Abstract
Background
Polycystic ovary syndrome (PCOS) often leads to insulin resistance, affecting glucose and fat metabolism. This study aimed to explore the impact of mineral supplements on insulin resistance, blood sugar, and lipid profiles in women with PCOS.
Methods
A systematic review of randomized controlled trials (RCTs) was conducted across four databases. The risk of bias was assessed using the Cochrane Risk of Bias 2 tool. Mineral supplements were compared with a placebo in all studies, and data were analyzed using fixed-effect and random-effects models to assess the impact on metabolic parameters.
Results
This meta-analysis included 11 RCTs involving 618 women with PCOS. Mineral supplementation was associated with significant reductions in fasting blood glucose (SMD = − 0.34, p < 0.001), fasting insulin (SMD = − 0.72, p < 0.001), and HOMA-IR (SMD = − 0.75, p < 0.001). In addition, total cholesterol (SMD = − 0.35, p < 0.001) and triglyceride levels (SMD = − 0.58, p < 0.001) were significantly reduced. A small but statistically significant reduction in HDL levels was also observed (SMD = − 0.19, p = 0.04). No significant effect was found on LDL-C (SMD = − 0.11, p = 0.55).
Conclusion
Mineral supplementation may improve insulin resistance and selected metabolic parameters in PCOS, with the most consistent effects observed for glycemic indices. Effects on lipid parameters were mixed. Further large-scale, well-designed trials are needed to clarify long-term benefits and optimal supplementation strategies.
Clinical trial number
Not applicable.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12902-025-02158-x.
Keywords: Polycystic ovary syndrome, Insulin resistance, Mineral supplements, Comprehensive treatment, Systematic evaluation, Meta-analysis
Introduction
Polycystic ovary syndrome (PCOS) is a prevalent public health concern and one of the most commonly occurring endocrine disorders [1]. The worldwide prevalence of PCOS is estimated to range between 5% and 18% [2]. PCOS primarily affects the endocrine system and reproductive function, with complex clinical symptoms that are difficult to treat, severely impacting patients’ quality of life [3, 4]. Clinical symptoms include irregular menstruation, ovulation disorders, excessive androgen secretion (e.g., obesity, hirsutism, acne), and ovarian polycystic changes detected by pelvic ultrasound [5]. The pathogenesis of PCOS remains unclear [6], but studies have linked it to genetic, environmental, and psychological factors [7]. The pathogenesis of PCOS is primarily attributed to disturbances in the endocrine system and dysfunction of the hypothalamic-pituitary-ovarian axis [8], leading to insulin resistance (IR) and elevated androgen production. IR plays a central role in the pathophysiological process of PCOS [9]. In PCOS patients, insulin receptor number and function are impaired, weakening the biological effect. Reduced insulin receptor sensitivity leads to a significant decline in insulin efficacy during glucose metabolism, further promoting hyperinsulinemia. This also affects ovarian function [10]. Studies have shown that 75% of lean PCOS women and 95% of obese women suffer from IR [11], indicating a high incidence of IR. It is estimated that the annual healthcare-related economic burden of PCOS in the United States is approximately USD 8.5 billion (2022 dollars). Of this total, about 46% (USD 3.9 billion in 2022) is attributable to the management of reproductive endocrine disorders, including menstrual dysfunction, hirsutism, and infertility [12].
Metformin is the first-line treatment for PCOS with insulin resistance (PCOS-IR) [13]. The mechanism of action involves enhancing insulin sensitivity, decreasing hepatic glucose production, and lowering androgen levels and body weight [14]. However, due to the difficulty in treating PCOS, long-term pharmacological intervention is often necessary [15]. long-term drug intervention may lead to gastrointestinal side effects (e.g., nausea and diarrhea), vitamin B12 deficiency (resulting in fatigue and anemia), and liver damage [16]. Treatment strategies for PCOS-IR patients are shifting from solely pharmacological interventions to integrated management models that include lifestyle changes and nutritional supplementation. Recent research18 has indicated that mineral supplements, as a novel adjunctive therapy, play a crucial role in managing PCOS19, obesity, 20metabolic syndrome, 21 and diabetes [17–21].
