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Background: Obesity and adverse lipid profile leads to female infertility. The cardiometabolic index (CMI) is a promising indicator for predicting obesity-related diseases. The correlation between CMI and female infertility merits further investigation. Methods: The data for this study were acquired from the 2013–2020 National Health and Nutrition Examination Survey (NHANES), with 2333 women enrolled. The cardiometabolic index (CMI) of each participant was calculated as the ratio of triglycerides and high-density lipoprotein cholesterol multiplied by waist-to-height ratio. Weighted multivariate logistic regression models were used to assess the independent correlation between the log-transformed CMI and infertility. Subgroup analyses were carried out to assess the reliability of the findings. Interaction tests were employed to determine whether variables affected infertility by interacting with log CMI. Results: A total of 2333 participants aged 18–45 years were enrolled, 274 of whom were infertile. Log CMI of the infertility group was significantly higher than that of the non-infertility group (P < 0.001). After adjustment for potential confounders, women with higher CMI were at an increased risk of infertility (OR = 2.411, 95% CI: 1.416–4.112), and this correlation was still consistent in subgroups aged under 35 years (P < 0.001). Furthermore, restricted cubic spline analysis showed a positive non-linear relationship between log CMI and infertility. Conclusions: Cardiometabolic index levels are positively correlated with increased risk of infertility in American females. Our study demonstrates the predictive capacity of CMI for female infertility. [ABSTRACT FROM AUTHOR]

ABSTRACT Literature on the association between the cardiometabolic index (CMI) and chronic kidney disease (CKD) risk is limited, especially in hypertensive populations. The objective of the present investigation was to assess the relationship between the CMI and CKD risk in a hypertensive population. The current cross‐sectional study included a total of 13 717 individuals with hypertension. The calculation of the CMI was based on the waist‐to‐height ratio and the triglyceride‐to‐high‐density lipoprotein cholesterol ratio. The definition of CKD was based on an estimated glomerular filtration rate (eGFR) of less than 60 mL/min/1.73 m2. The prevalence of CKD was found to be 4.24% in younger adults (aged < 65 years) and 14.93% in the elderly (aged ≥ 65 years). The results of the multivariate regression analysis indicated that in the elderly group, the CMI was positively associated with CKD risk (odd ratio [OR] 1.29; 95% confidence interval [CI]: 1.14, 1.46), while no significant relationship was observed in the younger group (OR 1.04, 95% CI: 0.85, 1.27). Furthermore, subgroup analyses did not identify any potential factors that could modify the relationship between the CMI and CKD risk (all p for interaction > 0.05). Among adults with hypertension, there was an independent and positive correlation between the CMI and CKD risk in the elderly, whereas such a correlation was not observed in younger adults.Trial Registration: ClinicalTrials.gov identifier: ChiCTR1800017274 [China Hypertension Registry Study] [ABSTRACT FROM AUTHOR]

Background: The cardiometabolic index (CMI) is a new predictor of obesity-related diseases, but its link to periodontitis is under-researched. This study aims to examine the potential association between the CMI and periodontitis. Methods: We conducted a cross-sectional study using the National Health and Nutrition Examination Survey (NHANES) database (2009–2014) to examine the potential association between CMI and periodontitis. The study utilized a weighted multivariate logistic model to assess the association between TG, HDL-C, TG/HDL-C, obesity indices (BMI, WC, WHtR, CMI), and periodontitis, employing the area under the receiver operating characteristic curves (ROC) to estimate areas under the curve (AUC). Furthermore, generalized smooth curve fitting was conducted to examine the relationship between CMI and periodontitis. Finally, the study incorporated subgroup analysis and interaction tests to examine consistency across different populations. Results: TG/HDL-C, WHtR, and CMI were positively associated with periodontitis in the fully adjusted classification model. It was observed that for each unit increase in CMI, there was a corresponding 17.8% increase in the prevalence of periodontitis [1.178 (1.049, 1.322) 0.00562] and 18.7% increase in the prevalence of moderate/severe [1.187 (1.057, 1.334) 0.00376] in the fully adjusted model. When CMI was used as a categorical variable, the adjusted OR for periodontitis prevalence increased significantly with increasing CMI after adjusting for all potential covariates (T3 vs. T1: OR, 1.28 [1.06, 1.55], p < 0.05). The ROC analysis indicated that a larger area under the curve was found in the CMI [0.554 (0.538–0.570)] than in the WC [0.544 (0.528–0.560)] and WHtR [0.544 (0.528–0.561)]. Nonetheless, the discrepancy observed did not reach statistical significance (all p > 0.05). Further generalized smooth curve fitting and threshold effect models indicated a positive linear correlation between CMI and periodontitis. Moreover, there is no interactive association between TG/HDL-C, WHtR, CMI, and periodontitis. Conclusions: This cross-sectional study revealed a positive relationship between CMI and periodontitis. Further prospective studies are warranted to validate our findings. [ABSTRACT FROM AUTHOR]

Purpose: The Cardiometabolic Index (CMI) is a novel marker of visceral obesity and dyslipidemia. Our study aimed to explore the association between CMI and kidney stones among US adults. Methods: This cross-sectional study was conducted among adults with complete records of CMI and kidney stones information from the 2011 to 2018 National Health and Nutrition Examination Survey (NHANES). Inverse probability treatment weighting (IPTW) was used to balance the baseline characteristics of the study population. The independent relationship between CMI and kidney stones was evaluated using IPTW-adjusted multivariate logistic regression, restricted cubic splines (RCS), and subgroup analysis. Results: A total of 9,177 participants, with an average CMI of 0.72 (0.99), were included in this study. The IPTW-adjusted logistic regression revealed that CMI was an independent risk factor for kidney stones. The adjusted odds ratio (OR) for kidney stones were 1.39 (95% CI: 1.24 - 1.56, P < 0.001) for the second CMI tertile and 1.31 (95% CI: 1.17 - 1.47, P < 0.001) for the third CMI tertile, compared with the first CMI tertile. A linear relationship between CMI levels and kidney stone risk was observed in the RCS analysis. Subgroup analysis showed that the association between CMI levels and kidney stone risk remained stable across groups. Conclusions: A positive association between CMI level and the risk of kidney stones was observed among US adults in our study. Further large-scale prospective studies are needed to validate our findings. [ABSTRACT FROM AUTHOR]

Background: The cardiometabolic index (CMI) is a novel metric for assessing cardiometabolic health and type 2 diabetes mellitus (DM), yet its relationship with insulin resistance (IR) and prediabetes (preDM) is not well-studied. There is also a gap in understanding the nonlinear associations between CMI and these conditions. Our study aimed to elucidate these associations. Methods: We included 13,142 adults from the National Health and Nutrition Examination Survey (NHANES) 2007–2020. CMI was calculated by multiplying the triglyceride-to-high density lipoprotein cholesterol (TG/HDL-C) by waist-to-height ratio (WHtR). Using weighted multivariable linear and logistic regression explored the relationships of CMI with glucose metabolism markers, IR, preDM, and DM. Nonlinear associations were assessed using generalized additive models (GAM), smooth curve fittings, and two-piecewise logistic regression. Results: Multivariate regression revealed positive correlations between CMI and glucose metabolic biomarkers, including FBG (β = 0.08, 95% CI: 0.06–0.10), HbA1c (β = 0.26, 95% CI: 0.22–0.31), FSI (β = 4.88, 95% CI: 4.23–5.54), and HOMA-IR (β = 1.85, 95% CI: 1.56–2.14). There were also significant correlations between CMI and increased risk of IR (OR = 3.51, 95% CI: 2.94–4.20), preDM (OR = 1.49, 95% CI: 1.29–1.71), and DM (OR = 2.22, 95% CI: 2.00-2.47). Inverse nonlinear L-shaped associations were found between CMI and IR, preDM, and DM, with saturation inflection points at 1.1, 1.45, and 1.6, respectively. Below these thresholds, increments in CMI significantly correlated with heightened risks of IR, preDM, and DM. Conclusions: CMI exhibited inverse L-shaped nonlinear relationships with IR, preDM, and DM, suggesting that reducing CMI to a certain level might significantly prevent these conditions. [ABSTRACT FROM AUTHOR]

Background: This study aimed to analyze the associations of cardiometabolic index (CMI) with diabetic statuses and insulin resistance (IR) using data from the National Health and Nutrition Examination Survey (NHANES) and examined the potential mediating role of inflammation in these correlations. Methods: This study enrolled 9477 participants across four NHANES cycles from 2011 to 2018. The primary outcomes of the study included the risk of having prediabetes, diabetes and the level of the homeostasis model assessment of IR (HOMA-IR). Other outcomes including the levels of fasting blood glucose (FBG), hemoglobin A1c (HbA1c), oral glucose tolerance test (OGTT) results, fasting insulin, the risk of oral hypoglycemic medicine use, insulin use, and retinopathy were also collected and analyzed. Logistic regression model, subgroup analysis, restricted cubic spine (RCS), and Pearson correlation coefficients were conducted to assess the associations of CMI with diabetic statuses and IR. The mediating role of inflammation was evaluated to investigate the potential mechanism of the associations between CMI and diabetic statuses. Results: Among included participants, the CMI levels in normal participants, prediabetes and diabetes in this study were 0.48, 0.73 and 1.07. After multivariable adjustment, CMI was positively associated with the risk of prediabetes (OR = 1.49, 95% CI = 1.24–1.79), diabetes (OR = 2.14, 95% CI = 1.82–2.50) and the level of HOMA-IR (β = 2.57, 95% CI = 2.14–3.01). Besides, an increased CMI was correlated with higher levels of FBG, HBA1c, OGTT results and fasting insulin as well as the greater risk of oral hypoglycemic medicine use and insulin use. The RCS showed an inverted L-shaped association of CMI with prediabetes and diabetes (P for non-linearity < 0.001). According to Pearson correlation coefficients, higher CMI was linked to higher rises in HOMA-IR (r = 0.224, P < 0.001). Inflammation-related indicators including leukocyte and neutrophil demonstrated significant mediating effects in the associations of CMI with prediabetes (15.5%, 9.8%), diabetes (5.1%, 6.0%) and HOMA-IR (3.3%, 2.6%). Conclusion: CMI was positively associated with the risk of worse diabetic statuses and higher level of IR while the associations may be partially mediated by inflammation-related indicators, suggesting that CMI could be a promising indicator for the prediction of severe diabetes and IR. [ABSTRACT FROM AUTHOR]

