Random Glucose Research Articles

Prognostic risk factors of catheter-related bloodstream infection in patients with maintenance hemodialysis

To analyze the pathogen distribution and prognostic risk factors of catheter-related bloodstream infection (CRBSI) in patients with maintenance hemodialysis (MHD) during non-hospitalization. A retrospective comparative study was conducted. Thirty-four patients of MHD with semi-permanent catheter admitted to the department of nephrology of Gansu Provincial Hospital from January 2020 to May 2023 due to CRBSI during non-hospitalization were enrolled. The distribution characteristics of pathogens causing CRBSI in MHD patients during non-hospital period were analyzed. All patients were actively given anti-infection treatment after admission. The general data, laboratory indicators and prognosis during hospitalization were collected through the electronic medical record system. Patients were divided into poor prognosis group (14 cases) and good prognosis group (20 cases) according to the treatment results during hospitalization. Univariate and binary Logistic regression were used to analyze the risk factors affecting the prognosis of patients, and receiver operator characteristic curve (ROC curve) was drawn to evaluate its predictive value for prognosis. A total of 28 pathogenic bacteria were isolated from 34 patients, of which 25 were Gram-positive, Staphylococcus was the most common pathogen, accounting for 82.15% of the total, and 16 strains of Staphylococcus aureus (57.15%), including 6 methicillin-resistant Staphylococcus aureus (MRSA, 21.43%). There were 7 strains of Staphylococcus epidermidis (25.00%), including 3 strains of methicillin-resistant Staphylococcus epidermidis (MRSE, 10.71%). There were 3 strains of Gram-negative bacteria, 1 strain each of Pseudomonas aeruginosa, Escherichia coli and Acinetobacter baumannii. Univariate analysis showed that the fever duration of MHD patients with CRBSI in the poor prognosis group was significantly longer than that in the good prognosis group [days: 8.50 (3.75, 45.00) vs. 2.50 (1.00, 4.75), P < 0.01], serum erythrocyte sedimentation rate (ESR), C-reactive protein (CRP) and random blood glucose (GLU) were significantly higher than those in the good prognosis group [ESR (mm/1 h): 82.36±24.98 vs. 56.95±35.65, CRP (mg/L): 123.45±74.10 vs. 67.35±55.22, GLU (mmol/L): 8.74±3.66 vs. 6.42±1.95, all P < 0.05]. Binary Logistic regression analysis showed that serum CRP was an independent risk factor for poor prognosis in MHD patients with CRBSI [odds ratio (OR) = 1.020, 95% confidence interval (95%CI) was 1.002-1.038, P = 0.025]. ROC curve analysis showed that the area under the curve (AUC) of serum CRP in predicting poor prognosis of MHD patients with CRBSI was 0.711; when the optimal cut-off value was 104.65 mg/L, the sensitivity was 64.3% and the specificity was 85.0%, indicating that it has good predictive value. Gram-positive bacteria are the main pathogens of CRBSI in MHD patients during non-hospital period. The poor prognosis is mainly related to the high level of serum CRP. Serum CRP level can effectively screen the high-risk group of MHD patients with CRBSI with poor prognosis.

Clinical care advice for monitoring of islet autoantibody positive individuals with presymptomatic type 1 diabetes.

Type 1 diabetes is an autoimmune disease that involves the development of autoantibodies against pancreatic islet beta-cell antigens, preceding clinical diagnosis by a period of preclinical disease activity. As screening activity to identify autoantibody-positive individuals increases, a rise in presymptomatic type 1 diabetes individuals seeking medical attention is expected. Current guidance on how to monitor these individuals in a safe but minimally invasive way is limited. This article aims to provide clinical guidance for monitoring individuals with presymptomatic type 1 diabetes to reduce the risk of diabetic ketoacidosis (DKA) at diagnosis. Expert consensus was obtained from members of the Fr1da, GPPAD, and INNODIA consortia, three European diabetes research groups. The guidance covers both specialist and primary care follow-up strategies. The guidance outlines recommended monitoring approaches based on age, disease stage and clinical setting. Individuals with presymptomatic type 1 diabetes are best followed up in specialist care. For stage 1, biannual assessments of random plasma glucose and HbA1c are suggested for children, while annual assessments are recommended for adolescents and adults. For stage 2, 3-monthly clinic visits with additional home monitoring are advised. The value of repeat OGTT in stage 1 and the use of continuous glucose monitoring in stage 2 are discussed. Primary care is encouraged to monitor individuals who decline specialist care, following the guidance presented. As type 1 diabetes screening programs become more prevalent, effective monitoring strategies are essential to mitigate the risk of complications such as DKA. This guidance serves as a valuable resource for clinicians, providing practical recommendations tailored to an individual's age and disease stage, both within specialist and primary care settings.

