Long-term lithium treatment in bipolar disorder is associated with longer leukocyte telomeres

Telomere shortening is a hallmark of aging and has been associated with oxidative stress, inflammation and chronic somatic, as well as psychiatric disorders, including schizophrenia and depression. Additionally, antidepressants have been found to protect against telomere shortening. However, pharmacological telomere studies are lacking in bipolar disorder (BD). Therefore, the objective of this study was to explore telomere length (TL) in patients with BD in the context of lithium treatment. We determined TL by quantitative real-time PCR using peripheral blood leukocytes. Participants were outpatients diagnosed with BD type 1 or 2 (n=256) and healthy controls (n=139). Retrospective case-control and case-case study designs were applied. Lithium response (LiR) was scored using the Alda-Scale. Lithium-treated BD patients overall, as well as those on lithium monotherapy, had 35% longer telomeres compared with controls (P<0.0005, partial η(2)=0.13). TL correlated positively with lithium treatment duration of >30 months (P=0.031, R(2)=0.13) and was negatively associated with increasing number of depressive episodes (P<0.007). BD patients responding well to lithium treatment had longer telomeres than those not responding well. This is the first study to report a positive effect of long-term lithium treatment on TL. Importantly, longer TL was also associated with a better LiR in BD patients. These data suggest that lithium exerts a protective effect against telomere shortening especially when therapeutically efficacious. We hypothesize that induction of telomerase activity may be involved in LiR in BD.

Prediction of lithium response using clinical data

OBJECTIVE

Promptly establishing maintenance therapy could reduce morbidity and mortality in patients with bipolar disorder. Using a machine learning approach, we sought to evaluate whether lithium responsiveness (LR) is predictable using clinical markers.

METHOD

Our data are the largest existing sample of direct interview-based clinical data from lithium-treated patients (n = 1266, 34.7% responders), collected across seven sites, internationally. We trained a random forest model to classify LR-as defined by the previously validated Alda scale-against 180 clinical predictors.

RESULTS

Under appropriate cross-validation procedures, LR was predictable in the pooled sample with an area under the receiver operating characteristic curve of 0.80 (95% CI 0.78-0.82) and a Cohen kappa of 0.46 (0.4-0.51). The model demonstrated a particularly low false-positive rate (specificity 0.91 [0.88-0.92]). Features related to clinical course and the absence of rapid cycling appeared consistently informative.

CONCLUSION

Clinical data can inform out-of-sample LR prediction to a potentially clinically relevant degree. Despite the relevance of clinical course and the absence of rapid cycling, there was substantial between-site heterogeneity with respect to feature importance. Future work must focus on improving classification of true positives, better characterizing between- and within-site heterogeneity, and further testing such models on new external datasets.

Clinical factors associated with lithium response in bipolar disorders

BACKGROUND

Bipolar disorder is a common chronic illness characterized by high levels of morbidity and all-cause mortality. Lithium is one of the gold standard mood stabilizer treatments, but the identification of good, partial and non-responders in clinical settings is inconsistent.

METHODS

We used an established rating scale (the Alda scale) to classify the degree of lithium response (good response, partial response, non-response) in a large, multicentre clinically representative sample of well-characterized cases of bipolar disorders I and II. Next, we examined previously reported clinical predictors of response to determine which factors significantly differentiated between the three response groups.

RESULTS

Of 754 cases, 300 received lithium, for at least 6 months, as a treatment for bipolar disorder (40%). Of these cases, 17% were classified as good response, 52% as partial response and 31% as non-response. Lifetime history of mixed episodes ( p = 0.017) and alcohol use disorders ( p = 0.015) both occurred in >20% of partial response and non-response groups but <10% of good response cases. Family history of bipolar disorder I was of borderline statistical significance, being more frequent in the good response group (38%) compared with the non-response group (18%). There was a trend ( p = 0.06) for bipolar disorder II to be associated with non-response.

CONCLUSIONS

Only three factors previously identified as predictors of lithium response significantly differentiated the response groups identified in our sample. Interestingly, these factors have all been found to co-occur more often than expected by chance, and it can be hypothesized that they may represent a shared underlying factor or dimension. Further prospective studies of predictors and the performance of the Alda scale are recommended.

Childhood trauma and mixed episodes are associated with poor response to lithium in bipolar disorders

OBJECTIVES

Reliable predictors of response to lithium are still lacking in bipolar disorders (BDs). However, childhood trauma has been hypothesized to be associated with poor response to lithium.

