Abstract
Background
The greater presence of neurodevelopmental antecedants may differentiate schizophrenia from bipolar disorders (BD). Machine learning/pattern recognition allows us to estimate the ...biological age of the brain from structural magnetic resonance imaging scans (MRI). The discrepancy between brain and chronological age could contribute to early detection and differentiation of BD and schizophrenia.
Methods
We estimated brain age in 2 studies focusing on early stages of schizophrenia or BD. In the first study, we recruited 43 participants with first episode of schizophrenia-spectrum disorders (FES) and 43 controls. In the second study, we included 96 offspring of bipolar parents (48 unaffected, 48 affected) and 60 controls. We used relevance vector regression trained on an independent sample of 504 controls to estimate the brain age of study participants from structural MRI. We calculated the brain-age gap estimate (BrainAGE) score by subtracting the chronological age from the brain age.
Results
Participants with FES had higher BrainAGE scores than controls (F(1, 83) = 8.79, corrected P = .008, Cohen’s d = 0.64). Their brain age was on average 2.64 ± 4.15 years greater than their chronological age (matched t(42) = 4.36, P < .001). In contrast, participants at risk or in the early stages of BD showed comparable BrainAGE scores to controls (F(2,149) = 1.04, corrected P = .70, η2 = 0.01) and comparable brain and chronological age.
Conclusions
Early stages of schizophrenia, but not early stages of BD, were associated with advanced BrainAGE scores. Participants with FES showed neurostructural alterations, which made their brains appear 2.64 years older than their chronological age. BrainAGE scores could aid in early differential diagnosis between BD and schizophrenia.
Abstract Objectives Impaired response inhibition underlies symptoms and altered functioning in patients with bipolar disorders (BD). The interpretation of fMRI studies requires an accurate estimation ...of neurocognitive performance, for which individual studies are typically underpowered. Thus, we performed the first combined meta-analysis of fMRI activations and neurocognitive performance in studies investigating response inhibition in BD. Methods We used signed differential mapping to combine anatomical coordinates of activation and standardized differences between means to evaluate neurocognitive performance in 30 fMRI studies of response inhibition comparing controls ( n = 667) and patients with BD ( n = 635). Results Relative to controls, BD patients underactivated the right inferior frontal gyrus (rIFG) regardless of current mood state and behavioral performance. Unique to euthymia were cortical hyperactivations (left superior temporal, right middle frontal gyri) combined with subcortical hypoactivations (basal ganglia), whereas unique to mania were subcortical hyperactivations (bilateral basal ganglia), combined with cortical hypoactivations (right inferior and medial frontal gyri). The fMRI changes in euthymia were associated with normal cognitive performance, whereas manic patients committed more errors during response inhibition. Conclusions The rIFG hypoactivations were congruent with a BD trait, which may underlie the impaired response inhibition in mania. Euthymic BD subjects may compensate for the rIFG hypoactivations by hyperactivations of adjacent cortical areas, yielding comparable performance in inhibitory functions and suggesting possibilities for neuromodulation treatment of these cognitive impairments. The reversal of the activation pattern between mania and euthymia has implications for monitoring of treatment response and identification of imminent relapse.
Peat bogs have historically represented exceptional carbon (C) sinks because of their extremely low decomposition rates and consequent accumulation of plant remnants as peat. Among the factors ...favoring that peat accumulation, a major role is played by the chemical quality of plant litter itself, which is poor in nutrients and characterized by polyphenols with a strong inhibitory effect on microbial breakdown. Because bogs receive their nutrient supply solely from atmospheric deposition, the global increase of atmospheric nitrogen (N) inputs as a consequence of human activities could potentially alter the litter chemistry with important, but still unknown, effects on their C balance. Here we present data showing the decomposition rates of recently formed litter peat samples collected in nine European countries under a natural gradient of atmospheric N deposition from approximately equal to 0.2 to 2 g·m⁻²·yr⁻¹. We found that enhanced decomposition rates for material accumulated under higher atmospheric N supplies resulted in higher carbon dioxide (CO₂) emissions and dissolved organic carbon release. The increased N availability favored microbial decomposition (i) by removing N constraints on microbial metabolism and (ii) through a chemical amelioration of litter peat quality with a positive feedback on microbial enzymatic activity. Although some uncertainty remains about whether decay-resistant Sphagnum will continue to dominate litter peat, our data indicate that, even without such changes, increased N deposition poses a serious risk to our valuable peatland C sinks.
