Association of serum calcium and metabolically healthy obese in US adults: a cross-sectional study.

OBJECTIVES: The relationship between serum calcium and occurrence of MHO (metabolically healthy obesity) and MUNO (metabolically unhealthy non-obesity) remains unclear, and distinguishing these two phenotypes is difficult within primary healthcare units. This study explores that relationship. METHODS: This survey included 28590 adults from the National Health and Nutrition Examination Survey (NHANES) 2001-2018. Obesity phenotypes were categorized based on BMI and presence or absence of metabolic syndrome components. Weighted multivariate logistic regression analyses were used to assess the association between serum calcium levels and the obesity phenotype. Restricted cubic spline analysis characterized dose-response relationships, and stratified analyses explored these relationships across sociodemographic and lifestyle factors. RESULTS: The overall prevalence of MHO and MUNO were 2.6% and 46.6%, respectively. After adjusting for covariates, serum calcium exhibited a negative association with MHO [OR (95%): 0.49 (0.36,0.67), p < 0.001], while exhibiting a positive association with MUNO [OR (95%): 1.48 (1.26,1.84), p < 0.001]. Additionally, we found a non-linear association between serum calcium levels and the incidences of MHO and MUNO. Stratified analyses demonstrated a strong negative correlation between serum calcium levels and MHO occurrence across various subgroups. There was no significant interaction between calcium and stratified variables except sex; the association between calcium and the occurrence of MHO was remarkable in female patients. Meanwhile, the predictive ability of serum calcium level for the occurrence of MUNO among all patients was consistent across various subgroups. There was a significant interaction between calcium level and stratified variables based on age, sex, race, and smoking status; the association was remarkable in older (≥ 40 years old), white, none or less smoking, and female patients. CONCLUSIONS: A significant correlation was identified between serum calcium levels and MHO or MUNO. The findings suggest that serum calcium levels may serve as an indicator for more accurate assessment and diagnosis of MUNO and MHO, especially among individuals with abdominal obesity.

Serum calcium levels exhibited an inverse relationship with metabolically healthy obesity (MHO) and a positive relationship with metabolically unhealthy non-obese (MUNO).A nonlinear association exists between serum calcium levels and the incidence of both MHO and MUNO.Serum calcium has the potential to enhance evaluation and screening for MUNO or MHO in the general US adult population.

Association between transitions in metabolic health and colorectal cancer across categories of body size phenotype: a prospective cohort study.

OBJECTIVE: We aimed to investigate the associations of changes in metabolic health across categories of body size phenotype with the risk of colorectal cancer in a community-based prospective cohort. METHODS: In the current study, a total of 70,987 participants were included. Changes in metabolic health across categories of body size phenotype were assessed between the health examination for the first time in the years 2006 through 2009 and a 2010/2011 health examination. A multivariate Cox proportional hazards model was used to assess the associations of changes in metabolic health across body size phenotype categories with risk of colorectal cancer. RESULTS: During the median follow-up time of 11.04 years, 428 (0.60%) participants developed colorectal cancer. Compared with metabolically healthy normal-weight (MHNW) participants who remained MH, the risk of colorectal cancer was increased by 144% (95% CI: 1.21-4.95) for participants with metabolically healthy obesity (MHO) who converted to a metabolically unhealthy (MU) phenotype. Participants who were MU at baseline were still at increased risk of colorectal cancer, regardless of obesity status. CONCLUSIONS: The MHO phenotype was a dynamic status over time, and converting to MU during follow-up and being initially MU were associated with having an increased risk of colorectal cancer, regardless of degree of obesity and body size phenotype.

Metabolically healthy obesity and left ventricular geometric remodelling in Chinese children.

