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Abstract

Objective

To investigate the association between the cholesterol, high-density lipoprotein, and glucose (CHG) index and type 2 diabetes mellitus prevalence in US adults.

Methods

This cross-sectional study included 11,390 participants from the National Health and Nutrition Examination Survey 2009–2018 cycles. The CHG index was calculated using total cholesterol, high-density lipoprotein, and fasting blood glucose levels. Weighted logistic regression was used to assess the association between the CHG index and type 2 diabetes mellitus prevalence, and restricted cubic spline analysis was applied to examine potential nonlinear relationships. Receiver operating characteristic curves were used to evaluate the diagnostic performance of the CHG index, and the results were compared with those of the triglyceride–glucose index.

Results

A higher CHG index was significantly associated with increased type 2 diabetes mellitus prevalence (odds ratio: 4.30, 95% confidence interval: 3.21–5.77, p < 0.001) after adjusting for multiple confounders. Restricted cubic spline analysis showed a nonlinear relationship, demonstrating an elevated risk of type 2 diabetes mellitus in patients with a CHG index of >5.24. The association remained consistent across all subgroups. The CHG index demonstrated good predictive value (area under the curve = 0.721; optimal cutoff = 5.47; sensitivity = 0.53; specificity = 0.80), comparable to that of the triglyceride–glucose index (area under the curve = 0.730; optimal cutoff = 8.84; sensitivity = 0.62; specificity = 0.73).

Conclusion

The CHG index may be associated with type 2 diabetes mellitus prevalence and could serve as a simple, accessible biomarker for early risk identification. Further prospective studies are needed to validate its clinical utility.

Keywords

Cholesterol high-density lipoprotein and glucose index type 2 diabetes mellitus cross-sectional study National Health and Nutrition Examination Survey

Introduction

Type 2 diabetes mellitus (T2DM) is a prevalent chronic metabolic disorder characterized by insulin resistance (IR) and insufficient insulin secretion.¹ It accounts for approximately 90% of adult diabetes cases, with a steadily rising global incidence.² By 2040, the number of individuals affected by T2DM is projected to reach 642 million, posing a substantial public health challenge.³ Moreover, T2DM is associated with multiple complications, including renal dysfunction, ophthalmic diseases, hypoglycemia, ketoacidosis, neuropathy, and cardiovascular diseases.^4,5 These complications not only diminish patients’ quality of life but also impose a significant burden on healthcare systems worldwide.⁶ Given these challenges, early identification and accurate diagnosis of T2DM are crucial for effective disease management and public health intervention.^7,8

Recently, the cholesterol, high-density lipoprotein, glucose (CHG) index has emerged as a promising biomarker for T2DM diagnosis.⁹ This index integrates total cholesterol (TC), high-density lipoprotein (HDL), and fasting blood glucose (FBG) levels and is calculated using the following formula: ln (TC (mg/dL) × FBG (mg/dL)/(2 × HDL (mg/dL))). As a comprehensive metabolic marker, the CHG index reflects both glucose and lipid metabolism disorders, providing a holistic assessment of metabolic health. Prior studies, particularly those based on Iranian cohorts, have demonstrated the potential value of the CHG index in detecting T2DM.⁹

However, the predictive performance and generalizability of the CHG index remain unclear in other populations, especially in the United States, where ethnic heterogeneity and lifestyle factors may influence its diagnostic utility. To date, no study has systematically evaluated the association between the CHG index and T2DM prevalence in a nationally representative US population. To bridge this knowledge gap, this study utilizes the National Health and Nutrition Examination Survey (NHANES) database,¹⁰ a comprehensive repository of health and demographic data on the US population, to investigate the association between the CHG index and T2DM prevalence. Furthermore, this study aimed to evaluate the broader applicability of the CHG index and explore its potential role in early detection and intervention strategies for T2DM across diverse populations.

