MicroRNA and Protein Cargos of Human Limbal Epithelial Cell-Derived Exosomes and Their Regulatory Roles in Limbal Stromal Cells of Diabetic and Non-Diabetic Corneas.

Epithelial and stromal/mesenchymal limbal stem cells contribute to corneal homeostasis and cell renewal. Extracellular vesicles (EVs), including exosomes (Exos), can be paracrine mediators of intercellular communication. Previously, we described cargos and regulatory roles of limbal stromal cell (LSC)-derived Exos in non-diabetic (N) and diabetic (DM) limbal epithelial cells (LECs). Presently, we quantify the miRNA and proteome profiles of human LEC-derived Exos and their regulatory roles in N- and DM-LSC. We revealed some miRNA and protein differences in DM vs. N-LEC-derived Exos' cargos, including proteins involved in Exo biogenesis and packaging that may affect Exo production and ultimately cellular crosstalk and corneal function. Treatment by N-Exos, but not by DM-Exos, enhanced wound healing in cultured N-LSCs and increased proliferation rates in N and DM LSCs vs. corresponding untreated (control) cells. N-Exos-treated LSCs reduced the keratocyte markers ALDH3A1 and lumican and increased the MSC markers CD73, CD90, and CD105 vs. control LSCs. These being opposite to the changes quantified in wounded LSCs. Overall, N-LEC Exos have a more pronounced effect on LSC wound healing, proliferation, and stem cell marker expression than DM-LEC Exos. This suggests that regulatory miRNA and protein cargo differences in DM- vs. N-LEC-derived Exos could contribute to the disease state.

A Complete Workflow for High Throughput Human Single Skeletal Muscle Fiber Proteomics.

Skeletal muscle is a major regulatory tissue of whole-body metabolism and is composed of a diverse mixture of cell (fiber) types. Aging and several diseases differentially affect the various fiber types, and therefore, investigating the changes in the proteome in a fiber-type specific manner is essential. Recent breakthroughs in isolated single muscle fiber proteomics have started to reveal heterogeneity among fibers. However, existing procedures are slow and laborious, requiring 2 h of mass spectrometry time per single muscle fiber; 50 fibers would take approximately 4 days to analyze. Thus, to capture the high variability in fibers both within and between individuals requires advancements in high throughput single muscle fiber proteomics. Here we use a single cell proteomics method to enable quantification of single muscle fiber proteomes in 15 min total instrument time. As proof of concept, we present data from 53 isolated skeletal muscle fibers obtained from two healthy individuals analyzed in 13.25 h. Adapting single cell data analysis techniques to integrate the data, we can reliably separate type 1 and 2A fibers. Ninety-four proteins were statistically different between clusters indicating alteration of proteins involved in fatty acid oxidation, oxidative phosphorylation, and muscle structure and contractile function. Our results indicate that this method is significantly faster than prior single fiber methods in both data collection and sample preparation while maintaining sufficient proteome depth. We anticipate this assay will enable future studies of single muscle fibers across hundreds of individuals, which has not been possible previously due to limitations in throughput.

High-Throughput Single-Cell Proteomic Analysis of Organ-Derived Heterogeneous Cell Populations by Nanoflow Dual-Trap Single-Column Liquid Chromatography.

Identification and proteomic characterization of rare cell types within complex organ-derived cell mixtures is best accomplished by label-free quantitative mass spectrometry. High throughput is required to rapidly survey hundreds to thousands of individual cells to adequately represent rare populations. Here we present parallelized nanoflow dual-trap single-column liquid chromatography (nanoDTSC) operating at 15 min of total run time per cell with peptides quantified over 11.5 min using standard commercial components, thus offering an accessible and efficient LC solution to analyze 96 single cells per day. At this throughput, nanoDTSC quantified over 1000 proteins in individual cardiomyocytes and heterogeneous populations of single cells from the aorta.

Protein biomarkers of disease progression in patients with systemic sclerosis associated interstitial lung disease.

