Heritability and genetic correlations of obesity indices and cardiometabolic traits in the Northern Chinese families.

OBJECTIVE: This study aimed to investigate the heritability of various obesity indices and their shared genetic factors with cardiometabolic traits in the Chinese nuclear family. METHODS: A total of 1270 individuals from 538 nuclear families were included in this cross-sectional study. Different indices were used to quantify fat mass and distribution, including body index mass (BMI), visceral fat index (VFI), and body fat percent (BFP). Heritability and genetic correlations for all quantitative traits were estimated using variance component models. The susceptibility-threshold model was utilized to estimate the heritability for binary traits. RESULTS: Heritability estimates for obesity indices were highest for BMI (59%), followed by BFP (49%), and VFI (40%). Heritability estimates for continuous cardiometabolic traits varied from 24% to 50%. All obesity measures exhibited consistently significant positive genetic correlations with blood pressure, fasting blood glucose, and uric acid (rG range: 0.26-0.57). However, diverse genetic correlations between various obesity indices and lipid profiles were observed. Significant genetic correlations were limited to specific pairs: BFP and total cholesterol (rG = 0.24), BFP and low-density lipoprotein cholesterol (rG = 0.25), and VFI and triglyceride (rG = 0.33). CONCLUSION: The genetic overlap between various obesity indices and cardiometabolic traits underscores the importance of pleiotropic genes. Further studies are warranted to investigate specific shared genetic and environmental factors between obesity and cardiometabolic diseases.

Metabolically healthy/unhealthy obesity and breast cancer: A possible role of plasma-derived extracellular vesicles on the cancerous behavior of triple-negative breast cancer.

PURPOSE: Obesity has known detrimental effects on breast cancer (BC) development and progression. However, it's essential to consider the obesity phenotype based on metabolic health. This study aims to evaluate the impact of circulating extracellular vesicles (EVs) from women with metabolically healthy or unhealthy normal weight, overweight, and obesity on MDA-MB-231 cell migration, invasion, and apoptosis. METHODS: Plasma EVs were isolated from different obesity phenotypes in women. EVs were characterized and EVs uptake by MDA-MB-231 cells was assessed. MDA-MB-231 cell lines were treated with EVs obtained from various studied groups, and migration, invasion, MMP-2 and MMP-9 activity, Bax and Bcl-2 mRNA expression, p-53 and Thr55 p-p53 protein expression, and apoptosis were assessed. RESULTS: EVs from obese individuals, regardless of phenotype, increased invasion and MMP-2 activity compared to healthy normal-weight EVs. Normal-weight EVs led to higher invasion under unhealthy conditions. BC cell migration was enhanced by EVs from healthy obese individuals compared to healthy normal-weight EVs. EVs from unhealthy obese women exhibited significantly lower p53/p-p53 levels and reduced apoptosis compared to healthy obese groups. CONCLUSION: It appears that EVs from both normal-weight women with unhealthy conditions and those with obesity or overweight, irrespective of metabolic status, worsened the cancerous behavior of TNBC cells. Therefore, considering metabolic health, in addition to BMI, is crucial for understanding obesity-related disorders.

Revisiting the beyond BMI paradigm in excess weight diagnosis and management: A call to action.

Adolphe Quételet, a 19th-century Belgian sociologist and statistician, pioneered the incorporation of statistics into social sciences. He initiated the development of anthropometry since he was interested in identifying the proportions of the 'ideal man'. He devised a ratio between weight and height, originally termed the Quételet Index, and today widely known and used as the body mass index or BMI. In 1835, he demonstrated that a normal curve accommodates the distribution of human traits articulating his reasoning on human variance around the average. Quételet's long-lasting legacy of the establishment of a simple measure to classify people's weight relative to an ideal for their height endures today with minor variations having dramatically influenced public health agendas. While being very useful, the limitations of the BMI are well known. Thus, revisiting the beyond BMI paradigm is a necessity in the era of precision medicine with morphofunctional assessment representing the way forward via incorporation of body composition and functionality appraisal. While healthcare systems were originally designed to address acute illnesses, today's demands require a radical rethinking together with an original reappraisal of our diagnosis and treatment approaches from a multidimensional perspective. Embracing new methodologies is the way forward to advance the field, gain a closer look at the underlying pathophysiology of excess weight, keep the spotlight on improving diagnostic performance and demonstrate its clinical validity. In order to provide every patient with the most accurate diagnosis together with the most appropriate management, a high degree of standardization and personalization is needed.

