Surrogate Index Research Articles

Comparative study of insulin resistance surrogate indices to predict mild cognitive impairment among Chinese non-diabetic adults

ObjectiveThe study aims to investigate the associations of triglyceride glucose (TyG), TyG combined with body mass index (TyG-BMI), metabolic score for insulin resistance (METS-IR), the triglyceride to high-density lipoprotein cholesterol ratio (TG/HDL-C), and the risk of mild cognitive impairment (MCI) in individuals without diabetes aged 45 and above.MethodsThe most recent data in this study were from a cohort study, which sourced samples from the China Health and Retirement Longitudinal Study, spanning 2011 to 2018. The four indices’ associations with MCI risk were analyzed using logistic regression. The predictive capacity was measured using the receiver operating characteristic (ROC) curve.ResultsOver a 7-year follow-up, 1,261 individuals (31.34%) of the 4,027 participants developed MCI. Logistic regression analysis revealed significant associations between these surrogate indices and MCI. The findings for the highest quartile compared with the lowest quartile were as follows: TyG: 1.24 (95% CI: 1.02, 1.49); TyG-BMI: 1.38 (95% CI: 1.13, 1.68); METS-IR: 1.50 (95% CI: 1.09, 2.06); TG/HDL-C: 1.34 (95% CI: 1.10, 1.64). ROC analysis revealed that TyG, TyG-BMI, TG/HDL-C, and METS-IR demonstrated excellent discriminatory power for MCI, with area under the curve (AUC) values of 0.82 (95% CI: 0.80, 0.83), 0.82 (95% CI: 0.80, 0.83), 0.83 (95% CI: 0.80, 0.84), and 0.83 (95% CI: 0.80, 0.84), respectively. The four indices showed stronger ability to predict MCI risk in females compared to males.ConclusionElevated levels of four indices are positively correlated with MCI risk. TyG-BMI and METS-IR demonstrate stronger capabilities in identifying MCI across both male and female populations. This suggests that early intervention in patients with elevated IR surrogate indices may help reduce the MCI.

Associations of four surrogate insulin resistance indexes with non-alcoholic steatohepatitis in Chinese patients with obesity: a cross-sectional study.

This study was designed to evaluate the association of four surrogate indexes of IR with NASH in patients with obesity. A total of 270 patients who underwent bariatric surgery, were included in this cross-sectional study. NASH was diagnosed based on liver biopsies. Binary logistics regression analyses were performed to assess the associations of four surrogate indexes of IR (HOMA-IR, Matsuda index, TyG, and TG/HDL-C) with NASH in patients with obesity. The restricted cubic spline was used to assess the dose-response associations of surrogate indexes of IR with NASH after adjusting for confounding factors. NASH was diagnosed in 136 patients, with a prevalence of 50.37%. Compared with tertile 1, the fully adjusted ORs (95% CIs) of NASH for tertile 3 were 2.711(1.113-6.608) and 0.297 (0.152-0.579) for TyG and Matsuda index. Consistently, per SD increment of TyG were still significantly associated with 64% increased risks of NASH, and per SD increment of Matsuda index were still significantly associated with 38% decreased risks of NASH. In contrast, no significant associations were found between HOMA-IR and TG/HDL-C and the risk of NASH in patients with obesity (all P > 0.05). After adjusting covariates in restricted cubic splines, the risk of NASH decreased with the increment of Matsuda Index levels (P-nonlinear = 0.442, P-overall = 0.007) and with the decrement of TyG levels (P-nonlinear = 0.004, P-overall = 0.001). In patients with obesity, TyG and Matsuda index were independently related to the risk of NASH after adjustment for traditional risk factors. In addition, compared with HOMA-IR and TG/HDL-C, the Matsuda index and TyG may be more suitable for NASH prediction in patients with obesity.

Calculated whole blood viscosity in non-diabetic subjects with asymptomatic carotid atherosclerosis: How insulin resistance may affect blood viscosity.

Asymptomatic atherosclerosis is an important early marker of vascular damage and, among its risk factors, hemorheological alterations play an important role. In a cohort of 85 non-diabetic subjects with asymptomatic carotid atherosclerosis (ACA), we have measured whole blood viscosity (cWBV) according to the haematocrit and plasma fibrinogen level. The cWBV distinguish the subgroup of ACA subjects with 3-5 cardiovascular risk factors (CRFs) from that with 1-2 CRFs and the same behavior is present for haematocrit and plasma fibrinogen level. Therefore, we divided the whole group of ACA subjects according to the medians of the four surrogate indexes with an insulin resistance degree of TG/HDL-C, TyG, VAI and LAP. The analysis of the correlation between cWBV and each index of insulin resistance has shown that no correlation is present in the whole group and in the group of ACA subjects with 1-2 CRFs, while in the subgroup with 3-5 CRFs there is a positive correlation between cWBV with TG/HDL-C and TyG at a low degree of statistical significance. The date underline that subjects with this clinical condition have an unaltered evaluation of the cWBV compared to the other indices.

