Zinc supplementation for the prevention of type 2 diabetes mellitus in adults with insulin resistance

Regina El Dib; Orsi LF Gameiro; Matheus SP Ogata; Norma SP Módolo; Leandro G Braz; Eliane C Jorge; Paulo do Nascimento Junior; Vânia Beletate

doi:10.1002/14651858.CD005525.pub3

Penambahan zink untuk pencegahan diabetes mellitus jenis 2 dalam kalangan dewasa dengan kerintangan insulin

Declaraciones de intereses de los autores

Versión publicada: 28 mayo 2015 Historial de versiones

https://doi.org/10.1002/14651858.CD005525.pub3

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Abstract

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Background

Diabetes is associated with long‐term damage, dysfunction and failure of various organs, especially the eyes, kidneys, nerves, heart and blood vessels. The risk of developing type 2 diabetes increases with age, obesity and lack of physical activity. Insulin resistance is a fundamental aspect of the aetiology of type 2 diabetes. Insulin resistance has been shown to be associated with atherosclerosis, dyslipidaemia, glucose intolerance, hyperuricaemia, hypertension and polycystic ovary syndrome. The mineral zinc plays a key role in the synthesis and action of insulin, both physiologically and in diabetes mellitus. Zinc seems to stimulate insulin action and insulin receptor tyrosine kinase activity.

Objectives

To assess the effects of zinc supplementation for the prevention of type 2 diabetes mellitus in adults with insulin resistance.

Search methods

This review is an update of a previous Cochrane systematic review published in 2007. We searched the Cochrane Library (2015, Issue 3), MEDLINE, EMBASE, LILACS and the ICTRP trial register (from inception to March 2015). There were no language restrictions. We conducted citation searches and screened reference lists of included studies.

Selection criteria

We included studies if they had a randomised or quasi‐randomised design and if they investigated zinc supplementation compared with placebo or no intervention in adults with insulin resistance living in the community.

Data collection and analysis

Two review authors selected relevant trials, assessed risk of bias and extracted data.

Main results

We included three trials with a total of 128 participants in this review. The duration of zinc supplementation ranged between four and 12 weeks. Risk of bias was unclear for most studies regarding selection bias (random sequence generation, allocation concealment) and detection bias (blinding of outcome assessment). No study reported on our key outcome measures (incidence of type 2 diabetes mellitus, adverse events, health‐related quality of life, all‐cause mortality, diabetic complications, socioeconomic effects). Evaluation of insulin resistance as measured by the Homeostasis Model Assessment of Insulin Resistance (HOMA‐IR) showed neutral effects when comparing zinc supplementation with control (two trials; 114 participants). There were neutral effects for trials comparing zinc supplementation with placebo for total cholesterol, high‐density lipoprotein (HDL) cholesterol, low‐density lipoprotein (LDL) cholesterol and triglycerides (2 studies, 70 participants). The one trial comparing zinc supplementation with exercise also showed neutral effects for total cholesterol, HDL and LDL cholesterol, and a mean difference in triglycerides of ‐30 mg/dL (95% confidence interval (CI) ‐49 to ‐10) in favour of zinc supplementation (53 participants). Various surrogate laboratory parameters were also analysed in the included trials.

Authors' conclusions

There is currently no evidence on which to base the use of zinc supplementation for the prevention of type 2 diabetes mellitus. Future trials should investigate patient‐important outcome measures such as incidence of type 2 diabetes mellitus, health‐related quality of life, diabetic complications, all‐cause mortality and socioeconomic effects.

PICO

Population

Intervention

Comparison

Outcome

El uso y la enseñanza del modelo PICO están muy extendidos en el ámbito de la atención sanitaria basada en la evidencia para formular preguntas y estrategias de búsqueda y para caracterizar estudios o metanálisis clínicos. PICO son las siglas en inglés de cuatro posibles componentes de una pregunta de investigación: paciente, población o problema; intervención; comparación; desenlace (outcome).

Para saber más sobre el uso del modelo PICO, puede consultar el Manual Cochrane.

