Visceral fat accumulation determines postprandial lipemic response, lipid peroxidation, DNA damage, and endothelial dysfunction in nonobese Korean men
2003; Elsevier BV; Volume: 44; Issue: 12 Linguagem: Inglês
10.1194/jlr.m300233-jlr200
ISSN1539-7262
AutoresYangsoo Jang, Oh Yoen Kim, Ha Jung Ryu, Ji Young Kim, Sang Hoon Song, José M. Ordovás, Jong Ho Lee,
Tópico(s)Diabetes, Cardiovascular Risks, and Lipoproteins
ResumoVisceral fat has been associated with multiple cardiovascular disease (CVD) risk factors. The aim of this study was to identify anthropometrical measures most closely associated with some well-known CVD risk factors. Because most Asians at risk have normal body mass index (BMI) according to Western standards, we studied healthy nonobese Korean males (n = 102; age: 36.5 ± 0.8 years, BMI: 23.8 ± 0.2 kg/m2). Visceral fat area (VFA) at the fourth lumbar vertebra was associated with increased postprandial triglyceride (TG) response (r = 0.53, P < 0.001) and with plasma malondialdehyde (MDA) (r = 0.36, P < 0.01) and PGF2α (r = 0.24, P < 0.05). When matched for BMI and age, men with high VFA (HVFA) (≥100 cm2; n = 27) had higher blood pressure (P < 0.01), increased consumption of cigarettes (P < 0.01), and lower ratio of energy expenditure to calorie intake (P < 0.01) as compared with low VFA men (<100 cm2; n = 27). Men with HVFA showed higher TG, glucose, and insulin responses following fat and oral glucose tolerance tests respectively higher plasma concentrations of MDA (P < 0.001), urinary PGF2α (P < 0.05), and lymphocytes deoxyribonucleic acid tail moments (P < 0.01). Conversely, HVFA was associated with lower testosterone, insulin-like growth factor-1, and brachial artery flow-mediated dilation (P < 0.001).In conclusion, our data indicate that visceral fat accumulation, even in nonobese men, is a major factor contributing to increased CVD risk. Visceral fat has been associated with multiple cardiovascular disease (CVD) risk factors. The aim of this study was to identify anthropometrical measures most closely associated with some well-known CVD risk factors. Because most Asians at risk have normal body mass index (BMI) according to Western standards, we studied healthy nonobese Korean males (n = 102; age: 36.5 ± 0.8 years, BMI: 23.8 ± 0.2 kg/m2). Visceral fat area (VFA) at the fourth lumbar vertebra was associated with increased postprandial triglyceride (TG) response (r = 0.53, P < 0.001) and with plasma malondialdehyde (MDA) (r = 0.36, P < 0.01) and PGF2α (r = 0.24, P < 0.05). When matched for BMI and age, men with high VFA (HVFA) (≥100 cm2; n = 27) had higher blood pressure (P < 0.01), increased consumption of cigarettes (P < 0.01), and lower ratio of energy expenditure to calorie intake (P < 0.01) as compared with low VFA men (<100 cm2; n = 27). Men with HVFA showed higher TG, glucose, and insulin responses following fat and oral glucose tolerance tests respectively higher plasma concentrations of MDA (P < 0.001), urinary PGF2α (P < 0.05), and lymphocytes deoxyribonucleic acid tail moments (P < 0.01). Conversely, HVFA was associated with lower testosterone, insulin-like growth factor-1, and brachial artery flow-mediated dilation (P < 0.001). In conclusion, our data indicate that visceral fat accumulation, even in nonobese men, is a major factor contributing to increased CVD risk. The most recent figures about the epidemic of obesity and overweight in the US and other industrialized countries are appalling. Thirty-four percent of US adults are considered overweight, and an additional 31 percent are obese. The consequences of this are far beyond the esthetics of the population. Being overweight or obese increases the risk of hypertension, heart disease, stroke, diabetes, and some cancers. In the US alone, 300,000 people die each year due to obesity-related causes, making it the second-leading cause of death after smoking. Asians experience similar risk of obesity-related diseases, but the problem is more insidious, as the risk threshold at which body mass index (BMI) appears to trigger the disease may be much lower for Asians (∼23 kg/m2) than for white populations (30 kg/m2) (1Choo V. WHO reassesses appropriate body-mass index for Asian populations.Lancet. 