Association between healthy beverage index and odds of diminished ovarian reserve in a case-control study.

This case-control study involved 370 women, comprising 120 participants with DOR and 250 age- and BMI-matched controls with normal ovarian reserve. The participants aged 18 to 45 years and had a BMI between 20 and 35 kg/m². They were recruited using purposive sampling method from Shahid-Beheshti Women’s Hospital, affiliated with Isfahan University of Medical Sciences in Isfahan, Iran. The diagnosis of DOR was determined by an expert gynecologist (H.GT). Women with either an AMH level of ≤ 0.7 ng/mL, an antral follicle count (AFC) of ≤ 4 across both ovaries, or both criteria met, were classified as patients with DOR 22 . Women with normal ovarian reserve were randomly selected from the same infertility centers. The sample size calculation was meticulously carried out using an appropriate formula, ensuring statistical robustness. The key parameters included a 95% confidence interval, 90% power, a 30% expected exposure ratio in the control group, and an assumed odds ratio of 2. This resulted in a total of 120 participants in the DOR group and 250 participants in the control group 9 . The study carefully established exclusion criteria to ensure the accuracy of its findings regarding DOR. Women were considered ineligible if they had: (1) a medical history of conditions like chemotherapy, radiotherapy, premature ovarian failure, infertility treatment, ovarian surgery, endometriosis, endocrine disorders (e.g., polycystic ovary syndrome, thyroid disorders, diabetes, Cushing’s syndrome, hyperprolactinemia, androgenic disorders), or major chronic diseases (e.g., gastrointestinal diseases, cancer, cardiovascular diseases, liver or kidney diseases, mood disorders), (2) current or recent (within the past three months) use of oral contraceptive medications, multivitamin mineral supplements, hormone treatments, or weight-loss interventions, (3) specific dietary or physical activity programs, and (4) were current smokers or alcohol consumers. Moreover, women who answered less than 35 items of food frequency questionnaire (FFQ) and had energy intake outside the range of 500 to 3500 kcal per day were excluded from the study as well. This survey was carried out in accordance with the Declaration of Helsinki 23 and all participants provided a written informed consent prior to participation in this research. The local Ethics Committee of Isfahan University of Medical Sciences approved the study protocol (IR.ARI.MUI.REC.1401.297). In this study, dietary intake over the past year was meticulously assessed using a validated 80-item FFQ 24 . Participants were asked to report their average intake of each food item on a daily, weekly, monthly and yearly basis. Portion sizes were standardized using Iranian household measurements and converted into grams. Daily food intake for each item was calculated by multiplying the reported frequency of consumption by the portion size. These data was then analyzed using Nutritionist IV software (First Databank Inc., San Bruno, CA), with some adjustments made to align with Iranian food composition 25 in order to estimate daily energy and nutrient intake. A modified version of the method suggested by Dufey and Davy was used to evaluate HBI 19 . All components of the HBI and their scoring system applied in the analysis are detailed in Table 1 . The final HBI score ranges from 0 to 95, with higher scores indicating better adherence to beverage standards. Table 1 Components of the healthy beverage index (HBI) and their scoring system as applied in the current analysis. Component Description Scores Water 20% of fluid requirement ≤ 15 No water consumption 0  0 and < 15 based on reported intake Low fat milk < 2% fat or fat-free milk: 0–16% of fluid requirements. 5 Diet drinks Artificially-sweetened beverages: 0–16% of fluid requirements 5 Natural fruit juice Fruit and vegetable juices: 0–8% of fluid requirements 5 Full fat milk 0% of fluid requirements coming from ≥ 2.0% fat milk, sweetened milks, and soy beverage 5 Unsweetened coffee and tea Unsweetened coffee and tea: 0–40% of fluid requirements 5 Alcohol a From 0–1 standard glass/day for women and from 0 to 2 standard glasses/day for men 5 Sugar-sweetened beverages 0–8% of fluid requirements coming from sugar-sweetened beverages, fruit drinks, and other sweetened beverages 15 Beverage energy total b Energy from beverages < 10% of total energy intake 20 Energy from beverages ≥ 10% but  0 and < 20 