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Diabetes Care. 2009 April; 32(4): 644–646.
Published online 2009 January 8. doi:  10.2337/dc08-1631
PMCID: PMC2660452

Combined Measurement of Fasting Plasma Glucose and A1C Is Effective for the Prediction of Type 2 Diabetes

The Kansai Healthcare Study
Kyoko Kogawa Sato, MD, PHD,1 Tomoshige Hayashi, MD, PHD,1 Nobuko Harita, MD,1 Takeshi Yoneda, MD,1 Yoshiko Nakamura, MD, PHD,2 Ginji Endo, MD, PHD,1 and Hiroshi Kambe, MD1,2

Abstract

OBJECTIVE

We prospectively assessed whether the combined measurements of fasting plasma glucose (FPG) and A1C were effective for predicting type 2 diabetes.

RESEARCH DESIGN AND METHODS

Study participants included 6,736 nondiabetic Japanese men aged 40–55 years. Type 2 diabetes was diagnosed in those who had an FPG ≥126 mg/dl or who were being treated with an oral antidiabetic agent or insulin. The models including FPG, A1C, and both were compared using the area under the receiver operating characteristic (AUROC) curves.

RESULTS

During the 4-year follow-up period, we confirmed 659 diabetes cases. In multivariate analysis, both FPG and A1C were independently associated with the risk of type 2 diabetes. The model including both FPG and A1C had a greater AUROC curve than that including FPG alone (0.853 vs. 0.818; P < 0.001) or A1C alone (0.853 vs. 0.771; P < 0.001).

CONCLUSIONS

The combined measurement of FPG and A1C was effective for predicting type 2 diabetes.

It is urgently necessary and important to identify individuals who might develop type 2 diabetes in order to prevent and delay its development. A1C has been used as an indicator to monitor glycemic control in patients with known diabetes (1,2). It is not clear whether A1C measurement is useful for detecting subjects with pre-diabetes independent of fasting plasma glucose (FPG). Our specific purposes were 1) to examine whether both FPG and A1C were independently associated with the risk of type 2 diabetes and 2) to assess the utility of both FPG and A1C measurements for predicting the incidence of type 2 diabetes using receiver operating characteristic curve analysis.

RESEARCH DESIGN AND METHODS

The Kansai Healthcare Study is an ongoing cohort investigation designed to clarify the risk factors for cardiometabolic diseases (3,4). The details of the study have been described previously (3,4). The protocol for the Kansai Healthcare Study was reviewed by the human subjects review committee at Osaka City University.

For the current analysis, study participants consisted of 9,116 Japanese men aged 40–55 years with FPG <126 mg/dl who were not taking an oral antidiabetic agent or insulin at study entry. The follow-up examination was conducted annually, and the follow-up period was 4 years. We excluded 2,312 men because of loss to follow-up. Thus, the study population consisted of 6,804 men.

Blood samples were drawn after an overnight 12-h fast. A1C was measured in the same laboratory by high-performance liquid chromatography standardized to the Japan Diabetes Society Committee for the Standardization of Glycohemoglobin, using an HA-8150 automatic glycohemoglobin analyzer (Kyoto Daiichi Kagaku, Kyoto, Japan) (5). The relationship is described by the following regression equation: A1C (%) of the National Glycohemoglobin Standardization Program = 0.0915 × {10.39 × [A1C (%) of Japan Diabetes Society] − 16.8} + 2.15 (6).

Participants were questioned about physical activity including the duration of their walk to work and how often they engaged in leisure-time physical activity. Participants were classified as engaging in regular leisure-time physical activity at least once weekly or less than once weekly. The validation of these questionnaire measures has been described in detail previously (3,4). Regarding smoking habits, participants were classified as nonsmokers, past smokers, or current smokers. Alcohol intake assessed by questionnaire was converted to total alcohol consumption (in grams of ethanol per day) using standard Japanese tables.

Type 2 diabetes at baseline and follow-up examinations was diagnosed in participants who had an FPG ≥126 mg/dl or were taking an oral antidiabetic agent or insulin (7).

