Impact of Transition in Metabolic Health and Obesity on the Incident Chronic Kidney Disease: A Nationwide Cohort Study

Metabolically healthy obesity and chronic kidney disease risk: exploring the dynamics

Obesity represents a prevalent global health concern with significant implications for various diseases, including chronic kidney disease (CKD). Within this landscape, the phenomenon of metabolically healthy obesity has emerged, challenging traditional notions about the health risks associated with excess weight. While traditional CKD risk factors involve obesity, metabolic syndrome, diabetes, and hypertension, the metabolically healthy obese (MHO) subgroup disrupts these assumptions. Our main objective in this study is to integrate existing literature on CKD in MHO individuals. In this endeavor, we delve into the pathophysiological foundations, the transition between obesity phenotypes and their impact on renal health, examine the implications of their metabolic resilience on mortality within a renal context, and explore potential management strategies specifically designed for MHO individuals. Offering a comprehensive overview of the pathophysiology, we cover various factors contributing to the risk of CKD in the metabolically healthy obese setting, including inflammation, cytokines, hemodynamics, and the renin-angiotensin-aldosterone system, gastrointestinal microbiota, diet, exercise, adipose distribution, and lipotoxicity. Through this synthesis, we aim to provide a comprehensive understanding of the risk of CKD in those classified as MHO.

The Kidney in Obesity: Current Evidence, Perspectives and Controversies

PURPOSE OF REVIEW

As obesity and chronic kidney disease (CKD) remain a public health issue, we aim to elaborate on their complex relationship regarding pathogenetic mechanisms and therapeutic potential as well. The purpose of this review is to enhance our understanding of the interplay between obesity and CKD in order to timely diagnose and treat obesity-related CKD.

RECENT FINDINGS

Obesity and CKD pose significant intertwined challenges to global health, affecting a substantial portion of the population worldwide. Obesity is recognized as an independent risk factor, intricately contributing to CKD pathogenesis through mechanisms such as lipotoxicity, chronic inflammation, and insulin resistance. Recent evidence highlights additional factors including hemodynamic changes and intestinal dysbiosis that exacerbate kidney dysfunction in obese individuals, leading to histologic alterations known as obesity-related glomerulopathy (ORG). This narrative review synthesizes current knowledge on the prevalence, pathophysiology, clinical manifestations, and diagnostic strategies of obesity-related kidney disease. Furthermore, it explores mechanistic insights to delineate current therapeutic approaches, future directions for managing this condition and controversies. By elucidating the multifaceted interactions between obesity and kidney health, this review aims to inform clinical practice and stimulate further research to address this global health epidemic effectively.

Metabolic health's central role in chronic kidney disease progression: a 20-year study of obesity-metabolic phenotype transitions

This study investigates the risk of chronic kidney disease (CKD) across four metabolic phenotypes: Metabolically Healthy-No Obesity (MH-NO), Metabolically Unhealthy-No obesity (MU-NO), Metabolically Healthy-Obesity (MH-O), and Metabolically Unhealthy-Obesity (MU-O). Data from the Tehran Lipid and Glucose Study, collected from 1999 to 2020, were used to categorize participants based on a BMI ≥ 30 kg/m2 and metabolic health status, defined by the presence of three or four of the following components: high blood pressure, elevated triglycerides, low high-density lipoprotein, and high fasting blood sugar. CKD, characterized by a glomerular filtration rate < 60 ml/min/1.72 m2. The hazard ratio (HR) of CKD risk was evaluated using Cox proportional hazard models. The study included 8731 participants, with an average age of 39.93 years, and identified 734 incidents of CKD. After adjusting for covariates, the MU-O group demonstrated the highest risk of CKD progression (HR 1.42-1.87), followed by the MU-NO group (HR 1.33-1.67), and the MH-O group (HR 1.18-1.54). Persistent MU-NO and MU-O posed the highest CKD risk compared to transitional states, highlighting the significance of exposure during early adulthood. These findings emphasize the independent contributions of excess weight and metabolic health, along with its components, to CKD risk. Therefore, preventive strategies should prioritize interventions during early-adulthood.

