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.

Association between metabolically healthy obesity and metastasis in lung cancer patients - a systematic review and meta-analysis.

Background: Many clinical trials have looked at the relationship between obesity and lung cancer (LC), however, there is scarcity of literature specifically addressing the association between metabolically healthy obesity and metastasis in LC patients. To address this gap in the body of evidence, the study was conducted to observe the association between metabolically healthy obesity and metastasis in LC patients. Methods: We conducted a pre-registered systematic review by searching six major online databases to identify studies relevant related to our investigation, in adherence with the PRISMA guidelines. A proper data extraction protocol was further established to synthesize the findings from the selected papers through a meta-analysis. Results: Eleven (11) studies met the requisite selection criterion and were included in the study. A random-effect model was used. Obesity was found to have a significant impact on readmission in LC patients. The combined analysis showed a significant effect size of 0.08 (95% CI 0.07 to 0.08), indicating a noticeable impact of obesity. It was also assessed that obese individuals had a 34% reduced risk of LC compared to normal weight individuals. Obesity was associated with a lower risk of surgical complications with a pooled risk ratio of 0.13 (95% CI 0.12 to 0.14). A statistically significant decreased risk of LC (pooled RR = 0.72, 95% CI: 0.68 to 0.77) was also observed in the obese individuals. Conclusion: The analysis reveals that obesity is associated with a noticeable increase in readmissions, although the impact on LC risk itself is negligible. Moreover, obesity appears to have a beneficial effect by reducing the risk of surgical complications. These results highlight the complex relationship between the two aforementioned factors, emphasizing the importance of considering obesity as a significant factor in patient management and healthcare decision-making. Systematic review registration: https://www.crd.york.ac.uk/prospero/, identifier CRD42023427612.

Adherence to the Mediterranean diet as a possible additional tool to be used for screening the metabolically unhealthy obesity (MUO) phenotype.

BACKGROUND: The terms metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUO) categorize subjects with obesity based on the presence or absence of cardio-metabolic risk factors. Detecting MUO phenotype is crucial due to the high risk of cardio-metabolic complications, requiring tailored and intensive follow-up. However, diagnosing MUO is time-consuming and costly. Thus, we aimed to investigate the role of Mediterranean diet (MD) in determining MHO/MUO phenotypes and whether adherence to MD could serve as an additional screening tool for MUO phenotype. METHODS: The study population of this cross-sectional observational study consisted of 275 subjects with obesity. We assessed their lifestyle habits (physical activity and smoking habits), anthropometric measurements (weight, height, waist circumference, body mass index), blood pressure, metabolic parameters, inflammatory marker (high sensitivity C reactive protein levels), adherence to MD (by PREvención con DIetaMEDiterránea (PREDIMED) questionnaire), and MHO/MUO phenotypes. RESULTS: The study included 275 individuals with obesity (256F/19M; 34.0 ± 10.5 years; BMI 38.3 ± 5.95 kg/m2). Among them, 114 (41.5%) exhibited MHO phenotype, while 161 (58.5%) had MUO phenotype. MHO phenotype exhibited favorable anthropometric and cardio-metabolic profiles, characterized by lower waist circumference (p < 0.001), BMI (p < 0.001), insulin resistance (p < 0.001), blood pressure (p < 0.001), inflammation (p < 0.001), and lipid levels (p < 0.001) compared to MUO phenotype. Notably, we found that MHO phenotype had higher adherence to MD (p < 0.001) and consumed more extra virgin olive oil (EVOO) (p < 0.001), vegetables (p < 0.001), fruits (p < 0.001), legumes (p = 0.001), fish (p < 0.001), wine (p = 0.008), and nuts (p = 0.001), while reporting lower intake of red/processed meats (p < 0.001), butter, cream, margarine (p = 0.008), soda drinks (p = 0.006), and commercial sweets (p = 0.002) compared to MUO phenotype. Adherence to MD (p < 0.001) and EVOO (p = 0.015) intake were identified as influential factors in determining the presence of MUO/MHO phenotypes. Furthermore, a PREDIMED score < 5 proved to be the most sensitive and specific cut-point value for predicting the presence of MUO phenotype (p < 0.001). CONCLUSION: High adherence to MD was associated with MHO phenotype. Moreover, we suggest that a specific cut-off of the PREDIMED score could be an indicator to discriminate patients with MUO/MHO phenotypes and therefore help in identifying patients at higher cardiovascular risk who will require specific dietary intervention.

