Aim To systematically analyze the risk factors for comorbid ischemic cardiovascular and cerebrovas-cular diseases using principal component analysis.It seeks to identify key risk factors influencing comorbidity and to con-struct a predictive model to serve as a screening tool for the prevention and management of comorbidities.Methods This retrospective study included patients diagnosed with coronary artery disease from December 1,2009 to June 30,2020,at the First Affiliated Hospital of Xinjiang Medical University,using data from the hospital's integrated coronary heart dis-ease prevention platform.Patients were divided into two groups:those with both cardiovascular and cerebrovascular disea-ses and those with only coronary artery disease based on inclusion and exclusion criteria.Clinical indicators during hospi-tal admission were collected.The sample was randomly divided into a modeling set and a validation set in a 7∶3 ratio.In the modeling set,principal component analysis was used to explore the distribution differences in risk factors between the comorbid group and the single-disease group.By analyzing factor load contributions,the most significant variables were i-dentified.Logistic regression was then used to construct a predictive model,and a nomogram was generated.The model's predictive ability and robustness were evaluated in the validation set.Results A total of 11 808 participants were included,with 2 781(23.6%)in the comorbid group and 9 027(76.4%)in the coronary heart disease-only group.Compared with the single-disease group,the comorbid group had a higher average age,a greater proportion of females,and higher systolic blood pressure levels at admission(P<0.001).Additionally,thelevelsof hemoglobin,platelet distri-bution width,low density lipoprotein,and blood sodium were significantly higher in the comorbid group(P<0.05).A total of 8 265 participants were randomly assigned to the modeling set.Principal component analysis identified seven key factors with factor load contributions greater than 5:sodium level,systolic blood pressure,diastolic blood pressure,age,hemoglobin concentration,platelet distribution width,and total bilirubin level.Using these factors,a nomogram was con-structed via Logistic regression.The nomogram's area under the receiver operating characteristic curve for predicting co-morbid ischemic cardiovascular and cerebrovascular diseases was 0.630(95%CI:0.600～0.768,P<0.001).A total of 3 543 participants were randomly assigned to the validation set.In the validation set,the receiver operating characteristic curve was 0.628.Conclusion Elevated sodium level,higher systolic and diastolic blood pressure at admission,older age,increased hemoglobin concentration,higher platelet distribution width,and lower total bilirubin level are risk factors for comorbid ischemic cardiovascular and cerebrovascular diseases.The nomogram constructed has clinical value for screening patients with such comorbidities.

Aim To systematically analyze the risk factors for comorbid ischemic cardiovascular and cerebrovas-cular diseases using principal component analysis.It seeks to identify key risk factors influencing comorbidity and to con-struct a predictive model to serve as a screening tool for the prevention and management of comorbidities.Methods This retrospective study included patients diagnosed with coronary artery disease from December 1,2009 to June 30,2020,at the First Affiliated Hospital of Xinjiang Medical University,using data from the hospital's integrated coronary heart dis-ease prevention platform.Patients were divided into two groups:those with both cardiovascular and cerebrovascular disea-ses and those with only coronary artery disease based on inclusion and exclusion criteria.Clinical indicators during hospi-tal admission were collected.The sample was randomly divided into a modeling set and a validation set in a 7∶3 ratio.In the modeling set,principal component analysis was used to explore the distribution differences in risk factors between the comorbid group and the single-disease group.By analyzing factor load contributions,the most significant variables were i-dentified.Logistic regression was then used to construct a predictive model,and a nomogram was generated.The model's predictive ability and robustness were evaluated in the validation set.Results A total of 11 808 participants were included,with 2 781(23.6%)in the comorbid group and 9 027(76.4%)in the coronary heart disease-only group.Compared with the single-disease group,the comorbid group had a higher average age,a greater proportion of females,and higher systolic blood pressure levels at admission(P<0.001).Additionally,thelevelsof hemoglobin,platelet distri-bution width,low density lipoprotein,and blood sodium were significantly higher in the comorbid group(P<0.05).A total of 8 265 participants were randomly assigned to the modeling set.Principal component analysis identified seven key factors with factor load contributions greater than 5:sodium level,systolic blood pressure,diastolic blood pressure,age,hemoglobin concentration,platelet distribution width,and total bilirubin level.Using these factors,a nomogram was con-structed via Logistic regression.The nomogram's area under the receiver operating characteristic curve for predicting co-morbid ischemic cardiovascular and cerebrovascular diseases was 0.630(95%CI:0.600～0.768,P<0.001).A total of 3 543 participants were randomly assigned to the validation set.In the validation set,the receiver operating characteristic curve was 0.628.Conclusion Elevated sodium level,higher systolic and diastolic blood pressure at admission,older age,increased hemoglobin concentration,higher platelet distribution width,and lower total bilirubin level are risk factors for comorbid ischemic cardiovascular and cerebrovascular diseases.The nomogram constructed has clinical value for screening patients with such comorbidities.