Diyabet Aşikar Olmasa da Komplikasyonları Aşikar Olabilir, Prediyabet Hastalarında Nefropatinin Sıklığı

BACKGROUND/AIMS: Prediabetes is considered a risk factor for diabetes mellitus (DM) and cardiovascular diseases. Complications are also detected during diagnosis in 10-40% of diabetes patients. Diabetic nephropathy is one of the critical microvascular complications of diabetes. Studies have shown that prediabetes is associated with the development of glomerular hyperfiltration and albuminuria, as in the early stages of diabetes. Identifying patients before overt DM occurs is important for early diagnosis and treatment of nephropathy and DM. The aim of our study is to investigate the presence and frequency of nephropathy in prediabetes patients. METHODS: Patients who applied to the outpatient internal medicine and endocrinology outpatient clinics and whose random fasting blood sugar was ≥100 mg/dl were evaluated. Oral glucose tolerance test (OGTT) was performed and HbA1C values were measured. 280 patients diagnosed with prediabetes and evaluated for nephropathy were included in the study. RESULTS: Nephropathy was detected in 81 (28.9%) of the patients. The average HbA1C value of the group with nephropathy was 6.28% (sd: 0.52) and the average HbA1C value of the group without nephropathy was 6.05% (sd: 0.29). The average HbA1C value was found to be significantly higher in the group with nephropathy (p=0.000). CONCLUSIONS: The fact that nephropathy was detected in 28.9% of the patients showed once again the seriousness of prediabetes. Therefore, patients with prediabetes, especially those with higher HbA1C values, need to be evaluated more carefully in terms of nephropathy and CKD.

Performance comparison of stress hyperglycemia ratio for predicting fatal outcomes in patients with thrombolyzed acute ischemic stroke.

The stress hyperglycemia ratio (SHR), a newly developed metric, is used to assess adverse outcomes in patients with acute ischemic stroke (AIS). However, the relationship between SHR and fatal outcomes (in-hospital mortality [IHM], malignant cerebral edema [MCE], symptomatic intracerebral hemorrhage [sICH], 3-month mortality, and poor functional outcome) in AIS patients receiving recombinant tissue plasminogen activator (rt-PA) treatment is unclear, and determining the optimal threshold remains incomplete. We retrospectively enrolled a total of 345 AIS patients treated with rt-PA during 2015-2022 and collected data on various glucose metrics, including different types of SHR, glycemic gap (GG), random plasma glucose (RPG), fasting plasma glucose (FPG), and hemoglobin A1c (HbA1c). SHR and GG were calculated using these equations: SHR1, [FPG]/[HbA1c]; SHR2, [admission RPG]/[HbA1c]; SHR3, FPG/[(1.59 × HbA1c)-2.59]; SHR4, [admission RPG]/[(1.59 × HbA1c)-2.59]; GG, admission RPG - [(1.59 × HbA1c)-2.59]. We used multivariable logistic regression analysis (MVLR) to identify the association between different glucose metrics and outcomes while comparing their predictive values. SHR1 had the greatest predictive power and a more significant correlation with fatal outcomes than other continuous glucose metrics. The area under the curve of the SHR1 for IHM, MCE, and sICH, 3-month mortality, and poor functional outcome were 0.75, 0.77, 0.77, 0.76, and 0.73, respectively. SHR1 (per 1-point increases) was independently associated with IHM (Odds ratios [ORs] = 5.80; 95% CI [1.96, 17.17]; p = 0.001), MCE (ORs = 4.73; 95% CI [1.71, 13.04]; p = 0.003), sICH (ORs = 4.68, 95% CI [1.48-14.82]; p = 0.009), 3-month mortality (ORs = 10.87; 95% CI [3.56, 33.21]; p<0.001), and 3-month poor functional outcome (ORs = 8.05; 95% CI [2.77, 23.39]; p<0.001) after adjustment in MVLR. In subgroup analysis, elevated SHR1 was associated with fatal outcomes in patients with non-diabetes, SBP≥ 180 mmHg, and NIHSS <16. SHR1 demonstrates an independent association with fatal outcomes in AIS patients treated with rt-PA, exhibiting superior predictive ability over other glucose metrics.

Impact of personalized diabetes care on distress and treatment satisfaction in people with breast cancer.

