scholarly journals Obesity Risk Assessment Tool for Low-Income Spanish Speaking Immigrant Parents with Young Children: Validity with BMI and Biomarkers of Obesity

Nutrients ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3582
Author(s):  
Marilyn Townsend ◽  
Mical Shilts ◽  
Louise Lanoue ◽  
Christiana Drake ◽  
L. Díaz Rios ◽  
...  

Children of Hispanic origin bear a high risk of obesity. Child weight gain trajectories are influenced by the family environment, including parent feeding practices. Excessive body fat can result in unhealthful metabolic and lipid profiles and increased risk of metabolic diseases. The objective was to estimate criterion validity of an obesity risk assessment tool targeting Spanish-speaking families of Mexican origin using anthropometric measures and blood values of their young children. A cross-sectional study design with five data collection sessions was conducted over an eight-week period and involved 206 parent/child dyads recruited at Head Start and the Special Supplemental Nutrition Program for Women, Infants and Children in Northern California. Main outcome measures were criterion validity of Niños Sanos, a pediatric obesity risk assessment tool, using anthropometric measures and blood biomarkers. Niños Sanos scores were inversely related to child BMI-for-age percentiles (p = 0.02), waist-for-height ratios (p = 0.05) and inversely related to blood biomarkers for the metabolic index (p = 0.03) and lipid index (p = 0.05) and positively related to anti-inflammatory index (p = 0.047). Overall, children with higher Niños Sanos scores had more healthful lipid, metabolic and inflammatory profiles, as well as lower BMI-for-age percentiles and waist-to height ratios, providing evidence for the criterion validity of the tool. Niños Sanos can be used by child obesity researchers, by counselors and medical professionals during clinic visits as a screening tool and by educators as a tool to set goals for behavior change.

2018 ◽  
Vol 50 (7) ◽  
pp. 705-717 ◽  
Author(s):  
Marilyn S. Townsend ◽  
Mical K. Shilts ◽  
Dennis M. Styne ◽  
Christiana Drake ◽  
Louise Lanoue ◽  
...  

2010 ◽  
Vol 192 (4) ◽  
pp. 197-202 ◽  
Author(s):  
Lei Chen ◽  
Dianna J Magliano ◽  
Beverley Balkau ◽  
Stephen Colagiuri ◽  
Paul Z Zimmet ◽  
...  

2007 ◽  
Vol 21 (6) ◽  
Author(s):  
Tara Denise Young ◽  
Lorrene Ritchie ◽  
Lenna Ontai‐Grzebeck ◽  
Shannon Tierney Williams ◽  
Marilyn S Townsend

2015 ◽  
Vol 33 (8) ◽  
pp. 923-929 ◽  
Author(s):  
V. Shane Pankratz ◽  
Amy C. Degnim ◽  
Ryan D. Frank ◽  
Marlene H. Frost ◽  
Daniel W. Visscher ◽  
...  

Purpose Optimal early detection and prevention for breast cancer depend on accurate identification of women at increased risk. We present a risk prediction model that incorporates histologic features of biopsy tissues from women with benign breast disease (BBD) and compare its performance to the Breast Cancer Risk Assessment Tool (BCRAT). Methods We estimated the age-specific incidence of breast cancer and death from the Mayo BBD cohort and then combined these estimates with a relative risk model derived from 377 patient cases with breast cancer and 734 matched controls sampled from the Mayo BBD cohort to develop the BBD–to–breast cancer (BBD-BC) risk assessment tool. We validated the model using an independent set of 378 patient cases with breast cancer and 728 matched controls from the Mayo BBD cohort and compared the risk predictions from our model with those from the BCRAT. Results The BBD-BC model predicts the probability of breast cancer in women with BBD using tissue-based and other risk factors. The concordance statistic from the BBD-BC model was 0.665 in the model development series and 0.629 in the validation series; these values were higher than those from the BCRAT (0.567 and 0.472, respectively). The BCRAT significantly underpredicted breast cancer risk after benign biopsy (P = .004), whereas the BBD-BC predictions were appropriately calibrated to observed cancers (P = .247). Conclusion We developed a model using both demographic and histologic features to predict breast cancer risk in women with BBD. Our model more accurately classifies a woman's breast cancer risk after a benign biopsy than the BCRAT.


2010 ◽  
Vol 192 (5) ◽  
pp. 274-274 ◽  
Author(s):  
Lei Chen ◽  
Dianna J Magliano ◽  
Beverley Balkau ◽  
Stephen Colagiuri ◽  
Paul Z Zimmet ◽  
...  

2018 ◽  
Vol 50 (7) ◽  
pp. S109
Author(s):  
Louise Lanoue ◽  
Marilyn S. Townsend ◽  
Mical K. Shilts ◽  
Christiana Drake ◽  
Lenna Ontai ◽  
...  

2020 ◽  
Vol 16 (S1) ◽  
pp. S-23-S-32 ◽  
Author(s):  
Marilyn S. Townsend ◽  
Mical K. Shilts ◽  
Louise Lanoue ◽  
Christiana Drake ◽  
Dennis M. Styne ◽  
...  

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