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Short-term Variability of Vitamin D-Related Biomarkers.

Compelling observational evidence (1-6) that low 25-hydroxyvitamin D [25(OH)D] [7] is associated with diabetes, cardiovascular disease, and cancer has prompted the US National Institutes of Health to make strategic investments in randomized clinical trials to test whether vitamin D3 supplementation reduces the risk of developing type 2 diabetes (the D2D Trial) (7), and heart disease, stroke and cancer (the VITAL trial) (8). Existing research has focused on total 25(OH)D; however, as highlighted in a 2015 US Preventive Services Task Force Recommendation Statement (9), it is possible that forms of vitamin [D.sub.3] other than 25(OH)D may be relevant to human health. Some of the other biomarkers on the vitamin D pathway of potential clinical importance include fibroblast growth factor 23 (FGF23), vitamin D binding protein (VDBP), free 25(OH)D, bioavailable 25(OH)D, parathyroid hormone (PTH), calcium and phosphorus.

Biological measures often naturally vary within an individual on a day-to-day basis. This variation can result in misclassification in clinical settings, and in research may bias estimates of association. While the within person CV ([CV.sub.W]) of some biomarkers of vitamin D status such as 25(OH)D, calcium and phosphorus are long established, other biomarkers like VDBP, FGF23, free 25(OH)D and bioavailable 25(OH)D have only recently garnered interest (10, 11) and their short-term variability has not yet been studied. For the more established biomarkers, older evaluations of their variability also require reexamination due to changes in laboratory methodology. For example, historically most studies measured 25(OH)D using immunoassay methods, whereas LC-MS/MS is now considered to be far more accurate (12). The newer LC-MS/MS methods typically also have less laboratory analytical imprecision, which would result in lower total variability.

As we await results of randomized clinical trials that will provide insight into whether vitamin D is causally associated with health benefits (13), it is important to understand the biological variability of vitamin D biomarkers. This is especially relevant for 25(OH)D, which has a high prevalence of inadequacy (<20 ng/mL; 81% in blacks and 18% in whites according to the National Health and Nutrition Examination Survey (NHANES)) (14) and is frequently measured in clinical settings (15). Thus, the objective of this analysis was to quantify the short-term (6-week) within-person variability of fasting serum 25(OH)D, FGF23, VDBP, free 25(OH)D, bioavailable 25(OH)D, PTH, calcium and phosphorus in a community-based population. We also calculated the percent of the cohort for which the second measurement resulted in reclassification according to clinical cutpoints, laboratory reference values or concentrations outside the central 95 th percentile of the distribution.

Materials and Methods


The Atherosclerosis Risk in Communities (ARIC) study is a community-based prospective cohort that began in 1987-1989 when a total of 15 792 predominantly black and white individuals, aged 45-64 years, were recruited from 4 US field centers: suburban Minneapolis, Minnesota; Forsyth County, North Carolina; Washington County, Maryland; and Jackson, Mississippi. Participants have been followed continuously since study initiation, and many have taken part in additional clinic examinations. The 5th clinic visit took place in 2011-2013, and was attended by 6538 participants (66% of those still living). Institutional review boards at each site approved all procedures, and all study participants provided written informed consent.

During visit 5, a total of 200 participants (50 from each field center) were asked to return for a repeat QC visit approximately 4-8 weeks following the initial visit. These 200 individuals, who were representative of the age and sex distribution of the larger cohort of persons who attended ARIC visit 5, form the basis for the present analysis. We additionally excluded participants having a race with low sample numbers (Asians; n = 2), missing or suspect information on single nucleotide polymorphisms (SNPs) needed for the calculation of bioavailable and free 25(OH)D (i.e., rs7041, rs4588; n = 8), missing any biomarker data (n = 6), and those with data points that were severe outliers (n = 24) as identified by the iterative outlier removal approach (16). Our final analytic sample includes 160 participants.


Participants were asked to fast for 8 h before the blood draw for both ARIC visit 5 and the repeat visit. Serum was stored at-70[degrees]C before measurement. Previously unthawed samples were used. Below is further information on measurement:

* VDBP was quantified using trypsin digestion and LCMS/MS at the University of Washington. This approach directly measures VDBP peptides (11). The interassay analytical CV (calculated using laboratory controls) is 7.3-9.0%. VDBP was also measured using a monoclonal ELISA, produced by R&D Systems.

* 25(OH)[D.sub.2] and 25(OH)[D.sub.3] concentrations were measured using LC-MS/MS at the University of Minnesota (17). The interassay analytical CVs were 5.3% for 25(OH)[D.sub.2] and 3.4% for 25(OH)[D.sub.3]. Given the minimal (approximately 6-week) time span between measurements, 25(OH)[D.sub.3] concentrations were not corrected for known seasonality effects in the primary analyses. Total 25(OH)D was calculated as the sum of 25(OH)[D.sub.2] and 25(OH)[D.sub.3].

* Calcium and phosphorus were measured on the Roche Modular P800. Interassay analytical CVs were 2.3% and 2.2% for calcium and phosphorus, respectively.

