Association of Metabolically Healthy and Unhealthy Obesity Phenotype with Markers Related to Obesity, Diabetes among Young, Healthy Adult Men. Analysis of MAGNETIC Study

Adipose tissue secretes many regulatory factors called adipokines. Adipokines affect the metabolism of lipids and carbohydrates. They also influence the regulation of the immune system and inflammation. The current study aimed to evaluate the association between markers related to obesity, diabesity and adipokines and metabolically healthy and unhealthy obesity in young men. The study included 98 healthy participants. We divided participants into three subgroups based on body mass index and metabolic health definition: 49 metabolically healthy normal-weight patients, 27 metabolically healthy obese patients and 22 metabolically unhealthy obese patients. The 14 metabolic markers selected were measured in serum or plasma. The analysis showed associations between markers related to obesity, diabesity and adipokines in metabolically healthy and unhealthy obese participants. The decreased level of adipsin (p < 0.05) was only associated with metabolically healthy obesity, not with metabolically unhealthy obesity. The decreased level of ghrelin (p < 0.001) and increased level of plasminogen activator inhibitor-1 (p < 0.01) were only associated with metabolically unhealthy obesity, not with metabolically healthy obesity. The decreased level of adiponectin and increased levels of leptin, c-peptide, insulin and angiopoietin-like 3 protein were associated with metabolically healthy and unhealthy obesity. In conclusion, our data show that metabolically healthy obesity was more similar to metabolically unhealthy obesity in terms of the analyzed markers related to obesity and diabesity.

Metabolically healthy obesity, transition to unhealthy phenotypes and type 2 diabetes in 0.5 million Chinese adults: The China Kadoorie Biobank

Objectives: To prospectively assess the association of metabolic health status and its transition with incident diabetes risk across body mass index (BMI) categories. Design: Cohort study based on the China Kadoorie Biobank (CKB) Methods: The CKB study enrolled 512,715 adults aged 30-79 years from 10 diverse areas in China during 2004-2008. After exclusion, 432,763 participants were cross-classified by BMI categories and the metabolic status during followed-up for incident diabetes disease. The changes in BMI and metabolic health status were defined from baseline to the second resurvey. Results: Type 2 diabetes risk is higher for metabolically healthy obese (MHO) subjects than metabolically healthy normal weight (MHN) individuals (HR: 3.97, 95% CI: 3.64-3.66), and it is highest for those affected by metabolically healthy obese (MUO) (HR: 6.47, 95% CI: 6.17-6.79). About 15.26% of participants with MHN converted to metabolically healthy overweight or obesity (MHOO), whereas 48.40% of MHOO remained unconverted throughout the follow-up. In obese or overweight people, the conversion from metabolically healthy to unhealthy might increase the chances of developing diabetes as compared to those with a stable metabolic healthy state (HR: 3.70, 95% CI: 2.99-4.59), while those with persistent metabolic disorders are most likely to have diabetes (HR: 8.32, 95% CI: 7.08-9.78). Conclusions: Metabolic healthy is a transient state, and individuals converted from metabolically healthy status to unhealthy phenotypes across all BMI categories might raise the risk of diabetes.

Evolution of Metabolic Phenotypes of Obesity in Coronary Patients after 5 Years of Dietary Intervention: From the CORDIOPREV Study

Background: Obesity phenotypes with different metabolic status have been described previously. We analyzed metabolic phenotypes in obese coronary patients during a 5-year follow-up, and examined the factors influencing this evolution. Methods: The CORDIOPREV study is a randomized, long-term secondary prevention study with two healthy diets: Mediterranean and low-fat. All obese patients were classified as either metabolically healthy obese (MHO) or metabolically unhealthy obese (MUO). We evaluated the changes in the metabolic phenotypes and related variables after 5 years of dietary intervention. Results: Initially, 562 out of the 1002 CORDIOPREV patients were obese. After 5 years, 476 obese patients maintained their clinical and dietary visits; 71.8% of MHO patients changed to unhealthy phenotypes (MHO-Progressors), whereas the MHO patients who maintained healthy phenotypes (MHO-Non-Progressors) lost more in terms of their body mass index (BMI) and had a lower fatty liver index (FLI-score) (p < 0.05). Most of the MUO (92%) patients maintained unhealthy phenotypes (MUO-Non-Responders), but 8% became metabolically healthy (MUO-Responders) after a significant decrease in their BMI and FLI-score, with improvement in all metabolic criteria. No differences were found among dietary groups. Conclusions: A greater loss of weight and liver fat is associated with a lower progression of the MHO phenotype to unhealthy phenotypes. Likewise, a marked improvement in these parameters is associated with regression from MUO to healthy phenotypes.

Pattern of Adiponectin, Osteocalcin, Irisin, FGF-21, and MCP-1 According to the Body Size Phenotype: Could They Be Markers of Metabolic Health in Mexican-Mestizo Middle-Aged Women?

