MetS is associated with cognitive impairment amongst elderly people in the Chinese population.

Background and purposeHuntington's disease (HD) is a dominantly inherited neurodegenerative disorder with varied prevalence in different populations, which may be associated with specific haplotypes. This study aimed to explore the haplotypes encompassing the HTT gene in the Chinese population.MethodsA total of 406 individuals with HD and 59 normal relatives from 253 families with HD were enrolled. A total of 29 tag single nucleotide polymorphisms (tSNPs) were selected and genotyped for the haplotype analysis.ResultsIn stage one, we used 18 tSNPs to replicate the distribution of three major haplogroups (A, B, C). We found that risk‐associated haplogroup variants A1 and A2, enriched on Caucasian HD chromosomes, were totally absent from both Chinese HD and control chromosomes, and the distributions of haplogroups between HD and control chromosomes were similar. Therefore, in stage two, we used 29 tSNPs (including the18 tSNPs) to define new haplogroups (I, II, III) and found that haplogroup I accounted for 61.4% on HD chromosomes and 34.4% on control chromosomes, indicating that haplogroup I was enriched on Chinese HD chromosomes.ConclusionsThis is the first haplotype analysis encompassing HTT in the Chinese population. The results contribute to explaining the low prevalence of HD in China and provide a better understanding of genetic diversity in the HTT region.

ObjectiveCognitive impairment is a detrimental complication of stroke that compromises the quality of life of the patients and poses a huge burden on society. Due to the lack of effective early prediction tools in clinical practice, many researchers have introduced machine learning (ML) into the prediction of post-stroke cognitive impairment (PSCI). However, the mathematical models for ML are diverse, and their accuracy remains highly contentious. Therefore, this study aimed to examine the efficiency of ML in the prediction of PSCI.MethodsRelevant articles were retrieved from Cochrane, Embase, PubMed, and Web of Science from the inception of each database to 5 December 2022. Study quality was evaluated by PROBAST, and c-index, sensitivity, specificity, and overall accuracy of the prediction models were meta-analyzed.ResultsA total of 21 articles involving 7,822 stroke patients (2,876 with PSCI) were included. The main modeling variables comprised age, gender, education level, stroke history, stroke severity, lesion volume, lesion site, stroke subtype, white matter hyperintensity (WMH), and vascular risk factors. The prediction models used were prediction nomograms constructed based on logistic regression. The pooled c-index, sensitivity, and specificity were 0.82 (95% CI 0.77–0.87), 0.77 (95% CI 0.72–0.80), and 0.80 (95% CI 0.71–0.86) in the training set, and 0.82 (95% CI 0.77–0.87), 0.82 (95% CI 0.70–0.90), and 0.80 (95% CI 0.68–0.82) in the validation set, respectively.ConclusionML is a potential tool for predicting PSCI and may be used to develop simple clinical scoring scales for subsequent clinical use.Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?RecordID=383476.