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ISSN: 2155-6156
Journal of Diabetes & Metabolism

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Prevalence and Risk Factors associated with Impaired Fasting Glucose in Adults from Maracaibo City, Venezuela

Valmore Bermúdez1*, Juan Salazar1, Robys González1, Angel Ortega1, María Calvo1, Luis Carlos Olivar1, Jessenia Morillo1, Edgar Miquilena1, Mervin Chávez-Castillo1, Rendy Chaparro1, Mayela Cabrera1 and Joselyn Rojas1,2

1Endocrine and Metabolic Diseases Research Center, University of Zulia, Maracaibo, Venezuela

2Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA. 02115, USA

*Corresponding Author:
Valmore Bermúdez
University of Zulia, Endocrine and Metabolic Diseases Research Center “Dr. Félix Gómez” Av. 20
Maracaibo 4004, Venezuela
Tel: +582617597279
E-mail: [email protected]

Received date: April 25, 2016; Accepted date: June 29, 2016; Published date: June 30, 2016

Citation: Bermúdez V, Salazar J, González R, Ortega A, Calvo M, et al. (2016) Prevalence and Risk Factors associated with Impaired Fasting Glucose in Adults from Maracaibo City, Venezuela. J Diabetes Metab 7:683. doi:10.4172/2155-6156.1000683

Copyright: © 2016 Bermúdez 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 author and source are credited.

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Abstract

Objective: The purpose of this study was to evaluate the prevalence and risk factors associated with impaired fasting glucose (IFG) in adult individuals from Maracaibo city, Venezuela. Materials and methods: 2230 patients from the Maracaibo Metabolic Syndrome Prevalence Study were selected. IFG was defined according to the 2016 ADA criteria. A multiple logistic regression model was constructed in order to assess risk factors associated with IFG. Results: In the general population, the prevalence of IFG was 19.5% (n=435), with 46.4% (n=202) being women and 53.6% (n=233) being men, p=0.004. The main risk factors associated with IFG were age (≥60 years: OR=2.31; CI 95%=1.23-4.35; p<0.01), alcohol consumption, abdominal obesity and insulin resistance. After evaluating individuals with IFG exclusively, the major risk factor was the presence of elevated high-sensitivity C-Reactive Protein levels (OR=2.03; CI 95%=1.13-3.67; p<0.02). Conclusions: In Maracaibo the prevalence of IFG is similar to that of international reports. It is associated with a variety of risk factors, especially abdominal obesity, insulin resistance and low-grade inflammation, demonstrating the close link between adiposopathy and alterations in glucose metabolism.

Keywords

Impaired fasting glucose; Insulin resistance; Obesity; Physical activity; Hypertension; Inflammation; Diabetes

Introduction

Type 2 Diabetes Mellitus (T2D) is responsible for approximately 4.9 deaths a year worldwide, with a projected 59% increase in these estimates by year 2035 [1]. In 2003, the American Diabetes Association (ADA) recognized an intermediate metabolic stage in which blood glucose levels are higher than normal range but do not reach the diagnostic criteria for T2D, leading to the birth of the concepts of impaired fasting glucose (IFG) and impaired glucose tolerance (IGT) [2]; often grouped together and termed “Pre-Diabetes” [3]. Recently, the prevalence of this metabolic alteration in American adults was found to be 37% in the general population, and 51% in subjects older than 65 years of age [4]. In subjects classified as pre-diabetics, IFG appears to be twice as frequent as IGT [5], representing an important risk factor for T2D [6].

The pathophysiology of IFG has been described to involve hepatic insulin resistance and a deficient early phase insulin secretion [7]. Therefore, IFG is often associated with overweight and obesity, both common in populations with deleterious habits and risk factors such as physical inactivity, high-calorie diets, excessive alcohol consumption and smoking [8]; all of which contribute to progressive dysfunction of pancreatic beta cells, a mechanism shared with T2D, but present in a less severe form in IFG [9].

