Plasma proteomic associations with genetics and health in the UK Biobank

Associations between genetically predicted concentrations of circulating inflammatory cytokines and the risk of ten pregnancy-related adverse outcomes: A two-sample Mendelian randomization study

BACKGROUND

Evidence from increasing observational studies indicates that systemic inflammation plays a role in pregnancy-related adverse events. However, the causal associations between them are largely unclear. To investigate the potential causal effects of genetically regulated concentrations of inflammatory cytokines on the risk of adverse pregnancy outcomes, we performed a Mendelian randomization (MR) analysis.

METHODS

The cis-protein quantitative trait loci for the 47 inflammatory cytokines derived from the latest genome-wide association studies (GWASs) consisting of 31,112 European individuals were used as the instrumental variables. The latest GWAS summary data for the ten adverse pregnancy events were obtained from the FinnGen project (samples ranging from 141,014 to 190,879). The inverse-variance weighted regression or Ward ratio was used as the primary MR analysis method. Sensitivity analyses based on the other five methods were performed to verify MR results. A replication MR analysis was conducted to further clarify the significant associations using data from the UK Biobank.

RESULTS

Twenty-three of the 220 associations were nominally significant (P < 0.05). Among them, seven robust associations survived the Bonferroni correction and passed sensitivity analyses, including positive associations of soluble intercellular adhesion molecule (sICAM-1) with the risk of excessive vomiting in pregnancy, preeclampsia (PE), and pregnancy hypertension (PH), vascular endothelial growth factor with the risk of medical abortion, macrophage colony-stimulating factor (MCSF) with the risk of spontaneous abortion (SA), and an inverse association of macrophage inflammatory protein-1α with the risk of medical abortion. The associations of MCSF with SA, and sICAM-1 with both PE and PH were further confirmed in the replication analysis.

CONCLUSIONS

This study provides further evidence of the role of systemic inflammation, especially endothelial dysfunction in the pathology of adverse pregnancy events, and the identified cytokines warrant in-depth research to explore their underlying mechanisms of action and to evaluate their potential as targets for disease screening, prevention, and treatment in the future.

Living Biobanks of Organoids: Valuable Resource for Translational Research

The emergence of organoids is considered a revolutionary model, changing the landscape of traditional translational research. These three-dimensional miniatures of human organs or tissues, cultivated from stem cells or biospecimens obtained from patients, faithfully replicate the structural and functional characteristics of specific target organs or tissues. In this extensive review, we explore the profound impact of organoids and assess the current state of living organoid biobanks, which are essential repositories for cryopreserving organoids derived from a variety of diseases. These resources hold significant value for translational research. We delve into the diverse origins of organoids, the underlying technologies, and their roles in recapitulating human development, disease modeling, as well as their potential applications in the pharmaceutical field. With a particular emphasis on biobanking organoids for prospective applications, we discuss how these advancements expedite the transition from bench to bedside translational research, thereby fostering personalized medicine and enriching our comprehension of human health.

Identifying behaviour-related and physiological risk factors for suicide attempts in the UK Biobank

Suicide is a global public health challenge, yet considerable uncertainty remains regarding the associations of both behaviour-related and physiological factors with suicide attempts (SA). Here we first estimated polygenic risk scores (PRS) for SA in 334,706 UK Biobank participants and conducted phenome-wide association analyses considering 2,291 factors. We identified 246 (63.07%) behaviour-related and 200 (10.41%, encompassing neuroimaging, blood and metabolic biomarkers, and proteins) physiological factors significantly associated with SA-PRS, with robust associations observed in lifestyle factors and mental health. Further case-control analyses involving 3,558 SA cases and 149,976 controls mirrored behaviour-related associations observed with SA-PRS. Moreover, Mendelian randomization analyses supported a potential causal effect of liability to 58 factors on SA, such as age at first intercourse, neuroticism, smoking, overall health rating and depression. Notably, machine-learning classification models based on behaviour-related factors exhibited high discriminative accuracy in distinguishing those with and without SA (area under the receiver operating characteristic curve 0.909 ± 0.006). This study provides comprehensive insights into diverse risk factors for SA, shedding light on potential avenues for targeted prevention and intervention strategies.

Plasma proteomic biomarkers and the association between poor cardiovascular health and incident dementia: The UK Biobank study

BACKGROUND

The study examined how plasma proteome indicators may explain the link between poor cardiovascular health (CVH) and dementia risk.

METHODS

The present study involved 28,974 UK Biobank participants aged 50-74y at baseline (2006-2010) who were followed-up for ≤ 15 y for incidence of dementia. CVH was calculated using Life's Essential 8 (LE8) total scores. The scores were standardized and reverse coded to reflect poor CVH (LE8z_rev). OLINK proteomics was available on this sample (k = 1,463 plasma proteins). The study primarily tested the mediating effects of the plasma proteome in LE8z_rev-dementia effect. The total effect was decomposed into "mediation only" or pure indirect effect (PIE), "interaction only" or interaction referent (INTREF), "neither mediation nor interaction" or controlled direct effect (CDE), and "both mediation and interaction" or mediated interaction (INTMED).

RESULTS

The study found poorer CVH assessed by LE8z_rev increased the risk of all-cause dementia by 11 % [per 1 SD, hazard ratio, (HR) = 1.11, 95 % CI: 1.03-1.20, p = 0.005). The study identified 11 plasma proteins with strong mediating effects, with GDF15 having the strongest association with dementia risk (per 1 SD, HR = 1.24, 95 % CI: 1.16, 1.33, P < 0.001 when LE8z_rev is set at its mean value) and the largest proportion mediated combining PIE and INTMED (62.6 %; 48 % of TE is PIE), followed by adrenomedullin or ADM. A first principal component with 10 top mediators (TNFRSF1A, GDF15, FSTL3, COL6A3, PLAUR, ADM, GFRAL, ACVRL1, TNFRSF6B, TGFA) mediated 53.6 % of the LE8z_rev-dementia effect. Using all the significant PIE (k = 526) proteins, we used OLINK Insight pathway analysis to identify key pathways, which revealed the involvement of the immune system, signal transduction, metabolism, disease, protein metabolism, hemostasis, membrane trafficking, extracellular matrix organization, developmental biology, and gene expression among others. STRING analysis revealed that five top consistent proteomic mediators were represented in two larger clusters reflecting numerous interconnected biological gene ontology pathways, most notably cytokine-mediated signaling pathway for GDF15 cluster (GO:0019221) and regulation of peptidyl-tyrosine phosphorylation for the ADM cluster (GO:0050730).

CONCLUSION

Dementia is linked to poor CVH mediated by GDF15 and ADM among several key proteomic markers which collectively explained ∼ 54 % of the total effect.

Sequential immunotherapy: towards cures for autoimmunity

Despite major progress in the treatment of autoimmune diseases in the past two decades, most therapies do not cure disease and can be associated with increased risk of infection through broad suppression of the immune system. However, advances in understanding the causes of autoimmune disease and clinical data from novel therapeutic modalities such as chimeric antigen receptor T cell therapies provide evidence that it may be possible to re-establish immune homeostasis and, potentially, prolong remission or even cure autoimmune diseases. Here, we propose a 'sequential immunotherapy' framework for immune system modulation to help achieve this ambitious goal. This framework encompasses three steps: controlling inflammation; resetting the immune system through elimination of pathogenic immune memory cells; and promoting and maintaining immune homeostasis via immune regulatory agents and tissue repair. We discuss existing drugs and those in development for each of the three steps. We also highlight the importance of causal human biology in identifying and prioritizing novel immunotherapeutic strategies as well as informing their application in specific patient subsets, enabling precision medicine approaches that have the potential to transform clinical care.

Understanding the genetic complexity of puberty timing across the allele frequency spectrum

Pubertal timing varies considerably and is associated with later health outcomes. We performed multi-ancestry genetic analyses on ~800,000 women, identifying 1,080 signals for age at menarche. Collectively, these explained 11% of trait variance in an independent sample. Women at the top and bottom 1% of polygenic risk exhibited ~11 and ~14-fold higher risks of delayed and precocious puberty, respectively. We identified several genes harboring rare loss-of-function variants in ~200,000 women, including variants in ZNF483, which abolished the impact of polygenic risk. Variant-to-gene mapping approaches and mouse gonadotropin-releasing hormone neuron RNA sequencing implicated 665 genes, including an uncharacterized G-protein-coupled receptor, GPR83, which amplified the signaling of MC3R, a key nutritional sensor. Shared signals with menopause timing at genes involved in DNA damage response suggest that the ovarian reserve might signal centrally to trigger puberty. We also highlight body size-dependent and independent mechanisms that potentially link reproductive timing to later life disease.

