OBJECTIVE To systematically evaluate the efficacy and safety of 6 kinds of GLP-1RAs in the treatment of type 2 diabetes mellitus （T2DM） patients with overweight or obesity， and to provide evidence-based reference for clinical practice. METHODS A comprehensive search was conducted in PubMed， Embase， Web of Science， the Cochrane Library， CNKI， VIP， Wanfang Data， and CBM from the inception to December 1， 2025. Randomized controlled trials （RCTs） were screened according to inclusion and exclusion criteria. Data extraction and risk of bias assessment were performed on the included studies. Network meta-analysis was conducted using Stata 17.0 software. RESULTS A total of 29 eligible RCTs were included， involving 7 404 patients. Six GLP-1RAs were evaluated： semaglutide， liraglutide， exenatide， dulaglutide， polyethylene glycol loxenatide， and beinaglutide. In terms of glycemic control， semaglutide had the highest probability of ranking first in reducing glycated hemoglobin （HbA1c） and fasting plasma glucose levels， followed by polyethylene glycol loxenatide. In terms of weight management， semaglutide showed the highest probability of ranking first， followed by liraglutide and exenatide. Regarding safety， dulaglutide had the highest probability of ranking first in reducing the incidence of gastrointestinal adverse events； none of the GLP-1RAs significantly increased the risk of severe hypoglycemia. Subgroup analysis revealed that liraglutide 1.8 mg， qd and exenatide extend-release 2.0 mg， qw demonstrated superior efficacy in reducing HbA1c and body weight compared with other doses/dosage forms of the same agents. CONCLUSIONS For T2DM patients with overweight or obesity， semaglutide offers the greatest benefits in glycemic control and weight reduction， while dulaglutide demonstrates superior gastrointestinal tolerability. Liraglutide 1.8 mg， qd and exenatide extend-release 2.0 mg， qw show relatively better overall efficacy in glycemic control and weight reduction among the same agents.

Background: This study aimed to estimate temporal trends in metabolically unhealthy obesity (MUO) among United States (US) adults by age, sex, race/ethnicity, and income from 1999 to 2018. Methods: We included 17,230 non-pregnant adults from a nationally representative cross-sectional study, the National Health and Nutrition Examination Survey (NHANES). MUO was defined as body mass index ≥30 kg/m2 with any metabolic disorders in blood pressure, blood glucose, and blood lipids. The age-adjusted percentage of MUO was calculated, and linear regression models estimated trends in MUO. Results: The weighted mean age of adults was 47.28 years; 51.02% were male, 74.64% were non-Hispanic White. The age-adjusted percentage of MUO continuously increased in adults across all subgroups during 1999–2018, although with different magnitudes (all P<0.05 for linear trend). Adults aged 45 to 64 years consistently had higher percentages of MUO from 1999–2000 (34.25%; 95% confidence interval [CI], 25.85% to 42.66%) to 2017–2018 (42.03%; 95% CI, 35.09% to 48.97%) than the other two age subgroups (P<0.05 for group differences). The age-adjusted percentage of MUO was the highest among non-Hispanic Blacks while the lowest among non-Hispanic Whites in most cycles. Adults with high-income levels generally had lower MUO percentages from 1999–2000 (22.63%; 95% CI, 17.00% to 28.26%) to 2017–2018 (32.36%; 95% CI, 23.87% to 40.85%) compared with the other two subgroups. Conclusion This study detected a continuous linear increasing trend in MUO among US adults from 1999 to 2018. The persistence of disparities by age, race/ethnicity, and income is a cause for concern. This calls for implementing evidence-based, structural, and effective MUO prevention programs.

