Electronic Journal of the International Federation of Clinical Chemistry and Laboratory Medicine最新文献

Background: Type 2 Diabetes Mellitus (T2DM) prevalence is rising globally, especially in low- and middle-income countries, and many cases remain undiagnosed until complications occur. Early identification in community settings is crucial. The Triglyceride-Glucose (TyG) index has been proposed as a simple and low-cost surrogate marker of insulin resistance.

Objective: This systematic review aims to evaluate the performance and applicability of the TyG index as a community-based screening tool for identifying individuals at risk of T2DM in young and adult populations.

Methods: We conducted a systematic literature search in PubMed, Scopus, Web of Science, and Medline (2015-2025). Observational studies in community or primary care populations were included if they reported TyG cutoff and diagnostic accuracy metrics. Data extraction covered study design, population, TyG cutoff values, and performance metrics. Study quality was assessed using the QUADAS-2 tool.

Results: Seventeen studies conducted across Asia, Latin America, and Europe met the inclusion criteria. TyG cutoff values varied between 4.49-9.45. In nearly all studies, higher TyG values were significantly associated with insulin resistance, impaired fasting glucose, or incident T2DM. The TyG index frequently demonstrated comparable or superior diagnostic performance relative to HOMA-IR in prediction settings.

Conclusion: The TyG index is a feasible, reliable, and low-cost biomarker for community-level screening of T2DM risk. For implementation in settings like Indonesia, local validation of cutoff values and cost-effectiveness studies are needed. Implementation of the TyG index in primary-care screening could improve cost-effective detection of metabolic risk in resource-limited settings.

Background: Liver function test (LFT; including alanine aminotransferase, aspartate aminotransferase, gamma-glutamyl transferase, and alkaline phosphatase) results are modulated by multiple factors, but their temporal changes have been insufficiently explored, especially in relation to aging and adiposity. First, we assessed the trends of LFTs levels over time across different age groups and sexes. Second, we tested the cross-sectional and longitudinal associations between levels of LFTs and anthropometric measurements capturing various degree of adiposity.

Methods: 5171 participants (2393 males), aged 35-75 years at baseline (2003-2006), from a prospective population-based cohort (CoLaus|PsyCoLaus study), were included and followed up until 2019-2023. Anthropometric measurements included body mass index, waist-to-height ratio, waist-to-hip ratio, relative fat mass, body shape index, body roundness index, waist-to-weight ratio and body surface area. Boxplots presented changes of LFTs across age groups. Multiple linear regressions and multilevel mixed models were used to analyze the cross-sectional and longitudinal associations between levels of LFTs and anthropometric measurements, adjusting for a large range of variables.

Results: LFTs values showed distinct temporal changes between age groups and sexes. Anthropometric measurements capturing various degree of adiposity demonstrated a strong and significant association (p<0.001) with all four LFTs in both cross-sectional and longitudinal analyses. These associations remained robust even after adjusting for multiple covariates.

Conclusion: In a population-based study, LFTs changed over time according to age and sex. These changes were independently associated with markers of adiposity, showing the importance of interpreting LFTs based on the clinical context, especially in presence of overweight or obesity.

Background: Internal Quality Control (IQC) ensures accuracy and reliability in laboratory testing but traditionally relies on reactive, threshold-based methods. These approaches often fail to detect subtle process deviations in time, potentially compromising quality.

Objective: To develop and validate a machine learning-based predictive model for early detection of IQC deviations using Altair RapidMiner, enhancing proactive quality management in clinical laboratories.

Methods: A retrospective analytical study was conducted using 4,572 IQC records from Meenakshi Labs, covering 8 analytes across multiple instruments. Data preprocessing included cleaning, feature engineering, and encoding. Three classification algorithms - Decision Tree, Gradient Boosted Trees, and Random Forest - were developed using Altair RapidMiner's no-code environment. Models were evaluated via 10-fold cross-validation using accuracy, precision, recall, F1-score, and ROC-AUC metrics.

Results: The Random Forest model outperformed others with 92.0% accuracy, 91.0% precision, 89.4% recall, and an AUC of 0.932. Key predictive features included analyte type, control level, reagent lot, and operator ID. The model correctly predicted 68% of future out-of-control events within a 24-hour window, demonstrating potential for preventive action. Feature importance analysis enhanced model interpretability.

