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Integrative analysis of the plasma proteome and polygenic risk of cardiometabolic diseases

Cardiometabolic diseases are frequently polygenic in architecture, comprising a large number of risk alleles with small effects spread across the genome1–3. Polygenic scores (PGS) aggregate these into a metric representing an individual’s genetic predisposition to disease. PGS have shown promise for early risk prediction4–7 and there is an open question as to whether PGS can also be used to understand disease biology8. Here, we demonstrate that cardiometabolic disease PGS can be used to elucidate the proteins underlying disease pathogenesis. In 3,087 healthy individuals, we found that PGS for coronary artery disease, type 2 diabetes, chronic kidney disease and ischaemic stroke are associated with the levels of 49 plasma proteins. Associations were polygenic in architecture, largely independent of cis and trans protein quantitative trait loci and present for proteins without quantitative trait loci. Over a follow-up of 7.7 years, 28 of these proteins associated with future myocardial infarction or type 2 diabetes events, 16 of which were mediators between polygenic risk and incident disease. Twelve of these were druggable targets with therapeutic potential. Our results demonstrate the potential for PGS to uncover causal disease biology and targets with therapeutic potential, including those that may be missed by approaches utilizing information at a single locus.


PGS validation
The CAD PGS and IS PGS have been externally validated in the studies in which they were published 49,50 (see also their respective pages on the PGS Catalog, which also includes external validation by other independent studies, using their accession numbers PGS000018 and PGS00039 at https://www.pgscatalog.org/). The AF PGS, CKD PGS, and T2D PGS were derived de novo for this study. In each case, PGS derivation did not include INTERVAL participants, and thus was able to act as an external validation cohort. Here, we show strong association between AF PGS and 7.7 year risk of AF (Hazard Ratio: 1.72, 95% Confidence Interval: 1.20-2.47, two-sided P-value: 0.003; Extended Data Fig. 8b) and between T2D PGS and 7.7 year risk of T2D (Hazard Ratio: 2.00, 95% Confidence Interval: 1.36-2.94, two-sided Pvalue: 410 -4 ; Extended Data Fig. 8b) in Cox proportional hazard models adjusting for age and sex. There were insufficient events to test association between CKD PGS and 7.7 year disease risk (N=0 events; Extended Data Fig. 8a), however, we observed strong correlation between the CKD PGS and eGFR (Beta: −0.90 standard deviation change in eGFR per standard deviation increase in CKD PGS, 95% confidence interval: −1.45-−0.36, two-sided P-value: 0.001; Extended Data Fig. 8c). Decreased eGFR indicates decreased renal function, and is the primary method through which CKD is diagnosed in the clinic 98 .

Pathway enrichment
No pathways, gene sets, or protein-protein interaction networks were significant after multiple testing correction. An archive of the query can be found at https://biit.cs.ut.ee/gplink/l/aWke5_FsQ1 for the proteins associated with the CAD PGS, at https://biit.cs.ut.ee/gplink/l/UNpRmoI8QF for the proteins associated with the CKD PGS, and at https://biit.cs.ut.ee/gplink/l/3QIUMijpTa for the proteins associated with the T2D PGS.

Robustness to proteomics technology
To test the robustness of PGS to protein associations, we measured 1,463 proteins in 418 independent samples with no prevalent cardiometabolic disease using an orthogonal and complementary technology; the Illumina NovaSeq based Olink Explore quantitative protein assays. These allowed us to independently test 16 proteins with significant PGS to protein associations, showing that effect size estimates were strongly correlated between platforms (Extended Data Fig. 6a; Pearson r: 0.82, P-value: 9×10 -5 ). We also tested four of these proteins using the qPCR based Olink Target 96 (Olink T96) protein assays in 3,848 independent samples, where effect size estimates were again similar and three of four PGS associations remained statistically significant (Extended Data Fig. 6b).

Longitudinal stability
In a further 646 samples with SomaLogic protein data at baseline and Olink T96 protein data in samples taken two-years after baseline, we observed similar effect size estimates for these four PGS to protein associations (Extended Data Fig. 6c) with strong correlations in protein levels across time and between platforms (Pearson r: 0.50-0.70; Extended Data Fig. 6d), further indicating temporal stability of the effects of PGS on protein levels.

Replication of PGS to disease associations and mediation analysis
Replication of protein to disease associations were assessed in the 3,848 independent INTERVAL samples with Olink T96 assays (Supplementary Table 5). Participants with Olink T96 assay data (replication cohort) were non-overlapping with the 3,087 INTERVAL participants in the discovery cohort. Participants in the replication cohort were also older than those in the discovery cohort (median age 44.0), with median age 60.9 at time of protein quantification, and median follow-up was shorter (5.1 years) as protein quantification was performed approximately 2 years after that of the discovery cohort. Over this follow-up period, there were 31 CAD events and 50 T2D, approximately double that of the discovery cohort.
Four of the 49 PGS-associated proteins were quantified by the Olink T96: IGFBP2, IGFBP1, GRN, and TIMP4 (Extended Data Fig. 6b). In the discovery cohort, IGFBP1, IGFBP2, and TIMP4 were associated with incident T2D (Fig. 2b) and associations with incident T2D replicated for IGFBP1 and IGFBP2 in the replication cohort (Supplementary Table 5). The association between TIMP4 and incident T2D did not replicate and nor did the association between the T2D PGS and TIMP4 (Extended Data Fig. 6b, Supplementary Table 5). Associations between GRN and incident CAD were not significant in either the discovery or replication cohort (Fig. 2b, Supplementary Table 5). Mediation analysis supported causal mediation of T2D PGS to incident T2D associations for IGFBP1 and IGFBP2 in both the discovery and replication cohorts (Fig. 2d, Supplementary Table 5).

Supplementary Methods
Polygenic score derivation PGSs for AF, CKD, and T2D were derived by filtering their respective GWAS summary statistics to a highconfidence set of 2.3 million common variants LD-thinned at r 2 =0.9 threshold in the UK Biobank version 3 genotype data 100,101 (imputed to the 1000 Genomes, UK10K, and haplotype reference consortium (HRC) 102 panels). This variant set included only bi-allelic SNPs with minor allele frequency > 1% and imputation INFO score > 0.4, and excluded SNPs which could not reliably be matched between datasets due to strand ambiguity (A/T and G/C SNPs).