In patients with breast cancer (BCa) and diabetes (DM), diabetes distress (DD) and treatment satisfaction (DTS) can influence BCa management and outcomes. We assessed the impact of implementing a personalized diabetes care model in patients with BCa. Patients in active treatment or surveillance for BCa with an HbA1c > 53 mmol/mol (7%) or random blood glucose >11.1 mmol/L were included. Participants were offered continuous glucose monitoring (CGM), virtual care and a dedicated diabetes provider for 6 months. Primary outcomes included DD measured by the Diabetes Distress Survey (DDS) and DTS measured by the Diabetes Treatment Satisfaction Questionnaire (DTSQ). Questionnaires were conducted at 0, 3 and 6 months. Thirty-one women were enrolled (median age 61, IQR 49.0-69.0). Compared to baseline, the mean DDS score was lower at both 3 months (2.2 vs. 1.8 [n = 27], p = 0.004, SD = 0.70) and 6 months (2.3 vs. 1.8 [n = 23], p = 0.002, SD = 0.70). The mean DTSQ score was higher at 3 months (baseline: 20.5 vs. 3 months: 28.7 [n = 28], p < 0.001, SD = 9.2) and 6 months (baseline: 20.4 vs. 6 months: 30.0 [n = 26], p < 0.001, SD = 9.7). Personalized diabetes care models that emphasize remote management and optimize access for those with BCa may lower DD and improve DTS.

Covid-19 Pneumonia Presenting as a ‘Typical Radiological Bronchopneumonia’ Pattern in Clinical-Radiological-Pathological Phenotype with Transient Hyperglycemia

Radiological phenotypes are radiological patterns or observable characteristics of COVID19 pneumonia. Various phenotypic classifications have been reported in literature. CT severity radiological phenotypes are widely used and universally accepted radiological phenotypic methods. Radiological CT severity phenotypic differentiation has documented very crucial role in initial assessment and during triaging of these cases in indoor and outdoor setting. Typical COVID-19 lung parenchymal involvement described as predominant ground glass opacities (GGOs) and consolidations in peripheral and subpleural portion of any lobe, predominantly involving lower lobes. Atypical Radiological patterns in COVID-19 has been documented as bronchopneumonia, multifocal consolidations, necrotizing pneumonia, cavitations with GGOs with or without consolidations. In present case report, 43-year male, presented with acute febrile respiratory illness with acute hypoxic respiratory failure documented as oxygen saturation of 80% at room air with tachypnea and respiratory distress. HRCT thorax was showing Pleural based, peripheral, central, multifocal patchy, confluent &amp; ill-defined GGOs and consolidations in bilateral lung fields in upper, middle and lower lobes. These typical radiological features suggestive of bronchopneumonia like radiological pattern which was very unusual for typical COVID-19 radiology. His laboratory parameters have shown abnormally raised inflammatory markers like CRP, Ferritin, LDH, D-Dimer and IL-6. Importantly, his random blood sugars were raised with abnormally raised HbA1c levels to label as diabetes mellitus. He was diagnosed with diabetes mellitus this time with COVID-19 illness without use of steroids. He was treated with standardized COVID-19 management institutional protocol with combination of low molecular weight heparin, methylprednisolone, remdesivir, meropenem and teicoplanin. Patient required BIPAP support with higher oxygen requirement with nasals canula for one week due to advanced radiological disease with more anatomical involvement. We have documented successful treatment outcome with use of rational treatment in timely by predicting disease severity with use of composite analysis of clinical, laboratory and radiological markers of illness. Transient hyperglycemia is known to occur and reported after COVID-19 illness due to virus induced inflammatory response and pancreatopathy with beta cell dysfunction. Transient hyperglycemia can be easily managed with insulin during hospitalization and oral anti-diabetic agents after discharge for few weeks. Dietary and lifestyle modification will help in majority with complete reversal of abnormally high sugar levels with restoration of normal HbA1c levels to non-diabetic range.

Review on the screening of urine glucose for early diagnosis of type 2 diabetes mellitus in school children and adolescents with obesity in Hong Kong.

Obesity and type 2 diabetes mellitus (T2DM) are growing health concerns. Since 2005, Student Health Service (SHS) and Hong Kong Paediatric Society formulated a protocol on urine glucose screening (UGS) for early diagnosis of T2DM in students with obesity in Hong Kong. This study reviews students with T2DM captured by this screening program and compare the data with the Hong Kong Children Diabetes Registry (HKCDR) database, to see if the UGS program facilitates early diagnosis of T2DM. Students between the ages of 10-18 years old with age- and sex-specific body mass index (BMI) >97th percentile who attended SHS between the school years from 2005/06 to 2017/18 were recruited for UGS. Those tested positive for random urine glucose underwent diagnostic testing for T2DM according to ADA guidelines. Demographic data and investigatory results from UGS and HKCDR within the same time period were compared. A total of 216,526 students completed UGS in the said period; 415 (0.19 %) students were tested positive for urine glucose of which 121 students were diagnosed with T2DM. UGS picked up 23 % of the newly diagnosed T2DM cases. When compared to the HKCDR database, students diagnosed via UGS were significantly younger, less obese, and had fewer diabetic related complications. The negative predictive value of UGS is high and can effectively rule out T2DM. Urine glucose screening is an inexpensive and simple test that allows for early diagnosis of T2DM among obese school students. Other methods including POCT HbA1c can be explored to improve program effectiveness.

