The Clinical Application of Mealtime Whey Protein for the Treatment of Postprandial Hyperglycaemia for People With Type 2 Diabetes: A Long Whey to Go

© Copyright © 2020 Smith, Bowden Davies, Stevenson and West. Mitigating postprandial hyperglycaemic excursions may be effective in not only enhancing glycaemic control for people with type 2 diabetes but also reducing the onset of diabetes-related complications. However, there are growing concerns over the long-term efficacy of anti-hyperglycaemic pharmacotherapies, which coupled with their rising financial costs, underlines the need for further non-pharmaceutical treatments to regulate postprandial glycaemic excursions. One promising strategy that acutely improves postprandial glycaemia for people with type 2 diabetes is through the provision of mealtime whey protein, owing to the slowing of gastric emptying and increased secretion of insulin and the incretin peptides. The magnitude of this effect appears greater when whey protein is consumed before, rather than with, a meal. Herein, this dietary tool may offer a simple and inexpensive strategy in the management of postprandial hyperglycaemia for people with type 2 diabetes. However, there are insufficient long-term studies that have investigated the use of mealtime whey protein as a treatment option for individuals with type 2 diabetes. The methodological approaches applied in acute studies and outcomes reported may also not portray what is achievable long-term in practice. Therefore, studies are needed to refine the application of this mealtime strategy to maximize its clinical potential to treat hyperglycaemia and to apply these long-term to address key components of successful diabetes care. This review discusses evidence surrounding the provision of mealtime whey protein to treat postprandial hyperglycaemia in individuals with type 2 diabetes and highlights areas to help facilitate its clinical application

Pre-Meal Whey Protein Alters Postprandial Insulinemia by Enhancing β-Cell Function and Reducing Insulin Clearance in T2D

CONTEXT: Treatments that reduce postprandial glycemia (PPG) independent of stimulating insulin secretion are appealing for the management of type 2 diabetes (T2D). Consuming pre-meal whey protein (WP) reduces PPG by delaying gastric emptying and increasing plasma insulin concentrations. However, its effects on β-cell function and insulin kinetics remains unclear. OBJECTIVE: To examine the PPG-regulatory effects of pre-meal WP by modeling insulin secretion rates (ISR), insulin clearance, and β-cell function. METHODS: This was a single-blind, randomized, placebo-controlled, crossover design study in 18 adults with T2D (HbA1c, 56.7 ± 8.8 mmol/mol) who underwent 2 240-minute mixed-meal tolerance tests. Participants consumed WP (15 g protein) or placebo (0 g protein) 10 minutes before a mixed-macronutrient breakfast meal. PPG, pancreatic islet, and incretin hormones were measured throughout. ISR was calculated by C-peptide deconvolution. Estimates of insulin clearance and β-cell function were modeled from glucose, insulin, and ISR. Changes in PPG incremental area under the curve (iAUC; prespecified) and insulin clearance (post hoc) were measured. RESULTS: β-cell function was 40% greater after WP (P = .001) and was accompanied with a -22% reduction in postprandial insulin clearance vs placebo (P < .0001). Both the peak change and PPG iAUC were reduced by WP (-1.5 mmol/L and -16%, respectively; both P < .05). Pre-meal WP augmented a 5.9-fold increase in glucagon and glucagon-like peptide 1 iAUC (both P < .0001), and a 1.5-fold increase in insulin iAUC (P < .001). Although the plasma insulin response was greater following WP, ISR was unaffected (P = .133). CONCLUSION: In adults with T2D, pre-meal WP reduced PPG by coordinating an enhancement in β-cell function with a reduction in insulin clearance. This enabled an efficient postprandial insulinemic profile to be achieved without requiring further β-cell stimulation.Trial registry ISRCTN ID: ISRCTN17563146 Website link: www.isrctn.com/ISRCTN17563146

Sars-Cov-2 Infection in People with Type 1 Diabetes and Hospital Admission: An Analysis of Risk Factors for England