Systemic sclerosis is a rare connective tissue disease; and interstitial lung disease (SSc-ILD) is associated with significant morbidity and mortality. There are no clinical, radiologic features, nor biomarkers that identify the specific time when patients are at risk for progression at which the benefits from treatment outweigh the risks. Our study aimed to identify blood protein biomarkers associated with progression of interstitial lung disease in patients with SSc-ILD using an unbiased, high-throughput approach. We classified SSc-ILD as progressive or stable based on change in forced vital capacity over 12 months or less. We profiled serum proteins by quantitative mass spectrometry and analyzed the association between protein levels and progression of SSc-ILD via logistic regression. The proteins associated with at a p value of < 0.1 were queried in the ingenuity pathway analysis (IPA) software to identify interaction networks, signaling, and metabolic pathways. Through principal component analysis, the relationship between the top 10 principal components and progression was evaluated. Unsupervised hierarchical clustering with heatmapping was done to define unique groups. The cohort consisted of 72 patients, 32 with progressive SSc-ILD and 40 with stable disease with similar baseline characteristics. Of a total of 794 proteins, 29 were associated with disease progression. After adjusting for multiple testing, these associations did not remain significant. IPA identified five upstream regulators that targeted proteins associated with progression, as well as a canonical pathway with a higher signal in the progression group. Principal component analysis showed that the ten components with the highest Eigenvalues represented 41% of the variability of the sample. Unsupervised clustering analysis revealed no significant heterogeneity between the subjects. We identified 29 proteins associated with progressive SSc-ILD. While these associations did not remain significant after accounting for multiple testing, some of these proteins are part of pathways relevant to autoimmunity and fibrogenesis. Limitations included a small sample size and a proportion of immunosuppressant use in the cohort, which could have altered the expression of inflammatory and immunologic proteins. Future directions include a targeted evaluation of these proteins in another SSc-ILD cohort or application of this study design to a treatment naïve population.

Complete Workflow for High Throughput Human Single Skeletal Muscle Fiber Proteomics.

Skeletal muscle is a major regulatory tissue of whole-body metabolism and is composed of a diverse mixture of cell (fiber) types. Aging and several diseases differentially affect the various fiber types, and therefore, investigating the changes in the proteome in a fiber-type specific manner is essential. Recent breakthroughs in isolated single muscle fiber proteomics have started to reveal heterogeneity among fibers. However, existing procedures are slow and laborious requiring two hours of mass spectrometry time per single muscle fiber; 50 fibers would take approximately four days to analyze. Thus, to capture the high variability in fibers both within and between individuals requires advancements in high throughput single muscle fiber proteomics. Here we use a single cell proteomics method to enable quantification of single muscle fiber proteomes in 15 minutes total instrument time. As proof of concept, we present data from 53 isolated skeletal muscle fibers obtained from two healthy individuals analyzed in 13.25 hours. Adapting single cell data analysis techniques to integrate the data, we can reliably separate type 1 and 2A fibers. Sixty-five proteins were statistically different between clusters indicating alteration of proteins involved in fatty acid oxidation, muscle structure and regulation. Our results indicate that this method is significantly faster than prior single fiber methods in both data collection and sample preparation while maintaining sufficient proteome depth. We anticipate this assay will enable future studies of single muscle fibers across hundreds of individuals, which has not been possible previously due to limitations in throughput.

High Throughput Single Cell Proteomic Analysis of Organ Derived Heterogeneous Cell Populations by Nanoflow Dual Trap Single Column Liquid Chromatography.

Identification and proteomic characterization of rare cell types within complex organ derived cell mixtures is best accomplished by label-free quantitative mass spectrometry. High throughput is required to rapidly survey hundreds to thousands of individual cells to adequately represent rare populations. Here we present parallelized nanoflow dual-trap single-column liquid chromatography (nanoDTSC) operating at 15 minutes of total run time per cell with peptides quantified over 11.5 minutes using standard commercial components, thus offering an accessible and efficient LC solution to analyze 96 single-cells per day. At this throughput, nanoDTSC quantified over 1,000 proteins in individual cardiomyocytes and heterogenous populations of single cells from aorta.

A panel of blood biomarkers unique to sudden cardiac arrest.

