Current evidence shows that apolipoprotein E (APOE), apolipoprotein CI (APOC1) and low density lipoprotein receptor-related protein (LRP) variations are related to late-onset Alzheimer's disease. However, it remains unclear if genetic polymorphisms in these genes are associated with cognitive decline in late-onset Alzheimer's disease patients. We performed a 30-month longitudinal cohort study to investigate the relationship between Alzheimer's disease and APOE, APOC1, and LRP. In this study, 78 Chinese Han patients with late-onset Alzheimer's disease were recruited form Guangxi Zhuang Autonomous Region in China. APOE, APOC1, and LRP genotyping was performed using polymerase chain reaction-restriction fragment length polymorphisms. The Mini-Mental State Examination and Clinical Dementia Rating Scale were used to assess patients' cognitive function. After a 30-month follow-up period, we found a significant reduction in Mini-Mental State Examination total score, a higher proportion of patients fulfilling cognitive impairment progression criteria, and a higher proportion of APOC1 H2 carriers in APOE ε4 carriers compared with non-carriers. In addition, the APOE ε4 allele frequency was significantly higher in the cognitive impairment progression group compared with the non-cognitive impairment progression group. In conclusion, APOE ε4 plays an important role in augmenting cognitive decline, and APOC1 H2 may act synergistically with APOE ε4 in increasing the risk of cognitive decline in Chinese patients with late-onset Alzheimer's disease.

Taq IB polymorphism in the cholesteryl ester transfer protein (CETP) gene has been reported to be associated with serum high-density lipoprotein cholesterol (HDL-C) levels and longevity in several populations, but controversial results also arose probably due to racial/ethnic diversity. Bama is a remote and mountainous county located in the northwest of Guangxi, People's Republic of China, which has been well known for its longevity for centuries. The current study was to investigate the possible association of CETP Taq IB polymorphism with serum lipid levels and longevity in the Bama Zhuang population. The CETP Taq IB genotypes were determined by polymerase chain reaction and restriction fragment length polymorphism in 523 long-lived inhabitants (long-lived group, LG; aged 90-107 years) and 498 healthy controls without longevity family history (non-long-lived group, non-LG; aged 40-69 years) residing in Bama County. The levels of total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) were higher but TG, HDL-C/LDL-C ratio and the prevalence of dyslipidemia were lower in LG than in non-LG (P < 0.001 for all). There were no differences in the allelic and genotypic frequencies between the two groups (P > 0.05). Serum HDL-C levels and HDL-C/LDL-C ratio in LG were different among the genotypes (P < 0.01 for each), the subjects with B2B2 and B1B2 genotyes had higher HDL-C levels and HDL-C/LDL-C ratio than the subjects with B1B1genotye, whereas the levels of TC and HDL-C in non-LG were different among/between the genotypes (P < 0.01 for each), the B2 allele carriers had lower TC and higher HDL-C levels than the B2 allele noncarriers. Serum TG and HDL-C levels and HDL-C/LDL-C ratio were correlated with genotypes in LG, whereas serum TC and HDL-C levels were associated with genotypes in non-LG (P < 0.05-0.001). The association of CETP Taq IB polymorphism and serum lipid profiles is different between LG and non-LG in the Chinese Bama Zhuang population. CETP Taq IB polymorphism might be one of the longevity-related genetic factors in this population.

Background: The number of patients suffering from depression is continuously increasing in China. Demographic characteristics, physical health levels, and individual lifestyles/healthy behaviors are associated with the severity of depression. However, the major risk factor for depression remains unclear.Materials and methods: In this investigation, 16,512 patients were screened using the CHARLS (China Health and Retirement Longitudinal Study) database after being determined to be eligible based on the inclusion criteria. Depressive symptoms were evaluated through the CESD-10 (10-item Center for Epidemiological Studies Depression Scale). Consequently, various models were developed based on potential predictive factors, employing stepwise LR (Logistic Regression)/RF (Random Forests) models to examine the influence and weighting of candidate factors that affect depression.Results: Gender, residential address location, changes in health status following last interview, physical disabilities, chronic pain, childhood health status, ADL (activity of daily living), and social activity were all revealed to be independent risk factors for depression (p < 0.05) in this study. Depression has a synergic effect (across chronic pain and age groups). In comparison to other factors, RF results showed that chronic pain had a stronger impact on depression.Conclusion: This preliminary study reveals that chronic pain is a major risk factor for depression.