A single bout of physical exercise improves 1-hour post-load plasma glucose in healthy young adults.

Physical exercise is a key component in the treatment of type 2 diabetes and plays an important role in maintaining a healthy glucose metabolism even in healthy subjects. To date, no studies have investigated the effect of a single bout of aerobic physical exercise on glucose metabolism in young, moderately active, healthy adults. We performed an OGTT 7 days before and 24h after a single bout of physical exercise, to evaluate 1-hour post-load plasma glucose and surrogate indexes of insulin sensitivity and insulin secretion. Glucose levels were significantly reduced after exercise at baseline and one hour after glucose load; similarly, insulin was significantly lower 1h after glucose load. We found a significant increase in the Matsuda index, confirmed by OGIS index, QUICKI index, and by significant reduction in HOMA-IR. Conversely, we observed a trend to increase in HOMA-B. This is the first study to evaluate the effect of a single bout of exercise on 1-hour glucose levels following OGTT. We found a significant reduction in 1-hour glucose levels following OGTT together with an increased insulin sensitivity. A single 30-minute bout of aerobic exercise also seemed to improve the insulin secretion pattern. Modifications in beta cell secretory capacity during exercise are likely secondary to an improvement in insulin action in insulin dependent tissues.

Adipocytes Size of the Epicardial Adipose Tissue in Patients With Coronary Artery Disease and Coronary Atherosclerosis: Relationship With Parameters of Blood Lipid Transport Function, Carbohydrate Metabolism and Inflammatory Markers

Aim. To assess the potential relationship between morphometric characteristics of adipocytes in epicardial and subcutaneous adipose tissue (EAT, SAT) and the lipid transport function of the blood, glucose e metabolism, and inflammatory biomarkers in patients with ischemic heart disease (IHD) after coronary artery bypass grafting.Material and methods. The study included 47 patients (33 men and 14 women) aged 53-72 years with chronic IHD. The study material was EAT and SAT adipocytes obtained from intraoperative explants. The size of EAT and SAT adipocytes and the proportion of EAT and SAT adipocytes with ≥100 μm and ≤50 μm sizes were determined. Carbohydrate metabolism parameters, blood lipid transport function, and inflammatory biomarkers were assessed. Surrogate indices of insulin resistance (IR) were calculated.Results. The mean size of EAT adipocytes, in contrast to SAT adipocytes, correlated (p<0.05) with serum concentrations of tumor necrosis factor alpha (TNF-α) (rs=0.43), triglycerides (TG) (rs=0.36), interleukin (IL)-1β (rs=0.29), basal C-peptide (rs=0.40) and TyG index (rs=0.32). Male gender and blood concentrations of basal C-peptide and TG in combination were identified as statistically significant determinants of an increase in the mean size of EAT adipocytes. The threshold values of TG and non-high-density lipoprotein cholesterol (nHDL-C) associated with an increase in the mean size of EAT adipocytes ≥87.61 μm were 1.4 and 2.63 mM, respectively. Patients with TG concentrations ≥1.4 mM had a greater size and proportion of hypertrophied EAT adipocytes, IR indices, and higher blood concentrations of basal glucose, high-sensitivity C-reactive protein, TNF-α, and IL-1β. TG concentrations of 1.2-1.4 mM, compared with concentrations <1.2 mM, were associated with a greater mean size of EAT adipocytes and higher serum IL-10 concentrations. Patients with non-HDL-C ≥2.63 mM, compared with its lower concentrations, had more pronounced hypertrophy of EAT adipocytes.Conclusion. Disorders in the morphometric characteristics of EAT adipocytes, in contrast to SAT adipocytes, are associated with increased blood concentrations of TG, non-HDL-C, inflammatory markers, and increased IR indices. It was shown for the first time that a combination of three factors, male gender, blood concentrations of basal C-peptide, and TG, independently of other indicators describe the variability of the mean size of EAT adipocytes. Increases in TG from 1.2 to 1.4 mM are associated with a larger size of EAT adipocytes and cytokine imbalance. Our data confirm the validity of identifying optimal blood concentrations of TG and non-HDL-C in patients with IHD and coronary atherosclerosis.

