For early detection of Alzheimer’s disease, it is important to find biomarkers with predictive value for disease progression and clinical manifestations, such as cognitive decline. Individuals can now be profiled based on their biomarker status for Aβ42 (A) or tau (T) deposition and neurodegeneration (N). The aim of this study was to compare the cerebrospinal fluid (CSF) and imaging (PET/MR) biomarkers in each ATN category and to assess their ability to predict longitudinal cognitive decline. A subset of 282 patients, who had had at the same time PET investigations with amyloid-β and tau tracers, CSF sampling, and structural MRI (18% within 13 months), was selected from the ADNI dataset. The participants were grouped by clinical diagnosis at that time: cognitively normal, subjective memory concern, early or late mild cognitive impairment, or AD. Agreement between CSF (amyloid-β-1-42(A), phosphorylated-Tau181(T), total-Tau(N)), and imaging (amyloid-β PET (florbetaben and florbetapir)(A), tau PET (flortaucipir)(T), hippocampal volume (MRI)(N)) positivity in ATN was assessed with Cohen’s Kappa. Linear mixed-effects models were used to predict decline in the episodic memory. There was moderate agreement between PET and CSF for A biomarkers (Kappa = 0.39–0.71), while only fair agreement for T biomarkers (Kappa ≤ 0.40, except AD) and discordance for N biomarkers across all groups (Kappa ≤ 0.14) was found. Baseline PET tau predicted longitudinal decline in episodic memory irrespective of CSF p-Tau181 positivity (p ≤ 0.02). Baseline PET tau and amyloid-β predicted decline in episodic memory (p ≤ 0.0001), but isolated PET amyloid-β did not. Isolated PET Tau positivity was only observed in 2 participants (0.71% of the sample). While results for amyloid-β were similar using CSF or imaging, CSF and imaging results for tau and neurodegeneration were not interchangeable. PET tau positivity was superior to CSF p-Tau181 and PET amyloid-β in predicting cognitive decline in the AD continuum within 3 years of follow-up.

Objective: Insulin resistance is reflected by the rates of reduced glucose uptake (GU) into the key insulin-sensitive tissues, skeletal muscle, liver and adipose tissue. It is unclear whether insulin resistance occurs simultaneously in all these tissues or whether insulin resistance is tissue specific.Design and methods: We measured GU in skeletal muscle, adipose tissue and liver and endogenous glucose production (EGP), in a single session using¹⁸F-fluorodeoxyglucose with positron emission tomography (PET) and euglycemic–hyperinsulinemic clamp. The study population consisted of 326 subjects without diabetes from the CMgene study cohort.Results: Skeletal muscle GU less than 33 µmol/kg tissue/min and subcutaneous adipose tissue GU less than 11.5 µmol/kg tissue/min characterized insulin-resistant individuals. Men had considerably worse insulin suppression of EGP compared to women. By using principal component analysis (PCA), BMI inversely and skeletal muscle, adipose tissue and liver GU positively loaded on same principal component explaining one-third of the variation in these measures. The results were largely similar when liver GU was replaced by EGP in PCA. Liver GU and EGP were positively associated with aging.Conclusions: We have provided threshold values, which can be used to identify tissue-specific insulin resistance. In addition, we found that insulin resistance measured by GU was only partially similar across all insulin-sensitive tissues studied, skeletal muscle, adipose tissue and liver and was affected by obesity, aging and gender.

OBJECTIVE Insulin stimulates brain glucose metabolism, but this effect of insulin is already maximal at fasting concentrations in healthy subjects. It is not known whether insulin is able to stimulate glucose metabolism above fasting concentrations in patients with impaired glucose tolerance. RESEARCH DESIGN AND METHODS We studied the effects of insulin on brain glucose metabolism and cerebral blood flow in 13 patients with impaired glucose tolerance and nine healthy subjects using positron emission tomography (PET). All subjects underwent PET with both [¹⁸F]fluorodeoxyglucose (for brain glucose metabolism) and [¹⁵O]H₂O (for cerebral blood flow) in two separate conditions (in the fasting state and during a euglycemic-hyperinsulinemic clamp). Arterial blood samples were acquired during the PET scans to allow fully quantitative modeling. RESULTS The hyperinsulinemic clamp increased brain glucose metabolism only in patients with impaired glucose tolerance (whole brain: +18%, P = 0.001) but not in healthy subjects (whole brain: +3.9%, P = 0.373). The hyperinsulinemic clamp did not alter cerebral blood flow in either group. CONCLUSIONS We found that insulin stimulates brain glucose metabolism at physiological postprandial levels in patients with impaired glucose tolerance but not in healthy subjects. These results suggest that insulin stimulation of brain glucose metabolism is maximal at fasting concentrations in healthy subjects but not in patients with impaired glucose tolerance.

Context and Objective: To examine whether pericardial and myocardial fat depots may contribute to the association between diabetes and cardiovascular risk, including sex-related differences, and the role of adiponectin, we evaluated data in patients with obesity and without diabetes [nondiabetic (ND)] or with impaired glucose tolerance or type 2 diabetes and in lean ND controls. Methods: Magnetic resonance imaging and spectroscopy were used to measure left ventricular (LV) function and abdominal sc and visceral fat areas to estimate respective masses, pericardial fat depots, and myocardial triglyceride content in 53 subjects (10 lean ND, 25 obese ND, six impaired-glucose-tolerance, and 12 type 2 diabetic patients with macrovascular disease); gender effects and adiponectin levels were evaluated in the available subset of subjects. Results: Myocardial and pericardial fat increased progressively across study groups. They were lower in obese women than men (P = 0.002), but cardiac steatosis caught up in hyperglycemic women (+81% vs. ND, P = 0.01). Adiponectin was inversely related with both fat depots (P < 0.01) and LV mass (P = 0.003) and positively with LV function (P = 0.03). In multiple regression analysis, myocardial and pericardial fat were independently related with plasma glucose levels, only pericardial fat mass was associated with visceral adiposity and myocardial fat with cardiac output and work. Conclusions: We conclude that glycemia, gender, adiponectin, and cardiac workload are associated with, and hyperglycemia and male gender are independent positive predictors of, heart adiposity. Once glucose tolerance becomes impaired, the evolution of cardiac steatosis is more pronounced in women. Glycemia and gender are predictors of heart adiposity, and cardiac steatosis deteriorates rapidly with glucose tolerance in women.