Study Of Biological Variation Research Articles

A New Concept for Reference Change Values-Regression to the Population Mean.

Reference change values (RCV) are used to indicate a change in analyte concentration that is unlikely to be due to random variation in the patient or the measurement. Current theory describes RCV relative to a first measurement result (X1). We investigate an alternative view predicting the starting point for RCV calculations from X1 and its location in the reference interval. Data for serum sodium, calcium, and total protein from the European Biological Variation study and from routine clinical collections were analyzed for the effect of the position of X1 within the reference interval on the following result from the same patient. A model to describe the effect was determined, and an equation to predict the RCV for a sample in a population was developed. For all data sets, the midpoints of the RCVs were dependent on the position of X1 in the population. Values for X1 below the population mean were more likely to be followed by a higher result, and X1 results above the mean were more likely to be followed by lower results. A model using population mean, reference interval dispersion, and result diagnostic variation provided a good fit with the data sets, and the derived equation predicted the changes seen. We have demonstrated that the position of X1 within the reference interval creates an asymmetrical RCV. This can be described as a regression to the population mean. Adding this concept to the theory of RCVs will be an important consideration in many cases.

Serum free light chains among twin siblings: is the kappa/lambda ratio genetically determined?

Background Serum kappa, lambda, the K/λ light chain concentrations are used for screening, diagnosis, and monitoring of patients with multiple myeloma and other plasma cell disorders. Biological variation studies conducted on healthy subjects showed that free light chains have a low within and high between-individual variation. We determined if this variation were genetically linked. Methods We obtained a single serum sample from 16 pairs of identical twins, 8 neonate twins, and 19 presumed directly-related siblings children, measured Κ and λ light chains and computed the Κ/λ ratio. Results As expected, Κ/λ results from each twin neonate were near identical (reflecting maternal/placental transfer). For older children and adult twins, the Κ/λ ratio form a cluster of results that were a subset of the reference range. There was one outlier, a female with a high, different from her twin sister. She likely had a monoclonal gammopathy (no followup was possible). Excluding this pair, results from neonate twins (14.4% ±10.3%) and non-neonate twins (18.0 ± 15.3%) were not significantly different. Results between non-twin siblings were more scattered (53.2%±53.4%) and different from neonate and non-neonate twin adult and children. Conclusion We suggest that the Κ/λ free light chains may be genetically linked.

The European biological variation study (EuBIVAS): Biological variation data for testosterone, follicle stimulating hormone, prolactin, luteinizing hormone and dehydroepiandrosterone sulfate in men

BackgroundKnowledge of biological variation (BV) of hormones is essential for interpretation of laboratory tests and for diagnostics of endocrinological and reproductive diseases. There is a lack of robust BV data for many hormones in men. MethodsWe used serum samples collected weekly over 10 weeks from the European Biological Variation Study (EuBIVAS) to determine BV of testosterone, follicle-stimulating hormone (FSH), prolactin, luteinizing hormone (LH) and dehydroepiandrosterone sulfate (DHEA-S) in 38 men. We derived within-subject (CVI) and between-subject (CVG) BV estimates by CV-ANOVA after trend, outlier, and homogeneity analysis and calculated reference change values, index of individuality (II), and analytical performance specifications. ResultsThe CVI estimates were 10 % for testosterone, 8 % for FSH, 13 % for prolactin, 22 % for LH, and 9 % for DHEA-S, respectively. The IIs ranged between 0.14 for FSH to 0.66 for LH, indicating high individuality. ConclusionsIn this study, we have used samples from the highly powered EuBIVAS study to derive BV estimates for testosterone, FSH, prolactin, LH and DHEA-S in men. Our data confirm previously published BV estimates of testosterone, FSH and LH. For prolactin and DHEA-S BV data for men are reported for the first time.

Biological variation estimates of Alzheimer's disease plasma biomarkers in healthy individuals.