2002; 360: 235Abstract Full Text Full Text PDF PubMed Scopus (236) Google Scholar). This may result from differences in body frame that affect the relation between body fat and BMI. The population of Korea, similar to other industrialized countries in Asia, is experiencing dramatic and fast changes in dietary and physical activity habits that are the driving force behind the increase in obesity and obesity-related diseases. Korean adults have increased the percent of calories from fat in their diets from 6% in 1969 to 19% in 1998. Despite the greater than 3-fold increase in the dietary fat intake, their daily calorie intake (about 1,950 kcal/d) and BMI (about 22.5 kg/m2) have changed little over the same period (2South Korean Ministry of Health and Welfare Reports on 1969–1998 National Nutrition survey; Report on National Health and Nutrition Survey. Ministry of Health and Welfare, Seoul, South Korea2000Google Scholar). However, these lifestyle changes have dramatically affected the central adipose tissue distribution pattern as reflected by the global increase of waist-hip ratio (WHR). Among Koreans, the mortality rate (per 100,000 of the population) as a result of ischemic heart disease has rapidly increased from 6.8 in 1988 to 13.8 in 1997 (3South Korean National Statistical Office Annual Report on Cause of Death Statistics. National Statistical Office, Seoul, South Korea1999Google Scholar). The increase in cardiovascular disease (CVD) rates is partially related to the rapid increase in the number of elderly subjects; however, the increase of fat intake in Korean parallels also the disease trends (2South Korean Ministry of Health and Welfare Reports on 1969–1998 National Nutrition survey; Report on National Health and Nutrition Survey. Ministry of Health and Welfare, Seoul, South Korea2000Google Scholar, 3South Korean National Statistical Office Annual Report on Cause of Death Statistics. National Statistical Office, Seoul, South Korea1999Google Scholar). These temporal changes suggest that the increase in fat intake and central fat distribution may be partially responsible for the increasing CVD rates in Korea and other neighboring countries. Previous research has shown that adipose tissue distribution is more closely associated with risk of diabetes, hypertension, and hyperlipidemia than total body fat or BMI (4Kissebah A.H. Krakower G.R. Regional adiposity and morbidity.Physiol. Rev. 1994; 74: 761-811Crossref PubMed Scopus (995) Google Scholar). Thus, visceral fat values above 100–130 cm2 represent a strong risk for these metabolic disturbances (5Williams M.J. Hunter G.R. Kekes-Szabo T. Trueth M.S. Snyder S. Berland L. Bladeau T. Intra-abdominal adipose tissue cut-points related to elevated cardiovascular risk in women.Int. J. Obes. 1996; 20: 613-617PubMed Google Scholar, 6Despres J.P. Lamarche B. Effects of diet and physical activity on adiposity and body fat distribution: implications for the prevention of cardiovascular disease.Nutr. Res. Rev. 1993; 6: 137-159Crossref PubMed Scopus (245) Google Scholar). Moreover, both obesity and visceral fat accumulation have been reported to be related with increased postprandial lipemia (7Couillard C. Bergeron N. Prud'homme D. Bergeron J. Tremblay A. Bouchard C. Mauriege P. Despres J.P. Postprandial triglyceride response in visceral obesity in men.Diabetes. 