AIM: To investigate the association between metabolically healthy obesity (MHO) and left ventricular geometric remodelling in Chinese children. MATERIALS AND METHODS: This cross-sectional study used data from two population-based samples in China, including 2871 children aged 6-11 years. Weight status was defined based on body mass index according to the World Health Organization growth chart. Metabolic status was defined based on the 2018 consensus-based criteria proposed by Damanhoury et al. Obes Rev 2018;19:1476-1491 (blood pressure, lipids and glucose). Left ventricular geometric remodelling was determined as concentric remodelling, eccentric hypertrophy, and concentric hypertrophy. Multinomial logistic regression analysis was used to determine odds ratios (ORs) and 95% confidence intervals (CIs) for the association between categories of weight and metabolic status and left ventricular geometric remodelling. RESULTS: Compared with children with metabolically healthy normal weight, those with MHO had higher odds of left ventricular geometric remodelling, with adjusted ORs (95% CIs) of 2.01 (1.23-3.28) for concentric remodelling, 6.36 (4.03-10.04) for eccentric hypertrophy, and 17.07 (7.97-36.58) for concentric hypertrophy. Corresponding ORs (95% CIs) were 2.35 (1.47-3.75), 10.85 (7.11-16.55), and 18.56 (8.63-39.94), respectively, for children with metabolically unhealthy obesity. In contrast, metabolically unhealthy normal weight was not associated with higher odds of left ventricular geometric remodelling. Findings were consistent in sensitivity analyses that used different definitions of weight and metabolic status and left ventricular geometric remodelling. CONCLUSIONS: Children with MHO had higher odds of left ventricular geometric remodelling than their metabolically healthy normal weight counterparts. Our findings suggest MHO may not be a benign condition for cardiac health in children.

Association between metabolic phenotypes of overweight/obesity and cardiovascular diseases in postmenopausal women.

BACKGROUND AND AIM: Obesity and metabolic abnormalities were associated with an increased risk of cardiovascular disease. However, it is unclear how metabolic weight phenotypes relate to cardiovascular diseases in postmenopausal women. This study aimed to explore the relationships in postmenopausal women. METHODS AND RESULTS: We included 15,575 postmenopausal women aged 35-75 years (median age, 60.6) without cardiovascular disease at baseline from a subcohort of the China Patient-centered Evaluative Assessment of Cardiac Events Million Persons Project. Metabolically unhealthy phenotype was defined as having ≥2 risk factors of metabolic syndrome: blood pressure ≥130/85 mm Hg or current use of antihypertensive drugs, fasting glucose ≥5.6 mmol/L or current use of antidiabetic agents, triglycerides ≥1.7 mmol/L, and high-density lipoprotein cholesterol <1.3 mmol/L. Cox regression analysis was used to evaluate the risks of cardiovascular diseases. Over a median follow-up period of 3.55 (interquartile range, 2.59-4.44) years, a total of 1354 cardiovascular events occurred. Compared to metabolically healthy normal weight, the multivariate-adjusted hazard ratios and their 95% confidence intervals were 1.41 (1.16-1.72) for metabolically unhealthy normal weight, 1.42 (1.16-1.73) for metabolically healthy overweight/obesity, and 1.75 (1.48-2.08) for metabolically unhealthy overweight/obesity. Subdividing overweight/obesity into separate groups revealed higher total cardiovascular disease risk only in metabolically unhealthy individuals across body mass index categories. CONCLUSION: In postmenopausal women, both metabolically healthy overweight/obesity and metabolically unhealthy normal weight were associated with a higher risk of cardiovascular disease compared to metabolically healthy normal weight, and the greatest risk was observed in the metabolically unhealthy overweight/obesity category.

Metabolically healthy obesity in adults with X-linked hypophosphatemia.

OBJECTIVES: X-linked hypophosphatemia (XLH) is characterized by increased concentrations of circulating fibroblast growth factor 23 (FGF-23) resulting in phosphate wasting, hypophosphatemia, atypical growth plate and bone matrix mineralization. Epidemiologic studies suggest a relationship between FGF-23, obesity, and metabolic dysfunction. The prevalence of overweight and obesity is high in children with XLH. We aimed to evaluate the prevalence of obesity and metabolic complications in adults with XLH. METHODS: We conducted a prospective cohort study in adult XLH patients from a single tertiary referral center. The proportion of patients with a BMI >25 kg/m2 was the main outcome measure. Body fat mass percentage (FM%) and adipose tissue surfaces were secondary outcome measures. Glucose homeostasis (plasma glucose and insulin concentrations after fasting and 2 hours after an oral glucose tolerance test) was explored in a subgroup of patients and compared with age-, sex-, and BMI-matched healthy controls. RESULTS: Among 113 evaluated patients, 85 (75%) were female and 110 (97%) carried a PHEX mutation. Sixty-three (56%) patients were overweight or obese, with a median BMI of 25.3 [IQR, 22.7; 29.2] kg/m2. BMI was correlated with FM%, abdominal and thigh subcutaneous and intra-abdominal adipose tissue surfaces. The prevalence of impaired fasting glucose, impaired glucose tolerance, and diabetes was not different between XLH patients and matched controls. CONCLUSION: The prevalence of overweight and obesity is high among XLH patients and is associated with excess fat mass. However, the prevalence of glucose homeostasis abnormalities is not increased in patients compared to healthy controls, suggesting that metabolically healthy overweight or obesity predominates.

