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Farashi S, Bonelli R, Jackson VE, Ansell BR, Guymer RH, Bahlo M. Decreased Circulating Very Small Low-Density Lipoprotein is Likely Causal for Age-Related Macular Degeneration. OPHTHALMOLOGY SCIENCE 2024; 4:100535. [PMID: 39091897 PMCID: PMC11292535 DOI: 10.1016/j.xops.2024.100535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 04/04/2024] [Accepted: 04/15/2024] [Indexed: 08/04/2024]
Abstract
Objective Abnormal changes in metabolite levels in serum or plasma have been highlighted in several studies in age-related macular degeneration (AMD), the leading cause of irreversible vision loss. Specific changes in lipid profiles are associated with an increased risk of AMD. Metabolites could thus be used to investigate AMD disease mechanisms or incorporated into AMD risk prediction models. However, whether particular metabolites causally affect the disease has yet to be established. Design A 3-tiered analysis of blood metabolites in the United Kingdom (UK) Biobank cohort to identify metabolites that differ in AMD patients with evidence for a putatively causal role in AMD. Participants A total of 72 376 donors from the UK Biobank cohort including participants with AMD (N = 1353) and non-AMD controls (N = 71 023). Methods We analyzed 325 directly measured or derived blood metabolites from the UK Biobank for 72 376 donors to identify AMD-associated metabolites. Genome-wide association studies for 325 metabolites in 98 316 European participants from the UK Biobank were performed. The causal effects of these metabolites in AMD were tested using a 2-sample Mendelian randomization approach. The predictive value of these measurements together with sex and age was assessed by developing a machine learning classifier. Main Outcome Measures Evaluating metabolic biomarkers associated with AMD susceptibility and investigating their potential causal contribution to the development of the disease. Results This study noted age to be the prominent risk factor associated with AMD development. While accounting for age and sex, we identified 84 metabolic markers as significantly (false discovery rate-adjusted P value < 0.05) associated with AMD. Lipoprotein subclasses comprised the majority of the AMD-associated metabolites (39%) followed by several lipoprotein to lipid ratios. Nineteen metabolites showed a likely causative role in AMD etiology. Of these, 6 lipoproteins contain very small, very low-density lipoprotein (VLDL), and phospholipids to total lipid ratio in medium VLDL. Based on this we postulate that depletion of circulating very small VLDLs is likely causal for AMD. The risk prediction model constructed from the metabolites, age and sex, identified age as the primary predictive factor with a much smaller contribution by metabolites to AMD risk prediction. Conclusions This study underscores the pronounced role of lipids in AMD susceptibility and the likely causal contribution of particular subclasses of lipoproteins to AMD. Our study provides valuable insights into the metabopathological mechanisms of AMD disease development and progression.
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Affiliation(s)
- Samaneh Farashi
- Population Health and Immunity Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria 3052, Australia
- Department of Medical Biology, University of Melbourne, 3052, Parkville, Victoria, Australia
| | - Roberto Bonelli
- Population Health and Immunity Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria 3052, Australia
- Department of Medical Biology, University of Melbourne, 3052, Parkville, Victoria, Australia
- The Lowy Medical Research Institute, La Jolla, California
| | - Victoria E. Jackson
- Population Health and Immunity Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria 3052, Australia
- Department of Medical Biology, University of Melbourne, 3052, Parkville, Victoria, Australia
| | - Brendan R.E. Ansell
- Population Health and Immunity Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria 3052, Australia
- Department of Medical Biology, University of Melbourne, 3052, Parkville, Victoria, Australia
| | - Robyn H. Guymer
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Victoria 3002, Australia
- Department of Surgery, (Ophthalmology), University of Melbourne, East Melbourne, Victoria 3002, Australia
| | - Melanie Bahlo
- Population Health and Immunity Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria 3052, Australia
- Department of Medical Biology, University of Melbourne, 3052, Parkville, Victoria, Australia
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Guo C, Liu Z, Fan H, Wang H, Zhang X, Zhao S, Li Y, Wang T, Dai L, Huang J, Chen X, Zhang T. Nonlinear relationships of circulating polyunsaturated fatty acids with the complications of liver cirrhosis: A prospective, longitudinal cohort study. Clin Nutr 2024; 43:2083-2091. [PMID: 39094473 DOI: 10.1016/j.clnu.2024.07.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2024] [Revised: 07/24/2024] [Accepted: 07/24/2024] [Indexed: 08/04/2024]
Abstract
BACKGROUND & AIMS The role of circulating polyunsaturated fatty acids (PUFAs) in preventing liver cirrhosis complications remains unclear. METHODS Between 2006 and 2010, 273,834 UK Biobank participants with plasma PUFA quantification data were enrolled and followed up until October 31, 2022. Plasma PUFAs were quantified using a high-throughput nuclear magnetic resonance-based metabolic profiling platform. Liver cirrhosis complications were defined as hospitalization for liver cirrhosis or presentation with hepatocellular carcinoma. RESULTS During a median follow-up of 13.9 years, 2026 participants developed liver cirrhosis complications. Total plasma PUFAs, omega-3 PUFAs, docosahexaenoic acid (DHA), omega-6 PUFAs, and linoleic acid (LA) were inversely associated with the risk of liver cirrhosis complications, whereas the plasma omega-6/omega-3 ratio was positively associated. Nonparametrically restricted cubic spline regression showed nonlinear associations of plasma PUFAs with liver cirrhosis complications. The inflection points were 4.78 mmol/L for total PUFAs, 0.73 mmol/L for omega-3 PUFAs, 0.25 mmol/L for DHA, 4.07 mmol/L for omega-6 PUFAs, and 2.99 mmol/L for LA. Plasma omega-3 PUFAs were negatively associated with the risk of liver cirrhosis complications when omega-3 PUFAs were <0.73 mmol/L (adjusted hazard ratio [HR], 0.11 [0.08-0.16]), whereas the association was inverted when omega-3 PUFAs were ≥0.73 mmol/L (adjusted HR, 1.87 [1.20-2.92]). CONCLUSIONS The protective effect of plasma omega-3 PUFAs on liver cirrhosis complications is reversed after passing the corresponding inflection point, suggesting an optimal dietary omega-3 PUFA supplementation dose.
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Affiliation(s)
- Chengnan Guo
- Shanghai Institute of Infectious Disease and Biosecurity, School of Public Health, Fudan University, Shanghai, China; Department of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety (Fudan University), Ministry of Education, Fudan University, Shanghai, China
| | - Zhenqiu Liu
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, and School of Life Sciences, Fudan University, Shanghai, China; Fudan University Taizhou Institute of Health Sciences, Taizhou, China
| | - Hong Fan
- Department of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety (Fudan University), Ministry of Education, Fudan University, Shanghai, China
| | - Haili Wang
- Department of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety (Fudan University), Ministry of Education, Fudan University, Shanghai, China
| | - Xin Zhang
- Department of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety (Fudan University), Ministry of Education, Fudan University, Shanghai, China
| | - Shuzhen Zhao
- Department of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety (Fudan University), Ministry of Education, Fudan University, Shanghai, China
| | - Yi Li
- Department of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety (Fudan University), Ministry of Education, Fudan University, Shanghai, China
| | - Tianye Wang
- Department of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety (Fudan University), Ministry of Education, Fudan University, Shanghai, China
| | - Luojia Dai
- Department of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety (Fudan University), Ministry of Education, Fudan University, Shanghai, China
| | - Jiayi Huang
- Department of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety (Fudan University), Ministry of Education, Fudan University, Shanghai, China
| | - Xingdong Chen
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, and School of Life Sciences, Fudan University, Shanghai, China; Fudan University Taizhou Institute of Health Sciences, Taizhou, China.
| | - Tiejun Zhang
- Shanghai Institute of Infectious Disease and Biosecurity, School of Public Health, Fudan University, Shanghai, China; Department of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety (Fudan University), Ministry of Education, Fudan University, Shanghai, China; Fudan University Taizhou Institute of Health Sciences, Taizhou, China.
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Liang N, Nho K, Newman JW, Arnold M, Huynh K, Meikle PJ, Borkowski K, Kaddurah-Daouk R. Peripheral inflammation is associated with brain atrophy and cognitive decline linked to mild cognitive impairment and Alzheimer's disease. Sci Rep 2024; 14:17423. [PMID: 39075118 PMCID: PMC11286782 DOI: 10.1038/s41598-024-67177-5] [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: 02/01/2024] [Accepted: 07/09/2024] [Indexed: 07/31/2024] Open
Abstract
Inflammation is an important factor in Alzheimer's disease (AD). An NMR measurement in plasma, glycoprotein acetyls (GlycA), captures the overall level of protein production and glycosylation implicated in systemic inflammation. With its additional advantage of reducing biological variability, GlycA might be useful in monitoring the relationship between peripheral inflammation and brain changes relevant to AD. However, the associations between GlycA and these brain changes have not been fully evaluated. Here, we performed Spearman's correlation analyses to evaluate these associations cross-sectionally and determined whether GlycA can inform AD-relevant longitudinal measurements among participants in the Alzheimer's Disease Neuroimaging Initiative (n = 1506), with additional linear models and stratification analyses to evaluate the influences of sex or diagnosis status and confirm findings from Spearman's correlation analyses. We found that GlycA was elevated in AD patients compared to cognitively normal participants. GlycA correlated negatively with multiple concurrent regional brain volumes in females diagnosed with late mild cognitive impairment (LMCI) or AD. Baseline GlycA level was associated with executive function decline at 3-9 year follow-up in participants diagnosed with LMCI at baseline, with similar but not identical trends observed in the future decline of memory and entorhinal cortex volume. Results here indicated that GlycA is an inflammatory biomarker relevant to AD pathogenesis and that the stage of LMCI might be relevant to inflammation-related intervention.
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Affiliation(s)
- Nuanyi Liang
- West Coast Metabolomics Center, Genome Center, University of California-Davis, Davis, CA, 95616, USA
| | - Kwangsik Nho
- Department of Radiology and Imaging Sciences and the Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - John W Newman
- West Coast Metabolomics Center, Genome Center, University of California-Davis, Davis, CA, 95616, USA
- Department of Nutrition, University of California-Davis, Davis, CA, 95616, USA
- Western Human Nutrition Research Center, United States Department of Agriculture-Agriculture Research Service, Davis, CA, 95616, USA
| | - Matthias Arnold
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, 27708, USA
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Kevin Huynh
- Baker Heart and Diabetes Institute, Melbourne, VIC, 3004, Australia
- Baker Department of Cardiovascular Research, Translation and Implementation, La Trobe University, Bundoora, VIC, 3086, Australia
| | - Peter J Meikle
- Baker Heart and Diabetes Institute, Melbourne, VIC, 3004, Australia
- Baker Department of Cardiovascular Research, Translation and Implementation, La Trobe University, Bundoora, VIC, 3086, Australia
| | - Kamil Borkowski
- West Coast Metabolomics Center, Genome Center, University of California-Davis, Davis, CA, 95616, USA.
| | - Rima Kaddurah-Daouk
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, 27708, USA.
- Duke Institute of Brain Sciences, Duke University, Durham, NC, USA.
- Department of Medicine, Duke University, Durham, NC, USA.
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Taeubert MJ, Kuipers TB, Zhou J, Li C, Wang S, Wang T, Tobi EW, Belsky DW, Lumey LH, Heijmans BT. Adults prenatally exposed to the Dutch Famine exhibit a metabolic signature associated with a broad spectrum of common diseases. BMC Med 2024; 22:309. [PMID: 39075494 PMCID: PMC11287851 DOI: 10.1186/s12916-024-03529-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Accepted: 07/12/2024] [Indexed: 07/31/2024] Open
Abstract
BACKGROUND Exposure to famine in the prenatal period is associated with an increased risk of metabolic disease, including obesity and type 2 diabetes. We employed nuclear magnetic resonance (NMR) metabolomic profiling to identify the metabolic changes that are associated with survival of prenatal famine exposure during the Dutch Famine at the end of World War II and subsequently assess their link to disease. METHODS NMR metabolomics data were generated from serum in 480 individuals prenatally exposed to famine (mean 58.8 years, 0.5 SD) and 464 controls (mean 57.9 years, 5.4 SD). We tested associations of prenatal famine exposure with levels of 168 individual metabolic biomarkers and compared the metabolic biomarker signature of famine exposure with those of 154 common diseases. RESULTS Prenatal famine exposure was associated with higher concentrations of branched-chain amino acids ((iso)-leucine), aromatic amino acid (tyrosine), and glucose in later life (0.2-0.3 SD, p < 3 × 10-3). The metabolic biomarker signature of prenatal famine exposure was positively correlated to that of incident type 2 diabetes from the UK Biobank (r = 0.77, p = 3 × 10-27), also when re-estimating the signature of prenatal famine exposure among individuals without diabetes (r = 0.67, p = 1 × 10-18). Remarkably, this association extended to 115 common diseases for which signatures were available (0.3 ≤ r ≤ 0.9, p < 3.2 × 10-4). Correlations among metabolic signatures of famine exposure and disease outcomes were attenuated when the famine signature was adjusted for body mass index. CONCLUSIONS Prenatal famine exposure is associated with a metabolic biomarker signature that strongly resembles signatures of a diverse set of diseases, an observation that can in part be attributed to a shared involvement of obesity.
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Affiliation(s)
- M Jazmin Taeubert
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - Thomas B Kuipers
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - Jiayi Zhou
- Butler Columbia Aging Center, Columbia University, New York, NY, USA
| | - Chihua Li
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
- State Key Laboratory of Quality Research in Chinese Medicine, University of Macau, Macao SAR, China
| | - Shuang Wang
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Tian Wang
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Elmar W Tobi
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - Daniel W Belsky
- Butler Columbia Aging Center, Columbia University, New York, NY, USA
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - L H Lumey
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Bastiaan T Heijmans
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands.
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Tao HW, Liu ZY, Jiang W, Miao MY, Lyu JQ, Zhao M, Zhu J, Chen JS, Du HZ, Li ZN, Zhu Z, Yang J, Qin LQ, Chen W, Chen GC. Lower plasma linoleic acids as a risk factor for gout: an integrated analysis of population-based cohort and genetic data. Food Funct 2024; 15:7567-7576. [PMID: 38934729 DOI: 10.1039/d4fo00987h] [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: 06/28/2024]
Abstract
Background: Gout is a nutrition-related, highly prevalent inflammatory arthritis with undesirable effects on the quality of life. The relationships between circulating fatty acids (FAs) and gout remain poorly understood. Method: We included 268 174 participants with plasma FAs measured using nuclear magnetic resonance at the baseline (2006-2010) from the UK Biobank, of which 15 194 participants had repeated measures of FAs between 2012 and 2013. Cox proportional hazards models were used to assess the association of the baseline and longitudinal changes in relative levels of plasma FAs (% total FAs) with incident gout. Mendelian randomization (MR) analyses were conducted to assess the potential causality of the examined association. Results: Over a median follow-up of 12.8 years, 5160 incident cases of gout occurred. Baseline polyunsaturated fatty acids (PUFAs), n-6 PUFAs, and linoleic acids (LAs) were inversely associated with incident gout (all P-trend values < 0.0001). Baseline monounsaturated fatty acids (MUFAs), n-3 PUFAs, and docosahexaenoic acids (DHAs) were positively associated with incident gout (all P-trend values < 0.0001). Longitudinal increments of n-6 PUFAs and LAs were associated with a lower risk of subsequent gout, whereas an increment of n-3 PUFAs was associated with a higher risk. In two-sample MR analyses, genetically determined higher levels of PUFAs, n-6 PUFAs, and LAs were associated with a decreased risk of gout (all P values < 0.05). Conclusions: Our findings consistently indicate a causal relationship of elevated levels of n-6 PUFAs, especially LAs, with a reduced risk of gout.
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Affiliation(s)
- Hao-Wei Tao
- Department of Nutrition and Food Hygiene, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Major Chronic Non-communicable Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou, China.
| | - Zhong-Yue Liu
- Department of Nutrition and Food Hygiene, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Major Chronic Non-communicable Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou, China.
| | - Wei Jiang
- Department of Hepatopancreatobiliary Surgery, Ningbo Medical Center Lihuili Hospital, Ningbo University, Ningbo, China
| | - Meng-Yuan Miao
- Department of Nutrition and Food Hygiene, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Major Chronic Non-communicable Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou, China.
| | - Jie-Qiong Lyu
- Department of Nutrition and Food Hygiene, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Major Chronic Non-communicable Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou, China.
| | - Miao Zhao
- Department of Nutrition and Food Hygiene, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Major Chronic Non-communicable Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou, China.
| | - Jie Zhu
- Department of Nutrition and Food Hygiene, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Major Chronic Non-communicable Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou, China.
| | - Jin-Si Chen
- Department of Nutrition and Food Hygiene, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Major Chronic Non-communicable Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou, China.
| | - Hong-Zhen Du
- Department of Nutrition, The First Hospital of Hebei Medical University, Shijiazhuang, China
- Hebei Key Laboratory of Nutrition and Health, Shijiazhuang, China
| | - Zeng-Ning Li
- Department of Nutrition, The First Hospital of Hebei Medical University, Shijiazhuang, China
- Hebei Key Laboratory of Nutrition and Health, Shijiazhuang, China
| | - Zhengbao Zhu
- Department of Epidemiology, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Suzhou Medical College of Soochow University, Suzhou, China
| | - Jing Yang
- Department of Nutrition and Food Hygiene, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Major Chronic Non-communicable Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou, China.
- Department of Clinical Nutrition, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Li-Qiang Qin
- Department of Nutrition and Food Hygiene, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Major Chronic Non-communicable Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou, China.
| | - Wei Chen
- Department of Clinical Nutrition, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Guo-Chong Chen
- Department of Nutrition and Food Hygiene, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Major Chronic Non-communicable Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou, China.
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Li J, Sun Y, Yu B, Cai L, Shen W, Wang B, Tan X, Guo Y, Wang N, Lu Y. Association patterns of ketone bodies with the risk of adverse outcomes according to diabetes status. Diabetes Obes Metab 2024. [PMID: 39010294 DOI: 10.1111/dom.15782] [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: 05/21/2024] [Revised: 06/24/2024] [Accepted: 06/25/2024] [Indexed: 07/17/2024]
Abstract
AIM To investigate the associations between ketone bodies (KB) and multiple adverse outcomes including cardiovascular disease (CVD), chronic kidney disease (CKD) and all-cause mortality according to diabetes status. METHODS This prospective study included 222 824 participants free from CVD and CKD at baseline from the UK Biobank. Total KB including β-hydroxybutyrate, acetoacetate and acetone were measured by nuclear magnetic resonance. Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the association between KB and adverse outcomes among participants with normoglycaemia, prediabetes and type 2 diabetes, respectively. RESULTS During a mean follow-up of 14.1 years, 24 088 incident CVD events (including 17 303 coronary heart disease events, 5172 stroke events and 5881 heart failure [HF] events), 8605 CKD events and 15 813 deaths, were documented. Higher total KB significantly increased the risk of HF among participants with normoglycaemia (HR, 1.32 [95% CI, 1.17-1.49], per 10-fold increase in total KB) and prediabetes (1.35 [1.04-1.76]), and increased the risk of CKD among those with normoglycaemia (1.20 [1.09-1.33]). Elevated KB levels were associated with an increased risk of all-cause mortality across the glycaemic spectrum (1.32 [1.23-1.42] for normoglycaemia, 1.45 [1.24-1.71] for prediabetes and 1.47 [1.11-1.94] for diabetes). Moreover, a significant additive interaction between KB and diabetes status was observed on the risk of death (P = .009), with 4.9% of deaths attributed to the interactive effects. CONCLUSIONS Our study underscored the variation in association patterns between KB and adverse outcomes according to diabetes status and suggested that KB could interact with diabetes status in an additive manner to increase the risk of mortality.
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Affiliation(s)
- Jiang Li
- Institute and Department of Endocrinology and Metabolism, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ying Sun
- Institute and Department of Endocrinology and Metabolism, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Bowei Yu
- Institute and Department of Endocrinology and Metabolism, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lingli Cai
- Institute and Department of Endocrinology and Metabolism, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wenqi Shen
- Institute and Department of Endocrinology and Metabolism, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Bin Wang
- Institute and Department of Endocrinology and Metabolism, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiao Tan
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden
- Department of Big Data in Health Science, School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
| | - Yuyu Guo
- Institute and Department of Endocrinology and Metabolism, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ningjian Wang
- Institute and Department of Endocrinology and Metabolism, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yingli Lu
- Institute and Department of Endocrinology and Metabolism, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Chen Z, Fung E, Wong CK, Ling L, Lui G, Lai CKC, Ng RWY, Sze RKH, Ho WCS, Hui DSC, Chan PKS. Early Metabolomic and Immunologic Biomarkers as Prognostic Indicators for COVID-19. Metabolites 2024; 14:380. [PMID: 39057703 PMCID: PMC11278819 DOI: 10.3390/metabo14070380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2024] [Revised: 07/05/2024] [Accepted: 07/06/2024] [Indexed: 07/28/2024] Open
Abstract
This prospective study in Hong Kong aimed at identifying prognostic metabolomic and immunologic biomarkers for Coronavirus Disease 2019 (COVID-19). We examined 327 patients, mean age 55 (19-89) years, in whom 33.6% were infected with Omicron and 66.4% were infected with earlier variants. The effect size of disease severity on metabolome outweighed others including age, gender, peak C-reactive protein (CRP), vitamin D and peak viral levels. Sixty-five metabolites demonstrated strong associations and the majority (54, 83.1%) were downregulated in severe disease (z score: -3.30 to -8.61). Ten cytokines/chemokines demonstrated strong associations (p < 0.001), and all were upregulated in severe disease. Multiple pairs of metabolomic/immunologic biomarkers showed significant correlations. Fourteen metabolites had the area under the receiver operating characteristic curve (AUC) > 0.8, suggesting a high predictive value. Three metabolites carried high sensitivity for severe disease: triglycerides in medium high-density lipoprotein (MHDL) (sensitivity: 0.94), free cholesterol-to-total lipids ratio in very small very-low-density lipoprotein (VLDL) (0.93), cholesteryl esters-to-total lipids ratio in chylomicrons and extremely large VLDL (0.92);whereas metabolites with the highest specificity were creatinine (specificity: 0.94), phospholipids in large VLDL (0.94) and triglycerides-to-total lipids ratio in large VLDL (0.93). Five cytokines/chemokines, namely, interleukin (IL)-6, IL-18, IL-10, macrophage inflammatory protein (MIP)-1b and tumour necrosis factor (TNF)-a, had AUC > 0.8. In conclusion, we demonstrated a tight interaction and prognostic potential of metabolomic and immunologic biomarkers enabling an outcome-based patient stratification.
