Association between Screen Time and Sociodemographic Factors, Physical Activity, and BMI among Children in Six European Countries (Feel4Diabetes): A Cross-Sectional Study.

Screen time among children in most European countries is notably high and is influenced by various sociodemographic and other factors. Our study aimed to explore the associations between parents' sociodemographic characteristics, socioeconomic status, body mass index (BMI), physical activity, risk status for type 2 diabetes, and their children's BMI, physical activity, and screen time. The data were sourced from the 2016 Feel4Diabetes study, involving 12,280 parents and 12,211 children aged 6-9 years (average age 8.21 years) in a cross-sectional study design. We used a logistic regression model to identify potential factors associated with children's screen time. The results showed that mothers with tertiary education (OR = 0.64; 95%CI = 0.49-0.82; p < 0.001), the middle age group (45-54 years) (OR = 0.81 95%CI = 0.66-0.98; p = 0.033), and families with higher incomes (middle-OR = 0.85; 95%CI = 0.75-0.97; p = 0.014; high-OR = 0.8; 95%CI = 0.69-0.93; p = 0.003) were associated with a decreased chance of children spending more than 2 h/day in front of the screen. In contrast, maternal overweight/obesity (OR = 1.15; 95%CI = 1.03-1.29; p = 0.013) and lower physical activity in children were linked to an increased likelihood of more than 2 h of screen time per day. Our findings suggest that targeted interventions should be developed to mitigate excessive screen time, particularly focusing on low-income families and mothers with low educational levels.

Effectiveness of a Digital Health Application for the Treatment of Diabetes Type II-A Pilot Study.

(1) Background: This study aimed at providing preliminary evidence for mebix, an app-based treatment program for patients with diabetes mellitus type II. The main target was to show a positive healthcare impact as defined by improved blood glucose control, i.e., reduced HbA1c values. (2) Methods: For this, a 3-month, prospective, open-label trial with an intraindividual control group was conducted. Participants received the mebix intervention for 3 months. HbA1c values were observed every 3 months: retrospectively, at baseline, and 3 months after the start of using the app. Additionally, weight and patients' reported outcomes (well-being, diabetes-related distress, and self-management) were assessed. Data generated within the app were summarized and analyzed (steps, physical activity, fulfilled tasks, and food logs). (3) Results: After the usage of mebix for 3 months, participants significantly reduced their HbA1c levels (-1.0 ± 0.8%). Moreover, improvements in weight, well-being, and self-management as well as a reduction in diabetes-related distress were observed. App-generated data mainly supported the other main finding, that higher baseline HbA1c values lead to higher reductions. Overall, the study provided preliminary evidence that mebix can help patients improve metabolic and psychological health outcomes.

Mechanisms of weight loss-induced remission in people with prediabetes: a post-hoc analysis of the randomised, controlled, multicentre Prediabetes Lifestyle Intervention Study (PLIS).

