Prejudice and the Plate: Effects of Weight Bias in Nutrition Judgments

As millions of people turn to social media for health information, better understanding the factors that guide health-related judgments and perceptions in this context is imperative. We report on two Web experiments (n > 400 total) examining the power of society’s widespread weight bias and related stereotypes to influence nutrition judgments in social media spaces. In Experiment 1, meals were judged as lower in nutritional quality when the person who recommended them (the source) was depicted as obese rather than of normal weight, an effect mediated by stereotypic beliefs about the source as a generally unhealthy person. Experiment 2 replicated this effect, which—notably—remained significant when controlling for objective nutritional information (calories and fat content). Results highlight spillover effects of weight bias that extend beyond person perception to color impressions of objects (here, food) that are associated with stigmatized attributes. Implications for everyday nutrition judgments and public health are considered.     

Keeping Secrets or Educating Others: A Dyadic Analysis of Group Entitativity’s Influence on Spouses’ Label Management Connected to AATD

In 1963, Goffman argued that forming a group based on shared stigma may provide benefits. However, there is no empirical research on whether perception that a separate, unique, coherent group exists (i.e., group entitativity) influences coping, such as educating others or secrecy, for the stigmatized individual or his or her spouse. Further, little is known about how spouses influence each other in terms of promoting the education of others about a stigmatizing condition, especially when it comes to the role of believing that stigma-based groups, to which they may both belong, exist. This study provides a step toward bridging this gap in the research by applying the label management model in efforts to understand coping for couples in which one spouse is diagnosed with genetic mutations leading to alpha-1 antitrypsin deficiency (AATD). This study included 50 married couples in which one spouse is diagnosed with genetic mutations leading to alpha-1 antitrypsin deficiency (AATD). We found that group entitativity related to those with AATD counterbalanced the influence of genetic stigma on spouses’ intentions to keep the diagnosis secret or to educate others about it. Intrapersonal and interpersonal influences appeared among spouses. Attention is needed on the power of creating groups for stigmatized persons and their relatives. Indeed, people live within a dynamic world of group entities, and multiple social identities including spousal and familial. While attention has been paid to the diffusion of stigmas to loved ones, less has been paid to the uplift of group entities for them.     

Epithelial ovarian cancer risk: A review of the current genetic landscape

Ovarian cancer is the fourth most common cause of cancer-related death in women in the developed world, and one of the most heritable cancers. One of the most significant risk factors for epithelial ovarian cancer (EOC) is a family history of breast and/or ovarian cancer. Combined risk factors can be used in models to stratify risk of EOC, and aid in decisions regarding risk-reduction strategies. Germline pathogenic variants in EOC susceptibility genes including those involved in homologous recombination and mismatch repair pathways are present in approximately 22% to 25% of EOC. These genes are associated with an estimated lifetime risk of EOC of 13% to 60% for BRCA1 variants and 10% to 25% for BRCA2 variants, with lower risks associated with remaining genes. Genome-wide association studies have identified single nucleotide polymorphisms (SNPs) thought to explain an additional 6.4% of the familial risk of ovarian cancer, with 34 susceptibility loci identified to date. However, an unknown proportion of the genetic component of EOC risk remains unexplained. This review comprises an overview of individual genes and SNPs suspected to contribute to risk of EOC, and discusses use of a polygenic risk score to predict individual cancer risk more accurately.