Connecting diet and disease: Using Mendelian randomisation to bridge the gap

Abstract

Establishing causality in nutrition research is challenging. While randomised controlled trials (RCTs) provide robust evidence, long-term dietary intervention studies with disease endpoints are often impractical. Short-term RCTs can instead identify intermediate traits that may lie on the causal pathway between diet and disease. Mendelian randomisation (MR) is an epidemiological approach that uses genetic variants as proxies for modifiable exposures to estimate the effects of lifelong differences in exposure on disease risk. However, the utility of MR is limited for complex dietary patterns because genetic variants typically reflect biological mechanisms rather than specific diets.

We propose a two-step framework integrating dietary RCTs with MR to infer potential lifetime effects of dietary interventions. First, RCT data identify molecular traits altered by an intervention. Second, MR evaluates whether these traits are associated with long-term disease risk.

We demonstrate this framework using the Diabetes Remission Clinical Trial (DiRECT), which measured circulating proteins and diabetes remission. Using protein data alone, 216 of 4,601 proteins changed following the intervention (step 1), and 10 were associated with diabetes risk using MR (step 2). We then compared these MR estimates with observed protein-remission associations from DiRECT. The broad agreement between the two (r{approx}-0.645, R2=0.416) supports this framework as a useful approach for estimating long-term effects of dietary interventions.

Estimating dose-response relationships for vitamin D with coronary heart disease, stroke, and all-cause mortality: observational and Mendelian randomisation analyses
Eleni Sofianopoulou, Stephen Kaptoge, Shoaib Afzal +83
The Lancet. Diabetes & Endocrinology3 Sep 2026Peer-reviewedCC BY 4.0Full text79 citedGeneral biology Epidemiology & public healthHAL
A review of the relationship between dimensions of alcohol consumption and the burden of disease: 2026 update including Mendelian randomisation studies
Sinclair Carr, Ana Lucia Espinosa Dice, Gerhard E. Gmel +3
Addiction13 May 2026Peer-reviewedCC BY-NC-ND12 citedGeneral medicineOpenAlex
Short telomeres, but not accelerated epigenetic or proteomic age, increase fibrotic lung disease risk: Mendelian randomisation with pleiotropy dissection and independent replication
S. Hanke
medRxiv14 Oct 2026PreprintCC BYFull textHuman geneticsmedRxiv
Metabolic syndrome and colorectal cancer share GPAT3 and PLAU as candidate genes: Mendelian randomisation, machine learning, single-cell knockout and docking
Li Zhou, Yongbin Hou, Leilei Zhang +3
Scientific Reports24 Oct 2026Peer-reviewedCC BY-NC-ND 4.0Full textOncologyCrossref
Evaluating Diabetes Drug Targets for Vascular Dementia Prevention Using Drug Target Mendelian Randomisation
A. Packer, L. Austin, D. M. Williams +2
medRxiv22 Sep 2026PreprintCC BYFull textHuman geneticsmedRxiv

Cite

BibTeX RIS

BibTeX

@misc{deslandes2026connecting,
  title = {{Connecting diet and disease: Using Mendelian randomisation to bridge the gap}},
  author = {B. Deslandes and L. J. Corbin and L. J. Goudswaard and M. R. Sandu and M. A. Lee and R. A. Beynon and L. McGeagh and G. D. Smith and N. Sattar and M. E. Lean and R. Taylor and J. A. Lane and N. J. Timpson and R. M. Martin and G. Richenberg and M. J. Gunter and J. Yarmolinsky and F. Koumanov and J. T. Gonzalez and R. C. Richmond and E. E. Vincent},
  howpublished = {medRxiv},
  year = {2026},
  month = sep,
  doi = {10.64898/2026.09.02.26362020},
  url = {https://www.medrxiv.org/content/10.64898/2026.09.02.26362020v1},
  note = {CC BY},
}

RIS

TY  - UNPB
TI  - Connecting diet and disease: Using Mendelian randomisation to bridge the gap
AU  - B. Deslandes
AU  - L. J. Corbin
AU  - L. J. Goudswaard
AU  - M. R. Sandu
AU  - M. A. Lee
AU  - R. A. Beynon
AU  - L. McGeagh
AU  - G. D. Smith
AU  - N. Sattar
AU  - M. E. Lean
AU  - R. Taylor
AU  - J. A. Lane
AU  - N. J. Timpson
AU  - R. M. Martin
AU  - G. Richenberg
AU  - M. J. Gunter
AU  - J. Yarmolinsky
AU  - F. Koumanov
AU  - J. T. Gonzalez
AU  - R. C. Richmond
AU  - E. E. Vincent
PY  - 2026
DA  - 2026/09/02
PB  - medRxiv
DO  - 10.64898/2026.09.02.26362020
UR  - https://www.medrxiv.org/content/10.64898/2026.09.02.26362020v1
AB  - Establishing causality in nutrition research is challenging. While randomised controlled trials (RCTs) provide robust evidence, long-term dietary intervention studies with disease endpoints are often impractical. Short-term RCTs can instead identify intermediate traits that may lie on the causal pathway between diet and disease. Mendelian randomisation (MR) is an epidemiological approach that uses genetic variants as proxies for modifiable exposures to estimate the effects of lifelong differences in exposure on disease risk. However, the utility of MR is limited for complex dietary patterns because genetic variants typically reflect biological mechanisms rather than specific diets.  We propose a two-step framework integrating dietary RCTs with MR to infer potential lifetime effects of dietary interventions. First, RCT data identify molecular traits altered by an intervention. Second, MR evaluates whether these traits are associated with long-term disease risk.  We demonstrate this framework using the Diabetes Remission Clinical Trial (DiRECT), which measured circulating proteins and diabetes remission. Using protein data alone, 216 of 4,601 proteins changed following the intervention (step 1), and 10 were associated with diabetes risk using MR (step 2). We then compared these MR estimates with observed protein-remission associations from DiRECT. The broad agreement between the two (r{approx}-0.645, R2=0.416) supports this framework as a useful approach for estimating long-term effects of dietary interventions.
ER  -

Abstract

Related

Cite