Remnant cholesterol can identify individuals at higher risk of metabolic syndrome in the general population.

Remnant cholesterol (RC) is a highly atherogenic lipid. Previous studies have shown that RC was closely associated with many metabolism-related diseases. However, the relationship of RC with metabolic syndrome (MetS) remains unclear. This study's objective is to investigate the relationship of RC with MetS. A total of 60,799 adults who received health assessments were included in this study. RC was calculated by subtracting the directly measured values for low-density lipoprotein cholesterol (LDL-C) and high-density lipoprotein cholesterol (HDL-C) from total cholesterol (TC) and divided into 5 groups according to its quintile. MetS diagnosis according to National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATP III) definitions. Application of receiver operating characteristic (ROC) curve analysis and multivariate logistic regression to assess the association of RC with MetS. In RC quintile groups, the prevalence of MetS was 0.84, 1.10, 1.92, 3.87 and 37.71%, respectively. Multivariate logical regression analysis showed that RC and MetS maintained a stable independent positive correlation between both sexes. An interaction test further showed that the MetS risk associated with RC was significantly higher in women than in men. Moreover, ROC analysis results showed that RC had high accuracy in identifying MetS, especially among young and middle-aged men [(area under the curve: AUC) < 30 years: 0.9572, 30-39 years: 0.9306, 40-49 years: 0.9067]. The current study provided the first evidence of a positive association between RC and MetS, and that this correlation was stronger in women than in man, which may be due to the relative deficiency of estrogen in women.

Evaluation of the value of conventional and unconventional lipid parameters for predicting the risk of diabetes in a non-diabetic population.

BACKGROUND: Conventional and unconventional lipid parameters are associated with diabetes risk, the comparative studies on lipid parameters for predicting future diabetes risk, however, are still extremely limited, and the value of conventional and unconventional lipid parameters in predicting future diabetes has not been evaluated. This study was designed to determine the predictive value of conventional and unconventional lipid parameters for the future development of diabetes. METHODS: The study was a longitudinal follow-up study of 15,464 participants with baseline normoglycemia. At baseline, conventional lipid parameters such as low-density lipoprotein cholesterol (LDL-C), triglyceride (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C) were measured/calculated, and unconventional lipid parameters such as non-HDL-C, remnant cholesterol (RC), LDL/HDL-C ratio, TG/HDL-C ratio, non-HDL/HDL-C ratio, TC/HDL-C ratio and RC/HDL-C ratio were calculated. Hazard ratio (HR) and 95% confidence interval (CI) were estimated by Cox proportional hazard regression adjusting for demographic and diabetes-related risk factors. The predictive value and threshold fluctuation intervals of baseline conventional and unconventional lipid parameters for future diabetes were evaluated by the time-dependent receiver operator characteristics (ROC) curve. RESULTS: The incidence rate of diabetes was 3.93 per 1000 person-years during an average follow-up period of 6.13 years. In the baseline non-diabetic population, only TG and HDL-C among the conventional lipid parameters were associated with future diabetes risk, while all the unconventional lipid parameters except non-HDL-C were significantly associated with future diabetes risk. In contrast, unconventional lipid parameters reflected diabetes risk better than conventional lipid parameters, and RC/HDL-C ratio was the best lipid parameter to reflect the risk of diabetes (HR: 6.75, 95% CI 2.40-18.98). Sensitivity analysis further verified the robustness of this result. Also, time-dependent ROC curve analysis showed that RC, non-HDL/HDL-C ratio, and TC/HDL-C ratio were the best lipid parameters for predicting the risk of medium-and long-term diabetes. CONCLUSIONS: Unconventional lipid parameters generally outperform conventional lipid parameters in assessing and predicting future diabetes risk. It is suggested that unconventional lipid parameters should also be routinely evaluated in clinical practice.

Utility of Non-High-Density Lipoprotein Cholesterol to High-Density Lipoprotein Cholesterol Ratio in Evaluating Incident Diabetes Risk.