Trends and characteristics of the metabolically healthy obese phenotype in an Arab population.

The metabolically healthy obesity (MHO) phenotype represents a complex and distinctive trait, the trends and characteristics of which remain unknown in the Saudi Arabian adult population. The present study aims to fill that gap. A combined total of 10,220 Saudi adults from 2 independent cohorts [2008-2019, N = 7,896 (2,903 males and 4,993 females), and 2021-2023, N = 2,324 (830 males and 1,494 females)] aged 19-70 years old was screened, of whom 9,631 (3,428 males and 6,203 females) were included. Anthropometric data were measured, and fasting blood samples were collected to assess glucose, lipids, adipocytokines and inflammatory markers using routine methods and commercially available assays. Obesity was defined as a body mass index (BMI) ≥30 kg/m2. Screening for MHO was done using the empiric definition proposed by Zembic and colleagues and the by the National Cholesterol Education Program's Adult Treatment Panel III (NCEP ATPIII). Of the 3,949 (41.0%) participants with obesity, 33.4% (95% confidence interval, CI, 32-35) were considered MHO using the empiric definition, and 32.8% (95% CI, 31-34) using NCEP-ATPIII. The overall age and gender adjusted prevalence of MHO in the Saudi adult population was 31.6% (95% CI, 30-33) and 30.1% (29-31) by the two definitions, respectively. Females had a higher age-adjusted prevalence of MHO than males (OR = 1.22, 95% CI 1.1-1.4, p = 0.009) as per the ATPIII criteria. MHO prevalence substantially increased over time from 2008 to 2023 (p < 0.001) for both definitions. Circulating leptin levels and insulin resistance were significantly higher in the MUO group than the MHO group independent of the definition used, suggesting the presence of a more severe form of leptin resistance in the MUO group which may explain the worse cardiometabolic profile as compared to the MHO group. In summary, the study highlights the first time the characteristics and trends of the MHO phenotype among Saudi Arabian adults. The pluripotent effects of leptin and its resistance may be central to MHO's progression, or lack thereof, to the MUO phenotype, and this needs further investigation.

Acute ischemic stroke in young adults: comparative analysis of outcomes and mortality in metabolically healthy obese or overweight vs. non-obese or overweight hospitalizations, 2016-2019 cohort.

INTRODUCTION: Rising obesity rates and the increasing prevalence of stroke in the metabolically healthy obese and overweight (MHOO) necessitate examining its association in younger (18-44 year) populations and analyzing acute ischemic stroke (AIS) trends and outcomes in MHOO vs. metabolically healthy non-obese or overweight (MHnOO). METHODS: Data from the United States National Inpatient Sample (2016-2019) was analyzed to identify young MHOO and MHnOO AIS patients using ICD-10-CM codes. Metabolically healthy status was defined by excluding hospitalization records with diagnostic codes for hypertension, diabetes, and dyslipidemia. Demographics, trends, and outcomes were compared using appropriate statistical approaches. RESULTS: Of 26,949,310 young metabolically healthy hospitalizations between 2016 and 2019, 47,795 had AIS, of which 4,985 were MHOO and 42,810 were MHnOO. The median age of AIS hospitalization was 35 years, and primarily female and white. From 2016 to 2019, AIS incidence rose slightly, which was significant only for the MHnOO cohort. The in-hospital mortality rate was significantly lower in the MHOO cohort (6.0 % vs. 8.6 %, p < 0.001). Hospitalization length and cost did not differ substantially between groups. Adjusted multivariable analysis revealed no significant difference in AIS hospitalization risk between MHOO and MHnOO (aOR: 1.02, p=0.701), with subgroup analysis in males (aOR: 0.88, p=0.161) or females (aOR: 1.06, p=0.363). However, all-cause in-hospital mortality (ACIHM) in AIS had lower odds in the MHOO vs. MHnOO cohorts (aOR: 0.60, p=0.021). CONCLUSION: Our study finds a rising trend of AIS hospitalizations in young metabolically healthy adults, with obesity or overweight status not being associated with AIS hospitalization. We identify an "obesity paradox" of lower odds for ACIHM for AIS hospitalizations in the MHOO cohort.

