total:

I1:

I2:

C1:

C2:

Total:

I1:

I2:

C1:

C2:

Total:

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I2:

C1:

C2:

Total:

I1:

I2:

C1:

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Total:

ID7

Intervention 1 (I1)

Intervention 2 (I2)

Control 1 (C1)

Control 2 (C2)

I1:

I2:

C1:

C2:

Total:

I1:

I2:

C1:

C2:

Total:

I1:

I2:

C1:

C2:

Total:

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I2:

C1:

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Total:

I1:

I2:

C1:

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Total:

ID8

Intervention 1 (I1)

Intervention 2 (I2)

Control 1 (C1)

Control 2 (C2)

I1:

I2:

C1:

C2:

Total:

I1:

I2:

C1:

C2:

Total:

I1:

I2:

C1:

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Total:

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C1:

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Total:

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C1:

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Total:

ID9

Intervention 1 (I1)

Intervention 2 (I2)

Control 1 (C1)

Control 2 (C2)

I1:

I2:

C1:

C2:

Total:

I1:

I2:

C1:

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Total:

I1:

I2:

C1:

C2:

Total:

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I2:

C1:

C2:

Total:

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I2:

C1:

C2:

Total:

ITT = intention‐to‐treat

Assessment of risk of bias in included studies

Methodological quality will be defined as the control of bias (Gluud 2006; Kjaergard 2001; Moher 1998; Schulz 1995). The control of bias will be assessed by the randomisation methods (allocation sequence generation, allocation concealment) and blinding. The allocation sequence generation will be classified as adequate if based on a computer or random number table. The allocation concealment will be classified as adequate if the allocation was performed through an independent central unit, serially numbered opaque sealed envelopes, or identically appearing sealed numbered drug bottles. Blinding was classified as adequate if the trial was described as double blind using an identically appearing active treatments or placebo. We will also extract whether the outcome assessment or data analyses were blinded. The reported follow up and sample size calculations will be extracted to assess the risk of attrition bias. To assess the risk of bias in the reporting of outcome measures, we will extract which outcome measures were defined as the primary by the original investigators. Two authors (GB, LLG) will assess risk of bias of each trial independently. Possible disagreement will be resolved by consensus, or with consultation of a third party in case of disagreement. The authors will explore the influence of individual quality criteria in a sensitivity analysis.

Dealing with missing data

Relevant missing data will be obtained from authors, if feasible. Evaluation of important numerical data such as screened, eligible and randomised patients as well as intention‐to‐treat (ITT) and per‐protocol (PP) population will be performed. Attrition rates, for example drop‐outs, losses to follow‐up and withdrawals will be investigated. Issues of missing data, ITT and PP will be critically appraised and compared to specification of primary outcome parameters and power calculation.

Dealing with duplicate publications

In the case of duplicate publications and companion papers of a primary study, we will try to maximise yield of information by simultaneous evaluation of all available data. In cases of doubt, the original publication (usually the oldest version) will obtain priority.

Assessment of heterogeneity

In the event of substantial clinical or methodological or statistical heterogeneity, study results will not be combined by means of meta‐analysis. Heterogeneity will be identified by visual inspection of the forest plots, by using a standard χ2‐test and a significance level of α = 0.1, in view of the low power of such tests. Heterogeneity will be specifically examined with I2 (Higgins 2002), where I2‐values of 50% and more indicate a substantial level of heterogeneity (Higgins 2003). When heterogeneity is found, we will attempt to determine potential reasons for it by examining individual study characteristics and those of subgroups of the main body of evidence.

Assessment of reporting biases

Funnel plots will be used in an exploratory data analysis to assess for the potential existence of small study bias. There are a number of explanations for the asymmetry of a funnel plot, including true heterogeneity of effect with respect to study size, poor methodological design of small studies and publication bias. We will perform adjusted rank correlation (Begg 1994) and regression asymmetry test (Egger 1997) for detection of small study bias. A P‐value < 0.10 will be considered significant in the latter analyses. We will carefully interpret results of funnel plots (Lau 2006).

Data synthesis

Data will be summarised statistically if they are available, sufficiently similar and of sufficient quality. Statistical analysis will be performed according to the statistical guidelines referenced in the newest version of The Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2008).
For the statistical analyses, we will use RevMan Analyses, STATA 8.2 (STATA Corp, College Station, Tex), Sigma Stat 3.0 (SPSS Inc, Chicago, Ill), and StatsDirect (StatsDirect Ltd, Altrincham, England). We will analyse the data with both fixed‐effect (DeMets 1987) and random‐effects (DerSimonian 1986) model meta‐analyses. We will present the results of the random‐effects model analyses. If results differ regarding statistical significance, we will present both analyses. Results will be presented as the relative risk (RR) with 95% confidence intervals (CI). The I2‐statistic will be presented as a measure of the percentage of variation due to heterogeneity rather than chance (Higgins 2002). The analyses will be performed using the intention‐to‐treat principle including all randomised participants irrespective of follow‐up. Participants with missing data will be included in the analyses using imputation methods. Accordingly, patients who are lost to follow‐up will be counted as survivors.

For zero event trials we will use risk difference (RD) as outcome measure instead of relative risk (Friedrich 2007; Higgins 2008). The influence of trials with zero events in the treatment or control group will be assessed by re‐calculating the random‐effects meta‐analyses with 0.5, 0.05, and 0.005 continuity corrections (Bradburn 2007; Sweeting 2004). For trials using a factorial design, 'at the margins' analysis combining all participants randomised to vitamin D will be performed (McAlister 2003). Due to the risk of interaction between different treatment regimens, we will also perform 'inside the table' analysis in which we will compare vitamin D only with placebo or no intervention. In the trials with parallel group design with more than two arms and additional therapy, we will compare the vitamin D arm only with placebo or no intervention. For cross‐over trials we will include only data from the first period (Higgins 2008). Trial sequential analysis will be performed to avoid random error due to repetitive analyses of accumulated data and prevent premature statements of superiority of intervention (Wetterslev 2008). We will compare the intervention effects in subgroups with tests for interaction (Altman 2003).

