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.

The transition of metabolic phenotypes and cardiovascular events: Panasonic cohort study 16.

OBJECTIVE: The study objective was to investigate whether changes in metabolic phenotype affect the risk of cardiovascular events. METHODS: All 117,589 participants were included in this retrospective cohort study. The metabolic phenotypes of the participants were assessed at two points (the second evaluation was set 2 years after the first evaluation), and the incidence rate of cardiovascular events was observed for 11 years. The main outcome was 3-point major adverse cardiac events (MACE), which comprises cardiovascular death, nonfatal coronary artery disease, and nonfatal stroke incidence. RESULTS: Of the participants, 2748 (2.3%) cases of 3-point MACE were identified during follow-up. The stable metabolically healthy obesity group had a higher risk of 3-point MACE than those with stable metabolically healthy nonobesity (MHNO). Additionally, the change from metabolically healthy obesity to MHNO for 2 years decreased the risk of 3-point MACE (hazard ratio [HR], 1.12: 95% CI: 0.84-1.47) to the same level as stable MHNO. However, the change from metabolically abnormal nonobesity and metabolically abnormal obesity to MHNO for 2 years maintained a higher risk of 3-point MACE (HR, 1.66 [95% CI: 1.36-2.01]; HR, 1.91 [95% CI: 1.22-2.81]) than those with stable MHNO. CONCLUSIONS: Change in metabolic phenotype is associated with incident 3-point MACE.

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.

Association between alternative healthy eating index (AHEI) with metabolic health status in adolescents with overweight and obesity.

BACKGROUND: There has been lack of evidence on the association between healthy dietary patterns and metabolic health status of adolescents. The present study aimed to evaluate the association between alternative healthy eating index (AHEI) and metabolic health status among a relatively representative sample of Iranian adolescents with overweight/obesity. METHODS: Adolescents with extra body weight (n = 203, aged 12-18 y), were selected for this cross-sectional study by a multistage cluster random-sampling method. Habitual dietary intakes and diet quality of individuals were assessed using validated food frequency questionnaire and AHEI-2010, respectively. Data on other covariates were also gathered by pre-tested questionnaires. To determine fasting glucose, insulin and lipid profiles, fasting blood samples were collected. Participants were categorized as having metabolically healthy overweight/obesity (MHO) or metabolically unhealthy overweight/obesity (MUO) phenotypes, based on two approaches (International Diabetes Federation (IDF) and combination of IDF with Homeostasis Model Assessment Insulin Resistance (HOMA-IR)). RESULTS: The overall prevalence of MUO was 38.9% (based on IDF criteria) and 33.0% (based on IDF/HOMA-IR criteria). After considering all potential confounders, participants in highest tertiles of AHEI-2010 had lower odds of MUO profile according to both IDF (OR = 0.05; 95% CI: 0.01-0.15) and IDF/HOMA-IR (OR = 0.05; 95% CI: 0.02-0.19) definitions. This association was stronger in adolescents with overweight compared with obese ones and also among girls than boys. Moreover, each unit increase in AHEI-2010 score was associated with lower risk of MUO based on both criteria. CONCLUSIONS: Higher adherence to AHEI-2010 was inversely associated with odds of MUO in Iranian adolescents with overweight/obesity.

Risk of incident chronic kidney disease in metabolically healthy obesity and metabolically unhealthy normal weight: A systematic review and meta-analysis.

