CLIN.CHEM.38/3, 329-333
(1992)
Broadly Pertinent Chronobiology Methods Quantify Phosphate Dynamics (Ohronome)
in Blood and Urine
Biological variability,
as well as technical (analytical)
sources of variation, determines
the width of the reference interval and can be a terrible foe of clinical chemistry. It is all too often ignored by implication
if not by
explicit lip-service
to homeostasis.
Like the deus &
machina, the organism is presumed to be able to “right”
itself, irrespective
of time (1, 2). So far, so good. The
trouble arises when the investigator
of a putative homeostasis
also assumes that the mechanism
of righting
assures relative constancy
in a time-invariant
fashion
and hence undertakes
a study at some convenient but
unspecified and not necessarily pertinent time.
Alternatively,
as in the paper by Kemp et al. in this
issue (3), variability can be resolved into dynamic characteristics
of ever-present,
partly periodic change: the
amplitude and acrophase assess the extent and timing
of reproducible,
rhythmic
change. By comparison
with
the arithmetic
mean, a usually more accurate and more
precise location index is provided by the rhythm-adjusted mean (MESOR; midline-estimating
statistic
of
rhythm),
which takes into consideration
the time structore of a physiological
variable. Relations among physiological mechanisms
in health and disease can then be
quantified
within the very range in which everyday
functions occur. A new dimension is added to clinical
chemistry
(4-9) by the methods of chronobiology,
the
science (logos) of life’s (bios) structure in time (chronos)
(4).
Most if not all variables in clinical chemistry undergo
rhythmic
changes
with different
frequencies.
The
rhythms can be algorithmically
formulated and validated by inferential
methods. Circadian rhythms (with
one cycle in 20-28 h), ultradian rhythms (with one cycle
in <20 h), and infradian rhythms (with one cycle in >28
h) all characterize,
with age trends, the body’s time
structure: the chronome. The word chronome (6) was
coined from “chronos” (time) and “nomos” (rule), in
analogy to genome, from “gene” and “chromosome.”
In
this editorial
we will amplify on the approach and
methods of Kemp et al. (3), who quantified changes in
phosphate metabolism
(Pi) along the 24-h scale-circadiana-and
the attendant uncertainties.
The prefix circa was introduced for several reasons
(10). First, it indicates the genetic basis of rhythms
indirectly supported by their free run under constant
environmental
conditions with an average period close
to, yet statistically
significantly
different from, an environmental match (10-12). Free-running,
which is being
shown for a growing number of variables, remains to be
investigated
for the case of changes in N. Second, the
definition
of circa-rhythms
further emphasizes
the statistical uncertainty
with which a period length can be
estimated
(10). Given the definition of circadian, the
meaning of diurnal was restricted
to the daylight span
of the 24-h day, so as to avoid constructions
such as “a
diurnal [24-h] rhythm in diurnal [daytime] epilepsy.”
PAMetabolism
Without any assessment
of uncertainty,
a detailed
historical
review of N metabolism
up to 1965 by Doe
(13) found mostly controversial descriptions of circadian
variations
in blood and urine. Reports as far back as
1854 mention lowest urinary N excretion in the morning. By 1965, 13 reports had found high N excretion at
night, whereas in eight the N excretion was reportedly
increased in the morning. The studies with the greatest
sampling rate (at half-hourly or hourly intervals) or the
longest monitoring span report the highest N excretion
occurring in the afternoon and early evening (14-16). In
contrast to circadian rhythms
in other electrolytes,
N
excretion reportedly
adjusts promptly after a shift in the
rest-activity
schedule (13). A failure to standardize the
living routine, the sole visualization
of data in a tabular
or graphic form without further
analysis, and accordingly indications
of local peaks or troughs rather than
patterns
characterizing
a data set as a whole, all contributed to the controversy.
Since 1965, several investigators
have published results on circadian rhythms in urinary N excretion, with
the timing of overall high values within a cycle-the
acrophase-reportedly
occurring
in the evening (8, 17,
18). In a longitudinal
self-study by a clinically healthy
man who collected
urine samples around the clock
between January
1968 and September
1969(18), results
from monthly pools yielded a circadian amplitude
of
13.0 mg/h (95% confidence interval: 10.5-15.4 mg/h) and
an acrophase
around
2145 h (95% confidence interval:
2100-2245).
