Reproducibility and Variability of Protein Analytes Measured Using a Multiplexed Modified Aptamer Assay

Integrated Multi-Omics for Novel Aging Biomarkers and Antiaging Targets

Aging is closely related to the occurrence of human diseases; however, its exact biological mechanism is unclear. Advancements in high-throughput technology provide new opportunities for omics research to understand the pathological process of various complex human diseases. However, single-omics technologies only provide limited insights into the biological mechanisms of diseases. DNA, RNA, protein, metabolites, and microorganisms usually play complementary roles and perform certain biological functions together. In this review, we summarize multi-omics methods based on the most relevant biomarkers in single-omics to better understand molecular functions and disease causes. The integration of multi-omics technologies can systematically reveal the interactions among aging molecules from a multidimensional perspective. Our review provides new insights regarding the discovery of aging biomarkers, mechanism of aging, and identification of novel antiaging targets. Overall, data from genomics, transcriptomics, proteomics, metabolomics, integromics, microbiomics, and systems biology contribute to the identification of new candidate biomarkers for aging and novel targets for antiaging interventions.

Proteomics and Risk of Atrial Fibrillation in Older Adults (From the Atherosclerosis Risk in Communities [ARIC] Study)

Plasma proteomic profiling may aid in the discovery of novel biomarkers upstream of the development of atrial fibrillation (AF). We used data from the Atherosclerosis Risk in Communities study to examine the relation between large-scale proteomics and incident AF in a cohort of older-aged adults in the United States. We quantified 4,877 plasma proteins in Atherosclerosis Risk in Communities participants at visit 5 (2011-2013) using an aptamer-based proteomic profiling platform. We used Cox proportional hazards models to assess the association between protein levels and incident AF, and explored relation of selected protein biomarkers using annotated pathway analysis. Our study included 4,668 AF-free participants (mean age 75 ± 5 years; 59% female; 20% Black race) with proteomic measures. A total of 585 participants developed AF over a mean follow-up of 5.7 ± 1.7 years. After adjustment for clinical factors associated with AF, N-terminal pro-B-type natriuretic peptide (NT-proBNP) was associated with the risk of incident AF (hazard ratio, 1.82; 95% CI, 1.68 to 1.98; p, 2.91 × 10-45 per doubling of NT-proBNP). In addition, 36 other proteins were also significantly associated with incident AF after Bonferroni correction. We further adjusted for medication use and estimated glomerular filtration rate and found 17 proteins, including angiopoietin-2 and transgelin, that remained significantly associated with incident AF. Pathway analyses implicated the inhibition of matrix metalloproteases as the top canonical pathway in AF pathogenesis. In conclusion, using a large-scale proteomic platform, we identified both novel and established proteins associated with incident AF and explored mechanistic pathways of AF development.

Polygenic Risk Scores for Kidney Function and Their Associations with Circulating Proteome, and Incident Kidney Diseases

BACKGROUND

Genome-wide association studies (GWAS) have revealed numerous loci for kidney function (eGFR). The relationship between polygenic predictors of eGFR, risk of incident adverse kidney outcomes, and the plasma proteome is not known.

METHODS

We developed a genome-wide polygenic risk score (PRS) for eGFR by applying the LDpred algorithm to summary statistics generated from a multiethnic meta-analysis of CKDGen Consortium GWAS ( n =765,348) and UK Biobank GWAS (90% of the cohort; n =451,508), followed by best-parameter selection using the remaining 10% of UK Biobank data ( n =45,158). We then tested the association of the PRS in the Atherosclerosis Risk in Communities (ARIC) study ( n =8866) with incident CKD, ESKD, kidney failure, and AKI. We also examined associations between the PRS and 4877 plasma proteins measured at middle age and older adulthood and evaluated mediation of PRS associations by eGFR.

RESULTS

The developed PRS showed a significant association with all outcomes. Hazard ratios per 1 SD lower PRS ranged from 1.06 (95% CI, 1.01 to 1.11) to 1.33 (95% CI, 1.28 to 1.37). The PRS was significantly associated with 132 proteins at both time points. The strongest associations were with cystatin C, collagen α -1(XV) chain, and desmocollin-2. Most proteins were higher at lower kidney function, except for five proteins, including testican-2. Most correlations of the genetic PRS with proteins were mediated by eGFR.

CONCLUSIONS

A PRS for eGFR is now sufficiently strong to capture risk for a spectrum of incident kidney diseases and broadly influences the plasma proteome, primarily mediated by eGFR.