Artificial intelligence-enhanced electrocardiography derived body mass index as a predictor of future cardiometabolic disease

The electrocardiogram (ECG) can capture obesity-related cardiac changes. Artificial intelligence-enhanced ECG (AI-ECG) can identify subclinical disease. We trained an AI-ECG model to predict body mass index (BMI) from the ECG alone. Developed from 512,950 12-lead ECGs from the Beth Israel Deaconess Medical Center (BIDMC), a secondary care cohort, and validated on UK Biobank (UKB) (n = 42,386), the model achieved a Pearson correlation coefficient (r) of 0.65 and 0.62, and an R2 of 0.43 and 0.39 in the BIDMC cohort and UK Biobank, respectively for AI-ECG BMI vs. measured BMI. We found delta-BMI, the difference between measured BMI and AI-ECG-predicted BMI (AI-ECG-BMI), to be a biomarker of cardiometabolic health. The top tertile of delta-BMI showed increased risk of future cardiometabolic disease (BIDMC: HR 1.15, p < 0.001; UKB: HR 1.58, p < 0.001) and diabetes mellitus (BIDMC: HR 1.25, p < 0.001; UKB: HR 2.28, p < 0.001) after adjusting for covariates including measured BMI. Significant enhancements in model fit, reclassification and improvements in discriminatory power were observed with the inclusion of delta-BMI in both cohorts. Phenotypic profiling highlighted associations between delta-BMI and cardiometabolic diseases, anthropometric measures of truncal obesity, and pericardial fat mass. Metabolic and proteomic profiling associates delta-BMI positively with valine, lipids in small HDL, syntaxin-3, and carnosine dipeptidase 1, and inversely with glutamine, glycine, colipase, and adiponectin. A genome-wide association study revealed associations with regulators of cardiovascular/metabolic traits, including SCN10A, SCN5A, EXOG and RXRG. In summary, our AI-ECG-BMI model accurately predicts BMI and introduces delta-BMI as a non-invasive biomarker for cardiometabolic risk stratification.

Protocol for genome-wide association study of human blood metabolites

Genetic variations influence the levels of blood metabolites. We present analytical pipelines for assessing genetic influences on human blood metabolites. We describe steps for the normalization of metabolome data, genome-wide association studies, and the identification of metabolite quantitative trait loci (mQTLs). We then detail procedures for functional enrichment analysis of mQTLs. This protocol could be applicable to other quantitative traits, such as clinical measurements or proteome data. For complete details on the use and execution of this protocol, please refer to Iwasaki et al.1.

Assessment of circulating proteins in thyroid cancer: Proteome-wide Mendelian randomization and colocalization analysis

The causality between circulating proteins and thyroid cancer (TC) remains unclear. We employed five large-scale circulating proteomic genome-wide association studies (GWASs) with up to 100,000 participants and a TC meta-GWAS (nCase = 3,418, nControl = 292,703) to conduct proteome-wide Mendelian randomization (MR) and Bayesian colocalization analysis. Protein and gene expressions were validated in thyroid tissue. Through MR analysis, we identified 26 circulating proteins with a putative causal relationship with TCs, among which NANS protein passed multiple corrections (P BH = 3.28e-5, 0.05/1,525). These proteins were involved in amino acids and organic acid synthesis pathways. Colocalization analysis further identified six proteins associated with TCs (VCAM1, LGMN, NPTX1, PLEKHA7, TNFAIP3, and BMP1). Tissue validation confirmed BMP1, LGMN, and PLEKHA7's differential expression between normal and TC tissues. We found limited evidence for linking circulating proteins and the risk of TCs. Our study highlighted the contribution of proteins, particularly those involved in amino acid metabolism, to TCs.

Prioritization of drug targets for thyroid cancer: a multi-omics Mendelian randomization study

OBJECTIVES

Recurrence or tumor metastasis and drug resistance remain the major challenge in the treatment of thyroid cancer. It is needed to identify novel drug targets for thyroid cancer.

METHODS

Summary data-based Mendelian randomization (SMR) and colocalization analysis were performed to evaluate the associations between gene methylation, expression, protein levels with thyroid cancer. We additionally performed protein-protein interaction (PPI) network, gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) analyses to further explore the potential roles of identified genes in thyroid cancer.

RESULTS

SDCCAG8 and VCAM1 genes were associated with risk of thyroid cancer with tier 1 evidence, while TCN2 gene was with tier 3 evidence. SDCCAG8 gene was associated with risk of papillary thyroid cancer with tier 1 evidence. At the level of circulating proteins, genetically predicted higher levels of SDCCAG8 (OR = 0.46, 95% CI 0.34-0.64) and VCAM1 (OR = 0.21, 95% CI 0.10-0.45) were inversely associated with thyroid cancer risk; higher level of TCN2 was associated with an increased risk of thyroid cancer (OR = 1.30, 95% CI 1.15-1.47); and the higher level of SDCCAG8 (OR = 0.40, 95% CI 0.28-0.58) was associated with a decreased risk of papillary thyroid cancer. The bioinformatics analysis showed that SDCCAG8, VCAM1 and TCN2 might play roles in immune-related pathways.

CONCLUSION

SDCCAG8, VCAM1 and TCN2 genes were associated with thyroid cancer risk with evidence at multi-omics levels. There were potential roles of SDCCAG8, VCAM1 and TCN2 in immune-related pathways. Our findings might improve the understanding of the pathogenesis of thyroid cancer and discovery of novel potential drug targets for this disease.

Identification of novel therapeutic targets for chronic kidney disease and kidney function by integrating multi-omics proteome with transcriptome

BACKGROUND

Chronic kidney disease (CKD) is a progressive disease for which there is no effective cure. We aimed to identify potential drug targets for CKD and kidney function by integrating plasma proteome and transcriptome.

METHODS

We designed a comprehensive analysis pipeline involving two-sample Mendelian randomization (MR) (for proteins), summary-based MR (SMR) (for mRNA), and colocalization (for coding genes) to identify potential multi-omics biomarkers for CKD and combined the protein-protein interaction, Gene Ontology (GO), and single-cell annotation to explore the potential biological roles. The outcomes included CKD, extensive kidney function phenotypes, and different CKD clinical types (IgA nephropathy, chronic glomerulonephritis, chronic tubulointerstitial nephritis, membranous nephropathy, nephrotic syndrome, and diabetic nephropathy).

RESULTS

Leveraging pQTLs of 3032 proteins from 3 large-scale GWASs and corresponding blood- and tissue-specific eQTLs, we identified 32 proteins associated with CKD, which were validated across diverse CKD datasets, kidney function indicators, and clinical types. Notably, 12 proteins with prior MR support, including fibroblast growth factor 5 (FGF5), isopentenyl-diphosphate delta-isomerase 2 (IDI2), inhibin beta C chain (INHBC), butyrophilin subfamily 3 member A2 (BTN3A2), BTN3A3, uromodulin (UMOD), complement component 4A (C4a), C4b, centrosomal protein of 170 kDa (CEP170), serologically defined colon cancer antigen 8 (SDCCAG8), MHC class I polypeptide-related sequence B (MICB), and liver-expressed antimicrobial peptide 2 (LEAP2), were confirmed. To our knowledge, 20 novel causal proteins have not been previously reported. Five novel proteins, namely, GCKR (OR 1.17, 95% CI 1.10-1.24), IGFBP-5 (OR 0.43, 95% CI 0.29-0.62), sRAGE (OR 1.14, 95% CI 1.07-1.22), GNPTG (OR 0.90, 95% CI 0.86-0.95), and YOD1 (OR 1.39, 95% CI 1.18-1.64,) passed the MR, SMR, and colocalization analysis. The other 15 proteins were also candidate targets (GATM, AIF1L, DQA2, PFKFB2, NFATC1, activin AC, Apo A-IV, MFAP4, DJC10, C2CD2L, TCEA2, HLA-E, PLD3, AIF1, and GMPR1). These proteins interact with each other, and their coding genes were mainly enrichment in immunity-related pathways or presented specificity across tissues, kidney-related tissue cells, and kidney single cells.

CONCLUSIONS

Our integrated analysis of plasma proteome and transcriptome data identifies 32 potential therapeutic targets for CKD, kidney function, and specific CKD clinical types, offering potential targets for the development of novel immunotherapies, combination therapies, or targeted interventions.

Genetic variants of LEAP2 are associated with anthropometric traits and circulating insulin-like growth factor-1 concentration: A UK Biobank study

AIM

To test the hypothesis that liver-expressed antimicrobial peptide 2 (LEAP2) genetic variants might influence the susceptibility to human obesity.

METHODS

Using data from the UK Biobank, we identified independent LEAP2 gene single nucleotide polymorphisms (SNPs) and examined their associations with obesity traits and serum insulin-like growth factor-1 (IGF-1) concentration. These associations were evaluated for both individual SNPs and after combining them into a genetic risk score (GRSLEAP2) using linear and logistic regression models. Sex-stratified analyses were also conducted.