Background: Endometriosis is intricately linked to metabolic health. The Cardiometabolic Index (CMI), a novel and readily accessible indicator, is utilized to evaluate metabolic status. This study seeks to investigate the potential correlation between CMI and endometriosis. Methods: Data from four consecutive survey cycles of the National Health and Nutrition Examination Survey (NHANES) conducted between 1999 and 2006 were utilized. This included adult females with self-reported diagnoses of endometriosis and complete information required for calculating the CMI. The calculation formula for CMI is Triglycerides(TG) / High-density lipoprotein cholesterol (HDL-C) × WHtR (WHtR = waist circumference / height). A multivariable logistic regression model was employed to investigate the linear association between CMI and endometriosis. Subgroup analyses were performed to explore potential influencing factors. Additionally, the linear relationship was validated using restricted cubic spline (RCS) curve plotting and threshold effect analysis. Results: This study, based on the National Health and Nutrition Examination Survey (NHANES), included a cohort of 2,224 adult women. The multivariable logistic regression analysis demonstrated that in the fully adjusted model, individuals with the highest CMI exhibited a 78% elevated likelihood of endometriosis compared to those with the lowest CMI (OR = 1.78; 95% CI, 1.02–3.11, P < 0.05). The subgroup analysis indicated that there were no significant interactions between CMI and specific subgroups (all interaction P > 0.05), except for the subgroup stratified by stroke status (P < 0.05). Additionally, the association between CMI and endometriosis was linear, with a 20% increase in the association for each unit increase in CMI when CMI > 0.67 (OR = 1.20; 95% CI, 1.05–1.37, P < 0.01). Conclusion: The study found that CMI levels are closely correlated with endometriosis, with this correlation increasing when the CMI exceeds 0.67. This finding implies that by regularly monitoring CMI levels, physicians may be able to screen women at risk for endometriosis at an earlier stage, thereby enabling the implementation of early interventions to slow the progression of the disease. To further validate these findings, larger-scale cohort studies are required to support the results of this research. [ABSTRACT FROM AUTHOR]

Background: Both lipid metabolism and obesity are crucial factors in osteoporosis, influencing the relevance of the cardiometabolic index (CMI), a new body fat index incorporating obesity and lipid metrics. Our study aims to explore the relationship between CMI and lumbar spine bone mineral density (BMD) and the mediating role of body fat percentage. Methods: Utilizing the National Health and Nutrition Examination Survey (NHANES) data from 2011 to 2018, we conducted a cross-sectional analysis. We employed multiple linear regression models, subgroup analyses, generalized additive models (GAM), smooth curve fitting, and mediation analysis to evaluate the linear and nonlinear relationships between CMI and lumbar spine BMD. Results: The study involved 5,124 participants with an average lumbar spine BMD of 1.03 ± 0.15 g/cm2. We identified a negative correlation between CMI and lumbar spine BMD (β = -0.015; 95% CI: -0.023, -0.008). Nonlinear associations were evident, with inflection points at CMI values of 1.12 and 2.86. Subgroup analyses showed consistent negative correlations across all categories without significant differences (p for interaction > 0.05). Moreover, body fat percentage negatively correlated with BMD (β = -0.005; 95% CI: -0.006, -0.004) and mediated 9.41% of the relationship between CMI and BMD. Conclusion: Increased CMI levels are associated with lower lumbar spine BMD, with body fat percentage significantly mediating this relationship. This underscores the importance of managing body composition in the context of bone health, highlighting CMI's potential utility in osteoporosis risk assessment. [ABSTRACT FROM AUTHOR]

Background: Infertility is a fertility disorder caused by various factors, with lipid metabolism playing a crucial role in its development. The cardiometabolic index (CMI), which combines blood lipids (TG/HDL-C) and obesity-related parameters (WHtR), is a new quantitative indicator. This study used NHANES data to investigate the relationship between CMI and the incidence of infertility. Methods: We utilized data from women who took part in the National Health and Nutrition Examination Survey (NHANES) from 2015 to March 2020 to calculate the CMI index. Subsequently, we used multivariate logistic regression, smooth curve fitting, and subgroup analysis to investigate the relationship between the CMI index and infertility. Results: The logistic regression model revealed a significant positive correlation between the CMI index and infertility, even after adjusting for all confounding variables (OR=3.23, 95%CI: 1.55-6.73, p=0.0017). This association remained consistent across all subgroups (p>0.05 for all interactions). Smooth curve fitting demonstrated a positive nonlinear relationship between CMI and infertility. However, the CMI index had limited diagnostic power for infertility (AUC=0.60, 95%CI: 0.56-0.65). However, the CMI-BMI index combined with BMI had good predictive performance (AUC=0.722, 95%CI: 0.676-0.767). Conclusion: The CMI index shows a positive correlation with infertility, but its diagnostic value is restricted. The combination with BMI has good diagnostic value. Further investigation is required to determine the effectiveness of the CMI index as an early indicator of infertility. [ABSTRACT FROM AUTHOR]

Introduction: Diabetes is a significant risk factor for cognitive impairment. Therefore, early identification of cognitive impairment in diabetic patients is particularly important. The aim of this study was to assess the relationship between Cardiometabolic index (CMI) and cognitive function in a diabetic population. Methods: A cross-sectional study was conducted by collecting information from the National Health and Nutrition Examination Survey (NHANES) 2011–2014. Multiple linear regression models were used to investigate the correlation between CMI and low cognitive function in a diabetic population. Threshold effects analysis and fitted smoothing curves were used to describe the nonlinear links. Interaction tests and subgroup analyses were also performed. Results: A total of 1,050 people participated in this study, including 561 men and 489 women. In the fully corrected model, CMI was positively associated with low cognitive performance as assessed by CERAD Word List Learning Test (CERAD W-L), Animal Fluency Test (AFT), and Digit Symbol Substitution Test (DSST) (OR = 1.37 [1.14, 1.72], p = 7.4 × 10−3), (OR = 1.21 [1.04, 1.51], p = 1.26 × 10−2), and (OR = 1.27 [1.08, 1.63], p = 2.53 × 10−2). Our study found that diabetic patients with higher CMI were at greater risk of developing low cognitive function. The effect of the subgroups on the positive association of CMI with cognitive impairment was not significant. A non-linear association between low cognitive performance and CMI was determined by CERAD W-L, AFT, and DSST (log-likelihood ratio <5 × 10−2). In addition, our also study found that CMI was a better predictor of cognitive impairment in diabetes than weight-adjusted waist index (WWI). Conclusion: Increased CMI is associated with an increased risk of cognitive impairment in people with diabetes. CMI can be used as a new anthropometric measure for predicting cognitive impairment in diabetes, with stronger predictive power than WWI. [ABSTRACT FROM AUTHOR]

Abstract Background Obesity and adverse lipid profile leads to female infertility. The cardiometabolic index (CMI) is a promising indicator for predicting obesity-related diseases. The correlation between CMI and female infertility merits further investigation. Methods The data for this study were acquired from the 2013–2020 National Health and Nutrition Examination Survey (NHANES), with 2333 women enrolled. The cardiometabolic index (CMI) of each participant was calculated as the ratio of triglycerides and high-density lipoprotein cholesterol multiplied by waist-to-height ratio. Weighted multivariate logistic regression models were used to assess the independent correlation between the log-transformed CMI and infertility. Subgroup analyses were carried out to assess the reliability of the findings. Interaction tests were employed to determine whether variables affected infertility by interacting with log CMI. Results A total of 2333 participants aged 18–45 years were enrolled, 274 of whom were infertile. Log CMI of the infertility group was significantly higher than that of the non-infertility group (P

Abstract Background The cardiometabolic index (CMI) is a new predictor of obesity-related diseases, but its link to periodontitis is under-researched. This study aims to examine the potential association between the CMI and periodontitis. Methods We conducted a cross-sectional study using the National Health and Nutrition Examination Survey (NHANES) database (2009–2014) to examine the potential association between CMI and periodontitis. The study utilized a weighted multivariate logistic model to assess the association between TG, HDL-C, TG/HDL-C, obesity indices (BMI, WC, WHtR, CMI), and periodontitis, employing the area under the receiver operating characteristic curves (ROC) to estimate areas under the curve (AUC). Furthermore, generalized smooth curve fitting was conducted to examine the relationship between CMI and periodontitis. Finally, the study incorporated subgroup analysis and interaction tests to examine consistency across different populations. Results TG/HDL-C, WHtR, and CMI were positively associated with periodontitis in the fully adjusted classification model. It was observed that for each unit increase in CMI, there was a corresponding 17.8% increase in the prevalence of periodontitis [1.178 (1.049, 1.322) 0.00562] and 18.7% increase in the prevalence of moderate/severe [1.187 (1.057, 1.334) 0.00376] in the fully adjusted model. When CMI was used as a categorical variable, the adjusted OR for periodontitis prevalence increased significantly with increasing CMI after adjusting for all potential covariates (T3 vs. T1: OR, 1.28 [1.06, 1.55], p 0.05). Further generalized smooth curve fitting and threshold effect models indicated a positive linear correlation between CMI and periodontitis. Moreover, there is no interactive association between TG/HDL-C, WHtR, CMI, and periodontitis. Conclusions This cross-sectional study revealed a positive relationship between CMI and periodontitis. Further prospective studies are warranted to validate our findings.