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Penambahan zink untuk pencegahan diabetes mellitus jenis 2

Soalan ulasan

Apakah kesan penambahan zink berbanding plasebo atau tanpa rawatan untuk pencegahan penyakit diabetes jenis 2 dalam kalangan dewasa dengan kerintangan insulin?

Latar belakang

Beberapa kajian telah menunjukkan bahawa zink menambahbaik paras glukosa (kawalan glisemik) dalam penghidap diabetes. Akibat diabetes, komplikasi jangka panjang boleh terjadi seperti penyakit buah pinggang, saraf dan mata. Risiko komplikasi kardiovaskular seperti serangan jantung dan strok juga meningkat. Diabetes jenis 1 ialah satu bentuk diabetes di mana badan tidak boleh menghasilkan insulin. Risiko mendapat diabetes jenis 2 meningkat dengan usia, obesiti dan kekurangan aktiviti fizikal dan dicirikan oleh peningkatan ketidakmampuan badan untuk memanfaatkan insulin (kerintangan insulin). Mineral zink memain peranan penting dalam tindakan insulin dan secara teori penambahan zink digunakan oleh orang dengan kerintangan insulin dapat mencegah permulaan diabetes.

Ciri‐ciri kajian

Kami memasukkan tiga kajian rawak terkawal dengan sejumlah 128 peserta dalam ulasan ini. Tempoh penambahan zink adalah di antara empat dan 12 minggu.

Keputusan utama

Tiada kajian melaporkan hasil utama pesakit yang penting (permulaan baru diabetes jenis 2, kesan sampingan, kualiti hidup berkaitan dengan kesihatan, kematian semua punca, komplikasi diabetes, kesan sosioekonomi). Kesan penambahan zink adalah tidak pasti terhadap tahap kerintangan dan lipid dalam darah (terutamanya kolesterol dan trigliserida).

Kualiti bukti

Kualiti keseluruhan kajian tyang dimasukkan adalah tidak jelas kerana penulis kajian tidak memberi maklumat penting untuk kami menilai cara kajian dilaksanakan (risiko yang tidak jelas dalam kebanyakan kes). Tambahan pula, bilangan kajian dan peserta adalah rendah dan para pengarang kajian tidak menyiasat hasil penting seperti permulaan baru diabetes jenis 2 atau kesan sampingan penambahan zink.

Status bukti

Bukti ini adalah terkini sehingga Mac 2015.

Authors' conclusions

Implications for practice

There is currently no evidence on which to base the use of zinc supplementation for the prevention of type 2 diabetes mellitus.

Implications for research

There are only three included studies evaluating the effects of zinc supplementation for the prevention of type 2 diabetes mellitus. Future randomised controlled clinical trials should have standardised outcome measures, such as incidence of type 2 diabetes mellitus, insulin resistance, diabetic complications, health‐related quality of life, all‐cause mortality and socioeconomic outcome measures.

Summary of findings

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Summary of findings for the main comparison.

Zinc supplementation for the prevention of type 2 diabetes mellitus
Population: non‐diabetic adults with insulin resistance Settings: not specified/outpatients Intervention: zinc supplementation Comparison: placebo or exercise
Outcomes	*Illustrative comparative risks (95% CI)**		Relative effect (95% CI)	No of participants (studies)	Quality of the evidence (GRADE)	Comments
	Assumed risk	Corresponding risk
	Placebo or exercise	Zinc supplementation
Incidence of type 2 diabetes mellitus	See comment	See comment	See comment	See comment	See comment	Outcome not reported
Diabetic complications	See comment	See comment	See comment	See comment	See comment	Outcome not reported
Adverse events	See comment	See comment	See comment	See comment	See comment	Outcome not reported
Health‐related quality of life	See comment	See comment	See comment	See comment	See comment	Outcome not reported
All‐cause mortality	See comment	See comment	See comment	See comment	See comment	Outcome not reported
Socioeconomic effects	See comment	See comment	See comment	See comment	See comment	Outcome not reported
The basis for the assumed risk* (e.g. the median control group risk across studies) is provided in footnotes. The corresponding risk (and its 95% confidence interval) is based on the assumed risk in the comparison group and the relative effect of the intervention (and its 95% CI). CI: confidence interval
GRADE Working Group grades of evidence High quality: Further research is very unlikely to change our confidence in the estimate of effect. Moderate quality: Further research is likely to have an important impact on our confidence in the estimate of effect and may change the estimate. Low quality: Further research is very likely to have an important impact on our confidence in the estimate of effect and is likely to change the estimate. Very low quality: We are very uncertain about the estimate.