RESULTS

Five SNPs showed positive associations with obesity-related traits. rs57880964 was associated with body mass index (BMI) and waist-to-hip ratio adjusted for BMI (WHRadjBMI), in the total population and among women. Four independent SNPs were positively associated with higher serum IGF-1 concentrations in both men and women. GRSLEAP2 was associated with BMI and WHRadjBMI only in women and with serum IGF-1 concentration in both sexes.

CONCLUSIONS

These findings reveal sex-specific associations between key LEAP2 gene variants and several obesity traits, while also indicating a strong independent association of LEAP2 variants with serum IGF-1 concentration.

Fine-mapping causal tissues and genes at disease-associated loci

Heritable diseases often manifest in a highly tissue-specific manner, with different disease loci mediated by genes in distinct tissues or cell types. We propose Tissue-Gene Fine-Mapping (TGFM), a fine-mapping method that infers the posterior probability (PIP) for each gene-tissue pair to mediate a disease locus by analyzing GWAS summary statistics (and in-sample LD) and leveraging eQTL data from diverse tissues to build cis-predicted expression models; TGFM also assigns PIPs to causal variants that are not mediated by gene expression in assayed genes and tissues. TGFM accounts for both co-regulation across genes and tissues and LD between SNPs (generalizing existing fine-mapping methods), and incorporates genome-wide estimates of each tissue's contribution to disease as tissue-level priors. TGFM was well-calibrated and moderately well-powered in simulations; unlike previous methods, TGFM was able to attain correct calibration by modeling uncertainty in cis-predicted expression models. We applied TGFM to 45 UK Biobank diseases/traits (average N = 316K) using eQTL data from 38 GTEx tissues. TGFM identified an average of 147 PIP > 0.5 causal genetic elements per disease/trait, of which 11% were gene-tissue pairs. Implicated gene-tissue pairs were concentrated in known disease-critical tissues, and causal genes were strongly enriched in disease-relevant gene sets. Causal gene-tissue pairs identified by TGFM recapitulated known biology (e.g., TPO-thyroid for Hypothyroidism), but also included biologically plausible novel findings (e.g., SLC20A2-artery aorta for Diastolic blood pressure). Further application of TGFM to single-cell eQTL data from 9 cell types in peripheral blood mononuclear cells (PBMC), analyzed jointly with GTEx tissues, identified 30 additional causal gene-PBMC cell type pairs at PIP > 0.5-primarily for autoimmune disease and blood cell traits, including the biologically plausible example of CD52 in classical monocyte cells for Monocyte count. In conclusion, TGFM is a robust and powerful method for fine-mapping causal tissues and genes at disease-associated loci.

Plasma IL-6 levels and their association with brain health and dementia risk: A population-based cohort study

BACKGROUND

Recent studies have associated immune abnormalities with dementia. IL-6 is a crucial cytokine in inflammatory responses, and recent evidence has linked elevated IL-6 levels to changes in brain structure and cognitive decline. However, the connection between IL-6 levels, cognition, brain volumes, and dementia risk requires exploration in large prospective cohorts.

METHODS

This study utilized a longitudinal cohort from the UK Biobank to analyze the correlation between IL-6 expression levels, cognitive performance, and cortical and subcortical brain volumes through linear regression. Additionally, we assessed the association between IL-6 levels and long-term dementia risk using Cox regression analysis. We also used one-sample Mendelian randomization to analyze the impact of genetic predisposition of dementia on elevated IL-6 levels.

RESULTS

A total of 50,864 participants were included in this study, with 1,391 new cases of all-cause dementia identified. Higher plasma IL-6 levels are associated with cortical and subcortical atrophy in regions such as the fusiform, thalamus proper, hippocampus, and larger ventricle volumes. IL-6 levels are negatively associated with cognitive performance in pair matching, numeric memory, prospective memory, and reaction time tests. Furthermore, elevated IL-6 levels are linked to a 23-35 % increased risk of all-cause dementia over an average follow-up period of 13.2 years. The one-sample Mendelian randomization analysis did not show associations between the genetic predisposition of dementia and elevated IL-6 levels.

CONCLUSIONS

Increased IL-6 levels are associated with worse cognition, brain atrophy, and a heightened risk of all-cause dementia. Our study highlights the need to focus on the role of peripheral IL-6 levels in managing brain health and dementia risk.

Genetic-informed proteome-wide scan reveals potential causal plasma proteins for idiopathic pulmonary fibrosis

Idiopathic pulmonary fibrosis (IPF) is a lethal lung disease for which there are no reliable biomarkers or disease-modifying drugs. Here, we integrated human genomics and proteomics to investigate the causal associations between 2769 plasma proteins and IPF. Our Mendelian randomisation analysis identified nine proteins associated with IPF, of which three (FUT3, ADAM15 and USP28) were colocalised. ADAM15 emerged as the top candidate, supported by expression quantitative trait locus analysis in both blood and lung tissue. These findings provide novel insights into the aetiology of IPF and offer translational opportunities in response to the clinical challenges of this devastating disease.

Identification of biomarkers and potential therapeutic targets for pancreatic cancer by proteomic analysis in two prospective cohorts

Pancreatic cancer (PC) is the deadliest malignancy due to late diagnosis. Aberrant alterations in the blood proteome might serve as biomarkers to facilitate early detection of PC. We designed a nested case-control study of incident PC based on a prospective cohort of 38,295 elderly Chinese participants with ∼5.7 years' follow-up. Forty matched case-control pairs passed the quality controls for the proximity extension assay of 1,463 serum proteins. With a lenient threshold of p < 0.005, we discovered regenerating family member 1A (REG1A), REG1B, tumor necrosis factor (TNF), and phospholipase A2 group IB (PLA2G1B) in association with incident PC, among which the two REG1 proteins were replicated using the UK Biobank Pharma Proteomics Project, with effect sizes increasing steadily as diagnosis time approaches the baseline. Mendelian randomization analysis further supported the potential causal effects of REG1 proteins on PC. Taken together, circulating REG1A and REG1B are promising biomarkers and potential therapeutic targets for the early detection and prevention of PC.

Ribosomal DNA copy number variation associates with hematological profiles and renal function in the UK Biobank

The phenotypic impact of genetic variation of repetitive features in the human genome is currently understudied. One such feature is the multi-copy 47S ribosomal DNA (rDNA) that codes for rRNA components of the ribosome. Here, we present an analysis of rDNA copy number (CN) variation in the UK Biobank (UKB). From the first release of UKB whole-genome sequencing (WGS) data, a discovery analysis in White British individuals reveals that rDNA CN associates with altered counts of specific blood cell subtypes, such as neutrophils, and with the estimated glomerular filtration rate, a marker of kidney function. Similar trends are observed in other ancestries. A range of analyses argue against reverse causality or common confounder effects, and all core results replicate in the second UKB WGS release. Our work demonstrates that rDNA CN is a genetic influence on trait variance in humans.

Plasma Proteomics of Exercise Blood Pressure and Incident Hypertension

Importance

Blood pressure response during acute exercise (exercise blood pressure [EBP]) is associated with the future risk of hypertension and cardiovascular disease (CVD). Biochemical characterization of EBP could inform disease biology and identify novel biomarkers of future hypertension.

Objective

To identify protein markers associated with EBP and test their association with incident hypertension.

Design, Setting, and Participants

This study assayed 4977 plasma proteins in 681 healthy participants (from 763 assessed) of the Health, Risk Factors, Exercise Training and Genetics (HERITAGE; data collection from January 1993 to December 1997 and plasma proteomics from January 2019 to January 2020) Family Study at rest who underwent 2 cardiopulmonary exercise tests. Individuals were free of CVD at the time of recruitment. Individuals with resting SBP ≥160 mm Hg or DBP ≥100 mm Hg or taking antihypertensive drug therapy were excluded from the study. The association between resting plasma protein levels to both resting BP and EBP was evaluated. Proteins associated with EBP were analyzed for their association with incident hypertension in the Framingham Heart Study (FHS; n = 1177) and validated in the Jackson Heart Study (JHS; n = 772) and Multi-Ethnic Study of Atherosclerosis (MESA; n = 1367). Proteins associated with incident hypertension were tested for putative causal links in approximately 700 000 individuals using cis-protein quantitative loci mendelian randomization (cis-MR). Data were analyzed from January 2023 to January 2024.

Exposures

Plasma proteins.

Main Outcomes and Measures

EBP was defined as the BP response during a fixed workload (50 W) on a cycle ergometer. Hypertension was defined as BP ≥140/90 mm Hg or taking antihypertensive medication.