Methods

Study design and data sources and processing

To explore the relationship between the CHG index and T2DM prevalence, we utilized data from the NHANES, a nationally representative cross-sectional survey conducted by the Centers for Disease Control and Prevention in the United States (https://www.cdc.gov/nchs/nhanes/). The NHANES program combines detailed interviews with physical and laboratory examinations. All participants provided written informed consent, and the survey was approved by the Research Ethics Review Board of the National Center for Health Statistics.¹¹ This study was conducted in accordance with the ethical principles of the Declaration of Helsinki (1975), as revised in 2024. The requirement for ethical approval was waived by the Ethics Committee of the Affiliated People’s Hospital of Fujian University of Traditional Chinese Medicine, as the NHANES database is publicly available and contains fully deidentified data. The reporting of this study conforms to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines.¹²

We analyzed data from five NHANES cycles (2009–2010, 2011–2012, 2013–2014, 2015–2016, and 2017–2018), yielding data from a total of 49,693 adult participants aged ≥20 years. Participants were excluded based on the following criteria: (a) 34,426 individuals with missing data required to calculate the CHG index; (b) 2 with missing T2DM status; (c) 2706 with missing educational level data; (d) 1138 with missing alcohol consumption data; (e) 18 with missing hypertension data; and (f) 8 with missing smoking status data. After applying these exclusion criteria, 11,390 eligible participants remained for the final analysis, including 2710 individuals with T2DM and 8680 without T2DM (Figure 1).

Figure 1.

Flowchart of participant selection. CHG: cholesterol, high-density lipoprotein, and glucose; T2DM: type 2 diabetes mellitus.

Definition of primary variables

The CHG index⁹ was developed by integrating TC, HDL, and FBG into the following formula: ln (TC (mg/dL) × FBG (mg/dL)/(2 × HDL (mg/dL))).

T2DM was defined according to the following criteria:¹⁰ (a) doctor informed that you have diabetes; (b) glycated hemoglobin (HbA1c) level ≥6.5 mmol/L; (c) fasting plasma glucose level ≥126 mg/dL; (d) taking insulin currently; and (e) current use of antidiabetic medications.

Definition of covariates

Covariates included sex, age, race, educational level, marital status, poverty-to-income ratio (PIR), body mass index (BMI), fasting insulin levels, smoking status, triglycerides (TG), hypertension, and alcohol consumption. Previous research has identified liver enzyme levels as significant risk factors for T2DM.^13,14 Therefore, alanine aminotransferase (ALT), aspartate aminotransferase (AST), gamma-glutamyl transferase (GGT), and alkaline phosphatase (ALP) were also included as covariates.

According to the World Health Organization guidelines, participants’ BMI was categorized into three groups: <25 (normal weight), 25–29.9 (overweight), and ≥30 kg/m² (obese).¹⁵ Income levels were stratified according to the poverty line to assess socioeconomic status. Households with a PIR ≤1 were classified as low income, those with a PIR between 1 and <4 as middle income, and those with a PIR ≥4 as high income.¹⁶

Statistical methods

The analysis utilized data from five 2-year cycles of the NHANES survey (2009–2018). Due to the inherent complexity of the NHANES sampling design, all analyses incorporated sampling weights. In accordance with the NHANES analytical guidelines, the original 2-year cycle weights were recalibrated by multiplying them by one-fifth (1/5) to represent the 10-year analytical period.^17,18

Baseline characteristics of all participants were summarized as means (mean ± SD) for continuous variables and as frequencies and proportions (N (%)) for categorical variables, and differences across groups for continuous and categorical variables were assessed using weighted t-tests and chi-square tests, respectively. To elucidate the association between the CHG index and T2DM prevalence, we developed three weighted logistic regression models. Model 1 was not adjusted for any confounding variables. Model 2 was adjusted for age, sex, race, educational level, marital status, and PIR. Model 3 introduced additional adjustments for alcohol consumption, smoking status, ALT, AST, ALP, GGT, TG, fasting insulin, hypertension, and BMI, building upon the adjustments made in Model 2. Odds ratios (ORs) and 95% confidence intervals (CIs) were computed to quantify the associations within the three models. Restricted cubic spline (RCS) plots were applied to assess potential nonlinear associations between the CHG index and T2DM prevalence. Furthermore, subgroup analyses were performed to investigate the potential moderating effects of demographic and clinical factors on the association between the CHG index and T2DM prevalence. The subgroup variables included age, sex, race, educational level, marital status, PIR, ALT, AST, ALP, GGT, TG, BMI, hypertension, insulin levels, alcohol consumption, and smoking status. Multicollinearity among covariates was assessed using variance inflation factors (VIFs). VIF values below 5 were considered acceptable and indicative of no severe multicollinearity.^19,20

