The Effects of Time of Venipuncture on
Variation of Serum Constituents
Consideration of Within-day and Day-to-day Changes in a
Group of Healthy Young Men
P E R W I N K E L , M.D.,
B E R N A R D E. S T A T L A N D , M.D.,
PH.D.,
AND H E N N I N G BOKELUND, P H . D .
Department of Clinical Chemistry, Finsensinstitute, Copenhagen, Denmark; Department of Laboratory Medicine and
Pathology, University of Minnesota Medical School, Minneapolis, Minnesota; Department of
Clinical Chemistry, Odense University Hospital, Odense, Denmark
ABSTRACT
Winkel, Per, Statland, Bernard E., and Bokelund, Henning: T h e effects of
time of venipuncture on variation of serum constituents. Consideration of
within-day and day-to-day changes in a group of healthy young men. Am J
Clin Pathol 64: 4 3 3 - 4 4 7 , 1975. Within-day and the day-to-day variations of
serum constituents were evaluated in 11 healthy young men. Eighteen constituents, including electrolytes, metabolites, proteins, and enzymes, were
assayed using the AutoChemist Multichannel Analytic System. Venipunctures were performed at three hours of the day, 8 A.M., 11 A.M., and 2 P.M.,
on five separate experimental days. A three-factor analysis of variance model
was employed to separate analytic variation from biological sources of variation.
Statistically significant (p < .05) group diurnal patterns (main effect of hour)
during the six-hour period were found for serum lipids, iron, urea, albumin,
total protein, and chloride. A unique individual diurnal pattern (subjecthour interaction) was statistically significant for serum potassium. Statistically
significant main effect of month (main effect of day) for the group of subjects
was seen for total lipids and potassium; however, the subject-day interaction
term, which is an index of the day-to-day variation for the subjects, was
significant (p < .05) for all of the constituents except for sodium ion. T h e
comparison of the variation expected within-day versus the variation seen
day-to-day over four months was made by pooling the sources of within-day
variation (main effect of hour + subject-hour interaction + subject-dayhour interaction) and by pooling the day-to-day variation terms (main effect
of day + subject-month interaction). For serum cholesterol, potassium, acid
phosphatase, and phosphate ion, the within-day variation was greater than the
day-to-day variation occurring over four months, while the other constituents
showed day-to-day variations of a greater magnitude than that experienced
during the six-hour period. (Key words: Variations of serum chemistries;
Serum electrolytes; Serum protein; Enzymes.)
Received December 20, 1974; received revised
manuscript February 20, 1975; accepted for publication February 20, 1975.
Supported in part by the Danish Research Council.
Dr. Statland was a recipient of the George C. Marshall
433
Fellowship administered by the American Scandinavian Foundation during the study.
Address reprint requests to Dr. Statland: Departmentof Laboratory Medicine and Pathology, Box 198,
Mayo Memorial Building, University of Minnesota
Hospitals, Minneapolis, Minnesota 55455.
434
WINKEL, STATLAND AND BOKELUND
IN INTERPRETING the significance of a
change in the value of a serum constituent
over time, the clinician is often faced with
the problem of deciding whether the difference between two measured values
reflects a change in the patient's condition
or could be explained on the basis of the
expected biological fluctuations occurring
in healthy subjects. T o make this decision,
he must be equipped with knowledge of
the usual biologic variations occurring both
within-day and from day-to-day. We
recently reported the within-day variation
of selected serum constituents occurring
over a six-hour period in a group of
healthy young men. 17 Using a two-factor
analysis of variance model, we were able to
distinguish the main effects of subject and
hour and the interaction term from the
analytic error. However, in that the study
was done on only one day, we could not
determine the individual (unique subject)
diurnal variation occurring over the sixhour period. That is, each individual may
have a unique diurnal pattern that would
be masked by the subject-hour interaction
component of variation. In addition, we
wanted to correlate the within-day biologic
variation with the day-to-day variation
occurring over a longer time interval, for
example, a number of months. In order to
determine both the day-to-day variations
and the possible individual within-day
variations, we performed numerous venipunctures on the same group of subjects,
at 8 A.M., 11 A.M., and 2 P.M. on five
separate days at approximately monthly
intervals, and on the serum specimens so
obtained, we assayed for 18 constituents.
Using a three-factor analysis of variance
model, we are able to determine the nature
of the within-day changes {i.e., group,
individual, or random) and also to compute
the relative contributions of the day-to-day
changes to the within-day changes of serum
constituents in this group of healthy
young men. Furthermore, we evaluated
the analytic error, both within-batch and
A.J.C.P. —Vol. 64
batch-to-batch, and compared the analytic
variation with the biologic variations.
Methods
Studies were performed on 11 male
students from the Technical University of
Denmark, Lundtofte, Denmark. T h e
student volunteers were from 21 to 27
years of age, in good health, were not
taking any drugs, and were not cigarette
smokers. T h e same blood sampling protocol was done on February 5, March 12,
March 27, May 7, and May 25, 1974. On
each of the five days, the volunteers arrived
at school after an overnight fast and remained fasting during the six hours of the
blood sampling period. Blood was drawn
at 8 A.M., 11 A.M., and 2 P.M. T h e same
technologist drew all the blood samples
on all five days. T h e regimen of venipunctures was performed while the students were attending classes at the school.
