Secondary Logo

Journal Logo

Research Papers: Gynecological Cancer

Dietary glycemic index, glycemic load, and the risk of endometrial cancer

a case–control study and meta-analysis

Galeone, Carlottaa,b; Augustin, Livia S.A.e,f; Filomeno, Mariaa,b; Malerba, Stefanoa; Zucchetto, Antonellae; Pelucchi, Claudioa; Montella, Mauriziod; Talamini, Renatoc; Franceschi, Silviag; La Vecchia, Carloa,b

Author Information
European Journal of Cancer Prevention: January 2013 - Volume 22 - Issue 1 - p 38-45
doi: 10.1097/CEJ.0b013e328354d378
  • Free



Endometrial cancer is associated with unopposed estrogens, overweight, and obesity, with consequent high circulating levels of estrogens, as well as nulliparity, polycystic ovary syndrome, and other menstrual and reproductive factors related to high estrogen/low progesterone levels (Parazzini et al., 1991; Cook et al., 2006). Hypertension and a low level of physical activity are also likely to play some role (Cook et al., 2006; Friedenreich et al., 2011; Rosato et al., 2011), whereas the issue of diet and endometrial cancer remains open to discussion (World Cancer Research Fund/American Institute for Cancer Research, 2007). However, there is some suggestion of a reduced risk of endometrial cancer with the intake of diets rich in vegetables and fruit (Bandera et al., 2007b; World Cancer Research Fund/American Institute for Cancer Research, 2007), and of an increased risk with high intakes of total fat, saturated, and animal fat (Bandera et al., 2007a; World Cancer Research Fund/American Institute for Cancer Research, 2007). Furthermore, diabetes may independently contribute to a two-fold higher risk of endometrial cancer (Friberg et al., 2007), which has been related to insulin and insulin-like growth factors (IGF).

Carbohydrate foods increase postprandial glycemia and insulin secretion at different rates, depending on the nature of the carbohydrates, the amount and type of fiber contained, processing method, and the presence of nutrients/antinutrients. These differences are captured by the glycemic index (GI), a classification of carbohydrate foods on the basis of the food’s potential to increase postprandial blood glucose. Hyperglycemia, hyperinsulinemia, and high-GI diets have been implicated in the etiology of many chronic diseases, such as diabetes, coronary heart disease, and cancer (Barclay et al., 2008).

Over the last decade, at least six epidemiological studies, five cohort (Folsom et al., 2003; Silvera et al., 2005; Cust et al., 2007; Larsson et al., 2007; George et al., 2009) and one case–control study (Augustin et al., 2003), have considered the role of dietary GI in the risk of endometrial cancer. However, the findings from these studies are inconsistent. Two recent meta-analyses, published in 2008 and based on four cohort and one case–control study, concluded that a high dietary glycemic load (GL, i.e. the product of the GI and total available carbohydrates) is positively associated with the risk of endometrial cancer, but the role of the GI was unclear (Gnagnarella et al., 2008; Mulholland et al., 2008).

To better understand the role of GI and GL in endometrial cancer, we examined these associations using data from an Italian multicenter case–control study and by updating a meta-analysis.

Materials and methods

Case–control study

A case–control study on endometrial cancer was carried out between 1992 and 2006, in three Italian areas, including the greater Milan area, the provinces of Udine and Pordenone in Northern Italy, and the urban area of Naples in Southern Italy.

Cases were 454 women (median age 60 years, range 18–79 years) with incident, histologically confirmed endometrial cancer, admitted to major teaching and general hospitals of the study areas. Controls were 908 women (median age 61 years, range 19–80 years) admitted to the same hospitals as cases for a wide spectrum of acute non-neoplastic conditions. Controls were matched with cases by 5-year age groups and study center, with a case to control ratio of 1 : 2. Fewer than 5% of both cases and controls approached refused the interview.