Hyperglycemia in hospital: an independent marker of infection, acute kidney injury & stroke for hospital inpatients.

Hyperglycemia in hospital inpatients without pre-existing diabetes is associated with increased mortality. However, the independent contribution of hyperglycemia to healthcare-associated infection (HAI), acute kidney injury (AKI), and stroke is unclear. To investigate the relationship between hyperglycemia and adverse clinical outcomes in hospital for patients with and without diabetes. Diabetes IN-hospital: Glucose and Outcomes (DINGO) was a 26-week (October 2019 - March 2020) prospective cohort study. Clinical and glucose data were collected up to the 14th day of admission. Primary stratification was by hyperglycemia, defined as ≥2 random capillary blood glucose (BG) measurements ≥11.1 mmol/L (≥200 mg/dL). Propensity weighting for nine clinical characteristics, was performed to allow interrogation of causality. To maintain the positivity assumption, patients with HbA1c > 12.0% were excluded and pre-hospital treatment not adjusted for. The Royal Melbourne Hospital, a quaternary referral hospital in Melbourne, Australia. Admissions with at least two capillary glucose values and length of stay >24 hours were eligible, with half randomly sampled. HAI, AKI, stroke, and mortality. Of 2,558 included admissions, 1,147 (45%) experienced hyperglycemia in hospital. Following propensity-weighting and adjustment, hyperglycemia in hospital was found to, independently of nine covariables, contribute an increased risk of in-hospital HAI (130 [11.3%] vs.100 [7.1%], adjusted odds ratio [aOR] 1.03, 95% confidence interval [95%CI] 1.01-1.05, p = 0.003), AKI (120 [10.5%] vs. 59 [4.2%], aOR 1.07, 95%CI 1.05-1.09, p < 0.001), and stroke (10 [0.9%] vs. 1 [0.1%], aOR 1.05, 95%CI 1.04-1.06, p < 0.001). In hospital inpatients (HbA1c ≤ 12.0%), irrespective of diabetes status and pre-hospital glycaemia, hyperglycemia increases the risk of in-hospital HAI, AKI, and stroke compared with those not experiencing hyperglycemia.

Development and validation of outcome prediction model for reperfusion therapy in acute ischemic stroke using nomogram and machine learning.

To develop logistic regression nomogram and machine learning (ML)-based models to predict 3-month unfavorable functional outcome for acute ischemic stroke (AIS) patients undergoing reperfusion therapy. Patients undergoing reperfusion therapy (intravenous thrombolysis and/or endovascular treatment) were prospectively recruited. Unfavorable outcome was defined as 3-month modified Rankin Scale (mRS) score 3-6. The independent risk factors associated with unfavorable outcome were obtained by regression analysis and included in the prediction model. The performance of nomogram was assessed by the area under the curve (AUC), calibration curve, and decision curve analysis (DCA). ML models were compared with nomogram using AUC; the generalizability of all models was ascertained in an external cohort. A total of 505 patients were enrolled, with 256 in the model construction, and 249 in the external validation. Five variables were identified as prognostic factors: baseline NIHSS, D-dimer level, random blood glucose (RBG), blood urea nitrogen (BUN), and systolic blood pressure (SBP) before reperfusion. The AUC values of nomogram were 0.865, 0.818, and 0.779 in the training set, test set, and external validation, respectively. The calibration curve and DCA indicated appreciable reliability and good net benefits. The best three ML models were extra trees (ET), CatBoost, and random forest (RF) models; all of them showed favorable discrimination in the training cohort, and confirmed in the test and external sets. Baseline NIHSS, D-dimer, RBG, BUN, and SBP before reperfusion were independent predictors for 3-month unfavorable outcome after reperfusion therapy in AIS patients. Both nomogram and ML models showed good discrimination and generalizability.

