Massachusetts General Hospital (MGH) scientists have developed a molecular imaging radiotracer that identifies Alzheimer's disease-related gamma-secretase in rodents and macaques that has translational potential for humans.
In the new study, Yulong Xu, PhD, and colleagues used recently developed gamma-secretase modulators (GSMs) and synthesized their GSM-based imaging agent, carbon-11 SGSM-15606. After performing molecular imaging in rodents, including Alzheimer's disease transgenic animals and macaques, they found the radiotracer displayed good brain uptake and selectivity, stable metabolism, and appropriate kinetics and distribution for imaging gamma-secretase in the brain (Journal of Experimental Medicine, 16 September 2020).
Alzheimer's disease is characterized by the buildup of amyloid plaques and neurofibrillary tangles in several brain regions. It is believed the beta-amyloid protein, and particularly the beta- amyloid 42 peptide, initiates the disease process. The imbalance between producing amyloid plaques and the inability to clear them leads to brain-cell death and thus Alzheimer's disease. GSMs preferentially reduce beta-amyloid 42 peptide levels by modulating, without totally suppressing, the enzyme's activity.
The PET tracer will "open new avenues for us to better understand the complex puzzle and facilitate drug discovery" in Alzheimer's disease, according to the authors.
![Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
