Unhealthy Overweight Research Articles

Associations of Metabolically Healthy Obesity and Retinal Age Gap.

We investigated the association between metabolically healthy obesity (MHO) and retinal age gap and explored potential sex differences in this association. This study included 30,335 participants from the UK Biobank. Body mass index (BMI) was classified into normal weight, overweight, and obesity. Metabolic health (MH) was defined as meeting the following criteria: systolic blood pressure of <130 mmHg, no antihypertensive drugs, waist-to-hip ratio of <0.95 for women or 1.03 for men, and the absence of diabetes. Participants were categorized as MH normal weight (MHN), MH overweight (MHOW), MHO, metabolically unhealthy normal weight, metabolically unhealthy (MU) overweight, and MU obesity. Retinal age gap was defined as the difference between retinal age and chronological age. Linear regression models were used to investigate the association of metabolic phenotypes of obesity with retinal age gap. Compared with MHN, individuals with MHOW (β, 0.17; 95% confidence interval [CI], 0.01-0.32; P = 0.039) and MHO (β, 0.23; 95% CI, 0.02-0.44; P = 0.031) were associated with increased retinal age gap. Furthermore, individuals classified as metabolic unhealthy were also associated with higher retinal age gap, irrespective of body mass index categories (β for MU normal weight, 0.23; 95% CI, 0.08-0.38; P = 0.003; β for MU overweight: 0.31; 95% CI, 0.18-0.45; P < 0.001; β for MU obesity, 0.50; 95% CI, 0.36-0.65; P < 0.001). No significant sex difference was observed in the association between metabolic phenotypes of obesity and retinal age gap (all P for interaction > 0.05). MHOW and MHO were associated significantly with an increased retinal age gap compared with MHN individuals. Weight management should be recommended for individuals who are overweight or obese, even in the absence of metabolic unhealth. Retinal age gap provides a simple tool for identifying early health risks for MHOW and MHO individuals.

The association of obesity phenotypes and risk of cardiovascular disease using time-varying and time-invariant approaches: An 18-year follow-up cohort study

Background and aimOur aim was risk estimation for cardiovascular disease (CVD) across obesity phenotypes over 18 years of follow-up using both time-invariant and time-varying approaches. Methods and resultsThis prospective cohort study included 9752 participants aged ≥30 years examined in the first and second phases of the Tehran Lipid and Glucose Study (1999–2001 and 2002–2005). Six phenotypes [i.e., metabolically healthy normal weight (MHNW), overweight (MHOW), and obese (MHO), as well as metabolically unhealthy normal weight (MUNW), overweight (MUOW), and obese (MUO)] were defined based on the body mass index (BMI) and metabolic status. Incident CVD was documented until March 2018. Time-invariant and time-varying Cox regression models were used to estimate CVD hazard ratio (HRs) for obesity phenotypes.Mean age of the participants was 46.6 ± 12.0 years, and 53.9 % of them were women. During 18 years of follow-up, 1083 new CVD events occurred. In metabolically unhealthy individuals, but not metabolically healthy people, multivariable-adjusted HRs for CVD events increased by BMI according to time-varying (HR = 1.6, 95 % CI = 1.13–2.26 for MUNW; HR = 1.92, 95 % CI = 1.43–2.58 for MUOW; HR = 1.94, 95 % CI = 1.4–2.68 for MUO) and time-invariant (HR = 1.85, 95 % CI = 1.01–3.39 for MUNW; HR = 2.75, 95 % CI = 1.63–4.63 for MUOW, and HR = 3.26, 95 % CI = 1.95–5.47 for MUO) models. ConclusionMetabolically unhealthy overweight and obese individuals are at increased risk of CVD and should be regularly screened to prevent possible cardiovascular events.

Irruption of Network Analysis to Explain Dietary, Psychological and Nutritional Patterns and Metabolic Health Status in Metabolically Healthy and Unhealthy Overweight and Obese University Students: Ecuadorian Case.

