OBJECTIVE—The provariant of the Pro12Ala polymorphism in peroxisome proliferator–activated receptor (PPAR)γ has been identified as a risk allele for type 2 diabetes. The purpose of the present study was to reveal a significant association with pre-diabetic phenotypes in nondiabetic individuals based on a systematic meta-analysis of all available published evidence.

RESEARCH DESIGN AND METHODS—We performed a classical meta-analysis of data from ∼32,000 nondiabetic subjects in 57 studies to assess the effect of the Pro12Ala polymorphism on pre-diabetic traits.

RESULTS—In the global comparison, there were no differences in BMI, glucose, insulin, or homeostasis model assessment of insulin resistance between the Pro/Pro and X/Ala genotype. However, in the Caucasian subgroup, the X/Ala genotype was associated with significantly increased BMI. In the obese subgroup (BMI >30 kg/m2), fasting glucose (P = 0.041) and insulin resistance (by homeostasis model analysis) (P = 0.020) were significantly greater in the Pro/Pro group. In subjects with the homozygous Ala/Ala genotype, fasting insulin was significantly lower compared with the Pro/Pro genotype (P = 0.040, NAla/Ala = 154).

CONCLUSIONS—Across all studies, the Pro12Ala polymorphism had no significant effect on diabetes-related traits. Only in selected subgroups, such as Caucasians and obese subjects, did we see an association of the Ala allele with greater BMI and greater insulin sensitivity. This demonstrates the importance for appropriate stratification of analyses by environmental or other genetic factors. Meta-analysis of Ala/Ala homozygotes more clearly demonstrated the association with greater insulin sensitivity of carriers of the Ala allele.

The Pro12Ala polymorphism in peroxisome proliferator–activated receptor (PPAR)γ, caused by a missense mutation in exon B of the adipocyte-specific γ2 isoform, was identified in 1997 and is thought to confer reduced transcriptional activity (1). Several more genetic variants in PPARγ are known but are much less frequent (2). The prevalence of the Ala allele varies from ∼4% in Asian (3) to ∼28% in Caucasian (4) populations. Since PPARγ2 is exclusively expressed in adipose tissue, the prevalent Pro12Ala polymorphism was originally studied for an association with obesity. Soon, evidence emerged suggesting that the Ala variant is associated with increased insulin sensitivity in nondiabetic Caucasians (5). Consequently, association with type 2 diabetes was examined, and an early meta-analysis (6) strongly suggested that the Pro variant is a risk allele. This analysis included 16 genetic association studies and used a family-based design to control for population stratification. A more recent meta-analysis (7) confirmed these findings, and the Pro12Ala polymorphism is now considered the best replicated genetic risk factor of common type 2 diabetes.

The key components in the pathogenesis of type 2 diabetes are insulin resistance and β-cell dysfunction (8,9). To further clarify the mechanisms underlying the association of Pro12Ala with type 2 diabetes, however, it is necessary to examine nondiabetic populations, because hyperglycemia itself can induce insulin resistance and secretory dysfunction (10,11). Therefore, we performed a meta-analysis of 57 suitable studies containing data related to the insulin resistance of cohorts with normal or impaired glucose tolerance. The hypothesis is that the Pro/Pro genotype is associated with a more “diabetic” phenotype in this population (e.g., greater insulin resistance).

Appropriate studies were collected by a PubMed search (before July 2005) using the phrase “(pro12ala or p12a) and (stud* or diabetes or insulin sensitivity or insulin secretion).” Additionally, reference lists from articles were searched for further suitable studies. Two articles could not be obtained, one from a Chinese journal (12) and the other one from a Polish journal (13). The retrieval contained 151 articles.

We used the following exclusion criteria: review articles without new data; multifold data or interim data of studies, except when interim analyses contained data not presented in the final analysis; studies only including patients with type 2 diabetes; patients with polycystic ovarian syndrome; hyperlipidemia; studies including children; missing case numbers or measurements of deviation; and missing genotype-specific data of the quantities of interest (BMI, fasting plasma glucose, and insulin; 2-h glucose and insulin during oral glucose tolerance test [OGTT]; and homeostasis model assessment of insulin resistance [HOMA-IR]). The 57 studies suitable for meta-analysis are shown in Table 1.

Data analysis

We assumed normal distribution for fasting glucose and 2-h glucose during OGTT and a log normal distribution for all other quantities, compatible with the original data from Tschritter et al. (14). Hence, means and SDs of BMI, fasting insulin, 2-h insulin during OGTT, and HOMA-IR were transformed to means and SDs of corresponding logarithmic quantities using standard formulas.

Logarithmic transformation provided the mean of log insulin when geometric means were presented. In the study of Erikson et al. (15), means and SDs for log glucose were calculated under the assumption of log normally distributed glucose. We calculated the corresponding means and SDs for glucose but treated the later quantities as parameter estimates for a normally distributed quantity according to our distribution assumption.

