Middle-aged Adult Population Research Articles

Combined effect of triglyceride glucose-body mass index and hypertension on new-onset stroke: evidence from the China health and retirement longitudinal study.

The aim of this study is to investigate the combined impact of the triglyceride glucose-body mass index (TyG-BMI) and hypertension on the risk of stroke among the middle-aged and older adult population in China. This study included 6,922 participants aged 45 and above from the China Health and Retirement Longitudinal Study, utilizing a multivariate Cox proportional hazards regression model to explore the relationship between TyG-BMI, hypertension, and the incidence of new-onset stroke events, as well as conducting Net Reclassification Improvement (NRI) and Integrated Discrimination Improvement (IDI) analyses to evaluate the predictive utility of TyG-BMI. During a 7-year follow-up period, a total of 401 stroke events were recorded. Compared to patients with lower TyG-BMI (TyG-BMI < 199.74) levels and non-hypertension, those with elevated TyG-BMI levels and non-hypertension had an adjusted hazard ratio (HR) and 95% confidence intervals (95%CI) were 1.47 (1.05-2.05). The adjusted HR and 95%CI for the group with lower TyG-BMI levels and hypertension was 2.99 (2.17-4.12), and for those with elevated TyG-BMI levels and hypertension, the adjusted HR and 95%CI was 3.49 (2.63-4.62). In a multivariate Cox proportional hazards regression model, the combination of elevated TyG-BMI levels and hypertension, treated as routine variables, was still significantly associated with the risk of stroke. NRI and IDI analyses showed significant improvements in risk prediction with the inclusion of TyG-BMI. Furthermore, in all subgroup analyses conducted, individuals with elevated TyG-BMI levels and hypertension nearly exhibited the highest risk for incident stroke. Our study reveals that the combined effect of TyG-BMI and hypertension may increase the risk of incident stroke in the middle-aged and older adult Chinese population. TyG-BMI correlates with comorbid conditions and enhances traditional risk assessment. Future research will require validation through larger sample sizes or diverse populations to further confirm this finding.

Health literacy demand and attitudes toward COVID-19 prevention measures among Korean American older adults and their caregivers

BackgroundHealth literacy has been linked to positive attitudes toward COVID-19 preventive measures among adolescents and young- or middle-aged adult populations. This study examined the relationship between health literacy and attitudes toward COVID-19 preventive measures among non-English speaking Korean American older adults and their caregivers. The study additionally investigated how sociodemographic characteristics were associated with attitudes.MethodsCOVID-19 survey data was collected from potential participants for an ongoing randomized controlled trial involving both Korean American older adults and their caregivers in the Baltimore-Washington and the New York Metropolitan areas (ClinicalTrials.gov Identifier: NCT03909347). Korean American older adults with normal cognition and their caregivers were allowed to participate in the survey. We used latent profile analysis to find unique clusters of participants with a similar pattern of responses to attitudes toward COVID-19 preventive measures. Based on the analysis, we employed multinomial logistic regression to investigate how health literacy and sociodemographic characteristics were associated with the clusters.ResultsWe found three clusters based on participant responses to COVID-19 preventive measures—Positive, Negative, or Mixed. Health literacy was not associated with COVID-19 related attitudes in the study sample. Men were 2.37 times more likely to be categorized as Mixed than having Positive Attitudes compared to women. The odds of a person living in the New York metropolitan area being categorized as having Mixed Attitudes compared to Positive Attitudes were also 2.67 times more than for a person living in the Baltimore-Washington area.ConclusionsDifferences in attitudes toward COVID-19 preventive measures were found among sociodemographic variables but not health literacy. Investigating what information channels or methods drive perception of public health information such as COVID-19 may help identify effective dissemination strategies for non-English speaking Korean older adults.

Relationships between altitude and depressive symptoms among middle-aged and older adults in China: a longitudinal study from the China health and retirement longitudinal study.

Previous studies have consistently documented positive associations between depressive symptoms and altitude; however, a longitudinal study of these relationships among middle-aged and older adult community populations in China has not been previously reported. We screened 17,705 subjects who met the inclusion criteria from the China Health and Retirement Longitudinal Study database 2011. The altitude is the altitude at which the participants lived in our study area. We estimated the prospective associations between altitude and depressive symptoms among middle-aged and older adults. The 10-item Center for Epidemiological Studies Depression Scale short form was used to measure depressive symptoms. A total of 6,594 participants without depressive symptoms were recruited from the same cohort in 2011 and were followed up in 2018. Multivariate logistic regression was used to assess the associations between altitude and depressive symptoms among middle-aged and older adults. The prevalence of depressive symptoms was 27.3% in our study. The prevalence of depressive symptoms increased with increasing altitude, and the prevalence of depressive symptoms in women was greater than that in men. Multivariate logistic regression adjusted for all other relevant variables showed that sex, altitude, education level, professional status and marital status were associated with depressive symptoms. This finding provides evidence of the relationship between altitude and depressive symptoms among middle-aged and older adult community populations in China and shows that depressive symptoms are significantly positively correlated with altitude and other factors, including sex, education level, professional status, and marital status.

