Undiagnosed Type Research Articles

Evaluation of Madras Diabetes Research Foundation-Indian Diabetes Risk Score in detecting undiagnosed diabetes in the Indian population: Results from the Indian Council of Medical Research-INdia DIABetes population-based study (INDIAB-15).

Screening of individuals for early detection and identification of undiagnosed diabetes can help in reducing the burden of diabetic complications. This study aimed to evaluate the performance of Madras Diabetes Research Foundation (MDRF)-Indian Diabetes Risk Score (IDRS) to screen for undiagnosed type 2 diabetes in a large representative population in India. Data were acquired from the Indian Council of Medical Research-INdia DIABetes (ICMR-INDIAB) study, a large national survey that included both urban and rural populations from 30 states/union territories in India. Stratified multistage design was followed to obtain a sample of 113,043 individuals (94.2% response rate). MDRF-IDRS used four simple parameters, viz. age, waist circumference, family history of diabetes and physical activity to detect undiagnosed diabetes. Receiver operating characteristic (ROC) with area under the curve (AUC) was used to assess the performance of MDRF-IDRS. We identified that 32.4, 52.7 and 14.9 per cent of the general population were under high-, moderate- and low-risk category of diabetes. Among the newly diagnosed individuals with diabetes [diagnosed by oral glucose tolerance test (OGTT)], 60.2, 35.9 and 3.9 per cent were identified under high-, moderate- and low-risk categories of IDRS. The ROC-AUC for the identification of diabetes was 0.697 (95% confidence interval: 0.684-0.709) for urban population and 0.694 (0.684-0.704) for rural, as well as 0.693 (0.682-0.705) for males and 0.707 (0.697-0.718) for females. MDRF-IDRS performed well when the population were sub-categorized by state or by regions. Performance of MDRF-IDRS is evaluated across the nation and is found to be suitable for easy and effective screening of diabetes in Asian Indians.

Undiagnosed Type 2 Diabetes Mellitus Prevalence and Associated Risk Factors in an Urban and Rural Metropolis in Ghana

Diabetes mellitus (Type 2) is a chronic disease characterized by high fasting blood glucose that can affect people without detection at onset until after some time in life. Early diagnoses and management usually lead to a better quality of life of an individual. The predisposing factors associated with the disease among same population can vary as a result of genetic composition and susceptibility of every single individual.

A non-invasive risk score including skin autofluorescence predicts diabetes risk in the general population

Increased skin autofluorescence (SAF) predicts the development of diabetes-related complications and cardiovascular disease. We assessed the performance of a simple model which includes SAF to identify individuals at high risk for undiagnosed and incident type 2 diabetes, in 58,377 participants in the Lifelines Cohort Study without known diabetes. Newly-diagnosed diabetes was defined as fasting blood glucose ≥ 7.0 mmol/l and/or HbA1c ≥ 6.5% (≥ 48 mmol/mol) or self-reported diabetes at follow-up. We constructed predictive models based on age, body mass index (BMI), SAF, and parental history of diabetes, and compared to results with the concise FINDRISC model. At 2nd visit to Lifelines, 1113 (1.9%) participants were identified with undiagnosed diabetes and 1033 (1.8%) participants developed diabetes during follow-up. A model comprising age, BMI and SAF yielded an AUC of 0.783 and was non-inferior to the concise FINDRISC model, which had an AUC of 0.797 to predict new diabetes. At a score of 5.8, sensitivity was 78% and specificity of 66%. Model 2 which also incorporated parental diabetes history, had an AUC of 0.792, and a sensitivity of 74% and specificity of 70% at a score of 6.5. Net reclassification index (NRI) did not improve significantly (NRI 1.43% (− 0.50–3.37 p = 0.15). The combination of an easy to perform SAF measurement with age and BMI is a good alternative screening tool suitable for medical and non-medical settings. Parental history of diabetes did not significantly improve model performance in this homogeneous cohort.