In all cases, the volunteers assumed the
sitting position 30 minutes before venipuncture; the tourniquet was applied by
the technologist for approximately 30
seconds, and 30 ml. of blood were drawn
through an 18-gauge needle to fill two
glass tubes. These two tubes of blood were
uniquely labeled and are referred to as the
"paired blood duplicates." T h e order in
which the students had their blood drawn
was randomized before each venipuncture
session. T h e blood specimens were allowed to clot at room temperature and
then centrifuged within an hour of venipuncture. T h e sera obtained from the
paired blood duplicates were then stored
at - 2 0 C. for one day before being assayed.
All 66 serum samples from one day's
experiment (11 subjects X 3 venipunctures
X 2 duplicates) were randomized and
assayed on one occasion on the AutoChemist Multichannel Analytical System
(AutoChem Instrument AB, Lindingo,
Sweden) 7 for the 18 serum constituents
listed in Table 1. T h e procedures for these
assays are referenced in the same table. A
435
VARIATIONS OF SERUM CONSTITUENTS
October 1975
Table 1. Grand Mean, Total Variations, and Analytic Variations for Serum Constituents
Total
Variation
Constituent
Grand Mean
Units
(%)
Sodium
Potassium
Calcium
Chloride
Phosphate
Urea
Creatinine
Uric acid
Iron
Cholesterol
Albumin
Total protein
Total lipids
Aspartate aminotransferase
Alanine aminotransferase
Acid phosphatase
Alkaline phosphatase
Lactate dehydrogenase
141.2
4.44
2.70
102.2
1.22
4.95
90.8
0.333
20.7
5.00
45.1
73.1
5.31
8.95
6.25
2.58
62.6
195.0
mmol. per I.
mmol. per 1.
mmol. per 1.
mmol. per 1.
mmol. per 1.
mmol. per 1.
/xmol. per 1.
mmol. per 1.
/nmol. per 1.
mmol. per 1.
Gm. p e r l .
Gm. per 1.
Gm. per 1.
u. per 1.
u. per 1.
u. p e r l .
u. per 1.
u. per 1.
1.92*
7.10
3.23
3.84
10.66
22.50
14.49
11.45
36.60
14.78
5.46
4.84
24.98
25.28
55.80
14.94
19.99
15.90
Analytic
Error
(%)
1.76t
2.75
2.73
3.44
2.44
2.49
6.26
2.55
3.37
5.66
3.85
1.69
3.60
6.41
16.67
7.86
2.84
11.78
Methodology
(Reference)
1
1
1
25
13
8
6
10
11
26
15
20
9
12
12
2
14
1
SD
* Coefficient of variation = I
1
Mean
X 100 I where SD is the square root of the total variance of the measurement.
'
t Coefficient of variation = I
x 100 I where SD is the square root of the variance of the analytic error (both within-batch and batch\^ Mean
'
to-batch). See text for details.
commercial quality control serum, Seronorm (Nyegaard and Co. A/S, Oslo, Norway) was assayed in the AutoChemist in
defined positions of the batch; specifically, every tenth sample was a control
serum specimen.
On each of the five days of the study, one
additional tube of blood was drawn from
six of the subjects at 8 A.M. T h e latter
specimens were processed as described
above with the exception that the sera were
not assayed the day after venipuncture,
but instead stored at —80 C. until two days
after the fifth (and last) experimental
day, when all the sera that had been stored
at —80 C. were randomized, thawed at
room temperature, and then assayed on
one occasion on the AutoChemist.
sists of all the results for the 11 volunteers
where three venipunctures were performed on each of five days and for which
the specimens were frozen at - 2 0 C. overnight and then assayed on five separate
runs. In the statistical analysis of Data
Set A, a three-factor analysis of variance
was performed for each of the 18 serum
constituents. T h r e e sources of variation
were included in the statistical model,
namely that d u e to differences among subjects (the effect of "subject"), that due to
the particular day of sampling (the effect
of "month"),* and that due to the time-ofday (the effect of "hour"). T h e latter effect
was considered fixed, while the former two
were considered random. The details of
the statistical analysis have been described. 16,23
Statistical Analysis
Three sets of data were analyzed separately. They are referred to as Data Set A,
Data Set B, and Data Set C. Data Set A con-
* In this paper, we use the term "main effect of
month" and the term "main effect of day" equivalently, as the interval between consecucive experimental days in the study is approximately one month.
