AUTHOR=Li Weiqin , Du Yuexin , Feng Lingyan , Song Panpan , Wang Leishen , Zhang Shuang , Li Wei , Zhu Dandan , Liu Huikun TITLE=Genetic and non-genetic factors in prediction of early pubertal development in Chinese girls JOURNAL=Frontiers in Endocrinology VOLUME=15 YEAR=2024 URL=https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2024.1413528 DOI=10.3389/fendo.2024.1413528 ISSN=1664-2392 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%) (P=0.020), and the reclassification significantly improved, with NRI of 8.19% (P= 0.023) and IDI of 4.22% (P <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.