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遗传和非遗传因素在预测中国女孩青春期早期发育中的作用。

Genetic and non-genetic factors in prediction of early pubertal development in Chinese girls.

机构信息

Institute of Maternal and Child Health, Tianjin Women and Children's Health Center, Tianjin, China.

Child Health Care, Tianjin Women and Children's Health Center, Tianjin, China.

出版信息

Front Endocrinol (Lausanne). 2024 Jul 1;15:1413528. doi: 10.3389/fendo.2024.1413528. eCollection 2024.

Abstract

OBJECTIVE

The objective of this study is to develop a combined predictive model for early pubertal development (EPD) in girls based on both non-genetic and genetic factors.

METHODS

The case-control study encompassed 147 girls diagnosed with EPD and 256 girls who exhibited normal pubertal development. The non-genetic risk score (NGRS) was calculated based on 6 independent biochemical predictors screened by multivariate logistic regressions, and the genetic risk score (GRS) was constructed using 28 EPD related single-nucleotide polymorphisms (SNPs). Area under receiver operator characteristic curve (AROC), net reclassification optimization index (NRI) and integration differentiation index (IDI) were used to evaluate the improvement of adding genetic variants to the non-genetic risk model.

RESULTS

Overweight (OR=2.74), longer electronic screen time (OR=1.79) and higher ratio of plastic bottled water (OR=1.01) were potential risk factors, and longer exercise time (OR=0.51) and longer day sleeping time (OR=0.97) were protective factors for EPD, and the AROC of NGRS model was 83.6% (79.3-87.9%). The GRS showed a significant association with EPD (OR=1.90), and the AROC of GRS model was 65.3% (59.7-70.8%). After adding GRS to the NGRS model, the AROC significantly increased to 85.7% (81.7-89.6%) (=0.020), and the reclassification significantly improved, with NRI of 8.19% (= 0.023) and IDI of 4.22% (0.001).

CONCLUSIONS

We established a combined prediction model of EPD in girls. Adding genetic variants to the non-genetic risk model brought modest improvement. However, the non-genetic factors such as overweight and living habits have higher predictive utility.

摘要

目的

本研究旨在建立一种基于非遗传和遗传因素的女孩早期青春期发育(EPD)综合预测模型。

方法

本病例对照研究纳入了 147 名确诊为 EPD 的女孩和 256 名表现为正常青春期发育的女孩。非遗传风险评分(NGRS)是根据多元逻辑回归筛选的 6 个独立生化预测因素计算得出的,遗传风险评分(GRS)是使用 28 个 EPD 相关单核苷酸多态性(SNP)构建的。接收者操作特征曲线下面积(AROC)、净重新分类优化指数(NRI)和综合区分度指数(IDI)用于评估遗传变异对非遗传风险模型的改善程度。

结果

超重(OR=2.74)、更长的电子屏幕时间(OR=1.79)和更高的塑料瓶装水比例(OR=1.01)是潜在的危险因素,而更长的运动时间(OR=0.51)和更长的白天睡眠时间(OR=0.97)是 EPD 的保护因素,NGRS 模型的 AROC 为 83.6%(79.3-87.9%)。GRS 与 EPD 显著相关(OR=1.90),GRS 模型的 AROC 为 65.3%(59.7-70.8%)。将 GRS 添加到 NGRS 模型后,AROC 显著增加至 85.7%(81.7-89.6%)(=0.020),重新分类明显改善,NRI 为 8.19%(=0.023),IDI 为 4.22%(0.001)。

结论

我们建立了一种女孩 EPD 的综合预测模型。将遗传变异添加到非遗传风险模型中带来了适度的改善。然而,超重和生活习惯等非遗传因素具有更高的预测效用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f4ac/11246873/00c5ad4e691d/fendo-15-1413528-g001.jpg

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