Metabolic Phenotypes Of Obesity Research Articles

Exploring the link between obesity and hypothyroidism in autoimmune thyroid diseases: a metabolic perspective.

Background: The management of primary hypothyroidism demands a comprehensive approach that encompasses both the implications of autoimmune thyroid disease and the distinct effects posed by obesity and metabolic irregularities. Despite its clinical importance, the interplay between obesity and hypothyroidism, especially in the context of metabolic perspectives, is insufficiently explored in existing research. This study endeavors to classify hypothyroidism by considering the presence of autoimmune thyroid disease and to examine its correlation with various metabolic obesity phenotypes. Method: This research was conducted by analyzing data from 1,170 individuals enrolled in the Thyroid Disease Database of Shandong Provincial Hospital. We assessed four distinct metabolic health statuses among the participants: Metabolically Healthy No Obese Metabolically Healthy Obese Metabolically Unhealthy No Obese and Metabolically Unhealthy Obese Utilizing logistic regression, we investigated the association between various metabolic obesity phenotypes and hypothyroidism. Results: The study revealed a significant correlation between the Metabolically Unhealthy Obese (MUO) phenotype and hypothyroidism, particularly among women who do not have thyroid autoimmunity. Notably, the Metabolically Unhealthy No Obese (MUNO) phenotype showed a significant association with hypothyroidism in individuals with thyroid autoimmunity, with a pronounced prevalence in women. Furthermore, elevated levels of triglycerides and blood glucose were found to be significantly associated with hypothyroidism in men with thyroid autoimmunity and in women without thyroid autoimmunity. Conclusion: Effective treatment of hypothyroidism requires a thorough understanding of the process of thyroid autoimmune development. In patients without concurrent thyroid autoimmunity, there is a notable correlation between obesity and metabolic issues with reduced thyroid function. Conversely, for patients with thyroid autoimmunity, a focused approach on managing metabolic abnormalities, especially triglyceride levels, is crucial.

Metabolic Obesity Phenotypes and Incident Cardiovascular Outcomes in Middle-Aged and Older Korean Adults: A Longitudinal 10-Year Analysis of the Korean Genome and Epidemiology Study.

Background: This study investigated the association of four metabolic obesity phenotypes with incident coronary artery disease and stroke in a large-scale, community population-based, prospective Korean cohort observed for over 10 years. Methods: The study participants included 7374 adults aged 40-69 years, drawn from the Korean Genome and Epidemiology Study. Participants with different metabolic obesity phenotypes were categorized according to body weight and metabolic health status into four groups: metabolically healthy nonobese (MHNO), metabolically healthy obese (MHO), metabolically unhealthy nonobese (MUHNO), and metabolically unhealthy obese (MUHO). Combined cardiovascular events were defined as coronary artery disease and stroke. We used multivariate Cox proportional hazards regression models to prospectively assess hazard ratios (HRs) with 95% confidence intervals (CIs) for incident coronary artery disease or stroke over 10 years after the baseline survey. Results: During the follow-up period, newly developed coronary artery disease, stroke, and combined cardiovascular events were diagnosed in 151 (2.0%), 137 (1.9%), and 283 (3.8%) participants, respectively. After adjusting for confounding variables, the HRs (95% CIs) for incident combined cardiovascular events were 1.81 (1.34-2.46) in the MUHO group, 1.29 (0.92-1.81) in the MUHNO group, and 1.21 (0.81-1.79) in the MHO group compared with those in the MHNO group. Conclusions: This study revealed distinct risks associated with four metabolic obesity phenotypes concerning incident coronary artery disease and stroke. After adjusting for potential confounding variables, the results indicated that MUHO, but not MUHNO or MHO, showed a higher risk of developing coronary artery disease and stroke than MHNO.