Studies have demonstrated that mineral supplements play a significant role in insulin signaling pathways and metabolic regulation, with substantial therapeutic effects [22]. Jamilian et al. [23] reported that chromium may improve IR, while other studies have found that mineral supplements have no significant effects on blood sugar, lipids, or PCOS [24]. Recently, Kanafchian et al. [25] conducted a case-control study showing that reduced serum zinc concentrations in women with PCOS positively impact metabolic and endocrine systems. However, it has been noted that while mineral supplements improve insulin sensitivity, insufficient evidence exists to support their effectiveness in alleviating PCOS-IR patients [26]. The therapeutic efficacy of mineral supplements in PCOS-IR therefore remains controversial. While several studies have investigated the effects of individual mineral supplements on PCOS-related outcomes [27], no comprehensive systematic review or meta-analysis has yet evaluated their overall impact on insulin resistance–related metabolic parameters in this population. Consequently, we conducted the present meta-analysis to systematically assess the effects of mineral supplementation in women with PCOS-IR. By evaluating key glycemic and metabolic markers, this study aims to provide clinicians with more robust evidence on treatment responses and to support the development of more personalized therapeutic strategies, thereby informing both clinical practice and future research.
Methods
Trial registration number
The study is registered with PROSPERO (registration number: CRD42024627634).
Literature search strategy
Two researchers independently performed a search across PubMed, Embase, Web of Science, and Cochrane. In PubMed, MeSH terms were utilized to broaden the scope of the search, with the following search terms: magnesium, chromium, zinc, selenium, chromium picolinate, polycystic ovary syndrome, and insulin resistance. There were no restrictions on publication date or study type; however, only studies published in English were included. The literature search was conducted up to August 31, 2025. The two authors independently reviewed the titles and abstracts of the identified articles, excluded duplicates and irrelevant studies, and reached a consensus on which studies to include or exclude based on the inclusion criteria. One author extracted basic information, study objectives, outcomes, and follow-up data, which was then reviewed by the second author. In cases of disagreement, a third-party expert made the final determination. This systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [28].
Inclusion and exclusion criteria
The inclusion criteria for this study were as follows: Study participants comprised individuals diagnosed with PCOS according to the 2003 Rotterdam criteria established by the European Society of Human Reproduction and Embryology (ESHRE) and the American Society of Reproductive Medicine (ASRM) [29], requiring at least two of the following: oligo- or anovulation, clinical or biochemical hyperandrogenism, or polycystic ovarian morphology (defined as the presence of ≥ 12 follicles measuring 2–9 mm in diameter in either or both ovaries and/or an ovarian volume > 10 cm³). The intervention involved mineral supplementation (magnesium, chromium, zinc, selenium, and chromium picolinate) in the experimental group compared with a placebo in the control group. Eligible literature was restricted to English-language publications reporting at least one parameter of metabolic dysfunction or insulin resistance, such as FBG, FINS, HOMA-IR, TC, TG, HDL-C, or LDL-C. Only RCTs with a sample size greater than 20 were included.
The exclusion criteria were systematically defined as follows: studies were excluded if the participants failed to meet the established diagnostic criteria for PCOS or did not demonstrate complete clinical remission; investigations with methodological inconsistencies in intervention protocols, insufficient critical data, or incomplete outcome reporting were also excluded; duplicate publications and studies with substantially incomplete information were not considered; all non-comparative research designs—including animal studies, reviews, letters, clinical guidelines, case reports, mechanistic investigations, conference abstracts, expert opinions, and editorial comments—were excluded; additionally, studies were ineligible if the full text was unavailable or if they were published in languages other than English.
Data extraction
Two investigators independently assessed the references according to the established inclusion and exclusion criteria, and extracted relevant data using standardized extraction forms. The extracted data were subsequently verified by both reviewers, with any discrepancies resolved through discussion. Studies lacking necessary data were excluded from the analysis. For each included study, the following information was gathered: (1) study details, including the first author, publication year, and trial protocol; (2) baseline patient characteristics: study design, sample size, age, intervention type, dosage of mineral supplements (magnesium, chromium, zinc, selenium, chromium picolinate), and placebo; (3) outcome measures. In cases where the measurement units for the outcome variables differed, they were converted to uniform units.
