Complicated curve association of body weight at diagnosis with C‐peptide in children and adults with new‐onset type 1 diabetes

Gong, Siyuan; Wu, Chao; Zhong, Ting; Xie, Yuting; Liu, Fang; Li, Juan; Li, Xia; Zhang, Zhou

doi:10.1002/dmrr.3285

Cited by 4 publications

(6 citation statements)

References 28 publications

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“…The association between BMI and T1DM progression is conflicting. Some studies showed that patients with higher BMI had better beta cell function at diagnosis and after follow-up (16)(17)(18). However, other studies showed that BMI might be a risk factor for developing T1DM and for accelerating T1DM progression (19)(20)(21).…”

Section: Discussionmentioning

confidence: 99%

A clinical diagnostic model based on an eXtreme Gradient Boosting algorithm to distinguish type 1 diabetes

Tang¹,

Tang²,

Sun³

et al. 2021

Ann Transl Med

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Background: Accurate classification of type 1 diabetes (T1DM) and type 2 diabetes (T2DM) in the early phase is crucial for individual precision treatment. This study aimed to develop a classification model having fewer and easier to access clinical variables to distinguish T1DM in newly diagnosed diabetes in adults.Methods: Clinical and laboratory data were collected from 15,206 adults with newly diagnosed diabetes in this cross-sectional study. This cohort represented 20 provinces and 4 municipalities in China. Types of diabetes were determined based on postprandial C-peptide (PCP) level and glutamic acid decarboxylase autoantibody (GADA) titer. We developed multivariable clinical diagnostic models using the eXtreme Gradient Boosting (XGBoost) algorithm. Classification variables included in the final model were based on their scores of importance. Model performance was evaluated by area under the receiver operating characteristic curve (ROC AUC), sensitivity, and specificity. The performance of models with different variable combinations was compared. Calibration intercept and slope were evaluated for the final model.Results: Among the newly diagnosed diabetes cohort, 1,465 (9.63%) persons had T1DM and 13,741 (90.37%) had T2DM. Body mass index (BMI) contributed the most to the model, followed by age of onset and hemoglobin A1c (HbA1c). Compared with models with other clinical variable combinations, a final model that integrated age of onset, BMI and HbA1c had relatively higher performance. The ROC AUC, sensitivity, and specificity for this model were 0.83 (95% CI, 0.80 to 0.85), 0.77, and 0.76, respectively.The calibration intercept and slope were 0.02 (95% CI, -0.03 to 0.06) and 0.90 (95% CI, 0.79 to 1.02), respectively, which suggested a good calibration performance.Conclusions: Our classification model that integrated age of onset, BMI, and HbA1c could distinguish T1DM from T2DM, which provides a useful tool in assisting physicians in subtyping and precising treatment in diabetes.

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Section: Discussionmentioning

confidence: 99%

A clinical diagnostic model based on an eXtreme Gradient Boosting algorithm to distinguish type 1 diabetes

Tang¹,

Tang²,

Sun³

et al. 2021

Ann Transl Med

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“…Analysis of U.K., TrialNet, and Chinese cohorts has identified two distinct phases of C-peptide decline in stage 3 disease: an initial exponential fall followed by a period of relative stability. Along with initial differences at the time of clinical diagnosis, the rate of decline over 2-4 years was inversely related to age at onset (10,(34)(35)(36). Furthermore, the U.S. T1D Exchange Study found that glycemic control was better in adults with type 1 diabetes than in children and adolescents with type 1 diabetes (37).…”

Section: Metabolic Characteristics Of Adult-onset Type 1 Diabetesmentioning

confidence: 99%

Adult-Onset Type 1 Diabetes: Current Understanding and Challenges

et al. 2021

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Recent epidemiological data have shown that more than half of all new cases of type 1 diabetes occur in adults. Key genetic, immune, and metabolic differences exist between adult-and childhood-onset type 1 diabetes, many of which are not well understood. A substantial risk of misclassification of diabetes type can result. Notably, some adults with type 1 diabetes may not require insulin at diagnosis, their clinical disease can masquerade as type 2 diabetes, and the consequent misclassification may result in inappropriate treatment. In response to this important issue, JDRF convened a workshop of international experts in November 2019. Here, we summarize the current understanding and unanswered questions in the field based on those discussions, highlighting epidemiology and immunogenetic and metabolic characteristics of adult-onset type 1 diabetes as well as disease-associated comorbidities and psychosocial challenges. In adultonset, as compared with childhood-onset, type 1 diabetes, HLA-associated risk is lower, with more protective genotypes and lower genetic risk scores; multiple diabetes-associated autoantibodies are decreased, though GADA remains dominant. Before diagnosis, those with autoantibodies progress more slowly, and at diagnosis, serum C-peptide is higher in adults than children, with ketoacidosis being less frequent. Tools to distinguish types of diabetes are discussed, including body phenotype, clinical course, family history, autoantibodies, comorbidities, and C-peptide. By providing this perspective, we aim to improve the management of adults presenting with type 1 diabetes.Clinically, it has been relatively easy to distinguish the acute, potentially lethal, childhood-onset diabetes from the less aggressive condition that affects adults. However, experience has taught us that not all children with diabetes are insulin dependent and not all adults are non-insulin dependent. Immune, genetic, and metabolic analysis of these two, apparently distinct, forms of diabetes revealed inconsistencies, such that insulin-dependent and immune-mediated diabetes was redefined as type 1 diabetes, while most other forms were relabeled as type 2 diabetes. Recent data suggest a further shift in our thinking, with the recognition that more than half of all new cases of type 1 diabetes occur in adults. However, many adults may not require insulin at diagnosis of type 1 diabetes and have a more gradual onset of hyperglycemia, often leading to misclassification and inappropriate care. Indeed, misdiagnosis occurs in nearly 40% of adults with new type 1 diabetes, with the risk of error increasing with age (1,2). To consider this important issue, JDRF convened a workshop of international experts in November 2019 in New York, NY. In this Perspective, based on that workshop, we outline the evidence for

