Next Article in Journal
Nonalcoholic Fatty Liver Disease and Risk of Early-Onset Vasomotor Symptoms in Lean and Overweight Premenopausal Women
Previous Article in Journal
Sociocultural Influences Contribute to Overeating and Unhealthy Eating: Creating and Maintaining an Obesogenic Social Environment in Indigenous Communities in Urban Fiji
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

The Relationship of Physical Activity and Dietary Quality with Android Fat Composition and Distribution in US Adults

1
School of Education, University of Rhode Island, Kingston, RI 02881, USA
2
Department of Kinesiology, University of Connecticut, Storrs, CT 06269, USA
3
Department of Health and Exercise Science, Rowan University, Glassboro, NJ 08028, USA
4
Department of Nutrition and Food Sciences, University of Rhode Island, Kingston, RI 02881, USA
*
Author to whom correspondence should be addressed.
Nutrients 2022, 14(14), 2804; https://doi.org/10.3390/nu14142804
Submission received: 13 June 2022 / Revised: 6 July 2022 / Accepted: 6 July 2022 / Published: 8 July 2022
(This article belongs to the Section Nutrition and Public Health)

Abstract

:
This study examined the relationship of physical activity (PA) and dietary quality to android fat composition and distribution using a national representative adult sample and determined sex-based differences in these relationships. It is a cross-sectional (n = 10,014) analysis of the 2011–2018 National Health and Nutrition Examination Survey and the US department of Agriculture’s Food Patterns Equivalents datasets. Variables utilized for this analysis include PA, 24-h dietary recalls, android percent fat, and android-gynoid (A/G) ratio measured by dual-energy X-ray absorptiometry. Multiple linear regression was performed to examine the relationship between PA and/or dietary quality and android percent fat and A/G ratio adjusted for confounding factors. The study results revealed that PA and/or dietary quality were inversely related to android percent fat and A/G ratio (p < 0.05), but the sex effect was only seen between PA and A/G ratio (p = 0.003). Participants who met PA recommendations and had higher dietary quality had 2.12% lower android fat than those who did not meet PA recommendations and had lower dietary quality (p < 0.001). Both PA and dietary quality are associated with android fat reduction regardless of sex. Given the direct connection between android fat and cardiovascular and metabolic diseases, it is important to increase both PA and dietary quality.

1. Introduction

The rapidly increasing rate of obesity is anticipated to have serious negative health consequences [1,2]. Research has indicated that excess body fat is associated with many metabolic and cardiovascular risk factors [3]. However, more recent research indicated that fat centralized in the abdominal/android region is a more sensitive indicator of metabolic or cardiovascular risk for chronic diseases than overall body fat percentage or fat located in the hip/gynoid region [4,5,6,7,8]. Specifically, studies have found that android accumulation was associated with metabolic risk factors [4,5], was a better estimator for the risk of type II diabetes than overall fat mass [6,7,8] and was a risk factor for stroke independent of body mass index [9]. It is important to understand how health behaviors influence android fat composition and its ratio to gynoid fat mass (A/G ratio), thus indirectly affecting related health risks.
Previous studies have found that higher levels of physical activity are related to reduced abdominal fat and A/G ratio [10,11,12,13,14]. Previous studies also found dietary quality, which refers to the balanced and diversified dietary patterns, used to evaluate compliance to dietary guidelines [15], is related to abdominal fat and A/G ratio [16,17,18,19,20]. While larger scale studies investigating the influence of health behaviors on A/G ratio are lacking, two studies examined such relationships with consideration of physical activity intensity [11,18]. More specifically, one study reported moderate to vigorous physical activity is inversely associated with A/G ratio in adults, 60–64 years, in mainland Britain, [11] while another study reported inverse relationships between dietary quality and A/G ratio in 60 years of age or older Australian males [18]. However, to our knowledge, neither of these previous studies examined the relationship between physical activity or dietary quality and android fat or the A/G ratio using a representative sample of US young and middle-aged adults. In addition, studies are also lacking in examining dose-response relationship between physical activity or dietary quality and android fat composition and distribution in this population. This is an important research gap to address as young and middle-aged adults are also age groups facing obesity related health concerns as they age [21,22,23]. Lifestyle habits developed during this age period can be maintained in older age [11,24], thus can help better reduce health challenges in an aging population. Furthermore, it is presently unknown if sex influences the relationships between health behaviors and fat distribution (A/G ratio). A better knowledge base of sex-based differences can help practitioners and policy makers prioritize lifestyle modifications and guide investment in public health campaigns with the greatest epidemiological benefit. Accordingly, more studies are needed on the relationship of physical activity and/or dietary quality with android fat and the A/G ratio using a representative sample of US adults.

