Glucose Intolerance and Physical Inactivity: The Relative Importance of Low Habitual Energy Expenditure and Cardiorespiratory Fitness
Nicholas J. Wareham1,
Man-Yu Wong2 and
Nicholas E. Day1,3
1 Department of Community Medicine, Institute of Public Health, University of Cambridge, Cambridge CB2 2SR, United Kingdom.
2 Department of Mathematics, Hong Kong University of Science and Technology, Hong Kong, People's Republic of China.
3 Biostatistics Unit, Medical Research Council, Cambridge CB2 2SR, United Kingdom.
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ABSTRACT
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Glucose intolerance and diabetes mellitus are associated with physical inactivity, but it is unclear whether preventive interventions should aim at increasing overall energy expenditure or increasing participation in vigorous, fitness-enhancing activities. Studies aimed at separating and quantifying the effects of these two dimensions of physical activity should use well-validated measurement instruments and employ a study design in which the bivariate error structure of these instruments is determined. In the Isle of Ely Study (Cambridgeshire, United Kingdom), 775 individuals aged 4570 years in 19941997 completed a glucose tolerance test and assessment of 4-day physical activity level (total energy expenditure/basal metabolic rate) by heart rate monitoring, a technique that has been validated against doubly labeled water and whole-body calorimetry. Cardiorespiratory fitness (maximum oxygen uptake (VO2max) per kg)) was measured in a submaximal test. To correct for measurement error, the authors had 190 individuals repeat both tests on three occasions at 4-month intervals. Two-hour glucose level was negatively correlated with physical activity level (men: r = -0.22, p < 0.001; women: r = -0.11, p < 0.05) and VO2max per kg (men: r = -0.18, p < 0.01; women: r = -0.19, p < 0.001) and was positively correlated with age and obesity. The model incorporating bivariate adjustment for measurement error showed that energy expenditure had a major effect on glucose tolerance, but there was less of an effect for cardiorespiratory fitness. These data provide support for public health strategies aimed at increasing overall energy expenditure.
energy metabolism; glucose intolerance; physical fitness
Abbreviations:
MET(s), metabolic equivalent(s); VO2max, maximum oxygen uptake
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INTRODUCTION
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The rise in obesity and related metabolic disorders recently observed in many developed countries has occurred despite a reduction in population-level consumption of total calories, and implies a trend towards reducing average energy expenditure (1
). This trend has been poorly documented, since energy expenditure is difficult to measure at the population level, and until recently only indirect markers of sedentary living such as car ownership or hours spent watching television were available (2
, 3
). Changes in population-level energy expenditure may be particularly important for the development of disorders such as type 2 diabetes mellitus, for which there is epidemiologic and experimental evidence of an association with physical inactivity (4

7
). Physical activity is therefore an important part of primary prevention strategies aimed at reducing the increasing global burden of diabetes (8
10
). However, there is currently epidemiologic uncertainty about whether the primary aim of increasing population-level physical activity should be to increase overall energy expenditure or to promote participation in vigorous, fitness-enhancing activity (11
). Many of the studies in previous reports used subjective questionnaires to describe self-reported physical activity (11
). Although they are sufficient to demonstrate the overall association between inactivity and diabetes, these questionnaires cannot clearly distinguish between the different dimensions of physical activity. In particular, they cannot separate the effects of vigorous activity, which is associated with increased cardiorespiratory fitness, from overall total energy expenditure (12
).
