Biernacka, Joanna M.

AbstractIntroductionMAPT H1 haplotype is implicated as a risk factor for neurodegenerative diseases including Alzheimer's disease (AD).MethodsUsing Alzheimer's Disease Genetics Consortium (ADGC) genome‐wide association study (GWAS) data (n = 18,841), we conducted a MAPT H1/H2 haplotype–stratified association to discover MAPT haplotype–specific AD risk loci.ResultsWe identified 11 loci—5 in H2‐non‐carriers and 6 in H2‐carriers—although none of the MAPT haplotype–specific associations achieved genome‐wide significance. The most significant H2 non‐carrier–specific association was with a NECTIN2 intronic (P = 1.33E‐07) variant, and that for H2 carriers was near NKX6‐1 (P = 1.99E‐06). The GABRG2 locus had the strongest epistasis with MAPT H1/H2 variant rs8070723 (P = 3.91E‐06). Eight of the 12 genes at these loci had transcriptome‐wide significant differential expression in AD versus control temporal cortex (q < 0.05). Six genes were members of the brain transcriptional co‐expression network implicated in “synaptic transmission” (P = 9.85E‐59), which is also enriched for neuronal genes (P = 1.0E‐164), including MAPT.DiscussionThis stratified GWAS identified loci that may confer AD risk in a MAPT haplotype–specific manner. This approach may preferentially enrich for neuronal genes implicated in synaptic transmission.

Objective Our aim was to evaluate the relationship between evening chronotype, a proxy marker of circadian system dysfunction, and disordered eating behavior and poor dietary habits in individuals with bipolar disorder (BD). Methods In this cross‐sectional study, we evaluated 783 adults with BD. Chronotype was determined using item 5 from the reduced Morningness‐Eveningness Questionnaire. The Eating Disorder Diagnostic Scale (EDDS) and the Rapid Eating Assessment for Participants—Shortened Version (REAP‐S) were used to assess disordered eating behavior and dietary habits respectively. General linear models and logistic regression models were utilized to evaluate differences between chronotype groups. Results Two hundred and eight (27%) BD participants self‐identified as having evening chronotypes. Compared to non‐evening types, evening types were younger ( P < 0.01) and, after controlling for age, had higher mean EDDS composite z‐scores ( P < 0.01); higher rates of binge‐eating (BE) behavior ( P = 0.04), bulimia nervosa ( P < 0.01), and nocturnal eating binges ( P < 0.01); and a higher body mass index ( P = 0.04). Compared to non‐evening types, evening chronotypes had a lower REAP‐S overall score ( P < 0.01) and scored lower on the ‘healthy foods’ and ‘avoidance of unhealthy food’ factors. Evening types also skipped breakfast more often ( P < 0.01), ate less fruit ( P = 0.02) and vegetables ( P = 0.04), and consumed more fried foods ( P < 0.01), unhealthy snacks ( P = 0.02), and soft drinks ( P = 0.01). Conclusions Our findings suggest that the circadian system plays a role in the disordered eating and unhealthy dietary behaviors observed in BD patients. The circadian system may therefore represent a therapeutic target in BD‐associated morbidity that warrants further investigation.

Abstract Eating disorders and substance use disorders frequently co‐occur. Twin studies reveal shared genetic variance between liabilities to eating disorders and substance use, with the strongest associations between symptoms of bulimia nervosa and problem alcohol use (genetic correlation [ r g ], twin‐based = 0.23‐0.53). We estimated the genetic correlation between eating disorder and substance use and disorder phenotypes using data from genome‐wide association studies (GWAS). Four eating disorder phenotypes (anorexia nervosa [AN], AN with binge eating, AN without binge eating, and a bulimia nervosa factor score), and eight substance‐use‐related phenotypes (drinks per week, alcohol use disorder [AUD], smoking initiation, current smoking, cigarettes per day, nicotine dependence, cannabis initiation, and cannabis use disorder) from eight studies were included. Significant genetic correlations were adjusted for variants associated with major depressive disorder and schizophrenia. Total study sample sizes per phenotype ranged from ~2400 to ~537 000 individuals. We used linkage disequilibrium score regression to calculate single nucleotide polymorphism‐based genetic correlations between eating disorder‐ and substance‐use‐related phenotypes. Significant positive genetic associations emerged between AUD and AN ( r g = 0.18; false discovery rate q = 0.0006), cannabis initiation and AN ( r g = 0.23; q < 0.0001), and cannabis initiation and AN with binge eating ( r g = 0.27; q = 0.0016). Conversely, significant negative genetic correlations were observed between three nondiagnostic smoking phenotypes (smoking initiation, current smoking, and cigarettes per day) and AN without binge eating ( r gs = −0.19 to −0.23; qs < 0.04). The genetic correlation between AUD and AN was no longer significant after co‐varying for major depressive disorder loci. The patterns of association between eating disorder‐ and substance‐use‐related phenotypes highlights the potentially complex and substance‐specific relationships among these behaviors.

