scholarly journals Superior Role of Waist Circumference to Body-Mass Index in the Prediction of Cardiometabolic Risk in Dyslipidemic Patients

2019 ◽  
pp. 931-938
Author(s):  
Ľ. Cibičková ◽  
K. Langová ◽  
H. Vaverková ◽  
J. Lukeš ◽  
N. Cibiček
Keyword(s):  
Insulin Resistance ◽  
Metabolic Syndrome ◽  
Body Mass Index ◽  
Waist Circumference ◽  
Lipid Profile ◽  
Body Mass ◽  
Cardiometabolic Risk ◽  
Predictive Values ◽  
High Bmi

Coronary risk evaluation by conventional factors (age, gender, smoking, blood pressure and cholesterol) may further be specified by facets of the metabolic syndrome, namely insulin resistance, hypertriglyceridemia and obesity. Although obesity is usually defined as elevated body mass index (BMI), recent data indicate a superior role of waist circumference or hypertri-glyceridemic waist (HTGW) over BMI in the assessment of cardiometabolic risk. In dyslipidemic patients, the specific contributions of risky waist, HTGW or BMI have not been evaluated as yet. 686 dyslipidemic subjects (322 males and 364 females) were enrolled into a cross-sectional study. In each subject basic antropometry (i.e. waist circumference, HTGW, BMI) and laboratory parameters of lipid profile and insulin resistance were determined. Cardiometabolic risk was given by fulfilling the criteria (harmonized definition) of metabolic syndrome. The significance of risky waist, HTGW and BMI were assessed by comparing the respective predictive values for the presence of metabolic syndrome. Dyslipidemic patients with risky waist, HTGW or high BMI have a more atherogenic lipid profile and higher insulin resistance compared to those without risky waist, HTGW or high BMI. Risky waist is stronger predictor of metabolic syndrome (PPV 66 %, NPV 90 %) and thus posesa greater cardiometabolic risk than higher BMI per se does (PPV 42 %, NPV 97 %). The contribution of triglycerides (i.e. HTGW) to these predictive values is marginal (PPV 66 %, NPV 92 %). The present results highlight the superior role of waist circumference as a screening tool over BMI for the evaluation of cardiometabolic risk in dyslipidemic subjects. HTGW brings little additional benefit in risk stratification. Lower BMI proved to be optimal for identifying the subjects with inferior risk.

scholarly journals The effects of metformin plus sulfonylurea therapy on clinical features of the metabolic syndrome

2010 ◽  
Vol 63 (9-10) ◽  
pp. 611-615 ◽  
Author(s):  
Branka Koprivica ◽  
Teodora Beljic-Zivkovic ◽  
Tatjana Ille
Keyword(s):  
Insulin Resistance ◽  
Blood Pressure ◽  
Type 2 Diabetes ◽  
Metabolic Syndrome ◽  
Body Mass Index ◽  
Waist Circumference ◽  
Glycemic Control ◽  
Body Mass ◽  
Combined Therapy

Introduction. Insulin resistance is a well-known leading factor in the development of metabolic syndrome. The aim of this study was to evaluate metabolic effects of metformin added to sulfonylurea in unsuccessfully treated type 2 diabetic patients with metabolic syndrome. Material and methods. A group of thirty subjects, with type 2 diabetes, secondary sulfonylurea failure and metabolic syndrome were administered the combined therapy of sulfonylurea plus metformin for six months. Metformin 2000 mg/d was added to previously used sulfonylurea agent in maximum daily dose. Antihypertensive and hypolipemic therapy was not changed. The following parameters were assessed at the beginning and after six months of therapy: glycemic control, body mass index, waist circumference, blood pressure, triglycerides, total cholesterol and its fractions, homeostatic models for evaluation of insulin resistance and secretion (HOMA R, HOMA B) and C- peptide. Results. Glycemic control was significantly improved after six months of the combined therapy: (fasting 7.89 vs. 10.61 mmol/l. p<0.01; postprandial 11.12 vs. 12.61 mmol/l. p<0.01, p<0.01; glycosylated hemoglobin 6.81 vs. 8.83%. p<0.01). the body mass index and waist circumference were significantly lower (26.7 vs. 27.8 kg/m2, p<0.01 and 99.7 vs. 101.4 cm for men, p<0.01; 87.2 vs. 88.5 for women, p<0.01). Fasting plasma triglycerides decreased from 3.37 to 2.45 mmol/l (p<0.001) and HOMA R from 7.04 to 5.23 (p<0.001). No treatment effects were observed on blood pressure, cholesterol, and residual insulin secretion. Conclusion. Administration of metformin in type 2 diabetes with metabolic syndrome decreased cardiovascular risk factors by reducing glycemia, triglycerides, BMI, central obesity and insulin resistance.