Obesity, abdominal obesity, metabolic obesity phenotypes, and Helicobacter pylori infection: results from NHANES 1999-2000.

BACKGROUND: Recent studies on the association between Helicobacter pylori (H. pylori) infection and obesity have reported conflicting results. Therefore, the purpose of our study was to investigate the association of obesity, abdominal obesity, and metabolic obesity phenotypes with H. pylori infection. METHODS: A cross-sectional study of 1568 participants aged 20 to 85 was conducted using the National Health and Nutrition Examination Survey (NHANES) cycle 1999-2000. Logistic regression models were employed to evaluate the association of general obesity as defined by body mass index (BMI), abdominal obesity as defined by waist circumference (WC) and waist-height ratio (WHtR), and metabolic obesity phenotypes with H. pylori seropositivity. Subgroup analyses stratified by age were conducted to explore age-specific differences in this association. RESULTS: After grouping individuals according to their WHtR, the prevalence rate of WHtR ≥ 0.5 in H. pylori-seropositive participants was significantly higher than that in H. pylori-seronegative participants (79.75 vs. 68.39, P < 0.001). The prevalence of H. pylori seropositivity in non-abdominal obesity and abdominal obesity defined by WHtR was 24.97% and 31.80%, respectively (P < 0.001). In the subgroup analysis, the adjusted association between abdominal obesity, as defined by the WHtR, and H. pylori seropositivity was significant in subjects aged < 50 years (OR = 2.23; 95% CI, 1.24-4.01; P = 0.01) but not in subjects aged ≥ 50 years (OR = 0.84; 95% CI, 0.35-1.99; P = 0.66). Subjects older than 50 years old had an OR (95% CI) for metabolically healthy obesity of 0.04 (0.01-0.35) compared with the control group. H. pylori seropositivity was consistently not associated with obesity as defined by BMI. CONCLUSIONS: Abdominal obesity, as defined by the WHtR, was associated with H. pylori infection in subjects aged ≤ 50 years.

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

OBJECTIVE: The 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. METHODS: This 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. RESULTS: Relative 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. CONCLUSION: The 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.

The effect of obesity phenotype changes on cardiovascular outcomes in adults older than 40 years in the prospective cohort of the Tehran lipids and glucose study (TLGS): joint model of longitudinal and time-to-event data.