436
WINKEL, STATLAND AND BOKELUND
A.J.C.P.—Vol.64
FIG. 1. Main effects present in a three-way ANOVA
model. The main effects of
subject, day, and hour. The
constructed examples include
the values of the serum constituent for two subjects at
d,J
three different hours on each
of two days. The dotted line
in frame A represents the
situation where there are no
main effects present. A shows
a main effect of subject only;
in B, the levels of both subI-Day I-I
1-Day 2-1
I-Dayl-I
1-Day 2-1
I—Day I —I
|-Day 2 - |
jects are equally influenced on
Day 1, and on Day 2, by the
In addition to A, there
In addition to B, there
The two subjects show
incremental differences "di"
is a main hour effect.
a mam subject affect.
is a mam day effect.
and "d2," respectively. In C,
it can be seen that at a given
time of day, the values are
changed by the same quantity on both days and for both subjects. These three quantities are "h,," "h2,"
and "h3" for the three times of day, respectively.
Figures 1 and 2 illustrate the biologically
meaningful main effects and interaction
terms present in the three factor ANOVA
model that considers the factors of subject,
day, and hour.
For our experimental design described
above, we briefly explain the biologic
meanings of the various terms included
in the ANOVA model. For a given serum
constituent, a significant main effect of
subject indicates that the subjects differ
in regard to the average concentration of
that serum constituent. A significant main
effect of month (day) implies that the
mean values computed for the various days
differ. A significant main effect of hour
indicates that the mean values for the three
hours of the day as computed for all subjects and for all days differ.
T h e implication of a significant subjecthour interaction is that the subjects show
hourly variations that vary from subject
to subject, but are consistent from day to
day for a given subject. A significant subj e c t - m o n t h (subject-day) interaction
indicates that the individual's daily level of
the serum constituent varies from month
to month. A significant m o n t h - h o u r interaction (day-hour interaction) implies that
the values of the serum constituent vary
as a function of the hour and of the day on
which the corresponding serum samples
were obtained. Such effects are probably
an indication of improperly controlled
e x p e r i m e n t s . A significant s u b j e c t m o n t h - h o u r interaction indicates a variation of a serum constituent that cannot
be explained on the basis of a general
group variation or on the basis of a consistent individual subject's variation (subj e c t - h o u r interaction) occurring over the
six-hour period of sampling. Such threefactor interactions are considered to be
random biologic fluctuations.
Unbiased estimates of all components of
variance were computed on the basis of
the expected values of the mean squares
of main effects and interactions. 16
T o compute the within-day and the dayto-day variation for each subject, a twofactor ANOVA was performed. For each
subject and for each constituent, a total of
30 serum measurements was usually used
(5 days X 3 venipunctures x 2 duplicates)
in the analysis. For these analyses, the
effects of day and hour were examined.
T h e factor of "day" was considered random
and "hour" considered fixed. Details of
the analysis have been described. 23 In the
above-mentioned analysis, the main effect
of hour is a measure of the diurnal variation of the subjects. T h e subject-hour
October
1975
VARIATIONS OF SERUM C O N S T I T U E N T S
interaction measures the random biologic
variation within the six-hour period of a
day (8 A.M. to 2 P.M.) while the main effect
of day is a measure of the subject's day-today variation. It should be noted that in
both analyses (two-factor and three-factor)
for Data Set A, the long-term batch-tobatch analytic variation is confounded
with the biologic day-to-day variation.
Additional data (Data Set B) were, therefore, needed to resolve this problem.
Data Set B consists of observations from
six of the subjects from whom three rather
than two serum samples were obtained
at the 8-A.M. venipuncture session on each
of the five experimental days. For each of
the six subjects and for each of the five
experimental days, two of the 8 A.M. set of
observations were included in Data Set B.
The first set of observations was obtained
from the measurements on serum sample
2, which was stored at - 2 0 C. and assayed
24 hours after venipuncture; the second
437
set of observations was obtained from the
determinations of serum sample 3 (the
third blood specimen of the triplicate
drawn at 8 A.M. for six of the subjects),
which was stored at - 8 0 C. and assayed on a subsequent occasion (i.e.,
on one assay run). Thus, Data Set B includes two categories of 8-A.M. values:
those based on serum sample 2 (run on
five separate assays) and those based on
serum sample 3 (run on one occasion after
being stored at —80 C. for a variable length
of time). T h e day-to-day variation of the
first category would logically include the
effect due to the batch-to-batch analytic
error, while the day-to-day variation of
the second category would include any
effect secondary to the influence of a
variable storage time on the serum samples.
In the analysis of Data Set B, a three-way
ANOVA test was also used. T h e three
factors considered were the effect of subject, the effect of month (day), and the
>m\j
FIG. 2. Individual variations superimposed
on
group
variations.
Illustration of the subject-time interactions. A, values of the serum constituent for Subject 1 when only the
main effects of day and hour are included. In B, a subject-day interaction
is introduced. On Day 1 and Day 2,
the values are changed by the quantities "s,di" and "sid 2 ," respectively. In
C, the effect of a subject-hour interaction is added. T h e values at three
hours are changed by the quantities
"sih,," "sih 2 ," and "Sih3," respectively,
on each of two days. In addition to the
main effects common to a group of
subjects and the effects common to a
particular subject, there are deviations not related to analytic variation.