To minimize potential bias caused by missing data, we performed multiple imputation using the ‘MICE’ package in R, consistent with previous studies.^21–23 Additionally, a sensitivity analysis was conducted to determine whether the imputed dataset significantly differed from the original preimputation data. The results indicated no significant differences between the imputed and original datasets (Table S1). Therefore, all multivariable analyses were based on the imputed dataset.²⁴ To assess the stability of our results, two sensitivity analyses were performed: (a) an analysis based on the unweighted dataset and (b) an analysis based on the unimputed dataset. Finally, we assessed and compared the diagnostic performance of the CHG index and triglyceride–glucose (TyG) index²⁵ for T2DM using the receiver operating characteristic (ROC) curve analysis and area under the curve (AUC) values.

The optimal diagnostic cutoff was determined by maximizing the Youden index.²⁶ A p value of <0.05 was regarded as the threshold for statistical significance. This cross-sectional study utilized R software (version 4.4.1) for all statistical analyses.

Results

Baseline characteristics of NHANES participants

A total of 11,390 eligible participants were included in this analysis. Table 1 presents their general characteristics, including age, sex, race, educational level, marital status, PIR, smoking status, alcohol consumption, BMI, hypertension, TG, and liver enzyme levels. The mean age of the participants was >50 years. Among them, 2710 individuals (23.79%) were classified into the T2DM group, whereas 8680 individuals (76.21%) were assigned to the non-T2DM group. The prevalence of T2DM was higher in men than in women, and the mean age in the T2DM group was greater than that in the non-T2DM group. In addition, compared with the non-T2DM group, participants in the T2DM group exhibited significantly higher levels of PIR, ALT, AST, GGT, ALP, TG, fasting insulin, and CHG index. Furthermore, a greater proportion of individuals in the T2DM group were smokers, had hypertension, were obese, and were married. Conversely, compared with the T2DM group, a higher proportion of individuals in the non-T2DM group reported alcohol consumption and attainment of at least some college education.

Table 1.

Weighted baseline characteristics of NHANES participants.

