The Effect of Chromium Supplements on Insulin Resistance: A Systematic Review and Meta-analysis of Randomized Controlled Trials

1. Introduction

The insulin hormone interacts with receptors on target cells and triggers an anabolic response (Petersen et al., 2017). Primarily, insulin in skeletal muscles and the liver increases glucose consumption through glycogen synthesis while suppressing lipolysis in white adipose tissue. The substance indirectly inhibits hepatic gluconeogenesis by reducing hepatic acetyl-CoA levels and diminishing pyruvate carboxylase activity (Schinner et al., 2005). Insulin resistance (IR) is marked by reduced responses of target cells accompanied by compensatory hyperinsulinemia. Potential contributing factors could include the down-regulation of insulin receptors, or genetic variations affecting tyrosine phosphorylation of these receptors, or may involve abnormalities of  GLUT 4 (Glucose transporter proteins) function (Wilcox et al., 2005).

IR is associated with overweight, hypertension (HTN), type 2 diabetes mellitus (T2DM), and hyperlipidemia; however, some mechanisms remain unclear (Rao et al., 2006). Diminished muscle glucose metabolism in response to insulin may cause hepatic steatosis and, along with a reduction in substrate oxidation, may be linked to mitochondrial dysfunction (Petersen et al., 2006). There may be a connection between polycystic ovary syndrome (PCOS) and IR, but the importance of this relationship remains complex and not fully understood (Moghetti et al., 2021). Ongoing research suggests that IR may be a contributing element in the development of cancer by mechanisms that include hyperinsulinemia, which provokes increasing levels of insulin-like growth factor 1 (IGF-1), potentially influencing the initiation and progression of tumors in individuals with IR. Moreover, there is frequently an excessive generation of reactive oxygen species that can be harmful to DNA and potentially contribute to the onset of cancer. (Arcidiacono et al., 2012). Interestingly, brain neurons possess insulin receptors, and insulin is integral to neuronal growth, synaptic development, and mitogenesis. Various preclinical studies have shown impaired insulin signaling to be associated with neurodegenerative diseases of the brain  (Cholerton et al., 2011. Hölscher et al., 2020). Insulin resistance exerts a multifaceted influence on the body, potentially leading to long-term complications. Therefore, multiple strategies have been proposed to mitigate insulin resistance. These strategies include using antidiabetic agents, anti-inflammatory medications, and the supplementation of vitamins and minerals, among others (Lebovitz et al., 2004. McDonald et al., 2007. Tong et al., 2022. Zhao et al., 2023). Chromium is one of the medications that has been used for reducing insulin resistance (Nishimura et al., 2021). The exact mechanism by which chromium affects insulin metabolism is not entirely understood. It has been suggested that trivalent chromium improves insulin function in peripheral tissues (Lipko et al., 2018). In vitro research suggests that chromium may enhance insulin sensitivity by stimulating the function of insulin receptors (Sahin et al., 2007). It has been demonstrated that chromium enhances insulin receptor β activity. Additionally, chromium promotes the movement of Glut4, a protein that helps cells take glucose to the cell surface. Chromium reduces the activity of PTP-1B (protein tyrosine phosphatase-1B), which normally slows down insulin signaling. It also helps reduce stress within cells and helps move cholesterol out of cell membranes, which supports the movement of Glut4 and increases glucose uptake (Hua et al., 2011).

Animal studies have shown an increase in chromium losses in diabetic rats (Clodfelder et al., 2017) and have expressed the positive impact of chromium in decreasing insulin resistance in obese mice  (Wang et al., 2006. Sreejayan et al., 2008. Król et al., 2010). Clinical trials have examined the effects of chromium, either alone or in combination with other interventions, on glucose metabolism (Lai et al., 2008. Dou et al., 2016. Saiyed et al., 2016. Yao et al., 2021). In addition, some meta-analyses have demonstrated the effects of chromium on reducing insulin resistance in T2DM (Balk et al., 2007. Heshmati et al., 2018.), and some showed a decrease in fasting glucose (Abdollahi et al., 2017. Zhao et al., 2022). While the majority of current research investigates the impact of supplementation on insulin and glucose profiles, in this review, we aimed to explore and compare the impact of chromium on insulin resistance, evaluating its effects across different populations.

