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. Author manuscript; available in PMC: 2011 Mar 29.
Published in final edited form as: Eur J Clin Nutr. 2007 Sep 19;63(1):78–86. doi: 10.1038/sj.ejcn.1602904

A prospective study of dietary carbohydrate quantity and quality in relation to risk of ovulatory infertility

Jorge E Chavarro 1,2, Janet W Rich-Edwards 2,3,4, Bernard A Rosner 2,5, Walter C Willett 1,2,4
PMCID: PMC3066074  NIHMSID: NIHMS281384  PMID: 17882137

Abstract

Objective

To evaluate whether the amount or quality of carbohydrate in diet is associated with ovulatory infertility.

Subjects and Methods

18,555 married, pre-menopausal women without a history of infertility were followed as they attempted a pregnancy or became pregnant during an eight-year period. Diet was assessed twice during follow-up using a validated food-frequency questionnaire and prospectively related to the incidence of infertility due ovulatory disorder.

Results

During follow-up, 438 women reported ovulatory infertility. Total carbohydrate intake and dietary glycemic load were positively related to ovulatory infertility in analyses adjusted for age, body mass index, smoking, parity, physical activity, recency of contraception, total energy intake, protein intake, and other dietary variables. The multivariable-adjusted risk ratio [RR] (95% confidence interval [CI]) of ovulatory infertility comparing the highest to lowest quintile of total carbohydrate intake was 1.91 (1.27 – 3.02). The corresponding RR (95% CI) for dietary glycemic load was 1.92 (1.26 – 2.92). Dietary glycemic index was positively related to ovulatory infertility only among nulliparous women. Intakes of fiber from different sources were unrelated to ovulatory infertility risk.

Conclusions

The amount and quality of carbohydrate in diet may be important determinants of ovulation and fertility in healthy women.

Keywords: Diet, carbohydrates, glycemic index, glycemic load, infertility, ovulation, epidemiology

INTRODUCTION

Infertility is a common condition affecting up to 15% of couples during their reproductive lifetime (Hull MG et al. 1985). In the United States alone 7 million women have an impaired ability to bear children (Chandra et al. 2005). Despite the magnitude of the problem, few modifiable risk factors for infertility have been identified and therefore prevention of this condition has been largely ignored.

Much evidence suggests that insulin sensitivity may be an important determinant of ovulatory function and fertility (Azziz et al. 2001; Hjollund et al. 1999; Vrbikova et al. 2002). Both the quality and quantity of carbohydrate in diet influence glucose metabolism, affecting insulin demand or sensitivity in healthy individuals (Jeppesen et al. 1997; Murakami et al. 2006) as well as in diabetics and women with polycystic ovary syndrome (PCOS) (Douglas et al. 2006a; Garg et al. 1994). However, it is not currently known whether the effects of carbohydrate intake on glucose and insulin metabolism lead to changes in ovulatory function or fertility in healthy women. To answer this question we prospectively evaluated whether dietary glycemic index, glycemic load and the intakes of carbohydrates and fiber from different sources were associated with infertility due to anovulation in a group of apparently healthy women.

SUBJECTS AND METHODS

The Nurses’ Health Study II (NHS II) started in 1989 when over 116,000 female registered nurses completed a mailed questionnaire. Participants have been followed since then with mailed questionnaires every other year. Here we present a prospective analysis of incident ovulatory infertility among participants of this cohort who provided dietary information as part of their participation in the NHS II. The study was approved by the Institutional Review Board of Brigham and Women’s Hospital.

Follow-up for the current analysis started in 1991, when diet was first measured, and concluded in 1999. Every two years participants were asked if they had tried to become pregnant for more than 1 year without success since the previous questionnaire administration, and to indicate whether their inability to conceive was caused by tubal blockage, ovulatory disorder, endometriosis, cervical mucous factor, male factor, was not found, was not investigated or was due to another reason. In a validation sub-study, self-reported diagnosis of ovulatory infertility was confirmed by review of medical records in 95% of the cases (Rich-Edwards et al. 1994). Women were also asked if they became pregnant during the preceding two-year period, including pregnancies resulting in miscarriages or induced abortions. Using this information we simulated a cohort of women attempting to become pregnant. Only married women, with available dietary information and without a history of infertility, were eligible to enter the analysis. These women contributed information to the analysis during each two-year period in which they reported a pregnancy or a failed pregnancy attempt, and were followed until they reported an infertility event from any cause, reached menopause or underwent a sterilization procedure (themselves or their partner), whichever came first. Ten diabetic women met these criteria. Since the small number of diabetics would preclude meaningful statistical adjustment or exploration of modification of the associations by diabetes, diabetic women were excluded from the analysis. After exclusions, we identified 18,555 women without a history of infertility who tried to become pregnant or became pregnant during the study period.

