Optimal Obesity- and Lipid-Related Indices for Predicting Metabolic Syndrome in Chronic Kidney Disease Patients with and without Type 2 Diabetes Mellitus in China

Chronic kidney disease combined with metabolic syndrome is a non-negligible risk factor

Metabolic syndrome (MetS) is a group of conditions characterized by hypertension (HTN), hyperglycaemia or insulin resistance (IR), hyperlipidaemia, and abdominal obesity. MetS is associated with a high incidence of cardiovascular events and mortality and is an independent risk factor for chronic kidney disease (CKD). MetS can cause CKD or accelerate the progression of kidney disease. Recent studies have found that MetS and kidney disease have a cause-and-effect relationship. Patients with CKD, those undergoing kidney transplantation, or kidney donors have a significantly higher risk of developing MetS than normal people. The present study reviewed the possible mechanisms of MetS in patients with CKD, including the disorders of glucose and fat metabolism after kidney injury, IR, HTN and the administration of glucocorticoid and calcineurin inhibitors. In addition, this study reviewed the effect of MetS in patients with CKD on important target organs such as the kidney, heart, brain and blood vessels, and the treatment and prevention of CKD combined with MetS. The study aims to provide strategies for the diagnosis, treatment and prevention of CKD in patients with MetS.

Antioxidant and anti-inflammatory effects of allicin in the kidney of an experimental model of metabolic syndrome

Background

Recent studies have suggested that metabolic syndrome (MS) encompasses a group of risk factors for developing chronic kidney disease (CKD). This work aimed to evaluate the antioxidant and anti-inflammatory effects of allicin in the kidney from an experimental model of MS.

Methods

Male Wistar rats (220-250 g) were used, and three experimental groups (n = 6) were formed: control (C), metabolic syndrome (MS), and MS treated with allicin (16 mg/Kg/day, gastric gavage) (MS+A). MS was considered when an increase of 20% in at least three parameters (body weight, systolic blood pressure (SBP), fasting blood glucose (FBG), or dyslipidemia) was observed compared to the C group. After the MS diagnosis, allicin was administered for 30 days.

Results

Before the treatment with allicin, the MS group showed more significant body weight gain, increased SBP, and FBG, glucose intolerance, and dyslipidemia. In addition, increased markers of kidney damage in urine and blood. Moreover, the MS increased oxidative stress and inflammation in the kidney compared to group C. The allicin treatment prevented further weight gain, reduced SBP, FBG, glucose intolerance, and dyslipidemia. Also, markers of kidney damage in urine and blood were decreased. Further, the oxidative stress and inflammation were decreased in the renal cortex of the MS+A compared to the MS group.

Conclusion

Allicin exerts its beneficial effects on the metabolic syndrome by considerably reducing systemic and renal inflammation as well as the oxidative stress. These effects were mediated through the Nrf2 pathway. The results suggest allicin may be a therapeutic alternative for treating kidney injury induced by the metabolic syndrome risk factors.

Relationship between lipid accumulation product and new-onset diabetes in the Japanese population: a retrospective cohort study

Background

Diabetes has become a global public health problem. Obesity has been established as a risk factor for diabetes. However, it remains unclear which of the obesity indicators (BMI, WC, WhtR, ABSI, BRI, LAP, VAI) is more appropriate for monitoring diabetes. Therefore, the objective of this investigation is to compare the strength of the association of these indicators and diabetes and reveal the relationship between LAP and diabetes.

Methods

15,252 people took part in this research. LAP was quartered and COX proportional risk model was applied to explore the relationship between LAP and new-onset diabetes. Smooth curve fitting was employed to investigate the non-linear link between LAP and diabetes mellitus. Finally, the receiver operating characteristic (ROC) curve was used to evaluate the predictive ability of the aforementioned indicators for diabetes.

Results

After adjusting for confounding factors, multiple linear regression analysis showed that each unit increase in LAP was associated with a 76.8% increase in the risk of developing diabetes (HR=1.768, 95% CI: 1.139 to 2.746, P=0.011). In addition, LAP predicted new-onset diabetes better than other indicators, and the AUC was the largest [HR: 0.713, 95% CI: 0.6806-0.7454, P<0.001, in women; HR: 0.7922, 95% CI: 0.7396-0.8447; P<0.001, in men]. When LAP was used as a lone predictor, its AUC area was largest both men and women. However, after adding classical predictors (FPG, HbA1c, SBP, exercise, age) to the model, the LAP is better than the ABSI, but not better than the other indicators when compared in pairs.

Conclusions

High levels of LAP correlate very strongly with diabetes and are an important risk factor for diabetes, especially in women, those with fatty liver and current smokers. LAP was superior to other indicators when screening for diabetes susceptibility using a single indicator of obesity, both in men and in women. However, when obesity indicators were added to the model together with classical predictors, LAP did not show a significant advantage over other indicators, except ABSI.