Background

Description of the condition

This is an update of a Cochrane Review first published in the Cochrane Library in 2007 (Beletate 2007), which included one study that did not provide evidence on patient‐important outcome measures such as incidence of new‐onset type 2 diabetes (Marreiro 2006).

Diabetes mellitus is a metabolic disorder resulting from a defect in insulin secretion, insulin action, or both. A consequence of this is chronic hyperglycaemia (that is elevated levels of plasma glucose) with disturbances of carbohydrate, fat and protein metabolism. Long‐term complications of diabetes mellitus include retinopathy, nephropathy and neuropathy. The risk of cardiovascular disease is increased (ADA 1999). There are two major forms of diabetes: type 1 diabetes (insulin‐dependent diabetes mellitus (IDDM)) and type 2 diabetes (non‐insulin‐dependent diabetes mellitus (NIDDM) (ADA 1999). Type 2 diabetes mellitus, the most prevalent form of the disease, is often asymptomatic and may remain undiagnosed for many years (ADA 1998). Type 2 diabetes may be seen in children and young adults. In type 2 diabetes the pancreatic islet cells are capable of compensating insulin resistance (see below) and producing larger quantities of insulin, at least at the beginning of the disease (Chausmer 1998).

Prevalence and costs

The worldwide prevalence of diabetes has been estimated to be around 347 million people (Danaei 2011). In 2004, an estimated 3.4 million people died from consequences of high blood sugar (WHO 2009). The World Health Organization (WHO) projects that diabetes will be the 7th leading cause of death in 2030 (WHO 2011). Diabetes accounts for over EUR 77 billion (USD 98) in healthcare costs (Tracey 2003).

The risk of developing type 2 diabetes and insulin resistance

The risk of developing type 2 diabetes increases with age, obesity and lack of physical activity. Insulin resistance is a fundamental aspect of the aetiology of type 2 diabetes (Kahn 2000). The majority of people who develop type 2 diabetes are insulin resistant, a condition in which the pancreas produces insulin but the body's cells become increasingly resistant to the regular effects of insulin, which results in hyperglycaemia (Tracey 2003). Insulin resistance has been shown to be associated with atherosclerosis (Howard 1996), dyslipidaemia (Moro 2003), glucose intolerance, hyperuricaemia, hypertension (Bonora 1998), and polycystic ovary syndrome (Kahn 2000).

Insulin resistance can be measured by using the glucose clamp technique (DeFronzo 1979), which is considered the gold standard in the assessment of insulin sensitivity (Karelis 2004). However, this method is laborious, expensive and inadequate for large‐scale or epidemiological studies (Bonora 2000). Matthews et al developed the Homeostasis Model Assessment (HOMA) method, which derives an estimate of insulin sensitivity by taking fasting plasma glucose and insulin concentrations into account. HOMA is supposed to evaluate insulin resistance and the function of ß‐cells. Insulin resistance is calculated with the following formula: (fasting serum insulin (µU/ml) x fasting plasma glucose (mmol/L)/22.5 (Matthews 1985).

Impaired glucose tolerance (IGT) and impaired fasting glucose (IFG) are associated with insulin resistance. IFG and IGT refer to an intermediate metabolic stage between normal glucose homeostasis and diabetes. This stage includes individuals with fasting glucose levels equal to or greater than 110 mg/dL but lower than 126 mg/dL or after an orale glucose tolerance test with two‐hour values equal to or greater than 140 mg/dL but lower than 200 mg/dL.

