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Jin T, Wu Y, Zhang S, Peng Y, Lin Y, Zhou S, Liu H, Yu P. The association between metabolomic profiles of lifestyle and the latent phase of incident chronic kidney disease in the UK Population. Sci Rep 2025; 15:2299. [PMID: 39824917 PMCID: PMC11742403 DOI: 10.1038/s41598-025-86030-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2024] [Accepted: 01/07/2025] [Indexed: 01/20/2025] Open
Abstract
Chronic kidney disease (CKD) is a global health challenge associated with lifestyle factors such as diet, alcohol, BMI, smoking, sleep, and physical activity. Metabolomics, especially nuclear magnetic resonance(NMR), offers insights into metabolic profiles' role in diseases, but more research is needed on its connection to CKD and lifestyle factors. Therefore, we utilized the latest metabolomics data from the UK Biobank to explore the relationship between plasma metabolites and lifestyle factors, as well as to investigate the associations between various factors, including lifestyle-related metabolites, and the latent phase of CKD onset. The study enrolled approximately 500,000 participants from the UK Biobank (UKB) between 2006 and 2010, excluding 447,163 individuals with missing data for any metabolite in the NMR metabolomics, any biomarker in the blood chemistry (including eGFR, albumin, or cystatin C), any factor required for constructing the lifestyle score, or a baseline diagnosis of CKD. Lifestyle scores (LS) were calculated based on several factors, including diet, alcohol consumption, smoking, BMI, physical activity, and sleep. Each healthy lifestyle component contributed to the overall score, which ranged from 0 to 6. A total of 249 biological metabolites covering multiple categories were determined by the NMR Metabolomics Platform. Random forest algorithms and LASSO regression were employed to identify lifestyle-related metabolites. Subsequently, accelerated failure time models(AFT) were used to assess the relationship between multiple factors, including traditional CKD-related biomarkers (such as eGFR, cystatin C, and albumin) and lifestyle-related metabolites, with the latent phase of incident CKD. Finally, we performed Kaplan-Meier survival curve analysis on the significant variables identified in the AFT model. Over a mean follow-up period of 13.86 years, 2,279 incident chronic kidney disease (CKD) cases were diagnosed. Among the 249 metabolites analyzed, 15 were identified as lifestyle-related, primarily lipid metabolites. Notably, among these metabolites, each 1 mmol/L increase in triglycerides in large LDL particles accelerated the onset of CKD by 24%. Diabetes, hypertension, and smoking were associated with a 56.6%, 31.5% and 22.3% faster onset of CKD, respectively. Additionally, each unit increase in age, BMI, TDI, and cystatin C was linked to a 3.2%, 1.4%, 1.6% and 32.3% faster onset of CKD. In contrast, higher levels of albumin and eGFR slowed the onset of CKD, reducing the speed of progression by 3.0% and 3.9% per unit increase, respectively. Nuclear magnetic resonance metabolomics offers new insights into renal health, though further validation studies are needed in the future.
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Affiliation(s)
- Tingting Jin
- NHC Key Lab of Hormones and Development and Tianjin Key Lab of Metabolic Diseases, Tianjin Medical University Chu Hsien-I Memorial Hospital & Institute of Endocrinology, Tianjin, 300134, China
| | - Yunqi Wu
- NHC Key Lab of Hormones and Development and Tianjin Key Lab of Metabolic Diseases, Tianjin Medical University Chu Hsien-I Memorial Hospital & Institute of Endocrinology, Tianjin, 300134, China
| | - Siyi Zhang
- NHC Key Lab of Hormones and Development and Tianjin Key Lab of Metabolic Diseases, Tianjin Medical University Chu Hsien-I Memorial Hospital & Institute of Endocrinology, Tianjin, 300134, China
| | - Ya Peng
- NHC Key Lab of Hormones and Development and Tianjin Key Lab of Metabolic Diseases, Tianjin Medical University Chu Hsien-I Memorial Hospital & Institute of Endocrinology, Tianjin, 300134, China
| | - Yao Lin
- NHC Key Lab of Hormones and Development and Tianjin Key Lab of Metabolic Diseases, Tianjin Medical University Chu Hsien-I Memorial Hospital & Institute of Endocrinology, Tianjin, 300134, China
| | - Saijun Zhou
- NHC Key Lab of Hormones and Development and Tianjin Key Lab of Metabolic Diseases, Tianjin Medical University Chu Hsien-I Memorial Hospital & Institute of Endocrinology, Tianjin, 300134, China
| | - Hongyan Liu
- NHC Key Lab of Hormones and Development and Tianjin Key Lab of Metabolic Diseases, Tianjin Medical University Chu Hsien-I Memorial Hospital & Institute of Endocrinology, Tianjin, 300134, China.
| | - Pei Yu
- NHC Key Lab of Hormones and Development and Tianjin Key Lab of Metabolic Diseases, Tianjin Medical University Chu Hsien-I Memorial Hospital & Institute of Endocrinology, Tianjin, 300134, China.
- Department of Nephrology & Blood Purification Center, The Second Hospital of Tianjin Medical University, Tianjin, 300211, China.
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Ji G, Wang Y, Lu Z, Long G, Xu C. Associations between ambient benzene and stroke, and the mediating role of accelerated biological aging: Findings from the UK biobank. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2025; 367:125656. [PMID: 39793648 DOI: 10.1016/j.envpol.2025.125656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2024] [Revised: 12/31/2024] [Accepted: 01/06/2025] [Indexed: 01/13/2025]
Abstract
Benzene can cause respiratory diseases. However, the associations between benzene and stroke are unclear. A total of 13,116 patients with stroke and 377,120 controls from the UK Biobank were included. The benzene exposure concentrations were matched on the basis of the address information of each participant via a data form from the UK Department for Environment, Food and Rural Affairs. Weighted Cox regression was used to investigate the association between benzene and stroke risk. The polygenic risk score (PRS) was used to observe the joint effects of benzene exposure and genetic factors on stroke risk. We conducted a mediation analysis to investigate the mediating role of accelerated biological aging in this cohort study. After adjusting for covariates, every 1 μg/m3 increase in benzene exposure increased the risk of stroke by 70%, which may be mediated by accelerated biological aging. The population with high benzene exposure concentrations and high PRSs had a 44% greater risk of stroke than did those with low benzene exposure concentrations and low PRSs. Benzene exposure and the PRS have joint effects on the risk of stroke. Benzene exposure was associated with stroke risk, possibly through increased biological aging, and the PRS modified this association.
