Metabolically Healthy Obese Individuals Research Articles

Association of metabolically healthy obesity with risk of heart failure and left ventricular dysfunction among older adults.

Obesity is major cause of heart failure (HF), but it is related with a better prognosis among the elderly. Therefore, we aimed to examine whether metabolically healthy obesity (MHO) in late life increases HF risk and is reflected in impaired left ventricular (LV) function. The participants were grouped into four metabolic phenotypes based on obesity and metabolic status: metabolically healthy non-obesity (MHN), MHO, metabolically unhealthy non-obesity (MUN), metabolically unhealthy obesity (MUO). Association of metabolic phenotypes with LV function was evaluated using multiple linear regression models. And association between metabolic phenotypes and risk of HF was assessed using multivariable logistic regression models. In addition, we validated the association of metabolic phenotypes and HF risk in a separate longitudinal cohort. In the primary cohort of 6335 participant, there were 434 participants diagnosed with HF. Compared to MHN participants, the risk of HF was higher among older individuals with MUN (OR = 1.51 [95% CI: 1.14-1.99]) and MUO (OR = 2.01 [95% CI: 1.39-2.91]), but not older individuals with MHO (OR = 0.86 [95% CI: 0.30-2.43). Regarding to LV function, worse LV diastolic function was noted among MUN and MUO individuals rather than MHO individuals. Older adults with MHO were also not associated with risk of HF in the validation cohort. Among older individuals, the metabolic health status might modify the association of obesity with risk of HF and LV diastolic dysfunction. Worse LV diastolic function and higher risk of HF were just noted in individuals with MUO, but not in those with MHO.

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

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

A Bayesian network analysis of the probabilistic relationships between metabolically healthy obesity and cardiovascular disease risk under new diagnostic criteria.

The relationship between metabolically healthy obesity (MHO) and cardiovascular disease (CVD) risk remains debated. The critical point may be the lack of consensus on MHO's definition and diagnostic criteria. This study aimed to investigate the association of MHO status with arteriosclerosis-CVD (ASCVD) risk in Chinese under new diagnostic criteria. Participants aged 35-79 in the 2009 China Health and Nutrition Survey cohort were included. The 10-year ASCVD risk was predicted by the prediction for ASCVD risk in China, and participants with a predicted risk of ⩾ 10% were classified into the high-risk group. The Bayesian network (BN) models were constructed to characterize the multivariable probabilistic connections between metabolically obesity phenotypes and ASCVD risk. The 10-year ASCVD risk score and the proportion of individuals at ASCVD high risk were significantly different between metabolically obesity phenotypes (P< 0.001). BN reasoning results showed that MHO individuals were not significantly associated with a 10-year ASCVD risk. Among metabolically unhealthy individuals, the conditional probability of high ASCVD risk increased with the Body Mass Index (BMI), with the conditional probability of high ASCVD risk was 24.63% (95% CI: 22.81-26.55%), 32.97% (95% CI: 30.75-35.27%) and 40.2% (95% CI: 36.64-43.86%) for metabolically unhealthy normal weight (MUNW), metabolically healthy overweight weight (MHOW), and metabolically unhealthy obesity (MUO) group, respectively. Subgroup analysis showed that MHO individuals were at increased risk of CVD compared with metabolically healthy normal weight (MHNW) individuals only in females. These results showed that there was no significant increase in ASCVD risk of MHO phenotype based on the new diagnostic criteria, suggesting that MHO is in a relatively healthy state.

Association between metabolic obesity phenotypes and the risk of developing prostate cancer: a propensity score matching study based on Xinjiang.

