Objective:Sepsis exhibits remarkable heterogeneity in disease progression trajectories,and accurate identificationof distinct trajectory-based phenotypes is critical for implementing personalized therapeutic strategie...Objective:Sepsis exhibits remarkable heterogeneity in disease progression trajectories,and accurate identificationof distinct trajectory-based phenotypes is critical for implementing personalized therapeutic strategies and prognostic assessment.However,trajectory clustering analysis of time-series clinical data poses substantial methodological challenges for researchers.This study provides a comprehensive tutorial framework demonstrating six trajectory modeling approaches integrated with proteomic analysis to guide researchers in identifying sepsis subtypes after laparoscopic surgery.Methods:This study employs simulated longitudinal data from 300 septic patients after laparoscopic surgery to demonstrate six trajectory modeling methods(group-based trajectory modeling,latent growth mixture modeling,latent transition analysis,time-varying effect modeling,K-means for longitudinal data,agglomerative hierarchical clustering)for identifying associations between predefinedsequential organ failure assessment trajectories and 25 proteomic biomarkers.Clustering performance was evaluated via multiple metrics,and a biomarker discovery pipeline integrating principal component analysis,random forests,feature selection,and receiver operating characteristic analysis was developed.Results:The six methods demonstrated varying performance in identifying trajectory structures,with each approach exhibiting distinct analytical characteristics.The performance metrics revealed differences across methods,which may inform context-specificmethod selection and interpretation strategies.Conclusion:This study illustrates practical implementations of trajectory modeling approaches under controlled conditions,facilitating informed method selection for clinical researchers.The inclusion of complete R code and integrated proteomics workflows offers a reproducible analytical framework connecting temporal pattern recognition to biomarker discovery.Beyond sepsis,this pipeline-oriented approach may be adapted to diverse clinical scenarios requiring longitudinal disease characterization and precision medicine applications.The comparative analysis reveals that each method has distinct strengths,providing a practical guide for clinical researchers in selecting appropriate methods based on their specificstudy goals and data characteristics.展开更多
Objective:To preliminarily construct and apply a longitudinal trajectory model for the prognosis of intracerebral hemorrhage(ICH)based on blood urea nitrogen(BUN)characteristics.Methods:Clinical data from 320 ICH pati...Objective:To preliminarily construct and apply a longitudinal trajectory model for the prognosis of intracerebral hemorrhage(ICH)based on blood urea nitrogen(BUN)characteristics.Methods:Clinical data from 320 ICH patients admitted to our hospital between 2020 and 2024 were collected,including demographic information,National Institutes of Health Stroke Scale(NIHSS)scores at admission,dynamic changes in BUN levels during treatment,and 30-day survival outcomes.A latent class growth model(LCGM)was first used for preliminary modeling,followed by a latent growth mixture modeling(GMM)approach to determine the final model.Three classes of BUN trajectories for ICH prognosis were identified,and latent classes were established.GMM modeling was then performed on these latent classes,considering linear,quadratic,and cubic polynomial forms;six GMM models were constructed and individuals were assigned to latent trajectory groups for validation.Results:LCGM analysis ultimately identified three dynamic BUN trajectory groups:Sustained low-level group(76 cases,23.8%):BUN remained stable between 3.1-9.0 mmol/L,with the highest 30-day survival rate(98.7%).Fluctuating-declining group(222 cases,69.4%):BUN initially increased and then slowly decreased(peak at day 3:15.2 mmol/L),with a 30-day mortality of 8.1%(18/222),higher than the sustained low-level group.Sustained high-level group(22 cases,6.9%):BUN mean>9.0 mmol/L,with a 30-day mortality of 41.7%(P=0.000).GMM model fitting showed that the cubic polynomial GMM model was optimal(AIC=6754.474,BIC=6852.450,Entropy=0.905).Incorporating gender,age,and BMI as covariates revealed significant effects for gender(Estimate=0.045,-0.011,P=0.000,0.000).The AUC for predicting 30-day mortality was 0.88(sensitivity 82.8%,specificity 77.9%),which increased to 0.89 when combined with admission NIHSS scores.Conclusion:The LCGM+GMM model based on dynamic BUN trajectories effectively distinguishes prognostic subgroups in ICH patients.Patients with persistently elevated or fluctuating-rising BUN levels have a significantly higher mortality risk compared to those with sustained low levels.This model provides a new quantitative tool for early identification of high-risk patients and poor prognoses.展开更多
