Abstract
Introduction
Type 2 diabetes is a chronic metabolic disorder that has been associated with alterations in the status of trace elements, including zinc, copper, iron and manganese. However, clinical studies reporting statuses of these trace elements in type 2 diabetes patients compared to controls have shown conflicting results.
Objective
This meta-analysis aimed to summarize the existing literature on the statuses of zinc, copper, iron, and manganese in Type 2 diabetes mellitus patients.
Methods
A literature search of Embase, PubMed, EBSCOHost, ScienceDirect, Scopus, Cochrane library and Web of Science electronic databases was conducted to find studies published from 1970 to November 2016 that compared the trace elements of interest between type 2 diabetic patients and healthy controls. Keywords used were type 2 diabetes, diabetes, hyperglycemia, insulin, glucose, HbA1c, trace elements, micronutrients, zinc, manganese, copper, ceruloplasmin, iron and ferritin. The bias corrected Hedges’ g, was utilized as the effect sizes. Due to the biological interaction between trace elements, it is important to collectively evaluate the statuses of these minerals in type 2 diabetes. Thus, the robust variance estimation method was chosen to handle dependency between multiple outcomes.
Results
A total of 52 studies met the inclusion criteria, amounting to 98 effect sizes. Diabetic patients (n=20183) had significantly lower zinc status when compared to controls (effect size = −1.73, p<0.01); whereas copper (effect size = 1.10, p<0.05) and ferritin levels (effect size = 1.05, p<0.01) were significantly higher. Although not significant, ceruloplasmin (effect size = 1.85, p=0.06) and iron (effect size = 1.42, p=0.06) levels were higher, and manganese (effect size = 0.27, p=0.34) was lower in patients.
Conclusion
Results from this meta-analysis indicate lower zinc status accompanied by increased copper and ferritin levels in patients with type 2 diabetes when compared to controls.
INTRODUCTION
Diabetes mellitus is a chronic metabolic disorder that arises due to absolute or relative lack of insulin production by the beta-cells of the pancreas. Impaired secretion of this protein affects glucose metabolism, and consequently, results in hyperglycemia. (1) Unregulated levels of blood glucose can lead to several debilitating conditions such as nephropathy, neuropathy, retinopathy, cardiovascular disease, stroke, and amputations of extremities.(2) About 90 to 95% of the patients are affected by type 2 diabetes (2) which is characterized primarily by insulin resistance, hyperinsulinemia, and beta-cell dysfunction.(3)
Trace elements facilitate numerous biochemical reactions (4), including those related to insulin and glucose metabolism. The transport of zinc into the beta-cells of the pancreas is essential for insulin production and its efficient packaging into vesicles.(5) Moreover, zinc finger protein 407 (6) and zinc-alpha2-glycoprotein (7, 8) have been shown to enhance the expression of the Glucose transporter type 4 (GLUT 4) protein in adipocytes and skeletal muscles, thereby mediating insulin-induced glucose uptake into these cells. Manganese is another trace element involved in carbohydrate metabolism.(9) Animal studies have indicated an association of manganese with optimal insulin synthesis and secretion.(10) Manganese also increases the binding of insulin to its receptor, thereby facilitating the physiological action of this hormone.(11) In a study conducted by Baly et al., manganese deficient rats had diminished insulin-stimulated glucose oxidation in the adipose tissue, and fewer insulin receptors compared to controls.(12) In contrast, elevated levels of copper (13) and iron (14, 15) have been linked to increased oxidative stress, which in turn could lead to insulin resistance, impaired glucose tolerance, and type 2 diabetes.(16) Consistently, greater copper and iron statuses have been associated with increased insulin resistance in humans.(17, 18) Thus, copper and iron also are important while investigating trace element status in type 2 diabetes.