Quality evaluation
The full texts of the 11 included RCTs were individually examined by two researchers, who utilized the Cochrane risk of bias assessment tool to evaluate study quality. The Risk of Bias (ROB 1.0) tool [30], as recommended by the Cochrane Collaboration, was employed for the assessment of study quality following data extraction. This tool assesses several key factors, including the generation of random sequences, allocation concealment, blinding of participants and outcome assessors, completeness of outcome data, selective reporting, and potential sources of bias. The quality assessment was categorized into three levels: “low risk,” “high risk,” or “unclear risk.” Any disagreements were resolved by consultation with a third-party researcher. The strength of the evidence for all outcomes included in the meta-analysis was evaluated using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) system [31].
Statistical analysis
Statistical analysis was conducted using RewMan5.4.0. For continuous variables, the weighted mean difference (WMD) and 95% confidence interval (95% CI) were calculated. The standard mean difference (SMD) and 95% CI were also computed for continuous data. Heterogeneity was evaluated using I² values, where I² ≤ 30%, 30% < I² < 75%, and I² ≥ 75% were considered to represent low, moderate, and high heterogeneity, respectively [32]. A fixed-effect model was applied when I² < 50%, while a random-effects model was employed if I² ≥ 50% [33]. Subgroup analyses based on dosage and intervention duration were conducted to ensure the robustness of the results. Sensitivity analysis was performed for outcomes with substantial heterogeneity, and the sources of this heterogeneity were explored. Publication bias was assessed using Begg’s funnel plot and Egger’s test [34, 35]. A P-value < 0.05 was considered to indicate a statistically significant difference between the two groups.
Results
Search results
During the initial literature search, a total of 4,366 articles were identified. After excluding 1,110 duplicate studies, 3,256 articles were removed based on the exclusion criteria following a review of their titles and abstracts. This left 24 studies for further consideration. A full-text review was subsequently conducted, leading to the exclusion of 13 studies that did not meet the inclusion criteria. Ultimately, 11 studies [23, 36–45] were included in the meta-analysis, all of which were RCTs. The process and outcomes of the literature screening are depicted in Fig. 1.
Fig. 1.
Flow diagram for literature search
Characteristics of included studies
Table S1 summarizes the characteristics of the included studies, which assessed a total of 618 women with PCOS. Of these, 310 received mineral supplementation, while 308 received a placebo. Participants were from two countries, with the majority of studies conducted in Iran (10 studies, 83.3%), followed by Egypt (1 study, 16.7%). In terms of interventions, all studies compared mineral supplementation with a placebo.icip
Risk of bias of included studies
All included studies were RCT with comparable baseline characteristics. Regarding allocation concealment, one of the 11 studies explicitly stated a low risk, while the remaining studies were assessed as “unclear risk” due to the lack of information on concealment methods. In terms of blinding, nine studies out of 11 implemented blinding for participants, which was assessed as low risk, while the remaining two studies did not specify whether blinding was applied, resulting in a high risk assessment. All 11 studies applied blinding for outcome assessment, which was considered low risk. For data integrity, all studies reported pre-specified outcome measures, with no other biases identified, thus assessed as low risk. Overall, the evidence is generally at low risk in several domains, but uncertainty remains primarily due to incomplete reporting of allocation concealment (Fig. 2).
Fig. 2.
Risk of bias of included studies
Results of the meta-analyses
Fasting blood glucose (FBG)
Ten studies [23, 36–44] involving 565 participants assessed the effect of mineral supplements on blood glucose levels in PCOS patients. Data analysis was conducted using a fixed-effect model (I² = 13%, p = 0.33). The results indicated that, compared to placebo, mineral supplements significantly reduced blood glucose levels in PCOS patients (SMD = -0.34, 95% CI: -0.50 to -0.17, p < 0.001) (Fig. 3A). This finding suggests that mineral supplements have a statistically significant effect on improving blood glucose levels.
Fig. 3.
