Association between weight status, metabolic syndrome, and chronic kidney disease among middle-aged and elderly Chinese

Effects of Metabolic Syndrome and Its Components on Chronic Kidney Disease and Renal Function: A Two-Sample Mendelian Randomization Study

Objective: The association of metabolic syndrome (MetS) and its components with chronic kidney disease (CKD) and renal function remains controversial in observational studies. To comprehensively investigate the association between MetS and its components with CKD and renal function, a Mendelian randomization (MR) study was performed. Methods: The inverse variance weighting (IVW) of random effects was used as the main estimation method, while MR-Egger and weighted median analysis results were used for auxiliary judgments. Cochran's Q test, MR-Egger intercept test, leave-one-out analysis, and funnel plots were used to assess heterogeneity and pleiotropy. Results: The MR analyses of genetically predicted MetS and its components' association with CKD risk and renal function showed the following causal associations: hypertension with CKD risk; MetS and obesity with increased blood urea nitrogen and decreased estimated glomerular filtration rate based on cystatin C; hypertension and diabetes with increased urine albumin-creatinine ratio and increased risk of microalbuminuria; and CKD with increased triglyceride. Conclusion: Based on genetic data, this study demonstrated an association between hypertension and CKD risk and a causal association between other MetS components and renal function. The early diagnosis and prevention of MetS and its components might be essential for CKD management.

Metabolic phenotypes and risk of end-stage kidney disease in patients with type 2 diabetes

Background

Obesity often initiates or coexists with metabolic abnormalities. This study aimed to investigate the pathological characteristics and the independent or mutual relations of obesity and metabolic abnormalities with end-stage kidney disease (ESKD) in patients with type 2 diabetes (T2D) and associated diabetic kidney disease (DKD).

Methods

A total of 495 Chinese patients with T2D and biopsy-confirmed DKD between 2003 and 2020 were enrolled in this retrospective study. The metabolic phenotypes were based on the body weight index (BMI)-based categories (obesity, BMI ≥ 25.0 kg/m²) and metabolic status (metabolically unhealthy status, ≥ 1 criterion National Cholesterol Education Program Adult Treatment Panel III (NCEP/ATP III) excluding waist circumference and hyperglycemia) and were categorized into four types: metabolically healthy non-obesity (MHNO), metabolically healthy obesity (MHO), metabolically unhealthy non-obesity (MUNO), and metabolically unhealthy obesity (MUO). The pathological findings were defined by the Renal Pathology Society classification. Cox proportional hazards models were used to estimate hazard ratios (HRs) for ESKD.

Results

There are 56 (11.3%) MHNO patients, 28 (5.7%) MHO patients, 176 (35.6%) MUNO patients, and 235 (47.5%) MUO patients. The high prevalence of the Kimmelstiel-Wilson nodule and severe mesangial expansion were associated with obesity, whereas severe IFTA was related to metabolically unhealthy status. In the multivariate analysis, the adjusted HR (aHR) was 2.09 [95% confidence interval (CI) 0.99-4.88] in the MHO group, 2.16 (95% CI 1.20-3.88) in the MUNO group, and 2.31 (95% CI 1.27-4.20) in the MUO group compared with the MHNO group. Furthermore, the presence of obesity was insignificantly associated with ESKD compared with non-obese patients (aHR 1.22, 95% CI 0.88-1.68), while the metabolically unhealthy status was significantly associated with ESKD compared to the metabolically healthy status in the multivariate analysis (aHR 1.69, 95% CI 1.10-2.60).

Conclusion

Obesity itself was insignificantly associated with ESKD; however, adding a metabolically unhealthy status to obesity increased the risk for progression to ESKD in T2D and biopsy-proven DKD.

