Pub Date : 2025-09-24DOI: 10.1007/s10654-025-01300-2
Daniel Dyball,Susie Schofield,Howard Burdett,Christopher J Boos,Anthony M J Bull,Paul Cullinan,Nicola T Fear,Alexander N Bennett,
The ADVANCE cohort study is a prospective cohort study investigating the impact of sustaining a serious physical combat injury whilst on deployment to Afghanistan on long-term health outcomes. The cohort will provide essential data on medical/psychosocial risk factors and outcomes associated with combat injury and establish injury-specific mechanisms of disease. Participants include UK Armed Forces personnel who sustained serious physical combat injuries and a frequency-matched comparison group who sustained no such injuries (uninjured group). The cohort consists of 1145 participants, with a baseline response rate of 59.6% for the injured group (n = 579) and 56.3% for the uninjured group (n = 566). The first follow up of this cohort retained 92% of the sample (n = 1053/1145). This cohort profile describes the baseline and first follow up demographics for ADVANCE, as well as details on published research projects spanning military epidemiology, physical health, including cardiovascular, respiratory, musculoskeletal and neurological, mental health and health-related behaviours. These projects comprise identification of early risk factors for disease and observed differences in health-characteristics between groups. Since baseline assessment of the cohort, ADVANCE has expanded investigations across physical and mental health domains, utilising advice from participant engagement and an international scientific advisory group. Researchers working on the ADVANCE cohort continue to engage with policy makers, clinicians and participants to ensure a wide-ranging impact from work conducted.Registration: The ADVANCE Study is registered at ISRCTN ID: ISRCTN57285353.
{"title":"Evaluating the physical and psychosocial impact of serious physical combat injuries in UK armed forces personnel-the ADVANCE cohort study.","authors":"Daniel Dyball,Susie Schofield,Howard Burdett,Christopher J Boos,Anthony M J Bull,Paul Cullinan,Nicola T Fear,Alexander N Bennett, ","doi":"10.1007/s10654-025-01300-2","DOIUrl":"https://doi.org/10.1007/s10654-025-01300-2","url":null,"abstract":"The ADVANCE cohort study is a prospective cohort study investigating the impact of sustaining a serious physical combat injury whilst on deployment to Afghanistan on long-term health outcomes. The cohort will provide essential data on medical/psychosocial risk factors and outcomes associated with combat injury and establish injury-specific mechanisms of disease. Participants include UK Armed Forces personnel who sustained serious physical combat injuries and a frequency-matched comparison group who sustained no such injuries (uninjured group). The cohort consists of 1145 participants, with a baseline response rate of 59.6% for the injured group (n = 579) and 56.3% for the uninjured group (n = 566). The first follow up of this cohort retained 92% of the sample (n = 1053/1145). This cohort profile describes the baseline and first follow up demographics for ADVANCE, as well as details on published research projects spanning military epidemiology, physical health, including cardiovascular, respiratory, musculoskeletal and neurological, mental health and health-related behaviours. These projects comprise identification of early risk factors for disease and observed differences in health-characteristics between groups. Since baseline assessment of the cohort, ADVANCE has expanded investigations across physical and mental health domains, utilising advice from participant engagement and an international scientific advisory group. Researchers working on the ADVANCE cohort continue to engage with policy makers, clinicians and participants to ensure a wide-ranging impact from work conducted.Registration: The ADVANCE Study is registered at ISRCTN ID: ISRCTN57285353.","PeriodicalId":11907,"journal":{"name":"European Journal of Epidemiology","volume":"12 1","pages":""},"PeriodicalIF":13.6,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145127285","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}
Pub Date : 2025-09-24DOI: 10.1007/s10654-025-01305-x
Soohyeon Ko
{"title":"Null within-twin estimates on education and dementia: cautions for within-family contrasts.","authors":"Soohyeon Ko","doi":"10.1007/s10654-025-01305-x","DOIUrl":"https://doi.org/10.1007/s10654-025-01305-x","url":null,"abstract":"","PeriodicalId":11907,"journal":{"name":"European Journal of Epidemiology","volume":"38 1","pages":""},"PeriodicalIF":13.6,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145127287","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}
{"title":"Sex, male origin microchimerism, and mortality in a Danish cohort.","authors":"Gitte Lindved Petersen,Tri-Long Nguyen,Rune Lindahl-Jacobsen,Anne Tjønneland,Mads Kamper-Jørgensen","doi":"10.1007/s10654-025-01309-7","DOIUrl":"https://doi.org/10.1007/s10654-025-01309-7","url":null,"abstract":"","PeriodicalId":11907,"journal":{"name":"European Journal of Epidemiology","volume":"156 1","pages":""},"PeriodicalIF":13.6,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145127289","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}
