Increased incidence of bladder cancer with metabolically unhealthy status: analysis from the National Health Checkup database in Korea

Association of metabolic obesity phenotypes with risk of overall and site-specific cancers: a systematic review and meta-analysis of cohort studies

BACKGROUND

Adiposity is a known risk factor for certain cancers; however, it is not clear whether the risk of cancer differs between individuals with high adiposity but different metabolic health status. The aim of this systematic literature review and meta-analysis of cohort studies was to evaluate associations between metabolic obesity phenotypes and overall and site-specific cancer risk.

METHODS

PubMed and Embase databases were used to identify relevant cohort studies up to the 6th of June 2023. Random-effects models were used to estimate summary relative risks (SRRs) and 95% confidence intervals (CIs) for the association between metabolic obesity phenotypes and cancer risk. Certainty of evidence was assessed using the Cochrane methods and the GRADE tool. This study is registered with PROSPERO, number CRD42024549511.

RESULTS

A total of 15,556 records were screened, and 31 publications covering 15 unique cohort studies were included in this analysis. Of these studies, 22 were evaluated as being at low risk of bias and 9 at moderate risk of bias. Compared to metabolically healthy normal-weight individuals (MHNW), metabolically unhealthy overweight/obese (MUOW/OB) individuals had a higher risk of overall (SRR = 1.21, 95% CI = 1.02-1.44, n = 3 studies, high certainty) and obesity-related cancers (SRR = 1.42, 95% CI = 1.15-1.74, n = 3, very low certainty). Specifically, MUOW/OB individuals were at higher risk of cancers of the postmenopausal breast (SRR = 1.32, 95% CI = 1.17-1.48, n = 7, low certainty), colorectum (SRR = 1.24, 95% CI = 1.16-1.31, n = 6, moderate certainty), endometrium (SRR = 2.31, 95% CI = 2.08-2.57, n = 4, high certainty), thyroid (SRR = 1.42, 95% CI = 1.29-1.57, n = 4, moderate certainty), kidney (SRR = 1.71, 95% CI = 1.40-2.10, n = 3, low certainty), pancreas (SRR = 1.35, 95% CI = 1.24-1.47, n = 3, high certainty), liver (SRR = 1.81, 95% CI = 1.36-2.42, n = 2, moderate certainty), gallbladder (SRR = 1.42, 95% CI = 1.17-1.73, n = 2, high certainty), bladder (SRR = 1.36, 95% CI = 1.19-1.56, n = 2, moderate certainty), and stomach (SRR = 1.50, 95% CI = 1.12-2.01, n = 2, high certainty). In addition, we found elevated risks of most of these cancers among individuals classified as MUNW and MHOW/OB phenotypes compared to those with MHNW phenotype. Our stratified analyses according to metabolic obesity phenotypes suggested that the elevated risks of some cancers were stronger in individuals with MUOW/OB versus those with MHOW/OB or MUNW phenotypes.

CONCLUSION

These findings suggest that both higher adiposity and metabolic dysfunction were independently associated with increased risk of several cancers, with the strongest associations generally observed among those with both metabolic dysfunction and obesity.

Transitions in metabolic syndrome and metabolic obesity status over time and risk of urologic cancer: A prospective cohort study

BACKGROUND AND AIMS

The effects of metabolic obesity (MO) phenotypes status and their dynamic changes on urologic cancer (UC) is ignored. We aimed to investigate the association between metabolic syndrome (MetS) and MO status at baseline, their dynamic changes and UC risk.

METHODS

This paper studied 97,897 subjects who were free of cancers at baseline (2006-2007). Individuals were classified into four MO phenotypes by MetS and obesity at baseline. Transitions in MetS and MO status from 2006-2007 to 2008-2009 were considered. The hazard ratios (HRs) and 95% confidence intervals (CIs) for UC were assessed by multifactorial Cox proportional risk regression models. The main limitations of this study are as follows: the ratio of men to women in the cohort is unbalanced; the impacts of MetS and MO on each cancer type (kidney cancer, prostate cancer, bladder cancer) have not been analyzed separately; the transition intervals of MetS and MO phenotypes are relatively short.

