Demographics, Symptoms, Psychotropic Use, and Caregiver Distress in Patients With Early vs Late Onset Dementia.

BACKGROUND: Understanding experiences and challenges faced by persons living with Early-Onset Dementia (EOD) compared to individuals diagnosed with Late-Onset Dementia (LOD) is important for the development of targeted interventions. OBJECTIVE: Describe differences in sociodemographic, neuropsychiatric behavioral symptoms, caregiver characteristics, and psychotropic use. DESIGN, SETTING, PARTICIPANTS: Cross-sectional, retrospective study including 908 UCLA Alzheimer's Dementia Care Program participants (177 with EOD and 731 with LOD). MEASUREMENTS: Onset of dementia was determined using age at program enrollment, with EOD defined as age <65 years and LOD defined as age >80 years. Sociodemographic and clinical characteristics were measured once at enrollment. Behavioral symptoms were measured using the Neuropsychiatric Inventory Questionnaire (NPI-Q) severity score and caregiver distress was measured using the NPI-Q distress score. Medications included antipsychotic, antidepressant, benzodiazepines and other hypnotics, antiepileptics, and dementia medications. RESULTS: EOD compared to LOD participants were more likely men, college graduates, married, live alone, and have fewer comorbidities. EOD caregivers were more often spouses (56% vs 26%, p <0.01), whereas LOD caregivers were more often children (57% vs 10%, p <0.01). EOD was associated with lower odds of being above the median (worse) NPI-Q severity (adjusted odds ratio [aOR], 0.58; 95% CI 0.35-0.96) and NPI-Q distress scores (aOR, 0.53; 95% CI 0.31-0.88). Psychotropic use did not differ between groups though symptoms were greater for LOD compared to EOD. CONCLUSION: Persons with EOD compared to LOD had sociodemographic differences, less health conditions, and fewer neuropsychiatric symptoms. Future policies could prioritize counseling for EOD patients and families, along with programs to support spousal caregivers of persons with EOD.

Patient-reported sleep and physical function during and after hospitalization.

PURPOSE: Poor sleep is associated with morbidity and mortality in the community; however, the health impact of poor sleep during and after hospitalization is poorly characterized. Our purpose was to describe trends in patient-reported sleep and physical function during and after hospitalization and evaluate sleep as a predictor of function after discharge. METHODS: This is a secondary analysis of trial data with 232 adults followed for 3months after hospital discharge. Main measures were patient-reported surveys on sleep (Pittsburgh Sleep Quality Index) and physical function (Katz Activities of Daily Living, Lawton Instrumental Activities of Daily Living, and Nagi Mobility Scale) were collected during hospitalization and at 1, 5, 9, and 13weeks postdischarge. RESULTS: Patient-reported sleep declined significantly during hospitalization and remained worse for 3months postdischarge (median Pittsburgh Sleep Quality Index=8 vs. 6, p < .001). In parallel, mobility declined significantly from baseline and remained worse at each follow-up time (median Nagi score=2 vs. 0, p < .001). Instrumental activities of daily living similarly decreased during and after hospitalization, but basic activities of daily living were unaffected. In adjusted time-series logistic regression models, the odds of mobility impairment were 1.48 times higher for each 1-point increase in Pittsburgh Sleep Quality Index score over time (95% CI 1.27-1.71, p < .001). CONCLUSIONS: Patient-reported sleep worsened during hospitalization, did not improve significantly for 3months after hospitalization, and poor sleep was a significant predictor of functional impairment over this time. Sleep dysfunction that begins with hospitalization may persist and prevent functional recovery after discharge. TRIAL REGISTRATION: The primary study was registered at ClinicalTrials.gov NCT03321279.

Measuring Outcomes in Psoriatic Arthritis: Comparing Routine Assessment of Patient Index Data and Psoriatic Arthritis Impact of Disease.