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“…12 For adults (>18 years old), BMI was converted to BMI-z score as [BMI-mean (BMI)]/SD (BMI). 13 Subjects had blood drawn for plasma glucose, glycated haemoglobin A 1c (HbA 1c ), triglycerides, total cholesterol, high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C). The urine sample was also collected for a test of glycosuria and ketonuria.…”

Section: Data Collectionmentioning

confidence: 99%

“…4 However, it is important to note that the knowledge acquired from studies based on these registries cannot be directly applied to the Chinese population due to differences in the incidence of T1D, the associations of genetic background, the frequency of islet-related autoantibodies, and the distribution of clinical subtypes between East Asian and Caucasian populations. [5][6][7][8][9][10][11][12][13][14][15][16][17] Several regional studies have been conducted within China, including the Guangdong Provincial Translational Medicine of Type 1 Diabetes, 7 a single-centre retrospective hospital study in Henan, 8 and the 3C study in Beijing and Shantou. 9 These studies have reported the demographic and informatics features, clinical characteristics at onset, complications, and comorbidities of Chinese T1D patients.…”

Section: Introductionmentioning

confidence: 99%

“…Epidemiological, clinical, and translational research in T1D has greatly benefited from extensive population and clinical centre‐based patient registries, such as the DPV Scientific Initiative of Germany and Austria, 1 the Hvidore Study Group, 2 T1D Exchange clinic register, 3 and The SEARCH for Diabetes in Youth 4 . However, it is important to note that the knowledge acquired from studies based on these registries cannot be directly applied to the Chinese population due to differences in the incidence of T1D, the associations of genetic background, the frequency of islet‐related autoantibodies, and the distribution of clinical subtypes between East Asian and Caucasian populations 5‐17 . Several regional studies have been conducted within China, including the Guangdong Provincial Translational Medicine of Type 1 Diabetes, 7 a single‐centre retrospective hospital study in Henan, 8 and the 3C study in Beijing and Shantou 9 .…”

Section: Introductionmentioning

confidence: 99%

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Care, control and complications of hospitalised patients with type 1 diabetes in China: A nationwide‐based registry study

Deng,

Xie,

et al. 2024

Diabetes Metabolism Res

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AimsTo evaluate the status quo of type 1 diabetes (T1D) management and characteristics of hospitalised patients with T1D in China through a nationwide multicentre registry study, the China Diabetes Type 1 Study (CD1S).Materials and MethodsClinical data from the electronic hospital records of all people with T1D were retrospectively collected in 13 tertiary hospitals across 7 regions of China from January 2016 to December 2021. Patients were defined as newly diagnosed who received a diagnosis of diabetes for less than 3 months.ResultsAmong the 4993 people with T1D, the median age (range) at diagnosis was 23.0 (1.0‐87.0) years and the median disease duration was 2.0 years. The median haemoglobin A1c (HbA1c) level was 10.7%. The prevalence of obesity, overweight, dyslipidemia, and hypertension were 2.5%, 10.8%, 62.5% and 25.9%, respectively. The incidence rate of diabetic ketoacidosis at disease onset was 41.1%, with the highest in children <10 years of age (50.6%). In patients not newly diagnosed, 60.7% were diagnosed with at least one chronic diabetic complication, with the highest proportion (45.3%) of diabetic peripheral neuropathy. Chronic complications were detected in 79.2% of people with T1D duration ≥10 years.ConclusionsIn the most recent years, there were still unsatisfactory metabolic control and high incidence of diabetic ketoacidosis as well as chronic diabetic complications among inpatients with T1D in China. The ongoing CD1S prospective study aims to improve the quality of T1D management nationally.

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Complicated curve association of body weight at diagnosis with C‐peptide in children and adults with new‐onset type 1 diabetes

Cited by 4 publications

References 28 publications

A clinical diagnostic model based on an eXtreme Gradient Boosting algorithm to distinguish type 1 diabetes

A clinical diagnostic model based on an eXtreme Gradient Boosting algorithm to distinguish type 1 diabetes

Adult-Onset Type 1 Diabetes: Current Understanding and Challenges

Care, control and complications of hospitalised patients with type 1 diabetes in China: A nationwide‐based registry study

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