* FGF23 was measured using a 2 site ELISA, manufactured by Kainos Incorporated. The interassay analytical CVs were 21.0% at a concentration of 23 pg/mL, 9.7% at a concentration of 42 pg/mL and 7.8% at a concentration of 82 pg/mL.

* Albumin was measured as part of the glycated albumin assay by Asahi Kasei Pharma adapted to the Roche Modular P800 Chemistry Analyzer (Roche Diagnostics Corporation). Interassay analytical CVs were 1.9% at a concentration of 4.48 g/dL and 4.0% at a concentration of 2.5 g/dL.

* PTH was measured on a Roche Elecsys 2010. The interassay analytical CVs were 5.1% at a concentration of 33.10 pg/mL and 2.9% at a concentration of 204 pg/mL.

* Bioavailable and free 25(OH)D were calculated using a general formula (18) that has been tailored for 25(OH)D (19). It incorporates serum VDBP, 25(OH)D and albumin concentrations, along with the binding affinities for 25(OH)D to albumin and VDBP. Binding affinities/constants are genotype-specific for VDBP.


For each biomarker, we calculated the mean concentration at ARIC visit 5, the mean at the 6-week follow-up visit, the mean difference (repeat minus original), and the standardized mean difference (repeat/SD--original/ SD). We also calculated the following measures of within-person total variability: [CV.sub.W], intraclass correlation coefficient (ICC), index of individuality, and Spearman rank correlation coefficient (r).

As we have done previously (20), to partition the total variance of the repeated measurements into the between-subject variance ([mathematical expression not reproducible]) and within-subject variance ([mathematical expression not reproducible]), we used linear mixed effects models with each biomarker as the dependent variable and the participant as a random effect. The between-person CV ([CV.sub.G]) was calculated as follows: [mathematical expression not reproducible], where [mu] is the mean of all values (both original and repeat measurements). Similarly, we calculated the [mathematical expression not reproducible]. The [CV.sub.W] is a function of the within-person biological CV and the analytical CV (or the methods CV reported from the lab). We then calculated the index of individuality as [CV.sub.W]/[CV.sub.G] (21, 22). We also calculated the ICC as [mathematical expression not reproducible]. We used bootstrapping to estimate the 95% CIs for estimates of [CV.sub.W], ICC, r, and the index of individuality using 200 replications. We also report the percent reclassified between the 2 visits according to clinical cutpoints or laboratory reference values, when they existed. For biomarkers that do not presently have clinical cutpoints, values falling within the central 95th percentiles (i.e., between 2.5% and 97.5%) were defined as normal. The distributions were defined based on the original exam values.

In sensitivity analyses, we additionally stratified by time between the initial and repeat visit (e.g., <6 weeks vs [greater than or equal to] 6 weeks) and participant race (black vs white). Further sensitivity analyses were conducted where we accounted for seasonal variation in 25(OH)D using the residuals approach (17). All statistical analyses were conducted using Stata, version 13.0 (Stata Corp).


The 160 individuals in our final analytic sample were 60% female, 27.5% black, and had a mean (SD) age of 76.3 (4.9) years. The median age was 76 years with a range of 68-88 years. Participants who attended the repeat study visit were similar to the overall cohort who attended visit 5 in terms of age [75.8 (5.3) years], and percent female (58.8%) and black (23.6%). Relative to whites, black participants in the repeat study tended to have lower concentrations of 25(OH)D and higher concentrations of PTH, but had similar concentrations of other biomarkers (Table 1). Distributions of biomarkers at the original and repeat visits are presented in Table 1 in the Data Supplement that accompanies the online version of this article at vol62/issue12.

Between the ARIC visit 5 blood draw and the repeat visit there were on average 6.4 weeks [+ or-] 2.1 [range: 3.3-13.3 weeks; 25th and 75th percentiles: 4.9 and 7.1 weeks]. Standardized differences between the visits were all near 0, indicating that there were no systematic blood processing or laboratory measurement issues between the visits (Table 2). The 6-week CVW was the lowest for serum calcium (2.0%), albumin (3.6%), 25(OH)D (6.9%), VDBP (7.0%) and phosphorus (7.6%); intermediate for free 25(OH)D (9.0%) and bioavailable 25(OH)D (9.9%); and highest for PTH (16.7%) and FGF23 (17.8%). The ICC and r were highest for 25(OH)D, bioavailable 25(OH)D, free 25(OH)D, and PTH (all [greater than or equal to] 0.80), but somewhat lower for VDBP, FGF23, calcium and phosphorus (approximately 0.60-0.75). For the index of individuality, low values (defined by <0.6) were observed for 25(OH)D, free 25(OH)D, bioavailable 25(OH)D, PTH, calcium and albumin, while intermediate values (defined as 0.6 to <1.4) were observed for FGF23, phosphorus, and VDBP.