Associations Between Metabolic Obesity Phenotypes and Pathological Characteristics of Papillary Thyroid Carcinoma

ObjectiveThe association between obesity, metabolic dysregulation, and the aggressive pathological traits of papillary thyroid carcinoma (PTC) continues to be a contentious issue. To date, no investigations have examined the impact of metabolic status on the malignant pathological features of PTC in relation to obesity. MethodsThis research involved 855 adult patients with PTC from Shandong Provincial Hospital, classified into 4 groups based on metabolic and obesity status: metabolically healthy nonobese, metabolically unhealthy nonobese (MUNO), metabolically healthy obese, and metabolically unhealthy obese. We employed logistic regression to investigate the relationship between these metabolic obesity phenotypes and PTC's pathological characteristics. Mediation analysis was also performed to determine metabolic abnormalities' mediating role in the nexus between obesity and these characteristics. ResultsRelative to metabolically healthy nonobese individuals, the metabolically unhealthy obese group was significantly associated with an elevated risk of larger tumor sizes and a greater number of tumor foci in PTC. Mediation analysis indicated that obesity directly influences tumor size, whereas its effect on tumor multifocality is mediated through metabolic dysfunctions. Specifically, high-density lipoprotein cholesterol levels were notably associated with tumor multifocality within obese subjects, serving as a mediator in obesity's impact on this trait. ConclusionThe concurrent presence of obesity and metabolic dysregulation is often connected to more aggressive pathological features in PTC. The mediation analysis suggests obesity directly affects tumor size and indirectly influences tumor multifocality via low high-density lipoprotein cholesterol levels.

Abstract 4772: Metabolic phenotype, age acceleration, and obesity-related cancer risk in Women's Health Initiative and Atherosclerosis Risk in Communities cohorts

Abstract Introduction: While normal physiologic aging causes metabolic decline, metabolic dysregulation owing to excess nutrient intake, physical inactivity, and obesity may drive accelerated aging. However, the relationship of accelerated aging with metabolic dysfunction across different levels of BMI (i.e., metabolic obesity phenotype) and whether it may mediate the relationship with obesity-related cancer (ORC) risk is not well established. Methods: To test this hypothesis, we are leveraging existing blood-based DNA methylation (DNAm) data within the Women’s Health Initiative (WHI) and the Atherosclerosis Risk in Communities (ARIC) cohorts. Weight, height and blood pressure were measured by trained technicians in the respective cohorts and fasting blood glucose, triglycerides and HDL cholesterol were quatified by standard clinican assays. Metabolic obesity phenotypes are classified as metabolically healthy normal weight (MHNW), metabolically unhealthy normal weight (MUNW), metabolically healthy overweight/obese (MHO), and metabolically unhealthy overweight/obese (MUO) when defined by ATP III criteria. Age acceleration was measured as a residual from regressing pan-tissue Horvath’s DNAmAge on chronological age. Results: The mean baseline age was 64 ± 6.6 years and 54 ± 5.7 year among WHI and ARIC participants, respectively and the age acceleration was measured at an mean follow-up time of 16 years from baseline for WHI and 3.5 years from baseline for ARIC cohort. The outcome of interest, ORC incidence was observed among 474 participants of which 387 were women. The median time for cancer diagnosis from baseline was 25 year and from DNAm measurement was 18.6 years. Of the 4341 (70% women) participants included in the analysis, 23.9% were MHNW, 3.2% MUNW, 39% MHO, and 33.5% MUO. A 31.5% of MHNW, 40.4% of MUNW, 28.7% of MHO, and 35.2% of MUO showed age acceleration. MUNW (OR 1.47, 95% CI: 1.03-2.11) and MUO (OR 1.18, 95% CI: 1.00-1.40) individuals at baseline had increased odds of experiencing age acceleration at a follow-up visit. The risk of all ORC combined was significantly elevated among MUO (HR 1.63, 95% CI: 1.27-2.09) compared to MHNW. Age acceleration at the follow-up visit was not significantly associated with ORC risk (HR 0.91, 95% CI: 0.75-1.11). These analyses were not adjusted for confounders. Conclusions: MUNW and MUO individuals had increased odds of age acceleration at a follow-up visit. Furthermore, MUO individuals were at an elevated risk of ORC, compared to individuals with MHNW. Accelerated aging does not appear to mediate this association, as accelerated aging was not associated with ORC. Citation Format: Prasoona Karra, Garnet L. Anderson, Mary C. Playdon, Sheetal Hardikar, Aladdin Shadyab, Kari E. North, Anne E. Justice, Elizabeth A. Platz, Jan Bressler, Anna Prizment, Weihua Guan, Dominique S. Michaud, Eric A. Whitsel, Brock C. Christensen, Lucas A. Salas. Metabolic phenotype, age acceleration, and obesity-related cancer risk in Women's Health Initiative and Atherosclerosis Risk in Communities cohorts [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4772.

