Association between the cardiometabolic index and all-cause and cardiovascular mortality in diabetes and prediabetes

Introduction

In recent years, diabetes and its related complications have become one of the leading causes of death and disability, posing a tremendous global public health challenge. According to the International Diabetes Federation, an estimated 537 million individuals had diabetes worldwide in 2021, and this figure is expected to reach 783 million by 2045 (1,2). About 12.2% of all deaths each year are due to diabetes and its complications. In consideration of these data, diabetes represents a serious threat to global healthcare systems (2-4).

Prediabetes, defined as blood sugar levels higher than normal but below the diabetes threshold, is associated with a substantial risk of the development of diabetes and its complications (5). The continuous progression of diabetes is closely related to the increased risk of cardiovascular disease (CVD), stroke, kidney disease, and retinopathy (6-8). Epidemiological data show that patients with diabetes have higher CVD mortality and all-cause mortality rates (9). Notably, the heart, brain vessels, and kidneys of patients with prediabetes may demonstrate damage (10,11). Insulin resistance (IR) is a state characterized by decreased sensitivity and responsiveness to insulin, which typically occurs several years before the onset of diabetes (12). Current evidence suggests that IR and related diseases can lead to CVD in both diabetic and non - diabetic subjects (13). Identifying residual risk factors in patients with different glucose metabolic states is critical to reducing morbidity and mortality, particularly the risk of CVD mortality. Therefore, the identification of intervenable risk factors and protective factors in individuals with prediabetes and diabetes is critical to reducing the incidence of CVD, the risk of death, and improving the global public health burden.

A study has shown that unbalanced body fat distribution and related abnormal lipid metabolism are closely related to the occurrence and deterioration of metabolic diseases, such as metabolic syndrome, diabetes, and CVD (14). Traditional anthropometric indicators, such as the body mass index (BMI), waist circumference, triglyceride (TG), and waist-to-height ratio (WHtR), have often been used to quantify obesity (15-17), and CVD. However, the relationships of CMI with different diabetic statuses and all-cause mortality and CVD mortality have not been explored. As an accessible and effective tool, the potential value of CMI in predicting different diabetic statuses, all-cause mortality, and CVD mortality merits further investigation.

Wakabayashi et al. proposed a metabolic marker, the cardiometabolic index (CMI) (18), which combines the WHtR, TG, and high-density lipoprotein cholesterol (HDL-C). The CMI was originally proposed as a predictor of diabetes mellitus (DM) risk. The CMI, which effectively integrates three indicators, is considered to be a more accurate evaluation indicator of abdominal obesity and abnormal lipid metabolism than other indicators, and thus is a more convenient, sensitive, and specific clinical indicator of metabolic diseases (19).

In-depth research on the CMI revealed an association between the CMI and hypertension, metabolic syndrome, CVD, stroke, hyperuricemia, kidney disease, peripheral artery disease, obstructive sleep apnea, and non-alcoholic fatty liver disease (NAFLD) (19-28). In addition, a high CMI score has been reported to be associated with elevated systemic inflammation, which increases the risk of diabetes progression, as well as the long-term complications of diabetes. These factors in turn increase the long-term risk of death (29). Similarly, IR is an important feature of diabetes and prediabetes, which can lead to metabolic syndrome, including hyperglycemia, dyslipidemia, and hypertension (30,31), and is closely related to vascular diseases (32,33). Previous studies have demonstrated that CMI is positively correlated with IR, which can be used to assess diabetes status and IR (34,35). However, there is still debate as to whether the CMI can predict long-term all-cause death and CVD death in individuals with different glucose metabolic states.

This study aimed to assess the association between the CMI and all-cause death and CVD death in individuals with prediabetes and diabetes, and thus provide valuable insights into its clinical application. We present this article in accordance with the STROBE reporting checklist (available at https://cdt.amegroups.com/article/view/10.21037/cdt-2025-100/rc).

Methods

Study population

The data for the study were collected from the National Health and Nutrition Examination Survey (NHANES), a nationwide study designed to assess the health and nutrition status of children and adults in the United States (U.S.). The NHANES employs a stratified and multistage random sampling method to ensure the high representativeness of the national sample (36). The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The National Center for Health Statistics Research Ethics Review Board granted its approval, and all participants provided written informed consent (Protocol Number: Protocol #98-12, Protocol #2005-06, Protocol #2011-17, Protocol #2018-01). All the data can be easily accessed via the NHANES website (https://wwwn.cdc.gov/nchs/nhanes/Default.aspx).

