The metabolic basis of Alzheimer disease (AD) is poorly understood, and the relationships between systemic abnormalities in metabolism and AD pathogenesis are unclear. Understanding how global ...perturbations in metabolism are related to severity of AD neuropathology and the eventual expression of AD symptoms in at-risk individuals is critical to developing effective disease-modifying treatments. In this study, we undertook parallel metabolomics analyses in both the brain and blood to identify systemic correlates of neuropathology and their associations with prodromal and preclinical measures of AD progression.
Quantitative and targeted metabolomics (Biocrates AbsoluteIDQ identification and quantification p180) assays were performed on brain tissue samples from the autopsy cohort of the Baltimore Longitudinal Study of Aging (BLSA) (N = 44, mean age = 81.33, % female = 36.36) from AD (N = 15), control (CN; N = 14), and "asymptomatic Alzheimer's disease" (ASYMAD, i.e., individuals with significant AD pathology but no cognitive impairment during life; N = 15) participants. Using machine-learning methods, we identified a panel of 26 metabolites from two main classes-sphingolipids and glycerophospholipids-that discriminated AD and CN samples with accuracy, sensitivity, and specificity of 83.33%, 86.67%, and 80%, respectively. We then assayed these 26 metabolites in serum samples from two well-characterized longitudinal cohorts representing prodromal (Alzheimer's Disease Neuroimaging Initiative ADNI, N = 767, mean age = 75.19, % female = 42.63) and preclinical (BLSA) (N = 207, mean age = 78.68, % female = 42.63) AD, in which we tested their associations with magnetic resonance imaging (MRI) measures of AD-related brain atrophy, cerebrospinal fluid (CSF) biomarkers of AD pathology, risk of conversion to incident AD, and trajectories of cognitive performance. We developed an integrated blood and brain endophenotype score that summarized the relative importance of each metabolite to severity of AD pathology and disease progression (Endophenotype Association Score in Early Alzheimer's Disease EASE-AD). Finally, we mapped the main metabolite classes emerging from our analyses to key biological pathways implicated in AD pathogenesis. We found that distinct sphingolipid species including sphingomyelin (SM) with acyl residue sums C16:0, C18:1, and C16:1 (SM C16:0, SM C18:1, SM C16:1) and hydroxysphingomyelin with acyl residue sum C14:1 (SM (OH) C14:1) were consistently associated with severity of AD pathology at autopsy and AD progression across prodromal and preclinical stages. Higher log-transformed blood concentrations of all four sphingolipids in cognitively normal individuals were significantly associated with increased risk of future conversion to incident AD: SM C16:0 (hazard ratio HR = 4.430, 95% confidence interval CI = 1.703-11.520, p = 0.002), SM C16:1 (HR = 3.455, 95% CI = 1.516-7.873, p = 0.003), SM (OH) C14:1 (HR = 3.539, 95% CI = 1.373-9.122, p = 0.009), and SM C18:1 (HR = 2.255, 95% CI = 1.047-4.855, p = 0.038). The sphingolipid species identified map to several biologically relevant pathways implicated in AD, including tau phosphorylation, amyloid-β (Aβ) metabolism, calcium homeostasis, acetylcholine biosynthesis, and apoptosis. Our study has limitations: the relatively small number of brain tissue samples may have limited our power to detect significant associations, control for heterogeneity between groups, and replicate our findings in independent, autopsy-derived brain samples.
We present a novel framework to identify biologically relevant brain and blood metabolites associated with disease pathology and progression during the prodromal and preclinical stages of AD. Our results show that perturbations in sphingolipid metabolism are consistently associated with endophenotypes across preclinical and prodromal AD, as well as with AD pathology at autopsy. Sphingolipids may be biologically relevant biomarkers for the early detection of AD, and correcting perturbations in sphingolipid metabolism may be a plausible and novel therapeutic strategy in AD.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
It is unclear whether abnormalities in brain glucose homeostasis are associated with Alzheimer's disease (AD) pathogenesis.
Within the autopsy cohort of the Baltimore Longitudinal Study of Aging, we ...measured brain glucose concentration and assessed the ratios of the glycolytic amino acids, serine, glycine, and alanine to glucose. We also quantified protein levels of the neuronal (GLUT3) and astrocytic (GLUT1) glucose transporters. Finally, we assessed the relationships between plasma glucose measured before death and brain tissue glucose.
