open access
What are the real associations of homeostasis model assessment (HOMA) with body mass index and age?
- Department of Public Health, Faculty of Medicine and Dentistry, Palacký University Olomouc, Olomouc, Czech Republic
- Department of Occupational Medicine, University Hospital Olomouc and Faculty of Medicine and Dentistry, Palacký University Olomouc, Olomouc, Czech Republic
- Institute of Biophysics and Informatics, 1st Medical Faculty Charles University, Prague, Czech Republic
- Department of Rehabilitation, Faculty of Medicine, Ostrava University, Ostrava, Czech Republic
- Department of Healthcare Management, Faculty of Health Sciences, Palacký University Olomouc, Olomouc, Czech Republic
- Science and Research Centre, Faculty of Health Sciences, Palacký University Olomouc, Olomouc, Czech Republic
- Department of Medical Biology, Faculty of Science, University of South Bohemia, České Budějovice, Czech Republic
- Clinic for Metabolic Assessment of Prof. MUDr. Karel Martiník, Hradec Králové, Czech Republic
- Institute of Laboratory Diagnostics, Faculty of Health and Social Sciences, University of South Bohemia, České Budějovice, Czech Republic
- Central Laboratories, Hospital of České Budějovice, České Budějovice, Czech Republic
open access
Abstract
Introduction: Insulin resistance (IR), a key pathogenesis mechanism of metabolic disorders, can be tested using homeostatic model assessment (HOMA). HOMA-IR quantifies peripheral tissue IR, whereas HOMA-β determines insulin secretion. The cross-sectional study aimed to examine non-linear associations of HOMA indices with age when adjusting for body mass index (BMI), and thus to investigate the indices’ ability to reflect the real development of glucose metabolism disorders over time.
Material and methods: The sample comprised 3406 individuals without diabetes mellitus (DM) divided into those with normal glucose metabolism (NGT, n = 1947) and prediabetes (n = 1459) after undergoing biochemical analyses. Polynomial multiple multivariate regression was applied to objectify associations of HOMA with both age and BMI.
Results: Mean values of HOMA-IR and HOMA-β in individuals with NGT were 1.5 and 82.8, respectively, while in prediabetics they were 2.2 and 74.3, respectively. The regression proved an inverse non-linear dependence of pancreatic b dysfunction, expressed by HOMA-β, on age, but did not prove a dependence on age for HOMA-IR. Both indices were positively, statistically significantly related to BMI, with a unit increase in BMI representing an increase in HOMA-IR by 0.1 and in HOMA-β by 3.2.
Conclusions: The mean values of HOMA indices showed that, compared with NGT, prediabetes is associated with more developed IR but lower insulin secretion. Both HOMA-IR and HOMA-b are predicted by BMI, but only HOMA-β is predicted by age. HOMA indices can reflect non-linear, closer-to-reality dependencies on age, which in many epidemiological studies are simplified to linear ones. The assessment of glucose metabolism using HOMA indices is beneficial for the primary prevention of IR and thus DM.
Abstract
Introduction: Insulin resistance (IR), a key pathogenesis mechanism of metabolic disorders, can be tested using homeostatic model assessment (HOMA). HOMA-IR quantifies peripheral tissue IR, whereas HOMA-β determines insulin secretion. The cross-sectional study aimed to examine non-linear associations of HOMA indices with age when adjusting for body mass index (BMI), and thus to investigate the indices’ ability to reflect the real development of glucose metabolism disorders over time.
Material and methods: The sample comprised 3406 individuals without diabetes mellitus (DM) divided into those with normal glucose metabolism (NGT, n = 1947) and prediabetes (n = 1459) after undergoing biochemical analyses. Polynomial multiple multivariate regression was applied to objectify associations of HOMA with both age and BMI.
Results: Mean values of HOMA-IR and HOMA-β in individuals with NGT were 1.5 and 82.8, respectively, while in prediabetics they were 2.2 and 74.3, respectively. The regression proved an inverse non-linear dependence of pancreatic b dysfunction, expressed by HOMA-β, on age, but did not prove a dependence on age for HOMA-IR. Both indices were positively, statistically significantly related to BMI, with a unit increase in BMI representing an increase in HOMA-IR by 0.1 and in HOMA-β by 3.2.
Conclusions: The mean values of HOMA indices showed that, compared with NGT, prediabetes is associated with more developed IR but lower insulin secretion. Both HOMA-IR and HOMA-b are predicted by BMI, but only HOMA-β is predicted by age. HOMA indices can reflect non-linear, closer-to-reality dependencies on age, which in many epidemiological studies are simplified to linear ones. The assessment of glucose metabolism using HOMA indices is beneficial for the primary prevention of IR and thus DM.
Keywords
insulin resistance; prediabetes; diabetes mellitus; homeostasis model assessment
Title
What are the real associations of homeostasis model assessment (HOMA) with body mass index and age?
Journal
Issue
Article type
Original paper
Pages
736-742
Published online
2022-06-29
Page views
4306
Article views/downloads
751
DOI
Pubmed
Bibliographic record
Endokrynol Pol 2022;73(4):736-742.
