The search for the genetic factors underlying complex neuropsychiatric disorders has proceeded apace in the past decade. Despite some advances in identifying genetic variants associated with psychiatric disorders, most variants have small individual contributions to risk. By contrast, disease risk increase appears to be less subtle for disease-predisposing environmental insults. In this study, we sought to identify associations between environmental pollution and risk of neuropsychiatric disorders. We present exploratory analyses of 2 independent, very large datasets: 151 million unique individuals, represented in a United States insurance claims dataset, and 1.4 million unique individuals documented in Danish national treatment registers. Environmental Protection Agency (EPA) county-level environmental quality indices (EQIs) in the US and individual-level exposure to air pollution in Denmark were used to assess the association between pollution exposure and the risk of neuropsychiatric disorders. These results show that air pollution is significantly associated with increased risk of psychiatric disorders. We hypothesize that pollutants affect the human brain via neuroinflammatory pathways that have also been shown to cause depression-like phenotypes in animal studies.

The highly lethal brain cancer glioblastoma (GBM) poses a daunting challenge because the blood-brain barrier renders potentially druggable amplified or mutated oncoproteins relatively inaccessible. Here, we identify sphingomyelin phosphodiesterase 1 (SMPD1), an enzyme that regulates the conversion of sphingomyelin to ceramide, as an actionable drug target in GBM. We show that the highly brain-penetrant antidepressant fluoxetine potently inhibits SMPD1 activity, killing GBMs, through inhibition of epidermal growth factor receptor (EGFR) signaling and via activation of lysosomal stress. Combining fluoxetine with temozolomide, a standard of care for GBM, causes massive increases in GBM cell death and complete tumor regression in mice. Incorporation of real-world evidence from electronic medical records from insurance databases reveals significantly increased survival in GBM patients treated with fluoxetine, which was not seen in patients treated with other selective serotonin reuptake inhibitor (SSRI) antidepressants. These results nominate the repurposing of fluoxetine as a potentially safe and promising therapy for patients with GBM and suggest prospective randomized clinical trials.

The transition to daylight saving time (DST) is beneficial for energy conservation but at the same time it has been reported to increase the risk of cerebrovascular and cardiovascular problems. Here, we evaluate the effect of the DST shift on a whole spectrum of diseases—an analysis we hope will be helpful in weighing the risks and benefits of DST shifts. Our study relied on a population-based, cross-sectional analysis of the IBM Watson Health MarketScan insurance claim dataset, which incorporates over 150 million unique patients in the US, and the Swedish national inpatient register, which incorporates more than nine million unique Swedes. For hundreds of sex- and age-specific diseases, we assessed effects of the DST shifts forward and backward by one hour in spring and autumn by comparing the observed and expected diagnosis rates after DST shift exposure. We found four prominent, elevated risk clusters, including cardiovascular diseases (such as heart attacks), injuries, mental and behavioral disorders, and immune-related diseases such as noninfective enteritis and colitis to be significantly associated with DST shifts in the United States and Sweden. While the majority of disease risk elevations are modest (a few percent), a considerable number of diseases exhibit an approximately ten percent relative risk increase. We estimate that each spring DST shift is associated with negative health effects–with 150,000 incidences in the US, and 880,000 globally. We also identify for the first time a collection of diseases with relative risks that appear to decrease immediately after the spring DST shift, enriched with infections and immune system-related maladies. These diseases’ decreasing relative risks might be driven by the documented boosting effect of a short-term stress (such as that experienced around the spring DST shift) on the immune system. Over a quarter of the world population is subjected to the daylight saving time (DST) shift twice a year, which disrupts both human work and rest schedules and possibly the body’s biological clock. Several clinical studies have reported an increased risk of cerebrovascular and cardiovascular problems with DST shifts but little is known about other potential health effects. The DST shift represents a natural exposure experiment which allows us the unique opportunity of linking health outcomes to an external, state-wide event in the US and Sweden. We performed a comprehensive, phenome-wide screening of the putative health effects of the DST shift by analyzing two independent, country-scale health datasets, and found both adverse and protective associations with DST shifts in several clusters of conditions. We successfully verified previously reported associations, such as heart diseases and injuries, and identified new signals–for example, immune-related conditions. We suggest that the ramifications of daylight-saving time shifts should be acknowledged and further tested.

