Introduction: Laboratory professionals (LP) are exposed to various hazards in the workplace, whose direct and/or cumulative effects can lead to the development of health disorders of varying severity. Our study aims to assess the level of occupational risk in biomedical laboratories. Subjects and methods: A cross-sectional study was conducted between November 2020 and February 2021. The study included LP of all profiles in Europe, and the territorial affiliation of the respondents formed the basis for the formation of the groups studied. A validated questionnaire used for data collection was distributed online through the networks of professional associations. Based on the type of agent, frequency of exposure, characteristics of the workplace and work process, and individual factors, an occupational risk assessment matrix was created in categories ranging from low to very high. Descriptive and inferential statistical methods with a statistical significance threshold of 5% (p ≤ 0.05) were used for the statistical analysis. Results: Significant differences in risk categorization were found between the groups studied (p < 0.001). Overall, 81.2% of LP in the European Union fall into the medium risk category, while more than half (52.1%) of LP and 1.7% of LP in Bosnia and Herzegovina fall into the high and very high risk categories. Higher education, service longer than 21 years, public sector, and biochemistry laboratory were identified as predictors of high risk, while predictors of very high risk were higher education, service of 21 to 30 years, public sector, and histopathology and molecular laboratories. Conclusions: Lack of equipment, organizational issues and working conditions were identified as weak points that directly correlate with risk levels in biomedical laboratory workplaces. Additional efforts to control exposure in biomedical laboratories are needed to maintain the health of LP.

Uvod: Povećanje koncentracije olova u okolišu posljedica je intenzivne industrijalizacije i urbanizacije. Toksična djelovanja olova zasnivaju se na inhibiciji aktivnosti velikog broja enzima, indukciji oksidativnog stresa i disregulaciji biosinteze proteina. Najčešći put unosa u općoj populaciji je ingestija kontaminirane hrane i vode, dok je inhalatorni put najčešće povezan sa profesionalnom izloženošću u različitim zanimanjima. Cilj istraživanja je evaluirati laboratorijske metode i biomarkere u procjeni izloženosti olovu. Materijal i metode: Za potrebe neeksperimentalnog kvalitativnog istraživanja korišteni su dostupni naučni članci publicirani na engleskom jeziku u relevantnim bazama podataka (MEDLINE i ScienceDirect). Pretraga baza provedena je upotrebom ključnih riječi: „laboratory diagnostics“, „occupational exposure“, „lead“. Rezultati: Najširu primjenu u laboratorijskoj dijagnostici kod procjene profesionalne izloženosti olovu imaju atomska apsorpciona spektrometrija (AAS) kao zlatni standard i induktivno spregnuta plazma sa masenom spektrometrijom, koja se zbog niske granice detekcije opisuje kao senzitivnija metoda u poređenju sa AAS. Koncentracija olova može se odrediti u brojnim biološkim uzorcima, ali se u laboratorijskoj praksi najčešće upotrebljavaju krv i urin. Kao najznačajniji biomarker u praćenju izloženosti koristi se enzim dehidrataza δ-aminolevulinske kiseline (ALAD) u krvi, kojeg karakterizira progresivna inaktivacija olovom i negativna korelacija sa koncentracijom olova. Također, koncentracija delta-aminolevulinske kiseline u urinu (δ-ALA-U) odražava stanje narušene funkcije enzima u biosintezi hema, te se smatra da dodatno određivanje cink protoporfirina u krvi i koproporfirina u urinu značajno doprinose u procjeni poremećaja izazvanih profesionalnom izloženošću olovu. Zaključak: Adekvatno praćenje izloženosti olovu ovisi o dostupnosti i karakteristikama primijenjenih laboratorijskih metoda, te specifičnosti i osjetljivosti biomarkera. Zbog toga, precizno određivanje koncentracije ALAD i δ-ALA-U, uz dodatne biomarkere, postaje imperativ za poboljšanu evaluaciju profesionalne izloženosti i omogućava pravovremeno poduzimanje preventivnih mjera.

Introduction: Insulin resistance (IR) is a complex pathophysiological condition multifactorial etiology characterized by diminished responsiveness of insulin target tissues. Today, various diagnostic approaches involving different laboratory parameters are available, but simple and non-invasive indices based on mathematical models are increasingly used in practice. This study aims to assess the effectiveness of various clinical surrogate indices in predicting IR across a population with varying body weights. Methods: The matched case-control study was conducted between January 2021 and December 2022. Secondary data extracted from the medical records of 129 subjects was analyzed, including demographic characteristics (age and gender), anthropometric measures (height and weight), and biochemical laboratory test results. y further divided into two subgroups based on body mass index (BMI): overweight (BMI between 25 and 29.9 kg/m2) and obese (BMI of 30 kg/m2 or higher). Using laboratory data values for six widely used clinical surrogate markers were calculated: Homeostatic model assessment for IR (HOMA-IR), quantitative insulin sensitivity check index (QUICKI), Mcauley index (MCAi), metabolic score for IR (METS-IR), Triglyceride to Glucose Index (TyG), and TyG to BMI (TyG-BMI). Results: Significant differences in HOMA-IR levels were observed between the groups (p < 0.001). A similar pattern was found for the TyG-BMI, with notable differences (p < 0.001). The obese participants had the highest mean levels for METS-IR and the TyG index while the control group had the highest mean values for the QUICKI and MCAi indices (p < 0.001). According to the analysis, three indices showed statistical significance in predicting IR independent of BMI (p < 0.05). Sensitivity and specificity were higher in the obese group (0.704 and 0.891) than in the overweight group (0.631 and 0.721). Conclusion: Given that IR is a multifactorial disease, using derived indices based on a combination of biochemical parameters and anthropometric indicators can significantly aid in predicting and mitigating numerous complications.