Rates of aging vary markedly among individuals, and biological age serves as a more reliable predictor of current health status than does chronological age. As such, the ability to predict biological age can support appropriate and timely active interventions aimed at improving coping with the aging process. However, the aging process is highly complex and multifactorial. Therefore, it is more scientific to construct a prediction model for biological age from multiple dimensions systematically. Physiological and biochemical parameters were evaluated to gage individual health status. Then, age-related indices were screened for inclusion in a model capable of predicting biological age. For subsequent modeling analyses, samples were divided into training and validation sets for subsequent deep learning model-based analyses (e.g. linear regression, lasso model, ridge regression, bayesian ridge regression, elasticity network, k-nearest neighbor, linear support vector machine, support vector machine, and decision tree models, and so on), with the model exhibiting the best ability to predict biological age thereby being identified. First, we defined the individual biological age according to the individual health status. Then, after 22 candidate indices (DNA methylation, leukocyte telomere length, and specific physiological and biochemical indicators) were screened for inclusion in a model capable of predicting biological age, 14 age-related indices and gender were used to construct a model via the Bagged Trees method, which was found to be the most reliable qualitative prediction model for biological age (accuracy=75.6%, AUC=0.84) by comparing 30 different classification algorithm models. The most reliable quantitative predictive model for biological age was found to be the model developed using the Rational Quadratic method (R²=0.85, RMSE=8.731 years) by comparing 24 regression algorithm models. Both qualitative model and quantitative model of biological age were successfully constructed from a multi-dimensional and systematic perspective. The predictive performance of our models was similar in both smaller and larger datasets, making it well-suited to predicting a given individual's biological age.

Background: The -493G/T polymorphism in the microsomal triglyceride transfer protein (MTP) gene is associated with lower serum low-density lipoprotein cholesterol (LDL-C) and triglyceride (TG) levels and longevity in several populations, but the results are inconsistent in different racial/ethnic groups. The current study was to investigate the plausible association of MTP -493G/T polymorphism with serum lipid levels and longevity in Zhuang long-lived families residing in Bama area, a famous home of longevity in Guangxi, China. Methods: The MTP -493G/T was genotyped by PCR-restriction fragment length polymorphism in 391 Bama Zhuang long-lived families (BLF, n = 1467, age 56.60 ± 29.43 years) and four control groups recruited from Bama and out-of-Bama area with or without a familial history of exceptional longevity: Bama non-long-lived families (BNLF, n = 586, age 44.81 ± 26.83 years), Bama non-Zhuang long-lived families (BNZLF, n = 444, age 52.09 ± 31.91 years), Pingguo long-lived families (PLF, n = 658, age 50.83 ± 30.30 years), and Pingguo non-long-lived families (PNLF, n = 539, age 38.74 ± 24.69 years). Correlation analyses between genotypes and serum lipid levels and longevity were then performed. Results: No particularly favorable lipoprotein and clinical phenotypes were seen in BLF as compared to general families in the same area. Instead, the levels of total cholesterol (TC), TG, LDL-C, and the prevalence of dyslipidemia were significantly higher in the three Bama families as compared to the two non-Bama families (P < 0.01 for all). There were no differences in the allelic and genotypic frequencies among the tested cohorts (P > 0.05 for all), but the TT genotype tended to enrich in the three long-lived cohorts from both areas. In addition, the individuals harboring TT genotype exhibited lower LDL-C and TC levels in the overall populations and Bama populations with a region- and sex-specific pattern. Multiple linear regression analyses unraveled that LDL-C levels were correlated with genotypes in Bama combined population, BNLF, and the total population (P < 0.05 for each) but not in Pingguo populations; TC and HDL-C levels were correlated with genotypes in Bama combined population and BLF, respectively (P < 0.05 for each). Conclusions: MTP -493G/T polymorphism may play an important role in fashioning the serum lipid profiles of Bama populations, despite no direct association between MTP -493G/T and longevity was detected.