If only quartile range or CI as a measure of deviation were available, SD was estimated under the above distribution assumptions. More precisely, for normally distributed quantities, we used the differences of quartile range or CI boundaries to calculate SD. For log normally distributed quantities, the differences of the logarithmic boundaries of quartile range or CI were taken to calculate SD.

Altshuler et al. (6) did not provide genotype-specific case numbers after a written request. In this case, we used the available allele frequencies to estimate a lower boundary of the case numbers. Hence, the influence of this study in our analysis is underestimated.

Subjects with Pro/Ala and Ala/Ala were referred to as X/Ala for the pooled analysis. Where possible, a separate analysis of the Ala/Ala genotype in comparison with Pro/Pro was performed. In some studies, sex and age subgroups were pooled by calculating the common mean and SD. Analysis was globally performed for all eligible studies and within the subgroups of Caucasian, Asian, lean, and obese individuals. To avoid overlap of subgroups, instead of via a cutoff point, we determined obesity by classifying mean BMI <25 kg/m2 as lean, mean BMI >30 kg/m2 as obese, and discharged groups in between.

Statistics

For statistical analysis, we applied the general approach for meta-analyses proposed by Whitehead (16,17). Hence, test statistic is the χ12-distributed U:

\[U{=}\ \left(\ {{\sum}_{i}}{\hat{{\theta}}}_{i}w_{i}\right)^{2}/{{\sum}_{i}}w_{i}\]

where θ̂i is the effect size and wi is the weight of the i-th study.

We calculated dimensionless standardized effect sizes θ̂i, which are the differences of the means of the compared groups divided by their common SD. Consequently, the weights depend only on case numbers. This method is advised when different unit systems are used, consistency among laboratories cannot be guaranteed, or if absolute study results cannot be directly compared (e.g., because of large differences between study populations and/or different adjustment methods). Results are shown in Figs. 13, providing standardized effect sizes and corresponding CIs.

We tested for the null hypothesis of no difference between the genotypes. The effect of the meta-analysis and its CI is also shown in comparison with the effects of the single studies. Negative values for the meta-effect size indicate that the corresponding quantity is smaller in Pro/Pro genotypes compared with X/Ala genotypes.

Inhomogeneity is analyzed by Q statistics. We calculated fixed-effects models and, in the case of significant inhomogeneity, random-effects models. Studies inconsistent with the other results were identified by radial plots (18) (data not shown). Regression of these plots were used to detect a possible publication bias (65).

Finally, robustness of significant results was investigated by a jackknife procedure. If significance was lost by leaving out single studies, we interpreted the result as having insufficient evidence for an effect. A substantial effect is indicated in cases of improvement of homogeneity and significance by leaving out a single study. All calculations except the power calculations were performed using MATLAB 7.0.4.365 (The MathWorks, Natick, MA). The powers were calculated with nQuery Advisor 6.0 (Statistical Solutions, Saugus, MA).

The Eggers test did not indicate any obvious publication bias.

X/Ala versus Pro/Pro (Table 2)

BMI was not different in the global analysis. In the Caucasian subgroup, however, BMI was greater in X/Ala compared with Pro/Pro (P = 0.015), which held up to jackknifing (Fig. 1). The BMI in Asians was also greater in the X/Ala group, but after leaving out one of the studies of Tai et al. (19), Hara et al. (20), Kawasaki et al. (21), or Wang et al. (22), the effect became nonsignificant. We found no effect of the Pro12Ala polymorphism with respect to fasting insulin, neither globally nor in any of the subgroups.

Fasting glucose was not different in the global comparison. In the obese subgroup, however, fasting glucose was higher in the Pro/Pro group (P = 0.041), and leaving out the study of Lindi et al. (23) substantially increased significance (standardized effect size 0.127, P = 0.0033) and homogeneity (P = 0.76). There was no significant effect on 2-h insulin and glucose during OGTT, neither globally nor in any subgroup.

HOMA-IR was significantly higher in Pro/Pro compared with X/Ala in the obese subjects, though not globally, indicating greater insulin sensitivity in obese carriers of the Ala allele. This effect became stronger (standardized effect size 0.148, P = 0.0025) with higher homogeneity of the remaining studies after leaving out the study of Lindi et al. (23) (Fig. 2).

Analysis of Ala/Ala

In 12 studies, the authors presented full genotype information, which allowed the analysis of the homozygous Ala/Ala genotype. Because of insufficient data quantity, the parameters HOMA-IR, 2-h glucose, and insulin were not analyzed in this part. Furthermore, only global testing of all pooled subgroups was performed. A total of up to 160 subjects with the Ala/Ala genotype were available for meta-analysis. Neither BMI (standardized effect size −0.125, P = 0.353) nor fasting glucose (standardized effect size −0.088, P = 0.282) were significantly different between the genotypes after global testing.

