Relationship Between Four Non-Insulin-Based Indexes of Insulin Resistance and Serum Uric Acid in Patients with Type 2 Diabetes: A Cross-Sectional Study

The predictive value of TyG and lipid ratios on the development of complications and hyperuricemia in patients with type 2 diabetes mellitus

Investigate the predictive value of TyG and lipid ratios on the development of complications and HUA in patients with T2DM. A retrospective cross-sectional study involving 9488 T2DM patients was conducted. They were divided into HUA and NUA group base on SUA level and divided into with and without complications groups according to the diagnosis of the endocrinologist. Necessary information and biochemical parameters were recorded during outpatient visit. TyG index and lipid ratios were calculated, and statistical analysis was carried out to correlate the calculated values and HUA using SPSS version 26.0 for Windows. TyG and lipid ratios were significantly higher in T2DM with HUA or with complications than those with NUA or without complications (p < 0.05). Regression analysis adjusting for confounding factors found TyG (adjusted OR = 1.54; 95% CI: 1.31-1.82; p < 0.05), TG/HDL-C (adjusted OR = 1.21; 95% CI: 1.04-1.40; p < 0.05) and TC/HDL (adjusted OR = 1.36; 95% CI: 1.17-1.57; p < 0.05) was risk factor of HUA in T2DM patients. TyG (adjusted OR = 1.21; 95% CI: 1.02-1.44; p < 0.05), TG/HDL (adjusted OR = 1.19; 95% CI: 1.03-1.38; p < 0.05) and Apo A/Apo B (adjusted OR = 1.41; 95% CI: 1.26-1.58; p < 0.05) was risk factor of complications in T2DM patients. TyG, TG/HDL-C, and TC/HDL can be used as early sensitive target in the occurrence of HUA in T2DM patients and TyG was the most influential risk factor. TyG, TG/HDL-C, and Apo A/Apo B can be used as early sensitive target in the occurrence of complications in T2DM patients and Apo A/Apo B was the most influential risk factor.

Construction of a nomogram for early diagnosis of refractory Mycoplasma pneumoniae pneumonia in children

Background

Refractory Mycoplasma pneumoniae pneumonia (RMPP) has a serious, rapid progression that can easily cause a variety of extra-pulmonary complications. Therefore, the early identification of RMPP is crucial. This study aimed to construct and validate a risk prediction model based on clinical manifestations, laboratory blood indicators, and radiological findings to help clinicians identify patients who are at high risk of RMPP.

Methods

We retrospectively analyzed the medical records of 369 children with Mycoplasma pneumoniae pneumonia (MPP) admitted to Xi'an Children's Hospital, China. The demographics, clinical features, laboratory data, and radiological findings between the RMPP group and the general Mycoplasma pneumoniae pneumonia (GMPP) group were compared and subjected to univariate and multivariate logistic regression analyses.

Results

The fever peak and duration of the children in the RMPP group (n=86) were higher and longer compared with those in the GMPP group (n=283) (P<0.05). There was a significant difference in the incidence of lobar pneumonia and pleural effusion in pulmonary imaging between the two groups (P<0.05). Laboratory tests showed that the children with RMPP had lower serum uric acid (SUA) and albumin (ALB) as compared with the GMPP group (P<0.05). White blood cells (WBCs), neutrophil count (NEP), erythrocyte sedimentation rate (ESR), procalcitonin (PCT), C-reactive protein (CRP), and neutrophil-to-lymphocyte ratio (NLR) were higher in the RMPP group (P<0.05). Binary logistic regression analysis showed that the fever duration, pleural effusion, WBC, NEP, lactate dehydrogenase (LDH), CRP, NLR, and SUA levels were independent predictors of RMPP (P<0.05). The receiver operator characteristic (ROC) curve results showed fever duration, WBC, NEP, CRP, LDH, SUA, and NLR had good predictive value. The areas under the curve (AUCs) were 0.861, 0.730, 0.758, 0.837, 0.868, 0.744, and 0.713 and the best cutoff values were 10.50, 10.13, 6.43, 29.45, 370.50, 170.50, and 3.47, respectively. Finally, fever duration of more than 10.5 days, pleural effusion, WBC >10.13×109/L, NEP >6.43×109/L, CRP >29.45 mg/L, LDH >370.50 U/L, NLR >3.47, and SUA <170.5 µmol/mL constructed a prediction model of RMPP. According to internal validation, the mean AUC of the nomogram based on the development dataset was 0.956 [95% confidence interval (CI): 0.937-0.974] with good discrimination ability for predicting RMPP patients. The calibration plot and Hosmer-Lemeshow test (P=0.70) of the prediction model showed good consistency between the predicted probability and actual probability. Decision curve analysis (DCA) showed that the nomogram is clinically useful.