The data of 80,312 participants across eight cycles of the NHANES from 2003 to 2018 were downloaded and screened for this study. Participants were excluded from the study if they met any of the following exclusion criteria: (I) were younger than 18 years of age (n=32,549); (II) had missing CMI data (n=7,077); (III) had a lack of follow-up data (n=67); (IV) were pregnant (n=864); (V) had a daily dietary energy intake <800 or >4,200 kcal for men and <500 or >3,500 kcal for women (n=3,301); and/or (VI) did not have diabetes or prediabetes (n=18,462). Ultimately, 17,992 participants were included in the study (Figure 1).

Figure 1 Flow chart of inclusion and exclusion criteria for participants. CMI, cardiometabolic index; NHANES, National Health and Nutrition Examination Survey.

Evaluation of diabetes and prediabetes

Diabetes was defined as a self-reported physician diagnosis of diabetes, the use of insulin or oral hypoglycemic drugs, fasting blood glucose (FBG) ≥126 mg/dL, randomized blood glucose or 2-h postprandial glucose ≥200 mg/dL, or glycated hemoglobin A1c (HbA1c) level ≥6.5% (37). Prediabetes was defined as self-reported prediabetic status, FBG between 100 and 125 mg/dL, or HbA1c levels between 5.7% and 6.4% (38).

CMI assessment

The CMI was used as an exposure factor, and the CMI was calculated using anthropometric and laboratory data (18). The CMI formula was expressed as follows: CMI = TG (mmol/L)/HDL-C (mmol/L) ´ WHtR. The WHtR was calculated as follows: WHtR = waist circumference (cm)/height (cm). All the enrolled participants were divided into quartiles based on the CMI values for the follow-up analysis.

Assessment of mortality

The mortality rates of the included participants were determined based on National Death Index records as of December 31, 2019. The causes of death were determined according to the International Statistical Classification of Diseases, 10^th revision (ICD-10), in which CVD mortality was defined as heart disease (I00–I09, I11, I13, and I20–I51) and cerebrovascular disease (I60–I69).

Assessment of covariates

Demographic information, including age, sex, race/ethnicity, education level, family education, smoking status, and disease status, was collected from the participants through home interviews. Race/ethnicity was divided into the following categories: non-Hispanic White, non-Hispanic Black, Mexican American, and other. Educational level was classified as high-school diploma, high school/equivalent, college or above. The household income-to-poverty ratio was divided into ≤1.3, >1.3, ≤3.5, and >3.5. Smoking status was divided into never-smokers, former smokers, and current smokers. Alcohol consumption was defined as non-drinkers, moderate drinking (0.1–13.9 g/day for women and 0.1–27.9 g/day for men), and heavy drinking (≥14 g/day for women and ≥28 g/day for men). Hypertension was defined as a self-reported diagnosis of high blood pressure, the use of antihypertensive medications, mean systolic blood pressure ≥140 mmHg, or mean diastolic blood pressure ≥90 mmHg. The BMI was calculated by dividing weight (kg) by the square of height (m). The estimated glomerular filtration rate (eGFR) was calculated using the Chronic Kidney Disease Epidemiology Collaboration equation (39). The Healthy Eating Index (HEI)-2015 includes 13 dietary components, and has a total score ranging from 0 to 100, with higher scores indicating higher dietary quality (40). In addition, the laboratory indicators of the participants were collected, including total cholesterol (TC), TG, blood urea nitrogen (BUN), HDL-C, low-density lipoprotein cholesterol (LDL-C), and uric acid.

Statistical analysis

Due to the complex multistage stratified probability survey design of the NHANES, sample weighting, stratification, and clustering were appropriately used in this study according to the NHANES analysis guidelines. Continuous variables are expressed as the weighted mean ± standard deviation (SD), and categorical variables are expressed as the number of cases (weighted percentage).