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Affiliation(s)
- Guixiang Ji
- Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment, Nanjing, 210042, Jiangsu, China
| | - Yiyi Wang
- School of Energy and Environment, Anhui University of Technology, Maanshan, 243002, China
| | - Zhixi Lu
- School of Public Health, Nanjing Medical University, Nanjing, China
| | - Guangfeng Long
- Department of Clinical Laboratory, Children's Hospital of Nanjing Medical University, Guangzhou Road #72, Nanjing, 210008, China.
| | - Cheng Xu
- Department of Toxicology, School of Public Health, Guangxi Medical University, Nanning, Guangxi, China; Guangxi Colleges and Universities Key Laboratory of Prevention and Control of Highly Prevalent Diseases, Guangxi Medical University, Nanning, 530021, China.
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Tian F, Wang Y, Qian ZM, Ran S, Zhang Z, Wang C, McMillin SE, Chavan NR, Lin H. Plasma metabolomic signature of healthy lifestyle, structural brain reserve and risk of dementia. Brain 2025; 148:143-153. [PMID: 39324695 DOI: 10.1093/brain/awae257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Revised: 06/18/2024] [Accepted: 07/18/2024] [Indexed: 09/27/2024] Open
Abstract
Although the association between healthy lifestyle and dementia risk has been documented, the relationship between a metabolic signature indicative of healthy lifestyle and dementia risk and the mediating role of structural brain impairment remain unknown. We retrieved 136 628 dementia-free participants from UK Biobank. Elastic net regression was used to obtain a metabolic signature that represented lifestyle behaviours. Cox proportional hazard models were fitted to explore the associations of lifestyle-associated metabolic signature with incident dementia. Causal associations between identified metabolites and dementia were investigated using Mendelian randomization. Mediation analysis was also conducted to uncover the potential mechanisms involving 19 imaging-derived phenotypes (brain volume, grey matter volume, white matter volume and regional grey matter volumes). During a follow-up of 12.55 years, 1783 incident cases of all-cause dementia were identified, including 725 cases of Alzheimer's dementia and 418 cases of vascular dementia. We identified 83 metabolites that could represent healthy lifestyle behaviours using elastic net regression. The metabolic signature was associated with a lower dementia risk, and for each standard deviation increment in metabolic signature, the hazard ratio was 0.89 [95% confidence interval (CI): 0.85, 0.93] for all-cause dementia, 0.95 (95% CI: 0.88, 1.03) for Alzheimer's dementia and 0.84 (95% CI: 0.77, 0.91) for vascular dementia. Mendelian randomization revealed potential causal associations between the identified metabolites and risk of dementia. In addition, the specific structural brain reserve, including the hippocampus, grey matter in the hippocampus, parahippocampal gyrus and middle temporal gyrus, were detected to mediate the effects of metabolic signature on dementia risk (mediated proportion ranging from 6.21% to 11.98%). The metabolic signature associated with a healthy lifestyle is inversely associated with dementia risk, and greater structural brain reserve plays an important role in mediating this relationship. These findings have significant implications for understanding the intricate connections between lifestyle, metabolism and brain health.
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Affiliation(s)
- Fei Tian
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Yuhua Wang
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Zhengmin Min Qian
- Department of Epidemiology and Biostatistics, College for Public Health & Social Justice, Saint Louis University, Saint Louis, MO 63104, USA
| | - Shanshan Ran
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Zilong Zhang
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Chongjian Wang
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou 450001, China
| | | | - Niraj R Chavan
- Department of Obstetrics, Gynecology and Women's Health, School of Medicine Saint Louis University, Saint Louis, MO 63117, USA
| | - Hualiang Lin
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
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Wang Y, Tian F, Qian Z(M, Ran S, Zhang J, Wang C, Chen L, Zheng D, Vaughn MG, Tabet M, Lin H. Healthy Lifestyle, Metabolic Signature, and Risk of Cardiovascular Diseases: A Population-Based Study. Nutrients 2024; 16:3553. [PMID: 39458547 PMCID: PMC11510148 DOI: 10.3390/nu16203553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2024] [Revised: 10/16/2024] [Accepted: 10/17/2024] [Indexed: 10/28/2024] Open
Abstract
BACKGROUND Although healthy lifestyle has been linked with a reduced risk of cardiovascular diseases (CVDs), the potential metabolic mechanism underlying this association remains unknown. METHODS We included 161,018 CVD-free participants from the UK Biobank. Elastic net regression was utilized to generate a healthy lifestyle-related metabolic signature. The Cox proportional hazards model was applied to investigate associations of lifestyle-related metabolic signature with incident CVDs, and mediation analysis was conducted to evaluate the potential mediating role of metabolic profile on the healthy lifestyle-CVD association. Mendelian randomization (MR) analysis was conducted to detect the causality. RESULTS During 13 years of follow-up, 17,030 participants developed incident CVDs. A healthy lifestyle-related metabolic signature comprising 123 metabolites was established, and it was inversely associated with CVDs. The hazard ratio (HR) was 0.83 (95% confidence interval [CI]: 0.81, 0.84) for CVD, 0.83 (95% CI: 0.81, 0.84) for ischemic heart disease (IHD), 0.86 (95% CI: 0.83, 0.90) for stroke, 0.86 (95% CI: 0.82, 0.89) for myocardial infarction (MI), and 0.75 (95% CI: 0.72, 0.77) for heart failure (HF) per standard deviation increase in the metabolic signature. The metabolic signature accounted for 20% of the association between healthy lifestyle score and CVD. Moreover, MR showed a potential causal association between the metabolic signature and stroke. CONCLUSIONS Our study revealed a potential link between a healthy lifestyle, metabolic signatures, and CVD. This connection suggests that identifying an individual's metabolic status and implementing lifestyle modifications may provide novel insights into the prevention of CVD.
