La diabetes tipo 3c. Abordaje desde el médico del primer nivel / Type 3 c diabetes: Approach from the first level doctor

La diabetes mellitus tipo 3c (DM3c) es una diabetes (DM) del páncreas exocrino que habrá que sospechar siempre que existan antecedentes de pancreatitis crónica (PC), pancreatitis aguda (PA) o recidivante (PAR) (80% de los casos) o una DM de nueva aparición en individuos a partir de los 50 años sin otra justificación (pruebas de autoinmunidad negativas, anticuerpos contra la descarboxilasa del ácido glutámico). Se trata de una entidad mal diagnosticada como diabetes tipo 2 (DM2) (90%) y, por ello, de no sospecharla puede pasar inadvertida. Para su diagnóstico son de utilidad la ecografía abdominal, la determinación del antígeno tumoral carbohydrate antigen 19-9 (CA 19.9), la resonancia magnética nuclear (RMN) o la tomografía axial computarizada (TAC). El tratamiento es el mismo de la DM2, aunque con ciertas especificaciones según el tipo de fármacos y con la particularidad de que al tratarse de una «diabetes frágil» habrá que tener mayor precaución con las hipoglucemias (monitorización). Asimismo, al ser una enfermedad del páncreas exocrino habrá que tratar específicamente esta para evitar las alteraciones metabólicas, malabsortivas y/o nutricionales (AU)

DM3c is diabetes (DM) of the exocrine pancreas that must be suspected whenever there is a history of chronic pancreatitis (CP), acute pancreatitis (AP) or recurrence (80% of cases) or new-onset DM in individuals from over 50 years of age without any other justification (negative autoimmunity tests, Glutamic Acid Decarboxylase antibodies). It is an entity misdiagnosed as type 2 diabetes (DM2) (90%) and therefore, if it is not suspected, it can go unnoticed. For its diagnosis, abdominal ultrasound, determination of the CA 19.9 tumor antigen (carbohydrate antigen 19-9), nuclear magnetic resonance (NMR) or computerized axial tomography (CT) are useful. The treatment is the same as DM2, although certain specifications depend on the type of drugs and with the particularity that in dealing with «fragile diabetes» greater caution must be taken with hypoglycemia (monitoring). Likewise, as it is a disease of the exocrine pancreas, it will have to be specifically treated to avoid metabolic, malabsorptive and/or nutritional alterations (AU)

Clinical characteristics, growth patterns, and long-term diabetes complications of 24 patients with neonatal diabetes mellitus: A single center experience.

OBJECTIVES: Neonatal diabetes mellitus (NDM) is a rare form of monogenic diabetes, diagnosed before age 6 months. We aimed to describe the clinical characteristics, molecular genetics, and long-term follow-up of NDM patients from a single pediatric endocrine center in Israel. METHODS: Retrospective study (1975-2020) of all patients diagnosed with diabetes before 6 months of age, who tested negative for pancreatic autoantibodies. Medical records were reviewed for demographic, familial and medical history, and clinical and biochemical features; a genetic analysis was performed. RESULTS: Of 24 patients, nine had transient neonatal diabetes (TNDM) and 15 permanent neonatal diabetes (PNDM), of whom five had rare syndromic causes. Genetic etiology was revealed in 87.5% of the NDM cohort, and the most common causes were ABCC8 mutations in TNDM and KCNJ11 and insulin gene mutations in PNDM. The switch from insulin to off-label sulfonylurea therapy was successful for 5/9 (56%) of the qualifying candidates. Severe hypoglycemia and diabetic ketoacidosis developed in 2 (8%) patients, and chronic diabetes complications in 5 (21%) patients with more than 10 years NDM. At last follow-up, weight and height of all but two syndromic PNDM patients were normal. The median height-SDS of the TNDM subgroup was significantly taller and the mean weight-SDS significantly heavier than those of the PNDM subgroup (-0.52 (-0.67, -0.09) vs. -0.9 (-1.42, -0.3) (p = 0.035) and 0.22 ± 0.69 vs. -0.89 ± 1.21 (p = 0.02), respectively). PNDM patients showed no incremental change in mean weight SDS over the time. CONCLUSION: The Israeli NDM cohort has clinical and genetic characteristics comparable with other populations. Patients with TNDM were taller and heavier than those diagnosed with PNDM, although both show rapid catch-up growth and reached normal growth parameters. Chronic diabetes complications developed in patients with long-standing NDM.