based on reported intake Energy from beverages ≥ 15% of total energy intake 0 Total fluid requirement Amount of beverages (mL) consumed ≥ to fluid requirements 20 Amount of beverages (mL) consumed  0 and < 20 based on reported intake a) Alcohol was not consumed by our target group and were not included in scoring system. b) Proportional scores were assigned as follows: proportional points for water = [(mL of water × 15)/(0.20 × total fluid requirements)]; proportional points for total energy from beverages = [(15–percentage of energy from beverages) × 10/3] when energy from beverages is between and 14%; proportional points for meeting total fluid requirements = [(total fluids consumed/total fluid requirements) × 20] where total fluid. requirements = 1 mL per 1 kcal of food consumed. Components of the healthy beverage index (HBI) and their scoring system as applied in the current analysis. Artificially-sweetened beverages: 0–16% of fluid requirements From 0–1 standard glass/day for women and from 0 to 2 standard glasses/day for men Proportional points between > 0 and < 20 based on reported intake Amount of beverages (mL) consumed  0 and < 20 based on reported intake a) Alcohol was not consumed by our target group and were not included in scoring system. b) Proportional scores were assigned as follows: proportional points for water = [(mL of water × 15)/(0.20 × total fluid requirements)]; proportional points for total energy from beverages = [(15–percentage of energy from beverages) × 10/3] when energy from beverages is between and 14%; proportional points for meeting total fluid requirements = [(total fluids consumed/total fluid requirements) × 20] where total fluid. requirements = 1 mL per 1 kcal of food consumed. By performing transvaginal ultrasound by an infertility gynecologist (H.GT), AFC was calculated as the sum of antral follicles measuring 2–10 mm in both ovaries on the third day of an unstimulated menstrual cycle. Using an ELISA kit (Monobind, California, USA), AMH levels were evaluated according to the manufacture protocols. All demographic data such as age, education, occupation and obstetric history was obtained from each participant using a demographic questionnaire. A short form of the International Physical Activity Questionnaire (IPAQ) was utilized to evaluate physical activity (PA) 26 . The frequency of days and duration of physical activity were multiplied by the activity’s MET value to get the MET hour per day (MET/h/day). Using a digital Seca scale (Saca 831, Hamburg, Germany), body weight was measured with minimal clothing and without shoes to the nearest 0.1 kg. Height was measured in a standing position without shoes by a portable stadiometer (Seca, Hamburg, Germany) to the nearest 0.5 cm. To calculate BMI, weight in kilogram was divided by height squared in meter (kg/m 2 ). Waist circumference (WC) was assessed at the midpoint between the lowest rib and iliac crest and hip circumference (HC) was determined at the largest circumference around the buttocks to the nearest 0.1 cm with a tape measure. Then, waist to hip ratio (WHR) was determined by dividing measured WC in centimeter by measured HC in centimeter. A Bio-Impedance Analyzer (BIA) (Inbody 770, Inbody Co, Seoul, Korea) was used to evaluate fat mass (FM) and fat free mass (FFM). Blood pressure assessed in the sitting position and after 5 min of rest by an automated digital sphygmomanometer (Microlife Blood Pressure Monitor A100- 30, Berneck, Switzerland). All statistical analyses in this study were performed using SPSS software version 21.0 (IBM, Chicago, IL), with a significance threshold set at P-values < 0.05. To evaluate adherence to HBI score, participants were categorized into tertiles based on their HBI scores. Higher tertiles represented individuals with better adherence to beverage quality, while lower tertiles indicated poorer adherence. The one-way ANOVA test was applied for comparing quantitative variables (presented as mean ± SD) across tertiles of HBI scores, while chi-square tests were used for assessing categorical variables (expressed as percentages). The Multivariable logistic regression was used to obtain odds ratio (OR) and 95% confidence interval (CI) for DOR across tertiles of HBI. First model was adjusted for energy intake and physical activity. Further adjustment for FM was done in the second model. Participants in the first tertile of HBI were considered as reference category. Tertiles of HBI were regarded as an ordinary variable to evaluate P for trend.