Description of the intervention

Every single cell in the body needs zinc for structural and energy‐producing functions. Zinc is an essential trigger for many biochemical reactions and also for protein production. Zinc plays an important role in cell division, growth and repair. It helps with wound healing and maintaining a normal sense of taste and smell. Zinc works as an immune booster and can be instrumental in fighting colds, flu and other infections. Zinc is a component of more than 200 enzymes, most of them involved in protein and DNA synthesis. Zinc has beneficial effects on sex and thyroid hormones. The human body does not produce zinc on its own, so it must be obtained from outside sources. The mineral zinc can be found in both animal and plant food sources, but the richest source of zinc comes from animal food.

The daily recommended dose of zinc is 12 mg for women and 15 mg for men. Studies suggest oral supplementation of zinc sulphate from 30 mg to 200 mg per day. The effect of supplementation with zinc was assessed in 10 patients with liver cirrhosis (Marchesini 1998). The patients presented with impaired glucose tolerance and zinc deficiency. Supplementation was approximately 136 mg zinc per day. The study showed that zinc supplementation produced a significant improvement in glucose disposal. The action of zinc seemed to be related to the increased activities of insulin independent glucose transporters (Marchesini 1998).

Adverse effects of the intervention

Amounts of zinc of two or more grams per day can cause gastrointestinal irritation and vomiting (Marchesini 1998; Marreiro 2004).

How the intervention might work

Zinc ions have an insulin‐like effect. A particularly sensitive target of zinc ions is protein tyrosine phosphatase 1B, a key regulator of the phosphorylation state of the insulin receptor (Haase 2005). Several studies have investigated the role of zinc status in insulin secretion and metabolism. In vitro studies show that insulin may form a complex with zinc, improving the solubility of this hormone in the pancreatic β‐cells (Rossetti 1990). Moreover, the binding ability of insulin to its receptor may be increased (Rossetti 1990). Alterations in zinc concentration and distribution in tissues, as well as improvements in insulin sensitivity after supplementation with this element have been demonstrated (Marchesini 1998).

Why it is important to do this review

Given the worldwide public health relevance of type 2 diabetes mellitus any intervention thought to be associated with only minor or no adverse effects should be investigated by means of a systematic review of the available evidence.

Objectives

To assess the effects of zinc supplementation for the prevention of type 2 diabetes mellitus in adults with insulin resistance.

Methods

Criteria for considering studies for this review

Types of studies

We included randomised and quasi‐randomised controlled clinical trials with a minimum duration of four weeks.

Types of participants

Non‐diabetic adults (18 years or older) living in the community with insulin resistance.

Diagnostic criteria

Insulin resistance had to be measured by the Homeostasis Model Assessment of Insulin Resistance (HOMA‐IR) or the glucose clamp technique.

To be consistent with changes in the classification of and diagnostic criteria for diabetes mellitus over the years, the diagnosis should have been established using the standard criteria valid at the time of the trial commencing (for example ADA 1999; WHO 1999). Ideally, the diagnostic criteria should have been described. If necessary, we would have used the study authors' definition of diabetes mellitus. We planned to subject diagnostic criteria to a sensitivity analysis.

Types of interventions

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

Intervention

Zinc

Comparator

Placebo.
No intervention.
Another dose of zinc.

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

Types of outcome measures

Primary outcomes

Incidence of type 2 diabetes mellitus.
Adverse events.

Secondary outcomes

Insulin resistance.
Health‐related quality of life.
All‐cause mortality.
Diabetic complications.
Socioeconomic effects.
Lipid levels.

Method of outcome measurement

Incidence of type 2 diabetes mellitus: new onset of type 2 diabetes mellitus as defined by standard diagnostic criteria (such as ADA 1997; ADA 1999; ADA 2003; WHO 1980; WHO 1985).
Adverse events: for example, diarrhoea, nausea, fatigue, gastrointestinal discomfort, anaemia.
Insulin resistance: estimated by the HOMA‐IR or the glucose clamp technique.
Health‐related quality of life: measured with a validated instrument such as the Quality of Well‐Being Scale (QWB), Medical Outcomes Study 36‐item Short‐Form Health Survey (SF‐36), EuroQol (EQ‐5D) and specific instruments such as the Diabetes Care Profile (DCP) and the Diabetes Quality of Life Measure (DQOL).
All‐cause mortality: defined as death from any cause.
Diabetic complications: defined as hypoglycaemia, diabetic ketoacidosis, hyperosmolar hyperglycaemic state, retinopathy, coronary heart disease, nephropathy, neuropathy, stroke and amputation.
Socioeconomic effects: such as consequences on income, wealth, education, occupation.
Lipid levels: as measured by total cholesterol, LDL and HDL cholesterol, triglycerides and leptin concentration.