The baseline characteristics of the included participants were compared according to the CMI quartile (Q1–Q4) and survival status. The weighted student t-test, Kruskal-Wallis test, or Chi-squared test were used to compare differences between groups as appropriate. A Kaplan-Meier curve analysis and logarithmic rank test were used for the primary outcome analysis to assess the CMI quartile array differences in terms of overall survival. Cox proportional hazard regression models were developed to assess the independent predictive value of the CMI for all-cause mortality and CVD mortality. The results are expressed as the hazard ratios (HRs) and 95% confidence intervals (CIs).

Specifically, the following three statistical inference models were constructed: Model 1: no adjustment covariates; Model 2: adjusted for sex, age, race, education level, and family income-to-poverty ratios; and Model 3: further adjusted for smoking status, alcohol consumption, BMI, hypertension, eGFR, the HEI-2015, HbA1c, LDL-C, and TC.

To explore the quantity-response relationship between the CMI and mortality risk, a restricted cubic spline (RCS) analysis was performed on the 5^th, 35^th, 65^th, and 95^th percentiles of the CMI distribution. Notably, the covariates were adjusted according to Model 3. Potential nonlinear relationships between the CMI and mortality were evaluated using likelihood ratio tests. Based on this result, we further conducted a threshold effect analysis. They are: (I) CMI value to inflection point, between two inflection points (reference), after inflection point; (II) when the CMI value reaches the lowest inflection point (reference) and the CMI is greater than the lowest inflection point; and (III) CMI values to the left inflection point (reference), after the right inflection point.

Sex (male or female), age (<60 or ≥60 years), race (non-Hispanic White or other), education level (less than high school, college or above), BMI (≤30 or >30 kg/m²), smoking status (never or former/current), alcohol consumption (never or moderate/heavy), hypertension (no or yes), and diabetic status (prediabetes or diabetes) were analyzed by stratification. Potential modification effects were detected by examining the corresponding multiplicative interaction terms.

Some sensitivity analyses were then performed to check the stability of the findings. Baseline malignancy, CVD, and participants who died within the first 2 years of follow-up were excluded to minimize potential reverse causal bias. Missing data were addressed using multiple imputation by chained equations (MICE).

A bilateral P value <0.05 was considered statistically significant. R (version 4.3.2; https://www.r-project.org/) was used for all analyses.

Results

Clinical characteristics of prediabetes and diabetes participants based on the CMI quartile

A total of 17,992 participants were included in the study, of whom 48.25% were female. The participants had a mean age of 54.01±15.97 years. Table 1 shows the baseline characteristics of participants with prediabetes and diabetes at different levels of the CMI (Q1: CMI ≤0.56; Q2: 0.56< CMI ≤0.62; Q3: 0.62< CMI ≤0.68; and Q4: CMI >0.68). Participants in the lowest 25% of CMI were on average 5 years younger than the rest, and were more likely to be non-Hispanic White, female, never smoker, non-drinker, lower household income, to have hypertension and diabetes. In addition, these participants were less likely to use oral hypoglycemic agents and insulin, appeared to have a longer course of diabetes, and had higher HbA1c and FBG levels. They also tended to have a lower LDL-C, eGFR, and HEI-2015 score.

Clinical characteristics of prediabetes and diabetes participants based on survival status

During 137,687 person-years of follow-up (median: 7.4 years), a total of 2,718 all-cause deaths and 891 CVD-related deaths were recorded. Table 2 compares baseline characteristics of participant follow-up status. Compared with the participants in the survival group, those in the non-survivors group were older, non-Hispanic White, male, never drinker, smoked, had lower household income, had lower education levels, were more likely to have diabetes, and were less likely to have prediabetes. In addition, these participants appeared to have a longer course of diabetes, and were more likely to take hypoglycemic drugs and use insulin. Meanwhile, non-survivors group had higher FBG, HbA1c, BUN, and uric acid levels, higher CMI scores, lower LDL-C and TC levels, and lower HEI-2015 scores.

Relationship between the CMI and mortality

As the Kaplan-Meier survival plots show (Figure 2), the participants with prediabetes and diabetes in the CMI low-quartile array had significantly better overall survival, including all-cause death and CVD death, than those in the CMI high-quartile array. Three Cox regression risk models were developed for all participants to explore the relationship between the CMI and the risk of mortality. After multivariate adjustment (Model 3), an increased CMI score was significantly associated with an increased risk of all-cause mortality and CVD mortality in the prediabetes and diabetes participants (Table 3). The HRs and 95% CIs for all-cause mortality in the low to high CMI quartiles were 1.00 (reference), 1.056 (0.875–1.274), 1.156 (0.912–1.464), and 1.42 (1.08–1.867), respectively. While those for CVD mortality were 1.00 (reference), 1.041 (0.768–1.41), 1.077 (0.771–1.503), and 1.29 (0.836–1.99), respectively.