Variables	All(N = 11,390)	Non-T2DM group(N = 8680)	T2DM group(N = 2710)	p
Age, years, mean (SD)	50.18 (17.67)	47.42 (17.51)	59.01 (15.12)	<0.001
ALT, IU/L, mean (SD)	24.77 (18.03)	24.44 (18.35)	25.82 (16.92)	0.003
AST, IU/L, mean (SD)	25.16 (19.63)	25.04 (19.30)	25.53 (20.66)	0.305
ALP, IU/L, mean (SD)	70.48 (25.20)	68.63 (22.57)	76.41 (31.48)	<0.001
GGT, IU/L, mean (SD)	29.43 (40.23)	27.29 (37.84)	36.30 (46.40)	<0.001
TG, mmol/L, mean (SD)	1.47 (1.38)	1.37 (1.08)	1.79 (2.04)	<0.001
Insulin, µU/mL, mean (SD)	14.33 (19.42)	12.10 (10.54)	21.46 (34.10)	<0.001
CHG index, (mean (SD))	5.26 (0.44)	5.17 (0.35)	5.55 (0.54)	<0.001
Sex, N (%)				0.07
Female	5800 (50.9)	4492 (51.8)	1308 (48.3)
Male	5590 (49.1)	4188 (48.2)	1402 (51.7)
PIR, N (%)				<0.001
High income	2838 (24.9)	2267 (26.1)	571 (21.1)
Middle income	6010 (52.8)	4505 (51.9)	1505 (55.5)
Low income	2542 (22.3)	1908 (22.0)	634 (23.4)
BMI, N (%)				<0.001
Normal weight	3229 (28.3)	2792 (32.2)	437 (16.1)
Overweight	3732 (32.8)	2934 (33.8)	798 (29.4)
Obese	4429 (38.9)	2954 (34.0)	1475 (54.4)
Smoking status, N (%)				0.001
No	6343 (55.7)	4934 (56.8)	1409 (52.0)
Yes	5047 (44.3)	3746 (43.2)	1301 (48.0)
Alcohol consumption, N (%)				<0.001
No	2788 (24.5)	2043 (23.5)	745 (27.5)
Yes	8602 (75.5)	6637 (76.5)	1965 (72.5)
Hypertension, N (%)				<0.001
No	7077 (62.1)	5982 (68.9)	1095 (40.4)
Yes	4313 (37.9)	2698 (31.1)	1615 (59.6)
Race, N (%)				<0.001
Mexican American	1688 (14.8)	1229 (14.2)	459 (16.9)
Non-Hispanic Black	2294 (20.1)	1666 (19.2)	628 (23.2)
Non-Hispanic White	4703 (41.3)	3740 (43.1)	963 (35.5)
Other Hispanic	1223 (10.7)	905 (10.4)	318 (11.7)
Other race, including multi-racial	1482 (13.0)	1140 (13.1)	342 (12.6)
Educational level, N (%)				<0.001
Less than 9th grade	1091 (9.6)	709 (8.2)	382 (14.1)
9th to 11th grade	1551 (13.6)	1122 (12.9)	429 (15.8)
High school graduate	2560 (22.5)	1924 (22.2)	636 (23.5)
Some college or AA degree	3433 (30.1)	2651 (30.5)	782 (28.9)
College graduate or above	2755 (24.2)	2274 (26.2)	481 (17.7)
Marital status, N (%)				<0.001
Divorced	1257 (11.0)	918 (10.6)	339 (12.5)
Living with partner	977 (8.6)	812 (9.4)	165 (6.1)
Married	5801 (50.9)	4349 (50.1)	1452 (53.6)
Never married	2089 (18.3)	1759 (20.3)	330 (12.2)
Separated	384 (3.4)	279 (3.2)	105 (3.9)
Widowed	882 (7.7)	563 (6.5)	319 (11.8)

ALP: alkaline phosphatase; ALT: alanine aminotransferase; AST: aspartate aminotransferase; BMI: body mass index; CHG: cholesterol, high-density lipoprotein, and glucose; GGT: gamma-glutamyltransferase; PIR: poverty-to-income ratio; T2DM: type 2 diabetes mellitus; TG: triglycerides; AA degree: Associate’s degree.

The relationship between the CHG index and T2DM prevalence

Table 2 displays the association between CHG index quartiles and T2DM prevalence among participants. In Model 1, without adjusting for covariates, the prevalence of T2DM increased significantly with higher CHG index (OR: 6.90, 95% CI: 5.57–8.54, p < 0.001). After adjusting for key demographic variables (age, sex, race, educational level, marital status, and PIR) in Model 2, this association remained strong and statistically significant (OR: 6.89, 95% CI: 5.46–8.69, p < 0.001). In Model 3, additional adjustments for ALT, AST, ALP, GGT, TG, BMI, hypertension, insulin, alcohol consumption, and smoking status did not substantially attenuate this association. Moreover, participants in the highest quartile of the CHG index had a 4.30-fold higher prevalence of T2DM than those in the lowest quartile (OR: 4.30, 95% CI: 3.21–5.77, p < 0.001). VIF analysis showed that all covariates had VIF values below 5, indicating no significant multicollinearity. Detailed VIF results are presented in Table S2.

Table 2.

Weighted logistic regression between the CHG index and T2DM prevalence.