Joint effect of BMI and metabolic status on mortality among adults: a population-based longitudinal study in United States

We explored the joint effects of different metabolic obesity phenotypes on all-cause and disease-specific mortality risk among the American population. Data were obtained from the National Health and Nutrition Examination Survey (NHANES) 1999–2018. Mortality outcome data were from mortality files linked to National Death Index record and follow-up information was up to December 31, 2019. 50,013 participants were finally included. Four metabolic obesity phenotypes were defined based on obesity and metabolic status: metabolically healthy obese (MHO), metabolically unhealthy obese (MUO), metabolically healthy non-obese (MHNO), and metabolically unhealthy non-obese (MUNO). Population-weighted Cox proportional hazards models were used to explore the all-cause and disease-specific mortality risk of metabolic obesity phenotypes. The all-cause mortality risk of MUO and MUNO was significantly higher than MHNO. MUNO was associated with a significantly increased risk of death from heart disease (HR: 1.40, 95% CI 1.16–1.70), hypertension (HR: 1.68, 95% CI 1.34–2.12), diabetes (HR: 2.29, 95% CI 1.67–3.15), and malignant neoplasms (HR:1.29, 95% CI 1.09–1.53). Metabolic unhealth significantly increased the risk of all-cause mortality, regardless of obesity status. Among individuals with metabolic unhealthy status, obesity significantly reduced the risk of all-cause mortality (HR: 0.91, 95% CI 0.85–0.98). Our study highlights the importance of identifying and characterizing metabolic obesity phenotypes in obese and metabolically abnormal patients, as well as healthy adults. Comprehensive evaluation of obesity and metabolic status is necessary to adopt appropriate interventions and treatment measures and maximize patient benefit.

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

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. This cross-sectional study was based on a national representative survey that included 50,885 community participants aged ≥40years. 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 2years 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. 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 2years, 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. 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.

Association of metabolic health and obesity with coronary heart disease in adult cancer survivors.

The metabolically healthy obese (MHO) phenotype is associated with an increased risk of coronary heart disease (CHD) in the general population. However, association of metabolic health and obesity phenotypes with CHD risk in adult cancer survivors remains unclear. We aimed to investigate the associations between different metabolic health and obesity phenotypes with incident CHD in adult cancer survivors. We used National Health Insurance Service (NHIS) to identify a cohort of 173,951 adult cancer survivors aged more than 20 years free of cardiovascular complications. Metabolically healthy nonobese (MHN), MHO, metabolically unhealthy nonobese (MUN), metabolically unhealthy obese (MUO) phenotypes were created using as at least three out of five metabolic health criteria along with obesity (body mass index ≥ 25.0 kg/m2). We used Cox proportional hazards model to assess CHD risk in each metabolic health and obesity phenotypes. During 1,376,050 person-years of follow-up, adult cancer survivors with MHO phenotype had a significantly higher risk of CHD (hazard ratio [HR] = 1.52; 95% confidence intervals [CI]: 1.41 to 1.65) as compared to those without obesity and metabolic abnormalities. MUN (HR = 1.81; 95% CI: 1.59 to 2.06) and MUO (HR = 1.92; 95% CI: 1.72 to 2.15) phenotypes were also associated with an increased risk of CHD among adult cancer survivors. Adult cancer survivors with MHO phenotype had a higher risk of CHD than those who are MHN. Metabolic health status and obesity were jointly associated with CHD risk in adult cancer survivors.

Is metabolic-healthy obesity associated with risk of dementia? An age-stratified analysis of the Whitehall II cohort study.

