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. 2023 Nov 30;18(11):e0287069.
doi: 10.1371/journal.pone.0287069. eCollection 2023.

Optimization of nutritional strategies using a mechanistic computational model in prediabetes: Application to the J-DOIT1 study data

Julia H Chen  1 , Momoko Fukasawa  2 , Naoki Sakane  3 , Akiko Suganuma  3 , Hideshi Kuzuya  3 , Shikhar Pandey  1 , Paul D'Alessandro  1 , Sai Phanindra Venkatapurapu  1 , Gaurav Dwivedi  1
Affiliations

Optimization of nutritional strategies using a mechanistic computational model in prediabetes: Application to the J-DOIT1 study data

Julia H Chen et al. PLoS One. .

Abstract

Lifestyle interventions have been shown to prevent or delay the onset of diabetes; however, inter-individual variability in responses to such interventions makes lifestyle recommendations challenging. We analyzed the Japan Diabetes Outcome Intervention Trial-1 (J-DOIT1) study data using a previously published mechanistic simulation model of type 2 diabetes onset and progression to understand the causes of inter-individual variability and to optimize dietary intervention strategies at an individual level. J-DOIT1, a large-scale lifestyle intervention study, involved 2607 subjects with a 4.2-year median follow-up period. We selected 112 individuals from the J-DOIT1 study and calibrated the mechanistic model to each participant's body weight and HbA1c time courses. We evaluated the relationship of physiological (e.g., insulin sensitivity) and lifestyle (e.g., dietary intake) parameters with variability in outcome. Finally, we used simulation analyses to predict individually optimized diets for weight reduction. The model predicted individual body weight and HbA1c time courses with a mean (±SD) prediction error of 1.0 kg (±1.2) and 0.14% (±0.18), respectively. Individuals with the most and least improved biomarkers showed no significant differences in model-estimated energy balance. A wide range of weight changes was observed for similar model-estimated caloric changes, indicating that caloric balance alone may not be a good predictor of body weight. The model suggests that a set of optimal diets exists to achieve a defined weight reduction, and this set of diets is unique to each individual. Our diabetes model can simulate changes in body weight and glycemic control as a result of lifestyle interventions. Moreover, this model could help dieticians and physicians to optimize personalized nutritional strategies according to their patients' goals.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Simulation model.
Schematic representing a previously developed and validated mechanistic model of diabetes onset and progression. (Adapted from [20]).
Fig 2
Fig 2. Subject selection and study design.
112 unique subjects were selected for individual-level analysis. 59 subjects were selected from the intervention arm of J-DOIT1 with a nearly equal distribution over three call frequency groups and two response categories within each call frequency group. 53 subjects from the control arm were found to be the best baseline-matched pairs of the 59 subjects from the intervention arm.
Fig 3
Fig 3. An example of model prediction for a pair of baseline-matched training and test subjects.
Panels on the left-hand side represent a subject from the training data set. Panels on the right-hand side show the baseline-matched subject from the test data set. The test subject is from the high call frequency group and was classified in the best responder category. The error bars around the measured values are assumed measurement errors, ±1 kg for body weight and ±0.15 points for HbA1c, as described under model calibration in the Materials and methods section.
Fig 4
Fig 4. Model-predicted caloric change versus weight change for subjects in the intervention arm.
The measured change in body weight from baseline to the first follow-up during the J-DOIT1 intervention (median duration 1 year) is plotted on the y-axis for subjects in the intervention arm. The x-axis shows model-estimated change in calories per day due to both diet and exercise changes averaged over the same period. The gray number in each quadrant is the fraction of data points in that quadrant. The data points fit a linear regression model (solid gray line) with r2 = 0.67 and a residual standard error of 2.5 kg, indicating a relatively wide spread around the line of best fit. Best and worst responders were defined based on the total percent change in body weight and HbA1c one year after the end of the J-DOIT1 intervention.
Fig 5
Fig 5. Optimal changes in carbohydrate and fat intake for targeted weight reduction.
Monte Carlo simulations identified a unique set of “optimal” carbohydrate and fat changes required for each subject that were predicted to lead to a targeted 5–7% reduction in body weight.
Fig 6
Fig 6. Including additional biomarker targets further narrows the predicted optimal diets.
The subset (pink circles) of optimal diets identified for subject Test-041 (Fig 5) to achieve a 5–7% reduction in body weight (gray and pink circles) was predicted to additionally reduce HbA1c by 0.1–0.2%.
Fig 7
Fig 7. Optimal diet trajectories and relative sensitivity to macronutrients.
A line was fit to the set of optimal diets predicted for each subject. The slopes of the lines were used to classify subjects into carbohydrate or fat sensitive categories. Lines that tend to be more horizontal (green lines; slope > -1) indicate individuals with greater sensitivity to fat change. Lines that tend to be more vertical (pink lines; slope < -1) indicate individuals with greater sensitivity to carbohydrate change.
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Substances

Grants and funding

This work was supported by JSPS KAKENHI Grant Number 18k01988.The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. PricewaterhouseCoopers, LLP provided support in the form of salaries for the following authors - [JHC, MF, SP, PMD, SPV, GD] but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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