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Research Article

Biochemical and clinical characterization of metabolic phenotypes: a cross-sectional study from Maracaibo city, Venezuela

[version 1; peer review: 1 approved with reservations]
Valmore Bermudez
https://orcid.org/0000-0003-1880-8887
1,2, Joselyn Rojas2,3, Juan Salazar2, [...] Maria Sofia Martinez2, Luis Carlos Olivar2, Maria Jose Calvo2, Andres Mindiola4, Roberto Añez
https://orcid.org/0000-0001-6363-2767
2, Sandra Wilches-Duran1, Marcos Cerda1, Modesto Graterol1, Rosemily Graterol1, Juan Diego Hernandez1, Carlos Garicano1, Manuel Velasco5
Valmore Bermudez
https://orcid.org/0000-0003-1880-8887
1,2, Joselyn Rojas2,3, [...] Juan Salazar2, Maria Sofia Martinez2, Luis Carlos Olivar2, Maria Jose Calvo2, Andres Mindiola4, Roberto Añez
https://orcid.org/0000-0001-6363-2767
2, Sandra Wilches-Duran1, Marcos Cerda1, Modesto Graterol1, Rosemily Graterol1, Juan Diego Hernandez1, Carlos Garicano1, Manuel Velasco5
PUBLISHED 27 Feb 2018
Author details Author details
OPEN PEER REVIEW
REVIEWER STATUS

Abstract

Background: In 1980, Reuben Andresen observed that in certain individuals, obesity did not increase mortality, introducing an atypical phenotype called “healthy obese”. Other studies reported that 10-15 % of lean individuals presented insulin resistance, hyperglycemia and dyslipidemia. The objective of this study was to evaluate biochemical and clinical characteristics of metabolic phenotypes in Maracaibo city.
Methods: A descriptive, cross-sectional study with a randomized multistage sampling was performed including 1226 non diabetic individuals from both sexes. For phenotype definition, the subjects were first classified according to their BMI into Normal-Weight, Overweight and Obese; then divided in metabolically healthy and unhealthy using a two-step analysis cluster. To evaluate the relationship with coronary risk, a multiple logistic regression model was performed.
Results: In the studied population, 5.2% (n=64) corresponded to unhealthy lean subjects, and 17.4% (n=217) to healthy obese subjects. Metabolically unhealthy normal-weight (MUNW) phenotype was found in males in 53.3% in contrast to 51.3% of metabolically unhealthy obese (MUO) phenotype found in females. An association between metabolically unhealthy phenotypes and a higher risk of a coronary event was found, especially for obese individuals (MHO: OR=1.85 CI95%: 1.11-3.09; p=0.02 and MUO: OR=2.09 CI95%: 1.34-3.28; p<0.01).
Conclusion: Individuals with atypical metabolic phenotypes exist in Maracaibo city. Related factors may include insulin resistance, basal glucose levels, and triglycerides levels. Lastly, cardiovascular risk exhibited by healthy obese individuals should be classified in categories of major coronary risk related to lean subjects.

Keywords

Metabolic phenotypes, two-step cluster, metabolically unhealthy lean, metabolically healthy obese, coronary risk

Corresponding authors: Valmore Bermudez, Juan Salazar
Competing interests: No competing interests were disclosed.
Grant information: This work was supported by research grant Nº CC-0437-10-21-09-10 from the Technological, Humanistic, and Scientific Development Council (Consejo de Desarrollo Científico, Humanístico y Tecnológico; CONDES), University of Zulia.
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Copyright:  © 2018 Bermudez V et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Data associated with the article are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).
How to cite: Bermudez V, Rojas J, Salazar J et al. Biochemical and clinical characterization of metabolic phenotypes: a cross-sectional study from Maracaibo city, Venezuela [version 1; peer review: 1 approved with reservations]. F1000Research 2018, 7:230 (https://doi.org/10.12688/f1000research.13897.1) First published: 27 Feb 2018, 7:230 (https://doi.org/10.12688/f1000research.13897.1) Latest published: 24 Mar 2021, 7:230 (https://doi.org/10.12688/f1000research.13897.3)

Introduction

Obesity is considered an entity with major morbi-mortality in the world since the end of the 20th century1. Multiples studies have shown its role as an independent risk factor for various cardiometabolic disorders such as hypertension (HTN), dyslipidemias, Type 2 Diabetes Mellitus (T2DM) and cardiovascular disease (CVD)2. For this reason, the actual clinical practice catalogues an obese patient as an “unhealthy” patient and a lean patient is considered “healthy”.

In spite of this, in 1980, Reuben Andresen discovered that in certain groups of individuals the obesity was not a mortality increasing factor, introducing the subtype “Healthy Obese”3. Around 20 years later, Ferranini et al. observed that a group of certain obese nondiabetic non-hypertensive subjects presented low insulin resistance (IR) prevalence, suggesting that this subtype must have a different risk of having T2DM and CVD from the IR obese; also suggesting a different management for them4.

