OBJECTIVES: To investigate how abdominal adiposity assessed by different anthropometric measurements and dual- energy X-ray absorptiometry ...
International Journal of Obesity (1997) 21, 367±371 ß 1997 Stockton Press All rights reserved 0307±0565/97 $12.00
Relationship of metabolic variables to abdominal adiposity measured by different anthropometric measurements and dual-energy X-ray absorptiometry in obese middle-aged women P Rissanen1, P HaÈmaÈlaÈinen2, E Vanninen3, M Tenhunen-Eskelinen3 and M Uusitupa2 1 Department of Clinical Nutrition, University of Kuopio and Obesity Research Group, Helsinki; 2 Department of Clinical Nutrition, University of Kuopio; and 3 Department of Clinical Physiology, Kuopio University Hospital
OBJECTIVES: To investigate how abdominal adiposity assessed by different anthropometric measurements and dualenergy X-ray absorptiometry measurements is associated with metabolic risk factors for cardiovascular disease and non-insulin-dependent diabetes mellitus in obese women. DESIGN: Cross-sectional study. SUBJECTS: Forty-three healthy, obese, middle-aged women (age: 29±64 y, BMI: 28±42 kg/m2). MEASUREMENTS: (1) Anthropometry: waist circumference, waist-to-hip ratio, waist-to-height ratio, abdominal sagittal and transverse diameters and their ratio. (2) Dual-energy X-ray absorptiometry: the amount of total and regional abdominal fat. (3) Metabolic measurements: serum total, VLDL, LDL, HDL cholesterol, triglycerides, fasting and postglucose serum insulin and glucose. RESULTS: After adjustment for age and BMI, all the anthropometric measurements except waist-to-hip ratio and waist-to-height ratio related signi®cantly to HDL and LDL cholesterol. On the other hand, waist-to-hip ratio and waistto-height ratio showed an association with triglycerides. In addition, all the anthropometric measurements except transverse diameter correlated signi®cantly with fasting insulin and fasting glucose. Waist-to-hip ratio was the only measure that associated with 2 h glucose concentration. The differences between the correlation coef®cients were not statistically signi®cant in the z-transformed correlation coef®cient test. As to dual-energy X-ray absorptiometry results, the region from the dome of diaphragm to the top of femur (`abdominal fat') and the area between the ®rst and the fourth lumbal vertebrae (`upper lumbal fat') inversely related to HDL cholesterol and positively to triglycerides. Both of these regions correlated signi®cantly with fasting insulin, and `upper lumbal fat' associated also with fasting glucose even after adjustment for age and BMI. CONCLUSION: None of the anthropometric measurements (waist circumference, waist-to-hip ratio, waist-to-height ratio or sagittal diameter) was signi®cantly superior to others to assess the metabolic risk pro®le. `Upper lumbal fat' (the area between the ®rst and the fourth lumbal vertebrae) measured by dual-energy X-ray absorptiometry discerned obese women with elevated fasting insulin and fasting glucose. Keywords: abdominal obesity, anthropometry, dual-energy X-ray absorptiometry, metabolic risk factors
Introduction The strong risk of central obesity for coronary heart disease (CHD) and non-insulin-dependent diabetes mellitus (NIDDM) is mediated via different metabolic alterations. It has been suggested that increased lipolytic activity of abdominal fat, especially visceral fat results in excess release of free fatty acids (FFAs) into portal vein.1 Increased portal FFA availability may result in reduced hepatic insulin clearance,2,3 Correspondence: P Rissanen, Obesity Research Group, Paasikivenkatu 4, FIN-00250 Helsinki, Finland. Received 25 July 1996; revised 5 December 1996; accepted 20 January 1997
increased hepatic glucose production4 and increased very-low-density-lipoprotein (VLDL) triglyceride synthesis.5 All these abnormalities provide a conceptual rationale for examining the relationship between various metabolic disturbances and adipose tissue distribution. Waist-to-hip ratio (WHR) is the most commonly used anthropometric measure in the estimation of abdominal obesity. In recent studies also sagittal and transverse diameters were assessed, but they were measured by computerized tomography (CT)6 or by magnetic resonance imaging (MRI).7 Thus, little information is available on the usefulness of different anthropometric methods in evaluation of the association between fat distribution and metabolic risk factors for CHD and NIDDM.