Timing of outcome measurement

We planned to collect data for all primary and secondary outcomes for any time of outcome measurement.

'Summary of findings' table

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

Incidence of type 2 diabetes mellitus.
Diabetic complications.
Adverse events.
Health‐related quality of life.
All‐cause mortality.
Socioeconomic effects.

Search methods for identification of studies

Electronic searches

We searched the following sources from inception to the specified date without language restriction.

Cochrane Library (2015, Issue 3)
- 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 (until March 2015)
EMBASE (until March 2015)
LILACS (until March 2015)
International Clinical Trials Registry Platform (ICTRP) Search Portal (http://apps.who.int/trialsearch/):
- Australian New Zealand Clinical Trials Registry (2 March 2015)
- ClinicalTrials.gov (2 March 2015)
- EU Clinical Trials Register (2 March 2015)
- International Standard Randomised Controlled Trial Number (ISRCTN) (2 March 2015)
- Brazilian Clinical Trials Registry (2 March 2015)
- Chinese Clinical Trial Registry (2 March 2015)
- Clinical Trials Registry ‐ India (2 March 2015)
- Clinical Research Information Service ‐ Republic of Korea (2 March 2015)
- Cuban Public Registry of Clinical Trials (2 March 2015)
- German Clinical Trials Register (2 March 2015)
- Iranian Registry of Clinical Trials (2 March 2015)
- Japan Primary Registries Network (2 March 2015)
- Pan African Clinical Trial Registry (2 March 2015)
- Sri Lanka Clinical Trials Registry (2 March 2015)
- The Netherlands National Trial Register (2 March 2015)
- Thai Clinical Trials Register (2 March 2015)

For detailed search strategies see Appendix 1. We continuously applied a MEDLINE (via Ovid SP) email alert service to identify newly published studies (Appendix 1). If we detected new studies for inclusion we would have evaluated these and incorporated findings in our review before submission of the final review draft (Beller 2013).

Searching other resources

We tried to identify other potentially eligible trials or ancillary publications by searching the reference lists of retrieved included trials, (systematic) reviews, meta‐analyses and health technology assessment reports.

Data collection and analysis

Selection of studies

Two review authors (LFOG, MSPO) independently scanned the title or abstract of every record retrieved. We retrieved full articles for further assessment if the information given suggested that the study: 1) included patients with insulin resistance, 2) compared a zinc intervention with placebo. Where differences in opinion existed, they were resolved by a third party (RED). If resolving disagreement was not possible, we planned to add the article to those 'awaiting classification' and contact the study authors for clarification. We present an adapted Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) flow diagram to show the process of study selection (Liberati 2009).

Data extraction and management

For studies that fulfilled the inclusion criteria, two review authors (LFOG, MSPO) independently abstracted relevant population and intervention characteristics, using standard data extraction templates as supplied by the Cochrane Metabolic and Endocrine Disorders (CMED) Group, with any disagreements to be resolved by discussion, or, if required, by a third review author (RED) (for details see Table 1; Appendix 2; Appendix 3; Appendix 4; Appendix 5; Appendix 6;Appendix 7; Appendix 8; Appendix 9; Appendix 10).

