Research Article

Metabolic Alterations in Mexican Young Adults: Sensitivity and Specificity Approaches between Anthropometric Parameters and Biochemical Markers

Roberto Reyes-Márquez 1, Beatriz Adriana Aguilar-Galarza 1 , Miriam Aracely Anaya-Loyola 1 , Ulisses Moreno-Celis 1, Juana Elizabeth Elton-Puente 1, Miguel Lloret 2, Carlos F. Sosa-Ferreyra 2,* , Teresa García-Gasca 1,*
1Faculty of Natural Sciences. Autonomous University of Queretaro. P. O. Box 184, Queretaro 76010, Qro., Mexico
2School of Medicine. Autonomous University of Queretaro. P. O. Box 184, Querétaro 76010, Qro. México

*Corresponding author:

Teresa García-Gasca, Faculty of Natural Sciences. Autonomous University of Queretaro, Email:
Carlos F. Sosa-Ferreyra. School of Medicine. Autonomous University of Queretaro, Email:


Non-communicable diseases, Anthropometric parameters, Dyslipidemias, Sensitivity, Specificity, Young adults


Previous studies have shown important prevalence for low-HDL, hypertriglyceridemia and insulin resistance in young adults between 17 to 25 years old, including normal BMI subjects.


Our goal was to determine the relationship between anthropometric or body composition parameters with biochemical markers for with non-communicable diseases (NCD), including sensitivity and specificity of anthropometric markers in apparently healthy young adults

Materials and methods:

Women (n=636) and men (n=624) between 18-30 years old voluntarily participated, anthropometric parameters (weight, height, body mass index (BMI), waist circumference (WC), hip circumference (HC), waist-hip ratio (WHR), waist-height index (WHI)), and body fat percentage (BFP) were taken. Biochemical markers (glucose, total cholesterol (TC), high density lipoproteins (HDL), low density lipoproteins (LDL), and triglycerides (TG)) were determined.


Metabolic alterations prevalence in women and men were WHI 55.3% and 61%; fat percentage 28.3% and 31.5%; HDL 40.5% and 21.9%; LDL 24% and 23%, TG 10% and 20%, respectively. BFP showed the highest sensitivity for biochemical alterations in women and men.


Significant prevalence of alterations in young adults was found, despite of BMI or BFP, high sensitivity was achieved using BFP but important differences were found by sex that must be taken in count in the clinical diagnosis.

The prevalence of non-communicable diseases (NCD) is associated with several metabolic disorders. Most frequently reported alterations are hyperglycemia, insulin resistance (IR), changes on lipid metabolism, coagulation disorders and nonalcoholic hepatic steatosis. These alterations are related to risk factors such as different degrees of body composition disorders, inappropriate lifestyles, bad eating habits, physical inactivity, addictions, increased blood pressure, age, sex, genetic factors and metabolic syndrome [1-3]. In America, NCD are responsible for 75% deaths and 34% of them are premature deaths (between 30 and 69 years of age), resulting in increased health care costs [4, 5].

Dyslipidemias and altered lipid metabolism are the main risk factors, which represent approximately 50% of the risk for developing cardiovascular disease. In fact, in 1984, Castelli proposed the atherogenic index, representing the ratio of total cholesterol and high-density lipoproteins (TC/HDL) [6-7] and the National Cholesterol Education Program has updated the basis for dyslipidemia treatment (Adult Treatment Panel or ATP) [8].

Metabolic obesity is related with visceral fat accumulation, adipocyte hypertrophy and hyperplasia, chronic inflammation and lipotoxicity [9-11] and it could be present in both thin and fat people. It has been observed that about 25% of the normal weight population has metabolic disorders, including young people between 18 to 35 years old, which are called “thin outside-fat inside”. This refers to individuals with low or normal body weight but with an increase in visceral fat, hyperglycemia, impaired carbohydrate metabolism, IR, high levels of TG, low HDL, high blood pressure and low-grade inflammation. These subjects are undetectable using only anthropometric measures and, usually, they are not early diagnosed and treated [12-14]. Additionally, it has been disclosed that about 30% of overweight individuals are not at risk of metabolic disorders. These subjects are called "fat outside-thin inside" and they include young people, mainly women, who have less visceral fat. These subjects are detected by anthropometry but not necessarily require immediate treatment [15-17].

