Dietary inflammatory index is associated with female infertility: a cross-sectional analysis of the Fasa adults cohort study

The present analysis included a total of 2,036 women aged 35–45 years. Of these, 595 participants (29.2%) reported a history of infertility. Baseline demographic, anthropometric, biochemical, and dietary characteristics of the study population, stratified by infertility status, are summarized in Table  1 . As shown in Table  1 , women with and without a history of infertility were closely matched in terms of mean age (39.58 ± 3.26 years versus 39.68 ± 3.21 years, respectively; P  = 0.53), indicating no significant age-related confounding. However, infertile women presented with modestly but significantly higher mean body weight and waist circumference compared with fertile women (both P  < 0.05). Women with infertility exhibited slightly higher mean BMI compared with fertile women (27.66 ± 5.06 vs. 27.02 ± 4.63; P  = 0.006), indicating a statistically significant difference. Systolic and diastolic blood pressure values were comparable between the two groups, with no notable differences observed. With respect to biochemical markers, infertile participants exhibited significantly lower mean high-density lipoprotein (HDL) cholesterol concentrations than their fertile counterparts ( P  < 0.05). In contrast, no meaningful differences were detected in fasting blood glucose, triglycerides, low-density lipoprotein (LDL) cholesterol, total cholesterol, blood urea nitrogen, or serum creatinine levels between the groups. Regarding dietary patterns, total energy intake and macronutrient composition were broadly comparable across both groups, with no statistically significant differences. Notably, however, the mean Dietary Inflammatory Index (DII) score was markedly higher among women reporting infertility (0.27 ± 0.43) compared with fertile women (− 1.35 ± 0.65; P  = 0.001). This substantial disparity suggests that women with a history of infertility adhered to dietary patterns with greater pro-inflammatory potential. As detailed in Table  2 , participant characteristics across quartiles of the Dietary Inflammatory Index (DII) revealed several notable patterns. No statistically significant differences were observed in age, body weight, waist circumference, body mass index (BMI), systolic blood pressure, fasting blood glucose, or the majority of biochemical parameters across the four quartiles. Table 2 Distribution of characteristics and dietary intakes across tertiles of the DII ( n  = 2036) Variables Q 1 ( n  = 509) Q 2 ( n  = 509) Q3 ( n  = 509) Q 4 ( n  = 509) P -value b Age (year) 39.61 ± 3.15 39.67 ± 3.22 39.83 ± 3.24 39.49 ± 3.28 0.39 Weight (kg) 67.69 ± 12.17 66.82 ± 12.94 67.14 ± 11.46 67.35 ± 12.72 0.72 Waist circumference (cm) 96.03 ± 10.79 95.40 ± 11.44 95.72 ± 11.08 96.00 ± 11.57 0.78 BMI (kg/m 2 ) 27.24 ± 4.77 27.12 ± 4.85 27.23 ± 4.59 27.26 ± 4.88 0.96 SBP (mmHg) 106.12 ± 14.94 104.52 ± 14.82 105.93 ± 14.91 105.01 ± 14.62 0.26 DBP (mmHg) 73.53 ± 11.60 71.45 ± 11.12 72.74 ± 11.25 71.84 ± 11.17 0.016 FBS (mg/dl) 87.13 ± 20.15 86.68 ± 20.13 87.04 ± 21.98 86.72 ± 21.58 0.98 TG (mg/dl) 120.96 ± 82.72 115.76 ± 68.23 122.31 ± 76.97 117.11 ± 59.99 0.43 LDL (mg/dl) 102.83 ± 31.36 106.02 ± 31.27 106.72 ± 31.02 108.04 ± 30.97 0.045 HDL (mg/dl) 54.80 ± 18.66 53.16 ± 15.57 52.06 ± 15.48 52.46 ± 14.85 0.038 Cholesterol (mg/dl) 181.83 ± 37.30 182.34 ± 36.05 183.25 ± 36.14 183.92 ± 36.52 0.802 BUN (mg/dl) 10.78 ± 2.84 10.99 ± 3.11 10.81 ± 2.88 10.87 ± 3.01 0.69 Cr (mg/dl) 0.89 ± 0.09 0.90 ± 0.14 0.91 ± 0.14 0.91 ± 0.17 0.77 Energy, (Kcal/day) 2683.58 ± 796.59 2415.61 ± 745.82 2602.81 ± 782.55 2863.52 ± 790.63 < 0.001 Fat (g/day) 66.02 ± 22.52 60.29 ± 23.24 64.43 ± 23.50 68.40 ± 24.57 < 0.001 Carbohydrates (g/day) 449.10 ± 139.56 400.27 ± 128.20 430.49 ± 137.93 481.38 ± 140.10 < 0.001 Protein (g/day) 90.23 ± 28.31 78.69 ± 26.09 83.08 ± 27.71 85.88 ± 28.25 < 0.001 Fiber (g/day) 36.15 ± 11.52 27.51 ± 9.28 25.94 ± 8.95 25.00 ± 8.30 < 0.001 Cholesterol (mg/day) 275.03 ± 140.67 233.73 ± 138.94 235.74 ± 125.56 209.65 ± 115.61 < 0.001 Na (mg/day) 4399.74 ± 1795.22 4107.75 ± 1503.1 4326.30 ± 1502.78 4791.25 ± 1681.92 < 