Blood biomarkers have proven useful in Alzheimer's disease (AD) research. However, little is known about their biological variation (BV), which improves the interpretation of individual-level data. We measured plasma amyloid beta (Aβ42, Aβ40), phosphorylated tau (p-tau181, p-tau217, p-tau231), glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL) in plasma samples collected weekly over 10weeks from 20 participants aged 40 to 60 years from the European Biological Variation Study. We estimated within- (CVI ) and between-subject (CVG ) BV, analytical variation, and reference change values (RCV). Biomarkers presented considerable variability in CVI and CVG . Aβ42/Aβ40 had the lowest CVI (≈ 3%) and p-tau181 the highest (≈ 16%), while others ranged from 6% to 10%. Most RCVs ranged from 20% to 30% (decrease) and 25% to 40% (increase). BV estimates for AD plasma biomarkers can potentially refine their clinical and research interpretation. RCVs might be useful for detecting significant changes between serial measurements when monitoring early disease progression or interventions. Highlights Plasma amyloid beta (Aβ42/Aβ40) presents the lowest between- and within-subject biological variation, but also changes the least in Alzheimer's disease (AD) patients versus controls. Plasma phosphorylated tau variants significantly vary in their within-subject biological variation, but their substantial fold-changes in AD likely limits the impact of their variability. Plasma neurofilament light chain and glial fibrillary acidic protein demonstrate high between-subject variation, the impact of which will depend on clinical context. Reference change values can potentially be useful in monitoring early disease progression and the safety/efficacy of interventions on an individual level. Serial sampling revealed that unexpectedly high values in heathy individuals can be observed, which urges caution when interpreting AD plasma biomarkers based on a single test result.

Biological Normalcy

Contesting ideas about what is “normal” human behavior or biology is a core contribution of anthropology. In efforts to provide more inclusive views of what it means to be human, anthropologists challenge judgments about diverse ways of being, which include assumptions about what it means to be normal. Meanings of the term normal encompass the descriptive (statistical) and the evaluative (normative), i.e., judgments about a given characteristic. In biomedicine, “healthy” is often the value ascribed to normal, but embedded in healthy are biases that derive from particular cultural and historical contexts. Here I review how the term normal is understood and used in anthropological and related studies of human biology and biological variation. I propose the biological normalcy framework for understanding how the statistical and normative meanings of normal mutually inform each other and their consequences for human population biology. Several examples provide illustrations of the framework. Expected final online publication date for the Annual Review of Anthropology, Volume 52 is October 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Multivariate Models of Animal Sex: Breaking Binaries Leads to a Better Understanding of Ecology and Evolution.

"Sex" is often used to describe a suite of phenotypic and genotypic traits of an organism related to reproduction. However, these traits-gamete type, chromosomal inheritance, physiology, morphology, behavior, etc.-are not necessarily coupled, and the rhetorical collapse of variation into a single term elides much of the complexity inherent in sexual phenotypes. We argue that consideration of "sex" as a constructed category operating at multiple biological levels opens up new avenues for inquiry in our study of biological variation. We apply this framework to three case studies that illustrate the diversity of sex variation, from decoupling sexual phenotypes to the evolutionary and ecological consequences of intrasexual polymorphisms. We argue that instead of assuming binary sex in these systems, some may be better categorized as multivariate and nonbinary. Finally, we conduct a meta-analysis of terms used to describe diversity in sexual phenotypes in the scientific literature to highlight how a multivariate model of sex can clarify, rather than cloud, studies of sexual diversity within and across species. We argue that such an expanded framework of "sex" better equips us to understand evolutionary processes, and that as biologists, it is incumbent upon us to push back against misunderstandings of the biology of sexual phenotypes that enact harm on marginalized communities.

Analytical performance specifications for the measurement uncertainty of 24,25-dihydroxyvitamin D examinations.