Conclusions

The simple and easy-to-use nomogram can help clinicians, especially primary doctors, to make early diagnoses of RMPP.

Association between triglyceride glucose index and hyperuricemia: a new evidence from China and the United States

Background

Hyperuricemia (HUA) is a glo\bal public health problem. The etiology of HUA is complex and efficient and accurate assessment metrics are still lacking when conducting large-scale epidemiologic screening. The aim of this study was to evaluate the association of the triglyceride glucose (TyG) index, TyG-body mass index (BMI), TyG-waist-to-height ratio (WHtR) with the risk of HUA.

Methods

Based on data collected from the National Health and Nutrition Examination Survey (NHANES) in the United States and the China Health and Aging Longitudinal Study (CHARLS) in China, a total of 14,286 U.S. adults and 4,620 Chinese adults were included in the analysis. The study examined the levels of TyG, TyG-BMI, TyG-WHtR, and TyG-WC. Multivariate logistic regression was utilized to investigate the relationships between these variables and hyperuricemia (HUA), separately. Additionally, the study used restricted cubic splines (RCS) to explore the linear associations of TyG, TyG-BMI, TyG-WHtR, TyG-WC, and HUA, separately.

Results

The NHANES results showed that TyG [Q2, 1.58(1.26, 1.98); Q3, 2.36 (1.94, 2.88); Q4, 3.21 (2.61, 3.94)], TyG-BMI [Q2, 2.14 (1.74, 2.65); Q3, 3.38 (2.74, 4.17); Q4, 6.70 (5.55, 8.02)], TyG-WHtR [Q2, 1.92 (1.56, 2.36); Q3, 3.14 (2.56, 3.85); Q4, 6.28 (5.12, 7.69)], TyG-WC [Q2, 2.32 (1.85, 2.90); Q3, 3.51 (2.84, 4.34); Q4, 7.32 (5.95, 9.02)] were identified as risk factors for hyperuricemia (HUA). Similarly, the CHARLS results, when fully adjusted for covariates, indicated that TyG [Q4, 2.36 (1.08, 5.15)], TyG-BMI [Q3, 2.60 (1.05, 6.41); Q4, 3.70 (1.64, 8.32)], TyG-WHtR (Q4, 2.84 (1.23, 6.55), TyG-WC [Q4, 2.85 (1.23, 6.5)] were also risk factors for HUA. The predictive ability of each indicator for the risk of developing HUA was stronger in women than in men. Furthermore, there was an observed nonlinear relationship between TyG, TyG-BMI, TyG-WHtR, TyG-WC, and HUA in both the NHANES and CHARLS datasets (P-nonlinearity < 0.05).

Conclusion

These findings suggest that TyG, TyG-BMI, TyG-WHtR and TyG-WC are associated with an increased risk of HUA. They are potential indicators for screening HUA status in the general population in China and the United States.

Nonlinear association of triglyceride-glucose index with hyperuricemia in US adults: a cross-sectional study

BACKGROUND

Despite abundant evidence on the epidemiological risk factors of metabolic diseases related to hyperuricemia, there is still insufficient evidence regarding the nonlinear relationship between triglyceride-glucose (TyG) index and hyperuricemia. Thus, the purpose of this research is to clarify the nonlinear connection between TyG and hyperuricemia.

METHODS

From 2011 to 2018, a cross-sectional study was carried out using data from the National Health and Nutrition Examination Survey (NHANES). This study had 8572 participants in all. TyG was computed as Ln [triglycerides (mg/dL) × fasting glucose (mg/dL)/2]. The outcome variable was hyperuricemia. The association between TyG and hyperuricemia was examined using weighted multiple logistic regression, subgroup analysis, generalized additive models, smooth fitting curves, and two-piecewise linear regression models.