Changes in biochemical profiles of children with severe acute malnutrition on admission and after nutritional rehabilitation

Purpose: The given study was done to find out the changes in biochemical profiles before and after nutritional rehabilitation of the children admitted at the Department of Paediatrics, Assam Medical College with severe acute malnutrition (SAM). Materials and Methods: A hospital based observational study was conducted from May 2020 to April 2021 with 55 consecutive cases of SAM. Vitros System 5600 was used for analysis of 13 biochemical parameters. Results: The mean values of serum sodium, potassium, and chloride at admission were 134.59±19.37 mEq/L, 4.22±0.80 mEq/L, and 101.12±16.36 mEq/L and that after rehabilitation were 136.66±19.95 mEq/L, 4.42±0.68 mEq/L, and 103.97±4.83 mEq/L. While that for magnesium, calcium, and phosphorous on admission were 2.11±0.38 mg/dL, 8.70±1.00 mg/dL, and 4.33±1.23 mg/dL and that after rehabilitation were 2.10±0.29 mg/dL, 9.45±0.65 mg/dL, and 4.76±0.84 mg/dL, respectively. The mean values of serum total protein, albumin, globulin, and A: G on admission were 6.53±1.44 g/dL, 3.30±0.88 g/dL, 3.23±0.87 g/dL, and 1.06±0.33, respectively, and that after rehabilitation were 6.84±0.98 g/dL, 3.86±0.57 g/dL, 2.98±0.69 g/dL, and 1.34±0.28, respectively. The mean of urea and creatinine on admission were found to be 26.25±19.30 mg/dL and 0.34±0.24 mg/dL, respectively, and that after rehabilitation were 23.91±11.76 mg/dL and 0.31±0.15 mg/dL respectively. Random blood sugar (RBS) on admission was 85.20±17.88 mg/dL and that after rehabilitation was 94.18±14.82 mg/dL. After rehabilitation, the mean value of calcium, phosphorous, albumin, A: G, and RBS were significantly raised. The minimum duration required for rehabilitation was 12 days with prevalence being maximum among the Tea-tribes (80.00%). Conclusion: The biochemical derangements in a patient with SAM are associated with significant morbidity and get improved after nutritional rehabilitation.

The Association of the Neutrophil-Lymphocyte Ratio With Diabetes Mellitus and Diabetic Foot Ulcer: A Review Article

Introduction: Diabetes mellitus (DM) is inappropriately elevated blood glucose levels. Diabetic foot ulcer (DFU) is the most common complication of diabetes mellitus. Many research studies was found that neutrophil lymphocyte ratio (NLR) have positive correlation with HBA1C, blood glucose, and outcome of diabetic foot ulcer. Methods: The literature searches using online databases such as PubMed, ScienceDirect Elsevier, and Google Schoolar. Result: After doing an online search, we found 39 articles were relevant for this review topic. Analysis: Many research studies have found increased HbA1C (≥6,5) and increased blood glucose (random blood glucose ≥ 200mg/dL and/or fasting blood glucose ≥126 mg/dL) in patients with diabetes mellitus have a positive correlation with increased NLR. And there was increased NLR values in patients with diabetic foot ulcer and NLR values was higher (NLR≥6) in non-healing ulcers than in healing ulcers. Conclusion: NLR can be used as screening tool for determining evaluation of diabetes mellitus and as screening tool for determining outcome of diabetic foot ulcers.

Association between blood glucose levels at admission and severity of COVID-19 patients

Hyperglycemia can be experienced by corona virus disease (COVID-19) patients due to the invasion of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) into pancreatic cells or other mechanisms such as insulin resistance, counter-regulatory, stress induction, and glucocorticoid therapy. Hyperglycemia can stimulate the production of reactive oxygen species (ROS) and pro-inflammatory cytokines leading to an increase in the disease severity. Based on the clinical and laboratory criteria, the severity of COVID-19 patients is classified into asymptomatic, mild, moderate, severe, and critical. This study aimed to investigate the association between blood glucose levels at admission and the severity of COVID-19 patients with and without diabetes mellitus (DM). It was a cross-sectional study using secondary data from COVID-19 patients in Pondok Kopi Jakarta Islamic Hospital from April to June 2021. This study involved 340 patients with comorbid DM (n=78) and without comorbid DM (n=262). The Mann-Whitney and Spearman correlation test were used. A significant difference between random blood glucose levels in comorbid DM patients and patients without comorbid DM (p&lt;0.05). However, there is a weak correlation between random blood glucose levels and severity with comorbid DM (r=0.112) and without comorbid DM (r=0.129). In conclusion, a positive and weak correlation between blood glucose levels at admission and the severity of COVID-19. The severity increases as the blood sugar level increases. Further study needs to be performed considering other comorbid conditions.