The exploration of the metabolites in the degradation pathways of vitamin D (VTD) has gained importance in recent years and simultaneous quantitation of twenty-five-hydroxy vitamin D (25(OH)D) mass concentration together with 24,25-dihydroxyvitamin D (24,25(OH)2D) has been proposed as a newer approach to define VTD deficiency. Yet, no data are available on 24,25(OH)2D biological variation (BV). In this study, we evaluated 24,25(OH)2D's BV on the European Biological Variation Study (EuBIVAS) cohort samples to determine if analytical performance specifications (APS) for 24,25(OH)2D could be generated. Six European laboratories recruited 91 healthy participants. 25(OH)D and 24,25(OH)2D concentrations in K3-EDTA plasma were examined weekly for up to 10weeks in duplicate with a validated LC-MS/MS method. The Vitamin D Metabolite Ratio (24,25(OH)2D divided by 25(OH)D×100) was also calculated at each time point. Linear regression of the mean 24,25(OH)2D concentrations at each blood collection showed participants were not in steady state. Variations of 24,25(OH)2D over time were significantly positively associated with the slopes of 25(OH)D concentrations over time and the concentration of 25(OH)D of the participant at inclusion, and negatively associated with body mass index (BMI), but not with age, gender, or location of the participant. The variation of the 24,25(OH)2D concentration in participants over a 10weeks period was 34.6%. Methods that would detect a significant change linked to the natural production of 24,25(OH)2D over this period at p<0.05 would need a relative measurement uncertainty (u%)<14.9% while at p<0.01, relative measurement uncertainty should be <10.5%. We have defined for the first time APS for 24,25(OH)2D examinations. According to the growing interest in this metabolite, several laboratories and manufacturers might aim to develop specific methods for its determination. The results presented in this paper are thus necessary prerequisites for the validation of such methods.

Biological variation: recent development and future challenges.

Biological variation (BV) data have many applications in laboratory medicine. However, these depend on the availability of relevant and robust BV data fit for purpose. BV data can be obtained through different study designs, both by experimental studies and studies utilizing previously analysed routine results derived from laboratory databases. The different BV applications include using BVdata for setting analytical performance specifications, tocalculate reference change values, to define the index ofindividuality and to establish personalized reference intervals. In this review, major achievements in the area of BV from last decade will be presented and discussed. These range from new models and approaches to derive BV data, the delivery of high-quality BV data by the highly powered European Biological Variation Study (EuBIVAS), the Biological Variation Data Critical Appraisal Checklist (BIVAC) and other standards for deriving and reporting BV data, the EFLM Biological Variation Database and new applications of BV data including personalized reference intervals and measurement uncertainty.

Biological variation of Alzheimer’s disease plasma biomarkers in healthy individuals

AbstractBackgroundPlasma biomarkers for Alzheimer’s disease (AD) have demonstrated great performance to identify AD pathology in research cohorts, and recent studies also support their potential to monitor effects of disease‐modifying treatments. To properly interpret these biomarkers in research settings and to inform power calculations of future trials using them as surrogate outcomes, information on their physiological variability over time is needed. We conducted a 10‐week biological variation (BV) study to assess the within‐individual (CVI; CV: coefficient of variation), between‐individual (CVG) and analytical variability (CVA) of blood‐based AD biomarkers. These parameters can provide the reference change value (RCV), which determines how much biomarker values must change to represent a significant abnormality‐related change, i.e. exceeding analytical and biological variation.MethodPlasma samples were collected weekly for 10 weeks from 20 healthy individuals from the European Biological Variation Study (n=20; 50% female, median age 46.4). Biomarkers for tau pathology (p‐tau181), brain amyloidosis (Aβ42, Aβ40, Aβ42/40), glial activation (GFAP) and neurodegeneration (NfL) were quantified in duplicate using the Simoa technology. The CV‐ANOVA statistical method was used to compute BV estimates. To evaluate diurnal rhythm of these biomarkers, we also conducted a study with non‐demented older adults (n=24; 58‐82yo), with hourly blood collections during 26h in strictly‐controlled environmental conditions (NCT02091427). P‐tau217 (Lily) analyses are ongoing for both studies.ResultIndividual‐level (Figure 1) and pooled biomarker levels are shown (Figure 2), as well as BV estimates (Table 1). The 10‐week within‐individual physiological variation (CVI) was lower for Aβ42/40 (4.3%), followed by Aβ42 (6.0%), Aβ40 (6.4%), NfL (7.9%), GFAP (9.3%) and p‐tau181 (15.8%). Between‐individual variation (CVG) was lower for Aβ42/40 (8.8%), followed by Aβ42 (13.3%), Aβ40 (17.0%), p‐tau181 (20.5%), NfL (23.0%) and GFAP (31.5%). Analytical variation was low for all biomarkers (CVA range: 2.5‐6.6%). The RCV was higher for p‐tau181 (45.6%), followed by GFAP (31.7%), NfL (27.6%), Aβ40 (19.0%), Aβ42 (18.8%) and Aβ42/40 (15.6%).ConclusionBlood‐based AD biomarkers demonstrate good week‐to‐week stability, but their CVG and RCV’s can be rather high, with implications both for use in clinical routine (misclassification risk) and for future trial designs (high numbers of participants needed). Results for the diurnal variation study will also be presented.