1998; 47: 953-960Crossref PubMed Scopus (245) Google Scholar), an underestimated CVD risk factor. However, it remains uncertain which measure of obesity represents the best predictor of postprandial lipemia response. Moreover, other behavioral factors, such as alcohol consumption, cigarette smoking, or poor eating patterns such as excessive calorie or high fat intake, are also significant determinants of postprandial lipemia, as well as lipid peroxidation, and deoxyribonucleic acid (DNA) damage (8Parks E.J. Recent findings in the study of postprandial lipemia.Curr. Atheroscler. Rep. 2001; 3: 462-470Crossref PubMed Scopus (68) Google Scholar, 9Dhawan A. Mathur N. Seth P.K. The effect of smoking and eating habits on DNA damage in Indian population as measured in the Comet assay.Mutat. Res. 2001; 474: 121-128Crossref PubMed Scopus (100) Google Scholar, 10Axelsen M. Eliasson B. Joheim E. Lenner R.A. Taskinen M.R. Smith U. Lipid intolerance in smokers.J. Intern. Med. 1995; 237: 449-455Crossref PubMed Scopus (89) Google Scholar, 11Henning B. Toborek M. McClain C.J. High-energy diets, fatty acids and endothelial cell function: implications for atherosclerosis.J. Am. Coll. Nutr. 2001; 20: 97-105Crossref PubMed Scopus (140) Google Scholar, 12Williams M.J.A. Sutherland W.H.F. McCormick M.P. de Jong S.A. Walker R.J. Wilkins G.T. Impaired endothelial function following a meal rich in used cooking fat.J. Am. Coll. Cardiol. 1999; 33: 1050-1055Crossref PubMed Scopus (170) Google Scholar, 13Jang Y. Lee J.H. Huh K.B. Kim O.Y. Topham D. Balderston B. Influence of alcohol consumption and smoking habits on cardiovascular risk factors and antioxidant status in healthy Korean men.Nutr. Res. 2000; 20: 1213-1227Crossref Scopus (5) Google Scholar). The objectives of this study were to identify the anthropometrical factor most closely associated with variability on the postprandial lipid response to a fat load challenge and to investigate the relationships between this variability, lipid peroxides, and DNA damage of lymphocytes in groups of healthy nonobese men categorized according to the relevant anthropometrical factor emerging from our research. Our findings will contribute to a better understanding of the metabolic alterations and behavioral factors responsible for the rapid increase in CVD mortality in Korea and to provide guidance for preventive and therapeutic interventions. Study subjects were recruited from volunteers who responded to advertisements for a nutrition study conducted by the Clinical Nutrition Research Team at Yonsei University in 2001. All subjects had normal glucose tolerance tests and electrocardiograms. None of them was taking any medication or had clinical evidence of CVD or cancer. Finally, 102 healthy men were selected as study subjects. The ranges for age and BMI were 21–53 years and 18.2–27.6 kg/m2, respectively. Written informed consent was obtained from all subjects and the protocol was approved from the Ethical Committee of the Yonsei University. A 6 h postprandial lipemia response test was carried out starting at 8:30 AM after an overnight fast of greater than 12 h. A standardized test meal was prepared in the metabolic ward using common food items. It consisted of a sandwich containing white bread, lettuce, ham, and soybean oil-based mayonnaise. The energy content, calculated from the computerized database Korean food-code based on food composition tables from the National Rural Living Science Institute (6th edition, 2000) in Korea, was 608 kcal (2.54 mJ), representing the average calorie intake from a traditional breakfast. Fat represented 41.4% (28 g) of the calories, carbohydrates made up 45.4% (69 g), and 13.2% of calories (20 g) were derived from