Trained interviewers interviewed cases and controls during their hospital stay. The structured questionnaire included information on sociodemographic factors, anthropometric variables, smoking, alcohol, and other lifestyle habits, a problem-oriented medical history, physical activity and history of cancer in first-degree relatives, menstrual and reproductive factors, and the use of oral contraceptives (OC) and hormone replacement therapy (HRT). A food frequency questionnaire (FFQ) was used to assess the usual diet during the 2 years before diagnosis (or hospital admission, for control patients). The FFQ included 78 foods, food groups, or recipes divided into six sections: (i) bread, cereals, first courses; (ii) second courses (i.e. meat, fish, and other main dishes); (iii) side dishes (i.e. vegetables); (iv) fruits; (v) sweets, desserts, and soft drinks; and (vi) milk, hot beverages, and sweeteners. At the end of each section, one or two open-ended questions were used to include other foods eaten at least once per week. Participants were asked to indicate the average weekly frequency of consumption for each dietary item; intakes less than once a week, but at least once a month, were coded as 0.5 per week. The validity and reproducibility of the FFQ has been shown previously (Franceschi et al., 1993; Decarli et al., 1996). Intakes of total energy and total carbohydrates were computed using an Italian food composition database (Salvini et al., 1996; Gnagnarella et al., 2004).

For the GI values, we primarily used international GI tables (Foster-Powell et al., 2002). For these calculations, we used the carbohydrate content of 50 foods or recipes, because the remaining 28 foods or recipes (chiefly meat-based and fish-based foods and cheese) contained negligible amounts of carbohydrates (Salvini et al., 1996). To take into account Italian cooking habits (e.g. pasta “al dente”), Italian sources were used for a few local recipes (Brighenti and Casiraghi, 1992). Food items for which a GI had not been determined were assigned the GI of the nearest comparable food (e.g. tangerines were assigned the GI of oranges). GI was expressed as a percent of the glycemic response elicited by white bread as a standard food. The average daily GI for each participant was calculated by summing the products of the available carbohydrate content per serving for each food or recipe, times the average number of servings per day, times its GI, all divided by the total daily available carbohydrate intake (Wolever et al., 1994; Foster-Powell et al., 2002; Augustin et al., 2004).

The average daily GL was calculated by summing the products of the available carbohydrate content per serving for each food or recipe, times the average number of servings of that food per day, times the food’s GI. Thus, each GL unit represents the equivalent of 1 g of carbohydrate from white bread.

Odds ratios (OR) for endometrial cancer and the corresponding 95% confidence intervals (CI) were derived using multiple logistic regression, conditioned on quinquennia of age and study center, and adjusted for year at interview, years of education, menopausal status and age at menopause, age at menarche, parity, OC and HRT use, history of hypertension and diabetes, noncarbohydrate energy intake, alcohol consumption, smoking habit, BMI, and occupational physical activity. GI and GL were also introduced in the model as continuous variables, and the unit of measurement was the difference between the 80th and the 20th percentile, that is the upper cut point of the fourth and the first quintiles, computed on the distribution of controls. Because diabetes is known to be associated with GL (Barclay et al., 2008) and is also positively associated with the risk of endometrial cancer (Cook et al., 2006; Lucenteforte et al., 2007), we also repeated the analyses excluding 53 diabetic cases and 54 diabetic control patients.


We used the same search strategies as those used by the two previous meta-analyses, described in detail elsewhere (Gnagnarella et al., 2008; Mulholland et al., 2008). Thus, a MEDLINE search in PubMed restricted to the period December 2007–September 2011 was performed, limiting the search to English language and following the Meta-analysis of Observational Studies in Epidemiology guidelines (Stroup et al., 2000). Two of the authors (C.G. and C.P.) independently selected the articles reporting information on the association between dietary GI, GL, and incidence of endometrial cancer or mortality. No study was excluded a priori for weakness of design or data quality. The flow chart of publication selection is shown in Fig. 1.

Fig. 1
Fig. 1:
Flow chart of the selection of publications included in the meta-analysis. GI, glycemic index; GL, glycemic load.

For each study included in this meta-analysis, we extracted data on study design, country, number of participants (cases, controls, or cohort size), duration of follow-up (for cohort studies), or calendar years of participant inclusion (for case–control studies), variables adjusted for in the analysis, relative risk (RR) estimates for categories of dietary GI and GL and the corresponding 95% CIs, and, when available, the upper cutpoints of dietary GI and GL level. In all studies, GI values were expressed using the bread scale, except in the NIH-AARP study (George et al., 2009), which used the glucose scale. Discrepancies between the two authors extracting the information were discussed and adjudicated.