0.001 SFA (g/day) 22.08 ± 9.06 21.26 ± 9.78 23.01 ± 10.24 25.18 ± 10.84 < 0.001 MUFA (g/day) 18.63 ± 7.18 16.43 ± 7.38 17.22 ± 7.87 17.24 ± 8.01 < 0.001 PUFA (g/day) 11.70 ± 4.63 9.15 ± 4.12 8.94 ± 4.09 7.99 ± 3.53 < 0.001 Vitamin E (mg/day) 10.27 ± 3.56 7.71 ± 2.77 6.98 ± 2.43 6.11 ± 2.12 < 0.001 Vitamin D (mcg/day) 1.57 ± 1.02 1.15 ± 0.85 1.14 ± 0.76 0.98 ± 0.67 < 0.001 Zinc (mg/day) 11.49 ± 3.44 9.85 ± 3.17 10.13 ± 3.28 10.14 ± 3.17 < 0.001 Selenium (mcg/day) 125.05 ± 48.93 118.70 ± 48.05 132.07 ± 53.12 149.84 ± 59.52 < 0.001 a All data are means ± standard deviations unless indicated. b One-way ANOVA was used for continuous variables BMI Body mass index, DBP Diastolic blood pressure, DAI Dietary Antioxidant Inde, mg/dl milligram/deciliter, FBS Fasting blood sugar, HDL High density lipoprotein, LDL Low density lipoprotein, Na Sodium, SFA Saturated fatty acid, MUFA Mono unsaturated fatty acid, PUFA Poly unsaturated fatty acid, TG Triglyceride, TC Total cholesterol Distribution of characteristics and dietary intakes across tertiles of the DII ( n  = 2036) Cholesterol (mg/day) a All data are means ± standard deviations unless indicated. b One-way ANOVA was used for continuous variables BMI Body mass index, DBP Diastolic blood pressure, DAI Dietary Antioxidant Inde, mg/dl milligram/deciliter, FBS Fasting blood sugar, HDL High density lipoprotein, LDL Low density lipoprotein, Na Sodium, SFA Saturated fatty acid, MUFA Mono unsaturated fatty acid, PUFA Poly unsaturated fatty acid, TG Triglyceride, TC Total cholesterol However, individuals in the highest DII quartile (indicating the most pro-inflammatory dietary pattern) displayed significantly higher mean low-density lipoprotein (LDL) cholesterol concentrations and lower high-density lipoprotein (HDL) cholesterol levels compared with those in the lowest quartile ( P  < 0.05). Diastolic blood pressure also showed modest but statistically significant variation across DII quartiles. Dietary intake profiles differed markedly with increasing inflammatory potential of the diet. Participants in the higher DII quartiles reported significantly greater consumption of total energy, carbohydrates, sodium, and saturated fatty acids. Conversely, intakes of protein, dietary fiber, monounsaturated fatty acids, polyunsaturated fatty acids, vitamin E, vitamin D, zinc, and selenium were substantially lower among those with more pro-inflammatory diets (all P  < 0.05). These dietary differences align closely with the theoretical framework of the DII, confirming that higher scores correspond to distinct and recognizable dietary patterns characterized by greater pro-inflammatory potential. Odds ratios (ORs) and 95% confidence intervals (CIs) for infertility according to DII scores are presented in Table  3 . In the logistic regression analyses, odds ratios (ORs) for infertility were calculated both for a one-unit increase in DII (continuous analysis) and for comparisons across quartiles of DII, using the lowest quartile (Q1) as the reference category. In the continuous model, each one-unit increase in DII was associated with 1.29 times higher odds of infertility (95% CI: 1.13–1.63). Table 3 Logistic regression analysis of infertility according to Dietary Inflammatory Index (DII) quartiles Crude OR (CI) P 1.06(0.97, 1.16) 0.17 Model 1 1.21(1.10, 1.43) 0.01 Model 2 1.29 (1.13, 1.63) 0.001 DII Quartile  Q1 (ref) 1.00 -  Q2 1.08 (0.82–1.41) 0.58  Q3 1.26 (0.97–1.64) 0.08  Q4 1.68 (1.24–2.28) 0.001 P for trend - 0.002 Odds ratios (ORs) and 95% confidence intervals (CIs) are shown for each quartile compared with the lowest quartile (Q1) as the reference category. The table also presents results for the continuous DII variable and a test for linear trend across quartiles. Model 1: adjusted for BMI and smoking status; Model 2: further adjusted for age, education, waist circumference, and total energy intake Logistic regression analysis of infertility according to Dietary Inflammatory Index (DII) quartiles Odds ratios (ORs) and 95% confidence intervals (CIs) are shown for each quartile compared with the lowest quartile (Q1) as the reference category. The table also presents results for the continuous DII variable