Cognitive impairment is a common feature of the major psychotic disorders, with deficits often present in at risk individuals and unaffected first-degree relatives. Previous studies have suggested that polygenic risk scores (PRS) for schizophrenia (SCZ) are associated with cognitive deficits, but there has been little examination of this association in longitudinal datasets, or comparison with other disorders. We used mixed models to study the association between PRS for 4 adult onset psychiatric disorders with cross-sectional cognitive performance and longitudinal cognitive decline in 8616 older adults from the Health and Retirement Study (HRS), followed for an average of 10 years. PRS were computed for SCZ, bipolar disorder (BD), Major Depressive Disorder (MDD), and Alzheimer’s disease (ALZ). SCZ PRS associated with decreased cognitive function (z = −3.00, P = .001, ΔR ² = 0.04%), which was largely driven by an association with impaired attention and orientation (z = −3.33, P = 4.3×10⁻⁴, ΔR ² = 0.08%). We found no effect of BD or MDD PRS on cognition, in contrast to a robust effect of the APOE4/TOMM40 locus (z = −5.05, P = 2.2×10⁻⁷, ΔR ² = 0.36%), which was primarily associated with impaired verbal memory (z = −5.15, P = 1.3×10⁻⁷, ΔR ² = 0.21%). APOE4/TOMM40 locus and the ALZ PRS, but not the PRS for SCZ, were associated with greater cognitive decline. In summary, using a large, representative sample of older adults, we found evidence for different degrees of association between polygenic risk for SCZ and genetic risk factors for ALZ on cognitive function and decline, highlighting potential differences in the pathophysiology of cognitive deficits seen in SCZ and ALZ.

Background: Distinguishing bipolar disorder (BP) from major depressive disorder (MDD) has important relevance for prognosis and treatment. Prior studies have identified clinical features that differ between these two diseases but have been limited by heterogeneity and lack of replication. We sought to identify depression-related features that distinguish BP from MDD in large samples with replication.Method: Using a large, opportunistically ascertained collection of subjects with BP and MDD we selected 34 depression-related clinical features to test across the diagnostic categories in an initial discovery dataset consisting of 1228 subjects (386 BPI, 158 BPII and 684 MDD). Features significantly associated with BP were tested in an independent sample of 1000 BPI cases and 1000 MDD cases for classifying ability in receiver operating characteristic (ROC) analysis.Results: Seven clinical features showed significant association with BPI compared with MDD: delusions, psychomotor retardation, incapacitation, greater number of mixed symptoms, greater number of episodes, shorter episode length, and a history of experiencing a high after depression treatment. ROC analyses of a model including these seven factors showed significant evidence for discrimination between BPI and MDD in an independent dataset (area under the curve = 0.83). Only two features (number of mixed symptoms, and feeling high after an antidepressant) showed an association with BPII versus MDD.Conclusions: Our study suggests that clinical features distinguishing depression in BPI versus MDD have important classification potential for clinical practice, and should also be incorporated as ‘baseline’ features in the evaluation of novel diagnostic biomarkers.

Background: Although accurate differentiation between bipolar disorder (BD) and unipolar major depressive disorder (MDD) has important prognostic and therapeutic implications, the distinction is often challenging based on clinical grounds alone. In this study, we tested whether psychiatric polygenic risk scores (PRSs) improve clinically based classification models of BD v. MDD diagnosis.Methods: Our sample included 843 BD and 930 MDD subjects similarly genotyped and phenotyped using the same standardized interview. We performed multivariate modeling and receiver operating characteristic analysis, testing the incremental effect of PRSs on a baseline model with clinical symptoms and features known to associate with BD compared with MDD status.Results: We found a strong association between a BD diagnosis and PRSs drawn from BD (R² = 3.5%, p = 4.94 × 10⁻¹²) and schizophrenia (R² = 3.2%, p = 5.71 × 10⁻¹¹) genome-wide association meta-analyses. Individuals with top decile BD PRS had a significantly increased risk for BD v. MDD compared with those in the lowest decile (odds ratio 3.39, confidence interval 2.19–5.25). PRSs discriminated BD v. MDD to a degree comparable with many individual symptoms and clinical features previously shown to associate with BD. When compared with the full composite model with all symptoms and clinical features PRSs provided modestly improved discriminatory ability (ΔC = 0.011, p = 6.48 × 10⁻⁴).Conclusions: Our study demonstrates that psychiatric PRSs provide modest independent discrimination between BD and MDD cases, suggesting that PRSs could ultimately have utility in subjects at the extremes of the distribution and/or subjects for whom clinical symptoms are poorly measured or yet to manifest.

This article describes an original methodology for integrating global SIR-like epidemic models with spatial interaction models, which enables the forecasting of COVID-19 dynamics in Poland through time and space. Mobility level, estimated by the regional population density and distances among inhabitants, was the determining variable in the spatial interaction model. The spatiotemporal diffusion model, which allows the temporal prediction of case counts and the possibility of determining their spatial distribution, made it possible to forecast the dynamics of the COVID-19 pandemic at a regional level in Poland. This model was used to predict incidence in 380 counties in Poland, which represents a much more detailed modeling than NUTS 3 according to the widely used geocoding standard Nomenclature of Territorial Units for Statistics. The research covered the entire territory of Poland in seven weeks of early 2021, just before the start of vaccination in Poland. The results were verified using official epidemiological data collected by sanitary and epidemiological stations. As the conducted analyses show, the application of the approach proposed in the article, integrating epidemiological models with spatial interaction models, especially unconstrained gravity models and destination (attraction) constrained models, leads to obtaining almost 90% of the coefficient of determination, which reflects the quality of the model’s fit with the spatiotemporal distribution of the validation data.

Novel treatment strategies that refract existing treatment algorithms for depressive disorders are being sought. Abnormal brain bioenergetic metabolism may represent an alternative, therapeutically targetable neurobiological basis for depression. A growing body of research points to endogenous ketones as candidate neuroprotective metabolites with the potential to enhance brain bioenergetics and improve mood. Sodium-glucose cotransporter-2 (SGLT2) inhibitors, originally approved for the treatment of diabetes, induce ketogenesis and are associated with mood improvement in population-based studies. In this column, we highlight the rationale for the hypothesis that ketogenesis induced by SGLT2 inhibitors may be an effective treatment for depressive disorders.