The value of early detection of type 2 diabetes

Predicting the Onset of Diabetes with Machine Learning Methods

The number of people suffering from diabetes in Taiwan has continued to rise in recent years. According to the statistics of the International Diabetes Federation, about 537 million people worldwide (10.5% of the global population) suffer from diabetes, and it is estimated that 643 million people will develop the condition (11.3% of the total population) by 2030. If this trend continues, the number will jump to 783 million (12.2%) by 2045. At present, the number of people with diabetes in Taiwan has reached 2.18 million, with an average of one in ten people suffering from the disease. In addition, according to the Bureau of National Health Insurance in Taiwan, the prevalence rate of diabetes among adults in Taiwan has reached 5% and is increasing each year. Diabetes can cause acute and chronic complications that can be fatal. Meanwhile, chronic complications can result in a variety of disabilities or organ decline. If holistic treatments and preventions are not provided to diabetic patients, it will lead to the consumption of more medical resources and a rapid decline in the quality of life of society as a whole. In this study, based on the outpatient examination data of a Taipei Municipal medical center, 15,000 women aged between 20 and 80 were selected as the subjects. These women were patients who had gone to the medical center during 2018–2020 and 2021–2022 with or without the diagnosis of diabetes. This study investigated eight different characteristics of the subjects, including the number of pregnancies, plasma glucose level, diastolic blood pressure, sebum thickness, insulin level, body mass index, diabetes pedigree function, and age. After sorting out the complete data of the patients, this study used Microsoft Machine Learning Studio to train the models of various kinds of neural networks, and the prediction results were used to compare the predictive ability of the various parameters for diabetes. Finally, this study found that after comparing the models using two-class logistic regression as well as the two-class neural network, two-class decision jungle, or two-class boosted decision tree for prediction, the best model was the two-class boosted decision tree, as its area under the curve could reach a score of 0.991, which was better than other models.

Polish Patients' Needs and Opinions about the Implementation of Pharmaceutical Care in Diabetes

The study aimed to get to know patients' opinions on implementing pharmaceutical care for diabetic patients in a community pharmacy to prevent and effectively and holistically approach the treatment of people with diabetes. It was based on an authorial survey form and conducted from August to October 2021 in a community pharmacy in Poznan, Poland. A total of 131 pharmacy patients over 18 years were included in the study. Results showed that the vast majority of patients confirmed their interest in pharmaceutical care in diabetes conducted by pharmacists. Moreover, 79.4% of respondents would like to benefit from medicines use review, while 87.0% confirmed an interest in the 'New Drug' service, with diabetic patients being particularly interested in this (p = 0.2447). Most respondents were also interested in education on how to use a glucose meter, administer insulin and use a lancing device. In addition, the study showed patients' insufficient knowledge about risk factors and prevention of diabetes with the need for patient education. As the source of funding, 91.7% of diabetic patients indicated the National Health Fund. Given that such a service has not been implemented in Poland yet, this study may support established teams at the Supreme Pharmaceutical Chamber or the Ministry of Health in introducing such new services.

Pelvic Avulsion Fractures in Adolescent Athletes: Analyzing the Effect of Delay in Diagnosis

OBJECTIVE

To evaluate whether delay in the diagnosis of pelvic avulsion fractures in young athletes leads to prolonged treatment and prolonged return toward sport activities, whether fractures at certain locations are associated with a greater risk of diagnostic delay, and what reasons may exist for delay in diagnosis.

DESIGN

Retrospective chart review of young patients who presented with pelvic region avulsion fracture to a community-based sports medicine clinic over a 19-year period.

SETTING

Private practice, primary care sports medicine clinic.

PATIENTS

Patients younger than 20 years diagnosed with pelvic region avulsion fracture.

INTERVENTIONS

None, this was a retrospective study.

MAIN OUTCOME MEASURES

Clearance for return toward sport activities.

RESULTS

Two hundred twenty-five cases were reviewed for reasons for delay in diagnosis; 208 cases met criteria for the duration of treatment and return to play activities portions of the study. The mean time from date of injury diagnosis was 19.59 days, and the mean duration from date of injury to clearance for return to play advancement was 67.20 days. Duration of treatment varied slightly depending on timing of diagnosis, whereas duration from date of injury to clearance for return to play advancement varied greatly depending on diagnostic delay. Those who did not sense a "pop" at the time of injury were more likely to experience diagnostic delay, as were athletes with ischial tuberosity fractures. The most common cause of diagnostic delay was patient/family decision on when to seek care; misdiagnosis as a muscle strain was also common.