Metabolically healthy obesity is hypothesized to be a benign condition but whether this is the case for dementia remains debated. We examined the role of age at assessment of metabolic-obesity phenotypes in associations with incident dementia. Obesity (body mass index ≥ 30kg/m2) and poor metabolic health (≥ 2 of elevated serum triglycerides, low HDL-C, elevated blood pressure, and elevated serum fasting glucose) were used to define four metabolic-obesity phenotypes (metabolically healthy (MHNO) and unhealthy non-obesity (MUNO), metabolically healthy (MHO) and unhealthy obesity (MUO)) at < 60, 60 to < 70, and ≥ 70years using 6 waves of data from the Whitehall II study and their associations with incident dementia was examined using Cox regression. Analyses with exposures measured < 60, 60 to < 70, and ≥ 70years involved 410 (5.8%), 379 (5.6%), and 262 (7.4%) incident dementia cases over a median follow-up of 20.8, 10.3, and 4.2years respectively. In analyses of individual components, obesity before 60years (HR 1.41, 95% CI: [1.08, 1.85]) but not at older ages was associated with dementia; unhealthy metabolic status when present < 60years (HR 1.33, 95% CI: [1.08, 1.62]) and 60 to < 70years (HR 1.32, 95% CI: [1.07, 1.62]) was associated with dementia. Compared to the metabolically healthy non-obesity group, the risk of dementia was higher in those with metabolically healthy obesity before 60years (1.69; 95% CI: [1.16, 2.45]); this was not the case when metabolic-obesity phenotype was present at 60 to < 70years or ≥ 70years. Analyses at older ages were on smaller numbers due to death and drop-out but inverse probability weighting to account for missing data yielded similar results. Individuals with metabolically healthy obesity before age 60 had a higher risk of incident dementia over a 27-year follow-up; the excess risk dissipates when metabolic health and obesity are measured after 70years.

The short- and long-term readmission of four major categories of digestive system cancers: does obesity or metabolic disorder matter?

Patients with digestive system cancers (DSCs) are at a high risk for hospitalizations; however, the risk factors for readmission remain unknown. Here, we established a retrospective cohort study to assess the association between metabolic obesity phenotypes and readmission risks of DSC. A total of 142,753 and 74,566 patients at index hospitalization were ultimately selected from the Nationwide Readmissions Database (NRD) 2018 to establish the 30-day and 180-day readmission cohorts, respectively. The study population was classified into four groups: metabolically healthy non-obese (MHNO), metabolically healthy obese (MHO), metabolically unhealthy non-obese (MUNO), and metabolically unhealthy obese (MUO). Multivariate Cox regression analysis was used to estimate the effect of metabolic obesity phenotypes on DSC readmission. The MUNO phenotype had 1.147-fold (95% CI: 1.066, 1.235; p < 0.001) increased 180-day readmission risks in patients with neoplasm of the upper digestive tract. The MUNO phenotype had 1.073-fold (95% CI: 1.027, 1.121; p = 0.002) increased 30-day readmission risks and 1.067-fold (95% CI: 1.021, 1.115; p = 0.004) increased 180-day readmission risks in patients with neoplasm of the lower digestive tract. The MUNO and MUO phenotypes were independent risk factors of readmission in patients with liver or pancreatic neoplasm. Metabolic obesity status was independently associated with a high risk of severe and unplanned hospitalization within 30 days or 180 days. Both obesity and metabolic abnormalities are associated with a high risk for the poor prognosis of DSC patients. The effect of metabolic categories on the short- or long-term readmission of liver or pancreas cancers may be stronger than that of obesity.

Hypothalamic astrocytic-BMAL1 regulates energy homeostasis in a sex-dependent manner

Here, we demonstrate that hypothalamic astrocytic BMAL1 computes cyclic metabolic information to optimize energetic resources in a sexually dimorphic manner. Knockdown of BMAL1 in female astrocytes leads to negative energy balance and alters basal metabolic cycles without affecting circadian locomotor activity. Thus, astrocytic BMAL1 contributes to the control of energy balance through the modulation of the metabolic rate, hepatic and white adipose tissue lipogenesis, and the activity of brown adipose tissue. Importantly, most of these alterations are specific to hypothalamic astrocytic BMAL1. Moreover, female mice with BMAL1 knockdown in astrocytes exhibited a "male-like" metabolic obese phenotype when fed a high-fat diet. Overall, our results suggest a sexually dimorphic effect of astrocytic BMAL1 on the regulation of energy homeostasis, which may be of interest in the physiopathology of obesity and related comorbidities.

