In the current study, odds ratios (ORs) were used to estimate the association of obesity with outcomes. The potential between study heterogeneity was estimated by Cochran’s Q-statistic (P value < 0.10 was considered as statistically significant) and I-squared (I2) tests. Because of a remarkable evidence for heterogeneity, the random-effected model was applied [27, 28]. To assessed the predefined sources of heterogeneity among included studies, subgroup analysis based on obesity severity, study design (cohort vs. non-cohort), ethnicity (Caucasian vs. East-Asian), age category (≥ 50 years vs. ˂ 50 years), and adjustment for covariates (Adjusted vs. Non-adjusted effect size) and univariate random effects meta-regressions based on sex and age of participants were used. Additionally, in sensitivity analysis, we evaluated the conclusiveness and robustness of results by excluding each of the studies from the pooled estimate and analyzing the rest of them. This method enables the assessment of whether the pooled estimates were affected by any individual studies. To discover the risk of publication bias and the small-study effect, Begg’s funnel plots and Egger’s regression test were estimated (P value < 0.05 was considered as statistically significant) [29, 30]. The funnel plot asymmetry was interpreted as follow: in case of no evidence of publication bias, studies with high precision (large study effects) will be located near the average line, and studies with low precision (small-study effects) will be spread equally on both sides of the average line; any deviation from this shape can indicate publication bias. In the forest plot figures, the areas of the squares for individual studies or diamond-shaped for overall results are inversely proportional to the variances of the log odds ratio estimates, and horizontal lines display CIs. The data analyses were carried out using STATA (version 14.0; Stata Corporation, College Station, TX) and SPSS (version 23.0; SPSS, Inc. Chicago, IL) software.

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