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Table 1. Overview of study populations

	Intervention(s) and comparator(s)	Sample size^a	Screened/eligible [N]	Randomised [N]	ITT [N]	Analysed [N]	Finishing study [N]	Randomised finishing study [%]	Follow‐up time^b
(1) Gómez‐García 2006	I: 100 mg/day zinc sulfate	12 (6 participants per group; 80% power)	14	7	‐	7	7	100	1 month
	C: placebo	12 (6 participants per group; 80% power)	14	7	‐	7	7	100
	total:			14	‐	14	14	100
(2) Marreiro 2006	I: 30 mg/day zinc amino chelate	‐	‐	28	‐	28	28	100	1 month
	C: placebo	‐	‐	28	‐	28	28	100
	total:			56	‐	56	56	100
(3) Soheilykhah 2012	I: 50 mg/day zinc sulfate	‐	‐	‐	‐	28	28	100	12 weeks
	C: regular exercise and weight control	‐	‐	‐	‐	25	25	100
	total:			58	‐	53	53	91.4
*Grand total*	*All interventions*			63			63
	*All comparators*			60			60
	*All interventions and comparators*			*128*			*123*

^aAccording to power calculation in study publication or report
^bDuration of intervention and/or follow‐up under randomised conditions until end of study

"‐" denotes not reported

C: comparator; I: intervention; ITT: intention‐to‐treat.

We planned to provide information including trial identifier about potentially relevant ongoing studies in the 'Characteristics of ongoing studies' table and in a joint appendix. We attempted to find the protocol for each included study and had planned to 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 sent an email request to the authors of included studies to enquire whether they were willing to answer questions regarding their trials. Appendix 11 shows the results of this survey. Thereafter, we sought relevant missing information on the trial from the study authors 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 maximised the yield of information by collating all available data. In case of doubt the publication reporting the longest follow‐up associated with our primary or secondary outcomes had priority.

Assessment of risk of bias in included studies

Two review authors (LFOG, MSPO) assessed each trial independently. We resolved possible disagreement by consensus, or with consultation of a third review author in case of disagreement (RED). In cases of disagreement, we consulted the rest of the group and made a judgement based on consensus.

We used the 'Risk of bias' assessment toll of the Cochrane Collaboration (Higgins 2011a; Higgins 2011b) and evaluated the following bias criteria:

Random sequence generation (selection bias)
Allocation concealment (selection bias)
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 judged the above 'Risk of bias' criteria as 'low risk', 'high risk' or 'unclear risk' and evaluated individual bias items as described in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011a). We have presented a 'Risk of bias' graph and a 'Risk of bias' summary. We assessed the impact of individual bias domains on study results at endpoint and study levels.

We planned to assess outcome reporting bias by integrating the results of the appendix 'Matrix of study endpoints (publications and trial documents)' and the appendix 'Examination of outcome reporting bias' (Kirkham 2010). This analysis would have formed the basis of the judgement of selective reporting (reporting bias).

For blinding of participants and personnel (performance bias), detection bias (blinding of outcome assessors) and attrition bias (incomplete outcome data) we intended to evaluate risk of bias separately for subjective and objective outcomes (Hróbjartsson 2013). We considered the implications of missing outcome data from individual participants.

We defined the following endpoints as subjective outcomes.

Adverse events
Health‐related quality of life

We defined the following endpoints as objective outcomes.

Incidence of type 2 diabetes mellitus
Diabetic complications
All‐cause mortality
Lipid levels
Socioeconomic effects

Measures of treatment effect

We planned to express dichotomous data as risk ratios (RRs) with 95% confidence intervals (CIs). We planned to express continuous data as mean differences (MDs) with 95% CIs.

Unit of analysis issues

We planned to 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 planned to obtain relevant missing data from authors, if feasible, and we evaluated important numerical data such as screened, eligible and randomised participants, as well as intention‐to‐treat (ITT), as‐treated and per‐protocol populations. We investigated attrition rates, for example drop‐outs, losses to follow‐up and withdrawals, and critically appraised issues of missing data and imputation methods (e.g. last observation carried forward (LOCF)).

Where standard deviations for outcomes were not reported, we planned to 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 planned to 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 planned not to report study results as the pooled effect estimate in a meta‐analysis.

We wanted to identify heterogeneity by 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 also planned to examine heterogeneity using 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 inconsistency (Higgins 2011a).