Features of bacterial DNA taxonomy in blood of patients with various metabolic phenotypes of obesity

Aim. To study the blood microbiome taxonomy in patients with metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUHO).Materials and methods. The study included healthy donors without obesity (n = 116) and obese patients who were divided into subgroups with MHO (n = 36) and MUHO (n = 53). Bacterial DNA isolated from blood samples was subject to metagenomic sequencing of the v3–v4 variable region in the 16S rRNA gene. We compared the frequency of isolating certain taxa from the samples and the proportion of these taxa in the total pool of bacterial DNA in the blood.Results. MUHO patients showed an increase in Lachnospiraceae, Ruminococcaceae, and Prevotellaceae, which are the main taxa in gut microbiota. This may indicate greater intestinal permeability in such patients. Obese patients, regardless of the metabolic phenotype of obesity, more often had Rhodobacteraceae, Streptomycetaceae, Leuconostocaceae, and Burkholderiaceae DNA in their blood. Nocardioidaceae, Flavobacteriaceae, Hyphomicrobiaceae, and Gaiellaceae DNA were more frequently present in the blood microbiome of patients with MHO, whereas MUHO patients more often had S24-7, Nocardiaceae, and Helicobacteraceae DNA in their blood. Many members of these families inhabit soil and water, which may indicate increased skin barrier permeability in obese patients. Additionally, a higher number of Helicobacteraceae-positive blood samples in the MUHO patient group may indicate increased translocation from the stomach.Conclusion. Obesity is accompanied by changes in the taxonomic composition of the blood microbiome. Moreover, the nature of the changes depends on the metabolic phenotype of obesity and the permeability of external barriers.

Association between Asymptomatic Hyperuricemia and Metabolic Obesity Phenotypes in Middle Aged Adults

Association between Asymptomatic Hyperuricemia and Metabolic Obesity Phenotypes in Middle Aged Adults

Metabolic obesity phenotypes and obesity-related cancer risk in the National Health and Nutrition Examination Survey.

Body mass index (BMI) fails to identify up to one-third of normal weight individuals with metabolic dysfunction who may be at increased risk of obesity-related cancer (ORC). Metabolic obesity phenotypes, an alternate metric to assess metabolic dysfunction with or without obesity, were evaluated for association with ORC risk. National Health and Nutrition Examination Survey participants from 1999 to 2018 (N = 19,500) were categorized into phenotypes according to the metabolic syndrome (MetS) criteria and BMI: metabolically healthy normal weight (MHNW), metabolically unhealthy normal weight (MUNW), metabolically healthy overweight/obese (MHO) and metabolically unhealthy overweight/obese (MUO). Adjusted multivariable logistic regression models were used to evaluate associations with ORC. With metabolic dysfunction defined as ≥1 MetS criteria, ORC cases (n = 528) had higher proportions of MUNW (28.2% vs. 17.4%) and MUO (62.6% vs. 60.9%) phenotypes than cancer-free individuals (n = 18,972). Compared with MHNW participants, MUNW participants had a 2.2-times higher ORC risk [OR (95%CI) = 2.21 (1.27-3.85)]. MHO and MUO participants demonstrated a 43% and 56% increased ORC risk, respectively, compared to MHNW, but these did not reach statistical significance [OR (95% CI) = 1.43 (0.46-4.42), 1.56 (0.91-2.67), respectively]. Hyperglycaemia, hypertension and central obesity were all independently associated with higher ORC risk compared to MHNW. MUNW participants have a higher risk of ORC than other abnormal phenotypes, compared with MHNW participants. Incorporating metabolic health measures in addition to assessing BMI may improve ORC risk stratification. Further research on the relationship between metabolic dysfunction and ORC is warranted.