Association between Metabolically Healthy Status and Risk of Gastrointestinal Cancer

PURPOSE

Although obesity is associated with numerous diseases, the risks of disease may depend on metabolically healthy status. Nevertheless, it is unclear to whether metabolically healthy status affects risk of gastrointestinal (GI) cancer in general Chinese population.

MATERIALS AND METHODS

A total of 114,995 participants who met the criteria were included from the Kailuan Study. The study participants were divided into four groups according to body mass index (BMI)/waist circumference (WC) and metabolic status. Incident of GI cancer (esophageal cancer, gastric cancer, liver cancer, biliary cancer, pancreatic cancer, and colorectal cancer) during 2006-2020 were confirmed by review of medical records. The Cox proportional hazard regression models were used to assess the association metabolically healthy status with the risk of GI cancer by calculating the hazard ratios (HR) and 95% confidence interval (CI).

RESULTS

During a mean 13.76 years of follow-up, we documented 2,311 GI cancers. Multivariate Cox regression analysis showed that compared with the metabolically healthy normal-weight group, metabolically healthy obese (MHO) participants demonstrated an increased risk of developing GI cancer (HR, 1.54; 95% CI, 1.11 to 2.13) by BMI categories. However, such associations were not found for WC category. These associations were moderated by age, sex, and anatomical site of the tumor. Individuals with metabolic unhealthy normal-weight or metabolic unhealthy obesity phenotype also have an increased risk of GI cancer.

CONCLUSION

MHO phenotype was associated with increased risk of GI cancer. Moreover, individuals who complicated by metabolic unhealthy status have an increased risk of developing GI cancer. Hence, clinicians should consider the risk of incident GI cancer in people with abnormal metabolically healthy status and counsel them about metabolic fitness and weight control.

Transitions in Metabolic Health and Onset Age of Cardiovascular Diseases

INTRODUCTION

The cardiometabolic risk associated with metabolically healthy obesity remains the subject of debate. It is unclear whether changes in metabolically healthy obesity status affect premature cardiovascular disease (CVD) risk. Authors aimed to investigate the association of metabolically healthy obesity and its transition over time with incident CVD by age at onset.

METHODS

In a community-based, prospective cohort study, 54,441 adults without CVD in or before 2010 were followed for incident CVD until 2020. This sample was analyzed in 2022. Four age groups were examined (<55, 55-65, 65-75, and ≥75 years) for CVD onset. In each age group, participants were cross-classified by BMI categories and metabolic health. The Cox proportional hazards model with age as the underlying time scale was used to examine the associations of metabolic health status and its transition with CVD across BMI categories.

RESULTS

During a median follow-up of 9.59 years, 3,038 participants developed CVD. Individuals with metabolically unhealthy obesity at baseline had the highest hazard ratio for CVD onset at any age, ranging from 2.68 (95% CI=2.02, 3.55) for CVD onset in those aged <55 years to 1.55 (95% CI=1.09, 2.10) for CVD onset in those aged ≥75 years. Individuals who had metabolically healthy obesity at baseline or even remained metabolically healthy during 2006-2010 were still at increased risk of premature CVD, and the association attenuated with increasing age of CVD onset.

CONCLUSIONS

The metabolically healthy obesity phenotype is dynamic and its transition to a metabolically unhealthy phenotype or even stable metabolically healthy obesity is associated with an increased risk of CVD. The associations were more evident for CVD onset at younger ages.

Obesity, metabolic dysfunction, and risk of kidney stone disease: a national cross-sectional study

BACKGROUND

This study aimed to investigate the association between different metabolic syndrome-body mass index (MetS-BMI) phenotypes and the risk of kidney stones.