Had we found heterogeneity, we would have attempted to determine potential reasons for it by examining individual study and subgroup characteristics.

Assessment of reporting biases

We planned to use funnel plots to assess small study effects, if there were at least 10 studies for a particular outcome. There can be several explanations for funnel plot asymmetry, including true heterogeneity of effect with respect to trial size, poor methodological design (and hence bias of small trials) and publication bias. We planned therefore to interpret the results carefully (Sterne 2011).

Data synthesis

Unless there was good evidence for homogeneous effects across studies we planned primarily to summarise low risk of bias data by means of a random‐effects model (Wood 2008). We planned to 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 planned to perform statistical analyses according to the statistical guidelines referenced in the latest version of the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011a).

Quality of evidence

We wanted to 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 (LFOG, MSPO) would have independently rated the quality of evidence for each outcome. We planned to 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 as 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 created the 'Summary of findings' table based on the methods described in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011a). We presented results for the outcomes as described in Types of outcome measures. If meta‐analysis was not possible, we planned to present the results in a narrative 'Summary of findings' table.

Subgroup analysis and investigation of heterogeneity

There were insufficient data to allow for any subgroup analyses. Should sufficient data in the future permit, we plan to carry out the following subgroup analyses and plan to investigate interaction.

Gender (female/male)
Age (depending on data but especially older versus younger participants)
Participants with or without comorbidities (for example, heart attack, stroke, peripheral vascular disease)
Duration of intervention
Different doses of zinc

Sensitivity analysis

There were insufficient data to allow for any sensitivity analyses to explore the influence of the following factors on effect size by restricting the analysis to:

Published studies.
Taking into account risk of bias, as specified in the 'Assessment of risk of bias in included studies' section.
Very long or large studies to establish how much they dominate the results.
Studies using the following filters: diagnostic criteria, language of publication, source of funding (industry versus other), or country.

We also planned to 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).

Results

Description of studies

For a detailed description of studies, see the tables Characteristics of included studies, Characteristics of excluded studies and Characteristics of ongoing studies.

Results of the search

In the first version of the review (2007) we evaluated 192 records. Following the assessment of the full articles, we considered only two publications for inclusion. We excluded one study (Marchesini 1998), and included one study that met our inclusion criteria (Marreiro 2006). For this first update, our comprehensive literature searches identified 132 records; from these, we identified 14 full‐text publications for further examination. We excluded the other studies on the basis of their titles or abstracts, because they did not meet the inclusion criteria or were not relevant to the question under study (Figure 1). After screening the full text of the selected publications, two new studies (Gómez‐García 2006; Soheilykhah 2012), and a further unpublished manuscript of Marreiro 2006 met the inclusion criteria for this update. We excluded nine studies (Czernichow 2006; Czernichow 2009; Islam 2013; Kelishadi 2010; Kim 2012; Marreiro 2002; Marchesini 1998; Ramos 2007; Stewart 2011). One further study is an ongoing trial (Ranasinghe 2013).

Figure 1

Study flow diagram.

Included studies

In this first update we included two published studies (Gómez‐García 2006; Soheilykhah 2012), and a further manuscript (Marreiro 2006), in addition to the original included study (Marreiro 2002), with a total of 128 randomised participants.

Overview of study populations

The Gómez‐García 2006 study enrolled 14 obese men. The Marreiro 2006 study included 56 normal glucose‐tolerant obese women, while the Soheilykhah 2012 study assessed 58 overweight or obese participants with a normal oral glucose tolerance test.

Study design

All included studies claimed to be randomised controlled trials.

Settings

Two of the included studies did not report the setting where the trials were conducted (Gómez‐García 2006; Soheilykhah 2012). One study involved outpatients visiting hospital clinics (Marreiro 2006).

Participants

Gómez‐García 2006 evaluated 14 men between 21 and 30 years old, while Marreiro 2006 assessed 56 women aged 25 to 45 years. The mean age of participants in Soheilykhah 2012 was 38 years.

