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Nutrición Hospitalaria

versión On-line ISSN 1699-5198versión impresa ISSN 0212-1611

Nutr. Hosp. vol.29 no.2 Madrid feb. 2014

http://dx.doi.org/10.3305/nh.2014.29.2.7121 

OTROS

 

Usual dietary glycemic load is associated with cardiometabolic risk factors in physically active Brazilian middle-aged men

La carga glucémica dietética habitual se asocia con factores de riesgo cardiometabólico en hombres brasileños de mediana edad físicamente activos

 

 

Paula G. Cocate1, Antônio J. Natali2, Alessandro de Oliveira1, Helen Hermana M. Hermsdorff1, Maria do Carmo G. Peluzio1, Giana Z. Longo1, Jéssica M. Buthers1, Eliziária C. dos Santos, Leandro L. de Oliveira and Rita de Cássia G. Alfenas1

1Departament of Nutrition and Health. Universidade Federal de Viçosa. Viçosa. Brazil.
2Departament of Physical Education. Universidade Federal de Viçosa. Viçosa. Brazil.

This study was supported by the Foundation for Research Support of the State of Minas Gerais (FAPEMIG, Brazil, CDS-APQ-02189-10).

Correspondence

 

 


ABSTRACT

Introduction: The effects of dietary glycemic load (GL) on cardiometabolic risk factors in physically active subjects are not completely known.
Objective: This cross-sectional study assessed the association of habitual dietary GL with cardiometabolic risk factors in physically active Brazilian middle-aged men.
Methods: One-hundred seventy-six subjects (Age: 50.6 ± 5.0 years, BMI: 25.5 ± 3.6 kg/m2) were evaluated. Anthropometry, lifestyle features, insulin resistance, oxidative stress biomarkers (8-iso-prostaglandin F2α; 8-iso-PGF2α and 8-hydroxydeoxyguanosine; 8-OHdG) and lipid profile were assessed. Dietary intake was estimated through a quantitative food frequency questionnaire.
Results: The dietary GL was positively associated with free fatty acid concentrations (β = 0.311, r2 = 0.13, P-value = 0.034) and triglycerides/HDL cholesterol ratio (β = 0.598, r2 = 0.19, P-value = 0.028) regardless of confounding factors (central obesity, red meat consumption, age and energy intake). The oxidative stress biomarker, 8-OHdG, was associated with habitual dietary GL (β = 0.432, r2 = 0.11, P-value = 0.004), regardless of previous confounding factors plus excessive alcohol consumption, iron intake and current smoking status.
Conclusions: The dietary GL was positively associated with lipid profile (free fatty acid concentrations and triglycerides/HDL cholesterol ratio) and oxidative stress biomarker (8-OHdG). These results indicate potential harmfulness of diet with higher GL to cardiometabolic risk factors in middle-aged men, even in physically active individuals.

Key words: Food habits. Carbohydrate. Triglycerides. High-density lipoproteins. Oxidative stress.


RESUMEN

Introducción: Los efectos de la carga glucémica (CG) de la dieta sobre los factores de riesgo cardiometabólico en sujetos físicamente activos no están establecidos por completo.
Objetivo: Este estudio transversal evaluó la asociación entre la CG de la dieta habitual y los factores de riesgo cardiometabólico en hombres brasileños de mediana edad físicamente activos.
Métodos: Ciento setenta y seis sujetos (índice de masa corporal: 25,5 ± 3,6 kg/m2; edad: 50,6 ± 5,0 años) fueron evaluados. Antropometría, características del estilo de vida, la resistencia a la insulina, biomarcadores del estrés oxidativo (8-iso-prostaglandina F2α, 8-iso-PGF2α y 8 hidroxideoxiguanosina, 8-OHdG) y el perfil lipídico fueron evaluados. La ingesta dietética se estimó por medio de un cuestionario cuantitativo de frecuencia de consumo.
Resultados: La CG de la dieta se asoció positivamente con las concentraciones de ácidos grasos libres (β = 0,311, r2 = 0,13, P = 0,034) y la razón triglicéridos/colesterol HDL (β = 0,598, r2 = 0,19, P = 0,028), independientemente de los factores de confusión (obesidad central, consumo de carne roja, edad e ingesta calórica). El biomarcador del estrés oxidativo, 8-OHdG, también se asoció con CG de la dieta habitual (β = 0,432, r2 = 0,11, P = 0,004), independientemente de los factores de confusión anteriores más el consumo excesivo de alcohol, la ingesta de hierro y tabaquismo actual.
Conclusiones: La CG de la dieta se asoció positivamente con el perfil lipídico (concentraciones de ácidos grasos libres y razón triglicéridos/HDL colesterol) y el biomarcador de estrés oxidativo 8-OHdG. Estos resultados indican el potencial de nocividad de una dieta con mayor CG respecto a los factores de riesgo cardiometabólico en hombres de mediana edad, incluso en aquellos físicamente activos.