The association between dietary nutritional patterns, psychological factors, and metabolic health status has not been investigated in university students. There are studies that include numerous variables to test hypotheses from various theoretical bases, but due to their complexity, they have not been studied in combination. The scientific community recognizes the use of Gaussian graphical models (GGM) as a set of novel methods capable of addressing this. To apply GGMs to derive specific networks for groups of healthy and unhealthy obese individuals that represent nutritional, psychological, and metabolic patterns in an Ecuadorian population. This was a quantitative, non-experimental, cross-sectional, correlational study conducted on a sample of 230 obese/overweight university students, selected through a multi-stage random sampling method. To assess usual dietary intake, a Food Frequency Questionnaire (FFQ) was used; to evaluate psychological profiles (anxiety, depression, and stress), the DASS-21 scale was employed; blood pressure and anthropometric data were collected; and insulin levels, lipid profiles, and glucose levels were determined using fasting blood samples. The International Diabetes Federation (IDF) criteria were applied to identify metabolically healthy and unhealthy individuals. Statistical analysis relied on univariate methods (frequencies, measures of central tendency, and dispersion), and the relationships were analyzed through networks. The Mann-Whitney U test was used to analyze differences between groups. In metabolically unhealthy obese individuals, GGMs identified a primary network consisting of the influence of waist circumference on blood pressure and insulin levels. In the healthy obese group, a different network was identified, incorporating stress and anxiety variables that influenced blood pressure, anthropometry, and insulin levels. Other identified networks show the dynamics of obesity and the effect of waist circumference on triglycerides, anxiety, and riboflavin intake. GGMs are an exploratory method that can be used to construct networks that illustrate the behavior of obesity in the studied population. In the future, the identified networks could form the basis for updating obesity management protocols in Primary Care Units and supporting clinical interventions in Ecuador.

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

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. 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. 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. A metabolically healthy phenotype is associated with lower frequency of IFG, IGT, and IFG + IGT status across all body weight categories.

Myocardial mechano-energetic efficiency is not impaired in patients with metabolically healthy overweight and obesity.

Reduced myocardial mechano-energetic efficiency (MEE) was associated with BMI. Subgroups of individuals with increased BMI but favorable cardiovascular risk profile were identified as individuals with "metabolically healthy overweight" (MHOW) and "metabolically healthy obesity" (MHO), respectively. We aim to investigate whether those with MHOW/MHO, defined as those having none of the components of metabolic syndrome, exhibit impaired MEE compared with their unhealthy counterparts. Myocardial MEE per gram of left ventricular mass (MEEi) was assessed by echocardiography in 2190 nondiabetic individuals participating in the CATAnzaro MEtabolic RIsk factors (CATAMERI) study who were divided, according to BMI and metabolic status, into groups of individuals with metabolically healthy normal weight (MHNW), metabolically unhealthy normal weight (MUNW), MHOW, metabolically unhealthy overweight (MUOW), MHO, and metabolically unhealthy obesity (MUO). After adjusting for age and sex, no differences in myocardial MEEi were observed among individuals with MHNW, MHOW, and MHO (p = 0.56). Myocardial MEEi was comparable among individuals with MUNW, MUOW, and MUO (p = 0.21). Individuals with MHNW, MHOW, and MHO displayed significantly higher myocardial MEEi compared with their unhealthy counterparts. Increased BMI is not an obligate determinant for reduced myocardial MEEi. Other known components of metabolic syndrome rather than increased BMI contributed to reduced myocardial MEEi.

Metabolically Unhealthy Normal Weight: Prevalence and Associated Factors in an Adult Population from Northwest Colombia.

Individuals with a normal weight may have metabolic alterations at risk for chronic non-communicable diseases. The prevalence of this condition and associated factors have not been reported in Latin American populations. We aimed to estimate the presence and associated factors of Metabolically Unhealthy Normal Weight (MUNW) in adults from a public program for the control and prevention of chronic diseases in Medellín, Colombia. Cross-sectional study. Overweight and normal weight were characterized according to the absence or presence of one or more components of the metabolic syndrome, obtaining four phenotypes: Metabolically Healthy Normal Weight (MHNW), MUNW (phenotype of interest), Metabolically Healthy Overweight (MHO), and Metabolically Unhealthy Overweight (MUO). The association of these phenotypes with sociodemographic variables of lifestyles and increased waist circumference was conducted by using logistic regression. In 37,558 individuals (72.7% women), the prevalence of MUNW was 23.3%. Among the additional phenotypes, MUO was found to be more prevalent (71.6%), while MHNW and MHO were very slightly common, 2% and 3.1%, respectively. In a multiple model, the factors associated with MUNW were age over 60 years (trend [OR 1.56 95% CI 0.97-2.52] p-value = 0.066), living in a rural area ([OR 1.58 95% CI 1.09-2.29] p-value = 0.015), and increased waist circumference ([OR 1.68 95% CI 1.45-1.95] p-value < 0.001). Male gender was inversely associated with all phenotypes (P < 0.05). Almost a quarter of the analyzed population presented MUNW. People living in a rural area and over 60 years old were more likely to present MUNW. Men were less likely to present the weight phenotypes studied, although they could have been underrepresented.