Obesity-induced metabolic dysfunction increases the risk of developing tumors, however, the relationship between metabolic obesity phenotypes and prostate cancer (PCa) remains unclear. The term metabolic obesity phenotypes was introduced based on metabolic status and BMI categories. Participants were categorized into four groups: metabolically healthy nonobesity (MHNO), metabolically healthy obesity (MHO), metabolically unhealthy nonobesity (MUNO), and metabolically unhealthy obesity (MUO). Propensity score matching was conducted based on age, ethnicity, marriage, etc. Univariate and multivariate conditional logistic regression analyses were used to assess the relationship between metabolic obesity phenotypes, metabolic risk factors, and PCa. Sensitivity analysis was performed to verify the robustness of the results. After propensity score matching among 564 PCa patients and 1418 healthy individuals, 209 were selected for each of the case and control groups. There were no statistically significant differences in the basic characteristics between the two groups. Univariate and multivariate conditional logistic regression suggested that the risk of developing PCa in both MHO and MUO individuals was higher than in MHNO individuals. Specifically, the risk of developing PCa in MHO individuals was 2.166 times higher than in MHNO individuals (OR=2.166, 95%CI: 1.133-4.139), and the risk in MUO individuals was is 2.398 times higher than in MHNO individuals(OR=2.398, 95%CI:1.271-4.523). Individuals with hyperglycemia and elevated triglycerides also had a higher risk of developing PCa (hyperglycemia:OR=1.488, 95%CI: 1.001-2.210; elevated triglycerides: OR=2.292, 95%CI: 1.419-3.702). Those with more than or equal to three metabolic risk factors had an increased risk of PCa (OR=1.990, 95%CI: 1.166-3.396). Sensitivity analysis indicated an increased risk of PCa in MUO individuals compared to MHNO individuals. In this retrospective study, individuals with MHO and MUO had a higher risk of developing PCa.

Distinguishing health-related parameters between metabolically healthy and metabolically unhealthy obesity in women.

Obesity represents a global health crisis, yet a dichotomy is emerging with classification according to the metabolic state into metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUO). This study aimed to identify distinctive systemic clinical/endocrinological parameters between MHO individuals, employing a comprehensive comparative analysis of 50 biomarkers. Our emphasis was on routine analytes, ensuring cost-effectiveness for widespread use in diagnosing metabolic health. The study included 182 women diagnosed with obesity referred for bariatric surgery at the Endocrinology, Diabetes, and Metabolism Service of São João Hospital and University Centre in Portugal. MUO was defined by the presence of at least one of the following metabolic disorders: diabetes, hypertension, or dyslipidemia. Patients were stratified based on the diagnosis of these pathologies. Significantly divergent health-related parameters were observed between MHO and MUO patients. Notable differences included: albumin (40.1 ± 2.2 vs 40,98 ± 2.6 g/L, p value = 0.017), triglycerides (110.7 ± 51.1 vs 137.57 ± 82.6 mg/dL, p value = 0.008), glucose (99.49 ± 13.0 vs 119.17 ± 38.9 mg/dL, p value < 0.001), glycated hemoglobin (5.58 ± 0.4 vs 6.15 ± 1.0%, p value < 0.001), urea (31.40 ± 10.0 vs 34.61 ± 10.2 mg/dL, p value = 0.014), total calcium (4.64 ± 0.15 vs 4.74 ± 0.17 mEq/L, 1 mEq/L = 1 mg/L, p value < 0.001), ferritin (100.04 ± 129.1 vs 128.55 ± 102.1 ng/mL, p value = 0.005), chloride (104.68 ± 1.5 vs 103.04 ± 2.6 mEq/L, p value < 0.001), prolactin (13.57 ± 6.3 vs 12.47 ± 7.1 ng/mL, p value = 0.041), insulin (20.36 ± 24.4 vs 23.87 ± 19.6 μU/mL, p value = 0.021), c peptide (3.78 ± 1.8 vs 4.28 ± 1.7 ng/mL, p value = 0.003), albumin/creatinine ratio (15.41 ± 31.0 vs 48.12 ± 158.7 mg/g creatinine, p value = 0.015), and whole-body mineral density (1.27 ± 0.1 vs 1.23 ± 0.1 g/cm2, p value = 0.016). Our findings highlight potential additional parameters that should be taken into consideration alongside the commonly used biomarkers for classifying metabolic health in women. These include albumin, urea, total calcium, ferritin, chloride, prolactin, c-peptide, albumin-creatinine ratio, and whole-body mineral density. Moreover, our results also suggest that MHO may represent a transitional phase preceding the development of the MUO phenotype.