and a test for linear trend across quartiles. Model 1: adjusted for BMI and smoking status; Model 2: further adjusted for age, education, waist circumference, and total energy intake When DII was categorized into quartiles, the odds of infertility increased progressively across higher DII categories. Compared with women in the lowest DII quartile (Q1), the adjusted odds ratios (ORs) for infertility were estimated as follows: Q2: 1.08 (95% CI: 0.82–1.41), Q3: 1.26 (95% CI: 0.97–1.64), and Q4: 1.68 (95% CI: 1.24–2.28). A test for linear trend indicated a statistically significant dose–response relationship (P for trend = 0.002). These results suggest that women with more pro-inflammatory diets had higher odds of infertility, consistent with the continuous DII analysis.

This cross-sectional analysis used data from female participants in the Fasa Adults Cohort Study, affiliated with Fasa University of Medical Sciences. The Fasa Adult Cohort Study recruited participants from November 2014 to June 2019. As a large population-based cohort, the study was designed to investigate determinants of non-communicable diseases among Iranian adults. It included 10,035 individuals aged 35–70 years without major physical or cognitive disabilities, who were randomly selected from the rural Sheshdeh district in southern Iran, provided that they had lived in the area for at least nine months before enrollment. Of the 2,036 women included in the analytic cohort, complete data for all variables presented in Table  1 were available for 1,737 participants. Participants with missing values for demographic, anthropometric, biochemical, or dietary variables were excluded from the corresponding analyses. The number of participants included in each comparison is clearly indicated in the tables. This approach ensured valid comparisons while minimizing bias due to missing data. Table 1 Baseline characteristics and dietary intakes of participants Variable Infertility P * Yes ( n  = 595) No ( n  = 1142) Age (year) 39.58 ± 3.26 39.68 ± 3.21 0.53 Weight (kg) 68.19 ± 13.13 66.86 ± 11.96 0.033 Waist circumference (cm) 96.62 ± 11.70 95.44 ± 11 0.029 BMI (kg/m 2 ) 27.66 ± 5.06 27.02 ± 4.63 0.006 SBP (mmHg) 105.52 ± 15.64 105.34 ± 14.48 0.79 DBP (mmHg) 71.79 ± 11.71 72.64 ± 11.13 0.85 FBS (mg/dl) 87.35 ± 22.13 86.70 ± 20.44 0.54 TG (mg/dl) 122.75 ± 67.11 117.46 ± 74.58 0.13 LDL (mg/dl) 107.90 ± 30.05 105.06 ± 31.61 0.062 HDL (mg/dl) 51.42 ± 14.95 53.83 ± 16.69 0.001 Cholesterol (mg/dl) 183.88 ± 35.53 182.40 ± 36.86 0.404 BUN (mg/dl) 10.67 ± 2.98 10.94 ± 2.45 0.055 Cr (mg/dl) 0.901 ± 0.096 0.906 ± 0.15 0.44 Dietary intake  DII 0.27 ± 0.43 -1.35 ± 0.65 0.001  Energy, Kcal/day 2681.09 ± 821.72 2624.98 ± 783.49 0.14  Protein (g/day) 85.22 ± 28.68 84.16 ± 27.58 0.437  Fat (g/day) 65.33 ± 24.44 64.55 ± 23.30 0.49  Carbohydrate (g/day) 447.89 ± 143.29 437.18 ± 137.94 0.115  Vitamin D (mcg/day) 1.20 ± 0.78 1.22 ± 0.9 0.68  Fiber (g/day) 28.62 ± 10.79 28.66 ± 10.46 0.92  Calcium (mg/day) 1279.62 ± 502.18 1266.20 ± 490.82 0.57  Fe (mg/day) 21.59 ± 8.08 21.18 ± 7.73 0.28  Vit B6 (mg/day) 10.59 ± 7.85 10.53 ± 8.93 0.87  Vit B12 (mcg/day) 5.75 ± 3.48 5.92 ± 4.24 0.39 Baseline characteristics and dietary intakes of participants Women aged 35–45 years with complete dietary and nutritional data were eligible for the present analysis. Participants were excluded if they were unmarried or divorced, pregnant at the time of assessment, had a history of hysterectomy or oophorectomy, had active malignancy, had neurological, inflammatory, hepatic, or endocrine disorders, or reported implausible total energy intake ( 4,500 kcal/day). Infertility status was assessed using a self-reported fertility questionnaire administered to participants. After applying the inclusion and exclusion criteria, 2,036 women were included in the final analytic sample. This study was approved by the Ethics Committee of Fasa University of Medical Sciences under the code IR.FUMS.REC.1403.036 and was conducted in accordance with the Declaration of Helsinki. Details of the questionnaires used in the Fasa cohort have been described previously [ 19 ]. Infertility status was ascertained via a structured fertility