Abstract
Patients with chronic kidney disease (CKD) are frequently exposed to environmental heavy metals, which can easily aggravate the condition or even accelerate death. The association between the metal mixture inflammatory index (MMII) and mortality in CKD patients has not been investigated yet. The MMII was developed using reduced rank regression models to gauge the systemic inflammatory potential of a multi-metal mixture. We analyzed data from 2569 CKD patients who participated in the National Health and Nutrition Examination Survey (1999–2018) with follow-up until December 31, 2019. The association between MMII and mortality was explored via multivariate COX regressions. Patients were divided into 3 groups according to tertiles of MMII: MMII-1 (−0.699 to 0.06), MMII-2 (0.060–0.268), and MMII-3 (0.268–1.329). Compared to MMII-1, the hazard ratios for all-cause, non-cardiovascular disease (CVD), and cancer mortality in MMII-3 were 1.30 (1.01–1.67), 1.49 (1.09–2.03), and 2.44 (1.33–4.49), respectively. In this nationally representative sample of U.S. CKD patients, MMII was closely associated with an increased risk of all-cause, non-CVD, and cancer mortality.
Keywords: chronic kidney disease (CKD), inflammation, metal mixture inflammatory index (MMII), mortality risk, survival
1. Introduction
Despite significant advances in nephrology care and early detection strategies over recent decades, chronic kidney disease (CKD) has emerged as a silent epidemic, affecting approximately 13.4% of the global adult population and representing a leading cause of premature mortality worldwide.[1,2] End-stage renal disease and CKD-related cardiovascular complications remain the primary drivers of morbidity and mortality in affected patients.[3] Additionally, CKD ranks among the top 10 leading causes of global disease burden, with kidney diseases now representing the seventh leading risk factor for death globally.[4] At its core, CKD progression is largely preventable and modifiable through early detection, optimal management of underlying conditions, and lifestyle modifications, particularly in the early stages (G1–G3a) when interventions are most cost-effective.[5,6] Current CKD surveillance and risk stratification focus on key biomarkers (estimated glomerular filtration rate [eGFR], albuminuria, proteinuria) and modifiable risk factors (diabetes control, blood pressure management, nephrotoxin avoidance, dietary modifications) that collectively determine disease progression and cardiovascular outcomes.[7–10]
Research has indicated that multiple metal exposure-induced inflammation is highly prevalent among CKD patients and is a leading risk factor for accelerated disease progression and increased mortality.[11,12] Recent studies have reported that cadmium (Cd), mercury (Hg), lead (Pb), and thallium (Tl) are associated with the highest risk of developing CKD, with significant accumulation in CKD patients compared to healthy controls.[12–14] These findings underscore the high prevalence of metal exposure-induced inflammatory responses among CKD patients and highlight multi-metal exposure as a significant clinical concern in this vulnerable population.[15,16] The extant literature has indicated that CKD patients with chronic multi-metal exposure have a greater risk for accelerated kidney function decline, cardiovascular complications, and increased mortality.[11,13,14] Consequently, determining optimal inflammatory biomarkers for monitoring multi-metal exposure effects could provide significant clinical advantages to these high-risk CKD patients.[15,17]
The metal mixture inflammatory index (MMII) represents a composite indicator derived from multiple heavy metal concentrations to evaluate the systemic inflammatory capacity of combined metal exposures.[14] This index was created to assess potential whole-body inflammatory responses from multi-metal exposure and examine relationships with overall mortality risk.[18] Elevated MMII values have shown significant correlations with increased overall mortality, establishing its clinical prognostic utility.[14] The index provides a holistic framework for evaluating simultaneous exposures and their health implications, advancing beyond conventional single-metal assessments to encompass the intricate dynamics of metal combinations in real-world exposure environments.[18]
Despite increasing attention to these factors, a significant research gap remains: the absence of studies exploring the association between MMII and mortality in CKD patients. Considering the existing discrepancies in research findings and the identified gaps in the literature, our study explored the prevalence of MMII status among a nationally representative sample of CKD patients in the United States. Specifically, we examined its associations with all-cause, cardiovascular disease (CVD), non-CVD, and cancer-related mortality.
2. Methods
2.1. Study design and patients
The current research utilized data from the National Health and Nutrition Examination Survey (NHANES) (https://www.cdc.gov/nchs/nhanes.htm), which is an ongoing study focused on assessing the health and nutritional status of the U.S. population. The NHANES protocols were reviewed and approved by the National Center for Health Statistics Research Ethics Review Board, and all participants provided written informed consent. This study analyzed data from the 1999 to 2018 survey cycles. Exclusion criteria were as follows: patients with missing age data or those younger than 20 years old; patients with missing CKD diagnoses and those without CKD; patients without MMII data; and patients without follow-up data.