Variations in levels of some adipokines, myokines, osteokines, hepatokines and inflammatory cytokines contribute to abnormal glucose and lipid metabolism. The aim of this study was to determine the pattern of adiponectin, osteocalcin (OCN), irisin, FGF-21, and MCP-1 according to the body size phenotype of middle-aged women, and their associations with BMI, visceral adipose tissue (VAT), and HOMA-IR. A cross-sectional study in 265 women aged from 40 to 65 years was performed. The biochemical characteristics were evaluated in metabolically healthy normal weight, metabolically unhealthy normal weight, metabolically healthy obese, and metabolically unhealthy obese women. There was an association of OCN with BMI (r = −0.107; p = 0.047); adiponectin with BMI (r = −0.217; p = 0.001), insulin (r = −0.415; p = 0.0001), HOMA-IR (r = −0.429; p = 0.0001), and VAT (r = −0.134; p = 0.025); irisin with BMI (r = 0.604; p = 0.001), insulin (r = 0.446; p = 0.0001), HOMA-IR (r = 0.452; p = 0.0001), and VAT (r = 0.645; p = 0.0001); FGF−21 with insulin (r = −0.337; p= 0.030) and HOMA-IR (r = −0.341; p = 0.03); and MCP-1 with BMI (r = 0.481; p = 0.0001), VAT (r = 0.497; p = 0.001), insulin (r = 0.298; p= 0.001), and HOMA-IR (r = 0.255; p = 0.004). A multivariate analysis showed that an elevation of OCN (OR 1.4 (95%CI 1.06–1.81)) and a reduction of adiponectin (OR 0.9 (0.84–0.96)) were associated factors for a metabolic unhealthy phenotype in normal weight participants. Likewise, higher irisin (OR 1.007 (1.003–1.011)) and MCP-1 (1.044 (1.008–1.083)) were risk factors for a metabolic unhealthy phenotype in woman with obesity. OCN, adiponectin, irisin, FGF-21, and MCP-1 are associated with some metabolic parameters such as BMI, HOMA-IR, and VAT, and could be possible biomarkers of an unhealthy metabolic phenotype in middle-aged women.

Magnesium intake is associated with the metabolically healthy obese phenotype

Although magnesium intake is inversely associated with the risk of metabolic abnormalities, whether magnesium intake plays a role on metabolically healthy obese (MHO) phenotype has not been explored. Therefore, the purpose of this study was to determine whether the magnesium intake is associated with the MHO phenotype. Apparently, healthy women and men aged 20–65 years with obesity were enrolled in a cross-sectional study. Subjects were allocated into MHO (n=124) and metabolically unhealthy obese (MUO) (n=123) groups. MHO phenotype was defined by abdominal obesity (waist circumference ≥90 cm in men and ≥80 cm in women) and none, or not more than one of the following risk factors: triglyceride levels ≥150 mg/dL; high-density lipoprotein cholesterol (HDL-C) levels <40 mg/dL in men and <50 mg/dL in women; fasting glucose ≥100 mg/dL; and systolic blood pressure ≥130 mm Hg and/or diastolic blood pressure ≥85 mm Hg. The MUO individuals were characterized by abdominal obesity and the presence of two or more of the aforementioned criteria. The proportion of individuals with high blood pressure (40.7% vs 5.6%, p<0.001), hyperglycemia (69.1% vs 16.9%, p<0.001), hypertriglyceridemia (84.6% vs 36.3%, p<0.001), and low HDL-C (51.2% vs 12.9%, p<0.001) was significantly higher in the MUO individuals as compared with individuals in the MHO group. The logistic regression analysis adjusted by sex and age showed that dietary magnesium intake is significantly associated with the MHO phenotype (OR=1.17; 95% CI 1.07 to 1.25, p=0.005). Our results show that magnesium intake is significantly associated with the MHO phenotype.

Transitions in Metabolic Health and Associations With Arterial Stiffness Progression Across Body Mass Index Categories

Metabolically healthy obesity is an unstable state and its transition to a metabolically unhealthy phenotype confers an increased risk of cardiovascular disease. However, it remains unclear whether changes in metabolic health over time are associated with arterial stiffness progression, a key player in the pathophysiology of cardiovascular disease. We aimed to investigate the associations of changes in metabolic health across body mass index categories with arterial stiffness and its progression. This study included 22 153 participants without cardiovascular disease or cancer at baseline from the Kailuan Study. Arterial stiffness was assessed using brachial-ankle pulse wave velocity at baseline and repeated after a mean follow-up of 3.1 years. Changes in metabolic health across body mass index categories were evaluated between the first survey (2006–2007) and the first brachial-ankle pulse wave velocity measurement. Multivariate linear regression models were used. Among initial metabolically healthy obese individuals, 53.4% (n=928) converted to a metabolically unhealthy phenotype. Compared with metabolically healthy normal-weight individuals who remained metabolically healthy, metabolically healthy obese individuals who converted to a metabolically unhealthy phenotype showed a 110.7 (95% CI, 90.8–130.6) cm/s higher increase in baseline brachial-ankle pulse wave velocity and a 22.8 (95% CI, 12.4–33.2) cm/s per year higher acceleration in arterial stiffness progression. Individuals who were initially metabolically unhealthy or converted so during follow-up across body mass index categories had higher baseline brachial-ankle pulse wave velocity and arterial stiffness progression than those who remained metabolically healthy. These data suggest that metabolically healthy individuals who develop an unhealthy phenotype across all body mass index categories are at increased risks of arterial stiffness and its progression.

