Weight loss success predicted by brain networks
Over 50% of Americans report that they want to lose weight. However, the success of lifestyle weight loss interventions varies considerably, indicating unique phenotypes associated with the ability to lose weight. A recent study by investigators funded by NHLBI, NIBIB, NIA, and NCATS examined if brain connectivity could predict successful weight loss.
The sample included 52 overweight and obese older adults (65 – 79 y) who participated in one of three 18-month weight loss interventions (diet only, diet plus aerobic exercise, or diet plus resistance training). MRI data were collected at baseline, and participants were required to fast and complete two rs-fMRI series with a food visualization fMRI in-between. Prediction models were created by combining machine learning and brain connectivity networks.
The model predicted successful weight loss in 96.8% of individuals. Connectivity patterns found to predict successful weight loss included brain network components associated with behavior emergence, self-regulation, body awareness, and food sensory features. These results provide insight into neurobehavioral phenotypes associated with weight loss success. Such brain network phenotypes may be a useful tool for tailoring obesity interventions and treatments.
Mokhtari F, Rejeski WJ, Zhu Y, Wu G, Simpson SL, Burdette JH, Laurienti PJ. 2018. Dynamic fMRI networks predict success in a behavioral weight loss program among older adults. Neuroimage 173:421-33.
Identifying geographic hot spots of intentional injury may serve prevention efforts
Geographic information systems (GIS) are effective for studying intentional injury, which includes both assault and self-harm, in different communities, but trauma centers have not widely adopted their use. A recent NIGMS-funded study used patient data to map the distribution of incidents and correlated socioeconomic factors.
Data from University of South Alabama Medical Center trauma registries was used to map the location of injury for 1,009 incidents between 2005-2015. Significant clustering of incidents was identified and mapped to display hot spots of trauma. Using census block groups, socioeconomic data were then correlated with high-risk incident areas.
Four geographic areas were identified to have a significant clustering of incidents. Socioeconomic factors associated with these high-risk areas included unemployment, single-parent household, and lack of a high school degree. Race, proximity to liquor stores and bars, median household income, per capita income, rate with public assistance, and population density did not correlate with the distribution of intentional injury by these geographic clusters.
Level I trauma centers may benefit from GIS analysis to identify high-risk areas in the community. An understanding of local factors that contribute to hot spots of intentional injury can then be used in trauma prevention efforts.
Lasecki CH, Mujica FC, Stutsman S, Williams AY, Ding L, Simmons JD, Brevard SB. 2018. Geospatial mapping can be used to identify geographic areas and social factors associated with intentional injury as targets for prevention efforts distinct to a given community. J Trauma Acute Care Surg 84(1):70-74.
Failing to report adverse events is not just about losing compensation
Losing out on financial incentives was thought to be the main driver deterring healthy volunteers from disclosing adverse events in clinical trials, but a recent NIGMS-funded study suggests that in addition to economic reasons, health risks and study integrity are also important factors in willingness to report adverse events.
The study conducted semi-structured qualitative interviews with 131 healthy U.S. volunteers currently enrolled in Phase I trials. Participants were asked open-ended questions regarding adverse event experiences and questions about their perceptions of clinical trials and their participation. Answers to interviews from the one-year follow-up were used to understand participant rationale for reporting or withholding information about adverse events.
One quarter of respondents admitted to not reporting an adverse event or describing a scenario in which they would not report. Reporting behavior was based on economic, health-oriented, and data integrity factors. Answers were highly context- specific and depended on healthy volunteers’ perceptions of the clinical trial and how their reporting would be perceived. These results can inform how clinical trials incentivize adverse event reporting and inform participants on the importance of reporting for their safety and the overall integrity of the study.
McManus L, Fisher JA. 2018. To Report or Not to Report: Exploring Healthy Volunteers’ Rationales for Disclosing Adverse Events in Phase I Drug Trials. AJOB Empir Bioeth 25:1-17