BACKGROUND: The identification of circulating biomarkers specific for sudden cardiac arrest (SCA) could enhance risk prediction. Of particular interest are biomarkers specific to SCA, independent of coronary artery disease (CAD). OBJECTIVE: The purpose of this study was to identify biomarkers of SCA obtained close to the SCA event. METHODS: Twenty cases (survivors of SCA) and 40 age- and sex-matched controls were compared, with a replication analysis of 29 cases matched to 57 controls. A secondary analysis compared 20 SCA cases to 20 controls with CAD. Blood samples were obtained from SCA survivors at a median of 11 months after the SCA event. Proteins were analyzed on a mass spectrometer using data-independent acquisition; a subset of cytokines were analyzed using immunoassays; and 1153 lipids (13 classes) were analyzed. A false discovery rate P value of <.05 identified associated proteins. RESULTS: Patients had a mean age of 58 years (range 25-87 years), and 70% were male. A total of 26 protein biomarkers associated with SCA when cases were compared with controls, of which 20 differentiated SCA from CAD. The replication analysis identified 8 of 26 biomarkers, of which 6 were not overlapping with CAD. The top identified biological processes involved the extracellular matrix, coagulation cascades, and platelet activation. Lipids in the lysophosphatidylcholine class were implicated in SCA through the CAD pathway. CONCLUSION: We identified a panel of novel blood biomarkers specifically associated with SCA, including several that may be involved outside the CAD pathway. These biomarkers could have mechanistic significance and the potential to enhance clinical prediction of SCA.

Ultra-high sensitivity cardiac troponin-I concentration and left ventricular structure and function in women with ischemia and no obstructive coronary artery disease.

Aims: Women are disproportionally impacted by ischemia and no obstructive coronary artery disease (INOCA), and such women are at increased risk of developing heart failure with preserved ejection fraction (HFpEF), however the mechanisms linking these conditions remain poorly understood. The aim of this study was to determine whether ultra-high sensitivity cardiac troponin I (u-hscTnI), an indicator of cardiomyocyte injury, is associated with abnormalities in myocardial perfusion and left ventricular (LV) structure and function in women with INOCA. Methods: 327 women with INOCA enrolled in the Women's Ischemia Syndrome Evaluation-Coronary Vascular Dysfunction (WISE-CVD) study underwent vasodilator stress cardiac magnetic resonance imaging (CMRI) and u-hscTnI measurements (Simoa HD-1 Analyzer, Quanterix Corporation). Multivariable linear regression was used to evaluate associations between u-hscTnI concentrations and myocardial perfusion (MPRI), LV mass index and feature-tracking derived strain measures of LV function. Results: u-hscTnI concentrations were quantifiable in 100% of the cohort and ranged from 0.004 to 79.6 pg/mL. In adjusted models, u-hscTnI was associated with LV mass index (+2.03; 95% CI 1.17, 2.89; p < 0.01) and early diastolic radial strain rate (SR) (+0.13; 95% CI 0.01, 0.25; p = 0.03), early diastolic circumferential SR (-0.04; 95% CI -0.08, 0.002; p = 0.06) and early diastolic longitudinal SR (-0.03; 95% CI -0.07, 0.002; p = 0.06). u-hscTnI was not associated with MPRI (p = 0.39) in adjusted models. Conclusion: Together, these findings support cardiomyocyte injury as a putative pathway towards adverse LV remodeling and dysfunction; however, further research is needed to define the specific mechanism(s) driving myocellular injury in INOCA.

Sex-based differences in remote monitoring of biometric, psychometric and biomarker indices in stable ischemic heart disease.

BACKGROUND: Sex-based differences are crucial to consider in the formulation of a personalized treatment plan. We evaluated sex-based differences in adherence and remotely monitored biometric, psychometric, and biomarker data among patients with stable ischemic heart disease (IHD). METHODS: The Prediction, Risk, and Evaluation of Major Adverse Cardiac Events (PRE-MACE) study evaluated patients with stable IHD over a 12-week period. We collected biometric and sleep data using remote patient monitoring via FitBit and psychometric data from Patient-Reported Outcomes Measurement Information System (PROMIS), Kansas City Cardiomyopathy (KCC) and Seattle Angina Questionnaire-7 (SAQ-7) questionnaires. Serum biomarker levels were collected at the baseline visit. We explored sex-based differences in demographics, adherence to study protocols, biometric data, sleep, psychometric data, and biomarker levels. RESULTS: There were 198 patients enrolled, with mean age 65.5 ± 11 years (± Standard deviation, SD), and 60% were females. Females were less adherent to weekly collection of PROMIS, KCC and SAQ-7 physical limitations questionnaires (all p < 0.05), compared to males. There was no difference in biometric physical activity. There was a statistically significant (p < 0.05) difference in sleep duration between sexes, with females sleeping 6 min longer. However, females reported higher PROMIS sleep disturbance scores (p < 0.001) and poorer psychometric scores overall (p < 0.05). A higher proportion of males had clinically significant elevations of median N-terminal pro-brain natriuretic peptide (p = 0.005) and high-sensitivity cardiac troponin levels (p < 0.001) compared to females. CONCLUSIONS: Among females and males with stable IHD, there are sex-based differences in remote monitoring behavior and data. Females are less adherent to psychometric data collection and report poorer psychometric and sleep quality scores than males. Elevated levels of biomarkers for MACE are more common in males. These findings may improve sex-specific understanding of IHD using remote patient monitoring.