The problem of separating the health benefits of complex and interrelated exposures presents considerable difficulties for epidemiologic studies. One possible approach to this problem is to use methods for assessing the exposure that most closely measure the true exposure of interest, and to design the study and analyze the data in such a way that the etiologic effects of related exposures can be separated. In this paper, we describe a study designed to quantify the association of habitual energy expenditure and fitness with glucose intolerance, in which we have utilized objective and validated methods for estimating energy expenditure and fitness. In addition, a repeated-measures substudy was undertaken in order to adjust for the effects of measurement error in the assessment of usual energy expenditure and fitness. The method employed to assess total energy expenditure, heart rate monitoring with individual calibration, has previously been shown to be feasible in medium-sized field studies (13
, 14
). The technique relies on the fact that there is a linear relation between heart rate and oxygen consumption, and therefore energy expenditure, above a definable critical level (13
, 14
) below which energy expenditure can be assumed to be equal to resting. This method has been validated by comparison with the gold standard techniques of whole-body calorimetry and doubly labeled water (15
, 16
). As an objective, cheap, and noninvasive method, it is potentially suitable for small and medium-sized epidemiologic studies in which prospective assessment of energy expenditure is required (13

16
). However, few studies have used this technique or alternative means of assessing total energy expenditure in population-based cohorts (17
). Methods for assessing cardiorespiratory fitness (defined as a health-related component of physical fitness that relates to the ability of the circulatory and respiratory systems to supply oxygen during sustained physical activity), such as maximum oxygen uptake (VO2max), have been more commonly used (17
), but these methods assess a separate dimension of physical activity.
The true exposures of interest in this study are usual level of energy expenditure and cardiorespiratory fitness, both of which are unmeasurable or latent variables. In general, if an exposure is both difficult to measure and inherently variable, a single measure will estimate the usual level with considerable imprecision, resulting in a misleading estimate of the relation between habitual exposure and disease. If no account is taken of measurement error, the relative precision with which the two variables are measured may determine the apparent strength of association. The standard methods for correcting for this measurement error (18


22
) are essentially univariate, and when the exposure is multidimensional, this approach can cause serious bias if the underlying variables are substantially correlatedparticularly if the variables have different degrees of measurement error (23
25
). Therefore, we have employed a bivariate procedure using variance-covariance modeling, which allows unbiased estimation of the separate contribution of each exposure (23
). We examine the sensitivity of our estimates to the principal assumption underlying the variance-covariance modelingnamely, that the errors of measurements are independent when the measurements are repeated. This process is applied to the data in this study to illustrate how the etiologic importance of different components of complex and uncertain exposure variables can be assessed. In particular, we investigate the roles of total energy expenditure and cardiorespiratory fitness in glucose intolerance.
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MATERIALS AND METHODS
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Selection of the subjects and metabolic tests
The volunteers in this analysis were all participants in the Isle of Ely Study, a continuing population-based cohort study in Ely, Cambridgeshire, United Kingdom. The design of the Isle of Ely Study has been described previously (26
, 27
). The original sample, comprising 1,122 individuals without known diabetes, was recruited between 1990 and 1992 at random from a population-based sampling frame consisting of all people in Ely aged 4065 years in 1990 (26
). The initial response rate was 74 percent. Between 1994 and 1997, a 4.5-year follow-up study was undertaken of all individuals who did not have diabetes by World Health Organization criteria at baseline (n = 1,071). Twenty subjects had died in the interim, and 937 of the remaining volunteers participated in follow-up (89 percent restudy rate) (27
). These individuals constituted the sample for this particular study, and 83 percent of the group agreed to participate. A total of 162 individuals who did not undertake all of the tests were excluded, 64 of them for medical reasons, including angina or dysrhythmia, treatment with beta-blocking agents, and the presence of a pacemaker. The remaining 775 people aged 4570 years appeared at the clinic at 8:30 a.m., having fasted since 10 p.m. the previous evening, and underwent a standard 75-g oral glucose tolerance test. Blood samples were taken at fasting and 30 and 120 minutes following oral glucose administration. Plasma glucose was measured in the routine National Health Service laboratory at Addenbrooke's Hospital using the hexokinase method (28
). In this analysis, 2-hour plasma glucose level following the glucose load was used as the main outcome, since it is a continuously distributed measure of glucose tolerance and the basis of World Health Organization definitions for diabetes (29
, 30
). Height and weight were measured while the participant stood in light clothing. Body circumferences were measured in duplicate using a metal tape. Body fat percentage was obtained using a standard impedance technique (Bodystat Ltd., Douglas, Isle of Man). Ethical permission for the study was granted by the Cambridge Local Research Ethics Committee.
