Biomedical subjects

Stephan Ripke

Publications and source records attributed to Stephan Ripke.

3 recordsLinked to original sources

PubMed2026

Distinguishing different psychiatric disorders using DDx-PRS.

Despite great progress on case-control polygenic prediction, an unmet need remains for a method that genetically distinguishes clinically related disorders (e.g., schizophrenia (SCZ) versus bipolar disorder (BIP) versus major depressive disorder (MDD) versus controls). We introduce differential diagnosis-polygenic risk score (DDx-PRS), which jointly estimates the posterior probabilities of each diagnostic category (e.g., SCZ = 50%, BIP = 25%, MDD = 15%, control = 10%) by modeling variance-covariance structure across disorders, leveraging case-control polygenic risk scores and prior clinical probabilities for each diagnostic category. We applied DDx-PRS to Psychiatric Genomics Consortium SCZ, BIP, MDD and control data, including summary-level training data from three case-control genome-wide association studies (n = 41,917-173,140 cases; total n = 1,048,683) and held-out test data from different cohorts with equal numbers for each diagnostic category (total n = 11,460). DDx-PRS was well calibrated and well powered (consistent with simulations) and produced comparable results to methods that require tuning data. True diagnosis probabilities in the top deciles of predicted diagnosis probabilities were considerably larger than prior baseline probabilities, implying appreciable potential for clinical utility in certain settings.

Humans↗

PubMed2025

Polygenic Contributions to Lithium Augmentation Outcomes in Unipolar Depression.

IMPORTANCE: Lithium augmentation is an effective treatment for patients with major depression after inadequate antidepressant response, but therapeutic outcomes vary considerably between individuals. Molecular studies may provide novel insights into treatment prediction and guide personalized therapy. OBJECTIVE: To investigate the association of polygenic risk scores (PRS) for schizophrenia (SCZ), major depressive disorder (MDD), and bipolar disorder (BIP) with clinical outcomes after lithium augmentation. DESIGN, SETTING, AND PARTICIPANTS: This cohort study analyzed prospectively assessed treatment outcomes in patients who underwent lithium augmentation. Disorder-specific PRS were calculated using well-powered genome-wide association study summary statistics. Participants were recruited from 13 psychiatric hospitals, primarily in the greater Berlin area, between 2008 and 2020. They were patients with MDD who showed inadequate response to at least 1 antidepressant, a baseline score of 12 or more on the 17-item Hamilton Depression Rating Scale (HAMD-17), adequate treatment duration (≥4 weeks), and no diagnostic or co-medication changes. Data analysis was conducted between June 2022 and November 2023. EXPOSURE: Polygenic risk scores for MDD, SCZ, or BIP. MAIN OUTCOMES AND MEASURES: Response was defined as a 50% or greater reduction in HAMD-17 score, remission as a HAMD-17 score of 7 or less. Cox proportional hazards models, adjusted for ancestry, demographic, and clinical covariates, were used to estimate hazard ratios (HRs) for favorable outcomes. RESULTS: Among 193 patients (mean [SD] age, 49.5 [13.4] years; 118 [61.1%] female and 75 [38.9%] male), higher BIP-PRS were associated with both response (HR, 1.29; 95% CI, 1.02-1.63; P = .03) and remission (HR, 1.52; 95% CI, 1.14-2.04; P = .004), explaining 2.51% and 4.53% of the variability in treatment outcomes, respectively. Individuals in the highest tertile of the BIP-PRS distribution had a 2.02-fold (95% CI, 1.15-3.53) higher likelihood of response and a 2.26-fold (95% CI, 1.17-4.36) higher chance of remission compared with those in the lowest tertile. Additionally, lower MDD-PRS was associated with better response to lithium augmentation (HR, 0.81; 95% CI, 0.66-1.00; P = .048; Nagelkerke R2 = 1.99%). No significant associations were observed between SCZ-PRS and response (HR, 1.00; 95% CI, 0.80-1.24; P = .97) or remission (HR, 1.12; 95% CI, 0.85-1.48; P = .42). CONCLUSIONS AND RELEVANCE: Individuals carrying a higher polygenic burden for BIP and lower polygenic risk for MDD are more likely to benefit from lithium augmentation. Our findings suggest that disease-related PRS may aid in developing treatment prediction models for lithium augmentation response in depression, potentially informing clinical decision-making.

Humans↗

PubMed2024

Distinguishing different psychiatric disorders using DDx-PRS.

Despite great progress on methods for case-control polygenic prediction (e.g. schizophrenia vs. control), there remains an unmet need for a method that genetically distinguishes clinically related disorders (e.g. schizophrenia (SCZ) vs. bipolar disorder (BIP) vs. depression (MDD) vs. control); such a method could have important clinical value, especially at disorder onset when differential diagnosis can be challenging. Here, we introduce a method, Differential Diagnosis-Polygenic Risk Score (DDx-PRS), that jointly estimates posterior probabilities of each possible diagnostic category (e.g. SCZ=50%, BIP=25%, MDD=15%, control=10%) by modeling variance/covariance structure across disorders, leveraging case-control polygenic risk scores (PRS) for each disorder (computed using existing methods) and prior clinical probabilities for each diagnostic category. DDx-PRS uses only summary-level training data and does not use tuning data, facilitating implementation in clinical settings. In simulations, DDx-PRS was well-calibrated (whereas a simpler approach that analyzes each disorder marginally was poorly calibrated), and effective in distinguishing each diagnostic category vs. the rest. We then applied DDx-PRS to Psychiatric Genomics Consortium SCZ/BIP/MDD/control data, including summary-level training data from 3 case-control GWAS ( N =41,917-173,140 cases; total N =1,048,683) and held-out test data from different cohorts with equal numbers of each diagnostic category (total N =11,460). DDx-PRS was well-calibrated and well-powered relative to these training sample sizes, attaining AUCs of 0.66 for SCZ vs. rest, 0.64 for BIP vs. rest, 0.59 for MDD vs. rest, and 0.68 for control vs. rest. DDx-PRS produced comparable results to methods that leverage tuning data, confirming that DDx-PRS is an effective method. True diagnosis probabilities in top deciles of predicted diagnosis probabilities were considerably larger than prior baseline probabilities, particularly in projections to larger training sample sizes, implying considerable potential for clinical utility under certain circumstances. In conclusion, DDx-PRS is an effective method for distinguishing clinically related disorders.

Journal Article↗