METS-IR as an important predictor of neurological impairment severity in patients with severe cerebral infarction: a multicenter study based on the Chinese population.

Insulin resistance (IR) is linked to an increased risk of neurological impairment following a stroke and may contribute to poor neurological prognosis in affected patients. The metabolic score for the insulin resistance index, shortened as the METS-IR, generally serves as a surrogate index for IR. However, its association with the severity of neurological impairment in patients with severe cerebral infarction (CI) in neurological intensive care units (ICU) has not been fully established. Patients with a diagnosis of CI, admitted to the neurological ICUs of Yangzhou University's Affiliated Hospital and Xuzhou Medical University's Affiliated Hospital, were included in the study. A multivariate logistic regression model and restricted cubic splines (RCS) were employed to explore the relationship between the METS-IR index and the severity of neurological impairment in these patients. The predictive capabilities of the METS-IR index and the triglyceride-glucose (TyG) index for outcome measures were compared through the ROC curve. Furthermore, a decision curve analysis was executed, and the integrated discrimination improvement (IDI) index was computed to evaluate the enhancements in predictive performance and clinical utility of various scoring systems with the inclusion of the METS-IR index. Subgroup analysis was conducted regarding age, BMI, and smoking status. The study ultimately included 504 participants. Adjusted logistic regression and RCS results showed that as the METS-IR index increases, the risk of neurological impairment in patients with severe CI consistently grows (P for overall = 0.0146, P-nonlinear: 0.0689). The METS-IR index's predictive capability for neurological impairment (AUC = 0.669) was superior to that of the TyG index (AUC = 0.519). From the study results, the METS-IR index can serve as an important predictor for neurological impairment in ICU patients with severe CI. It can aid in the identification and early intervention of neurological impairment in these patients.

Respiratory muscle strength and oxygenation as predictors of length of hospital stay in patients with COVID-19

The aim was to analyze the prognostic value of ventilation/ gas exchange dissociation markers to predict the treatment length in patients with the acute phase of COVID-19.Materials and methods: The analysis was performed using a database of 384 randomly selected patients from December 2021 to May 2022 with a confirmed diagnosis of the acute phase of COVID-19, aged 61±16 years. Spirometry of maximal inspiratory pressure (MIP) and maximal expiratory pressure (MEP) was performed on 2,2±0,2 day of hospitalization with a portable MicroRPM device (CareFusion, UK) and measurement of surrogate oxygenation index (SpO2/FiO2), ROX index (SpO2/ FiO2)/respiratory rate) was carried out in all patients. Log-regression models were used (STATISTICA 10) to determine cutoff values for these functional variables and their ability to predict the patients length of hospital stay from the date of examination (≤ 7 vs > 7 days).Results: The lowest sensitivity (SE) and specificity (SP) were shown in models using only the respiratory muscle strength markers MIP (SE=54%, SP=70%) and MEP (SE=73%, SP=47%), that were the same in the combination of MIP and MEP (SE=65%, SP=58%). The areas under ROC were equal to 0.6 in all three models. The models based on hypoxia markers had a higher classification power (AUC 0,7) compared to the previous three, and the sensitivity value was higher in the model using the ROX index (SE = 58% and SP = 78%), but the specificity was better in the surrogate oxygenation index model (SE = 48% and SP = 88%). A complex model based on a combination of the two integral hypoxia indices, as well as a diaphragm strength marker (SpO2/FiO2+MIP*ROX+MIP) had the best sensitivity (67%) and specificity (84%), and the area under ROC reached 0.8.Conclusion: Indicators of respiratory muscle strength and oxygenation are suitable markers for combined models and algorithms for predicting hospital length of stay in COVID-19 patients.

