Insulin resistance: regression and clustering.

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 lar...

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Autores principales:	Sangho Yoon, Themistocles L Assimes, Thomas Quertermous, Chin-Fu Hsiao, Lee-Ming Chuang, Chii-Min Hwu, Bala Rajaratnam, Richard A Olshen
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/23c1e9fe7dbe4af29eae4f0576b17958
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spelling	oai:doaj.org-article:23c1e9fe7dbe4af29eae4f0576b179582021-11-18T08:17:31ZInsulin resistance: regression and clustering.1932-620310.1371/journal.pone.0094129https://doaj.org/article/23c1e9fe7dbe4af29eae4f0576b179582014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24887437/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203In 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.Sangho YoonThemistocles L AssimesThomas QuertermousChin-Fu HsiaoLee-Ming ChuangChii-Min HwuBala RajaratnamRichard A OlshenPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 6, p e94129 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Sangho Yoon Themistocles L Assimes Thomas Quertermous Chin-Fu Hsiao Lee-Ming Chuang Chii-Min Hwu Bala Rajaratnam Richard A Olshen Insulin resistance: regression and clustering.
description	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.
format	article
author	Sangho Yoon Themistocles L Assimes Thomas Quertermous Chin-Fu Hsiao Lee-Ming Chuang Chii-Min Hwu Bala Rajaratnam Richard A Olshen
author_facet	Sangho Yoon Themistocles L Assimes Thomas Quertermous Chin-Fu Hsiao Lee-Ming Chuang Chii-Min Hwu Bala Rajaratnam Richard A Olshen
author_sort	Sangho Yoon
title	Insulin resistance: regression and clustering.
title_short	Insulin resistance: regression and clustering.
title_full	Insulin resistance: regression and clustering.
title_fullStr	Insulin resistance: regression and clustering.
title_full_unstemmed	Insulin resistance: regression and clustering.
title_sort	insulin resistance: regression and clustering.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/23c1e9fe7dbe4af29eae4f0576b17958
work_keys_str_mv	AT sanghoyoon insulinresistanceregressionandclustering AT themistocleslassimes insulinresistanceregressionandclustering AT thomasquertermous insulinresistanceregressionandclustering AT chinfuhsiao insulinresistanceregressionandclustering AT leemingchuang insulinresistanceregressionandclustering AT chiiminhwu insulinresistanceregressionandclustering AT balarajaratnam insulinresistanceregressionandclustering AT richardaolshen insulinresistanceregressionandclustering
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Insulin resistance: regression and clustering.

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