OBJECTIVE: To examine the construct validity of Routine Assessment of Patient Index Data 3 (RAPID3) and Psoriatic Arthritis Impact of Disease (PsAID) in patients with psoriatic arthritis (PsA). In examining construct validity, we also addressed scores among subgroups with severe psoriasis, poly articular disease, enthesitis, and dactylitis, and evaluated influences of sociodemographic factors and comorbidities (contextual factors) on these patient-reported outcomes (PRO). METHODS: Patients with PsA were enrolled in the Psoriatic Arthritis Research Consortium (PARC) between 2014 and 2016. PARC is a longitudinal observational cohort study conducted at 4 US institutions. In this cross-sectional study, construct validity was assessed by examining Spearman correlation coefficients for RAPID3 and PsAID with physician-reported disease activity measures and other PRO [e.g., Medical Outcomes Study Short Form-12 physical component summary/mental component summary (SF-12 PCS/MCS), Functional Assessment of Chronic Illness Therapy-Fatigue scale (FACIT-F)]. Contextual factors and disease subgroups were assessed in multivariable linear regression models with RAPID3 or PsAID12 as outcomes of interest and the hypothesized contextual factors as covariates. RESULTS: Among 401 patients enrolled in PARC, 347 completed RAPID3 or PsAID12. Of these, most were white females with a mean age of 51.7 years (SD 14.02). RAPID3 and PsAID were highly correlated (r = 0.90). These measures were also correlated with the SF-12 PCS (r = -0.67) and FACIT-F (r = -0.77). Important contextual factors and disease subgroups included enthesitis, joint counts, education, insurance type, and depression. CONCLUSION: RAPID3 and PsAID12 have excellent construct validity in PsA and are strongly correlated despite differing items. Contextual factors (i.e., the presence of depression and obesity) should be considered when interpreting raw scores of the RAPID3 and PsAID12.

Lipidomic profiling identifies signatures of metabolic risk.

BACKGROUND: Metabolic syndrome (MetS), the clustering of metabolic risk factors, is associated with cardiovascular disease risk. We sought to determine if dysregulation of the lipidome may contribute to metabolic risk factors. METHODS: We measured 154 circulating lipid species in 658 participants from the Framingham Heart Study (FHS) using liquid chromatography-tandem mass spectrometry and tested for associations with obesity, dysglycemia, and dyslipidemia. Independent external validation was sought in three independent cohorts. Follow-up data from the FHS were used to test for lipid metabolites associated with longitudinal changes in metabolic risk factors. RESULTS: Thirty-nine lipids were associated with obesity and eight with dysglycemia in the FHS. Of 32 lipids that were available for replication for obesity and six for dyslipidemia, 28 (88%) replicated for obesity and five (83%) for dysglycemia. Four lipids were associated with longitudinal changes in body mass index and four were associated with changes in fasting blood glucose in the FHS. CONCLUSIONS: We identified and replicated several novel lipid biomarkers of key metabolic traits. The lipid moieties identified in this study are involved in biological pathways of metabolic risk and can be explored for prognostic and therapeutic utility.

MicroRNA Signature of Cigarette Smoking and Evidence for a Putative Causal Role of MicroRNAs in Smoking-Related Inflammation and Target Organ Damage.

BACKGROUND: Cigarette smoking increases risk for multiple diseases. MicroRNAs regulate gene expression and may play a role in smoking-induced target organ damage. We sought to describe a microRNA signature of cigarette smoking and relate it to smoking-associated clinical phenotypes, gene expression, and lung inflammatory signaling. METHODS AND RESULTS: Expression profiling of 283 microRNAs was conducted on whole blood-derived RNA from 5023 Framingham Heart Study participants (54.0% women; mean age, 55±13 years) using TaqMan assays and high-throughput reverse transcription quantitative polymerase chain reaction. Associations of microRNA expression with smoking status and associations of smoking-related microRNAs with inflammatory biomarkers and pulmonary function were tested with linear mixed effects models. We identified a 6-microRNA signature of smoking. Five of the 6 smoking-related microRNAs were associated with serum levels of C-reactive protein or interleukin-6; miR-1180 was associated with pulmonary function measures at a marginally significant level. Bioinformatic evaluation of smoking-associated genes coexpressed with the microRNA signature of cigarette smoking revealed enrichment for immune-related pathways. Smoking-associated microRNAs altered expression of selected inflammatory mediators in cell culture gain-of-function assays. CONCLUSIONS: We characterized a novel microRNA signature of cigarette smoking. The top microRNAs were associated with systemic inflammatory markers and reduced pulmonary function, correlated with expression of genes involved in immune function, and were sufficient to modulate inflammatory signaling. Our results highlight smoking-associated microRNAs and are consistent with the hypothesis that smoking-associated microRNAs serve as mediators of smoking-induced inflammation and target organ damage. These findings call for further mechanistic studies to explore the diagnostic and therapeutic use of smoking-related microRNAs.

Association of Body Mass Index with DNA Methylation and Gene Expression in Blood Cells and Relations to Cardiometabolic Disease: A Mendelian Randomization Approach.

