Background: Cardiovascular disease is the leading cause of death among women worldwide, yet, women have historically been underrepresented in cardiovascular trials. Methods: We systematically assessed the participation of women in completed cardiovascular trials registered in ClinicalTrials.gov between 2010 and 2017, and extracted publicly available information including disease type, sponsor type, country, trial size, intervention type, and the demographic characteristics of trial participants. We calculated the female-to-male ratio for each trial and determined the prevalence-adjusted estimates for participation of women by dividing the percentage of women among trial participants by the percentage of women in the disease population (participation prevalence ratio; a ratio of 0.8 to 1.2 suggests comparable prevalence and good representation). Results: We identified 740 completed cardiovascular trials including a total of 862 652 adults, of whom 38.2% were women. The median female-to-male ratio of each trial was 0.51 (25th quartile, 0.32; 75th quartile, 0.90) overall and varied by age group (1.02 in ≤55 year old group versus 0.40 in the 61- to 65-year-old group), type of intervention (0.44 for procedural trials versus 0.78 for lifestyle intervention trials), disease type (0.34 for acute coronary syndrome versus 3.20 for pulmonary hypertension), region (0.45 for Western Pacific versus 0.55 for the Americas), funding/sponsor type (0.14 for government-funded versus 0.73 for multiple sponsors), and trial size (0.56 for smaller [n≤47] versus 0.49 for larger [n≥399] trials). Relative to their prevalence in the disease population, participation prevalence ratio was higher than 0.8 for hypertension, pulmonary arterial hypertension and lower (participation prevalence ratio 0.48 to 0.78) for arrhythmia, coronary heart disease, acute coronary syndrome, and heart failure trials. The most recent time period (2013 to 2017) saw significant increases in participation prevalence ratios for stroke ( P =0.007) and heart failure ( P =0.01) trials compared with previous periods. Conclusions: Among cardiovascular trials in the current decade, men still predominate overall, but the representation of women varies with disease and trial characteristics, and has improved in stroke and heart failure trials.

Background Apolipoprotein E (APOE) ε4 is the single most important genetic risk factor for cognitive impairment and Alzheimer disease (AD), while lifestyle factors such as smoking, drinking, diet, and physical activity also have impact on cognition. The goal of the study is to investigate whether the association between lifestyle and cognition varies by APOE genotype among the oldest old. Methods and findings We used the cross-sectional data including 6,160 oldest old (aged 80 years old or older) from the genetic substudy of the Chinese Longitudinal Healthy Longevity Survey (CLHLS) which is a national wide cohort study that began in 1998 with follow-up surveys every 2–3 years. Cognitive impairment was defined as a Mini-Mental State Examination (MMSE) score less than 18. Healthy lifestyle profile was classified into 3 groups by a composite measure including smoking, alcohol consumption, dietary pattern, physical activity, and body weight. APOE genotype was categorized as APOE ε4 carriers versus noncarriers. We examined the associations of cognitive impairment with lifestyle profile and APOE genotype using multivariable logistic regressions, controlling for age, sex, education, marital status, residence, disability, and numbers of chronic conditions. The mean age of our study sample was 90.1 (standard deviation [SD], 7.2) years (range 80–113); 57.6% were women, and 17.5% were APOE ε4 carriers. The mean MMSE score was 21.4 (SD: 9.2), and 25.0% had cognitive impairment. Compared with those with an unhealthy lifestyle, participants with intermediate and healthy lifestyle profiles were associated with 28% (95% confidence interval [CI]: 16%–38%, P < 0.001) and 55% (95% CI: 44%–64%, P < 0.001) lower adjusted odds of cognitive impairment. Carrying the APOE ε4 allele was associated with 17% higher odds (95% CI: 1%–31%, P = 0.042) of being cognitively impaired in the adjusted model. The association between lifestyle profiles and cognitive function did not vary significantly by APOE ε4 genotype (noncarriers: 0.47 [0.37–0.60] healthy versus unhealthy; carriers: 0.33 [0.18–0.58], P for interaction = 0.30). The main limitation was the lifestyle measurements were self-reported and were nonspecific. Generalizability of the findings is another limitation because the study sample was from the oldest old in China, with unique characteristics such as low body weight compared to populations in high-income countries. Conclusions In this study, we observed that healthier lifestyle was associated with better cognitive function among the oldest old regardless of APOE genotype. Our findings may inform the cognitive outlook for those oldest old with high genetic risk of cognitive impairment.