Abstract The Cardiometabolic Index (CMI) represents an innovative metric that encompasses abdominal obesity and lipid levels, offering a comprehensive assessment of cardiometabolic health. It is derived through the multiplication of the waist-to-height ratio by the triglyceride-to-high-density lipoprotein cholesterol ratio. Although obesity and blood lipid levels are known factors affecting sleep quality, the direct correlation between CMI and sleep quality has yet to be elucidated. This study uses NHANES data to explore the potential correlation between CMI and sleep quality. Our research employed a cross-sectional design, utilizing data from the National Health and Nutrition Examination Survey (NHANES) spanning 2011 to 2014. CMI was used as the exposure variable, with sleep quality assessed through three self-reported measures: trouble sleeping, sleep disorders, and sleep duration. We employed multivariate logistic regression models and linear regression model as well as subgroup analyses to explore the independent relationship between CMI and sleep quality. Additionally, interaction tests were conducted to examine differences across various subgroups. The study included 4298 participants, comprising 2134 males and 2164 females. There was a positive correlation between CMI and trouble sleeping (OR = 1.63, 95% CI 1.28–2.08) and sleep disorders (OR = 1.96, 95% CI 1.43–2.67), while there was a negative correlation between CMI and sleep duration (β = − 0.17, 95% CI − 0.31 to − 0.03), indicating that a higher CMI was associated with shorter sleep duration. Subgroup analyses and interaction tests revealed a consistently positive association between CMI and sleep difficulty across various populations, including gender, age, hypertension, diabetes, and cardiovascular disease history. However, the relationship between CMI and sleep disorders was more prominent among participants aged 50 and above. In American adults, a higher CMI is linked to an increased prevalence of sleep disturbances. In clinical practice, CMI can be considered as a supplementary factor in the assessment and management of sleep problems. Our study also provided new insights for improving sleep quality.

Abstract Background The cardiometabolic index (CMI) is a novel metric for assessing cardiometabolic health and type 2 diabetes mellitus (DM), yet its relationship with insulin resistance (IR) and prediabetes (preDM) is not well-studied. There is also a gap in understanding the nonlinear associations between CMI and these conditions. Our study aimed to elucidate these associations. Methods We included 13,142 adults from the National Health and Nutrition Examination Survey (NHANES) 2007–2020. CMI was calculated by multiplying the triglyceride-to-high density lipoprotein cholesterol (TG/HDL-C) by waist-to-height ratio (WHtR). Using weighted multivariable linear and logistic regression explored the relationships of CMI with glucose metabolism markers, IR, preDM, and DM. Nonlinear associations were assessed using generalized additive models (GAM), smooth curve fittings, and two-piecewise logistic regression. Results Multivariate regression revealed positive correlations between CMI and glucose metabolic biomarkers, including FBG (β = 0.08, 95% CI: 0.06–0.10), HbA1c (β = 0.26, 95% CI: 0.22–0.31), FSI (β = 4.88, 95% CI: 4.23–5.54), and HOMA-IR (β = 1.85, 95% CI: 1.56–2.14). There were also significant correlations between CMI and increased risk of IR (OR = 3.51, 95% CI: 2.94–4.20), preDM (OR = 1.49, 95% CI: 1.29–1.71), and DM (OR = 2.22, 95% CI: 2.00-2.47). Inverse nonlinear L-shaped associations were found between CMI and IR, preDM, and DM, with saturation inflection points at 1.1, 1.45, and 1.6, respectively. Below these thresholds, increments in CMI significantly correlated with heightened risks of IR, preDM, and DM. Conclusions CMI exhibited inverse L-shaped nonlinear relationships with IR, preDM, and DM, suggesting that reducing CMI to a certain level might significantly prevent these conditions.

Abstract Background This study aimed to analyze the associations of cardiometabolic index (CMI) with diabetic statuses and insulin resistance (IR) using data from the National Health and Nutrition Examination Survey (NHANES) and examined the potential mediating role of inflammation in these correlations. Methods This study enrolled 9477 participants across four NHANES cycles from 2011 to 2018. The primary outcomes of the study included the risk of having prediabetes, diabetes and the level of the homeostasis model assessment of IR (HOMA-IR). Other outcomes including the levels of fasting blood glucose (FBG), hemoglobin A1c (HbA1c), oral glucose tolerance test (OGTT) results, fasting insulin, the risk of oral hypoglycemic medicine use, insulin use, and retinopathy were also collected and analyzed. Logistic regression model, subgroup analysis, restricted cubic spine (RCS), and Pearson correlation coefficients were conducted to assess the associations of CMI with diabetic statuses and IR. The mediating role of inflammation was evaluated to investigate the potential mechanism of the associations between CMI and diabetic statuses. Results Among included participants, the CMI levels in normal participants, prediabetes and diabetes in this study were 0.48, 0.73 and 1.07. After multivariable adjustment, CMI was positively associated with the risk of prediabetes (OR = 1.49, 95% CI = 1.24–1.79), diabetes (OR = 2.14, 95% CI = 1.82–2.50) and the level of HOMA-IR (β = 2.57, 95% CI = 2.14–3.01). Besides, an increased CMI was correlated with higher levels of FBG, HBA1c, OGTT results and fasting insulin as well as the greater risk of oral hypoglycemic medicine use and insulin use. The RCS showed an inverted L-shaped association of CMI with prediabetes and diabetes (P for non-linearity

Abstract Background Endometriosis is intricately linked to metabolic health. The Cardiometabolic Index (CMI), a novel and readily accessible indicator, is utilized to evaluate metabolic status. This study seeks to investigate the potential correlation between CMI and endometriosis. Methods Data from four consecutive survey cycles of the National Health and Nutrition Examination Survey (NHANES) conducted between 1999 and 2006 were utilized. This included adult females with self-reported diagnoses of endometriosis and complete information required for calculating the CMI. The calculation formula for CMI is Triglycerides(TG) / High-density lipoprotein cholesterol (HDL-C) × WHtR (WHtR = waist circumference / height). A multivariable logistic regression model was employed to investigate the linear association between CMI and endometriosis. Subgroup analyses were performed to explore potential influencing factors. Additionally, the linear relationship was validated using restricted cubic spline (RCS) curve plotting and threshold effect analysis. Results This study, based on the National Health and Nutrition Examination Survey (NHANES), included a cohort of 2,224 adult women. The multivariable logistic regression analysis demonstrated that in the fully adjusted model, individuals with the highest CMI exhibited a 78% elevated likelihood of endometriosis compared to those with the lowest CMI (OR = 1.78; 95% CI, 1.02–3.11, P 0.05), except for the subgroup stratified by stroke status (P 0.67 (OR = 1.20; 95% CI, 1.05–1.37, P

Abstract Background: Both lipid metabolism and obesity are crucial factors in osteoporosis, influencing the relevance of the cardiometabolic index (CMI), a new body fat index incorporating obesity and lipid metrics. Our study aims to explore the relationship between CMI and lumbar spine bone mineral density (BMD) and the mediating role of body fat percentage. Methods: Utilizing the National Health and Nutrition Examination Survey (NHANES) data from 2011 to 2018, we conducted a cross-sectional analysis. We employed multiple linear regression models, subgroup analyses, generalized additive models (GAM), smooth curve fitting, and mediation analysis to evaluate the linear and nonlinear relationships between CMI and lumbar spine BMD. Results: The study involved 5,124 participants with an average lumbar spine BMD of 1.03 ± 0.15 g/cm2. We identified a negative correlation between CMI and lumbar spine BMD (β = -0.015; 95% CI: -0.023, -0.008). Nonlinear associations were evident, with inflection points at CMI values of 1.12 and 2.86. Subgroup analyses showed consistent negative correlations across all categories without significant differences (p for interaction > 0.05). Moreover, body fat percentage negatively correlated with BMD (β = -0.005; 95% CI: -0.006, -0.004) and mediated 9.41% of the relationship between CMI and BMD. Conclusion: Increased CMI levels are associated with lower lumbar spine BMD, with body fat percentage significantly mediating this relationship. This underscores the importance of managing body composition in the context of bone health, highlighting CMI’s potential utility in osteoporosis risk assessment.

The cardiometabolic index (CMI) is an emerging and effective indicator for predicting the presence of metabolic-associated fatty liver disease (MAFLD). This study aims to investigate the relationship between CMI and MAFLD using data from NHANES 2017–2020. In this cross-sectional study, a total of 3,749 subjects were included. The study conducted a thorough analysis of CMI with three multivariate logistic regression models, subgroup analyses, and restricted cubic splines (RCS) were utilized. Using multifactorial logistic regression as the primary method of analysis, we found that a higher CMI was also significantly associated with an increased risk of MAFLD (OR = 1.45, 95% CI (1.05–2.01)). This result was further visualized by the RCS curve: There was a non-linear positive correlation between CMI and MAFLD incidence (the turning point is CMI = 0.4554). These findings were strongly reinforced by subsequent subgroup and sensitivity analyses. There is a robust positive relationship between the CMI and the risk of MAFLD, providing valuable clinical benefits for early detection and screening of MAFLD. It is important to highlight the presence of a non-linear association between CMI and MAFLD, with an inflection point identified at CMI = 0.4554.

