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Original Studies |
Institut für Klinische Chemie und Laboratoriumsmedizin, Zentrallaboratorium, Westfälische Wilhelms-Universität Münster, D-48129 Münster, Germany; and Institut für Arterioskleroseforschung an der Universität Münster, D-48149 Münster, Germany
Address all correspondence and requests for reprints to: Dr. Arnold von Eckardstein, Institut für Klinische Chemie und Laboratoriumsmedizin, Zentrallaboratorium, Westfälische Wilhelms Universität Münster, Albert Schweitzer Strasse 33, D-48129 Munster, Germany. E-mail: vonecka{at}uni-muenster.de
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Abstract |
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Global risk estimation by multiple risk factors does not improve the prediction of diabetes mellitus by fasting glucose in middle-aged men. The lower discriminatory cut-off of 5.72 mmol/L glucose may help to reduce the previously reported discordance between impaired fasting glucose (American Diabetes Association) and impaired glucose tolerance (WHO) in diagnosis of the prediabetic state.
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Introduction |
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TopAbstractIntroductionSubjects and MethodsResultsDiscussionReferences |
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Finally, the American Diabetes Association (ADA) and the WHO have defined new criteria for the diagnosis of diabetes mellitus and the prediabetic state (30, 31). ADA defines diabetes mellitus by the presence of clinical symptoms and/or the (repeated) finding of fasting glucose levels of 7 mmol/L or more. This cut-off was chosen because it has the best agreement with the diagnosis of diabetes mellitus by the finding of postprandial glucose levels of 11.2 mmol/L or more in the oral glucose tolerance test, which in epidemiological studies of the occurrence of microangiopathic complications of diabetes mellitus has evolved as a gold standard in the diagnosis of diabetes mellitus (32). By consensus, but without any epidemiological data basis, the ADA has defined the prediabetic state as the finding of impaired fasting glucose levels of 6.1–7 mmol/L (30). Although accepting impaired glucose tolerance as a risk factor for diabetes mellitus, the ADA no longer recommends oral glucose testing because of practical reasons (30). By contrast, the WHO continues to require the performance of oral glucose tolerance testing for individuals with a fasting glucose level between 5.6 and 11.2 mmol/L and extends the definition of diabetes to the finding of 2 h glucose levels being higher than 11.2 mmol/L. A prediabetic state is defined as impaired glucose tolerance, with 2 h glucose levels ranging from 7.8–11.2 mmol/L (31, 33). Previous studies, which compared the WHO and ADA criteria, showed a various degree of concordance with respect to diagnosis of manifest diabetes mellitus and great discordance with respect to diagnosis of the prediabetic state (34, 35, 36, 37, 38). Moreover, compared to impaired glucose tolerance (WHO), impaired fasting glucose (ADA) had a weaker association with cardiovascular morbidity and all-cause mortality (39, 40). It is hence assumed that the ADA criteria underestimate the burden of glucose disorders (41). Therefore and because of the lack of epidemiological data on this issue, we also analyzed the data for the best cut-off of fasting glucose that identifies men at risk for diabetes mellitus.
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Subjects and Methods |
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The PROCAM study began in 1979 and examined people at work
(employees of 52 companies and authorities) for cardiovascular risk
factors, mortality, and cardiovascular events, including myocardial
infarction and stroke (42). The recruitment phase was completed at the
end of 1985. Full data records are held for 13,737 male participants,
aged 41.4 ± 11.2 yr, and 5,961 women, aged 36.6 ± 12.5 yr.
The examination at study entry included case history using standardized
questionnaires, measurement of blood pressure and anthropometric data,
a resting electrocardiogram, and collection of a blood sample after a
12-h fast for the determination of more than 20 laboratory parameters.
The examination was carried out during paid working hours.