In our analysis, the reference category was the lowest level of GI and GL in each study. The highest categories were the fourth quartiles in two studies (Silvera et al., 2005; Cust et al., 2007) and the fifth quintiles for the other studies. RRs for the highest versus the lowest category were calculated using random-effects models, which consider both within-study and between-study variations (DerSimonian and Laird, 1986). Summary estimates for cohort and case–control studies were computed separately as well as combined. We quantified the degree of heterogeneity between studies using the I2 statistic (Higgins et al., 2003). The presence of publication bias and outlier studies was evaluated using funnel plots (Thornton and Lee, 2000) and Begg’s and Egger’s tests (Egger et al., 1997).


Case–control study

Table 1 presents the distribution of cases and controls and the multivariate ORs of endometrial cancer according to quintiles of GI and GL overall and separately for nondiabetic participants. The overall ORs for the highest versus the lowest quintile were 1.03 (95% CI: 0.67–1.58) for GI and 1.01 (95% CI: 0.64–1.61) for GL. Continuous ORs for a difference between the upper cut point of the fourth and the first quintiles were 1.06 (95% CI: 0.85–1.32) for GI and 1.04 (95% CI: 0.83–1.29) for GL. These results were not materially changed when 53 diabetic cases and 54 controls were excluded from the analysis.

Table 1
Table 1:
Odds ratios and 95% confidence intervals of endometrial cancer according to glycemic index and glycemic load overall and among patients without a history of diabetes: Italy, 1992–2006

Table 2 shows the ORs for GI and GL in separate strata of age, parity, OC use, HRT use, menopausal status, age at menopause, and BMI. No significant heterogeneity emerged across any of the strata considered.

Table 2
Table 2:
Continuous odds ratiosa and 95% confidence intervals of endometrial cancer according to glycemic index and glycemic load in separate strata of selected covariates, overall and among patients without a history of diabetes: Italy, 1992–2006


Two studies examining the association between dietary GI and GL and the risk of endometrial cancer were identified between December 2007 and September 2011: the NIH-AARP Diet and Health Study (George et al., 2009) and our current study. The main characteristics of these and of previously identified studies from the two meta-analysis are presented in Table 3. There were 3200 cases from cohort studies and 864 from case–control studies, resulting in a total of 4064 cases. The NIH-AARP Diet and Health Study (George et al., 2009) contributed about a quarter of all cases. There was no evidence of publication bias overall, as tested using Egger’s test (P=0.08 for GI and P=0.70 for GL) and Begg’s test (P=0.45 for GI and P=0.88 for GL).

Table 3
Table 3:
Main characteristics of the studies included in the meta-analysis

Figure 2 shows the risk estimates of endometrial cancer for the highest versus the lowest GI level from cohort studies (RR=1.00, 95% CI: 0.87–1.14, I2=20.5%), case–control studies (OR=1.40, 95% CI: 0.77–2.54, I2=74.4%), and from all the studies combined (RR=1.09, 95% CI: 0.92–1.29, I2=55.5%).

Fig. 2
Fig. 2:
Relative risks (RR) and 95% confidence intervals (CI) of endometrial cancer for the highest versus the lowest glycemic index level. The combined RRs and 95% CI were calculated using random-effects models. NR, not reported.

Figure 3 reports the risk estimates for the highest as compared with the lowest GL level. The summary RR was 1.21 (95% CI: 1.07–1.36, I2=0.0%) for cohort studies, 1.04 (95% CI: 0.72–1.51, I2=0.0%) for case–control studies, and 1.19 (95% CI: 1.06–1.34, I2=0.0%) overall.

Fig. 3
Fig. 3:
Relative risks (RR) and 95% confidence intervals (CI) of endometrial cancer for the highest versus the lowest glycemic load level. The combined RRs and 95% CI were calculated using random-effects models. NR, not reported.


This case–control study shows no association between dietary GI and GL and risk of endometrial cancer overall and in strata of covariates, whereas the updated meta-analysis is consistent with an increased risk with high GL, but not GI.

