A Metabolically Unhealthy Phenotype Is Associated with &lt;b&gt;&lt;i&gt;ADIPOQ&lt;/i&gt;&lt;/b&gt; Genetic Variants and Lower Serum Adiponectin Levels

Torres-Castillo, Nathaly; Campos-Pérez, Wendy; Rodríguez-Echevarría, Roberto; Rodríguez-Reyes, Sarai Citlalic; Martínez-López, Érika

doi:10.1159/000510021

Cited by 7 publications

(3 citation statements)

References 39 publications

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“…Consistently, the community-based Framingham Third Generation Cohort found that in age‐ and sex‐adjusted models, adiponectin levels were significantly lower in the MHO group than in the MHNW group ( 54 ). However, data on 345 adults from western Mexico showed that lower serum adiponectin levels were only found in the metabolic unhealthy phenotype in subjects with excess weight (EW), and no differences were observed between the normal-weight metabolically healthy (NW-MH) and the EW-MH phenotype ( 55 ). A cross-sectional study included a total of 2486 white individuals, who have been stratified into metabolically healthy or unhealthy.…”

Section: Discussionmentioning

confidence: 99%

Early prediction of body composition parameters on metabolically unhealthy in the Chinese population via advanced machine learning

Deng,

Qiu,

Sun

et al. 2023

Front. Endocrinol.

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BackgroundMetabolic syndrome (Mets) is considered a global epidemic of the 21st century, predisposing to cardiometabolic diseases. This study aims to describe and compare the body composition profiles between metabolic healthy (MH) and metabolic unhealthy (MU) phenotype in normal and obesity population in China, and to explore the predictive ability of body composition indices to distinguish MU by generating machine learning algorithms.MethodsA cross-sectional study was conducted and the subjects who came to the hospital to receive a health examination were enrolled. Body composition was assessed using bioelectrical impedance analyser. A model generator with a gradient-boosting tree algorithm (LightGBM) combined with the SHapley Additive exPlanations method was adapted to train and interpret the model. Receiver-operating characteristic curves were used to analyze the predictive value.ResultsWe found the significant difference in body composition parameters between the metabolic healthy normal weight (MHNW), metabolic healthy obesity (MHO), metabolic unhealthy normal weight (MUNW) and metabolic unhealthy obesity (MUO) individuals, especially among the MHNW, MUNW and MUO phenotype. MHNW phenotype had significantly lower whole fat mass (FM), trunk FM and trunk free fat mass (FFM), and had significantly lower visceral fat areas compared to MUNW and MUO phenotype, respectively. The bioimpedance phase angle, waist-hip ratio (WHR) and free fat mass index (FFMI) were found to be remarkably lower in MHNW than in MUNW and MUO groups, and lower in MHO than in MUO group. For predictive analysis, the LightGBM-based model identified 32 status-predicting features for MUNW with MHNW group as the reference, MUO with MHO as the reference and MUO with MHNW as the reference, achieved high discriminative power, with area under the curve (AUC) values of 0.842 [0.658, 1.000] for MUNW vs. MHNW, 0.746 [0.599, 0.893] for MUO vs. MHO and 0.968 [0.968, 1.000] for MUO and MHNW, respectively. A 2-variable model was developed for more practical clinical applications. WHR > 0.92 and FFMI > 18.5 kg/m2 predict the increased risk of MU.ConclusionBody composition measurement and validation of this model could be a valuable approach for the early management and prevention of MU, whether in obese or normal population.

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Section: Discussionmentioning

confidence: 99%

Early prediction of body composition parameters on metabolically unhealthy in the Chinese population via advanced machine learning

Deng,

Qiu,

Sun

et al. 2023

Front. Endocrinol.

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“…In this work, we found ADIPOQ gene displayed remarkably differential both in RNA expression ( P = 7.65E−14) and m 6 A methylation ( P = 5.09E−05). ADIPOQ has been identified as candidate gene for the metabolic syndrome and T2DM by genome wide associated study [ 60 , 61 ]. Previous work also indicated ADIPOQ exhibited higher expression in both intramuscular fat and subcutaneous fat than LDM in the same swine population [ 23 ].…”

Section: Discussionmentioning

confidence: 99%

Profiling of N6-methyladenosine methylation in porcine longissimus dorsi muscle and unravelling the hub gene ADIPOQ promotes adipogenesis in an m6A-YTHDF1–dependent manner

