GLUT-1, HK2, CD68, and cathepsin K remained in both multivariate models and thus provided independent information regarding FDG uptake. We suggest that FDG uptake is a composite indicator of macrophage load, overall inflammatory activity and collagenolytic plaque destabilization. Accordingly, FDG-PET could prove to be an important predictor of cerebrovascular events in patients with carotid plaques.

The somatostatin receptor subtype 2 is expressed on macrophages, an abundant cell type in the atherosclerotic plaque. Visualization of somatostatin receptor subtype 2, for oncologic purposes, is frequently made using the DOTA-derived somatostatin analogs DOTATOC or DOTATATE for PET. We aimed to compare the uptake of the PET tracers 68 Ga-DOTATOC and 64 Cu-DOTATATE in large arteries, in the assessment of atherosclerosis by noninvasive imaging technique, combining PET and CT. Further, the correlation of uptake and cardiovascular risk factors was investigated. Methods: Sixty consecutive patients with neuroendocrine tumors underwent both 68 Ga-DOTATOC and 64 Cu-DOTATATE PET/CT scans, in random order. For each scan, the maximum and mean standardized uptake values (SUVs) were calculated in 5 arterial segments. In addition, the blood-pool-corrected target-to-background ratio was calculated. Uptake of the tracers was correlated with cardiovascular risk factors collected from medical records. Results: We found detectable uptake of both tracers in all arterial segments studied. Uptake of 64 Cu-DOTATATE was significantly higher than 68 Ga-DOTATOC in the vascular regions both when calculated as maximum and mean uptake. There was a significant association between Framingham risk score and the overall maximum uptake of 64 Cu-DOTATATE using SUV (r 5 0.4; P 5 0.004) as well as target-to-background ratio (r 5 0.3; P 5 0.04), whereas no association was found with 68 Ga-DOTATOC. The association of risk factors and maximum SUV of 64 Cu-DOTATATE was found driven by body mass index, smoking, diabetes, and coronary calcium score (P , 0.001, P 5 0.01, P 5 0.005, and P 5 0.03, respectively). Conclusion: In a series of oncologic patients, vascular uptake of 68 Ga-DOTATOC and 64 Cu-DOTATATE was found, with highest uptake of the latter. Uptake of 64 Cu-DOTATATE, but not of 68 Ga-DOTATOC, was correlated with cardiovascular risk factors, suggesting a potential role for 64 Cu-DOTATATE in the assessment of atherosclerosis.

AimTo study whether 18F-FDG can be used for in vivo imaging of atherogenesis by examining the correlation between 18F-FDG uptake and gene expression of key molecular markers of atherosclerosis in apoE−/− mice.MethodsNine groups of apoE−/− mice were given normal chow or high-fat diet. At different time-points, 18F-FDG PET/contrast-enhanced CT scans were performed on dedicated animal scanners. After scans, animals were euthanized, aortas removed, gamma counted, RNA extracted from the tissue, and gene expression of chemo (C-X-C motif) ligand 1 (CXCL-1), monocyte chemoattractant protein (MCP)-1, vascular cell adhesion molecule (VCAM)-1, cluster of differentiation molecule (CD)-68, osteopontin (OPN), lectin-like oxidized LDL-receptor (LOX)-1, hypoxia-inducible factor (HIF)-1α, HIF-2α, vascular endothelial growth factor A (VEGF), and tissue factor (TF) was measured by means of qPCR.ResultsThe uptake of 18F-FDG increased over time in the groups of mice receiving high-fat diet measured by PET and ex vivo gamma counting. The gene expression of all examined markers of atherosclerosis correlated significantly with 18F-FDG uptake. The strongest correlation was seen with TF and CD68 (p<0.001). A multivariate analysis showed CD68, OPN, TF, and VCAM-1 to be the most important contributors to the uptake of 18F-FDG. Together they could explain 60% of the 18F-FDG uptake.ConclusionWe have demonstrated that 18F-FDG can be used to follow the progression of atherosclerosis in apoE−/− mice. The gene expression of ten molecular markers representing different molecular processes important for atherosclerosis was shown to correlate with the uptake of 18F-FDG. Especially, the gene expressions of CD68, OPN, TF, and VCAM-1 were strong predictors for the uptake.

Tendon pathology is related to metabolic disease and mechanical overloading, but the effect of metabolic disease on tendon mechanics is unknown. This study investigated the effect of diet and apolipoprotein E deficiency (ApoE(-/-)) on mechanical properties and advanced glycation end product (AGE) cross-linking of non-weight-bearing mouse tail tendons. Twenty ApoE(-/-) male mice were used as a model for hypercholesterolemia along with 26 wild-type (WT) mice. One-half of the mice from each group was fed a normal diet (ND) and the other half was fed a high-fat diet (HFD) to induce obesity. All were killed at 40 wk, and tail tendon fascicles were mechanically tested to failure and analyzed for AGEs. Diets were also analyzed for AGEs. ApoE(-/-) mice displayed a 14% increase in plateau modulus compared with WT mice (P < 0.05), whereas HFD mice displayed a 13% decrease in plateau modulus (P < 0.05) and a 12% decrease in total modulus (P < 0.05) compared with ND mice. Tail tendons of HFD mice had significantly lower concentrations of AGEs [carboxymethyllysine (CML): 26%, P < 0.0001; methylglyoxal-derived hydroimidazolone 1 (MG-H1): 15%, P < 0.005; pentosidine: 13%, P < 0.0005]. The HFD had ∼44-fold lower content of CML (P < 0.01), ∼29-fold lower content of carboxyethyllysine (P < 0.005), and ∼16-fold lower content of MG-H1 (P < 0.05) compared with ND. ApoE(-/-) increased, whereas HFD decreased mouse tail tendon stiffness. Dietary AGE content may be a crucial determinant for accumulation of AGE cross-links in tendons and for tissue compliance. The results demonstrate how systemic metabolic factors may influence tendon health.