To develop a guideline terminology system and promote its standardization, thereby enhancing medical staff's accurate understanding and correct application of guidelines.

To introduce and analyze guideline terminology related to clinical question formulation, evidence retrieval and appraisal, and recommendation development.

OBJECTIVE To standardize the main processes and related technical links of the clinical comprehensive evaluation of drugs， and provide guidance and reference for improving the quality of comprehensive evaluation evidence and its transformation and application value. METHODS The construction of Guideline for the Workflow of Clinical Comprehensive Evaluation of Drugs was based on the standard guideline formulation method of the World Health Organization （WHO）， strictly followed the latest definition of guidelines by the Institute of Medicine of the National Academy of Sciences of the United States， and conformed to the six major areas of the Guideline Research and Evaluation Tool Ⅱ. Delphi method was adopted to construct the research questions； research evidence was established by applying the research methods of evidence-based medicine. The evidence quality classification system of the Chinese Evidence-Based Medicine Center was adopted for evidence classification and evaluation. The recommendation strength was determined by the recommendation strength classification standard formulated by the Oxford University Evidence-Based Medicine Center， and the recommendation opinions were formed through the expert consensus method. RESULTS & CONCLUSIONS The Guideline for the Workflow of Clinical Comprehensive Evaluation of Drugs covers 4 major categories of research questions， including topic selection， evaluation implementation， evidence evaluation， and application and transformation of results. The formulation of this guideline has standardized the technical links of the entire process of clinical comprehensive evaluation of drugs， which can effectively guide the high-quality and high-efficient development of this work， enhance the standardized output and transformation application value of evaluation evidence， and provide high-quality evidence support for the scientific decision-making of health and the rationalization of clinical medication.

To explore the role of the "Clinical Practice Guidelines" column and others in the Medical Joumnal of Peking Union Medical College Hospital (Med J PUMCH) in promoting diseiplinary development.

Objective:To explore the impact of sleep duration, physical exercise, and their interactions on the risk of dyslipidemia in older adults aged ≥80 (the oldest old) in China.Methods:The study subjects were the oldest old from four rounds of Healthy Aging and Biomarkers Cohort Study (2008-2009, 2011-2012, 2014 and 2017-2018). The information about their demographic characteristics, lifestyles, physical examination results and others were collected, and fasting venous blood samples were collected from them for blood lipid testing. Competing risk model was used to analyze the causal associations of sleep duration and physical exercise with the risk for dyslipidemia. Restricted cubic spline (RCS) function was used to explore the dose-response relationship between sleep duration and the risk for dyslipidemia. Additive and multiplicative interaction model were used to explore the interaction of sleep duration and physical exercise on the risk for dyslipidemia.Results:The average age of 1 809 subjects was (93.1±7.7) years, 65.1% of them were women. The average sleep duration of the subjects was (8.0±2.5) hours/day, 28.1% of them had sleep duration for less than 7 hours/day, and 27.2% had sleep for duration more than 9 hours/day at baseline survey. During the 9-year cumulative follow-up of 6 150.6 person years (follow-up of average 3.4 years for one person), there were 304 new cases of dyslipidemia, with an incidence density of 4 942.6/100 000 person years. The results of competitive risk model analysis showed that compared with those who slept for 7-9 hours/day, the risk for dyslipidemia in oldest old with sleep duration >9 hours/day increased by 22% ( HR=1.22, 95% CI: 1.07-1.39). Compared with the oldest old having no physical exercise, the risk for dyslipidemia in the oldest old having physical exercise decreased by 33% ( HR=0.67, 95% CI: 0.57-0.78). The RCS function showed a linear positive dose-response relationship between sleep duration and the risk for hyperlipidemia. The interaction analysis showed that physical exercise and sleep duration had an antagonistic effect on the risk for hyperlipidemia. Conclusion:Physical exercise could reduce the adverse effects of prolonged sleep on blood lipids in the oldest old.