RESULTS

In the regression model adjusting for all confounding variables, the OR (95% CI) for the association between TyG and hyperuricemia was 2.34 (1.70, 3.21). There is a nonlinear and reverse U-shaped association between TyG and hyperuricemia, with a inflection point of 9.69. The OR (95% CI) before the inflection point was 2.64 (2.12, 3.28), and after the inflection point was 0.32 (0.11, 0.98). The interaction in gender, BMI, hypertension, and diabetes analysis was statistically significant.

CONCLUSION

Additional prospective studies are required to corroborate the current findings, which indicate a strong positive connection between TyG and hyperuricemia among adults in the United States.

The prognostic impact of insulin resistance surrogates in patients with acute myocardial infarction with and without type 2 diabetes

BACKGROUND

Cardiovascular disease is the major cause of morbidity and mortality, particularly in type 2 diabetes mellitus (T2DM). Novel markers of insulin resistance and progression of atherosclerosis include the triglycerides and glucose index (TyG index), the triglycerides and body mass index (Tyg-BMI) and the metabolic score for insulin resistance (METS-IR). Establishing independent risk factors for in-hospital death and major adverse cardiac and cerebrovascular events (MACCE) in patients with myocardial infarction (MI) remains critical. The aim of the study was to assess the risk of in-hospital death and MACCE within 12 months after ST-segment elevation myocardial infarction (STEMI) and non-ST-segment elevation myocardial infarction (NSTEMI) in patients with and without T2DM based on TyG index, Tyg-BMI and METS-IR.

METHODS

Retrospective analysis included 1706 patients with STEMI and NSTEMI hospitalized between 2013 and 2021. We analyzed prognostic value of TyG index, Tyg-BMI and METS-IR for in-hospital death and MACCE as its components (death from any cause, MI, stroke, revascularization) within 12 months after STEMI or NSTEMI in patients with and without T2DM.

RESULTS

Of 1706 patients, 58 in-hospital deaths were reported (29 patients [4.3%] in the group with T2DM and 29 patients [2.8%] in the group without T2DM; p = 0.1). MACCE occurred in 18.9% of the total study population (25.8% in the group with T2DM and 14.4% in the group without T2DM; p < 0.001). TyG index, Tyg-BMI and METS-IR were significantly higher in the group of patients with T2DM compared to those without T2DM (p < 0.001). Long-term MACCE were more prevalent in patients with T2DM (p < 0.001). The area under the ROC curve (AUC-ROC) for the prediction of in-hospital death and the TyG index was 0.69 (p < 0.001). The ROC curve for predicting in-hospital death based on METS-IR was 0.682 (p < 0.001). The AUC-ROC values for MACCE prediction based on the TyG index and METS-IR were 0.582 (p < 0.001) and 0.57 (p < 0.001), respectively.

CONCLUSIONS

TyG index was an independent risk factor for in-hospital death in patients with STEMI or NSTEMI. TyG index, TyG-BMI and METS-IR were not independent risk factors for MACCE at 12 month follow-up. TyG index and METS-IR have low predictive value in predicting MACCE within 12 months after STEMI and NSTEMI.

Assessing the predictive value of insulin resistance indices for metabolic syndrome risk in type 2 diabetes mellitus patients

Limited research has explored the effectiveness of insulin resistance (IR) in forecasting metabolic syndrome (MetS) risk, especially within the Iranian population afflicted with type 2 diabetes mellitus (T2DM). The present investigation aimed to assess the efficacy of IR indices in predicting the risk of MetS among T2DM patients. Convenient sampling was utilized to select four hundred subjects with T2DM. Metabolic factors and IR indices, including the Waist Circumference-Triglyceride Index (WTI), Triglyceride and Glucose Index (TyG index), the product of TyG index and abdominal obesity indices, and the Metabolic Score for Insulin Resistance (METS-IR), were evaluated. Logistic regression, coupled with modeling, was employed to explore the risk of MetS. The predictive performance of the indices for MetS stratified by sex was evaluated via receiver operating characteristic (ROC) curve analysis and estimation of the area under the curve (AUC) values. The TyG-Waist Circumference (TyG-WC) index exhibited the largest AUCs in both males (0.91) and females (0.93), while the TyG-Body Mass Index (TyG-BMI) demonstrated the smallest AUCs (0.77 in males and 0.74 in females). All indices significantly predicted the risk of MetS in all subjects before and after adjustment (p < 0.001 for all). The TyG-WC index demonstrated the highest odds ratios for MetS (8.06, 95% CI 5.41-12.00). In conclusion, all IR indices assessed in this study effectively predicted the risk of MetS among Iranian patients with T2DM, with the TyG-WC index emerging as the most robust predictor across both genders.