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Affiliation(s)
- Yuhua Wang
- Department of Epidemiology, School of Public Health, Sun Yat-Sen University, Guangzhou 510080, China; (Y.W.); (F.T.); (S.R.); (J.Z.); (L.C.); (D.Z.)
| | - Fei Tian
- Department of Epidemiology, School of Public Health, Sun Yat-Sen University, Guangzhou 510080, China; (Y.W.); (F.T.); (S.R.); (J.Z.); (L.C.); (D.Z.)
| | - Zhengmin (Min) Qian
- Department of Epidemiology and Biostatistics, College for Public Health & Social Justice, Saint Louis University, Saint Louis, MO 63104, USA;
| | - Shanshan Ran
- Department of Epidemiology, School of Public Health, Sun Yat-Sen University, Guangzhou 510080, China; (Y.W.); (F.T.); (S.R.); (J.Z.); (L.C.); (D.Z.)
| | - Jingyi Zhang
- Department of Epidemiology, School of Public Health, Sun Yat-Sen University, Guangzhou 510080, China; (Y.W.); (F.T.); (S.R.); (J.Z.); (L.C.); (D.Z.)
| | - Chongjian Wang
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou 450001, China;
| | - Lan Chen
- Department of Epidemiology, School of Public Health, Sun Yat-Sen University, Guangzhou 510080, China; (Y.W.); (F.T.); (S.R.); (J.Z.); (L.C.); (D.Z.)
| | - Dashan Zheng
- Department of Epidemiology, School of Public Health, Sun Yat-Sen University, Guangzhou 510080, China; (Y.W.); (F.T.); (S.R.); (J.Z.); (L.C.); (D.Z.)
| | - Michael G. Vaughn
- School of Social Work, Saint Louis University, St. Louis, MO 63103, USA;
| | - Maya Tabet
- College of Global Population Health, University of Health Sciences and Pharmacy in St. Louis, St. Louis, MO 63110, USA;
| | - Hualiang Lin
- Department of Epidemiology, School of Public Health, Sun Yat-Sen University, Guangzhou 510080, China; (Y.W.); (F.T.); (S.R.); (J.Z.); (L.C.); (D.Z.)
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Lu X, Zhu X, Li G, Wu L, Shao L, Fan Y, Pan CW, Wu Y, Borné Y, Ke C. Habitual Coffee, Tea, and Caffeine Consumption, Circulating Metabolites, and the Risk of Cardiometabolic Multimorbidity. J Clin Endocrinol Metab 2024:dgae552. [PMID: 39287934 DOI: 10.1210/clinem/dgae552] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Indexed: 09/19/2024]
Abstract
CONTEXT Cardiometabolic multimorbidity (CM) is an increasing public health concern. Previous observational studies have suggested inverse associations between coffee, tea, and caffeine intake and risks of individual cardiometabolic diseases; however, their associations with CM and related biological markers are unknown. METHODS This prospective study involved 172 315 (for caffeine analysis) and 188 091 (tea and coffee analysis) participants free of any cardiometabolic diseases at baseline from the UK Biobank; 168 metabolites were measured among 88 204 and 96 393 participants. CM was defined as the coexistence of at least 2 of the following conditions: type 2 diabetes, coronary heart disease, and stroke. RESULTS Nonlinear inverse associations of coffee, tea, and caffeine intake with the risk of new-onset CM were observed. Compared with nonconsumers or consumers of less than 100 mg caffeine per day, consumers of moderate amount of coffee (3 drinks/d) or caffeine (200-300 mg/d) had the lowest risk for new-onset CM, with respective hazard ratios (95% CIs) of 0.519 (0.417-0.647) and 0.593 (0.499-0.704). Multistate models revealed that moderate coffee or caffeine intake was inversely associated with risks of almost all developmental stages of CM, including transitions from a disease-free state to single cardiometabolic diseases and subsequently to CM. A total of 80 to 97 metabolites, such as lipid components within very low-density lipoprotein, histidine, and glycoprotein acetyls, were identified to be associated with both coffee, tea, or caffeine intake and incident CM. CONCLUSION Habitual coffee or caffeine intake, especially at a moderate level, was associated with a lower risk of new-onset CM and could play important roles in almost all transition phases of CM development. Future studies are warranted to validate the implicated metabolic biomarkers underlying the relation between coffee, tea, and caffeine intake and CM.