Evaluation of the Randox and Fuji Dri-Chem vcCRP-P assays of canine C-reactive protein.

In veterinary medicine, measurement of canine C-reactive protein (cCRP) is used widely to detect inflammatory diseases. We evaluated the precision of Randox and Fuji assays for cCRP, as well as accuracy, correlation, and agreement compared to a reference ELISA. Blood samples from 71 client-owned dogs (20 healthy, 51 diseased) were analyzed with the 3 assays. Inter-assay CVs were ~3.5% with both the Randox and Fuji assays. The mean biases were -1.90% for the Randox and -5.93% for the Fuji test; the targeted biases were ~8.5% for both assays. The CV, bias, and observed total error were acceptable for the 2 assays compared to ASVCP recommendations based on biological variation studies. The Spearman correlation coefficient for cCRP concentration compared with the reference ELISA was 0.83 for the Randox test and 0.92 for the Fuji test. Both assays measured cCRP precisely at intermediate and increased concentrations. Correlation with the reference ELISA was good, and both assays could be used to evaluate cCRP concentrations in veterinary practice. However, the assays did not reach analytical agreement; hence the results obtained by these assays are not interchangeable, and serial monitoring of cCRP requires the use of the same assay.

Short-term biological variation study of plasma hemophilia and thrombophilia parameters in a population of apparently healthy Caucasian adults

Biological variation (BV) data obtained in a standardized way is valuable to assess the analytical requirements and the utility of a reference interval. Our study aimed to determine the short-term BV of thrombophilia (protein S, protein C, activated protein C resistance (APCR) and factor VIII) and hemophilia (factors VIII, IX and XI) parameters in plasma. Coagulation factors V and XII were also evaluated. Based on the obtained data, we assessed analytical performance specifications for the parameters. Finally, we intended to provide a robust tool for comparison of serial measurements of factors V, VIII, IX and XI. A blood draw was performed weekly in 19 apparently healthy Caucasian adults for five weeks at Saint-Luc University Hospital (Brussels, Belgium). Parameters were measured in duplicate. BV components were calculated with a nested analysis of variance after exclusion of outliers. The analytical coefficient of variation (CV) varied from 1.5 to 4.6%, the within-subject CV from 1.6 to 8.9% and the between-subject CV from 3.8 to 24.1%. All parameters showed high individuality. For most parameters, theanalytical goal was met with our assays. Reference changevalues (RCV) of-16.7% to +20.0%,-20.7% to +26.0%,-15.3% to +18.1% and-13.1% to +15.1% were obtained for factors V, VIII, IX and XI respectively. All studied parameters were highly individualized. The assessment of BV data can guide setting analytical goal specifications. Comparison of serial measurements in the follow-up of patients suffering from hepatic failure or mild hemophilia is facilitated by evaluation of the RCV.

Comparison of 8 methods for univariate statistical exclusion of pathological subpopulations for indirect reference intervals and biological variation studies

BackgroundIndirect reference intervals and biological variation studies heavily rely on statistical methods to separate pathological and non-pathological subpopulations within the same dataset. In recognition of this, we compare the performance of eight univariate statistical methods for identification and exclusion of values originating from pathological subpopulations. MethodsThe eight approaches examined were: Tukey’s rule with and without Box-Cox transformation; median absolute deviation; double median absolute deviation; Gaussian mixture models; van der Loo (Vdl) methods 1 and 2; and the Kosmic approach. Using four scenarios including lognormal distributions and varying the conditions through the number of pathological populations, central location, spread and proportion for a total of 256 simulated mixed populations. A performance criterion of ± 0.05 fractional error from the true underlying lower and upper reference interval was chosen. ResultsOverall, the Kosmic method was a standout with the highest number of scenarios lying within the acceptable error, followed by Vdl method 1 and Tukey’s rule. Kosmic and Vdl method 1 appears to discriminate better the non-pathological reference population in the case of log-normal distributed data. When the proportion and spread of pathological subpopulations is high, the performance of statistical exclusion deteriorated considerably. DiscussionsIt is important that laboratories use a priori defined clinical criteria to minimise the proportion of pathological subpopulation in a dataset prior to analysis. The curated dataset should then be carefully examined so that the appropriate statistical method can be applied.