Studies have reported inconsistent results about the risk of incident chronic kidney disease (CKD) in people with metabolically healthy obesity (MHO). We designed this systematic review and meta-analysis to evaluate the risk of developing CKD in people with MHO and metabolically unhealthy normal weight (MUNW). We used a predefined search strategy to retrieve eligible studies from multiple databases up to June 20, 2022. Random-effects model meta-analyses were implied to estimate the overall hazard ratio (HR) of incident CKD in obesity phenotypes. Eight prospective cohort studies, including approximately 5 million participants with a median follow-up ranging between 3 and 14 years, were included in this meta-analysis. Compared to the metabolically healthy normal weight (MHNW), the mean differences in cardiometabolic and renal risk factors in MHO, MUNW, and metabolically unhealthy obesity (MUO) were evaluated with overall HR of 1.42, 1.49, and 1.84, respectively. Compared to MHNW, the mean estimated glomerular filtration rate (eGFR) and high-density lipoprotein (HDL) were significantly lower, and low-density lipoprotein (LDL), blood pressure, blood glucose, and triglycerides were higher in MHO and MUNW. In conclusion, MHO and MUNW are not benign conditions and pose a higher risk for incident CKD. Obesity, whether in the presence or absence of metabolic health, is a risk factor for CKD.

Metabolically healthy obese individuals are still at high risk for diabetes: Application of the marginal structural model.

AIM: To assess the effect of obesity phenotype on the incidence of diabetes, considering phenotype as a time-varying exposure. METHODS: We used community-based cohort data from the Korean Genome and Epidemiology Study, with a 16-year follow-up period. Obesity phenotype was determined using body mass index and metabolic syndrome criteria. The influence of obesity phenotype on the occurrence of diabetes was evaluated using a Cox proportional hazard model and a marginal structural model (MSM). RESULTS: Obesity phenotypes were defined in 6265 individuals, with diabetes identified in 903 (14.4%) during the follow-up period. Individuals with metabolically healthy obesity (MHO) exhibited a higher risk of diabetes compared to those with metabolically healthy normal weight (MHNW), with a hazard ratio (HR) of 1.48 (95% confidence interval [CI] 1.15-1.90). This association remained significant after applying the MSM (HR 1.49, 95% CI 1.01-2.20). Moreover, various sensitivity analyses consistently demonstrated a higher risk of diabetes in individuals with MHO compared to those with MHNW. CONCLUSIONS: Even when obesity phenotype was treated as a time-varying exposure, individuals with MHO were still at higher risk for developing diabetes than those with MHNW. Consequently, such individuals should aim to avoid transitioning to a metabolically unfavourable state and strive to reduce their body weight to a normal range.

The triglycerides and glucose index is more strongly associated with metabolically healthy obesity phenotype than the lipid and obesity indices.

PURPOSE: The triglycerides and glucose (TyG) index is a reliable biomarker for estimating insulin resistance; however, evidence regarding the use of the TyG index in individuals with metabolically healthy obesity (MHO) is scarce. Thus, we examined the association between the TyG index and the MHO phenotype. METHODS: Apparently healthy men and women aged 18 years or more with obesity (body mass index [BMI] ≥ 30 kg/m2) were allocated into the following groups: MHO and metabolically unhealthy obesity (MUO). The MHO phenotype was defined by obesity and the absence of the following metabolic disorders: elevated triglyceride concentrations, elevated glucose levels, elevated blood pressure, and low HDL-C. The MUO was defined by individuals with obesity and at least one of the aforementioned cardiovascular risk factors. RESULTS: A total 827 individuals, 605 (73.1%) women and 222 (26.9%) men were enrolled and allocated into the MHO (n = 104) and MUO (n = 723) groups. The adjusted regression analysis by age, sex, BMI, and waist circumference showed that fasting glucose (OR = 0.90; 95% CI: 0.88-0.93), and triglycerides (OR = 0.97; 95% CI: 0.96-0.98), as well as the triglycerides/HDL-C (OR = 0.18; 95% CI: 0.13-0.26), lipid accumulation product (OR = 0.95; 95% CI: 0.93-0.96), visceral adipose index (OR = 0.38; 95% CI: 0.31-0.46), and TyG index (OR = 0.001; 95% CI: 0.000-0.004) are inversely associated with the MHO, while the HDL-C (OR = 1.10; 95% CI: 1.07-1.12) had a direct association. CONCLUSIONS: Our results show that the TyG index is more strongly associated with the MHO phenotype than the lipid and obesity indices.