Figure 2 Kaplan-Meier curves of the survival rates of participants based on the CMI quartiles. (A) All-cause mortality. (B) CVD mortality. Q1: CMI ≤0.56; Q2: 0.56< CMI ≤0.62; Q3: 0.62< CMI ≤0.68; and Q4: CMI >0.68. CMI, cardiometabolic index; CVD, cardiovascular disease.

In addition, we performed a multivariable-adjusted RCS analysis to assess the dose-response relationship between the CMI and mortality risk rates (Figures 3,4). The RCS curve showed a significant nonlinear U-shaped relationship between the CMI, all-cause mortality, and CVD mortality (P_nonlinear<0.001 and P_nonlinear=0.03, respectively). Based on the two-piecewise Cox proportional hazards regression models, we determined the inflection points as 0.57 and 0.62 for all-cause mortality, and as 0.54 and 0.62 for CVD mortality, respectively. Table 4 shows that the risk of all-cause mortality in the rising stage of the right inflection point is higher than that of the left inflection point. However, this trend was not seen for CVD mortality. Further, we explored the dose-response relationship between the CMI and mortality in the prediabetic and diabetic populations, respectively. The results revealed a U-shaped relationship between the CMI and the all-cause mortality of the prediabetic participants (P_nonlinear=0.01) and an approximately linear relationship between the CMI and CVD mortality (P_nonlinear=0.050). A U-shaped relationship was also found between the CMI and all-cause mortality and CVD mortality of the diabetic participants (P_nonlinear=0.01 and P_nonlinear=0.01, respectively).

Figure 3 RCS analysis between the CMI and mortality risk. (A) All-cause mortality. (B) CVD mortality. The solid red/blue lines correspond to the central estimates, and the red/blue-shaded regions indicate the 95% CIs. CI, confidence interval; CMI, cardiometabolic index; CVD, cardiovascular disease; HR, hazard ratio; RCS, restricted cubic spline.

Figure 4 RCS analysis between the CMI and all-cause mortality (A) and CVD mortality (B) in diabetic patients. Dose-response relationship between the CMI and all-cause (C) and CVD mortality (D) in prediabetic patients. The solid red/blue lines correspond to the central estimates, and the red/blue-shaded regions indicate the 95% CIs. CI, confidence interval; CMI, cardiometabolic index; CVD, cardiovascular disease; HR, hazard ratio; RCS, restricted cubic spline.

Subgroup analysis

In our subgroup analyses and interaction tests, we investigated the relationship between the CMI and the risk of all-cause mortality and CVD mortality across different population subgroups (Figures 5,6). The results showed that the association between CMI and all-cause mortality was significantly associated among male, ≤60 years old, non-Hispanic White, education level group, BMI group, never drinking alcohol, and previous/current smokers. However, the association between CMI and CVD mortality was only observed to be significantly correlated in the prediabetic population. There was a significant interaction between the CMI and all-cause mortality based on age and sex. There was also a significant interaction between the CMI and CVD mortality based on smoking status and diabetes status.

Figure 5 Stratified analyses of the associations between the CMI and all-cause mortality. Adjusted for sex, age, race, education level, smoking status, alcohol consumption, BMI, and hypertension. When analyzed, the stratified variables were not included in the model. BMI, body mass index; CI, confidence interval; CMI, cardiometabolic index; HR, hazard ratio.

Figure 6 Stratified analyses of the associations between the CMI and CVD mortality. Adjusted for sex, age, race, education level, smoking status, alcohol consumption, BMI, and hypertension. When analyzed, the stratified variables were not included in the model. BMI, body mass index; CI, confidence interval; CMI, cardiometabolic index; CVD, cardiovascular disease; HR, hazard ratio.

Sensitivity analysis

In the sensitivity analysis, populations might have influenced the results were separately excluded. The results remained consistent (Tables S1-S3).