Ultra-highly sensitive cardiac troponin I: Age and sex differences in healthy individuals.

Background: Associations between elevated circulating cardiac troponin I (cTnI) levels and adverse cardiac outcomes were established prior to the ability to measure extremely low levels of cTnI. Immunoassays that achieve precise ultra-highly sensitive quantification of cTnI (u-hs-cTnI) will allow accurate measurement in healthy subjects. We aimed to evaluate the distribution of u-hs-cTnI values measured by (Simoa HD-1 Analyzer, Quanterix Corporation, Lexington, MA) in healthy subjects and characterize relations to sex and age. Methods: Two independent, healthy cohorts (total of 200 women, 200 men) aged 18-86 years were analyzed in duplicate using the u-hs-cTnI Immunoassay. The u-hs-cTnI 99th percentiles were calculated as the upper limits considering a robust estimation against outliers with 90% confidence intervals. The Quanterix immunoassay analytical performance was established and compared to an existing clinical assay (ARCHITECT STAT High Sensitivity Troponin I, Abbott Laboratories, Wiesbaden, Germany). Results: The lower limit of detection of the u-hs-cTnI assay was calculated to be 0.005 ng/L; we accurately quantified u-hs-cTnI in 95% of healthy individuals. The Quanterix immunoassay within overlapping concentrations correlated with the Abbott assay (R2 = 0.932). The calculated combined 99th percentile was 7.94 ng/L (90% Confidence Interval [CI], 5.47-10.52). Women had lower mean u-hs-cTnI concentrations than men under the age of 40 years. The sex-specific 99th percentile for female vs. male individuals was 4.89 ng/L (90%CI, 3.71-6.25) and 10.49 ng/L (90%CI, 5.19-15.06), respectively. Conclusion: The Quanterix immunoassay provides precise quantification in 95% of healthy individuals. Women under the age of 40 years have significantly lower levels of u-hs-cTnI than men.

Parallels between retinal and brain pathology and response to immunotherapy in old, late-stage Alzheimer's disease mouse models.

Despite growing evidence for the characteristic signs of Alzheimer's disease (AD) in the neurosensory retina, our understanding of retina-brain relationships, especially at advanced disease stages and in response to therapy, is lacking. In transgenic models of AD (APPSWE/PS1∆E9; ADtg mice), glatiramer acetate (GA) immunomodulation alleviates disease progression in pre- and early-symptomatic disease stages. Here, we explored the link between retinal and cerebral AD-related biomarkers, including response to GA immunization, in cohorts of old, late-stage ADtg mice. This aged model is considered more clinically relevant to the age-dependent disease. Levels of synaptotoxic amyloid ß-protein (Aß)1-42, angiopathic Aß1-40, non-amyloidogenic Aß1-38, and Aß42/Aß40 ratios tightly correlated between paired retinas derived from oculus sinister (OS) and oculus dexter (OD) eyes, and between left and right posterior brain hemispheres. We identified lateralization of Aß burden, with one-side dominance within paired retinal and brain tissues. Importantly, OS and OD retinal Aß levels correlated with their cerebral counterparts, with stronger contralateral correlations and following GA immunization. Moreover, immunomodulation in old ADtg mice brought about reductions in cerebral vascular and parenchymal Aß deposits, especially of large, dense-core plaques, and alleviation of microgliosis and astrocytosis. Immunization further enhanced cerebral recruitment of peripheral myeloid cells and synaptic preservation. Mass spectrometry analysis identified new parallels in retino-cerebral AD-related pathology and response to GA immunization, including restoration of homeostatic glutamine synthetase expression. Overall, our results illustrate the viability of immunomodulation-guided CNS repair in old AD model mice, while shedding light onto similar retino-cerebral responses to intervention, providing incentives to explore retinal AD biomarkers.