Investigation of a surrogate measure-based safety index for predicting injury crashes at signalized intersections

Objectives The paper develops a machine learning-based safety index for classifying traffic conflicts that can be used to estimate the frequency of signalized intersection crashes, with a focus on the more severe ones that result in fatal and severe injury. The number of conflicts in different severity levels categorized by the safety index is used as an explanatory variable for developing statistical models for pro-actively estimating crashes. Methods Video-derived conflicts in different severity levels between left-turning vehicles and opposing through vehicles, a well-recognized severe injury crash typology at signalized intersections, were identified by jointly integrating the indicators of frequency and severity, using an autoencoder neural network integration method to develop anomaly scores. Regression models were then developed to relate crashes at the same intersections to the classified conflicts based on the value of their safety indexes. Cumulative Residual plots were investigated. Finally, equations defining the boundary between consecutive anomaly score levels were developed to facilitate application in practice. Results Regression models for total and fatal plus severe (FSI) crashes utilizing classified extreme conflicts based on anomaly scores were found to outperform the models using total conflicts. The improvement is more pronounced for FSI crashes. The results also suggest that the machine learning integration method can efficiently classify conflicts accurately according to crash severity levels since the higher anomaly score is associated with a higher crash severity level (i.e., FSI). Conclusions The proposed framework represents a methodological advancement in traffic conflict-based estimation of crashes using a machine learning model to classify conflicts by their anomaly scores. For jurisdictions without the resources to develop such a model to classify conflicts for their own datasets, the simple equations defining the boundary between consecutive anomaly score levels could be used as an approximation.

Single and mixed associations of composite antioxidant diet on triglyceride-glucose index

BackgroundAlthough the relationship between oxidative stress and insulin resistance (IR) has been established, the associations of the composite dietary antioxidant index (CDAI) and its components with the surrogate index of insulin resistance (IR), triglyceride-glucose index (TyG), is still not clear.MethodsThis study analyzed the cross-sectional data of the National Health and Nutrition Examination Survey (NHANES) from 2003 to 2018. Multivariate linear regression, weighted quantile sum (WQS) regression, and Bayesian kernel machine regression (BKMR) were used to analyze the associations of the CDAI and its components with the TyG. In addition, subgroup analysis and several sensitivity analyses were conducted.ResultsA total of 14,673 participants with complete data were included, with a median age of 50 years and 7,257 women (49%). Multivariate linear regression showed that after full adjustment, the CDAI was significantly negatively associated with the TyG [β: -0.005, 95% CI: (-0.008, -0.002), p = 0.002]. The model in which six nutrients were mutually corrected showed that vitamin E (per-SD increase) was most strongly associated with the TyG [β: -0.062, 95% CI: (-0.074, -0.050), p < 0.0001]. In the WQS model, the WQS index of the antioxidant diet was negatively associated with the TyG (β: -0.060; P < 0.0001). Similar effects were observed in the BKMR analysis. Notably, in the WQS and BKMR models, vitamin E became the most influential component. In addition, in the subgroup analysis, the association between the CDAI and the TyG in overweight or obese and diabetic populations was significantly weaker.ConclusionAntioxidant diets, especially vitamin E, are significantly negatively correlated with TyG. This study emphasizes the important value of supplementing vitamin E to improve IR. However, patients with poor weight management and diabetes seem to benefit less from antioxidant diets.

Association between metabolic score for insulin resistance and regression to normoglycemia from prediabetes in Chinese adults: A retrospective cohort study.

Metabolic score for insulin resistance (METS-IR) is a surrogate index to estimate insulin sensitivity. The aim of this study was to examine the association between METS-IR and regression to normoglycemia in Chinese adults with prediabetes. A total of 15,415 Chinese adults with prediabetes defined by their fasting blood glucose were included in this retrospective study. The association between METS-IR and regression to normoglycemia from prediabetes was evaluated using the Cox proportional hazards regression model. A Cox proportional hazards regression with cubic spline function was performed to explore the nonlinear association between METS-IR and regression to normoglycemia. Kaplan-Meier curves was used to describe the probability of regression to normoglycemia from prediabetes. In multivariate Cox proportional hazards regression analyses, the increase in METS-IR was independently associated with a reduced probability of regression to normoglycemia from prediabetes (all p < 0.01 in models 1-3). A nonlinear association between METS-IR and the probability of regression to normoglycemia was observed, with an inflection point of 49.3. The hazard ratio on the left side of the inflection point was 0.965 (95% CI 0.953-0.976). Subgroup analyses demonstrated the robustness of our findings. This study demonstrated a negative and nonlinear association between METS-IR and regression to normoglycemia in Chinese adults with prediabetes. When METS-IR is below 49.3, reducing METS-IR could significantly increase the probability of regression to normoglycemia from prediabetes.

Insight into the Predictive Power of Surrogate Diagnostic Indices for Identifying Individuals with Metabolic Syndrome.