Research comparing zinc, manganese, copper and iron levels between type 2 diabetes patients and healthy individuals have reported inconsistent results. For example, some studies have indicated greater levels of these trace elements in type 2 diabetes patients compared to controls (4, 19, 20); whereas other investigations have demonstrated reduced status. (20–25) Thus, this meta-analysis aimed to summarize the existing literature on difference in zinc, manganese, copper and iron levels between type 2 diabetic and nondiabetic individuals. Ceruloplasmin and ferritin levels were additionally utilized to indicate copper and iron statuses, respectively. It is hypothesized that diabetic patients will have lower zinc and manganese concentrations, and elevated copper and iron statuses compared to nondiabetic controls.
A secondary goal of this research is to explore the influence of age, body mass index (BMI) and gender on the primary outcomes of interest by a moderator analysis. It is well established that age > 45 years, (26) gender (27) and overweight/ obesity (28) may influence trace element concentrations in humans, and are associated with a higher incidence of type 2 diabetes.(29–31) Moreover, the presence of fat depots around organs has been reported to negatively impact the status of minerals when compared to subcutaneous fat. Consequently, gender may influence the development of type 2 diabetes since the incidence of visceral adiposity is higher in men as compared to women.(32)
MATERIAL AND METHODS
Search Strategy
A literature search of Embase, PubMed, EBSCOHost, ScienceDirect, Scopus, Cochrane library and Web of Science electronic databases was conducted to identify studies published between 1970 and November 2016 investigating trace element concentrations in type 2 diabetes mellitus patients and healthy controls. Keywords used were type 2 diabetes, diabetes, hyperglycemia, insulin, glucose, HbA1c, trace elements, micronutrients, zinc, manganese, copper, ceruloplasmin, iron and ferritin.
Data Extraction
Two investigators independently carried out the literature search and recorded the data in the database. The studies recorded by each investigator were then matched to remove duplicate studies. The titles and abstracts were read for each retrieved record to select studies meeting the inclusion criteria. The reference lists of all the retrieved studies also were reviewed by each investigator to minimize the chance of missing relevant studies.
Inclusion and Exclusion Criteria
The inclusion criteria for the studies that were retrieved using the search terms were: a) use of a cross-sectional or case-control design b) reporting of plasma/ serum values of zinc/ copper/ ceruloplasmin/ iron/ ferritin/ manganese of type 2 diabetic patients and healthy controls and b) absence of any diabetes-related complications in patients.
Studies were excluded if: a) they reported statistics jointly for patients with and without complications in type 2 diabetes group; b) the text was not available in English; and c) adequate information was not available in the text to calculate effect sizes.
All articles were assessed based on the inclusion/exclusion criteria and any disagreements were resolved by discussion with a nutrition professor having expertise in field of trace elements.
Statistical analysis
The author, year and country for investigations that met the inclusion criteria were identified. The primary outcome, status of trace elements (zinc, copper, iron and manganese), in the control and diabetic groups were extracted. Other characteristics included for the analysis were type of biomarker (serum/plasma), age, BMI, and percentage of men in the control and diabetic arms. Difference in BMI between the control and diabetic groups was documented. The weighted average of age and proportion of males were determined, with the number of participants in each group representing the weights.
The primary studies reported trace element status in varying units of concentrations. Thus, a standardized mean difference, the bias corrected Hedges’ g, (33) was utilized as the effect size for the meta-analysis. The hypothesis to be tested was that the status of zinc and manganese would be lower in diabetic subjects, whereas that of copper, ceruloplasmin, and iron and ferritin would be higher. Since these trace elements have been shown to interact with each other, the use of a univariate model to determine their effect sizes could inflate Type I error rates. Methods utilized to handle dependency of multiple related outcomes in meta-analysis included robust variance estimation (RVE) method and generalized least squares (GLS) estimation. Unlike GLS estimation, the RVE approach does not require use of the correlations between pairs of multivariate outcomes. Thus, the RVE method was chosen for this meta-analysis due to the lack of reporting of correlations between trace elements in the primary studies.
