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Insulin resistance as a mediator in the association between nickel exposure and metabolic dysfunction-associated steatotic liver disease

Diabetology & Metabolic Syndrome volume 17, Article number: 8 (2025) Cite this article

Abstract

Background

The myriad implications of heavy metal pollution on human health have garnered substantial attention within the academic domain. Nevertheless, a notable research gap persists, as there is currently insufficient direct investigation elucidating the intricate interplay between nickel exposure and the risk of metabolic dysfunction-associated steatotic liver disease (MASLD).

Methods

The data utilized in this study was sourced from the National Health and Nutrition Examination Survey 2017–2020. Hepatic steatosis was evaluated utilizing controlled attenuation parameters (CAP), and nickel exposure level was reflected by urinary nickel concentration. To analyze the association between nickel exposure and MASLD, three multiple logistic regression models with weights were developed. Furthermore, a mediation analysis was performed to examine insulin resistance’s potential mediating role.

Results

There were a total of 1,187 participants in the study, of which 548 (46.17%) had MASLD. MASLD individuals had a significantly higher urinary nickel concentration than non-MASLD individuals (P = 0.008). After accounting for demographic factors, biochemical indicators, and metabolic conditions, the odds ratio (OR) and 95% confidence interval (CI) for MASLD were 2.10 (1.09–4.05) per onefold increase in urinary nickel concentration and 2.61 (1.22–5.55) for the highest tertile versus the lowest tertile. Insulin resistance was found to mediate approximately 73.69% of the total association between nickel exposure and MASLD (P = 0.004).

Conclusions

Nickel exposure was independently associated with the prevalence of MASLD. Excessive exposure to nickel may promote the occurrence of MASLD by enhancing insulin resistance.

Introduction

In the era of modern industrialization, the increasing environmental risk posed by prolonged human exposure to heavy metals has become a significant concern. The complex effects of heavy metal pollution on health are now a major focus of both academic research and public interest [1]. Within the field of hepatology, strong association between heavy metal exposure and liver health has been established. Studies reveal that exposure to heavy metals not only induces liver dysfunction and inflammatory responses but also increases the susceptibility to metabolic dysfunction-associated steatotic liver disease (MASLD) [2,3,4]. MASLD is the most recent nomenclature for non-alcoholic fatty liver disease (NAFLD), characterized by the presence of at least one cardiometabolic risk factor and the absence of other causes of hepatic steatosis [5]. In 2019, the global prevalence rate of MASLD exceeded 30%, with an estimated incidence rate of 4.9% [6]. Since MASLD can progress to cirrhosis and hepatocellular carcinoma, and significantly increase the risk of cardiovascular disease, it poses a major health and economic burden worldwide [7]. The treatment of MASLD is facing considerable challenges at present, despite sustained efforts by researchers to identify viable therapeutic strategies [8]. Therefore, to alleviate the disease burden of MASLD, it is crucial to identify additional modifiable environmental risk factors and intervene early.

Nickel is ubiquitous in natural environments and industrial commodities. Human exposure to nickel occurs through various pathways, including dietary ingestion, inhalation of airborne particulate matter, and direct dermal contact with alloyed substances [9, 10]. Regardless of the mechanism of nickel absorption, renal excretion is the primary route for eliminating this metal [11]. Consequently, urinary nickel levels could serve as a fundamental metric in assessing and quantifying the extent of nickel exposure across various populations [12]. Previous research has demonstrated that nickel influences various aspects of human metabolism, such as the metabolism of uric acid (UA) and glucose [12,13,14]. Specifically, exposure to nickel can trigger abnormal glucose metabolism and increase the risk of diabetes [12, 13]. Yang et al. revealed that occupational workers with prolonged nickel exposure exhibited gut microbiota alterations and developed UA metabolism disorders compared to individuals without such exposure [14]. Metabolic abnormalities, particularly glucose metabolism disorders and insulin resistance, are well-established as closely associated with the occurrence of MASLD [15, 16]. In the state of insulin resistance, the inhibitory effect of insulin on hepatic gluconeogenesis is impaired, de novo lipogenesis in the liver is abnormally enhanced, and free fatty acids from peripheral adipose tissue breakdown increase, all of which contribute to the liver’s inability to maintain glucose homeostasis and the accumulation of lipids [17]. Therefore, the above evidence suggests a potential link between nickel exposure and MASLD. Nevertheless, there is currently a lack of direct research elucidating the intricate relationship between nickel and MASLD.