Introduction: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus (coronavirus disease 2019 [COVID-19]) pandemic revealed the vulnerability of specific population groups in relation to susceptibility to acute deterioration in their health, including hospital admission and mortality. There is less data on outcomes for people with type 1 diabetes (T1D) following SARS-CoV-2 infection than for those with type 2 diabetes (T2D). In this study we set out to determine the relative likelihood of hospital admission following SARS-CoV-2 infection in people with T1D when compared to those without T1D. Methods: This study was conducted as a retrospective cohort study and utilised an all-England dataset. Electronic health record data relating to people in a national England database (NHS England’s Secure Data Environment, accessed via the BHF Data Science Centre's CVD-COVID-UK/COVID-IMPACT consortium) were analysed. The cohort consisted of patients with a confirmed SARS-CoV-2 infection, and the exposure was whether or not an individual had T1D prior to infection (77,392 patients with T1D). The patients without T1D were matched for sex, age and approximate date of the positive COVID-19 test, with three SARS-CoV-2-infected people living without diabetes (n = 223,995). Potential factors influencing the relative likelihood of the outcome of hospital admission within 28 days were ascertained using univariable and multivariable logistic regression. Results: Median age of the people living with T1D was 37 (interquartile range 25–52) years, 47.4% were female and 89.6% were of white ethnicity. Mean body mass index was 27 (standard error [SE] 0.022) kg/m2, and mean glycated haemoglobin (HbA1c) was 67.3 (SE 0.069) mmol/mol (8.3%). A significantly higher proportion of people with T1D (10.7%) versus matched non-diabetes individuals (3.9%) were admitted to hospital. In combined analysis including individuals with T1D and matched controls, multiple regression modelling indicated that the factors independently relating to a higher likelihood of hospital admission were: T1D (odds ratio [OR] 1.71, 95% confidence interval [CI] 1.62–1.80]), age (OR 1.02, 95% CI 1.02–1.03), social deprivation (higher Townsend deprivation score: OR 1.07, 95% CI 1.06–1.08), lower estimated glomerular filtration rate (eGFR) value (OR 0.975, 95% CI 0.974–0.976), non-white ethnicity (OR black 1.19, 95% CI 1.06–1.33/OR Asian 1.21, 95% CI 1.05–1.39) and having asthma (OR 1.27, 95% CI 1.19–1.35]), chronic obstructive pulmonary disease (OR 2.10, 95% CI 1.89–2.32), severe mental illness (OR 1.83, 95% CI 1.57–2.12) or hypertension (OR 1.44, 95% CI 1.37–1.52). Conclusion: In this all-England study, we describe that, following confirmed infection with SARS-CoV-2, the risk factors for hospital admission for people living with T1D are similar to people without diabetes following confirmed SARS-CoV-2 infection, although the former were more likely to be admitted to hospital. The younger age of individuals with T1D in relation to risk stratification must be taken into account in any ongoing risk reduction strategies regarding COVID-19/future viral pandemics

Correction to: Sars-Cov-2 Infection in People with Type 1 Diabetes and Hospital Admission: An Analysis of Risk Factors for England

The article “Sars-Cov-2 Infection in People with Type 1 Diabetes and Hospital Admission: An Analysis of Risk Factors for England”, written by Adrian H. Heald, David A. Jenkins, Richard Williams, Rajshekhar N. Mudaliar, Amber Khan, Akheel Syed, Naveed Sattar, Kamlesh Khunti, Asma Naseem, Kelly A. Bowden-Davies, J. Martin Gibson, William Ollier, on behalf of the CVD-COVID-UK/COVID-IMPACT Consortium was originally published electronically on the publisher’s Internet portal (currently SpringerLink) on August 25, 2023, without open access. Now, the article is updated with open access as This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The original article has been corrected

Physical Activity and Sedentary Time: Association with Metabolic Health and Liver Fat.

INTRODUCTION/PURPOSE: To investigate whether a) lower levels of daily physical activity (PA) and greater sedentary time accounted for contrasting metabolic phenotypes (higher liver fat/presence of metabolic syndrome [MetS+] vs lower liver fat/absence of metabolic syndrome [MetS-]) in individuals of similar BMI and b) the association of sedentary time on metabolic health and liver fat. METHODS: Ninety-eight habitually active participants (53 female, 45 male; age 39±13 years; BMI 26.9±5.1 kg/m), underwent assessments of PA (SenseWear armband; wear time ~98%), cardio-respiratory fitness (V[Combining Dot Above]O2 peak), body composition (MRI and MRS) and multi-organ insulin sensitivity (OGTT). We undertook a) cross-sectional analysis comparing four groups: non-obese or obese, with and without metabolic syndrome (MetS+ vs MetS-) and b) univariate and multivariate regression for sedentary time and other levels of PA in relation to liver fat. RESULTS: Light, moderate and vigorous PA did not account for differences in metabolic health between individuals, whether non-obese or obese, although MetS+ individuals were more sedentary, with a higher number, and prolonged bouts (~1-2 hours). Overall, sedentary time, average daily METS and V[Combining Dot Above]O2 peak were each independently associated with liver fat percentage. Each additional hour of daily sedentary time was associated with a 1.15% (95% CI, 1.14-1.50%) higher liver fat content. CONCLUSIONS: Greater sedentary time, independent of other levels of PA, is associated with being metabolically unhealthy; even in habitually active people, lesser sedentary time, and higher cardio-respiratory fitness and average daily METS is associated with lower liver fat.This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Metabolic syndrome is associated with reduced flow mediated dilation independent of obesity status.