2. Methods

The current study is a secondary data analysis utilizing four cycles and eight years of data (2011–2018) from two datasets: (1) National Health and Nutrition Examination Survey (NHANES) and (2) the US Department of Agriculture’s Food Patterns Equivalents [25,26]. The inclusion criteria for this study are adults who have (1) dual-energy X-ray absorptiometry (DXA) data (18–59 years old; n = 16,142), and data for (2) body mass index (n = 15,338), (3) physical activity, two 24-h dietary recalls (n = 11,374), and android and gynoid fat mass (n = 10,014). Accordingly, 10,014 out of 39,156 respondents met all the above inclusion criteria and have been included for the current study (see Figure 1). The University of Rhode Island Institutional Review Board has approved the current study under exemption category (IRB reference#: 1849196-1).

2.1. Android Fat Composition and Distribution

Android and gynoid fat were measured by using DXA and analyzed via the Hologic APEX software [25,27]. In the Hologic software, both android and gynoid regions were defined in the same method as utilized in Shepherd and colleagues’ study [28]. The android area was defined as the lower trunk area above the pelvic line and 20% of the distance between this line and the neck cut line, and gynoid area was defined as twice the height of the android region below the pelvic line [28]. Android percent fat was defined as android fat mass divided by android total mass; and A/G ratio was calculated by the Hologic APEX software used in the scan analysis [25].

2.2. Physical Activity

The Global Physical Activity questionnaire was used to measure a typical week of physical activity which was analyzed following the World Health Organization analysis guide for Global Physical Activity questionnaire [25,29]. Physical activity results are reported as metabolic equivalent (MET) minutes of moderate to vigorous physical activity per week. MET-minutes per week was used as opposed to physical activity time (min/week) or energy expenditure (kcal/week) as this variable accounts for the variation in metabolic demands between a wide range of common physical activities that is independent of body mass of the individual [29]. Results were classified as three categories: insufficiently active (<600 MET-minutes/week), active (600–1200 MET-minutes/week) and highly active (>1200 MET-minutes/week) based on U.S. Department of Health and Human Services’ physical activity guidelines for Americans [30]. Respondents classified as active and highly active met the current physical activity recommendation of 600 MET-minutes/week for adults [30].

2.3. Dietary Quality

Dietary quality was measured using the Healthy Eating Index 2015 (HEI-2015) which is based on adherence to the 2015–2020 Dietary Guidelines for Americans [31]. Assessing adherence to the dietary guidelines provides a broad picture of dietary patterns and consumption, which is more predictive of disease risks than focusing on consumption of individual nutrients [32]. The HEI-2015 analysis used data from two datasets: National Health and Nutrition Examination Survey’s two 24-h dietary recalls and US Department of Agriculture’s Food Patterns Equivalents dataset [25,26]. The HEI-2015 scoring metric contains 13 dietary components that assess either adequacy (9 components) or moderation of intake (4 components) [31]. The maximum score for HEI-2015 is 100 which has been classified into three categories for this study based on the score distribution in this sample: Higher dietary quality (the highest tertile, 58.1 < HEI-2015 ≤ 95.8), Lower dietary quality (the lower two tertiles, 10 ≤ HEI-2015 ≤ 58.1). This approach has been used previously to analyze HEI-2015 data [33].

2.4. Lifestyle Groups

Four lifestyle groups were categorized utilizing the criteria for physical activity recommendation (met vs. did not meet) and dietary quality score distribution (higher vs. lower) [34]. More specifically, (1) Group 1: did not meet physical activity recommendation + lower dietary quality, (2) Group 2: did not meet physical activity recommendation + higher dietary quality, (3) Group 3: met physical activity recommendation + lower dietary quality, (4) Group 4: met physical activity recommendation + higher dietary quality.