scholarly journals Insulin resistance in relation to clinical, endocrine and metabolic profile of infertile women with polycystic ovary syndrome

2021 ◽  
Vol 14 (1) ◽  
pp. 1-6
Author(s):  
Shakeela Ishrat ◽  
Marufa Hossain ◽  
Subrata Kumar Biswas
Keyword(s):  
Insulin Resistance ◽  
Metabolic Syndrome ◽  
Body Mass Index ◽  
Waist Circumference ◽  
Polycystic Ovary Syndrome ◽  
Body Mass ◽  
Polycystic Ovary ◽  
Ovary Syndrome ◽  
Infertile Women ◽  
Significant Difference

The objective of this study is to explore how hyperinsulinemia and insulin resistance relate to the clinical, endocrine and metabolic factors in the infertile women with polycystic ovary syndrome. This study was conducted on 121 consecutive infertile women with polycystic ovary syndrome attending the Infertility unit from January 2017 to December 2017. They were divided into two groups: insulin resistant and insulin sensitive. There was significant difference in body mass index and waist circumference between the two groups. Serum lipids were not associated with insulin resistance. Hyperinsulinemia was significantly associated with metabolic syndrome. Reducing body mass index and waist circumference may improve insulin resistance in infertile women with polycystic ovary syndrome. Screening the infertile women with polycystic ovary syndrome for hyperinsulinemia and insulin resistance and subsequent counseling is recommended to address the long-term risks of metabolic syndrome.

scholarly journals Correlations of Anthropometric and Calculation-Modified Anthropometric Measurements with Cardiometabolic Risk Factors in Korean Adults: A National Survey-Based Study

2021 ◽  
Author(s):  
Kihong Hong
Keyword(s):  
Risk Factors ◽  
Metabolic Syndrome ◽  
Body Mass Index ◽  
Waist Circumference ◽  
Body Mass ◽  
Central Obesity ◽  
Cardiometabolic Risk ◽  
Anthropometric Measurements ◽  
Cardiometabolic Risk Factors ◽  
Male And Female

Abstract Background and Aims: There are studies that show NC measurements are associated with central obesity and upper body fat distribution and are strongly associated with metabolic markers. Recently, studies have been conducted regarding neck circumference (NC) as a novel index to screen for obesity and cardiometabolic risk factors. Here, we investigated various anthropometric measurements and their correlations with cardiometabolic risk factors, pulmonary function test (PFT) results and metabolic syndrome among the adult Korean population. Methods: This study was based on data acquired from the 8th edition (2019) of the Korea National Health and Nutrition Examination Survey (KNHANES), consisting of survey data on smoking and alcohol consumption, cardiometabolic risk factors, PFT results, metabolic syndrome profile and baseline characteristics such as age, gender and blood pressure. Continuous variables were analyzed by independent t-test, while categorical variables were analyzed using the Rao-Scott chi-square test Precision-recall (PR) plots were used to assess the diagnostic value of anthropometric measurements for cardiometabolic risk profiles such as insulin resistance, impaired glucose tolerance, dyslipidemia, hypertension, central obesity and PFT results. Area under the curve (AUC) was used to evaluate the predictive ability of the anthropometric measurements and calculation-modified anthropometric measurements (CMAMs) such as NC(neck circumference) devided by BMI(body mass index) and WC(waist circumference) and BMI devided by WC at different preset thresholds. Calculation was used to distinguish each anthropometric measurements aside from common features they may share. Results: A total of 3525study subjects aged over 40 years were included in the study the mean age for male and female were 56.8 and 58.5 respectively, percentage of male and female was 42% and 58% respectively. PR plots of anthropometric measurements and the metabolic syndrome profile indicated that in the presence of 3 metabolic syndrome criteria, waist circumference (WC) and body mass index (BMI) had the highest AUCs 0.62 and 0.587 respectively, in the presence of 4 metabolic syndrome criteria, waist circumference (WC) and body mass index (BMI) had the highest AUCs 0.342 and 0.317 respectively whereas in the presence of more than 5 metabolic syndrome criteria, NC and WC had the highest AUCs 0.09 and 0.083 respectively.Conclusion: This study showed that NC is correlated with metabolic syndrome. Additionally, each anthropometric measurement was uniquely correlated with specific cardiometabolic risk factor and differed according to sex. In such cases, applications in smartphones could be used to calculate for cardiometabolic risk assessments. Also it may be necessary to screen for metabolic syndrome in people whose NC is over 39.9cm for males and 34.5cm for females.

