Identifying the potential role of insomnia on multimorbidity: A Mendelian randomization phenome-wide association study in UK Biobank

Objectives: To identify the breadth of potential causal effects of insomnia on health outcomes and hence its possible role in multimorbidity. Design: Mendelian randomisation (MR) Phenome-wide association study (MR-PheWAS) with two-sample Mendelian randomisation follow-up. Setting: Individual data from UK Biobank and summary data from a number of genome-wide association studies. Participants: 336,975 unrelated white-British UK Biobank participants. Exposures: Standardised genetic risk of insomnia for the MR-PheWAS and genetically predicted insomnia for the two-sample MR follow-up, with insomnia instrumented by a genetic risk score (GRS) created from 129 single-nucleotide polymorphisms (SNPs). Main outcomes measures: 11,409 outcomes from UK Biobank extracted and processed by an automated pipeline (PHESANT). Potential causal effects (i.e., those passing a Bonferroni-corrected significance threshold) were followed up with two-sample MR in MR-Base, where possible. Results: 437 potential causal effects of insomnia were observed for a number of traits, including anxiety, stress, depression, mania, addiction, pain, body composition, immune, respiratory, endocrine, dental, musculoskeletal, cardiovascular and reproductive traits, as well as socioeconomic and behavioural traits. We were able to undertake two-sample MR for 71 of these 437 and found evidence of causal effects (with directionally concordant effect estimates across all analyses) for 25 of these. These included, for example, risk of anxiety disorders (OR=1.55 [95% confidence interval (CI): 1.30, 1.86] per category increase in insomnia), diseases of the oesophagus/stomach/duodenum (OR=1.32 [95% CI: 1.14, 1.53]) and spondylosis (OR=1.57 [95% CI: 1.22, 2.01]). Conclusion: Insomnia potentially causes a wide range of adverse health outcomes and behaviours. This has implications for developing interventions to prevent and treat a number of diseases in order to reduce multimorbidity and associated polypharmacy.

Genetic Risk Score Enhances Coronary Artery Disease Risk Prediction in Individuals With Type 1 Diabetes

OBJECTIVE Individuals with type 1 diabetes are at a high lifetime risk of coronary artery disease (CAD) calling for early interventions. This study explores the use of a genetic risk score (GRS) for CAD risk prediction, compares it to established clinical markers and investigates its performance according to the age and pharmacological treatment. <p>RESEARCH DESIGN AND METHODS This study in 3,295 individuals with type 1 diabetes from the Finnish Diabetic Nephropathy Study (467 incident CAD, 14.8 years follow-up) employed three risk scores: a GRS, a validated clinical score and their combined score. Hazard ratios (HR) were calculated with Cox regression and model performances compared with Harrel’s C-index. </p> <p>RESULTS A HR of 6.7 for CAD was observed between the highest and the lowest 5^th percentile of the GRS (<i>P</i>=1.8×10^-6). The performance of GRS (C-index [C] 0.562) was similar to HbA_1c (C=0.563, <i>p</i>-value for difference 0.96), HDL (C=0.571, <i>P</i>=0.6) and total cholesterol (C=0.594, <i>P</i>=0.1). The GRS was not correlated with the clinical score (<i>r</i>=-0.013, <i>P</i>=0.5). The combined score outperformed the clinical score (C=0.813 vs C=0.820, <i>P</i>=0.003). The GRS performed better in individuals below the median age (38.6 years) compared to those above (C=0.637 vs C=0.546). </p> <p>CONCLUSIONS A GRS identified individuals at high risk of CAD and worked better in younger individuals. GRS was also an independent risk factor for CAD with a predictive power comparable to that of HbA_1c, HDL and total cholesterol and, when incorporated into a clinical model, modestly improved the predictions. The GRS promises early risk stratification in clinical practice by enhancing the prediction of CAD. </p>

Genetic Risk Score Enhances Coronary Artery Disease Risk Prediction in Individuals With Type 1 Diabetes

