Ralf Kellmann

Background: Adrenal-related biomarkers are central in evaluating endocrine causes of secondary hypertension, but their interpretation is influenced by biological variation (BV). In this study, we aimed to estimate the within-subject (CVI) and between-subject (CVG) BV and derive the reference change values (RCVs), the Index of Individuality (II), and the Harris–Brown heterogeneity ratio (HBR) for aldosterone, renin, and the aldosterone–renin ratio (ARR), ACTH, the cortisol/ACTH ratio, metanephrines, and normetanephrines. Methods: Weekly blood samples were collected from 30 healthy volunteers for 10 consecutive weeks under standardized conditions and were analyzed using state-of-the-art analytical methods. A Bayesian hierarchical model was applied to estimate the posterior distributions of the BV components, yielding estimates of the CVI, CVG, RCVs, II, and HBR. Results: High BV was observed for aldosterone, renin, and the ARR. Aldosterone showed the highest CVI (48.8%), while metanephrine showed the lowest (13.5%). The CVG was high for most measurands, whereas metanephrines showed comparatively low CVG (30.5%). No major sex differences were observed for the biomarkers after performing sex-stratified estimates. The HBR indicated population homogeneity for ARR (female), ACTH (male), and both metanephrines, while aldosterone, renin, ARR (male and mixed), and ACTH (mixed and female) showed heterogeneity. All biomarkers had an II < 1, and RCVs ranged from −72.3% to +261.2% (aldosterone) and −32.9% to +48.9% (metanephrine). Conclusions: This study provides BV estimates for multiple biomarkers relevant to endocrine secondary hypertension. The results underscore the importance of applying BV-derived tools, including RCVs and II, to improve the interpretation of serial measurements in diagnosis and follow-up.

Background: Population-wide reference intervals (RIpop) are commonly used in laboratory medicine but may not reflect an individual’s tightly regulated homeostatic interval. Personalized reference intervals (RIper) could enhance diagnostic precision by accounting for individual variability. A parametric empirical Bayes (PEB) framework stabilizes individual estimates using population parameters, enabling reliable RIper even from a limited number of individual results. Methods: We applied the PEB framework to estimate RIper for 9 biomarkers: albumin, creatinine, phosphate, cortisone, cortisol, testosterone, androstenedione, 17-hydroxyprogesterone, and 11-deoxycortisol. The PEB parameters tested were derived from both routine Laboratory Information System (LIS) data and a local biological variation (BV) study. Using serial samples from healthy adults, we assessed the proportion of results flagged with a 95% prediction interval and compared RIper to conventional RIpop and reference change values (RCVs). Results: LIS parameters were based on data from 1986 to 185 488 patients. PEB-based RIper were consistently narrower than RIpop while maintaining or reducing the proportion of flagged results. For example, albumin flagging decreased from 4.7% (RIpop) to 0.3% (RIper), phosphate from 5.4% to 3.7%, and cortisone from 7.1% to 3.9%. Conversely, 17-hydroxyprogesterone increased from 0.0% to 5.5% but remained close to the expected 5%. PEB thresholds were narrower than standard RCV estimates by correcting for regression toward the mean without increasing flagged results. Conclusions: The PEB framework effectively provides personalized cutoffs for laboratory tests even when few individual patient results are available. PEB parameters can be established using LIS or BV data, offering a feasible and cost-effective implementation pathway.

The reference change value (RCV) is calculated by combining the within-subject biological variation (CV_I) and local analytical variation (CV_A). These calculations do not account for the variation seen in preanalytical conditions in routine practice or CV_I in patients presenting for treatment. As a result, the RCVs may not reflect routine practice or align with clinicians’ experiences. We propose a novel RCV approach based on routine patient data that is potentially more clinically relevant. This study used the refineR algorithm to determine RCVs using serial patient data extracted from a local Laboratory Information System (LIS). The model was applied to biomarkers with a range of result ratio distributions varying from normal to log-normal. Results were compared against conventional formula-based RCVs using CV_I estimates from a state-of-the-art biological variation study. Monte Carlo simulations were also used to validate the LIS data approach. The RCVs estimated from LIS data were: 11-deoxycortisol (men): −70%/+196%, 17-hydroxyprogesterone (men): −49%/+100%, albumin: −10%/+11%, androstenedione (men): −47%/+96%, cortisol (men): −54%/+51%, cortisone (men): −32%/+51%, creatinine: −16%/+14%, phosphate (women): −23%/+29%, phosphate (men): −27%/+29%, testosterone (men): −38%/+60%. The formula-based RCV estimates showed similar but slightly lower results, and the Monte Carlo simulations confirmed the applicability of the new approach. RCVs may be estimated from patient results without prior assumptions about the shape of the ratios between serial results. Laboratories can determine RCVs based on local practice and population.