Background: Data suggest that metabolic health status, incorporating components of metabolic syndrome (MetS), predicts cardiovascular disease (CVD) risk better than BMI. This study explored the association of MetS and obesity with endothelial function, a prognostic risk factor for incident CVD. Methods: Forty-four participants were phenotyped according to BMI as non-obese vs obese (30 kg/m2) and according to the International Diabetes Federation criteria of MetS: ≤2 criteria MetS (MetS-) vs ≥3 criteria MetS (MetS+); (1.)non-obese MetS- vs (2.) non-obese MetS+ and (3.) obese MetS- vs (4.) obese MetS+. Flow-mediated dilation (FMD), body composition including liver fat (MRI and spectroscopy), dietary intake, intensities of habitual physical activity and cardio-respiratory fitness were determined. Variables were analysed using a one-factor between-groups ANOVA and linear regression; mean (95% CI) are presented. Results: Individuals with MetS+ displayed lower FMD than those with MetS-. For non-obese individuals mean difference between MetS+ and MetS- was 4.1% ((1.0, 7.3); P = 0.004) and obese individuals had a mean difference between MetS+ and MetS- of 6.2% ((3.1, 9.2); P < 0.001). Although there was no association between BMI and FMD (P = 0.27), an increased number of MetS components was associated with a lower FMD (P = 0.005), and after adjustment for age and sex, 19.7% of the variance of FMD was explained by MetS, whereas only 1.1% was explained by BMI. Conclusions: In this study cohort, components of MetS, rather than obesity per se, contribute to reduced FMD, which suggests a reduced bioavailability of nitric oxide and thus increased risk of CVD

Short-Term Physical Inactivity Induces Endothelial Dysfunction

Objective: This study examined the effects of a short-term reduction in physical activity, and subsequent resumption, on metabolic profiles, body composition and cardiovascular (endothelial) function. Design: Twenty-eight habitually active (≥10,000 steps/day) participants (18 female, 10 male; age 32 ± 11 years; BMI 24.3 ± 2.5 kg/m2) were assessed at baseline, following 14 days of step-reduction and 14 days after resuming habitual activity. Methods: Physical activity was monitored throughout (SenseWear Armband). Endothelial function (flow mediated dilation; FMD), cardiorespiratory fitness (V.O2 peak) and body composition including liver fat (dual-energy x-ray absorptiometry and magnetic resonance spectroscopy) were determined at each assessment. Statistical analysis was performed using one-way within subject’s ANOVA; data presented as mean (95% CI). Results: Participants decreased their step count from baseline by 10,111 steps/day (8949, 11,274; P < 0.001), increasing sedentary time by 103 min/day (29, 177; P < 0.001). Following 14 days of step-reduction, endothelial function was reduced by a 1.8% (0.4, 3.3; P = 0.01) decrease in FMD. Following resumption of habitual activity, FMD increased by 1.4%, comparable to the baseline level 0.4% (–1.8, 2.6; P = 1.00). Total body fat, waist circumference, liver fat, whole body insulin sensitivity and cardiorespiratory fitness were all adversely affected by 14 days step-reduction (P < 0.05) but returned to baseline levels following resumption of activity. Conclusion: This data shows for the first time that whilst a decline in endothelial function is observed following short-term physical inactivity, this is reversed on resumption of habitual activity. The findings highlight the need for public health interventions that focus on minimizing time spent in sedentary behavior

Short-term decreased physical activity with increased sedentary behaviour causes metabolic derangements and altered body composition: effects in individuals with and without a first-degree relative with type 2 diabetes.