Results

Among the 681 participants in the HERITAGE Family Study, the mean (SD) age was 34 (13) years; 366 participants (54%) were female; 238 (35%) were self-reported Black and 443 (65%) were self-reported White. Proteomic profiling of EBP revealed 34 proteins that would not have otherwise been identified through profiling of resting BP alone. Transforming growth factor β receptor 3 (TGFBR3) and prostaglandin D2 synthase (PTGDS) had the strongest association with exercise systolic BP (SBP) and diastolic BP (DBP), respectively (TGFBR3: exercise SBP, β estimate, -3.39; 95% CI, -4.79 to -2.00; P = 2.33 × 10-6; PTGDS: exercise DBP β estimate, -2.50; 95% CI, -3.29 to -1.70; P = 1.18 × 10-9). In fully adjusted models, TGFBR3 was inversely associated with incident hypertension in FHS, JHS, and MESA (hazard ratio [HR]: FHS, 0.86; 95% CI, 0.75-0.97; P = .01; JHS, 0.87; 95% CI, 0.77-0.97; P = .02; MESA, 0.84; 95% CI, 0.71-0.98; P = .03; pooled cohort, 0.86; 95% CI, 0.79-0.92; P = 6 × 10-5). Using cis-MR, genetically predicted levels of TGFBR3 were associated with SBP, hypertension, and CVD events (SBP: β, -0.38; 95% CI, -0.64 to -0.11; P = .006; hypertension: odds ratio [OR], 0.99; 95% CI, 0.98-0.99; P < .001; heart failure with hypertension: OR, 0.86; 95% CI, 0.77-0.97; P = .01; CVD: OR, 0.84; 95% CI, 0.77-0.92; P = 8 × 10-5; cerebrovascular events: OR, 0.77; 95% CI, 0.70-0.85; P = 5 × 10-7).

Conclusions and Relevance

Plasma proteomic profiling of EBP identified a novel protein, TGFBR3, which may protect against elevated BP and long-term CVD outcomes.

Novel Protein Biomarkers and Therapeutic Targets for Type 1 Diabetes and Its Complications: Insights from Summary-Data-Based Mendelian Randomization and Colocalization Analysis

Millions of patients suffer from type 1 diabetes (T1D) and its associated complications. Nevertheless, the pursuit of a cure for T1D has encountered significant challenges, with a crucial impediment being the lack of biomarkers that can accurately predict the progression of T1D and reliable therapeutic targets for T1D. Hence, there is an urgent need to discover novel protein biomarkers and therapeutic targets, which holds promise for targeted therapy for T1D. In this study, we extracted summary-level data on 4907 plasma proteins from 35,559 Icelanders and 2923 plasma proteins from 54,219 UK participants as exposures. The genome-wide association study (GWAS) summary statistics on T1D and T1D with complications were obtained from the R9 release results from the FinnGen consortium. Summary-data-based Mendelian randomization (SMR) analysis was employed to evaluate the causal associations between the genetically predicted levels of plasma proteins and T1D-associated outcomes. Colocalization analysis was utilized to investigate the shared genetic variants between the exposure and outcome. Moreover, transcriptome analysis and a protein-protein interaction (PPI) network further illustrated the expression patterns of the identified protein targets and their interactions with the established targets of T1D. Finally, a Mendelian randomization phenome-wide association study evaluated the potential side effects of the identified core protein targets. In the primary SMR analysis, we identified 72 potential protein targets for T1D and its complications, and nine of them were considered crucial protein targets. Within the group were five risk targets and four protective targets. Backed by evidence from the colocalization analysis, the protein targets were classified into four tiers, with MANSC4, CTRB1, SIGLEC5 and MST1 being categorized as tier 1 targets. Delving into the DrugBank database, we retrieved 11 existing medications for T1D along with their therapeutic targets. The PPI network clarified the interactions among the identified potential protein targets and established ones. Finally, the Mendelian randomization phenome-wide association study corroborated MANSC4 as a reliable target capable of mitigating the risk of various forms of diabetes, and it revealed the absence of adverse effects linked to CTRB1, SIGLEC5 and MST1. This study unveiled many protein biomarkers and therapeutic targets for T1D and its complications. Such advancements hold great promise for the progression of drug development and targeted therapy for T1D.

Integrating plasma protein-centric multi-omics to identify potential therapeutic targets for pancreatic cancer

BACKGROUND

Deciphering the role of plasma proteins in pancreatic cancer (PC) susceptibility can aid in identifying novel targets for diagnosis and treatment.

METHODS

We examined the relationship between genetically determined levels of plasma proteins and PC through a systemic proteome-wide Mendelian randomization (MR) analysis utilizing cis-pQTLs from multiple centers. Rigorous sensitivity analyses, colocalization, reverse MR, replications with varying instrumental variable selections and additional datasets, as well as subsequent meta-analysis, were utilized to confirm the robustness of significant findings. The causative effect of corresponding protein-coding genes' expression and their expression pattern in single-cell types were then investigated. Enrichment analysis, between-protein interaction and causation, knock-out mice models, and mediation analysis with established PC risk factors were applied to indicate the pathogenetic pathways. These candidate targets were ultimately prioritized upon druggability and potential side effects predicted by a phenome-wide MR.

RESULTS

Twenty-one PC-related circulating proteins were identified in the exploratory phase with no evidence for horizontal pleiotropy or reverse causation. Of these, 11 were confirmed in a meta-analysis integrating external validations. The causality at a transcription level was repeated for neutrophil elastase, hydroxyacylglutathione hydrolase, lipase member N, protein disulfide-isomerase A5, xyloside xylosyltransferase 1. The carbohydrate sulfotransferase 11 and histo-blood group ABO system transferase exhibited high-support genetic colocalization evidence and were found to affect PC carcinogenesis partially through modulating body mass index and type 2 diabetes, respectively. Approved drugs have been established for eight candidate targets, which could potentially be repurposed for PC therapies. The phenome-wide investigation revealed 12 proteins associated with 51 non-PC traits, and interference on protein disulfide-isomerase A5 and cystatin-D would increase the risk of other malignancies.

CONCLUSIONS

By employing comprehensive methodologies, this study demonstrated a genetic predisposition linking 21 circulating proteins to PC risk. Our findings shed new light on the PC etiology and highlighted potential targets as priorities for future efforts in early diagnosis and therapeutic strategies of PC.

Identification of distinct and shared biomarker panels in different manifestations of cerebral small vessel disease through proteomic profiling

The pathophysiology underlying various manifestations of cerebral small vessel disease (cSVD) remains obscure. Using cerebrospinal fluid proximity extension assays and co-expression network analysis of 2,943 proteins, we found common and distinct proteomic signatures between white matter lesions (WML), microbleeds and infarcts measured in 856 living patients, and validated WML-associated proteins in three additional datasets. Proteins indicative of extracellular matrix dysregulation and vascular remodeling, including ELN, POSTN, CCN2 and MMP12 were elevated across all cSVD manifestations, with MMP12 emerging as an early cSVD indicator. cSVD-associated proteins formed a co-abundance network linked to metabolism and enriched in endothelial and arterial smooth muscle cells, showing elevated levels at early disease manifestations. Later disease stages involved changes in microglial proteins, associated with longitudinal WML progression, and changes in neuronal proteins mediating WML-associated cognitive decline. These findings provide an atlas of novel cSVD biomarkers and a promising roadmap for the next generation of cSVD therapeutics.

Therapeutic targets for age-related macular degeneration: proteome-wide Mendelian randomization and colocalization analyses

Background

Currently, effective therapeutic drugs for age-related macular degeneration (AMD) are urgently needed, and it is crucial to explore new treatment targets. The proteome is indispensable for exploring disease targets, so we conducted a Mendelian randomization (MR) of the proteome to identify new targets for AMD and its related subtypes.

Methods

The plasma protein level data used in this study were obtained from two large-scale studies of protein quantitative trait loci (pQTL), comprising 35,559 and 54,219 samples, respectively. The expression quantitative trait loci (eQTL) data were sourced from eQTLGen and GTEx Version 8. The discovery set for AMD data and subtypes was derived from the FinnGen study, consisting of 9,721 AMD cases and 381,339 controls, 5,239 wet AMD cases and 273,920 controls, and 6,651 dry AMD cases and 272,504 controls. The replication set for AMD data was obtained from the study by Winkler TW et al., comprising 14,034 cases and 91,234 controls. Summary Mendelian randomization (SMR) analysis was employed to assess the association between QTL data and AMD and its subtypes, while colocalization analysis was performed to determine whether they share causal variants. Additionally, chemical exploration and molecular docking were utilized to validate potential drugs targeting the identified proteins.

Results

SMR and colocalization analysis jointly identified risk-associated proteins for AMD and its subtypes, including 5 proteins (WARS1, BRD2, IL20RB, TGFB1, TNFRSF10A) associated with AMD, 2 proteins (WARS1, IL20RB) associated with Dry-AMD, and 9 proteins (COL10A1, WARS1, VTN, SDF2, LBP, CD226, TGFB1, TNFRSF10A, CSF2) associated with Wet-AMD. The results revealed potential therapeutic chemicals, and molecular docking indicated a good binding between the chemicals and protein structures.