To investigate the potential role of nickel in the development of MASLD, we analyzed data from the National Health and Nutrition Examination Survey (NHANES) database to assess the association between nickel exposure and MASLD. Furthermore, we performed a mediation analysis to determine the mediating effects of insulin resistance on this relationship.

Methods

Study population

Our data were derived from the NHANES database. The NHANES is a nationally representative health survey conducted by the National Center for Health Statistics (NCHS) in the United States. The survey aims to investigate and monitor disease and health trends to provide data support for public health policy development. There was only access to nickel levels in 2017–2018 and 2019–2020. Consequently, participants selected from NHANES 2017–2020 were included in this research. In addition, to mitigate potential confounding factors and enhance validity, the following exclusion criteria were applied to choose study population: (a) heavy alcohol consumption [18]; (b) infection with viral hepatitis (including hepatitis B virus surface antigen positive or hepatitis C virus confirmation antibody positive); (c) blood ferritin saturation exceeding 50%; (d) presence of other self-reported liver diseases (including self-reported liver cancer, autoimmune hepatitis); (e) missing urine sample for detection of nickel or levels of nickel below the lower limit of detection; (f) missing information about covariates; (g) possible renal insufficiency identified by serum creatinine (Scr) greater than 133 µmol/L. The detailed process of sample selection is shown in Fig. 1. The NCHS Institutional Review Board has approved NHANES’s investigation, and all participants have provided written informed consent. Our research is an analysis conducted using public resources published by NHANES and therefore does not require submission to an institutional internal review board number.

Fig. 1
figure 1

Flowchart of sample selection from the NHANES 2017–2020. NHANES: National Health and Nutrition Examination Survey; CAP: controlled attenuation parameters; MASLD: metabolic dysfunction-associated steatotic liver disease

Full size image

Nickel exposure

Given the short half-life of nickel in blood, blood nickel levels just reflect recent nickel exposure [19]. In contrast, nickel concentrations in urine samples are a more accurate measure of long-term exposure when exposed to stable conditions. This is due to the urinary excretion of nickel reflects a balance between ongoing exposure and elimination processes [20]. Therefore, this study mainly relied on urinary nickel concentration to represent participants’ nickel exposure levels. Urine specimens from participants were collected at the mobile examination center and subjected to specific processing procedures before transportation to the American Centers for Disease Control and Prevention for analysis. The quantification of urinary nickel concentration was predominantly conducted through inductively coupled plasma mass spectrometry (ICP-MS). The detailed laboratory procedures obtained from the NHANES manual are provided in the supplementary materials.

Definition of MASLD

According to the latest international consensus [5], MASLD is defined by the concurrent presence of hepatic steatosis along with at least one of the five established cardiometabolic risk factors, while simultaneously excluding alternative etiologies for hepatic steatosis. In this study, hepatic steatosis is evaluated utilizing controlled attenuation parameters (CAP), which are acquired through vibration-controlled transient elastography (VCTE). According to the research by Eddowes et al., hepatic steatosis is discerned by a CAP value equal to or exceeding 248 dB/m [21]. Consequently, after the exclusion of other specific factors contributing to hepatic steatosis, the population meeting the above criteria will be defined as MASLD patients.

Definition of insulin resistance

The homeostatic model assessment of insulin resistance (HOMA-IR) stands as a widely utilized methodological approach deployed across both research and clinical domains for the assessment of insulin resistance magnitude [22]. By integrating measurements of fasting plasma glucose and insulin levels, HOMA-IR provides a comprehensive index reflective of insulin sensitivity. A higher HOMA-IR value corresponds to greater resistance to insulin within the body. Therefore, it serving as a cornerstone in the elucidation of metabolic dysfunction and its implications for various health outcomes, including metabolic disorders and cardiovascular disease. Its calculation formula is as follows:

$$\:HOMA-IR\:=\:\frac{fasting\:glucose\:\left(FPG,\:\:\:mmol/L\right)\:\times\:\:fasting\:insulin\:level\:(FINS,\:\:\:\mu\:U/ml)}{22.5}$$

Other covariates

Based on previous studies, we selected other covariates to adjust. Demographic information was collected through questionnaire, including age, gender (Male and Female), race (Non-Hispanic White, Non-Hispanic Black, Mexican American, and other), smoking history (Never, Former, and Now), and education attainment (Less than high school, High school, More than high school, and Unknown). Body mass index (BMI), liver function tests (alanine aminotransferase [ALT] and aspartate aminotransferase [AST]), UA, and Scr were measured at the mobile examination center. In addition, we collected diet information (including intake level of energy [kcal/d] and dietary fiber [g/d]), and chronic disease information (including hypertension, hyperlipidemia, and diabetes).