Computations in the ensuing section were performed using the Statistical Package for Social Sciences (SPSS Version 22, Armonk, NY, 2013). The standard deviations of the trace element biomarker values in the control and diabetic groups, and the associated sample sizes were used to calculate the pooled sample variances. Trace element values of the diabetic subjects were subtracted from the controls when calculating effect sizes. Negative effects indicated that the trace element levels were lower in the diabetic arm as compared to the nondiabetic control. The resulting differences were divided by the pooled sample variances to compute Hedges g, to which the small-sample bias correction was applied. Bias-corrected Hedges g, thus obtained, represented the effect size for each study. The variance and 95% confidence intervals estimates for the effect sizes were calculated using the associated standardized mean difference and sample sizes in each group.
The statistical software, R with macro robumeta, were used to calculate the pooled average effect size and variance using the RVE method to apply Tipton’s (34) small sample bias correction. Statistics reported were 95% CIs, and two-sided Z-statistic to assess the statistical significance of the pooled average effect size. The effects of type of biomarker, age, difference in BMI, and percent males were evaluated by including these variables in the mixed-effects, meta-regression model. Publication bias was assessed by visualization of funnel plots. The I2 statistic was used to assess heterogeneity across studies. Cut-off values of 0, 25, 50 and 75% indicated no, low, moderate and high heterogeneity. A symmetrical funnel plot evidence for lack of publication bias in the meta-analysis. The Egger’s regression test also was performed, with p<0.05 suggesting significant publication bias.
RESULTS
Literature search
Table/Figure 1 illustrates the flow diagram of the screening involved in the selection of articles for the meta-analysis. A total of 97 articles were retrieved, with a final number of 52 case-control studies (1, 4, 19, 20, 22–25, 35–79) with 20183 participants were included in the meta-analysis.
Table 1.
Characteristics of studies included
| First Author | Year | Country | Biomarker | Agea | Proportion of malesa |
Difference in body mass indexb |
Sample size | Effect size | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
|
|
|||||||||||||
| Control | Diabetic | Zinc | Copper | Ceruloplasmin | Iron | Ferritin | Manganese | |||||||
| Pidduck | 1970 | England | Plasma | 52.95 | 100 | --c | 40 | 36 | 5.71 | 0.18 | -- | -- | -- | -- |
| Pidduck | 1970 | England | Plasma | 57.43 | 0 | -- | 46 | 42 | -4.07 | 1.26 | -- | -- | -- | -- |
| Mateo | 1978 | Spain | Serum | -- | -- | -- | 10 | 49 | 1.2 | 1.29 | 0.39 | -- | -- | -- |
| Raz | 1989 | Israel | Serum | 50.51 | 49.15 | 1.7 | 21 | 38 | 0.58 | -0.9 | -- | -- | -- | -- |