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.

Neurological-related proteomic profiling in plasma of children with metabolic healthy and unhealthy overweight/obesity.

OBJECTIVE: Children with overweight/obesity (OW/OB) exhibit poor cardiometabolic health, yet mechanisms influencing brain health remain unclear. We examined the differences in neurological-related circulating proteins in plasma among children with metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUO) and the association with metabolic syndrome markers. METHODS: In this cross-sectional study, we included 84 Caucasian children (39% girls), aged 10.1 ± 1.1 years, from the ActiveBrains project (NCT02295072). A ninety-two-protein targeted approach using Olink's® technology was used. RESULTS: We identified distinct concentrations of CD38, LAIR2, MANF and NRP2 proteins in MHO compared with MUO. Moreover, individual metabolic syndrome (MS) markers were linked to nine proteins (CD38, CPM, EDA2R, IL12, JAMB, KYNU, LAYN, MSR1 and SMOC2) in children with OW/OB. These proteins play crucial roles in diverse biological processes (e.g., angiogenesis, cholesterol transport, nicotinamide adenine dinucleotide (NAD+) catalysis and maintenance of blood-brain barrier) related to brain health. CONCLUSION: Our proteomics study suggests that cardiometabolic health (represented by MHO/MUO or individual MS markers) is associated with the concentration in plasma of several proteins involved in brain health. Larger-scale studies are needed to contrast/confirm these findings, with CD38 standing out as a particularly noteworthy and robust discovery.

Predicting the Transition to Metabolically Unhealthy Obesity Among Young Adults With Metabolically Healthy Obesity in South Korea: Nationwide Population-Based Study.

BACKGROUND: Globally, over 39% of individuals are obese. Metabolic syndrome, usually accompanied by obesity, is regarded as a major contributor to noncommunicable diseases. Given this relationship, the concepts of metabolically healthy and unhealthy obesity, considering metabolic status, have been evolving. Attention is being directed to metabolically healthy people with obesity who have relatively low transition rates to noncommunicable diseases. As obesity rates continue to rise and unhealthy behaviors prevail among young adults, there is a growing need for obesity management that considers these metabolic statuses. A nomogram can be used as an effective tool to predict the risk of transitioning to metabolically unhealthy obesity from a metabolically healthy status. OBJECTIVE: The study aimed to identify demographic factors, health behaviors, and 5 metabolic statuses related to the transition from metabolically healthy obesity to unhealthy obesity among people aged between 20 and 44 years and to develop a screening tool to predict this transition. METHODS: This secondary analysis study used national health data from the National Health Insurance System in South Korea. We analyzed the customized data using SAS (SAS Institute Inc) and conducted logistic regression to identify factors related to the transition from metabolically healthy to unhealthy obesity. A nomogram was developed to predict the transition using the identified factors. RESULTS: Among 3,351,989 people, there was a significant association between the transition from metabolically healthy to unhealthy obesity and general characteristics, health behaviors, and metabolic components. Male participants showed a 1.30 higher odds ratio for transitioning to metabolically unhealthy obesity than female participants, and people in the lowest economic status were also at risk for the transition (odds ratio 1.08, 95% CI 1.05-1.1). Smoking status, consuming >30 g of alcohol, and insufficient regular exercise were negatively associated with the transition. Each relevant variable was assigned a point value. When the nomogram total points reached 295, the shift from metabolically healthy to unhealthy obesity had a prediction rate of >50%. CONCLUSIONS: This study identified key factors for young adults transitioning from healthy to unhealthy obesity, creating a predictive nomogram. This nomogram, including triglycerides, waist circumference, high-density lipoprotein-cholesterol, blood pressure, and fasting glucose, allows easy assessment of obesity risk even for the general population. This tool simplifies predictions amid rising obesity rates and interventions.