Objective:To investigate the distribution characteristics of skeletal muscle mass and strength in the older adults over 65 years old in 18 longevity areas in China.Methods:The subjects were selected from the Healthy Aging and Biomarkers Cohort Study conducted in 18 longevity areas of China. A total of 4 662 older adults over 65 years old from a cross- sectional survey in 2021 were included in the study. The information about their sociodemographic characteristics, lifestyle, nutrient intake and other factors were collected through questionnaire surveys and physical examinations. Grip strength was measured by using professional electronic grip dynamometer. Total skeletal muscle mass (TSM) was measured using bioelectrical impedance analysis, and TSM was adjusted by height squared and BMI to obtain TSM Ht2 and TSM BMI. The proportion of individuals with low muscle mass and strength was determined according to the recommended method by the Asian Working Group for Sarcopenia (AWGS). Descriptive analysis was conducted on the population and regional distribution characteristics of people with different muscle mass and grip strength. A generalized additive model was used to analyze the age-related trends of muscle mass and grip strength. Results:The age of 4 662 study subjects was (82.69±10.54) years, men accounted for 46.85% (2 184 cases) and Han Chinese accounted for 96.27% (4 488 cases). The M( Q1, Q3) of TSM, TSM Ht2 and TSM BMI in men were 23.30 (20.50, 26.20) kg, 9.02 (8.13, 9.89) kg/m 2, and 1.01 (0.90, 1.13) kg·(kg/m 2) -1, respectively, which were all higher than those in women [TSM: 18.20 (15.70, 20.70) kg, TSM Ht2: 8.18 (7.42, 9.07) kg/m 2 and TSM BMI: 0.79 (0.69, 0.90) kg·(kg/m 2) -1], the differences were significant (all P<0.001). The grip strength of men [ M( Q1, Q3): 24.50 (17.80, 30.80) kg] was higher than that of women [ M( Q1, Q3): 15.60 (11.10, 19.90) kg], the difference was significant ( P<0.001). Southern elderly men had lower TSM and TSM Ht2 compared with northern elderly men (all P<0.001), while there was no significant regional difference in TSM BMI ( P>0.05). Southern elderly women had higher TSM Ht2 and TSM BMI compared with northern elderly women (all P<0.001), while there was no significant regional difference in TSM ( P>0.05). Furthermore, according to the method recommended by AWGS, the elderly with low muscle mass and grip strength were characterized by older age, illiteracy, being unmarried/divorced/widowed, poor chewing ability, impaired activity of daily living and living in southern region. Conclusion:There were population and regional differences in muscle mass and grip strength in the older adults over 65 years in 18 longevity areas of China, and these differences showed decreasing trends with age.

Objective:To develop a prediction tool for 6-year incident risk of frailty among Chinese older adults aged 65 years or above.Methods:Data from the Chinese Longitudinal Healthy Longevity Survey from 2002 to 2018 was used, including 13 676 older adults aged 65 years or above who were free of frailty at baseline. Key predictors of frailty were identified via the least absolute shrinkage and selection operator (LASSO) method, and were thereafter used to predict the incident frailty based on the Cox proportional hazards regression model. The model was internally validated by 2 000 Bootstrap resamples and evaluated for the performance of discrimination and calibration using the area under the receiver operating characteristic curve (AUC) and calibration curve, respectively. The net benefit of the developed prediction tool was evaluated by decision-curve analysis.Results:The M( Q1, Q3) age and follow-up time of the participants were 81.0 (71.0, 90.0) years and 6.0 (4.1, 9.2) years, respectively. A total of 4 126 older persons (30.2%) were recorded with frailty incidents during the follow-up, with the corresponding incidence density of 41.8/1 000 person-years. A total of 15 key predictors of frailty were selected by LASSO, namely, age, sex, race, education years, meat consumption, tea drinking, performing housework, raising domestic animals, playing cards or mahjong, and baseline status of visual function, activities of the daily living score, instrumental activities of the daily living score, hypertension, heart disease, and self-rated health. The prediction model was internally validated with an AUC of 0.802, with the max Youden's index of 0.467 at a risk threshold of 19.0%. The calibration curve showed high consistency between predicted probabilities and observed proportions of frailty events. The decision curve indicated that higher net benefits could be obtained via the prediction model than did strategies based on intervention in all or none participants for any risk threshold less than 59%, and the model-based net benefit was estimated to be 0.10 at a risk threshold of 19.0%. Conclusions:The herein developed 6-year incident risk prediction model of frailty, based on easily accessible questionnaires and physical examination variables, has good predictive performance. It has application potential in identifying populations at high risk of incident frailty.

With the development of the Scientific, Transparent and Applicable Rankings tool for clinical practice guidelines(STAR), the publication of evaluation and ranking for scientificity, transparency and applicability of Chinese guidelines and consensus published in the medical journals in 2021and 2022, as well as the publication of the STAR evaluation and ranking for some specialities, the STAR evaluation and ranking has received widespread attention in the medical community. In order to further enhance its scientificity and transparency, Methodology and Technology Specialization Committee of the STAR Working Group presents this article to introduce sample identification and speciality assignment in the evaluation and ranking process.