Higher brain tissue glucose concentration, reduced glycolytic flux, and lower GLUT3 are related to severity of AD pathology and the expression of AD symptoms. Longitudinal increases in fasting plasma glucose levels are associated with higher brain tissue glucose concentrations.
Impaired glucose metabolism due to reduced glycolytic flux may be intrinsic to AD pathogenesis. Abnormalities in brain glucose homeostasis may begin several years before the onset of clinical symptoms.
•Brain tissue glucose is associated with severity of Alzheimer's disease (AD) pathology and symptom onset.•Reduced brain glycolytic flux is associated with severity of AD pathology and symptom onset.•Neuronal glucose transporter-3 is lower in AD.•Lower glucose transporter-3 levels are associated with more severe AD pathology.•Increase in plasma glucose decades before death is related to higher brain glucose.
Objective
The aim of this study was to examine the putative adverse effects of ambient fine particulate matter (PM2.5: PM with aerodynamic diameters <2.5μm) on brain volumes in older women.
Methods
...We conducted a prospective study of 1,403 community‐dwelling older women without dementia enrolled in the Women's Health Initiative Memory Study, 1996–1998. Structural brain magnetic resonance imaging scans were performed at the age of 71–89 years in 2005–2006 to obtain volumetric measures of gray matter (GM) and normal‐appearing white matter (WM). Given residential histories and air monitoring data, we used a spatiotemporal model to estimate cumulative PM2.5 exposure in 1999–2006. Multiple linear regression was employed to evaluate the associations between PM2.5 and brain volumes, adjusting for intracranial volumes and potential confounders.
Results
Older women with greater PM2.5 exposures had significantly smaller WM, but not GM, volumes, independent of geographical region, demographics, socioeconomic status, lifestyles, and clinical characteristics, including cardiovascular risk factors. For each interquartile increment (3.49μg/m3) of cumulative PM2.5 exposure, the average WM volume (WMV; 95% confidence interval) was 6.23cm3 (3.72–8.74) smaller in the total brain and 4.47cm3 (2.27–6.67) lower in the association areas, equivalent to 1 to 2 years of brain aging. The adverse PM2.5 effects on smaller WMVs were present in frontal and temporal lobes and corpus callosum (all p values <0.01). Hippocampal volumes did not differ by PM2.5 exposure.
Interpretation
PM2.5 exposure may contribute to WM loss in older women. Future studies are needed to determine whether exposures result in myelination disturbance, disruption of axonal integrity, damages to oligodendrocytes, or other WM neuropathologies. Ann Neurol 2015;78:466–476
Abstract
Objectives
Genetic risks for cognitive decline are not modifiable; however their relative importance compared to modifiable factors is unclear. We used machine learning to evaluate ...modifiable and genetic risk factors for Alzheimer’s disease (AD), to predict cognitive decline.
Methods
Health and Retirement Study participants, aged 65–90 years, with DNA and >2 cognitive evaluations, were included (n = 7,142). Predictors included age, body mass index, gender, education, APOE ε4, cardiovascular, hypertension, diabetes, stroke, neighborhood socioeconomic status (NSES), and AD risk genes. Latent class trajectory analyses of cognitive scores determined the form and number of classes. Random Forests (RF) classification investigated predictors of cognitive trajectories. Performance metrics (accuracy, sensitivity, and specificity) were reported.
Results
Three classes were identified. Discriminating highest from lowest classes produced the best RF performance: accuracy = 78% (1.0%), sensitivity = 75% (1.0%), and specificity = 81% (1.0%). Top ranked predictors were education, age, gender, stroke, NSES, and diabetes, APOE ε4 carrier status, and body mass index (BMI). When discriminating high from medium classes, top predictors were education, age, gender, stroke, diabetes, NSES, and BMI. When discriminating medium from the low classes, education, NSES, age, diabetes, and stroke were top predictors.
Discussion
The combination of latent trajectories and RF classification techniques suggested that nongenetic factors contribute more to cognitive decline than genetic factors. Education was the most relevant predictor for discrimination.