2.2. Definition of CKD
The identification of CKD patients was conducted according to KDIGO 2012 Clinical Practice Guideline, requiring either decreased kidney function or the presence of albuminuria.[19] Kidney function assessment utilized the CKD-EPI equation for eGFR calculation from serum creatinine values.[20] Impaired kidney function was characterized by eGFR < 60 mL/min/1.73 m2. Positive albuminuria was determined by urinary albumin-to-creatinine ratio ≥ 30 mg/g. CKD staging was assigned to patients based on eGFR: G1: eGFR ≥ 90 mL/min/1.73 m2; G2: eGFR: 60 to 89 mL/min/1.73 m2; G3a: eGFR: 45 to 59 mL/min/1.73 m2; G3b: eGFR: 30 to 44 mL/min/1.73 m2; G4: eGFR: 15 to 29 mL/min/1.73 m2; G5: eGFR < 15 mL/min/1.73 m2. In our study, we included all CKD stages (G1–G5) patients based on either eGFR < 60 mL/min/1.73m2 or the albuminuria ≥ 30 mg/g with preserved or mildly impaired eGFR. Urinary albumin quantification employed solid phase fluorescence immunoassay methodology, while urinary creatinine was analyzed using enzymatic techniques.[21]
2.3. Definition of MMII
To explore the association between metal mixtures and mortality outcomes, we developed the MMII to evaluate the combined inflammatory impact of various heavy metals using reduced rank regression (RRR) modeling combined with stepwise linear regression techniques.[22,23] RRR functions as a data-driven approach that constructs linear combinations of exposure variables (heavy metals) to optimize the explained variance in outcome variables (systemic inflammatory biomarkers).[24] The primary factor derived from RRR analysis was chosen to represent the metal exposure profile associated with low-grade systemic inflammation for subsequent investigation. We then employed stepwise linear regression procedures to identify the most influential heavy metals within this exposure profile, applying P = .10 as the inclusion criterion. The selected metals received weights based on their respective regression coefficients, and their weighted combination constituted the MMII score. This index quantifies the inflammatory potential of an individual’s cumulative metal exposure, with higher scores indicating a greater pro-inflammatory state. Using this methodology, we employed weighted multivariate Cox proportional hazards models to assess the association between the standardized MMII and all-cause mortality, thereby conducting a comprehensive examination of the intricate relationship between metal mixture exposure and mortality risk.
2.4. Covariates and classification
The analysis incorporated several covariates, including age, sex, race and ethnicity (classified as non-Hispanic White, non-Hispanic Black, Hispanic, and others), educational background (categorized as less than high school, high school or equivalent, and college or above), marital status (grouped as married/living with partner, and divorced/separated/widowed/never married), family poverty income ratio (calculated by dividing total family income by the poverty threshold, with categories of <1.3, 1.3 to <3.5, and ≥3.5), body mass index (BMI) (kg/m2, with ranges of <25, 25.0–29.9, and ≥30), smoking history (defined as having smoked at least 100 cigarettes in one’s lifetime, with responses of yes or no), alcohol consumption (indicated by having at least 12 alcoholic drinks per year, with responses of yes or no), diabetes, and cancer.
2.5. Assessment of mortality
Mortality status determination for the follow-up cohort was accomplished using the NHANES public use linked mortality dataset, updated through December 31, 2019. The National Center for Health Statistics linked this dataset to the National Death Index through probabilistic matching procedures. Causes of death were classified using the International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10) coding system. Cardiovascular mortality encompassed deaths from heart disease (ICD-10: I00–I09, I11, I13, I20–I51) and cerebrovascular conditions (ICD-10: I60–I69). We also categorized specific mortality types including malignant neoplasms (codes 019-043) and other causes (code 010) based on ICD-10 classifications. Follow-up duration was measured in years from the initial interview date to either death occurrence or, for surviving participants, until December 31, 2019.
