Displaying all 6 publications

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  1. Bauer M, Glenn T, Alda M, Andreassen OA, Angelopoulos E, Ardau R, et al.
    Eur. Psychiatry, 2015 Jan;30(1):99-105.
    PMID: 25498240 DOI: 10.1016/j.eurpsy.2014.10.005
    PURPOSE: Two common approaches to identify subgroups of patients with bipolar disorder are clustering methodology (mixture analysis) based on the age of onset, and a birth cohort analysis. This study investigates if a birth cohort effect will influence the results of clustering on the age of onset, using a large, international database.

    METHODS: The database includes 4037 patients with a diagnosis of bipolar I disorder, previously collected at 36 collection sites in 23 countries. Generalized estimating equations (GEE) were used to adjust the data for country median age, and in some models, birth cohort. Model-based clustering (mixture analysis) was then performed on the age of onset data using the residuals. Clinical variables in subgroups were compared.

    RESULTS: There was a strong birth cohort effect. Without adjusting for the birth cohort, three subgroups were found by clustering. After adjusting for the birth cohort or when considering only those born after 1959, two subgroups were found. With results of either two or three subgroups, the youngest subgroup was more likely to have a family history of mood disorders and a first episode with depressed polarity. However, without adjusting for birth cohort (three subgroups), family history and polarity of the first episode could not be distinguished between the middle and oldest subgroups.

    CONCLUSION: These results using international data confirm prior findings using single country data, that there are subgroups of bipolar I disorder based on the age of onset, and that there is a birth cohort effect. Including the birth cohort adjustment altered the number and characteristics of subgroups detected when clustering by age of onset. Further investigation is needed to determine if combining both approaches will identify subgroups that are more useful for research.

  2. Bauer M, Glenn T, Alda M, Andreassen OA, Angelopoulos E, Ardau R, et al.
    J Affect Disord, 2014;167:104-11.
    PMID: 24953482 DOI: 10.1016/j.jad.2014.05.032
    The onset of bipolar disorder is influenced by the interaction of genetic and environmental factors. We previously found that a large increase in sunlight in springtime was associated with a lower age of onset. This study extends this analysis with more collection sites at diverse locations, and includes family history and polarity of first episode.
  3. Bauer M, Glenn T, Alda M, Andreassen OA, Angelopoulos E, Ardau R, et al.
    J Psychiatr Res, 2015 May;64:1-8.
    PMID: 25862378 DOI: 10.1016/j.jpsychires.2015.03.013
    Environmental conditions early in life may imprint the circadian system and influence response to environmental signals later in life. We previously determined that a large springtime increase in solar insolation at the onset location was associated with a younger age of onset of bipolar disorder, especially with a family history of mood disorders. This study investigated whether the hours of daylight at the birth location affected this association.
  4. Bauer M, Glenn T, Alda M, Aleksandrovich MA, Andreassen OA, Angelopoulos E, et al.
    Acta Psychiatr Scand, 2017 Dec;136(6):571-582.
    PMID: 28722128 DOI: 10.1111/acps.12772
    OBJECTIVE: To confirm prior findings that the larger the maximum monthly increase in solar insolation in springtime, the younger the age of onset of bipolar disorder.

    METHOD: Data were collected from 5536 patients at 50 sites in 32 countries on six continents. Onset occurred at 456 locations in 57 countries. Variables included solar insolation, birth-cohort, family history, polarity of first episode and country physician density.

    RESULTS: There was a significant, inverse association between the maximum monthly increase in solar insolation at the onset location, and the age of onset. This effect was reduced in those without a family history of mood disorders and with a first episode of mania rather than depression. The maximum monthly increase occurred in springtime. The youngest birth-cohort had the youngest age of onset. All prior relationships were confirmed using both the entire sample, and only the youngest birth-cohort (all estimated coefficients P < 0.001).

    CONCLUSION: A large increase in springtime solar insolation may impact the onset of bipolar disorder, especially with a family history of mood disorders. Recent societal changes that affect light exposure (LED lighting, mobile devices backlit with LEDs) may influence adaptability to a springtime circadian challenge.

  5. Bauer M, Glenn T, Alda M, Andreassen OA, Angelopoulos E, Ardau R, et al.
    J Psychiatr Res, 2019 06;113:1-9.
    PMID: 30878786 DOI: 10.1016/j.jpsychires.2019.03.001
    In many international studies, rates of completed suicide and suicide attempts have a seasonal pattern that peaks in spring or summer. This exploratory study investigated the association between solar insolation and a history of suicide attempt in patients with bipolar I disorder. Solar insolation is the amount of electromagnetic energy from the Sun striking a surface area on Earth. Data were collected previously from 5536 patients with bipolar I disorder at 50 collection sites in 32 countries at a wide range of latitudes in both hemispheres. Suicide related data were available for 3365 patients from 310 onset locations in 51 countries. 1047 (31.1%) had a history of suicide attempt. There was a significant inverse association between a history of suicide attempt and the ratio of mean winter solar insolation/mean summer solar insolation. This ratio is smallest near the poles where the winter insolation is very small compared to the summer insolation. This ratio is largest near the equator where there is relatively little variation in the insolation over the year. Other variables in the model that were positively associated with suicide attempt were being female, a history of alcohol or substance abuse, and being in a younger birth cohort. Living in a country with a state-sponsored religion decreased the association. (All estimated coefficients p 
  6. Amare AT, Schubert KO, Hou L, Clark SR, Papiol S, Cearns M, et al.
    Mol Psychiatry, 2021 Jun;26(6):2457-2470.
    PMID: 32203155 DOI: 10.1038/s41380-020-0689-5
    Lithium is a first-line medication for bipolar disorder (BD), but only one in three patients respond optimally to the drug. Since evidence shows a strong clinical and genetic overlap between depression and bipolar disorder, we investigated whether a polygenic susceptibility to major depression is associated with response to lithium treatment in patients with BD. Weighted polygenic scores (PGSs) were computed for major depression (MD) at different GWAS p value thresholds using genetic data obtained from 2586 bipolar patients who received lithium treatment and took part in the Consortium on Lithium Genetics (ConLi+Gen) study. Summary statistics from genome-wide association studies in MD (135,458 cases and 344,901 controls) from the Psychiatric Genomics Consortium (PGC) were used for PGS weighting. Response to lithium treatment was defined by continuous scores and categorical outcome (responders versus non-responders) using measurements on the Alda scale. Associations between PGSs of MD and lithium treatment response were assessed using a linear and binary logistic regression modeling for the continuous and categorical outcomes, respectively. The analysis was performed for the entire cohort, and for European and Asian sub-samples. The PGSs for MD were significantly associated with lithium treatment response in multi-ethnic, European or Asian populations, at various p value thresholds. Bipolar patients with a low polygenic load for MD were more likely to respond well to lithium, compared to those patients with high polygenic load [lowest vs highest PGS quartiles, multi-ethnic sample: OR = 1.54 (95% CI: 1.18-2.01) and European sample: OR = 1.75 (95% CI: 1.30-2.36)]. While our analysis in the Asian sample found equivalent effect size in the same direction: OR = 1.71 (95% CI: 0.61-4.90), this was not statistically significant. Using PGS decile comparison, we found a similar trend of association between a high genetic loading for MD and lower response to lithium. Our findings underscore the genetic contribution to lithium response in BD and support the emerging concept of a lithium-responsive biotype in BD.
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