protein. In contrast, the macronutrient composition of the subjects' usual diet was that of a typical diet with cooked refined rice, consumed by a substantial number of Koreans, that is, about 57% of energy from carbohydrate, 22% from fat, 16% from protein, and 5% from others (mainly alcohol). Body weight and height were measured in the morning, unclothed and without shoes. BMI was calculated as body weight in kilograms divided by height in square meters. Waist and hip circumferences were combined into the WHR representing a commonly used surrogate of body fat distribution. Blood pressure was read from the left arm while subjects remained seated. An average of three measurements was recorded for each subject. We performed computerized tomography (CT) scanning using a General Electric (GE) High Speed Advantage 9800 scanner (Milwaukee, WI) to measure fat and muscle areas. Four cross-sectional images were obtained from each subject. Two abdominal ones at the level of the first lumbar (L1) and fourth lumbar (L4) vertebras, one from the thigh (midway between patella and pubis), and one from the calf (at the most protruding area). Each CT slice was analyzed for the cross-sectional area of fat using a density control program available in the standard GE computer software. Parameters for total abdominal fat density at the levels of L1 and L4 were selected between the range of −150 and −50 Hounsfield units (HU). Total abdominal fat area was divided into visceral and subcutaneous fat areas to calculate specific fat areas. Parameters for thigh and calf muscle areas were selected as between the range of −49 and +100 HU and for fat areas between −150 and −50 HU. Venous blood samples were obtained from the forearm and collected into EDTA-treated and plain tubes during fasting (baseline), and at 2 h, 3 h, 4 h, and 6 h after breakfast for assessment of glucose, insulin, free fatty acids (FFAs), and triglycerides (TGs). Tubes were immediately covered with aluminum foil and placed on ice until they arrived at the analytical laboratory (within 1–3 h) and were stored at −70°C. Subjects were asked to refrain from performing strenuous exercise or drinking alcoholic beverages 24 h prior to the fat tolerance test. They were also instructed to avoid eating or drinking anything except water during the test period. Fasting serum concentrations of total cholesterol and TG were measured using commercially available kits on a Hitachi 7150 Autoanalyzer (Hitachi Ltd., Tokyo, Japan). After precipitation of serum chylomicron, LDL, and VLDL with dextran sulfate-magnesium, HDL left in the supernatant was measured by an enzymatic method. LDL cholesterol concentrations were estimated indirectly using the Friedwald formula for subjects with serum TG < 4.52 mmol/l (400 mg/ml), or otherwise directly using commercially available kits on a Hitachi 7150 Autoanalyzer. Serum apolipoprotein A-I (apoA-I) and apoB were determined by turbidometry at 340 nm using a specific anti-serum (Roche, Switzerland). At a different time than the fat tolerance test, we carried out a glucose load test in order to investigate glucose tolerance. Each subject received a 75 g glucose solution after an overnight fast. Venous specimens were collected before and 30 min, 60 min, 90 min, and 120 min after the glucose load test. We used criteria, newly developed and modified by the National Diabetes Data Group and the World Health Organization Expert Committee on Diabetes Mellitus, to categorize subjects according to their glucose status (14The Expert Committee on the Diagnosis and Classification of Diabetes Mellitus Report of the Expert Committee on the Diagnosis and Classification of Diabetes Mellitus.Diabetes Care. 1997; 20: 1183-1197Crossref PubMed Scopus (7497) Google Scholar). Glucose