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Affiliation(s)
- Xujia Lu
- Department of Epidemiology and Biostatistics, School of Public Health, Suzhou Medical College of Soochow University, Suzhou 215123, China
- MOE Key Laboratory of Geriatric Diseases and Immunology, Soochow University, Suzhou 215123, Jiangsu, China
| | - Xiaohong Zhu
- Suzhou Centers for Disease Control and Prevention, Suzhou 215000, Jiangsu, China
| | - Guochen Li
- Department of Epidemiology and Biostatistics, School of Public Health, Suzhou Medical College of Soochow University, Suzhou 215123, China
| | - Luying Wu
- Department of Epidemiology and Biostatistics, School of Public Health, Suzhou Medical College of Soochow University, Suzhou 215123, China
- MOE Key Laboratory of Geriatric Diseases and Immunology, Soochow University, Suzhou 215123, Jiangsu, China
| | - Liping Shao
- Department of Epidemiology and Biostatistics, School of Public Health, Suzhou Medical College of Soochow University, Suzhou 215123, China
- MOE Key Laboratory of Geriatric Diseases and Immunology, Soochow University, Suzhou 215123, Jiangsu, China
| | - Yulong Fan
- Department of Epidemiology and Biostatistics, School of Public Health, Suzhou Medical College of Soochow University, Suzhou 215123, China
- MOE Key Laboratory of Geriatric Diseases and Immunology, Soochow University, Suzhou 215123, Jiangsu, China
| | - Chen-Wei Pan
- School of Public Health, Suzhou Medical College of Soochow University, Suzhou 215123, China
| | - Ying Wu
- Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - Yan Borné
- Department of Clinical Sciences Malmö, Lund University, Malmö 20502, Sweden
| | - Chaofu Ke
- Department of Epidemiology and Biostatistics, School of Public Health, Suzhou Medical College of Soochow University, Suzhou 215123, China
- MOE Key Laboratory of Geriatric Diseases and Immunology, Soochow University, Suzhou 215123, Jiangsu, China
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Rakusanova S, Cajka T. Metabolomics and Lipidomics for Studying Metabolic Syndrome: Insights into Cardiovascular Diseases, Type 1 & 2 Diabetes, and Metabolic Dysfunction-Associated Steatotic Liver Disease. Physiol Res 2024; 73:S165-S183. [PMID: 39212142 PMCID: PMC11412346 DOI: 10.33549/physiolres.935443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/04/2024] Open
Abstract
Metabolomics and lipidomics have emerged as tools in understanding the connections of metabolic syndrome (MetS) with cardiovascular diseases (CVD), type 1 and type 2 diabetes (T1D, T2D), and metabolic dysfunction-associated steatotic liver disease (MASLD). This review highlights the applications of these omics approaches in large-scale cohort studies, emphasizing their role in biomarker discovery and disease prediction. Integrating metabolomics and lipidomics has significantly advanced our understanding of MetS pathology by identifying unique metabolic signatures associated with disease progression. However, challenges such as standardizing analytical workflows, data interpretation, and biomarker validation remain critical for translating research findings into clinical practice. Future research should focus on optimizing these methodologies to enhance their clinical utility and address the global burden of MetS-related diseases.
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Affiliation(s)
- S Rakusanova
- Laboratory of Translational Metabolism, Institute of Physiology of the Czech Academy of Sciences, Prague, Czech Republic.
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Wu TT, Pan Y, Zhi XY, Deng CJ, Wang S, Guo XX, Hou XG, Yang Y, Zheng YY, Xie X. Association between extremely high prognostic nutritional index and all-cause mortality in patients with coronary artery disease: secondary analysis of a prospective cohort study in China. BMJ Open 2024; 14:e079954. [PMID: 38885991 PMCID: PMC11184201 DOI: 10.1136/bmjopen-2023-079954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 05/19/2024] [Indexed: 06/20/2024] Open
Abstract
OBJECTIVES Decreased prognostic nutritional index (PNI) was associated with adverse outcomes in many clinical diseases. This study aimed to evaluate the relationship between baseline PNI value and adverse clinical outcomes in patients with coronary artery disease (CAD). DESIGN The Personalized Antiplatelet Therapy According to CYP2C19 Genotype in Coronary Artery Disease (PRACTICE) study, a prospective cohort study of 15 250 patients with CAD, was performed from December 2016 to October 2021. The longest follow-up period was 5 years. This study was a secondary analysis of the PRACTICE study. SETTING The study setting was Xinjiang Medical University Affiliated First Hospital in China. PARTICIPANTS Using the 50th and 90th percentiles of the PNI in the total cohort as two cut-off limits, we divided all participants into three groups: Q1 (PNI <51.35, n = 7515), Q2 (51.35 ≤ PNI < 59.80, n = 5958) and Q3 (PNI ≥ 59.80, n = 1510). The PNI value was calculated as 10 × serum albumin (g/dL) + 0.005 × total lymphocyte count (per mm3). PRIMARY OUTCOME The primary outcome measure was mortality, including all-cause mortality (ACM) and cardiac mortality (CM). RESULTS In 14 983 participants followed for a median of 24 months, a total of 448 ACM, 333 CM, 1162 major adverse cardiovascular events (MACE) and 1276 major adverse cardiovascular and cerebrovascular events (MACCE) were recorded. The incidence of adverse outcomes was significantly different among the three groups (p <0.001). There were 338 (4.5%), 77 (1.3%) and 33 (2.2%) ACM events in the three groups, respectively. A restricted cubic spline displayed a J-shaped relationship between the PNI and worse 5-year outcomes, including ACM, CM, MACE and MACCE. After adjusting for traditional cardiovascular risk factors, we found that only patients with extremely high PNI values in the Q3 subgroup or low PNI values in the Q1 subgroup had a greater risk of ACM (Q3 vs Q2, HR: 1.617, 95% CI 1.012 to 2.585, p=0.045; Q1 vs Q2, HR=1.995, 95% CI 1.532 to 2.598, p <0.001). CONCLUSION This study revealed a J-shaped relationship between the baseline PNI and ACM in patients with CAD, with a greater risk of ACM at extremely high PNI values. TRIAL REGISTRATION NUMBER NCT05174143.
