Approach to the patient with atypical diabetes

Identification of atypical pediatric diabetes mellitus cases using electronic medical records

INTRODUCTION

There are no established methods to identify children with atypical diabetes for further study. We aimed to develop strategies to systematically ascertain cases of atypical pediatric diabetes using electronic medical records (EMR).

RESEARCH DESIGN AND METHODS

We tested two strategies in a large pediatric hospital in the USA. Strategy 1: we designed a questionnaire to rule out typical diabetes and applied it to the EMR of 100 youth with diabetes. Strategy 2: we built three electronic queries to generate reports of three atypical pediatric diabetes phenotypes: unknown type, type 2 diabetes (T2D) diagnosed <10 years old and autoantibody-negative type 1 diabetes (AbNegT1D).

RESULTS

Strategy 1 identified six cases (6%) of atypical diabetes (mean diagnosis age=11±2.6 years, 16.6% men, 33% non-Hispanic white (NHW) and 66.6% Hispanic). Strategy 2: unknown diabetes type: n=68 (1%) out of 6676 patients with diabetes; mean diagnosis age=12.6±3.3 years, 32.8% men, 23.8% NHW, 47.6% Hispanic, 25.4% African American (AA), 3.2% other. T2D <10 years old: n=64 (6.6%) out of 1142 patients with T2D; mean diagnosis age=8.6±1.6 years, 20.3% men, 4.7% NHW, 65.6% Hispanic, 28.1% AA, 1.6% other. AbNegT1D: n=38 (5.6%) out of 680 patients with new onset T1D; mean diagnosis age=11.3±3.8 years; 57.9% men, 50% NHW, 19.4% Hispanic, 22.3% AA, 8.3% other.

CONCLUSIONS

In sum, we identified 1%-6.6% of atypical diabetes cases in a pediatric diabetes population with high racial and ethnic diversity using systematic review of the EMR. Better identification of these cases using unbiased approaches may advance precision diabetes.

Secondary diabetes due to different etiologies: Four case reports

BACKGROUND

As research on diabetes continues to advance, more complex classifications of this disease have emerged, revealing the existence of special types of diabetes, and many of these patients are prone to misdiagnosis and underdiagnosis, leading to treatment delays and increased health care costs. The purpose of this study was to identify four causes of secondary diabetes.

CASE SUMMARY

Secondary diabetes can be caused by various factors, some of which are often overlooked. These factors include genetic defects, autoimmune disorders, and diabetes induced by tumours. This paper describes four types of secondary diabetes caused by Williams-Beuren syndrome, Prader-Willi syndrome, pituitary adenoma, and IgG4-related diseases. These cases deviate significantly from the typical progression of the disease due to their low incidence and rarity, often leading to their neglect in clinical practice. In comparison to regular diabetes patients, the four individuals described here exhibited distinct characteristics. Standard hypoglycaemic treatments failed to effectively control the disease. Subsequently, a series of examinations and follow-up history confirmed the diagnosis and underlying cause of diabetes. Upon addressing the primary condition, such as excising a pituitary adenoma, providing glucocorticoid supplementation, and implementing symptomatic treatments, all patients experienced a considerable decrease in blood glucose levels, which were subsequently maintained within a stable range. Furthermore, other accompanying symptoms improved.

CONCLUSION

Rare diseases causing secondary diabetes are often not considered in the diagnosis of diabetes. Therefore, it is crucial to conduct genetic tests, antibody detection and other appropriate diagnostic measures when necessary to facilitate early diagnosis and intervention through proactive and efficient management of the underlying condition, ultimately improving patient outcomes.

Investigator and participant expectations for returning non-genetic results: insights from the Rare and Atypical Diabetes Network (RADIANT) study

Objectives/Goals

The Rare and Atypical DIAbetes NeTwork (RADIANT) aims to discover the underlying pathoetiology of atypical diabetes by conducting both genotyping and non-genetic deep phenotyping. While the return of genetic test results in research settings has been investigated, the return of non-genetic results (RoR-NG) has received less attention. We explore the RoR-NG with RADIANT investigators and participants.