Several tools are currently used to assess the health and nutritional status through anthropometric and body composition parameters as well as biochemical markers. Body mass index (BMI) is one of the most used, however, it cannot differentiate the percentages of fat and lean mass and it is not always associated with metabolic risk. Additionally, the increase in waist circumference (WC), waist-hip ratio (WHR), waist-height index (WHI), body fat percentage (BFP) and the presence of dyslipidemia are also associated to NCD risk [18-21]. Therefore, our goal was to study the relationship between anthropometric and body composition parameters respect to biochemical blood markers in order to find the best indicators of metabolic risk through the estimation of sensitivity and specificity in apparently healthy young individuals.

2.1 Study group

It was a descriptive, observational and cross-sectional study where 1260 participants between 18 and 30 years of age, of both sexes, participated voluntarily by signing the informed consent. The study was approved by the Bioethics Committee of the Natural Sciences Faculty-UAQ, following the guidelines of the World Medical Association declaration of Helsinki, which pointed out in detail the ethical principles for medical research involving human subjects [22]. Inclusion criteria were: age between 18 and 30 years, free of previous illnesses (cardiovascular, diabetes, cancer, hypothyroidism), free of any medical treatment, non-pregnant nor lactating, free of polycystic ovary syndrome, not practicing strenuous exercise and not missing a limb.

2.2 Anthropometric and body composition measurements

Participants fasted for 12 h before taking the blood samples, a medical record was filled up and a physical examination was performed, including anthropometric and body composition measurements (weight, height, waist circumference (WC), hip circumference (HC) and fat percentage). All these measurements were taken following standard procedures of the World Health Organization [23]. Height was measured using a stadiometer (Holtain Limited, Crosswell, Crymych, and Pembs). Waist circumference was measured standing with both feet together and arms relaxed at the sides by placing a measure tape (SECA) on a line located at the midpoint between the upper iliac crest and the costal lower margin, at the end of a normal exhalation. The hip circumference was measured standing with both feet together and arms relaxed at the sides by placing a measure tape (SECA) on the most prominent part of the buttocks [23]. BMI was calculated according to the formula of Quetelet and classified for diagnosis according to WHO cut-off values [23, 24]. Weight and body composition determination were performed using multi frequency bioelectrical impedance equipment (Body Composition Analyzed X-Scan Plus II; Jawson Medical Co., Ltd.). Blood pressure was measured idle and after a rest of 15 min, sitting with back and arms supported.

2.3 Biochemical markers determination

Two blood samples were taken to analyse the biochemical markers; one of 7.2 mL using BD Vacutainer Plus plastic tubes containing anticoagulant EDTAK2 and another one of 5-mL using a Vacutainer SST II tube with gel. The samples were processed in a 1400 Cel-Dyn analyzer (Abbott, USA), Mindray BS 120 (Medical International Limited, China) and multiparametric SPINTROL (SPINREACT S.A. /S.A.U. Girona, Spain). LDL cholesterol for individuals presenting TG <400 mg/dL was calculated using the Fridel wald formula: LDL = TC- (TG/5 + HDL); and for individuals presenting TG >400 mg/dL, LDL cholesterol was determined by a colorimetric enzymatic method in a spectrophotometer (Thermo Spectronic Genesis 20, Madison, U.S.A.). Cut-off values used for this study were based on those recommended by WHO, National Institutes of Health (NIH), Treatment of High Blood Cholesterol in Adults (ATP III), Mexican Society of Hypertension (SMH) International Diabetes Federation (IDF), the Latin American Diabetes Association (ALAD), Guidelines for BMI and Gallager, considering the ethnic and regional characteristics [1, 20, 21, 25-28].

