Predictors of incident obesity phenotype in nonobese healthy adults

Eftekharzadeh, Anita; Asghari, Golaleh; Serahati, Sara; Hosseinpanah, Farhad; Azizi, Ali; Barzin, Maryam; Mirmiran, Parvin; Azizi, Fereidoun

doi:10.1111/eci.12743

Cited by 15 publications

(15 citation statements)

References 22 publications

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“…Obesity is not a uniform condition [8,9,10]. Generally, obesity is categorized into four phenotypes according to metabolic and anthropometric status: metabolically unhealthy obesity (MUO), metabolically unhealthy normal weight, metabolically healthy obesity (MHO), and metabolically healthy normal weight (MHNW) [9]. Individuals with MHO have been shown to have a lower risk of CVD and mortality compared with those with MUO [11,12].…”

Section: Introductionmentioning

confidence: 99%

The Relationship between Metabolically Healthy Obesity and the Risk of Cardiovascular Disease: A Systematic Review and Meta-Analysis

Yeh

Chen

Tsai

et al. 2019

JCM

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Cardiovascular disease (CVD) risk in individuals with metabolically healthy obesity (MHO) is unclear. We searched databases from inception to May 2019. Data were pooled using a random effects model. Newcastle-Ottawa Scale assessment was performed. Primary and secondary outcomes were CVD risk and all-cause mortality. Forty-three studies involving 4,822,205 cases were included. The median percentage of females, age and duration of follow-up was 52%, 49.9 years and 10.6 years, respectively. The mean Newcastle-Ottawa Scale score of the articles was 7.9 ± 1.0. Compared to individuals with a metabolically healthy normal weight, individuals with MHO had higher adjusted risk of CVD and all-cause mortality. We identified a significant linear dose-response relationship between body mass index (BMI) and CVD risk among metabolically healthy individuals (p < 0.001); every unit increase in BMI increased the CVD risk. Multivariate meta-regression analysis showed that an increased proportion of women and age resulted in the risk of CVD affected by MHO reduction (p = 0.014, p = 0.030, respectively). Age and sex explained the observed heterogeneity and reported the adjusted R2. MHO resulted in a significantly increased risk for CVD; therefore, long-term weight loss should be encouraged.

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Section: Introductionmentioning

confidence: 99%

The Relationship between Metabolically Healthy Obesity and the Risk of Cardiovascular Disease: A Systematic Review and Meta-Analysis

Yeh

Chen

Tsai

et al. 2019

JCM

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“…The most common cause of metabolic syndrome has been presumed to be abdominal obesity [11]. In recent years, however, the need to consider nonobese metabolic syndrome has received attention [12]. Unlike obese metabolic syndrome, for which body weight reductions can decrease the risk and which has received much attention in health care, nonobese metabolic syndrome has no common cause, such as obesity.…”

Section: Introductionmentioning

confidence: 99%

Metabolic Syndrome Prediction Using Machine Learning Models with Genetic and Clinical Information from a Nonobese Healthy Population

et al. 2018

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The prevalence of metabolic syndrome (MS) in the nonobese population is not low. However, the identification and risk mitigation of MS are not easy in this population. We aimed to develop an MS prediction model using genetic and clinical factors of nonobese Koreans through machine learning methods. A prediction model for MS was designed for a nonobese population using clinical and genetic polymorphism information with five machine learning algorithms, including naïve Bayes classification (NB). The analysis was performed in two stages (training and test sets). Model A was designed with only clinical information (age, sex, body mass index, smoking status, alcohol consumption status, and exercise status), and for model B, genetic information (for 10 polymorphisms) was added to model A. Of the 7,502 nonobese participants, 647 (8.6%) had MS. In the test set analysis, for the maximum sensitivity criterion, NB showed the highest sensitivity: 0.38 for model A and 0.42 for model B. The specificity of NB was 0.79 for model A and 0.80 for model B. In a comparison of the performances of models A and B by NB, model B (area under the receiver operating characteristic curve [AUC] = 0.69, clinical and genetic information input) showed better performance than model A (AUC = 0.65, clinical information only input). We designed a prediction model for MS in a nonobese population using clinical and genetic information. With this model, we might convince nonobese MS individuals to undergo health checks and adopt behaviors associated with a preventive lifestyle.