Identification of patients with IFG is clinically relevant, considering that changes in lifestyle and potential pharmacological intervention could reduce the risk of advancing into T2D and favor regression to euglycemia [10,11]. In view of the scarcity of reports on the epidemiological behavior of this metabolic disorder in our locality, the purpose of this study was to evaluate the prevalence and risk factors associated with impaired fasting glucose (IFG) in adult individuals from the Maracaibo city, Venezuela.

Materials and Methods

Subjects selection

The sample method has been already published in the Maracaibo City Metabolic Syndrome Prevalence Study cross-sectional proposal [12], yet the main aspects will be mentioned. This was a cross-sectional, descriptive, randomized, multistage study which enrolled a total of 2,230 subjects. The study was approved by the Bioethics Committee of the Endocrine and Metabolic Diseases Research Center – University of Zulia, and all participants signed a written consent before being interrogated and physically examined by a trained team.

Subjects evaluation

Data were collected through completion of a full clinical record carried out by trained personnel, who encompassed interrogation on personal and family history of endocrine and cardiovascular disease, and determination of socioeconomic status by the Graffar scale modified by Méndez-Castellano [13].

Based on information obtained during the clinical interview, subjects were categorized by their smoking habits as follows [14]: a) Non-Smokers, individuals who had never smoked, or had smoked <100 cigarettes in their lifetime; b) Current Smokers, subjects who had smoked ≥100 cigarettes in their lifetime, or reported current habitual smoking at the time of evaluation, or had quit smoking less than one year prior to our assessment; and c) Past Smokers, individuals who had consumed ≥100 cigarettes in their lifetime and quit over one year prior to our questioning. Regarding alcohol intake, drinkers were defined as subjects who consumed ≥1 gram of alcohol daily [15].

Physical activity (PA) was assessed with the International Physical Activity Questionnaire (IPAQ) [16]. For statistical analysis, PA was evaluated in 4 domains: Occupational, Household, Transport and Leisure. In each of these domains, subjects were categorized as: 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.

Clinical evaluation

Blood pressure (BP) was taken with subjects sitting down with their feet on the floor following 15 minutes of rest, determined through the auscultatory method with a calibrated mercury sphygmomanometer; identifying Korotkoff’s phases I and V as systolic and diastolic BP respectively. BP was determined 3 times, with 15 minutes in between each take, on two different days; results were classified by the Eighth Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure (JNC-8) guidelines [17].

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. Body Mass Index (BMI) was calculated with the formula: [weight/height2] expressing results as kg/m2. According to their BMI, subjects were sorted in 3 categories: a) BMI ≤ 24.9; b) 25- 29.9; and c) ≥30 [18]. Waist circumference (WC) was evaluated with calibrated measuring tapes in accordance to the anatomical landmarks proposed by the USA National Institutes of Health protocol [19].

Laboratory analysis

Overnight fasting determination of glucose, total cholesterol, triacylglycerides (TAG), and HDL-C was done with an automated analyzer (Human Gesellschaft für Biochemica und Diagnostica mbH, Germany); the intra-assay variation coefficients for total cholesterol, TAG, and HDL-C were 3%, 5%, and 5%, respectively. LDL-C and VLDL-C levels were calculated applying Friedewald’s formula [20] but when TAG levels were <400 mg/dL. LDL-C concentrations were directly measured through lipoprotein electrophoresis and densitometry with a BioRad GS-800 optical densitometer. Insulin was quantified using ultrasensitive ELISA double-sandwich methodology (DRG Instruments GmbH, Germany, Inc.). Serum high-sensitivity C-Reactive Protein (hs- CRP) was quantified through immunoturbidimetric assays (Human Gesellschaft fur¨Biochemica und Diagnostica mbH, Germany).

Definitions

Subjects having the following characteristics were operatively classified as having T2D: a) Those with a previously established diagnosis of T2D; and b) Those without personal history of T2D but with fasting glucose ≥ 126 mg/dL (2). On the other hand, non-diabetic subjects were classified as follows: a) normoglycemic (NG), individuals with fasting glucose<100 mg/dL; and b) Impaired Fasting Glucose (IFG) those fasting glycemia 100-126 mg/dL [21].

