Nutrition and the science of disease prevention: a systems approach to support metabolic health

Abstract

Progress in nutritional science, genetics, computer science, and behavioral economics can be leveraged to address the challenge of noncommunicable disease. This report highlights the connection between nutrition and the complex science of preventing disease and discusses the promotion of optimal metabolic health, building on input from several complementary disciplines. The discussion focuses on (1) the basic science of optimal metabolic health, including data from gene-diet interactions, microbiome, and epidemiological research in nutrition, with the goal of defining better targets and interventions, and (2) how nutrition, from pharma to lifestyle, can build on systems science to address complex issues.

Keywords: body weight; diabetes; gene-diet interactions; gut microbiome; obesity; systems science.

Figure 1

Summary of meta-analyses of prospective cohort studies on food and beverage intake and type 2 diabetes. Relative risks are a comparison of extreme categories, except for processed meat (per 50 g/day increase), unprocessed red meat and fish or seafood (per 100 g/day), white rice (per each serving/day), wholegrains (per 3 servings/day), sugar-sweetened beverages in European cohorts (per 336 g/day), and alcohol (22 g/day for men or 24 g/day for women, with abstainers). Adapted, with permission, from Ley et al.

Figure 2

The future of research on stratified diabetes medicine: a systems epidemiology approach to the discovery of interactions between the exposome (all nongenetic elements to which we are exposed) and the quantifiable elements of the human physiome.

Figure 3

Difference in body mass index (BMI) associated with one serving of a sugar-sweetened beverage per day, according to the quartile of the genetic predisposition score. The data show effect sizes (β coefficients (± SE)) of sugar-sweetened beverage intake (one serving/day) on BMI (the weight in kilograms divided by the square of the height in meters), stratified according to the quartile of the genetic predisposition score. In the Nurses’ Health Study (NHS) cohort, the median scores across the quartiles were 24.5 (range: 13.1–26.3), 27.8 (range: 26.4–29.0), 30.3 (range: 29.1–31.7), and 33.6 (range: 31.8–43.4); in the Health Professionals Follow-up Study (HPFS) cohort, the median scores were 24.9 (range: 16.0–26.5), 27.9 (range: 26.6–29.1), 30.4 (range: 29.2–31.7), and 33.6 (range: 31.8–41.9); and in the Women’s Genome Health Study (WGHS) cohort, the median scores were 24.7 (range: 15.3–26.5), 27.8 (range: 26.6–29.1), 30.3 (range: 29.2–31.6), and 33.4 (range: 31.7–43.4). In the NHS and Health Professionals Follow-up Study (HPFS) cohorts, the analyses were based on data collected from the first 4 years of the studies in women (1980–1984) and men (1986–1990), respectively, with adjustment for age, source of genotyping data, physical activity levels, time spent watching television, current smoking status, alcohol intake, and Alternative Healthy Eating Index score. In the Women’s Genome Health Study (WGHS) cohort, the analyses were based on data collected from the first 3 years, with adjustment for age, geographic region, eigenvectors, physical activity levels, current smoking status, and alcohol intake. P values shown are for interactions, and I bars indicate standard error. Reproduced, with permission, from Qi et al.