Transformation of Sequential Hospital and Outpatient Laboratory Data into Between-Day Reference Change Values.

Serial differences between intrapatient consecutive measurements can be transformed into Taylor series of variation vs time with the intersection at time = 0 (y0) equal to the total variation (analytical + biological + preanalytical). With small preanalytical variation, y0, expressed as a percentage of the mean, is equal to the variable component of the reference change value (RCV) calculation: (CVA2 + CVI2)1/2. We determined the between-day RCV of patient data for 17 analytes and compared them to healthy participants' RCVs. We analyzed 653 consecutive days of Dartmouth-Hitchcock Roche Modular general chemistry data (4.2 million results: 60% inpatient, 40% outpatient). The serial patient values of 17 analytes were transformed into 95% 2-sided RCV (RCVAlternate), and 3 sets of RCVhealthy were calculated from 3 Roche Modular analyzers' quality control summaries and CVI derived from biological variation (BV) studies using healthy participants. The RCVAlternate values are similar to RCVhealthy derived from known components of variation. For sodium, chloride, bicarbonate calcium, magnesium, phosphate, alanine aminotransferase, albumin, and total protein, the RCVs are equivalent. As expected, increased variation was found for glucose, aspartate aminotransferase, creatinine, and potassium. Direct bilirubin and urea demonstrated lower variation. Our RCVAlternate values integrate known and unknown components of analytic, biologic, and preanalytic variation, and depict the variations observed by clinical teams that make medical decisions based on the test values. The RCVAlternate values are similar to the RCVhealthy values derived from known components of variation and suggest further studies to better understand the results being generated on actual patients tested in typical laboratory environments.

Evaluation of Measurement Uncertainties of Immunoassays Analytes According to Biological Variation Databases.

Quality control (QC) of analytical measurement is a stage used for the evaluation of whether the measurement method is appropriate to the standards required for patient care. QC encompasses internal (IQC) and external laboratory content (EQC). Measurement uncertainty (MU) is an important issue that expresses each analyte's measurement. Autoanalyzer's, calibrators, and control materials of analytes, reagents, measures contribute to MU. The aim of this study was to calculate the measurement uncertainty of nineteen immunoassay analytes in the biochemistry laboratory and to evaluate the compatibility of these measurements with the analytical quality targets derived from different biological variations. In the study, Cobas 6000 Modular Analysis Series (Roche Diagnostics, USA) autoanalyzer was used. IQC data of the analytes between August 2017 and January 2018 were used together with the monthly EQC data of June 2017 to January 2018. NORDTEST guideline was used to calculate the MU. "Desirable" analytical quality goals of each analyte with Fraser's formula were calculated and evaluated the limits from EuBIVAS (The European Biological Variation Study) and Ricos's biological variation databases. MU of fT4 was seen to be higher than TEa (Total Allowable Analytical Error)Ricos in our study. MU of fT4 was seen to be lower than TEaEuBIVAS (MUfT4: 9.20%, TEaRicos: 8.00%; TEaEuBIVAS: 9.92%). The analytical quality performances of the analytes included in the study turned out to be sufficient in our laboratory.

The European Biological Variation Study (EuBIVAS): Biological Variation Data for Coagulation Markers Estimated by a Bayesian Model.