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Affiliation(s)
- Ting-Ting Wu
- Department of Cardiology, Xinjiang Medical University Affiliated First Hospital, No. 137, Liyushan Road, Urumqi, China
- Key Laboratory of High Incidence Disease Research in Xingjiang (Xinjiang Medical University, Ministry of Education), Urumqi, China
- Key Laboratory of Hypertension Research of Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Ying Pan
- Department of Cardiology, Xinjiang Medical University Affiliated First Hospital, No. 137, Liyushan Road, Urumqi, China
- Key Laboratory of Hypertension Research of Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Xiao-Yu Zhi
- Department of Cardiology, Xinjiang Medical University Affiliated First Hospital, No. 137, Liyushan Road, Urumqi, China
- Key Laboratory of Hypertension Research of Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Chang-Jiang Deng
- Department of Cardiology, Xinjiang Medical University Affiliated First Hospital, No. 137, Liyushan Road, Urumqi, China
- Key Laboratory of Hypertension Research of Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Shun Wang
- Department of Cardiology, Xinjiang Medical University Affiliated First Hospital, No. 137, Liyushan Road, Urumqi, China
- Key Laboratory of Hypertension Research of Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Xiao-Xia Guo
- Department of Cardiology, Xinjiang Medical University Affiliated First Hospital, No. 137, Liyushan Road, Urumqi, China
- Key Laboratory of Hypertension Research of Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Xian-Geng Hou
- Department of Cardiology, Xinjiang Medical University Affiliated First Hospital, No. 137, Liyushan Road, Urumqi, China
- Key Laboratory of Hypertension Research of Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Yi Yang
- Department of Cardiology, Xinjiang Medical University Affiliated First Hospital, No. 137, Liyushan Road, Urumqi, China
- Key Laboratory of Hypertension Research of Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Ying-Ying Zheng
- Department of Cardiology, Xinjiang Medical University Affiliated First Hospital, No. 137, Liyushan Road, Urumqi, China
- Key Laboratory of High Incidence Disease Research in Xingjiang (Xinjiang Medical University, Ministry of Education), Urumqi, China
- Key Laboratory of Hypertension Research of Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Xiang Xie
- Department of Cardiology, Xinjiang Medical University Affiliated First Hospital, No. 137, Liyushan Road, Urumqi, China
- Key Laboratory of High Incidence Disease Research in Xingjiang (Xinjiang Medical University, Ministry of Education), Urumqi, China
- Key Laboratory of Hypertension Research of Xinjiang Medical University, Urumqi, Xinjiang, China
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Li S, Che J, Gu B, Li Y, Han X, Sun T, Pan K, Lv J, Zhang S, Wang C, Zhang T, Wang J, Xue F. Metabolites, Healthy Lifestyle, and Polygenic Risk Score Associated with Upper Gastrointestinal Cancer: Findings from the UK Biobank Study. J Proteome Res 2024; 23:1679-1688. [PMID: 38546438 DOI: 10.1021/acs.jproteome.3c00827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/04/2024]
Abstract
Previous metabolomics studies have highlighted the predictive value of metabolites on upper gastrointestinal (UGI) cancer, while most of them ignored the potential effects of lifestyle and genetic risk on plasma metabolites. This study aimed to evaluate the role of lifestyle and genetic risk in the metabolic mechanism of UGI cancer. Differential metabolites of UGI cancer were identified using partial least-squares discriminant analysis and the Wilcoxon test. Then, we calculated the healthy lifestyle index (HLI) score and polygenic risk score (PRS) and divided them into three groups, respectively. A total of 15 metabolites were identified as UGI-cancer-related differential metabolites. The metabolite model (AUC = 0.699) exhibited superior discrimination ability compared to those of the HLI model (AUC = 0.615) and the PRS model (AUC = 0.593). Moreover, subgroup analysis revealed that the metabolite model showed higher discrimination ability for individuals with unhealthy lifestyles compared to that with healthy individuals (AUC = 0.783 vs 0.684). Furthermore, in the genetic risk subgroup analysis, individuals with a genetic predisposition to UGI cancer exhibited the best discriminative performance in the metabolite model (AUC = 0.770). These findings demonstrated the clinical significance of metabolic biomarkers in UGI cancer discrimination, especially in individuals with unhealthy lifestyles and a high genetic risk.
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Affiliation(s)
- Shuting Li
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
| | - Jiajing Che
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
| | - Bingbing Gu
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
| | - Yunfei Li
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
| | - Xinyue Han
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
| | - Tiantian Sun
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
| | - Keyu Pan
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
| | - Jiali Lv
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
| | - Shuai Zhang
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
| | - Cheng Wang
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
| | - Tao Zhang
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
| | - Jialin Wang
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan 250117, China
| | - Fuzhong Xue
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
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9
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Fotakis C, Amanatidou AI, Kafyra M, Andreou V, Kalafati IP, Zervou M, Dedoussis GV. Circulatory Metabolite Ratios as Indicators of Lifestyle Risk Factors Based on a Greek NAFLD Case-Control Study. Nutrients 2024; 16:1235. [PMID: 38674925 PMCID: PMC11055137 DOI: 10.3390/nu16081235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Revised: 04/16/2024] [Accepted: 04/18/2024] [Indexed: 04/28/2024] Open
Abstract
An ensemble of confounding factors, such as an unhealthy diet, obesity, physical inactivity, and smoking, have been linked to a lifestyle that increases one's susceptibility to chronic diseases and early mortality. The circulatory metabolome may provide a rational means of pinpointing the advent of metabolite variations that reflect an adherence to a lifestyle and are associated with the occurrence of chronic diseases. Data related to four major modifiable lifestyle factors, including adherence to the Mediterranean diet (estimated on MedDietScore), body mass index (BMI), smoking, and physical activity level (PAL), were used to create the lifestyle risk score (LS). The LS was further categorized into four groups, where a higher score group indicates a less healthy lifestyle. Drawing on this, we analyzed 223 NMR serum spectra, 89 MASLD patients and 134 controls; these were coupled to chemometrics to identify "key" features and understand the biological processes involved in specific lifestyles. The unsupervised analysis verified that lifestyle was the factor influencing the samples' differentiation, while the supervised analysis highlighted metabolic signatures. Τhe metabolic ratios of alanine/formic acid and leucine/formic acid, with AUROC > 0.8, may constitute discriminant indexes of lifestyle. On these grounds, this research contributed to understanding the impact of lifestyle on the circulatory metabolome and highlighted "prudent lifestyle" biomarkers.