Objective·To explore the relationship between sleep quality and carotid atherosclerosis in the population with low or moderate risk of cardiovascular and cerebrovascular diseases.Methods·Based on the population-based cohort study of chronic diseases in Xinjiang,the researchers selected residents aged 35-75 from two fixed communities in Urumqi and Korla,in Northern and Southern Xinjiang,respectively,using a two-stage random cluster sampling method from July 2019 to September 2021.In the population without a history of coronary heart disease and cerebrovascular events,the prediction model for atherosclerotic cardiovascular disease(ASCVD)risk in China(China-PAR)was used to evaluate the risk of cardiovascular and cerebrovascular diseases.Low and moderate risk population of cardiovascular and cerebrovascular diseases were included.Participants completed physical examinations,questionnaires[including the Risk Factors Assessment Scale of Cardiovascular and Cerebrovascular Diseases in Xinjiang,Pittsburgh Sleep Quality Index(PSQI),International Physical Activity Questionnaire(IPAQ),and Food Frequency Questionnaire],cardiovascular and metabolic biochemical examinations,and carotid color doppler.Carotid intima-media thickness(CIMT)and plaque formation were used to determine the carotid atherosclerosis of the study subjects.Multivariate Logistic regression model and restricted cubic spline(RCS)were used to analyze the relationship between sleep quality and carotid intima-media thickening/plaque formation in the population with low and moderate risk of cardiovascular and cerebrovascular diseases.Results·A total of 1 528 subjects were included in the study,the mean age was(49.4±8.2)years,and 685(44.8％)were male.In the included population,there were 581(38.0％)subjects with carotid intima-media thickening and 305(20.0％)subjects with carotid plaque formation.Among them,intima-media thickening and plaque formation both occurred in 154(10.1％)people.Therefore,the prevalence of carotid atherosclerosis was 47.9％(732 subjects).Compared with the group without carotid artery thickening,the group with carotid intima-media thickening/plaque formation had higher levels of general cardiovascular and cerebrovascular risk factors,including age,male ratio,blood lipid levels and obesity,and higher PSQ1 sleep score[(7.06±2.13)vs(7.43±2.51),P=0.001].The proportion of patients with poor sleep quality was higher(6.5％vs 12.1％,P=0.001).Multivariate Logistic regression analysis showed that poor sleep quality was an independent risk factor for carotid atherosclerosis[adjusted OR(aOR)=1.22,95％CI 1.004-1.492,P=0.040].RCS analysis suggested that PSQI and the risk of carotid atherosclerosis showed a positive linear correlation,that is,the worse quality of sleep,the higher risk of carotid atherosclerosis.Conclusion·Although the traditional metabolic risk factors are at a low risk level,the prevalence of carotid atherosclerosis is high and poor sleep quality is an independent risk factor for carotid atherosclerosis in the low and moderate risk population of cardiovascular and cerebrovascular diseases.