This study aimed to assess the diagnostic capability of insulin surrogate measurements in identifying individuals with metabolic syndrome (MetS) and propose applicable indices derived from fasting values, particularly in large study populations. Data were collected from the datasets of the Surveillance of Risk Factors of NCDs in Iran Study (STEPS). MetS was defined based on the National Cholesterol Education Program (NCEP) criteria. Various insulin surrogate indices, including Homeostasis Model Assessment (HOMA), Quantitative Insulin Sensitivity Check Index (QUICKI), Fasting glucose to insulin ratio (FGIR), Reynaud, Reciprocal insulin, McAuley, Metabolic Score for Insulin Resistance (METS-IR), Triglyceride-glucose index (TyG), TG/ HDL-C, TG/ BMI, and TG/ WC ratio were assessed. Receiver Operating Characteristic (ROC) curves were used to assess pathologic conditions and determine the optimal cut-off through the highest score of the Youden index. Also, Area Under the Curve (AUC) values were established for each index totally and according to sex, age, and BMI differences. The study population consisted of 373 individuals (49.9% women; 75.1% middle age, 39.1% obese, and 27.3% overweight), of whom 117 (31.4%) had MetS. The METS-IR (AUC: 0.856; 95% CI: 0.817-0.895), TG/ HDL-C (AUC: 0.820; 95% CI: 0.775-0.886), TyG (AUC: 0.808; 95% CI: 0.759-0.857), and McAuley (AUC: 0.804; 95% CI: 0.757-0.852) indices provided the greatest AUC respectively for detection of MetS. The values of AUC for all the indices were higher in men than women. This trend was consistent after data stratification based on BMI categories, middle age, and senile individuals. The present study indicated that indices of insulin, including METS-IR, TG/HDLC, TyG, and McAuley, have an equal or better capacity in determining the risk of MetS than HOMA-IR, are capable of identifying individuals with MetS and may provide a simple approach for identifying populations at risk of insulin resistance.

Potential relationships between apelin and metabolic-associated indices in transition dairy cows.

Metabolic disorders pose significant challenges in transition dairy cows. Numerous parameters have been investigated in this context, and apelin has recently emerged as a potential metabolic indicator. Accordingly, this study aimed to assess the associations between this hormone and other metabolic parameters. Twenty-two adult Holstein-Friesian dairy cows, 21 days before their expected calving date, were selected for blood sampling and serum separation at four time points: 21 and 10 days before calving and 10 and 21 days after parturition. Serum concentrations of apelin, leptin, insulin, cortisol, T3, T4, non-esterified fatty acids, glucose, total protein, albumin, globulin, aspartate aminotransferase, alanine transaminase, triglycerides, cholesterol, high, low and very low-density lipoproteins, total, direct and indirect bilirubin were measured in these samples. Surrogate indices for insulin resistance, body condition score, and milk production were also evaluated. Throughout the transition period, a significant increase in apelin levels was observed. Various models were employed to identify associations between apelin and the studied metabolic parameters. Notably, significant correlations between apelin and Leptin, Insulin, Cortisol, T3, T4, NEFA, Cholesterol, LDL, VLDL, Total Protein, Albumin, Globulin, Total Bilirubin, Direct Bilirubin and Indirect Bilirubin were observed, with some being immediate while others developed over time. These findings indicate a mutual influence between apelin and specific metabolic indices. Changes in any component of the metabolic profile at one stage can lead to alterations in apelin levels in subsequent stages. The correlations uncovered between apelin and other components of the metabolic profile in transitioning dairy cows offer valuable insights, contributing to a better understanding of the potential effects of apelin on the studied indicators and vice versa.

Which surrogate insulin resistance indices best predict coronary artery disease? A machine learning approach

