Weight cycling may cause a redistribution of body fat to the upper body fat compartments. We investigated the distribution of subcutaneous adipose tissue (SAT) in 30 overweight women with a history of weight-cycling and age-matched controls (167 normal weight and 97 overweight subjects). Measurements of SAT were performed using an optical device, the Lipometer. The SAT topography describes the thicknesses of SAT layers at 15 anatomically well-defined body sites from neck to calf. The overweight women with a history of weight cycling had significantly thicker SAT layers on the upper body compared to the overweight controls, but even thinner SAT layers on their legs than the normal weight women. An android fat pattern was attributed to overweight females and, even more pronounced, to the weight cyclers. The majority of normal weight women showed a gynoid fat pattern. Using stepwise discriminant analysis, 89.0% of all weight cyclers and overweight controls could be classified correctly into the two groups. These findings show the importance of normal weight maintenance as a health-promoting factor.
Obesity has become a worldwide epidemic causing many prevalent diseases, particularly in association with increased upper body fat distribution.1, 2, 3, 4 As a consequence, dieting is common, but long-term weight-loss is seldom achieved.5, 6 Weight cycling occurs when frequent attempts of weight loss are followed by a return to the pre-diet weight or even more.7
There are contradictory theories about the adverse health consequences of weight cycling, such as a decreased basal metabolic rate,8, 9 alterations in fatty acid metabolism,10, 11, 12 bone density loss,13, 14, 15 and a change in body composition to a higher proportion of body fat.8, 16, 17 Epidemic research found fairly consistent relationships between weight variability and cardiovascular morbidity and mortality.18, 19, 20, 21, 22 However, recent studies discuss the weak adverse effects of weight cycling on cardiovascular risk factors.23, 24 Several reviews remark that these studies have some limitations and that information about the causes of the weight change is missing.18, 25 It has been suggested that the adverse effects of weight cycling could be due to a redistribution of body fat to the upper body compartments.26, 27 In turn, an upper body obesity strengthens the risk of the ‘Metabolic Syndrome’, a constellation of metabolic diseases including dyslipidemia, insulin resistance, hypertension and its clinical consequences like cardiovascular disease and fatty liver.1, 2, 28, 29
Imaging techniques, that is, magnetic resonance imaging (MRI), computer tomography (CT) and dual-energy X-ray absorptiometry (DEXA), are precise and accurate techniques used to study lean body mass and adipose tissue distribution. However, these techniques are not applicable as field methods because of the risk of radiological burden (CT, DEXA), and the high costs (MRI) limit the measurement to a restricted area.2, 30 The optical device Lipometer has been developed at the Medical University of Graz, on which Möller has a patent on EU. Pat. No. 0516251. It is only used for research without commercial interest. The Lipometer enables a noninvasive quick, precise (resolution range 0.1–50 mm) and safe determination of subcutaneous adipose tissue (SAT) thickness at any given site of the human body. The implement of the Lipometer measurement at 15 well-distributed anatomically specified body sites provides the estimation of a SAT topography (SAT-Top), which gives detailed information of an individual body fat distribution pattern.31, 32, 33, 34, 35, 36
The aim of the present study was to investigate the body fat distribution of women who had repeatedly lost weight by dieting without lasting success. These women were examined in order to test if their body fat distribution patterns could classify them clearly as a homogenous group, when compared with SAT-Tops of randomly selected overweight women (matched by age and BMI) and normal weight women (matched by age).
Overweight women with a history of weight cycling
A total of 30 overweight women with a history of weight cycling were selected from the obesity research unit at the Clinic of Internal Medicine in order to provide their SAT-Top values. The weight remained stable for at least 3 months prior to the measurements. Their personal characteristics are presented in Table 1.
Weight cyclers were identified by their answer to the question, ‘Within the last 4 y, how many times did you lose each of the amounts of weight on purpose?’ The responses were 0, 1–2, 3–4, 5–6, and ≥7 times for each of the magnitudes of weight loss 2–4, 4–8, 9–22, and >22 kg. Women who intentionally lost more than 4 kg weight at least three times were classified as weight cyclers.24
No subject suffered from any metabolic disease other than obesity, as checked by medical history.
Overweight women and normal weight subjects
In all, 167 normal weight subjects (BMI<25 kg/m2) with a comparable age and 97 overweight subjects with a comparable BMI and age were recruited from health and fitness checks for comparison of the SAT-Tops with obese women with a history of weight cycling.
Measurement of SAT-Tops
The thicknesses of SAT layers were measured by the Lipometer (EU. Pat. No. 0516251) at 15 specified body sites from 1-neck to 15-calf rendering SAT-Tops.
