Detection Of Type 2 Diabetes Mellitus Research Articles

Incident Cancer Risk in Patients with Incident Type 2 Diabetes Mellitus in Hungary (Part 1).

(1) Background: Patients with type 2 diabetes mellitus (T2DM) are at higher risk of cancer but how these two diseases associate is still debated. The goal of this study was the assessment of the overall incidence of cancer among patients with newly diagnosed T2DM in Hungary. (2) Methods: A nationwide, retrospective, longitudinal study was performed using a Hungarian database. After exclusion of cases of age < 18 years, with gestational diabetes, with polycystic ovary syndrome, and with type 1 and prevalent type 2 diabetes mellitus, the incident T2DM (approx. 50,000 cases yearly) and for comparison, the diabetes-free Hungarian adult population (approx. 7,000,000 cases yearly) was included in the study. The primary endpoints were the overall and site-specific incidence and annual percentage change of the incidence of cancer in both populations. (3) Results: The overall incidence of cancer in patients amounted to 29.4/1000 and 6.6/1000 with or without T2DM, respectively, and the OR (95%CI) of cancer of the T2DM group was 4.32 (4.14-4.53), p < 0.0001. The risk of having cancer was age dependent. The incidence of cancer was declining in the non-diabetic but was unchanged in the T2DM population. The average lag time of diagnosing cancer after the detection of T2DM was 3.86 months. (4) Conclusions: Incident T2DM is associated with a significantly higher overall risk of incident cancer, with a reverse correlation of age. Newly registered T2DM patients were suggested to be screened for cancer within 6 months.

Exploring the effectiveness of machine learning algorithms for early detection of Type-2 Diabetes Mellitus

Type 2 Diabetes Mellitus (T2DM) poses a significant global health challenge, greatly impacting the well-being and longevity of individuals. Detecting T2DM early on is of utmost importance as it prevents or delays associated complications. This research study seeks to assess the effectiveness of employing ma-chine learning algorithms for the early identification of T2DM. A classification model is constructed utilizing a dataset comprising T2DM-diagnosed patients and healthy controls, incorporating advanced feature selection techniques. The model is subjected to rigorous training and testing, utilizing machine learning algorithms such as Logistic Regression, K-Nearest Neighbors, Decision Trees, Random Forest, and Support Vector Machines. The results demonstrate that the Random Forest algorithm achieves an impressive accuracy of 98 % in detecting T2DM. This remarkable accuracy underscores the immense potential of machine learning algorithms in facilitating the early detection of T2DM. It emphasizes the significance of integrating these methods into the clinical decision-making process. The findings from this study will significantly contribute to developing an enhanced precision medicine screening process for T2DM, empowering healthcare providers to identify the disease at its nascent stages, thereby leading to improved patient outcomes.

A Comparison of Machine Learning Techniques for the Detection of Type-2 Diabetes Mellitus: Experiences from Bangladesh

Diabetes is a chronic disease caused by a persistently high blood sugar level, causing other chronic diseases, including cardiovascular, kidney, eye, and nerve damage. Prompt detection plays a vital role in reducing the risk and severity associated with diabetes, and identifying key risk factors can help individuals become more mindful of their lifestyles. In this study, we conducted a questionnaire-based survey utilizing standard diabetes risk variables to examine the prevalence of diabetes in Bangladesh. To enable prompt detection of diabetes, we compared different machine learning techniques and proposed an ensemble-based machine learning framework that incorporated algorithms such as decision tree, random forest, and extreme gradient boost algorithms. In order to address class imbalance within the dataset, we initially applied the synthetic minority oversampling technique (SMOTE) and random oversampling (ROS) techniques. We evaluated the performance of various classifiers, including decision tree (DT), logistic regression (LR), support vector machine (SVM), gradient boost (GB), extreme gradient boost (XGBoost), random forest (RF), and ensemble technique (ET), on our diabetes datasets. Our experimental results showed that the ET outperformed other classifiers; to further enhance its effectiveness, we fine-tuned and evaluated the hyperparameters of the ET. Using statistical and machine learning techniques, we also ranked features and identified that age, extreme thirst, and diabetes in the family are significant features that prove instrumental in the detection of diabetes patients. This method has great potential for clinicians to effectively identify individuals at risk of diabetes, facilitating timely intervention and care.