CONCLUSIONS

Diagnostic delay of adolescent pelvic avulsion fractures may unnecessarily prevent athletes from returning to play within an optimal time frame. Our observations highlight a need for educating athletes and their families on when to seek initial or follow-up medical care as well as educating medical providers regarding the diagnosis of pelvic avulsion fractures.

Extracellular vesicles-incorporated microRNA signature as biomarker and diagnosis of prediabetes state and its complications

Extracellular vesicles (EVs) are small anuclear vesicles, delimited by a lipid bilayer, released by almost all cell types, carrying functionally active biological molecules that can be transferred to the neighbouring or distant cells, inducing phenotypical and functional changes, relevant in various physio-pathological conditions. The microRNAs are the most significant active components transported by EVs, with crucial role in intercellular communication and significant effects on recipient cells. They may also server as novel valuable biomarkers for the diagnosis of metabolic disorders. Moreover, EVs are supposed to mediate type 2 diabetes mellitus (T2DM) risk and its progress. The T2DM development is preceded by prediabetes, a state that is associated with early forms of nephropathy and neuropathy, chronic kidney disease, diabetic retinopathy, and increased risk of macrovascular disease. Although the interest of scientists was focused not only on the pathogenesis of diabetes, but also on the early diagnosis, little is known about EVs-incorporated microRNA involvement in prediabetes state and its microvascular and macrovascular complications. Here, we survey the biogenesis, classification, content, biological functions and the most popular primary isolation methods of EVs, review the EVs-associated microRNA profiling connexion with early stages of diabetes and discuss the role of EVs containing specific microRNAs in prediabetes complications.

Serological Phenotyping Analysis Uncovers a Unique Metabolomic Pattern Associated With Early Onset of Type 2 Diabetes Mellitus

Background: Type 2 diabetes mellitus (T2DM) is a multifaceted disorder affecting epidemic proportion at global scope. Defective insulin secretion by pancreatic β-cells and the inability of insulin-sensitive tissues to respond effectively to insulin are the underlying biology of T2DM. However, circulating biomarkers indicative of early diabetic onset at the asymptomatic stage have not been well described. We hypothesized that global and targeted mass spectrometry (MS) based metabolomic discovery can identify novel serological metabolic biomarkers specifically associated with T2DM. We further hypothesized that these markers can have a unique pattern associated with latent or early asymptomatic stage, promising an effective liquid biopsy approach for population T2DM risk stratification and screening.

Methods: Four independent cohorts were assembled for the study. The T2DM cohort included sera from 25 patients with T2DM and 25 healthy individuals for the biomarker discovery and sera from 15 patients with T2DM and 15 healthy controls for the testing. The Pre-T2DM cohort included sera from 76 with prediabetes and 62 healthy controls for the model training and sera from 35 patients with prediabetes and 27 healthy controls for the model testing. Both global and targeted (amino acid, acylcarnitine, and fatty acid) approaches were used to deep phenotype the serological metabolome by high performance liquid chromatography-high resolution mass spectrometry. Different machine learning approaches (Random Forest, XGBoost, and ElasticNet) were applied to model the unique T2DM/Pre-T2DM metabolic patterns and contrasted with their effectiness to differentiate T2DM/Pre-T2DM from controls.

Results: The univariate analysis identified unique panel of metabolites (n = 22) significantly associated with T2DM. Global metabolomics and subsequent structure determination led to the identification of 8 T2DM biomarkers while targeted LCMS profiling discovered 14 T2DM biomarkers. Our panel can effectively differentiate T2DM (ROC AUC = 1.00) or Pre-T2DM (ROC AUC = 0.84) from the controls in the respective testing cohort.

Conclusion: Our serological metabolite panel can be utilized to identifiy asymptomatic population at risk of T2DM, which may provide utility in identifying population at risk at an early stage of diabetic development to allow for clinical intervention. This early detection would guide ehanced levels of care and accelerate development of clinical strategies to prevent T2DM.