Prevalence and factors associated with undiagnosed type 2 diabetes among adults in Iraq: analysis of cross-sectional data from the 2015 STEPS survey

ObjectiveThe purpose of the study was to assess the prevalence and correlates of undiagnosed type 2 diabetes (UT2D) among adults (aged 18 years and older) in Iraq.DesignCross-sectional, population-based study.SettingNationally representative...

ODP205 Hepatic steatosis induced by Nicotine plus Coca Cola is prevented by Nicotinamide riboside (NR) that increases mitochondrial NAD+

Abstract Type 2 diabetes has emerged as an important risk factor for poor prognosis in hospitalized patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Hyperglycemia can independently predispose the patients with SARS-CoV-2 to adverse outcomes, irrespective of the diagnosis of diabetes.1 Despite the accentuated risk, there is limited evidence about the complex interplay of undiagnosed diabetes and SARS-CoV-2 pneumonia on the clinical outcomes. We propose to investigate the cardiovascular events and clinical outcomes in patients with undiagnosed and type 2 diabetes hospitalized with SARS-CoV-2 pneumonia. Methods This retrospective study included patients hospitalized with SARS-CoV-2 pneumonia between March 7, 2020, through March 30, 2021. The diagnosis of SARS-CoV-2 pneumonia required a positive reverse transcriptase polymerase chain reaction (RT-PCR) for SARS-CoV-2, presence of new or worsening pulmonary infiltrates on computed tomography scan or chest X-ray, and at least one of the following: new or increased cough, a temperature of >37.8 °C or dyspnea. The diagnosis of undiagnosed diabetes was determined in patients with no previous history of diabetes and glycated hemoglobin (HbA1c) ≥ 6.5% (48 mmol/mol) or an admission serum glucose ≥ 200 mg/dL (≥ 11.1 mmol/mol). The outcomes included in-hospital cardiovascular events, intensive care unit (ICU) admissions and in-hospital mortality. Logistic regression was used to estimate adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for association of undiagnosed diabetes and type 2 diabetes for individual outcomes. Results Among the 1,645 adults hospitalized with SARS-CoV-2 pneumonia, 107 (10.8%) patients were identified with undiagnosed diabetes and 634 (39%) had established diagnosis of type 2 diabetes. Patients with undiagnosed diabetes had increased odds of developing cardiovascular events (OR: 1.73, 95% CI 1. 07, 2.80), ICU admissions (OR: 1.61, 95% CI 1.10, 2.34), and mortality (OR: 1.77, 95% CI 1. 02, 3. 07), compared to patients type 2 diabetes and no diabetes. A prior history of type 2 diabetes was not significantly associated with primary study outcomes. Conclusions Patients with undiagnosed diabetes hospitalized with SARS-CoV-2 pneumonia have an increased risk of developing cardiovascular complications and overall poor clinical outcomes compared to patients with type 2 diabetes and no diabetes. Implementation of healthcare-wide strategies for screening and early intervention of prediabetes and diabetes are needed to improve the disease outcomes in the current pandemic and future public health threats. References Khunti, K., et al., COVID-19, Hyperglycemia, and New-Onset Diabetes. Diabetes Care, 2021. 44(12): p. 2645-2655. Presentation: Saturday, June 11, 2022 1:00 p.m. - 2:00 p.m., Sunday, June 12, 2022 12:30 p.m. - 2:30 p.m.

ODP633 Impact of Undiagnosed Diabetes on Cardiovascular Events and Clinical outcomes in Patients Hospitalized with COVID-19 Pneumonia