Tirzepatide and potential use for metabolically healthy obesity.

Metabolically healthy obesity or metabolically healthy overweight (MHO) is best described as being absent of any major metabolic disorder or cardiovascular diseases such as type 2 diabetes mellitus, dyslipidemia, hypertension, and atherosclerotic cardiovascular disease despite being obese or overweight. Nevertheless, MHO is being recognized as an important risk factor for the development of cardiovascular, cerebrovascular, and peripheral artery disease. In addition, these patients are at a high risk of conversion to the metabolically unhealthy phenotype. Tirzepatide is a newly developed glucose-lowering agent which acts on the glucose-dependent insulinotropic peptide (GIP) and glucagon-like peptide-1 (GLP-1) receptors. It has been shown to result in several highly beneficial outcomes including weight loss and a significant improvement in important metabolic parameters such as HbA1c, fasting serum glucose, and triglyceride/lipoprotein levels. These findings suggest that tirzepatide could potentially be beneficial to metabolically healthy obese or metabolically healthy overweight patients in reducing their risk of adverse cardiovascular outcomes and conversion to the metabolically unhealthy phenotype. In this review, we aim to discuss the potential benefits of using the novel anti-diabetic tirzepatide in the management of MHO to prevent the development of cardiovascular events and to decrease the likelihood of conversion to the unhealthy phenotype. We initially describe the clinical outcomes of MHO as well as the association of MHO with developing future cardiovascular events. We then delineate the currently available evidence behind the clinical effects of tirzepatide. We finally discuss the potential advantages of using tirzepatide in the management of MHO.

Metabolically healthy obesity is associated with higher risk of both hyperfiltration and mildly reduced estimated glomerular filtration rate: the role of serum uric acid in a cross-sectional study.

BACKGROUND: The impact of metabolically healthy obesity (MHO) on kidney dysfunction remains debatable. Moreover, few studies have focused on the early stages of kidney dysfunction indicated by hyperfiltration and mildly reduced eGFR. Thus, we aimed to investigate the association between the MHO and early kidney dysfunction, which is represented by hyperfiltration and mildly reduced estimated glomerular filtration rate (eGFR), and to further explore whether serum uric acid affects this association. METHODS: This cross-sectional study enrolled 1188 residents aged ≥ 40 years old from Yonghong Communities. Metabolically healthy phenotypes were categorized based on Adult Treatment Panel III criteria. Obesity was defined as body mass index (BMI) ≥ 25 kg/m2. Mildly reduced eGFR was defined as being in the range 60 < eGFR ≤ 90 ml/min/1.73m2. Hyperfiltration was defined as eGFR > 95th percentile after adjusting for sex, age, weight, and height. RESULTS: Overall, MHO accounted for 12.8% of total participants and 24.6% of obese participants. Compared to metabolically healthy non-obesity (MHNO), MHO was significantly associated with an increased risk of mildly reduced eGFR (odds ratio [OR] = 1.85, 95% confidence interval [CI] 1.13-3.01) and hyperfiltration (OR = 2.28, 95% CI 1.03-5.09). However, upon further adjusting for uric acid, the association between the MHO phenotype and mildly reduced eGFR was reduced to null. Compared with MHNO/non-hyperuricemia, MHO/non-hyperuricemia was associated with an increased risk of mildly reduced eGFR (OR = 2.04, 95% CI 1.17-3.58), whereas MHO/hyperuricemia was associated with an observably increased risk (OR = 3.07, 95% CI 1.34-7.01). CONCLUSIONS: MHO was associated with an increased risk of early kidney dysfunction, and the serum uric acid partially mediated this association. Further prospective studies are warranted to clarify the causality.

Inflammation biomarkers and inflammatory genes expression in metabolically healthy obese patients.