RELATIONSHIP BETWEEN SKELETAL MASS INDEX AND DIFFERENT OBESITY METABOLIC PHENOTYPES AMONG ADOLESCENTS IN CHINA

Objective: To explore the relationship between skeletal mass index (SMI) and different obesity metabolic phenotypes among adolescents,and to provide a theoretical basis for the prevention and control of obesity and related metabolic diseases in adolescents. Design and method: A total of 1352 adolescents aged 12-18 were randomly selected for questionnaire survey,physical measurements,body composition determination and laboratory testing. Multiple logistic regression analysis was used to analyze the correlation intensity between SMI and different metabolic phenotypes of obesity. Results: The metabolically unhealthy normal weight (MUNW) phenotype, metabolically healthy obesity(MHO) phenotype and metabolically unhealthy obesity(MUO) phenotype had lower levels of SMI compared to the metabolically healthy normal weight(MHNW) phenotype among adolescents,with all differences being statistically significant(P-values were all less than 0.001). Similar results were found after stratification by sex. Multiple logistic regression analysis showed that after adjusting for sex, SMI were protective factors for MUNW phenotype, MHO phenotype and MUO phenotype in adolescents(P-values were all less than 0.001),with the 0.86(95%CI:0.85,0.92),0.78(0.75,0.82) and 0.73(0.70,0.76) increased risks of MUNW phenotype, MHO phenotype and MUO phenotype associated with each unit rise in SMI, similar results were found after stratification by sex. Conclusions: SMI were protective factors for MUNW phenotype, MHO phenotype and MUO phenotype among adolescents. Attention should be paid to the assessment and supervision of adolescent skeletal muscle quality when prevented obesity and related metabolic risks,reducing the risk of abnormal cardiovascular metabolism in adolescents.

Relationship between skeletal muscle mass index and metabolic phenotypes of obesity in adolescents

To study the relationship between skeletal muscle mass index (SMI) and metabolic phenotypes of obesity in adolescents, and to provide a basis for the prevention and control of adolescent obesity and related metabolic diseases. A total of 1 352 adolescents aged 12 to 18 years were randomly selected by stratified cluster sampling in Yinchuan City from October 2017 to September 2020, and they were surveyed using questionnaires, physical measurements, body composition measurements, and laboratory tests. According to the diagnostic criteria for metabolic abnormalities and the definition of obesity based on the body mass index, the subjects were divided into four metabolic phenotypes: metabolically healthy normal weight, metabolically healthy obesity, metabolically unhealthy normal weight, and metabolically unhealthy obesity. The association between SMI and the metabolic phenotypes was analyzed using multivariate logistic regression. The SMI level in the metabolically unhealthy normal weight, metabolically healthy obesity, and metabolically unhealthy obesity groups was lower than that in the metabolically healthy normal weight group (P<0.001). Multivariate logistic regression analysis showed that after adjusting for gender and age, a higher SMI level was a protective factors for adolescents to develop metabolic unhealthy normal weight, metabolically healthy obesity, and metabolically unhealthy obesity phenotypes (OR=0.74, 0.60, and 0.54, respectively; P<0.001). Increasing SMI can reduce the risk of the development of metabolic unhealthy/obesity.

Association between different metabolic phenotypes of obesity and thyroid disorders among Chinese adults: a nationwide cross-sectional study.

Increased body mass index (BMI) and metabolic abnormalities both have potential associations with thyroid disease. The aim of this study was to investigate the correlation between different metabolic phenotypes of obesity and thyroid disorders using nationwide data from China. Data were collected from a cross-sectional survey called the Thyroid Disorders, Iodine Status, and Diabetes Epidemiological Survey conducted between 2015 and 2017 in China. A total of 69007 subjects aged 18 years or older were defined and divided into six groups on the basis of BMI and metabolic health status: metabolically healthy normal weight (MHNW), metabolically unhealthy normal weight (MUNW), metabolically healthy overweight (MHOW), metabolically unhealthy overweight (MUOW), metabolically healthy obesity (MHO), and metabolically unhealthy obesity (MUO). We estimated the odds ratios (ORs) and confidence intervals [CIs] for different thyroid disorders according to metabolic phenotypes using multivariate logistic regression models. In our study, we found that the prevalence of subclinical hypothyroidism was almost as high in the MUNW group as in the MUO group, the prevalence of overt hyperthyroidism was highest in the MUNW group and Graves' disease was highest in the MHO group. Our results also suggested that the prevalence of goiter and thyroid nodules increased with increased BMI values and that the MUO group had the highest incidence. Multivariate logistic regression analysis indicated that subjects with unhealthy metabolic phenotypes (MUNW, MUOW, and MUO) all had an increased risk of subclinical hypothyroidism, regardless of their BMI. MUNW subjects had an approximately 1.6-fold higher risk of overt hyperthyroidism and a 1.8-fold higher risk of Graves' disease than their metabolically healthy counterparts (MHNW). The present study also demonstrated that the MUO group had the highest risk of goiter and thyroid nodules among the metabolic phenotypes of obesity. Based on our study, we found that metabolic abnormalities and obesity play different roles in various thyroid diseases. Metabolically unhealthy individuals, both with and without obesity, have a higher risk of thyroid disorders than metabolically healthy individuals without obesity.