Relationship of Mitochondrial DNA Oxidation and Content with Metabolic Syndrome and Cardiovascular Risk in Obesity Phenotypes.

Obesity, chronic inflammation, and oxidative stress can influence mitochondrial DNA (mtDNA) content. Our objective was to evaluate the oxidation level and content of mtDNA and its relationship with metabolic parameters in metabolically healthy obese (MHO) compared to metabolically unhealthy obese (MUO) and normal weight (NW) controls. We studied 94 NW, 95 MHO, and 97 MUO individuals between 18 and 80 years old. Relative mtDNA content and mtDNA oxidation level (8-oxoguanine, 8-OxoG) were determined in peripheral blood leukocytes by the SYBR Green method of real-time PCR. One-way ANOVA and Tukey test were used to compare biochemical, clinical, and anthropometric characteristics, as well as mtDNA content and 8-OxoG. A progressive decrease in mtDNA content was observed between NW, MHO, and MUO with significant differences in MUO vs. NW (p: 0.04). An increase in 8-OxoG was observed in MUO patients compared to the other groups (MUO vs. MHO p: 0.01; MUO vs. NW p: 0.04). mtDNA content was directly correlated with HDL-c (p < 0.01) and inversely with waist circumference (p: 0.01) and LDL-c (p: 0.05). mtDNA content decreased, and the oxidation level increased concomitantly with the presence of obesity, the number of MS components, higher coronary risk, and insulin resistance parameters. MHO presented a similar mtDNA oxidation level to NW and mtDNA content to the MUO, placing the MHO individuals as having an intermediate phenotype. Changes in mtDNA content and oxidation were correlated to the lipid profile related to obesity and/or MS presence, probably associated with oxidative stress and chronic low-grade inflammation.

Exploring metabolically healthy obesity: prevalence, characteristics, and cardiovascular risk in the Iranian population based on the STEPS 2021.

Regarding the rapidly increasing prevalence of obesity throughout the globe, it remains a serious public health concern. A subgroup of obesity that does not meet metabolic syndrome criteria is called metabolically healthy obesity (MHO). However, whether the MHO phenotype increases cardiovascular disease (CVD) risk is controversial. This study aimed to evaluate the prevalence of MHO and its 10-year CVD risk in Iranian populations. Based on the STEPS 2021 project in Iran, we collected data on 18119 Iranians 25 years and older from all 31 provinces after applying many statistical factors. Using the Framingham score, we evaluated the 10-year cardiovascular risk associated with the various MHO definition criteria for Iranian populations. The prevalence of MHO was 6.42% (5.93-6.91) at the national level according to the AHA-NHLBI definition, and 23.29% of obese women and 24.55% of obese men were classified as MHOs. Moreover, the MHO group was younger than the metabolically unhealthy obesity (MUO) group based on all definitions (p < 0.001). The odds ratio of MUO individuals being classified as high-risk individuals by the Framingham criteria for CVD was significantly higher than that of MHO individuals by all definitions, with a crude odds ratio of 3.55:1 based on AHA-NHLBI definition. This study reveals a significant prevalence of MHO in the Iranian population, with approximately 25% of obese individuals classified as MHO. While MHO is associated with a lower risk of cardiovascular disease compared to MUO, MHO carries the potential for transitioning to an unhealthy state. The online version contains supplementary material available at 10.1007/s40200-023-01364-5.

Computed tomography evaluation of skeletal muscle quality and quantity in people with morbid obesity with and without metabolic abnormality.