BACKGROUND AND AIMS: Obesity without metabolic alterations (Metabolically Healthy Obesity, MHO) is a condition with a risk of death and cardiovascular disease lower than that of obesity associated with metabolic alterations (Metabolically Unhealthy Obesity, MUO) and similar to that of healthy non obese individuals. Inflammation is considered as a key risk factor mediating the adverse health outcomes in obesity. METHODS AND RESULTS: We compared circulating levels of thirteen major cytokines and adipokines and the expression profiles of fifteen pro-inflammatory and two anti-inflammatory genes in visceral and subcutaneous adipose tissue in a series of 16 MHO patients and in 32 MUO patients that underwent bariatric surgery. MHO was defined according to the most applied definition in current literature. Serum levels of a large set of major cytokines and adipokines did not differ between MHO and MUO patients (p ≥ 0.15). Analyses of the expression profile of pro-inflammatory and anti-inflammatory genes in subcutaneous and visceral adipose tissue failed to show differences between MHO and MUO patients (p ≥ 0.07). Sensitivity analyses applying two additional definitions of MHO confirmed the results of the primary analysis. CONCLUSION: In a series of metabolically healthy obese patients neither circulating levels of major cytokines and adipokines nor the gene expression profile of a large set of pro-inflammatory and anti-inflammatory genes in subcutaneous and visceral fat differed from those in metabolically unhealthy obese patients.

Metabolically (un)healthy obesity and risk of obesity-related cancers: a pooled study.

BACKGROUND: Studies of obesity with or without metabolic aberrations, commonly termed metabolically unhealthy or healthy obesity, in relation to cancer risk are scarce. METHODS: We investigated body mass index (normal weight, overweight, obesity) jointly and in interaction with metabolic health status in relation to obesity-related cancer risk (n = 23 630) among 797 193 European individuals. A metabolic score comprising mid-blood pressure, plasma glucose, and triglycerides was used to define metabolically healthy and unhealthy status. Hazard ratios (HRs) and multiplicative interactions were assessed using Cox regression, and additive interactions were assessed using the relative excess risk for interaction. All statistical tests were 2-sided. RESULTS: Metabolically unhealthy obesity, with a baseline prevalence of 7%, was, compared with metabolically healthy normal weight, associated with an increased relative risk of any obesity-related cancer and of colon, rectal, pancreas, endometrial, liver, gallbladder, and renal cell cancer (P < .05), with the highest risk estimates for endometrial, liver, and renal cell cancer (HR = 2.55-3.00). Metabolically healthy obesity showed a higher relative risk for any obesity-related cancer and colon (in men), endometrial, renal cell, liver, and gallbladder cancer, though the risk relationships were weaker. There were no multiplicative interactions, but there were additive, positive interactions between body mass index and metabolic health status on obesity-related and rectal cancer among men and on endometrial cancer (P < .05). CONCLUSIONS: This study highlights that the type of metabolic obesity phenotype is important when assessing obesity-related cancer risk. In general, metabolic aberrations further increased the obesity-induced cancer risk, suggesting that obesity and metabolic aberrations are useful targets for prevention.

Associations of metabolic heterogeneity of obesity with frailty progression: Results from two prospective cohorts.

BACKGROUND: Previous studies indicated that obesity would accelerate frailty progression. However, obesity is heterogeneous by different metabolic status. The associations of metabolic heterogeneity of obesity with frailty progression remain unclear. METHODS: A total of 6730 participants from the China Health and Retirement Longitudinal Study (CHARLS) and 4713 from the English Longitudinal Study of Ageing (ELSA) were included at baseline. Metabolic heterogeneity of obesity was evaluated based on four obesity and metabolic phenotypes as metabolically healthy normal weight (MHNW), metabolically unhealthy normal weight (MUNW), metabolically healthy overweight/obesity (MHOO), and metabolically unhealthy overweight/obesity (MUOO). Frailty status was assessed by the frailty index (FI) ranging from 0 to 100 and frailty was defined as FI ≥ 25. Linear mixed-effect models were used to analyse the associations of metabolic heterogeneity of obesity with frailty progression. RESULTS: In the CHARLS, MUOO and MUNW presented the accelerated FI progression with additional annual increases of 0.284 (95% CI: 0.155 to 0.413, P < 0.001) and 0.169 (95% CI: 0.035 to 0.303, P = 0.013) as compared with MHNW. MHOO presented no accelerated FI progression (ß: -0.011, 95% CI: -0.196 to 0.173, P = 0.904) as compared with MHNW. In the ELSA, the accelerated FI progression was marginally significant for MUOO (ß: 0.103, 95% CI: -0.005 to 0.210, P = 0.061) and MUNW (ß: 0.157, 95% CI: -0.011 to 0.324, P = 0.066), but not for MHOO (ß: -0.047, 95% CI: -0.157 to 0.062, P = 0.396) in comparison with MHNW. The associations of MUOO and MUNW with the accelerated FI progression were stronger after excluding the baseline frail participants in both cohorts. The metabolic status changed over time. When compared with stable MHNW, participants who changed from MHNW to MUNW presented the accelerated FI progression with additional annual increases of 0.356 (95% CI: 0.113 to 0.599, P = 0.004) and 0.255 (95% CI: 0.033 to 0.477, P = 0.024) in the CHARLS and ELSA, respectively. The accelerated FI progression was also found in MHOO participants who transitioned to MUOO (CHARLS, ß: 0.358, 95% CI: 0.053 to 0.663, P = 0.022; ELSA, ß: 0.210, 95% CI: 0.049 to 0.370, P = 0.011). CONCLUSIONS: Metabolically unhealthy overweight/obesity and normal weight, but not metabolically healthy overweight/obesity, accelerated frailty progression as compared with metabolically healthy normal weight. Regardless of obesity status, transitions from healthy metabolic status to unhealthy metabolic status accelerated frailty progression as compared with stable metabolically healthy normal weight. Our findings highlight the important role of metabolic status in frailty progression and recommend the stratified management of obesity based on 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.