| Car | 1992 | Yugoslavia | Serum | 45.5 | 46.67 | 4.3 | 15 | 15 | −1.42 | −2.86 | -- | -- | -- | -- |
| Williams | 1995 | UK | Plasma | 51.55 | 46.15 | -- | 26 | 26 | −6.09 | −1.99 | -- | -- | -- | -- |
| Hughes | 1998 | Singapore | Serum | -- | 100 | 1.3 | 248 | 72 | -- | -- | -- | -- | 0.44 | -- |
| Hughes | 1998 | Singapore | Serum | -- | 0 | 1.5 | 282 | 54 | -- | -- | -- | -- | 0.46 | -- |
| Daimon | 1998 | Japan | Serum | 57.05 | -- | -- | 158 | 637 | -- | -- | 2.58 | -- | -- | -- |
| Ekmekcioglu | 2000 | Austria | Plasma | 67.05 | -- | 0 | 50 | 53 | 0.31 | 0.2 | -- | --d | -- | 0.2 |
| Kim | 2000 | Korea | Serum | 57.03 | 46.67 | 2.4 | 25 | 50 | -- | -- | -- | -- | 0.32 | -- |
| Olaniyan | 2001 | Nigeria | Serum | 57.03 | 39.81 | -- | 50 | 53 | −0.99 | 0.74 | -- | -- | -- | -- |
| Ekin | 2003 | Turkey | Serum | 56.98 | 0 | 1.2 | 29 | 108 | −4.27 | --d | -- | 5.86 | -- | --d |
| Ekin | 2003 | Turkey | Serum | 56.28 | 100 | 1.6 | 21 | 92 | −4.64 | --d | -- | 4.83 | -- | --d |
| Memisogullari | 2003 | Turkey | Serum | -- | -- | -- | 18 | 21 | -- | -- | 0.86 | -- | -- | -- |
| Memisogullari | 2004 | Turkey | Serum | 54.49 | -- | -- | 21 | 29 | -- | -- | 1.35 | -- | -- | -- |
| Qin | 2004 | China | Serum | -- | -- | -- | 110 | 104 | -- | -- | 2.33 | -- | -- | -- |
| Elis | 2004 | Israel | Serum | 63 | 40.59 | -- | 40 | 29 | -- | -- | -- | −0.04 | 0.15 | -- |
| Jiang | 2004 | USA | Plasma | 56.45 | 0 | 4.1 | 716 | 698 | -- | -- | -- | -- | 0.42 | -- |
| Elsammak | 2005 | Egypt | Serum | -- | -- | -- | 18 | 22 | -- | -- | -- | -- | −1.19 | -- |
| Kaviarasan | 2005 | India | Plasma | 54.24 | -- | 5.21 | 18 | 20 | -- | -- | 3.76 | -- | -- | -- |
| Virgolici | 2005 | Romania | Plasma | 53.4 | 50 | 11.4 | 20 | 20 | -- | -- | 1.02 | -- | -- | -- |
| Al-Maroof | 2006 | Iraq | Serum | 45.73 | -- | 1.69 | 133 | 101 | −1.18 | -- | -- | -- | -- | -- |
| Abou-Shousha | 2006 | Egypt | Serum | 48.52 | 0 | -- | 10 | 20 | -- | -- | -- | -- | 0.65 | -- |
| Lee | 2006 | Korea | Serum | 58.6 | 67.35 | -- | 47 | 48 | -- | -- | -- | 0.15 | 0.86 | -- |
| Adewumi | 2007 | Nigeria | Serum | 46.67 | 100 | 6.25 | 50 | 28 | -- | --d | -- | -- | -- | −0.89 |
| Adewumi | 2007 | Nigeria | Serum | 44.12 | 0 | 3.43 | 40 | 62 | -- | --d | -- | -- | -- | −1.03 |
| Viktorinova | 2009 | Slovak | Plasma | 42.49 | 47.46 | 0.55 | 34 | 25 | −0.49 | 0.53 | -- | -- | -- | -- |
| Rajpathak | 2009 | USA | Serum | 50.3 | 36.4 | 1.5 | 280 | 280 | -- | -- | -- | -- | 0.11 | -- |
| Flores | 2010 | Mexico | Serum | -- | -- | -- | 12 | 76 | 0.04 | 1.05 | -- | -- | -- | 1.15 |
| Sarkar | 2010 | India | Plasma | 54.39 | 62.19 | -- | 78 | 123 | -- | 2.94 | −1.3 | -- | -- | -- |
| Ashourpour | 2010 | Iran | Serum | 53.29 | -- | 0.29 | 53 | 54 | -- | -- | -- | -- | 3.41 | -- |
