Effect of excess body weight on the genetic susceptibility to cancer

Host Genetic Background Effect on Body Weight Changes Influenced by Heterozygous Smad4 Knockout Using Collaborative Cross Mouse Population

Obesity and its attendant conditions have become major health problems worldwide, and obesity is currently ranked as the fifth most common cause of death globally. Complex environmental and genetic factors are causes of the current obesity epidemic. Diet, lifestyle, chemical exposure, and other confounding factors are difficult to manage in humans. The mice model is helpful in researching genetic BW gain because genetic and environmental risk factors can be controlled in mice. Studies in mouse strains with various genetic backgrounds and established genetic structures provide unparalleled opportunities to find and analyze trait-related genomic loci. In this study, we used the Collaborative Cross (CC), a large panel of recombinant inbred mouse strains, to present a predictive study using heterozygous Smad4 knockout profiles of CC mice to understand and effectively identify predispositions to body weight gain. Male C57Bl/6J Smad4+/- mice were mated with female mice from 10 different CC lines to create F1 mice (Smad4+/-x CC). Body weight (BW) was measured weekly until week 16 and then monthly until the end of the study (week 48). The heritability (H2) of the assessed traits was estimated and presented. Comparative analysis of various machine learning algorithms for predicting the BW changes and genotype of mice was conducted. Our data showed that the body weight records of F1 mice with different CC lines differed between wild-type and mutant Smad4 mice during the experiment. Genetic background affects weight gain and some lines gained more weight in the presence of heterozygous Smad4 knockout, while others gained less, but, in general, the mutation caused overweight mice, except for a few lines. In both control and mutant groups, female %BW had a higher heritability (H2) value than males. Additionally, both sexes with wild-type genotypes showed higher heritability values than the mutant group. Logistic regression provides the most accurate mouse genotype predictions using machine learning. We plan to validate the proposed method on more CC lines and mice per line to expand the literature on machine learning for BW prediction.

Pre-pregnancy obesity and childhood malignancies: A population-based cohort study

OBJECTIVE

Exploring the effect of maternal obesity during pregnancy on the long-term health of offspring is of great importance. The aim of this study was to evaluate the association between maternal pre-pregnancy obesity and future risk of childhood malignancies.

STUDY DESIGN

A population-based cohort analysis comparing the risk for long-term childhood malignancies (up to the age of 18 years) in children born (1991-2014) to mothers with and without pre-pregnancy obesity (body mass index > 30) was conducted in July 2017. Childhood malignancies were predefined based on ICD-9 codes, as recorded in the hospital medical files. Children with congenital malformations and multiple gestations were excluded from the analysis. The Kaplan-Meier survival curve was constructed to compare cumulative oncological morbidity in both groups over time. The Cox proportional hazards model was used to control for confounders.

RESULTS

During the study period, 241 273 infants met the inclusion criteria; 3268 were born to mothers with pre-pregnancy obesity. Children of obese women had significantly increased risk for several childhood malignancies (including brain tumors) as well as increased risk for total hospitalizations with malignancy diagnoses, even after controlling for several confounders (adjusted HR 1.90, 95% CI 1.07-3.37, P = 0.028). Cumulative incidence of oncological morbidity was also significantly increased over time in the studied group (log-rank P = 0.023).

CONCLUSION

Maternal pre-pregnancy obesity is significantly associated with an increased long-term risk for general childhood malignancies, and specifically brain tumors in the offspring. These results are important when counseling mothers regarding potential future risks and recommended lifestyle modifications.

Prepregnancy obesity: a risk factor for future development of ovarian and breast cancer

The aim of this study was to investigate the association between a history of prepregnancy obesity and a woman's future long-term risk for the development of female malignancies. A population-based study compared the incidence of long-term female malignancies in a cohort of consecutive women with and without a diagnosis of prepregnancy obesity. Deliveries occurred between the years 1988 and 2013, with a mean follow-up duration of 11.6 years. Women with known malignancies before the index pregnancy and known genetic predisposition for malignancy were excluded from the study. Female malignancies were divided according to specific type (ovary, uterine, breast, and uterine cervix). A Kaplan-Meier survival curve was used to estimate the cumulative incidence of malignancies. A Cox proportional hazards model was used to estimate the adjusted hazard ratios for female malignancy. During the study period, 106 251 deliveries fulfilled the inclusion criteria; 2.2% (n=2360) occurred in patients with a history of prepregnancy obesity. During the follow-up period, patients with prepregnancy obesity had a significantly increased risk for hospitalization because of female malignancies as a group and specifically ovarian and breast cancer. Using a Kaplan-Meier survival curve, patients with a previous diagnosis of prepregnancy obesity had a significantly higher cumulative incidence of female malignancies. Using a Cox proportional hazards model, adjusted for confounders such as gestational diabetes mellitus and maternal age, prepregnancy obesity remained independently associated with long-term risk for female malignancies (adjusted hazard ratio: 1.4; 95% confidence interval: 1.1-1.9; P=0.045). Prepregnancy obesity is an independent risk factor for long-term female malignancies such as ovarian and breast cancer.

Identification of genetic factors that modify motor performance and body weight using Collaborative Cross mice

Evidence has emerged that suggests a link between motor deficits, obesity and many neurological disorders. However, the contributing genetic risk factors are poorly understood. Here we used the Collaborative Cross (CC), a large panel of newly inbred mice that captures 90% of the known variation among laboratory mice, to identify the genetic loci controlling rotarod performance and its relationship with body weight in a cohort of 365 mice across 16 CC strains. Body weight and rotarod performance varied widely across CC strains and were significantly negatively correlated. Genetic linkage analysis identified 14 loci that were associated with body weight. However, 45 loci affected rotarod performance, seven of which were also associated with body weight, suggesting a strong link at the genetic level. Lastly, we show that genes identified in this study overlap significantly with those related to neurological disorders and obesity found in human GWA studies. In conclusion, our results provide a genetic framework for studies of the connection between body weight, the central nervous system and behavior.