Mapping variants between datasets
Variants in PGSs or in GWAS summary statistics (for Mendelian randomisation; see below) were mapped to variants in the INTERVAL genotype data by genomic location and effect allele (or by rsid if genomic location not provided). If a variant matched by genomic location but the effect allele matched no alleles in the genotype data, the strand orientation of the alleles were flipped and checked for match again. Variants with potential for allele mismatch due to strand ambiguity (e.g. palindromic A/T and G/C variants) were excluded unless effect allele frequency information was provided in the PGS or GWAS summary statistics. Where effect allele frequency was provided, alleles were matched additionally by frequency (yes/no below 50%), and excluded where the minor allele frequency was greater than 42%.
When mapping GWAS summary statistics to INTERVAL for Mendelian randomisation (see below), multiallelic variants were removed prior to the procedure described above, and effect alleles were oriented to the minor allele in INTERVAL.

Estimated Glomerular Filtration Rate
Serum creatinine was quantified in 3,307 INTERVAL participants by Metabolon HD4 metabolomics in mg/dL units, and adjusted for sample measurement batch, sample measurement plate, and days between blood draw and sample processing. Subsequently, eGFR was quantified from serum creatinine using the CKD-EPI equation 99  Where SCr = serum creatinine in mg/dL. For men, k = 0.9 and α = −0.411, and for women, k = 0.7 and α = −0.329. All participants analysed were white British.

Averaging of association summary statistics across aptamers
Many proteins were targeted by more than one SomaLogic aptamer. For these proteins, each aptamer may target different components of the protein, with varying degrees of measurement error. Throughout, multiple aptamers targeting the same protein have been treated as independent measurements, and coefficients from statistical models have been averaged to obtain a single robust estimate for the respective protein. For linear coefficients (e.g. linear regression) the mean of the beta coefficient and 95% confidence intervals was taken across the multiple aptamers. For exponent coefficients (e.g. hazards and odds ratios) a log transform was applied prior to the mean, and exponent taken of the result.
To obtain an average of P-values across aptamers, the P-values were converted to Z-scores, the mean of Zscores computed, then converted back into a P-value. To ensure correct averaging of P-values from discordant directions of effect, two-sided P-values were converted to one-sided P-values with the alternate hypothesis chosen based on the sign of the beta coefficient (or log transformed coefficient in the case of hazard ratio or odds ratio) prior to Z-score conversion, then the resulting Z-score converted back to a two-sided P-value.

Pathway enrichment
We used the gProfiler tool 103 (https://biit.cs.ut.ee/gprofiler/gost) to test whether PGS to protein associations were enriched for known pathways (KEGG, Reactome, WikiPathways), gene ontology (GO) terms (biological process, molecular function, and cellular compartment), or protein-protein interaction networks (CORUM, Human Protein Atlas). The set of 3,424 UniProtKB identifies covering all measured proteins and protein complexes was used as the reference (background) set for each test.
Quantification of proteins using the qPCR-based Olink T96 platform was performed in 5,000 INTERVAL participants using Olink's cardiovascular II, cardiovascular III, and inflammation panels (https://www.olink.com/). In total 265 proteins were quantified by the three Olink T96 panels, of which 224 were also quantified on the SomaLogic platform, among which four were associated with any PGS (Extended Data Fig. 6b-e). All four of these proteins were quantified by the Olink T96 cardiovascular III panel. For this panel, protein levels on the log2 scale were regressed on age, sex, sample measurement plate, days between blood draw to sample processing, and season, then the residuals were inverse rank normal transformed.
Quantification of proteins using the Illumina NovaSeq-based Olink Explore platform was performed in 500 INTERVAL participants with Olink T96 data (Extended Data Fig. 6a,e). In total 1,463 proteins were quantified. Samples in the first column of each plate (N=50) were removed due to quality issues. No participants with Olink Explore data had SomaLogic protein data at baseline. Protein levels on the log2 scale were regressed on age, sex, sample measurement plate, and days between blood draw to sample processing, then the residuals were inverse rank normal transformed.

Physiological and environmental confounders
Sensitivity analysis were performed by additionally adjusting the PGS to protein aptamer linear regression models for BMI, circadian effects, and seasonal effects separately (Extended Data Fig. 6f).
BMI was calculated from self-reported weight and height. Fifteen samples with outlier BMI were removed by excluding samples with self-reported weight < 50 kg or > 160 kg; weights outside the range for NHS blood donation eligibility criteria (https://my.blood.co.uk/knowledgebase/Index/W), and samples with selfreported height < 1.47 m or > 2.1 m; clinical thresholds for dwarfism and gigantism respectively. After outlier exclusion the range of participant BMI was 17.0-55.8 (median 25.5). BMI was subsequently log transformed prior to model fitting. Mediation analysis was performed for six PGS to protein associations attenuated by BMI (Extended Data Fig. 6g), using BMI as the mediator and protein levels as the outcome.
To capture the potentially non-linear effects of circadian rhythm and season on protein levels, both were treated as a categorical variables by grouping samples into 10 equal duration bins, with the largest sample size bin acting as the reference group in the model. To model circadian effects, the time of day of sample draw was split into 10 bins, each 73.5 minutes in length, with sample sizes ranging from 62 to 405 (median: 267, interquartile range: 237-325) samples. Bin six, covering 2:08pm-3:21pm with 405 samples, was used as the reference group. Samples with no time of blood draw recorded (n=468; 15%) were excluded when adjusting for circadian effects. To model seasonal effects, the date of blood draw was split into 10 bins, each 50 days in length, with sample sizes ranging from 117 to 446 (median: 283, interquartile range: 274-396) samples. Bin four, covering the 9 th November 2012 -30 th December 2012 with 446 samples, was used as the reference group.

Cis-pQTL mapping
Previous pQTL mapping studies have focused on the identification of pQTLs genome-wide, requiring conservative P-value thresholds meaning many cis-pQTLs have been missed. Here, we applied a hierarchical multiple testing procedure 55 that has shown to best control false discovery rates in cis-eQTL mapping 56 to pQTL summary statistics for each SomaLogic aptamer published by Sun et al. 2018 26 . First, (1) P-values within 1Mb of any gene encoding the target protein were Bonferroni corrected for the number of tests within the respective cis window(s) to obtain locally corrected P-values, then (2) the smallest locally corrected Pvalue was taken for each of the 3,793 aptamers, then (3) FDR correction was applied across all aptamers to obtain a single globally corrected P-value for each aptamer, then (4) globally corrected P-values were filtered at FDR < 0.05 to identify aptamers with cis-pQTLs, finally (5) the locally corrected P-value corresponding to the global FDR = 0.05 threshold (locally corrected P = 0.0124) was used as a significance threshold to identify significant cis-variants for each of the aptamers with cis-pQTLs. Cis-pQTLs were identified for 890 proteins (26% of the measured proteome), an increase of 369 proteins over those reported by Sun et al. 2018 26 using a genome-wide P-value threshold of P < 1.5×10 -11 . Significant cis-pQTLs mapped here are provided in Supplementary Data 4.