High rates of undiagnosed diabetes mellitus among patients with active tuberculosis in Addis Ababa, Ethiopia

Background

Tuberculosis (TB) and diabetes mellitus (DM) have negative synergistic impact on each other. Global guidelines recommend collaborative efforts to address this synergy, but implementation has been slow. Part of the reason is lack of adequate evidence on the operational feasibility of existing tools and mechanisms of collaboration. The objective of this study was to assess the yield of DM screening among TB patients using risk scoring tools combined with blood tests as a feasible strategy for early detection to improve TB/DM treatment outcomes.

Methods

Between September 2020 and December 2021, we conducted a cross-sectional study among patients receiving TB treatment in public health facilities in Addis Ababa, Ethiopia. Trained health workers collected data on symptoms and risk scoring checklists before testing for random and fasting blood glucose levels. We used logistic regression analyses techniques to determine factors associated with increased DM detection. A receiver-operating characteristic curve was constructed to determine the performance of the risk scoring checklist.

Results

Of 2381 TB patients screened, 197 (8.3%) had DM of which 48.7% were newly diagnosed. Having a family history of DM predicted DM with Odds Ratio (OR) of 5.36 (95% Confidence Interval, [3.67, 7.83]) followed by age ≥ 45 years (OR = 4.64, [3.18, 6.76]). Having one or more “symptoms” of DM was a significant predictor (OR 2.88, 95% CI, 2.06–4.01). Combining risk scores with symptoms predicted DM diagnosis with sensitivity of 94.7%, but specificity was low at 29.4%. In patients with known treatment outcome status, death rate was high.

Conclusions

Almost a half of TB patients with DM did not know their status. A simple tool that combined risk factors with symptoms accurately predicted a subsequent diagnosis of DM. Such tools can help avoid high rates of death among TB patients suffering from DM through early detection.

Machine Learning Approaches to Predict Risks of Diabetic Complications and Poor Glycemic Control in Nonadherent Type 2 Diabetes

Purpose: The objective of this study was to evaluate the efficacy of machine learning algorithms in predicting risks of complications and poor glycemic control in nonadherent type 2 diabetes (T2D). Materials and Methods: This study was a real-world study of the complications and blood glucose prognosis of nonadherent T2D patients. Data of inpatients in Sichuan Provincial People's Hospital from January 2010 to December 2015 were collected. The T2D patients who had neither been monitored for glycosylated hemoglobin A nor had changed their hyperglycemia treatment regimens within the last 12 months were the object of this study. Seven types of machine learning algorithms were used to develop 18 prediction models. The predictive performance was mainly assessed using the area under the curve of the testing set. Results: Of 800 T2D patients, 165 (20.6%) met the inclusion criteria, of which 129 (78.2%) had poor glycemic control (defined as glycosylated hemoglobin A ≥7%). The highest area under the curves of the testing set for diabetic nephropathy, diabetic peripheral neuropathy, diabetic angiopathy, diabetic eye disease, and glycosylated hemoglobin A were 0.902 ± 0.040, 0.859 ± 0.050, 0.889 ± 0.059, 0.832 ± 0.086, and 0.825 ± 0.092, respectively. Conclusion: Both univariate analysis and machine learning methods reached the same conclusion. The duration of T2D and the duration of unadjusted hypoglycemic treatment were the key risk factors of diabetic complications, and the number of hypoglycemic drugs was the key risk factor of glycemic control of nonadherent T2D. This was the first study to use machine learning algorithms to explore the potential adverse outcomes of nonadherent T2D. The performances of the final prediction models we developed were acceptable; our prediction performances outperformed most other previous studies in most evaluation measures. Those models have potential clinical applicability in improving T2D care.

Statins are associated with new onset type 2 diabetes mellitus (T2DM) in Medicare patients ≥65 years

BACKGROUND

To evaluate the association of statins and co-morbidities with new onset type 2 diabetes mellitus (T2DM) in patients 65 years and older.

METHODS

This retrospective study used de-identified administrative healthcare claims and enrolment data from a Medicare Advantage Prescription Drug (MAPD) health plan offered by a large multistate healthcare company. The plan covered >2.4 million individuals, of whom >1.7 million individuals were ≥65 years. Of these, 265 554 individuals had continuous MAPD enrolment January 2008 to December 2015. The unadjusted model assessed demographic, pharmacy and T2DM co-morbidities as covariates. Significant variables (P < .05) in the unadjusted model were then included in the adjusted model. The adjusted model used Cox proportional hazards to evaluate covariate effects. Matched propensity score analysis was used to analyse the association of statins and T2DM onset.