Insulin-sensitizing medications were originally used in psychiatric practice to treat weight gain and other metabolic side effects that accompany the use of mood stabilizers, antipsychotics, and some ...antidepressants. However, in recent studies these medications have been shown to cause improvement in depressive symptoms, creating a potential new indication outside of metabolic regulation. However, it is still unclear whether the antidepressant properties of these medications are associated with improvements in metabolic markers. We performed a systematic search of the literature following PRISMA guidelines of studies investigating antidepressant effects of insulin-sensitizing medications. We specifically focused on whether any improvements in depressive symptoms were connected to the improvement of metabolic dysfunction. Majority of the studies included in this review reported significant improvement in depressive symptoms following treatment with insulin-sensitizing medications. Nine out of the fifteen included studies assessed for a correlation between improvement in symptoms and changes in metabolic markers and only two of the nine studies found such association, with effect sizes ranging from R
= 0.26-0.38. The metabolic variables, which correlated with improvements in depressive symptoms included oral glucose tolerance test, fasting plasma glucose and glycosylated hemoglobin following treatment with pioglitazone or metformin. The use of insulin-sensitizing medications has a clear positive impact on depressive symptoms. However, it seems that the symptom improvement may be unrelated to improvement in metabolic markers or weight. It is unclear which additional mechanisms play a role in the observed clinical improvement. Some alternative options include inflammatory, neuroinflammatory changes, improvements in cognitive functioning or brain structure. Future studies of insulin-sensitizing medications should measure metabolic markers and study the links between changes in metabolic markers and changes in depression. Additionally, it is important to use novel outcomes in these studies, such as changes in cognitive functioning and to investigate not only acute, but also prophylactic treatment effects.
Abstract Background Numerous studies have observed that offspring of bipolar parents manifest a broad spectrum of psychiatric disorders. We tested the hypothesis that in high risk offspring, bipolar ...disorder evolves in a predictable clinical sequence from non-specific (non-mood) to specific (mood) psychopathology. Methods Offspring from well-characterized families with one bipolar parent (high risk) or two well parents (controls) were assessed annually or at anytime symptoms developed using KSADS-PL interviews for up to 15 years. DSM-IV diagnoses were made on blind consensus review using all available clinical material. We compared the age-adjusted risks of lifetime psychopathology between high risk and control subjects and assessed the conditional probability of developing a mood disorder given a history of non-mood disorders. In subjects meeting full DSM-IV criteria for bipolar disorder, we assessed the sequence of psychopathology against a clinical staging model. Results High risk offspring manifest higher rates of anxiety and sleep disorders, as well as major mood and substance use disorders compared to controls. Antecedent anxiety increased the age-adjusted risk of mood disorder from 40 to 85% (hazard ratio of 2.6). High risk subjects who developed a mood disorder had an increased risk of a substance use disorder (hazard ratio of 2.4), typically meeting diagnostic criteria during or after the first major mood episode. The evolution of psychopathology leading to bipolar disorder generally followed the proposed sequence, although not all subjects manifest all stages. Limitations Larger numbers of high risk offspring prospectively assessed over the risk period would allow confirmation of these preliminary findings. Conclusions Clinical staging may be a useful approach to refine the early diagnosis and facilitate research into the evolution of bipolar disorder in those at familial risk.
Little is known about the impact of insulin resistance on bipolar disorder.
To examine the relationships between insulin resistance, type 2 diabetes and clinical course and treatment outcomes in ...bipolar disorder.
We measured fasting glucose and insulin in 121 adults with bipolar disorder. We diagnosed type 2 diabetes and determined insulin resistance. The National Institute of Mental Health Life Chart was used to record the course of bipolar disorder and the Alda scale to establish response to prophylactic lithium treatment.
Patients with bipolar disorder and type 2 diabetes or insulin resistance had three times higher odds of a chronic course of bipolar disorder compared with euglycaemic patients (50% and 48.7% respectively v. 27.3%, odds ratio (OR) = 3.07, P = 0.007), three times higher odds of rapid cycling (38.5% and 39.5% respectively v. 18.2%, OR = 3.13, P = 0.012) and were more likely to be refractory to lithium treatment (36.8% and 36.7% respectively v. 3.2%, OR = 8.40, P<0.0001). All associations remained significant after controlling for antipsychotic exposure and body mass index in sensitivity analyses.