High GI and GL diets may increase the risk of endometrial cancer by raising postprandial glycemia, thereby increasing the exposure to possible endogenous mitogenic factors (e.g. insulin, IGF, and estrogen) that have been linked to endometrial carcinogenesis (Kaaks et al., 2002). A few studies have shown direct associations of the risk of endometrial cancer with GI, GL, insulin, and IGF and diabetes (Augustin et al., 2003; Silvera et al., 2005; Lucenteforte et al., 2007), although others have reported no association (Folsom et al., 2003; Cust et al., 2007). It has also been hypothesized that high levels of GI and GL may increase the level of markers of inflammation (Mann, 2007), such as C-reactive protein, and a recent nested case–control study reported an increased risk for endometrial cancer in patients with elevated prediagnostic levels of inflammatory markers (Dossus et al., 2010).

The results of this meta-analysis were similar to those of other female hormone-related cancers. In particular, dietary GL, but not GI, was positively associated with a risk of breast cancer (Gnagnarella et al., 2008). This underlines the complexity of the mechanism, and consequently of IGF signaling and the concentrations of IGF-binding protein (Gnagnarella et al., 2008). Dietary GL is based on both the GI and the amount of carbohydrate, which generally has larger ranges within a population, and appears to be a relevant factor.

Dietary carbohydrates, particularly refined ones, have been positively associated with a risk of endometrial cancer (Chatenoud et al., 1998 and 1999). In contrast, high intakes of fiber, vegetables, and fruit have been linked to lower risk estimates (La Vecchia et al., 1997; Bandera et al., 2007b). Carbohydrate intakes have been correlated positively with fiber intake (Biel et al., 2011). However, when we further adjusted for fiber intake in the multivariate models, the results for both GI and GL were materially unchanged.

In this case–control study, as well as in the epidemiological studies considered in the meta-analysis, the methods used to assess the dietary intake, mainly FFQs, were not specifically developed to assess dietary GI and GL intake. The estimated GI and GL values were derived from a limited variety of food items listed in the FFQs, and this should limit the corresponding detectable range, particularly for GI.

In our population, GI and GL were inversely related to BMI and the waist to hip ratio (Rossi et al., 2010), which are strongly related to the risk of endometrial cancer (Parazzini et al., 1991; Cook et al., 2006; Dal Maso et al., 2011). This may be because of dietary modifications in overweight women, or simply because of underreporting of carbohydrate intake, especially among individuals with a high BMI who may selectively reduce – or underreport – refined carbohydrate intake (Gaesser, 2007; Rossi et al., 2010). These many factors might lead to an underestimate of the association of dietary GI and GL with the risk of endometrial cancer.

Among the possible limitations of this case–control study, selection bias is unlikely to be major, as patients hospitalized for chronic or digestive tract conditions were excluded and the participation rate was satisfactory. A recent cancer diagnosis might have influenced recall of diet, although awareness of dietary hypotheses in endometrial cancer is uncommon to the general population. Further, by interviewing participants in the same hospital setting, the comparability of information between cases and controls is improved (D’Avanzo et al., 1997).

The strengths of this case–control study are its large size combined with the collection of extensive dietary information using a satisfactorily reproducible and valid FFQ (Franceschi et al., 1993; Decarli et al., 1996), the comparable catchment areas of cases and controls, the high participation rate (>95%), and the possibility of allowance for intake of energy and for several covariates in the analyses.


This work was supported by the Italian Association for Cancer Research (AIRC no. 10068). The authors thank I. Garimoldi for editorial assistance.

Conflicts of interest

There are no conflicts of interest.