Abstract Type 2 diabetes has emerged as an important risk factor for poor prognosis in hospitalized patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Hyperglycemia can independently predispose the patients with SARS-CoV-2 to adverse outcomes, irrespective of the diagnosis of diabetes 1. Despite the accentuated risk, there is limited evidence about the complex interplay of undiagnosed diabetes and SARS-CoV-2 pneumonia on the clinical outcomes. We propose to investigate the cardiovascular events and clinical outcomes in patients with undiagnosed and type 2 diabetes hospitalized with SARS-CoV-2 pneumonia. Methods This retrospective study included patients hospitalized with SARS-CoV-2 pneumonia between March 7, 2020, through March 30, 2021. The diagnosis of SARS-CoV-2 pneumonia required a positive reverse transcriptase polymerase chain reaction (RT-PCR) for SARS-CoV-2, presence of new or worsening pulmonary infiltrates on computed tomography scan or chest X-ray, and at least one of the following: new or increased cough, a temperature of >37.8 °C or dyspnea. The diagnosis of undiagnosed diabetes was determined in patients with no previous history of diabetes and glycated hemoglobin (HbA1c) ≥ 6.5% (48 mmol/mol) or an admission serum glucose ≥ 200 mg/dL (≥ 11.1 mmol/mol). The outcomes included in-hospital cardiovascular events, intensive care unit (ICU) admissions and in-hospital mortality. Logistic regression was used to estimate adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for association of undiagnosed diabetes and type 2 diabetes for individual outcomes. Results Among the 1,645 adults hospitalized with SARS-CoV-2 pneumonia, 107 (10.8%) patients were identified with undiagnosed diabetes and 634 (39%) had established diagnosis of type 2 diabetes. Patients with undiagnosed diabetes had increased odds of developing cardiovascular events (OR: 1.73, 95% CI 1. 07, 2.80), ICU admissions (OR: 1.61, 95% CI 1.10, 2.34), and mortality (OR: 1.77, 95% CI 1. 02, 3. 07), compared to patients type 2 diabetes and no diabetes. A prior history of type 2 diabetes was not significantly associated with primary study outcomes. Conclusions Patients with undiagnosed diabetes hospitalized with SARS-CoV-2 pneumonia have an increased risk of developing cardiovascular complications and overall poor clinical outcomes compared to patients with type 2 diabetes and no diabetes. Implementation of healthcare-wide strategies for screening and early intervention of prediabetes and diabetes are needed to improve the disease outcomes in the current pandemic and future public health threats. References Khunti, K., et al., COVID-19, Hyperglycemia, and New-Onset Diabetes. Diabetes Care, 2021. 44(12): p. 2645-2655. Presentation: Sunday, June 12, 2022 12:30 p.m. - 2:30 p.m.

External Validation of Finnish Diabetes Risk Score and Australian Diabetes Risk Assessment Tool Prediction Models to Identify People with Undiagnosed Type 2 Diabetes: A Cross-sectional Study in Iran.

Noninvasive risk prediction models have been widely used in various settings to identify individuals with undiagnosed diabetes. We aimed to evaluate the discrimination, calibration, and clinical usefulness of the Finnish Diabetes Risk Score (FINDRISC) and Australian Diabetes Risk Assessment (AUSDRISK) to screen undiagnosed diabetes in Kerman, Iran. We analyzed data from 2014 to 2018 in the second round of the Kerman Coronary Artery Disease Risk Factors Study (KERCADRS), Iran. Participants aged 35 - 65 with no history of confirmed diabetes were eligible. The area under the receiver operating characteristic curve (AUROC) and decision curve analysis were applied to evaluate the discrimination power and clinical usefulness of the models, respectively. The calibration was assessed by the Hosmer-Lemeshow test and the calibration plots. Out of 3262 participants, 145 (4.44%) had undiagnosed diabetes. The estimated AUROCs were 0.67 and 0.62 for the AUSDRISK and FINDRISC models, respectively (P < 0.001). The chi-square test results for FINDRISC and AUSDRISC were 7.90 and 16.47 for the original model and 3.69 and 14.61 for the recalibrated model, respectively. Based on the decision curves, useful threshold ranges for the original models of FINDRIS and AUSDRISK were 4% to 10% and 3% to 13%, respectively. Useful thresholds for the recalibrated models of FINDRISC and AUSDRISK were 4% to 8% and 4% to 9%, respectively. The original AUSDRISK model performs better than FINDRISC in identifying patients with undiagnosed diabetes and could be used as a simple and noninvasive tool where access to laboratory facilities is costly or limited.