BACKGROUND: The link between DNA methylation, obesity, and adiposity-related diseases in the general population remains uncertain. METHODS AND FINDINGS: We conducted an association study of body mass index (BMI) and differential methylation for over 400,000 CpGs assayed by microarray in whole-blood-derived DNA from 3,743 participants in the Framingham Heart Study and the Lothian Birth Cohorts, with independent replication in three external cohorts of 4,055 participants. We examined variations in whole blood gene expression and conducted Mendelian randomization analyses to investigate the functional and clinical relevance of the findings. We identified novel and previously reported BMI-related differential methylation at 83 CpGs that replicated across cohorts; BMI-related differential methylation was associated with concurrent changes in the expression of genes in lipid metabolism pathways. Genetic instrumental variable analysis of alterations in methylation at one of the 83 replicated CpGs, cg11024682 (intronic to sterol regulatory element binding transcription factor 1 [SREBF1]), demonstrated links to BMI, adiposity-related traits, and coronary artery disease. Independent genetic instruments for expression of SREBF1 supported the findings linking methylation to adiposity and cardiometabolic disease. Methylation at a substantial proportion (16 of 83) of the identified loci was found to be secondary to differences in BMI. However, the cross-sectional nature of the data limits definitive causal determination. CONCLUSIONS: We present robust associations of BMI with differential DNA methylation at numerous loci in blood cells. BMI-related DNA methylation and gene expression provide mechanistic insights into the relationship between DNA methylation, obesity, and adiposity-related diseases.

Metabolite Signatures of Metabolic Risk Factors and their Longitudinal Changes.

CONTEXT: Metabolic dysregulation underlies key metabolic risk factors­obesity, dyslipidemia, and dysglycemia. OBJECTIVE: To uncover mechanistic links between metabolomic dysregulation and metabolic risk by testing metabolite associations with risk factors cross-sectionally and with risk factor changes over time. DESIGN: Cross-sectional­discovery samples (n = 650; age, 36­69 years) from the Framingham Heart Study (FHS) and replication samples (n = 670; age, 61­76 years) from the BioImage Study, both following a factorial design sampled from high vs low strata of body mass index, lipids, and glucose. Longitudinal­FHS participants (n = 554) with 5­7 years of follow-up for risk factor changes. SETTING: Observational studies. PARTICIPANTS: Cross-sectional samples with or without obesity, dysglycemia, and dyslipidemia, excluding prevalent cardiovascular disease and diabetes or dyslipidemia treatment. Age- and sex-matched by group. INTERVENTIONS: None. MAIN OUTCOME MEASURE(S): Gas chromatography-mass spectrometry detected 119 plasma metabolites. Cross-sectional associations with obesity, dyslipidemia, and dysglycemia were tested in discovery, with external replication of 37 metabolites. Single- and multi-metabolite markers were tested for association with longitudinal changes in risk factors. RESULTS: Cross-sectional metabolite associations were identified with obesity (n = 26), dyslipidemia (n = 21), and dysglycemia (n = 11) in discovery. Glutamic acid, lactic acid, and sitosterol associated with all three risk factors in meta-analysis (P < 4.5 × 10−4). Metabolites associated with longitudinal risk factor changes were enriched for bioactive lipids. Multi-metabolite panels explained 2.5­15.3% of longitudinal changes in metabolic traits. CONCLUSIONS: Cross-sectional results implicated dysregulated glutamate cycling and amino acid metabolism in metabolic risk. Certain bioactive lipids were associated with risk factors cross-sectionally and over time, suggesting their upstream role in risk factor progression. Functional studies are needed to validate findings and facilitate translation into treatments or preventive measures.

Multiple imputation and analysis for high-dimensional incomplete proteomics data.

Multivariable analysis of proteomics data using standard statistical models is hindered by the presence of incomplete data. We faced this issue in a nested case-control study of 135 incident cases of myocardial infarction and 135 pair-matched controls from the Framingham Heart Study Offspring cohort. Plasma protein markers (K = 861) were measured on the case-control pairs (N = 135), and the majority of proteins had missing expression values for a subset of samples. In the setting of many more variables than observations (K ≫ N), we explored and documented the feasibility of multiple imputation approaches along with subsequent analysis of the imputed data sets. Initially, we selected proteins with complete expression data (K = 261) and randomly masked some values as the basis of simulation to tune the imputation and analysis process. We randomly shuffled proteins into several bins, performed multiple imputation within each bin, and followed up with stepwise selection using conditional logistic regression within each bin. This process was repeated hundreds of times. We determined the optimal method of multiple imputation, number of proteins per bin, and number of random shuffles using several performance statistics. We then applied this method to 544 proteins with incomplete expression data (≤ 40% missing values), from which we identified a panel of seven proteins that were jointly associated with myocardial infarction.