RESULTS

From baseline (2006-2007) survey to December 31, 2020, during a median follow-up of 14.02 years, 554 cases of UC were diagnosed. Participants with MetS [HRs (95% CI) = 1.26 (1.06-1.49)] and metabolically unhealthy obesity (MUO) [HRs (95% CI) = 1.49 (1.17-1.89)] had significantly higher risk of UC than those with non-MetS and metabolically healthy normal weight (MHN). Transitions in MetS and MO phenotypes over time were studied. Compared with non-MetS to non-MetS, the risks for UC in MetS to MetS [HRs (95% CI) = 1.45 (1.11-1.88)] was increased. Compared with MHN to MHN, both MUO to metabolically healthy obesity (MHO) [HRs (95% CI) = 2.65 (1.43-4.92)] and MUO to MUO [HRs (95% CI) = 1.60 (1.06-2.42)] had significantly higher UC risk.

CONCLUSIONS

MetS and MUO increased the UC risk at baseline. Transitions of MetS to MetS, MUO to MUO and even MUO to MHO over time significantly increased the risk of UC development.

Sex disparity in the association between metabolic-anthropometric phenotypes and risk of obesity-related cancer: a prospective cohort study

BACKGROUND

Sex disparity between metabolic-obesity (defined by body mass index, BMI) phenotypes and obesity-related cancer (ORC) remains unknown. Considering BMI reflecting overall obesity but not fat distribution, we aimed to systematically assess the association of our newly proposed metabolic-anthropometric phenotypes with risk of overall and site-specific ORC by sex.

METHODS

A total of 141,579 men (mean age: 56.37 years, mean follow-up time: 12.04 years) and 131,047 women (mean age: 56.22 years, mean follow up time: 11.82 years) from the UK Biobank was included, and designated as metabolic-anthropometric phenotypes based on metabolic status (metabolically healthy/unhealthy), BMI (non-obesity/obesity) and body shape (pear/slim/apple/wide). The sex-specific association of different phenotypes with overall and site-specific ORC was assessed by hazard ratios (HRs) and 95% confidence intervals (CIs) using Cox proportional hazards regression models.

RESULTS

We found metabolically unhealthy and/or obesity phenotypes conveyed a higher risk in men than in women for overall ORC and colorectal cancer compared with metabolically healthy non-obesity phenotype (Pinteraction < 0.05). Of note, metabolically healthy obesity phenotype contributed to increased risks of most ORC in men (HRs: 1.58 ~ 2.91), but only correlated with higher risks of endometrial (HR = 1.89, 95% CI: 1.54-2.32) and postmenopausal breast cancers (HR = 1.17, 95% CI: 1.05-1.31) in women. Similarly, even under metabolically healthy, men carrying apple and wide shapes phenotypes (metabolically healthy apple/wide and metabolically healthy non-obesity apple/wide) suffered an increased risk of ORC (mainly colorectal, liver, gastric cardia, and renal cancers, HRs: 1.20 ~ 3.81) in comparison with pear shape or non-obesity pear shape.

CONCLUSIONS

There was a significant sex disparity between metabolic-anthropometric phenotypes and ORC risk. We advised future ORC prevention and control worth taking body shape and sex disparity into account.