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Affiliation(s)
- Charalambos Fotakis
- Institute of Chemical Biology, National Hellenic Research Foundation, 48 Vas. Constantinou Ave., 11635 Athens, Greece; (C.F.); (V.A.)
| | - Athina I. Amanatidou
- Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University of Athens, 17671 Athens, Greece; (A.I.A.); (M.K.); (I.P.K.)
| | - Maria Kafyra
- Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University of Athens, 17671 Athens, Greece; (A.I.A.); (M.K.); (I.P.K.)
| | - Vasiliki Andreou
- Institute of Chemical Biology, National Hellenic Research Foundation, 48 Vas. Constantinou Ave., 11635 Athens, Greece; (C.F.); (V.A.)
| | - Ioanna Panagiota Kalafati
- Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University of Athens, 17671 Athens, Greece; (A.I.A.); (M.K.); (I.P.K.)
| | - Maria Zervou
- Institute of Chemical Biology, National Hellenic Research Foundation, 48 Vas. Constantinou Ave., 11635 Athens, Greece; (C.F.); (V.A.)
| | - George V. Dedoussis
- Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University of Athens, 17671 Athens, Greece; (A.I.A.); (M.K.); (I.P.K.)
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10
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Tessier AJ, Wang F, Liang L, Wittenbecher C, Haslam DE, Eliassen AH, Tobias DK, Li J, Zeleznik OA, Ascherio A, Sun Q, Stampfer MJ, Grodstein F, Rexrode KM, Manson JE, Balasubramanian R, Clish CB, Martínez-González MA, Chavarro JE, Hu FB, Guasch-Ferré M. Plasma metabolites of a healthy lifestyle in relation to mortality and longevity: Four prospective US cohort studies. MED 2024; 5:224-238.e5. [PMID: 38366602 PMCID: PMC10940196 DOI: 10.1016/j.medj.2024.01.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 11/09/2023] [Accepted: 01/18/2024] [Indexed: 02/18/2024]
Abstract
BACKGROUND A healthy lifestyle is associated with a lower premature mortality risk and with longer life expectancy. However, the metabolic pathways of a healthy lifestyle and how they relate to mortality and longevity are unclear. We aimed to identify and replicate a healthy lifestyle metabolomic signature and examine how it is related to total and cause-specific mortality risk and longevity. METHODS In four large cohorts with 13,056 individuals and 28-year follow-up, we assessed five healthy lifestyle factors, used liquid chromatography mass spectrometry to profile plasma metabolites, and ascertained deaths with death certificates. The unique healthy lifestyle metabolomic signature was identified using an elastic regression. Multivariable Cox regressions were used to assess associations of the signature with mortality and longevity. FINDINGS The identified healthy lifestyle metabolomic signature was reflective of lipid metabolism pathways. Shorter and more saturated triacylglycerol and diacylglycerol metabolite sets were inversely associated with the healthy lifestyle score, whereas cholesteryl ester and phosphatidylcholine plasmalogen sets were positively associated. Participants with a higher healthy lifestyle metabolomic signature had a 17% lower risk of all-cause mortality, 19% for cardiovascular disease mortality, and 17% for cancer mortality and were 25% more likely to reach longevity. The healthy lifestyle metabolomic signature explained 38% of the association between the self-reported healthy lifestyle score and total mortality risk and 49% of the association with longevity. CONCLUSIONS This study identifies a metabolomic signature that measures adherence to a healthy lifestyle and shows prediction of total and cause-specific mortality and longevity. FUNDING This work was funded by the NIH, CIHR, AHA, Novo Nordisk Foundation, and SciLifeLab.
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Affiliation(s)
- Anne-Julie Tessier
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
| | - Fenglei Wang
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Liming Liang
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | | | - Danielle E Haslam
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - A Heather Eliassen
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Deirdre K Tobias
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Jun Li
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Oana A Zeleznik
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Alberto Ascherio
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Qi Sun
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Meir J Stampfer
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Francine Grodstein
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Kathryn M Rexrode
- Division of Women's Health, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - JoAnn E Manson
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Raji Balasubramanian
- Department of Biostatistics and Epidemiology, University of Massachusetts Amherst, Amherst, MA, USA
| | - Clary B Clish
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Miguel A Martínez-González
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Department of Preventive Medicine and Public Health, University of Navarra, Pamplona, Spain
| | - Jorge E Chavarro
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Frank B Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Marta Guasch-Ferré
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Department of Public Health and Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
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11
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Jin J, Xu Z, Beevers SD, Huang J, Kelly F, Li G. Long-term ambient ozone, omega-3 fatty acid, genetic susceptibility, and risk of mental disorders among middle-aged and older adults in UK biobank. ENVIRONMENTAL RESEARCH 2024; 243:117825. [PMID: 38081346 DOI: 10.1016/j.envres.2023.117825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 11/14/2023] [Accepted: 11/29/2023] [Indexed: 12/17/2023]
Abstract
BACKGROUND Evidence linking ozone to depression and anxiety disorders remains sparse and results are heterogeneous. It remains unknown whether omega-3 fatty acid, or genetic susceptibility of mental disorders modify the impacts of ozone. The aim is to assess the associations of ambient ozone with depression and anxiety, and further explore the potential modification effects of omega-3 fatty acid and genetic susceptibility. METHODS In total of 257,534 participants were enrolled from 2006 to 2010 and followed up to 2016. Depression and anxiety were assessed using mental health questionnaires, primary care records and hospital admission records. The annual average concentrations of ozone were calculated and linked to individuals by home address. Dietary intake and plasma concentration were selected to reflect levels of omega-3 fatty acid. Polygenetic risk scores were selected to reflect genetic susceptibility. We examined the associations of ozone and incident mental disorders, and potential modification of omega-3 fatty acid and genetic susceptibility. RESULTS Incidences of depression (N = 6957) and anxiety (N = 6944) was associated with increase of ozone. Higher levels of omega-3 fatty acid might attenuate the ozone related depression risk. However, the modification effects of genetic susceptibility were not found. CONCLUSIONS Long-term exposure to ambient ozone increase the risk of mental disorders among the middle aged and older adults, and omega-3 fatty acid could reduce the adverse effects of ozone on mental health. Higher intake of omega-3 fatty acid is a potential strategy to prevent the risks caused by ozone on public mental health.