As per the IDF/NHLBI/AHA/WHF/IAS/IASO-2009 consensus criteria [22], abdominal obesity was defined as waist circumference≥80 cm in females or ≥90 cm in males. Likewise, we use local waist circumference cutoff points ≥ 90 cm (females) and ≥95 cm (males) in abdominal obesity definition [23].

Regarding a lipid definition, hypertriacylglyceridemia was defined as fasting TAG ≥150 mg/dL; and low HDL-C as fasting HDL-C<50 mg/ dL in females or <40 mg/dL in males. HOMA2-IR was utilized for the evaluation of insulin resistance (IR) as proposed by Levy et al. computed with the HOMA-Calculator v2.2.2 software application, IR was defined as HOMA2-IR ≥ 2 [24] and elevated hs-CRP was defined as levels ≥ 0.765 mg/L [25].

Statistical Analysis

Qualitative variables were expressed as absolute and relative frequencies, evaluating association through Pearson’s Chi-squared (χ2) test. Quantitative variables were evaluated for distribution normality with Geary’s test and were expressed as arithmetic means ± SD. Variables with non-normal distribution underwent logarithmic transformation; when normalization could not be achieved (TAG, HDL-C hs-CRP), these variables were expressed as medians (25th percentile-75th percentile). One-Way ANOVA or Kruskal-Wallis tests were applied to evaluate differences between means or medians, respectively.

A multiple logistic regression model was constructed in order to estimate odds ratios (Confidence Interval 95%) for the presence of IFG, adjusted by gender, age groups, ethnic groups, socioeconomic status, educational status, marital status, occupational status, smoking habits, leisure-domain PA, presence of high TAG, presence of low HDL-C, JNC-8 classification, presence of elevated WC, BMI classification, presence of elevated hs-CRP and presence of IR. A second model was constructed, introducing adjustment for presence of elevated hs- CRP, and local abdominal obesity definition to waist circumference (Females: ≥90 cm; Males: ≥95 cm).

In addition, an ordinal logistic regression model was constructed, with fasting glucose tertiles among subjects with IFG set as the dependent variable. The independent variables included gender, age groups, ethnic groups, abdominal obesity, hypertriglyceridemia, low HDL-C, BMI classification, JNC-8 classification, TAG/HDL-C ratio tertiles, elevated hs-CRP and presence of IR. Regression coefficients (β) were calculated with their corresponding confidence intervals (95% CI) along with Odds Ratios (eβ) and their 95% CI. Goodness-of-fit parameters were calculated and parallel line testing was performed. Data were analyzed with the Statistical Package for the Social Sciences (SPSS) v.21 for Windows (IBM Inc. Chicago, IL), and de results were considered statistically significant when p<0.05.

Results

Characteristics of the general population

The sample had 2,230 individuals, 52.6% were women (n=1172) with a mean age of 39.3 ± 15.4. The prevalence of DM2 was 8.4% (n=187) and the prevalence of IFG was 19.5% (n=435) (Figure 1).

diabetes-metabolism-impaired-fasting

Figure 1: Prevalence of type 2 diabetes mellitus and impaired fasting glucose. Maracaibo City Metabolic Syndrome Prevalence Study, 2016.

Sociodemographic and psycobiological characteristics according to IFG

Table 1 shows the prevalence of IFG according to sociodemographic and psychobiological habits of the population. Age groups showed the greatest degree of association, displaying a rising prevalence of IFG, from 12.1% (n=91) in individuals aged <30 and years, to 31% in those aged ≥ 60 years (n=54, χ2=63.47; p<0.001). In contrast, regarding leisure-time PA, prevalence of IFG was highest in subjects classified as inactive (22.1%; n=270), and lowest in subjects in Q5 (11%; n=20), χ2=15.80; p=0.007.