Association between Metabolically Healthy Status and Risk of Gastrointestinal Cancer.

PURPOSE: Although obesity is associated with numerous diseases, the risks of disease may depend on metabolically healthy status. Nevertheless, it is unclear to whether metabolically healthy status affects risk of gastrointestinal (GI) cancer in general Chinese population. MATERIALS AND METHODS: A total of 114,995 participants who met the criteria were included from the Kailuan Study. The study participants were divided into four groups according to body mass index (BMI)/waist circumference (WC) and metabolic status. Incident of GI cancer (esophageal cancer, gastric cancer, liver cancer, biliary cancer, pancreatic cancer, and colorectal cancer) during 2006-2020 were confirmed by review of medical records. The Cox proportional hazard regression models were used to assess the association metabolically healthy status with the risk of GI cancer by calculating the hazard ratios (HR) and 95% confidence interval (CI). RESULTS: During a mean 13.76 years of follow-up, we documented 2,311 GI cancers. Multivariate Cox regression analysis showed that compared with the metabolically healthy normal-weight group, metabolically healthy obese (MHO) participants demonstrated an increased risk of developing GI cancer (HR, 1.54; 95% CI, 1.11 to 2.13) by BMI categories. However, such associations were not found for WC category. These associations were moderated by age, sex, and anatomical site of the tumor. Individuals with metabolic unhealthy normal-weight or metabolic unhealthy obesity phenotype also have an increased risk of GI cancer. CONCLUSION: MHO phenotype was associated with increased risk of GI cancer. Moreover, individuals who complicated by metabolic unhealthy status have an increased risk of developing GI cancer. Hence, clinicians should consider the risk of incident GI cancer in people with abnormal metabolically healthy status and counsel them about metabolic fitness and weight control.

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

BACKGROUND: 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. METHODS: Obesity (body mass index ≥ 30 kg/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 ≥ 70 years using 6 waves of data from the Whitehall II study and their associations with incident dementia was examined using Cox regression. RESULTS: Analyses with exposures measured < 60, 60 to < 70, and ≥ 70 years 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 4.2 years respectively. In analyses of individual components, obesity before 60 years (HR 1.41, 95% CI: [1.08, 1.85]) but not at older ages was associated with dementia; unhealthy metabolic status when present < 60 years (HR 1.33, 95% CI: [1.08, 1.62]) and 60 to < 70 years (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 60 years (1.69; 95% CI: [1.16, 2.45]); this was not the case when metabolic-obesity phenotype was present at 60 to < 70 years or ≥ 70 years. 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. CONCLUSIONS: 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 70 years.

Longitudinal association of serum 25-hydroxyvitamin D levels with metabolically healthy body size transition in children and adolescents: A prospective cohort study with 2 years of follow-up.

BACKGROUND AND AIMS: Although the associations of vitamin D with obesity and metabolic abnormalities have been reported, the role of vitamin D in the transition of obesity phenotype remains unclear but is highly desired since it is crucial to identify potential methods for obesity management. Therefore, we aimed to investigate the relationship between vitamin D and the risk for metabolically unhealthy obesity (MUO) or metabolically healthy obesity (MHO) in metabolically healthy children with 2 years of follow-up. METHODS: Data were collected from a population-based cohort consisting of 6424 metabolically healthy children aged 6-16 years at baseline. Metabolic abnormalities including hypertension, high triglycerides (TG), low high-density lipoprotein cholesterol (HDL-C), hyperglycemia, and hyperuricemia were assessed both at baseline and follow-up. Baseline serum 25-hydroxyvitamin D (25[OH]D) concentrations were measured as exposure. The obesity phenotype transition was evaluated by weight status with the combination of metabolic health status from baseline to follow-up. RESULTS: During a 2-year follow-up, 889 (13.8 %) incident MUO cases occurred. For participants with obesity, each 10 nmol/L increment in 25(OH)D concentrations was associated with a 21 % (95%CI: 13 %∼43 %) and a 7 % (95%CI: 1 %∼14 %) decreased risk in high TG and hyperuricemia, respectively. A 51 % (95%CI: 22 %∼69 %) lower risk of MUO was observed in participants with sufficient vitamin D levels (≥50 nmol/L) compared to those with vitamin D deficiency (<30 nmol/L). Besides, among children who were MHO at baseline, those with sufficient vitamin D levels (≥50 nmol/L) were more likely to transition to metabolically healthy normal weight (MHNW) than vitamin D deficient individuals (<30 nmol/L). CONCLUSIONS: Vitamin D may prevent the development of MUO and help increase the transition from MHO to MHNW. The findings highlight that vitamin D might be an effective nutrient for obesity management.

