Literature DB >> 32340088

Risk for metabolic syndrome in the population with visceral fat area measured by bioelectrical impedance analysis.

Han Ho Jeon1, Yong Kang Lee1, Dong Hyun Kim1, Haeyong Pak2, Sang Yun Shin1, Jeong Hun Seo1.   

Abstract

BACKGROUND/AIMS: To investigate whether visceral fat area (VFA) measured by bioelectric impedance analysis (BIA) was associated with metabolic syndrome in subjects with and without obesity.
METHODS: A total 23,202 participants who underwent medical check-ups were assessed. Participants were stratified by body mass index (BMI) and VFA. We evaluated six different groups for metabolic syndrome: Group 1 (normal weight and low VFA), Group 2 (normal weight and high VFA), Group 3 (overweight and low VFA), Group 4 (overweight and high VFA), Group 5 (obesity and low VFA), and Group 6 (obesity and high VFA).
RESULTS: Metabolic syndrome traits and metabolic syndrome were significantly more prevalent in the high-VFA (≥ 100 cm2 ) subgroup in each BMI group. Adjusted logistic regression analyses revealed that the odds ratio for metabolic syndrome compared with Group 1 was the highest in Group 6 (24.53; 95% confidence interval [CI], 21.77 to 27.64). Notably, the odds ratio of Group 2 was higher than that of Group 3 (2.92; 95% CI, 2.30 to 3.69 vs. 2.57; 95% CI, 2.23 to 2.97).
CONCLUSION: Our study demonstrates that the combination of BMI assessment and VFA determination by BIA may be a useful method for predicting the risk of metabolic syndrome. The VFA by BIA may be a useful target for interventions to improve metabolic syndrome.

Entities:  

Keywords:  Electric impedance; Intra-abdominal fat; Metabolic syndrome

Mesh:

Year:  2020        PMID: 32340088      PMCID: PMC7820642          DOI: 10.3904/kjim.2018.427

Source DB:  PubMed          Journal:  Korean J Intern Med        ISSN: 1226-3303            Impact factor:   2.884


INTRODUCTION

Due to recent changes in eating habits and lifestyle, the prevalence of obesity is increasing worldwide [1,2], regardless of race, age, and gender, and the incidence rates and complications of obesity-related diseases are increasing. People with obesity are more likely to develop metabolic disorders such as metabolic syndrome, type 2 diabetes, hypertension, dyslipidemia, ischemic heart disease, stroke, fatty liver, gallbladder disease, and thyroid disease [2-4]. Visceral fat plays an important role in the development of metabolic and cardiovascular diseases [5-7]. However, body mass index (BMI), the main measure used to determine obesity, does not account for the amount of muscle and fat, but simply accounts for body weight [8]. Therefore, there is a limit to predicting obesity-related disease. In addition, it is possible to evaluate abdominal obesity through the measurement of waist circumference [9], but there are limitations in that body fat percentage cannot be measured directly, and the reproducibility is poor due to large errors in the measurements [10]. Methods for measuring visceral fat area (VFA) include dual energy X-ray absorptiometry, computed tomography (CT), and magnetic resonance imaging; however, these methods require expensive equipment and exposure to radiation, which limits their use. Current body composition analysis through bioelectrical impedance analysis (BIA) is the most widely used method because it has the advantage of measuring body fat and muscle mass easily and inexpensively [11-13]. In this study, we analyzed the relationship between metabolic syndrome in the obesity group defined by BMI and the obesity group defined by the VFA determined by bioelectrical impedance and assessed the risk of metabolic syndrome by subdividing the groups according to the combination of BMI and VFA.

METHODS

Study population

We identified 43,837 adults (≥ 18 years old) who underwent voluntary routine check-ups at the National Health Insurance Service Ilsan Hospital between January 2011 and December 2015. When participants underwent multiple examinations, we analyzed the data from their first visit. We excluded participants with BMI less than 18.5 kg/m2. In total, 23,202 participants were included in this study. This study was approved by the Institutional Review Board of the National Health Insurance Service Ilsan Hospital (IRB No.: 2017-04-043-002). Written informed consent by the patients was waived due to a retrospective nature of our study.

