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Chronic diseases have become one of the most important factors threatening human health. Subjective life expectancy (SLE) describes an individual's expectation or subjective perception of lifespan. This article aims to explore the relationship between chronic diseases and SLE, as well as the differences among different age groups and different types of chronic diseases in this relationship. China Health and Retirement Longitudinal Study (CHARLS) is a nationwide longitudinal study that evaluates the social, economic, and health conditions of middle-aged and older adult families and individuals aged 45 and above in China. In this study, CHARLS used probability proportional to size sampling (PPS sampling) to ensure the breadth and representativeness of the sample. This study selected cross-sectional data from CHARLS 2018, removed missing values, and obtained a valid sample of 10,658 middle-aged and older individuals, of whom 8564 had chronic diseases. After controlling demographic, health behavior, socioeconomic, psychological, and social security factors, an ordered logistic regression was performed to explore the relationship between chronic disease and SLE in middle-aged and older adults. The results show that chronic diseases negatively correlate with SLE in middle-aged and older adults. Middle-aged and older adults with chronic diseases are 36.2% less likely to have high life expectancy than those without chronic diseases. Many different types of chronic diseases are negatively correlated with SLE. Cancer is most negatively correlated with SLE, far exceeding other chronic diseases. Chronic disease and SLE of middle-aged and older adults have age-heterogeneous differences. For middle-aged people aged 45-59 and young older adults aged 60-79, there is a significant correlation between chronic diseases and SLE. However, there is no correlation between chronic diseases and subjective life expectancy in the older population aged 80 and above. The government and society should pay close attention to the prevention and treatment of chronic diseases among middle-aged and older adults and adjust policies and measures according to the population's age structure. In addition, the government and society should pay attention to the spiritual needs of middle-aged and older adults. The government and society should pay more attention to cancer patients. Finally, the scientific research team should also strengthen research on chronic diseases, research and development of specific drugs and vaccines, improve the cure rate of chronic diseases, promote people's health, and make people no longer afraid of diseases.
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BACKGROUND: In recent years, the incidence of rectal prolapse has increased significantly due to the sedentary lifestyle and irregular eating habits of modern life. However, there is a lack of clinical studies on the treatment of rectal prolapse with traditional Chinese medicine (TCM) with a large sample size. Therefore, this study investigated the characteristics of rectal prolapse treatment formulas and then studied the network pharmacology of their core therapeutic drugs, which can help to provide a reference for the treatment and postoperative care of rectal prolapse patients. OBJECTIVE: This study aimed to explore the prescription characteristics and the mechanism of action of core drugs in the treatment of rectal prolapse in Chinese medicine through data mining and bioinformatics techniques. METHODS: We collected the diagnosis and treatment information of patients with rectal prolapse from January 2014 to September 2021 in the electronic case database of Nanjing Hospital of TCM, mined the patient information and prescription features using R, screened the active ingredients of the core pairs of drugs and disease drug intersection targets using TCMSP and GnenCard databases, and constructed a Protein-protein interaction (PPI) network using STRING and Cytoscape, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of the intersecting targets were performed using Metascape and R. RESULTS: We found that prolapse is easy to occur in people over 50 years old, preferably in autumn and winter. Commonly used therapeutic Chinese medicines include Glycyrrhiza glabra, Radix angelicae sinensis, Radix astragali, Atractylodes macrocephala, and Pericarpium citri reticulatae, which are mostly deficiency tonic medicines, warm in nature, and belong to spleen meridian. The core therapeutic medicinal pair was "Bupleuri radix-Cimicifugae rhizoma". There were 190 common targets of Bupleuri radix and Cimicifugae rhizoma, and 71 intersection targets of the drug pair and prolapse. The main components of the core drugs for the treatment of prolapse may be quercetin, kaempferol, Stigmasterol, etc, and the core targets may be CASP3, AKT1, HIF1A, etc. The total number of GO entries for the intersection targets of "Bupleuri radix-Cimicifugae rhizoma" and diseases was 3495, among which the molecular functions accounted for the largest proportion, mainly Pathways in cancer, IL-18 signaling pathway, etc. KEGG enriched pathway analysis yielded 168 results, and the major pathways were pathways in cancer, lipid and atherosclerosis, IL-17 signaling pathway, etc. Conclusion: This study adopted real-world research methodology and used data mining and bioinformatics technology to mine the medication law of rectal prolapse and its core drug action mechanism from the clinical information of Chinese medicine.
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BACKGROUND: The objective of this research is to investigate the dynamic developmental trends between Age-Friendly Environments (AFE) and healthy aging in the Chinese population. METHODS: This study focused on a sample of 11,770 participants from the CHARLS and utilized the ATHLOS Healthy Aging Index to assess the level of healthy aging among the Chinese population. Linear mixed model (LMM) was used to explore the relationship between AFE and healthy aging. Furthermore, a cross-lagged panel model (CLPM) and a random-intercept cross-lagged panel model (RI-CLPM) were used to examine the dynamic developmental trends of healthy aging, taking into account both Between-Person effects and Within-Person effects. RESULTS: The results from LMM showed a positive correlation between AFE and healthy aging (ß = 0.087, p < 0.001). There was a positive interaction between the geographic distribution and AFE (central region * AFE: ß = 0.031, p = 0.038; eastern region * AFE: ß = 0.048, p = 0.003). In CLPM and RI-CLPM, the positive effect of healthy aging on AFE is a type of Between-Person effects (ß ranges from 0.147 to 0.159, p < 0.001), while the positive effect of AFE on healthy aging is Within-Person effects (ß ranges from 0.021 to 0.024, p = 0.004). CONCLUSION: Firstly, individuals with high levels of healthy aging are more inclined to actively participate in the development of appropriate AFE compared to those with low levels of healthy aging. Furthermore, by encouraging and guiding individuals to engage in activities that contribute to building appropriate AFE, can elevate their AFE levels beyond the previous average level, thereby improving their future healthy aging levels. Lastly, addressing vulnerable groups by reducing disparities and meeting their health needs effectively is crucial for fostering healthy aging in these populations.