MATERIALS AND METHODS

Participants aged 20-80 years from six consecutive cycles of the NHANES 2007-2018 were included in this study. According to their MetS status and BMI, the included participants were allocated into six mutually exclusive groups: metabolically healthy normal weight (MHN)/overweight (MHOW)/obesity (MHO) and metabolically unhealthy normal weight (MUN)/overweight (MUOW)/obesity (MUO). To explore the association between MetS-BMI phenotypes and the risk of kidney stones, binary logistic regression was used to determine the odds ratios (ORs).

RESULTS

A total of 13,589 participants were included. It was revealed that all the phenotypes with obesity displayed higher risks of kidney stones (OR = 1.38, p < 0.01 for MHO & OR = 1.80, p < 0.001 for MUO, in the fully adjusted model). The risk increased significantly when metabolic dysfunction coexisted with overweight and obesity (OR = 1.39, p < 0.05 for MUOW & OR = 1.80, p < 0.001 for MUO, in the fully adjusted model). Of note, the ORs for the MUO and MUOW groups were higher than those for the MHO and MHOW groups, respectively.

CONCLUSIONS

Obesity and unhealthy metabolic status can jointly increase the risk of kidney stones. Assessing the metabolic status of all individuals may be beneficial for preventing kidney stones.

An Overview of Chronic Kidney Disease Pathophysiology: The Impact of Gut Dysbiosis and Oral Disease

Chronic kidney disease (CKD) is a severe condition and a significant public health issue worldwide, carrying the burden of an increased risk of cardiovascular events and mortality. The traditional factors that promote the onset and progression of CKD are cardiometabolic risk factors like hypertension and diabetes, but non-traditional contributors are escalating. Moreover, gut dysbiosis, inflammation, and an impaired immune response are emerging as crucial mechanisms in the disease pathology. The gut microbiome and kidney disease exert a reciprocal influence commonly referred to as "the gut-kidney axis" through the induction of metabolic, immunological, and endocrine alterations. Periodontal diseases are strictly involved in the gut-kidney axis for their impact on the gut microbiota composition and for the metabolic and immunological alterations occurring in and reciprocally affecting both conditions. This review aims to provide an overview of the dynamic biological interconnections between oral health status, gut, and renal pathophysiology, spotlighting the dynamic oral-gut-kidney axis and raising whether periodontal diseases and gut microbiota can be disease modifiers in CKD. By doing so, we try to offer new insights into therapeutic strategies that may enhance the clinical trajectory of CKD patients, ultimately advancing our quest for improved patient outcomes and well-being.

Analysis of the association between glomerular filtration rate, proteinuria and metabolic syndrome in chronic kidney patients based on longitudinal data

Chronic kidney disease (CKD) is a group of chronic diseases caused by kidney damage from multiple causes. Metabolic syndrome (MS) manifests as dysfunction of endothelial cells and chronic functional inflammatory states, and may be involved in pathological changes related to renal impairment. Based on longitudinal data analysis of the association between estimated glomerular filtration rate (eGFR), proteinuria and MS in patients with CKD, this study aims to provide new ideas for the pathophysiological mechanism of CKD and a theoretical basis for the early prevention and effective intervention of MS-related kidney damage. A total of 126 patients with CKD were divided into non-MS group and MS group. According to the eGFR level, 126 patients with CKD were divided into G1 group, G2 group, G3a group, G3b group, G4 group and G5 group. Serum markers such as eGFR, urine protein, and triglycerides (TG) were collected. The correlation between eGFR, urine protein and MS-related indexes was analysed, and the risk factors affecting CKD complicated by MS were analysed. In patients with CKD, the levels of urine protein, abdominal circumference, TG, systolic blood pressure (SBP), diastolic blood pressure (DBP), and fasting blood glucose (FPG) were increased with the course of the disease, but the levels of eGFR and high density lipoprotein (HDL-C) were decreased (p < 0.05). Abdominal circumference, TG, SBP, DBP, FP were significantly negatively correlated with eGFR, but HDL-C was positively correlated with eGFR (p < 0.05). Diabetes, hyperlipidemia, UA, and SBP were independent risk factors affecting CKD complicated MS, and eGFR were independent protective factors (p < 0.05). The combination of diabetes, hyperlipidemia, UA, SBP, and eGFR exhibited higher prediction value for the CKD patients complicated by MS. There was a certain correlation between between MS components with eGFR and urinary protein in patients with CKD. The early intervention treatment of MS was helpful in delaying the development of CKD and reducing proteinuria.