Data collection

The participants arrived at the hospital after an overnight fast. Clinical and laboratory data were collected during the health examination. The height and weight of each participant were measured while he/she maintained a straight standing posture while wearing a light examination suit and no shoes, and the BMI was calculated. The waist circumference was measured mid-way between the lowest rib and the iliac crest. Visceral fat area was measured by a trained nurse using an InBody 720 (Biospace Co., Seoul, Korea) according to the manufacturer’s protocol. The following clinical and laboratory data were collected: age, body weight (kg), body mass index, smoking, drinking habits, waist circumference (cm), systolic blood pressure (mmHg), diastolic blood pressure (mmHg), uric acid (mg/dL), total cholesterol (mg/dL), high-density lipoprotein cholesterol (HDL-C; mg/dL), low-density lipoprotein cholesterol (LDL-C; mg/dL), triglycerides (mg/dL), fasting glucose (mg/dL), hemoglobin A1c (HbA1c, %), gamma-glutamyl transferase (GGT, U/L), aspartate aminotransferase (AST, U/L), and alanine aminotransferase (ALT, U/L).

Definitions

The cut-off point for obesity for Asians is defined by the World Health Organization as a BMI of 23.0 kg/m2 [14]. The participants were divided into three groups by BMI: normal weight (18.5 to 22.9 kg/m2), overweight (23 to 24.9 kg/m2), and obesity (≥ 25 kg/m2). The definition of obesity by VFA was set as VFA ≥ 100 cm2 [15]. Participants were categorized into six groups according to the combination of BMI and VFA. The six groups were (Fig. 1): (1) normal weight and VFA < 100 cm2 (Group 1); (2) normal weight and VFA ≥ 100 cm2 (Group 2); (3) overweight and VFA < 100 cm2 (Group 3); (4) overweight and VFA ≥ 100 cm2 (Group 4); (5) obesity and VFA < 100 cm2 (Group 5); and (6) obesity and VFA ≥ 100 cm2 (Group 6).
Figure 1.

Study participant and categorization. Normal weight, body mass index (BMI) 18.5 to 22.9 kg/m2; overweight, BMI 23 to 24.9 kg/m2; obesity, BMI ≥ 25 kg/m2. VFA, visceral fat area.

Metabolic syndrome was defined in accordance with the National Cholesterol Education Program Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults, Adult Treatment Panel III. Participants were defined as having metabolic syndrome if they had three or more of the following five factors: (1) waist circumference: male ≥ 90 cm, female ≥ 80 cm; (2) triglycerides: ≥ 150 mg/dL or use of medication; (3) HDL-C: male < 40 mg/dL, female < 50 mg/dL or use of medication; (4) blood pressure ≥ 130/85 mmHg (systolic blood pressure ≥ 130 mmHg or diastolic blood pressure ≥ 85 mmHg) or use of medication; and (5) fasting glucose ≥ 100 mg/dL or use of medication.

Statistical analysis

Continuous variables are presented as mean ± standard deviation. Categorical variables are presented as count (percentage). Differences between groups were tested by Student’s t test for continuous variables and the chi-square test for categorical variables. Multiple logistic regression analysis was used to determine the risk of metabolic syndrome in each of the six groups. First, we used the base model for the BMI and VFA at baseline. Model 1 was adjusted for age and sex, while Model 2 was adjusted for age, sex, diabetes mellitus, hypertension, alcohol intake, and smoking. The associations are presented as odds ratios (ORs) with 95% confidence intervals (CIs). All statistical analyses were performed with SAS 9.4 (SAS Institute, Cary, NC, USA). A p value below 0.05 was considered statistically significant.

RESULT

Baseline characteristics of study population

The demographic and clinical characteristics of the participants are shown in Table 1. The mean age was 47.6 years for women and 47.9 years for men. The mean VFA was 82.2 cm2 in women and 106.5 cm2 in men. The prevalence of metabolic syndrome in women and men was 19.6% and 29.7% respectively. The mean BMI, blood pressure, uric acid, total cholesterol, LDL-C, triglycerides, fasting blood glucose, GGT, AST, and ALT levels were significantly higher in men than in women. The mean HDL-C level was significantly higher in women than in men. Table 2 displays the correlations between VFA and various other parameters. The VFA correlated positively with BMI, blood pressure, uric acid, total cholesterol, LDL-C, triglycerides, fasting blood glucose, GGT, AST, and ALT levels, but negatively with HDL-C level.
Table 1.