Background Cardiovascular disease(CVD)and frailty are interrelated conditions prevalent in aging populations,yet their dynamic temporal relationship remains underexplored.This study investigates longitudinal changes i...Background Cardiovascular disease(CVD)and frailty are interrelated conditions prevalent in aging populations,yet their dynamic temporal relationship remains underexplored.This study investigates longitudinal changes in frailty trajectories before and after incident CVD across diverse cohorts.Methods Utilizing data from four longitudinal,multinational cohorts(ELSA,HRS,CHARLS,SHARE;n=66,537),we constructed the frailty index(FI)based on age-related health deficits,using 40,40,42,and 44 items from ELSA,HRS,CHARLS and SHARE,respectively.Linear mixed models assessed FI changes pre-and post-CVD,adjusting for demographics,lifestyle,and baseline FI.Sensitivity analyses excluded hypertension,diabetes,and arthritis to mitigate confounding.Results Frailty increased steadily before CVD onset(pre-CVD slope:ELSAβ=0.005,HRSβ=0.005,CHARLSβ=0.012,SHAREβ=0.007;all P<0.001),with an acute FI spike at diagnosis(post-CVD acute change:ELSAβ=0.024,HRSβ=0.031,CHARLSβ=0.046,SHAREβ=0.038;all P<0.001).Post-CVD,frailty progression further accelerated(ELSAβ=0.008,HRSβ=0.005,CHARLSβ=0.017,SHAREβ=0.010;all P<0.001).Sensitivity analyses confirmed robustness across age strata and FI definitions.Conclusions This first multinational study demonstrates bidirectional acceleration of frailty around CVD onset,highlighting their close temporal interplay.These findings suggest that incorporating frailty assessment into CVD management may help identify high-risk individuals and support timely,multidimensional care in aging populations.展开更多
Introduction:Obesity,particularly central adiposity,has been associated with elevated cancer risk.However,longitudinal data on adiposity trajectories and cancer incidence in Chinese populations remain limited.Methods:...Introduction:Obesity,particularly central adiposity,has been associated with elevated cancer risk.However,longitudinal data on adiposity trajectories and cancer incidence in Chinese populations remain limited.Methods:We analyzed data from 25,653 adults with≥10 health check-ups in the West China Hospital Alliance Longitudinal Epidemiology Wellness(WHALE)Study(2010–2023).Five adiposity indicators—body mass index(BMI),waist circumference(WC),waist-to-hip ratio(WHR),BMIadjusted WC(WCadjBMI),and BMI-adjusted WHR(WHRadjBMI)—were evaluated using Poisson regression and generalized linear mixed-effects models.Latent class mixed modeling identified long-term adiposity trajectories.Analyses were stratified by sex and age(≥50 years).Results:Over 14 years,393 participants developed cancer.Higher BMI[risk ratio(RR)=0.873,P=0.019]was associated with lower cancer risk,whereas central adiposity indicators(e.g.,WCadjBMI,RR=1.175,P=0.001)showed positive associations,particularly among men and those aged≥50 years.WCadjBMI was significantly associated with lung cancer(RR=1.246,P=0.009),with similar trends for breast and liver cancers.Inverted U-shaped trajectories of BMIadjusted waist measures were linked to elevated cancer risk,highlighting the relevance of long-term fat distribution.Conclusions:Central adiposity and its trajectories are associated with cancer risk in Chinese adults,supporting dynamic obesity monitoring and targeted prevention in older adults and men.展开更多
基金funding from the China National Key Research and Development Program(No.2023YFC3603104)the National Natural Science Foundation of China(Nos.82472243 and 82272180)+6 种基金the Fundamental Research Funds for the Central Universities(No.226-2025-00024)the Huadong Medicine Joint Funds of the Zhejiang Provincial Natural Science Foundation of China(No.LHDMD24H150001)the Key Research&Development Project of Zhejiang Province(No.2024C03240)a collaborative scientific project co-established by the Science and Technology Department of the National Administration of Traditional Chinese Medicine and the Zhejiang Provincial Administration of Traditional Chinese Medicine(No.GZY-ZJ-KJ-24082)he General Health Science and Technology Program of Zhejiang Province(No.2024KY1099)the Project of Zhejiang University Longquan Innovation Center(No.ZJDXLQCXZCJBGS2024016)Wu Jieping Medical Foundation Special Research Grant(No.320.6750.2024-23-07).
