Association between the novel dietary index for gut microbiota, biomarkers of hashimoto’s thyroiditis and metabolic parameters among women of reproductive age

Abstract

Background

Emerging evidence highlights the role of gut microbiota in autoimmune diseases, including Hashimoto’s thyroiditis (HT). Diet is a key modulator of gut microbial composition. This study investigated the association between a novel Dietary Index for Gut Microbiota (DIGM) and thyroid-related biomarkers among women with HT.

Methods

In this cross-sectional study, 162 women with clinically confirmed HT were enrolled. Dietary intake was assessed using a validated food frequency questionnaire. The DIGM score was constructed based on intake of foods known to influence gut microbial health, with higher scores indicating greater adherence to a microbiota-supportive diet. Anthropometric parameters and serum levels of thyroid hormones, thyroid autoantibodies, lipid profile, and oxidative stress markers were measured. Participants were categorized into tertiles based on DIGM score, and comparisons across groups were made using ANOVA and general linear models, adjusting for potential confounders.

Results

Participants in the highest DIGM tertile had significantly lower serum anti-thyroid peroxidase (anti-TPO) and anti-thyroglobulin (anti-Tg) antibody levels (P < 0.05), lower triglyceride concentrations (p = 0.017), and reduced waist-to-hip ratio (p = 0.030) compared to those in the lowest tertile. No significant differences were observed in TSH, T3, T4, anti-Tg, TAC, or MDA levels across DIGM categories.

Conclusion

Higher adherence to a microbiota-supportive dietary pattern, as reflected by the DIGM score, was associated with favorable immune and metabolic profiles in women with HT. Due to the cross-sectional design of the study, causal relationships cannot be inferred. Further longitudinal or intervention studies are needed to elucidate causality and to suggest dietary modulation of gut microbiota as a non-pharmacological approach to support management of autoimmune thyroid disease.

Clinical trial number

Not applicable.

Introduction

Hashimoto’s thyroiditis (HT), also known as chronic lymphocytic thyroiditis, is the most prevalent autoimmune thyroid disorder and a leading cause of hypothyroidism worldwide [1]. Characterized by lymphocytic infiltration and gradual destruction of thyroid tissue, HT predominantly affects women and is often associated with the presence of antithyroid antibodies, particularly anti-thyroid peroxidase (anti-TPO) and anti-thyroglobulin (anti-Tg) [2]. The global incidence of HT has been steadily rising, which may be partly attributed to environmental, genetic, and lifestyle-related factors [3]. HT exhibits a striking female predominance, with women being affected approximately 5 to 10 times more frequently than men [4]. This disparity is particularly evident during the reproductive years, suggesting that hormonal and immunological differences between sexes may play a crucial role in disease susceptibility [5]. Estrogen has been shown to modulate immune responses by enhancing B-cell activation and increasing the production of autoantibodies, which may partly explain the higher prevalence of autoimmune thyroid conditions in women [6]. Moreover, given the chronic nature of HT, potential complications, and the lifelong requirement for thyroid hormone replacement therapy in many patients, the identification of modifiable risk factors and supportive therapeutic strategies for managing HT is of growing clinical interest. In recent years, growing evidence has highlighted the critical role of gut microbiota in modulating immune homeostasis and the pathogenesis of autoimmune diseases, including HT [7]. The gut-associated lymphoid tissue (GALT), which interacts closely with intestinal microbes, plays a key role in maintaining immune tolerance and preventing aberrant autoimmune responses [8]. Dysbiosis, or imbalance in gut microbial composition, has been associated with increased intestinal permeability or leaky gut, systemic inflammation, and molecular mimicry — all of which are mechanisms implicated in thyroid autoimmunity [9]. Several studies have reported altered gut microbiota in HT, including increased relative abundance of Bacteroides species in patients compared to healthy controls, supporting the concept of a pro-inflammatory gut signature in HT [10–12]. These microbial alterations may contribute to the initiation or exacerbation of thyroid autoimmunity by promoting Th1/Th17 immune responses and elevating circulating autoantibodies. This growing body of evidence underscores the potential for microbiota-targeted strategies in modulating immune function and mitigating HT progression.