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Affiliation(s)
- Jianbo Jin
- Department of Occupational and Environmental Health Sciences, Peking University School of Public Health, Beijing, China.
| | - Zhihu Xu
- Department of Occupational and Environmental Health Sciences, Peking University School of Public Health, Beijing, China.
| | - Sean D Beevers
- Environmental Research Group, School of Public Health, Imperial College London, London, UK.
| | - Jing Huang
- Department of Occupational and Environmental Health Sciences, Peking University School of Public Health, Beijing, China; Peking University Institute for Global Health and Development, Beijing, China.
| | - Frank Kelly
- Environmental Research Group, School of Public Health, Imperial College London, London, UK.
| | - Guoxing Li
- Department of Occupational and Environmental Health Sciences, Peking University School of Public Health, Beijing, China; Environmental Research Group, School of Public Health, Imperial College London, London, UK.
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12
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Rios S, García-Gavilán JF, Babio N, Paz-Graniel I, Ruiz-Canela M, Liang L, Clish CB, Toledo E, Corella D, Estruch R, Ros E, Fitó M, Arós F, Fiol M, Guasch-Ferré M, Santos-Lozano JM, Li J, Razquin C, Martínez-González MÁ, Hu FB, Salas-Salvadó J. Plasma metabolite profiles associated with the World Cancer Research Fund/American Institute for Cancer Research lifestyle score and future risk of cardiovascular disease and type 2 diabetes. Cardiovasc Diabetol 2023; 22:252. [PMID: 37716984 PMCID: PMC10505328 DOI: 10.1186/s12933-023-01912-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 07/01/2023] [Indexed: 09/18/2023] Open
Abstract
BACKGROUND A healthy lifestyle (HL) has been inversely related to type 2 diabetes (T2D) and cardiovascular disease (CVD). However, few studies have identified a metabolite profile associated with HL. The present study aims to identify a metabolite profile of a HL score and assess its association with the incidence of T2D and CVD in individuals at high cardiovascular risk. METHODS In a subset of 1833 participants (age 55-80y) of the PREDIMED study, we estimated adherence to a HL using a composite score based on the 2018 Word Cancer Research Fund/American Institute for Cancer Research recommendations. Plasma metabolites were analyzed using LC-MS/MS methods at baseline (discovery sample) and 1-year of follow-up (validation sample). Cross-sectional associations between 385 known metabolites and the HL score were assessed using elastic net regression. A 10-cross-validation procedure was used, and correlation coefficients or AUC were assessed between the identified metabolite profiles and the self-reported HL score. We estimated the associations between the identified metabolite profiles and T2D and CVD using multivariable Cox regression models. RESULTS The metabolite profiles that identified HL as a dichotomous or continuous variable included 24 and 58 metabolites, respectively. These are amino acids or derivatives, lipids, and energy intermediates or xenobiotic compounds. After adjustment for potential confounders, baseline metabolite profiles were associated with a lower risk of T2D (hazard ratio [HR] and 95% confidence interval (CI): 0.54, 0.38-0.77 for dichotomous HL, and 0.22, 0.11-0.43 for continuous HL). Similar results were observed with CVD (HR, 95% CI: 0.59, 0.42-0.83 for dichotomous HF and HR, 95%CI: 0.58, 0.31-1.07 for continuous HL). The reduction in the risk of T2D and CVD was maintained or attenuated, respectively, for the 1-year metabolomic profile. CONCLUSIONS In an elderly population at high risk of CVD, a set of metabolites was selected as potential metabolites associated with the HL pattern predicting the risk of T2D and, to a lesser extent, CVD. These results support previous findings that some of these metabolites are inversely associated with the risk of T2D and CVD. TRIAL REGISTRATION The PREDIMED trial was registered at ISRCTN ( http://www.isrctn.com/ , ISRCTN35739639).
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Affiliation(s)
- Santiago Rios
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Alimentaciò, Nutrició Desenvolupament i Salut Mental ANUT-DSM, Reus, Spain
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain
| | - Jesús F García-Gavilán
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Alimentaciò, Nutrició Desenvolupament i Salut Mental ANUT-DSM, Reus, Spain.
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain.
- Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain.
| | - Nancy Babio
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Alimentaciò, Nutrició Desenvolupament i Salut Mental ANUT-DSM, Reus, Spain
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain
| | - Indira Paz-Graniel
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Alimentaciò, Nutrició Desenvolupament i Salut Mental ANUT-DSM, Reus, Spain
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain
| | - Miguel Ruiz-Canela
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Department of Preventive Medicine and Public Health, University of Navarra, IdiSNA, Pamplona, Spain
| | - Liming Liang
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Clary B Clish
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Estefania Toledo
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Department of Preventive Medicine and Public Health, University of Navarra, IdiSNA, Pamplona, Spain
| | - Dolores Corella
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Department of Preventive Medicine, University of Valencia, Valencia, Spain
| | - Ramón Estruch
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Department of Internal Medicine, Institut d'Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), Hospital Clínic, Barcelona, Spain
| | - Emilio Ros
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Department of Endocrinology and Nutrition, Lipid Clinic, Institut d'Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), Hospital Clínic, Barcelona, Spain
| | - Montserrat Fitó
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Unit of Cardiovascular Risk and Nutrition, Institut Hospital del Mar de Investigaciones Médicas Municipal d'Investigació Médica (IMIM), Barcelona, Spain
| | - Fernando Arós
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Department of Cardiology, Hospital Universitario de Álava, Vitoria, Spain
| | - Miquel Fiol
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Health Research Institute of the Balearic Islands (IdISBa), Hospital Son Espases, Palma de Mallorca, Spain
| | - Marta Guasch-Ferré
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Public Health and Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - José M Santos-Lozano
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Research Unit, Department of Family Medicine, Distrito Sanitario Atención Primaria Sevilla, Sevilla, Spain
| | - Jun Li
- Department of Preventive Medicine and Public Health, University of Navarra, IdiSNA, Pamplona, Spain
- Health Research Institute of the Balearic Islands (IdISBa), Hospital Son Espases, Palma de Mallorca, Spain
| | - Cristina Razquin
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Department of Preventive Medicine and Public Health, University of Navarra, IdiSNA, Pamplona, Spain
| | - Miguel Ángel Martínez-González
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Department of Preventive Medicine and Public Health, University of Navarra, IdiSNA, Pamplona, Spain
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Frank B Hu
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jordi Salas-Salvadó
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Alimentaciò, Nutrició Desenvolupament i Salut Mental ANUT-DSM, Reus, Spain.