High Fructose Corn Syrup Accelerates Kidney Disease and Mortality in Obese Mice with Metabolic Syndrome

The presence of obesity and metabolic syndrome is strongly linked with chronic kidney disease (CKD), but the mechanisms responsible for the association are poorly understood. Here, we tested the hypothesis that mice with obesity and metabolic syndrome might have increased susceptibility to CKD from liquid high fructose corn syrup (HFCS) by favoring the absorption and utilization of fructose. We evaluated the pound mouse model of metabolic syndrome to determine if it showed baseline differences in fructose transport and metabolism and whether it was more susceptible to chronic kidney disease when administered HFCS. Pound mice have increased expression of fructose transporter (Glut5) and fructokinase (the limiting enzyme driving fructose metabolism) associated with enhanced fructose absorption. Pound mice receiving HFCS rapidly develop CKD with increased mortality rates associated with intrarenal mitochondria loss and oxidative stress. In pound mice lacking fructokinase, the effect of HFCS to cause CKD and early mortality was aborted, associated with reductions in oxidative stress and fewer mitochondria loss. Obesity and metabolic syndrome show increased susceptibility to fructose-containing sugars and increased risk for CKD and mortality. Lowering added sugar intake may be beneficial in reducing the risk for CKD in subjects with metabolic syndrome.

Prevalence and Mortality Risk of Neurological Disorders during the COVID-19 Pandemic: An Umbrella Review of the Current Evidence

INTRODUCTION

Coronavirus disease 2019 (COVID-19), a global pandemic, has infected approximately 10% of the world's population. This comprehensive review aimed to determine the prevalence of various neurological disorders in COVID-19 without overlapping meta-analysis errors.

METHODS

We searched for meta-analyses on neurological disorders following COVID-19 published up to March 14, 2023. We obtained 1,184 studies, of which 44 meta-analyses involving 9,228,588 COVID-19 patients were finally included. After confirming the forest plot of each study and removing overlapping individual studies, a re-meta-analysis was performed using the random-effects model.

RESULTS

The summarized combined prevalence of each neurological disorder is as follows: stroke 3.39% (95% confidence interval, 1.50-5.27), dementia 6.41% (1.36-11.46), multiple sclerosis 4.00% (2.50-5.00), epilepsy 5.36% (-0.60-11.32), Parkinson's disease 0.67% (-1.11-2.45), encephalitis 0.66% (-0.44-1.77), and Guillain-Barré syndrome 3.83% (-0.13-7.80). In addition, the mortality risk of patients with comorbidities of COVID-19 is as follows: stroke OR 1.63 (1.23-2.03), epilepsy OR 1.71 (1.00-2.42), dementia OR 1.90 (1.31-2.48), Parkinson's disease OR 3.94 (-2.12-10.01).

CONCLUSION

Our results show that the prevalence and mortality risk may increase in some neurological diseases during the COVID-19 pandemic. Future studies should elucidate the precise mechanisms for the link between COVID-19 and neurological diseases, determine which patient characteristics predispose them to neurological diseases, and consider potential global patient management.

The Effect of Metabolic Syndrome and Its Individual Components on Renal Function: A Meta-Analysis

BACKGROUND

Observational studies have reported inconsistent findings in the relationship between metabolic syndrome (MetS), its components, and loss of renal function, mainly including eGFR decline, new-onset CKD, and ESRD. This meta-analysis was performed to investigate their potential associations.

METHODS

PubMed and EMBASE were systematically searched from their inception to 21 July 2022. Observational cohort studies in English assessing the risk of renal dysfunction in individuals with MetS were identified. Risk estimates and their 95% confidence intervals (CIs) were extracted and pooled using the random-effects approach.

RESULTS

A total of 32 studies with 413,621 participants were included in the meta-analysis. MetS contributed to higher risks of renal dysfunction (RR = 1.50, 95% CI = 1.39-1.61) and, specifically, rapid decline in eGFR (RR 1.31, 95% CI 1.13-1.51), new-onset CKD (RR 1.47, 95% CI 1.37-1.58), as well as ESRD (RR 1.55, 95% CI 1.08-2.22). Moreover, all individual components of MetS were significantly associated with renal dysfunction, while elevated BP conveyed the highest risk (RR = 1.37, 95% CI = 1.29-1.46), impaired fasting glucose with the lowest and diabetic-dependent risk (RR = 1.20, 95% CI = 1.09-1.33).

CONCLUSIONS

Individuals with MetS and its components are at higher risk of renal dysfunction.

