May Underdiagnosed Nutrition Imbalances Be Responsible for a Portion of So-Called Unexplained Infertility? From Diagnosis to Potential Treatment Options

Abstract

The aim of the study was to investigate whether women affected by unexplained infertility may have undiagnosed dietary imbalances which negatively affect fertility. Secondarily, we investigated whether varying degrees of nutritional abnormalities may benefit from different periconceptional dietary supplementations, evaluating the most effective intervention in improving pregnancy rate after in vitro fertilization (IVF). We conducted a survey on 2 cohorts of patients (group A: unexplained infertility and group B: healthy first trimester spontaneous pregnancies) with the scope of investigating and comparing their dietary status discriminating women without dietary abnormalities (cohort 1) from those with abnormalities exclusively in micronutrient intake (cohort 2) or combined abnormalities in both micronutrient and macronutrient intake and associated obesity (cohort 3). All women included in group A were offered the opportunity to receive a prescription for one of the 3 designated daily dietary supplementation schemes (subgroups A1, A2, and A3) which were to be implemented in the 3 months immediately prior to beginning IVF treatment. When compared with fertile women, patients having unexplained infertility showed significant abnormalities in dietary habits. These differences ranged from a minimal imbalance in micronutrient intake (potentially avoidable with dietary supplementation) to severe combined macronutrient and micronutrient imbalance frequently associated with obesity (partially amendable by inositol supplementation and frequently requiring long-term dietary reeducation before establishment of fertility). Nutritional investigation and treatment may explain and resolve a portion of cases of unexplained infertility, improving the outcome of IVF treatment and, with minimal imbalances, likely restore spontaneous fertility.

Keywords

unexplained infertility dietary imbalance assisted reproduction dietary supplementation obesity

Introduction

In the majority of developed countries, social trends have led many women to postpone marriage, childbearing, and first pregnancy due to career priorities, advanced education, contraception, artificial abortion, and financial concerns, causing an increase in childbearing in the third and fourth decades of life.¹ According to this observation, we may affirm that the percentage of couples having infertility which turn to assisted reproduction techniques (ARTs) will increase in the near future, confirming the current trend.

When stratified according to pathogenesis, the absence of a definable cause, also known as unexplained or idiopathic infertility, is observed in 15% to 30% of infertile couples.² The veracity of the term “unexplained infertility” has been challenged by many clinicians and researchers since they emphasize that the assignment of this title to an infertile couple is much dependent on the quantity, quality, and nature of the applied diagnostic tests.³

Since the inability to define the causes of couples’ infertility does not inevitably mean that there is no underlying cause for the disorder, extensive research should be conducted on other possible causes of failed conception rather than, as frequently occurs, immediately recommending assisted reproductive technology (ART) as a fallacious solution. Despite the current situation in which ART treatment is dominant in infertility care, when we consider the cohort of patients with a diagnosis of unexplained infertility, available data clearly show that ARTs will not increase the chance of pregnancy while remaining expensive, time consuming, and risky therapies.⁴

Although it is universally accepted that nutrition and lifestyle factors, such as diet, exercise, and obesity, affect reproductive performance, preconceptional nutritional care is often inadequate. Nonetheless, during assisted reproduction, there are few structured clinical initiatives aimed at offering couples preconceptional counseling.⁵ Screening is the first step in counseling. Although screening for lifestyle factors, such as smoking and obesity, is relatively straightforward, nutritional assessment is more complex. Despite the large number of questionnaire tools used to address nutrition (designed to measure diet and nutrient intake as accurately as possible), most of these tools are not suitable for clinical practice due to time and financial constraints.⁶ Furthermore, ensuring that an adequate nutritional status is attained through counseling is difficult and complicated by various factors such as nutrient interactions, ethnic variations in nutritional habits, day-to-day intake variations, misreporting, and method of evaluation. It is also impossible to ignore the fact that the recommended daily intake for specific nutrients is still debated.⁶

Investigation into a potential association between the above-mentioned factors with unexplained infertility represents a great challenge in clinical practice. The evaluation of dietary status in patients with “apparent” idiopathic infertility may potentially solve the diagnostic dilemma and restore spontaneous fertility or at least improve the success rate of assisted procedures.

Propelled by the expectation that dietary investigation may represent one of the most cost-effective strategies capable of improving the level of care offered to patients affected by unexplained infertility while allowing significant financial savings by avoiding ineffective ART treatments, we conducted this study with the aim of investigating whether asymptomatic dietary imbalances negatively affecting fertility may be present in such women. Secondarily, we investigated whether different degrees of abnormalities in macronutrient and micronutrient intake may benefit from distinct periconceptional nonpharmacological dietary supplementations, evaluating the most effective protocol in improving pregnancy rate in in vitro fertilization (IVF).