2. Method

The protocol for the study has been registered with the International Prospective Register of Systematic Reviews (PROSPERO), and it is assigned the registration number CRD420250655755. The research adhered to the guidelines established by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) (Page et al., 2021).

2.1. Search Strategy

An extensive search was performed across PubMed, Web of Science, Scopus, and Embase databases in January 2025. The objective was to identify randomized controlled trials assessing the impact of chromium supplementation on insulin sensitivity and resistance. We included a diverse population rather than narrowing it down to a particular group. The search utilized MeSH terms, synonyms, and related keywords for chromium, insulin resistance, and metabolic syndrome. In addition, a comprehensive manual search was conducted utilizing Google Scholar alongside an exploration of gray literature sources. The full search strategy on different databases is provided in Appendix 1.

Two independent reviewers examined and evaluated all identified articles to determine their eligibility according to the established inclusion and exclusion criteria. To guarantee the correctness of the chosen studies, any conflicts were addressed through discussions with a third reviewer.

2.2.  Inclusion and Exclusion Criteria

We employed the PICO framework to assess the population, intervention, comparison, and outcome as a guiding structure to clearly define the eligibility criteria (Table 1). We considered all randomized controlled trials (RCTs) published in English in peer-reviewed journals up to January 2025. These studies focused on the impact of chromium supplementation on insulin and glucose levels.

We excluded non-RCTs, observational studies, case reports, and review articles to ensure a focused analysis. Additionally, we excluded articles that did not report relevant outcomes.

Table 1: The population, intervention, comparison, outcome study design (PICO)

 2.3. Data Extraction

Two reviewers were tasked with extracting data, with a clear focus on enhancing the quality of our work. An Excel spreadsheet was used to systematically gather study characteristics, including the lead author's name, year of publication, sample size, and participant demographics. Information related to the intervention, such as the type of supplement, dosage, and treatment duration, was also recorded. The outcome results on fasting blood sugar levels (FBS), glucose tolerance test (GTT), HbA1C, insulin, values for insulin resistance, the methods used to determine insulin resistance, or insulin sensitivity. Furthermore, Additional outcomes, such as body mass index (BMI), blood pressure (BP), and lipid profiles, were recorded when accessible. Any discrepancies in the results were addressed by a third reviewer.

2.4. Quality Assessment

The quality of articles was assessed using the updated Cochrane Risk of Bias (RoB-2) tool. Each research study was categorized as having either a low or high risk of bias or exhibiting concerns in different areas. These key areas included the intriguing dynamics of random sequence generation, the essential practice of allocation concealment, the critical aspect of selective reporting, the various methods of blinding, and the exploration of potential biases (Sterne et al. 2019).

2.5.  Data Analysis

The mean change and SD (standard deviation) between the baseline and after intervention HOMA-IR were drawn out. The standardized mean difference (SMD) and 95% confidence interval (CI) were used to compare the effect size. For the studies that provided fasting insulin and glucose values, without stating HOMA-IR, we calculated it by using the following formula:

fasting insulin (mIU/L) ×fasting glucose (mg/dL)/405. For analysis, we included only the studies that were conducted among diabetes, prediabetes, and individuals with documented IR. Our main goal was to assess the efficacy of chromium on the HOMA-IR level. For primary analysis, we excluded the studies among healthy populations and overweight individuals. Then, we assessed the effectiveness of chromium on glycemic variables among women with PCOS as a separate group due to the sufficient included articles to conduct a meta-analysis. Regarding healthy and overweight groups, we did not have enough articles assessing the HOMA-IR index, so we did not include them in the analysis.

We employed a random-effect model using restricted maximum likelihood estimation. The between-study heterogeneity was assessed using Cochrane’s Q statistic and Hedges’ g I² estimation. I² values of 25-50% served as low, values of 50-75% medium, and more than 75% meant substantial heterogeneity.  Sensitivity analyses (small study effect) were investigated by the leave-one-out method, and subgroup analysis was conducted by the overall ROB result, age, intervention dose, and duration. Publication bias was studied using standard and contoured funnel plots, Egger’s test, and the non-parametric trim-and-fill test. The Grading Quality of Evidence and Strength of Recommendations for diagnostic tests and strategies (GRADE) checklist was used to determine the certainty of evidence. STATA version 17.0.0. Statistical software was used for all analyses.

