Background and aim: Non-alcoholic fatty liver disease (NAFLD) is defined as a liver fat content >= 5.56%. It is of clinical interest to know the prevalence of NAFLD in people with a combination... Show moreBackground and aim: Non-alcoholic fatty liver disease (NAFLD) is defined as a liver fat content >= 5.56%. It is of clinical interest to know the prevalence of NAFLD in people with a combination of metabolic risk factors. We aimed to examine the prevalence of NAFLD, including groups with metabolic risk factors.Methods and results: In this cross-sectional analysis of the Netherlands Epidemiology of Obesity (NEO) study, liver fat content was assessed using proton magnetic resonance spectroscopy (H-MRS). Participants with excessive alcohol consumption or missing values were excluded, leaving a total of 1570 participants for the analyses. Mean (SD) age of the population was 55 years, BMI 25.9 (4.0) kg/m(2) and 46% were men. The prevalence of NAFLD was 27% (95% CI 24-30). The prevalence of NAFLD was increased in participants with hypertriglyceridemia (57%, 52-63), obesity (62%, 58-66) and diabetes (69%, 61-77). The prevalence of NAFLD was highest in those with diabetes and obesity (79%, 71-87), obesity and hypertriglyceridemia (81%, 76-86) and with diabetes and hypertriglyceridemia (86%, 77-95). NAFLD was also present in 12% (8-16) of participants without overweight.Conclusions: The prevalence of NAFLD in a middle-aged population in the Netherlands in 2010 was 27%. The prevalence of NAFLD is particularly increased in individuals with diabetes, obesity, and hypertriglyceridemia. This information may help clinicians and general practitioners in the risk stratification of their patients in daily practice.(c) 2023 The Author(s). Published by Elsevier B.V. on behalf of The Italian Diabetes Society, the Italian Society for the Study of Atherosclerosis, the Italian Society of Human Nutrition and the Department of Clinical Medicine and Surgery, Federico II University. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Show less
Aims/hypothesis There is a lack of e-health systems that integrate the complex variety of aspects relevant for diabetes self-management. We developed and field-tested an e-health system (POWER2DM)... Show moreAims/hypothesis There is a lack of e-health systems that integrate the complex variety of aspects relevant for diabetes self-management. We developed and field-tested an e-health system (POWER2DM) that integrates medical, psychological and behavioural aspects and connected wearables to support patients and healthcare professionals in shared decision making and diabetes self-management.Methods Participants with type 1 or type 2 diabetes (aged >18 years) from hospital outpatient diabetes clinics in the Netherlands and Spain were randomised using randomisation software to POWER2DM or usual care for 37 weeks. This RCT assessed the change in HbA(1c) between the POWER2DM and usual care groups at the end of the study (37 weeks) as a primary outcome measure. Participants and clinicians were not blinded to the intervention. Changes in quality of life (QoL) (WHO-5 Well-Being Index [WHO-5]), diabetes self-management (Diabetes Self-Management Questionnaire - Revised [DSMQ-R]), glycaemic profiles from continuous glucose monitoring devices, awareness of hypoglycaemia (Clarke hypoglycaemia unawareness instrument), incidence of hypoglycaemic episodes and technology acceptance were secondary outcome measures. Additionally, sub-analyses were performed for participants with type 1 and type 2 diabetes separately.Results A total of 226 participants participated in the trial (108 with type 1 diabetes; 118 with type 2 diabetes). In the POWER2DM group (n=111), HbA(1c) decreased from 60.6 +/- 14.7 mmol/mol (7.7 +/- 1.3%) to 56.7 +/- 12.1 mmol/mol (7.3 +/- 1.1%) (means +/- SD, p<0.001), compared with no change in the usual care group (n=115) (baseline: 61.7 +/- 13.7 mmol/mol, 7.8 +/- 1.3%; end of study: 61.0 +/- 12.4 mmol/mol, 7.7 +/- 1.1%; p=0.19) (between-group difference 0.24%, p=0.008). In the sub-analyses in the POWER2DM group, HbA(1c) in participants with type 2 diabetes decreased from 62.3 +/- 17.3 mmol/mol (7.9 +/- 1.6%) to 54.3 +/- 11.1 mmol/mol (7.1 +/- 1.0%) (p<0.001) compared with no change in HbA(1c) in participants with type 1 diabetes (baseline: 58.8 +/- 11.2 mmol/mol [7.5 +/- 1.0%]; end