Furthermore, in 1975, Bernstein et al. observed that 11 normal-weight men with type IV or V dyslipidemia presented higher serum glucose levels; and also carried bigger sized adipocytes with respect to their healthy counterparts5. Years later, Ruderman et al. introduced the “Metabolically Unhealthy Normal-Weight” phenotype attributed to lean individuals with metabolic alterations associated to obesity6.

The importance of these atypical metabolic phenotypes lies in the fact that their diagnosis may be challenging for clinicians delaying their detection. Because of this, in recent years, multiple studies have been dedicated to the research of accurate clinical, biochemical, and genetic elements capable to detect these atypical metabolic states, and their evolution.

In this sense, these phenotypes determinants and frequencies have not been deeply researched in Latin-American populations7. The objective of this study is to characterize, from a clinical-biological point of view, the metabolic phenotypes in the population from Maracaibo city, Venezuela.

Materials and methods

Population selection

The Maracaibo City Metabolic Syndrome Prevalence Study (MMSPS) is a cross-sectional study whose purpose is to detect metabolic syndrome and cardiovascular disease risk factors in the adult population from Maracaibo, the second largest city of Venezuela, with approximately 2,500,000 inhabitants, during the period May 2007 – December 2009. The original study included a total of 2230 individuals of both genders, aged between 18–85 years old, and the study protocol was previously reported8. This sub-analysis excluded those individuals with no measurements of serum insulin levels. Patients with past history of diabetes were also excluded because their disease control, evolution and pharmacological treatments would affect the variables in the study.

These subjects were categorized into six groups, first according to their Body Mass Index (BMI) (normal-weight, overweight and obese) and second, to their healthy/unhealthy definition. This categorization was made using the protocol from two-step cluster analysis published previously9. The metabolic variables were chosen as possible metabolic predictors based on their physiological function and biological plausibility. These variables were: mean arterial pressure (MAP), triglycerides (TAG), total cholesterol, HDL-C, HOMA2-IR, HOMA2-βcell, HOMA2-S, fasting blood glucose, non-HDL-C cholesterol, TAG/HDL-C ratio, and high-sensitivity C-Reactive Protein (hs-CRP) levels; waist circumference (WC) was excluded and was assessed as a 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 most appropriate predictive variables selected for each group 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. The two-step cluster analysis with SPSS was conducted in two phases: during the first step (called “precluster”), the subjects were divided into several small subclusters. Then, the obtained subclusters were grouped in preferred number of clusters; if the desired number of clusters was unknown, the SPSS two-step cluster component would find the proper number of clusters automatically. Once the program analyzed the subclusters with the characteristics of each BMI category (as described previously), the subjects were categorized into 6 phenotypes: healthy normal-weight (HNW), metabolically unhealthy normal-weight (MUNW), healthy and metabolically disturbed overweight, metabolically unhealthy obese (MUO), and metabolically healthy obese (MHO). Overweight subjects were excluded from this secondary analysis since they represent a non-conventional group outside the metabolic phenotypes and require separate analysis. The final sample included 1226 subjects (Figure 1).

b340b8fc-3e30-4f32-b380-dd95d208851f_figure1.gif

Figure 1. Patient selection diagram. Maracaibo city, Venezuela.

During simple selection, subjects with no measurements of serum insulin levels and patients with past history of diabetes were excluded. These subjects were categorized into six groups, first according to their BMI and second to their healthy/unhealthy definition, using two-step cluster analysis.

Clinical evaluation

Data was collected through completion of a full clinical record carried out by trained personnel, which included interrogation regarding ethnic origin and socioeconomic status by the Graffar scale according to Méndez-Castellano10. The assessment of blood pressure was done by applying the auscultatory technique, and HTN classification was made using the criteria proposed in the VII Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure11.

For Anthropometric Analysis, an electrical bioelectric scale was used to obtain weight (Tanita, TBF-310 GS Body Composition Analyzer, Tokyo – Japan). Height was measured using a calibrated metric measurement tape, with the subject standing up barefoot. BMI formula (weight/height2) was applied, expressing the results as kg/m2. Obesity was classified applying the WHO criteria12 based on the BMI value. Finally, WC was measured using calibrated measuring tape in accordance to the anatomical landmarks proposed by the USA National Institutes of Health protocol13.

Physical activity. Physical activity (PA) was assessed with the International Physical Activity Questionnaire (IPAQ). For statistical analysis, PA was evaluated in 4 domains: occupational, household, transport, and leisure. In each of these domains, subjects were categorized as follows: (a) inactive, MET/week = 0, or (b) active, MET/week > 0. The latter were then subcategorized by gender-specific MET/week quintiles in each domain (Table 1), which were published previously14.

Table 1. Gender-specific MET quintiles for each domain of physical activity.

Maracaibo city, Venezuela.