We investigated the differences in quantity and quality of skeletal muscle between metabolically healthy obese (MHO) and metabolically unhealthy obese (MUO) individuals using abdominal CT. One hundred and seventy-two people with morbid obesity who underwent bariatric surgery and 64 healthy control individuals participated in this retrospective study. We divided the people with morbid obesity into an MHO and MUO group. In addition, nonobese metabolic healthy people were included analysis to provide reference levels. CT evaluation of muscle quantity (at the level of the third lumbar vertebra [L3]) was performed by calculating muscle anatomical cross-sectional area (CSA), which was normalized to patient height to produce skeletal muscle index (SMI). Muscle quality was assessed as skeletal muscle density (SMD), which was calculated from CT muscle attenuation. To characterize intramuscular composition, muscle attenuation was classified into three categories using Hounsfield unit (HU) thresholds: -190 HU to -30 HU for intermuscular adipose tissue (IMAT), -29 to +29 HU for low attenuation muscle (LAM), and +30 to +150 HU for normal attenuation muscle (NAM). People with morbid obesity comprised 24 (14%) MHO individuals and 148 (86%) MUO individuals. The mean age of the participants was 39.7 ± 12.5 years, and 154 (65%) participants were women. MUO individuals had a significantly greater total skeletal muscle CSA than MHO individuals in the model that adjusted for all variables. Total skeletal muscle SMI, SMD, NAM index, LAM index, and IMAT index did not differ between MHO and MUO individuals for all adjusted models. Total skeletal muscle at the L3 level was not different in muscle quantity, quality, or intramuscular composition between the MHO and MUO individuals, based on CT evaluation. MHO individuals who are considered "healthy" should be carefully monitored and can have a similar risk of metabolic complications as MUO individuals, at least based on an assessment of skeletal muscle.

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

The risk of kidney stones in metabolically healthy obesity (MHO) individuals is largely unexplored. This study using percent body fat (%BF) to categorize obesity, to investigate the association between MHO as well as other metabolic syndrome-obesity combined phenotypes and kidney stones in a national representative population. This cross-sectional study included 4,287 participants in the National Health and Nutrition Examination Survey from 2011 to 2018. Metabolically healthy status was defined as not having any component of metabolic syndrome or insulin resistance. Obesity was identified by %BF, which was measured and assessed by dual-energy x-ray absorptiometry (DXA) scan. Participants were cross-classified by metabolic health and obesity status. The outcome was self-report kidney stones. Multivariable logistic regression model was used to examine the association between MHO and kidney stones. A total of 358 participants had kidney stones [weighted prevalence (SE): 8.61% (0.56%)]. The weighted prevalence (SE) of kidney stones in MHN, MHOW, and MHO groups was 3.13% (1.10%), 4.97% (1.36%), and 8.55% (2.09%), respectively. After adjusting for age, sex, race and ethnicity, education level, smoking status, alcohol consumption, physical activity, daily water intake, CKD stage 3-5, and hyperuricemia, MHO individuals (OR: 2.90, 95% CI: 1.18, 7.0) had a significantly higher risk of kidney stones than those with metabolically healthy normal weight. In metabolically healthy participants, a 5% increment in %BF was associated with a significantly higher risk of kidney stones (OR: 1.60, 95% CI: 1.20, 2.14). Furthermore, a nonlinear dose-response relationship between %BF and the kidney stones was observed in metabolically healthy participants (P for non-linearity = 0.046). Using %BF to define obesity, MHO phenotype was significantly associated with higher risks of kidney stones, suggesting that obesity can independently contribute to kidney stones in the absence of metabolic abnormalities and insulin resistance. Regarding kidney stones prevention, MHO individuals might still benefit from lifestyle interventions aimed at healthy body composition maintenance.