3. Results

3.1. Study Selection

We initially discovered 2,363 articles, including 351 from PubMed, 484 from Scopus, 792 from the Web of Science, and 736 from Embase. After eliminating duplicate entries, 1,198 articles were screened. The first screening focused on titles, resulting in 155 articles. After reviewing the abstracts, 45 articles were chosen for full-text evaluation, ultimately bringing about the inclusion of 35 articles as shown in Figure 1.

Figure 1: PRISMA diagram for included articles

The RCTs included in our study were published between 1981 and 2024. A total of 35 studies assessing glucose profile and insulin levels were examined. Of these, 14 studies focused on patients with T2DM, three studies on individuals with glucose intolerance, two studies among those with IR, two investigations on HIV-positive cases with IR, and four studies targeted women with PCOS. While four studies involved healthy individuals, five studies involved overweight individuals, and one on non-alcoholic fatty liver patients. Three studies included more than one chromium supplementation arm. These arms were pooled into a single intervention group, as recommended in the Cochrane Handbook, to ensure a single independent comparison with the control group in each study. The interventions lasted 6 to 38 weeks, and the prescribed dosages of chromium supplementation varied from 50µg to 1000µg. The most used form of chromium was chromium picolinate. The trial by Silpa et al. (2024) enrolled 60 participants but did not report group sizes; therefore, an equal allocation of 30 patients in each group was assumed based on the total sample size and comparable baseline variables.  Table 2 demonstrates the included studies. Tables 3-5 outline the main findings of these studies based on the population.

Table 2: The characteristics of included studies.

T2DM: type 2 diabetes mellitus, IR: insulin resistance, PCOS: polycystic ovary syndrome, HIV: human immunodeficiency virus, NS: non-specified, CrPic: chromium picolinate, CrN: chromium nicotinate, CrCl: chromium chloride, CDNC: chromium dinicocysteinate, CY: chromium-enriched yeast

Table 3: The effects of chromium on the outcomes in those with diabetes, prediabetic conditions, or known insulin resistance

Cr: chromium, ISI: insulin sensitivity index,  FSIVGTT: frequently sampled intravenous glucose tolerance test, QUICKI: quantitative insulin sensitivity check index, HOMA-IR: Homeostatic Model Assessment for Insulin Resistance,  HDL: high-density lipoprotein, LDL: low-density lipoprotein, VLDL: very low-density lipoprotein, TG: triglyceride,  FBS: fasting blood sugar, HbA1C:  Hemoglobin A1c, AUC: area under the curve, P: P value

Table 4: The effects of chromium on the outcomes in overweight or healthy individuals.

Cr: chromium, ISI: insulin sensitivity index,  FSIVGTT: frequently sampled intravenous glucose tolerance test, QUICKI: quantitative insulin sensitivity check index, HOMA-IR: Homeostatic Model Assessment for Insulin Resistance,  HDL: high-density lipoprotein, LDL: low-density lipoprotein, VLDL: very low-density lipoprotein, TG: triglyceride,  FBS: fasting blood sugar, HbA1C:  Hemoglobin A1c, AUC: area under the curve, P: P value

Table 5: The effects of chromium on the outcomes in women with polycystic ovary syndrome.

Cr: chromium, ISI: insulin sensitivity index,  FSIVGTT: frequently sampled intravenous glucose tolerance test, QUICKI: quantitative insulin sensitivity check index, HOMA-IR: Homeostatic Model Assessment for Insulin Resistance,  HDL: high-density lipoprotein, LDL: low-density lipoprotein, VLDL: very low-density lipoprotein, TG: triglyceride,  FBS: fasting blood sugar, HbA1C:  Hemoglobin A1c, AUC: area under the curve, P: P value

3.3. Quality Assessment

Figure 2 illustrates the outcomes of the risk of bias assessment conducted on the selected articles. Out of the total articles reviewed, fifteen were identified as having a high risk of bias. Conversely, eleven articles were classified as having a low risk of bias, suggesting that they adhered to more rigorous research standards and produced reliable results. Additionally, eight articles were identified as having some concerns related to bias, highlighting specific areas where the research may be weakened or questioned.