RESULTS

The cumulative rate of diagnosed T2DM onset in the study cohort was 4.82% (4314/89 390). Annualised incidence of T2DM diagnosis was 0.82%, 0.88%, 1.04% and 2.09% in 2012, 2013, 2014 and 2015, respectively. T2DM onset was associated with male sex, non-white (African American or Hispanic ethnicity), statin use, hypertension, hyperlipidaemia, heart failure, lower limb ulceration, atherosclerosis, other retinopathy, angina pectoris, poor vision and blindness and absence ischaemic heart disease (IHD). Matched propensity score analysis showed that statin use was significantly associated with T2DM onset (Odds Ratio = 1.26, 95% Confidence Interval: 1.12-1.41, P < .0001) in the adjusted model.

CONCLUSIONS

Analyses indicated that statin usage was associated with new onset T2DM after adjusting for covariates.

Secondary prevention of diabetes through workplace health screening

Background

Workplace health screening offers a unique opportunity to assess individuals for type 2 diabetes mellitus.

Aims

To evaluate the association between workplace diabetes screening, subsequent diagnosis and changes in fasting plasma glucose (FPG), glycated haemoglobin (HbA1c) and body mass index (BMI) among individuals who screened positive for diabetes.

Methods

Employees without a prior diagnosis of diabetes participated in workplace health screening by 45 employers throughout the USA. Individuals screened positive for diabetes based on standard criteria (≥126 mg/dL FPG or ≥6.5% [48 mmol/mol] HbA1c). Diabetes diagnoses were identified after screening using claims-based ICD9-CM diagnosis codes. Discrete-time survival analysis estimated the monthly rate of new diabetes cases after screening, relative to the time period before screening. Paired t-tests evaluated 1-year changes in blood glucose measures and BMI among individuals with positive screenings.

Results

Of 22790 participating individuals, 900 (4%) screened positive for diabetes. A significantly greater rate of new diabetes diagnoses was observed during the first month after screening, compared to the 3-month period before screening (odds ratio [OR] 2.65, 95% confidence intervals [CIs] 2.02-3.47). Among 538 individuals with diabetes who returned for workplace screening 1 year later, significant improvements were observed in BMI (mean ± SD = -0.63 ± 2.56 kg/m2, P < 0.001) and FPG levels (mean ± SD = -9.3 ± 66.5 mg/dL, P < 0.01).

Conclusions

Workplace screening was associated with a reduction in the number of undiagnosed employees with diabetes and significant improvement in FPG and BMI at 1-year follow-up.

Multiple Glycation Sites in Blood Plasma Proteins as an Integrated Biomarker of Type 2 Diabetes Mellitus

Type 2 diabetes mellitus (T2DM) is one of the most widely spread metabolic diseases. Because of its asymptomatic onset and slow development, early diagnosis and adequate glycaemic control are the prerequisites for successful T2DM therapy. In this context, individual amino acid residues might be sensitive indicators of alterations in blood glycation levels. Moreover, due to a large variation in the half-life times of plasma proteins, a generalized biomarker, based on multiple glycation sites, might provide comprehensive control of the glycemic status across any desired time span. Therefore, here, we address the patterns of glycation sites in highly-abundant blood plasma proteins of T2DM patients and corresponding age- and gender-matched controls by comprehensive liquid chromatography-mass spectrometry (LC-MS). The analysis revealed 42 lysyl residues, significantly upregulated under hyperglycemic conditions. Thereby, for 32 glycation sites, biomarker behavior was demonstrated here for the first time. The differentially glycated lysines represented nine plasma proteins with half-lives from 2 to 21 days, giving access to an integrated biomarker based on multiple protein-specific Amadori peptides. The validation of this biomarker relied on linear discriminant analysis (LDA) with random sub-sampling of the training set and leave-one-out cross-validation (LOOCV), which resulted in an accuracy, specificity, and sensitivity of 92%, 100%, and 85%, respectively.

Prevalence of prediabetes and diabetes mellitus among adults residing in Cameroon: A systematic review and meta-analysis

AIMS

To summarize current data on the prevalence of prediabetes and diabetes mellitus in Cameroon.

METHODS

Population-based cross-sectional studies published between January 1, 2000 and April 30, 2017 including apparently healthy adults residing in Cameroon were searched in PubMed, EMBASE, African Journals Online, and African Index Medicus. We used a random-effects model to pool data.

