Biochemia medica最新文献

Thyroid function tests (TFTs) - thyroid stimulating hormone (TSH), total triiodothyronine (T3), total thyroxine (T4), free triiodothyronine (FT3), free thyroxine (FT4), thyroid peroxidase antibodies (anti-TPO), thyroglobulin antibodies (anti-Tg), TSH receptors antibodies (anti-TSHR), and thyroglobulin (Tg) - are used to diagnose thyroid disorders and are crucial biomarkers for monitoring and managing thyroid cancer. The 2022 national survey results revealed that thyroid function testing is not standardized among Croatian medical-biochemistry laboratories. Laboratories follow individual protocols at each testing stage, from patient preparation to result reporting. To address this, the Working group for laboratory endocrinology of the Croatian society of medical biochemistry and laboratory medicine has developed recommendations based on the latest national and international guidelines, research and the authors' expert opinion. The document aims to standardize all phases of thyroid function testing, with 7 preanalytical, 12 analytical, and 8 postanalytical recommendations, each supported by expert explanations. While primarily directed at Croatian laboratory professionals, this document is also relevant to other healthcare professionals managing thyroid-related health issues.

Introduction: Hepcidin (Hep), a key regulatory hormone of iron (Fe) homeostasis, governs its absorption and storage, and is influenced by inflammation and Fe status. This study investigated serum Hep concentrations and their associations with Fe markers and inflammation in patients with sporadic colorectal cancer (CRC).

Materials and methods: We compared serum concentrations of Hep, Fe, unsaturated and total iron binding capacity, transferrin, transferrin saturation, ferritin, C-reactive protein (CRP), interleukin-6 (IL-6) and tumor markers in 82 CRC patients and 58 controls. Statistically significant differences were tested using the Mann-Whitney U test and Student's t test. Additionally, Hep were analyzed according to tumor stage. Colorectal cancer was confirmed histopathologically after colonoscopy with biopsy (TNM staging).

Results: Colorectal cancer patients exhibited significantly lower Hep concentrations than controls (8.1 vs. 19.7 ng/mL, P = 0.020). Ferritin was also lower in CRC (109 vs. 250 µg/L, P = 0.002). Hepcidin showed the strongest positive correlation with ferritin in CRC. Inflammatory markers (CRP and IL-6) correlated moderately to weakly with hepcidin in both groups (controls: rho = 0.52 (P < 0.001); CRC: rho = 0.26 (P = 0.022) for CRP and CRC: rho = 0.30 (P = 0.033) for IL-6). Notably, Hep concentrations were lower in patients with advanced tumor stage (T0 vs. T3, P = 0.043).

Conclusion: These findings suggest that CRC is associated with lower hepcidin and ferritin concentrations, potentially reflecting complex and cancer-specific dysregulation in Fe metabolism beyond inflammation alone.

Atherosclerosis is an active interaction between lipoproteins and inflammatory cells. Monocytes and macrophages are the most important immune cells involved in the process of atherosclerosis. They interact with atherogenic lipoproteins, in particular low density lipoprotein (LDL) cholesterol and lipoprotein(a) (Lp(a)). The increased concentration of the LDL cholesterol and Lp(a) accelerates the polarization of monocytes and macrophages toward proinflammatory phenotype and the formation of the foam cells. These cells then release large quantities of inflammatory cytokines that stimulate the oxidation of atherogenic lipoproteins that are even more atherogenic and contribute to the formation of foam cells and the secretion of the pro-inflammatory cytokines, thus creating a vicious circle. Surface marker C-C chemokine receptor type 2, expressed on monocytes/macrophages, enables their adhesion and migration into the subendothelial layer. The rupture of the atherosclerotic plaque on one hand, and the ability of the oxidized LDL cholesterol and Lp(a) to trigger arterial thrombosis by different mechanisms on the other hand, result in acute cardiovascular event. Here, we summarize the role of the monocytes and macrophages in atherosclerosis and explore the influence of LDL cholesterol and Lp(a) on monocytes and macrophages during the entire process of atherosclerosis, from its initiation to progression.

