Literature DB >> 15161807

Use and abuse of HOMA modeling.

Tara M Wallace1, Jonathan C Levy, David R Matthews.   

Abstract

Homeostatic model assessment (HOMA) is a method for assessing beta-cell function and insulin resistance (IR) from basal (fasting) glucose and insulin or C-peptide concentrations. It has been reported in >500 publications, 20 times more frequently for the estimation of IR than beta-cell function. This article summarizes the physiological basis of HOMA, a structural model of steady-state insulin and glucose domains, constructed from physiological dose responses of glucose uptake and insulin production. Hepatic and peripheral glucose efflux and uptake were modeled to be dependent on plasma glucose and insulin concentrations. Decreases in beta-cell function were modeled by changing the beta-cell response to plasma glucose concentrations. The original HOMA model was described in 1985 with a formula for approximate estimation. The computer model is available but has not been as widely used as the approximation formulae. HOMA has been validated against a variety of physiological methods. We review the use and reporting of HOMA in the literature and give guidance on its appropriate use (e.g., cohort and epidemiological studies) and inappropriate use (e.g., measuring beta-cell function in isolation). The HOMA model compares favorably with other models and has the advantage of requiring only a single plasma sample assayed for insulin and glucose. In conclusion, the HOMA model has become a widely used clinical and epidemiological tool and, when used appropriately, it can yield valuable data. However, as with all models, the primary input data need to be robust, and the data need to be interpreted carefully.

Entities:  

Mesh:

Substances:

Year:  2004        PMID: 15161807     DOI: 10.2337/diacare.27.6.1487

Source DB:  PubMed          Journal:  Diabetes Care        ISSN: 0149-5992            Impact factor:   19.112


  1472 in total

1.  Metabolic syndrome is associated with exposure to organochlorine pesticides in Anniston, AL, United States.

Authors:  Paula F Rosenbaum; Ruth S Weinstock; Allen E Silverstone; Andreas Sjödin; Marian Pavuk
Journal:  Environ Int       Date:  2017-08-02       Impact factor: 9.621

2.  The association of breastfeeding with insulin resistance at 17 years: Prospective observations from Hong Kong's "Children of 1997" birth cohort.

Authors:  Lai Ling Hui; Man Ki Kwok; E Anthony S Nelson; So Lun Lee; Gabriel M Leung; C Mary Schooling
Journal:  Matern Child Nutr       Date:  2017-08-04       Impact factor: 3.092

3.  Persistence of skin-deep resilience in African American adults.

Authors:  Gene H Brody; Tianyi Yu; Edith Chen; Gregory E Miller
Journal:  Health Psychol       Date:  2020-06-29       Impact factor: 4.267

4.  Changes in stress, eating, and metabolic factors are related to changes in telomerase activity in a randomized mindfulness intervention pilot study.

Authors:  Jennifer Daubenmier; Jue Lin; Elizabeth Blackburn; Frederick M Hecht; Jean Kristeller; Nicole Maninger; Margaret Kuwata; Peter Bacchetti; Peter J Havel; Elissa Epel
Journal:  Psychoneuroendocrinology       Date:  2011-12-14       Impact factor: 4.905

5.  Improvement of nonalcoholic fatty liver disease after bariatric surgery in morbidly obese Chinese patients.

Authors:  Chi-Ming Tai; Chih-Kun Huang; Jau-Chung Hwang; Hung Chiang; Chi-Yang Chang; Ching-Tai Lee; Ming-Lung Yu; Jaw-Town Lin
Journal:  Obes Surg       Date:  2012-07       Impact factor: 4.129

6.  Ethnic and gender susceptibility to metabolic risk.

Authors:  Scott M Grundy; Ian J Neeland; Aslan T Turer; Gloria Lena Vega
Journal:  Metab Syndr Relat Disord       Date:  2013-12-10       Impact factor: 1.894

7.  Dietary Manganese, Plasma Markers of Inflammation, and the Development of Type 2 Diabetes in Postmenopausal Women: Findings From the Women's Health Initiative.

Authors:  Jung Ho Gong; Kenneth Lo; Qing Liu; Jie Li; Shuiqing Lai; Aladdin H Shadyab; Chrisa Arcan; Linda Snetselaar; Simin Liu
Journal:  Diabetes Care       Date:  2020-04-15       Impact factor: 19.112

8.  Genetic control of obesity, glucose homeostasis, dyslipidemia and fatty liver in a mouse model of diet-induced metabolic syndrome.

Authors:  D S Sinasac; J D Riordan; S H Spiezio; B S Yandell; C M Croniger; J H Nadeau
Journal:  Int J Obes (Lond)       Date:  2015-09-18       Impact factor: 5.095

9.  β-Cell dysfunction is associated with metabolic syndrome severity in adults.

Authors:  Steven K Malin; Stephen Finnegan; Ciaran E Fealy; Julianne Filion; Michael B Rocco; John P Kirwan
Journal:  Metab Syndr Relat Disord       Date:  2013-11-27       Impact factor: 1.894

10.  Polychlorinated biphenyl exposure and glucose metabolism in 9-year-old Danish children.

Authors:  Tina K Jensen; Amalie G Timmermann; Laura I Rossing; Mathias Ried-Larsen; Anders Grøntved; Lars B Andersen; Christine Dalgaard; Oluf H Hansen; Thomas Scheike; Flemming Nielsen; Philippe Grandjean
Journal:  J Clin Endocrinol Metab       Date:  2014-12       Impact factor: 5.958

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.