For biological variation (BV) data to be safely used, data must be reliable and relevant to the population in which they are applied. We used samples from the European Biological Variation Study (EuBIVAS) to determine BV of coagulation markers by a Bayesian model robust to extreme observations and used the derived within-participant BV estimates [CVP(i)] to assess the applicability of the BV estimates in clinical practice. Plasma samples were drawn from 92 healthy individuals for 10 consecutive weeks at 6 European laboratories and analyzed in duplicate for activated partial thromboplastin time (APTT), prothrombin time (PT), fibrinogen, D-dimer, antithrombin (AT), protein C, protein S free, and factor VIII (FVIII). A Bayesian model with Student t likelihoods for samples and replicates was applied to derive CVP(i) and predicted BV estimates with 95% credibility intervals. For all markers except D-dimer, CVP(i) were homogeneously distributed in the overall study population or in subgroups. Mean within-subject estimates (CVI) were <5% for APTT, PT, AT, and protein S free, <10% for protein C and FVIII, and <12% for fibrinogen. For APTT, protein C, and protein S free, estimates were significantly lower in men than in women ≤50 years. For most coagulation markers, a common CVI estimate for men and women is applicable, whereas for APTT, protein C, and protein S free, sex-specific reference change values should be applied. The use of a Bayesian model to deliver individual CVP(i) allows for improved interpretation and application of the data.

The multicenter European Biological Variation Study (EuBIVAS): a new glance provided by the Principal Component Analysis (PCA), a machine learning unsupervised algorithms, based on the basic metabolic panel linked measurands.

The European Biological Variation Study (EuBIVAS), which includes 91 healthy volunteers from five European countries, estimated high-quality biological variation (BV) data for several measurands. Previous EuBIVAS papers reported no significant differences among laboratories/population; however, they were focused on specific set of measurands, without a comprehensive general look. The aim of this paper is to evaluate the homogeneity of EuBIVAS data considering multivariate information applying the Principal Component Analysis (PCA), a machine learning unsupervised algorithm. The EuBIVAS data for 13 basic metabolic panel linked measurands (glucose, albumin, total protein, electrolytes, urea, total bilirubin, creatinine, phosphatase alkaline, aminotransferases), age, sex, menopause, body mass index (BMI), country, alcohol, smoking habits, and physical activity, have been used to generate three databases developed using the traditional univariate and the multivariate Elliptic Envelope approaches to detect outliers, and different missing-value imputations. Two matrix of data for each database, reporting both mean values, and "within-person BV" (CVP) values for any measurand/subject, were analyzed using PCA. A clear clustering between males and females mean values has been identified, where the menopausal females are closer to the males. Data interpretations for the three databases are similar. No significant differences for both mean and CVPs values, for countries, alcohol, smoking habits, BMI and physical activity, have been found. The absence of meaningful differences among countries confirms the EuBIVAS sample homogeneity and that the obtained data are widely applicable to deliver APS. Our data suggest that the use of PCA and the multivariate approach may be used to detect outliers, although further studies are required.

The European Biological Variation Study (EuBIVAS): a summary report.

Biological variation (BV) data have many important applications in laboratory medicine. Concerns about quality of published BV data led the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) 1st Strategic Conference to indicate need for new studies to generate BV estimates of required quality. In response, the EFLM Working Group on BV delivered themulticenter European Biological Variation Study (EuBIVAS). This review summarises the EuBIVAS and its outcomes. Serum/plasma samples were taken from 91 ostensibly healthy individuals for 10 consecutive weeks at 6 European centres. Analysis was performed by Siemens ADVIA 2400 (clinical chemistry), Cobas Roche 8000, c702 and e801 (proteins and tumor markers/hormones respectively), ACL Top 750 (coagulation parameters), and IDS iSYS or DiaSorin Liaison (bone biomarkers). A strict preanalytical and analytical protocol was applied. To determine BV estimates with 95% CI, CV-ANOVA after analysis of outliers, homogeneity and trend analysis or a Bayesian model was applied. EuBIVAS has so far delivered BV estimates for 80 different measurands. Estimates for 10measurands (non-HDL cholesterol, S100-β protein, neuron-specific enolase, soluble transferrin receptor, intact fibroblast growth-factor-23, uncarboxylated-unphosphorylated matrix-Gla protein, human epididymis protein-4, free, conjugated and %free prostate-specific antigen), prior to EuBIVAS, have not been available. BV data for creatinine and troponin I were obtained using two analytical methods in each case. The EuBIVAS has delivered high-quality BV data for a wide range of measurands. The BV estimates are for many measurands lower than those previously reported, having an impact on the derived analytical performance specifications and reference change values.

