Lars Fritsche

BackgroundChildbirth may be a traumatic experience and vulnerability to posttraumatic stress disorder (PTSD) may increase the risk of postpartum depression (PPD). We investigated whether genetic vulnerability to PTSD as measured by polygenic score (PGS) increases the risk of PPD and whether a predisposition to PTSD in PPD cases exceeds that of major depressive disorder (MDD) outside the postpartum period.MethodsThis case-control study included participants from the iPSYCH2015, a case-cohort of all singletons born in Denmark between 1981 and 2008. Restricting to women born between 1981 and 1997 and excluding women with a first diagnosis other than depression (N = 22 613), 333 were identified with PPD. For each PPD case, 999 representing the background population and 993 with MDD outside the postpartum were matched by calendar year at birth, cohort selection, and age. PTSD PGS …

Objective To explore the role of selection bias adjustment by weighting electronic health record (EHR)-linked biobank data for commonly performed analyses. Materials and methods We mapped diagnosis (ICD code) data to standardized phecodes from three EHR-linked biobanks with varying recruitment strategies: All of Us (AOU; n=244,071), Michigan Genomics Initiative (MGI; n=81,243), and UK Biobank (UKB; n=401,167). Using 2019 National Health Interview Survey data, we constructed selection weights for AOU and MGI to be more representative of the US adult population. We used weights previously developed for UKB to represent the UKB-eligible population. We conducted four common descriptive and analytic tasks comparing unweighted and weighted results. Results For AOU and MGI, estimated phecode prevalences decreased after weighting (weighted-unweighted median phecode prevalence ratio [MPR]: 0.82 and 0.61), while UKB estimates increased (MPR: 1.06). Weighting minimally impacted latent phenome dimensionality estimation. Comparing weighted versus unweighted PheWAS for colorectal cancer, the strongest associations remained unaltered and there was large overlap in significant hits. Weighting affected the estimated log-odds ratio for sex and colorectal cancer to align more closely with national registry-based estimates. Discussion Weighting had limited impact on dimensionality estimation and large-scale hypothesis testing but impacted prevalence and association estimation more. Results from untargeted association analyses should be followed by weighted analysis when effect size estimation is of interest for …

Anti-vascular endothelial growth factor (anti-VEGF) injections have revolutionized the field of ophthalmology, and their use in a variety of retinal diseases is growing. The authors performed a comprehensive search in the PubMed, Google Scholar, and Cochrane databases for published studies and case reports relating to the use of anti-VEGF injections in peripheral exudative hemorrhagic chorioretinopathy.

Genome-wide association studies have contributed extensively to the discovery of disease-associated common variants. However, the genetic contribution to complex traits is still largely difficult to interpret. We report a genome-wide association study of 2394 cases and 2393 controls for age-related macular degeneration (AMD) via whole-genome sequencing, with 46.9 million genetic variants. Our study reveals significant single-variant association signals at four loci and independent gene-based signals in CFH, C2, C3, and NRTN. Using data from the Exome Aggregation Consortium (ExAC) for a gene-based test, we demonstrate an enrichment of predicted rare loss-of-function variants in CFH, CFI, and an as-yet unreported gene in AMD, ORMDL2. Our method of using a large variant list without individual-level genotypes as an external reference provides a flexible and convenient approach to leverage the …

BackgroundGenetic variants can contribute differently to trait heritability by their functional categories, and recent studies have shown that incorporating functional annotation can improve the predictive performance of polygenic risk scores (PRSs). In addition, when only a small proportion of variants are causal variants, PRS methods that employ a Bayesian framework with shrinkage can account for such sparsity. It is possible that the annotation group level effect is also sparse. However, the number of PRS methods that incorporate both annotation information and shrinkage on effect sizes is limited. We propose a PRS method, PRSbils, which utilizes the functional annotation information with a bilevel continuous shrinkage prior to accommodate the varying genetic architectures both on the variant-specific level and on the functional annotation level.ResultsWe conducted simulation studies and investigated the …

Background Post-Acute Sequelae of COVID-19 (PASC) have emerged as a global public health and healthcare challenge. This study aimed to uncover predictive factors for PASC from multi-modal data to develop a predictive model for PASC diagnoses. Methods We analyzed electronic health records from 92,301 COVID-19 patients, covering medical phenotypes, medications, and lab results. We used a Super Learner-based prediction approach to identify predictive factors. We integrated the model outputs into individual and composite risk scores and evaluated their predictive performance. Results Our analysis identified several factors predictive of diagnoses of PASC, including being overweight/obese and the use of HMG CoA reductase inhibitors prior to COVID-19 infection, and respiratory system symptoms during COVID-19 infection. We developed a composite risk score with a moderate discriminatory ability for PASC (covariate-adjusted AUC (95% confidence interval): 0.66 (0.63, 0.69)) by combining the risk scores based on phenotype and medication records. The combined risk score could identify 10% of individuals with a 2.2-fold increased risk for PASC. Conclusions We identified several factors predictive of diagnoses of PASC and integrated the information into a composite risk score for PASC prediction, which could contribute to the identification of individuals at higher risk for PASC and inform preventive efforts.