文摘Objective:Sepsis exhibits remarkable heterogeneity in disease progression trajectories,and accurate identificationof distinct trajectory-based phenotypes is critical for implementing personalized therapeutic strategies and prognostic assessment.However,trajectory clustering analysis of time-series clinical data poses substantial methodological challenges for researchers.This study provides a comprehensive tutorial framework demonstrating six trajectory modeling approaches integrated with proteomic analysis to guide researchers in identifying sepsis subtypes after laparoscopic surgery.Methods:This study employs simulated longitudinal data from 300 septic patients after laparoscopic surgery to demonstrate six trajectory modeling methods(group-based trajectory modeling,latent growth mixture modeling,latent transition analysis,time-varying effect modeling,K-means for longitudinal data,agglomerative hierarchical clustering)for identifying associations between predefinedsequential organ failure assessment trajectories and 25 proteomic biomarkers.Clustering performance was evaluated via multiple metrics,and a biomarker discovery pipeline integrating principal component analysis,random forests,feature selection,and receiver operating characteristic analysis was developed.Results:The six methods demonstrated varying performance in identifying trajectory structures,with each approach exhibiting distinct analytical characteristics.The performance metrics revealed differences across methods,which may inform context-specificmethod selection and interpretation strategies.Conclusion:This study illustrates practical implementations of trajectory modeling approaches under controlled conditions,facilitating informed method selection for clinical researchers.The inclusion of complete R code and integrated proteomics workflows offers a reproducible analytical framework connecting temporal pattern recognition to biomarker discovery.Beyond sepsis,this pipeline-oriented approach may be adapted to diverse clinical scenarios requiring longitudinal disease characterization and precision medicine applications.The comparative analysis reveals that each method has distinct strengths,providing a practical guide for clinical researchers in selecting appropriate methods based on their specificstudy goals and data characteristics.
文摘Objective:To preliminarily construct and apply a longitudinal trajectory model for the prognosis of intracerebral hemorrhage(ICH)based on blood urea nitrogen(BUN)characteristics.Methods:Clinical data from 320 ICH patients admitted to our hospital between 2020 and 2024 were collected,including demographic information,National Institutes of Health Stroke Scale(NIHSS)scores at admission,dynamic changes in BUN levels during treatment,and 30-day survival outcomes.A latent class growth model(LCGM)was first used for preliminary modeling,followed by a latent growth mixture modeling(GMM)approach to determine the final model.Three classes of BUN trajectories for ICH prognosis were identified,and latent classes were established.GMM modeling was then performed on these latent classes,considering linear,quadratic,and cubic polynomial forms;six GMM models were constructed and individuals were assigned to latent trajectory groups for validation.Results:LCGM analysis ultimately identified three dynamic BUN trajectory groups:Sustained low-level group(76 cases,23.8%):BUN remained stable between 3.1-9.0 mmol/L,with the highest 30-day survival rate(98.7%).Fluctuating-declining group(222 cases,69.4%):BUN initially increased and then slowly decreased(peak at day 3:15.2 mmol/L),with a 30-day mortality of 8.1%(18/222),higher than the sustained low-level group.Sustained high-level group(22 cases,6.9%):BUN mean>9.0 mmol/L,with a 30-day mortality of 41.7%(P=0.000).GMM model fitting showed that the cubic polynomial GMM model was optimal(AIC=6754.474,BIC=6852.450,Entropy=0.905).Incorporating gender,age,and BMI as covariates revealed significant effects for gender(Estimate=0.045,-0.011,P=0.000,0.000).The AUC for predicting 30-day mortality was 0.88(sensitivity 82.8%,specificity 77.9%),which increased to 0.89 when combined with admission NIHSS scores.Conclusion:The LCGM+GMM model based on dynamic BUN trajectories effectively distinguishes prognostic subgroups in ICH patients.Patients with persistently elevated or fluctuating-rising BUN levels have a significantly higher mortality risk compared to those with sustained low levels.This model provides a new quantitative tool for early identification of high-risk patients and poor prognoses.