Abstract MP31: Serum Metabolite Signatures for Metabolic Healthy and Unhealthy Obesity: The Bogalusa Heart Study

Introduction: Mechanisms for metabolically healthy obesity (MHO) are not well studied. Methods: We performed cross-sectional analyses among 1,261 participants of the Bogalusa Heart Study 2013-2016 survey. A total of 887 known serum metabolites were profiled using an untargeted approach. Participants who met all the following five criteria were considered metabolically healthy, otherwise, unhealthy: 1) SBP &lt;130 mm Hg and DBP &lt;85 mm Hg; 2) fasting glucose &lt;100 mg/dl; 3) triglycerides &lt;150 mg/dl; 4) HDL ≥40 mg/dl in men and ≥50 mg/dl in women; and 5) not taking medications for hypertension, diabetes, or dyslipidemia. We tested associations of metabolites with metabolic health status, controlling for age, sex, race, education, smoking, drinking, and kidney function. Results: Among 648 obese participants (BMI≥30 kg/m 2 ), 66 (10.2%) had MHO. After Bonferroni correction, 26 metabolites differed between MHO and metabolically unhealthy obesity (MUO) groups. Amino acids serine , glycine, and histidine , fatty acid N-linoleoylserine , 4 sphingolipids, and 3 plasmalogens were significantly higher among MHO individuals. These metabolites are functionally relevant to metabolic health ( Table ). Carbohydrates mannose , lactate, and pyruvate , urate , amino acids glutamate , 1-carboxyethylvaline , 1-carboxyethylleucine , 3-methyl-2-oxovalerate , and 3-methyl-2-oxobutyrate , PE(18:0/18:2(9Z,12Z)) , and 5 diacylglycerols were significantly higher in the MUO group. These metabolites are mostly food additives or reduce gut microbiota diversity ( Table ). Furthermore, histidine was associated with metabolic health only among overweight and obese participants. Odds ratios of being metabolically healthy per SD increase of histidine among normal weight, overweight, and obese participants were 1.05 (95% CI: 0.83-1.32), 1.54 (95% CI: 1.10-2.14), and 2.12 (95% CI: 1.56-2.89), respectively (P for group difference=1.41E-4). Conclusion: We identified potential intervention targets to improve metabolic health in obesity.

Association between metabolically healthy obesity and non-alcoholic fatty liver disease.

The risks of NAFLD and NAFLD with fibrosis progression among metabolically healthy obesity (MHO) individuals are largely unexplored. This cohort study investigated the association between MHO as well as other metabolic syndrome-obesity combined phenotypes and NAFLD and its fibrosis progression. Participants included 31,010 adults from a health check-up cohort free from NAFLD and intermediate or high probability of advanced fibrosis at baseline. Metabolically healthy was defined as not having any component of metabolic syndrome. Obesity was identified by body mass index (BMI) and waist circumference (WC). Participants were cross-classified by metabolic health and obesity at baseline. The outcomes were NAFLD, and NAFLD with fibrosis progression, as assessed by abdominal B-type ultrasound and noninvasive fibrosis score. During a median follow-up of 2.2 (interquartile range, 1.2-4.9) years, 7,393 participants developed NAFLD. MHO individuals (HR 5.51, 95% CI 4.98, 6.09 for BMI criteria; HR 6.76, 95% CI 6.04, 7.57 for WC criteria) had a significantly higher risk of NAFLD than those with metabolically healthy normal weight or low WC. The corresponding HRs (95% CIs) for metabolically healthy overweight (defined by BMI) and medium WC were 2.74 (2.49-3.02) and 2.93 (2.65-3.24), respectively. Furthermore, 557 participants developed NAFLD with fibrosis progression. The association between different obesity phenotypes and NAFLD with fibrosis progression also showed a similar pattern. MHO was associated with significantly higher risks of NAFLD and its fibrosis progression, suggesting that regarding NAFLD prevention, MHO individuals might still benefit from lifestyle interventions aimed at body weight and WC maintenance.

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.