Mendelian randomisation
The Ensembl variant effect predictor (VEP) 67 was used to identify significant cis-pQTLs that result in changes in protein structure, for which the association between the variant and protein may reflect differences in aptamer binding affinity rather changes in protein levels. These variants, and any in LD (r 2 > 0.1) were excluded from the candidate set of genetic instruments. LD was computed between all cis-pQTLs for each aptamer from the INTERVAL probabilistic genotype data using QCTOOL 105 version 2 and LDstore 106 . Among the 24 PGS associated proteins with any significant cis-pQTLs, 15 included those associated with changes in protein structure (26 variants in total, comprising 24 missense mutations, 1 splice acceptor variant, and 1 splice region variant), including 7 of the 11 proteins tested in Mendelian randomisation analysis (CFH, CFI, CST3, FAH, SHBG, VWC2, and WFIKKN2; Extended Data Fig. 9).
Genetic instruments for each aptamer were selected using a step-forward procedure that pruned the set of significant cis-pQTLs by LD after mapping pQTL to GWAS summary statistics. For each aptamer and GWAS, cis-pQTLs were sorted by P-value and the variant with the smallest P-value added to the set of independent cis-pQTLs. Then, the remaining list of cis-pQTLs was filtered to remove any variants with r 2 > 0.1 with any variants in the set of independent cis-pQTLs. This was repeated until no variants remained in the list of cis-pQTLs for the aptamer which mapped to the respective GWAS summary statistics (Supplementary Table 7).
Five Mendelian randomisation methods were used in parallel to estimate the causal effects of protein levels on disease (Supplementary Table 6), as each method makes different assumptions about instrument validity and are differentially robust to different sources of bias: (1) The inverse-variance weighted estimator, which takes the average causal estimate across all instruments: the average of their ratios of disease log odds over effect on protein levels multiplied by their standard errors 63 . It assumes all instruments are valid: the variant is associated with both the protein and disease, and that the variant has only a direct effect on that protein's levels; it can only effect the disease by modifying the protein's levels 63 .
(2) The simple median estimator takes the median causal effect across instruments and is robust to outliers and up to 50% invalid instruments 64 .
(3) The weighted median estimator, which weights each instrument by its inverse variance when calculating the median 64 . (4) The weighted mode estimator, which estimates the average causal effect from the largest set of instruments with consistent causal estimates 65 . (5) The Egger estimator which detects and corrects for bias arising from horizontal pleiotropy; where instruments are affecting other risk factors that may explain their observed association with the disease 66 . Where there were multiple aptamers meeting the criteria for Mendelian randomisation for a given protein, causal estimates from the five Mendelian randomisation methods as well as the Egger intercept were averaged across the multiple aptamers (Supplementary Table  6). A consensus causal estimate was subsequently obtained by taking the median estimate, standard error, 95% confidence interval, and P-value across the five Mendelian randomisation methods.

Colocalisation analysis
Bayesian colocalisation tests were performed between pQTL and GWAS signals to test whether both signals arose from a shared causal variant 69 . Tests were performed for cis-pQTLs used in Mendelian randomisation (Supplementary Table 7) with P-value < 1×10 -6 in the respective GWAS, the suggested threshold for colocalisation testing 69 . For each test, SNPs within 200Kb of the cis-pQTL that mapped to the GWAS summary statistics (see above) were used. Colocalisation was tested using the R package coloc 69 using summary-statistics based method (coloc.abf) with the default prior probabilities. For each test, five posterior probabilities were computed (Supplementary Table 7): PP.H0: no association with either the cis-pQTL or GWAS trait, PP.H1: significant cis-pQTL association but no association in GWAS, PP.H2: significant GWAS signal, but no association with the protein, PP.H3: cis-pQTL and GWAS signals arise from two independent causal variants, and PP.H4: cis-pQTL and GWAS signals arise from a shared causal variant.
Evidence of colocalisation was defined as previously suggested 107 as PP.H3 + PP.H4 ≥ 0.99 and PP4/PP3 ≥ 5: i.e. posterior probability of a shared causal variant 5-fold greater than posterior probability for associations arising from independent causal variants, and combined both posterior probabilities summing to 99%).

Supplementary Table Legends
Supplementary Hospital records in the 13.4 years prior to baseline assessment were summarised into 301 CALIBER phenotypes. Among those, we considered 48 to potentially indicate the presence of overt or sub-clinical cardiometabolic disease at baseline assessment and with potential to confound causal inference (Fig. 2a). Participants with hospital record for any of these events in the 13.4 years prior to baseline were excluded from analyses (N=87 of 3,174 participants excluded). This table details the 48 prevalent cardiometabolic disease phenotypes, the ICD-10 codes used to define them, and the number of participants hospitalised with that phenotype prior to baseline. Percentages given are relative to the total number of excluded participants. ICD-10 codes were obtained from https://github.com/spiros/chronological-mapphenotypes (git commit 32d55e48 on 23 rd March 2020).

Supplementary Table 2: Sensitivity and specificity of protein aptamers
For each of the 49 PGS-associated proteins (Fig. 1b, Extended Data Fig. 3), provides details of the intended target for each SomaLogic aptamer passing quality control. Aptamer: Sequence ID for the SomaLogic aptamer(s) targeting the protein. Intended Target: full name of the intended target for each aptamer as provided by SomaLogic. Note that these may be protein complexes or specific protein isoforms. UniProt: The UniProtKB identifier for the intended target protein. Gene: gene symbol for the encoding gene. Entrez ID: the Entrez identifier in NCBI Gene. Chr: the chromosome the gene is located on, as listed on the NCBI gene page for the gene. Start: the start position of the gene on that chromosome on human genome the build GRCh37. Cross-reactivity: summary of cross-reactivity testing performed by SomaLogic for the aptamer against any proteins with >40% sequence homology. Entries may be a blank cell, indicating the aptamer has not yet been tested by SomaLogic, "no closely related human proteins" where the aptamer has been tested but there were no proteins with >40% sequence homology to the target protein, "no binding observed", "binding observed with at least 10x weaker affinity", or "Binding observed with similar affinity". Mass Spec confirmation: indicates whether binding of the aptamer to the target protein has been confirmed by mass spectrometry pulldown experiments either by SomaLogic (see the technical note published by SomaLogic at https://somalogic.com/technology/our-platform/somamer-specificity/) or by Emilsson et al. 2018 108 (see Supplementary Tables 3 and 4 in Emilsson et al. 2018 108 ). cis pQTL: "yes" where aptamer had a significant cis-pQTL after hierarchical multiple testing correction (Supplementary Information), supporting the aptamer to target binding. In cases where aptamers bound another protein or differentially bound to specific protein isoforms the protein target in the main text (Extended Data Fig. 4) reflects this.