Social isolation, loneliness, and the risk of incident type 2 diabetes mellitus by glycemic status

AimThe modifying effect of prediabetic status on the association of social isolation and loneliness with the risk of type 2 diabetes mellitus (T2DM) remains uncertain. We aimed to explore whether prediabetic status modifies the association of social isolation and loneliness with incident T2DM. Methods358,951 participants with random blood glucose < 11.1 mmol/l, hemoglobin A1c < 6.5 % and without diagnosis of diabetes from the UK Biobank were included. Prediabetes was defined by hemoglobin A1c level at 5.7–6.4 %. Social isolation and loneliness were assessed using self-reported questionnaires. The study outcome was incident T2DM. ResultsDuring a median follow-up of 12.5 years, 13,213 (3.7 %) incident T2DM cases were documented. Social isolation and loneliness in subjects with normoglycemia (adjusted HR [95 %CI]: social isolation: 1.14 [1.07;1.23]; loneliness: 1.33 [1.20;1.47]) were more strongly associated with increased risk of T2DM than in those with prediabetes (adjusted HR [95 %CI]: social isolation: 0.97 [0.91;1.03]; loneliness: 1.04 [0.95;1.13]) (Both P for interaction < 0.001). Among individuals with prediabetes, alcoholic consumption (30.9 %), household income (23.3 %), healthy sleep (17.1 %), loneliness (14.9 %), and physical activity (12.6 %) mediated most of the variance in the association between social isolation and incident T2DM, while body mass index (17.9 %) and healthy sleep (17.6 %) mediated most of the variance in the association between loneliness and incident T2DM. ConclusionSocial isolation and loneliness were independently associated with a higher risk of T2DM among individuals without prediabetes. Among those with prediabetes, the association of social isolation and loneliness with incident T2DM were mainly mediated by some socioeconomic and lifestyle factors.

GRHPR gene variations in Iraqi patients infected with calcium oxalate kidney stones

The alterations in glyoxylate reductase and hydroxy-pyruvate reductase concentrations in the sera and the genetic alterations associated with calcium oxalate kidney stones in Iraqi patients were not studied previously so this study aimed to focus on these points. This study included 80 subjects; they were 50 patients with calcium oxalate stones compared to 30 apparently healthy controls. Biochemical investigations for kidney functions (creatinine, urea, and uric acid), were performed on the sera of both groups. Also, complete blood count, random blood sugar, and blood group tests. Furthermore, urine had been collected for General Urine Examination to visualize oxalate crystals in the urine of the patient. Also, the GRHPR enzyme concentration was measured by ELISA for both groups. The DNA was isolated from whole blood and the target DNA was amplified by PCR then the pathogenic mutations at c.295C&gt;T (rs119490108), c.165G&gt;A (rs180177314) and c.904C&gt;T (p. Arg302Cys) rs180177322 were investigated by direct sequencing of the product, and then the results were analyzed. This study found that the concentration of the enzyme in the controls (4.78 ± 1.06 mg/dl) was significantly higher than its concentration in the patients (0.411 ± 0.02mg/dl). The pathogenic mutations were not found in both studied groups, but other positions were found polymorphic; at exon 4 the rs2768659 (A&gt;G), rs1294628807 (G&gt;A) and rs2736664 (C&gt;T), at exon 6: c.579A&gt;G (p. Ala193=) rs309458 and c.494-68A&gt;G rs309459 and at exon 9 c.*146A&gt;G rs1057507. In conclusion, this study found that calcium oxalate stones were associated with decrease GRHPR enzyme concentration in the patients compared to the control group which may be caused by mutations or epigenetics masking of the gene expression.

DiaNet v2 deep learning based method for diabetes diagnosis using retinal images

Diabetes mellitus (DM) is a prevalent chronic metabolic disorder linked to increased morbidity and mortality. With a significant portion of cases remaining undiagnosed, particularly in the Middle East North Africa (MENA) region, more accurate and accessible diagnostic methods are essential. Current diagnostic tests like fasting plasma glucose (FPG), oral glucose tolerance tests (OGTT), random plasma glucose (RPG), and hemoglobin A1c (HbA1c) have limitations, leading to misclassifications and discomfort for patients. The aim of this study is to enhance diabetes diagnosis accuracy by developing an improved predictive model using retinal images from the Qatari population, addressing the limitations of current diagnostic methods. This study explores an alternative approach involving retinal images, building upon the DiaNet model, the first deep learning model for diabetes detection based solely on retinal images. The newly proposed DiaNet v2 model is developed using a large dataset from Qatar Biobank (QBB) and Hamad Medical Corporation (HMC) covering wide range of pathologies in the the retinal images. Utilizing the most extensive collection of retinal images from the 5545 participants (2540 diabetic patients and 3005 control), DiaNet v2 is developed for diabetes diagnosis. DiaNet v2 achieves an impressive accuracy of over 92%, 93% sensitivity, and 91% specificity in distinguishing diabetic patients from the control group. Given the high prevalence of diabetes and the limitations of existing diagnostic methods in clinical setup, this study proposes an innovative solution. By leveraging a comprehensive retinal image dataset and applying advanced deep learning techniques, DiaNet v2 demonstrates a remarkable accuracy in diabetes diagnosis. This approach has the potential to revolutionize diabetes detection, providing a more accessible, non-invasive and accurate method for early intervention and treatment planning, particularly in regions with high diabetes rates like MENA.