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.

Obesity and chronic kidney disease

The prevalence of obesity has increased dramatically during the past decades, which has been a major health problem. Since 1975, the number of people with obesity worldwide has nearly tripled. An increasing number of studies find obesity as a driver of chronic kidney disease (CKD) progression, and the mechanisms are complex and include hemodynamic changes, inflammation, oxidative stress, and activation of the renin-angiotensin-aldosterone system (RAAS). Obesity-related kidney disease is characterized by glomerulomegaly, which is often accompanied by localized and segmental glomerulosclerosis lesions. In these patients, the early symptoms are atypical, with microproteinuria being the main clinical manifestation and nephrotic syndrome being rare. Weight loss and RAAS blockers have a protective effect on obesity-related CKD, but even so, a significant proportion of patients eventually progress to end-stage renal disease despite treatment. Thus, it is critical to comprehend the mechanisms underlying obesity-related CKD to create new tactics for slowing or stopping disease progression. In this review, we summarize current knowledge on the mechanisms of obesity-related kidney disease, its pathological changes, and future perspectives on its treatment.

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.

Metabolically obese phenotype and its dynamic change are associated with increased carotid intima-media thickness: Results from a cohort study

BACKGROUND AND AIMS

To examine associations between metabolically obese phenotypes or their changes and increased carotid intima-media thickness (CIMT).

METHODS AND RESULTS

This prospective cohort included 13,681 Chinese adults aged 20-80 years who completed follow-up health examination with carotid ultrasound and were divided according to metabolic and weight status: metabolically healthy and normal weight (MHNW); metabolically obese but normal weight (MONW); metabolically healthy but obese (MHO); metabolically abnormal and obese (MAO). Cox and logistic regression were used to evaluate the associations of the phenotypes or their changes with increased CIMT. During a mean follow-up of 33 months, 1927 participants developed increased CIMT. After adjusting for age, sex and potential biochemical confounders, MAO was significantly associated with increased CIMT (HR 1.22, 95% CI [1.07, 1.4]); the association remained significant in those 40 years or older. Compared with stable MHNW, increased CIMT risk was higher for stable MAO (OR 1.35 [1.16, 1.57]), transitional MAO from MONW (OR 1.44 [1.04, 1.97]), and transitional MHO from MHNW (OR 1.59 [1.10, 2.26]) in demographic adjusted models; only stable MAO remained significant in the multivariate adjusted model (OR 1.23 [1.05, 1.45]).

CONCLUSION

MAO significantly elevated the risk of increased CIMT. Stable MAO and obese transitions also promoted CIMT progression.

Pathogenesis, Murine Models, and Clinical Implications of Metabolically Healthy Obesity

Although obesity is commonly associated with numerous cardiometabolic pathologies, some people with obesity are resistant to detrimental effects of excess body fat, which constitutes a condition called “metabolically healthy obesity” (MHO). Metabolic features of MHO that distinguish it from metabolically unhealthy obesity (MUO) include differences in the fat distribution, adipokine types, and levels of chronic inflammation. Murine models are available that mimic the phenotype of human MHO, with increased adiposity but preserved insulin sensitivity. Clinically, there is no established definition of MHO yet. Despite the lack of a uniform definition, most studies describe MHO as a particular case of obesity with no or only one metabolic syndrome components and lower levels of insulin resistance or inflammatory markers. Another clinical viewpoint is the dynamic and changing nature of MHO, which substantially impacts the clinical outcome. In this review, we explore the pathophysiology and some murine models of MHO. The definition, variability, and clinical implications of the MHO phenotype are also discussed. Understanding the characteristics that differentiate people with MHO from those with MUO can lead to new insights into the mechanisms behind obesity-related metabolic derangements and diseases.

