Objective: To analyze the association between insufficient sleep and score of depressive symptoms among junior and senior high school students, so as to provide a scientific reference for targeted early intervention measures of adolescents depressive symptoms. Methods: From September to November 2023, a stratified cluster random sampling method was used to select 96 080 junior and senior high school students from 409 schools in 113 districts and counties in Shaanxi Province. A questionnaire survey was conducted using the 2023 Shaanxi Provincial Common Student Diseases and Health Influencing Factors Survey Form, and their height and weight were measured. Propensity score (PS) matched (1∶1) analysis was used to match participants with insufficient sleep to those sufficient sleep students. Through the gradual correction of the confounders, three multilevel linear models were established to analyze the association between insufficient sleep and depressive symptoms score, and subgroup analysis was conducted afterward. Results: A total of 70 135 (73.00%) students had insufficient sleep. After PS matching, 25 894 pairs were matched. Before PS matching, after adjusting for gender, educational stage, region, adolescent characteristics, boarding status, smoking, alcohol consumption, outdoor activities and body mass index grouping, linear regression analysis results showed that compared with students who got adequate sleep, students who lacked sleep had an increase of 1.39 scores ( B=1.39, 95%CI =1.28-1.51) in depressive symptoms; after PS matching, students with insufficient sleep got an increase of 1.32 scores ( B=1.32, 95%CI =1.17- 1.45 ) in depressive symptoms score compared with those who had adequate sleep (both P <0.05). Conclusions The insufficient sleep is associated with the increase of the depressive symptoms score of junior and senior high school students. It is recommended that junior and senior high school students should keep a good sleeping habit, so as to reduce the prevalence of depressive symptoms.

Objective:To understand the prevalence of frailty and the importance of its influencing factors in adult population in Shaanxi Province.Methods:The data were from Shaanxi baseline survey of natural population cohort study in northwest China during 2018-2019. The frailty index (FI) was constructed to evaluate the frailty status of the population, and XGboost model combined with Shapley method was used to analyze the importance of the sociodemographic and life behavior factors affecting the prevalence of frailty by gender and age.Results:A total of 25 079 subjects were included, in whom 964 (3.8%) had frailty, and there was no significant difference in the overall prevalence of frailty between women (3.9%) and men (3.8%) ( P=0.629), but there was a gender specific difference in the distribution of FI ( P<0.001), and the proportion of the pre-frailty in men was higher than that in women. The prevalence of frailty increased with age ( P<0.001), the prevalence of frailty were 1.3%, 2.5% and 7.8% in young, middle-aged and elderly women, respectively, and 1.9%, 2.7% and 5.5% in young, middle-aged and elderly men, respectively. Sociodemographic characteristics and lifestyle patterns were both influencing factors for the prevalence of frailty, but their importance varied with gender and age. The top five contributing factors were education level, staying up late, annual family income level, sedentary time and marital status in young women, and staying up late, smoking, annual family income level, sedentary time and drinking in young men. The top five contributing factors were education level, annual family income level, passive exposure to smoking, staying up late, and sedentary time in middle-aged women, and annual family income level, education level, sedentary time, staying up late and drinking in middle-aged men. The top five contributing factors were annual family income level, passive exposure to smoking, sedentary time, marital status, and smartphone use in elderly women, and education level, annual family income level, smoking, smartphone use and sedentary time in elderly men. Conclusions:There are gender specific differences in the distribution of FI in Shaanxi. The prevalence of frailty increased with age, but young and middle-aged people also have frailty risk. The prevalence of frailty in young men was mainly related to unhealthy life behaviors, such as staying up late, smoking, sedentary behavior and drinking, while the prevalence of frailty in middle-aged and elderly men and women were more affected by sociodemographic factors, such as education level, economic status and marital status.