Injection drug use is a growing major public health concern. Injection drug users (IDUs) have a higher incidence of co-morbidities including HIV, Hepatitis, and other infections. An effective humoral response is critical for optimal homeostasis and protection from infection; however, the impact of injection heroin use on humoral immunity is poorly understood. We hypothesized that IDUs have altered B cell and antibody profiles. A comprehensive systems biology-based cross-sectional assessment of 130 peripheral blood B cell flow cytometry- and plasma- based features was performed on HIV-/Hepatitis C-, active heroin IDUs who participated in a syringe exchange program (n = 19) and healthy control subjects (n = 19). The IDU group had substantial polydrug use, with 89% reporting cocaine injection within the preceding month. IDUs exhibited a significant, 2-fold increase in total B cells compared to healthy subjects, which was associated with increased activated B cell subsets. Although plasma total IgG titers were similar between groups, IDUs had significantly higher IgG3 and IgG4, suggestive of chronic B cell activation. Total IgM was also increased in IDUs, as well as HIV Envelope-specific IgM, suggestive of increased HIV exposure. IDUs exhibited numerous features suggestive of systemic inflammation, including significantly increased plasma sCD40L, TNF-α, TGF-α, IL-8, and ceramide metabolites. Machine learning multivariate analysis distilled a set of 10 features that classified samples based on group with absolute accuracy. These results demonstrate broad alterations in the steady-state humoral profile of IDUs that are associated with increased systemic inflammation. Such dysregulation may impact the ability of IDUs to generate optimal responses to vaccination and infection, or lead to increased risk for inflammation-related co-morbidities, and should be considered when developing immune-based interventions for this growing population.

Seasonal affective disorder (SAD) famously follows annual cycles, with incidence elevation in the fall and spring. Should some version of cyclic annual pattern be expected from other psychiatric disorders? Would annual cycles be similar for distinct psychiatric conditions? This study probes these questions using 2 very large datasets describing the health histories of 150 million unique U.S. citizens and the entire Swedish population. We performed 2 types of analysis, using “uncorrected” and “corrected” observations. The former analysis focused on counts of daily patient visits associated with each disease. The latter analysis instead looked at the proportion of disease-specific visits within the total volume of visits for a time interval. In the uncorrected analysis, we found that psychiatric disorders’ annual patterns were remarkably similar across the studied diseases in both countries, with the magnitude of annual variation significantly higher in Sweden than in the United States for psychiatric, but not infectious diseases. In the corrected analysis, only 1 group of patients—11 to 20 years old—reproduced all regularities we observed for psychiatric disorders in the uncorrected analysis; the annual healthcare-seeking visit patterns associated with other age-groups changed drastically. Analogous analyses over infectious diseases were less divergent over these 2 types of computation. Comparing these 2 sets of results in the context of published psychiatric disorder seasonality studies, we tend to believe that our uncorrected results are more likely to capture the real trends, while the corrected results perhaps reflect mostly artifacts determined by dominantly fluctuating, health-seeking visits across a given year. However, the divergent results are ultimately inconclusive; thus, we present both sets of results unredacted, and, in the spirit of full disclosure, leave the verdict to the reader.

Traffic accident prediction is crucial to preventive measures against accidents and effective traffic management. Identifying hotspots can facilitate the selection of the most critical survey points to note the contributing features. In this research, an effort has been made to identify hotspots and predict traffic accident occurrences in an urban area. Accident data was obtained from the Rescue 1122 Emergency Services of Faisalabad, and hotspots were identified using Moran’s I in ArcGIS. Results showed that most hotspots were located around the General Transport Stand (GTS) area due to the maximum number of road users. The temporal investigations showed that the accident occurrence was significant from 1 to 2 p.m. The identified hotspots were further investigated by conducting a field survey. Essential features such as road geometric features, road furniture, and traffic data were used for developing Machine Learning Algorithms for accident prediction. Using Computer Vision, traffic data was extracted from recorded videos. Random forest, linear regression, and Decision tree algorithms were developed using Python in the Jupyter Notebook environment. The decision tree algorithm showed a maximum accuracy of 84.4%. The analysis of contributing factors revealed that road measurements had the maximum effect on accident occurrence. Doi: 10.28991/CEJ-2024-010-06-013 Full Text: PDF