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

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

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

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

Risk of Cardiovascular Diseases Among Different Metabolic Obesity Phenotypes: A Prospective Observational Study.

Objectives: Various diseases are associated with obesity and metabolism. We sought to investigate the risk of cardiovascular disease (CVD) in diverse metabolic obesity phenotypes. Methods and Results: A prospective observational study of 1517 participants ≥25 years of age without CVD at baseline was conducted. Participants were categorized into four groups based on the condition of central obesity and metabolic health status: metabolically healthy normal weight, metabolically healthy obesity (MHO), metabolically unhealthy normal weight, and metabolically unhealthy obese (MUO). A multivariate Cox regression analysis was used to analyze the relationship between different obesity phenotypes and CVD. During 14830.49 person-years of follow-up, there were 244 incident cases of CVD. Of the 1517 participants, 72 (4.75%) and 812 (53.53%) were classified as having MHO and MUO, respectively. MHO and MUO had a tendency toward a higher risk of CVD [adjusted hazard ratios (HRs) = 1.49, 95% confidence interval (CI): 1.11-2.02 and HR = 1.25, 95% CI: 1.00-1.55, respectively] based on the waist circumference criterion. Conclusion: MHO and MUO can increase the risk of CVD.

Metabolic phenotyping in people living with obesity: Implications for dietary prevention.

Given the increasing number of people living with obesity and related chronic metabolic disease, precision nutrition approaches are required to increase the effectiveness of prevention strategies. This review addresses these approaches in different metabolic phenotypes (metabotypes) in obesity. Although obesity is typically associated with an increased cardiometabolic disease risk, some people with obesity are relatively protected against the detrimental effects of excess adiposity on cardiometabolic health, also referred to as 'metabolically healthy obesity' (MHO). Underlying mechanisms, the extent to which MHO is a transient state as well as lifestyle strategies to counteract the transition from MHO to metabolically unhealthy obesity (MUO) are discussed. Based on the limited resources that are available for dietary lifestyle interventions, it may be reasonable to prioritize interventions for people with MUO, since targeting high-risk patients for specific nutritional, lifestyle or weight-loss strategies may enhance the cost-effectiveness of these interventions. Additionally, the concept of tissue insulin resistant (IR) metabotypes is discussed, representing distinct etiologies towards type 2 diabetes (T2D) as well as cardiovascular disease (CVD). Recent evidence indicates that these tissue IR metabotypes, already present in individuals with obesity with a normal glucose homeostasis, respond differentially to diet. Modulation of dietary macronutrient composition according to these metabotypes may considerably improve cardiometabolic health benefits. Thus, nutritional or lifestyle intervention may improve cardiometabolic health, even with only minor or no weight loss, which stresses the importance of focusing on a healthy lifestyle and not on weight loss only. Targeting different metabotypes towards T2D and cardiometabolic diseases may lead to more effective lifestyle prevention and treatment strategies. Age and sex-related differences in tissue metabotypes and related microbial composition and functionality (fermentation), as important drivers and/or mediators of dietary intervention response, have to be taken into account. For the implementation of these approaches, more prospective trials are required to provide the knowledge base for precision nutrition in the prevention of chronic metabolic diseases.