MET Quintiles*FemalesMales
Work
Domain		Lower
Limit		Upper
Limit		Lower
Limit		Upper
Limit
Very Low33.00385.9933.00714.99
Low386.001201.49715.002042.09
Moderate1201.502751.592042.103578.39
High2751.604546.793578.406495.59
Very High4546.806495.60
Transport
Domain		Lower
Limit		Upper
Limit		Lower
Limit		Upper
Limit
Very Low33.00131.9933.00164.99
Low132.00230.99165.00257.49
Moderate231.50346.49247.50521.09
High346.50700.79521.101385.99
Very High700.801386.00
Household
Domain		Lower
Limit		Upper
Limit		Lower
Limit		Upper
Limit
Very Low30.00539.9930.00269.99
Low540.001139.99270.00629.99
Moderate1140.001919.99630.001084.99
High1920.003779.991085.002429.99
Very High3780.002430.00
Leisure
Domain		Lower
Limit		Upper
Limit		Lower
Limit		Upper
Limit
Very Low33.00230.9933.00296.99
Low321.00445.49297.00791.99
Moderate445.50742.49792.001532.39
High742.501798.791532.402879.99
Very High1798.802880.00

*Obtained from IPAQ scoring. Subjects with 0 MET were excluded from quintiles and classified separately as Inactive.

Biochemical analyses

Fasting levels of glucose, cholesterol, TAG, HDL-C, and hs-CRP were assessed in our clinical laboratory using an automatized computer analyzer (Human Gesellschaft fur Biochemica und Diagnostica mbH). LDL-C and VLDL-C levels were calculated applying the Friedewald formulas15. When TAG were over 400 mg/dL measurement was done using lipoprotein electrophoresis and optical densitometry (BioRad GS-800 densitometer, USA). Lipoprotein (a) [Lp(a)] was estimated through the latex turbidimetric method, Human Gesellschaft für Biochemica and Diagnostica, Germany. Likewise, serum hs-CRP levels were quantified employing immunoturbidimetric essays (Human Gesellschaft für Biochemica and Diagnostica MBH). Insulin was determined using an ultrasensitive ELISA method (DRG Instruments GmbH, Germany, International DRG Division, Inc.). For the evaluation of insulin resistance (IR), the HOMA2-IR model proposed by Levy et al. was utilized16 determined through the HOMA-Calculator v2.2.2 program. Visceral Adiposity Index (VAI) calculation was performed with the gender-specific equations proposed by Amato et al.17. The Metabolic Syndrome (MS) diagnosis was done using the Harmonizing-2009 consensus criteria18.

Calibration of the Framingham-Wilson equation and coronary risk categorization for the population of Maracaibo city

For proper equation calibration, the constants in the formula regarding major cumulative coronary events (lethal and non-lethal myocardial infarction, symptomatic and no symptomatic angina) were substituted with the local statistics obtained from the Vital Statistics Yearbook of the State of Zulia from 2008, where the morbidity and mortality for cardiovascular diseases is registered, the calibration process has been detailed previously19. The coronary risk was classified in 2 categories: <5% in 10 years, and ≥5% in 10 years.

Statistical analysis

Normal distribution of continuous variables was assessed using Geary’s test; for normally distributed variables, the results were expressed as arithmetic mean ± SD (standard deviation). Variables without normal distribution were logarithmically transformed, and normal distribution subsequently corroborated. When normalization could not be achieved, these variables were expressed as medians (25th percentile–75th percentile). Student’s –test/One-way ANOVA or Mann-Whitney/Kruskal Wallis’s tests were applied to evaluate differences between means or medians, respectively. Qualitative variables were expressed as absolute and relative frequencies, assessed through the χ2 test and the Z test for Proportions.

A logistic regression model was constructed with coronary risk as dependent variable and independent variables: gender, age groups, ethnicity, socioeconomic status, smoking habit, physical activity in leisure time, elevated TAG, and metabolic phenotypes. Database construction and statistical analysis were done using the Statistical Package for the Social Sciences (SPSS) v22 for Windows (IBM Inc., Chicago, IL), results were considered statistically significant when p<0.05.

Results

Population general characteristics

A total of 1226 individuals were studied, 55.1% (n=676) corresponded to females and 44.9% (n=550) to males. The mean age (years) of the general population was 37.94±14.99. Subjects distribution according to their metabolic phenotype is shown in Figure 2 where the 5.2% (n=64) of the individuals were classified as MUNW, and 17.4% (n=213) as MHO, representing 34.13% from the total of obese subjects, while sociodemographic and metabolic characteristics from the studied simple are shown in Table 2.

b340b8fc-3e30-4f32-b380-dd95d208851f_figure2.gif

Figure 2. Distribution of individuals according to metabolic phenotypes. Maracaibo city, Venezuela.

For this sub-analysis overweight subjects were excluded, evaluating only the typical obesity phenotypes with 4 groups.

Table 2. General Characteristics of the studied sample.

Maracaibo city, Venezuela.

