Blood circulating proteins are confounded readouts of the biological processes that occur in different tissues and organs. Many proteins have been linked to complex disorders and are also under substantial genetic control. Here, we investigate the associations between over 1000 blood circulating proteins and body mass index (BMI) in three studies including over 4600 participants. We show that BMI is associated with widespread changes in the plasma proteome. We observe 152 replicated protein associations with BMI. 24 proteins also associate with a genome-wide polygenic score (GPS) for BMI. These proteins are involved in lipid metabolism and inflammatory pathways impacting clinically relevant pathways of adiposity. Mendelian randomization suggests a bi-directional causal relationship of BMI with LEPR/LEP, IGFBP1, and WFIKKN2, a protein-to-BMI relationship for AGER, DPT, and CTSA, and a BMI-to-protein relationship for another 21 proteins. Combined with animal model and tissue-specific gene expression data, our findings suggest potential therapeutic targets further elucidating the role of these proteins in obesity associated pathologies.

DNA methylation and blood circulating proteins have been associated with many complex disorders, but the underlying disease-causing mechanisms often remain unclear. Here, we report an epigenome-wide association study of 1123 proteins from 944 participants of the KORA population study and replication in a multi-ethnic cohort of 344 individuals. We identify 98 CpG-protein associations (pQTMs) at a stringent Bonferroni level of significance. Overlapping associations with transcriptomics, metabolomics, and clinical endpoints suggest implication of processes related to chronic low-grade inflammation, including a network involving methylation of NLRC5, a regulator of the inflammasome, and associated pQTMs implicating key proteins of the immune system, such as CD48, CD163, CXCL10, CXCL11, LAG3, FCGR3B, and B2M. Our study links DNA methylation to disease endpoints via intermediate proteomics phenotypes and identifies correlative networks that may eventually be targeted in a personalized approach of chronic low-grade inflammation.

Georgakis et al. Circulating monocyte chemoattractant protein-1 and risk of stroke: a meta-analysis of population-based studies involving 17,180 individuals. Appendix I. Search strategy. Online Table I. Summary of the study design, population characteristics, methods used for quantifying circulating MCP-1 levels, stroke outcome definitions, and assessments in the cohorts included in the meta-analysis. Online Table II. Quality characteristics of the included studies according to the Newcastle-Ottawa Scale. Online Table III. Associations between baseline circulating MCP-1 levels and risk of any stroke. Shown are the results from random-effects meta-analyses across the different models in the pooled sample consisting of six population-based studies. Online Table IV. Associations between baseline circulating MCP-1 levels and risk of ischemic stroke. Shown are the results from random-effects meta-analyses across the different models in the pooled sample consisting of six population-based studies. Online Table V. Associations between baseline circulating MCP-1 levels and risk of hemorrhagic stroke. Shown are the results from random-effects meta-analyses across the different models in the pooled sample consisting of six population-based studies. Online Table VI. Meta-regression analyses for the effect of different study characteristics on the association between ln-transformed MCP-1 circulating levels at baseline (1 SD increment) with any stroke and etiological stroke subtypes (ischemic and hemorrhagic stroke). Online Table VII. Associations between baseline circulating hsCRP, IL-6, and MCP-1 levels and risk of any stroke, ischemic stroke, and hemorrhagic stroke. Shown are the results from random-effects metaanalyses of the pooled sample consisting of four population-based studies, where both hsCRP and IL-6 levels were available. Online Figure I. Flowchart of the study selection for the systematic review. Online Figure II. Study-specific and pooled hazard ratios for incident any stroke per standard deviation increase in ln-transformed circulating MCP-1 levels and across MCP-1 level quartiles. Shown are the results from random-effects meta-analyses. Online Figure III. Study-specific and pooled hazard ratios for incident ischemic stroke per standard deviation increase in ln-transformed circulating MCP-1 levels and across MCP-1 level quartiles. Shown are the results from random-effects meta-analyses (Model 2). Online Figure IV. Study-specific and pooled hazard ratios for incident hemorrhagic stroke per standard deviation increase in ln-transformed circulating MCP-1 levels and across MCP-1 level quartiles. Shown are the results from random-effects meta-analyses (Model 2). Online Figure V. Pooled hazard ratios for incident fatal and non-fatal stroke per circulating MCP-1 levels, as derived from random-effects meta-analyses (Model 2). Online Figure VI. Pooled hazard ratios for incident any stroke per standard deviation increase in lntransformed circulating MCP-1 levels and across MCP-1 level quartiles in sensitivity analyses ...