Figure 2: Cochrane Risk of Bias (RoB 2 Tool) Summary

3.4. Effects of chromium supplementation on HOMA-IR

Twenty studies with 1147 participants compared chromium and placebo on the HOMA-IR index among diabetic and prediabetic individuals. Meta-analysis of these studies demonstrated (Figure 3) that chromium significantly reduced the HOMA-IR index (pooled MD= -1.29; 95%CI (-1.84 to -0.73), P_V= 0.00), but this analysis had a high level of heterogeneity (I²= 94.7%).

Figure 3: Forest plot for meta-analysis of HOMA-IR mean changes in the chromium group versus the control group

3.5. Effects of chromium supplementation on FBS and HbA1C

Seventeen studies evaluated the efficacy of the intervention on FBS levels and showed (Figure 4) a significant reduction in the FBS values (pooled MD= - 13.71; 95%CI (-26.29 to -1.12), P_V= 0.03, I²= 97.74%). Regarding the efficacy of chromium on the HbA1C levels, no significant changes were detected (pooled MD= - 0.17; 95%CI (-0.63 to 0.29), P_V= 0.42, I²= 96.03%) (Figure 5).

Figure 4: Forest plot for meta-analysis of FBS mean changes in the chromium group versus the control group

Figure 5: Forest plot for meta-analysis of HbA1C mean changes in the chromium group versus the control group

3.6. Effects of chromium supplementation on HOMA-IR and FBS among PCOS individuals

Four studies with 199 cases assessed the efficacy of chromium on fasting insulin and glucose among women with PCOS. No significant pooled reduction reported (HOMA-IR: (pooled MD= - 0.81; 95%CI (-4.01 to 2.38), P_V= 0.88, I²= 2.10%), FBS: (pooled MD= - 0.12; 95%CI (-2.46 to 2.22), P_V= 0.48, I²= 0.00%)) (Figure 6).

Figure 6: Forest plot for meta-analysis of HOMA-IR and FBS mean changes after intervention in women with PCOS

3.7. Subgroup analysis

Subgroup analysis didn’t reveal a significant difference in SMD for HOMA-IR changes based on the patient age, chromium type, chromium dose, or duration of supplementation. The graphs for the subgroup analyses are available in Appendix 2-5. Additionally, the subgroup analysis based on the underlying condition did not show any significant differences in the HOMA-IR SMD (Appendix 6). However, the results of subgroup analysis based on the quality of the included articles partially explained heterogeneity and showed that the quality of the studies is a significant moderator of the effects of chromium on HOMA-IR ratio (Appendix 7).

3.8. Sensitivity analysis

Leve one out sensitivity analyses on HOMA-IR confirmed the robustness of the outcome, as the removal of any study didn’t alter the pooled SMD. In contrast, the results for FBS change showed a weaker and less consistent negative effect (Appendix 8,9).

3.9. Publication bias

The standard and counter-enhanced funnel plot inspection demonstrated an asymmetric distribution (Appendix 10). However, several studies were located outside the contour for p > 10%, which suggests that the asymmetry may not be solely due to publication bias, but possibly other factors such as small-study effects. To statistically investigate the asymmetry, Egger's regression test for small-study effects was conducted. The results of the test showed a statistically significant asymmetry (beta1 = -11.43, SE = 1.887, z = -6.06, p < 0.0001). A nonparametric trim-and-fill analysis was performed to estimate the impact of publication bias on the overall effect size by imputing missing studies (Appendix 11). The analysis identified no missing studies (observed = 20, imputed = 0).  The pooled Hedges's g for both observed studies and the combination of observed and imputed studies was the same (-0.624, 95% CI [-0.748, -0.500])

3.10. GRADE

The GRADE checklist was applied for three outcomes, including HOMA-IR, FBS, and HbA1C changes. The final level of certainty for these outcomes was moderate, low, and moderate, respectively. The GRADE table is available at https://1drv.ms/x/c/7234bfd3278bfcf9/EaYLYCShZrZMnOqp7S0Hf4kB1TOba-i4QMSzzgcj7B6Kpw?e=HPR00O.