Supplementary Table 3: Summary statistics for PGS-associated proteins with multiple aptamers
Five of the 49 PGS-associated proteins (Fig. 1b) were targeted by multiple aptamers. Aptamer: sequence identifier for the SomaLogic SOMAscan aptamer targeting the listed protein. Columns and statistical tests are as described in Extended Data Fig. 3 and Extended Data Fig. 6f. From left to right; Beta: standard deviation change in protein aptamer levels per standard deviation increase in PGS in linear regression adjusting for age, sex, 10 genotype PCs, sample measurement batch, and time between blood draw and sample processing. 95% CI: 95% confidence interval for the beta estimate. P-value: two-sided P-value from linear regression, not corrected for multiple testing. HR: hazard ratio for 7.7 year risk of hospitalisation with the respective disease (indicated in sub-headings) conferred per standard deviation increase in protein aptamer levels in cox proportional hazard models using follow-up as time scale and adjusting for age, sex, sample measurement batch, and time between blood draw and sample processing. 95% CI: 95% confidence interval for the hazard ratio. P-value: Two-sided P-value, not corrected for multiple testing. % PGS Mediated: Percentage of total association between the respective PGS and 7.7 year risk of hospitalisation with the respective disease mediated by the respective protein aptamer. 95% CI: 95% confidence interval for the % PGS mediated. P-value: two-sided P-value, not corrected for multiple testing. Linear regression, cox proportional hazard models, and mediation analysis were all performed in the same n=3,087 independent INTERVAL participants. Rows under the "Effect of PGS through BMI" and "Effect of PGS independent of BMI" contain entries where the PGS-protein association was attenuated (two-sided P-value > 0.05) when additionally adjusting the linear regression described above for BMI in n=3,072 participants with nonmissing BMI (Extended Data Fig. 6f). Entries contain results from mediation analysis in n=3,072 participants testing whether PGS to protein aptamer associations were mediated by BMI. For "Effect of PGS through BMI": Beta: estimated effect of PGS on the protein aptamer levels through BMI (standard deviation change in protein levels through BMI per standard deviation increase in PGS). 95% CI: 95% confidence interval. P-value: two-sided P-value, not adjusted for multiple testing correction. For "Effect of PGS independent of BMI": estimated effect of PGS on the protein aptamer levels independent of BMI. 95% CI: 95% confidence interval. P-value: two-sided P-value, not adjusted for multiple testing correction. Lighter text indicates P > 0.05 for the respective statistical test.

Supplementary Table 4: Independent contributions of PGS and pQTLs to protein levels
Summary statistics from linear regression of protein aptamer levels on PGS levels and pQTLs adjusting for age, sex, 10 genotype PCs, sample measurement batch, and time between blood draw and sample processing in n=3,087 INTERVAL participants for the 49 PGS-associated proteins from Fig. 1b. PGS and protein columns indicate the PGS and protein in the respective statistical test, and the Aptamer column gives the SomaLogic sequence identifier for the specific aptamer used in the statistical test. Under the "protein information" heading, Full name gives the full name of intended target for the aptamer as provided by SomaLogic (note that these may be protein complexes or specific protein isoforms). UniProt: The UniProtKB identifier for the intended target protein. Gene: gene symbol for the encoding gene. chr: the chromosome the gene is located on, as listed on the NCBI gene page for the gene. start: the start position of the gene on that chromosome on human genome the build GRCh37. Entries in the coefficient column indicate which dependent variable in the linear regression the corresponding summary statistics pertain; either "PGS", or the rsID for a pQTL. For pQTLs, their chromosome (Chr) and position (Pos) on build GRCh37 are given, along with the effect allele (EA) whose probabilistic dosage was used as a dependent variable in the linear regression, the other allele (OA), effect allele frequency (EAF), and whether the pQTL is a cis-or trans-pQTL. PQTLs included as dependent variables were (1) conditionally independent cis-and trans-pQTLs reaching genome-wide significant (P < 1.5×10 -11 ) in Sun et al. 2018 26 and (2) additional cis-pQTLs mapped in this study (significant after hierarchical multiple testing correction). Beta: beta estimate for the dependent variable (coefficient column) in the linear regression, indicating standard deviation change in protein aptamer levels per standard deviation increase in PGS or per copy of the pQTL effect allele. SE: standard error of the beta estimate. L95: lower bound of the 95% confidence interval of the beta estimate. U95: upper bound of the 95% confidence interval of the beta estimate. P-value: two-sided P-value for the dependent variable in the linear regression, not adjusted for multiple testing.

