Demographic and clinical associations to employment status in older-age bipolar disorder: Analysis from the GAGE-BD database project

OBJECTIVE: The current literature on employment in older adults with bipolar disorder (OABD) is limited. Using the Global Aging and Geriatric Experiments in Bipolar Disorder Database (GAGE-BD), we examined the relationship of occupational status in OABD to other demographic and clinical characteristics. METHODS: Seven hundred and thirty-eight participants from 11 international samples with data on educational level and occupational status were included. Employment status was dichotomized as employed versus unemployed. Generalized linear mixed models with random intercepts for the study cohort were used to examine the relationship between baseline characteristics and employment. Predictors in the models included baseline demographics, education, psychiatric symptom severity, psychiatric comorbidity, somatic comorbidity, and prior psychiatric hospitalizations. RESULTS: In the sample, 23.6% (n = 174) were employed, while 76.4% were unemployed (n = 564). In multivariable logistic regression models, less education, older age, a history of both anxiety and substance/alcohol use disorders, more prior psychiatric hospitalizations, and higher levels of BD depression severity were associated with greater odds of unemployment. In the subsample of individuals less than 65 years of age, findings were similar. No significant association between manic symptoms, gender, age of onset, or employment status was observed. CONCLUSION: Results suggest an association between educational level, age, psychiatric severity and comorbidity in relation to employment in OABD. Implications include the need for management of psychiatric symptoms and comorbidity across the lifespan, as well as improving educational access for people with BD and skills training or other support for those with work-life breaks to re-enter employment and optimize the overall outcome

Sex Differences Among Older Adults With Bipolar Disorder: Results From the Global Aging & Geriatric Experiments in Bipolar Disorder (GAGE-BD) Project

OBJECTIVE: Sex-specific research in adult bipolar disorder (BD) is sparse and even more so among those with older age bipolar disorder (OABD). Knowledge about sex differences across the bipolar lifespan is urgently needed to target and improve treatment. To address this gap, the current study examined sex differences in the domains of clinical presentation, general functioning, and mood symptoms among individuals with OABD. METHODS: This Global Aging & Geriatric Experiments in Bipolar Disorder (GAGE-BD) study used data from 19 international studies including BD patients aged ≥50 years (N = 1,185: 645 women, 540 men).A comparison of mood symptoms between women and men was conducted initially using two-tailed t tests and then accounting for systematic differences between the contributing cohorts by performing generalized linear mixed models (GLMMs). Associations between sex and other clinical characteristics were examined using GLMM including: age, BD subtype, rapid cycling, psychiatric hospitalization, lifetime psychiatric comorbidity, and physical health comorbidity, with study cohort as a random intercept. RESULTS: Regarding depressive mood symptoms, women had higher scores on anxiety and hypochondriasis items. Female sex was associated with more psychiatric hospitalizations and male sex with lifetime substance abuse disorders. CONCLUSION: Our findings show important clinical sex differences and provide support that older age women experience a more severe course of BD, with higher rates of psychiatric hospitalization. The reasons for this may be biological, psychological, or social. These differences as well as underlying mechanisms should be a focus for healthcare professionals and need to be studied further

Bipolar symptoms, somatic burden and functioning in older-age bipolar disorder: A replication study from the global aging & geriatric experiments in bipolar disorder database (GAGE-BD) project

Objectives: The Global Aging & Geriatric Experiments in Bipolar Disorder Database (GAGE-BD) project pools archival datasets on older age bipolar disorder (OABD). An initial Wave 1 (W1; n = 1369) analysis found both manic and depressive symptoms reduced among older patients. To replicate this finding, we gathered an independent Wave 2 (W2; n = 1232, mean ± standard deviation age 47.2 ± 13.5, 65% women, 49% aged over 50) dataset. Design/Methods: Using mixed models with random effects for cohort, we examined associations between BD symptoms, somatic burden and age and the contribution of these to functioning in W2 and the combined W1 + W2 sample (n = 2601). Results: Compared to W1, the W2 sample was younger (p < 0.001), less educated (p < 0.001), more symptomatic (p < 0.001), lower functioning (p < 0.001) and had fewer somatic conditions (p < 0.001). In the full W2, older individuals had reduced manic symptom severity, but age was not associated with depression severity. Age was not associated with functioning in W2. More severe BD symptoms (mania p ≤ 0.001, depression p ≤ 0.001) were associated with worse functioning. Older age was significantly associated with higher somatic burden in the W2 and the W1 + W2 samples, but this burden was not associated with poorer functioning. Conclusions: In a large, independent sample, older age was associated with less severe mania and more somatic burden (consistent with previous findings), but there was no association of depression with age (different from previous findings). Similar to previous findings, worse BD symptom severity was associated with worse functioning, emphasizing the need for symptom relief in OABD to promote better functioning