Statistical analysis

Firstly, the study population was characterized based on liver status to provide a general description of the demographic characteristics of the study subjects. The chi-square test was used to examine categorical variables (weighted percentages), while continuous variables (weighted means and standard error) were analyzed using t-tests. Subsequently, due to the skewed distribution of urinary nickel, log2-transformed concentration value was used in this study, and it was categorized both as continuous and tertile variables. Weighted multiple logistic regression models were utilized to evaluate the association between nickel levels in these two different forms and MASLD, results are presented as odds ratios (ORs) and 95% confidence intervals (CIs). A total of three models were employed throughout this analysis. Model 1 was adjusted for Scr to eliminate the effect of renal function. Model 2 was additionally adjusted for age, gender, race, and education level based on model 1. Then we added BMI, ALT/AST, UA, hypertension, hyperlipidemia, and diabetes in model 3. At the same time, subgroup analysis was conducted based on race, smoking history, BMI, hyperlipidemia, and diabetes, and interaction tests were applied to the results of the subgroup analysis. Furthermore, a mediation analysis was conducted to discuss the potential influence of nickel on the development of MASLD through its impact on insulin resistance. All analyses mentioned above were conducted using R software (version 4.2.3). A P value of less than 0.05 was considered statistically significant.

Results

Characteristics of the study population

There were a total of 1,187 participants in the study, of which 548 (46.17%) had MASLD. As presented in Table 1, the mean log2-urinary nickel concentration value in the total study population was 1.19, with 1.13 in the non-MASLD individuals and 1.26 in the MASLD participants, and the difference was statistically significant (P = 0.008). In addition, compared with the non-MASLD participants, the MASLD individuals exhibited a tendency toward advanced age, BMI, ALT, AST, UA, Scr, fasting glucose, and HOMA-IR (all P < 0.05), a larger proportion of males (58.75% vs. 41.25%, P = 0.008), more white people (69.04% vs. 63.10%, P = 0.026), higher prevalence rates of hypertension (49.95% vs. 21.28%), hyperlipidemia (77.90% vs. 53.04%), and diabetes (30.56% vs. 2.76%) (all P < 0.001). Nevertheless, no significant differences were observed with respect to energy intake, dietary fiber consumption, and smoking history (all P > 0.05).

Table 1 Characteristics of including population
Full size table

Association between nickel exposure and MASLD

Three weighted multiple logistic regression models were constructed to explore the association between nickel exposure and the occurrence of MASLD, the specific results are detailed in Table 2. When urinary nickel concentration (after log2-transformed) was directly included as a continuous variable, the association with the development of MASLD remained significant even in the fully adjusted model 3 (OR = 2.10, 95% CI = 1.09–4.05, P = 0.028). Upon stratifying urinary nickel concentration into tertiles, it was observed that elevated levels of urinary nickel within both the middle tertile (T2) and the highest tertile (T3) exhibited statistically significant associations with MASLD in both model 1 and model 2, when contrasted with the lowest tertile (T1). However, within model 3, only the highest tertile of urinary nickel concentration demonstrated a significant association with MASLD, with an OR of 2.61 (95% CI = 1.22–5.55, P = 0.015). The trend tests in all three models were significant (all P < 0.05).

Table 2 Association between urinary nickel and MASLD
Full size table

Subgroup analysis

Table 3 summarizes the results of the subgroup analysis. When urinary nickel concentration was treated as a continuous variable, we found an interaction between race and nickel exposure (P for interaction < 0.05). The association between urinary nickel concentration and MASLD was significant only in the White and Other racial groups, but not in Black or Mexican populations. No significant interactions were observed in subgroups stratified by smoking history, BMI, hyperlipidemia, and diabetes (P for interaction > 0.05), indicating that the association between urinary nickel concentration and MASLD was consistent across these subgroups. When urinary nickel concentration was treated as a tertile variable, no significant interactions were observed in any of the five subgroups (P for interaction > 0.05).

