TY - JOUR
T1 - Model of personalized postprandial glycemic response to food developed for an Israeli cohort predicts responses in Midwestern American individuals
AU - Mendes-Soares, Helena
AU - Raveh-Sadka, Tali
AU - Azulay, Shahar
AU - Ben-Shlomo, Yatir
AU - Cohen, Yossi
AU - Ofek, Tal
AU - Stevens, Josh
AU - Bachrach, Davidi
AU - Kashyap, Purna
AU - Segal, Lihi
AU - Nelson, Heidi
N1 - Publisher Copyright:
© American Society for Nutrition 2019.
PY - 2019/7/1
Y1 - 2019/7/1
N2 - Background: Controlled glycemic concentrations are associated with a lower risk of conditions such as cardiovascular disease and diabetes. Models commonly used to guide interventions to control the glycemic response to food have low efficacy, with recent clinical guidelines arguing for the use of personalized approaches. Objective: We tested the efficacy of a predictive model of personalized postprandial glycemic response to foods that was developed with an Israeli cohort and that takes into consideration food components and specific features, including the microbiome, when applied to individuals from the Midwestern US. Design: We recruited 327 individuals for this study. Participants provided information regarding lifestyle, dietary habits, and health, as well as a stool sample for characterization of their gut microbiome. Participants were connected to continuous glucose monitors for 6 d, and the glycemic response to meals logged during this time was computed. The ability of a model trained using meals logged by the Israeli cohort to correctly predict glycemic responses in the Midwestern cohort was assessed and compared with that of a model trained using meals logged by both cohorts. Results: When trained on the Israeli cohort meals only, model performance for predicting responses of individuals in the Midwestern cohort was better (R = 0.596) than that observed for models taking into consideration the carbohydrate (R = 0.395) or calorie content of the meals alone (R = 0.336). Performance increased (R = 0.618) when the model was trained on meals from both cohorts, likely because of the observed differences in age distribution, diet, and microbiome. Conclusions: We show that the modeling framework described in Zeevi et al. for an Israeli cohort is applicable to a Midwestern population, and outperforms commonly used approaches for the control of blood glucose responses. The adaptation of the model to the Midwestern cohort further enhances performance and is a promising means for designing effective nutritional interventions to control glycemic responses to foods. This trial was registered at clinicaltrials. gov as NCT02945514.
AB - Background: Controlled glycemic concentrations are associated with a lower risk of conditions such as cardiovascular disease and diabetes. Models commonly used to guide interventions to control the glycemic response to food have low efficacy, with recent clinical guidelines arguing for the use of personalized approaches. Objective: We tested the efficacy of a predictive model of personalized postprandial glycemic response to foods that was developed with an Israeli cohort and that takes into consideration food components and specific features, including the microbiome, when applied to individuals from the Midwestern US. Design: We recruited 327 individuals for this study. Participants provided information regarding lifestyle, dietary habits, and health, as well as a stool sample for characterization of their gut microbiome. Participants were connected to continuous glucose monitors for 6 d, and the glycemic response to meals logged during this time was computed. The ability of a model trained using meals logged by the Israeli cohort to correctly predict glycemic responses in the Midwestern cohort was assessed and compared with that of a model trained using meals logged by both cohorts. Results: When trained on the Israeli cohort meals only, model performance for predicting responses of individuals in the Midwestern cohort was better (R = 0.596) than that observed for models taking into consideration the carbohydrate (R = 0.395) or calorie content of the meals alone (R = 0.336). Performance increased (R = 0.618) when the model was trained on meals from both cohorts, likely because of the observed differences in age distribution, diet, and microbiome. Conclusions: We show that the modeling framework described in Zeevi et al. for an Israeli cohort is applicable to a Midwestern population, and outperforms commonly used approaches for the control of blood glucose responses. The adaptation of the model to the Midwestern cohort further enhances performance and is a promising means for designing effective nutritional interventions to control glycemic responses to foods. This trial was registered at clinicaltrials. gov as NCT02945514.
KW - Carbohydrate content
KW - Continuous glucose monitors
KW - Diabetes
KW - Glycemic response
KW - Microbiome
KW - Personalized nutrition
UR - http://www.scopus.com/inward/record.url?scp=85068621508&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85068621508&partnerID=8YFLogxK
U2 - 10.1093/ajcn/nqz028
DO - 10.1093/ajcn/nqz028
M3 - Article
C2 - 31095300
AN - SCOPUS:85068621508
SN - 0002-9165
VL - 110
SP - 63
EP - 75
JO - American Journal of Clinical Nutrition
JF - American Journal of Clinical Nutrition
IS - 1
M1 - nzq028
ER -