Is normal body mass index a good indicator of metabolic health in Azar cohort population?
Mohammd Hossein SomiZeinab NikniazAlireza OstadrahimiAmir Taher Eftekhar SadatElnaz FaramarziPublished in: Journal of cardiovascular and thoracic research (2019)
Introduction: Metabolic syndrome (Mets) has become most important public health problem in the world. We examined the association between Mets and different cardiometabolic phenotype in Azar cohort population. Methods: In the present study, the data of 13099 subjects who participated in Azar cohort study were cross-sectionally analyzed. Mets was defined according to the National Cholesterol Education Program's Adult Treatment Panel III report (ATPIII) criteria. Participants were categorized into four cardiometabolic phenotypes including metabolically healthy Lean (MHL), metabolically unhealthy lean (MUHL), metabolically healthy Obese (MHO), metabolically unhealthy obese (MUHO) according to BMI cut-off point (25 kg/m2 ), and the presence of Mets. Results: Totally, the prevalence of Mets was 33.20% with the higher prevalence in women (40.1%). About 46.7% of participants were MHO and 1.6% of them were MHL. In both genders, MUHL had the highest prevalence of hyperglycemia, hypertrigliceridemia, hypo-HDL-cholestrolemia and Frahmingham 10-year CVD risk. In both MUHL and MUHO phenotypes, hypertriglyceridemia (OR: 31.97 [95% CI: 22.31, 45.81] and OR: 20.28 [95% CI: 17.32, 23.75]) and hypo-HDL cholestrolemia (OR:27.97 [95% CI: 17.35, 45.09] and OR:11.0 [95% CI: 9.62, 12.58]) are the strongest predictor of incidence of Mets. Also, the results of multinominal regression analyses indicated that in all cardiometabolic phenotypes, Framingham 10- year CVD risks had the lowest power for predicting of Mets incidence. Conclusion: Based on the results, in addition to obese individuals, multiple metabolic abnormalities were seen in normal weight individuals and these subjects are even at higher risk of developing Mets compared with metabolically obese individuals. So, it seems that decision on initiation of lifestyle interventions should not be only based on the BMI; rather metabolic status seems to be even more important.
Keyphrases
- metabolic syndrome
- body mass index
- weight loss
- risk factors
- public health
- adipose tissue
- physical activity
- weight gain
- quality improvement
- type diabetes
- healthcare
- bariatric surgery
- obese patients
- polycystic ovary syndrome
- uric acid
- cardiovascular disease
- machine learning
- decision making
- young adults
- skeletal muscle
- data analysis
- health promotion
- postmenopausal women