Characteristic	Model 1OR (95% CI)(p value)	Model 2OR (95% CI)(p value)	Model 3OR (95% CI)(p value)
CHG index
Quartiles
Q1	Reference	Reference	Reference
Q2	1.13 (1.08–1.61)(p = 0.009)	1.22 (0.99–1.50)(p = 0.064)	1.01 (0.81–1.24)(p = 0.956)
Q3	1.95 (1.61–2.35)(p < 0.001)	1.84 (1.48–2.28)(p < 0.001)	1.32 (1.04–1.68)(p = 0.026)
Q4	6.90 (5.57–8.54)(p < 0.001)	6.89 (5.46–8.69)(p < 0.001)	4.30 (3.21–5.77)(p < 0.001)

Model 1: Non-adjusted model.

Model 2: Adjusted for age, sex, race, educational level, marital status, and PIR.

Model 3: Adjusted for age, sex, race, educational level, marital status, PIR, ALT, AST, ALP, GGT, TG, BMI, hypertension, insulin, alcohol consumption and smoking status.

To further investigate this relationship, RCS analysis was conducted. The analysis revealed a significant nonlinear association between the CHG index and T2DM prevalence (p-nonlinearity < 0.05; Figure 2), with a notable increase in T2DM prevalence observed when the CHG index exceeded 5.24. These findings indicate that the association between the CHG index and T2DM prevalence is more complex than a simple linear relationship.

Figure 2.

Restricted cubic spline plot assessing the association between the CHG index and T2DM prevalence. The associations were adjusted for age, sex, race, educational level, marital status, PIR, ALT, AST, ALP, GGT, TG, BMI, hypertension, insulin, alcohol consumption, and smoking status. X-axis represents the CHG index, and Y-axis represents the ORs and 95% CIs for T2DM. CHG: cholesterol, high-density lipoprotein, and glucose; T2DM: type 2 diabetes mellitus; PIR: poverty-to-income ratio; ALT: alanine aminotransferase; AST: aspartate aminotransferase; ALP: alkaline phosphatase; GGT: gamma-glutamyl transferase; TG: triglycerides; BMI: body mass index; ORs: odds ratios; CIs: confidence intervals.

Subgroup analysis

To gain deeper insights into the factors influencing the relationship between the CHG index and T2DM prevalence, we conducted a comprehensive subgroup analysis. The data were stratified based on key covariates, including age, sex, race, educational level, marital status, PIR, ALT, AST, ALP, GGT, TG, BMI, hypertension, insulin levels, alcohol consumption, and smoking status. Despite stratification, the positive association between the CHG index and T2DM prevalence remained consistent across all subgroups, with no statistically significant variations observed (p > 0.05 for interaction; Figure 3).

Figure 3.

Subgroup analysis of the associations between the CHG index and T2DM prevalence. Stratified by age, sex, race, educational level, marital status, PIR, ALT, AST, ALP, GGT, TG, BMI, hypertension, insulin, alcohol consumption, and smoking status. CHG: cholesterol, high-density lipoprotein, and glucose; T2DM: type 2 diabetes mellitus; PIR: poverty-to-income ratio; ALT: alanine aminotransferase; AST: aspartate aminotransferase; ALP: alkaline phosphatase; GGT: gamma-glutamyl transferase; TG: triglycerides; BMI: body mass index.

Sensitivity analysis

We conducted a sensitivity analysis using the unweighted dataset, and the results indicated that the association between the CHG index and T2DM prevalence remained consistent (Table S3). Additionally, a weighted logistic regression based on the unimputed dataset demonstrated a stable association between the CHG index and T2DM prevalence (Table S4).

ROC curve analysis

The AUC for the CHG index in predicting T2DM was 0.721 (95% CI: 0.709–0.732), indicating a good discriminatory ability. The optimal cutoff value of the CHG index for predicting T2DM was 5.47, with a sensitivity of 0.53 and specificity of 0.80. Similarly, the TyG index showed an AUC of 0.730 (95% CI: 0.718–0.742) in predicting T2DM, with an optimal cutoff value of 8.84, sensitivity of 0.62, and specificity of 0.73 (Figure 4).