6004Background: To date there are no validated predictive tests to inform treatment selection for patients with oropharyngeal cancer (OPC). Currently treatment is decided on disease resectability, clinician preference and patient choice. Methods: Objective To develop a predictive test to select treatment for advanced OPC. Participants Training cohort: 543 cases from 10 cancer centres. External validation cohort: 442 cases from 3 centres. Design Multivariable logistic regression of 8 clinical parameters and 10 biomarkers to develop biomarker-only and composite clinical/biomarker predictive models; subsequently validated on a separate cohort. Biomarkers scored by ≥2 ‘blinded’ pathologists. Outcomes Primary: overall survival (OS). Results: 724 males, 261 females; Median follow-up =8.8 (6.86-10.47) years. More validation cases received surgery (53.5% vs 37.9%, p=0.001) and fewer received chemo/radiotherapy …

Purpose: The lack of well-established biomarkers and a wide variability in the progression speed makes predicting the conversion from intermediate to neovascular age-related macular degeneration (iAMD, nAMD) a challenging task. We developed an artificial intelligence (AI) method to predict the risk of conversion of an eye from iAMD to nAMD within the next 6, 12 and 18 months from OCT scans.Methods: We propose a two-stage Deep Learning method. First, a fully convolutional Encoder is learned for feature extraction through Self-Supervised learning (SSL) to overcome the paucity of annotated data. Next, a 3 layer Classifier is trained to predict the probability of the time to conversion P (T*≤ t) from the learned features (Fig. a).

There is a need to identify accurately prognostic factors that determine the progression of intermediate to late-stage age-related macular degeneration (AMD). Currently, clinicians cannot provide individualized prognoses of disease progression. Moreover, enriching clinical trials with rapid progressors may facilitate delivery of shorter intervention trials aimed at delaying or preventing progression to late AMD. Thus, we performed a systematic review to outline and assess the accuracy of reporting prognostic factors for the progression of intermediate to late AMD. A meta-analysis was originally planned. Synonyms of AMD and disease progression were used to search Medline and EMBASE for articles investigating AMD progression published between 1991 and 2021. Initial search results included 3229 articles. Predetermined eligibility criteria were employed to systematically screen papers by two reviewers working …

Background A growing number of Coronavirus Disease-2019 (COVID-19) survivors are affected by post-acute sequelae of SARS CoV-2 infection (PACS). Using electronic health record data, we aimed to characterize PASC-associated diagnoses and develop risk prediction models. Methods In our cohort of 63,675 patients with a history of COVID-19, 1724 (2.7%) had a recorded PASC diagnosis. We used a case–control study design and phenome-wide scans to characterize PASC-associated phenotypes of the pre-, acute-, and post-COVID-19 periods. We also integrated PASC-associated phenotypes into phenotype risk scores (PheRSs) and evaluated their predictive performance. Results In the post-COVID-19 period, known PASC symptoms (e.g., shortness of breath, malaise/fatigue) and musculoskeletal, infectious, and digestive disorders were enriched among PASC cases. We found seven phenotypes in the pre-COVID-19 period (e.g., irritable bowel syndrome, concussion, nausea/vomiting) and sixty-nine phenotypes in the acute-COVID-19 period (predominantly respiratory, circulatory, neurological) associated with PASC. The derived pre- and acute-COVID-19 PheRSs stratified risk well, e.g., the combined PheRSs identified a quarter of the cohort with a history of COVID-19 with a 3.5-fold increased risk (95% CI: 2.19, 5.55) for PASC compared to the bottom 50%. Conclusions The uncovered PASC-associated diagnoses across categories highlighted a complex arrangement of presenting and likely predisposing features, some with potential for risk stratification approaches.

Polygenic hazard score (PHS) models designed for European ancestry (EUR) individuals provide ample information regarding survival risk discrimination. Incorporating such information can improve the performance of risk discrimination in an internal small-sized non-EUR cohort. However, given that external EUR-based model and internal individual-level data come from different populations, ignoring population heterogeneity can introduce substantial bias. In this paper, we develop a Kullback-Leibler-based Cox model (CoxKL) to integrate internal individual-level time-to-event data with external risk scores derived from published prediction models, accounting for population heterogeneity. Partial-likelihood-based KL information is utilized to measure the discrepancy between the external risk information and the internal data. We establish the asymptotic properties of the CoxKL estimator. Simulation studies show that the integration model by the proposed CoxKL method achieves improved estimation efficiency and prediction accuracy. We applied the proposed method to develop a trans-ancestry PHS model for prostate cancer and found that integrating a previously published EUR-based PHS with an internal genotype data of African ancestry (AFR) males yielded considerable improvement on the prostate cancer risk discrimination.

