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Alterations in brain cholesterol homeostasis have been broadly implicated in neurological disorders. Notwithstanding the complexity by which cholesterol biology is governed in the mammalian brain, excess neuronal cholesterol is primarily eliminated by metabolic clearance via cytochrome P450 46A1 (CYP46A1). No methods are currently available for visualizing cholesterol metabolism in the living human brain; therefore, a noninvasive technology that quantitatively measures the extent of brain cholesterol metabolism via CYP46A1 could broadly affect disease diagnosis and treatment options using targeted therapies. Here, we describe the development and testing of a CYP46A1-targeted positron emission tomography (PET) tracer, 18 F-CHL-2205 ( 18 F-Cholestify). Our data show that PET imaging readouts correlate with CYP46A1 protein expression and with the extent to which cholesterol is metabolized in the brain, as assessed by cross-species postmortem analyses of specimens from rodents, nonhuman primates, and humans. Proof of concept of in vivo efficacy is provided in the well-established 3xTg-AD murine model of Alzheimer’s disease (AD), where we show that the probe is sensitive to differences in brain cholesterol metabolism between 3xTg-AD mice and control animals. Furthermore, our clinical observations point toward a considerably higher baseline brain cholesterol clearance via CYP46A1 in women, as compared to age-matched men. These findings illustrate the vast potential of assessing brain cholesterol metabolism using PET and establish PET as a sensitive tool for noninvasive assessment of brain cholesterol homeostasis in the clinic.

Aims Type 2 diabetes mellitus (T2DM) is now very prevalent in China. Due to the lower rate of controlled diabetes in China compared to that in developed countries, there is a higher incidence of serious cardiovascular complications, especially acute coronary syndrome (ACS). The aim of this study was to establish a potent risk predictive model in the economically disadvantaged northwest region of China, which could predict the probability of new-onset ACS in patients with T2DM. Methods Of 456 patients with T2DM admitted to the First Affiliated Hospital of Xi’an Jiaotong University from January 2018 to January 2019 and included in this study, 270 had no ACS, while 186 had newly diagnosed ACS. Overall, 32 demographic characteristics and serum biomarkers of the study patients were analysed. The least absolute shrinkage and selection operator regression was used to select variables, while the multivariate logistic regression was used to establish the predictive model that was presented using a nomogram. The area under the receiver operating characteristics curve (AUC) was used to evaluate the discriminatory capacity of the model. A calibration plot and Hosmer–Lemeshow test were used for the calibration of the predictive model, while the decision curve analysis (DCA) was used to evaluate its clinical validity. Results After random sampling, 319 and 137 T2DM patients were included in the training and validation sets, respectively. The predictive model included age, body mass index, diabetes duration, systolic blood pressure (SBP), diastolic blood pressure (DBP), low-density lipoprotein cholesterol, serum uric acid, lipoprotein(a), hypertension history and alcohol drinking status as predictors. The AUC of the predictive model and that of the internal validation set was 0.830 [95% confidence interval (CI) 0.786–0.874] and 0.827 (95% CI 0.756–0.899), respectively. The predictive model showed very good fitting degree, and DCA demonstrated a clinically effective predictive model. Conclusions A potent risk predictive model was established, which is of great value for the secondary prevention of diabetes. Weight loss, lowering of SBP and blood uric acid levels and appropriate control for DBP may significantly reduce the risk of new-onset ACS in T2DM patients in Northwest China.

This study aimed to assess the association between PAM single-nucleotide polymorphisms (SNPs) and T2DM risk in the Chinese population. We performed the genotype of PAM SNPs using Agena MassARRAY in 1002 subjects. The effect of PAM polymorphisms on T2DM occurrence was evaluated by logistic regression analysis. False-positive report probability (FPRP) was utilized to assess the noteworthiness of the significant results. This study showed that PAM rs406761, rs17154889, and rs6889592 were related to an increased risk of T2DM. The similar results were also in subjects with ≤ 60 years. Rs2431320 and rs406761 were related to an increased risk of T2DM in males, and rs6889592 was only found to be associated with T2DM risk in females. Rs2431320 and rs406761 increased T2DM risk in people with BMI 24, and rs6889592 and rs26431 significantly correlated with T2DM risk in people with BMI ≤ 24. By comparing patients with no retinopathy with controls, the correlation between PAM rs406761 and rs17154889 and T2DM risk was observed. The significant association between T2DM risk and PAM SNPs was remarkable by FPRP values. PAM SNPs were correlated with T2DM risk in the Chinese population, illustrating the importance of PAM SNPs in the pathogenesis of T2DM.