Abstract Objective The aim of this study was to evaluate the efficacy and safety of the dopaminergic‐enhancing agent modafinil/armodafinil (MoArm) as adjunctive treatment for bipolar depression. Methods A comprehensive search of major electronic databases was conducted to identify randomized controlled trials (RCTs) of adjunctive MoArm that included patients with bipolar I (BP‐I) or bipolar II (BP‐II) depression. Data for response/remission and all‐cause discontinuation were analyzed. Effect size was summarized by relative risk (RR) using a random effect model. Results Of 58 studies, five RCTs (N = 795 drug, N = 792 placebo) met inclusion criteria. Four armodafinil studies included only BP‐I patients and one modafinil study included both bipolar subtypes with limited heterogeneity ( I 2 = 34%, P = .19; I 2 = 18%, P = .30). Compared to placebo, augmentation with MoArm was associated with significantly greater rates of treatment response (RR, 1.18; 95% CI, 1.01‐1.37; P = .03) and remission (RR, 1.38; 95% CI, 1.10‐1.73; P = .005). All‐cause discontinuation was not different than placebo (RR, 1.08; 95% CI, 0.89‐1.30; P = .45) with no evidence of increased risk of mood switch or suicide attempts with MoArm (RR, 0.99; 95% CI, 0.39‐2.5; P = .98; RR, 1.02; 95% CI, 0.37‐2.85; P = .97). Conclusion This narrower scope meta‐analysis of one drug for one disease suggests that adjunctive MoArm may represent a novel therapeutic intervention. Further studies delineating the subtypes of bipolar depression responsive to these novel dopaminergic‐enhancing agents are encouraged.

We previously reported that testis‐specific Y‐encoded‐like protein (TSPYLs) are transcription regulators for CYP3A4, CYP2C9, and CYP2C19. Here, we observed dual roles for TSPYLs in mediating serotonin transport and the metabolism of selective serotonin reuptake inhibitors (SSRIs) in patients with major depressive disorder (MDD). The widely prescribed SSRIs, citalopram, and escitalopram are metabolized mainly by CYP2C19. The TSPYL1 rs3828743 single nucleotide polymorphism (SNP), which decreases its suppression of CYP2C19 expression, was associated with rapid escitalopram metabolism and worse treatment response in the Mayo PGRN‐AMPS clinical trial. We also found that TSPYLs can regulate expression of the serotonin transporter protein, SLC6A4, and, in turn, serotonin transport into cells. The SNPs in tight linkage disequilibrium with the TSPYL1 rs10223646 SNP were significantly correlated with baseline severity of depression in patients with MDD in the Sequenced Treatment Alternatives to Relieve Depression and International SSRI Pharmacogenomics Consortium clinical trials. Our findings suggest that genetic variation in TSPYL genes may be novel indicators for baseline severity of depression and SSRI poor response.

We set out to determine whether machine learning–based algorithms that included functionally validated pharmacogenomic biomarkers joined with clinical measures could predict selective serotonin reuptake inhibitor (SSRI) remission/response in patients with major depressive disorder (MDD). We studied 1,030 white outpatients with MDD treated with citalopram/escitalopram in the Mayo Clinic Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomic Study (PGRN‐AMPS; n = 398), Sequenced Treatment Alternatives to Relieve Depression (STAR*D; n = 467), and International SSRI Pharmacogenomics Consortium (ISPC; n = 165) trials. A genomewide association study for PGRN‐AMPS plasma metabolites associated with SSRI response (serotonin) and baseline MDD severity (kynurenine) identified single nucleotide polymorphisms (SNPs) in DEFB1, ERICH3, AHR, and TSPAN5 that we tested as predictors. Supervised machine‐learning methods trained using SNPs and total baseline depression scores predicted remission and response at 8 weeks with area under the receiver operating curve (AUC) > 0.7 (P < 0.04) in PGRN‐AMPS patients, with comparable prediction accuracies > 69% (P ≤ 0.07) in STAR*D and ISPC. These results demonstrate that machine learning can achieve accurate and, importantly, replicable prediction of SSRI therapy response using total baseline depression severity combined with pharmacogenomic biomarkers.