Overall results were similar in analyses stratified by race (see online Supplemental Table 2). The ICC and Spearman correlation (r)--both indices that are influenced by sample size (23)--were in some instances slightly higher in whites than blacks, which is consistent with our sample including more whites (n = 116) than blacks (n = 44). No noteworthy differences were observed when we stratified by length of time between measurements (see online Supplemental Table 3). Results for 25(OH)D were also virtually identical when we corrected for seasonal variation: [CV.sub.W] = 6.9, ICC = 0.95, Spearman rank = 0.95, index of individuality = 0.22. Lastly, in online Supplemental Table 4 we provide short-term variability results for VDBP as measured by the monoclonal immunoassay, which shows a higher ICC (0.95) and lower index of individuality (0.22) compared with LC-MS/MS.

To gauge the impact that individual-level variation may have on diagnostic misclassification, we examined the percent reclassified at the second measurement (Table 3). PTH and VDBP had 7.5% reclassified. For phosphorus, FGF23 and 25(OH)D about 4%-5% were classified, and for the other biomarkers approximately 3% or less were reclassified. Given our sample size relatively few individuals were reclassified, hence CIs for the percent reclassified are wide. Reclassification for VDBP as measured by the monoclonal immunoassay is provided in online Supplemental Table 5.


This evaluation of 9 vitamin D pathway biomarkers revealed that 6-week variability, as expressed by [CV.sub.W], was quite low for serum VDBP, 25(OH)D, calcium, phosphorus, and albumin, intermediate for free and bioavailable 25(OH)D, but fairly high for FGF23 and PTH. Reclassification was highest for PTH, VDBP and phosphorus, although these results should be interpreted cautiously as precision was poor for these analyses. Multiple measurements of some biomarkers, such as FGF23 and PTH, may be needed to minimize misclassification. Variability was similar between blacks and whites, suggesting that although there may be racial differences in the absolute concentrations and the interrelations of these biomarkers (24, 25), the short-term within-person stability of these biomarkers does not vary by race. These results provide insight into the extent of potential misclassification of vitamin D markers in both research and clinical settings.

CVw was identified a priori as the primary index of variability owing to its validity across a wide range of biomarker values. However, to allow for a more comprehensive understanding of the variability of these biomarkers, several other indicators of variability are reported herein, each with a different interpretation (23). Standardized mean differences were all small, suggesting that there were no systematic differences between the 2 sets of readings (e.g., related to variation in blood processing and/or laboratory drift) thereby facilitating a systematic evaluation of short-term variability. Spearman rank correlations assess monotonicity of the 2 biomarker measurements, whereas ICC is an estimate of the fraction of the total measurement variability caused by variation among individuals. Overall, in the present analysis Spearman rank correlations and ICCs resulted in similar inferences. Notably, these indices are both highly influenced by the range of a biomarker among individuals (23). Higher values are typically observed for biomarkers with a wider range, and correspondingly suggest that the within-person variance is low relative to the between person variance. The index of individuality, which is the ratio of total within-person CV to between-person CV, can be useful for determining the value of repeating tests of a biomarker. In the present analysis, low indices (<0.6) were observed for several biomarkers (i.e., free 25(OH)D, bioavailable 25(OH)D, PTH, calcium and albumin), which suggests that repeating tests of these biomarkers will result in concentrations close to the first, and therefore provide little new information (26). All of the other biomarkers explored had index of individuality values in the intermediate range, between 0.6-0.9, which suggests repeat measures may be somewhat useful to minimize misclassification. The monoclonal immunoassay showed excellent within-person variability, however as we have shown previously this assay is biased by genotype (10, 11); thus results for this assay should be interpreted with caution. The excellent within-person variability is most likely a result of the wide distribution of VDBP for this method, which results from the strong influence of genotype on the measurements (e.g., the poor reactivity of the Gc1f isoform in the assay).

This study provides the first evidence of the shortterm variability of VDBP, and calculated free and bioavailable 25(OH)D. Recent studies have evaluated daily variability of FGF23 (27-30), and most have found no diurnal variation; however, no studies as of yet have assessed its short-term variability. Approximately 2-week biologic variability of 25(OH)D, calcium, phosphorus and albumin has been previously reported using data from the NHANES III survey. (31 ) For 25(OH)D, the 2-week CVw and index of individuality reported in NHANES (i.e., 11.3, 0.30, respectively) (31) were roughly similar to those we observed in this ARIC sample (6.9, 0.22). The subsample of ARIC participants in the present study was on average 76 years old, while the NHANES population was, on average, 39 years old. The somewhat lower within-person variability among the older ARIC participants is not entirely surprising, since older populations may have less variability in their expo sure to the sun and are known to have reduced dermal conversion of 7-dehydrocholesterol to vitamin D3 (24). NHANES III also used an RIA to measure 25(OH)D, while in the present ARIC samples we used LC-MS/MS. Prior work has demonstrated that 25(OH)D tracks reasonably well across longer time-periods (32-34). Using data from more than 1700 ARIC participants who had 25(OH)D measured in serum collected in 1990-1992 and in 1993-1995, we recently reported the 3-year correlation of 25(OH)D concentrations to be r = 0.73 in whites and r = 0.66 in blacks (35). Our finding of high biological variability for PTH is consistent with that of prior work in individuals without chronic kidney disease, which has shown PTH to have a within-person variation of approximately 25% (36,37). For serum calcium, phosphorus, and albumin, short-term variability was comparable between the ARIC and NHANES (31) samples (CVw/index of individuality): calcium ARIC = 2.0/ 0.56, NHANES = 2.3/0.55, phosphorus ARIC = 7.6/ 0.77, NHANES = 9.5/0.58, albumin ARIC = 3.6/0.58, NHANES = 3.5/0.46.