Within- and between-subject biological variation data for tumor markers based on the European Biological Variation Study.

Reliable biological variation (BV) data are required for the clinical use of tumor markers in the diagnosis and monitoring of treatment effects in cancer. The European Biological Variation Study (EuBIVAS) was established by the EFLM Biological Variation Working Group to deliver BV data for clinically important measurands. In this study, EuBIVAS-based BV estimates are provided for cancer antigen (CA) 125, CA 15-3, CA 19-9, carcinoembryonic antigen, cytokeratin-19 fragment, alpha-fetoprotein and human epididymis protein 4. Subjects from five European countries were enrolled in the study, and weekly samples were collected from 91 healthy individuals (53 females and 38 males; 21-69 years old) for 10 consecutive weeks. All samples were analyzed in duplicate within a single run. After excluding outliers and homogeneity analysis, the BVs of tumor markers were determined by CV-ANOVA on trend-corrected data, when relevant (Røraas method). Marked individuality was found for all tumor markers. CYFRA 21-1 was the measurand with the highest index of individuality (II) at 0.67, whereas CA 19-9 had the lowest II at 0.07. The CV I s of HE4, CYFRA 21-1, CA 19-9, CA 125 and CA 15-3 of pre- and postmenopausal females were significantly different from each other. This study provides updated BV estimates for several tumor markers, and the findings indicate that marked individuality is characteristic. The use of reference change values should be considered when monitoring treatment of patients by means of tumor markers.

European Biological Variation Study (EuBIVAS): within- and between-subject biological variation estimates for serum thyroid biomarkers based on weekly samplings from 91 healthy participants.

Thyroid biomarkers are fundamental for the diagnosis of thyroid disorders and for the monitoring and treatment of patients with these diseases. The knowledge of biological variation (BV) is important to define analytical performance specifications (APS) and reference change values (RCV). The aim of this study was to deliver BV estimates for thyroid stimulating hormone (TSH), free thyroxine (FT4), free triiodothyronine (FT3), thyroglobulin (TG), and calcitonin (CT). Analyses were performed on serum samples obtained from the European Biological Variation Study population (91 healthy individuals from six European laboratories; 21-69 years) on the Roche Cobas e801 at the San Raffaele Hospital (Milan, Italy). All samples from each individual were evaluated in duplicate within a single run. The BV estimates with 95% CIs were obtained by CV-ANOVA, after analysis of variance homogeneity and outliers. The within-subject (CV I ) BV estimates were for TSH 17.7%, FT3 5.0%, FT4 4.8%, TG 10.3, and CT 13.0%, all significantly lower than those reported in the literature. No significant differences were observed for BV estimates between men and women. The availability of updated, in the case of CT not previously published, BV estimates for thyroid markers based on the large scale EuBIVAS study allows for refined APS and associated RCV applicable in the diagnosis and management of thyroid and related diseases.

Identifying adaptive alleles in the human genome: from selection mapping to functional validation.

The suite of phenotypic diversity across geographically distributed human populations is the outcome of genetic drift, gene flow, and natural selection throughout human evolution. Human genetic variation underlying local biological adaptations to selective pressures is incompletely characterized. With the emergence of population genetics modeling of large-scale genomic data derived from diverse populations, scientists are able to map signatures of natural selection in the genome in a process known as selection mapping. Inferred selection signals further can be used to identify candidate functional alleles that underlie putative adaptive phenotypes. Phenotypic association, fine mapping, and functional experiments facilitate the identification of candidate adaptive alleles. Functional investigation of candidate adaptive variation using novel techniques in molecular biology is slowly beginning to unravel how selection signals translate to changes in biology that underlie the phenotypic spectrum of our species. In addition to informing evolutionary hypotheses of adaptation, the discovery and functional annotation of adaptive alleles alsomay be of clinical significance. While selection mapping efforts in non-European populations are growing, there remains a stark under-representation of diverse human populations in current public genomic databases, of both clinical and non-clinical cohorts. This lack of inclusion limits the study of human biological variation. Identifying and functionally validating candidate adaptive alleles in more global populations is necessary for understanding basic human biology and human disease.