Metabolic obesity phenotypes and risk of colorectal cancer in postmenopausal women

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.

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.

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.

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.

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.

Plasma Concentrations of Gut Hormones Acyl Ghrelin and Peptide YY and Subsequent Risk of Colorectal Cancer and Molecular Tumor Subtypes

Obesity and metabolic dysfunction are implicated in colorectal cancer development. Appetite-regulating gut hormones might have a role in colorectal cancer risk. We investigated whether circulating levels of the gut hormones ghrelin (analyzed as acyl ghrelin) and Peptide YY (PYY) were associated with subsequent colorectal cancer risk, including clinical and molecular tumor subtypes. We also provide descriptive data on these hormones in relation to background participant characteristics and metabolic biomarkers. This population-based study included 1,010 matched case-control pairs with a median of 12.3 years of follow-up. Acyl ghrelin and PYY were measured by multiplex immunoassay. Data on KRAS and BRAF mutations and microsatellite instability (MSI) status were available for 704 and 708 cases, respectively. Conditional logistic regression models estimated association to colorectal cancer risk. Partial correlation and linear regression were used to investigate relationships between background and metabolic variables and variation in plasma gut hormone concentrations. Acyl ghrelin was not clearly associated with colorectal cancer risk (multivariable OR per 1 SD increase: 1.11; 95% CI, 1.00-1.23). Positive associations were observed for specific subtypes, in particular BRAF-mutated colorectal cancer and right-sided colon cancer, although with nonsignificant heterogeneity. PYY was not related to colorectal cancer risk (multivariable OR per 1 SD: 1.04; 95% CI, 0.95-1.14) or any tumor subtype. In the control participants, ghrelin was inversely correlated with BMI, and PYY was positively correlated with C-peptide and insulin levels. These findings provide limited support for a possible role for ghrelin in colorectal cancer development, primarily in specific anatomical and molecular tumor subtypes.

PREVENTION RELEVANCE

The findings of this study do not support a major role for the metabolic gut hormones ghrelin and PYY in colorectal cancer development but suggest the possibility of an involvement for ghrelin in specific tumor subtypes. Elucidating subtype-specific risk factors and mechanisms of carcinogenesis may have implications for precision prevention.

Association between metabolic syndrome and colorectal cancer incidence and all-cause mortality: a hospital-based observational study

Background

Metabolic syndrome (MetS) is a worldwide pandemic and complex disorder associated with colorectal cancer (CRC). This study aims to identify the influence of number of MetS components on CRC incidence and mortality, using a national, longitudinal dataset of hospital care in Taiwan.

Methods

Patient data from the Taiwan National Health Insurance Research Database (NHIRD) from 2001 to 2008 were extracted. Individuals with at least one inpatient diagnosis or 2 outpatient visits with any MetS component found within one year were identified and included. Subjects died within 12 months after the presence of MetS components or had any prior cancer were excluded. The study cohort were then divided into two groups: subjects who had more (i.e., 3 to 4) MetS components and those who had fewer (i.e., 1 to 2) MetS components. An 2:1 propensity score (PS) matching were performed to balance the baseline characteristics between the groups. Cox regression analyses were conducted to compare the CRC incidence and all-cause mortality at follow-up between subjects with more MetS components versus fewer components.

Results

After matching, a total of 119,843 subjects (78,274 with 1–2 and 41,569 with 3–4 MetS components) were analyzed. After adjusting for confounders, subjects with 3–4 MetS components had a significantly higher risk of CRC [adjusted hazard ratio (aHR), 1.28; 95% confidence interval (CI), 1.15–1.43, p < 0.001) and all-cause mortality (aHR, 1.13; 95% CI, 1.08–1.17, p < 0.001) than those with only 1–2 MetS components. In stratified analyses, the greatest increased risk of CRC incidence that 3–4 MetS components posed as compared to 1–2 MetS components was seen in subjects without CHD history (aHR, 1.41, 95% CI, 1.23–1.62, p < 0.001). In addition, 3–4 MetS components (vs. 1–2) led to greater all-cause mortality among the subjects < 65y, both genders, with or without CHD, subjects without CKD hisotry, both aspirin users and non-users, users of nonsteroidal anti-inflammatory drugs (NSAIDs), and users of statin.