Women who met the selection criteria for the study and reported infertility due to ovulatory disorder were considered cases. All other events (pregnancies – resulting in live births, miscarriages or induced abortions – and infertility due to other causes) were considered noncases.

Dietary assessment

Dietary information was collected in 1991 and 1995 using a validated food-frequency questionnaire (FFQ) with 133 and 142 food items, respectively (Liu et al. 2001; Willett et al. 1985). Participants were asked to report how often, on average, they consumed each of the foods and beverages included in the FFQ during the previous year. The questionnaire offered nine options for frequency of intake, ranging from never or less than once per month to six or more times per day. Nutrient intakes were estimated by summing the nutrient contribution of all food items in the questionnaire. The nutrient content of each food and specified portion size was obtained from a nutrient database derived from the US Department of Agriculture (2001) with supplemental information from food manufacturers and, in the case of glycemic index, from previously published databases (Jenkins et al. 1981; Miller et al. 1995; University of Sydney (Australia) (accessed 2002)). Each woman’s average dietary glycemic index was calculated by summing the product of the carbohydrate content of each food times its frequency of intake and glycemic index, divided by the total carbohydrate intake (Salmeron et al. 1997). To account for differences in overall carbohydrate intake we also calculated dietary glycemic load as the product of total carbohydrate intake times the average dietary glycemic index (Salmeron et al. 1997). The percentage of energy contributed by carbohydrates was calculated as the intake of energy from this nutrient divided by total energy intake. Dietary glycemic index, glycemic load and intakes of fiber and other nutrients were adjusted for total energy intake using the nutrient residual method (Willett & Stampfer 1998).

Statistical analyses

The risk ratio (estimated as an odds ratio) of ovulatory infertility in relation to dietary factors was estimated using logistic regression. The generalized estimating equation approach (Fitzmaurice et al. 2004) with an exchangeable working correlation structure, was used to account for the within-person correlation in outcomes at different time periods. We divided women into five groups according to quintiles of dietary glycemic index, glycemic load and intakes of total carbohydrates and fiber. In these models, the risk ratio was computed as the risk of infertility in a specific quintile of cumulative averaged intake compared to the risk in the lowest quintile. Tests for linear trend were conducted by using the median values of intake in each category as a continuous variable. The risk ratio associated with a 1-serving per day increase in consumption of specific carbohydrate-rich foods was estimated by modeling the intake of these foods as a continuous variable. All models were adjusted for total energy intake, age and calendar time at the beginning of each questionnaire cycle. Multivariable models included additional terms for body mass index (wt (kg)/ht2 (m)) (BMI), parity, smoking history, physical activity, history of oral contraceptive use and dietary factors found to be related to infertility in preliminary analyses (use of multivitamins and intakes of alcohol, coffee, iron, trans fatty acids, animal protein and vegetable protein) (Chavarro et al. 2006; Chavarro et al. 2007; Chavarro et al. in press). Multivariable models for total carbohydrate intake were fit with adjustment for protein and trans fatty acid intakes to simulate the substitution of carbohydrates for naturally occurring fats (saturated, mono-unsaturated and poly-unsaturated), and without adjustment for protein and trans fatty acid intakes to simulated the substitution for the average mixture of protein and fats in the study population. Values of the dietary and non-dietary variables were updated as new data became available from follow-up questionnaires.

Lastly, we examined whether the associations between dietary variables and ovulatory infertility were modified by participant characteristics (age, parity and BMI), or the presence of long menstrual cycles (>40 days), by introducing cross-product terms between carbohydrate and the variable of interest. All P values were two sided. Analyses were performed in SAS version 9.1.

RESULTS

Between 1991 and 1999, 26,971 eligible pregnancies and pregnancy attempts were accrued among 18,555 women. Of these, 3,430 (12.7% of all events) were incident reports of infertility, of which 2,165 were women reporting at least one medical diagnosis for infertility and 438 (1.6% of all events, 20.2% of investigated infertility cases) were incident reports of ovulatory infertility. Higher carbohydrate intake at baseline was associated with a generally healthy lifestyle. Women who consumed more carbohydrates, also consumed less fat, animal protein, alcohol and coffee while consuming more protein from vegetable sources, fiber and multivitamins (Table 1). These women were also less likely to be smokers, weighed less and were more physically active than women with lower carbohydrate intake. On the other hand, while women with a high glycemic index diet also consumed less saturated fat, animal protein, alcohol and coffee, they also had a higher intake of trans fat, lower intakes of fiber and multivitamins and were less physically active than women with lower glycemic index diets.

