The overview of analytical approaches and key findings were presented in figure 1. The literature search generated 14 932 records, and 85 studies were included in the final analysis, involving 50 prospective observational studies, 25 MR studies and 10 studies performing both observational and MR analyses (online supplemental figure S1). The characteristics of included studies were summarised in online supplemental table S2. For a full reference list, see online supplemental file 1.

A total of 60 studies examined the associations between proteins and incident CMDs, reporting results for 3788 protein-disease pairs. 2318 pairs with two or more reports were included in meta-analysis. Of these, the associations of 748 pairs remained significant in meta-analysis (figures 2 and 3A). The number of proteins included in each stage is summarised by diseases in figure 2. Among all stroke subtypes, only incident ischaemic stroke (IS) was investigated and included in meta-analysis. Moderate heterogeneity was observed for observational pooled results, and 45.8% pairs had I2≥80%. Detailed effect estimates of meta-analysis specific for each disease were summarised in online supplemental tables S3–S8.

Summary of proteins in observational and MR studies. The number of proteins in observational and MR pooled results following three steps (ie, reported by original studies, included in meta-analysis and significantly associated with cardiometabolic disease (CMD) in observational meta-analysis or with tier 1 or 2 evidence in MR pooled results), and comparison of proteins significant in observational meta-analysis and tier 1 or 2 proteins in MR pooled results. Consistent proteins denote protein biomarkers showing observational and genetic associations in the same direction, while inconsistent proteins denote protein biomarkers showing opposite associations in observational and genetic analyses. AF, atrial fibrillation; CHD, coronary heart disease; HF, heart failure; IS, ischaemic stroke; MR, Mendelian randomisation study; OB, observational study; T2D, type 2 diabetes.

Associations between proteins and cardiometabolic diseases (CMDs) in (A) observational meta-analysis and (B) Mendelian randomisation (MR) pooled results. Names were given for top 20 proteins with the lowest p value. AF, atrial fibrillation; CHD, coronary heart disease; HF, heart failure; IS, ischaemic stroke; T2D, type 2 diabetes.

In our pooled results, 133 proteins were associated with risk of two or more CMDs, referred to as ‘pleiotropic protein’ (figure 4). These included 94 proteins associated with 2 diseases, 27 proteins with 3 diseases, 9 proteins (FABP4, IBP2, IL6, MMP12, ANFB, TNR1B, TR10B, UPAR, HGF) with 4 diseases and 3 proteins (GDF15, HAVR1, MMP7) with 5 diseases. The directions and strengths of associations between single protein and different diseases differed. 111 showed directionally concordant associations with all disease types, including positive associations for 83 proteins and inverse associations for 28 proteins. In contrast, 22 proteins showed opposite associations with different diseases (ie, positive associations with some and inverse associations with the others).

Pleiotropy of proteins in observational pooled results. Grey colour denotes that the protein-disease pair was not available for meta-analysis. AF, atrial fibrillation; CHD, coronary heart disease; HF, heart failure; IS, ischaemic stroke; RR, relative risk; T2D, type 2 diabetes.

The evaluation of MR evidence included 35 studies assessing circulating proteins as possible causal biomarkers for CMDs, with 10 531 protein-disease pairs reported and 1614 pairs eligible for meta-analysis. Different from the observational studies, the genetic associations between proteins and six stroke subtypes were investigated, including total stroke, IS, large artery stroke (LAS), cardioembolic stroke (CES), small vessel stroke, haemorrhagic stroke (HS) and subarachnoid haemorrhage. The certainty of evidence derived from MR studies was divided into four tiers, and 245 proteins were graded as tier 1 and tier 2 (figures 1 and 2 and online supplemental figure S2). Moderate heterogeneity was observed for MR pooled results, and 14.2% pairs had I2≥80%. Detailed effect estimates for each disease were summarised in online supplemental tables S9–S21.

When comparing the observational and genetic associations in the same study, 39 of 246 protein-disease pairs (15.8%) showed consistent results (online supplemental table S22). Of 1731 protein-disease pairs investigated in both of observational and MR pooled analyses, only 22 pairs showed directionally consistent associations (ie, satisfying significant observational associations and tier 1–2 proteins on MR evidence, figure 2).

