CLIN. CHEM.25/10, 1799-1805(1979)

(I IPJI(’b.I (‘5.1LAITPV %JnI oc Pin IA 1070 1700

Intra-IndividualVariationin CommonlyAnalyzedSerum Constituents

Barbara Morrison,1 A. Shenkln,1 A. McLelland,’ D. A.Graham,1 Gillian Wuga,’ and K. J. M. Cunningham3

Concentrations of 27 commonly estimated serum con-stituents were measured in blood sampled from 20 ap-parently healthy volunteers at 0830, 1230, and 1630 hourson each of four days, at weekly intervals. Time-dependentstatistically significant (p � 0.01) variation was observedin 12 of the constituents. The 15 constituents that showedno statistically significant diurnal variation Included theimmunoglobulins, other specific proteins, enzymes,cholesterol, calcium, copper, and magnesium. On thedifferent days of the study consistent temporal patternswere observed in urea, creatinine, phosphate, zinc, bili-rubin, triglyceride, total protein, and albumin. The magni-tude of variation was particularly great in potassium, iron,zinc, and bilirubin. In general, day-to-day changes in con-centration exceeded within-day changes, except for po-tassium, phosphate, and zinc, for which greater changescould be observed within-day than from one day to anotherat the same time.

AdditIonal Keyphrases: diurnal and circadian variationday-to-day and within-day changes . normal valuesvariation, source of #{149} intra-indivk*jal variation . referenceranges

Many constituents of biological fluids are frequently seriallyestimated, to assess changes in the condition of a patient, orthe effect of therapy. If observed changes in concentration areto be correctly interpreted, it is necessary to know what vari-ation may normally occur in the concentration of that con-stituent when no pathological condition is present. Severalstudies of variation of serum constituents in individual normalsubjects have been reported. The relevance to population-based reference ranges has been discussed (1,2), together withmethods of assessing inter- and intra-individual variation.Statland and co-workers, in a series of studies (3-6), havediscussed various factors that contribute to intra-individualvariation, considering both within-day and short-term day-to-day variation, and various aspects of long-term variationhave been studied by Cotlove and co-workers (7-9). Althoughthese studies demonstrated significant intra-individualvariation in many serum constituents, the probability of thistype of variation occurring in a single individual was not in-vestigated. In this study we were concerned not only withwithin-day variation but also with the consistency on four

‘Department of Biochemistry, Royal Infirmary, Glasgow G4 OSF,Scotland.

2 Department of Biochemistry, Raigmore Hospital, Inverness,

Scotland3Department of Biochemistry, Western Infirmary, Glasgow,

Scotland.ReceivedJan. 31, 1979; accepted July 12, 1979.

Robertson,2 Margaret Barrowman,1 Sheila

different days of any variation observed during an 8-h periodin 27 serum constituents.

Materials and Methods

Subjects

Nine men and 11 women, ages 19 to 30 years, took part inthe study. Brief medical histories, including details of weightand height, diet, and medication, were recorded. None of thesubjects was a heavy smoker; alcohol consumption in thegroup was moderate and occasional. Two of the women tookoral contraceptives, and other medication was only such asmight be expected in any group of ostensibly healthy subjects.It was not considered to be necessary to exclude any subjectfrom the study on the basis of medical histories. Subjectsperformed their normal work, which involved no strenuousexercise, throughout the day.

SamplesTwenty-milliliter blood samples were drawn at 0830 hours

after an overnight fast, at 1230 hours, and again at 1630 hourson each of four days, at weekly intervals. Breakfast was takenbetween 0830 and 0900 hours and lunch from 1245 to 1345hours, no other food being consumed between the samplingtimes. All subjects were required to sit quietly for 10 mm be-fore each blood sample was collected, and the same vene-puncture technique was used throughout. All samples werecentrifuged for 10 mm at 3000 rpm as soon as clotting wascomplete (about 1 h after withdrawal) and the serum wasseparated and divided into aliquots. All aliquots of serum werestored at -20 #{176}Cuntil at least one week after the last sampleswere collected before analyses were begun.

Analytical MethodsThe serum constituents analyzed are shown in Table 1,

which also contains notes on the analytical methods used andtheir “within-batch” coefficients of variation (CVs) calculatedat nonpathological concentrations in our laboratory. All 12samples from each subject were analyzed in one batch for eachserum constituent, to obviate between-batch analyticalvariation.