Quantifying the amount of within-person short-term variability inherent in these vitamin D markers provides insight into the extent of potential misclassification of vitamin D markers in both research and clinical settings. In these data, reclassification was highest for PTH, VDBP and phosphorus, although imprecision was high given the relatively few individuals reclassified. High within-person variability, or random fluctuations around a set point, can lead to false positive results at the individual level, and substantial overestimates of disease prevalence on a population level, especially if the biomarker is only measured once (38, 39). Overestimates occur with greater short-term variability since the variance is larger, essentially flattening and widening the distribution and resulting in more individuals having concentrations beyond established cutpoints. It is also possible that measurements from this study and resulting estimates of short-term variability could be incorporated into regression models (i.e., regression calibration, which allows for the simultaneous correction of variation or measurement error in exposure(s) as well as accounts for the relationship of that variation with other risk factors) and could have a substantial effect on estimates of association (40). Of course, understanding within-person variation is only 1 aspect of variability. Interlaboratory bias remains a major concern in both clinical practice and research settings. As such, it is crucial that efforts by initiatives such as the Vitamin D Standardization Program, which seeks to standardize laboratory measurements such that results are accurate and comparable over time, location, and laboratory procedure, are implemented by laboratories (41, 42).

These data have notable strengths, as well as limitations. The time span between initial and repeat study visits varied between participants. Additionally, we may have been underpowered for the stratified analyses. Nevertheless, this study provides a comprehensive evaluation of the short-term variability of numerous markers of vitamin D metabolism. Also, our study sample was quite advanced in age (mean age 76.3 years). However, we believe our results are generalizable to younger populations since we would not expect that intraindividual variability of these biomarkers would be different among the elderly vs younger adults, and our results were similar for the biomarkers previously reported in the younger NHANES sample (31). Strengths of the study include the sample, which was community-based (as opposed to from a clinical setting) and was relatively large for a repeatability study. Presence of both blacks and whites in the sample is another important characteristic of the study, given the literature suggesting racial/ethnic differences in vitamin D metabolism (24, 25). For each biomarker, state-of-the-art assays for the samples from the original and repeat visits were conducted in the same laboratory and with the same instruments, which helped us isolate within-person variability from other sources of variability that could arise from storage time, freeze-thaw, machine calibration, and lot-to-lot variability of reagents.

In conclusion, over a 6-week time-frame, variability was quite low for serum VDBP, 25(OH)D, calcium, phosphorus, and albumin, intermediate for free and bioavailable 25(OH)D, but fairly high for FGF23 and PTH. As such, multiple measurements of some biomarkers, such as FGF23 and PTH, may be needed to minimize misclassification. These results provide insight into the extent of potential misclassification of vitamin D pathway biomarkers in research and clinical settings.

Author Contributions: All authors confirmed they have contributed to the intellectual content of this paper and have met the following 3 requirements: (a) significant contribution to the conception and design, acquisition of data, or analysis and interpretation of data; (b) drafting or revising the article for intellectual content; and (c) final approval of the published article.

Authors' Disclosures or Potential Conflicts of Interest: Upon manuscript submission, all authors completed the author disclosure form. Disclosures and/or potential conflicts of interest:

Employment or Leadership: A.N. Hoofnagle, Clinical Chemistry, AACC.

Consultant or Advisory Role: None declared.

Stock Ownership: None declared.

Honoraria: None declared.

Research Funding: The Atherosclerosis Risk in Communities Study is carried out as a collaborative study supported by National Heart, Lung, and Blood Institute contracts (HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN2682011 00008C,HHSN268201100009C, HHSN268201100010C, HHSN 268201100011C, and HHSN268201100012C). The University of Washington has received grant (Waters, Inc.) and instrumentation (Waters, Inc., and Thermo, Inc.) support from mass spectrometry com panies. Reagents for the glycated albumin (albumin) assays were donated by the Asahi Kasei Corporation. P.L. Lutsey, NIH grants HL103706 and HL103706-S1, nonsponsored McKnight Land-Grant Professorship funds; A.N. Hoofnagle, NIH grants CA160034 and DK035816; C.M. Henderson, NIH grant HL007028; E. Selvin, NIH grants K24DK106414 and 2R01DK089174.

Expert Testimony: None declared.

Patents: None declared.

Role of Sponsor: The funding organizations played no role in the design of study, choice of enrolled patients, review and interpretation of data, and final approval of manuscript.

Acknowledgments: The authors thank the staff and participants of the ARIC study for their important contributions.


(1.) Kunutsor S, Apekey T, Steur M. Vitamin D and risk of future hypertension: meta-analysis of 283,537 participants. Eur J Epidemiol 2013; 28:205-21.

