A metabolomic signature of weight-loss and its associations with BMI-linked diseases: A post-hoc analysis of two intervention trials and a mendelian randomisation study
10 affiliations
- 1MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- 2Population Health Sciences, University of Bristol, Bristol, United Kingdom
- 3Robertson Centre for Biostatistics, School of Health and Wellbeing, University of Glasgow, Glasgow, United Kingdom
- 4School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, United Kingdom
- 5Newcastle Magnetic Resonance Centre, Institute of Cellular Medicine, Campus for Ageing and Vitality, Newcastle University, Newcastle upon Tyne, United Kingdom
- 6Human Nutrition, School of Medicine, Dentistry and Nursing, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, United Kingdom
- 7Bristol Medical School, University of Bristol, Bristol, United Kingdom
- 8Exeter Medical School, University of Exeter and Exeter NIHR Biomedical Research Centre, University of Exeter and Royal Devon University Healthcare NHS Foundation Trust, Exeter, United Kingdom
- 9NIHR Bristol Biomedical Research Centre, University Hospitals Bristol NHS Foundation Trust and University of Bristol, Bristol, United Kingdom
- 10Institute of Health Informatics, University College London, London, United Kingdom
Abstract
Background: Obesity is a major risk factor for a broad spectrum of diseases, yet the molecular pathways underlying this risk remains poorly understood. Individual analytical approaches—interventional trials, observational data, and genetic methods—are each susceptible to distinct sources of bias. We aimed to identify a common metabolic perturbation signature of adiposity by triangulating evidence across multiple sources of body mass index (BMI) variation, and to characterise associations between this signature and contrasting adiposity-linked disease outcomes. Methods and findings: We integrated mass spectrometry and nuclear magnetic resonance metabolomic profiling from two weight-loss interventions—a structured diet programme (DiRECT trial, ISRCTN03267836) and metabolic and bariatric surgery (By-Band-Sleeve trial, ISRCTN00786323)—with mendelian randomisation (MR) estimates of the effect of lifetime BMI exposure on metabolite levels, in order to identify a consistent BMI-associated metabolite signature across sources with distinct error structures. Weight loss-associated metabolites were then instrumented using genetic variants and their associations with heart failure (HF), osteoarthritis, and kidney cancer estimated in a two-sample MR framework, with instruments grouped into clusters based on genetic similarity to aid biological interpretation. 242 metabolites were consistently altered across weight loss interventions, predominantly comprising lipids, lipoproteins, and amino acids. The majority of these showed concordant associations with lifetime BMI in MR, consistent with regulation by adiposity. Differential components of this signature were associated with disease-specific outcomes. For HF, low-density lipoprotein (LDL) measures were strongly associated with increased risk, consistent with ischaemic aetiology enrichment, whilst high-density lipoprotein (HDL) cholesterol-enrichment traits were protective, and markers of dysfunctional HDL remodelling were associated with increased risk. For osteoarthritis, lipoprotein associations were again associated with disease risk but in the opposite direction to that seen with HF; additional signals were observed in glycine metabolism pathways. For kidney cancer, the adiposity metabolite signature translated poorly into disease associations, with only 5-methyluridine and ursodeoxycholate associations identified. The main limitations of our study include the potential for residual horizontal pleiotropy with metabolite instruments despite multiple sensitivity analyses and the rank-normalised scale of metabolite measures, which limits direct physiological interpretability. Conclusions: Triangulating evidence across dietary intervention, metabolic and bariatric surgery, and MR of lifetime BMI identified a robust metabolic signature of adiposity. The association of components of this signature with obesity-related outcomes was disease-specific, supporting the notion that not all adiposity-associated metabolic perturbations are uniformly relevant across the spectrum of obesity-related diseases. Why was this study done?: What did the researchers do and find?: What do these findings mean?: Nicolas Sunderland and colleagues combined metabolomic data from weight-loss trials with genetic analyses to identify metabolic features consistently linked to adiposity. The study showed that these metabolic alterations were associated with obesity-related diseases in disease-specific ways, rather than reflecting a single shared pathway across all outcomes.