The Specialist Treatment of Inpatients: Caring for Diabetes in Surgery (STOIC-D Surgery) Trial: A Randomized Controlled Trial of Early Intervention With an Electronic Specialist-Led Model of Diabetes Care.

To investigate the effect of early intervention with an electronic specialist-led "proactive" model of care on glycemic and clinical outcomes. The Specialist Treatment of Inpatients: Caring for Diabetes in Surgery (STOIC-D Surgery) randomized controlled trial was performed at the Royal Melbourne Hospital. Eligible participants were adults admitted to a surgical ward during the study with either known diabetes or newly detected hyperglycemia (at least one random blood glucose result ≥11.1 mmol/L). Participants were randomized 1:1 to standard diabetes care or the intervention consisting of an early consult by a specialist inpatient diabetes team using electronic tools for patient identification, communication of recommendations, and therapy intensification. The primary outcome was median patient-day mean glucose (PDMG). The key secondary outcome was incidence of health care-associated infection (HAI). Between 12 February 2021 and 17 December 2021, 1,371 admissions met inclusion criteria, with 680 assigned to early intervention and 691 to standard diabetes care. Baseline characteristics were similar between groups. The early intervention group achieved a lower median PDMG of 8.2 mmol/L (interquartile range [IQR] 6.9-10.0 mmol/L) compared with 8.6 mmol/L (IQR 7.2-10.3 mmol/L) in the control group for an estimated difference of -0.3 mmol/L (95% CI -0.4 to -0.2 mmol/L, P < 0.0001). The incidence of HAI was lower in the intervention group (77 [11%] vs. 110 [16%]), for an absolute risk difference of -4.6% (95% CI -8.2 to -1.0, P = 0.016). In surgical inpatients, early diabetes management intervention with an electronic specialist-led diabetes model of care reduces glucose and HAI.

Evaluation of Diabetes Risk Score Tool for Detecting Undiagnosed Type 2 Diabetes Mellitus in Attendees of Referral Clinics at Primary Health Care Centers, Sudan

Introduction: The prevalence of diabetes in Sudan is increasing; however, suitable risk assessment and screening tools to identify at-risk individuals are lacking. Objective: To evaluate the Diabetes Risk Score (DRS) tool for detecting Type 2 Diabetes Mellitus among undiagnosed individuals. Methods: In this cross-sectional study, 214 individuals were recruited from Primary Health Care Referral Centers in Khartoum State. Attendees were interviewed to fill out the Diabetes Risk Score (DRS) questionnaire. Random blood glucose and glycosylated hemoglobin (HbA1c) were tested. Descriptive statistics and sensitivity analysis were performed to test the applicability of the DRS in Sudan. Results: The prevalence of undiagnosed diabetes was found to be 14%. Regarding blood tests, Random blood glucose (RBG) was normal in 93% of the participants (cutoff ≤140 mg/dl). The HbA1c result was normal in 86% of the participants (cutoff &lt; 6.4%). The DRS was high in 40.2% (cutoff ≥33), while 59.8% were considered moderate to low risk. The DRS had a sensitivity of 83.33% and a specificity of 66.85%. The positive and negative predictive values were 29.07% and 96.09%, respectively. The Area Under the Curve (AUC) for detecting undiagnosed diabetes was 0.751 (95% CI: 0.662-0.840). Conclusion: The DRS tool was found to be applicable with reference to the HbA1c test for predicting undiagnosed diabetic patients.