Abstract Background Previous findings have revealed that disorders of lipid metabolism may be a risk factor for pulmonary function damage; however, the combined effect of dyslipidemia and central obesity on pulmonary function is unclear. The cardiometabolic index (CMI) is a composite of serum lipids (triglyceride (TG)/high-density lipoprotein cholesterol (HDL-C)) and visceral fat parameters (waist-to-height ratio (WHtR)). This research aimed to investigate the link between CMI and pulmonary function, employing large-scale demographic data sourced from the National Health and Nutrition Examination Survey (NHANES) database. Methods This cross-sectional study used data involving 4125 adults aged 20 and above collected by NHANES between 2007 and 2012. We defined CMI as the exposure variable and measured outcomes using forced expiratory volume in one second (FEV1), forced vital capacity (FVC), and FEV1/FVC to evaluate pulmonary function. Weighted multiple linear regression models and subgroup analyses were employed to investigate separate relationships between CMI and pulmonary function. In addition, to investigate variations across different strata and evaluate the robustness of the findings, interaction tests and sensitivity analyses were conducted. Results Results from the weighted multiple linear regression analysis indicated a unit increase in log2-CMI was associated with a reduction of 82.63 mL in FEV1 and 112.92 mL in FVC. The negative association remained significant after transforming log2-CMI by quartile (Q). When the log2-CMI level reached Q4, β coefficients (β) were -128.49 (95% CI: -205.85, -51.13), -169.01 (95% CI: -266.72, -71.30), respectively. According to the interaction test findings, the negative association linking log2-CMI with FEV1 and FVC persists regardless of confounding factors including age, gender, BMI, physical activity (PA), and smoking status. A subsequent sensitivity analysis provided additional confirmation of the stability and reliability of the results. For females, the inflection points for the nonlinear relationships between log2-CMI and FEV1, as well as log2-CMI and FVC, were identified at 2.33 and 2.11, respectively. While in males, a consistent negative association was observed. Conclusions Our findings suggest that higher CMI is associated with lower FEV1 and FVC. CMI may serve as a complementary consideration to the assessment and management of pulmonary function in clinical practice.

Abstract Background This study sought to elucidate the associations of cardiometabolic index (CMI), as a metabolism-related index, with all-cause and cardiovascular mortality among the older population. Utilizing data from the National Health and Nutrition Examination Survey (NHANES), we further explored the potential mediating effect of inflammation within these associations. Methods A cohort of 3029 participants aged over 65 years old, spanning six NHANES cycles from 2005 to 2016, was enrolled and assessed. The primary endpoints of the study included all-cause mortality and cardiovascular mortality utilizing data from National Center for Health Statistics (NCHS). Cox regression model and subgroup analysis were conducted to assess the associations of CMI with all-cause and cardiovascular mortality. The mediating effect of inflammation-related indicators including leukocyte, neutrophil, lymphocyte, systemic immune-inflammation index (SII), neutrophil to lymphocyte ratio (NLR) were evaluated to investigate the potential mechanism of the associations between CMI and mortality through mediation package in R 4.2.2. Results The mean CMI among the enrolled participants was 0.74±0.66, with an average age of 73.28±5.50 years. After an average follow-up period of 89.20 months, there were 1,015 instances of all-cause deaths and 348 cardiovascular deaths documented. In the multivariable-adjusted model, CMI was positively related to all-cause mortality (Hazard Ratio (HR)=1.11, 95% CI=1.01-1.21). Mediation analysis indicated that leukocytes and neutrophils mediated 6.6% and 13.9% of the association of CMI with all-cause mortality. Conclusion Elevated CMI is positively associated with all-cause mortality in the older adults. The association appeared to be partially mediated through inflammatory pathways, indicating that CMI may serve as a valuable indicator for poor prognosis among the older population.

Abstract Composed of obesity and lipid parameters, the cardiometabolic index (CMI) has emerged as a novel diagnostic tool. Originally developed for diabetes diagnosis, its application has expanded to identifying patients with cardiovascular diseases, such as atherosclerosis and hypertension. However, the relationship between CMI and non-alcoholic fatty liver disease (NAFLD) and liver fibrosis in the US population remains unclear. This cross-sectional study analyzed data from the National Health and Nutrition Examination Survey (NHANES) spanning 2017–2020, involving 2996 participants aged 20 years or older. Vibration controlled transient elastography using a FibroScan® system (model 502, V2 Touch) with controlled attenuation parameter measurements identified NAFLD at a threshold of ≥ 274 dB/m, while liver stiffness measurement (LSM) results (median, ≥ 8.2 kPa) indicated fibrosis. A multifactorial logistic regression model explored the relationship between CMI and NAFLD and fibrosis. The effectiveness of CMI in detecting NAFLD and liver fibrosis was assessed through receiver operating characteristic curve analysis. Controlling for potential confounders, CMI showed a significant positive association with NAFLD (adjusted OR = 1.44, 95% CI 1.44–1.45) and liver fibrosis (adjusted OR = 1.84, 95% CI 1.84–1.85). The Areas Under the Curve for predicting NAFLD and fibrosis were 0.762 (95% CI 0.745 ~ 0.779) and 0.664(95% CI 0.633 ~ 0.696), respectively, with optimal cut-off values of 0.462 and 0.527. There is a positive correlation between CMI and NAFLD and fibrosis, which is a suitable and simple predictor of NAFLD and fibrosis.

Objective To study the relationship between the cardiometabolic index (CMI) and hyperuricemia (HUA) in Guizhou minorities. Methods Based on the H China Multi - ethnic Cohort Studyir, 16 630 Miao, Dong and Bouyei population aged 30 to 79 years were included in the study. Logistic regression model was used to analyze the association between CMI and HUA, restricted cubic spline was used to analyze the dose - response relationship between CMI and HUA, and ROC curve was used to analyze the predictive value of CMI for HUA. Results The overall detection rate of HUA was 22. 8%, and those of Miao, Dong and Bouyei nationalities were 24. 4%, 25. 2% and 18. 5%, respectively. In the total population, Miao, Dong and Bouyei populations, after adjusting for covariates, compared with QI, the ORs for HUA were 4. 39 (95% CI: 3. 76 - 5. 12), 3. 96 (95% CI: 3. 03 - 5. 18), 4. 28 (95% CI: 3. 36 - 5.46), and 5. 55 (95% CI: 4. 08 - 7. 54) in CMI Q4 group, respectively. There was a non - linear dose - response relationship between CMI and risk of HUA (P for nonlinear < 0. 001). ROC curve analysis showed that, the area under the curve (the best cut - off value, sensitivity and specificity) of CMI for predicting the risk of HUA in the total population, Miao, Dong and Bouyei populations were 0.742 (0.641, 68.3%, 68.8%), 0.724 (0.651, 63.8%, 70.8%) 0,741 (0.654, 68.5%, 68.1%), 0.775 (0.555, 68.8%, 73.0%). Conclusion CMI is associated with the increased risk of HUA in different ethnic populations, and can be used as an indicator for predicting HUA in ethnic minority populations in Guizhou province. [ABSTRACT FROM AUTHOR]

Background: Previous findings have revealed that disorders of lipid metabolism may be a risk factor for pulmonary function damage; however, the combined effect of dyslipidemia and central obesity on pulmonary function is unclear. The cardiometabolic index (CMI) is a composite of serum lipids (triglyceride (TG)/high-density lipoprotein cholesterol (HDL-C)) and visceral fat parameters (waist-to-height ratio (WHtR)). This research aimed to investigate the link between CMI and pulmonary function, employing large-scale demographic data sourced from the National Health and Nutrition Examination Survey (NHANES) database. Methods: This cross-sectional study used data involving 4125 adults aged 20 and above collected by NHANES between 2007 and 2012. We defined CMI as the exposure variable and measured outcomes using forced expiratory volume in one second (FEV1), forced vital capacity (FVC), and FEV1/FVC to evaluate pulmonary function. Weighted multiple linear regression models and subgroup analyses were employed to investigate separate relationships between CMI and pulmonary function. In addition, to investigate variations across different strata and evaluate the robustness of the findings, interaction tests and sensitivity analyses were conducted. Results: Results from the weighted multiple linear regression analysis indicated a unit increase in log2-CMI was associated with a reduction of 82.63 mL in FEV1 and 112.92 mL in FVC. The negative association remained significant after transforming log2-CMI by quartile (Q). When the log2-CMI level reached Q4, β coefficients (β) were -128.49 (95% CI: -205.85, -51.13), -169.01 (95% CI: -266.72, -71.30), respectively. According to the interaction test findings, the negative association linking log2-CMI with FEV1 and FVC persists regardless of confounding factors including age, gender, BMI, physical activity (PA), and smoking status. A subsequent sensitivity analysis provided additional confirmation of the stability and reliability of the results. For females, the inflection points for the nonlinear relationships between log2-CMI and FEV1, as well as log2-CMI and FVC, were identified at 2.33 and 2.11, respectively. While in males, a consistent negative association was observed. Conclusions: Our findings suggest that higher CMI is associated with lower FEV1 and FVC. CMI may serve as a complementary consideration to the assessment and management of pulmonary function in clinical practice. [ABSTRACT FROM AUTHOR]

AbstractObjectivesMethodsResultsConclusionCardiometabolic Index (CMI) is a surrogate marker for metabolic disorders. It is associated with various chronic diseases. This study aims to investigate the relationship between CMI and asthma.Data from seven consecutive National Health and Nutrition Examination Survey cycles between 2005 and 2018 were used. The study included adults with self-reported asthma diagnoses and complete information for CMI calculation. The formula for CMI is CMI = [WC (cm)/height (cm)] × [TG (mg/dL)/HDL-C (mg/dL)]. A multivariate logistic regression model was employed to examine the linear relationship between CMI and asthma. Subgroup analyses were conducted to explore potential influencing factors. Additionally, smooth curve fitting and threshold effect analysis were used to describe the non-linear relationship.A higher CMI was possibly associated with an increased prevalence of asthma. After adjusting for various covariates including marital status, Poverty Income Ratio, Body Mass Index, hypertension, diabetes, smoking, alcohol consumption, heart attack, and stroke, the results remained significant (OR = 1.03; 95%CI, 1.00-1.05, p = 0.0178, R2 = 0.52). Participants with the highest CMI had a 38% increased risk of asthma prevalence compared to those with the lowest CMI (OR = 1.38; 95%CI, 1.19–1.60, p < 0.0001).The findings reveal that elevated CMI levels correlate with an increased risk of asthma, highlighting CMI’s potential as a predictive marker for asthma, particularly in populations with a CMI below 1.97. These results suggest that interventions aimed at improving metabolic health may prove effective in managing or preventing asthma. [ABSTRACT FROM AUTHOR]