Conclusion

Proteome-wide MR have identified risk-associated proteins for AMD and its subtypes, suggesting that these proteins may serve as potential therapeutic targets worthy of further clinical investigation.

Unraveling the role of plasma proteins in dementia: insights from two cohort studies in the UK, with causal evidence from Mendelian randomization

Population-based proteomics offer a groundbreaking avenue to predict dementia onset. This study employed a proteome-wide, data-driven approach to investigate protein-dementia associations in 229 incident all-cause dementia (ACD) among 3,249 participants from the English Longitudinal Study of Ageing (ELSA) over a median 9.8-year follow-up, then validated in 1,506 incident ACD among 52,745 individuals from the UK Biobank (UKB) over median 13.7 years. NEFL and RPS6KB1 were robustly associated with incident ACD; MMP12 was associated with vascular dementia in ELSA. Additional markers EDA2R and KIM1 (HAVCR1) were identified from sensitivity analyses. Combining NEFL and RPS6KB1 with other factors yielded high predictive accuracy (area under the curve (AUC)=0.871) for incident ACD. Replication in the UKB confirmed associations between identified proteins with various dementia subtypes. Results from reverse Mendelian Randomization also supported the role of several proteins as early dementia biomarkers. These findings underscore proteomics' potential in identifying novel risk screening targets for dementia.

Genetic association of glycemic traits and antihyperglycemic agent target genes with the risk of lung cancer: A Mendelian randomization study

AIMS

To evaluate the potential causal effect of glycemic traits on lung cancer and investigate the impact of antihyperglycemic agent-target genes on lung cancer risk.

METHODS

Genetic variants associated with glycemic traits, antihyperglycemic agent-target genes, and lung cancer were extracted from the Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC), expression quantitative trait loci (eQTLs), protein quantitative trait loci (pQTLs), and the International Lung Cancer Consortium (ILCCO), respectively. Mendelian randomization (MR) analyses were performed to examine the associations of glycemic traits and antihyperglycemic agent-target genes with lung cancer. Mediation analysis was conducted to explore whether overweight operated as a mediator between antihyperglycemic agents and lung cancer outcomes.

RESULTS

Genetically determined glycated hemoglobin A1c levels were associated with squamous cell lung cancer (OR = 1.78; 95 % CI, 1.08-2.92; p = 0.023). The PRKAB1 gene (the target of metformin) was associated with a lower risk of developing lung adenocarcinoma (OR = 0.85; 95 % CI, 0.76-0.96; p = 0.006). Further mediation analyses did not support overweight as a mediator between PRKAB1 activation and lung adenocarcinoma.

CONCLUSION

Our analyses suggest an association of genetically determined abnormal glycemic traits with squamous cell lung cancer. The potential association between PRKAB1 activation and a reduced risk of developing lung adenocarcinoma appears to be independent of the anti-obesity effects of metformin, suggesting that PRKAB1 activation may have a direct protective effect on lung adenocarcinoma development.

An interactive atlas of genomic, proteomic, and metabolomic biomarkers promotes the potential of proteins to predict complex diseases

Multiomics analyses have identified multiple potential biomarkers of the incidence and prevalence of complex diseases. However, it is not known which type of biomarker is optimal for clinical purposes. Here, we make a systematic comparison of 90 million genetic variants, 1453 proteins, and 325 metabolites from 500,000 individuals with complex diseases from the UK Biobank. A machine learning pipeline consisting of data cleaning, data imputation, feature selection, and model training using cross-validation and comparison of the results on holdout test sets showed that proteins were most predictive, followed by metabolites, and genetic variants. Only five proteins per disease resulted in median (min-max) areas under the receiver operating characteristic curves for incidence of 0.79 (0.65-0.86) and 0.84 (0.70-0.91) for prevalence. In summary, our work suggests the potential of predicting complex diseases based on a limited number of proteins. We provide an interactive atlas (macd.shinyapps.io/ShinyApp/) to find genomic, proteomic, or metabolomic biomarkers for different complex diseases.

Proteomic analysis of cardiorespiratory fitness for prediction of mortality and multisystem disease risks

Despite the wide effects of cardiorespiratory fitness (CRF) on metabolic, cardiovascular, pulmonary and neurological health, challenges in the feasibility and reproducibility of CRF measurements have impeded its use for clinical decision-making. Here we link proteomic profiles to CRF in 14,145 individuals across four international cohorts with diverse CRF ascertainment methods to establish, validate and characterize a proteomic CRF score. In a cohort of around 22,000 individuals in the UK Biobank, a proteomic CRF score was associated with a reduced risk of all-cause mortality (unadjusted hazard ratio 0.50 (95% confidence interval 0.48-0.52) per 1 s.d. increase). The proteomic CRF score was also associated with multisystem disease risk and provided risk reclassification and discrimination beyond clinical risk factors, as well as modulating high polygenic risk of certain diseases. Finally, we observed dynamicity of the proteomic CRF score in individuals who undertook a 20-week exercise training program and an association of the score with the degree of the effect of training on CRF, suggesting potential use of the score for personalization of exercise recommendations. These results indicate that population-based proteomics provides biologically relevant molecular readouts of CRF that are additive to genetic risk, potentially modifiable and clinically translatable.

Identifying novel proteins for migraine by integrating proteomes from blood and CSF with genome-wide association data

BACKGROUND

Proteome-wide Mendelian randomization studies have been increasingly utilized to identify potential drug targets for diseases. We aimed to identify potential therapeutic targets for migraine and its subtypes through the application of Mendelian randomization and co-localization analysis methods.

METHODS

We utilized cis-protein quantitative trait loci data for 1378 plasma proteins available from two studies with 7213 individuals and 35,559 individuals, respectively. Summary data for migraine and its subtypes were obtained from a genetic study involving up to 1,339,303 individuals. Proteins that passed both the discovery and validation Mendelian randomization analysis, sensitivity analysis, heterogeneity test, and pleiotropy test, were associated with ≥2 outcomes, and received strong support from co-localization analysis (PP.H4.abf ≥0.80) and were classified as tier 1 proteins.

RESULTS

We identified three tier 1 proteins (LRP11, ITIH1, and ADGRF5), whose genes have not been previously identified as causal genes for migraine in genetic studies. LRP11 was significantly associated with the risk of any migraine (OR [odds ratio] = 0.968, 95% CI [confidence interval] = 0.955-0.981, p = 1.27 × 10-6) and significantly/suggestively associated with three migraine subtypes. ITIH1 was significantly associated with the risk of any migraine (OR = 1.044, 95% CI = 1.024-1.065, p = 1.08 × 10-5) and migraine with visual disturbances. ADGRF5 was significantly associated with the risk of any migraine (OR = 0.964, 95% CI = 0.946-0.982, p = 8.74 × 10-5) and suggestively associated with migraine with aura. The effects of LRP11 and ADGRF5 were further replicated using cerebrospinal fluid protein data. Apart from ADGRF5, there was no evidence of potential adverse consequences when modulating the plasma levels. We also identified another four proteins (PLCG1, ARHGAP25, CHGA, and MANBA) with no potential adverse consequences when modulating the plasma levels, and their genes were not reported by previous genetic studies.

CONCLUSIONS

We found compelling evidence for two proteins and suggestive evidence for four proteins that could be promising targets for migraine treatment without significant adverse consequences. The corresponding genes were not reported in previous genetic studies. Future studies are needed to confirm the causal role of these proteins and explore the underlying mechanisms.

High-throughput molecular assays for inclusion in personalised oncology trials - State-of-the-art and beyond

In the last decades, the development of high-throughput molecular assays has revolutionised cancer diagnostics, paving the way for the concept of personalised cancer medicine. This progress has been driven by the introduction of such technologies through biomarker-driven oncology trials. In this review, strengths and limitations of various state-of-the-art sequencing technologies, including gene panel sequencing (DNA and RNA), whole-exome/whole-genome sequencing and whole-transcriptome sequencing, are explored, focusing on their ability to identify clinically relevant biomarkers with diagnostic, prognostic and/or predictive impact. This includes the need to assess complex biomarkers, for example microsatellite instability, tumour mutation burden and homologous recombination deficiency, to identify patients suitable for specific therapies, including immunotherapy. Furthermore, the crucial role of biomarker analysis and multidisciplinary molecular tumour boards in selecting patients for trial inclusion is discussed in relation to various trial concepts, including drug repurposing. Recognising that today's exploratory techniques will evolve into tomorrow's routine diagnostics and clinical study inclusion assays, the importance of emerging technologies for multimodal diagnostics, such as proteomics and in vivo drug sensitivity testing, is also discussed. In addition, key regulatory aspects and the importance of patient engagement in all phases of a clinical trial are described. Finally, we propose a set of recommendations for consideration when planning a new precision cancer medicine trial.

