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Hughes DA, Li-Gao R, Bull CJ, de Mutsert R, Rosendaal FR, Mook-Kanamori DO, Willems van Dijk K, Timpson NJ. The association between body mass index and metabolite response to a liquid mixed meal challenge: a Mendelian randomization study. Am J Clin Nutr 2024; 119:1354-1370. [PMID: 38494119 PMCID: PMC11130664 DOI: 10.1016/j.ajcnut.2024.03.009] [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] [Revised: 01/30/2024] [Accepted: 03/12/2024] [Indexed: 03/19/2024] Open
Abstract
BACKGROUND Metabolite abundance is a dynamic trait that varies in response to environmental stimuli and phenotypic traits, such as food consumption and body mass index (BMI, kg/m2). OBJECTIVES In this study, we used the Netherlands Epidemiology of Obesity (NEO) study data to identify observational and causal associations between BMI and metabolite response to a liquid meal. METHODS A liquid meal challenge was performed, and Nightingale Health metabolite profiles were collected in 5744 NEO participants. Observational and one-sample Mendelian randomization (MR) analysis were conducted to estimate the effect of BMI on metabolites (n = 229) in the fasting, postprandial, and response (or change in abundance) states. RESULTS We observed 473 associations with BMI (175 fasting, 188 postprandial, and 110 response) in observational analyses. In MR analyses, we observed 20 metabolite traits (5 fasting, 12 postprandial, and 3 response) to be associated with BMI. MR associations included the glucogenic amino acid alanine, which was inversely associated with BMI in the response state (β: -0.081; SE: 0.023; P = 5.91 × 10-4), suggesting that as alanine increased in postprandial abundance, that increase was attenuated with increasing BMI. CONCLUSIONS Overall, this study showed that MR estimates were strongly correlated with observational effect estimates, suggesting that the broad associations seen between BMI and metabolite variation has a causal underpinning. Specific effects in previously unassessed postprandial and response states are detected, and these may likely mark novel life course risk exposures driven by regular nutrition.
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Affiliation(s)
- David A Hughes
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom; Population Health Science, Bristol Medical School, University of Bristol, Bristol, United Kingdom.
| | - Ruifang Li-Gao
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Caroline J Bull
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom; Population Health Science, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Renée de Mutsert
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Frits R Rosendaal
- 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
| | - Ko Willems van Dijk
- Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands; Division of Endocrinology, Department of Internal Medicine, Leiden University Medical Center, Leiden, the Netherlands
| | - Nicholas J Timpson
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom; Population Health Science, Bristol Medical School, University of Bristol, Bristol, United Kingdom
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Bull CJ, Hazelwood E, Legge DN, Corbin LJ, Richardson TG, Lee M, Yarmolinsky J, Smith-Byrne K, Hughes DA, Johansson M, Peters U, Berndt SI, Brenner H, Burnett-Hartman A, Cheng I, Kweon SS, Le Marchand L, Li L, Newcomb PA, Pearlman R, McConnachie A, Welsh P, Taylor R, Lean MEJ, Sattar N, Murphy N, Gunter MJ, Timpson NJ, Vincent EE. Impact of weight loss on cancer-related proteins in serum: results from a cluster randomised controlled trial of individuals with type 2 diabetes. EBioMedicine 2024; 100:104977. [PMID: 38290287 PMCID: PMC10844806 DOI: 10.1016/j.ebiom.2024.104977] [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: 07/19/2023] [Revised: 01/03/2024] [Accepted: 01/06/2024] [Indexed: 02/01/2024] Open
Abstract
BACKGROUND Type 2 diabetes is associated with higher risk of several cancer types. However, the biological intermediates driving this relationship are not fully understood. As novel interventions for treating and managing type 2 diabetes become increasingly available, whether they also disrupt the pathways leading to increased cancer risk is currently unknown. We investigated the effect of a type 2 diabetes intervention, in the form of intentional weight loss, on circulating proteins associated with cancer risk to gain insight into potential mechanisms linking type 2 diabetes and adiposity with cancer development. METHODS Fasting serum samples from participants with diabetes enrolled in the Diabetes Remission Clinical Trial (DiRECT) receiving the Counterweight-Plus weight-loss programme (intervention, N = 117, mean weight-loss 10 kg, 46% diabetes remission) or best-practice care by guidelines (control, N = 143, mean weight-loss 1 kg, 4% diabetes remission) were