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

Metabolic obesity phenotypes and obesity-related cancer risk in the National Health and Nutrition Examination Survey

INTRODUCTION

Body mass index (BMI) fails to identify up to one-third of normal weight individuals with metabolic dysfunction who may be at increased risk of obesity-related cancer (ORC). Metabolic obesity phenotypes, an alternate metric to assess metabolic dysfunction with or without obesity, were evaluated for association with ORC risk.

METHODS

National Health and Nutrition Examination Survey participants from 1999 to 2018 (N = 19,500) were categorized into phenotypes according to the metabolic syndrome (MetS) criteria and BMI: metabolically healthy normal weight (MHNW), metabolically unhealthy normal weight (MUNW), metabolically healthy overweight/obese (MHO) and metabolically unhealthy overweight/obese (MUO). Adjusted multivariable logistic regression models were used to evaluate associations with ORC.

RESULTS

With metabolic dysfunction defined as ≥1 MetS criteria, ORC cases (n = 528) had higher proportions of MUNW (28.2% vs. 17.4%) and MUO (62.6% vs. 60.9%) phenotypes than cancer-free individuals (n = 18,972). Compared with MHNW participants, MUNW participants had a 2.2-times higher ORC risk [OR (95%CI) = 2.21 (1.27-3.85)]. MHO and MUO participants demonstrated a 43% and 56% increased ORC risk, respectively, compared to MHNW, but these did not reach statistical significance [OR (95% CI) = 1.43 (0.46-4.42), 1.56 (0.91-2.67), respectively]. Hyperglycaemia, hypertension and central obesity were all independently associated with higher ORC risk compared to MHNW.

CONCLUSIONS

MUNW participants have a higher risk of ORC than other abnormal phenotypes, compared with MHNW participants. Incorporating metabolic health measures in addition to assessing BMI may improve ORC risk stratification. Further research on the relationship between metabolic dysfunction and ORC is warranted.

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.

Metabolically healthy obesity: Misleading phrase or healthy phenotype?

Obesity is a heterogenous condition with multiple different phenotypes. Among these a particular subtype exists named as metabolically healthy obesity (MHO). MHO has multiple definitions and its prevalence varies according to study. The potential mechanisms underlying the pathophysiology of MHO include the different types of adipose tissue and their distribution, the role of hormones, inflammation, diet, the intestinal microbiota and genetic factors. In contrast to the negative metabolic profile associated with metabolically unhealthy obesity (MUO), MHO has relatively favorable metabolic characteristics. Nevertheless, MHO is still associated with many important chronic diseases including cardiovascular disease, hypertension, type 2 diabetes, chronic kidney disease as well as certain types of cancer and has the risk of progression into the unhealthy phenotype. Therefore, it should not be considered as a benign condition. The major therapeutic alternatives include dietary modifications, exercise, bariatric surgery and certain medications including glucagon-like peptide-1 (GLP-1) analogs, sodium-glucose cotransporter-2 (SGLT-2) inhibitors and tirzepatide. In this review, we discuss the significance of MHO while comparing this phenotype with MUO.

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.

Koyanagi Y, Matsuo K, Mikami H, Kusakabe M, Takeuchi K, Wakai K. Associations of metabolic syndrome and metabolically unhealthy obesity with cancer mortality: The Japan Multi-Institutional Collaborative Cohort (J-MICC) study

Purpose

The association between metabolic syndrome (MetS) and the risk of death from cancer is still a controversial issue. The purpose of this study was to examine the associations of MetS and metabolically unhealthy obesity (MUHO) with cancer mortality in a Japanese population.

Methods

We used data from the Japan Multi-Institutional Collaborative Cohort Study. The study population consisted of 28,554 eligible subjects (14,103 men and 14,451 women) aged 35–69 years. MetS was diagnosed based on the criteria of the National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATP III) and the Japan Society for the Study of Obesity (JASSO), using the body mass index instead of waist circumference. The Cox proportional hazards analysis was used to estimate adjusted hazard ratios (HR) and 95% confidence intervals (CI) for total cancer mortality in relation to MetS and its components. Additionally, the associations of obesity and the metabolic health status with cancer mortality were examined.

Results

During an average 6.9-year follow-up, there were 192 deaths from cancer. The presence of MetS was significantly correlated with increased total cancer mortality when the JASSO criteria were used (HR = 1.51, 95% CI 1.04–2.21), but not when the NCEP-ATP III criteria were used (HR = 1.09, 95% CI 0.78–1.53). Metabolic risk factors, elevated fasting blood glucose, and MUHO were positively associated with cancer mortality (P <0.05).

Conclusion

MetS diagnosed using the JASSO criteria and MUHO were associated with an increased risk of total cancer mortality in the Japanese population.

Prolonged or Transition to Metabolically Unhealthy Status, Regardless of Obesity Status, Is Associated with Higher Risk of Cardiovascular Disease Incidence and Mortality in Koreans

