BACKGROUNDAlthough frailty has been identified as a potential risk factor for cancer, most previous studies have only considered frailty status at a single time point. The relationship between dynamic changes in frailty and incident cancer is less well understood. This study aimed to evaluate the associations of both baseline frailty status and changes in frailty status with subsequent cancer risk.METHODSData were derived from the Health and Retirement Study (HRS), a nationally representative prospective cohort in the United States. Frailty was assessed using a 29-item Rockwood frailty index and categorized as robust, pre-frail or frail. Changes in frailty status were determined over a 2-year period. Incident cancer was identified through self-reported physician diagnoses. Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs), adjusting for demographic, lifestyle and health-related covariates.RESULTSA total of 11 661 participants (63.1% female; mean age: 67.1 years) were included in the baseline frailty analysis, and 10 178 participants (63.8% female; mean age: 66.3 years) were included in the frailty change analysis. During a median follow-up of 7.2 years, baseline frailty was associated with a significantly increased risk of incident cancer (frail vs. robust: HR 1.61, 95% CI 1.27-2.02; pre-frail vs. robust: HR 1.46, 95% CI 1.17-1.83). Over the 2-year transition period, participants who progressed from robust to pre-frail/frail status had a higher cancer risk compared to those who remained robust (HR 2.50, 95% CI 1.74-3.61). Conversely, frail individuals who improved to pre-frail or robust status had a reduced cancer risk relative to those who remained frail (HR 0.66, 95% CI 0.48-0.90). Similar risk reduction was observed among pre-frail individuals who recovered to robust status (HR 0.51, 95% CI 0.34-0.76). Additionally, greater increases in frailty index over timeremained associated with elevated cancer risk after multivariable adjustment (highest vs. lowest quartile of ΔFI: HR 1.35, 95% CI 1.13-1.63; p for trend < 0.001).CONCLUSIONSBoth baseline frailty and changes in frailty status are independently associated with cancer risk. Frailty progression significantly increases the risk of incident cancer, whereas recovery from frailty is associated with reduced risk. These findings underscore the importance of dynamic frailty monitoring and suggest that interventions targeting frailty warrant investigation for potential cancer risk reduction.
虽然虚弱已被确定为癌症的潜在危险因素,但大多数先前的研究只考虑了单一时间点的虚弱状态。虚弱的动态变化和癌症的发生之间的关系还不太清楚。本研究旨在评估基线虚弱状态和虚弱状态变化与随后癌症风险的关系。方法数据来源于健康与退休研究(HRS),这是美国具有全国代表性的前瞻性队列研究。虚弱是用29项Rockwood虚弱指数来评估的,分为强壮、预虚弱和虚弱。衰弱状态的变化是在2年内确定的。偶发性癌症是通过自我报告的医生诊断来确定的。Cox比例风险模型用于估计风险比(hr)和95%置信区间(ci),并对人口统计学、生活方式和健康相关协变量进行调整。结果基线衰弱分析共纳入11 661例(女性63.1%,平均年龄67.1岁),衰弱改变分析纳入10 178例(女性63.8%,平均年龄66.3岁)。在中位随访7.2年期间,基线虚弱与癌症发生风险显著增加相关(虚弱vs强壮:HR 1.61, 95% CI 1.27-2.02;虚弱前期vs强壮:HR 1.46, 95% CI 1.17-1.83)。在2年的过渡期内,从健康状态进展到虚弱/虚弱前状态的参与者与保持健康状态的参与者相比具有更高的癌症风险(HR 2.50, 95% CI 1.74-3.61)。相反,体弱的个体改善到体弱前或健壮状态的癌症风险相对于那些仍然体弱的个体降低(HR 0.66, 95% CI 0.48-0.90)。在恢复到健壮状态的体弱前个体中观察到类似的风险降低(HR 0.51, 95% CI 0.34-0.76)。此外,在多变量调整后,随着时间的推移,虚弱指数的增加仍然与癌症风险升高有关(最高四分位数vs最低四分位数ΔFI: HR 1.35, 95% CI 1.13-1.63; p < 0.001)。结论基线虚弱和虚弱状态的改变与癌症风险独立相关。虚弱的进展会显著增加患癌症的风险,而从虚弱中恢复则与降低风险相关。这些发现强调了动态虚弱监测的重要性,并表明针对虚弱的干预措施值得调查,以降低潜在的癌症风险。
{"title":"Changes in Frailty and Incident Cancer: Evidence From the Health and Retirement Study.","authors":"Zhaoting Bu,Xinying Chen,Xiaoyue Liu,Bing Yin,Sanyu Ge,Xin Zheng,Changhong Xu,Hong Zhao,Yi Li,Xiangrui Li,Hanping Shi","doi":"10.1002/jcsm.70164","DOIUrl":"https://doi.org/10.1002/jcsm.70164","url":null,"abstract":"BACKGROUNDAlthough frailty has been identified as a potential risk factor for cancer, most previous studies have only considered frailty status at a single time point. The relationship between dynamic changes in frailty and incident cancer is less well understood. This study aimed to evaluate the associations of both baseline frailty status and changes in frailty status with subsequent cancer risk.METHODSData were derived from the Health and Retirement Study (HRS), a nationally representative prospective cohort in the United States. Frailty was assessed using a 29-item Rockwood frailty index and categorized as robust, pre-frail or frail. Changes in frailty status were determined over a 2-year period. Incident cancer was identified through self-reported physician diagnoses. Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs), adjusting for demographic, lifestyle and health-related covariates.RESULTSA total of 11 661 participants (63.1% female; mean age: 67.1 years) were included in the baseline frailty analysis, and 10 178 participants (63.8% female; mean age: 66.3 years) were included in the frailty change analysis. During a median follow-up of 7.2 years, baseline frailty was associated with a significantly increased risk of incident cancer (frail vs. robust: HR 1.61, 95% CI 1.27-2.02; pre-frail vs. robust: HR 1.46, 95% CI 1.17-1.83). Over the 2-year transition period, participants who progressed from robust to pre-frail/frail status had a higher cancer risk compared to those who remained robust (HR 2.50, 95% CI 1.74-3.61). Conversely, frail individuals who improved to pre-frail or robust status had a reduced cancer risk relative to those who remained frail (HR 0.66, 95% CI 0.48-0.90). Similar risk reduction was observed among pre-frail individuals who recovered to robust status (HR 0.51, 95% CI 0.34-0.76). Additionally, greater increases in frailty index over timeremained associated with elevated cancer risk after multivariable adjustment (highest vs. lowest quartile of ΔFI: HR 1.35, 95% CI 1.13-1.63; p for trend < 0.001).CONCLUSIONSBoth baseline frailty and changes in frailty status are independently associated with cancer risk. Frailty progression significantly increases the risk of incident cancer, whereas recovery from frailty is associated with reduced risk. These findings underscore the importance of dynamic frailty monitoring and suggest that interventions targeting frailty warrant investigation for potential cancer risk reduction.","PeriodicalId":186,"journal":{"name":"Journal of Cachexia, Sarcopenia and Muscle","volume":"09 1","pages":"e70164"},"PeriodicalIF":8.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145759926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
BACKGROUNDAI-driven automated body composition analysis (BCA) may provide quantitative prognostic biomarkers derived from routine staging CTs. This two-centre study evaluates the prognostic value of these volumetric markers for overall survival in lung cancer patients.METHODSLung cancer cohorts from Hospital A (n = 3345, median age 65, 86% NSCLC, 40% M1, 40% female) and B (n = 1364, median age 66, 87% NSCLC, 37% M1, 38% female) underwent automated BCA of abdominal CTs ±60 days of primary diagnosis. A deep learning network segmented muscle, bone and adipose tissues (visceral = VAT, subcutaneous = SAT, intra-/intermuscular = IMAT and total = TAT) to derive three markers: Sarcopenia Index (SI = Muscle/Bone), Myosteatotic Fat Index (MFI = IMAT/TAT) and Abdominal Fat Index (AFI = VAT/SAT). Kaplan-Meier survival analysis, Cox proportional hazards modelling and machine learning-based survival prediction were performed. A survival model including clinical data (BMI, ECOG, L3-SMI, -SATI, -VATI and -IMATI) was fitted on Hospital A data and validated on Hospital B data.RESULTSIn nonmetastatic NSCLC, high SI predicted longer survival across centres for males (Hospital A: 24.6 vs. 46.0 months; Hospital B: 13.3 vs. 28.9 months; both p < 0.001) and females (Hospital A: 37.9 vs. 53.6 months, p = 0.008; Hospital B: 23.0 vs. 28.6 months, p = 0.018). High MFI indicated reduced survival in males at both hospitals (Hospital A: 43.7 vs. 28.2 months; Hospital B: 28.8 vs. 14.3 months; both p ≤ 0.001) but showed center-dependent effects in females (significant only in Hospital A, p < 0.01). In metastatic disease, SI remained prognostic for males at both centres (p < 0.05), while MFI was significant only in Hospital A (p ≤ 0.001) and AFI only in Hospital B (p = 0.042). Multivariate Cox regression confirmed that higher SI was protective (A: HR 0.53, B: 0.59, p ≤ 0.001), while MFI was associated with shorter survival (A: HR 1.31, B: 1.12, p < 0.01). The multivariate survival model trained on Hospital A's data demonstrated prognostic differentiation of groups in internal (n = 209, p ≤ 0.001) and external (Hospital B, n = 361, p = 0.044) validation, with SI feature importance (0.037) ranking below ECOG (0.082) and M-status (0.078), outperforming all other features including conventional L3-single-slice measurements.CONCLUSIONCT-based volumetric BCA provides prognostic biomarkers in lung cancer with varying significance by sex, disease stage and centre. SI was the strongest prognostic marker, outperforming conventional L3-based measurements, while fat-related markers showed varying associations. Our multivariate model suggests that BCA markers, particularly SI, may enhance risk stratification in lung cancer, pending centre-specific and sex-specific validation. Integration of these markers into clinical workflows could enable personalized care and targeted interventions for high-risk patients.