Physical activity in relation to metabolic health and obesity: The Feel4Diabetes study.

AIM: To examine physical activity levels in association with metabolic health and estimate the stability of metabolically healthy obese (MHO) phenotypes over a 2-year period. METHODS: In total, 2848 men and women from families at risk of the development of diabetes were recruited. Participants were classified as obese or non-obese and metabolic health was defined using five existing definitions. Physical activity was estimated with the International Physical Activity Questionnaire and pedometers. RESULTS: Prevalence of the MHO phenotype varied among definitions (0% to 20.2%). Overall, the MHO were more active than the metabolically unhealthy obese (MUO). Daily sitting hours (odds ratio [OR] = 1.055, 95% confidence interval [CI]: 1.009-1.104) and daily steps (per 500; OR = 0.934, 95% CI: 0.896-0.973) were remarkable predictors of metabolic health in individuals with obesity; and likewise, in individuals without obesity. After 2 years, 44.1% of baseline MHO adults transitioned to MUO, while 84.0% of the MUO at baseline remained at the same phenotype. Although physical activity was not a major determinant in phenotype transitioning, daily steps were associated with the maintenance of metabolic health over time in the non-obese group. CONCLUSION: A universally accepted definition for MHO is needed. Being physically active can contribute to a metabolically healthy profile even in the presence of obesity; still, MHO is a transient condition and physical activity alone may not be an adequate factor for its maintenance.

Frequency of prediabetes in individuals with increased adiposity and metabolically healthy or unhealthy phenotypes.

AIMS: To utilize the estimated glucose disposal rate (eGDR) index of insulin sensitivity, which is based on readily available clinical variables, namely, waist circumference, hypertension and glycated haemoglobin, to discriminate between metabolically healthy and unhealthy phenotypes, and to determine the prevalence of prediabetic conditions. METHODS: Non-diabetic individuals (n = 2201) were stratified into quartiles of insulin sensitivity based on eGDR index. Individuals in the upper quartiles of eGDR were defined as having metabolically healthy normal weight (MHNW), metabolically healthy overweight (MHOW) or metabolically healthy obesity (MHO) according to their body mass index, while those in the lower quartiles were classified as having metabolically unhealthy normal weight (MUNW), metabolically unhealthy overweight (MUOW) and metabolically unhealthy obesity (MUO), respectively. RESULTS: The frequency of impaired fasting glucose (IFG), impaired glucose tolerance (IGT), and IFG + IGT status was comparable among the MHNW, MHOW and MHO groups, while it increased from those with MUNW status towards those with MUOW and MUO status. As compared with participants with MHNW, the odds ratio of having IFG, IGT, or IFG + IGT was significantly higher in participants with MUOW and MUO but not in those with MUNW, MHOW and MHO, respectively. CONCLUSIONS: A metabolically healthy phenotype is associated with lower frequency of IFG, IGT, and IFG + IGT status across all body weight categories.

Association between the transition to metabolically unhealthy obesity and lifestyle behavior: A nationwide cohort study.

OBJECTIVES: To identify the characteristics of individuals transitioning from metabolically healthy obesity (MHO) to unhealthy obesity and the factors influencing the change. DESIGN: This is a nationwide cohort study using data from the National Health Insurance Service in South Korea. SAMPLE: Individuals with obesity but metabolically healthy in 2009 and 2010 and those still obese 4 years later were selected. MEASUREMENTS: Sociodemographic, physical, metabolic, and health behavior variables were collected, and logistic regression was used to find an association with the transition. RESULTS: We analyzed 1,564,467 individuals, observing significant differences in all variables and the transition from MHO to unhealthy obesity. Among males, the transition was associated with smoking and drinking positively and physical activity negatively. Among females, drinking demonstrated a negative correlation. Regardless of age, regular exercise was negatively associated with the transition for all individuals. Except for older adults, all age groups showed a positive correlation with smoking and drinking. CONCLUSIONS: Considering the significant factors in the transition, it is essential to develop and implement interventions varied by gender and age to delay and prevent the change in metabolic status. The necessity of developing interventions enables individuals to engage in regular exercise, regardless of age and gender.