Forest plot showing the effect of mineral supplements on PCOS patients: (A) Fasting Blood Glucose (FBG); (B) Fasting Insulin (FINS); (C) Homeostasis Model Assessment of Insulin Resistance (HOMA-IR)
Fasting insulin (FINS)
Eight studies [23, 36–41, 43] involving 465 participants assessed the effect of mineral supplements on fasting insulin (FINS) in PCOS patients. Data analysis was performed using a fixed-effect model (I² = 28%). The results showed that, compared to placebo, mineral supplements significantly reduced fasting insulin levels in PCOS patients (SMD = -0.72, 95% CI: -0.91 to -0.53, p < 0.001) (Fig. 3B). This finding suggests that mineral supplements have a statistically significant effect on improving FINS levels.
Homeostasis model assessment of insulin resistance (HOMA-IR)
Eight studies [23, 37–41, 43, 44]involving 420 participants assessed the effect of mineral supplements on HOMA-IR in PCOS patients. Data analysis was conducted using a fixed-effect model (I² = 16%). The results indicated that, compared to placebo, mineral supplements significantly reduced HOMA-IR levels in PCOS patients (SMD = -0.75, 95% CI: -0.96 to -0.53, p < 0.001) (Fig. 3C). This finding suggests that mineral supplements have a statistically significant effect on improving HOMA-IR.
Total cholesterol (TC)
Ten studies [23, 37–45] involving 533 participants assessed the effect of mineral supplements on cholesterol levels in PCOS patients. Data analysis was performed using a fixed-effect model (I² = 41%). The results showed that, compared to placebo, mineral supplements significantly reduced cholesterol levels in PCOS patients (SMD = -0.35, 95% CI: -0.52 to -0.17, p < 0.001) (Fig. 4A). This finding suggests that mineral supplements have a statistically significant effect on improving cholesterol levels.
Fig. 4.
Forest plot showing the effect of mineral supplements on PCOS patients: (A) Total Cholesterol (TC); (B) Triglycerides (TG)
Triglyceride (TG)
Nine studies [23, 37–44] involving 480 participants assessed the effect of mineral supplements on triglyceride levels in PCOS patients. Data analysis was conducted using a random-effects model (I² = 58%). The results indicated that, compared to placebo, mineral supplements significantly reduced triglyceride levels in PCOS patients (SMD = -0.58, 95% CI: -0.87 to -0.29, p < 0.001) (Fig. 4A). This finding suggests that mineral supplements have a statistically significant effect on improving triglyceride levels.
High-density lipoprotein (HDL)
Nine studies [23, 37–44] involving 480 participants assessed the effect of mineral supplements on high-density lipoprotein (HDL) levels in PCOS patients. Data analysis was conducted using a fixed-effect model (I² = 33%). The results indicated that, compared to placebo, mineral supplements significantly reduced HDL levels in PCOS patients (SMD = -0.19, 95% CI: -0.37 to -0.01, p = 0.04) (Fig. 5A). These results demonstrate a statistically significant decrease in HDL levels following mineral supplementation compared with placebo.
Fig. 5.
Forest plot showing the effect of mineral supplements on PCOS patients: (A) High-Density Lipoprotein (HDL); (B) Low-Density Lipoprotein (LDL-C)
Low-density lipoprotein (LDL-C)
Nine studies [23, 37–44] involving 480 participants assessed the effect of mineral supplements on low-density lipoprotein (LDL-C) levels in PCOS patients. Data analysis was conducted using a random-effects model (I² = 73%). The results indicated that, compared to placebo, mineral supplements had no significant effect on LDL-C levels in PCOS patients (SMD = -0.11, 95% CI: -0.46 to 0.25, p = 0.55) (Fig. 5B). This finding suggests that mineral supplements do not significantly improve LDL-C levels.