Participation was voluntary (between 40–80% took part; average,
60%), and free of charge both to the volunteers and to their employers
(apart from loss of work). All findings were reported to the
participant’s general practitioner, and the volunteer was told whether
the results of the examination were normal or whether a check-up by the
general practitioner might be necessary. The investigators neither
carried out nor arranged for any intervention (42). For the present
study we selected the data of 3,951 men, aged 36–60 yr, who had at
least 1 follow-up examination within 4–10 yr. Data for 199 men were
excluded from the statistical analysis because they had diabetes
mellitus at entry into the study (Table 1
). Thus, we investigated the roles of
various risk factors in the incidence of diabetes mellitus in a cohort
of 3,737 men who had no manifest diabetes mellitus at baseline and who
underwent at least 1 other examination during follow-up. If a subject
underwent more than 1 examination during this period, we used the data
from the final visit for statistical evaluation.
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A family history of diabetes mellitus was defined as positive if at least one first or second degree relative had diabetes mellitus. Anyone smoking at least one cigarette per day within the last 12 months was considered a current smoker.
Systolic and diastolic readings were taken from the left arm with the subject seated and the arm at heart level. One measurement was taken at the start of the interview by the examining physician, and one was taken at the end of the interview. The second measurement was recorded (43).
For determination of body weight the probands were dressed in only underwear. Body mass index was calculated as the ratio of weight (kilograms) to the square of height (meters).
Blood taking and biochemical measurements
Serum was taken by venipuncture after at least 12 h of fasting. Serum was prepared after 1–2 h of clotting time by centrifugation at 2000 x g. All biochemical parameters were measured by enzymatic methods using assays and the Hitachi 737 autoanalyzer from Roche (Mannheim, Germany). Glucose was determined by the hexokinase method, uric acid by the uricase method, cholesterol by the CHOD-PAP method, triglycerides by the GPO-PAP method, and HDL cholesterol by the CHOD-PAP method after precipitation of apolipoprotein B-containing lipoproteins with phosphotungstic acid/MgCl2. LDL cholesterol was calculated by the Friedewald formula, if triglycerides were less than 4.6 mmol/L (44). Imprecision was below 2% for glucose and uric acid, below 3% for total cholesterol, below 4% for triglycerides, and below 5% for HDL cholesterol.
We compared glucose levels in serum and plasma of 50 volunteers with serum glucose levels ranging from 3.6–16.2 mmol/L. At a coefficient of correlation of 0.99, the linear regression equation was: plasma glucose (mmol/L) = 0.963 x serum glucose (mmol/L) - 0.04 (mmol/L).
Definitions of diabetes mellitus, impaired fasting glucose, and arterial hypertension
A subject was defined as affected by diabetes mellitus if this diagnosis was known to the patient or, according to the ADA definitions, if fasting serum glucose was 7 mmol/L or more. Impaired fasting glucose was defined as a fasting serum glucose level of 6.1 mmol/L or more but less than 7 mmol/L (30).
A proband was considered hypertensive when he knew this diagnosis and was treated with antihypertensive drugs or, in accordance with the definition by the WHO (45), when systolic and diastolic blood pressures were 160 and/or 95 mm Hg or more, respectively. Borderline hypertension was defined by a systolic blood pressure of 140 mm Hg or more, but less than 160 mm Hg and/or a diastolic blood pressure of 90 mm Hg or more, but less than 95 mm Hg.
Statistics
An explorative analysis was performed using the
statistical package for the social sciences (SPSS-X) (46) and the
statistical analysis system (SAS Institute, Inc., Cary,
NC) (47). Because of the strong age dependency of diabetes mellitus,
all data analyzed by univariate methods were adjusted for age. Because
of their non-Gaussian frequency distribution, triglycerides data were
evaluated after loge transformation. Comparisons
between groups were performed with Student’s t test for
continuous variables and the 
2 test for
discrete variables. The relative risks were calculated using the
category with the lowest incidence of diabetes mellitus as the
reference.