Genetic determinants of obesity in Korean populations: exploring genome-wide associations and polygenic risk scores

East Asian populations exhibit a genetic predisposition to obesity, yet comprehensive research on these traits is limited. We conducted a genome-wide association study (GWAS) with 93,673 Korean subjects to uncover novel genetic loci linked to obesity, examining metrics such as body mass index, waist circumference, body fat ratio, and abdominal fat ratio. Participants were categorized into non-obese, metabolically healthy obese (MHO), and metabolically unhealthy obese (MUO) groups. Using advanced computational methods, we developed a multifaceted polygenic risk scores (PRS) model to predict obesity. Our GWAS identified significant genetic effects with distinct sizes and directions within the MHO and MUO groups compared with the non-obese group. Gene-based and gene-set analyses, along with cluster analysis, revealed heterogeneous patterns of significant genes on chromosomes 3 (MUO group) and 11 (MHO group). In analyses targeting genetic predisposition differences based on metabolic health, odds ratios of high PRS compared with medium PRS showed significant differences between non-obese and MUO, and non-obese and MHO. Similar patterns were seen for low PRS compared with medium PRS. These findings were supported by the estimated genetic correlation (0.89 from bivariate GREML). Regional analyses highlighted significant local genetic correlations on chromosome 11, while single variant approaches suggested widespread pleiotropic effects, especially on chromosome 11. In conclusion, our study identifies specific genetic loci and risks associated with obesity in the Korean population, emphasizing the heterogeneous genetic factors contributing to MHO and MUO.

Metabolically defined body size and body shape phenotypes and risk of postmenopausal breast cancer in the European Prospective Investigation into Cancer and Nutrition

BACKGROUND

Excess body fatness and hyperinsulinemia are both associated with an increased risk of postmenopausal breast cancer. However, whether women with high body fatness but normal insulin levels or those with normal body fatness and high levels of insulin are at elevated risk of breast cancer is not known. We investigated the associations of metabolically defined body size and shape phenotypes with the risk of postmenopausal breast cancer in a nested case-control study within the European Prospective Investigation into Cancer and Nutrition.

METHODS

Concentrations of C-peptide-a marker for insulin secretion-were measured at inclusion prior to cancer diagnosis in serum from 610 incident postmenopausal breast cancer cases and 1130 matched controls. C-peptide concentrations among the control participants were used to define metabolically healthy (MH; in first tertile) and metabolically unhealthy (MU; >1st tertile) status. We created four metabolic health/body size phenotype categories by combining the metabolic health definitions with normal weight (NW; BMI < 25 kg/m2 , or WC < 80 cm, or WHR < 0.8) and overweight or obese (OW/OB; BMI ≥ 25 kg/m2 , or WC ≥ 80 cm, or WHR ≥ 0.8) status for each of the three anthropometric measures separately: (1) MHNW, (2) MHOW/OB, (3) MUNW, and (4) MUOW/OB. Conditional logistic regression was used to compute odds ratios (ORs) and 95% confidence intervals (CIs).

RESULTS

Women classified as MUOW/OB were at higher risk of postmenopausal breast cancer compared to MHNW women considering BMI (OR = 1.58, 95% CI = 1.14-2.19) and WC (OR = 1.51, 95% CI = 1.09-2.08) cut points and there was also a suggestive increased risk for the WHR (OR = 1.29, 95% CI = 0.94-1.77) definition. Conversely, women with the MHOW/OB and MUNW were not at statistically significant elevated risk of postmenopausal breast cancer risk compared to MHNW women.

CONCLUSION

These findings suggest that being overweight or obese and metabolically unhealthy raises risk of postmenopausal breast cancer while overweight or obese women with normal insulin levels are not at higher risk. Additional research should consider the combined utility of anthropometric measures with metabolic parameters in predicting breast cancer risk.

Association between metabolically healthy obesity and kidney stones: results from the 2011-2018 National Health and Nutrition Examination Survey

Introduction

The risk of kidney stones in metabolically healthy obesity (MHO) individuals is largely unexplored. This study using percent body fat (%BF) to categorize obesity, to investigate the association between MHO as well as other metabolic syndrome-obesity combined phenotypes and kidney stones in a national representative population.

Materials and methods

This cross-sectional study included 4,287 participants in the National Health and Nutrition Examination Survey from 2011 to 2018. Metabolically healthy status was defined as not having any component of metabolic syndrome or insulin resistance. Obesity was identified by %BF, which was measured and assessed by dual-energy x-ray absorptiometry (DXA) scan. Participants were cross-classified by metabolic health and obesity status. The outcome was self-report kidney stones. Multivariable logistic regression model was used to examine the association between MHO and kidney stones.