OBJECTIVE Individuals with type 1 diabetes are at a high lifetime risk of coronary artery disease (CAD) calling for early interventions. This study explores the use of a genetic risk score (GRS) for CAD risk prediction, compares it to established clinical markers and investigates its performance according to the age and pharmacological treatment. <p>RESEARCH DESIGN AND METHODS This study in 3,295 individuals with type 1 diabetes from the Finnish Diabetic Nephropathy Study (467 incident CAD, 14.8 years follow-up) employed three risk scores: a GRS, a validated clinical score and their combined score. Hazard ratios (HR) were calculated with Cox regression and model performances compared with Harrel’s C-index. </p> <p>RESULTS A HR of 6.7 for CAD was observed between the highest and the lowest 5^th percentile of the GRS (<i>P</i>=1.8×10^-6). The performance of GRS (C-index [C] 0.562) was similar to HbA_1c (C=0.563, <i>p</i>-value for difference 0.96), HDL (C=0.571, <i>P</i>=0.6) and total cholesterol (C=0.594, <i>P</i>=0.1). The GRS was not correlated with the clinical score (<i>r</i>=-0.013, <i>P</i>=0.5). The combined score outperformed the clinical score (C=0.813 vs C=0.820, <i>P</i>=0.003). The GRS performed better in individuals below the median age (38.6 years) compared to those above (C=0.637 vs C=0.546). </p> <p>CONCLUSIONS A GRS identified individuals at high risk of CAD and worked better in younger individuals. GRS was also an independent risk factor for CAD with a predictive power comparable to that of HbA_1c, HDL and total cholesterol and, when incorporated into a clinical model, modestly improved the predictions. The GRS promises early risk stratification in clinical practice by enhancing the prediction of CAD. </p>

A Levee to the Flood: Pre-injury Neuroinflammation and Immune Stress Influence Traumatic Brain Injury Outcome

Increasing evidence demonstrates that aging influences the brain's response to traumatic brain injury (TBI), setting the stage for neurodegenerative pathology like Alzheimer's disease (AD). This topic is often dominated by discussions of post-injury aging and inflammation, which can diminish the consideration of those same factors before TBI. In fact, pre-TBI aging and inflammation may be just as critical in mediating outcomes. For example, elderly individuals suffer from the highest rates of TBI of all severities. Additionally, pre-injury immune challenges or stressors may alter pathology and outcome independent of age. The inflammatory response to TBI is malleable and influenced by previous, coincident, and subsequent immune insults. Therefore, pre-existing conditions that elicit or include an inflammatory response could substantially influence the brain's ability to respond to traumatic injury and ultimately affect chronic outcome. The purpose of this review is to detail how age-related cellular and molecular changes, as well as genetic risk variants for AD affect the neuroinflammatory response to TBI. First, we will review the sources and pathology of neuroinflammation following TBI. Then, we will highlight the significance of age-related, endogenous sources of inflammation, including changes in cytokine expression, reactive oxygen species processing, and mitochondrial function. Heightened focus is placed on the mitochondria as an integral link between inflammation and various genetic risk factors for AD. Together, this review will compile current clinical and experimental research to highlight how pre-existing inflammatory changes associated with infection and stress, aging, and genetic risk factors can alter response to TBI.

Genetic Risk Score Enhances Coronary Artery Disease Risk Prediction in Individuals With Type 1 Diabetes

OBJECTIVE Individuals with type 1 diabetes are at a high lifetime risk of coronary artery disease (CAD), calling for early interventions. This study explores the use of a genetic risk score (GRS) for CAD risk prediction, compares it to established clinical markers, and investigates its performance according to the age and pharmacological treatment. RESEARCH DESIGN AND METHODS This study in 3,295 individuals with type 1 diabetes from the Finnish Diabetic Nephropathy Study (467 incident CAD, 14.8 years follow-up) used three risk scores: a GRS, a validated clinical score, and their combined score. Hazard ratios (HR) were calculated with Cox regression, and model performances were compared with the Harrell C-index (C-index). RESULTS A HR of 6.7 for CAD was observed between the highest and the lowest 5th percentile of the GRS (P = 1.8 × 10−6). The performance of GRS (C-index = 0.562) was similar to HbA1c (C-index = 0.563, P = 0.96 for difference), HDL (C-index = 0.571, P = 0.6), and total cholesterol (C-index = 0.594, P = 0.1). The GRS was not correlated with the clinical score (r = −0.013, P = 0.5). The combined score outperformed the clinical score (C-index = 0.813 vs. C-index = 0.820, P = 0.003). The GRS performed better in individuals below the median age (38.6 years) compared with those above (C-index = 0.637 vs. C-index = 0.546). CONCLUSIONS A GRS identified individuals at high risk of CAD and worked better in younger individuals. GRS was also an independent risk factor for CAD, with a predictive power comparable to that of HbA1c and HDL and total cholesterol, and when incorporated into a clinical model, modestly improved the predictions. The GRS promises early risk stratification in clinical practice by enhancing the prediction of CAD.

