Hematological Indices and Genetic Variants of Premature Ovarian Insufficiency: Machine Learning Approaches.

Mohammad Reza Mirinezhad, Malihe Aghasizadeh, Hamideh Ghazizadeh, Anahid Hemmatpur, Mohammad Reza Fazl Mashhadi, Hamed Khedmatgozar, Amir Kiyoumarsioskouei, Ali Ebrahimi Dabagh, Mohammad Amin Mohammadi, Arezoo Rastegarmoghadam Ebrahimian, Melika Malek, Sara Moazedi, Simin Rashidian, Gordon A Ferns, Tayebeh Hamzehloei, Alireza Pasdar, Majid Ghayour-Mobarhan
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Abstract

Background: Premature Ovarian Insufficiency (POI) is associated with infertility. Little is known about the potential circulating biomarkers that could be used to predict POI. We have investigated the possible association between white and red blood cells, platelet indices, and eight established single nucleotide polymorphisms (SNPs) associated with POI risk.

Methods: 117 women with premature menopause (PM) and 183 healthy women without a history of menopause before age 40 were recruited for this study. The tetra-primer amplification refractory mutation system-polymerase chain reaction (Tetra ARMS PCR) and allele-specific oligonucleotides- polymerase chain reaction (ASO-PCR) were carried out for genotyping for eight SNPs reported to be associated with POI. Decision tree analysis was applied to test the diagnostic value of hematological parameters to identify the risk of POI.

Results: Women with POI had lower neutrophil (NEUT) and white blood cell (WBC), whereas red blood cell (RBC), hemoglobin (HGB), hematocrit (HCT), mean corpuscular volume (MCV), and mean cell hemoglobin (MCH) were higher. Platelet (PLT) count was also lower in affected women. Our data also indicated that HGB and HCT count were significantly associated with rs16991615 and rs244715. Mean Platelet volume (MPV) and platelet distribution width (PDW) were associated with rs244715, rs1046089, rs4806660, and rs2303369. The rs16991615 was also associated with RBC count, and rs451417 was associated with NEUTs. The decision tree (DT) model reveals that women with the NEUT count at a cut-off value of less than 2.8 and HCT equal to or more than 38.7% could be identified as high-risk cases for POI. Overall, we found the DT approach had a sensitivity = 85%, specificity = 72%, and accuracy = 74%.

Conclusion: The genetic variants involved in POI are associated with changes in reproductive hormone levels and with changes in hematological indices.

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早发性卵巢功能不全的血液学指标和遗传变异:机器学习方法。
背景:卵巢早衰(POI)与不孕症有关。人们对可用于预测 POI 的潜在循环生物标志物知之甚少。我们研究了白细胞、红细胞、血小板指数和 8 个已确定的与 POI 风险相关的单核苷酸多态性(SNPs)之间可能存在的关联:本研究招募了 117 名更年期过早(PM)的妇女和 183 名 40 岁前无更年期史的健康妇女。采用四引物扩增难治性突变系统聚合酶链反应(Tetra ARMS PCR)和等位基因特异性寡核苷酸聚合酶链反应(ASO-PCR)对据报道与 POI 相关的 8 个 SNP 进行基因分型。应用决策树分析检验了血液学参数对确定 POI 风险的诊断价值:结果:患有 POI 的女性中性粒细胞(NEUT)和白细胞(WBC)较低,而红细胞(RBC)、血红蛋白(HGB)、血细胞比容(HCT)、平均血球容积(MCV)和平均细胞血红蛋白(MCH)较高。受影响妇女的血小板(PLT)计数也较低。我们的数据还表明,HGB 和 HCT 计数与 rs16991615 和 rs244715 显著相关。平均血小板体积(MPV)和血小板分布宽度(PDW)与 rs244715、rs1046089、rs4806660 和 rs2303369 相关。rs16991615 还与红细胞计数相关,而 rs451417 则与 NEUTs 相关。决策树(DT)模型显示,NEUT 数量的临界值小于 2.8 且 HCT 等于或大于 38.7% 的妇女可被确定为 POI 的高风险病例。总体而言,我们发现 DT 方法的灵敏度为 85%,特异度为 72%,准确度为 74%:结论:POI 所涉及的基因变异与生殖激素水平的变化和血液指标的变化有关。
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