Materials and Methods

We conducted an epidemiological survey followed by a prospective interventional study on Italian women affected by unexplained infertility and scheduled for fresh nondonor IVF treatment at the Assisted Reproduction Unit of Gynecology and Obstetrics Clinic, Department of Women’s and Children’s Health, University of Padua between January 2012 and October 2014. This study was conducted in collaboration with a team of dieticians from the Dietetics Unit of the University of Padua.

Study Design: First and Second Step

The first step of the study was to perform an epidemiological survey on separate cohorts of patients (group A and group B) in order to investigate their dietary status. In detail, group A (case group) was composed of healthy Italian women aged between 18 and 40 years affected by unexplained infertility referred to the IVF unit for assisted reproduction. Group B (control group) was comprised of healthy nulliparous women in the first trimester of a singleton pregnancy (spontaneous onset) who voluntarily participated in the survey. Women admitted to the control group were recruited from the Obstetrics Clinic of the Department of Women’s and Children’s Health, University of Padua during the same interval time of recruitment for group A patients. Cases and controls were matched for age. We differentiated women on the basis of an absence of dietary abnormalities (cohort 1) as opposed to those having abnormalities exclusively in micronutrient intake (cohort 2) or those having combined abnormalities in both micronutrient and macronutrient intake and associated with obesity (cohort 3).

In the second phase of the study, regardless of the outcome of the dietary investigation, all women included in group A were offered the opportunity to receive a prescription for one of the following schemes comprised a 3-month daily dietary supplementation given immediately prior to commencing IVF treatment:

supplementation with iron (7 mg) and folic acid (400 μg; subgroup A1);

supplementation with iron (30 mg) and folic acid (400 μg) plus lactoferrin (75 mg), fluorine (1 mg), docosahexaenoic acid (150 mg), zinc (7.5 mg), copper (1.2 mg), calcium (400 mg), magnesium (100 mg), manganese (2.5 mg), iodine (150 μg) vitamin D3 (10 μg), vitamin B6 (1.4 mg), vitamin B12 (2.5 μg), and vitamin C (60 mg; subgroup A2); and

supplementation similar to the subgroup A2 plus inositol (myo-inositol 550 mg, d-chiro-inositol 13.8 mg; subgroup A3).

Inclusion and Exclusion Criteria

Regarding group A patients, we considered as exclusion the following criteria: history of smoking in the previous 12 months, deep endometriosis with elevated CA125 serum values,⁷ karyotype abnormalities, acquired or inherited thrombophilia and immunological disorders, history of type 1 or 2 diabetes,⁸ previous chemotherapy and/or radiotherapy for neoplasia, and untreated uterine diseases (such as endometrial polyps, submucous myomas, intrauterine synechiae, and/or uterine septus).^9,10 We also excluded patients who received low-dose aspirin during treatment (case group)¹¹ or calcium supplementation during pregnancy (control group).¹²

Regarding group B patients, we considered only spontaneous physiological uncomplicated pregnancies up until the 12th week of gestation. We excluded patients with a personal history of restrictive dieting (gluten-free, vegan, or vegetarian diet) or who have experienced a considerable variation in body weight (20% or above) in the 6 months prior to enrollment. Moreover, we excluded patients who did not speak and understand Italian language since both personal history and investigation into dietary status were performed in the Italian language.

Interventions

At recruitment, patients of both groups underwent dietary evaluation through collection of personal history and dietary status using Recall 24-hour questionnaire. Simultaneously, all patients received an ad hoc “1-week dietary diary” to be completed in the following week and underwent body weight and height measurements (participants wore light clothing and no shoes). Waist circumference was measured at the midpoint between the lowest rib and the iliac crest during minimal respiration.

For both groups, abnormalities in macronutrient and/or micronutrient intake were ascertained by validated electronic software (METADIETA; Meteda SRL, San Benedetto Del Tronto, Italy) for dietary evaluation by comparison with standard values reported as normal for the Italian population—Livelli di Assunzione di Riferimento di Nutrienti ed energia per la popolazione italiana (LARN) (standard measure discriminating for sex, age, weight, physical activities, and physiological status such as pregnancy, breastfeeding, and menopause).¹³