Determinants of Visual Impairment Among Chinese Middle-Aged and Older Adults: Risk Prediction Model Using Machine Learning Algorithms.

Visual impairment (VI) is a prevalent global health issue, affecting over 2.2 billion people worldwide, with nearly half of the Chinese population aged 60 years and older being affected. Early detection of high-risk VI is essential for preventing irreversible vision loss among Chinese middle-aged and older adults. While machine learning (ML) algorithms exhibit significant predictive advantages, their application in predicting VI risk among the general middle-aged and older adult population in China remains limited. This study aimed to predict VI and identify its determinants using ML algorithms. We used 19,047 participants from 4 waves of the China Health and Retirement Longitudinal Study (CHARLS) that were conducted between 2011 and 2018. To envisage the prevalence of VI, we generated a geographical distribution map. Additionally, we constructed a model using indicators of a self-reported questionnaire, a physical examination, and blood biomarkers as predictors. Multiple ML algorithms, including gradient boosting machine, distributed random forest, the generalized linear model, deep learning, and stacked ensemble, were used for prediction. We plotted receiver operating characteristic and calibration curves to assess the predictive performance. Variable importance analysis was used to identify key predictors. Among all participants, 33.9% (6449/19,047) had VI. Qinghai, Chongqing, Anhui, and Sichuan showed the highest VI rates, while Beijing and Xinjiang had the lowest. The generalized linear model, gradient boosting machine, and stacked ensemble achieved acceptable area under curve values of 0.706, 0.710, and 0.715, respectively, with the stacked ensemble performing best. Key predictors included hearing impairment, self-expectation of health status, pain, age, hand grip strength, depression, night sleep duration, high-density lipoprotein cholesterol, and arthritis or rheumatism. Nearly one-third of middle-aged and older adults in China had VI. The prevalence of VI shows regional variations, but there are no distinct east-west or north-south distribution differences. ML algorithms demonstrate accurate predictive capabilities for VI. The combination of prediction models and variable importance analysis provides valuable insights for the early identification and intervention of VI among Chinese middle-aged and older adults.

Exploring key factors influencing depressive symptoms among middle-aged and elderly adult population: A machine learning-based method

ObjectiveThis paper aims to investigate the key factors, including demographics, socioeconomics, physical well-being, lifestyle, daily activities and loneliness that can impact depressive symptoms in the middle-aged and elderly population using machine learning techniques. By identifying the most important predictors of depressive symptoms through the analysis, the findings can have important implications for early depression detection and intervention. ParticipantsFor our cross-sectional study, we recruited a total of 976 volunteers, with a specific focus on individuals aged 50 and above. Each participant was requested to provide their demographic, socioeconomic information and undergo several physical health tests. Additionally, they were asked to respond to questionnaires that assessed their mental well-being. Furthermore, participants were requested to maintain an activity log for a continuous 14-day period, starting from the day after they signed up. They had the option to use either a provided mobile application or paper to record their activities. MethodsWe evaluated multiple machine learning models to find the best-performing one. Subsequently, we conducted post-hoc analysis to extract the variable significance from the selected model to gain deeper insights into the factors influencing depression. ResultsLogistic Regression was chosen as it exhibited superior performance across other models, with AUC of 0.807 ± 0.038, accuracy of 0.798 ± 0.048, specificity of 0.795 ± 0.061, sensitivity of 0.819 ± 0.097, NPV of 0.972 ± 0.013 and PPV of 0.359 ± 0.064. The top influential predictors identified in the model included loneliness, health indicator (i.e. frailty, eyesight, functional mobility), time spent on activities (i.e. staying home, doing exercises and visiting friends) and perceived income adequacy. ConclusionThese findings have the potential to identify individuals at risk of depression and prioritize interventions based on the influential factors. The amount of time dedicated to daily activities emerges as a significant indicator of depression risk among middle-aged and elderly individuals, along with loneliness, physical health indicators and perceived income adequacy.

Effectiveness of Catch-Up Vaccination Interventions Versus Standard or Usual Care Procedures in Increasing Adherence to Recommended Vaccinations Among Different Age Groups: Systematic Review and Meta-Analysis of Randomized Controlled Trials and Before-After Studies.