Comparison of proteomic methods in evaluating biomarker-AKI associations in cardiac surgery patients

Although immunoassays are the most widely used protein measurement method, aptamer-based methods such as the SomaScan platform can quantify up to 7000 proteins per biosample, creating new opportunities for unbiased discovery. However, there is limited research comparing the consistency of biomarker-disease associations between immunoassay and aptamer-based platforms. In a substudy of the TRIBE-AKI cohort, preoperative and postoperative plasma samples from 294 patients with previous immunoassay measurements were analyzed using the SomaScan platform. Inter-platform Spearman correlations (r_s) and biomarker-AKI associations were compared across 30 preoperative and 34 postoperative immunoassay-aptamer pairs. Possible factors contributing to inter-platform differences were examined including target protein characteristics, immunoassay, and SomaScan coefficients of variation, other assay characteristics, and sample storage time. The median r_s was 0.54 (interquartile range [IQR] 0.34-0.83) in postoperative samples and 0.41 (IQR 0.21-0.69) in preoperative samples. We observed a trend of greater r_s in biomarkers with greater concentrations; the Spearman correlation between the concentration of protein and the inter-platform correlation was 0.64 in preoperative pairs and 0.53 in postoperative pairs. Of proteins measured by immunoassays, we observed significant biomarker-AKI associations for 13 proteins preop and 24 postop; of all corresponding aptamers, 8 proteins preop and 12 postop. All proteins significantly associated with AKI as measured by SomaScan were also significantly associated with AKI as measured by immunoassay. All biomarker-AKI odds ratios were significantly different (P < 0.05) between platforms in 14% of aptamer-immunoassay pairs, none of which had high (r_s > 0.50) inter-platform correlations. Although similar biomarker-disease associations were observed overall, biomarkers with high physiological concentrations tended to have the highest-confidence inter-platform operability in correlations and biomarker-disease associations. Aptamer assays provide excellent precision and an unprecedented coverage and promise for disease associations but interpretation of results should keep in mind a broad range of correlations with immunoassays.

Proteins Associated with Risk of Kidney Function Decline in the General Population

BACKGROUND

Proteomic profiling may allow identification of plasma proteins that associate with subsequent changesin kidney function, elucidating biologic processes underlying the development and progression of CKD.

METHODS

We quantified the association between 4877 plasma proteins and a composite outcome of ESKD or decline in eGFR by ≥50% among 9406 participants in the Atherosclerosis Risk in Communities (ARIC) Study (visit 3; mean age, 60 years) who were followed for a median of 14.4 years. We performed separate analyses for these proteins in a subset of 4378 participants (visit 5), who were followed at a later time point, for a median of 4.4 years. For validation, we evaluated proteins with significant associations (false discovery rate <5%) in both time periods in 3249 participants in the Chronic Renal Insufficiency Cohort (CRIC) and 703 participants in the African American Study of Kidney Disease and Hypertension (AASK). We also compared the genetic determinants of protein levels with those from a meta-analysis genome-wide association study of eGFR.

RESULTS

In models adjusted for multiple covariates, including baseline eGFR and albuminuria, we identified 13 distinct proteins that were significantly associated with the composite end point in both time periods, including TNF receptor superfamily members 1A and 1B, trefoil factor 3, and β-trace protein. Of these proteins, 12 were also significantly associated in CRIC, and nine were significantly associated in AASK. Higher levels of each protein associated with higher risk of 50% eGFR decline or ESKD. We found genetic evidence for a causal role for one protein, lectin mannose-binding 2 protein (LMAN2).

CONCLUSIONS

Large-scale proteomic analysis identified both known and novel proteomic risk factors for eGFR decline.

The ARIC (Atherosclerosis Risk In Communities) Study: JACC Focus Seminar 3/8

ARIC (Atherosclerosis Risk In Communities) initiated community-based surveillance in 1987 for myocardial infarction and coronary heart disease (CHD) incidence and mortality and created a prospective cohort of 15,792 Black and White adults ages 45 to 64 years. The primary aims were to improve understanding of the decline in CHD mortality and identify determinants of subclinical atherosclerosis and CHD in Black and White middle-age adults. ARIC has examined areas including health disparities, genomics, heart failure, and prevention, producing more than 2,300 publications. Results have had strong clinical impact and demonstrate the importance of population-based research in the spectrum of biomedical research to improve health.