Patients in group A participating in the second phase of the study, following one of the above-mentioned 3-month dietary supplementation schemes, were scheduled for IVF treatments. After ovarian reserve assessment,¹ patients received standard controlled ovarian stimulation (COS) by long protocol using the gonadotropin-releasing hormone agonist (Decapeptyl; Ipsen, Paris, France) 0.1 mg daily beginning in the midluteal phase of the previous cycle for hypothalamic inhibition and recombinant follicle-stimulating hormone (rFSH, Gonal-F; Merck Serono, Geneva, Switzerland) at a starting dose of 225 IU daily, administered after confirmation of correct hypothalamic inhibition. Subsequent gonadotropin dose adjustments were performed by clinicians guided by biochemical and sonographical features of ovarian response beginning from stimulation day 5. When an adequate ovarian response was achieved (at least 3 follicles measuring over 16 mm in diameter or at least 1 follicle larger than 18 mm), we proceeded with ovulation induction by recombinant human chorionic gonadotropin (hCG) 250 μg (Ovitrelle; Merck Serono). Oocyte retrieval took place 35 hours after hCG administration. All oocytes were fertilized by Fertilizzazione In Vitro con Embryo Transfer/intracytoplasmic sperm injection technique depending on semen parameters. Double embryo transfer was performed 3 days later in all patients (after morphological assessment for quality).¹⁴ All patients received high-dose progesterone supplementation (600 mg vaginally and 100 mg intramuscularly per day) for luteal phase support until β-hCG assay was performed 14 days after embryo transfer.¹⁵ Clinical pregnancy was confirmed by positive serum β-hCG test 2 weeks after embryo transfer, and ongoing pregnancy was defined as an uncomplicated pregnancy over 12 gestational weeks.

Data Collection

All information regarding dietary status was stored in an electronic database using an individualized alphanumeric code assigned on recruitment. The same code was used for data collection in a different electronic database in which gynecologists reported clinical outcomes (see subsequently). Both databases were matched at the end of the trial by a statistician not involved in the clinical management. This was to maintain dieticians blinded to clinical outcomes and vice versa.

Data regarding personal history, general features, 24-hour recall, and 1-week dietary diary were analyzed using METADIETA software. For all patients, we collected data regarding general features (age, height, weight, body mass index [BMI], waist circumference, daily physical activity, and occupational status).

To overcome potential differences between group A and group B, which may emerge due to the entry of general features in the software (according to the suggestion of LARN¹³), we calculated the recommended average daily energy intake (estimated as kcal) for each individual woman according to basal metabolism and physical activities. For each patient, in both group A and group B, we calculated the daily energy intake comparing them with the estimated 50th, 25th and 75th centiles of the recommended energy intake charts (considered appropriate). Furthermore, we calculated Δ variation in calories introduced compared with what is estimated to be adequate, considering as minor dietary alterations in calorie intakes that fall within the 25th and the 75th centiles and as major those which fall above or below this range.

For all patients, we evaluated the number of meals and snacks per day and the relative percentage of calorie distribution as well as the percentage of calories produced by each class of macronutrients (carbohydrates considering the percentage of simple sugars, lipids considering the percentage of n-6 and n-3 polyunsaturated fatty acids, and proteins). Considering micronutrients, we estimated the daily intake and compared this value to the suggested standard for the population.

Solely for women belonging to group A and participating in phase 2 of the study, we collected data regarding number of years of infertility, antral follicle count (AFC) and anti-Mullerian hormone (AMH) serum value, length of ovarian stimulation (days), total dose of rFSH administered (IU), total number of Metaphase II (MII) oocytes collected, number of embryos obtained, and number and percentage of pregnancy. For group B, we considered data regarding gestational age at enrollment.

End Points

Primary end point of the study was to compare group A versus group B in terms of nutritional status. Secondary end point was to evaluate the outcome of IVF in the different subgroups of dietary supplementation schemes stratifying data according to initial dietary abnormalities.

Statistical Analysis

Statistical analysis was performed by SPSS software (Chicago, Illinois) for Windows version 19, applying parametric and nonparametric tests when appropriate. The Kolmogorov-Smirnov test was used to assess the normality of distribution. Continuous variables were expressed as absolute numbers or average ± standard deviation and analyzed by Student t test. Multiple comparisons within subgroups were made by analysis of variance test (using the Bonferroni test as post hoc test). Categorical variables were expressed as percentages and analyzed through the χ² test or the Fisher exact test when appropriate. Statistical significance was defined as P values < .05.

Ethical Issues

All women involved in the study were considered eligible only if they had voluntarily agreed to the aim of the study and, after adequate counseling, had given written signed consent and agreed to the use of their data in respect of Italian privacy law.