Conclusion

Compared with 1–2 components, subjects with 3–4 MetS components are at greater risk of CRC and death at follow-up. This study also demonstrates the risks for CRC and all-cause mortality in certain subgroups of individuals with 3–4 MetS components compared to 1–2 components. These findings may help clinicians on the CRC risk stratification according to individuals’ characteristics, as well as to optimize the strategy of MetS surveillance and control in order to prevent CRC.

Association of sleep duration and insomnia with metabolic syndrome and its components in the Women's Health Initiative

BACKGROUND

Epidemiological evidence suggests that inadequate sleep duration and insomnia may be associated with increased risk of metabolic syndrome (MetS). However, longitudinal data with repeated measures of sleep duration and insomnia and of MetS are limited. We examined the association of sleep duration and insomnia with MetS and its components using longitudinal data from the Women's Health Initiative (WHI).

METHODS

The study included postmenopausal women (ages 50-79 years) diabetes-free at enrollment in the WHI, with baseline data on sleep duration (n = 5,159), insomnia (n = 5,063), MetS, and its components. Repeated measures of self-reported sleep duration and insomnia were available from years 1 or 3 of follow-up and of the MetS components from years 3, 6 and 9. Associations were assessed using logistic regression and generalized estimating equations models, and odds ratios and 95% confidence intervals (CI) adjusted for major risk factors were calculated.

RESULTS

In cross-sectional analysis, baseline sleep duration ≥ 9 h was positively associated with MetS (OR = 1.51; 95%CI 1.12-2.04), while sleep duration of 8- < 9 h was associated with waist circumference > 88 cm and triglycerides ≥ 150 mg/dL (OR = 1.18; 95%CI 1.01-1.40 and OR = 1.23; 95%CI 1.05-1.46, respectively). Insomnia had a borderline positive association with MetS (OR = 1.14; 95%CI 0.99-1.31), and significant positive associations with waist circumference > 88 cm and glucose ≥ 100 mg/dL (OR = 1.18; 95%CI 1.03-1.34 and OR = 1.17; 95%CI 1.02-1.35, respectively). In the longitudinal analysis, change from restful sleep to insomnia over time was associated with increased odds of developing MetS (OR = 1.40; 95%CI 1.01-1.94), and of a triglyceride level ≥ 150 mg/dL (OR = 1.48; 95%CI 1.08-2.03).

CONCLUSIONS

Among postmenopausal women in the WHI, sleep duration and insomnia were associated with current and future risk of MetS and some of its components.

Metabolic Obesity Phenotypes and Risk of Lung Cancer: A Prospective Cohort Study of 450,482 UK Biobank Participants

(1) Background: The association between metabolic obesity phenotypes and incident lung cancer (LC) remains unclear. (2) Methods: Based on the combination of baseline BMI categories and metabolic health status, participants were categorized into eight groups: metabolically healthy underweight (MHUW), metabolically unhealthy underweight (MUUW), metabolically healthy normal (MHN), metabolically unhealthy normal (MUN), metabolically healthy overweight (MHOW), metabolically unhealthy overweight (MUOW), metabolically healthy obesity (MHO), and metabolically unhealthy obesity (MUO). The Cox proportional hazards model and Mendelian randomization (MR) were applied to assess the association between metabolic obesity phenotypes with LC risk. (3) Results: During a median follow-up of 9.1 years, 3654 incident LC patients were confirmed among 450,482 individuals. Compared with participants with MHN, those with MUUW had higher rates of incident LC (hazard ratio (HR) = 3.24, 95% confidence interval (CI) = 1.33-7.87, p = 0.009). MHO and MHOW individuals had a 24% and 18% lower risk of developing LC, respectively (MHO: HR = 0.76, 95% CI = 0.61-0.95, p = 0.02; MHO: HR = 0.82, 95% CI = 0.70-0.96, p = 0.02). No genetic association of metabolic obesity phenotypes and LC risk was observed in MR analysis. (4) Conclusions: In this prospective cohort study, individuals with MHOW and MHO phenotypes were at a lower risk and MUUW were at a higher risk of LC. However, MR failed to reveal any evidence that metabolic obesity phenotypes would be associated with a higher risk of LC.