To address the global challenge of vaccine hesitancy, the Strategic Advisory Group of Experts on Immunization strongly promotes vaccination reminder and recall interventions. Coupled with the new opportunities presented by scientific advancements, these measures are crucial for successfully immunizing target population groups. This systematic review and meta-analysis aims to assess the effectiveness of various interventions in increasing vaccination coverage compared with standard or usual care. The review will cover all vaccinations recommended for different age groups. In February 2022, 2 databases were consulted, retrieving 1850 studies. Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, 79 manuscripts were included after the assessment phase. These comprised 46 trials/randomized controlled trials (RCTs) and 33 before-after studies. A meta-analysis using a random-effects model was performed with STATA software (version 14.1.2). The selected outcome was the risk ratio (RR) of vaccination coverage improvement effectiveness. Additionally, meta-regression analyses were conducted for the included manuscripts. The analyses showed an overall efficacy of RR 1.22 (95% CI 1.19-1.26) for RCTs and RR 1.70 (95% CI 1.54-1.87) for before-after studies when considering all interventions cumulatively. Subgroup analyses identified multicomponent interventions (RR 1.58, 95% CI 1.36-1.85) and recall clinical interventions (RR 1.24, 95% CI 1.17-1.32) as the most effective in increasing vaccination coverage for RCTs. By contrast, educational interventions (RR 2.13, 95% CI 1.60-2.83) and multicomponent interventions (RR 1.61, 95% CI 1.43-1.82) achieved the highest increases for before-after studies. Meta-regression analyses indicated that the middle-aged adult population was associated with a higher increase in vaccination coverage (RCT: coefficient 0.54, 95% CI 0.12-0.95; before-after: coefficient 1.27, 95% CI 0.70-1.84). Community, family, and health care-based multidimensional interventions, as well as education-based catch-up strategies, effectively improve vaccination coverage. Therefore, their systematic implementation is highly relevant for targeting undervaccinated population groups. This approach aligns with national vaccination schedules and aims to eliminate or eradicate vaccine-preventable diseases.

Association between cadmium exposure and serum neurofilament light chain levels: A nationwide population-based survey

BackgroundAlthough cadmium exposure had been demonstrated to be toxic to the nervous system, little was known about the link between cadmium exposure and axonal injury. Therefore, the present study aimed to reveal whether there was any correlation between blood cadmium and serum neurofilament light chain (NfL) levels in the general population. MethodsThis study included 1040 participants with a median (IQR) age of 47 (35−60) years from the 2013–2014 National Health and Nutrition Examination Survey. Serum NfL levels were measured through immunoassay, and whole blood cadmium concentrations were detected by means of inductively coupled plasma mass spectrometry. Linear regression and restricted cubic spline model was applied to analyze the significance of relationship between blood cadmium and serum NfL levels. ResultsIn the full adjusted model, blood cadmium levels were found to be positively associated with serum NfL levels (Q4 vs Q1, β = 3.35, 95 %CI: 0.41, 6.30, p for trend = 0.014). A potential linear positive dose-effect relationship was discovered between blood cadmium and serum NfL levels (p for non-linearity = 0.15). According to the result of stratified analysis, the significant positive relationship between blood cadmium and serum NfL levels was present only in the population of middle-aged and older adults. ConclusionThe present study suggested a positive association between blood cadmium and serum NfL levels in the general US population.

Metabolic Score for Insulin Resistance and New-Onset Type 2 Diabetes in a Middle-Aged and Older Adult Population: Nationwide Prospective Cohort Study and Implications for Primary Care.

