Citations

ABSTRACT Background Mobility functional status is inconsistently recorded in electronic health records. Recent advances in Large Language Models (LLMs) enable automated extraction of functional information from unstructured clinical notes. Leveraging a validated functional LLM with high performance in mobility extraction, we aimed to study the association between LLM‐derived mobility functional status and clinical outcomes, including emergency department (ED) visits, hospitalizations, and mortality. Methods Individuals aged 60 years and older enrolled in the Mayo Clinic Study of Aging, with documented clinical notes in 2022 were included. Individuals without any identifiable functional information were excluded. The LLM‐derived mobility functional status categorized mobility as impaired or unimpaired, across the five mobility domains as classified by International Classification of Functioning, Disability, and Health. Clinical outcomes in 2023 were extracted from the Rochester Epidemiology Project database. Logistic regression models assessed associations between mobility impairment and outcomes, adjusted for age, sex, Elixhauser comorbidity index, and education. Results Three thousand three hundred thirty‐seven individuals were screened, of whom three hundred forty‐three had no functional information. Two thousand nine hundred ninety‐four individuals were analyzed. 79.4% had at least one mobility impairment. Higher mobility impairment is associated with older age and greater Elixhauser comorbidity. Individuals with 3 or more impairments had an increased adjusted odds of ED visits (Adjusted Odds Ratio (aOR): 2.18 (1.64, 2.90)) and hospitalizations (aOR: 2.35 (1.51, 3.65)) compared to unimpaired individuals ( p < 0.001), while mortality did not demonstrate a significant association with mobility impairment. Conclusion LLM‐derived mobility functional status assessment was associated with ED visits and hospitalization, but not with all‐cause mortality after adjustment. Prospective validation and evaluation of incremental discrimination, calibration, and clinical utility are needed before clinical implementation.

Abstract Background Sarcopenia is common in chronic obstructive pulmonary disease (COPD) and contributes to adverse outcomes. Alpha-1 antitrypsin deficiency (AATD) is the most common genetic cause of COPD, but its impact on skeletal muscle remains to be determined. Methods We performed a retrospective analysis of 487,207 participants in the UK Biobank with available SERPINA1 genotyping. Associations between AAT genotypes and measures of skeletal muscle mass were determined using multivariable linear and logistic regression models. Models adjusted for age, sex, smoking status, genetic principal components, lung function (FEV₁% predicted), and liver fibrosis risk (FIB-4). We determined that the Z-allele was associated with sarcopenia and mortality using regression models and mediation analysis. Results Compared with PI*MM individuals, carriers of the Z allele (PI*MZ, PI*SZ, PI*ZZ) demonstrated significantly lower fat free mass index (FFMI) and appendicular skeletal muscle index (ASMI), as well as higher odds of sarcopenia by binary definition. Associations persisted after adjustment for lung and liver function. A dose-dependent relationship was observed, with the greatest reductions in muscle mass and highest sarcopenia associated with PI*ZZ individuals (adjusted OR for FFMI-defined sarcopenia 2.02, 95% CI 1.16-3.32). Each additional Z allele was associated with increased mortality risk (adjusted OR 1.09, 95% CI 1.02-1.16), with sarcopenia mediating a modest proportion (∼2.6%) of this association. Conclusions. The SERPINA1 Z allele is associated with reduced muscle mass and increased risk for sarcopenia independent of lung and liver disease. Our findings expand the phenotypic spectrum of AATD-related diseases to include skeletal muscle as a modifiable target for intervention.

Abstract Clinical syndromes of frontotemporal dementia present both distinct and overlapping neuroimaging abnormalities and, consequently, domains of impairment. The aim of this study was to determine how well covariance patterns from multiple neuroimaging modalities discriminate across clinical frontotemporal dementia syndromes. Four-hundred participants with a clinical frontotemporal dementia diagnosis, including behavioral variant of frontotemporal dementia, semantic variant of primary progressive aphasia, right temporal variant of frontotemporal dementia, primary progressive aphasia, apraxia of speech with agrammatic aphasia, primary progressive apraxia of speech, progressive supranuclear palsy and corticobasal syndrome, and 109 cognitively unimpaired participants underwent extensive neurological and neuropsychological assessments, structural MRI, flortaucipir-PET for tau and [18F] fluorodeoxyglucose-PET. Multimodal imaging covariance was investigated at the voxel-level using principal component analysis after adjusting for age and sex effects. Linear regression models were fit to investigate the relationship between principal components and clinical scores. Multinomial logistic regression models investigated the ability of imaging principal components to discriminate across syndromes. The first principal component, which describes the largest source of variability in the data, was a pattern of widespread cortical atrophy (21% variability explained) and of frontoparietal and temporal hypometabolism (21%): scores were lowest in behavioral variant of frontotemporal dementia and correlated with general cognitive impairment. On tau-PET, the first principal component (43% variability) was not significantly different among syndromes and captured a widespread cortical tau uptake, with a focus on the temporal lobe. In all three modalities, the second principal component captured temporal lobe imaging abnormalities, contrasting the motor and semantic clinical syndromes. Subsequent components described smaller variability. On cross-validation, the classifiers based on principal components from MRI (multiclass area under the receiver operating characteristic: 0.76) and [18F]fluorodeoxyglucose-PET (0.79) outperformed tau-PET (0.71), while combining all three modalities did not significantly improve the classifier performance. On cross-validation, the sensitivity of the image-based classifiers was heterogeneous across syndromes and modalities, with highest values for progressive supranuclear palsy and semantic variant of primary progressive aphasia and lowest for primary progressive aphasia and corticobasal syndrome. Our study found that the covariance components of structural and molecular neuroimaging underlying frontotemporal dementia clinical syndromes exist both as a continuum and with syndrome-specific patterns. More than one imaging model was able to correctly classify several frontotemporal dementia clinical syndromes with good specificity and sensitivity, highlighting the value of imaging in aiding patient diagnosis.