Pancreatic fat fraction in dual-energy computed tomography as a potential quantitative parameter in the detection of type 2 diabetes mellitus

PurposeTo investigate the clinical value of measuring pancreatic fat fraction using dual-energy computed tomography (DECT) in association with type 2 diabetes mellitus (T2DM). Materials and methodsThis retrospective study included patients who underwent abdominal DECT between September 2021 and July 2022. The fat fractions in the head, body, and tail of the pancreas were calculated using fat maps generated from unenhanced DECT images, and CT values were measured at the same locations. The intraclass correlation coefficient (ICC) was used to analyze the reproducibility of measurements from two observers. Diagnostic performance was assessed using receiver operating characteristic curves. ResultsSeventy-eight patients, including 45 T2DM patients and 33 controls, were enrolled. The fat fractions of the pancreas were significantly higher in the T2DM group than in the control group (pancreatic head: 8.4 ± 6.3 % vs 5.1 ± 3.9 %; pancreatic body: 4.8 ± 4.0 % vs 2.7 ± 3.9 %; and pancreatic tail: 5.3 ± 3.2 % vs 2.7 ± 2.9 %, all p < 0.05). And the CT values of the pancreas were significantly lower in the T2DM group than in the control group (pancreatic head: 41.1 ± 8.5 HU vs 45.7 ± 4.6 HU; pancreatic body: 44.4 ± 5.0 HU vs 47.4 ± 3.7 HU; and pancreatic tail: 44.5 ± 5.0 HU vs 47.6 ± 3.2 HU, all p < 0.05).The fat fraction of the pancreatic tail was the best indicator for distinguishing T2DM patients from the controls (area under the curve: 0.716 (95 % CI: 0.601, 0.832), sensitivity: 64.4 % (95 % CI: 48.7 %, 77.7 %), and specificity: 78.8 % (95 % CI: 60.6 %, 90.4 %)). ConclusionThe DECT fat fractions of the pancreas could be a valuable additional parameter in the detection of T2DM.

Analysis of serum gamma glutamyl transpeptidase (GGT) level as a marker for the detection of type 2 diabetes mellitus in Okhla industrial area

Analysis of serum gamma glutamyl transpeptidase (GGT) level as a marker for the detection of type 2 diabetes mellitus in Okhla industrial area - IJCBR- Print ISSN No: - 2394-6369 Online ISSN No:- 2394-6377 Article DOI No:- 10.18231/j.ijcbr.2019.073, International Journal of Clinical Biochemistry and Research-Int J Clin

A New Metabolomic Signature in Type-2 Diabetes Mellitus and Its Pathophysiology

ObjectiveThe objective of the current study was to find a metabolic signature associated with the early manifestations of type-2 diabetes mellitus.Research Design and MethodModern metabolic profiling technology (MxP™ Broad Profiling) was applied to find early alterations in the plasma metabolome of type-2 diabetic patients. The results were validated in an independent study. Eicosanoid and single inon monitoring analysis (MxP™ Eicosanoid and MxP™ SIM analysis) were performed in subsets of samples.ResultsA metabolic signature including significantly increased levels of glyoxylate as a potential novel marker for early detection of type-2 diabetes mellitus was identified in an initial study (Study1). The signature was significantly altered in fasted diabetic and pre-diabetic subjects and in non-fasted subjects up to three years prior to the diagnosis of type-2 diabetes; most alterations were also consistently found in an independent patient group (Study 2). In Study 2 diabetic and most control subjects suffered from heart failure. In Study 1 a subgroup of diabetic subjects, with a history of use of anti-hypertensive medication further showed a more pronounced increase of glyoxylate levels, compared to a non-diabetic control group when tested in a hyperglycemic state. In the context of a prior history of anti-hypertensive medication, alterations in hexosamine and eicosanoid levels were also found.ConclusionA metabolic signature including glyoxylate was associated with type-2 diabetes mellitus, independent of the fasting status and of occurrence of another major disease. The same signature was also found to be associated with pre-diabetic subjects. Glyoxylate levels further showed a specifically strong increase in a subgroup of diabetic subjects. It could represent a new marker for the detection of medical subgroups of diabetic subjects.