The metabolic score for insulin resistance (METS-IR) has emerged as a noninsulin-based index for the approximation of insulin resistance (IR), yet longitudinal evidence supporting the utility of METS-IR in the primary prevention of type 2 diabetes mellitus (T2DM) remains limited. We aimed to investigate the longitudinal association between METS-IR, which combines fasting plasma glucose (FPG), lipid profiles, and anthropometrics that can be routinely obtained in resource-limited primary care settings, and the incidence of new-onset T2DM. We conducted a closed-cohort analysis of a nationwide, prospective cohort of 7583 Chinese middle-aged and older adults who were free of T2DM at baseline, sampled from 28 out of 31 provinces in China. We examined the characteristics of participants stratified by elevated blood pressure (BP) at baseline and new-onset T2DM at follow-up. We performed Cox proportional hazard regression analysis to explore associations of baseline METS-IR with incident T2DM in participants overall and in participants stratified by baseline BP. We also applied net reclassification improvement and integrated discrimination improvement to examine the incremental value of METS-IR. During a mean follow-up period of 6.3 years, T2DM occurred in 527 participants, among which two-thirds (332/527, 62.9%; 95% CI 58.7%-67.1%) had baseline FPG<110 mg/dL. A SD unit increase in baseline METS-IR was associated with the first incidence of T2DM (adjusted hazard ratio [aHR] 1.33, 95% CI 1.22-1.45; P<.001) in all participants. We obtained similar results in participants with normal baseline BP (aHR 1.41, 95% CI 1.22-1.62; P<.001) and elevated baseline BP (aHR 1.29, 95% CI 1.16-1.44; P<.001). The predictive capability for incident T2DM was improved by adding METS-IR to FPG. In study participants with new-onset T2DM whose baseline FPG was <126 mg/dL and <110 mg/dL, 62.9% (332/527; 95% CI 60%-65.9%) and 58.1% (193/332; 95% CI 54.3%-61.9%) of participants had baseline METS-IR above the cutoff values, respectively. METS-IR was significantly associated with new-onset T2DM, regardless of baseline BP level. Regular monitoring of METS-IR on top of routine blood glucose in clinical practice may add to the ability to enhance the early identification of primary care populations at risk for T2DM.

Sleep duration and the risk of new-onset arthritis in middle-aged and older adult population: results from prospective cohort study in China.

The pain and sleep disorders caused by arthritis are health issues that have been re-emphasized with the aging population. However, the majority of research on arthritis and sleep disorders has focused on cases that have already been diagnosed with arthritis. This research aims to explore the correlation between sleep duration and new-onset arthritis in middle-aged and older adult individuals. Utilizing data from the China Health and Retirement Longitudinal Study from baseline (2011) to the Wave 3 follow-up (2018), we conducted a 7-year longitudinal investigation targeting populations with valid sleep questionnaire records and without arthritis. Sleep duration was assessed from nighttime sleep and daytime nap records. The new-onset of arthritis was determined based on self-reported diagnosis. We employed different logistic regression models to consider the potential impact of sleep duration on arthritis and conducted mediation analyses to assess the involvement of BMI in the association between sleep duration and the new-onset risk of arthritis. Out of the 6,597 individuals analyzed in the cohort, 586 (8.9%) were diagnosed with new-onset arthritis. Median sleep duration was notably shorter in the new-onset arthritis group (6.63 vs. 6.41 h, p < 0.05). There was a notable negative correlation found between new-onset risk of arthritis and sleep duration, with each Interquartile Range (IQR) increment in sleep leading to a 16% risk reduction (OR: 0.864; 95% CI: 0.784-0.954). Stratified analyses revealed BMI as a potential modifier in the sleep-arthritis relationship (P for interaction = 0.05). Mediation analyses further showed that about 3.5% of the association was mediated by BMI. Additionally, the inclusion of sleep duration improved the arthritis predictive power of our model, with an IDI of 0.105 (0.0203, 0.1898) and an NRI of 0.0013 (0.0004, 0.0022) after adding sleep duration to the basic model. In the middle-aged and older adult demographic of China, increased sleep duration is associated with a decreased new-onset risk of arthritis, with BMI potentially playing a role in mediating this connection.

Correlation of cardiometabolic index and sarcopenia with cardiometabolic multimorbidity in middle-aged and older adult: a prospective study.

Research has demonstrated that sarcopenia and visceral obesity are significant risk factors for chronic disease in middle-aged and older adults. However, the relationship between sarcopenia, the cardiac metabolic index (CMI), a novel measure of visceral obesity, and cardiometabolic multimorbidity (CMM) remains unclear. In this study, data from the China Longitudinal Study of Health and Retirement (CHARLS) were analyzed to investigate the association between sarcopenia and CMI with CMM in the middle-aged and older adult population. The study included 4,959 participants aged 45 and over. Sarcopenia was defined using the criteria of the Asian Sarcopenia Working Group 2019. CMM is defined as having two or more of the following conditions: physician-diagnosed heart disease, diabetes, stroke, and/or hypertension. CMI was calculated using the formula: CMI = (TG/HDL-C) × WHtR. To explore the association between CMI and sarcopenia and CMM, cox proportional risk regression models were used. The median age of all participants was 57 years, with 47.1% being male. Over the 8-year follow-up, 1,362 individuals developed CMM. The incidence of CMM was 8.7/1,000 person-years in the group without sarcopenia or high CMI, 17.37/1,000 person-years in those with high CMI, 14.22/1,000 person-years in the sarcopenia group, and 22.34/1,000 person-years in the group with both conditions. After adjusting for covariates, the group with both sarcopenia and high CMI had a significantly increased risk of CMM (HR 2.48, 95% CI 1.12-5.51) and heart disease (HR 2.04, 95% CI 1.05-3.98). Among those over 65 years, sarcopenia was discovered to be associated with an increased risk of CMM [HR (95% CI: 4.83 (1.22, 19.06)]. The risk of CMM was further increased to 7.31-fold (95% CI:1.72, 31.15) when combined with high CMI. The combination of sarcopenia and high CMI is associated with an increased risk of developing CMM. Early identification and intervention of sarcopenia and CMI not only enable the development of targeted therapeutic strategies but also provide potential opportunities to reduce the morbidity and mortality of CMM.

