Translational Investigation of Insulin Resistance
Insulin resistance is a major cause of the epidemic diseases of our society: diabetes, heart attacks, strokes, and fatty liver. Our goal is to understand who develops insulin resistance, how, and why. We use human genetics, functional genomics, and cell-based assays to address this goal. In addition to this discovery science, we focus on clinical translation by developing clinical tools to interpret large scale genetic, genomic, and longitudinal health data to diagnose and treat people with diseases related to insulin resistance.
Insulin Resistance Gene & Biomarker Discovery
Insulin resistance remains clinically challenging to quantify and treat. The recent accumulation of genome sequences, serum samples, and clinical characterizations of large patient populations presents a unique opportunity to determine the genetic and molecular underpinnings of insulin resistance. We leverage these resources to identify insulin resistance genes and novel mechanisms using large scale human genetic analyses of (a) common and (b) rare genetic variants and (c) ‘omics’ on patient serum/plasma samples to rapidly identify and credential novel biomarkers.
(a) DeForest, N. et al. Genome-wide discovery and integrative genomic characterization of insulin resistance loci using serum triglycerides to HDL-cholesterol as a proxy. Nature Communications 15(1):8068 (2024).
(b) Majithia A. R. et al. Prospective functional classification of all possible missense variants in PPARG. Nat. Genet. 48, 1570-1575 (2016).
(c) Begzati A. et al. Plasma lipid metabolites, clinical glycemic predictors, and incident type 2 diabetes. Diabetes Care. 48(3):1-8 (2025).
Precision 'omics' for Metabolic Disease Subtyping
Diabetes is a heterogeneous disease with a polygenic basis. It is difficult to predict the severity of disease and patient risk of microvascular complications like kidney failure and macrovascular complications like heart attack and stroke. To differentiate individuals at high risk for serious complications from those with a more stable disease course, we conduct longitudinal analyses of the Diabetes Prevention Program (DPP), a landmark study of participants with pre-diabetes and their clinical outcomes over three decades (a). We also develop tools for classifying genetic variants found in the course of human genome sequencing powered by massively parallel experiments to provide molecular diagnoses, disease risk estimation, and guide pharmacotherapy (b, c, d).
(a) Kobayashi, E. et al. Longitudinal metabolic trajectories in Diabetes Prevention Program participants reveal subgroups with varying micro- and macrovascular complication risks. Diabetes Care dc250866 (2025) doi:10.2337/dc25-0866.
(b) Agostini, M. et al. A pharmacogenetic approach to the treatment of patients with PPARG mutations. Diabetes 67, 1086–1092 (2018).
(c) DeForest, N. et al. Human gain-of-function variants in HNF1A confer protection from diabetes but independently increase hepatic secretion of atherogenic lipoproteins. Cell Genom 3, 100339 (2023).
(d) Lal, D. et al. Gene family information facilitates variant interpretation and identification of disease-associated genes in neurodevelopmental disorders. Genome Med. 12, 28 (2020).
Adipose Tissue Function in Health & Disease
Why does overweight sometimes cause insulin resistance and metabolic diseases, while in other instances seem to cause no metabolic consequence? We think that the answer lies in adipose ‘fat’ tissue. The composition and function of adipose varies between different parts of the body and between individuals. We know that people with lipodystrophy, a rare genetic syndrome causing little to no adipose tissue, suffer from the most aggressive forms of metabolic disease. This demonstrates that fat tissue is necessary for health, but we lack a complete understanding of the mechanisms that distinguish metabolically healthy and unhealthy fat. We are using modern ‘omic’ techniques like single cell sequencing (a), high-throughput genome engineering (b), and new protein tagging methods to investigate the genetic basis of what distinguishes healthy and unhealthy adipocytes, how they signal to the rest of the body to prevent and cause metabolic disease, and how we could use this information to find the next generation of therapeutics.
(a) Du, X. et al. An alternatively translated isoform of PPARG suggests AF-1 domain inhibition as an insulin sensitization target. Diabetes 74, 651-663 (2025).
(b) Jiao, Y. et al. Discovering metabolic disease gene interactions by correlated effects on cellular morphology. Mol Metab 24, 108-119 (2019).
