Richard E. Kennedy

There is a growing interest in using ¹⁸F-DPA-714 PET to study neuroinflammation and microglial activation through imaging the 18-kDa translocator protein (TSPO). Although quantification of ¹⁸F-DPA-714 binding can be achieved through kinetic modeling analysis with an arterial input function (AIF) measured with blood sampling procedures, the invasiveness of such procedures has been an obstacle for wide application. To address these challenges, we developed an image-derived input function (IDIF) that noninvasively estimates the arterial input function from the images acquired for ¹⁸F-DPA-714 quantification. Methods: The method entails three fully automatic steps to extract the IDIF, including a segmentation of voxels with highest likelihood of being the arterial blood over the carotid artery, a model-based matrix factorization to extract the arterial blood signal, and a scaling optimization procedure to scale the extracted arterial blood signal into the activity concentration unit. Two cohorts of human subjects were used to evaluate the extracted IDIF. In the first cohort of five subjects, arterial blood sampling was performed, and the calculated IDIF was validated against the measured AIF through the comparison of distribution volumes from AIF (V_T,AIF) and IDIF (V_T,IDIF). In the second cohort, PET studies from twenty-eight healthy controls without arterial blood sampling were used to compare V_T,IDIF with V_T,REF measured using a reference region-based analysis to evaluate whether it can distinguish high-affinity (HAB) and mixed-affinity (MAB) binders. Results: In the arterial blood-sampling cohort, V_T derived from IDIF was found to be an accurate surrogate of the V_T from AIF. The bias of V_{T, IDIF} was −5.8 ± 7.8% when compared to V_T,AIF, and the linear mixed effect model showed a high correlation between V_T,AIF and V_{T, IDIF} (p < 0.001). In the nonblood-sampling cohort, V_{T, IDIF} showed a significance difference between the HAB and MAB healthy controls. V_{T, IDIF} and standard uptake values (SUV) showed superior results in distinguishing HAB from MAB subjects than V_T,REF. Conclusions: A novel IDIF method for ¹⁸F-DPA-714 PET quantification was developed and evaluated in this study. This IDIF provides a noninvasive alternative measurement of V_T to quantify the TSPO binding of ¹⁸F-DPA-714 in the human brain through dynamic PET scans.

This study aimed to determine the proportion of older adults who recovered community mobility after hospitalization and identify factors associated with recovery. Using a random sample of 1,000 Medicare beneficiaries ≥65 years of age, we identified individuals with at least one hospitalization over 8.5 years of follow-up. Data were collected at baseline and every 6 months, including demographics, function, social support, community mobility measured by the UAB Life-Space Assessment (LSA), and overnight hospital admissions. Recovery was defined as a LSA score no more than five points lower than the prehospitalization LSA score at last follow-up. Overall, 339 participants (M age = 75.4 [SD = 6.6] years, 44% African American, 48% female) had at least one hospitalization. In the full logistic regression model, younger age (p = .007) and religious service attendance (p = .001) remained independently associated with recovery. An understanding of factors associated with recovery after hospitalization may provide a target for future interventions.

Given the complex and progressive nature of Alzheimer’s disease (AD), a precision medicine approach for diagnosis and treatment requires the identification of patient subgroups with biomedically distinct and actionable phenotype definitions. Longitudinal patient-level data for 1160 AD patients receiving placebo or no treatment with a follow-up of up to 18 months were extracted from an integrated clinical trials dataset. We used latent class mixed modelling (LCMM) to identify patient subgroups demonstrating distinct patterns of change over time in disease severity, as measured by the Alzheimer’s Disease Assessment Scale—cognitive subscale score. The optimal number of subgroups (classes) was selected by the model which had the lowest Bayesian Information Criterion. Other patient-level variables were used to define these subgroups’ distinguishing characteristics and to investigate the interactions between patient characteristics and patterns of disease progression. The LCMM resulted in three distinct subgroups of patients, with 10.3% in Class 1, 76.5% in Class 2 and 13.2% in Class 3. While all classes demonstrated some degree of cognitive decline, each demonstrated a different pattern of change in cognitive scores, potentially reflecting different subtypes of AD patients. Class 1 represents rapid decliners with a steep decline in cognition over time, and who tended to be younger and better educated. Class 2 represents slow decliners, while Class 3 represents severely impaired slow decliners: patients with a similar rate of decline to Class 2 but with worse baseline cognitive scores. Class 2 demonstrated a significantly higher proportion of patients with a history of statins use; Class 3 showed lower levels of blood monocytes and serum calcium, and higher blood glucose levels. Our results, ‘learned’ from clinical data, indicate the existence of at least three subgroups of Alzheimer’s patients, each demonstrating a different trajectory of disease progression. This hypothesis-generating approach has detected distinct AD subgroups that may prove to be discrete endophenotypes linked to specific aetiologies. These findings could enable stratification within a clinical trial or study context, which may help identify new targets for intervention and guide better care.

Despite substantial research and development investment in Alzheimer’s disease (AD), effective therapeutics remain elusive. Significant emerging evidence has linked cholesterol, β-amyloid and AD, and several studies have shown a reduced risk for AD and dementia in populations treated with statins. However, while some clinical trials evaluating statins in general AD populations have been conducted, these resulted in no significant therapeutic benefit. By focusing on subgroups of the AD population, it may be possible to detect endotypes responsive to statin therapy. Here we investigate the possible protective and therapeutic effect of statins in AD through the analysis of datasets of integrated clinical trials, and prospective observational studies. Re-analysis of AD patient-level data from failed clinical trials suggested by trend that use of simvastatin may slow the progression of cognitive decline, and to a greater extent in ApoE4 homozygotes. Evaluation of continual long-term use of various statins, in participants from multiple studies at baseline, revealed better cognitive performance in statin users. These findings were supported in an additional, observational cohort where the incidence of AD was significantly lower in statin users, and ApoE4/ApoE4-genotyped AD patients treated with statins showed better cognitive function over the course of 10-year follow-up. These results indicate that the use of statins may benefit all AD patients with potentially greater therapeutic efficacy in those homozygous for ApoE4.