Karlson, E. | bioRxiv Science

bioRxiv2023

OMICmAge: An integrative multi-omics approach to quantify biological age with electronic medical records

Biological aging is a multifactorial process involving complex interactions of cellular and biochemical processes that is reflected in omic profiles. Using common clinical laboratory measures in ~30,000 individuals from the MGB-Biobank, we developed a robust, predictive biological aging phenotype, EMRAge, that balances clinical biomarkers with overall mortality risk and can be broadly recapitulated across EMRs. We then applied elastic-net regression to model EMRAge with DNA-methylation (DNAm) and multiple omics, generating DNAmEMRAge and OMICmAge, respectively. Both biomarkers demonstrated strong associations with chronic diseases and mortality that outperform current biomarkers across our discovery (MGB-ABC, n=3,451) and validation (TruDiagnostic, n=12,666) cohorts. Through the use of epigenetic biomarker proxies, OMICmAge has the unique advantage of expanding the predictive search space to include epigenomic, proteomic, metabolomic, and clinical data while distilling this in a measure with DNAm alone, providing opportunities to identify clinically-relevant interconnections central to the aging process.

bioinformatics↗

bioRxiv2019

Using genetics to differentiate patients with similar symptoms: application to inflammatory arthritis in the rheumatology outpatient clinic

Slow developing complex diseases are a clinical diagnostic challenge. Since genetic information is increasingly available prior to a patients first visit to a clinic, it might improve diagnostic accuracy. We aimed to devise a method to convert genetic information into simple probabilities discriminating between multiple diagnoses in patients presenting with inflammatory arthritis.\n\nWe developed G-Prob, which calculates for each patient the genetic probability for each of multiple possible diseases. We tested this for inflammatory arthritis-causing diseases (rheumatoid arthritis, systemic lupus erythematosus, spondyloarthropathy, psoriatic arthritis and gout). After validating in simulated data, we tested G-Prob in biobank cohorts in which genetic data were linked to electronic medical records: - 1,200 patients identified by ICD-codes within the eMERGE database (n= 52,623);\n- 245 patients identified through ICD codes and review of medical records within the Partners Biobank (n=12,604);\n- 243 patients selected prospectively with final diagnoses by medical record review within the Partners Biobank (n=12,604).\nThe calibration of G-Prob with the disease status was high (with regression coefficients ranging from 0.90-1.08 (ideal would be 1.00) in all cohorts. G-Probs discriminative ability was high in all cohorts with pooled Area Under the Curve (AUC)=0.69 [95%CI 0.67-0.71], 0.81 [95%CI 0.76-0.84] and 0.84 [95%CI 0.81-0.86]. For all patients, at least one disease could be ruled out, and in 45% of patients a most likely diagnosis could be identified with an overall 64% positive predictive value. In 35% of instances the clinicians initial diagnosis was incorrect. Initial clinical diagnosis explained 39% of the variance in final disease prediction which improved to 51% (P<0.0001) by adding G-Prob genetic data.\n\nIn conclusion, by converting genotypes into an interpretable probability value for five different inflammatory arthritides, we can better discriminate and diagnose rheumatic diseases. Genotypes available prior to a clinical visit could be considered part of patients medical history and potentially used to improve precision and diagnostic efficiency in clinical practice.

genetics↗