Purpose: Diabetes is one of the most prevalent chronic diseases in the world, and its prevalence is expected to rise further. To help understand the utility of the ratio of non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol (NHHR) in diabetes prevention, this large-scale longitudinal cohort study aims to explore the association of NHHR with diabetes risk and compare it as a risk predictor with conventional lipid parameters. Patients and Methods: This observational study extracted data from the NAGALA longitudinal cohort study conducted in Japan between 2004 and 2015. Multivariate Cox regression analysis was used to evaluate the association between NHHR and the risk of diabetes. The dose-response relationship was analyzed by restricted cubic spline (RCS) regression and the potential risk threshold was estimated. The receiver operator characteristic curve (ROC) was used to analyze and calculate the predictive value and optimal threshold of NHHR and other conventional lipids for new-onset diabetes. Results: Of the 15,464 people aged 18-79, 373 (2.41%) were diagnosed with new-onset diabetes during the study period, with a median age of 46 years. The sensitivity analysis based on multivariate adjustment showed that the independent positive correlation between diabetes and NHHR was stable in different populations. RCS and ROC analysis indicated that the association between NHHR and diabetes was non-linear, and the NHHR was a better marker for predicting diabetes risk than other conventional lipid parameters; Additionally, it is worth noting that an NHHR of approximately 2.74 may be the optimal threshold for intervention in diabetes risk. Conclusion: In the general population, NHHR is a better marker for predicting diabetes risk than conventional lipid parameters, and an NHHR of about 2.74 may be the optimal threshold for assessing diabetes risk.

Association of LDL:HDL ratio with prediabetes risk: a longitudinal observational study based on Chinese adults.

BACKGROUND: Low-density lipoprotein:high-density lipoprotein cholesterol ratio (LDL:HDL ratio) has a good performance in identifying diabetes mellitus (DM) and insulin resistance. However, it is not yet clear whether the LDL:HDL ratio is associated with a high-risk state of prediabetes. METHODS: This cohort study retrospectively analyzed the data of 100,309 Chinese adults with normoglycemia at baseline. The outcome event of interest was new-onset prediabetes. Using multivariate Cox regression and smoothing splines to assess the association of LDL:HDL ratio with prediabetes. RESULTS: During an average observation period of 37.4 months, 12,352 (12.31%) subjects were newly diagnosed with prediabetes. After adequate adjustment for important risk factors, the LDL:HDL ratio was positively correlated with the prediabetes risk, and the sensitivity analysis further suggested the robustness of the results. Additionally, in stratified analysis, we discovered significant interactions between LDL:HDL ratio and family history of DM, sex, body mass index and age (all P-interaction < 0.05); among them, the LDL:HDL ratio-related prediabetes risk decreased with the growth of body mass index and age, and increased significantly in women and people with a family history of DM. CONCLUSIONS: The increased LDL:HDL ratio in the Chinese population indicates an increased risk of developing prediabetes, especially in women, those with a family history of DM, younger adults, and non-obese individuals.

Remnant Cholesterol is an Independent Predictor of New-Onset Diabetes: A Single-Center Cohort Study.

PURPOSE: Remnant cholesterol (RC) is the cholesterol of triglyceride-rich lipoproteins, which has a high degree of atherogenic effect. To date, epidemiological evidence supports that higher RC levels lead to a greater risk of adverse cardiovascular events in patients with diabetes, but the nature of the association between RC levels and diabetes risk remains unclear. This study was designed to assess the association of RC with the risk of new-onset diabetes and to investigate whether there is a causal relationship between the two. PATIENTS AND METHODS: The subjects included 15,464 individuals of the general population who participated in a health examination. Subjects were quartered according to the RC quartile, and the Cox proportional hazard model was used to assess the independent association between RC and new-onset diabetes. RESULTS: During an average observation period of 6.13 years, 2.41% of the subjects were diagnosed with new-onset diabetes. Kaplan-Meier analysis showed that the 13-year cumulative diabetes rates corresponding to the RC quartile were 8.62%, 2.49%, 12.78%, and 17.91%. Multivariate Cox regression analysis indicated that higher RC levels were independently associated with an increased risk of new-onset diabetes (HR: 2.44, 95% CI: 1.50-3.89). Additionally, according to the results of receiver operating characteristic curve analysis, RC had the largest area under the curve (0.7314) compared to traditional lipid parameters in predicting new-onset diabetes. CONCLUSION: These results indicated that RC is an important independent predictor of new-onset diabetes in the general population.

Association of remnant cholesterol with nonalcoholic fatty liver disease: a general population-based study.

