Components Of Biological Variation Research Articles

Biological Variation Data in Triathletes for Metabolism and Growth-Related Biomarkers Included in the Athlete Biological Passport.

When using biological variation (BV) data, BV estimates need to be robust and representative. High-endurance athletes represent a population under special physiological conditions, which could influence BV estimates. Our study aimed to estimate BV in athletes for metabolism and growth-related biomarkers involved in the Athlete Biological Passport (ABP), by 2 different statistical models. Thirty triathletes were sampled monthly for 11 months. The samples were analyzed for human growth hormone (hGH), insulin-like growth factor-1 (IGF-1), insulin-like growth factor binding protein 3 (IGFBP-3), insulin, and N-terminal propeptide of type III procollagen (P-III-NP) by immunoassay. Bayesian and ANOVA methods were applied to estimate within-subject (CVI) and between-subject BV. CVI estimates ranged from 7.8% for IGFBP-3 to 27.0% for insulin, when derived by the Bayesian method. The 2 models gave similar results, except for P-III-NP. Data were heterogeneously distributed for P-III-NP for the overall population and in females for IGF-1 and IGFBP-3. BV components were not estimated for hGH due to lack of steady state. The index of individuality was below 0.6 for all measurands, except for insulin. In an athlete population, to apply a common CVI for insulin would be appropriate, but for IGF-1 and IGFBP-3 gender-specific estimates should be applied. P-III-NP data were heterogeneously distributed and using a mean CVI may not be representative for the population. The high degree of individuality for IGF-1, IGFBP-3, and P-III-NP makes them good candidates to be interpreted through reference change values and the ABP.

Diurnal and day-to-day biological variation of salivary cortisol and cortisone.

There is a growing interest in the relevance of salivary cortisol and cortisone concentrations in stress-related research. To correctly attribute the magnitude of salivary cortisol and cortisone variation as an effect of a stressful event, a coherent understanding of the day-to-day intra-individual and inter-individual variability across the diurnal cycle of the two steroids is required. However, such information is currently lacking. This study aimed to overcome these existing limitations by performing an investigation of the biological variation (BV) of salivary cortisol and cortisone within one day and between five days using an LC-MS/MS method. Saliva samples were collected from 20 healthy volunteers immediately after waking up, at 8:00, 12:00, 15:00, 19:00 and 23:00 on each day over five days. All samples were analyzed in duplicate in one run. Nested ANOVA was used to calculate the sums of squares for analytical and biological components of variation. The within-subject BV of salivary cortisol and cortisone (CVI) ranged from a minimum of 29.3 and 19.0 % to a maximum of 56.5 and 49.1 %, respectively, while the between-subject biological variation (CVG) ranged from 29.7 and 29.0 % to 51.6 and 43.6 %. The reference change values (RCVs) ranged from 96 to 245 % for cortisol and from 55 to 194 % for cortisone. A medium index of individuality was observed for both compounds at all time points. This study provides updated BV estimates and RCVs for different times of day that can be used to assess the magnitude of change in biomarkers in future stress-related research.

Short-term biological variation of serum tryptase.

Serum tryptase is a biomarker of mast cell activation. Among others, it is used in the diagnosis of anaphylaxis where a significant increase during the acute phase supports the diagnosis. When evaluating changes in biomarker levels, it is of utmost importance to consider the biological variation of the marker. Therefore, the aim of this study was to evaluate the short-term biological variation of serum tryptase. Blood samples were drawn at 9 AM three days in a row from apparently healthy subjects. On day two, additional blood samples were drawn every third hour for 12 h. The tryptase concentration was measured in serum using a fluoroenzyme immunoassay (ImmunoCAP™, Thermo Fisher Scientific). Linear mixed-effects models were used to calculate components of biological variation. In 32 subjects, the overall mean concentration of tryptase was 4.0 ng/mL (range, 1.3-8.0 ng/mL). The within-subject variation was 3.7 % (95 % confidence interval (CI) 3.0-4.4 %), the between-subject variation was 31.5 % (95 % CI 23.1-39.8 %), and the analytical variation was 3.4 % (95 % CI 2.9-4.1 %). The reference change value was 13.3 % for an increase in tryptase at a 95 % level of significance. No significant day-to-day variation was observed (p=0.77), while a minute decrease in the serum concentration was observed during the day (p<0.0001). Serum tryptase is a tightly regulated biomarker with very low within-subject variation, no significant day-to-day variation, and only minor semidiurnal variation. In contrast, a considerable between-subject variation exists. This establishes serum tryptase as a well-suited biomarker for monitoring.

