Notes

Spirally-patterned pinhole arrays for long-term fluorescence mobile image resolution.
To analyse time-trends in BMI, obesity and cardiometabolic risk in adults with type 1 diabetes (T1DM) from the Diabetes Prospective Follow-up registry DPV.

Data from 62,519 individuals with T1DM (age≥18years, BMI≥18.5kg/m
) were analysed. Multivariable regression models were used to determine time-trends in BMI, obesity and cardiometabolic risk and to identify predictors for increasing BMI. Results were compared to the NCD Risk Factor Collaboration (NCD-RisC) data for Germany.

Between 1999 and 2018 mean BMI increased from 25.0kg/m
to 26.2kg/m
in individuals with T1DM. This trend was most pronounced in young and middle-aged individuals (>21-55years of age) and in those with higher baseline BMI. Insulin dose and diabetes duration were associated with increasing BMI. Between 1999 and 2016, the prevalence of obesity increased 1.8-fold in individuals with T1DM and 1.4-fold among the German population, respectively (NCD-RisC). Approximately 50-70% of individuals with obesity were insufficiently treated for hypertension and/or dyslipidaemia.

In adults with T1DM the prevalence of obesity is increasing at a faster pace than in the German population. BMI needs to be closely monitored, particularly during young adulthood, and cardiovascular risk factors need to be controlled better to prevent CVD and premature death.
In adults with T1DM the prevalence of obesity is increasing at a faster pace than in the German population. BMI needs to be closely monitored, particularly during young adulthood, and cardiovascular risk factors need to be controlled better to prevent CVD and premature death.
To investigate factors associated with health-related quality of life (HRQoL) in patients with type 2 diabetes mellitus (T2D) at initiation of second-line glucose-lowering therapy.

DISCOVER is a 3-year, prospective observational study of patients with T2D initiating second-line glucose-lowering therapy, conducted in 38 countries. HRQoL at baseline was assessed using the physical and mental component summary (PCS; MCS) scores of the 36-Item Short Form Health Survey version 2 (SF-36v2) in 31 countries (n=8309) and the Hypoglycaemia Fear Survey-II (HFS-II) in 23 countries (n=6516). Factors associated with differences in HRQoL were assessed using multivariable hierarchical regression models.

Mean PCS and MCS scores were 48.0 (standard deviation [SD] 7.8) and 45.5 (SD 10.4), respectively. Factors associated with significantly lower SF-36v2 scores included being female, having a history of macrovascular complications and first-line treatment with oral combinations (vs metformin monotherapy). Mean HFS-II behaviour and worry scores were 8.2 (SD 9.9) and 7.3 (SD 11.8), respectively. Increased fear of hypoglycaemia was significantly associated with lower SF-36v2 scores.

Several patient-, disease- and treatment-related characteristics correlated with HRQoL, indicating that a multifactorial approach is needed to maintain HRQoL in patients with T2D.
Several patient-, disease- and treatment-related characteristics correlated with HRQoL, indicating that a multifactorial approach is needed to maintain HRQoL in patients with T2D.
COVID-19 has spread globally with heavy impact on most countries and our therapeutic strategies in COVID-19 patients with diabetes are still limited. AMG900 Recently, some new information was added to this field. We performed this updated meta-analysis to reveal the underlying effect of metformin on COVID-19 patients with diabetes.

We searched the PubMed, Embase and CNKI (China National Knowledge Infrastructure) databases for all articles. The odds ratio (OR) corresponding to the 95% confidence interval (95% CI) was used to assess the effect of metformin on COVID-19 patients with diabetes. The statistical heterogeneity among studies was assessed with the Q-test and I
statistics.

We collected 17 studies including 20,719 COVID-19 patients with diabetes. Our results found that metformin was associated with significantly decreased mortality and severity in COVID-19 patients with diabetes (OR = 0.64, 95% CI = 0.51-0.79 for mortality, and OR = 0.81, 95% CI = 0.66-0.99 for severity).

Our meta-analysis indicated that following metformin treatment might benefit the patients with T2DM, both the mortality and severity. However, patients with severe COVID-19 should be monitored closely for the development of lactic acidosis, acidosis, and decreased kidney function.
Our meta-analysis indicated that following metformin treatment might benefit the patients with T2DM, both the mortality and severity. However, patients with severe COVID-19 should be monitored closely for the development of lactic acidosis, acidosis, and decreased kidney function.
/hypothesis. To determine the best cut-off threshold value of the Finnish Diabetes Risk Score (FINDRISC) for the detection of diabetes and non-diabetic hyperglycaemia in people 35years or older at primary health care settings in Europe.

Cross-sectional study in 11,444 adults from primary health care centres using community and opportunistic screening approaches. All participants completed the FINDRISC questionnaire and underwent a 2-hour oral glucose tolerance test (OGTT). The FINDRISC performance was assessed by the area under the curve (AUC) using receiver operating characteristics (ROC) analysis. The sensitivity, specificity, Youdeńs index, positive and negative prediction values for different FINDRISC cut-offs were calculated.

The optimal FINDRISC value for detecting both diabetes or glucose impairment in the community - screened sample was 14 point with the associated AUC 0.75,5 (95%CI 0.73,7-0.77,3). The optimal score in the opportunistic screening sample was 16 with the associated AUC only 0.60,4 (95% CI 0.56, 4-0.64, 4).

The FINDRISC is a non-invasive tool useful for detecting people with unknown diabetes and glucose impairment in people visiting primary health centres in Europe.
The FINDRISC is a non-invasive tool useful for detecting people with unknown diabetes and glucose impairment in people visiting primary health centres in Europe.
Using data from a large multi-centre cohort, we aimed to create a risk prediction model for large-for-gestational age (LGA) infants, using both logistic regression and naïve Bayes approaches, and compare the utility of these two approaches.

We have compared the two techniques underpinning machine learning logistic regression (LR) and naïve Bayes (NB) in terms of their ability to predict large-for-gestational age (LGA) infants. Using data from five centres involved in the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) study, we developed LR and NB models and compared the predictive ability and stability between the models. Models were developed combining the risks of hyperglycaemia (assessed in three forms IADPSG GDM yes/no, GDM subtype, OGTT z-score quintiles), demographic and clinical variables as potential predictors.

The two approaches resulted in similar estimates of LGA risk (intraclass correlation coefficient 0.955, 95% CI 0.952, 0.958) however the AUROC for the LR model was significantly higher (0.
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