Of the 35 proteins significant in the meta-analysis of observational studies for CHD, only MMP12 was tier 1 or 2 targets in MR studies (figure 2), but the directions of associations were inconsistent with observational studies (OR 1.29; 95% CI 1.09 to 1.52; p=0.003) and MR studies (OR 0.97; 95% CI 0.94 to 1.00; p=0.022).

Within the set of 31 proteins exhibiting significance in observational results for IS, ADML and MMP12 were also identified as tier 1 or 2 targets (figure 2). ADML and MMP12 were associated with higher risk of IS in observational meta-analysis, while both of them were associated with lower risk of IS in MR studies (figure 3).

Among the 323 proteins found significant in observational studies for T2D, 15 proteins belonged to tier 1 or 2 targets (figure 2). Nine proteins showed directionally consistent associations with risk of T2D between observational and MR studies, and the remaining six proteins showed opposite associations (figure 3).

In the set of 286 HF-associated proteins identified in the meta-analysis of observational studies, the MR evidence of 27 proteins was graded as tier 1 or 2 (figure 2). The results of 12 proteins were directionally consistent in observational and MR analyses, and the results of 15 proteins were directionally opposite (figure 3).

There were 57 proteins significant in the observational results for AF, among which only three proteins were classified with tier 1 or 2 MR evidence (figure 2). SPON1 was directionally consistent (RR 1.37; 95% CI 1.11 to 1.69 in observational studies vs OR 1.08; 95% CI 1.02 to 1.15 in MR studies); the remaining two were directionally inconsistent, namely FBLN3 (RR 1.80; 95% CI 1.50 to 2.17 in observational studies vs OR 0.94; 95% CI 0.90 to 0.97 in MR studies) and LEP (RR 0.90; 95% CI 0.81 to 1.00 in observational studies vs OR 1.14; 95% CI 1.00 to 1.29 in MR studies).

Of the 16 proteins significantly associated with atherosclerosis in observational studies, only ANFB was considered as tier 1 or 2 in MR pooled results (figure 2), which was inversely associated with risk of atherosclerosis in MR studies (β, −0.006; 95% CI −0.009 to −0.003; p=4.40×10−5), but showed positive association in observational pooled results (β, 0.006; 95% CI 0.001 to 0.010; p=0.014).

Combining the associations between a single protein and various CMDs, we identified 23 tier 1 and 2 proteins associated with risk of two or more CMDs, referred to as ‘pleiotropic protein’ (online supplemental figure S2). These included 14 proteins associated with 2 diseases, 3 proteins with 3 diseases, 3 proteins (TMPS5, TNF12, TNR5) with 4 diseases, 1 protein (LPA) with 6 diseases and 2 proteins (IL6RA and MMP12) associated with 7 diseases. The directions and strengths of associations between single protein with different diseases differed. Of these 23 proteins, 18 showed directionally concordant associations with all disease types, including positive associations for eight proteins (LPA, BGAT, FGF5, HSPB1, I15RA, MMP3, NELL1, TMPS5) and inverse associations for 10 proteins (CATD, DHPR, ERAP1, FCG2A, IL6RA, MMP12, QSOX2, SCAR5, TFPI1, TNR5). In contrast, five proteins showed directionally opposite associations with different diseases (CFAI, IL1R2, MANBA, SPON1, TNF12).

Of the proteins identified as tier 1 or tier 2, there were 4, 8, 19, 85 and 12 GO biological processes identified for CHD, IS, T2D, HF and AF (p<0.05), and 9 processes were related to ≥2 diseases (online supplemental table S27). The top 20 GO biological processes (ie, terms with lowest p value) were shown in figure 6A. There were 5, 2, 6, 11 and 2 KEGG pathways identified for CHD, IS, T2D, HF and AF (p<0.05), respectively, and 4 pathways were related to ≥2 diseases (online supplemental table S28, figure 6B).

Chord diagrams of enriched in GO biological processes and KEGG pathways for cardiometabolic disease (CMD). (A) shows the top 20 GO biological processes and (B) shows significant KEGG pathways. AF, atrial fibrillation; CHD, coronary heart disease; HF, heart failure; IS, ischaemic stroke; T2D, type 2 diabetes.