Digital medicine, digital research and artificial intelligence (AI) have the power to transform the field of diabetes with continuous and no-burden remote monitoring of patients’ symptoms, ...physiological data, behaviours, and social and environmental contexts through the use of wearables, sensors and smartphone technologies. Moreover, data generated online and by digital technologies – which the authors suggest be grouped under the term ‘digitosome’ – constitute, through the quantity and variety of information they represent, a powerful potential for identifying new digital markers and patterns of risk that, ultimately, when combined with clinical data, can improve diabetes management and quality of life, and also prevent diabetes-related complications. Moving from a world in which patients are characterized by only a few recent measurements of fasting glucose levels and glycated haemoglobin to a world where patients, healthcare professionals and research scientists can consider various key parameters at thousands of time points simultaneously will profoundly change the way diabetes is prevented, managed and characterized in patients living with diabetes, as well as how it is scientifically researched. Indeed, the present review looks at how the digitization of diabetes can impact all fields of diabetes – its prevention, management, technology and research – and how it can complement, but not replace, what is usually done in traditional clinical settings. Such a profound shift is a genuine game changer that should be embraced by all, as it can provide solid research results transferable to patients, improve general health literacy, and provide tools to facilitate the everyday decision-making process by both healthcare professionals and patients living with diabetes.
A socioeconomic gradient related to type 2 diabetes (T2D) prevalence has been demonstrated in high-income countries. However, there is no evidence of such a socioeconomic gradient regarding diabetes ...complications. Thus, the aim of this systematic review was to collect data on risk of complications according to socioeconomic status in patients with T2D.
PubMed and EMBASE were searched for English-language observational studies evaluating the prevalence or incidence of micro- and macrovascular complications according to individual and geographical socioeconomic status (SES). Observational studies reporting the prevalence and risk of micro- and macrovascular diabetes complications, according to an individual or geographical index of deprivation, were selected, and estimated crude and adjusted risks for each complication were reported.
Among the 28 included studies, most described a clear relationship between SES and diabetes complications, especially retinopathy (in 9 of 14 studies) and cardiopathy (in 8 of 9 studies). Both individual and area-based low SES was associated with an increased risk of complications. However, very few studies adjusted their analyses according to HbA1c level.
Evaluation of SES is necessary for every T2D patient, as it appears to be a risk factor for diabetes complications. However, the available studies are insufficient for gradation of the impact of low socioeconomic level on each of these complications. Regardless, strategies for the improved screening, follow-up and care of high-risk patients should now be implemented.
Fruit and vegetable intake (FVI) may reduce the risk of type 2 diabetes (T2D), but the epidemiological evidence is inconclusive. The aim of this study is to examine the prospective association of FVI ...with T2D and conduct an updated meta-analysis. In the European Prospective Investigation into Cancer-InterAct (EPIC-InterAct) prospective case-cohort study nested within eight European countries, a representative sample of 16,154 participants and 12,403 incident cases of T2D were identified from 340,234 individuals with 3.99 million person-years of follow-up. For the meta-analysis we identified prospective studies on FVI and T2D risk by systematic searches of MEDLINE and EMBASE until April 2011. In EPIC-InterAct, estimated FVI by dietary questionnaires varied more than twofold between countries. In adjusted analyses the hazard ratio (95% confidence interval) comparing the highest with lowest quartile of reported intake was 0.90 (0.80-1.01) for FVI; 0.89 (0.76-1.04) for fruit and 0.94 (0.84-1.05) for vegetables. Among FV subtypes, only root vegetables were inversely associated with diabetes 0.87 (0.77-0.99). In meta-analysis using pooled data from five studies including EPIC-InterAct, comparing the highest with lowest category for FVI was associated with a lower relative risk of diabetes (0.93 (0.87-1.00)). Fruit or vegetables separately were not associated with diabetes. Among FV subtypes, only green leafy vegetable (GLV) intake (relative risk: 0.84 (0.74-0.94)) was inversely associated with diabetes. Subtypes of vegetables, such as root vegetables or GLVs may be beneficial for the prevention of diabetes, while total FVI may exert a weaker overall effect.
•Objective sleep measures are more frequently associated with HbA1c levels than are subjective sleep measures.•Sleep patterns and HbA1c are mostly associated with men, and with those who are either ...overweight and/or depressed.•Use of accelerometers to assess sleep patterns could help to better target subjects at high risk of diabetes.