The risk of chronic disease and mortality may differ by metabolic health and obesity status and its transition. We investigated the risk of cardiovascular disease (CVD) and cancer incidence and mortality according to metabolic health and obesity status and their transition using the nationally representative Korea National Health and Nutrition Examination Survey (KNHANES) and the Ansan-Ansung (ASAS) cohort of the Korean Genome and Epidemiology Study. Participants that agreed to mortality linkage (n = 28,468 in KNHANES and n = 7530 adults in ASAS) were analyzed (mean follow-up: 8.2 and 17.4 years, respectively). Adults with no metabolic risk factors and BMI <25 or ≥25 kg/m2 were categorized as metabolically healthy non-obese (MHN) or metabolically healthy obese (MHO), respectively. Metabolically unhealthy non-obese (MUN) and metabolically unhealthy obese (MUO) adults had ≥1 metabolic risk factor and a BMI < or ≥25 kg/m2, respectively. In KNHANES participants, MUN, and MUO had higher risks for cardiovascular mortality, but not cancer mortality, compared with MHN adults. MHO had 47% and 35% lower risks of cancer mortality and all-cause mortality, respectively, compared to MHN. Similar results were observed in the ASAS participants. Compared to those persistently MHN, the risk of CVD was greater when continuously MUN or MUO. Transitioning from a metabolically healthy state to MUO also increased the risk of CVD. Few associations were found for cancer incidence. Using a nationally representative cohort and an 18-year follow-up cohort, we observed that the risk of CVD incidence and mortality and all-cause mortality, but not cancer incidence or mortality, increases with a continuous or a transition to an unhealthy metabolic status in Koreans.

The Association Between Metabolic Status and Risk of Cancer Among Patients With Obesity: Metabolically Healthy Obesity vs. Metabolically Unhealthy Obesity

Background

Controversial evidence about the association between cancer risk and metabolic status among individuals with obesity has been reported, but pooled data remain absent. This study aims to present pooled data comparing cancer risk between patients with metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUO).

Methods

The current study systematically searched pieces of literature on January 4, 2021, of prospective cohorts that compare the incidence of cancer between MHO and MUO. The quality of included studies was assessed using Newcastle-Ottawa scale, and publication bias was evaluated using funnel plots.

Results

Eleven high-quality studies were eventually selected. Quantitative analysis indicates that a lower cancer incidence exists for MHO phenotype than that for MUO (odds ratio [OR], 0.71; 95% confidential interval [CI], 0.61-0.84). Consistent outcomes are presented by subgroup analyses, which are grouped by cohort region (western population: [OR, 0.84; 95% CI, 0.75-0.93]; Asian population: [OR, 0.64; 95% CI, 0.54-0.77]); definition of metabolic unhealthiness (≥3 metabolic abnormalities: [OR, 0.62; 95% CI, 0.54-0.71]; ≥1 metabolic abnormality: [OR, 0.76; 95% CI, 0.62-0.94]); and definition of obesity (body mass index (BMI), ≥30 kg/m2: [OR, 0.84; 95% CI, 0.73-0.98]; BMI, ≥25 kg/m2: [OR, 0.53; 95% CI, 0.52-0.55]).

Conclusion

In conclusion, this study suggests a reduced cancer risk for MHO compared to MUO regardless of population heterogeneity, or the definitions of obesity and metabolic status.

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.

The Adipokine Component in the Molecular Regulation of Cancer Cell Survival, Proliferation and Metastasis

A hormonal imbalance may disrupt the rigorously monitored cellular microenvironment by hampering the natural homeostatic mechanisms. The most common example of such hormonal glitch could be seen in obesity where the uprise in adipokine levels is in virtue of the expanding bulk of adipose tissue. Such aberrant endocrine signaling disrupts the regulation of cellular fate, rendering the cells to live in a tumor supportive microenvironment. Previously, it was believed that the adipokines support cancer proliferation and metastasis with no direct involvement in neoplastic transformations and tumorigenesis. However, the recent studies have reported discrete mechanisms that establish the direct involvement of adipokine signaling in tumorigenesis. Moreover, the individual adipokine profile of the patients has never been considered in the prognosis and staging of the disease. Hence, the present manuscript has focused on the reported extensive mechanisms that culminate the basis of poor prognosis and diminished survival rate in obese cancer patients.

Association between cardiovascular risk factors and colorectal cancer: A systematic review and meta-analysis of prospective cohort studies

BACKGROUND

Emerging data have suggested colorectal cancer (CRC) often coexists with cardiovascular diseases, but whether cardiovascular risk factors play a role in CRC remains unclear. We performed a systematic review and meta-analysis to better illustrate the associations between cardiovascular risk factors and CRC.

METHODS

We searched EMBASE, MEDLINE and Web of Science databases from inception up to June 14, 2020. Prospective cohort studies were included if they evaluated the association between at least one of cardiovascular risk factors and CRC incidence, containing sufficient data to obtain relative risk (RR) and 95% confidence interval (CI). We performed separate meta-analyses for each cardiovascular risk factor using random-effect model. PROSPERO registration number: CRD42020175537.

FINDINGS

Data from 84 studies, reporting 52, 348, 827 individuals and 384, 973 incident cases were included in the analysis. Overall, the risk of CRC was 1.31(95% CI, 1.21-1.42) for obesity, 1.14 (95% CI, 1.09-1.20) for per 5 kg/m² increase in body mass index, 1.18 (95% CI, 1.14-1.23) for former smoker, 1.20 (95% CI, 1.11-1.30) for current smoker, 1.25 (95% CI, 1.16-1.35) for diabetes, 1.07 (95% CI, 1.02-1.12) for hypertension. The summary RRs of CRC for the highest versus lowest quartiles of total cholesterol, triglyceride, low-density lipoprotein were 1.12 (95% CI, 1.03-1.22), 1.18 (95% CI, 1.04-1.35), 0.85 (95% CI, 0.62-1.17) respectively and the pooled RR for the lowest versus highest quartile of high-density lipoprotein was 1.14 (95% CI, 1.02-1.28).

INTERPRETATION

Unfavorable cardiovascular risk factors are associated with increased risk of CRC, which may provide novel insight into the screening strategies of CRC in patient with these risk factors.