Routine measurements of the brain found on MR or PET imaging could help identify which older adults are likely to gain cognitive protection from a structured, intensive lifestyle program, according to a study published April 20 in JAMA Neurology.
The finding points toward a more personalized approach to dementia prevention, wrote a team led by Theresa M. Harrison, PhD, of the University of California, Berkeley.
"[Our study found that] older adults with specific at-risk brain characteristics, including lower baseline [hippocampal] volume, showed a greater cognitive benefit [from] structured intervention," the group noted.
Brain imaging biomarkers may identify underlying mechanisms of the effects of intervention and define the brain characteristics of those most likely to benefit cognitively from the intervention, Harrison and colleagues explained. The team conducted a study that explored whether a lifestyle intervention is related to brain changes; whether any brain changes correlate with intervention-specific cognition changes; and whether brain imaging biomarkers offer insight into who is likely to benefit.
Harrison and colleagues used data from the Protect Brain Health Through Lifestyle Intervention to Reduce Risk (U.S. POINTER) study. For this research, the investigators included information from a subset of 983 POINTER participants (mean age, 68 years) who had sedentary lifestyles, less-than-ideal diets, two additional risk factors for cognitive decline, and no neuroimaging contraindications.
Patients were randomized to receive either a structured (n = 516) or self-guided (n = 467) intervention; both encouraged increased physical and cognitive activity, healthy nutrition, social engagement, and cardiovascular health monitoring, although they differed in intensity and accountability. All participants underwent at least one MRI or PET exam, and the team assessed the following primary imaging outcomes: global beta-amyloid burden, tau burden in the entorhinal cortex, hippocampal volume, and white matter hyperintensity volume.
The team reported that, overall, the lifestyle intervention did not affect biomarker changes and was not associated with beta-amyloid pathology. But it did find that a lower baseline hippocampal volume was associated with greater cognitive benefit for participants in the structured group (lower hippocampal volume hazard ratio [HR] of 0.077 versus higher hippocampal volume HR of 0.002; p = 0.03).
These results are congruent with the parent trial subgroup findings, which also showed that "an at-risk group, those with lower baseline global cognitive performance, significantly benefitted from the structured intervention," Harrison and colleagues noted.
"Further work and longitudinal follow-up will further define profiles of older adults who require differing levels of intervention support and intensity to avoid cognitive decline," they concluded.
Access the full study here.














![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.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&fit=crop&h=112&q=70&w=112)