Subgroup analysis
Table 1 presents the subgroup analysis of triglyceride and LDL-C. The subgroup analysis results indicated that triglyceride exhibited statistically significant effects across different intervention time and dose subgroups (all P < 0.01): for the intervention time subgroup, the SMD was − 0.43 (95% CI [-0.64, -0.22], I²=42%) when the intervention duration was ≤ 8 weeks, and − 1.09 (95% CI [-1.49, -0.70], I²=0%) when the duration was > 8 weeks; for the dose subgroup, the SMD was − 0.53 (95% CI [-0.80, -0.25], I²=0%) at a dose of ≤ 200 mg, and − 0.62 (95% CI [-1.12, -0.12], I²=75%, P = 0.01) at a dose of > 200 mg. In contrast, LDL-C did not show statistically significant effects in any of the subgroups (all P > 0.05): the SMD was − 0.03 (95% CI [-0.25, 0.20], I²=1%, P = 0.80) for an intervention duration of ≤ 8 weeks, -0.22 (95% CI [-1.29, 0.86], I²=92%, P = 0.69) for > 8 weeks, -0.08 (95% CI [-0.35, 0.19], I²=34%, P = 0.55) at a dose of ≤ 200 mg, and − 0.09 (95% CI [-0.71, 0.53], I²=84%, P = 0.77) at a dose of > 200 mg.
Table 1.
Subgroup analysis of triglyceride and low density lipoprotein
| Subgroup | SMD (95% CI) | I² | p value |
|---|---|---|---|
| Triglyceride | |||
| Intervention time | |||
| ≤ 8 weeks | -0.43 [-0.64, -0.22] | 42 | < 0.01 |
| > 8 weeks | -1.09 [-1.49, -0.7] | 0 | < 0.01 |
| Dose | |||
| ≤ 200 mg | -0.53 [-0.80, -0.25] | 0 | < 0.01 |
| > 200 mg | -0.62 [-1.12, -0.12] | 75 | 0.01 |
| Low density lipoprotein | |||
| Intervention time | |||
| ≤ 8 weeks | -0.03 [-0.25, 0.2] | 1 | 0.8 |
| > 8 weeks | -0.22 [-1.29, 0.86] | 92 | 0.69 |
| Dose | |||
| ≤ 200 mg | -0.08 [-0.35, 0.19] | 34 | 0.55 |
| > 200 mg | -0.09 [-0.71, 0.53] | 84 | 0.77 |
Certainty of the evidence
According to the GRADE criteria, the certainty of the evidence was rated as moderate. This rating was mainly downgraded due to concerns regarding the risk of bias in the included studies. A detailed GRADE assessment can be found in Table S2.
Discussion
Our systematic review and meta-analysis suggests that mineral supplementation is associated with improvements in biochemical markers related to insulin resistance in women with PCOS. Across 11 RCTs, mineral supplements significantly reduced FBG (SMD = − 0.34, p < 0.001) and FINS (SMD = − 0.72, p < 0.001), and were associated with a significant reduction in HOMA-IR (SMD = − 0.75, p < 0.001). Collectively, these findings indicate that mineral supplementation may contribute to improved insulin sensitivity in PCOS, supporting its potential role as an adjunctive strategy for managing PCOS-related insulin resistance. With respect to lipid outcomes, mineral supplementation was associated with significant reductions in total cholesterol (SMD = − 0.35, p < 0.001) and triglycerides (SMD = − 0.58, p < 0.001). In contrast, a small but statistically significant reduction in HDL-C was observed (SMD = − 0.19, p = 0.04), and no significant effect was found for LDL-C (SMD = − 0.11, p = 0.55). Therefore, the overall effects of mineral supplementation on lipid parameters appear mixed, and the clinical significance of this modest change in HDL-C as a meaningful improvement remains uncertain. Future well-designed trials with longer follow-up, standardized supplement regimens, and clinically meaningful lipid endpoints are warranted to clarify the impact of mineral supplementation on lipid metabolism, particularly LDL-C and HDL-related outcomes.
In PCOS patients, approximately 50%-70% of women experience varying degrees of IR [46]. Even women with normal body weight may face a higher risk of IR [47]. Insulin promotes glucose uptake and utilization by binding to insulin receptors on target tissues, but in PCOS patients, the efficiency of insulin action is reduced, leading to insulin resistance [48, 49]. IR creates a positive feedback loop through the hypothalamic-pituitary-ovarian axis, promoting excessive androgen production and exacerbating insulin secretion, forming a vicious cycle that leads to metabolic disturbances and reproductive dysfunction [50, 51]. PCOS patients with IR often present with glucose metabolism abnormalities (such as elevated fasting blood glucose and impaired glucose tolerance) and lipid metabolism disorders (such as increased triglycerides, decreased high-density lipoprotein cholesterol, and increased low-density lipoprotein cholesterol), which increase the risk of type 2 diabetes, hyperlipidemia, and fatty liver disease [52, 53]. Consistent with this metabolic perspective, adherence to healthy dietary patterns such as the DASH and Mediterranean diets has been shown to be associated with lower blood pressure and reduced cardiometabolic risk in non-hypertensive populations, highlighting the importance of dietary components—including micronutrients—in metabolic regulation [54]. Therefore, intervening in insulin resistance not only improves the metabolic issues in PCOS patients but also helps prevent related metabolic diseases.