Higher Sensitivity to Thyroid Hormones May Be Linked to Maintaining the Healthy Metabolic Condition in People with Obesity: New Insight from NHANES.

INTRODUCTION: Obesity contributes to the pathogenesis of diverse metabolic diseases, yet the mechanism underlying metabolically healthy obesity (MHO) remains elusive. Thyroid hormones and sensitivity to them have a major impact on metabolism. Our study aimed to investigate the association between MHO and thyroid hormone sensitivity. METHODS: Thyroid hormone indices, including the thyroid-stimulating hormone (TSH) index (TSHI), the Thyrotroph Thyroxine Sensitivity Index (TTSI), the Thyroid Feedback Quantile-Based Index (TFQI), and the Parametric Thyroid Feedback Quantile-Based Index (PTFQI), were calculated based on a non-institutionalized US sample in the National Health and Nutrition Examination Survey (NHANES, 2007-2012). Participants were divided into four groups (metabolically healthy non-obesity [MHNO], metabolically unhealthy non-obesity [MUNO], MHO, and metabolically unhealthy obesity [MUO]) according to their body mass index and metabolic profiles. Linear regression, logistic regression, and restricted cubic splines were employed to analyze the association between thyroid hormone indices and metabolic phenotypes. RESULTS: A total of 4,857 participants (49.6% men; mean age, 42.6 years) were included, with 1,539 having obesity and 235 identified as MHO. Participants in the MHO group exhibited lower levels of TSH, TSHI, TTSI, TFQI, and PTFQI compared with the MHNO group (all p < 0.05), while the differences among MHNO, MUNO, and MUO groups were not statistically significant (all p > 0.05). Among participants with obesity, TSH, TSHI, TTSI, TFQI, and PTFQI were positively associated with metabolic abnormality (all p < 0.05). CONCLUSION: Participants with MHO exhibited higher thyroid hormone sensitivity among various obesity phenotypes, even when compared with those with MHNO. A positive association was observed between metabolic abnormality and thyroid hormone sensitivity, while the trend of TSH was observed to be consistent with sensitivity to thyroid hormone indices in discriminating metabolic abnormality. Hence, TSH has the potential to serve as a convenient index for detecting sensitivity to thyroid hormones and further metabolic conditions.

Association between metabolic healthy obesity and female infertility: the national health and nutrition examination survey, 2013-2020.

BACKGROUND: Obesity has been confirmed to be associated with infertility. However, the association between metabolically healthy obesity (MHO), a subset of obesity with no metabolic abnormalities, and female infertility has not yet been investigated. This study aimed to examine the association between MHO and the risk of female infertility among United States. METHODS: This study utilized a cross-sectional design and included 3542 women aged 20-45 years who were selected from the National Health and Nutrition Examination Survey (NHANES) 2013-2020 database. The association between MHO and the risk of infertility was evaluated using risk factor-adjusted logistic regression models. RESULTS: Higher BMI and WC were associated with increased infertility risk after adjusting for potential confounding factors (OR (95% CI): 1.04(1.02, 1.06), P = 0.001; OR (95% CI): 1.02 (1.01, 1.03), P < 0.001; respectively). After cross-classifying by metabolic health and obesity according to BMI and WC categories, individuals with MHO had a higher risk of infertility than those with MHN (OR (95% CI): 1.75(0.88, 3.50) for BMI criteria; OR (95% CI): 2.01(1.03, 3.95) for WC criteria). A positive linear relationship was observed between BMI/WC and infertility risk among metabolically healthy women (Pnon-linearity=0.306, 0.170; respectively). CONCLUSIONS: MHO was associated with an increased risk of infertility among reproductive-aged women in the US. Obesity itself, regardless of metabolic health status, was associated with a higher infertility risk. Our results support implementing lifestyle changes aimed at achieving and maintaining a healthy body weight in all individuals, even those who are metabolically healthy.