Short-term biological variation of plasma uracil in a Caucasian healthy population.

Plasma uracil is a new biomarker to assess theactivity of dihydropyrimidine dehydrogenase before cancer treatment with fluoropyrimidine drugs. Knowledge on the biological variation of plasma uracil is important toassess the applicability of plasma uracil as a biomarker of drug tolerance and efficacy. A total of 33 apparently healthy individuals were submitted to sequential blood draws for three days. On thesecond day, blood draws were performed every third hour for 12h. Plasma uracil was quantified by LC-MS/MS. The within-subject (CVI) and between-subject (CVG) biological variation estimates were calculated using linear mixed-effects models. The overall median value of plasma uracil was 10.6ng/mL (range 5.6-23.1ng/mL). The CVI and CVG were 13.5 and 22.1%, respectively. Plasma uracil remained stable during the day, and there was no day-to-day variation observed. No differences in biological variation components were found between sex and no correlation to age was found. Four samples were calculated to be required toestimate the homeostatic set-point ±15% with 95% confidence. Plasma uracil is subject to tight homeostatic regulation without semidiurnal and day-to-day variation, however between-subject variation exists. This emphasizes plasma uracil as a well-suited biomarker for evaluation ofdihydropyrimidine dehydrogenase activity, but four samples are required to establish the homeostatic set-point in a patient.

Short-term biological variation of serum glial fibrillary acidic protein.

Serum glial fibrillary acidic protein (GFAP) is an emerging biomarker for intracerebral diseases and is approved for clinical use in traumatic brain injury. GFAP is also being investigated for several other applications, where the GFAP changes are not always outstanding. It is thus essential for the interpretation of GFAP to distinguish clinical relevant changes from natural occurring biological variation. This study aimed at estimating the biological variation of serum GFAP. Apparently healthy subjects (n=33) had blood sampled for three consecutive days. On the second day, blood was also drawn every third hour from 9 AM to 9 PM. Serum GFAP was measured by Single Molecule Array (Simoa™). Components of biological variation were estimated in a linear mixed-effects model. The overall median GFAP value was 92.5pg/mL (range 34.4-260.3pg/mL). The overall within- (CVI) and between-subject variations (CVG) were 9.7 and 39.5%. The reference change value was 36.9% for an increase. No day-to-day variation was observed, however semidiurnal variation was observed with increasing GFAP values between 9AM and 12 PM (p<0.00001) and decreasing from 12 to 9 PM (p<0.001). Serum GFAP exhibits a relatively low CVI but a considerable CVG and a marked semidiurnal variation. This implies caution on the timing of blood sampling and when interpreting GFAP in relation to reference intervals, especially in conditions where only small GFAP differences are observed.

Biological Variation in Rotational Thromboelastometry in Patients with Atrial Fibrillation Receiving Rivaroxaban.