The association between hyperuricemia and insulin resistance surrogates, dietary- and lifestyle insulin resistance indices in an Iranian population: MASHAD cohort study

BACKGROUND

Previous studies have reported insulin resistance (IR) to be associated with hyperuricemia. In this study, we aimed to assess the possible associations between the empirical dietary index for IR (EDIR), the empirical lifestyle index for IR (ELIR), and non-insulin-based surrogates (triglyceride-glucose (TyG) index, triglyceride-to-high-density-lipoprotein-cholesterol (TG/HDL-C) ratio, metabolic score for insulin resistance (METS-IR) and TyG with body mass index (TyG-BMI)) and hyperuricemia in an Iranian population.

METHODS

In this cross-sectional study, 6457 participants aged 35-65 years were recruited as part of the MASHAD cohort study. EDIR and ELIR were calculated using dietary intakes, body mass index, and physical activity information. Insulin resistance surrogates including TyG, TyG-BMI, TG/HDL-C, and METS-IR were calculated for all participants. Hyperuricemia was defined as serum uric acid ≥ 7 mg/dl in men or ≥ 6 mg/dl in women. Multivariable logistic regression models were applied to determine the association between indexes of IR and hyperuricemia.

RESULTS

The mean ELIR and IR surrogates (TyG, TyG-BMI, TG/ HDL, and METS-IR) were significantly higher in subjects with hyperuricemia compared to non-hyperuricemic subjects (p < 0.001). After adjusting for confounding variables, the association between hyperuricemia and EDIR was not significant, but ELIR had a significant association in all models (p < 0.001). All four IR surrogates (TyG, TyG-BMI, TG/ HDL, and METS-IR) showed a significant association with hyperuricemia (p < 0.001).

CONCLUSION

There was a significant association between indexes of insulin resistance: TyG, TyG-BMI, TG/HDL-c, METS-IR, and ELIR with hyperuricemia, in a population sample from northeastern Iran.

Insulin resistance is associated with the presence and severity of retinopathy in patients with type 2 diabetes

BACKGROUND

To assess the relationship between novel insulin resistance (IR) indices and the presence and severity of diabetic retinopathy (DR) in patients with type 2 diabetes.

METHODS

This is a cross-sectional study involving 2211 patients. The study outcomes were DR events. The study exposures were IR indices including estimated glucose disposal rate (eGDR), natural logarithm of glucose disposal rate (lnGDR), metabolic insulin resistance score (METS-IR), triglyceride glucose index-body mass index (TyG-BMI), triglyceride glucose index-waist-to-hip ratio (TyG-WHR), and triglyceride/high-density lipoprotein cholesterol(TG/HDL-c ratio). We used binary and multivariate ordered logistic regression models to estimate the association between different IR indices and the presence and severity of DR. Subject work characteristic curves were used to assess the predictive power of different IR indices for DR.

RESULTS

DR was present in 25.4% of participants. After adjusting for all covariates, per standard deviation (SD) increases in eGDR (ratio [OR] 0.38 [95% CI 0.32-0.44]), lnGDR (0.34 [0.27-0.42]) were negatively associated with the presence of DR. In contrast, per SD increases in METS-IR (1.97 [1.70-2.28]), TyG-BMI (1.94 [1.68-2.25]), TyG-WHR (2.34 [2.01-2.72]) and TG/HDL-c ratio (1.21 [1.08-1.36]) were positively associated with the presence of DR. eGDR was strongly associated with severity of DR. Of all variables, eGDR had the strongest diagnostic value for DR (AUC = 0.757).

CONCLUSIONS

Of the six IR indices, eGDR was significantly associated with the presence and severity of DR in patients with type 2 diabetes. eGDR has a good predictive value for DR. Thus, eGDR maybe a stronger marker of DR.