Clinical characteristics of the study population stratified by gender

CharacteristicMale (n = 12,772)Female (n = 10,430)p value
Age, yr47.9 ± 11.947.6 ± 12.50.028
Body weight, kg73.5 ± 10.758.4 ± 8.3< 0.001
BMI, kg/m225.0 ± 3.023.3 ± 3.2< 0.001
Waist circumference, cm86.6 ± 7.880.6 ± 8.2< 0.001
VFA, cm2106.5 ± 35.682.2 ± 31.3< 0.001
Hypertension14,02 (11.0)674 (6.5)< 0.001
Diabetes mellitus2,229 (17.5)1,111 (10.7)< 0.001
Smokinga9,666 (75.7)745 (7.1)< 0.001
Alcohol drinkingb4,842 (37.9)3,298 (31.6)< 0.001
Systolic BP, mmHg123.7 ± 13.9117.39 ± 15.1< 0.001
Diastolic BP, mmHg75.9 ± 10.671.49 ± 10.5< 0.001
Total cholesterol, mg/dL198.7 ± 37.5195.8 ± 36.3< 0.001
HDL-C, mg/dL47.9 ± 11.456.67 ± 13.4< 0.001
LDL-C, mg/dL121.6 ± 33.2116.85 ± 32.3< 0.001
Triglyceride, mg/dL153.2 ± 112.798.23 ± 65.5< 0.001
HbA1c, %5.8 ± 0.85.7 ± 0.7< 0.001
Metabolic syndrome3,799 (29.7)2,046 (19.6)< 0.001
Fasting blood glucose, mg/dL99.1 ± 23.594.1 ± 17.5< 0.001
Uric acid, mg/dL6.0 ± 1.24.4 ± 0.9< 0.001
GGT, IU/L39.8 ± 64.217.5 ± 15.7< 0.001
AST, IU/L27.5 ± 14.522.7 ± 9.9< 0.001
ALT, IU/L30.8 ± 23.520.0 ± 13.4< 0.001

Values are presented as mean ± SD or number (%).

BMI, body mass index; VFA, visceral fat area; BP, blood pressure; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; HbA1c, hemoglobin A1c; GGT, gamma-glutamyl transferase; AST, aspartate transaminase; ALT, alanine transaminase.

Smoking: past smoker and current smoker.

Alcohol drinking: drink more than once a week.

Table 2.

Correlation between VFA and other variables

VariableCorrelation coefficient
Age0.209
BMI0.701
Waist circumference0.793
Systolic BP0.349
Diastolic BP0.307
Total cholesterol0.148
HDL-C–0.303
LDL-C0.174
Triglyceride0.322
Fasting glucose0.229
HbA1c0.228
Uric acid0.340
GGT0.166
AST0.223
ALT0.303

Values are the correlation coefficients and p < 0.001 for all analyses.

VFA, visceral fat area; BMI, body mass index; BP, blood pressure; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; HbA1c, hemoglobin A1c; GGT, gamma-glutamyl transferase; AST, aspartate transaminase; ALT, alanine transaminase.

Main characteristics of the groups defined by BMI and VFA

We divided participants into six groups according to the combination of BMI and VFA. The clinical characteristics of participants by gender according to BMI and VFA are shown in Tables 3 and 4. In men, the prevalence of metabolic syndrome was 6.1% in Group 1, 13.3% in Group 2, 13.1% in Group 3, 21.5% in Group 4, 25.4% in Group 5, and 56.2% in Group 6. In women, the prevalence of metabolic syndrome was 4.3% in Group 1, 21.6% in Group 2, 12.1% in Group 3, 30.9% in Group 4, 33.9% in Group 5 and 62.2% in Group 6. The subgroups of participants with VFA ≥ 100 cm2 were generally older and had greater BMI and waist circumference than those with VFA < 100 cm2. In addition, VFA ≥ 100 cm2 was associated with the presence of hypertension, diabetes mellitus, metabolic syndrome, and metabolic parameters.
Table 3.

