Genetic variation within the pri-let-7f-2 in the X chromosome predicting stroke risk in a Chinese Han population from Liaoning, China: From a case-control study to a new predictive nomogram

Yaxuan Wang; Luying Qiu; Yuye Wang; Zhiyi He; Xue Lan; Lei Cui; Yanzhe Wang

doi:10.3389/fmed.2022.936249

Genetic variation within the pri-let-7f-2 in the X chromosome predicting stroke risk in a Chinese Han population from Liaoning, China: From a case-control study to a new predictive nomogram

Front Med (Lausanne). 2022 Nov 30:9:936249. doi: 10.3389/fmed.2022.936249. eCollection 2022.

Authors

Yaxuan Wang¹, Luying Qiu², Yuye Wang², Zhiyi He², Xue Lan³, Lei Cui³, Yanzhe Wang²

Affiliations

¹ Department of Anesthesiology, The First Affiliated Hospital of China Medical University, Shenyang, China.
² Department of Neurology, Key Laboratory for Neurological Big Data of Liaoning Province, The First Affiliated Hospital of China Medical University, Shenyang, China.
³ School of Health Management, China Medical University, Shenyang, China.

Abstract

Background and objectives: Stroke is the most common cause of disability and the second cause of death worldwide. Therefore, there is a need to identify patients at risk of developing stroke. This case-control study aimed to create and verify a gender-specific genetic signature-based nomogram to facilitate the prediction of ischemic stroke (IS) risk using only easily available clinical variables.

Materials and methods: A total of 1,803 IS patients and 1,456 healthy controls from the Liaoning province in China (Han population) were included which randomly divided into training cohort (70%) and validation cohort (30%) using the sample function in R software. The distribution of the pri-let-7f-2 rs17276588 variant genotype was analyzed. Following genotyping analysis, statistical analysis was used to identify relevant features. The features identified from the multivariate logistic regression, the least absolute shrinkage and selection operator (LASSO) regression, and univariate regression were used to create a multivariate prediction nomogram model. A calibration curve was used to determine the discrimination accuracy of the model in the training and validation cohorts. External validity was also performed.

Results: The genotyping analysis identified the A allele as a potential risk factor for IS in both men and women. The nomogram identified the rs17276588 variant genotype and several clinical parameters, including age, diabetes mellitus, body mass index (BMI), hypertension, history of alcohol use, history of smoking, and hyperlipidemia as risk factors for developing IS. The calibration curves for the male and female models showed good consistency and applicability.

Conclusion: The pri-let-7f-2 rs17276588 variant genotype is highly linked to the incidence of IS in the northern Chinese Han population. The nomogram we devised, which combines genetic fingerprints and clinical data, has a lot of promise for predicting the risk of IS within the Chinese Han population.

Keywords: case-control study; ischemic stroke; nomogram; predictors; pri-let-7f-2; pri-microRNA; rs17276588; single nucleotide polymorphism (SNP).