Objective: To explore the relationship between sleep/physical activity and metabolic syndrome (MS) in urban population of Xinjiang. Methods: This is a prospective, cross-sectional study. From July 2019 to September 2021, a two-stage random sampling method was used to randomly select residents aged 30-74 years from two communities in Urumqi of northern Xinjiang and Korla of southern Xinjiang. General situation questionnaire, Pittsburgh Sleep Quality Index Scale (PSQI) survey, International Physical Activity Questionnaire (IPAQ) survey, physical examination, physiological and biochemical indicators were obtained and analyzed. The dose-response curves of healthy sleep score and physical activity with metabolic syndrome were plotted using restricted cubic spline curves. Multivariate logistic regression model was used to analyze the independent and combined effects of sleep quality and physical activity on MS risk. Results: A total of 10 209 participants were included. The mean age of the subjects was (47.1±9.1) years, and males accounted for 51.3% (5 275/10 209). The prevalence of MS was significantly associated with the healthy sleep score and physical activity. Compared to the subjects with healthy sleep, OR (95%CI) of MS with intermediate, and poor sleep were 1.20(1.06-1.35), 1.23(1.04-1.45), respectively. Compared to the subjects with high physical activity, OR (95%CI) of MS with medium, low physical activity was 1.34(1.15-1.56), 1.42(1.19-1.70), respectively. There was a significant interaction between sleep and physical activity in MS (P for interaction=0.002). Compared to the subjects with high physical activity and healthy sleep, OR (95%CI) of MS with poor sleep and high physical activity was 2.03 (1.24-3.33, P for trend=0.016). Conclusion: Poor sleep quality and lack of physical activity are not only independent risk factors for an increased risk of MS but also have a combined effect with an increased risk of MS.
Male ; Humans ; Adult ; Middle Aged ; Sleep Quality ; Metabolic Syndrome/epidemiology* ; Urban Population ; Cross-Sectional Studies ; Prospective Studies ; Exercise

Objective: To explore the relationship between sleep/physical activity and metabolic syndrome (MS) in urban population of Xinjiang. Methods: This is a prospective, cross-sectional study. From July 2019 to September 2021, a two-stage random sampling method was used to randomly select residents aged 30-74 years from two communities in Urumqi of northern Xinjiang and Korla of southern Xinjiang. General situation questionnaire, Pittsburgh Sleep Quality Index Scale (PSQI) survey, International Physical Activity Questionnaire (IPAQ) survey, physical examination, physiological and biochemical indicators were obtained and analyzed. The dose-response curves of healthy sleep score and physical activity with metabolic syndrome were plotted using restricted cubic spline curves. Multivariate logistic regression model was used to analyze the independent and combined effects of sleep quality and physical activity on MS risk. Results: A total of 10 209 participants were included. The mean age of the subjects was (47.1±9.1) years, and males accounted for 51.3% (5 275/10 209). The prevalence of MS was significantly associated with the healthy sleep score and physical activity. Compared to the subjects with healthy sleep, OR (95%CI) of MS with intermediate, and poor sleep were 1.20(1.06-1.35), 1.23(1.04-1.45), respectively. Compared to the subjects with high physical activity, OR (95%CI) of MS with medium, low physical activity was 1.34(1.15-1.56), 1.42(1.19-1.70), respectively. There was a significant interaction between sleep and physical activity in MS (P for interaction=0.002). Compared to the subjects with high physical activity and healthy sleep, OR (95%CI) of MS with poor sleep and high physical activity was 2.03 (1.24-3.33, P for trend=0.016). Conclusion: Poor sleep quality and lack of physical activity are not only independent risk factors for an increased risk of MS but also have a combined effect with an increased risk of MS.
Male ; Humans ; Adult ; Middle Aged ; Sleep Quality ; Metabolic Syndrome/epidemiology* ; Urban Population ; Cross-Sectional Studies ; Prospective Studies ; Exercise