What is the relationship between serum uric acid level and insulin resistance?: A case-control study

Diabetes, arises from either an absolute or relative insufficiency of insulin or insulin resistance of peripheral tissues. For assessing long-term blood glucose concentration and insulin resistance, the utilization of glycosylated hemoglobin (HbA1c) and the Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) is widespread. Insulin resistance can lead to hyperuricemia by reducing the kidney ability to excrete urate, thus increasing sodium reabsorption. The aim of this study was to investigate the possible relationship between serum uric acid levels and insulin resistance. This was a retrospective case-control study. A total of 2530 applications in 2-year time were included in the study. Patient, known hypertension status, fasting plasma glucose, insulin, uric acid, HDL, low-density lipoprotein (LDL), triglyceride/Tg, HbA1c laboratory values and Tg/HDL ratio were examined. A statistically significant difference existed in the median uric acid values between the insulin-resistant and insulin-sensitive groups (P < .001). Additionally, a weak positive statistical correlation was identified between uric acid and HOMA-IR values (R = 0.299; P < .001) and uric acid and Tg/HDL values (R = 0.357; P < .001). This study concludes that there is a positive correlation between serum uric acid levels and insulin resistance.

Insulin resistance surrogate markers and risk of hyperuricemia among patients with and without coronary artery disease: a cross-sectional study

Background

Although emerging evidence emphasizes the associations between both insulin resistance and hyperuricemia with coronary artery disease (CAD) risk, no definite relationship has yet been established. In this respect, time-efficient and affordable methods to estimate insulin resistance (IR) status, and to predict risk of hyperuricemia, are needed. Thus, the goal of this investigation was to examine the associations between IR, as assessed by novel surrogate markers [triglyceride-glucose (TyG) and TyG-body mass index (TyG-BMI)], and risk of hyperuricemia in patients with and without diagnosed CAD.

Methods

This cross-sectional study used data from the medical records of 1,170 patients who were referred to the cardiology outpatient clinic. Medical records, anthropometrics, and serum analytes were determined at the initial visit. Hyperuricemia was defined as serum uric acid ≥ 5.6 mg/dL. IR was estimated through surrogate markers (TyG and TyG-BMI). Multiple regression analysis was performed to assess the relationship between these indices and odds of hyperuricemia among patients with and without CAD.

Results

Overall, 814 angiographically-confirmed CAD cases (mean age (SD) = 52 (8)yrs) were compared with 356 patients without CAD (mean age (SD) = 48 (8)yr). There were positive associations between TyG and TyG-BMI indices and odds of hyperuricemia in CAD patients after controlling for confounders (adjusted odds ratio (aOR) = 1.60; 95%CI: 1.02-2.51; p-value = 0.036; and aOR = 1.83; 95%CI: 1.24-2.70; p-value = 0.002, third tertiles for TYG and TYG-BMI, respectively).

Conclusion

The present findings suggest that higher levels of the IR surrogate markers, TyG and TyG-BMI, are associated with higher odds of hyperuricemia in patients with CAD. However, given the cross-sectional design of this study, the sensitivity and specificity of these novel markers could not be determined for confirming the diagnosis of IR and hyperuricemia, further studies are needed to determine such outcomes and to confirm the current findings.

Relationship between baseline and changed serum uric acid and the incidence of type 2 diabetes mellitus: a national cohort study

Objective

To explore the correlation between baseline serum uric acid (SUA) and SUA changes with the incidence of type 2 diabetes mellitus (T2DM) among middle-aged and older individuals.

Method

Binary logistic regression was used to calculate the odds ratio (ORs) and 95% confidence intervals (CIs) of the effects of baseline and changes in SUA on the incidence of T2DM. Stratified analysis was conducted based on sex, and the SUA levels were classified into four quartiles to assess the effect of baseline and relative changes in SUA on the incidence of T2DM. Furthermore, interaction analysis was performed between body mass index (BMI) and SUA, age and SUA, and sex and SUA.