Oxidative stress is increasingly recognized as a key contributor to the pathogenesis of HT [13]. Excess reactive oxygen species (ROS), generated during thyroid hormone synthesis, can exacerbate inflammatory signaling, promote thyrocyte damage, and enhance autoantigen presentation, thereby accelerating autoimmune responses [13]. Consistent with this mechanism, prior studies have reported reduced total antioxidant capacity (TAC) and elevated lipid peroxidation products—particularly malondialdehyde (MDA)—in patients with HT, indicating systemic antioxidant depletion and oxidative damage [14, 15]. TAC reflects the cumulative activity of enzymatic and non-enzymatic antioxidants and is frequently reduced in patients with HT, indicating impaired systemic antioxidant defense [16]. MDA is a well-established biomarker of lipid peroxidation and has been consistently reported to be elevated in HT, highlighting ongoing oxidative damage to cellular membranes [14]. Arylesterase (ARE) activity represents a major component of the paraoxonase-1 (PON1) antioxidant system, which plays a protective role against oxidative stress–driven inflammation, and reduced ARE activity has been observed in several autoimmune and metabolic disorders [17–19]. Because these biomarkers capture distinct but interrelated dimensions of oxidative stress—antioxidant capacity, lipid peroxidation, and PON1-related enzymatic defense—they are recognized as suitable indicators for studying redox alterations in HT pathophysiology.

Diet plays a pivotal role in shaping the composition and metabolic activity of the gut microbiota, thereby influencing immune regulation and systemic inflammation [20]. High-fiber, plant-based diets enriched with polyphenols, prebiotics, and fermented foods have been shown to enhance microbial diversity and promote the abundance of anti-inflammatory taxa such as Lactobacillus and Bifidobacterium [21]. Conversely, Western dietary patterns rich in saturated fats, sugars, and ultra-processed foods are linked with dysbiosis, elevated intestinal permeability, and systemic immune activation [22]. In the context of HT, dietary modulation of the microbiome may represent a promising adjunctive strategy for restoring immune tolerance and reducing thyroid-specific autoantibody production [23]. Emerging interventional studies suggest that targeted dietary changes can lead to favorable shifts in gut microbial composition and improvements in thyroid function parameters [24]. The dietary index for gut microbiota (DIGM) as a composite scoring system developed to quantify adherence to dietary patterns known to promote a healthy gut microbial profile. This index is based on the intake of food groups that have been shown to positively or negatively influence microbial diversity, short-chain fatty acid production, and anti-inflammatory microbial taxa. Typically, the index includes higher scores for consumption of microbiota-supporting foods such as avocado, broccoli chickpeas, coffee, fermented dairies, soybean and whole grains. At the while assigning lower scores to intake of red and processed meats, refined sugars, and ultra-processed foods [25]. DIGM score was originally developed and validated in the study by Kase BE et al. [25], where the index was evaluated in a large NHANES cohort (n = 3812). In that study, the DIGM score showed significant positive associations with established biomarkers of gut microbiota diversity, specifically urinary enterodiol and enterolactone. Based on the reviewed articles by the authors, fourteen foods and nutrients were identified as having beneficial or unfavorable effects on gut microbiota. Beneficial effects were an increase in α-diversity and β-diversity indices; an increase in total SCFA, butyrate, acetate, propionate, or isobutyrate; or balanced Firmicutes/Bacteroidetes ratio [25]. By capturing the overall quality of the diet in relation to microbial health, this index provides a practical tool for evaluating the potential of dietary interventions to modulate gut-immune interactions, particularly in immune-mediated conditions like Hashimoto’s thyroiditis [26]. These findings provide a scientific foundation for developing dietary indices that support microbiota health as a novel approach to attenuate biomarkers of thyroid autoimmunity. Given the strong connection between diet, gut microbiota, and thyroid autoimmunity, targeted dietary strategies may help manage HT. However, few studies have assessed diets specifically designed to support gut health in relation to HT biomarkers. This study aims to evaluate the association between a novel gut microbiota-targeted dietary index and thyroid status (e.g. thyroid hormones and autoantibodies [anti-TPO and anti-Tg]), metabolic profiles and oxidative stress biomarkers in women with HT.