StatisticalMethodsFor each of the 27 constituents studied the following cal-

culations were performed:i) The overall mean and standard deviation for each sam-

pling time was calculated for each serum constituent.ii) “Repeated measures” analysis of variance. The results

were set out in matrixes of three time points for each of thefour days of the study. Complete matrixes were obtained from14subjects, the others having failed to supply one or more ofthe blood samples. A repeated-measures analysis of variance(ANOvA) program was developed for a DEC PDP 11/34 pro-

Table 1. Serum Constituents Analyzed, Togetherwith Brief Descriptions of Methods Used, and Our

Within-batch CVs for ThemAnalytical Method

69.4047.841.86

10.211.183.210.273.54

3.192.660.671.920.500.630.070.66

70.7349.011.8410.23

1 .673.292.273.54

3.482.870.632.020.530.640.070.66

Sodian

Potassium

Chloride

Co2

Urea

Creatinine

Total Protein

Albumin

IgA

IgG

1gM

Iranoferrin

Ceruloplasmin

in

Calcium

Phosphate

Iron

Copper

Zinc

Magnesium

Ur.te

Aspartate alulnotransferase

Alanine a.tnotransferase

Alkaline phosphatase

RU irubln

Cholesterol

Triglyceride

Table 3. Population Means (±SD) for Each SerumConstituent at 0830, 1230, and 1630 Hours

08 123_0 l6

mean s.d. mean s.d. mean s.d.

Sodium ‘m,ol/L 140.7 1.64 140.2 1.65 140.8 1.72Potassium ‘ 4.10 0.26 4.07 0.23 3.95 0.26Chloride 103.6 1.79 102.7 1.55 102.8 1.73Total Co 24.35 2.14 24.17 2.21 24.97 2.08Urea 4.86 1.52 4.78 1.08 5.10 1.17Creatinine jasol/L 77.01 13.86 71.86 13.89 75.22 14.81

Total protein 3.14Albumin 2.67IgA 0.64IgG 1.941gM 0.54Transferrin 0.70Ceruloplasmin 0.07

2-Macg1obolin 0.69

Calcium 0.08Phosphate 0.17Iron 6.64Copper 3.95Zinc 2.42Magnesium 0.07orate 53.9

Aspartotn amino- U/I 22.03 8.31 22.58 8.53 22.21 8.41tronsferase

A1nine amino- #{149} 13.36 3.06 13.42 2.64 13.95 8.68Cra no Perase

Alkaline Phosphatose 163.6 42.72 173.8 51.05 172.6 82.99Bilirabin JIOl/L 8.46 4.83 8.21 4.83 7.23 3.91

CholesterOl iulfL 5.06 0.97 5.07 1.00 5.05 0.99Triglyceride ‘ 0.88 0.28 0.92 0.32 1.14 0.67

70.8249.06

1.8710.181.203.360.283.51

II 343 flame phOtter

IL 343 flame photometer

U II, 5(4-0005 P04

U II, S64-000B FJ4

U II. 564-0001 FD4

88 II, S(4-OOll PHd

U II. 564-0014 FC4

U II. 564-0030 P04

Automated imeunoprecipltation

Madification of Technicon AlP syst

U II. 5(4-0003 FJ4

U II, 5(4-0004 P44

U II. SF4-0025 fL,

Atomic absorption, Perkin Elmer 403

U II. plsosphotungstlcacid

LOBReaction Rate analyser at 37 C.optimised

U II. 5(4-0018 ((4

U I!, SE4-00l6 (144

U II. 5(4-0023 ((5

na1/L

isr1/L

n’nol/Lioiml/L

within-batchCVI

0.5

1 .0

1.0

3.0

2.5

2.5

1.0

1 .0

3.0

2.0

5.0

2.0

3.0

3.0

1.5

2.0

1.5

3.0

3.0

3.0

3.0

5.0

5.0

4.0

3.0

2.0

3.0

2.371.30

17.7714.9812.960.81

240.3

0.080.167.173.822.430.07

58.9

2.401.20

18.4715.4212.190.81

228.2

0.080.157.564.192.320.06

54.1

2.391.31

17.0715.2311.050.83

232.3

fourth day, we could detect no trends in the results for anyserum constituent that could be attributed to the differentstorage intervals. This concurs with the findings of variousworkers (11, 12) that all the constituents measured are stable

at -20 #{176}C.