Augmented Genetic Risk of Thrombophilia in Pediatric Acute Lymphoblastic Leukemia: Case report and Review of Literature

Acute Lymphoblastic Leukemia (ALL) is the most common malignancy in children. Venous thromboembolism (VTE) is relatively common in children with acute ALL that usually appears after the diagnosis or during therapy secondary to many associated risk factors. Here in we report for the first time a child who developed cerebral venous sinus thrombosis (CVST) prior to the diagnosis of ALL.

Improving Machine Learning Prediction of ADHD Using Gene Set Polygenic Risk Scores and Risk Scores from Genetically Correlated Phenotypes

Background: Polygenic risk scores (PRSs), which sum the effects of SNPs throughout the genome to measure risk afforded by common genetic variants, have improved our ability to estimate disorder risk for Attention-Deficit/Hyperactivity Disorder (ADHD) but the accuracy of risk prediction is rarely investigated. Methods: With the goal of improving risk prediction, we performed gene set analysis of GWAS data to select gene sets associated with ADHD within a training subset. For each selected gene set, we generated gene set polygenic risk scores (gsPRSs), which sum the effects of SNPs for each selected gene set. We created gsPRS for ADHD and for phenotypes having a high genetic correlation with ADHD. These gsPRS were added to the standard PRS as input to machine learning models predicting ADHD. We used feature importance scores to select gsPRS for a final model and to generate a ranking of the most consistently predictive gsPRS. Results: For a test subset that had not been used for training or validation, a random forest (RF) model using PRSs from ADHD and genetically correlated phenotypes and an optimized group of 20 gsPRS had an area under the receiving operating characteristic curve (AUC) of 0.72 (95% CI: 0.70 to 0.74). This AUC was a statistically significant improvement over logistic regression models and RF models using only PRS from ADHD and genetically correlated phenotypes. Conclusions: Summing risk at the gene set level and incorporating genetic risk from disorders with high genetic correlations with ADHD improved the accuracy of predicting ADHD. Learning curves suggest that additional improvements would be expected with larger study sizes. Our study suggests that better accounting of genetic risk and the genetic context of allelic differences results in more predictive models.

Gene-Environment Interaction Analysis Incorporating Sex, Cardiometabolic Diseases, and Multiple Deprivation Index Reveals Novel Genetic Associations With COVID-19 Severity

Increasing evidence indicates that specific genetic variants influence the severity of outcomes after infection with COVID-19. However, it is not clear whether the effect of these genetic factors is independent of the risk due to more established non-genetic demographic and metabolic risk factors such as male sex, poor cardiometabolic health, and low socioeconomic status. We sought to identify interactions between genetic variants and non-genetic risk factors influencing COVID-19 severity via a genome-wide interaction study in the UK Biobank. Of 378,051 unrelated individuals of European ancestry, 2,402 were classified as having experienced severe COVID-19, defined as hospitalization or death due to COVID-19. Exposures included sex, cardiometabolic risk factors [obesity and type 2 diabetes (T2D), tested jointly], and multiple deprivation index. Multiplicative interaction was tested using a logistic regression model, conducting both an interaction test and a joint test of genetic main and interaction effects. Five independent variants reached genome-wide significance in the joint test, one of which also reached significance in the interaction test. One of these, rs2268616 in the placental growth factor (PGF) gene, showed stronger effects in males and in individuals with T2D. None of the five variants showed effects on a similarly-defined phenotype in a lookup in the COVID-19 Host Genetics Initiative. These results reveal potential additional genetic loci contributing to COVID-19 severity and demonstrate the value of including non-genetic risk factors in an interaction testing approach for genetic discovery.