Cardiovascular events in metabolically healthy obese. A nationwide cohort study

Background Obesity is a risk factor for cardiovascular disease (CVD) and has been increasing globally over the past 40 years in many countries worldwide. Metabolic abnormalities such as hypertension, dyslipidemia and diabetes mellitus are commonly associated and may mediate some of the deleterious effects of obesity. A subset of obese individuals without obesity-related metabolic abnormalities may be classified as being “metabolically healthy obese” (MHO). We aimed to evaluate the associations among MHO individuals and different types of incident cardiovascular events in a contemporary population at a nationwide level. Methods From the national hospitalization discharge database, all patients discharged from French hospitals in 2013 with at least 5 years or follow-up and without a history of major adverse cardiovascular event (myocardial infarction, heart failure [HF], ischemic stroke or cardiovascular death, MACE-HF) or underweight/ malnutrition were identified. They were categorized by phenotypes defined by obesity and 3 metabolic abnormalities (diabetes mellitus, hypertension, and hyperlipidemia). In total, 2,953,816 individuals were included in the analysis, among whom 272,838 (9.5%) were obese. We evaluated incidence rates and hazard ratios for MACE-HF, cardiovascular death, myocardial infarction, ischemic stroke, new-onset HF and new-onset atrial fibrillation (AF). Adjustments were made on age, sex and smoking status at baseline. Results During a mean follow-up of 4.9 years, obese individuals with no metabolic abnormalities had a higher risk of MACE-HF (multivariate-adjusted hazard ratio [HR] 1.22, 95% confidence interval [CI]: 1.19–1.24), new-onset HF (HR 1.34, 95% CI 1.31–1.37), and AF (HR 1.33, 95% CI 1.30–1.37) compared with non-obese individuals with 0 metabolic abnormalities. By contrast, risks were not higher for myocardial infarction (HR 0.92, 95% CI 0.87–0.98), ischemic stroke (HR 0.93, 95% CI 0.88–0.98) and cardiovascular death (HR 0.99, 95% CI 0.93–1.04). In the models fully adjusted on all baseline characteristics, obesity was independently associated with a higher risk of MACE-HF events (HR 1.13, 95% CI 1.12–1.14), of new-onset HF (HR 1.19, 95% CI 1.18–1.20) and new-onset AF (HR 1.29, 95% CI 1.28–1.31). This was not the case for the association of obesity with cardiovascular death (HR 0.96, 95% CI 0.94–0.98), myocardial infarction (HR 0.93, 95% CI 0.91–0.95) and ischemic stroke (HR 0.93, 95% CI 0.91–0.96). Conclusions Metabolically healthy obese individuals do not have a higher risk of myocardial infarction, ischemic stroke or cardiovascular death than metabolically healthy non-obese individuals. By contrast they have a higher risk of new-onset HF and new onset AF. Even individuals who are non-obese can have metabolic abnormalities and be at high risk of cardiovascular disease events. Our observations suggest that specific studies investigating different aggressive preventive measures in specific subgroups of patients are warranted. FUNDunding Acknowledgement Type of funding sources: None.

Trends in Obesity and Metabolic Status Among a Health Check-Up Population in Beijing and Hunan Between 2012 and 2020

Background: Previous cross-sectional studies have reported the prevalence of obesity and metabolic status in China. However, the trend of change in obesity and metabolic status, especially in different sex and age groups are lacking. Methods: In a series cross-sectional study, data on 256,782 participants surveyed between 2014 and 2020 in Beijing and 697,170 participants surveyed between 2012 and 2020 in Hunan were analyzed. Anthropometrics, blood pressure measurements, and blood tests were performed according to standard protocols. Trends in obesity and metabolic status were evaluated using the Joinpoint software to estimate annual percentage changes in slopes.Results: Based on age- and sex-standardized values, the mean BMI values in Beijing and Hunan participants were 23.94 (95%CI: 23.93, 23.95) and 23.68 (95%CI: 23.67, 23.69) kg/m2, respectively. Between 2014-2020, the overall obesity prevalence among Beijing participants increased from 12.70% (95%CI: 12.17%, 13.23%) to 14.33% (95% CI: 13.97%, 14.70%) (P=0.009), mainly derived by the 20-39 and 40-59 age groups. Moreover, the prevalence of metabolically healthy obese significantly increased from 2.07% (95%CI: 1.84%, 2.30%) to 4.33% (95% CI: 4.13%, 4.53%) in Beijing. Between 2012-2020, no significant trend in obesity was found among overall Hunan participants, but the prevalence of metabolically unhealthy obese significantly increased from 5.36% (95% CI: 5.18%, 5.54%) to 7.35% (95% CI: 7.11%, 7.58%), mainly derived by the 20-39 and 40-59 age groups.Conclusions: The trends in obesity and metabolic status were different between Hunan and Beijing. National weight control plan is needed in China, with a particular focus on young and middle-aged population.