Population Means

The population mean and standard deviation for eachserum constituent at the three times are shown in Table 3.There is a strong suggestion of circadian variation in certain

cessor, and an example of the output is given in Table 2. The measurements-for example, phosphate, total protein, andmodel we used identifies times as a fixed effect and treats zinc. However, in most other constituents, inter-individualsubjects and days as random effects. Differences in results differences have affected the means to such an extent that anybetween different times of day are evaluated by the “be- such variation within particular individuals is obscured.tween-times” sum of squares, and consistency of responseamong the 14 subjects by the “subjects x times” interaction Repeated-Measures ANOVAsum of squares (SOS).The calculation is more fully described Results from the ANOVA program are summarized in Tableby Winer (10). Because the F-tests are only approximate 4. Significant variation with time was detected in 12 of the 27unless correlations between results at any two times of day are analytes, and consistency of response among subjects wasidentical for all subjects, significance was judged at the 1% found for all but six methods: potassium, urea, ceruloplasmin,rather than the 5% level, phosphate, bilirubin, and triglycerides (triacylglycerols).

The methods showing no significant time-to-time variation,Results with consistency across all 14 subjects, were the immuno-

Although the samples taken on the first day of the study globulins, transferrin and a2-macroglobulin, calcium, iron,had been stored three weeks longer than those taken on the copper, and magnesium, the three enzymes, and cholesterol.

These analytes will be excluded from further discussion, ex-Table 2. Example of “Repeated-Measures” cept for iron, which merits particular attention.

ANOVA Table Remaining assays can be divided into two groups: thoseshowing significant and consistent variation between times

Analysis of variance-SodiumSource of variation of day and those methods in which no consistent response was

T SOS DF M.s. F found.The source of the significant findings in the first group was

Between subjects T 285.25 13 21.94 9.30 elucidated by reference to overall mean-time values over allBetweentimes T 14.00 2 7.00 6.50SubjectX times 1 28.00 26 1.08 1.54 days and for all 20 subjects (Table 3). These overall means,Dayswithinsubjects 1 49.08 42 2.36 which parallel the meanscalculated during the ANOVA (TableResidual 1 58.67 84 0.70 2), clarify whether significance is due to results from oneOverall 1 485.00 167 particular time of day or whether results at all three times of

Mean time values: 0830- 140.607 1230- 140.018 1630- 140.643 day vary substantially from one another.Mean time valuesexpressed In mol/L One of the implications of the findings of consistent re-

Calculation completed. sponse among all days and subjects is that the “pattern” of____________________________________________________ the relationships of the overall means to one another will be

1800 CLINICAL CHEMISTRY. Vol. 25. No. 10. 1979

16Zinc

pmoljl

14

12

0

8

ZINCpmoI

Fig. 2. Complete sets of results for zinc for six subjects

CLINICAL CHEMISTRY, Vol. 25, No. 10, 1979 1801

Table 4. Instances of Significant Variationbetween Times of Day and Consistency ofResponse among Subjects, by “Repeated-

Measures” Analysis of VarianceVariation betweentimes Consistent response

Sodium P 0.01 YES

Potassium p 0.01 MO

Chloride p 0.01 YES

CO2

Urea

p 0.01

poo.Ol

YES

NA

Creatinine p 0.001 YES

Total Protein p #{149}0.001 YES

Albumin p 0.01 YES

IgA MS. YES

196 MS. YES

1gM N.S. YES

Iransferrin N.S. YES

Ceruloplasnin N.S. NO

a24iacroglobulin N.S. YES

Calcium MS. YES

Phosphate P 0.001 NO

Iron N.S. YES

Copper N.S. YEt

Zinc p 0.001 Oft

Magnesium N.S. Yb

Orate p 0.001 YES

44.5. YES

MS. YES

MS. YES

p#{176}000l NO

N.S. YES

N.S. NO

Aspartate aminotransferase

Al ani ian aminotrensferase

Alkaline phosphatase

Bilirsbin

Cholesterol

Triglyceride

reproducible on graphing the pattern of the mean time re-sponse of each subject over all four days of the trial, and thisapproach is further discussed below. The second group ofanalytes, which showed no consistent time-to-time variation,was also examined in this way.

Patterns of VariationWe prepared graphs of the mean values at the three times

of day for each individual subject for each serum constit-uent.