Table 3 Subgroup analysis of the association between urinary nickel and MASLD
Full size table

Mediation analysis

As shown in Table 4, the direct impact of nickel exposure on MASLD did not reach statistical significance (P = 0.116), but the mediated effect was found to be significant (P = 0.008). This suggests that nickel exposure potentially influences MASLD development through the mechanism of increased insulin resistance. In detail, HOMA-IR mediate approximately 73.69% of the total association between nickel exposure and MASLD (P = 0.004), underscoring the substantial role of insulin resistance as a mediator in the relationship between nickel exposure and MASLD pathogenesis.

Table 4 Mediation effect of HOMA-IR on the association between urinary nickel and MASLD
Full size table

Discussion

In this study, we explored the association between nickel exposure and the occurrence of MASLD. We found that the OR (95% CI) for MASLD was 2.10 (1.09–4.05) per onefold increase in urinary nickel concentration, and 2.61 (1.22–5.55) for the highest tertile individuals, compared with the lowest tertile individuals. These results revealed the potential role of nickel exposure in the development of MASLD, highlighting the significance of further investigation into its mechanisms and implications for public health.

The mean value of log2-urinary nickel concentration was 1.19 in the population included in this study. There may be some variation in urinary nickel concentration among people in different regions and races [12, 23, 24], which is related to population characteristics, exposure levels, and detection methods. Besides occupational exposure, the general population primarily encounters nickel exposure through ambient air, dietary intake, and drinking water sources [25]. Therefore, the level of nickel exposure is closely related to local air quality, soil pollution, and industrial development. In addition, smoking is also a small but should not be ignored source of nickel [26]. With the widespread use of nickel-based alloy products, especially those that are susceptible to prolonged exposure such as kitchen utensils and medical implants [27], the overall level of nickel exposure in the population has undoubtedly increased. As for the reference range of urinary nickel concentration, there is currently no unified standard, and previously more attention was paid to the toxic concentration of nickel, rather than the harmful concentration associated with the development of chronic diseases or cancer.

A previous cross-sectional study has shown a significant association between nickel concentrations in soil and NAFLD in men [28]. In this study, we looked at the association between urinary nickel concentration, which can reflect the level of long-term nickel exposure, and MASLD. This association was independent of common risk factors for MASLD. Thus, our study more directly illustrates the potential risk of excessive nickel exposure to promote the occurrence of MASLD. In the future, countries should strengthen the monitoring and control of nickel pollution in the environment, and formulate the reference range of urinary nickel concentration through further research, so as to evaluate the degree of nickel exposure of people in the region.

In the subgroup analysis, the results of urinary nickel concentration as a continuous variable differed from those as a tertile variable, particularly in the racial subgroups. Since the continuous variable assumes a linear relationship between urinary nickel concentration and the occurrence of MASLD, whereas the tertile variable allows for a nonlinear relationship, this difference may arise from statistical power associated with the variable handling. Meanwhile, genetic susceptibility and lifestyle factors may contribute to the lack of a significant association between nickel exposure and MASLD in specific racial groups.

As insulin resistance has a close relationship with the development and progression of MASLD [29], previous studies have proved that nickel exposure is not only associated with type 2 diabetes, but also with elevated fasting glucose, glycated hemoglobin (HbA1c), and HOMA-IR [30]. Hence, we conducted a mediation analysis and the results showed that insulin resistance was indeed a mediator between nickel exposure and MASLD. Nickel serves as a vital constituent of various enzymes within the human body, including acetyl-CoA synthase and Ni-superoxide dismutase. However, excessive exposure to nickel can have negative effects on the body [25]. Previous studies have demonstrated that nickel possesses the capacity to induce insulin resistance via its modulation of reactive oxygen species, the nitric oxide pathway, and the competitive mechanism involving Ca2+ and Ni2+ [31, 32]. In addition, the accumulation of nickel may also promote the occurrence of MASLD through the following approaches: (a) disrupting the equilibrium between glutathione reductase and the antioxidant defense system of mitochondrion by forming nickel-thiol complexes, leading to oxidative stress in liver or islet β cells [33]; (b) damaging the normal structure of the liver and contributing to fat accumulation and steatosis in the liver [34]; (c) causing increased iron content in the liver, leading to liver injury through mitochondrial damage and ferroptosis [35]. However, these mechanisms currently stand as potential explanations, and their ability to elucidate the association between nickel exposure and MASLD still needs to be verified by further basic research.