2.6. Statistical analysis
To maintain representativeness of the US population, NHANES employed sampling weights to adjust for unequal selection probabilities resulting from the cluster sampling design and intentional oversampling of specific subgroups. Following NHANES analytical guidelines, we integrated strata and primary sampling units alongside sampling weights to preserve national representativeness of the data. Participants were categorized based on MMII levels. Continuous variables were presented as mean ± standard error (SE), whereas categorical variables were displayed as frequencies and percentages. ANOVA (for continuous variables) and weighted chi-square tests (for categorical variables) were utilized to examine participant sociodemographic characteristics. Random forest multiple imputations were applied to address missing covariate values. The “jskm” package generated survival curves across different MMII categories and mortality outcomes, with weighted log-rank tests conducted to assess differences in cumulative mortality between MMII levels. Both weighted univariate and multivariate Cox proportional hazards regression models assessed associations between MMII and all-cause mortality, CVD mortality, non-CVD mortality, and cancer mortality. Model 1 performed unadjusted analyses. Model 2 adjusted for age, sex, race/ethnicity, education, marital status, BMI, alcohol use, and family poverty-income ratio. Model 3 additionally controlled for diabetes and cancer beyond Model 2 variables. Subgroup and interaction analyses examined whether MMII-mortality associations differed by age, sex, or BMI. Restricted cubic spline (RCS) analyses explored nonlinear relationships between MMII and mortality risk. Wald tests evaluated RCS model nonlinearity and overall associations. Statistical analyses used R software (version 4.4.1). Two-tailed P-values < .05 indicated statistical significance.
3. Results
3.1. Exclusion criteria and patients
A total of 101,316 participants were extracted from the NHANES database from 1999 to 2018. 46,235 patients with missing age information or younger than 20 years old were excluded; 6102 patients with missing CKD diagnoses were excluded; 39,470 non-CKD patients were excluded; 6939 patients without MMII data were excluded; 1 patient without follow-up data was excluded. Finally, a total of 2569 participants were enrolled in this study (weighted population: 12,782,595). The detailed process of participant exclusion criteria is illustrated in Figure 1.
Figure 1.
Flow chart for the selection of eligible participants for this study.
3.2. Patient characteristics and relationship between MMII and clinicopathological factors
The relationship between clinicopathological characteristics and different groups of MMII in the whole cohort (weighted) is presented in Table 1. Briefly, the weighted mean age (SE) was 59.73 (0.50) years; the weighted mean MMII (SE) was 0.16 (0.01). Regarding serum creatinine, MMII-3 group showed significantly elevated creatinine (103.05 (2.62) mg/dL) compared to MMII-1 (90.55 (1.75) mg/dL) and MMII-2 (98.23 (1.74) mg/dL) groups (P < .001). Weighted females accounted for 57.66% and males accounted for 42.34%. Among these patients, there were 1202 non-Hispanic Whites, accounting for 67.65%, while there were 3486 non-Hispanic Blacks, accounting for 13.18%, and 188 Hispanics, accounting for 4.99%. Besides, 49.72% were identified as current smokers, while 85.23% were classified as alcohol users. Patients with diabetes accounted for 33.44%.
Table 1.
Characteristics of the study patients according to the levels of MMII.
| Variable | Total (weighted %)* | Q1 | Q2 | Q3 | P value |
|---|---|---|---|---|---|
| Age | 59.73 (0.50) | 55.67 (0.97) | 61.74 (0.71) | 62.17 (0.62) | <.0001 |
| MMII | 0.16 (0.01) | −0.11 (0.01) | 0.17 (0.00) | 0.44 (0.01) | <.0001 |
| Creatinine (µmol/L) | 97.01 (1.30) | 90.55 (1.75) | 98.23 (1.74) | 103.05 (2.62) | <.0001 |
