
The U.S. Food and Drug Administration (FDA) on Tuesday issued a directive requiring that manufacturers of gadolinium-based contrast agents (GBCAs) add a new warning to their product labeling that notifies healthcare providers that patients could retain gadolinium long after an MRI scan.
The December 19 edict also mandates that manufacturers conduct human and animal studies to further evaluate the safety of the contrast agents. In addition, patients will receive a new medication guide with educational information about GBCAs before they undergo contrast-enhanced MRI scans. Finally, the agency is advising providers to consider the gadolinium retention issue when deciding whether to order contrast with a patient's MRI scan.
"We recommend that healthcare professionals consider the retention characteristics of each agent when choosing a GBCA for patients who may be at higher risk, such as those who may require repeat GBCA MRI scans to monitor a chronic condition," the FDA wrote in the announcement.
The new safety communiqué follows a year in which gadolinium and GBCA safety has been an FDA priority. In May, the agency completed a 21-month review of GBCA safety by concluding there were "no harmful effects to date" from gadolinium retention in the brain, and that it was not necessary to place any new restrictions on gadolinium contrast products.
The move also puts into action the September recommendation of its Medical Imaging Drugs Advisory Committee (MIDAC) to add a gadolinium retention warning to GBCA prescribing information and to have contrast agent manufacturers conduct additional research to gather more data on potential adverse effects from GBCA administration and gadolinium deposition.
The agency's approach is still more cautious than the path taken by European regulators, however. The Europe Medicines Agency (EMA) earlier this year recommended that three linear GBCA products be pulled from the market, giving member states one year to implement the decision. One country, the U.K., announced on December 14 its plans to pull several linear agents from the market.
The FDA's approach has drawn fire from patients who say they were sickened by gadolinium; however, the FDA has stated that it wants to see more research before it takes more drastic action. In July, the agency initiated another investigation into the risk of gadolinium deposits following a series of studies that detected higher than normal signal intensities in the brains of patients years after they had undergone four or more GBCA-enhanced MRI scans.
While again reiterating in the December 19 announcement that "gadolinium retention has not been directly linked to adverse health effects in patients with normal kidney function," the FDA also noted the occurrence of nephrogenic systemic fibrosis (NSF) in patients with pre-existing kidney failure and reports of adverse events in patients with normal kidney function.
But again, the FDA wrote that a "causal association between these adverse events and gadolinium retention could not be established."



![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=100&q=70&w=100)





![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)








