It is generally agreed that excess abdominal fat, in particular visceral abdominal fat (VAF), is related to an increased risk for obesity-related complications. We examined the association between metabolic risk factors and maintaining VAF after weight loss intervention.
A total of 54 postmenopausal, obese women who achieved a VAF loss of at least 10% from their baseline values during a 14-week intervention were enrolled as subjects. Body weight, VAF assessed by CT scans, and metabolic risk factors (that is, blood pressure, lipids and glucose) were measured at baseline (week 0), post-intervention (week 15), and at a 2-year follow-up (week 105). The subjects were divided into two groups according to their changes in VAF between weeks 15 and 105 (follow-up period): (1) VAF gainers (VAF changes >0 cm2, n=28) or (2) VAF maintainers (VAF changes ⩽0 cm2, n=26).
The mean change in VAF of all subjects during the 14-week intervention was −34±16 cm2 (−29.7±12.3%) (P<0.01). Along with this change, improvements (P<0.05) were observed in all metabolic risk factors except for high-density lipoprotein cholesterol (HDLC). During the follow-up period, there were interactions between the two VAF groups in HDLC, triglycerides (TG) and total cholesterol (TC)/HDLC ratio (all P<0.01). In particular, the HDLC of VAF maintainers improved, and the value at week 105 exceeded baseline level (P<0.01). However, systolic and diastolic blood pressure, TC and low-density lipoprotein cholesterol in the VAF maintainers increased (all P<0.05) back to their mean baseline level despite a further decrease in their VAF during the follow-up period (P<0.01).
This study shows that long-term maintenance of VAF after weight loss intervention is associated with improvements in HDLC and TG among obese, postmenopausal women.
Obesity is closely associated with various metabolic disorders such as hypertension, dyslipidemia, type 2 diabetes and cardiovascular disease,1, 2 and the prevalence of obesity continues to increase in many countries.3 It is well known that body weight reduction improves metabolic disorders related to obesity, and many studies4, 5, 6, 7 have indicated that metabolic risk factors, such as blood pressure, lipids and glucose, improve with weight reduction through a short-term weight-loss intervention. However, several studies8, 9, 10 have shown that acute and temporary energy restriction during a short-term intervention has a significant effect on the metabolic risk factors that are independent of weight loss, that is, improved risk factors can be shown even before any significant weight loss has occurred. This evidence confounds the effect that short-term weight-loss interventions have on the risk factors. Moreover, it is also well known that obese subjects who lose their weight through a short-term intervention are more likely to regain their weight.11, 12 With these points in mind, for human health, the effects of weight-loss treatment on metabolic risk factors should be discussed including the effects of long-term weight maintenance after interventions rather than just short-term weight loss.
Some studies13, 14 have indicated that favorable long-term maintenance of body weight after short-term weight loss prevented deterioration of metabolic risk factors for a prolonged period. However, there are other studies15, 16, 17 showing that risk factors such as blood pressure, lipid profile and insulin sensitivity returned to baseline level even though body weight remained essentially unchanged over the long term.
In recent years, there is general agreement that excess abdominal fat, in particular visceral abdominal fat (VAF), is related to increased risk for obesity-related complications.18, 19 Previous studies,13, 14, 15, 16, 17 which investigated associations between long-term changes in body weight and metabolic risk factors focused mainly on changes in body weight or abdominal circumference (AC). Until now, there have been no studies investigating the associations between long-term VAF maintenance after weight-loss intervention and changes in metabolic risk factors. In this study, we examined the associations between metabolic risk factors and VAF, measured using computed tomography (CT) scans, 2 years after an initial 14-week weight loss intervention among obese postmenopausal women. We hypothesized that the long-term maintenance of VAF would have beneficial effects on metabolic risk factors such as blood pressure, lipids and glucose.