    Normoglycemics ImpairedFastingGlucose χ2(p)
n % n %
Gender Females 870 81.2 202 18.8 8.21 (0.004)
  Males 736 76.0 233 24.0  
Agegroups (years) <30 664 87.9 91 12.1 63.47 (<0.001)
  30-59 822 73.9 290 26.1  
  ≥60 120 69.0 54 31.0  
Ethnicgroups Mixedrace 1224 78.7 332 21.3 6.09 (0.19)
  Hispanic White 248 77.3 73 22.7  
  Afro-Venezuelan 42 72.4 16 27.6  
  Amerindian 81 86.2 13 13.8  
  Others 11 91.7 1 8.3  
Socioeconomic status Classes I y II 319 75.8 102 24.2  4.71 (0.09)
  ClassIII 660 80.9 156 19.1  
  classes  IV y V 627 78.0 177 22.0  
Occupational Status Un- Employed 799 76.3 248 23.7  3.12 (0.08)
  Employed 668 79.7 170 20.3  
Familyhistory No 859 79.2 226 20.8  3.95 (0.27)
  T2D 702 78.5 192 21.5  
  Type 1 Diabetes Mellitus 25 80.6 6 19.4  
  Both of them 20 64.5 11 35.5  
Smoking Habits Non-Smokers 1143 79.6 293 20.4 2.82 (0.24) 
  CurrentSmokers 226 75.6 73 24.4  
  PastSmokers 230 77.2 68 22.8  
Alcohol Consumption No 1139 80.9 269 19.1 13.19 (<0.001) 
  Yes 467 73.8 166 26.2  
Physical activity  (Work) Inactive 1187 78.3 329 21.7 1.90 (0.86) 
  Q1 83 79.8 21 20.2  
  Q2 82 76.6 25 23.4  
  Q3 83 79.8 21 20.2  
  Q4 89 83.2 18 16.8  
  Q5 82 79.6 21 20.4  
Physical activity (Transportation) Inactive 569 78.6 155 21.4 3.92 (0.56) 
  Q1 203 81.5 46 18.5  
  Q2 206 76.6 63 23.4  
  Q3 210 81.4 48 18.6  
  Q4 214 78.1 60 21.9  
  Q5 180 76.3 56 23.7  
Physical activity (Household) Inactive 402 75.0 134 25.0 11.78 (0.04) 
  Q1 236 81.1 55 18.9  
  Q2 252 81.8 56 18.2  
  Q3 257 82.1 56 17.9  
  Q4 220 75.1 73 24.9  
  Q5 239 79.7 61 20.3  
Physicalactivity (Leisure) Inactive 949 77.9 270 22.1 15.80 (0.007) 
  Q1 113 72.4 43 27.6  
  Q2 127 77.9 36 22.1  
  Q3 130 78,8 35 21,2  
  Q4 126 80,3 31 19.7  
  Q5 161 89.0 20 11.0  
Total§   1606 78.7 435 21.3  

Table 1: Sociodemographic and psychobiological characteristics associated with the diagnosis of Impaired Fasting Glucose. Maracaibo City Metabolic Syndrome Prevalence Study, 2016.

Clinical characteristics according to IFG

Among all clinical features studied (Table 2), IR appeared to be the most closely related to IFG, with these disorders coexisting in 31.4% of subjects (n=252) (χ2=75.44; p<0.0001). BP classification by the JNC-8 was also tightly linked to IFG, with a 15.7% prevalence in normotensive subjects vs 32.9% in hypertensives; χ2=50.01; p<0.0001.

  Normoglycemics ImpairedFastingGlucose. χ2(p)
n % n %
Waistcircumference         30.53 (<0.0001)
Normal 470 87.0 70 13.0  
Elevated 1136 75.7 365 24.3  
Waistcircumference         48.49 (<0.0001)
Normal 856 85.1 150 14.9  
Elevated 750 72.5 285 27.5  
Triacylglycerides (mg/dL)         47.25 (<0.0001)
<150 1256 82.3 270 17.7  
≥150 350 68.0 165 32.0  
HDL-C         3.43 (0.06)
Normal 711 80.6 171 19.4  
Lower 895 77.2 264 22.8  
BMI         28.78 (<0.001)
Normal weight 566 84.2 106 15.8  
Overweight 579 79.3 151 20.7  
Obesity 461 72.1 178 27.9  
JNC-8 Classification         50.01 (<0.0001)
Normotension 708 84.3 132 15.7  
Prehypertension 613 79.0 163 21.0  
Hypertension 285 67.1 140 32.9  
hs-CRP (mg/L)         4.86 (0.03)
<0.765 817 83.0 167 17.0  
≥0.765 237 77.5 69 22.5  
InsulinResistance¶         75.44 (<0.0001)
Absent 892 85.4 152 14.6  
Present 550 68.6 252 31.4  
Total§ 1606 78.7 435 21.3  

Table 2: Clinical features associated with Impaired Fasting Glucose. Maracaibo City Metabolic Syndrome Prevalence Study, 2016.