Association between weight change, waist circumference change, and the risk of nonalcoholic fatty liver disease in individuals with metabolically healthy overweight or obesity and metabolically unhealthy overweight or obesity

BackgroundThis study aimed to explore and compare the effect of weight change, and waist circumference (WC) change, on the risk of nonalcoholic fatty liver disease (NAFLD) in individuals with metabolically healthy overweight or obesity (MHOW/O) and metabolically unhealthy overweight or obesity (MUOW/O) in a health check-up cohort in China. Methods5625 adults with overweight or obesity, and free from NAFLD at baseline were included. Metabolically healthy was defined as not having any components of metabolic syndrome. Weight/WC changes were calculated as the relative difference between the first and second visits of check-up. NAFLD was assessed based on abdominal ultrasound. ResultsDuring a median follow-up of 2.1 (IQR: 1.1–4.3) years, 1849 participants developed NAFLD. In MHOW/O participants, the multivariable adjusted HRs (95 % CIs) for NAFLD in weight change ≤ −5.0 %, and − 4.9–− 1.0 % were 0.36 (0.23–0.59), 0.59 (0.43–0.80), respectively, compared to the weight stable group (−0.9% to 0.9 %). The corresponding HRs (95 % CIs) for the association between WC change (≤ 6.0 %, - 5.9 to −3.0 %) and NAFLD in MHOW/O participants were 0.41 (0.27–0.62), and 0.74 (0.54–1.01), respectively, compared to the WC stable group (−2.9–2.9 %). Similar patterns were observed in MUOW/O participants. A more marked gradient of cumulative incidence of NAFLD across weight/WC change categories was observed in MHOW/O than in MUOW/O individuals. ConclusionsA more evident association between weight/WC loss and risk of NAFLD was observed in MHOW/O than in MUOW/O individuals. Our findings indicate the practical significance of encouraging all individuals with overweight and obesity to achieve a clinically relevant level of weight/WC loss to prevent NAFLD, even among metabolic healthy groups.

The various facets of orthorexic eating behavior: five case reports of individuals with supposed orthorexia nervosa

BackgroundOrthorexia nervosa, defined as a fixation on eating healthy according to subjective criteria, is recently being discussed as another variant of disordered eating behavior. Further characteristics are rigid adherence to nutritional rules, anxieties and avoidance behavior in the context of eating as well as a focus on health, not on body shape or weight loss, which is supposed to differentiate orthorexic from other disordered eating behavior. Although diagnostic criteria have been suggested, they have rarely been used in case reports published to date. Hence, the aim of this study was to present five individuals with supposed orthorexia nervosa, using preliminary diagnostic criteria to assess their eating behavior.Case presentationThe five cases (three females, two males) reveal the great variety of disordered eating behavior. Fear of unhealthy overweight (case A), supposed orthorexic eating behavior as a coping strategy for anorexia nervosa (case B), the exclusive consumption of animal products with a total exclusion of fruits and vegetables (case C), the fixation on exercise and athletic goals (case D) as well as a focus on a vegan diet and unprocessed foods (case E) are facets of orthorexia nervosa with varying degrees of impairment.ConclusionsIt is concluded that orthorexia nervosa manifests itself in very different ways and that more research is needed in order to determine whether it could be a useful additional category of disordered eating behavior.

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 between sugar-free beverage intake and childhood obesity among Chinese children and adolescents.