subject to proteomic analysis using the Olink Oncology-II platform (48% of participants were female; 52% male). To identify proteins which may be altered by the weight-loss intervention, the difference in protein levels between groups at baseline and 1 year was examined using linear regression. Mendelian randomization (MR) was performed to extend these results to evaluate cancer risk and elucidate possible biological mechanisms linking type 2 diabetes and cancer development. MR analyses were conducted using independent datasets, including large cancer meta-analyses, UK Biobank, and FinnGen, to estimate potential causal relationships between proteins modified during intentional weight loss and the risk of colorectal, breast, endometrial, gallbladder, liver, and pancreatic cancers. FINDINGS Nine proteins were modified by the intervention: glycoprotein Nmb; furin; Wnt inhibitory factor 1; toll-like receptor 3; pancreatic prohormone; erb-b2 receptor tyrosine kinase 2; hepatocyte growth factor; endothelial cell specific molecule 1 and Ret proto-oncogene (Holm corrected P-value <0.05). Mendelian randomization analyses indicated a causal relationship between predicted circulating furin and glycoprotein Nmb on breast cancer risk (odds ratio (OR) = 0.81, 95% confidence interval (CI) = 0.67-0.99, P-value = 0.03; and OR = 0.88, 95% CI = 0.78-0.99, P-value = 0.04 respectively), though these results were not supported in sensitivity analyses examining violations of MR assumptions. INTERPRETATION Intentional weight loss among individuals with recently diagnosed diabetes may modify levels of cancer-related proteins in serum. Further evaluation of the proteins identified in this analysis could reveal molecular pathways that mediate the effect of adiposity and type 2 diabetes on cancer risk. FUNDING The main sources of funding for this work were Diabetes UK, Cancer Research UK, World Cancer Research Fund, and Wellcome.
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Affiliation(s)
- Caroline J Bull
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK; School of Translational Health Sciences, Dorothy Hodgkin Building, University of Bristol, Bristol, UK
| | - Emma Hazelwood
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Danny N Legge
- School of Translational Health Sciences, Dorothy Hodgkin Building, University of Bristol, Bristol, UK
| | - Laura J Corbin
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Tom G Richardson
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Matthew Lee
- Section of Nutrition and Metabolism, International Agency for Research on Cancer, WHO, Lyon, France
| | - James Yarmolinsky
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Karl Smith-Byrne
- Cancer Epidemiology Unit, Oxford Population Health, University of Oxford, UK
| | - David A Hughes
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Mattias Johansson
- Section of Nutrition and Metabolism, International Agency for Research on Cancer, WHO, Lyon, France
| | - Ulrike Peters
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Sonja I Berndt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany; Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | - Iona Cheng
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - Sun-Seog Kweon
- Department of Preventive Medicine, Chonnam National University Medical School, Gwangju, Korea; Jeonnam Regional Cancer Center, Chonnam National University Hwasun Hospital, Hwasun, Korea
| | | | - Li Li
- Department of Family Medicine, University of Virginia, Charlottesville, VA, USA
| | - Polly A Newcomb
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA; School of Public Health, University of Washington, Seattle, WA, USA
| | - Rachel Pearlman
- Division of Human Genetics, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center, Columbus, OH, USA
| | - Alex McConnachie
- Robertson Centre for Biostatistics, Institute of Health and Wellbeing, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Paul Welsh
- School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, UK
| | - Roy Taylor
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Mike E J Lean
- Human Nutrition, School of Medicine, Dentistry and Nursing, College of Medical, Veterinary & Life Sciences, University of Glasgow, Glasgow, UK
| | - Naveed Sattar
- School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, UK
| | - Neil Murphy
- Section of Nutrition and Metabolism, International Agency for Research on Cancer, WHO, Lyon, France
| | - Marc J Gunter
- Section of Nutrition and Metabolism, International Agency for Research on Cancer, WHO, Lyon, France; Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, UK
| | - Nicholas J Timpson
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Emma E Vincent
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK; School of Translational Health Sciences, Dorothy Hodgkin Building, University of Bristol, Bristol, UK.