Clinical characteristics of male according to BMI and VFA

CharacteristicNormal weight (n = 3,178)
Overweight (n = 3,548)
Obesity (n = 6,046)
VFA < 100 (n = 2,599)VFA ≥ 100 (n = 579)p valueVFA < 100 (n = 1,959)VFA ≥ 100 (n = 1,589)p valueVFA < 100 (n = 1,396)VFA ≥ 100 (n = 4,650)p value
Age, yr47.3 ± 13.150.6 ± 12.2< 0.00147.3 ± 11.650.3 ± 11.6< 0.00147.1 ± 11.248.0 ± 11.20.009
Body weight, kg62.5 ± 5.665.9 ± 5.5< 0.00169.5 ± 5.071.0 ± 5.4< 0.00176.0 ± 6.582.5 ± 9.7< 0.001
BMI, kg/m221.4 ± 1.122.1 ± 0.8< 0.00123.9 ± 0.624.1 ± 0.6< 0.00126.4 ± 1.327.8 ± 2.4< 0.001
Hypertension158 (6.1)64 (11.1)< 0.001156 (8.0)202 (12.7)< 0.001132 (9.5)690 (14.8)< 0.001
Diabetes mellitus228 (8.8)73 (12.6)0.004249 (12.7)269 (16.9)< 0.001230 (16.5)1180 (25.4)< 0.001
Smokinga1871 (72.0)469 (81.0)< 0.0011380 (70.4)1284 (80.8)< 0.001993 (71.1)3669 (78.9)< 0.001
Alcohol drinkingb836 (32.2)209 (36.1)< 0.001706 (36.0)608 (38.3)0.173531 (38.0)1952 (42.0)0.009
Waist circumference, cm77.1 ± 3.481.6 ± 2.6< 0.00182.9 ± 2.385.7 ± 2.6< 0.00188.1 ± 3.494.0 ± 6.1< 0.001
Metabolic syndrome159 (6.1)77 (13.3)< 0.001256 (13.1)341 (21.5)< 0.001355 (25.4)2611 (56.2)< 0.001
Systolic BP, mmHg118.1 ± 3.5122.0 ± 13.3< 0.001121.3 ± 13.5123.5 ± 13.2< 0.001124.4 ±13.3128.0 ± 13.3< 0.001
Diastolic BP, mmHg72.1 ± 10.174.9 ± 9.9< 0.00174.3 ± 10.475.8 ± 10.0< 0.00176.3 ± 10.478.8 ± 10.3< 0.001
Total cholesterol, mg/dL189.8 ± 34.7199.7 ± 39.1< 0.001195.9 ± 36.2204.2 ± 35.8< 0.001199.6 ± 35.9203.2 ± 39.00.001
HDL -C, mg/dL52.5 ± 13.049.6 ± 12.3< 0.00149.0 ± 11.248.3 ± 11.10.06146.8 ± 10.845.0 ± 9.6< 0.001
LDL-C, mg/dL114.0 ± 30.9122.0 ± 34.4< 0.001120.0 ± 31.2126.4 ± 32.9< 0.001122.9 ± 32.9124.8 ± 34.5< 0.001
Triglyceride, mg/dL108.9 ± 75.8144.0 ± 105.0< 0.001131.9 ± 110.2157.2 ± 104.0< 0.001151.2 ± 104.4188.2 ± 125.7< 0.001
HbA1c, %5.6 ± 0.85.8 ± 0.8< 0.0015.7 ± 0.85.8 ± 0.9< 0.0015.7 ± 0.85.9 ± 0.9< 0.001
Fasting blood glucose, mg/dL94.3 ± 22.898.1 ± 23.4< 0.00196.5 ± 23.8100.5 ± 23.8< 0.00197.9 ± 20.0103.0 ± 24.2< 0.001
Uric acid, mg/dL5.6 ± 1.15.8 ± 1.1< 0.0015.9 ±1.16.0 ± 1.2< 0.0016.1 ± 1.26.3 ± 1.3< 0.001
GGT, IU/L34.1 ± 108.240.0 ± 77.00.15732.0 ± 38.542.5 ± 47.6< 0.00135.7 ± 42.946. 9± 45.6< 0.001
AST, IU/L25.0 ± 17.526.3 ± 14.10.04925.3 ± 11.127. 1 ± 11.6< 0.00127.0 ± 13.530.3 ± 14.8< 0.001
ALT, IU/L23.6 ± 28.527.4 ± 22.1< 0.00126.2 ± 15.230.1 ± 19.0< 0.00129.8 ± 17.937.8 ± 24.5< 0.001

Values are presented as mean ± SD or number (%). Normal weight, BMI 18.5 kg/m2–22.9 kg/m2; Overweight, BMI 23 kg/m2–24.9 kg/m2; Obesity, BMI ≥ 25 kg/m2.

