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Clouds in the Sky
Soumyadutta Basak

Soumyadutta Basak 

Jr. Bioinformatician

Effects of social participation patterns and living arrangement on mental health of Chinese older adults: A latent class analysis.

Published on: May 22, 2024

Original author: Chen J, et al. (2022) (DOI: 10.3389/fpubh.2022.915541)

According to the World Health Organization, health is a complete state of physical, mental, and social well-being. Mental health is extremely important, especially for older people who, because of physical frailty and cognitive decline, frequently feel negative emotions like depression and loneliness. Positive emotions, in contrast, contribute to better physical functioning and longevity. To enhance older individuals' quality of life, it is crucial to research their mental health. Living arrangements and family have a big influence on the mental health of older people. Living alone makes one more vulnerable to unpleasant emotions while living with family often results in greater mental and physical health. In order to age healthily, one must participate in social activities that promote support and connections with others. It lowers the risk of depression, prevents social isolation, and improves general well-being. Most research has not made a distinction between different forms of social participation. In order to categorize older persons according to their social participation patterns, this study uses latent class analysis (LCA), and it then looks at the effects on mental health. The study intends to give a thorough understanding of the relationship between living arrangements, mental health, and social participation in older individuals, as well as the impact of individual characteristics, utilizing data from nationwide surveys called the Chinese Longitudinal Healthy Longevity Survey. Methods: The Chinese Longitudinal Healthy Longevity Survey (CLHLS) collected baseline data from 2537 Chinese elders who were 60 years of age or older in the waves of 2014 and 2018. The study then employed latent class analysis to ascertain social participation patterns. The association between older persons' sociodemographic and health factors and their patterns of social engagement was investigated using multinomial logistic regression. The differences and relationships between these categories and living arrangements on mental health status—which is reflected by good and negative emotions—were investigated using binary logistic regression. Results: Low activity (17.5%, n = 443), moderate activity (36.2%, n = 1,176), and high activity (46.3%, n = 918) were the three patterns of social participation that were identified. These patterns were substantially correlated with mental health conditions at baseline. Compared to the other two groups, the high-activity group had significantly better levels of both positive and negative emotions (OR = 1.36, 95% CI = 1.05–1.76 and OR = 1.50, 95% CI = 1.16–1.93). Only negative feelings were significantly impacted by living arrangements (OR=1.25, 95% CI = 1.02–1.53). Social participation patterns were influenced by several factors, including age, gender, education, marital status, self-rated health, and limitations on daily activities. Conclusion: There is a strong correlation between Chinese older citizens' living arrangements, social involvement patterns, and their mental health. Population-tailored interventions may help liberate older adults from domestic labor and improve social participation. Support from family members can also reduce the harm that negative emotions do as people age, improving their health.

Association between gut microbiota, and longevity: A genetic correlation, and Mendelian randomization study.

Published on: January 31, 2024

Original author: Dan He, et al. (2022) (DOI: 10.1186/s12866-022-02703-x)

Longevity is amongst the complex traits whose genetic foundation is yet unknown. The purpose of this study was to investigate the possible causal relationship and genetic correlation between gut microbiota and longevity. Numerous studies have been conducted on the relationship between gut microbiota and aging and multiple investigations have revealed a connection between aging and the gut microbiome's composition and metabolites, mainly through immune control mechanisms, nutritional signaling pathways, and epigenetic mechanisms. It is still unclear how the gut microbiota affects longevity-related features like healthspan, lifespan etc and longevity biologically and this study is being carried out to find out the answers about the genetic correlation between gut microbiome and longevity. Methodology: A lot of research has been done on candidate genetic variations for a variety of complex features and disorders, such as gut microbiota and longevity phenotypes, using genome-wide association studies, or GWAS. In the present study data has been analyzed to identify any association between the gut microbiome and longevity. The two major analyses employed in this study are LDSC regression analyses and Mendelian Randomization Analyses. LDSC analysis is used to find out the genetic correlation which is a novel technique for the understanding of heredity and pathogenesis mechanisms for complex diseases. One of the major benefits of using LDSC analysis is that it allows the use of summary statistics data instead of genotype data at the individual level. This simplifies the data analysis process, as most of the GWAS analyses offer summary statistical results. This is followed by MR analysis, an epidemiological method that allows an evaluation of possible causal relationships between exposures and outcomes using observational data. In contrast to the traditional epidemiological research, it is capable of preventing confounding variables from having an impact on the study's findings and reverse causality. This study utilized two-sample MR analysis and LDSC regression using GWAS data from European ancestry to investigate the relationships between gut microbiota and four longevity-related characteristics. Results: All of the GWAS data was obtained from publicly accessible databases, and various software and algorithms were employed to ensure quality assurance and address any gaps in the data. To avoid bias from varying imputation quality, the study only included data containing SNPs with imputation quality scores > 0.9 and MAF (Minor Allele Frequency) > 0.01. The LDSC analysis included 157 gut microbiota and three longevity-related phenotype data sets for the analysis. Four potential genetic associations were identified by LDSC analysis: Sporobacter for health span, Collinsella for parental lifespan, and Veillonella and Roseburia for longevity. Additional MR analysis revealed a possible causal relationship between Collinsella and the father's age of death, or parental longevity. Reverse MR research also found various causal impacts of longevity-related variables on gut microbiota, such as longevity and Sporobacter. The pleiotropic and heterogeneity tests' statistical insignificance confirmed the MR study's validity. Conclusion: Chronic diseases have become more common as the population ages, and this has put more strain on developing countries' healthcare systems. As a result, research into ways to prolong a healthy lifespan is now essential. Thus, this study examined the ways in which gut microbiota composition and structure are significantly influenced by lifespan, as demonstrated by reverse MR analysis that analyze the causal effects on traits associated with longevity. In conclusion, this study used LDSC regression and MR analysis of huge GWAS data to assess genetic linkage and causal connection between gut microbiota and longevity. The findings provide credence to the hypothesis that gut bacteria play a role in the evolution of lifespan.


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