Methods

Patients’ recruitment

This cross-sectional study was conducted among 162 women of reproductive age (18–49 years) diagnosed with HT. Participants were recruited from outpatient endocrine clinics of Prince Sattam Bin Abdulaziz University Hospital in Al-Kharj, Saudi Arabia between December 2023 and June 2024. Diagnosis of HT was confirmed by an endocrinologist based on elevated thyroid-stimulating hormone (TSH) levels, positive anti-TPO and/or anti-Tg antibodies, and supportive ultrasonographic features of thyroiditis.

Sample size estimation was performed using G*Power software (version 3.1.9.7), targeting a two-tailed test to detect a minimum correlation coefficient (r) of 0.22 between the dietary index and Hashimoto’s thyroiditis-related biomarkers [27]. With a desired statistical power of 80% and a significance level of 0.05, the minimum required sample size was calculated to be 157 participants. To accommodate potential dropouts or incomplete data, a total of 162 women were ultimately recruited.

Participants were included if they had no history of other autoimmune, metabolic, or gastrointestinal disorders. Exclusion criteria included pregnancy, lactation, recent use of antibiotics or probiotics (within the past 3 months), adherence to specific therapeutic diets, or intake of medications known to affect thyroid function or gut microbiota composition (e.g., corticosteroids, immunosuppressants). All participants provided written informed consent prior to enrollment.

Anthropometric assessments

Anthropometric measurements were conducted using standardized protocols while participants wore light clothing and no shoes. Body weight was measured to the nearest 0.1 kg using a calibrated digital scale (Seca 803, Germany), and height was measured to the nearest 0.1 cm using a stadiometer (Seca 206, Germany). Body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared (kg/m²). Waist circumference (WC) was measured to the nearest 0.1 cm at the midpoint between the lowest rib and the iliac crest using a non-elastic measuring tape, with the participant in a standing position and at the end of a normal expiration. Hip circumference (HC) was measured at the widest part of the buttocks. Waist-to-hip ratio (WHR) was then calculated as WC divided by hip circumference. All measurements were taken twice, and the average was recorded for analysis.

Dietary and physical activity assessment

Usual dietary intake was assessed using a validated semi-quantitative food frequency questionnaire (FFQ) designed to capture participants’ habitual food consumption over the past 12 months [28]. Portion sizes were converted into grams using standard household measures, and nutrient intakes were analyzed using Nutritionist IV software (First Databank, San Bruno, CA), adapted for regional food items.

Dietary Index for Gut Microbiota was constructed based on the current scientific literature linking specific food groups with gut microbial composition, diversity, and metabolite profiles. The DIGM score incorporated both beneficial and harmful dietary components known to influence microbial richness and the abundance of health-related taxa [25, 29, 30]. To score the DI-GM, sex-specific median intakes of each component were computed except for a high-fat diet for which a fixed cutoff, i.e., 40% energy from fat was used [25]. Favorable food items, including avocado, broccoli, chickpeas, coffee, fermented dairy products, cranberries, fiber, green tea, soy bean, and whole grains, received a score of 1 if the individual’s intake exceeded the sex-specific median, and 0 otherwise. In contrast, unfavorable components—including red and processed meats, refined grains, and high-fat diets—were scored in reverse, with lower intakes receiving higher scores. The total DIGM score ranged from 0 to 14, with favorable components contributing up to 10 points and unfavorable items up to 4 points. A higher DIGM score represented a dietary pattern more conducive to gut microbial health. Physical activity (PA) was assessed using a validated International Physical Activity Questionnaire (IPAQ) [31].