Metabolic obesity phenotypes: a friend or foe of digestive polyps?-An observational study based on National Inpatient Database

OBJECTIVE

Obesity is associated with an increased risk of digestive polyps, whereas all obesity are not created equally. The role of metabolic states in occurrence risks of polyps among individuals with varying degrees of obesity remains unknown. Our study aimed to evaluate the association between metabolic obesity phenotypes and the occurrence of digestive polyps.

RESEARCH DESIGN AND METHODS

Data from 9,278,949 patients between 2016 and 2018 from the National Inpatient Sample (NIS) database, a nationally representative database of all discharges from US health-care hospitals, were analyzed. According to obesity phenotype, the study population was classified into four groups: metabolically healthy nonobese (MHNO), metabolically unhealthy nonobese (MUNO), metabolically healthy obese (MHO) and metabolically unhealthy obese (MUO). We calculated the incidence rates of various digestive polyps (stomach/duodenum, colon and rectum polyps) among these participants by searching the hospital records for ICD-10 diagnosis codes indicating each gastric, duodenum, colon or rectal polyps. The multiple stepwise regression analysis and further in-depth subgroup analysis were used to determine the associations between metabolic obesity phenotypes and the occurrence of digestive polyps.

RESULTS

In the total or female population, those with the MUNO and MUO phenotypes had significantly higher prevalence of digestive polyps compared with individuals with the MHNO or MHO phenotypes (all p < 0.05) and a significant difference was not found between MUNO and MUO phenotypes (p > 0.05). Obese subjects seem to be more likely to develop stomach and duodenum polyps or colon polyps than non-obese subjects in metabolically healthy people of males (MHO vs. MHNO, p < 0.05), whereas obesity status seems to have little effect on the occurrence of digestive polyps in metabolically healthy people of females (MHO vs. MHNO, p>0.05). After adjusting for the potential confounders, the MHO, MUNO and MUO phenotypes were all risk factors for stomach and duodenum polyps (OR = 1.46, 95% CI: 1.36-1.58, p< 0.01; OR = 1.19, 95% CI: 1.14-1.25, p< 0.01; OR = 1.44, 95% CI: 1.35-1.55, p< 0.01, respectively) or colon polyps (OR = 1.28, 95% CI: 1.21-1.35, p< 0.01; OR = 1.18, 95% CI: 1.14-1.22, p< 0.01; OR = 1.46, 95% CI: 1.38-1.54, p< 0.01, respectively) compared with the MHNO phenotype，especially in menopausal female. Interestingly, we also observed in further in-depth subgroup analysis that metabolic abnormalities may have a greater impact on the occurrence of digestive polyps than obesity (all p < 0.05).

CONCLUSIONS

Both metabolic abnormities and obesity were associated with a higher risk of digestive polyps. The effect of metabolism on digestive polyp occurrence may be stronger than that of obesity, highlighting the importance of abnormal metabolic status modification regardless of obesity status. Clinical intervention should not only focus on obesity, but also on metabolic abnormalities to decrease digestive polyp risk.

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.

Abdominal obesity phenotypes are associated with the risk of developing non-alcoholic fatty liver disease: insights from the general population

Background

The diversity of obesity-related metabolic characteristics generates different obesity phenotypes and corresponding metabolic diseases. This study aims to explore the correlation of different abdominal obesity phenotypes with non-alcoholic fatty liver disease (NAFLD).

Methods

The current study included 14,251 subjects, 7411 males and 6840 females. Abdominal obesity was defined as waist circumference ≥ 85 cm in males and ≥ 80 cm in females; according to the diagnostic criteria for metabolic syndrome recommended by the National Cholesterol Education Program Adult Treatment Panel III, having more than one metabolic abnormality (except waist circumference criteria) was defined as metabolically unhealthy. All subjects were divided into 4 abdominal obesity phenotypes based on the presence ( +) or absence (− ) of metabolically healthy/unhealthy (MH) and abdominal obesity (AO) at baseline: metabolically healthy + non-abdominal obesity (MH⁻AO⁻); metabolically healthy + abdominal obesity (MH⁻AO⁺); metabolically unhealthy + non-abdominal obesity (MH⁺AO⁻); metabolically unhealthy + abdominal obesity (MH⁺AO⁺). The relationship between each phenotype and NAFLD was analyzed using multivariate logistic regression.