Table 1.

Age-standardized baseline* characteristics of the study population by quintiles of total carbohydrate and glycemic index intake.

Carbohydrate intake Glycemic index
Q1 Q3 Q5 Q1 Q3 Q5
Age, years 32.5 32.5 32.9 33.2 32.5 32.0
Saturated fat intake, g/day 26.8 22.3 16.9 23.3 22.4 21.0
Monounsaturated fat intake, g/day 28.0 23.2 18.0 23.2 23.5 23.0
Polyunsaturated fat intake, g/day 12.3 10.8 9.0 10.8 10.9 10.5
Trans unsaturated fat intake, g/day 3.7 3.1 2.4 2.8 3.1 3.3
Animal protein intake, g/day 76.0 64.1 49.9 71.3 63.9 57.7
Vegetable protein intake, g/day 19.8 22.5 24.7 21.6 22.7 22.1
Fiber intake, g/day 15.4 18.1 21.1 19.0 18.3 16.6
Cereal fiber intake, g/day 4.5 5.9 7.1 5.4 5.9 5.8
Alcohol intake, g/day 4.4 2.6 1.8 4.2 2.8 1.7
Coffee intake ≥ 2 cups/day. % 30 23 18 32 23 16
Multivitamin use, % 48 58 63 59 58 53
Current smoker, % 11 5 6 8 6 8
Body Mass Index, kg/m2 24.8 23.8 23.0 24.2 23.9 23.6
Physical activity, METs/week 17.8 20.6 27.0 26.1 20.9 16.9
Cycles ≥ 40 days, % 3 4 4 4 3 4
Nulliparous, % 23 21 29 28 22 21
Oral contraceptive use at the beginning of the mailing cycle, % 20 16 14 16 16 17
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*

Values are presented as age-standardized means and proportions. Values for age are not age-standardized

Total carbohydrate intake was unrelated to ovulatory infertility in models simulating the substitution of carbohydrates for the average mixture of other energy sources in the study population (Table 2). Nevertheless, there was a positive association between total carbohydrate intake and ovulatory infertility in the models where this nutrient is increased at the expense of naturally occurring fats. In the multivariable-adjusted model, women in the highest quintile of total carbohydrate intake had a 78% greater risk of ovulatory infertility than women in the lowest quintile (RR [95% CI] = 1.78 [1.14–2.78]) and there was a statistically significant linear trend towards greater risk of ovulatory infertility with increasing carbohydrate intake (P, trend = 0.005). Further adjustment for cereal fiber intake made this association even stronger.

Table 2.

Risk ratios and 95% confidence intervals for ovulatory infertility by quintiles of carbohydrate, glycemic index and glycemic load intake

Quintile of Intake
1 2 3 4 5 P, trend*
     Total Carbohydrate
Median Intake (% of calories) 42 47 51 54 60
Cases / non-cases 92 / 5,302 90 / 5,304 79 / 5,316 90 / 5,305 87 / 5,306
Substitution for fats and protein
Age and energy adjusted RR† 1.00 (referent) 1.01 (0.76–1.36) 0.90 (0.66–1.22) 1.03 (0.77–1.38) 1.00 (0.74–1.35) 0.99
Multivariable adjusted RR 1‡
1.00 (referent) 1.08 (0.80–1.46) 1.00 (0.73–1.37) 1.18 (0.87–1.61) 1.03 (0.74–1.41) 0.74
Substitution for naturally occurring fats
Age and energy adjusted RR§ 1.00 (referent) 1.16 (0.85–1.58) 1.11 (0.79–1.55) 1.37 (0.96–1.94) 1.54 (1.02–2.33) 0.03
Multivariable adjusted RR 2∥ 1.00 (referent) 1.28 (0.93–1.76) 1.30 (0.92–1.85) 1.69 (1.17–2.45) 1.78 (1.14–2.78) 0.005
Multivariable adjusted RR 3 1.00 (referent) 1.30 (0.94–1.80) 1.35 (0.94–1.92) 1.78 (1.22–2.60) 1.91 (1.21–3.02) 0.003
     Glycemic index
Median Intake 50 53 54 56 58
Cases / non-cases 111 / 5,285 79 / 5,325 68 / 5,329 82 / 5,298 98 / 5,296
Age and energy adjusted RR† 1.00 (referent) 0.73 (0.54–0.97) 0.62 (0.46–0.84) 0.75 (0.56–1.00) 0.88 (0.67–1.16) 0.35
Multivariable adjusted RR 2∥ 1.00 (referent) 0.82 (0.61–1.10) 0.72 (0.52–0.99) 0.90 (0.66–1.22) 1.08 (0.79–1.48) 0.68
Multivariable adjusted RR 3 1.00 (referent) 0.82 (0.61–1.10) 0.72 (0.52–0.99) 0.90 (0.66–1.23) 1.09 (0.79–1.48) 0.67
     Glycemic load
Median Intake 100 114 123 133 149
Cases / non-cases 94 / 5,297 102 / 5,298 69 / 5,322 73 / 5,320 100 / 5,296
Age and energy adjusted RR† 1.00 (referent) 1.10 (0.83–1.47) 0.74 (0.54–1.01) 0.77 (0.57–1.05) 1.04 (0.78–1.38) 0.65
Multivariable adjusted RR 2∥ 1.00 (referent) 1.44 (1.07–1.96) 1.02 (0.73–1.44) 1.25 (0.85–1.82) 1.82 (1.21–2.75) 0.02
Multivariable adjusted RR 3 1.00 (referent) 1.47 (1.08–1.99) 1.05 (0.75–1.48) 1.29 (0.88–1.90) 1.92 (1.26–2.92) 0.01
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*