Association of Metabolic Syndrome and Other Factors with the Presence of Diabetic Nephropathy in Type 2 Diabetic Patients

INTRODUCTION

Diabetic nephropathy (DN) is a severe diabetes mellitus (DM) complication that contributes to medical and financial burdens. This study aimed to investigate risk factors for DN among type 2 diabetes mellitus (T2DM) patients by stratifying the participants based on the presence of metabolic syndrome (MetS).

MATERIALS AND METHODS

Between June 2017 and June 2022, Taiwan Hospital was chosen for this retrospective case-control study. Following the completion of a standardized interview and the donation of blood samples for this study, participants were divided into two groups according to whether they had MetS. We contrasted how the potential DN-related factors impacted these two groups.

RESULTS

A total of 1212 patients were included, and 639 patients (52.7%) had MetS. Multivariable analysis showed that the level of educational qualifications, fasting glucose, and uric acid (UA) were associated with DN. However, chewing betel nut behavior, higher systolic blood pressure (SBP), and higher glycated hemoglobin (HbA1c) were found to be risk factors of DN among the patients who had both T2DM and MetS. Notably, betel nut chewing increased the chance of DN in T2DM patients with MetS.

CONCLUSIONS

This study found that the level of education, chewing betel nut behavior, HbA1c, fasting glucose, SBP, and UA were significant risk factors for the development of DN in diabetic individuals with concurrent MetS. Our research reveals that managing the aforementioned risk factors is crucial to lowering the prevalence of DN, particularly in individuals with lower levels of education.

The risk for chronic kidney disease in metabolically healthy obese patients: A systematic review and meta-analysis

BACKGROUND

Chronic kidney disease (CKD) is associated with obesity and metabolic syndrome. Nevertheless, the association of CKD with phenotype referred as metabolically healthy obese or overweight is unclear. In this this systematic review and meta-analysis, we investigate the relationships between obesity and CKD independent of metabolic syndrome by appraising published evidence in studies focusing on metabolically healthy obese people.

MATERIALS AND METHODS

We performed a literature search through three databases Embase (Elsevier), the Cochrane Central Register of Controlled Trials (Wiley) and PubMed/Medline Web of Science up to March 2022 with the following terms: "chronic kidney disease", "kidney function", "obesity", "metabolic syndrome", "metabolically healthy obesity", "metabolically healthy overweight". Metabolically unhealthy was defined an individual having at least 3 of the following: abdominal obesity, high blood pressure, hypertriglyceridemia, low HDL cholesterol and hyperglycaemia. We used Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) for reporting. Prospective, retrospective, randomized and nonrandomized studies fitting the search criteria were included in our results.

RESULTS

Our final analysis included 16 studies with a total number of 4.965.285 participants. There is considerable heterogeneity in terms of study design, participant characteristics and number of participants across individual studies. In comparison to healthy normal weight patients, the risk was progressively higher in overweight (RR 1.29, 95% CI 1.27 to 1.32, p < 0.001) and obese patients (RR 1.47, 95% CI 1.31 to 1.65, p < 0.001).

CONCLUSION

Metabolically healthy overweight and obese individuals have higher risk of CKD compared to individuals without weight excess.

A Hybrid Risk Factor Evaluation Scheme for Metabolic Syndrome and Stage 3 Chronic Kidney Disease Based on Multiple Machine Learning Techniques

With the rapid development of medicine and technology, machine learning (ML) techniques are extensively applied to medical informatics and the suboptimal health field to identify critical predictor variables and risk factors. Metabolic syndrome (MetS) and chronic kidney disease (CKD) are important risk factors for many comorbidities and complications. Existing studies that utilize different statistical or ML algorithms to perform CKD data analysis mostly analyze the early-stage subjects directly, but few studies have discussed the predictive models and important risk factors for the stage-III CKD high-risk health screening population. The middle stages 3a and 3b of CKD indicate moderate renal failure. This study aims to construct an effective hybrid important risk factor evaluation scheme for subjects with MetS and CKD stages III based on ML predictive models. The six well-known ML techniques, namely random forest (RF), logistic regression (LGR), multivariate adaptive regression splines (MARS), extreme gradient boosting (XGBoost), gradient boosting with categorical features support (CatBoost), and a light gradient boosting machine (LightGBM), were used in the proposed scheme. The data were sourced from the Taiwan health examination indicators and the questionnaire responses of 71,108 members between 2005 and 2017. In total, 375 stage 3a CKD and 50 CKD stage 3b CKD patients were enrolled, and 33 different variables were used to evaluate potential risk factors. Based on the results, the top five important variables, namely BUN, SBP, Right Intraocular Pressure (R-IOP), RBCs, and T-Cho/HDL-C (C/H), were identified as significant variables for evaluating the subjects with MetS and CKD stage 3a or 3b.