We recruited 84 postmenopausal, obese (body mass index (BMI) >25 kg m–2, which is the JASSO criteria of obesity20), Japanese women through advertisements in local newspapers to participate in a 14-week weight loss intervention at Sodegaura Healthcare Center, Chiba, Japan between 2004 and 2006. Of the participants, 83 women completed the 14-week intervention. Of these women, 69 consented to a follow-up measurement session after 2 years of self-management (no further intervention). In this study, because we focused on whether a change in VAF after an intervention had significant effects on the metabolic risk factors, we excluded 15 subjects who did not achieve at least a 10% loss of VAF during the 14-week intervention. Consequently, 54 postmenopausal, obese women (55.6±4.8 years) were included in the final analysis. To investigate the association between changes in VAF and metabolic risk factors during the follow-up period (week 15–week 105), the subjects were divided into two groups according to changes in their VAF areas between weeks 15 and 105: (1) the VAF gainer group with VAF changes >0 cm2, or (2) the VAF maintainer group with VAF changes ⩽0 cm2.
We monitored the subjects’ medication status during the study period. At baseline, there were 15 subjects (VAF gainer, n=7; VAF maintainer, n=8) who were taking medication for hypertension. Of these 15 subjects being treated for hypertension, 14 continued the medication during the intervention and the follow-up period, whereas one subject (VAF maintainer) discontinued taking the medication before week 15 and did not use it during the follow-up period. Similarly, there were six subjects (VAF gainer, n=2; VAF maintainer, n=4) who were taking medication for dyslipidemia and two subjects (VAF gainer, n=1; VAF maintainer, n=1) who were taking medication for hyperglycemia. All of the dyslipidemia and hyperglycemia subjects continued their medications during the follow-up period. On the other hand, two other subjects (VAF gainer) started to take medication for dyslipidemia during the follow-up period.
The aim and design of this study were explained to every subject before each gave her written, informed consent. This study was conducted in accordance with the guidelines proposed in the Declaration of Helsinki. The Ethical Committee of the University of Tsukuba, Japan also reviewed and approved the study protocol.
The subjects took part in a 14-week lifestyle intervention program. The program mainly comprised of dietary modifications with a physical activity program (90 min per session, 12 times in 14 weeks). One class consisted of approximately 25 subjects and 6 staff members (dieticians and physical fitness trainers).
The dietary modifications program consisted of 12 lectures, including practical training sessions (30–90 min per lecture) and individual counseling by skilled dieticians. The program was based on the Four-Food-Group Point Method,21 otherwise known as Kagawa Nutrition University Diet (KNUD) (http://www.co-4gun.eiyo.ac.jp/Index1-English.html). The KNUD is organized into four food groups (FG) based on their nutrient contents: FG 1 (dairy products and eggs), FG 2 (meat, fish and beans), FG 3 (vegetables and fruits), and FG 4 (grains, oil and sugar). To calculate energy intake and nutrient balance easily, every food is portioned into 80 kcal increments, which is considered one point in the method. To consume a well-balanced daily diet, each person chooses three points worth of foods from FGs 1, 2 and 3 (9 points in total), and 6 points from FG 4. Therefore, in this study, the subjects roughly consumed a total of 15 points or 1200 kcal day–1 from the four FGs. The subjects kept a daily diary in which they recorded in detail every food they ate, any transitional changes in body weight and their health and mental conditions during the 14-week intervention period. At every diet counseling class, dietitians checked each subject's diary as precisely as possible, adding their advice, whereas subjects were taking part in their classes, or they provided face-to-face individual counseling based on the diary after the classes.
The physical-activity program consisted of lectures on exercise and physical activity (45 min per lecture, 2 times in 12 sessions) and supervised exercise sessions (60 min per session, 2 times in 12 sessions) by fitness experts. The lectures consisted of basic exercise instruction, such as the proper way of walking and how to prevent injury during exercise, and on how to increase physical activity during daily life. The supervised exercise sessions consisted of stretching, calisthenics, brisk walking and light resistance training.