Subgroup analysis and investigation of heterogeneity

Subgroup analyses will be mainly performed if one of the primary outcome parameters demonstrates statistically significant differences between intervention groups. In any other case subgroup analyses will be clearly marked as a hypothesis generating exercise.

The following subgroup analyses are planned:

  • low‐bias risk trials compared to high‐bias risk trials;

  • placebo controlled trials compared to no intervention trials;

  • healthy participants or participants recruited among the general population compared to participants diagnosed with a specific disease in a stable phase compared to participants diagnosed with vitamin D deficiency;

  • vitamin D monotherapy compared to vitamin D and calcium compared to vitamin D, calcium, and other vitamins and minerals;

  • high dose of vitamin D compared to low dose of vitamin D;

  • daily regimen compared to intermittent regimen;

  • oral vitamin D compared to parenteral vitamin D;

  • form of vitamin D, ergocalciferol compared to cholecalciferol.

Sensitivity analysis

We will perform sensitivity analyses in order to explore the influence of the following factors on effect size:

  • repeating the analysis excluding unpublished trials;

  • repeating the analysis taking account of trial quality and hence risk of bias, as specified above;

  • repeating the analysis excluding trials using the following filters: diagnostic criteria, language of publication, source of funding (industry versus other), country.

The robustness of the results will also be tested by repeating the analysis using different measures of effects size (relative risk, odds ratio) and different statistical models (fixed‐ and random‐effects models).

Table 1. Study populations

study ID

intervention

[n] screened

[n] randomised

[n] safety

[n] ITT

[n] finishing study

comments

ID1

Intervention 1 (I1)

Intervention 2 (I2)

Control 1 (C1)

Control 2 (C2)

I1:

I2:

C1:

C2:

Total:

I1:

I2:

C1:

C2:

Total:

I1:

I2:

C1:

C2:

Total:

I1:

I2:

C1:

C2:

Total:

I1:

I2:

C1:

C2:

Total:

ID2

Intervention 1 (I1)

Intervention 2 (I2)

Control 1 (C1)

Control 2 (C2)

I1:

I2:

C1:

C2:

Total:

I1:

I2:

C1:

C2:

Total:

I1:

I2:

C1:

C2:

Total:

I1:

I2:

C1:

C2:

Total:

I1:

I2:

C1:

C2:

Total:

ID3

Intervention 1 (I1)

Intervention 2 (I2)

Control 1 (C1)

Control 2 (C2)

I1:

I2:

C1:

C2:

Total:

I1:

I2:

C1:

C2:

Total:

I1:

I2:

C1:

C2:

Total:

I1:

I2:

C1:

C2:

Total:

I1:

I2:

C1:

C2:

Total:

ID3

Intervention 1 (I1)

Intervention 2 (I2)

Control 1 (C1)

Control 2 (C2)

I1:

I2:

C1:

C2:

Total:

I1:

I2:

C1:

C2:

Total:

I1:

I2:

C1:

C2:

Total:

I1:

I2:

C1:

C2:

Total:

I1:

I2:

C1:

C2:

Total:

ID4

Intervention 1 (I1)

Intervention 2 (I2)

Control 1 (C1)

Control 2 (C2)

I1:

I2:

C1:

C2:

Total:

I1:

I2:

C1:

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Total:

I1:

I2:

C1:

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Total:

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I2:

C1:

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Total:

I1:

I2:

C1:

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Total:

ID5

Intervention 1 (I1)

Intervention 2 (I2)

Control 1 (C1)

Control 2 (C2)

I1:

I2:

C1:

C2:

Total:

I1:

I2:

C1:

C2:

Total:

I1:

I2:

C1:

C2:

Total:

I1:

I2:

C1:

C2:

Total:

I1:

I2:

C1:

C2:

Total:

ID6

Intervention 1 (I1)

Intervention 2 (I2)

Control 1 (C1)

Control 2 (C2)

I1:

I2:

C1:

C2:

Total:

I1:

I2:

C1:

C2:

Total:

I1:

I2:

C1:

C2:

Total:

I1:

I2:

C1:

C2:

Total:

I1:

I2:

C1:

C2:

Total:

ID7

Intervention 1 (I1)

Intervention 2 (I2)

Control 1 (C1)

Control 2 (C2)

I1:

I2:

C1:

C2:

Total:

I1:

I2:

C1:

C2:

Total:

I1:

I2:

C1:

C2:

Total:

I1:

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C1:

C2:

Total:

I1:

I2:

C1:

C2:

Total:

ID8

Intervention 1 (I1)

Intervention 2 (I2)

Control 1 (C1)

Control 2 (C2)

I1:

I2:

C1:

C2:

Total:

I1:

I2:

C1:

C2:

Total:

I1:

I2:

C1:

C2:

Total:

I1:

I2:

C1:

C2:

Total:

I1:

I2:

C1:

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ID9

Intervention 1 (I1)

Intervention 2 (I2)

Control 1 (C1)

Control 2 (C2)

I1:

I2:

C1:

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Total:

I1:

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C1:

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Total:

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C1:

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ITT = intention‐to‐treat
Figures and Tables -
Table 1. Study populations
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