To our knowledge, this study contributes novel insights into the potential role of nickel in the development of MASLD. By utilizing the robust study design of NHANES, this finding can be extrapolated to the wider population of the United States. Compared to the utilization of ultrasound, computed tomography, and the United States fatty liver index for assessing hepatic steatosis, the use of CAP in this study demonstrated enhanced sensitivity. However, it is imperative to recognize certain limitations in this research endeavor. First, as our study only obtained urinary nickel concentration from participants, without data on nickel concentration or content in other body components (such as peripheral blood and hair), it may not fully reflect the participants’ total nickel exposure. Second, this study employed a cross-sectional design, and therefore, further longitudinal research is needed to establish a causal relationship between nickel exposure and MASLD.

Conclusions

Our study demonstrates a positive correlation between the level of the nickel exposure and the risk of MASLD. Excessive exposure to nickel may promote the occurrence of MASLD by enhancing insulin resistance. Hence, increased attention should be directed towards understanding the contribution of environmental factors, specifically nickel exposure, in the pathogenesis of MASLD.

Data availability

The datasets utilized in this study are publicly available and can be accessed at the National Health and Nutrition Examination Surveys database (https://www.cdc.gov/nchs/nhanes).

References

  1. Fuller R, Landrigan PJ, Balakrishnan K, Bathan G, Bose-O’Reilly S, Brauer M, et al. Pollution and health: a progress update. Lancet Planet Health. 2022;6:e535–47.

    Article  PubMed  Google Scholar 

  2. Hyder O, Chung M, Cosgrove D, Herman JM, Li Z, Firoozmand A, et al. Cadmium exposure and liver disease among US adults. J Gastrointest Surg. 2013;17:1265–73.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Cave M, Appana S, Patel M, Falkner KC, McClain CJ, Brock G. Polychlorinated biphenyls, lead, and mercury are associated with liver disease in American adults: NHANES 2003–2004. Environ Health Perspect. 2010;118:1735–42.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  4. Li Y, Liu Z, Chang Y, Chen N, Zhang R, Liu X, et al. Associations of multiple toxic metal exposures with metabolic dysfunction-associated fatty liver disease: NHANES 2011–2018. Front Nutr. 2023;10:1301319.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Rinella ME, Lazarus JV, Ratziu V, Francque SM, Sanyal AJ, Kanwal F, et al. A multisociety Delphi consensus statement on new fatty liver disease nomenclature. J Hepatol. 2023;79:1542–56.

    Article  PubMed  CAS  Google Scholar 

  6. Younossi ZM, Golabi P, Paik JM, Henry A, Van Dongen C, Henry L. The global epidemiology of nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH): a systematic review. Hepatology. 2023;77:1335–47.

    Article  PubMed  Google Scholar 

  7. Miao L, Targher G, Byrne CD, Cao YY, Zheng MH. Current status and future trends of the global burden of MASLD. Trends Endocrinol Metab. 2024;35:697–707.

    Article  PubMed  CAS  Google Scholar 

  8. Zeng J, Fan JG, Francque SM. Therapeutic management of metabolic dysfunction associated steatotic liver disease. United Eur Gastroenterol J. 2024;12:177–86.

    Article  CAS  Google Scholar 

  9. Cubadda F, Iacoponi F, Ferraris F, D’Amato M, Aureli F, Raggi A, et al. Dietary exposure of the Italian population to nickel: the national total Diet Study. Food Chem Toxicol. 2020;146:111813.

    Article  PubMed  CAS  Google Scholar 

  10. Beattie H, Keen C, Coldwell M, Tan E, Morton J, McAlinden J, et al. The use of bio-monitoring to assess exposure in the electroplating industry. J Expo Sci Environ Epidemiol. 2017;27:47–55.

    Article  PubMed  CAS  Google Scholar 

  11. Patriarca M, Lyon TD, Fell GS. Nickel metabolism in humans investigated with an oral stable isotope. Am J Clin Nutr. 1997;66:616–21.

