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Weight loss at your fingertips: personalized nutrition with fasting glucose and insulin using a novel statistical approach

Christian Ritz, Arne Astrup, Thomas M Larsen, Mads F Hjorth
European Journal of Clinical Nutrition 2019 April 9

BACKGROUND/OBJECTIVES: Precision medicine is changing the way people are diagnosed and treated into a more personalized approach. Using a novel statistical approach, we demonstrate how two diets cause differential weight loss depending on pre-treatment fasting plasma glucose (FPG) and fasting insulin (FI) levels.

SUBJECTS/METHODS: One hundred and eighty-one overweight people with increased waist circumference were randomly assigned to receive an ad libitum New Nordic Diet (NND) high in dietary fiber and whole grain or an Average Danish (Western) Diet (ADD) for 26 weeks. All foods were provided free of charge. Body weight was measured throughout the study and blood was drawn before randomization from where FPG and FI were analyzed. Weight was described by linear mixed models including biomarker (FPG or FI) diet group interactions. Individualized predictions were estimated as contrasts of intercepts and slopes of pre-treatment biomarkers.

RESULTS: Every mmol/L increase in baseline FPG predicted a between-diet difference of 3.00 (1.18;4.83, n = 181, P = 0.001) kg larger weight loss from choosing NND over ADD. For instance, a baseline FPG level of 4.7 mmol/L would lead to an average of 1.42 kg larger weight loss on NND vs. ADD (above 0.41 kg with 95% certainty), whereas the average effect size would be 8.33 kg (above 5.50 kg with 95% certainty) among subjects with FPG level of 7.0 mmol/L. Among individuals with FPG <5.6 mmol/L, each pmol/L lower baseline FI predicted a 0.039 (95% CI 0.017;0.061, n = 143, P < 0.001) kg larger weight loss from choosing NND over ADD.

CONCLUSIONS: Use of pre-treatment FPG and FI led to truly individualized predictions of treatment effect of introducing more fiber and whole grain in the diet on weight loss, ranging from almost no effect to losing >8 kg. These findings suggest that this novel statistical approach has great potential when re-evaluating data from existing randomized controlled trials.


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