BackgroundVarious surrogate markers of insulin resistance have been developed, capable of predicting coronary artery disease (CAD) without the need to detect serum insulin. For accurate prediction, they depend only on glucose and lipid profiles, as well as anthropometric features. However, there is still no agreement on the most suitable one for predicting CAD.MethodsWe followed a cohort of 2,000 individuals, ranging in age from 20 to 74, for a duration of 9.9 years. We utilized multivariate Cox proportional hazard models to investigate the association between TyG-index, TyG-BMI, TyG-WC, TG/HDL, plus METS-IR and the occurrence of CAD. The receiver operating curve (ROC) was employed to compare the predictive efficacy of these indices and their corresponding cutoff values for predicting CAD. We also used three distinct embedded feature selection methods: LASSO, Random Forest feature selection, and the Boruta algorithm, to evaluate and compare surrogate markers of insulin resistance in predicting CAD. In addition, we utilized the ceteris paribus profile on the Random Forest model to illustrate how the model’s predictive performance is affected by variations in individual surrogate markers, while keeping all other factors consistent in a diagram.ResultsThe TyG-index was the only surrogate marker of insulin resistance that demonstrated an association with CAD in fully adjusted model (HR: 2.54, CI: 1.34–4.81). The association was more prominent in females. Moreover, it demonstrated the highest area under the ROC curve (0.67 [0.63–0.7]) in comparison to other surrogate indices for insulin resistance. All feature selection approaches concur that the TyG-index is the most reliable surrogate insulin resistance marker for predicting CAD. Based on the Ceteris paribus profile of Random Forest the predictive ability of the TyG-index increased steadily after 9 with a positive slope, without any decline or leveling off.ConclusionDue to the simplicity of assessing the TyG-index with routine biochemical assays and given that the TyG-index was the most effective surrogate insulin resistance index for predicting CAD based on our results, it seems suitable for inclusion in future CAD prevention strategies.

Accessing the relationship between six surrogate insulin resistance indexes and the incidence of rapid kidney function decline and the progression to chronic kidney disease among middle-aged and older adults in China: Results from the China health and retirement longitudinal study

AimsInsulin resistance is closely related to kidney function decline, but which insulin resistance index could better predict rapid kidney function decline (RKFD) remains unclear. We aimed to evaluate the prospective association between six insulin resistance indexes: Chinese Visceral Adiposity Index (CVAI), Lipid Accumulation Product (LAP), Atherogenic Index of Plasma (AIP), triglyceride-glucose (TyG) index, triglyceride-glucose × Body Mass Index (TyGBMI) and triglyceride-glucose × waist circumference (TyGWC) with RKFD and further the progression to chronic kidney disease (CKD). Methods and measurementsData were obtained from the China Health and Retirement Longitudinal Study. Participants with normal kidney function (eGFRcr-cys ≥60 ml/min per 1.73 m2) and ≥45 years old were included at the baseline (year 2011). The eGFR was estimated by a combination of serum creatinine and cystatin C. The primary outcome was RKFD, defined as an annualized decline in eGFRcr-cys of 5 ml/min per 1.73 m2 or more. Secondary outcome was progression to CKD under the condition of RKFD, defined as an annualized decline in eGFRcr-cys of 5 ml/min per 1.73 m2 or more combined with eGFRcr-cys <60 ml/min per 1.73 m2 at the exit visit. Logistic analysis was applied for analysis of the association between six insulin resistance indexes and RKFD or progression to CKD. We use receiver operating characteristic curves to study the predictive performance of six insulin resistance indexes. Subgroup analysis were conducted by diabetes or hypertension status of the participants. ResultsA total of 3899 participants with normal kidney function were included in this study. After a 3.99 years follow-up, 191 of them ended up with RKFD. Among them, 66 participants progressed to CKD. Logistic analysis showed that per SD increase of all the six insulin resistance indexes were significantly associated with the incidence of RKFD (all P < 0.01), among which, TyGWC had the best predictive value for RKFD. There were significant association between per SD increase of CVAI, LAP, TyGBMI and TyGWC with progression to CKD (all P < 0.01), and CVAI had better predictive role than other indexes. In subgroup analysis, we found that the association between insulin resistance indexes and progression to CKD was more significant in subjects with hypertension or without diabetes. However, no significant differences were observed in the RKFD group. ConclusionsIn this study we proved six insulin resistance indexes were predictively associated with RKFD in Chinese with normal renal function over age 45. TyGWC is the best insulin resistance index for predicting RKFD. CVAI is the best index for predicting further progression to CKD.

Association of Triglyceride Glucose-Derived Indices with Recurrent Events Following Atherosclerotic Cardiovascular Disease.