was measured by a glucose oxidase method using the Beckman Glucose Analyzer (Beckman Instruments, Irvine, CA). Insulin was measured by radioimmuno assays with commercial kits from Immuno Nucleo Corporation (Stillwater, MN). FFA was analyzed with a Hitachi 7150 autoanalyzer (Hitachi Ltd., Tokyo, Japan). Each response of glucose, insulin, and FFA was calculated with the area under each response curve. Insulin resistance (IR) was also calculated with the homeostasis model assessment (HOMA) (15Haffner S.M. Kennedy E. Gonzalez C. Stern M.P. Miettinen H. A prospective analysis of the HOMA model.Diabetes Care. 1996; 19: 1138-1141Crossref PubMed Scopus (507) Google Scholar) using the following equation: IR = [fasting insulin (μU/ml) × fasting glucose (mmol/l)]/22.5. Immunoradiometric assays (IRMAs) were used to measure serum total testosterone using RIA-mat 280 (Byk-Sangtec Diagnostica, Germany) with Cost-A-Count total testosterone. Serum insulin like growth factor-1 (IGF-1) was measured using the IRMA kit from Diagnostic System Laboratories. Serum leptin was measured using Packard Cobra II 5005 R-Counter with the human leptin RIA kit from Linco. Urine was collected after a 12 h fast in polyethylene bottles containing 1% butylated hydroxytoluene before blood collection. The tubes were immediately covered with aluminum foil and stored at −70°C until extraction. Urinary 8-epi-prostaglandin F2α (8-epi-PGF2α) was quantified with gas chromatography (Hewlett Packard 6890, Wilmington, DE) and mass selective detector (Hewlett Packard 3973), according to the modified method of Pratico, Lawson, and Fitzgerald (16Pratico D. Lawson J.A. Fitzgerald G.A. Cyclooxygenase-dependent formation of the isoprostane, 8-epi-prostaglandin F2α.J. Biol. Chem. 1995; 270: 980-1008Abstract Full Text Full Text PDF Scopus (241) Google Scholar) and Mori et al. (17Mori T.A. Croft K.D. Puddey I.B. Beilin L.J. An improved method for the measurement of urinary and plasma F2-isoprostanes using gas chromatography-mass spectrometry.Anal. Biochem. 1999; 268: 11-25Crossref Scopus (193) Google Scholar). Urinary creatinines were determined by the alkaline picrated (Jeffe) reaction (18Liobat-Estelles M. Sevillano-Cabeja A. Campins-Falco P. Kinetic chemometric studies of the determination of creatinine using the Jaffe reaction. Part I: kinetics of the reaction; analytical conclusion.Analyst. 1989; 11: 597-602Crossref Scopus (18) Google Scholar), and urinary 8-epi-PGF2α levels were expressed as picogram per milligram creatinine (pgl/mg of creatinine). Plasma malondialdehyde (MDA) was assayed according to the fluorometric method described by Buckingham (19Buckingum K.W. Effect of dietary polyunsaturated/saturated fatty acid ratio and dietary vitamin E on lipid peroxidation in the rat.J. Nutr. 1985; 115: 1425-1435Crossref PubMed Scopus (59) Google Scholar). For the comet assay, 120 μl whole blood was mixed with 900 μl PBS and poured gently over 150 μl lymphocyte separation solution (HISTOPAQUE-1077). After centrifugation at 1,450 rpm for 4 min, lymphocytes were pipetted out and transferred to another tube. DNA damage was analyzed basically as described by Green et al. (20Green M.H. Lowe J.E. Harcourt S.A. Akinluyi P. Rowe T. Cole J. Anstey A.V. Arlett C.F. UV-C sensitivity of unstimulated and stimulated human lymphocytes from normal and Xeroderma pigmentosum donors in the comet assay: a potential diagnostic technique.Mutat. Res. 1992; 279: 137-144Crossref Scopus (146) Google Scholar). All steps were performed under dimmed light and the electrophoresis tank was covered with black paper to avoid additional light-induced DNA damage. Using high-resolution ultrasound (GE Vigmed Ultrasound, Horten, Norway), we assessed brachial arterial vasoreactivity to reactive hyperemia (flow-mediated dilation; FMD) and sublingual nitroglycerin (nitroglycerin-mediated dilation; NMD) with the method described by Kang et al. (21Kang S.M. Jang Y. Kim J.Y. Chung N. Cho S.Y. Chae J.S. Lee J.H. Effect of oral administration of testosterone on brachial arterial vasoreactivity in men with coronary artery disease.Am. J. Cardiol. 