Calculated in a separate regression model with median protein intake in each quintile as a continuous variable and the same group of covariates specified for the corresponding model.

†

Adjusted for age (continuous), calendar time (4 two-year intervals) and total energy intake (continuous).

‡

Age and energy adjusted model further adjusted for body mass index (<20, 20–24.9, 25–29.9, ≥30 and missing), parity (0, 1, ≥2 and missing), smoking history (never, past 1–4 cig/day, past 5–14 cig/day, past 15–24 cig/day, past ≥ 25 cig/day or unknown amount, current 1–4 cig/day, current 5–14 cig/day, current 15–24 cig/day and current ≥ 25 cig/day or unknown amount), physical activity (<3 MET-h/wk, 3–8.9 MET-h/wk, 9–17.9 MET-h/wk, 18–26.9 MET-h/wk, 27–41.9 MET-h/wk, ≥ 42 MET-h/wk and missing), oral contraceptive use (current user, never user, past user 0–23 months ago, past user 24–47 months ago, past user 48–71 months ago, past user 72–95 months ago, past user 96–119 months ago, past user ≥ 120 months ago and missing), frequency of multivitamin use (non-users, ≤2 tablets/week, 3 to 5 tablets/week, ≥6 tablets/week and missing), intake of alcohol (no intake, < 2 g/day, 2–4.9 g/day, ≥ 5 g/day), coffee (<1 serving/month, 1 serving/month, 2–6 servings/week, 1 serving/day, 2–3 servings/day, ≥ 4 servings/day) and iron (quintiles)

§

Age and energy adjusted model further adjusted for trans fatty acids (continuous), animal protein (continuous) and vegetable protein (continuous).

∥

Multivariable adjusted model 1 further adjusted for trans fatty acids (continuous), animal protein (continuous) and vegetable protein (continuous).

Multivariable adjusted model 2 further adjusted for cereal fiber intake.

Dietary glycemic index and glycemic load were unrelated to ovulatory infertility in age and energy-adjusted analyses (Table 2). After adjustment for potential confounders, particularly after adjustment for animal and vegetable protein intake, dietary glycemic load was associated with a higher risk of infertility due to anovulation. Statistical adjustment for potential confounders had no impact in the results for dietary glycemic index. Similarly, total fiber intake and intake of fiber from different sources were unrelated to ovulatory infertility both in age and energy-adjusted analyses and in multivariable-adjusted analyses (Table 3).

Table 3.

Risk ratios and 95% confidence intervals for ovulatory infertility by quintiles of fiber intake