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Dual-energy X-ray absorptiometry (DEXA) is a sophisticated method which is a cheaper and a more frequently available technique than MRI and CT. It also involves only a negligible radiation dose compared to CT,8,9 but there has been little interest in using it to examine the relation of central obesity to the levels of insulin, glucose and serum lipids. Thus, the aim of the present study was to investigate how abdominal adiposity measured by different anthropometric and DEXA methods is associated with metabolic risk factors for CHD and NIDDM in obese women. We also evaluated which region of DEXA measurements is most strongly related to the metabolic variables.
Materials Subjects
The subjects were recruited from occupational health care. After exclusions of 17 women, 43 women entered this cross-sectional study. The mean age (s.d.) was 45.6 8.9 y, range 29±64 y). The mean body mass index was 34.0 3.4 kg/m2, range 28±42. The main exclusion criteria were previously known or newly diagnosed diabetes, signi®cant thyroid, liver and kidney diseases, eating disorders and markedly elevated blood pressure (diastolic blood pressure 105 mmHg). Written consent was obtained from all subjects after a detailed explanation of the study protocol. The study was approved by the Ethics Committee at the University of Kuopio.
Methods Study design
All the measurements were made within ®ve weeks. The subjects performed no strenuous physical activity for 24 h and fasted for 12 h before the measurements and the blood tests. At the ®rst visit height and weight were measured. DEXA-measurements performed and serum lipids and lipoproteins were analysed. Oral glucose tolerance test and anthropometric measurements were made 4±5 weeks later than the other measurements. The subjects lost weight 2.4 kg on the average, range 0.1±4.4 kg during this period of time. Anthropometric measurements
Weight was measured to the nearest 0.1 kg using an electronic scale (Vogel & Halke GmbH & Co, Germany) and height was measured to the nearest 0.1 cm using a well-mounted stadiometer. Waist circumference was measured in standing position at the level midway between the lateral lower rib margin and the iliac crest and hip circumference at the levels of the trochanters, through the
pubic symphysis.10 The supine transverse diameter and the supine sagittal diameter were measured at the same level as waist circumference by a slide gauge on a hard couch.11 DEXA measurements
DEXA measurements were made by a densitometer (Lunar DPX, Lunar Radiation Corp, Madison, WI) using a medium speed total body scanning acquisition mode.8,9 The software version 3.6z was employed for subsequent analyses. Each patient was positioned with the arms suf®ciently separated from the trunk. The abdominal region of interest (ROI) was de®ned manually by adjusting the lines of rib box in four different ways. The `abdominal fat' was measured from the dome of the diaphragm to the top of the femur. The `upper lumbal fat' was measured between the ®rst and the fourth lumbal vertebrae. The `lower lumbal fat' was measured between the fourth and the ®fth lumbal vertebrae. The `hip fat' was gauged downwards the trochanter major so that the height of the area was as high as the height of `lower lumbal fat' area. Biochemical analyses
Serum and lipoprotein lipids were analysed after ultracentrifugation and precipitation by enzymatic methods,12,13 CHOD-PAP method for cholesterol and HDL cholesterol and GPO-PAP method for triglycerides. Oral glucose tolerance test (OGTT) was performed and samples were collected at 0, 30, 60, 90, 120 and 180 min to determine glucose and insulin levels. Serum glucose was determined by glucosedehydrogenase method14 and insulin by a radioimmunoassay with double antibody-PEG technique CIS (CIS biointernational, B.P. 32, F-91192, Git-surYvette Codex, France). Statistical methods
Statistical analyses were carried out using the SPSSX-program.15 Means and standard deviations (s.d.) were calculated for each variable. Bivariate and partial Pearson correlation coef®cients and scatter plot were used to quantify the relation between different measurements and metabolic variables. Correlation coef®cients were compared by a test based on ztransformed correlation coef®cients.16
Results The mean body mass index (mean s.d.) was 34.0 3.4 kg/m2, the body fat percentage 45.7 3.6% measured by DEXA and WHR 0.92 0.07 (Table 1). The results of the biochemical analyses are shown in Table 2. One woman had impaired glucose tolerance.