Development and External Validation of a Multivariable Prediction Model to Identify Nondiabetic Hyperglycemia and Undiagnosed Type 2 Diabetes: Diabetes Risk Assessment in Dentistry Score (DDS)

The aim of this study was to develop and externally validate a score for use in dental settings to identify those at risk of undiagnosed nondiabetic hyperglycemia (NDH) or type 2 diabetes (T2D). The Studies of Health in Pomerania (SHIP) project comprises 2 representative population-based cohort studies conducted in northeast Germany. SHIP-TREND-0, 2008 to 2012 (the development data set) had 3,339 eligible participants, with 329 having undiagnosed NDH or T2D. Missing data were replaced using multiple imputation. Potential covariates were selected for inclusion in the model using backward elimination. Heuristic shrinkage was used to reduce overfitting, and the final model was adjusted for optimism. We report the full model and a simplified paper-based point-score system. External validation of the model and score employed an independent data set comprising 2,359 participants with 357 events. Predictive performance, discrimination, calibration, and clinical utility were assessed. The final model included age, sex, body mass index, smoking status, first-degree relative with diabetes, presence of a dental prosthesis, presence of mobile teeth, history of periodontal treatment, and probing pocket depths ≥5 mm as well as prespecified interaction terms. In SHIP-TREND-0, the model area under the curve (AUC) was 0.72 (95% confidence interval [CI] 0.69, 0.75), calibration in the large was −0.025. The point score AUC was 0.69 (95% CI 0.65, 0.72), with sensitivity of 77.0 (95% CI 76.8, 77.2), specificity of 51.5 (95% CI 51.4, 51.7), negative predictive value of 94.5 (95% CI 94.5, 94.6), and positive predictive value of 17.0 (95% CI 17.0, 17.1). External validation of the point score gave an AUC of 0.69 (95% CI 0.66, 0.71), sensitivity of 79.2 (95% CI 79.0, 79.4), specificity of 49.9 (95% CI 49.8, 50.00), negative predictive value 91.5 (95% CI 91.5, 91.6), and positive predictive value of 25.9 (95% CI 25.8, 26.0). A validated prediction model involving dental variables can identify NDH or undiagnosed T2DM. Further studies are required to validate the model for different European populations.

Socio-economic gradients in diagnosed and undiagnosed Type 2 diabetes and its related health complications

Background and aimsDiagnosed and undiagnosed Type 2 Diabetes (T2D) remains a challenge in high-income countries. In addition, the presence of T2D can cause further disease burden because of its high susceptibility to complications. Nevertheless, there is limited evidence of socio-economic gradients in undiagnosed T2D and its complications in a large population cohort. We investigated this using the Dutch Lifelines Cohort Study (Lifelines). Methods and resultsWithin Lifelines, baseline data of 102 163 adults aged 30 and above were collected from 2007 to 2013. The associations of Socio-Economic Status (SES), indicated by monthly household income, with the prevalence of T2D status and the number of T2D complications were assessed using multinomial Poisson and linear regressions with adjustments for age and sex. The prevalence of diagnosed and undiagnosed T2D was, respectively, 3.0% and 3.0% in the low SES group compared to 1.1% and 1.8% in the high SES group. Individuals with lower SES were at higher risk of having undiagnosed T2D (relative risk ratio (rrr) [95% CI]: 1.63 [1.47–1.81] for low SES and 1.16 [1.05–1.29] for middle SES) and diagnosed T2D, compared with those with high SES. Lower SES was positively associated with the number of T2D complications (low SES vs. high SES (ref); B [95% CI]: 0.15 [0.13–0.16]). ConclusionComplementing the known socio-economic gradients in diagnosed T2D, we document socio-economic gradients in undiagnosed T2D and T2D complications in a single, large general representative population. Furthermore, individuals with low SES with diagnosed or undiagnosed T2D were more susceptible to T2D complications.