FemaleMaleTotal
n%n%n%
Age Group
(years)
<3023534.822841.546337.8
30–4925337.422040.047338.6
≥5018827.810218.529023.7
Ethnic Groups
Mixed51275.742777.693976.6
White Hispanic11116.48014.519115.6
Afrodescendant152.2213.8362.9
Indian-American304.4213.8514.2
Other81.210.290.7
Socioeconomic
Status
Class I152.291.6242.0
Class II11617.211320.522918.7
Class III25337.423743.149040.0
Class IV25137.117231.342334.5
Class V416.1193.5604.9
Smoking Habit
No Smoker52377.535164.387471.6
Smoker7611.310519.218114.8
Past Smoker7611.39016.516613.6
Hypertension‡12618.614426.227022.0
Elevated
Triglycerides		13920.617030.930925.2
Low HDL-C42963.527049.169957.0
Metabolic
Syndrome*		25037.023342.448339.4
Insulin
Resistance†		31746.925746.757446.8
Total676100.0550100.01226100.0

‡ Past history and Diagnosed in the Study

* Metabolic Syndrome Diagnosis according to 2009 Harmonizing Consensus

† HOMA2-IR ≥2

Metabolic phenotypes and sociodemographic characteristics

In the evaluation of the epidemiologic behavior of the metabolic phenotypes according to sex, we found that HNW and MUO individuals were predominately females (62.5%, n=336; 51.3%, n=211 respectively), while the atypical phenotypes were predominately males (MUNW: 56.3%, n=36; MHO: 52.6%, n=112. χ2=22.53, p<0.001). Likewise, a statistically significant association was found between age groups and metabolic phenotypes (χ2= 211.91, p<0.001), observing a predominance in the < 30 years age group in the normal-weight phenotype (HNW: 56.1%, n=302; MUNW: 57.8%, n=37), whereas the 30–49 age group was predominately obese phenotypes (MHO: 47.9%, n=102; MUO: 50.1%, n=106). There was no statistically significant association between metabolic phenotypes, ethnic groups (χ2= 20.96, p=0.05) and socioeconomic status (χ2= 14.56, p=0.27) (Table 3).

Table 3. Sociodemographic characteristics according to metabolic phenotypes.

Maracaibo city, Venezuela.

(HNW)
A		(MUNW)
B		(MHO)
C		(MUO)
D		A vs. BA vs. CA vs. DB vs. CB vs. DC vs. D
n%n%n%n%χ2 (p)*p**p**p**p**p**p**
Gender22.53
(<0.001)
Female33662.52843.810147.421151.3<0.05<0.05<0.05NSNSNS
Male20237.53656.311252.620048.7<0.05<0.05<0.05NSNSNS
Age Group
(years)		176.63
(<0.001)
<3030256.13757.84621.67819.0NS<0.05<0.05<0.05<0.05NS
30–4915328.41218.810247.920650.1NS<0.05<0.05<0.05<0.05NS
≥508315.51523.46530.512730.9NS<0.05<0.05NSNSNS
Ethnic Group20.96
(0.05)
Mixed41276.65078.116979.330874.9NSNSNSNSNSNS
White Hispanic7413.869.43114.68019.5NSNSNSNSNSNS
Afrodescendant163.034.762.8112.7NSNSNSNSNSNS
Indian-American325.957.862.881.9NSNS<0.05NS<0.05NS
Others40.700.010.541.0NSNSNSNSNSNS
Socioeconomic
Status		14.56
(0.27)
Class I122.200.020.9102.4NSNSNSNSNSNS
Class II9617.81523.43516.48320.2NSNSNSNSNSNS
Class III21339.62132.810247.915437.5NSNSNSNSNSNS
Class IV18734.82539.16229.114936.3NSNSNSNSNSNS
Class V305.634.7125.6153.6NSNSNSNSNSNS
Total53810064100213100411100

HNW (Healthy Normal Weight); MUNW (Metabolically Unhealthy Normal Weight); MHO (Metabolically Healthy Obese); MUO (Metabolically Unhealthy Obese).

* Chi-Square Test.

** Z-test of proportions.

Metabolic phenotypes and psychobiologic habits

Initially, in relation to the smoking habit, the non-smokers were the most frequent group (χ2=30.91; p<0.001), despite the fact MUNW phenotype consisted of the highest percentage of smoking individuals (18.8%, n=12), whereas MUO subjects consisted of the highest proportion of past smoking subjects (20.2%, n=83). On the other side, in the evaluation of the metabolic phenotypes according to PA there was a statistically significant association in the transport-related physical activity (χ2=43.39; p<0.001) and leisure activities (χ2=50.48; p<0.001) (Table 4).

Table 4. Psychobiologic Habits according to metabolic phenotypes.

Maracaibo city, Venezuela.