BACKGROUND: Obesity is a worldwide health concern with serious clinical effects, including myocardial infarction (MI), stroke, cardiovascular diseases (CVDs), and all-cause mortality. The present study aimed to assess the association of obesity phenotypes and different CVDs and mortality in males and females by simultaneously considering the longitudinal and survival time data. METHODS: In the Tehran Lipid and Glucose Study (TLGS), participants older than three years were selected by a multi-stage random cluster sampling method and followed for about 19 years. In the current study, individuals aged over 40 years without a medical history of CVD, stroke, MI, and coronary heart disease were included. Exclusions comprised those undergoing treatment for CVD and those with more than 30% missing information or incomplete data. Joint modeling of longitudinal binary outcome and survival time data was applied to assess the dependency and the association between the changes in obesity phenotypes and time to occurrence of CVD, MI, stroke, and CVD mortality. To account for any potential sex-related confounding effect on the association between the obesity phenotypes and CVD outcomes, sex-specific analysis was carried out. The analysis was performed using packages (JMbayes2) of R software (version 4.2.1). RESULTS: Overall, 6350 adults above 40 years were included. In the joint modeling of CVD outcome among males, literates and participants with a family history of diabetes were at lower risk of CVD compared to illiterates and those with no family history of diabetes in the Bayesian Cox model. Current smokers were at higher risk of CVD compared to non-smokers. In a logistic mixed effects model, odds of obesity phenotype was higher among participants with low physical activity, family history of diabetes and older age compared to males with high physical activity, no family history of diabetes and younger age. In females, based on the results of the Bayesian Cox model, participants with family history of diabetes, family history of CVD, abnormal obesity phenotype and past smokers had a higher risk of CVD compared to those with no history of diabetes, CVD and nonsmokers. In the obesity varying model, odds of obesity phenotype was higher among females with history of diabetes and older age compared to those with no history of diabetes and who were younger. There was no significant variable associated with MI among males in the Bayesian Cox model. Odds of obesity phenotype was higher in males with low physical activity compared to those with high physical activity in the obesity varying model, whereas current smokers were at lower odds of obesity phenotype than nonsmokers. In females, risk of MI was higher among those with family history of diabetes compared to those with no history of diabetes in the Bayesian Cox model. In the logistic mixed effects model, a direct and significant association was found between age and obesity phenotype. In males, participants with history of diabetes, abnormal obesity phenotype and older age were at higher risk of stroke in the Bayesian Cox model compared to males with no history of diabetes, normal obesity phenotype and younger persons. In the obesity varying model, odds of obesity phenotype was higher in males with low physical activity, family history of diabetes and older age compared to those with high physical activity, no family history of diabetes and who were younger. Smokers had a lower odds of obesity phenotype than nonsmokers. In females, past smokers and those with family history of diabetes were at higher risk of stroke compared to nonsmokers and females with no history of diabetes in the Bayesian Cox model. In the obesity varying model, females with family history of diabetes and older ages had a higher odds of obesity phenotype compared to those with no family history of diabetes and who were younger. Among males, risk of CVD mortality was lower in past smokers compared to nonsmokers in the survival model. A direct and significant association was found between age and CVD mortality. Odds of obesity phenotype was higher in males with a history of diabetes than in those with no family history of diabetes in the logistic mixed effects model. CONCLUSIONS: It seems that modifications to metabolic disorders may have an impact on the heightened incidence of CVDs. Based on this, males with obesity and any type of metabolic disorder had a higher risk of CVD, stroke and CVD mortality (excluding MI) compared to those with a normal body mass index (BMI) and no metabolic disorders. Females with obesity and any type of metabolic disorder were at higher risk of CVD(, MI and stroke compared to those with a normal BMI and no metabolic disorders suggesting that obesity and metabolic disorders are related. Due to its synergistic effect on high blood pressure, metabolic disorders raise the risk of CVD.

Association of major dietary patterns and different obesity phenotypes in Southwest China: the China Multi-Ethnic Cohort (CMEC) Study.

PURPOSE: Dietary behavior is an important part of lifestyle interventions for obesity and its cardiovascular comorbidities. However, little is known about associations between dietary patterns and obesity phenotypes in Southwest China, a region with unique dietary patterns and significant heterogeneity in obesity. METHODS: Data from the baseline survey of the China Multi-Ethnic Cohort in Southwest China were analyzed (n = 64,448). Dietary intakes during the past year were measured with the semi-quantitative Food Frequency Questionnaire (s-FFQ). Principal component factor analysis (PCFA) was used to identify dietary patterns. Multinomial logistic regressions were used to examine the associations between dietary patterns and obesity phenotypes and stratified analyses were performed to assess whether the associations differed across demographic variables. RESULTS: Three dietary patterns were identified and then named according to their apparent regional gathering characteristics: the Sichuan Basin dietary pattern (characterized by high intakes of various foods), the Yunnan-Guizhou Plateau dietary pattern (characterized by agricultural lifestyles), and the Qinghai-Tibet Plateau dietary pattern (characterized by animal husbandry lifestyles), respectively. Higher adherence to the Sichuan Basin dietary pattern was positively associated with metabolically healthy overweight/obesity (MHO, OR 1.13, 95% CI 1.05-1.21) but negatively associated with metabolically unhealthy normal weight (MUNW, OR 0.78, 95% CI 0.65-0.95). Higher adherence to the other two dietary patterns was positively associated with MHO and metabolically unhealthy overweight/obesity (MUO). Besides, differences in socioeconomic status also affected the relationship between dietary patterns and obesity phenotypes. CONCLUSIONS: Adherence to the more diverse Sichuan basin dietary pattern performed a mixed picture, while the other two may increase the risk of obesity phenotypes, which indicates nutritional interventions are urgently needed.

