Effects of Age and Sex on Estimated Diabetes Prevalence Using Different Diagnostic Criteria: The Tromsø OGTT Study

Impact of Education as a Social Determinant on the Risk of Type 2 Diabetes Mellitus in Korean Adults

Education is correlated with health literacy, which is a combination of reading and listening skills, data analysis, and decision-making during the necessary health situations. This study aims to evaluate the effect of education on the risk of type 2 diabetes mellitus (T2DM). This is a population-based cross-sectional study using the 2019 nationwide survey data in Korea. There were 3951 study subjects, after excluding participants with missing data for key exposures and outcome variables. Descriptive statistics, χ2 (chi-square) test, and logistic regression were performed to analyze the data. The prevalence of T2DM was associated with educational attainment, sex, age, smoking status, physical activity, carbohydrate intake, and obesity. In the logistic regression model, the odds ratio (OR) of having T2DM was much lower among people educated in college or higher (OR = 0.49, 95% confidence interval [95% CI] = 0.34-0.64) than those with only or without primary education after adjusting for biological factors (sex, age) and health behaviors (smoking status, physical activity, carbohydrate intake, and obesity). This study shows that educational attainment is a significant social determinant influencing health outcomes both directly and indirectly. Therefore, it is necessary to develop policies to reduce the health inequity of T2DM caused by differences in educational attainment.

Deep learning to estimate impaired glucose metabolism from Magnetic Resonance Imaging of the liver: An opportunistic population screening approach

AIM

Diabetes is a global health challenge, and many individuals are undiagnosed and not aware of their increased risk of morbidity/mortality although dedicated tests are available, which indicates the need for novel population-wide screening approaches. Here, we developed a deep learning pipeline for opportunistic screening of impaired glucose metabolism using routine magnetic resonance imaging (MRI) of the liver and tested its prognostic value in a general population setting.

METHODS

In this retrospective study a fully automatic deep learning pipeline was developed to quantify liver shape features on routine MR imaging using data from a prospective population study. Subsequently, the association between liver shape features and impaired glucose metabolism was investigated in individuals with prediabetes, type 2 diabetes and healthy controls without prior cardiovascular diseases. K-medoids clustering (3 clusters) with a dissimilarity matrix based on Euclidean distance and ordinal regression was used to assess the association between liver shape features and glycaemic status.

RESULTS

The deep learning pipeline showed a high performance for liver shape analysis with a mean Dice score of 97.0±0.01. Out of 339 included individuals (mean age 56.3±9.1 years; males 58.1%), 79 (23.3%) and 46 (13.6%) were classified as having prediabetes and type 2 diabetes, respectively. Individuals in the high risk cluster using all liver shape features (n = 14) had a 2.4 fold increased risk of impaired glucose metabolism after adjustment for cardiometabolic risk factors (age, sex, BMI, total cholesterol, alcohol consumption, hypertension, smoking and hepatic steatosis; OR 2.44 [95% CI 1.12-5.38]; p = 0.03). Based on individual shape features, the strongest association was found between liver volume and impaired glucose metabolism after adjustment for the same risk factors (OR 1.97 [1.38-2.85]; p<0.001).

CONCLUSIONS

Deep learning can estimate impaired glucose metabolism on routine liver MRI independent of cardiometabolic risk factors and hepatic steatosis.

Transitions in Metabolic Health Status and Obesity Over Time and Risk of Diabetes: The Dongfeng-Tongji Cohort Study

CONTEXT

Evidence regarding the association between metabolically healthy overweight or obesity (MHOO) and diabetes is controversial, and mostly ignores the dynamic change of metabolic health status and obesity.

OBJECTIVE

To explore the association between transitions of metabolic health status and obesity over 5 years and diabetes incidence.