| Urea_nitrogen (mmol/L) | 6.24 (0.08) | 5.93 (0.12) | 6.55 (0.13) | 6.24 (0.12) | .002 |
| Albumin (g/L) | 41.59 (0.09) | 41.89 (0.13) | 41.40 (0.15) | 41.45 (0.17) | .03 |
| Sex | |||||
| Female | 1336 (57.66) | 443 (56.33) | 459 (60.55) | 434 (56.16) | .3 |
| Male | 1233 (42.34) | 413 (43.67) | 396 (39.45) | 424 (43.84) | |
| Race and ethnicity | |||||
| Non-Hispanic White | 1202 (67.65) | 427 (68.31) | 393 (66.74) | 382 (67.84) | <.0001 |
| Other Hispanic | 188 (4.99) | 80 (7.18) | 68 (5.28) | 40 (2.24) | |
| Non-Hispanic Black | 580 (13.18) | 127 (8.88) | 200 (13.82) | 253 (17.30) | |
| Other† | 599 (14.19) | 222 (15.63) | 194 (14.16) | 183 (12.62) | |
| PIR | |||||
| <1.3 | 940 (28.10) | 279 (24.95) | 310 (27.94) | 351 (31.78) | .02 |
| 1.3 to <3.5 | 1092 (43.63) | 382 (44.90) | 351 (40.81) | 359 (45.12) | |
| ≥3.5 | 537 (28.27) | 195 (30.14) | 194 (31.24) | 148 (23.11) | |
| Education | |||||
| <High school | 888 (25.11) | 282 (23.22) | 285 (23.95) | 321 (28.43) | .003 |
| High school | 610 (26.36) | 180 (22.93) | 214 (26.87) | 216 (29.65) | |
| >High school | 1071 (48.53) | 394 (53.85) | 356 (49.18) | 321 (41.93) | |
| Marital status | |||||
| Married or living with partner | 1403 (58.90) | 490 (61.23) | 475 (60.82) | 438 (54.30) | .03 |
| Divorced/Separated/Widowed/Never married | 1166 (41.10) | 366 (38.77) | 380 (39.18) | 420 (45.70) | |
| BMI, kg/m2 | |||||
| <25 | 667 (26.12) | 208 (25.42) | 203 (24.59) | 256 (28.47) | .64 |
| 25 to <30 | 771 (28.19) | 263 (28.00) | 262 (29.90) | 246 (26.62) | |
| ≥30 | 1131 (45.70) | 385 (46.58) | 390 (45.51) | 356 (44.91) | |
| Smoking | |||||
| No | 1308 (50.28) | 563 (63.73) | 465 (52.67) | 280 (32.80) | <.0001 |
| Yes | 1261 (49.72) | 293 (36.27) | 390 (47.33) | 578 (67.20) | |
| Alcohol use | |||||
| No | 439 (14.77) | 168 (16.69) | 165 (16.23) | 106 (11.11) | .03 |
| Yes | 2130 (85.23) | 688 (83.31) | 690 (83.77) | 752 (88.89) | |
| Diabetes | |||||
| No | 1554 (66.56) | 532 (69.82) | 516 (65.84) | 506 (63.65) | .1 |
| Yes | 1015 (33.44) | 324 (30.18) | 339 (34.16) | 352 (36.35) | |
| Hypertension | |||||
| No | 756 (33.86) | 301 (41.97) | 236 (30.14) | 219 (28.67) | <.0001 |
| Yes | 1813 (66.14) | 555 (58.03) | 619 (69.86) | 639 (71.33) | |
| Cancer | |||||
| No | 2128 (83.01) | 715 (84.50) | 709 (82.09) | 704 (82.30) | .42 |
| Yes | 441 (16.99) | 141 (15.50) | 146 (17.91) | 154 (17.70) | |
| Death_All cause | |||||
| 0 | 1769 (73.31) | 632 (78.23) | 564 (71.64) | 573 (69.57) | .003 |
| 1 | 800 (26.69) | 224 (21.77) | 291 (28.36) | 285 (30.43) | |
| Death_CVD | |||||
| 0 | 2277 (90.30) | 760 (91.38) | 755 (88.72) | 762 (90.74) | .27 |
| 1 | 292 (9.70) | 96 (8.62) | 100 (11.28) | 96 (9.26) | |
| Death_non-CVD | |||||
| 0 | 2061 (83.01) | 728 (86.85) | 664 (82.92) | 669 (78.83) | .001 |
| 1 | 508 (16.99) | 128 (13.15) | 191 (17.08) | 189 (21.17) | |
| Death_Cancer | |||||
| 0 | 2419 (94.66) | 828 (96.58) | 796 (94.66) | 795 (92.52) | .01 |
| 1 | 150 (5.34) | 28 (3.42) | 59 (5.34) | 63 (7.48) | <.0001 |
BMI = body mass index, CVD = cardiovascular disease, MMII = metal mixture inflammatory index, PIR = family poverty income ratio.
Weighted to be nationally representative. The weighted percentage may not sum to 100% because of missing data.
Including American Indian/Alaska Native/Pacific Islander, Asian, and multiracial.
3.3. Survival analysis of MMII-groups
The range of MMII was −0.699 to 1.329. Patients were divided into 3 groups according to tertiles of MMII: MMII-1 (−0.699 to 0.06), MMII-2 (0.060–0.268), and MMII-3 (0.268–1.329). The results showed that the cumulative rates of all-cause (Log-rank P < .001), non-CVD (Log-rank P < .001), and cancer (Log-rank P < .001) mortality were highest in the MMII-3 group compared to the other 2 groups; there was no difference in the cumulative rate of CVD-related mortality among the 3 groups (Fig. 2).