T h e s e deviations are d e n o t e d as
subject-day-hour interactions, and are
illustrated in D.
I-Ooyl-I
1-Day 2-1
I-Dayl-I
Subject I demonstrates both main
doy I group} and
main hour (group) effects.
/
%
I-Day2-|
In addition to A, we note
o subject-doy
interaction.
D
s . d j h , <!
s.Ojhj
|-Doy I - I
I-Day 2 - |
In addition to 8, we note
a subject-hour interaction.
|-Doyl-|
a
|-
Day 2-1
tn addition to C, we note
subject-day-hour interaction
438
AJ.C.P.—Vol. 64
WINKEL, STATLAND AND BOKELUND
effect of storage and analytic procedure.
All effects were considered random. The
main effect of month and the interaction
between the effect of month and the effect
of storage and analytic procedure (monthanalytic procedure interaction) were
studied. A main effect of monthf would
signify a probable biologic month-tomonth change that would be independent
of batch-to-batch analytic error and of
t T h e expected value of the mean squares of the
main effect of month includes the three-factor interaction, the subject-month interaction, and the
month-analytic procedure interaction. T h e test of
main effect of month was, therefore, performed in
the following way: if neither of the two-factor
interactions was significantly different from zero,
the significance of the main effect of month was
tested using the three-factor interaction mean squares.
If only one of the two-factor interactions was significantly different from zero, the mean squares of that
interaction were used in the test. If both the twofactor interactions differed significantly from zero, an
approximate F-test with approximate degrees of
freedom was used. 16
long-term storage effect. A significant
month-analytic procedure interaction
would imply that the storage at - 8 0 C.
and/or the batch-to-batch error affected
the five assays unequally.
Data Set C includes the results of tests
of the commercial control serum, Seronorm. A total of 40 observations was used
for the analysis (5 batches X 8 Seronorm
samples run per batch). A one-way
ANOVA was performed on the Seronorm
data in order to separate the batch-tobatch (month-to-month) variation from the
within-batch variation.
Results
The grand means of the 18 serum
constituents of all 11 subjects over all 15
sampling sessions are presented in Table
1. In the same table, we compare the
analytic variation to the total variation.
Table 2. Biological Components of Variance, All Values Presented as
Ratio to the Total Biological Variance
Inter-subject
Variation
Intra-subject Variation
Individual
Group
Sodium
Potassium
Calcium
Chloride
Phosphate
Urea
Creatinine
Uric acid
Iron
Cholesterol
Albumin
Total protein
Total lipids
Aspartate aminotransferase
Alanine aminotransferase
Acid phosphatase
Alkaline phosphatase
Lactate dehydrogenase
Main
Subject
Effect
Main
Hour
Effect
Main
Month
Effect
SubjectHour
Interaction
SubjectMonth
Interaction
SubjectHourMonth
Interaction
0.357*
0.074f
0.695t
0.535t
0.164*
0.509t
0.787t
0.622t
0.375t
0.912t
0.365t
0.685t
0.449t
0.299f
0.595t
0.616t
0.810t
0.526t
0.076
0.002
0.001
0.062*
0.037
0.027t
0.000
0.000
0.023t
0.001
0.086t
0.021*
0.060t
0.000
0.002
0.062
0.001
0.008
0.000
0.086tt
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.158tt
0.000
0.000
0.000
0.000
0.000
0.055
0.708t
0.060
0.000
0.184f
0.009*
0.029*
0.001
0.015
0.006
0.016
0.019
0.010
0.000
0.000
0.08 I t
0.014t
0.007
0.218
0.052t
0.244t
0.176*
0.336f
0.418t
0.120t
0.289t
0.504t
0.021t
0.323t
0.177f
0.221t
0.685t
0.357t
0.103t
0.163t
0.319t
0.294
0.078*
0.000
0.227
0.279t
0.036t
0.065t
0.048t
0.084f
0.060t
0.204t
0.097t
0.102t
0.016*
0.046t
0.138t
0.012t
0.140t
* p < 0.05.
tp<0.01.
X Includes analytic batch-to-batch variation.
October 1975
439
VARIATIONS OF SERUM CONSTITUENTS
Total Lipids
T h e analytic variation includes the withinbatch variation derived from the replication term of the three-factor "ANOVA
and the batch-to-batch variation derived
from the main month variance term from
the three-factor ANOVA. In the cases of
potassium and total lipids, the batch-tobatch variations were based on the Seronorm data (Data Set C, see Statistical
Methods for details). The total variation
consists of both the analytic variation and
the biological variation. The latter is the
algebraic sum of the inter-subject and the
intra-subject components of variation.
In Table 2 we present the various contributions to the biologic variation in terms
of their ratio to the total biologic variance,
as well as their statistical significance. As
noted in Table 2, a main effect of subject
(inter-subject variation) was significant for
all constituents studied but varied from
7.4% for potassium to 91.2% for cholesterol. The main hour effect common to
the group was significant for six of the
variates: total lipids, urea, iron, albumin,
total protein, and chloride. In Figure 3 we
present the mean values for the three
times-of-day for these six serum constituents.