METHODS

We examined 17 309 participants derived from the Dongfeng-Tongji cohort and followed from 2008 to 2018 (median follow-up 9.9 years). All participants were categorized into 4 phenotypes based on body mass index (BMI) and metabolic health status: metabolically healthy normal weight (MHNW), metabolically unhealthy normal weight (MUNW), MHOO, and metabolically unhealthy overweight or obesity (MUOO). The associations of changes in BMI-metabolic health status (2008-2013) with diabetes incidence (2018) were performed among 12 206 individuals with 2 follow-up examinations.

RESULTS

Compared with stable MHNW, stable MHOO (hazard ratio [HR] 1.76; 95% CI 1.26, 2.45) and transition from MHOO to metabolically unhealthy phenotypes were associated with higher risk for diabetes (HR 2.97; 95% CI 1.79, 4.93 in MHOO to MUNW group and HR 3.38; 95% CI 2.54, 4.49 in MHOO to MUOO group). Instead, improvements to metabolic healthy phenotypes or weight loss occurring in MUOO reduced the risk of diabetes compared with stable MUOO, changing from MUOO to MHNW, MUNW, and MHOO resulted in HRs of 0.57 (95% CI 0.37, 0.87), 0.68 (95% CI 0.50, 0.93), and 0.45 (95% CI 0.34, 0.60), respectively.

CONCLUSION

People with MHOO, even stable MHOO, or its transition to metabolically unhealthy phenotypes were at increased risk of diabetes. Metabolic improvements and weight control may reduce the risk of diabetes.

Criteria for prediabetes and posttransplant diabetes mellitus after kidney transplantation: A 2-year diagnostic accuracy study of participants from a randomized controlled trial

Posttransplant diabetes mellitus (PTDM) and prediabetes (impaired glucose tolerance [IGT] and impaired fasting glucose [IFG]) are associated with cardiovascular events. We assessed the diagnostic performance of fasting plasma glucose (FPG) and HbA1c as alternatives to oral glucose tolerance test (OGTT)-derived 2-hour plasma glucose (2hPG) using sensitivity and specificity in 263 kidney transplant recipients (KTRs) from a clinical trial. Between visits at 6, 12, and 24 months after transplantation, 28%-31% of patients switched glycemic category (normal glucose tolerance [NGT], IGT/IFG, PTDM). Correlations of FPG and HbA1c against 2hPG were lower at 6 months (r = 0.59 [FPG against 2hPG]; r = 0.45 [HbA1c against 2hPG]) vs. 24 months (r = 0.73 [FPG against 2hPG]; r = 0.74 [HbA1c against 2hPG]). Up to 69% of 2hPG-defined PTDM cases were missed by conventional HbA1c and FPG thresholds. For prediabetes, concordance of FPG and HbA1c with 2hPG ranged from 6%-9%. In conclusion, in our well-defined randomized trial cohort, one-third of KTRs switched glycemic category over 2 years and although the correlations of FPG and HbA1c with 2hPG improved with time, their diagnostic concordance was poor for PTDM and, especially, prediabetes. Considering posttransplant metabolic instability, FPG's and HbA1c 's diagnostic performance, the OGTT remains indispensable to diagnose PTDM and prediabetes after kidney transplantation.

MRI-Derived Radiomics Features of Hepatic Fat Predict Metabolic States in Individuals without Cardiovascular Disease

RATIONALE AND OBJECTIVES

To investigate radiomics features of hepatic fat as potential biomarkers of type 2 diabetes mellitus (T2DM) and metabolic syndrome (MetS) in individuals without overt cardiovascular disease, and benchmarking against hepatic proton density fat fraction (PDFF) and the body mass index (BMI).