Prevalence and correlates of undiagnosed, diagnosed, and total type 2 diabetes among adults in Morocco, 2017

The study aimed to estimate the prevalence and associated factors of undiagnosed type 2 diabetes (T2D) among adults in Morocco. Cross-sectional data were analyzed from 4779 people (≥ 18 years, mean age 41.7 years) who participated in the Morocco STEPS nationally representative survey in 2017 and had completed fasting blood glucose measurement. The results indicate that the prevalence of undiagnosed T2D was 5.9% (44.7% of total T2D), diagnosed T2D 7.3% and total T2D 13.2%. In the adjusted multinomial logistic regression analysis, older age (≥ 50 years), receipt of health care advice, and obesity were positively associated with undiagnosed T2D. Older age (≥ 50 years), urban residence, receipt of health care advice, ever cholesterol screening, moderate sedentary behaviour, obesity, hypertension, and elevated total cholesterol were positively associated with diagnosed T2D. In adjusted logistic regression analysis, older age (≥ 50 years), receipt of health care advice and cholesterol screening were negatively associated with undiagnosed T2D versus diagnosed T2D. A significant proportion of adults in Morocco had undiagnosed T2D and several associated factors were identified that can help guide interventions.

Quantification of glycated hemoglobin and glucose in vivo using Raman spectroscopy and artificial neural networks

Undiagnosed type 2 diabetes (T2D) remains a major public health concern. The global estimation of undiagnosed diabetes is about 46%, being this situation more critical in developing countries. Therefore, we proposed a non-invasive method to quantify glycated hemoglobin (HbA1c) and glucose in vivo. We developed a technique based on Raman spectroscopy, RReliefF as a feature selection method, and regression based on feed-forward artificial neural networks (FFNN). The spectra were obtained from the forearm, wrist, and index finger of 46 individuals. The use of FFNN allowed us to achieve an error in the predictive model of 0.69% for HbA1c and 30.12 mg/dL for glucose. Patients were classified according to HbA1c values into three categories: healthy, prediabetes, and T2D. The proposed method obtained a specificity and sensitivity of 87.50% and 80.77%, respectively. This work demonstrates the benefit of using artificial neural networks and feature selection techniques to enhance Raman spectra processing to determine glycated hemoglobin and glucose in patients with undiagnosed T2D.

Progression likelihood score identifies substages of presymptomatic type 1 diabetes in childhood public health screening

Aims/hypothesisThe aim of this study was to develop strategies that identify children from the general population who have late-stage presymptomatic type 1 diabetes and may, therefore, benefit from immune intervention.MethodsWe tested children from Bavaria, Germany, aged 1.75–10 years, enrolled in the Fr1da public health screening programme for islet autoantibodies (n=154,462). OGTT and HbA1c were assessed in children with multiple islet autoantibodies for diagnosis of presymptomatic stage 1 (normoglycaemia) or stage 2 (dysglycaemia) type 1 diabetes. Cox proportional hazards and penalised logistic regression of autoantibody, genetic, metabolic and demographic information were used to develop a progression likelihood score to identify children with stage 1 type 1 diabetes who progressed to stage 3 (clinical) type 1 diabetes within 2 years.ResultsOf 447 children with multiple islet autoantibodies, 364 (81.4%) were staged. Undiagnosed stage 3 type 1 diabetes, presymptomatic stage 2, and stage 1 type 1 diabetes were detected in 41 (0.027% of screened children), 30 (0.019%) and 293 (0.19%) children, respectively. The 2 year risk for progression to stage 3 type 1 diabetes was 48% (95% CI 34, 58) in children with stage 2 type 1 diabetes (annualised risk, 28%). HbA1c, islet antigen-2 autoantibody positivity and titre, and the 90 min OGTT value were predictors of progression in children with stage 1 type 1 diabetes. The derived progression likelihood score identified substages corresponding to ≤90th centile (stage 1a, n=258) and >90th centile (stage 1b, n=29; 0.019%) of stage 1 children with a 4.1% (95% CI 1.4, 6.7) and 46% (95% CI 21, 63) 2 year risk of progressing to stage 3 type 1 diabetes, respectively.Conclusions/interpretationPublic health screening for islet autoantibodies found 0.027% of children to have undiagnosed clinical type 1 diabetes and 0.038% to have undiagnosed presymptomatic stage 2 or stage 1b type 1 diabetes, with 50% risk to develop clinical type 1 diabetes within 2 years.Graphical abstract

Translation and performance of the Finnish Diabetes Risk Score for detecting undiagnosed diabetes and dysglycaemia in the Indonesian population.

