K Giannakou1, E Evangelou2,3, S I Papatheodorou1. 1. Cyprus International Institute for Environmental & Public Health, Cyprus University of Technology, Limassol, Cyprus. 2. Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece. 3. Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK.
Abstract
OBJECTIVE: To summarize evidence from the literature on genetic and non-genetic risk factors associated with pre-eclampsia (PE), assess the presence of statistical bias in the studies and identify risk factors for which there is robust evidence supporting their association with PE. METHODS: PubMed and ISI Web of Science were searched from inception to October 2016, to identify systematic reviews and meta-analyses of observational studies examining associations between genetic or non-genetic risk factors and PE. For each meta-analysis, the summary-effect size was estimated using random-effects and fixed-effects models, along with 95% CIs and the 95% prediction interval. Between-study heterogeneity was expressed using the I2 statistic, and evidence of small-study effects (large studies had significantly more conservative results than smaller studies) and evidence of excess significance bias (too many studies with statistically significant results) were estimated. RESULTS: Fifty-eight eligible meta-analyses were identified, which included 1466 primary studies and provided data on 130 comparisons of risk factors associated with PE, covering a wide range of comorbid diseases, genetic factors, exposure to environmental agents and biomarkers. Sixty-five (50%) associations had nominally statistically significant findings at P < 0.05, while 16 (12%) were significant at P < 10-6 . Sixty-five (50%) associations had large or very large heterogeneity. Evidence for small-study effects and excess significance bias was found in 10 (8%) and 26 (20%) associations, respectively. The only non-genetic risk factor with convincing evidence for an association with PE was oocyte donation vs spontaneous conception, which had a summary odds ratio of 4.33 (95% CI, 3.11-6.03), was supported by 2712 cases with small heterogeneity (I2 = 26%) and 95% prediction intervals excluding the null value, and without hints of small-study effects (P for Egger's test > 0.10) or excess of significance (P > 0.05). Of the statistically significant (P < 0.05) genetic risk factors for PE, only PAI-1 4G/5G (recessive model) polymorphism was supported by strong evidence for a contribution to the pathogenesis of PE. Eleven factors (serum iron level, pregnancy-associated plasma protein-A, chronic kidney disease, polycystic ovary syndrome, mental stress, bacterial and viral infections, cigarette smoking, oocyte donation vs assisted reproductive technology, obesity vs normal weight, severe obesity vs normal weight and primiparity) presented highly suggestive evidence for an association with PE. CONCLUSIONS: A large proportion of meta-analyses of genetic and non-genetic risk factors for PE have caveats that threaten their validity. Oocyte donation vs spontaneous conception and PAI-1 4G/5G polymorphism (recessive model) showed the strongest consistent evidence for an association with risk for PE.
OBJECTIVE: To summarize evidence from the literature on genetic and non-genetic risk factors associated with pre-eclampsia (PE), assess the presence of statistical bias in the studies and identify risk factors for which there is robust evidence supporting their association with PE. METHODS: PubMed and ISI Web of Science were searched from inception to October 2016, to identify systematic reviews and meta-analyses of observational studies examining associations between genetic or non-genetic risk factors and PE. For each meta-analysis, the summary-effect size was estimated using random-effects and fixed-effects models, along with 95% CIs and the 95% prediction interval. Between-study heterogeneity was expressed using the I2 statistic, and evidence of small-study effects (large studies had significantly more conservative results than smaller studies) and evidence of excess significance bias (too many studies with statistically significant results) were estimated. RESULTS: Fifty-eight eligible meta-analyses were identified, which included 1466 primary studies and provided data on 130 comparisons of risk factors associated with PE, covering a wide range of comorbid diseases, genetic factors, exposure to environmental agents and biomarkers. Sixty-five (50%) associations had nominally statistically significant findings at P < 0.05, while 16 (12%) were significant at P < 10-6 . Sixty-five (50%) associations had large or very large heterogeneity. Evidence for small-study effects and excess significance bias was found in 10 (8%) and 26 (20%) associations, respectively. The only non-genetic risk factor with convincing evidence for an association with PE was oocyte donation vs spontaneous conception, which had a summary odds ratio of 4.33 (95% CI, 3.11-6.03), was supported by 2712 cases with small heterogeneity (I2 = 26%) and 95% prediction intervals excluding the null value, and without hints of small-study effects (P for Egger's test > 0.10) or excess of significance (P > 0.05). Of the statistically significant (P < 0.05) genetic risk factors for PE, only PAI-1 4G/5G (recessive model) polymorphism was supported by strong evidence for a contribution to the pathogenesis of PE. Eleven factors (serum iron level, pregnancy-associated plasma protein-A, chronic kidney disease, polycystic ovary syndrome, mental stress, bacterial and viral infections, cigarette smoking, oocyte donation vs assisted reproductive technology, obesity vs normal weight, severe obesity vs normal weight and primiparity) presented highly suggestive evidence for an association with PE. CONCLUSIONS: A large proportion of meta-analyses of genetic and non-genetic risk factors for PE have caveats that threaten their validity. Oocyte donation vs spontaneous conception and PAI-1 4G/5G polymorphism (recessive model) showed the strongest consistent evidence for an association with risk for PE.
Authors: Anum S Minhas; Wendy Ying; S Michelle Ogunwole; Michael Miller; Sammy Zakaria; Arthur J Vaught; Allison G Hays; Andreea A Creanga; Ari Cedars; Erin D Michos; Roger S Blumenthal; Garima Sharma Journal: Curr Treat Options Cardiovasc Med Date: 2020-10-31
Authors: Perrine Janiaud; Arnav Agarwal; Ioanna Tzoulaki; Evropi Theodoratou; Konstantinos K Tsilidis; Evangelos Evangelou; John P A Ioannidis Journal: BMC Med Date: 2021-07-06 Impact factor: 11.150