Anthropometric measurements were performed at baseline (week 0), post-intervention (week 15), and at the 2-year follow-up (week 105). Body weight was measured once to the nearest 0.1 kg using a digital scale (TBF-551; Tanita, Tokyo, Japan), and height was measured once to the nearest 0.1 cm using a wall-mounted stadiometer (YG-200; Yagami, Nagoya, Japan) with the subjects in underwear and barefooted while fasting in the morning. BMI was calculated as weight (in kilograms) divided by height (in meters) squared. AC was measured directly on the skin at the level of the umbilicus in the standing position. The AC measurements were taken in duplicate to the nearest 0.1 cm. Body composition, recorded as percentage fat mass, fat mass (kg), and fat-free mass (kg), was assessed by a bioelectrical impedance analysis. This was measured at 50 kHzm, standing foot to foot, and body fat was calculated using the manufacturer's algorithm (TBF-551; Tanita, Tokyo, Japan).
We acquired CT images for each subject using a CT scanner (TSX-002A; Toshiba, Tokyo, Japan) to calculate VAF, subcutaneous abdominal fat (SAF) and total abdominal fat (TAF) areas. A single cross-sectional CT slice (120 kV, 200 mA, field of view of 500 mm, image matrix of 512 × 512, section thickness of 5 mm, scanning time of 5 s) was obtained with the subject in the supine position, arms extended above the head and centered at the level of the umbilicus (fourth and fifth lumbar vertebrae). The hard copy film prints (window level=0, window width=500 in Hounsfield units) were then scanned using a flat bed scanner (GT-X970; SEIKO EPSON, Nagano, Japan) for image analysis. A single trained technician performed blinded image analyses to determine the VAF, SAF and TAF areas using a computer software program (Fat Scan; N2 system, Osaka, Japan). Detailed descriptions of the program (Fat Scan) have been published elsewhere.22 Briefly, the attenuation range of CT numbers (in Hounsfield units) for fat tissue was calculated first. A histogram for fat tissue was then computed on the basis of mean attenuation plus or minus 2 s.d. The technician then defined the intraperitoneal tissue by tracing its contour on the scan; within that region of interest, we designated tissue with attenuation within the mean plus or minus 2 s.d. as the VAF area. Next, the TAF area was calculated in the region outlining the circumference of the abdominal wall. Finally, the VAF area was subtracted from the TAF area, and the remainder was defined as the SAF area. The method for calculating abdominal fat areas using CT scanning has been validated22 and used in some pertinent studies.4, 5, 6, 23 The intra-class correlation for repeated VAF area determinations in our laboratory was 0.98.
Blood pressure and biochemical assays of blood
Blood pressure and biochemical assays of blood were measured at weeks 0, 15 and 105. One trained nurse measured the systolic and diastolic blood pressures (SBP and DBP) of subjects at the right arm using a mercury manometer and a standard protocol after the subjects rested for at least 20 min in the sitting position. Using the first and the fifth Korotkoff sounds as indicative of SBP and DBP, respectively, these values were estimated as the mean of two readings. Cuff sizes were selected based on upper arm girth and length. A blood sample was drawn from each subject after a 12-h fast. Serum glucose and lipids were assayed by routine automated laboratory methods. Plasma samples were stored at −80 °C until analyzed. The inter- and intra-assay coefficients of variation were <5% for all blood parameters. Low-density lipoprotein cholesterol (LDLC) was calculated according to Friedewald’s formula.24
Total energy intake in kilocalories and the amounts of each nutrient (carbohydrate, fat and protein in grams) were assessed at week 0, during intervention (week 10), and week 105 by both three-day weighed dietary records (3-day WDR) and dietary recall interviews performed by one skilled dietician with each subject. The dietician explained how to complete the 3-day WDR in detail before the measurements, and the subjects learned how to use a digital cooking scale for weighing food. The dietitian collected the recorded sheets from the 3-day WDR and codified the food items and food weights. Thereafter, the dietician interviewed each subject to elicit more information about the subject's food intake for the three-day period. The food data of the dietary records were converted to energy and nutrient data by the dietician and analyzed using the Eiyoukun (Kenpakusya, version 4.0, Tokyo, Japan), which are based on the Standard Tables of Food Composition in Japan (fourth revised edition).25
