J Chron Dis 1976, Vol. 29, pp. 667-676. Pergamon

Press. Printed in Great Britain

A STUDY OF THE EFFECTS OF FASTING TIME ON THE ONE HOUR GLUCOSE TOLERANCE TEST RODGER D. PARKER

Operations Research Division, School of Hygiene and Public Health, The Johns Hopkins University, Baltimore, Maryland, U.S.A.

and LAWRENCE A. YAMAMOTO

Health Services Research Department, US. Public Health Service Hospital, Baltimore, Maryland, U.S.A. (Received 3

April

1976)

Abstract-The effect of time since last food intake upon the value of glucose one hour

post challenge is examined on a large group of asymptomatic patients. A number of multivariate statistical techniques are employed, and after a series of determinations, the conclusion is reached that the time since last eating has a relatively marginal effect on the glucose tolerance test.

INTRODUCTION

AND

OBJECTIVES

ALTHOUGH the glucose tolerance test is one of the most time honored and thoroughly studied of all laboratory procedures, there are still a number of unknowns related to the differentiation of diabetic and non-diabetic subjects using this test. A number of studies have been performed to determine the effect of previous food ingestion on the results of the timed glucose tolerance. Hayner et al. [l] concluded that a detectable level of depression on the one hour value occurred if food was ingested by the subject within four hours of the test. However, a study [2] performed by the National Health Examination Survey Team on a group of male inmates at a correctional institution notes that no significant correlation between times of eating and values of glucose tolerance was found. It has been established that blood glucose has a decreased elevation after repeated challenges within a short time interval [3-6]. This effect is possibly due to increased intestinal absorption producing increased portal circulation when a second challenge occurs, or it could also be due to the activation of insulin by the first challenge, lowering blood glucose. 667

668

RODGERD. PARKERand LAWRENCEA. YAMAMOTO

It remains to be conclusively shown however that these studies can be analogously utilized to conclude that fasting time has an effect on the oral glucose tolerance test when performed on asymptomatic and presumably non-diabetic patients. The comprehensive effects of the carbohydrate and iron carbohydrate elements of diet on absorption, the interval since last eating as well as demographic factors, such as age, sex and race tend to complicate the situation in a natural environment. In the operation of the Health Evaluation Center* (a multiphasic screening clinic for asymptomatic patients in which our study was conducted) criteria established by Hayner et al. to distinguish between normal and abnormal values of the one hour glucose tolerance have been used for the past 2 yr. Age, sex, and dichotomized fasting time (whether or not the patient has eaten within 4 hr) are taken into consideration in determining whether or not a patient warrants a full 3 hr glucose tolerance test. Because of some doubt as to the validity of the pre and post four hour cuttoff we have undertaken a study of our own patients to determine the impact of food history. The goal of this study is to determine by multivariate statistical techniques whether or not fasting time (i.e. time since last eating) emerges naturally as a significant predictor of the one hour glucose tolerance test. METHOD

Data from 1358 asymptomatic patient visits to the Health Evaluation Center including age, sex, race, fasting time, and blood chemistries (SMA-12, Technicon Corporation) forms the basis for our study. The patients were given a 75 g dose of glucose in a cola form (Glucola, the Ames Corporation) and 1 hr after challenge their blood was drawn. The glucose method was that of Bittner and Manning [7, S] and relies on the reduction of a copperneocuproine complex. TABLE1. VARIABLES INCLUDEDIN THE Variable Age Cholesterol Calcium Phosphorous Bilirubin Albumin Total protein Uric acid BUN Glucose* LDH SGOT Fasting time

Mean

STUDY

Standard deviation

43.0 225.0 10.0 3.7 0.42 4.6 1.5 5.6 14.0 144.0 147.0 37.0 4.0

Number of cases: 13.58. *One hour glucose tolerance.

*US. Public Health Service Hospital, Baltimore, Maryland.

14.0 46.0 0.53 0.60 0.29 0.49 0.49 1.6 6.1 51.0 47.0 21.0 2.3

The One Hour

Glucose

Tolerance

Test

669

The variables included in the multivariate analysis are shown in Table l.* ‘Glucose’ denotes the value of glucose 1 hr post challenge and will be treated as the dependent variable in the analysis. The variable Fasting Time is the time since last food intake and is the primary independent variable. The concomitant impact of age and the eleven other variables will be investigated as well. The stages in our data analysis were as follows: 1. Perform a stepwise linear regression using glucose tolerance as the dependent variable and all other variables as independent ones; 2. Graph glucose tolerance vs age, the most significant predictor variable found in stage 1; 3. Perform a stepwise linear regression using glucose tolerance as the dependent variable and all other variables except age as the independent ones; on all patients between 40 and 60 yr of age; 4. Graph glucose tolerance vs time since last eating for all patients in the 40-60 yr age interval; 5. Form the dichotomization of the variable, time since last eating, which is most predictive of glucose tolerance, and find the probability density function of glucose tolerance for patients in each of the two groups; 6. Determine how significant is the predictability of glucose tolerance resulting from the dichotomization of time since last eating. RESULTS

