Purpose: Sarcopenia is a pathological change characterized by muscle loss in older people. According to the reports, there is controversy on the relationship between dyslipidemia and sarcopenia. Therefore, this meta-analysis aimed to explore the association between sarcopenia and dyslipidemia. Methods: We searched the Cochrane Library, Web of Science, PubMed, China National Knowledge Infrastructure (CNKI), Wan Fang, China Science and Technology Journal Database (VIP Database) for case‒control studies to extract data on the odds ratio (OR) between sarcopenia and dyslipidemia and the MD(mean difference) of TC, LDL-C, HDL-C, TG, and TG/HDL-C between sarcopenia and nonsarcopenia. The JBI(Joanna Briggs) guidelines were used to evaluate the quality. Excel 2021, Review Manager 5.3 and Stata 16.0 were used for the statistical analysis. Results: Twenty studies were included in the meta-analysis, 19 of which were evaluated as good quality. The overall OR of the relationship between sarcopenia and dyslipidemia was 1.47, and the MD values of TC, LDL-C, HDL-C, TG, and TG/HDL-C were 1.10, 1.95, 1.27, 30.13, and 0.16 respectively. In female, compared with the non-sarcopnia, the MD of TC, LDL-C, HDL-C, TG of sarcopenia were − 1.67,2.21,1.02,-3.18 respectively. In male, the MD of TC, LDL-C, HDL-C, TG between sarcopenia and non-sarcopenia were − 0.51, 1.41, 5.77, -0.67. The OR between sarcopenia and dyslipidemia of the non-China region was 4.38, and it was 0.9 in China. In the group(> 60), MD of TC between sarcopenia and non-sarcopenia was 2.63, while it was 1.54 in the group(20–60). Conclusion: Dyslipidemia was associated with sarcopenia in the elderly, which was affected by sex, region and age.

To characterize the indications approved by the US Food and Drug Administration (FDA) on the basis of early phase clinical trials (EPCTs) and compared with that of phase three randomized controlled trials. We collected the publicly available FDA documents of targeted anticancer drugs approved between January 2012 and December 2021. We identified 95 targeted anticancer drugs with 188 indications approved by the FDA. One hundred and twelve (59.6%) indications were approved on the basis of EPCTs, with a significant increase of 22.2% per year. Of 112 EPCTs, 32 (28.6%) were dose-expansion cohort trials and 75 (67.0%) were single-arm phase 2 trials, respectively, with a significant increase of 29.7% and 18.7% per year. Compared with indications approved on the basis of phase three randomized controlled trials, the indications approved on the basis of EPCTs had significantly higher odds in receiving accelerated approval and lower odds in the number of entered patients of pivotal trials. Dose-expansion cohort trials and single-arm phase 2 trials played a critical role in EPCTs. EPCT was a major trial type in providing evidences for the FDA approvals of targeted anticancer drugs.

Objective: To compare the screening ability of the phase Angle (PhA) and the Short-Form Mini Nutritional Assessment (MNA-SF) alone and combined detection in the different stages of sarcopenia among the older adults in the community. Methods: The older adults aged 65 and above were enlisted during community outpatient service and their nutritional status was evaluated by MNA-SF scale. PhA was measured by bioelectrical impedance analysis (BIA). AWGS2019 and EWGSOP2010 were used to define the different stages of sarcopenia. We measured skeletal mass index (SMI) and grip strength with BIA and electronic grip apparatus and measured body function with 6-m pace, SPPB test, and standing test. Results: The AUC of PhA in the screening of possible sarcopenia was 0.640, the sensitivity was 58.49%, the specificity was 66.67%, and the cut-off value was 4.5. The AUC of the combined PhA and MNA-SF for possible sarcopenia was 0.642, the sensitivity was 57.55%, and the specificity was 70.00%. The AUC of MNA-SF for the screening of pre-sarcopenia was 0.805, the sensitivity was 66.67%, the specificity was 85.83%, and the cut-off value was 12. The AUC of the combined PhA and MNA-SF was 0.826, the sensitivity was 75.00%, and the specificity was 85.00%. The AUC of PhA in the screening of sarcopenia (common type) was 0.808, the sensitivity was 82.35%, the specificity was 73.33%, the cut-off value was 4.4. The AUC of the combined PhA and MNA-SF for sarcopenia (common type) was 0.835, the sensitivity was 76.47% and the specificity was 81.67%. The AUC of PhA and for the screening of severe sarcopenia was 0.935, the sensitivity was 93.33%, the specificity was 92.50%, and the cut-off value was 4.1. The AUC of the combined PhA and MNA-SF was 0.943, the sensitivity was 86.67%, and the specificity was 93.33%. Conclusion: The screening ability of PhA alone or in combination was higher than that of MNA-SF in the screening of possible sarcopenia. The screening ability of the combined detection was higher than that of PhA alone in the screening of pre-sarcopenia. The combination of PhA and MNA-SF or PhA alone all performed better value in the screening of sarcopenia (common type). Compared to MNA-SF, the PhA performed better in the screening of severe sarcopenia, which provided references for identifying patients with different stages of sarcopenia in the community.