To analyze the association of objective and subjective sleep measures with HbA1c and insulin sensitivity in the general population.
Using a cross-sectional design, data from 1028 participants in the ORISCAV-LUX-2 study from the general population in Luxembourg were analyzed. Objective sleep measures were assessed using accelerometers whereas subjective measures were assessed using the Pittsburgh Sleep Quality Index (PSQI) questionnaire. Sleep measures were defined as predictors, while HbA1c and quantitative insulin sensitivity check index (QUICKI) scores were considered outcomes. Linear and spline regression models were fitted by progressively adjusting for demographic and lifestyle variables in the total sample population as well as by stratified analyses using gender, obesity status, depressive symptoms and diabetes status.
In fully adjusted models, total and deep sleep durations were associated with lower HbA1c (mmol/mol) levels, whereas sleep coefficients of variation (%) and poor sleep efficiency, as measured by PSQI scores (units), were associated with higher HbA1c levels. In stratified models, such associations were observed mainly in men, and in subjects who had depressive symptoms, were overweight and no diabetes. In addition, total sleep, deep sleep, coefficients of variation and poor sleep efficiency as measured by PSQI revealed non-linear associations. Similarly, greater insulin sensitivity was associated with longer total sleep time and with PSQI-6 (use of sleep medication).
Associations were more frequently observed between sleep characteristics and glycaemic control with the use of objective sleep measures. Also, such associations varied within subgroups of the population. Our results highlight the relevance of measuring sleep patterns as key factors in the prevention of diabetes.
Evidence on associations between self-reported diabetes mellitus, diabetes duration, age at diabetes diagnosis, insulin treatment, and risk of biliary tract cancer (BTC) and hepatocellular carcinoma ...(HCC), independent of general and abdominal obesity is scarce.
We conducted a prospective analysis in the EPIC-cohort study among 363 426 participants with self-reported diabetes data. Multivariable adjusted relative risks and 95% confidence intervals were estimated from Cox regression models. In a nested case–control subset, analyses were carried out in HCV/HBV-negative individuals.
During 8.5 years of follow-up, 204 BTC cases including 75 gallbladder cancer (GBC) cases, and 176 HCC cases were identified. Independent of body mass index and waist-to-height ratio diabetes status was associated with higher risk of BTC and HCC 1.77 (1.00–3.13) and 2.17 (1.36–3.47). For BTC, the risk seemed to be higher in participants with shorter diabetes duration and those not treated with insulin. Regarding cancer subsites, diabetes was only associated with GBC 2.72 (1.17–6.31). The risk for HCC was particularly higher in participants treated with insulin. The results were not appreciably different in HCV/HBV-negative individuals.
This study supports the hypothesis that diabetes is a risk factor for BTC (particularly GBC) and HCC. Further research is required to establish whether diabetes treatment or duration is associated with these cancers.
To document the family transmission of Type 2 diabetes to men and women.
The French D.E.S.I.R. cohort followed men and women over 9 years, with 3-yearly testing for incident Type 2 diabetes. First- ...and/or second-degree family histories of diabetes were available for 2187 men and 2282 women. Age-adjusted hazard ratios were estimated for various family members and groupings of family members, as well as for a genetic diabetes risk score, based on 65 diabetes-associated loci.
Over 9 years, 136 men and 63 women had incident Type 2 diabetes. The hazard ratios for diabetes associated with having a first-degree family member with diabetes (parents, siblings, children) differed between men 1.21 (95% CI 0.80, 1.85) and women 3.02 (95% CI 1.83, 4.99); P
=0.006. The genetic risk score was predictive of diabetes in both men and women, with similar hazard ratios 1.10 (95% CI 1.06, 1.15) and 1.08 (95% CI 1.02, 1.14) respectively, for each additional at-risk allele. In women, the risk associated with having a family member with diabetes persisted after adjusting for the genetic score.
Women with a family history of diabetes (paternal or maternal) were at risk of developing Type 2 diabetes and this risk was independent of a genetic score; in contrast, for men, there was no association. Diabetes screening and prevention may need to more specifically target women with diabetes in their family, but further studies are required as the number of people with diabetes in this study was small.
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