Keywords
insulin resistance
prediabetes
diabetes mellitus
homeostasis model assessment
Authors
Dagmar Horáková
Ladislav Štěpánek
Lubomír Štěpánek
Dalibor Pastucha
Jana Janoutová
Vladimír Janout
Vladimír Kron
Miroslav Verner
Daniel Martiník
- International Diabetes Federation. IDF Diabetes Atlas, 9th edn. Brussels, Belgium: 2019. https://www.diabetesatlas.org/en/resources/.
- Cypryk K, Małecki P. A review of cardiovascular outcome trials in type 2 diabetes. Endokrynol Pol. 2018; 69(4).
- Stepanek L, Horakova D, Nakladalova M, et al. Significance of prediabetes as a nosological entity. Biomed Pap Med Fac Univ Palacky Olomouc Czech Repub. 2018 [Epub ahead of print].
- Stern SE, Williams K, Ferrannini E, et al. Identification of individuals with insulin resistance using routine clinical measurements. Diabetes. 2005; 54(2): 333–339.
- Wallace TM, Levy JC, Matthews DR. Use and abuse of HOMA modeling. Diabetes Care. 2004; 27(6): 1487–1495.
- Horáková D, Štěpánek L, Janout V, et al. Optimal Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) Cut-Offs: A Cross-Sectional Study in the Czech Population. Medicina (Kaunas). 2019; 55(5).
- Štěpánek L, Horáková D, Štěpánek L, et al. Associations Between Homeostasis Model Assessment (HOMA) and Routinely Examined Parameters in Individuals With Metabolic Syndrome. Physiol Res. 2019; 68(6): 921–930.
- Chia CW, Egan JM, Ferrucci L. Age-Related Changes in Glucose Metabolism, Hyperglycemia, and Cardiovascular Risk. Circ Res. 2018; 123(7): 886–904.
- Hirose H, Takayama M, Iwao Y, et al. Effects of Aging on Visceral and Subcutaneous Fat Areas and on Homeostasis Model Assessment of Insulin Resistance and Insulin Secretion Capacity in a Comprehensive Health Checkup. J Atheroscler Thromb. 2016; 23(2): 207–215.
- Hulman A, Simmons RK, Brunner EJ, et al. Trajectories of glycaemia, insulin sensitivity and insulin secretion in South Asian and white individuals before diagnosis of type 2 diabetes: a longitudinal analysis from the Whitehall II cohort study. Diabetologia. 2017; 60(7): 1252–1260.
- Li CL, Tsai ST, Chou P. Relative role of insulin resistance and beta-cell dysfunction in the progression to type 2 diabetes--The Kinmen Study. Diabetes Res Clin Pract. 2003; 59(3): 225–232.
- Ha CHo, Swearingin B, Jeon YK. Relationship of visfatin level to pancreatic endocrine hormone level, HOMA-IR index, and HOMA β-cell index in overweight women who performed hydraulic resistance exercise. J Phys Ther Sci. 2015; 27(9): 2965–2969.
- Ruiz-Ojeda FJ, Méndez-Gutiérrez A, Aguilera CM, et al. Extracellular Matrix Remodeling of Adipose Tissue in Obesity and Metabolic Diseases. Int J Mol Sci. 2019; 20(19).
- Lin SF, Fan YC, Chou CC, et al. Body composition patterns among normal glycemic, pre-diabetic, diabetic health Chinese adults in community: NAHSIT 2013-2016. PLoS One. 2020; 15(11): e0241121.
- Chen M, Yang R, Wang Y, et al. Non-linear associations of body mass index with impaired fasting glucose, β-cell dysfunction, and insulin resistance in nondiabetic Chinese individuals: a cross-sectional study. Endokrynol Pol. 2021; 72(6): 618–624.
- Petersen MC, Shulman GI. Mechanisms of Insulin Action and Insulin Resistance. Physiol Rev. 2018; 98(4): 2133–2223.
- Wu H, Ballantyne CM, Khan IM, et al. CD11c expression in adipose tissue and blood and its role in diet-induced obesity. Arterioscler Thromb Vasc Biol. 2010; 30(2): 186–192.
- Poon AK, Whitsel EA, Heiss G, et al. Insulin resistance and reduced cardiac autonomic function in older adults: the Atherosclerosis Risk in Communities study. BMC Cardiovasc Disord. 2020; 20(1): 217.
- Tang Qi, Li X, Song P, et al. Optimal cut-off values for the homeostasis model assessment of insulin resistance (HOMA-IR) and pre-diabetes screening: Developments in research and prospects for the future. Drug Discov Ther. 2015; 9(6): 380–385.
- Townsend DK, McGregor K, Wu E, et al. Insulin resistance and metabolic syndrome criteria in lean, normoglycemic college-age subjects. Diabetes Metab Syndr. 2018; 12(5): 609–616.
- Cederholm J, Zethelius B. SPISE and other fasting indexes of insulin resistance: risks of coronary heart disease or type 2 diabetes. Comparative cross-sectional and longitudinal aspects. Ups J Med Sci. 2019; 124(4): 265–272.
- Gierach M, Junik R. Insulin resistance in metabolic syndrome depending on the occurrence of its components. Endokrynol Pol. 2021; 72(3): 243–248.
- Gesteiro E, Megía A, Guadalupe-Grau A, et al. Early identification of metabolic syndrome risk: A review of reviews and proposal for defining pre-metabolic syndrome status. Nutr Metab Cardiovasc Dis. 2021; 31(9): 2557–2574.