BACKGROUND: Remnant cholesterol (RC) mediates the progression of coronary artery disease, diabetic complications, hypertension, and chronic kidney disease. Limited information is available on the association of RC with nonalcoholic fatty liver disease (NAFLD). This study aimed to explore whether RC can be used to independently evaluate the risk of NAFLD in the general population and to analyze the predictive value of RC for NAFLD. METHODS: The study included 14,251 subjects enrolled in a health screening program. NAFLD was diagnosed by ultrasound, and the association of RC with NAFLD was assessed using the receiver operating characteristic (ROC) curve and logistic regression equation. RESULTS: Subjects with elevated RC had a significantly higher risk of developing NAFLD after fully adjusting for potential confounding factors (OR 1.77 per SD increase, 95% CI 1.64-1.91, P trend< 0.001). There were significant differences in this association among sex, BMI and age stratification. Compared with men, women were facing a higher risk of RC-related NAFLD. Compared with people with normal BMI, overweight and obesity, the risk of RC-related NAFLD was higher in thin people. In different age stratifications, when RC increased, young people had a higher risk of developing NAFLD than other age groups. Additionally, ROC analysis results showed that among all lipid parameters, the AUC of RC was the largest (women: 0.81; men: 0.74), and the best threshold for predicting NAFLD was 0.54 in women and 0.63 in men. CONCLUSIONS: The results obtained from this study indicate that (1) in the general population, RC is independently associated with NAFLD but not with other risk factors. (2) Compared with traditional lipid parameters, RC has a better predictive ability for NAFLD in men.

Triglyceride glucose-body mass index and the risk of diabetes: a general population-based cohort study.

BACKGROUND: Triglyceride glucose-body mass index (TyG-BMI) has been proven to be a reliable substitute for insulin resistance. However, whether a causal association exists between TyG-BMI and new-onset diabetes remains uncertain. The purpose of this study was to investigate the causal association and predictive performance between TyG-BMI and diabetes. METHODS: A total of 116,661 subjects who underwent a physical examination were included in this study. The subjects were divided into five equal points according to the quintile of TyG-BMI, and the outcome of interest was the occurrence of diabetic events. TyG-BMI = ln [fasting plasma glucose (mg/dL) × fasting triglycerides (mg/dL)/2] × BMI. RESULTS: During the average follow-up period of 3.1 (0.95) years, 1888 men (1.61 %) and 793 women (0.68 %) were newly diagnosed with diabetes. Multivariate Cox regression analysis showed that TyG-BMI was an independent predictor of new-onset diabetes (HR 1.50 per SD increase, 95 %CI: 1.40 to 1.60, P-trend < 0.00001), and the best TyG-BMI cutoff value for predicting new-onset diabetes was 213.2966 (area under the curve 0.7741, sensitivity 72.51 %, specificity 69.54 %). Additionally, the results of subgroup analysis suggested that the risk of TyG-BMI-related diabetes in young and middle-aged people was significantly higher than that in middle-aged and elderly people, and the risk of TyG-BMI-related diabetes in non-obese people was significantly higher than that in overweight and obese people (P for interaction < 0.05). CONCLUSIONS: This cohort study of the Chinese population shows that after excluding other confounding factors, there is a causal association of TyG-BMI with diabetes, and this independent association is more obvious in young, middle-aged and non-obese people.

Usefulness of the triglyceride glucose-body mass index in evaluating nonalcoholic fatty liver disease: insights from a general population.

BACKGROUND: Triglyceride glucose-body mass index (TyG-BMI) is a recently developed alternative indicator to identify insulin resistance. However, few studies have investigated the association between the TyG-BMI and nonalcoholic fatty liver disease (NAFLD). Therefore, this study aimed to study the relationship between NAFLD and the TyG-BMI in the general population and its predictive value. METHODS: A cross-sectional study was conducted on 14,251 general subjects who took part in a comprehensive health examination. The anthropological characteristics and many risk factors for NAFLD were measured. RESULTS: After fully adjusting for confounding variables, a stable positive correlation was found between NAFLD and the TyG-BMI (OR: 3.90 per SD increase; 95% CI: 3.54 to 4.29; P-trend< 0.00001). This positive correlation was not simply linear but a stable non-linear correlation. Additionally, obvious threshold effects and saturation effects were found, in which a threshold effect occurred when the TyG-BMI was between 100 and 150; when the TyG-BMI was between 300 and 400, the corresponding NAFLD risk appeared saturated. Furthermore, receiver operating characteristic analysis showed that the TyG-BMI could better predict the risk of NAFLD than other traditional indicators [TyG-BMI (AUC): 0.886; 95% CI: 0.8797-0.8927; P < 0.0001], particularly among young and middle-aged and non-obese people. CONCLUSIONS: This epidemiological study is the first on the association between the TyG-BMI and NAFLD risk in the general population. In this large data set from the general population, the TyG-BMI showed an independent positive correlation with NAFLD. The discovery of the threshold effect and saturation effect between them provides a new idea to prevent and treat NAFLD.