Association between metabolic phenotype and urinary albumin-creatinine ratio in Chinese community adults: A cross-sectional study

BACKGROUND

Urinary albumin-creatinine ratio (UACR) is a sensitive marker of kidney injury. This study analyzed the prevalence of different metabolic phenotypes and investigated their relationship with UACR in Chinese community adults.

METHODS

This study involved 33 303 participants over 40 years old from seven centers across China. They were stratified into six groups according to their body mass index (BMI) and metabolic status: metabolically healthy normal weight (MHNW), metabolically healthy overweight (MHOW), metabolically healthy obesity (MHO), metabolically unhealthy normal weight (MUNW), metabolically unhealthy overweight (MUOW), and metabolically unhealthy obesity (MUO). Increased albuminuria was defined as a UACR ≥30 mg/g.

RESULTS

The percentages of MHNW, MHOW, MHO, MUNW, MUOW, and MUO were 27.6%, 15.9%, 4.1%, 19.8%, 22.5%, and 9.6%, respectively. Multiple logistic regression analysis showed that the MHO group (odds ratio [OR] 1.205; 95% CI, 1.081-1.343), MUNW group (OR 1.232; 95% CI, 1.021-1.486), MUOW group (OR 1.447; 95% CI, 1.303-1.607), and MUO group (OR 1.912; 95% CI, 1.680-2.176) were at higher risk of increased albuminuria compared to the MHNW group. Subgroup analysis indicated that the risk of increased albuminuria was further elevated among regular smokers in men aged 40 to 55 years old with abdominal obesity.

CONCLUSIONS

Among Chinese community adults, increased albuminuria was associated with increased BMI whether metabolism was normal or not, and those with abnormal metabolism were at greater risk of increased albuminuria than those with normal metabolism. These findings suggest that overweight or obesity or metabolic abnormalities are risk factors for chronic kidney disease.

Transition of Metabolic Phenotypes and Risk of Atrial Fibrillation According to BMI: Kailuan Study

Objective

Atrial fibrillation (AF) is associated with both obesity and its metabolic consequences. However, there is a paucity of information on whether the dynamic change of metabolic health and obesity phenotypes affect the risk of AF. We aimed to prospectively examine the association between metabolic health and its change over time and AF risk across body mass index (BMI) categories.

Methods

A total of 58,483 participants without history of cancer, and cardiovascular diseases from the Kailuan study were included in the present study. Transition of metabolic phenotypes was evaluated between 1st survey (2006–2007) and the 2nd survey (2008–2009). The hazard ratios (HRs) and 95% confidence intervals (CIs) for AF were assessed by Cox proportional hazards regression.

Results

During a median follow-up of 3 years, we documented 580 cases of AF. Compared with metabolically healthy individuals with normal weight, the multivariable-adjusted hazard ratios for metabolically healthy and unhealthy overweight/obese were 1.27 (95% CI: 1.01, 1.59) and 1.37 (95% CI: 1.09, 1.72), respectively. However, when transition was taken into account, overweight/obese people who maintained metabolically healthy status were not associated with increased long-term risk (HR, 1.11;95% CI: 0.70, 1.78), whereas participants who converted from metabolically healthy overweight/obese status to an unhealthy phenotype had higher AF risk than those who maintained metabolically healthy normal weight (HR 1.59, 95% CI: 1.11, 2.26). When BMI and metabolically healthy status were updated over the course of the study, significant short-term elevations in AF risk were associated with individuals with stable MU-OW/OB status.

Conclusion

In this community-based cohort study, metabolically healthy overweight/obese individuals have increased risks of AF. Obesity remains a risk factor for AF independent of major metabolic factors. Our data further suggested that metabolic phenotype was a dynamic condition, and maintenance of metabolic health and normal weight might alleviate the risk of AF.