Crowdsourcing assessment of maternal blood multi-omics for predicting gestational age and preterm birth

Identification of pregnancies at risk of preterm birth (PTB), the leading cause of newborn deaths, remains challenging given the syndromic nature of the disease. We report a longitudinal multi-omics study coupled with a DREAM challenge to develop predictive models of PTB. The findings indicate that whole-blood gene expression predicts ultrasound-based gestational ages in normal and complicated pregnancies (r = 0.83) and, using data collected before 37 weeks of gestation, also predicts the delivery date in both normal pregnancies (r = 0.86) and those with spontaneous preterm birth (r = 0.75). Based on samples collected before 33 weeks in asymptomatic women, our analysis suggests that expression changes preceding preterm prelabor rupture of the membranes are consistent across time points and cohorts and involve leukocyte-mediated immunity. Models built from plasma proteomic data predict spontaneous preterm delivery with intact membranes with higher accuracy and earlier in pregnancy than transcriptomic models (AUROC = 0.76 versus AUROC = 0.6 at 27-33 weeks of gestation).

Plasma Proteomic Profile Predicts Survival in Heart Failure With Reduced Ejection Fraction

BACKGROUND

It remains unclear whether the plasma proteome adds value to established predictors in heart failure (HF) with reduced ejection fraction (HFrEF). We sought to derive and validate a plasma proteomic risk score (PRS) for survival in patients with HFrEF (HFrEF-PRS).

METHODS

Patients meeting Framingham criteria for HF with EF<50% were enrolled (N=1017) and plasma underwent SOMAscan profiling (4453 targets). Patients were randomly divided 2:1 into derivation and validation cohorts. The HFrEF-PRS was derived using Cox regression of all-cause mortality adjusted for clinical score and NT-proBNP (N-terminal pro-B-type natriuretic peptide), then was tested in the validation cohort. Risk stratification improvement was evaluated by C statistic, integrated discrimination index, continuous net reclassification index, and median improvement in risk score for 1-year and 3-year mortality.

RESULTS

Participants' mean age was 68 years, 48% identified as Black, 35% were female, and 296 deaths occurred. In derivation (n=681), 128 proteins associated with mortality, 8 comprising the optimized HFrEF-PRS. In validation (n=336) the HFrEF-PRS associated with mortality (hazard ratio, 2.27 [95% CI, 1.84-2.82], P=6.3×10^-14), Kaplan-Meier curves differed significantly between HFrEF-PRS quartiles (P=2.2×10^-6), and it remained significant after adjustment for clinical score and NT-proBNP (hazard ratio, 1.37 [95% CI, 1.05-1.79], P=0.021). The HFrEF-PRS improved metrics of risk stratification (C statistic change, 0.009, P=0.612; integrated discrimination index, 0.041, P=0.010; net reclassification index=0.391, P=0.078; median improvement in risk score=0.039, P=0.016) and associated with cardiovascular death and HF phenotypes (eg, 6-minute walk distance, EF change). Most HFrEF-PRS proteins had little known connection to HFrEF.

CONCLUSIONS

A plasma multiprotein score improved risk stratification in patients with HFrEF and identified novel candidates.

Large-scale plasma proteomic analysis identifies proteins and pathways associated with dementia risk

The plasma proteomic changes that precede the onset of dementia could yield insights into disease biology and highlight new biomarkers and avenues for intervention. We quantified 4,877 plasma proteins in nondemented older adults in the Atherosclerosis Risk in Communities cohort and performed a proteome-wide association study of dementia risk over five years (n = 4,110; 428 incident cases). Thirty-eight proteins were associated with incident dementia after Bonferroni correction. Of these, 16 were also associated with late-life dementia risk when measured in plasma collected nearly 20 years earlier, during mid-life. Two-sample Mendelian randomization causally implicated two dementia-associated proteins (SVEP1 and angiostatin) in Alzheimer's disease. SVEP1, an immunologically relevant cellular adhesion protein, was found to be part of larger dementia-associated protein networks, and circulating levels were associated with atrophy in brain regions vulnerable to Alzheimer's pathology. Pathway analyses for the broader set of dementia-associated proteins implicated immune, lipid, metabolic signaling and hemostasis pathways in dementia pathogenesis.