Diagnostic value of visceral adiposity index in chronic kidney disease: a meta-analysis

AIMS

Several studies have revealed inconsistencies about the predictive properties of visceral adiposity index (VAI) in identifying chronic kidney disease (CKD). To date, it is unclear whether the VAI is a valuable diagnostic tool for CKD. This study intended to evaluate the predictive properties of the VAI in identifying CKD.

METHODS

The PubMed, Embase, Web of Science, and Cochrane databases were searched for all studies that met our criteria from the earliest available article until November 2022. Articles were assessed for quality using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). The heterogeneity was explored with the Cochran Q test and I² test. Publication bias was detected using Deek's Funnel plot. Review Manager 5.3, Meta-disc 1.4, and STATA 15.0 were used for our study.

RESULTS

Seven studies involving 65,504 participants met our selection criteria and were therefore included in the analysis. Pooled sensitivity (Sen), specificity (Spe), positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR) and area under the curve (AUC) were 0.67 (95%CI: 0.54-0.77), 0.75 (95%CI: 0.65-0.83), 2.7 (95%CI: 1.7-4.2), 0.44 (95%CI: 0.29-0.66), 6 (95%CI:3.00-14.00) and 0.77 (95%CI: 0.74-0.81), respectively. Subgroup analysis indicated that mean age of subjects was the potential source of heterogeneity. The Fagan diagram found that the predictive properties of CKD were 73% when the pretest probability was set to 50%.

CONCLUSIONS

The VAI is a valuable agent in predicting CKD and may be helpful in the detection of CKD. More studies are needed for further validation.

Prediction of MAFLD and NAFLD using different screening indexes: A cross-sectional study in U.S. adults

INTRODUCTION

Metabolic dysfunction-associated fatty liver disease (MAFLD), formerly known as non-alcoholic fatty liver disease (NAFLD), has become the most common chronic liver disease worldwide. We aimed to explore the gender-related association between nine indexes (BMI/WC/VAI/LAP/WHtR/TyG/TyG-BMI/TyG-WC/TyG-WHtR) and MAFLD/NAFLD and examine their diagnostic utility for these conditions.

METHODS

Eligible participants were screened from the 2017-2018 cycle data of National Health and Nutrition Examination Survey (NHANES). Logistic regression and receiver operating characteristic (ROC) curve were used to assess the predictive performance of 9 indexes for MAFLD/NAFLD.

RESULTS

Among the 809 eligible individuals, 478 had MAFLD and 499 had NAFLD. After adjusting for gender, age, ethnicity, FIPR and education level, positive associations with the risk of MAFLD/NAFLD were found for all the nine indexes. For female, TyG-WHtR presented the best performance in identifying MAFLD/NAFLD, with AUC of 0.845 (95% CI = 0.806-0.879) and 0.831 (95% CI = 0.791-0.867) respectively. For male, TyG-WC presented the best performance in identifying MAFLD/NAFLD, with AUC of 0.900 (95% CI = 0.867-0.927) and 0.855 (95% CI = 0.817-0.888) respectively.

CONCLUSION

BMI/WC/VAI/LAP/WHtR/TyG/TyG-BMI/TyG-WC/TyG-WHtR are important indexes to identify the risk of MAFLD and NAFLD.

Predictive ability of obesity- and lipid-related indicators for metabolic syndrome in relatively healthy Chinese adults

BACKGROUND AND OBJECTIVE

Metabolic syndrome (MetS) is an important risk factor for cardiovascular complications and kidney damage. Obesity- and lipid-related indices are closely related to MetS, and different indices have different predictive abilities for MetS. This study aimed to evaluate the predictive value of eight obesity- and lipid-related indicators, namely, body mass index (BMI), lipid accumulation product (LAP), body roundness index (BRI), Chinese visceral adiposity index (CVAI), body adiposity index (BAI), abdominal volume index (AVI), triglyceride glucose index (TYG), and visceral adiposity index (VAI), for MetS.

METHODS

A total of 1,452 relatively healthy people in Beijing were enrolled in 2016, and the correlation between the eight indicators and MetS was analyzed by multivariate logistic regression. The receiver operating characteristic (ROC) curve and the area under the curve (AUC) were used to analyze the predictive ability of the eight indicators for MetS. The Delong test was used to compare the AUC values of the eight indicators. MetS was defined according to the Chinese Guidelines for the Prevention and Treatment of Type 2 Diabetes (2020 edition), the revised National Cholesterol Education Program Adult Treatment Group (NCEP-ATPIII), and the International Diabetes Federation (IDF).

RESULTS

Using these three sets of criteria, LAP, TYG, CVAI, and VAI, which are based on blood lipids, had higher AUC values for MetS prediction than BMI, BRI, AVI, and BAI, which are based on anthropometry. LAP had the highest AUC values of 0.893 (0.874-0.912), 0.886 (0.869-0.903), and 0.882 (0.864-0.899), separately, based on the three sets of criteria.

CONCLUSION

The eight obesity- and lipid-related indicators had screening value for MetS in relatively healthy people, and of the eight indicators, LAP performed the best.