2.4 Statistical analysis

The variables obtained were compiled into a database in MS-Excel 2010 for further analysis with SPSS version 18. Descriptive statistics of anthropometric parameters, body composition and metabolic variables were calculated. Differences between sexes were calculated by student t-test (p ≤ 0.05) and subsequent analyses were performed by sex based on the results. Correlation analyses within and between groups were performed. Multicollinearity was avoided in order not to include redundant variables in the multiple regression models. Sensitivity and specificity of anthropometric variables with regard to the biochemical and metabolic markers were calculated.

3.1 Descriptive data and prevalence of metabolic alterations

Table 1shows the descriptive data of the studied population by sex. Significant differences between averages were found. For men, the highest levels were systolic and diastolic blood pressure, WC, WHR, weight, hemoglobin, glucose, creatinine, serum albumin, TG, TG/HDL and TC/HDL ratios. Meanwhile, for women, the highest levels were fat percentage, HDL, and HDL/TC index. Figure 1A shows the prevalence of alterations by sex where the highest alterations were WHI, with 55% for women and 61% for men, fat percentage with 28.3% for women and 31.57% for men, followed by BMI. In case of biochemical markers, low levels of HDL with 40.57%, was the highest prevalence for women and for men the highest prevalence was high LDL levels with 24%. According to the lipid indexes, the most altered ones were TG/HDL with 31.6% for women and 50% for men, followed by HDL/TC. Figures 1B and 1C show the prevalence of metabolic alterations in women and men with normal BMI or BFP. It is seen that the most affected parameters in thin people were low HDL, high LDL and TG/HDL index. In the case of women, neither normal BMI nor normal BFP classifications showed differences, however, in the case of men the highest prevalence’s were found in subjects with normal BMI.

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Figure 1. Prevalence for metabolic alterations in young adults. Young women (n=636) and men (n=624) participated in the study after fasting for 12 h. A) Total metabolic alterations in the studied population. B) Metabolic alterations in women with normal BMI or normal BFP. C) Metabolic alterations in men with normal BMI or normal BFP. BP, blood pressure; BMI, body mass index; BFP, body fat percentage; WC, waist circumference; WHR, waist-hip ratio; WHI, waist-height index; Gluc, glucose, TC, total cholesterol; TG, triglycerides; HDL, high density lipoproteins; LDL, low density lipoproteins. Indexes for TG/HDL, TC/HDL, LDL/HDL, HDL/TC.

Table 1. General characteristics of the studied population by sex.


Women (636)


Men (624)




S D ±


S D ±

P value

Age, years






Systolic blood pressure, mmHg






Diastolic blood pressure, mmHg






Waist circumference (WC), cm






Hip circumference (HC), cm






Waist-hip ratio (WHR)






Height, cm






Waist-height index (WHI)






Weight, Kg






Body mass index (BMI), Kg/m2






Body Fat percentage (BFP), %






Hemoglobin, g/dL






Glucose, mg/dL






Creatinine, mg/dL






Albumin, g/dL






Total Cholesterol (TC), mg/dL






Triglycerides (TG), mg/dL






HDL mg/dL






LDL mg/dL






TG/HDL index






TC/HDL index






LDL/HDL index






HDL/TC index






NSD: No significant difference

3.2 Correlations analysis.

Table 2 shows significant correlations (R> 0.11) between variables. Regarding biochemical markers and anthropometric parameters, greater significance was found for TG-BMI and TG-WHI for women, while for men were TG-WHI and TG-WC. The most significant correlations between lipid indexes and anthropometric parameters were TG/HDL-BMI and TG/HDL-WHI for women and men, respectively. Based on this, the sensitivities and specificities of the different anthropometric variables with biochemical markers and lipid indexes were estimated by sex.

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3.3 Sensitivity analysis.

