Cardiometabolic multimorbidity (CMM) is an increasing burden in ageing populations. Cardiovascular-kidney-metabolic (CKM) stages 0–3 provide an upstream framework for identifying metabolic, renal, and cardiovascular vulnerability before overt multimorbidity. We evaluated whether routinely derived metabolic–adiposity indices add useful risk-phenotype information beyond established clinical factors.
This prospective CHARLS cohort included 7,062 adults aged ≥ 45 years who were free of CMM and had CKM stages 0–3 at baseline. Twenty-nine cholesterol–high-density lipoprotein cholesterol–glucose (CHG)-derived, triglyceride–glucose (TyG)-derived, and adiposity/body-shape indices were evaluated; the main text focused on RFM, CHG-RFM, TyG-RFM, BMI, and WC. Model 3 was used to anchor the primary association interpretation, while Model 4 was retained as an extended clinical-adjustment model to assess robustness after broader adjustment. Benjamini–Hochberg correction, inter-index correlations, paired CHG–TyG models, proportional-hazards diagnostics, prediction analyses, and sensitivity analyses were performed.
Among 7,062 participants, 613 developed incident CMM (8.7%). In Model 3, the per-SD HRs (95% CIs) were 1.85 (1.52–2.25) for RFM, 1.72 (1.47–2.03) for CHG-RFM, 1.67 (1.41–1.98) for TyG-RFM, 1.30 (1.20–1.41) for BMI, and 1.34 (1.23–1.46) for WC. The corresponding estimates remained broadly consistent in Model 4. After Benjamini–Hochberg correction, 28 of 29 Model 3 per-SD associations remained supported; ABSI was the exception. Correlations were substantial among structurally related indices, and paired CHG–TyG models showed marked mutual attenuation. The clinical baseline AUC was 0.787, whereas selected index-extended models achieved AUCs of approximately 0.799–0.800, indicating modest absolute improvement.
Selected routine metabolic–adiposity indices were associated with incident CMM among adults with CKM stages 0–3. RFM-related indices showed stronger association signals, whereas BMI and WC remain more accessible measures. The broadly consistent Model 3 and Model 4 estimates supported the overall association pattern. However, because inter-index overlap was substantial and incremental predictive gains were modest, these indices should be viewed as candidate adjunctive risk markers rather than independent biomarkers or clinically validated decision tools.
Zhenyu Geng, Yawei Rong, Jiahao Liu et al.· BMC Cardiovascular Disorders· 0 citations
Background Coronary atherosclerosis (CA) is a leading cause of cardiovascular morbidity and mortality worldwide. This study is aimed at identifying candidate plasma proteins and potential therapeutic targets for CA. Methods We performed a proteome‐wide Mendelian randomization (MR) analysis using integrated protein quantitative trait loci (pQTLs) and genome‐wide association study (GWAS) summary data. Forward two‐sample MR was first performed using cis‐pQTLs from the UK Biobank Pharma Proteomics Project (UKB‐PPP), followed by reverse MR analysis to exclude potential reverse causality. Bayesian colocalization analysis was conducted to ensure that the associations between proteins and CA were driven by shared genetic variants. Summary‐data‐based MR (SMR) combined with HEIDI testing was used to prioritize proteins and eliminate linkage bias. Significant proteins were cross‐referenced with a curated druggable genome to identify their potential therapeutic relevance. Cross‐platform validation was performed using the SomaScan‐based pQTL dataset from deCODE genetics. An oxidized low‐density lipoprotein (ox‐LDL)–induced human umbilical vein endothelial cell (HUVEC) injury model was used for the evaluation of prioritized proteins. Results Forward MR analysis using UKB‐PPP cis‐pQTL data identified 45 CA‐associated proteins (23 protective, 22 risk‐related; FDR < 0.05), with no reverse causality observed. Five proteins—PARP1, SDCCAG8, FST, FOLH1, and NCAN—were prioritized through MR (FDR < 0.05), colocalization analysis (PP.H4 > 0.50), and SMR analysis with HEIDI filtering (p_SMR < 0.05; p_HEIDI > 0.05). All five proteins were included in the druggable genome list, supporting their potential therapeutic relevance. PARP1 showed consistent associations across Olink and SomaScan platforms and was upregulated in an ox‐LDL‐induced HUVEC model as assessed by western blot. Conclusions This study identified PARP1, SDCCAG8, FST, FOLH1, and NCAN as genetically prioritized candidate proteins for CA, with PARP1 showing the most consistent evidence across analyses. Further validation and mechanistic studies are warranted.
Da Gao, Hai-Yan Lin, Sheng-Jie Wang et al.· Cardiovascular Therapeutics· 0 citations
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