BACKGROUND: Remission of type 2 diabetes can occur as a result of weight loss and is characterised by liver fat and pancreas fat reduction and recovered insulin secretion. In this analysis, we aimed to investigate the mechanisms of weight loss- induced remission in people with prediabetes. METHODS: In this prespecified post-hoc analysis, weight loss-induced resolution of prediabetes in the randomised, controlled, multicentre Prediabetes Lifestyle Intervention Study (PLIS) was assessed, and the results were validated against participants from the Diabetes Prevention Program (DPP) study. For PLIS, between March 1, 2012, and Aug 31, 2016, participants were recruited from eight clinical study centres (including seven university hospitals) in Germany and randomly assigned to receive either a control intervention, a standard lifestyle intervention (ie, DPP-based intervention), or an intensified lifestyle intervention for 12 months. For DPP, participants were recruited from 23 clinical study centres in the USA between July 31, 1996, and May 18, 1999, and randomly assigned to receive either a standard lifestyle intervention, metformin, or placebo. In both PLIS and DPP, only participants who were randomly assigned to receive lifestyle intervention or placebo and who lost at least 5% of their bodyweight were included in this analysis. Responders were defined as people who returned to normal fasting plasma glucose (FPG; <5·6 mmol/L), normal glucose tolerance (<7·8 mmol/L), and HbA1c less than 39 mmol/mol after 12 months of lifestyle intervention or placebo or control intervention. Non-responders were defined as people who had FPG, 2 h glucose, or HbA1c more than these thresholds. The main outcomes for this analysis were insulin sensitivity, insulin secretion, visceral adipose tissue (VAT), and intrahepatic lipid content (IHL) and were evaluated via linear mixed models. FINDINGS: Of 1160 participants recruited to PLIS, 298 (25·7%) had weight loss of 5% or more of their bodyweight at baseline. 128 (43%) of 298 participants were responders and 170 (57%) were non-responders. Responders were younger than non-responders (mean age 55·6 years [SD 9·9] vs 60·4 years [8·6]; p<0·0001). The DPP validation cohort included 683 participants who lost at least 5% of their bodyweight at baseline. Of these, 132 (19%) were responders and 551 (81%) were non-responders. In PLIS, BMI reduction was similar between responders and non-responders (responders mean at baseline 32·4 kg/m2 [SD 5·6] to mean at 12 months 29·0 kg/m2 [4·9] vs non-responders 32·1 kg/m2 [5·9] to 29·2 kg/m2 [5·4]; p=0·86). However, whole-body insulin sensitivity increased more in responders than in non-responders (mean at baseline 291 mL/[min × m2], SD 60 to mean at 12 months 378 mL/[min × m2], 56 vs 278 mL/[min × m2], 62, to 323 mL/[min × m2], 66; p<0·0001), whereas insulin secretion did not differ within groups over time or between groups (responders mean at baseline 175 pmol/mmol [SD 64] to mean at 12 months 163·7 pmol/mmol [60·6] vs non-responders 158·0 pmol/mmol [55·6] to 154·1 pmol/mmol [56·2]; p=0·46). IHL decreased in both groups, without a difference between groups (responders mean at baseline 10·1% [SD 8·7] to mean at 12 months 3·5% [3·9] vs non-responders 10·3% [8·1] to 4·2% [4·2]; p=0·34); however, VAT decreased more in responders than in non-responders (mean at baseline 6·2 L [SD 2·9] to mean at 12 months 4·1 L [2·3] vs 5·7 L [2·3] to 4·5 L [2·2]; p=0·0003). Responders had a 73% lower risk of developing type 2 diabetes than non-responders in the 2 years after the intervention ended. INTERPRETATION: By contrast to remission of type 2 diabetes, resolution of prediabetes was characterised by an improvement in insulin sensitivity and reduced VAT. Because return to normal glucose regulation (NGR) prevents development of type 2 diabetes, we propose the concept of remission of prediabetes in analogy to type 2 diabetes. We suggest that remission of prediabetes should be the primary therapeutic aim in individuals with prediabetes. FUNDING: German Federal Ministry for Education and Research via the German Center for Diabetes Research; the Ministry of Science, Research and the Arts Baden-Württemberg; the Helmholtz Association and Helmholtz Munich; the Cluster of Excellence Controlling Microbes to Fight Infections; and the German Research Foundation.

Loci for insulin processing and secretion provide insight into type 2 diabetes risk.

Insulin secretion is critical for glucose homeostasis, and increased levels of the precursor proinsulin relative to insulin indicate pancreatic islet beta-cell stress and insufficient insulin secretory capacity in the setting of insulin resistance. We conducted meta-analyses of genome-wide association results for fasting proinsulin from 16 European-ancestry studies in 45,861 individuals. We found 36 independent signals at 30 loci (p value < 5 × 10-8), which validated 12 previously reported loci for proinsulin and ten additional loci previously identified for another glycemic trait. Half of the alleles associated with higher proinsulin showed higher rather than lower effects on glucose levels, corresponding to different mechanisms. Proinsulin loci included genes that affect prohormone convertases, beta-cell dysfunction, vesicle trafficking, beta-cell transcriptional regulation, and lysosomes/autophagy processes. We colocalized 11 proinsulin signals with islet expression quantitative trait locus (eQTL) data, suggesting candidate genes, including ARSG, WIPI1, SLC7A14, and SIX3. The NKX6-3/ANK1 proinsulin signal colocalized with a T2D signal and an adipose ANK1 eQTL signal but not the islet NKX6-3 eQTL. Signals were enriched for islet enhancers, and we showed a plausible islet regulatory mechanism for the lead signal in the MADD locus. These results show how detailed genetic studies of an intermediate phenotype can elucidate mechanisms that may predispose one to disease.