2.5. Confounding Variables

Respondents’ demographics characteristics were reported for the current study are age, race/ethnicity (White, Black, Hispanic, others), education (high school or less, some college or more), and ratio of family income to poverty [25]. Body mass index was also reported and has been further classified as underweight, normal, overweight, and obese based on Centers for Disease Control and Prevention’s body mass index interpretation for adults [35]. Additionally, daily energy intake has also been included due to its possible influence on our study variables [36].

2.6. Data Analysis

All analyses were performed using the combined 8-year sample weights. The dietary two-day sample weight was selected to construct weights for the combination of four data cycles (2 years per cycle & 8 years in total) based on National Health and Nutrition Examination Survey Methods and Analytic Guidelines regarding weight selection and weight construction for combined data cycles [37]. Multicollinearity among independent variables (physical activity or HEI score) and control covariates were checked using PROC REG with weight statement, no collinearities were observed using the criteria based on the condition index exceeding 30 [38]. Sample characteristics are expressed as weighted means ± standard errors or count (percentage). p values for continuous variables were obtained by performing t-test (PROC SURVEYREG), and p values for categorical variables were obtained by performing Chi-Squared test (PROC SURVEYREQ). For the relationships between physical activity and/or dietary quality and android fat composition and distribution, adjusted β (95% confidence interval), p values and R-square were obtained performing multiple linear regression (PROC SURVEYREG) adjusted for age, sex, race/ethnicity, education, family income to poverty ratio, body mass index and daily energy intake. The interaction terms were added into the model to examine the modification effect of sex to investigate whether the association between physical activity and/or dietary quality and android fat composition and distribution differed by male and female. Statistical Analysis Software 9.4 (SAS Institute Inc., Cary, NC, USA) was utilized to analyze the data considering the complex sample design, and p < 0.05 was chosen as the statistically significant level.

3. Results

Out of 10,014 respondents, approximately half (48.7%) of them are females, 39.3% are racial/ethnic minorities; 32.9% have high school or less education, 15.9% live below poverty level; 1.6%, 31.5% and 37.3% are respondents whose body mass index categorized as underweight, overweight and obese respectively. Additionally, 31% do not meet physical activity recommendation, and by definition 66.0% had HEI scores in the lower dietary quality category. Males have lower android fat percentage and are more physically active than females whereas females have a lower A/G ratio and better dietary quality than males (see Table 1). Moreover, the descriptive results in Table 2 indicated that there was a linear decrease in android fat percentage from lower to higher physical activity and dietary quality groups for both sexes with the lowest android fat percentage seen in the met physical activity recommendations and higher dietary quality group (see Table 2).
There was an inverse relationship between physical activity and android fat composition and distribution (see Table 3 and Table 4). For every 100 MET-minutes/week increase, the percent android fat was reduced by 0.0103. Respondents who were classified as highly active had 1.64 percent lower android fat on average than those who were classified as insufficiently active. No statistically significant difference was observed between physical activity and the A/G ratio (see Table 3). There was a similar pattern observed in males and females; however, there were differences in A/G ratio as there was a significant interaction by sex (see Table 4). For every 100 MET-minutes/week physical activity increase, males had a greater A/G ratio reduction (β = −0.0002, 95% CI: −0.0004, −0.0001) than females (see Table 4).
Similar to physical activity, there was an inverse relationship between dietary quality and android fat composition and distribution (see Table 3). For every 10-point HEI score increase, android fat decreased by 0.34%, and the A/G ratio decreased 0.01 on average. In comparison to those with a lower dietary quality score, respondents who had higher dietary quality scores showed lower android percent fat. No statistically significant differences in the A/G ratio were observed between higher and lower dietary quality score groups (see Table 3). However, the results in sex specific analyses revealed that females with higher HEI scores showed lower A/G ratio compared to those who had lower HEI scores but there was no sex by score group interaction (see Table 4).
Physical activity and diet integrated lifestyle group analyses indicated that in comparison to respondents who were in group 1: did not meet physical activity recommendation and had lower dietary quality, those in other lifestyle groups had a lower percentage of android fat. The difference was greatest (2.12% for android fat) between group 1 (not meet physical activity recommendation and lower dietary quality) and group 4 (met physical activity recommendation and had higher dietary quality). There was no difference in A/G ratio between lifestyle groups (see Table 3). There was a similar pattern observed in analysis specifically for males. For females, there was A/G ratio difference between group 4 and group 1. Additionally, compared to their counterparts in group 1, males who met physical activity recommendations but had a lower dietary quality had lower android fat percentage than females (β = −0.86, 95% CI: −1.58, −0.15) (see Table 4).