Studies suggest a positive association between ultra-processed food (UPF) intake and type 2 diabetes risk. However, studies were primarily conducted in Western populations with diets and disease profiles different from populations living elsewhere. In addition, the dose-response relationship needs to be further elucidated. We conducted an individual-level pooled analysis of three Korean prospective cohorts (n = 72,776). UPF intake (in the percentage of g/d as the main UPF unit) was assessed using validated food frequency questionnaires and categorized according to Nova classification. Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). We subsequently conducted a meta-analysis of prospective studies, including recent studies from both Western and non-Western populations, to assess the dose-response relationship. In a pooled analysis of Korean cohorts (up to 18 years of follow-up), the highest (vs. lowest) quartile of UPF intake was associated with an increased type 2 diabetes risk (pooled HR [95% CI] = 1.11 [1.02, 1.21] , p-trend = 0.03). The positive association persisted after additional adjustment for BMI, nutritional factors (fiber, sodium, and carbohydrate intakes), or diet quality score. Among individual UPF subgroups, processed meats, ready-to-eat/heat mixed dishes, and ice cream were positively associated with diabetes risk. In a meta-analysis of 17 prospective cohorts, every 10% (of g/d) increment in UPF intake was associated with a 10% (summary RR [95% CI] = 1.10 [1.08, 1.12] ) higher risk in a dose-response fashion. Our meta-evidence supports that higher UPF intake may monotonically increase type 2 diabetes risk.
{"title":"Ultra-processed food intake and risk of type 2 diabetes: a pooled analysis of three prospective cohorts of Korean adults and an updated meta-analysis.","authors":"Yujin Kim,Yoonkyoung Cho,Bonjae Koo,Zhangling Chen,Qi Sun,Hannah Oh","doi":"10.1007/s10654-025-01297-8","DOIUrl":"https://doi.org/10.1007/s10654-025-01297-8","url":null,"abstract":"Studies suggest a positive association between ultra-processed food (UPF) intake and type 2 diabetes risk. However, studies were primarily conducted in Western populations with diets and disease profiles different from populations living elsewhere. In addition, the dose-response relationship needs to be further elucidated. We conducted an individual-level pooled analysis of three Korean prospective cohorts (n = 72,776). UPF intake (in the percentage of g/d as the main UPF unit) was assessed using validated food frequency questionnaires and categorized according to Nova classification. Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). We subsequently conducted a meta-analysis of prospective studies, including recent studies from both Western and non-Western populations, to assess the dose-response relationship. In a pooled analysis of Korean cohorts (up to 18 years of follow-up), the highest (vs. lowest) quartile of UPF intake was associated with an increased type 2 diabetes risk (pooled HR [95% CI] = 1.11 [1.02, 1.21] , p-trend = 0.03). The positive association persisted after additional adjustment for BMI, nutritional factors (fiber, sodium, and carbohydrate intakes), or diet quality score. Among individual UPF subgroups, processed meats, ready-to-eat/heat mixed dishes, and ice cream were positively associated with diabetes risk. In a meta-analysis of 17 prospective cohorts, every 10% (of g/d) increment in UPF intake was associated with a 10% (summary RR [95% CI] = 1.10 [1.08, 1.12] ) higher risk in a dose-response fashion. Our meta-evidence supports that higher UPF intake may monotonically increase type 2 diabetes risk.","PeriodicalId":11907,"journal":{"name":"European Journal of Epidemiology","volume":"72 1","pages":""},"PeriodicalIF":13.6,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145068440","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}
Social isolation is recognized as a public health emergency. However, major guidelines provide vastly different recommendations on how to target it, and no strategy has been substantiated on firm theoretical or empirical grounds, yet. Rose's seminal The Strategy of Preventive Medicine provided a theoretical framework for such arbitrations between approaches. Therein, determining the shape of the relationship between risk factor and outcome is of paramount importance. However, quantitative approaches immediately applying this theory to evidence are still lacking. Thus, in this pre-registered analysis, we pursued a novel approach and employed generalized additive mixed models to model the shape of social isolation's Links to brain, cognitive and mental health outcomes in a well-characterised population-based sample. We derived brain measures from 3T MRIs, assessed cognitive functions with extensive neuropsychological testing and measured social isolation and mental health outcomes using established questionnaires. Overall, we studied over 10,000 (mean age 58a, 53% women) participants at baseline and over 5500 (mean age 64a, 53% women) at follow-up after ~ 6 years. The relationship of social contact with almost all outcomes was firmly linear and did not differ above and below the standard threshold for social isolation. Only for processing speed did we detect a steeper slope amongst socially isolated individuals. Hence, most of the health effects of social contact were observed in individuals that would not be categorised as socially isolated. Applying advanced statistical methods to a large and well-characterised dataset we provide evidence in support of a shift in focus away from individual-level and towards population-level preventive approaches.