Figure 4.

ROC curve analysis of the CHG and TyG indices for predicting T2DM. ROC: receiver operating characteristic; CHG: cholesterol, high-density lipoprotein, and glucose; TyG: triglyceride–glucose index; T2DM: type 2 diabetes mellitus.

Discussion

This study employed a cross-sectional analysis of NHANES data from 2009 to 2018 to provide compelling evidence regarding a significant positive association between the CHG index and the prevalence of T2DM in the US population. Importantly, this association remained robust after rigorous adjustment for a wide range of potential confounders.

Several indices have previously been used to estimate the risk of IR and T2DM, including the triglyceride-to-HDL cholesterol (TG/HDL-C) ratio, TC-to-HDL-C ratio (TC/HDL-C), TyG index, and homeostasis model assessment for IR (HOMA-IR).^27–29 Although these indices are clinically relevant and have demonstrated utility, some may be constrained by factors such as variable predictive power across populations, higher costs, technical complexity, or suboptimal reproducibility in certain settings.^10,27,28 In contrast, the CHG index integrates three readily available parameters (TC, HDL-C, and FBG) into a single metric that reflects both lipid and glucose metabolism. This integrative quality may enhance its clinical utility and predictive accuracy.

Our ROC analysis showed that the CHG index had an AUC of 0.721 (95% CI: 0.709–0.732) in predicting T2DM, with an optimal cutoff of 5.47, yielding a sensitivity of 0.53 and specificity of 0.80. Similarly, the TyG index showed an AUC of 0.730 (95% CI: 0.718–0.742) in predicting T2DM, with an optimal cutoff value of 8.84, sensitivity of 0.62, and specificity of 0.73. The AUC of the TyG index was slightly higher than that of the CHG index, although the difference was small and the CIs overlapped, suggesting comparable predictive performance between the two indices. Although the TyG index exhibited better sensitivity, the CHG index demonstrated superior specificity, indicating that each index offers distinct advantages depending on whether the clinical aim is early detection or confirmatory diagnosis. These findings closely align with those of a previous study conducted in an Iranian population, which reported a cutoff of 5.57 with an AUC of 0.864 and specificity of 89.82%.⁹ Although the AUC and specificity were higher in the Iranian cohort, likely due to population differences or clinical settings, both studies support the CHG index as a viable and accessible screening tool for T2DM.

IR and pancreatic β-cell dysfunction are key drivers of T2DM pathogenesis,^29,30 and the CHG index—comprising TC, FBG, and HDL-C—reflects metabolic alterations linked to these mechanisms. Chronic hyperglycemia initiates compensatory insulin secretion, but prolonged stress impairs β-cell function.^31,32 Concurrently, inflammatory cytokines—such as tumor necrosis factor-alpha, interleukin-6, and interleukin-1 beta—triggered by elevated glucose levels, disrupt insulin signaling and exacerbate IR.^33–37 Oxidative stress further amplifies this effect, as excessive intracellular glucose promotes the generation of reactive oxygen species, which damage the insulin pathways.^30,38,39 These two processes are mutually reinforcing, forming a vicious cycle that accelerates β-cell failure and IR progression.⁴⁰ Additionally, lipid disorders contribute significantly—high TC impairs insulin secretion, whereas low HDL-C compromises β-cell survival.^41–44 A higher TC/HDL-C ratio is also associated with more severe IR and elevated T2DM risk.^45–48 By integrating lipid and glucose biomarkers implicated in these interrelated pathways, the CHG index may serve as a mechanistically relevant predictor of T2DM.

This study has several notable strengths. First, it demonstrates a significant association between the CHG index and T2DM prevalence in the US population, identifying an optimal cutoff value of 5.47, thus highlighting its potential and practicality as a novel biomarker. Second, the study employed multiple statistical models with rigorous adjustments for demographic variables, lifestyle factors (such as alcohol consumption and smoking status), and clinical biomarkers (such as liver enzymes and TG). These comprehensive adjustments ensure that the observed association between the CHG index and T2DM prevalence remains robust after controlling for various confounders. Finally, subgroup and sensitivity analyses further confirmed the robustness of the study’s findings.