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Affiliation(s)
- Zigui Chen
- Department of Microbiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR 999077, China; (Z.C.); (C.K.C.L.); (R.W.Y.N.); (R.K.H.S.); (W.C.S.H.)
| | - Erik Fung
- Cardiovascular Science Center and Division of Cardiology, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China;
- School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR 999077, China
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, White City, London SW7 2AZ, UK
| | - Chun-Kwok Wong
- Department of Chemical Pathology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR 999077, China;
| | - Lowell Ling
- Department of Anaesthesia and Intensive Care, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR 999077, China;
| | - Grace Lui
- Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR 999077, China; (G.L.); (D.S.C.H.)
| | - Christopher K. C. Lai
- Department of Microbiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR 999077, China; (Z.C.); (C.K.C.L.); (R.W.Y.N.); (R.K.H.S.); (W.C.S.H.)
| | - Rita W. Y. Ng
- Department of Microbiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR 999077, China; (Z.C.); (C.K.C.L.); (R.W.Y.N.); (R.K.H.S.); (W.C.S.H.)
| | - Ryan K. H. Sze
- Department of Microbiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR 999077, China; (Z.C.); (C.K.C.L.); (R.W.Y.N.); (R.K.H.S.); (W.C.S.H.)
| | - Wendy C. S. Ho
- Department of Microbiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR 999077, China; (Z.C.); (C.K.C.L.); (R.W.Y.N.); (R.K.H.S.); (W.C.S.H.)
| | - David S. C. Hui
- Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR 999077, China; (G.L.); (D.S.C.H.)
| | - Paul K. S. Chan
- Department of Microbiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR 999077, China; (Z.C.); (C.K.C.L.); (R.W.Y.N.); (R.K.H.S.); (W.C.S.H.)
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8
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Gilliland T, Dron JS, Selvaraj MS, Trinder M, Paruchuri K, Urbut SM, Haidermota S, Bernardo R, Uddin MM, Honigberg MC, Peloso GM, Natarajan P. Genetic Architecture and Clinical Outcomes of Combined Lipid Disturbances. Circ Res 2024; 135:265-276. [PMID: 38828614 PMCID: PMC11223949 DOI: 10.1161/circresaha.123.323973] [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: 11/10/2023] [Accepted: 05/20/2024] [Indexed: 06/05/2024]
Abstract
BACKGROUND Dyslipoproteinemia often involves simultaneous derangements of multiple lipid traits. We aimed to evaluate the phenotypic and genetic characteristics of combined lipid disturbances in a general population-based cohort. METHODS Among UK Biobank participants without prevalent coronary artery disease, we used blood lipid and apolipoprotein B concentrations to ascribe individuals into 1 of 6 reproducible and mutually exclusive dyslipoproteinemia subtypes. Incident coronary artery disease risk was estimated for each subtype using Cox proportional hazards models. Phenome-wide analyses and genome-wide association studies were performed for each subtype, followed by in silico causal gene prioritization and heritability analyses. Additionally, the prevalence of disruptive variants in causal genes for Mendelian lipid disorders was assessed using whole-exome sequence data. RESULTS Among 450 636 UK Biobank participants: 63 (0.01%) had chylomicronemia; 40 005 (8.9%) had hypercholesterolemia; 94 785 (21.0%) had combined hyperlipidemia; 13 998 (3.1%) had remnant hypercholesterolemia; 110 389 (24.5%) had hypertriglyceridemia; and 49 (0.01%) had mixed hypertriglyceridemia and hypercholesterolemia. Over a median (interquartile range) follow-up of 11.1 (10.4-11.8) years, incident coronary artery disease risk varied across subtypes, with combined hyperlipidemia exhibiting the largest hazard (hazard ratio, 1.92 [95% CI, 1.84-2.01]; P=2×10-16), even when accounting for non-HDL-C (hazard ratio, 1.45 [95% CI, 1.30-1.60]; P=2.6×10-12). Genome-wide association studies revealed 250 loci significantly associated with dyslipoproteinemia subtypes, of which 72 (28.8%) were not detected in prior single lipid trait genome-wide association studies. Mendelian lipid variant carriers were rare (2.0%) among individuals with dyslipoproteinemia, but polygenic heritability was high, ranging from 23% for remnant hypercholesterolemia to 54% for combined hyperlipidemia. CONCLUSIONS Simultaneous assessment of multiple lipid derangements revealed nuanced differences in coronary artery disease risk and genetic architectures across dyslipoproteinemia subtypes. These findings highlight the importance of looking beyond single lipid traits to better understand combined lipid and lipoprotein phenotypes and implications for disease risk.
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Affiliation(s)
- Thomas Gilliland
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA
- Department of Medicine, Harvard Medical School, Boston, MA
| | - Jacqueline S. Dron
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
| | - Margaret Sunitha Selvaraj
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA
- Department of Medicine, Harvard Medical School, Boston, MA
| | - Mark Trinder
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA
- Centre for Heart Lung Innovation, University of British Columbia, Vancouver, BC
| | - Kaavya Paruchuri
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA
- Department of Medicine, Harvard Medical School, Boston, MA
| | - Sarah M. Urbut
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA
- Department of Medicine, Harvard Medical School, Boston, MA
| | - Sara Haidermota
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA
| | - Rachel Bernardo
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA
| | - Md Mesbah Uddin
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA
| | - Michael C. Honigberg
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA
- Department of Medicine, Harvard Medical School, Boston, MA
| | - Gina M. Peloso
- Department of Biostatistics, Boston University School of Public Health, Boston, MA
| | - Pradeep Natarajan
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA
- Department of Medicine, Harvard Medical School, Boston, MA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
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9
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Lau CE, Manou M, Markozannes G, Ala‐Korpela M, Ben‐Shlomo Y, Chaturvedi N, Engmann J, Gentry‐Maharaj A, Herzig K, Hingorani A, Järvelin M, Kähönen M, Kivimäki M, Lehtimäki T, Marttila S, Menon U, Munroe PB, Palaniswamy S, Providencia R, Raitakari O, Schmidt AF, Sebert S, Wong A, Vineis P, Tzoulaki I, Robinson O. NMR metabolomic modeling of age and lifespan: A multicohort analysis. Aging Cell 2024; 23:e14164. [PMID: 38637937 PMCID: PMC11258446 DOI: 10.1111/acel.14164] [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: 11/03/2023] [Revised: 03/05/2024] [Accepted: 03/19/2024] [Indexed: 04/20/2024] Open
Abstract
Metabolomic age models have been proposed for the study of biological aging, however, they have not been widely validated. We aimed to assess the performance of newly developed and existing nuclear magnetic resonance spectroscopy (NMR) metabolomic age models for prediction of chronological age (CA), mortality, and age-related disease. Ninety-eight metabolic variables were measured in blood from nine UK and Finnish cohort studies (N ≈31,000 individuals, age range 24-86 years). We used nonlinear and penalized regression to model CA and time to all-cause mortality. We examined associations of four new and two previously published metabolomic age models, with aging risk factors and phenotypes. Within the UK Biobank (N ≈102,000), we tested prediction of CA, incident disease (cardiovascular disease (CVD), type-2 diabetes mellitus, cancer, dementia, and chronic obstructive pulmonary disease), and all-cause mortality. Seven-fold cross-validated Pearson's r between metabolomic age models and CA ranged between 0.47 and 0.65 in the training cohort set (mean absolute error: 8-9 years). Metabolomic age models, adjusted for CA, were associated with C-reactive protein, and inversely associated with glomerular filtration rate. Positively associated risk factors included obesity, diabetes, smoking, and physical inactivity. In UK Biobank, correlations of metabolomic age with CA were modest (r = 0.29-0.33), yet all metabolomic model scores predicted mortality (hazard ratios of 1.01 to 1.06/metabolomic age year) and CVD, after adjustment for CA. While metabolomic age models were only moderately associated with CA in an independent population, they provided additional prediction of morbidity and mortality over CA itself, suggesting their wider applicability.
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Affiliation(s)
- Chung‐Ho E. Lau
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public HealthImperial College LondonLondonUK
| | - Maria Manou
- Department of Hygiene and EpidemiologyUniversity of Ioannina Medical SchoolIoanninaGreece
| | - Georgios Markozannes
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public HealthImperial College LondonLondonUK
- Department of Hygiene and EpidemiologyUniversity of Ioannina Medical SchoolIoanninaGreece
| | - Mika Ala‐Korpela
- Systems Epidemiology, Faculty of MedicineUniversity of OuluOuluFinland
- Research Unit of Population Health, Faculty of MedicineUniversity of OuluOuluFinland
- Biocenter OuluUniversity of OuluOuluFinland
- NMR Metabolomics Laboratory, School of Pharmacy, Faculty of Health SciencesUniversity of Eastern FinlandKuopioFinland
| | | | - Nish Chaturvedi
- MRC Unit for Lifelong Health and Ageing at UCLUniversity College LondonLondonUK
| | - Jorgen Engmann
- UCL Institute of Cardiovascular Science, Population Science and Experimental Medicine, Centre for Translational GenomicsLondonUK
| | - Aleksandra Gentry‐Maharaj
- MRC Clinical Trials Unit at UCL, Institute of Clinical Trials and MethodologyUniversity College LondonLondonUK
- Department of Women's Cancer, Elizabeth Garrett Anderson Institute for Women's HealthUniversity College LondonLondonUK
| | - Karl‐Heinz Herzig
- Institute of Biomedicine and Internal Medicine, Biocenter of Oulu, Medical Research Center Oulu, Oulu University Hospital, Faculty of MedicineOulu UniversityOuluFinland
- Department of Pediatric Gastroenterology and Metabolic DiseasesPoznan University of Medical SciencesPoznanPoland
| | - Aroon Hingorani
- UCL Institute of Cardiovascular Science, Population Science and Experimental Medicine, Centre for Translational GenomicsLondonUK
| | - Marjo‐Riitta Järvelin
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public HealthImperial College LondonLondonUK
- Research Unit of Population Health, Faculty of MedicineUniversity of OuluOuluFinland
- Department of Life Sciences, College of Health and Life SciencesBrunel University LondonLondonUK
| | - Mika Kähönen
- Department of Clinical PhysiologyTampere University HospitalTampereFinland
- Faculty of Medicine and Health TechnologyTampere UniversityTampereFinland
| | | | - Terho Lehtimäki
- Faculty of Medicine and Health Technology and Finnish Cardiovascular Research Center TampereTampere UniversityTampereFinland
- Department of Clinical Chemistry Fimlab LaboratoriesTampereFinland
| | - Saara Marttila
- Molecular Epidemiology, Faculty of Medicine and Health TechnologyTampere UniversityTampereFinland
- Gerontology Research Center (GEREC)Tampere UniversityTampereFinland
| | - Usha Menon
- MRC Clinical Trials Unit at UCL, Institute of Clinical Trials and MethodologyUniversity College LondonLondonUK
| | - Patricia B. Munroe
- William Harvey Research Institute, Barts and the London Faculty of Medicine and DentistryQueen Mary University of LondonLondonUK
- National Institute of Health and Care Research, Barts Cardiovascular Biomedical Research CentreQueen Mary University of LondonLondonUK
| | - Saranya Palaniswamy
- Research Unit of Population Health, Faculty of MedicineUniversity of OuluOuluFinland
| | - Rui Providencia
- Institute of Health Informatics Research, University College LondonLondonUK
- Barts Heart Centre, Barts Health NHS TrustLondonUK
| | - Olli Raitakari
- Centre for Population Health Research, University of Turku and Turku University HospitalTurkuFinland
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of TurkuTurkuFinland
- Department of Clinical Physiology and Nuclear MedicineTurku University HospitalTurkuFinland
| | - Amand Floriaan Schmidt
- Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College LondonLondonUK
- Department of Cardiology, Amsterdam Cardiovascular Science, Amsterdam University Medical CentersUniversity of AmsterdamAmsterdamThe Netherlands
- UCL BHF Research Accelerator CentreLondonUK
| | - Sylvain Sebert
- Research Unit of Population Health, Faculty of MedicineUniversity of OuluOuluFinland
| | - Andrew Wong
- MRC Unit for Lifelong Health and Ageing at UCLUniversity College LondonLondonUK
| | - Paolo Vineis
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public HealthImperial College LondonLondonUK
| | - Ioanna Tzoulaki
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public HealthImperial College LondonLondonUK
- Biomedical Research Foundation, Academy of AthensAthensGreece
| | - Oliver Robinson
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public HealthImperial College LondonLondonUK
- Ageing Epidemiology (AGE) Research Unit, School of Public HealthImperial College LondonLondonUK
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10
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Liu H, Zhang Q, Hao Q, Li Q, Yang L, Yang X, Wang K, Teng J, Gong Z, Jia Y. Associations between sarcopenia and circulating branched-chain amino acids: a cross-sectional study over 100,000 participants. BMC Geriatr 2024; 24:541. [PMID: 38907227 PMCID: PMC11193178 DOI: 10.1186/s12877-024-05144-5] [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] [Received: 10/09/2023] [Accepted: 06/13/2024] [Indexed: 06/23/2024] Open
Abstract
BACKGROUND Emerging evidence suggests that alterations in BCAA metabolism may contribute to the pathogenesis of sarcopenia. However, the relationship between branched-chain amino acids (BCAAs) and sarcopenia is incompletely understood, and existing literature presents conflicting results. In this study, we conducted a community-based study involving > 100,000 United Kingdom adults to comprehensively explore the association between BCAAs and sarcopenia, and assess the potential role of muscle mass in mediating the relationship between BCAAs and muscle strength. METHODS Multivariable linear regression analysis examined the relationship between circulating BCAAs and muscle mass/strength. Logistic regression analysis assessed the impact of circulating BCAAs and quartiles of BCAAs on sarcopenia risk. Subgroup analyses explored the variations in associations across age, and gender. Mediation analysis investigated the potential mediating effect of muscle mass on the BCAA-muscle strength relationship. RESULTS Among 108,017 participants (mean age: 56.40 ± 8.09 years; 46.23% men), positive associations were observed between total BCAA, isoleucine, leucine, valine, and muscle mass (beta, 0.56-2.53; p < 0.05) and between total BCAA, leucine, valine, and muscle strength (beta, 0.91-3.44; p < 0.05). Logistic regression analysis revealed that increased circulating valine was associated with a 47% reduced sarcopenia risk (odds ratio = 0.53; 95% confidence interval = 0.3-0.94; p = 0.029). Subgroup analyses demonstrated strong associations between circulating BCAAs and muscle mass/strength in men and individuals aged ≥ 60 years. Mediation analysis suggested that muscle mass completely mediated the relationship between total BCAA, and valine levels and muscle strength, partially mediated the relationship between leucine levels and muscle strength, obscuring the true effect of isoleucine on muscle strength. CONCLUSION This study suggested the potential benefits of BCAAs in preserving muscle mass/strength and highlighted muscle mass might be mediator of BCAA-muscle strength association. Our findings contribute new evidence for the clinical prevention and treatment of sarcopenia and related conditions involving muscle mass/strength loss.
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Affiliation(s)
- HuiMin Liu
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China
| | - Qiang Zhang
- School of Nursing and Health, Zhengzhou University, High-Tech Development Zone of States, 101 Kexue Road, Zhengzhou, NO, China
| | - QianMeng Hao
- Department of Blood Transfusion, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou, 450053, Henan, China
| | - QingSheng Li
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China
| | - LingFei Yang
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China
| | - Xuan Yang
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China
| | - KaiXin Wang
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China
| | - JunFang Teng
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China
| | - Zhe Gong
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China.
| | - YanJie Jia
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China.
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11
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He B, Lam HS, Sun Y, Kwok MK, Leung GM, Schooling CM, Au Yeung SL. Association of childhood food consumption and dietary pattern with cardiometabolic risk factors and metabolomics in late adolescence: prospective evidence from 'Children of 1997' birth cohort. J Epidemiol Community Health 2024:jech-2023-221245. [PMID: 38857919 DOI: 10.1136/jech-2023-221245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 05/30/2024] [Indexed: 06/12/2024]
Abstract
BACKGROUND Healthy diet might protect against cardiometabolic diseases, but uncertainty exists about its definition and role in adolescence. METHOD In a subset of Hong Kong's 'Children of 1997' birth cohort (n=2844 out of 8327), we prospectively examined sex-specific associations of food consumption and dietary pattern, proxied by the Global Diet Quality Score (GDQS) at~12.0 years, with cardiometabolic risk factors and metabolomics at~17.6 years. RESULT Higher vegetable (-0.04 SD, 95% CIs: -0.09 to 0.00) and soy consumption (-0.05 SD, 95% CI: -0.09 to -0.01) were associated with lower waist-to-hip ratio. Higher fruit and vegetable consumption were associated with lower fasting glucose (p<0.05). Higher fish consumption was associated with 0.06 SD (95% CI: 0.01 to 0.10) high-density lipoprotein cholesterol and -0.07 SD (95% CI: -0.11 to -0.02) triglycerides. After correcting for multiple comparisons (p<0.001), higher fish, fruit and vegetable consumption were associated with higher fatty acid unsaturation, higher concentration and percentage of omega-3 and a lower ratio of omega-6/omega-3. At nominal significance (p<0.05), higher fish consumption was associated with lower very-low-density lipoprotein and triglycerides relevant metabolomics. Higher vegetable and fruit consumption were associated with lower glycolysis-related metabolomics. Lower sugar-sweetened beverages (SSBs) consumption was associated with lower branched-chain amino acids. Similar associations with adiposity and metabolomics biomarkers were observed for GDQS. CONCLUSIONS Higher consumption of fruit, vegetables and fish and lower ice cream and SSBs consumption were associated with lower cardiometabolic risk in adolescents.
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Affiliation(s)
- Baoting He
- School of Public Health, Li Ka Shing Faculty of Medcine, The University of Hong Kong, Hong Kong Special Administrative Region, People's Republic of China
| | - Hugh Simon Lam
- Department of Paediatrics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, People's Republic of China
| | - Yangbo Sun
- Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis, Tennessee, US
| | - Man Ki Kwok
- School of Public Health, Li Ka Shing Faculty of Medcine, The University of Hong Kong, Hong Kong Special Administrative Region, People's Republic of China
| | - Gabriel M Leung
- School of Public Health, Li Ka Shing Faculty of Medcine, The University of Hong Kong, Hong Kong Special Administrative Region, People's Republic of China
| | - C Mary Schooling
- School of Public Health, Li Ka Shing Faculty of Medcine, The University of Hong Kong, Hong Kong Special Administrative Region, People's Republic of China
- City University of New York, Graduate School of Public Health and Health Policy, New York, US
| | - Shiu Lun Au Yeung
- School of Public Health, Li Ka Shing Faculty of Medcine, The University of Hong Kong, Hong Kong Special Administrative Region, People's Republic of China
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12
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Nummela AJ, Scheinin H, Perola M, Joensuu A, Laitio R, Arola O, Grönlund J, Roine RO, Bäcklund M, Vahlberg TJ, Laitio T. A metabolic profile of xenon and metabolite associations with 6-month mortality after out-of-hospital cardiac arrest: A post-hoc study of the randomised Xe-Hypotheca trial. PLoS One 2024; 19:e0304966. [PMID: 38833442 PMCID: PMC11149864 DOI: 10.1371/journal.pone.0304966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 05/18/2024] [Indexed: 06/06/2024] Open
Abstract
PURPOSE Out-of-hospital cardiac arrest (OHCA) carries a relatively poor prognosis and requires multimodal prognostication to guide clinical decisions. Identification of previously unrecognized metabolic routes associated with patient outcome may contribute to future biomarker discovery. In OHCA, inhaled xenon elicits neuro- and cardioprotection. However, the metabolic effects remain unknown. MATERIALS AND METHODS In this post-hoc study of the randomised, 2-group, single-blind, phase 2 Xe-Hypotheca trial, 110 OHCA survivors were randomised 1:1 to receive targeted temperature management (TTM) at 33°C with or without inhaled xenon during 24 h. Blood samples for nuclear magnetic resonance spectroscopy metabolic profiling were drawn upon admission, at 24 and 72 h. RESULTS At 24 h, increased lactate, adjusted hazard-ratio 2.25, 95% CI [1.53; 3.30], p<0.001, and decreased branched-chain amino acids (BCAA) leucine 0.64 [0.5; 0.82], p = 0.007, and valine 0.37 [0.22; 0.63], p = 0.003, associated with 6-month mortality. At 72 h, increased lactate 2.77 [1.76; 4.36], p<0.001, and alanine 2.43 [1.56; 3.78], p = 0.001, and decreased small HDL cholesterol ester content (S-HDL-CE) 0.36 [0.19; 0.68], p = 0.021, associated with mortality. No difference was observed between xenon and control groups. CONCLUSIONS In OHCA patients receiving TTM with or without xenon, high lactate and alanine and decreased BCAAs and S-HDL-CE associated with increased mortality. It remains to be established whether current observations on BCAAs, and possibly alanine and lactate, could reflect neural damage via their roles in the metabolism of the neurotransmitter glutamate. Xenon did not significantly alter the measured metabolic profile, a potentially beneficial attribute in the context of compromised ICU patients. TRIAL REGISTRATION Trial Registry number: ClinicalTrials.gov Identifier: NCT00879892.