Supplementary Table 5: Replication of PGS to protein to disease associations and mediation analysis
For the four PGS-associated proteins from Fig. 1b quantified by Olink T96 protein assays, shows associations with PGS, incident disease, and mediation analysis in the n=3,087 INTERVAL participants with SomaLogic proteomics ("discovery cohort", i.e. results from main analyses detailed in Extended Data Fig. 3) alongside results in an independent set of 3,848 independent INTERVAL participants with Olink T96 protein assays quantified 2 years after baseline assessment ("replication cohort"). Participants in the replication cohort were not part of the discovery cohort, and people with prevalent cardiometabolic disease at time of Olink T96 protein quantification were excluded. Participants in the replication cohort were older than those in the discovery cohort (median age 44.0 in the discovery cohort, 60.9 in the replication cohort) and had shorter follow-up time (median follow-up 7.7 years in the discovery cohort, 5.1 years in the replication cohort) as protein quantification in the replication cohort was performed approximately 2 years after that of the discovery cohort. Over this follow-up period, there were 31 CAD events and 50 T2D events in the replication cohort, and 15 CAD events and 27 T2D events in the discovery cohort. Under the "PGS to protein association" heading; beta: standard deviation change in protein levels per standard deviation increase in PGS (from Fig. 1b) in linear regression adjusting for age, sex, 10 genotype PCs, and platform specific technical covariates. 95% CI: 95% confidence interval of the beta estimate. P-value: two-sided P-value. FDR: Benjamini-Hochberg false discovery rate corrected P-value. FDR correction was applied separately for each PGS to all 3,438 P-values from linear regression of each of the 3,438 measured proteins on the respective PGS in the discovery cohort. Polygenicity: proportion of the genome (%) required to explain the PGS to protein association (from Fig. 1c). Replicates: "Yes" or "No" depending on whether the two-sided P-value < 0.05 in the replication cohort. Under the "Protein to incident disease association heading": HR: hazard ratio for risk of hospitalisation with the respective disease over the respective follow-up period conferred per standard deviation increase in protein levels in cox proportional hazard models using follow-up as time scale and adjusting for age at protein measurement, sex, and platform specific technical covariates. 95% CI: 95% confidence interval for the hazard ratio. P-value: Two-sided P-value, not corrected for multiple testing. Replicates: "Yes" or "No" depending on whether the two-sided P-value < 0.05 in the replication cohort, or "-" where the association was not significant in the discovery cohort. Under the "Causal effect of PGS on disease risk through protein in mediation analysis" heading: % PGS Mediated: Percentage of total association between the respective PGS and risk of hospitalisation with the respective disease over the respective followup period mediated by the respective protein. In the discovery cohort, the total odds ratio for CAD conferred per standard deviation increase in CAD PGS was 2.51 (95% CI: 1.50-4.22, P-value: 510 −4 ) and the total odds ratio for diabetes conferred per standard deviation increase T2D PGS was 1.97 (95% CI: 1.34-2.91, Pvalue: 610 −4 ) in logistic regression adjusting for age, sex, and 10 genotype PCs in n=3,087 participants. In the replication cohort, the total odds ratio for CAD conferred per standard deviation increase in CAD PGS was 1.33 (95% CI: 0.94-1.89, P-value: 0.11) and the total odds ratio for diabetes conferred per standard deviation increase T2D PGS was 1.76 (95% CI: 1.32-2.34, P-value: 110 −4 ) in logistic regression adjusting for age, sex, and 10 genotype PCs in n=3,848 participants. 95% CI: 95% confidence interval for the % PGS mediated. P-value: two-sided P-value. Replicates: "Yes" where the causal mediation analysis had P < 0.05 in the replication cohort, "-" where the PGS to protein association or protein to disease association was either not significant, or did not replicate (and thus mediation analysis P > 0.05 in both cohorts).

Supplementary Table 6: Causal estimates from Mendelian randomisation analysis
From right to left: entries under the "Aptamer specific estimates" detail the causal estimates from five Mendelian randomisation methods for each PGS-associated protein aptamer with at least three independent genetic instruments (Supplementary Table 7), which were optimised per-aptamer. All five methods are twosample; utilising information from pQTL summary statistics and GWAS summary statistics (Supplementary Table 7). Estimate: estimated causal effect of a standard deviation increase in the respective protein levels on the log odds for the risk of the respective disease (as defined in the respective GWAS; Supplementary Table 7) using the respective Mendelian randomisation method. L95: lower bound for the 95% confidence interval of the causal estimate. U95: upper bound for the 95% confidence interval. P-value: two-sided Pvalue for the causal estimate. Rows with "(Intercept)" in the "Method" column detail the estimate of the intercept in Egger regression. Under the "Causal estimate from each Mendelian randomisation method" heading, causal estimates and 95% confidence intervals for each method have been averaged where there were multiple aptamers targeting the protein and P-values obtained by averaging corresponding Z-scores. Under the "Consensus Causal Estimate" heading the median estimated causal effect across the five Mendelian randomisation methods is given (excluding the Egger intercept term). These correspond also to the summary statistics detailed in Extended Data Fig. 9a. The Pleiotropy P-value column corresponds to the P-value from the Egger intercept term, which indicates where P < 0.05, confounding of the causal estimate by associations between genetic instruments (cis-pQTLs) with multiple disease risk factors (horizontal pleiotropy). The Colocalisation column indicates whether there was evidence of pQTL and GWAS signals arising from a shared causal variant for any of the genetic instruments used for Mendelian randomisation (Supplementary Table 7).

Supplementary Table 7: Genetic instruments for Mendelian randomisation analysis
Genetic instruments used in Mendelian randomisation analysis for aptamers with three or more independent by LD (r 2 < 0.1) cis-pQTLs that mapped to disease GWAS summary statistics. Entries under the "Protein information" column give the chromosome and start location of the encoding gene on human genome build GRCh37. See . In all cases, we used the GWAS summary statistics for the samples of recent European ancestry. For T2D, we used the BMI-adjusted GWAS summary statistics in order to avoid false positive causal estimates arising where pQTL variants influence T2D risk through BMI rather than through the tested protein (horizontal pleiotropy). The EAF columns give the frequency of the effect allele in the pQTL summary statistics and GWAS summary statistics respectively. Colocalization of pQTL and GWAS signals was tested where the two-sided P-value < 110 -6 for the instrument in the GWAS summary statistics. Colocalisation was tested using variants within 200Kb of the listed instrument. "Variants" lists the number of variants in that window that mapped between the GWAS and pQTL summary statistics. PP.H0, PP.H1, PP.H2, PP.H3, and PP.H4 give posterior probabilities from the colocalisation test for: (PP.H0) no association with either the cis-pQTL or GWAS trait, (PP.H1) significant cis-pQTL association but no association in GWAS, (PP.H2): significant GWAS signal, but no association with the protein, (PP.H3) cis-pQTL and GWAS signals arise from two independent causal variants, and (PP.H4) cis-pQTL and GWAS signals arise from a shared causal variant. Evidence of colocalisation (Yes/No in the colocalises column) was defined as PP.H3 + PP.H4 ≥ 0.99 and PP4/PP3 ≥ 5 (posterior probability of a shared causal variant 5-fold greater than posterior probability for associations arising from independent causal variants, and combined both posterior probabilities summing to 99%).

