Weight Discrimination Experienced Prior to Enrolling in a Behavioral Obesity Intervention is Associated with Treatment Response Among Black and White Adults in the Southeastern U.S

Author(s):  
Kaylee B. Crockett ◽  
Alena Borgatti ◽  
Fei Tan ◽  
Ziting Tang ◽  
Gareth Dutton
2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 296-296
Author(s):  
Caroline Hartnett

Abstract Cognitive decline common in the U.S. and greatly impacts quality of life, both for those who experience it and for those who care for them. Black Americans experience higher burdens of cognitive decline but the mechanisms underlying this disparity have not been fully elucidated. Stress experienced in early life is a promising explanatory factor, since stress and cognition are linked, childhood stressors been shown to have a range of negative implications later in life, and Black children experience more childhood stressors than White children, on average. In this paper, we use data from the Behavioral Risk Factor Surveillance System (BRFSS) to examine whether stressful experiences in childhood help explain Black-White disparities in memory loss. These data were available for 5 state-years between 2011 and 2017 (n=11,708). Preliminary results indicate that, while stressful childhood experiences are strongly associated with memory loss, stressful experiences do not mediate the association between race and memory loss. However, race does appear to moderate the association between stressful childhood experiences and memory loss. Specifically, stressful experiences are associated with a higher likelihood of memory loss for Black adults compared to White adults.In addition, there seem to be some noteworthy patterns across different types of experiences (i.e. parental drinking may predict later memory loss more strongly for Black adults than White adults, but parental hitting may predict memory loss more strongly for White adults than Black adults).


Diabetes Care ◽  
2013 ◽  
Vol 36 (11) ◽  
pp. 3557-3565 ◽  
Author(s):  
C. L. Jackson ◽  
S. Redline ◽  
I. Kawachi ◽  
F. B. Hu

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Matthew W Segar ◽  
Byron Jaeger ◽  
Kershaw V Patel ◽  
Vijay Nambi ◽  
Chiadi E Ndumele ◽  
...  

Introduction: Heart failure (HF) risk and the underlying biological risk factors vary by race. Machine learning (ML) may improve race-specific HF risk prediction but this has not been fully evaluated. Methods: The study included participants from 4 cohorts (ARIC, DHS, JHS, and MESA) aged > 40 years, free of baseline HF, and with adjudicated HF event follow-up. Black adults from JHS and white adults from ARIC were used to derive race-specific ML models to predict 10-year HF risk. The ML models were externally validated in subgroups of black and white adults from ARIC (excluding JHS participants) and pooled MESA/DHS cohorts and compared to prior established HF risk scores developed in ARIC and MESA. Harrell’s C-index and Greenwood-Nam-D’Agostino chi-square were used to assess discrimination and calibration, respectively. Results: In the derivation cohorts, 288 of 4141 (7.0%) black and 391 of 8242 (4.7%) white adults developed HF over 10 years. The ML models had excellent discrimination in both black and white participants (C-indices = 0.88 and 0.89). In the external validation cohorts for black participants from ARIC (excluding JHS, N = 1072) and MESA/DHS pooled cohorts (N = 2821), 131 (12.2%) and 115 (4.1%) developed HF. The ML model had adequate calibration and demonstrated superior discrimination compared to established HF risk models (Fig A). A consistent pattern was also observed in the external validation cohorts of white participants from the MESA/DHS pooled cohorts (N=3236; 100 [3.1%] HF events) (Fig A). The most important predictors of HF in both races were NP levels. Cardiac biomarkers and glycemic parameters were most important among blacks while LV hypertrophy and prevalent CVD and traditional CV risk factors were the strongest predictors among whites (Fig B). Conclusions: Race-specific and ML-based HF risk models that integrate clinical, laboratory, and biomarker data demonstrated superior performance when compared to traditional risk prediction models.


2020 ◽  
Vol 29 (12) ◽  
pp. 1823-1831
Author(s):  
Blessing Osemengbe Ahiante ◽  
Wayne Smith ◽  
Leandi Lammertyn ◽  
Aletta Elisabeth Schutte

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