A significant main effect of month was
noted for seven of the serum constituents.
These variates are presented in Table 3.
In order to rule out the possibility that
this observation may have resulted from a
batch-to-batch analytic error (long-term
analytic variation), we performed a threeway ANOVA on Data Set B, i.e., the data
from the results of the sera stored at - 8 0 C.
and assayed on one occasion, as well as
on the sera stored at - 2 0 C. and assayed
on five separate occasions. As indicated in
Table 3, of the seven variates, only potassium and total lipids showed a significant
main effect of month using this analysis.
Figures 4 and 5 depict the average values
for potassium and total lipids, respectively,
for six of the subjects on each of the five
sampling days (8-A.M. sample) for both
Urea
J_
0800
1100
1400
Albumin
L
0800
75
1100
1400
0800
Total Protein
103.0
74
2 102.5
73
o I02.0t-
72
E
-U-L
0800
1100
Time
1400
_
1100
Chloride
101.51—
V
0800
1100
1400
Time
FIG. 3. Serum constituents having a significant
main effect of hour. Mean values for six variates,
showing a significant group within-day (hour-tohour) effect. T h e values for total lipids, urea, iron,
albumin, total protein, and chloride at 8 A.M.,
11 A.M., and 2 P.M. for all subjects on all five experimental days.
types of specimens, namely that sera stored
at —20 C. and assayed on five separate
runs, and the sera stored at - 8 0 C. and
assayed on one occasion. Thus, only for
potassium and total lipids are we able to
rule out the possibility that the main effect
of month was due entirely to the long-term
analytic variation.
For the remaining five serum constituents, the long-term analytic variation is
a likely explanation for the main month
effect noted originally. It appears from
Table 3 that for these five variates, the SD
of the main month effect as based on values
from the 11 subjects' sera determined on
five separate assay runs is of the same order
of magnitude as the SD of the five corresponding control sera Seronorm mean
values corrected for the within-batch
analytic variation. Furthermore, the high
r values determined on the five mean
WINKEL, STATLAND AND BOKELUND
440
values of the 11 subjects' sera and the
corresponding control sera means indicate
that the variation of the Seronorm mean
values parallels that of the mean values
for the 11 subjects' sera. For serum cholesterol, albumin, and chloride, the above is
a very likely explanation, as may be seen
from Table 3. T h e SD of the main month
effect as based on values from the 11
subjects' sera determined on five separate
assay runs is compared with the SD of the
five corresponding control sera mean
values corrected for the within-batch
analytic error. Furthermore, the r values
computed from the five mean values for
the 11 subjects' sera and the corresponding
five control sera means are also very impressive in their high degree of correspondence, suggesting again that the
variation of the Seronorm control sera
mean values parallels that of the mean
values for the 11 subjects' sera.
T h e individual day-to-day variations as
expressed by the subject-month interaction term are very pronounced, especially for most of the enzymes, total lipids,
urea, and serum iron. It should be noted
that this term does not represent a general
group month-to-month effect, but the
A.J.C.P.—Vol.
64
average of the individual month-to-month
fluctuations. T h e relative magnitudes of
the individual variations and the group
variation are illustrated in Figure 6, which
depicts the changes in the mean levels for
uric acid in each of four of the subjects
on the five days of the study. T h e corresponding mean levels for all 11 subjects
and the average values for the commercial
quality control serum, which are also shown
in the figure, are relatively constant. By
contrast, the four subjects showed considerable changes during the course of the
day-to-day experiment.
As noted in Table 2, the subject-hour
interaction term (unique individual diurnal variation) contributes a relatively
small portion of the total biologic variation
for most of the variates studied. Serum
potassium is the notable exception. More
than 70% of the total biologic variation
for potassium is related to the subject's
personal diurnal variation, i.e., that part
of his within-day variation that is consistent
from day-to-day and also independent of
any group diurnal variation. Figure 7 presents the average values for each of three
subjects at 8 A.M., 11 A.M., and 2 P.M.
for the five days of the experiment. It
Table 3. Values in Commercial Control Sera (Seronorm) and in the Eleven Subjects' Sera,
Five Assay Runs for Seven Constituents Showing a Significant Main Month Effect
Seronorm
Sera of
Eleven Subjects
Ratio of
Correlation
Coefficient§
SD s u bjects tO
Potassium
Total lipids
Sodium
Cholesterol
Albumin
Chloride
Calcium
Mean*
SDt
Mean*
SD*
^Useronorm
x Mean Subject
4.6511
2.92
136.3
2.90
32.1
102.0
2.71
0.00414
0.0792
1.084
0.229
1.75
2.82
0.0316
4.44
5.31
141.2
5.00
45.1
102.2
2.70
0.0640
0.524
0.8045
0.209
1.17
2.65
0.0358
15.46
6.61
0.742
0.912
0.671
0.941
1.132
+0.454
-0.432
+ 0.742
+0.906
+0.806
+0.941
+0.658
* Grand mean of five means determined on each of five experimental days.
t T h e square root of the variance of the batch-to-batch Seronorm variation as determined by one-way ANOVA.