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

OBJECTIVES: 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. METHODS: 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. RESULTS: 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 (ß 22.04, 95% CI 14.33-29.75, P < 0.001) and sE-selectin (ß 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 (ß 0.70, 95% CI - 8.91 to 10.32, P = 0.886) and sE-selectin (ß 3.69, 95% CI - 1.13 to 8.51, P = 0.133) in the participants with MHO compared to the non-obese participants. CONCLUSIONS: 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.

'Obesities': Position statement on a complex disease entity with multifaceted drivers.

Academic medicine fosters research that moves from discovery to translation, at the same time as promoting education of the next generation of professionals. In the field of obesity, the supposed integration of knowledge, discovery and translation research to clinical care is being particularly hampered. The classification of obesity based on the body mass index does not account for several subtypes of obesity. The lack of a universally shared definition of "obesities" makes it impossible to establish the real burden of the different obesity phenotypes. The individual's genotype, adipotype, enterotype and microbiota interplays with macronutrient intake, appetite, metabolism and thermogenesis. Further investigations based on the concept of differently diagnosed "obesities" are required.

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

BACKGROUND: The diversity of obesity-related metabolic characteristics generates different obesity phenotypes and corresponding metabolic diseases. This study aims to explore the correlation of different abdominal obesity phenotypes with non-alcoholic fatty liver disease (NAFLD). METHODS: The current study included 14,251 subjects, 7411 males and 6840 females. Abdominal obesity was defined as waist circumference ≥ 85 cm in males and ≥ 80 cm in females; according to the diagnostic criteria for metabolic syndrome recommended by the National Cholesterol Education Program Adult Treatment Panel III, having more than one metabolic abnormality (except waist circumference criteria) was defined as metabolically unhealthy. All subjects were divided into 4 abdominal obesity phenotypes based on the presence ( +) or absence (- ) of metabolically healthy/unhealthy (MH) and abdominal obesity (AO) at baseline: metabolically healthy + non-abdominal obesity (MH-AO-); metabolically healthy + abdominal obesity (MH-AO+); metabolically unhealthy + non-abdominal obesity (MH+AO-); metabolically unhealthy + abdominal obesity (MH+AO+). The relationship between each phenotype and NAFLD was analyzed using multivariate logistic regression. RESULTS: A total of 2507 (17.59%) subjects in this study were diagnosed with NAFLD. The prevalence rates of NAFLD in female subjects with MH-AO-, MH-AO+, MH+AO-, and MH+AO+ phenotypes were 1.73%, 24.42%, 7.60%, and 59.35%, respectively. Among male subjects with MH-AO-, MH-AO+, MH+AO-, and MH+AO+ phenotypes, the prevalence rates were 9.93%, 50.54%, 25.49%, and 73.22%, respectively. After fully adjusting for confounding factors, with the MH-AO- phenotype as the reference phenotype, male MH-AO+ and MH+AO+ phenotypes increased the risk of NAFLD by 42% and 47%, respectively (MH-AO+: OR 1.42, 95%CI 1.13,1.78; MH+AO+: OR 1.47, 95%CI 1.08,2.01); the corresponding risks of MH-AO+ and MH+AO+ in females increased by 113% and 134%, respectively (MH-AO+: OR 2.13, 95%CI 1.47,3.09; MH+AO+: OR 2.34, 95%CI 1.32,4.17); by contrast, there was no significant increase in the risk of NAFLD in the MH+AO- phenotype in both sexes. CONCLUSIONS: This first report on the relationship of abdominal obesity phenotypes with NAFLD showed that both MH-AO+ and MH+AO+ phenotypes were associated with a higher risk of NAFLD, especially in the female population. These data provided a new reference for the screening and prevention of NAFLD.

Metabolic obesity phenotypes and chronic kidney disease: a cross-sectional study from the RaNCD cohort study.