Biometric and Psychometric Remote Monitoring and Cardiovascular Risk Biomarkers in Ischemic Heart Disease.

Background Patients with stable ischemic heart disease represent a heterogeneous population at variable risk for major adverse cardiac events (MACE). Because MACE typically occurs outside the hospital, we studied whether biometric and psychometric remote patient monitoring are associated with MACE risk biomarkers. Methods and Results In 198 patients with stable ischemic heart disease (mean age 65±11 years, 60% women), we evaluated baseline measures, including biometric (FitBit 2) and psychometric (acquired via smartphone-administered patient-reported outcomes) remote monitoring, in the PRE-MACE (Prediction, Risk, and Evaluation of Major Adverse Cardiac Events) study. In multivariable adjusted regression analyses, we examined the association of these measures with biomarkers of MACE risk, including NT-proBNP (N-terminal pro-b-type natriuretic peptide), u-hs-cTnI (ultra-high sensitivity cardiac-specific troponin I), and hs-CRP (high-sensitivity C-reactive) protein. Both biometric and psychometric measures were associated with NT-proBNP. Specifically, step count, heart rate, physical activity, global health score, and physical function score were all inversely related, whereas physical limitation score was directly related (P≤0.05 for all). However, only biometric measures (step count and heart rate) were associated with u-hs-cTnI (inversely related, P<0.05), while only the psychometric measures of physical limitation were associated with hs-CRP (directly related, P≤0.05). Conclusions In stable ischemic heart disease patients, remotely monitored measures were associated with MACE risk biomarkers. Both biometric and psychometric measures were related to NT-proBNP. In contrast, biometric measures were uniquely related to u-hs-cTnI, while psychometric indices were uniquely related to hs-CRP. Further investigation could assess the predictive value of these metrics for MACE in ischemic heart disease.

Quality Control and Outlier Detection of Targeted Mass Spectrometry Data from Multiplex Protein Panels.

Increased throughput as well as increased multiplexing of liquid chromatography coupled to selected reaction monitoring mass spectrometry (LC-SRM-MS) assays for protein quantification challenges routine data analysis. Despite the measurement of multiple transitions from multiple peptides, for clinical applications a single (quantifier) transition from one (quantifier) signature peptide is used to represent the protein quantity with most data used solely to validate the quantifier result. To support the generation of reliable protein results from multiplexed LC-SRM-MS assays with large sample numbers, we developed a data analysis process for quality control and outlier detection using data from an 11-protein multiplex LC-SRM-MS method for dried blood samples (195 492 chromatographic peaks from 1481 samples * 11 proteins * 2 peptides * 3 transitions * 2 isotopologues). The 2-tiered data analysis process detects outliers for ion transition ratio, peptide ratio, and % difference between duplicates, applying less stringent criteria to samples with a small % difference between duplicates (Tier 1) and more stringent criteria to samples with unassessed or a large % difference between duplicates (Tier 2). After manual peak review, 1127 samples (76%) were selected based on the sample quality. The data analysis process thereafter automatically selected quantifier transitions/peptides, removed quality control failures and outliers (8%), averaged duplicates, and generated a comprehensive report listing 6085 quality controlled protein-level results. The proposed data analysis process serves as a starting point toward standardized data analysis of multiplexed LC-SRM-MS protein assays.

Identification of Putative Early Atherosclerosis Biomarkers by Unsupervised Deconvolution of Heterogeneous Vascular Proteomes.