Patients (1358) were tested; the means and standard deviations of the variables of the study for the total sample population are given in Table 1.t Fifty-one per cent of the population was female, 49% male; 74% was Caucasian. The correlation of each of the variables with 1 hr glucose tolerance and the order of entry of the first five variables in the stepwise linear regression with glucose tolerance as dependent variable are given m Table 2. TABLE OTHER

2. CORRELATION VARIABLES, AND

Variable

Age Phos Cholesterol Uric acid Fast time SGOT LDH BUN Bilirubin Calcium Total protein Albumin

OF GLUCOSE TOLERANCE WITH THE ORDER OF ENTRY IN THE STEP-WISE REGRESSION

Correlation with glucose tolerance 0.30

_-0.27

0.21 0.16 0.15 0.10 0.08 0.06 0.03 -0.02 -0.02 0.02

Entry number in the regression 1 2 5 3 4

*LDH-Lactic Dehydrogenase; SGOT-Serum glutamic oxalacetic transaminase; BUN-Blood nitrogen. t Known diabetics as well as those with suspicious findings were excluded from the study.

urea

670

RODGER D. PARKER and LAWRENCE A. YAMAMOTO TABLE 3. RESULTS OF THE STEP-WISE REGRESSION OF ALL VARIABLES ON GLUCOSE TOLERANCE Step

Variable

entered

Multiple

1 Age 2 Phos 3 Uric acid 4 Fasting time 5 Cholesterol All variables included

R

0.30 0.36 0.39 0.40 0.41 0.42

The correlations are not especially high in general; fasting time, i.e. time since last eating, is only fifth on the list with a correlation of 0.15 and is only in fourth place in order of entry into the regression. Table 3 gives the multiple correlation coefficient, R, for the regression run. The R’s are not large enough to indicate good predictability of the dependent variable, glucose tolerance. That is, all the independent variables together are really inadequate (R = 0.42) to explain satisfactorily most of the variation found in the glucose tolerances among the patients in our sample. Virtually all of the variation which is explainable is explained by the leading five independent variables which entered first. In particular, fasting time is not very important in its contribution to R. At this stage we have a negative finding for our study in the sense that food history does not seem to be an important factor in explaining glucose tolerances. It is, however, possible that food history is important for certain types of patients since our results are for the total population. To investigate this possibility, we examine the relationship of glucose tolerance to age since age is the single most important predictor variable. It may be possible that for patients in certain age groupings fasting time turns out to be an important predictor, i.e. removing the variation of fasting time with age may remove most of the effect of all independent variables except food history. To investigate further these possibilities, we graph glucose tolerance vs age. Table 4 gives the data for the graph which is then plotted in Fig. 1. The data is tabulated in eight age intervals: Ck-20,2@30, etc. The second column in Table 4 gives the fraction of the patients in the eight age intervals; the third column the average age of all patients within an interval; the fourth column the TABLE 4. RELATIONSHIP OF GLUCOSE TOLERANCE TO AGE

Age

interval t&20 20-30 3&40 4(r50 5&60 60-70 7&80 >80 All

Fraction of patients

0.06 0.20 0.12 0.24 0.25 0.09 0.04 0.00 1.00

age

Average glucose tolerance

Standard deviation glucose tolerance

18.5 23.5 34.7 45.0 54.2 63.4 13.6 84.5 43.0

121.7 120.9 128.7 151.2 151.6 170.6 174.3 219.5 144.3

34.0 41.0 40.7 54.9 52.3 49.9 59.6 23.3 50.8

Average

The One Hour

3

Glucose

Tolerance

I

I

I

I

15

25

35

45

Age. FIG. 1. Glucose

tolerance

671

Test

I

I

I

I

55

65

75

85

yr

vs age for all patients.