BMI, body mass index; VFA, visceral fat area; BP, blood pressure; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; HbA1c, hemoglobin A1c; GGT, gamma-glutamyl transferase; AST, aspartate transaminase; ALT, alanine transaminase.

Smoking: past smoker and current smoker.

Alcohol drinking: drink more than once a week.

Table 4.

Clinical characteristics of female according to BMI and VFA

CharacteristicNormal weight (n = 5,509)
Overweight (n = 2,220)
Obesity (n = 2,701)
VFA < 100 (n = 5,361)VFA ≥ 100 (n = 148)p valueVFA < 100 (n = 1,689)VFA ≥ 100 (n = 531)p valueVFA < 100 (n = 933)VFA ≥ 100 (n = 1,768)p value
Age, yr43.8 ± 11.155.1 ± 11.5< 0.00148.6 ± 11.455.4 ± 12.2< 0.00149.1 ± 11.755.4 ± 12.4< 0.001
Body weight, kg53.3 ± 4.555.9 ± 4.5< 0.00159.3 ± 4.360.0 ± 4.80.00364.8 ± 5.969.5 ± 8.8< 0.001
BMI, kg/m220.9 ± 1.222.0 ± 0.7< 0.00123.8 ± 0.624.1 ± 0.5< 0.00126.4 ± 1.428.3 ± 2.7< 0.001
Hypertension117 (2.2)14 (9.5)< 0.00190 (5.3)66 (12.4)< 0.00174 (7.9)313 (17.7)< 0.001
Diabetes mellitus263 (4.9)30 (20.3)< 0.001162 (9.6)85 (16.0)< 0.001133 (14.3)438 (24.8)< 0.001
Smokinga433 (8.1)17 (11.5)< 0.00195 (5.6)34 (6.4)0.50448 (5.1)118 (6.7)0.116
Alcohol drinkingb1,941 (36.2)37 (25.0)< 0.001511 (30.3)109 (20.5)< 0.001289 (31.0)411 (23.2)< 0.001
Waist circumference, cm74.6 ± 3.580.0 ± 2.2< 0.00181.4 ± 2.284.2 ± 2.2< 0.00187.1 ± 3.793.3 ± 6.6< 0.001
Metabolic syndrome229 (4.3)32 (21.6)< 0.001205 (12.1)164 (30.9)< 0.001316 (33.9)1,100 (62.2)< 0.001
Systolic BP, mmHg112.4 ± 13.5122.0 ± 16.2< 0.001118.2 ± 14.2122.9 ± 14.6< 0.001121.3 ± 14.7127.9 ± 14.3< 0.001
Diastolic BP, mmHg68.8 ± 9.875.2 ± 11.2< 0.00171.8 ± 10.175.2 ± 10.0< 0.00173.2 ± 10.377.3 ± 9.9< 0.001
Total cholesterol, mg/dL190.7 ± 33.6209.1 ± 38.5< 0.001197.3 ± 37.1206.5 ± 38.2< 0.001199.2 ± 37.0203.7 ± 38.90.004
HDL-C, mg/dL59.9 ± 13.756.1 ± 13.10.00155.5 ± 12.754.3 ± 11.80.05053.3 ± 12.250.7 ±+ 11.7< 0.001
LDL-C, mg/dL110.6 ± 29.7130.7 ± 34.0< 0.001119.5 ± 32.2127.4 ± 34.6< 0.001121.7 ± 31.7126.4 ± 35.20.001
Triglyceride, mg/dL80.0 ± 47.3108.6 ± 57.4< 0.00199.0 ± 62.8119.5 ± 70.9< 0.001115.9 ± 79.2135.7 ± 83.2< 0.001
HbA1c, %5.5 ± 0.55.8 ± 1.0< 0.0015.6 ± 0.65.9 ± 0.9< 0.0015.8 ± 0.76.0 ± 0.9< 0.001
Fasting blood glucose, mg/dL90.0 ± 12.299.6 ± 32.3< 0.00193.5 ± 14.598.6 ± 18.9< 0.00196.8 ± 18.0104.0 ± 25.2< 0.001
Uric acid, mg/dL4.2 ± 0.84.4 ± 1.00.0084.3 ± 0.94.6 ± 1.0< 0.0014.6 ± 1.04.8 ± 1.0< 0.001
GGT, IU/L15.0 ± 13.120.6 ± 16.2< 0.00116.9 ± 12.419.3 ± 11.6< 0.00119.5 ± 16.823.7 ± 22.9< 0.001
AST, IU/L21.3 ± 8.023.0 ± 6.30.00222.2 ± 8.123.9 ± 8.8< 0.00123.3 ± 8.926.9 ± 15.1< 0.001
ALT, IU/L17.2 ± 9.320.1 ± 7.7< 0.00119.7 ± 10.922.2 ± 11.6< 0.00121.9 ± 12.727.3 ± 21.8< 0.001