This case-control study included 120 women diagnosed with DOR as the case group, and 250 age- and BMI-matched women with normal ovarian reserve as controls. Initially, 382 women were recruited for participation. However, 12 participants were excluded; 6 due to refusal to continue the study and 6 for not completing the questionnaire (Fig.  1 ). Fig. 1 Participants were matched based on age and BMI at the time of recruitment to control for caseline confounding factors. Participants were matched based on age and BMI at the time of recruitment to control for caseline confounding factors. Sociodemographic and anthropometric characteristics of case and control groups are provided in Table 2 .  In comparison with controls, women with DOR had significantly higher FM (38.47 ± 7.05 vs. 36.47 ± 8.91; P  = 0.020), WC (102.23 ± 35.95 vs. 91.70 ± 12.43; P  = 0.002), and WHR (0.90 ± 0.12 vs. 0.86 ± 0.08; P  = 0.003) and were more likely to be higher educated ( P  < 0.001). Moreover, women with DOR had significantly lower AFC (2.34 ± 1.19 vs. 9.59 ± 2.24; P  < 0.001) and AMH levels (0.56 ± 0.71 vs. 4.11 ± 1.18; P  < 0.001) than controls. Regarding occupation, controls were more likely to be housewives than women with DOR ( P  < 0.001). There were no other significant differences between cases and controls. Table 2 General characteristic of study participants . Variable Case (N=120) Control (N=250) P-value a Age (years) 33.37 ± 3.24 32.91 ± 3.15 0.196 BMI (kg/m 2 ) 29.85 ± 2.49 27.75 ± 3.45 0.235 Weight (kg) 80.96 ± 4.78 79.26 ± 8.41 0.487 FM (kg) 38.47 ± 7.05 36.47 ± 8.91 0.020 FFM (kg) 57.99 ± 11.33 60.12 ± 11.97 0.098 WC (cm) 102.23 ± 35.95 91.70 ± 12.43 0.002 HC (cm) 109.10 ± 31.59 106.10 ± 11.57 0.316 WHR 0.90 ± 0.12 0.86 ± 0.08 0.003 SBP (mmHg) 122.18 ± 12.77 123.58 ± 14.03 0.341 DBP (mmHg) 79.41 ± 11.67 81.85 ± 10.48 0.056 Physical activity (MET/h/day) 19.05 ± 4.12 18.98 ± 4.51 0.896 Socioeconomic status (%) 10 (8.30) 19 (7.60) 0.252 50 (41.70) 127 (50.80) < 0.001 60 (50.00) 104 (41.60) < 0.001 Education (%) 14 (11.70) 34 (13.60) 0.441 31 (25.80) 121 (48.40) < 0.001 75 (62.50) 95 (38.00) < 0.001 Occupation (%) 82 (68.30) 184 (73.60) 26 (21.70) 10 (4.00) 12 (10.00) 56 (22.40) Pervious Pregnancy (%) 99 (82.50) 203 (81.20) 21 (17.50) 47 (18.80) AFC count 2.34 ± 1.19 9.59 ± 2.24 AMH (ng/ml) 0.56 ± 0.71 4.11 ± 1.18 Quantitative variables are expressed as mean ± SD and qualitative variables expressed as n (%). The socioeconomic status scored was evaluated based on education level of both subjects and the family head, job of both subjects and the family head family size, home status and home type by using self-reported questionnaire.Abbreviation: BMI body mass index, DBP diastolic blood pressure, FFM fat free mass, FM fat mass, HC hip circumference, SBP systolic blood pressure, WC waist circumference, WHR waist to hip ratio, AFC antral follicle count.   P- valuesa resulted from independent t-tests for quantitative and Chi-square for qualitative variables between the two groups. General characteristic of study participants . Quantitative variables are expressed as mean ± SD and qualitative variables expressed as n (%). The socioeconomic status scored was evaluated based on education level of both subjects and the family head, job of both subjects and the family head family size, home status and home type by using self-reported questionnaire.Abbreviation: BMI body mass index, DBP diastolic blood pressure, FFM fat free mass, FM fat mass, HC hip circumference, SBP systolic blood pressure, WC waist circumference, WHR waist to hip ratio, AFC antral follicle count. P- valuesa resulted from independent t-tests for quantitative and Chi-square for qualitative variables between the two groups. The general characteristics of participants across tertiles of HBI are presented in Table 3  As illustrated, women with DOR in the third tertile (≥ 74) of HBI compared to those in the first tertile (< 47) had significantly higher HC ( P  = 0.010). Besides, among controls, those who were assigned to the second tertile (47–73) of HBI had a higher level of AMH compared to those in first category ( P  = 0.021). Table 3 Characteristics of study participants across tertiles of healthy beverage index. Variable Case (120) Control (250) T1<47 T2 (47–73) T3≥74 P* T1<47 T2 (47–73) T3≥74 P* Age (years) 33.10 ± 3.29 33.55 ± 3.29 33.47 ± 3.18 0.794 33.10 ± 3.27 32.99 ± 2.99 32.63 ± 3.24 0.617 BMI (kg/m 2 ) 29.68 ± 2.13 30.22 ± 2.84 29.52 ± 2.42 0.408 27.91 ± 3.52 27.77 ± 3.31 27.56 ± 3.58 0.822 Weight (kg) 82.51 ± 4.09 81.98 ± 3.60 82.66 ± 4.82 0.733 78.07 ± 4.46 77.63 ± 5.31 78.90 ± 4.85 0.243 Height (cm) 164.66 ± 4.34 163.63 ± 6.07 164.72 ± 5.66 0.582 160.26 ± 8.51 162.79 ± 8.31 162.70 ± 8.13 0.089 FM (kg) 37.10 ± 5.98 38.44 ± 6.13 40.29 ± 9.10 0.157 37.10 ± 9.93 35.79 ± 8.66 36.64 ± 8.12 0.622 FFM (kg) 58.50 ± 11.73 58.75 ± 11.49 56.24 ± 10.74 0.594 60.06 ± 12.25 59.32 ± 11.76 61.14 ± 12.02 0.617 WC (cm) 103.37 ± 34.32 97.79 ± 33.42 107.31 ± 41.55 0.501 91.79 ± 12.55 91.47 ± 11.31 91.88 ± 13.68 0.975 HC (cm) 108.45 ± 30.44 100.61 ± 18.22 122.41 ± 43.04 0.010 105.62 ± 11.14 105.48 ± 11.90 107.35 ± 11.69 0.522 WHR 0.90 ± 0.12 0.90 ± 0.15 0.91 ± 0.10 0.985 0.87 ± 0.08 0.87 ± 0.09 0.86 ± 0.08 0.423 SBP (mmHg) 123.15 ± 14.48 123.60 ± 12.51 118.88 ± 10.38 0.230 125.86 ± 14.07 122.47 ± 13.83 122.50 ± 14.14 0.205 DBP (mmHg) 80.34 ± 13.19 79.40 ± 11.03 78.25 ± 10.76 0.753 83.27 ± 10.61 82.24 ± 10.57 79.94 ± 11.27 0.140 AFC count 2.49 ± 1.25 2.19 ± 1.15 2.38 ± 1.21 0.507 9.53 ± 2.13 9.81 ± 2.27 9.40 ± 2.32 0.466 AMH (ng/ml) 0.66 ± 1.19 0.52 ± 0.23 0.50 ± 0.20 0.546 3.97 ± 1.19 4.39 ± 1.19 3.94 ± 1.14 0.021 Physical activity (MET/h/day) 18.68 ± 4.04 19.96 ± 3.80 18.19 ± 4.53 0.135 19.14 ± 4.44 19.23 ± 4.46 18.55 ± 4.68 0.585 Low Socioeconomic status (%) 1 (2.40) 5 (10.60) 4 (12.50) 0.175 7 (8.60) 5 (5.50) 7 (9.00) 0.247 Middle 16 (39.00) 17 (36.20) 17 (53.10) 0.297 37 (45.70) 55 (60.40) 35 (44.90) High 24 (58.50) 25 (53.20) 11 (34.40) 0.397 37 (45.70) 31 (34.10) 36 (46.20) Under diploma Education (%) 4 (9.80) 3 (6.40) 7 (21.90) 0.918 9 (11.10) 16 (17.60) 9 (11.50) 0.630 Diploma 10 (24.40) 14 (29.80) 7 (21.90) 43 (53.10) 40 (44.00) 38 (48.70) University education 27 (65.90) 30 (63.80) 18 (56.30) 29 (35.80) 35 (38.50) 31 (39.70) Housewife Occupation (%) 31 (75.60) 29 (61.70) 22 (68.80) 57 (70.40) 70 (76.90) 57 (73.10) 0.849 Employed 5 (12.20) 14 (29.80) 7 (21.90) 3 (3.70) 3 (3.30) 4 (5.10) Student 5 (12.20) 4 (8.50) 3 (9.40) 21 (25.90) 18 (19.80) 17 (21.80) No Pervious Pregnancy (%) 34 (82.90) 38 (80.90) 27 (84.40) 69 (85.20) 68 (74.70) 66 (84.60) 0.140 Yes 7 (17.10) 9 (19.10) 5 (15.60) 12 (14.80) 23 (25.30) 12 (15.40) Quantitative variables are expressed as mean ± SD and qualitative variables expressed as n (%). The SES scored was evaluated based on education level of both subjects and the family head, job of both subjects and the family head family size, home status and home type by using self-reported questionnaire. AFC antral follicle count, BMI body mass index, DBP diastolic blood pressure, FFM fat free mass, FM fat mass, HC hip circumference, SBP systolic blood pressure, WC waist circumference, WHR waist to hip ratio. ** p values resulted from independent t-tests for quantitative and Chi-square for qualitative variables between the two groups. * p values resulted from ANOVA test for quantitative and Chi-square for qualitative variables across tertiles. Characteristics of study participants across tertiles of healthy beverage index. The crude and multivariable-adjusted odds ratio (95% CI) for the association between HBI and DOR are shown in Table 4 . In the crude model, no significant association was found between HBI and DOR. This association remained non-significant after controlling for potential confounders in model I. After adjusting for confounders in model II (adjusting for energy intake, physical activity level, and FM), a modest inverse association was revealed between the highest tertile (≥ 74) of HBI score and the risk of DOR (OR 0.92; 95%CI 0.41–0.96, P  = 0.042), compared to the lowest tertile (< 47).

To our knowledge, this is the first study to examine the association between the Healthy Beverage Index (HBI) and diminished ovarian reserve (DOR) among women. Our findings indicate that higher adherence to HBI was modestly associated with reduced odds of DOR in women attending a fertility clinic. Specifically, women in the highest HBI tertile had 8% lower odds of DOR. However, the small effect size and borderline statistical significance limit the clinical implications of this finding. Table 4 The crude and adjusted odds ratio (95% CI) for the associations between healthy beverage index (HBI) and the risk of Diminished Ovarian Reserve (DOR). DOR/control T1 < 47 T2 (47–73) T3 ≥ 74 P -trend 41/81 47/91 32/78 Crude Ref (1.00) 1.02 (0.31–1.91) 