Both cardiometabolic and chronic inflammatory diseases pose a significant challenge to global public health, particularly among older adults. Here, we investigated the interplay between systemic inflammatory status and the cardiometabolic index (CMI) in older men with adequate weight or obesity. In this observational cross-sectional study, older men (71.79 ± 7.35 years) were separated into groups with normal weight (NW, n = 34) and obesity (O, n = 32) to assess circulating levels of pro- and anti-inflammatory cytokines and CMI. Overall, the O group showed not only a higher inflammatory status but also increased CMI (p < 0.0001) compared with the NW group. Interestingly, only positive correlations were found between pro- and anti-inflammatory cytokines in both groups. Through multivariate regression analysis, IL-6 (β = −0.2276, p = 0.0003) and IL-10 (β = 0.2023, p = 0.0030) significantly influenced CMI in the NW group. No significant results were found in the O group. Our findings reinforce the effects of obesity in inflammaging, as well as suggesting that the influence of cytokines in CMI occurs in older men with normal weight, since the elevated pro-inflammatory profile observed in older men with obesity can interfere in this effect. [ABSTRACT FROM AUTHOR]

Obesity and insulin resistance are associated with left ventricular diastolic dysfunction (LVDD) and increased risk of heart failure. Cardiometabolic index (CMI) and triglyceride glucose (TyG) are new indexes to assess visceral obesity and insulin resistance, respectively. The study aimed to investigate the clinical usefulness of these indexes for identifying LVDD individuals. Overall, 1898 asymptomatic individuals were included in this cross-sectional study. Participants underwent anthropometrics, serum biochemical evaluation, and echocardiography. Multiple linear regression analysis revealed that both indexes were independent determinants of diastolic parameters among females; while for males, CMI and TyG were not associated with A velocity. In the multivariate logistic analysis, the proportion of LVDD in the third and fourth quartiles of CMI remained significantly greater than that in the lowest quartile in females (Q3 vs. Q1: odds ratio (OR) = 2.032, 95% confidence interval (CI): 1.181–3.496; Q4 vs. Q1: OR = 2.393, 95% CI: 1.347–4.249); while in males, the incidence of LVDD was significantly greater only in the fourth quartile. For TyG, the presence of LVDD in the fourth quartile was significantly greater in both genders. The discriminant values between the CMI (AUC: 0.704, 95% CI: 0.668–0.739) and TyG (AUC: 0.717, 95% CI: 0.682–0.752) were similar in females. Both indexes performed better in females than in males to identify LVDD. The CMI and TyG might both serve as effective tools to identify LVDD in routine health check-ups in primary care, mainly in females. With simpler parameters, the CMI could be utilized in medically resource-limited areas. • CMI and TyG are novel markers incorporated anthropometric and biochemical indexes. • We explored the clinical usefulness of CMI and TyG in identifying LVDD individuals. • The degree of LVDD in response to increased indexes is more pronounced in females. • CMI and TyG might be effective tools to identify LVDD in routine health check-ups. • CMI might assist with the screening of LVDD in medically resource-limited areas. [ABSTRACT FROM AUTHOR]

Background: This study sought to elucidate the associations of cardiometabolic index (CMI), as a metabolism-related index, with all-cause and cardiovascular mortality among the older population. Utilizing data from the National Health and Nutrition Examination Survey (NHANES), we further explored the potential mediating effect of inflammation within these associations. Methods: A cohort of 3029 participants aged over 65 years old, spanning six NHANES cycles from 2005 to 2016, was enrolled and assessed. The primary endpoints of the study included all-cause mortality and cardiovascular mortality utilizing data from National Center for Health Statistics (NCHS). Cox regression model and subgroup analysis were conducted to assess the associations of CMI with all-cause and cardiovascular mortality. The mediating effect of inflammation-related indicators including leukocyte, neutrophil, lymphocyte, systemic immune-inflammation index (SII), neutrophil to lymphocyte ratio (NLR) were evaluated to investigate the potential mechanism of the associations between CMI and mortality through mediation package in R 4.2.2. Results: The mean CMI among the enrolled participants was 0.74±0.66, with an average age of 73.28±5.50 years. After an average follow-up period of 89.20 months, there were 1,015 instances of all-cause deaths and 348 cardiovascular deaths documented. In the multivariable-adjusted model, CMI was positively related to all-cause mortality (Hazard Ratio (HR)=1.11, 95% CI=1.01-1.21). Mediation analysis indicated that leukocytes and neutrophils mediated 6.6% and 13.9% of the association of CMI with all-cause mortality. Conclusion: Elevated CMI is positively associated with all-cause mortality in the older adults. The association appeared to be partially mediated through inflammatory pathways, indicating that CMI may serve as a valuable indicator for poor prognosis among the older population. [ABSTRACT FROM AUTHOR]

Composed of obesity and lipid parameters, the cardiometabolic index (CMI) has emerged as a novel diagnostic tool. Originally developed for diabetes diagnosis, its application has expanded to identifying patients with cardiovascular diseases, such as atherosclerosis and hypertension. However, the relationship between CMI and non-alcoholic fatty liver disease (NAFLD) and liver fibrosis in the US population remains unclear. This cross-sectional study analyzed data from the National Health and Nutrition Examination Survey (NHANES) spanning 2017–2020, involving 2996 participants aged 20 years or older. Vibration controlled transient elastography using a FibroScan® system (model 502, V2 Touch) with controlled attenuation parameter measurements identified NAFLD at a threshold of ≥ 274 dB/m, while liver stiffness measurement (LSM) results (median, ≥ 8.2 kPa) indicated fibrosis. A multifactorial logistic regression model explored the relationship between CMI and NAFLD and fibrosis. The effectiveness of CMI in detecting NAFLD and liver fibrosis was assessed through receiver operating characteristic curve analysis. Controlling for potential confounders, CMI showed a significant positive association with NAFLD (adjusted OR = 1.44, 95% CI 1.44–1.45) and liver fibrosis (adjusted OR = 1.84, 95% CI 1.84–1.85). The Areas Under the Curve for predicting NAFLD and fibrosis were 0.762 (95% CI 0.745 ~ 0.779) and 0.664(95% CI 0.633 ~ 0.696), respectively, with optimal cut-off values of 0.462 and 0.527. There is a positive correlation between CMI and NAFLD and fibrosis, which is a suitable and simple predictor of NAFLD and fibrosis. [ABSTRACT FROM AUTHOR]

BackgroundEndometriosis is an estrogen-dependent gynecological endocrine condition and a systemic inflammatory disease associated to improper lipid metabolism and increased cardiovascular risk. The Cardiometabolic Index (CMI) is a novel indicator representing visceral adipose tissue distribution and metabolic dysfunction, integrating lipid metabolism indicators and the waist-to-height ratio. While anomalies in lipid metabolism are often associated with (BMI) Body Mass Index, literature consistently shows a negative link between endometriosis and female BMI, and some studies have found that endometriosis is one of the few reproductive diseases not persistently positively correlated with obesity. Given the limitations of BMI, a comprehensive index like CMI is crucial for exploring the incidence of endometriosis. Currently, research on the correlation between CMI and endometriosis is lacking, prompting this study to investigate this association.ObjectiveTo investigate the association between the CMI and the risk of having endometriosis in a sample representing the entire U.S. population.Study designA cross-sectional analysis was conducted using data from four cycles of the National Health and Nutrition Examination Survey (NHANES) spanning the years 1999 to 2006. The study included individuals aged 20 to 54 with a documented history of endometriosis and complete CMI data. Logistic regression analysis, subgroup and interaction analyses, smooth curve fitting, and restricted cubic splines (RCS) were utilized to examine the association between CMI and endometriosis.ResultsThe study found that individuals with higher CMI had an increased probability of developing endometriosis. This relationship remained significant after adjusting for potential confounders such as age, ethnicity, Poverty Income Ratio (PIR), drinking, smoking, education level, and marital status. The fully adjusted model revealed a positive correlation between CMI and endometriosis (OR = 1.21; 95% CI, 1.04–1.40, p < 0.05). Subgroup and interaction analyses showed no significant effect modification by age, BMI, PIR, hypertension, drinking, smoking, or menarche age (all p-values for interaction > 0.05).ConclusionOur study shows a link between CMI and the chance of getting endometriosis.Due to the common occurrence of endometriosis and the lack of clarity surrounding their cause, more study is needed to confirm our results and find out if CMI could be used as a warning sign for endometriosis.