RESULTS

All included studies had a low risk of bias. Six studies were conducted in an urban setting only, one in a rural setting only, and five in both settings. The overall prevalence of diabetes mellitus was 5.8% (95%CI 4.1-7.9; 12 studies) in a pooled sample of 37,147 participants. The prevalence of prediabetes was 7.1% (95%CI: 3.0-21.9; 4 studies) in a pooled sample of 5,872 people. In univariable meta-regression analysis, the prevalence of diabetes mellitus increased with age, hypertension, overweight and obesity. There was no difference for sex and settings (rural versus urban).

CONCLUSIONS

This study reports a relatively high prevalence of diabetes mellitus and prediabetes in Cameroon, with no difference between urban and rural settings and between sexes. The main drivers include increasing age, overweight and obesity. Community-based educational programs are needed to tackle the burden of the disease in the country.

Google Trends can improve surveillance of Type 2 diabetes

Recent studies demonstrate that people are increasingly looking online to assess their health, with reasons varying from personal preferences and beliefs to inability to book a timely appointment with their local medical practice. Records of these activities represent a new source of data about the health of populations, but which is currently unaccounted for by disease surveillance models. This could potentially be useful as evidence of individuals' perception of bodily changes and self-diagnosis of early symptoms of an emerging disease. We make use of the Experian geodemographic Mosaic dataset in order to extract Type 2 diabetes candidate risk variables and compare their temporal relationships with the search keywords, used to describe early symptoms of the disease on Google. Our results demonstrate that Google Trends can detect early signs of diabetes by monitoring combinations of keywords, associated with searches for hypertension treatment and poor living conditions; Combined search semantics, related to obesity, how to quit smoking and improve living conditions (deprivation) can be also employed, however, may lead to less accurate results.

Differentially expressed haptoglobin as a potential biomarker for type 2 diabetic mellitus in Hispanic population

Glycosylated hemoglobin (HbA1c) measurement is currently a primary tool for diagnosis of type 2 diabetes mellitus (T2DM), especially for the assessment of chronic hyperglycemia. However, many studies reported the limitation of using HbA1c for T2DM diagnosis/prognosis, such as poor sensitivities, difficult standardization, and variable cut points across ethnic groups. Therefore, the aim of this study was to discover novel biomarkers associated with elevated HbA1c levels as complementary T2DM diagnostic tools. Two-dimensional difference gel electrophoresis combined with mass spectrometry were applied for protein profile analyses of two pooled serum samples collected from Hispanic T2DM subjects (n = 74) with HbA1c ≥7 and HbA1c< 7, respectively. Isoforms of haptoglobin (Hp) α1/α2 chains were significantly altered in pooled serum samples from T2DM subjects with HbA1c ≥7 compared to those with HbA1c< 7. Hp genotypes of 262 Hispanic subjects, including 109 T2DM and 153 nondiabetic controls, were further determined by PCRs and western blotting analysis. Meanwhile, a new droplet digital PCR method for Hp genotyping was also established. The distribution of Hp2 allele was higher in T2DM subjects compared to nondiabetic controls and the HbA1c levels of T2DM subjects carrying at least one Hp2 allele tended to be higher than T2DM subjects with Hp 1-1. In summary, our results indicate that differentially expressed serum Hp protein isoforms could be associated with HbA1c levels and subjects with Hp2 allele have a higher risk for the occurrence of T2DM in Hispanic population. © 2016 BioFactors, 43(3):424-433, 2017.

Perceived risk of diabetes seriously underestimates actual diabetes risk: The KORA FF4 study

OBJECTIVE

Early detection of diabetes and prediabetic states is beneficial for patients, but may be delayed by patients´ being overly optimistic about their own health. Therefore, we assessed how persons without known diabetes perceive their risk of having or developing diabetes, and we identified factors associated with perception of diabetes risk.

RESEARCH DESIGN AND METHODS

1,953 participants without previously known diabetes from the population-based, German KORA FF4 Study (59.1 years, 47.8% men) had an oral glucose tolerance test. They estimated their probability of having undiagnosed diabetes mellitus (UDM) on a six category scale, and assessed whether they were at risk of developing diabetes in the future. We cross-tabulated glycemic status with risk perception, and fitted robust Poisson regression models to identify determinants of diabetes risk perception.