Risk factors for IFG

Evaluation of the correlation between risk factors in a multivariate context revealed the presence of IR to be the most important risk factor for IFG in our population (OR=2.51; 95%CI=1.79-3.52; p<0.01), followed by age groups (≥60 years: OR=2.31; 95%CI=1.23-4.35; p<0.01) (Table 3).

  Model 1* Modelo 2**
  Crude Odds Ratio
(CI 95%a)
pb Adjusted
Odds Ratio
(CI 95%a)
pb Adjusted
Odds Ratio
(CI 95%a)
pb
Agegroups (years)            
<30 1.00 - 1.00 - 1.00 -
30-49 2.57 (1.99 - 3.33) <0.01 1.99 (1.46 - 2.71) <0.01 2.09 (1.39 - 3.14) <0.01
≥60 3.28 (2.23 - 4.84) <0.01 2.47 (1.54 - 3.96) <0.01 2.31 (1.23 - 4.35) <0.01
Alcohol consumptionc            
No 1.00 - 1.00 - 1.00 -
Yes 1.51 (1.21 - 1.88) <0.01 1.28 (0.98 - 1.67) 0.07 1.49 (1.04 - 2.12) 0.03
Hypertriglyceridemiaf            
Absent 1.00 - 1.00 - 1.00 -
Present 2.19 (1.75 - 2.75) <0.01 1.42 (1.09 - 1.86) 0.01 1.35 (0.95 - 1.94) 0.09
JNC-8 Classification            
Normotense 1.00 - 1.00 - 1.00 -
Pre-hypertensive 1.43 (1.11 - 1.84) <0.01 1.05 (0.78 - 1.40) 0.77 0.93 (0.54 - 1.35) 0.68
Hypertensive 2.64 (2.00 - 3.47) <0.01 1.61 (1.15 - 2.25) <0.01 1.19 (0.77 - 1.85) 0.44
ElevatedWaistcircumference            
Absent - - 1.00 - 1.00 -
Present - - 1.13 (0.79 - 1.59) 0.51 1.62 (1.07 - 2.45) 0.02
Insulin-resistanceg            
Absent 1.00 - 1.00 - 1.00 -
Present 2.69 (2.14 - 3.38) <0.01 2.33 (1.81 - 2.99) <0.01 2.51 (1.79 - 3.52) <0.01

Table 3: Logistic regression models of risk factors for Impaired Fasting Glucose. Maracaibo City Metabolic Syndrome Prevalence Study, 2016.

Clinical and biochemical characteristics of subjects with IFG

After assessing the behavior of various clinical and biochemical variables according to fasting glucose tertiles among subjects with IFG, a progressive worsening across tertiles was observed in most variables (Table 4). Although many variables showed a significant association with these fasting glucose tertiles, the presence of elevated hs-CRP was the main risk factor identified in the multivariate analysis (OR=2.03; 95%CI=1.13 to 3.67; p<0.02).

