Cut-off points of anthropometric markers associated with hypertension and diabetes in Peru: Demographic and Health Survey 2018

Hernández-Vasquéz, Akram; Azañedo, Diego; Vargas-Fernández, Rodrigo; Aparco, Juan Pablo; Chaparro, Raúl; Santero, Marilina

doi:10.1017/s1368980020004036

Cited by 8 publications

(24 citation statements)

References 44 publications

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“…Conicity index has been associated with an excellent predictive capacity of metabolic syndrome in adolescents (Cristine Silva et al, 2020) and in older people (Ceolin et al, 2019). Furthermore, CI has demonstrated to be a good predictor of future diabetes (Andrade et al, 2016;Wang et al, 2019;Hernández-Vásquez et al, 2020) and hypertension (Andrade et al, 2016;Hernández-Vásquez et al, 2020). Thus, CI can be considered a good indicator of high cardiovascular risk (Tonding et al, 2014;Motamed et al, 2015), although it was not associated with coronary heart disease incidence in the population from the Framingham Heart Study (Kim et al, 2000).…”

Section: Associated Health Aspectsmentioning

confidence: 99%

Anthropometric Indicators as a Tool for Diagnosis of Obesity and Other Health Risk Factors: A Literature Review

et al. 2021

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Obesity is characterized by the accumulation of an excessive amount of fat mass (FM) in the adipose tissue, subcutaneous, or inside certain organs. The risk does not lie so much in the amount of fat accumulated as in its distribution. Abdominal obesity (central or visceral) is an important risk factor for cardiovascular diseases, diabetes, and cancer, having an important role in the so-called metabolic syndrome. Therefore, it is necessary to prevent, detect, and appropriately treat obesity. The diagnosis is based on anthropometric indices that have been associated with adiposity and its distribution. Indices themselves, or a combination of some of them, conform to a big picture with different values to establish risk. Anthropometric indices can be used for risk identification, intervention, or impact evaluation on nutritional status or health; therefore, they will be called anthropometric health indicators (AHIs). We have found 17 AHIs that can be obtained or estimated from 3D human shapes, being a noninvasive alternative compared to X-ray-based systems, and more accessible than high-cost equipment. A literature review has been conducted to analyze the following information for each indicator: definition; main calculation or obtaining methods used; health aspects associated with the indicator (among others, obesity, metabolic syndrome, or diabetes); criteria to classify the population by means of percentiles or cutoff points, and based on variables such as sex, age, ethnicity, or geographic area, and limitations.

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Section: Associated Health Aspectsmentioning

confidence: 99%

Anthropometric Indicators as a Tool for Diagnosis of Obesity and Other Health Risk Factors: A Literature Review

et al. 2021

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“…Nevertheless, southern European populations were under-represented in these meta-analyses, as one study from Italy and one from Turkey were included, and neither meta-analysis examined the performance of CI. CI performed well in predicting HTN in some populations [11,12], but fared poorly in our analysis, registering the lowest AUCs in both sexes. This conforms with earlier cross-sectional analyses [43,44] where performance of CI in terms of AUC appeared inferior to BMI, WHtR and WC.…”

Section: Discussionmentioning

confidence: 71%

“…The optimal cut-offs for BMI identified in this study (27.01 among females and 25.64 kg/m 2 among males) were lower than the 30 kg/m 2 WHO cut-off for defining obesity [17], and those reported for Peruvian [12] and Jordanian adults [47]; but higher than those reported in studies from China [37,50], South Korea [51] and Nigeria [44]. The optimal WC cut-off among males in this analysis (86.25 cm) was lower than the 94 cm International Diabetes Federation (IDF) cut-off defining central adiposity.…”