Primary Arterial Hypertension Associated with Cognitive Dysfunction in Young Adults: Results from a Cross-Sectional Controlled Study.

Despite evidence of primary hypertension (PH)-associated cognitive dysfunction in pediatric, middle-aged, and older adult populations, respective data in young adults remains scarce. We aimed to define differences in cognitive performance between individuals with PH and healthy controls in early adulthood. A convenience sample of young adults (age 18-45 years) with PH and their healthy sex, age, education, and household income matched counterparts were cross-sectionally tested for verbal fluency, verbal memory, general intelligence, reaction speed, attention, visual memory, and executive functioning. Between-group differences were determined using Student's t and Mann-Whitney U tests. Sensitivity analysis was performed by adjusting for body mass index (BMI) in analysis of covariance (ANCOVA) and regression models. Thirty-three adults with PH (22, 66.7% male, median age 38.8, interquartile range (IQR) = 33.2-41.6 years) and twenty-two healthy controls (9, 40.9% male, median age 36.1, IQR = 27.5-39.8 years) completed the neuropsychological assessment. Participants with PH performed worse on computerized tasks of reaction time (median response time (Z = -2.019, p = 0.044), median time for release of response button (Z = -2.509, p = 0.012)) and sustained attention (signal detection measure, RVPA (t = 2.373, p = 0.021), false alarms ÷ (false alarms + correct rejections), RVPPFA (Z = -2.052, p = 0.040)). The group variable was not a statistically significant predictor of performance in these domains after adjustment for BMI (p > 0.05). In regression analyses, high office systolic blood pressure (oSBP) was independently associated with poor sustained attention (βSBP(st.) = -0.283, multiple R2 = 0.252 (RVPA), βSBP(st.) = 0.551, multiple R2 = 0.386 (RVPPFA)). Young adults with PH were found to perform worse in tasks of response speed and sustained attention. While the difference between neuropsychological evaluation results in PH and control groups was confounded by BMI, oSBP measures were independently related to sustained attention. The selectivity of PH-associated cognitive profile in young adults has to be confirmed in larger trials.

Post-COVID-19 Effects & Prevalence of Healthier Lifestyle Behaviors Across Young to Middle-Aged Adult Population

Post-COVID-19 Effects & Prevalence of Healthier Lifestyle Behaviors Across Young to Middle-Aged Adult Population

Linking sarcopenia, brain structure and cognitive performance: a large-scale UK Biobank study.

Sarcopenia refers to age-related loss of muscle mass and function and is related to impaired somatic and brain health, including cognitive decline and Alzheimer's disease. However, the relationships between sarcopenia, brain structure and cognition are poorly understood. Here, we investigate the associations between sarcopenic traits, brain structure and cognitive performance. We included 33 709 UK Biobank participants (54.2% female; age range 44-82 years) with structural and diffusion magnetic resonance imaging, thigh muscle fat infiltration (n = 30 561) from whole-body magnetic resonance imaging (muscle quality indicator) and general cognitive performance as indicated by the first principal component of a principal component analysis across multiple cognitive tests (n = 22 530). Of these, 1703 participants qualified for probable sarcopenia based on low handgrip strength, and we assigned the remaining 32 006 participants to the non-sarcopenia group. We used multiple linear regression to test how sarcopenic traits (probable sarcopenia versus non-sarcopenia and percentage of thigh muscle fat infiltration) relate to cognitive performance and brain structure (cortical thickness and area, white matter fractional anisotropy and deep and lower brain volumes). Next, we used structural equation modelling to test whether brain structure mediated the association between sarcopenic and cognitive traits. We adjusted all statistical analyses for confounders. We show that sarcopenic traits (probable sarcopenia versus non-sarcopenia and muscle fat infiltration) are significantly associated with lower cognitive performance and various brain magnetic resonance imaging measures. In probable sarcopenia, for the included brain regions, we observed widespread significant lower white matter fractional anisotropy (77.1% of tracts), predominantly lower regional brain volumes (61.3% of volumes) and thinner cortical thickness (37.9% of parcellations), with |r| effect sizes in (0.02, 0.06) and P-values in (0.0002, 4.2e-29). In contrast, we observed significant associations between higher muscle fat infiltration and widespread thinner cortical thickness (76.5% of parcellations), lower white matter fractional anisotropy (62.5% of tracts) and predominantly lower brain volumes (35.5% of volumes), with |r| effect sizes in (0.02, 0.07) and P-values in (0.0002, 1.9e-31). The regions showing the most significant effect sizes across the cortex, white matter and volumes were of the sensorimotor system. Structural equation modelling analysis revealed that sensorimotor brain regions mediate the link between sarcopenic and cognitive traits [probable sarcopenia: P-values in (0.0001, 1.0e-11); muscle fat infiltration: P-values in (7.7e-05, 1.7e-12)]. Our findings show significant associations between sarcopenic traits, brain structure and cognitive performance in a middle-aged and older adult population. Mediation analyses suggest that regional brain structure mediates the association between sarcopenic and cognitive traits, with potential implications for dementia development and prevention.