Results

A total of 358 participants had kidney stones [weighted prevalence (SE): 8.61% (0.56%)]. The weighted prevalence (SE) of kidney stones in MHN, MHOW, and MHO groups was 3.13% (1.10%), 4.97% (1.36%), and 8.55% (2.09%), respectively. After adjusting for age, sex, race and ethnicity, education level, smoking status, alcohol consumption, physical activity, daily water intake, CKD stage 3-5, and hyperuricemia, MHO individuals (OR: 2.90, 95% CI: 1.18, 7.0) had a significantly higher risk of kidney stones than those with metabolically healthy normal weight. In metabolically healthy participants, a 5% increment in %BF was associated with a significantly higher risk of kidney stones (OR: 1.60, 95% CI: 1.20, 2.14). Furthermore, a nonlinear dose-response relationship between %BF and the kidney stones was observed in metabolically healthy participants (P for non-linearity = 0.046).

Conclusion

Using %BF to define obesity, MHO phenotype was significantly associated with higher risks of kidney stones, suggesting that obesity can independently contribute to kidney stones in the absence of metabolic abnormalities and insulin resistance. Regarding kidney stones prevention, MHO individuals might still benefit from lifestyle interventions aimed at healthy body composition maintenance.

Metabolically Defined Body Size Phenotypes and Risk of Endometrial Cancer in the European Prospective Investigation into Cancer and Nutrition (EPIC)

BACKGROUND

Obesity is a risk factor for endometrial cancer but whether metabolic dysfunction is associated with endometrial cancer independent of body size is not known.

METHODS

The association of metabolically defined body size phenotypes with endometrial cancer risk was investigated in a nested case-control study (817 cases/ 817 controls) within the European Prospective Investigation into Cancer and Nutrition (EPIC). Concentrations of C-peptide were used to define metabolically healthy (MH; <1st tertile) and metabolically unhealthy (MU; ≥1st tertile) status among the control participants. These metabolic health definitions were combined with normal weight (NW); body mass index (BMI)<25 kg/m2 or waist circumference (WC)<80 cm or waist-to-hip ratio (WHR)<0.8) and overweight (OW; BMI≥25 kg/m2 or WC≥80 cm or WHR≥0.8) status, generating four phenotype groups for each anthropometric measure: (i) MH/NW, (ii) MH/OW, (iii) MU/NW, and (iv) MU/OW.

RESULTS

In a multivariable-adjusted conditional logistic regression model, compared with MH/NW individuals, endometrial cancer risk was higher among those classified as MU/NW [ORWC, 1.48; 95% confidence interval (CI), 1.05-2.10 and ORWHR, 1.68; 95% CI, 1.21-2.35] and MU/OW (ORBMI, 2.38; 95% CI, 1.73-3.27; ORWC, 2.69; 95% CI, 1.92-3.77 and ORWHR, 1.83; 95% CI, 1.32-2.54). MH/OW individuals were also at increased endometrial cancer risk compared with MH/NW individuals (ORWC, 1.94; 95% CI, 1.24-3.04).

CONCLUSIONS

Women with metabolic dysfunction appear to have higher risk of endometrial cancer regardless of their body size. However, OW status raises endometrial cancer risk even among women with lower insulin levels, suggesting that obesity-related pathways are relevant for the development of this cancer beyond insulin.

IMPACT

Classifying women by metabolic health may be of greater utility in identifying those at higher risk for endometrial cancer than anthropometry per se.