Quintile of Intake
1 2 3 4 5 P, trend*
     Total fiber
Median Intake (g/day) 12 15 17 20 24
Cases / non-cases 84 / 5,213 91 / 5,412 85 / 5,359 90 / 5,278 88 / 5,271
Age and energy adjusted RR† 1.00 (referent) 1.05 (0.78–1.42) 1.00 (0.73–1.35) 1.07 (0.79–1.45) 1.03 (0.75–1.40) 0.86
Multivariable adjusted RR 1‡ 1.00 (referent) 1.14 (0.83–1.56) 1.11 (0.78–1.56) 1.22 (0.84–1.77) 1.26 (0.81–1.95) 0.31
Multivariable adjusted RR 2§ 1.00 (referent) 1.15 (0.84–1.57) 1.11 (0.78–1.57) 1.22 (0.84–1.78) 1.23 (0.79–1.91) 0.38
     Cereal fiber
Median Intake (g/day) 3 4 5 7 9
Cases / non-cases 85 / 5,224 109 / 5,555 93 / 5,108 66 / 5,327 85 / 5,319
Age and energy adjusted RR† 1.00 (referent) 1.21 (0.91–1.61) 1.10 (0.82–1.49) 0.76 (0.55–1.05) 0.94 (0.69–1.27) 0.13
Multivariable adjusted RR 1‡ 1.00 (referent) 1.34 (0.98–1.82) 1.25 (0.90–1.74) 0.86 (0.59–1.26) 1.10 (0.74–1.63) 0.64
Multivariable adjusted RR 2§ 1.00 (referent) 1.31 (0.96–1.79) 1.20 (0.86–1.68) 0.82 (0.56–1.20) 1.00 (0.67–1.49) 0.33
     Vegetable fiber
Median Intake (g/day) 3 5 6 7 10
Cases / non-cases 89 / 5,422 73 / 5,311 96 / 5,193 92 / 5,329 88 / 5,278
Age and energy adjusted RR† 1.00 (referent) 0.84 (0.61–1.14) 1.13 (0.84–1.51) 1.07 (0.79–1.43) 1.02 (0.75–1.38) 0.56
Multivariable adjusted RR 1‡ 1.00 (referent) 0.90 (0.66–1.25) 1.21 (0.89–1.63) 1.11 (0.81–1.53) 1.04 (0.72–1.51) 0.64
Multivariable adjusted RR 2§ 1.00 (referent) 0.92 (0.67–1.27) 1.24 (0.92–1.68) 1.15 (0.84–1.59) 1.10 (0.76–1.60) 0.44
     Fruit fiber
Median Intake (g/day) 1 2 3 4 6.1
Cases / non-cases 84 / 5,309 94 / 5,404 87 / 5,286 79 / 5,210 94 / 5,324
Age and energy adjusted RR† 1.00 (referent) 1.08 (0.81–1.46) 1.05 (0.78–1.42) 0.96 (0.71–1.31) 1.09 (0.80–1.47) 0.88
Multivariable adjusted RR 1‡ 1.00 (referent) 1.18 (0.87–1.60) 1.14 (0.83–1.56) 1.08 (0.78–1.50) 1.20 (0.86–1.67) 0.49
Multivariable adjusted RR 2§ 1.00 (referent) 1.18 (0.87–1.60) 1.14 (0.83–1.56) 1.08 (0.78–1.50) 1.19 (0.85–1.66) 0.52
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*

Calculated in a separate regression model with median protein intake in each quintile as a continuous variable and the same group of covariates specified for the corresponding model.

†

Adjusted for age (continuous), calendar time (4 two-year intervals) and total energy intake (continuous).

‡

Age and energy adjusted model further adjusted for body mass index (<20, 20–24.9, 25–29.9, ≥30 and missing), parity (0, 1, ≥2 and missing), smoking history (never, past 1–4 cig/day, past 5–14 cig/day, past 15–24 cig/day, past ≥ 25 cig/day or unknown amount, current 1–4 cig/day, current 5–14 cig/day, current 15–24 cig/day and current ≥ 25 cig/day or unknown amount), physical activity (<3 MET-h/wk, 3–8.9 MET-h/wk, 9–17.9 MET-h/wk, 18–26.9 MET-h/wk, 27–41.9 MET-h/wk, ≥ 42 MET-h/wk and missing), oral contraceptive use (current user, never user, past user 0–23 months ago, past user 24–47 months ago, past user 48–71 months ago, past user 72–95 months ago, past user 96–119 months ago, past user ≥ 120 months ago and missing), frequency of multivitamin use (non-users, ≤2 tablets/week, 3 to 5 tablets/week, ≥6 tablets/week and missing), intake of alcohol (no intake, < 2 g/day, 2–4.9 g/day, ≥ 5 g/day), coffee (<1 serving/month, 1 serving/month, 2–6 servings/week, 1 serving/day, 2–3 servings/day, ≥ 4 servings/day), iron (quintiles) trans fatty acids (continuous), animal protein (continuous) and vegetable protein (continuous).

§

Multivariable adjusted model 1 further adjusted glycemic load.

We then examined the association between the main carbohydrate foods sources in this population and ovulatory infertility. Overall, high glycemic index foods (cold breakfast cereals, white rice and potatoes) were associated with a greater risk of ovulatory infertility while low glycemic index foods (brown rice, pasta and dark bread) were associated with a reduced risk of this condition (Table 4). This pattern was not consistent across all foods examined, however, and only the association between cold breakfast cereal intake and ovulatory infertility was statistically significant (p = 0.02).

Table 4.

Risk ratios (95% CIs) of ovulatory infertility associated with increasing the intake of specific carbohydrate-rich foods by 1 serving/day.