Biochemical assays

After an overnight fast of at least 10 h, venous blood samples were drawn from participants and centrifuged at 3000 ×g for 10 min. The serum was separated and stored at − 70 °C until analysis. Thyroid hormones, including TSH, triiodothyronine (T3), and thyroxine (T4), were measured using commercially available enzyme-linked immunosorbent assay (ELISA) kits (e.g., Thermo Fisher Scientific, USA). Serum levels of thyroid autoantibodies—including anti-TPO and anti-Tg — were quantified using solid-phase ELISA kits (e.g., Abcam or BioLegend, USA).

Serum lipid profile parameters, including total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C) were analyzed by enzymatic colorimetric methods using an automated clinical chemistry analyzer (e.g., Hitachi 902, Roche Diagnostics, Germany). Low-density lipoprotein cholesterol (LDL-C) was calculated using the Friedewald formula [32].

Oxidative stress markers were assessed using standard validated laboratory procedures. TAC was measured using the ferric reducing antioxidant power (FRAP) assay described by Benzie and Strain et al. [33]. MDA, as an index of lipid peroxidation, was quantified using the thiobarbituric acid reactive substances (TBARS) method according to Ohkawa et al. [34]. ARE was determined spectrophotometrically using phenylacetate as substrate following the method introduced by Eckerson et al. [35]. All measurements were performed using a calibrated UV–Vis spectrophotometer (e.g., Shimadzu UV-1800, Japan).

Statistical analysis

Data analysis was performed using SPSS software version 26.0 (IBM Corp., Armonk, NY, USA). The distribution of variables was evaluated for normality using the Shapiro-Wilk test. Descriptive statistics for continuous variables are presented as means with standard deviations (SD), while categorical variables are reported as frequencies and percentages. Non-normally distributed variables are presented as median and interquartile range (Q1-Q3). Participants were categorized into tertiles based on DIGM score as follows: first tertile as low DIGM score (e.g. 1–4); second tertile as medium DIGM score (e.g. 4–8) and third tertile as high DIGM score (e.g. ≥ 8). To compare continuous and categorical variables across different tertiles of the DIGM, one-way analysis of variance (ANOVA) and Chi-square tests were employed, respectively. Post-hoc test of Tukey was used for between tertile’ comparisons. Adjustment for potential confounders (e.g. age, BMI, PA and smoking status) was performed with analysis of covariance (ANCOVA). For non-normally distributed variables, instead of ANOVA, Kruskall-Wallis followed by Mann-Whitney U test were applied. Also, the Quade nonparametric ANCOVA alternative was used for confounders’ adjustment. Statistical significance was set at a two-sided p-value less than 0.05.

Results

A total of 162 women with HT were categorized into three groups based on their DIGM scores of low (1–4), medium (4–8), and high (≥ 8). No significant differences were observed in age, weight, height, BMI, WC, or HC across DIGM categories (p > 0.05). However, WHR was significantly lower in the high DIGM group compared to the other groups (p = 0.030), suggesting a more favorable fat distribution profile among those with a microbiota-supportive diet. In terms of sociodemographic factors, no significant differences were found among DIGM groups for marital status, education level, employment status or physical activity (p > 0.05). However, the prevalence of smoking was significantly lower among participants in the high DIGM group (5.7%) compared to the low DIGM group (16.6%) (p = 0.049) (Table 1).

Table 1.