{"title":"Generalized additive mixed models to discern data-driven theoretically informed strategies for public brain, cognitive and mental health.","authors":"Laurenz Lammer,Frauke Beyer,Steffi Riedel-Heller,Julia Sacher,Heide Glaesmer,Arno Villringer,A Veronica Witte","doi":"10.1007/s10654-025-01296-9","DOIUrl":"https://doi.org/10.1007/s10654-025-01296-9","url":null,"abstract":"Social isolation is recognized as a public health emergency. However, major guidelines provide vastly different recommendations on how to target it, and no strategy has been substantiated on firm theoretical or empirical grounds, yet. Rose's seminal The Strategy of Preventive Medicine provided a theoretical framework for such arbitrations between approaches. Therein, determining the shape of the relationship between risk factor and outcome is of paramount importance. However, quantitative approaches immediately applying this theory to evidence are still lacking. Thus, in this pre-registered analysis, we pursued a novel approach and employed generalized additive mixed models to model the shape of social isolation's Links to brain, cognitive and mental health outcomes in a well-characterised population-based sample. We derived brain measures from 3T MRIs, assessed cognitive functions with extensive neuropsychological testing and measured social isolation and mental health outcomes using established questionnaires. Overall, we studied over 10,000 (mean age 58a, 53% women) participants at baseline and over 5500 (mean age 64a, 53% women) at follow-up after ~ 6 years. The relationship of social contact with almost all outcomes was firmly linear and did not differ above and below the standard threshold for social isolation. Only for processing speed did we detect a steeper slope amongst socially isolated individuals. Hence, most of the health effects of social contact were observed in individuals that would not be categorised as socially isolated. Applying advanced statistical methods to a large and well-characterised dataset we provide evidence in support of a shift in focus away from individual-level and towards population-level preventive approaches.","PeriodicalId":11907,"journal":{"name":"European Journal of Epidemiology","volume":"20 1","pages":""},"PeriodicalIF":13.6,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145059190","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}
Pub Date : 2025-09-11DOI: 10.1007/s10654-025-01298-7
Erin E Gabriel,Alex Ocampo,Arvid Sjölander
Although the ideal randomized clinical trial is the gold standard for causal inference, real randomized trials often suffer from imperfections that may hamper causal effect estimation. Stating the estimand of interest can help reduce confusion about what is being estimated, but it is often difficult to determine what is and is not identifiable given a trial's specific imperfections. We demonstrate how directed acyclic graphs can be used to elucidate the consequences of common imperfections, such as noncompliance, unblinding, and drop-out, for the identification of the intention-to-treat effect, the total treatment effect and the physiological treatment effect. We assert that the physiological treatment effect is not identifiable outside a trial with perfect compliance and no dropout, where blinding is perfectly maintained.