Palabras clave: Hábitos alimentares. Hidratos de carbono. Triglicéridos. Lipoproteínas de alta densidad. Estrés oxidativo.


Abbreviations
8-iso-PGF2α: 8-iso-prostaglandin F2α.
8-OHdG: 8-hydroxydeoxyguanosine.
BMI: body mass index.
CVD: Cardiovascular diseases.
DM2: type 2 diabetes.
FFA: Free fatty acids.
FFQ: food frequency questionnaire.
GI: glycemic index.
GL: glycemic load.
HDL-C: high density lipoprotein.
HOMA-IR: Homeostatic model of assessment of insulin resistance.
NADH: Reduced nicotinamide adenine dinucleotide.
NAD+: Oxidized nicotinamide adenine dinucleotide.
TG/HDL-C: ratio between triglycerides and high density lipoprotein.
TG: triglycerides.

 

Introduction

Cardiovascular diseases (CVD) are the leading cause of mortality worldwide. The World Health Organization estimated that approximately 30% of deaths from CVD occurred in the world in 2008 and that this rate will reach higher proportions in 20301. The occurrence of these diseases is highly influenced by environmental factors, particularly, the quality of food intake2 and the level of physical activity3.

The role of carbohydrate intake as a risk factor for manifestation of chronic diseases has received important attention in the scientific community. The dietary glycemic load (GL), obtained by multiplying the glycemic index (GI) of a food/meal by its available carbohydrate content4, reflects the quality and the amount of consumed carbohydrate. The consumption of high-GL diets leads to postprandial hyperglycemia/ hyperinsulinemia, increasing the risk for CVD5. Moreover, the increase of dietary GL has been associated with CVD risk factors, such as reduced concentrations of high density lipoprotein (HDL-C), high concentrations of triglycerides6-8 and higher concentrations of oxidative stress marker9. Thus the dietary pattern adopted by the modern society, characterized by high consumption of carbohydrate-rich foods and high-GI diet10, may increase the risk for CVD in the population.

Most of the previous studies associating dietary GI/GL with cardiovascular risk factors were typically conducted in women,8,11 type 2 diabetes (DM2)12 and obese subjects13. On the other hand, the relationship between dietary GL and cardiometabolic risk factors in physically active individuals, to our knowledge, is not clear yet. Taking into consideration that regular physical activity has beneficial effects against CVD14 and that aging is a potent cardiovascular risk factor1 this cross-sectional study was designed to assess the association of habitual dietary GL with cardiometabolic risk factors in physically active middle-aged men.

 

Materials and methods

Study population

One-hundred seventy-six men aged between 40 and 59 years participated in this study. The staff members of Universidade Federal de Viçosa, Brazil were recruited by systematic sampling using interview as previously described15. The following exclusion criteria were considered: body weight changes greater than 3 kg in the three months preceding the beginning of the study, thyroid diseases, heart failure, cerebrovascular diseases, infectious diseases, inflammatory diseases, gastrointestinal tract diseases, liver disease, chronic kidney disease and/or history of kidney stones, cancer in the previous ten years, eating disorders (anorexia and bulimia), food allergies, changes in the level of physical activity and in the eating habit in the three months preceding the study. Subjects using vitamin supplements, diuretics or drugs that affect food intake and/or the metabolism of nutrients, pacemaker and/or prosthetic limbs users, elite athletes and subjects who were not physically active (number of steps < 10,000)16 were also excluded.

The study was conducted according to the Declaration of Helsinki guidelines and all procedures involving human subjects were approved by the Ethics Committee in Human Research of the Federal University of Viçosa (Reference no 069/2010). Written informed consent was obtained from all participants of the study.