RESULTS

74% (95% CI: 65-82) of persons with UDM believed that their probability of having undetected diabetes was low or very low. 72% (95% CI: 69-75) of persons with prediabetes believed that they were not at risk of developing diabetes. In people with prediabetes, seeing oneself at risk of diabetes was associated with self-rated poor general health (prevalence ratio (PR) = 3.1 (95% CI: 1.4-6.8), parental diabetes (PR = 2.6, 1.9-3.4), high educational level (PR = 1.9 (1.4-2.5)), lower age (PR = 0.7, 0.6-0.8, per 1 standard deviation increase), female sex (PR = 1.2, 0.9-1.5) and obesity (PR = 1.5, 1.2-2.0).

CONCLUSIONS

People with undiagnosed diabetes or prediabetes considerably underestimate their probability of having or developing diabetes. Contrary to associations with actual diabetes risk, perceived diabetes risk was lower in men, lower educated and older persons.

Update on pre-diabetes: Focus on diagnostic criteria and cardiovascular risk

Pre-diabetes, which is typically defined as blood glucose concentrations higher than normal but lower than the diabetes threshold, is a high-risk state for diabetes and cardiovascular disease development. As such, it represents three groups of individuals: Those with impaired fasting glucose (IFG), those with impaired glucose tolerance (IGT) and those with a glycated haemoglobin (HbA_1c) between 39-46 mmol/mol. Several clinical trials have shown the important role of IFG, IGT and HbA_1c-pre-diabetes as predictive tools for the risk of developing type 2 diabetes. Moreover, with regard to cardiovascular disease, pre-diabetes is associated with more advanced vascular damage compared with normoglycaemia, independently of confounding factors. In view of these observations, diagnosis of pre-diabetes is mandatory to prevent or delay the development of the disease and its complications; however, a number of previous studies reported that the concordance between pre-diabetes diagnoses made by IFG, IGT or HbA_1c is scarce and there are conflicting data as to which of these methods best predicts cardiovascular disease. This review highlights recent studies and current controversies in the field. In consideration of the expected increased use of HbA_1c as a screening tool to identify individuals with alteration of glycaemic homeostasis, we focused on the evidence regarding the ability of HbA_1c as a diagnostic tool for pre-diabetes and as a useful marker in identifying patients who have an increased risk for cardiovascular disease. Finally, we reviewed the current evidence regarding non-traditional glycaemic biomarkers and their use as alternatives to or additions to traditional ones.

Does Thiazolidinedione therapy exacerbate fluid retention in congestive heart failure?

The ever-growing global burden of congestive heart failure (CHF) and type 2 diabetes mellitus (T2DM) as well as their co-existence necessitate that anti-diabetic pharmacotherapy will modulate the cardiovascular risk inherent to T2DM while complying with the accompanying restrictions imposed by CHF. The thiazolidinedione (TZD) family of peroxisome proliferator-activated receptor γ (PPARγ) agonists initially provided a promising therapeutic option in T2DM owing to anti-diabetic efficacy combined with pleiotropic beneficial cardiovascular effects. However, the utility of TZDs in T2DM has declined in the past decade, largely due to concomitant adverse effects of fluid retention and edema formation attributed to salt-retaining effects of PPARγ activation on the nephron. Presumably, the latter effects are potentially deleterious in the context of pre-existing fluid retention in CHF. However, despite a considerable body of evidence on mechanisms responsible for TZD-induced fluid retention suggesting that this class of drugs is rightfully prohibited from use in CHF patients, there is a paucity of experimental and clinical studies that investigate the effects of TZDs on salt and water homeostasis in the CHF setting. In an attempt to elucidate whether TZDs actually exacerbate the pre-existing fluid retention in CHF, our review summarizes the pathophysiology of fluid retention in CHF. Moreover, we thoroughly review the available data on TZD-induced fluid retention and proposed mechanisms in animals and patients. Finally, we will present recent studies challenging the common notion that TZDs worsen renal salt and water retention in CHF.

Adapting existing diabetes risk scores for an Asian population: a risk score for detecting undiagnosed diabetes in the Mongolian population

Background

Most of the commonly used diabetes mellitus screening tools and risk scores have been developed with American or European populations in mind. Their applicability, therefore, to low and middle-income countries remains unquantified. Simultaneously, low and middle-income countries including Mongolia are currently witnessing rising diabetes prevalence. This research aims to develop and validate a diabetes risk score for the screening of undiagnosed type 2 diabetes mellitus in the Mongolian adult population.