Coronary artery disease remains a leading cause of death in industrialized nations, and early detection of disease is a critical intervention target to effectively treat patients and manage risk. Proteomic analysis of mixed tissue homogenates may obscure subtle protein changes that occur uniquely in underlying tissue subtypes. The unsupervised 'convex analysis of mixtures' (CAM) tool has previously been shown to effectively segregate cellular subtypes from mixed expression data. In this study, we hypothesized that CAM would identify proteomic information specifically informative to early atherosclerosis lesion involvement that could lead to potential markers of early disease detection. We quantified the proteome of 99 paired abdominal aorta (AA) and left anterior descending coronary artery (LAD) specimens (N = 198 specimens total) acquired during autopsy of young adults free of diagnosed cardiac disease. The CAM tool was then used to segregate protein subsets uniquely associated with different underlying tissue types, yielding markers of normal and fibrous plaque (FP) tissues in LAD and AA (N = 62 lesions markers). CAM-derived FP marker expression was validated against pathologist estimated luminal surface involvement of FP, as well as in an orthogonal cohort of "pure" fibrous plaque, fatty streak, and normal vascular specimens. A targeted mass spectrometry (MS) assay quantified 39 of 62 CAM-FP markers in plasma from women with angiographically verified coronary artery disease (CAD, N = 46) or free from apparent CAD (control, N = 40). Elastic net variable selection with logistic regression reduced this list to 10 proteins capable of classifying CAD status in this cohort with <6% misclassification error, and a mean area under the receiver operating characteristic curve of 0.992 (confidence interval 0.968-0.998) after cross validation. The proteomics-CAM workflow identified lesion-specific molecular biomarker candidates by distilling the most representative molecules from heterogeneous tissue types.

A protocol integrating remote patient monitoring patient reported outcomes and cardiovascular biomarkers.

We describe the protocol, design, and methodology of the Prediction, Risk, and Evaluation of Major Adverse Cardiac Events (PRE-MACE) study as a multicomponent remote patient monitoring in cardiology. Using biosensor, biomarkers, and patient-reported outcomes in participants with stable ischemic heart disease, the PRE-MACE study is designed to measure cross-sectional correlations and establish the ability of remote monitoring to predict major adverse cardiovascular event (MACE) biomarkers and incident MACE at baseline and 12-month follow-up. It will further assess the adherence and cost-effectiveness of remote monitoring and blood sampling over the initial months. Despite medication and lifestyle changes, patients with cardiovascular disease can experience MACE due to undertreatment, poor adherence, or failure to recognize clinical or biochemical changes that presage MACE. Identifying patients using remote monitoring to detect MACE forerunners has potential to improve outcomes, avoid MACE, and reduce resource utilization. Data collection will include: (1) continuous remote monitoring using wearable biosensors; (2) biomarker measurements using plasma and at-home micro-sampling blood collection; and (3) patient-reported outcomes to monitor perceived stress, anxiety, depression, and health-related quality of life. Two hundred participants will be followed for 90 days with a subset (n = 80) monitored for 180 days. All participants will be followed up for MACE at 12 months.The PRE-MACE study will utilize remote monitoring with biosensors, biomarkers, and patient-reported outcomes to identify intermediate biomarkers of MACE in patients with stable ischemic heart disease. If shown to be effective, this intervention can be utilized between health visits to predict MACE and reduce financial impact of MACE.

Serum NfL (Neurofilament Light Chain) Levels and Incident Stroke in Adults With Diabetes Mellitus.

Background and Purpose- Effective stroke prevention depends on accurate stroke risk prediction. We determined the discriminative ability of NfL (neurofilament light chain) levels for distinguishing between adults with diabetes mellitus who develop incident stroke and those who remain stroke free during a 7-year follow-up period. Methods- We performed a case-control study of participants selected from the previously completed ACCORD trial (Action to Control Cardiovascular Risk in Diabetes). Cases were all ACCORD subjects who were stroke free at enrollment and developed incident stroke during follow-up (n=113). Control subjects (n=250) were randomly selected ACCORD subjects who had no stroke events either before or after randomization. NfL was measured in baseline samples using Single Molecule Array technology (Quanterix). Results- Baseline NfL levels were higher in stroke subjects, compared to controls, after adjusting for age, race, blood pressure, weight, and the Framingham Stroke Risk Score. Relative to the subjects in the lowest quintile of NfL levels, the hazard ratios of incident stroke for subjects in the second to fifth quintiles were 3.91 (1.45-10.53), 4.05 (1.52-10.79), 5.63 (2.16-14.66), and 9.75 (3.84-27.71), respectively, after adjusting for race and Framingham Stroke Risk Score. Incorporating NfL levels into a predictive score that already included race and Framingham Stroke Risk Score increased the score's C statistic from 0.71 (95% CI, 0.66-0.77) to 0.78 (95% CI, 0.73-0.83), P<0.001. Older age, nonwhite race, higher systolic blood pressure, glomerular filtration rate <60, and higher hemoglobin A1C were independent predictors of serum NfL in this cohort but diastolic blood pressure, durations of hypertension or diabetes mellitus, and lipid levels were not. In total, cardiovascular disease risk factors explained 19.2% of the variability in baseline NfL levels. Conclusions- Serum NfL levels predict incident stroke and add considerably to the discriminatory power of the Framingham Stroke Risk Score in a cohort of middle-aged and older adults with diabetes mellitus.