Conclusion: Machine learning, particularly Random Forest, effectively augments IQC by enabling predictive monitoring. Altair RapidMiner offers a user-friendly platform, making advanced analytics accessible even without programming skills. This approach aligns with Quality 4.0 initiatives, promoting data-driven, real-time decision-making in laboratory quality assurance.

Introduction: In low-resource settings, reusing blood collection tubes cleaned with detergents such as 'Teepol' is common due to economic constraints. However, residual detergent may interfere with biochemical assays. This study evaluated the effect of residual 'Teepol' on serum electrolyte, total protein, and cholesterol, with emphasis on direct ion-selective electrode (ISE) methods.

Method: A controlled interference experiment was conducted using pooled human serum spiked with increasing concentrations of 'Teepol' (0%, 0.2%, 0.5%, and 1.0% v/v of original detergent). Serum sodium and potassium were measured using direct ISE (Ortho_Vitros^® 4600), while total protein and cholesterol were measured via colorimetric methods (BS 800M). All analytes were tested in a single run to avoid inter-run variability. Statistical significance was assessed via Pearson correlation and comparison against 95% confidence intervals derived from quality control data.

Results: Serum sodium and potassium showed a concentration-dependent decline with increasing 'Teepol'. At 1.0%, sodium decreased by ~12% and potassium by ~43% compared to control, with values falling outside the 95% confidence intervals, confirming significant interference. Total protein and cholesterol measurements remained within expected analytical variation. Strong negative correlations were observed for sodium (R=-0.966) and potassium (R=-0.989) with 'Teepol' concentration (p< 0.05).

Conclusion: Residual 'Teepol' ≥0.5% v/v significantly interferes with serum sodium and potassium measurements using direct ISE. These findings highlight the importance of strict tube-washing protocols or the use of disposable tubes for critical assays. Inconsistent cleaning practices in low-resource laboratories may allow such interference, posing a risk to result accuracy and clinical decision-making.

Aim: Diabetic nephropathy (DN) is one of the major contributors to end stage kidney disease globally. Reliable biomarkers for early diagnosis of DN still exists as a major challenge. Serum soluble CD36 (sCD36) involved in lipid metabolism and oxidative stress has been identified as a likely biomarker of DN. Herein, we assess the relationship between sCD36 and clinical worsening of DN, to determine its potential diagnostic value.

Material and methods: A case- control study involving 160 participants, categorized into four groups was conducted i.e., healthy individuals, diabetics with normo-albuminuria, microalbuminuria, and macroalbuminuria (n = 40).Demographic variables and biochemical parameters were compared. The concentrations of sCD36 in serum samples were determined, as well as correlation analysis between sCD36, fasting plasma glucose (FPG), HbA1c, urine albumin creatinine ratio (UACR), and estimated glomerular filtration rate (eGFR) were performed. The diagnostic performance of sCD36 was determined using receiver operating characteristic (ROC) curve.

Results: Serum sCD36 levels rose progressively from 9.97 ng/mL in the control group to 12.13 ng/mL in the macroalbuminuria group, p < 0.001. Patients with higher sCD36 levels also had higher fasting plasma glucose, HbA1C, and UACR with a lower eGFR. The ROC analysis of sCD36 gave an AUC of 0.908, showing excellent diagnostic capability of the model. The optimal cut-off value of 10.6 ng/mL yielded 87.5 % sensitivity and 80.83% specificity for detecting advanced DN.

Conclusion: Increased serum sCD36 levels correlates directly to DN progression, hence being a promising candidate biomarker for diagnosis and prognosis. The possible direct application of sCD36 into clinical practice might help improve DN management and treatment.

Introduction: Gestational diabetes mellitus is a condition in which glucose intolerance develops during pregnancy. Sortilin is a type I transmembrane protein, that belongs to the VPS10 family of post-Golgi trafficking receptors that is involved in signaling, intracellular sorting, and transport of proteins. Sortilin is required for the storage of glucose and co-expressed with the glucose transporter GLUT4 in differentiated adipocytes. This study aimed to evaluate serum sortilin level in individuals with GDM and to elucidate its relation with insulin resistance.

Methodology: This was a case-control study. It involved 80 pregnant women, 40 with GDM (case group) and 40 healthy pregnant women (control group). Fasting blood glucose, HbA1c, insulin, HOMA-IR, and sortilin were analyzed in all the participants. Statistical analysis was performed with SPSS, version 26.0.