The study aims to present a case study of a patient with supratherapeutic serum gentamicin concentration. An 83-year-old male was admitted to the Department of Internal Medicine for persistent loss of appetite, decompensated heart failure, and pneumonia. He was treated with 240 mg gentamicin daily alongside ampicillin/sulbactam penicillin antibiotic. The trough gentamicin concentrations and estimated glomerular filtration rate from creatinine (eGFRcrea) and cystatin C (eGFRcys) were performed. The patient had the supratherapeutic trough gentamicin concentration of 2.5 mg/L. eGFRcrea was 62 mL/min/1.73m² and eGFRcys was 25 mL/min/1.73m². The difference between eGFRcrea and eGFRcys was 148%. Falsely high eGFRcrea in elderly patient led to the supratherapeutic gentamicin concentration even after the standard 240 mg gentamicin dose.

The alcohol dehydrogenase (ADH) method is commonly used to measure serum alcohol concentration (SAC) and plasma alcohol concentration (PAC) for the rapid detection of ethanol intoxication in emergency medical departments. Alcohol dehydrogenase methods are sometimes used in forensic laboratories as a preliminary screening test prior to confirmation by gas chromatographic (GC) methods. This review identifies critical factors affecting results of ADH methods of analysis including clinical reliability and forensic defensibility. Key considerations include intra-analytical factors (method chemistry, calibration, analytical performance, interferences, calibrator stability, and sample matrix effects) and post-analytical factors (measurement units, reference ranges, performance specifications, uncertainty budget, medical decision levels, legal intoxication thresholds, ADH-GC agreement, and SAC/PAC to blood alcohol concentration (BAC) conversion). The yeast ADH method demonstrates high selectivity for ethanol with no assay-specific bias, and measurement error and uncertainty meet regulatory standards. However, ADH methods are prone to interferences, particularly from lactate dehydrogenase/lactic acid (LD/LA), leading to potential false positive results. Free hemoglobin (hemolysis) is another problem with ADH methods introducing a negative bias. When results provided by hospital laboratories are interpreted in a legal context, care is needed because ethanol concentrations in plasma/serum are about 15% higher than in whole blood (range 10-20%). Although less important in clinical practice, these differences are important to consider in a forensic context. The ADH method is not inherently a forensic assay, but these limitations can be mitigated by refining laboratory procedures and standardizing the assay methodology and quality control, thus strengthening forensic reliability and boosting confidence in the analytical results.

Introduction: The ISO 15189:2022 standard considers both the robustness of analytical methods and the risk of erroneous results in the quality control plan (QCP) design. Westgard et al.'s nomogram recommends quality control (QC) rules based on sample run size to ensure that the maximum expected number of unreliable patient results remains below one. This study aimed to implement a standardized, risk-based QC strategy across multiple analyzers without integrated on board QC, ensuring practical quality assurance.

Material and methods: Thirty-two biochemistry parameters on Alinity c systems and three on Cobas Pro systems were included. The analytical performance of each parameter on each analyzer was assessed using sigma metric. Workload requirements were considered to determine the desired run size. Based on the "sigma metric statistical QC run size nomogram" proposed by Westgard et al., a multistage bracketed QCP was designed for each parameter. When multiple designs were available, the simplest QC rule was prioritized.

Results: Seven QCPs were initially established for 35 parameters. In the absence of automation, practical adaptations based on sigma metrics were implemented. Additionally, to streamline management, the QCP covering the greatest number of parameters per analyzer was prioritized, which ultimately resulted in the adoption of only two general QCP. Only 4 individualized QCP were required to cover 10 parameters with lower sigma values.

Conclusions: This approach demonstrates the feasibility of implementing a refined QC strategy for parameters with sigma ≥ 4 in a highly automated laboratory, ensuring consistent quality assurance and efficient resource allocation for higher-risk tests.

Introduction: High-density lipoprotein (HDL) particles are key participants in reverse cholesterol transport. Cholesterol efflux capacity (CEC) and apolipoprotein A1 (Apo A1) are HDL-related biomarkers often used to evaluate HDL particle functionality and quantity. This study aimed to assess the correlation between CEC and Apo A1 concentrations and explore whether methodological aspects influence the correlation results.

Materials and methods: This meta-analysis was prospectively registered in the PROSPERO database (registration number CRD42024552535). Three databases, PubMed, Web of Science, and Cochrane Library, were screened for the studies published between January 2000 and May 2024. The correlation results were analyzed using a random-effects model, and sensitivity and subgroup analyses were performed.