Association between obese phenotypes and risk of carotid artery plaque among chinese male railway drivers.

BACKGROUND: China has the world's highest rail transportation network density, and the prevalence of obesity among railway workers in China is more than twice that of adults in the world. Carotid artery plaque (CAP) is a simple and noninvasive predictor of early atherosclerosis, while the association between different obese phenotypes and CAP risk among Chinese male railway drivers is unclear. METHODS: This cross-sectional study was performed among 8,645 Chinese male railway drivers. Obese phenotypes were assessed based on the obesity status (the body mass index ≥ 28 kg/m2 as obesity vs. < 28 kg/m2 as non-obesity) and metabolic status (metabolically healthy vs. metabolically unhealthy). Metabolically unhealthy was defined as the presence of at least one dysfunction, including elevated blood pressure, elevated fasting blood glucose, elevated triglyceride, and reduced high-density-lipoprotein cholesterol. Four obese phenotypes were defined based on the body mass index and metabolic status, i.e., metabolically healthy non-obesity (MHNO), metabolically healthy obesity (MHO), metabolically unhealthy obesity (MUO), and metabolically unhealthy non-obesity (MUNO). Multivariable logistic regression was employed to estimate the association between different obese phenotypes and the risk of CAP. Subgroup analysis was performed to examine the variation of the association by age, circadian rhythm disorders, and history of smoking and drinking. RESULTS: The prevalence of CAP among male railway drivers in MHO, MUO, MUNO, and MHNO was 8.75%, 18.67%, 17.82%, and 5.36%, respectively. Compared to those with MHNO, an increased risk for CAP was observed among those with MHO (OR = 2.18, 95% CI: 0.82, 5.10), MUO (OR = 1.78, 95% CI:1.44, 2.21), and MUNO (OR = 2.20, 95% CI: 1.67, 2.89). The subgroup analysis showed that both of the metabolically unhealthy groups (MUNO and MUO) aged < 45 years were prone to a higher risk of CAP (for the MUNO group, OR = 4.27, 95% CI:2.71, 7.10; for the MUO group, OR = 4.00, 95%CI: 2.26, 7.17). CONCLUSION: The obese phenotypes are associated with CAP risk in male railway drivers, especially those with metabolically unhealthy conditions aged < 45 years.

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.

The Association Between Metabolic Derangement and Wound Complications in Elective Plastic Surgery.