The comparison of general characteristics of HT patients across DIGM categories

Variable		N	Mean	SD	P value*
Age (y)	1st tertile	54	34.15	8.94	0.637
	2nd tertile	53	33.49	8.68
	3rd tertile	55	35.11	9.05
Weight (kg)	1st tertile	54	63.79	10.53	0.752
	2nd tertile	53	65.41	8.41
	3rd tertile	55	63.69	5.65
Height (cm)	1st tertile	54	158.96	5.19	0.362
	2nd tertile	53	159.81	6.95
	3rd tertile	55	160.56	5.24
BMI (kg/m2)	1st tertile	54	25.29	1.95	0.574
	2nd tertile	53	25.72	1.55
	3rd tertile	55	24.78	1.91
WC (cm)	1st tertile	54	84.24	5.73	0.281
	2nd tertile	53	85.91	8.21
	3rd tertile	55	82.07	7.47
HC (cm)	1st tertile	54	102.36	8.80	0.920
	2nd tertile	53	102.99	4.50
	3rd tertile	55	103.04	5.24
WHR †	1st tertile	54	0.81	0.06	0.030
	2nd tertile	53	0.82	0.07
	3rd tertile	55	0.79	0.06
PA (MET. Min/week)	1st tertile	54	38.30	5.97	0.411
	2nd tertile	53	36.84	5.88
	3rd tertile	55	37.80	5.34
Marriage [n%, married]	1st tertile	54	42	77.8	0.874
	2nd tertile	53	35	66
	3rd tertile	55	42	76.04
Education [n%, ≥ 12 years]	1st tertile	54	37	68.5	0.402
	2nd tertile	53	29	547
	3rd tertile	55	37	67.26
Job [n%, unemployed]	1st tertile	54	37	68.5	0.670
	2nd tertile	53	27	50.94
	3rd tertile	55	35	63.63
Smoking [n%, smokers]‡	1st tertile	54	9	16.6	0.049
	2nd tertile	53	5	9.43
	3rd tertile	55	3	5.66

DIGM, dietary index for gut microbiota; BMI, body mass index; WC, waist circumference; WHR, waist to hip ratio; PA, physical activity; MET, metabolic equivalent. * P values derived from one way ANOVA followed by Tukey’s post-hoc test. † Significant difference between third and second tertile. ‡ Significant difference between all of tertiles. First tertile: low DIGM score (e.g. 1–4); second tertile: medium DIGM score (e.g. 4–8) and third tertile: high DIGM score (e.g. ≥ 8)

The results of thyroid function and biochemical assessments are presented in Table 2. Serum levels of anti-TPO antibodies were significantly lower in the high DIGM group compared to the low DIGM group after adjustment for confounders (p = 0.044). For Anti-Tg, the difference between DIGM categories were statistically significant before and after adjustment for confounders (P = 0.037; P = 0.011 respectively). No significant differences were observed in serum TSH, T3, or T4 levels across DIGM groups (p > 0.05). Regarding lipid profile, a significant difference was observed in serum triglyceride levels, with participants in the high DIGM group having lower TG concentrations (79.83 ± 23.26 mg/dl) compared to those in the low DIGM group (104.66 ± 17.71 mg/dl) (p = 0.017). Other lipid parameters, including total cholesterol, HDL-C, LDL-C, did not differ significantly between groups. Also oxidative stress markers including TAC, MDA and ARE activity showed no statistically significant differences across DIGM categories (p > 0.05). Table 3 represents the median and interquartile range of intake of dietary components favorable and unfavorable for gut microbiota among the study participants. The findings reflect the variability in consumption patterns of gut microbiota–related foods within the study population (Table 3).

Table 2.