    Article  PubMed  CAS  Google Scholar 

  12. Liu G, Sun L, Pan A, Zhu M, Li Z, Wang Z, et al. Nickel exposure is associated with the prevalence of type 2 diabetes in Chinese adults. Int J Epidemiol. 2015;44:240–8.

    Article  PubMed  Google Scholar 

  13. Titcomb TJ, Liu B, Lehmler HJ, Snetselaar LG, Bao W. Environmental Nickel Exposure and Diabetes in a nationally Representative Sample of US adults. Expo Health. 2021;13:697–704.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  14. Yang J, Feng P, Ling Z, Khan A, Wang X, Chen Y, et al. Nickel exposure induces gut microbiome disorder and serum uric acid elevation. Environ Pollut. 2023;324:121349.

    Article  PubMed  CAS  Google Scholar 

  15. Sun Q, Zhang T, Manji L, Liu Y, Chang Q, Zhao Y, et al. Association between serum uric acid and non-alcoholic fatty liver disease: an updated systematic review and Meta-analysis. Clin Epidemiol. 2023;15:683–93.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Powell EE, Wong VW, Rinella M. Non-alcoholic fatty liver disease. Lancet. 2021;397:2212–24.

    Article  PubMed  CAS  Google Scholar 

  17. Bo T, Gao L, Yao Z, Shao S, Wang X, Proud CG, et al. Hepatic selective insulin resistance at the intersection of insulin signaling and metabolic dysfunction-associated steatotic liver disease. Cell Metab. 2024;36:947–68.

    Article  PubMed  CAS  Google Scholar 

  18. Rattan P, Penrice DD, Ahn JC, Ferrer A, Patnaik M, Shah VH, et al. Inverse Association of Telomere length with Liver Disease and Mortality in the US Population. Hepatol Commun. 2022;6:399–410.

    Article  PubMed  CAS  Google Scholar 

  19. Sunderman FW. Jr. Biological monitoring of nickel in humans. Scand J Work Environ Health. 1993;19(Suppl 1):34–8.

    PubMed  CAS  Google Scholar 

  20. Wilhelm M, Wittsiepe J, Seiwert M, Hünken A, Becker K, Conrad A, et al. Levels and predictors of urinary nickel concentrations of children in Germany: results from the German Environmental Survey on children (GerES IV). Int J Hyg Environ Health. 2013;216:163–9.

    Article  PubMed  CAS  Google Scholar 

  21. Eddowes PJ, Sasso M, Allison M, Tsochatzis E, Anstee QM, Sheridan D, et al. Accuracy of FibroScan Controlled Attenuation parameter and liver stiffness measurement in assessing steatosis and fibrosis in patients with nonalcoholic fatty liver disease. Gastroenterology. 2019;156:1717–30.

    Article  PubMed  Google Scholar 

  22. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia. 1985;28:412–9.

    Article  PubMed  CAS  Google Scholar 

  23. Ohashi F, Fukui Y, Takada S, Moriguchi J, Ezaki T, Ikeda M. Reference values for cobalt, copper, manganese, and nickel in urine among women of the general population in Japan. Int Arch Occup Environ Health. 2006;80:117–26.

    Article  PubMed  CAS  Google Scholar 

  24. Smith-Sivertsen T, Tchachtchine V, Lund E, Bykov V, Thomassen Y, Norseth T. Urinary nickel excretion in populations living in the proximity of two Russian nickel refineries: a norwegian-russian population-based study. Environ Health Perspect. 1998;106:503–11.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Begum W, Rai S, Banerjee S, Bhattacharjee S, Mondal MH, Bhattarai A, et al. A comprehensive review on the sources, essentiality and toxicological profile of nickel. RSC Adv. 2022;12:9139–53.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. Cempel M, Nikel G, Nickel. A review of its sources and environmental toxicology. Pol J Environ Stud. 2006;15:375–82.

    CAS  Google Scholar 

  27. Genchi G, Carocci A, Lauria G, Sinicropi MS, Catalano A. Nickel: Human Health and Environmental Toxicology. Int J Environ Res Public Health. 2020;17:679.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. Lin YC, Lian IB, Kor CT, Chang CC, Su PY, Chang WT, et al. Association between soil heavy metals and fatty liver disease in men in Taiwan: a cross sectional study. BMJ Open. 2017;7:e014215.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Watt MJ, Miotto PM, De Nardo W, Montgomery MK. The liver as an endocrine organ-linking NAFLD and insulin resistance. Endocr Rev. 2019;40:1367–93.