{"title":"Elucidating some common biases in randomized controlled trials using directed acyclic graphs.","authors":"Erin E Gabriel,Alex Ocampo,Arvid Sjölander","doi":"10.1007/s10654-025-01298-7","DOIUrl":"https://doi.org/10.1007/s10654-025-01298-7","url":null,"abstract":"Although the ideal randomized clinical trial is the gold standard for causal inference, real randomized trials often suffer from imperfections that may hamper causal effect estimation. Stating the estimand of interest can help reduce confusion about what is being estimated, but it is often difficult to determine what is and is not identifiable given a trial's specific imperfections. We demonstrate how directed acyclic graphs can be used to elucidate the consequences of common imperfections, such as noncompliance, unblinding, and drop-out, for the identification of the intention-to-treat effect, the total treatment effect and the physiological treatment effect. We assert that the physiological treatment effect is not identifiable outside a trial with perfect compliance and no dropout, where blinding is perfectly maintained.","PeriodicalId":11907,"journal":{"name":"European Journal of Epidemiology","volume":"33 1","pages":""},"PeriodicalIF":13.6,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145031999","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}
Pub Date : 2025-09-04DOI: 10.1007/s10654-025-01299-6
Karoline Moe,Eivind Schjelderup Skarpsno,Tom Ivar Lund Nilsen,Silje L Kaspersen,Solveig Osborg Ose,David Carslake,Paul Jarle Mork,Lene Aasdahl
A more comprehensive understanding of the causal relationships between body mass index (BMI) and sick leave is needed. We aimed to examine the effect of BMI on the risk of cause-specific and all-cause long-term sick leave using an instrumental variable approach. The study included 21,918 adults participating in the two latest surveys of the population-based HUNT Study (HUNT3, 2006-2008 and HUNT4, 2017-2019) linked with registry data on cause-specific sick leave, including musculoskeletal and mental disorders. We used Cox regression to estimate risk of long-term sick leave per standard deviation (SD) increase in z-score of BMI, applying both conventional analysis of own BMI and instrumental variable analysis based on offspring BMI. In the conventional analyses, hazard ratios per SD increase in z-score of BMI ranged from 1.04 (95% confidence interval (CI) 0.99-1.08) for mental health disorders in women to 1.17 (95% CI 1.13-1.22) for musculoskeletal disorders in men. The instrumental variable approach supported that higher BMI increased the risk of long-term sick leave, except for sick leave due to mental health disorders in men. The analyses suggested that offspring BMI as an instrument is not independent of shared confounding. The results from both the conventional and instrumental variable analyses show that higher BMI increases the risk of long-term sick leave, except for sick leave due to mental health disorders in men. The instrumental variable method is likely to remove bias due to reverse causation, but residual bias due to shared confounding factors cannot be ruled out.
需要更全面地了解身体质量指数（BMI）与病假之间的因果关系。我们的目的是使用工具变量方法检查BMI对特定原因和全原因长期病假风险的影响。该研究包括21918名成年人，他们参加了基于人群的HUNT研究（HUNT3, 2006-2008年和HUNT4, 2017-2019年）的两项最新调查，这些调查与特定原因病假的登记数据有关，包括肌肉骨骼和精神疾病。我们采用常规的BMI分析和基于子代BMI的工具变量分析，采用Cox回归来估计长期病假对BMI z得分每标准差（SD）增加的风险。在常规分析中，BMI z得分每SD增加的风险比范围从女性精神健康障碍的1.04（95%可信区间（CI） 0.99-1.08）到男性肌肉骨骼疾病的1.17 （95% CI 1.13-1.22）。工具变量方法支持较高的BMI增加了长期病假的风险，但男性因精神健康障碍而请病假的情况除外。分析表明，后代BMI作为一种工具并不是独立于共同的混杂因素。常规变量分析和工具变量分析的结果表明，高BMI会增加长期病假的风险，但男性因精神健康障碍而请病假的情况除外。工具变量法有可能消除反向因果关系造成的偏倚，但不能排除共同混杂因素造成的残留偏倚。