Dietary intake assessment

A food frequency questionnaire (FFQ), developed for the Brazilian population, was used to assess the usual dietary intake of the participants17. Daily food consumption was estimated as frequency x portion x size for each consumed food item. Nutrient intake was assessed using the software Dietpro® version 5.5i (AS Systems, Viçosa, Brazil), using mainly two Brazilian nutritional composition tables or an international composition table when the needed nutritional information was not described in these tables conforming described previously18.

The GI values for most foods listed on the FFQ were obtained from the University of Sydney GI data base website19. The GI of foods not listed in that database was estimated considering the GI of foods that had similar nutritional composition and methods of preparation. Dietary GI and GL were calculated using the formulas described by Levitan et al.:11 [Dietary GI = Σfoods Carbohydrate (g) in a serving of food x Frequency of consumed food x GI Σfoods Carbohydrate (g) in a serving of food x Frequency of consumed food; Dietary GL = Σfoods Carbohydrate (g) in a serving of food x Frequency of consumed food x GI/100]

It should be noted, however, that we considered only the available carbohydrate for these calculations.

Anthropometric and body composition assessments

Body weight, height and waist circumference were taken using standard measurement procedures, as previously described15. Body mass index was calculated as weight (kg) divided by height squared (m2).The cut-off used for central obesity was waist circumference ≥ 94 cm20.

Total body fat percentage was determined by total body scanning with a dual energy X-ray absorptiometry (GE/Lunar, Madison, WI, USA; enCORE software version 13.31).

Lifestyle co-variables

The participants were asked about their current smoking status (yes/no) and habitual quantity/ frequency of alcohol consumption. Excessive alcohol consumption was defined as a daily ingestion above 21 units per week21. So, in the statistical analysis, were considered excessive alcohol consumer (yes) or no alcohol consumer (no).

The habitual physical activity was estimated by the mean number of daily steps (7 consecutive days) measured by Digi-Walker SW-200 pedometer (Yamax Corporation, Tokyo, Japan), according to the instructions previously described15. A minimum of 10,000 steps/day was considered the cut-off point to classify individuals as active16.

Sample collection and analysis

Blood samples were collected from the antecubital vein after 12-hour overnight fasting. The serum was separated from whole blood by centrifugation at 2.225 g for 15 min at room temperature (2-3 Sigma, Sigma Laborzentrifuzen, Osterodeam Harz, Germany) and was immediately frozen at -80o C until analysis.

Glucose, insulin and the Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) index were assessed by protocol previously described15.

Free fatty acids (FFA) were determined by a kinetic spectrophotometry method using the kit EnzyChrom Free Fatty Acid Assay (Bioassay Systems, Hayward, CA). Serum HDL-C and triglyceride (TG) were determined by the enzymatic colorimetric method (Cobas Mira Plus - Roche Diagnostics GmbH, Montclair, NJ, USA). The atherogenic index was calculated by the ratio between triglycerides and HDL-C (TG/HDL-C ratio)22.

The urine samples were collected after 12-hour overnight fasting and frozen at -80o C until analysis. The concentrations of 8-iso-prostaglandin F2α (8-iso-PGF2α) (Oxford, MI, USA) and 8-hydroxydeoxyguano-sine (8-OHdG) (Cayman, MI, USA) were determined by competitive ELISA according to manufacturer's instructions. Although Cayman's kit recognizes the 8-OHdG from DNA, the ELISA values are always higher than LC/MS inasmuch as this method also detects 8-hydro-xyguanosine and 8-hydroxyguanine from either DNA or RNA. The values for urinary 8-iso-PGF2α and 8-OHdG were normalized by milligrams of urinary creatinine which was measured by a kinetic colorimetric method using a Bioclin commercial kit (Cobas Mira Plus - Roche Diagnostics GmbH, Montclair, NJ, USA).

Statistical analysis

Normal distribution of data was assessed by the Shapiro-Wilk test. Non-normally distributed variables were log-transformed before statistical analysis. The nutrients, dietary GI, and dietary GL were energy adjusted using the residual method as previously applied11,23. The comparison of nutrients consumption among lower or higher dietary GL (estimated by median) was performed by t test. Such methodological procedure of dividing participants into groups of risk has been used in epidemiologic study previously8.