Figure 2.
Kaplan–Meier survival curves for mortality, stratified by MMII. (A) All-cause death; (B) CVD death; (C) Non-CVD death; (D) Cancer death. Different groups are based on tertiles of MMII. MMII = metal mixture inflammatory index.
3.4. Multivariate Cox regression analysis
The MMII-3 group had the highest risk of all-cause, non-CVD, and cancer mortality in model 3 (Fig. 3). Compared to the MMII-1 and MMII-2 groups, the hazard ratios for all-cause, non-CVD, and cancer mortality in the MMII-3 group were 1.30 (1.01, 1.67), 1.49 (1.09, 2.03), and 2.44 (1.33, 4.49), respectively.
Figure 3.
Association of different groups based on MMII score with all-cause, CVD, non-CVD, and cancer mortality. (A) Crude model. (B) Adjusted for several covariates, including age, sex, race and ethnicity, education level, marital status, BMI, alcohol consumption, and family PIR. (C) Further adjusted for diabetes and cancer in addition to the variables included in Model 2. BMI = body mass index, CVD = cardiovascular disease, MMII = metal mixture inflammatory index, PIR = poverty income ratio.
3.5. Restricted cubic spline analyses
RCS analysis showed that MMII was positively correlated with the risk of death. That is, as the MMII score increased, the risk of non-CVD (P for overall = .012) and cancer (P for overall = .002) mortality gradually increased (Fig. 4).
Figure 4.
Restricted cubic splines for the associations of the MMII score with the risk of all-cause, CVD, non-CVD, and cancer mortality. Cox proportional hazard models adjusted for age, sex, race and ethnicity, education level, marital status, BMI, alcohol consumption, family PIR, diabetes, and cancer. BMI= body mass index, CVD = cardiovascular disease, MMII = metal mixture inflammatory index, PIR = poverty income ratio.
3.6. Subgroup analysis
In BMI-stratified analyses, the association between MMII and mortality was more pronounced in CKD patients with BMI < 30 than those with BMI ≥ 30, especially for cancer death (Fig. S1, Supplemental Digital Content, https://links.lww.com/MD/R378). This suggested that the association between MMII and mortality risk might vary by BMI. In age-stratified analyses, the interaction effect was statistically significant, especially for non-CVD and cancer death (Fig. S2, Supplemental Digital Content, https://links.lww.com/MD/R378). This suggested that the association between MMII and mortality risk might vary by age. The association did not differ by sex (Fig. S3, Supplemental Digital Content, https://links.lww.com/MD/R378).
4. Discussion
This study was the first to investigate the effect of MMII on survival in a US nationally representative CKD population. Among these CKD patients, the average MMII value was 0.16 ± 0.26. Over the 13.2-year follow-up period, MMII was associated with a higher risk of all-cause, non-CVD, and cancer mortality. For the RCS results, the results of our analysis were consistent with expectations. Figure 4C and D (P for nonlinear: .091, .055) showed that there was a linear relationship between MMII and the risk of non-CVD and cancer mortality; that is, the higher the MMII score, the higher the risk of death. Indeed, in the long term, a high MMII score was positively correlated with the risk of death, and a high MMII score was a risk factor for all-cause, non-CVD, and cancer mortality.