Background The emergence and advancement of mobile technologies offer a promising opportunity for people with diabetes to improve their self-management. Despite the proliferation of mobile apps, few studies have evaluated the apps that are available to the millions of people with diabetes in China. Objective This study aimed to conduct a systematic search of Chinese mobile apps for diabetes self-management and to evaluate their quality, functionality, and features by using validated rating scales. Methods A systematic search was conducted to identify Chinese apps for diabetes self-management in the four most popular Chinese language mobile app stores. Apps were included if they were designed for diabetes self-management and contained at least one of the following components: blood glucose management, dietary and physical activity management, medication taking, and prevention of diabetes-related comorbidities. Apps were excluded if they were unrelated to health, not in Chinese, or the targeted users are health care professionals. Apps meeting the identified inclusion criteria were downloaded and evaluated by a team of 5 raters. The quality, functionalities, and features of these apps were assessed by using the Mobile App Rating Scale (MARS), the IMS Institute for Healthcare Informatics Functionality score, and a checklist of self-management activities developed based on the Chinese diabetes self-management guideline, respectively. Results Among 2072 apps searched, 199 were eligible based on the inclusion criteria, and 67 apps were successfully downloaded for rating. These 67 apps had an average MARS score of 3.42 out of 5, and 76% (51/67) of the apps achieved an acceptable quality (MARS score >3.0). The scores for the four subdomains of MARS were 3.97 for functionality, 3.45 for aesthetics, 3.21 for information, and 3.07 for engagement. On average, reviewed apps applied five out of the 19 examined behavior change techniques, whereas the average score on the subjective quality for the potential impact on behavior change is 3 out of 5. In addition, the average score on IMS functionality was 6 out of 11. Functionalities in collecting, recording, and displaying data were mostly presented in the reviewed apps. Most of the apps were multifeatured with monitoring blood glucose and tracking lifestyle behaviors as common features, but some key self-management activities recommended by clinical guidelines, such as stress and emotional management, were rarely presented in these apps. Conclusions The general quality of the reviewed apps for diabetes self-management is suboptimal, although the potential for improvement is significant. More attention needs to be paid to the engagement and information quality of these apps through co-design with researchers, public health practitioners, and consumers. There is also a need to promote the awareness of the public on the benefit and potential risks of utilizing health apps for self-management.

Objective: Biological age (BA) has been accepted as a more accurate proxy of aging than chronological age (CA). This study aimed to use machine learning (ML) algorithms to estimate BA in the Chinese population.Materials and methods: We used data from 9,771 middle-aged and older Chinese adults (≥45 years) in the 2011/2012 wave of the China Health and Retirement Longitudinal Study and followed until 2018. We used several ML algorithms (e.g., Gradient Boosting Regressor, Random Forest, CatBoost Regressor, and Support Vector Machine) to develop new measures of biological aging (ML-BAs) based on physiological biomarkers. R-squared value and mean absolute error (MAE) were used to determine the optimal performance of these ML-BAs. We used logistic regression models to examine the associations of the best ML-BA and a conventional aging measure—Klemera and Doubal method-BA (KDM-BA) we previously developed—with physical disability and mortality, respectively.Results: The Gradient Boosting Regression model performed the best, resulting in an ML-BA with an R-squared value of 0.270 and an MAE of 6.519. This ML-BA was significantly associated with disability in basic activities of daily living, instrumental activities of daily living, lower extremity mobility, and upper extremity mobility, and mortality, with odds ratios ranging from 1 to 7% (per 1-year increment in ML-BA, all P &lt; 0.001), independent of CA. These associations were generally comparable to that of KDM-BA.Conclusion: This study provides a valid ML-based measure of biological aging for middle-aged and older Chinese adults. These findings support the application of ML in geroscience research and may help facilitate preventive and geroprotector intervention studies.

Objective To examine the association of baseline body mass index (BMI) and BMI change with cognitive impairment among older adults in China.Methods The study included data from the Chinese Longitudinal Healthy Longevity Study, a national community-based prospective cohort study from 2002-2018. Baseline BMI and BMI change measurements were available for 12,027 adults aged older than 65 years. Cognitive impairment was de ned as Chinese version of the Mini Mental State Examination score less than 18. Multivariable Cox proportional hazard model was used.Results Among 12027 participants (mean age was 81.23 years old and 47.48% were male), the proportion of underweight, normal, overweight and obese at baseline was 33.87%, 51.39%, 11.39% and 3.34%, respectively. During an average of 5.9 years' follow-up, 3086 participants (4.35 per 100 person-years) with incident cognitive impairment were identi ed. Compared with normal weight group, adjusted hazard ratio (AHR) for cognitive impairment was 0.86 (95% CI 0.75-0.99) among overweight group, whereas corresponding AHR was 1.02 (95% CI 0.94-1.10) in underweight and 1.01 (95% CI 0.80-1.28) in obese. Large weight loss (<-10%) was signi cantly associated with an increased risk of cognitive impairment (AHR, 1.42, 95% CI 1.29-1.56), compared to stable weight status group (-5%~5%). In the restricted cubic spline models, BMI change showed a L-shaped association with cognitive impairment.Conclusions BMI-de ned overweight is associated with a reduced risk of cognitive impairment among Chinese older adults, while large weight loss is associated with increased risk. More attention should be paid to older adults with signi cant weight loss.We are grateful to all cooperating organizations and their staff in CLHLS whose hard work made this study possible. We thank the interviewees and their families for their voluntary participation in the CLHLS study.