BACKGROUND: Investigating the effect of metabolic disorders on chronic kidney disease (CKD) in the presence or the absence of obesity is of great importance. This study aimed to examine the independent and joint relationships of obesity and metabolic syndrome (MetS) with CKD.  METHODS : The present study was performed on 9,762 participants from the baseline phase of the Ravansar non- communicable diseases (RaNCD) study. Thereafter, the CKD was estimated by glomerular filtration rate (eGFR) using the Modification of Diet in Renal Disease (MDRD) equation. All the included participants were categorized into the following four phenotypes: metabolically healthy non-overweight/obesity (MHNO), metabolically unhealthy non-overweight/obesity (MUNO), metabolically healthy overweight/obesity (MHO), and metabolically unhealthy overweight/obesity (MUO). Finally, Logistic regression analysis was used to estimate the odds ratio (ORs). RESULTS: The mean age of the included participants was 47.33 ± 8.27 years old, %48.16 (4,701) of whom were men. As well, 1,058(10.84%) participants had CKD (eGFR less than 60 ml/min/1.73m2). The overweight/obesity was not significantly associated with odds of CKD. The odds of CKD in male subjects with MetS was 1.48 times higher than non-MetS ones (95% CI: 1.10, 2.01). After adjusting the confounders, the odds of CKD were 1.54 times (95% CI: 1.12, 2.11) higher in the MUNO and 2.22 times (95% CI: 1.44, 3.41) higher in the MUO compared to MHNO phenotype in male subjects. The odds of CKD in the MUNO and MUO was 1.31 times (95% CI: 1.10, 1.60) and 1.23 times (95% CI: 1.01, 1.54) higher than MHNO phenotype in female subjects, respectively. CONCLUSION: The odds of CKD were higher in MUNO and MUO phenotypes. Therefore, lifestyle modification is recommended to control normal weight and healthy metabolism.

METABOLOMIC ANALYSIS OF NORMAL WEIGHT, HEALTHY AND UNHEALTHY OBESITY: AMINO ACID CHANGE ACROSS THE SPECTRUM OF METABOLIC WELLBEING IN WOMEN.

Context: Obesity is a complex and heterogeneous disorder with multiple phenotypes described. Although metabolomic biomarkers of obesity have been extensively studied, biomarkers of obesity phenotypes and differences between these phenotypes and normal-weight (NW) persons have been less investigated. Objective: The objective of this cross-sectional analysis was to investigate serum amino acids (AA) as markers of metabolic alterations in obesity phenotypes and NW. Design: Cross-sectional. Subjects and Methods: By targeted metabolomics we analyzed serum samples of 70 women using ultrahigh-performance liquid chromatography/mass spectrometry. Participants were divided into 3 groups: NW, metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUHO). Results: Five AAs were significantly different between study groups: cysteine, methionine, asparagine, glutamine, and lysine (p-value <0.05 and variable importance in the projection >1). Cysteine increased linearly with metabolic unwellness from NW to MUHO. Lysine and glutamine were significantly higher, and asparagine was significantly lower in NW and MHO than in MUHO. Conclusions: By trend and group analysis we identified specific changes in serum AAs along with the progression of metabolically unwellness.

Psychological features in obesity: A network analysis.

OBJECTIVE: Enhanced understanding of psychosocial factors associated with obesity may improve knowledge of their interplay mechanisms. The aim of this study was to assess the relationship between psychosocial variables in individuals with obesity using a network analysis. METHODS: Patients seeking treatment for obesity were consecutively recruited from a rehabilitative residential treatment program for severe obesity between January 2016 and March 2019. Each patient completed the following questionnaires: Eating Disorder Examination Questionnaire, Symptom Checklist-90, Obesity Related Well-Being, and Weight Bias Internalization Scale. In addition, current body mass index (BMI) was measured, and maximum acceptable and dream BMI were assessed. RESULTS: The sample comprised 996 patients with obesity (age 52.3 [SD = 16.0] years; BMI 41.8 [SD = 7.8] kg/m2 ; 65.7% women; 52.2% married or living with a partner). Network analysis showed that interpersonal sensitivity and shape-weight concern, but also internalized weight stigma, were the most central and highly interconnected nodes in the network. In contrast, objective binge-eating episodes and dietary restraint were the most peripheral and least connected nodes. Eating disorder features and psychological distress formed two clearly separate clusters. No difference in network structure was found between men and women. CONCLUSIONS: The pattern of network node connections supports the importance of assessing psychological distress, interpersonal sensitivity, shape-weight concern, and internalized weight stigma in patients seeking treatment for obesity.

