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OBJECTIVE: This study explored the determinants of post-stroke depression (PSD) in ischemic stroke (AIS) patients and its association with the burden score of cerebral small vessel disease (CSVD). PATIENTS AND METHODS: We analyzed 374 AIS patients treated between January 2020 and January 2022. Patients were categorized into 90 with PSD and 284 without PSD, enabling an investigation into PSD risk factors and the CSVD-PSD relationship. RESULTS: There was no significant difference in health factors between PSD and non-PSD patients (p>0.05). However, significant disparities were noted in age, gender, initial Barthel Index (BI), Mini-Mental State Examination (MMSE) score, plasma fibrinogen, homocysteine, red cell distribution width, National Institutes of Health Stroke Scale (NIHSS) score, and CSVD burden score (p<0.05). Regression analysis indicated that these variables were pivotal PSD predictors (OR>1, p<0.05). Surprisingly, a positive correlation with PSD occurrence was found for age, NIHSS score, plasma fibrinogen, homocysteine levels, red cell distribution width, CSVD burden score (r=0.565, 0.615, 0.482, 0.514, 0.572, 0.608, respectively; p<0.05). Meanwhile, the MMSE score and BI index were inversely related to PSD onset (r=-0.604, -0.590; p<0.05). The ROC curve analysis of the combination model based on MMSE, NIHSS and CSVD score revealed an AUC of 0.926 and Youden's index of 0.744. CONCLUSIONS: Age, MMSE score, BI index, NIHSS score, plasma fibrinogen concentration, homocysteine level, red blood cell distribution width, and CSVD burden score are all major influencing factors in the occurrence of PSD. The combination model based on MMSE, NIHSS, and CSVD scores presented a valuable approach to predicting PSD. [ABSTRACT FROM AUTHOR]

Polygenic risk scores (PRSs) aggregate the effects of genetic variants across the genome and are used to predict risk of complex diseases, such as obesity. Current PRSs only include common variants (minor allele frequency (MAF) ≥1%), whereas the contribution of rare variants in PRSs to predict disease remains unknown. Here, we examine whether augmenting the standard common variant PRS (PRScommon) with a rare variant PRS (PRSrare) improves prediction of obesity. We used genome-wide genotyped and imputed data on 451,145 European-ancestry participants of the UK Biobank, as well as whole exome sequencing (WES) data on 184,385 participants. We performed single variant analyses (for both common and rare variants) and gene-based analyses (for rare variants) for association with BMI (kg/m2), obesity (BMI ≥ 30 kg/m2), and extreme obesity (BMI ≥ 40 kg/m2). We built PRSscommon and PRSsrare using a range of methods (Clumping+Thresholding [C+T], PRS-CS, lassosum, gene-burden test). We selected the best-performing PRSs and assessed their performance in 36,757 European-ancestry unrelated participants with whole genome sequencing (WGS) data from the Trans-Omics for Precision Medicine (TOPMed) program. The best-performing PRScommon explained 10.1% of variation in BMI, and 18.3% and 22.5% of the susceptibility to obesity and extreme obesity, respectively, whereas the best-performing PRSrare explained 1.49%, and 2.97% and 3.68%, respectively. The PRSrare was associated with an increased risk of obesity and extreme obesity (ORobesity = 1.37 per SDPRS, Pobesity = 1.7x10-85; ORextremeobesity = 1.55 per SDPRS, Pextremeobesity = 3.8x10-40), which was attenuated, after adjusting for PRScommon (ORobesity = 1.08 per SDPRS, Pobesity = 9.8x10-6; ORextremeobesity= 1.09 per SDPRS, Pextremeobesity = 0.02). When PRSrare and PRScommon are combined, the increase in explained variance attributed to PRSrare was small (incremental Nagelkerke R2 = 0.24% for obesity and 0.51% for extreme obesity). Consistently, combining PRSrare to PRScommon provided little improvement to the prediction of obesity (PRSrare AUC = 0.591; PRScommon AUC = 0.708; PRScombined AUC = 0.710). In summary, while rare variants show convincing association with BMI, obesity and extreme obesity, the PRSrare provides limited improvement over PRScommon in the prediction of obesity risk, based on these large populations.