4. Discussion

The key finding of the present study was that both physical activity and dietary quality were inversely related to android percent fat in men and women. Meeting physical activity recommendations and having higher dietary quality provided a compounding impact on reducing android fat percentage. This finding supports multi-component lifestyle modifications that focus on both lifetime physical activity and overall dietary quality in order to reduce android fat and reduce the risk of obesity related chronic diseases. To the authors’ knowledge, this is the first large scale study utilizing a nationally representative sample measuring fat using DXA, leisure time physical activity using a validated measure and dietary quality using the HEI-2015 based on two 24-h recalls. In addition, the study sample of 18–59-year-old adults, is the appropriate target population for primary prevention programs.
In the present study, leisure time physical activity was inversely associated with android percent fat for both sexes. Specifically, respondents showed 0.0103 percent android fat reduction for every 100 MET-minutes/week physical activity time increase which is approximately 123 kcal/week expended for a person who weighs 70 kg, this is equivalent to 25 min/week of moderate intensity (e.g., walking) or 12.5 min/week of vigorous intensity (e.g., running) physical activity [39]. This finding has been supported by previous studies [10,11,12] but adds to the literature with a large representative sample of general adults in comparison to previous studies either using smaller sample sizes [10,11], or special populations such as ethnic Greenlanders [12] or older adults in Britain [11]. The current study extends previous studies by examining a dose-response relationship between android percent fat and physical activity levels (insufficiently active, active, highly active) as defined by U.S. Department of Health and Human Services’ physical activity guidelines for Americans [30]. While the beneficial effects of physical activity on android fat were observed similarly between sexes, people who were categorized as highly active (>1200 MET-minutes/week) have lower android fat than those who were insufficiently active. Moreover, we found that an increase in physical activity time provided greater reductions in the A/G ratio in men than in women. However, it should also be noted that categorical comparisons between sexes revealed that among participants who met physical activity recommendations but had lower dietary quality, men had lower android fat percentage than women, suggesting the effect of physical activity may be stronger in men than women. Nevertheless, these results suggest that both men and women will likely benefit from having an active lifestyle. We believe, these results relate to the importance of physical activity promotion for public health given one third of respondents in the present study reported being insufficiently active.
Similar to physical activity, there was an inverse relationship between dietary quality and android fat composition and distribution. For every 10-point HEI-2015 score increase, android fat decreased by 0.34% and A/G ratio decreased by 0.01. Since the HEI is intended to evaluate consumption of a set of foods in relation to the dietary guidelines, rather than dietary quantity, scores can be interpreted using a graded approach to qualitatively describe adherence to the 2015–2020 Dietary Guidelines for Americans (A = 90–100, B = 80–89, C = 70–79, D = 60–69, and F = 0–59) [40]. Therefore, a 10-point increase can be viewed as an increase in a letter grade and thus steps towards adhering to the dietary guidelines, with scores >80, indicative of close adherence. Typically, individuals can improve their scores by increasing foods from food sources in the adequacy dietary component categories while simultaneously decreasing food sources in the moderation component categories [40]. Regardless, this finding is in line with observations from previous studies [16,18]. Direct comparisons are not possible due to the sample differences (adults vs. older adults) [18] or national vs. regional samples [16] or different dietary quality measures (HEI-2015 scores vs. Mediterranean-style diet) [16]. The study sample utilized NHANES data collected 2011–2018 thus the use of HEI-2015 reflecting 2015–2020 Dietary Guidelines for Americans is appropriate [31]. NHANES dietary data collection methodology is rigorous, and the dietary database was concurrent with data collection adding strength to study findings [25,26]. This study determined the influence of compliance with dietary guidelines, as measured by HEI-2015 scores, on android fat across a representative sample of young to middle aged Americans. The finding that increased HEI-2015 scores were associated with decreased android fat likely reflects the cumulative effects of consumption of a healthier diet because HEI-2015 scores are dependent upon the consumption of a balanced diet, high in nutrient dense foods (e.g., fruits, vegetables, whole grains, dairy, lean proteins and unsaturated fats) and low in refined grains, sodium, sugar and saturated fats [31]. However, future research is needed to investigate if certain dietary components or total diet are more closely related to android fat.
Another important finding of the present study was that the strength of the healthy lifestyle (physical activity and diet) on android fat composition and distribution. The summative effects of both physical activity and dietary quality provide strong evidence that healthy behaviors are multifaceted and that lifestyle modifications made to improve overall health and curb the progression of obesity related illnesses should reflect both physical activity and diet. This finding provides evidence-based justification for health practitioners to prioritize healthy lifestyle that encompasses both physical activity and dietary behaviors. Additionally, while beneficial effects of physical activity and dietary quality on abdominal fat percentage were seen across sexes, it is noteworthy that the A/G ratio differences between lifestyle groups was only observed in females, but not males. When these relationships were directly compared between sexes, the comparison failed to reach significance. These results revealed the variation of android fat or A/G ratio related to dietary quality and physical activity regardless of weight status or energy consumption since both body mass index and daily energy intake were adjusted for in all analyses. It is possible that adjustment might not fully address overall adiposity. For instance, body mass index may over-represent adiposity in those with high muscle mass with low fat mass and under-represent adiposity of those with low muscle mass and high fat mass. In conclusion, further research is needed to determine if changing physical activity and diet will affect android fat similarly in men and women.