Supplementary Table 8: Comparison to proteome-wide T2D associations in AGES-Reykjavik
For the 31 proteins associated with the T2D PGS in Fig. 1b, compares associations between protein levels and incident T2D in INTERVAL from Fig. 2b, causal mediation analyses from Fig. 2d, and Mendelian randomisation analyses from Extended Data Fig. 9 to associations with prevalent and incident T2D and Mendelian randomisation analyses of the AGES-Reykjavik cohort published by Gudmundsdottir et al. 2020 35 . The first set of columns contain details of the cox proportional hazards models testing the association between the protein levels and 7.7 year risk of hospitalisation with T2D adjusting for age, sex, sample measurement batch, and time between blood draw and sample measurement in n=3,087 INTERVAL participants from Fig. 2b. Here, the log of the hazard ratio (log HR) is detailed, along with its 95% confidence interval (L95: lower bound, U95: upper bound) and two-sided P-value. The "P < 0.0012" column indicates whether the P-value was significant in Fig. 2b after Bonferroni correction for the 42 protein to disease association tests. The second set of columns contain details from the logistic regression of incident T2D on protein levels adjusting for age and sex performed by Gudmundsdottir et al. 2020  give the lower and upper bounds of the 95% confidence interval of the % PGS mediated respectively. Pvalue: two-sided P-value. The "P < 0.0012" column indicates whether the P-value was significant in Fig. 2d after Bonferroni correction for the 42 mediation analyses. The fifth set of columns contains details from the two-sample Mendelian randomisation analysis performed here and detailed in Extended Data Fig. 5 and Supplementary  Table 6, indicating the estimated causal effect on the log odds of T2D per standard deviation increase in the respective protein levels. L95 and U95 give the lower and upper bounds of the 95% confidence interval of the causal estimate respectively. P-value: two-sided P-value. The "P < 0.0038" column indicates whether the P-value was significant in Extended Data Fig. 9 after Bonferroni correction for the 13 Mendelian randomisation performed tests. The consistent direction column indicates (with "yes") whether the direction of the estimated causal effect is consistent with the direction of the T2D PGS to protein association and protein to incident disease association. Proteins missing entries lacked sufficient genetic instruments for our Mendelian randomisation analysis. Finally, the sixth column set contains summary statistics from the Mendelian randomisation analysis performed by Gudmundsdottir et al.

Supplementary Table 9: Drugs interacting with druggable causal PGS-associated proteins
Information on the 76 drugs in DrugBank (https://go.drugbank.com/) that interact with the druggable causal PGS-associated proteins listed in Table 1. Columns under the "Effect of drug on PGS-associated protein" heading detail the effect the listed drug has on the PGS-associated protein. Proteins may either be targets of the drug, carriers of the drug, enzymes modulated by the drug, or cellular transporters affected by the drug. The "Method of drug action" column indicates whether the PGS-associated protein is among the proteins through which the drug's method of pharmacological action is known to take place. Columns under the "Drug details" heading give the description of the drug provided by DrugBank, the type of the drug (small molecule or biotech), and the groups the drug belongs to in DrugBank (approved, nutraceutical, investigational, experimental, and/or withdrawn). Columns under the "Pharmacology" heading provided information from the following fields in DrugBank. "Indication": a descriptive field describing the approved conditions, diseases, or states for which a drug can safely and effectively be used. "Pharmacodynamics": A description of how the drug modifies or affects the body. "Mechanism": a description of the biochemical interaction through which the drug produces its intended effect. Columns under the "Proteins interacting with drug" heading provide a listing of human proteins that are known to interact with the drug in some way. Columns and rows may need to be resized to view the full contents of descriptive fields of interest. Pharmacological targets: a comma separate list of drug to molecule (e.g. protein) binding or interactions describing how the drug exerts its pharmacological action. Other targets: a comma separate list of drug to molecule (e.g. protein) binding or interactions excluding targets through which the drug exerts its pharmacological action (targets to which the drug binds but has no effect, or unknown effect). Enzymes: a comma separated list of enzymes (e.g. proteins) which metabolise the drug. Carriers: a list of molecules (e.g. proteins) that bind the drug and facilitate its movement within the body to the target molecule. Transporters: a comma separated list of cell membrane bound proteins that facilitate transport of the drug into and out of cells. Columns under the "Maximum phase clinical trials" heading provide details on the set of clinical trials that have reached (or are underway) the most advanced phase among all clinical trials listed on the DrugBank page for the drug. Max phase: maximum phase reached in any clinical trial listed under the Clinical Trials heading on the drugs DrugBank page. Status: status or outcome of that clinical trial. Purpose: purpose of that clinical trial. Indications: list of conditions the clinical trial was testing the drug for. Distinct clinical trials are separated by a " | ", while clinical trials that target multiple conditions have those conditions separated by a " / ".

Supplementary Data 1: Curated SomaLogic aptamer information.
Annotations for the 4,034 SOMAscan aptamers quantified in the INTERVAL cohort. QC tag: reason for aptamer exclusion. The 3,793 high-quality aptamers are tagged "Protein", with other values indicating the exclusion reason provided by SomaLogic. Under the "Protein Information" heading: full name of the intended target for each aptamer as provided by SomaLogic. Note that these may be protein complexes or specific protein isoforms. UniProt: The UniProtKB identifier for the intended target protein. Under the "Encoding Gene" heading: Gene: Gene(s) encoding the targeted protein. Entrez: the Entrez identifier in NCBI Gene. Chr: the chromosome the gene is located on, as listed on the NCBI gene page for the gene. Start: the start position of the gene on that chromosome on human genome the build GRCh37. Where multiple proteins were targeted, their corresponding UniProt identifiers and encoding genes are separated by a "|", as are their respective chromosomes, start, and end positions on build GRCh37. Genes with multiple start sites have these separated by a ";". Under the "aptamer specificity" heading: Cross-reactivity: summary of cross-reactivity testing performed by SomaLogic for the aptamer against any proteins with >40% sequence homology. Entries may be a blank cell, indicating the aptamer has not yet been tested by SomaLogic; "under investigation", indicating ongoing experiments by SomaLogic; "no closely related human proteins" where the aptamer has been tested but there were no proteins with >40% sequence homology to the target protein; "no binding observed", "binding observed with at least 10x weaker affinity", or "Binding observed with similar affinity" for proteins with >40% sequence homology with the intended target. Mass Spec confirmation: indicates whether binding of the aptamer to the target protein has been confirmed by mass spectrometry pulldown experiments either by SomaLogic (see the technical note published by SomaLogic at https://somalogic.com/technology/our-platform/somamer-specificity/) or by Emilsson et al. 2018 108 (see Supplementary Tables 3 and 4

