The purpose of this study was to determine the levels of trace metals in the blood of the general Korean population. A total of 258 healthy individuals, according to their regular medical check-ups, (119 males and 139 females, age ranging from 12 to 78 years old) were enrolled from December 2014 to December 2016. Levels of 10 trace elements were determined using inductively coupled plasma mass spectrometry (ICP-MS). The geometric mean (GM) levels for lead, arsenic, cesium, mercury, aluminum, cadmium, copper, manganese, selenium, and zinc were 15.97 μg/L, 7.19 μg/L, 2.39 μg/L, 3.41 μg/L, 10.57 μg/L, 0.78 μg/L, 979.8 μg/L, 11.06 μg/L, 111.37 μg/L, and 872.7 μg/L, respectively. There were significant gender-related differences in the levels of several metals; male individuals had higher Pb, As, Cs, Hg, and Se than females, while females had higher Cd, Cu, and Mn than males. We noticed remarkably high blood levels of Hg, As and Al in the Korean population. The element concentrations reported represent a new contribution to the knowledge of the blood chemistry for the Korea population. The data can be used to assess the clinical health of this population.

Han SH, et al. Korean J Lab Med. 2008 Oct;28(5):378-385. https://doi.org/10.3343/kjlm.2008.28.5.378

Background: Metabolic syndrome (MetS) is a cluster of conditions associated with glucose intolerance, hypertension, abdominal obesity, dyslipidemia, and insulin resistance that increase the risk of cardiovascular diseases (CVD) and type 2 diabetes (T2D). Since MetS is known as a complex symptom with a high incidence of genetic factors, it is important to identify genetic variants for each clinical characteristic of MetS. Methods: We performed targeted next-generation sequencing (NGS) to identify genetic variants related to obesity, blood glucose, triacylglycerol (TG), and high-density lipoprotein (HDL)-cholesterol level, and hypertension in 48 subjects with MetS and in 48 healthy subjects. Results: NGS analysis revealed that 26 of 48 subjects (54.2%) with MetS had putative non-synonymous variants related to the clinical features of MetS. Of the subjects with MetS, 8 (16.7%) had variants in 4 genes (COL6A2, FTO, SPARC, and MTHFR) related to central obesity, 17 (35.4%) had variants in 6 genes (APOB, SLC2A2, LPA, ABCG5, ABCG8, and GCKR) related to hyperglycemia, 3 (6.3%) had variants in 4 genes (APOA1, APOC2, APOA4, and LMF1) related to hypertriglyceridemia, 8 (16.7%) had variants in 4 genes (ABCA1, CETP, SCARB1, and LDLR) related to low HDL-cholesterolemia, and 5 (10.4%) had variants in ADD1 related to hypertension. Conclusions: Our findings may contribute to broadening the genetic spectrum of risk variants related to the development of MetS.

Urinary tract infections (UTIs) are pervasive and prevalent in both community and hospital settings. Recent trends in the changes of the causative microorganisms in these infections could affect the effectiveness of urinalysis (UA). We aimed to evaluate the predictive performance of UA for urinary culture test results according to the causative microorganisms. In addition, UA results were integrated with artificial intelligence (AI) methods to improve the predictive power. A total of 360,376 suspected UTI patients were enrolled from two university hospitals and one commercial laboratory. To ensure broad model applicability, only a limited range of clinical data available from commercial laboratories was used in the analyses. Overall, 53,408 (14.8%) patients were identified as having a positive urine culture. Among the UA tests, the combination of leukocyte esterase and nitrite tests showed the highest area under the curve (AUROC, 0.766; 95% CI, 0.764–0.768) for predicting urine culture positivity but performed poorly for Gram-positive bacteriuria (0.642; 0.637–0.647). The application of an AI model improved the predictive power of the model for urine culture results to an AUROC of 0.872 (0.870–0.875), and the model showed superior performance metrics not only for Gram-negative bacteriuria (0.901; 0.899–0.902) but also for Gram-positive bacteriuria (0.745; 0.740–0.749) and funguria (0.872; 0.865–0.879). As the prevalence of non- Escherichia coli -caused UTIs increases, the performance of UA in predicting UTIs could be compromised. The addition of AI technologies has shown potential for improving the predictive performance of UA for urine culture results. IMPORTANCE: UA had good performance in predicting urine culture results caused by Gram-negative bacteria, especially for Escherichia coli and Pseudomonas aeruginosa bacteriuria, but had limitations in predicting urine culture results caused by Gram-positive bacteria, including Streptococcus agalactiae and Enterococcus faecalis . We developed and externally validated an AI model incorporating minimal demographic information of patients (age and sex) and laboratory data for UA, complete blood count, and serum creatinine concentrations. The AI model exhibited improved performance in predicting urine culture results across all the causative microorganisms, including Gram-positive bacteria, Gram-negative bacteria, and fungi.