Development and validation of a new diabetes index for the risk classification of present and new-onset diabetes: multicohort study.

In this study, we aimed to propose a novel diabetes index for the risk classification based on machine learning techniques with a high accuracy for diabetes mellitus. Upon analyzing their demographic and biochemical data, we classified the 2013-16 Korea National Health and Nutrition Examination Survey (KNHANES), the 2017-18 KNHANES, and the Korean Genome and Epidemiology Study (KoGES), as the derivation, internal validation, and external validation sets, respectively. We constructed a new diabetes index using logistic regression (LR) and calculated the probability of diabetes in the validation sets. We used the area under the receiver operating characteristic curve (AUROC) and Cox regression analysis to measure the performance of the internal and external validation sets, respectively. We constructed a gender-specific diabetes prediction model, having a resultant AUROC of 0.93 and 0.94 for men and women, respectively. Based on this probability, we classified participants into five groups and analyzed cumulative incidence from the KoGES dataset. Group 5 demonstrated significantly worse outcomes than those in other groups. Our novel model for predicting diabetes, based on two large-scale population-based cohort studies, showed high sensitivity and selectivity. Therefore, our diabetes index can be used to classify individuals at high risk of diabetes.

Diabetes Patient Surveillance in the Emergency Department: Proof of Concept and Opportunities.

INTRODUCTION: The purpose of this study was to characterize the at-risk diabetes and prediabetes patient population visiting emergency department (ED) and urgent care (UC) centers in upstate South Carolina. METHODS: We conducted this retrospective study at the largest non-profit healthcare system in South Carolina, using electronic health record (EHR) data of patients who had an ED or UC visit between February 2, 2016-July 31, 2018. Key variables including International Classification of Diseases, 10th Revision codes, laboratory test results, family history, medication, and demographic characteristics were used to classify the patients as healthy, having prediabetes, having diabetes, being at-risk for prediabetes, or being at-risk for diabetes. Patients who were known to have diabetes were classified further as having controlled diabetes, management challenged, or uncontrolled diabetes. Population analysis was stratified by the patient's annual number of ED/UC visits. RESULTS: The risk stratification revealed 4.58% unique patients with unrecognized diabetes and 10.34% of the known patients with diabetes considered to be suboptimally controlled. Patients identified as diabetes management challenged had more ED/UC visits. Of note, 33.95% of the patients had unrecognized prediabetes/diabetes risk factors identified during their ED/UC with 87.95% having some form of healthcare insurance. CONCLUSION: This study supports the idea that a single ED/UC unscheduled visit can identify individuals with unrecognized diabetes and an at-risk prediabetes population using EHR data. A patient's ED/UC visit, regardless of their primary reason for seeking care, may be an opportunity to provide early identification and diabetes disease management enrollment to augment the medical care of our community.

Genetics of canine diabetes mellitus part 1: Phenotypes of disease.

This two-part article discusses the mechanisms by which genetic variation can influence the risk of complex diseases, with a focus on canine diabetes mellitus. In Part 1, presented here, the importance of accurate methods for classifying different types of diabetes will be discussed, since this underpins the selection of cases and controls for genetic studies. Part 2 will focus on our current understanding of the genes involved in diabetes risk, and the way in which new genome sequencing technologies are poised to reveal new diabetes genes in veterinary species.

Genetics of canine diabetes mellitus part 2: Current understanding and future directions.

Part 1 of this 2-part review outlined the importance of disease classification in diabetes genetic studies, as well as the ways in which genetic variants may contribute to risk of a complex disease within an individual, or within a particular group of individuals. Part 2, presented here, describes in more detail our current understanding of the genetics of canine diabetes mellitus compared to our knowledge of the human disease. Ongoing work to improve our knowledge, using new technologies, is also introduced.

Differences between transient neonatal diabetes mellitus subtypes can guide diagnosis and therapy.