Values are presented as mean ± SD or number (%). Normal weight, BMI 18.5–22.9 kg/m2; overweight, BMI 23–24.9 kg/m2; obesity, BMI ≥ 25 kg/m2.

BMI, body mass index; VFA, visceral fat area; BP, blood pressure; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; HbA1c, hemoglobin A1c; GGT, gamma-glutamyl transferase; AST, aspartate transaminase; ALT, alanine transaminase.

Smoking: past smoker and current smoker.

Alcohol drinking: drink more than once a week.

Presence of metabolic syndrome in groups defined by BMI and VFA

In variable-adjusted models, we estimated the risk of metabolic syndrome in each group defined by BMI and VFA (Table 5). In Model 1, the odds ratios for metabolic syndrome compared with Group 1 were 2.91 (95% CI, 2.31 to 3.67), 2.64 (95% CI, 2.29 to 3.04), 5.27 (95% CI, 4.55 to 6.11), 7.62 (95% CI, 6.63 to 8.75), and 25.44 (95% CI, 22.60 to 28.63) in Groups 2, 3, 4, 5, and 6, respectively. In Model 2, the respective odds ratios for metabolic syndrome compared with Group 1 were 2.92 (95% CI, 2.30 to 3.69), 2.57 (95% CI, 2.23 to 2.97), 5.16 (95% CI, 4.44 to 6.00), 7.45 (95% CI, 6.47 to 8.56), and 24.53 (95% CI, 21.77 to 27.64) in Groups 2, 3, 4, 5, and 6.
Table 5.

The odds ratio of metabolic syndrome in the group stratified by the combination of body mass index and visceral fat area

VariableNo.Crude OR (95% CI)p valueModel 1
Model 2
Adjusted OR (95% CI)p valueAdjusted OR (95% CI)p value
Group 17,9601.001.001.00
Group 27273.44 (2.74–4.32)< 0.0012.91 (2.31–3.67)< 0.0012.92 (2.30–3.69)< 0.001
Group 33,6482.82 (2.45–3.25)< 0.0012.64 (2.29–3.04)< 0.0012.57 (2.23–2.97)< 0.001
Group 42,1206.10 (5.29–7.04)< 0.0015.27 (4.55–6.11)< 0.0015.16 (4.44–6.00)< 0.001
Group 52,3297.90 (6.89–9.05)< 0.0017.62 (6.63–8.75)< 0.0017.45 (6.47–8.56)< 0.001
Group 66,41826.75 (23.88–29.96)< 0.00125.44 (22.60–28.63)< 0.00124.53 (21.77–27.64)< 0.001

Model 1. Adjust variable: age, sex; Model 2. Adjust variable: Model 1 + diabetes mellitus, hypertension, alcohol, smoking.

OR, odds ratio; CI, confidence interval.