To evaluate the associations of dietary GL, available carbohydrate and dietary GI with FFA and with TG/HDL-c ratio we used linear multiple regression controlled by the occurrence of central obesity (waist circumference ≥ 94 cm), red meat consumption (g/d), age (years) and energy intake (kcal/d). To evaluate the associations of dietary GL, available carbohydrate and dietary GI with 8-iso-PGF2α and with 8-OHdG we used linear multiple regression controlled by previous confounding factors plus others important confounding factors for oxidative stress such as excessive alcohol consumption (yes/no), daily iron intake (mg/d) and current smoking status (yes/no).

In addition, the Spearman correlation coefficient was used to investigate the correlation of HOMA-IR with FFA and with TG/HDL as well as that of dietary GL with available carbohydrate and with dietary GI.

Data processing and analysis were performed using the software STATA version 9.1 (Stata Corp., College Station, TX, USA). The P-value <0.05 was considered as statistically significant.

 

Results

Anthropometric, clinical and lifestyle characteristics of the participants are shown in table I. The mean BMI was equivalent to 25.5 kg/m2 and the number of steps per day corresponded to 13,591. The central obesity occurrence (waist circumference ≥ 94 cm) was 31.2%.

 

 

Regarding the dietary habits, according to the median of dietary GL consumed by participants, those subjects who consumed a diet with lower GL (<105.2 units) also consumed a lower GI diet, lower amount of carbohydrate, as well as higher amount of protein, fat, red meat and iron than those who consumed a diet with higher GL (≥ 105.2 units) (Table II).

Multiple linear regression models were applied to assess the relationship of dietary GL, dietary GI and available carbohydrate with blood lipid profile (FFA concentrations and TG/HDL-C ratio) and with the concentrations of oxidative stress markers. Interestingly, TG/HDL-C ratio and concentrations of FFA and of 8-OHdG, a marker of oxidative DNA damage were positively associated with dietary GL, regardless of interfering variables (Table III). Moreover, there was a positive association of TG/HDL-C ratio and 8-OHdG concentrations with the dietary available carbohydrate, regardless of interfering variables. However, there were no associations between dietary GI and the evaluated variables (Table III).

Finally, was verified a positive correlation of blood lipid profile (TG/HDL-C ratio and FFA concentrations) with HOMA-IR (Fig. 1) as well as of dietary GL with available carbohydrate and dietary GI (Fig. 2).

 

Discussion

This cross-sectional study was carried out to evaluate the association of habitual dietary GL with cardiometabolic risk factors in physically active Brazilian middle-aged men.

An important finding was the positive association of dietary GL with the TG/HDL-C ratio in this population, after adjustment for the confounding variables. The increased concentrations of TG and the reduced concentrations of HDL-C promoted by the consumption of high-carbohydrate diets may be related to decreased clearance of TG-rich particles. The clearance of TG occurs through the action of lipoprotein lipase (LPL) both in the TG stored in adipose tissue or skeletal muscle. In the skeletal muscle LPL is also involved in TG oxidation. However, the competition between chylomicron from enterocytes and VLDL-C particles (from liver) by LPL leads to its accumulation in plasma, which stimulates cholesterol ester transfer from HDL-C and LDL-C to those TG-rich lipoproteins. Subsequently, the content of TG from HDL-C particles could be hydrolyzed by hepatic lipase forming small particles of such lipoprotein that are rapidly removed via circulation24.

Similar results were reported in the Health Workers Cohort Study7 with men and women aged 20 to 70 years and by other11 in women (≥ 45 years) without diabetes/DCV showing a positive association of dietary GL with TG concentrations and a negative association with HDL-C concentrations. In contrast, a randomized clinical trial demonstrated that despite the reduction in dietary GL, no association of this change with improvements in TG and HDL-C concentrations was found in middle-aged men and women23. It is possible that the results reported by Lin et al.23 reflects the impact of the food intake under-reporting on GL classification and on its association with risk factors.

Hypertriglyceridemia induced by high-carbohydrate diets can also be triggered by insulin resistance due to stimulation of lipolysis and consequently an endogenous overproduction of FFA and VLDL-C25. Moreover, the glucose and insulin concentrations increase considerably in the period immediately after consumption of high GI26/GL5 diet while in the period 4 to 6 hours postprandial there is glycaemia reduction and possibility hypoglycemia, which leads to increased release of counterregulatory hormones, and consequently the increase in glycaemia and FFA levels27. In fact our results show a positive association of FFA levels with dietary GL, as well as, a positive correlation of insulin resistance (HOMA-IR) with FFA levels and with TG/HDL-C ratio.