(2.) Song Y, Wang L, Pittas AG, Del Gobbo LC, Zhang C, Manson JE, Hu FB. Blood 25-hydroxy vitamin D levels and incident type 2 diabetes: a meta-analysis of prospective studies. Diabetes Care 2013; 36:1422-8.

(3.) Brondum-Jacobsen P, Benn M, Jensen GB, Nordestgaard BG. 25-hydroxyvitamin D levels and risk of ischemic heart disease, myocardial infarction, and early death: population-based study and meta-analyses of 18 and 17 studies. Arterioscler Thrombo Vasc Biol 2012; 32:2794-802.

(4.) Meredith AJ, McManus BM. Vitamin D in heart failure. J Card Fail 2013; 19:692-711.

(5.) Wang L, Song Y, Manson JE, Pilz S, Marz W, Michaelsson K, et al. Circulating 25-hydroxy-vitamin D and risk of cardiovascular disease: a meta-analysis of prospective studies. Circ Cardiovasc Qual Outcomes 2012; 5:819-29.

(6.) Lutsey P, Michos E. Vitamin D, calcium, and atherosclerotic risk: evidence from serum levels and supplementation studies. Curr Atheroscler Rep 2012; 15:1-8.

(7.) Pittas AG, Dawson-Hughes B, Sheehan PR, Rosen CJ, Ware JH, Knowler WC, et al. Rationale and design of the vitamin D and type 2 diabetes (D2D) study: a diabetes prevention trial. Diabetes Care 2014; 37:3227-34.

(8.) Manson JE, Bassuk SS, Lee IM, Cook NR, Albert MA, Gordon D, et al. The vitamin D and omega-3 trial (VITAL): rationale and design of a large randomized controlled trial of vitamin D and marine omega-3 fatty acid supplements for the primary prevention of cancer and cardiovascular disease. Contemp Clin Trials 2012; 33:159-71.

(9.) LeFevre ML. Screening for vitamin D deficiency in adults: U.S. Preventive Services Task Force recommendation statement screening for vitamin D deficiency in adults. Ann Intern Med 2015; 162:133-40.

(10.) Hoofnagle AN, Eckfeldt JH, Lutsey PL. Vitamin D-binding protein concentrations quantified by mass spectrometry. N Engl J Med 2015; 373:1480-2.

(11.) Henderson CM, Lutsey PL, Misialek JR, Laha TJ, Selvin E, Eckfeldt JH, Hoofnagle AN. Measurement by a novel LC-MS/MS methodology reveals similar serum concentrations of vitamin D-binding protein in blacks and whites. Clin Chem 2016; 62:179-87.

(12.) Roth HJ, Schmidt-Gayk H, Weber H, Niederau C. Accuracy and clinical implications of 7 25-hydroxyvitamin D methods compared with liquid chromatography-tandem mass spectrometry as a reference. Ann Clin Biochem 2008; 45:153-9.

(13.) Manson JE, Bassuk SS. Vitamin D research and clinical practice: at a crossroads. JAMA 2015; 313:1311-2.

(14.) Gutierrez OM, Farwell WR, Kermah D, Taylor EN. Racial differences in the relationship between vitamin D, bone mineral density, and parathyroid hormone in the National Health and Nutrition Examination Survey. Osteoporos Int 2011; 22:1745-53.

(15.) Rosen C, moderator, Thadhani RI, Millard PS, Rowe SB, panelists. Panel: what is the vitamin D experience? [Panel Discussion]. Vitamin D: Moving Toward Evidence-based Decision Making in Primary Care Conference; 2014 Dec 2-4; Bethesda, MD.

(16.) Parrinello CM, Grams ME, Sang Y, Couper D, Wruck LM, Li D, et al. Iterative outlier removal: a method for identifying outliers in laboratory recalibration studies. Clin Chem 2016; 62:966-72.

(17.) Lutsey PL, Eckfeldt JH, Ogagarue ER, Folsom AR, Michos ED, Gross M. The 25-hydroxyvitamin D C-3 epimer: Distribution, correlates, and reclassification of 25-hydroxyvitamin D status in the population-based Atherosclerosis Risk in Communities Study (ARIC). Clin Chim Acta 2015; 442:75-81.

(18.) Vermeulen A, Verdonck L, Kaufman JM. A critical evaluation of simple methods for the estimation of free testosterone in serum. J Clin Endocrinol Metab 1999; 84: 3666-72.

(19.) Powe CE, Evans MK, Wenger J, Zonderman AB, Berg AH, Nalls M, et al. Vitamin D-binding protein and vitamin D status of black Americans and white Americans. N Engl J Med 2013; 369:1991-2000.

(20.) Parrinello CM, Lutsey PL, Couper D, Eckfeldt JH, Steffes MW, Coresh J, Selvin E. Total short-term variability in biomarkers of hyperglycemia in older adults. Clin Chem 2015; 61:1540-1.

(21.) Omar F. Essential laboratory knowledge for the clinician. Continuing Medical Education. 2012; 30:244-8.

(22.) Fraser CG. Biological variation: from principles to practice. Washington (DC): AACC Press 2001.