Supplementary Data 2: Aptamer distributions and covariate associations a) Aptamer distributions and covariate associations in the raw data.
For each aptamer, summarises the distribution of the aptamer's levels prior to quality control, covariate adjustment, and normalisation (columns F to M), and details the association in linear regression (n=3,087 participants) between the raw aptamer levels and the covariates that the aptamer levels were adjusted for prior to inverse rank normalisation by Sun et al. 2018 26 (age, sex, first three genotype PCs, time between blood draw and sample processing >1 day, columns N to AK), sample measurement batch (columns BN-BQ) which was additionally regressed out in this study, and covariates adjusted for in sensitivity analysis (Extended Data Fig. 6f) (BMI, season, time of day; columns BR to ES). The Aptamer Annotation columns (columns B to D) provide details on the intended target of each Aptamer (column A): the intended protein target as provided by SomaLogic, the UniProt KB identifier for the target protein(s), and the gene(s) encoding the protein. Where multiple proteins were targeted, their corresponding UniProt identifiers and encoding genes are separated by a "|", as are their respective chromosomes and start positions on build GRCh37. Genes with multiple start sites have these separated by a ";". Under the "Summary of apatamer level distribution" heading: Minimum: the smallest value for the aptamer level in the 3,087 samples without prevelant cardiometabolic disease. N < 5 SD: number of samples at least 5 standard deviations below the mean. N > 5 SD number of samples at least 5 standard deviations above the mean. Under the covariate association headings: Beta: beta estimate in linear regression model fit for the apatamer levels on that covariate in the n=3,087 INTERVA participants, indicating change in protein levels per unit increase in the covariate. L95: lower bound of the 95% confidence interval. U95: upper bound of the 95% confidence interval. P-value: two-sided P-value for the beta-estimate in linear regression. Point estimates are shown in black where P < 0.05, and are dulled in grey where P > 0.05. For covariates that were fit as factors (sex, batch, time between blood draw and sample processing, date of sample collection, time of day of sample collection) the group to which the beta estimate pertains is detailed in the second header row, as is the reference group to which it is compared to in the linear regression model. Aptamers pertaining to the PGS associated proteins (Supplementary Table 2) are detailed at the top of the table, and are indicated by the "Associated with PGS" column (column E). Proteins are ordered top to bottom in ascending order by association with any PGS (smallest FDR correct P-value across the five PGSs from Supplementary Data 3a). b) Aptamer distributions and covariate associations after quality control and normalisation. Columns are as described in Supplementary Data 2a. Here, the unit of effects of covariates on normalised aptamer levels (Beta, 95% confidence interval) are standard deviation change in protein levels per unit increase in covariates. All P-values are two-sided and associations were fit with linear regression in n=3,087 participants, as described in Supplementary Data 2a.

Supplementary Data 3: Summary statistics for statistical tests.
Each sheet a-j contains a distinct set of summary statistics. The figures which they underlie are noted in the first sentence of each caption. a) Summary statistics underlying Fig. 1a-b. Details associations between the each of the five cardiometabolic PGSs with each of the 3,793 SomaLogic aptamers targeting 3,438 proteins in linear regression adjusting for age, sex, 10 genotype PCs, sample measurement batch, and time between blood draw and sample processing in n=3,087 INTERVAL participants free of cardiometabolic disease. Under the "Protein Information" heading: full name of the intended target for each aptamer as provided by SomaLogic. Note that these may be protein complexes or specific protein isoforms. UniProt: The UniProtKB identifier for the intended target protein. Gene: gene symbol for the gene(s) encoding for the protein. Entrez ID: the Entrez identifier in NCBI Gene. Chr: the chromosome the gene is located on, as listed on the NCBI gene page for the gene. Start: the start position of the gene on that chromosome on human genome the build GRCh37. Where multiple proteins were targeted, their corresponding UniProt identifiers and encoding genes are separated by a "|", as are their respective chromosomes and start positions on build GRCh37. Genes with multiple start sites have these separated by a ";". Under the "Aptamer association" heading: beta: beta estimate from linear regression described above, indicating standard deviation change in protein aptamer levels per standard deviation increase in the respective PGS. L95: lower bound of the 95% confidence interval. U95: upper bound of the 95% confidence interval. P-value: two-sided P-value. The "Aptamer ID" gives the SomaLogic sequence ID for the aptamer tested as the dependent variable. For proteins targeted by more than one aptamer, beta estimates and 95% confidence intervals were averaged, and two-sided P-value obtained by averaging Z-scores. These are reported under the "Protein association" heading. FDR: Benjamini-Hochberg false-discovery rate adjusted two-sided P-value, applied across all 3,438 protein associations for each PGS separately. Fig. 6a-c. Details PGS to protein associations assessed using orthogonal proteomics technology (Olink Explore and Olink T96). Note each figure panel comprises PGS to protein associations assessed in different groups of samples using different proteomics technology, as described in Extended Data Fig. 6a-c. For rows corresponding to Extended Data Fig. 6a, linear regression were fit for each protein in an independent set of n=418 INTERVAL participants (participants not included in the main analyses, e.g. Fig. 1) in which protein levels were measured with the Olink Explore platform. For rows corresponding to Extended Data Fig. 6b, linear regression were fit for each protein in an independent set of n=3,848 INTERVAL participants (participants not included in the main analyses, e.g. Fig.  1) in which protein levels were measured with the Olink T96 platform. For rows corresponding to Extended Data Fig. 6c, linear regression were fit for each protein in n=646 INTERVAL participants for which protein levels were measured with both the SomaLogic platform and Olink Explore platform (these 646 participants were included in the main analyses, e.g. Fig. 1). Associations were adjusted for age, sex, 10 genotype PCs, and platform specific technical covariates. Beta: beta estimate from linear regression, indicating standard deviation change in protein levels per standard deviation increase in the PGS. L95: lower bound of the 95% confidence interval. U95: upper bound of the 95% confidence interval. P-value: two-sided P-value. Fig. 6f. Details PGS to protein associations from Fig. 1b and Extended Data Fig. 3 in linear regression after (1) additionally adjusted for circadian effects (time of day of blood draw; n=3,087 participants), (2) additionally adjusted for seasonal effects (date of blood data; n=3,087 participants), (3) when including 87 additional participants with prevalent cardiometabolic disease (n=3,174 participants), and (4) when adjusting for BMI (n=3,072 participants with non-missing BMI). All associations were testing using linear regression adjusting for age, sex, 10 genotype PCs, sample measurement batch, and time between blood draw and sample measurement in addition to the covariates noted above. Under the "Aptamer association" heading: beta: beta estimate from linear regression described above, indicating standard deviation change in protein aptamer levels per standard deviation increase in the respective PGS. L95: lower bound of the 95% confidence interval. U95: upper bound of the 95% confidence interval. P-value: two-sided P-value. The "Aptamer ID" gives the SomaLogic sequence ID for the aptamer tested as the dependent variable. For proteins targeted by more than one aptamer, beta estimates and 95% confidence intervals were averaged, and two-sided P-value obtained by averaging Z-scores. These are reported under the "Protein association" heading. Fig. 6g. Extended details from mediation analysis of T2D PGS to protein associations by BMI in n=3,072 INTERVAL participants. The natural direct effect corresponds to the effect of T2D PGS on protein levels independent of BMI. The natural indirect effect corresponds to the effect of T2D PGS on protein levels through BMI. The total effect, not detailed in Extended Data Fig. 6g, corresponds to the sum of the natural direct effect and natural indirect effect. Units of point estimates (Beta) are standard deviation change in protein levels per standard deviation increase in T2D PGS. L95 and U95 correspond to the lower and upper bounds of the 95% confidence interval respectively. P-value: two-sided P-value. The % of total effect was calculated by dividing the corresponding point estimate by the point estimate for the total effect for the PGS to protein association, and is reported as % PGS mediated in Extended Data Fig. 6g for the natural indirect effect. For proteins targeted by more than one aptamer (WFIKKN2), beta estimates and 95% confidence intervals were averaged, and two-sided Pvalue obtained by averaging Z-scores. Fig. 7 and polygenicity calculations. Each PGS was partitioned into 1,703 approximately independent LD blocks then tested for association with each protein in linear regression of protein aptamer levels on the PGS computed from the LD block, adjusting for age, sex, 10 genotype PCs, sample measurement batch, and time-between blood draw and sample processing in n=3,087 INTERVAL participants. Columns under the "LD block" heading provide information on the genomic location (human genome build GRCh37) and total size (in base pairs) for each of these 1,703 LD blocks. Under the "Aptamer association" heading: beta: beta estimate from linear regression described above, indicating standard deviation change in protein aptamer levels per standard deviation increase in the respective PGS computed from the respective LD block. L95: lower bound of the 95% confidence interval. U95: upper bound of the 95% confidence interval. P-value: two-sided P-value. The "Aptamer ID" gives the SomaLogic sequence ID for the aptamer tested as the dependent variable. For proteins targeted by more than one aptamer, beta estimates and 95% confidence intervals were averaged, and two-sided P-value obtained by averaging Z-scores. These are reported under the "Protein association" heading. Fig 1c. Following on from Supplementary Data 3e, LD blocks were sequentially removed from each PGS to protein association in ascending order of two-sided P-value (see Supplementary Data 3e) until the PGS to protein association was attenuated (two-sided P-value > 0.05) when fitting linear regression in n=3,087 participants adjusting for age, sex, 10 genotype PCs, sample measurement batch, and time-between blood draw and sample processing. This table details the PGS to protein association at each step in this process until the association was attenuated. At each step, the number of LD blocks removed from the PGS are given, along with the total base pairs in the genome these LD blocks covered, and the total % of the genome removed from the PGS. Under the "Aptamer association" heading: beta: beta estimate from linear regression described above, indicating standard deviation change in protein aptamer levels per standard deviation increase in the respective PGS computed after removing the respective LD blocks. L95: lower bound of the 95% confidence interval. U95: upper bound of the 95% confidence interval. P-value: two-sided P-value. The "Aptamer ID" gives the SomaLogic sequence ID for the aptamer tested as the dependent variable. For proteins targeted by more than one aptamer, beta estimates and 95% confidence intervals were averaged, and two-sided P-value obtained by averaging Z-scores. These are reported under the "Protein association" heading. The final row for each PGS to protein association, where the P-value > 0.05, gives the polygenicity of the PGS to protein association reported in Fig. 1c in the % removed column.