A diabetes risk score cannot directly be translated and applied in different populations, and its performance should be evaluated in the target population. This study aimed to translate the Finnish Diabetes Risk Score (FINDRISC) instrument and compare its performance with the modified version for detecting undiagnosed type 2 diabetes mellitus (T2DM) and dysglycaemia among the Indonesian adult population. Forward and backward translations were performed and followed by cultural adaptation. In total, 1,403 participants were recruited. The FINDRISC-Bahasa Indonesia (FINDRISC-BI) was scored according to the original FINDRISC instrument, while a Modified FINDRISC-BI was analyzed using a specific body mass index and waist circumference classification for Indonesians. The area under the receiver operating characteristic curve, sensitivity, specificity, and the optimal cut-offs of both instruments were estimated. The area under the receiver operating characteristic curve for detecting undiagnosed T2DM was 0.73 (0.67–0.78) for the FINDRISC-BI with an optimal cut-off score of ≥9 (sensitivity = 63.0%; specificity = 67.3%) and 0.72 (0.67–0.78) for the Modified FINDRISC-BI with an optimal cut-off score of ≥11 (sensitivity = 59.8%; specificity = 74.9%). The area under the receiver operating characteristic curve for detecting dysglycaemia was 0.72 (0.69–0.75) for the FINDRISC-BI instrument with an optimal cut-off score of ≥8 (sensitivity = 66.4%; specificity = 67.0%), and 0.72 (0.69–0.75) for the Modified FINDRISC-BI instrument with an optimal cut-off score ≥9 (sensitivity = 63.8%; specificity = 67.6%). The Indonesian version of the FINDRISC instrument has acceptable diagnostic accuracy for screening people with undiagnosed T2DM or dysglycaemia in Indonesia. Modifying the body mass index and waist circumference classifications in the Modified FINDRISC-BI results in a similar diagnostic accuracy; however, the Modified FINDRISC-BI has a higher optimal cut-off point than the FINDRISC-BI. People with an above optimal cut-off score are suggested to take a further blood glucose test.

0839 Complex Sleep Apnea Improved with Decompression of a Chiari I Malformation in a Pediatric Patient

Abstract Introduction Chiari malformation (CM) occurs when a portion of the cerebellum herniates through the foramen magnum. CM is categorized as two types. Type 1 involves the cerebellar tonsils and type 2 involves the cerebellum and brain stem. Those with CM can be asymptomatic to having debilitating neurologic symptoms such as dysphagia, tinnitus, emesis, balance difficulty, muscle weakness, and/or headache. Central sleep apnea (CSA) and obstructive sleep apnea (OSA) have been associated with CM. It is postulated that the sleep disordered breathing (SDB) is due to compression of the medulla which houses the breathing center and cranial nerves that play a role in nocturnal breathing. OSA has been thought to be related to muscle weakness of the lower airway in those with CM. After resolving CM with surgical decompression, residual OSA is common requiring treatment with positive airway pressure therapy. Report of Cases: A 3-year-old male with a history of OSA and tonsillar hypertrophy. Polysomnography (PSG) was performed after tonsillectomy and adenoidectomy indicating severe OSA (oAHI 17.7/hr) and CSA (CSAI 29.9/hr). A titration study was conducted and bilevel positive airway pressure spontaneous/timed (BPAP ST) 10/6 cmH2O with a backup rate of 12 breaths per minute was utilized further resolving his SDB and daytime sleepiness. After months of BPAP ST therapy, he presented to an urgent care with symptoms of somnolence, emesis, and nystagmus of the left eye. Magnetic resonance imaging of the brain revealed a 2 cm herniation of the cerebellar tonsils indicating CM type 1. Neurosurgery performed surgical decompression without complication. Post-operative PSG indicated significant improvement of OSA (oAHI of 3.45/hr) and resolution of CSA (0.9/hr). BPAP ST therapy was discontinued with the resolution of his daytime sleepiness and SDB. Conclusion This case demonstrates a patient with severe OSA and CSA due to an undiagnosed CM type 1. OSA and CSA are associated with CM; however, residual OSA typically exists status post decompression and can require positive airway pressure therapy for treatment. Our case demonstrates the need for consideration of CM in patients with complex sleep apnea and that surgical decompression can improve both OSA and CSA in these patients. Support (If Any)