2002; 89: 862-864Abstract Full Text Full Text PDF PubMed Scopus (88) Google Scholar). A 10 MHz linear phased array ultrasound transducer (GE Vigmed Ultrasound, Horten, Norway) was used to image the dominant arm branchial artery longitudinally 3–5 cm just above the antecubital fossa. All subjects rested in the supine position for 10 min in a quiet room. The straight segment of the artery was chosen. After the depth and gain setting were optimized to identify the vessel wall, lumen interface and baseline brachial artery diameter were obtained. Brachial artery diameter was measured from the anterior to the posterior interface between the media and the adventitia and determined at end diastole on 2D image. Reactive hyperemia was induced by inflation and then deflation of a pneumatic cuff placed around the forearm portion. The blood pressure cuff was inflated to 250 mm Hg for 5 min, creating distal limb ischemia. After release of the cuff, brachial artery diameter was measured within the first 15 s of reactive hyperemia. FMD was then used as a measure of endothelium-dependent vasodilation. The brachial artery was allowed to return to the initial baseline level 10 min after cuff release. A further baseline brachial artery diameter was obtained. A 0.6 mg of nitroglycerin was then given sublingually, and the brachial artery diameter was then measured for the ensuing 3 min. The NMD was used as a measure of endothelium-independent vasodilation. The percent change in diameter caused by reactive hyperemia was calculated by dividing the difference from baseline end-diastolic diameter by the baseline value (FMD%). The percent change in diameter caused by nitroglycerin administration was also calculated in the same way (NMD%). Blood pressure and heart rate were measured before the examination. All data were calculated as an average from four consecutive cardiac cycles. Usual food intake was assessed with a 24 h recall method and a semi-quantitative food frequency questionnaire. Nutrient intake data were calculated as mean values from the same data base as referred above. Total calorie expenditure (kcal/day) was calculated from activity patterns including basal metabolic rate, physical activity for 24 h (22Christian J.L. Greger J.H. Nutrition for Living. The Benjamin/Cummings Publishing Company, Inc., San Francisco1991: 111Google Scholar), and specific dynamic action of food. Basal metabolic rate for each subject was calculated with the Harris-Benedict equation (23The American Dietetic Association Handbook of Clinical Dietetics. 2nd edition. Yale University Press, New Haven, CT1992: 5-39Google Scholar). We used SPSS version 11.0 for Windows (Statistical Package for the Social Science, SPSS Inc., Chicago, IL) for all our statistical analyses. Each variable was examined for normal distribution and significantly skewed variables were log transformed. For descriptive purposes, mean values were presented on untransformed and unadjusted variables. Results were expressed as mean ± SE. We used Pearson correlation coefficient to evaluate the correlation of the variables, and multiple regression analysis to investigate main factors influencing postprandial lipid response. Following identification of the main factor, subjects in upper 25th percentile of the factor were selected and individually matched by age (within a 2 year difference) and BMI (within a 1 kg/m2 difference) to subjects within the remaining 