Results

In the cohort study, the highest quartiles of SUA were significantly correlated with an increased incidence of T2DM among females in model 1 [OR = 2.231 (1.631, 3.050)], model 2 [OR = 2.090 (1.523, 2.867)], model 3 [OR = 2.075 (1.511, 2.849)], and model 4 [OR = 1.707 (1.234, 2.362)]. The highest quartiles of SUA had a statistically significant effect on the incidence of T2DM among all participants in model 1 [OR = 1.601 (1.277, 2.008)], model 2 [OR = 1.519 (1.204, 1.915)], model 3 [OR = 1.597 (1.257, 2.027)], and model 4 [OR = 1.380 (1.083, 1.760)]. Regarding the relative change of SUA, the highest quantiles of SUA were significantly correlated with an increased incidence of T2DM among females in model 1 [OR = 1.409 (1.050, 1.890)], model 2 [OR = 1.433 (1.067, 1.926)], and model 3 [OR = 1.420 (1.056, 1.910)], and there was a statistically significant correlation with incident T2DM among all participants in model 4 [OR = 1.346 (1.079, 1.680)] after adjusting for all covariates. However, there was no significant correlation between baseline, relative, and absolute changes in SUA and the incidence of T2DM among males. The interaction analysis demonstrated that sex, BMI, and the relative changes in SUA had a combined effect on the incidence of T2DM, while age and the changes in SUA had a joint effect on the incidence of T2DM only in females.

Conclusion

There was a positive association between SUA and the incidence of T2DM for all participants. However, significant sex differences in incidence were observed only in women, not men.

The potential relationship of coronary artery disease and hyperuricemia: A cardiometabolic risk factor

Coronary arterial disease (CAD) is the leading cause of mortality in the world. Hyperuricemia has recently emerged as a novel independent risk factor of CAD, in addition to the traditional risk factors such as hyperlipidemia, smoking, and obesity. Several clinical studies have shown that hyperuricemia is strongly associated with the risk, progression and poor prognosis of CAD, as well as verifying an association with traditional CAD risk factors. Uric acid or enzymes in the uric acid production pathway are associated with inflammation, oxidative stress, regulation of multiple signaling pathways and the renin-angiotensin-aldosterone system (RAAS), and these pathophysiological alterations are currently the main mechanisms of coronary atherosclerosis formation. The risk of death from CAD can be effectively reduced by the uric acid-lowering therapy, but the interventional treatment of uric acid levels in patients with CAD remains controversial due to the diversity of co-morbidities and the complexity of causative factors. In this review, we analyze the association between hyperuricemia and CAD, elucidate the possible mechanisms by which uric acid induces or exacerbates CAD, and discuss the benefits and drawbacks of uric acid-lowering therapy. This review could provide theoretical references for the prevention and management of hyperuricemia-associated CAD.

Association between triglyceride glucose-body mass index and cardiovascular outcomes in patients undergoing percutaneous coronary intervention: a retrospective study

BACKGROUND

The triglyceride glucose-body mass index (TyG-BMI index) has been considered a reliable surrogate measure of insulin resistance; however, its ability to predict the incidence of cardiovascular disease in individuals with coronary artery disease (CAD) remains uncertain. The aim of this study was to demonstrate the correlation between the TyG-BMI index and cardiovascular incidence.

METHODS

A total of 2533 consecutive participants who underwent percutaneous coronary intervention (PCI) and drug-eluting stent (DES) implantation were included. Data from 1438 patients was analyzed in the study. The endpoint was defined as a composite of acute myocardial infarction, repeat revascularization, stroke, and all-cause mortality (major adverse cardiac and cerebrovascular events, MACCEs) at 34-month follow-up. The formula for calculating the TyG-BMI index is ln [fasting triglyceride (mg/dL) × fasting blood glucose (mg/dL)/2] × BMI.

RESULTS

Among the 1438 participants, 195 incident patient cases of MACCEs were ascertained. The incidence of MACCEs showed no statistically significant differences in the TyG-BMI index tertiles in the overall population. Further exploratory subgroup analysis and multivariable logistic regression analysis revealed a linear relationship between the TyG-BMI index (per 1 SD increased) and MACCEs in the elderly patients (OR = 1.22, 95% CI 1.011-1.467, p = 0.038) and in the female patients (OR = 1.33, 95% CI 1.004-1.764, p = 0.047). The addition of the TyG-BMI index to traditional risk factor models in elderly and female patients did not improve risk prediction for MACCEs.

CONCLUSION

A higher TyG-BMI index was proportionally related to an increased incidence of MACCEs in the elderly or female patients. However, the inclusion of the TyG-BMI index did not provide better predictive performance for MACCEs in the elderly, specifically in female patients.

