BMJ Open Diabetes Research & Care最新文献

Introduction: The rate of progression to complete insulin deficiency varies greatly in type 1 diabetes. This constitutes a challenge, especially when randomizing patients in intervention trials aiming to preserve beta cell function. This study aimed to identify biomarkers predictive of either a rapid or slow disease progression in children with new-onset type 1 diabetes.

Research design and methods: A retrospective, longitudinal cohort study of children (<18 years) with type 1 diabetes (N=46) was included at diagnosis and followed until complete insulinopenia (C-peptide <0.03 nmol/L). Children were grouped into rapid progressors (n=20, loss within 30 months) and slow progressors (n=26). A sex-matched control group of healthy children (N=45) of similar age was included for comparison. Multiple biomarkers were assessed by proximity extension assay (PEA) at baseline and follow-up.

Results: At baseline, rapid progressors had lower C-peptide and higher autoantibody levels than slow. Three biomarkers were higher in the rapid group: carbonic anhydrase 9, corticosteroid 11-beta-dehydrogenase isozyme 1, and tumor necrosis factor receptor superfamily member 21. In a linear mixed model, 25 proteins changed over time, irrespective of group. One protein, a coxsackievirus B-adenovirus receptor (CAR) increased over time in rapid progressors. Eighty-one proteins differed between type 1 diabetes and healthy controls. Principal component analysis could not distinguish between rapid, slow, and healthy controls.

Conclusions: Despite differences in individual proteins, the combination of multiple biomarkers analyzed by PEA could not distinguish the rate of progression in children with new-onset type 1 diabetes. Only one marker was altered significantly when considering both time and group effects, namely CAR, which increased significantly over time in the rapid group. Nevertheless, we did find some markers that may be useful in predicting the decline of the C-peptide. Moreover, these could potentially be important for understanding type 1 diabetes pathogenesis.

Introduction: Diabetes mellitus is known to increase the risk of cancer. Fasting blood glucose (FBG) levels can be changed over time. However, the association between FBG trajectory and cancer risk has been insufficiently studied. This research aims to examine the relationship between FBG trajectories and cancer risk in the Korean population.

Research design and methods: We analyzed data from the National Health Insurance Service-National Health Screening Cohort collected between 2002 and 2015. Group-based trajectory modeling was performed on 256,271 Koreans aged 40-79 years who had participated in health examinations at least three times from 2002 to 2007. After excluding patients with cancer history before 2008, we constructed a cancer-free cohort. The Cox proportional hazards model was applied to examine the association between FBG trajectories and cancer incidence by cancer type, after adjustments for covariates. Cancer case was defined as a person who was an outpatient thrice or was hospitalized once or more with a cancer diagnosis code within the first year of the claim.

Results: During the follow-up time (2008-2015), 18,991 cancer cases were identified. Four glucose trajectories were found: low-stable (mean of FBG at each wave <100 mg/dL), elevated-stable (113-124 mg/dL), elevated-high (104-166 mg/dL), and high-stable (>177 mg/dL). The high-stable group had a higher risk of multiple myeloma, liver cancer and gastrointestinal cancer than the low-stable group, with HR 4.09 (95% CI 1.40 to 11.95), HR 1.68 (95% CI 1.25 to 2.26) and HR 1.27 (95% CI 1.11 to 1.45), respectively. In elevated-stable trajectory, the risk increased for all cancer (HR 1.08, 95% CI 1.02 to 1.16) and stomach cancer (HR 1.24, 95% CI 1.07 to 1.43). Significant associations were also found in the elevated-high group with oral (HR 2.13, 95% CI 1.01 to 4.47), liver (HR 1.50, 95% CI 1.08 to 2.08) and pancreatic cancer (HR 1.99, 95% CI 1.20 to 3.30).

Conclusions: Our study highlights that the uncontrolled high glucose level for many years may increase the risk of cancer.

Introduction: Severe hypoglycemia (SH) in older adults (OAs) with type 1 diabetes is associated with profound morbidity and mortality, yet its etiology can be complex and multifactorial. Enhanced tools to identify OAs who are at high risk for SH are needed. This study used machine learning to identify characteristics that distinguish those with and without recent SH, selecting from a range of demographic and clinical, behavioral and lifestyle, and neurocognitive characteristics, along with continuous glucose monitoring (CGM) measures.

Research design and methods: Data from a case-control study involving OAs recruited from the T1D Exchange Clinical Network were analyzed. The random forest machine learning algorithm was used to elucidate the characteristics associated with case versus control status and their relative importance. Models with successively rich characteristic sets were examined to systematically incorporate each domain of possible risk characteristics.

Results: Data from 191 OAs with type 1 diabetes (47.1% female, 92.1% non-Hispanic white) were analyzed. Across models, hypoglycemia unawareness was the top characteristic associated with SH history. For the model with the richest input data, the most important characteristics, in descending order, were hypoglycemia unawareness, hypoglycemia fear, coefficient of variation from CGM, % time blood glucose below 70 mg/dL, and trail making test B score.

Conclusions: Machine learning may augment risk stratification for OAs by identifying key characteristics associated with SH. Prospective studies are needed to identify the predictive performance of these risk characteristics.

Type 2 diabetes mellitus (T2DM) is characterized by persistent hyperglycemia which is further associated with hyperactivity of the hypothalamic-pituitary-adrenal (HPA) axis. Several studies have shown that HPA axis hyperactivity is heightened in the chronic hyperglycemic state with severe hyperglycemic events more likely to result in a depressive disorder. The HPA axis is also regulated by the immune system. Upon stress, under homeostatic conditions, the immune system is activated via the sympatho-adrenal-medullary axis resulting in an immune response which secretes proinflammatory cytokines. These cytokines aid in the activation of the HPA axis during stress. However, in T2DM, where there is persistent hyperglycemia, the immune system is dysregulated resulting in the elevated concentrations of these cytokines. The HPA axis, already activated by the hyperglycemia, is further activated by the cytokines which all contribute to a diagnosis of depression in patients with T2DM. However, the onset of T2DM is often preceded by pre-diabetes, a reversible state of moderate hyperglycemia and insulin resistance. Complications often seen in T2DM have been reported to begin in the pre-diabetic state. While the current management strategies have been shown to ameliorate the moderate hyperglycemic state and decrease the risk of developing T2DM, research is necessary for clinical studies to profile these direct effects of moderate hyperglycemia in pre-diabetes on the HPA axis and the indirect effects moderate hyperglycemia may have on the HPA axis by investigating the components of the immune system that play a role in regulating this pathway.