Objective To study the age-dependent relationship between body mass index(BMI)and cognitive impairment in rural population aged 40 years and above.Methods From October 2014 to March 2015,people aged 40 years and above,who lived in two natural villages in Huyi District of Xi'an,were selected as the research subjects.Their general demographic information,lifestyle,medical history,family history,physical examination,and biochemical examination were collected.Mini-Mental State Examination(MMSE)was used to evaluate global cognitive function.Cognitive impairment was defined as an MMSE score lower than the cutoff value,specifically,scores ≤17 for subjects who were illiterate,scores ≤20 for subjects with primary school education,and scores ≤24 for subjects with junior high school education or above.The age-dependent relationship between BMI and cognitive impairment was discussed using stratified analysis,restricted cubic spline(RCS),and multivariate Logistic regression.Results We included a total of 1 792 subjects in the analysis,of whom 230(12.8％)were diagnosed with cognitive impairment.There were 726 males(40.5％);the average age was(55.53±9.92)years,ranging from 40 to 85 years,1 193 subjects aged 40-59 years(66.6％),and 599 subjects aged ≥60 years(33.4％).The average BMI was(25.29±3.14)kg/m2.In the total population,BMI index was fitted as restricted cubic splines in the Logistic regression model,and other confounding factors were corrected.The results showed that BMI index was significantly correlated with cognitive impairment(Poverall=0.023),and there was a trend of nonlinear relationship(P nonlinear=0.097).The specific relationship was that with BMI=25 kg/m2 as the reference(OR=1),when BMI index was＜25 kg/m2,the OR value increased as BMI index decreased.However,when BMI index was ≥25 kg/m2,the OR value did not change significantly as BMI index increased.The population was divided into two subgroups according to age(40-59 years vs.≥60 years).Stratified analysis showed that in the ≥60 years old subgroup,cognitive impairment had significant correlation with BMI index(Poverall=0.038,Pnonlinear=0.097),and the changing trend of the correlation was similar to that of the overall population.By contrast,in the 40-59 years old subgroup,BMI index was not significantly associated with cognitive impairment(Poverall=0.722,Pnonlinear=0.738).Conclusion The relationship between BMI and cognitive impairment is affected by age.No significant association is found in the middle-aged population of 40-59 years old,but there may be a nonlinear association in the elderly population over 60 years old.Specifically,with BMI=25 kg/m2 as the boundary,as BMI decreases,the risk of cognitive impairment gradually increases.As BMI further increases,the risk of cognitive impairment does not change significantly even though it reaches the obesity level.

Objective To explore the time cross-lagged effect between carotid intima-media thickness(CIMT)and bone mineral density(BMD)and to assess whether CIMT can be used as an early predictor of osteoporosis.Methods Based on the retrospective cohort study involved,people who underwent health checkups at The First Affiliated Hospital of Xi'an Jiaotong University from January 2019 to December 2023 were selected,and data related to CIMT and BMD were collected.The time-series relationship between CIMT and BMD was explored by cross-lagged modeling.Meanwhile,the effects of CIMT on BMD and its dose-response relationship were assessed using multiple linear regression and restricted cubic spline regression models.Results Analysis of 2 453 study subjects revealed a significant negative relationship between prior physical examination CIMT and subsequent BMD,and this relationship remained significant after controlling for confounders.For every 1-unit increase in CIMT,there was a mean decrease in second-stage BMD T-values of 0.113.Restricted cubic spline regression analysis showed a maximum decrease in BMD T-values of 0.121 for every 1.00 mm increase in CIMT.Conclusion The present study found that there was a significant negative cross-lag effect between CIMT and BMD,and that there was a dose-response between an increase in CIMT and a decrease in BMD.CIMT,as an easy-to-measure indicator,may be a potential marker for early prediction of osteoporosis,especially in the elderly population.