Does Pre-existing Diabetes Correlate with Long COVID-19 in Europe? Evidence from the Analysis of the Survey of Health, Ageing and Retirement in Europe's Corona Surveys.

A substantial proportion of those infected with COVID-19 are presenting with persistent symptoms, referred to as long COVID-19. Emerging evidence suggests that the presence of pre-existing chronic conditions, such as diabetes, may increase the risk of long COVID-19. To investigate whether having pre-existing diabetes increases the risk of developing long COVID-19 in the population of middle-aged and older adults (≥50 years old) in Europe, while assessing if this relationship can be accounted for or is modified by the known long COVID-19 and diabetes risk factors (age, sex, hospitalization, pre-existing hypertension, and weight status). A population-based longitudinal prospective study involving a sample of respondents aged 50 years and older (n = 4,004) with probable or confirmed COVID-19 infection from 27 countries that participated in both waves 7 and 8 of the Survey of Health, Ageing and Retirement in Europe and its 2020 and 2021 Corona Surveys. Logistic regression modeling was performed. Overall, 66.8% of the respondents affected by COVID-19 infection reported at least one long COVID-19 symptom; 55.2% were female, and the average age was 64.6 years; 13.2% had pre-existing diabetes. Respondents with pre-existing diabetes had significantly higher odds of developing long COVID-19, compared to those without diabetes (OR = 1.37; 95% CI = 1.12, 1.68). This relationship remained significant (OR = 2.00; 98% CI = 0.25, 1.14) after adjusting for sex (OR = 1.64 for females; 95% CI = 1.43, 1.88), hospitalization for COVID-19 illness (OR = 3.19; 95% CI = 2.41, 4.23), pre-existing hypertension (OR = 1.17; 95% CI = 1.01, 1.36), and overweight (OR = 1.31; 95% CI = 1.11, 1.56) and obese (OR = 1.77; 95% CI = 1.44, 2.19) weight status. The effect of pre-existing diabetes on the risk of long COVID-19 is moderated by age; it was highest at the age of 50 (OR = 2.00; 95% CI = 1.28, 3.14), and then, it declined with age. There is a relationship between pre-existing diabetes and long COVID-19, even after controlling for literature-based confounding factors, with age having a moderating effect on this relationship.

Cardiovascular health awareness, risk perception, behavioural intention and INTERHEART risk stratification among middle-aged adults in Malaysia

Objective: To investigate the interrelationship between cardiovascular health awareness, risk perception, behavioural intention, and INTERHEART risk stratification in a middle-aged adult population in Malaysia. Methods: A cross-sectional survey with convenience sampling was conducted during November 2022 and January 2023. Participants completed validated questionnaires assessing cardiovascular health awareness, risk perception of cardiovascular diseases, behavioural intention towards adopting healthy habits, and INTERHEART risk stratification score (IHRS) based on established risk factors. A total of 602 respondents were included in the final analysis. Data were analysed with independent t-test/one-way ANOVA or Mann- Whitney/Kruskai-Wailis to test the differences, Pearson correlation or linear regression test to analyze the association of independent and dependent variables. Results: There was a significant positive correlation between medical knowledge related to cardiovascular disease (CVD) and knowledge related to CVD risk prevention, risk perception, behavioural intention and IHRS (P&lt;0.05, Pearson correlation). Notably, individuals with higher IHRS tended to have lower knowledge related to CVD and CVD risk prevention, risk perception, and behavioural intention. Males, laborers, active/former smokers, individuals with lower household income and educational levels, those involved in occupations not related to the healthcare sector, and those who did not receive the CVD health brochure or are unaware of health self-assessment tools are likely to have lower levels of knowledge, risk perception, and poorer behavioural intention regarding cardiovascular health (P&lt;0.05, one-way ANOVA). While educational level, smoking status, awareness about CVD poster, self-assessment tools were repeatedly significantly associated with knowledge related to CVD and CVD risk prevention, risk perception, behavioral intention and/or IHRS (P&lt;0.05, linear regression). Conclusions: These findings underscore the importance of promoting cardiovascular health awareness and risk perception among middle-aged adults to foster positive BI and reduce CVD risk. Tailored interventions targeting specific risk factors identified by INTERHEART may enhance risk stratification accuracy and facilitate targeted preventive strategies.