(HNW)
A		(MUNW)
B		(MHO)
C		(MUO)
D		A vs. BA vs. CA vs. DB vs. CB vs. DC vs. D
n%n%n%n%χ2 (p)*p**p**p**p**p**p**
Smoking Habit30.91
(<0.001)
No Smoker41577.74468.815472.626163.5NSNS<0.05NSNSNS
Smoker7213.51218.83014.26716.3NSNSNSNSNSNS
Past Smoker478.8812.52813.28320.2NSNS<0.05NSNSNS
Physical Activity
Work Sphere		14.17
(0.51)
Inactive40875.85078.115974.630774.7NSNSNSNSNSNS
Very Low305.634.7104.7184.4NSNSNSNSNSNS
Low325.957.8125.6174.1NSNSNSNSNSNS
Moderate254.623.173.3215.1NSNSNSNSNSNS
High264.834.794.2266.3NSNSNSNSNSNS
Very High173.211.6167.5225.4NSNSNSNSNSNS
Physical Activity
Transport Sphere		43.39
(<0.001)
Inactive16330.61930.28741.018846.4NS<0.05<0.05NSNSNS
Very Low5810.934.83215.1317.7NSNSNSNSNS<0.05
Low7013.2711.12511.84711.6NSNSNSNSNSNS
Moderate7313.7914.32612.34410.9NSNSNSNSNSNS
High9016.91422.22511.84711.6NSNSNSNSNSNS
Very High7814.71117.5178.04811.9NSNSNSNSNSNS
Physical Activity
Household Sphere		24.33
(0.06)
Inactive12523.21523.47535.212630.7NS<0.05NSNSNSNS
Very Low9517.71117.22210.34811.7NSNSNSNSNSNS
Low8616.0914.12411.36014.6NSNSNSNSNSNS
Moderate8315.41117.23817.86315.3NSNSNSNSNSNS
High7313.61015.6219.95413.1NSNSNSNSNSNS
Very High7614.1812.53315.56014.6NSNSNSNSNSNS
Physical Activity
Leisure Sphere		50.48
(<0.001)
Inactive30556.73757.813462.929070.6NSNSNSNSNSNS
Very Low427.8710.9104.7276.6NSNSNSNSNSNS
Low438.023.12310.8266.3NSNSNSNSNSNS
Moderate438.034.7198.9317.5NSNSNSNSNSNS
High417.669.4209.4215.1NSNSNSNSNSNS
Very High6411.9914.173.3163.9NS<0.05<0.05<0.05<0.05NS
Total53810064100213100411100

HNW (Healthy Normal Weight); MUNW (Metabolically Unhealthy Normal Weight); MHO (Metabolically Healthy Obese); MUO (Metabolically Unhealthy Obese).

* Chi-Square Test.

** Z-test of proportions.

Phenotypes and endocrine-metabolic alterations

Distribution of subjects according to phenotypes and endocrine-metabolic alterations are shown in Table 5. A high percentage of MUNW and MUO individuals with insulin resistance was found in contrast to healthy subjects (79.7%, n=51 and 97.1%, n=399, respectively). On the other side, a higher percentage of MUNW with high TAG was found (34.4% n=22 vs 9.5% n=51 HNW; p<0.05) and also a higher prevalence of MS (29.7% n=19 vs 12.3% n=66; p<0.05 HNW); similar findings were observed in the obese phenotypes, where a minor prevalence of these alterations were found in the MHO subjects (high TAG levels: 28.8% n=60 vs 42.8% n=176, p<0.05; MS: 53.1% n=113 vs 69.3% n=285, p<0.05). Finally, a significant association was found between the metabolic phenotypes with low HDL-C (χ2=44.08; p<0.0001) and HTN (χ2= 182.22, p<0.0001).

Table 5. Endocrine-Metabolic Alterations according to metabolic phenotypes Maracaibo city, Venezuela.

(HNW)
A		(MUNW)
B		(MHO)
C		(MUO)
D		A vs. BA vs. CA vs. DB vs. CB vs. DC vs. D
n%n%n%n%χ2 (p)*p**p**p**p**p**p**
HOMA2-IR727.9
(<0.0001)
<243480.71320.319390.6122.9<0.05NS<0.05NS<0.05<0.05
≥210419.35179.7209.439997.1<0.05<0.05<0.05<0.05<0.05<0.05
Hypertension182.22
(<0.0001)
Absent33187.33282.15343.19639.8NS<0.05<0.05<0.05<0.05NS
Present‡4812.7717.97056.914560.2NS<0.05<0.05<0.05<0.05NS
Triglycerides142.09
(<0.0001)
Normal48790.54265.615371.823557.2<0.05<0.05<0.05NSNS<0.05
High519.52234.46028.217642.8<0.05<0.05<0.05NSNS<0.05
HDL-C44.08
(<0.0001)
Normal28352.63046.98539.912931.4NS<0.05<0.05NSNSNS
Low25547.43453.112860.128268.6NS<0.05<0.05NSNSNS
Metabolic
Syndrome		339.38
(<0.0001)
Absent47287.74570.310046.912630.7<0.05<0.05<0.05<0.05<0.05<0.05
Present6612.31929.711353.128569.3<0.05<0.05<0.05<0.05<0.05<0.05
Total53810064100213100411100

HNW (Healthy Normal Weight); MUNW (Metabolically Unhealthy Normal Weight); MHO (Metabolically Healthy Obese); MUO (Metabolically Unhealthy Obese).