Purpose: Topographic variations in the risk of age-related macular degeneration (AMD)-associated lesions were reported. In this study we performed microperimetry (MP) subfield analyses in intermediate age-related macular degeneration (iAMD) to investigate regional differences in photoreceptor functionMethods: This is a cross-sectional analysis of baseline data from 247 patient eyes of the longitudinal, prospective, multicenter and non-interventional PINNACLE study. Mesopic MP was conducted with the MAIA (Centervue, Italy) device, which predominantly measures cone function. The Visual Field Modeling and Analysis software was applied to calculate MP-derived hill-of-vison surface plots and the volume beneath the plots. MP regional subfields were defined as quadrants and cubes (see Figure 1). We calculated paired non-parametric statistics to account for comparisons of subfields within same eyes. Where appropriate, Friedman and multiple-comparison tests were used.Results: Based on the mean sensitivity (MS)(p= 0.005) and volumetric (V)(p= 0.015) analysis, the retinal sensitivity in the superior half of the PINNACLE standard grid was significantly higher than in the inferior half. Similarly, retinal sensitivity in the nasal half was higher than in the temporal half (MS: p= 0.007; V: p= 0.003). A Friedman test revealed that retinal sensitivity varied between the quadrants significantly in MS-based (p= 0.0003) and in V-based analysis (p= 0.007). Specifically, the superior nasal quadrant (SNQ) exhibited significantly higher retinal sensitivities than all other quadrants in MS. In V analysis, the SNQ showed significantly higher retinal sensitivities …

We investigated the design and analysis of observational booster vaccine effectiveness (VE) studies by performing a scoping review of booster VE literature with a focus on study design and analytic choices. We then applied 20 different approaches, including those found in the literature, to a single dataset from Michigan Medicine. We identified 80 studies in our review, including over 150 million observations in total. We found that while protection against infection is variable and dependent on several factors including the study population and time period, both monovalent boosters and particularly the bivalent booster offer strong protection against severe COVID-19. In addition, VE analyses with a severe disease outcome (hospitalization, intensive care unit admission, or death) appear to be more robust to design and analytic choices than an infection endpoint. In terms of design choices, we found that test-negative …

PurposeTo study the individual course of retinal changes caused by healthy aging using deep learning.DesignRetrospective analysis of a large data set of retinal OCT images.ParticipantsA total of 85 709 adults between the age of 40 and 75 years of whom OCT images were acquired in the scope of the UK Biobank population study.MethodsWe created a counterfactual generative adversarial network (GAN), a type of neural network that learns from cross-sectional, retrospective data. It then synthesizes high-resolution counterfactual OCT images and longitudinal time series. These counterfactuals allow visualization and analysis of hypothetical scenarios in which certain characteristics of the imaged subject, such as age or sex, are altered, whereas other attributes, crucially the subject’s identity and image acquisition settings, remain fixed.Main Outcome MeasuresUsing our counterfactual GAN, we investigated …

Biobanks of linked clinical patient histories and biological samples are an efficient strategy to generate large cohorts for modern genetics research. Biobank recruitment varies by factors such as geographic catchment and sampling strategy, which affect biobank demographics and research utility. Here, we describe the Michigan Genomics Initiative (MGI), a single-health-system biobank currently consisting of >91,000 participants recruited primarily during surgical encounters at Michigan Medicine. The surgical enrollment results in a biobank enriched for many diseases and ideally suited for a disease genetics cohort. Compared with the much larger population-based UK Biobank, MGI has higher prevalence for nearly all diagnosis-code-based phenotypes and larger absolute case counts for many phenotypes. Genome-wide association study (GWAS) results replicate known findings, thereby validating the genetic and …

Background Studies have shown an increased risk of severe SARS-CoV-2–related (COVID-19) disease outcome and mortality for patients with cancer, but it is not well understood whether associations vary by cancer site, cancer treatment, and vaccination status. Methods Using electronic health record data from an academic medical center, we identified a retrospective cohort of 260,757 individuals tested for or diagnosed with COVID-19 from March 10, 2020, to August 1, 2022. Of these, 52,019 tested positive for COVID-19 of whom 13,752 had a cancer diagnosis. We conducted Firth-corrected logistic regression to assess the association between cancer status, site, treatment, vaccination, and four COVID-19 outcomes: hospitalization, intensive care unit admission, mortality, and a composite “severe COVID” outcome. Results Cancer diagnosis was …