Expression of ICAM-1 and E-selectin in different metabolic obesity phenotypes: discrepancy for endothelial dysfunction.

Endothelial dysfunction, the earliest vascular alteration, is a consequence of metabolic disorders associated with obesity. However, it is still unclear whether a proportion of obese individuals without metabolic alterations associated with obesity, defined as "metabolically healthy obesity (MHO)", exhibit better endothelial function. We therefore aimed to investigate the association of different metabolic obesity phenotypes with endothelial dysfunction. The obese participants without clinical cardiovascular disease from the MESA (Multi-Ethnic Study of Atherosclerosis) were allocated to the different metabolic obesity phenotypes based on their metabolic status, including MHO and metabolically unhealthy obesity (MUO). Associations of metabolic obesity phenotypes with the biomarkers of endothelial dysfunction, including soluble intercellular adhesion molecule-1 (sICAM-1) and soluble E-selectin (sE-selectin), were evaluated using multiple linear regression models. Plasma levels of sICAM-1 and sE-selectin were respectively measured in 2371 and 968 participants. Compared to the non-obese participants, those with MUO were associated with higher concentrations of sICAM-1 (β 22.04, 95% CI 14.33-29.75, P < 0.001) and sE-selectin (β 9.87, 95% CI 6.00-13.75, P < 0.001) after adjusting for confounders. However, no differences were found for the concentrations of sICAM-1 (β 0.70, 95% CI -8.91 to 10.32, P = 0.886) and sE-selectin (β 3.69, 95% CI -1.13 to 8.51, P = 0.133) in the participants with MHO compared to the non-obese participants. Individuals with MUO were associated with elevated biomarkers of endothelial dysfunction, but the association with endothelial dysfunction was not found in those with MHO, indicating that the individuals with MHO might exhibit better endothelial function.

Abstract 6454: Metabolic obesity phenotype and risk of obesity-related cancers in the women's health initiative

Abstract Introduction: Body mass index (BMI) may misclassify obesity-related cancer (ORC) risk since metabolic dysfunction can exist at any BMI. We measured the association of metabolic dysfunction, independent of BMI with risk of ORC. Methods: Study included 60% white and 34% black Women’s Health Initiative participants with available baseline cardiovascular disease biomarkers. Metabolic obesity phenotypes were classified as normal weight with no metabolic dysfunction (NWNMD), normal weight with metabolic dysfunction (NWMD), overweight/obese with no metabolic dysfunction (OONMD) and overweight/obese with metabolic dysfunction (OOMD) when defined by Wildman and ATP III criteria. We performed a Cox proportional hazards regression with death as a competing risk adjusting for confounders. Results: Defining by Wildman and ATP III criteria, 17.0%, 6.5%, 31.0%, 45.0%; and 22.2%, 2.4%, 41% and 35% were NWNMD, NWMD, OONMD and OOMD respectively. The risk of all ORC combined was elevated among NWMD (HR 1.12, 95% CI: 0.90-1.39), OONMD (HR 1.15, 95% CI: 1.00-1.32) and OOMD (HR 1.35, 95% CI: 1.18-1.54) when compared to NWNMD using Wildman criteria. Results were similar when defined by ATP III, hs-CRP and HOMA-IR. When stratified by cancer type, NWMD were at increased risk of colorectal cancer when compared to NWNMD (HR 1.70, 95% CI: 1.02-2.82). Individuals who were overweight/obese were at increased risk of cancer for all cancer types though the effect estimates were lower among OONMD phenotype. Conclusions: NWMD and OOMD individuals are at increased risk of all ORC combined, independent of BMI status when compared to individuals with NWNMD. Citation Format: Prasoona Karra, Maci Winn, Garnet Anderson, Benjamin Haaland, Aladdin H. Shadyab, Marian Neuhouser, Rebecca Seguin-Fowler, Cynthia A. Thomson, Mace Coday, Jean Wactawski-Wende, Marcia L. Stefanick, Xiaochen Zhang, Ting-Yuan David Cheng, Shama Karanth, Yangbo Sun, Nazmus Saquib, Margaret Pichado, Su Yon Jung, Fred Tabung, Scott A. Summers, William L. Holland, Thunder Jalili, Marc Gunter, Sheetal Hardikar, Mary C. Playdon. Metabolic obesity phenotype and risk of obesity-related cancers in the women's health initiative [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6454.

