(untitled) (Precision Medicine Communications, 2022)
The most common cause of disability and the second leading cause of death worldwide is stroke. Hence, the identification of patients at risk of having a stroke is of paramount importance. Wang and associates aimed to create a gender-specific method founded on genetic markers nomogram that would use easily available clinical lab data to predict patients at risk of Ischemic Stroke (IS). In their investigation, 1456 healthy volunteers and 1803 IS patients from the Han population of the Huang province of China were selected. They were divided into two major groups Training (70%) and Validation (30%). pri-let-7f-2 rs17276588 variant gene distribution was measured via statistical methods. A multivariate prediction nomogram was generated using characteristics from univariate regression and the least absolute shrinkage and selection operator (LASSO) regression. The genotyping results identified the A allele as a potential IS risk factor. Many clinical factors, body mass index (BMI), diabetes mellitus, smoking, history of alcohol abuse, hypertension, hyperlipidemia, and age, were identified as risk factors for IS by the nomogram. Moreover, the rs17276588 genotype was also determined to be a risk factor. The model calibration curves exhibited strong consistency and applicability. It was reasonably inferred that in the Northern Chinese Han population pri-let-7f-2 rs17276588 variant gene was associated with IS. The combination of relevant clinical data and genetic profiles in the shape of a nomogram bears a lot of promise for IS risk prediction in the Chinese Han population. Front. Med. (2022) DOI:.org/10.3389/fmed.2022.936249