The comparison of biochemical variables of HT patients across DIGM categories

Biochemical variable		N	Mean	SD	P- value *	P-value **
TSH (mIU/l)€	1st tertile	54	2.35	1.10–6.10	0.883	0.534
	2nd tertile	53	2.20	1.10–4.50
	3rd tertile	55	3.50	1.15–5.15
T3 (nmol/l)	1st tertile	54	1.24	0.34	0.198	0.255
	2nd tertile	53	1.39	0.42
	3rd tertile	55	1.50	0.28
T4 (nmol/l)	1st tertile	54	6.88	0.56	0.321	0.306
	2nd tertile	53	6.99	0.52
	3rd tertile	55	7.27	0.12
Anti-TPO (IU/ml)† €	1st tertile	54	815.90	127.40–1200	0.076	0.044
	2nd tertile	53	385.70	112.30–1068.0
	3rd tertile	55	294.80	23.55-815.08
Anti-Tg (IU/ml) †€	1st tertile	54	48.95	14.60–292.20	0.037	0.011
	2nd tertile	53	15.50	8.70–89.90)
	3rd tertile	55	29.00	12.30–252.30
HDL (mg/dl)	1st tertile	54	56.35	5.18	0.234	0.210
	2nd tertile	53	57.90	2.41
	3rd tertile	55	63.18	8.66
TC (mg/dl)	1st tertile	54	174.01	15.33	0.632	0.757
	2nd tertile	53	167.84	13.46
	3rd tertile	55	172.72	16.22
TG (mg/dl) ‡	1st tertile	54	104.66	17.71	0.017	0.008
	2nd tertile	53	102.43	13.10
	3rd tertile	55	79.83	23.26
LDL (mg/dl)	1st tertile	54	96.72	18.64	0.604	0.742
	2nd tertile	53	89.46	16.64
	3rd tertile	55	93.57	15.19
TAC (mmol/L)	1st tertile	54	1.50	0.34	0.309	0.206
	2nd tertile	53	1.46	0.25
	3rd tertile	55	1.41	0.32
MDA (µmol/L)	1st tertile	54	2.79	1.05	0.730	0.828
	2nd tertile	53	2.85	1.09
	3rd tertile	55	2.70	0.85
ARE (U/l)	1st tertile	54	1.25	0.22	0.884	0.780
	2nd tertile	53	1.24	0.22
	3rd tertile	55	1.23	0.23

DIGM, dietary index for gut microbiota; TSH, thyroid stimulating hormone; Anti-TPO, particularly anti-thyroid peroxidase; Anti-Tg, anti-thyroglobulin; HDL, high density lipoprotein cholesterol; TC, total cholesterol; TG, triglyceride; LDL, low density lipoprotein cholesterol; TAC, total antioxidant capacity; MDA, malondialdehyde; ARE, arylesterase; * P values derived from one way ANOVA; €, data for non-normal variables are presented as median (interquartile range Q1-Q3) ** P -values derived from analysis of covariance after adjusting for the confounders of age, BMI, physical activity and smoking status. † Significant difference between first and second tertiles. ‡ Significant difference between third and second tertile. For non-normally distributed variables Kruskall-Wallis followed by Mann-Whitney U test and Quade test were applied. First tertile: low DIGM score (e.g. 1–4); second tertile: medium DIGM score (e.g. 4–8) and third tertile: high DIGM score (e.g. ≥ 8)

Table 3.

The median (interval) of favorite and unfavorate dietary ingredients for gut microbiota among study participants

Food item	Median (IQR, Q1-Q3)
Favorite for gut microbiota
Avocado (g/d)	0.279 (0 -1.14)
Broccoli (g/d)	6.20 (1.50-13.28)
Chickpeas (g/d)	4.43 (2.39–12.45)
Coffees (g/d)	1.53 (0–14)
Fermented dairies (g/d)	107.55 (58.42-241.46)
Cranberries (g/d)	0.4 (0.04–1.28)
Fiber (g/d)	50.43 (36.30-76.74)
Green teas (g/d)	0.68 (0-8.33)
Soybeans (g/d)	0.575 (0-4.29)
Whole grains (g/d)	218.24 (282.48-666.59)
Unfavorate for gut microbiota
Processed meat (g/d)	2 (0.0-5.5)
Red meat (g/d)	18.87 (12.73–34.07)
Refined grains (g/d)	456.11 (283.53–665.60)
High fat diet (%)	31.80 (26.91–35.36)

IQR, interquartile range. Non-normal variables are presented as median (interquartile range Q1-Q3)

Discussion

In this cross-sectional study of women with HT, higher adherence to a gut microbiota-supportive dietary pattern, as measured by the DIGM score, was significantly associated with lower serum levels of anti-TPO and anti-Tg antibodies, reduced triglyceride levels, and lower waist-to-hip ratio (Fig. 1). These findings suggest a potential link between dietary patterns favorable to gut microbial health and immune-metabolic parameters in HT, which could complement existing disease management strategies.