0.81 (0.36–1.36) 0.066 Model 1 Ref (1.00) 1.08 (0.38–1.96) 0.88 (0.45–1.32) 0.062 Model 2 Ref (1.00) 1.19 (0.40–1.99) 0.92 (0.41–0.96) 0.042 Model I was adjusted for energy intake (kcal/day) and physical activity level and Model II was adjusted for: energy intake (kcal/day), physical activity level, and fat Mass (kg). HBI: healthy beverage index, OR: odds ratio, CI: confidence interval. The crude and adjusted odds ratio (95% CI) for the associations between healthy beverage index (HBI) and the risk of Diminished Ovarian Reserve (DOR). Model I was adjusted for energy intake (kcal/day) and physical activity level and Model II was adjusted for: energy intake (kcal/day), physical activity level, and fat Mass (kg). HBI: healthy beverage index, OR: odds ratio, CI: confidence interval. Ovarian reserve, a key determinant of female fertility, reflects the remaining follicular pool, which declines with advancing age in the absence of pathological conditions 8 . Although the precise etiology of DOR remains unclear, factors such as ovarian surgery, gonadotoxic treatments, genetic predispositions 27 , and environmental or lifestyle exposures (e.g., smoking, poor diet, stress) may contribute 28 . The loss of normal reproductive potential impacts not only fertility but also broader aspects of women’s health 29 . While AMH and AFC are established markers for estimating ovarian reserve, recent evidence suggests that AMH may have physiological roles beyond the reproductive system 30 – 32 . However, in the context of this study, AMH is primarily utilized to assess ovarian function and its relevance to ovulatory health 31 . In the current study, despite lack of association between these markers and HBI among women with DOR, healthy subjects who were assigned to the second tertile of HBI had a higher concentration of AMH compared with those in first catgory, suggesting that moderate HBI adherence may support ovarian reserve. The lack of significant differences in DOR markers across HBI categories in the case group may be influenced by unaccounted confounding variables, including environmental pollutants, stress, psychoactive substance use, and endocrine, functional, or autoimmune disturbances affecting the reproductive system 8 , 27 . Genetic factors not assessed in this study may also contribute. Notably, genes such as FOXL2, which regulates folliculogenesis and granulosa cell development 33 , and FMR1, linked to diminished ovarian reserve 34 , have been implicated in ovarian dysfunction. Additionally, emerging evidence suggests that BRCA1/2 mutations may contribute to early ovarian aging and reduced reproductive lifespan, independent of their role in cancer susceptibility 35 . Considering these reproductive-specific genetic influences may offer deeper insights into the mechanisms and management of ovulatory disorders. The HBI, as a holistic dietary index containing eight beverage groups, total beverage energy, and fluid consumption, may provide better identification of improvements in health-associated conditions and it can be utilized as a counseling instrument for promotion of healthy beverage choices 36 , 37 . To our knowledge, this is the first study to explore the role of overall beverage quality in relation to ovarian reserve, limiting direct comparability with previous research. However, there is some evidence focusing on the effects of HBI, which comprehensively consider both the quality and quantity of all beverage types, on health outcomes like metabolic syndrome 38 , obesity 1 , cancer 39 , chronic kidney disease (CKD) progression and all-cause mortality 40 , and all of them have revealed an inverse relationship between the HBI and these health conditions. In the current study, water was the most essential source of fluid consumption and a decreased intake of SSBs was seen at the top category of compliance with the HBI which might illustrate causal relations of our results. In this regard, although animal models have suggested that intakes of energy drinks, as a category of SSBs, can result in a reduction in ovarian reserve and AMH levels 41 , the results of epidemiologic studies on humans have reported inconclusive findings 11 . Only two investigations have evaluated the link of SSBs with ovarian reserve markers and both of them have found no relations 11 , 16 . In the more recent one, the EARTH (Environment and Reproductive Health) study, which