Objective:This study utilizes data from the 2021 Chinese Longitudinal Healthy Longevity and Happy Family Survey(CLHLS-HF)to examine the current status of Long-Term Care Insurance(LTCI)implementation and to identify the factors influencing whether disabled elderly individuals receive formal care services.The study aims to provide policy recommendations to enhance the effectiveness and equity of the system.Methods:In this cross-sectional study, a sample of 1 447 older participants with dependency, residing in LTCI pilot areas and meeting the inclusion criteria from the 2021 CLHLS-HF, was selected.Chi-square tests and binary logistic regression analyses were employed to explore the factors influencing the receipt of formal care by dependent older individuals.Results:Among the 1 447 participants, there were 496 males with an average age of 92 years(SD 9)and 951 females with an average age of 95 years(SD 9). Of these, 701 received formal care.The logistic regression analysis revealed that factors influencing the receipt of formal care included urban residence( OR=2.237, 95% CI: 1.675-2.987, P<0.001), residing in the eastern region( OR=2.907, 95% CI: 1.747-4.837, P<0.001), living in the western region( OR=3.132, 95% CI: 1.816-5.501, P<0.001), having no children( OR=2.478, 95% CI: 1.108-5.540, P=0.027), and the degree of disability, with severe disability being more likely to receive care compared to mild( OR=0.497, 95% CI: 0.388-0.637, P<0.001)and moderate disabilities( OR=0.589, 95% CI: 0.433-0.801, P=0.001). Conclusions:Dependent older individuals in the eastern and western regions, particularly those without children or with severe disabilities, are more likely to receive formal care through the LTCI system.However, there are substantial inequities in LTCI coverage among individuals with varying degrees of disability.To enhance the effectiveness of the LTCI system, greater efforts should be directed towards economically disadvantaged regions and older individuals with mild to moderate disabilities, thereby ensuring better protection for the disabled population.

Background: This study aimed to estimate temporal trends in metabolically unhealthy obesity (MUO) among United States (US) adults by age, sex, race/ethnicity, and income from 1999 to 2018. Methods: We included 17,230 non-pregnant adults from a nationally representative cross-sectional study, the National Health and Nutrition Examination Survey (NHANES). MUO was defined as body mass index ≥30 kg/m2 with any metabolic disorders in blood pressure, blood glucose, and blood lipids. The age-adjusted percentage of MUO was calculated, and linear regression models estimated trends in MUO. Results: The weighted mean age of adults was 47.28 years; 51.02% were male, 74.64% were non-Hispanic White. The age-adjusted percentage of MUO continuously increased in adults across all subgroups during 1999–2018, although with different magnitudes (all P<0.05 for linear trend). Adults aged 45 to 64 years consistently had higher percentages of MUO from 1999–2000 (34.25%; 95% confidence interval [CI], 25.85% to 42.66%) to 2017–2018 (42.03%; 95% CI, 35.09% to 48.97%) than the other two age subgroups (P<0.05 for group differences). The age-adjusted percentage of MUO was the highest among non-Hispanic Blacks while the lowest among non-Hispanic Whites in most cycles. Adults with high-income levels generally had lower MUO percentages from 1999–2000 (22.63%; 95% CI, 17.00% to 28.26%) to 2017–2018 (32.36%; 95% CI, 23.87% to 40.85%) compared with the other two subgroups. Conclusion This study detected a continuous linear increasing trend in MUO among US adults from 1999 to 2018. The persistence of disparities by age, race/ethnicity, and income is a cause for concern. This calls for implementing evidence-based, structural, and effective MUO prevention programs.