Impact of metabolically healthy obesity on carotid intima-media thickness - The Brazilian Longitudinal Study of Adult Health.

BACKGROUND AND AIMS: Obesity increases the risk of metabolic abnormalities, which contributes to elevated cardiovascular risk. However, the independent role of obesity in the development of cardiovascular disease is still debatable. There are individuals with an obesity phenotype without metabolic abnormalities: "metabolically healthy obesity" (MHO). This study evaluates the association between MHO and carotid intima-media thickness (CIMT), an early marker of subclinical atherosclerosis. METHODS AND RESULTS: This is a cross-sectional analysis of the baseline data from the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). We used a strict definition to classify MHO: body mass index ≥30 kg/m2 and meeting none of the four metabolic syndrome criteria. Data from 10,335 participants were analyzed. The obesity prevalence in our population was 21.2% (n = 2191). The prevalence of MHO was 5.6% (n = 124). When individuals were stratified according to metabolic health, we found the metabolically healthy individuals were younger, more likely to be women and never smokers. The mean CIMT of the sample was 0.81 mm (±0.20). The mean CIMT of the metabolically healthy subsample was 0.70 mm (±0.13) in individuals without obesity and 0.76 mm (±0.13) in individuals with obesity (p < 0.001). The mean CIMT of the metabolically unhealthy subsample was 0.81 mm (±0.20) in individuals without obesity and 0.88 mm (±0.20) in individuals with obesity (p < 0.001). These findings remained essentially unchanged after multivariate adjustment for confounding factors. CONCLUSION: The concept of MHO, even with the strict definition, seems inadequate, as even in this population, obesity is associated with higher CIMT levels.

Abdominal Obesity Phenotypes and Incidence of Thyroid Autoimmunity: A 9-Year Follow-up.

PURPOSE: The association between obesity and autoimmune diseases has been suggested by several previous studies. The objective of our study was to assess the association of abdominal obesity phenotypes with thyroid autoimmunity. MATERIALS AND METHODS: This study was conducted within the framework of a population-based cohort study, Tehran Thyroid Study (TTS) on 4708 subjects without thyroid autoimmunity at baseline. Participants were categorized into four abdominal obesity phenotypes according to waist circumference (WC) and other metabolic syndrome components. Serum concentrations of thyroid peroxidase antibody (TPOAb), free T4 (FT4), thyrotropin (TSH), glucose, and lipid profiles were measured after 3, 6 and 9 years of follow-up. Cox proportional hazard models were used to evaluate associations of different phenotypes with the incidence of thyroid autoimmunity, adjusted for age, sex, FT4, and TSH. RESULTS: Highest and lowest incidence rates of TPOAb positivity were observed among metabolically unhealthy, non-abdominally obese (MUNAO) [8.78 (7.31-10.55) per 1000 person-years of follow-up] and metabolically unhealthy abdominally obese (MUAO) [4.98 (3.88-6.41) per 1000 person-years of follow-up] phenotypes. Considering the metabolically healthy non-abdominal obese (MHNAO) individuals as reference, none of metabolically healthy abdominally obese (MHAO), MUNAO, and MUAO phenotypes were associated with increased risk of developing TPOAb positivity. Compared to individuals with high WC, the incidence rate (95%CI) of TPOAb positivity was higher among those with normal WC: 8.44 (7.13-10.0) vs 5.11 (4.01-6.51) per 1000 person-years, respectively. Higher WC was not associated with incident TPOAb positivity. CONCLUSION: There was no significant association between baseline abdominal obesity phenotype status and development of TPOAb positivity over 9 years of follow-up.

Evaluation of Transcriptomic Regulations behind Metabolic Syndrome in Obese and Lean Subjects.