Anthropometric and DEXA measurements in obesity P Rissanen et al Table 1 Age and obesity indexes in study subjects Variable
Mean
s.d.
Range
Age (y) BMI (kg/m2) Total fat mass (%)a Waist circumference (cm) WHR WHTR Sagittal diameter (cm) Transverse diameter (cm) STR
45.6 34.0 45.7 102.5 0.92 0.64 22.9 35.6 0.64
8.9 3.4 3.6 9.7 0.07 0.07 2.7 2.8 0.05
29.0±64.0 28.1±42.2 40.1±52.9 74.8±116.0 0.68±1.06 0.46±0.79 16.2±28.0 30.2±43.1 0.53±0.75
BMI body mass index. a Measured by dual-energy X-ray absorptiometry; WHR waistto-hip ratio; WHTR waist-to-height ratio; STR sagittaltransverse ratio.
Table 2 Metabolic variables of study subjects Variable
Mean
s.d.
Range
LDL-cholesterol (mmol/l) HDL-cholesterol (mmol/l) Triglycerides (mmol/l) Fasting glucose (mmol/l) 2 h glucose (mmol/l) Fasting insulin (pmol/l)
3.7 1.2 1.6 5.0 6.1 73.8
0.9 0.3 0.7 0.5 1.4 32.4
2.2±6.0 0.8±1.9 0.6±3.7 4.0±6.2 3.2±8.6 24.0±156.0
Table 3 presents the relationship between metabolic variables and anthropometric measurements after controlling for age and BMI. The differences between correlation coef®cients were not statistically signi®-
cant in the z-transformed correlation coef®cient test. All the measurements were signi®cantly related to HDL (r 70.31±70.53). Waist circumference, sagittal diameter and transverse diameter associated with LDL cholesterol (r 0.34±0.36). WHR and WHTR correlated with triglycerides (r 0.38, 0.40). In addition, fasting insulin associated signi®cantly (r 0.32±0.50) with all the measurements except transverse diameter. Moreover, fasting glucose correlated signi®cantly (r 0.39±0.52) with all the measurements except transverse diameter. WHR was the only measurement that also related to 2 h glucose concentration (r 0.34). The bivariate correlation coef®cients and the respective adjusted correlations (after controlling the effect of age and BMI) between metabolic variables and DEXA measurements are summarized in Table 4. The differences between correlation coef®cients were not statistically signi®cant in the z-transformed correlation coef®cient test. The area from the dome of diaphragm to the top of femur (abdominal fat), the area between ®rst and fourth lumbal vertebrae (upper lumbal fat) and the area between fourth and ®fth lumbal vertebrae (lower lumbal fat) and its ratio to hip fat related signi®cantly to HDL cholesterol (r 70.35±0.57). Triglycerides were correlated with `abdominal fat', `upper lumbal fat' and the ratio of lower lumbal fat to hip fat (r 0.30±0.35). In addition, any DEXA-region, except hip fat, correlated positively with fasting insulin (r 0.40±0.54). The ratio of lower lumbal fat to hip fat related to metabolic
Table 3 Partial Pearson correlation coef®cients between the metabolic variables and anthropometric measurements adjusted for age and BMI Variable HDL-C LDL-C Tg f-Ins f-G 2hG
Waist 70.46** 0.36* 0.20 0.36* 0.41** 0.14
WHR 70.53** 0.38 0.40* 0.39* 0.49** 0.34*
WHTR 70.39* 0.22 0.38* 0.32* 0.52** 0.29
Sagitt. diam.
