Context: There are discrepancies in the seasonality of insulin resistance (IR) across the literature, probably due to age-related differences in the seasonality of lifestyle factors and thermoregulation mechanisms. Objective: To estimate the seasonality of IR according to the homeostatic model assessment-IR (HOMA-IR), glucose, and insulin levels and to examine the role of lifestyle markers [body mass index (BMI) and physical activity] and meteorological factors, according to age. Design, Setting, and Participants: Seasonality was examined using cosinor analysis among middle-aged (45 to 65 years) and elderly (≥65 years) participants of a population-based Dutch cohort. We analyzed 13,622 observations from 8979 participants (57.6% women) without diagnosis of diabetes and fasting glucose <7 mmol/L. BMI was measured, physical activity was evaluated using a validated questionnaire, and meteorological factors (daily mean ambient temperature, mean relative humidity, total sunlight hours, and total precipitation) were obtained from local records. Seasonality estimates were adjusted for confounders. Results: Among the middle-aged participants, seasonal variation estimates were: 0.11 units (95% confidence interval: 0.03, 0.20) for HOMA-IR, 0.28 µIU/mL (-0.05, 0.69) for insulin, and 0.05 mmol/L (0.01, 0.09) for glucose. These had a summer peak, and lifestyle markers explained the pattern. Among the elderly, seasonal variations were: 0.29 units (0.21, 0.37) for HOMA-IR, 0.96 µIU/mL (0.58, 1.28) for insulin, and 0.01 mmol/L (-0.01, 0.05) for glucose. These had a winter peak and ambient temperature explained the pattern. Conclusion: Impaired thermoregulation mechanisms could explain the winter peak of IR among elderly people without diabetes. The seasonality of lifestyle factors may explain the seasonality of glucose.
Context: There are discrepancies in the seasonality of insulin resistance (IR) across the literature, probably due to age-related differences in the seasonality of lifestyle factors and thermoregulation mechanisms. Objective: To estimate the seasonality of IR according to the homeostatic model assessment-IR (HOMA-IR), glucose, and insulin levels and to examine the role of lifestyle markers [body mass index (BMI) and physical activity] and meteorological factors, according to age. Design, Setting, and Participants: Seasonality was examined using cosinor analysis among middle-aged (45 to 65 years) and elderly (≥65 years) participants of a population-based Dutch cohort. We analyzed 13,622 observations from 8979 participants (57.6% women) without diagnosis of diabetes and fasting glucose <7 mmol/L. BMI was measured, physical activity was evaluated using a validated questionnaire, and meteorological factors (daily mean ambient temperature, mean relative humidity, total sunlight hours, and total precipitation) were obtained from local records. Seasonality estimates were adjusted for confounders. Results: Among the middle-aged participants, seasonal variation estimates were: 0.11 units (95% confidence interval: 0.03, 0.20) for HOMA-IR, 0.28 µIU/mL (-0.05, 0.69) for insulin, and 0.05 mmol/L (0.01, 0.09) for glucose. These had a summer peak, and lifestyle markers explained the pattern. Among the elderly, seasonal variations were: 0.29 units (0.21, 0.37) for HOMA-IR, 0.96 µIU/mL (0.58, 1.28) for insulin, and 0.01 mmol/L (-0.01, 0.05) for glucose. These had a winter peak and ambient temperature explained the pattern. Conclusion: Impaired thermoregulation mechanisms could explain the winter peak of IR among elderly people without diabetes. The seasonality of lifestyle factors may explain the seasonality of glucose.
Authors: Raymond Noordam; Ashna Ramkisoensing; Nellie Y Loh; Matt J Neville; Frits R Rosendaal; Ko Willems van Dijk; Diana van Heemst; Fredrik Karpe; Constantinos Christodoulides; Sander Kooijman Journal: J Clin Endocrinol Metab Date: 2019-07-01 Impact factor: 5.958
Authors: M Arfan Ikram; Guy Brusselle; Mohsen Ghanbari; André Goedegebure; M Kamran Ikram; Maryam Kavousi; Brenda C T Kieboom; Caroline C W Klaver; Robert J de Knegt; Annemarie I Luik; Tamar E C Nijsten; Robin P Peeters; Frank J A van Rooij; Bruno H Stricker; André G Uitterlinden; Meike W Vernooij; Trudy Voortman Journal: Eur J Epidemiol Date: 2020-05-04 Impact factor: 8.082