  Tertile 1
(100-102.9 mg/dL)
[A]
Tertile 2
(103-107.9 mg/dL)
[B]
Tertile 3
(≥108 mg/dL)
[C]
p* A vs B A vs C B vs C
Media DE Media DE Media DE
Age (years) 40.6 15.1 44.2 14.9 46.2 14.2 0.003 0.08 0.002 0.41
BMI (Kg/m2) 28.5 6.5 29.1 5.6 31.5 7.9 <0.001 0.75 0.001 0.006
WaistCircumference (cm) 95.6 15.1 97.7 13.3 104.0 18.8 <0.001 0.40 <0.001 0.004
Basal glycemia (mg/dL) 100.9 0.8 104.8 1.5 113.8 4.8 <0.001 <0.001 <0.001 <0.001
Insulin (UI/L) 15.8 8.6 18.2 14.3 18.7 10.9 0.100 0.60 0.09 0.43
HOMA2-βcell 133.2 48.2 134.9 65.5 120.2 46.9 0.03 0.91 0.05 0.09
HOMA2-S 53.1 26.9 53.1 47.9 45.3 25.1 0.03 0.51 0.02 0.22
HOMA2-IR 2.36 1.21 2.70 1.89 2.88 1.59 0.03 0.49 0.02 0.24
Total cholesterol (mg/dL) 193.4 42.0 203.9 60.1 208.1 47.7 0.03 0.24 0.02 0.52
Triacilglycerides (mg/dL) 132.8 96.7 143.8 112.8 167.2 109.5 <0.001 0.57 <0.001 0.008
HDL-C (mg/dL) 45.1 11.5 43.1 13.2 41.3 10.5 0.02 0.19 0.02 0.53
VLDL-C (mg/dL) 26.6 19.4 28.8 22.6 33.6 21.9 0.02 0.66 0.02 0.13
LDL-C (mg/dL) 122.8 36.2 130.9 44.2 134.5 39.9 0.09 0.51 0.07 0.51
Cholesterol Non HDL 148.2 41.5 160.8 60.2 166.8 48.0 0.009 0.11 0.007 0.56
Index TAG/HDL† 2.4 1.5-4.3 2.9 1.9-4.4 3.5 2.3-5.5 <0.001 - - -
Lipoprotein(a) (mg/dL) 28.6 12.9 26.9 15.9 25.4 14.5 0.209 0.62 0.18 0.65
Systolicbloodpressure (mmHg) 120.9 17.9 122.8 17.6 125.7 16.7 0.04 0.59 0.03 0.24
Diastolicbloodpressure (mmHg)  78.8  12.5  79.7  11.9  80.6  11.8 0.40 0.75 0.37 0.79
hs-CRP (mg/L)† 0.397 0.208-0.758 0.383 0.139-0.674 0.607 0.231-1.027 0.05 - - -

Table 4: Clinical and biochemical characteristics in subjects with Impaired Fasting Glucose according to fasting glucose tertiles. Maracaibo City Metabolic Syndrome Prevalence Study, 2016.

Discussion

IFG is an intermediate disglycemic stage that precedes T2D [5], which has displayed an alarming rise in prevalence in recent years [26,27]. Because of the possible disease-modifying potential of early therapeutic intervention in these subjects, it is of vital importance to evaluate the epidemiological behavior of this metabolic alteration and determine the main risk factors associated with it in our population.

In Latin America, the CARMELA study reported a prevalence of IFG of only 2%, with Mexico and Bogota boasting the highest figures. Meanwhile, the city of Barquisimeto in Venezuela reported only 1% [28] of IFG prevalence. All of these are lower than estimations in the USA (26%) [4]. In contrast to the CARMELA study, the PERUDIAB study recently evaluated 1,677 Peruvian individuals aged ≥ 25 years, estimating a general IFG prevalence of 22.4% [29]. Reports on prediabetes are scarce in Venezuela, although there is a report from a rural population in Merida State, in the Andean Region, which found 18.6% of the individuals to have IFG [30], a similar rate to the findings in our study (19.5%).

Evaluation of IFG by gender showed a higher prevalence in males as has been described in other populations [31,32], similarly echoing previous reports [27,28,33], prevalence increasing with age in both univariate and multivariate analysis (Table 5). This might be associated with senescent changes, such as increased visceral adiposity [34] decreased lean mass [35] and reduced PA [36] all having a direct influence on IR development. The decrease in PA may play a particularly important role in this process, as PA improves glucose metabolism by favoring its uptake by target organs, depleting muscular glycogen, and inducing favorable changes in lipid metabolism [37,38].