{"title":"An instrumental variable analysis of body mass index and risk of long-term sick leave: the HUNT Study, Norway.","authors":"Karoline Moe,Eivind Schjelderup Skarpsno,Tom Ivar Lund Nilsen,Silje L Kaspersen,Solveig Osborg Ose,David Carslake,Paul Jarle Mork,Lene Aasdahl","doi":"10.1007/s10654-025-01299-6","DOIUrl":"https://doi.org/10.1007/s10654-025-01299-6","url":null,"abstract":"A more comprehensive understanding of the causal relationships between body mass index (BMI) and sick leave is needed. We aimed to examine the effect of BMI on the risk of cause-specific and all-cause long-term sick leave using an instrumental variable approach. The study included 21,918 adults participating in the two latest surveys of the population-based HUNT Study (HUNT3, 2006-2008 and HUNT4, 2017-2019) linked with registry data on cause-specific sick leave, including musculoskeletal and mental disorders. We used Cox regression to estimate risk of long-term sick leave per standard deviation (SD) increase in z-score of BMI, applying both conventional analysis of own BMI and instrumental variable analysis based on offspring BMI. In the conventional analyses, hazard ratios per SD increase in z-score of BMI ranged from 1.04 (95% confidence interval (CI) 0.99-1.08) for mental health disorders in women to 1.17 (95% CI 1.13-1.22) for musculoskeletal disorders in men. The instrumental variable approach supported that higher BMI increased the risk of long-term sick leave, except for sick leave due to mental health disorders in men. The analyses suggested that offspring BMI as an instrument is not independent of shared confounding. The results from both the conventional and instrumental variable analyses show that higher BMI increases the risk of long-term sick leave, except for sick leave due to mental health disorders in men. The instrumental variable method is likely to remove bias due to reverse causation, but residual bias due to shared confounding factors cannot be ruled out.","PeriodicalId":11907,"journal":{"name":"European Journal of Epidemiology","volume":"15 1","pages":""},"PeriodicalIF":13.6,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144962717","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}
The associations of colorectal cancer (CRC) risk with metabolites, lifestyle factors and their joint effects have not been fully elucidated. Therefore, we conducted a prospective cohort study to estimate the associations of CRC risk with metabolites, metabolic risk score (MRS) and its joint associations with lifestyle factors. This study included 82,514 participants with plasma metabolites data in the UK Biobank. LASSO-COX and Random Forest was used to select metabolites. Cox regression was utilized to construct MRS and estimate the associations of CRC risk with metabolites, MRS and its joint associations with lifestyle factors. Single-cell RNA sequencing data were analyzed to identify metabolism-related genes and metabolic pathways during CRC progression. During a median follow-up of 13.28 years, 1151 incident CRC cases were identified. MRS, constructed using 6 metabolites, was significantly associated with increased CRC risk (HR = 1.39, 95% CI 1.22-1.56 for high vs. low MRS), with the strongest association for proximal colon cancer (HR = 1.51, 95% CI 1.24-1.84), followed by distal colon cancer and rectal cancer (HR = 1.35, 95% CI 1.05-1.72; HR = 1.37, 95% CI 1.11-1.69). Joint associations were identified between MRS and lifestyle factors with CRC risk. Individuals with healthy sleep, never smoking, healthy diet, and healthy lifestyle but high MRS also exhibited elevated CRC risk. Linoleic acid, histidine and tyrosine metabolism pathways played important roles during normal intestinal mucosa to CRC progression. Pre-diagnostic metabolites and MRS were significantly associated with increased CRC risk, especially proximal colon cancer. Individuals should maintain normal metabolite levels and healthy lifestyles for CRC prevention.
结直肠癌（CRC）风险与代谢物、生活方式因素及其联合效应的关系尚未完全阐明。因此，我们进行了一项前瞻性队列研究，以估计结直肠癌风险与代谢物、代谢风险评分（MRS）及其与生活方式因素的联合关系。这项研究包括82514名参与者，他们的血浆代谢物数据来自英国生物银行。使用LASSO-COX和Random Forest筛选代谢物。利用Cox回归构建MRS，估计CRC风险与代谢物、MRS及其与生活方式因素的联合关系。分析单细胞RNA测序数据，以确定CRC进展过程中的代谢相关基因和代谢途径。在中位随访13.28年期间，确定了1151例CRC病例。使用6种代谢物构建的MRS与CRC风险增加显著相关（高MRS vs低MRS的HR = 1.39, 95% CI 1.22-1.56），其中与近端结肠癌的相关性最强（HR = 1.51, 95% CI 1.24-1.84），其次是远端结肠癌和直肠癌（HR = 1.35, 95% CI 1.05-1.72; HR = 1.37, 95% CI 1.11-1.69）。MRS和生活方式因素与结直肠癌风险之间存在联合关联。睡眠健康、从不吸烟、饮食健康、生活方式健康但MRS较高的个体也表现出较高的结直肠癌风险。亚油酸、组氨酸和酪氨酸代谢途径在正常肠黏膜向结直肠癌进展过程中起重要作用。诊断前代谢物和MRS与结直肠癌风险增加显著相关，尤其是近端结肠癌。为了预防结直肠癌，个人应该保持正常的代谢物水平和健康的生活方式。