Food Age and energy-adjusted* Multivariable-adjusted†
RR (95% CI) p RR (95% CI) p
Cold breakfast cereal 1.10 (0.88 – 1.38) 0.39 1.31 (1.04 – 1.63) 0.02
White rice 1.24 (0.84 – 1.82) 0.28 1.19 (0.78 – 1.81) 0.41
Brown rice 0.83 (0.36 – 1.94) 0.67 0.73 (0.29 – 1.84) 0.50
Pasta 0.95 (0.57 – 1.58) 0.84 0.92 (0.52 – 1.62) 0.78
Potatoes (baked, boiled or mashed) 1.34 (0.82 – 2.18) 0.24 1.27 (0.80 – 2.03) 0.32
French fried potatoes 1.97 (0.87 – 4.49) 0.10 1.74 (0.63 – 4.81) 0.29
White bread 0.91 (0.77 – 1.07) 0.24 0.98 (0.83 – 1.15) 0.80
Dark bread 0.90 (0.79 – 1.03) 0.14 0.99 (0.86 – 1.15) 0.93
English muffins or bagels 1.08 (0.76 – 1.54) 0.67 0.97 (0.67 – 1.41) 0.87
Muffins or biscuits 0.80 (0.42 – 1.52) 0.50 0.71 (0.37 – 1.33) 0.28
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*

Adjusted for age (continuous), calendar time (4 two-year intervals) and total energy intake (continuous).

†

Age and energy adjusted model further adjusted for body mass index (<20, 20–24.9, 25–29.9, ≥30 and missing), parity (0, 1, ≥2 and missing), smoking history (never, past 1–4 cig/day, past 5–14 cig/day, past 15–24 cig/day, past ≥ 25 cig/day or unknown amount, current 1–4 cig/day, current 5–14 cig/day, current 15–24 cig/day and current ≥ 25 cig/day or unknown amount), physical activity (<3 MET-h/wk, 3–8.9 MET-h/wk, 9–17.9 MET-h/wk, 18–26.9 MET-h/wk, 27–41.9 MET-h/wk, ≥ 42 MET-h/wk and missing), oral contraceptive use (current user, never user, past user 0–23 months ago, past user 24–47 months ago, past user 48–71 months ago, past user 72–95 months ago, past user 96–119 months ago, past user ≥ 120 months ago and missing), frequency of multivitamin use (non-users, ≤2 tablets/week, 3 to 5 tablets/week, ≥6 tablets/week and missing), intake of alcohol (no intake, < 2 g/day, 2–4.9 g/day, ≥ 5 g/day), coffee (<1 serving/month, 1 serving/month, 2–6 servings/week, 1 serving/day, 2–3 servings/day, ≥ 4 servings/day), iron (quintiles) trans fatty acids (continuous), animal protein (continuous) and vegetable protein (continuous).

Lastly, we examined whether the associations of dietary glycemic index, glycemic load, total carbohydrate intake and fiber intake with ovulatory infertility were different in subgroups of women defined by age, menstrual cycle length, BMI and parity. Age modified the association between fiber intake, and particularly cereal fiber intake, and ovulatory infertility (P, interaction = 0.02). An increase in cereal fiber intake of 10 g/day, while holding caloric intake constant, was associated with a 44% lower risk of ovulatory infertility (p = 0.02) among women older than 32 years while the same increase in cereal fiber intake was unrelated to infertility due to anovulation among younger women (p = 0.78). Parity appeared to modify the associations of fiber intake and dietary glycemic index with ovulatory infertility. Fiber intake was associated with a small, yet not statistically significant, reduction in the risk of ovulatory infertility among nulliparous women while it was unrelated to this disease among parous women (Table 5). Dietary glycemic index was positively related to the risk of ovulatory infertility among nulliparous women but unrelated to this condition among parous women (P, interaction = 0.02). The risk ratio (95% CI) comparing top to bottom quintiles of dietary glycemic index was 1.55 (1.02–2.37) among nulliparous women (p, trend = 0.05) and 0.78 (0.51–1.18) among parous women (p, trend = 0.22).

Table 5.

Risk ratios (RR) and 95% confidence intervals (CI)* of ovulatory infertility associated with increasing fiber intake by 10 g/day in subgroups of the study population.