Musculoskeletal Biorepository: Establishment, Sustainment, and Tips for Success

A biorepository, also referred to as a "biobank," is a collection of biologic samples that are stored for laboratory research. With the emergence of precision medicine, the importance of leveraging individual patient biomolecular signatures to improve diagnosis, prognosis, and treatment is becoming increasingly recognized. Successful development and sustainment of a biorepository provides the potential for transformative preclinical research. Establishing a biobank requires a team approach with involvement of the institutions' research laboratory team and regulatory body. Execution of research activities requires a coordinated team approach for case identification, consent process, data and specimen collection, specimen processing, and storage and archiving. The advancing fields of precision medicine and orthobiologics provide incredible opportunities for institutions to generate novel lines of inquiry in musculoskeletal diseases through a multiomics approach (genomic, transcriptomic, proteomic, microbiomic). In addition, a biobank is an important component of post-market surveillance for the rapidly emerging field of orthobiologics.

A genome-wide association study of mass spectrometry proteomics using the Seer Proteograph platform

Genome-wide association studies (GWAS) with proteomics are essential tools for drug discovery. To date, most studies have used affinity proteomics platforms, which have limited discovery to protein panels covered by the available affinity binders. Furthermore, it is not clear to which extent protein epitope changing variants interfere with the detection of protein quantitative trait loci (pQTLs). Mass spectrometry-based (MS) proteomics can overcome some of these limitations. Here we report a GWAS using the MS-based Seer Proteograph™ platform with blood samples from a discovery cohort of 1,260 American participants and a replication in 325 individuals from Asia, with diverse ethnic backgrounds. We analysed 1,980 proteins quantified in at least 80% of the samples, out of 5,753 proteins quantified across the discovery cohort. We identified 252 and replicated 90 pQTLs, where 30 of the replicated pQTLs have not been reported before. We further investigated 200 of the strongest associated cis-pQTLs previously identified using the SOMAscan and the Olink platforms and found that up to one third of the affinity proteomics pQTLs may be affected by epitope effects, while another third were confirmed by MS proteomics to be consistent with the hypothesis that genetic variants induce changes in protein expression. The present study demonstrates the complementarity of the different proteomics approaches and reports pQTLs not accessible to affinity proteomics, suggesting that many more pQTLs remain to be discovered using MS-based platforms.

Diffusion imaging genomics provides novel insight into early mechanisms of cerebral small vessel disease

Cerebral small vessel disease (cSVD) is a leading cause of stroke and dementia. Genetic risk loci for white matter hyperintensities (WMH), the most common MRI-marker of cSVD in older age, were recently shown to be significantly associated with white matter (WM) microstructure on diffusion tensor imaging (signal-based) in young adults. To provide new insights into these early changes in WM microstructure and their relation with cSVD, we sought to explore the genetic underpinnings of cutting-edge tissue-based diffusion imaging markers across the adult lifespan. We conducted a genome-wide association study of neurite orientation dispersion and density imaging (NODDI) markers in young adults (i-Share study: N = 1 758, (mean[range]) 22.1[18-35] years), with follow-up in young middle-aged (Rhineland Study: N = 714, 35.2[30-40] years) and late middle-aged to older individuals (UK Biobank: N = 33 224, 64.3[45-82] years). We identified 21 loci associated with NODDI markers across brain regions in young adults. The most robust association, replicated in both follow-up cohorts, was with Neurite Density Index (NDI) at chr5q14.3, a known WMH locus in VCAN. Two additional loci were replicated in UK Biobank, at chr17q21.2 with NDI, and chr19q13.12 with Orientation Dispersion Index (ODI). Transcriptome-wide association studies showed associations of STAT3 expression in arterial and adipose tissue (chr17q21.2) with NDI, and of several genes at chr19q13.12 with ODI. Genetic susceptibility to larger WMH volume, but not to vascular risk factors, was significantly associated with decreased NDI in young adults, especially in regions known to harbor WMH in older age. Individually, seven of 25 known WMH risk loci were associated with NDI in young adults. In conclusion, we identified multiple novel genetic risk loci associated with NODDI markers, particularly NDI, in early adulthood. These point to possible early-life mechanisms underlying cSVD and to processes involving remyelination, neurodevelopment and neurodegeneration, with a potential for novel approaches to prevention.

A plasma protein-based risk score to predict hip fractures

As there are effective treatments to reduce hip fractures, identification of patients at high risk of hip fracture is important to inform efficient intervention strategies. To obtain a new tool for hip fracture prediction, we developed a protein-based risk score in the Cardiovascular Health Study using an aptamer-based proteomic platform. The proteomic risk score predicted incident hip fractures and improved hip fracture discrimination in two Trøndelag Health Study validation cohorts using the same aptamer-based platform. When transferred to an antibody-based proteomic platform in a UK Biobank validation cohort, the proteomic risk score was strongly associated with hip fractures (hazard ratio per s.d. increase, 1.64; 95% confidence interval 1.53-1.77). The proteomic risk score, but not available polygenic risk scores for fractures or bone mineral density, improved the C-index beyond the fracture risk assessment tool (FRAX), which integrates information from clinical risk factors (C-index, FRAX 0.735 versus FRAX + proteomic risk score 0.776). The developed proteomic risk score constitutes a new tool for stratifying patients according to hip fracture risk; however, its improvement in hip fracture discrimination is modest and its clinical utility beyond FRAX with information on femoral neck bone mineral density remains to be determined.

Systems biology dissection of PTSD and MDD across brain regions, cell types, and blood

The molecular pathology of stress-related disorders remains elusive. Our brain multiregion, multiomic study of posttraumatic stress disorder (PTSD) and major depressive disorder (MDD) included the central nucleus of the amygdala, hippocampal dentate gyrus, and medial prefrontal cortex (mPFC). Genes and exons within the mPFC carried most disease signals replicated across two independent cohorts. Pathways pointed to immune function, neuronal and synaptic regulation, and stress hormones. Multiomic factor and gene network analyses provided the underlying genomic structure. Single nucleus RNA sequencing in dorsolateral PFC revealed dysregulated (stress-related) signals in neuronal and non-neuronal cell types. Analyses of brain-blood intersections in >50,000 UK Biobank participants were conducted along with fine-mapping of the results of PTSD and MDD genome-wide association studies to distinguish risk from disease processes. Our data suggest shared and distinct molecular pathology in both disorders and propose potential therapeutic targets and biomarkers.

Exploration of potential novel drug targets for diabetic retinopathy by plasma proteome screening

The aim of this study is to identify novel potential drug targets for diabetic retinopathy (DR). A bidirectional two-sample Mendelian randomization (MR) analysis was performed using protein quantitative trait loci (pQTL) of 734 plasma proteins as the exposures and clinically diagnosed DR as the outcome. Genetic instruments for 734 plasma proteins were obtained from recently published genome-wide association studies (GWAS), and external plasma proteome data was retrieved from the Icelandic Decoding Genetics Study and UK Biobank Pharma Proteomics Project. Summary-level data of GWAS for DR were obtained from the Finngen Consortium, comprising 14,584 cases and 202,082 population controls. Steiger filtering, Bayesian co-localization, and phenotype scanning were used to further verify the causal relationships calculated by MR. Three significant (p < 6.81 × 10-5) plasma protein-DR pairs were identified during the primary MR analysis, including CFH (OR = 0.8; 95% CI 0.75-0.86; p = 1.29 × 10-9), B3GNT8 (OR = 1.09; 95% CI 1.05-1.12; p = 5.9 × 10-6) and CFHR4 (OR = 1.11; 95% CI 1.06-1.16; p = 1.95 × 10-6). None of the three proteins showed reverse causation. According to Bayesian colocalization analysis, CFH (coloc.abf-PPH4 = 0.534) and B3GNT8 (coloc.abf-PPH4 = 0.638) in plasma shared the same variant with DR. All three identified proteins were validated in external replication cohorts. Our research shows a cause-and-effect connection between genetically determined levels of CFH, B3GNT8 and CFHR4 plasma proteins and DR. The discovery implies that these proteins hold potential as drug target in the process of developing drugs to treat DR.

Proteogenomic analyses identify coagulation factor XI as a thromboinflammatory mediator of long COVID

SARS-CoV-2 infection can result in long COVID, characterized by post-acute symptoms from multiple organ systems. Current hypotheses on mechanisms underlying long COVID include persistent inflammation and dysregulated coagulation; however, precise mechanisms and causal mediators remain unclear. Here, we tested the associations of genetic instruments for 49 complement and coagulation factors from the UK Biobank ( N =34,557) with long COVID in the Long COVID Host Genetics Initiative ( N =997,600). Primary analyses revealed that genetically predicted higher factor XI increased long COVID risk (odds ratio, 1.17 [95% confidence interval, 1.08-1.27] per standard deviation; P =1.7×10 -4 ). This association was robust to sensitivity analyses using pleiotropy-robust methods and different genetic instruments and was replicated using proteogenomic data from an Icelandic cohort. Genetically predicted factor XI was also associated with venous thromboembolism, but not with acute COVID-19 or long COVID-resembling conditions. Collectively, these findings provide genetic evidence implicating factor XI in the biology of long COVID.