Design of the DAVOS Study: Diabetes Smartphone App, a Fully Automatic Transmission of Data From the Blood Glucose Meter and Insulin Pens Using Wireless Technology to Enhance Diabetes Self-Management-A Study Protocol for a Randomized Controlled Trial.

BACKGROUND: In the treatment of diabetes mellitus, the challenge is to integrate adequate self-management into clinical care. Customization including goal setting, monitoring, and feedback could be achieved through digitization. Digital linking between different devices could simplify and promote self-management. The aim of this study is to evaluate the outcome of diabetes treatment assisted by a digital health application compared with standard diabetes therapy. METHODS: The DAVOS study is a 6-month-period prospective, multicentric, randomized controlled trial. In total, 154 diabetes patients (age ≥18; treated with insulin) will be recruited and randomized into control group or intervention group. Both groups will receive standard diabetes care. The intervention group will additionally use a diabetes app. HbA1c value will be monitored on three separate defined visits. Primary endpoint is the overall reduction of HbA1c value. Secondary endpoints (eg, usability of the app) will be determined through patient-reported outcome questionnaires. DISCUSSION: Through enhanced interaction of health care professionals, providers of the app, and patients, the study aims to demonstrate improvement in the self-management of diabetes. As part of the closure management, all patients will be invited to use the examined application after completion of the study. The DAVOS study will be conducted in accordance with the valid version of the present study protocol and the internationally recognized International Conference on Harmonization-Good Clinical Practice (ICH-GCP) Guidelines. Special attention will be paid to European, national, and regional requirements for the approval, provision, and use of medical devices. The study was registered in the German Register of Clinical Trials (DRKS) with number DRKS00025996.

Low IGF1 and high IGFBP1 predict diabetes onset in prediabetic patients.

Objectives: Some individuals develop type 2 diabetes mellitus (T2DM) despite significant metabolic improvements through lifestyle intervention. We tested the hypotheses that insulin growth factor 1 (IGF1) and its binding proteins 1 and 2 predict the onset of T2DM in prediabetes patients and determine the capacity for metabolic regeneration. Design: We measured fasting serum IGF1, insulin growth factor-binding protein 1 (IGFBP1) and IGFBP2 in three randomized controlled lifestyle intervention trials, covering at least 1 year of intervention period and 1 year of additional follow-up. Methods: Within a sample of 414 high-risk prediabetes patients (58% women; 28-80 years), we analyzed fasting serum concentrations of IGF1, IGFBP1 and IGFBP2 in relation to diabetes incidence and metabolic parameters over 2 years. Three hundred and forty-five subjects finished the first year of intervention. Results: The interventions significantly improved body weight (BMI: -3.24%, P < 0.001), liver fat (-36.8%, P < 0.001), insulin sensitivity (IS) (homeostatic model assessment-insulin resistance: -6.3%, P < 0.001) and insulin secretion (disposition index: +35%, P < 0.001) in the cohort. Fourteen percent developed T2DM within 2 years. Mean IGFBP1 levels at baseline were lower in prediabetes compared to a healthy population. Also, prediabetes patients with obesity and nonalcoholic fatty liver disease had lower IGFBP1. Those with impaired glucose tolerance had higher IGFBP1 compared to those with only impaired fasting glucose. Baseline IGF1 was lower (122.5 vs 146.6 µg/L) and IGFBP1 was higher (3.32 vs 2.09 µg/L) in subjects who developed T2DM (n = 57), resulting in a significant prediction of diabetes incidence (hazard ratio (HR) IGF1: 0.991 µg/L, P = 0.003; HR IGFBP1: 1.061 µg/L, P = 0.002). This translates into a 20% and 9% difference in T2DM incidence for IGF1 and IGFBP1, respectively. Despite reduced weight, visceral fat and hepatic fat in response to 1 year of lifestyle intervention, those who developed T2DM had not improved insulin sensitivity, glucose tolerance or IGFBP1. Conclusions: Lower IGF1 and higher IGFBP1 in prediabetes predicted the incidence of T2DM, indicating an impairment of beta-cell function, which explains the unresponsiveness to lifestyle intervention.