Fasting insulin was significantly lower in Ala/Ala compared with the Pro/Pro genotype (standardized effect size 0.168, P = 0.040). However, this effect vanished after jackknifing. The effect also vanished after calculating a random-effects model, which might have been more appropriate in view of the borderline study homogeneity (P = 0.052 for test of inhomogeneity). On the other hand, excluding the study of Rosmond et al. (24), which reported extremely high fasting insulin values in the Ala/Ala group discrepant from all other studies, makes the effect highly significant (standardized effect size 0.227, P = 0.0067) and the remaining studies highly homogeneous (P = 0.81 for inhomogeneity test; Fig. 3).

The BMI of individuals with the Pro/Pro genotype did not significantly differ from individuals with the X/Ala genotype in the global analysis. Interestingly, in the Caucasian subgroup, the X/Ala genotype was associated with significantly increased BMI. The absence of a statistical effect in the Asian subgroup might be explained by gene-gene and gene-environmental interactions (25). Also, the lower frequency of the Ala allele in Asians and the lower sample size in comparison with the Caucasian subgroup may play a role. Masud et al. (64) found a greater BMI in Ala carriers in obese subjects, but this study included patients with diabetes.

Surrogate markers of insulin resistance, such as fasting insulin and HOMA-IR, were not significantly different between the Pro/Pro and X/Ala genotypes globally. In the obese subgroup (BMI >30 kg/m2), however, fasting glucose was significantly higher in the Pro/Pro group. Analogously, HOMA-IR was also significantly higher in the obese subgroup with the Pro/Pro genotype. These effects became even stronger after leaving out the study of Lindi et al. (23), which only included obese subjects with impaired glucose tolerance. Two-hour postchallenge glucose and insulin concentrations were not significantly different between genotypes, neither in the global nor in the subgroup analysis, but the case numbers are much lower.

Subsequently, we investigated the influence of Ala/Ala homozygosity when full genotype information was given. Fasting insulin was higher in the Pro/Pro compared with the Ala/Ala group. Jackknifing revealed that the study of Rosmond et al. (24) was responsible for significant inhomogeneity among the studies. That study reported extremely high fasting insulin values in the Ala/Ala group, in discordance with all other studies. After excluding the study of Rosmond et al. (24), which contained only six individuals with Ala/Ala, the effect became highly significant and the remaining studies highly homogeneous. It is important to note that these six individiuals had a significantly higher BMI, which would easily explain the differences in fasting insulin and insulin sensitivity. Therefore, we interpret the results of the Ala/Ala meta-analysis as an indication of greater insulin sensitivity of this genotype.

To ultimately assess the role of greater insulin resistance in the Pro/Pro group in comparison with the Ala/Ala group, a meta-analysis of euglycemic-hyperinsulinemic clamp data would be desirable. This could not be performed because the four available studies (2629) were too heterogenous and could not be subjected to jackknifing. Two-hour glucose and insulin were not analyzed in Ala/Ala individuals due to insufficient data.

Another shortcoming of our meta-analysis is the classical meta-analysis approach with respect to the quantities considered. Because several studies used different adjustment methods, a meta-regression of the data might have been more appropriate. However, this would require access to original data of the individual studies, which was not possible. Because of different adjustment methods or different study populations, we always calculated standardized effect measures, which are more robust than absolute effect measures.

To evaluate the sensitivity of our meta-analysis, we estimated the statistical power and the absolute effects under the assumption of variance homogeneity among the studies. With a power of 80% for log(BMI), a global effect size of 0.8% difference (related to the group with the greater value) between the Pro/Pro and X/Ala groups could be discovered. On the other hand, with calculation of the common variance, the estimated effect was only a 0.7% difference. For fasting glucose, the discoverable effect was 0.045 mmol/l but the estimated effect was only 0.023 mmol/l. For logarithm of fasting insulin, the discoverable effect was a 2.7% difference but the estimated effect was only 2%. Consequently, even very small effects could be detected with our analysis but were not found globally. Furthermore, the estimates of the global effects were very precise (narrow CI) despite some inhomogeneity of the studies.

In conclusion, across all studies the Pro12Ala polymorphism had no significant effect on diabetes-related traits within a nondiabetic population. Only in selected subgroups, such as Caucasians and obese subjects, did we see an association of the Ala allele with greater BMI and greater insulin sensitivity. This demonstrates the importance for appropriate stratification of analyses by as many factors as possible. Meta-analysis of Ala/Ala homozygotes more clearly demonstrated the association with greater insulin sensitivity of the Ala allele.

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A.T. and M.S. contributed equally to this work.

A table elsewhere in this issue shows conventional and Système International (SI) units and conversion factors for many substances.

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