Profiling of Urine Carbonyl Metabolic Fingerprints in Bladder Cancer Based on Ambient Ionization Mass Spectrometry

The diagnosis of bladder cancer (BC) is currently based on cystoscopy, which is invasive and expensive. Here, we describe a noninvasive profiling method for carbonyl metabolic fingerprints in BC, which is based on a desorption, separation, and ionization mass spectrometry (DSI-MS) platform with N,N-dimethylethylenediamine (DMED) as a differential labeling reagent. The DSI-MS platform avoids the interferences from intra- and/or intersamples. Additionally, the DMED derivatization increases detection sensitivity and distinguishes carboxyl, aldehyde, and ketone groups in untreated urine samples. Carbonyl metabolic fingerprints of urine from 41 BC patients and 41 controls were portrayed and 9 potential biomarkers were identified. The mechanisms of the regulations of these biomarkers have been tentatively discussed. A logistic regression (LR) machine learning algorithm was applied to discriminate BC from controls, and an accuracy of 85% was achieved. We believe that the method proposed here may pave the way toward the point-of-care diagnosis of BC in a patient-friendly manner.

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.

The ambiguous role of obesity in oncology by promoting cancer but boosting antitumor immunotherapy

Obesity is nowadays considered a pandemic which prevalence's has been steadily increasingly in western countries. It is a dynamic, complex, and multifactorial disease which propitiates the development of several metabolic and cardiovascular diseases, as well as cancer. Excessive adipose tissue has been causally related to cancer progression and is a preventable risk factor for overall and cancer-specific survival, associated with poor prognosis in cancer patients. The onset of obesity features a state of chronic low-grade inflammation and secretion of a diversity of adipocyte-derived molecules (adipokines, cytokines, hormones), responsible for altering the metabolic, inflammatory, and immune landscape. The crosstalk between adipocytes and tumor cells fuels the tumor microenvironment with pro-inflammatory factors, promoting tissue injury, mutagenesis, invasion, and metastasis. Although classically established as a risk factor for cancer and treatment toxicity, recent evidence suggests mild obesity is related to better outcomes, with obese cancer patients showing better responses to treatment when compared to lean cancer patients. This phenomenon is termed obesity paradox and has been reported in different types and stages of cancer. The mechanisms underlying this paradoxical relationship between obesity and cancer are still not fully described but point to systemic alterations in metabolic fitness and modulation of the tumor microenvironment by obesity-associated molecules. Obesity impacts the response to cancer treatments, such as chemotherapy and immunotherapy, and has been reported as having a positive association with immune checkpoint therapy. In this review, we discuss obesity's association to inflammation and cancer, also highlighting potential physiological and biological mechanisms underlying this association, hoping to clarify the existence and impact of obesity paradox in cancer development and treatment.

The relationship between metabolic syndrome and its components with bladder cancer: a systematic review and meta-analysis of cohort studies

A previous meta-analysis, entitled "The association between metabolic syndrome and bladder cancer susceptibility and prognosis: an updated comprehensive evidence synthesis of 95 observational studies involving 97,795,299 subjects," focused on all observational studies, whereas in the present meta-analysis, we focused on cohort studies to obtain more accurate and stronger evidence to evaluate the association between metabolic syndrome and its components with bladder cancer. PubMed, Embase, Scopus, and Web of Science were searched to identify studies on the association between metabolic syndrome and its components with bladder cancer from January 1, 2000 through May 23, 2021. The pooled relative risk (RR) and 95% confidence intervals (CI) were used to measure this relationship using a random-effects meta-analytic model. Quality appraisal was undertaken using the Newcastle-Ottawa Scale. In total, 56 studies were included. A statistically significant relationship was found between metabolic syndrome and bladder cancer 1.09 (95% CI, 1.02 to 1.17), and there was evidence of moderate heterogeneity among these studies. Our findings also indicated statistically significant relationships between diabetes (RR, 1.23; 95% CI, 1.16 to 1.31) and hypertension (RR, 1.07; 95% CI, 1.01 to 1.13) with bladder cancer, but obesity and overweight did not present a statistically significant relationship with bladder cancer. We found no evidence of publication bias. Our analysis demonstrated statistically significant relationships between metabolic syndrome and the risk of bladder cancer. Furthermore, diabetes and hypertension were associated with the risk of bladder cancer.