Association of hyperuricemia with cardiovascular diseases: current evidence

The aim of the present study is to present a historical and unified perspective on the association of serum uric acid (SUA) in the cause of cardiovascular diseases (CVDs). The association of hyperuricemia (HUC) with CVD begun to be appreciated in the middle 1950s and early 1990s when clinical evidence was shown on the association of HUC with CVD. However, this association was disputed by several investigators including the Framingham group and by professional societies, like the American Heart Association and the American Society of Hypertension. This dispute was weakened or reversed by later studies, which showed a positive association of HUC with CVD, CHD, HF, CKD, and stroke, mediated by several risk factors, both molecular such as, oxidative stress, inflammatory stress, insulin resistance, and endothelial dysfunction, as well as clinical factors such as, atherosclerosis, hypertension, metabolic syndrome, and type 2 diabetes mellitus. The great majority of recent studies show a positive association of HUC with CVDs, and CKD. However, the cutoff of the damaging levels of SUA have not been established as yet. The European Society of Hypertension (ESH) Treatment Guidelines have proposed a cutoff level of SUA for CVD > 7 mg/dl for men and > 6 mg/dl for women. In contrast, the URRAH study has shown a SUA level of 4.7 mg/dl for all-cause mortality and 5.6 mg/dl for CV mortality. These levels are lower than the SUA levels proposed by the ESH, which are consistent with HUC. For a better understanding of this association, a Medline search of the English literature was conducted between 2015 and 2022 and 44 pertinent papers were selected. These papers together with collateral literature will be discussed in this review.

Simple Method to Predict Insulin Resistance in Children Aged 6-12 Years by Using Machine Learning

BACKGROUND

Due to the increasing insulin resistance (IR) in childhood, rates of diabetes and cardiovascular disease may rise in the future and seriously threaten the healthy development of children. Finding an easy way to predict IR in children can help pediatricians to identify these children in time and intervene appropriately, which is particularly important for practitioners in primary health care.

PATIENTS AND METHODS

Seventeen features from 503 children 6-12 years old were collected. We defined IR by HOMA-IR greater than 3.0, thus classifying children with IR and those without IR. Data were preprocessed by multivariate imputation and oversampling to resolve missing values and data imbalances; then, recursive feature elimination was applied to further select features of interest, and 5 machine learning methods-namely, logistic regression (LR), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), and gradient boosting with categorical features support (CatBoost)-were used for model training. We tested the trained models on an external test set containing information from 133 children, from which performance metrics were extracted and the optimal model was selected.

RESULTS

After feature selection, the numbers of chosen features for the LR, SVM, RF, XGBoost, and CatBoost models were 6, 9, 10, 14, and 6, respectively. Among them, glucose, waist circumference, and age were chosen as predictors by most of the models. Finally, all 5 models achieved good performance on the external test set. Both XGBoost and CatBoost had the same AUC (0.85), which was highest among those of all models. Their accuracy, sensitivity, precision, and F1 scores were also close, but the specificity of XGBoost reached 0.79, which was significantly higher than that of CatBoost, so XGBoost was chosen as the optimal model.

CONCLUSION

The model developed herein has a good predictive ability for IR in children 6-12 years old and can be clinically applied to help pediatricians identify children with IR in a simple and inexpensive way.

Association between the metabolic score for insulin resistance (METS-IR) and estimated glomerular filtration rate (eGFR) among health check-up population in Japan: A retrospective cross-sectional study

AIM

This study aimed to investigate the relationship between a new metric-metabolic score for insulin resistance (METS-IR)-and estimated glomerular filtration rate (eGFR) among Japanese participants who underwent health check-ups.

METHODS

We conducted a cross-sectional study that involved participants in a medical health screening program, which was conducted at the Medical Health Check-up Center in Japan. This retrospective study examined the relationship between METS-IR and eGFR among 881 individuals that joined the program between March 1, 2004, and December 31, 2012. Covariates consisted of serum laboratory tests and lifestyle questionnaires. Multivariate linear regression analysis was used to explore the association between METS-IR and eGFR. In addition, subgroup and interaction analyses were done based on age, sex, body mass index (BMI), alcohol use, smoking status, and hyperuricemia.

