Intra-abdominal adipose tissue deposition and parity

Abstract

Objective:

To determine the relationship between body composition/fat distribution and parity after adjusting for potential confounders: age, smoking, and physical activity.

Design:

Cross-sectional.

Subjects:

A total of 170 Caucasian women between the ages of 18 and 76 years, who were non-smokers with no cardiovascular disease, diabetes, metabolic, or endocrine disorders.

Measurements:

Physical activity assessment (Baecke Physical Activity Questionnaire), anthropometric measures, and body composition (dual-energy X-ray absorptiometry, computed tomography).

Results:

Although percent body fat was related to parity (r=0.26, P<0.01), after adjusting for age, physical activity index, and smoking, the parity–percent body fat relationship was no longer significant. Multiple regression analysis for modeling intra-abdominal adipose tissue demonstrated that parity and intra-abdominal adipose tissue were significantly related after adjusting for percent body fat, physical activity index, and smoking (partial r=0.18, P=0.02, unstandardized β=5.22±2.26, intercept=−37.32±24.63).

Conclusion:

Our data suggest that intra-abdominal adipose tissue increases with increasing parity, even after adjusting for potential confounders: age, percent body fat, physical activity, and smoking.

Introduction

The increasing rise in the incidence of obesity, along with the risks for cardiovascular disease and diabetes, and certain types of cancer associated with it, is a major worldwide health concern, with the prevalence of obesity among women increasing by almost 15% over the last decade.1, 2 Postpartum weight retention has been identified as a contributor, and studies indicate that these associated increases in weight tend to be between 0.5 and 3.8 kg and cumulative with each pregnancy.3 The accumulation of central adiposity, particularly that which is deposited intra-abdominally, has proven to be more metabolically active and to carry more significant health risks than fat accumulation in other parts of the body. It is recognized that total body fat increases with parity,4 and that several studies have reported proportionally large increases in waist circumference and/or waist–hip ratio (WHR) with regard to postpartum weight retention.5, 6, 7, 8 Although circumference measures provide some insight into where fat is deposited with regard to girth, they provide no insight into whether the deposition is subcutaneous or intra-abdominal. In one study, magnetic resonance imaging (MRI) was used to quantify centrally located fat and identify it as either subcutaneous or non-subcutaneous fat volume.9 The results indicated that 1-year postpartum women had a larger expansion of lower trunk non-subcutaneous fat volume than subcutaneous fat volume. Although it is not clear exactly what fat depot is represented by the non-subcutaneous fat volume, it seems reasonable to assume that it is primarily comprised of intra-abdominal adipose tissue (IAAT). This small study of 15 postpartum subjects suggests that IAAT may increase following pregnancy, potentially increasing metabolic disease risk. Therefore, the purpose of this study was to determine the relationship between parity, body composition, and fat distribution after adjusting for the potential confounders: age, percent body fat (%BF), an index of physical activity (PA), and smoking. Independent of these potential confounders, we hypothesize that parity is positively related to %BF and a disproportionately large IAAT.

Methods

Subjects

The 181 subjects in this study were derived from a larger sample of 228 healthy Caucasian women. These women, aged 18–76 years, were recruited with the help of local media services to participate in a study designed to examine IAAT distribution with age. The 181 subjects represent the subset of participants on which parity information was available. The study was approved by the appropriate institutional review board (University of Alabama at Birmingham, Birmingham, AL, USA). Informed consent was given, and a medical history and a physical activity questionnaire completed. Eleven smokers were not included in the analysis since their number was insufficient to independently assess the impact of smoking on the association between parity and IAAT. Of the 170 subjects retained, 116 had never smoked and 54 were previous smokers. Criteria for exclusion, determined via questionnaire, were presence of cardiovascular disease, diabetes, any metabolic or endocrine disorders, the intake of cholesterol-lowering medications, and pregnancy. Body composition measurements using dual-energy X-ray absorptiometry (DXA) and computed tomography (CT) scans were performed in the morning after a 12-h fast. To ensure that the sample represented a full range of age and adiposity, five age groups (18–29, 30–39, 40–49, 50–59 and >59 years) and five body fat groups (<20.0, 20.1–25.0, 25.1–30.0, 30.1–35.0 and >35%) were selected. A maximum of 47 subjects and a minimum of 20 subjects were included in each of the age groups.

Medical history

All subjects provided information concerning their medical history. These questions pertained to disease and family history; smoking status (1 – never smoked, 2 – previous smoker, 3 – smoker) and number of children. For the purpose of this study, parity is defined as number of live births.

Physical activity assessment

The Baecke Physical Activity Questionnaire10 was used to assess physical activity. The Baecke includes 16 questions reflecting three habitual physical activity scores from the past 12 months: (1) occupational physical activities (OPA; eight questions); (2) physical exercises in leisure (PEL; four questions), and (3) leisure and locomotion physical activities (LLA; four questions). Subjects rated their normal physical activity using a scale of 1–5 (five being the most active). The total score (TS) was calculated as (TS=OPA+PEL+LLA) and reported as an index. Baecke et al.10 report an average 3-month test–retest reliability coefficient of 0.81. Jacobs et al.11, in a similar population (20–59 years old women), reported a test–retest reliability coefficient of 0.93, and Mahoney and Freedson12 reported a validation of 0.53 with the CALTRAC activity counter and the Baecke in women aged 18–38 years.