However, some limitations should also be acknowledged. First, although the NHANES dataset is representative of the US population, these findings might not be directly generalizable to populations in other regions or countries with different racial, socioeconomic, or healthcare characteristics; thus, validation in diverse populations is warranted. Second, given the cross-sectional nature of the NHANES data, causal relationships cannot be established. Therefore, future prospective cohort studies or clinical trials are required to further validate the predictive utility of the CHG index. Third, some data (e.g. smoking status, alcohol consumption, and diabetes diagnosis) were self-reported, introducing potential recall bias or misclassification. Fourth, despite rigorous adjustment for multiple covariates, residual confounding from unmeasured factors such as physical activity and dietary habits may still influence the observed associations. In addition, future studies should compare the CHG index with other established metabolic indicators, such as the HOMA-IR, to better understand their relative diagnostic value.

Conclusion

Our results suggest that the CHG index is associated with T2DM prevalence in the US population, highlighting its potential as a biomarker candidate. However, given the cross-sectional design of this study, prospective cohort studies are needed to further validate its predictive utility.

Supplemental Material

sj-pdf-1-imr-10.1177_03000605251375557 - Supplemental material for Association between the cholesterol, high-density lipoprotein, and glucose index and type 2 diabetes mellitus prevalence in US adults: A cross-sectional study based on National Health and Nutrition Examination Survey 2009–2018

Supplemental material, sj-pdf-1-imr-10.1177_03000605251375557 for Association between the cholesterol, high-density lipoprotein, and glucose index and type 2 diabetes mellitus prevalence in US adults: A cross-sectional study based on National Health and Nutrition Examination Survey 2009–2018 by Zhe Zhang, Mengting Chen, Dong Cai, Mengzhen Fan and Yang Wang in Journal of International Medical Research

Footnotes

Acknowledgments

We thank all the authors who participated in this study. We thank the NHANES database () for providing summary-level data and the efforts of all the researchers.

Author contributions

ZZ and YW contributed to the study design, conceptualization, manuscript drafting, and interpretation of results. MTC, DC, and MZF were responsible for data collection and analysis. YW performed supervision and reviewed and edited the manuscript. All authors participated in critical revisions and approved the final version of the manuscript.

Data availability

The datasets of the cross-sectional studies can be found in the NHANES datasets ().

Declaration of conflicting interests

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

Ethical approval

This study involved human participants and was approved by the Research Ethics Review Board (ERB) of the National Health and Nutrition Examination Survey (NHANES). The protocol approval numbers for each survey cycle are as follows: Continuation of Protocol #2005-06, Protocol #2011-17, Continuation of Protocol #2011-17, and Protocol #2018-01. For more information, please visit .

Funding

This study was not supported by any funds.

Informed consent statement

Informed consent was obtained from all participants involved in the study.

ORCID iD

Yang Wang

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Supplementary Material

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Association between the cholesterol,high-density lipoprotein,and glucose index and type 2 diabetes mellitus prevalence in US adults: A cross-sectional study based on National Health and Nutrition Examination Survey 2009–2018

Abstract

Objective

Methods

Results

Conclusion

Keywords

Introduction

Methods

Study design and data sources and processing

Definition of primary variables

Definition of covariates

Statistical methods

Results

Baseline characteristics of NHANES participants

The relationship between the CHG index and T2DM prevalence

Subgroup analysis

Sensitivity analysis

ROC curve analysis

Discussion

Conclusion

Supplemental Material

sj-pdf-1-imr-10.1177_03000605251375557 - Supplemental material for Association between the cholesterol, high-density lipoprotein, and glucose index and type 2 diabetes mellitus prevalence in US adults: A cross-sectional study based on National Health and Nutrition Examination Survey 2009–2018

Footnotes

Acknowledgments

Author contributions

Data availability

Declaration of conflicting interests

Ethical approval

Funding

Informed consent statement

ORCID iD

References

Supplementary Material