OBJECTIVE: Transient neonatal diabetes mellitus (TNDM) is caused by activating mutations in ABCC8 and KCNJ11 genes (KATP/TNDM) or by chromosome 6q24 abnormalities (6q24/TNDM). We wanted to assess whether these different genetic aetiologies result in distinct clinical features. DESIGN: Retrospective analysis of the Italian data set of patients with TNDM. METHODS: Clinical features and treatment of 22 KATP/TNDM patients and 12 6q24/TNDM patients were compared. RESULTS: Fourteen KATP/TNDM probands had a carrier parent with abnormal glucose values, four patients with 6q24 showed macroglossia and/or umbilical hernia. Median age at diabetes onset and birth weight were lower in patients with 6q24 (1 week; -2.27 SD) than those with KATP mutations (4.0 weeks; -1.04 SD) (P = 0.009 and P = 0.007, respectively). Median time to remission was longer in KATP/TNDM than 6q24/TNDM (21.5 weeks vs 12 weeks) (P = 0.002). Two KATP/TNDM patients entered diabetes remission without pharmacological therapy. A proband with the ABCC8/L225P variant previously associated with permanent neonatal diabetes entered 7-year long remission after 1 year of sulfonylurea therapy. Seven diabetic individuals with KATP mutations were successfully treated with sulfonylurea monotherapy; four cases with relapsing 6q24/TNDM were treated with insulin, metformin or combination therapy. CONCLUSIONS: If TNDM is suspected, KATP genes should be analyzed first with the exception of patients with macroglossia and/or umbilical hernia. Remission of diabetes without pharmacological therapy should not preclude genetic analysis. Early treatment with sulfonylurea may induce long-lasting remission of diabetes in patients with KATP mutations associated with PNDM. Adult patients carrying KATP/TNDM mutations respond favourably to sulfonylurea monotherapy.

DIAGNOSIS OF ENDOCRINE DISEASE: Diagnosing and classifying diabetes in diseases of the exocrine pancreas.

Diabetes in the setting of diseases of the exocrine pancreas has long existed as a known, but underdiagnosed or misdiagnosed, disorder. It currently finds itself in a state of taxonomic dereliction and requires a long overdue refurbishment. Correct conceptualisation is a key precondition for knowledge development in this disorder. This article lays out the epistemological foundation for diabetes of the exocrine pancreas (DEP) and presents a synthesis of the current interdisciplinary discourse on diagnosing and classifying DEP. A diagnosis of DEP in people with no medical records of pre-existing diabetes is generally based on the most up-to-date biochemical criteria endorsed by the American Diabetes Association and European Association for the Study of Diabetes. The presence of exocrine pancreatic dysfunction is not considered a mandatory diagnostic criterion for DEP but is rather a significant risk factor for developing DEP. DEP principally comprises post-pancreatitis diabetes mellitus, pancreatic cancer-related diabetes, and cystic fibrosis-related diabetes, which are mutually exclusive with autoimmune diabetes and type 2 diabetes. Other exclusions and stipulations apply. The DEP criteria will be instrumental in aiding optimal design and conduct of clinical studies, uniform collection of health utilisation data, meaningful comparison of scientific findings across countries, and clear communication among stakeholders (healthcare providers, patients, medical regulatory authorities, pharmaceutical industry).

A Classification Algorithm for Types of Diabetes in Chronic Pancreatitis Using Epidemiological Characteristics: Outcome of a Longitudinal Cohort Study.

OBJECTIVES: We developed an epidemiological algorithm to classify types of diabetes mellitus (DM) in chronic pancreatitis (CP), and applied it to a nationwide prospective longitudinal cohort of CP patients. METHODS: Patients with definite CP (M-ANNHEIM criteria) were classified as having DM types 1, 2, or 3c, or no DM using an algorithm based on epidemiological characteristics: DM onset in relation to age, CP onset, exocrine insufficiency. Variables associated with development of DM were identified. RESULTS: Of 1130 included patients with CP between 2011 and 2018, 368 patients (33%) had DM at inclusion. Among patients with DM, 11 were classified as having type 1 (3%), 159 as type 2 (43%), and 191 as type 3c (52%). Patients with DM type 3c had longer duration of CP, more severe pain and lower physical quality of life. During longitudinal follow-up of median 47 months, 120 (20%) patients developed DM, of which 99 patients were classified as type 3c. This was independently associated with pancreatic endoscopy and surgery. CONCLUSIONS: The described algorithm based on epidemiological characteristics can help to classify types of DM in patients with CP. Diabetes mellitus type 3c is associated with longer duration of CP and more severe CP sequelae.

Defining and Classifying New Subgroups of Diabetes.