The relationship between sugar-free beverage (SFB) intake and childhood obesity among Chinese children is unknown. To describe the status of SFB consumption among children and adolescents in China and assess the association between SFB intake and different types of obesity. The study was based on the baseline data of an ongoing cohort project named Evaluation and Monitoring on School-based Nutrition and Growth in Shenzhen (EMSNGS). Food frequency questionnaires were used to collect information on SFB consumption in 3227 students aged 9-17. Physical and clinical examinations were conducted by trained investigators and clinicians. Multivariable binary logistic regression models were performed to assess the association between SFB intake and general obesity, overweight/obesity, abdominal obesity, metabolically unhealthy overweight (MUOW)/metabolically unhealthy obesity (MUO). The median age of the participants was 13.28 years. Among the participants, 55.2% were boys, and 66.1% were adolescents. The median SFB consumption was 16.67 mL/d. After adjusting for potential confounding factors, each 100 mL increase in daily SFB intake was associated with an increased risk of overweight/obesity (OR = 1.14; 95%CI: 1.06-1.23), abdominal obesity (OR = 1.12; 95%CI: 1.03-1.23), and MUOW/MUO (OR = 1.12; 95%CI: 1.02-1.21), respectively. Stratified analyses showed that family income may have an impact on the association between SFB intake and overweight/obesity (P for interaction = 0.021) and abdominal obesity (P for interaction = 0.031). SFB intake was positively associated with childhood obesity in Chinese children, particularly among individuals with high-income families.

Metabolic health status and renal disorders: a cross-sectional study

Previous surveys suggests that body mass index (BMI) may be positively related to development of chronic kidney disease (CKD). However, this association might be altered by metabolic syndrome. Therefore, we aimed to evaluate the association of metabolic health status with CKD. The present cross-sectional study was carried out on 3322 representative sample of Iranian adults. Metabolic syndrome was identified based on the National Cholesterol Education Program (NCEP) Adult Treatment Panel III (ATP III) and BMI was assessed by anthropometric measurements. Estimated glomerular filtration rate (eGFR) was calculated by modification of diet in renal disease-Chronic Kidney Disease Epidemiology Collaboration (MDRD-EPI) formula. Subjects were categorized into four phenotypes: metabolically healthy normal weight (MHNW), metabolically healthy overweight and obesity (MHO), metabolically unhealthy normal weight (MUHNW), and metabolically unhealthy overweight and obesity (MUHO). Based on multivariate-adjusted models, the risk of CKD was significantly higher in MUHO compared with MHNW (OR: 1.48; p < 0.05). Although MUHNW and MUHO were associated with lower eGFR and albuminuria, the significant association was not observed in case of hematuria. Furthermore, subjects with kidney stones tended to be in MHO (OR: 1.42; p < 0.05) and MUHO phenotypes (OR: 1.64; p < 0.05), in comparison to the MHNW phenotype. The odds of kidney disorders were higher in adults with metabolic syndrome, regardless of BMI. However, this relationship might be strengthened by the concomitance of metabolic syndrome and obesity. To verify our findings, clarify the causality, and elucidate the underlying mechanisms, further research are warranted.

Associations between metabolic phenotypes and diabetes risk: A systematic review and meta-analysis

IntroductionIt seems that the risk of developing diabetes cannot be predicted solely based on weight or BMI. Metabolic phenotypes might offer a more precise tool for identifying patients at higher risk of diabetes, thus enabling more effective and targeted preventive interventions. ObjectiveTo determine the association between these metabolic phenotypes and the risk of diabetes. MaterialsSystematic Review (SR) with a meta-analysis of cohort studies. The search was carried out in four databases: Pubmed/Medline, SCOPUS, Web of Science, and EMBASE. Metabolic states were classified into six groups: 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). Association measures were presented as odds ratios (OR) and hazard ratios (HR) along with their 95 % confidence intervals (CI95%). ResultsA total of six studies were evaluated. For the meta-analysis, only studies using OR as the measure of association were included. Compared with individuals with MHNW, a statistically significant association was found for MUNW (OR: 1.82; CI95% 1.62, 2.04), MHOW (OR: 1.19; CI95% 1.07, 1.32), MUOW (OR: 2.44; CI95% 2.19, 2.72), MHO (OR: 2.14; CI95% 1.52, 3.01), and MUO (OR: 3.94; CI95% 3.28, 4.74). ConclusionsMetabolic phenotypes are significantly associated with the risk of diabetes, regardless of BMI. Further research in this field is required, and should be conducted in other regions of the world where obesity and diabetes rates are rapidly increasing.