Treating Insulin Resistance with Intermittent Personalized Nutrition to Sustain Metabolic Health and Functioning Immune System for Risk Reduction of Viral Diseases Especially COVID-19 and Long-COVID.

The Covid-19 pandemic has provided new and strong evidence for poor outcomes of viral infection in patients with poor metabolic health. Insulin resistance is at the root of many metabolic conditions and a key driver of their progression as it promotes ineffectual inflammation whilst impairing immune functions. In a vicious circle, insulin resistance facilitates SARS-CoV-2 infection, whilst infection drives insulin resistance. We discuss the underlying mechanisms and explore ways to improve metabolic health and prevent insulin resistance through early detection and targeted nutritional interventions. With proven efficacy in prediabetes, type 2 diabetes, and their cardiovascular and organ complications, as much as non-alcoholic liver disease, we argue to extend such approaches to ensure resilience to the current pandemic and viral challenges beyond.

Impact of a Digital Lifestyle Intervention on Diabetes Self-Management: A Pilot Study.

The aim of this study was to provide preliminary evidence on the impact of the digital health application Vitadio on improving glycemic control in patients with type 2 diabetes mellitus. This was a 3-month, prospective, multicenter, open-label trial with an intraindividual control group. Participants received a digital lifestyle intervention. HbA1c levels were observed at 3 time points: retrospectively, at 3 months before app use; at baseline, at the start of usage; and 3 months after the start of use. In addition, changes in other metabolic parameters (fasting glucose, body weight, and waist circumference), patient reported outcomes (quality of life, self-efficacy, and depression), and data generated within the app (frequency of use, steps, and photos of meals) were evaluated. Repeated measures analysis of variance with the Bonferroni correction was used to assess the overall difference in HbA1c values between the intervention and the intraindividual control group, with p < 0.05 considered significant. Participants (n = 42) were 57 ± 7.4 years old, 55% male, and with a mean baseline HbA1c of 7.9 ± 1.0%. An average HbA1c reduction of −0.9 ± 1.1% (p < 0.001) was achieved. The digital health application was effective in significantly reducing body weight (−4.3 ± 4.5 kg), body mass index (−1.4 ± 1.5 kg/m2), waist circumference (−5.7 ± 15 cm), and fasting glucose (−0.6 ± 1.3 mmol/L). The digital therapy achieved a clinically meaningful and significant HbA1c reduction as well as a positive effect on metabolic parameters. These results provide preliminary evidence that Vitadio may be effective in supporting patient diabetes management by motivating patients to adopt healthier lifestyles and improving their self-management.

The interface of COVID-19, diabetes, and depression.

Comorbid diabetes with depression is a challenging and often under-recognized clinical problem. During the current COVID-19 pandemic, a communicable disease is thriving on the increasing incidences of these non-communicable diseases. These three different health problems are bidirectionally connected forming a vicious cycle. Firstly, depressed individuals show a higher risk of developing diabetes and patients with diabetes have a higher risk of developing symptoms of depression. Secondly, patients with diabetes have a higher risk of developing severe COVID-19 as well as of experiencing breakthrough infections. Thirdly, in both patients with type 2 diabetes and in COVID-19 survivors the prevalence of depression seems to be increased. Fourthly, lockdown and quarantine measurements during the COVID-19 pandemic has led to an increase in depression. Therefore, it is of importance to increase the awareness of this interface between depression, diabetes and COVID-19. Finally, as symptoms of post-COVID, diabetes and depression may be overlapping, there is a need for educating skilled personnel in the management of these comorbidities.

Video-based smartphone app ('VIDEA bewegt') for physical activity support in German adults: a single-armed observational study.

