Impact of intra-category food substitutions on the risk of type 2 diabetes: a modelling study on the pizza category

Abstract

Advice on replacing unhealthy foods with healthier alternatives within the same food category may be more acceptable and might ease the transition towards a healthy diet. Here, we studied the potential impact of substitutions within the pizza category on the risk of type 2 diabetes (T2D). The study sample consisted of 2,510 adults from the INCA2 French national survey. Based on their nutritional characteristics, the 353 pizzas marketed in France were grouped in 100 clusters that were used to run various scenarios of pizza substitutions, which were either isoenergetic (IE) or non-isoenergetic (NIE). We then used a model structurally similar to the Preventable Risk Integrated ModEl (PRIME) to assess the expected rate of change in risk of T2D. Pizzas characterized by a low energy, high vegetable content and whole grain dough were associated with a greater reduction in the risk of T2D. The rates of change in risk of T2D were markedly stronger in men and for NIE substitutions. When the rates of change were estimated in the subsample of pizza consumers, replacing the observed pizzas with the best pizza resulted in a T2D risk reduction of −6.7% (−8.4%; −4.9%, IE) and −8.9% (−11.2%; −6.3%, NIE), assuming that this is their usual diets. The greatest risk reduction induced by an IE substitution of the observed pizza with a mixed dish was similar to that observed with the best pizzas. Overall, this modelling study suggests that healthy swaps within a category can effectively supplement broader dietary changes toward a healthier diet.

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“…These simulations are essential for policy-makers as they provide useful estimates of the potential health-related gains from a given change [19]. PRIME-Diabetes allows us to estimate the variation in the T2DM risk related to dietary changes [20].…”

“…We evaluated the potential impact of dietary pattern substitution on the T2DM risk using a model structurally similar to the Preventable Risk Integrated ModEl (PRIME) [19] as developed by Adjibade et al [20]. PRIME is a comparative risk assessment model designed to estimate the impact of changes in the age-and sex-specific distribution of one or more of twelve behavioural risk factors of non-communicable diseases mortality.…”

“…developed PRIME-Diabetes, a model estimating the variation in T2DM risk related to lifestyle changes [20]. As originally reported [19], risk relationships were drawn from published metaanalyses and included parameters appropriately adjusted for other behavioural risk factors to minimize the risk of double-counting of effect size.…”

“…As originally reported [19], risk relationships were drawn from published metaanalyses and included parameters appropriately adjusted for other behavioural risk factors to minimize the risk of double-counting of effect size. PRIME-Diabetes was set up using relative risk data on the dose-response associations between the T2DM risk and each food group or nutrient identified as having an impact on the risk of T2DM: whole grains, refined grains, fruits, green leafy vegetables, other vegetables, nuts, potatoes, dairy products, red meats, processed meats, butter, fish, eggs, olive oil, tea, coffee, chocolate, sugar-sweetened beverages, daily energy, carbohydrates, magnesium [20] (Supplementary Figure 1). Additionally, change in energy intake is an important factor for T2DM risk estimates as energy imbalance is associated with being overweight, itself associated with the T2DM risk [20].…”

“…PRIME-Diabetes was set up using relative risk data on the dose-response associations between the T2DM risk and each food group or nutrient identified as having an impact on the risk of T2DM: whole grains, refined grains, fruits, green leafy vegetables, other vegetables, nuts, potatoes, dairy products, red meats, processed meats, butter, fish, eggs, olive oil, tea, coffee, chocolate, sugar-sweetened beverages, daily energy, carbohydrates, magnesium [20] (Supplementary Figure 1). Additionally, change in energy intake is an important factor for T2DM risk estimates as energy imbalance is associated with being overweight, itself associated with the T2DM risk [20]. Thus, the model was parameterized so that a change in energy intake resulted in a change in the T2DM risk as a function of the BMI and the level of physical activity of the participants [20].…”