  Tertile 1
(100-102.9 mg/dL)
Tertile 2
(103-107.9 mg/dL)
Tertile 3
(≥108 mg/dL)
χ2 (p) β*(CI95%); p OR (CI95%)
n % n % n %
Waistcircumference             11.26 (0.004)    
Normal 55 36.7 53 35.3 42 28.0   0  
Elevated 68 23.9 96 33.7 121 42.5   0.12 (-0.88 – 0.65); 0.76 1.13 (0.41 – 1.91)
Triacilglycerides (mg/dL)              9.04 (0.01)    
<150 86 31.9 97 35.9 87 32.2   0 0
≥150 37 22.4 52 31.5 76 46.1   0.18 (-1.09 – 0.73); 0.69 1.19 (0.33 – 2.07)
HDL-C              4.42 (0.11)    
Normal 58 33.9 54 31.6 59 34.5   0 0
Low 65 24.6 95 36.0 104 39.4   -0.13 (-0.48 – 0.74); 0.67 0.87 (0.61 – 2.09)
BMI Classification             11.56 (0.02)     
Normal weight 37 34.9 39 36.8 30 28.3   0 0
Overweight 47 31.1 53 35.1 51 33.8   -0.15 (-0.95 – 0.62); 0.63 0.86 (0.39 – 1.86)
Obesity 39 21.9 57 32.0 82 46.1   0.63 (-0.82 – 0.95); 0.86 1.88 (0.44 – 2.59)
JNC-8 Classification             6.46 (0.17)     
Normotense 43 32.6 47 35.6 42 31.8   0 0
Pre-hypertensive 49 30.1 56 34.4 58 35.6   0.03 (-0.57 – 0.61); 0.96 1.03 (0.56 – 1.84)
Hypertensive 31 22.1 46 32.9 63 45.0   0.50 (-0.22 – 1.17); 0.17 1.64 (0.80 – 3.22)
hs-CRP (mg/L)             12.02 (0.002)     
<0.765 54 32.3 65 38.9 48 28.7   0 0
≥0.765 17 24.6 16 23.2 36 52.2   0.71 (0.12 – 1.30); 0.02 2.03 (1.13 – 3.67)
Insulinresistence¶             2.09 (0.35)     
Absent 44 28.9 58 38.2 50 32.9   0 0
Present 66 26.2 85 33.7 101 40.1   0.39 (-0.19 – 0.96); 0.17 1.48 (0.82 – 2.61)
TAG/HDL ratio             16.59 (<0.01)    
<1.74 39 31.7 34 22.8 22 13.5   0  
1.74-3.36 39 31.7 55 36.9 54 33.1   0.28 (-1.23 – 1.12); 0.93 1.32 (0.29 – 3.07)
>3.36 45 36.6 60 40.3 87 53.4   -0.05 (-0.49 – 1.04); 0.48 0.95 (0.61 – 2.83)
Total§ 123 28.3 149 34.3 163 37.4      

Table 5: Clinical characteristics in patients with Impaired Fasting Glucose according to fasting glucose tertiles. Maracaibo City Metabolic Syndrome Prevalence Study, 2016.

Furthermore, alcohol consumption was associated with a higher risk of having this metabolic alteration, similar to findings reported by Cullmann et al. [39] in 111 pre-diabetic Swedish individuals, where alcohol consumption was recognized as a risk factor in men, mainly in beer drinkers (OR: 1.84, 95% CI: 1.13-3.01, p<0,05). This effect appears to be dependent on the amount and type of alcohol consumed, as for women, for example, the moderate consumption of wine was found to be protective, while the excessive consumption of spirituous beverages was identified as a risk factor (OR: 2.41, 95% CI 1.47-3.96). Additionally, our research group has demonstrated that the effect of alcohol consumption on the components of the metabolic syndrome appears to be dose-dependent in our population, with an approximate intake of 4-6 beers, 3-5 spirituous beverages or 4-7 cups of wine found to be a risk factor for hyperglycemia among males (OR: 1.99, 95%CI: 1.20-3.33; p<0.01) [40].