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 phenotypes and risk of colorectal cancer: a systematic review and meta-analysis of cohort studies

BACKGROUND

The association of obesity with colorectal cancer (CRC) may vary depending on metabolic status.

OBJECTIVE

This meta-analysis aimed to investigate the combined impacts of obesity and metabolic status on CRC risk.

METHODS

The Scopus, PubMed, and web of sciences databases were systematically searched up to Jun 2021 to find all eligible publications examining CRC risk in individuals with metabolically unhealthy normal-weight (MUHNW), metabolically healthy obesity (MHO), and metabolically unhealthy obesity (MUHO) phenotypes.

RESULTS

A total of 7 cohort studies with a total of 759,066 participants were included in this meta-analysis. Compared with healthy normal-weight people, MUHNW, MHO, and MUHO individuals indicated an increased risk for CRC with a pooled odds ratio of 1.19 (95% CI = 1.09-1.31) in MUHNW, 1.14 (95% CI = 1.06-1.22) in MHO, and 1.24 (95% CI = 1.19-1.29) in MUHO subjects. When analyses were stratified based on gender, associations remained significant for males. However, the elevated risk of CRC associated with MHO and MUHO was not significant in female participants.

CONCLUSIONS

The individuals with metabolic abnormality, although at a normal weight, have an increased risk for CRC. Moreover, obesity is associated with CRC irrespective of metabolic status.

Implications of metabolic health status and obesity on the risk of kidney cancer: A nationwide population-based cohort study

PURPOSE

This study evaluated the association between metabolic health status and incident kidney cancer among obese participants.

MATERIALS AND METHODS

A total of 514,866 individuals were included from the Korean National Health Insurance Service-National Health Screening Cohort. Changes in metabolic health status and obesity from the baseline examination in 2009-2010 to the next biannual examination in 2011-2012 were determined. Based on the status change, obese participants were divided into four groups: stable metabolically healthy obesity, metabolically healthy obesity to metabolically unhealthy obesity, metabolically unhealthy obesity to metabolically healthy obesity, and stable metabolically unhealthy obesity.

RESULTS

The stable metabolically healthy obesity phenotype did not confer an increased risk of incident kidney cancer, compared to the stable metabolically healthy non-obese group. In contrast, the metabolically healthy obesity to metabolically unhealthy obesity group had a significantly higher risk of incident kidney cancer than the stable metabolically healthy non-obese group. Among patients with metabolically unhealthy obesity at baseline, those who transitioned to the metabolically healthy obese group had no increased risk of kidney cancer, whereas those who remained in metabolically unhealthy obesity status had a higher risk of incident kidney cancer than the stable metabolically healthy non-obese group. The transition or maintenance of metabolic health was a decisive factor for kidney cancer in obese patients.

CONCLUSIONS

Maintaining or restoring metabolic health should be stressed upon in obese patients to reduce the risk of kidney cancer.

Metabolically healthy obesity is associated with increased risk of lower urinary tract symptoms secondary to benign prostatic hyperplasia: A cohort study of Chinese elderly males

OBJECTIVES

Obesity and metabolic status are both modifiable risk factors of lower urinary tract symptoms secondary to benign prostatic hyperplasia (LUTS/BPH). However, the association between metabolically healthy obesity (MHO) and LUTS/BPH is largely unexplored. This study aimed to investigate the risk of LUTS/BPH among different metabolic syndrome-body mass index (MetS-BMI) phenotypes in a cohort of Chinese males.

METHODS

A total of 3321 males from the China Health and Retirement Longitudinal Study (CHARLS) without history of LUTS/BPH at baseline were included into the analyses. Participants were categorized into six mutually exclusive groups according to presence or absence of MetS combined with BMI status: metabolically healthy normal weight/overweight/obesity (MHN/MHOW/MHO) and metabolically unhealthy normal weight/overweight/obesity (MUN/MUOW/MUO). Adjusted odds ratios (OR) and 95% CI of LUTS/BPH across MetS-BMI categories were estimated with multivariable logistic regression models.