Abstract Biallelic colony‐stimulating factor 1 receptor ( CSF1R ) variants are a major cause of early‐onset CSF1R ‐related disorder (CSF1R‐RD), although fewer than 30 cases have been reported and genotype–phenotype correlations remain unclear. The aim of this study was to characterize the clinical, radiological, and genetic features of patients with brain abnormalities, neurodegeneration, and dysosteosclerosis (BANDDOS). Six patients with biallelic CSF1R variants underwent detailed clinical, neurological, and magnetic resonance imaging evaluation. Three siblings with a homozygous p.Gly585Val variant showed marked phenotypic variability, ranging from severe childhood‐onset neurodegeneration to an asymptomatic presentation, all with white matter abnormalities. Three unrelated patients carried different biallelic CSF1R variants and presented with congenital skeletal abnormalities or early‐onset neurological disease of variable severity. These cases broaden the phenotypic spectrum of biallelic CSF1R‐RD and demonstrate that disease severity cannot be predicted solely by genotype, suggesting an important role for genetic or environmental modifiers.

Background Alzheimer's disease (AD) and related dementias are leading causes of death worldwide. While socioeconomic status (SES) is a critical determinant, its impact on mortality after diagnosis remains understudied. Objective To investigate the roles of neighborhood-level and individual-level SES in predicting mortality risk among older adults with incident dementia. Methods We analyzed data from 924 participants with incident dementia from the Mayo Clinic Study of Aging (N = 6909), a cohort with clinical follow-up visits every 15 months. We assessed mortality risk using Cox's time-varying survival regression, adjusting for time-fixed and time-varying covariates. Primary predictors were neighborhood-level SES (Area Deprivation Index [ADI]) and individual-level SES (housing-based SES index [HOUSES], education, and occupation). We also applied a random survival forest model to assess the predictive contribution of SES indicators and identify influential predictors. Results 857 participants (92.7%) died, with a median time from dementia diagnosis to death of 29 months (IQR: 12–54). Lower SES measured by HOUSES was associated with increased mortality risk (HR 1.28, 95% CI 1.07–1.52). Conversely, ADI, education, and occupation were not associated with mortality. Male sex, older age, congestive heart failure and diabetes were associated with higher mortality risk. In predictive analysis, HOUSES ranked among the top 10 contributors to mortality risk. Conclusions Among older adults with dementia, lower HOUSES was associated with higher mortality risk beyond neighborhood-level deprivation, education, and occupation. Integrating this housing-based individual-level SES measure into dementia care planning may help identify patients with greater socioeconomic vulnerability and inform mortality risk stratification.

Abstract Purpose The devastation and impact of a new cancer diagnosis can be compounded by psychosocial challenges that can significantly affect patient outcomes. Historically at Mayo Clinic Florida, social work contact with newly diagnosed hematology oncology patients occurred at various and inconsistent times during treatment. Baseline data revealed that 2.16% of these patients received social work contact ranging from 2 to 43 days after diagnosis. Referrals were typically initiated following elevated distress screening or provider-identified needs. This quality improvement initiative explored the impact of embedding early clinical social work services to increase timely social work support for patients and awareness of the role of social work and resources. Methods All patients newly diagnosed with hematological malignancy were scheduled for first contact with social work within two weeks of meeting with their medical provider. During the first social work appointment, psychosocial screening was performed and social work role and resource education was provided. A follow-up consultation was scheduled if needs were identified. Results 96 out of 122 (78.6%) newly diagnosed patients had social work contact for an initial screen during this quality improvement project. There was a 76.44% increase from baseline in the number of patients receiving social work support as a result of this initiative. 52.08% of the newly diagnosed patients proceeded from this initial screening to a scheduled social worker consult. Conclusion Early and proactive social work outreach through a Responsive Practical Proactive (RPP) approach improved access to psychosocial care and helped address gaps between distress screening and timely intervention.