Trans. diam.
70.48** 0.34* 70.00 0.42** 0.39* 0.11
70.31* 0.35* 0.10 0.03 0.04 70.00
STR 70.36* 0.16 70.06 0.50** 0.45** 0.13
HDL-C HDL cholesterol; LDL-C LDL cholesterol; Tg triglycerides; f-Ins fasting insulin; f-G fasting glucose; 2 h G 2 h glucose during OGTT; Waist waist circumference; WHR waist-to-hip ratio; WHTR waist-to-height ratio; Sagitt. diam. sagittal diameter; Trans. diam. transverse diameter; STR sagittal to transverse ratio. * P < 0.05; ** P < 0.01; *** P < 0.001.
Table 4 Bivariate and partial Pearson correlation coef®cients between the metabolic variables and DEXA measurements Variable
Abdominal fat (kg)
HDL-C
70.41** (70.11) 0.04 (0.13) 0.34* (70.04) 0.45** (0.32*) 0.20 (0.18)
LDL-C Tg f-Ins f-G
Upper lumbal fat (kg) 70.49** (70.29) 0.15 (0.28) 0.30* (70.04) 0.54*** (0.47**) 0.32* (0.37*)
Lower lumbal fat (kg) 70.35* (70.10) 70.00 (0.10) 0.26 (70.09) 0.40** (0.22) 0.06 (0.02)
Abbreviations as in Table 3; adjustment for age and BMI in parenthesis.
Hip fat (kg)
Lower lumbal fat : hip fat
70.02 (70.39*) 70.17 (70.09) 0.04 (70.42**) 0.14 (70.18) 70.21 (70.30)
70.57*** (70.52**) 0.21 (0.16) 0.35* (0.30) 0.47** (0.45**) 0.40** (0.32*)
369
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370
parameters comparable to WHR in anthropometric measurements. In age and BMI adjusted partial Pearson correlation coef®cient analysis the association between metabolic variables and DEXA measurements remained statistically signi®cant only for some variables (Table 4). Fasting insulin related to abdominal fat, to upper lumbal fat and to the ratio of lower lumbal fat to hip fat (r 0.32±0.47). Interestingly, hip fat correlated negatively with triglycerides (r 70.42) and positively with HDL cholesterol (r 0.39). The correlations between the ratio of lower lumbal fat to hip fat and metabolic variables remained statistically signi®cant even after adjustment for age and BMI.
Discussion In the present study, the abdominal fat accumulation was examined by both conventional (WHR, waist circumference) and by some more recent anthropometric indices (WHTR, sagittal diameter, transverse diameter, STR). Taken together, all of them except transverse diameter, related signi®cantly to serum lipids or fasting and postglucose insulin and glucose levels even after adjusting for age and BMI. These results can not be directly compared to the data from previous studies, since there are no earlier studies where all the anthropometric measurements have been made anthropometrically. Moreover, our measurement sites differed from those of previous studies.6,17,18 Instead of skin landmarks, we used bone landmarks, the midway between the lower rib margin and the superior anterior iliac crest as the level for measuring waist circumference, transverse and sagittal diameters and the level of throchanters for measuring hip circumference. These levels are recommended by WHO10 and they were shown to be associated with metabolic variables by Seidell and co-workers.19 Skin landmarks, for example the umbilicus level, is dif®cult to determine, since it varies among obese individuals. In a recent study, the minimal waist girth and the girth at the level of umbilicus differed by up to 26 cm in the same individual.20 Our results indicated that none of the anthropometric measurements was signi®cantly better than others on the basis of the z-transformed correlation coef®cient test. However, Pouliot and co-workers6 suggested that the waist circumference and the abdominal sagittal diameter may be superior to the WHR and van der Kooy et al7 found that the waist circumference and WHR are more closely related to the metabolic variables than sagittal diameter. Their conclusions were based on the strength of correlations, but no P-values were calculated between the correlation coef®cients to demonstrate the statistical signi®cance of the differences. Richelsen and Pedersen21 found that sagittal diameter and the ratio of