(23.) Szklo M, Nieto FJ. Epidemiology: beyond the basics. 3rd ed. Burlington (MA): Jones & Bartlett Learning; 2014.

(24.) Institute of Medicine (US) Committee to Review Dietary Reference Intakes for Vitamin D and Calcium. Dietary reference intakes for calcium and vitamin D. Ross AC, Taylor CL, Yaktine AL, Del Valle HB, editors. Washington (DC): National Academies Press; 2011.

(25.) Chun RF, Peercy BE, Orwoll ES, Nielson CM, Adams JS, Hewison M. Vitamin D and DPB: the free hormone hypothesis revisited. J Steroid Biochem Mol Biol 2014; 144, Part A:132-7.

(26.) Petersen PH, Fraser CG, Sandberg S, Goldschmidt H. The index of individuality is often a misinterpreted quantity characteristic. Clin Chem Lab Med 1999; 37: 655-61.

(27.) Vervloet MG, van Ittersum FJ, Buttler RM, Heijboer AC, Blankenstein MA, ter Wee PM. Effects of dietary phosphate and calcium intake on fibroblast growth factor-23. Clin J Am Soc Nephrol 2011; 6:383-9.

(28.) Isakova T, Xie H, Barchi-Chung A, Smith K, Sowden N, Epstein M, et al. Daily variability in mineral metabolites in CKD and effects of dietary calcium and calcitriol. Clin J Am Soc Nephrol 2012; 7:820-8.

(29.) Larsson T, Nisbeth ULF, Ljunggren O, Juppner H, Jonsson KB. Circulating concentration of FGF-23 increases as renal function declines in patients with chronic kidney disease, but does not change in response to variation in phosphate intake in healthy volunteers. Kidney Int 2003; 64:2272-9.

(30.) Carpenter TO, Insogna KL, Zhang JH, Ellis B, Nieman S, Simpson C, et al. Circulating levels of soluble klotho and FGF23 in x-linked hypophosphatemia: circadian variance, effects of treatment, and relationship to parathyroid status. J Clin Endocrinol Metab 2010; 95: E352-7.

(31.) Lacher DA, Hughes JP, Carroll MD. Biological variation of laboratory analytes based on the 1999-2002 National Health and Nutrition Examination Survey. Natl Health Stat Report 2010; (21):1-7.

(32.) Berger C, Greene-Finestone LS, Langsetmo L, Kreiger N, Joseph L, Kovacs CS, et al. Temporal trends and determinants of longitudinal change in 25-hydroxyvitamin D and parathyroid hormone levels. J Bone Miner Res 2012; 27:1381-9.

(33.) van Schoor NM, Knol DL, Deeg DJ, Peters FP, Heijboer AC, Lips P. Longitudinal changes and seasonal variations in serum 25-hydroxyvitamin D levels in different age groups: results of the Longitudinal Aging Study Amsterdam. Osteoporos Int 2014; 25:1483-91.

(34.) Jorde R, Sneve M, Torjesen P, Figenschau Y. No improvement in cardiovascular risk factors in overweight and obese subjects after supplementation with vitamin D3 for 1 year. J Intern Med 2010; 267:462-72.

(35.) McKibben RA, Zhao D, Lutsey PL, Schneider ALC, Guallar E, Mosley TH, Michos ED. Factors associated with change in 25-hydroxyvitamin D levels over longitudinal follow up in the ARIC study. J Clin Endocrinol Metab 2016; 101:33-43.

(36.) Viljoen A, Singh Dhruv K, Twomey Patrick J, Farrington K. Analytical quality goals for parathyroid hormone based on biological variation. Clin Chem Lab Med 2008; 46:1438-42.

(37.) Ankrah-Tetteh T, Wijeratne S, Swaminathan R. Intraindividual variation in serum thyroid hormones, parathyroid hormone and insulin-like growth factor-1. Ann Clin Biochem 2008; 45:167-9.

(38.) Selvin E, Crainiceanu CM, Brancati FL, Coresh J. Short-term variability in measures of glycemia and implications for the classification of diabetes. Arch Intern Med 2007; 167:1545-51.

(39.) Selvin E, Juraschek SP, Eckfeldt J, Levey AS, Inker LA, Coresh J. Within-person variability in kidney measures. Am J Kidney Dis 2013; 61:716-22.

(40.) Paynter NP, Crainiceanu CM, Sharrett AR, Chambless LE, Coresh J. Effect of correcting for long-term variation in major coronary heart disease risk factors: relative hazard estimation and risk prediction in the Atherosclerosis Risk in Communities Study. Ann Epidemiol 2012; 22:191-7.

(41.) Sempos CT, Durazo-Arvizu RA, Binkley N, Jones J, Merkel JM, Carter GD. Developing vitamin D dietary guidelines and the lack of 25-hydroxyvitamin D assay standardization: the ever-present past. J Steroid Biochem Mol Biol [Epub ahead of print 2015 Aug 28].

(42.) Sempos CT, Vesper HW, Phinney KW, Thienpont LM, Coates PM. Vitamin D status as an international issue: national surveys and the problem of standardization. Scand J Clin Lab Invest Suppl 2012; 243:32-40.