DISCUSSION

The following results were obtained through this study. First, VFA estimated by BIA correlated well with waist circumference and metabolic syndrome components. Although previous studies have shown similar results regarding VFA [11,12,16,17], our study demonstrated the usefulness of VFA estimation by BIA in a large number of participants. Visceral fat is known to be associated with insulin resistance and diabetes [18]. Our study revealed that VFA was also associated with HbA1c level. Additionally, we found that VFA was associated with the level of uric acid. Uric acid is not included in the diagnostic criteria for metabolic syndrome, but recent studies have revealed that increased uric acid level plays a causative role in the pathogenesis of metabolic, renal, and cardiovascular diseases and leads to increased mortality [19-21]. Second, when participants were divided into six groups, the odds ratio for metabolic syndrome tended to increase with group number, and the odds ratio for metabolic syndrome in Group 6 was very high at 24.53. Interestingly, when the normal-weight group was divided based on VFA, the odds ratio for metabolic syndrome in Group 2 (normal weight and VFA ≥ 100 cm2) was 2.92 compared with Group 1 (normal weight and VFA < 100 cm2). Additionally, the odds ratio for metabolic syndrome in Group 2 was higher than that in Group 3 (overweight and VFA < 100 cm2). The odds ratio for metabolic syndrome increased dramatically from 7.45 to 24.53 when participants with obesity based on BMI were further divided based on VFA (Group 5 vs. Group 6). These results suggest that VFA is an important component and cause of metabolic syndrome. Previous studies have demonstrated that VFA is associated with metabolic syndrome components and have proposed 100 cm2 as a reasonable cut-off point for VFA, suggesting that VFA < 100 cm2 may be an important factor for improvement [15,22]. Visceral fat is associated with a greater cardiometabolic risk than subcutaneous fat or high BMI [23,24]. The adipocytokines induced by visceral fat are closely related with metabolic disorders [25], which develop into various metabolic and cardiovascular diseases. Visceral fat reduces the production of defensive adipocytokines such as adiponectin [25,26]. Changes in adiponectin in obesity play an important role in the development of metabolic and cardiovascular complications [25,27]. Circulating adiponectin level was found to correlate with VFA but not BMI in subjects with obesity [28]. The clinical significance of our study is as follows. The combination of BMI assessment and VFA determination by the BIA method allowed the diagnosis of individuals with an unrecognized but relatively high risk of metabolic syndrome (such as those with normal BMI and VFA greater than 100 cm2). In addition, our study confirmed that the risk of metabolic syndrome was significantly lower (OR, 7.45 vs. 24.53) when VFA was less than 100 cm2 in participants with obesity based on BMI. In other words, VFA (cut-off point of 100 cm2) may be a useful target for interventions to improve metabolic syndrome. The present study had several limitations. First, our study was limited by its cross-sectional and single-center retrospective design. Second, exercise as a habitual parameter was not evaluated in the present study because the data was not correctly acquired. However, the influence of this limitation was reduced by the large sample size in our study. Third, VFA was not measured by a standard method such as CT, which is the most accurate and reliable method. However, VFA determined by BIA correlates well with that determined by CT [17,29]. In conclusion, this study demonstrated that the combination of BMI assessment and VFA determination by BIA may be a simple and useful method for predicting the risk of metabolic syndrome. The VFA by BIA may be a useful target for interventions to improve metabolic syndrome. Future interventional trials are needed to confirm the usefulness of targeting the VFA to improve metabolic syndrome.
  29 in total

Review 1.  The concept of metabolic syndrome: contribution of visceral fat accumulation and its molecular mechanism.

Authors:  Yuji Matsuzawa; Tohru Funahashi; Tadashi Nakamura
Journal:  J Atheroscler Thromb       Date:  2011-07-08       Impact factor: 4.928

2.  Low plasma adiponectin concentration is an indicator of the metabolic syndrome.

Authors:  Merja Santaniemi; Y Antero Kesäniemi; Olavi Ukkola
Journal:  Eur J Endocrinol       Date:  2006-11       Impact factor: 6.664

3.  Serum uric acid concentration as a risk factor for cardiovascular mortality: a longterm cohort study of atomic bomb survivors.

Authors:  Masayuki Hakoda; Naomi Masunari; Michiko Yamada; Saeko Fujiwara; Gen Suzuki; Kazunori Kodama; Fumiyoshi Kasagi
Journal:  J Rheumatol       Date:  2005-05       Impact factor: 4.666

4.  Hyperuricemia causes glomerular hypertrophy in the rat.

Authors:  Takahiko Nakagawa; Marilda Mazzali; Duk-Hee Kang; John Kanellis; Susumu Watanabe; Laura G Sanchez-Lozada; Bernardo Rodriguez-Iturbe; Jaime Herrera-Acosta; Richard J Johnson
Journal:  Am J Nephrol       Date:  2003 Jan-Feb       Impact factor: 3.754

5.  The utility of visceral fat level by bioelectrical impedance analysis in the screening of metabolic syndrome - the results of the Kyushu and Okinawa Population Study (KOPS).