METHODS

We included 148 patients with BD, euthymic when retrospectively and clinically assessed for response to lithium and childhood trauma using reliable scales.

RESULTS

According to the 'Alda scale', the sample consisted in 20.3% of excellent responders, 49.3% of partial responders and 30.4% of non-responders to lithium. A higher level of physical abuse significantly correlated with a lower level of response to lithium (P = 0.009). As compared to patients not exposed to any abuse, patients with at least two trauma abuses (emotional, physical or sexual) were more at risk of belonging to the non-responders group (OR = 4.91 95% CI (1.01-27.02)). Among investigated clinical variables, lifetime presence of mixed episodes and alcohol misuse were associated with non-response to lithium. Multivariate analyses demonstrated that physical abuse and mixed episodes were independently associated with poor response to lithium (P = 0.005 and P = 0.013 respectively).

CONCLUSIONS

Childhood physical abuse might be involved in a poor future response to lithium prophylaxis, this effect being independent of the association between clinical expression of BD and poor response to lithium.

An examination of the quality and performance of the Alda scale for classifying lithium response phenotypes

OBJECTIVES

The Retrospective Assessment of the Lithium Response Phenotype Scale (Alda scale) is the most widely used clinical measure of lithium response phenotypes. We assess its performance against recommended psychometric and clinimetric standards.

METHODS

We used data from the Consortium for Lithium Genetics and a French study of lithium response phenotypes (combined sample >2500) to assess reproducibility, responsiveness, validity, and interpretability of the A scale (assessing change in illness activity), the B scale, and its items (assessing confounders of response) and the previously established response categories derived from the Total Score for the Alda scale.

RESULTS

The key findings are that the B scale is vulnerable to error measurement. For example, some items contribute little to overall performance of the Alda scale (eg, B2) and that the B scale does not reliably assess a single construct (uncertainty in response). Machine learning models indicate that it may be more useful to employ an algorithm for combining the ratings of individual B items in a sequence that clarifies the noise to signal ratio instead of using a composite score.

CONCLUSIONS

This study highlights three important topics. First, empirical approaches can help determine which aspects of the performance of any scale can be improved. Second, the B scale of the Alda is best applied as a multidimensional index (identifying several independent confounders of the assessment of response). Third, an integrated science approach to precision psychiatry is vital, otherwise phenotypic misclassifications will undermine the reliability and validity of findings from genetics and biomarker studies.

A glutamatergic network mediates lithium response in bipolar disorder as defined by epigenome pathway analysis

AIM

A regulatory network in the human brain mediating lithium response in bipolar patients was revealed by analysis of functional SNPs from genome-wide association studies (GWAS) and published gene association studies, followed by epigenome mapping.

METHODS

An initial set of 23,312 SNPs in linkage disequilibrium with lead SNPs, and sub-threshold GWAS SNPs rescued by pathway analysis, were studied in the same populations. These were assessed using our workflow and annotation by the epigenome roadmap consortium.

RESULTS

Twenty-seven percent of 802 SNPs that were associated with lithium response (13 published studies gene association studies and two GWAS) were shared in common with 1281 SNPs from 18 GWAS examining psychiatric disorders and adverse events associated with lithium treatment. Nineteen SNPs were annotated as active regulatory elements such as enhancers and promoters in a tissue-specific manner. They were located within noncoding regions of ten genes: ANK3, ARNTL, CACNA1C, CACNG2, CDKN1A, CREB1, GRIA2, GSK3B, NR1D1 and SLC1A2. Following gene set enrichment and pathway analysis, these genes were found to be significantly associated (p = 10(-27); Fisher exact test) with an AMPA2 glutamate receptor network in human brain. Our workflow results showed concordance with annotation of regulatory elements from the epigenome roadmap. Analysis of cognate mRNA and enhancer RNA exhibited patterns consistent with an integrated pathway in human brain.

CONCLUSION

This pharmacoepigenomic regulatory pathway is located in the same brain regions that exhibit tissue volume loss in bipolar disorder. Although in silico analysis requires biological validation, the approach provides value for identification of candidate variants that may be used in pharmacogenomic testing to identify bipolar patients likely to respond to lithium.