Our study was defined exempt from institutional review board since it was not randomized (patients blinded to results of dietary investigation) and all women who agreed to the second phase of the study spontaneously chose one of the 3 dietary supplementation schemes routinely prescribed in our unit in the event of unexplained infertility (after adequate counseling regarding the lack of evidence concerning the efficacy).

Results

In the interval time considered, 257 patients were deemed eligible and were enrolled into the study. Among these, 198 women were assigned to group A and 59 to group B. The 2 groups (group A vs group B) were homogeneous for age (35.02 ± 3.3 vs 34.76 ± 4.5; P = not significant [NS]) and height (1.67 ± 0.08 vs 1.65 ± 0.9; P = NS), whereas differences were found in terms of weight (79.34 ± 12.59 vs 66.44 ± 8.89; P < .001), BMI (28.26 ± 3.39 vs 24.28 ± 2.18; P < .001), and waist circumference (81.12 ± 10.46 vs 65.03 ± 6.62; P < .001). In group B, mean value of gestational age was 8.4 ± 2.7 weeks.

In group A, 33.3% (66 patients) of women reported having regular daily physical activity, whereas in group B, 69.5% (41 pregnant women) reported such activity (P < .001). Regarding occupational status, 16.2% of group A (32 women) and 20.3% of group B (12 women) declared employment (P = NS).

Despite the distribution of adequate energy intake for the reference population was comparable in both groups (r2 = .159; P = NS; Figure 1), the average value of calories introduced daily was significantly different when we compared group A versus group B (2688.64 ± 580.78 vs 2115.44 ± 326.63; P < .001; Figure 2). Additionally, the 2 groups showed significant differences in terms of Δ variation in calories introduced compared to the recommended values (388.61 ± 531.03 vs 56.84 ± 239.47; P < .001; Figure 3).

Figure 1.

Group A versus group B: estimation of 50th, 25th, and 75th centiles of energy intake (reported as kcal) according to basal metabolism and physical activities (estimated by the software according to patients features and personal history).

Figure 2.

Group A versus group B: calories introduced daily with diet and relative distribution with respect to the estimated 25th and 75th centile.

Figure 3.

Group A versus group B: Δ variation in calories introduced with respect to those estimated adequate.

When we compared group A versus group B, we found that 43% (86 women) versus 25.4% (15 women) had minor dietary alterations (P < .05), whereas 30.8% (61 women, of which 55 with higher and 6 with lower intake) versus 3.4% (2 women) had severe alterations (P < .001). Interestingly, although the 2 groups were comparable in terms of number of meals and snacks per day (2.96 ± 0.83 vs 3.20 ± 0.97, with 43.9% vs 35.6%, respectively, having at least 1 snack during day; P = NS), the distribution of calories (in percentage) resulted significantly different: 12.87 ± 5.16 versus 18.02 ± 3.38 at breakfast, 42.71 ± 6.03 versus 38.69 ± 5.90 at lunch, 36.73 ± 8.24 versus 30.41 ± 7.80 at dinner, and 7.69 ± 9.10 versus 12.88 ± 9.96 at snack(s) (P < .05). Additionally, considering the recommended daily intake of macronutrients and micronutrients, significant differences were found in both groups (Table 1).

Table 1.

Macronutrients and Micronutrients Intake: Comparison Between Group A Versus Group B in Relation to the Reference Standard of Recommended Intake for Italian Population.