Screening for Renal Function Impairment in Patients with Chronic Type II Diabetes Mellitus in a Tertiary Care Hospital of Pakistan

Objective: The objective of the present study was to detect early biochemical changes, in type II diabetics, indicating renal function impairment leading to Chronic Kidney Disease (CKD). Methodology: This study was conducted in the Department of Biochemistry at Shaikh Zayed FPGMI, Lahore during September 2016 to April 2017. It was a cross-sectional analytical study and was done through nonprobability convenient sampling The study comprised of 50 diagnosed patients of type II diabetes mellitus (for the last 10 years) and 50 healthy subjects. Both male and female participants in equal number between the age of 35-75 were included in the study. Blood glucose level (random), blood urea, serum creatinine, urinary albumin and urinary creatinine were estimated and albumin to creatinine ratio (ACR) in mg/g was calculated. eGFR was also calculated using S/creatinine. The lab values of the study parameters were recorded on the designed proforma after getting proper written consent from the participants. SPSS 20.0 was used for data entry and analysis. Results: Mean values of random plasma glucose level, blood urea, serum creatinine, urine albumin, urine creatinine, ACR and eGFR of patients when compared with those of healthy group showed significant p-values (≤0.05). Conclusion: There was a significant difference in renal laboratory parameters between diabetic patients and healthy controls indicating the presence of CKD. In a developing country like Pakistan type II diabetes mellitus is an incidental finding due to lack of regular health monitoring and it is very difficult to determine the exact duration of the disease. Patients of type II DM develop CKD (Chronic Kidney Disease) due to persistent high plasma glucose levels. Therefore good control of DM and regular monitoring of renal parameters will be helpful in preventing the development of CKD. Key Words: Diabetes Mellitus, Nephropathy, Chronic Kidney Disease, Urinary Albumin to Creatinine ratio.

Body indices based receiver operating characteristics curve models are important risk assessing tools for metabolic diseases among Asian women

PurposeThe right way to measure obesity is still a matter of debate. This study will look at the prevalence of obesity, anthropometrics, and body composition as screening tools for obesity and adiposity among adult women in urban Prayagraj (Allahabad), Uttar Pradesh, India. It will also try to figure out exactly what level of obesity is linked to a metabolic risk. MethodsA Cross-sectional study comprising 570 urban women of Prayagraj (Allahabad), Uttar Pradesh, India aged 20–49 years were examined for anthropometry, body composition analysis, blood pressure, random blood sugar, and haemoglobin. ResultsExcept for total body water (TBW), all measures of obesity and health markers increased with age (p < 0.000, 95% CI-confidence interval). Appropriate cutoffs calculated with model for adult women for body fat (%), muscle mass (kg), total body water (%), and visceral fat (kg) were 33.5, 34.5, 46.5, and 4.5 respectively. Using stepwise logistic regression, two models eliminating waist circumference (WC) and wait to hip ratio (WHR), respectively, were created. Age, WHR, and visceral fat (VF) for systolic blood pressure; age and TBW for diastolic blood pressure; age and VF for random blood sugar; WHR, body fat% (BF %), Muscle mass (MM), and age for haemoglobin, were all significantly associated with the presence of metabolic risk variables in Model 1. In model 2, only age was significant for predicting systolic blood pressure; age, TBW, and WC for diastolic blood pressure; age and VF for random blood sugar; BF%, WC, and age for haemoglobin were shown to be significantly associated with metabolic risk variables. ConclusionsTwo basic models for predicting metabolic risk in Asian Indians were studied. Both models can be used to assess metabolic risk in them.

Risk factors for ketoacidosis in children/adolescents with type 1 diabetes mellitus and establishment of a predictive model

To investigate the risk factors for diabetic ketoacidosis (DKA) in children/adolescents with type 1 diabetes mellitus (T1DM) and to establish a model for predicting the risk of DKA. A retrospective analysis was performed on 217 children/adolescents with T1DM who were admitted to General Hospital of Ningxia Medical University from January 2018 to December 2021. Among the 217 children/adolescents,169 cases with DKA were included as the DKA group and 48 cases without DKA were included as the non-DKA group. The risk factors for DKA in the children/adolescents with T1DM were analyzed, and a nomogram model was established for predicting the risk of DKA in children/adolescents with T1DM. For the 217 children/adolescents with T1DM, the incidence rate of DKA was 77.9% (169/217). The multivariate logistic regression analysis showed that high levels of random blood glucose, hemoglobin A1c (HbA1c), blood ketone body, and triglyceride on admission were closely associated with the development of DKA in the children/adolescents with T1DM (OR=1.156, 3.2031015, 20.131, and 9.519 respectively; P<0.05). The nomogram prediction model had a C-statistic of 0.95, with a mean absolute error of 0.004 between the risk of DKA predicted by the nomogram model and the actual risk of DKA, indicating that the model had a good overall prediction ability. High levels of random blood glucose, HbA1c, blood ketone body, and triglyceride on admission are closely associated with the development of DKA in children/adolescents with T1DM, and targeted intervention measures should be developed to reduce the risk of DKA.