Health consequences of obesity and projected future obesity health burden in China

OBJECTIVE

This study examined the effects of overweight/obesity on mortality and morbidity outcomes and the disparities, time trends, and projected future obesity health burden in China.

METHODS

Cohort studies that were conducted in China and published in English or Chinese between January 1, 1995, and July 31, 2021, were systematically searched. This study focused on overweight/obesity, type 2 diabetes mellitus (T2DM), hypertension, cardiovascular diseases, metabolic syndrome, cancers, and chronic kidney disease.

RESULTS

A total of 31 cohorts and 50 cohort studies reporting on mortality (n = 20) and morbidities (n = 30) associated with obesity met study inclusion criteria. Overall, BMI was nonlinearly (U-shaped) associated with all-cause mortality and linearly associated with risks of T2DM, cardiovascular diseases, hypertension, cancer, metabolic syndrome, and chronic kidney disease. In 2018, among adults, the prevalence of overweight/obesity, hypertension, and T2DM was 51.2%, 27.5%, and 12.4%, respectively. Their future projected prevalence would be 70.5%, 35.4%, and 18.5% in 2030, respectively. The projected number of adults having these conditions would be 810.65 million, 416.47 million, and 217.64 million, respectively. The urban-rural disparity in overweight/obesity prevalence was projected to shrink and then reverse over time.

CONCLUSIONS

The current health burden of obesity in China is high and it will sharply increase in coming years and affect population groups differently. China needs to implement vigorous interventions for obesity prevention and treatment.

Metabolic obesity phenotypes and chronic kidney disease: a cross-sectional study from the RaNCD cohort study

BACKGROUND

Investigating the effect of metabolic disorders on chronic kidney disease (CKD) in the presence or the absence of obesity is of great importance. This study aimed to examine the independent and joint relationships of obesity and metabolic syndrome (MetS) with CKD.  METHODS : The present study was performed on 9,762 participants from the baseline phase of the Ravansar non- communicable diseases (RaNCD) study. Thereafter, the CKD was estimated by glomerular filtration rate (eGFR) using the Modification of Diet in Renal Disease (MDRD) equation. All the included participants were categorized into the following four phenotypes: metabolically healthy non-overweight/obesity (MHNO), metabolically unhealthy non-overweight/obesity (MUNO), metabolically healthy overweight/obesity (MHO), and metabolically unhealthy overweight/obesity (MUO). Finally, Logistic regression analysis was used to estimate the odds ratio (ORs).

RESULTS

The mean age of the included participants was 47.33 ± 8.27 years old, %48.16 (4,701) of whom were men. As well, 1,058(10.84%) participants had CKD (eGFR less than 60 ml/min/1.73m2). The overweight/obesity was not significantly associated with odds of CKD. The odds of CKD in male subjects with MetS was 1.48 times higher than non-MetS ones (95% CI: 1.10, 2.01). After adjusting the confounders, the odds of CKD were 1.54 times (95% CI: 1.12, 2.11) higher in the MUNO and 2.22 times (95% CI: 1.44, 3.41) higher in the MUO compared to MHNO phenotype in male subjects. The odds of CKD in the MUNO and MUO was 1.31 times (95% CI: 1.10, 1.60) and 1.23 times (95% CI: 1.01, 1.54) higher than MHNO phenotype in female subjects, respectively.

CONCLUSION

The odds of CKD were higher in MUNO and MUO phenotypes. Therefore, lifestyle modification is recommended to control normal weight and healthy metabolism.