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Affiliation(s)
- Aleksi J. Nummela
- Department of Internal Medicine, Turku University Hospital, University of Turku, Turku, Finland
| | - Harry Scheinin
- Department of Perioperative Services, Intensive Care and Pain Management, Turku University Hospital, University of Turku, Turku, Finland
- Integrative Physiology and Pharmacology, Institute of Biomedicine, University of Turku, Turku, Finland
| | - Markus Perola
- Finnish Institute for Health and Welfare, Helsinki, Finland
- Faculty of Medicine, Research Program for Clinical and Molecular Metabolism, University of Helsinki, Helsinki, Finland
| | - Anni Joensuu
- Finnish Institute for Health and Welfare, Helsinki, Finland
- Faculty of Medicine, Research Program for Clinical and Molecular Metabolism, University of Helsinki, Helsinki, Finland
| | - Ruut Laitio
- Department of Perioperative Services, Intensive Care and Pain Management, Turku University Hospital, University of Turku, Turku, Finland
| | - Olli Arola
- Department of Perioperative Services, Intensive Care and Pain Management, Turku University Hospital, University of Turku, Turku, Finland
| | - Juha Grönlund
- Department of Perioperative Services, Intensive Care and Pain Management, Turku University Hospital, University of Turku, Turku, Finland
| | - Risto O. Roine
- Division of Clinical Neurosciences, University of Turku, Turku University Hospital, Turku, Finland
| | - Minna Bäcklund
- Department of Anesthesiology, Intensive Care and Pain Medicine, Division of Intensive Care Medicine, University of Helsinki and HUS Helsinki University Hospital, Helsinki, Finland
| | - Tero J. Vahlberg
- Department of Biostatistics, University of Turku and Turku University Hospital, Turku, Finland
| | - Timo Laitio
- Department of Perioperative Services, Intensive Care and Pain Management, Turku University Hospital, University of Turku, Turku, Finland
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13
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Dalamaga M. Clinical metabolomics: Useful insights, perspectives and challenges. Metabol Open 2024; 22:100290. [PMID: 39011161 PMCID: PMC11247213 DOI: 10.1016/j.metop.2024.100290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/17/2024] Open
Abstract
Metabolomics, a cutting-edge omics technique, is a rapidly advancing field in biomedical research, concentrating on the elucidation of pathogenetic mechanisms and the discovery of novel metabolite signatures predictive of disease risk, aiding in earlier disease detection, prognosis and prediction of treatment response. The capacity of this omics approach to simultaneously quantify thousands of metabolites, i.e. small molecules less than 1500 Da in samples, positions it as a promising tool for research and clinical applications in personalized medicine. Clinical metabolomics studies have proven valuable in understanding cardiometabolic disorders, potentially uncovering diagnostic biomarkers predictive of disease risk. Liquid chromatography-mass spectrometry is the predominant analytical method used in metabolomics, particularly untargeted. Metabolomics combined with extensive genomic data, proteomics, clinical chemistry data, imaging, health records, and other pertinent health-related data may yield significant advances beneficial for both public health initiatives, clinical applications and precision medicine, particularly in rare disorders and multimorbidity. This special issue has gathered original research articles in topics related to clinical metabolomics as well as research articles, reviews, perspectives and highlights in the broader field of translational and clinical metabolic research. Additional research is necessary to identify which metabolites consistently enhance clinical risk prediction across various populations and are causally linked to disease progression.
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Affiliation(s)
- Maria Dalamaga
- Department of Biological Chemistry, School of Medicine, National and Kapodistrian University of Athens, 75 Mikras Asias, 11527, Athens, Greece
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14
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Sluiskes M, Goeman J, Beekman M, Slagboom E, van den Akker E, Putter H, Rodríguez-Girondo M. The AccelerAge framework: a new statistical approach to predict biological age based on time-to-event data. Eur J Epidemiol 2024; 39:623-641. [PMID: 38581608 PMCID: PMC11249598 DOI: 10.1007/s10654-024-01114-8] [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: 11/27/2023] [Accepted: 03/06/2024] [Indexed: 04/08/2024]
Abstract
Aging is a multifaceted and intricate physiological process characterized by a gradual decline in functional capacity, leading to increased susceptibility to diseases and mortality. While chronological age serves as a strong risk factor for age-related health conditions, considerable heterogeneity exists in the aging trajectories of individuals, suggesting that biological age may provide a more nuanced understanding of the aging process. However, the concept of biological age lacks a clear operationalization, leading to the development of various biological age predictors without a solid statistical foundation. This paper addresses these limitations by proposing a comprehensive operationalization of biological age, introducing the "AccelerAge" framework for predicting biological age, and introducing previously underutilized evaluation measures for assessing the performance of biological age predictors. The AccelerAge framework, based on Accelerated Failure Time (AFT) models, directly models the effect of candidate predictors of aging on an individual's survival time, aligning with the prevalent metaphor of aging as a clock. We compare predictors based on the AccelerAge framework to a predictor based on the GrimAge predictor, which is considered one of the best-performing biological age predictors, using simulated data as well as data from the UK Biobank and the Leiden Longevity Study. Our approach seeks to establish a robust statistical foundation for biological age clocks, enabling a more accurate and interpretable assessment of an individual's aging status.
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Affiliation(s)
- Marije Sluiskes
- Medical Statistics, Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands.
| | - Jelle Goeman
- Medical Statistics, Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - Marian Beekman
- Molecular Epidemiology, Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - Eline Slagboom
- Molecular Epidemiology, Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
- Max Planck Institute for the Biology of Ageing, Cologne, Germany
| | - Erik van den Akker
- Molecular Epidemiology, Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
- Pattern Recognition and Bioinformatics, Delft University of Technology, Delft, The Netherlands
| | - Hein Putter
- Medical Statistics, Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - Mar Rodríguez-Girondo
- Medical Statistics, Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands.
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Han L, Chen X, Wang Y, Zhang R, Zhao T, Pu L, Huang Y, Sun H. A machine learning algorithm based on circulating metabolic biomarkers offers improved predictions of neurological diseases. Clin Chim Acta 2024; 558:119671. [PMID: 38621587 DOI: 10.1016/j.cca.2024.119671] [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] [Received: 01/04/2024] [Revised: 04/01/2024] [Accepted: 04/10/2024] [Indexed: 04/17/2024]
Abstract
BACKGROUND AND AIMS A machine learning algorithm based on circulating metabolic biomarkers for the predictions of neurological diseases (NLDs) is lacking. To develop a machine learning algorithm to compare the performance of a metabolic biomarker-based model with that of a clinical model based on conventional risk factors for predicting three NLDs: dementia, Parkinson's disease (PD), and Alzheimer's disease (AD). MATERIALS AND METHODS The eXtreme Gradient Boosting (XGBoost) algorithm was used to construct a metabolic biomarker-based model (metabolic model), a clinical risk factor-based model (clinical model), and a combined model for the prediction of the three NLDs. Risk discrimination (c-statistic), net reclassification improvement (NRI) index, and integrated discrimination improvement (IDI) index values were determined for each model. RESULTS The results indicate that incorporation of metabolic biomarkers into the clinical model afforded a model with improved performance in the prediction of dementia, AD, and PD, as demonstrated by NRI values of 0.159 (0.039-0.279), 0.113 (0.005-0.176), and 0.201 (-0.021-0.423), respectively; and IDI values of 0.098 (0.073-0.122), 0.070 (0.049-0.090), and 0.085 (0.068-0.101), respectively. CONCLUSION The performance of the model based on circulating NMR spectroscopy-detected metabolic biomarkers was better than that of the clinical model in the prediction of dementia, AD, and PD.
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Affiliation(s)
- Liyuan Han
- Key Laboratory of Diagnosis and Treatment of Digestive System Tumors of Zhejiang Province, Ningbo No 2 Hospital, Ningbo 315000, China; Center for Cardiovascular and Cerebrovascular Epidemiology and Translational Medicine, Ningbo Institute of Life and Health Industry, University of Chinese Academy of Sciences, Ningbo 315000, China
| | - Xi Chen
- Department of Economics, Yale University, USA; Yale Alzheimer's Disease Research Center, Yale University, USA
| | - Yue Wang
- School of Public Health, Medical College of Soochow University, Suzhou, Jiangsu province, China
| | - Ruijie Zhang
- Key Laboratory of Diagnosis and Treatment of Digestive System Tumors of Zhejiang Province, Ningbo No 2 Hospital, Ningbo 315000, China; Center for Cardiovascular and Cerebrovascular Epidemiology and Translational Medicine, Ningbo Institute of Life and Health Industry, University of Chinese Academy of Sciences, Ningbo 315000, China
| | - Tian Zhao
- Key Laboratory of Diagnosis and Treatment of Digestive System Tumors of Zhejiang Province, Ningbo No 2 Hospital, Ningbo 315000, China; Center for Cardiovascular and Cerebrovascular Epidemiology and Translational Medicine, Ningbo Institute of Life and Health Industry, University of Chinese Academy of Sciences, Ningbo 315000, China
| | - Liyuan Pu
- Key Laboratory of Diagnosis and Treatment of Digestive System Tumors of Zhejiang Province, Ningbo No 2 Hospital, Ningbo 315000, China; Center for Cardiovascular and Cerebrovascular Epidemiology and Translational Medicine, Ningbo Institute of Life and Health Industry, University of Chinese Academy of Sciences, Ningbo 315000, China
| | - Yi Huang
- Laboratory of Neurological Diseases and Brain Function, Department of Neurosur-gery, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang 315010, China; Key Laboratory of Precision Medicine for Atherosclerotic Diseases of Zhejiang Province, Ningbo, Zhejiang 315010, China.
| | - Hongpeng Sun
- School of Public Health, Medical College of Soochow University, Suzhou, Jiangsu province, China.
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Aldoori J, Zulyniak MA, Toogood GJ, Hull MA. Fish oil supplement use modifies the relationship between dietary oily fish intake and plasma n-3 PUFA levels: an analysis of the UK Biobank. Br J Nutr 2024; 131:1608-1618. [PMID: 38220216 PMCID: PMC11043909 DOI: 10.1017/s0007114524000138] [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: 10/02/2023] [Revised: 12/17/2023] [Accepted: 01/08/2024] [Indexed: 01/16/2024]
Abstract
Observational evidence linking dietary n-3 PUFA intake and health outcomes is limited by a lack of robust validation of dietary intake using blood n-3 PUFA levels and potential confounding by fish oil supplement (FOS) use. We investigated the relationship between oily fish intake, FOS use and plasma n-3 PUFA levels in 121 650 UK Biobank (UKBB) participants. Ordinal logistic regression models, adjusted for clinical and lifestyle factors, were used to quantify the contribution of dietary oily fish intake and FOS use to plasma n-3 PUFA levels (measured by NMR spectroscopy). Oily fish intake and FOS use were reported by 38 % and 31 % of participants, respectively. Increasing oily fish intake was associated with a higher likelihood of FOS use (P < 0·001). Oily fish intake ≥ twice a week was the strongest predictor of high total n-3 PUFA (OR 6·7 (95 % CI 6·3, 7·1)) and DHA levels (6·6 (6·3, 7·1). FOS use was an independent predictor of high plasma n-3 PUFA levels (2·0 (2·0, 2·1)) with a similar OR to that associated with eating oily fish < once a week (1·9 (1·8, 2·0)). FOS use was associated with plasma n-3 PUFA levels that were similar to individuals in the next highest oily fish intake category. In conclusion, FOS use is more common in frequent fish consumers and modifies the relationship between oily fish intake and plasma n-3 PUFA levels in UKBB participants. If unaccounted for, FOS use may confound the relationship between dietary n-3 PUFA intake, blood levels of n-3 PUFAs and health outcomes.
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Affiliation(s)
- Joanna Aldoori
- Leeds Institute of Medical Research, University of Leeds, LeedsLS9 7TF, UK
- St James’s University Hospital, Leeds Teaching Hospitals NHS Trust, LeedsLS9 7TF, UK
| | | | - Giles J. Toogood
- Leeds Institute of Medical Research, University of Leeds, LeedsLS9 7TF, UK
- St James’s University Hospital, Leeds Teaching Hospitals NHS Trust, LeedsLS9 7TF, UK
| | - Mark A. Hull
- Leeds Institute of Medical Research, University of Leeds, LeedsLS9 7TF, UK
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17
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Jin Q, Lau ESH, Luk AO, Tam CHT, Ozaki R, Lim CKP, Wu H, Chow EYK, Kong APS, Lee HM, Fan B, Ng ACW, Jiang G, Lee KF, Siu SC, Hui G, Tsang CC, Lau KP, Leung JY, Tsang MW, Cheung EYN, Kam G, Lau IT, Li JK, Yeung VTF, Lau E, Lo S, Fung S, Cheng YL, Chow CC, Yu W, Tsui SKW, Tomlinson B, Huang Y, Lan HY, Szeto CC, So WY, Jenkins AJ, Fung E, Muilwijk M, Blom MT, 't Hart LM, Chan JCN, Ma RCW. Circulating metabolomic markers linking diabetic kidney disease and incident cardiovascular disease in type 2 diabetes: analyses from the Hong Kong Diabetes Biobank. Diabetologia 2024; 67:837-849. [PMID: 38413437 PMCID: PMC10954952 DOI: 10.1007/s00125-024-06108-5] [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: 07/27/2023] [Accepted: 01/03/2024] [Indexed: 02/29/2024]
Abstract
AIMS/HYPOTHESIS The aim of this study was to describe the metabolome in diabetic kidney disease (DKD) and its association with incident CVD in type 2 diabetes, and identify prognostic biomarkers. METHODS From a prospective cohort of individuals with type 2 diabetes, baseline sera (N=1991) were quantified for 170 metabolites using NMR spectroscopy with median 5.2 years of follow-up. Associations of chronic kidney disease (CKD, eGFR<60 ml/min per 1.73 m2) or severely increased albuminuria with each metabolite were examined using linear regression, adjusted for confounders and multiplicity. Associations between DKD (CKD or severely increased albuminuria)-related metabolites and incident CVD were examined using Cox regressions. Metabolomic biomarkers were identified and assessed for CVD prediction and replicated in two independent cohorts. RESULTS At false discovery rate (FDR)<0.05, 156 metabolites were associated with DKD (151 for CKD and 128 for severely increased albuminuria), including apolipoprotein B-containing lipoproteins, HDL, fatty acids, phenylalanine, tyrosine, albumin and glycoprotein acetyls. Over 5.2 years of follow-up, 75 metabolites were associated with incident CVD at FDR<0.05. A model comprising age, sex and three metabolites (albumin, triglycerides in large HDL and phospholipids in small LDL) performed comparably to conventional risk factors (C statistic 0.765 vs 0.762, p=0.893) and adding the three metabolites further improved CVD prediction (C statistic from 0.762 to 0.797, p=0.014) and improved discrimination and reclassification. The 3-metabolite score was validated in independent Chinese and Dutch cohorts. CONCLUSIONS/INTERPRETATION Altered metabolomic signatures in DKD are associated with incident CVD and improve CVD risk stratification.
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Affiliation(s)
- Qiao Jin
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
| | - Eric S H Lau
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China
| | - Andrea O Luk
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China
| | - Claudia H T Tam
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China
- CUHK-SJTU Joint Research Centre in Diabetes Genomics and Precision Medicine, Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China
| | - Risa Ozaki
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China
| | - Cadmon K P Lim
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China
- CUHK-SJTU Joint Research Centre in Diabetes Genomics and Precision Medicine, Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China
| | - Hongjiang Wu
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China
| | - Elaine Y K Chow
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China
| | - Alice P S Kong
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China
| | - Heung Man Lee
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
| | - Baoqi Fan
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China
- CUHK-SJTU Joint Research Centre in Diabetes Genomics and Precision Medicine, Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China
| | - Alex C W Ng
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
| | - Guozhi Jiang
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, Guangdong, China
| | - Ka Fai Lee
- Department of Medicine and Geriatrics, Kwong Wah Hospital, Hong Kong, China
| | - Shing Chung Siu
- Diabetes Centre, Tung Wah Eastern Hospital, Hong Kong, China
| | - Grace Hui
- Diabetes Centre, Tung Wah Eastern Hospital, Hong Kong, China
| | - Chiu Chi Tsang
- Diabetes and Education Centre, Alice Ho Miu Ling Nethersole Hospital, Hong Kong, China
| | | | - Jenny Y Leung
- Department of Medicine and Geriatrics, Ruttonjee Hospital, Hong Kong, China
| | - Man-Wo Tsang
- Department of Medicine and Geriatrics, United Christian Hospital, Hong Kong, China
| | - Elaine Y N Cheung
- Department of Medicine and Geriatrics, United Christian Hospital, Hong Kong, China
| | - Grace Kam
- Department of Medicine and Geriatrics, United Christian Hospital, Hong Kong, China
| | - Ip Tim Lau
- Tseung Kwan O Hospital, Hong Kong, China
| | - June K Li
- Department of Medicine, Yan Chai Hospital, Hong Kong, China
| | - Vincent T F Yeung
- Centre for Diabetes Education and Management, Our Lady of Maryknoll Hospital, Hong Kong, China
| | - Emmy Lau
- Department of Medicine, Pamela Youde Nethersole Eastern Hospital, Hong Kong, China
| | - Stanley Lo
- Department of Medicine, Pamela Youde Nethersole Eastern Hospital, Hong Kong, China
| | - Samuel Fung
- Department of Medicine and Geriatrics, Princess Margaret Hospital, Hong Kong, China
| | - Yuk Lun Cheng
- Department of Medicine, Alice Ho Miu Ling Nethersole Hospital, Hong Kong, China
| | - Chun Chung Chow
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
| | - Weichuan Yu
- Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong, China
| | - Stephen K W Tsui
- School of Biomedical Sciences, The Chinese University of Hong Kong, Hong Kong, China
| | - Brian Tomlinson
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
- Faculty of Medicine, Macau University of Science and Technology, Macau, China
| | - Yu Huang
- School of Biomedical Sciences, The Chinese University of Hong Kong, Hong Kong, China
- Department of Biomedical Sciences, City University of Hong Kong, Hong Kong, China
| | - Hui-Yao Lan
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China
| | - Cheuk Chun Szeto
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China
| | - Wing Yee So
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China
| | - Alicia J Jenkins
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
- NHMRC Clinical Trials Centre, University of Sydney, Sydney, NSW, Australia
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
| | - Erik Fung
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China
- School of Biomedical Sciences, The Chinese University of Hong Kong, Hong Kong, China
| | - Mirthe Muilwijk
- Department of Epidemiology and Data Science, Amsterdam UMC - Location Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Health Behaviors & Chronic Diseases Research Program, Amsterdam Public Health, Amsterdam UMC, Amsterdam, the Netherlands
| | - Marieke T Blom
- Health Behaviors & Chronic Diseases Research Program, Amsterdam Public Health, Amsterdam UMC, Amsterdam, the Netherlands
- Department of General Practice, Amsterdam UMC - Location Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Leen M 't Hart
- Department of Epidemiology and Data Science, Amsterdam UMC - Location Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Health Behaviors & Chronic Diseases Research Program, Amsterdam Public Health, Amsterdam UMC, Amsterdam, the Netherlands
- Department of Biomedical Data Sciences, Section Molecular Epidemiology, Leiden University Medical Centre, Leiden, the Netherlands
- Department of Cell and Chemical Biology, Leiden University Medical Centre, Leiden, the Netherlands
| | - Juliana C N Chan
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China
- CUHK-SJTU Joint Research Centre in Diabetes Genomics and Precision Medicine, Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China
| | - Ronald C W Ma
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China.
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China.
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China.
- CUHK-SJTU Joint Research Centre in Diabetes Genomics and Precision Medicine, Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China.
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18
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Shang X, Liu J, Zhu Z, Zhang X, Huang Y, Liu S, Wang W, Zhang X, Ma S, Tang S, Hu Y, Ge Z, Yu H, He M. Metabolomic age and risk of 50 chronic diseases in community-dwelling adults: A prospective cohort study. Aging Cell 2024; 23:e14125. [PMID: 38380547 PMCID: PMC11113347 DOI: 10.1111/acel.14125] [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: 07/26/2023] [Revised: 01/25/2024] [Accepted: 02/11/2024] [Indexed: 02/22/2024] Open
Abstract
It is unclear how metabolomic age is associated with the risk of a wide range of chronic diseases. Our analysis included 110,692 participants (training: n = 27,673; testing: n = 27,673; validating: n = 55,346) aged 39-71 years at baseline (2006-2010) from the UK Biobank. Incident chronic diseases were identified using inpatient records, or death registers until January 2021. Predicted metabolomic age was trained and tested based on 168 metabolomics. Metabolomic age was linked to the risk of 50 diseases in the validation dataset. The median follow-up duration for individual diseases ranged from 11.2 years to 11.9 years. After controlling for false discovery rate, chronological age-adjusted age gap (CAAG) was significantly associated with the incidence of 25 out of 50 chronic diseases. After adjustment for full covariates, associations with 15 chronic diseases remained significant. Greater CAAG was associated with increased risk of eight cardiometabolic disorders (including cardiovascular diseases and diabetes), some cancers, alcohol use disorder, chronic obstructive pulmonary disease, chronic kidney disease, chronic liver disease and age-related macular degeneration. The association between CAAG and risk of peripheral vascular disease, other cardiac diseases, fracture, cataract and thyroid disorder was stronger among individuals with unhealthy diet than in those with healthy diet. The association between CAAG and risk of some conditions was stronger in younger individuals, those with metabolic disorders or low education. Metabolomic age plays an important role in the development of multiple chronic diseases. Healthy diet and high education may mitigate the risk for some chronic diseases due to metabolomic age acceleration.