The inverse relationship between HDL-C and CVD risk is well established inasmuch as HDL-C exerts antioxidant, anticoagulant, antiplatelet and antiatherogenic functions28. Moreover, high concentrations of FFA29 and TG/HDL-C ratio30 may favor the increase of CVD risk. Thus, our results indicate that the increase in dietary GL is a possible risk factor for CVD, even in physically active individuals.

The practice of walking (metabolic equivalent hours per day walk) was associated with lower CVD risk in a cohort study that included the participation of men and women14. In addition, daily walking can attenuate the fasting TG concentrations and remnants particles in response to high-carbohydrate intake31. However, dietary GL was not assessed by Koutsari et al.31 and in other study14 the association of physical activity with dietary intake was not investigated. Thus, it is still not yet clear whether the walking (directly related to number of steps) may actually attenuate the deleterious effects of dietary high GL on cardiometabolic risk factors manifestation.

Postprandial hyperglycemia also has been related to oxidative stress3, 2 possibly due to increased production of free radicals by the non-enzimatic glycation; autooxidation of glucose; and intracellular activation of the polyol pathway, which produces an imbalance in the NADH/NAD+ ratio33. In this context, another important result obtained in this study was the positive association between dietary GL and urinary 8-OHdG, an oxidative DNA damage marker.

Besides postprandial hyperglycemia, oxidative stress may be a consequence of a reduction in the antioxidant activity34. However, since HDL-C has antioxidant function35 and a negative association of dietary GL with this lipoprotein was demonstrated in the present study, we performed complementary analysis and observed a negative correlation between 8-OHdG and HDL-C (R = -0.153, P-value = 0.04). This outcome indicates that the pro-oxidant effect of dietary GL could also be related to reduced HDL-C concentrations.

Our results showed no association between dietary GL and the lipid peroxidation marker, 8-iso-PGF2 . In concert, a crossover study showed no significant changes in the fasting urinary isoprostane concentrations in young men (29.4 ± 4.4 years) after the consumption of high or low GI36. Another cross-sectional study reported that plasma malondialdehyde concentrations, but not isoprostane, increased linearly with the increase in GL in healthy men and women (46.7 ± 13.5 years)9. In view of these results, investigations are needed to further test the relationship between dietary GL and changes in the concentrations of 8-iso-PGF2α.

It is noteworthy that in the present study, we observed a strong correlation of dietary GL with available carbohydrate consumption and a moderate correlation between dietary GI and dietary GL. Similar results were obtained in the Women's Health Study conducted in postmenopausal women without diagnosis of CVD or cancer11 and in another study involving healthy men and women (18-75 years)9. These outcomes suggest that the available carbohydrate but not the dietary GI explains better the statistical results obtained for dietary GL.

Some limitations of the current study should be noted. First, since this is a cross-sectional, the results showed here must be cautiously considered as we cannot assure that the observed associations show a cause/effect relationship, although we controlled potential interfering variables. Second, further studies involving larger numbers of physically active individuals and/or with longitudinal design should be conducted before considering the application of these results at the population level.

In summary, the data presented in this crosssectional study showed that dietary GL was positively associated with blood lipid profile (TG/HDL-C ratio and FFA concentrations) and with oxidative stress marker (8-OHdG) in physically active men. These results indicate the potentially harmful influence of the consumption of higher GL diets on cardiometabolic risk factors, even in physically active subjects.

 

Acknowledgments

The authors thank the nursing staff for excellent technical assistance and the students, who helped in the fieldwork of the study. AJN, MCGP and RCGA are CNPq fellows.

