title: Insulin resistance: regression and clustering.
abstract: In this paper we try to define insulin resistance (IR) precisely for a group of Chinese women. Our definition deliberately does not depend upon body mass index (BMI) or age, although in other studies, with particular random effects models quite different from models used here, BMI accounts for a large part of the variability in IR. We accomplish our goal through application of Gauss mixture vector quantization (GMVQ), a technique for clustering that was developed for application to lossy data compression. Defining data come from measurements that play major roles in medical practice. A precise statement of what the data are is in Section 1. Their family structures are described in detail. They concern levels of lipids and the results of an oral glucose tolerance test (OGTT). We apply GMVQ to residuals obtained from regressions of outcomes of an OGTT and lipids on functions of age and BMI that are inferred from the data. A bootstrap procedure developed for our family data supplemented by insights from other approaches leads us to believe that two clusters are appropriate for defining IR precisely. One cluster consists of women who are IR, and the other of women who seem not to be. Genes and other features are used to predict cluster membership. We argue that prediction with "main effects" is not satisfactory, but prediction that includes interactions may be.
authors.affiliation: Google Inc., Mountain View, California, United States of America; Department of Health Research and Policy, Stanford, California, United States of America.
authors.forename: Sangho
authors.initials: S
authors.lastname: Yoon
authors.affiliation: Division of Cardiovascular Medicine, Department of Medicine, Falk Cardiovascular Research Center, Stanford, California, United States of America.
authors.forename: Themistocles L
authors.initials: TL
authors.lastname: Assimes
authors.affiliation: Division of Cardiovascular Medicine, Department of Medicine, Falk Cardiovascular Research Center, Stanford, California, United States of America.
authors.forename: Thomas
authors.initials: T
authors.lastname: Quertermous
authors.affiliation: Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Insititues, Miaoli County, Taiwan.
authors.forename: Chin-Fu
authors.initials: CF
authors.lastname: Hsiao
authors.affiliation: Graduate Institute of Clinical Medicine, National Taiwan University, Taipei, Taiwan.
authors.forename: Lee-Ming
authors.initials: LM
authors.lastname: Chuang
authors.affiliation: School of Medicine, National Yang-Ming University, Taipei, Taiwan.
authors.forename: Chii-Min
authors.initials: CM
authors.lastname: Hwu
authors.affiliation: Department of Statistics, Stanford, California, United States of America; Department of Environmental Earth System Sciences, Stanford, California, United States of America.
authors.forename: Bala
authors.initials: B
authors.lastname: Rajaratnam
authors.affiliation: Department of Health Research and Policy, Stanford, California, United States of America; Department of Electrical Engineering, Stanford, California, United States of America; Department of Statistics, Stanford, California, United States of America.
authors.forename: Richard A
authors.initials: RA
authors.lastname: Olshen
mesh_terms: D001786:Blood Glucose / Q000378:metabolism; D016000:Cluster Analysis; D005260:Female; D005951:Glucose Tolerance Test; D006801:Humans; D007333:Insulin Resistance / Q000235:genetics; D008297:Male; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D025341:Principal Component Analysis; D012044:Regression Analysis; D015203:Reproducibility of Results; D060388:Support Vector Machine
chemical_list: D001786:Blood Glucose