Methods/Study Population

We conducted one-on-one interviews with 10 adult RADIANT participants and 10 RADIANT investigators. Participants also completed two health literacy screening tools and a survey on perspectives regarding return of results (RoR). Investigators completed one survey on experience and confidence in explaining clinical tests utilized in the RADIANT study and another survey on perspectives regarding RoR.

Results

Most participants were non-Hispanic White. All participants had high scores on health literacy screens. Both RADIANT participants and investigators expressed strong support for RoR-NG. RADIANT participants and investigators acknowledged the different roles and responsibilities between research and clinical care for interpreting and acting on non-genetic results. However, the lines between clinical care and research in returning and acting on results were often blurred by both participants and investigators.

Discussion/Significance

Our study provides important insight into how both investigators and participants simultaneously distinguish and blur clinical and research roles and responsibilities when discussing non-genetic research results and the return of these results. Further study should engage individuals from diverse racial and ethnic backgrounds and with varying levels of health literacy to understand how best to support all participants when returning research results.

The Rare and Atypical Diabetes Network (RADIANT) Study: Design and Early Results

OBJECTIVE

The Rare and Atypical Diabetes Network (RADIANT) will perform a study of individuals and, if deemed informative, a study of their family members with uncharacterized forms of diabetes.

RESEARCH DESIGN AND METHODS

The protocol includes genomic (whole-genome [WGS], RNA, and mitochondrial sequencing), phenotypic (vital signs, biometric measurements, questionnaires, and photography), metabolomics, and metabolic assessments.

RESULTS

Among 122 with WGS results of 878 enrolled individuals, a likely pathogenic variant in a known diabetes monogenic gene was found in 3 (2.5%), and six new monogenic variants have been identified in the SMAD5, PTPMT1, INS, NFKB1, IGF1R, and PAX6 genes. Frequent phenotypic clusters are lean type 2 diabetes, autoantibody-negative and insulin-deficient diabetes, lipodystrophic diabetes, and new forms of possible monogenic or oligogenic diabetes.

CONCLUSIONS

The analyses will lead to improved means of atypical diabetes identification. Genetic sequencing can identify new variants, and metabolomics and transcriptomics analysis can identify novel mechanisms and biomarkers for atypical disease.

Data Mining Framework for Discovering and Clustering Phenotypes of Atypical Diabetes

CONTEXT

Some individuals present with forms of diabetes that are "atypical" (AD), which do not conform to typical features of either type 1 diabetes (T1D) or type 2 diabetes (T2D). These forms of AD display a range of phenotypic characteristics that likely reflect different endotypes based on unique etiologies or pathogenic processes.

OBJECTIVE

To develop an analytical approach to identify and cluster phenotypes of AD.

METHODS

We developed Discover Atypical Diabetes (DiscoverAD), a data mining framework, to identify and cluster phenotypes of AD. DiscoverAD was trained against characteristics of manually classified patients with AD among 278 adults with diabetes within the Cameron County Hispanic Cohort (CCHC) (Study A). We then tested DiscoverAD in a separate population of 758 multiethnic children with T1D within the Texas Children's Hospital Registry for New-Onset Type 1 Diabetes (TCHRNO-1) (Study B).

RESULTS

We identified an AD frequency of 11.5% in the CCHC (Study A) and 5.3% in the pediatric TCHRNO-1 (Study B). Cluster analysis identified 4 distinct groups of AD in Study A: cluster 1, positive for the 65 kDa glutamate decarboxylase autoantibody (GAD65Ab), adult-onset, long disease duration, preserved beta-cell function, no insulin treatment; cluster 2, GAD65Ab negative, diagnosed at age ≤21 years; cluster 3, GAD65Ab negative, adult-onset, poor beta-cell function, lacking central obesity; cluster 4, diabetic ketoacidosis (DKA)-prone participants lacking a typical T1D phenotype. Applying DiscoverAD to the pediatric patients with T1D in Study B revealed 2 distinct groups of AD: cluster 1, autoantibody negative, poor beta-cell function, lower body mass index (BMI); cluster 2, autoantibody positive, higher BMI, higher incidence of DKA.

CONCLUSION

DiscoverAD can be adapted to different datasets to identify and define phenotypes of participants with AD based on available clinical variables.