sagittal diameter to height (SDH) correlated slightly more strongly with metabolic risk factors than WHR and waist circumference. However, using the multiple regression analysis they were not able to show the absolute superiority of sagittal diameter and SDH, since the indices of abdominal fatness are closely intercorrelated. DEXA measurements indicated no stronger association with the metabolic variables than the anthropometric measurements, even though the total amount of abdominal fat was estimated directly. This ®nding may be explained by the dif®culty to de®ne the anatomically correct area which contains the highest amount of abdominal fat and a relatively high amount of visceral fat but a low amount of subcutaneous fat. For example `abdominal fat' (from the dome of diaphragm to the top of femur) could contain substantial amount of subcutaneous gluteal fat which has not been shown to contribute to CHD metabolic risk pro®le.22 `Lower lumbal fat' has been strongly associated with total adiposity in a previous study23 and now also in our study. It is also dif®cult to adjust the lines of rib box in such a narrow region between L4 and L5. Despite these dif®culties, DEXA measured total abdominal fat is well associated with insulin resistance, since the area between the ®rst and the fourth lumbal vertebrae (upper lumbal fat) correlated signi®cantly with both fasting insulin and fasting glucose even after controlling for age and BMI. This con®rms the recent result in non-obese women.24 The average weight loss of 2.4 kg during the study period can be assumed to affect the associations between metabolic variables and the anthropometric measurements since the latter ones were determined 4±5 weeks after the beginning of the study. As the concentrations of fasting insulin and fasting glucose were analysed both at the onset of the study and in OGTT 4±5 weeks later we could calculate the effect of weight loss on correlations with anthropometric measurements. The correlation coef®cients were slightly smaller in the beginning of the study than in OGTT, but the differences were not statistically signi®cant (data not shown). Thus the results of study are reliable.
Conclusions The DEXA measurements did not show any stronger association with metabolic variables than anthropometric measurements. As disturbances of both lipoprotein metabolism and serum insulin and glucose homeostasis are examined then WHR, WHTR, waist circumference and sagittal diameter are the most relevant simple measures for obese women. The amount of abdominal fat in the region between the ®rst and the fourth lumbal vertebrae is a good indicator of fasting insulin and fasting glucose levels.
Anthropometric and DEXA measurements in obesity P Rissanen et al
Acknowledgements
This study was funded by a research grant from F. Hoffmann-La Roche Ltd. and by the EVO fund of the Kuopio University Hospital. References
1 Ostman J, Arner P, Engfeldt P, Kager L. Regional differences in the control of lipolysis in human adipose tissue. Metabolism 1979; 28: 1198±1205. 2 Peiris A, Mueller RA, Smith GA, Struve MF, Kissebah AH. Splanchnic insulin metabolism in obesity: In¯uence of body fat distribution. J Clin Invest 1986; 78: 1648±1657. 3 Peiris AN, Struve MF, Kissebah AH. Relationship of body fat distribution to the metabolic clearance of insulin in premenopausal women. Int J Obes 1987; 11: 581±589. 4 Bevilacqua A, Bonadonna R, Buzzigoli G. Acute elevation of free fatty acid levels leads to hepatic insulin resistance in obese subjects. Metabolism 1987; 36: 502±506. 5 Kissebah AH, Alfarsi S, Adams PW, Wynn V. Role of insulin resistance in adipose tissue and liver in the pathogenesis of endogenous hypertriglyceridemia in man. Diabetologia 1976; 12: 563±571. 6 Pouliot MC, Despres JP, Lemieux S, Moorjani S, Bouchard C, Tremblay A, Nadeau A, Lupien P. Waist circumference and abdominal sagittal diameter: best simple anthropometric indexes of abdominal visceral adipose tissue accumulation and related cardiovascular risk in men and women. Am J Cardiol 1994; 73 (7): 460±468. 7 Van der Kooy K, Leenen R, Seidell JC, Deurenberg P, Visser M. Abdominal diameters as indicators of visceral fat: comparison between magnetic resonance imaging and anthropometry. Br J Nutr 1993; 70 (1): 47±58. 8 Mazess RB, Barden HS, Bidek JP, Hanson J. Dual energy Xray absorptiometry for total-body and regional bone-mineral and soft-tissue composition. Am J Clin Nutr 1990; 51: 1106± 1112. 9 Haarbo J, Gotfredsen A, Hassager C, Christiansen C. Validation of body composition measurement by dual energy X-ray absorptiometry (DEXA). Clin Physiol 1991; 11: 331±341. 10 WHO Regional Of®ce for Europe, Nutrition Unit. Measuring obesityÐclassi®cation and description of anthropometric data. #EUR/ICP/NUT 125, Copenhagen, 1988. 11 SjoÈstrm L. A computer-tomography based multi-compartment body composition technique and anthropometric predictions of lean body mass, total and subcutaneous adipose tissue. Int J Obes 1991; 15 (2): 19±30.