Rotational thromboelastometry (ROTEM) is a viscoelastic hemostasis test used primarily in the management of bleeding after trauma or in cardiac surgery. To allow safe and valid clinical interpretation of test results, objective specifications for analytical performance are needed, which are generally based on biological variation within (CVI) and between (CVG) individuals. The aim of this study was to evaluate biological variation in ROTEM in patients receiving rivaroxaban. Sixty patients with atrial fibrillation on stable rivaroxaban therapy were included, from whom blood was collected on six occasions: three times at trough and three at peak rivaroxaban concentrations. ROTEM® Extem and LowTF were measured as well as rivaroxaban concentration, PT, APTT, and anti-Xa. Within- (CVI) and between-subject (CVG) biological estimates were calculated. Knowledge of these biological variation components will help to establish the appropriate objective analytical performance specifications for ROTEM analysis.

Comparison of four indirect (data mining) approaches to derive within-subject biological variation.

Within-subject biological variation (CVi ) is a fundamental aspect of laboratory medicine, from interpretation of serial results, partitioning of reference intervals and setting analytical performance specifications. Four indirect (data mining) approaches in determination of CVi were directly compared. Paired serial laboratory results for 5,000 patients was simulated using four parameters, d the percentage difference in the means between the pathological and non-pathological populations, CVi the within-subject coefficient of variation for non-pathological values, f the fraction of pathological values, and e the relative increase in CVi of the pathological distribution. These parameters resulted in a total of 128 permutations. Performance of theExpected Mean Squares method (EMS), the median method, a result ratio method with Tukey's outlier exclusion method and a modified result ratio method with Tukey's outlier exclusion were compared. Within the 128 permutations examined in this study, the EMS method performed the best with 101/128 permutations falling within ±0.20 fractional error of the 'true' simulated CVi , followed by the result ratio method with Tukey's exclusion method for 78/128 permutations. The median method grossly under-estimated the CVi . The modified result ratio with Tukey's rule performed best overall with 114/128 permutations within allowable error. This simulation study demonstrates that with careful selection of the statistical approach the influence of outliers from pathological populations can be minimised, and it is possible to recover CVi values close to the 'true' underlying non-pathological population. This finding provides further evidence for use of routine laboratory databases in derivation of biological variation components.

Reference Interval, Biological Variations and Distribution of Creatinine Level in Fasting Urine: A Cross-sectional Study

Introduction: Serum creatinine, urine creatinine, and rate of urine formation are components of creatinine clearance and are under multiple influences. Of these, urine creatinine has the widest variability, especially in the tropical region. Reference intervals, biological variations, and distributions of urine creatinine in the reference individuals are related and important for clinical interpretations of kidney functions. Aim: To establish the reference intervals, biological variations, and distributions of creatinine in urine formed during morning fasting state from reference individuals in a tropical location. Materials and Methods: This observational cross-sectional study of the sample population was done from January 2017 to November 2021 for the establishment of reference interval. Reference individuals, defined by inclusion and exclusion criteria, were from the population at near sea levels plains, in Thrissur district, Kerala, avoiding the mountainous Western Ghats. Morning fasting, third sample of urine was collected to ensure that the urine collected was formed in the morning (n=156). A second set of 44 urine samples, collected from 10 reference individuals consecutively for 4 to 5 days, was used for estimating the analytical Coefficient of Variation (CVA), within-subject (CVI ), and between-subject (CVG) components of biological variation. Outliers in the creatinine data were identified and removed by the Box-Whisker plot to exclude the subclinical influences that affected the urine creatinine, followed by BoxCox transformation to bring the distribution to Gaussian and reference interval calculations of fasting urine creatinine. Results: As the total sample number was &gt;120, non parametric percentile method was used to calculate the reference interval of 17.14 to 325.89 mg/dL. As the female sample number was between 40 and 120, double-sided, 95% reference interval, based on normal distribution was calculated as 12.41 to 328.71 mg/dL. As the male sample was &lt;50, method of robust calculation was used to define the reference interval of 21.91 to 379.12 mg/dL. The reference interval and biological variation data showed gender differences in the distribution, CVI , and CVG due to lower concentration of fasting urine creatinine in females resulting from lower muscle mass. Conclusion: Reference intervals of creatinine in fasting urine formed in the morning were established. Gender differences were observed in the distribution of data, CVI , and CVG.