X T h e square root of the main variance as determined by the three-way ANOVA where the main month effect is equivalent to the main
day effect where the three factors subject, day, and hour are present.
§ Correlation coefficient determined on the five Seronorm means (one for each experimental day) versus the five mean values for the
eleven subjects' samples. For four degrees of freedom, when r > 0.811, p < 0.05; and when r > 0.917, p < 0.01.
1 For units, see Table 1.
October 1975
441
VARIATIONS OF SERUM C O N S T I T U E N T S
6.4
6.C
4.7
E
e
5 6
'
o
4.5
~
H
E
A,
4.3
5.2
Q.
4.8
O—^3 One assay run
• — • Five separate assays
4.1
Feb.
Mar.
Apr.
May
June
Time of Year (month)
FIG. 4. Main effect of month on serum potassium.
Mean values of serum potassium when the analyses
were performed both on five separate assays and
also on one assay run.
V
Feb.
Mar.
-•-o One assay run
» ^ — • Five separate assays
Apr.
May
Jjne
Time of Year (month)
FIG. 5. Main effect of month on serum lipids. Mean
values of serum lipids for six subjects when the
analyses were performed both on five separate assays
and also on one assay run.
should be noted that the group diurnal
0.460
variation for potassium represents only
0.2% of the total biologic variation. The
consistency of the diurnal variation for
each of these three subjects can be appreciated in Figure 8. For Subjects 1 and 3,
the peak potassium value was always at
11 A.M., while for Subject 6, the peak value
was consistently at 8 A.M.
SeronornP
ammo Average of IISubjects
T h e intra-individual biologic variation,
•
• Individual subjects
i.e., the total biologic variation excluding
the subject-to-subject differences, can be
Feb.
Mar.
Apr.
May
June
divided into two components: within-day
Time of Year (month)
variation (main hour effect, subject-hour
FIG. 6. Subject-month interaction for serum uric
interaction, and s u b j e c t - m o n t h - h o u r acid.
Mean values of three duplicate samples taken
interaction — these t h r e e factors can each day on five experimental days at approximately
equivalently be referred to as group monthly intervals. T h e average values for the 11
subjects as a group, for four of the individual
diurnal variation, individual diurnal varia- subjects, and for the commercial quality control serum
tion, and random biological fluctuations) are presented.
and day-to-day variation (main month and
subject-month interaction—these two
factors may be referred to as group day-to- [(SD/grand mean) X 100%] for both the
day variation and individual day-to-day within-day and day-to-day variadon on the
variation). T h e total intra-individual bio- nomogram in Figure 9. Each serum conlogical variance then equals the sum of the stituent is depicted as a point. T h e total
within-day biological variance and the day- variadon can be determined by nodng the
to-day biological variance. We have pre- straight line distance from any point to the
sented the estimated biological variability in origin; the coefficient of variadon of the
terms of average coefficient of variadon day-to-day and the within-day variation can
442
WINKEL, STATLAND AND BOKELUND
4.7
4.6
4.5
Subject
#3
• Subject
*6
4.4
4.3
4.2
o
a.
Subject * I
4.1
U-
0800
1100
1400
Time of Day
(nour i
FIG. 7. Mean values for serum potassium at three
times of day on five days for three subjects.
be read off the x and y axes, respectively, by
drawing the appropriate perpendicular
lines. T h e above relationship holds in that
the square of the hypotenuse equals the
sum of the squares of the two legs of
the right triangle; or in other words, the
total biological variance equals the sum of
SUBJECT #1
4.75
SUBJECT # 3
A.J.C.P.—Vol.
64
the biological day-to-day variance and the
biological within-day variance. T h e equivalence line (coefficient of variation of dayto-day = coefficient of variation of withinday) forms a 45-degree angle with the
x axis. All points below that line represent
serum constituents where the average dayto-day coefficient of variation is greater
than the within-day coefficient of variation.
As noted in the figure, the serum variates
fall into groupings on the basis of total
biologic variation: (1) less than 1% coefficient of variation: sodium, chloride, and
calcium; (2) approximately 3%: total protein and albumin; (3) 4 to 10% coefficient
of variation: cholesterol, creatinine, potassium, uric acid, the enzymes—acid phosphatase, alkaline phosphatase, and phosphate ion; (4) 15 to 2 1 % : urea, total lipids,
and aspartate aminotransferase; (5) approximately 30%; serum iron and alanine
aminotransferase. Four of the variates
have a higher within-day biologic variation
SUBJECT # 6
Feb.-5
-
4.50
1
4.25
•
/
Mar. 12
4.75
ler)
4.50
o
i
1
4.25
i
< •
Mar.
4.75
e
e
4.50
£
4.25
i
27
FIG. 8. Subject-hour interaction for
serum potassium. Serum potassium
values at 8 A.M., 11 A.M., and 2 P.M. on
each of five days for three subjects.
Potos
3
May
4.75
450
4.25
7
:
•"-»
""*•-*"
May 15
4.75 f -
A.