Developing a risk score for undiagnosed prediabetes or type 2 diabetes among Saharawi refugees in Algeria

AimsTo prevent type 2 diabetes mellitus (T2D) and reduce the risk of complications, early identification of people at risk of developing T2D, preferably through simple diabetes risk scores, is essential. The aim of this study was to create a risk score for identifying subjects with undiagnosed prediabetes or T2D among Saharawi refugees in Algeria and compare the performance of this score to the Finnish diabetes risk score (FINDRISC).MethodsA cross-sectional survey was carried out in five Saharawi refugee camps in Algeria in 2014. A total of 180 women and 175 men were included. HbA1c and cut-offs proposed by the American Diabetes Association (ADA) were used to define cases. Variables to include in the risk score were determined by backwards elimination in logistic regression. Simplified scores were created based on beta coefficients from the multivariable model after internal validation with bootstrapping and shrinkage. The empirical cut-off value for the simplified score and FINDRISC was determined by Area Under the Receiver Operating Curve (AUROC) analysis.ResultsVariables included in the final risk score were age, body mass index (BMI), and waist circumference. The area under the curve (AUC) (C.I) was 0.82 (0.76, 0.88). The sensitivity, specificity, and positive and negative predictive values were 89, 65, 28, and 97%, respectively. AUC and sensitivity were slightly higher and specificity somewhat lower than for FINDRISC.ConclusionsThe risk score developed is a helpful tool to decide who should be screened for prediabetes or T2D by blood sample analysis. The performance of the risk score was adequate based on internal validation with bootstrap analyses, but should be confirmed in external validation studies.

Unexplained Anasarca in Type 1 Diabetes Mellitus

Background: Undiagnosed Type 1 diabetes mellitus (T1DM) often presents as diabetic ketoacidosis (DKA). We report a child with newly diagnosed T1DM who developed anasarca and persistent diarrhea following resolution of DKA and was referred to us for the same. Clinical Description: We reviewed the clinical history, examination, and investigations that had been undertaken. Our clinical evaluation was in concurrence with the referring hospital-anasarca with probable partially treated spontaneous bacterial peritonitis (SBP). However, the cause of the subacute anasarca and persistent diarrhea was unclear. The child was empirically started on broad-spectrum antibiotics for the SBP, a high-protein diet to build up the protein, and continued the same subcutaneous insulin, on which he was euglycemic. After ruling out usual causes, i.e., renal, hepatic, and cardiac, we reviewed the possibility of celiac disease, tuberculosis, insulin edema, and hypothyroidism. Management: When he did not improve despite a good appetite, adherence to management, and all tests were inconclusive, we reviewed the etiopathogenesis. Untreated T1DM had led to chronic negative catabolism that had precipitated severe hypoproteinemia. A vicious cycle had set in which hypoalbuminemia was leading to bowel wall edema, resulting in protein malabsorption, perpetuation of diarrhea, and further hypoproteinemia. Our assumption proved to be correct when a single dose of parenteral albumin broke the cycle, and the child improved drastically with the resolution of diarrhea within 24 h and the edema in a few days. Conclusions: This case highlights the implications of severe catabolism in a patient with untreated diabetes and how this may be a self-perpetuating condition.