Results

A total of 2507 (17.59%) subjects in this study were diagnosed with NAFLD. The prevalence rates of NAFLD in female subjects with MH⁻AO⁻, MH⁻AO⁺, MH⁺AO⁻, and MH⁺AO⁺ phenotypes were 1.73%, 24.42%, 7.60%, and 59.35%, respectively. Among male subjects with MH⁻AO⁻, MH⁻AO⁺, MH⁺AO⁻, and MH⁺AO⁺ phenotypes, the prevalence rates were 9.93%, 50.54%, 25.49%, and 73.22%, respectively. After fully adjusting for confounding factors, with the MH⁻AO⁻ phenotype as the reference phenotype, male MH⁻AO⁺ and MH⁺AO⁺ phenotypes increased the risk of NAFLD by 42% and 47%, respectively (MH⁻AO⁺: OR 1.42, 95%CI 1.13,1.78; MH⁺AO⁺: OR 1.47, 95%CI 1.08,2.01); the corresponding risks of MH⁻AO⁺ and MH⁺AO⁺ in females increased by 113% and 134%, respectively (MH⁻AO⁺: OR 2.13, 95%CI 1.47,3.09; MH⁺AO⁺: OR 2.34, 95%CI 1.32,4.17); by contrast, there was no significant increase in the risk of NAFLD in the MH⁺AO⁻ phenotype in both sexes.

Conclusions

This first report on the relationship of abdominal obesity phenotypes with NAFLD showed that both MH⁻AO⁺ and MH⁺AO⁺ phenotypes were associated with a higher risk of NAFLD, especially in the female population. These data provided a new reference for the screening and prevention of NAFLD.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12876-022-02393-9.

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.

Metabolically healthy overweight/obesity and cancer risk: A representative cohort study in Taiwan

OBJECTIVE

Many cancers are caused by overweight; however, cancer risk varies among individuals with obesity. Few studies are addressing the relationship between metabolic obesity phenotypes and cancer. This study investigates the association between metabolically healthy overweight (MHOW) or metabolically healthy obesity (MHO) and cancer incidence.

METHODS

In a nationwide, representative community-based prospective cohort study, 5734 Taiwanese adults were classified into eight phenotypes according to body mass index (underweight <18.5; normal weight 18.5-23.9; overweight 24-26.9; and obese ≥27 kg/m²) and metabolic status (healthy/unhealthy). Participants with healthy cardiometabolic blood profiles included in the metabolic syndrome criteria and an absence of hypertension, diabetes, and hyperlipidemia were considered metabolically healthy. We used the Cox proportional hazards models to estimate the adjusted hazard ratio (HR) and 95% confidence intervals (95% CI).

RESULTS

During 73,389 person-years of follow-up, 428 incident cancers were identified. Compared to the participants with metabolically healthy normal weight, participants with MHOW (adjusted HR 1.39, 95% CI, 0.90-2.13) or MHO (adjusted HR 1.07, 95% CI, 0.51-2.22) had a tendency toward a higher risk of cancer. These associations were stronger in MHOW (adjusted HR 1.77, 95% CI, 1.09-2.86) or MHO (adjusted HR 1.39, 95% CI, 0.66-2.93) participants younger than 65 years.

CONCLUSIONS

This study was the first to investigate the impact of metabolic obesity phenotype on the incidence of cancer in the Taiwanese population. Even in the absence of metabolic abnormalities, overweight, and obesity may cause a modest increase in the risk of developing cancer.