Results: A total of 19 studies with 4967 participants were included. This meta-analysis's results indicated a statistically significant positive moderate strength correlation between CEC and Apo A1 concentrations. A high level of study heterogeneity was observed among the included studies. Further exploration into this heterogeneity revealed that different cell culture lines and cholesterol acceptors used to evaluate CEC impact the overall result of the pooled correlation estimate. The methods used to evaluate Apo A1 did not significantly affect the correlation estimate between CEC and Apo A1 concentrations.

Conclusions: The correlation between CEC and Apo A1 lacks strength and consistency for Apo A1 being used as a surrogate marker for HDL function in a clinical setting. Currently, there is a high need for the standardization of CEC measurement methodologies that impact the overall results and comparability of the studies that have already been conducted.

Spurious elevations in intact parathyroid hormone (iPTH) can lead to unnecessary interventions. We describe a dialysis patient who developed unexpectedly high iPTH concentrations months after total parathyroidectomy with forearm autotransplantation (TPTX-AT). Blood samples had been collected from the grafted forearm, resulting in falsely elevated iPTH values. Once the sample was collected from the non-grafted arm, iPTH concentrations normalized. This case highlights the importance of sampling site awareness in laboratory diagnostics and demonstrates how implementing a simple laboratory information system (LIS)-based protocol can prevent misinterpretation.

Introduction: Traditional internal quality control (IQC) has limitations in detecting systematic errors in clinical laboratories. Patient-Based Real-Time Quality Control (PBRTQC) has emerged as a complementary method, offering new approaches for quality monitoring. Among these, monitoring daily positivity rates provides meaningful insights into laboratory performance.

Materials and methods: This study highlights a case in which PBRTQC was implemented to detect and address a reagent batch issue in thyroid peroxidase antibody (TPO-Ab) testing. Over one year (July 2023 to July 2024), daily positivity rates and their fluctuations were retrospectively analyzed and daily positivity rate alarm limits were established for monitoring.

Results: A notable increase in the TPO-Ab positivity rate was identified starting in June 2024. For outpatients and inpatients, the positivity rates in June and July 2024 were 46.1% ± 7.8% (N = 9039) and 61.4% ± 12.0% (N = 8735), respectively. For the physical examination population, the positivity rates during the same months were 30.0% ± 11.7% (N = 4754) and 52.5% ± 18.1% (N = 5726), respectively. These rates were significantly higher than the pre-June 2024 average monthly positivity rates of 30.0% ± 2.9% (N = 9070 per month) for patients and 11.0% ± 2.4% (N = 4663 per month) for the physical examination population.

Conclusions: PBRTQC, particularly monitoring daily positivity rates, is a valuable tool for early detection of systematic errors. Establishing PBRTQC systems can supplement traditional IQC to improve laboratory test quality.

Introduction: A paradigm shift is occurring in lipid testing, as fasting is no longer required. We aimed to determine whether low-density lipoprotein cholesterol (LDL-C) concentrations calculated using three different equations, along with the components used in these calculations, vary with different fasting durations in routine clinical practice.

Materials and methods: The concentrations of LDL-C were calculated using the Friedewald, Martin-Hopkins, and Sampson/NIH equations, along with the lipid components involved in these equations, depending on time since the last meal in a cohort of 77,300 outpatients at a community hospital. The study population was divided into groups according to fasting durations by 2-hour intervals. A general linear model was applied to identify differences between fasting and nonfasting groups.

Results: Regardless of the calculation method, LDL-C concentrations varied with fasting duration for up to 8-10 hours. The greatest absolute mean differences in LDL-C concentrations between fasting and nonfasting states were - 0.32, - 0.30, and - 0.26 mmol/L when using the Friedewald, Sampson/NIH, and Martin-Hopkins equations, respectively. Among the equation components, triglyceride concentrations were the most sensitive to fasting duration, remaining elevated for 4-6 hours after the last meal, while total cholesterol and non-high-density lipoprotein cholesterol (HDL-C) concentrations decreased for up to 8-10 hours postprandially. However, HDL-C concentrations remained relatively stable.

Conclusions: The variation in postprandial LDL-C concentrations was observed not to differ between the three calculation methods and reached negligible concentrations after at least 8 hours of fasting. If LDL-C concentrations measured in a nonfasting state are near clinical decision thresholds, subsequent lipid measurement should be performed in a fasting state.