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Bayes Conditional Probability-Based Causation Analysis between Gestational Diabetes Mellitus (GDM) and Pregnancy-Induced Hypertension (PIH): A Statistic Case Study in Harbin, China
Both gestational diabetes mellitus (GDM) and pregnancy-induced hypertension (PIH) would influence the gestation significantly. However, the causation between these two symptoms remains speculative. 16,404 pregnant women were identified in Harbin, China, in this study. We investigated and evaluated t...
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Published in: | Journal of diabetes research 2022, Vol.2022, p.2590415-7 |
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description | Both gestational diabetes mellitus (GDM) and pregnancy-induced hypertension (PIH) would influence the gestation significantly. However, the causation between these two symptoms remains speculative. 16,404 pregnant women were identified in Harbin, China, in this study. We investigated and evaluated the causal effect of GDM on PIH based on the Bayes conditional probability. The statistical results indicated that PIH might cause GDM, but not vice versa. Also, this case study demonstrated that the decrease temperature might also cause hypertension during pregnancy, and the prevalence rate of GDM increased with age. However, the prevalence of diabetes did not show a remarkable difference in varied areas and ages. This study could provide some essential information that will help to investigate the mechanism for GDM and PIH. |
doi_str_mv | 10.1155/2022/2590415 |
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However, the causation between these two symptoms remains speculative. 16,404 pregnant women were identified in Harbin, China, in this study. We investigated and evaluated the causal effect of GDM on PIH based on the Bayes conditional probability. The statistical results indicated that PIH might cause GDM, but not vice versa. Also, this case study demonstrated that the decrease temperature might also cause hypertension during pregnancy, and the prevalence rate of GDM increased with age. However, the prevalence of diabetes did not show a remarkable difference in varied areas and ages. This study could provide some essential information that will help to investigate the mechanism for GDM and PIH.</description><identifier>ISSN: 2314-6745</identifier><identifier>EISSN: 2314-6753</identifier><identifier>DOI: 10.1155/2022/2590415</identifier><identifier>PMID: 35493606</identifier><language>eng</language><publisher>England: Hindawi</publisher><subject>Bayes Theorem ; Causality ; China - epidemiology ; Diabetes, Gestational - epidemiology ; Eclampsia ; Female ; Gestational diabetes ; Humans ; Hypertension ; Hypertension, Pregnancy-Induced - epidemiology ; Hypertension, Pregnancy-Induced - etiology ; Insulin resistance ; Pregnancy ; Probability ; Risk Factors</subject><ispartof>Journal of diabetes research, 2022, Vol.2022, p.2590415-7</ispartof><rights>Copyright © 2022 Dan Diao et al.</rights><rights>Copyright © 2022 Dan Diao et al. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>Copyright © 2022 Dan Diao et al. 2022</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c545t-712265c6bad70f00a82b24b04a0cbe97aa5da338f98ea465fafae135324752663</citedby><cites>FETCH-LOGICAL-c545t-712265c6bad70f00a82b24b04a0cbe97aa5da338f98ea465fafae135324752663</cites><orcidid>0000-0001-7266-3821</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2658000170/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2658000170?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35493606$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Sharif, Ali</contributor><creatorcontrib>Diao, Dan</creatorcontrib><creatorcontrib>Diao, Fang</creatorcontrib><creatorcontrib>Xiao, Bin</creatorcontrib><creatorcontrib>Liu, Ning</creatorcontrib><creatorcontrib>Zheng, Dan</creatorcontrib><creatorcontrib>Li, Fengjuan</creatorcontrib><creatorcontrib>Yang, Xu</creatorcontrib><title>Bayes Conditional Probability-Based Causation Analysis between Gestational Diabetes Mellitus (GDM) and Pregnancy-Induced Hypertension (PIH): A Statistic Case Study in Harbin, China</title><title>Journal of diabetes research</title><addtitle>J Diabetes Res</addtitle><description>Both gestational diabetes mellitus (GDM) and pregnancy-induced hypertension (PIH) would influence the gestation significantly. However, the causation between these two symptoms remains speculative. 16,404 pregnant women were identified in Harbin, China, in this study. We investigated and evaluated the causal effect of GDM on PIH based on the Bayes conditional probability. The statistical results indicated that PIH might cause GDM, but not vice versa. Also, this case study demonstrated that the decrease temperature might also cause hypertension during pregnancy, and the prevalence rate of GDM increased with age. However, the prevalence of diabetes did not show a remarkable difference in varied areas and ages. 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subjects | Bayes Theorem Causality China - epidemiology Diabetes, Gestational - epidemiology Eclampsia Female Gestational diabetes Humans Hypertension Hypertension, Pregnancy-Induced - epidemiology Hypertension, Pregnancy-Induced - etiology Insulin resistance Pregnancy Probability Risk Factors |
title | Bayes Conditional Probability-Based Causation Analysis between Gestational Diabetes Mellitus (GDM) and Pregnancy-Induced Hypertension (PIH): A Statistic Case Study in Harbin, China |
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