questionnaire delivered by experienced interviewers [ 19 ]. Consistent with standard clinical definitions, women were classified as having a history of infertility if they reported inability to conceive after at least 12 months of regular unprotected sexual intercourse. This definition captured “ever infertility” and may have included both primary and secondary infertility. Participants who did not meet this criterion were classified as fertile. Written informed consent was obtained from all participants before data collection. Dietary habits were evaluated through a reliable and validated 118-item semi-quantitative food frequency questionnaire (FFQ) tailored specifically for Iranian communities [ 19 ]. Participants reported their usual consumption frequency for each food item over the past year, and standardized portion sizes were applied to estimate daily nutrient intake. To quantify the inflammatory potential of participants’ diets, DII was calculated according to established literature-derived methods. The DII scoring system synthesizes evidence on the effects of dietary components on key inflammatory biomarkers, including C-reactive protein (CRP), interleukins (IL-1β, IL-6, IL-10), and tumor necrosis factor-alpha (TNF-α). Although the full DII includes 45 dietary parameters, our analysis incorporated 31 components available from the FFQ, consistent with prior epidemiological studies. For each dietary component, individual intake was first standardized using a global comparative database to generate z-scores. These z-scores were then converted into centered percentile ranks and multiplied by the corresponding inflammatory effect score derived from the literature. The weighted values for all components were summed to yield the total DII score for each participant. Higher DII scores reflect diets with greater pro-inflammatory potential, while lower or negative scores indicate more anti-inflammatory diets. In our cohort, DII scores ranged approximately from − 4.5 (most anti-inflammatory) to + 4.2 (most pro-inflammatory), with fertile women averaging − 1.35 ± 0.65 and infertile women averaging 0.27 ± 0.43. These values fall within the expected range and confirm the validity of the DII calculation in this population. This detailed methodology ensures transparency regarding standardization, weighting, and aggregation processes, and clarifies how observed differences between fertile and infertile women are consistent with the established DII framework. Data on demographic features, educational level, smoking habits, and medical background were gathered via structured questionnaires [ 19 ]. Anthropometric measurements, including weight, height, and waist circumference, were obtained by trained staff using standardized procedures. Body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared. Blood pressure was measured using a calibrated sphygmomanometer after an appropriate resting period. Fasting venous blood samples were collected to measure biochemical markers, including fasting plasma glucose and serum lipid concentrations. Total energy intake was estimated from the FFQ and included as a covariate to account for differences in overall dietary intake. Continuous variables were summarized as means with standard deviations, whereas categorical variables were reported as counts and percentages. Group differences in baseline features between women with and without infertility were examined via independent samples t-tests for continuous measures and chi-square tests for categorical ones. One-way analysis of variance (ANOVA) was employed to explore variations in participant attributes across DII quartiles. To investigate the link between DII and infertility, logistic regression analyses were performed. DII was analyzed both as a continuous variable and as a categorical variable divided into quartiles, with the lowest quartile (Q1) serving as the reference group. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated for each quartile relative to Q1. For tests of linear trend, the median value of each quartile was assigned to participants in that quartile and modeled as a continuous variable. This approach allows for evaluation of both dose–response and non-linear associations. The unadjusted model evaluated the crude association, Model 1 adjusted for BMI and smoking status, and Model 2 further adjusted for age, educational level, waist circumference, and total energy intake. All analyses were performed using SPSS, with statistical significance defined as a two-tailed p-value < 0.05.