Analysis of the Influence of microRNAs in Lithium Response in Bipolar Disorder

Bipolar disorder (BD) is a common, highly heritable neuropsychiatric disease characterized by recurrent episodes of mania and depression. Lithium is the best-established long-term treatment for BD, even though individual response is highly variable. Evidence suggests that some of this variability has a genetic basis. This is supported by the largest genome-wide association study (GWAS) of lithium response to date conducted by the International Consortium on Lithium Genetics (ConLiGen). Recently, we performed the first genome-wide analysis of the involvement of miRNAs in BD and identified nine BD-associated miRNAs. However, it is unknown whether these miRNAs are also associated with lithium response in BD. In the present study, we therefore tested whether common variants at these nine candidate miRNAs contribute to the variance in lithium response in BD. Furthermore, we systematically analyzed whether any other miRNA in the genome is implicated in the response to lithium. For this purpose, we performed gene-based tests for all known miRNA coding genes in the ConLiGen GWAS dataset (n = 2,563 patients) using a set-based testing approach adapted from the versatile gene-based test for GWAS (VEGAS2). In the candidate approach, miR-499a showed a nominally significant association with lithium response, providing some evidence for involvement in both development and treatment of BD. In the genome-wide miRNA analysis, 71 miRNAs showed nominally significant associations with the dichotomous phenotype and 106 with the continuous trait for treatment response. A total of 15 miRNAs revealed nominal significance in both phenotypes with miR-633 showing the strongest association with the continuous trait (p = 9.80E-04) and miR-607 with the dichotomous phenotype (p = 5.79E-04). No association between miRNAs and treatment response to lithium in BD in either of the tested conditions withstood multiple testing correction. Given the limited power of our study, the investigation of miRNAs in larger GWAS samples of BD and lithium response is warranted.

Alterations in phosphorylated cAMP response element-binding protein (pCREB) signaling: an endophenotype of lithium-responsive bipolar disorder?

OBJECTIVES

Abnormalities of signal transduction are considered among the susceptibility factors for bipolar disorder (BD). These include changes in G-protein-mediated signaling and subsequent modification of gene expression via transcription factors such as cAMP response element-binding protein (CREB).

METHODS

We investigated levels of CREB in lymphoblasts from patients with BD, all responders to lithium prophylaxis (n = 13), and healthy control subjects (n = 15). Phosphorylated CREB (pCREB) was measured by immunoblotting in subjects with BD (n = 15) as well as in their affected (n = 17) and unaffected (n = 18) relatives, and healthy controls (n = 16).

RESULTS

Basal CREB levels were comparable in patients and control subjects and were not changed by lithium treatment. pCREB levels were increased in both patients and their relatives compared to controls (p = 0.003). Forskolin stimulation led to a 24% increase in pCREB levels in cells from healthy subjects (p = 0.002) but not in the other three groups. When using basal and stimulated pCREB levels as a biochemical phenotype in a preliminary linkage study, we found the strongest support for linkage in regions largely overlapping with those showing linkage with the clinical phenotype (3p, 6p, 16p, 17q, 19q, and 21q).

CONCLUSIONS

Abnormal pCREB signaling could be considered a biochemical phenotype for lithium-responsive BD.

Can network analysis shed light on predictors of lithium response in bipolar I disorder?

OBJECTIVE

To undertake a large-scale clinical study of predictors of lithium (Li) response in bipolar I disorder (BD-I) and apply contemporary multivariate approaches to account for inter-relationships between putative predictors.

METHODS

We used network analysis to estimate the number and strength of connections between potential predictors of good Li response (measured by a new scoring algorithm for the Retrospective Assessment of Response to Lithium Scale) in 900 individuals with BD-I recruited to the Consortium of Lithium Genetics.

RESULTS

After accounting for co-associations between potential predictors, the most important factors associated with the good Li response phenotype were panic disorder, manic predominant polarity, manic first episode, age at onset between 15-32 years and family history of BD. Factors most strongly linked to poor outcome were comorbid obsessive-compulsive disorder, alcohol and/or substance misuse, and/or psychosis (symptoms or syndromes).

CONCLUSIONS

Network analysis can offer important additional insights to prospective studies of predictors of Li treatment outcomes. It appears to especially help in further clarifying the role of family history of BD (i.e. its direct and indirect associations) and highlighting the positive and negative associations of different subtypes of anxiety disorders with Li response, particularly the little-known negative association between Li response and obsessive-compulsive disorder.