recruited women seeking fertility care, indicated that usual intake of this type of beverages may not adversely influence ovarian reserve which was measured by AFC 11 . Women included in this research, in one hand, had much lower consumption of SSBs and, on the other hand, they had higher means of AFC than the patients in our study (13 versus 2.34) which may explain these discrepant results. Besides, they only look at AFC while AMH is considered a more reliable indicator of DOR 11 . Given the limited epidemiologic evidence on beverage consumption and its association with DOR and related markers, further research is needed to clarify these relationships. Nonetheless, there is growing evidence showing the association of HBI and its components with various outcomes 39 , 40 . Recently, the results of a cohort study in Iran 42 has reported an inverse relationship between the quality of overall beverage intake and nonalcoholic fatty liver disease; suggesting that healthy beverages have a favorable influence on metabolic system. Moreover, the beneficial effects of healthier beverage choices on obesity and weight gain—both of which are linked to reproductive disorders 43 —have been frequently reported 20 , 44 . The role of BMI in pathomechanism of reproductive disorders appears to be important. Most participants in the current study were overweight or obese which can illustrate partially observed relations between higher adherence to HBI and lower risk of DOR. Underlying mechanisms through which higher HBI score contributes positively to this type of ovulation disorder may partially be attributed to the lower intake of SSBs containing sweeteners like sucrose and high-fructose corn syrup (HFCS), which have been shown to increase insulin resistance, leading to oxidative stress. More specifically, insulin resistance amplifies the synthesis of androgens and insulin-like growth factor I (IGFI), resulting in an impaired oocyte development 45 . On the other hand, enhanced oxidative stress and chronic low-grade systemic inflammation following consumption of diets containing high glycemic index foods with high carbohydrate content such as SSBs, as the main sources of added sugars, have been considered as a crucial feature of reproductive dysfunctions 46 . Another probable mechanism relating HBI score to DOR seems to be a higher intake of water and maintaining optimal hydration among those with the highest adherence to this indicator as the proinflammatory effects of hypohydration has already been evident 47 . Besides, intakes of healthy liquids including water and unsweetened coffee and tea may increase feelings of fullness and satiety and decrease food intake and sugary drinks consumption which are known as contributors to weight gain and adiposity 48 . Accordingly, obesity has been associated with an enhancement in the prevalence of poor reproductive health outcome 43 . This research has several strengths that should be noted including offering novel insights into potential modifiable risk factors particularly diet for prevention and management of DOR. Furthermore, homogenization of the two groups through matching by age and BMI, large sample size and comprehensive consideration of potential confounding variables in the analyses are other its advantages. Nevertheless, some certain limitations require to be considered when interpreting our findings. A key limitation of this study is the modest effect size and borderline statistical significance, which may limit the clinical relevance and generalizability of the findings. Another important limitation of this study is that due to its retrospective nature, causality cannot be inferred. Moreover, since dietary intake was assessed after the diagnosis of DOR, there is a potential for reverse causality—participants may have altered their beverage consumption in response to their diagnosis. As a result, the observed associations may not accurately represent long-term beverage habits prior to the development of DOR. Future prospective studies are necessary to clarify the temporal relationship and more effectively evaluate causality. Additionally, despite adjustment for a number of confounders, this study did not account for certain potentially influential factors such as psychological stress, environmental exposures (e.g., endocrine-disrupting chemicals), genetic predispositions, and endocrine-related comorbidities (e.g., polycystic