An etiologically based classification of diabetes is needed to account for the heterogeneity of type 1 and type 2 diabetes (T1D and T2D) and emerging forms of diabetes worldwide. It may be productive for both classification and clinical discovery to consider variant forms of diabetes as a spectrum. Maturity onset diabetes of youth and neonatal diabetes serve as models for etiologically defined, rare forms of diabetes in the spectrum. Ketosis-prone diabetes is a model for more complex forms, amenable to phenotypic dissection. Bioinformatic approaches such as clustering analyses of large datasets and multi-omics investigations of rare and atypical phenotypes are promising avenues to explore and define new subgroups of diabetes.

Imbalanced learning: Improving classification of diabetic neuropathy from magnetic resonance imaging.

One of the fundamental challenges when dealing with medical imaging datasets is class imbalance. Class imbalance happens where an instance in the class of interest is relatively low, when compared to the rest of the data. This study aims to apply oversampling strategies in an attempt to balance the classes and improve classification performance. We evaluated four different classifiers from k-nearest neighbors (k-NN), support vector machine (SVM), multilayer perceptron (MLP) and decision trees (DT) with 73 oversampling strategies. In this work, we used imbalanced learning oversampling techniques to improve classification in datasets that are distinctively sparser and clustered. This work reports the best oversampling and classifier combinations and concludes that the usage of oversampling methods always outperforms no oversampling strategies hence improving the classification results.

Implications of Integrated Pancreatic Microcirculation: Crosstalk between Endocrine and Exocrine Compartments.

The endocrine and exocrine pancreas have been studied separately by endocrinologists and gastroenterologists as two organ systems. The pancreatic islet, consisting of 1-2% mass of the whole pancreas, has long been believed to be regulated independently from the surrounding exocrine tissues. Particularly, islet blood flow has been consistently illustrated as one-way flow from arteriole(s) to venule(s) with no integration of the capillary network between the endocrine and exocrine pancreas. It is likely linked to the long-standing dogma that the rodent islet has a mantle of non-ß-cells and that the islet is completely separated from the exocrine compartment. A new model of islet microcirculation is built on the basis of analyses of in vivo blood flow measurements in mice and an in situ three-dimensional structure of the capillary network in mice and humans. The deduced integrated blood flow throughout the entire pancreas suggests direct interactions between islet endocrine cells and surrounding cells as well as the bidirectional blood flow between the endocrine and exocrine pancreas, not necessarily a unidirectional blood flow as in a so-called insuloacinar portal system. In this perspective, we discuss how this conceptual transformation could potentially affect our current understanding of the biology, physiology, and pathogenesis of the islet and pancreas.

Classification of Diabetes Using Photoplethysmogram (PPG) Waveform Analysis: Logistic Regression Modeling.

In this research, the photoplethysmogram (PPG) waveform analysis is utilized to develop a logistic regression-based predictive model for the classification of diabetes. The classifier has three predictors age, b/a, and SP indices in which they achieved an overall accuracy of 92.3% in the prediction of diabetes. In this study, a total of 587 subjects were enrolled. A total of 459 subjects were used for model training and development, while the rest of the 128 subjects were used for model testing and validation. The classifier was able to diagnose 63 patients correctly as diabetes while 27 subjects were wrongly classified as nondiabetes with an accuracy of 70%. Again, the model classified 479 subjects as nondiabetes correctly while it incorrectly classified 18 subjects as diabetes with an accuracy of 96.4%. Finally, the proposed model revealed an overall predictive accuracy of 92.3% which makes it a reliable surrogate measure for diabetes classification and prediction in clinical settings.

Histological validation of a type 1 diabetes clinical diagnostic model for classification of diabetes.

AIMS: Misclassification of diabetes is common due to an overlap in the clinical features of type 1 and type 2 diabetes. Combined diagnostic models incorporating clinical and biomarker information have recently been developed that can aid classification, but they have not been validated using pancreatic pathology. We evaluated a clinical diagnostic model against histologically defined type 1 diabetes. METHODS: We classified cases from the Network for Pancreatic Organ donors with Diabetes (nPOD) biobank as type 1 (n = 111) or non-type 1 (n = 42) diabetes using histopathology. Type 1 diabetes was defined by lobular loss of insulin-containing islets along with multiple insulin-deficient islets. We assessed the discriminative performance of previously described type 1 diabetes diagnostic models, based on clinical features (age at diagnosis, BMI) and biomarker data [autoantibodies, type 1 diabetes genetic risk score (T1D-GRS)], and singular features for identifying type 1 diabetes by the area under the curve of the receiver operator characteristic (AUC-ROC). RESULTS: Diagnostic models validated well against histologically defined type 1 diabetes. The model combining clinical features, islet autoantibodies and T1D-GRS was strongly discriminative of type 1 diabetes, and performed better than clinical features alone (AUC-ROC 0.97 vs. 0.95; P = 0.03). Histological classification of type 1 diabetes was concordant with serum C-peptide [median < 17 pmol/l (limit of detection) vs. 1037 pmol/l in non-type 1 diabetes; P < 0.0001]. CONCLUSIONS: Our study provides robust histological evidence that a clinical diagnostic model, combining clinical features and biomarkers, could improve diabetes classification. Our study also provides reassurance that a C-peptide-based definition of type 1 diabetes is an appropriate surrogate outcome that can be used in large clinical studies where histological definition is impossible. Parts of this study were presented in abstract form at the Network for Pancreatic Organ Donors Conference, Florida, USA, 19-22 February 2019 and Diabetes UK Professional Conference, Liverpool, UK, 6-8 March 2019.

