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Studying phenotypic and genetic characteristics of age at onset (AAO) and polarity at onset (PAO) in bipolar disorder can provide new insights into disease pathology and facilitate the development of screening tools.
Aims
To examine the genetic architecture of AAO and PAO and their association with bipolar disorder disease characteristics.
Method
Genome-wide association studies (GWASs) and polygenic score (PGS) analyses of AAO (n = 12 977) and PAO (n = 6773) were conducted in patients with bipolar disorder from 34 cohorts and a replication sample (n = 2237). The association of onset with disease characteristics was investigated in two of these cohorts.
Results
Earlier AAO was associated with a higher probability of psychotic symptoms, suicidality, lower educational attainment, not living together and fewer episodes. Depressive onset correlated with suicidality and manic onset correlated with delusions and manic episodes. Systematic differences in AAO between cohorts and continents of origin were observed. This was also reflected in single-nucleotide variant-based heritability estimates, with higher heritabilities for stricter onset definitions. Increased PGS for autism spectrum disorder (β = −0.34 years, s.e. = 0.08), major depression (β = −0.34 years, s.e. = 0.08), schizophrenia (β = −0.39 years, s.e. = 0.08), and educational attainment (β = −0.31 years, s.e. = 0.08) were associated with an earlier AAO. The AAO GWAS identified one significant locus, but this finding did not replicate. Neither GWAS nor PGS analyses yielded significant associations with PAO.
Conclusions
AAO and PAO are associated with indicators of bipolar disorder severity. Individuals with an earlier onset show an increased polygenic liability for a broad spectrum of psychiatric traits. Systematic differences in AAO across cohorts, continents and phenotype definitions introduce significant heterogeneity, affecting analyses.
We aimed to evaluate the prevalence, clinical determinants, and consequences (falls and hospitalization) of frailty in older adults with mental illness.
Design:
Retrospective clinical cohort study.
Setting:
We collected the data in a specialized psychogeriatric ward, in Boston, USA, between July 2018 and June 2019.
Participants:
Two hundred and fourty-four inpatients aged 65 years old and over.
Measurements:
Psychiatric diagnosis was based on a multi-professional consensus meeting according to DSM-5 criteria. Frailty was assessed according to two common instruments, that is, the FRAIL questionnaire and the deficit accumulation model (aka Frailty Index [FI]). Multiple linear regression analyses were conducted to evaluate the association between frailty and sample demographics (age, female sex, and non-Caucasian ethnicity) and clinical characteristics (dementia, number of clinical diseases, current infection, number of psychotropic, and non-psychotropic medications in use). Multiple regression between frailty assessments and either falls or number of hospital admissions in the last 6 and 12 months, respectively, were analyzed and adjusted for covariates.
Results:
Prevalence of frailty was high, that is, 83.6% according to the FI and 55.3% according to the FRAIL questionnaire. Age, the number of clinical (somatic) diseases, and the number of non-psychotropic medications were independently associated with frailty identified by the FRAIL. Dementia, current infection, the number of clinical (somatic) diseases, and the number of non-psychotropic medications were independently associated with frailty according to the FI. Falls were significantly associated with both frailty instruments. However, we found only a significant association for the number of hospital admissions with the FI.
Conclusion:
Frailty is highly prevalent among geriatric psychiatry inpatients. The FRAIL questionnaire and the FI may capture different forms of frailty dimensions, being the former probably more associated with the phenotype model and the latter more associated with multimorbidity.
Mathematical discussions of models of functional response (predation rate as a function of prey density) have usually emphasized description of the shape of the functional-response curve. However, lack of congruence between experimental design and data analysis and under-utilization of appropriate statistical methods of analysis have hindered an empirical synthetic treatment of such feeding behavior. Here we review existing experimental and statistical procedures with reference to Holling's generalized model of functional response, and describe: (1) an experimental design compatible with the assumptions of the model; (2) a maximum-likelihood method for fitting the model; (3) several methods for statistical comparison of sets of functional-response curves; and (4) an exploratory graphical method for examining patterns of variation among larger numbers of samples.
We analyze the properties of quasar variability using repeated SDSS imaging data in five UV-to-far red photometric bands, accurate to 0.02 mag, for ∼13,000 spectroscopically confirmed quasars. The observed time lags span the range from 3 hours to over 3 years, and constrain the quasar variability for rest-frame time lags of up to two years, and at rest-frame wavelengths from 1000Å to 6000Å. We demonstrate that ∼66,000 SDSS measurements of magnitude differences can be described within the measurement noise by a simple function of only three free parameters. The addition of POSS data constrains the long-term behavior of quasar variability and provides evidence for a turn-over in the structure function. This turn-over indicates that the characteristic time scale for optical variability of quasars is of the order 1 year.To search for other articles by the author(s) go to: http://adsabs.harvard.edu/abstract_service.html
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