average glucose tolerance of all patients within an interval; and the fifth column the corresponding standard deviation. There are seven points on the graph corresponding to the first seven age intervals, the values of the x-coordinates of the points are the average ages, of the y-coordinates the average glucose tolerances. Table 4 reveals that the 323 patients (0.24 of the sample size) in age group 4&50 have average glucose tolerances of 151.2; whereas, the 343 patients (0.25 of the sample size) in age group 50-60 have average glucose tolerances of 151.6. Thus, since the standard deviations are also quite similar (54.9 and 52.3) it is seen that between ages 40 and 60 there is no discernible variation of glucose tolerance with age. Thus for these patients, age is not predictive of glucose tolerance, and therefore fasting time might well be a leading factor in determining their glucose tolerances. To investigate this possibility a regression was run on just the patients in the age group 40-60. Table 5 gives the correlation with glucose tolerance of the five leading variables in this regression (age of course is no longer a variable), and the entry number of the variable in the regression. Comparing Table 5 with Table 2 we see that the same variables (minus age) are still the leading ones and that the new correlations do not differ much from the earlier ones. Thus removing the variation of glucose tolerance with age does not raise the correlations of glucose tolerance with the other variables and in particular not with fasting time. Indeed even here time since last eating only enters in third place in the regression. TAIILE~. HIGHESTCORRELAT~ONSOFGLUCOSE TOLERANCE WITH OIXERVARIABLESFOR THE 666 PATIENTS BETWEEN 40 AND 60 yr OLD

Variable Phos Uric acid Fasting time SGOT Cholesterol

Correlation with glucose tolerance -0.22 0.17 0.13 0.12 0.08

Entry number in the regression 1 2 3 5 4

RODGER D. PARKER and LAWRENCE A. YAMAMOTO

672

TABLE 6. RESULTS OF THE STEP-WISE REGRESSIONOF ALL VARIABLESEXCEPT AGE ON GLUCOSE TOLERANCE OF PATIENTS BETWEEN 40 AND 60 yr OLD step

Variable

1 2 3 4 All variables

entered

Multiple

R

0.22 0.28 0.29 0.30 0.32

Pbos Uric acid Fasting time Cholesterol included

Table 6 gives the multiple correlation coefficient R for this regression run. On this age-restricted population, the R’s are not high enough to indicate good predictability of the dependent variable, glucose tolerance. At this stage, we have another negative finding. Even on the restricted population of 4MO yr olds, food history is not instrumental in explaining glucose tolerances. Figure 2 gives the graph of glucose tolerance vs fasting time for the patients between 40 and 60 yr old. Inspection of Fig. 2 suggests that if one thinks of dichotomizing the variable fasting time for the purpose of investigating what effect if any it has on glucose tolerance, the probable best point would be at 4 hr. The shape of the curve is like a ‘dipper’ but all points to the right of 4 hr do have values of glucose tolerance high than do the points to the left of 4 hr. Our decision to dichotomize fasting time at 4 hr does coincide with that of past investigators [ 11. Using all 1331 patients (27 members of the initial sample had time since last eating not measured) we form Table 7. Table 7 gives the percentage of the 778 patients with fasting time less than or equal 4 hr in the various glucose tolerance ranges, as well as the percentages for the 553 patients with fasting time greater than 4 hr. Perusal of the table reveals that higher percentages of patients with fasting time less than or equal 4 hr occur for all glucose tolerance ranges up to 150; whereas, from 150 on, the percentages are higher for patients with fasting time greater than 4 hr. Figure 3 contains a ‘smoothed’ version of the data contained in Table 7. In Fig. 3 we see that the probability density function for patients with fasting time less than or equal to 4 hr is above the probability density function of patients

Time FIG. 2. Glucose

tolerance

since

lost eating,

vs meal for all patients

hr between

40 and 60 yr old.

The One Hour

Glucose

Tolerance

Test

673

TABLE 7. PERCENTAGE DECOMPOSITION OF VALUES OF GLUCOSE

TOLERANCE

Patients with fasting time 250 Total

Patients with fasting time >4

5 19 11 11 9 9 8 5 5 4 3 3 4 2 2 loo

5 6 7 5 8 I 5 100

with fasting times greater than 4 hr to the left of the value 150, which is a cross-over point. To the right of 150 the relative positions of the two probability density functions are reversed. It follows that the dichotomization of glucose tolerance, which will yield the highest predictive relationship with the dichotomized fasting time variable, occurs at 150. Table 8 contains a two-way cross tabulation of glucose tolerance dichotomized at 150 vs fasting time dichotomized at 4 hr. If these dichotomizations resulted in perfect predictability, the diagonal terms in the percentage tabulation would sum to lOO%, i.e. the off-diagonal terms would be zero. In actuality the diagonal terms sum to 63% so that 37% are ‘misclassified’ under this dichotomization. Although this result is not striking, (as we might suspect from the negative results in the regression analyses); nevertheless, it is apparent that some predictive relationship exists.

L

C.D. 29/10-E

I

50

I

75

I

100

I

I25

I-hr

glucose

FIG. 3. Smoothed

estimate

I

150

I

175

tolerance.

I

200

I

225

I

250

mg/dl

of the probability

density.