Masqueraders: how to identify atypical diabetes in primary care

Diabetes mellitus is a complex set of conditions that impacts 34 million Americans. While type 1 diabetes, type 2 diabetes, and gestational diabetes are most frequently encountered, there are many other types of diabetes with which healthcare providers are less familiar. These atypical forms of diabetes make up nearly 10% of diabetes cases and can masquerade as type 1 or 2 diabetes mellitus (T1DM or T2DM), and the treatment may not be optimized if the diagnosis is not accurate. Atypical forms include monogenic diabetes (formally known as maturity-onset diabetes of the young [MODY]), latent autoimmune diabetes of the adult (LADA), ketosis-prone diabetes, and secondary diabetes. This paper will detail the defining characteristics of each atypical form and demonstrate how they can masquerade as type 1 or 2 diabetes mellitus. Gestational diabetes mellitus will not be discussed in this article.

Management of ketosis-prone type 2 diabetes mellitus

Diabetic ketoacidosis (DKA) has largely been considered unique to type 1 diabetes because of the absolute lack of insulin production secondary to beta-cell dysfunction. However, a relatively new diabetes subtype known as ketosis-prone type 2 diabetes mellitus (DM) may also elicit diabetic ketoacidosis. Ketosis-prone type 2 DM shares a similar pathophysiology as type 2 DM, but presents initially with signs and symptoms consistent with type 1 DM. Patients with ketosis-prone type 2 DM often present with elevated glucose levels of 500-700 mg/dl, elevated ketone levels, and elevations in hemoglobin A1C. Unlike DKA seen in type 1 DM, they do not exhibit autoantibodies to beta cells. The similarity with type 1 DM exists in their impaired insulin secretion, which, when combined with extreme insulin resistance, will lead to ketoacidosis. Despite the initial clinical presentation that resembles type 1 DM, patients may not require lifelong insulin and achieve appropriate glycemic control with oral agents. Nurse practitioners must recognize the clinical picture of ketosis-prone type 2 DM and use a multifaceted approach, encouraging dietary changes, increased physical activity, and medication adherence to build the self-management skills of the patient and ultimately decrease the long-term disease burden.

Uncommon Presentations of Diabetes: Zebras in the Herd

The majority of patients with diabetes are diagnosed as having either type 1 or type 2 diabetes. However, when encountered in clinical practice, some patients may not match the classic diagnostic criteria or expected clinical presentation for either type of the disease. Latent autoimmune, ketosis-prone, and monogenic diabetes are nonclassical forms of diabetes that are often misdiagnosed as either type 1 or type 2 diabetes. Recognizing the distinguishing clinical characteristics and understanding the diagnostic criteria for each will lead to appropriate treatment, facilitate personalized medicine, and improve patient outcomes.

HNF1A gene p.I27L is associated with early-onset, maturity-onset diabetes of the young-like diabetes in Turkey

BACKGROUND

The molecular basis of the Turkish population with suspected maturity-onset diabetes of the young (MODY) has not been identified. This is the first study to investigate the association between HNF1A-gene single-nucleotide polymorphisms (SNPs) and having early-onset, MODY-like diabetes mellitus in the Turkish population.

METHODS

All diabetic patients (N = 565) who presented to our clinic between 2012 and 2015 with a clinical suspicion of MODY were included in the study. Analysis of HNF1A, HNFB, HNF4A, GCK gene mutations was performed using real-time polymerase chain reaction sequencing. After genetic analysis, diabetics (n = 46) with HNF1A, HNF1B, HNF4A, GCK gene mutations (diagnosed as MODY) and diabetics (n = 30) with HNF1B, HNF4A, GCK gene SNPs were excluded. Patients with early-onset, MODY-like diabetes (n = 486) and non-diabetic controls (n = 263) were included. Genetic analyses for the HNF1A gene p.S487 N (rs2464196), p.A98V (rs1800574) and p.I27L (rs1169288) SNPs were performed using Sanger-based DNA sequencing among the control group.