Cancer Risk in Normal Weight Individuals with Metabolic Obesity: A Narrative Review

Obesity represents one of the most significant public health challenges worldwide. Current clinical practice relies on body mass index (BMI) to define the obesity status of an individual, even though the index has long been recognized for its limitations as a measure of body fat. In normal BMI individuals, increased central adiposity has been associated with worse health outcomes, including increased risks of cardiovascular disease and metabolic disorders. The condition leading to these outcomes has been described as metabolic obesity in the normal weight (MONW). More recent evidence suggests that MONW is associated with increased risk of several obesity-related malignancies, including postmenopausal breast, endometrial, colorectal, and liver cancers. In MONW patients, the false reassurance of a normal range BMI can lead to lost opportunities for implementing preventive interventions that may benefit a substantial number of people. A growing body of literature has documented the increased risk profile of MONW individuals and demonstrated practical uses for body composition and biochemical analyses to identify this at-risk population. In this review, we survey the current literature on MONW and cancer, summarize pathophysiology and oncogenic mechanisms, highlight potential strategies for diagnosis and treatment, and suggest directions for future research.

Sex Differences in the Incidence of Obesity-Related Gastrointestinal Cancer

Cancer is the second leading cause of death worldwide, with 9.6 million people estimated to have died of cancer in 2018. Excess body fat deposition is a risk factor for many types of cancer. Men and women exhibit differences in body fat distribution and energy homeostasis regulation. This systematic review aimed to understand why sex disparities in obesity are associated with sex differences in the incidence of gastrointestinal cancers. Cancers of the esophagus, liver, and colon are representative gastrointestinal cancers, and obesity is a convincing risk factor for their development. Numerous epidemiological studies have found sex differences in the incidence of esophageal, liver, and colorectal cancers. We suggest that these sexual disparities are partly explained by the availability of estrogens and other genetic factors regulating inflammation, cell growth, and apoptosis. Sex differences in gut microbiota composition may contribute to differences in the incidence and phenotype of colorectal cancer. To establish successful practices in personalized nutrition and medicine, one should be aware of the sex differences in the pathophysiology and associated mechanisms of cancer development.

Metabolic Syndrome and Colorectal Cancer Risk: Results of Propensity Score-Based Analyses in a Community-Based Cohort Study

Background: This study aimed to determine the effects of metabolic syndrome (MetS) on colorectal cancer (CRC) using propensity score (PS) methods. Methods: The study subjects were 2417 men and 4568 women from the Korean National Cancer Center (KNCC) Community Cohort enrolled between 2003 and 2010. Odds risks (ORs) and 95% confidence intervals (CIs) using PS matching analysis, regression models adjusted by the PS or stratified into five strata according to PS, and PS weighting methods were calculated. Results: In women, MetS and abnormally high triglyceride (TG) levels were associated with CRC risk using the PS matching analysis (ORs, for MetS, 2.19 (95% CI, 1.10–4.33); for abnormal TG levels, 2.08 (95% CI, 1.07–4.02)). However, there were no significant associations between MetS and TG levels and CRC risk in men. Conclusions: Our study might provide additional evidence that deteriorated metabolic profiles increase the risk of CRC in women rather than men. Thus, this may have an important role in effective population-level interventions for deteriorated metabolic profiles at an early stage.

Joint associations of physical activity and body mass index with the risk of established excess body fatness-related cancers among postmenopausal women

PURPOSE

Excess body fatness and physical activity independently influence the risk of several types of cancer. However, few studies have examined whether physical activity mitigates the excess risk associated with higher body mass index (BMI).

METHODS

We examined the individual and joint associations between BMI (kg/m²) and leisure-time moderate-to-vigorous physical activity (MVPA, MET-hours/week) with the risk of three established excess body fatness-related cancers (breast, colon, and endometrial) among 43,795 postmenopausal women in the Cancer Prevention Study II (CPS-II) Nutrition Cohort (1992/1993-2015). Further exclusions for women without an intact uterus resulted in 31,805 women for endometrial cancer analyses. Multivariable Cox proportional hazards regression was used to calculate hazard ratio (HR) and 95% confidence intervals (CIs) with interaction terms to assess multiplicative interaction. The relative excess risk due to interaction (RERI) was calculated to assess additive interaction.