Infertility is recognized as a major public health concern worldwide, affecting a substantial proportion of couples during their reproductive years [ 1 ]. Global estimates indicate that approximately one in six couples experience infertility at some point in their lives, with a considerable burden in low- and middle-income countries. Beyond its biological dimensions, infertility has profound psychological, social, and economic consequences, particularly for women, who often experience a disproportionate share of the emotional and societal pressures associated with childbearing [ 1 , 2 ]. Infertility has also been associated with anxiety, depression, and reduced quality of life, highlighting the importance of identifying potentially modifiable factors related to reproductive health [ 3 , 4 ]. Female infertility arises from a complex interplay of biological, environmental, and lifestyle-related factors. Although established causes include ovulatory disorders, tubal abnormalities, and endocrine dysfunction, increasing attention has been directed toward modifiable lifestyle factors that may be associated with reproductive function [ 5 , 6 ]. Among these, dietary habits have emerged as a potentially important but still underexplored factor. Unlike genetic or anatomical determinants, diet represents a modifiable exposure that may offer practical opportunities for prevention and risk reduction at the population level [ 7 – 10 ]. In recent years, growing evidence has highlighted the role of chronic low-grade inflammation in female reproductive disorders. Inflammatory pathways may affect key reproductive processes, including follicular development, ovulation, endometrial receptivity, and embryo implantation [ 11 , 12 ]. Conditions such as endometriosis, polycystic ovary syndrome (PCOS), and recurrent pregnancy loss have been linked to dysregulated inflammatory signaling. Nutrition plays an important role in the regulation of systemic inflammation, as some dietary patterns may promote inflammatory responses, whereas others may exert anti-inflammatory effects through modulation of cytokine activity, oxidative stress, and metabolic homeostasis [ 13 – 15 ]. The Dietary Inflammatory Index (DII) is a validated, literature-derived tool designed to assess the overall inflammatory potential of an individual’s dietary pattern. The DII integrates evidence on the effects of nutrients, foods, and bioactive compounds on inflammatory biomarkers, including C-reactive protein, interleukins, and tumor necrosis factor-alpha [ 16 ]. Higher DII scores indicate more pro-inflammatory diets, which are typically characterized by higher intake of refined carbohydrates, saturated fats, and sodium, and lower intake of fiber, antioxidants, and unsaturated fatty acids [ 17 ]. A limited number of epidemiological studies have examined the association between DII and female infertility. Most available studies suggest that more pro-inflammatory dietary patterns are associated with higher odds of infertility, even after adjustment for major confounders. However, the current evidence remains limited, geographically restricted, and largely derived from Western or East Asian populations. Therefore, additional studies are needed in Middle Eastern populations, where dietary patterns, reproductive profiles, and lifestyle factors may differ substantially [ 18 ]. Accordingly, the present study was designed to examine the association between DII and female infertility among women aged 35–45 years enrolled in the Fasa Adults Cohort Study in Fasa, Iran. Using data from a large population-based cohort and adjusting for relevant demographic, anthropometric, and lifestyle factors, this study aimed to contribute to the growing evidence on the relationship between diet-related inflammation and female reproductive health.