The patterns shown by these graphs reflect the patternssuggested by the overall means of the methods in the firstgroup of interest outlined above, and give the clinical bio-chemist a better appreciation of the range of results to beexpected in these apparently normal, healthy subjects. Forexample, the concentration of zinc in serum appears to declinesteadily throughout the day, the highest values being foundin the samples collected during fasting (Figure 1). In addition,the graphs of all 12 results for several subjects suggest thatserum zinc follows the same pattern of variation on each dayof the study in most individuals (Figure 2). The graph of meanresults for creatinine in the serum of each subject at each timeof day (Figure 3) again reflects the overall means in thatthe mid-day sample shows lower results than the other two(Table 3).

Similar parallels were seen in each constituent in this groupof analytes that showed significant between-time variationwith consistent patterns.

However, it should not be assumed that, where no signifi-

0830 12.30 16.30 time

Mean values for each subject

Fig. 1. Means for the results on the four days (at 0830, 1230,and1630 hours) for zinc In each subject

cant between-time variation has been reported, we saw nofluctuations in results. In serum iron, for example, a sub-stantial and unpredictable variation is seen on plotting all 12values for several individuals (Figure 4) but this is masked bythe large residual term in the ANOVA.

The constituents showing no consistent time-to-timevariation were: potassium, urea, ceruloplasmin, phosphate,bilirubin, and triglycerides.

Potassium shows considerable variation in individuals(Figure 5) and within different days of the study, as does hi-lirubin, with a trend towards an overall decrease throughoutthe day.

Phosphate (Figure 6) and urea dip at mid-day, like creati-nine, but here the size of the changes are sufficiently unpre-

mmol/l Creatinine Kmmol/l

4.4

4.3

42

4’l

4’O

3.9

3’8#{149}

3.7

100

80

60

Fig. 3. Means for the results on the four days for creatinine ineach subject

dictable to indicate a lack of consistency between individ-uals.

Ceruloplasmin and triglycerides are the only analytesshowing no significant time-to-time variation and yet an in-consistent response. For ceruloplasmin a graph of the meantimes for all subjects shows a generally flat curve, except fora single subject who showed a substantial increase in the valueat 1630 hours on all four days of the study. Triglycerides, notsurprisingly, do show some increase in the value at 1630 hours,but again the magnitude of response is variable, almost cer-tainly depending on the size of the lunchtime meal.

Reproducibility of Pattern of Variation in IndividualSubjects

In an attempt to assess the reproducibility of variation inany one serum constituent from one day to another, we re-corded the pattern of change for each subject on each day. Forthis purpose, a change was considered to have taken place only

16.30

0830

21

Iron

dayl d2 doy3

10

Fig. 4. Complete sets of results for iron for three subjects withnormal iron concentrations and for two subjects with low serumironReferencerange,9-30 cmoI/L

1802 CLINICAL CHEMISTRY, Vol. 25. No. 10, 1979

08’30 12’30

1230 163O

Mean value for each subjectFig. 5. Means of results on the four days for potassium in eachsubject

when the difference between two consecutive results wasgreater than the analytical variation of the method. The re-sults for all 20 subjects were included.

Nine patterns can readily be defined in this way (Figure 7).The patterns observed on each day of the study for eachsubject (each “subject-day”) were recorded [detailed data canbe obtained from the authors or the editorial office of thisjournal], and the overall frequency with which the nine pat-terns occur is shown in Table 5.

There was no statistically significant difference (by chi-square test) between the distribution of these patterns in menand women for any of the serum constituents. The smallnumbers of subjects of each sex make it undesirable to drawconclusions from this analysis.

The discounting of changes that were within the limits ofanalytical variation for the methods has resulted in a flat-tening of the curves for some constituents, and pattern 5,where there is no significant change throughout the day, isseen to be the dominant pattern for many serum constituents.It was the case for more than half of the subject-days withrespect to sodium, chloride, C02, IgA, ceruloplasmin, calcium,copper, urate, the transaminases, and cholesterol, and whenother patterns did occur in these constituents, they were withfew exceptions either 6, 7, 8, or 9-i.e., the patterns in which

--.-.,____ there is no change between two consecutive times.In some constituents-for example, transferrin-dis-