Can Gamma-glutamyl Transferase Predict Unhealthy Metabolic Phenotypes Among Healthcare Workers in Azar Cohort Study?

Background: Although various studies have assessed the correlation between gamma-glutamyl transferase (GGT) and cardiometabolic risk factors in obesity, no research has differentiated among metabolically-healthy obese (MHO) and metabolically unhealthy obese (MUHO), metabolically-healthy lean (MHL), and metabolically-unhealthy lean (MUHL). Objectives: Accordingly, this study evaluated the correlation between GGT and cardiometabolic phenotypes among healthcare workers. Methods: In this study, there were anthropometric measurements as well as the measurements of fasting blood sugar (FBS), GGT, cholesterol, triglyceride (TG), high lipoprotein density (HDL), and blood pressure in 1458 healthcare workers enrolled in the Azar Cohort Study. Metabolic syndrome (MetS) was defined according to the National Cholesterol Education Program Adult Treatment Panel III (ATP III). Accordingly, the participants were divided into four cardiometabolic phenotypes. Results: In this cross-sectional study, there was a significant difference in the prevalence of cardiometabolic phenotypes regarding the GGT tertiles (P ≤ 0.001). The highest prevalence of MHO was observed in the third GGT tertile. The mean waist circumference, TG, FBS, HDL, and systolic and diastolic blood pressure levels increased in the MHO, MUHO, and MHL groups in a dose dependent manner with an increase in the GGT tertiles (P &lt; 0.05). In comparing the highest and lowest GGT tertile, the risk of MHO and MUHO increased by 2.84 (95%CI 2.01 - 4.01) and 9.12 (95%CI 5.54 - 15), respectively. However, the correlation between the GGT tertile and MUHL did not reveal a similar trend. The ROC curve shows the cutoff value of 18.5 U/L for GGT, which allowed us to distinguish between the MUHO and MHO individuals. Conclusions: The findings revealed that GGT can indicate the risk of MetS as such, it can be used to detect at-risk MHO individuals and administer proper interventions.

Metabolically healthy obesity: Is it really healthy for type 2 diabetes mellitus?

Metabolically healthy obese (MHO) individuals are reported to have a lower risk of developing cardiovascular diseases in comparison with individuals with metabolic syndrome. However, the association between MHO and type 2 diabetes (T2DM) is still controversial. Some studies indicated that MHO is a favorable phenotype for T2DM, but more studies showed that MHO individuals have an increased risk of developing T2DM compared with metabolically healthy normal-weight individuals, especially among those who would acquire metabolically unhealthy obesity. This has been supported by finding insulin resistance and low-grade inflammatory responses in MHO individuals with a tendency for impaired beta-cell dysfunction. Studies also showed that liver fat accumulation increased the risk of incidence of T2DM in MHO. Here, we reviewed current literature on the relationship between MHO and T2DM, discussed the determinants for the development of diabetes in MHO, and summarized the measures for the prevention of T2DM in MHO.

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

BackgroundThe association of obesity with colorectal cancer (CRC) may vary depending on metabolic status.ObjectiveThis meta-analysis aimed to investigate the combined impacts of obesity and metabolic status on CRC risk.MethodsThe 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.ResultsA 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.ConclusionsThe 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.

Evaluation of Liver Metabolism Biomarkers in Metabolic Associated Fatty Liver Disease According to Obesity Phenotype