Total fiber Cereal fiber
Subgroup Cases (n) RR (95% CI) P, interaction RR (95% CI) P, interaction
Age ≤ 32 years 214 1.03 (0.79 – 1.34) 0.02 0.94 (0.61 – 1.44) 0.02
Age > 32 years 224 0.82 (0.62 – 1.08) 0.56 (0.34 – 0.93)
Cycles ≥ 40 days 52 0.89 (0.69 – 1.16) 0.75 0.77 (0.52 – 1.14) 0.55
Cycles < 40 days 386 0.99 (0.62– 1.57) 0.60 (0.22 – 1.66)
BMI < 25 248 0.88 (0.65 – 1.19) 0.16 0.78 (0.49 – 1.22) 0.44
BMI ≥ 25 190 0.96 (0.71 – 1.30) 0.65 (0.36 – 1.20)
Nulliparous 208 0.75 (0.54 – 1.05) 0.03 0.79 (0.50 – 1.23) 0.39
Parous 230 1.06 (0.80 – 1.41) 0.66 (0.37 – 1.19)
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*

Adjusted for age (continuous), calendar time (4 two-year intervals), total energy intake (continuous), body mass index (<20, 20–24.9, 25–29.9, ≥30 and missing), parity (0, 1, ≥2 and missing), smoking history (never, past 1–4 cig/day, past 5–14 cig/day, past 15–24 cig/day, past ≥ 25 cig/day or unknown amount, current 1–4 cig/day, current 5–14 cig/day, current 15–24 cig/day and current ≥ 25 cig/day or unknown amount), physical activity (<3 MET-h/wk, 3–8.9 MET-h/wk, 9–17.9 MET-h/wk, 18–26.9 MET-h/wk, 27–41.9 MET-h/wk, ≥ 42 MET-h/wk and missing), oral contraceptive use (current user, never user, past user 0–23 months ago, past user 24–47 months ago, past user 48–71 months ago, past user 72–95 months ago, past user 96–119 months ago, past user ≥ 120 months ago and missing), frequency of multivitamin use (non-users, ≤2 tablets/week, 3 to 5 tablets/week, ≥6 tablets/week and missing), intake of alcohol (no intake, < 2 g/day, 2–4.9 g/day, ≥ 5 g/day), coffee (<1 serving/month, 1 serving/month, 2–6 servings/week, 1 serving/day, 2–3 servings/day, ≥ 4 servings/day), iron (quintiles) trans fatty acids (continuous), animal protein (continuous), vegetable protein (continuous), and glycemic load (continuous).

DISCUSSION

We prospectively examined the associations of the quality and quantity of dietary carbohydrates with ovulatory infertility among a group of apparently healthy women and found that the quantity of carbohydrate was positively related to this condition when consumed at the expense of naturally occurring fats. In addition, we found a positive association between the quality of carbohydrate and ovulatory infertility among nulliparous women. These associations were independent of other characteristics of diet previously found to be associated with fertility and insulin sensitivity (Chavarro et al. 2006; Chavarro et al. 2007; Liese et al. 2005). We have previously reported how factors known to increase insulin resistance such as overweight and obesity, physical inactivity and intake of trans fatty acids (Lefevre et al. 2005; The Diabetes Prevention Program Research Group 2005), are also associated with a greater risk of ovulatory infertility (Chavarro et al. 2007; Rich-Edwards et al. 1994; Rich-Edwards et al. 2002). Others have found that greater levels of HbA1c are associated with decreased fertility and sub-clinical metabolic characteristics resembling PCOS among apparently healthy women (Hjollund et al. 1999). Further, insulin sensitizers improve reproductive metabolic parameters and ovulatory function in PCOS women (Brettenthaler et al. 2004; Moghetti et al. 2000). Our results are consistent with these previous findings and with the hypothesis that insulin sensitivity may be a key factor regulating ovulatory function and fertility in healthy women.

Although we are unaware of previous studies examining the association between carbohydrate intake and ovulatory infertility in apparently healthy women, studies among women with PCOS, the most common cause of anovulation, suggest that the amount and quality of carbohydrate in diet may influence reproductive function. In a small retrospective case-control study, Douglas and collaborators found that women with PCOS had a greater intake of high glycemic index foods, particularly white bread and fried potatoes, than age, race and BMI-matched controls (Douglas et al. 2006b). In a small feeding trial among women with PCOS conducted by the same group (Douglas et al. 2006a), consuming a low carbohydrate diet (43% vs. 56% of energy) led to changes that would be expected to result in improved reproductive and metabolic outcomes including significant reductions in fasting and post glucose challenge insulin levels and a reduction in free testosterone levels of borderline statistical significance (Douglas et al. 2006a). Our results are consistent with the findings of these two studies.