The widespread occurrence of metal-induced inflammation in CKD patients could be attributed to impaired renal clearance mechanisms and increased susceptibility to environmental toxins due to compromised glomerular filtration.[12,25] Environmental toxicants accumulate in CKD patients due to reduced kidney function, leading to persistent activation of inflammatory pathways and oxidative stress.[16,25,26] Studies have found that CKD patients exhibit sustained inflammatory responses to metal exposure due to compromised antioxidant defense systems and impaired detoxification mechanisms.[15,16] A high MMII can be detrimental to CKD patients as it might accelerate glomerular sclerosis, exacerbate systemic inflammation, and contribute to cardiovascular complications.[27,28] A recent study by Wang et al found that higher MMII was associated with increased all-cause mortality in the general population, suggesting that metal-induced inflammation plays a crucial role in adverse health outcomes.[29] However, several questions remain to be explored regarding its specific impact on CKD patients. First, Wang et al focused primarily on the general population without stratifying by kidney function status, which might have introduced heterogeneity bias as CKD patients have fundamentally different metal clearance capacities and inflammatory responses. Shi et al reported that mixed metal exposure increased CKD risk in patients with type 2 diabetes, demonstrating complex metal interactions.[30] Gu et al found that systemic inflammatory markers were strongly associated with mortality in CKD patients, particularly in early stages.[31] However, these studies examined either traditional inflammatory biomarkers or individual metal effects rather than a comprehensive metal mixture inflammatory approach. This discrepancy in stage-specific effects might be because the relationship between metal toxicity and kidney damage becomes more pronounced in early-stage CKD when residual nephron function allows for greater metal accumulation and inflammatory response amplification. Additionally, patients with early-stage CKD and elevated MMII were more likely to develop cardiovascular complications before reaching end-stage renal disease, as the kidney’s reduced capacity to eliminate metals leads to systemic inflammation and endothelial dysfunction.[32]
Our findings have several possible biological explanations for the association between MMII and CKD progression. Elevated MMII reflects mixed heavy metal exposure, which can induce multiple pathophysiological pathways detrimental to kidney function. First, heavy metal exposure triggers extensive oxidative stress through increased reactive oxygen species production and depletion of antioxidant defense systems, leading to direct nephron damage and accelerated CKD progression.[16,30] Second, mixed metal exposure activates chronic inflammatory cascades, characterized by elevated levels of pro-inflammatorycytokines including tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6), and C-reactive protein (CRP), which contribute to renal interstitial fibrosis and glomerular sclerosis.[33–36] Third, certain heavy metals such as cadmium and lead exhibit direct nephrotoxic effects by accumulating in renal tubular cells, causing tubular epithelial cell apoptosis and irreversible structural damage to the kidneys.[30,37] Additionally, metal mixture exposure impairs endothelial function through nitric oxide pathway disruption, resulting in renal vascular dysfunction and hypertension, which further exacerbates CKD progression.[38]
This investigation had several notable strengths. A key advantage was the significant observation that elevated MMII scores were associated with increased mortality risk in CKD patients, particularly for cancer-related deaths. This research filled important knowledge gaps in the current literature regarding MMII applications in CKD populations. Additionally, the study utilized a representative cohort of US CKD patients from diverse ethnic backgrounds, enhancing the external validity and applicability of the results to broader CKD populations. Nevertheless, certain limitations must be acknowledged despite these advantages. First, missing data regarding MMII measurements and mortality outcomes could introduce selection bias. Second, the observational study design cannot entirely eliminate the influence of unmeasured confounding factors and unknown variables in the analytical framework. Moreover, MMII scores were assessed only at study initiation, without capturing temporal variations in these parameters throughout the follow-up period. Finally, the relatively limited sample size in subgroup analyses may have constrained the statistical power to identify meaningful differences between participant groups. Consequently, prospective studies with expanded sample sizes are warranted to establish whether the observed relationships represent true causal associations.
5. Conclusion
This study demonstrated that CKD patients who had higher MMII faced an increased risk of all-cause mortality, as well as non-CVD and cancer mortality. These results emphasize the importance of considering MMII scores in developing targeted intervention strategies to improve survival outcomes among CKD patients.
Acknowledgments
The authors thank the participants of the NHANES databases.
Author contributions
Conceptualization: Jinmei Tang, Qingfang Lin, Qin Pan.
Data curation: Jinmei Tang, Kangqin Cai, Qingfang Lin.
Formal analysis: Qingfang Lin.
Software: Kangqin Cai.
Supervision: Qingfang Lin.
Writing – original draft: Jinmei Tang, Kangqin Cai.
Writing – review & editing: Qin Pan.
Supplementary Material
Abbreviations:
- BMI
- body mass index
- CKD
- chronic kidney disease
- CVD
- cardiovascular disease
- MMII
- metal mixture inflammatory index
- NHANES
- National Health and Nutrition Examination Survey
- RCS
- restricted cubic spline
- SE
- standard error
This study analyzed publicly available, de-identified data from the National Health and Nutrition Examination Survey (NHANES) 1999–2018. The NHANES protocols were reviewed and approved by the National Center for Health Statistics Research Ethics Review Board, and all participants provided written informed consent. As this study involved secondary analysis of de-identified public data, additional ethics approval was not required.
Supplemental Digital Content is available for this article.
The authors have no funding and conflicts of interest to disclose.
The datasets analyzed during the current study are available in the National Health and Nutrition Examination Survey (NHANES) repository, https://www.cdc.gov/nchs/nhanes/index.htm.
How to cite this article: Tang J, Cai K, Lin Q, Pan Q. Metal mixture inflammatory index and mortality risk in chronic kidney disease patients: A nationally representative cohort study from the NHANES 1999 to 2018. Medicine 2026;105:7(e47693).