12 Wahlefeld AW. Triglycerides determination after enzymatic hydrolysis. In: Bergmeyer H, Gawehn K (eds). Methods of Enzymatic Analysis. Chemie: Weinheim, 1974, pp 1831±1835. 13 Siedel J, Hagele EO, Ziegenhorn J, Wahlefeld AW. Reagent for the enzymatic determination of serum total cholesterol with improved lipolytic ef®ciency. Clin Chem 1983; 29 (6): 1075±1080. 14 Barham D, Trinder P. An improved colour reagent for the determination of blood glucose by the oxidase system. Analyst 1972; 97 (151): 142±145. 15 SPSS-X User's Guide. 3rd Edn. SPSS Inc.: Chicago, 1988. 16 Zar JH. Biostatistical Analysis, 2nd Edn. Prentice-Hall: Englewood Cliffs, New Jersey, 1984. 17 Seidell JC, Oosterlee A, Thijssen MAO, Burema J, Deurenberg P, Hautvast J, Ruijs JHJ. Assessment of intra-abdominal and subcutaneous abdominal fat: relation between anthropometry and computed tomography. Am J Clin Nutr 1987; 45: 7± 13. 18 Zamboni M, Armellini F, Milani MP, Todesco T, de Marchi M, Robbi R, Montessor G, Bergamo AIA, Bosello O. Evaluation of regional fat distribution: comparison between W/H ratio and computed tomography in obese women. Int J Med 1992; 232: 341±347. 19 Seidell JC, Cigolini M, Charzewska J, Ellisinger B-M, Biase G. Fat distribution in European women: A comparison of anthropometric measurements in relation to cardiovascular risk factors. Int J Epidemiol 1990; 19: 303±308. 20 Weidner MD, Gavigar KE, Tyndall GL, Hickey MS, McCammon MR, Houmard JA. Which anthropometric indeces of regional adiposity are related to the insulin resistance of aging? Int J Obes 1995; 19: 325±330. 21 Richelsen B, Pedersen SB. Associations between different anthropometric measurements of fatness and metabolic risk parameters in non-obese, healthy, middle-aged men. Int J Obes 1995; 19: 169±174. 22 Despres J-P, Moorjani S, Lupien PJ, Tremblay A, Nadeau A, Bouchard C. Regional distribution of body fat, plasma lipoproteins and cardiovascular disease. Arteriosclerosis 1990; 10: 497±511. 23 Kvist H, SjoÈstroÈm L, Tyfelen U. Adipose tissue volume determinations in women by computed tomography; technical considerations. Int J Obes 1986; 10: 53±67. 24 Carey DG, Jenkins AJ, Campbell LV, Freund J, Chisholm DJ. Abnormal fat and insulin resistance in nonobese women: direct measurements reveal a strong relationship in subjects at both high and low risk of diabetes. Diabetes 1996; 45: 633± 638.
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