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Affiliation(s)
- Xianwen Shang
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical SciencesSouthern Medical UniversityGuangzhouChina
- Guangdong Cardiovascular InstituteGuangdong Provincial People's Hospital, Guangdong Academy of Medical SciencesGuangzhouChina
- The Ophthalmic Epidemiology DepartmentCentre for Eye Research AustraliaMelbourneVictoriaAustralia
- Department of Medicine, Royal Melbourne HospitalUniversity of MelbourneMelbourneVictoriaAustralia
| | - Jiahao Liu
- The Ophthalmic Epidemiology DepartmentCentre for Eye Research AustraliaMelbourneVictoriaAustralia
| | - Zhuoting Zhu
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical SciencesSouthern Medical UniversityGuangzhouChina
- Guangdong Cardiovascular InstituteGuangdong Provincial People's Hospital, Guangdong Academy of Medical SciencesGuangzhouChina
- The Ophthalmic Epidemiology DepartmentCentre for Eye Research AustraliaMelbourneVictoriaAustralia
| | - Xueli Zhang
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical SciencesSouthern Medical UniversityGuangzhouChina
- Medical Research Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical SciencesSouthern Medical UniversityGuangzhouChina
| | - Yu Huang
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical SciencesSouthern Medical UniversityGuangzhouChina
- Guangdong Cardiovascular InstituteGuangdong Provincial People's Hospital, Guangdong Academy of Medical SciencesGuangzhouChina
| | - Shunming Liu
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical SciencesSouthern Medical UniversityGuangzhouChina
| | - Wei Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic CenterSun Yat‐sen UniversityGuangzhouChina
| | - Xiayin Zhang
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical SciencesSouthern Medical UniversityGuangzhouChina
- Guangdong Cardiovascular InstituteGuangdong Provincial People's Hospital, Guangdong Academy of Medical SciencesGuangzhouChina
| | - Shuo Ma
- Medical Big Data Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical SciencesSouthern Medical UniversityGuangzhouChina
| | - Shulin Tang
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical SciencesSouthern Medical UniversityGuangzhouChina
| | - Yijun Hu
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical SciencesSouthern Medical UniversityGuangzhouChina
| | - Zongyuan Ge
- Monash e‐Research Center, Faculty of Engineering, Airdoc Research, Nvidia AI Technology Research CenterMonash UniversityMelbourneVictoriaAustralia
| | - Honghua Yu
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical SciencesSouthern Medical UniversityGuangzhouChina
| | - Mingguang He
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical SciencesSouthern Medical UniversityGuangzhouChina
- The Ophthalmic Epidemiology DepartmentCentre for Eye Research AustraliaMelbourneVictoriaAustralia
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic CenterSun Yat‐sen UniversityGuangzhouChina
- Experimental OphthalmologyThe Hong Kong Polytechnic UniversityHong KongChina
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Jung CY, Koh HB, Heo GY, Ko B, Kim HW, Park JT, Yoo TH, Kang SW, Han SH. Association of ketone bodies with incident CKD and death: A UK Biobank study. DIABETES & METABOLISM 2024; 50:101527. [PMID: 38447817 DOI: 10.1016/j.diabet.2024.101527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 02/26/2024] [Accepted: 03/03/2024] [Indexed: 03/08/2024]
Abstract
AIMS Although cellular and animal models have suggested a protective effect of ketone bodies (KBs), clinical data are still lacking to support these findings. This study aimed to investigate the association of KB levels with incident chronic kidney disease (CKD) and death. METHODS This was a prospective cohort study of 87,899 UK Biobank participants without baseline CKD who had plasma levels of β-hydroxybutyrate, acetoacetate, and acetone levels measured at the time of enrollment. The main predictor was plasma total KB, which was the sum of the aforementioned three KBs. The primary outcome was a composite of incident CKD, or all-cause mortality. Secondary outcomes included the individual components of the primary outcome. RESULTS During a median follow-up of 11.9 years, a total of 8,145 primary outcome events occurred (incidence rate 8.0/1,000 person-years). In the multivariable Cox model, a 1-standard deviation increase in log total KB was associated with a 7 % [adjusted hazard ratio (aHR), 1.07; 95 % confidence interval (CI), 1.05-1.10] higher risk of the primary outcome. When stratified into quartiles, the aHR (95 % CI) for Q4 versus Q1 was 1.18 (1.11-1.27). This association was consistent for incident CKD (aHR, 1.04; 95 % CI, 1.01-1.07), and all-cause mortality (aHR, 1.10; 95 % CI, 1.07-1.13). Compared with Q1, Q4 was associated with a 12 % (aHR 1.12; 95 % CI 1.02-1.24) and 26 % (aHR 1.26; 95 % CI 1.15-1.37) higher risk of incident CKD and all-cause mortality, respectively. CONCLUSIONS Higher KB levels were independently associated with higher risk of incident CKD and death.
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Affiliation(s)
- Chan-Young Jung
- Division of Nephrology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Hee Byung Koh
- Department of Internal Medicine, International St. Mary's Hospital, Catholic Kwandong University, Incheon, Republic of Korea
| | - Ga Young Heo
- Department of Internal Medicine, Yonsei University College of Medicine, Institute of Kidney Disease Research, Seoul, Republic of Korea
| | - Byounghwi Ko
- Department of Internal Medicine, Yonsei University College of Medicine, Institute of Kidney Disease Research, Seoul, Republic of Korea
| | - Hyung Woo Kim
- Department of Internal Medicine, Yonsei University College of Medicine, Institute of Kidney Disease Research, Seoul, Republic of Korea
| | - Jung Tak Park
- Department of Internal Medicine, Yonsei University College of Medicine, Institute of Kidney Disease Research, Seoul, Republic of Korea
| | - Tae-Hyun Yoo
- Department of Internal Medicine, Yonsei University College of Medicine, Institute of Kidney Disease Research, Seoul, Republic of Korea
| | - Shin-Wook Kang
- Department of Internal Medicine, Yonsei University College of Medicine, Institute of Kidney Disease Research, Seoul, Republic of Korea
| | - Seung Hyeok Han
- Department of Internal Medicine, Yonsei University College of Medicine, Institute of Kidney Disease Research, Seoul, Republic of Korea.
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20
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Wadström BN, Pedersen KM, Wulff AB, Nordestgaard BG. Remnant Cholesterol, Not LDL Cholesterol, Explains Peripheral Artery Disease Risk Conferred by apoB: A Cohort Study. Arterioscler Thromb Vasc Biol 2024; 44:1144-1155. [PMID: 38511326 DOI: 10.1161/atvbaha.123.320175] [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] [Received: 09/14/2023] [Accepted: 03/06/2024] [Indexed: 03/22/2024]
Abstract
BACKGROUND Elevated apoB-containing lipoproteins (=remnants+LDLs [low-density lipoproteins]) are a major risk factor for atherosclerotic cardiovascular disease, including peripheral artery disease (PAD) and myocardial infarction. We tested the hypothesis that remnants and LDL both explain part of the increased risk of PAD conferred by elevated apoB-containing lipoproteins. For comparison, we also studied the risk of chronic limb-threatening ischemia and myocardial infarction. METHODS apoB, remnant cholesterol, and LDL cholesterol were measured in 93 461 individuals without statin use at baseline from the Copenhagen General Population Study (2003-2015). During up to 15 years of follow-up, 1207 had PAD, 552 had chronic limb-threatening ischemia, and 2022 had myocardial infarction in the Danish National Patient Registry. Remnant and LDL cholesterol were calculated from a standard lipid profile. Remnant and LDL particle counts were additionally measured with nuclear magnetic resonance spectroscopy in 25 347 of the individuals. Results were replicated in 302 167 individuals without statin use from the UK Biobank (2004-2010). RESULTS In the Copenhagen General Population Study, multivariable adjusted hazard ratios for risk of PAD per 1 mmol/L (39 mg/dL) increment in remnant and LDL cholesterol were 1.9 (95% CI, 1.5-2.4) and 1.1 (95% CI, 1.0-1.2), respectively; corresponding results in the UK Biobank were 1.7 (95% CI, 1.4-2.1) and 0.9 (95% CI, 0.9-1.0), respectively. In the association from elevated apoB to increased risk of PAD, remnant and LDL cholesterol explained 73% (32%-100%) and 8% (0%-46%), respectively; corresponding results were 63% (30%-100%) and 0% (0%-33%) for risk of chronic limb-threatening ischemia and 41% (27%-55%) and 54% (38%-70%) for risk of myocardial infarction; results for remnant and LDL particle counts corroborated these findings. CONCLUSIONS PAD risk conferred by elevated apoB-containing lipoproteins was explained mainly by elevated remnants, while myocardial infarction risk was explained by both elevated remnants and LDL.
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Affiliation(s)
- Benjamin N Wadström
- Department of Clinical Biochemistry and the Copenhagen General Population Study, Copenhagen University Hospital-Herlev and Gentofte, Denmark. Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark
| | - Kasper M Pedersen
- Department of Clinical Biochemistry and the Copenhagen General Population Study, Copenhagen University Hospital-Herlev and Gentofte, Denmark. Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark
| | - Anders B Wulff
- Department of Clinical Biochemistry and the Copenhagen General Population Study, Copenhagen University Hospital-Herlev and Gentofte, Denmark. Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark
| | - Børge G Nordestgaard
- Department of Clinical Biochemistry and the Copenhagen General Population Study, Copenhagen University Hospital-Herlev and Gentofte, Denmark. Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark
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21
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Guo C, Liu Z, Fan H, Wang H, Zhang X, Zhao S, Li Y, Han X, Wang T, Chen X, Zhang T. Machine-learning-based plasma metabolomic profiles for predicting long-term complications of cirrhosis. Hepatology 2024:01515467-990000000-00850. [PMID: 38630500 DOI: 10.1097/hep.0000000000000879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 03/24/2024] [Indexed: 06/04/2024]
Abstract
BACKGROUND AND AIMS The complications of liver cirrhosis occur after long asymptomatic stages of progressive fibrosis and are generally diagnosed late. We aimed to develop a plasma metabolomic-based score tool to predict these events. APPROACH AND RESULTS We enrolled 64,005 UK biobank participants with metabolomic profiles. Participants were randomly divided into the training (n=43,734) and validation cohorts (n=20,271). Liver cirrhosis complications were defined as hospitalization for liver cirrhosis or presentation with HCC. An interpretable machine-learning framework was applied to learn the metabolomic states extracted from 168 circulating metabolites in the training cohort. An integrated nomogram was developed and compared to conventional and genetic risk scores. We created 3 groups: low-risk, middle-risk, and high-risk through selected cutoffs of the nomogram. The predictive performance was validated through the area under a time-dependent receiver operating characteristic curve (time-dependent AUC), calibration curves, and decision curve analysis. The metabolomic state model could accurately predict the 10-year risk of liver cirrhosis complications in the training cohort (time-dependent AUC: 0.84 [95% CI: 0.82-0.86]), and outperform the fibrosis-4 index (time-dependent AUC difference: 0.06 [0.03-0.10]) and polygenic risk score (0.25 [0.21-0.29]). The nomogram, integrating metabolomic state, aspartate aminotransferase, platelet count, waist/hip ratio, and smoking status showed a time-dependent AUC of 0.930 at 3 years, 0.889 at 5 years, and 0.861 at 10 years in the validation cohort, respectively. The HR in the high-risk group was 43.58 (95% CI: 27.08-70.12) compared with the low-risk group. CONCLUSIONS We developed a metabolomic state-integrated nomogram, which enables risk stratification and personalized administration of liver-related events.
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Affiliation(s)
- Chengnan Guo
- Shanghai Institute of Infectious Disease and Biosecurity, School of Public Health, Fudan University, Shanghai, China
- Department of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety (Fudan University), Ministry of Education, Fudan University, Shanghai, China
| | - Zhenqiu Liu
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, and School of Life Sciences, Fudan University, Shanghai, China
- Fudan University Taizhou Institute of Health Sciences, Taizhou, China
| | - Hong Fan
- Department of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety (Fudan University), Ministry of Education, Fudan University, Shanghai, China
- Fudan University Taizhou Institute of Health Sciences, Taizhou, China
| | - Haili Wang
- Department of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety (Fudan University), Ministry of Education, Fudan University, Shanghai, China
| | - Xin Zhang
- Department of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety (Fudan University), Ministry of Education, Fudan University, Shanghai, China
| | - Shuzhen Zhao
- Department of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety (Fudan University), Ministry of Education, Fudan University, Shanghai, China
| | - Yi Li
- Department of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety (Fudan University), Ministry of Education, Fudan University, Shanghai, China
| | - Xinyu Han
- Department of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety (Fudan University), Ministry of Education, Fudan University, Shanghai, China
| | - Tianye Wang
- Department of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety (Fudan University), Ministry of Education, Fudan University, Shanghai, China
| | - Xingdong Chen
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, and School of Life Sciences, Fudan University, Shanghai, China
- Fudan University Taizhou Institute of Health Sciences, Taizhou, China
| | - Tiejun Zhang
- Shanghai Institute of Infectious Disease and Biosecurity, School of Public Health, Fudan University, Shanghai, China
- Department of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety (Fudan University), Ministry of Education, Fudan University, Shanghai, China
- Fudan University Taizhou Institute of Health Sciences, Taizhou, China
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22
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Mäkinen VP, Ala-Korpela M. Influence of age and sex on longitudinal metabolic profiles and body weight trajectories in the UK Biobank. Int J Epidemiol 2024; 53:dyae055. [PMID: 38641429 PMCID: PMC11031410 DOI: 10.1093/ije/dyae055] [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: 12/01/2023] [Accepted: 04/04/2024] [Indexed: 04/21/2024] Open
Abstract
BACKGROUND Accurate characterization of how age influences body weight and metabolism at different stages of life is important for understanding ageing processes. Here, we explore observational longitudinal associations between metabolic health and weight from the fifth to the seventh decade of life, using carefully adjusted statistical designs. METHODS Body measures and biochemical data from blood and urine (220 measures) across two visits were available from 10 104 UK Biobank participants. Participants were divided into stable (within ±4% per decade), weight loss and weight gain categories. Final subgroups were metabolically matched at baseline (48% women, follow-up 4.3 years, ages 41-70; n = 3368 per subgroup) and further stratified by the median age of 59.3 years and sex. RESULTS Pulse pressure, haemoglobin A1c and cystatin-C tracked ageing consistently (P < 0.0001). In women under 59, age-associated increases in citrate, pyruvate, alkaline phosphatase and calcium were observed along with adverse changes across lipoprotein measures, fatty acid species and liver enzymes (P < 0.0001). Principal component analysis revealed a qualitative sex difference in the temporal relationship between body weight and metabolism: weight loss was not associated with systemic metabolic improvement in women, whereas both age strata converged consistently towards beneficial (weight loss) or adverse (weight gain) phenotypes in men. CONCLUSIONS We report longitudinal ageing trends for 220 metabolic measures in absolute concentrations, many of which have not been described for older individuals before. Our results also revealed a fundamental dynamic sex divergence that we speculate is caused by menopause-driven metabolic deterioration in women.
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Affiliation(s)
- Ville-Petteri Mäkinen
- Systems Epidemiology, Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, University of Oulu, Oulu, Finland
| | - Mika Ala-Korpela
- Systems Epidemiology, Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, University of Oulu, Oulu, Finland
- NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland
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23
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Zhang Y, Sun Y, Yu Q, Song S, Brenna JT, Shen Y, Ye K. Higher ratio of plasma omega-6/omega-3 fatty acids is associated with greater risk of all-cause, cancer, and cardiovascular mortality: A population-based cohort study in UK Biobank. eLife 2024; 12:RP90132. [PMID: 38578269 PMCID: PMC10997328 DOI: 10.7554/elife.90132] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/06/2024] Open
Abstract
Background Circulating omega-3 and omega-6 polyunsaturated fatty acids (PUFAs) have been associated with various chronic diseases and mortality, but results are conflicting. Few studies examined the role of omega-6/omega-3 ratio in mortality. Methods We investigated plasma omega-3 and omega-6 PUFAs and their ratio in relation to all-cause and cause-specific mortality in a large prospective cohort, the UK Biobank. Of 85,425 participants who had complete information on circulating PUFAs, 6461 died during follow-up, including 2794 from cancer and 1668 from cardiovascular disease (CVD). Associations were estimated by multivariable Cox proportional hazards regression with adjustment for relevant risk factors. Results Risk for all three mortality outcomes increased as the ratio of omega-6/omega-3 PUFAs increased (all Ptrend <0.05). Comparing the highest to the lowest quintiles, individuals had 26% (95% CI, 15-38%) higher total mortality, 14% (95% CI, 0-31%) higher cancer mortality, and 31% (95% CI, 10-55%) higher CVD mortality. Moreover, omega-3 and omega-6 PUFAs in plasma were all inversely associated with all-cause, cancer, and CVD mortality, with omega-3 showing stronger effects. Conclusions Using a population-based cohort in UK Biobank, our study revealed a strong association between the ratio of circulating omega-6/omega-3 PUFAs and the risk of all-cause, cancer, and CVD mortality. Funding Research reported in this publication was supported by the National Institute of General Medical Sciences of the National Institute of Health under the award number R35GM143060 (KY). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
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Affiliation(s)
- Yuchen Zhang
- Department of Epidemiology and Biostatistics, College of Public Health, University of GeorgiaAthens, GeorgiaUnited States
| | - Yitang Sun
- Department of Genetics, University of GeorgiaAthens, GeorgiaUnited States
| | - Qi Yu
- Department of Biostatistics and Bioinformatics, Emory UniversityAtlanta, GeorgiaUnited States
| | - Suhang Song
- Department of Health Policy and Management, College of Public Health, University of GeorgiaAthens, GeorgiaUnited States
| | - J Thomas Brenna
- Division of Nutritional Sciences, Cornell UniversityIthaca, New YorkUnited States
- Dell Pediatric Research Institute and the Depts of Pediatrics, of Nutrition, and of Chemistry, University of Texas at AustinAustin, TexasUnited States
| | - Ye Shen
- Department of Epidemiology and Biostatistics, College of Public Health, University of GeorgiaAthens, GeorgiaUnited States
| | - Kaixiong Ye
- Department of Genetics, University of GeorgiaAthens, GeorgiaUnited States
- Institute of Bioinformatics, University of GeorgiaAthens, GeorgiaUnited States
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24
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Taeubert MJ, Kuipers TB, Zhou J, Li C, Wang S, Wang T, Tobi EW, Belsky DW, Lumey LH, Heijmans BT. Adults prenatally exposed to the Dutch Famine exhibit a metabolic signature associated with a broad spectrum of common diseases. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.04.24305284. [PMID: 38633796 PMCID: PMC11023671 DOI: 10.1101/2024.04.04.24305284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/19/2024]
Abstract
Background Exposure to famine in the prenatal period is associated with an increased risk of metabolic disease, including obesity and type-2 diabetes. We employed nuclear magnetic resonance (NMR) metabolomic profiling to provide a deeper insight into the metabolic changes associated with survival of prenatal famine exposure during the Dutch Famine at the end of World War II and explore their link to disease. Methods NMR metabolomics data were generated from serum in 480 individuals prenatally exposed to famine (mean 58.8 years, 0.5 SD) and 464 controls (mean 57.9 years, 5.4 SD). We tested associations of prenatal famine exposure with levels of 168 individual metabolic biomarkers and compared the metabolic biomarker signature of famine exposure with those of 154 common diseases. Results Prenatal famine exposure was associated with higher concentrations of branched-chain amino acids ((iso)-leucine), aromatic amino acid (tyrosine), and glucose in later life (0.2-0.3 SD, p < 3x10-3). The metabolic biomarker signature of prenatal famine exposure was positively correlated to that of incident type-2 diabetes (r = 0.77, p = 3x10-27), also when re-estimating the signature of prenatal famine exposure among individuals without diabetes (r = 0.67, p = 1x10-18). Remarkably, this association extended to 115 common diseases for which signatures were available (0.3 ≤ r ≤ 0.9, p < 3.2x10-4). Correlations among metabolic signatures of famine exposure and disease outcomes were attenuated when the famine signature was adjusted for body mass index. Conclusions Prenatal famine exposure is associated with a metabolic biomarker signature that strongly resembles signatures of a diverse set of diseases, an observation that can in part be attributed to a shared involvement of obesity.