Locus for severity implicates CNS resilience in progression of multiple sclerosis

Multiple sclerosis (MS) is an autoimmune disease of the central nervous system (CNS) that results in significant neurodegeneration in the majority of those affected and is a common cause of chronic neurological disability in young adults(1,2). Here, to provide insight into the potential mechanisms involved in progression, we conducted a genome-wide association study of the age-related MS severity score in 12,584 cases and replicated our findings in a further 9,805 cases. We identified a significant association with rs10191329 in the DYSF-ZNF638 locus, the risk allele of which is associated with a shortening in the median time to requiring a walking aid of a median of 3.7 years in homozygous carriers and with increased brainstem and cortical pathology in brain tissue. We also identified suggestive association with rs149097173 in the DNM3-PIGC locus and significant heritability enrichment in CNS tissues. Mendelian randomization analyses suggested a potential protective role for higher educational attainment. In contrast to immune-driven susceptibility(3), these findings suggest a key role for CNS resilience and potentially neurocognitive reserve in determining outcome in MS

Mining Complex Genetic Patterns Conferring Multiple Sclerosis Risk

(1) Background: Complex genetic relationships, including gene-gene (G × G; epistasis), gene(n), and gene-environment (G × E) interactions, explain a substantial portion of the heritability in multiple sclerosis (MS). Machine learning and data mining methods are promising approaches for uncovering higher order genetic relationships, but their use in MS have been limited. (2) Methods: Association rule mining (ARM), a combinatorial rule-based machine learning algorithm, was applied to genetic data for non-Latinx MS cases (n = 207) and controls (n = 179). The objective was to identify patterns (rules) amongst the known MS risk variants, including HLA-DRB1*15:01 presence, HLA-A*02:01 absence, and 194 of the 200 common autosomal variants. Probabilistic measures (confidence and support) were used to mine rules. (3) Results: 114 rules met minimum requirements of 80% confidence and 5% support. The top ranking rule by confidence consisted of HLA-DRB1*15:01, SLC30A7-rs56678847 and AC093277.1-rs6880809; carriers of these variants had a significantly greater risk for MS (odds ratio = 20.2, 95% CI: 8.5, 37.5; p = 4 × 10−9). Several variants were shared across rules, the most common was INTS8-rs78727559, which was in 32.5% of rules. (4) Conclusions: In summary, we demonstrate evidence that specific combinations of MS risk variants disproportionately confer elevated risk by applying a robust analytical framework to a modestly sized study population

Random Forests for Genetic Association Studies

The Random Forests (RF) algorithm has become a commonly used machine learning algorithm for genetic association studies. It is well suited for genetic applications since it is both computationally efficient and models genetic causal mechanisms well. With its growing ubiquity, there has been inconsistent and less than optimal use of RF in the literature. The purpose of this review is to breakdown the theoretical and statistical basis of RF so that practitioners are able to apply it in their work. An emphasis is placed on showing how the various components contribute to bias and variance, as well as discussing variable importance measures. Applications specific to genetic studies are highlighted. To provide context, RF is compared to other commonly used machine learning algorithms.

Testing the Relative Performance of Data Adaptive Prediction Algorithms: A Generalized Test of Conditional Risk Differences

Comparing the relative fit of competing models can be used to address many different scientific questions. In classical statistics one can, if appropriate, use likelihood ratio tests and information based criterion, whereas clinical medicine has tended to rely on comparisons of fit metrics like C-statistics. However, for many data adaptive modelling procedures such approaches are not suitable. In these cases, statisticians have used cross-validation, which can make inference challenging. In this paper we propose a general approach that focuses on the “conditional” risk difference (conditional on the model fits being fixed) for the improvement in prediction risk. Specifically, we derive a Wald-type test statistic and associated confidence intervals for cross-validated test sets utilizing the independent validation within cross-validation in conjunction with a test for multiple comparisons. We show that this test maintains proper Type I Error under the null fit, and can be used as a general test of relative fit for any semi-parametric model alternative. We apply the test to a candidate gene study to test for the association of a set of genes in a genetic pathway