Comorbid insulin resistance may be an important factor in resistance to treatment in bipolar disorder.
Background To translate our knowledge about neuroanatomy of bipolar disorder (BD) into a diagnostic tool, it is necessary to identify the neural signature of predisposition for BD and separate it ...from effects of long-standing illness and treatment. Thus, we examined the associations among genetic risk, illness burden, lithium treatment, and brain structure in BD. Methods This is a two-center, replication-design, structural magnetic resonance imaging study. First, we investigated neuroanatomic markers of familial predisposition by comparing 50 unaffected and 36 affected relatives of BD probands as well as 49 control subjects using modulated voxel-based morphometry. Second, we investigated effects of long-standing illness and treatment on the identified markers in 19 young participants early in the course of BD, 29 subjects with substantial burden of long-lasting BD and either minimal lifetime ( n = 12), or long-term ongoing ( n = 17) lithium treatment. Results Five groups, including the unaffected and affected relatives of BD probands from each center as well as participants early in the course of BD showed larger right inferior frontal gyrus (rIFG) volumes than control subjects (corrected p < .001). The rIFG volume correlated negatively with illness duration (corrected p < .01) and, relative to the controls, was smaller among BD individuals with long-term illness burden and minimal lifetime lithium exposure (corrected p < .001). Li-treated subjects had normal rIFG volumes despite substantial illness burden. Conclusions Brain structural changes in BD may result from interplay between illness burden and compensatory processes, which may be enhanced by lithium treatment. The rIFG volume could aid in identification of subjects at risk for BD even before any behavioral manifestations.
Abstract
Objectives. Converging evidence suggests that the brain-derived neurotrophic factor (BDNF) gene Val66Met polymorphism affects brain structure. Yet the majority of studies have shown no ...effect of this polymorphism on hippocampal volumes, perhaps due to small effect size. Methods. We performed a meta-analysis of studies investigating the association between Val66Met BDNF polymorphism and hippocampal volumes in healthy subjects by combining standardized differences between means (SDM) from individual studies using random effect models. Results. Data from 399 healthy subjects (255 Val-BDNF homozygotes and 144 carriers of at least one Met-BDNF allele) in seven studies were meta-analysed. Both the left and right hippocampi were significantly larger in Val-BDNF homozygotes than in carriers of at least one Met-BDNF allele (SDM = 0.41, 95% Confidence Interval = 0.20; 0.62, z = 3.86, P = 0.0001; SDM = 0.41; 95% Confidence Interval = 0.20; 0.61, z = 3.81, P = 0.0001, respectively), with no evidence of publication bias. Conclusions. Healthy carriers of BDNF gene Val66Met polymorphism show bilateral hippocampal volume reduction. The effect size was small, but the same direction of effect was seen in all meta-analyzed studies. The association with the BDNF gene Val66Met polymorphism makes hippocampal volume a potential candidate for an endophenotype of disorders presenting with reduced hippocampal volumes.
Objective:
Bipolar disorders increase the risk of dementia and show biological and brain alterations, which resemble accelerated aging. Lithium may counter some of these processes and lower the risk ...of dementia. However, until now no study has specifically investigated the effects of Li on brain age.
Methods:
We acquired structural magnetic resonance imaging scans from 84 participants with bipolar disorders (41 with and 43 without Li treatment) and 45 controls. We used a machine learning model trained on an independent sample of 504 controls to estimate the individual brain ages of study participants, and calculated BrainAGE by subtracting chronological from the estimated brain age.
Results:
BrainAGE was significantly greater in non-Li relative to Li or control participants, F(2, 125) = 10.22, p < 0.001, with no differences between the Li treated and control groups. The estimated brain age was significantly higher than the chronological age in the non-Li (4.28 ± 6.33 years, matched t(42) = 4.43, p < 0.001), but not the Li-treated group (0.48 ± 7.60 years, not significant). Even Li-treated participants with partial prophylactic treatment response showed lower BrainAGE than the non-Li group, F(1, 64) = 4.80, p = 0.03.
Conclusions:
Bipolar disorders were associated with greater, whereas Li treatment with lower discrepancy between brain and chronological age. These findings support the neuroprotective effects of Li, which were sufficiently pronounced to affect a complex, multivariate measure of brain structure. The association between Li treatment and BrainAGE was independent of long-term thymoprophylactic response and thus may generalize beyond bipolar disorders, to neurodegenerative disorders.
PDF