Gong

Liu

et al. 2023

J Animal Sci Biotechnol

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Background Intramuscular fat (IMF) content is a critical indicator of pork quality, and abnormal IMF is also relevant to human disease as well as aging. Although N6-methyladenosine (m6A) RNA modification was recently found to regulate adipogenesis in porcine intramuscular fat, however, the underlying molecular mechanisms was still unclear. Results In this work, we collected 20 longissimus dorsi muscle samples with high (average 3.95%) or low IMF content (average 1.22%) from a unique heterogenous swine population for m6A sequencing (m6A-seq). We discovered 70 genes show both differential RNA expression and m6A modification from high and low IMF group, including ADIPOQ and SFRP1, two hub genes inferred through gene co-expression analysis. Particularly, we observed ADIPOQ, which contains three m6A modification sites within 3′ untranslated and protein coding region, could promote porcine intramuscular preadipocyte differentiation in an m6A-dependent manner. Furthermore, we found the YT521‑B homology domain family protein 1 (YTHDF1) could target and promote ADIPOQ mRNA translation. Conclusions Our study provided a comprehensive profiling of m6A methylation in porcine longissimus dorsi muscle and characterized the involvement of m6A epigenetic modification in the regulation of ADIPOQ mRNA on IMF deposition through an m6A-YTHDF1-dependent manner.

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“… 34 It has been proposed that adipose tissue as an endocrine organ produces different adipocytokines, which could be associated with wide range of metabolic diseases. 37 Therefore, the diagnosis of “obesity phenotypes” should remain an indication to initiate prevention, even though the individuals with certain phenotypes without DM at the time of diagnosis. 7 In recent years, studies have explored the effectiveness of weight loss by stratifying individuals according to obesity phenotypes.…”

Section: Discussionmentioning

confidence: 99%

Effect of Metabolic Health and Obesity Phenotype on Risk of Diabetes Mellitus: A Population-Based Longitudinal Study

Zhu¹,

Hu²,

Guo³

et al. 2021

DMSO

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Background: Epidemiologic evidence on body mass index (BMI)-metabolic status phenotypes and diabetes risk remains controversial, especially for metabolically healthy obesity (MHO). We aimed to examine the effect of metabolic health and obesity phenotype on diabetes risk in the Chinese population. Methods: A population-based cohort study was carried out. The baseline survey was conducted in 2017, with two follow-up visits in 2018 and 2020. Diabetes was defined based on the criteria of the World Health Organization. Robust generalized estimating equation models with a binary distribution using a log link and exchange structure were applied for the pooled analysis sample. Results: A total sample of 9623 observations was pooled for the longitudinal data analysis. The average follow-up time was 1.64 years per person and the overall incidence density of diabetes was 6.94% person-years. Decreased diabetes risk was found in metabolically healthy overweight phenotype (RR = 0.65; 95% CI = 0.47-0.90) and no significant associations were detected for the MHO individuals (RR = 0.99; 95% CI = 0.63-1.53) compared with those of metabolically healthy normal weight, in contrast to metabolically unhealthy normal weight (MU-NW) (RR = 1.81; 95% CI = 1.28-2.55), metabolically unhealthy overweight (MU-OW) (RR = 2.02; 95% CI = 1.57-2.61) and metabolically unhealthy obesity (MUO) (RR = 2.48; 95% CI = 1.89-3.26) phenotypes. Significant associations between BMI-metabolic status phenotypes and diabetes were found in both males and females. Conclusion:The MUO phenotype needs to be accorded much more importance. MU-NW and MU-OW are also important component for targeted prevention. Our findings can be targeted for optimizing preventive strategies to mitigate the obviously increased prevalence of diabetes.

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A Metabolically Unhealthy Phenotype Is Associated with <b><i>ADIPOQ</i></b> Genetic Variants and Lower Serum Adiponectin Levels

Cited by 7 publications

References 39 publications

Early prediction of body composition parameters on metabolically unhealthy in the Chinese population via advanced machine learning

Early prediction of body composition parameters on metabolically unhealthy in the Chinese population via advanced machine learning

Profiling of N6-methyladenosine methylation in porcine longissimus dorsi muscle and unravelling the hub gene ADIPOQ promotes adipogenesis in an m6A-YTHDF1–dependent manner

Effect of Metabolic Health and Obesity Phenotype on Risk of Diabetes Mellitus: A Population-Based Longitudinal Study

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