______ crepancies between the findings of the ANOVA and this studyof patterns may be accounted for by the fact that patternswere recorded for all 20 subjects, whereas ANOVA was only

performed for the subjects who had complete matrixes.No dominant pattern can be detected in some of the pa-

rameters in Table 5. However, when some of the patterns are

Phosphatem mcI/I

1’5

1’4

12,

/;\ \

1’l,

1’0

1’9

o‘8

.;/

#{149}sss,44. #{149},/#{149}#{149}#{149}

8 9

Fig. 7. Nine possible patterns of variation

08’30 12.30 16.30

Mean values for each subject

Fig. 6. Means of results on the four days for phosphate in eachsubject

Table 5. Number of Observations of Each Patternfor Each Serum Constituent

Pattern 1 2 3 4 5 6 7 8 9

Sodi noPotassiumChlorideCOAriaCreatiol,e

Total proteinAlbumin19*IgO1q11TrunsferrinCoruloplasmln,2-Macroglobulln

CalciumPhosphateIronCopperZincMaqnesinoOrate

Aspartate i notransferaseIlanine #ini,otransferaseAlkaline phosphataseBill robin

CholesterolTriglyceride

O 0 0 7 50 7 7 1 11 18 15 18 1 5 1 7 5O 0 0 1 62 5 2 2 01 1 0 6 38 6 11 1 81 0 3 17 14 9 27 0 1O 1 3 26 9 23 2 S 2

6 13 0 3 23 3 5 4 16is in 2 8 5 2 4 7 ii3 2 1 6 39 5 6 4 73 11 3 6 17 4 6 8 141 6 0 9 30 6 11 7 210 10 1 6 16 7 5 3 152 3 0 4 42 3 13 0 61 2 1 1 28 4 6 15 6

0 5 0 1 49 1 2 44 92 1 0 33 12 11 7 2 42 13 7 0 16 5 7 13 91 a 0 1 43 3 3 2 120 7 25 6 6 7 0 19 26 3 3 8 12 5 19 5 120 1 4 5 41 17 3 2 0

2 7 02 3 02 15 03 14 23

3 38 10 4 4 34 40 7 9 2 59 16 5 15 2 81 7 9 4 10 1

0 2 0 2 55 1 4 6 39 7 0 13 13 1 20 4 A

CLINICAL CHEMISTRY, Vol. 25, No. 10, 1979 1803

grouped together, such as those that show a morning increase(patterns 1, 2, and 9) or those that show an overall fallthroughout the day (patterns 3, 6, and 8), certain trends ap-pear.

Urea and creatinine commonly show lowet values at mid-day (patterns 4, 6, and 7). However, in urea this is due mostoften to an increase during the afternoon (patterns 4 and 7),whereas in creatinine it is due mainly to a decline during themorning (patterns 4 and 6).

Total protein, albumin, and transferrin all show increasingvalues in the morning (patterns 1, 2, and 9), this being mostmarked in albumin, where pattern 5 seldom occurs. It can beconcluded that the increase in total protein in the morning isaccounted for largely by an increase in serum albumin.

The pattern of variation in serum phosphate, which isclearly shown in Figure 6 and in the population means (Table3), is pattern 4, where lowest values are found only at mid-day.When individual results are examined, on only 33 of 73subject-days was this pattern observed. However, patterns 6and 7 also have lowest values at mid-day, as well as at oneother time of day, and when occurrences of these two patternswere included with pattern 4, phosphate was seen to be lowestat mid-day on 70% of subject-days.

In serum zinc, pattern 5 and those patterns that show anoverall increase throughout the day (patterns 1, 7, and 9)seldom occurred. The dominant patterns were those in whichvalues during fasting were highest, i.e., patterns 3, 6, and 8.Similarly bilirubin tended to decrease from the fastingvalue-patterns 3, 6, and 8 on most occasions-but here otherpatterns were not uncommon. Afternoon increases in tri-glycerides were more commonly seen than morning increases,this probably reflecting the type of dietary intake at lunch.

\/ \._.4 65

A specimen for “lipid” analysis must therefore be collectedduring fasting for triglycerides estimation, but not necessarilyfor cholesterol estimation.

In the remaining analytes-potassium, IgG, 1gM, a2-ma-croglobulin, iron, magnesium, and alkaline phosphat#{225}se-nogrouping of patterns reveals any characteristic trends. Someof these, however, are not without interest. For example,serum iron, which is widely believed to decline throughout theday (13), was frequently observed to increase above the fastingvalues, and the highest values occurred at mid-day (patterns2, 84 and 9) on nearly half the occasions. Another interestingfeature of this diurnal variation in serum iron was that itgenerally paralleled the variation in bilirubin. No simple ar-ithmetical relationship could be established between iron andbilirubin values, but in only nine of the 144 (i.e., morningchanges and afternoon changes on 72 subject days) compari-sons we made did iron values change in the opposite directionto bilirubin.