Objective:To investigate the association of urinary cadmium level with body mass index (BMI) and body circumferences among the older adults over 65 years old in 9 longevity areas of China.Methods:Subjects were older adults over 65 years old from the Healthy Aging and Biomarkers Cohort Study (HABCS) between 2017 and 2018 conducted in 9 longevity areas in China. A total of 1 968 older adults were included in this study. Information including socio-demographic characteristics, lifestyles, diet intake, and health status was collected by using questionnaires and physical examinations. Urine samples were collected to detect urinary cadmium and creatinine levels. Body circumferences included waist circumference, hip circumference and calf circumference. Subjects were divided into three groups (low:<0.77 μg/g·creatinine, middle:0.77-1.69 μg/g·creatinine, high:≥1.69 μg/g·creatinine) by tertiles of creatinine-adjusted urinary cadmium concentration. Multiple linear regression models were used to analyze the association of creatinine-adjusted urinary cadmium level with BMI and body circumferences. The dose-response relationship of creatinine-adjusted urinary cadmium concentration with BMI and body circumferences was analyzed by using restrictive cubic splines fitting multiple linear regression model.Results:The mean age of subjects was (83.34±11.14) years old. The median (Q1, Q3) concentration of creatinine-adjusted urinary cadmium was 1.13 (0.63, 2.09) μg/g·creatinine, and the BMI was (22.70±3.82) kg/m 2. The mean values of waist circumference, hip circumference, and calf circumference were (85.42±10.68) cm, (92.67±8.90) cm, and (31.08±4.76) cm, respectively. After controlling confounding factors, the results of the multiple linear regression model showed that for each increment of 1 μg/g·creatinine in creatinine-adjusted urinary cadmium, the change of BMI, waist circumference, hip circumference, and calf circumference in the high-level group was -0.28 (-0.37, -0.19) kg/m 2, -0.74 (-0.96, -0.52) cm, -0.78 (-0.96, -0.61) cm, and -0.20 (-0.30, -0.11) cm, respectively. The restrictive cubic splines curve showed a negative nonlinear association of creatinine-adjusted urinary cadmium with BMI ( Pnonlinear<0.001) and negative linear associations of creatinine-adjusted urinary cadmium with waist circumference ( Plinear<0.001), hip circumference ( Plinear<0.001), and calf circumference ( Plinear<0.001). Conclusion:Urinary cadmium level is significantly associated with decreased BMI, waist circumference, hip circumference and calf circumference among older adults over 65 years old in 9 longevity areas of China.

Objective:To explore a definition of healthy longevity in the Chinese population based on the Delphi method.Methods:Through a comprehensive literature review and expert consultation, the dimensions in the definition of healthy longevity were identified, and a preliminary list of questions was created. Experts in clinical medicine, public health, basic research, and the elderly care service industry, who had been working in the field of geriatric health for at least 5 years, were invited to participate in the Delphi survey from August to December 2022. The survey questionnaires were administered via email in two rounds, and experts were asked to select the optimal options from the provided questions. The active coefficients were expressed by the response rate, and a consensus was reached when the largest number of experts agreed for single-choice questions and more than 70% agreed for multiple-choice questions.Results:In the two rounds, the active coefficients were 96.00% (24/25) and 79.17% (19/24), respectively, and a consensus was finally reached on nine items, including age, physical health, common metabolic indicators, mental health, cognitive function, functional ability, social activity, self-rated health, and subjective well-being. Following discussions among the research team and experts, a final definition of healthy longevity was determined. Healthy longevity could refer to a state of good physical, psychological, cognitive function and social adaptation, as well as subjective well-being, in individuals aged 90 and above. Specifically, individuals with healthy longevity should be free from diseases associated with high disability rates and mortality, such as stroke, cancer, and Parkinson′s disease. They should also maintain reasonable levels of common non-communicable disease indicators, such as blood pressure and blood glucose, and exhibit favorable mental health and cognitive function using validated measurement tools. In addition, individuals with healthy longevity should engage in social interactions with friends and relatives, care for family members, and go out to do things. Meanwhile, with the ability to complete the visual and hearing functions of daily life and communication, and the ability to complete basic activities such as walking, eating, bathing, toileting, dressing, continence of urination, and bowel movement independently, they could rate themselves to be in good health and experience a relatively high level of life satisfaction.Conclusion:A definition of healthy longevity in the Chinese population is established through the two-round Delphi consultation.