Pamela L. Lutsey, [1] * Christina M. Parrinello, [2] Jeffrey R. Misialek, [1] Andy N. Hoofnagle, [3] Clark M. Henderson, [3] Thomas J. Laha, [3] Erin D. Michos, [4,5] John H. Eckfeldt, [6] and Elizabeth Selvin [5]

[1] Division of Epidemiology and Community Health, University of Minnesota School of Public Health, Minneapolis, MN; [2] Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY; [3] Department of Laboratory Medicine, University of Washington, Seattle, WA; [4] Division of Cardiology, Johns Hopkins University, Baltimore, MD; [5] Department of Epidemiology and the Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; [6] Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN.

* Address correspondence to this author at: Division of Epidemiology and Community Health, University of Minnesota, 1300 S 2nd St, Suite 300, Minneapolis, MN 55454. Fax 612-624-0315; e-mail

Received May 31, 2016; accepted August 19, 2016.

Previously published online at DOI: 10.1373/clinchem.2016.261461

[7] Nonstandard abbreviations: 25(OH)D, 25-hyrdoxyvitamin D; FGF23, fibroblast growth factor 23; VDBP, vitamin D binding protein; PTH, parathyroid hormone; [CV.sub.W], within person CV; NHANES, National Health and Nutrition Examination Survey; ARIC study, Atherosclerosis Risk in Communities study; SNP, single nucleotide polymorphism; ICC, intraclass correlation coefficient; t, Spearman rank correlation coefficient; [mathematical expression not reproducible], between subject variance; [mathematical expression not reproducible], within-subject variance; CVG, between-person CV.
Table 1. Characteristics of repeatability study participants, overall
and by race: the ARIC study, visit 5 (2011-2013). (a)

                                Overall        Whites        Blacks

n                                 160           116            44
Age, years                     76.3(4.9)     76.2 (4.8)    76.5 (5.2)
Female, %                        60.0           57.8          65.9
VDBP, pg/mL                   264.5(31.6)   265.7 (33.5)   261.3(26.2)
25(OH)D, ng/mL                34.0(11.1)     35.7(10.4)    29.5(11.6)
Calcium, mg/dL                 9.3 (0.4)     9.4 (0.4)      9.2 (0.4)
Phosphorus, mg/dL              3.6 (0.4)     3.5 (0.4)      3.6 (0.4)
FGF23, pg/mL                  61.1 (20.9)   62.2 (22.5)    58.2 (15.8)
Albumin, g/dL                  4.1 (0.3)     4.2 (0.3)      3.9 (0.3)
PTH, pg/mL                    45.7(18.5)     43.4(17.0)    51.6(21.0)
Free 25(OH)D, pg/mL           10.4 (5.0)     11.8(4.7)      6.9 (3.9)
Bioavailable 25(OH)D, ng/mL    3.9 (1.9)     4.4 (1.7)      2.5 (1.6)

(a) Valuesare mean (SD)or percentage. To convert mg/dLto mmol/L,
multiply by 0.2495 for calcium and by 0.3229 for phosphorus.

Table 2. Short-term variability in vitamin D-related biomarkers in
160 older adults: the ARIC study, visit 5 (2011-2013).

                              Original exam,   Repeat exam,
                                mean (SD)       mean (SD)

VDBP, [micro]g/ml                265 (32)        257(31)
25(OH)D, ng/mL                  34.0(11.1)      33.5(11.0)
Calcium, mg/dL (c)              9.3 (0.4)       9.3 (0.4)
Phosphorus, mg/dL (c)           3.6 (0.4)       3.5 (0.5)
FGF23, pg/mL                   61.1 (20.9)      59.7(19.4)
Albumin, g/dL                   4.1 (0.3)       4.0 (0.3)
PTH, pg/mL                      45.7(18.5)     45.1 (19.2)
Free 25(OH)D, pg/mL             10.4 (5.0)      10.6(5.2)
Bioavailable 25(OH)D, ng/mL     3.9 (1.9)       3.9 (2.0)

                                Difference      Standardized
                                 (repeat--       difference,
                              original), mean   mean (SD) (a)

VDBP, [micro]g/ml                 -8(25)         -0.1 (0.8)
25(OH)D, ng/mL                  -0.5 (3.3)        0.0 (0.3)
Calcium, mg/dL (c)               0.0 (0.3)        0.4 (0.7)
Phosphorus, mg/dL (c)            0.0 (0.4)       -0.4 (0.9)
FGF23, pg/mL                    -1.4(15.2)        0.2 (0.8)
Albumin, g/dL                   -0.1 (0.2)        0.1 (0.7)
PTH, pg/mL                      -0.6(10.7)       -0.1 (0.6)
Free 25(OH)D, pg/mL              0.1 (1.3)        0.0 (0.3)
Bioavailable 25(OH)D, ng/mL      0.0 (0.5)       -0.1 (0.3)

                               CVW (95% Cl)     ICC (95% Cl) (b)