Activity energy expenditure assessments
The total energy expenditure (TEE) and activity energy expenditure (AEE) were assessed by a uniaxial accelerometer (Lifecorder; Suzuken Co. Ltd., Nagoya, Japan) and by keeping a diary of exercise. Accelerometer measurements were validated using the doubly-labeled water method26 and the metabolic chamber method.27 The accelerometer can assess two types of activity-related energy expenditure, namely, the energy expenditure of activities (EEAct) indicating ≥1.8 metabolic equivalents and energy expenditure of minor activities (EEminorAct) indicating <1.8 metabolic equivalents.28 In this study, AEE was estimated as a total of these two EEs (AEE=EEAct+EEminorAct).29 The TEE was calculated from the sum of the basal metabolic rate, the diet-induced thermogenesis (10% TEE), the EEAct and the EEminorAct. The basal metabolic rate was estimated from sex, age, weight and height using a standard Japanese formula.28 Detailed descriptions of the accelerometer have been published elsewhere.27 Subjects wore the Lifecorder constantly (except while sleeping or bathing) during a 1 week period before the intervention, for 1 week during the intervention (week 10), and for 1 week before the follow-up measurement. During these 7-day segments, the subjects were also instructed to keep a diary of their exercise, which consisted of the status of wearing the Lifecorder and detailed exercise information on the type, time and ratings of perceived exertion.30 During times of exceptional physical exertion, such as swimming, we estimated this exceptional EE by its metabolic equivalents from the diary entries. For these measurement periods, we calculated TEE and AEE from both the Lifecorder data and the diary entries.
Values are expressed as the mean±s.d. Paired Student's t-tests were performed to test the significance of changes in values. Unpaired Student’s t-tests were used to compare values between two groups. To compare any change in each item between groups, a two-way repeated measures analysis of variance (time × group) was applied. The relationship between two measurement values was assessed by Pearson's product moment correlation. The data were analyzed with the Statistical Analysis System (SAS), version 9.13 (SAS Institute Inc., Cary, NC, USA). We considered a P-value <0.05 as statistically significant.
Table 1 shows measurement variables of all the subjects (n=54) at weeks 0, 15 and 105 along with changes in the measurement variables. The mean changes in body weight and VAF during the 14-week intervention were −8.3±2.4 kg (−12.5±3.1%) and −34±16 cm2 (−29.7±12.3%), respectively. Along with the reductions in body weight, VAF, and the other body fat-related variables (BMI, percentage fat mass, fat mass, TAF, SAF and AC) during the intervention, improvements were observed in all metabolic risk factors except for high-density lipoprotein cholesterol (HDLC). As for the follow-up period, while there was an increase in the mean body weight from week 15 to week 105 (+2.2±3.3 kg or +3.3±4.8%), the mean VAF did not change (+4±20 cm2 or +2.4±16.7%). The mean values of triglycerides (TG), fasting plasma glucose (FPG), and total cholesterol (TC)/HDLC ratio at week 105 were lower than their baseline values (week 0). Although we did not observe an improvement in HDLC during the intervention period, an improvement was observed at week 105. On the other hand, the mean values of SBP, DBP, TC and LDLC had increased back to baseline values.
Table 2 shows Pearson's correlation coefficients between percent changes during the follow-up period relative to the baseline values of the anthropometric variables and metabolic risk factors. The percent changes in TC and LDLC correlated moderately with several anthropometric variables, such as TAF, SAF and body weight, but we observed no correlation between changes in these two risk factors and VAF, whereas, the change in HDLC correlated with the change in VAF. The percent change in TG and the TC/HDLC ratio correlated with almost all the anthropometric variables. On the other hand, the percent changes in SBP, DBP and FPG did not correlate with any percent changes in the anthropometric variables.