Results: Age, gestational week, and blood pressure showed no significant difference in both groups. Body mass Index (BMI), fasting blood glucose, glycated hemoglobin (HbA1c), fasting insulin, homeostasis model assessment of insulin resistance (HOMA-IR), and sortilin were significantly higher in the GDM group. Maternal serum sortilin showed a statistically significant positive correlation with BMI, fasting plasma glucose, serum insulin, HOMA-IR, and HbA1c. (r:0.37, p:<0.05; r:0.64, p:<0.001; r:0.38, p:<0.001; r:0.42, p<0.001, r:0.68, p<0.001 respectively). The Receiver Operating Characteristics (ROC) curve indicated sortilin as a potential biomarker for the prediction of cases with an area under a curve of 0.98 (p-value<0.001) and for the cut-off point value of >2.6ng/ml, the sensitivity was 97.5%, specificity was 97.5%, false negative rate was 2.5% and false positive rate was 2.5%. The Youden's index was 0.95, thus indicating the diagnostic accuracy of sortilin.

Conclusion: This study aimed to evaluate serum sortilin level in individuals with GDM and to elucidate its relation with insulin resistance. In this study, we observed that serum sortilin concentration was significantly higher in individuals with gestational GDM than in healthy pregnant women. Also, serum sortilin had a statistically significant positive correlation with fasting blood glucose, fasting insulin, HOMA-IR, and glycated hemoglobin. The area under the curve of ROC and the Youden's index shows the impeccable diagnostic accuracy of sortilin.

Objective: This article emphasizes the need to integrate cardiovascular, renal, and metabolic assessments into routine clinical practice, with the aim of improving the prevention, diagnosis, and management of chronic non-communicable diseases (NCDs).

Methods: We conducted a comprehensive review of the current medical literature on cardio-renal-metabolic syndrome, alongside a critical analysis of traditional clinical profiles. Based on these findings, we propose a novel integrated laboratory profile that captures the interconnections between these systems, aiming to enhance diagnostic accuracy and patient management.

Results: Significant gaps in current fragmented assessments were identified, leading to the development of a profile that integrates key biomarkers from all three systems for a more comprehensive and accurate evaluation. This profile will be implemented according to the complexity of the care levels and according to the patient's stage of cardio-renalmetabolic syndrome.

Conclusion: Implementation of the new integrated cardiorenal-metabolic profile could likely optimize clinical care, reduce healthcare costs, and improve patient outcomes. However, its success will depend on the technical and logistical capabilities of clinical laboratory networks, as well as the stage of the patient's disease and the level of care at which it is implemented.

Background: Internal Quality Control (IQC) is a cornerstone of clinical laboratory operations, ensuring reliability in diagnostic testing. Commercial IQC materials, though effective, pose challenges of high cost, limited availability, and susceptibility to matrix effects. Pooled sera (PS), derived from discarded patient samples, offer a cost-effective alternative. However, the stability and performance of pooled sera as IQC material in immunoassays need robust evaluation, particularly when combined with advanced statistical tools like Exponentially Weighted Moving Average (EWMA).

Objective: To evaluate the utility of pooled sera as IQC material for immunoassay parameters using the EWMA statistical approach and compare its performance against commercially available IQC materials.

Method: A study was conducted in the clinical biochemistry laboratory at AIIMS Bhubaneswar. Pooled sera were prepared from discarded patient samples, aliquoted, and stored at -5°C. Five immunoassay parameters (Free T3, Free T4, TSH, Cortisol, and Ferritin) were monitored over 60 days using both pooled sera and commercial IQC materials. EWMA charts with a 7-day smoothing window were employed to assess error detection. Performance metrics, including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Concordance Correlation Coefficient (CCC), were analyzed. Statistical significance was set at p<0.05.

Results: The EWMA analysis of pooled sera (ePSIQC) closely mirrored commercial IQC performance. Concordance was significant for all parameters except Free T4. The ePSIQC method demonstrated superior early error detection compared to traditional Westgard Multirules. Pooled sera remained stable throughout the study duration, with deviations observed only in a few instances.

Conclusion: Pooled sera, combined with EWMA, provides a cost-effective, stable, and reliable alternative to commercial IQC materials for immunoassays. The enhanced error detection capability of EWMA strengthens laboratory quality control, offering a viable solution for resource-limited settings.