Monthly acrophases
were all within 3.5 h,
indicating
the consistency (stability) of the individual
circadian pattern. The biological variation in urinary
variables such as N, magnesium, other electrolytes, and
catecholamines
is large: on the average there is a 50% to
100% change for data normalized
as a relative difference
between the highest and lowest values assumed by the
periodic
function
fitted by least squares to the data (8).
Changes in N concentration
in blood, though of a lesser
magnitude,
are also eminently
periodic. Table 1 documents the reproducibility
of the circadian
characteristics of serum N concentrations
across sex, age, and
geographic location (19), once data obtained under standardized
conditions
are analyzed
by time-microscopic
methods (4).
Pi in Blood and Urine
Doe (13) compared the circadian
in 10 individuals
serum and urine
variation in Pi of
who provided both
CLINICALCHEMISTRY,Vol.38, No.3, 1992
329
Table 1. Clrcadlan
Change In ConcentratIon of Phosphate In Blood
P1,mg/L
Acrophase, ‘h
Location and
rsfersnce
sex
Age, years
England (3)
Minnesota (13)
Minnesota (19)
Minnesota (19)
Romania (19)
Romania (19)
Romania (19)
Romania (19)
Romania (19)
Romania (19)
M&F
M
M&F
M&F
M
F
M
F
M
F
22-40
25-55
24 ± 10
71 ± 5
21 ± 2
21 ± 2
11.0 ± 1.5
11.0 ± 1.5
76 ± 8
76 ± 8
No. of
subjects
Double
<0.001
40.0
23
<0.001
39.0
87
107
83
117
0.001
48.9
ampiftude
5.3 (14.9)a
2.9 (6.2)
4.6 (10.7)
1.0 (2.5)
3.3 (8.3)
4.0 (10.3)
5.3 (10.8)
01:08
Degree.
-30
-36
-21
+16 (-344)
-19
-31
-17
0.001
48.6
5.4 (11.1)
01:12
-18
0.001
32.1
32.7
1.0 (3.1)
1.0 (3.1)
23:20
02:04
+10
-31
9
10
24
23
20
P
MER
<0.05
0.103
<0.001
0.154
35.3
0.001
46.7
43.1
39.8
h:mln
02:01
02:24
01:24
22:56
01:16
02:06
(-350)
Changeassessedby the cosinor method (4, 20-24). This method yields a probability(F) for rhythm detection bya zero-amplitudetest and quantifies circadian
rhythm characteristics: MES0R-a rhythm-adjusted mean; amplitude-measure of predictable extent of change (half the difference betweenthehighest and lowest
values assumed by the 24-h cosine curve fitted by least.squares to the data); and acrophase-measure of tIming of overall high values, expressed in hours and
minutes as a lag from local midnight or in degrees, with 360’ equated to 24 h. Note that all acrophases are within 520 (or 3 h and 30 mm).
Amplitude as percent of MESOR is listed in parentheses.
blood and urine samples during the same 24 h under
highly standardized
conditions of diet. Expressing
the
data as a percentage
of each individual’s 24-h mean and
pooling all data in a single series, demonstrated statistically significant
circadian
rhythms in blood and urine
(P <0.001). The single 24-h cosine model accounted for
22% and 48% of the total variance,
and the extent of
predictable
circadian
change,
gauged by the double
amplitude, was 13% and 54% in blood and urine, respectively. The timing of overall high values, as gauged by
the acrophase, occurred earlier in urine (around 2115 h)
than in blood (0240 h; P <0.05). When the results were
summarized by population-mean
cosinor, then, on the
average,
the 24-h cosine model accounted
for 49%
(blood; P = 0.103) and 59% (urine; P <0.001) of the
variance. These results agree with those of Kemp et al.
(3) in terms of timing and extent of change and in terms
of relative timing between blood and urine. The timing
of changes from blood to tissue, bone in particular,
enters into the relation between blood and urine and
awaits chronobiological
scrutiny.