RESULTS

BMI and MVPA were individually associated with breast and endometrial cancer risk, but only BMI was associated with colon cancer risk. In joint analyses, increasing levels of MVPA did not lower the risk of these cancers among obese women. For example, compared to the common referent (BMI 18.5- < 25 kg/m², MVPA > 0- < 7.5 MET-hours/week), BMI ≥ 30 kg/m² was associated with a higher risk of breast cancer among women with low MVPA (> 0-< 7.5 MET-hours/week: HR = 1.42, 95% CI: 1.22 - 1.67) and high MVPA (≥ 15 MET-hours/week: HR = 1.53, 95% CI: 1.25 - 1.87; RERI = 0.20, 95% CI: -0.14, 0.54, multiplicative P_interaction = 0.64).

CONCLUSION

Our results do not support the hypothesis that leisure-time physical activity mitigates the excess risk associated with higher BMI for risk of breast, endometrial, or colon cancer among postmenopausal women.

The association between metabolically healthy obesity and risk of cancer: A systematic review and meta-analysis of prospective cohort studies

The risk of cancer among adults with metabolically healthy obesity (MHO) has not yet been established. We systematically searched from inception to 15 March 2020. We included prospective cohort studies that compared participants with MHO and participants with metabolically healthy non-obesity (MHNO) for incidence of any type of cancer. Benign tumors, cancer mortality or cancer prognosis were not in the scope of our analysis. The Newcastle-Ottawa Scale was used for quality assessment. Ultimately, eight studies with a total of 12 542 390 participants were included. The pooled meta-analysis using random effect model showed participants with MHO demonstrated a significantly increased risk of developing cancer (odds ratio [OR], 1.14; 95% confidence interval [CI], 1.05 to 1.23; and I² = 39%) than those with MHNO. The subgroup analysis revealed a higher pooled estimate (OR, 1.17; 95% CI, 1.01-1.35; and I² = 56%) in comparison with metabolically healthy normal weight. No evidence of effect modification by age, sex, ethnicity, smoking, sample size or length of follow-up was found. In conclusion, the present study reports a positive association between MHO and cancer incidence. All individuals with obesity, even in the absence of metabolic dysfunction, should be encouraged to lose weight.

Diabetes and kidney cancer risk among post-menopausal women: The Iowa women's health study

BACKGROUND

Many studies have reported a positive association between diabetes and kidney cancer. However, it is unclear whether diabetes is a risk factor for kidney cancer independent of other risk factors, such as obesity and hypertension. We comprehensively examined the association of diabetes and its duration with incident kidney cancer in the prospective cohort Iowa Women's Health Study (1986-2011).

METHODS

Diabetes status was self-reported at baseline (1986) and on five follow-up questionnaires. Cox proportional hazards regression was used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) for the associations of baseline and time-dependent diabetes with the risk of incident kidney cancer.

RESULTS

During the 25 years of follow-up, 245 cases of kidney cancer occurred among 36,975 post-menopausal women. In an age-adjusted model, there was a significant association between time-dependent diabetes and the risk of kidney cancer [HR (95% CI) = 1.76 (1.26, 1.45)]; the association was attenuated after multivariable adjustment for age, body mass index (BMI), waist-to-hip ratio (WHR), hypertension, physical activity, diuretic use, pack-years of smoking, alcohol intake, and total caloric intake [HR = 1.35 (0.94, 1.94)]. However, among non-obese women or women with a waist circumference less than 34.6 in., diabetes was significantly associated with kidney cancer risk: for time-dependent diabetes, HRs (95% CIs) were 1.82 (1.10, 3.00) among those with BMI < 30 kg/m² and 2.18 (1.08, 4.38) among those with a waist circumference <34.6 in..

CONCLUSIONS

Our results suggest that diabetes is associated with kidney cancer risk among non-obese post-menopausal women.

Association of obesity status and metabolic syndrome with site-specific cancers: a population-based cohort study

Background

Obesity and metabolic syndrome (MetS) appear in clusters and are both associated with an increased risk of cancer. However, it remains unknown whether obesity status with or without MetS increases the risk of site-specific cancers.