Discussion

To our knowledge, this was the first study to report that a higher CMI score is independently associated with a long-term mortality risk in U.S. adults with prediabetes and diabetes. In addition, we also found a similar U-shaped association between the CMI and all-cause mortality and CVD mortality, and a dose-response relationship was observed in both the prediabetes and diabetic populations. The results of the subgroup analysis showed that this trend was similar in different populations. There was no significant interaction between the baseline CMI and stratification variables. Together, our findings showed that the CMI is an effective predictor of all-cause mortality and risk among U.S. adults with diabetes and prediabetes.

As an anthropometric indicator, the CMI was initially proposed by Wakabayashi et al. for the identification of DM, and was shown to be significantly correlated with elevated blood sugar and diabetes (18). Previous clinical studies have explored the relationship between the CMI and various populations and patient groups, including those with NAFLD, DM, atherosclerosis, and CVD (41,42). The findings suggest that the CMI is a favorable predictor of metabolic disease and patient prognosis.

To date, several studies have confirmed the relationship between the CMI and the incidence of DM (41,43). A previous study showed that the CMI could serve as an effective alternative method for predicting metabolic syndrome, and had an area under the curve of 0.86 (26). A retrospective study of 15,453 Japanese adults showed a significant nonlinear relationship between the CMI and DM (44). In addition, Acosta-Garcia et al. explored the potential association between the CMI, dyslipidemia, and changes in blood pressure (45). Zou et al. found a significant positive correlation between the CMI and an increased risk of NAFLD (27). However, an increased CMI score was only significantly associated with an increased risk of NAFLD in women (24).

Research has found and association between the CMI, and the risk of CVD and mortality. Specifically, one study found that high CMI scores were positively associated with the risk of new CVD in participants with obstructive sleep apnea (adjusted HRs: 1.31; 95% CI: 1.20–1.43) (46). Another study of a general population in rural China reported a strong and independent association between the CMI and ischemic stroke (47). Notably, recent epidemiological studies have also focused on the potential predictive value of the CMI for mortality risk, including all-cause mortality and CVD mortality. A study of 3,029 participants aged over 65 years found that an increased CMI score was positively associated with all-cause mortality (HR: 1.11; 95% CI: 1.01–1.21) (29). Another study simultaneously compared eight anthropometric indices, including the CMI, which was the weakest in predicting the risk of all-cause death and CVD death (48). However, the association between the CMI and the risk of all-cause death and CVD death in the prediabetic and diabetic populations was unclear.

Our study included 19,772 adults with prediabetes and diabetes from a representative sample of the U.S. population, and sought to determine whether the CMI was associated with all-cause death and CVD death in prediabetes and diabetes participants. We found that a high CMI score was associated with an increased risk of all-cause mortality and CVD mortality. The CMI is based on a combination of the TG/HDL-C ratio and the WHtR, and provides an integrated approach for assessing dyslipidemia and abdominal obesity, parameters that are critical drivers of metabolic disorders. A previous study has shown that low HDL-C levels are associated with an increased risk of CVD (49). A 9.6-year follow-up study of 7,175 Japanese community residents with no prior history of CVD showed that HDL-C levels were negatively associated with all-cause mortality, which was consistent with findings for European and American populations (50). The mechanism may be related to the relationship between HDL-C and the extracellular matrix, cell differentiation, and proliferation (51). Similarly, high plasma TG levels reduce the number of insulin receptors on fat cells and prevent insulin from binding to insulin receptors, leading to diabetes. In recent years, the combined TG and the HDL-C (TG/HDL-C) index has been used to predict metabolic diseases, which have been confirmed to be closely related to IR and metabolic disorders (52). The TG/HDL-C index is also considered an appropriate indicator of CVD risk, and is used to predict CVD risk (53,54). In the past, the TG/HDL-C index was used to evaluate small atherogenic LDL-C particles, which were closely related to IR, DM, and metabolic syndrome (55,56).

Similarly, another critical component of the CMI, the WHtR, plays an essential role in assessing obesity and metabolic diseases. Compared with traditional indicators, such as the BMI, the WHtR is a more accurate assessment of abdominal obesity regardless of height (57,58). Obese individuals have excess free fatty acid circulation and an elevated WHtR, which can prevent glucose transport activity and limit the effect of insulin on glucose metabolism, resulting in IR (59). This study showed the prognostic value of the CMI as an easily measured and calculable indicator of adults with prediabetes or diabetes. However, further studies need to be conducted to verify the association of the CMI in the general population and other specific populations.