Kemp et al. (3) computed a correlation
coefficient,
presumably
between the original values for plasma Pi
concentrations
and the corresponding
urinary N excretions. The matter of correlation between two rhythmic
variables requires
amplification,
If an association
between two variables
is sought to draw a statistical
inference, it is important
that the values assumed
by
any one variable are independent
and that the individuals studied represent a random sample from a given
population.
If one or both variables are serially correlated, the assumption
of independence
is violated. This
is the case for rhythmic
variables
such as N. The
correlation
can then be studied for each rhythm characteristic separately.
For a comparison
of overall values,
the MESOR is a more appropriate
endpoint than the
original data collected around the clock, because the
actual
values change
predictably
(rather than randomly) as a function of time. In the study by Doe (13), in
330
CLINICALCHEMISTRY,Vol.38, No.3, 1992
which the number of subjects was similar to that of the
study by Kemp et a!. (3), the Pearson product-moment
correlation coefficient computed
between the MESOR of
plasma [N] and that of urinary N excretion is not
statistically
significantly
different from zero (r = 0.357;
P = 0.31). By contrast, if the original data were used,
the correlation
between
the two variables
would be
statistically
highly significant
(r = 0.696; P <0.001).
This result corresponds to that of Kemp et a!. (3) (r =
0.42; P <0.05).
In dealing with periodic variables,
apart from the
Pearson
product-moment
correlation
coefficient,
it is
also pertinent to compute the autocorrelation
and crosscorrelation functions.
These serve to measure the correlation between observations
on the same or different
variables at different
distances
apart. For instance,
a
peak in the estimated
cross-correlation
function at lag k
may indicate that one variable is related to the other
when it is delayed by a time interval k. The autocorrelation and cross-correlation
functions,
however, do not
provide estimates
of rhythm parameters
such as the
acrophase or amplitude; moreover, as a rule, no dispersion estimates are provided. Given variables observed at
a high enough sampling
rate and for a long enough
span, an approach by spectral and cross-spectral
analysis leads to the computation
of the coherence function.
The latter yields information concerning the correlation
specified for a given frequency for the relations between
two variables. A large coherence at a given frequency
between two variables indicates that they are closely
related for that particular frequency component. With
limited time series and sufficient available prior information, as is the case for Pi (13), the cosinor approach by
Kemp et a!. (3) has the merit of providing dynamic
endpoints (amplitude and acrophase) with their uncertainties [see Figures 1 and 2 (discrepancies
with results
published by the original authors stem from inaccuracies in our taking mean values off the authors’ graphs)].
Figure 1 illustrates the 24-h cosine model fitted to the
E
0.
a
E
a
a
0.
00:00 04:00 08:00
12:00 16:00
20:00
lime (Clock Hours)
Fig. 1. Illustration of the single cosinor method on plasma Pi
concentration: plot of original data [from Kemp et al. (3)j and fitted
curve,
as a function of time
This method examines whethera cosinefunctionwiththeanticipatedperiodIs
a better approximationof the data than is a horizontal line (meanvalue, ‘?). To
test for the presenceof a rhythm,we partitionthe total sum of squares.i.e., the
sum of squared deviations of the data from ‘V [distance a at data point =
t,)1, into the sum of squares due to the regressionmodel (distance bat t =
t,) and the residual sum of squares (distance cat t = t,). A comparison of the
latter two terms with their respective number of degrees of freedom yields a
test of significance for the zero-amplitude (no-rhythm) assumption; it also
serves to derive confidenceintervalsfor the MESOR (a rhythm-adjustedmean),
amplitude, and acrophase (measures of predictable extent and timing of
change) when a rhythm is detected by the rejection of the no-rhythm
hypothesis. Printed with permissionfrom Chronobiologia
data
as a function
of time; Figure 2 provides a polar
the e!!ipse around the vector tip represents a 95% confidence region for the circadian
amplitude-acrophase
pair. When this error ellipse does not
cover the pole, i.e., the center of the graph, the “norhythm” or zero-amplitude
assumption
is rejected and
the rhythm
characteristics
can be quantified
with a
measure of their uncertainties.