{"title":"Exploration of Fully-Automated Body Composition Analysis Using Routine CT-Staging of Lung Cancer Patients for Survival Prognosis.","authors":"Marc-David Künnemann,Christian Römer,Anne Helfen,Annalen Bleckmann,Marcel Kemper,Walter Heindel,Tobias J Brix,Michael Forsting,Johannes Haubold,Marcel Opitz,Martin Schuler,Felix Nensa,Katarzyna Borys,René Hosch","doi":"10.1002/jcsm.70021","DOIUrl":"https://doi.org/10.1002/jcsm.70021","url":null,"abstract":"BACKGROUNDAI-driven automated body composition analysis (BCA) may provide quantitative prognostic biomarkers derived from routine staging CTs. This two-centre study evaluates the prognostic value of these volumetric markers for overall survival in lung cancer patients.METHODSLung cancer cohorts from Hospital A (n = 3345, median age 65, 86% NSCLC, 40% M1, 40% female) and B (n = 1364, median age 66, 87% NSCLC, 37% M1, 38% female) underwent automated BCA of abdominal CTs ±60 days of primary diagnosis. A deep learning network segmented muscle, bone and adipose tissues (visceral = VAT, subcutaneous = SAT, intra-/intermuscular = IMAT and total = TAT) to derive three markers: Sarcopenia Index (SI = Muscle/Bone), Myosteatotic Fat Index (MFI = IMAT/TAT) and Abdominal Fat Index (AFI = VAT/SAT). Kaplan-Meier survival analysis, Cox proportional hazards modelling and machine learning-based survival prediction were performed. A survival model including clinical data (BMI, ECOG, L3-SMI, -SATI, -VATI and -IMATI) was fitted on Hospital A data and validated on Hospital B data.RESULTSIn nonmetastatic NSCLC, high SI predicted longer survival across centres for males (Hospital A: 24.6 vs. 46.0 months; Hospital B: 13.3 vs. 28.9 months; both p < 0.001) and females (Hospital A: 37.9 vs. 53.6 months, p = 0.008; Hospital B: 23.0 vs. 28.6 months, p = 0.018). High MFI indicated reduced survival in males at both hospitals (Hospital A: 43.7 vs. 28.2 months; Hospital B: 28.8 vs. 14.3 months; both p ≤ 0.001) but showed center-dependent effects in females (significant only in Hospital A, p < 0.01). In metastatic disease, SI remained prognostic for males at both centres (p < 0.05), while MFI was significant only in Hospital A (p ≤ 0.001) and AFI only in Hospital B (p = 0.042). Multivariate Cox regression confirmed that higher SI was protective (A: HR 0.53, B: 0.59, p ≤ 0.001), while MFI was associated with shorter survival (A: HR 1.31, B: 1.12, p < 0.01). The multivariate survival model trained on Hospital A's data demonstrated prognostic differentiation of groups in internal (n = 209, p ≤ 0.001) and external (Hospital B, n = 361, p = 0.044) validation, with SI feature importance (0.037) ranking below ECOG (0.082) and M-status (0.078), outperforming all other features including conventional L3-single-slice measurements.CONCLUSIONCT-based volumetric BCA provides prognostic biomarkers in lung cancer with varying significance by sex, disease stage and centre. SI was the strongest prognostic marker, outperforming conventional L3-based measurements, while fat-related markers showed varying associations. Our multivariate model suggests that BCA markers, particularly SI, may enhance risk stratification in lung cancer, pending centre-specific and sex-specific validation. Integration of these markers into clinical workflows could enable personalized care and targeted interventions for high-risk patients.","PeriodicalId":186,"journal":{"name":"Journal of Cachexia, Sarcopenia and Muscle","volume":"27 1","pages":"e70021"},"PeriodicalIF":8.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144787090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pamela N. Klassen, Vera C. Mazurak, Jessica Thorlakson, Stephane Servais
Investigators are increasingly measuring skeletal muscle (SM) and adipose tissue (AT) change during cancer treatment to understand impact on patient outcomes. Recent meta‐analyses have reported high heterogeneity in this literature, representing uncertainty in the resulting estimates. Using the setting of palliative‐intent chemotherapy as an exemplar, we aimed to systematically summarize the sources of variability among studies evaluating SM and AT change during cancer treatment and propose standards for future studies to enable reliable meta‐analysis. Studies that measured computed tomography‐defined SM and/or AT change in adult patients during palliative‐intent chemotherapy for solid tumours were included, with no date or geographical limiters. Of 2496 publications screened by abstract/title, 83 were reviewed in full text and 38 included for extraction, representing 34 unique cohorts across 8 tumour sites. The timing of baseline measurement was frequently defined as prior to treatment, while endpoint timing ranged from 6 weeks after treatment start to time of progression. Fewer than 50% specified the actual time interval between measurements. Measurement error was infrequently discussed (8/34). A single metric (cm2/m2, cm2 or %) was used to describe SM