We found that total carbohydrate intake and dietary glycemic load were positively related to ovulatory infertility and that dietary glycemic index was associated to this condition among nulliparous women. Since greater insulin sensitivity and improved glucose homeostasis have been previously linked to improved ovulatory function and fertility in healthy women and women with PCOS (Azziz et al. 2001; Brettenthaler et al. 2004; Hjollund et al. 1999), it is possible that the observed associations are mediated through the effects of carbohydrate intake on glucose metabolism. In randomized crossover feeding trials among healthy subjects, increasing total carbohydrate intake, in an amount equivalent to going from the lowest to the highest quintile of intake in this study population, increased post-prandial insulin response to test meals after a few days on this diet (Coulston et al. 1983) as well as day-long insulin and glucose concentrations after 3 weeks of following this diet (Jeppesen et al. 1997). Similar effects have been observed in type 2 diabetics (Chen et al. 1995; Garg et al. 1994) PCOS women (Douglas et al. 2006a) and hypertriglyceridemic individuals (Liu et al. 1983). Low-glycemic index diets significantly decrease HbA1c levels and improve insulin sensitivity among diabetics (Brand-Miller et al. 2003; Jimenez-Cruz et al. 2003; Rizkalla et al. 2004), women with impaired glucose tolerance (Ostman et al. 2006) and are related to fasting glucose and HbA1c levels among healthy women (Murakami et al. 2006). In addition, higher dietary glycemic load has been associated with higher fasting glucose levels (Murakami et al. 2006) and greater insulin resistance (McKeown et al. 2004), although these findings have not been consistent across studies (Lau et al. 2005; Liese et al. 2005; Mayer-Davis et al. 2006). Increased insulin levels resulting from greater carbohydrate intake could also lead to increased free IGF-I and androgen levels as a consequence of insulin action on the production of IGF-I, its binding proteins and SHBG (Kaaks & Lukanova 2001), thus creating an endocrine environment similar to that suggested to be responsible for the clinical manifestations of PCOS (Dunaif 1997; Ehrmann 2005). Whether these or other mechanisms explain the observed associations should be explored in future studies.

An alternative explanation for our results is that the positive association between increasing carbohydrate intake at the expense of natural fats and ovulatory infertility is not a result of metabolic effects of carbohydrate intake but rather due to a beneficial effect on ovulatory function of consuming naturally occurring fats. This possibility is consistent with our previous findings of an inverse association between intakes of specific fatty acids, particularly of saturated and mono-unsaturated fats (Chavarro et al. 2007), and with previous reports suggesting that intakes of total and saturated fat may have beneficial effects on menstrual cycle characteristics and ovulatory function (Deuster et al. 1986; Hill et al. 1984; Reichman et al. 1992; Snow et al. 1990). Although we cannot distinguish between a beneficial effect of naturally occurring fats and an adverse effect of carbohydrates from these data, this distinction is not practically important because, with total calories fixed, an increase in one nutrient largely implies a decrease in the other.

Our study has several strengths including its prospective design. Dietary information was collected two to four years before outcome events were reported, minimizing the possibility that our results were affected by fertility status at the time information on diet was obtained. The use of previously validated instruments for the assessment of diet and infertility due to ovulatory problems add strength to our results. The most important limitation is that our study was not a cohort of women known to be planning a pregnancy. Cases were clearly trying to conceive, but some pregnancy non-cases may have conceived accidentally. Nevertheless, the study was conducted in a cohort of women whose pregnancies are likely to be planned given their socioeconomic characteristics (i.e. professional women in their late 20s and early 30s) (Chandra et al. 2005), and was restricted to married women, whose pregnancies are even more likely to be intentional (Chandra et al. 2005). We also considered women diagnosed with infertility from other causes as non-cases, making it less likely that pregnancy intention affected our results. Another potential limitation is the possibility that our findings are due to unmeasured factors related both to carbohydrate intake and ovulatory infertility. However, we accounted statistically for a variety of known and suspected risk factors for infertility in our analyses and only two of these factors (protein intake and parity) had major impact on the results.

In conclusion, we found that greater carbohydrate intake and dietary glycemic load were associated with an increased risk of infertility due to anovulation in a cohort of apparently healthy women and that dietary glycemic index was positively related to this condition among nulliparous women in this cohort. These findings are consistent with multiple lines of evidence suggesting a role of insulin and glucose metabolism on fertility but they need to be confirmed by other studies. In the meantime, as lower intakes of refined starch are associated with reduced risks of major chronic diseases (Liu et al. 2000; Oh et al. 2005; Schulze et al. 2004), reducing intakes of carbohydrates from these sources is sensible for women attempting to become pregnant as it may also improve fertility.

Acknowledgments

Financial Support:

The work reported in this manuscript was supported by CA50385, the main Nurses’ Health Study II grant, by the training grant T32 DK-007703 and by the Yerby Postdoctoral Fellowship Program.

The Nurses Health Study II is supported for other specific projects by the following NIH grants: CA55075, CA67262, AG/CA14742, CA67883, CA65725, DK52866, HL64108, HL03804.

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