The simultaneous contributions of several factors to the risk of diabetes mellitus were analyzed using a multiple logistic model of those factors that upon univariate analysis had a significant association with the incidence of diabetes mellitus, namely age, body mass index (BMI), HDL cholesterol, triglycerides, uric acid, blood pressure, glucose levels, and family history positive for diabetes mellitus. Forward and backward selections were used to build up the logistic regression model. Both procedures were modified in that at each point of the selection process the partial significance of each term included in or excluded from the model was reviewed. In the analyses the criterion for a variable to enter and to remain in the model was that its initial probability value as well as its partial probability value in the presence of other variables should not exceed 0.05. Maximum likelihood statistics were used for the selection process. Initially, the population was split in half to derive multiple logistic models from the data of one half of the population and to test it in the other half. As the results of the two procedures did not differ significantly, only results from a third model are presented, which included the data for the entire population.
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Results |
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At entry into the PROCAM study 3737 men, aged 36–60 yr, were free
of diabetes mellitus as defined by their unawareness of this diagnosis
and by the finding of fasting glucose levels being below 7 mmol/L. At a
repeat examination after 4–10 yr of follow-up (mean, 6.3 yr), 200 men
were diagnosed to have developed diabetes mellitus either because in
the meantime an antidiabetic treatment has been started or because they
had fasting glucose levels of 7 mmol/L or more (Table 1
). Men who
developed diabetes mellitus had a 2 months longer mean follow-up
interval than men who did not develop diabetes mellitus
(P < 0.001).
Compared to nondiabetic men, men who had become diabetic during
follow-up had a 7% higher mean BMI, 5% higher mean systolic and
diastolic blood pressures, 11% higher mean fasting serum levels of
glucose, 35% higher median triglyceride levels, and 7% higher mean
levels of uric acid as well as 7% lower mean levels of HDL cholesterol
(Table 2; all P < 0.001,
by t test). Fasting glucose was impaired in 49% of men who
later became diabetic compared to 9.6% who did not develop diabetes
mellitus during follow-up (P < 0.001, by
2 test). Thirty-one percent of men with
subsequent diabetes mellitus, but only 19.4% of men without subsequent
diabetes mellitus (P < 0.001, by
2 test), had at least one first or second
degree relative with diabetes mellitus.
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describes the relative
risks for future diabetes mellitus associated with tertiles of various
potential risk factors for diabetes mellitus. As expected, the highest
relative risk was associated with increasing glucose levels. The
relative risk for diabetes mellitus also significantly increased with
increasing age, BMI, systolic and diastolic blood pressures,
triglycerides, and uric acid as well as with decreasing HDL
cholesterol. Total cholesterol and LDL cholesterol had no statistically
significant association with the risk of diabetes mellitus.
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Upon multiple logistic function (MLF) analysis glucose, BMI (both
P < 0.001), age, glucose, HDL cholesterol,
hypertension, and family history (all P < 0.05), but
not triglycerides and uric acid, were significantly and independently
associated with the future occurrence of diabetes mellitus (Table 4).
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we stratified the incidence
rate of diabetes mellitus for quintiles of estimated risk, which was
calculated by MLF analysis. According to this model, 69.6% of all new
diabetes mellitus cases occurred in the highest quintile, whereas only
3.6%, 8.2%, 5.2%, and 13.6% occurred in the first to fourth
quintiles. Thus, the risk of future diabetes mellitus was increased by
a factor of 19.3 in individuals in the highest quintile compared to
individuals in the lowest quintile or by a factor of 8.7 compared to
individuals in the first to fourth quintiles. In Fig. 2 we compared the receiver operating
characteristics (ROC) curves of the MLF model and of some independent
single risk factors for diabetes mellitus. The areas under the curve,
which reflect the performance of a test, were similar for the MLF risk
estimates [Fig. 2A; 79.3%; 95% confidence interval (CI),
78.0–80.6%] and glucose (Fig. 2B; 79.9%; 95% CI, 78.6–81.2%),
but significantly higher than those for BMI (Fig. 2C; 66.3%; 95% CI,
64.7–67.8%) and HDL cholesterol (Fig. 2D; 59.5%; 95% CI,
79.7–82.3%). In agreement with the similar areas under the ROC
curves, sensitivity and positive predictive value of glucose and MLF
risk estimates were similar when specificity was defined as 80% or
90% (Table 5). Impaired fasting glucose,
defined according to the ADA guidelines (31), had a diagnostic
sensitivity and specificity toward the prediction of diabetes mellitus
of 51% and 91%, respectively. At the same specificity, the diagnostic
sensitivity of the MLF risk estimate was 53%. At this cut-off of
estimated MLF risk, 76% of men had a fasting glucose of 6.1 mmol/L or
more and less than 7 mmol/L; 28.5% of men with this cluster developed
diabetes mellitus compared to 9.4% of men who had fasting glucose of
6.1 mmol/L or more but a MLF risk estimate below this threshold value.