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Corbin LJ, Hughes DA, Bull CJ, Vincent EE, Smith ML, McConnachie A, Messow CM, Welsh P, Taylor R, Lean MEJ, Sattar N, Timpson NJ. The metabolomic signature of weight loss and remission in the Diabetes Remission Clinical Trial (DiRECT). Diabetologia 2024; 67:74-87. [PMID: 37878066 PMCID: PMC10709482 DOI: 10.1007/s00125-023-06019-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 08/04/2023] [Indexed: 10/26/2023]
Abstract
AIMS/HYPOTHESIS High-throughput metabolomics technologies in a variety of study designs have demonstrated a consistent metabolomic signature of overweight and type 2 diabetes. However, the extent to which these metabolomic patterns can be reversed with weight loss and diabetes remission has been weakly investigated. We aimed to characterise the metabolomic consequences of a weight-loss intervention in individuals with type 2 diabetes. METHODS We analysed 574 fasted serum samples collected within an existing RCT (the Diabetes Remission Clinical Trial [DiRECT]) (N=298). In the trial, participating primary care practices were randomly assigned (1:1) to provide either a weight management programme (intervention) or best-practice care by guidelines (control) treatment to individuals with type 2 diabetes. Here, metabolomics analysis was performed on samples collected at baseline and 12 months using both untargeted MS and targeted 1H-NMR spectroscopy. Multivariable regression models were fitted to evaluate the effect of the intervention on metabolite levels. RESULTS Decreases in branched-chain amino acids, sugars and LDL triglycerides, and increases in sphingolipids, plasmalogens and metabolites related to fatty acid metabolism were associated with the intervention (Holm-corrected p<0.05). In individuals who lost more than 9 kg between baseline and 12 months, those who achieved diabetes remission saw greater reductions in glucose, fructose and mannose, compared with those who did not achieve remission. CONCLUSIONS/INTERPRETATION We have characterised the metabolomic effects of an integrated weight management programme previously shown to deliver weight loss and diabetes remission. A large proportion of the metabolome appears to be modifiable. Patterns of change were largely and strikingly opposite to perturbances previously documented with the development of type 2 diabetes. DATA AVAILABILITY The data used for analysis are available on a research data repository ( https://researchdata.gla.ac.uk/ ) with access given to researchers subject to appropriate data sharing agreements. Metabolite data preparation, data pre-processing, statistical analyses and figure generation were performed in R Studio v.1.0.143 using R v.4.0.2. The R code for this study has been made publicly available on GitHub at: https://github.com/lauracorbin/metabolomics_of_direct .
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Affiliation(s)
- Laura J Corbin
- MRC Integrative Epidemiology Unit at University of Bristol, Bristol, UK.
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
| | - David A Hughes
- MRC Integrative Epidemiology Unit at University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Caroline J Bull
- MRC Integrative Epidemiology Unit at University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Emma E Vincent
- MRC Integrative Epidemiology Unit at University of Bristol, Bristol, UK
- School of Translational Health Sciences, Dorothy Hodgkin Building, University of Bristol, Bristol, UK
| | - Madeleine L Smith
- MRC Integrative Epidemiology Unit at University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Alex McConnachie
- Robertson Centre for Biostatistics, Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - Claudia-Martina Messow
- Robertson Centre for Biostatistics, Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - Paul Welsh
- School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, UK
| | - Roy Taylor
- Newcastle Magnetic Resonance Centre, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Michael E J Lean
- Human Nutrition, School of Medicine, Dentistry and Nursing, College of Medical, Veterinary & Life Sciences, University of Glasgow, Glasgow, UK
| | - Naveed Sattar
- School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, UK
| | - Nicholas J Timpson
- MRC Integrative Epidemiology Unit at University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
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Goudswaard LJ, Smith ML, Hughes DA, Taylor R, Lean M, Sattar N, Welsh P, McConnachie A, Blazeby JM, Rogers CA, Suhre K, Zaghlool SB, Hers I, Timpson NJ, Corbin LJ. Using trials of caloric restriction and bariatric surgery to explore the effects of body mass index on the circulating proteome. Sci Rep 2023; 13:21077. [PMID: 38030643 PMCID: PMC10686974 DOI: 10.1038/s41598-023-47030-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 11/08/2023] [Indexed: 12/01/2023] Open
Abstract
Thousands of proteins circulate in the bloodstream; identifying those which associate with weight and intervention-induced weight loss may help explain mechanisms of diseases associated with adiposity. We aimed to identify consistent protein signatures of weight loss across independent studies capturing changes in body mass index (BMI). We analysed proteomic data from studies implementing caloric restriction (Diabetes Remission Clinical trial) and bariatric surgery (By-Band-Sleeve), using SomaLogic and Olink Explore1536 technologies, respectively. Linear mixed models were used to estimate the effect of the interventions on circulating proteins. Twenty-three proteins were altered in a consistent direction after both bariatric surgery and caloric restriction, suggesting that these proteins are modulated by weight change, independent of intervention type. We also integrated Mendelian randomisation (MR) estimates of the effect of BMI on proteins measured by SomaLogic from a UK blood donor cohort as a third line of causal evidence. These MR estimates provided further corroborative evidence for a role of BMI in regulating the levels of six proteins including alcohol dehydrogenase-4, nogo receptor and interleukin-1 receptor antagonist protein. These results indicate the importance of triangulation in interrogating causal relationships; further study into the role of proteins modulated by weight in disease is now warranted.
