Network structures and temporal stability of self- and informant-rated affective symptoms in Alzheimer's disease

Background: Affective symptoms in Alzheimer's disease (AD) can be rated with both informantand self-ratings. Information from these two modalities may not converge. We estimated network structures of affective symptoms in AD with both rating modalities and assessed the longitudinal stability of the networks. Methods: Network analyses combining self-rated and informant-rated affective symptoms were conducted in 3198 individuals with AD at two time points (mean follow-up 387 days), drawn from the NACC database. Self rated symptoms were assessed by Geriatric Depression Scale, and informant-rated symptoms included depression, apathy and anxiety questions from Neuropsychiatric Inventory Questionnaire. Results: Informant-rated symptoms were mainly connected to symptoms expressing lack of positive affect, but not to the more central symptoms of self-rated worthlessness and helplessness. Networks did not differ in structure (p = .71), or connectivity (p = .92) between visits. Symptoms formed four clinically meaningful clusters of depressive symptoms and decline, lack of positive affect, informant-rated apathy and anxiety and informant-rated depression. Limitations: The symptom dynamics in our study could have been present before AD diagnosis. The lack of positive affect cluster may represent a methodological artefact rather than a theoretically meaningful subgroup. Requiring follow-up lead to a selection of patients with less cognitive decline. Conclusions: Informant rating may only capture the more visible affective symptoms, such as not being in good spirits, instead of more central and severe symptoms, such as hopelessness and worthlessness. Future research should continue to be mindful of differences between self- and informant-rated symptoms even in earlier stages of AD.Peer reviewe

Depressive symptoms are associated with analgesic use in people with Alzheimer's disease: Kuopio ALSOVA study.

Neuropsychiatric symptoms of Alzheimer's disease (AD) such as depression may be associated with pain, which according to the literature may be inadequately recognized and managed in this population. This study aimed to identify the factors associated with analgesic use in persons with AD; in particular, how AD severity, functional status, neuropsychiatric symptoms of AD, co-morbidities and somatic symptoms are associated with analgesic use. 236 community-dwelling persons with very mild or mild AD at baseline, and their caregivers, were interviewed over five years as part of the prospective ALSOVA study. Generalized Estimating Equations (GEEs) were used to estimate unadjusted and adjusted odds ratios (ORs) for the factors associated with analgesic use over a five year follow-up. The proportion of persons with AD using any analgesic was low (13.6%) at baseline and remained relatively constant during the follow-up (15.3% at Year 5). Over time, the most prevalent analgesic changed from non-steroidal anti-inflammatories (8.1% of persons with AD at Year 1) to acetaminophen (11.1% at Year 5). Depressive symptoms (measured by the Beck Depression Inventory, BDI) were independently associated with analgesic use, after effects of age, gender, education, AD severity, comorbidities and somatic symptoms were taken into account. For every one unit increase in BDI, the odds of analgesic use increased by 4% (OR = 1.04, 95% confidence interval CI = 1.02-1.07). Caregiver depressive symptoms were not statistically significantly associated with analgesic use of the person with AD. Depressive symptoms were significantly associated with analgesic use during the five year follow-up period. Possible explanations warranting investigation are that persons with AD may express depressive symptoms as painful somatic complaints, or untreated pain may cause depressive symptoms. Greater awareness of the association between depressive symptoms and analgesic use may lead to safer and more effective prescribing for these conditions

The association between distinct frontal brain volumes and behavioral symptoms in mild cognitive impairment, alzheimer's disease, and frontotemporal dementia