Diabetic retinopathy (DR) is one of the leading causes of blindness in the United States and world-wide. DR is a silent disease that may go unnoticed until it is too late for effective treatment. ...Therefore, early detection could improve the chances of therapeutic interventions that would alleviate its effects.
Graded fundus photography and systemic data from 3443 ACCORD-Eye Study participants were used to estimate Random Forest (RF) and logistic regression classifiers. We studied the impact of sample size on classifier performance and the possibility of using RF generated class conditional probabilities as metrics describing DR risk. RF measures of variable importance are used to detect factors that affect classification performance.
Both types of data were informative when discriminating participants with or without DR. RF based models produced much higher classification accuracy than those based on logistic regression. Combining both types of data did not increase accuracy but did increase statistical discrimination of healthy participants who subsequently did or did not have DR events during four years of follow-up. RF variable importance criteria revealed that microaneurysms counts in both eyes seemed to play the most important role in discrimination among the graded fundus variables, while the number of medicines and diabetes duration were the most relevant among the systemic variables.
We have introduced RF methods to DR classification analyses based on fundus photography data. In addition, we propose an approach to DR risk assessment based on metrics derived from graded fundus photography and systemic data. Our results suggest that RF methods could be a valuable tool to diagnose DR diagnosis and evaluate its progression.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Abstract Introduction Recently, quantitative metabolomics identified a panel of 10 plasma lipids that were highly predictive of conversion to Alzheimer's disease (AD) in cognitively normal older ...individuals (n = 28, area under the curve AUC = 0.92, sensitivity/specificity of 90%/90%). Methods Quantitative targeted metabolomics in serum using an identical method as in the index study. Results We failed to replicate these findings in a substantially larger study from two independent cohorts—the Baltimore Longitudinal Study of Aging (BLSA, n = 93, AUC = 0.642, sensitivity/specificity of 51.6%/65.7%) and the Age, Gene/Environment Susceptibility-Reykjavik Study (AGES-RS, n = 100, AUC = 0.395, sensitivity/specificity of 47.0%/36.0%). In analyses applying machine learning methods to all 187 metabolite concentrations assayed, we find a modest signal in the BLSA with distinct metabolites associated with the preclinical and symptomatic stages of AD, whereas the same methods gave poor classification accuracies in the AGES-RS samples. Discussion We believe that ours is the largest blood biomarker study of preclinical AD to date. These findings underscore the importance of large-scale independent validation of index findings from biomarker studies with relatively small sample sizes.
Abstract Introduction The Mediterranean and Dietary Approaches to Stop Hypertension diets have been associated with lower dementia risk. We evaluated dietary inflammatory potential in relation to ...mild cognitive impairment (MCI)/dementia risk. Methods Baseline food frequency questionnaires from n = 7085 women (aged 65–79 years) were used to calculate Dietary Inflammatory Index (DII) scores that were categorized into four groups. Cognitive function was evaluated annually, and MCI and all-cause dementia cases were adjudicated centrally. Mixed effect models evaluated cognitive decline on over time; Cox models evaluated the risk of MCI or dementia across DII groups. Results Over an average of 9.7 years, there were 1081 incident cases of cognitive impairment. Higher DII scores were associated with greater cognitive decline and earlier onset of cognitive impairment. Adjusted hazard ratios (HRs) comparing lower (anti-inflammatory; group 1 referent) DII scores to the higher scores were group 2-HR: 1.01 (0.86–1.20); group 3-HR: 0.99 (0.82–1.18); and group 4-HR: 1.27 (1.06–1.52). Conclusions Diets with the highest pro-inflammatory potential were associated with higher risk of MCI or dementia.
