Association between the trajectory of ideal cardiovascular health metrics and incident chronic kidney disease among 27,635 older adults in northern China-a prospective cohort study

• Published in: BMC geriatrics 2024-02, Vol.24 (1), p.193-9, Article 193
• Main Authors: Bai, Pufei, Shao, Xian, Ning, Xiaoqun, Jiang, Xi, Liu, Hongyan, Lin, Yao, Hou, Fang, Zhang, Yourui, Zhou, Saijun, Yu, Pei
• Format: Article
• Language: English
• Subjects: Aged; Blood pressure; Body mass index; Cardiovascular diseases; Cardiovascular Diseases - diagnosis; Cardiovascular Diseases - epidemiology; Cardiovascular Diseases - prevention & control; Cardiovascular health metrics; Care and treatment; China - epidemiology; Cholesterol; Chronic kidney disease; Chronic kidney failure; Cohort analysis; Cohort Studies; Confounding (Statistics); Demographic aspects; Development and progression; Exercise; Glucose; Group-based trajectory model; Health aspects; Health behavior; Health Status; Humans; Kidney diseases; Life style; Middle Aged; Older people; Physical fitness; Prevention; Prospective cohort study; Prospective Studies; Quality Indicators, Health Care; Questionnaires; Renal Insufficiency, Chronic - diagnosis; Renal Insufficiency, Chronic - epidemiology; Risk Factors; Sensitivity analysis; Urine; Variables
• ISSN: 1471-2318; 1471-2318
• DOI: 10.1186/s12877-024-04760-5

There is a lack of relevant studies evaluating the long-term impact of cardiovascular health factor (CVH) metrics on chronic kidney disease (CKD). This study investigates the long-term change in CVH metrics in older people and explores the relationship between CVH metrics trajectory and CKD. In total, 27,635 older people aged over 60 from the community-based Tianjin Chronic Kidney Disease Cohort study were enrolled. The participants completed five annual physical examinations between January 01, 2014, and December 31, 2018, and a subsequent follow-up between January 01, 2019, and December 31, 2021. CVH metrics trajectories were established by the group-based trajectory model to predict CKD risk. The relationships between baseline CVH, CVH change (ΔCVH), and CKD risk were also explored by logistic regression and restricted cubic spline regression model. In addition, likelihood ratio tests were used to compare the goodness of fit of the different models. Six distinct CVH metrics trajectories were identified among the participants: low-stable (11.19%), low-medium-stable (30.58%), medium-stable (30.54%), medium-high-decreased (5.46%), medium-high-stable (18.93%), and high-stable (3.25%). After adjustment for potential confounders, higher CVH metrics trajectory was associated with decreased risk of CKD (P for trend