Details of the procedures
are given elsewhere (4,21-24).
Rhythmic
components
other than circadian characterize Pi. Eleven subjects who were studied longitudinally three times a week (in the morning) for three
months,
starting
at the end of November,
showed a
statistically
significant
near-weekly
(circaseptan)
rhythm (P = 0.0 11) with a relative double amplitude of
7.1% and an acrophase
occurring around 2300 h on
Mondays (19). The reported
lack of statistical
significance of circannuals (19) may be accounted for in part by
the fact that analyses
were carried out in a serially
independent
fashion, wherein the circadian
MESOR
of
each individual was assigned to the date of blood sampling to constitute
a single time-series
amenable
to
circannual
testing.
Interindividual
variability
may
have obscured circannual changes, which are best eva!uated by repeated monitoring
of the same individuals
around the seasons (e.g., 18).
display, where
Implicationsfor Clinical Chemistry
Multifrequency
rhythms in urinary and blood Pi account for a large portion of the intra-individual
variabil-
For% In O.gres.: 360#{176}C
24 Nani
Fig. 2. Illustrationof the singlecosinormethodon plasmaP1
concentration [datafrom Kempet al. (3)1:polar display
The circular scale represents one cycle, with 3600 equated to 24 h and the
reference time chosen as local midnight The circadlan amplitude and acrophaseof the fitted curve shown in Fig. 1 are representedhere asa directed
line (vecto,). The vector points to about 0200 h, the time at which high Pm
concentrationscan be anticipatedin plasma. The ellipseshown around the tip
of the vector is the 95% confidence region for the joint estimation of the
amplitudeand acrophase.The statisticalsignificanceof the circadlanrhythm In
plasma P1concentratIonis shown by the fact that the error ellipse does not
coverthe pole (the center of the graph, correspondingto an amplitudeof zero).
Pole overlap by the error ellipse means compatibilitywith the test criterion of
thezero-amplitude(no-rhythm)assumption,and the null hypothesiscannot be
rejected. Printed with permission from Chronoblologia
ity. Unless these predictably
large changes are considered and exploited in the clinical or research laboratory,
a reduction of the technical error from 10% to 1% is not
likely to amount to considerable progress. Benefit from
advances in analytical chemistry depends upon concurrent progress in chronobio!ogy.
Rhythm characteristics
provide new diagnostic
tools in addition to usually rendering the mean value more nearly accurate (when serial
data are collected at irregular intervals) and more precise
(when serial data are collected at equal intervals) (7).
Amplitudes
(A) and acrophases
(4,), measures
of extent
and timing of rhythmic change, provide diagnostic tools
that recognize disease earlier: in the case of a patient
with hypercortisolism,
whose circulating cortisol values
are mostly outside the time-qualified
range, the absolute
values of the data from his son are all within the
physiological range, but the circadian (A,4,) pair for the
son is outside the 90% prediction limits (20). This example illustrates
the broader applications of chronobiology,
beyond the 0800- and 2000-h cortiso! determinations,
in
testing for deficiency or excess at times of expected
overall high or low values, respectively (4, 7).
Once rhythms
are mapped, reference limits-chronodesms (25, 26)-can be defined for their characteristics
as well as for time-specified
single samples. The interpretation of ensuing single samples as well as single
time-series
thus becomes cost-effective.
The systematic
coding of time as an additional variable constitutes the
basis for constructing
large data banks collected from
clinically
healthy individuals
stratified
by sex, age,
ethnicity, and pertinent disease risk. In addition to such
CLINICALCHEMISTRY,Vol.38, No.3, 1992 331
life span studies with
as a
long-term
goal. The immediate
promise from a chronobiological approach is the improvement of (a) the sensitivity of screening; (b) the reliability of diagnosis; (c) the
accuracy of prognosis; and (d) the timing and efficacy of
treatment,
possibly
leading to (e) the prevention of
civilization-related
diseases, as a new task in which the
clinical laboratory
can play a major role (5-8). It is
critical
to estimate
cost in this context, when each
drawing of blood adds to the already inflated fee for
service. Even for blood, however, the specification of the
sampling time and of the subject’s routine can be relatively cost-effective,
once chronodesms
have been
mapped from appropriate reference groups.