Background: This study aimed to estimate temporal trends in metabolically unhealthy obesity (MUO) among United States (US) adults by age, sex, race/ethnicity, and income from 1999 to 2018. Methods: We included 17,230 non-pregnant adults from a nationally representative cross-sectional study, the National Health and Nutrition Examination Survey (NHANES). MUO was defined as body mass index ≥30 kg/m2 with any metabolic disorders in blood pressure, blood glucose, and blood lipids. The age-adjusted percentage of MUO was calculated, and linear regression models estimated trends in MUO. Results: The weighted mean age of adults was 47.28 years; 51.02% were male, 74.64% were non-Hispanic White. The age-adjusted percentage of MUO continuously increased in adults across all subgroups during 1999–2018, although with different magnitudes (all P<0.05 for linear trend). Adults aged 45 to 64 years consistently had higher percentages of MUO from 1999–2000 (34.25%; 95% confidence interval [CI], 25.85% to 42.66%) to 2017–2018 (42.03%; 95% CI, 35.09% to 48.97%) than the other two age subgroups (P<0.05 for group differences). The age-adjusted percentage of MUO was the highest among non-Hispanic Blacks while the lowest among non-Hispanic Whites in most cycles. Adults with high-income levels generally had lower MUO percentages from 1999–2000 (22.63%; 95% CI, 17.00% to 28.26%) to 2017–2018 (32.36%; 95% CI, 23.87% to 40.85%) compared with the other two subgroups. Conclusion This study detected a continuous linear increasing trend in MUO among US adults from 1999 to 2018. The persistence of disparities by age, race/ethnicity, and income is a cause for concern. This calls for implementing evidence-based, structural, and effective MUO prevention programs.

Turning to critical illness is a common stage of various diseases and injuries before death. Patients usually have complex health conditions, while the treatment process involves a wide range of content, along with high requirements for doctor′s professionalism and multi-specialty teamwork, as well as a great demand for time-sensitive treatments. However, this is not matched with critical care professionals and the current state of medical care in China. Telemedicine, which shortens the distance of medical professionals and the gap of disease diagnosis and treatments in various regions through electronic information, can effectively solve the current problem. Therefore, there is an urgent need to develop a standardized, high-quality visualization telemedicine round system .Therefore, experts have been organized to search domestic and foreign literature on telemedicine round for critically ill patients and to form this consensus based on clinical experiences so as to further improve the level of critical care treatments in regions.

Background: This study aimed to estimate temporal trends in metabolically unhealthy obesity (MUO) among United States (US) adults by age, sex, race/ethnicity, and income from 1999 to 2018. Methods: We included 17,230 non-pregnant adults from a nationally representative cross-sectional study, the National Health and Nutrition Examination Survey (NHANES). MUO was defined as body mass index ≥30 kg/m2 with any metabolic disorders in blood pressure, blood glucose, and blood lipids. The age-adjusted percentage of MUO was calculated, and linear regression models estimated trends in MUO. Results: The weighted mean age of adults was 47.28 years; 51.02% were male, 74.64% were non-Hispanic White. The age-adjusted percentage of MUO continuously increased in adults across all subgroups during 1999–2018, although with different magnitudes (all P<0.05 for linear trend). Adults aged 45 to 64 years consistently had higher percentages of MUO from 1999–2000 (34.25%; 95% confidence interval [CI], 25.85% to 42.66%) to 2017–2018 (42.03%; 95% CI, 35.09% to 48.97%) than the other two age subgroups (P<0.05 for group differences). The age-adjusted percentage of MUO was the highest among non-Hispanic Blacks while the lowest among non-Hispanic Whites in most cycles. Adults with high-income levels generally had lower MUO percentages from 1999–2000 (22.63%; 95% CI, 17.00% to 28.26%) to 2017–2018 (32.36%; 95% CI, 23.87% to 40.85%) compared with the other two subgroups. Conclusion This study detected a continuous linear increasing trend in MUO among US adults from 1999 to 2018. The persistence of disparities by age, race/ethnicity, and income is a cause for concern. This calls for implementing evidence-based, structural, and effective MUO prevention programs.

