Life’s Essential 8 cardiovascular health, cardiovascular-kidney-metabolic syndrome stages, and incident cardiovascular events: a nationwide 10-year prospective cohort study in China | Cardiovascular Diabetology

Study design and population

The prospective China Cardiometabolic Disease and Cancer Cohort (4C) Study is a multicenter, population-based, prospective cohort study designed to investigate the risk factors of cardiovascular, cancer, and all-cause mortality in the Chinese population. The objectives and design have been described in detail in previous publications [16]. Briefly, the baseline survey was conducted in 2011–2012, and 193,846 participants aged 40 years or older were recruited from 20 communities covering 16 provinces in mainland China. During a median follow-up time of 10.1 years, 4430 (2.3%) participants were lost to follow-up due to incomplete death surveillance coverage in certain regions and 26,057 participants were further excluded due to incomplete information on CVD events during the follow-up period. Finally, 100,727 participants with complete baseline data to define CKM stage and LE 8 CVH score were included in the current analysis (eFigure 1 in the Supplement). Baseline key characteristics between the participants recruited and those excluded due to missing data were generally similar (eTable 1 in the Supplement). The protocol of 4C Study was approved by the Ethical Review Committee of Ruijin Hospital. Written informed consent was obtained from all participants.

Measurements

Data collection was conducted face-to-face at community clinics at baseline and follow-up visits. Baseline characteristics, including sociodemographic information, lifestyle habits, and medical history, were assessed by a standard questionnaire. Skilled nurses measured height, weight, systolic blood pressure (SBP), and diastolic blood pressure (DBP) according to standard protocols. Blood samples were collected after overnight fasting. Then, a standard oral glucose tolerance test (OGTT) was conducted, and 2-hour post-load blood specimens were collected. Fasting and 2-hour post-load plasma glucose were measured at local hospitals. Blood specimens and first-morning spot urine samples were collected and aliquoted into 0.5-mL Eppendorf tubes within 2 h of collection and shipped in dry ice to the central laboratory of the study located at Shanghai Institute of Endocrine and Metabolic Diseases, which is accredited by the College of American Pathologists. At the central laboratory, glycated hemoglobin (HbA1c) was determined using finger capillary whole blood by high-performance liquid chromatography (VARIANT™ II Systems, BIO-RAD, Hercules, CA, USA), serum lipids profiles and creatinine were measured by an autoanalyzer (Abbott Laboratories, IL, USA), and urinary albumin (immunonephelometry using Siemens BNII and BN ProSpec nephelometers [Siemens Healthcare Diagnostics, Marburg, Germany]) and urinary creatinine concentrations was also tested (by enzymatic method [ADVIA Chemistry XPT System; Siemens Healthcare, Erlangen, Germany]) for calculation of Urinary albumin-to-creatinine ratio (ACR). Estimated glomerular filtration rate (eGFR) was calculated based on age, sex, race, and centrally measured serum creatinine using the Chronic Kidney Disease Epidemiology Collaboration equation (CKD-EPI 2009) for Asian individuals [17].

Definition of LE 8 CVH score and status

According to the definition proposed by AHA in 2022 [10], we scored the CVH score based on 8 components, including 2 domains of health behaviors (nicotine exposure, diet, physical activity, sleep) and health factors (BMI, blood lipids, blood glucose, and blood pressure). Health behavior information was collected by a standardized questionnaire. Health factors metrics were measured in the study center (BMI, blood pressure, and plasma glucose level) or central laboratory (blood lipids and HbA1c). Detailed methods and scoring criteria of CVH were presented in eTable 2 in the Supplement. The overall CVH score was calculated as the average of 8 component metric scores. Participants with high CVH scores of 80–100 were considered optimal CVH, while moderate CVH referred to 50–79 and low CVH of 0–49. For each CVH domain/metric, participants with a score of 80–100 were considered to have a corresponding optimal CVH domain/metric, while those with a score of 0–79 were regarded as suboptimal.

Definition of CKM stages

According to the definition proposed by AHA in 2023 [7, 8], we defined CKM syndrome as 5 stages: Stage 0: absence of any CKM risk factors; Stage 1: having excess body weight, abdominal obesity, or dysfunctional adipose tissue (clinically manifest as prediabetes), without the presence of other metabolic risk factors or CKD; Stage 2: presence of metabolic risk factors or moderate- to high-risk CKD; Stage 3: presence of subclinical CVD or its risk equivalents; Stage 4: clinical CVD including coronary heart disease, myocadiac infarction, stroke or peripheral artery disease. Detailed methods and staging rationales were presented in eTable 3 in the Supplement.

Follow-up and outcome assessment

Morbidity and mortality data until November 30, 2021 were obtained through linkage using the unique identification number of each participant to the local death registries of the National Disease Surveillance Point System, the registries of cardiovascular diseases, and the National Health Insurance System. Two members of the outcome adjudication committee, who were blinded to the baseline characteristics, independently verified each clinical event. Discrepancies were adjudicated through discussion involving other members of the committee. Incident CVD was defined as a composite of non-fatal myocardial infarction (I21–I22), non-fatal stroke (I60–I64, I69), and cardiovascular death (I00–I99).

Statistical analyses

Baseline characteristics of participants were summarized as mean ± standard deviation (SD), or number of participants and percentage, or median with interquartile range. The distributions of CKM stages were visualized by density ridgeline plot of overall CVH score, health behavior score, and health factor score range, respectively. The Fine-Gray hazard model was used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) of CKM stages or CVH status associated with incident CVD events, considering the competing risk of non-CVD death. A multivariable model was utilized to control potential confounding factors with adjustments for age, sex, and education duration based on biological plausibility. When analyzing the association between CVH domains/metrics and CVD events, we further adjusted for all other CVH metrics simultaneously. We also analyzed the combination association of CKM stages and different CVH strata (High [optimal] versus low and median) or different number of optimal CVH metrics (≥ 6, 5, 4, 3, < 2) with CVD events. We used cubic spline analysis to explore the association between continuous CVH score and incident CVD events among populations with different CKM stages. Average population attributable fractions (PAFs) of each suboptimal CVH metric, as well as clusters of CVH behavior and factor domains, were calculated using the approach described by Eide and Gefeller and implemented in the “averisk” R package [18, 19], to quantify the proportional reduction in disease prevalence that would be achieved if the risk factor is theoretically removed from the population. PAFs were calculated with adjustments for age, sex, education duration, and 8 individual-level CVH metrics simultaneously.

The main analysis was conducted in the unimputed dataset with complete information on key exposures and outcomes, and several sensitivity analyses were performed: (1) The main analysis separately in women and men (eTables 4 and 5 in the Supplement). (2) The main analysis among participants without diagnosed cancer at baseline (eTable 6 in the Supplement). (3) The main analysis after excluding participants who underwent CVD events or died within the first 2 years of observation time (eTable 7 in the Supplement). (4) The main analysis for non-fatal and fatal CVD events, respectively (eTables 8 and 9 in the Supplement). (5) The main analysis separately in participants aged < 60 years and ≥ 60 years, respectively (eTables 10–12 in the Supplement). (6) The analysis for CKM stages and the association with incident CVD events after excluding participants aged 80 years or older (eTable 13 in the Supplement). The primary analyses were conducted among participants without missing data, and the validity of the findings was tested in multiple imputation data sets imputed for missing baseline information and outcome, using PROC MI in SAS by fully conditional specification (FCS) method which assumed missing at random (MAR). (eTables 14–17 in the Supplement).

We used SAS software (version 9.4), and R software (version 4.1.1) for statistical analyses. All reported P values are nominal. Statistical significance was a two-tailed P < 0.05.