Proteomic aging clock (PAC) predicts age-related outcomes in middle-aged and older adults

Beyond mere prognostication, optimal biomarkers of aging provide insights into qualitative and quantitative features of biological aging and might, therefore, offer useful information for the testing and, ultimately, clinical use of gerotherapeutics. We aimed to develop a proteomic aging clock (PAC) for all-cause mortality risk as a proxy of biological age. Data were from the UK Biobank Pharma Proteomics Project, including 53,021 participants aged between 39 and 70 years and 2923 plasma proteins assessed using the Olink Explore 3072 assay®. 10.9% of the participants died during a mean follow-up of 13.3 years, with the mean age at death of 70.1 years. The Spearman correlation between PAC proteomic age and chronological age was 0.77. PAC showed robust age-adjusted associations and predictions for all-cause mortality and the onset of various diseases in general and disease-free participants. The proteins associated with PAC proteomic age deviation were enriched in several processes related to the hallmarks of biological aging. Our results expand previous findings by showing that biological age acceleration, based on PAC, strongly predicts all-cause mortality and several incident disease outcomes. Particularly, it facilitates the evaluation of risk for multiple conditions in a disease-free population, thereby, contributing to the prevention of initial diseases, which vary among individuals and may subsequently lead to additional comorbidities.

Identifying proteomic risk factors for cancer using prospective and exome analyses of 1463 circulating proteins and risk of 19 cancers in the UK Biobank

The availability of protein measurements and whole exome sequence data in the UK Biobank enables investigation of potential observational and genetic protein-cancer risk associations. We investigated associations of 1463 plasma proteins with incidence of 19 cancers and 9 cancer subsites in UK Biobank participants (average 12 years follow-up). Emerging protein-cancer associations were further explored using two genetic approaches, cis-pQTL and exome-wide protein genetic scores (exGS). We identify 618 protein-cancer associations, of which 107 persist for cases diagnosed more than seven years after blood draw, 29 of 618 were associated in genetic analyses, and four had support from long time-to-diagnosis ( > 7 years) and both cis-pQTL and exGS analyses: CD74 and TNFRSF1B with NHL, ADAM8 with leukemia, and SFTPA2 with lung cancer. We present multiple blood protein-cancer risk associations, including many detectable more than seven years before cancer diagnosis and that had concordant evidence from genetic analyses, suggesting a possible role in cancer development.

Transcript and protein signatures derived from shared molecular interactions across cancers are associated with mortality

BACKGROUND

Characterization of shared cancer mechanisms have been proposed to improve therapy strategies and prognosis. Here, we aimed to identify shared cell-cell interactions (CCIs) within the tumor microenvironment across multiple solid cancers and assess their association with cancer mortality.

METHODS

CCIs of each cancer were identified by NicheNet analysis of single-cell RNA sequencing data from breast, colon, liver, lung, and ovarian cancers. These CCIs were used to construct a shared multi-cellular tumor model (shared-MCTM) representing common CCIs across cancers. A gene signature was identified from the shared-MCTM and tested on the mRNA and protein level in two large independent cohorts: The Cancer Genome Atlas (TCGA, 9185 tumor samples and 727 controls across 22 cancers) and UK biobank (UKBB, 10,384 cancer patients and 5063 controls with proteomics data across 17 cancers). Cox proportional hazards models were used to evaluate the association of the signature with 10-year all-cause mortality, including sex-specific analysis.

RESULTS

A shared-MCTM was derived from five individual cancers. A shared gene signature was extracted from this shared-MCTM and the most prominent regulatory cell type, matrix cancer-associated fibroblast (mCAF). The signature exhibited significant expression changes in multiple cancers compared to controls at both mRNA and protein levels in two independent cohorts. Importantly, it was significantly associated with mortality in cancer patients in both cohorts. The highest hazard ratios were observed for brain cancer in TCGA (HR [95%CI] = 6.90[4.64-10.25]) and ovarian cancer in UKBB (5.53[2.08-8.80]). Sex-specific analysis revealed distinct risks, with a higher mortality risk associated with the protein signature score in males (2.41[1.97-2.96]) compared to females (1.84[1.44-2.37]).

CONCLUSION

We identified a gene signature from a comprehensive shared-MCTM representing common CCIs across different cancers and revealed the regulatory role of mCAF in the tumor microenvironment. The pathogenic relevance of the gene signature was supported by differential expression and association with mortality on both mRNA and protein levels in two independent cohorts.

From genetic associations to genes: methods, applications, and challenges

Genome-wide association studies (GWASs) have identified numerous genetic loci associated with human traits and diseases. However, pinpointing the causal genes remains a challenge, which impedes the translation of GWAS findings into biological insights and medical applications. In this review, we provide an in-depth overview of the methods and technologies used for prioritizing genes from GWAS loci, including gene-based association tests, integrative analysis of GWAS and molecular quantitative trait loci (xQTL) data, linking GWAS variants to target genes through enhancer-gene connection maps, and network-based prioritization. We also outline strategies for generating context-dependent xQTL data and their applications in gene prioritization. We further highlight the potential of gene prioritization in drug repurposing. Lastly, we discuss future challenges and opportunities in this field.

A comprehensive genetic map of cytokine responses in Lyme borreliosis

The incidence of Lyme borreliosis has risen, accompanied by persistent symptoms. The innate immune system and related cytokines are crucial in the host response and symptom development. We characterized cytokine production capacity before and after antibiotic treatment in 1,060 Lyme borreliosis patients. We observed a negative correlation between antibody production and IL-10 responses, as well as increased IL-1Ra responses in patients with disseminated disease. Genome-wide mapping the cytokine production allowed us to identify 34 cytokine quantitative trait loci (cQTLs), with 31 novel ones. We pinpointed the causal variant at the TLR1-6-10 locus and validated the regulation of IL-1Ra responses at transcritpome level using an independent cohort. We found that cQTLs contribute to Lyme borreliosis susceptibility and are relevant to other immune-mediated diseases. Our findings improve the understanding of cytokine responses in Lyme borreliosis and provide a genetic map of immune function as an expanded resource.

Determinants of plasma levels of proglucagon and the metabolic impact of glucagon receptor signalling: a UK Biobank study

AIMS/HYPOTHESES

Glucagon and glucagon-like peptide-1 (GLP-1) are derived from the same precursor; proglucagon, and dual agonists of their receptors are currently being explored for the treatment of obesity and metabolic dysfunction-associated steatotic liver disease (MASLD). Elevated levels of endogenous glucagon (hyperglucagonaemia) have been linked with hyperglycaemia in individuals with type 2 diabetes but are also observed in individuals with obesity and MASLD. GLP-1 levels have been reported to be largely unaffected or even reduced in similar conditions. We investigated potential determinants of plasma proglucagon and associations of glucagon receptor signalling with metabolic diseases based on data from the UK Biobank.

METHODS

We used exome sequencing data from the UK Biobank for ~410,000 white participants to identify glucagon receptor variants and grouped them based on their known or predicted signalling. Data on plasma levels of proglucagon estimated using Olink technology were available for a subset of the cohort (~40,000). We determined associations of glucagon receptor variants and proglucagon with BMI, type 2 diabetes and liver fat (quantified by liver MRI) and performed survival analyses to investigate if elevated proglucagon predicts type 2 diabetes development.

RESULTS

Obesity, MASLD and type 2 diabetes were associated with elevated plasma levels of proglucagon independently of each other. Baseline proglucagon levels were associated with the risk of type 2 diabetes development over a 14 year follow-up period (HR 1.13; 95% CI 1.09, 1.17; n=1562; p=1.3×10-12). This association was of the same magnitude across strata of BMI. Carriers of glucagon receptor variants with reduced cAMP signalling had elevated levels of proglucagon (β 0.847; 95% CI 0.04, 1.66; n=17; p=0.04), and carriers of variants with a predicted frameshift mutation had higher levels of liver fat compared with the wild-type reference group (β 0.504; 95% CI 0.03, 0.98; n=11; p=0.04).

CONCLUSIONS/INTERPRETATION

Our findings support the suggestion that glucagon receptor signalling is involved in MASLD, that plasma levels of proglucagon are linked to the risk of type 2 diabetes development, and that proglucagon levels are influenced by genetic variation in the glucagon receptor, obesity, type 2 diabetes and MASLD. Determining the molecular signalling pathways downstream of glucagon receptor activation may guide the development of biased GLP-1/glucagon co-agonist with improved metabolic benefits.

