# Effects of alcohol, coffee, and milk intake on cardiometabolic disease via observational analysis and Mendelian randomization

> **NIH NIH F32** · STANFORD UNIVERSITY · 2020 · $78,558

## Abstract

Project Summary
 Cardiovascular diseases are the global leading cause of morbidity and mortality, accounting for 32% of
deaths (> 17 million) annually as of 2017. Diabetes is also a major health problem, with a prevalence of 463
million globally and 29 million in the US. Obesity is thought to be a risk factor for both cardiovascular diseases
(e.g. coronary heart disease, atrial fibrillation, ischemic stroke) and type 2 diabetes. Furthermore, obesity has
continued to rise in the US population.
 Diet is an important risk factor for obesity and cardiometabolic outcomes, as well as a modifiable factor
for intervention that could reduce disease risk. The contribution of specific dietary factors to cardiometabolic
disease is poorly understood due to ethical, economical, and practical issues with randomizing a sufficient
number of individuals to dietary interventions.
 Thus, we rely on observational data, which is prone to confounding by factors such as socioeconomic
status and lifestyle choices. Because genes are fixed at conception and randomly assorted within a population,
Mendelian Randomization (MR) mimics a randomized controlled trial on the basis of genetic makeup, allowing
inference of causality. MR requires large-scale data, and with the availability of data such as that in the UK
Biobank (> 500k participants), we can now use MR to address questions regarding dietary exposures that have
so far been beyond our reach. I will combine observational, genetic, and MR analyses in the UK Biobank with
external genome-wide association study (GWAS) meta-analyses of risk factors and cardiometabolic disease
outcomes to answer important dietary questions that have so far evaded us.
 I aim to elucidate the relationship of several dietary factors with cardiometabolic disease using
observational and MR studies. In Aim 1, I will characterize alcohol using observational association, standard MR,
and non-linear MR analyses based on variation in the alcohol dehydrogenase gene. In Aim 2, I will characterize
coffee using observational analysis, perform GWAS to create a genetic risk score for coffee intake and subtypes
of coffee, and use the resulting genetic risk scores for MR and non-linear MR analyses. In Aim 3, I will
characterize various dairy intake types using observational analysis and perform an MR analysis based on
variation in the lactase persistence gene.
 In summary, the combination of observational, genetic, and MR analyses will allow us to characterize the
risk associated with ubiquitous alcohol, caffeine, and dairy consumption in a much more meaningful way than
correlations drawn from observational analyses alone. This has tremendous potential to influence the advice
given by nutritionists and physicians to the hundreds of millions of people who suffer from or are at risk of
cardiovascular disease, diabetes, and obesity.

## Key facts

- **NIH application ID:** 10021413
- **Project number:** 5F32HL149254-02
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Joanna Lankester
- **Activity code:** F32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $78,558
- **Award type:** 5
- **Project period:** 2019-09-01 → 2021-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10021413

## Citation

> US National Institutes of Health, RePORTER application 10021413, Effects of alcohol, coffee, and milk intake on cardiometabolic disease via observational analysis and Mendelian randomization (5F32HL149254-02). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10021413. Licensed CC0.

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