Biological variation and reference change value data for serum copper, zinc and selenium in Turkish adult population

Abstract Objectives Calculation of biological variation (BV) components is very important in evaluating whether a test result is clinically significant. The aim of this study is to analyze BV components for copper, zinc and selenium in a cohort of healthy Turkish participants. Methods A total of 10 serum samples were collected from each of the 15 healthy individuals (nine female, six male), once a week, during 10 weeks. Copper, zinc and selenium levels were analyzed by atomic absorption spectrometer. BV parameters were calculated with the approach suggested by Fraser. Results Analytical variation (CVA), within-subject BV (CVI), between-subject BV (CVG) values were 8.4, 7.1 and 4.3 for copper; 4.2, 9.1 and 13.7 for zinc; 7.6, 2.5 and 6.9 for selenium, respectively. Reference change values (RCV) were 30.46, 27.56 and 22.16% for copper, zinc and selenium, respectively. The index of individuality (II) values were 1.65, 0.66 and 0.36 for copper, zinc and selenium, respectively. Conclusions According to the results of this study, traditional reference intervals can be used for copper but we do not recommend using it for zinc and selenium. We think that it would be more accurate to use RCV value for zinc and selenium in terms of following significant changes in recurrent results of a patient.

Performance specifications for measurement uncertainty of common biochemical measurands according to Milan models.

Definition and fullfillment of analytical performance specifications (APS) for measurement uncertainty (MU) allow to make laboratory determinations clinically usable. The 2014 Milan Strategic Conference have proposed models to objectively derive APS based on: (a)the effect of analytical performance on clinical outcome; (b)biological variation components; and (3) the state of the art of the measurement, defined as the highest level of analytical performance technically achievable. Using these models appropriately, we present here a proposal for defining APS for standard MU for some common biochemical measurands. We allocated a group of 13 measurands selected among the most commonly laboratory requested tests to each of the three Milan models on the basis of their biological and clinical characteristics. Both minimum and desirable levels of quality of APS for standard MU of clinical samples were defined by using information obtained from available studies. Blood total hemoglobin, plasma glucose, blood glycated hemoglobin, and serum 25-hydroxyvitamin D3 were allocated to the model 1 and the corresponding desirable APS were 2.80, 2.00, 3.00, and 10.0%, respectively. Plasma potassium, sodium, chloride, total calcium, alanine aminotransferase, creatinine, urea, and total bilirubin were allocated to the model 2 and the corresponding desirable APS were 1.96, 0.27, 0.49, 0.91, 4.65, 2.20, 7.05, and 10.5%, respectively. For C-reactive protein, allocated to the model 3, a desirable MU of 3.76% was defined. APS for MU of clinical samples derived in this study are essential to objectively evaluate the reliability of results provided by medical laboratories.

Models to estimate biological variation components and interpretation of serial results: strengths and limitations.

Biological variation (BV) has multiple applications in a variety of fields of clinical laboratory. The use of BV in statistical modeling is twofold. On the one hand, some models are used for the generation of BV estimates (within- and between-subject variability). Other models are built based on BV in combination with other factors to establish ranges of normality that will help the clinician interpret serial results for the same subject. There are two types of statistical models for the calculation of BV estimates: A. Direct methods, prospective studies designed to calculate BV estimates; i. Classic model: developed by Harris and Fraser, revised by the Working Group on Biological Variation of the European Federation of Laboratory Medicine. ii. Mixed-effect models. iii. Bayesian model. B. Indirect methods, retrospective studies to derive BV estimates from large databases of results. Big data. Understanding the characteristics of these models is crucial as they determine their applicability in different settings and populations. Models for defining ranges that help in the interpretation of individual serial results include: A. Reference change value and B. Bayesian data network. In summary, this review provides an overview of the models used to define BV components and others for the follow-up of patients. These models should be exploited in the future to personalize and improve the information provided by the clinical laboratory and get the best of the resources available.

