ArticleLipids in health and disease2025
Longitudinal changes and patterns in cardiometabolic index and the natural course of prediabetes in the China health and retirement longitudinal study.
Article in Lipids in health and disease, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Abstract
backgroundPrediabetes is one of the most common metabolic disorders in the aging process. This study aims to investigate the longitudinal changes in the Cardiometabolic Index (CMI) and their relationship with the natural course of prediabetes in middle-aged and elderly populations.
methodsThis study used longitudinal data from the China Health and Retirement Longitudinal Study. The natural course of prediabetes was used to describe the trend in glycemic development during follow-up, defined as progression to diabetes or regression to normoglycaemia. Longitudinal changes in CMI were categorized into CMI transition patterns (consistently-low, low-to-high, high-to-low, and consistently-high) and cumulative CMI (CumCMI) exposure. CumCMI was calculated as the ratio of the mean CMI values measured during the longitudinal surveys to the total duration of exposure.
resultsAccording to the inclusion and exclusion criteria, a total of 2,544 prediabetic participants from the China Health and Retirement Longitudinal Study cohort were included in the study. During a median follow-up of 3 years, the rates of progression and regression of prediabetes were as follows in the consistently-low, low-to-high, high-to-low, and consistently-high CMI pattern groups: 9.94%, 16.55%, 11.72%, 20.32% for progression; and 24.97%, 22.37%, 23.81%, 20.42% for regression, respectively. Regarding prediabetes progression, our results found that a high baseline CMI level and high CumCMI exposure during follow-up significantly increased the risk of developing diabetes in prediabetic patients. Furthermore, during follow-up, compared to the low-to-high CMI pattern group, the consistently-low CMI pattern was protective for prediabetic patients. Concerning prediabetes regression, we only observed a negative correlation between baseline CMI and follow-up CumCMI exposure with outcomes in the elderly (age ≥ 60 years). Specifically, high baseline CMI levels and high follow-up CumCMI exposure significantly hindered prediabetes regression in the elderly.
conclusionIn this prospective cohort study of middle-aged and elderly populations, we found that longitudinal changes in CMI were associated with the progression and regression of prediabetes. High CumCMI exposure during follow-up significantly increased the risk of diabetes events and hindered the recovery of normoglycaemia in the elderly. Moreover, maintaining a consistently-low CMI pattern during follow-up reduced the risk of diabetes in prediabetic patients.
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