As we were and as we Should Be, Combined Exercise Training in Adults with Schizophrenia: CORTEX-SP Study Part I.

Given the health benefits and the role of exercise as an anti-inflammatory adjuvant program, this study aimed to determine the effectiveness of a combined exercise program on cardiorespiratory fitness (CRF), body composition, and biochemical levels in adults with schizophrenia (SZ) characterized at baseline as metabolically unhealthy overweight with low CRF. Participants diagnosed with SZ (n = 112, 41.3 ± 10.4 yr, 28.7% women) were randomly assigned into a Treatment-As-Usual (TAU) control group (n = 53) or a supervised exercise group (n = 59, 3 days/week). Each combined exercise session consisted of both a low-volume high-intensity interval training (less than 10 min of high-intensity time per session) and a resistance circuit-training program. All variables were assessed pre-and post-intervention (20 weeks). For the assessment of CRF, a peak cardiopulmonary exercise test on a cycle ergometer was used. Following the intervention, participants from the exercise group (n = 51) showed increases in CRF (P < 0.001) through peak oxygen uptake (L·min-1; Δ = 17.6%; mL·kg.1·min-1, Δ = 19.6%) and the metabolic equivalent of task (Δ = 19%), with no significant changes (P > 0.05) in body composition and biochemical variables. However, the TAU group (n = 38) did not show any significant change in the study variables (P > 0.05). Between-group significant differences (P ≤ 0.05) were observed in CRF, first ventilatory threshold, and heart rate peak after the intervention period favoring the exercise group. This study demonstrated that a supervised combined exercise program in people with SZ helps to maintain body composition values and improve CRF levels. This could lead to an important clinical change in the characterization from metabolically unhealthy overweight to a metabolically healthy overweight population. Hence, exercise should be considered a co-adjuvant program in the treatment of the SZ population.

Investigation the interaction of dietary fat quality indices and the MC4R gene in metabolically healthy and unhealthy overweight and obese women

Obesity has become a common global problem. Some obese people can be metabolically healthy. Gene-environment interaction can be important in this context. This study aimed to assess the interaction between dietary fat quality indices and the Melanocortin 4 receptor (MC4R) gene in metabolically healthy and unhealthy overweight and obese women. This cross-sectional study was conducted on 279 women with overweight and obesity. The definition of metabolically healthy and unhealthy phenotypes was done according to Karelis criteria. Dietary assessment was done using a 147-item validated semi-quantitative food frequency questionnaire and dietary fat quality was assessed by cholesterol-saturated fat index (CSI) and the ratio of omega-6/omega-3 (N6/N3) essential fatty acids. MC4R was genotyped by polymerase chain reaction-restriction fragment length polymorphism technique. A generalized linear model was used to evaluate the interaction between dietary fat quality indices and the MC4R gene in both crude and adjusted models. Study subjects with higher ratio of N6/N3 had higher homeostatic model assessment for insulin resistance (HOMA IR) index (P = 0.03) and other variables showed no difference according to the tertile of CSI and N6/N3. Participants with the C allele of MC4R rs17782313 had lower height (P < 0.001) and higher HOMA index (P = 0.01). We found that the CC genotype of MC4R interacts with the N6/N3 ratio on the metabolically unhealthy phenotype in the crude model (β = 9.94, CI 2.49–17.39, P = 0.009) and even after adjustment for all confounders (β = 9.002, CI 1.15–16.85, P = 0.02, β = − 12.12, CI 2.79–21.46, P = 0.01). The data of this study can justify one inconsistency observed in society, regarding dietary recommendations about metabolic health status. Those with CC genotype, are more likely to have an unhealthy phenotype with an increase in N6/N3 as one fat quality indices than those who do not have CC genotype. We found the interaction of dietary fat quality indices such as N6/N3 and the MC4R gene in metabolically unhealthy overweight and obese women.

Clinical Blood Metabogram: Application to Overweight and Obese Patients.