Triglyceride glucose (TyG) and TyG-body mass index (TyG-BMI) are reliable surrogate indices of insulin resistance and used for risk stratification and outcome prediction in patients with atherosclerotic cardiovascular disease (ASCVD). Here, we inserted estimated average glucose (eAG) into the TyG (TyAG) and TyG-BMI (TyAG-BMI) as derived parameters and explored their clinical significance in cardiovascular risk prediction. This was a population-based cohort study of 9,944 Chinese patients with ASCVD. The baseline admission fasting glucose and A1C-derived eAG values were recorded. Cardiovascular events (CVEs) that occurred during an average of 38.5 months of follow-up were recorded. We stratified the patients into four groups by quartiles of the parameters. Baseline data and outcomes were analyzed. Distribution of the TyAG and TyAG-BMI indices shifted slightly toward higher values (the right side) compared with TyG and TyG-BMI, respectively. The baseline levels of cardiovascular risk factors and coronary severity increased with quartile of TyG, TyAG, TyG-BMI, and TyAG-BMI (all P<0.001). The multivariate-adjusted hazard ratios for CVEs when the highest and lowest quartiles were compared from low to high were 1.02 (95% confidence interval [CI], 0.77 to 1.36; TyG), 1.29 (95% CI, 0.97 to 1.73; TyAG), 1.59 (95% CI, 1.01 to 2.58; TyG-BMI), and 1.91 (95% CI, 1.16 to 3.15; TyAG-BMI). The latter two showed statistical significance. This study suggests that TyAG and TyAG-BMI exhibit more information than TyG and TyG-BMI in disease progression among patients with ASCVD. The TyAG-BMI index provided better predictive performance for CVEs than other parameters.

Surrogate indices of insulin resistance using the Matsuda index as reference in adult men-a computational approach.

Overweight and obesity, high blood pressure, hyperglycemia, hyperlipidemia, and insulin resistance (IR) are strongly associated with non-communicable diseases (NCDs), including type 2 diabetes, cardiovascular disease, stroke, and cancer. Different surrogate indices of IR are derived and validated with the euglycemic-hyperinsulinemic clamp (EHC) test. Thus, using a computational approach to predict IR with Matsuda index as reference, this study aimed to determine the optimal cutoff value and diagnosis accuracy for surrogate indices in non-diabetic young adult men. A cross-sectional descriptive study was carried out with 93 young men (ages 18-31). Serum levels of glucose and insulin were analyzed in the fasting state and during an oral glucose tolerance test (OGTT). Additionally, clinical, biochemical, hormonal, and anthropometric characteristics and body composition (DEXA) were determined. The computational approach to evaluate the IR diagnostic accuracy and cutoff value using difference parameters was examined, as well as other statistical tools to make the output robust. The highest sensitivity and specificity at the optimal cutoff value, respectively, were established for the Homeostasis model assessment of insulin resistance index (HOMA-IR) (0.91; 0.98; 3.40), the Quantitative insulin sensitivity check index (QUICKI) (0.98; 0.96; 0.33), the triglyceride-glucose (TyG)-waist circumference index (TyG-WC) (1.00; 1.00; 427.77), the TyG-body mass index (TyG-BMI) (1.00; 1.00; 132.44), TyG-waist-to-height ratio (TyG-WHtR) (0.98; 1.00; 2.48), waist-to-height ratio (WHtR) (1.00; 1.00; 0.53), waist circumference (WC) (1.00; 1.00; 92.63), body mass index (BMI) (1.00; 1.00; 28.69), total body fat percentage (TFM) (%) (1.00; 1.00; 31.07), android fat (AF) (%) (1.00; 0.98; 40.33), lipid accumulation product (LAP) (0.84; 1.00; 45.49), leptin (0.91; 1.00; 16.08), leptin/adiponectin ratio (LAR) (0.84; 1.00; 1.17), and fasting insulin (0.91; 0.98; 16.01). The computational approach was used to determine the diagnosis accuracy and the optimal cutoff value for IR to be used in preventive healthcare.

Impaired Cholesterol Uptake Capacity in Patients with Hypertriglyceridemia and Diabetes Mellitus.

Although low high-density lipoprotein cholesterol (HDL-C) levels are a common metabolic abnormality associated with insulin resistance, their role in cardiovascular risk stratification remains controversial. Recently, we developed a simple, high-throughput, cell-free assay system to evaluate the "cholesterol uptake capacity (CUC)" as a novel concept for HDL functionality. In this study, we assessed the CUC in patients with hypertriglyceridemia and diabetes mellitus. The CUC was measured using cryopreserved serum samples from 285 patients who underwent coronary angiography or percutaneous coronary intervention between December 2014 and May 2019 at Kobe University Hospital. The CUC was significantly lower in diabetic patients (n = 125) than in nondiabetic patients (93.0 vs 100.7 arbitrary units (A.U.), P = 0.002). Patients with serum triglyceride (TG) levels >150 mg/dL (n = 94) also had a significantly lower CUC (91.8 vs 100.0 A.U., P = 0.004). Furthermore, the CUC showed a significant inverse correlation with TG, hemoglobin A1c (Hb A1c), homeostasis model assessment of insulin resistance (HOMA-IR), and body mass index (BMI). Finally, the HDL-C/Apolipoprotein A1 (ApoA1) ratio, calculated as a surrogate index of HDL particle size, was significantly positively correlated with the CUC (r2 = 0.49, P < 0.001), but inversely correlated with TG levels (r2 = -0.30, P < 0.001). The CUC decreased in patients with hypertriglyceridemia and diabetes mellitus, and HDL particle size was a factor defining the CUC and inversely correlated with TG levels, suggesting that impaired CUC in insulin-resistant states was partially due to the shift in HDL towards smaller particles. These findings provide a better understanding of the mechanisms underlying impaired HDL functionality.