Multiple mechanisms have been suggested to confer to the pathophysiology of metabolic syndrome (MetS), however despite great interest from the scientific community, the exact contribution of each of MetS risk factors still remains unclear. The present study aimed to investigate molecular signatures in peripheral blood of individuals affected by MetS and different degrees of obesity. Metabolic health of 1204 individuals from 1000PLUS cohort was assessed, and 32 subjects were recruited to four study groups: MetS lean, MetS obese, "healthy obese", and healthy lean. Whole-blood transcriptome next generation sequencing with functional data analysis were carried out. MetS obese and MetS lean study participants showed the upregulation of genes involved in inflammation and coagulation processes: granulocyte adhesion and diapedesis (p < 0.0001, p = 0.0063), prothrombin activation pathway (p = 0.0032, p = 0.0091), coagulation system (p = 0.0010, p = 0.0155). The results for "healthy obese" indicate enrichment in molecules associated with protein synthesis (p < 0.0001), mitochondrial dysfunction (p < 0.0001), and oxidative phosphorylation (p < 0.0001). Our results suggest that MetS is related to the state of inflammation and vascular system changes independent of excess body weight. Furthermore, "healthy obese", despite not fulfilling the criteria for MetS diagnosis, seems to display an intermediate state with a lower degree of metabolic abnormalities, before they proceed to a full blown MetS.

Protective role of skeletal muscle mass against progression from metabolically healthy to unhealthy phenotype.

OBJECTIVE: Metabolically healthy individuals are known to be resistant to cardiovascular disease development. However, a considerable fraction of those individuals shows deteriorated metabolic health over time. Although skeletal muscle is the primary insulin-responsive target organ, a longitudinal investigation of the skeletal muscle mass in relation to the development of metabolically unhealthy phenotype has not been performed. We aimed to evaluate whether greater skeletal muscle mass is an independent protective factor for the development of metabolically unhealthy phenotype. DESIGN, PARTICIPANTS AND MEASUREMENTS: We conducted a retrospective cohort study with 9033 metabolically healthy volunteers who underwent routine health examinations in 2012 and a follow-up examination in 2016. Obesity was defined as Asian-Pacific body mass index criterion ≥25 kg/m2 . Subjects with fewer than two risk factors (elevated blood pressure, triglyceride, glucose, high-sensitivity C-reactive protein, insulin resistance and decreased high-density lipoprotein cholesterol levels) were characterized as metabolically healthy using Wildman criteria. RESULTS: At the 4-year follow-up, approximately one-fourth of the nonobese participants and half of the participants with obesity showed metabolic deterioration. In nonobese men and women, higher appendicular skeletal muscle mass (ASM)/weight at baseline was significantly associated with decreased risk of metabolic deterioration. Compared to the lowest quartile of ASM/weight, the adjusted odds ratios (95% confidence intervals) of the highest quartile were 0.68 (0.52-0.89) in nonobese men and 0.64 (0.46-0.90) in nonobese women. However, this association was not observed in obese subjects. CONCLUSIONS: Greater skeletal muscle mass at baseline is significantly associated with maintenance of metabolically healthy status, especially in nonobese individuals.

Association between obesity phenotypes in adolescents and adult metabolic syndrome: Tehran Lipid and Glucose Study.

Obesity phenotypes can be regarded as an indicator of CVD risk factors. The aim of the present study was to determine the prevalence of adolescents with different obesity phenotypes and the role of obesity phenotypes in prediction of the metabolic syndrome (MetS) in adults. For this population-based cohort study, 2159 adolescents aged 11-18 years were included. Subjects were divided into four obesity phenotype groups: metabolically healthy normal weight (MHNW), metabolically healthy obese (MHO), metabolically unhealthy normal weight (MUNW) and metabolically unhealthy obese (MUO). Cox proportional hazard modelling was used to estimate the incidence of the MetS in adults after a median follow-up of 11·3 years. The incidence rate of the MetS in early adulthood was 111·6 (95 % CI 98·7, 126·3) per 10 000 person-years, with higher values in boys (210·1 (95 % CI 183·0, 241·3)), compared with girls (39·7 (95 % CI 30·2, 52·1)). In the age- and adult BMI-adjusted model, the hazard ratio of the MetS in adulthood for boys was 3·33 (95 % CI 2·08, 5·32) among MUO phenotype followed less than 6 years, 1·71 (95 % CI 1·01, 2·90) among MHO, and 2·52 (95 % CI 1·72, 3·68) among MUNW. All associations were attenuated in girls except for MUO phenotype followed less than 6 years (5·72 (95 % CI 2·14, 15·3)). In conclusion, MUNW and MHO phenotypes in boys, but not in girls, and MUO phenotype in both sexes with less than 6 years of follow-up increased the risk of adult MetS compared with MHNW. It seems that lack of obesity at least in boys does not protect them from MetS development in adulthood.