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BibTeX
@article{sunderland2026metabolomic,
title = {{A metabolomic signature of weight-loss and its associations with BMI-linked diseases: A post-hoc analysis of two intervention trials and a mendelian randomisation study}},
author = {Nicholas Sunderland and Madeleine L. Smith and Alex McConnachie and Paul Welsh and Roy Taylor and Michael E. J. Lean and Chris A. Rogers and Jane M. Blazeby and Naveed Sattar and Lavinia Paternoster and R. Thomas Lumbers and Nicholas J. Timpson and Laura J. Corbin},
journal = {PLOS Medicine},
year = {2026},
month = nov,
doi = {10.1371/journal.pmed.1005290},
url = {https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005290},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - A metabolomic signature of weight-loss and its associations with BMI-linked diseases: A post-hoc analysis of two intervention trials and a mendelian randomisation study
AU - Nicholas Sunderland
AU - Madeleine L. Smith
AU - Alex McConnachie
AU - Paul Welsh
AU - Roy Taylor
AU - Michael E. J. Lean
AU - Chris A. Rogers
AU - Jane M. Blazeby
AU - Naveed Sattar
AU - Lavinia Paternoster
AU - R. Thomas Lumbers
AU - Nicholas J. Timpson
AU - Laura J. Corbin
PY - 2026
DA - 2026/11/04
JO - PLOS Medicine
DO - 10.1371/journal.pmed.1005290
UR - https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005290
AB - Background: Obesity is a major risk factor for a broad spectrum of diseases, yet the molecular pathways underlying this risk remains poorly understood. Individual analytical approaches—interventional trials, observational data, and genetic methods—are each susceptible to distinct sources of bias. We aimed to identify a common metabolic perturbation signature of adiposity by triangulating evidence across multiple sources of body mass index (BMI) variation, and to characterise associations between this signature and contrasting adiposity-linked disease outcomes. Methods and findings: We integrated mass spectrometry and nuclear magnetic resonance metabolomic profiling from two weight-loss interventions—a structured diet programme (DiRECT trial, ISRCTN03267836) and metabolic and bariatric surgery (By-Band-Sleeve trial, ISRCTN00786323)—with mendelian randomisation (MR) estimates of the effect of lifetime BMI exposure on metabolite levels, in order to identify a consistent BMI-associated metabolite signature across sources with distinct error structures. Weight loss-associated metabolites were then instrumented using genetic variants and their associations with heart failure (HF), osteoarthritis, and kidney cancer estimated in a two-sample MR framework, with instruments grouped into clusters based on genetic similarity to aid biological interpretation. 242 metabolites were consistently altered across weight loss interventions, predominantly comprising lipids, lipoproteins, and amino acids. The majority of these showed concordant associations with lifetime BMI in MR, consistent with regulation by adiposity. Differential components of this signature were associated with disease-specific outcomes. For HF, low-density lipoprotein (LDL) measures were strongly associated with increased risk, consistent with ischaemic aetiology enrichment, whilst high-density lipoprotein (HDL) cholesterol-enrichment traits were protective, and markers of dysfunctional HDL remodelling were associated with increased risk. For osteoarthritis, lipoprotein associations were again associated with disease risk but in the opposite direction to that seen with HF; additional signals were observed in glycine metabolism pathways. For kidney cancer, the adiposity metabolite signature translated poorly into disease associations, with only 5-methyluridine and ursodeoxycholate associations identified. The main limitations of our study include the potential for residual horizontal pleiotropy with metabolite instruments despite multiple sensitivity analyses and the rank-normalised scale of metabolite measures, which limits direct physiological interpretability. Conclusions: Triangulating evidence across dietary intervention, metabolic and bariatric surgery, and MR of lifetime BMI identified a robust metabolic signature of adiposity. The association of components of this signature with obesity-related outcomes was disease-specific, supporting the notion that not all adiposity-associated metabolic perturbations are uniformly relevant across the spectrum of obesity-related diseases. Why was this study done?: What did the researchers do and find?: What do these findings mean?: Nicolas Sunderland and colleagues combined metabolomic data from weight-loss trials with genetic analyses to identify metabolic features consistently linked to adiposity. The study showed that these metabolic alterations were associated with obesity-related diseases in disease-specific ways, rather than reflecting a single shared pathway across all outcomes.
SN - 1549-1676
ER -