 

References

1. World Health Organization. Global atlas on cardiovascular disease prevention and control. World Health Organization: Geneva, Switzerland, 2011.         [ Links ]

2. Sanchez-Muniz FJ. Dietary fibre and cardiovascular health. Nutr Hosp 2012; 27: 31-45.         [ Links ]

3. Rique ABR, Soares EA, Meirelles CM. Nutrition and exercise on cardiovascular disease prevention and control. Rev Bras Med Esporte 2002; 8: 244-54.         [ Links ]

4. Foster-Powell K, Holt SH, Brand-Miller JC. International table of glycemic index and glycemic load values: 2002. Am J Clin Nutr 2002; 76: 5-56.         [ Links ]

5. Brand-Miller JC. Glycemic load and chronic disease. Nutr Rev 2003; 61: S49-55.         [ Links ]

6. Radhika G, Ganesan A, Sathya RM, Sudha V, Mohan V. Dietary carbohydrates, glycemic load and serum high-density lipoprotein cholesterol concentrations among South Indian adults. Eur J Clin Nutr 2009; 63: 413-20.         [ Links ]

7. Denova-Gutierrez E, Huitron-Bravo G, Talavera JO, Castanon S, Gallegos-Carrillo K, Flores Y, et al. Dietary glycemic index, dietary glycemic load, blood lipids, and coronary heart disease. J Nutr Metab 2010. DOI: 10.1155/2010/170680.         [ Links ]

8. Liu S, Manson JE, Stampfer MJ, Holmes MD, Hu FB, Hankinson SE, et al. Dietary glycemic load assessed by food-frequency questionnaire in relation to plasma high-density-lipoprotein cholesterol and fasting plasma triacylglycerols in postmenopausal women. Am J Clin Nutr 2001; 73: 560-6.         [ Links ]

9. Hu Y, Block G, Norkus EP, Morrow JD, Dietrich M, Hudes M. Relations of glycemic index and glycemic load with plasma oxidative stress markers. Am J Clin Nutr 2006; 84: 70-6; quiz 266-7.         [ Links ]

10. Brand-Miller JC, Griffin HJ, Colagiuri S. The carnivore connection hypothesis: revisited. J Obes 2012; 2012: 258624.         [ Links ]

11. Levitan EB, Cook NR, Stampfer MJ, Ridker PM, Rexrode KM, Buring JE, et al. Dietary glycemic index, dietary glycemic load, blood lipids, and C-reactive protein. Metabolism 2008; 57: 437-43.         [ Links ]

12. Afaghi A, Ziaee A, Afaghi M. Effect of low-glycemic load diet on changes in cardiovascular risk factors in poorly controlled diabetic patients. Indian J Endocrinol Metab 2012; 16: 991-5.         [ Links ]

13. Armendariz-Anguiano AL, Jimenez-Cruz A, Bacardi-Gascon M, Hurtado-Ayala L. Effect of a low glycemic load on body composition and Homeostasis Model Assessment (HOMA) in overweight and obese subjects. Nutr Hosp 2011; 26: 170-5.         [ Links ]

14. Williams PT, Thompson PD. Walking Versus Running for Hypertension, Cholesterol, and Diabetes Mellitus Risk Reduction. Arteriosclerosis, Thrombosis, and Vascular Biology 2013; 33: 1085-91.         [ Links ]

15. Cocate PG, de Oliveira A, Hermsdorff HH, Alfenas RD, Amorim PR, Longo GZ, et al. Benefits and relationship of steps walked per day to cardiometabolic risk factor in Brazilian middle-aged men. J Sci Med Sport 2013. DOI: 10.1016/j.jsams. 2013.04.017.         [ Links ]

16. Tudor-Locke C, Bassett DR, Jr. How many steps/day are enough? Preliminary pedometer indices for public health. Sports Med 2004; 34: 1-8.         [ Links ]

17. Ribeiro AB, Cardoso MA. Development of a food frequency questionnaire as a tool for programs of chronic diseases prevention. Rev Nutr 2002; 15: 239-45.         [ Links ]

18. Cocate PG, Natali AJ, de Oliveira A, Alfenas RC, Peluzio MCG, Longo GZ, et al. Red but not white meat consumption is associated with metabolic syndrome, insulin resistance and lipid peroxidation in Brazilian middle-aged men Eur J Prev Cardiol 2013. DOI: 10.1177/2047487313507684.         [ Links ]

19. Sydney University Glycemic Index Research Service (SUGiRS) GI database. Sydney: University of Sydney; 2012. http://www.glycemicindex.com/ (2012, accessed 08 January 2012).         [ Links ]

20. Aschner P, Buendia R, Brajkovich I, Gonzalez A, Figueredo R, Juarez XE, et al. Determination of the cutoff point for waist circumference that establishes the presence of abdominal obesity in Latin American men and women. Diabetes Res Clin Pract 2011; 93: 243-7.         [ Links ]