Biological variation of immunological blood biomarkers in healthy individuals and quality goals for biomarker tests

BackgroundCytokines, chemokines, adipocytokines, soluble cell receptors, and immune activation markers play an important role in immune responsiveness and can provide prognostic value since they reflect underlying conditions and disease states. This study was undertaken to investigate the components of biological variation for various laboratory tests of blood immunological biomarkers.ResultsEstimates of intra-individual coefficient of variation (CVI) and inter-individual coefficient of variation (CVG) were examined for blood immunological biomarkers. Biomarkers with CVI < 10% for both genders were CD3, CD4, and CD8 T-cells, serum levels of soluble cluster of differentiation 14 (sCD14), sCD163, and soluble glycoprotein 130 (sgp130). The CVI for serum levels of adiponectin, interleukin-1 receptor antagonist (IL-1Ra), macrophage inflammatory protein 1 beta (MIP-1β), soluble CD40 Ligand (sCD40L), soluble interleukin-2 receptor alpha (sIL-2Rα), soluble interleukin-6 receptor (sIL-6R), soluble tumor necrosis factor receptor II (sTNF-RII), and tumor necrosis factor alpha (TNF-α) were between 11 and 20%. Biomarkers with CVG < 20% were CD3 T-cell, and serum concentrations of sCD14, sCD40L, and sgp130. The biomarkers with CVG > 40% were adiponectin, IL-1ra, leptin, MIP-1β, sCD163, and sIL-2Rα.ConclusionThe biological variations of biomarkers have important monitoring value for longitudinal investigation and are essential for quality specification of tests that are performed in the laboratory. The CVI was relatively small while CVG was comparatively large and mean values of each biomarker vary between subjects. The individuality of biomarkers significantly influences reference interval values. A majority of the biomarkers in this study had strong individuality and the result of each biomarker should be cautiously interpreted if using established reference interval values. Comparison of a patient’s test result with previous ones may be more useful than the usage of conventional reference values.

Why Analytical Performance Specifications are Important and How They Can Be Established

Performance specifications are important to be able to judge how good our analytical quality has to be. When we know the performance specifications, we can make rules for internal and external quality assurance, for calculating six sigma and ‘total allowable error’. How to set analytical performance specifications was discussed at the Strategic Conference of European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) in 2014. Four models were recommended. Model 1: based on the effect of analytical performance on clinical outcomes. Model 2: based on components of biological variation of the measurand. Model 3: based on state-of-the-art. Preference should be given to models 1 and 2.1 Recommendations have been made for how to allocate different measurands to different models.2 The work is now being taken forward by two groups in EFLM: the task group for setting analytical performance specifications based on outcome studies, and the task group for the biological variation database.

Analytical and Biological Variability of Urinary Epidermal Growth Factor-to-Creatinine Ratio in Patients with Chronic Kidney Disease and in Healthy Volunteers.

Urinary levels of epidermal growth factor to creatinine ratio are considered as an early predictor of interstitial kidney fibrosis but so far no data are available on their biological variation (BV) and derived parameters. The aim of this study is to determine the BV of epidermal growth factor to creatinine ratio in patients with chronic kidney disease and in healthy volunteers. This cross-sectional study included 150 patients with chronic kidney disease (CKD) and 150 healthy volunteers (HV). In both groups, spot second-morning urine samples were collected once a week for 4 consecutive weeks. Measurements of EGF were done by ELISA and expressed as EGF/creatinine ratio. The components of BV, individuality index (II), and reference change values (RCV) were calculated. The analytical coefficient of variation (CVa) of EGF/creatinine ratio was 3.8% in patients with CKD and 3.9% in HV. The within-patient variation coefficient (CVw) was 11.2% in CKD and 12.1% in HV. The between-patient coefficient of variation (CVg) was 34% in CKD and 22% in HV. In both groups, CVa met the optimum analytical quality specifications for imprecision, since it was lower than 25% of CVw. There were no significant differences between CKD and HV in the analytical or in the within-patient variances of the EGF/creatinine ratio. The between-patient EGF/creatinine ratio variance was significantly higher in patients with CKD than in HV (F: 48.3, p: 0.000). The EGF/creatinine ratio showed an individuality index (II) of 0.3 in CKD and 0.5 in HV. The reference change value (RCV) was 29.2% in CKD and 31.6% in HV. The CVa associated with the measurement techniques used in our study meets the optimal criteria of analytical imprecision. The urine EGF/creatinine ratio shows a high index of individuality both in patients with CKD and in HV, so the comparison of an isolated value with a reference interval is of little use. In the monitoring of repeated levels in the same individual or patient, the changes can only be considered significant when they are greater than 30% in relation to the previous values.