RESULTS

A total of 394 (11.86%) participants developed LUTS/BPH during the follow-up. After adjusting for age, educational level, smoking status, drinking status, and BMI change, the multivariable-adjusted OR (95% CI) for incident LUTS/BPH comparing MUO, MHO, MUOW, MHOW, and MUN with MHN were 1.99 (1.23-3.22), 2.04 (1.14-3.66), 1.61 (1.11-2.34), 1.45 (1.02-2.05), and 0.91 (0.54-1.56), respectively.

CONCLUSIONS

MHO and MHOW were risk populations of LUTS/BPH, suggesting that overweight and obesity can independently contribute to LUTS/BPH, even among metabolically healthy individuals. These findings emphasize metabolically healthy individuals may still benefit from maintaining normal body weight to prevent LUTS/BPH. Our findings also support that those recommendations for LUTS/BPH should highlight the importance of maintaining metabolic health across all BMI groups among Chinese males.

Transitions in metabolic health status over time and risk of heart failure: a prospective study

AIMS

- Evidence for the effects of metabolically healthy obese (MHO) status on heart failure (HF) is limited and ignores the dynamic change of metabolic health and obesity phenotypes. We aimed to investigate the associations of metabolic health and its transition with HF across body mass index (BMI) and waist circumference (WC) categories.

METHODS

- This prospective cohort study was conducted with 93,288 Chinese adults who were free of cardiovascular disease, cancer or HF at baseline (2006-2007). Metabolic health was defined as having no or only one abnormality in blood pressure, glucose, high-density lipoprotein cholesterol, or triglyceride levels. Participants were cross-classified at baseline by metabolic health and obesity (defined by BMI and WC criteria). Transitions in metabolic health status from 2006-2007 to 2010-2011 were considered. The hazard ratios (HRs) and 95% confidence intervals (CIs) for HF were assessed by Cox proportional hazards regression.

RESULTS

- During a mean ± standard deviation follow-up of 9.7 ± 1.5 years, 1,628 participants developed HF. Individuals with MHO (HR: 1.78, 95% CI: 1.45, 2.19 for BMI criteria; HR: 1.51, 95% CI: 1.30, 1.76 for WC criteria) had higher risk of HF than those with metabolically healthy normal weight (MH-NW). Individuals with initial MHO who shifted to metabolically unhealthy phenotype during follow-up had higher risk of HF compared with stable MH-NW individuals (HR 3.12; 95% CI: 2.01, 4.85 for BMI categories; HR 1.98; 95% CI: 1.42, 2.77 for WC categories). Even stable MHO individuals were at an increased risk of HF compared with stable MH-NW individuals (HR: 2.17; 95% CI: 1.39, 3.39 for BMI categories; HR: 1.33; 95% CI: 0.96, 1.85 for WC categories).

CONCLUSIONS

- MHO phenotype is dynamic and its transition to metabolically unhealthy phenotype or even stable MHO is associated with increased risk of HF. Maintaining metabolic health may provide a clue for preventing HF.

The risk of Alzheimer's disease according to dynamic changes in metabolic health and obesity: a nationwide population-based cohort study

We evaluated the association of metabolic health and obesity phenotypes with the risk of Alzheimer's disease (AD). This study enrolled 136,847 elderly participants aged 60 or above from the Korean National Health Insurance System. At baseline examinations in 2009 and 2010, subjects were categorized into four groups: the metabolically healthy non-obese (MHNO), metabolically healthy obese (MHO), metabolically unhealthy non-obese (MUNO), and metabolically unhealthy obese (MUO) groups. Based on the phenotypic transition after 2 years, the subjects were further categorized into 16 subgroups. They were followed from 2009 to 2015 to monitor for AD development. The MHO phenotype protected subjects from AD, relative to the MHNO phenotype (HR, 0.73; 95% CI, 0.65-0.81). Among subjects initially classified as MHO, 41.8% remained MHO, with a significantly lower risk of AD compared with the stable MHNO group (HR, 0.62; 95% CI, 0.50-0.77). Among MUO subjects at baseline, those who changed phenotype to MUNO were at higher risk of AD (HR, 1.47; 95% CI, 1.28-1.70), and the transition to the MHO phenotype protected subjects from AD (HR, 0.62; 95% CI, 0.50-0.78). The MHO phenotype conferred a decreased risk of AD. Maintenance or recovery of metabolic health might mitigate AD risk among obese individuals.