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Affiliation(s)
- M. Jazmin Taeubert
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - Thomas B. Kuipers
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - Jiayi Zhou
- Butler Columbia Aging Center, Columbia University, New York, United States
| | - Chihua Li
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, United States
| | - Shuang Wang
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, United States
| | - Tian Wang
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, United States
| | - Elmar W. Tobi
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | | | - Daniel W. Belsky
- Butler Columbia Aging Center, Columbia University, New York, United States
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, United States
| | - L. H. Lumey
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, United States
| | - Bastiaan T. Heijmans
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
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25
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Lian J, Vardhanabhuti V. Metabolic biomarkers using nuclear magnetic resonance metabolomics assay for the prediction of aging-related disease risk and mortality: a prospective, longitudinal, observational, cohort study based on the UK Biobank. GeroScience 2024; 46:1515-1526. [PMID: 37648937 PMCID: PMC10828466 DOI: 10.1007/s11357-023-00918-y] [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] [Received: 05/29/2023] [Accepted: 08/21/2023] [Indexed: 09/01/2023] Open
Abstract
The identification of metabolic biomarkers for aging-related diseases and mortality is of significant interest in the field of longevity. In this study, we investigated the associations between nuclear magnetic resonance (NMR) metabolomics biomarkers and aging-related diseases as well as mortality using the UK Biobank dataset. We analyzed NMR samples from approximately 110,000 participants and used multi-head machine learning classification models to predict the incidence of aging-related diseases. Cox regression models were then applied to assess the relevance of NMR biomarkers to the risk of death due to aging-related diseases. Additionally, we conducted survival analyses to evaluate the potential improvements of NMR in predicting survival and identify the biomarkers most strongly associated with negative health outcomes by dividing participants into health, disease, and death groups for all age groups. Our analysis revealed specific metabolomics profiles that were associated with the incidence of age-related diseases, and the most significant biomarker was intermediate density lipoprotein cholesteryl (IDL-CE). In addition, NMR biomarkers could provide additional contributions to relevant mortality risk prediction when combined with conventional risk factors, by improving the C-index from 0.813 to 0.833, with 17 NMR biomarkers significantly contributing to disease-related death, such as monounsaturated fatty acids (MUFA), linoleic acid (LA), glycoprotein acetyls (GlycA), and omega-3. Moreover, the value of free cholesterol in very large HDL particles (XL-HDL-FC) in the healthy control group demonstrated significantly higher values than the disease and death group across all age groups. This study highlights the potential of NMR metabolomics profiling as a valuable tool for identifying metabolic biomarkers associated with aging-related diseases and mortality risk, which could have practical implications for aging-related disease risk and mortality prediction.
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Affiliation(s)
- Jie Lian
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Room 406, Block K, Queen Mary Hospital, Pokfulam Road, Hong Kong, SAR, China
| | - Varut Vardhanabhuti
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Room 406, Block K, Queen Mary Hospital, Pokfulam Road, Hong Kong, SAR, China.
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26
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Karjalainen MK, Karthikeyan S, Oliver-Williams C, Sliz E, Allara E, Fung WT, Surendran P, Zhang W, Jousilahti P, Kristiansson K, Salomaa V, Goodwin M, Hughes DA, Boehnke M, Fernandes Silva L, Yin X, Mahajan A, Neville MJ, van Zuydam NR, de Mutsert R, Li-Gao R, Mook-Kanamori DO, Demirkan A, Liu J, Noordam R, Trompet S, Chen Z, Kartsonaki C, Li L, Lin K, Hagenbeek FA, Hottenga JJ, Pool R, Ikram MA, van Meurs J, Haller T, Milaneschi Y, Kähönen M, Mishra PP, Joshi PK, Macdonald-Dunlop E, Mangino M, Zierer J, Acar IE, Hoyng CB, Lechanteur YTE, Franke L, Kurilshikov A, Zhernakova A, Beekman M, van den Akker EB, Kolcic I, Polasek O, Rudan I, Gieger C, Waldenberger M, Asselbergs FW, Hayward C, Fu J, den Hollander AI, Menni C, Spector TD, Wilson JF, Lehtimäki T, Raitakari OT, Penninx BWJH, Esko T, Walters RG, Jukema JW, Sattar N, Ghanbari M, Willems van Dijk K, Karpe F, McCarthy MI, Laakso M, Järvelin MR, Timpson NJ, Perola M, Kooner JS, Chambers JC, van Duijn C, Slagboom PE, Boomsma DI, Danesh J, Ala-Korpela M, Butterworth AS, Kettunen J. Genome-wide characterization of circulating metabolic biomarkers. Nature 2024; 628:130-138. [PMID: 38448586 PMCID: PMC10990933 DOI: 10.1038/s41586-024-07148-y] [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: 11/07/2022] [Accepted: 02/01/2024] [Indexed: 03/08/2024]
Abstract
Genome-wide association analyses using high-throughput metabolomics platforms have led to novel insights into the biology of human metabolism1-7. This detailed knowledge of the genetic determinants of systemic metabolism has been pivotal for uncovering how genetic pathways influence biological mechanisms and complex diseases8-11. Here we present a genome-wide association study for 233 circulating metabolic traits quantified by nuclear magnetic resonance spectroscopy in up to 136,016 participants from 33 cohorts. We identify more than 400 independent loci and assign probable causal genes at two-thirds of these using manual curation of plausible biological candidates. We highlight the importance of sample and participant characteristics that can have significant effects on genetic associations. We use detailed metabolic profiling of lipoprotein- and lipid-associated variants to better characterize how known lipid loci and novel loci affect lipoprotein metabolism at a granular level. We demonstrate the translational utility of comprehensively phenotyped molecular data, characterizing the metabolic associations of intrahepatic cholestasis of pregnancy. Finally, we observe substantial genetic pleiotropy for multiple metabolic pathways and illustrate the importance of careful instrument selection in Mendelian randomization analysis, revealing a putative causal relationship between acetone and hypertension. Our publicly available results provide a foundational resource for the community to examine the role of metabolism across diverse diseases.
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Affiliation(s)
- Minna K Karjalainen
- Systems Epidemiology, Faculty of Medicine, University of Oulu and Biocenter Oulu, Oulu, Finland.
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland.
- Northern Finland Birth Cohorts, Arctic Biobank, Infrastructure for Population Studies, Faculty of Medicine, University of Oulu, Oulu, Finland.
| | - Savita Karthikeyan
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Clare Oliver-Williams
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Public Health Specialty Training Programme, Cambridge, UK
| | - Eeva Sliz
- Systems Epidemiology, Faculty of Medicine, University of Oulu and Biocenter Oulu, Oulu, Finland
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Elias Allara
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Wing Tung Fung
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Praveen Surendran
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Rutherford Fund Fellow, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
| | - Weihua Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- Department of Cardiology, Ealing Hospital, London North West University Healthcare NHS Trust, London, UK
| | - Pekka Jousilahti
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Kati Kristiansson
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Veikko Salomaa
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Matt Goodwin
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
| | - David A Hughes
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Lilian Fernandes Silva
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland
| | - Xianyong Yin
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
- Department of Epidemiology, School of Public Health, Nanjing Medical University, Jiangsu, China
| | - Anubha Mahajan
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Genentech, South San Francisco, CA, USA
| | - Matt J Neville
- NIHR Oxford Biomedical Research Centre, OUHFT Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Natalie R van Zuydam
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Renée de Mutsert
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Ruifang Li-Gao
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Dennis O Mook-Kanamori
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
- Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, The Netherlands
| | - Ayse Demirkan
- Surrey Institute for People-Centred AI, University of Surrey, Guildford, UK
- Section of Statistical Multi-Omics, Department of Clinical and Experimental Medicine, University of Surrey, Guildford, UK
| | - Jun Liu
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Raymond Noordam
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, The Netherlands
| | - Stella Trompet
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, The Netherlands
- Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Zhengming Chen
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- MRC Population Health Research Unit, University of Oxford, Oxford, UK
| | - Christiana Kartsonaki
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- MRC Population Health Research Unit, University of Oxford, Oxford, UK
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases, Peking University, Ministry of Education, Beijing, China
| | - Kuang Lin
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Fiona A Hagenbeek
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Jouke Jan Hottenga
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - René Pool
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Joyce van Meurs
- Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Toomas Haller
- Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Yuri Milaneschi
- Department of Psychiatry, Amsterdam Neuroscience and Amsterdam Public Health, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Mika Kähönen
- Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Department of Clinical Physiology, Tampere University Hospital, Tampere, Finland
| | - Pashupati P Mishra
- Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Department of Clinical Chemistry, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
| | - Peter K Joshi
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, Scotland
| | - Erin Macdonald-Dunlop
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, Scotland
| | - Massimo Mangino
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
- NIHR Biomedical Research Centre at Guy's and St Thomas' Foundation Trust, London, UK
| | - Jonas Zierer
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Ilhan E Acar
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
- Department of Ophthalmology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Carel B Hoyng
- Department of Ophthalmology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Yara T E Lechanteur
- Department of Ophthalmology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Lude Franke
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Alexander Kurilshikov
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Alexandra Zhernakova
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Marian Beekman
- Section of Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - Erik B van den Akker
- Section of Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
- Center for Computational Biology, Leiden University Medical Center, Leiden, The Netherlands
- The Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands
| | - Ivana Kolcic
- Department of Public Health, School of Medicine, University of Split, Split, Croatia
| | - Ozren Polasek
- Department of Public Health, School of Medicine, University of Split, Split, Croatia
| | - Igor Rudan
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, Scotland
| | - Christian Gieger
- Research Unit Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | - Melanie Waldenberger
- Research Unit Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | - Folkert W Asselbergs
- Amsterdam University Medical Centers, Department of Cardiology, University of Amsterdam, Amsterdam, The Netherlands
- Health Data Research UK and Institute of Health Informatics, University College London, London, UK
| | - Caroline Hayward
- Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Jingyuan Fu
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Department of Pediatrics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Anneke I den Hollander
- Department of Ophthalmology, Radboud University Medical Center, Nijmegen, The Netherlands
- Genomics Research Center, Abbvie, Cambridge, MA, USA
| | - Cristina Menni
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Tim D Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - James F Wilson
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, Scotland
- Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Terho Lehtimäki
- Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Department of Clinical Chemistry, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
| | - Olli T Raitakari
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
- Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland
- Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland
- InFLAMES Research Flagship, University of Turku, Turku, Finland
| | - Brenda W J H Penninx
- Department of Psychiatry, Amsterdam Neuroscience and Amsterdam Public Health, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Tonu Esko
- Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Robin G Walters
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- MRC Population Health Research Unit, University of Oxford, Oxford, UK
| | - J Wouter Jukema
- Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands
- Netherlands Heart Institute, Utrecht, The Netherlands
| | - Naveed Sattar
- School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, UK
| | - Mohsen Ghanbari
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Ko Willems van Dijk
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
- Department of Internal Medicine, Division Endocrinology, Leiden University Medical Center, Leiden, The Netherlands
- Leiden Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, The Netherlands
| | - Fredrik Karpe
- NIHR Oxford Biomedical Research Centre, OUHFT Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Mark I McCarthy
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Genentech, South San Francisco, CA, USA
| | - Markku Laakso
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland
- Kuopio University Hospital, Kuopio, Finland
| | - Marjo-Riitta Järvelin
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- Department of Life Sciences, College of Health and Life Sciences, Brunel University London, Uxbridge, UK
- Unit of Primary Health Care, Oulu University Hospital, OYS, Oulu, Finland
| | - Nicholas J Timpson
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
| | - Markus Perola
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
- Diabetes and Obesity Research Program, University of Helsinki, Helsinki, Finland
- Estonian Genome Center, University of Tartu, Tartu, Estonia
| | - Jaspal S Kooner
- Department of Cardiology, Ealing Hospital, London North West University Healthcare NHS Trust, London, UK
- Imperial College Healthcare NHS Trust, Imperial College London, London, UK
- MRC-PHE Centre for Environment and Health, Imperial College London, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | - John C Chambers
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- Department of Cardiology, Ealing Hospital, London North West University Healthcare NHS Trust, London, UK
- Imperial College Healthcare NHS Trust, Imperial College London, London, UK
- MRC-PHE Centre for Environment and Health, Imperial College London, London, UK
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Cornelia van Duijn
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - P Eline Slagboom
- Section of Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
- Amsterdam Reproduction and Development (AR&D) Research Institute, Amsterdam, The Netherlands
| | - John Danesh
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- Department of Human Genetics, Wellcome Sanger Institute, Hinxton, UK
| | - Mika Ala-Korpela
- Systems Epidemiology, Faculty of Medicine, University of Oulu and Biocenter Oulu, Oulu, Finland
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
- NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland
| | - Adam S Butterworth
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
| | - Johannes Kettunen
- Systems Epidemiology, Faculty of Medicine, University of Oulu and Biocenter Oulu, Oulu, Finland
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
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Nielsen RL, Monfeuga T, Kitchen RR, Egerod L, Leal LG, Schreyer ATH, Gade FS, Sun C, Helenius M, Simonsen L, Willert M, Tahrani AA, McVey Z, Gupta R. Data-driven identification of predictive risk biomarkers for subgroups of osteoarthritis using interpretable machine learning. Nat Commun 2024; 15:2817. [PMID: 38561399 PMCID: PMC10985086 DOI: 10.1038/s41467-024-46663-4] [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: 08/04/2023] [Accepted: 03/04/2024] [Indexed: 04/04/2024] Open
Abstract
Osteoarthritis (OA) is increasing in prevalence and has a severe impact on patients' lives. However, our understanding of biomarkers driving OA risk remains limited. We developed a model predicting the five-year risk of OA diagnosis, integrating retrospective clinical, lifestyle and biomarker data from the UK Biobank (19,120 patients with OA, ROC-AUC: 0.72, 95%CI (0.71-0.73)). Higher age, BMI and prescription of non-steroidal anti-inflammatory drugs contributed most to increased OA risk prediction ahead of diagnosis. We identified 14 subgroups of OA risk profiles. These subgroups were validated in an independent set of patients evaluating the 11-year OA risk, with 88% of patients being uniquely assigned to one of the 14 subgroups. Individual OA risk profiles were characterised by personalised biomarkers. Omics integration demonstrated the predictive importance of key OA genes and pathways (e.g., GDF5 and TGF-β signalling) and OA-specific biomarkers (e.g., CRTAC1 and COL9A1). In summary, this work identifies opportunities for personalised OA prevention and insights into its underlying pathogenesis.
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Affiliation(s)
| | | | | | - Line Egerod
- Novo Nordisk Research Centre Oxford, Oxford, UK
| | - Luis G Leal
- Novo Nordisk Research Centre Oxford, Oxford, UK
| | | | | | - Carol Sun
- Novo Nordisk Research Centre Oxford, Oxford, UK
| | | | | | | | | | - Zahra McVey
- Novo Nordisk Research Centre Oxford, Oxford, UK
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28
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Crick DCP, Khandaker GM, Halligan SL, Burgner D, Mansell T, Fraser A. Comparison of the stability of glycoprotein acetyls and high sensitivity C-reactive protein as markers of chronic inflammation. Immunology 2024; 171:497-512. [PMID: 38148627 DOI: 10.1111/imm.13739] [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: 05/31/2023] [Accepted: 11/29/2023] [Indexed: 12/28/2023] Open
Abstract
It has been suggested that glycoprotein acetyls (GlycA) better reflects chronic inflammation than high sensitivity C-reactive protein (hsCRP), but paediatric/life-course data are sparse. Using data from the Avon Longitudinal Study of Parents and Children (ALSPAC) and UK Biobank, we compared short- (over weeks) and long-term (over years) correlations of GlycA and hsCRP, cross-sectional correlations between GlycA and hsCRP, and associations of pro-inflammatory risk factors with GlycA and hsCRP across the life-course. GlycA showed high short-term (weeks) stability at 15 years (r = 0.75; 95% CI = 0.56, 0.94), 18 years (r = 0.74; 0.64, 0.85), 24 years (r = 0.74; 0.51, 0.98) and 48 years (r = 0.82 0.76, 0.86) and this was comparable to the short-term stability of hsCRP at 24 years. GlycA stability was moderate over the long-term, for example between 15 and 18 years r = 0.52; 0.47, 0.56 and between 15 and 24 years r = 0.37; 0.31, 0.44. These were larger than equivalent correlations of hsCRP. GlycA and concurrently measured hsCRP were moderately correlated at all ages, for example at 15 years (r = 0.44; 0.40, 0.48) and at 18 years (r = 0.55; 0.51, 0.59). We found similar associations of known proinflammatory factors and inflammatory diseases with GlycA and hsCRP. For example, BMI was positively associated with GlycA (mean difference in GlycA per standard deviation change in BMI = 0.08; 95% CI = 0.07, 0.10) and hsCRP (0.10; 0.08, 0.11). This study showed that GlycA has greater long-term stability than hsCRP, however associations of proinflammatory factors with GlycA and hsCRP were broadly similar.
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Affiliation(s)
- Daisy C P Crick
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
| | - Golam M Khandaker
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- NIHR Bristol Biomedical Research Centre, Bristol, UK
- Avon and Wiltshire Mental Health Partnership NHS Trust, Bristol, UK
- Centre for Academic Mental Health, University of Bristol, Bristol, UK
| | - Sarah L Halligan
- Department of Psychology, University of Bath, Bath, UK
- Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa
- Department of Psychiatry, Stellenbosch University, Stellenbosch, South Africa
| | - David Burgner
- Murdoch Children's Research Institute, Royal Children's Hospital, Parkville, VIC, Australia
- Department of Paediatrics, Melbourne University, Parkville, VIC, Australia
| | - Toby Mansell
- Murdoch Children's Research Institute, Royal Children's Hospital, Parkville, VIC, Australia
- Department of Paediatrics, Melbourne University, Parkville, VIC, Australia
| | - Abigail Fraser
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
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29
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Oexner RR, Ahn H, Theofilatos K, Shah RA, Schmitt R, Chowienczyk P, Zoccarato A, Shah AM. Serum metabolomics improves risk stratification for incident heart failure. Eur J Heart Fail 2024; 26:829-840. [PMID: 38623713 DOI: 10.1002/ejhf.3226] [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: 10/26/2023] [Revised: 02/01/2024] [Accepted: 03/26/2024] [Indexed: 04/17/2024] Open
Abstract
AIMS Prediction and early detection of heart failure (HF) is crucial to mitigate its impact on quality of life, survival, and healthcare expenditure. Here, we explored the predictive value of serum metabolomics (168 metabolites detected by proton nuclear magnetic resonance [1H-NMR] spectroscopy) for incident HF. METHODS AND RESULTS Leveraging data of 68 311 individuals and >0.8 million person-years of follow-up from the UK Biobank cohort, we (i) fitted per-metabolite Cox proportional hazards models to assess individual metabolite associations, and (ii) trained and validated elastic net models to predict incident HF using the serum metabolome. We benchmarked discriminative performance against a comprehensive, well-validated clinical risk score (Pooled Cohort Equations to Prevent HF [PCP-HF]). During a median follow-up of ≈12.3 years, several metabolites showed independent association with incident HF (90/168 adjusting for age and sex, 48/168 adjusting for PCP-HF). Performance-optimized risk models effectively retained key predictors representing highly correlated clusters (≈80% feature reduction). Adding metabolomics to PCP-HF improved predictive performance (Harrel's C: 0.768 vs. 0.755, ΔC = 0.013, [95% confidence interval [CI] 0.004-0.022], continuous net reclassification improvement [NRI]: 0.287 [95% CI 0.200-0.367], relative integrated discrimination improvement [IDI]: 17.47% [95% CI 9.463-27.825]). Models including age, sex and metabolomics performed almost as well as PCP-HF (Harrel's C: 0.745 vs. 0.755, ΔC = 0.010 [95% CI -0.004 to 0.027], continuous NRI: 0.097 [95% CI -0.025 to 0.217], relative IDI: 13.445% [95% CI -10.608 to 41.454]). Risk and survival stratification was improved by integrating metabolomics. CONCLUSION Serum metabolomics improves incident HF risk prediction over PCP-HF. Scores based on age, sex and metabolomics exhibit similar predictive power to clinically-based models, potentially offering a cost-effective, standardizable, and scalable single-domain alternative.
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Affiliation(s)
- Rafael R Oexner
- King's College London British Heart Foundation Centre of Research Excellence, School of Cardiovascular and Metabolic Medicine and Sciences, King's College London, London, UK
| | - Hyunchan Ahn
- King's College London British Heart Foundation Centre of Research Excellence, School of Cardiovascular and Metabolic Medicine and Sciences, King's College London, London, UK
| | - Konstantinos Theofilatos
- King's College London British Heart Foundation Centre of Research Excellence, School of Cardiovascular and Metabolic Medicine and Sciences, King's College London, London, UK
| | - Ravi A Shah
- University College Hospital, University College London Hospitals NHS Foundation Trust, London, UK
| | - Robin Schmitt
- King's College London British Heart Foundation Centre of Research Excellence, School of Cardiovascular and Metabolic Medicine and Sciences, King's College London, London, UK
| | - Philip Chowienczyk
- King's College London British Heart Foundation Centre of Research Excellence, School of Cardiovascular and Metabolic Medicine and Sciences, King's College London, London, UK
| | - Anna Zoccarato
- King's College London British Heart Foundation Centre of Research Excellence, School of Cardiovascular and Metabolic Medicine and Sciences, King's College London, London, UK
| | - Ajay M Shah
- King's College London British Heart Foundation Centre of Research Excellence, School of Cardiovascular and Metabolic Medicine and Sciences, King's College London, London, UK
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30
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Kapp M, Holtfreter B, Kocher T, Friedrich N, Pink C, Völzke H, Nauck M. Serum lipoprotein subfractions are associated with the periodontal status: Results from the population-based cohort SHIP-TREND. J Clin Periodontol 2024; 51:390-405. [PMID: 38098273 DOI: 10.1111/jcpe.13902] [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: 07/25/2023] [Revised: 10/19/2023] [Accepted: 10/28/2023] [Indexed: 03/16/2024]
Abstract
AIM To investigate the medium-term associations of serum protein subfractions derived from proton nuclear magnetic resonance (1 H-NMR) spectroscopy with periodontitis and tooth loss. MATERIALS AND METHODS A total of 3031 participants of the cohort Study of Health in Pomerania (SHIP-TREND) were included. In addition to conventional serum testing, serum lipoprotein contents and subfractions were analysed by 1 H-NMR spectroscopy. Confounder-adjusted associations of lipoprotein variables with periodontitis and the number of missing teeth variables were analysed using mixed-effects models with random intercepts for time across individuals, accounting for multiple testing. RESULTS While only spurious associations between lipoprotein levels from conventional blood tests were found-that is, triglycerides were associated with mean clinical attachment level (CAL) and low-density lipoprotein cholesterol/high-density lipoprotein cholesterol (LDL-C/HDL-C) ratio with the number of missing teeth - several associations emerged from serum lipoprotein subfractions derived from 1 H-NMR analysis. Specifically, elevated LDL triglycerides were associated with higher levels of mean probing depth (PD), mean CALs, and increased odds of having <20 teeth. HDL-4 cholesterol levels were inversely associated with mean PD. Systemic inflammation (C-reactive protein) might mediate the effects of LDL and HDL triglyceride contents on periodontitis severity. CONCLUSIONS Several associations between serum lipoprotein subfractions and periodontitis were observed. As the underlying biochemical mechanisms remain unclear, further research is needed.