In this study, while the mean VAF did not change during the 2-year follow-up period (+4±20 cm2), we did observe a notable variability in VAF change between individuals. Therefore, we divided the subjects into two groups according to their change in VAF during the follow-up period: (1) VAF gainers (n=28), or (2) VAF maintainers (n=26) (Figure 1).
Table 3 compares subjects’ dietary intakes and energy expenditures between the two VAF groups at weeks 0, 10 and 105. We observed no differences between the two groups in any of the analyses. From week 0 to week 10, reductions were observed in total energy intake, carbohydrate intake, fat intake, protein intake and TEE, and an increase was observed in the ratio of protein intake to total energy intake in both groups. From week 10 to week 105, increases were observed in total energy intake, carbohydrate intake and fat intake, and a reduction was observed in the ratio of protein intake to total energy intake, although the energy intake, carbohydrate intake, fat intake and protein intake at week 105 were still lower than at week 0 in both groups. On the other hand, we observed no AEE changes in either of the two groups during the intervention and follow-up period.
Table 4 compares subjects’ anthropometric measurements and metabolic risk factors between the 2 groups at weeks 0 and 15. There were no differences between the 2 groups except for DBP at week 15. Table 4 also shows the results of two-way repeated measures analysis of variance. The changes in these values during the intervention period were not different between the 2 groups except for DBP and LDLC.
Figure 2a presents changes in body weight and VAF during the follow-up period by group; there were interactions between the 2 groups in both of these values. Figure 2b presents changes in metabolic risk factors during the same period by group. Interactions were observed between the 2 groups in HDLC, TG and TC/HDLC ratio: the HDLC in the VAF gainer group remained unchanged while an improvement was observed in HDLC in the VAF maintainer group; the TG and TC/HDLC ratio in the VAF gainer group increased while those in the VAF maintainer group remained unchanged. On the other hand, we observed no interaction between the 2 groups in SBP, DBP, TC, LDLC and FPG. These variables (except for FPG) in both groups increased from week 15 to week 105, and the variables at week 105 had returned to baseline levels, that is, there were no differences between week 0 and week 105 in SBP, DBP, TC and LDLC in both groups.
Metabolic risk factors improved along with the loss of body weight during the 14-week lifestyle intervention in this study. However, many of the values at week 105 were back to the baseline level even though subjects’ mean body weight and VAF remained at a level lower than baseline from week 15 to week 105 (Table 1). In this study, the subjects reduced their energy intake (approximately −700 kcal day–1, Table 3) through the 14-week intervention. As indicated by previous studies,8, 9, 10 it is possible that the initial improvements in metabolic risk factors may occur in part due to calorie restriction rather than from body weight or VAF reductions themselves.
The strength of this study is that many subjects succeeded in long-term maintenance of their weight loss during the 2-year follow-up period. Although the increase in mean body weight (+2.2±3.3 kg) of all 54 subjects was small, but significant, from week 15 to week 105, the mean VAF remained unchanged (+4±20 cm2) over the same period. There were 26 subjects who kept their VAF below the post-intervention (week 15) level. This made it possible to divide subjects into two groups, that is, VAF gainers and VAF maintainers. To our knowledge, this is the first study focusing on long-term VAF maintenance and changes in metabolic risk factors after short-term weight-loss intervention.