Epidemiology of undiagnosed type 2 diabetes mellitus among hill tribe adults in Thailand

A community-based cross-sectional study was performed to estimate the prevalence of and determine factors associated with undiagnosed type 2 diabetes (T2DM) among hill tribe adults aged 30 years and over. Data were collected from the target hill tribe people living in the 30 selected villages in Chiang Rai Province in northern Thailand. A total of 2365 people were invited to participate in the study; 65.9% were female, 72.4% were aged 40–69 years, 0.9% regularly received preventive healthcare, and only 41.2% understood Thai. The overall prevalence of undiagnosed T2DM was 7.5%. After adjusting for age and sex, two factors were found to be associated with T2DM: triglycerides and high-density lipoprotein cholesterol (HDL-C). Those with high triglyceride levels were 2.80 (95% CI 1.99–3.94) times more likely to be suffering from T2DM than those with a normal triglyceride level. Those with low HDL-C levels were 1.65 (95% CI 1.16–2.34) times more likely to be suffering from T2DM than those with normal HDL-C. Appropriate public health interventions should be developed and implemented to reduce T2DM by providing regular preventive healthcare, educating patients on DM prevention and control, and encouraging regular exercise and reduced consumption of fatty food.

Extracting New Temporal Features to Improve the Interpretability of Undiagnosed Type 2 Diabetes Mellitus Prediction Models.

Type 2 diabetes mellitus (T2DM) often results in high morbidity and mortality. In addition, T2DM presents a substantial financial burden for individuals and their families, health systems, and societies. According to studies and reports, globally, the incidence and prevalence of T2DM are increasing rapidly. Several models have been built to predict T2DM onset in the future or detect undiagnosed T2DM in patients. Additional to the performance of such models, their interpretability is crucial for health experts, especially in personalized clinical prediction models. Data collected over 42 months from health check-up examinations and prescribed drugs data repositories of four primary healthcare providers were used in this study. We propose a framework consisting of LogicRegression based feature extraction and Least Absolute Shrinkage and Selection operator based prediction modeling for undiagnosed T2DM prediction. Performance of the models was measured using Area under the ROC curve (AUC) with corresponding confidence intervals. Results show that using LogicRegression based feature extraction resulted in simpler models, which are easier for healthcare experts to interpret, especially in cases with many binary features. Models developed using the proposed framework resulted in an AUC of 0.818 (95% Confidence Interval (CI): 0.812−0.823) that was comparable to more complex models (i.e., models with a larger number of features), where all features were included in prediction model development with the AUC of 0.816 (95% CI: 0.810−0.822). However, the difference in the number of used features was significant. This study proposes a framework for building interpretable models in healthcare that can contribute to higher trust in prediction models from healthcare experts.

Prevalence and trends of type 2 diabetes mellitus and prediabetes among community-dwelling heart failure patients in the United States

AimsThis study estimated national prevalence and trends of diagnosed and undiagnosed type 2 diabetes mellitus (T2DM) and prediabetes among heart failure (HF) patients in the U.S. MethodsThis cross-sectional study included 527 participants aged 20+ years with a diagnosis of HF, using data from the National Health and Nutrition Examination Survey 2005–2016. We assessed prevalence estimates of diagnosed and undiagnosed T2DM and prediabetes stratified by age-standardized sociodemographic and health characteristics. Trends of T2DM and prediabetes prevalence were examined using logistic regressions. ResultsPrevalence rates of diagnosed and undiagnosed T2DM among HF patients were 34.7% (95% confidence interval (CI), 29.2–40.3%) and 12.8% (95% CI, 9.2–16.9%), respectively. Prediabetes affected 39.1% (95% CI, 33.6–44.9%) of HF patients. Prevalence estimates of diagnosed T2DM were significantly different between non-Hispanic White (20.1% [95% CI, 13.5–27.6%]) and Hispanic participants (52.1% [95% CI, 35.9–68.0%]) (P < 0.001). The prevalence of T2DM and prediabetes did not significantly change between 2005 and 2016. ConclusionsPrevalence rates of T2DM and prediabetes among community-dwelling HF patients in the U.S. remained high between 2005 and 2016. Prevention of and targeted intervention for T2DM among at-risk HF patients is needed, particularly among those of Hispanic origin.

Performance of the FINDRISC tool in screening for diabetes in a primary care setting

Introduction: the global prevalence of diabetes mellitus is on the rise worldwide. Identification of persons at high risk for the disease may aid in disease prevention. The study aimed to ascertain the relationship of FINDRISC score with fasting plasma glucose in detecting prediabetes and undiagnosed type 2 diabetes mellitus among adults.