Metabolically healthy obesity and risks of cardiovascular disease and all-cause mortality, a matched cohort study: the Shizuoka study.

BACKGROUND: Metabolically healthy obesity is not always a benign condition. It is associated with an increased incidence of cardiovascular disease and all-cause mortality. We investigated the prognostic significance of metabolically healthy obesity by comparing clinical profile-matched metabolically healthy obesity and non-obesity groups. METHODS: We analyzed a health insurance dataset with annual health checkup data from Japan. The analyzed data included 168,699 individuals aged <65 years. Obesity was defined as ≥25 kg/m2 body mass index. Metabolically healthy was defined as ≤1 metabolic risk factor (high blood pressure, low high-density lipoprotein cholesterol, high low-density lipoprotein cholesterol, or high hemoglobin A1c). Incidence rates of stroke, myocardial infarction, and all-cause mortality identified from the insurance data were compared between metabolically healthy obesity and non-obesity groups (n = 8644 each) using a log-rank test. RESULTS: The stroke (obesity: 9.2 per 10,000 person-years; non-obesity: 10.5; log-rank test p = 0.595), myocardial infarction (obesity: 3.7; non-obesity: 3.1; p = 0.613), and all-cause mortality (obesity: 26.6; non-obesity: 23.2; p = 0.304) incidence rates did not differ significantly between the metabolically healthy obesity and non-obesity groups, even when the abdominal obesity was considered in the analysis. The lack of association was also observed in the comparison between the metabolically unhealthy obesity and non-obesity groups (n = 10,965 each). The population with metabolically healthy obesity reported negligibly worse metabolic profiles than the population with non-obesity at the 5.6-year follow-up. CONCLUSION: Obesity, when accompanied by a healthy metabolic profile, did not increase the risk of cardiovascular outcomes and all-cause mortality.

Metabolic health's central role in chronic kidney disease progression: a 20-year study of obesity-metabolic phenotype transitions.

This study investigates the risk of chronic kidney disease (CKD) across four metabolic phenotypes: Metabolically Healthy-No Obesity (MH-NO), Metabolically Unhealthy-No obesity (MU-NO), Metabolically Healthy-Obesity (MH-O), and Metabolically Unhealthy-Obesity (MU-O). Data from the Tehran Lipid and Glucose Study, collected from 1999 to 2020, were used to categorize participants based on a BMI ≥ 30 kg/m2 and metabolic health status, defined by the presence of three or four of the following components: high blood pressure, elevated triglycerides, low high-density lipoprotein, and high fasting blood sugar. CKD, characterized by a glomerular filtration rate < 60 ml/min/1.72 m2. The hazard ratio (HR) of CKD risk was evaluated using Cox proportional hazard models. The study included 8731 participants, with an average age of 39.93 years, and identified 734 incidents of CKD. After adjusting for covariates, the MU-O group demonstrated the highest risk of CKD progression (HR 1.42-1.87), followed by the MU-NO group (HR 1.33-1.67), and the MH-O group (HR 1.18-1.54). Persistent MU-NO and MU-O posed the highest CKD risk compared to transitional states, highlighting the significance of exposure during early adulthood. These findings emphasize the independent contributions of excess weight and metabolic health, along with its components, to CKD risk. Therefore, preventive strategies should prioritize interventions during early-adulthood.

Prevalence, Risk Factors, and Metabolic Characteristics of Metabolically Healthy Obesity in Patients Seeking Bariatric Surgery: A Cohort Study.