* Chi-Square Test.

** Z-test of proportions.

‡Personal history and Diagnosis in the Study

Metabolic phenotypes and biologic-anthropometric variables

Biochemical and clinical characteristics according to metabolic phenotypes are shown in Table 6. An increasing tendency of their variable levels was observed, except on HOMA2-IR, HOMA2-βcell, HOMA2-S, insulin y glucose levels.

Table 6. Clinical and biochemical characteristics according to metabolic phenotypes.

Maracaibo city, Venezuela.

HNWMUNWMHOMUO
MeanSDMeanSDMeanSDMeanSDp*
Age (years)32,514,734,116,542,913,543,113,2<0.001
Body Mass Index
(Kg/m2)		21,92,122,91,734,54,735,45,6<0.001
Waist Circunference (cm)
   Female79,38,277,27,1104,410,6105,510,1<0.001
   Male81,56,986,97,6109,211,9116,015,3<0.001
HOMA2-βcell127,240,4204,588,2118,937,0188,780,8<0.001
HOMA2-S81,944,641,027,380,636,932,810,5<0.001
HOMA2-IR1,50,53,21,61,40,43,51,6<0.001
Insulin (µU/mL)9,93,622,311,99,62,923,711,8<0.001
Glucose (mg/dL)89,310,194,922,791,911,3103,228,9<0.001
Total Cholesterol
(mg/dL)		174,938,8180,144,9196,552,3200,845,4<0.001
Triglycerides (mg/dL) 73.453.0–106.099.167.9–209.0107.775.0–164.0135.297.0–193.0<0.001
HDL-C (mg/dL)
   Female49,311,851,611,545,613,044,111,5<0.001
   Male46,011,239,511,840,29,936,78,5<0.001
VLDL-C (mg/dL)17,19,331,028,526,720,432,521,5<0.001
LDL-C (mg/dL)109,834,5106,440,2126,335,1128,037,2<0.001
Lipoprotein(a) (mg/dL)26,114,022,214,728,713,429,314,1<0.001
hs-C Reactive Protein
(mg/L) 		0.2970.070–0.5980.2350.099–0.5800.4350.177–0.8140.5620.195–1.222<0.001
Non HDL Cholesterol126,938,6135,345,5153,851,9160,345,1<0.001
Triacylglicerides/
HDL-C Index		1.51.0–2.42.41.4–5.52.81.7–4.13.52.3–5.5<0.001
Visceral Adiposity
Index		1.70.7–1.81.60.9–3.31.81.2–2.92.41.7–3.9<0.001
Systolic Blood
Pressure (mmHg)		111,913,3115,215,3125,318,4125,617,3<0.001
Diastolic Blood
Pressure (mmHg)		71,79,473,910,981,512,381,911,2<0.001

HNW (Healthy Normal Weight); MUNW (Metabolically Unhealthy Normal Weight); MHO (Metabolically Healthy Obese); MUO (Metabolically Unhealthy Obese).

SD=Standar Deviation;

* One-way ANOVA Test

¶ As Median (p25–p75th) Comparison: Kruskal Wallis Test

Metabolic phenotypes and coronary risk classification

An association between metabolically unhealthy phenotypes and a higher risk of a coronary event was found. This association was stronger for unhealthy phenotypes than for their healthy counterparts. However, results were statistically significant for obese individuals (MHO: OR=1.85 CI95%: 1.11-3.09; p=0.02 and MUO: OR=2.09 CI95%: 1.34-3.28; p<0.01) (Table 7).

Table 7. Logistic regression model for metabolic phenotypes and coronary risk categories.

Maracaibo city, Venezuela.

Crude Odds Ratio
(IC 95%a)		pbAdjusted Odds
Ratio*
(IC 95%a)		pb
Metabolic Phenotypes
Metabolically Healthy
Normal Weight		1,00-1,00-
Metabolically
Unhealthy Normal
Weight		3,41 (1,46 - 7,98)< 0,012.24 (0,89 - 5.56)0,08
Metabolically Healthy
Obese		2,26 (1,40 - 3,64)< 0,011.85 (1.11 - 3.09)0,02
Metabolically
Unhealthy Obese		2,85 (1,89 - 4,29)< 0,012.09 (1.34 - 3.28)< 0,01

a Confidence Interval (95%); b Level of significance

Dependent Variable: Coronary risk: <5% in 10 years vs ≥5% in 10 years

* Adjusted Model for: sex, age, ethnic group, socioeconomic status, smoking habit, physical activity in leisure dimension according to IPAQ, high TAG, and metabolic phenotypes.