	Group	Recommended Intake	Mean Intake	SD	P
Macronutrients
Carbohydrates	A	45%-60%	42.72	11.23	<.01
Carbohydrates	B	45%-60%	52.51	6.22	<.01
Of which simple sugars	A	<15%	18.48	4.41	<.001
Of which simple sugars	B	<15%	12.42	3.08	<.001
Lipids	A	20%-35%	33.81	5.80	<.001
Lipids	B	20%-35%	23.95	4.04	<.001
Of which PUFA N-6	A	4%-8%	2.70	1.58	<.001
Of which PUFA N-6	B	4%-8%	6.08	1.45	<.001
Of which PUFA N-3	A	0.5%-2%	0.72	0.45	<.001
Of which PUFA N-3	B	0.5%-2%	1.21	0.44	<.001
Proteins	A	10%-35%	23.47	14.38	NS
Proteins	B	10%-35%	23.54	7.58	NS
Micronutrients: vitamins
C (ascorbic acid), mg	A	60	47.68	7.77	<.001
C (ascorbic acid), mg	B	60	71.22	6.30	<.001
B1 (thiamine), mg	A	0.9	0.80	0.14	<.05
B1 (thiamine), mg	B	0.9	1.06	0.15	<.05
B2 (riboflavin), mg	A	1.1	1.03	0.15	<.05
B2 (riboflavin), mg	B	1.1	1.32	0.15	<.05
B3 (niacin), mg	A	14	11.13	1.995	<.001
B3 (niacin), mg	B	14	17.69	1.653	<.001
B6 (pyridoxine), mg	A	1.1	1.12	0.17	NS
B6 (pyridoxine), mg	B	1.1	1.37	0.16	NS
B12 (cobalamin), mg	A	2.0	1.80	0.23	<.05
B12 (cobalamin), mg	B	2.0	2.27	0.21	<.05
Folic acid, μg	A	320	260.85	43.32	<.01
Folic acid, μg	B	320	313.07	11.26	<.01
A (carotenoids), μg	A	400	349.57	40.83	<.001
A (carotenoids), μg	B	400	481.31	38.35	<.001
D (cholecalciferol), μg	A	10	7.56	1.546	<.05
D (cholecalciferol), μg	B	10	9.25	1.238	<.05
Micronutrients: oligoelements
Calcium, mg	A	1000	857.05	197.695	<.01
Calcium, mg	B	1000	1015.76	81.559	<.01
Sodium, g	A	1.5	1.56	0.02	NS
Sodium, g	B	1.5	1.48	0.07	NS
Potassium, g	A	3.9	3.58	0.28	NS
Potassium, g	B	3.9	3.80	0.18	NS
Phosphorus, mg	A	700	650.20	70.90	NS
Phosphorus, mg	B	700	675.42	39.59	NS
Iron, mg	A	18	14.46	3.95	<.001
Iron, mg	B	18	19.14	1.42	<.001
Magnesium, mg	A	240	217.90	40.99	<.05
Magnesium, mg	B	240	244.22	16.85	<.05
Zinc, mg	A	8	6.67	1.55	<.01
Zinc, mg	B	8	8.15	0.68	<.01
Copper, mg	A	0.9	1.22	0.16	<.001
Copper, mg	B	0.9	0.89	0.08	<.001
Selenium, μg	A	55	81.90	8.65	<.001
Selenium, μg	B	55	55.53	7.62	<.001

Abbreviations: NS, not significant; PUFA, polyunsaturated fatty acids; SD, standard deviation.

Regarding macronutrients, group A as opposed to group B women showed a significantly lower intake of carbohydrates (P < .01) with a higher percentage of simple sugars (P < .001). Otherwise, group A women showed that one-third of their dietary calories derived from the metabolism of lipids: when compared with group B, group A showed a significantly higher lipid intake (P < .001) with a significantly lower percentage of n-6 and n-3 polyunsaturated fatty acid (P < .001). No differences were found between groups regarding the percentage of protein intake.

Regarding vitamin intake, differences between the 2 groups were found regarding vitamin C (ascorbic acid; P < .001), vitamin B1 (thiamine; P < .05), vitamin B2 (riboflavin; P < .05), vitamin B3 (niacin; P < .001), vitamin B12 (cobalamin; P < .05), folic acid (P < .01), vitamin A (carotenoids; P < .001), and vitamin D (cholecalciferol; P < .05), whereas no differences were found in terms of vitamin B6 (pyridoxine) intake. Interestingly, we found that vitamin intake in group A women was below the recommended daily dose for all the considered vitamins with the exception of vitamin B6, whereas both groups showed inadequate intake of folic acid and vitamin D.

Regarding the intake of trace minerals, group A showed a significantly lower intake of calcium (P < .01), iron (P < .001), magnesium (P < .05), and zinc (P < .01), whereas a significantly higher intake of copper (P < .001) and selenium (P < .001) was found when compared with group B women. No differences were found between groups for sodium, potassium, and phosphorus intake. All detailed data are reported in Table 1.

Among the 198 women assigned to group A, only 122 were eligible for inclusion in the second phase of the study. In detail, 41 women were admitted to subgroup A1, 43 to subgroup A2, and 38 to subgroup A3. The comparison among subgroups showed no differences in terms of years of infertility, AFC, and AMH serum value (P = NS). However, further stratification of subgroups according to dietary evaluation (cohort 1, cohort 2, and cohort 3) showed significant differences within groups. We found that in all subgroups, more than 40% of patients were affected by micronutrient abnormalities (cohort 2), followed by more severe dietary imbalances (abnormalities in macronutrients, micronutrients, and obesity: cohort 3), whereas only about 25% of women are free of dietary abnormalities (cohort 1; P < .05). We found within all subgroups a significant association between years of infertility and severity of dietary abnormalities (P < .001).