RESULTS

p.S487 N and p.A98V was similar between the diabetics and controls in dominant and recessive models with no association (each, p > 0.05). p.I27L GT/TT carriers (GT/TT vs. GG, OR = 1.68, 95% CI: [1. 21-2.13]; p = 0.035) and p.I27L TT carriers had increased risk of having MODY-like diabetes (GT/GG vs. TT, OR = 1.56, 95% CI: [1. 14-2.57]; p = 0.048). Family inheritance of diabetes was significantly more common in patients with the p.I27L TT genotype. The p.I27L SNP was modestly associated with having diabetes after adjusting for body mass index and age (β = 1.45, 95% CI: [1. 2-4.2]; p = 0.036).

CONCLUSIONS

The HNF1A gene p.I27L SNP was modestly associated with having early-onset, MODY-like diabetes in the Turkish population. HNF1A gene p.I27L SNP might contribute to age at diabetes diagnosis and family inheritance.

Ketosis-Prone Type 2 Diabetes: A Case Series

Ketosis-prone type 2 diabetes ("Flatbush diabetes") carries features of both classical type 1 and type 2 diabetes and is highly prevalent in African populations. The disease, which is highly ketosis-prone, but neither chronically insulinopenic nor autoimmune, is discussed regarding pathogenesis, diagnosis and treatment from a patient case perspective.

Association of TCF7L2 mutation and atypical diabetes in a Uruguayan population

AIM

To investigate if mutations in TCF7L2 are associated with “atypical diabetes” in the Uruguayan population.

METHODS

Healthy, nondiabetic controls (n = 133) and patients with type 2 diabetes (n = 177) were selected from among the presenting population at level-3 referral healthcare centers in Uruguay. Patients with type 2 diabetes were subgrouped according to “atypical diabetes” (n = 92) and “classical diabetes” (n = 85). Genotyping for the rs12255372 and rs7903146 single nucleotide polymorphisms (SNPs) in the TCFTL2 gene was carried out with TaqMan^® probes. Random samples were sequenced by Macrogen Ltd. (South Korea). Statistical analysis of the SNP data was carried out with the SNPStats online tool (http://bioinfo.iconcologia.net/SNPstats). The best inheritance model was chosen according to the lowest values of Akaike’s information criterion and Bayesian information criterion. Differences between groups were determined by unpaired t-tests after checking the normal distribution or were converted to normalize the data. The association of SNPs was tested for matched case-control samples by using χ² analysis and calculation of odds ratios (ORs) with 95% confidence intervals (CIs). All statistical tests were performed using SPSS v10.0 and EpiInfo7 statistical packages. Significant statistical differences were assumed in all cases showing adjusted P < 0.05.

RESULTS

We genotyped two TCF7L2 SNPs (rs7903146 and rs12255372) in a population-based sample of 310 Uruguayan subjects, including 133 healthy control subjects and 177 clinical diagnosed with type 2 diabetes. For both SNPs analyzed, the best model was the dominant type: rs12255372 = G/G vs G/T+T/T, OR = 0.63, 95%CI: 0.40-0.98, P < 0.05 and rs7903146 = C/C vs C/T+T/T, OR = 0.79, 95%CI: 0.41-1.55, P = 0.3. The rs12255372 SNP showed high association with the type 2 diabetes cases (OR = 1.60, 95%CI: 1.20-2.51, P < 0.05). However, when the type 2 diabetics group was analyzed according to the atypical and classical subgroupings, the association with diabetes existed only for rs12255372 and the classical subgroup (vs controls: OR = 2.1, 95%CI: 1.21-3.75, P < 0.05); no significant differences were found for either SNP or atypical diabetes.

CONCLUSION

This is the first time SNPs_TCF7L2 were genotyped in a diabetic population stratified by genotype instead of phenotype. Classical and atypical patients showed statistical differences.

Diabetic Ketoacidosis Post Bariatric Surgery

In patients with type 2 diabetes, bariatric surgery can lead to significant improvements in glycemic control and diabetes remission. We present a case of a Hispanic female with type 2 diabetes phenotype who underwent bariatric surgery and post-operatively stopped her insulin therapy due to multiple reasons, including decreased oral intake and concern for hypoglycemia. Ultimately, she developed diabetic ketoacidosis. She does not fit into the classical type 2 diabetes or type 1 diabetes definition but into the heterogeneous subgroup of diabetes called ketosis-prone diabetes.