Section: Discussionmentioning

confidence: 78%

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Relationship of Anthropometric Indicators of General and Abdominal Obesity with Hypertension and Their Predictive Performance among Albanians: A Nationwide Cross-Sectional Study

et al. 2021

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Anthropometric indicators of general and abdominal obesity can predict cardiovascular disease outcomes. Their performance in predicting hypertension (HTN) varies across populations. We aimed to analyze the relationship of body mass index (BMI), waist circumference (WC), waist-to-height ratio (WHtR) and conicity index (CI) with HTN, to examine their predictive performance and to determine their optimal cut-offs in a nationally representative sample of Albanians aged 15–59 years (n = 20,635). Logistic regression models were fitted and sex-specific receiver-operating characteristic (ROC) curves were constructed. The indicators were positively associated with HTN. Sex modified the relationships, as associations appeared significantly stronger among females than males in the highest categories of the indicators. The area under ROC curves (AUCs) for BMI were 0.729 (95% confidence interval (CI): 0.720–0.738) among females and 0.648 (95% CI: 0.633–0.663) among males, and AUCs for WHtR were 0.725 (95% CI: 0.716–0.734) among females and 0.637 (95% CI: 0.622–0.652) among males. However, the AUCs for BMI and WHtR did not differ significantly among females (p = 0.279) and males (p = 0.227). BMI outperformed WC and CI in both sexes. The optimal BMI cut-offs were 27.0 kg/m2 among females and 25.6 kg/m2 among males, and that for WHtR were 0.53 among females and 0.54 among males. BMI and WHtR demonstrated similar discriminatory power, and the identified cut-offs may inform initiatives for structured HTN screening in Albania.

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“…Different studies have evaluated anthropometric indices in order to identify the best predictor of cardiometabolic risk. The weight–height ratio (WHtR) has shown better predictive power of cardiometabolic risk in different populations, including Peru, than other indices such as BMI or WC [ 16 , 17 , 18 ]. Likewise, changes in body composition patterns have been reported in different populations due to the COVID-19 pandemic [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

The Association between Altitude and Waist–Height Ratio in Peruvian Adults: A Cross-Sectional Data Analysis of a Population-Based Survey

Hernández-Vasquéz

Azañedo

2022

IJERPH

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To evaluate the association between altitude and cardiometabolic risk calculated with the weight–height ratio (WHtR) in the Peruvian adult population via the cross-sectional data analysis of the Peruvian Demographic and Health Survey 2021. A total of 26,117 adults from 18 to 64 years of age were included in the analysis. The dependent variable was cardiometabolic risk, defined as “Yes” if the WHtR was ≥0.5 and “No” if the WHtR was <0.5. Exposure was altitude of residence categorized as: <1500 meters above sea level (masl); 1500 to 2499 masl; 2500 to 3499 masl; and ≥3500 masl. Crude and adjusted Poisson regression models were used to calculate prevalence ratios (PR) with 95% confidence intervals (CI). The mean WHtR in the population was 0.59 (standard deviation: 0.08), and 87.6% (95% CI: 86.9–88.2) were classified as at risk. After adjusting for sex, age, education level, well-being index, and area of residence, living at altitudes between 2500 and 3499 masl (aPR: 0.98; 95% CI: 0.96–1.00) and ≥3500 masl (aPR: 0.95; 95% CI: 0.93–0.97) were associated with lower cardiometabolic risk in comparison with living at <1500 masl. An inverse association was identified between living at a higher altitude and the proportion of cardiometabolic risk in the Peruvian adult population. However, at least 8 out of 10 people were identified as at risk in all categories of altitude.

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Cut-off points of anthropometric markers associated with hypertension and diabetes in Peru: Demographic and Health Survey 2018

Cited by 8 publications

References 44 publications

Anthropometric Indicators as a Tool for Diagnosis of Obesity and Other Health Risk Factors: A Literature Review

Anthropometric Indicators as a Tool for Diagnosis of Obesity and Other Health Risk Factors: A Literature Review

Relationship of Anthropometric Indicators of General and Abdominal Obesity with Hypertension and Their Predictive Performance among Albanians: A Nationwide Cross-Sectional Study

The Association between Altitude and Waist–Height Ratio in Peruvian Adults: A Cross-Sectional Data Analysis of a Population-Based Survey

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