Review of Genetic and Artificial Intelligence approaches to improving Gestational Diabetes Mellitus Screening and Diagnosis in sub-Saharan Africa.

IF 2.5 3区 工程技术 Q2 BIOLOGY Yale Journal of Biology and Medicine Pub Date : 2024-03-29 eCollection Date: 2024-03-01 DOI:10.59249/ZBSC2656
Vansh V Gadhia, Jaspreet Loyal
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Abstract

Background: Adverse outcomes from gestational diabetes mellitus (GDM) in the mother and newborn are well established. Genetic variants may predict GDM and Artificial Intelligence (AI) can potentially assist with improved screening and early identification in lower resource settings. There is limited information on genetic variants associated with GDM in sub-Saharan Africa and the implementation of AI in GDM screening in sub-Saharan Africa is largely unknown. Methods: We reviewed the literature on what is known about genetic predictors of GDM in sub-Saharan African women. We searched PubMed and Google Scholar for single nucleotide polymorphisms (SNPs) involved in GDM predisposition in a sub-Saharan African population. We report on barriers that limit the implementation of AI that could assist with GDM screening and offer possible solutions. Results: In a Black South African cohort, the minor allele of the SNP rs4581569 existing in the PDX1 gene was significantly associated with GDM. We were not able to find any published literature on the implementation of AI to identify women at risk of GDM before second trimester of pregnancy in sub-Saharan Africa. Barriers to successful integration of AI into healthcare systems are broad but solutions exist. Conclusions: More research is needed to identify SNPs associated with GDM in sub-Saharan Africa. The implementation of AI and its applications in the field of healthcare in the sub-Saharan African region is a significant opportunity to positively impact early identification of GDM.

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审查改进撒哈拉以南非洲妊娠糖尿病筛查和诊断的遗传和人工智能方法。
背景:妊娠糖尿病(GDM)对母亲和新生儿造成的不良后果已得到公认。遗传变异可预测妊娠糖尿病,而人工智能(AI)可帮助改善资源较少地区的筛查和早期识别。在撒哈拉以南非洲地区,与 GDM 相关的遗传变异信息非常有限,而在撒哈拉以南非洲地区,人工智能在 GDM 筛查中的应用情况在很大程度上也是未知的。方法:我们查阅了有关撒哈拉以南非洲妇女 GDM 遗传预测因素的文献。我们在 PubMed 和 Google Scholar 上搜索了撒哈拉以南非洲人群中与 GDM 易感性有关的单核苷酸多态性 (SNP)。我们报告了限制人工智能(AI)实施的障碍,人工智能可帮助进行 GDM 筛查,并提供了可能的解决方案。结果在一个南非黑人队列中,存在于 PDX1 基因中的 SNP rs4581569 小等位基因与 GDM 显著相关。在撒哈拉以南非洲地区,我们未能找到任何已发表的关于实施人工智能以在妊娠后三个月前识别有 GDM 风险的妇女的文献。将人工智能成功融入医疗保健系统的障碍很多,但解决方案是存在的。结论需要开展更多研究,以确定与撒哈拉以南非洲 GDM 相关的 SNPs。在撒哈拉以南非洲地区实施人工智能及其在医疗保健领域的应用,是对 GDM 早期识别产生积极影响的重要机会。
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来源期刊
Yale Journal of Biology and Medicine
Yale Journal of Biology and Medicine Biochemistry, Genetics and Molecular Biology-General Biochemistry,Genetics and Molecular Biology
CiteScore
5.00
自引率
0.00%
发文量
41
期刊介绍: The Yale Journal of Biology and Medicine (YJBM) is a graduate and medical student-run, peer-reviewed, open-access journal dedicated to the publication of original research articles, scientific reviews, articles on medical history, personal perspectives on medicine, policy analyses, case reports, and symposia related to biomedical matters. YJBM is published quarterly and aims to publish articles of interest to both physicians and scientists. YJBM is and has been an internationally distributed journal with a long history of landmark articles. Our contributors feature a notable list of philosophers, statesmen, scientists, and physicians, including Ernst Cassirer, Harvey Cushing, Rene Dubos, Edward Kennedy, Donald Seldin, and Jack Strominger. Our Editorial Board consists of students and faculty members from Yale School of Medicine and Yale University Graduate School of Arts & Sciences. All manuscripts submitted to YJBM are first evaluated on the basis of scientific quality, originality, appropriateness, contribution to the field, and style. Suitable manuscripts are then subject to rigorous, fair, and rapid peer review.
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