Methods

Blood glucose measurements from 1018 Mongolians, as well as information on demography and risk factors prevalence was drawn from 2009 STEPS data. Existing risk scores were applied, measuring sensitivity using area under ROC-curves. Logistic regression models were used to identify additional independent predictors for undiagnosed diabetes. Finally, a new risk score was developed and Hosmer-Lemeshow tests were used to evaluate the agreement between the observed and predicted prevalence.

Results

The performance of existing risk scores to identify undiagnosed diabetes was moderate; with the area under ROC curves between 61–64 %. In addition to well-established risk factors, three new independent predictors for undiagnosed diabetes were identified. Incorporating these into a new risk score, the area under ROC curves increased to 77 % (95 % CI 71 %–82 %).

Conclusions

Existing European or American diabetes risk tools cannot be adopted in Asian countries without prior validation in the specific population. With this in mind, a low-cost, reliable screening tool for undiagnosed diabetes was developed and internally validated for Mongolians. The potential for cost and morbidity savings could be significant.

TNF-alpha levels in tears: a novel biomarker to assess the degree of diabetic retinopathy

We assess the level of tumour necrosis factor alpha (TNF-alpha) in tear fluids and other serum parameters associated with diabetes in different degrees of diabetic retinopathy. We have performed a prospective, nonrandomized, observational study. Study population consisted of 16 healthy subjects (controls) and 32 type 2 diabetic patients: 16 affected by proliferative diabetic retinopathy (PDR) and 16 with nonproliferative retinopathy (NDPR, background/preproliferative). Body mass index, urinary albumin, blood glucose, HbA1c, and tear levels of TNF-alpha were measured in all subjects. The value of glycaemia, microalbuminurea, and Body mass index in diabetic retinopathy groups were higher than those in control group (P < 0.05). Glycemia in NPDR: 6.6 mmol/L (range: 5.8-6.3); in PDR: 6.7 mmol/L (range: 6.1-7.2); in control: 5.7 mmol/L (range: 4.9-6.1); microalbuminurea in NPDR: 10.6 mg/L (range: 5.6-20); in PDR: 25.2 mg/L (range: 17-40); in control: 5.3 mg/L (range: 2.6-10); Body mass index in NPDR: 26 Kg/m(2) (range: 20.3-40); in PDR: 28 Kg/m(2) (range 20.3-52); in control: 21 Kg/m(2) (range 19-26). The TNF-alpha concentrations in tears increase with the severity of pathology and were lower in control group than in diabetic subjects. In the end, the level of TNF-alpha is highly correlated with severity of diabetic retinopathy and with nephropathy. Tear fluid collection may be a useful noninvasive method for the detection of proliferative diabetic retinopathy.

Social status and participation in health checks in men and women in Germany: results from the German Health Update (GEDA), 2009 and 2010

BACKGROUND

Representative data for Germany were used to determine whether a person's social-status characteristics affect the likelihood of having a check-up for the early detection of disease.

METHODS

The analyses reported here were performed on data obtained in the Robert Koch Institute's German Health Update (GEDA) in 2009 and 2010. The survey responses of 26 555 people about whether they had had a check-up were evaluated. For inclusion, participants were required to be over age 35 and covered by statutory health insurance. To study the potential links between social status and check-up participation rates, a multidimensional social-status index was used in addition to three individual components of social status (highest educational level attained, occupational status, income).

RESULTS

50.8% of the men and 49.8% of the women surveyed had had a check-up in the two years before the survey. Low social status was associated with a lower rate of check-ups for both sexes (for men, odds ratio [OR] 0.59, 95% confidence interval [CI] 0.50-0.70, p<0.001; for women, OR 0.63, 95% CI 0.55-0.72, p<0.001). For both men and women, occupational status and income each had independent effects on check-up participation, while educational level was not found to have any independent effect.

CONCLUSION

These findings indicate that socially disadvantaged persons are less likely to have check-ups than others. Efforts to increase check-up rates should take account of the demonstrated effects of income level and occupational status.

Diabetes screening among immigrants: a population-based urban cohort study

OBJECTIVE

To examine diabetes screening, predictors of screening, and the burden of undiagnosed diabetes in the immigrant population and whether these estimates differ by ethnicity.