Thus, despite similar performance characteristics, the MLF risk
estimate appears to provide prognostic information for diabetes
mellitus in addition to that provided by fasting glucose testing. It is
however important to note that a glucose level of 5.72 mmol/L, rather
than 6.1 mmol/L, was the best discriminatory cut-off for the
identification of men with future diabetes mellitus. At this cut-off,
sensitivity and specificity were 75.0% and 72.7%, respectively.
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Further examples for the interaction of risk factors in the
prognosis of diabetes mellitus are shown in Figs. 3 and 4.
Figure 3 demonstrates the interaction between glucose levels and other
risk factors for diabetes mellitus. In both groups of individuals with
normal (<6.1 mmol/L) and impaired fasting glucose (6.1–7 mmol/L),
increases in BMI (Fig. 3A) or blood pressure (Fig. 3B) as well as a
decrease in HDL cholesterol (Fig. 3C) increased the risk of diabetes
mellitus. The joint occurrence of impaired fasting glucose with
overweight, hypertension, or low HDL cholesterol had prevalences of
2.0%, 4.8%, and 3.0%, respectively, but accounted for about 30%,
38%, and 32% of all new cases of diabetes mellitus. Also,
interactions among BMI, blood pressure, and HDL cholesterol formed
gradients of increasing risk for diabetes mellitus (Fig. 4). The risk
of diabetes mellitus associated with decreasing levels of HDL
cholesterol was further increased by increasing BMI (Fig. 4A) and
increasing blood pressure (Fig. 4B).
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Discussion |
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Upon multivariate analysis, only glucose, BMI, hypertension, low HDL cholesterol, and family history positive for diabetes mellitus were independent risk factors for diabetes mellitus. Of course, statistical independence does not imply biological independence, as obesity and insulin resistance play important roles in the pathogenesis of both diabetes mellitus and the above-mentionned metabolic cardiovascular risk factors (25, 26, 27). Clustering of several risk factors within one individual aggravates the risk of diabetes mellitus in a dose dependent-manner. Mykkänen and colleagues previously associated the number of risk factors with the risk of diabetes mellitus in 65- to 74-yr-old Finnish individuals (18). They found that the relative risk of diabetes mellitus increased from 3.6 in individuals with a single risk factor to 59 in individuals with four risk factors, namely impaired glucose tolerance, hypertriglyceridemia, low HDL cholesterol, and hypertension. However, this dichotomic approach does not take into account the graded relationships between risk factors and disease. Moreover, despite the strong associated relative risk, this approach has only limited sensitivity. For example, in the PROCAM study, 38% of individuals with impaired fasting glucose and hypertension developed diabetes mellitus. However, as this condition had a prevalence of only 2.9%, nearly 80% of individuals with future diabetes mellitus did not present with this cluster. These disadvantages in risk assessment by counting risk factors can be overcome by the use of MLF algorithms that take into account the graded relationship between risk factors and disease. On the basis of the coefficients of our multivariate analysis of single risk factors we calculated an MLF algorithm that helped to estimate the risk of 35- to 60-yr-old men to develop diabetes mellitus in the next 4–10 yr (mean, 6.3 yr). Thereby, we characterized a 20% subgroup of the population in which the incidence of diabetes mellitus was 18.0% compared to 2.07% in the residual population (relative risk, 8.7). In fact, 69% of all incidences of diabetes mellitus occurred in this subgroup.
