Growth mixture modeling as an exploratory analysis tool in longitudinal quantitative trait loci analysis

We examined the properties of growth mixture modeling in finding longitudinal quantitative trait loci in a genome-wide association study. Two software packages are commonly used in these analyses: Mplus and the SAS TRAJ procedure. We analyzed the 200 replicates of the simulated data with these programs using three tests: the likelihood-ratio test statistic, a direct test of genetic model coefficients, and the chi-square test classifying subjects based on the trajectory model's posterior Bayesian probability. The Mplus program was not effective in this application due to its computational demands. The distributions of these tests applied to genes not related to the trait were sensitive to departures from Hardy-Weinberg equilibrium. The likelihood-ratio test statistic was not usable in this application because its distribution was far from the expected asymptotic distributions when applied to markers with no genetic relation to the quantitative trait. The other two tests were satisfactory. Power was still substantial when we used markers near the gene rather than the gene itself. That is, growth mixture modeling may be useful in genome-wide association studies. For markers near the actual gene, there was somewhat greater power for the direct test of the coefficients and lesser power for the posterior Bayesian probability chi-square test

Sleep Disturbance and Strain Among Caregivers of Persons Living With Dementia

ObjectiveThe study objective was to examine predictors of sleep disturbance and strain among caregivers of persons living with dementia (PLWD).MethodsThis cross-sectional study utilized a sample of community-dwelling older adults and their family caregivers drawn from the 2017 National Health and Aging Trends Study and National Study of Caregiving. Multivariable logistic regression was used to assess the association between caregiver and PLWD characteristics and a composite measure of caregiving strain. High caregiving strain was defined as a total score of >= 5 on the 6 caregiving strain items (e.g., emotional difficulty, no time for self). We used multivariable proportional odds models to examine predictors of caregiver sleep-related outcomes (trouble falling back to sleep and interrupted sleep), after adjusting for other caregiver and PLWD factors.ResultsOf the 1,142 family caregivers, 65.2% were female, 15% were Black, and 14% were Hispanic. Average age was 60 years old. Female caregivers were more likely to report high level of strain compared to male caregivers (OR: 2.61, 95% CI = 1.56, 4.39). Compared to non-Hispanic Whites, non-Hispanic Black and Hispanic caregivers had reduced odds of reporting greater trouble falling back asleep [OR = 0.55, CI (0.36, 0.82) and OR = 0.56, CI (0.34, 0.91), respectively]. The odds of reporting greater trouble falling back asleep was significantly greater among caregivers with high blood pressure vs. caregivers without high blood pressure [OR = 1.62, CI (1.12, 2.33)].ConclusionIn this cross-sectional study, caregivers with greater sleep difficulty (trouble falling back asleep) were more likely to report having high blood pressure. We found no racial/ethnic differences in interrupted sleep among caregivers to PLWD. These results suggest that interventions to improve sleep among caregivers to PLWD may decrease poor cardiovascular outcomes in this group