Mylène Aubertin-Leheudre1, Stephen Anton2, Daniel P Beavers3, Todd M Manini2, Roger Fielding4, Ann Newman5, Tim Church6, Stephen B Kritchevsky7, David Conroy8, Mary M McDermott9, Anda Botoseneanu10, Michelle E Hauser11, Marco Pahor2. 1. Department of Aging and Geriatric Research, University of Florida, Gainesville, FL; Department of Sciences of Physical Activity, Université du Québec à Montréal, CRIUGM, Montréal, Québec, Canada. Electronic address: aubertin.mylene@gmail.com. 2. Department of Aging and Geriatric Research, University of Florida, Gainesville, FL. 3. Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, NC. 4. Nutrition, Exercise Physiology, and Sarcopenia Laboratory, Jean Mayer USDA Human Nutrition Research Center on Aging, Tufts University, Boston, MA. 5. Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA. 6. Preventive Medicine Laboratory, Pennington Biomedical Research Center, Baton Rouge, LA. 7. Sticht Center on Aging, School of Medicine, Wake Forest University, Winston-Salem, NC. 8. Department of Kinesiology, Pennsylvania State University, University Park, PA. 9. Department of Internal Medicine, Northwestern Medical Faculty Foundation, Chicago, IL. 10. Institute of Gerontology, University of Michingan, Ann Arbor, MI. 11. Stanford Prevention Research Center, Stanford University, Palo Alto, CA.
Abstract
OBJECTIVE: The purpose of this study was to examine the relationship between dynapenia and metabolic risk factors in obese and nonobese older adults. METHODS: A total of 1453 men and women (age ≥70 years) from the Lifestyle Interventions and Independence for Elders (LIFE) Study were categorized as (1) nondynapenic/nonobese (NDYN-NO), (2) dynapenic/nonobese (DYN-NO), (3) nondynapenic/obese (NDYN-O), or (4) dynapenic/obese (DYN-O), based on muscle strength (Foundation for the National Institute of Health criteria) and body mass index. Dependent variables were blood lipids, fasting glucose, blood pressure, presence of at least 3 metabolic syndrome (MetS) criteria, and other chronic conditions. RESULTS: A significantly higher likelihood of having abdominal obesity criteria in NDYN-NO compared with DYN-NO groups (55.6 vs 45.1%, P ≤ .01) was observed. Waist circumference also was significantly higher in obese groups (DYN-O = 114.0 ± 12.9 and NDYN-O = 111.2 ± 13.1) than in nonobese (NDYN-NO = 93.1 ± 10.7 and DYN-NO = 92.2 ± 11.2, P ≤ .01); and higher in NDYN-O compared with DYN-O (P = .008). Additionally, NDYN-O demonstrated higher diastolic blood pressure compared with DYN-O (70.9 ± 10.1 vs 67.7 ± 9.7, P ≤ .001). No significant differences were found across dynapenia and obesity status for all other metabolic components (P > .05). The odds of having MetS or its individual components were similar in obese and nonobese, combined or not with dynapenia (nonsignificant odds ratio [95% confidence interval]). CONCLUSION: Nonobese dynapenic older adults had fewer metabolic disease risk factors than nonobese and nondynapenic older adults. Moreover, among obese older adults, dynapenia was associated with lower risk of meeting MetS criteria for waist circumference and diastolic blood pressure. Additionally, the presence of dynapenia did not increase cardiometabolic disease risk in either obese or nonobese older adults.
OBJECTIVE: The purpose of this study was to examine the relationship between dynapenia and metabolic risk factors in obese and nonobese older adults. METHODS: A total of 1453 men and women (age ≥70 years) from the Lifestyle Interventions and Independence for Elders (LIFE) Study were categorized as (1) nondynapenic/nonobese (NDYN-NO), (2) dynapenic/nonobese (DYN-NO), (3) nondynapenic/obese (NDYN-O), or (4) dynapenic/obese (DYN-O), based on muscle strength (Foundation for the National Institute of Health criteria) and body mass index. Dependent variables were blood lipids, fasting glucose, blood pressure, presence of at least 3 metabolic syndrome (MetS) criteria, and other chronic conditions. RESULTS: A significantly higher likelihood of having abdominal obesity criteria in NDYN-NO compared with DYN-NO groups (55.6 vs 45.1%, P ≤ .01) was observed. Waist circumference also was significantly higher in obese groups (DYN-O = 114.0 ± 12.9 and NDYN-O = 111.2 ± 13.1) than in nonobese (NDYN-NO = 93.1 ± 10.7 and DYN-NO = 92.2 ± 11.2, P ≤ .01); and higher in NDYN-O compared with DYN-O (P = .008). Additionally, NDYN-O demonstrated higher diastolic blood pressure compared with DYN-O (70.9 ± 10.1 vs 67.7 ± 9.7, P ≤ .001). No significant differences were found across dynapenia and obesity status for all other metabolic components (P > .05). The odds of having MetS or its individual components were similar in obese and nonobese, combined or not with dynapenia (nonsignificant odds ratio [95% confidence interval]). CONCLUSION: Nonobese dynapenic older adults had fewer metabolic disease risk factors than nonobese and nondynapenic older adults. Moreover, among obese older adults, dynapenia was associated with lower risk of meeting MetS criteria for waist circumference and diastolic blood pressure. Additionally, the presence of dynapenia did not increase cardiometabolic disease risk in either obese or nonobese older adults.
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