A novel criterion of metabolically healthy obesity could effectively identify individuals with low cardiovascular risk among Chinese cohort.

Background and objective: Obesity has become a serious public health problem and brings a heavy burden of cardiovascular disease. Metabolically healthy obesity (MHO) is defined as individuals with obesity with no or only minor metabolic complications. Whether individuals with MHO have a lower cardiovascular risk remains controversial. In this study, a new criterion was used to define MHO and assess its predictive value for cardiovascular events and death. At the same time, the new criterion and the traditional criterion are compared to analyze the differences between different diagnostic criteria. Methods: A prospective cohort was established in northeast rural China from 2012 to 2013. Follow-up was conducted in 2015 and 2018 to investigate the incidence of cardiovascular events and survival. Subjects were grouped according to the metabolic health and obesity status. Kaplan-Meier curves were drawn to describe the cumulative risk of endpoint events in the four groups. Cox regression analysis model was constructed to evaluate the risk of endpoint events. Analysis of variance and post hoc analyses were used to calculate and compare differences in metabolic markers between MHO subjects diagnosed by novel and traditional criteria. Results: A total of 9345 participants 35 years of age or older without a history of cardiovascular disease were included in this study. After a median follow-up of 4.66 years, the data showed that participants in the MHO group had no significant increase in the risk of composite cardiovascular events and stroke, but had a 162% increase in the risk of coronary heart disease (HR: 2.62; 95%CI: 1.21-5.67). However, when using conventional criteria for metabolic health, mMHO group had a 52% increase in combined CVD risk (HR: 1.52; 95%CI: 1.14-2.03). By comparing the differences of metabolic indicators between MHO subjects diagnosed by the two criteria, MHO subjects diagnosed by the new criterion had higher WC, WHR, TG, FPG, and lower HDL-C levels except for lower blood pressure, showing more exposure to cardiovascular risk factors. Conclusions: The risk of combined CVD and stroke was not increased in MHO subjects. The new metabolic health criterion is superior to the traditional criterion and can effectively identify individuals with obesity with a lower risk of combined CVD. Blood pressure levels may be responsible for the inconsistent risk of combined CVD in MHO subjects diagnosed with both criteria.

Transitions in Metabolic Health and Onset Age of Cardiovascular Diseases.

INTRODUCTION: The cardiometabolic risk associated with metabolically healthy obesity remains the subject of debate. It is unclear whether changes in metabolically healthy obesity status affect premature cardiovascular disease (CVD) risk. Authors aimed to investigate the association of metabolically healthy obesity and its transition over time with incident CVD by age at onset. METHODS: In a community-based, prospective cohort study, 54,441 adults without CVD in or before 2010 were followed for incident CVD until 2020. This sample was analyzed in 2022. Four age groups were examined (<55, 55-65, 65-75, and ≥75 years) for CVD onset. In each age group, participants were cross-classified by BMI categories and metabolic health. The Cox proportional hazards model with age as the underlying time scale was used to examine the associations of metabolic health status and its transition with CVD across BMI categories. RESULTS: During a median follow-up of 9.59 years, 3,038 participants developed CVD. Individuals with metabolically unhealthy obesity at baseline had the highest hazard ratio for CVD onset at any age, ranging from 2.68 (95% CI=2.02, 3.55) for CVD onset in those aged <55 years to 1.55 (95% CI=1.09, 2.10) for CVD onset in those aged ≥75 years. Individuals who had metabolically healthy obesity at baseline or even remained metabolically healthy during 2006-2010 were still at increased risk of premature CVD, and the association attenuated with increasing age of CVD onset. CONCLUSIONS: The metabolically healthy obesity phenotype is dynamic and its transition to a metabolically unhealthy phenotype or even stable metabolically healthy obesity is associated with an increased risk of CVD. The associations were more evident for CVD onset at younger ages.