The biological mechanism underlying the association between the CMI and mortality in prediabetes and diabetes is not yet fully understood; however, the potential critical pathway may be related to lipid metabolism disorders. Chronic inflammation, oxidative stress, and IR are all involved in the occurrence and progression of DM and CVD (60-62). IR patients are more likely to develop metabolic disorders, such as diabetes, dyslipidemia, and metabolic syndrome, which are closely associated with CVD. From the pathological point of view, IR increases sympathetic nerve excitation, renal sodium retention, and blood pressure elevation, thereby increasing the cardiac load, and leading to an increased risk of cardiovascular and cerebrovascular diseases (63). Similarly, sustained IR can exacerbate the inflammatory response, thereby damaging endothelial cells and promoting foam cell formation, leading to atherosclerosis (64).

In addition, the analysis of the participants’ baseline characteristics showed that the prediabetic or diabetic participants with a high CMI score had higher HbA1c, FBG, and TG levels, a higher BMI, and lower HDL-C and eGFR levels. The control of traditional risk factors for diabetes, such as adequate diet and physical activity, is also critical for the prevention of diabetes and its complications (65,66). These results suggest that the association between the CMI and all-cause death and CVD death is also related to the presence of traditional CVD risk factors. Taken together, our findings support the use of the CMI as a predictor of risk in adults with diabetes and prediabetes. The inclusion of the CMI in the assessment of the long-term outcomes of individuals with prediabetes and diabetes could play a crucial role in public health and prevention strategies.

The U-shaped association between the CMI and death risk indicates the need for individualized risk stratification in the management of diabetes and prediabetes. Clinicians may consider incorporating the CMI threshold (optimal range: 0.57–0.62) into existing risk prediction models to improve the accuracy of prognosis for patients with abnormal blood glucose. Although the data are from a nationally representative U.S. cohort, the pathophysiological mechanisms underlying the CMI-mortality association (e.g., visceral obesity, IR) are universal metabolic determinants. This biological plausibility suggests potential applicability to other populations, although validation in different ethnic groups and healthcare settings remains necessary. The real innovation lies in demonstrating the nonlinear risk pattern of the CMI, which fundamentally challenges the traditional linear assumption in anthropometric risk assessment and paves the way for precise preventive strategies.

Study limitations

Despite the advantages and clinical implications of this study, it had a number of limitations. First, cause-and-effect relationships could not be established because this study was observational. Second, the results were based on U.S. adults with prediabetes and diabetes, which somewhat limits the generalizability of the CMI to other populations, although subgroup and sensitivity analyses were performed. Third, we could not wholly exclude the potential confounding factors. We established different risk models and a series of sensitivity analyses to adjust for confounding factors; however, residual confounding factors might still have affected prognosis. Fourth, the data from NHANES did not distinguish between people with different types of diabetes. However, we included patients 18 years of age and older, so the results of the study may be more representative of people with type 2 diabetes. Finally, the study only looked at the baseline CMI, and it did not examine time changes in the CMI.

Conclusions

Our results showed that a high CMI score was associated with an increased risk of all-cause mortality and CVD mortality in U.S. participants with prediabetes and diabetes, and the association was nonlinear and approximately U-shaped. Therefore, CMI assessments may help predict the long-term prognostic risk of individuals with prediabetes and diabetes. However, further research needs to be conducted to confirm these findings.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://cdt.amegroups.com/article/view/10.21037/cdt-2025-100/rc

Peer Review File: Available at https://cdt.amegroups.com/article/view/10.21037/cdt-2025-100/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://cdt.amegroups.com/article/view/10.21037/cdt-2025-100/coif). K.C.F. reports consulting fees from Eli Lilly, Boehringer Ingelheim, Novartis and Janssen, unrelated to this submitted work. The other authors have no other conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The National Center for Health Statistics Research Ethics Review Board granted its approval, and all participants provided written informed consent (Protocol Number: Protocol #98-12, Protocol #2005-06, Protocol #2011-17, Protocol #2018-01). All the data can be easily accessed via the NHANES website (https://wwwn.cdc.gov/nchs/nhanes/Default.aspx).

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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(English Language Editor: L. Huleatt)