A higher DII score was independently associated with higher odds of infertility in this Iranian cohort, reinforcing the hypothesis that dietary inflammation may be a significant and potentially modifiable factor in female infertility, while underscoring the necessity for prospective and mechanistic studies to elucidate directionality and underlying mechanisms.

In this cross-sectional analysis of women aged 35–45 years from the Fasa Adults Cohort Study, higher DII scores were associated with greater odds of self-reported infertility after adjustment for relevant demographic, anthropometric, and lifestyle variables. Although the crude association was not statistically significant, the association became evident after adjustment and remained stable in the fully adjusted model. These findings suggest that the inflammatory potential of diet may be related to infertility in this population. However, because of the cross-sectional design, the direction of the association cannot be determined. Our results are broadly aligned with emerging epidemiologic evidence indicating that more pro-inflammatory diets are associated with infertility [ 20 ]. Analyses based on NHANES data have reported a positive association between higher DII scores and infertility among reproductive-age women, even after multivariable adjustment [ 21 ]. Similar patterns have been described in Iranian populations; notably, Moludi and colleagues, using data from the RaNCD cohort, observed higher infertility risk among women with more pro-inflammatory diets [ 22 ]. A recent systematic review and meta-analysis of existing data determined that a higher Dietary Inflammatory Index (DII) correlates with an elevated risk of infertility, corroborating the overall trajectory of our findings [ 15 ]. It is important to note that the strength of the link has not been entirely constant across research. Variations in age range, infertility assessment, adjustment variables, and cultural food habits can all alter effect estimates. In our population, the association became evident post-adjustment, potentially indicating confounding by obesity, smoking, education, or energy intake—variables that frequently associated with both dietary quality and reproductive health [ 23 , 24 ]. While several biological mechanisms plausibly link pro-inflammatory diets to female infertility, it is important to interpret these mechanistic explanations with caution, as inflammatory biomarkers (e.g., CRP, IL-6, TNF-α) were not measured in our cohort. The pathways described, including ovarian immunological disruption, altered endometrial receptivity, and metabolic disturbances, are supported by prior literature, but our study only assessed dietary inflammatory potential and self-reported infertility. Therefore, these mechanistic interpretations should be considered hypothesis-generating rather than confirmatory, and future studies incorporating direct measurement of inflammatory biomarkers are needed to validate these pathways. Chronic low-grade inflammation can disrupt ovarian immunological homeostasis, impairing follicular development and ovulation. Elevated levels of pro-inflammatory cytokines such as IL-6, TNF-α, and CRP may interfere with ovarian signaling pathways, altering hormone production and ovulatory function [ 25 ]. In addition, systemic inflammation can reduce endometrial receptivity by affecting the local immune environment, which is critical for successful embryo implantation. Dysregulated cytokine signaling may lead to implantation failure or early pregnancy loss [ 26 ]. Metabolic pathways provide another link: diets high in refined carbohydrates, saturated fats, and sodium may exacerbate insulin resistance and oxidative stress, further perturbing reproductive hormone regulation. These interconnected inflammatory and metabolic disruptions illustrate how dietary patterns may influence multiple levels of the reproductive axis, from ovarian function to endometrial receptivity and embryo implantation [ 27 ]. Importantly, these pathways are not independent. Inflammatory, metabolic, and immunological disruptions often coexist, creating intersecting effects on multiple levels of the reproductive axis. For instance, obesity and central adiposity can amplify systemic inflammation and insulin resistance, further aggravating ovarian and endometrial dysfunction. This integrative framework highlights that dietary inflammation may simultaneously influence ovarian, endometrial, and metabolic domains, providing a coherent biological rationale for the observed association between higher DII scores and infertility in our cohort [ 28 ]. Overall, incorporating these specific mechanisms strengthens the biological plausibility of our findings and underscores