f) Summary statistics underlying polygenicity calculations shown in
g) Summary statistics underlying Fig. 2b. Association between PGS-associated proteins with 7.7 year risk of hospitalisation with CAD or T2D in n=3,087 INTERVAL participants in cox proportional hazards models adjusting for age, sex, sample measurement batch, and time between blood draw and sample processing. Under the "Aptamer association" heading: HR: hazard ratio for 7.7 year risk of hospitalisation with the respective disease per standard deviation increase in protein aptamer levels. L95: lower bound of the 95% confidence interval. U95: upper bound of the 95% confidence interval. P-value: two-sided P-value. The "Aptamer ID" gives the SomaLogic sequence ID for the aptamer tested as the dependent variable. For proteins targeted by more than one aptamer, hazard ratios and 95% confidence intervals were obtained by averaging the log hazard ratio and its 95% confidence interval, and two-sided P-value obtained by averaging Z-scores. These are reported under the "Protein association" heading.
h) Summary statistics underlying Fig. 2d. Extended details from mediation analysis of PGS to protein to incident disease associations in n=3,087 INTERVAL participants adjusting for age, sex, 10 genotype PCs, sample measurement batch, and time between blood draw and sample processing. The natural direct effect corresponds to the effect of the respective PGS on the log odds of 7.7 year risk of hospitalisation with the respective disease independent of the respective protein. The natural indirect effect corresponds to the effect of the respective PGS on the log odds of 7.7 year risk of hospitalisation through the respective protein. The total effect corresponds to the sum of the natural direct effect and natural indirect effect. Units of point estimates (log OR) are change in log odds of the respective disease per standard deviation increase in the respective PGS. L95 and U95 correspond to the lower and upper bounds of the 95% confidence interval respectively. P-value: two-sided P-value. The % of total effect was calculated by dividing the corresponding point estimate by the point estimate for the total effect. The % PGS mediated reported in Extended Data Fig. 3 and shown in Fig. 2d corresponds to the % of total effect due to the natural indirect effect (% of PGS to disease association mediated by the protein). For proteins targeted by more than one aptamer (GPD1, IGFBP1, IGFBP2, and WFIKKN2), log odds and 95% confidence intervals were averaged, and two-sided Pvalue obtained by averaging Z-scores. Supplementary Table 5. Extended details from mediation analysis of PGS to protein to incident disease associations for the four PGS-associated proteins measured with the Olink T96 platform in n=3,848 INTERVAL participants. Mediation analysis was adjusted for age, sex, 10 genotype PCs, and platform-specific technical covariates (season, sample measurement batch, and time between blood draw and sample processing). The natural direct effect corresponds to the effect of the respective PGS on the log odds of 5.1 year risk of hospitalisation with the respective disease independent of the respective protein. The natural indirect effect corresponds to the effect of the respective PGS on the log odds of 5.1 year risk of hospitalisation through the respective protein. The total effect corresponds to the sum of the natural direct effect and natural indirect effect. Units of point estimates (log OR) are change in log odds of the respective disease per standard deviation increase in the respective PGS. L95 and U95 correspond to the lower and upper bounds of the 95% confidence interval respectively. P-value: two-sided P-value. The % of total effect was calculated by dividing the corresponding point estimate by the point estimate for the total effect. The % PGS mediated reported in Supplementary  Table 5 corresponds to the % of total effect due to the natural indirect effect (% of PGS to disease association mediated by the protein).