Yingting Zuo1,2, Anxin Wang3,4, Shuohua Chen5, Xue Tian1,2, Shouling Wu5, Yan He1,2. 1. Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China. 2. Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China. 3. Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China. 4. China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China. 5. Department of Cardiology, Kailuan Hospital, North China University of Science and Technology, Tangshan, China.
Abstract
BACKGROUND: The relationship between estimated glomerular filtration rate (eGFR) trajectories and myocardial infarction (MI) has so far been unclear in people with diabetes or prediabetes. We aimed to identify common eGFR trajectories in people with diabetes or prediabetes and to examine their association with MI risk. METHODS: The data of this analysis were derived from the Kailuan study, which was a prospective community-based cohort study. The eGFR trajectories of 24 723 participants from the year 2006 to 2012 were generated by latent mixture modeling. Cox proportional hazards models were used to calculate hazard ratios (HR) and their 95% CI for the subsequent risk of MI of different eGFR trajectories. RESULTS: We identified five distinct eGFR trajectories during 2006 to 2012 and named them according to their eGFR range and pattern over time: low-stable (9.4%), moderate-stable (31.4%), moderate-increasing (29.5%), high-decreasing (13.9%), and high-stable (15.8%). During a mean follow-up of 4.61 years, there were a total of 235 incident MI. Although the high-decreasing group had similar eGFR levels to the moderate-stable group during the last exposure period, the risk was much higher (adjusted HR, 3.57; 95% CI, 1.63-7.85 vs adjusted HR, 2.88; 95% CI, 1.36-6.08). Notably, the moderate-increasing group had reached the normal range, but still had a significantly increased risk (adjusted HR, 2.63; 95% CI, 1.24-5.55). CONCLUSIONS: eGFR trajectories were associated with MI risk in people with diabetes or prediabetes. These observations suggest that long-term trajectories of eGFR may be important for risk prediction of MI and should be highlighted in primary prevention.
BACKGROUND: The relationship between estimated glomerular filtration rate (eGFR) trajectories and myocardial infarction (MI) has so far been unclear in people with diabetes or prediabetes. We aimed to identify common eGFR trajectories in people with diabetes or prediabetes and to examine their association with MI risk. METHODS: The data of this analysis were derived from the Kailuan study, which was a prospective community-based cohort study. The eGFR trajectories of 24 723 participants from the year 2006 to 2012 were generated by latent mixture modeling. Cox proportional hazards models were used to calculate hazard ratios (HR) and their 95% CI for the subsequent risk of MI of different eGFR trajectories. RESULTS: We identified five distinct eGFR trajectories during 2006 to 2012 and named them according to their eGFR range and pattern over time: low-stable (9.4%), moderate-stable (31.4%), moderate-increasing (29.5%), high-decreasing (13.9%), and high-stable (15.8%). During a mean follow-up of 4.61 years, there were a total of 235 incident MI. Although the high-decreasing group had similar eGFR levels to the moderate-stable group during the last exposure period, the risk was much higher (adjusted HR, 3.57; 95% CI, 1.63-7.85 vs adjusted HR, 2.88; 95% CI, 1.36-6.08). Notably, the moderate-increasing group had reached the normal range, but still had a significantly increased risk (adjusted HR, 2.63; 95% CI, 1.24-5.55). CONCLUSIONS: eGFR trajectories were associated with MI risk in people with diabetes or prediabetes. These observations suggest that long-term trajectories of eGFR may be important for risk prediction of MI and should be highlighted in primary prevention.
Authors: Yuan Wang; Eric Yuk Fai Wan; Ivy Lynn Mak; Margaret Kay Ho; Weng Yee Chin; Esther Yee Tak Yu; Cindy Lo Kuen Lam Journal: PLoS One Date: 2022-01-27 Impact factor: 3.240