scholarly journals Changes in Body Composition and Cardiometabolic Risk Factors in Relation to the Reduction in Body Mass Index in Adolescents with Obesity

2021 ◽  
Author(s):  
Teodoro Durá-Travé ◽  
Fidel Gallinas-Victoriano ◽  
María Malumbres-Chacón ◽  
María Urretavizcaya-Martinez ◽  
Paula Moreno-González ◽  
...  
Keyword(s):  
Risk Factors ◽  
Insulin Resistance ◽  
Blood Pressure ◽  
Body Mass Index ◽  
Body Composition ◽  
Lipid Profile ◽  
Body Mass ◽  
Cardiometabolic Risk ◽  
Cardiometabolic Risk Factors ◽  
Blood Testing

Abstract Background. There are controversial data in relation to the reduction in BMI-SDS needed to improve adiposity in the pediatric population with obesity. The aim of this work is to determine the minimum variation in body mass index standard deviation score (BMI-SDS) required to improve the values of adiposity markers and cardiometabolic risk factors in growing adolescents with obesity. Methods. A longitudinal study consisting in clinical evaluation (waist circumference, waist-to-height ratio and fat mass index, and blood pressure) and blood testing (insulin resistance and lipid profile) was conducted in 350 adolescents with obesity (152 boys and 198 girls), aged 10.2–14.3 years, that went through a combined intervention (12 months). Results. A decrease in SDS-BMI ≤0.5 was not associated with any significant improvement in the clinical features and blood testing recorded. A decrease in BMI-SDS >0.5, and especially if >1.0, was linked to a significant improvement in adiposity markers. A decrease in BMI-SDS >0.5 was associated with a significant improvement in insulin resistance, and a decrease in BMI-SDS >1.0 was associated with a significant decrease in the percentage of patients who showed high values of systolic blood pressure, HOMA-IR and lipid profile Conclusions. Improvement in body composition, insulin resistance and lipid profile can be observed with reductions in BMI-SDS of ≥0.5 in obese adolescents, while extended benefits are obtained by losing at least 1.0 BMI-SDS.

scholarly journals Combination of sleep duration, TV time and body mass index is associated with cardiometabolic risk moderated by age in youth

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Ana P. Sehn ◽  
Anelise R. Gaya ◽  
Caroline Brand ◽  
Arieli F. Dias ◽  
Roya Kelishadi ◽  
...  
Keyword(s):  
Body Mass Index ◽  
Risk Factor ◽  
Sleep Duration ◽  
Body Mass ◽  
Cardiometabolic Risk ◽  
High Systolic Blood Pressure ◽  
Television Time ◽  
The Relationship ◽  
Moderating Role