BackgroundThe cardiometabolic index (CMI) is a novel metabolic biomarker, but research on its association with osteporosis (OP) is limited. The objective of this research was to clarify the relationship between CMI and OP in the older adult population of the United States.MethodsThis study conducted a cross-sectional analysis using NHANES data 2007–2018 with exclusion of 2011–2012 and 2015–2016 cycles. Logistic regression was used to investigate the relationship between CMI and OP prevalence. Restricted cubic spline curve (RCS) and threshold saturation analyses were performed to explore the nonlinear association between CMI and OP prevalence. Subgroup analyses, sensitivity analyses, and additional analyses were conducted to ensure the robustness and reliability of the findings.ResultsThe study included 4,191 participants, revealing that those with OP had significantly lower CMI levels. Logistic regression revealed a strong inverse correlation between Log CMI and OP (OR = 0.72, 95% CI = 0.59–0.88), which persisted after adjusting for covariates. RCS analysis revealed a nonlinear inverse relationship with the critical threshold at CMI = 0.93. Below this threshold, each unit increase in CMI was associated with a 37% reduction in OP prevalence, but changes above this threshold were not significant. Subgroup and sensitivity analyses confirmed the robustness of the findings.ConclusionElevated CMI exhibited a robust inverse correlation with the prevalence of OP in the older adult U.S. population. Maintaining a moderate CMI significantly diminishes the risk of developing OP.

BackgroundThe risk of congestive heart failure (CHF) is significantly affected by obesity. However, data on the association between visceral obesity and the risk of CHF remain limited. We explored the relationship between CHF and cardiometabolic index (CMI).MethodsDrawing from the National Health and Nutrition Examination Survey (NHANES) for 2011–2018, we enrolled 9,008 participants in a cross-sectional study. We calculated the CMI as triglyceride (TG)/high density lipid-cholesterol (HDL-C) × weight-to-height ratio (WHtR), and CMI-age as CMI × age. Then, we analyzed CMI and CMI-age as categorical and continuous variables to assess its correlation with CHF. To assess the relationships of CMI and CMI-age with CHF, we used multiple logistic regression models and performed subgroup analysis. To examine the predictive ability of CMI and CMI-age on patients with CHF, we used receiver operating characteristic (ROC) curves.ResultsThe overall prevalence of CHF was 3.31%. The results revealed significant differences in demographic data, comorbidities, lifestyle variables, standing height, BMI, WC, WHtR, TG, and HDL-C among the four groups classified by CMI quartile and CMI-age quartile. When indicators were analyzed as continuous variables, CMI and CMI-age showed positive correlations with CHF in both the crude and adjusted models (all P 0.05). The areas under the ROC curve (AUCs) of CMI and CMI-age in predicting CHF were 0.610 (95% CI, 0.578–0.642) and 0.697 (95% CI, 0.668–0.725) separately, suggesting that CMI-age was significantly better than the CMI in predicting CHF (P

Background The prevalence of hyperuricemia (HUA) is increasing year by year, and fewer studies have been conducted for the Xinjiang region. Because the population in Xinjiang has special regional and dietary characteristics, regional studies are necessary. Objective To investigate the current status of HUA among people aged≥35 years in Xinjiang Uygur Autonomous Region, and explore the correlation of cardiometabolic index (CMI) with HUA among healthy population undergoing health examinations of different ethnicities and genders, so as to provide a reference basis for the development of personalized preventive measures for HUA patients in the region. Methods This was a cross-sectional study, and 9 429 medical examiners aged≥35 years who participated in health checkups at the First Affiliated Hospital of Xinjiang Medical University from October 2021 to October 2022 were selected as the study subjects and divided into the HUA group (n=527) and the non-HUA group (n=8 902) according to the combination of HUA; and the study subjects were stratified into Q1 (CMI≤0.34) , Q2 (0.340.99) according to the CMI. General demographic information (gender, age, ethnicity) , past medical history (coronary heart disease, hypertension, diabetes mellitus, stroke) , physical examination and laboratory test indices of the study subjects were collected and compared by reviewing the medical record system. A multivariate Logistic regression model was used to analyze the effect of CMI on the risk of HUA (model Ⅰ adjusted for age, gender, gender and BMI; model Ⅱ adjusted for age, gender, past history, urea nitrogen, etc.) , and the correlation between CMI and HUA was analyzed bystratification according to gender (male/female) and ethnicity (Han/Uyghur/Kazak) . Results There were 527 (5.58%) HUA patients among 9 429 medical examiners, with an average age of (54.3±12.9) years; 434 (82.4%) were male and 93 (17.6%) were female; 402 (76.3%) of them were Han nationality, 82 (15.5%) were Uyghurs, and 43 (8.2%) were Kazakhs. In the comparison between the HUA group and non-HUA group, there was no statistically significant difference in stroke history and HDL-C levels (P>0.05) ; the differences in gender, age, ethnicity, history of coronary heart disease, hypertension and diabetes, diastolic blood pressure (DBP) , systolic blood pressure (SBP) , urea nitrogen (UN) , blood uric acid (SUA) , creatinine, fasting plasma glucose (FPG) , triacylglycerol (TG) , total cholesterol (TC) , low-density lipoprotein cholesterol (LDL-C) , BMI, waist-to-height ratio (WHtR) , and CMI were statistically significant (P

Abstract Background The cardiometabolic index (CMI) is a new metric derived from the triglyceride-glucose index and body mass index and is considered a potential marker for cardiovascular risk assessment. This study aimed to examine the correlation between the CMI and the presence and severity of arteriosclerosis in patients with type 2 diabetes mellitus (T2DM). Methods This study involved 2243 patients with T2DM. The CMI was derived by dividing the triglyceride level (mmol/L) by the high-density lipoprotein level (mmol/L) and then multiplying the quotient by the waist-to-height ratio. Multivariate logistic regression was used to analyze the correlations between the CMI and BMI blood biomarkers, blood pressure, and brachial-ankle pulse wave velocity (baPWV). Results Patients were categorized into three groups based on their CMI: Group C1 (CMI 1.355; n = 750). Increased BMI, fasting glucose, insulin (at 120 min), total cholesterol (TC), and baPWV values were observed in Groups C2 and C3, with statistically significant trends (all trends P

Background: The concomitant occurrence of obesity and metabolic syndrome (MetS) causes a significant worsening of a patient's clinical condition. Indexes that employ anthropometric measurements alone or associated with blood parameters have been investigated for their ability to identify MetS. This study aimed to evaluate the diagnostic accuracy of three of these indexes, the body adiposity index (BAI), the lipid accumulation product index (LAP), and the cardiometabolic index (CMI), in a cohort of 1912 adult subjects with obesity. Methods and Results: MetS was found in 62.3% of the enrolled subjects, with a higher prevalence in males (72.5%) than females (60.9%). Receiver operating characteristic (ROC) analysis was used to define which index performed better. The BAI was found to be the lowest-performing index, with an ROC area of 0.50, a sensitivity of 30.31%, a specificity of 74.48%, and a likelihood ratio of 1.19. On the contrary, the LAP and the CMI showed a comparable ROC area of 0.82. The LAP had a sensitivity of 63.06%, a specificity of 86.55%, and a likelihood ratio of 4.69, while the CMI had a sensitivity of 67.59%, specificity of 81.55%, and a likelihood ratio of 3.66. The analysis was also performed in the group divided into males and females, with overlapping results. Conclusions: The LAP and the CMI performed better than the BAI in detecting MetS both in the general population with obesity and in the male/female subgroups. In the future, it will be important to validate these useful diagnostic tools in order to employ them in clinical practices. [ABSTRACT FROM AUTHOR]

Background: Central obesity is a risk factor for chronic kidney disease (CKD). However, the exact correlation between the cardiometabolic index (CMI), an indicator of central obesity, and CKD remains unclear. Here, we aimed to investigate the correlation between the CMI and CKD in the general American population. Methods: This cross-sectional study involved 64,313 members of the general population (≥ 20 years of age) with data in the National Health and Nutrition Examination Survey (NHANES) 1999–2020. The individuals were grouped into three categories by CMI tertile: T1 group (n = 7,029), T2 group (n = 7,356), and T3 group (n = 7,380). Logistic regression analysis was performed, with NHANES recommended weights, to assess the association between the CMI and CKD. Results: A total of 21,765 participants were included; the overall prevalence of CKD was 12.2%. From the low to the high CMI tertile, the prevalence of CKD increased from 8.9% to 16.0% (P < 0.001). After full adjustment for confounders, the higher tertile of CMI (OR: 1.08, 95% CI: 1.03 − 1.13, P = 0.002) had the higher risk of CKD. Compared with the T1 group, the groups with higher CMI levels had a higher CKD risk (T2: OR: 1.01, 95%CI: 0.87–1.18, P = 0.812; T3: OR: 1.22, 95%CI: 1.05–1.43, P = 0.013). Conclusions: Higher CMI was independently associated with higher CKD risk in the general population. [ABSTRACT FROM AUTHOR]

Emerging evidence suggests a common pathophysiological basis for metabolic disorders and mental diseases. Despite the existence of reports suggesting a strong connection between dyslipidemia and depression, a comprehensive and reliable indicator to identify depression is still lacking. Cardiometabolic index (CMI) is an integrated index calculated from three vital metabolic indicators, including triglyceride (TG), high-density lipoprotein cholesterol (HDL C) and waist height ratio (WHtR). This study aims to explore the association between CMI and depression. Cross-sectional data of participants with complete information of CMI, depression, and other covariates were obtained from the National Health and Nutrition Examination Survey (NHANES). Weighted student's t -test and Chi-square test were used to identify the differences between two groups. Weighted multivariate logistic regression model, restricted cubic spline (RCS) regression analysis, subgroup analysis and interaction tests were conducted to explore the association between CMI and depression. Receiver operating curve (ROC) analysis and area under the curve (AUC) were also utilized to evaluate the performance of CMI in identifying depression. A positive correlation between CMI and depression was observed in 3794 participants included in the study, which was further confirmed to be non-linear via RCS regression analysis, with two significant inflection points being identified, including 0.9522 and 1.58. In the crude or adjusted models, individuals with a CMI level ≥ 0.9522 exhibited remarkably increased risk for developing depression. CMI got an AUC of 0.748 in identifying depression. Subgroup analyses and interaction tests indicate that the association between CMI and depression remained consistent across different subgroups and was not modified by other covariates except drinking. Those who are current drinkers and with a high CMI are more susceptible to suffer depression. An elevated CMI is linked to increased risk for depression. Addressing dyslipidemia and improving lipid levels may potentially lower the risk for depression. • Higher levels of CMI were associated with increased depression prevalence. • CMI was non-linearly associated with depression, with inflection points being identified at 0.9522 and 1.58. • A CMI level below 0.9522 may potentially help to reduce the risk of developing depression. [ABSTRACT FROM AUTHOR]