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain.
- Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain.
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13
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Zhang J, Fang XY, Leng R, Chen HF, Qian TT, Cai YY, Zhang XH, Wang YY, Mu M, Tao XR, Leng RX, Ye DQ. Metabolic signature of healthy lifestyle and risk of rheumatoid arthritis: observational and Mendelian randomization study. Am J Clin Nutr 2023:S0002-9165(23)48892-2. [PMID: 37127109 DOI: 10.1016/j.ajcnut.2023.04.034] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Revised: 04/10/2023] [Accepted: 04/26/2023] [Indexed: 05/03/2023] Open
Abstract
BACKGROUND While substantial evidence reveals that healthy lifestyle behaviors are associated with a lower risk of rheumatoid arthritis (RA), the underlying metabolic mechanisms remain unclear. OBJECTIVES This study aimed to identify the metabolic signature reflecting a healthy lifestyle and investigate its observational and genetic linkage with RA risk. METHODS This study included 87,258 UK Biobank participants (557 cases of incident RA) aged 37 to 73 years with complete lifestyle, genotyping and nuclear magnetic resonance (NMR) metabolomics data. A healthy lifestyle was assessed based on five factors: healthy diet, regular exercise, not smoking, moderate alcohol consumption, and normal body mass index. The metabolic signature was developed by summing selected metabolites' concentrations weighted by the coefficients using elastic net regression. We used multivariate Cox model to assess the associations between metabolic signatures and RA risk, and examined the mediating role of the metabolic signature in the impact of a healthy lifestyle on RA. We performed genome-wide association analysis (GWAS) to obtain genetic variants associated with the metabolic signature, then conducted Mendelian randomization (MR) analyses to detect causality. RESULTS The metabolic signature comprised of 81 metabolites, robustly correlated with healthy lifestyle ( r = 0.45, P = 4.2 × 10-15). The metabolic signature was inversely associated with RA risk (HR per SD increment: 0.76, 95% CI: 0.70-0.83), and largely explained protective effects of healthy lifestyle on RA with 64% (95%CI: 50.4-83.3) mediation proportion. One and two-sample MR analyses also consistently showed the associations of genetically inferred per SD increment in metabolic signature with a reduction in RA risk (HR: 0.84, 95% CI: 0.75-0.94, P = 0.002 and OR: 0.84, 95% CI: 0.73-0.97, P = 0.02 respectively). CONCLUSION Our findings implicate the metabolic signature reflecting healthy lifestyle as a potential causal mediator in the development of RA, highlighting the importance of early lifestyle intervention and metabolic tracking for precise prevention of RA.
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Affiliation(s)
- Jie Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, 230032, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, 230032, China
| | - Xin-Yu Fang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, 230032, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, 230032, China
| | - Rui Leng
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, 230032, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, 230032, China
| | - Hai-Feng Chen
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, 230032, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, 230032, China
| | - Ting-Ting Qian
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, 230032, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, 230032, China
| | - Yu-Yu Cai
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, 230032, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, 230032, China
| | - Xin-Hong Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, 230032, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, 230032, China
| | - Yi-Yu Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, 230032, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, 230032, China
| | - Min Mu
- School of Public Health, Anhui University of Science and Technology, Huainan, Anhui, 232001, China
| | - Xin-Rong Tao
- School of Public Health, Anhui University of Science and Technology, Huainan, Anhui, 232001, China
| | - Rui-Xue Leng
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, 230032, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, 230032, China.
| | - Dong-Qing Ye
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, 230032, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, 230032, China; School of Public Health, Anhui University of Science and Technology, Huainan, Anhui, 232001, China.
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14
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Sudevan R. Can conventional cardiovascular risk prediction models be improved by nuclear magnetic resonance (NMR) metabolomic signatures? Eur J Prev Cardiol 2023; 30:241-242. [PMID: 36545900 DOI: 10.1093/eurjpc/zwac306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 12/15/2022] [Accepted: 12/19/2022] [Indexed: 12/24/2022]
Affiliation(s)
- Remya Sudevan
- Health Sciences Research & Cardiology, Amrita Vishwa Vidyapeetham, Brahmasthanam, Edappally North P.O, Kochi, Kerala 682024, India
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Tesorio T, Mone P, de Donato A, Trimarco V, Santulli G. Linking lifestyle factors to cardiovascular risk through metabolomics: Insights from a large population of diabetic patients followed-up for 11 years. Atherosclerosis 2023; 367:37-39. [PMID: 36725416 PMCID: PMC9957959 DOI: 10.1016/j.atherosclerosis.2023.01.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 01/17/2023] [Indexed: 01/22/2023]
Affiliation(s)
- Tullio Tesorio
- Casa di Cura "Montevergine", Mercogliano (Avellino), Italy
| | - Pasquale Mone
- Department of Medicine - Wilf Family Cardiovascular Research Center, Institute for Aging Research, Fleischer Institute for Diabetes and Metabolism (FIDAM), Institute for Neuroimmunology and Inflammation (INI), Albert Einstein College of Medicine, New York City, NY, USA; University of Campania "Luigi Vanvitelli", Naples, Italy
| | | | | | - Gaetano Santulli
- Department of Medicine - Wilf Family Cardiovascular Research Center, Institute for Aging Research, Fleischer Institute for Diabetes and Metabolism (FIDAM), Institute for Neuroimmunology and Inflammation (INI), Albert Einstein College of Medicine, New York City, NY, USA; "Federico II" University, Naples, Italy; Department of Molecular Pharmacology - Einstein/Sinai Diabetes Research Center (ES-DRC), Albert Einstein College of Medicine, New York City, NY, USA.
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