The comparison of subgroups showed significant differences in terms of length of stimulation (9.51 ± 1.5 vs 8.72 ± 1.4 vs 9.05 ± 1.1; P < .05) and, consequently, in terms of total dose of rFSH administered (2619.59 ± 436.57 vs 2374.16 ± 374.1 vs 2494.05 ± 371.9; P < .05). When subgroups were stratified according to dietary abnormalities, we found an increasing trend in the length of stimulation and the rFSH administered in subgroup A1 from cohort 1 to cohort 3 (P < .001). Differently, in subgroup A2 and subgroup A3, differences were found only between cohort 3 and the remaining 2 cohorts, although with an opposite trend: subgroup A2 patients with severe dietary abnormalities showed the worst trend, whereas the best was observed in subgroup A3 (P < .001).

Considering the outcome of controlled ovarian stimulation, we found significant differences among subgroups in terms of MII oocytes (7.78 ± 2.5 vs 8.98 ± 3.6 vs 7.32 ± 2.1; P < .05) and embryos (6.15 ± 2.6 vs 7.58 ± 3.6 vs 5.26 ± 1.7; P < .01). Considering MII oocytes, after the stratification of data among dietary cohorts, we found that patients without abnormalities showed significantly worse outcomes in subgroup A3 when compared with subgroup A1 and subgroup A2 (P < .001); patients having exclusively micronutrient abnormalities showed the best outcome in subgroup A2 (comparable with patients without dietary abnormalities), whereas both subgroup A1 and subgroup A3 showed worse outcomes (P < .001). Finally, patients with severe dietary abnormalities had poor outcomes in all subgroups, although in subgroup A3, the outcome was better than in both subgroup A1 and subgroup A2 (P < .001).

Regarding the number of embryos, the trend was similar to that observed for MII oocytes for all subgroups and cohorts of patients. Regarding pregnancy rate, statistical differences were found after comparison between groups (19.5% vs 25.5% vs 13.1%; P < .05). Interestingly, when patients were stratified according to dietary cohort, we found that the best outcomes were obtained in patients without dietary abnormalities only in subgroup A1 and subgroup A2 (P < .01). Regarding patients having abnormalities exclusively in micronutrient intake, we observed a good outcome in subgroup A2 (comparable to one of the patients without dietary abnormalities), an intermediate outcome in subgroup A1, and a poor outcome in subgroup A3 (P < .05). Regarding the cohort of patients affected by severe dietary abnormalities, the outcome was poor in all subgroups, although a significant trend was observed: 0% in subgroup A1 versus 7.1% in subgroup A2 versus 15.3% in subgroup A3 (P < .05). All data are summarized in Table 2.

Table 2.

General Features and IVF Outcomes: Comparison Among All Subgroups Stratified for Dietitian Status (Cohort 1 Versus Cohort 2 Versus Cohort 3).

