The association between a dietary habits score and the risk of metabolic syndrome: A cohort study

Sarebanhassanabadi, Mohammadtaghi; Mirhosseini, Seyed Jalil; Mirzaei, Masoud; Namayandeh, Seyedeh Mahdieh; Soltani, Mohammad Hossein; Salehi‐Abargouei, Amin

doi:10.1016/j.clnu.2019.02.005

Cited by 11 publications

(16 citation statements)

References 47 publications

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“…In addition, it has been reported that diet, exercise habits and nutritional status all affect the blood lipids levels and composition of subjects [26][27][28][29]. The focus of present study is to screen out FFAs with significant differences between NW and OB individuals of different nationalities, and these different FFAs may be a potential biomarker for predicting metabolic obesity and related diseases.…”

Section: Discussionmentioning

confidence: 99%

Serum FFAs profile analysis of Normal weight and obesity individuals of Han and Uygur nationalities in China

Qiu

Zhu

et al. 2020

Lipids Health Dis

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Background: Han and Uygur are the two main nationalities living in Xinjiang, China. There are significant differences in the incidence of metabolic diseases for two nationalities, but the specific reasons are not clear. Obesity is an important risk factor for the development of metabolic syndrome, which may be closely related to the increase of serum free fatty acids (FFAs) content. This study aims to use metabolomics to compare the changes of serum FFAs profiles between normal weight (NW) and obese (OB) individuals of two nationalities, screening out the differential FFAs, predicting and evaluating their relationship with diseases. Methods: Thirty-four kinds of FFAs in serum were detected by ultra-high-pressure liquid chromatography-mass spectrometry (UHPLC-MS) and distinctions in FFAs profiles were evaluated using a metabolomics method while Receiver operating characteristics (ROC) and logistic regression models were used to explore FFAs significant for diagnosing obesity and obesity-associated comorbidities.

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Section: Discussionmentioning

confidence: 99%

Serum FFAs profile analysis of Normal weight and obesity individuals of Han and Uygur nationalities in China

Qiu

Zhu

et al. 2020

Lipids Health Dis

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“…Despite moderate levels of physical activity, the WC and BP increased slightly, and the HDL-C and TG levels increased significantly, suggesting challenges related to dietary habits. Dietary habits are associated with hyperglycemia, hypertriglyceridemia, hypertension, low HDL cholesterol levels, and abdominal obesity [ 35 , 36 ]. In the non-improvement group of the MetS participants, the five risk factors did not change or changed slightly.…”

Section: Discussionmentioning

confidence: 99%

Effective Prevention and Management Tools for Metabolic Syndrome Based on Digital Health-Based Lifestyle Interventions Using Healthcare Devices

Lee

Kim

et al. 2022

Diagnostics

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Digital health-based lifestyle interventions (e.g., mobile applications, short messaging service, wearable devices, social media, and interactive websites) are widely used to manage metabolic syndrome (MetS). This study aimed to confirm the utility of self-care for prevention or management of MetS. We recruited 106 participants with one or more MetS risk factors from December 2019 to September 2020. Participants were provided five healthcare devices and applications. Characteristics were compared at baseline and follow-up to examine changes in risk factors, engagement, persistence, and physical activity (analyzed through device use frequency and lifestyle interventions performed). Participants with 1–2 MetS risk factors showed statistically significant reductions in waist circumference (WC) and blood pressure (BP). Participants with ≥3 MetS risk factors showed statistically significant reductions in risk factors including weight, body mass index, WC, BP, and fasting blood sugar (FBS). The prevention and improvement groups used more healthcare devices than the other groups. Smartwatch was the most frequently used device (5 times/week), and physical activity logged more than 7000 steps/week. WC, BP, and FBS of the improvement group were reduced by more than 40%. Based on engagement, persistence, and physical activity, digital health-based lifestyle interventions could be helpful for MetS prevention and management.

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“…The YHHP is a population-based epidemiological study evaluating cardiovascular diseases and metabolic disorders. [26].…”

Section: Study Populationmentioning

confidence: 99%

“…In YHHP, 100 clusters, and 20 families from each cluster were defined, and one adult (aged 20 to 74 years) from each family was randomly selected for participation and evaluation in first phase in 2005-2006(n=2000, men=1000, women=1000). [26] After ten years of follow-up (2015-2016), the participants were re-invited to Yazd cardiovascular research centre (YCRC) to be re-evaluated. [26] Ethical approval…”

Section: Study Populationmentioning

confidence: 99%

An innovative model for predicting coronary heart disease using TyG-index: A machine learning-based cohort study

Mirjalili,

Soltani,

HeidaryMeibodi

et al. 2023

Preprint

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Background Various coronary heart disease (CHD) predictive models have been developed for predicting CHD incidence, but none of them has optimal predictive value. Although these models consider diabetes as an important CHD risk factor, they did not consider insulin resistance or Triglyceride. Methods Two-thousand participants of a community-based Iranian population, aged 20–74 years, were investigated with a mean follow-up of 9.9 years (range: 7.6 to 12.2). The association between TyG-index (a logarithmised combination of fasting blood glucose and triglyceride that demonstrates insulin resistance) and CHD was investigated using multivariate Cox proportional hazard models. Diabetes was substituted for TyG-index in CHD prediction models developed using machine learning. CHD-predicting TyG-index cut-off points were calculated. Results The incidence of CHD was 14.5%.As compared to the lowest quartile of TyG-index, the fourth quartile had a fully adjusted hazard ratio of 2.32 (CI: 1.16–4.68, p-trend 0.04). In order to predict coronary heart disease, TyG-index > 8.42 had the highest negative predictive value. Machine learning models that predicted CHD based on TyG-index performed significantly better than those based on diabetes. TyG-index was not only more important than diabetes in prediction of CHD; it was the most important factor in machine learning models. Conclusion TyG-index can be used in clinical practice and predictive models as a highly valuable index for predicting and preventing CHD.

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The association between a dietary habits score and the risk of metabolic syndrome: A cohort study

Cited by 11 publications

References 47 publications

Serum FFAs profile analysis of Normal weight and obesity individuals of Han and Uygur nationalities in China

Serum FFAs profile analysis of Normal weight and obesity individuals of Han and Uygur nationalities in China

Effective Prevention and Management Tools for Metabolic Syndrome Based on Digital Health-Based Lifestyle Interventions Using Healthcare Devices

An innovative model for predicting coronary heart disease using TyG-index: A machine learning-based cohort study

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