BACKGROUND: Bariatric surgery is an effective treatment for morbid obesity. However, a subset of individuals seeking bariatric surgery may exhibit a metabolically healthy obesity (MHO) phenotype, suggesting that they may not experience metabolic complications despite being overweight. OBJECTIVE: This study aimed to determine the prevalence and metabolic features of MHO in a population undergoing bariatric surgery. METHODS: A representative sample of 665 participants aged 14 or older who underwent bariatric surgery at our center from January 1, 2010 to January 1, 2020 was included in this cohort study. MHO was defined based on specific criteria, including blood pressure, waist-to-hip ratio, and absence of diabetes. RESULTS: Among the 665 participants, 80 individuals (12.0%) met the criteria for MHO. Female gender (P = .021) and younger age (P < .001) were associated with a higher likelihood of MHO. Smaller weight and BMI were observed in individuals with MHO. However, a considerable proportion of those with MHO exhibited other metabolic abnormalities, such as fatty liver (68.6%), hyperuricemia (55.3%), elevated lipid levels (58.7%), and abnormal lipoprotein levels (88%). CONCLUSION: Approximately 1 in 8 individuals referred for bariatric surgery displayed the phenotype of MHO. Despite being metabolically healthy based on certain criteria, a significant proportion of individuals with MHO still exhibited metabolic abnormalities, such as fatty liver, hyperuricemia, elevated lipid levels, and abnormal lipoprotein levels, highlighting the importance of thorough metabolic evaluation in this population.

Transition in metabolic health phenotypes across general adiposity categories and association with the risk of depression: a prospective analysis.

BACKGROUND: The association between obesity and depression may partly depend on the contextual metabolic health. The effect of change in metabolic health status over time on subsequent depression risk remains unclear. We aimed to assess the prospective association between metabolic health and its change over time and the risk of depression across body mass index (BMI) categories. METHODS: Based on a nationally representative cohort, we included participants enrolled at the wave 2 (2004-2005) of the English Longitudinal Study of Ageing and with follow-up for depression at wave 8 (2016-2017). Participants were cross-classified by BMI categories and metabolic health (defined by the absence of hypertension, diabetes, and hypercholesterolemia) at baseline or its change over time (during waves 3-6). Logistic regression model was used to calculate odds ratios (ORs) and 95% confidence intervals (CIs) for the risk of depression at follow-up stratified by BMI category and metabolic health status with adjustment for potential confounders. RESULTS: The risk of depression was increased for participants with metabolically healthy obesity compared with healthy nonobese participants, and the risk was highest for those with metabolically unhealthy obesity (OR 1.62, 95% CI 1.18-2.20). Particularly hypertension and diabetes contribute most to the increased risk. The majority of metabolically healthy participants converted to unhealthy metabolic phenotype (50.1% of those with obesity over 8 years), which was associated with an increased risk of depression. Participants who maintained metabolically healthy obesity were still at higher risk (1.99, 1.33-2.72), with the highest risk observed for those with stable unhealthy metabolic phenotypes. CONCLUSIONS: Obesity remains a risk factor for depression, independent of whether other metabolic risk factors are present or whether participants convert to unhealthy metabolic phenotypes over time. Long-term maintenance of metabolic health and healthy body weight may be beneficial for the population mental well-being.

Relationship between metabolically healthy obesity and coronary artery calcification.

There is a lack of consensus regarding universally accepted criteria for metabolic health (MH). A simple definition of MH was systematically derived in a recent prospective cohort study. The present cross-sectional study aimed to explore the applicability of these criteria in Korean population, using coronary calcification as an indicator of cardiovascular risk. In total, 1049 healthy participants, who underwent coronary artery calcification testing at university hospital health promotion centers between January and December 2022, were included. Applying the main components of the newly derived definition, MH was defined as follows: (1) systolic blood pressure < 130 mmHg and no use of blood pressure-lowering medication; (2) waist circumference < 90 cm for males and < 85 cm for females; and (3) absence of diabetes. Multivariate logistic regression was conducted to examine the odds ratio (OR) and 95 % confidence interval (CI) for coronary artery calcium score across different phenotypes. The prevalence of coronary artery calcification in this study was 41.1 %. Compared with metabolically healthy, normal weight subjects, those with the metabolically healthy obesity phenotype did not exhibit increased odds for coronary atherosclerosis. (OR 0.93 [95 % CI 0.48-1.79]) Conversely, metabolically unhealthy subjects had increased risk, regardless of their body mass index category (OR 3.10 [95 % CI 1.84-5.24] in metabolically unhealthy normal weight; OR 3.21 [95 % CI 1.92-5.37] in metabolically unhealthy overweight; OR 2.73 [95 % CI 1.72-4.33] in metabolically unhealthy obese phenotype). These findings suggest that the new definition for MH has the potential to effectively distinguish individuals at risk for cardiovascular disease from those who are not.