the need for future longitudinal studies that combine dietary assessment with inflammatory biomarkers, metabolic profiling, and detailed reproductive phenotyping [ 29 ]. These paths are not mutually exclusive. In practical contexts, dietary inflammation frequently coexists with additional exposures sedentary behavior, central obesity, and cardiometabolic risk resulting in intersecting molecular pathways to reproductive dysfunction. This also shows why it’s so important to carefully control for confounders and why residual confounding is still possible even in models that are well-adjusted [ 28 ]. This research possesses numerous advantages. The analysis utilizes a substantial sample of women from a reputable population-based cohort framework (Fasa Adults Cohort Study), hence improving the internal consistency of data collecting and assessment techniques. Moreover, employing a dietary index that encompasses overall inflammatory potential rather than isolated nutrients may more accurately represent actual dietary practices and their cumulative physiological impacts. However, constraints must be acknowledged while analyzing the results. It is important to emphasize that due to the cross-sectional design of our study, causal relationships between dietary inflammatory potential and female infertility cannot be firmly established. While we observed a positive association between higher DII scores and infertility, it is possible that women experiencing infertility may have modified their diet in attempts to improve fertility, introducing the potential for reverse causation. Therefore, these findings should be interpreted as indicative of association rather than causation. Longitudinal studies and prospective cohort analyses are warranted to clarify the directionality of this relationship. Second, although we adjusted for several important confounders including BMI, smoking, age, waist circumference, and total energy intake, we did not have comprehensive data on critical reproductive factors such as polycystic ovary syndrome (PCOS), endometriosis, and detailed reproductive history including previous ART treatments. These unmeasured confounders could potentially bias the observed association between dietary inflammatory potential and infertility. Future studies should aim to incorporate these variables to better isolate the effect of diet on reproductive outcomes. Third, infertility status was determined solely by self-report without clinical confirmation, introducing the potential for recall bias and misclassification. In addition, our data did not differentiate between primary, secondary, or male-factor infertility. Consequently, some cases of reported infertility may reflect male or combined-factor causes, and misclassification could bias the observed associations. Future studies incorporating clinically verified infertility and detailed reproductive history are warranted to validate these findings. Finally, although our models were adjusted for several relevant demographic, anthropometric, and lifestyle factors, including age, BMI, smoking status, waist circumference, educational level, and total energy intake, we were unable to control for some important infertility-related factors. In particular, information on PCOS, endometriosis, thyroid disorders, physical activity, and detailed socioeconomic status was not available in the analytic dataset. These factors are closely related to both inflammatory status and reproductive health and may therefore confound the observed association between DII and infertility. As a result, residual confounding cannot be excluded, and the findings should be interpreted cautiously as associations rather than causal effects. Future prospective studies should incorporate detailed reproductive, endocrine, lifestyle, and socioeconomic variables to better clarify the independent relationship between dietary inflammatory potential and infertility. Notwithstanding these reservations, our findings contribute to an expanding corpus of research indicating that diets with heightened inflammatory potential may be associated with an increased risk of infertility. Should future longitudinal studies validate temporality, dietary interventions designed to reduce inflammatory potential—by increasing consumption of fiber-rich plant foods, unsaturated fats, and foods high in antioxidants—may emerge as a viable element of comprehensive infertility prevention and preconception care. Prospective analyses in cohort studies that include inflammatory biomarkers (e.g., CRP, IL-6) could elucidate whether systemic inflammation mediates the relationship between the DII and infertility, and whether the effects vary by adiposity, metabolic health, or specific infertility phenotypes.