Augustin LS, Gallus S, Bosetti C, Levi F, Negri E, Franceschi S, et al. Glycemic index and glycemic load in endometrial cancer. Int J Cancer. 2003;105:404–407
Augustin LS, Galeone C, Dal Maso L, Pelucchi C, Ramazzotti V, Jenkins DJ, et al. Glycemic index, glycemic load and risk of prostate cancer. Int J Cancer. 2004;112:446–450
Bandera EV, Kushi LH, Moore DF, Gifkins DM, McCullough ML. Dietary lipids and endometrial cancer: the current epidemiologic evidence. Cancer Causes Control. 2007a;18:687–703
Bandera EV, Kushi LH, Moore DF, Gifkins DM, McCullough ML. Fruits and vegetables and endometrial cancer risk: a systematic literature review and meta-analysis. Nutr Cancer. 2007b;58:6–21
Barclay AW, Petocz P, McMillan-Price J, Flood VM, Prvan T, Mitchell P, et al. Glycemic index, glycemic load, and chronic disease risk – a meta-analysis of observational studies. Am J Clin Nutr. 2008;87:627–637
Biel RK, Friedenreich CM, Csizmadi I, Robson PJ, McLaren L, Faris P, et al. Case-control study of dietary patterns and endometrial cancer risk. Nutr Cancer. 2011;63:673–686
Brighenti F, Casiraghi M. Influence of transformation processes on glycemic response to starch foods. Giornale Italiano Nutr Clin Prev. 1992;1:79–87
Chatenoud L, Tavani A, La Vecchia C, Jacobs DR Jr., Negri E, Levi F, et al. Whole grain food intake and cancer risk. Int J Cancer. 1998;77:24–28
Chatenoud L, La Vecchia C, Franceschi S, Tavani A, Jacobs DR Jr., Parpinel MT, et al. Refined-cereal intake and risk of selected cancers in Italy. Am J Clin Nutr. 1999;70:1107–1110
Cook L, Weiss N, Doherthy J, Chen CSchottenfeld D, Fraumeni JF Jr.. Endometrial cancer. Cancer epidemiology and prevention. 2006 New York Oxford University Press:1027–1043
Cust AE, Slimani N, Kaaks R, van Bakel M, Biessy C, Ferrari P, et al. Dietary carbohydrates, glycemic index, glycemic load, and endometrial cancer risk within the European Prospective Investigation into Cancer and Nutrition cohort. Am J Epidemiol. 2007;166:912–923
D’Avanzo B, La Vecchia C, Katsouyanni K, Negri E, Trichopoulos D. An assessment, and reproducibility of food frequency data provided by hospital controls. Eur J Cancer Prev. 1997;6:288–293
Dal Maso L, Tavani A, Zucchetto A, Montella M, Ferraroni M, Negri E, et al. Anthropometric measures at different ages and endometrial cancer risk. Br J Cancer. 2011;104:1207–1213
Decarli A, Franceschi S, Ferraroni M, Gnagnarella P, Parpinel MT, La Vecchia C, et al. Validation of a food-frequency questionnaire to assess dietary intakes in cancer studies in Italy. Results for specific nutrients. Ann Epidemiol. 1996;6:110–118
DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials. 1986;7:177–188
Dossus L, Rinaldi S, Becker S, Lukanova A, Tjonneland A, Olsen A, et al. Obesity, inflammatory markers, and endometrial cancer risk: a prospective case-control study. Endocr Relat Cancer. 2010;17:1007–1019
Egger M, Davey Smith G, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ. 1997;315:629–634
Folsom AR, Demissie Z, Harnack L. Glycemic index, glycemic load, and incidence of endometrial cancer: the Iowa women's health study. Nutr Cancer. 2003;46:119–124
Foster-Powell K, Holt SH, Brand-Miller JC. International table of glycemic index and glycemic load values: 2002. Am J Clin Nutr. 2002;76:5–56
Franceschi S, Negri E, Salvini S, Decarli A, Ferraroni M, Filiberti R, et al. Reproducibility of an Italian food frequency questionnaire for cancer studies: results for specific food items. Eur J Cancer. 1993;29A:2298–2305
Friberg E, Orsini N, Mantzoros CS, Wolk A. Diabetes mellitus and risk of endometrial cancer: a meta-analysis. Diabetologia. 2007;50:1365–1374