Spatial analysis of the prevalence of abdominal obesity in middle-aged and older adult people in China: exploring the relationship with meteorological factors based on gender differences.

In recent years, the incidence of abdominal obesity among the middle-aged and older adult population in China has significantly increased. However, the gender disparities in the spatial distribution of abdominal obesity incidence and its relationship with meteorological factors among this demographic in China remain unclear. This gap in knowledge highlights the need for further research to understand these dynamics and inform targeted public health strategies. This study utilized data from the 2015 China Health and Retirement Longitudinal Study (CHARLS) to analyze the incidence of abdominal obesity among the middle-aged and older adult population in China. Additionally, meteorological data were collected from the National Meteorological Information Center. Using Moran's I index and Getis-Ord Gi* statistical methods, the spatial distribution characteristics of abdominal obesity incidence were examined. The influence of various meteorological factors on the incidence of abdominal obesity in middle-aged and older adult males and females was investigated using the q statistic from the Geodetector method. Furthermore, Multi-Scale Geographically Weighted Regression (MGWR) analysis was employed to explore the impact of meteorological factors on the spatial heterogeneity of abdominal obesity incidence from a gender perspective. The spatial distribution of abdominal obesity among middle-aged and older adult individuals in China exhibits a decreasing trend from northwest to southeast, with notable spatial autocorrelation. Hotspots are concentrated in North and Northeast China, while cold spots are observed in Southwest China. Gender differences have minimal impact on spatial clustering characteristics. Meteorological factors, including temperature, sunlight, precipitation, wind speed, humidity, and atmospheric pressure, influence incidence rates. Notably, temperature and sunlight exert a greater impact on females, while wind speed has a reduced effect. Interactions among various meteorological factors generally demonstrate bivariate enhancement without significant gender disparities. However, gender disparities are evident in the influence of specific meteorological variables such as annual maximum, average, and minimum temperatures, as well as sunlight duration and precipitation, on the spatial heterogeneity of abdominal obesity incidence. Meteorological factors show a significant association with abdominal obesity prevalence in middle-aged and older adults, with temperature factors playing a prominent role. However, this relationship is influenced by gender differences and spatial heterogeneity. These findings suggest that effective public health policies should be not only gender-sensitive but also locally adapted.

Does Physical Exercise Modify the Pathophysiology of Alzheimer's Disease in Older Persons?

Physical exercise is well known for its benefits on brain health. However, the mechanisms through which these benefits occur remain discussed, especially in the context of cognitive conditions such as Alzheimer's disease. The present short review summarizes the findings of interventional studies that examined the effects of exercise training on the specific and non-specific biomarkers of Alzheimer's disease. Controlled exercise intervention studies published in the English language were selected if they assessed the effects of a physical exercise intervention of at least 2 weeks in middle-aged or older adults on one of the following biomarkers measured either in the brain, the cerebrospinal fluid or the blood: beta-amyloid, tau, neurofilament light chain, and glial fibrillary acidic protein. Overall, there was no strong evidence of significant effects of exercise interventions on any of the selected biomarkers. However, in specific populations, such as women with obesity, pre-diabetes, or depression, favorable changes in blood beta-amyloid concentrations were reported. Further benefits on cerebrospinal fluid beta-amyloid were also demonstrated in APOE-ε4 allele carriers with Alzheimer's disease. In conclusion, the current evidence suggests that physical exercise does not modulate the pathophysiology of Alzheimer's disease in the overall population of middle-aged and older adults. Nonetheless, some specific populations, such as women with metabolic disorders and Alzheimer's disease patients with APOE-ε4 genotype, seem to be favorably affected. Further studies, including long follow-ups, large sample sizes, and concomitantly assessing the effects of other factors such as sedentary behavior and diet, are required to bring further evidence to the field.

Dietary vitamin K intake is associated with decreased neurofilament light chain among middle-aged and older adults from the NHANES.