Assessment of the oxygen consumption-heart rate relation at rest and during exercise
The protocol for undertaking the individual calibration between heart rate and energy expenditure has been reported previously (13

16
). The oxygen consumption-heart rate relation was assessed at rest while the subject lay prone and then while the subject was seated, employing an oxygen analyzer that was calibrated daily using 100 percent nitrogen and fresh air as standard gases. Subjects bicycled on a cycle ergometer at several different workloads to provide the slope and intercept of the line relating energy expenditure to heart rate. Each subject cycled at 50 revolutions per minute, and the workload (in watts) was progressively increased from 0 W through 37.5 W, 75 W, and 125 W in stages lasting 5 minutes each. At each workload, three separate readings were made of heart rate, minute volume, and expired air oxygen concentration. The 125 W level was only undertaken if the heart rate had not reached 120 beats per minute by the end of the 5 minutes at 75 W. The oxygen concentration in the expired air and minute volume data were used to calculate oxygen consumption after correction for standard temperature and pressure. Energy expenditure (kJ/minute) was calculated at each time point as oxygen consumption (ml/minute) x 20.35. Mean resting energy expenditure was taken as the average of the lying and sitting values. Flex heart rate was calculated as the mean of the highest resting pulse rate and the lowest pulse rate upon exercise. Finally, the slope and intercept of the least squares regression line of the exercise points were calculated. VO2max was measured from the linear regression as predicted oxygen consumption at maximal heart rate (220 - age), and results are expressed per unit of body weight (kg). The volunteers wore the heart rate monitor (Polar Electro Oy, Kempele, Finland) continuously during their waking hours over the following 4 days. Heart rate readings were directly downloaded into a computer via a serial interface, and the individual calibration data were used to predict minute energy expenditure for each person. Sleeping energy expenditure was calculated as 95 percent of basal metabolic rate, where this was derived from published prediction equations (31
, 32
). A physical activity level, defined as the ratio of total energy expenditure to basal metabolic rate (31
), was computed for each day and averaged over the 4-day period.
Repeated-measures substudy
A random group of 190 subjects in the cohort revisited the testing site for measurements on three further occasions over the following year. Volunteers attended at 4-month intervals, and on each occasion measurements were taken of height, weight, and impedance using the methods described previously. The calibration between heart rate and resting and exercise energy expenditure was performed as before; the volunteers then underwent 4-day heart rate monitoring.
Statistical analysis
The reliability coefficients for physical activity level, height, weight, body mass index (weight (kg)/height (m)2), percentage of body fat, and VO2max per kg were estimated using the formulae described by Armstrong et al. (18
). By this method, each of n subjects is measured k times, with Xij being the jth measure of subject i.
is the mean of k measurements in subject i, and
is the overall mean. The reliability coefficient is given as (BMS - WMS)/(BMS + (k - 1)WMS), where BMS is between-subject mean square, which equals between-subject sum of squares/degrees of freedom
. Simple linear regression and multiple regression analyses were undertaken using SAS (SAS Institute, Inc., Cary, North Carolina), and the regression coefficients are presented per standard deviation for each variable. The univariate correction was undertaken using the univariate reliability coefficients. The bivariate correction factors for physical activity level and VO2max per kg were estimated from the variance-covariance matrices using the method described by Wong et al. (25
). Both the univariate and the bivariate correction factors were calculated under the assumption that the errors associated with repeated measures in the same individual were independent. In the absence of other methods for measuring physical activity level or VO2max per kg, the validity of this assumption cannot be assessed. We investigated the effect of departures from this independence assumption by assuming that the measurement errors between any two repeated measures are linearly related, with a correlation coefficient ranging from 0.0 to 0.2. The reliability will be overestimated if the correlation coefficient of measurement errors between repeated measures is positive and underestimated if this correlation is negative.