Sufficient physical activity (PA) reduces the risk of a myriad of diseases and preserves physical capabilities in later life. While there have been significant achievements in mapping accelerations ...to real-life movements using machine learning (ML), errors continue to be common, particularly for wrist-worn devices. It remains unknown whether ML models are robust for estimating age-related loss of physical function. In this study, we evaluated the performance of ML models (XGBoost and LASSO) to estimate the hallmark measures of PA in low physical performance (LPP) and high physical performance (HPP) groups. Our models were built to recognize PA types and intensities, identify each individual activity, and estimate energy expenditure (EE) using wrist-worn accelerometer data (33 activities per participant) from a large sample of participants (n = 247, 57% females, aged 60+ years). Results indicated that the ML models were accurate in recognizing PA by type and intensity while also estimating EE accurately. However, the models built to recognize individual activities were less robust. Across all tasks, XGBoost outperformed LASSO. XGBoost obtained F1-Scores for sedentary (0.932 ± 0.005), locomotion (0.946 ± 0.003), lifestyle (0.927 ± 0.006), and strength flexibility exercise (0.915 ± 0.017) activity type recognition tasks. The F1-Scores for recognizing low, light, and moderate activity intensity were (0.932 ± 0.005), (0.840 ± 0.004), and (0.869 ± 0.005), respectively. The root mean square error for EE estimation was 0.836 ± 0.059 METs. There was no evidence showing that splitting the participants into the LPP and HPP groups improved the models’ performance on estimating the hallmark measures of physical activities. In conclusion, using features derived from wrist-worn accelerometer data, machine learning models can accurately recognize PA types and intensities and estimate EE for older adults with high and low physical function.
Our understanding of the specific aspects of vascular contributions to dementia remains unclear.
We aim to identify the correlates of incident dementia in a multi-ethnic cardiovascular cohort.
A ...total of 6806 participants with follow-up data for incident dementia were included. Probable dementia diagnoses were identified using hospitalization discharge diagnoses according to the International Classification of Diseases Codes (ICD). We used Random Forest analyses to identify the correlates of incident dementia and cognitive function from among 198 variables collected at the baseline MESA exam entailing demographic risk factors, medical history, anthropometry, lab biomarkers, electrocardiograms, cardiovascular magnetic resonance imaging, carotid ultrasonography, coronary artery calcium and liver fat content. Death and stroke were considered competing events.
Over 14 years of follow-up, 326 dementia events were identified. Beyond age, the top correlates of dementia included coronary artery calcification, high sensitivity troponin, common carotid artery intima to media thickness, NT-proBNP, physical activity, pulse pressure, tumor necrosis factor-α, history of cancer, and liver to spleen attenuation ratio from computed tomography. Correlates of cognitive function included income and physical activity, body size, serum glucose, glomerular filtration rate, measures of carotid artery stiffness, alcohol use, and inflammation indexed as IL-2 and TNF soluble receptors and plasmin-antiplasmin complex.
In a deeply phenotyped cardiovascular cohort we identified the key correlates of dementia beyond age as subclinical atherosclerosis and myocyte damage, vascular function, inflammation, physical activity, hepatic steatosis, and history of cancer.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Accelerometer-based fitness trackers and smartwatches are proliferating with incessant attention towards health tracking. Despite their growing popularity, accurately measuring hallmark measures of ...physical activities has yet to be accomplished in adults of all ages. In this work, we evaluated the performance of four machine learning models: decision tree, random forest, extreme gradient boosting (XGBoost) and least absolute shrinkage and selection operator (LASSO), to estimate the hallmark measures of physical activities in young (20-50 years), middle-aged (50-70 years, and older adults (70-89 years. Our models were built to recognize physical activity types, recognize physical activity intensities, estimate energy expenditure (EE) and recognize individual physical activities using wrist-worn tri-axial accelerometer data (33 activities per participant) from a large sample of participants (
= 253, 62% women, aged 20-89 years old). Results showed that the machine learning models were quite accurate at recognizing physical activity type and intensity and estimating energy expenditure. However, models performed less optimally when recognizing individual physical activities. F1-Scores derived from XGBoost's models were high for sedentary (0.955-0.973), locomotion (0.942-0.964) and lifestyle (0.913-0.949) activity types with no apparent difference across age groups. Low (0.919-0.947), light (0.813-0.828) and moderate (0.846-0.875) physical activity intensities were also recognized accurately. The root mean square error range for EE was approximately 1 equivalent of resting EE 0.835-1.009 METs. Generally, random forest and XGBoost models outperformed other models. In conclusion, machine learning models to label physical activity types, activity intensity and energy expenditure are accurate and there are minimal differences in their performance across young, middle-aged and older adults.
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