4.50 | 4.251—
><
i
0800
1100 1400
*
0800
'.
1100
1400
Time of Day inour,
0 8 0 0 1100
1400
443
VARIATIONS OF SERUM CONSTITUENTS
October 1975
Alanine A - T
• Total L tpid
- Phosphate
%AadP'tase
^Potassium
\
c
0)
7
[u
mLDH
_
/^Creatinine
^Cholesterol
*•*—
<v
o
u
Na
0
•^Albumin
J*TP
fea_l
o
m
\ , „,
•AlkPtose
• Aspartate
A-T
Unc Acid
\
12
20
24
28
32
Coefficient of Variation : Day to Day
(Percent)
FIG. 9. Nomogram comparing within-day variation with day-to-day variation. Mean values of 11 subjects
for each of 18 variates are represented.
as compared with the day-to-day variation.
They are cholesterol, potassium, acid
phosphatase, and phosphate ion. Six of the
variates, uric acid, alkaline phosphatase,
urea, iron, alanine aminotransferase, and
aspartate aminotransferase, have day-today variations at least twice as great as the
within-day variations. Figures 10 and 11
depict the biological within-day variation
versus the biological day-to-day variation
in terms of coefficients of variation in a
manner analogous to that described for
Figure 9, but in these cases, each of the 11
subjects is represented uniquely by a
separate point along with the mean coefficient of variation. T h e two figures present
the individual variations found for alkaline
phosphatase and phosphate ion.
drew blood samples at one specified timeof-day and did not explore any common
effect of "day" that could be present for
the group as a whole. In order to investigate both the day-to-day changes and the
within-day changes, we used ANOVA
procedure to evaluate effects both for
individual subjects and for the group as a
whole. T h e availability of the AutoChemist
permitted us the opportunity of assaying
numerous constituents on many serum
samples on one occasion.
T h e analytic variation, as determined on
the basis of paired blood duplicate samples,
includes preinstrumental as well as instrumental sources of error. T h e preinstrumental sources consist of those procedural
factors of variation in venipuncture
technic, storage and processing of whole
Discussion
blood specimens, centrifugation step, and
T h e problem of intra-individual varia- separation of the sera. Thus, the magnition of serum constituents was first studied tude of the analytic variation, as deterprospectively by Williams and associates, 21 mined in this study, will be greater than the
who investigated the week-to-week changes variation attributable to instrumental error
in a group of healthy volunteers. They alone, but should depict the total uncer-
WINKEL, STATLAND AND BOKELUND
444
• Individual values
O Mean value
1 2-o
-
Q
—
"~~ ~~
^
c
o
O
A.J.C.P. —Vol. 64
y/
\
FIG. 10. Nomogram comparing within-day
variation with day-to-day variation for alkaline
phosphatase in serum. Values for each of the
11 subjects are represented.
V
v.
\
o—
/
»•
\
o
1
\
o
2-/
1
2
1
4
1
6
II
8
1
10
12
II
Coefficient of Variation : Day-to-Day
6r C8 P '
• Individual values
O /Vfeo/7 values
Fig. 11. Nomogram comparing withinday variation with day-to-day variation for
phosphate ion. Values for each of the 11
subjects are represented.
Coefficient of Variation : Day-to-Day
v D eice pM
tainty encountered in routine blood
drawing. 3
T h e most dramatic changes in serum
constituents as a function of time-of-day
were found for total lipids, urea, and iron.
There were slight but statistically significant increases in serum albumin and total
protein. On a previous occasion, we were
able to note an effect of time-of-day for
serum sodium, potassium, and chloride;
however, the present study revealed a
main effect for chloride only. 17 This
apparent discrepancy can be explained on
the basis of a storage artifact that probably
October 1975
VARIATIONS OF SERUM CONSTITUENTS
occurred in the earlier experiment, where
refrigeration of the whole-blood sample
caused inhibition of the N a - K pump; thus,
the 8-A.M. samples, which had been stored
for a slightly longer time, had a much
higher serum potassium value. 17
A main effect of hour for serum iron,
with highest values found at 2 P.M., has
been recently reported by Wiltink and coworkers, 22 who also examined healthy subjects. The changes in serum urea and total
lipids may be explained by metabolic
effects induced by the prolonged fast that
the subjects had to endure, i.e., from 8
A.M. to 2 P.M.
T h e estimation of the biological day-today (month-to-month) variation for the
group as a whole presents a difficult
analytic problem. When samples are
assayed on separate runs, one run per day
(as described for Data Set A), the biological main effect of day will be confounded
with the long-term analytical variation. On
the other hand, if all the samples drawn
on various days are stored and assayed on
one occasion, the biological main effect of
day may be confounded with an effect of
storage. In the present study, this problem
was solved by designing the experiment in
such a way that the long-term analytical
variation and the effect of storage could
be studied as one factor while the biological
day-to-day variation entered the analysis
as the second factor. As it turned out, this
precaution was necessary. Thus, for serum
potassium and total lipids, a biological main
effect of day was demonstrated. However,
the very significant interaction between the
above-mentioned two factors in the case of
total lipids (Fig. 5), and the variance of the
batch-to-batch Seronorm values (Table 3),
d e m o n s t r a t e a significant long-term
analytical variation for total lipids.