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RESULTS
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Table 1 shows the characteristics of the 334 men and 441 women who participated in this study. The mean body mass index was not significantly different from that reported from national monitoring studies carried out in England and Wales (33
). Subjects were grouped into sex-specific quintiles for physical activity level. As figure 1 shows, there was a negative relation between physical activity level and the main outcome variable, 2-hour plasma glucose level. Table 2 shows the Pearson correlation coefficients between 2-hour glucose level, physical activity level, VO2max per kg, and anthropometric variables. In both men and women, 2-hour glucose level was positively correlated with age and each of the measures of adiposity. There was a significant negative correlation between 2-hour glucose and physical activity level and VO2max per kg in both sexes. The measure of fitness, VO2max per kg, was strongly negatively correlated with age and with the measures of adiposity. Physical activity level and VO2max per kg were positively correlated with each other. The measure of energy expenditure, physical activity level, was not correlated with age, but it was weakly negatively related to each of the measures of adiposity.

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FIGURE 1. Mean 2-hour plasma glucose levels (mmol/liter) by sex-specific quintile of physical activity level (PAL) (n = 775): The Isle of Ely Study, 19941997. Bars, standard error.
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TABLE 2. Pearson correlation coefficients for 2-hour plasma glucose concentration, physical activity level, maximum oxygen uptake, and other anthropometric variables (n = 775): The Isle of Ely Study, 19941997
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Before undertaking multivariate analysis to separate the relative effects of each of these variables, we examined the results of the repeated-measures substudy. Table 3 shows the within-subject and between-subject mean squares, as defined by Armstrong et al. (18
), and the reliability coefficients for each variable, stratified by sex.
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TABLE 3. Reliability coefficients for measurements of energy expenditure and fitness, by sex (n = 190): The Isle of Ely repeated-measures substudy, 19941997
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The data show that height and weight (and therefore body mass index) have extremely high reliability, as these variables are stable and are measured precisely. Therefore, when these variables are used in epidemiologic studies, the observed measure of effect equates closely with the true underlying association. By contrast, physical activity level has a relatively low reliability. This is the consequence not just of imprecision in the measurement but also of biologic variability. Thus, if a single measure were used as an indicator of usual or habitual physical activity level, a considerable degree of underestimation of the true effect would result. The same is true for VO2max per kg in women, which also has a low reliability. The between-subject and within-subject covariances of physical activity level and VO2max per kg were 3.669 and 0.1606 for men and 1.881 and 0.0433 for women. The true between-subject covariances were calculated, from the between-subject and within-subject covariances, to be 0.877 in men and 0.459 in women. Hence, the deattenuated true between-subject correlation in the substudy between physical activity level and VO2max per kg was 0.54 in both men and women.
With knowledge of the reliability coefficients and of the variance-covariance matrices, one can estimate the relation of physical activity level and VO2max per kg with 2-hour plasma glucose in a number of different ways. Six possible models are shown in table 4, in order of increasing complexity, to illustrate how the estimated effect is altered by the different models. In each case, the data have been stratified by sex because of the possibility of differences in effect size between men and women or differences in the reliability coefficients. The unadjusted regression coefficients, standardized to the sex-specific standard deviation for each variable, are large for both physical activity level and VO2max per kg, but adjustment for age and age plus body mass index reduces the effect size for VO2max per kg and has little effect on the coefficient for physical activity level. For men, the standard deviations of physical activity level and VO2max per kg are 0.364 and 7.84 ml/kg/minute, respectively. In women, the standard deviations are 0.296 and 5.81 ml/kg/minute. In the model that includes age, body mass index, and both physical activity level and VO2max per kg, the coefficient for physical activity level is diminished but is still significant, whereas that for VO2max per kg is close to and not statistically different from 0. No statistically significant interaction terms were demonstrable. We repeated these analyses using the impedance-predicted percentage of body fat as the measure of obesity rather than body mass index, because of the stronger correlations with percentage of fat demonstrated in table 2. Because the magnitude and direction of the estimated effect sizes were similar, we show only the results from the analysis using body mass index as a covariate. Similarly, there was no effect on the results when the analyses were repeated using VO2max rather than VO2max per kg.
