The goal of this study is to investigate the relationship between vitamin D status and a diabetes risk factor, postprandial glycaemic response in White, South Asian and Black African Caribbean populations in London.

The main question[s] it aims to answer are:

• Are there differences in postprandial glycaemic response to a sugar water drink or orange juice between White, South Asian and Black African Caribbean people in London?
• Is there a relationship plasma 25(OH)D and the postprandial glycaemic response to a sugar water drink or orange juice consumption?
• What are the knowledge and perceptions of vitamin D intake between White, South Asian and Black African Caribbean people in London?
• Is there a difference in dietary vitamin D intake between the three ethnic groups?

Participants will make two visits to the lab, approximately 2.5 hours each. The order of the two drinks will be randomized via Excel Random function. For each visit, the blood sugar levels will be measured at fasting (0) and every 30 minutes up to 2 hours postprandially (5 times total) using a finger prick. At baseline only (visit 1), participants will fill out a knowledge and perception survey, provide a 4-day estimated food diary, provide a 7 ml blood sample via phlebotomy, and have their height, weight and body fat percentage measured.

Background

Health patterns differ significantly between ethnic minority groups and the white population. In the United Kingdom (UK), the risk of developing diabetes is two-to-six times higher in South Asians (SAs) and up to three times higher in Black African-Caribbeans (ACs) than in white Caucasians and people in these groups develop the condition at a younger age. Parts of London are among the areas of the UK with the highest proportion of minority groups and highest prevalence of Type-2 Diabetes Mellitus (T2DM). Although multiple factors have been identified including socio-economic standing, diet, culture and attitude, language barriers, genetic, epi-genetic and lifestyles, research into biological diversity is much under-investigated. Recent research found that the postprandial glucose peak in SAs is two-three times larger than that in white Caucasians after identical carbohydrate loads. Our unpublished data also showed the postprandial glucose peak was significantly higher in non-white (Chinese, Indian and Black) than white participants (6.11 nmol/L vs 5.15 nmol/L, P=0.015) after white bread consumption. Though obesity is believed to account for 80-85% of the risk of developing T2DM due to obesity causing insulin resistance, and some minority groups e.g. the Black African and Caribbean population have a higher overweight and obesity prevalence than white Caucasians (73.6% vs. 63.3%), other biological mechanisms have been sparsely investigated, among which is vitamin D (vitD). VitD deficiency in minority groups in the UK is well known and is described as an unrecognized epidemic. In the UK, 50% of SAs and 33% of black ACs demonstrate vitD deficiency vs. 17.5% in white Caucasians, primarily due to more subcutaneous pigmentation that absorbs Ultraviolet B from the sunlight and reduces vitD production in the skin and high latitudes in the UK. This situation is worse in East London in particular because 47% of AC people and 42% of SA people had vitD deficiency compared with 17% of white population in Tower Hamlets. An inverse association of serum 25(OH)D levels with insulin resistance was observed in healthy adults and diabetic patients. Recent evidence shows vitD supplementation may help improve glycaemic control and insulin resistance in T2DM patients. However, little evidence is available for minority groups or residents in London, indicating London (particularly East London) AC and SA communities being underrepresented in the evidence base surrounding diabetes and vitamin D. VitD plays important roles in calcium metabolism and involves in modulation of cell growth, neuromuscular and immune function, and reduction of inflammation due to its receptors expressed ubiquitously in nearly all human cells including the pancreatic β-cells. Animal studies show vitD treatment improved insulin production and sensitivity, and the increased insulin secretion may be caused by a higher intracellular calcium. Moreover, 1, 25(OH)2D (the active form of vitD) may modulate β-cell growth and differentiation. The secondary high parathyroid hormone (PTH) concentration and increased inflammatory markers associated with vitD deficiency may also cause glucose intolerance. VitD may have indirect impact on glycaemic control via obesity. Our research and many others have shown a significant inverse association of body mass index (BMI) and serum 25(OH)D. This is thought to be due to a complex of mutual influence because vitD receptors are expressed on adipose cells and regulate their functions, indicating vitD deficiency might be one of the causes of obesity, thus indirectly leading to increased risk of T2DM.

Postprandial glycaemic response (PGR) has implications in T2DM development. The oral glucose tolerance test (OGTT) is widely used to assess the insulin sensitivity, the pancreatic β-cell function, and judge an individual's metabolic capacity to handle carbohydrate foods. However, a recently published study indicated that within- subject variations of the PGR pattern may exist between OGTT and food intake, suggesting a necessity of combining OGTT and a meal/drink tolerance test for individualized glycemic management. The awareness of vitD and its impact on health is poor in the UK. Though the COVID-19 pandemic has brought vitD to the public attention, a recent UK survey reveals that 49% adults are unaware of UK Government guidelines for vitamin D. There is no such survey available on the minority groups, nor in residents of London. The investigators are interested in the dietary vitamin D intake between different ethnic groups in London, which will be partly explanatory to vitD status in the target population and there is an urgent call for research on minority population to address health inequality.