Fig. 1.

Summary of study findings

Microbiota-related modulation of Th17 and regulatory T cell balance is considered a core immunological mechanism in autoimmune diseases like HT [36]. SCFAs, particularly butyrate, downregulate inflammatory cytokines (e.g., IL-17, TNF-α) and promote Treg expansion, thus reducing the production of thyroid autoantibodies [37]. Moreover, dysbiosis has been linked with increased intestinal permeability which facilitates translocation of microbial antigens and stimulates autoimmune responses through molecular mimicry with thyroid antigens [37]. These mechanisms support the idea that improving gut microbial ecology through diet could attenuate autoimmune responses in HT.

Participants with higher DIGM scores had significantly lower serum TG levels. This observation is consistent with prior research showing that diets rich in fiber, unsaturated fats, and fermented foods modulate gut microbiota toward increased production of SCFAs, which enhance lipid metabolism and reduce hepatic lipogenesis [38]. SCFAs activate G-protein-coupled receptors (GPR41 and GPR43), improving insulin sensitivity and lipid clearance [39]. Furthermore, high-fiber and polyphenol-rich diets are associated with lower expression of lipogenic genes (e.g., SREBP-1c, FAS) and reduced very-low-density lipoprotein (VLDL) secretion [40]. Given that dyslipidemia is a frequent comorbidity in HT, this finding reinforces the metabolic benefit of microbiota-targeted dietary strategies in this population. Waist-to-hip ratio, an indicator of visceral fat accumulation, was significantly lower in individuals with higher DIGM scores. Diet-induced shifts in gut microbiota have been shown to reduce systemic endotoxemia and improve adipose tissue inflammation, contributing to reductions in central obesity [41]. SCFA-producing bacteria, particularly Akkermansia muciniphila, have been negatively associated with visceral fat and WHR [42]. Increased WHR has been linked with both thyroid dysfunction and systemic inflammation, possibly due to adipose-derived cytokines such as leptin and IL-6 that can influence autoimmune processes [43]. Therefore, the observed association may reflect both anti-inflammatory and metabolic benefits of DIGM adherence. It should be noted that while previous literature has proposed various mechanisms linking gut microbiota with immune regulation, lipid metabolism, and adiposity—including SCFA-mediated anti-inflammatory effects, modulation of Th17/Treg balance, and effects on intestinal permeability—our study did not directly measure microbiome composition, SCFA levels, or immune cell subsets. Therefore, any discussion of mechanistic pathways in the context of our findings remains hypothetical. These interpretations are based on existing research and should be considered as a foundation for future studies rather than confirmed causal relationships.

Although oxidative stress plays a central role in HT pathogenesis, no significant differences were observed in TAC, MDA levels and ARE activity across DIGM categories. This may be due to insufficient statistical power or the relatively narrow range of antioxidant intake in the studied population. Previous trials have suggested that longer-term dietary interventions or supplementation may be required to elicit measurable changes in redox status [44]. Also, evidence from observational and intervention studies suggests that healthy dietary patterns, such as Mediterranean or dietary approach to stop hypertension diets, can reduce oxidative stress, though effects on specific biomarkers are often heterogeneous [45, 46]. For instance, meta-analyses have shown reductions in MDA but inconsistent effects on TAC. Similarly, a case–control study in hypothyroid patients found a positive association with TAC but not MDA [47]. Also, the lack of significant differences in ARE across DIGM tertile may indicate that dietary modulation of paraxonase 1-related antioxidant activity is less pronounced due to relatively stable, host-determined nature of ARE activity, which is strongly influenced by genetic polymorphisms and less sensitive to short-term or moderate dietary variations [48]. These findings align with our results, indicating that dietary influence on oxidative stress may be modest and context-dependent. Future studies with larger samples, repeated biomarker measures, or longitudinal designs may better capture subtle but meaningful effects.