VDBP, [micro]g/ml              7.0 (6.3-7.7)    0.67(0.62-0.71)
25(OH)D, ng/mL                 6.9 (6.2-7.7)    0.95(0.95-0.96)
Calcium, mg/dL (c)              2.0(17-2.2)     0.76(0.72-0.80)
Phosphorus, mg/dL (c)          7.6 (67-8.5)     0.63(0.56-0.69)
FGF23, pg/mL                  17.8(16.1-19.6)   0.71 (0.67-0.76)
Albumin, g/dL                  3.6 (3.1-4.0)    0.75(0.70-0.80)
PTH, pg/mL                    16.7(14.6-18.8)   0.84 (0.81-0.87)
Free 25(OH)D, pg/mL            9.0(7.9-10.1)    0.97(0.96-0.97)
Bioavailable 25(OH)D, ng/mL   9.9 (8.9-11.0)    0.96(0.95-0.97)

                               r(95% Cl) (b)         Index of
                                                   (95% Cl) (b)

VDBP, [micro]g/ml             0.63 (0.53-0.72)   0.71 (0.63-0.79)
25(OH)D, ng/mL                0.95 (0.93-0.96)   0.22 (0.19-0.24)
Calcium, mg/dL (c)            0.71 (0.63-0.78)   0.56 (0.49-0.63)
Phosphorus, mg/dL (c)         0.61 (0.51-0.70)   0.77(0.67-0.87)
FGF23, pg/mL                  0.66 (0.56-0.74)   0.63 (0.56-0.70)
Albumin, g/dL                 0.75 (0.67-0.81)   0.58 (0.49-0.66)
PTH, pg/mL                    0.80 (0.74-0.85)   0.44 (0.38-0.50)
Free 25(OH)D, pg/mL           0.96 (0.94-0.97)   0.19(0.17-0.21)
Bioavailable 25(OH)D, ng/mL   0.95 (0.93-0.96)   0.21 (0.18-0.23)

(a) (Repeat value/SD)--(original value/SD).

(b) The 95% CIs were bootstrapped with 200 replications.

(c) To convert mg/dLto mmol/L, multiply by 0.2495 for calcium
and by 0.3229 for phosphorus.

Table 3. Percentage of participants reclassified with repeat
biomarker measurements: The ARIC study, visit 5 (2011-2013).


Biomarker                          Determination           Value

VDBP, pg/mL                   Central 95th percentile   207.3-345.4
25(OH)D, ng/mL                       Clinical               <20
Calcium, mg/dL (b)                 Lab reference         8.4-10.2
Phosphorus, mg/dL (b)              Lab reference          2.7-4.5
FGF23, pg/mL                  Central 95th percentile   28.6-116.6
Albumin, g/dL                      Lab reference          3.5-5.0
PTH, pg/mL                           Clinical               >65
Free 25(OH)D, pg/mL           Central 95th percentile    2.6-22.1
Bioavailable 25(OH)D, ng/mL   Central 95th percentile     0.9-8.5

                                           Abnormal, n (%)

Biomarker                     Original exam   Repeat exam   Both exams

VDBP, pg/mL                      8 (5%)        10(6.3%)      3(1.9%)
25(OH)D, ng/mL                  17(10.6%)      19(11.9%)     15(9.4%)
Calcium, mg/dL (b)               3(1.9%)        3(1.9%)      2 (1.3%)
Phosphorus, mg/dL (b)            3(1.9%)        5(3.1%)         0
FGF23, pg/mL                     8 (5%)         3(1.9%)      2 (1.3%)
Albumin, g/dL                   1 (0.6%)       2 (1.3%)         0
PTH, pg/mL                     22 (13.8%)       16(10%)      13(8.1%)
Free 25(OH)D, pg/mL              8 (5%)        9 (5.6%)      6 (3.8%)
Bioavailable 25(OH)D, ng/mL      8 (5%)         8 (5%)       6 (3.8%)

                                Reclassified (a)

Biomarker                     n    Percentage (95% CI)

VDBP, pg/mL                   12      7.5(4.3-12.8)
25(OH)D, ng/mL                6       3.8 (1.7-8.2)
Calcium, mg/dL (b)            2       1.3 (0.3-4.9)
Phosphorus, mg/dL (b)         8       5.0 (2.5-9.7)
FGF23, pg/mL                  7       4.4 (2.1-9.0)
Albumin, g/dL                 3       1.9 (0.6-5.7)
PTH, pg/mL                    12      7.5 (4.3-2.8)
Free 25(OH)D, pg/mL           5       3.1 (1.3-7.4)
Bioavailable 25(OH)D, ng/mL   4       2.5 (0.9-6.5)

(a) In a different category (abnormal vs normal) at the original exam
and repeat visits.

(b) To convert mg/dLto mmol/L, multiply by 0.2495 for calcium and by
0.3229 for phosphorus.
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Title Annotation:Nutrition
Author:Lutsey, Pamela L.; Parrinello, Christina M.; Misialek, Jeffrey R.; Hoofnagle, Andy N.; Henderson, Cl
Publication:Clinical Chemistry
Date:Dec 1, 2016
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