K

4.1

3.

\mm#{231}vl

\

\

2’

UI

a\

\\

I”

Table 6. Maximum Changes Recorded in Any OneIndividual, and Means of All Subjects’ Maximum

ChangesMaximum Change Means of all

Recorded Subjects.Maximumbetween Changes between

Times Days Times Days

UI

‘S\

\a 4.0 5.0 2.251.2 1.1 0.584.0 4.0 2.7b6.0 5.0 2.901.5 3.1 0.7127 26 13.5

10.07.00.827.00.441 . 390.141.35

4.353.450.271.811.280.580.050.50

Sodium nmlol/LPotassium mmol/LChloride rnmol/LTotal CO nnuol/LUrea lrnno/LCreatjnjne jmol/L

Total protein gILAlbumin g/LIgA (J/LIqG gIL1gM gILTransferrin g/LCeruloplasmin g/L

2-Macroglobu1in g/L

Calcium mol/LPhosphate mol/LIron ljmol/LCopper pmol/LZinc jjmol/LMagnesium nnnol/LUrate pmol/L

Aspartate aminotransferase U/LAlanine aminotransferase OILAlkaline phosphatase O/LBilirubin jmol/L

Cholesterol mol/LTriglyceride mol/L

2.650.553.053.201.4614.3

5.184.100.362.150.310.560.070.64

0.120.2512.33.373.841,1268.5

7,7)5.00.666.20.411.180.161.23

0.20(1.4522.07.112.40.22go

0.20 0.100.35 0.2825.0 6.9012.3 1.9710.7 4.161.31 0.11180 34.5

11 20 7 914 16 5 6164 165 53 636 13 4 5

0.50 1.8 0.31 0.682.35 1.6 0.56 0.59

KmmoVI

‘#{188}

.

3

//

-

day 1 doy 2 doy3 doy4

1804 CLINICALCHEMISTRY,Vol. 25, No. 10, 1979

doyl doy2 day3 doy4

Fig. 8. Complete sets of results for potassium for three subjectsshowing consistent patterns of variation on the four days

Potassium is particularly interesting in that pattern 5 doesnot occur here, and patterns 2 and 4 are found in equal num-bers. A morning decrease (patterns 3, 4, and 6) was observedon about half of subject-days, and an increase (patterns 1, 2,and 9) during the morning on a third, reflecting the lack ofuniformity in diurnal variation of potassium shown in Figures8 and 9.

Magnitude of VariationFor many of the constituents where statistically significant

variation can be demonstrated, the actual changes recordedare too small to be of routine clinical interest. The magnitudeof the variation in sodium, chloride, and C02, for example, wasusually within the limits of analytical variation, and whenchanges within the limits of the precision of the method areexcluded (Table 5), these analytes appear to remain constantthroughout the day. In other serum constituents, some ofwhich do not show statistically significant variation, the dif-ference between two samples taken at different times of daymay still be large. Nonetheless, it may be difficult to interpretthe clinical significance of such a change in concentration.

Serum iron, for example, frequently changed by as muchas 10 zmol/L during the course of a day (Figure 4). In sevenof the subjects, iron values fell outside our reference range(9-30 zmolfL) at one or more times during the study; in onesubject, two of the 12 results were above the upper limit of thereference range and one below the lower limit. One low value

for serum iron is therefore not necessarily representative ofthe individual’s iron status. In only two subjects was serumiron found to be relatively constant, and in these two mdi-

Fig. 9. Complete sets of results far potassium for three subjectsin whom no consistent pattern of variation was detected

viduals the iron concentration was very low and suggestive ofiron deficiency (Figure 4).

To assess the magnitude of variatiOn for each serum con-stituent, we recorded the maximum range of results observedin each subject within a day, and from one day to another atthe same time of day. Table 6 shows the means of these figures,together with the greatest changes recorded in any one indi-vidual.

In general, the maximum changes observed were greater ina between-day comparison than for the between-times com-parison. However, it is noteworthy that phosphate and zinc,the two serum constituents that show the most distinct pat-terns of diurnal variation, vary more during the course of a daythan they do from day to day. Potassium also varies morewithin a day than from day to day, but in this case it is theactual, rather than the relative, magnitude of the variationthat is important, because changes of more than 0.5 mmol/Lin a day were encountered in more than half the subjects.Statland et al. noted similar changes in potassium values (3)and found greater changes when strenuous exercise was taken

(4).

