A vast new population study funded by the German Ministry of Education and Research aims to explore the development of chronic diseases in more than 200,000 participants, and features whole-body MRI in more than 30,000 individuals as a key research component.
Over the next 30 years, the National Cohort study (NAKO) aims to use MRI in its detailed exploration of chronic disease development, designed to create effective preventive strategies and examine the relationship between disease, lifestyle, and environmental factors.
The project is expected to cost 210 billion euros, of which 10% is devoted to radiology, the German Radiology Society (Deutsche Röntgengesellschaft, DRG) reported.
Among a total 200,000 expected participants, 30,000 will undergo whole-body MRI, according to the DRG report. The project was spurred by increases in the development of chronic diseases, including cancer, diabetes mellitus, and cardiovascular diseases, owing to population changes that have increased the percentage of older people.
Outcomes data have traditionally been hard to produce, but this project requires it in order to demonstrate the benefit of new imaging technologies, DRG said. For radiology, the project is expected to increase the number of disease markers in MRI.
![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)