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Affiliation(s)
- Marius Kapp
- Department of Restorative Dentistry, Periodontology, Endodontology, and Preventive and Pediatric Dentistry, Dental School, University Medicine Greifswald, Greifswald, Germany
| | - Birte Holtfreter
- Department of Restorative Dentistry, Periodontology, Endodontology, and Preventive and Pediatric Dentistry, Dental School, University Medicine Greifswald, Greifswald, Germany
| | - Thomas Kocher
- Department of Restorative Dentistry, Periodontology, Endodontology, and Preventive and Pediatric Dentistry, Dental School, University Medicine Greifswald, Greifswald, Germany
| | - Nele Friedrich
- Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Christiane Pink
- Department of Orthodontics, Dental School, University Medicine Greifswald, Greifswald, Germany
| | - Henry Völzke
- German Centre for Cardiovascular Research (DZHK), Partner Site Greifswald, Greifswald, Germany
- Institute for Community Medicine, SHIP/Clinical-Epidemiological Research, University Medicine Greifswald, Greifswald, Germany
| | - Matthias Nauck
- Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany
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31
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Yurkovich JT, Evans SJ, Rappaport N, Boore JL, Lovejoy JC, Price ND, Hood LE. The transition from genomics to phenomics in personalized population health. Nat Rev Genet 2024; 25:286-302. [PMID: 38093095 DOI: 10.1038/s41576-023-00674-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/03/2023] [Indexed: 03/21/2024]
Abstract
Modern health care faces several serious challenges, including an ageing population and its inherent burden of chronic diseases, rising costs and marginal quality metrics. By assessing and optimizing the health trajectory of each individual using a data-driven personalized approach that reflects their genetics, behaviour and environment, we can start to address these challenges. This assessment includes longitudinal phenome measures, such as the blood proteome and metabolome, gut microbiome composition and function, and lifestyle and behaviour through wearables and questionnaires. Here, we review ongoing large-scale genomics and longitudinal phenomics efforts and the powerful insights they provide into wellness. We describe our vision for the transformation of the current health care from disease-oriented to data-driven, wellness-oriented and personalized population health.
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Affiliation(s)
- James T Yurkovich
- Phenome Health, Seattle, WA, USA
- Center for Phenomic Health, The Buck Institute for Research on Aging, Novato, CA, USA
- Department of Bioengineering, University of Texas at Dallas, Richardson, TX, USA
| | - Simon J Evans
- Phenome Health, Seattle, WA, USA
- Center for Phenomic Health, The Buck Institute for Research on Aging, Novato, CA, USA
| | - Noa Rappaport
- Center for Phenomic Health, The Buck Institute for Research on Aging, Novato, CA, USA
- Institute for Systems Biology, Seattle, WA, USA
| | - Jeffrey L Boore
- Phenome Health, Seattle, WA, USA
- Center for Phenomic Health, The Buck Institute for Research on Aging, Novato, CA, USA
| | - Jennifer C Lovejoy
- Phenome Health, Seattle, WA, USA
- Center for Phenomic Health, The Buck Institute for Research on Aging, Novato, CA, USA
- Institute for Systems Biology, Seattle, WA, USA
| | - Nathan D Price
- Institute for Systems Biology, Seattle, WA, USA
- Thorne HealthTech, New York, NY, USA
- Department of Bioengineering, University of Washington, Seattle, WA, USA
- Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA, USA
| | - Leroy E Hood
- Phenome Health, Seattle, WA, USA.
- Center for Phenomic Health, The Buck Institute for Research on Aging, Novato, CA, USA.
- Institute for Systems Biology, Seattle, WA, USA.
- Department of Bioengineering, University of Washington, Seattle, WA, USA.
- Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA, USA.
- Department of Immunology, University of Washington, Seattle, WA, USA.
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32
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Yao Z, Jia X, Chen Z, Zhang T, Li X, Zhang L, Chen F, Zhang J, Zhang Z, Liu Z, Chen Z. Dietary patterns, metabolomics and frailty in a large cohort of 120 000 participants. Food Funct 2024; 15:3174-3185. [PMID: 38441259 DOI: 10.1039/d3fo03575a] [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: 03/19/2024]
Abstract
Objective: To examine the associations of dietary patterns with frailty and whether metabolic signatures (MSs) mediate these associations. Methods: We used UK Biobank data to examine (1) the associations of four dietary patterns (i.e., alternate Mediterranean diet [aMED], Recommended Food Score [RFS], Dietary Approaches to Stop Hypertension [DASH] and Mediterranean-DASH Intervention for Neurodegenerative Delay [MIND] diet) with frailty (measured by the frailty phenotype and the frailty index) using multivariable logistic regression (analytic sample 1: N = 124 261; mean age = 57.7 years), and (2) the mediating role of MSs (weighted sums of the metabolites selected from 168 plasma metabolites using the LASSO algorithm) in the above associations via mediation analysis (analytic sample 2: N = 26 270; mean age = 57.7 years). Results: Four dietary patterns were independently associated with frailty (all P < 0.001). For instance, compared to participants in the lowest tertile for RFS, those in the intermediate (odds ratio [OR]: 0.81; 95% confidence interval [CI]: 0.74, 0.89) and highest (OR: 0.62; 95% CI: 0.56, 0.68) tertiles had a lower risk of frailty. We found that 98, 68, 123 and 75 metabolites were associated with aMED, RFS, DASH and MIND, respectively, including 16 common metabolites (e.g., fatty acids, lipoproteins, acetate and glycoprotein acetyls). The MSs based on these metabolites partially mediated the association of the four dietary patterns with frailty, with the mediation proportion ranging from 26.52% to 45.83%. The results were robust when using another frailty measure, the frailty index. Conclusions: The four dietary patterns were associated with frailty, and these associations were partially mediated by MSs. Adherence to healthy dietary patterns may potentially reduce frailty development by modulating metabolites.
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Affiliation(s)
- Zhao Yao
- Department of Rehabilitation Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, Zhejiang, China.
- The Second Affiliated Hospital and Yuying Children's Hospital of, Wenzhou Medical University, Wenzhou 325000, Zhejiang, China
| | - Xueqing Jia
- The Second Affiliated Hospital and School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Zhuoneng Chen
- Department of Gastroenterology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, Zhejiang, China
| | - Tianfang Zhang
- Department of Rehabilitation Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, Zhejiang, China.
| | - Xin Li
- Department of Exercise and Nutrition Science, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
| | - Liming Zhang
- The Second Affiliated Hospital and School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Fenfen Chen
- The Second Affiliated Hospital and Yuying Children's Hospital of, Wenzhou Medical University, Wenzhou 325000, Zhejiang, China
- Department of Rehabilitation Medicine, Taizhou Hospital Affiliated to Wenzhou Medical University, China
| | - Jingyun Zhang
- The Second Affiliated Hospital and School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Ziwei Zhang
- Department of Rehabilitation Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, Zhejiang, China.
| | - Zuyun Liu
- The Second Affiliated Hospital and School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Zuobing Chen
- Department of Rehabilitation Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, Zhejiang, China.
- The Second Affiliated Hospital and Yuying Children's Hospital of, Wenzhou Medical University, Wenzhou 325000, Zhejiang, China
- Department of Rehabilitation Medicine, Taizhou Hospital Affiliated to Wenzhou Medical University, China
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Sluiskes MH, Goeman JJ, Beekman M, Slagboom PE, Putter H, Rodríguez-Girondo M. Clarifying the biological and statistical assumptions of cross-sectional biological age predictors: an elaborate illustration using synthetic and real data. BMC Med Res Methodol 2024; 24:58. [PMID: 38459475 PMCID: PMC10921716 DOI: 10.1186/s12874-024-02181-x] [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] [Received: 06/22/2023] [Accepted: 02/15/2024] [Indexed: 03/10/2024] Open
Abstract
BACKGROUND There is divergence in the rate at which people age. The concept of biological age is postulated to capture this variability, and hence to better represent an individual's true global physiological state than chronological age. Biological age predictors are often generated based on cross-sectional data, using biochemical or molecular markers as predictor variables. It is assumed that the difference between chronological and predicted biological age is informative of one's chronological age-independent aging divergence ∆. METHODS We investigated the statistical assumptions underlying the most popular cross-sectional biological age predictors, based on multiple linear regression, the Klemera-Doubal method or principal component analysis. We used synthetic and real data to illustrate the consequences if this assumption does not hold. RESULTS The most popular cross-sectional biological age predictors all use the same strong underlying assumption, namely that a candidate marker of aging's association with chronological age is directly informative of its association with the aging rate ∆. We called this the identical-association assumption and proved that it is untestable in a cross-sectional setting. If this assumption does not hold, weights assigned to candidate markers of aging are uninformative, and no more signal may be captured than if markers would have been assigned weights at random. CONCLUSIONS Cross-sectional methods for predicting biological age commonly use the untestable identical-association assumption, which previous literature in the field had never explicitly acknowledged. These methods have inherent limitations and may provide uninformative results, highlighting the importance of researchers exercising caution in the development and interpretation of cross-sectional biological age predictors.
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Affiliation(s)
- Marije H Sluiskes
- Medical Statistics, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands.
| | - Jelle J Goeman
- Medical Statistics, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
| | - Marian Beekman
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
| | - P Eline Slagboom
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
- Max Planck Institute for the Biology of Ageing, Cologne, Germany
| | - Hein Putter
- Medical Statistics, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
| | - Mar Rodríguez-Girondo
- Medical Statistics, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
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Wong THT, Mo JMY, Zhou M, Zhao JV, Schooling CM, He B, Luo S, Au Yeung SL. A two-sample Mendelian randomization study explores metabolic profiling of different glycemic traits. Commun Biol 2024; 7:293. [PMID: 38459184 PMCID: PMC10923832 DOI: 10.1038/s42003-024-05977-1] [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] [Received: 01/02/2023] [Accepted: 02/27/2024] [Indexed: 03/10/2024] Open
Abstract
We assessed the causal relation of four glycemic traits and type 2 diabetes liability with 167 metabolites using Mendelian randomization with various sensitivity analyses and a reverse Mendelian randomization analysis. We extracted instruments for fasting glucose, 2-h glucose, fasting insulin, and glycated hemoglobin from the Meta-Analyses of Glucose and Insulin-related traits Consortium (n = 200,622), and those for type 2 diabetes liability from a meta-analysis of multiple cohorts (148,726 cases, 965,732 controls) in Europeans. Outcome data were from summary statistics of 167 metabolites from the UK Biobank (n = 115,078). Fasting glucose and 2-h glucose were not associated with any metabolite. Higher glycated hemoglobin was associated with higher free cholesterol in small low-density lipoprotein. Type 2 diabetes liability and fasting insulin were inversely associated with apolipoprotein A1, total cholines, lipoprotein subfractions in high-density-lipoprotein and intermediate-density lipoproteins, and positively associated with aromatic amino acids. These findings indicate hyperglycemia-independent patterns and highlight the role of insulin in type 2 diabetes development. Further studies should evaluate these glycemic traits in type 2 diabetes diagnosis and clinical management.
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Affiliation(s)
- Tommy H T Wong
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Jacky M Y Mo
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Mingqi Zhou
- Department of Biological Chemistry, School of Medicine, University of California Irvine, Irvine, CA, USA
- Center for Epigenetics and Metabolism, University of California Irvine, Irvine, CA, USA
| | - Jie V Zhao
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - C Mary Schooling
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- School of Public Health and Health Policy, City University of New York, New York, NY, USA
| | - Baoting He
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Shan Luo
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
| | - Shiu Lun Au Yeung
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
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Manninen S, Tilles-Tirkkonen T, Aittola K, Männikkö R, Karhunen L, Kolehmainen M, Schwab U, Lindström J, Lakka T, Pihlajamäki J. Associations of Lifestyle Patterns with Glucose and Lipid Metabolism in Finnish Adults at Increased Risk of Type 2 Diabetes. Mol Nutr Food Res 2024; 68:e2300338. [PMID: 38308150 DOI: 10.1002/mnfr.202300338] [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] [Received: 05/24/2023] [Revised: 10/18/2023] [Indexed: 02/04/2024]
Abstract
SCOPE Various lifestyle and sociodemographic factors have been associated with risk factors for type 2 diabetes (T2D). However, their combined associations with T2D risk factors have been studied much less. MATERIALS AND RESULTS This study investigates cross-sectional associations of lifestyle patterns with T2D risk factors among 2925 adults at increased risk participating in the Stop Diabetes study. Lifestyle patterns are determined using principal component analysis (PCA) with several lifestyle and sociodemographic factors. The associations of lifestyle patterns with measures of glucose and lipid metabolism and serum metabolites analyzed by nuclear magnetic resonance (NMR) spectroscopy are studied using linear regression analysis. "Healthy eating" pattern is associated with better glucose and insulin metabolism, more favorable lipoprotein and fatty acid profiles and lower serum concentrations of metabolites related to inflammation, insulin resistance, and T2D. "High socioeconomic status and low physical activity" pattern is associated with increased serum concentrations of branched-chain amino acids, as are "Meat and poultry" and "Sleeping hours" patterns. "Snacks" pattern is associated with lower serum concentrations of ketone bodies. CONCLUSIONS Our results show, in large scale primary care setting, that healthy eating is associated with better glucose and lipid metabolism and reveal novel associations of lifestyle patterns with metabolites related to glucose metabolism.
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Affiliation(s)
- Suvi Manninen
- Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, 70211, Finland
| | - Tanja Tilles-Tirkkonen
- Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, 70211, Finland
| | - Kirsikka Aittola
- Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, 70211, Finland
| | - Reija Männikkö
- Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, 70211, Finland
| | - Leila Karhunen
- Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, 70211, Finland
| | - Marjukka Kolehmainen
- Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, 70211, Finland
| | - Ursula Schwab
- Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, 70211, Finland
- Department of Medicine, Endocrinology and Clinical Nutrition, Kuopio University Hospital, Kuopio, KYS, 70029, Finland
| | - Jaana Lindström
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, 00271, Finland
| | - Timo Lakka
- Institute of Biomedicine, School of Medicine, University of Eastern Finland, Kuopio, 70211, Finland
- Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, Kuopio, KYS, 70029, Finland
- Foundation for Research in Health Exercise and Nutrition, Kuopio Research Institute of Exercise Medicine, Kuopio, 70100, Finland
| | - Jussi Pihlajamäki
- Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, 70211, Finland
- Department of Medicine, Endocrinology and Clinical Nutrition, Kuopio University Hospital, Kuopio, KYS, 70029, Finland
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Skytte HN, Roland MCP, Christensen JJ, Holven KB, Lekva T, Gunnes N, Michelsen TM. Maternal metabolic profiling across body mass index groups: An exploratory longitudinal study. Acta Obstet Gynecol Scand 2024; 103:540-550. [PMID: 38083835 PMCID: PMC10867396 DOI: 10.1111/aogs.14750] [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: 03/29/2023] [Revised: 11/20/2023] [Accepted: 11/24/2023] [Indexed: 02/16/2024]
Abstract
INTRODUCTION Increased BMI has been identified as a risk factor for most pregnancy complications, but the underlying metabolic factors mediating the detrimental effects of BMI are largely unknown. We aimed to compare metabolic profiles in overweight/obese women (body mass index [BMI] ≥ 25 kg/m2 ) and normal weight/underweight women (BMI < 25 kg/m2 ) across gestation. We also explored how gestational weight gain (GWG) affected maternal metabolic profiles. MATERIAL AND METHODS Exploratory nested case-control study based on a prospective longitudinal cohort of women who were healthy prior to pregnancy and gave birth at Oslo University Hospital from 2002 to 2008. The sample consisted of 48 women who were overweight/obese and 59 normal-weight/underweight women. Plasma samples from four time points in pregnancy (weeks 14-16, 22-24, 30-32 and 36-38) were analyzed by nuclear magnetic resonance spectroscopy and 91 metabolites were measured. Linear regression models were fitted for each of the metabolites at each time point. RESULTS Overweight or obese women had higher levels of lipids in very-low-density lipoprotein (VLDL), total triglycerides, triglycerides in VLDL, total fatty acids, monounsaturated fatty acids, saturated fatty acids, leucine, valine, and total branched-chain amino acids in pregnancy weeks 14-16 compared to underweight and normal-weight women. Docosahexaenoic acid and degree of unsaturation were significantly lower in overweight/obese women in pregnancy weeks 36-38. In addition, overweight or obese women had higher particle concentration of XXL-VLDL and glycoprotein acetyls (GlycA) at weeks 14-16 and 30-32. GWG did not seem to affect the metabolic profile, regardless of BMI group when BMI was treated as a dichotomous variable, ≥25 kg/m2 (yes/no). CONCLUSIONS Overweight or obese women had smaller pregnancy-related metabolic alterations than normal-weight/underweight women. There was a trend toward higher triglyceride and VLDL particle concentration in overweight/obese women. As this was a hypothesis-generating study, the similarities with late-onset pre-eclampsia warrant further investigation. The unfavorable development of fatty acid composition in overweight/obese women, with possible implication for the offspring, should also be studied further in the future.
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Affiliation(s)
- Hege Nyhus Skytte
- Norwegian Research Center for Women's HealthOslo University HospitalOsloNorway
- Faculty of MedicineUniversity of OsloOsloNorway
| | | | | | - Kirsten Bjørklund Holven
- Department of NutritionUniversity of OsloOsloNorway
- Norwegian National Advisory Unit on Familial HypercholesterolemiaOslo University HospitalOsloNorway
| | - Tove Lekva
- Research Institute of Internal MedicineOslo University HospitalOsloNorway
| | - Nina Gunnes
- Norwegian Research Center for Women's HealthOslo University HospitalOsloNorway
| | - Trond Melbye Michelsen
- Faculty of MedicineUniversity of OsloOsloNorway
- Division of Obstetrics and GynecologyOslo University HospitalOsloNorway
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Compton H, Smith ML, Bull C, Korologou-Linden R, Ben-Shlomo Y, Bell JA, Williams DM, Anderson EL. Life course plasma metabolomic signatures of genetic liability to Alzheimer's disease. Sci Rep 2024; 14:3896. [PMID: 38365930 PMCID: PMC10873397 DOI: 10.1038/s41598-024-54569-w] [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: 11/13/2023] [Accepted: 02/14/2024] [Indexed: 02/18/2024] Open
Abstract
Mechanisms through which most known Alzheimer's disease (AD) loci operate to increase AD risk remain unclear. Although Apolipoprotein E (APOE) is known to regulate lipid homeostasis, the effects of broader AD genetic liability on non-lipid metabolites remain unknown, and the earliest ages at which metabolic perturbations occur and how these change over time are yet to be elucidated. We examined the effects of AD genetic liability on the plasma metabolome across the life course. Using a reverse Mendelian randomization framework in two population-based cohorts [Avon Longitudinal Study of Parents and Children (ALSPAC, n = 5648) and UK Biobank (n ≤ 118,466)], we estimated the effects of genetic liability to AD on 229 plasma metabolites, at seven different life stages, spanning 8 to 73 years. We also compared the specific effects of APOE ε4 and APOE ε2 carriage on metabolites. In ALSPAC, AD genetic liability demonstrated the strongest positive associations with cholesterol-related traits, with similar magnitudes of association observed across all age groups including in childhood. In UK Biobank, the effect of AD liability on several lipid traits decreased with age. Fatty acid metabolites demonstrated positive associations with AD liability in both cohorts, though with smaller magnitudes than lipid traits. Sensitivity analyses indicated that observed effects are largely driven by the strongest AD instrument, APOE, with many contrasting effects observed on lipids and fatty acids for both ε4 and ε2 carriage. Our findings indicate pronounced effects of the ε4 and ε2 genetic variants on both pro- and anti-atherogenic lipid traits and sphingomyelins, which begin in childhood and either persist into later life or appear to change dynamically.
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Affiliation(s)
- Hannah Compton
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK
| | - Madeleine L Smith
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Caroline Bull
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- School of Translational Health Sciences, University of Bristol, Bristol, UK
| | - Roxanna Korologou-Linden
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Yoav Ben-Shlomo
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK
| | - Joshua A Bell
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Dylan M Williams
- MRC Unit for Lifelong Health & Ageing at UCL, University College London, London, UK
| | - Emma L Anderson
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK.
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
- Division of Psychiatry, University College London, 149 Tottenham Court Road, London, W1T 7NF, UK.