Objective:To understand the prevalence of frailty and the importance of its influencing factors in adult population in Shaanxi Province.Methods:The data were from Shaanxi baseline survey of natural population cohort study in northwest China during 2018-2019. The frailty index (FI) was constructed to evaluate the frailty status of the population, and XGboost model combined with Shapley method was used to analyze the importance of the sociodemographic and life behavior factors affecting the prevalence of frailty by gender and age.Results:A total of 25 079 subjects were included, in whom 964 (3.8%) had frailty, and there was no significant difference in the overall prevalence of frailty between women (3.9%) and men (3.8%) ( P=0.629), but there was a gender specific difference in the distribution of FI ( P<0.001), and the proportion of the pre-frailty in men was higher than that in women. The prevalence of frailty increased with age ( P<0.001), the prevalence of frailty were 1.3%, 2.5% and 7.8% in young, middle-aged and elderly women, respectively, and 1.9%, 2.7% and 5.5% in young, middle-aged and elderly men, respectively. Sociodemographic characteristics and lifestyle patterns were both influencing factors for the prevalence of frailty, but their importance varied with gender and age. The top five contributing factors were education level, staying up late, annual family income level, sedentary time and marital status in young women, and staying up late, smoking, annual family income level, sedentary time and drinking in young men. The top five contributing factors were education level, annual family income level, passive exposure to smoking, staying up late, and sedentary time in middle-aged women, and annual family income level, education level, sedentary time, staying up late and drinking in middle-aged men. The top five contributing factors were annual family income level, passive exposure to smoking, sedentary time, marital status, and smartphone use in elderly women, and education level, annual family income level, smoking, smartphone use and sedentary time in elderly men. Conclusions:There are gender specific differences in the distribution of FI in Shaanxi. The prevalence of frailty increased with age, but young and middle-aged people also have frailty risk. The prevalence of frailty in young men was mainly related to unhealthy life behaviors, such as staying up late, smoking, sedentary behavior and drinking, while the prevalence of frailty in middle-aged and elderly men and women were more affected by sociodemographic factors, such as education level, economic status and marital status.

Objective To explore the time cross-lagged effect between carotid intima-media thickness(CIMT)and bone mineral density(BMD)and to assess whether CIMT can be used as an early predictor of osteoporosis.Methods Based on the retrospective cohort study involved,people who underwent health checkups at The First Affiliated Hospital of Xi'an Jiaotong University from January 2019 to December 2023 were selected,and data related to CIMT and BMD were collected.The time-series relationship between CIMT and BMD was explored by cross-lagged modeling.Meanwhile,the effects of CIMT on BMD and its dose-response relationship were assessed using multiple linear regression and restricted cubic spline regression models.Results Analysis of 2 453 study subjects revealed a significant negative relationship between prior physical examination CIMT and subsequent BMD,and this relationship remained significant after controlling for confounders.For every 1-unit increase in CIMT,there was a mean decrease in second-stage BMD T-values of 0.113.Restricted cubic spline regression analysis showed a maximum decrease in BMD T-values of 0.121 for every 1.00 mm increase in CIMT.Conclusion The present study found that there was a significant negative cross-lag effect between CIMT and BMD,and that there was a dose-response between an increase in CIMT and a decrease in BMD.CIMT,as an easy-to-measure indicator,may be a potential marker for early prediction of osteoporosis,especially in the elderly population.

Objective To study the age-dependent relationship between body mass index(BMI)and cognitive impairment in rural population aged 40 years and above.Methods From October 2014 to March 2015,people aged 40 years and above,who lived in two natural villages in Huyi District of Xi'an,were selected as the research subjects.Their general demographic information,lifestyle,medical history,family history,physical examination,and biochemical examination were collected.Mini-Mental State Examination(MMSE)was used to evaluate global cognitive function.Cognitive impairment was defined as an MMSE score lower than the cutoff value,specifically,scores ≤17 for subjects who were illiterate,scores ≤20 for subjects with primary school education,and scores ≤24 for subjects with junior high school education or above.The age-dependent relationship between BMI and cognitive impairment was discussed using stratified analysis,restricted cubic spline(RCS),and multivariate Logistic regression.Results We included a total of 1 792 subjects in the analysis,of whom 230(12.8％)were diagnosed with cognitive impairment.There were 726 males(40.5％);the average age was(55.53±9.92)years,ranging from 40 to 85 years,1 193 subjects aged 40-59 years(66.6％),and 599 subjects aged ≥60 years(33.4％).The average BMI was(25.29±3.14)kg/m2.In the total population,BMI index was fitted as restricted cubic splines in the Logistic regression model,and other confounding factors were corrected.The results showed that BMI index was significantly correlated with cognitive impairment(Poverall=0.023),and there was a trend of nonlinear relationship(P nonlinear=0.097).The specific relationship was that with BMI=25 kg/m2 as the reference(OR=1),when BMI index was＜25 kg/m2,the OR value increased as BMI index decreased.However,when BMI index was ≥25 kg/m2,the OR value did not change significantly as BMI index increased.The population was divided into two subgroups according to age(40-59 years vs.≥60 years).Stratified analysis showed that in the ≥60 years old subgroup,cognitive impairment had significant correlation with BMI index(Poverall=0.038,Pnonlinear=0.097),and the changing trend of the correlation was similar to that of the overall population.By contrast,in the 40-59 years old subgroup,BMI index was not significantly associated with cognitive impairment(Poverall=0.722,Pnonlinear=0.738).Conclusion The relationship between BMI and cognitive impairment is affected by age.No significant association is found in the middle-aged population of 40-59 years old,but there may be a nonlinear association in the elderly population over 60 years old.Specifically,with BMI=25 kg/m2 as the boundary,as BMI decreases,the risk of cognitive impairment gradually increases.As BMI further increases,the risk of cognitive impairment does not change significantly even though it reaches the obesity level.