MATERIALS AND METHODS

This study collected liver radiomics features of 310 individuals that were part of a case-controlled imaging substudy embedded in a prospective cohort. Individuals had known T2DM (n = 39; 12.6 %) and MetS (n = 107; 34.5 %) status, and were divided into stratified training (n = 232; 75 %) and validation (n = 78; 25 %) sets. Six hundred eighty-four MRI radiomics features were extracted for each liver volume of interest (VOI) on T₁-weighted dual-echo Dixon relative fat water content (rfwc) maps. Test-retest and inter-rater variance was simulated by additionally extracting radiomics features using noise augmented rfwc maps and deformed volume of interests. One hundred and seventy-one features with test-retest reliability (ICC(1,1)) and inter-rater agreement (ICC(3,k)) of ≥0.85 on the training set were considered stable. To construct predictive random forest (RF) models, stable features were filtered using univariate RF analysis followed by sequential forward aggregation. The predictive performance was evaluated on the independent validation set with area under the curve of the receiver operating characteristic (AUROC) and balanced accuracy (Accuracy_B).

RESULTS

On the validation set, the radiomics RF models predicted T2DM with AUROC of 0.835 and Accuracy_B of 0.822 and MetS with AUROC of 0.838 and Accuracy_B of 0.787, outperforming the RF models trained on the benchmark parameters PDFF and BMI.

CONCLUSION

Hepatic radiomics features may serve as potential imaging biomarkers for T2DM and MetS.

Prediction of Type 2 Diabetes Based on Machine Learning Algorithm

Prediction of type 2 diabetes (T2D) occurrence allows a person at risk to take actions that can prevent onset or delay the progression of the disease. In this study, we developed a machine learning (ML) model to predict T2D occurrence in the following year (Y + 1) using variables in the current year (Y). The dataset for this study was collected at a private medical institute as electronic health records from 2013 to 2018. To construct the prediction model, key features were first selected using ANOVA tests, chi-squared tests, and recursive feature elimination methods. The resultant features were fasting plasma glucose (FPG), HbA1c, triglycerides, BMI, gamma-GTP, age, uric acid, sex, smoking, drinking, physical activity, and family history. We then employed logistic regression, random forest, support vector machine, XGBoost, and ensemble machine learning algorithms based on these variables to predict the outcome as normal (non-diabetic), prediabetes, or diabetes. Based on the experimental results, the performance of the prediction model proved to be reasonably good at forecasting the occurrence of T2D in the Korean population. The model can provide clinicians and patients with valuable predictive information on the likelihood of developing T2D. The cross-validation (CV) results showed that the ensemble models had a superior performance to that of the single models. The CV performance of the prediction models was improved by incorporating more medical history from the dataset.

Plasma glucose in screening for diabetes and pre-diabetes: how much is too much? Analysis of fasting plasma glucose and oral glucose tolerance test in Sri Lankans

BACKGROUND

Fasting plasma glucose (FPG) is the most commonly used screening tool for diabetes in Sri Lanka. Cut-off values from American Diabetes Association recommendations are adopted in the absence of local data. We aimed to establish FPG cut offs for Sri Lankans to screen for diabetes and pre-diabetes.

METHODS

Data on FPG and diabetes/pre-diabetes status were obtained from Sri Lanka Diabetes and Cardiovascular Study (SLDCS), a community based island wide observational study conducted in 2005-6. Sensitivity specificity and area under the ROC curve were calculated for different FPG values.

RESULTS

Study included 4014 community dwelling people after excluding people already on treatment for diabetes or pre-diabetes. Mean age was 45.3 (± 15) years and 60.4% were females. FPG cut off of 5.3 mmol/L showed better sensitivity and specificity than 5.6 mmol/L in detecting diabetes (87.8% and 84.4% Vs 80.8% and 92.1%) and pre-diabetes (54.7% and 87.0% Vs 43.8% and 94.2%).

CONCLUSIONS

A lower FPG cut off of 5.3 mmol/L has a better sensitivity and acceptable specificity in screening for diabetes and pre-diabetes in Sri Lankan adults.

Cost of diabetes in Norway 2011

AIMS

To quantify the excess cost of diabetes in Norway in 2011.

