Determining the Development of Insulin Resistance in Older Adults of the NuAge cohort Using Trajectory Modeling of the Homeostatic Model Assessment of Insulin Resistance Score
Joane Matta1,4,7, Nancy Mayo1,6, Isabelle J. Dionne2, Pierrette Gaudreau3, Tamàs Fulop2, Daniel Tessier2, Katherine Gray-Donald4, Bryna Shatenstein5, Susan C. Scott6, José A. Morais1,4*
Affiliation
- 1MUHC – Division of Geriatric Medicine and McGill University, Quebec, Canada
- 2CIUSSS-IUGS and University of Sherbrooke,Quebec, Canada
- 3CUSM – Research Center and Faculty of Medicine, University of Montreal, Quebec, Canada
- 4School of Dietetics and Human Nutrition, McGill University, Quebec, Canada
- 5Department of Nutrition, Universityof Montréal, Quebec, Canada
- 6MUHC – Division of Clinical Epidemiology and McGill University, Quebec, Canada
- 7Department of Nutrition and Dietetics, Holy Spirit University, Jounieh, Lebanon
Corresponding Author
José A. Morais, MD, FRCPC, Division of Geriatric Medicine of McGill University Health Centre, Montreal General Hospital, 1650 Cedar Avenue, Room E16.124.1, Montréal, Quebec, Canada H3G 1A4, Tel: (514) 934-1934/ext. 34499; Fax: (514) 843-1400; E-mail: jose.morais@mcgill.ca
Citation
Morais, J.A., et al. Determining the Development of Insulin Resistance in Older Adults of the NuAge Cohort Using Trajectory Modeling of the Homeostatic Model Assessment of Insulin Resistance Score. (2016) J Diab Obes 3(2): 43- 50.
Copy rights
© 2016 Morais, J.A. This is an Open access article distributed under the terms of Creative Commons Attribution 4.0 International License.
Keywords
Abstract
Background: Age-associated body composition changes increase the risk of developing insulin resistance. Identifying these subjects in epidemiological studies is challenging.
Objective: Identify insulin-resistant subjects over a 3-year period and determine predictors.
Methods: Data on 649 non-diabetic participants of the Quebec Longitudinal Study on Nutrition and Successful Aging (NuAge) Cohort were analyzed. Muscle mass index (kg/height in m²) and %body fat were derived from dual X-ray absorptiometry or bioimpedancemetry. Insulin resistance was based on the Homeostatic Model Assessment of insulin resistance HOMA-IR score. Physical activity was assessed by questionnaire. Protein and fat intake were obtained from three 24-h food recalls. Developmental trajectories over 4 time points were used to determine insulin sensitivity status. Logistic regression analyses serve to determine baseline variables affecting change over time.
Results: Seven group-based trajectories were identified from a model with good fit. Curve inspection allowed for the classification of insulin sensitive and resistant subjects. Predictors of insulin resistance were: muscle mass index [OR (95% CI): 1.72 (1.26 - 2.3)]; %body fat [1.18 (1.12 - 1.25)]; male sex [OR for women versus men: 0.145 (0.04 - 0.45)].
Conclusion: Greater muscle mass index and % body fat contribute to higher odds of insulin resistance with aging in man whereas being a woman decreases these odds. The relationship between muscle mass and the development of insulin resistance is counterintuitive and requires further exploration since it suggests that maintenance of muscle mass with aging is a contributor. Our probabilistic approach addresses one of the challenges in determining insulin-resistant subjects in epidemiological studies.