Acute neuropathological consequences of short-term mechanical ventilation in wild-type and Alzheimer's disease mice.

BACKGROUND: Mechanical ventilation is strongly associated with cognitive decline after critical illness. This finding is particularly evident among older individuals who have pre-existing cognitive impairment, most commonly characterized by varying degrees of cerebral amyloid-ß accumulation, neuroinflammation, and blood-brain barrier dysfunction. We sought to test the hypothesis that short-term mechanical ventilation contributes to the neuropathology of cognitive impairment by (i) increasing cerebral amyloid-ß accumulation in mice with pre-existing Alzheimer's disease pathology, (ii) increasing neurologic and systemic inflammation in wild-type mice and mice with pre-existing Alzheimer's disease pathology, and (iii) increasing hippocampal blood-brain barrier permeability in wild-type mice and mice with pre-existing Alzheimer's disease pathology. METHODS: We subjected double transgenic Alzheimer's disease (APP/PSEN1) and wild-type mice to mechanical ventilation for 4 h and compared to non-mechanically ventilated Alzheimer's disease model and wild-type mice. Cerebral soluble/insoluble amyloid-ß1-40/amyloid-ß1-42 and neurological and systemic markers of inflammation were quantified. Hippocampal blood-brain barrier permeability was quantified using a novel methodology that enabled assessment of small and large molecule permeability across the blood-brain barrier. RESULTS: Mechanical ventilation resulted in (i) a significant increase in cerebral soluble amyloid-ß1-40 (p = 0.007) and (ii) significant increases in neuroinflammatory cytokines in both wild-type and Alzheimer's disease mice which, in most cases, were not reflected in the plasma. There were (i) direct correlations between polymorphonuclear cells in the bronchoalveolar fluid and cerebral soluble amyloid-ß1-40 (p = 0.0033), and several Alzheimer's disease-relevant neuroinflammatory biomarkers including cerebral TNF-α and IL-6; (iii) significant decreases in blood-brain barrier permeability in mechanically ventilated Alzheimer's disease mice and a trend towards increased blood-brain barrier permeability in mechanically ventilated wild-type mice. CONCLUSIONS: These results provide the first evidence that short-term mechanical ventilation independently promotes the neuropathology of Alzheimer's disease in subjects with and without pre-existing cerebral Alzheimer's disease pathology. Future studies are needed to further clarify the specific mechanisms by which this occurs and to develop neuroprotective mechanical ventilation strategies that mitigate the risk of cognitive decline after critical illness.

Feasibility of Patient-Centric Remote Dried Blood Sampling: The Prediction, Risk, and Evaluation of Major Adverse Cardiac Events (PRE-MACE) Study.

Background: Remote patient monitoring can shift important data collection opportunities to low-cost settings. Here, we evaluate whether the quality of blood-samples taken by patients at home differs from samples taken from the same patients by clinical staff. We examine the effects of socio-demographic and patient reported outcomes (PRO) survey data on remote blood sampling compliance and quality. Methods: Samples were collected both in-clinic by study-staff and remotely by subjects at home. During cataloging the samples were graded for quality. We used chi-squared tests and logistic regressions to examine differences in quality and compliance between samples taken in-clinic versus samples taken by subjects at-home. Results: 64.6% of in-clinic samples and 69.7% of samples collected remotely at home received a Good (compared to Not Good) quality grade (chi2 = 4.91; p =.03). Regression analysis found remote samples had roughly 1.5 times higher odds of being Good quality compared to samples taken in-clinic (p <.001; 95% CI 1.18-2.03). Increased anxiety reduced odds of contributing a Good sample (p =.04; 95% CI.95-1.0). Response rates were significantly higher for in-clinic sampling (95.8% vs 89.8%; p <.001). Conclusion: Blood-samples taken by patients at home using a microsampling device yielded higher quality samples than those taken in-clinic.