metabolically healthy
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Nutrients ◽  
2022 ◽  
Vol 14 (2) ◽  
pp. 373
Author(s):  
Davide Masi ◽  
Renata Risi ◽  
Filippo Biagi ◽  
Daniel Vasquez Barahona ◽  
Mikiko Watanabe ◽  
...  

The key factors playing a role in the pathogenesis of metabolic alterations observed in many patients with obesity have not been fully characterized. Their identification is crucial, and it would represent a fundamental step towards better management of this urgent public health issue. This aim could be accomplished by exploiting the potential of machine learning (ML) technology. In a single-centre study (n = 2567), we used an ML analysis to cluster patients with metabolically healthy (MHO) or metabolically unhealthy (MUO) obesity, based on several clinical and biochemical variables. The first model provided by ML was able to predict the presence/absence of MHO with an accuracy of 66.67% and 72.15%, respectively, and included the following parameters: HOMA-IR, upper body fat/lower body fat, glycosylated haemoglobin, red blood cells, age, alanine aminotransferase, uric acid, white blood cells, insulin-like growth factor 1 (IGF-1) and gamma-glutamyl transferase. For each of these parameters, ML provided threshold values identifying either MUO or MHO. A second model including IGF-1 zSDS, a surrogate marker of IGF-1 normalized by age and sex, was even more accurate with a 71.84% and 72.3% precision, respectively. Our results demonstrated high IGF-1 levels in MHO patients, thus highlighting a possible role of IGF-1 as a novel metabolic health parameter to effectively predict the development of MUO using ML technology.


2022 ◽  
pp. 1-29
Author(s):  
Dan Tang ◽  
Xiong Xiao ◽  
Liling Chen ◽  
Yixi kangzhu ◽  
Wei Deng ◽  
...  

Abstract Metabolically healthy obesity (MHO) might be an alternative valuable target in obesity treatment. We aimed to assess whether alternative Mediterranean (aMED) diet and Dietary Approaches to Stop Hypertension (DASH) diet were favorably associated with obesity and MHO phenotype in a Chinese Multi-Ethnic population. We conducted this cross-sectional analysis using the baseline data of the China Multi-Ethnic Cohort (CMEC) study that enrolled 99 556 participants from seven diverse ethnic groups. Participants with self-reported cardiometabolic diseases were excluded to eliminate possible reverse causality. Marginal structural logistic models were used to estimate the associations, with confounders determined by directed acyclic graph (DAG). Among 65 699 included participants, 11.2% were with obesity. MHO phenotype was present in 5.7% of total population and 52.7% of population with obesity. Compared with the lowest quintile, the highest quintile of DASH diet score had 23% decreased odds of obesity (OR = 0.77, 95% CI: 0.71-0.83, Ptrend <0.001), and 27% increased odds of MHO (OR = 1.27, 95% CI: 1.10-1.48, Ptrend =0.001) in population with obesity. However, aMED diet showed no obvious favorable associations. Further adjusting for BMI did not change the associations between diet scores and MHO. Results were robust to various sensitivity analyses. In conclusion, DASH diet rather than aMED diet is associated with reduced risk of obesity and presents BMI-independent metabolic benefits in this large population-based study. Recommendation for adhering to DASH diet may benefit the prevention of obesity and related metabolic disorders in Chinese population.


PLoS ONE ◽  
2022 ◽  
Vol 17 (1) ◽  
pp. e0262246
Author(s):  
Ozra Tabatabaei-Malazy ◽  
Sahar Saeedi Moghaddam ◽  
Masoud Masinaei ◽  
Nazila Rezaei ◽  
Sahar Mohammadi Fateh ◽  
...  

Introduction The prevalence of metabolically healthy obesity (MHO) varies based on different criteria. We assessed the prevalence of MHO and metabolic unhealthiness based on body mass index (BMI) and their association with metabolic syndrome (MetS) in a nation-wide study. Methods Data were taken from the STEPs 2016 study, from 18,459 Iranians aged ≥25 years. Demographic, metabolic, and anthropometric data were collected. Subjects were stratified by BMI, metabolic unhealthiness, and having MetS. The latter was defined based on National Cholesterol Education Program Adult Treatment Panel III 2004 (NCEP ATP III), was then assessed. Results The prevalence of MHO and metabolic unhealthiness in obese subjects was 7.5% (about 3.6 million) and 18.3% (about 8.9 million), respectively. Most of the metabolic unhealthy individuals were female (53.5%) or urban residents (72.9%). Low physical activity was significantly and positively associated (Odds Ratio: 1.18, 95% CI: 1.04–1.35) with metabolic unhealthiness, while being a rural residence (0.83, 0.74–0.93), and having higher education (0.47, 0.39–0.58) significantly but negatively affected it. Dyslipidemia was the most frequent MetS component with a prevalence rate of 46.6% (42.1–51.1), 62.2% (60.8–63.6), 76.3% (75.1–77.5), and 83.4% (82.1–84.6) among underweight, normal weight, overweight and obese phenotypes, respectively. Conclusion BMI aside, an additional set of criteria such as metabolic markers should be taken into account to identify normal weight but metabolically unhealthy individuals. Given the highest prevalence of dyslipidemia among obese subjects, further interventions are required to raise public awareness, promote healthy lifestyles and establish lipid clinics.