Metabolically healthy obesity, transition from metabolic healthy to unhealthy status, and carotid atherosclerosis.

BACKGROUND: Evidence of the effects of metabolically healthy obesity (MHO) on atherosclerosis is limited; the transition effects of metabolic health and obesity phenotypes have been ignored. We examined the association between metabolic health and the transition to atherosclerosis risk across body mass index (BMI) categories in a community population. METHODS: This cross-sectional study was based on a national representative survey that included 50,885 community participants aged ≥40 years. It was conducted from 01 December 2017 to 31 December 2020, in 13 urban and 13 rural regions across Hunan China. Metabolic health was defined as meeting less than three abnormalities in blood pressure, glucose, high-density lipoprotein cholesterol, triglycerides, or waist circumference. The participants were cross-classified at baseline based on their metabolic health and obesity. In addition, the relationship between atherosclerosis and transitions in metabolic health status based on 4733 participants from baseline to the second survey after 2 years was considered. The relationship between metabolic health status and the risk of transition to Carotid atherosclerosis (CA) was assessed using logistic regression and Cox proportional hazards regression analyses. RESULTS: In this study, the mean age of the participants was 60.7 years (standard deviation [SD], 10.91), 53.0% were female, and 51.2% had CA. As compared with metabolically healthy normal weight (MHN), those with MHO phenotype (odd ratio [OR] 1.10, 95% confidence interval [CI] 1.02-1.21), metabolically unhealthy normal weight (OR 1.27, 95% CI 1.19-1.35), metabolically unhealthy overweight (OR 1.41, 95% CI 1.33-1.48), and metabolically unhealthy obese (OR 1.54, 95% CI 1.44-1.64) had higher risk for CA. However, during the follow-up of 2 years, almost 33% of the participants transitioned to a metabolically unhealthy status. As compared with stable healthy normal weight, transition from metabolically healthy to unhealthy status (hazard ratios [HR] 1.21, 95% [CI] 1.02-1.43) and stable metabolically unhealthy overweight or obesity (MUOO) (HR 1.32, 95% CI 1.17-1.48) were associated with higher risk of CA. CONCLUSIONS: In the community population, obesity remains a risk factor for CA despite metabolic health. However, the risks were highest for metabolically unhealthy status across all BMI categories. A large proportion of metabolically healthy overweight or participants with obesity converts to an unhealthy phenotype over time, which is associated with an increased risk of CA.

Healthy overweight and obesity in the young: Prevalence and risk of major adverse cardiovascular events.

AIMS: To investigate the prevalence of metabolically healthy overweight/obesity and to study its longitudinal association with major adverse cardiovascular and renal events (MARCE). METHODS AND RESULTS: The study was conducted in 1210 young-to-middle-age subjects grouped according to their BMI and metabolic status. The risk of MARCE was evaluated during 17.4 years of follow-up. Forty-eight-percent of the participants had normal weight, 41.9% had overweight, and 9.3% had obesity. Metabolically healthy status was found in 31.1% of subjects with normal weight and in 20.0% of those with overweight/obesity. During the follow-up, there were 108 MARCE. In multivariate Cox analysis adjusted for confounders and risk factors, no association was found between MARCE and overweight/obesity (p = 0.49). In contrast, metabolic status considered as a two-class variable (0 versus at least one metabolic abnormality) was a significant predictor of MARCE (HR, 2.11; 95%CI, 1.21-3.70, p = 0.009). Exclusion of atrial fibrillation from MARCE (N = 87) provided similar results (HR, 2.11; 95%CI, 1.07-4.16, p = 0.030). Inclusion of average 24 h BP in the regression model attenuated the strength of the associations. Compared to the group with healthy metabolic status, the metabolically unhealthy overweight/obesity participants had an increased risk of MARCE with an adjusted HR of 2.33 (95%CI, 1.05-5.19, p = 0.038). Among the metabolically healthy individuals, the CV risk did not differ according to BMI group (p = 0.53). CONCLUSION: The present data show that the risk of MARCE is not increased in young metabolically healthy overweight/obesity suggesting that the clinical approach to people with high BMI should focus on parameters of metabolic health rather than on BMI.