Regarding clinical characteristics, in the multivariate context the results showed that only subjects with abdominal obesity had a higher risk of IFG similar to results by Diaz et al. [8] in the PREDAPS study, where 1184 Spanish pre-diabetic individuals were evaluated. In this study, abdominal obesity was considered a risk factor in both genres, indeed obesity is closely related to metabolic disorders of carbohydrates, especially visceral adiposity. In the context of adiposopathy –characterized by increased signaling of proinflammatory cytokines such as TNF-α, MCP-1, IL-1β and macrophage infiltration in adipose tissue-[41] TNF-α interrupts the insulin signaling cascade by phosphorylating serine-threonine sidechains in the insulin receptor substrate (IRS) altering its enzymatic activity and preventing GLUT4 translocation in insulin-dependent tissues, being a molecular mechanism of utmost importance in IFG appearance [42]. In addition, TNF-α increases lipolysis and decreases biosynthesis of triacylglycerols via peroxisome proliferator-activated receptor gamma (PPARγ) signaling in adipose tissue, consequently causing an increase in free fatty acids (FFA) release into splanchnic circulation (43). According to Shulman et al. [43] this phenomenon favors FFA storage in ectopic tissues such as the liver and muscle, increasing the intracellular concentration of intermediates of fatty acid metabolism such as acyl-CoA, ceramides and diacylglycerol which are also involved in the phosphorylation of IRS and therefore in inhibiting effects [44], suggesting a close relationship between obesity, the presence of IR and the appearance of IFG.

Hypertension was also found to be a risk factor for IFG in our population, in resemblance to the PREDAPS study, where hypertensive subjects had a risk 2.33 times higher risk of IFG [8]. Nevertheless, this association was no longer significant after adjusting the model by abdominal circumference and elevated hs-CRP, which might imply that this co-relation between hypertension and alteration of IFG depends on other factors different from IR, such as a certain level of chronic inflammation and oxidative stress, as has been proposed in previous publications [45].

Finally, when evaluating individuals with IFG exclusively, we observed that clinical and metabolic parameters are altered by increasing levels of glycemia. Moreover, when multivariate analysis revealed the presence of elevated hs-CRP to be the factor to most closely related to the highest tertile of fasting glycemia, similar to the findings of Jaiswal et al. [46]. Indeed, prospective studies have described elevated hs-CRP as an independent risk factor for the development of both T2D [47] and pre-diabetes [48], being considered as a chronic inflammation marker closely related to IR, and playing a central role in the development and progression of IFG, metabolic syndrome and T2D [49]. It is also important to note that elevated hs- CRP was a significant risk factor only when analyzing subjects with IFG. Molecular mechanisms that could explain this unique correlation in this group of subjects may be an upregulation of CRP expression by IR state, counteracting the physiological effects of insulin in early phase synthesis of hepatic proteins [50], existing major acute phase protein synthesis, which might explain why hs-CRP is a better predicting factor only in patients with established metabolic disorders.

Limitations of this study include its cross-sectional design, which does not allow establishment of causality, and the lack of assessment of nutritional habits, which repr sent an important factor contributing to dysglycemia. Likewise, even though there are many markers for low grade inflammation, hs-CRP was the only one who rendered significant results in the multivariant analysis. However, more research is required in order to properly depict the mechanistics of this association in our population. Finally, the design of the study did not originally include the evaluation of 2 hr oral glucose tolerance test or postpandrial measurements. Nevertheless, in the upcoming second phase of the project, we are considering this variable as a new inclusion in our research.

In conclusion, our results highlight the responsibility of clinicians for the early detection of subjects with IFG, especially in populations with high prevalence of risk factors such as obesity, physical inactivity and metabolic syndrome [51-53]. IFG prevalence in our population is similar to worldwide reports, in contrast to the low prevalence previously reported in Latin America in past decades. IFG is also linked to various cardiovascular risk factors in our population such as age, alcohol consumption, abdominal obesity; suggesting these patients as candidates for early therapeutic intervention as recommended by the ADA [54]. Lastly, hs-CRP may be a useful predicting factor for T2D among subjects with IFG.

Funding

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, and research grant Nº. FZ-0058-2007 from Fundacite-Zulia.

Disclosure

The authors have are no conflicts of interest to disclose.

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