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TABLE 4. Effect of adjustment for confounding and measurement error on the standardized linear regression coefficients relating variation in energy expenditure (physical activity level) and cardio-respiratory fitness (VO2max*) to variation in 2-hour plasma glucose level (mmol/liter) (n = 775): The Isle of Ely Study, 19941997
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The final two models in the table show the effect of adjustment for measurement error. In the first model, the adjusted regression coefficients for physical activity level and VO2max per kg have been corrected using the simple reliability coefficients (18
). If this analysis were assumed to be appropriate, one would conclude that the effect of a 1-standard-deviation increase in physical activity level would be a decrease in 2-hour plasma glucose level of 0.97 mmol/liter in men and 0.31 mmol/liter in women. We repeated these analyses using the correction methods proposed by Fuller (21
) and Spiegelman et al. (22
), and the point estimates and their confidence intervals were not significantly different (data not shown). Finally, we utilized the multivariate model to adjust the data using the multivariate correction factor. These results suggest that the effect size for physical activity level is even greater than would be estimated from the simple univariate correction factor and that the data are compatible with no effect for VO2max per kg. Figure 2 shows the 95 percent confidence limits for these point estimates (25
) for the simple multiple regression analysis with no correction for measurement error (dotted line) and the multivariate correction model (solid line). For simplicity, the data are shown for both sexes combined. The figure demonstrates that the results are compatible with no effect for VO2max per kg, since the 0 point on the x axis is included in both circles. However, the data are not compatible with a null effect for physical activity level, because the 0 point on the y axis is outside of both circles. One assumption underlying this analysis is that there is no correlated error between the repeated measures of VO2max per kg and physical activity level. Therefore, we tested this assumption by estimating the regression coefficients using correlated error ranging from 0.0 to 0.2 (table 5). The results of this sensitivity analysis suggest that the conclusion would be unchanged even with correlated error of 0.2.

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FIGURE 2. Ninety-five percent confidence interval bounds for standardized regression coefficients for physical activity level (PAL) and maximum oxygen uptake (VO2max) per kg, using multiple regression without correction for measurement error (dotted line) and a bivariate correction model (solid line) (n = 775): The Isle of Ely Study, 19941997.
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TABLE 5. Effect of different degrees of correlated error on the standardized linear regression coefficients relating variation in energy expenditure (physical activity level) and cardiorespiratory fitness (VO2max*) to variation in 2-hour plasma glucose level (mmol/liter) (n = 775): The Isle of Ely Study, 19941997
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DISCUSSION
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Previous epidemiologic studies have shown that physical activity is important in the etiology of glucose intolerance but have treated it as a unidimensional exposure. To our knowledge, assessment of the relative importance of the different subcomponents of this exposure has not previously been attempted (7
). The study described here was designed to examine the effects of two specific components of physical activity using objective measures of the true exposures of interest, a study design in which the measurement error of the objective measures could be estimated, and an analytical strategy of separating the contributions of the different variables using a bivariate correction approach. Although complex, this approach should identify more accurately the component of physical activity most closely related to glucose intolerance and provide more realistic estimates of the true effect size.
Using imprecise information may lead one to focus on the wrong element of an exposure and expect too much or too little from intervention. There have been at least seven prospective cohort studies reporting a relation between physical activity and the incidence of non-insulin-dependent diabetes; all of them concluded that physical activity is protective (34




40
). The observed associations are consistent and are not confounded by other known risk factors for diabetes such as age and obesity. However, most of these studies use questionnaire-based methods which focus on recreational physical activity, and none of them can easily be interpreted in terms of total energy expenditure, because the validation studies have been against other measures of self-reported behavior (17
) or VO2max (41
). From the currently available epidemiologic data, one might conclude that the goal of any preventive strategy should be to increase the frequency of vigorous activity that makes the heart thump or produces a sweat, as shown in the Nurses' and Physicians' Health Studies (34
, 35
). Such a conclusion might be supported by recent observations that cardiorespiratory fitness is predictive of the future development of glucose intolerance (42
) but may not necessarily be appropriate if the focus of the methods of exposure has been limited. The important public health question is whether the epidemiologic evidence is sufficient to formulate recommendations promoting periods of vigorous activity leading to increased fitness, as opposed to an overall increase in energy expenditure. Since primary prevention studies in individuals at high risk of type 2 diabetes are already under way (43
45
), this is a real consideration rather than a theoretical one. Experimental evidence suggests that low-level physical activity may improve glucose tolerance and insulin action without effects on cardiorespiratory fitness or body weight (46
). Focusing on the wrong element of physical activity in an intervention program could have counterproductive results, because studies have shown (especially in the elderly) that attempts to increase fitness by training may well be successful in improving cardiorespiratory fitness but do not necessarily result in increases in overall energy expenditure, as patients compensate for the periods of intense activity by resting more during the other parts of the day (47
). Specifying which dimension of this complex exposure is most important for a given outcome is therefore an important goal (12
).