Objective To analyze the relationship between the biochemical markers of liver metabolism in different stages of Metabolic Associated Fatty Liver Disease (MAFLD) according to the obesity phenotype. Methodology This is a cross-sectional study with individuals with class III obesity classified according to the obesity phenotypes proposed by the National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) criteria. Biochemical and anthropometric variables were analyzed according to the staging of MAFLD and obesity phenotype. Results A total of 50 subjects with MAFLD, 62% (n = 31) with steatosis and 38% (n = 19) with steatohepatitis without fibrosis; 36% were classified as metabolically healthy obesity (MHO) and 64% as metabolically unhealthy obesity (MUHO), respectively. Mean values of alkaline phosphatase were 85.44 ± 27.27 vs. 61.92 ± 17.57 (p = 0.006); gamma-glutamyl transpeptidase, 25.77 ± 15.36 vs. 30.63 ± 19.49 (p = 0.025); and albumin, 3.99 ± 0.34 vs. 4.24 ± 0.23 (p = 0.037), were lower and statistically significant in the MHO group with steatosis. The results show when considering individuals with IR, only AP is a predictor of unhealthy phenotype (B-0.934, 0.848- 1.029, p = 0.031). Conclusion MHO individuals with steatosis present lower severe changes related to markers of liver damage and function and AP is considered the predictor of MUHO phenotype.

Proteomic and Metabolomic Characterization of Metabolically Healthy Obesity: A Descriptive Study from a Swedish Cohort

Method Associations between different biomarkers (proteomics, lipidomics, and metabolomics) coupled to either MHO or metabolically unhealthy obese (MUO) individuals were analyzed through principal component analysis (PCA). Subjects were identified from a subsample of 416 obese individuals, selected from the Malmö Diet and Cancer study—Cardiovascular arm (MDCS-CV, n = 3,443). They were further divided into MHO (n = 143) and MUO (n = 273) defined by a history of hospitalization, or not, at baseline inclusion, and nonobese subjects (NOC, n = 3,027). Two distinctive principle components (PL2, PP5) were discovered with a significant difference and thus further investigated through their main loadings. Results MHO individuals had a more metabolically favorable lipid and glucose profile than MUO subjects, that is, lower levels of traditional blood glucose and triglycerides, as well as a trend of lower metabolically unfavorable lipid biomarkers. PL2 (lipidomics, p=0.02) showed stronger associations of triacylglycerides with MUO, whereas phospholipids correlated with MHO. PP5 (proteomics, p=0.01) included interleukin-1 receptor antagonist (IL-1ra) and leptin with positive relations to MUO and galanin that correlated positively to MHO. The group differences in metabolite profiles were to a large extent explained by factors included in the metabolic syndrome. Conclusion Compared to MUO individuals, corresponding MHO individuals present with a more favorable lipid metabolic profile, accompanied by a downregulation of potentially harmful proteomic biomarkers. This unique and extensive biomarker profiling presents novel data on potentially differentiating traits between these two obese phenotypes.

Current prospects of metabolically healthy obesity

Obesity is a heterogeneous disorder posing a great risk of developing cardiovascular disease and type 2 diabetes. Metabolically healthy obesity (MHO) is a phenotype claimed to be protected against these risks, but its mechanisms remain unclear. Overview of current evidence indicating MHO and its metabolic characteristics, highlighting possible factors underlying MHO. PubMed, Medline, Clinical trials.gov, Science Direct, ADI, and WHO databases were searched through to June 2021 . Recent evidence shows MHO phenotype with a prevalence of 6–75% and claims protection from cardiometabolic complications, though its precise definition and diagnosis are not yet established. Although MHO individuals are not healthy and may represent a subclinical state of obesity, they are closer to normal than those with other obesity phenotypes concerning adipocyte size and distribution, immune cell infiltration, adipokine secretion, cardiorespiratory fitness, proinflammatory biomarkers, insulin sensitivity, and dietary response. Notably, 36–52% of MHO individuals may become unhealthy later in life, though the evidence is inconclusive. MHO phenotype remains a controversial scientific issue, especially for its definition. MHO individuals are classified to have fewer or milder metabolic risks than those with unhealthy phenotypes; some maintain a healthy lifestyle and fat and fit. Thus, in-depth research is warranted. • Metabolic healthy obesity (MHO) shows reduced cardiometabolic risks. • There is no precise definition and diagnosis of MHO yet. • Although not healthy, MHO individuals are closer to normal than other phenotypes. • Evidence that MHO individuals may become unhealthy later in life is inconclusive. • The MHO phenotype remains a controversial scientific issue.