Predictors of incident obesity phenotype in nonobese healthy adults.

BACKGROUND: Despite their different cardiovascular consequences, little is known about predictors of metabolically healthy (MHO) and metabolically unhealthy obesity (MUHO). This cohort study was designed to address this question in participants of the Tehran Lipid and Glucose Study. MATERIALS AND METHODS: Employing the Joint Interim Statement (JIS) metabolic syndrome criteria to define MHO/MUHO phenotypes, nonobese, otherwise healthy individuals, aged > 20 years (n = 3489) were recruited and followed up for a median of 13·4 years. RESULTS: At the follow-up, MHO incidence rate in obese individuals was 36·6%. Comparing MHO vs. MUHO, female gender [odds ratio (OR) = 3·28, 95% confidence interval (CI) 1·27, 8·46)], increased body mass index (BMI; OR = 1·32, 95% CI: 1·12, 1·60) and elevated high-density lipoprotein cholesterol (HDL-C) levels (OR = 1·04, 95% CI: 1·02, 1·07) were related to higher odds of incident MHO, while older age (OR = 0·95, 95% CI: 0·92, 0·98), increased waist circumference (WC; OR = 0·86, 95% CI: 0·81, 0·91), higher WC gain (OR = 0·91, 95% CI: 0·87, 0·95) and increased diastolic blood pressure (DBP; OR = 0·94, 95% CI: 0·91, 0·98) prevented progression towards MHO. CONCLUSIONS: While baseline BMI and WC were detrimental for developing MHO vs. MUHO, gender was the strongest predictor of incident obesity phenotype in healthy nonobese individuals.

Effect of Different Obesity Phenotypes on Incidence of Chronic Kidney Disease in Tehranian Adults.

BACKGROUND: The aim of this community-based study is to ascertain the effect of different obesity phenotypes on the incidence of chronic kidney disease in Iranian adults. STUDY DESIGN: A prospective cohort study, the Tehran Lipid Glucose Study (TLGS). SETTING AND PARTICIPANTS: Adults aged ≥ 20 years with a mean age of 40.38 years (54.8% female) who were free from chronic kidney disease (CKD) at baseline (phase 1) and were followed up at 3 time stages (phases 2, 3, and 4) for a mean duration of 9.4 years to assess the risk for CKD. PREDICTOR: Obesity phenotypes. OUTCOME: Incidence of chronic kidney disease. MEASUREMENTS: Glomerular filtration rate (GFR) was estimated from the simplified equation developed using data from the Modification of Diet in Renal Disease (MDRD) Study. RESULTS: CKD events occurred in 1162 participants. The prevalence of the 2 known obesity phenotypes (metabolically obese normal weight [MONW] and metabolically healthy but obese [MHO]) in the overall population was 3.5% and 8.8%, respectively. According to Kaplan-Meier curves, rates of freedom from CKD in the MHO and MONW obesity phenotypes were 75.3% and 60.6%, respectively (p < 0.0001). Age- and sex-adjusted (model 1) hazard ratios for participants with MHO or MONW obesity phenotype were 1.14 (95% confidence interval [CI], 0.91-1.43) and 1.43 (95% CI, 1.09-1.88), respectively. After further adjustment for confounder variables (model 2), multivariate-adjusted hazard ratios for CKD for participants with MHO or MONW obesity phenotypes were 1.23 (95% CI, 0.93-1.62) and 1.43 (95% CI, 1.08-1.90), respectively. CONCLUSION: Adults with the MONW obesity phenotype compared to those with MHO obesity phenotype have a higher risk for incidence of CKD. The results indicate that having a normal weight is not the only factor to protect against incidence of CKD.