75th percentile. Selected subjects were grouped for subsequent analyses. Chi-squared tests were used to compare differences in frequencies for categorical variables. A two tailed value of P < 0.05 was considered statistically significant. Table 1 shows the basal characteristics of the 102 subjects initially selected to participate in this study. Their mean BMI was 23.8 ± 0.21 with a range of 18.2 to 27.6 years. Seventy-four men reported consumption of alcoholic beverages and 39 were current smokers. These subjects had similar habitual dietary fat intake, physical activity, and socioeconomic status.TABLE 1General characteristics of the sample of 102 nonobese healthy menMin–MaxAge (years) 36.5 ± 0.82 (21–53)BMI (kg/m2) 23.8 ± 0.21 (18.2–27.6)WHR 0.86 ± 0.00 (0.75–0.97)Body fat (%) 23.5 ± 0.56 (11.0–38.0)Blood pressure SBP (mmHg) 123.2 ± 1.30 (102–171) DBP (mmHg) 77.0 ± 1.09 (55–119)Current smoking (%)38 Tobacco consumption (cigarettes/day) 7.90 ± 0.95 (0–40)Current drinking (%)72 Alcohol intake (g/day) 18.6 ± 2.48 (0.0–153.9)Hypertension (%)16Total energy expenditure (kcal/day)2465.8 ± 36.7(1862–2943)Total calorie intake (kcal/day)2402.8 ± 51.7(1112–3104)% fat from total calorie intake 21.9 ± 0.88 (11.9–33.8)Adipose tissue areas (cm2) at first lumbar vertebra Subcutaneous 72.1 ± 3.23 (12.8–182.7) Visceral 98.7 ± 4.89 (13.3–227.0)Adipose tissue areas (cm2) at fourth lumbar vertebra Subcutaneous 76.2 ± 2.92 (17.3–139.4) Visceral 126.2 ± 4.47 (44.4–266.6)Postprandial TG area 972.4 ± 59.0 (225–3174)MDA (nmol/ml) 3.11 ± 0.17 (0.40–7.31)8-epi-PGF2α (pg/mg creatinine) 251.8 ± 24.0 (20.2–1156.4)BMI, body mass index; DBP, diastolic blood pressure; 8-epi-PGF2α, 8-epi prostaglandin F2α; MDA, malondialdehyde; SBP, systolic blood pressure; TG, triacylglycerol; WHR, waist-hip ratio. Mean ± SE. Open table in a new tab BMI, body mass index; DBP, diastolic blood pressure; 8-epi-PGF2α, 8-epi prostaglandin F2α; MDA, malondialdehyde; SBP, systolic blood pressure; TG, triacylglycerol; WHR, waist-hip ratio. Mean ± SE. The average mean values of subcutaneous and visceral fat areas (VFAs) at L1 and L4 vertebrae, postprandial TG area, MDA, and 8-epi-PGF2α of the 102 subjects are shown in Table 1. Table 2 shows that some anthropometrical parameters such as body fatness and abdominal adipose tissue distribution (WHR and VFA at L1 and L4 vertebrae) were positively correlated with postprandial lipemia, whereas all variables reflecting body fatness and adipose tissue distribution were positively associated with plasma MDA concentrations. BMI, WHR, subcutaneous fat areas at L1 and L4 levels, and VFA at L4 showed positive correlation with urinary excretion of 8-epi-PGF2α. Postprandial lipemia showed positively significant correlation with plasma MDA concentration (R = 0.332, P = 0.005, data was not shown in Table 2). Age was associated with neither postprandial lipemia nor lipid peroxides.TABLE 2Pearson correlations of age, body fat distribution, total triglyceride response area, and lipid peroxides in 102 nonobese healthy menPostprandial TG AreaaLN, log transformed.MDA8-epi-PGF2αaLN, log transformed.RPRPRPAge0.1660.138−0.0320.771−0.1020.348SBP0.4210.0000.2640.0150.1600.138DBP0.4240.0000.2440.0210.1150.289BMI0.1970.0780.3080.0040.2480.021WHR0.3460.0020.2920.0070.2430.023L1VF0.4610.0000.3270.0020.2010.064L1SF0.0830.4640.3210.0030.4140.000L4VF0.5860.0000.3560.0010.2420.025L4SF0.0360.7540.3150.0030.3590.001L1VF, visceral fat area at first lumbar vertebra; L1SF, subcutaneous fat area at first lumbar vertebra; L4VF, visceral fat area at fourth lumbar vertebra; L4SF, subcutaneous fat area at fourth lumbar vertebra.a LN, log transformed. Open table in a new tab L1VF, visceral fat area at first lumbar vertebra; L1SF, subcutaneous fat area at first lumbar