Authors:  Mami Unno; Norihiro Furusyo; Haru Mukae; Tsunehisa Koga; Kunimitsu Eiraku; Jun Hayashi
Journal:  J Atheroscler Thromb       Date:  2012-01-31       Impact factor: 4.928

6.  Abdominal visceral and subcutaneous adipose tissue compartments: association with metabolic risk factors in the Framingham Heart Study.

Authors:  Caroline S Fox; Joseph M Massaro; Udo Hoffmann; Karla M Pou; Pal Maurovich-Horvat; Chun-Yu Liu; Ramachandran S Vasan; Joanne M Murabito; James B Meigs; L Adrienne Cupples; Ralph B D'Agostino; Christopher J O'Donnell
Journal:  Circulation       Date:  2007-06-18       Impact factor: 29.690

7.  How useful is body mass index for comparison of body fatness across age, sex, and ethnic groups?

Authors:  D Gallagher; M Visser; D Sepúlveda; R N Pierson; T Harris; S B Heymsfield
Journal:  Am J Epidemiol       Date:  1996-02-01       Impact factor: 4.897

8.  Impact of the Visceral Fat Area Measured by Dual Impedance Method on the Diagnostic Components of Metabolic Diseases in a Middle-aged Japanese Population.

Authors:  Koji Sakamaki; Yuko Maejima; Yoshiharu Tokita; Yasuhiro Masamura; Kensuke Kumamoto; Masako Akuzawa; Nobuo Nagano; Katsuyuki Nakajima; Kenju Shimomura; Seiichi Takenoshita; Yohnosuke Shimomura
Journal:  Intern Med       Date:  2016-07-01       Impact factor: 1.271

9.  Comorbidity of overweight and obesity in a nationally representative sample of German adults aged 18-79 years.

Authors:  Anja Schienkiewitz; Gert B M Mensink; Christa Scheidt-Nave
Journal:  BMC Public Health       Date:  2012-08-15       Impact factor: 3.295

10.  Risk for metabolic diseases in normal weight individuals with visceral fat accumulation: a cross-sectional study in Japan.

Authors:  Yukako Tatsumi; Yoko M Nakao; Izuru Masuda; Aya Higashiyama; Misa Takegami; Kunihiro Nishimura; Makoto Watanabe; Takayoshi Ohkubo; Tomonori Okamura; Yoshihiro Miyamoto
Journal:  BMJ Open       Date:  2017-01-16       Impact factor: 2.692

View more
  3 in total

1.  Association of obesity, visceral adiposity, and sarcopenia with an increased risk of metabolic syndrome: A retrospective study.

Authors:  Su Hwan Kim; Hyoun Woo Kang; Ji Bong Jeong; Dong Seok Lee; Dong-Won Ahn; Ji Won Kim; Byeong Gwan Kim; Kook Lae Lee; Sohee Oh; Soon Ho Yoon; Sang Joon Park
Journal:  PLoS One       Date:  2021-08-17       Impact factor: 3.240

2.  Association between sarcopenia level and metabolic syndrome.

Authors:  Su Hwan Kim; Ji Bong Jeong; Jinwoo Kang; Dong-Won Ahn; Ji Won Kim; Byeong Gwan Kim; Kook Lae Lee; Sohee Oh; Soon Ho Yoon; Sang Joon Park; Doo Hee Lee
Journal:  PLoS One       Date:  2021-03-19       Impact factor: 3.240

3.  Bioelectrical impedance analysis for early screening of upper limb subclinical lymphedema: A case-control study.

Authors:  Linli Zhuang; Huaying Chen; Xuemei Zheng; Shaoyong Wu; Youhui Yu; Lu Lan; Liang Xu; Jumei Xu; Hongying Fan
Journal:  PLoS One       Date:  2022-09-19       Impact factor: 3.752

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.