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Affiliation(s)
- Lucy J Goudswaard
- Population Health Sciences, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK.
- MRC Integrative Epidemiology Unit, Bristol, UK.
- Physiology, Pharmacology & Neuroscience, University of Bristol, Biomedical Sciences Building, University Walk, Bristol, BS8 1TD, UK.
| | - Madeleine L Smith
- Population Health Sciences, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- MRC Integrative Epidemiology Unit, Bristol, UK
| | - David A Hughes
- Population Health Sciences, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- MRC Integrative Epidemiology Unit, Bristol, UK
| | - Roy Taylor
- Newcastle Magnetic Resonance Centre, Translational and Clinical Research Institute, Newcastle University, Newcastle Upon Tyne, NE4 5PL, UK
| | - Michael Lean
- Human Nutrition, School of Medicine, Dentistry and Nursing, College of Medical, Veterinary & Life Sciences, University of Glasgow, Glasgow, G31 2ER, UK
| | - Naveed Sattar
- School of Cardiovascular and Medical Science, University of Glasgow, Glasgow, G12 8TA, UK
| | - Paul Welsh
- School of Cardiovascular and Medical Science, University of Glasgow, Glasgow, G12 8TA, UK
| | - Alex McConnachie
- Robertson Centre for Biostatistics, School of Health and Wellbeing, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Jane M Blazeby
- Population Health Sciences, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Chris A Rogers
- Bristol Medical School, Bristol Trials Centre, University of Bristol, Bristol, BS8 1NU, UK
| | - Karsten Suhre
- Department of Biophysics and Physiology, Weill Cornell Medicine - Qatar, Doha, Qatar
| | - Shaza B Zaghlool
- Department of Biophysics and Physiology, Weill Cornell Medicine - Qatar, Doha, Qatar
| | - Ingeborg Hers
- Physiology, Pharmacology & Neuroscience, University of Bristol, Biomedical Sciences Building, University Walk, Bristol, BS8 1TD, UK
| | - Nicholas J Timpson
- Population Health Sciences, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- MRC Integrative Epidemiology Unit, Bristol, UK
| | - Laura J Corbin
- Population Health Sciences, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- MRC Integrative Epidemiology Unit, Bristol, UK
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Goulding NJ, Goudswaard LJ, Hughes DA, Corbin LJ, Groom A, Ring S, Timpson NJ, Fraser A, Northstone K, Suderman M. Inflammation proteomics datasets in the ALSPAC cohort. Wellcome Open Res 2022. [DOI: 10.12688/wellcomeopenres.18482.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
Abstract
Proteomics is the identification, detection and quantification of proteins within a biological sample. The complete set of proteins expressed by an organism is known as the proteome. The availability of new high-throughput proteomic technologies, such as Olink Proteomic Proximity Extension Assay (PEA) technology has enabled detailed investigation of the circulating proteome in large-scale epidemiological studies. In particular, the Olink® Target 96 inflammatory panel allows the measurement of 92 circulating inflammatory proteins. The Avon Longitudinal Study of Parents and Children (ALSPAC) is a prospective population-based cohort study which recruited pregnant women in 1991-1992 and has followed these women, their partners, and their offspring ever since. In this data note, we describe the proteomic data available in ALSPAC. Ninety-two proteins were analysed in 9000 blood plasma samples using the Olink® Target 96 inflammatory panel. Samples were derived from 2968 fasted mothers (mean age 47.5; Focus on Mothers 1 (FOM1)), 3005 non-fasted offspring at age 9 (Focus@9) and 3027 fasted offspring at age 24 (Focus@24). Post sample filtering, 1834 offspring have data at both timepoints and 1119 of those have data from their mother available. We performed quality control analyses using a standardised data processing workflow (metaboprep) to produce a filtered dataset of 8983 samples for researchers to use in future analyses. Initial validation analyses indicate that IL-6 measured using the Olink® Target 96 inflammatory panel is highly correlated with IL-6 previously measured by clinical chemistry (Pearson’s correlation = 0.77) and we are able to reproduce the reported positive correlation between body mass index (BMI) and IL-6. The pre-processing and validation analyses indicate a rich proteomic dataset to further characterise the role of inflammation in health and disease.