Objective:To investigate the relationship between triglyceride glucose index (TyG) and body inflammation.Methods:The data were obtained from a baseline survey in population in Xi'an in natural population cohort study in northwest China established in 2018-2019. Based on TG and FPG, TyG/TyG-BMI was constructed to reflect insulin resistance (IR) in the body, and systemic immune-inflammation index (SII) reflecting inflammation in the body was constructed using neutrophil, lymphocyte, and platelet counts. A logistic regression model was used to explore the relationship between the TyG and the SII.Results:A total of 11 491 subjects were included in the analysis. After adjusting for covariates, each unit increase in the TyG increased the risk of high SII by 21% ( OR=1.21, 95% CI:1.12-1.30). The risk of high SII in the group with the TyG in Q4 was 1.34 times higher than that in the group Q1 ( OR=1.34, 95% CI:1.18-1.52). Both sensitivity analysis and subgroup analysis further confirmed the stability of the association between the TyG and the SII. In the population with a BMI ranging from 18.5 to 23.9 kg/m 2, for every unit increase in the TyG as a continuous variable, the risk for high SII increased by 31% ( OR=1.31, 95% CI:1.18-1.45). As a categorical variable, the risk for high SII in the Q4 group was 1.52 times higher than that in the Q1 group ( OR=1.52, 95% CI:1.27-1.83). In a population with BMIs ranging from 24.0 to 27.9 kg/m 2, for every unit increase in the TyG as a continuous variable, the risk for high SII increased by 20% ( OR=1.20, 95% CI:1.07-1.35), and there was no significant difference when it was a categorical variable. Conclusions:The increase in IR is closely related to the development of inflammation in the body, and BMI may regulate their relationship. Early prevention of elevated IR levels before overweight or obesity may have a positive effect on the control of inflammation in the body.

Cohort study is one of the important research methods in analytical epidemiology because of its clear time sequence relationship, which is better than other observational studies in demonstrating causal association. However, screening diagnosis or other methods are often used to exclude the individuals with outcome events during the enrollment process of the subjects in cohort studies. The accuracy of screening diagnosis and the effectiveness of exclusion will affect the accuracy of the baseline status assessment of the subjects included in the study, which may lead to the causal time sequence reversal of exposure-outcome in the estimation of causal effect. Landmark analysis can be used to control reverse causality by excluding subjects with potentially unknown expose-outcome timing. In this paper, we describe the basic principles and analytical steps of landmark analysis, and use data from the Chinese Longitudinal Healthy Longevity Survey to explore the relationship between physical activity and frailty, and introduce the specific application of landmark analysis for the purpose of facilitating its application and inferring causal effects more accurately in cohort studies.

With completing a baseline survey of a large natural population cohort, conducting regular follow-up has become a key factor in further improving the quality of cohort construction and ensuring its sustainable development. Typical cohort follow-up methods include repeat surveys, routine monitoring, and community-oriented surveillance. However, in practical applications, there are often issues such as high costs, difficulty, and high error rates. Telephone follow-up is an important supplementary method to the methods mentioned above, as it has the characteristics of low cost, fast response, and high quality. However, the with difficult organization, quality control is challenging, response rates are low, and management levels vary widely, which limits its widespread use in large-scale population cohort studies. Given the above problems, this study draws on customer relationship management based on the actual needs of the China Northwest Cohort follow-up. It relies on the REDCap electronic data collection platform to build a telephone follow-up management and quality control system. Targeted solutions are provided for key issues in telephone follow-up implementation, including organizational structure, project management, data collection, and process quality control, to improve the quality control level of telephone follow-up comprehensively and thereby enhance the quality and efficiency of follow-up. We hope to provide standardized follow-up programs and efficient quality control tools for newly established and existing cohort studies.