Methods

We used data derived from 390,575 individuals (37–73 years old) from the UK Biobank who were enrolled from 2006–2016 with a median of 7.8 years of follow-up. Obesity was defined by BMI ≥ 30 kg/m² and MetS was defined by the criteria of the Adult Treatment Panel-III (ATP-III). Cox proportional hazards models were used to investigate the associations of BMI and MetS with 22 cancers.

Results

Metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUO) phenotypes represented 6.7% and 17.9% of the total analytic samples and 27.1% and 72.9% of the included subpopulation with obesity, respectively. Obesity was independently associated with higher risks of 10 of 22 cancers. Stratified by metabolic status, the MUO phenotype was consistently associated with 10 obesity-related cancers. In contrast, the MHO phenotype was only associated with increased risks of five cancers: endometrium, oesophagus, kidney, pancreas and postmenopausal breast cancers.

Conclusion

Even in metabolically healthy individuals, obesity was associated with increased risks of five cancers, whereas we did not find that these individuals were associated with increased risks of several other obesity-related cancers.

Mendelian Randomization Study: The Association Between Metabolic Pathways and Colorectal Cancer Risk

Background: The roles of obesity-related biomarkers and their molecular pathways in the development of postmenopausal colorectal cancer (CRC) have been inconclusive. We examined insulin resistance (IR) as a major hormonal pathway mediating the association between obesity and CRC risk in a Mendelian randomization (MR) framework.

Methods: We performed MR analysis using individual-level data of 11,078 non-Hispanic white postmenopausal women from our earlier genome-wide association study. We identified four independent single-nucleotide polymorphisms associated with fasting glucose (FG), three with fasting insulin (FI), and six with homeostatic model assessment–IR (HOMA-IR), which were not associated with obesity. We estimated hazard ratios (HRs) for CRC by adjusting for potential confounding factors plus genetic principal components.

Results: Overall, we observed no direct association between combined 13 IR genetic instruments and CRC risk (HR = 0.96, 95% confidence interval [CI]: 0.78–1.17). In phenotypic analysis, genetically raised HOMA-IR exhibited its effects on the increased risk and FG and FI on the reduced risk for CRC, but with a lack of statistical power. Subgroup analyses by physical activity level and dietary fat intake with combined phenotypes showed that genetically determined IR was associated with reduced CRC risk in both physical activity-stratified (single contributor: MTRR rs722025; HR = 0.12, 95% CI: 0.02–0.62) and high-fat diet subgroups (main contributor: G6PC2 rs560887; HR = 0.59, 95% CI: 0.37–0.94).

Conclusions: Complex evidence was observed for a potential causal association between IR and CRC risk. Our findings may provide an additional value of intervention trials to lower IR and reduce CRC risk.

Synergism of Adipocytokine Profile and ADIPOQ/TNF-α Polymorphisms in NAFLD-associated MetS Predict Colorectal Liver Metastases Outgrowth

BACKGROUND/AIM

The aim of this study was to evaluate whether the altered profile of adipocytokine and genetic fingerprint in NAFLD-associated metabolic syndrome "cluster" represents synergistic risk factors predicting onset of liver colorectal cancer metastases.

MATERIALS AND METHODS

A total of 165 colorectal cancer patients were enrolled, 56,3% were with metabolic syndrome/NAFLD. Serum samples were assayed for ADIPOQ, leptin and TNF-a levels by ELISA. ADIPOQ rs266729 C/G and TNF-308 A/G genotypes were analyzed in DNA isolated from whole blood.

RESULTS

Reduction in adiponectin levels and increase in leptin and TNF-α was shown in patients with liver metastases. This trend was influenced by BMI, MetS/NAFLD, and insulin resistance. ADIPOQ G rs266729 and TNF- 308 A allele are associated with obesity, MetS/NAFLD and insulin resistance. ADIPOQ CG/GG and GA/AA TNF-alpha genotypes confer susceptibility to liver metastases.

CONCLUSION

Obesity and hepatic steatosis significantly favor the development of colorectal cancer liver metastases and the individual adipocytokines genetic profile may play an important predictive role.

Combined test of third lumbar skeletal muscle index and prognostic nutrition index improve prognosis prediction power in resected colorectal cancer liver metastasis

Background: In this paper, we aim to explore clinical value of skeletal muscle index (SMI) and prognostic nutrition index (PNI) on resected colorectal cancer liver metastasis (CRLM).