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RODGER D. PARKER and LAWRENCEA. YAMAMOTO TABLE 8. CROSSTABULATIONOF GLUCOSETOLERANCE DICHOTOMIZED AT 150 VS FASTING TIME DICHOTOMIZED AT 4 hr Glucose tolerance

Q150 ,150 Total % 150 1150 Total

Fasting time >4

0.05 or R > 0.225 is considered marginally significant in most statistical analyses. Therefore, our split into groups would not appear to be important by an R2 criterion. On the other hand, a further analysis of significance of a split employs the use of the confidence interval on the difference of the true means of the upper and lower groups. Let A$ be the sampled mean of the upper group minus the sampled mean of the lower group. Let Ap be the difference of the true mean of the upper group minus the true mean of the lower group. A 95% confidence interval for Ap is an interval which has 0.95 probability of containing Ap. Assuming that the values of the dependent variable, glucose tolerance, are normally distributed for the two sub-populations defined by the groupings, the 95% confidence interval may be determined. Afi is 22.8 and the 95% confidence interval for the difference, Ap, of the true means is 17.2-28.4. This means that we have a 95% ‘expectation’ that the true

The One Hour Glucose Tolerance Test

675

TABLE 9. VARIATION IN ONE HOUR GLUCOSE TOLERANCE

EXPLAINED

BY

DICHOTOMIZING

ING TIME OF PATIENTS

AT

FAST-

4 hr “() =553 yu =157.3 S” = 54.7

” = 1331 7 = 144.3 s = 50.0 n,=770 yL = 134.5 S, = 46.9

R=O.l9

95% confidence

interval

for Ap is[l7.2,28.4]

difference, Ap, (which is an unknown constant), lies between 17.2 and 28.4. Since the sampled mean for the total population is 144.3. a difference of means as small as 17.2 would seem tangible.

DISCUSSION

AND

CONCLUSIONS

In this study, a data base of 1358 asymptomatic patients with medical profiles of thirteen variables has been used to determine if time since last eating emerges as a significant predictor of 1 hr glucose tolerance. Applying the most natural technique, stepwise regression, food history comes out as only marginally predictive. Age is the most important single predictor variable; however, the graph of age vs glucose tolerance does reveal a ‘plateau’ for the large group of patients between ages 40-60. The conjecture that time since last eating ought to be a significant predictor variable of glucose tolerance on this group of patients, since the variation of glucose tolerance with age is essentially constant for them, is not verified. The potential relationship of time since last eating to glucose tolerance is then pursued by examining the graph of these two variables for all patients. The graph is ‘dipper shaped’ but does indicate that four hours since eating is a natural point at which to dichotomize patients. The 553 patients with more than four hours since last eating exhibit a mean value of glucose tolerance of 157.3, whereas those who have eaten less than or equal to 4 hr ago have a mean of 134.5. The 957:) confidence interval for the difference of the true means of these two groups is 17.2-28.4. Nevertheless ordinary statistical considerations expressed in table eight. and the smallness of R = 0.19 suggest only marginal importance of the dichotomization of fasting time at 4 hr to the prediction of 1 hr glucose tolerance.

676

RODGERD. PARKERand LAWRENCEA. YAMAMOTO

REFERENCES 1. Hayner NE, Kjelsberg MO, Epstein FH, Francis T: Carbohydrate tolerance and diabetes in a total community, Tecumseh, Michigan. Diabetes 14: 413, 1965 2. Gordon T: Glucose tolerance of adults, United States, 196G-1962; Diabetes prevalence and results of glucose tolerance test, by age and sex. Vital and Health Statistics, Series 11, No. 2, Washington, D.C., U.S. Government Printing Office, 1964 3. Somersalo 0: Staub effect in children: Studies of the blood sugar regulation by means of double and triple glucose tolerance tests. Acta Paedia (Stockholm), Suppl. 78, 1950 4. Staub H: Untersuchungen uber den Zuckerstoffwechsel des Menchen-1. Uber das Verhalten des Blutzuckers nach peroraler Zufuhr kleiner Glucosemengen. Z Klin Med 91: 44, 1921 5. Hamman L, Hirschman I: Studies on blood sugar-IV. Effects upon the blood sugar of the repeated ingestion of glucose. Bull Johns Hopkins Hosp 30: 306, 1919 6. Traugott K: Uber des Verhalten des blut Zuckerspiegels bei wiederholter und verschiedener Art enteraler Zuckerzufuhr und dessen Bedeutung fur die Leberfunction. Klin Wschr 1: 892, 1922 7. Bittner D L, Manning J: Automated neocuproine glucose method. Critical factors and normal values. In Automation in Clinical Chemistry. Technicon Symposia, 1966, 1, Scova NB et al. (Eds.), Mediad, White Plains, N.Y., 1967, p. 33 8. Technicon Method Files, SMA 12/60, Technicon Instrument Corporation, Tarrytown, N.Y., 10591