Estimating insulin sensitivity and β-cell function from the oral glucose tolerance test: validation of a new insulin sensitivity and secretion (ISS) model.

Efficient and accurate methods to estimate insulin sensitivity (SI) and β-cell function (BCF) are of great importance for studying the pathogenesis and treatment effectiveness of type 2 diabetes (T2D). Existing methods range in sensitivity, input data, and technical requirements. Oral glucose tolerance tests (OGTTs) are preferred because they are simpler and more physiological than intravenous methods. However, current analytical methods for OGTT-derived SI and BCF also range in complexity; the oral minimal models require mathematical expertise for deconvolution and fitting differential equations, and simple algebraic surrogate indices (e.g., Matsuda index, insulinogenic index) may produce unphysiological values. We developed a new insulin secretion and sensitivity (ISS) model for clinical research that provides precise and accurate estimates of SI and BCF from a standard OGTT, focusing on effectiveness, ease of implementation, and pragmatism. This model was developed by fitting a pair of differential equations to glucose and insulin without need of deconvolution or C-peptide data. This model is derived from a published model for longitudinal simulation of T2D progression that represents glucose-insulin homeostasis, including postchallenge suppression of hepatic glucose production and first- and second-phase insulin secretion. The ISS model was evaluated in three diverse cohorts across the lifespan. The new model had a strong correlation with gold-standard estimates from intravenous glucose tolerance tests and insulin clamps. The ISS model has broad applicability among diverse populations because it balances performance, fidelity, and complexity to provide a reliable phenotype of T2D risk.NEW & NOTEWORTHY The pathogenesis of type 2 diabetes (T2D) is determined by a balance between insulin sensitivity (SI) and β-cell function (BCF), which can be determined by gold standard direct measurements or estimated by fitting differential equation models to oral glucose tolerance tests (OGTTs). We propose and validate a new differential equation model that is simpler to use than current models and requires less data while maintaining good correlation and agreement with gold standards. Matlab and Python code is freely available.

Future clinical prospects of C-peptide testing in the early diagnosis of gestational diabetes.

Gestational diabetes is typically diagnosed in the late second or third trimester of pregnancy. It is one of the most common metabolic disorders among expectant mothers, with potential serious short- and long-term complications for both maternal and offspring health. C-peptide is secreted from pancreatic beta-cells into circulation in equimolar amounts with insulin. It is a useful biomarker to estimate the beta-cell function because it undergoes negligible hepatic clearance and consequently it has a longer half-life compared to insulin. Pregnancy induces increased insulin resistance due to physiological changes in hormonal and metabolic homeostasis. Inadequate compensation by islet beta-cells results in hyperglycemia. The standard oral glucose tolerance test at 24-28 wk of gestation sets the diagnosis. Accumulated evidence from prospective studies indicates a link between early pregnancy C-peptide levels and the risk of subsequent gestational diabetes. Elevated C-peptide levels and surrogate glycemic indices at the beginning of pregnancy could prompt appropriate strategies for secondary prevention.

Is liver fibrosis a risk factor for gynecological cancers?

A recent study by Crudele et al. reported on the association between surrogate indices of liver fibrosis and risk of gynecological cancers among dysmetabolic women. To put this study in context, notions regarding sex dimorphism in nonalcoholic fatty liver disease (NAFLD) are discussed. Additionally, meta-analytic reviews regarding the risk of extrahepatic cancers are reviewed. Next, I discuss the relationship of metabolic dysfunction-associated fatty liver disease (MAFLD) with extrahepatic cancers, notably including the breast and cancers of the female reproductive systems in humans. The pathomechanisms potentially accounting for this association include genetics, deregulated sex hormones, chronic subclinical inflammatory state, dysmetabolic milieu, oxidative stress, gut dysbiosis, environmental pollution, and altered immune surveillance.