The sensor head of the Lipometer contains a set of light-emitting diodes (λ=660 nm) and a photodetector.37 To measure the thickness of a SAT layer, the sensor head is held perpendicular to the selected body site. The SAT layer is illuminated by different light patterns varying in time. A photodiode measures the corresponding back-scattered light intensities of the light patterns and calculates the thickness of the SAT layer in mm. Technical characteristics of the device and the first validation of the results using CT as a reference for comparison have been reported.37 To describe the complete subcutaneous fat distribution, 15 evenly distributed and anatomically well-defined body sites were specified from neck to calf on the right side of the body. The coefficients of variation of these 15 body sites sorted from 1-neck to 15-calf (in %), which were also previously published,33 are 4.8, 6.0, 9.5, 2.2, 1.9, 3.2, 3.8, 3.2, 6.7, 3.7, 5.5, 5.6, 12.2, 4.2, and 5.8. The measurement cycle takes about 2 min, during which the subject is standing.34, 35, 38
Statistical analysis was carried out using SPSS for windows. The hypothesis of variables being normally distributed was tested by the Kolmogorov–Smirnov test. Differences in the distribution of variables between weight cyclers, overweight, and normal weight women were tested by the Student's t-test for independent samples in case of normally distributed variables (as in Table 1) and by the Mann–Whitney U-test if variables were not normally distributed (as in Table 2). Stepwise discriminant analysis was applied in order to investigate whether the knowledge of single SAT layers or the combination of SAT layers enables a correct classification between different groups.
A relative SAT-Top plot was constructed to visualize the SAT-Top patterns of the obese groups. The medians of each of the 15 body sites of normal weight subjects were set to 100% and sorted top–down from neck to calf. The medians of the obese groups were calculated as percent of the normal weight medians and show the deviation of SAT-layer thicknesses from normal weight subjects, as well as the difference in the SAT patterns of the two obese groups.
Women after weight cycling were compared to overweight women and women with normal weight. The medians and ranges of SAT-layer thicknesses are presented in Table 2.
Compared to normal weight subjects, the SAT layers of the upper body were significantly thicker in both obese groups (P<0.001). Overweight women with a history of weight cycling had higher SAT-layer thicknesses at the upper body compared to simply obese women (2-triceps, 3-biceps, 6-lateral chest, and 10-hip: P<0.01; 4-upper back, and 7-upper abdomen: P<0.05). The SAT layers of the legs were considerably thicker in simply overweight women (11-front thigh, 13-rear thigh, and 15-calf: P<0,01), whereas two SAT layers of the legs of weight cyclers were even thinner than in normal weight subjects (11-front thigh: P=0.023 and 15-calf: P=0.007). The other SAT layers of the legs in weight cyclers did not differ particularly from the normal weight subjects, but were thinner than the SAT layers of simply overweight women.
The relative SAT-Top plot (Figure 1) shows the differences in the SAT pattern of the two obese groups. The medians of the SAT-layer thicknesses of the normal weight subjects were set to 100%. Women with weight cycling showed an increase to 303.7% at body site 6-lateral chest, and in the simply overweight group an increase up to 207.5% at the same body site compared to the normal weight subjects was observed. All SAT-layer thicknesses on the legs were increased up to 118.4% in the overweight women, whereas in the group of women with weight cycling four SAT layers of the legs were even decreased down to 72.8% in comparison to the normal weight women.
In summary, we observed an android fat pattern with strong upper body obesity and decreased SAT layers on the legs in women with weight cycling, in contrast to a gynoid body fat pattern with increased SAT-layer thicknesses on the legs in the majority of normal weight women and a less distinctive body fat distribution of simply overweight women.
By stepwise discriminant analysis, a correct classification between the two overweight groups was possible in 89.0% of all cases using a set of five significant body sites (P<0.05) for discrimination (2-triceps, 6-lateral chest, 10-hip, 11-front thigh, and 15-calf). In all, 16.7% of the women with weight cycling were classified as simply obese, whereas 8.9% of simply obese women were classified as weight cyclers. The discrimination power between normal weight subjects and obese women with weight cycling was: 95.4% correctly classified, using the SAT layers 1-neck, 3-biceps, 4-upper back and 8-lower abdomen (P<0.05). Altogether, 3.0% of the normal weight subjects and 13.3% of the women with weight cycling were falsely classified. A correct discrimination between simply overweight women and normal weight subjects was possible in 89.8% of all cases, with 88.6% of simply obese and 90.4% of all normal weight women being correctly classified.