Metabolic health is a determining factor for incident colorectal cancer in the obese population: A nationwide population-based cohort study

Background

The association of the risk of colorectal cancer (CRC) with obesity or obesity‐induced metabolic disturbances remains controversial. We assessed the association of metabolic health status with incident CRC among subjects with obesity.

Methods

This study included 319,397 subjects from the Korean National Health Insurance Service‐National Health Screening Cohort. Transitions in metabolic health status and obesity were examined during 2009–2010 and 2011–2012. We categorized subjects with obesity into four separate groups according to their dynamic metabolic health status: metabolically healthy obesity (MHO), MHO to metabolically unhealthy obesity (MUO), MUO to MHO, and stable MUO. Subjects were followed up from 2009 to 2015 for incident CRC.

Results

The stable MHO group showed no increased risk of incident CRC (hazard ratio [HR], 0.97; 95% confidence interval [CI], 0.83–1.14). However, the MHO to MUO group had a higher risk of incident CRC than the stable metabolically healthy nonobese (MHNO) group (HR, 1.34; 95% CI, 1.15–1.57). Among patients with MUO at baseline, those in the subgroup who transitioned to MHO group were not at increased risk of CRC (HR, 1.06; 95% CI, 0.91–1.25), whereas those who remained in the stable MUO group had a higher risk of incident CRC than those in the stable MHNO group (HR, 1.29; 95% CI, 1.19–1.41).

Conclusions

The transition of metabolic health was a determining factor for CRC among subjects with obesity. Hence, maintenance or recovery of metabolic health should be addressed to prevent CRC in individuals with obesity.

Range of adiposity and cardiorenal syndrome

Obesity and obesity-related co-morbidities, diabetes mellitus, and hypertension are among the fastest-growing risk factors of heart failure and kidney disease worldwide. Obesity, which is not a unitary concept, or a static process, ranges from alterations in distribution to the amount of adiposity. Visceral adiposity, which includes intraabdominal visceral fat mass and ectopic fat deposition such as hepatic, cardiac, or renal, was robustly associated with a greater risk for cardiorenal morbidity than subcutaneous adiposity. In addition, morbid obesity has also demonstrated a negative effect on cardiac and renal functioning. The mechanisms by which adipose tissue is linked with the cardiorenal syndrome (CRS) are hemodynamic and mechanical changes, as well neurohumoral pathways such as insulin resistance, endothelial dysfunction, nitric oxide bioavailability, renin-angiotensin-aldosterone, oxidative stress, sympathetic nervous systems, natriuretic peptides, adipokines and inflammation. Adiposity and other associated co-morbidities induce adverse cardiac remodeling and interstitial fibrosis. Heart failure with preserved ejection fraction has been associated with obesity-related functional and structural abnormalities. Obesity might also impair kidney function through hyperfiltration, increased glomerular capillary wall tension, and podocyte dysfunction, which leads to tubulointerstitial fibrosis and loss of nephrons and, finally, chronic kidney disease. The development of new treatments with renal and cardiac effects in the context of type 2 diabetes, which improves mortality outcome, has highlighted the importance of CRS and its prevalence. Increased body fat triggers cellular, neuro-humoral and metabolic pathways, which create a phenotype of the CRS with specific cellular and biochemical biomarkers. Obesity has become a single cardiorenal umbrella or type of cardiorenal metabolic syndrome. This review article provides a clinical overview of the available data on the relationship between a range of adiposity and CRS, the support for obesity as a single cardiorenal umbrella, and the most relevant studies on the recent therapeutic approaches.