Our study suggests that mineral supplements can regulate metabolism in PCOS patients with IR. Minerals improve physical and hormonal balance by promoting the conversion of cholesterol into testosterone via steroid hormone pathways, while also regulating the synthesis and secretion of insulin, thereby facilitating metabolic regulation [55]. Minerals are known to be involved in neurotransmitter regulation; zinc affects the secretion of gonadotropin-releasing hormone (GnRH) in the hypothalamus, influencing the reproductive axis [56]. By modulating NMDA and GABA receptors, magnesium reduces excessive excitation of hypothalamic neurons, alleviating anxiety and depression symptoms in PCOS patients [57]. Chromium improves insulin function and leptin signaling, indirectly affecting the hypothalamus’ regulation of appetite and energy expenditure, thus reducing body mass index (BMI) [58]. Selenium influences the hypothalamic-pituitary-thyroid axis, regulating thyroid hormone metabolism, energy metabolism, and body temperature [59]. Chromium picolinate improves insulin sensitivity by reducing the breakdown of TG in adipose tissue, lowering free fatty acid (FFA) levels, triglyceride levels, and cholesterol production [60].
Our study indicates that mineral supplements significantly reduce TC and TG levels, and also lead to a modest but statistically significant reduction in HDL-C levels, with no significant effect on LDL-C levels. Insulin resistance plays a central role in lipid metabolism disorders [61], as it increases lipase activity and the release of FFA, promoting the breakdown of triglycerides, leading to fatty liver and hypertriglyceridemia [62]. Research shows that mineral supplements improve lipid metabolism disorders through various mechanisms and may help prevent atherosclerosis [63]. Zinc improves insulin sensitivity by participating in energy and lipid metabolism, reducing total cholesterol and LDL-C levels, and preventing atherosclerosis [64]. A meta-analysis has shown that zinc can improve insulin resistance in PCOS [65], though its mechanism remains unclear. Magnesium can reduce total cholesterol and triglyceride levels, while protecting endothelial cells from oxidative stress, and selenium helps reduce the risk of cardiovascular disease [66, 67]. However, evidence for these effects is still limited. Another study reported that magnesium, when combined with other nutritional supplements, can lower total cholesterol, triglycerides, and LDL-C levels [68]. Chromium picolinate reduces total cholesterol and LDL-C, improves lipid profiles, lowers triglyceride levels, and may increase HDL-C, potentially preventing atherosclerosis [69]. However, some studies report that chromium picolinate, while improving insulin resistance, may increase free testosterone levels and BMI [70], and can lower fasting insulin, regulating glucose metabolism [71–73]. In addition, a separate meta-analysis indicated that chromium supplementation exerts no beneficial effects on blood lipid profiles, and thus it cannot be used as a monotherapy for managing dyslipidemia in adults [74]. Additionally, combining different mineral supplements may have synergistic effects [75]. For example, the combined supplementation of magnesium, zinc, calcium, and vitamins D or E has been shown to improve lipid profiles, enhance glucose metabolism, and modulate the inflammatory response in PCOS patients [76].