DATA AVAILABILITY

All coding is available through https://github.com/nicwin98/UK-Biobank-GCG.

SharePro: an accurate and efficient genetic colocalization method accounting for multiple causal signals

MOTIVATION

Colocalization analysis is commonly used to assess whether two or more traits share the same genetic signals identified in genome-wide association studies (GWAS), and is important for prioritizing targets for functional follow-up of GWAS results. Existing colocalization methods can have suboptimal performance when there are multiple causal variants in one genomic locus.

RESULTS

We propose SharePro to extend the COLOC framework for colocalization analysis. SharePro integrates linkage disequilibrium (LD) modeling and colocalization assessment by grouping correlated variants into effect groups. With an efficient variational inference algorithm, posterior colocalization probabilities can be accurately estimated. In simulation studies, SharePro demonstrated increased power with a well-controlled false positive rate at a low computational cost. Compared to existing methods, SharePro provided stronger and more consistent colocalization evidence for known lipid-lowering drug target proteins and their corresponding lipid traits. Through an additional challenging case of the colocalization analysis of the circulating abundance of R-spondin 3 GWAS and estimated bone mineral density GWAS, we demonstrated the utility of SharePro in identifying biologically plausible colocalized signals.

AVAILABILITY AND IMPLEMENTATION

SharePro for colocalization analysis is written in Python and openly available at https://github.com/zhwm/SharePro_coloc.

Harnessing the power of proteomics in precision diabetes medicine

Precision diabetes medicine (PDM) aims to reduce errors in prevention programmes, diagnosis thresholds, prognosis prediction and treatment strategies. However, its advancement and implementation are difficult due to the heterogeneity of complex molecular processes and environmental exposures that influence an individual's disease trajectory. To address this challenge, it is imperative to develop robust screening methods for all areas of PDM. Innovative proteomic technologies, alongside genomics, have proven effective in precision cancer medicine and are showing promise in diabetes research for potential translation. This narrative review highlights how proteomics is well-positioned to help improve PDM. Specifically, a critical assessment of widely adopted affinity-based proteomic technologies in large-scale clinical studies and evidence of the benefits and feasibility of using MS-based plasma proteomics is presented. We also present a case for the use of proteomics to identify predictive protein panels for type 2 diabetes subtyping and the development of clinical prediction models for prevention, diagnosis, prognosis and treatment strategies. Lastly, we discuss the importance of plasma and tissue proteomics and its integration with genomics (proteogenomics) for identifying unique type 2 diabetes intra- and inter-subtype aetiology. We conclude with a call for action formed on advancing proteomics technologies, benchmarking their performance and standardisation across sites, with an emphasis on data sharing and the inclusion of diverse ancestries in large cohort studies. These efforts should foster collaboration with key stakeholders and align with ongoing academic programmes such as the Precision Medicine in Diabetes Initiative consortium.

Genome-wide association analyses identify 95 risk loci and provide insights into the neurobiology of post-traumatic stress disorder

Post-traumatic stress disorder (PTSD) genetics are characterized by lower discoverability than most other psychiatric disorders. The contribution to biological understanding from previous genetic studies has thus been limited. We performed a multi-ancestry meta-analysis of genome-wide association studies across 1,222,882 individuals of European ancestry (137,136 cases) and 58,051 admixed individuals with African and Native American ancestry (13,624 cases). We identified 95 genome-wide significant loci (80 new). Convergent multi-omic approaches identified 43 potential causal genes, broadly classified as neurotransmitter and ion channel synaptic modulators (for example, GRIA1, GRM8 and CACNA1E), developmental, axon guidance and transcription factors (for example, FOXP2, EFNA5 and DCC), synaptic structure and function genes (for example, PCLO, NCAM1 and PDE4B) and endocrine or immune regulators (for example, ESR1, TRAF3 and TANK). Additional top genes influence stress, immune, fear and threat-related processes, previously hypothesized to underlie PTSD neurobiology. These findings strengthen our understanding of neurobiological systems relevant to PTSD pathophysiology, while also opening new areas for investigation.

Pre-diagnostic plasma proteomics profile for hepatocellular carcinoma

OBJECTIVE

Proteomics may discover pathophysiological changes related to hepatocellular carcinoma (HCC), an aggressive and lethal type of cancer with low sensitivity for early-stage diagnosis.

DESIGN

We measured 1,305 pre-diagnostic (median 12.7 years) SOMAscan proteins from 54 pairs of healthy individuals who subsequently developed HCC and matched non-HCC controls from the Nurses' Health Study (NHS) and the Health Professionals Follow-up Study (HPFS). Candidate proteins were validated in the independent, prospective UK Biobank Pharma Proteomics Project (UKB-PPP).

RESULTS

In NHS/HPFS, we identified 56 elevated proteins in HCC with absolute fold-change >1.2 and Wald test P < .05 in conditional logistic regression analysis. Ingenuity Pathway Analysis identified enrichment of pathways associated with cell viability, adhesion, proteolysis, apoptosis, and inflammatory response. Four proteins, chitinase-3-like protein 1, growth/differentiation factor 15, interleukin-1 receptor antagonist protein, and E-selectin, showed strong positive associations with HCC and were thus validated by ELISA (odds ratio ranged 2.48-14.7, all P < .05) in the NHS/HPFS and by Olink platform (hazard ratio ranged 1.90-3.93, all P < .05) in the UKB-PPP. Adding these four proteins to a logistic regression model of traditional HCC risk factors increased the area under the curve (AUC) from 0.67 to 0.87 in the NHS/HPFS. Consistently, model AUC was 0.88 for HCC risk prediction in the UKB-PPP.

CONCLUSION

However, the limited number of HCC cases in the cohorts necessitates caution in interpreting our findings, emphasizing the need for further validation in high-risk populations.

Evaluation of Plasma Biomarkers for Causal Association With Peripheral Artery Disease

BACKGROUND

Hundreds of biomarkers for peripheral artery disease (PAD) have been reported in the literature; however, the observational nature of these studies limits causal inference due to the potential of reverse causality and residual confounding. We sought to evaluate the potential causal impact of putative PAD biomarkers identified in human observational studies through genetic causal inference methods.

METHODS

Putative circulating PAD biomarkers were identified from human observational studies through a comprehensive literature search based on terms related to PAD using PubMed, Cochrane, and Embase. Genetic instruments were generated from publicly available genome-wide association studies of circulating biomarkers. Two-sample Mendelian randomization was used to test the association of genetically determined biomarker levels with PAD using summary statistics from a genome-wide association study of 31 307 individuals with and 211 753 individuals without PAD in the Veterans Affairs Million Veteran Program and replicated in data from FinnGen comprised of 11 924 individuals with and 288 638 individuals without PAD.

RESULTS

We identified 204 unique circulating biomarkers for PAD from the observational literature, of which 173 were genetically instrumented using genome-wide association study results. After accounting for multiple testing (false discovery rate, <0.05), 10 of 173 (5.8%) biomarkers had significant associations with PAD. These 10 biomarkers represented categories including plasma lipoprotein regulation, lipid homeostasis, and protein-lipid complex remodeling. Observational literature highlighted different pathways including inflammatory response, negative regulation of multicellular organismal processes, and regulation of response to external stimuli.

CONCLUSIONS

Integrating human observational studies and genetic causal inference highlights several key pathways in PAD pathophysiology. This work demonstrates that a substantial portion of biomarkers identified in observational studies are not well supported by human genetic evidence and emphasizes the importance of triangulating evidence to understand PAD pathophysiology. Although the identified biomarkers offer insights into atherosclerotic development in the lower limb, their specificity to PAD compared with more widespread atherosclerosis requires further study.

Integrative common and rare variant analyses provide insights into the genetic architecture of liver cirrhosis

We report a multi-ancestry genome-wide association study on liver cirrhosis and its associated endophenotypes, alanine aminotransferase (ALT) and γ-glutamyl transferase. Using data from 12 cohorts, including 18,265 cases with cirrhosis, 1,782,047 controls, up to 1 million individuals with liver function tests and a validation cohort of 21,689 cases and 617,729 controls, we identify and validate 14 risk associations for cirrhosis. Many variants are located near genes involved in hepatic lipid metabolism. One of these, PNPLA3 p.Ile148Met, interacts with alcohol intake, obesity and diabetes on the risk of cirrhosis and hepatocellular carcinoma (HCC). We develop a polygenic risk score that associates with the progression from cirrhosis to HCC. By focusing on prioritized genes from common variant analyses, we find that rare coding variants in GPAM associate with lower ALT, supporting GPAM as a potential target for therapeutic inhibition. In conclusion, this study provides insights into the genetic underpinnings of cirrhosis.