Body measures and composition

Height, weight, and waist circumference

All anthropometric measures were performed in duplicate by one observer the morning after a 12-h fast. Body height was measured to the nearest 0.25 in without shoes on a stadiometer. Body weight was measured to nearest 0.25 lb on a calibrated clinical scale, while the subject was wearing a swimsuit. Waist circumference was measured at the level of the umbilicus while the subject was standing.

DXA

The Lunar DPX-L densitometer using the body composition Adult Software Version 3.1 (Lunar Corp., Madison WI, USA) was used to determine total %BF by providing measures of bone mineral content and density, fat mass (FM), and fat-free mass (FFM). Measures were made with subjects lying supine on a padded table, wearing a hospital gown. The scan required approximately 25–30 min to perform.

Computed tomography

A single CT scan was taken at the level of the fourth to fifth lumbar vertebrae (L4–L5) with a HiLight/HTD Advantage Scanner (General Electric, Milwaukee). Radiographic factors were 120 kVp (peak kilovolts) and 40 mA. The subjects were examined in a supine position with their arms stretched above their heads. Previous studies indicate that visceral fat areas from a single scan in the L4–L5 region are highly correlated with total visceral fat volume.13, 14 Therefore, a single 5-mm scan taken for 2 s at the umbilicus level (which usually corresponds to the level of the fourth lumbar vertebra) was obtained. Attenuation ranges were −30 to −190 Hounsfield units for adipose tissue. Cross-sectional areas of adipose tissue were determined by using an adipose tissue highlighting technique. IAAT and subcutaneous adipose tissue (SAT) were measured separating adipose tissue areas by encircling the muscle wall surrounding the abdominal cavity with a cursor. Both intra- and inter-observer test–retest reliability were r=0.99 with a coefficient of variation less than 2% based on the re-evaluation of 20 single scans.

Data analysis

Descriptive statistics were calculated for all subjects according to four parity categories: zero children, one child, two children, and three or more children. Too few observations were found for women with four (N=15), five (N=2), and six (N=2) children, therefore all subjects with three or more children were included in one group for analysis. Parity is considered a continuous variable, 0–6 births for correlation and regression analysis. Simple Pearson correlations were used to determine relationships between parity, CT-determined IAAT, and age with physical activity index, DXA-determined %BF, CT-determined abdominal SAT, waist circumference (WC in cm), and CT-determined fat-free abdominal cross-section. In order to understand the independent relationship of parity with body composition and fat distribution, several potential confounders were included in the multiple regression models: age, physical activity index, smoking, and %BF. Since menopause and hormone replacement therapy are suspected to be associated with increased IAAT,15 they were included as potential confounders in our initial analyses. However, they were later excluded because their inclusion did not affect any of the results, as long as age, which is strongly related to fat distribution, was also included in the model. Because %BF, WC, SAT, and IAAT were all considered to play a contributory role in circumference measures of the mid-section of the body, these variables were included in the models as dependent variables. Linear regression was performed separately for each dependent variable. Two-way analysis of variance (ANOVA) was used to demonstrate changes in IAAT across four parity categories while adjusting for age, %BF, physical activity index, and smoking. Significance was set at P<0.05 for all analysis. All statistics were performed using SPSS version 11.5 for Windows.

Results

Table 1 contains descriptives on the 170 Caucasian women included in the study according to their parity status, and they are presented as unadjusted means±s.d. The sample was heterogeneous for age (range 18–76) and %BF (range 32.83±9.08). Table 2 contains simple Pearson product correlations between parity, IAAT, and age, with each other, and with the physical activity index, % BF, SAT, and WC. Among the remaining simple correlations, IAAT and age were significantly related to all variables, and parity was positively and significantly related to all variables except physical activity index.

Table 1 Descriptive unadjusted means by parity status with standard deviations (s.d.±), N=170
Table 2 Simple Pearson product correlations and (probability), N=170

Tables 3 and 4 present linear regression models for predicting %BF, WC, SAT, and IAAT. All regression models include the predictors age, parity, physical activity index, smoking, and %BF (with the exception of Model 1, in which %BF is the dependent variable, and Model 4, in which IAAT is the dependent variable). Model 1, predicting %BF, indicates that age and physical activity index are both significant independent predictors in the model (Standardized Beta: age 0.34, physical activity index −0.38 with P levels <0.01). Model 2, predicting WC, indicates that %BF is independently related to WC (Standardized Beta: %BF 0.55, P<0.01).

Table 3 Regression models for predicting DXA determined %body fat and waist circumference (WC in cm), N=170
Table 4 Regression models for predicting CT determined abdominal subcutaneous adipose tissue (SAT), and CT determined intra-abdominal adipose tissue (IAAT), N=170

Table 4 presents regression Models 3 and 4 for predicting SAT and IAAT. In Model 3, only %BF was significantly and independently related to SAT (Standardized Beta: 0.82, P<0.01). However, in Model 4, all variables except smoking contributed significantly to the equation (Standardized Betas: age 0.23, physical activity index −0.18, %BF 0.49 all with P-values <0.01; parity 0.13, P=0.02). Figure 1 illustrates the increase in IAAT across four parity categories after adjusting for age, %BF, physical activity index, and smoking.