Sensitivity analysis identifies non-healthy people; in our study corresponds to subjects who present both, anthropometric and metabolic alterations (Figure 2). Tabulated data show the specific sensitivity for each parameter and graphical data show the mean value for each case. BFP showed the highest sensitivity to detect subjects with metabolic disorders, mainly glucose, TC, HDL, LDL and TG for both sexes, with a mean of 95% for women and 75% for men. The sensitivity of anthropometric parameters to detect subjects with altered lipid indexes showed values lower than 33% in women; while in men BFP was the most sensitive indicator (67%). These results suggest that both, fat distribution (WHI) and percentage (BFP) are related with metabolic alterations although other anthropometric parameters were into normal range, in such case known as “thin outside-fat inside”. It is important to highlight that BFP can detect a high number of non-healthy people in spite of their anthropometric characteristics.

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Figure 2. Sensitivity analysis between anthropometric and body composition parameters respect to biochemical markers. Tabulated data show the individual values for the sensitivity analysis. Graphical data show the mean values for the anthropometric or body composition parameters respect to biochemical markers or lipid indices.

3.4 Specificity analysis.

Specificity identifies healthy people. In our study, this corresponds to subjects with normal anthropometric parameters and normal metabolic markers (Figure 3). Tabulated data show the specific sensitivity for each parameter and the graphical data show the mean value for each case. BFP showed the lowest level for the identification of healthy people, mostly for women with 7% and 42% for men. These results suggest that BFP is a hard parameter for detecting healthy people, which mean that it can avoid false positives. In this sense, 93% of women and 58% of men were identified as no healthy people, many of them without metabolic alterations but with high fat percentage, which have been called "fat outside-thin inside". Regarding the specificity of anthropometric parameters to detect normal lipid indexes, values were similar between women and men. BFP showed a value of 42% for women and 37% for men while all other anthropometric parameters were between 70-87% for women and 60-82% for men.

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Figure 3. Specificity analysis between anthropometric and body composition parameters respect to biochemical markers. Tabulated data show the individual values for the specificity analysis. Graphical data show the mean values for the anthropometric or body composition parameters respect to biochemical markers or lipid indices.

Systemic arterial hypertension in this population showed similar prevalence than that reported in studies with young Mexicans (10%), this alteration is a disease and a risk factor associated to the development of NCD [29, 30] therefore special attention must be taken in young people. A greater number of altered parameters for men were observed. Overweight and obesity prevalence’s were similar to those found in some other groups of young Mexican populations [30, 31]. These alterations are closely related to consumption of high-caloric diets, sugary carbonated beverages, excessive intake of alcohol or tobacco and physical inactivity [3].

Although BMI is one of the most globally accepted anthropometric parameter to classify subjects, it does not differentiate the proportion of fat and lean mass and is not related with metabolic obesity; therefore, it shows a poor sensitivity to diagnose the metabolic status. Other anthropometric parameters (WC, WHR and WHI) are better indicators of the amount and distribution of body fat and their alteration has been associated with risk factors for the development of NCD. In our study, high fat percentage was between 22-29%. The central fat distribution measured by WC and WHR was higher for women. WHI was high in approximately 60% of the studied subjects and showed a greater association with NCD like hyperglycemia, IR and dyslipidemia, which increase the predictive ability for cardio metabolic risk [19, 20, and 32]. Among biochemical markers, the average of TC and LDL were similar for both sexes. Glucose and TG were higher in men, while HDL was lower in a higher proportion of women. These data were also reported in other studies with Mexican youth [33].

Lipid indexes provide a good predictive value for metabolic alterations, even if the lipid profile is found in an appropriate range. In our study, TG/HDL and HDL/TC ratios were higher, which is in accordance with the prevalence of high TG and low HDL depending on the sex. These ratios do not correspond to those reported in other studies in adults, where prevalence was higher for TC/HDL and LDL/HDL [6, 7, 33]. These differences may be related with the subject’s age, so our results suggest that earlier alterations can be found by using TG/HDL and HDL/TC ratios in young people.

