The cut-off values of dietary energy intake for determining metabolic syndrome in hemodialysis patients: A clinical cross-sectional study

Duong, Tuyen Van; Wong, Te-Chih; Chen, Hsi Hsien; Chen, Tzen‐Wen; Chen, Tso-Hsiao; Hsu, Yung‐Ho; Peng, Sheng-Jeng; Kuo, Ko‐Lin; Wang, Chi-Sin; Tseng, I-Hsin; Feng, Yiwei; Chang, Tai-Yue; Su, Chien‐Tien; Yang, Shumei

doi:10.1371/journal.pone.0193742

Cited by 6 publications

(4 citation statements)

References 38 publications

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“…In addition, has been observed association between high energy intake, high carbohydrate, fat and the MetS [13,23,24]. A study by Trevino et al with 90 days of intervention and giving a high-calorie diet to rats demonstrated, a high-calorie diet has led to the MetS in rats [25].…”

Section: Discussionmentioning

confidence: 99%

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Application of Machine Learning Algorithm to Predicting Metabolic Syndrome in Adults

Mirzaei

Mostadaei

Najafi

et al. 2021

Preprint

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Background The prevalence of the metabolic syndrome (MetS) is increasing worldwide. Early detection of the MetS by valid and available indicators can help prevent, control and reduce its complications. This study aims to identify of most important anthropometric, biochemical and nutritional indices for predicting MetS. Methods This study conducted on 9,602 participants from baseline data of the Ravansar Non-Communicable Disease (RaNCD) cohort study including of adults aged 35–65 years. The reference model for MetS was considered according to International Diabetes Federation (IDF) criteria. We used a wrapper algorithm and area under ROC curve (AUC) for selection and assessing most important predictors of MetS. Results The importance value (IV) for components of the models for prediction of MetS was confirmed, before implementing the models. Identified model with components of age, waist circumference (WC), body mass index (BMI), fasting blood sugar (FBS), systolic-diastolic blood pressure (SBP-DBP), triglyceride (TG), hip circumference (HC) and AUC of 0.893 (95% CI: 0.884–0.902) for men and 0.867 (95% CI: 0.853–0.881) for women was a strongest model for predictive of MetS risk. The AUC (95% CI) for non-invasive model was 0.756 (0.746–0.766) in total population has a good predictive power for MetS risk with components of age, WC, BMI, SBP, DBP. Conclusion This study demonstrated that in addition to aggressive models, models non-invasive (anthropometric indices, blood pressure and energy intake) can be also a good and convenience screening tool to predict the MetS. The models, in addition to the application of clinical diagnosis, can be widely used in researches on large populations.

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Section: Discussionmentioning

confidence: 99%

“…Improving the diet with the approach of reducing in ammatory foods decrease the MetS [12]. A multi-center study in Taiwan found that energy intake could be a good predictor of the MetS, and optimal cut-off points set the energy intake for predicting the MetS at 26.2 kcal/kg/day [13].…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning Algorithm to Predicting Metabolic Syndrome in Adults

Mirzaei

Mostadaei

Najafi

et al. 2021

Preprint

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“…Volume overload or edema closely linked with other clinical instability [20]. Therefore, patients with evidence of edema were excluded in the current study and in previous studies [21–23].…”

Section: Methodsmentioning

confidence: 99%

Insulin Resistance and Cardiovascular Risks in Different Groups of Hemodialysis Patients: A Multicenter Study

Duong

Shih

Wong

et al. 2019

BioMed Research International

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Background. To investigate the association between insulin resistance (IR) and cardiovascular disease (CVD) risks among hemodialysis patients. Methods. We conducted a cross-sectional study between 2013 and 2017, on 384 hemodialysis patients from seven hospital-based-dialysis centers. HOMA-IR is classified according to median value. The CVD risks were defined by the K/DOQI Guidelines. Logistic regression analysis was used. Results. Patients’ age was 60.9 ± 11.8, 58.1% men, and 40.3% overweight/obese. The median of HOMA-IR was 5.4, 82.8% high systolic blood pressure, and 85.7% hyperhomocysteinemia. In multivariate analysis, IR was significantly associated with higher odds of low high-density lipoprotein cholesterol, high triglyceride, and impaired fasting glucose in groups of normal weight, overweight/obese, nondiabetes, diabetes, and overall sample. IR linked with elevated high-sensitive C-reactive protein in normal weight patients (odd ratio, OR=2.21, 95% confidence interval, 1.16-4.22, p < .05), with hypoalbuminemia in normal weight patients (OR=8.31, 95% CI, 2.35-29.37, p < .01), in nondiabetes patients (OR=6.59, 95% CI, 1.81-23.95, p < .01), and overall sample (OR=3.07, 1.51-6.23, p < .01). Conclusions. The level of IR and prevalence of CVD risks were high in hemodialysis patients. IR was independently associated with CVD risks.

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“…The Healthy Eating Index for HemoDialysis patients (HEI-HD) was developed in 2014 [ 11 ]. It was constructed based on the healthy eating pattern of HD patients [ 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ]. HEI-HD contains 16 diet items.…”

Section: Introductionmentioning

confidence: 99%

Effect of Different Nutritional Education Based on Healthy Eating Index for HemoDialysis Patients on Dietary Quality and Muscle Mass

et al. 2022

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Background: Hemodialysis patients are at high risk of muscle loss as a result of aging and disease, and combined with inadequate dietary intake. The Healthy Eating Index for HemoDialysis patients (HEI-HD) was developed to assess the dietary quality of hemodialysis patients. The purposes of this study were to examine the effects of different nutritional education models using HEI-HD-based education on dietary quality and muscle mass in hemodialysis patients. Methods: A quasi-experimental study was conducted from May 2019 to April 2021, with four groups, including no course for patients and nurses (Non-C), course for nurses (CN), course for patients (CP), and course for patients and nurses (CPN). The courses were delivered by registered dietitians. The data of 94 patients were collected and analyzed at baseline, after 2 months of intervention, and 2 months follow-up, including demographics, body composition, 3-day dietary records, and hemodialysis dietary knowledge. The HEI-HD index score was calculated. Results: Patients aged 58.3 ± 10.1 years. The dietary quality change in the CPN group was improved as compared with the Non-C group (−3.4 ± 9.5 vs. 3.0 ± 5.5, 0.04). The skeletal muscle mass of the Non-C group at intervention was also significantly lower than baseline, but the CPN group was not. Conclusions: The HEI-HD-based nutritional education for both patients and nurses showed a positive effect on improving the dietary quality and maintaining muscle mass in hemodialysis patients.

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The cut-off values of dietary energy intake for determining metabolic syndrome in hemodialysis patients: A clinical cross-sectional study

Cited by 6 publications

References 38 publications

Application of Machine Learning Algorithm to Predicting Metabolic Syndrome in Adults

Application of Machine Learning Algorithm to Predicting Metabolic Syndrome in Adults

Insulin Resistance and Cardiovascular Risks in Different Groups of Hemodialysis Patients: A Multicenter Study

Effect of Different Nutritional Education Based on Healthy Eating Index for HemoDialysis Patients on Dietary Quality and Muscle Mass

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