Objective: Caregiver burden is a factor that caregivers undertake for patients with chronic diseases and individuals in need of care. Depending on the symptoms of the disease and the processes following the diagnosis, depression problems may develop in caregivers. In this study, it was aimed at determining the caregiving burden of parents who have children with chronic diseases, the condition of special needs, and the factors that affect this burden. Materials and Methods: This study is a descriptive cross-sectional study conducted with 206 parents of children who had been diagnosed with a chronic disease for at least 1 year. The sociodemographic data, the Patient Health Questionnaire 2 & 9, and the Burden Scale for Family Caregivers were provided to the parents. Results: Eighty-four of the participants had little or no caregiver burden, while 122 had moderate and severe caregiver burden. The mean caregiving burden score of mothers was higher (P = .004). Fathers’ caregiver burden scores varied depending on the level of education (P = .031). Caregiving burden score was found to differ according to income level (P < .001). The mean caregiving burden score of individuals with chronic disease in the family was higher (P < .001). It was determined that individuals with higher Patient Health Questionnaire 2 and 9 scores and those who did not receive psychological support were higher (P < .001). Conclusion: Parents who are providing care for children with chronic diseases should be supported and closely monitored for signs of depression. [ABSTRACT FROM AUTHOR]

Polygenic risk scores (PRSs) aggregate the effects of genetic variants across the genome and are used to predict risk of complex diseases, such as obesity. Current PRSs only include common variants (minor allele frequency (MAF) ≥1%), whereas the contribution of rare variants in PRSs to predict disease remains unknown. Here, we examine whether augmenting the standard common variant PRS (PRScommon) with a rare variant PRS (PRSrare) improves prediction of obesity. We used genome-wide genotyped and imputed data on 451,145 European-ancestry participants of the UK Biobank, as well as whole exome sequencing (WES) data on 184,385 participants. We performed single variant analyses (for both common and rare variants) and gene-based analyses (for rare variants) for association with BMI (kg/m2), obesity (BMI ≥ 30 kg/m2), and extreme obesity (BMI ≥ 40 kg/m2). We built PRSscommon and PRSsrare using a range of methods (Clumping+Thresholding [C+T], PRS-CS, lassosum, gene-burden test). We selected the best-performing PRSs and assessed their performance in 36,757 European-ancestry unrelated participants with whole genome sequencing (WGS) data from the Trans-Omics for Precision Medicine (TOPMed) program. The best-performing PRScommon explained 10.1% of variation in BMI, and 18.3% and 22.5% of the susceptibility to obesity and extreme obesity, respectively, whereas the best-performing PRSrare explained 1.49%, and 2.97% and 3.68%, respectively. The PRSrare was associated with an increased risk of obesity and extreme obesity (ORobesity = 1.37 per SDPRS, Pobesity = 1.7x10-85; ORextremeobesity = 1.55 per SDPRS, Pextremeobesity = 3.8x10-40), which was attenuated, after adjusting for PRScommon (ORobesity = 1.08 per SDPRS, Pobesity = 9.8x10-6; ORextremeobesity= 1.09 per SDPRS, Pextremeobesity = 0.02). When PRSrare and PRScommon are combined, the increase in explained variance attributed to PRSrare was small (incremental Nagelkerke R2 = 0.24% for obesity and 0.51% for extreme obesity). Consistently, combining PRSrare to PRScommon provided little improvement to the prediction of obesity (PRSrare AUC = 0.591; PRScommon AUC = 0.708; PRScombined AUC = 0.710). In summary, while rare variants show convincing association with BMI, obesity and extreme obesity, the PRSrare provides limited improvement over PRScommon in the prediction of obesity risk, based on these large populations.