Results: Among the 539 patients, 355 were males. Baseline lower SMI was associated with smaller BMI, smaller PNI, smaller pre-albumin and longer hospitalization days (P<0.05). Patients with lower SMI and PNI had significantly shorter duration of PFS and OS (P<0.05). SMI can reflect the postoperative treatment response. Postoperative 6-month’s and 12-month’s SMI and PNI can indicate overall prognosis. When combined SMI and PNI, prognostic AUC of ROC curves improved significantly.

Conclusion: Combined monitor of SMI and PNI can improve the power at predicting prognosis. Postoperative 6-month’s record of SMI and PNI was more accurate and predictive for CRLM prognosis.

Method: A total of 539 resected CRLM patients between January 2013 to December 2016 with complete clinical data were included. Computed tomography image was collected from each patient. Receiver-operating characteristic (ROC) curves were constructed; area under curves (AUC) were also determined. All clinical variables were analyzed in proper way.

Lipid and metabolic profiles in female dogs with mammary carcinoma receiving dietary fish oil supplementation

Background

Dyslipidemias induce angiogenesis and accelerate the development and in vitro growth of breast tumors. The aim of this study was to assess the lipid and metabolic profile of female dogs with mammary carcinomas and their correlations with body condition score and degree of tumor malignancy, as well as to study the effect of dietary fish oil supplementation on these animals.

Results

Overweight or obese dogs had more aggressive carcinomas and higher triglyceride (p = 0.0363), VLDL (p = 0.0181), albumin (p = 0.0188), globulin (p = 0.0145) and lactate (p = 0.0255) concentrations. There was no change in the lipid profile after supplementation with fish oil at any concentration. However, in relation to the metabolic profile, glucose (p = 0.0067), total protein (p = 0.0002) and globulin (p = 0.0002) concentrations were increased when 90% omega-3 fish oil was used as a dietary supplement.

Conclusion

Obese dogs showed altered lipid and metabolic profiles and more aggressive tumors, suggesting an important relationship between dyslipidemia and tumor aggressiveness. Supplementation with fish oil, rich in omega-3 fatty acids, may alter metabolic parameters in cancer patients.

The Association between Prebiotic Fiber Supplement Use and Colorectal Cancer Risk and Mortality in the Women's Health Initiative

BACKGROUND

Fiber-based prebiotic supplements are marketed for maintaining bowel health and promoting beneficial gut bacteria. However, the association between prebiotic supplement use and colorectal cancer risk and mortality is unknown.

METHODS

The association between prebiotic use and colorectal cancer risk and mortality was evaluated in postmenopausal women in the Women's Health Initiative study. Self-reported prebiotic use was documented at study enrollment. Adjudicated colorectal cancer cases and mortality were captured using medical and death records. Cox proportional hazards models were used to estimate the HR related to prebiotic use and colorectal cancer risk and mortality.

RESULTS

In total, 3,032 colorectal cancer cases were diagnosed during an average 15.4 years of follow-up. Overall, 3.7% of women used a prebiotic with psyllium, the major fiber type. Use of any prebiotic supplement was not associated with colorectal cancer risk or mortality. The type of prebiotic supplement (none vs. insoluble or soluble) was not associated with colorectal cancer risk; however, use of insoluble fiber prebiotics compared with none was associated with higher colorectal cancer mortality [HR, 2.79; 95% confidence interval (CI), 1.32-5.90; P = 0.007]. Likelihood ratio tests indicated no significant interactions between prebiotic use and other colorectal cancer risk factors, including metabolic syndrome.

CONCLUSIONS

Prebiotic fiber supplement use was not associated with colorectal cancer risk. Insoluble, but not soluble, prebiotic fiber use was associated with higher colorectal cancer mortality. These findings do not support the promotion of prebiotic fiber supplements to reduce colorectal cancer risk or colorectal cancer mortality.

IMPACT

Further investigation is warranted for findings regarding insoluble prebiotic fiber and higher colorectal cancer mortality in postmenopausal women.