4. Discussion

This meta-analysis demonstrated that chromium supplementation significantly decreased IR in populations with diabetes and those experiencing IR. Furthermore, a significant reduction in FBS was observed. However, the analysis did not detect a significant change in HbA1c levels, suggesting that while chromium may have an acute effect on insulin resistance and fasting glucose, its long-term impact on overall glycemic control, as measured by HbA1c, remains inconclusive. The results of the analysis are consistent with several previous studies and reviews that have supported the beneficial role of chromium in managing insulin resistance (Balk et al., 2007. Abdollahi et al., 2013. Asbaghi et al., 2020). Accordingly, Balk et al. (2007) meta-analysis stated that chromium decreased glycemic indices in diabetics without any effect on glucose metabolism in those with normal blood glucose. Although our results contrast with the Zhao et al. (2022) meta-analysis, which stated that the only glycemic index significantly affected by chromium is HbA1c. This discrepancy may be attributed to differences in the inclusion criteria of the studies, the specific patient populations analyzed, or the high degree of heterogeneity noted in our analysis, which could mask an effect on HbA1c.

While chromium supplementation did not show any significant effect on the HOMA-IR index or FBS in women with PCOS, a non-significant reduction in this population may suggest the benefits of chromium may extend beyond individuals with diabetes and prediabetes, offering a potential therapeutic avenue for improving metabolic health in PCOS patients. Two previous meta-analyses stated the significant effect of chromium on HOMA-IR, with fewer included articles (Tang et al., 2018. Heshmati et al., 2018). Additionally, Fazelian et al. (2017) showed improvement in insulin sensitivity after chromium treatment in women with PCOS. Based on our results, it appears that the statistical significance of this finding diminished as more RCTs were included in the analysis.

A subgroup analysis by intervention duration suggested that the beneficial effects of chromium on IR may be more pronounced at 12 weeks, with no meaningful effects observed at 8 or 24-week durations. These results align with Asbaghi et al. (2020) on the duration of supplementation. Chromium doses up to 500µg were associated with a greater improvement in HOMA-IR levels.

The bioavailability of chromium might be affected by different variables. Certain elements have been demonstrated to affect chromium absorption, as a diet rich in phytate and simple sugars may reduce it (Anderson et al., 2003).  Some studies have confirmed that additional trace elements can enhance the beneficial effects of chromium on metabolic health.  Zhao et al. (2024) noted that the combined supplementation of chromium and magnesium enhances glucose and lipid levels while decreasing inflammation and oxidative stress markers. Imanparast et al. (2020) showed that co-supplementation of chromium and vitamin D3 significantly decreases HOMA-IR. Lai et al. (2008) demonstrated that combining vitamin C or vitamin E with chromium is as effective as chromium supplementation in improving insulin resistance. Chromium's bioavailability varies by form, with picolinate seeming to be the most stable and bioavailable (Anderson et al., 2008); however, our subgroup analysis did not find any significant differences based on the form of supplementation. Furthermore, given the natural decline in chromium levels with aging, supplementation needs may vary from person to person (Schinner et al., 2005). This is supported by our finding of a non-statistically significant reduction in HOMA-IR in those older than 60, indicating that further research is needed to determine the optimal dosage and duration for this specific population.

5. Limitations

Our results showed that despite a significant pooled reduction in HOMA-IR and FBS with chromium supplementation, the wide 95% prediction interval (PrI) suggests limited generalizability to new populations or settings. Furthermore, there were an insufficient number of studies reporting IR to perform a subgroup analysis in overweight and healthy populations. Further investigation through well-structured RCTs, which modify a range of variables, may be instrumental in accurately assessing chromium's efficacy across diverse populations.

6. Conclusion

Chromium supplementation has been demonstrated to reduce insulin resistance in patients with T2DM and those who already exhibit significant insulin resistance. However, individuals without established insulin resistance may not experience benefits from chromium.

Conflict of interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors

Ethical Approval: As this article is a review of previously published articles, an ethics approval statement is not applicable.

Declaration of Generative AI and AI-assisted Technologies: This study has not used any generative AI tools or technologies in the preparation of this manuscript.