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“…Eftekharzade et al 36 found that among all suggested predictors of metabolic outcome, female gender played the strongest role. Also, in our study all genes were significant only in women.…”

Section: Discussionmentioning

confidence: 99%

Genetic markers and continuity of healthy metabolic status: Tehran cardio-metabolic genetic study (TCGS)

Ahangar

Javanrouh

Daneshpour

et al. 2020

Sci Rep

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obese individuals can be categorized as "healthy obese" (MHo) and "unhealthy obese" (MUo) based on the presence or absence of metabolic abnormality. this study sets out to assess potential genetic causes behind persistence of healthy metabolic status in individuals categorized as "healthy obese". This study was conducted in the framework of the Tehran cardio-metabolic genetic study (TCGS). 766 MHO subjects at the start of the study followed up 15 years for occurrence of metabolic unhealthy status. these two groups (persistent MHo, MUo) were compared regarding the presence or absence of 16 single nucleotide polymorphisms (SNPs) identified as being associated with obesity phenotype in previous studies. We used logistic regression model for assessing the association between MHo/ MUO with candidate SNPs. By the end of the follow up, 206 (27%) were categorized as the persistent MHO and 560 (73%) as MUO groups. Considering interaction effect between some SNP and sex, a sex stratification analysis was applied. When the analysis was performed by gender, rs1121980 associated with a decrease, and rs7903146 with an increase in the likelihood of persistent MHO individuals. Another analysis was separately performed on postmenopausal women from both groups; it showed that rs13107325 was associated with an increase in the likelihood of persistent MHO status in this subgroup of woman. In all cases, the markers had dominant inheritance. This findings suggest that the expression of some genetic markers are associated with persistence of healthy metabolic status, in female obese individuals. Obesity and overweight have a clear association with long-term morbidity and mortality 1,2. There is a wide range of heterogeneity among obese individuals with the combination of obesity and metabolic status causing a wide spectrum of obesity phenotypes 3,4. Prevalence of "healthy obese" is reported to be between 9 and 34% in different populations depending on various definitions of metabolic healthy status 5-8. Whether "healthy obese" has a benign course is controversial and different studies have reported varying results 9-12. Some studies have shown that this obesity phenotype is a dynamic entity and in cohort studies up to 50% of individuals who were "healthy obese" turned into "unhealthy obese" at the end of the follow-up period 5-8. Some studies have assessed the predictors of changes in metabolic health in healthy obese individuals. Achilike et al. 13 suggested that lipid profile and insulin resistance predict the metabolic status in obesity. Appleton et al. 6 reported that younger age and lower waist circumference (WC) at baseline of obese individuals were significantly associated with maintaining the metabolic healthy state in a longitudinal follow-up. In addition, a study on Tehranian healthy abdominal obese individuals showed that baseline hypertriglyceridemia, low HDL-C, and HOMA-IR were independent predictors of metabolic changes in this population 5. However, it has been known that obesity and the resulting metabolic abnormality a...

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Predictors of incident obesity phenotype in nonobese healthy adults

Abstract: While baseline BMI and WC were detrimental for developing MHO vs. MUHO, gender was the strongest predictor of incident obesity phenotype in healthy nonobese individuals.

Cited by 15 publications

References 22 publications

The Relationship between Metabolically Healthy Obesity and the Risk of Cardiovascular Disease: A Systematic Review and Meta-Analysis

The Relationship between Metabolically Healthy Obesity and the Risk of Cardiovascular Disease: A Systematic Review and Meta-Analysis

Metabolic Syndrome Prediction Using Machine Learning Models with Genetic and Clinical Information from a Nonobese Healthy Population

Genetic markers and continuity of healthy metabolic status: Tehran cardio-metabolic genetic study (TCGS)

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