METHODS

A national cross-sectional cost-of-illness analysis of direct and indirect diabetes-related healthcare costs, based on pseudonymised data from six public national registers, international studies, and clinical expertise. Direct medical costs are estimated from primary and secondary health care registers and the national prescription database. Indirect costs include social and productivity costs.

RESULTS

The total excess cost of diabetes in Norway in 2011 was €516 million. Direct costs amounted to €408 million and indirect costs amounted to €108 million. Scenario analysis proposes an upper boundary of total cost at €575 million, direct costs at €428 million and indirect costs at €161 million. Expenditure on blood glucose lowering agents was €71 million and expenditure on blood glucose monitoring strips was €55 million. Blood glucose lowering agents-, lipid lowering agents, and antihypertensives represented 28% of the direct costs. Loss of productivity (€0.9 million) scored highest among the indirect costs.

CONCLUSIONS

The cost implications of diabetes in Norway in 2011 were high and comparable to previous studies in Scandinavia. Prevention of complications contributed to a higher cost than treating diabetes-related complications. The more than five-fold higher expenditure in other countries might be due to differences in budget priorities, efficacy of healthcare, indirect healthcare cost applications, or research methodology.

Prevalence of metabolic syndrome and diabetes mellitus in Sami and Norwegian populations. The SAMINOR-a cross-sectional study

OBJECTIVES

Metabolic syndrome (MetS) is recognised as a reliable long-term predictor of adverse health outcomes. Elevated prevalence rates of MetS and chronic lifestyle diseases have been documented in different indigenous groups. We aimed to evaluate the prevalence of MetS and diabetes mellitus in relation to ethnicity in Northern Norway. In addition, we discussed different cut-off values for waist circumference (WC) and what impact this has on the prevalence of MetS.

MATERIALS AND METHODS

SAMINOR is a population-based study of health and living conditions in areas home to Sami and non-Sami populations. The survey was carried out in 2003-2004. All eligible residents in specific age groups were invited. In total, 16,538 males and females aged 36-79 years participated and gave informed consent for medical research.

RESULTS

This study involved a total of 7822 female and 7290 male participants. Sami affiliation was reported by 5141 participants (34%). The prevalence of MetS was high in both ethnic groups independent of which WC cut-off value was used. No ethnic differences in prevalence of diabetes mellitus were demonstrated. However, ethnicity appeared to affect diabetes treatment, which was more prevalent among Sami than non-Sami women.

CONCLUSIONS

In this study, there was no ethnic difference in diabetes prevalence, but ethnicity appeared to affect diabetes treatment. Tablet treatment was more commonly in use among Sami women than among non-Sami women. We demonstrated a high share of negative metabolic components. These metabolic components have important health implications. Therefore, determining preventive initiatives is important in the primary and specialist healthcare system. These initiatives must be made culture and linguistic specific, in order to reduce differences and improve health status in the whole population.

Glycemic Allostasis during Mental Activities on Fasting in Non-alcohol Users and Alcohol Users with Different Durations of Abstinence

Glycemic allostasis is the process by which blood glucose stabilization is achieved through the balancing of glucose consumption rate and release into the blood stream under a variety of stressors. This paper reviews findings on the dynamics of glycemic levels during mental activities on fasting in non-alcohol users and alcohol users with different periods of abstinence. Referred articles for this review were searched in the databases of PubMed, Scopus, DOAJ and AJOL. The search was conducted in 2013 between January 20 and July 31. The following keywords were used in the search: alcohol action on glycemia OR brain glucose OR cognitive functions; dynamics of glycemia, dynamics of glycemia during mental activities; dynamics of glycemia on fasting; dynamics of glycemia in non-alcohol users OR alcohol users; glycemic regulation during sobriety. Analysis of the selected articles showed that glycemic allostasis during mental activities on fasting is poorly regulated in alcohol users even after a long duration of sobriety (1-4 weeks after alcohol consumption), compared to non-alcohol users. The major contributor to the maintenance of euglycemia during mental activities after the night's rest (during continuing fast) is gluconeogenesis.