Previous studies reported that weight cycling is highly correlated with an increased waist/hip ratio (WHR) and is thus consistent with an increased upper body fat distribution.26, 27 Interestingly, a highly significant correlation between BMI and WHR was found only in weight cyclers but not among nonweight cyclers.26
In contrast, no adverse effects of weight cycling on body fat distribution were found by other authors.8 Van der Kooy reported neither a change in body fatness nor a preferential deposition of visceral fat after weight regain using MRI to study the effect of a single cycle of weight loss and regain on three fat depots.39 It has to be mentioned that these studies have several limitations. First, the change of body fat distribution caused by only one single cycle was investigated. The measurement of the body fat distribution after a long period of frequent weight fluctuations would probably give another impression of the impact of weight cycling. Second, on the basis of a single-slice sampling strategy, the large fluctuation of the whole body fat cannot be predicted.30
The complications of obesity have often been attributed to visceral adipose tissue (VAT) with a concomitant increase in portal vein free fatty acids.1, 40 A heterogeneity of adipose tissue metabolism also exists among the different subcutaneous fat compartments.41 The differential deposition of adipose tissue between the abdominal subcutaneous compartment and the gluteal fat depot is associated with variations in cardiovascular risk factors.29 Tai et al concluded that there needs to be a shift of focus away from intra-abdominal fat to subcutaneous fat when dealing with the features of the metabolic syndrome.
To investigate whether the women with a history of weight cycling had a body fat distribution pattern which was in accordance with the body fat distribution of women having metabolic complications, we employed a factor plot (Figure 2) which has been partly published.35
We supplemented this factor plot with the data of the three groups investigated in the present study. The group of simply overweight women provided a similar body fat distribution pattern comparable to 60–70-y-old women even though the women in the group of simply overweight were 20 y younger on average. The means of the weight cyclers, on the other hand, were situated closely to obese women with polycystic ovary syndrome (PCOSo), showing a typical android body fat distribution. The upper body factor (factor 1) of the group of women with weight cycling was very high, whereas the lower body factor (factor 2) showed a very small value for the female group. The SAT layers of the legs of PCOSo were even more decreased. Diabetic females (Wd) also had a poor development of SAT layers on the extremities comparable to PCOSo, but less fat on the trunk.33, 34, 35
Although the factor plot demonstrates that the total development of SAT layers was comparable, discriminant analysis demonstrated that the groups have different body fat distribution patterns, classifying 81.3% of all women correctly as weight cyclers and PCOSo. The PCOSo had thicker SAT layers of the neck and calf, whereas the SAT layers of 2-biceps and 10-hip were significantly thinner in than in women with weight cycling. The discrimination power between diabetic women and women with weight cycling was even greater: 90.0% were correctly classified with only one diabetic woman and four women of the group with weight cycling were falsely classified. This might indicate that the weight cyclers who were falsely classified could be prone to develop insulin resistance. The great discrimination power between the weight cyclers and the diabetic females does not imply necessarily that weight cycling has no effect on glycemic control. It has to be mentioned that the group of weight cyclers were much younger on average than the group of diabetic females (mean age 62.4±6.6). Therefore we cannot preclude that weight cycling increases the risk of becoming diabetic. A large longitudinal study of aging could show that weight variability over time was related to greater decreases in glucose tolerance.19
However, several studies found no association between weight cycling and fasting glucose values.42, 43 We postulate that not only the increase in upper body SAT layers alone but also the decrease in the lower body SAT layers is associated with metabolic and endocrine diseases. A value for factor 2 (related mainly to the legs) smaller than zero and a value for factor 1 (related to the upper body compartments) above zero could be used as an indicator of metabolic and/or endocrine abnormalities.
Factor 2 of the group with weight cycling was beyond zero. Although the serum parameters of these women were still in the normal range, their body fat distribution indicated a shift to the metabolic syndrome. However, the conclusion that ‘weight cycling per se’ leads to metabolic abnormalities cannot be secure due to the presented data. It is also possible that a third variable caused an inability in the observed weight cyclers that prevented the maintenance of a weight reduction with a concomitant android body fat distribution.
The mechanisms that might mediate a plausible relationship between weight cycling and the adverse health consequences that are under discussion such as a change in body fat distribution have not been identified yet. Nevertheless, the data of our study as well as previous studies suggest that a stable weight over time is associated with best health.39 Many obesity-related health conditions can be ameliorated independently of weight loss through changes in lifestyle.44, 45
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Cite this article
Wallner, S., Luschnigg, N., Schnedl, W. et al. Body fat distribution of overweight females with a history of weight cycling. Int J Obes 28, 1143–1148 (2004). https://doi.org/10.1038/sj.ijo.0802736
- body fat distribution
- weight cycling
- discriminant analysis
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