OBJECTIVES: The primary objective of this study was to investigate the effect of the video-based smartphone app 'VIDEA bewegt' over eight programme weeks on physical activity in German adults. DESIGN: The study used a single-arm observational design, assessing the app's effectiveness under real-life conditions. Data were collected from July 2019 to July 2020. SETTING: The app is enabling users to access video-based educational content via their smartphone. A clinical visit or in-person contact was not required. PARTICIPANTS: All individuals registered in the freely available app were invited to take part in the study. INTERVENTIONS: The app aims to increase physical activity in everyday life. It combines educative videos on lifestyle-related benefits and instructional videos of strength and endurance exercises to do at home with motivational components like goal setting, documentation of progress and personalised messages. PRIMARY AND SECONDARY OUTCOME MEASURES: Primary outcomes were physical activity based one MET minutes per week (metabolic equivalent) and step numbers.Secondary outcomes included physical self-efficacy (motivational, maintenance, recovery self-efficacy), health-related quality of life: Mental Health Component Summary score and Physical Health Component Summary score. RESULTS: Of 97 people included in the data analysis, 55 successfully completed the programme and all questionnaires. Significant increases over eight programme weeks (between T0 and T2) were observed in physical activity based on MET minutes per week, health-related quality of life, and recovery self-efficacy. Time spent sitting and body mass index significantly decreased for those completing the programme. CONCLUSIONS: Although significant benefits of physical activity were observed following a complete-case analysis, results should be dealt with caution. Studies with a larger and less heterogeneous sample and robust study designs able to measure causal effects would be desirable. TRIAL REGISTRATION NUMBER: DRKS00017392.

Long-Term Effects of a Video-Based Smartphone App ("VIDEA Bewegt") to Increase the Physical Activity of German Adults: A Single-Armed Observational Follow-Up Study.

As physical inactivity is one of the four leading risk factors for mortality, it should be intensively treated. Therefore, this one-year follow-up study aimed to evaluate the long-term effects of a preventive app to increase physical activity in German adults under real-life circumstances. Data collection took place from July 2019 to July 2021 and included six online questionnaires. Physical activity was studied as the primary outcome based on MET-minutes per week (metabolic equivalent). Secondary outcomes included health-related quality of life based on a mental (MCS) and physical health component summary score (PCS). At the time of publication, 46/65 participants completed the study (median 52 years, 81.5% women). A significant increase of physical activity was observed in people with a low/moderate baseline activity during the first four months of follow-up (median increase by 490 MET-minutes per week, p < 0.001, r = 0.649). Both MCS (median increase by 2.8, p = 0.006, r = 0.344) and PCS (median increase by 2.6, p < 0.001, r = 0.521) significantly increased during the first two months and the BMI significantly decreased during the first six months after the intervention (median decrease by 0.96 kg/m2, p < 0.001, r = 0.465). Thus, this study provides evidence for the medium-term impact of the app, since the effects decreased over time. However, due to the chosen study design and a sizeable loss to follow-up, the validity of these findings is limited.

Machine Learning Refutes Loss of Smell as a Risk Indicator of Diabetes Mellitus.

Because it is associated with central nervous changes, and olfactory dysfunction has been reported with increased prevalence among persons with diabetes, this study addressed the question of whether the risk of developing diabetes in the next 10 years is reflected in olfactory symptoms. In a cross-sectional study, in 164 individuals seeking medical consulting for possible diabetes, olfactory function was evaluated using a standardized clinical test assessing olfactory threshold, odor discrimination, and odor identification. Metabolomics parameters were assessed via blood concentrations. The individual diabetes risk was quantified according to the validated German version of the "FINDRISK" diabetes risk score. Machine learning algorithms trained with metabolomics patterns predicted low or high diabetes risk with a balanced accuracy of 63-75%. Similarly, olfactory subtest results predicted the olfactory dysfunction category with a balanced accuracy of 85-94%, occasionally reaching 100%. However, olfactory subtest results failed to improve the prediction of diabetes risk based on metabolomics data, and metabolomics data did not improve the prediction of the olfactory dysfunction category based on olfactory subtest results. Results of the present study suggest that olfactory function is not a useful predictor of diabetes.