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Fang S, Wade KH, Hughes DA, Fitzgibbon S, Yip V, Timpson NJ, Corbin LJ. A multivariant recall-by-genotype study of the metabolomic signature of BMI. Obesity (Silver Spring) 2022; 30:1298-1310. [PMID: 35598895 PMCID: PMC9324973 DOI: 10.1002/oby.23441] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 03/14/2022] [Accepted: 03/15/2022] [Indexed: 12/12/2022]
Abstract
OBJECTIVE This study estimated the effect of BMI on circulating metabolites in young adults using a recall-by-genotype study design. METHODS A recall-by-genotype study was implemented in the Avon Longitudinal Study of Parents and Children. Samples from 756 participants were selected for untargeted metabolomics analysis based on low versus high genetic liability for higher BMI defined by a genetic risk score (GRS). Regression analyses were performed to investigate associations between BMI GRS group and relative abundance of 973 metabolites. RESULTS After correction for multiple testing, 29 metabolites were associated with BMI GRS group. Bilirubin was among the most strongly associated metabolites, with reduced levels measured in individuals in the high-BMI GRS group (β = -0.32, 95% CI: -0.46 to -0.18, Benjamini-Hochberg adjusted p = 0.005). This study observed associations between BMI GRS group and the levels of several potentially diet-related metabolites, including hippurate, which had lower mean abundance in individuals in the high-BMI GRS group (β = -0.29, 95% CI: -0.44 to -0.15, Benjamini-Hochberg adjusted p = 0.008). CONCLUSIONS Together with existing literature, these results suggest that a genetic predisposition to higher BMI captures differences in metabolism leading to adiposity gain. In the absence of prospective data, separating these effects from the downstream consequences of weight gain is challenging.
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Affiliation(s)
- Si Fang
- MRC Integrative Epidemiology Unit at the University of BristolBristolUK
- Population Health ScienceBristol Medical SchoolUniversity of BristolBristolUK
| | - Kaitlin H. Wade
- MRC Integrative Epidemiology Unit at the University of BristolBristolUK
- Population Health ScienceBristol Medical SchoolUniversity of BristolBristolUK
| | - David A. Hughes
- MRC Integrative Epidemiology Unit at the University of BristolBristolUK
- Population Health ScienceBristol Medical SchoolUniversity of BristolBristolUK
| | - Sophie Fitzgibbon
- Bristol Bioresource LaboratoriesPopulation Health ScienceBristol Medical SchoolUniversity of BristolBristolUK
| | - Vikki Yip
- Bristol Bioresource LaboratoriesPopulation Health ScienceBristol Medical SchoolUniversity of BristolBristolUK
| | - Nicholas J. Timpson
- MRC Integrative Epidemiology Unit at the University of BristolBristolUK
- Population Health ScienceBristol Medical SchoolUniversity of BristolBristolUK
| | - Laura J. Corbin
- MRC Integrative Epidemiology Unit at the University of BristolBristolUK
- Population Health ScienceBristol Medical SchoolUniversity of BristolBristolUK
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