We conclude that some serum constituents remain pre-dictably constant throughout the day (sodium, chloride, C02,IgA, ceruloplasmin, calcium, copper, urate, aspartate and al-anine aminotransferases, and cholesterol) or else vary byamounts that are too small to be of interest in routine clinicalbiochemistry.

For some constituents, however, it may be possible to pre-dict the extent and direction of the diurnal variation that maybe encountered. Urea, creatinine, total protein, albumin,transferrin, phosphate, zinc, bilirubin, and triglyceridesgenerally vary in characteristic patterns during the course ofa day. For interpretation of the results of these analyses it is

CLINICAL CHEMISTRY, Vol. 25, No. 10, 1979 1805

necessary to know the time of day at which samples are drawn.When results for samples taken on different days are to becompared, blood should be collected at the same time eachday.

As for the remaining constituents, we cannot predict theextent or direction of diurnal variation on the basis of the datapresented here. Attempts to standardize blood collection willreduce, but not exclude, intra-individual variation, which maystill be considerable.

The magnitude of the variation found in many serum con-stituents is very large. If variation occurs to the same extentin bed-rest patients as in the healthy subjects, it must be takeninto account when interpreting biochemistry results obtainedin clinical laboratories. A study of intra-individual variationin bed-rest patients is therefore now in progress.

We thank the technicians of this department who performed theanalyses,and the 20students who, without payment, took part in thestudy.

References1. Pickup, J. F., Harris, E. K., Kearns, M., and Brown, S. S., Intra-individual variation of some serum constituents and its relevance to

population-based reference ranges. Clin. Chem. 23,842 (1977).2. Harris, E. K., Effects of intra- and inter-individual variation onthe appropriate use of normal ranges. Clin. Chem. 20, 1535 (1974).3. Statland, B. E., Winkel, P., and Bokelund, H., Factors contributingto intra-individual variation of serum constituents. 1. Within-dayvariation of serum constituents in healthy subjects. Clin. Chem. 19,1374 (1973).4. Statland, B. E., Winkel, P., and Bokelund, H., Factorscontributing

to intra-individual variation of serum constituents. 2. Effects of ex-erciseand diet on variation of serum constituents in healthy subjects.Clin. C/hem. 19, 1380 (1973).5. Statland, B. E., Winkel, P., and Bokelund, H., Factors contributingto intra-individual variation of serum constituents. 4. Effects ofposture and tourniquet application on variation of serum constituentsin healthy subjects. Clin. Chem. 20, 1513 (1974).6. Winkel, P., Statland, B. E., andBokelund, H., Factorscontributing

to intra-individual variation of serum constituents. 5. Short-termday-to-day and within-hourvariationofserum constituentsinhealthysubjects. Clin. Chem. 20, 1520 (1974).

7. Williams, G. Z., Young, D. S., Stein, M. R., and Cotlove, E., Bio-logical and analytic components of variation in long-term studies ofserum constituents in normal subjects. I. Objectives, subject selection,laboratory procedures and estimation of analytical deviation. Clin.(‘hem. 16, 1016 (1970).8. Harris, E. K., Kanofsky, P., Shakarji, G., and Cotlove, E., Studiesof serum constituentsin normal subjects.II. Estimating biologicalcomponents of variation.Clin. Chem. 16, 1022 (1970).9. Cotlove, E., Harris, E. K., and Williams, G. Z., Biological and an-alytic components of variation in long-term studies of serum con-stituents in normal subjects. Ill. Physiological and medical implica-tions. Clin. Chem. 16, 1028 (1970).

10. Winer, B. S., chap. 7 in Statistical Principles in ExperimentalDesign, 1st ed., McGraw-Hill, New York, NY, p 3181.II. Wild ing, P., Zilva, J. E., and Wilde, C. E., Transport ofspecimensfor clinical chemistry analysis.Ann. Clin. Biochem. 14, 301 (1977).l2. Ballantyne, F. C., Morrison, B. A., and Ballantyne, D., Effect ofstorage on estimatesof protein concentrations in human plasma. Clin.(‘him. Acta 87,455 (1978).

13. Henry, R. J., Cannon, D. C., and Winkelman, J. W., Clinical(‘hemistrv, Principles and Tech nics, 2nd ed., Harper and Row, NewYork, NY, p 685.