AbstractObjectivesThe combination of sleep duration, television (TV) time and body mass index (BMI) may be related to the alteration of cardiometabolic risk. However, there are few studies that use these variables grouped, and showing the moderating role of age. This study aimed to verify if the combination of sleep duration, TV time and BMI is associated with cardiometabolic risk and the moderating role of age in this relationship in youth.MethodsCross-sectional study conducted with 1411 adolescents (611 male), aged 10–17 years. Sleep duration, TV time and BMI were assessed and grouped into eight categories. Cardiometabolic risk was assessed by a continuous metabolic risk score, including the following variables: low HDL-cholesterol, elevated triglycerides, dysglycemia, high systolic blood pressure, high waist circumference and low cardiorespiratory fitness. Generalized linear models were used to test moderation of age in the relationship between the eight categories of sleep duration/television time/BMI with cardiometabolic risk.ResultsCardiometabolic risk factor showed association with all overweight or obesity independent of sleep time and TV time. Age moderated the relationship between sleep duration/television time/BMI with cardiometabolic risk. This association was stronger in younger adolescents (11 and 13 years), indicating that individuals with inadequate sleep, prolonged TV time and overweight/obesity present higher cardiometabolic risk values when compared to 15-year-old adolescents.ConclusionOverweight/obesity, independently of sleep duration and TV time, is the main risk factor for cardiometabolic disorders in adolescence. When moderated by age, younger adolescents that presented the combination of risk factors had higher cardiometabolic risk.

scholarly journals Effects of Triphala on Lipid and Glucose Profiles and Anthropometric Parameters: A Systematic Review

2021 ◽  
Vol 26 ◽  
pp. 2515690X2110110
Author(s):  
Wiraphol Phimarn ◽  
Bunleu Sungthong ◽  
Hiroyuki Itabe
Keyword(s):  
Body Mass Index ◽  
Body Weight ◽  
Blood Glucose ◽  
Waist Circumference ◽  
Lipid Profile ◽  
Body Mass ◽  
The Body ◽  
Current Evidence ◽  
Diabetic Patients ◽  
Anthropometric Parameters

Aim. The efficacy of triphala on lipid profile, blood glucose and anthropometric parameters and its safety were assessed. Methods. Databases such as PubMed, ScienceDirect, Web of Science, and Thai Library Integrated System (ThaiLIS) were systematically searched to review current evidence of randomized controlled trials (RCT) on triphala. RCTs investigating the safety and efficacy of triphala on lipid profile, blood glucose and anthropometric parameters were included. Study selection, data extraction, and quality assessment were performed independently by 2 authors. Results. Twelve studies on a total of 749 patients were included. The triphala-treated groups showed significantly reduced low-density lipoprotein-cholesterol, total cholesterol and triglyceride in 6 studies. Five RCTs demonstrated triphala-treated groups led to statistically significant decrease in body weight, body mass index and waist circumference of obese patients. Moreover, triphala significantly decreased fasting blood glucose level in diabetic patients but not in people without diabetes. No serious adverse event associated with triphala was reported during treatment. Conclusions. This review summarized a current evidence to show triphala might improve the lipid profile, blood glucose, the body weight, body mass index and waist circumference under certain conditions. However, large well-designed RCTs are required to confirm this conclusion.

scholarly journals Body mass index and waist circumference are better predictors of insulin resistance than total body fat percentage in middle-aged and elderly Taiwanese

Medicine ◽  
2017 ◽  
Vol 96 (39) ◽  
pp. e8126 ◽  
Author(s):  
Yiu-Hua Cheng ◽  
Yu-Chung Tsao ◽  
I-Shiang Tzeng ◽  
Hai-Hua Chuang ◽  
Wen-Cheng Li ◽  
...  
Keyword(s):  
Insulin Resistance ◽  
Body Mass Index ◽  
Waist Circumference ◽  
Body Mass ◽  
Body Fat ◽  
Body Fat Percentage ◽  
Middle Aged ◽  
Fat Percentage ◽  
Total Body Fat ◽  
Total Body

scholarly journals Body Mass Index, Waist Circumference, and the Clustering of Cardiometabolic Risk Factors in Early Childhood

2015 ◽  
Vol 30 (2) ◽  
pp. 160-170 ◽  
Author(s):  
Laura N. Anderson ◽  
Gerald Lebovic ◽  
Jill Hamilton ◽  
Anthony J. Hanley ◽  
Brian W. McCrindle ◽  
...  
Keyword(s):  
Risk Factors ◽  
Body Mass Index ◽  
Early Childhood ◽  
Waist Circumference ◽  
Body Mass ◽  
Cardiometabolic Risk ◽  
Cardiometabolic Risk Factors
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