Objective To overcome the limitations associated with the collection and curation of COVID-19 outcome data in biobanks, this study proposes the use of polygenic risk scores (PRS) as reliable proxies of COVID-19 severity across three large biobanks: the Michigan Genomics Initiative (MGI), UK Biobank (UKB), and NIH All of Us. The goal is to identify associations between pre-existing conditions and COVID-19 severity. Methods Drawing on a sample of more than 500,000 individuals from the three biobanks, we conducted a phenome-wide association study (PheWAS) to identify associations between a PRS for COVID-19 severity, derived from a genome-wide association study on COVID-19 hospitalization, and clinical pre-existing, pre-pandemic phenotypes. We performed cohort-specific PRS PheWAS and a subsequent fixed-effects meta-analysis. Results The current study uncovered 23 pre-existing conditions significantly associated with the COVID-19 severity PRS in cohort-specific analyses, of which 21 were observed in the UKB cohort and two in the MGI cohort. The meta-analysis yielded 27 significant phenotypes predominantly related to obesity, metabolic disorders, and cardiovascular conditions. After adjusting for body mass index, several clinical phenotypes, such as hypercholesterolemia and gastrointestinal disorders, remained associated with an increased risk of hospitalization following COVID-19 infection. Conclusion By employing PRS as a proxy for COVID-19 severity, we corroborated known risk factors and identified novel associations between pre-existing clinical phenotypes and COVID-19 severity. Our study highlights the …

Purpose: To investigate and compare novel volumetric microperimetry (MP)–derived metrics in intermediate age-related macular degeneration (iAMD), as current MP metrics show high variability and low sensitivity.Methods: This is a cross-sectional analysis of microperimetry baseline data from the multicenter, prospective PINNACLE study (ClinicalTrials. gov NCT04269304). The Visual Field Modeling and Analysis (VFMA) software and an open-source implementation (OSI) were applied to calculate MP-derived hill-of-vison (HOV) surface plots and the total volume (VTOT) beneath the plots. Bland–Altman plots were used for methodologic comparison, and the association of retinal sensitivity metrics with explanatory variables was tested with mixed-effects models.Results: In total, 247 eyes of 189 participants (75±7.3 years) were included in the analysis. The VTOT output of VFMA and OSI exhibited a significant difference (P< 0.0001). VFMA yielded slightly higher coefficients of determination than OSI and mean sensitivity (MS) in univariable and multivariable modeling, for example, in association with low-luminance visual acuity (LLVA)(marginal R 2/conditional R 2: VFMA 0.171/0.771, OSI 0.162/0.765, MS 0.133/0.755). In the multivariable analysis, LLVA was the only demonstrable predictor of VFMA VTOT (t-value, P-value:− 7.5,< 0.001) and MS (− 6.5,< 0.001).Conclusions: The HOV-derived metric of VTOT exhibits favorable characteristics compared to MS in evaluating retinal sensitivity. The output of VFMA and OSI is not exactly interchangeable in this cross-sectional analysis. Longitudinal analysis is necessary to assess their performance in …

Understanding patterns of drug‐gene interactions (DGIs) is important for advancing the clinical implementation of pharmacogenetics (PGx) into routine practice. Prior studies have estimated the prevalence of DGIs, but few have confirmed DGIs in patients with known genotypes and prescriptions, nor have they evaluated clinician characteristics associated with DGI‐prescribing. This retrospective chart review assessed prevalence of DGI, defined as a medication prescription in a patient with a PGx phenotype that has a clinical practice guideline recommendation to adjust therapy or monitor drug response, for patients enrolled in a research genetic biorepository linked to electronic health records (EHRs). The prevalence of prescriptions for medications with pharmacogenetic (PGx) guidelines, proportion of prescriptions with DGI, location of DGI prescription, and clinical service of the prescriber were evaluated …

Background: Exposure phenotype risk scores (E-PRS) use genetic variation as a natural experiment to examine the causal relationship between risk or protective factors and diseases. A recent review summarized 76 studies using this approach, finding that several factors (alcohol consumption, BMI, telomere length, hormones) likely cause cancer. However, this article also highlighted the need for larger studies incorporating more, newly discovered associated, variants in the E-PRS and investigating specific cancer types vs. all cancers and cancer subtypes. Methods: We used 21 published E-PRS that were derived from summary statistics of large genome-wide association studies using PRSCS. We evaluated 15 continuous and 6 binary E-PRS for factors that are known or hypothesized to contribute to cancer risk or progression and their association with prostate cancer aggressiveness and death. Our sample …