Recently, the concept of a mass spectrometric blood metabogram was introduced, which allows the analysis of the blood metabolome in terms of the time, cost, and reproducibility of clinical laboratory tests. It was demonstrated that the components of the metabogram are related groups of the blood metabolites associated with humoral regulation; the metabolism of lipids, carbohydrates, and amines; lipid intake into the organism; and liver function, thereby providing clinically relevant information. The purpose of this work was to evaluate the relevance of using the metabogram in a disease. To do this, the metabogram was used to analyze patients with various degrees of metabolic alterations associated with obesity. The study involved 20 healthy individuals, 20 overweight individuals, and 60 individuals with class 1, 2, or 3 obesity. The results showed that the metabogram revealed obesity-associated metabolic alterations, including changes in the blood levels of steroids, amino acids, fatty acids, and phospholipids, which are consistent with the available scientific data to date. Therefore, the metabogram allows testing of metabolically unhealthy overweight or obese patients, providing both a general overview of their metabolic alterations and detailing their individual characteristics. It was concluded that the metabogram is an accurate and clinically applicable test for assessing an individual's metabolic status in disease.

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.

Transitions in Metabolic Health Status and Obesity Over Time and Risk of Diabetes: The Dongfeng-Tongji Cohort Study.

Evidence regarding the association between metabolically healthy overweight or obesity (MHOO) and diabetes is controversial, and mostly ignores the dynamic change of metabolic health status and obesity. To explore the association between transitions of metabolic health status and obesity over 5 years and diabetes incidence. We examined 17 309 participants derived from the Dongfeng-Tongji cohort and followed from 2008 to 2018 (median follow-up 9.9 years). All participants were categorized into 4 phenotypes based on body mass index (BMI) and metabolic health status: metabolically healthy normal weight (MHNW), metabolically unhealthy normal weight (MUNW), MHOO, and metabolically unhealthy overweight or obesity (MUOO). The associations of changes in BMI-metabolic health status (2008-2013) with diabetes incidence (2018) were performed among 12 206 individuals with 2 follow-up examinations. Compared with stable MHNW, stable MHOO (hazard ratio [HR] 1.76; 95% CI 1.26, 2.45) and transition from MHOO to metabolically unhealthy phenotypes were associated with higher risk for diabetes (HR 2.97; 95% CI 1.79, 4.93 in MHOO to MUNW group and HR 3.38; 95% CI 2.54, 4.49 in MHOO to MUOO group). Instead, improvements to metabolic healthy phenotypes or weight loss occurring in MUOO reduced the risk of diabetes compared with stable MUOO, changing from MUOO to MHNW, MUNW, and MHOO resulted in HRs of 0.57 (95% CI 0.37, 0.87), 0.68 (95% CI 0.50, 0.93), and 0.45 (95% CI 0.34, 0.60), respectively. People with MHOO, even stable MHOO, or its transition to metabolically unhealthy phenotypes were at increased risk of diabetes. Metabolic improvements and weight control may reduce the risk of diabetes.

Hyperuricemia as an effect modifier of the association between metabolic phenotypes and nonalcoholic fatty liver disease in Chinese population

BackgroundDifferent metabolic phenotypes may be related to nonalcoholic fatty liver disease (NAFLD), but such association whether modified by serum uric acid levels is unknown. We examined the association between different metabolic phenotypes and NAFLD and further explore whether hyperuricemia could modify this association.MethodsA total of 2959 participants (mean age: 55.02 years) with medical checkups were recruited from Tianjin Medical University General Hospital. Participants were categorized into four groups according to their BMI levels and metabolically healthy status: metabolically healthy normal weight (MHNW), metabolically healthy overweight or obese (MHO), metabolically unhealthy normal weight (MUNW), and metabolically unhealthy overweight or obese (MUO). Blood samples (including serum uric acid) were collected from participants after an overnight fast. NAFLD was diagnosed based on abdominal ultrasonography scanning. Data were analyzed using logistic regression models and the interaction effect model.ResultsThe prevalence of NAFLD in MHNW, MHO, MUNW, and MUO groups was 9.9% (7.9–12.0%), 42.8% (39.5–46.1%), 36.5% (31.2–41.9%), and 69.7% (66.8–72.6%), respectively. In multi-adjusted logistic models, the ORs (95% CIs) of NAFLD were 5.32 (4.01–7.04) for participants with MHO, 4.51 (3.17–6.40) for those with MUNW, and 13.68 (10.23–18.30) for those with MUO compared to those with MHNW. In the stratified analysis by uric acid levels, the prevalence of NAFLD was significantly higher in participants with MHO, MUNW, and MUO in the hyperuricemia group than those in the normal uric acid group, and the interaction effect of metabolic phenotypes and uric acid on NAFLD was statistical significant (P < 0.05).ConclusionsMHO, MUNW, and MUO were associated with higher prevalence of NAFLD. Serum uric acid levels may modify the association between metabolically phenotypes and NAFLD.