| Chacko | 2010 | India | Serum | -- | 52.87 | -- | 47 | 40 | -- | -- | 11.13 | -- | -- | -- |
| Nasli-Esfahani | 2011 | Iran | Serum | 50.6 | 34.96 | -- | 151 | 150 | −1.05 | --d | -- | -- | -- | −0.13 |
| Kim | 2011 | Korea | Serum | 51.27 | 100 | 0.8 | 3384 | 727 | -- | -- | -- | -- | 0.3 | -- |
| Kim | 2011 | Korea | Serum | 50.17 | 0 | 2.2 | 3869 | 327 | -- | -- | -- | -- | 0.54 | -- |
| Ferdousi | 2012 | Bangladesh | Serum | -- | -- | 5.15 | 60 | 60 | −0.38 | 0.46 | -- | -- | -- | -- |
| Savu | 2012 | Romania | Plasma | 40.99 | 41 | -- | 15 | 15 | -- | 1.56 | 1.55 | -- | -- | -- |
| Ganesh | 2012 | India | Serum | 42.7 | 38.35 | −1.86 | 30 | 30 | -- | -- | -- | 0.39 | 0.6 | -- |
| Praveeena | 2013 | India | Serum | -- | -- | -- | 40 | 40 | −3.41 | -- | -- | -- | -- | -- |
| Farid | 2013 | Saudi Arabia | Serum | 59.51 | 100 | -- | 55 | 55 | −1.19 | 0.84 | -- | -- | -- | --d |
| Dosa | 2013 | Romania | Plasma | -- | -- | 3.86 | 17 | 30 | −2.53 | 0.44 | -- | -- | -- | -- |
| Kundu | 2013 | India | Serum | -- | -- | 30 | 30 | -- | -- | -- | 0.33 | 2.1 | -- | |
| Santa | 2014 | India | Serum | -- | -- | 5.42 | 40 | 60 | −4.66 | 5.5 | -- | -- | -- | -- |
| Oyedeji | 2014 | Nigeria | Plasma | -- | 0 | -- | 35 | 50 | −2.03 | --d | -- | 2.66 | -- | -- |
| Kumar | 2014 | India | Serum | 48.5 | 74 | -- | 50 | 50 | −1.86 | 2.07 | -- | -- | -- | -- |
| Alam | 2014 | Pakistan | Serum | 40.4 | -- | -- | 44 | 67 | -- | -- | -- | -- | 2.4 | -- |
| Nagarajrao | 2015 | Saudi Arabia | Serum | 49.43 | 55.24 | 0.04 | 58 | 47 | −2.18 | 4.09 | -- | 3.33 | -- | -- |
| Maheshwari | 2015 | India | Serum | 48.28 | 50 | 50 | -- | 2.58 | ||||||
| Badran | 2016 | Egypt | Serum | 52.88 | 57.89 | 0.96 | 36 | 40 | −4.51 | --d | -- | --d | -- | --d |
| Atari-Hajipirlo | 2016 | Iran | Serum | 52.46 | 53.12 | -- | 50 | 46 | −1.25 | 1.21 | -- | 1.35 | -- | -- |
| Wang | 2016 | China | Serum | 67.58 | 100 | 0.87 | 529 | 289 | -- | -- | -- | -- | -- | 0.15 |
| Wang | 2016 | China | Serum | 67.63 | 0 | −2 | 751 | 494 | -- | -- | -- | -- | -- | −0.29 |
| Devi | 2016 | India | Serum | -- | 47.5 | -- | 40 | 40 | −0.67 | 4.06 | -- | -- | -- | -- |
| Goud | 2016 | India | Serum | -- | -- | -- | 20 | 20 | -- | -- | 0.97 | −0.51 | 2.17 | -- |
| Jeppu | 2016 | Malaysia | Serum | -- | -- | -- | 50 | 50 | -- | -- | −2.21 | -- | -- | -- |
| Borah | 2016 | India | Serum | 53.93 | 56.5 | 2.17 | 92 | 92 | -- | -- | -- | -- | 1.87 | -- |
| Eva | 2016 | Banglade | Serum | 47.79 | 54 | 0.36 | 50 | 50 | −0.96 | -- | -- | -- | -- | −0.9 |
| Zhang | 2017 | China | Serum | 55.91 | 34.68 | 1.2 | 1327 | 510 | 0.16 | 0.15 | -- | -- | -- | 0.02 |
Represents the weighted average where the weights are the number of individuals in control and diabetic groups
Represents the difference in the body mass index between diabetic and control groups
Not reported in the primary study
Data did not meet the inclusion criteria
Figure 1.