The Association Between Visceral Adiposity Index and Worsening Renal Function in the Elderly

Background

Visceral adiposity index (VAI) is an indicator of visceral fat accumulation and dysfunction. However, little is known about whether VAI is associated with worsening renal function (WRF) in the elderly. Therefore, our study aimed to explore the association between VAI and WRF among the elderly population.

Methods

In total, 5,583 elderly participants (aged ≥ 65 years) who participated in the annual health checkups at least twice between January 2017 and July 2021 were enrolled and divided into four groups according to the VAI quartiles. The primary endpoint was incident chronic kidney disease (CKD), defined as incident estimated glomerular filtration rate (eGFR) < 60 ml/min/1.73 m². The secondary endpoint was rapid kidney function decline (RKFD), defined as decline in eGFR of 40%. To evaluate the association between VAI and WRF, three Cox regression models were conducted, where VAI was treated as a continuous variable and a categorical variable (Q1 as reference), respectively. Subgroup analysis in participants with different baseline characteristics was also performed.

Results

During a median of 2.46 year follow-up, 931 (16.68%) participants developed CKD. After fully adjusting for confounding factors, VAI was significantly associated with incident CKD (HR, 1.052; 95% CI: 1.029–1.076, p < 0.001), and RKFD (HR, 1.077; 95% CI: 1.041–1.114, p < 0.001). Moreover, compared to those with the lowest VAI quartiles, subjects with the highest quartiles had a higher risk of incident CKD (HR, 1.286; 95% CI: 1.033–1.601, p = 0.024), and RKFD (HR, 1.895; 95% CI: 1.086–3.307, p = 0.025). The risk of incident CKD also tended to increase with elevated VAI quartiles (all p-values for trend <0.05). This positive association remained consistent among participants with different genders, baseline weights, or kidney functions.

Conclusion

In our study, elevated VAI was associated with increased risk of incident CKD and RKFD in the elderly population.

The Impact of Hepatitis C Virus, Metabolic Disturbance, and Unhealthy Behavior on Chronic Kidney Disease: A Secondary Cross-Sectional Analysis

BACKGROUND

Hepatitis C virus (HCV) infection is associated with a higher risk of chronic kidney disease (CKD). This study investigates the relationship among HCV, CKD, and understudied confounders, such as unhealthy behaviors and metabolic disturbances.

METHODS

This cross-sectional study was conducted as part of a community health promotion program in an HCV endemic area of Taiwan from June to December 2019. Multivariable logistic regression analyses adjusted for demographic and clinical characteristics were performed to investigate the association between CKD and HCV seropositivity.

RESULTS

Of 2387 participants who underwent health check-ups, the mean age was 64.1 years old; females predominated (63.2%), and 306 (12.8%) subjects were seropositive for HCV. CKD, defined as a lower estimated glomerular filtration rate (eGFR) was associated with unhealthy dietary habits, metabolic syndrome, and HCV. Less frequent exercise, higher waist circumference (WC) and HbA1c all affected risk of CKD; HCV increased risk of CKD by 44% compared to non-HCV (OR 1.44, 95% confidence interval (CI) 1.05-1.98) in the multivariable analysis. In the HCV group, lower eGFR was also significantly associated with the severity of metabolic syndrome (MetS) (median eGFR was 86.4, 77.1, and 64.5 mL/min/1.73 m² for individuals with three and five MetS components, respectively).

CONCLUSIONS

Beyond metabolic disturbance and irregular exercise, HCV seropositivity is independently associated with CKD in a community survey. Healthy lifestyle promotion might protect against renal function decline in HCV; however, the mechanisms underlying the association need further large-scale investigation.

Metabolically healthy obesity: Is it really healthy for type 2 diabetes mellitus?