Effect of Metabolic Health and Obesity Phenotype on Risk of Diabetes Mellitus: A Population-Based Longitudinal Study.

BackgroundEpidemiologic evidence on body mass index (BMI)-metabolic status phenotypes and diabetes risk remains controversial, especially for metabolically healthy obesity (MHO). We aimed to examine the effect of metabolic health and obesity phenotype on diabetes risk in the Chinese population.MethodsA population-based cohort study was carried out. The baseline survey was conducted in 2017, with two follow-up visits in 2018 and 2020. Diabetes was defined based on the criteria of the World Health Organization. Robust generalized estimating equation models with a binary distribution using a log link and exchange structure were applied for the pooled analysis sample.ResultsA total sample of 9623 observations was pooled for the longitudinal data analysis. The average follow-up time was 1.64 years per person and the overall incidence density of diabetes was 6.94% person-years. Decreased diabetes risk was found in metabolically healthy overweight phenotype (RR = 0.65; 95% CI = 0.47–0.90) and no significant associations were detected for the MHO individuals (RR = 0.99; 95% CI = 0.63–1.53) compared with those of metabolically healthy normal weight, in contrast to metabolically unhealthy normal weight (MU-NW) (RR = 1.81; 95% CI = 1.28–2.55), metabolically unhealthy overweight (MU-OW) (RR = 2.02; 95% CI = 1.57–2.61) and metabolically unhealthy obesity (MUO) (RR = 2.48; 95% CI = 1.89–3.26) phenotypes. Significant associations between BMI-metabolic status phenotypes and diabetes were found in both males and females.ConclusionThe MUO phenotype needs to be accorded much more importance. MU-NW and MU-OW are also important component for targeted prevention. Our findings can be targeted for optimizing preventive strategies to mitigate the obviously increased prevalence of diabetes.

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

Aims- Evidence for the effects of metabolically healthy obese (MHO) status on heart failure (HF) is limited and ignores the dynamic change of metabolic health and obesity phenotypes. We aimed to investigate the associations of metabolic health and its transition with HF across body mass index (BMI) and waist circumference (WC) categories. Methods- This prospective cohort study was conducted with 93,288 Chinese adults who were free of cardiovascular disease, cancer or HF at baseline (2006–2007). Metabolic health was defined as having no or only one abnormality in blood pressure, glucose, high-density lipoprotein cholesterol, or triglyceride levels. Participants were cross-classified at baseline by metabolic health and obesity (defined by BMI and WC criteria). Transitions in metabolic health status from 2006 to 2007 to 2010 to 2011 were considered. The hazard ratios (HRs) and 95% confidence intervals (CIs) for HF were assessed by Cox proportional hazards regression. Results- During a mean ± standard deviation follow-up of 9.7 ± 1.5 years, 1,628 participants developed HF. Individuals with MHO (HR: 1.78, 95% CI: 1.45, 2.19 for BMI criteria; HR: 1.51, 95% CI: 1.30, 1.76 for WC criteria) had higher risk of HF than those with metabolically healthy normal weight (MHNW). Individuals with initial MHO who shifted to metabolically unhealthy phenotype during follow-up had higher risk of HF compared with stable MHNW individuals (HR 3.12; 95% CI: 2.01, 4.85 for BMI categories; HR 1.98; 95% CI: 1.42, 2.77 for WC categories). Even stable MHO individuals were at an increased risk of HF compared with stable MHNW individuals (HR: 2.17; 95% CI: 1.39, 3.39 for BMI categories; HR: 1.33; 95% CI: 0.96, 1.85 for WC categories). Conclusions- MHO phenotype is dynamic and its transition to metabolically unhealthy phenotype or even stable MHO is associated with increased risk of HF. Maintaining metabolic health may provide a clue for preventing HF.