For practical reasons, the clinical laboratory today
places its major emphasis on tests of blood rather than
urine. If urine is collected, a 24-h sample is usually
thought to eliminate rhythms. Kemp et a!. (3) deserve
credit for assessing circadian rhythms in both urine and
blood. Their approach will have to be extended
to a
quantification of the chronome as a whole. Once this
quantification
is achieved, single-sample
spot-checks
can be designed for a cost-effective single-sample test or
for a test on a few samples that exploit rather than
eliminate
the dynamics within the physiological range.
The atom has been split; by fission, energy has been
released, and by fusion, even more energy has been
produced. The usual value range can also be split into
rhythms (the analogy to fission), yielding novel information, with the relations among rhythms (by analogy
to fusion) adding to such information, e.g., the steps
initiated by the paper of Kemp et a!. (3) and, it is hoped,
by many other endeavors in clinical chemistry.
Current focus on phosphate homeostasis
(27) can only
gain by using, as reference, the characteristics
of the
phosphate chronome. Until automatic sampling becomes biophysically practical (28), urine is the vehicle
for nomnvasive long-term monitoring over a span of
time-varying
peer-group
limits,
outcomes may develop continuous risk functions
more than a year. Cost is reduced if patients learn to
collect the samples themselves and to carry out some
tests themselves, as is done by patients with diabetes or
high blood pressure (29,30). Education and the design of
instrumentation
for simplif’ing
self-help (31) are the
ways to reduce cost while improving the quality of care
(Figure 3) in clinical chemistry and in preventive med-
icine broadly.
It took nearly a century and a half to follow up the
findings of early reports concerning
circadian
variation
such as that in Pi and so many other variables. But to
prove any benefit from focusing on the broader chronome and to derive new reference intervals, studies on
at least a few biomedical ‘pilot tests by a longitudinal
approach (18) will have to be extended to many clinical
chemical variables
determined in urine. Roads are being built and maintained
in good repair for ready
driving. Reference intervals could also be so built. This
suggestion does not mean that for each laboratory
determination
the entire chronome
has to be mapped,
just as the driver does not build the road while driving
along. This analogy
should dispel many reservations
concerning
the
cost
of the
approach
to the
chronome.
Notably for a preventive medicine, extensive mapping
has revealed the importance
of circannual rhythms (32).
At the outset, such large international
studies are
critical. These serve to specify single-sample spot-checks
(32-34) that allow the early recognition of increased
risk and lead to preventive intervention for lowering
risk and improving health. A human chronome initiative (5,6) seeks outcomes as test criteria in this scenario
for the clinical laboratory of the future.
Physical and eventually chemical monitoring and
time series analysis are aims of the human chronome
initiative (35-37). Physiological monitoring of humans
could rely on the computer for automated
data collection
and analysis and has led to the biophysical detection of
the earliest (e.g., neonatal) risk of developing civilization-related diseases later in life. This task will have to
High
High
1A]
I
I
ciwsn.Il.l.#{216}S
I’v
B
JBJ
FWV,srRsaCen
B’
?ash.u’Lcss
-
C
Low
Low
lmprov.m.nt
RedUCtIon
Qualityof Care
Fig. 3. Cost andqualitytrade-offs (left) or utilization of chronobiological concepts for preventive as well as curative medical devices (right)
Pertinenceof the chronome to everyday heaithcare poses challengesto the clinical laboratory,the device industry, and the educationalsystem.The mapping of
the chronome is critical for quantifying,understanding,and Improving healthby the recognitionand reductionof disease nsk. A concerted effort by educators and
medical professionalsto implement the concepts of chronoblology broadly offers a solutionto reduce cost while improving the quality of heafthcare by placing
emphasis on prevention rather than continuing the nearly exclusive focus on the cure of disease, with an unavoidablecompromisein qualityso as to stem cost.