INTRODUCTION: The incidence of metabolically unhealthy obesity is rising nationally. In this study, we compare wound and overall complications between metabolically unhealthy obese and healthy patients undergoing elective plastic surgery and model how operative time influences a complication risk. METHODS: Patients undergoing elective breast and body plastic surgery procedures in the 2009-2019 National Surgical Quality Improvement Program (NSQIP) dataset were identified. Complications were compared between metabolically unhealthy obese (body mass index [BMI] > 30 with diabetes and/or hypertension) versus metabolically healthy obese patients (BMI > 30 without diabetes or hypertension). Logistic regression was used to model the probability of wound complications across operative times stratified by metabolic status. RESULTS: Of 139,352 patients, 13.4% (n = 18,663) had metabolically unhealthy obesity and 23.8% (n = 33,135) had metabolically healthy obesity. Compared to metabolically healthy patients, metabolically unhealthy patients had higher incidence of wound complications (6.9% versus 5.6%; P < 0.001) and adverse events (12.4% versus 9.6%; P < 0.001), in addition to higher 30-d readmission, returns to the operating room, and length of stay (all P < 0.001). After adjustment, BMI (Odds ratio [OR] 7.86), hypertension (OR 1.15), and diabetes (OR 1.25) were independent risk factors for wound complications (all P < 0.001). Among metabolically unhealthy patients, the operative time was log-linear with a wound complication risk (OR 1.21; P < 0.001). CONCLUSIONS: Diabetes and hypertension are additive risk factors with obesity for wound complications in elective plastic surgery. Among patients with metabolically unhealthy obesity, a risk of wound complications increases logarithmically with operative time. This distinction with regard to metabolic state might explain the unclear impact of obesity on surgical outcomes within existing surgical literature.

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.

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.

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.

Perinatal socio-behavioral and obstetric predictors of metabolically healthy and unhealthy obesity in adult offspring.

OBJECTIVE: The purpose of this study was to investigate whether obstetric and perinatal socio-behavioral characteristics at the time of pregnancy predict obesity phenotypes of adult offspring. METHODS: The Jerusalem Perinatal Study was conducted among 17,003 deliveries during 1974 to 1976. Follow-up studies were conducted during 2007 to 2009 and 2017 to 2019 among 1,440 offspring undergoing examinations. Offspring were classified into four phenotypes according to obesity and metabolic status: metabolically healthy normal weight (MHNW, reference group), unhealthy normal weight, healthy obesity (MHO), and unhealthy obesity (MUO). Regression models were carried out to identify perinatal predictors for risk phenotypes at age 30 to 35 years, emphasizing the differentiation between socio-behavioral and obstetric features. RESULTS: A total of 15.7% of participants were classified as MUO, and 5.4% were classified as MHO. Low socioeconomic status was associated with both obesity phenotypes (e.g., odds ratio [OR]MHO/MHNW = 2.98, p < 0.001). High socioeconomic status was associated with MUO (ORMUO/MHNW = 1.93, p = 0.002). Maternal low education was also associated with both obesity phenotypes (ORMUO/MHNW = 2.46, p < 0.001, ORMHO/MHNW = 2.45, p = 0.005). Participants with MUO were more likely to have a smoking father (ORMUO/MHNW = 1.48, p = 0.021). CONCLUSIONS: Perinatal socio-behavioral characteristics are associated with adult obesity phenotypes. The findings point to possible mechanisms underlying the development of obesity in young adults and, thus, contribute toward identifying high-risk groups that would mostly benefit from obesity risk-reduction interventions.

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

OBJECTIVES: Obesity and metabolic status are both modifiable risk factors of lower urinary tract symptoms secondary to benign prostatic hyperplasia (LUTS/BPH). However, the association between metabolically healthy obesity (MHO) and LUTS/BPH is largely unexplored. This study aimed to investigate the risk of LUTS/BPH among different metabolic syndrome-body mass index (MetS-BMI) phenotypes in a cohort of Chinese males. METHODS: A total of 3321 males from the China Health and Retirement Longitudinal Study (CHARLS) without history of LUTS/BPH at baseline were included into the analyses. Participants were categorized into six mutually exclusive groups according to presence or absence of MetS combined with BMI status: metabolically healthy normal weight/overweight/obesity (MHN/MHOW/MHO) and metabolically unhealthy normal weight/overweight/obesity (MUN/MUOW/MUO). Adjusted odds ratios (OR) and 95% CI of LUTS/BPH across MetS-BMI categories were estimated with multivariable logistic regression models. RESULTS: A total of 394 (11.86%) participants developed LUTS/BPH during the follow-up. After adjusting for age, educational level, smoking status, drinking status, and BMI change, the multivariable-adjusted OR (95% CI) for incident LUTS/BPH comparing MUO, MHO, MUOW, MHOW, and MUN with MHN were 1.99 (1.23-3.22), 2.04 (1.14-3.66), 1.61 (1.11-2.34), 1.45 (1.02-2.05), and 0.91 (0.54-1.56), respectively. CONCLUSIONS: MHO and MHOW were risk populations of LUTS/BPH, suggesting that overweight and obesity can independently contribute to LUTS/BPH, even among metabolically healthy individuals. These findings emphasize metabolically healthy individuals may still benefit from maintaining normal body weight to prevent LUTS/BPH. Our findings also support that those recommendations for LUTS/BPH should highlight the importance of maintaining metabolic health across all BMI groups among Chinese males.