Aims

To investigate the difference in postprandial glycemic response to oral glucose tolerance tests and orange juice consumption between white Caucasian, South Asian and Black African and Caribbean adults

To investigate the association of serum 25(OH)D concentration with postprandial glycemic response to oral glucose tolerance tests and orange juice consumption between white Caucasian, South Asian and Black African and Caribbean adults

To assess the knowledge and perception of vitamin D, and dietary vitamin D intake in white Caucasian, South Asian and Black African and Caribbean adults

Study design

This is an acute, randomized, repeated measures cross-over design study. 126 healthy adults living in London will be recruited from white Caucasian, South Asian and Black African and Caribbean populations (n=42 in each group).

Recruitment

Participants will be recruited in two ways:

• From staff and students who live in London with gatekeeper permissions Recruitment adverts will be circulated to staff and students at City, University of London.
• From local communities in London with gatekeeper permissions. More communities will be identified in London to help us with recruitment by circulating the project advert, e.g., East London Community College, East London Community Band, and various community centers in London. In addition, the advert leaflets will be posted at the local supermarkets, libraries, hairdressers and barbers, food outlets etc. with permission of gatekeepers.

Treatments

Participants will consume a glucose drink (75g glucose in 300 ml water, 281 kcal) used for oral glucose tolerance test (OGTT) and pure orange juice (OJ, 100% Pure Squeezed Orange Juice Smooth 300 ml containing 129 kcal, 30 g sugar, 0.3 g fibre, 1.8 g protein and 90 mg vitamin C) on separate occasions with at least 48-hour interval and in a random order. Participants are instructed to consumed each drink consumption within a 5 minute period (time taken is recorded). During the two hour study period, participants remain sedentary and cannot eat and drink anything additional.

Sample and data collection

On the night prior to each of their study visit, participants are asked to follow their normal dietary pattern, have a good night's sleep and avoid alcohol and intensive exercise. Participants are instructed to fast for at least eight hours prior to their visit start time.

Blood glucose concentration is measured at baseline (0 minutes), +30 minutes, +60 minutes, +90 minutes and 120 min by finger prick blood sampling and measured using the HemoCue glucose analyser (HemoCue Ltd, Angelholm, Sweden). For the first visit only, a 7-ml fasting blood is collected at baseline (0 minutes) via phlebotomy to measure serum 25(OH)D, C-reactive protein (CRP), parathyroid hormone (PTH), calcium, cholesterol and high-density lipoprotein (HDL). Body mass index (BMI) and fat composition will be measured using a stadiometer and bioelectric impendence analyzing scale (Tanita, Amsterdam). Participants will complete a vitamin D knowledge questionnaire (at baseline) that includes demographics questions including ethnicity. Dietary vitamin D intake is assessed through an estimated food diary that participants complete over four consecutive days (between visits 1 and 2).

Randomization

The order of the drink consumption is randomized by using Excel Random function. Details are below. In Excel spreadsheet, a list of participants (from 1-126) is shown in one column. On the next column, the RANDBETWEEN function is used, and choosing 0 and 1 as the range to randomly generate the value of 0 or 1. Participants with a value of 0 will consume glucose drink while participants with a value of 1 will consume pure orange juice as their first drink respectively.

Statistical analysis

This study aims to achieve a minimum of 25% variability in postprandial glycaemic response among three ethnic groups considering the response has taken from five different time points with 30 min interval for each person and to achieve an 90% power of the study, 42 people are required in each group (n=126 in total) at 5% level of significance. Continuous data will be presented as mean±standard deviation. Categorical data will be presented as percentage. A two-way repeated measures ANOVA is used to assess the treatment (two drinks) effect (within-subject), the effect-time interaction, and between-subject effects (ethnicity, gender, BMI categories) on blood glucose concentrations (five time-points). Continuous variables (e.g., 25(OH)D etc.) between groups are analyzed by two-way ANOVA. Dietary vitD intake between groups will be analyzed by one-way ANOVA (three groups) or independent t-test (two groups) should data are normally distributed otherwise Mann-Whitney test will be used. Categorical variables e.g. the percentage of vitD deficiency are compared by Chi-Square between groups. Data normality will be tested by Kolmogorov-Smirnov test. The generalized linear mixed models (GLMMs) for longitudinal data will also be used for modelling glycaemic response over time because this model accounts for the correlation between observations within each individual and adjusts confounding factors (e.g. BMI, age). The statistical significance will be reported with a p-value and a 95% confidence interval at a 5% significance level. The statistical software International Business Machines (IBM) SPSS 29 will be used to analyze data. Regression imputation will be used to deal with missing data. Both Intention-to-treat analysis and per protocol analysis will be used for the data analysis.

Inclusion Criteria:

• Age 18-65 years
• Live in any London Borough
• Participants ethnicity is either White Caucasian, South Asian, or Black African-Caribbean

Exclusion Criteria:

• Mixed or multiple ethnicities
• Diagnosis of diabetes
• Diagnosis of a digestive system diseases
• Diagnosis of a liver or kidney disease
• Diagnosis of any other chronic diseases
• Diagnosis of any blood clotting disorders
• Are currently pregnant or breastfeeding
• A body mass index (BMI) <18.5 kg/m2
• Consume alcohol more than 14 units per week
• Regular smoker (one or more cigarettes per day)
• On special diet
• Gluten sensitivity or celiac disease
• Fasting blood glucose ≥ 11 mmol/L