Blood Glucose Levels After Bread Consumption Between Participants With Normal Weight and Overweight/Obesity

Overview

Obesity and increased blood glucose peaks are risk factors for type 2 diabetes mellitus (T2DM). Few studies have explored whether increased body fat contributes to higher blood glucose peaks after food consumption. This study aimed to investigate differences in blood glucose levels between healthy adults with normal weight and those with overweight/obesity after consuming two commercially available breads (white and wholemeal) with different dietary fibre contents. In this study, 20 healthy adults (10 normal weight, 10 overweight/obese) consumed two slices of white bread (100 g, fibre 3.6 g) or wholemeal bread (88 g, fibre 5.6 g) alongside 150 ml of orange juice and 10 g of butter on separate visits in random order after fasting for 8-12 hours. Blood glucose concentration was measured while fasting (before bread consumption) and at 30, 60, 90, and 120 minutes after consumption using finger-prick tests. Information on age, gender, ethnicity, body mass index (BMI), and body fat percentage (BF%) was also collected.

Introduction Postprandial glycaemic response (PPGR) is an independent risk factor for type 2 diabetes mellitus (T2DM), and postprandial hyperglycaemia is the first detectable metabolic alteration in the progression from prediabetes to T2DM. T2DM is a multifactorial disease, with overweight and obesity being major risk factors. Previous evidence shows inconsistent results regarding the association between BMI (few studies used BF%) and PPGRs. Some studies found no significant difference in the incremental area under the curve (iAUC) of PPGRs between participants with normal weight and overweight/obesity after bread consumption or glucose solution ingestion. Other studies similarly reported no differences after processed orange juice ingestion or a high-fat meal. Bread is a staple food in the UK, with white bread being the most consumed, followed by wholemeal bread. Wholemeal bread is generally considered healthier than white bread due to its higher content of dietary fibre, phytochemicals, and some minerals and vitamins. Interestingly, some studies found no significant differences in PPGRs between wholemeal and white breads, despite the higher fibre content of wholemeal bread. This may be due to the predominance of insoluble fibre in wholemeal flour, which does not affect postprandial glycaemia, whereas soluble, viscous fibre is known to slow gastric emptying and glucose absorption, thereby attenuating glycaemic spikes. However, few studies have tested commercially available breads, which typically contain around 7 g (wholemeal) and 2.9 g (white) of dietary fibre per 100 g. The primary aim of this study is to determine whether individuals with overweight or obesity exhibit higher PPGRs compared to individuals with normal weight following the consumption of commercially available white and wholemeal breads sold in UK supermarkets. The secondary aim is to assess whether wholemeal bread consumption attenuates PPGRs compared to white bread, irrespective of BMI or BF%. Methods Ethics approval The study was approved by the Coventry University Ethics Committee (Ref P136390). Participants Twenty participants will be recruited using purposive and convenience sampling via a recruitment advert circulated by email and word of mouth among university staff and students. Normal weight (BMI 18.5-24.9 kg/m²) and overweight/obesity (BMI ≥ 25 kg/m²) were defined according to National Institute for Health and Care Excellence (NICE). Inclusion criteria: healthy adults aged 18-50 years. Exclusion criteria: diabetes, digestive system diseases, BMI \< 18.5 kg/m², coeliac disease, other chronic diseases, blood clotting disorders, or inability to consume the study meals due to food allergy or other reasons. Eligibility will be screened via a health and lifestyle questionnaire. Study design This is an acute, randomised, non-blinded crossover trial. Eligible participants will attend two visits (at least 48 hours apart). They will be asked to fast for 8-12 hours (water only) prior to each visit. On each visit, participants will be provided with meals containing either white bread (WB) or wholemeal bread (WMB) in random order. The bread type for the first visit will be determined by coin toss: heads = WB, tails = WMB. Participants will consume two slices of bread (white or wholemeal) alongside 150 ml of pure orange juice and 10 g of butter to mimic a real-life scenario and enhance palatability. All food and drink items will be purchased from TESCO supermarket (Welwyn Garden City, United Kingdom). Postprandial blood glucose will be measured at 0 (fasting), 30, 60, 90, and 120 minutes by finger prick, performed by the researcher using the BIOSEN Blood Glucose/Lactate Analyser (EKF Diagnostics, Cardiff). Participants will be instructed to consume the meal within 10 minutes, remain sedentary, and refrain from eating or drinking during the study period. Mobile alarms will be used to ensure blood samples are collected on time. Participants will also be asked to follow a similar dinner, avoid intensive physical activity and alcohol, and obtain adequate sleep the night before each visit. Names, email addresses, and mobile numbers will be collected for appointment purposes, and self-reported demographic variables (age, gender, and ethnicity) will also be collected. The treatment allocation (bread type) will not be blinded to AC and YX, who conducted the study, or to participants (non-blinded), but will be blinded to HD, who analysed the data. Outcome measures PPGRs will be reported as iAUCs and peak values (PVs) after the meals for up to 2 hours. iAUC will be calculated using trapezoidal numerical integration, including only the incremental area above the fasting level. Body height will be measured with a stadiometer, and body weight and BF% will be measured using the TANITA MC-980MA PLUS (TANITA Company, Tokyo) before the meal on the first visit only. Data analysis The sample size was informed by a similar study design and calculated using G\*Power software (version 3.1.9.7, Düsseldorf, Germany), selecting F-test, ANOVA: repeated measures, within-between interaction. Twenty participants were required to detect an effect size of 0.3 with 90% power at a significance level of 0.05. Categorical data will be presented as frequency (n) and percentage (%). Continuous data will be presented as mean ± standard deviation (SD). Differences in age, BMI, and BF% between the two groups will be analysed using independent-samples t-tests. Gender and ethnicity distributions between groups will be analysed using the Chi-squared test. Differences in iAUC and PVs between the two body weight groups (between-subject factor) and between WB and WMB conditions (within-subject factor) will be analysed using a two-way repeated-measures ANOVA. Pearson's correlation will be used to analyse associations of BMI and BF% with iAUCs and PVs after white and wholemeal bread consumption separately. Normality of continuous data will be assessed using the Kolmogorov-Smirnov test. Statistical significance is set at P ≤ 0.05 (two-tailed).