N = 122	Subgroup A1 (41)				Subgroup A2 (43)				Subgroup A3 (38)				P (All Subgroups)
Dietary Abnormality	Cohort 1 (None)	Cohort 2 (Micronutrients Only)	Cohort 3 (Macronutrients + Micronutrients + Obesity)	P (Subcohorts of Subgroup A1)	Cohort 1 (None)	Cohort 2 (Micronutrients Only)	Cohort 3 (Macronutrients + Micronutrients + Obesity)	P (Subcohorts of Subgroup A2)	Cohort 1 (None)	Cohort 2 (Micronutrients Only)	Cohort 3 (Macronutrients + Micronutrients + Obesity)	P (Subcohorts of Subgroup A3)	P (All Subgroups)
Number of patients (%)	11 (26.8)	17 (41.4)	13 (31.8)	<.05	10 (23.3%)	19 (44.2)	14 (32.5)	<.05	10 (26.3)	15 (39.35)	13 (34.2)	<.05	NS
Years of infertility	1.91 ± 0.8	3.12 ± 0.9	4.12 ± 1.1	<.001	1.90 ± 0.7	2.89 ± 0.8	4.02 ± 1.3	<.001	2.40 ± 0.6	3.07 ± 0.7	3.23 ± 1.4	<.001	NS
AFC	12.64 ± 3.1	12.88 ± 2.5	12.92 ± 3.4	NS	13.10 ± 2.5	13.16 ± 3.8	12.43 ± 3.0	NS	11.50 ± 2.9	12.07 ± 3.1	12.23 ± 2.7	NS	NS
AMH (ng/ml)	1.82 ± 0.4	1.84 ± 0.5	1.93 ± 0.1	NS	1.82 ± 0.3	1.91 ± 0.5	1.87 ± 0.6	NS	1.83 ± 0.3	1.97 ± 0.4	1.94 ± 0.2	NS	NS
Length of stimulation, days	7.82 ± 0.6	9.71 ± 1.1	10.69 ± 1.4	<.001	7.70 ± 0.6	8.05 ± 0.9	10.36 ± 0.9	<.001	9.40 ± 0.9	9.47 ± 1.2	8.31 ± 0.7	<.05	<.05
Total rFSH, IU	2198.91 ± 180.8	2652.65 ± 404.9	2932.31 ± 346.0	<.001	2116.80 ± 252.8	2209.32 ± 275.7	2781.71 ± 182.0	<.001	2537.90 ± 354.0	2638.47 ± 421.3	2293.69 ± 232.9	<.05	<.05
Total number of oocytes	11.27 ± 2.9	10.06 ± 2.0	9.77 ± 2.4	<.05	12.20 ± 2.1	12.11 ± 3.8	8.57 ± 1.9	<.01	10.60 ± 2.8	9.47 ± 2.6	10.62 ± 2.2	NS	NS
Number of MII oocytes	10.18 ± 2.8	7.65 ± 1.8	5.92 ± 1.4	<.001	11.0 ± 1.7	10.68 ± 3.5	5.21 ± 1.1	<.001	6.40 ± 1.6	7.40 ± 2.3	7.92 ± 2.1	<.05	<.05
Number of embryos	8.18 ± 2.8	5.82 ± 1.3	4.00 ± 0.9	<.001	8.90 ± 1.9	8.37 ± 3.1	3.50 ± 0.6	<.001	4.30 ± 1.2	5.27 ± 1.8	6.00 ± 1.7	<.01	<.01
Pregnancy (%)	*4 (36.3%)	**4 (23.5%)	***0 (0%)	* versus *: <.001; versus **: <.001; versus **: <.01	*3 (30%)	**7 (36.8%)	***1 (7.1%)	* versus *: <.001; versus **: <.001; versus **: NS	*1 (10%)	**2 (13.3%)	***2 (15.3%)	* versus *: <.01; versus **: <.05; versus **: NS	<.05

Abbreviations: AFC, antral follicle count; AMH, anti-Mullerian hormone; IVF, in vitro fertilization; NS, not significant; rFSH, recombinant follicle-stimulating hormone. *, **, *** refer to signifcance between the cohorts 1,2,3 in term of pregnancy rate (%).

Discussion

It has been accepted that food customs are closely associated with the quality of life in both men’s and women’s reproductive life.¹⁶ The Western pattern diet, also called Western dietary pattern or the meat-sweet diet, is a dietary habit chosen by many people in the developed countries and increasingly in the developing countries. It is characterized by high intakes of red meat, sugary desserts, high-fat foods, and refined grains and typically contains high-fat dairy products, high-sugar drinks, and higher intakes of processed meat. The Western pattern diet is composed of foods that are rich in fat and sugar. Several important vitamins and minerals that are essential for good health are deficient in the Western pattern diet.¹⁷

In addition to diet, lifestyle factors, which are behaviors and circumstances that are, or were once, modifiable, can be a contributing factor to subfertility/infertility. In fact, our results clearly showed that women affected by unexplained infertility, when compared with those with spontaneous pregnancy, showed higher weight, BMI, waist circumference, and, as expected, a lower prevalence of regular daily physical activity.

The deleterious effects of overweight, obesity, and absent physical activity on fertility are well confirmed,¹⁸ and emerging results emphasize the fact that these factors may contribute to poor outcomes after ART.¹⁹ Our data regarding the cohort of obese women undergoing IVF treatment confirmed the poor outcomes reported in the literature independent of dietary supplementation and with minimal beneficial effects seen only after supplementation with inositol. The observed improvement linked to inositol supplementation in obese women with insulin resistance (such as a large part of women with polycystic ovarian disease) is supported by the literature.²⁰ Furthermore, our data are in line with the emerging hypothesis suggesting potential adverse effects of inositol supplementation in the absence of obesity and insulin resistance, showing worse outcomes in nonobese women receiving inositol compared with untreated controls.²¹

Diet-induced obesity is linked to an inappropriate calorie intake related to basal metabolism and physical activity. In our survey, conducted on an Italian cohort, we compared the dietary information collected with national population reference values for energy and nutrient intake¹³ and found that women affected by unexplained infertility compared to pregnant participants had a significantly higher incidence of both minor (43% vs 25%) and major dietary alterations (30% vs 3%), including total energy intake and its distribution throughout the day, and macronutrient percentage contribution.