Flow diagram of the screening process involved in inclusion of articles
Summary of included studies
The characteristics of the included studies are presented in Table/Figure 2. The number of included studies reporting values for zinc, copper, ceruloplasmin, iron, ferritin, and manganese were 25, 20, 12, 9, 16 and 7, respectively. A few investigations reported mean trace element levels separately for men and women, thereby resulting in two effect sizes per study. Thus, the total number of effect sizes for zinc, copper, ceruloplasmin, iron, ferritin and manganese were 27, 21, 12, 10, 19 and 9, respectively.
Figure 2.
Forest plot of effect size estimates and its 95% confidence intervals representing difference in zinc levels between healthy and diabetic subjects
aThe weight of each study indicates its influence on the pooled effect size. A higher percentage weight is shown by a larger box and narrower confidence interval for the respective study in the forest plot
bData represented as weighted average of the effect size for men and women
Pooled average effect size for zinc, copper, ceruloplasmin, iron, ferritin and manganese
The Forest plots in Tables/Figures 3–8 depict the average pooled effect size, 95% CI and statistical significance for zinc, copper, ceruloplasmin, iron, ferritin and manganese, respectively. For investigations that report separate values for men and women, a weighted average of the effect size was calculated with the number in each gender as weights. Zinc levels were significantly lower in type 2 diabetic subjects when compared to non-diabetic individuals (pooled effect size = −1.73, p<0.01), whereas that for copper (pooled effect size = 1.10, p<0.05) and ferritin (pooled effect size = 1.05, p<0.05) were significantly higher. Patients diagnosed with type 2 diabetes also had higher values for ceruloplasmin (pooled effect size = 1.85, p=0.06) and iron (pooled effect size = 1.42, p=0.06), but lower manganese concentrations (pooled effect size = −0.27, p=0.34) than nondiabetic controls. However, these effect sizes were not significant. The I2 statistic for the meta-analysis was 97.6%.
Figure 3.
Forest plot of effect size estimates and its 95% confidence intervals representing difference in copper levels between healthy and diabetic subjects
aThe weight of each study indicates its influence on the pooled effect size. A higher percentage weight is shown by a larger box and narrower confidence interval for the respective study in the forest plot
bData represented as weighted average of the effect size for men and women
Figure 8.
Funnel plot
Moderator analysis
The influence of biomarker type for quantification of trace element status, age, gender and BMI on effect size were carried out as separate, additional analyses. The weighted average of age and percentage of men were used to represent age and gender, respectively, with the number of individuals in the control and diabetic groups serving as the weights. The weighted mean of the difference in BMI also was used as a moderator.
Z-statistics for testing the type of biomarker, age, gender and BMI as moderator effects were −0.504, −0.219, 0.982, and 0.459 respectively. Furthermore, none of the moderators had a significant influence on any of the effect sizes.
Publication bias
Figure 8 indicates a fairly symmetrical funnel plot. Furthermore, the Egger’s regression showed no evidence for significant publication bias in the included studies.
DISCUSSION
This meta-analysis found that zinc and manganese concentrations were lower in type 2 diabetic individuals compared to nondiabetic controls; while levels of copper, ceruloplasmin, iron and ferritin were higher. However, the bias-corrected Hedges g was not significant for ceruloplasmin, iron and manganese. The inconsistent findings between copper and ceruloplasmin, and iron and ferritin could due to difference in the number of studies investigating levels of these biomarkers. It is important to note that the greatest effect size was observed for ceruloplasmin, followed by iron. The smaller number of studies reporting ceruloplasmin and iron values could have resulted in lower statistical power, and subsequently, non-significant effect sizes. The use of plasma vs serum, age, BMI, and gender did not significantly influence the effect sizes in the present meta-analysis.