Can actigraphy be used to define lithium response dimensions in bipolar disorders?

BACKGROUND

Actigraphy is commonly used in case-control studies to explore sleep-wake patterns and circadian rhythmicity in bipolar disorders (BD). However, there is limited ecological research regarding actigraphy parameters associated with response to lithium (Li_Resp).

METHODS

Outpatients with BD-I (n=70) and BD-II (n=20) who were all prescribed prophylactic Li undertook 21 consecutive days of actigraphy recording. The Retrospective Assessment of Response to Lithium Scale (also referred as the Alda scale) was rated on a 0-10 continuum. We used principal component analysis (PCA) to summarize interrelationships among clinical and actigraphic variables and Li_Resp.

RESULTS

PCA demonstrated the existence of a Li_Resp dimension (accounting for >20% explained variance) characterized by 5 markers of circadian timing and rhythmicity. Replication of the PCA, using the resampling procedure, confirmed this model was robust for the BD-I but not for BD-II (which showed weaker associations between Li_Resp and sleep variables). These circadian rhythm markers identified by PCA correctly classified 64% (95% Confidence Intervals: 52-76%; p<0.03) of all BD cases as Li responders or non-responders.

LIMITATIONS

Although we attempted to minimize risk of statistical error, the small BD-II subsample may have undermined the ability of PCA to identify a robust Li_Resp dimension for this subtype.

CONCLUSIONS

Our findings are compatible with circadian models of BD and with putative mechanisms of action of Li. If confirmed in prospective studies, the study offers support for use of actigraphy as a relevant method for real time objective monitoring of Li_Resp, with few concerns regarding reliability and validity.

Association study of BDNF Val66Met gene polymorphism with bipolar disorder and lithium treatment response in Indian population

BACKGROUND

The association of the Val66Met (rs6265) polymorphism in the brain-derived neurotrophic factor (BDNF) gene with bipolar disorder (BD) and response to lithium treatment has been suggested, though inconsistently. The considerable diversity of allele frequency across different populations contributes to this. There is no data from South Asia till date. Hence, we examined the association of this polymorphism in BD cases from India, and its association with lithium treatment response.

METHODS

BD patients (N = 301) were recruited from the clinical services of National Institute of Mental Health and Neurosciences (NIMHANS), India. Lithium treatment response for 190 BD subjects was assessed using Alda scale by NIMH life charts. Patients with total score ⩾7 were defined as lithium responders (N = 115) and patients with score <7 were defined as lithium non-responders (N = 75). Healthy controls (N = 484) with no lifetime history of neuropsychiatric illness or a family history of mental illness were recruited as control set. Genotyping was performed by TaqMan genotyping assay.

RESULTS

Genotype and allele frequency of BDNF Val66Met SNP was significantly different (χ2 = 7.78, p = 0.02) in cases compared to controls, and the Val(G) allele was more frequent (χ2 = 7.08, p = 0.008) in BD patients. However, no significant difference is noted in genotype or allele frequencies of this polymorphism between the lithium responders and non-responders.

CONCLUSIONS

The Val(G) allele of BDNF Val66Met polymorphism is associated with risk of BD in this sample, but it is not related to response to lithium.

The Mechanisms of Lithium Action: The Old and New Findings

Despite lithium's presence in modern psychiatry for three-quarters of a century, the mechanisms of its therapeutic action have not been fully elucidated. This article presents the evolution of the views on these mechanisms, and both the old and new findings are discussed. Among the old mechanisms, lithium's effect on the purinergic system; electrolyte metabolism; membrane transport; and second messenger systems, namely, cyclic nucleotide and phosphatidylinositol (PI), glycogen synthase kinase-3beta (GSK-3β), brain-derived neurotrophic factor, and neurotransmitters, are discussed. The new data were obtained from in vitro studies, molecular biology, and genetic research. They showed the effects of lithium on the immune system, biological rhythms, telomere functions, and mitochondria. In this article, each lithium mechanism is considered in the light of its association with the pathogenesis of bipolar disorder or/and as a marker of the lithium response. Although not exhaustive, this review elucidates the multiple potential mechanisms of lithium action. It was also observed that many seemingly "old" mechanisms have experienced a resurgence in research conducted during the 21st century. Additionally, many studies converged on the previously postulated mechanisms of lithium inhibiting GSK-3β and PI.