Biomarker clustering in autosomal dominant Alzheimer's disease.

INTRODUCTION: As the number of biomarkers used to study Alzheimer's disease (AD) continues to increase, it is important to understand the utility of any given biomarker, as well as what additional information a biomarker provides when compared to others. METHODS: We used hierarchical clustering to group 19 cross-sectional biomarkers in autosomal dominant AD. Feature selection identified biomarkers that were the strongest predictors of mutation status and estimated years from symptom onset (EYO). Biomarkers identified included clinical assessments, neuroimaging, cerebrospinal fluid amyloid, and tau, and emerging biomarkers of neuronal integrity and inflammation. RESULTS: Three primary clusters were identified: neurodegeneration, amyloid/tau, and emerging biomarkers. Feature selection identified amyloid and tau measures as the primary predictors of mutation status and EYO. Emerging biomarkers of neuronal integrity and inflammation were relatively weak predictors. DISCUSSION: These results provide novel insight into our understanding of the relationships among biomarkers and the staging of biomarkers based on disease progression.

Modeling autosomal dominant Alzheimer's disease with machine learning.

INTRODUCTION: Machine learning models were used to discover novel disease trajectories for autosomal dominant Alzheimer's disease. METHODS: Longitudinal structural magnetic resonance imaging, amyloid positron emission tomography (PET), and fluorodeoxyglucose PET were acquired in 131 mutation carriers and 74 non-carriers from the Dominantly Inherited Alzheimer Network; the groups were matched for age, education, sex, and apolipoprotein ε4 (APOE ε4). A deep neural network was trained to predict disease progression for each modality. Relief algorithms identified the strongest predictors of mutation status. RESULTS: The Relief algorithm identified the caudate, cingulate, and precuneus as the strongest predictors among all modalities. The model yielded accurate results for predicting future Pittsburgh compound B (R2  = 0.95), fluorodeoxyglucose (R2  = 0.93), and atrophy (R2  = 0.95) in mutation carriers compared to non-carriers. DISCUSSION: Results suggest a sigmoidal trajectory for amyloid, a biphasic response for metabolism, and a gradual decrease in volume, with disease progression primarily in subcortical, middle frontal, and posterior parietal regions.

Type 2 diabetes, obesity, and sex difference affect the fate of glucose in the human heart.

Type 2 diabetes, obesity, and sex difference affect myocardial glucose uptake and utilization. However, their effect on the intramyocellular fate of glucose in humans has been unknown. How the heart uses glucose is important, because it affects energy production and oxygen efficiency, which in turn affect heart function and adaptability. We hypothesized that type 2 diabetes, sex difference, and obesity affect myocardial glucose oxidation, glycolysis, and glycogen production. In a first-in-human study, we measured intramyocardiocellular glucose metabolism from time-activity curves generated from previously obtained positron emission tomography scans of 110 subjects in 3 groups: nonobese, obese, and diabetes. Group and sex difference interacted in the prediction of all glucose uptake, utilization, and metabolism rates. Group independently predicted fractional glucose uptake and its components: glycolysis, glycogen deposition, and glucose oxidation rates. Sex difference predicted glycolysis rates. However, there were fewer differences in glucose metabolism between diabetic patients and others when plasma glucose levels were included in the modeling. The potentially detrimental effects of obesity and diabetes on myocardial glucose metabolism are more pronounced in men than women. This sex difference dimorphism needs to be taken into account in the design, trials, and application of metabolic modulator therapy. Slightly higher plasma glucose levels improve depressed glucose oxidation and glycogen deposition rates in diabetic patients.

Positron emission tomography and magnetic resonance imaging methods and datasets within the Dominantly Inherited Alzheimer Network (DIAN).

The Dominantly Inherited Alzheimer Network (DIAN) is an international collaboration studying autosomal dominant Alzheimer disease (ADAD). ADAD arises from mutations occurring in three genes. Offspring from ADAD families have a 50% chance of inheriting their familial mutation, so non-carrier siblings can be recruited for comparisons in case-control studies. The age of onset in ADAD is highly predictable within families, allowing researchers to estimate an individual's point in the disease trajectory. These characteristics allow candidate AD biomarker measurements to be reliably mapped during the preclinical phase. Although ADAD represents a small proportion of AD cases, understanding neuroimaging-based changes that occur during the preclinical period may provide insight into early disease stages of 'sporadic' AD also. Additionally, this study provides rich data for research in healthy aging through inclusion of the non-carrier controls. Here we introduce the neuroimaging dataset collected and describe how this resource can be used by a range of researchers.

Phase 1 Evaluation of 11C-CS1P1 to Assess Safety and Dosimetry in Human Participants.

This study evaluated the safety, dosimetry, and characteristics of 3-((2-fluoro-4-(5-(2'-methyl-2-(trifluoromethyl)-[1,1'-biphenyl]-4-yl)-1,2,4-oxadiazol-3-yl)benzyl)(methyl-11C)amino)propanoic acid (11C-CS1P1), a radiotracer targeting sphingosine-1-phosphate receptor (S1PR) 1 (S1PR1). S1PR1 is of clinical interest because of its role in multiple sclerosis (and other conditions), with an expanding class of S1PR modulators approved for relapsing multiple sclerosis. 11C-CS1P1 binds S1PR1 with high specificity and has shown promise in animal models of inflammatory diseases. Methods: 11C-CS1P1 was injected into 5 male and 6 female healthy participants. Ten participants were imaged with PET using a multipass whole-body continuous-bed-motion acquisition, and one had dedicated head and neck PET and MRI. Participants were continuously monitored for safety events. Organ time-activity curve data were collected, integrated, and normalized to the injected activity. Organ radiation doses and effective dose were computed using the adult male and female models in OLINDA, version 2.2. SUV images were evaluated for qualitative biodistribution. Results: No adverse events were observed after the dose, including no bradycardia. The liver was the critical organ from dosimetry analysis (mean ± SD: female, 23.12 ± 5.19 µSv/MBq; male, 21.06 ± 1.63 µSv/MBq). The whole-body effective dose (as defined by International Commission on Radiological Protection publication 103) was 4.18 ± 0.30 µSv/MBq in women and 3.54 ± 0.14 µSv/MBq in men. Using a maximum delivered dose of 740 MBq (20 mCi), the effective dose for women would be 3.1 mSv (0.31 rem), with a liver dose of 17.1 mSv (1.7 rem); the effective dose for men would be 2.6 mSv (0.26 rem), with a liver dose of 15.6 mSv (1.56 rem). Brain uptake was seen predominantly in gray matter and correlated with regional S1PR1 RNA expression (r = 0.84). Conclusion: These results support the safety of 11C-CS1P1 for evaluation of inflammation in human clinical populations. Dosimetry permits repeated measures in the same participants. Brain uptake correlates well with known target topography.