According to the area under the ROC curve the risk estimation by MLF was equal to that by fasting glucose (79.3% vs. 79.9%). Impaired fasting glucose, as defined by the new ADA recommendations (30), is thought to reflect a prediabetic state and hence to have a high sensitivity and specificity toward the identification of individuals with future diabetes mellitus. In the PROCAM population, a fasting glucose level of 6.1–7 mmol/L was associated with a relative risk of 8.9 and had a diagnostic sensitivity of 51% and a specificity of 91% in the prediction of future diabetes mellitus. At the same specificity, the sensitivity of the risk estimation by MLF was slightly higher, namely 53%, and the associated relative risk was 9. This underscores the predictive value of glucose levels in the identification of men at risk for diabetes mellitus, even if they are measured only in the fasting state (30). Using ROC analysis, we found evidence that the optimal cut-off for the identification of men at increased risk for diabetes mellitus should be even lower, namely 5.72 mmol/L. Several studies revealed the reduced sensitivity of impaired fasting glucose (ADA) and impaired glucose tolerance (WHO) toward the prediction of diabetes mellitus (34, 35, 36, 37, 38) and death and the association with cardiovascular morbidity (39, 40). The lower discriminatory cut-off of 5.72 mmol/L found in our study may hence help to overcome this shortcoming of the ADA criteria. By method comparison we found that glucose levels in plasma are about 4% lower than those in serum, which was used for our measurement of glucose. As a consequence, the cut-off for fasting plasma glucose may be even lower, namely 5.5 mmol/L.
The PROCAM study is a field study. As a consequence, we only measured fasting glucose and did not repeat glucose measurements for confirmation of the diagnosis. Because of the first limitation, we probably included diabetic individuals at baseline and overlooked other diabetic individuals during follow-up who would have been diagnosed as diabetic by oral glucose tolerance testing. For the same reason we probably also misclassified some individuals as glucose tolerant. Because of the second limitation we may have made a premature diagnosis of diabetes mellitus in some individuals. These limitations obviously weaken the strength of our data. However, it is also important to note that in previous studies, the relative risk associated with glucose intolerance, as defined by elevated fasting or postprandial glucose levels, ranged between 6–15 and was hence not much higher than that estimated by MLF or fasting glucose in our study (9, 17, 18, 48). Moreover, it is important to note that oral glucose tolerance tests are impractical in many clinical and out-patient situations, especially as screening tests in the general population. Actually, this problem has been a major motivation for the ADA not to demand glucose tolerance testing (30). The multiparametric screening of anthropometric and metabolic parameters needed for risk estimation by MLF analysis may turn out to be a more practical surrogate test instead of glucose tolerance testing or at least may serve as a kind of quality control for risk assessment by fasting glucose. Moreover, it is important to emphasize that our findings have been made in middle-aged Caucasian men and may not extend to women and individuals of different age and ethnicity.
In conclusion, fasting glucose is a powerful measure to identify middle-aged Caucasian men at increased risk of diabetes mellitus. The predictive value would be even improved by the definition of a lower cut-off of 5.72 mmol/L. Finally, age, BMI, elevated blood pressure, low HDL cholesterol, as well as the occurrence of diabetes mellitus in the family history are independent factors that characterize a prediabetic state. The parallel assessment of these independent and easy to measure risk factors and the use of a statistical algorithm help to identify individuals who are at high risk for diabetes mellitus.
Received December 22, 1999.
Revised April 12, 2000.
Accepted May 16, 2000.
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