Background: Cardiometabolic multimorbidity (CMM) has emerged as a prominent public health concern. Hypertensive patients are prone to develop comorbidities. Moreover, the accumulation of visceral adipose tissue is the main cause for the development of cardiometabolic diseases. The cardiometabolic index (CMI), lipid accumulation product (LAP), visceral adiposity index (VAI), and Chinese visceral adiposity index (CVAI) not only assess adipose tissue mass but also reflect adipose tissue dysfunction. So far, no study has been reported to evaluate the association of CMI, LAP, VAI, and CVAI with CMM risk in hypertensive patients. Therefore, this study aimed to assess the association between these adiposity indicators and the risk of CMM among Chinese hypertensive patients. Methods: In this cross-sectional study, a total of 229,287 hypertensive patients aged 35 years and older were included from the National Basic Public Health Service Project. All participants underwent a face-to-face questionnaire survey, physical examination, and the collection of fasting venous blood samples. Multivariable logistic regression models were performed to estimate odds ratios (ORs) and 95% confidence intervals (CIs). Receiver operating characteristic curve was utilized to evaluate the identification ability for CMM. Results: After adjusting for confounders, each 1-standard deviation increase in CMI, LAP, VAI, and CVAI was associated with a 14%, 8%, 12%, and 54% increased risk of CMM, respectively. When comparing the highest quartile of these indicators with the lowest quartile, individuals in the highest quartile of CMM, LAP, VAI, and CVAI had a 1.39-fold (95% CI 1.30, 1.48), 1.28-fold (95% CI 1.19, 1.37), 1.37-fold (95% CI 1.29, 1.46), and 2.56-fold (95% CI 2.34, 2.79) increased risk of CMM after adjusting for potential confounders. Notably, a nonlinear association was observed for CMI, LAP, and VAI with the risk of CMM (all P nonlinearity < 0.001). CVAI exhibited the highest area under the receiver operating characteristic curve (AUC) among all the included adiposity indices in this analysis. Conclusion: This study indicated the significant positive association of CMI, LAP, VAI, and CVAI with the risk of CMM in hypertensive patients. Among these indicators, CVAI demonstrated the most robust performance in predicting CMM risk and may serve as a valuable tool for identifying CMM risk in Chinese hypertensive patients. [ABSTRACT FROM AUTHOR]

Background: Cardiovascular and metabolic diseases include a large group of pathologies and constitute one of the most serious chronic health problems facing the 21st century, with high rates of morbidity and mortality worldwide. Unhealthy diets influence the development of these pathologies. The Mediterranean diet can be an important part in the treatment of these diseases. The objective of this study was to assess the effect of a program that aims to increase adherence to the Mediterranean diet on the improvement of different cardiometabolic risk parameters. Methods: A prospective intervention study was carried out on 7034 Spanish workers. Prior to the intervention, 22 cardiometabolic risk scales were evaluated. Participants in this study were informed both orally and in writing of the characteristics and benefits of the Mediterranean diet and were given the website of the Ministry of Health, Consumption and Social Welfare of Spain, which provides advice on nutrition. Adherence to the Mediterranean diet was reinforced by sending a monthly SMS to their mobile phones. After six months of follow-up, the 22 risk scales were re-evaluated to assess changes. Means and standard deviations were calculated using Student's t test to analyse quantitative variables. Prevalence was calculated using the Chi-square test when the variables were qualitative. Results: All the cardiometabolic risk scales studied decreased after implementing a program to improve and enhance adherence to the Mediterranean diet. The number of losses in the sample was very low, standing at 4.31%. Conclusions: The Mediterranean diet is effective in reducing all cardiovascular risk scales evaluated. The mean values and prevalence of high values of the different cardiometabolic risk scales analysed led to lower values after the implementation of the program to increase adherence to the Mediterranean diet. We observed a significant positive difference in metabolic age in both sexes. We have obtained a significant improvement in the insulin resistance index, especially in the SPISE-IR index, data that we have not found in previous publications. Easy access to the Internet and new information and communication technologies facilitate adherence to a diet and can reduce the number of losses. [ABSTRACT FROM AUTHOR]

Background: The cardiometabolic index (CMI) is a new metric derived from the triglyceride-glucose index and body mass index and is considered a potential marker for cardiovascular risk assessment. This study aimed to examine the correlation between the CMI and the presence and severity of arteriosclerosis in patients with type 2 diabetes mellitus (T2DM). Methods: This study involved 2243 patients with T2DM. The CMI was derived by dividing the triglyceride level (mmol/L) by the high-density lipoprotein level (mmol/L) and then multiplying the quotient by the waist-to-height ratio. Multivariate logistic regression was used to analyze the correlations between the CMI and BMI blood biomarkers, blood pressure, and brachial-ankle pulse wave velocity (baPWV). Results: Patients were categorized into three groups based on their CMI: Group C1 (CMI < 0.775; n = 750), Group C2 (CMI: 0.775–1.355; n = 743), and Group C3 (CMI > 1.355; n = 750). Increased BMI, fasting glucose, insulin (at 120 min), total cholesterol (TC), and baPWV values were observed in Groups C2 and C3, with statistically significant trends (all trends P < 0.05). The CMI was positively correlated with systolic blood pressure (r = 0.74, P < 0.001). Multivariate analysis revealed that an increased CMI contributed to a greater risk for arteriosclerosis (OR = 1.87, 95%CI: 1.66–2.10, P < 0.001). Compared to the C1 group, the C2 group and C3 group had a greater risk of developing arteriosclerosis, with ORs of 4.55 (95%CI: 3.57–5.81, P<0.001) and 5.56 (95%CI: 4.32–7.17, P<0.001), respectively. The association was notably stronger in patients with a BMI below 21.62 kg/m² than in those with a BMI of 21.62 kg/m² or higher (OR = 4.53 vs. OR = 1.59). Conclusions: These findings suggest that the CMI is a relevant and independent marker of arteriosclerosis in patients with T2DM and may be useful in the risk stratification and management of these patients. [ABSTRACT FROM AUTHOR]

Donghao Wang,1,&ast; Yating Chen,1,&ast; Yutong Ding,1,&ast; Yongkang Tang,1,&ast; Xiaofen Su,1,&ast; Shiwei Li,1 Haojie Zhang,1,2 Yanyan Zhou,1 Zhiyang Zhuang,1 Qiming Gan,1 Jingcun Wang,1 Yuting Zhang,1 Dongxing Zhao,1 Nuofu Zhang1 1State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Sleep Medicine Center, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, 510160, People’s Republic of China; 2The Clinical Medicine Department, Henan University, Zhengzhou, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Nuofu Zhang, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Sleep Medicine Center, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, 510160, People’s Republic of China, Tel +86-13600460056, Email nfzhanggird@163.comBackground: Obstructive sleep apnea (OSA) is a common chronic disease with various comorbidities. The cardiometabolic index (CMI) reflects visceral fat tissue distribution and function, assessing the risk of obesity-related conditions such as metabolic syndrome (MetS) and stroke, which are strongly connected to OSA. The relationship between CMI with OSA and OSA combined with MetS (OMS) remains unclear. This study aims to evaluate the screening value of CMI for OSA and OMS, compared to the lipid accumulation product (LAP).Methods: A total of 280 participants who underwent polysomnography were finally included, with the measurements of metabolic-related laboratory test results such as total cholesterol and triglyceride. Receiver operating curve (ROC) analysis and calculation of the area under the curve (AUC) were conducted to assess the screening potential of CMI, LAP, and the logistic regression models established based on them for OSA and OMS. The Youden index, sensitivity, and specificity were used to determine the optimal cutoff points.Results: ROC curve analysis revealed that the AUCs for CMI in screening OSA and OMS were 0.808 and 0.797, and the optimal cutoff values were 0.71 (sensitivity 0.797, specificity 0.776) and 0.89 (sensitivity 0.830, specificity 0.662), respectively, showing higher Youden index than LAP. The AUCs of screening models based on CMI for OSA and OMS were 0.887 and 0.824, respectively.Conclusion: CMI and LAP can effectively screen for OSA and OMS, while CMI has more practical cutoff values for identifying the diseased states. Screening models based on CMI demonstrate a high discriminatory ability for OSA and OMS, which needs verification in a large-scale population.Keywords: obstructive sleep apnea, metabolic syndrome, cardiometabolic index, lipid accumulation products, screening model