Effect of metabolic health and obesity on all-cause death and CVD incidence in Korean adults: a retrospective cohort study

This study aimed to investigate the risk of all-cause mortality and incidence of CVD according to metabolic health and body mass index (BMI) in Korean adults. This study was retrospectively designed using the National Health Insurance Service-National Health Screening Cohort data. Participants were divided into six groups according to two category of metabolic syndrome and three categories of BMI. Hazard ratios (HRs) and 95% confidence intervals (CIs) for the composite outcome (all-cause mortality and incidence of CVDs) were estimated using multivariable Cox proportional hazards regression models. 151,706 participants aged ≥ 40 years were enrolled; median follow-up period was 9.7 years in the study. Compared to metabolically healthy normal weight, the fully adjusted HRs (95% CIs) of metabolically healthy overweight, metabolically healthy obese, metabolically unhealthy normal weight, metabolically unhealthy overweight, and metabolically unhealthy obese for composite outcome were 1.07 (1.03–1.12), 1.12 (1.07–1.17), 1.33 (1.25–1.41), 1.28 (1.22–1.34), and 1.31 (1.26–1.37), respectively, in men, and 1.10 (1.05–1.16), 1.22 (1.16–1.29), 1.34 (1.26–1.43), 1.27 (1.19–1.34), and, 1.40 (1.34–1.47), respectively, in women. High BMI and metabolic unhealthiness were associated with an increased risk on the composite of all-cause mortality and incidence of CVD in both sexes.

Transition patterns of metabolism-weight phenotypes over time: A longitudinal study using the multistate Markov model in China.

A change in weight or metabolic status is a dynamic process, yet most studies have focused on metabolically healthy obesity (MHO) and the transition between MHO and metabolically unhealthy obesity (MUO); therefore, they have not fully revealed the nature of all possible transitions among metabolism-weight phenotypes over the years. This was a longitudinal study based on a retrospective health check-up cohort. A total of 9,742 apparently healthy individuals aged 20-60 years at study entry were included and underwent at least two health check-ups. Six metabolism-weight phenotypes were cross-defined by body mass index (BMI) categories and metabolic status as follows: metabolically healthy normal weight (MHNW), metabolically healthy overweight (MHOW), MHO, metabolically unhealthy normal weight (MUNW), metabolically unhealthy overweight (MUOW), and MUO. A multistate Markov model was used to analyse all possible transitions among these phenotypes and assess the effects of demographic and blood indicators on the transitions. The transition intensity from MUNW to MHNW was the highest (0.64), followed by the transition from MHO to MUO (0.56). The greatest sojourn time appeared in the MHNW state (3.84 years), followed by the MUO state (2.34 years), and the shortest sojourn time appeared in the MHO state (1.16 years). Transition intensities for metabolic improvement gradually decreased with BMI level as follows: 0.64 for MUNW to MHNW, 0.44 for MUOW to MHNW, and 0.27 for MUO to MHO; however, transition intensities for metabolic deterioration, including MHNW to MUNW, MHOW to MUOW, and MHO to MUO, were 0.15, 0.38, and 0.56, respectively. In the middle-aged male group, elevated alanine aminotransferase (ALT), aspartate aminotransferase (AST), and uric acid (UA) increased the risk of deterioration in weight and metabolic status and decreased the possibility of improvement. Maintaining a normal and stable BMI is important for metabolic health. More attention should be given to males and elderly people to prevent their progression to an unhealthy metabolic and/or weight status. MHO is the most unstable phenotype and is prone to convert to the MUO state, and individuals with abnormal ALT, AST and UA are at an increased risk of transitioning to an unhealthy weight and/or metabolic status; therefore, we should be alert to abnormal indicators and MHO. Intervention measures should be taken early to maintain healthy weight and metabolic status.