of study: 59.2 +/- 12.7 mmol/mol [7.6 +/- 1.2%]; p=0.84). There was an increase in the time during which interstitial glucose levels were between 3.0 and 3.9 mmol/l in the POWER2DM group, but no increase in clinically relevant hypoglycaemia (interstitial glucose level below 3.0 mmol/l). QoL improved in participants with type 1 diabetes in the POWER2DM group compared with the usual care group (baseline: 15.7 +/- 3.8; end of study: 16.3 +/- 3.5; p=0.047 for between-group difference). Diabetes self-management improved in both participants with type 1 diabetes (from 7.3 +/- 1.2 to 7.7 +/- 1.2; p=0.002) and those with type 2 diabetes (from 6.5 +/- 1.3 to 6.7 +/- 1.3; p=0.003) within the POWER2DM group. The POWER2DM integrated e-health support was well accepted in daily life and no important adverse (or unexpected) effects or side effects were observed.Conclusions/interpretation POWER2DM improves HbA(1c) levels compared with usual care in those with type 2 diabetes, improves QoL in those with type 1 diabetes, improves diabetes self-management in those with type 1 and type 2 diabetes, and is well accepted in daily life.Trial registration ClinicalTrials.gov NCT03588104.Funding This study was funded by the European Union's Horizon 2020 Research and Innovation Programme (grant agreement number 689444). Show less
Introduction Little is known about the comparative effects of sodium glucose cotransporter-2 inhibitors (SGLT2i), glucagon-like peptide-1 receptor agonists (GLP1-RA), or dipeptidyl peptidase-4... Show moreIntroduction Little is known about the comparative effects of sodium glucose cotransporter-2 inhibitors (SGLT2i), glucagon-like peptide-1 receptor agonists (GLP1-RA), or dipeptidyl peptidase-4 inhibitors (DPP-4i) on the risk of acute kidney injury (AKI) in routine care, which may differ from the controlled setting of trials.Methods Observational study comparing risks of AKI among new users of SGLT2i, GLP1-RA or DPP-4i in the region of Stockholm, Sweden, during 2008-2018. AKI was defined by ICD-10 codes and creatinine-based KDIGO criteria. We used inverse probability of treatment weighting (IPTW) to adjust for 60 potential confounders, weighted Kaplan-Meier curves and Cox regression to estimate hazard ratios and absolute risks.Results We included 17,407 participants who newly initiated DPP-4i (N = 10,605), GLP1-RA (N = 4448) or SGLT2i (N = 2354). Mean age was 63 years (39% women) and median (IQR) eGFR was 89 (73-100) ml/min/1.73 m(2). During a median follow-up of 2.5 years, 1411 participants experienced AKI. SGLT2i users had the lowest incidence rate of AKI, 18.3 [CI 95% 14.1-23.4] per 1000 person years, followed by GLP1-RA (22.5; 19.9-25.3) and DPP-4i (26.6; 25-28.2). The weighted 3-year absolute risk for AKI was 5.79% [3.63-8.52] in the SGLT2i group, compared with 7.03% [5.69-8.69] and 7.00% [6.43-7.58] in the GLP1-RA and DPP-4i groups, respectively. The adjusted hazard ratio was 0.73 [CI 95% 0.45-1.16] for SGLT2i vs. DPP-4i, and 0.98 [CI 95% 0.82-1.18] for GLP1-RA vs. DPP-4i.Conclusion This study of routine care patients initiating novel glucose-lowering drugs showed similar occurrence of AKI between therapies, and suggests lower risk for SGLT2i. Show less
Huang, Z.L.; Lum, E.; Jimenez, G.; Semwal, M.; Sloot, P.; Car, J. 2019
BackgroundSmartphone apps are becoming increasingly popular for supporting diabetes self-management. A key aspect of diabetes self-management is appropriate medication-taking. This study aims to... Show moreBackgroundSmartphone apps are becoming increasingly popular for supporting diabetes self-management. A key aspect of diabetes self-management is appropriate medication-taking. This study aims to systematically assess and characterise the medication management features in diabetes self-management apps and their congruence with best-practice evidence-based criteria.MethodsThe Google Play and Apple app stores were searched in June 2018 using diabetes-related terms in the English language. Apps with both medication and blood glucose management features were downloaded and evaluated against assessment criteria derived from international medication management and diabetes guidelines.ResultsOur search yielded 3369 Android and 1799 iOS potentially relevant apps; of which, 143 apps (81 Android, 62 iOS) met inclusion criteria and were downloaded and assessed. Over half 58.0% (83/143) of