Background: Biological variation (BV) data may be used to develop analytical performance specifications (APS), reference change values (RCV), and support the applicability of population reference intervals. This study estimates within-subject BV (CVI) for several endocrine biomarkers using 3 different methodological approaches. Methods: For the direct method, 30 healthy volunteers were sampled weekly for 10 consecutive weeks. Samples were analyzed in duplicate for 17-hydroxyprogesterone (17-OHP), androstenedione, cortisol, cortisone, estradiol, follicle-stimulating hormone (FSH), luteinizing hormone (LH), sex hormone-binding globulin (SHBG), and testosterone. A CV-ANOVA with outlier removal and a Bayesian model were applied to derive the CVI. For estradiol, FSH and LH, only the male subgroup was included. In the indirect method, using the same analytes and groups, pairs of sequential results were extracted from the laboratory information system. The total result variation for individual pairs was determined by identifying a central gaussian distribution in the ratios of the result pairs. The CVI was then estimated by removing the effect of analytical variation. Results: The estimated CVI from the Bayesian model (μCVP(i)) in the total cohort was: 17-OHP, 23%; androstenedione, 20%; cortisol, 18%; cortisone, 11%; SHBG, 7.4%; testosterone, 16%; and for the sex hormones in men: estradiol, 14%; FSH, 8%; and LH, 26%. CVI-heterogeneity was present for most endocrine markers. Similar CVI data were estimated using the CV-ANOVA and the indirect method. Conclusions: Similar CVI data were obtained using 2 different direct and one indirect method. The indirect approach is a low-cost alternative ensuring implementation of CVI data applicable for local conditions.

Background: Diagnosing Cushing syndrome (CS) can be challenging. The 24-hour urine free cortisol (UFC) measurement is considered gold standard. This is a laborious test, dependent on correct urine collection. Late-night salivary cortisol is easier and is used as a screening test for CS in adults, but has not been validated for use in children. Objective: To define liquid chromatography tandem mass spectrometry (LC-MS/MS)-based cutoff values for bedtime and morning salivary cortisol and cortisone in children, and validate the results in children with and without CS. Methods: Bedtime and morning salivary samples were collected from 320 healthy children aged 4 to 16 years. Fifty-four patients from the children’s outpatient obesity clinic and 3 children with pituitary CS were used for validation. Steroid hormones were assayed by LC-MS/MS. Cutoff levels for bedtime salivary cortisol and cortisone were defined by the 97.5% percentile in healthy subjects. Results: Bedtime cutoff levels for cortisol and cortisone were 2.4 and 12.0 nmol/L, respectively. Applying these cutoff levels on the verification cohort, 1 child from the obesity clinic had bedtime salivary cortisol exceeding the defined cutoff level, but normal salivary cortisone. All 3 children with pituitary CS had salivary cortisol and cortisone far above the defined bedtime cutoff levels. Healthy subjects showed a significant decrease in salivary cortisol from early morning to bedtime. Conclusions: We propose that bedtime salivary cortisol measured by LC-MS/MS with a diagnostic threshold above 2.4 nmol/L can be applied as a screening test for CS in children. Age- and gender-specific cutoff levels are not needed.

Paralytic shellfish toxins (PSTs) are neurotoxic alkaloids produced by freshwater cyanobacteria and marine dinoflagellates. Due to their antagonism of voltage‐gated sodium channels in excitable cells, certain analogues are of significant pharmacological interest. The biosynthesis of the parent compound, saxitoxin, is initiated with the formation of 4‐amino‐3‐oxo‐guanidinoheptane (ethyl ketone) by an unusual polyketide synthase‐like enzyme, SxtA. We have heterologously expressed SxtA from Raphidiopsis raciborskii T3 in Escherichia coli and analysed its activity in vivo. Ethyl ketone and a truncated analogue, methyl ketone, were detected by HPLC‐ESI‐HRMS analysis, thus suggesting that SxtA has relaxed substrate specificity in vivo. The chemical structures of these products were further verified by tandem mass spectrometry and labelled‐precursor feeding with [guanidino‐¹⁵N₂] arginine and [1,2‐¹³C₂] acetate. These results indicate that the reactions catalysed by SxtA could give rise to multiple PST variants, including analogues of ecological and pharmacological significance.

Background: Current analytical routine methods lack the sensitivity to monitor plasma estrogen levels in breast cancer patients treated with aromatase inhibitors. Such monitoring is warranted for premenopausal patients treated with an aromatase inhibitor and an LH-releasing hormone analogue in particular. Therefore, we aimed to develop a routine tandem mass spectroscopy combined with liquid chromatography (LC-MS/MS) method for estradiol (E2) and estrone (E1) for use in the sub-picomolar range. Method: Calibrators, quality controls (QC), or serum samples were spiked with isotope-labeled internal standard and purified by liquid-liquid extraction. The reconstituted extracts were analyzed by LC-MS/MS in negative electrospray ionization mode. QCs at 6 levels made from pooled patient sera were used to validate the accuracy, sensitivity, and precision of the method. Results: We achieved limits of quantification of 0.6 pmol/L (0.16 pg/mL) for E2 and 0.3 pmol/L (0.07 pg/mL) for E1. The coefficient of variation was below 9.0% at all QC levels for E2 (range, 1.7-153 pmol/L), and below 7.8% for E1 (range, 1.7-143 pmol/L). The method is traceable to the E2 reference standard BCR576. Reference ranges for E2 and E1 in healthy, postmenopausal women were obtained, for E2: 3.8 to 36 pmol/L, for E1: 22 to 122 pmol/L. We measured and confirmed ultra-low E2 and E1 concentrations in sera from patients on the aromatase inhibitors letrozole or exemestane. Conclusion: This ultrasensitive LC-MS/MS method is suitable for routine assessment of serum E1 and E2 levels in breast cancer patients during estrogen suppression therapy. The method satisfies all requirements for measurement of E2 in the clinical setting as stated by the Endocrine Society in 2013. Precis: We report an ultrasensitive LCMS/MS routine assay that measures pretreatment and suppressed levels of estradiol/estrone during aromatase inhibitor treatment of postmenopausal breast cancer patients.