5. Limitations and Strength

The strengths of the present study are (1) we examined the association of integrated physical activity and dietary quality with android fat using nationally representative data, (2) we were able to effectively compare these associations between sexes, (3) android fat and A/G ratio were measured by using DXA. The limitations of the present study are: (1) its cross-sectional study nature which did not allow determination of causality; (2) the physical activity instrument and two 24-h dietary recalls might possess certain limitations due to the nature of self-report, although these instruments have been validated and widely used in large studies like ours [29,31]; (3) the average HEI score of 52.67 is slightly lower than the national average, thus findings might not be fully representative and generalizable to the US population; (4) there might be a risk of residual confounding factors even though we adjusted for appropriate confounders including body mass index.

6. Conclusions

The present study found that physical activity and dietary quality were inversely associated with percent of android fat regardless of sex. However, males experienced greater benefits to the android fat distribution (A/G ratio) than females through increasing weekly physical activity time. Furthermore, respondents who met physical activity recommendations and had higher dietary quality had lowest percentage of android fat (men and women both) and a lower A/G ratio (women only) than respondents who did not meet physical activity recommendations and had lower dietary quality.

Author Contributions

Conceptualization, F.X., J.E.E., G.W.G. and D.L.L.; Methodology, F.X., J.E.E., G.W.G. and D.L.L.; Data Curation, F.X., J.E.E. and G.W.G.; Formal Analysis, F.X.; Writing—Original Draft Preparation, F.X., J.E.E., G.W.G. and D.L.L.; Writing—Review & Editing, F.X., J.E.E., G.W.G. and D.L.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Data are publicly accessible data and obtained from Centers for Disease Control and Prevention and the US Department of Agriculture websites. This study was approved by the University of Rhode Island Institutional Review Board under exemption category (IRB reference#: 1849196-1).

Informed Consent Statement

Not applicable.

Data Availability Statement

Data used for the present study from two publicly accessible datasets: (1) Centers for Disease Control website: https://wwwn.cdc.gov/nchs/nhanes/sasviewer.aspx (accessed on 2 August 2021); and (2) U.S. Department of agriculture website: https://www.ars.usda.gov/northeast-area/beltsville-md-bhnrc/beltsville-human-nutrition-research-center/food-surveys-research-group/docs/fped-data-tables/ (accessed on 2 August 2021).

