Down syndrome is associated with several comorbidities, including intellectual disability, growth restriction, and congenital heart defects. The prevalence of Down syndrome–associated comorbidities is highly variable, and intellectual disability, although fully penetrant, ranges from mild to severe. Understanding the basis of this interindividual variability might identify predictive biomarkers of in utero and postnatal outcomes that could be used as endpoints to test the efficacy of future therapeutic interventions.

The main objective of this study was to examine if antenatal interindividual variability exists in mouse models of Down syndrome and whether applying statistical approaches to clinically relevant measurements (ie, the weights of the embryo, placenta, and brain) could define cutoffs that discriminate between subgroups of trisomic embryos.

Three commonly used mouse models of Down syndrome (Dp(16)1/Yey, Ts65Dn, and Ts1Cje) and a new model (Ts66Yah) were used in this study. Trisomic and euploid littermate embryos were used from each model with total numbers of 102 for Ts66Yah, 118 for Dp(16)1/Yey, 92 for Ts65Dn, and 126 for Ts1Cje. Placental, embryonic, and brain weights and volumes at embryonic day 18.5 were compared between genotypes in each model. K-mean clustering analysis was applied to embryonic and brain weights to identify severity classes in trisomic embryos, and brain and placental volumetric measurements were compared between genotypes and classes for each strain. In addition, Ts66Yah embryos were examined for malformations because embryonic phenotypes have never been examined in this model.

Reduced body and brain weights were present in Ts66Yah, Dp(16)1/Yey, and Ts65Dn embyos. Cluster analysis identified 2 severity classes in trisomic embryos—mild and severe—in all 4 models that were distinguishable using a putative embryonic weight cutoff of <0.5 standard deviation below the mean. Ts66Yah trisomic embryos develop congenital anomalies that are also found in humans with Down syndrome, including congenital heart defects and renal pelvis dilation.

Statistical approaches applied to clinically relevant measurements revealed 2 classes of phenotypic severity in trisomic mouse models of Down syndrome. Analysis of severely affected trisomic animals may facilitate the identification of biomarkers and endpoints that can be used to prenatally predict outcomes and the efficacy of treatments.