Calculation of Biological Variation Components and Reference Change Value for Serum Copper and Zinc.

Zinc and copper are among the most important trace elements. Deficiencies of these trace elements cause a wide variety of disorders. The present study aims to report the definitive assessment of biological variation (BV) parameters for these elements as within-subject BV (CVI), between subject BV (CVG), index of individuality (II), and reference change value (RCV) in a Turkish cohort study group. Ten blood specimens were collected weekly from 20 healthy volunteers (13 women, 7 men) for 10 weeks. Collected sera were stored at -80°C until the time of analysis. Serum zinc and copper levels were analyzed with atomic absorption spectrometry and ANOVA test was used to calculate the variations. The CVI and CVG for zinc were 6.26% and 23.27%, respectively. Analytical variation (CVA) was calculated as 4.24%. II and RCV for zinc were calculated as 0.26 and 21.51%, respectively. The CVI and CVG for copper were 6.05% and 19.64%, respectively. CVA was calculated as 4.24%. II and RCV for copper were calculated as 0.31 and 20.47%, respectively. Since II values were less than 0.6 for both analytes, the reference values will be of little use. RCV might be preferred for better evaluation instead.

Biological variation components of glycated hemoglobin HbA1c on tosoh G7 HPLC

Biological variation components of glycated hemoglobin HbA1c on tosoh G7 HPLC

Implementation of biological variation-based analytical performance specifications in the laboratory: Stringent evaluation of Improvacutor blood collection tubes.

Recently, because the quality of laboratory analyses has increased along with the need for quality improvement, several external quality control bodies have adapted performance specifications using the Desirable Biological Variation Database, termed “Ricos goals”; these criteria are more stringent than those presented in CLIA 88. In this study, we aimed to validate newly introduced serum separator tubes, Improvacutor, for routine clinical chemistry testing in accordance with Ricos goals and CLIA 88. Blood samples were collected from 100 volunteers into three types of serum vacuum tubes: Greiner Vacuette, Becton Dickinson (BD) Vacutainer, and Improve Improvacutor. The samples were subjected to 16 routine chemistry tests using a TBA-200fr NEO chemistry autoanalyzer. In the comparison analysis, all 16 test results were acceptable according to CLIA 88. However, in the comparison of Improve and BD tubes, creatinine showed 4.31% (+0.08 μmol/L) bias. This slightly exceeded the Desirable Specification for Inaccuracy Ricos limit of ±3.96%, but still satisfied the CLIS88 limit of ±26.52 μmol/L. The remaining 15 analytes performed acceptably according to the Desirable Specifications of Ricos. The correlation coefficient of 12 analytes was greater than 0.95 in Passing-Bablok regression analysis among the three tubes, but was lower for four analytes: calcium, sodium, potassium, and chloride. In the stability assay, only potassium tested in the Greiner tube revealed a larger positive bias (2.18%) than the Ricos Desirable Specification for Inaccuracy based on biologic variation (1.8%). The BD tube also showed a positive bias of 1.74%, whereas the new Improve tube showed the smallest positive bias of 1.17% in potassium level after 72 h storage. Thus, the results of this study demonstrate that recently introduced analytical performance specifications based on components of biological variation (Rico's goal) could be extended to criterion for performance evaluation and applied.

