Etiologies underlying subtypes of long-standing type 2 diabetes.

BACKGROUND: Attempts to subtype, type 2 diabetes (T2D) have mostly focused on newly diagnosed European patients. In this study, our aim was to subtype T2D in a non-white Emirati ethnic population with long-standing disease, using unsupervised soft clustering, based on etiological determinants. METHODS: The Auto Cluster model in the IBM SPSS Modeler was used to cluster data from 348 Emirati patients with long-standing T2D. Five predictor variables (fasting blood glucose (FBG), fasting serum insulin (FSI), body mass index (BMI), hemoglobin A1c (HbA1c) and age at diagnosis) were used to determine the appropriate number of clusters and their clinical characteristics. Multinomial logistic regression was used to validate clustering results. RESULTS: Five clusters were identified; the first four matched Ahlqvist et al subgroups: severe insulin-resistant diabetes (SIRD), severe insulin-deficient diabetes (SIDD), mild age-related diabetes (MARD), mild obesity-related diabetes (MOD), and a fifth new subtype of mild early onset diabetes (MEOD). The Modeler algorithm allows for soft assignments, in which a data point can be assigned to multiple clusters with different probabilities. There were 151 patients (43%) with membership in cluster peaks with no overlap. The remaining 197 patients (57%) showed extensive overlap between clusters at the base of distributions. CONCLUSIONS: Despite the complex picture of long-standing T2D with comorbidities and complications, our study demonstrates the feasibility of identifying subtypes and their underlying causes. While clustering provides valuable insights into the architecture of T2D subtypes, its application to individual patient management would remain limited due to overlapping characteristics. Therefore, integrating simplified, personalized metabolic profiles with clustering holds greater promise for guiding clinical decisions than subtyping alone.

Immunological and Some Biological Biomarkers in Patients Infected with Moraxella Catarrhalis.

BACKGROUND: Moraxella catarrhalis is the most common gram-negative bacteria pathogen that affects the human. The current study was carried out to investigate M. catarrhalis infection and how it modulates some biomarkers. METHODS: The samples were collected from 100 patients diagnosed with respiratory tract infections such as pneumonia, otitis media, and tonsillitis. Cultural characteristics were for the colonies cultured on blood and hot blood media. Microscopic method, biochemical tests, and Vitek 2 system was tested and they showed that ten isolates were M. catarrhalis. RESULTS: Out of 10 isolates, 8 isolates (80%) were ß-lactamase-producing. The sensitivity of the isolates was deter-mined against seven antibiotics, and they showed multidrug resistance (MDR). All isolates showed 100% resistance to Ampicillin and Ceftazidime; however, the isolates showed less resistance to Meropenem and Imipenem. Enzyme-linked immunosorbent assay was used to determine the levels of Anti-DNA, IgM, IgG, IL-1ß and hs-CRP in the patient serum. The infected serum with M. catarrhalis showed normal levels of Anti-DNA and IgM compared to control group, while the serum with high levels of IgG, IL-1ß, and hs-CRP were recorded (p < 0.001). CONCLUSIONS: The multi-antibiotic resistance of M. catarrhalis plays an important role in raising pro-inflammatory markers such as IgG, IL-1ß, and hs-CRP levels, which may subsequently affect the respiratory tract.

Risk and predictors of severity and mortality in patients with type 2 diabetes and COVID-19 in Dubai.

BACKGROUND: Globally, patients with diabetes suffer from increased disease severity and mortality due to coronavirus disease 2019 (COVID-19). Old age, high body mass index (BMI), comorbidities, and complications of diabetes are recognized as major risk factors for infection severity and mortality. AIM: To investigate the risk and predictors of higher severity and mortality among in-hospital patients with COVID-19 and type 2 diabetes (T2D) during the first wave of the pandemic in Dubai (March-September 2020). METHODS: In this cross-sectional nested case-control study, a total of 1083 patients with COVID-19 were recruited. This study included 890 men and 193 women. Of these, 427 had T2D and 656 were non-diabetic. The clinical, radiographic, and laboratory data of the patients with and without T2D were compared. Independent predictors of mortality in COVID-19 non-survivors were identified in patients with and without T2D. RESULTS: T2D patients with COVID-19 were older and had higher BMI than those without T2D. They had higher rates of comorbidities such as hypertension, ischemic heart disease, heart failure, and more life-threatening complications. All laboratory parameters of disease severity were significantly higher than in those without T2D. Therefore, these patients had a longer hospital stay and a significantly higher mortality rate. They died from COVID-19 at a rate three times higher than patients without. Most laboratory and radiographic severity indices in non-survivors were high in patients with and without T2D. In the univariate analysis of the predictors of mortality among all COVID-19 non-survivors, significant associations were identified with old age, increased white blood cell count, lym-phopenia, and elevated serum troponin levels. In multivariate analysis, only lymphopenia was identified as an independent predictor of mortality among T2D non-survivors. CONCLUSION: Patients with COVID-19 and T2D were older with higher BMI, more comorbidities, higher disease severity indices, more severe proinflammatory state with cardiac involvement, and died from COVID-19 at three times the rate of patients without T2D. The identified mortality predictors will help healthcare workers prioritize the management of patients with COVID-19.