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Sensitivity and Specificity Improvement in Abdominal Obesity Diagnosis Using Cluster Analysis during Waist Circumference Cut-Off Point Selection.

Bermúdez V, Rojas J, Salazar J, Añez R, Toledo A, Bello L, Apruzzese V, González R, Chacín M, Cabrera M, Cano C, Velasco M, López-Miranda J - J Diabetes Res (2015)

Bottom Line: TSCA in the selection of the groups used in ROC curves construction proved to be an important tool, aiding in the detection of MOWN and MHO which cannot be identified with WC alone.The resulting WC cutpoints were <91.00 cm for women and <98.00 cm for men.Furthermore, anthropometry is insufficient to determine healthiness, and, biochemical analysis is needed to properly filter subjects during classification.

View Article: PubMed Central - PubMed

Affiliation: Endocrine and Metabolic Diseases Research Center, The University of Zulia, 20th Avenue, Maracaibo 4004, Venezuela.

ABSTRACT

Introduction: The purpose of this study was to analyze the influence of metabolic phenotypes during the construction of ROC curves for waist circumference (WC) cutpoint selection.

Materials and methods: A total of 1,902 subjects of both genders were selected from the Maracaibo City Metabolic Syndrome Prevalence Study database. Two-Step Cluster Analysis (TSCA) was applied to select metabolically healthy and sick men and women. ROC curves were constructed to determine WC cutoff points by gender.

Results: Through TSCA, metabolic phenotype predictive variables were selected: HOMA2-IR and HOMA2-βcell for women and HOMA2-IR, HOMA2-βcell, and TAG for men. Subjects were classified as healthy normal weight, metabolically obese normal weight, healthy and metabolically disturbed overweight, and healthy and metabolically disturbed obese. Final WC cutpoints were 91.50 cm for women (93.4% sensitivity, 93.7% specificity) and 98.15 cm for men (96% sensitivity, 99.5% specificity).

Conclusions: TSCA in the selection of the groups used in ROC curves construction proved to be an important tool, aiding in the detection of MOWN and MHO which cannot be identified with WC alone. The resulting WC cutpoints were <91.00 cm for women and <98.00 cm for men. Furthermore, anthropometry is insufficient to determine healthiness, and, biochemical analysis is needed to properly filter subjects during classification.

No MeSH data available.


Related in: MedlinePlus

Diagramshowing the two-stage clustering method to properly categorize the subjects into healthy and sick groups according to the selected predictors.
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Related In: Results  -  Collection


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fig1: Diagramshowing the two-stage clustering method to properly categorize the subjects into healthy and sick groups according to the selected predictors.

Mentions: Each BMI category was submitted independently to the cluster analysis, categorizing the subjects as metabolically healthy or sick; see Figure 1. The metabolic variables evaluated as possible metabolic predictors based on their physiological function and biological plausibility were MAP, TAG, total cholesterol, HDL-C, HOMA2-IR, HOMA2-βcell, HOMA2-S, fasting blood glucose, non-HDL-C cholesterol, TAG/HDL-C index, and hs-CRP; WC was excluded because it was the assessed dependent variable. The predictive strength of these variables was analyzed in accordance to cluster ability and quality, ranging from 0.0 to 1.0. The best metabolic predictive variables selected were (a) HOMA2-IR and HOMA2-βcell for normal weight women; (b) HOMA2-IR, HOMA2-βcell and TAG for normal weight men; (c) HOMA2-IR and HOMA2-βcell for overweight women; (d) HOMA2-IR, HOMA2-βcell, and TAG for overweight men; and (e) HOMA2-IR for male and female obese patients (Table 1).


Sensitivity and Specificity Improvement in Abdominal Obesity Diagnosis Using Cluster Analysis during Waist Circumference Cut-Off Point Selection.

Bermúdez V, Rojas J, Salazar J, Añez R, Toledo A, Bello L, Apruzzese V, González R, Chacín M, Cabrera M, Cano C, Velasco M, López-Miranda J - J Diabetes Res (2015)

Diagramshowing the two-stage clustering method to properly categorize the subjects into healthy and sick groups according to the selected predictors.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4402167&req=5

fig1: Diagramshowing the two-stage clustering method to properly categorize the subjects into healthy and sick groups according to the selected predictors.
Mentions: Each BMI category was submitted independently to the cluster analysis, categorizing the subjects as metabolically healthy or sick; see Figure 1. The metabolic variables evaluated as possible metabolic predictors based on their physiological function and biological plausibility were MAP, TAG, total cholesterol, HDL-C, HOMA2-IR, HOMA2-βcell, HOMA2-S, fasting blood glucose, non-HDL-C cholesterol, TAG/HDL-C index, and hs-CRP; WC was excluded because it was the assessed dependent variable. The predictive strength of these variables was analyzed in accordance to cluster ability and quality, ranging from 0.0 to 1.0. The best metabolic predictive variables selected were (a) HOMA2-IR and HOMA2-βcell for normal weight women; (b) HOMA2-IR, HOMA2-βcell and TAG for normal weight men; (c) HOMA2-IR and HOMA2-βcell for overweight women; (d) HOMA2-IR, HOMA2-βcell, and TAG for overweight men; and (e) HOMA2-IR for male and female obese patients (Table 1).

Bottom Line: TSCA in the selection of the groups used in ROC curves construction proved to be an important tool, aiding in the detection of MOWN and MHO which cannot be identified with WC alone.The resulting WC cutpoints were <91.00 cm for women and <98.00 cm for men.Furthermore, anthropometry is insufficient to determine healthiness, and, biochemical analysis is needed to properly filter subjects during classification.

View Article: PubMed Central - PubMed

Affiliation: Endocrine and Metabolic Diseases Research Center, The University of Zulia, 20th Avenue, Maracaibo 4004, Venezuela.

ABSTRACT

Introduction: The purpose of this study was to analyze the influence of metabolic phenotypes during the construction of ROC curves for waist circumference (WC) cutpoint selection.

Materials and methods: A total of 1,902 subjects of both genders were selected from the Maracaibo City Metabolic Syndrome Prevalence Study database. Two-Step Cluster Analysis (TSCA) was applied to select metabolically healthy and sick men and women. ROC curves were constructed to determine WC cutoff points by gender.

Results: Through TSCA, metabolic phenotype predictive variables were selected: HOMA2-IR and HOMA2-βcell for women and HOMA2-IR, HOMA2-βcell, and TAG for men. Subjects were classified as healthy normal weight, metabolically obese normal weight, healthy and metabolically disturbed overweight, and healthy and metabolically disturbed obese. Final WC cutpoints were 91.50 cm for women (93.4% sensitivity, 93.7% specificity) and 98.15 cm for men (96% sensitivity, 99.5% specificity).

Conclusions: TSCA in the selection of the groups used in ROC curves construction proved to be an important tool, aiding in the detection of MOWN and MHO which cannot be identified with WC alone. The resulting WC cutpoints were <91.00 cm for women and <98.00 cm for men. Furthermore, anthropometry is insufficient to determine healthiness, and, biochemical analysis is needed to properly filter subjects during classification.

No MeSH data available.


Related in: MedlinePlus