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.
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.
METHODS: Eighteen male healthy subjects volunteered to participate in the experiment. Walking gait analysis was conducted with eight different levels of insole to simulate the LLD, starting from 0 cm until 4.0 cm with 0.5 cm increment. Qualisys Track Manager System and C-motion Visual 3D biomechanical tools were used to analyse the results. Four joints (ankle, knee, hip, and pelvis) of lower limb of two legs were investigated. The increment of insoles was placed on the right leg to represent the long leg.
RESULTS: The results suggest that the mean contact forces for all joints in the short leg were increased as the increment level increased. On the contrary, the mean contact forces in the long leg decreased when the LLD level increased. Among these four joints, JCF in hip shows a positive increment based on the ASI value. Means that hip shows the most affected joint as the LLD level increase.
CONCLUSIONS: The result obtained in this study might help clinicians treat patients with a structural LLD for treatment plan including surgical intervention.