ovary syndrome or thyroid disorders), which may affect both HBI and ovarian reserve. The absence of these variables from our analytical models may have introduced residual confounding, and future research should consider their inclusion to enhance model precision and validity. Despite using a reliable and validated FFQ to assess dietary intake, the possibility of recall bias and measurement error remains inevitable. Last, our results cannot be extrapolated to women in general population because study participants comprised women presenting to a fertility clinic. In conclusion, our findings suggest that higher adherence to the HBI may be modestly associated with reduced odds of DOR among women attending a fertility clinic. However, the observed effect size was small and the statistical significance borderline, indicating that the clinical relevance of this association remains uncertain. These findings highlight a potential link between beverage quality and reproductive health, but further investigations—particularly prospective studies across diverse populations and age groups—are warranted. Additionally, exploring the underlying biological mechanisms through biomarkers such as inflammatory and oxidative stress markers may offer valuable insights into how beverage consumption patterns influence reproductive capacity.

Diminished ovarian reserve is characterized by a decline in both the quantity and quality of follicles and oocytes, adversely impacting female reproductive health and fertility 1 , 2 . Recent data indicate a concerning increase in the global prevalence of DOR 3 , 4 , which is associated with reduced fertility, premature menopause, higher miscarriage rates, and suboptimal responses to assisted reproductive technologies 5 , 6 . Consequently, identifying effective strategies for managing DOR and its related complications is of considerable clinical importance. While non-modifiable factors such as age, genetic predisposition, and iatrogenic causes contribute to DOR development, modifiable lifestyle factors present valuable opportunities for intervention. Among these, diet, obesity, and physical activity have been shown to influence ovarian reserve and reproductive outcomes 7 , 8 . Dietary intake, in particular, plays a critical role in supporting female reproductive health and mitigating DOR. Notably, beverages—constituting approximately 20–25% of overall energy intake—may impact ovarian reserve and fertility, highlighting the need to examine their role in reproductive health 9 , 10 . Most existing studies investigating beverage consumption and reproductive outcomes have focused on individual beverage types rather than overall drinking patterns assessed through comprehensive scoring systems. This narrow focus may miss broader insights into how combined beverage choices impact ovarian health 11 – 13 . For instance, previous evidence has revealed an association of sugar-sweetened beverages (SSBs) intakes with lower fecundability 14 . On the other hand, caffeinated energy drinks have been proposed to influence negatively fertility 15 . Conversely, dairy intake has been associated with a slower decline in anti-Müllerian hormone (AMH), a key biomarker of ovarian reserve 16 . However, findings across studies remain inconsistent 17 , 18 , underscoring the importance of a holistic approach that considers the combined impact of diverse beverage types in the management of fertility disorders. So, understanding these combined effects could inform public health recommendations aimed at promoting healthier beverage choices. The Healthy Beverage Index (HBI), proposed by and Dufey and Davy et al. ., offers a robust tool for assessing beverage quality and consumption patterns in epidemiological research. The HBI offers a comprehensive assessment of beverage quality by evaluating fluid intake patterns across eight categories and considering total beverage energy, thereby linking drink choices to dietary quality and health outcomes 19 . Although prior research has linked HBI scores with conditions such as obesity and metabolic syndrome, its relationship with reproductive health remains unexplored 20 , 21 . To the best of our knowledge, no study has previously investigated the association between HBI and fertility disturbances. Therefore, the present case-control study aimed to explore the relationship between HBI scores and DOR among Iranian adults.