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The major objective of this research work was to evaluate various physico-chemical characteristics, such as, chemical composition, antioxidant capacity, objective color and texture profile analysis (TPA) of the wheat flour/chickpea flour (CF) blends, so that nutritious baked products could be consumed by the type-2 diabetic persons.

Wholegrain wheat flour (WGF) and white wheat flour (WWF) were substituted with CF at 0 to 40% levels. These wheat flour/CF blends were analyzed for proximate composition, the prepared dough and baked breads were tested for objective color, antioxidant capacity as trolox equivalent antioxidant capacity (TEAC), malondialdehyde (MDA) and total phenolic content (TPC) and TPA.

WGF had the highest TEAC (117.42 mM/100g) value, followed by WWF (73.98 mM/100g) and CF (60.67 mM/100g). TEAC, MDA and TPC values varied significantly among all the three flour samples.

Inclusion of whole chickpea (without dehulling) flour in such type of blends would be another interesting investigation during the future research studies.

These research findings have a great potential for the production of these baked products for human consumption on an industrial scale.

Production of breads using wheat flour and CF blends would benefits the consumers.

Production of Arabic and pan breads using wheat flour and CF blends would, therefore, combine the benefits of both the needed proteins of plant origin and the health-promoting bioactive compounds, in a most sustainable way for the consumers.

The aim of the present study was to investigate the association of the empirical dietary index for insulin resistance (EDIR) and empirical lifestyle index for insulin resistance (ELIR) with the risk of nonalcoholic fatty liver disease (NAFLD) in Iranian adults.

In this case-control study, 120 cases of NAFLD and 240 controls aged ≥20 years were included. NAFLD was detected by a gastroenterologist using an ultrasonography test. The food frequency questionnaire was used to collect nutritional data and determine the score of EDIR in participants. ELIR was determined based on body mass index, physical activity and dietary pattern. The odds ratios (ORs) of NAFLD were reported across tertiles of EDIR and ELIR using a logistic regression test.

The mean±SD age and BMI of subjects were 41.8 ± 7.5 years and 27.4 ± 2.2 kg/m², respectively. In the age and sex-adjusted model, the odds of NAFLD were increased across tertiles of ELIR (OR = 3.00; 95% CI: 1.63–5.55, P_trend = 0.001). Also, based on the fully adjusted model, the odds of NAFLD were increased according to tertiles of ELIR (OR = 2.66; 95% CI: 1.38–5.10, P_trend = 0.006). However, no significant association was found between the higher score of EDIR and odds of NAFLD based on the age and sex-adjusted model (OR = 1.18; 95% CI: 0.68–2.05, P_trend = 0.52) and the multivariable-adjusted model (OR = 0.91; 95% CI: 0.48–1.70, P_trend = 0.87).

To the best of the authors’ knowledge, this was the first study to examine the role of the insulinemic potential of diet and lifestyle in predicting NAFLD risk. Our findings suggested that a lifestyle with a higher score of ELIR was positively associated with NAFLD risk. However, a diet with a higher score of EDIR was not related to the odds of NAFLD.

Food choices profoundly affect one's dietary, nutritional and health outcomes. Using alcoholic beverages as a case study, the authors assess the potential of genetic data in predicting consumers' food choices combined with conventional socio-demographic data.

A discrete choice experiment was conducted to elicit the underlying preferences of 484 participants from seven provinces in China. By linking three types of data (—data from the choice experiment, socio-demographic information and individual genotyping data) of the participants, the authors employed four machine learning-based classification (MLC) models to assess the performance of genetic information in predicting individuals' food choices.

The authors found that the XGBoost algorithm incorporating both genetic and socio-demographic data achieves the highest prediction accuracy (77.36%), significantly outperforming those using only socio-demographic data (permutation test p-value = 0.033). Polygenic scores of several behavioral traits (e.g. depression and height) and genetic variants associated with bitter taste perceptions (e.g. TAS2R5 rs2227264 and TAS2R38 rs713598) offer contributions comparable to that of standard socio-demographic factors (e.g. gender, age and income).

This study is among the first in the economic literature to empirically demonstrate genetic factors' important role in predicting consumer behavior. The findings contribute fresh insights to the realm of random utility theory and warrant further consumer behavior studies integrating genetic data to facilitate developments in precision nutrition and precision marketing.