Association of metabolic health phenotypes, obesity, and hepatocellular carcinoma risk.

BACKGROUND: The obesity and hepatocellular carcinoma (HCC) risk association may differ by individuals' metabolic health status. AIM: To investigate the association between obesity categories and HCC risk among individuals with different metabolic health phenotypes. METHODS: A case-control study among 518 HCC cases and 1,036 frequency-matched controls was conducted. Body mass index (BMI) was assessed before diagnosis. Pre-diagnosis data on dyslipidemia, hypertension, and diabetes were used to categorize participants as metabolically healthy or metabolically unhealthy. Participants were further categorized into metabolically healthy normal weight (MHNW), metabolically healthy overweight (MHOW), metabolically healthy obese (MHO), metabolically unhealthy normal weight (MUNW), metabolically unhealthy overweight (MUOW), and metabolically unhealthy obese (MHO). We used logistic regression to calculate multivariable-adjusted odds ratios (ORs) and 95% confidence intervals (CIs). RESULTS: Being overweight (OR=1.68, 95%CI=1.21-2.34) or obese (OR=1.49, 95%CI=1.11-1.89) was associated with higher HCC risk. Among metabolically healthy participants, no association was found between being overweight or obese and HCC risk. However, among the metabolically unhealthy participants, being overweight (OR=1.89, 95%CI=1.31-2.72) or obese (OR=1.50, 95%CI=1.07-2.09) was associated with higher HCC risk. Compared to the MHNW phenotype, no association was found between the MHOW and MHO phenotypes and HCC risk, but the MUNW (OR=1.94, 95%CI=1.09-3.43), MUOW (OR=3.78, 95%CI=2.15-6.65), and MUO (OR=2.93, 95%CI=1.70-5.05) phenotypes were associated with higher HCC risk. CONCLUSION: The association between BMI and HCC appears to be restricted to individuals with underlying metabolic abnormalities.

Metabolically healthy obesity and cardiovascular events: A nationwide cohort study.

AIM: To evaluate the associations between metabolically healthy obesity (MHO) and different types of incident cardiovascular events in a contemporary population. MATERIALS AND METHODS: All patients discharged from French hospitals in 2013 with at least 5 years of follow-up and without a history of major adverse cardiovascular event (MACE; myocardial infarction, heart failure [HF], ischaemic stroke or cardiovascular death [MACE-HF]) or underweight/malnutrition were identified. They were categorized by phenotypes defined by obesity and three metabolic abnormalities (diabetes, hypertension and hyperlipidaemia). Hazard ratios (HRs) for cardiovascular events during follow-up were adjusted on age, sex and smoking status at baseline. RESULTS: In total, 2 873 039 individuals were included in the analysis, among whom 272 838 (9.5%) had obesity. During a mean follow-up of 4.9 years, when pooling men and women, individuals with MHO had a higher risk of MACE-HF (multivariate-adjusted HR 1.22, 95% confidence interval [CI]: 1.19-1.24), new-onset HF (HR 1.34, 95% CI 1.31-1.37) and atrial fibrillation (AF; HR 1.33, 95% CI 1.30-1.37) compared with individuals with no obesity and zero metabolic abnormalities. By contrast, risks were not higher for myocardial infarction (HR 0.92, 95% CI 0.87-0.98), ischaemic stroke (HR 0.93, 95% CI 0.88-0.98) and cardiovascular death (HR 0.99, 95% CI 0.93-1.04). MHO in men was associated with a higher risk of clinical events compared with metabolically healthy men of normal weight (HR 1.12-1.80), while women with MHO had a lower risk for most events than metabolically healthy women of normal weight (HR 0.49-0.99). CONCLUSIONS: In a large and contemporary analysis of patients seen in French hospitals, individuals with MHO did not have a higher risk of myocardial infarction, ischaemic stroke or cardiovascular death than metabolically healthy individuals with no obesity. By contrast, they had a higher risk of new-onset HF and new-onset AF. However, notable differences were observed in men and women in the sex-stratified analysis.

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.