Neurofilament-light chain (NfL) is associated with neurodegenerative diseases, which are increasingly prevalent with aging. Vitamin K has been shown a neuroprotective effect. Therefore, we aimed to explore the potential relationship between dietary vitamin K intake and serum NfL. This study was conducted on the 2013-2014 cycles of the National Health and Nutrition Examination Survey, a multi-site population-based study of the US general population. Serum NfL level was measured using a highly sensitive immunoassay. Dietary vitamin K intake was estimated from two-day dietary recall interviews, and its relationship with NfL was determined using linear regression models. The study included a total of 1,533 participants with a median age of 46 years, comprising 801 women (52.2%) and 732 men (47.8%). The median dietary intake of vitamin K was 81.6 μg/d, and the median serum NfL was 12 pg./mL. After adjusting for potential confounding factors in the full model, individuals with higher dietary vitamin K intake had lower serum NfL levels (Q4 vs. Q1, β = -4.92, 95%CI: -7.66, -2.19, p = 0.002). A non-linear negative dose-response association is found between dietary vitamin K intake and serum NfL levels (P for non-linearity = 0.008); this association reaches a plateau when the dietary vitamin K intake is higher than 200 μg/d. According to the results of stratified analysis, the relationship between dietary vitamin K intake and serum NfL levels was stronger in the population of middle-aged and older adults. The present study suggested a negative association between dietary vitamin K intake and serum NfL levels in the general US population, especially in middle-aged and older adults. This study might offer a novel nutritional idea for the primary prevention and mechanism exploration of neurodegenerative diseases.

Does subjective life expectancy matter in purchasing life insurance among middle-aged and older adult? Evidence from China.

Population-wide increase in life expectancy is a source of aggregate longevity risk. Life insurance is a natural instrument to manage the risk. Previous studies used chronological age to examine the relationship between aging and life insurance purchase, which ignored the impact of subjective life expectancy-the real perception of remaining time. Therefore, this study aims to fill the lack in this area and to explore in depth the relationship between subjective life expectancy and purchasing life insurance among middle-aged and older adult at micro perspective. This paper utilizes data from the China Health and Retirement Longitudinal Study (CHARLS) over a period of 4 years to construct both Probit and Tobit models. The findings reveal that subjective life expectancy positively affects the likelihood of participation and the extent of life insurance among the middle-aged and older adult population in China. IV model estimation results show good robustness of the results. Meanwhile, there is also heterogeneity in the effect with respect to gender, hukou, education and wealth. The findings provide new perspective to explain the subjective motivation of purchasing life insurance in China.

Decoding emotional resilience in aging: unveiling the interplay between daily functioning and emotional health.

EPs pose significant challenges to individual health and quality of life, attracting attention in public health as a risk factor for diminished quality of life and healthy life expectancy in middle-aged and older adult populations. Therefore, in the context of global aging, meticulous exploration of the factors behind emotional issues becomes paramount. Whether ADL can serve as a potential marker for EPs remains unclear. This study aims to provide new evidence for ADL as an early predictor of EPs through statistical analysis and validation using machine learning algorithms. Data from the 2018 China Health and Retirement Longitudinal Study (CHARLS) national baseline survey, comprising 9,766 samples aged 45 and above, were utilized. ADL was assessed using the BI, while the presence of EPs was evaluated based on the record of "Diagnosed with Emotional Problems by a Doctor" in CHARLS data. Statistical analyses including independent samples t-test, chi-square test, Pearson correlation analysis, and multiple linear regression were conducted using SPSS 25.0. Machine learning algorithms, including Support Vector Machine (SVM), Decision Tree (DT), and Logistic Regression (LR), were implemented using Python 3.10.2. Population demographic analysis revealed a significantly lower average BI score of 65.044 in the "Diagnosed with Emotional Problems by a Doctor" group compared to 85.128 in the "Not diagnosed with Emotional Problems by a Doctor" group. Pearson correlation analysis indicated a significant negative correlation between ADL and EPs (r = -0.165, p < 0.001). Iterative analysis using stratified multiple linear regression across three different models demonstrated the persistent statistical significance of the negative correlation between ADL and EPs (B = -0.002, β = -0.186, t = -16.476, 95% CI = -0.002, -0.001, p = 0.000), confirming its stability. Machine learning algorithms validated our findings from statistical analysis, confirming the predictive accuracy of ADL for EPs. The area under the curve (AUC) for the three models were SVM-AUC = 0.700, DT-AUC = 0.742, and LR-AUC = 0.711. In experiments using other covariates and other covariates + BI, the overall prediction level of machine learning algorithms improved after adding BI, emphasizing the positive effect of ADL on EPs prediction. This study, employing various statistical methods, identified a negative correlation between ADL and EPs, with machine learning algorithms confirming this finding. Impaired ADL increases susceptibility to EPs.