Objective:This study utilizes data from the 2021 Chinese Longitudinal Healthy Longevity and Happy Family Survey(CLHLS-HF)to examine the current status of Long-Term Care Insurance(LTCI)implementation and to identify the factors influencing whether disabled elderly individuals receive formal care services.The study aims to provide policy recommendations to enhance the effectiveness and equity of the system.Methods:In this cross-sectional study, a sample of 1 447 older participants with dependency, residing in LTCI pilot areas and meeting the inclusion criteria from the 2021 CLHLS-HF, was selected.Chi-square tests and binary logistic regression analyses were employed to explore the factors influencing the receipt of formal care by dependent older individuals.Results:Among the 1 447 participants, there were 496 males with an average age of 92 years(SD 9)and 951 females with an average age of 95 years(SD 9). Of these, 701 received formal care.The logistic regression analysis revealed that factors influencing the receipt of formal care included urban residence( OR=2.237, 95% CI: 1.675-2.987, P<0.001), residing in the eastern region( OR=2.907, 95% CI: 1.747-4.837, P<0.001), living in the western region( OR=3.132, 95% CI: 1.816-5.501, P<0.001), having no children( OR=2.478, 95% CI: 1.108-5.540, P=0.027), and the degree of disability, with severe disability being more likely to receive care compared to mild( OR=0.497, 95% CI: 0.388-0.637, P<0.001)and moderate disabilities( OR=0.589, 95% CI: 0.433-0.801, P=0.001). Conclusions:Dependent older individuals in the eastern and western regions, particularly those without children or with severe disabilities, are more likely to receive formal care through the LTCI system.However, there are substantial inequities in LTCI coverage among individuals with varying degrees of disability.To enhance the effectiveness of the LTCI system, greater efforts should be directed towards economically disadvantaged regions and older individuals with mild to moderate disabilities, thereby ensuring better protection for the disabled population.

Turning to critical illness is a common stage of various diseases and injuries before death. Patients usually have complex health conditions, while the treatment process involves a wide range of content, along with high requirements for doctor′s professionalism and multi-specialty teamwork, as well as a great demand for time-sensitive treatments. However, this is not matched with critical care professionals and the current state of medical care in China. Telemedicine, which shortens the distance of medical professionals and the gap of disease diagnosis and treatments in various regions through electronic information, can effectively solve the current problem. Therefore, there is an urgent need to develop a standardized, high-quality visualization telemedicine round system .Therefore, experts have been organized to search domestic and foreign literature on telemedicine round for critically ill patients and to form this consensus based on clinical experiences so as to further improve the level of critical care treatments in regions.

Objective To analyze the medication rules of Professor HUANG Feng for the treatment of low back pain using data mining methods.Methods The information of prescriptions for the effective cases of outpatients with low back pain treated by Professor HUANG Feng were collected and screened.Microsoft Excel 2019 was used to analyze the frequency of medication and the distribution of properties,flavors and meridian tropism of the drugs in the included prescription.IBM SPSS Modeler 18.0 was used for association rule analysis,and IBM Statistics 26.0 was used for cluster analysis.Results A total of 239 prescriptions and 75 Chinese medicines were included.There were 23 high-frequency Chinese medicines with the medication frequency being or over 20 times,and the top 10 Chinese medicines were Glycyrrhizae Radix et Rhizoma,vinegar-processed Corydalis Rhizoma,Cibotii Rhizoma,Atractylodis Macrocephalae Rhizoma,Zanthoxyli Radix,salt-processed Achyranthis Bidentatae Radix,Rehmanniae Radix,Dipsaci Radix,Coicis Semen,and Salviae Miltiorrhizae Radix et Rhizoma.The medicines were mainly warm in nature,and were sweet,bitter and pungent in flavor.Most of the drugs had the meridian tropism of liver,stomach and spleen meridians.Among the drug combinations obtained from association rule analysis with the top 20 highest support,vinegar-processed Corydalis Rhizoma,Cibotii Rhizoma,Atractylodis Macrocephalae Rhizoma and Zanthoxyli Radix were the core drugs.Cluster analysis yielded 6 clustering combinations.Conclusion For the treatment of low back pain,Professor HUANG Feng follows the principle of"treatment adapting to the climate,individuality,and environment"and"treating the root cause of the disease",usually adopts the drugs for activating blood,moving qi and relieving pain,nourishing the liver and kidney,and also uses the medicines for replenishing qi and strengthening the spleen.The ideas of HUANG Feng for the treatment of low back pain can be used as a reference for the clinical treatment.