In addition to their metabolic roles, several minerals possess notable antioxidant properties. Evidence from other chronic inflammatory conditions further supports the relevance of antioxidant capacity to disease severity. For example, a cross-sectional study in patients with knee osteoarthritis demonstrated that higher dietary total antioxidant capacity was inversely associated with disease severity as well as inflammatory and oxidative stress biomarkers, including IL-6, TNF-α, MMP-1, and NF-κB [77]. In one study, magnesium supplementation in women with PCOS significantly increased total antioxidant capacity (TAC) (522 mmol/L vs. 590 mmol/L; β = 66.3), suggesting that these beneficial effects may be attributable, at least in part, to the antioxidant activity of magnesium [78] In another randomized controlled trial, zinc supplementation significantly reduced hirsutism compared with placebo, as evidenced by a decrease in the Ferriman–Gallwey score by 1.71 points, which is considered the gold standard for the assessment of hirsutism (p < 0.001). Moreover, zinc supplementation was associated with a significant reduction in circulating malondialdehyde (MDA) concentrations, a well-established biomarker of oxidative stress (− 0.09 µmol/L, p = 0.04) [79]. These findings were further supported by another study in which women with PCOS aged 18–40 years received selenium supplementation at a dose of 200 µg/day for 8 weeks. This intervention resulted in significant improvements in reproductive outcomes, along with marked reductions in hirsutism scores, serum dehydroepiandrosterone (DHEA) levels, high-sensitivity C-reactive protein (hs-CRP), and MDA concentrations [80]. Additionally, a randomized controlled trial involving 54 infertile women with PCOS aged 18–40 years demonstrated that supplementation significantly reduced fasting plasma glucose (FPG), insulin levels, serum triglycerides, total cholesterol, and MDA concentrations, while plasma TAC was significantly increased [81].
Given that most of the trials included in our meta-analysis were conducted in Iran, it is reasonable to question whether genetic or biochemical backgrounds specific to Iranian women might modulate the efficacy of mineral supplementation. An association study investigating polymorphisms in the H19 imprinted gene in an Iranian population reported that the H19 rs217727 C allele significantly increased susceptibility to PCOS (OR ≈ 2.0), suggesting that this imprinting gene variant may serve as a genetic risk marker [82]. Another study in an Iranian cohort found that the IGF2BP2 rs4402960 T allele significantly elevated PCOS risk (OR = 1.52), indicating that this locus may contribute to insulin resistance and aberrant follicular development through disruption of IGF-2 regulation [83]. In contrast, a separate Iranian study reported that genotype and allele distributions of RETN − 420 C/G and + 299 A/G polymorphisms did not differ significantly between PCOS and control groups (p > 0.05), showing no evident association with PCOS risk [84]. Such discrepancies highlight the complex etiology of PCOS and the potential influence of ethnic and genetic diversity on disease susceptibility. Integrating these genetic data into future mineral-supplementation trials could help elucidate gene–nutrient interactions, enable stratified efficacy analyses, and ultimately enhance the external validity of findings beyond the Iranian population.
This study provides a comprehensive evaluation of the effects of mineral supplements (Mg, Cr, Zn, Se, CrP) on PCOS-IR and treatment outcomes for FBS, FINS, HOMA-IR, TC, TG, HDL-C, and LDL-C. However, there are some limitations in our study: (1) The evidence base regarding the effects of mineral supplements on PCOS-related complications is still limited. (2) Due to insufficient reporting of intervention details in the primary studies, we were unable to conduct subgroup and heterogeneity analyses based on key supplementation parameters (including dosage, duration, and administration frequency). (3) Since most of the studies included in our meta-analysis were conducted in Iran, the results may only apply to the Iranian population, potentially introducing selection bias. More in-depth research in Eastern populations and other countries is needed. Therefore, future studies should focus on high-quality, large-scale trials.
Conclusion
In conclusion, mineral supplementation may improve IR in patients with PCOS; however, the evidence should be interpreted cautiously due to the predominance of trials from a limited geographic region. Further large-scale, multicenter, long-term RCTs in more diverse populations are needed to confirm these findings, evaluate the durability of effects, and clarify potential synergistic benefits of combined mineral supplementation, thereby supporting more individualized clinical decision-making.
Supplementary Information
Below is the link to the electronic supplementary material.
Author contributions
JLW contributed to conception and design of the study. JY, RW organized the database. JY, SC, JW performed the statistical analysis. JY, RW, JW wrote the first draft of the manuscript. JY, QQ, CW modified the manuscript. All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.
Funding
The authors declare that no funding was received for this work.
Data availability
The datasets used and analyzed during the current study are available from the corresponding authors on reasonable request.
Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets used and analyzed during the current study are available from the corresponding authors on reasonable request.