Abstract The effect of single nucleotide variants (SNVs) in coding and noncoding regions is of great interest in genetics. Although many computational methods aim to elucidate the effects of SNVs on cellular mechanisms, it is not straightforward to comprehensively cover different molecular effects. To address this, we compiled and benchmarked sequence and structure‐based variant effect predictors and we computed the impact of nearly all possible amino acid and nucleotide variants in the reference genomes of Homo sapiens, Saccharomyces cerevisiae and Escherichia coli. Studied mechanisms include protein stability, interaction interfaces, post‐translational modifications and transcription factor binding sites. We apply this resource to the study of natural and disease coding variants. We also show how variant effects can be aggregated to generate protein complex burden scores that uncover protein complex to phenotype associations based on a set of newly generated growth profiles of 93 sequenced S. cerevisiae strains in 43 conditions. This resource is available through mutfunc ( www.mutfunc.com ), a tool by which users can query precomputed predictions by providing amino acid or nucleotide‐level variants.

Background: Huntington disease (HD) is an inherited neurodegenerative disorder most commonly manifesting in adulthood. Identification of biomarkers tracking neurodegeneration before the onset of motor symptoms is important for future interventional studies. Our study aimed to contribute in the phenotypic characterization of the premanifest HD phase.Methods: 28 premanifest subjects (preHD), 25 age-matched controls, and 12 manifest HD patients were enrolled for the study. The participants underwent a multimodal protocol including cognitive evaluations, arithmetic ability test, posturography, composite cerebellar functional test (CCFS), and brain 3T-MRI. PreHD were divided at the group median for predicted years to expected onset into "far-from-onset" (>15 years, PreHD-far), and "close-to-onset" (≤15 years, preHD-close). Basal ganglia volumes and cortical thickness were computed using FreeSurfer.Results: PreHD-close showed significantly lower scores than controls in Symbol Digit Modalities Test (p = 0.017), Arithmetic subtraction task (p = 0.04), and MMSE (p < 0.006). At posturography, preHD-close showed increased sway velocity (<0.04) and distance (p < 0.02) compared to controls. PreHD-close had reduced striatum and globus pallidus volumes and left occipital cortical thinning compared to controls. Compared to PreHD far-from-onset, PreHD-close showed bilateral cortical thinning in occipital and parahippocampal regions, inversely correlating with burden score and prognostic index for HD. CCFS only differed between controls and manifest HD. PreHD far-from-onset did not show significant differences in comparison with controls.Conclusions: We confirmed that quantitative brain MRI represents a valid biomarker of neurodegeneration in preHD. Posturography and Arithmentic tests seem promising tools for detecting early changes in premanifest HD, but need to be further confirmed in large cohorts. [ABSTRACT FROM AUTHOR]

The effect of single nucleotide variants (SNVs) in coding and noncoding regions is of great interest in genetics. Although many computational methods aim to elucidate the effects of SNVs on cellular mechanisms, it is not straightforward to comprehensively cover different molecular effects. To address this, we compiled and benchmarked sequence and structure‐based variant effect predictors and we computed the impact of nearly all possible amino acid and nucleotide variants in the reference genomes of Homo sapiens, Saccharomyces cerevisiae and Escherichia coli. Studied mechanisms include protein stability, interaction interfaces, post‐translational modifications and transcription factor binding sites. We apply this resource to the study of natural and disease coding variants. We also show how variant effects can be aggregated to generate protein complex burden scores that uncover protein complex to phenotype associations based on a set of newly generated growth profiles of 93 sequenced S. cerevisiae strains in 43 conditions. This resource is available through mutfunc (www.mutfunc.com), a tool by which users can query precomputed predictions by providing amino acid or nucleotide‐level variants. Synopsis: This study presents mutfunc, a resource that includes the pre‐computed impact of genetic variants in three model organisms (human, yeast and E. coli). These predictions can be used to prioritize genetic variants and compute gene burden scores. Mutfunc is a resource that includes predictions on the impact of single nucleotide substitution across three organisms.Genetic variants can be prioritized by their likely impact on various molecular processes.A gene/complex burden score can be computed from these predictions and used to associate genotype with phenotype. This study presents mutfunc, a resource that includes the pre‐computed impact of genetic variants in three model organisms (human, yeast and E. coli). These predictions can be used to prioritize genetic variants and compute gene burden scores. [ABSTRACT FROM AUTHOR]