Plasma Dehydroepiandrosterone Sulfate and Cardiovascular Disease Risk in Older Men and Women

CONTEXT

Lower dehydroepiandrosterone-sulfate (DHEA-S) levels have been inconsistently associated with coronary heart disease (CHD) and mortality. Data are limited for heart failure (HF) and association between DHEA-S change and events.

OBJECTIVE

Assess associations between low DHEA-S/DHEA-S change and incident HF hospitalization, CHD, and mortality in older adults.

DESIGN

DHEA-S was measured in stored plasma from visits 4 (1996-1998) and 5 (2011-2013) of the Atherosclerosis Risk in Communities study. Follow-up for incident events: 18 years for DHEA-S level; 5.5 years for DHEA-S change.

SETTING

General community.

PARTICIPANTS

Individuals without prevalent cardiovascular disease (n = 8143, mean age 63 years).

MAIN OUTCOME MEASURE

Associations between DHEA-S and incident HF hospitalization, CHD, or mortality; associations between 15-year change in DHEA-S (n = 3706) and cardiovascular events.

RESULTS

DHEA-S below the 15th sex-specific percentile of the study population (men: 55.4 µg/dL; women: 27.4 µg/dL) was associated with increased HF hospitalization (men: hazard ratio [HR] 1.30, 95% confidence interval [CI], 1.07-1.58; women: HR 1.42, 95% CI, 1.13-1.79); DHEA-S below the 25th sex-specific percentile (men: 70.0 µg/dL; women: 37.1 µg/dL) was associated with increased death (men: HR 1.12, 95% CI, 1.01-1.25; women: HR 1.19, 95% CI, 1.03-1.37). In men, but not women, greater percentage decrease in DHEA-S was associated with increased HF hospitalization (HR 1.94, 95% CI, 1.11-3.39). Low DHEA-S and change in DHEA-S were not associated with incident CHD.

CONCLUSIONS

Low DHEA-S is associated with increased risk for HF and mortality but not CHD. Further investigation is warranted to evaluate mechanisms underlying these associations.

Systematic review and analysis of human proteomics aging studies unveils a novel proteomic aging clock and identifies key processes that change with age

The development of clinical interventions that significantly improve human healthspan requires robust markers of biological age as well as thoughtful therapeutic targets. To promote these goals, we performed a systematic review and analysis of human aging and proteomics studies. The systematic review includes 36 different proteomics analyses, each of which identified proteins that significantly changed with age. We discovered 1,128 proteins that had been reported by at least two or more analyses and 32 proteins that had been reported by five or more analyses. Each of these 32 proteins has known connections relevant to aging and age-related disease. GDF15, for example, extends both lifespan and healthspan when overexpressed in mice and is additionally required for the anti-diabetic drug metformin to exert beneficial effects on body weight and energy balance. Bioinformatic enrichment analyses of our 1,128 commonly identified proteins heavily implicated processes relevant to inflammation, the extracellular matrix, and gene regulation. We additionally propose a novel proteomic aging clock comprised of proteins that were reported to change with age in plasma in three or more different studies. Using a large patient cohort comprised of 3,301 subjects (aged 18-76 years), we demonstrate that this clock is able to accurately predict human age.

Comparison of Proteomic Assessment Methods in Multiple Cohort Studies

Novel proteomics platforms, such as the aptamer-based SOMAscan platform, can quantify large numbers of proteins efficiently and cost-effectively and are rapidly growing in popularity. However, comparisons to conventional immunoassays remain underexplored, leaving investigators unsure when cross-assay comparisons are appropriate. The correlation of results from immunoassays with relative protein quantification is explored by SOMAscan. For 63 proteins assessed in two chronic obstructive pulmonary disease (COPD) cohorts, subpopulations and intermediate outcome measures in COPD Study (SPIROMICS), and COPDGene, using myriad rules based medicine multiplex immunoassays and SOMAscan, Spearman correlation coefficients range from -0.13 to 0.97, with a median correlation coefficient of ≈0.5 and consistent results across cohorts. A similar range is observed for immunoassays in the population-based Multi-Ethnic Study of Atherosclerosis and for other assays in COPDGene and SPIROMICS. Comparisons of relative quantification from the antibody-based Olink platform and SOMAscan in a small cohort of myocardial infarction patients also show a wide correlation range. Finally, cis pQTL data, mass spectrometry aptamer confirmation, and other publicly available data are integrated to assess relationships with observed correlations. Correlation between proteomics assays shows a wide range and should be carefully considered when comparing and meta-analyzing proteomics data across assays and studies.