Paulo H N Harada1, Olga V Demler2, Sagar B Dugani3, Akintunde O Akinkuolie2, Manickavasagar V Moorthy4, Paul M Ridker5, Nancy R Cook6, Aruna D Pradhan7, Samia Mora8. 1. Division of Preventive Medicine, Center for Lipid Metabolomics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Division of Preventive Medicine, Brigham and Women's Hospital, Harvard Medical School, Harvard Medical School, Boston, MA, USA; Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Center for Clinical and Epidemiological Research, Hospital Universitario at University of Sao Paulo, Sao Paulo, SP, Brazil. 2. Division of Preventive Medicine, Center for Lipid Metabolomics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Division of Preventive Medicine, Brigham and Women's Hospital, Harvard Medical School, Harvard Medical School, Boston, MA, USA. 3. Division of Preventive Medicine, Center for Lipid Metabolomics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Division of Preventive Medicine, Brigham and Women's Hospital, Harvard Medical School, Harvard Medical School, Boston, MA, USA; Division of Internal Medicine, St. Michael's Hospital, University of Toronto, Toronto, Canada. 4. Division of Preventive Medicine, Brigham and Women's Hospital, Harvard Medical School, Harvard Medical School, Boston, MA, USA. 5. Division of Preventive Medicine, Brigham and Women's Hospital, Harvard Medical School, Harvard Medical School, Boston, MA, USA; Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. 6. Division of Preventive Medicine, Brigham and Women's Hospital, Harvard Medical School, Harvard Medical School, Boston, MA, USA; Harvard T. H. Chan School of Public Health, Boston, MA, USA. 7. Division of Preventive Medicine, Brigham and Women's Hospital, Harvard Medical School, Harvard Medical School, Boston, MA, USA; Veterans Affairs Boston Healthcare System, Boston, MA, USA. 8. Division of Preventive Medicine, Center for Lipid Metabolomics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Division of Preventive Medicine, Brigham and Women's Hospital, Harvard Medical School, Harvard Medical School, Boston, MA, USA; Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. Electronic address: smora@bwh.harvard.edu.
Abstract
BACKGROUND: Type II diabetes (T2D) is preceded by prolonged insulin resistance and relative insulin deficiency incompletely captured by glucose metabolism parameters, high-density lipoprotein (HDL) cholesterol and triglycerides. OBJECTIVE: Whether lipoprotein insulin resistance (LPIR) score, a metabolomic marker, is associated with incident diabetes and improves risk reclassification over traditional markers on extended follow-up. METHODS: Among 25,925 nondiabetic women aged 45 years or older, LPIR was measured by nuclear magnetic resonance spectroscopy as a weighted score of very low density lipoprotein, low-density lipoprotein, and HDL particle sizes, and their subsets concentrations. We run adjusted cox regression models for LPIR with incident T2D (20.4 years median follow-up). RESULTS: Adjusting for demographics, body mass index, life style factors, blood pressure, and T2D family history, the LPIR hazard ratio for T2D (hazard ratio [HR] per standard deviation, 95% confidence interval) was 1.95 (1.85, 2.06). Further adjusting for HbA1c, C-reactive protein, triglycerides, HDL and low-density lipoprotein cholesterol, LPIR HR was attenuated to 1.41 (1.31, 1.53) and had the strongest association with T2D after HbA1C in mutually adjusted models. The association persisted even in those with optimal clinical profiles, adjusted HR per standard deviation 1.91 (1.17, 3.13). In participants deemed at intermediate T2D risk by the Framingham Offspring T2D score, LPIR led to a net reclassification of 0.145 (0.117, 0.175). CONCLUSION: In middle-aged or older healthy women followed prospectively for over 20 years, LPIR was robustly associated with incident T2D, including among those with an optimal clinical metabolic profile. LPIR improved T2D risk classification and may guide early and targeted prevention strategies.
BACKGROUND:Type II diabetes (T2D) is preceded by prolonged insulin resistance and relative insulin deficiency incompletely captured by glucose metabolism parameters, high-density lipoprotein (HDL) cholesterol and triglycerides. OBJECTIVE: Whether lipoprotein insulin resistance (LPIR) score, a metabolomic marker, is associated with incident diabetes and improves risk reclassification over traditional markers on extended follow-up. METHODS: Among 25,925 nondiabetic women aged 45 years or older, LPIR was measured by nuclear magnetic resonance spectroscopy as a weighted score of very low density lipoprotein, low-density lipoprotein, and HDL particle sizes, and their subsets concentrations. We run adjusted cox regression models for LPIR with incident T2D (20.4 years median follow-up). RESULTS: Adjusting for demographics, body mass index, life style factors, blood pressure, and T2D family history, the LPIR hazard ratio for T2D (hazard ratio [HR] per standard deviation, 95% confidence interval) was 1.95 (1.85, 2.06). Further adjusting for HbA1c, C-reactive protein, triglycerides, HDL and low-density lipoprotein cholesterol, LPIR HR was attenuated to 1.41 (1.31, 1.53) and had the strongest association with T2D after HbA1C in mutually adjusted models. The association persisted even in those with optimal clinical profiles, adjusted HR per standard deviation 1.91 (1.17, 3.13). In participants deemed at intermediate T2D risk by the Framingham Offspring T2D score, LPIR led to a net reclassification of 0.145 (0.117, 0.175). CONCLUSION: In middle-aged or older healthy women followed prospectively for over 20 years, LPIR was robustly associated with incident T2D, including among those with an optimal clinical metabolic profile. LPIR improved T2D risk classification and may guide early and targeted prevention strategies.
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