Dataset 1.MMSPS metabolic phenotype dataset.
BMI: Body Mass Index, WaistC: Waist Circumference, HDL-C: High Density Lipoprotein Cholesterol, VLDL-C: Very Low Density Lipoprotein Cholesterol, LDL-C: Low Density Lipoprotein Cholesterol, Lp(a): Lipoprotein (a), hs-CRP: high Sensitivity C Reactive Protein, Non-HDL-Col: Non-High Density Lipoprotein Cholesterol, TAG/HDL ratio: Triglycerides/High Density Lipoprotein ratio VAI: Visceral Adiposity Index, BP: Blood Pressure, HNW: Healthy Normal-Weight, MUNW: Metabolically Unhealthy Normal-Weight, MUO: Metabolically Unhealthy Obese, MHO: Metabolically Healthy Obese.

Discussion

Obesity is a prioritized area for the world health systems because of its increasing prevalence, incidence, and associated costs in the last decade20. This disease has been defined classically as “excessive presence of adipose tissue that is injurious for health” and given its association to other chronic-degenerative diseases3,21 has been stereotyped as “more adiposity, more risk”. All the classic methods employed for obesity diagnosis, even central and global, are indirect measurements. For different populations they do not allow to determine the adipose tissue functioning from individuals, even though they have high sensitivity, specificity, and predictive values. Based on this, multiple epidemiologic studies have detected a considerable percentage of individuals who did not enter in the classic “HNW” and “MUO” phenotypes, showing the existence of atypical metabolic phenotypes called “MUNW” and “MHO”3. The defining criteria of these metabolic states differ significantly between studies and are defined under highly subjectivity levels, nonetheless insulin sensitivity and lipid profile are often used to define healthy and unhealthy phenotypes22–24.

Giving this criteria and methods discrepancy, such as the psychobiologic, sociodemographic, and genetic patterns according to latitudes, the phenotype frequency presents high variability25. This could bias the study by selecting predetermined variables and cut-off points to consider an individual as healthy or unhealthy. In this sense, data mining techniques were proposed to avoid potential bias. The program would group subjects according to spontaneous tendencies and biologic behavior of related variables.

Applied studies in Asia reported a prevalence of 8.7%–13.07% and 3.9%–15.5% for MUNW and MHO phenotypes, respectively26,27. Likewise, studies conducted in Europe reported frequencies ranging between 18.9% and 45.8% for the MUNW phenotype, and between 2.1% and 18.5% for the MHO phenotype28–30; a similar variability was observed in American research studies31,32. Latin American reports are scant, however Fanghanel et al.33 showed a 5.8% prevalence of the MUNW phenotype for the Mexico City, similar to the one showed in the present study, whereas contrasting the obese phenotypes the Maracaibo population exhibited the highest prevalence of MHO subjects (17% vs 10.8% of the Mexican population).

The atypical metabolic phenotypes, as MUNW and MHO, tend to be observed in females with more frequency32,34. However, the present study reported these phenotypes were more frequent in males. Significant difference between sexes was found in the MUNW group, similar to the study by Hinnouko et al.35. Smoking habit, age, and physical activity values, were discovered as influencing factors in these findings.

In the same manner, multiple studies have reported that healthy phenotype prevalence decreases with age27,29, but in our population an increase was observed in the frequency of MHO individuals older than 30 years old. Yoo et al.36 did not report differences in this phenotype prevalence between subjects older and younger than 35 years. Regarding the MUNW phenotype in the Maracaibo population, a higher frequency was found in subjects younger than 30 years. A considerable part of epidemiologic studies that evaluate this association possessed samples conformed by subjects older than 35 years. This may limit the establishment of a tendency in frequency of healthy phenotypes according to age. Similarly, factors such as ethnicity from African descendants37 and socioeconomical status38 have been related to the presence of atypical phenotypes, but no relationship was found between these variables in Maracaibo population.

One of the greatest enigmas formulated in relation to the atypical metabolic phenotypes, is focused on its conditioning factors. Psychobiologic habits have been considered key elements in comprehension of its biology and behavior related to time. Diniz et al.39 found a significant association between healthy metabolic phenotypes with absence of smoking habit, also with increased PA levels, such as the present study. Ortega et al.40 reported that MHO subjects present with better cardiorespiratory fitness profiles than their unhealthy counterpart, and by adjusting for this variable the MHO individuals showed less mortality. Other studies report that the phenotypes progression from health to unhealthiness is not related to the smoking habit, alcohol, or quantified PA through indirect methods30 and depends fundamentally on abdominal circumference and visceral adiposity increment.

Regarding to cardiometabolic profiles, our study showed evidence of significantly higher HOMA2-βcell values in all of the unhealthy phenotypes, described previously by the NHANES study41 and by Madeira et al.42. Also higher HOMA2-IR and a lower HOMA2-S demonstrate again the importance to define metabolic states in lean and obese individuals. They could also elevate the risk of developing T2DM and CVD in the unhealthy phenotypes, given their hyper functioning pancreatic beta cell and hyperinsulinemia43.