All-cause mortality risk with different metabolic abdominal obesity phenotypes: the Rural Chinese Cohort Study.

We aimed to investigate the association of metabolic obesity phenotypes with all-cause mortality risk in a rural Chinese population. This prospective cohort study enrolled 15704 Chinese adults (38·86 % men) with a median age of 51·00 (interquartile range: 41·00-60·00) at baseline (2007-2008) and followed up during 2013-2014. Obesity was defined by waist circumference (WC: ≥ 90 cm for men and ≥ 80 cm for women) or waist-to-height ratio (WHtR: ≥ 0·5). The hazard ratio (HR) and 95 % CI for the risk of all-cause mortality related to metabolic obesity phenotypes were calculated using the Cox hazards regression model. During a median follow-up of 6·01 years, 864 deaths were identified. When obesity was defined by WC, the prevalence of participants with metabolically healthy non-obesity (MHNO), metabolically healthy obesity (MHO), metabolically unhealthy non-obesity (MUNO) and metabolically unhealthy obesity (MUO) at baseline was 12·12 %, 2·80 %, 41·93 % and 43·15 %, respectively. After adjusting for age, sex, alcohol drinking, smoking, physical activity and education, the risk of all-cause mortality was higher with both MUNO (HR = 1·20, 95 % CI 1·14, 1·26) and MUO (HR = 1·20, 95 % CI 1·13, 1·27) v. MHNO, but the risk was not statistically significant with MHO (HR = 0·99, 95 % CI 0·89, 1·10). This result remained consistent when stratified by sex. Defining obesity by WHtR gave similar results. MHO does not suggest a greater risk of all-cause mortality compared to MHNO, but participants with metabolic abnormality, with or without obesity, have a higher risk of all-cause mortality. These results should be cautiously interpreted as the representation of MHO is small.

Impact of Early Life Factors on Metabolic Phenotypes of Obesity in Preschool Children.

To investigate the impact of factors in the first 1,000 days of life on metabolic phenotypes of obesity in preschool children in a cohort study. We recruited 3-year-old children for the study. Early life factors included maternal age at delivery, maternal education, prepregnancy BMI, gestational weight gain, gravidity, history of gestational diabetes mellitus, delivery mode, gestational age, family history of metabolic disorders, paternal education, annual family income, child sex, birth weight, and breastfeeding duration. According to BMI and metabolic status, children were classified as metabolically healthy (no metabolic risk factors) with normal weight (MHNW), metabolically unhealthy (one or more metabolic risk factors) with normal weight (MUNW), metabolically healthy with overweight or obesity (MHO), and metabolically unhealthy with overweight or obesity (MUO). We recruited 3,822 children for the study, with 3,015 analyzed. Accelerated BMI z score growth rate between 6 and 24 months was associated with MHO (β = 0.022; 95% CI 0.009, 0.036) and MUO (β = 0.037; 95% CI 0.018, 0.056). Maternal overweight (odds ratio [OR] 3.16; 95% CI 1.55, 6.42) and obesity (OR 8.14; 95% CI 3.73, 17.76) before pregnancy and macrosomia (OR 2.47; 95% CI 1.32, 4.59) were associated with MHO, and maternal obesity before pregnancy (OR 6.35; 95% CI 2.17, 18.52) increased the risk of MUO. Early life factors, such as maternal obesity and accelerated BMI growth rate between 6 and 24 months, were related not only to MHO but also to MUO. Children with these early life factors should be given interventions for weight control to prevent metabolic abnormalities.