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Bernhardsen GP, Thomas O, Mäntyselkä P, Niskanen L, Vanhala M, Koponen H, Lehto SM. Metabolites and depressive symptoms: Network- and longitudinal analyses from the Finnish Depression and Metabolic Syndrome in Adults (FDMSA) Study. J Affect Disord 2024; 347:199-209. [PMID: 38000471 DOI: 10.1016/j.jad.2023.11.070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 10/20/2023] [Accepted: 11/18/2023] [Indexed: 11/26/2023]
Abstract
BACKGROUND Depression is associated with metabolic abnormalities linked to metabolic syndrome and tissue inflammation, but the interplay between metabolic markers and their association with subsequent depression is unknown. Therefore, we aimed to describe the network of metabolites and their prospective association with depressive symptoms. METHODS The Finnish Depression and Metabolic Syndrome in Adults (FDMSA) cohort, originally a prospective case-control study, comprised a group with Beck Depression Inventory (BDI)-I scores ≥10 at baseline, and controls (n = 319, BDI-I < 10); mean (sd) follow-up time: 7.4 (0.7) years. Serum metabolic biomarkers were determined by proton nuclear magnetic resonance (NMR), and depressive symptoms sum-score by using the BDI-I. We examined the prospective associations between metabolites at baseline and BDI score at follow-up utilizing multivariate linear regression, parsimonious predictions models and network analysis. RESULTS Some metabolites tended to be either negatively (e.g. histidine) or positively associated (e.g. glycoprotein acetylation, creatinine and triglycerides in very large high density lipoproteins [XL-HDL-TG]) with depressive symptoms. None of the associations were significant after correction for multiple testing. The network analysis suggested high correlation among the metabolites, but that none of the metabolites directly influenced subsequent depressive symptoms. LIMITATIONS Although the sample size may be considered satisfactory in a prospective context, we cannot exclude the possibility that our study was underpowered. CONCLUSIONS Our results suggest that the investigated metabolic biomarkers are not a driving force in the development of depressive symptoms. These findings should be confirmed in studies with larger samples and studies that account for the heterogeneity of depressive disorders.
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Affiliation(s)
- Guro Pauck Bernhardsen
- Department of Research and Development, Division of Mental Health Services, Akershus University Hospital, Lørenskog, Norway.
| | - Owen Thomas
- Division of Research and Innovation, Akershus University Hospital, Lørenskog, Norway
| | - Pekka Mäntyselkä
- Institute of Public Health and Clinical Nutrition, General Practice, University of Eastern Finland, Kuopio, Finland; Clinical Research and Trials Centre, Kuopio University Hospital, Wellbeing Services County of North Savo, Kuopio, Finland
| | - Leo Niskanen
- Institute of Public Health and Clinical Nutrition, General Practice, University of Eastern Finland, Kuopio, Finland; Departments of Internal Medicine, Endocrinology/Diabetology, Päijät-Häme Central Hospital, Lahti, Finland; Eira Medical Center and Hospital, Helsinki, Finland
| | - Mauno Vanhala
- Institute of Public Health and Clinical Nutrition, General Practice, University of Eastern Finland, Kuopio, Finland
| | - Hannu Koponen
- Psychiatry, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Soili M Lehto
- Department of Research and Development, Division of Mental Health Services, Akershus University Hospital, Lørenskog, Norway; Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychiatry, University of Helsinki, Helsinki, Finland
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Tsermoula P, Kristensen NB, Mobaraki N, Engelsen SRB, Khakimov B. Efficient Quantification of Milk Metabolites from 1H NMR Spectra Using the Signature Mapping (SigMa) Approach: Chemical Shift Library Development for Cows' Milk and Colostrum. Anal Chem 2024; 96:1861-1871. [PMID: 38277502 DOI: 10.1021/acs.analchem.3c03449] [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: 01/28/2024]
Abstract
Cow milk contains essential nutrients for humans, and its bulk composition is usually analyzed using Fourier transform infrared spectroscopy. The higher sensitivity of nuclear magnetic resonance (NMR) spectroscopy can augment the extractible qualitative and quantitative information from milk to nearly 60 compounds, enabling us to monitor the health of cows and milk quality. Proton (1H) NMR spectroscopy produces complex spectra that require expert knowledge for identifying and quantifying metabolites. Therefore, an efficient and reproducible methodology is required to transform complex milk 1H NMR spectra into annotated and quantified milk metabolome data. In this study, standard operating procedures for screening the milk metabolome using 1H NMR spectra are developed. A chemical shift library of 63 milk metabolites was established and implemented in the open-access Signature Mapping (SigMa) software. SigMa is a spectral analysis tool that transforms 1H NMR spectra into a quantitative metabolite table. The applicability of the proposed methodology to whole milk, skim milk, and ultrafiltered milk is demonstrated, and the method is tested on ultrafiltered colostrum samples from dairy cows (n = 88) to evaluate whether metabolic changes in colostrum may reflect the metabolic status of cows.
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Affiliation(s)
- Paraskevi Tsermoula
- Department of Food Science, University of Copenhagen, Rolighedsvej 26, Frederiksberg 1958, Denmark
| | | | - Nabiollah Mobaraki
- Institute for Medicinal and Pharmaceutical Chemistry, University of Technology Braunschweig, Beethovenstraße 55, Braunschweig 38106, Germany
| | - So Ren B Engelsen
- Department of Food Science, University of Copenhagen, Rolighedsvej 26, Frederiksberg 1958, Denmark
| | - Bekzod Khakimov
- Department of Food Science, University of Copenhagen, Rolighedsvej 26, Frederiksberg 1958, Denmark
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Clayton GL, Borges MC, Lawlor DA. The impact of reproductive factors on the metabolic profile of females from menarche to menopause. Nat Commun 2024; 15:1103. [PMID: 38320991 PMCID: PMC10847109 DOI: 10.1038/s41467-023-44459-6] [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: 06/02/2022] [Accepted: 12/14/2023] [Indexed: 02/08/2024] Open
Abstract
We explore the relation between age at menarche, parity and age at natural menopause with 249 metabolic traits in over 65,000 UK Biobank women using multivariable regression, Mendelian randomization and negative control (parity only). Older age of menarche is related to a less atherogenic metabolic profile in multivariable regression and Mendelian randomization, which is largely attenuated when accounting for adult body mass index. In multivariable regression, higher parity relates to more particles and lipids in VLDL, which are not observed in male negative controls. In multivariable regression and Mendelian randomization, older age at natural menopause is related to lower concentrations of inflammation markers, but we observe inconsistent results for LDL-related traits due to chronological age-specific effects. For example, older age at menopause is related to lower LDL-cholesterol in younger women but slightly higher in older women. Our findings support a role of reproductive traits on later life metabolic profile and provide insights into identifying novel markers for the prevention of adverse cardiometabolic outcomes in women.
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Affiliation(s)
- Gemma L Clayton
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
| | - Maria Carolina Borges
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- NIHR Bristol Biomedical Research Centre, Bristol, UK
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Copetti M, Baroni MG, Buzzetti R, Cavallo MG, Cossu E, D'Angelo P, Cosmo SD, Leonetti F, Morano S, Morviducci L, Napoli N, Prudente S, Pugliese G, Savino AF, Trischitta V. Validation in type 2 diabetes of a metabolomic signature of all-cause mortality. Diabetes Metab Res Rev 2024; 40:e3734. [PMID: 37839040 DOI: 10.1002/dmrr.3734] [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: 01/23/2023] [Revised: 08/29/2023] [Accepted: 09/25/2023] [Indexed: 10/17/2023]
Abstract
CONTEXT Mortality in type 2 diabetes is twice that of the normoglycemic population. Unravelling biomarkers that identify high-risk patients for referral to the most aggressive and costly prevention strategies is needed. OBJECTIVE To validate in type 2 diabetes the association with all-cause mortality of a 14-metabolite score (14-MS) previously reported in the general population and whether this score can be used to improve well-established mortality prediction models. METHODS This is a sub-study consisting of 600 patients from the "Sapienza University Mortality and Morbidity Event Rate" (SUMMER) study in diabetes, a prospective multicentre investigation on all-cause mortality in patients with type 2 diabetes. Metabolic biomarkers were quantified from serum samples using high-throughput proton nuclear magnetic resonance metabolomics. RESULTS In type 2 diabetes, the 14-MS showed a significant (p < 0.0001) association with mortality, which was lower (p < 0.0001) than that reported in the general population. This difference was mainly due to two metabolites (histidine and ratio of polyunsaturated fatty acids to total fatty acids) with an effect size that was significantly (p = 0.01) lower in diabetes than in the general population. A parsimonious 12-MS (i.e. lacking the 2 metabolites mentioned above) improved patient discrimination and classification of two well-established mortality prediction models (p < 0.0001 for all measures). CONCLUSIONS The metabolomic signature of mortality in the general population is only partially effective in type 2 diabetes. Prediction markers developed and validated in the general population must be revalidated if they are to be used in patients with diabetes.
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Affiliation(s)
- Massimiliano Copetti
- Fondazione IRCCS Casa Sollievo della Sofferenza, Unit of Biostatistics, San Giovanni Rotondo, Italy
| | - Marco Giorgio Baroni
- Department of Clinical Medicine, Public Health, Life and Environmental Sciences (MeSVA), University of L'Aquila, L'Aquila, Italy
- Neuroendocrinology and Metabolic Diseases, IRCCS Neuromed, Pozzilli, Italy
| | - Raffaella Buzzetti
- Department of Experimental Medicine, Sapienza University of Rome, Rome, Italy
| | | | - Efiso Cossu
- Department of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italy
| | - Paola D'Angelo
- Department of Clinical Medicine and Health Service Integration, Diabetology and Nutrition Unit, Sandro Pertini Hospital - aslrm2, Rome, Italy
| | - Salvatore De Cosmo
- Department of Medicine, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
| | - Frida Leonetti
- Department of Medical and Surgical Sciences and Biotechnologies, Sapienza University of Rome, Rome, Italy
| | - Susanna Morano
- Department of Experimental Medicine, Sapienza University of Rome, Rome, Italy
| | - Lelio Morviducci
- Unit of Diabetology, Santo Spirito Hospital - ASL RM1, Rome, Italy
| | - Nicola Napoli
- Unit of Endocrinology and Diabetes, Department of Medicine, Campus Bio-medico University of Rome, Rome, Italy
| | - Sabrina Prudente
- Fondazione IRCCS Casa Sollievo della Sofferenza, Research Unit of Metabolic and Cardiovascular diseases, San Giovanni Rotondo, Italy
| | - Giuseppe Pugliese
- Department of Clinical and Molecular Medicine, Sapienza University of Rome, Rome, Italy
| | - Antonio Fernando Savino
- Fondazione IRCCS Casa Sollievo della Sofferenza, Laboratory of Clinical Chemistry, San Giovanni Rotondo, Italy
| | - Vincenzo Trischitta
- Department of Experimental Medicine, Sapienza University of Rome, Rome, Italy
- Fondazione IRCCS Casa Sollievo della Sofferenza, Research Unit of Diabetes and Endocrine Diseases, San Giovanni Rotondo, Italy
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Li T, Ihanus A, Ohukainen P, Järvelin MR, Kähönen M, Kettunen J, Raitakari OT, Lehtimäki T, Mäkinen VP, Tynkkynen T, Ala-Korpela M. Clinical and biochemical associations of urinary metabolites: quantitative epidemiological approach on renal-cardiometabolic biomarkers. Int J Epidemiol 2024; 53:dyad162. [PMID: 38030573 PMCID: PMC10859141 DOI: 10.1093/ije/dyad162] [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: 09/04/2022] [Accepted: 11/17/2023] [Indexed: 12/01/2023] Open
Abstract
BACKGROUND Urinary metabolomics has demonstrated considerable potential to assess kidney function and its metabolic corollaries in health and disease. However, applications in epidemiology remain sparse due to technical challenges. METHODS We added 17 metabolites to an open-access urinary nuclear magnetic resonance metabolomics platform, extending the panel to 61 metabolites (n = 994). We also introduced automated quantification for 11 metabolites, extending the panel to 12 metabolites (+creatinine). Epidemiological associations between these 12 metabolites and 49 clinical measures were studied in three independent cohorts (up to 5989 participants). Detailed regression analyses with various confounding factors are presented for body mass index (BMI) and smoking. RESULTS Sex-specific population reference concentrations and distributions are provided for 61 urinary metabolites (419 men and 575 women), together with methodological intra-assay metabolite variations as well as the biological intra-individual and epidemiological population variations. For the 12 metabolites, 362 associations were found. These are mostly novel and reflect potential molecular proxies to estimate kidney function, as the associations cannot be simply explained by estimated glomerular filtration rate. Unspecific renal excretion results in leakage of amino acids (and glucose) to urine in all individuals. Seven urinary metabolites associated with smoking, providing questionnaire-independent proxy measures of smoking status in epidemiological studies. Common confounders did not affect metabolite associations with smoking, but insulin had a clear effect on most associations with BMI, including strong effects on 2-hydroxyisobutyrate, valine, alanine, trigonelline and hippurate. CONCLUSIONS Urinary metabolomics provides new insight on kidney function and related biomarkers on the renal-cardiometabolic system, supporting large-scale applications in epidemiology.
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Affiliation(s)
- Tianqi Li
- Systems Epidemiology, Faculty of Medicine, University of Oulu, Oulu, Finland
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, University of Oulu, Oulu, Finland
| | - Andrei Ihanus
- Systems Epidemiology, Faculty of Medicine, University of Oulu, Oulu, Finland
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, University of Oulu, Oulu, Finland
- NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland
| | - Pauli Ohukainen
- Systems Epidemiology, Faculty of Medicine, University of Oulu, Oulu, Finland
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, University of Oulu, Oulu, Finland
| | - Marjo-Riitta Järvelin
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
- Unit of Primary Health Care, Oulu University Hospital, OYS, Oulu, Finland
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, Imperial College London, London, UK
- Department of Life Sciences, College of Health and Life Sciences, Brunel University London, London, UK
| | - Mika Kähönen
- Department of Clinical Physiology, Tampere University Hospital, and Finnish Cardiovascular Research Center Tampere, Tampere University, Tampere, Finland
| | - Johannes Kettunen
- Systems Epidemiology, Faculty of Medicine, University of Oulu, Oulu, Finland
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, University of Oulu, Oulu, Finland
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Olli T Raitakari
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
- Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland
- Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories, and Finnish Cardiovascular Research Center Tampere, Tampere University, Tampere, Finland
| | - Ville-Petteri Mäkinen
- Systems Epidemiology, Faculty of Medicine, University of Oulu, Oulu, Finland
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, University of Oulu, Oulu, Finland
| | - Tuulia Tynkkynen
- Systems Epidemiology, Faculty of Medicine, University of Oulu, Oulu, Finland
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, University of Oulu, Oulu, Finland
- NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland
| | - Mika Ala-Korpela
- Systems Epidemiology, Faculty of Medicine, University of Oulu, Oulu, Finland
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, University of Oulu, Oulu, Finland
- NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland
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Smit AP, Herber GCM, Kuiper LM, Loef B, Picavet HSJ, Verschuren WMM. Past or Present; Which Exposures Predict Metabolomic Aging Better? The Doetinchem Cohort Study. J Gerontol A Biol Sci Med Sci 2024; 79:glad202. [PMID: 37642222 PMCID: PMC10799759 DOI: 10.1093/gerona/glad202] [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: 05/08/2023] [Indexed: 08/31/2023] Open
Abstract
People age differently. Differences in aging might be reflected by metabolites, also known as metabolomic aging. Predicting metabolomic aging is of interest in public health research. However, the added value of longitudinal over cross-sectional predictors of metabolomic aging is unknown. We studied exposome-related exposures as potential predictors of metabolomic aging, both cross-sectionally and longitudinally in men and women. We used data from 4 459 participants, aged 36-75 of Round 4 (2003-2008) of the long-running Doetinchem Cohort Study (DCS). Metabolomic age was calculated with the MetaboHealth algorithm. Cross-sectional exposures were demographic, biological, lifestyle, and environmental at Round 4. Longitudinal exposures were based on the average exposure over 15 years (Round 1 [1987-1991] to 4), and trend in these exposure over time. Random Forest was performed to identify model performance and important predictors. Prediction performances were similar for cross-sectional and longitudinal exposures in both men (R2 6.8 and 5.8, respectively) and women (R2 14.8 and 14.4, respectively). Biological and diet exposures were most predictive for metabolomic aging in both men and women. Other important predictors were smoking behavior for men and contraceptive use and menopausal status for women. Taking into account history of exposure levels (longitudinal) had no added value over cross-sectionally measured exposures in predicting metabolomic aging in the current study. However, the prediction performances of both models were rather low. The most important predictors for metabolomic aging were from the biological and lifestyle domain and differed slightly between men and women.
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Affiliation(s)
- Annelot P Smit
- Center for Prevention, Lifestyle and Health, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Gerrie-Cor M Herber
- Center for Prevention, Lifestyle and Health, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
| | - Lieke M Kuiper
- Center for Prevention, Lifestyle and Health, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
- Department of Internal Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - Bette Loef
- Center for Prevention, Lifestyle and Health, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
| | - H Susan J Picavet
- Center for Prevention, Lifestyle and Health, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
| | - W M Monique Verschuren
- Center for Prevention, Lifestyle and Health, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
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Zhao S, Hörkkö S, Savolainen MJ, Koivukangas V, Mäkinen VP, Ala-Korpela M, Hukkanen J. Short-Term Metabolic Changes and Their Physiological Mediators in the Roux-en-Y Gastric Bypass Bariatric Surgery. Obes Surg 2024; 34:625-634. [PMID: 38191968 PMCID: PMC10810963 DOI: 10.1007/s11695-023-07042-y] [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: 11/08/2023] [Revised: 12/18/2023] [Accepted: 12/28/2023] [Indexed: 01/10/2024]
Abstract
BACKGROUND The Roux-en-Y gastric bypass (RYGB) is a common bariatric surgery to treat obesity. Its metabolic consequences are favourable and long-term clinical corollaries beneficial. However, detailed assessments of various affected metabolic pathways and their mediating physiological factors are scarce. METHODS We performed a clinical study with 30 RYGB patients in preoperative and 6-month postoperative visits. NMR metabolomics was applied to profiling of systemic metabolism via 80 molecular traits, representing core cardiometabolic pathways. Glucose, glycated haemoglobin (HbA1c), insulin, and apolipoprotein B-48 were measured with standard assays. Logistic regression models of the surgery effect were used for each metabolic measure and assessed individually for multiple mediating physiological factors. RESULTS Changes in insulin concentrations reflected those of BMI with robust decreases due to the surgery. Six months after the surgery, triglycerides, remnant cholesterol, and apolipoprotein B-100 were decreased -24%, -18%, and -14%, respectively. Lactate and glycoprotein acetyls, a systemic inflammation biomarker, decreased -16% and -9%, respectively. The concentrations of branched-chain (BCAA; leucine, isoleucine, and valine) and aromatic (phenylalanine and tyrosine) amino acids decreased after the surgery between -17% for tyrosine and -23% for leucine. Except for the most prominent metabolic changes observed for the BCAAs, all changes were almost completely mediated by weight change and insulin. Glucose and type 2 diabetes had clearly weaker effects on the metabolic changes. CONCLUSIONS The comprehensive metabolic analyses indicate that weight loss and improved insulin sensitivity during the 6 months after the RYGB surgery are the key physiological outcomes mediating the short-term advantageous metabolic effects of RYGB. The clinical study was registered at ClinicalTrials.gov as NCT01330251.
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Affiliation(s)
- Siyu Zhao
- Systems Epidemiology, Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, University of Oulu, Oulu, Finland
| | - Sohvi Hörkkö
- Medical Microbiology and Immunology, Research Unit of Biomedicine, University of Oulu, Oulu, Finland
- Medical Research Center Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland
| | - Markku J Savolainen
- Medical Research Center Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland
- Research Unit of Internal Medicine, University of Oulu, Oulu, Finland
| | - Vesa Koivukangas
- Medical Research Center Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland
- Department of Surgery, Oulu University Hospital and University of Oulu, Oulu, Finland
| | - Ville-Petteri Mäkinen
- Systems Epidemiology, Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, University of Oulu, Oulu, Finland
| | - Mika Ala-Korpela
- Systems Epidemiology, Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland.
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland.
- Biocenter Oulu, University of Oulu, Oulu, Finland.
- NMR Metabolomics Laboratory, School of Pharmacy, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland.
| | - Janne Hukkanen
- Biocenter Oulu, University of Oulu, Oulu, Finland.
- Medical Research Center Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland.
- Research Unit of Internal Medicine, University of Oulu, Oulu, Finland.
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Zhang Y, Sun Y, Yu Q, Song S, Brenna JT, Shen Y, Ye K. Higher ratio of plasma omega-6/omega-3 fatty acids is associated with greater risk of all-cause, cancer, and cardiovascular mortality: a population-based cohort study in UK Biobank. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.01.16.23284631. [PMID: 36711941 PMCID: PMC9882493 DOI: 10.1101/2023.01.16.23284631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Background Circulating omega-3 and omega-6 polyunsaturated fatty acids (PUFAs) have been associated with various chronic diseases and mortality, but results are conflicting. Few studies examined the role of omega-6/omega-3 ratio in mortality. Methods We investigated plasma omega-3 and omega-6 PUFAs and their ratio in relation to all-cause and cause-specific mortality in a large prospective cohort, the UK Biobank. Of 85,425 participants who had complete information on circulating PUFAs, 6,461 died during follow-up, including 2,794 from cancer and 1,668 from cardiovascular disease (CVD). Associations were estimated by multivariable Cox proportional hazards regression with adjustment for relevant risk factors. Results Risk for all three mortality outcomes increased as the ratio of omega-6/omega-3 PUFAs increased (all Ptrend < 0.05). Comparing the highest to the lowest quintiles, individuals had 26% (95% CI, 15-38%) higher total mortality, 14% (95% CI, 0-31%) higher cancer mortality, and 31% (95% CI, 10-55%) higher CVD mortality. Moreover, omega-3 and omega-6 PUFAs in plasma were all inversely associated with all-cause, cancer, and CVD mortality, with omega-3 showing stronger effects. Conclusions Using a population-based cohort in UK Biobank, our study revealed a strong association between the ratio of circulating omega-6/omega-3 PUFAs and the risk of all-cause, cancer, and CVD mortality.