vertebra; L4VF, visceral fat area at fourth lumbar vertebra; L4SF, subcutaneous fat area at fourth lumbar vertebra. Based on the information generated from the correlation analyses between body fat measures and postprandial TG area, we carried out stepwise multiple regression analysis in order to identify the most significant anthropometrical predictor of postprandial lipid response. Measures of body fatness, alcohol consumption, and cigarette smoking were used as independent variables, and postprandial TG area was the dependent variable. The data presented in Table 3 show that the most significant predictor of postprandial lipemic response was VFA at L4 level followed by total fat area at L4 level.TABLE 3Stepwise multiple regression for 102 nonobese healthy men using age, blood pressure, anthropometric parameter, fat areas at different levels of body, alcohol consumption, and cigarette smoking as independent variables and postprandial TG area as dependent variableDependent VariableStepIndependent VariableR 2Adjusted R 2PPostprandial TG areaaLN, log transformed.1 stepL4VF0.3440.3360.0002 stepL4TF0.3960.3810.000L4TF, total fat area at 4th lumbar vertebra. Fat areas at different levels of body: total, visceral, and subcutaneous fat areas at both 1st and 4th lumbar vertebra.a LN, log transformed. Open table in a new tab L4TF, total fat area at 4th lumbar vertebra. Fat areas at different levels of body: total, visceral, and subcutaneous fat areas at both 1st and 4th lumbar vertebra. Figure 1shows the distribution of VFA at L4 level in all 102 nonobese healthy men. Subjects in upper 25th percentile of the main factor (VFA at L4 level) were categorized as high VFA (HVFA, n = 27). The lowest VFA in this group was over 100 cm2 (100.3 cm2) and the average area was 114 ± 2 cm2. Subjects with HVFA were age- (within a 1 year difference) and BMI- (within a 1 kg/m2 difference) matched to control subjects selected for having low VFA (LVFA; <100 cm2, n = 27). The average area of the control group was 74 ± 3 cm2. Men within the HVFA were characterized by higher WHR and blood pressure and higher consumption of cigarettes than those within the LVFA group (Table 4). Among subjects within the HVFA, 38.5% had hypertension. No differences were found in subcutaneous fat area at both L1 and L4 vertebrae (Table 4). Nonobese men with HVFA showed higher ratio of visceral to subcutaneous fat area and lower muscle area of midthigh compared with those with LVFA.TABLE 4General characteristics, fat, and muscle areas in two groups of nonobese men matched on the basis of age and BMI but with low versus high visceral fat areaLow VFAHVFAAge (years)40.2 ± 1.7140.1 ± 1.07BMI (kg/m2)25.2 ± 0.2525.3 ± 0.30Waist-hip ratio0.86 ± 0.010.91 ± 0.01cP < 0.001 compared with low VFA.Body fat (%)24.1 ± 0.8726.0 ± 0.80Blood pressure SBP (mmHg)122.7 ± 1.94132.7 ± 2.79bP < 0.01 compared with low VFA. DBP (mmHg)75.0 ± 1.7885.3 ± 2.23cP < 0.001 compared with low VFA.Current drinking (%)75.0 88.9 Alcohol (g/day)17.1 ± 5.4925.2 ± 5.11Current smoking (%)39.370.4aP < 0.05 compared with low VFA. Tobacco (cigarettes/day)3.50 ± 1.0813.4 ± 2.02bP < 0.01 compared with low VFA.Hypertension (%)10.738.5aP < 0.05 compared with low VFA.First lumbar vertebra Total fat (cm2)177.6 ± 8.68237.0 ± 10.5bP < 0.01 compared with low VFA. Visceral fat (cm2)102.6 ± 5.92149.8 ± 7.57bP < 0.01 compared with low VFA. Subcutaneous fat (cm2)75.1 ± 4.0687.2 ± 5.92 Visceral-subcutaneous fat ratio1.40 ± 0.081.87 ± 0.14aP < 0.05 compared with low VFA.Fourth lumbar vertebra Total fat (cm2)209.6 ± 7.83259.8 ± 8.30bP < 0.01 compared with low VFA. Visceral fat (cm2)73.7 ± 3.03114.1 ± 2.03bP < 0.01 compared with low VFA. Subcutaneous fat (cm2)135.8 ± 6.60
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