The methods for separating the etiologic effects of complex exposures used in this study are similar to all correction procedures in that they involve assumptions. Testing the sensitivity of the results to deviations from these assumptions is an integral part of the analytical process. However, in general, the results will be less vulnerable to deviations in the underlying model assumptions if the exposure measurement instruments that are used are reasonably precise estimates of the true exposures of interest (24
, 25
). Thus, the importance of this study lies not only in its analytical strategy but also in the application of objective methods for assessing total energy expenditure and cardiorespiratory fitness which are well validated. The data provided by this study strongly suggest that increasing overall energy turnover is of considerably greater importance in determining glucose tolerance than improving cardiorespiratory fitness, even given the limitations of the cross-sectional study design. Although the inference of the direction of causality is weaker from such a study, none of the subjects in this study had known diabetes and the association was seen across the spectrum of glucose intolerance. Therefore, it is unlikely that the association could be in the reverse direction, i.e., that poor glucose tolerance led to low physical activity. The individuals studied are part of a continuing population-based cohort study. Although the cohort study had a high initial response rate and a high restudy rate, the volunteers in this study were a subgroup of those who attended. The likely direction of selection bias is towards recruitment of a relatively healthy cohort. Although this bias might limit the generalizability of the estimates of mean energy expenditure to the population as a whole, they should not have affected the validity of the observations of associations within the cohort.
A quantitative estimate of the effect of population behavior changes can be made using these data. Physical activity level is the ratio of total energy expenditure to basal metabolic rate. All specific physical activities can be described in terms of metabolic equivalents (METs); listings of these are available in compendia such as that published by Ainsworth et al. (48
). An MET is the ratio of the energy cost of an activity relative to basal metabolic rate. Thus, the energy cost of episodes of physical activity can be expressed in MET-hours. There are 168 hours in a week; therefore, the denominator of the equation defining physical activity level is 168, as the energy cost of basal metabolic rate is, by definition, 1 MET. Thus, a 0.1 increase in physical activity level is equivalent to 16.8 MET-hours per week, or 2.4 MET-hours per day. Since the energy cost of walking is approximately 4 METs, an additional 2.4 MET-hours could be accumulated by 36 extra minutes of walking. If the population were to increase their physical activity level by 0.1which could be achieved in any number of different ways, including walking for approximately 30 minutes extra each daythen even without a change in weight or fitness, one would expect the population mean 2-hour glucose level to decrease by 0.29 mmol/liter. This quantitation of the likely benefit of population-level changes will be of use in planning intervention studies.
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ACKNOWLEDGMENTS
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The Isle of Ely Study was funded by the British Diabetic Association, the Anglia and Oxford Regional Health Authority, and the Medical Research Council. N. J. W. is a Medical Research Council Clinician Scientist Fellow. The work of Dr. Wong was supported by the British Council.
The authors are grateful to the staff of the St. Mary's Street Surgery, Isle of Ely, and to H. Shannasy, S. Curran, S. Hennings, P. Murgatroyd, J. Mitchell, and Drs. M. Hennings and A. M. Prentice for their help with the fieldwork for this study. The staff of the National Health Service Department of Clinical Biochemistry, Addenbrooke's Hospital, Cambridge, led by Professor C. N. Hales, carried out the glucose analyses.
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NOTES
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Reprint requests to Dr. N. J. Wareham, Department of Community Medicine, Institute of Public Health, University of Cambridge, Robinson Way, Cambridge CB2 2SR, United Kingdom (e-mail: njw1004{at}medschl.cam.ac.uk).
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Received for publication November 16, 1998.
Accepted for publication September 7, 1999.