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Affiliation(s)
- Yuchen Zhang
- Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, Georgia, US
| | - Yitang Sun
- Department of Genetics, University of Georgia, Athens, Georgia, US
| | - Qi Yu
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, US
| | - Suhang Song
- Department of Health Policy and Management, College of Public Health, University of Georgia, Athens, Georgia, US
| | - J. Thomas Brenna
- Division of Nutritional Sciences, Cornell University, Ithaca, NY, US
- Dell Pediatric Research Institute and the Depts of Pediatrics, of Nutrition, and of Chemistry, University of Texas at Austin, Austin, TX, US
| | - Ye Shen
- Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, Georgia, US
| | - Kaixiong Ye
- Department of Genetics, University of Georgia, Athens, Georgia, US
- Institute of Bioinformatics, University of Georgia, Athens, Georgia, US
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Yang CR, Lin WJ, Shen PC, Liao PY, Dai YC, Hung YC, Lai HC, Mehmood S, Cheng WC, Ma WL. Phenotypic and metabolomic characteristics of mouse models of metabolic associated steatohepatitis. Biomark Res 2024; 12:6. [PMID: 38195587 PMCID: PMC10777576 DOI: 10.1186/s40364-023-00555-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Accepted: 12/29/2023] [Indexed: 01/11/2024] Open
Abstract
BACKGROUND Metabolic associated steatohepatitis (MASH) is metabolic disease that may progress to cirrhosis and hepatocellular carcinoma. Mouse models of diet-induced MASH, which is characterized by the high levels of fats, sugars, and cholesterol in diets, are commonly used in research. However, mouse models accurately reflecting the progression of MASH in humans remain to be established. Studies have explored the potential use of serological metabolites as biomarkers of MASH severity in relation to human MASH. METHODS We performed a comparative analysis of three mouse models of diet-induced MASH in terms of phenotypic and metabolomic characteristics; MASH was induced using different diets: a high-fat diet; a Western diet; and a high-fat, high-cholesterol diet. Liver cirrhosis was diagnosed using standard clinical approaches (e.g., METAVIR score, hyaluronan level, and collagen deposition level). Mouse serum samples were subjected to nuclear magnetic resonance spectroscopy-based metabolomic profiling followed by bioinformatic analyses. Metabolomic analysis of a retrospective cohort of patients with hepatocellular carcinoma was performed; the corresponding cirrhosis scores were also evaluated. RESULTS Using clinically relevant quantitative diagnostic methods, the severity of MASH was evaluated. Regarding metabolomics, the number of lipoprotein metabolites increased with both diet and MASH progression. Notably, the levels of very low-density lipoprotein (VLDL) and low-density lipoprotein (LDL) significantly increased with fibrosis progression. During the development of diet-induced MASH in mice, the strongest upregulation of expression was noted for VLDL receptor. Metabolomic analysis of a retrospective cohort of patients with cirrhosis indicated lipoproteins (e.g., VLDL and LDL) as predominant biomarkers of cirrhosis. CONCLUSIONS Our findings provide insight into the pathophysiology and metabolomics of experimental MASH and its relevance to human MASH. The observed upregulation of lipoprotein expression reveals a feedforward mechanism for MASH development that may be targeted for the development of noninvasive diagnosis.
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Affiliation(s)
- Cian-Ru Yang
- Program for Health Science and Industry, Graduate Institute of Biomedical Sciences, and Department of Medicine, and Tumor Biology Center, School of Medicine, China Medical University, Taichung, Taiwan
- Department of Medical Research, Department of Gynecology and Obstetrics, and Department of Gastroenterology, China Medical University Hospital, Taichung, Taiwan
| | - Wen-Jen Lin
- Program for Health Science and Industry, Graduate Institute of Biomedical Sciences, and Department of Medicine, and Tumor Biology Center, School of Medicine, China Medical University, Taichung, Taiwan
- Department of Medical Research, Department of Gynecology and Obstetrics, and Department of Gastroenterology, China Medical University Hospital, Taichung, Taiwan
| | - Pei-Chun Shen
- Program for Health Science and Industry, Graduate Institute of Biomedical Sciences, and Department of Medicine, and Tumor Biology Center, School of Medicine, China Medical University, Taichung, Taiwan
- Department of Medical Research, Department of Gynecology and Obstetrics, and Department of Gastroenterology, China Medical University Hospital, Taichung, Taiwan
| | - Pei-Yin Liao
- Program for Health Science and Industry, Graduate Institute of Biomedical Sciences, and Department of Medicine, and Tumor Biology Center, School of Medicine, China Medical University, Taichung, Taiwan
- Department of Medical Research, Department of Gynecology and Obstetrics, and Department of Gastroenterology, China Medical University Hospital, Taichung, Taiwan
| | - Yuan-Chang Dai
- Department of Pathology, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chia-Yi City, Taiwan
| | - Yao-Ching Hung
- Department of Gynecology and Obstetrics, Asia University Hospital, Taichung, Taiwan
| | - Hsueh-Chou Lai
- Graduate Institute of Integrated Medicine, College of Chinese Medicine, China Medical University, Taichung, Taiwan
- Center for Digestive Medicine, Department of Internal Medicine, China Medical University Hospital, Taichung, Taiwan
| | - Shiraz Mehmood
- Program for Health Science and Industry, Graduate Institute of Biomedical Sciences, and Department of Medicine, and Tumor Biology Center, School of Medicine, China Medical University, Taichung, Taiwan
| | - Wei-Chung Cheng
- Program for Health Science and Industry, Graduate Institute of Biomedical Sciences, and Department of Medicine, and Tumor Biology Center, School of Medicine, China Medical University, Taichung, Taiwan.
| | - Wen-Lung Ma
- Program for Health Science and Industry, Graduate Institute of Biomedical Sciences, and Department of Medicine, and Tumor Biology Center, School of Medicine, China Medical University, Taichung, Taiwan.
- Department of Medical Research, Department of Gynecology and Obstetrics, and Department of Gastroenterology, China Medical University Hospital, Taichung, Taiwan.
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Lehtovirta M, Pahkala K, Rovio SP, Magnussen CG, Laitinen TT, Niinikoski H, Lagström H, Viikari JSA, Rönnemaa T, Jula A, Ala-Korpela M, Raitakari OT. Association of tobacco smoke exposure with metabolic profile from childhood to early adulthood: the Special Turku Coronary Risk Factor Intervention Project. Eur J Prev Cardiol 2024; 31:103-115. [PMID: 37655930 DOI: 10.1093/eurjpc/zwad285] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 08/04/2023] [Accepted: 08/29/2023] [Indexed: 09/02/2023]
Abstract
AIMS To investigate the associations between passive tobacco smoke exposure and daily smoking with a comprehensive metabolic profile, measured repeatedly from childhood to adulthood. METHODS AND RESULTS Study cohort was derived from the Special Turku Coronary Risk Factor Intervention Project (STRIP). Smoking status was obtained by questionnaire, while serum cotinine concentrations were measured using gas chromatography. Metabolic measures were quantified by nuclear magnetic resonance metabolomics at 9 (n = 539), 11 (n = 536), 13 (n = 525), 15 (n = 488), 17 (n = 455), and 19 (n = 409) years. Association of passive tobacco smoke exposure with metabolic profile compared participants who reported less-than-weekly smoking and had serum cotinine concentration <1 ng/mL (no exposure) with those whose cotinine concentration was ≥10 ng/mL (passive tobacco smoke exposure). Associations of daily smoking with metabolic profile in adolescence were analysed by comparing participants reporting daily smoking with those reporting no tobacco use and having serum cotinine concentrations <1 ng/mL. Passive tobacco smoke exposure was directly associated with the serum ratio of monounsaturated fatty acids to total fatty acids [β = 0.34 standard deviation (SD), (0.17-0.51), P < 0.0001] and inversely associated with the serum ratios of polyunsaturated fatty acids. Exposure to passive tobacco smoke was directly associated with very-low-density lipoprotein particle size [β = 0.28 SD, (0.12-0.45), P = 0.001] and inversely associated with HDL particle size {β = -0.21 SD, [-0.34 to -0.07], P = 0.003}. Daily smokers exhibited a similar metabolic profile to those exposed to passive tobacco smoke. These results persisted after adjusting for body mass index, STRIP study group allocation, dietary target score, pubertal status, and parental socio-economic status. CONCLUSION Both passive and active tobacco smoke exposures during childhood and adolescence are detrimentally associated with circulating metabolic measures indicative of increased cardio-metabolic risk.
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Affiliation(s)
- Miia Lehtovirta
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Kiinamyllynkatu 10, Turku FI-20520, Finland
- Centre for Population Health Research, Turku University Hospital, University of Turku, Turku, Finland
| | - Katja Pahkala
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Kiinamyllynkatu 10, Turku FI-20520, Finland
- Centre for Population Health Research, Turku University Hospital, University of Turku, Turku, Finland
- Paavo Nurmi Centre, Unit for Health and Physical Activity, University of Turku, Turku, Finland
| | - Suvi P Rovio
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Kiinamyllynkatu 10, Turku FI-20520, Finland
- Centre for Population Health Research, Turku University Hospital, University of Turku, Turku, Finland
| | - Costan G Magnussen
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Kiinamyllynkatu 10, Turku FI-20520, Finland
- Centre for Population Health Research, Turku University Hospital, University of Turku, Turku, Finland
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
| | - Tomi T Laitinen
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Kiinamyllynkatu 10, Turku FI-20520, Finland
- Centre for Population Health Research, Turku University Hospital, University of Turku, Turku, Finland
- Paavo Nurmi Centre, Unit for Health and Physical Activity, University of Turku, Turku, Finland
| | - Harri Niinikoski
- Centre for Population Health Research, Turku University Hospital, University of Turku, Turku, Finland
- Department of Pediatrics and Adolescent Medicine, Turku University Hospital, University of Turku, Turku, Finland
| | - Hanna Lagström
- Centre for Population Health Research, Turku University Hospital, University of Turku, Turku, Finland
- Department of Public Health, Turku University Hospital, University of Turku, Turku, Finland
| | - Jorma S A Viikari
- Division of Medicine, Turku University Hospital, Turku, Finland
- Department of Medicine, University of Turku, Turku, Finland
| | - Tapani Rönnemaa
- Division of Medicine, Turku University Hospital, Turku, Finland
- Department of Medicine, University of Turku, Turku, Finland
| | - Antti Jula
- Department of Chronic Disease Prevention, Institute for Health and Welfare, Turku, Finland
| | - Mika Ala-Korpela
- Systems Epidemiology, Research Unit of Population Health, Faculty of Medicine, University of Oulu & Biocenter Oulu, Oulu, Finland
- NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland
| | - Olli T Raitakari
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Kiinamyllynkatu 10, Turku FI-20520, Finland
- Centre for Population Health Research, Turku University Hospital, University of Turku, Turku, Finland
- Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland
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Geng TT, Chen JX, Lu Q, Wang PL, Xia PF, Zhu K, Li Y, Guo KQ, Yang K, Liao YF, Zhou YF, Liu G, Pan A. Nuclear Magnetic Resonance-Based Metabolomics and Risk of CKD. Am J Kidney Dis 2024; 83:9-17. [PMID: 37678743 DOI: 10.1053/j.ajkd.2023.05.014] [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] [Received: 11/09/2022] [Revised: 05/07/2023] [Accepted: 05/18/2023] [Indexed: 09/09/2023]
Abstract
RATIONALE & OBJECTIVE Chronic kidney disease (CKD) leads to lipid and metabolic abnormalities, but a comprehensive investigation of lipids, lipoprotein particles, and circulating metabolites associated with the risk of CKD has been lacking. We examined the associations of nuclear magnetic resonance (NMR)-based metabolomics data with CKD risk in the UK Biobank study. STUDY DESIGN Observational cohort study. SETTING & PARTICIPANTS A total of 91,532 participants in the UK Biobank Study without CKD and not receiving lipid-lowering therapy. EXPOSURE Levels of metabolites including lipid concentration and composition within 14 lipoprotein subclasses, as well as other metabolic biomarkers were quantified via NMR spectroscopy. OUTCOME Incident CKD identified using ICD codes in any primary care data, hospital admission records, or death register records. ANALYTICAL APPROACH Cox proportional hazards regression models were used to estimate hazard ratios and 95% confidence intervals. RESULTS We identified 2,269 CKD cases over a median follow-up period of 13.1 years via linkage with the electronic health records. After adjusting for covariates and correcting for multiple testing, 90 of 142 biomarkers were significantly associated with incident CKD. In general, higher concentrations of very-low-density lipoprotein (VLDL) particles were associated with a higher risk of CKD whereas higher concentrations of high-density lipoprotein (HDL) particles were associated with a lower risk of CKD. Higher concentrations of cholesterol, phospholipids, and total lipids within VLDL were associated with a higher risk of CKD, whereas within HDL they were associated with a lower risk of CKD. Further, higher triglyceride levels within all lipoprotein subclasses, including all HDL particles, were associated with greater risk of CKD. We also identified that several amino acids, fatty acids, and inflammatory biomarkers were associated with risk of CKD. LIMITATIONS Potential underreporting of CKD cases because of case identification via electronic health records. CONCLUSIONS Our findings highlight multiple known and novel pathways linking circulating metabolites to the risk of CKD. PLAIN-LANGUAGE SUMMARY The relationship between individual lipoprotein particle subclasses and lipid-related traits and risk of chronic kidney disease (CKD) in general population is unclear. Using data from 91,532 participants in the UK Biobank, we evaluated the associations of metabolites measured using nuclear magnetic resonance testing with the risk of CKD. We identified that 90 out of 142 lipid biomarkers were significantly associated with incident CKD. We found that very-low-density lipoproteins, high-density lipoproteins, the lipid concentration and composition within these lipoproteins, triglycerides within all the lipoprotein subclasses, fatty acids, amino acids, and inflammation biomarkers were associated with CKD risk. These findings advance our knowledge about mechanistic pathways that may contribute to the development of CKD.
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Affiliation(s)
- Ting-Ting Geng
- Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan
| | - Jun-Xiang Chen
- Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan
| | - Qi Lu
- Department of Nutrition and Food Hygiene, Hubei Key Laboratory of Food Nutrition and Safety, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan
| | - Pei-Lu Wang
- Department of Epidemiology, T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts
| | - Peng-Fei Xia
- Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan
| | - Kai Zhu
- Department of Nutrition and Food Hygiene, Hubei Key Laboratory of Food Nutrition and Safety, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan
| | - Yue Li
- Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan
| | - Kun-Quan Guo
- Department of Endocrinology, Sinopharm Dongfeng General Hospital, Hubei University of Medicine, Shiyan
| | - Kun Yang
- Department of Endocrinology, Sinopharm Dongfeng General Hospital, Hubei University of Medicine, Shiyan
| | - Yun-Fei Liao
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yan-Feng Zhou
- Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan
| | - Gang Liu
- Department of Nutrition and Food Hygiene, Hubei Key Laboratory of Food Nutrition and Safety, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan.
| | - An Pan
- Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan.
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Salminen A, Määttä A, Mäntylä P, Leskelä J, Pietiäinen M, Buhlin K, Suominen A, Paju S, Sattler W, Sinisalo J, Pussinen P. Systemic Metabolic Signatures of Oral Diseases. J Dent Res 2024; 103:13-21. [PMID: 37968796 PMCID: PMC10734208 DOI: 10.1177/00220345231203562] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2023] Open
Abstract
Systemic metabolic signatures of oral diseases have been rarely investigated, and prospective studies do not exist. We analyzed whether signs of current or past infectious/inflammatory oral diseases are associated with circulating metabolites. Two study populations were included: the population-based Health-2000 (n = 6,229) and Parogene (n = 452), a cohort of patients with an indication to coronary angiography. Health-2000 participants (n = 4,116) provided follow-up serum samples 11 y after the baseline. Serum concentrations of 157 metabolites were determined with a nuclear magnetic resonance spectroscopy-based method. The associations between oral parameters and metabolite concentrations were analyzed using linear regression models adjusted for age, sex, number of teeth, smoking, presence of diabetes, and education (in Health-2000 only). The number of decayed teeth presented positive associations with low-density lipoprotein diameter and the concentrations of pyruvate and citrate. Negative associations were found between caries and the unsaturation degree of fatty acids (FA) and relative proportions of docosahexaenoic and omega-3 FAs. The number of root canal fillings was positively associated with very low-density lipoprotein parameters, such as diameter, cholesterol, triglycerides, and number of particles. Deepened periodontal pockets were positively associated with concentrations of cholesterol, triglycerides, pyruvate, leucine, valine, phenylalanine, and glycoprotein acetyls and negatively associated with high-density lipoprotein (HDL) diameter, FA unsaturation degree, and relative proportions of omega-6 and polyunsaturated FAs. Bleeding on probing (BOP) was associated with increased concentrations of triglycerides and glycoprotein acetyls, as well as decreased proportions of omega-3 and omega-6 FAs. Caries at baseline predicted alterations in apolipoprotein B-containing lipoproteins and HDL-related metabolites in the follow-up, and both caries and BOP were associated with changes in HDL-related metabolites and omega-3 FAs in the follow-up. Signs of current or past infectious/inflammatory oral diseases, especially periodontitis, were associated with metabolic profiles typical for inflammation. Oral diseases may represent a modifiable risk factor for systemic chronic inflammation and thus cardiometabolic disorders.
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Affiliation(s)
- A. Salminen
- Oral and Maxillofacial diseases, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - A.M. Määttä
- Oral and Maxillofacial diseases, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - P. Mäntylä
- Institute of Dentistry, University of Eastern Finland, Kuopio, Finland
- Odontology Education, Kuopio University Hospital, Kuopio, Finland
| | - J. Leskelä
- Oral and Maxillofacial diseases, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - M. Pietiäinen
- Oral and Maxillofacial diseases, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - K. Buhlin
- Oral and Maxillofacial diseases, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Division of Periodontology, Department of Dental Medicine, Karolinska Institutet, Huddinge, Sweden
| | - A.L. Suominen
- Institute of Dentistry, University of Eastern Finland, Kuopio, Finland
- Odontology Education, Kuopio University Hospital, Kuopio, Finland
- Department of Public Health and Welfare, National Institute for Health and Welfare, Helsinki, Finland
| | - S. Paju
- Oral and Maxillofacial diseases, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - W. Sattler
- Division of Molecular Biology and Biochemistry, Gottfried Schatz Research Center, Medical University of Graz, Graz, Austria
| | - J. Sinisalo
- HUCH Heart and Lung Center, Helsinki University Hospital and University of Helsinki, Finland
| | - P.J. Pussinen
- Oral and Maxillofacial diseases, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Institute of Dentistry, University of Eastern Finland, Kuopio, Finland
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Drouard G, Hagenbeek FA, Whipp AM, Pool R, Hottenga JJ, Jansen R, Hubers N, Afonin A, Willemsen G, de Geus EJC, Ripatti S, Pirinen M, Kanninen KM, Boomsma DI, van Dongen J, Kaprio J. Longitudinal multi-omics study reveals common etiology underlying association between plasma proteome and BMI trajectories in adolescent and young adult twins. BMC Med 2023; 21:508. [PMID: 38129841 PMCID: PMC10740308 DOI: 10.1186/s12916-023-03198-7] [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: 07/03/2023] [Accepted: 11/27/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND The influence of genetics and environment on the association of the plasma proteome with body mass index (BMI) and changes in BMI remains underexplored, and the links to other omics in these associations remain to be investigated. We characterized protein-BMI trajectory associations in adolescents and adults and how these connect to other omics layers. METHODS Our study included two cohorts of longitudinally followed twins: FinnTwin12 (N = 651) and the Netherlands Twin Register (NTR) (N = 665). Follow-up comprised 4 BMI measurements over approximately 6 (NTR: 23-27 years old) to 10 years (FinnTwin12: 12-22 years old), with omics data collected at the last BMI measurement. BMI changes were calculated in latent growth curve models. Mixed-effects models were used to quantify the associations between the abundance of 439 plasma proteins with BMI at blood sampling and changes in BMI. In FinnTwin12, the sources of genetic and environmental variation underlying the protein abundances were quantified by twin models, as were the associations of proteins with BMI and BMI changes. In NTR, we investigated the association of gene expression of genes encoding proteins identified in FinnTwin12 with BMI and changes in BMI. We linked identified proteins and their coding genes to plasma metabolites and polygenic risk scores (PRS) applying mixed-effects models and correlation networks. RESULTS We identified 66 and 14 proteins associated with BMI at blood sampling and changes in BMI, respectively. The average heritability of these proteins was 35%. Of the 66 BMI-protein associations, 43 and 12 showed genetic and environmental correlations, respectively, including 8 proteins showing both. Similarly, we observed 7 and 3 genetic and environmental correlations between changes in BMI and protein abundance, respectively. S100A8 gene expression was associated with BMI at blood sampling, and the PRG4 and CFI genes were associated with BMI changes. Proteins showed strong connections with metabolites and PRSs, but we observed no multi-omics connections among gene expression and other omics layers. CONCLUSIONS Associations between the proteome and BMI trajectories are characterized by shared genetic, environmental, and metabolic etiologies. We observed few gene-protein pairs associated with BMI or changes in BMI at the proteome and transcriptome levels.
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Affiliation(s)
- Gabin Drouard
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland.
| | - Fiona A Hagenbeek
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Alyce M Whipp
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - René Pool
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Jouke Jan Hottenga
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Rick Jansen
- Department of Psychiatry, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Mood, Anxiety, Psychosis, Sleep & Stress Program, Amsterdam, The Netherlands
| | - Nikki Hubers
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
- Amsterdam Reproduction & Development (AR&D) Research Institute, Amsterdam, The Netherlands
| | - Aleksei Afonin
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Gonneke Willemsen
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Eco J C de Geus
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Department of Public Health, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Matti Pirinen
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Department of Public Health, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
| | - Katja M Kanninen
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
- Amsterdam Reproduction & Development (AR&D) Research Institute, Amsterdam, The Netherlands
| | - Jenny van Dongen
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
- Amsterdam Reproduction & Development (AR&D) Research Institute, Amsterdam, The Netherlands
| | - Jaakko Kaprio
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland.
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