Similarly, no significant associations were observed between the DIGM score and serum TSH, T3, or T4 levels. This may reflect that dietary modulation primarily affects autoimmune or metabolic pathways rather than directly altering thyroid hormones. Supporting this, in a cross-sectional study, cardiometabolic dietary patterns were linked to TSH but not consistently with T4 [49] and a Mediterranean diet study reported modest changes in free T3 and T4 within the normal range [50] Additionally, food-group patterns showed differential associations with thyroid hormones in healthy adults [51] and narrative reviews emphasize micronutrient-mediated influences rather than gross hormonal shifts [52]. Thus, our null findings may reflect stable thyroid regulation, measurement limits, or stronger dietary effects on immune rather than endocrine aspects of Hashimoto’s thyroiditis.

This study is among the first to examine a microbiota-based dietary index in relation to thyroid autoimmunity. Its strengths include a validated FFQ, comprehensive biomarker assessment, and focus on a clinically relevant female population. Also, the DIGM score used in the present study is based on the index originally developed and evaluated in a NHANES cohort, where it demonstrated positive associations with urinary enterodiol and enterolactone—biomarkers commonly used as indicators of gut microbiota diversity [25]. This provides preliminary biological validation of the index. Despite the novel approach and clinically relevant findings, several limitations must be acknowledged. First, regarding the study design, the cross-sectional nature of this work precludes any causal inference. Therefore, the observed associations between the DIGM score and metabolic or inflammatory parameters should not be interpreted as cause–effect relationships. Second, the study relied on self-reported dietary intake, which may be subject to recall bias. Third, this study did not include measurements of free thyroid hormones (FT3 and FT4), as they were not part of the routine laboratory panel in the parent project. Although total T3, total T4, and TSH were assessed, the absence of FT3 and FT4 may limit the precision of thyroid function evaluation. Finally, the sample included only women of reproductive age, limiting generalizability to other demographic groups, including men and older adults.

Conclusion

This study demonstrates that adherence to a gut microbiota-supportive dietary pattern, as reflected by a higher DIGM score is associated with reduced levels of anti-thyroid peroxidase antibodies, lower triglyceride concentrations, and lower WHR among women with Hashimoto’s thyroiditis. Promoting dietary patterns that enhance gut microbial diversity—characterized by high intake of plant-based, fiber-rich, and fermented foods—may offer a low-risk, adjunctive approach to managing Hashimoto’s thyroiditis. Future longitudinal and interventional studies are needed to confirm these associations and to determine whether dietary interventions can lead to meaningful clinical improvements in thyroid autoimmunity and associated metabolic complications.

Author contributions

AAMAES and HG were the supervisors. They generated the first hypothesis of the work and managed all of the project. Additionally, they were in close contact to supervise the work progression. Data collection and sample gathering was done by MSM, AH and ZAAK. While MJS, IAAAK, JR, MSK and AE performed the data entry into software, data analysis and figure illustration. AAMAE and MSJ also wrote the first draft of the manuscript, revised all parts of the paper and finalized it. All of the authors were involved in article writing and confirmed the final draft of the paper for submission.

Funding

The work was granted by Prince Sattam Bin Abdulaziz University for financial support of the work (under project number: PSAU/2023/R/1447).

Data availability

The datasets of the current study are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

Written informed consent was obtained from all of the participants of the study. All methods in the current research were performed in accordance with the declaration of Helsinki’s guidelines and regulations. The protocol of the current study is approved by the ethics committee of Prince Sattam Bin Abdulaziz University under Project Number (PSAU/2023/R/1447).

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.