Citations

AbstractAimsLong‐term lithium therapy (LTLT) has been associated with kidney insufficiency in bipolar disorder (BD). We aimed to investigate the risk factors of chronic kidney disease (CKD) development and progression among BD patients receiving LTLT.MethodsWe included adult patients with BD on LTLT (≥1 year) who were enrolled in the Mayo Clinic Bipolar Biobank, Rochester, Minnesota. We reviewed electronic medical records to extract information related to lithium therapy and kidney‐related data to assess changes in the estimated glomerular filtration rate (eGFR). CKD severity was assessed based on eGFR.ResultsAmong 154 patients who received LTLT, 41 patients (27%) developed CKD, of whom 20 (49%) patients continued lithium (continuers) and 19 (46%) discontinued it (discontinuers). The median time to stage 3 CKD development was 21.7 years from the start of Li treatment. Type‐2 diabetes mellitus and benzodiazepine use were independent predictors for CKD development in the survival analysis, after controlling for age. The subsequent CKD progression rate did not differ between continuers and discontinuers (mean GFR 48.6 vs. 44.1, p = 0.13) at the end of follow‐up duration (mean duration: 3.5 ± 4.4 years for continuers and 4.9 ± 5.3 years for discontinuers).ConclusionCKD was observed in one fourth of patients with BD receiving LTLT. There was no significant difference in the progression of CKD among Li continuers versus discontinuers, at the mean follow‐up duration of 4.2 years, after the CKD diagnosis. Progression of CKD could be influenced by existing comorbidities and may not necessarily be due to lithium alone.

Objectives: Prior studies demonstrate elevated cortical glutamate (Glu) in patients with bipolar disorder (BD). Studies assessing neurochemistry in early stages of bipolar illness before the emergence of manic symptoms are lacking. This study aimed to examine neurochemical correlates measured by proton magnetic resonance spectroscopy ( 1 H-MRS) and a dimensional measure of bipolarity in a sample of depressed adolescents. Methods: Adolescent participants (aged 13–21 years) underwent a semistructured diagnostic interview and clinical assessment, which included the General Behavior Inventory Parent Version (P-GBI), a 73-item, parent-rated assessment of symptoms and behaviors. 1 H-MRS scans of a left dorsolateral prefrontal cortex (L-DLPFC) voxel (8 cm 3 ) were collected using a two-dimensional J -averaged sequence to assess N -acetylaspartate (NAA), Glu, Glx (glutamate + glutamine), and NAA/Glx concentrations. We used generalized linear models to assess the relationships between P-GBI scores and metabolite levels in L-DLPFC. Results: Thirty-six participants (17 healthy controls, 19 depressed) underwent 1 H-MRS scans and clinical evaluation with the P-GBI. There was a significant negative relationship between P-GBI score and L-DLPFC NAA/Glx in the whole sample. However, the magnitude of the effect was small and statistical significance was lost after correcting for multiple comparisons. Conclusions: These preliminary results suggest that NAA/Glx may have utility as a marker of bipolar traits in healthy and depressed adolescents. If replicated, 1 H-MRS measures of glutamatergic metabolism anomalies might have a role in identifying depressed adolescents at risk for mixed symptom presentations or BD. Identifying bipolarity in the early stages of the disease would have a significant impact on treatment planning and prognosis. Further longitudinal studies should examine neurochemical correlates of mood state during the developmental emergence of BD.

Several antiepileptic drugs (AEDs) have US Food and Drug Administration (FDA) approval for use as mood stabilizers in bipolar disorder (BD), but not all BD patients respond to these AED mood stabilizers (AED‐MSs). To identify genetic polymorphisms that contribute to the variability in AED‐MS response, we performed a discovery genome‐wide association study (GWAS) of 199 BD patients from the Mayo Clinic Bipolar Disorder Biobank. Most of these patients had been treated with the AED‐MS valproate/divalproex and/or lamotrigine. AED‐MS response was assessed using the Alda scale, which quantifies clinical improvement while accounting for potential confounding factors. We identified two genome‐wide significant single‐nucleotide polymorphism (SNP) signals that mapped to the THSD7A (rs78835388, P = 7.1E‐09) and SLC35F3 (rs114872993, P = 3.2E‐08) genes. We also identified two genes with statistically significant gene‐level associations: ABCC1 (P = 6.7E‐07; top SNP rs875740, P = 2.0E‐6), and DISP1 (P = 8.9E‐07; top SNP rs34701716, P = 8.9E‐07). THSD7A SNPs were previously found to be associated with risk for several psychiatric disorders, including BD. Both THSD7A and SLC35F3 are expressed in excitatory/glutamatergic and inhibitory/γ‐aminobutyric acidergic (GABAergic) neurons, which are targets of AED‐MSs. ABCC1 is involved in the transport of valproate and lamotrigine metabolites, and the SNPs in ABCC1 and DISP1 with the strongest evidence of association in our GWAS are strong splicing quantitative trait loci in the human gut, suggesting a possible influence on drug absorption. In conclusion, our pharmacogenomic study identified novel genetic loci that appear to contribute to AED‐MS treatment response, and may facilitate precision medicine in BD.

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.

Suicide is a major public health concern; nevertheless, its neurobiology remains unknown. An area of interest in suicide research is the dorsolateral prefrontal cortex (DLPFC). We aimed to identify altered proteins and potential biological pathways in the DLPFC of individuals who died by suicide employing mass spectrometry-based untargeted proteomics. Postmortem DLPFC from age-matched male suicide mood disorder cases (n = 5) and non-suicide mood disorder cases (n = 5) were compared. The proteins that differed between groups at false discovery rate (FDR) adjusted p-values (Benjamini–Hochberg–Yekutieli) <0.3 and Log2 fold change (FC) >|0.4| were considered statistically significant and were subjected to pathway analysis by Qiagen Ingenuity software. Thirty-three of the 5162 detected proteins showed significantly altered expression levels in the suicide cases and two of them after adjustment for body mass index. The top differentially expressed protein was potassium voltage-gated channel subfamily Q member 3 (KCNQ3) (Log2FC = −0.481, p = 2.10 × 10−09, FDR = 5.93 × 10−06), which also showed a trend to downregulation in Western blot (p = 0.045, Bonferroni adjusted p = 0.090). The most notably enriched pathway was the GABA receptor signaling pathway (p < 0.001). Here, we report a reduction trend of KCNQ3 levels in the DLPFC of male suicide victims with mood disorders. Further studies with a larger sample size and equal sex representation are needed.

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.