Contributor Information
Jinmei Tang, Email: 1067653984@qq.com.
Kangqin Cai, Email: zgckq@126.com.
Qingfang Lin, Email: 615934060@qq.com.
References
- [1].Hill NR, Fatoba ST, Oke JL, et al. Global prevalence of chronic kidney disease – a systematic review and meta-analysis. PLoS One. 2016;11:e0158765. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [2].Kovesdy CP. Epidemiology of chronic kidney disease: an update 2022. Kidney Int Suppl (2011). 2022;12:7–11. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [3].Francis A, Harhay MN, Ong ACM, et al. ; American Society of Nephrology. Chronic kidney disease and the global public health agenda: an international consensus. Nat Rev Nephrol. 2024;20:473–85. [DOI] [PubMed] [Google Scholar]
- [4].Jager KJ, Kovesdy C, Langham R, Rosenberg M, Jha V, Zoccali C. A single number for advocacy and communication-worldwide more than 850 million individuals have kidney diseases. Kidney Int. 2019;96:1048–50. [DOI] [PubMed] [Google Scholar]
- [5].Chadban S, Arici M, Power A, et al. Projecting the economic burden of chronic kidney disease at the patient level (inside CKD): a microsimulation modelling study. EClinicalMedicine. 2024;72:102615. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [6].Palmer SC, Maggo JK, Campbell KL, et al. Dietary interventions for adults with chronic kidney disease. Cochrane Database Syst Rev. 2017;4:CD011998. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [7].Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group. KDIGO 2024 clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney Int. 2024;105:S117–314. [DOI] [PubMed] [Google Scholar]
- [8].Inker LA, Titan S. Measurement and estimation of GFR for use in clinical practice: core curriculum 2021. Am J Kidney Dis. 2021;78:736–49. [DOI] [PubMed] [Google Scholar]
- [9].Murton M, Goff-Leggett D, Bobrowska A, et al. Burden of chronic kidney disease by KDIGO categories of glomerular filtration rate and albuminuria: a systematic review. Adv Ther. 2021;38:180–200. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [10].Kidney Disease: Improving Global Outcomes (KDIGO) Diabetes Work Group. KDIGO 2022 clinical practice guideline for diabetes management in chronic kidney disease. Kidney Int. 2022;102:S1–127. [DOI] [PubMed] [Google Scholar]
- [11].Lee BT, Ahmed FA, Hamm LL, et al. Association of C-reactive protein, tumor necrosis factor-alpha, and interleukin-6 with chronic kidney disease. BMC Nephrol. 2015;16:77. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [12].Smereczanski NM, Brzoska MM, Rogalska J, Hutsch T. Current levels of environmental exposure to cadmium in industrialized countries as a risk factor for kidney damage in the general population: a comprehensive review of available data. Int J Mol Sci. 2023;24:8413. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [13].Tsai HJ, Hung CH, Wang CW, et al. Associations among heavy metals and proteinuria and chronic kidney disease. Diagnostics (Basel). 2021;11:282. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [14].Moody EC, Coca SG, Sanders AP. Toxic metals and chronic kidney disease: a systematic review of recent literature. Curr Environ Health Rep. 2018;5:453–63. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [15].Rapa SF, Di Iorio BR, Campiglia P, Heidland A, Marzocco S. Inflammation and oxidative stress in chronic kidney disease-potential therapeutic role of minerals, vitamins and plant-derived metabolites. Int J Mol Sci. 2019;21:263. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [16].Kim NH, Hyun YY, Lee KB, et al. Environmental heavy metal exposure and chronic kidney disease in the general population. J Korean Med Sci. 2015;30:272–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [17].Tarng DC, Liu TY, Huang TP. Protective effect of vitamin C on 8-hydroxy-2’-deoxyguanosine level in peripheral blood lymphocytes of chronic hemodialysis patients. Kidney Int. 2004;66:820–31. [DOI] [PubMed] [Google Scholar]
- [18].Orr SE, Bridges CC. Chronic kidney disease and exposure to nephrotoxic metals. Int J Mol Sci. 2017;18:1039. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [19].Stevens PE, Levin A; Kidney Disease: Improving Global Outcomes Chronic Kidney Disease Guideline Development Work Group Members. Evaluation and management of chronic kidney disease: synopsis of the kidney disease: improving global outcomes 2012 clinical practice guideline. Ann Intern Med. 2013;158:825–30. [DOI] [PubMed] [Google Scholar]