    Article  PubMed  Google Scholar 

  30. Qu Y, Ji S, Sun Q, Zhao F, Li Z, Zhang M, et al. Association of urinary nickel levels with diabetes and fasting blood glucose levels: a nationwide Chinese population-based study. Ecotoxicol Environ Saf. 2023;252:114601.

    Article  PubMed  CAS  Google Scholar 

  31. Chen YW, Yang CY, Huang CF, Hung DZ, Leung YM, Liu SH. Heavy metals, islet function and diabetes development. Islets. 2009;1:169–76.

    Article  PubMed  Google Scholar 

  32. Das KK, Das SN, Dhundasi SA. Nickel, its adverse health effects & oxidative stress. Indian J Med Res. 2008;128:412–25.

    PubMed  CAS  Google Scholar 

  33. Valko M, Morris H, Cronin MT. Metals, toxicity and oxidative stress. Curr Med Chem. 2005;12:1161–208.

    Article  PubMed  CAS  Google Scholar 

  34. Das KK, Gupta AD, Dhundasi SA, Patil AM, Das SN, Ambekar JG. Effect of L-ascorbic acid on nickel-induced alterations in serum lipid profiles and liver histopathology in rats. J Basic Clin Physiol Pharmacol. 2006;17:29–44.

    Article  PubMed  CAS  Google Scholar 

  35. Wei L, Zuo Z, Yang Z, Yin H, Yang Y, Fang J, et al. Mitochondria damage and ferroptosis involved in Ni-induced hepatotoxicity in mice. Toxicology. 2022;466:153068.

    Article  PubMed  CAS  Google Scholar 

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Acknowledgements

Gratitude is extended to all participants and investigators involved in the National Health and Nutrition Examination Surveys for their invaluable contributions to the study.

Funding

This study was financially supported by The Key Project of Education Foundation (No.2023HX0054), Guang Zhou 21st century Education Foundation; and The Cross Innovation Talent Project (No. JCRCYG-2022-005), Renmin Hospital of Wuhan University.

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Author notes

  1. Zhou Liu, Liang Zhang and Yanrui Wu contributed equally to this work.

Authors and Affiliations

  1. Department of Intensive Care Unit, Renmin Hospital of Wuhan University, Wuhan, China

    Zhou Liu, Guang Li, Zhenwen Li & Liying Zhan

  2. Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China

    Liang Zhang

  3. Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China

    Yanrui Wu, Zongbiao Tan & Weiguo Dong

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Contributions

(I) Conception and design: Zhou Liu, Liang Zhang, Yanrui Wu, Zongbiao Tan; (II) Administrative support: Liying Zhan, Weiguo Dong; (III) Provision of study materials: Liying Zhan, Weiguo Dong, Guang Li, Zhenwen Li; (IV) Collection and assembly of data: Zhou Liu, Liang Zhang, Yanrui Wu, Zongbiao Tan; (V) Data analysis and interpretation: Zhou Liu, Liang Zhang, Yanrui Wu; (VI) Manuscript writing: Zhou Liu, Liang Zhang, Yanrui Wu; (VII) Final approval of manuscript: All authors.

Corresponding authors

Correspondence to Liying Zhan or Weiguo Dong.

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Ethics approval and consent to participate

The NCHS Institutional Review Board has approved NHANES’s investigation, and all participants have provided written informed consent. Our research is an analysis conducted using public resources published by NHANES and therefore does not require submission to an institutional internal review board number.

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Not Applicable.

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The authors declare no competing interests.

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Supplementary Material 1

: Determination process of urinary nickel concentration (obtained from the NHANES manual).pdf

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Liu, Z., Zhang, L., Wu, Y. et al. Insulin resistance as a mediator in the association between nickel exposure and metabolic dysfunction-associated steatotic liver disease. Diabetol Metab Syndr 17, 8 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13098-024-01567-7

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  • Published:

  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13098-024-01567-7

Keywords

Diabetology & Metabolic Syndrome

ISSN: 1758-5996

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