Friedenreich CM, Biel RK, Lau DC, Csizmadi I, Courneya KS, Magliocco AM, et al. Case-control study of the metabolic syndrome and metabolic risk factors for endometrial cancer. Cancer Epidemiol Biomarkers Prev. 2011;20:2384–2395
Gaesser GA. Carbohydrate quantity and quality in relation to body mass index. J Am Diet Assoc. 2007;107:1768–1780
George SM, Mayne ST, Leitzmann MF, Park Y, Schatzkin A, Flood A, et al. Dietary glycemic index, glycemic load, and risk of cancer: a prospective cohort study. Am J Epidemiol. 2009;169:462–472
Gnagnarella P, Parpinel M, Salvini S, Franceschi S, Palli D, Boyle P. The update of the Italian food composition database. J Food Composition Anal. 2004;17:509–522
Gnagnarella P, Gandini S, La Vecchia C, Maisonneuve P. Glycemic index, glycemic load, and cancer risk: a meta-analysis. Am J Clin Nutr. 2008;87:1793–1801
Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. BMJ. 2003;327:557–560
Kaaks R, Lukanova A, Kurzer MS. Obesity, endogenous hormones, and endometrial cancer risk: a synthetic review. Cancer Epidemiol Biomarkers Prev. 2002;11:1531–1543
La Vecchia C, Negri E, Franceschi S, Levi F. An epidemiological study of endometrial cancer, nutrition and health. Eur J Cancer Prev. 1997;6:171–174
Larsson SC, Friberg E, Wolk A. Carbohydrate intake, glycemic index and glycemic load in relation to risk of endometrial cancer: a prospective study of Swedish women. Int J Cancer. 2007;120:1103–1107
Lucenteforte E, Bosetti C, Talamini R, Montella M, Zucchetto A, Pelucchi C, et al. Diabetes and endometrial cancer: effect modification by body weight, physical activity and hypertension. Br J Cancer. 2007;97:995–998
Mann J. Dietary carbohydrate: relationship to cardiovascular disease and disorders of carbohydrate metabolism. Eur J Clin Nutr. 2007;61(Suppl 1):S100–S111
Mulholland HG, Murray LJ, Cardwell CR, Cantwell MM. Dietary glycaemic index, glycaemic load and endometrial and ovarian cancer risk: a systematic review and meta-analysis. Br J Cancer. 2008;99:434–441
Parazzini F, La Vecchia C, Bocciolone L, Franceschi S. The epidemiology of endometrial cancer. Gynecol Oncol. 1991;41:1–16
Rosato V, Zucchetto A, Bosetti C, Dal Maso L, Montella M, Pelucchi C, et al. Metabolic syndrome and endometrial cancer risk. Ann Oncol. 2011;22:884–889
Rossi M, Bosetti C, Talamini R, Lagiou P, Negri E, Franceschi S, et al. Glycemic index and glycemic load in relation to body mass index and waist to hip ratio. Eur J Nutr. 2010;49:459–464
Salvini S, Gnagnarella P, Parpinel MT, Boyle P, Decarli A, Ferraroni M, et al. The food composition database for an Italian food frequency questionnaire. J Food Composition Anal. 1996;9:57–71
Silvera SA, Rohan TE, Jain M, Terry PD, Howe GR, Miller AB. Glycaemic index, glycaemic load and risk of endometrial cancer: a prospective cohort study. Public Health Nutr. 2005;8:912–919
Stroup DF, Berlin JA, Morton SC, Olkin I, Williamson GD, Rennie D, et al. Meta-analysis of observational studies in epidemiology: a proposal for reporting. Meta-analysis Of Observational Studies in Epidemiology (MOOSE) group. JAMA. 2000;283:2008–2012
Thornton A, Lee P. Publication bias in meta-analysis: its causes and consequences. J Clin Epidemiol. 2000;53:207–216
Wolever TM, Nguyen PM, Chiasson JL, Hunt JA, Josse RG, Palmason C, et al. Determinants of diet glycemic index calculated retrospectively from diet records of 342 individuals with non-insulin-dependent diabetes mellitus. Am J Clin Nutr. 1994;59:1265–1269
Food, nutrition, physical activity, and the prevention of cancer: a global perspective. 2007 Washington, DC AICR

cancer risk; diet; endometrial cancer; glycemic index; glycemic load; meta-analysis

© 2013 Lippincott Williams & Wilkins, Inc.