T h e biologic month-to-month change in
total lipids has been reported by Fuller,
Grainger, and Jarret, 4 who also found a
minimum value in the late winter months.
It has been suggested that both serum
445
potassium and sodium are increased
during the warmer months. 5
For the remaining serum constituents, a
biological main effect of day could not be
demonstrated, and we elected to interpret
the total main effect of day as being due
entirely to the long-term analytical variation. However, this decision is somewhat
arbitrary, being based as it is on the presence or absence of statistical significance.
If anything, we are probably overestimating the analytical variation. Yet, for most
of the serum constituents studied, except
for the electrolytes sodium, calcium, and
chloride, the total contribution of batchto-batch and within-batch analytic error
is relatively small compared with the biological variation seen in healthy subjects.
T h e magnitude of the intra-individual
variation must be known before we are
able to use a patient's baseline values as his
own control, i.e., in assessing whether a
change in a patient's values should be
considered a sign of improved or of
deteriorating health or merely a change
within expected normal intra-individual
variation.
We have pooled the various within-day
factors and the day-to-day factors to gain
insight into the relationship of the timed e p e n d e n t factors of intra-individual
variations. It should be emphasized that
the data presented here are constrained by
the experimental design, which defines
the six-hour period from 8 A.M. to 2
P.M. to be the range of the within-day time
span. Yet, this period is clinically relevant,
in that most venipunctures are performed
during those hours. T h e magnitude of the
day-to-day variation based on a four-month
time span compares favorably with day-today fluctuations we previously found over
a two-week period. 23 Thus, these monthto-month variations should be considered
not as seasonal, but rather as variations
reflecting relatively frequent changes from
day-to-day within a month.
Although the magnitude and relative dis-
446
WINKEL, STATLAND AND BOKELUND
tribution of the within-day and day-to-day
variation may be appreciated for the group
as a whole (Fig. 9), it should be emphasized that these are average results. T h e
subjects differ not only in their mean
levels, but also in their relative and absolute
within-day and day-to-day variations. This
last point is made obvious in the cases of
alkaline phosphatase and phosphate ion,
as illustrated in Figures 10 and 11. Presendy, we are investigating the interrelationships of selected serum variates in the
same subjects using a multivariate a p proach. 24
Previously, we have reported the effects
of prior diet and prior exercise, 18 as well
as various posture and tourniquet stresses,
on the variation of serum constituents in
healthy subjects. 19 For certain serum
variates, these factors contribute significant effects on the net variation. In the
present study, we have standardized the
preparation of the subject regarding
those factors to minimize any variation
not related to the effect of time of venipuncture. In an inadequately defined
experiment, i.e., without a p p r o p r i a t e
concern for posture, tourniquet application time, etc., one would expect more
random biological variation, i.e., the subj e c t - m o n t h - h o u r interaction term would
be greater.
It should be emphasized that in the
present study we have attempted to simulate the clinical situation for the ambulatory patient who goes to the clinic for
venipuncture. T h e hospitalized subject
may very well experience different levels
of biological within-day a n d day-to-day
variations.
T h e rigidity of the hospital routine, e.g.,
the fixed times of meals and arousal from
sleep, may serve to decrease the magnitude
of these variations. Conversely, however,
the hospitalized patient may experience
additional changes d u e to the drugs he
takes, the added anxiety of the hospital
environment, and, of course, the nature of
A.J.C.P. —Vol. 64
the clinical problem from which he suffers.
Application of the studies described in this
paper to hospitalized well subjects and to
hospitalized sick subjects is needed, and
would complete the picture. Then we could
better evaluate the clinical significance of
changes in concentrations of serum constituents occurring from hour-to-hour
and from day-to-day in the inpatient.
Acknowledgments. Steen Laier, Head of the Department of Mechanical Engineers of the Academy
of Engineers, Technical University of Denmark,
Lundtofte, Denmark, cooperated in this study.
August Johnson recruited the volunteers a n d organized the blood drawing sessions. Liff Kistrup,
Connie Hansen, and the other technologists at the
Odense Central Laboratory operated the AutoChemist and processed the serum samples. Nancy
Wassmann performed the venipunctures a n d prepared the specimens for analysis. Virginia Mackenzie
assisted in preparation of the manuscript. Professor
Eugene Johnson, Department of Biometry, University
of Minnesota, offered helpful suggestions in presenting the results of the statistical analysis. Dr. Ellis S. Benson, Department of Laboratory Medicine and Pathology, University of Minnesota; Dr. Jens Lintrup, Department of Clinical Chemistry, Finseninstitute,
Copenhagen; and Professor Magnus Hjelm, Department of Clinical Chemistry, Odense State University Hospital, Odense, Denmark, offered support
and encouragement of this project.
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