Modulatory Properties of Vitamin D in Type 2 Diabetic Patients: A Focus on Inflammation and Dyslipidemia

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Abstract

Background: Evidence from preclinical studies has found a correlation between the development of type 2 diabetes (T2D) and vitamin D deficiency. However, evidence from randomized controlled trials (RCTs) revealed inconclusive results on vitamin D supplementation. We explored the effect of vitamin D on inflammation and dyslipidemia in T2D. Methods: We comprehensively searched for RCTs evaluating the effect of vitamin D in T2D on PubMed. Data were analyzed using Review Manager 5.3 and reports, such as standardized mean difference (SMD) and 95% confidence intervals (CI) at a 5% significant level using a random effect model. Results: This study revealed a significant reduction in tumor necrosis factor-alpha (TNF-α) SMD = (−0.51, 95%CI (−0.93, −0.09); p = 0.02), high sensitivity C-reactive protein (hs-CRP) SMD = (−1.06, 95%CI (−1.67, −0.45); p < 0.05) in vitamin D compared to placebo. Additionally, interleukin-6 (IL-6) exhibited a marginal effect SMD = (−0.52, 95%CI (−1.05, 0.01), p = 0.05). Furthermore, a significant reduction in the level of triglycerides SMD = (−0.65, 95%CI (−1.11, −0.18), p < 0.05) was observed, concomitant to a significantly increased high-density lipoprotein (HDL) level SMD = (0.53, 95%CI (0.08, 0.98), p = 0.02). However, no statistically significant changes were observed in total cholesterols SMD = (−0.16, 95%CI (−0.57, 0.24), p = 0.43) and low-density lipoprotein (LDL) SMD = (−0.06, 95%CI (−0.37, 0.24), p = 0.67). Conclusions: These findings suggest that vitamin D supplementation may be beneficial in ameliorating inflammation and dyslipidemia in T2D patients.

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The PRISMA 2020 statement: an updated guideline for reporting systematic reviews

Matthew J L Page, Joanne E McKenzie, Patrick Bossuyt … (2025)

The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement, published in 2009, was designed to help systematic reviewers transparently report why the review was done, what the authors did, and what they found. Over the past decade, advances in systematic review methodology and terminology have necessitated an update to the guideline. The PRISMA 2020 statement replaces the 2009 statement and includes new reporting guidance that reflects advances in methods to identify, select, appraise, and synthesise studies. The structure and presentation of the items have been modified to facilitate implementation. In this article, we present the PRISMA 2020 27-item checklist, an expanded checklist that details reporting recommendations for each item, the PRISMA 2020 abstract checklist, and the revised flow diagrams for original and updated reviews.

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Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range

Xiang Wan, Wenqian Wang, Jiming Liu … (2014)

Background In systematic reviews and meta-analysis, researchers often pool the results of the sample mean and standard deviation from a set of similar clinical trials. A number of the trials, however, reported the study using the median, the minimum and maximum values, and/or the first and third quartiles. Hence, in order to combine results, one may have to estimate the sample mean and standard deviation for such trials. Methods In this paper, we propose to improve the existing literature in several directions. First, we show that the sample standard deviation estimation in Hozo et al.’s method (BMC Med Res Methodol 5:13, 2005) has some serious limitations and is always less satisfactory in practice. Inspired by this, we propose a new estimation method by incorporating the sample size. Second, we systematically study the sample mean and standard deviation estimation problem under several other interesting settings where the interquartile range is also available for the trials. Results We demonstrate the performance of the proposed methods through simulation studies for the three frequently encountered scenarios, respectively. For the first two scenarios, our method greatly improves existing methods and provides a nearly unbiased estimate of the true sample standard deviation for normal data and a slightly biased estimate for skewed data. For the third scenario, our method still performs very well for both normal data and skewed data. Furthermore, we compare the estimators of the sample mean and standard deviation under all three scenarios and present some suggestions on which scenario is preferred in real-world applications. Conclusions In this paper, we discuss different approximation methods in the estimation of the sample mean and standard deviation and propose some new estimation methods to improve the existing literature. We conclude our work with a summary table (an Excel spread sheet including all formulas) that serves as a comprehensive guidance for performing meta-analysis in different situations. Electronic supplementary material The online version of this article (doi:10.1186/1471-2288-14-135) contains supplementary material, which is available to authorized users.

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Estimating the mean and variance from the median, range, and the size of a sample

Stela Pudar Hozo, Benjamin Djulbegovic, Iztok Hozo (2005)

Background Usually the researchers performing meta-analysis of continuous outcomes from clinical trials need their mean value and the variance (or standard deviation) in order to pool data. However, sometimes the published reports of clinical trials only report the median, range and the size of the trial. Methods In this article we use simple and elementary inequalities and approximations in order to estimate the mean and the variance for such trials. Our estimation is distribution-free, i.e., it makes no assumption on the distribution of the underlying data. Results We found two simple formulas that estimate the mean using the values of the median (m), low and high end of the range (a and b, respectively), and n (the sample size). Using simulations, we show that median can be used to estimate mean when the sample size is larger than 25. For smaller samples our new formula, devised in this paper, should be used. We also estimated the variance of an unknown sample using the median, low and high end of the range, and the sample size. Our estimate is performing as the best estimate in our simulations for very small samples (n ≤ 15). For moderately sized samples (15 70), the formula range/6 gives the best estimator for the standard deviation (variance). We also include an illustrative example of the potential value of our method using reports from the Cochrane review on the role of erythropoietin in anemia due to malignancy. Conclusion Using these formulas, we hope to help meta-analysts use clinical trials in their analysis even when not all of the information is available and/or reported.