Implementation and Analysis of Classification Algorithms for Diabetes.

BACKGROUND: In this era of cutting edge research, though one of the ubiquitous facilities accessible to modern man is state of the art medical care yet diabetes has emerged as one of the major ailments afflicting the present generation. So the prime necessity of this age has transformed into providing cheap and sustainable medical care against such major diseases like diabetes. In layman's terms Diabetes may be defined as a physiological condition wherein the blood glucose level is more than the prescribed level on a regular basis. OBJECTIVES: So the prime objective of this work is to provide a novel classification technique for detection of diabetes in a timely and effective manner. METHODS: The proposed work comprises of four phases: In the first phase a "Localized Diabetes Dataset" has been compiled and collected from Bombay Medical Hall, Mahabir Chowk, Pyada Toli, Upper Bazar, Jharkhand, Ranchi, India. In the second phase various classification techniques namely RBF NN, MLP NN, NBs, and J48graft DT have been applied on the Localized Diabetes Dataset. In the third phase, Genetic algorithm (GA) has been utilized as an attribute selection technique through which six attributes among twelve attributes have been filtered. Lastly in the fourth phase RBF NN, MLP NN, NBs and J48graft DT has been utilized for classification on relevant attributes obtained by GA. RESULTS: In this study, comparative analysis of outcomes obtained by with and without the use of GA for the same set of classification technique has been done w.r.t performance assessment. It has been conclusively inferred that GA is helpful in removing insignificant attributes, reducing the cost and computation time while enhancing ROC and accuracy. CONCLUSION: The utilized strategy may likewise be executed for other medical issues.

[Reclassification of diabetes mellitus based on pathophysiologic and genetic features]. / Pathologisch-genetisch orientierte Diabetes-Reklassifizierung.

Diabetes mellitus has been defined by hyperglycemia, but in addition to hyperglycemia, there are several other factors determining the clinical course and complications. We review the current classification of diabetes and recent attempts to identify new subphenotypes. Notably, there are anthropometry-pathophysiology based and genome-based subphenotyping approaches. They aim to improve the prediction of disease course and complications and could pave the way for precision medicine in the therapy of diabetes.

Congenital diabetes mellitus.

Congenital diabetes mellitus is a rare disorder characterized by hyperglycemia that occurs shortly after birth. We define "Diabetes of Infancy" if hyperglycemia onset before 6 months of life. From the clinical point of view, we distinguish two main types of diabetes of infancy: transient (TNDM), which remits spontaneously, and permanent (PNDM), which requires lifelong treatment. TNDM may relapse later in life. About 50% of cases are transient (TNDM) and 50% permanent. Clinical manifestations include severe intrauterine growth retardation, hyperglycemia and dehydration. A wide range of different associated clinical signs including facial dysmorphism, deafness and neurological, cardiac, kidney or urinary tract anomalies are reported. Developmental delay and learning difficulties may also be observed. In this paper we review all the causes of congenital diabetes and all genes and syndromes involved in this pathology. The discovery of the pathogenesis of most forms of congenital diabetes has made it possible to adapt the therapy to the diagnosis and in the forms of alteration of the potassium channels of the pancreatic Beta cells the switch from insulin to glibenclamide per os has greatly improved the quality of life. Congenital diabetes, although it is a very rare form, has been at the must of research in recent years especially for pathogenesis and pharmacogenetics. The most striking difference compared to the more frequent autoimmune diabetes in children (type 1 diabetes) is the possibility of treatment with hypoglycemic agents and the apparent lower frequency of chronic complications.