One of our primary findings is that subsequent HDLC increase after weight loss intervention is associated with long-term VAF maintenance. The HDLC in VAF maintainers remained unchanged during the 14-week intervention; however, it improved during the 2-year follow-up period, and the value at week 105 exceeded the baseline level. Consistent with our results, several previous studies31, 32, 33 indicated that improvement of HDLC did not occur during the weight loss intervention, but rather during the weight maintenance period after the intervention; this tendency was especially evident in women.32
Previous studies31, 33 explained this process by focusing on the activation of lipoprotein lipase or hepatic lipase. Tissue levels of lipoprotein lipase may fall by 50–80% during acute caloric restriction,34 but during a weight maintenance period its activation may cause tissue levels to exceed baseline levels considerably. Furthermore, long-term reduction of body fat is associated with reductions in hepatic lipase activation, which explains the elevation in HDLC that accompanies weight loss.35 Moreover, Wing and Jeffery32 showed that female subjects who maintained their weight loss for 18 months had incremental improvements in HDLC and waist-to-hip ratio with time. The results from that study suggest that a change in visceral fat may be associated with a change in HDLC, even though waist-to-hip ratio might not have a strong association with VAF.36 In our study, we assessed subjects’ VAF with CT scans, and the results were consistent with Wing and Jeffery.32 Changes in VAF-induced hormone levels in women may affect their HDLC status. Additional research is needed to clarify the pathway by which this occurs. With the favorable changes in HDLC among VAF maintainers, a group difference was observed in changes in TC/HDLC ratio between the two groups. In addition, this study also showed that long-term VAF maintenance was associated with improvements in TG level over time, which are consistent with previous studies.16, 32
As for TC and LDLC, a systematic review by Poobalan et al.14 shows the beneficial effects of long-term weight maintenance after weight-loss treatment on these two lipids’ levels. However, some studies16, 17 showed that TC and LDLC improved significantly during short-term weight loss intervention, and thereafter, they increased back to the baseline levels even though subjects did not regain their body weight during a follow-up period. Our results are consistent with those findings:16, 17 the TC and LDLC of the VAF maintainers increased back to the mean baseline level during the follow-up period, even though the VAF in this group decreased further during the same period (Figure 2). Thus, from our results we found no associations between VAF maintenance and changes in TC and LDLC. Similarly, we observed no associations between VAF maintenance and changes in SBP, DBP and FPG. Some researchers33, 37 indicated that there are a number of potential confounding effects due to macronutrient composition of the diet, exercise and biological variability of the subject, which together may affect the changes in these risk factors over time.
Our study did have several limitations. First, although we focused on postmenopausal women because it is known that menopause is associated with increases in VAF,38 the study’s sample size was relatively small. Insufficient sample size may be concomitant with a type II error. Further research is needed to confirm our results. Second, there were no associations between dietary measurements and changes in risk factors except for the association between changes in FPG and fat intake during the follow-up period (r=0.24, P=0.08, data not shown). Furthermore, we found no difference in any of the dietary measurements between the two VAF groups. In this study, the subject's calorie and macronutrient intake were measured by 3-day WDRs. Although this method is considered the gold standard for estimating dietary intake, there may have been an under- or overestimation of total energy intake along with carbohydrate, fat and protein intake.39 Additional research is needed to increase our understanding of the association between dietary values and changes in metabolic risk factors.
In summary, our hypothesis is partly supported by the significant effects of VAF maintenance after short-term weight loss intervention on improvements of HDLC and TG during the follow-up period. However, we did not observe any associations between maintaining VAF and the other risk factors, that is, SBP, DBP, TC, LDLC and FPG, which challenges our hypothesis. Consequently, this study shows that long-term maintenance of reduced VAF is associated with improvements in HDLC and TG among obese postmenopausal women, suggesting that long-term weight maintenance after short-term weight loss intervention should be encouraged for obese people.
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We are grateful to the participants and staff members in the study. The Sodegaura Health Promotion Project, Sodegaura, Chiba, Japan supported this research.
The authors declare no conflict of interest.
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Cite this article
Matsuo, T., Kato, Y., Murotake, Y. et al. An increase in high-density lipoprotein cholesterol after weight loss intervention is associated with long-term maintenance of reduced visceral abdominal fat. Int J Obes 34, 1742–1751 (2010). https://doi.org/10.1038/ijo.2010.95
- visceral abdominal fat
- high-density lipoprotein cholesterol
- weight maintenance
- metabolic risk factors
- weight loss