Modifiable and Non-Modifiable Risk Factors for Premature Coronary Heart Disease (PCHD): Systematic Review and Meta-Analysis

Prediction of coronary heart disease based on klotho levels using machine learning

The diagnostic accuracy for coronary heart disease (CHD) needs to be improved. Some studies have indicated that klotho protein levels upon admission comprise an independent risk factor for CHD and have clinical value for predicting CHD. This study aimed to construct a tool to predict CHD risk by analyzing klotho levels and clinically relevant indicators by using a machine learning (ML) method. We randomly assigned the dataset of the National Health and Nutrition Examination Survey (NHANES) 2007-2016 to training and test sets at a ratio of 70:30. We evaluated the ability of five models constructed using logistic regression, neural networks, random forest, support vector machine, and eXtreme Gradient Boosting to predict CHD. We determined their predictive performance using the following parameters: area under the receiver operating characteristic curve, accuracy, precision, recall, F1, and Brier scores. We analyzed data from 11,583 persons in US NHANES and entered 13 potential predictive variables, including klotho and other clinically relevant indicators, into the feature screening process. We established that the five ML models could predict the onset of CHD. The RF model showed the best predictive performance among the five ML models.

Association analysis of factors influencing psychosomatic diseases among middle-aged and elderly people in China

OBJECTIVE

This study aimed to explore the association between psychological factors and noncommunicable chronic diseases (NCDs).

METHODS

This study used CLHLS database and included a sample of 10,702 cases. Single-factor and multi-factor logistic regression analyses were used to determine the possible risk factors for developing psychosomatic diseases.

RESULTS

Univariate logistic regression found statistically significant associations between the development of psychosomatic disorders and depression, anxiety, sleep duration, and dyslipidemia (P < 0.05). Multivariate logistic regression concluded that depression, anxiety, sleep duration <7 h, and dyslipidemia were risk factors for developing psychosomatic disorders. Depression was a significant risk factor for the development of diabetes (OR = 1.419; 95 %CI: 1.034, 1.946), bronchitis (OR = 1.416; 95 %CI: 1.025, 1.956), gastrointestinal ulcers (OR = 1.541; 95 %CI: 1.223, 1.942), and arthritis (OR = 1.225; 95 %CI: 1.047, 1.433). Anxiety was a significant risk factor for developing bronchitis (OR = 1.240; 95 %CI: 1.061, 1.449), gastrointestinal ulcers (OR = 1.257; 95 %CI: 1.017, 1.554), and arthritis (OR = 1.303; 95 %CI: 1.123, 1.512). Sleep duration <7 h was associated with the development of diabetes (OR = 1.473; 95 %CI: 1.200, 1.808), bronchitis (OR = 1.213; 95 %CI: 1.011, 1.455), gastrointestinal ulcers (OR = 1.795; 95 %CI: 1.356, 2.377), and arthritis (OR = 1.532; 95 %CI: 1.272, 1.845). Dyslipidemia was a significant risk factor for developing diabetes (OR = 3.945; 95%CI: 3.254, 4.783), bronchitis (OR = 1.769; 95 %CI: 1.398, 2.239), gastrointestinal ulcers (OR = 3.244; 95 %CI: 2.491, 4.226), and arthritis (OR = 3.733; 95 %CI: 3.114, 4.571).

CONCLUSIONS

The monitoring of psychological factors as a prospective observation sentinel for NCDs prevention and control should be improved, the traditional intervention model of NCDs prevention and control should be changed.

Dyslipidemia Impacts Cardiometabolic Health and CVD Risk in a Relatively Young Otherwise Healthy Population

Dyslipidemia, abnormal levels of lipids in the bloodstream, is associated with cardiovascular disease risk (CVD). The purpose of this study was to evaluate the effects of dyslipidemia on cardiometabolic health in relatively young, healthy adults. Participants were 54 healthy males and females aged 18-60 years. Participants were assessed for anthropometrics, body composition, blood pressure and vascular stiffness indicators, microvascular health, and glucose and lipid levels. Using a cross-sectional approach, participants were characterized and grouped as having dyslipidemia or not, and then statistically assessed to determine whether differences in other cardiometabolic health measures existed between the groups. There were significant differences between groups for body weight and composition (total mass, muscle mass, visceral fat, bone mass, and body mass index, all, p < 0.027, Cohen's d > 0.605) with the dyslipidemia group being higher. There were significant differences between groups for peripheral and central blood pressures (all, p < 0.003, Cohen's d > 0.899), as well as for vascular stiffness indicators (pulse pressure, augmentation pressure, augmentation index, augmentation index 75) (all, p < 0.022, Cohen's d > 0.672) with elevations noted in the dyslipidemia group. Ten-year CVD risk was significantly different between groups, with an average risk of 0.8% in the normal lipids group and a risk of 5.4% in the dyslipidemia group (p < 0.001, Cohen's d = 1.260). However, there were no significant differences in macro- or micronutrient intake between groups (all, p > 0.166, Cohen's d < 0.412). There is a significant impact on cardiometabolic health in individuals with dyslipidemia who are otherwise healthy, which may increase individual risk for CVD. Trial Registration: ClinicalTrials.gov identifier: NCT06544915.

Apolipoprotein E E3/E4 genotype is associated with an increased risk of premature coronary artery disease

OBJECTIVE

Dyslipidemia is one of the causes of coronary heart disease (CAD), and apolipoprotein E (APOE) gene polymorphism affects lipid levels. However, the relationship between APOE gene polymorphisms and premature CAD (PCAD, male CAD patients with ≤ 55 years old and female with ≤ 65 years old) risk had different results in different studies. The aim of this study was to assess this relationship and to further evaluate the relationship between APOE gene polymorphisms and PCAD risk in the Hakka population.

METHODS

This study retrospectively analyzed 301 PCAD patients and 402 age matched controls without CAD. The APOE rs429358 and rs7412 polymorphisms were genotyped by polymerase chain reaction (PCR) -chip technique. The distribution of APOE genotypes and alleles between the case group and the control group was compared. The relationship between APOE genotypes and PCAD risk was obtained by logistic regression analysis.

RESULTS

The frequency of the APOE ɛ3/ɛ4 genotype (18.9% vs. 10.2%, p = 0.001) and ε4 allele (11.1% vs. 7.0%, p = 0.007) was higher in the PCAD patients than that in controls, respectively. PCAD patients with ɛ2 allele had higher TG level than those with ɛ3 allele, and controls carried ɛ2 allele had higher HDL-C level and lower LDL-C level than those carried ɛ3 allele. Regression logistic analysis showed that BMI ≥ 24.0 kg/m2 (BMI ≥ 24.0 kg/m2 vs. BMI 18.5-23.9 kg/m2, OR: 1.763, 95% CI: 1.235-2.516, p = 0.002), history of smoking (Yes vs. No, OR: 5.098, 95% CI: 2.910-8.930, p < 0.001), ɛ3/ɛ4 genotype (ɛ3/ɛ4 vs. ɛ3/ɛ3, OR: 2.203, 95% CI: 1.363-3.559, p = 0.001), ε4 allele (ε4 vs. ε3, OR: 2.125, 95% CI: 1.333-3.389, p = 0.002), and TC level (OR: 1.397, 95% CI: 1.023-1.910, p = 0.036) were associated with PCAD.

CONCLUSIONS

In summary, BMI ≥ 24.0 kg/m2, history of smoking, APOE ɛ3/ɛ4 genotype, and TC level were independent risk factors for PCAD. It means that young individuals who are overweight, have a history of smoking, and carried APOE ɛ3/ɛ4 genotype had increased risk of PCAD.