{"title":"Plasma metabolites, metabolic risk score and colorectal cancer risk: a prospective cohort study.","authors":"Ying Deng,Miaomiao Yang,Panxin Peng,Ying Lin,Jiaqi Lin,Jingyao Huang,Kejia Wu,Xingxing Hu,Zibo Ni,Dongsheng Hu,Ming Zhang,Baochang He,Yinggang Chen,Lin Tian,Chunsheng Cheng,Qingtian Luo,Pei Qin,Xiuyun Chen,Jian Yang,Fulan Hu","doi":"10.1007/s10654-025-01284-z","DOIUrl":"https://doi.org/10.1007/s10654-025-01284-z","url":null,"abstract":"The associations of colorectal cancer (CRC) risk with metabolites, lifestyle factors and their joint effects have not been fully elucidated. Therefore, we conducted a prospective cohort study to estimate the associations of CRC risk with metabolites, metabolic risk score (MRS) and its joint associations with lifestyle factors. This study included 82,514 participants with plasma metabolites data in the UK Biobank. LASSO-COX and Random Forest was used to select metabolites. Cox regression was utilized to construct MRS and estimate the associations of CRC risk with metabolites, MRS and its joint associations with lifestyle factors. Single-cell RNA sequencing data were analyzed to identify metabolism-related genes and metabolic pathways during CRC progression. During a median follow-up of 13.28 years, 1151 incident CRC cases were identified. MRS, constructed using 6 metabolites, was significantly associated with increased CRC risk (HR = 1.39, 95% CI 1.22-1.56 for high vs. low MRS), with the strongest association for proximal colon cancer (HR = 1.51, 95% CI 1.24-1.84), followed by distal colon cancer and rectal cancer (HR = 1.35, 95% CI 1.05-1.72; HR = 1.37, 95% CI 1.11-1.69). Joint associations were identified between MRS and lifestyle factors with CRC risk. Individuals with healthy sleep, never smoking, healthy diet, and healthy lifestyle but high MRS also exhibited elevated CRC risk. Linoleic acid, histidine and tyrosine metabolism pathways played important roles during normal intestinal mucosa to CRC progression. Pre-diagnostic metabolites and MRS were significantly associated with increased CRC risk, especially proximal colon cancer. Individuals should maintain normal metabolite levels and healthy lifestyles for CRC prevention.","PeriodicalId":11907,"journal":{"name":"European Journal of Epidemiology","volume":"27 1","pages":""},"PeriodicalIF":13.6,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144962723","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}
Pub Date : 2025-09-01Epub Date: 2025-07-12DOI: 10.1007/s10654-025-01270-5
Jadwiga Buchwald, Terho Lehtimäki, Olli Raitakari, Veikko Salomaa, Jaakko Kaprio, Matti Pirinen
Faster nicotine metabolism, defined as the nicotine metabolite ratio (NMR), is known to associate with heavier smoking and challenges in smoking cessation. However, the broader health implications of genetically determined nicotine metabolism are not well characterized. We performed a hypothesis-free phenome-wide association study (PheWAS) of over 21,000 outcome variables from UK Biobank (UKB) to explore how the NMR (measured as the 3-hydroxycotinine-to-cotinine ratio) associates with the phenome. As the exposure variable, we used a genetic score for faster nicotine metabolism based on 10 putative causal genetic variants, explaining 33.8 % of the variance in the NMR. We analysed ever and never smokers separately to assess whether a causal pathway through nicotine metabolism is plausible. A total of 57 outcome variables reached phenome-wide significance at a false discovery rate of 5 %. We observed expected associations with several phenotypes related to smoking and nicotine, but could not replicate prior findings on cessation. Importantly, we found novel associations between genetically determined faster nicotine metabolism and adverse health outcomes, including unfavourable liver enzyme and lipid values, as well as increased caffeine consumption. These associations did not appear to differ between ever and never smokers, suggesting the corresponding pathways may not involve nicotine metabolism. No favourable health outcomes were linked to genetically determined faster nicotine metabolism. Our findings support a possibility that a future smoking cessation therapy converting fast metabolizers of nicotine to slower ones could work without adverse side effects and potentially even provide other health-related benefits.
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