Metabolically healthy obese (MHO) individuals are reported to have a lower risk of developing cardiovascular diseases in comparison with individuals with metabolic syndrome. However, the association between MHO and type 2 diabetes (T2DM) is still controversial. Some studies indicated that MHO is a favorable phenotype for T2DM, but more studies showed that MHO individuals have an increased risk of developing T2DM compared with metabolically healthy normal-weight individuals, especially among those who would acquire metabolically unhealthy obesity. This has been supported by finding insulin resistance and low-grade inflammatory responses in MHO individuals with a tendency for impaired beta-cell dysfunction. Studies also showed that liver fat accumulation increased the risk of incidence of T2DM in MHO. Here, we reviewed current literature on the relationship between MHO and T2DM, discussed the determinants for the development of diabetes in MHO, and summarized the measures for the prevention of T2DM in MHO.

Metabolic Syndrome Components and Chronic Kidney Disease in a Community Population Aged 40 Years and Older in Southern China: A Cross-Sectional Study

PURPOSE

To investigate the correlation between metabolic syndrome components and chronic kidney disease (CKD) among a community population aged 40 years and older in Southern China.

PATIENTS AND METHODS

From December 2017 to March 2018, 1969 participants (male n = 715, female n = 1254) aged 40 years and older were recruited in Southern China for a cross-sectional survey. A logistic regression model was established to analyze the correlation between metabolic syndrome components and CKD.

RESULTS

Among the 1969 subjects, 407 (20.7%) were CKD patients, including 152 males (prevalence rate 21.3%) and 255 females (prevalence rate 20.3%). Anthropometric data (waist circumference, age, systolic and diastolic blood pressure), serum/plasma data (serum creatinine, serum uric acid, fasting plasma glucose, C-reactive protein, serum triglyceride), urinary and other findings (body mass index, waist-to-hip and waist-to-height ratios, urinary albumin to creatinine ratio, homeostasis model assessment of insulin resistance) were significantly higher in patients with than without CKD (P < 0.05). Metabolic syndrome and at least some of its components were statistically significant risk factors for CKD in models with and without adjustment for diabetes, obesity and hypertension.

CONCLUSION

Metabolic syndrome and its single or combined components are independently associated with CKD in community populations aged 40 years and older. The correlation between some components and CKD remained significant in both non-diabetic and non-obese subjects. Correlations between components of metabolic syndrome and CKD show that it is feasible and necessary to carry out targeted screening and intervention tests in people aged 40 and over.

Health Data-Driven Machine Learning Algorithms Applied to Risk Indicators Assessment for Chronic Kidney Disease

PURPOSE

As global aging progresses, the health management of chronic diseases has become an important issue of concern to governments. Influenced by the aging of its population and improvements in the medical system and healthcare in general, Taiwan's population of patients with chronic kidney disease (CKD) has tended to grow year by year, including the incidence of high-risk cases that pose major health hazards to the elderly and middle-aged populations.

METHODS

This study analyzed the annual health screening data for 65,394 people from 2010 to 2015 sourced from the MJ Group - a major health screening center in Taiwan - including data for 18 risk indicators. We used five prediction model analysis methods, namely, logistic regression (LR) analysis, C5.0 decision tree (C5.0) analysis, stochastic gradient boosting (SGB) analysis, multivariate adaptive regression splines (MARS), and eXtreme gradient boosting (XGboost), with estimated glomerular filtration rate (e-GFR) data to determine G3a, G3b & G4 stage CKD risk factors.

RESULTS

The LR analysis (AUC=0.848), SGB analysis (AUC=0.855), and XGboost (AUC=0.858) generated similar classification performance levels and all outperformed the C5.0 and MARS methods. The study results showed that in terms of CKD risk factors, blood urea nitrogen (BUN) and uric acid (UA) were identified as the first and second most important indicators in the models of all five analysis methods, and they were also clinically recognized as the major risk factors. The results for systolic blood pressure (SBP), SGPT, SGOT, and LDL were similar to those of a related study. Interestingly, however, socioeconomic status-related education was found to be the third important indicator in all three of the better performing analysis methods, indicating that it is more important than the other risk indicators of this study, which had different levels of importance according to the different methods.

CONCLUSION

The five prediction model methods can provide high and similar classification performance in this study. Based on the results of this study, it is recommended that education as the socioeconomic status should be an important factor for CKD, as high educational level showed a negative and highly significant correlation with CKD. The findings of this study should also be of value for further discussions and follow-up research.