We used multiple logistic regression analysis to estimate the odds ratio (OR) for the incidence of type 2 diabetes in relation to baseline variables, and we calculated the 95% CI for each OR. The models including FPG, A1C, and both for predicting type 2 diabetes were compared using the area under the receiver operating characteristic (AUROC) curves. The AUROC curve is used to evaluate clinical utility for predictive models (8,9). It can range from 0.5 (no predictive ability) to 1 (perfect discrimination) (8). A value 0.8 ≤ AUROC < 0.9 is considered excellent discrimination (10). All P values were two tailed. Statistical analyses were performed using Stata SE, version 10.0 (Stata, College Station, TX).

RESULTS

During the 4-year follow-up period, we confirmed 659 cases of type 2 diabetes. The baseline characteristics of the study population are summarized in Table A1 (available in an online appendix at http://care.diabetesjournals.org/cgi/content/full/dc08-1631/DC1).

In multiple logistic regression models, FPG was divided into three categories: ≤99, 100–109, and 110–125 mg/dl; and A1C was divided into five categories: ≤4.9, 5.0–5.4, 5.5–5.9, 6.0–6.4, and ≥6.5%. Both FPG and A1C were independently associated with the risk of type 2 diabetes. Even after stratifying participants by FPG (≤99 or ≥100 mg/dl), elevated A1C had an increased risk of type 2 diabetes (Table 1). The Pearson correlation coefficient between FPG and A1C at baseline was not strong (r = 0.305; P < 0.001). There was no multicollinearity in all models of Table 1 because the variance inflation factor for independent variables was <4.0 (11). There was no interaction between FPG and A1C at baseline.

Table 1
ORs of FPG with A1C and AUROC curve for various models to predict incidence of type 2 diabetes

In the AUROC curve analyses used to predict the incidence of type 2 diabetes, FPG and A1C were treated as continuous variables. In all participants, the model including both FPG and A1C had a greater AUROC curve than those including FPG alone (0.853 vs. 0.818; P < 0.001) or A1C alone (0.853 vs. 0.771; P < 0.001) (Table 1). After stratifying participants according to FPG, the combined measurement of FPG and A1C was effective to predict type 2 diabetes in each group. After excluding 17 participants who were confirmed as having type 2 diabetes based on use of an oral antidiabetic agent or insulin, we examined all analyses again, and the associations did not change.

CONCLUSIONS

Three prospective studies have reported the utility of A1C in predicting type 2 diabetes (1214). Of these, two studies did not show whether both FPG and A1C were independently associated with the risk of type 2 diabetes (12,13). Droumaguet et al. (14) showed that A1C was a significant risk factor for type 2 diabetes in French men and women with FPG ≥110 mg/dl, not with FPG <110 mg/dl. In our study, even in those with FPG <100 mg/dl, A1C was associated with an increased risk of type 2 diabetes. This might have been due to ethnicity differences.

A1C is commonly considered to reflect the previous 8–12 weeks' average blood glucose concentrations. Several previous studies in diabetic patients have shown the contribution of FPG and postprandial blood glucose to A1C (15). In the present study, the correlation between A1C and FPG at baseline was not strong; therefore, A1C is not a surrogate marker of FPG. We think that it is effective to use the combined measurement of FPG and A1C to predict type 2 diabetes.

Because all participants were registered employees of the same company and of a single ethnic group, our results may not be representative of the general population but may apply to Japanese-American men and also, possibly, other Asian-American and native Asian men. In conclusion, our results provide evidence that the combined measurement of FPG and A1C is effective for predicting type 2 diabetes.

Supplementary Material

Online-Only Appendix:

Acknowledgments

This work was supported by a Grant-in-Aid for Scientific Research (17390177, 20390187) from the Ministry of Education, Culture, Sports, Science and Technology.

This work was also supported by facilities and services provided by the Kansai Health Administration Center at Nippon Telegraph and Telephone West Corporation. The funding sources had no role in the collection of the data or in the decision to submit the manuscript for publication. No other potential conflicts of interest relevant to this article were reported.

Parts of this study were presented in abstract form at the 68th Scientific Sessions of the American Diabetes Association, San Fransisco, California, 6–10 June 2008.

We thank the participants in the Kansai Healthcare Study for their dedication.

Footnotes

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked “advertisement” in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

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