Taking into account the BMI, 66.5% of the men showed normal weight as well as 75.31% of the women, however, of these, 28.52% and 35.39%, respectively, resulted with high BFP. In contrast, 68.42% of the men and 71.69% of the women presented normal BFP and of these, 2.51% and 3.77%, respectively, were overweight or obese according to BMI. This shows that BMI does not accurately detect people with possible metabolic obesity, while the BFP leaves a smaller percentage of people with overweight or obesity that is, people with high BMI but normal BFP. When determining the prevalence of metabolic alterations in women with normal BMI or BFP (Figure 1B), high prevalence’s of low HDL, high LDL, and TG/HDL were observed, where values were similar when classified using BMI or BFP, suggesting that BMI could have a similar predictive ability as BFP in young women. In the case of men (Figure 1C), the highest prevalence’s for metabolic alterations were also observed in the same parameters but, in this case, they were higher for subjects with normal BMI, suggesting that this anthropometric parameter is not capable of detecting thin people with metabolic obesity in young men.

Sensitivity detects non-healthy subjects and our results showed the highest sensitivity for BFP, detecting a greater percentage of subjects with both, anthropometric and metabolic alterations. That suggests a direct relation between fat percentage alterations and metabolic disorders. A significant percentage of subjects with metabolic alterations but with normal anthropometry ("thin outside-fat inside") were not detected by using the anthropometric parameters. In example, the sensitivity of BFP for glucose level in women was of 94.11% in comparison with BMI of 17.65%, which means that glucose alteration will not be detected in a higher percent of subjects with normal BMI rather than with BFP. If we take the average sensitivity of the different anthropometric parameters, BFP showed 94.67% while BMI was of 30.86%. The sensitivity average for anthropometric parameters in ascendant order in women was BMI

Specificity detects healthy people, which in our study are subjects that present normal anthropometric and metabolic parameters. For both sexes, the BFP showed the lowest specificity, that is, the average for biochemical parameters in women was of 6.87% and for men of 42.04%. The specificity average for biochemical markers for women in ascendant order was BFP

Our results are in accordance with reports showing different lipid profile, as well as different fat distribution between sexes, where women presented low levels of HDL and higher percentage of fat in hips, while men present hypertriglyceridemia and a higher percentage of abdominal fat [15-17]. Therefore, anthropometric parameters will not have the same ability to predict metabolic alterations between women and men. This fact has to be taken in count in order to improve strategies for clinical diagnosis.

A significant prevalence for both, anthropometric and biochemical alterations, was observed in Mexican young adults despite their BMI or their BFP. However, these alterations are not necessarily the same, since WHI for both sexes, HDL for women and TG/HDL for men were the most remarkable prevalent alterations. The sensitivity analysis showed that BFP presented the best approach in order to detect biochemical alterations in both sexes and for lipid indexes alterations in men, showing the greatest accuracy to detect people with altered anthropometric and biochemical markers. WHI showed the highest prevalence for NCD risk and the second in sensitivity for altered biochemical markers for women. In terms of specificity, BFP presented the highest capacity to detect subjects with altered anthropometric parameters without biochemical alterations for both sexes.

Anthropometric parameters showed differential ability to detect metabolic alterations, been the best WHI for women and BMI for men. The results suggest that BFP and WHI could be used as parameters for the detection of metabolic alterations in young women better than BMI. BFP must be used as the most important parameter in order to detect people with metabolic alterations in spite of their anthropometric status, but also healthy people with anthropometric alterations, an important factor to consider for public health strategies. The results show significant differences between sexes that must be taken in count in the clinical diagnosis. Likewise, our results show differences between young people and adults, suggesting that metabolic alterations might be detected using different parameters.

The authors declare no conflict of interest.
This study was supported by to UAQ SU-Salud, FOPER and PFCE-2017 programs.

We are grateful for the technical assistance support of the Nutrition Clinic (Natural Sciences Faculty-UAQ). We are also grateful to MSc. María de Lourdes Maqueda Rascón for the language revision support.

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Published: 21 June 2017

Reviewed By : Dr.Antonio M. Esquinas, Dr. Nieves González Gómez,


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