the apps had a medication reminder feature; 16.8% (24/143) had a feature to review medication adherence; 39.9% (57/143) allowed entry of medication-taking instructions; 5.6% (8/143) provided information about medication; and 4.2% (6/143) displayed motivational messages to encourage medication-taking. Only two apps prompted users on the use of complementary medicine. Issues such as limited medication logging capacity, faulty reminder features, unclear medication adherence assessment, and visually distracting excessive advertising were observed during app assessments.ConclusionsA large proportion of diabetes self-management apps lacked features for enhancing medication adherence and safety. More emphasis should be given to the design of medication management features in diabetes apps to improve their alignment to evidence-based best practice. Show less
This review discusses the relevant metabolic pathways and their regulators which show potential for T cell metabolism-based immunotherapy in diseases hallmarked by both metabolic disease and... Show moreThis review discusses the relevant metabolic pathways and their regulators which show potential for T cell metabolism-based immunotherapy in diseases hallmarked by both metabolic disease and autoimmunity. Multiple therapeutic approaches using existing pharmaceuticals are possible from a rationale in which T cell metabolism forms the hub in dampening the T cell component of autoimmunity in metabolic diseases. Future research into the effects of a metabolically aberrant micro-environment on T cell metabolism and its potential as a therapeutic target for immunomodulation could lead to novel treatment strategies for metabolic disease-associated autoimmunity. Show less
This review discusses the relevant metabolic pathways and their regulators which show potential for T cell metabolism-based immunotherapy in diseases hallmarked by both metabolic disease and... Show moreThis review discusses the relevant metabolic pathways and their regulators which show potential for T cell metabolism-based immunotherapy in diseases hallmarked by both metabolic disease and autoimmunity. Multiple therapeutic approaches using existing pharmaceuticals are possible from a rationale in which T cell metabolism forms the hub in dampening the T cell component of autoimmunity in metabolic diseases. Future research into the effects of a metabolically aberrant micro-environment on T cell metabolism and its potential as a therapeutic target for immunomodulation could lead to novel treatment strategies for metabolic disease-associated autoimmunity. Show less
This review discusses the relevant metabolic pathways and their regulators which show potential for T cell metabolism-based immunotherapy in diseases hallmarked by both metabolic disease and... Show moreThis review discusses the relevant metabolic pathways and their regulators which show potential for T cell metabolism-based immunotherapy in diseases hallmarked by both metabolic disease and autoimmunity. Multiple therapeutic approaches using existing pharmaceuticals are possible from a rationale in which T cell metabolism forms the hub in dampening the T cell component of autoimmunity in metabolic diseases. Future research into the effects of a metabolically aberrant micro-environment on T cell metabolism and its potential as a therapeutic target for immunomodulation could lead to novel treatment strategies for metabolic disease-associated autoimmunity. Show less
This review discusses the relevant metabolic pathways and their regulators which show potential for T cell metabolism-based immunotherapy in diseases hallmarked by both metabolic disease and... Show moreThis review discusses the relevant metabolic pathways and their regulators which show potential for T cell metabolism-based immunotherapy in diseases hallmarked by both metabolic disease and autoimmunity. Multiple therapeutic approaches using existing pharmaceuticals are possible from a rationale in which T cell metabolism forms the hub in dampening the T cell component of autoimmunity in metabolic diseases. Future research into the effects of a metabolically aberrant micro-environment on T cell metabolism and its potential as a therapeutic target for immunomodulation could lead to novel treatment strategies for metabolic disease-associated autoimmunity. Show less