MHO subjects present with lower HOMA2-IR and higher TAG, LDL-C, PAS, PAD, and hs-CRP levels. In contrast to lean subjects, MHO has higher VAI. The latter constitutes an initial obesity state, without a significant risk of T2DM and CVD in the short term (7–11 years)44, but there is in the long term (>16–30 years)45. The natural history of the MHO is variable, only 16% of MHO individuals stay on that status without alteration for the following 7–8 years46. Those who progress to an unhealthy state present a higher risk of high blood pressure, low-grade inflammation, bad metabolic control and high TAG30. In spite of the metabolic “benign” state of the MHO adipose tissue, non-metabolic complications of obesity, do not exclude these subjects from getting T2DM, CVD, and chronic diseases associated with obesity in the future34,35.

Healthy obese individuals must be classified in categories with higher risk of a coronary event compared to lean subjects. This is consistent with previous reports related to metabolic phenotypes and CVD, suggesting that healthy obese subjects have a higher risk profile in comparison to those with lower BMI36; as well as an increased risk for CVD47 and metabolic disorders such as fatty liver and low-grade inflammation48. Given the above, a profound evaluation of these patients is recommended. This includes not only obese subjects but also those who are overweight, which can go unnoticed in a routine consultation and CVD could be subclinical; as it has been demonstrated by Khan et al. in 475 women from the SWAN study49.

Finally, despite the fact that our report presents a novel method to classify healthy and unhealthy subjects, it is important to mention the difficulty to follow-up these individuals. The latter would show the atypical phenotype stability related to time, as well as the incidence of T2DM and CVD. This was the main limitation of our study. In addition our study lacks nutritional data. For this reason, a thorough and constant evaluation of subjects with atypical metabolic phenotypes is recommended, given their demonstrated unsteadiness in time, and associated non metabolic comorbidities observed especially in the MHO individuals.

Data availability

Dataset 1: MMSPS metabolic phenotype dataset. BMI: Body Mass Index, WaistC: Waist Circumference, HDL-C: High Density Lipoprotein Cholesterol, VLDL-C: Very Low Density Lipoprotein Cholesterol, LDL-C: Low Density Lipoprotein Cholesterol, Lp(a): Lipoprotein (a), hs-CRP: high Sensitivity C Reactive Protein, Non-HDL-Col: Non-High Density Lipoprotein Cholesterol, TAG/HDL ratio: Triglycerides/High Density Lipoprotein ratio VAI: Visceral Adiposity Index, BP: Blood Pressure, HNW: Healthy Normal-Weight, MUNW: Metabolically Unhealthy Normal-Weight, MUO: Metabolically Unhealthy Obese, MHO: Metabolically Healthy Obese. 10.5256/f1000research.13897.d19335150

Ethics and consent

The study was approved by the Bioethics Committee of the Endocrine and Metabolic Research Center – University of Zulia (approval number: BEC-006-0305). This ethical approval included all future studies that used the data from the Maracaibo City Metabolic Syndrome Prevalence Study (MMSPS). All participants signed written a informed consent for participation in the study before being questioned and physically examined by a trained team.

Abbreviations

CVD: cardiovascular disease

HDL-C: High Density Lipoprotein - Cholesterol

HNW: healthy normal-weight

HOMA: Homeostasis Model Assesment

HTN: hypertension

hs-CRP: high-sensitivity C-Reactive Protein

IR: insulin resistance

LDL-C: Low Density Lipoprotein – Cholesterol

MAP: mean arterial pressure

MET: Metabolic Equivalent

MMSPS: Maracaibo City Metabolic Syndrome Prevalence Study

MHO: metabolically healthy obese

MS: Metabolic Syndrome

MUNW: metabolically unhealthy normal-weight

MUO: metabolically unhealthy obese

PA: Physical activity

SD: standard deviation

TAG: triglycerides

T2DM: Type 2 Diabetes Mellitus

VAI: Visceral Adiposity Index

Competing interests

The authors have no conflicts of interest to disclose.

Grant information

This work was supported by research grant Nº CC-0437-10-21-09-10 from the Technological, Humanistic, and Scientific Development Council (Consejo de Desarrollo Científico, Humanístico y Tecnológico; CONDES), University of Zulia.

The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Víctor A. Cortes, Department of Nutrition, Diabetes and Metabolism, School of Medicine, Pontifical Catholic University of Chile (UC), Santiago, Chile 
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https://doi.org/10.5256/f1000research.15107.r35881
The authors aimed to estimate the prevalence of “atypical metabolic phenotypes” i.e. lean metabolically unhealthy and obese metabolically healthy individuals in the city area of Maracaibo, Venezuela. For that, they analyzed previously generated data from a cross-sectional city-wide health survey ... Continue reading
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  1. Víctor A. Cortes, Pontifical Catholic University of Chile (UC), Santiago, Chile
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