Biological variation of holotranscobalamin and cobalamin in healthy individuals

Data on biological variation for cobalamin and holotranscobalamin (holoTC) are limited. The aim of this study was to determine within-subject (CVI) and between-subject (CVG) biological variations for these analytes in a healthy population. We collected blood samples from 15 healthy volunteers (12 women and three men, 22–66 years) on the same weekday for 10 consecutive weeks. Serum samples were stored at –80 °C until analysis in duplicate in a single analytical run. The CVI and CVG were estimated by nested ANOVA. The CVI (95% CI) for cobalamin and holoTC was 6.7% (5.7–7.7) and 13.0% (11.5–15.0), respectively. The corresponding CVG was 24.1% (16.4–36.0) and 24.6% (16.3–37.7). The analytical variation (CVA) (95% CI) was 3.5% (3.2–4.0) for cobalamin and 2.4% (2.1–2.6) for holoTC. The index of individuality (II) was low (<0.6) for both cobalamin and holoTC and the reference change value (RCV) was 20.1% for cobalamin and 36.6% for holoTC. Our study describes the components of biological variation of cobalamin and holoTC in a healthy population, contributing to a better clinical interpretation of these biomarkers.

Criteria for assigning laboratory measurands to models for analytical performance specifications defined in the 1st EFLM Strategic Conference.

This paper, prepared by the EFLM Task and Finish Group on Allocation of laboratory tests to different models for performance specifications (TFG-DM), is dealing with criteria for allocating measurands to the different models for analytical performance specifications (APS) recognized in the 1st EFLM Strategic Conference Consensus Statement. Model 1, based on the effect of APS on clinical outcome, is the model of choice for measurands that have a central role in the decision-making of a specific disease or clinical situation and where cut-off/decision limits are established for either diagnosing, screening or monitoring. Total cholesterol, glucose, HbA1c, serum albumin and cardiac troponins represent practical examples. Model 2 is based on components of biological variation and should be applied to measurands that do not have a central role in a specific disease or clinical situation, but where the concentration of the measurand is in a steady state. This is best achieved for measurands under strict homeostatic control in order to preserve their concentrations in the body fluid of interest, but it can also be applied to other measurands that are in a steady state in biological fluids. In this case, it is expected that the "noise" produced by the measurement procedure will not significantly alter the signal provided by the concentration of the measurand. This model especially applies to electrolytes and minerals in blood plasma (sodium, potassium, chloride, bicarbonate, calcium, magnesium, inorganic phosphate) and to creatinine, cystatin C, uric acid and total protein in plasma. Model 3, based on state-of-the-art of the measurement, should be used for all the measurands that cannot be included in models 1 or 2.

Spatial, Phylogenetic, Environmental and Biological Components of Variation in Extinction Risk: A Case Study Using Banksia

Comparative analyses of extinction risk routinely apply methods that account for phylogenetic non-independence, but few analyses of extinction risk have addressed the possibility of spatial non-independence. We explored patterns of extinction risk in Banksia, a plant genus largely endemic to Australia’s southwest biodiversity hotspot, using methods to partition the variance in two response variables (threat status and range size) into phylogenetic, spatial, and independent components. We then estimated the effects of a number of biological and external predictors on extinction risk independently of phylogeny and space. The models explained up to 34.2% of the variation in range size and up to 9.7% of the variation in threat status, nearly all of which was accounted for by the predictors, not by phylogeny or space. In the case of Banksia, therefore, high extinction risk can be clearly linked with biological syndromes (such as a brief flowering period) or geographic indicators of human impact (such as extensive habitat loss), but cannot be predicted from phylogenetic relatedness or geographic proximity.