2022 ◽  
Author(s):  
Sarang Jeong ◽  
Han Byul Jang ◽  
Hyo-Jin Kim ◽  
Hye-Ja Lee

Abstract BackgroundObesity is classified as metabolically unhealthy obesity (MUO) and metabolically healthy obesity (MHO). The current study aimed to screen for relationships and different potential metabolic biomarkers involved between MHO and MUO in adolescents.MethodsThe study included 148 obese adolescents aged between 14 and 16. The study participants were divided into MUO and MHO groups based on the age-specific adolescent metabolic syndrome (MetS) criteria of the International Diabetes Federation. The current study was conducted to investigate the clinical and metabolic differences (AbsoluteIDQ™ p180 kit) between adolescents in the MHO group and those in the MUO group. Multivariate analyses were conducted to investigate the metabolites as independent predictors for the odds ratio and the presence of the MetS in adolescents.ResultsThere were significant differences in the 3 acylcarnitines, 5 amino acids, glutamine/glutamate ratio, 3 biogenic amines, and 2 glycerophospholipids between the obese adolescents in the MUO group and those in the MHO group. Moreover, several metabolites were associated with the prevalence of MUO in adolescents. Additionally, several metabolites were inversely correlated with MHO in adolescents of the MUO group.ConclusionsWe observed that histidine, lysine, PCaaC34:1, and several clinical factors in adolescents of the MUO group were reverse correlated with the results in adolescents of the MHO group. In addition, the triglyceride-glucose index was related to MUO in adolescents, compared with the homeostasis model assessment of insulin resistance. Thus, the biomarkers found in this study have a potential to reflect the clinical outcomes of MUO in adolescents. These biomarkers will lead to a better understanding of MetS in obese adolescents.


Life ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 1350
Author(s):  
Mateusz Lejawa ◽  
Kamila Osadnik ◽  
Zenon Czuba ◽  
Tadeusz Osadnik ◽  
Natalia Pawlas

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.


Author(s):  
Jinyu Zhou ◽  
Ling Bai ◽  
Yangyang Dong ◽  
Rongrong Cai ◽  
Wenqing Ding

Abstract Objectives The association between metabolically healthy overweight/obesity (MHO) and inflammatory markers remains controversial. The aim of the present study was to describe the prevalence of different metabolic phenotypes and to examine the relationship of different metabolic phenotypes with inflammatory markers among Chinese children and adolescents. Methods The study included 1,125 children and adolescents aged 10–18 years using a cross-sectional survey, and all subjects were classified into four groups based on a combination of BMI and metabolic status. In addition, the inflammatory markers we measured were high-sensitivity C-reactive protein (hs-CRP), tumor necrosis factor-α (TNF-α), and interleukin-6 (IL-6). Results The prevalence of metabolically healthy with normal-weight (MHNW), MHO, metabolically unhealthy with normal-weight (MUNW), and metabolically unhealthy overweight/obesity (MUO) phenotypes was 38.76, 7.11, 38.67 and 15.47%, respectively. The results of logistic regression analysis showed that the MHO was associated with the z scores of hs-CRP in Chinese children and adolescents (OR=0.57, 95% CI: 0.39–0.83). Meanwhile, multivariate adjusted regression analysis showed that the relationship between hs-CRP and MHO among the overweight/obese was consistent with the results above, but among the normal-weight, only the highest quartile of TNF-α could increase the risk of MUNW (OR=1.65, 95% CI: 1.09–2.52). Conclusions MHO phenotypes were not common in Chinese children and adolescents. Individuals with MHO had a more beneficial hs-CRP profile than those with MUO.


2021 ◽  
Author(s):  
Zimin Song ◽  
Meng Gao ◽  
Jun Lv ◽  
Canqing Yu ◽  
Yu Guo ◽  
...  

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.


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