- [20].Levey AS, Levin A, Kellum JA. Definition and classification of kidney diseases. Am J Kidney Dis. 2013;61:686–8. [DOI] [PubMed] [Google Scholar]
- [21].Lu J, Li H, Wang S. Interaction effect of psoriasis and chronic kidney disease on the risk of all-cause mortality: a prospective cohort study of NHANES data. Nephrol Dial Transplant. 2023;38:2474–84. [DOI] [PubMed] [Google Scholar]
- [22].Tabung FK, Smith-Warner SA, Chavarro JE, et al. Development and validation of an empirical dietary inflammatory index. J Nutr. 2016;146:1560–70. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [23].Piernas C, Gao M, Jebb SA. Dietary patterns derived by reduced rank regression and non-communicable disease risk [published online ahead of print June 29, 2022]. Proc Nutr Soc. doi: 10.1017/S0029665122001094. [DOI] [PubMed] [Google Scholar]
- [24].Lucas M, Chocano-Bedoya P, Schulze MB, et al. Inflammatory dietary pattern and risk of depression among women. Brain Behav Immun. 2014;36:46–53. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [25].Yan LJ, Allen DC. Cadmium-induced kidney injury: oxidative damage as a unifying mechanism. Biomolecules. 2021;11:1575. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [26].Verma S, Singh P, Khurana S, et al. Implications of oxidative stress in chronic kidney disease: a review on current concepts and therapies. Kidney Res Clin Pract. 2021;40:183–93. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [27].Spence JD, Urquhart BL. Cerebrovascular disease, cardiovascular disease, and chronic kidney disease: interplays and influences. Curr Neurol Neurosci Rep. 2022;22:757–66. [DOI] [PubMed] [Google Scholar]
- [28].Kaesler N, Cheng M, Nagai J, et al. Mapping cardiac remodeling in chronic kidney disease. Sci Adv. 2023;9:eadj4846. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [29].Wang Y, Wang Y, Li R, et al. Low-grade systemic inflammation links heavy metal exposures to mortality: a multi-metal inflammatory index approach. Sci Total Environ. 2024;947:174537. [DOI] [PubMed] [Google Scholar]
- [30].Shi X, Wang X, Zhang J, et al. Associations of mixed metal exposure with chronic kidney disease from NHANES 2011–2018. Sci Rep. 2024;14:13062. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [31].Gu L, Xia Z, Qing B, et al. Systemic inflammatory response index (SIRI) is associated with all-cause mortality and cardiovascular mortality in population with chronic kidney disease: evidence from NHANES (2001–2018). Front Immunol. 2024;15:1338025. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [32].Lamas GA, Bhatnagar A, Jones MR, et al. ; American Heart Association Council on Epidemiology and Prevention; Council on Cardiovascular and Stroke Nursing; Council on Lifestyle and Cardiometabolic Health; Council on Peripheral Vascular Disease; and Council on the Kidney in Cardiovascular Disease. Contaminant metals as cardiovascular risk factors: a scientific statement from the American Heart Association. J Am Heart Assoc. 2023;12:e029852. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [33].Mihai S, Codrici E, Popescu ID, et al. Inflammation-related mechanisms in chronic kidney disease prediction, progression, and outcome. J Immunol Res. 2018;2018:2180373. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [34].Batra G, Lakic TG, Lindback J, et al. Interleukin 6 and cardiovascular outcomes in patients with chronic kidney disease and chronic coronary syndrome. JAMA Cardiol. 2021;6:1440–5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [35].Yeo ES, Hwang JY, Park JE, Choi YJ, Huh KB, Kim WY. Tumor necrosis factor (TNF-alpha) and C-reactive protein (CRP) are positively associated with the risk of chronic kidney disease in patients with type 2 diabetes. Yonsei Med J. 2010;51:519–25. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [36].He P, Zhang J, Tian N, et al. The relationship between C-reactive protein to lymphocyte ratio and the prevalence of chronic kidney disease in US adults: a cross-sectional study. Front Endocrinol (Lausanne). 2024;15:1469750. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [37].Jomova K, Alomar SY, Nepovimova E, Kuca K, Valko M. Heavy metals: toxicity and human health effects. Arch Toxicol. 2025;99:153–209. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [38].Choi YH, Hu H, Mukherjee B, Miller J, Park SK. Environmental cadmium and lead exposures and hearing loss in U.S. adults: the National Health and Nutrition Examination Survey, 1999 to 2004. Environ Health Perspect. 2012;120:1544–50. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.