Metabolic factors associated with incident fracture among older adults with type 2 diabetes mellitus: a nested case-control study.
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Older adults with type 2 diabetes mellitus have an increased risk of fracture despite a paradoxically higher average bone mineral density. This study identified additional markers of fracture risk in this at-risk population. Non-esterified fatty acids and the amino acids glutamine/glutamate and asparagine/aspartate were associated with incident fractures.
PurposeType 2 diabetes mellitus (T2D) is associated with an increased risk of fracture despite a paradoxically higher bone mineral density. Additional markers of fracture risk are needed to identify at-risk individuals.
MethodThe MURDOCK study is an ongoing study, initiated in 2007, of residents in central North Carolina. At enrollment, participants completed health questionnaires and provided biospecimen samples. In this nested case-control analysis, incident fractures among adults with T2D, age ≥ 50 years, were identified by self-report and electronic medical record query. Fracture cases were matched 1:2 by age, gender, race/ethnicity, and BMI to those without incident fracture. Stored sera were analyzed for conventional metabolites and targeted metabolomics (amino acids and acylcarnitines). The association between incident fracture and metabolic profile was assessed using conditional logistic regression, controlled for multiple confounders including tobacco and alcohol use, medical comorbidities, and medications.
Results107 incident fractures were identified with 210 matched controls. Targeted metabolomics analysis included 2 amino acid factors, consisting of: 1) the branched chain amino acids, phenylalanine and tyrosine; and 2) glutamine/glutamate, asparagine/aspartate, arginine, and serine [E/QD/NRS]. After controlling for multiple risk factors, E/QD/NRS was significantly associated with incident fracture (OR 2.50, 95% CI: 1.36-4.63). Non-esterified fatty acids were associated with lower odds of fracture (OR 0.17, 95% CI: 0.03-0.87). There were no associations with fracture among other conventional metabolites, acylcarnitine factors, nor the other amino acid factors.
ConclusionOur results indicate novel biomarkers, and suggest potential mechanisms, of fracture risk among older adults with T2D.
Published Version (Please cite this version)
Lee, Richard H, James Bain, Michael Muehlbauer, Olga Ilkayeva, Carl Pieper, Doug Wixted and Cathleen Colón-Emeric (2023). Metabolic factors associated with incident fracture among older adults with type 2 diabetes mellitus: a nested case-control study. Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA, 34(7). pp. 1263–1268. 10.1007/s00198-023-06763-1 Retrieved from https://hdl.handle.net/10161/29452.
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Olga Ilkayeva, Ph.D., is the Director of the Metabolomics Core Laboratory at Duke Molecular Physiology Institute. She received her Ph.D. training in Cell Regulation from UT Southwestern Medical Center at Dallas, TX. Her postdoctoral research in the laboratory of Dr. Chris Newgard at Duke University Medical Center focused on lipid metabolism and regulation of insulin secretion. As a research scientist at the Stedman Nutrition and Metabolism Center, Dr. Ilkayeva expanded her studies to include the development of targeted mass spectrometry analyses. Currently, she works on developing and validating quantitative mass spectrometry methods used for metabolic profiling of various biological models with emphasis on diabetes, obesity, cardiovascular disease, and the role of gut microbiome in both health and disease.
1) Issues in the Design of Medical Experiments: I explore the use of reliability/generalizability models in experimental design. In addition to incorporation of reliability, I study powering longitudinal trials with multiple outcomes and substantial missing data using Mixed models.
2) Issues in the Analysis of Repeated Measures Designs & Longitudinal Data: Use of Hierarchical Linear Models (HLM) or Mixed Models in modeling trajectories of multiple variables over time (e.g., physical and cognitive functioning and Blood Pressure). My current work involves methodologies in simultaneous estimation of trajectories for multiple variables within and between domains, modeling co-occuring change.
Areas of Substantive interest: (1) Experimental design and analysis in gerontology and geriatrics, and psychiatry,
(2) Multivariate repeated measures designs,
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