Improved Performance of Dynamic Measures of Insulin Response Over Surrogate Indices to Identify Genetic Contributors of Type 2 Diabetes: The GUARDIAN Consortium

Methods for estimating insulin resistance from untargeted metabolomics data

CONTEXT

Insulin resistance is associated with multiple complex diseases; however, precise measures of insulin resistance are invasive, expensive, and time-consuming.

OBJECTIVE

Develop estimation models for measures of insulin resistance, including insulin sensitivity index (SI) and homeostatic model assessment of insulin resistance (HOMA-IR) from metabolomics data.

DESIGN

Insulin Resistance Atherosclerosis Family Study (IRASFS).

SETTING

Community based.

PARTICIPANTS

Mexican Americans (MA) and African Americans (AA).

MAIN OUTCOME

Estimation models for measures of insulin resistance, i.e. SI and HOMA-IR.

RESULTS

Least Absolute Shrinkage and Selection Operator (LASSO) and Elastic Net regression were used to build insulin resistance estimation models from 1274 metabolites combined with clinical data, e.g. age, sex, body mass index (BMI). Metabolite data were transformed using three approaches, i.e. inverse normal transformation, standardization, and Box Cox transformation. The analysis was performed in one MA recruitment site (San Luis Valley, Colorado (SLV); N = 450) and tested in another MA recruitment site (San Antonio, Texas (SA); N = 473). In addition, the two MA recruitment sites were combined and estimation models tested in the AA recruitment sample (Los Angeles, California; N = 495). Estimated and empiric SI were correlated in the SA (r2 = 0.77) and AA (r2 = 0.74) testing datasets. Further, estimated and empiric SI were consistently associated with BMI, low-density lipoprotein cholesterol (LDL), and triglycerides. We applied similar approaches to estimate HOMA-IR with similar results.

CONCLUSIONS

We have developed a method for estimating insulin resistance with metabolomics data that has the potential for application to a wide range of biomedical studies and conditions.

Initiating Dialogue in Community-Partnered Participatory Research to Address Obesity in South Los Angeles

In South Los Angeles, a community-engaged research project on obesity was initiated between a translational research institute seeking to build community-based or partnered participatory research (CBPR/CPPR) capacity, and a community partner with extensive experience. This manuscript describes the partnership-building process and discusses results from a bi-directional knowledge transfer event.

Metabolomics Identifies Distinctive Metabolite Signatures for Measures of Glucose Homeostasis: The Insulin Resistance Atherosclerosis Family Study (IRAS-FS)

CONTEXT

Metabolomics provides a biochemical fingerprint that, when coupled with clinical phenotypes, can provide insight into physiological processes.

OBJECTIVE

Survey metabolites associated with dynamic and basal measures of glucose homeostasis.

DESIGN

Analysis of 733 plasma metabolites from the Insulin Resistance Atherosclerosis Family Study.

SETTING

Community based.

PARTICIPANTS

One thousand one hundred eleven Mexican Americans.

MAIN OUTCOME

Dynamic measures were obtained from the frequently sampled intravenous glucose tolerance test and included insulin sensitivity and acute insulin response to glucose. Basal measures included homeostatic model assessment of insulin resistance and β-cell function.

RESULTS

Insulin sensitivity was associated with 99 metabolites (P < 6.82 × 10-5) explaining 28% of the variance (R2adj) beyond 28% by body mass index. Beyond branched chain amino acids (BCAAs; P = 1.85 × 10-18 to 1.70 × 10-5, R2adj = 8.1%) and phospholipids (P = 3.51 × 10-17 to 3.00 × 10-5, R2adj = 14%), novel signatures of long-chain fatty acids (LCFAs; P = 4.49 × 10-23 to 4.14 × 10-7, R2adj = 11%) were observed. Conditional analysis suggested that BCAA and LCFA signatures were independent. LCFAs were not associated with homeostatic model assessment of insulin resistance (P > 0.024). Acute insulin response to glucose was associated with six metabolites; glucose had the strongest association (P = 5.68 × 10-16). Homeostatic model assessment of β-cell function had significant signatures from the urea cycle (P = 9.64 × 10-14 to 7.27 × 10-6, R2adj = 11%). Novel associations of polyunsaturated fatty acids (P = 2.58 × 10-13 to 6.70 × 10-5, R2adj = 10%) and LCFAs (P = 9.06 × 10-15 to 3.93 × 10-7, R2adj = 10%) were observed with glucose effectiveness. Assessment of the hyperbolic relationship between insulin sensitivity and secretion through the disposition index revealed a distinctive signature of polyunsaturated fatty acids (P = 1.55 × 10-12 to 5.81 × 10-6; R2adj = 3.8%) beyond that of its component measures.

CONCLUSIONS

Metabolomics reveals distinct signatures that differentiate dynamic and basal measures of glucose homeostasis and further identifies new metabolite classes associated with dynamic measures, providing expanded insight into the metabolic basis of insulin resistance.

Physiologic Interpretation of GWAS Signals for Type 2 Diabetes

This chapter reviews both statistical and physiologic issues related to the pathophysiologic effects of genetic variation in the context of type 2 diabetes. The goal is to review current methodologies used to analyze disease-related quantitative traits for those who do not have extensive quantitative and physiologic background, as an attempt to bridge that gap. We leverage mathematical modeling to illustrate the strengths and weaknesses of different approaches and attempt to reinforce with real data analysis. Topics reviewed include phenotype selection, phenotype specificity, multiple variant analysis via the genetic risk score, and consideration of multiple disease-related phenotypes. Type 2 diabetes is used as the example, not only because of the extensive existing knowledge at the genetic, physiologic, clinical, and epidemiologic levels, but also because type 2 diabetes has been at the forefront of complex disease genetics, with many examples to draw from.