21. Duncan BB, Schmidt MI, Giugliani ERJ. Medicina ambulatorial: condutas de atenção primaria baseada em evidências (Ambulatory medicine: conducts evidence-based primary care). Artmed: Porto Alegre, Brazil, 2004.         [ Links ]

22. Gaziano JM, Hennekens CH, O'Donnell CJ, Breslow JL, Buring JE. Fasting triglycerides, high-density lipoprotein, and risk of myocardial infarction. Circulation 1997; 96: 2520-5.         [ Links ]

23. Lin PH, Chen C, Young DR, Mitchell D, Elmer P, Wang Y, et al. Glycemic index and glycemic load are associated with some cardiovascular risk factors among the PREMIER study participants. Food Nutr Res 2012; 56.         [ Links ]

24. Polacow VO, Lancha Junior AH. High-carbohydrate diets: effects on lipid metabolism, body adiposity and its association with physical activity and cardiovascular disease risk. Arq Bras Endocrinol Metabol 2007; 51: 389-400.         [ Links ]

25. Parks EJ, Hellerstein MK. Carbohydrate-induced hypertriacyl-glycerolemia: historical perspective and review of biological mechanisms. Am J Clin Nutr 2000; 71: 412-33.         [ Links ]

26. Cocate PG, Pereira LG, Marins JC, Cecon PR, Bressan J, Alfenas RC. Metabolic responses to high glycemic index and low glycemic index meals: a controlled crossover clinical trial. Nutr J 2011; 10: 1.         [ Links ]

27. Ludwig DS. The glycemic index: physiological mechanisms relating to obesity, diabetes, and cardiovascular disease. JAMA 2002; 287: 2414-23.         [ Links ]

28. Natarajan P, Ray KK, Cannon CP. High-density lipoprotein and coronary heart disease: current and future therapies. J Am Coll Cardiol 2010; 55: 1283-99.         [ Links ]

29. Ebbert JO, Jensen MD. Fat depots, free fatty acids, and dyslipidemia. Nutrients 2013; 5: 498-508.         [ Links ]

30. Bittner V, Johnson BD, Zineh I, Rogers WJ, Vido D, Marroquin OC, et al. The triglyceride/high-density lipoprotein cholesterol ratio predicts all-cause mortality in women with suspected myocardial ischemia: a report from the Women's Ischemia Syndrome Evaluation (WISE). Am Heart J 2009; 157: 548-55.         [ Links ]

31. Koutsari C, Karpe F, Humphreys SM, Frayn KN, Hardman AE. Exercise prevents the accumulation of triglyceride-rich lipoproteins and their remnants seen when changing to a highcarbohydrate diet. Arterioscler Thromb Vasc Biol 2001; 21: 1520-5.         [ Links ]

32. Vidigal FC, Cocate PG, Pereira LG, Alfenas RCG. The role of hyperglycemia in the induction of oxidative stress and inflammatory process. Nutr Hosp 2012; 27: 1391-8.         [ Links ]

33. Ceriello A. Acute hyperglycaemia and oxidative stress generation. Diabet Med 1997; 14 (Suppl. 3): S45-9.         [ Links ]

34. Gohil JT, Patel PK, Gupta P. Evaluation of oxidative stress and antioxidant defence in subjects of preeclampsia. J Obstet Gynaecol India 2011; 61: 638-40.         [ Links ]

35. Badrnya S, Assinger A, Volf I. Native High Density Lipoproteins (HDL) Interfere with Platelet Activation Induced by Oxidized Low Density Lipoproteins (OxLDL). Int J Mol Sci 2013; 14:10107-21.         [ Links ]

36. Botero D, Ebbeling CB, Blumberg JB, Ribaya-Mercado JD, Creager MA, Swain JF, et al. Acute effects of dietary glycemic index on antioxidant capacity in a nutrient-controlled feeding study. Obesity (Silver Spring) 2009; 17: 1664-70.         [ Links ]

 

 

Correspondence:
Rita de Cássia Gonçalves Alfenas.
Universidade Federal de Viçosa.
Departamento de Nutrição e Saúde.
Av. PH Rolfs, s/n.
36570-000 Viçosa. Minas Gerais. Brasil.
E-mail: ralfenas@ufv.br

Recibido: 7-XI-2013.
Aceptado: 14-XI-2013.

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