RESULTS

A total of 881 individuals participated in this study. High METS-IR was highly linked with reduced eGFR (adjusted β = -5.04, 95% confidence interval (CI): -7.65 to -2.43), while METS-IR was utilized as a categorical variable inside the multiple regression analysis. A decrease in eGFR of 2.54 units was reported for every 10-unit rise in METS-IR (adjusted β = -2.54, 95% CI: -4.04 to -1.05, P-value = 0.001). Stratified analysis suggested no marked interaction between METS-IR and eGFR across age, sex, BMI, and alcohol consumption groups. However, there was an indication of interaction between METS-IR level, smoking status (P-value = 0.001), and uric level (P-value = 0.011) on eGFR decrease.

CONCLUSIONS

METS-IR is remarkably associated with eGFR among the participants who underwent health check-ups in Gifu, Japan. Although more studies are required to prove it, METS-IR could be applied as a monitoring index for early screening, primary prevention, and diagnostic and treatment management strategies for chronic kidney disease.

Relationship of the metabolic score for insulin resistance and the risk of stroke in patients with hypertension: A cohort study

BACKGROUND

The current status of the dose-response relationship between the metabolic score for insulin resistance (METS-IR) and new-onset stroke in hypertensive patients and its subtypes is unclear. This study aimed to determine the association between METS-IR and incident stroke and its subtypes within a cohort of Chinese hypertensive patients.

METHODS

A total of 14032 hospitalized patients with hypertension from January 1, 2010, to December 31, 2021, were included in this retrospective cohort study. Cox models and restricted cubic splines were applied to determine the association between METS-IR and the risk of stroke.

RESULTS

During a median follow-up of 4.80 years, 1067 incident stroke cases occurred. Patients in the highest quartile group of METS-IR levels exhibited a higher risk of stroke (HR, 1.80; 95% CI, 1.50-2.17) and ischemic stroke (HR, 1.96; 95% CI, 1.60-2.42) than those in the lowest quartile group. However, no significant associations were observed between METS-IR and the risk of hemorrhagic stroke. Restricted cubic spline analysis suggested a nearly J-shaped association between METS-IR and risk of stroke and ischemic stroke (P for nonlinearity < 0.001). METS-IR did produce a significant improvement in the C statistic when added to the basic model (from 0.637 to 0.664, P < 0.001). Notably, the addition of METS-IR to the basic model resulted in a significant improvement in predicting incident total stroke and ischemic stroke.

CONCLUSIONS

This cohort study suggests a relationship between METS-IR and the risk of stroke and ischemic stroke. Further studies are required to elucidate the underlying mechanisms.

Association Between Triglyceride-Glucose Index and Serum Uric Acid Levels: A Biochemical Study on Anthropometry in Non-Obese Type 2 Diabetes Mellitus Patients

PURPOSE

The triglyceride-glucose index (TyG) is positively correlated with serum uric acid (SUA) in patients with type 2 diabetes mellitus (T2DM). However, whether this relationship exists in non-obese T2DM patients remains unknown. The study investigated the relationship between TyG and SUA in Chinese non-obese T2DM patients and examined the prognostic value of TyG in hyperuricemia (HUA).

PATIENTS AND METHODS

In total, 719 T2DM patients who were not obese were enrolled from among those who visited the Hebei General Hospital. The patients were categorized into groups according to their SUA levels. The relationship between TyG and clinical parameters was examined through correlation analysis. To consider covariates and examine the independent impact of TyG on HUA, logistic regression was performed. The receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic value of TyG and homeostasis model assessment of insulin resistance (HOMA-IR) for HUA.

RESULTS

The HUA prevalence was 12.10%. TyG was statistically different among the four SUA groups, with lower TyG levels in the Q1, Q2, and Q3 groups than that in the Q4 group. TyG was positively correlated with SUA (r = 0.176, P < 0.001). Logistic regression exhibited that TyG and SUA were independently correlated (OR = 2.427, 95% CI = 1.134-5.195, P = 0.022) even after adjustment for confounding factors. The ROC curve showed that the predictive value of TyG for HUA was higher than that of HOMA-IR (AUROC = 0.613, P = 0.001).

CONCLUSION

TyG was positively correlated with SUA in non-obese T2DM patients. TyG may better predict HUA in non-obese T2DM patients than HOMA-IR.