The results of this meta-analysis suggest possible zinc deficiencies and elevated copper and iron (as indicated by ferritin) statuses in type 2 diabetic individuals. The findings are comparable to previous research which demonstrates abnormal metabolism of zinc in type 2 diabetes patients, marked by malabsorption and increased urinary losses of this micronutrient.(80) Further, the greater ceruloplasmin (40, 81) and lower transferrin (40) levels observed in type 2 diabetic individuals could result in elevated levels of free copper and iron, respectively. Increased levels of these transition metals have been linked to greater oxidative stress,(82) and subsequently, increased insulin resistance and type 2 diabetes (16). The concomitant decrease in trace elements that function as antioxidants, such as zinc (83) and manganese, (84, 85) may cause an impaired scavenging mechanism of free radicals, and may further exacerbate the prognosis of diabetes. Collectively, these results indicate the need for cohort studies to explore the role of these trace elements in the progression of type 2 diabetes.
The results of this meta-analysis are subject to certain limitations. Since the meta-analysis did not include longitudinal studies, it is not possible to establish causal inferences on the association between altered trace element status and type 2 diabetes. Another limitation is that the results could have been influenced by exclusion of articles that lacked availability of text in English. Further, the high level of heterogeneity in this meta-analysis could have affected the validity of the overall pooled effect size estimate. Nevertheless, the analysis is strengthened by the use of a model that handles dependency between multiple outcomes.
CONCLUSION
In conclusion, this meta-analysis found altered levels of trace minerals in type 2 diabetes patients in comparison with controls. Inadequate zinc and a concurrent excess of copper and iron levels may be associated with an increased level of oxidative stress, and may exacerbate the condition. Longitudinal studies could help in understanding the association between trace element variations and onset and progression of type 2 diabetes.
Figure 4.
Forest plot of effect size estimates and its 95% confidence intervals representing difference in ceruloplasmin levels between healthy and diabetic subjects
aThe weight of each study indicates its influence on the pooled effect size. A higher percentage weight is shown by a larger box and narrower confidence interval for the respective study in the forest plot
Figure 5.
Forest plot of effect size estimates and its 95% confidence intervals representing difference in iron levels between healthy and diabetic subjects
aThe weight of each study indicates its influence on the pooled effect size. A higher percentage weight is shown by a larger box and narrower confidence interval for the respective study in the forest plot
bData represented as weighted average of the effect size for men and women
Figure 6.
Forest plot of effect size estimates and its 95% confidence intervals representing difference in ferritin levels between healthy and diabetic subjects
aThe weight of each study indicates its influence on the pooled effect size. A higher percentage weight is shown by a larger box and narrower confidence interval for the respective study in the forest plot
bData represented as weighted average of the effect size for men and women
Figure 7.
Forest plot of effect size estimates and its 95% confidence intervals representing difference in manganese levels between healthy and diabetic subjects
aThe weight of each study indicates its influence on the pooled effect size. A higher percentage weight is shown by a larger box and narrower confidence interval for the respective study in the forest plot
bData represented as weighted average of the effect size for men and women
Acknowledgments
None.
Financial Support
This research received no specific grant from any funding agency, commercial or not-for-profit sectors. Lead author’s effort was supported in part by the intramural research program of the National Institutes of Health, Eunice Kennedy Shriver National Institute of Child Health and Human Development.
Footnotes
Financial interest disclosure:
None
Conflict of Interest
None
Contributor Information
Namrata Sanjeevi, Postdoctoral Fellow, Health Behavior Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, 6710B Rockledge Drive Room 3165A, MSC 7004, Bethesda, MD 20817, USA, namratas@utexas.edu.
Jeanne Freeland-Graves, Department of Nutritional Sciences, The University of Texas at Austin, 1 University Station A2703, Austin, TX 78712, USA, jfg@mail.utexas.edu.
S. Natasha Beretvas, Department of Educational Psychology, The University of Texas at Austin, 1 University Station D5800, Austin, TX 78712, USA, tberetvas@austin.utexas.edu.
Prageet K. Sachdev, Department of Nutritional Sciences, The University of Texas at Austin, 1 University Station A2703, Austin, TX 78712, USA, prageet@utexas.edu
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