Our aim was to investigate the association between behavioral symptoms of agitation, disinhibition, irritability, elation, and aberrant motor behavior to frontal brain volumes in a cohort with various neurodegenerative diseases. A total of 121 patients with mild cognitive impairment (MCI, n = 58), Alzheimer's disease (AD, n = 45) and behavioral variant frontotemporal dementia (bvFTD, n = 18) were evaluated with a Neuropsychiatric Inventory (NPI). A T1-weighted MRI scan was acquired for each participant and quantified with a multi-atlas segmentation method. The volumetric MRI measures of the frontal lobes were associated with neuropsychiatric symptom scores with a linear model. In the regression model, we included CDR score and TMT B time as covariates to account for cognitive and executive functions. The brain volumes were corrected for age, gender and head size. The total behavioral symptom score of the five symptoms of interest was negatively associated with the volume of the subcallosal area (β = −0.32, p = 0.002). High disinhibition scores were associated with reduced volume in the gyrus rectus (β = −0.30, p = 0.002), medial frontal cortex (β = −0.30, p = 0.002), superior frontal gyrus (β = −0.28, p = 0.003), inferior frontal gyrus (β = −0.28, p = 0.005) and subcallosal area (β = −0.28, p = 0.005). Elation scores were associated with reduced volumes of the medial orbital gyrus (β = −0.30, p = 0.002) and inferior frontal gyrus (β = −0.28, p = 0.004). Aberrant motor behavior was associated with atrophy of frontal pole (β = −0.29, p = 0.005) and the subcallosal area (β = −0.39, p < 0.001). No significant associations with frontal brain volumes were found for agitation and irritability. We conclude that the subcallosal area may be common neuroanatomical area for behavioral symptoms in neurodegenerative diseases, and it appears to be independent of disease etiology

Genome-wide association analyses of symptom severity among clozapine-treated patients with schizophrenia spectrum disorders

Clozapine is the most effective antipsychotic for patients with treatment-resistant schizophrenia. However, response is highly variable and possible genetic underpinnings of this variability remain unknown. Here, we performed polygenic risk score (PRS) analyses to estimate the amount of variance in symptom severity among clozapine-treated patients explained by PRSs (R2) and examined the association between symptom severity and genotype-predicted CYP1A2, CYP2D6, and CYP2C19 enzyme activity. Genome-wide association (GWA) analyses were performed to explore loci associated with symptom severity. A multicenter cohort of 804 patients (after quality control N = 684) with schizophrenia spectrum disorder treated with clozapine were cross-sectionally assessed using the Positive and Negative Syndrome Scale and/or the Clinical Global Impression-Severity (CGI-S) scale. GWA and PRS regression analyses were conducted. Genotype-predicted CYP1A2, CYP2D6, and CYP2C19 enzyme activities were calculated. Schizophrenia-PRS was most significantly and positively associated with low symptom severity (p = 1.03 × 10−3; R2 = 1.85). Cross-disorder-PRS was also positively associated with lower CGI-S score (p = 0.01; R2 = 0.81). Compared to the lowest tertile, patients in the highest schizophrenia-PRS tertile had 1.94 times (p = 6.84×10−4) increased probability of low symptom severity. Higher genotype-predicted CYP2C19 enzyme activity was independently associated with lower symptom severity (p = 8.44×10−3). While no locus surpassed the genome-wide significance threshold, rs1923778 within NFIB showed a suggestive association (p = 3.78×10−7) with symptom severity. We show that high schizophrenia-PRS and genotype-predicted CYP2C19 enzyme activity are independently associated with lower symptom severity among individuals treated with clozapine. Our findings open avenues for future pharmacogenomic projects investigating the potential of PRS and genotype-predicted CYP-activity in schizophrenia

Global snow mass measurements and the effect of stratigraphic detail on inversion of microwave brightness temperatures

Snow provides large seasonal storage of freshwater, and information about the distribution of snow mass as Snow Water Equivalent (SWE) is important for hydrological planning and detecting climate change impacts. Large regional disagreements remain between estimates from reanalyses, remote sensing and modelling. Assimilating passive microwave information improves SWE estimates in many regions but the assimilation must account for how microwave scattering depends on snow stratigraphy. Physical snow models can estimate snow stratigraphy, but users must consider the computational expense of model complexity versus acceptable errors. Using data from the National Aeronautics and Space Administration Cold Land Processes Experiment (NASA CLPX) and the Helsinki University of Technology (HUT) microwave emission model of layered snowpacks, it is shown that simulations of the brightness temperature difference between 19 GHz and 37 GHz vertically polarised microwaves are consistent with Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) and Special Sensor Microwave Imager (SSM/I) retrievals once known stratigraphic information is used. Simulated brightness temperature differences for an individual snow profile depend on the provided stratigraphic detail. Relative to a profile defined at the 10 cm resolution of density and temperature measurements, the error introduced by simplification to a single layer of average properties increases approximately linearly with snow mass. If this brightness temperature error is converted into SWE using a traditional retrieval method then it is equivalent to ±13 mm SWE (7% of total) at a depth of 100 cm. This error is reduced to ±5.6 mm SWE (3 % of total) for a two-layer model