Background: With changes in lifestyle, the issue of sleep disorders is becoming increasingly common. Diabetes and sarcopenia have been found to be independently associated with sleep disorders. However, fewer studies have explored the interaction between the combination of diabetes and sarcopenia at different stages and sleep disorders. Objective: This study aimed to explore the relationship between the combination of diabetes and sarcopenia and the incidence of sleep disorders in adults aged 45 years and older. Methods: Based on data from the CHARLS (China Health and Retirement Longitudinal Study), we selected participants with comprehensive diagnostic information on diabetes and sarcopenia from 2011 who had normal sleep duration at baseline and checked their follow-up information of sleep duration from 2013, 2015, 2018, and 2020. Diabetes was classified into diabetes (D), prediabetes (PD), and nondiabetes (ND), and sarcopenia was divided into sarcopenia (S), possible sarcopenia (PS), and nonsarcopenia (NS). The participants were divided into DS, DPS, DNS, PDS, PDPS, PDNS, NDS, NDPS, and NDNS groups. Kaplan-Meier survival curves, the log-rank test, Cox proportional hazards regression, and restricted cubic spline models were used for statistical analysis. Results: A total of 4936 participants were included in this study. The DS group had the highest incidence of sleep disorders: 49.32%, 28.57%, 36.36%, and 80.00% in 2013, 2015, 2018, and 2020 respectively. In the crude model, compared with the NDNS group, the risk of sleep disorders was increased in the DS group (hazard ratio [HR] 1.707, 95% CI 1.196‐2.437), PDS (HR 1.599, 95% CI 1.235‐2.071), NDS (HR 1.465, 95% CI 1.282‐1.674), and DPS group (HR 1.318, 95% CI 1.097‐1.583). The risk was increased but not statistically significant in the PDPS group (HR 1.160, 95% CI 0.987‐1.365). After adjusting for covariates, the risk of sleep disorders remained statistically significant in the DS group (HR 1.515, 95% CI 1.059‐2.167) and was significantly higher in the PDS (HR 1.423, 95% CI 1.096‐1.847) and NDS (HR 1.279, 95% CI 1.113‐1.468) groups than that in the NDNS group. The nonlinear associations between appendicular skeletal muscle mass, grip strength, 5-time chair test, fasting plasma glucose, and sleep disorders were observed and described. Conclusions: The combination of diabetes and sarcopenia significantly increases the risk of sleep disorders in adults aged 45 years and older. and the implementation of progression control of both diabetes and sarcopenia may be helpful to prevent sleep disorders in this population.

This study focuses on the role of primary care in China’s response to COVID-19. A retrospective, reflective approach was taken using data available to one of the authors who led the national community response to COVID-19, first in Wuhan and then multiple cities in ten provinces/municipalities across the country. At the peak of the pandemic, primary care providers shoulder various public health responsibilities and work in close partnerships with other key stakeholders in the local communities. Primary care providers keep playing a ‘sentinel’/surveillance role in identifying re-emerging cases after the elimination of community transmissions of COVID-19. Critically, however, the pandemic once again highlights some key limitations of the primary care sector, including the lack of gatekeeping, limited capacity and weak integration between medical care and public health.

What is already known about this topic? In December 2022, China revised its epidemic prevention and control strategy, leading to an increase in coronavirus disease 2019 (COVID-19) cases and a peak in medical consultations. Government departments implemented relevant policies to coordinate and allocate medical resources throughout China. However, there is a scarcity of research on the status of medical consultations and the factors influencing them. What is added by this report? In the study population, over 80% of individuals with COVID-19 chose not to pursue medical care, while more than 70% of patients who sought treatment opted for primary healthcare facilities. The decision to consult medical professionals was influenced by various factors, such as age, education level, employment status, urban-rural distribution, and the presence of symptoms following COVID-19 infection. What are the implications for public health practice? The implementation of tiered diagnostic and treatment approaches, aligned with guidelines issued by governing bodies, is essential for mitigating the strain on medical resources. Primary healthcare institutions serve as “gatekeepers” for public health and should be further expanded in the future.