基金Natural Science Foundation of Shandong Province(Grant No.ZR2021QH176)。
文摘Background Cardiovascular disease(CVD)and frailty are interrelated conditions prevalent in aging populations,yet their dynamic temporal relationship remains underexplored.This study investigates longitudinal changes in frailty trajectories before and after incident CVD across diverse cohorts.Methods Utilizing data from four longitudinal,multinational cohorts(ELSA,HRS,CHARLS,SHARE;n=66,537),we constructed the frailty index(FI)based on age-related health deficits,using 40,40,42,and 44 items from ELSA,HRS,CHARLS and SHARE,respectively.Linear mixed models assessed FI changes pre-and post-CVD,adjusting for demographics,lifestyle,and baseline FI.Sensitivity analyses excluded hypertension,diabetes,and arthritis to mitigate confounding.Results Frailty increased steadily before CVD onset(pre-CVD slope:ELSAβ=0.005,HRSβ=0.005,CHARLSβ=0.012,SHAREβ=0.007;all P<0.001),with an acute FI spike at diagnosis(post-CVD acute change:ELSAβ=0.024,HRSβ=0.031,CHARLSβ=0.046,SHAREβ=0.038;all P<0.001).Post-CVD,frailty progression further accelerated(ELSAβ=0.008,HRSβ=0.005,CHARLSβ=0.017,SHAREβ=0.010;all P<0.001).Sensitivity analyses confirmed robustness across age strata and FI definitions.Conclusions This first multinational study demonstrates bidirectional acceleration of frailty around CVD onset,highlighting their close temporal interplay.These findings suggest that incorporating frailty assessment into CVD management may help identify high-risk individuals and support timely,multidimensional care in aging populations.
基金Supported by the National Natural Science Foundation of China(32471519)National Natural Science Foundation of China(32171285)the 1.3.5 project for Disciplines of Excellence from West China Hospital of Sichuan University(ZYGD23039).
文摘Introduction:Obesity,particularly central adiposity,has been associated with elevated cancer risk.However,longitudinal data on adiposity trajectories and cancer incidence in Chinese populations remain limited.Methods:We analyzed data from 25,653 adults with≥10 health check-ups in the West China Hospital Alliance Longitudinal Epidemiology Wellness(WHALE)Study(2010–2023).Five adiposity indicators—body mass index(BMI),waist circumference(WC),waist-to-hip ratio(WHR),BMIadjusted WC(WCadjBMI),and BMI-adjusted WHR(WHRadjBMI)—were evaluated using Poisson regression and generalized linear mixed-effects models.Latent class mixed modeling identified long-term adiposity trajectories.Analyses were stratified by sex and age(≥50 years).Results:Over 14 years,393 participants developed cancer.Higher BMI[risk ratio(RR)=0.873,P=0.019]was associated with lower cancer risk,whereas central adiposity indicators(e.g.,WCadjBMI,RR=1.175,P=0.001)showed positive associations,particularly among men and those aged≥50 years.WCadjBMI was significantly associated with lung cancer(RR=1.246,P=0.009),with similar trends for breast and liver cancers.Inverted U-shaped trajectories of BMIadjusted waist measures were linked to elevated cancer risk,highlighting the relevance of long-term fat distribution.Conclusions:Central adiposity and its trajectories are associated with cancer risk in Chinese adults,supporting dynamic obesity monitoring and targeted prevention in older adults and men.