Gómez‐García 2006 recruited study participants with a prediabetic state and stable weight for at least three months before the beginning of the study. Marreiro 2006 recruited glucose‐tolerant obese women. Soheilykhah 2012 recruited overweight or obese participants. The mean body mass index (BMI) at baseline ranged from 31 kg/m² in Gómez‐García 2006 to 36 kg/m² in Marreiro 2006. Soheilykhah 2012 evaluated 58 first degree relatives of diabetic people with a normal oral glucose tolerance test and a BMI > 25 kg/m²; the mean BMI was 29 kg/m².

Interventions

In the Gómez‐García 2006 study participants received 100 mg zinc per day for four weeks (n = 7) or a matching placebo (n = 7). In the Marreiro 2006 study treatment with zinc consisted of 30 mg per day for four weeks (n = 28) or a matching placebo (n = 28). In the Soheilykhah 2012 study 28 participants received 50 mg zinc per day for 12 weeks and for 25 participants regular exercise and weight control were recommended.

Outcomes

None of the included studies investigated the incidence of type 2 diabetes mellitus, adverse events, health‐related quality of life, all‐cause mortality, diabetic complications or socioeconomic effects.

In the Gómez‐García 2006 study the authors evaluated insulin sensitivity, leptin levels, biochemical profile and androgens. Outcome measures in the Marreiro 2006 study were insulin resistance, anthropometric parameters, diet parameters, leptin levels, insulin levels, zinc levels, lipid metabolism and fasting plasma glucose. In the Soheilykhah 2012 study adiponectin, fasting blood glucose, insulin, insulin resistance and lipid profile were assessed.

Excluded studies

Nine studies are described in the Characteristics of excluded studies (Czernichow 2006; Czernichow 2009; Islam 2013; Kelishadi 2010; Kim 2012; Marreiro 2002; Marchesini 1998; Ramos 2007; Stewart 2011). The main reason for exclusion was that the study was not a randomised controlled trial (RCT) or it was a RCT evaluating a combination of another oxidant and vitamins or included only children.

The Ranasinghe 2013 study is described in detail under Characteristics of ongoing studies. This is a RCT performed in Sri Lanka that evaluated participants in a prediabetic stage. The anticipated end of study was the end of June 2012. Participants received 20 mg zinc per day or a matching placebo for a period of 12 months. The objectives of this study were as follows: "The study aims to evaluate the effects of zinc supplementation on the progression of disease in patients with pre‐diabetes from Sri Lanka and determine the metabolic effects of zinc supplementation on glycemic control. Furthermore, we aim to evaluate the effects of zinc supplementation on appetite and body weight in patients with pre‐diabetes" (Ranasinghe 2013). None of our review's primary or secondary outcome measures were planned to be analysed.

Risk of bias in included studies

For details on the risk of bias of the included studies see Characteristics of included studies. For an overview of review authors' judgements about each risk of bias item for individual studies and across all studies see Figure 2 and Figure 3.

Figure 2

'Risk of bias' graph: review authors' judgements about each risk of bias item presented as percentages across all included studies.

Figure 3

'Risk of bias' summary: review authors' judgements about each risk of bias item for each included study.

Allocation

All included studies had an unclear risk of selection bias with the exception of Gómez‐García 2006 that used opaque and sealed envelopes in a simple randomisation process.

Blinding

Two studies were double‐blinded for both investigators and participants resulting in a low risk of performance bias (Gómez‐García 2006; Marreiro 2006). Two of the included studies did not report on blinding of outcome assessors (Marreiro 2006; Soheilykhah 2012) and were ranked as unclear risk of bias for this domain. However, blinding of outcome assessors in Gómez‐García 2006 was done for both objective and subjective outcomes resulting in a low risk of detection bias.

Incomplete outcome data

All participants in the Gómez‐García 2006 and Marreiro 2006 studies completed the trial. In the Soheilykhah 2012 study 53 of 58 participants completed the study. Altogether we ranked attrition bias as low risk for all studies.

Selective reporting

We noted no evidence of selective reporting in any of the included studies.

Other potential sources of bias

We noted no evidence of other potential sources of bias in any of the included studies.

Effects of interventions

See: Summary of findings for the main comparison