AIMS/HYPOTHESIS: Low physical activity levels and sedentary behaviour are associated with obesity, insulin resistance and type 2 diabetes. We investigated the effects of a short-term reduction in physical activity with increased sedentary behaviour on metabolic profiles and body composition, comparing the effects in individuals with first-degree relatives with type 2 diabetes (FDR+ve) vs those without (FDR-ve). METHODS: Forty-five habitually active participants (16 FDR+ve [10 female, 6 male] and 29 FDR-ve [18 female, 11 male]; age 36 ± 14 years) were assessed at baseline, after 14 days of step reduction and 14 days after resuming normal activity. We determined physical activity (using a SenseWear armband), cardiorespiratory fitness ([Formula: see text]), body composition (dual-energy x-ray absorptiometry/magnetic resonance spectroscopy) and multi-organ insulin sensitivity (OGTT) at each time point. Statistical analysis was performed using a two-factor between-groups ANCOVA, with data presented as mean ± SD or (95% CI). RESULTS: There were no significant between-group differences in physical activity either at baseline or following step reduction. During the step-reduction phase, average daily step count decreased by 10,285 steps (95% CI 9389, 11,182; p < 0.001), a reduction of 81 ± 8%, increasing sedentary time by 223 min/day (151, 295; p < 0.001). Pooling data from both groups, following step reduction there was a significant decrease in whole-body insulin sensitivity (Matsuda index) (p < 0.001), muscle insulin sensitivity index (p < 0.001), cardiorespiratory fitness (p = 0.002) and lower limb lean mass (p = 0.004). Further, there was a significant increase in total body fat (p < 0.001), liver fat (p = 0.001) and LDL-cholesterol (p = 0.013), with a borderline significant increase in NEFA AUC during the OGTT (p = 0.050). Four significant between-group differences were apparent: following step reduction, FDR+ve participants accumulated 1.5% more android fat (0.4, 2.6; p = 0.008) and increased triacylglycerol by 0.3 mmol/l (0.1, 0.6; p = 0.044). After resuming normal activity, FDR+ve participants engaged in lower amounts of vigorous activity (p = 0.006) and had lower muscle insulin sensitivity (p = 0.023). All other changes were reversed with no significant between-group differences. CONCLUSIONS/INTERPRETATION: A short-term reduction in physical activity with increased sedentary behaviour leads to a reversible reduction in multi-organ insulin sensitivity and cardiorespiratory fitness, with concomitant increases in central and liver fat and dyslipidaemia. The effects are broadly similar in FDR+ve and FDR-ve individuals. Public health recommendations promoting physical activity should incorporate advice to avoid periods of sedentary behaviour

The risk factors potentially influencing hospital admission in people with diabetes, following SARS-CoV-2 infection : a population-level analysis

Introduction: Since early 2020 the whole world has been challenged by the SARS-CoV-2 virus and the associated global pandemic (Covid-19). People with diabetes are particularly at high risk of becoming seriously unwell after contracting this virus. Methods: This population-based study included people living in the Greater Manchester conurbation who had a recorded diagnosis of type 1 diabetes mellitus (T1DM) or type 2 diabetes mellitus (T2DM) and subsequent Covid-19 infection. Each individual with T1DM (n = 862) or T2DM (n = 13,225) was matched with three Covid-19-infected non-diabetes controls. Results: For individuals with T1DM, hospital admission rate in the first 28 days after a positive Covid-19 test was 10% vs 4.7% in age/gender-matched controls [relative risk (RR) 2.1]. For individuals with T2DM, hospital admission rate after a positive Covid-19 test was 16.3% vs 11.6% in age/gender-matched controls (RR 1.4). The average Townsend score was higher in T2DM (1.8) vs matched controls (0.4), with a higher proportion of people with T2DM observed in the top two quintiles of greatest disadvantage (p < 0.001). For Covid-19-infected individuals with T1DM, factors influencing admission likelihood included age, body mass index (BMI), hypertension, HbA1c, low HDL-cholesterol, lower estimated glomerular filtration rate (eGFR), chronic obstructive pulmonary disease (COPD) and being of African/mixed ethnicity. In Covid-19-infected individuals with T2DM, factors related to a higher admission rate included age, Townsend index, comorbidity with COPD/asthma and severe mental illness (SMI), lower eGFR. Metformin prescription lowered the likelihood. For multivariate analysis in combined individuals with T2DM/controls, factors relating to higher likelihood of admission were having T2DM/age/male gender/diagnosed COPD/diagnosed hypertension/social deprivation (higher Townsend index) and non-white ethnicity (all groups). Conclusion: In a UK population we have confirmed a significantly higher likelihood of admission in people with diabetes following Covid-19 infection. A number of factors mediate that increased likelihood of hospital admission. For T2DM, the majority of factors related to increased admission rate are common to the general population but more prevalent in T2DM. There was a protective effect of metformin in people with T2DM