change in 18/34 cohorts, while multiple metrics were presented for 10/34 and no descriptive metrics for 6/34. AT change metrics and sex‐specific reporting were available for 10/34 cohorts. Associations between SM loss and overall survival were evaluated in 24 publications, with classification of SM loss ranging from any loss to >14% loss over variable time intervals. Age and sex were the most common covariates, with disease response in 50% of models. Despite a wealth of data and effort, heterogeneity in study design, reporting and statistical analysis hinders evidence synthesis regarding the severity and outcomes of SM and AT change during cancer treatment. Proposed standards for study design include selection of homogenous cohorts, clear definition of baseline/endpoint timing and attention to measurement error. Standard reporting should include baseline SM and AT by sex, actual scan interval, SM and AT change using multiple metrics and visualization of the range of change observed. Reporting by sex would advance understanding of sexual dimorphism in SM and AT change. Evaluating the impact of tissue change on outcomes requires adjustment for relevant covariates and concurrent disease response. Adoption of these standards by researchers and publishers would alter the current paradigm to enable meta‐analysis of future studies and move the field towards meaningful application of SM and AT change to clinical care.
{"title":"Call for standardization in assessment and reporting of muscle and adipose change using computed tomography analysis in oncology: A scoping review","authors":"Pamela N. Klassen, Vera C. Mazurak, Jessica Thorlakson, Stephane Servais","doi":"10.1002/jcsm.13318","DOIUrl":"10.1002/jcsm.13318","url":null,"abstract":"Investigators are increasingly measuring skeletal muscle (SM) and adipose tissue (AT) change during cancer treatment to understand impact on patient outcomes. Recent meta‐analyses have reported high heterogeneity in this literature, representing uncertainty in the resulting estimates. Using the setting of palliative‐intent chemotherapy as an exemplar, we aimed to systematically summarize the sources of variability among studies evaluating SM and AT change during cancer treatment and propose standards for future studies to enable reliable meta‐analysis. Studies that measured computed tomography‐defined SM and/or AT change in adult patients during palliative‐intent chemotherapy for solid tumours were included, with no date or geographical limiters. Of 2496 publications screened by abstract/title, 83 were reviewed in full text and 38 included for extraction, representing 34 unique cohorts across 8 tumour sites. The timing of baseline measurement was frequently defined as prior to treatment, while endpoint timing ranged from 6 weeks after treatment start to time of progression. Fewer than 50% specified the actual time interval between measurements. Measurement error was infrequently discussed (8/34). A single metric (cm2/m2, cm2 or %) was used to describe SM change in 18/34 cohorts, while multiple metrics were presented for 10/34 and no descriptive metrics for 6/34. AT change metrics and sex‐specific reporting were available for 10/34 cohorts. Associations between SM loss and overall survival were evaluated in 24 publications, with classification of SM loss ranging from any loss to >14% loss over variable time intervals. Age and sex were the most common covariates, with disease response in 50% of models. Despite a wealth of data and effort, heterogeneity in study design, reporting and statistical analysis hinders evidence synthesis regarding the severity and outcomes of SM and AT change during cancer treatment. Proposed standards for study design include selection of homogenous cohorts, clear definition of baseline/endpoint timing and attention to measurement error. Standard reporting should include baseline SM and AT by sex, actual scan interval, SM and AT change using multiple metrics and visualization of the range of change observed. Reporting by sex would advance understanding of sexual dimorphism in SM and AT change. Evaluating the impact of tissue change on outcomes requires adjustment for relevant covariates and concurrent disease response. Adoption of these standards by researchers and publishers would alter the current paradigm to enable meta‐analysis of future studies and move the field towards meaningful application of SM and AT change to clinical care.","PeriodicalId":186,"journal":{"name":"Journal of Cachexia, Sarcopenia and Muscle","volume":"14 5","pages":"1918-1931"},"PeriodicalIF":8.9,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jcsm.13318","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10524503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nicolas Collao, Donna D'Souza, Laura Messeiller, Evan Pilon, Jessica Lloyd, Jillian Larkin, Matthew Ngu, Alexanne Cuillerier, Alexander E. Green, Keir J. Menzies, Yan Burelle, Michael De Lisio