RESEARCH DESIGN AND METHODS

A population-based retrospective cohort linking administrative health data to immigration files was used to follow the entire diabetes-free population aged 40 years and up in Ontario, Canada (N = 3,484,222) for 3 years (2004-2007) to determine whether individuals were screened for diabetes. Multivariate regression was used to determine predictors of having a diabetes test.

RESULTS

Screening rates were slightly higher in the immigrant versus the general population (76.0 and 74.4%, respectively; P < 0.001), with the highest rates in people born in South Asia, Mexico, Latin America, and the Caribbean. Immigrant seniors (age ≥65 years) were screened less than nonimmigrant seniors. Percent yield of new diabetes subjects among those screened was high for certain countries of birth (South Asia, 13.0%; Mexico and Latin America, 12.1%; Caribbean, 9.5%) and low among others (Europe, Central Asia, U.S., 5.1-5.2%). The number of physician visits was the single most important predictor of screening, and many high-risk ethnic groups required numerous visits before a test was administered. The proportion of diabetes that remained undiagnosed was estimated to be 9.7% in the general population and 9.0% in immigrants.

CONCLUSIONS

Overall diabetes-screening rates are high in Canada's universal health care setting, including among high-risk ethnic groups. Despite this finding, disparities in screening rates between immigrant subgroups persist and multiple physician visits are often required to achieve recommended screening levels.

Screening for type 2 diabetes and dysglycemia

Type 2 diabetes mellitus (T2DM) and dysglycemia (impaired glucose tolerance and/or impaired fasting glucose) are increasingly contributing to the global burden of diseases. The authors reviewed the published literature to critically evaluate the evidence on screening for both conditions and to identify the gaps in current understanding. Acceptable, relatively simple, and accurate tools can be used to screen for both T2DM and dysglycemia. Lifestyle modification and/or medication (e.g., metformin) are cost-effective in reducing the incidence of T2DM. However, their application is not yet routine practice. It is unclear whether diabetes-prevention strategies, which influence cardiovascular risk favorably, will also prevent diabetic vascular complications. Cardioprotective therapies, which are cost-effective in preventing complications in conventionally diagnosed T2DM, can be used in screen-detected diabetes, but the magnitude of their effects is unknown. Economic modeling suggests that screening for both T2DM and dysglycemia may be cost-effective, although empirical data on tangible benefits in preventing complications or death are lacking. Screening for T2DM is psychologically unharmful, but the specific impact of attributing the label of dysglycemia remains uncertain. Addressing these gaps will inform the development of a screening policy for T2DM and dysglycemia within a holistic diabetes prevention and control framework combining secondary and high-risk primary prevention strategies.

Nutritional risk factors of early development of postpartum prediabetes and diabetes in women with gestational diabetes mellitus

OBJECTIVE

Early detection of prediabetes and diabetes after delivery helps prevent and delay the development of overt type 2 diabetes in women with gestational diabetes mellitus (GDM). We sought to identify modifiable risk factors for the early development of postpartum type 2 diabetes in women with GDM that may help establish interventions for preventing or delaying the subsequent onset of type 2 diabetes.

METHODS

Three hundred eighty-one women who developed GDM during pregnancy were tested for 1) antepartum anthropometric and biochemical measurements, 2) pregnancy outcome, 3) oral glucose tolerance test at 6 to 12 wk after delivery, and 4) postpartum anthropometric, biochemical, and nutritional measurements. The subjects were divided into three groups on the basis of the postpartum oral glucose tolerance test results: normal glucose tolerance group (n=193), prediabetes (n=161), and diabetes (n=27).

RESULTS

The incidences of postpartum prediabetes and diabetes at 6 to 12 wk follow-up in Korean women with GDM were 44.8% and 5.2%, respectively. Antepartum modifiable risk factors for developing type 2 diabetes at early postpartum included higher body mass index, lower β-cell function, insulin dosage during late pregnancy, and the non-modifiable risk factor of family history of diabetes (R2=0.14). Postpartum risk factors included higher body mass index, serum triacylglycerols, hemoglobin A1c, and energy intake and lower insulin secretion capacity (R2=0.43). Animal fat intake was higher in the prediabetes and diabetes groups than in the normal glucose tolerance group, whereas breast-feeding did not alter the risk for the development of postpartum diabetes.

CONCLUSION

This study strongly suggests that the development of postpartum type 2 diabetes in women with GDM can be prevented and/or delayed by lifestyle and nutritional intervention during antepartum and postpartum.