Limin Wu,1 Jing Xu2 1Department of Gastroenterology, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Lucheng District, Wenzhou, Zhejiang Province, People’s Republic of China; 2Department of Endocrinology, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Lucheng District, Wenzhou, Zhejiang Province, People’s Republic of ChinaCorrespondence: Jing Xu, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, People’s Republic of China, Tel +86 15068256508, Email 47914057@qq.comPurpose: Cardiometabolic index (CMI) has been suggested as innovative measures for assessing the cardiometabolic status. However, there is a lack of relevant studies on exploring the relationship between CMI and insulin resistance (IR). Consequently, this study aims to examine the relationship between CMI and IR in subjects with type 2 diabetes mellitus (T2DM).Patients and Methods: A cross-sectional study was performed on 2493 patients with T2DM (including 1505 males and 988 females). IR was measured through the homeostatic model assessment of insulin resistance (HOMA-IR), which was defined as HOMI-IR≥ 2.69. The relationship between CMI and IR was evaluated with Spearman’s correlation, ROC analysis, multiple logistic regression, generalized smooth curve fitting and subgroup analysis.Results: CMI was correlated with HOMA-IR in patients with T2DM (Spearman correlation coefficient = 0.391 in females and 0.346 in males, P< 0.001). Through the multiple logistic regression analysis, CMI was significantly correlated with IR (OR=1.30, 95% CI=1.15– 1.47 in males and OR=1.62, 95% CI=1.32– 1.99 in females). In addition, a non-linear correlation between CMI and IR risk was identified. The AUC of CMI (AUC = 0.702 for males and 0.733 for females, all p < 0.01) was the largest compared with traditional indexes of adiposity and blood lipids. According to the subgroup analysis, the two had a more significantly positive correlation in females, the elderly and subjects with HbA1c < 7%.Conclusion: In patients with T2DM, elevated CMI is significantly correlated with IR, as a useful index of IR.Keywords: high density lipoprotein cholesterol, cardiometabolic index, waist-to-height ratio, insulin resistance, type 2 diabetes mellitus

Background: Cardiometabolic index (CMI) is a novel indicator for predicting the risk of obesity-related diseases. We aimed to determine the relationships of CMI with insulin resistance (IR), impaired fasting glucose (IFG), and type 2 diabetes mellitus (T2DM) using NHANES data from 1999 to 2020. Methods: After CMI values were estimated, weighted univariate and multivariate logistic regression analyses were used to ascertain whether CMI was an independent risk indicator for IR, IFG, and T2DM. Furthermore, stratified analyses and interaction analyses were carried out to investigate the heterogeneity of correlations across various subgroups. Subsequently, restricted cubic splines (RCS) were used to examine nonlinear relationships. Results: 21,304 US adults were enrolled in our study, of whom 5,326 (22.38%) had IR, 4,706 (20.17%) had IFG, and 3,724 (13.02%) had T2DM. In the studied population, a higher CMI index value was significantly associated with an elevated likelihood of IR, IFG, and T2DM. In the RCS regression model, the relationship between CMI and IR, IFG, and T2DM was identified as nonlinear. A nonlinear inverted U-shaped relationship was found between CMI and IFG, and an inverse L-shaped association was observed between CMI and IR, CMI and T2DM. The cut-off values of CMI were 1.35, 1.48, and 1.30 for IR, IFG, and T2DM, respectively. Conclusion: Our results indicate that CMI was positively correlated with an increase in IR, IFG, and T2DM in the studied population. CMI may be a simple and effective surrogate indicator of IR, IFG, and T2DM. [ABSTRACT FROM AUTHOR]

Background: Atherosclerosis is a primary risk factor for cardiovascular disease, but its pathogenesis is unclear. Previous studies reported that the prospero homeobox 1 (PROX1) gene is associated with lipid disorders and insulin resistance. However, few studies have been concerned about the relationship between PROX1 gene and atherosclerosis in Chinese individuals. Material and methods: A total of 2177 individuals with data on body indicators, pulse wave velocity, and genotype were included in this study. We estimated the cardiometabolic index (CMI) of the participants and analyzed the association between the rs340974 single nucleotide polymorphism (SNP) and CMI as well as pulse wave velocity (PWV). The interaction effects between the rs340974 SNP and metabolic factors for the early atherosclerosis index were also analyzed. Results: The rs340874 SNP CC genotype exhibits increased metabolism factors, such as carotid femoral PWV, body mass index (BMI), and CMI, compared with those in other groups. The prevalence ratios of hypertension in different genotype groups were significantly different. However, after adjusting for some metabolic factors, the difference disappeared. The associations between the rs340874 SNP and CMI as well as carotid femoral PWV were confirmed by multiple linear regression. The interaction effect indicates that sex and BMI modified the relation of the rs340874 SNP and CMI or carotid femoral PWV, which are indexes of early atherosclerosis. Conclusions: The PROX1 gene rs340874 SNP CC genotype is associated with CMI and cfPWV, and sex and obesity can modify this relationship in Chinese populations. [ABSTRACT FROM AUTHOR]

Background: Physical activity (PA) is widely recommended for preventing and combating obesity, but the most effective PA pattern for treating obesity remains unclear. Cardiometabolic index (CMI), derived from waist height ratio and triglycerides to high-density lipoprotein-cholesterol ratio, is a novel indicator for evaluating obesity. However, the relationship between different PA patterns and CMI remains unelucidated. Objective: This study aimed to explore the association between different PA patterns and CMI in U.S. adults. Methods: Participants with complete information in CMI, PA patterns, and other covariates in the National Health and Nutrition Examination Survey database (2007–2016) were included in this study. Multivariate linear regression models were utilized to explore the relationship between PA patterns and CMI. Moreover, stratified analyses, interaction tests and restricted cubic spline (RCS) regression analysis were used to investigate the stability and nonlinearity of the association, respectively. Results: A total of 16,442 adults were included in this study. After adjusting for all potential covariates, only the regularly active group was significantly associated with CMI reduction (β = −0.13, 95% CI: 0.19 to −0.07, P 60, with higher education level, and who are current drinkers. Furthermore, RCS analysis indicated that total PA in a week was significantly, nonlinearly associated with CMI in non-inactive adults, and that a total of PA more than 330 min can reap favorable CMI reduction. Conclusion: Being regularly active is associated with significant CMI reduction, while being weekend warriors and insufficiently active do not achieve equivalent benefits. For non-inactive individuals, engaging in PA for more than 330 min weekly helps to reduce CMI effectively.

Abstract Objective To investigate the correlation between anthropometric indexes [cardiometabolic index (CMI), lipid accumulation products (LAP), waist triglyceride index (WTI), and body mass index (BMI)] and acute pancreatitis (AP) in a Chinese adult population. Methodology The present investigation consisted of a prospective group including 117,326 subjects who were enrolled in the Kailuan investigation. The individuals were categorized into quartiles based on their baseline levels of CMI, LAP, and WIT. BMI was categorized into three distinctive groups: normal weight group (BMI

Dongxing Gu,1,&ast; Yayun Lu,1,&ast; Baiqing Xu,1 Xuefeng Tang2 1Health Examination Center, Huadong Sanatorium, Wuxi, People’s Republic of China; 2Department of Health Nursing, Huadong Sanatorium, Wuxi, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Baiqing Xu; Xuefeng Tang, Huadong Sanatorium, No. 67 Jinyuan Road, Wuxi, 214065, People’s Republic of China, Email doctorxbq@163.com; 978665164@qq.comBackground and Objective: Evidence suggests that cardiometabolic index (CMI) has been identified as a novel obesity-related index associated with diabetes, hypertension, and cardiovascular disease. Current evidence suggests that the differences in sex hormones and regional fat distribution in both sexes are directly correlated with metabolic dysfunction-associated fatty liver disease (MAFLD) risk. This study aimed to investigate the diagnostic value of CMI in MAFLD in both sexes.Methods: This retrospective study included 6107 subjects who underwent annual health check-ups from March 2021 to January 2022. CMI was calculated by multiplying the ratio of triglycerides and high-density lipoprotein cholesterol (TG/HDL-C) by waist-to-height ratio (WHtR). Multivariable logistic regression analysis and restricted cubic spline were used to investigate the association of CMI and MAFLD risk. Receiver operating characteristic curve analysis was conducted for the exploration of the diagnostic accuracies of obesity-related indicators. Areas under the curves (AUCs) with 95% CIs were calculated.Results: Prevalence of MAFLD increased with elevated quartiles of CMI in both sexes. The median (IQR) age was 46.00 (18.00) years. Multivariate logistic regression analyses showed that higher CMI was independently associated with MAFLD, in which every additional standard deviation (SD) of CMI increased the risk of MAFLD (OR=2.72, 95% CI:2.35– 3.15 for males; OR=3.26, 95% CI:2.36– 4.51 for females). Subjects in the fourth quartile of CMI had the highest odds of MAFLD for males (OR=15.82, 95% CI:11.84– 21.14) and females (OR=22.60, 95% CI:9.52– 53.65)(all P for trend< 0.001). Besides, CMI had a non-linearity association with MAFLD (all P for non-linearity< 0.001). Furthermore, CMI exhibited the largest AUC compared to other obesity-related indexes in terms of discriminating MAFLD in males (AUC=0.796, 95% CI:0.782– 0.810) and females (AUC=0.853, 95% CI:0.834– 0.872).Conclusion: CMI was a convenient indicator for the screening of MAFLD among Chinese adults. Females with high CMI had a better diagnostic value for MAFLD than males.Keywords: metabolic dysfunction-associated fatty liver disease, cardiometabolic index, sex, general population, predicting value

Both cardiometabolic and chronic inflammatory diseases pose a significant challenge to global public health, particularly among older adults. Here, we investigated the interplay between systemic inflammatory status and the cardiometabolic index (CMI) in older men with adequate weight or obesity. In this observational cross-sectional study, older men (71.79 ± 7.35 years) were separated into groups with normal weight (NW, n = 34) and obesity (O, n = 32) to assess circulating levels of pro- and anti-inflammatory cytokines and CMI. Overall, the O group showed not only a higher inflammatory status but also increased CMI (p < 0.0001) compared with the NW group. Interestingly, only positive correlations were found between pro- and anti-inflammatory cytokines in both groups. Through multivariate regression analysis, IL-6 (β = −0.2276, p = 0.0003) and IL-10 (β = 0.2023, p = 0.0030) significantly influenced CMI in the NW group. No significant results were found in the O group. Our findings reinforce the effects of obesity in inflammaging, as well as suggesting that the influence of cytokines in CMI occurs in older men with normal weight, since the elevated pro-inflammatory profile observed in older men with obesity can interfere in this effect.