A commitment to reducing cost In clinical chemistry by instrumentation
places the patient Into the healthcareloop as an active participant. In the case of diabetes,
therewas no choicebut to adopt thisapproach, which can stillbe chronobiologically
refined.Skyrocketingcostsargueforthe extensionof the educatedhealthy
subject’s participation In preventive healthcare.Source:Chronobiologla199118:112; reprintedwith permission
332
CLINICALCHEMISTRY,Vol.38, No.3, 1992
be integrated
with a time-targeted
clinical chemistry
for
the earliest noninvasive detection
of disease risk, e.g.,
serially
monitoring salivary or urinary markers as
indicators
for chemotherapy to be given before the
resurgence
of a metastatic
cancer.
Supported by the U.S. Public Health Service (GM-13981); Minnesota Medical Foundation
(SMF-745-88); and Dr. h.c. Dr. h.c.
Earl Bakken and Dr. Betty Sullivan Funds. Christopher Bingham
(Professor, Applied Statistics, University of Minnesota, St. Paul,
MN) further developed and presented the methodsdescribed here
(Halberg
F, Bingham C. The scope and promise of chronobiology
and biostatistics:
interpenetrating,
inseparable disciplines. Proc.
Biopharm. Section, Am. Statistical
Assoc., Chicago, IL, August
15-18, 1986; pp. 11-42, 1987). Data developed by Richard P. Doe
(Professor of Medicine Emeritus, University
of Minnesota) and
Erhard Haus (Professor of Laboratory
Medicine and Pathology,
University of Minnesota) are included in Table 1.
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27. Bronner F, Peterlik M. Extra- and intracellular calcium and
phosphate regulation. Boca Raton, FL: CRC Press, 1991:250 pp.
28. Halberg E, Jardetzky N, Halberg F, et al. Magnetic resonance
spectroscopy and ambulatory cardiovascular monitoring noninvasively gauge timing of phosphate metabolism and circulation.
Chronobiologia 1989;16:1-8.
29. del Pow F, Perez Subias M, Halberg F, Burillo V, Hermida
Dominguez RC. Microprocessor-based system for self-measurement applications. Proc 5th Ann Conf IEEE, Engineering in Med
Biol Soc, Columbus, OH, September 10-12, 1983:413-8.
30. del Pow F, Rodrigues MJ, Arredondo MT, Otsuka K, G#{243}mez
E,
Halberg F. Decimation of ambulatory blood pressure (BP) series,
Proc 26th Ann Mtg Expo, Washington, DC, May 11-15, 1991.
Arlington, VA: Assoc for Advancement of Medical Instrumentation, 1991:29.
31. Halberg F, Conn#{233}lissen
G, Bakken E. Caregiving merged with
chronobiologic outcome assessment, research and education in
health maintenance
organizations
(liMOs). In: Hayes DK, Pauly
JE, Reiter RJ, eds. Chronobiology: its role in clinical medicine,
general biology, and agriculture, Part B. New York: Wiley-Liss,
1990:491-549.
32. Halberg F, Corn#{233}lissen
G, Sothern RB, et al. International
geographic studies of oncological interest on chronobiological variables. In: Kaiser H, ed. Neoplasms-comparative
pathology of
growth in animals, plants and man. Baltimore: Williams and
Wilkins, 1981:553-96.
33. Hermida RC, Halberg F, Halberg E. Closer to a psychoneuroendocrine hemopsy? [Review]. Biochim Clin 1986;10:1053-66.
34. Halbeng F. Quo vadis basic and clinical chronobiology: promise for health maintenance [Review]. Am J Anat 1983;168:543-94.
35. Halberg F. Norberto Montalbetti: 1936-1991. Bioquimia 1991;
16:43-6.
36. Halbeng F, Cornelissen G, Carandente F, Csrandente A. A
welcome to the chronome as farewell to cari.ssimo Norberto.
Biochim Clin 1991;15:1239-47.
37. Halberg F, Connelissen G, Carandente F. On with the human
chronome initiative: the legacy of Norberto Montalbetti. Chronobiologia 1991;18:105-6.
Corn#{233}lissen
Franz Halberg
Germaine
Chronobiology Laboratories
University of Minnesota
5-187 Lyon Laboratories
420 Washington
Ave. SE.
Minneapolis, MN 55455
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