Association of plant-based dietary patterns with the risk of type 2 diabetes mellitus using cross-sectional results from RaNCD cohort.
Neda Heidarzadeh-EsfahaniMitra DarbandiFiroozeh KhamoushiFarid NajafiDavood SoleimaniMozhgan MoradiEbrahim ShakibaYahya PasdarPublished in: Scientific reports (2024)
The prevalence of type 2 diabetes mellitus (T2DM) is increasing in middle- and low-income countries, and this disease is a burden on public health systems. Notably, dietary components are crucial regulatory factors in T2DM. Plant-based dietary patterns and certain food groups, such as whole grains, legumes, nuts, vegetables, and fruits, are inversely correlated with diabetes incidence. We conducted the present study to determine the association between adherence to a plant-based diet and the risk of diabetes among adults. We conducted a cross-sectional, population-based RaNCD cohort study involving 3401 men and 3699 women. The plant-based diet index (PDI) was developed using a 118-item food frequency questionnaire (FFQ). Logistic regression models were used to evaluate the association between the PDI score and the risk of T2DM. A total of 7100 participants with a mean age of 45.96 ± 7.78 years were analysed. The mean PDI scores in the first, second, and third tertiles (T) were 47.13 ± 3.41, 54.44 ± 1.69, and 61.57 ± 3.24, respectively. A lower PDI was significantly correlated with a greater incidence of T2DM (T1 = 7.50%, T2 = 4.85%, T3 = 4.63%; P value < 0.001). Higher PDI scores were associated with significantly increased intakes of fibre, vegetables, fruits, olives, olive oil, legumes, soy products, tea/coffee, whole grains, nuts, vitamin E, vitamin C, and omega-6 fatty acids (P value < 0.001). After adjusting for confounding variables, the odds of having T2DM were significantly lower (by 30%) at T3 of the PDI than at T1 (OR = 0.70; 95% CI = 0.51, 0.96; P value < 0.001). Our data suggest that adhering to plant-based diets comprising whole grains, fruits, vegetables, nuts, legumes, vegetable oils, and tea/coffee can be recommended today to reduce the risk of T2DM.
Keyphrases
- glycemic control
- weight loss
- type diabetes
- risk factors
- cross sectional
- human health
- fatty acid
- cardiovascular disease
- physical activity
- healthcare
- cell wall
- health risk assessment
- risk assessment
- mental health
- electronic health record
- metabolic syndrome
- transcription factor
- emergency department
- machine learning
- big data
- deep learning
- drinking water
- artificial intelligence
- adverse drug