Infertile patients had a lower intake of carbohydrates, although these were composed of a higher percentage of simple sugars, and a higher intake of fatty acids, although with a significantly lower percentage of unsaturated components compared to the controls. If, on one hand, the adverse effects of simple sugar intake on fertility are well confirmed by a great deal of literature considering n women affected with polycystic ovarian disease and diabetic disease,^8,22,23 on the other hand, our data confirm evidence suggesting the potential role of dietary trans fatty acids in increasing the risk of ovulatory infertility when they replace carbohydrates or unsaturated fats commonly found in vegetable oils.²⁴ Certainly, it may be extremely difficult to explain idiopathic infertility exclusively by the analysis of single nutrients as single nutrient dietary imbalances are unusual. For example, in our study patients affected with infertility, we observed imbalances in all evaluated oligoelements and in large number of vitamins. This evidence assumes great importance when, for example, unsaturated fatty acid metabolism is worsened by a low iron intake^24,25 and the glucid metabolism may be negatively affected by imbalances in certain oligoelements such as magnesium and zinc.²⁶

Unfortunately, we are not able to explain the impact that an increased intake of copper and selenium had in our cohort of infertile women. Evidence suggesting the role of copper, zinc, and selenium in the destruction of free radicals through cascading enzyme systems (by superoxide dismutase in the presence of copper and zinc cofactors and by selenium glutathione peroxidase in the presence of selenium) leads to hypothesize that both the excess and defect in intake may have a role in affecting spontaneous fertility.^27,28

Regarding vitamins, we found that both the fertile and infertile cohort had inadequate intakes of folic acid and vitamin D. While avoiding digression concerning each vitamin, a good deal of literature is available regarding the role of vitamins in infertility and specifically in unexplained infertility.^29
–31

Our data regarding IVF outcomes after dietary supplementation unequivocally demonstrated that in patients without dietary abnormalities, iron and folic supplementation alone is sufficient to achieve a success rate comparable with standard IVF care, whereas additional micronutrient (vitamins and trace minerals) supplementation did not yield a further improvement in ART success. Moreover, patients with minimal dietary imbalance significantly benefit from micronutrient supplementation, achieving a success rate of patients without nutritional imbalances, whereas in cases of severe dietary abnormalities, this supplementation was incapable of improving ART outcome. As mentioned previously, the subcohort of women with severe nutritional imbalances and obesity showed minimal benefits from inositol supplementation, despite an overall poor outcome. The suspicion of potential negative effects of inositol supplementation in nonobese women originated by our data leads us to suggest investigation into dietary status rather than an empirical prescription of dietary supplementation in the absence of a proven necessity or in cases in which it may be deemed potentially insufficient due to the severity of the dietary deficiency.

Since clinicians involved in infertility care may benefit from expert support in excluding dietary abnormalities in patients affected by apparent unexplained infertility, it would be considered good medical practice to avoid empirical treatments since while they may be useful (as in the case of calcium supplementation and onset of preeclampsia),^12,32 they may also be damaging (as in aspirin supplementation during ovarian stimulation).¹¹

Finally our study, although pioneeristic and innovative both for aim and results, is not free of limitations potentially affecting the accuracy of the evidence. The relative small sample size, the absence of data regarding male dietary status, ongoing pregnancy rate, estimation of the effects of dietary supplementation on cumulative pregnancy rate, and the lack of an adequate follow-up period after dietary supplementation (to collect information regarding spontaneous restoration of fertility) lead us to recommend caution in the interpretation of our results which require further validation by large-scale prospective trials.

The exclusion of patients who did not speak and understand Italian language may potentially represent a bias since this fact did not allow us the opportunity to compare different dietary habits (due to different ethnicity). However, this very limitation has rendered our results highly accurate for the Italian population.

In conclusion, our results allowed us to postulate that when compared with fertile women, patients having unexplained infertility presented with significant abnormalities in dietary habits. These differences ranged from minimal micronutrient imbalances (potentially avoidable with dietary supplementation) to severe combined macronutrient and micronutrient imbalances frequently associated with obesity (partially amendable by inositol supplementation and frequently requiring long-term dietary education before establishment of fertility). Despite the above-mentioned limitations and difficulties encountered in introducing nutritional screening and counseling into infertility care, we may hypothesize that nutritional investigation and treatment may explain and solve a percentage of cases of infertility currently defined as infertility “without apparent cause” and frequently subjected to empiric “over or under treatment.”

Footnotes

Acknowledgements

The authors acknowledge all the equip of Obstetrics and Gynecology Clinic of Padua University for the precious collaboration in the development of the study.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

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