Figure 1
figure1

Intra-abdominal adipose tissue (IAAT) means across four parity categories. Adjusted for age, % body fat, physical activity index, and smoking.

Discussion

The primary purpose of this study was to not only determine whether total body fat was related to parity but also to further examine the relationship between parity and IAAT deposition using imaging technology. We found, as have others, that parity was associated with increases in %BF.5, 6, 7, 8, 9, 16 In addition, our results support the findings of Sohlström and Forsum,9 who found that women 1 year post-partum retained a proportionally larger lower trunk non-subcutaneous volume of adipose tissue (presumably primarily IAAT) compared to SAT volume. We have also extended these findings to show that the relationship between IAAT and parity is independent of the potential confounders: age, %BF, smoking, and physical activity index. These data, along with those of Sohlström and Forsum,9 are highly suggestive that parity may have some effect not only on overall body fat but also on fat distribution.

After adjusting for age, physical activity index, and smoking, parity was not significantly related to %BF. Only physical activity (index) and age entered the model as significant covariates. Since physical activity is independently related to %BF, these results suggest that physical activity may play an important role in preventing increased %BF following pregnancy. This is consistent with several studies that have shown a significant relationship between reduced physical activity (determined by doubly labeled water) and weight gain.17, 18

WC was not related to parity after adjusting for potential confounders. However, as would be expected, %BF (partial r=0.50, P<0.01) contributed independently and significantly to WC. None of the other potential confounders contributed to the model.

SAT was not significantly related to parity after adjusting for potential confounders, and with the exception to the adjustment made for %BF, none of the other variables contributed significantly to the model. This was not the case when predicting IAAT, in which all but one of the adjusting variables (smoking) significantly contributed to the estimation of IAAT. Although smoking has been found to be associated with larger waist circumferences and more IAAT,16, 19, 20, 21 we had so few smokers (n=11) in our original data pool of 181 that they were not included in the analysis, and only non-smokers (n=116) and those who had previously been smokers (n=54) were retained. Because of this we cannot speculate on the effect smoking has on fat distribution. However, it is important to point out that the data suggest that if there are smoking effects on IAAT, those who quit smoking eventually return to a fat distribution that is similar to that of non-smokers.

In conjunction with studies that have reported increases in anthropometric measures associated with parity, there are also a number that have reported a positive association between parity and coronary heart disease22, 23, 24 and artery disease.5, 25 Two biological mechanisms have been proposed as explanations for this relationship,22 both of which negatively influence blood lipids (reducing HDL). The first is based upon pregnancy's ability to reduce lifetime estrogen exposure, and the second upon the state of relative insulin sensitivity imposed by pregnancy. In contrast to the reported adverse effect of parity on IAAT, it is important to note that considerable research data indicate that the same proposed mechanism by which pregnancy reduces lifetime estrogen exposure is related to a long-term lifetime reduction in breast cancer risk.26, 27 Early age at first birth and increasing parity are also associated with decreased risk.26, 27

As is the case with parity and coronary heart and artery disease, only speculation can be offered regarding the relationship of IAAT to parity. IAAT tends to be highly sensitive to the effects of circulating cortisol and responds to its presence by increasing in size, thus increasing the risk of diseases like diabetes, high blood pressure, heart disease, and stroke. Cushing's syndrome and similar diseases have been cited as evidence for this theory because they cause extreme cortisol exposure, resulting in excessive IAAT accumulation.28 Motherhood is associated with a number of stressors, and it is possible that stress-induced cortisol release could contribute to this increase in IAAT.

Our study was cross-sectional, prohibiting conclusions of cause and effect. The inclusion of only Caucasian women who were non-smokers in our subject population limits the application to other populations. The impact of numerous dietary factors (including fat, saturated fat, and fiber) has been assessed in these subjects and found not to be independently related to IAAT.29 However, there are many other factors that we could not account for that have been cited as possible confounders for increases in waist circumference and/or IAAT. These include alcohol consumption,30, 31, 32 stress,28 socioeconomic status, vocational education, having been born small for gestational age, shift work, and diet and physical activity during adolescence.33 Nevertheless, the persistent relationship between parity and IAAT despite controlling for %BF, age, smoking (by including only nonsmokers), and physical activity is an intriguing finding that warrants further investigation.

In conclusion, our data suggest that IAAT increases with parity independent of age, %BF, physical activity, and smoking. As the data suggest that fat may be shifting from subcutaneous depots to an intra-abdominal depot with parity, it is possible that women who have had children should be even more conscious of maintaining healthful weights than women who have not had children.

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Blaudeau, T., Hunter, G. & Sirikul, B. Intra-abdominal adipose tissue deposition and parity. Int J Obes 30, 1119–1124 (2006). https://doi.org/10.1038/sj.ijo.0803252

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Keywords

  • physical activity
  • fat distribution

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