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Fryar, C.D.; Carroll, M.D.; Afful, J. Prevalence of Overweight, Obesity, and Severe Obesity Among Adults Aged 20 and Over: United States, 1960–1962 Through 2017–2018; NCHS Health: Hyattsville, MD, USA, 2020.
  2. Managing Overweight and Obesity in Adults: Systematic Evidence Reviews from the Obesity Expert Panel; Agency for Healthcare Research and Quality: Rockville, MD, USA, 2013. Available online: https://www.ahrq.gov/evidencenow/heart-health/overall/obesity.html (accessed on 22 August 2018).
  3. Knight, J.A. Diseases and disorders associated with excess body weight. Ann. Clin. Lab. Sci. 2011, 41, 107–121. [Google Scholar] [PubMed]
  4. Kotronen, A.; Yki-Järvinen, H.; Sevastianova, K.; Bergholm, R.; Hakkarainen, A.; Pietiläinen, K.H.; Juurinen, L.; Lundbom, N.; Sørensen, T.I. Comparison of the relative contributions of intra-abdominal and liver fat to components of the metabolic syndrome. Obesity 2011, 19, 23–28. [Google Scholar] [CrossRef] [PubMed]
  5. Kang, S.M.; Yoon, J.W.; Ahn, H.Y.; Kim, S.Y.; Lee, K.H.; Shin, H.; Choi, S.H.; Park, K.S.; Jang, H.C.; Lim, S. Android fat depot is more closely associated with metabolic syndrome than abdominal visceral fat in elderly people. PLoS ONE 2011, 6, e27694. [Google Scholar] [CrossRef] [PubMed]
  6. Min, K.B.; Min, J.Y. Android and gynoid fat percentages and serum lipid levels in United States adults. Clin. Endocrinol. 2015, 82, 377–387. [Google Scholar] [CrossRef]
  7. Wang, Y.; Rimm, E.B.; Stampfer, M.J.; Willett, W.C.; Hu, F.B. Comparison of abdominal adiposity and overall obesity in predicting risk of type 2 diabetes among men. Am. J. Clin. Nutr. 2005, 81, 555–563. [Google Scholar] [CrossRef] [Green Version]
  8. Sari, C.I.; Eikelis, N.; Head, G.A.; Schlaich, M.; Meikle, P.; Lambert, G.; Lambert, E. Android fat deposition and its association with cardiovascular risk factors in overweight young males. Front. Physiol. 2019, 10, 1162. [Google Scholar] [CrossRef]
  9. Toss, F.; Wiklund, P.; Franks, P.W.; Eriksson, M.; Gustafson, Y.; Hallmans, G.; Nordström, P.; Nordström, A. Abdominal and gynoid adiposity and the risk of stroke. Int. J. Obes. 2011, 35, 1427–1432. [Google Scholar] [CrossRef] [Green Version]
  10. Kay, S.J.; Fiatarone Singh, M.A. The influence of physical activity on abdominal fat: A systematic review of the literature. Obes. Rev. 2006, 7, 183–200. [Google Scholar] [CrossRef]
  11. Bann, D.; Kuh, D.; Wills, A.; Adams, J.; Brage, S.; Cooper, R. Physical activity across adulthood in relation to fat and lean body mass in early old age: Findings from the Medical Research Council National Survey of Health and Development, 1946–2010. Am. J. Epidemiol. 2014, 179, 197–1207. [Google Scholar] [CrossRef] [Green Version]
  12. Bann, D.; Kuh, D.; Wills, A.; Adams, J.; Brage, S.; Cooper, R. Physical activity and abdominal fat distribution in Greenland. Med. Sci. Sports Exerc. 2017, 9, 2064–2070. [Google Scholar]
  13. Dahl-Petersen, I.K.; Bjerregaard, P.; Brage, S.; Jorgensen, M.E. Physical activity energy expenditure is associated with 2-h insulin independently of obesity among Inuit in Greenland. Diabetes Res. Clin. Pract. 2013, 102, 242–249. [Google Scholar] [CrossRef] [PubMed]
  14. Irving, B.A.; Davis, C.K.; Brock, D.W.; Weltman, J.Y.; Swift, D.; Barrett, E.J.; Gaesser, G.A.; Weltman, A. Effect of exercise training intensity on abdominal visceral fat and body composition. Med. Sci. Sports Exerc. 2008, 40, 1863–1872. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  15. Wirt, A.; Collins, C.E. Diet quality—What is it and does it matter? Public Health Nutr. 2009, 12, 2473–2492. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  16. Hennein, R.; Liu, C.; McKeown, N.M.; Hoffmann, U.; Long, M.T.; Levy, D.; Ma, J. Increased Diet Quality is Associated with Long-Term Reduction of Abdominal and Pericardial Fat. Obesity 2019, 27, 670–677. [Google Scholar] [CrossRef]
  17. Wolongevicz, D.M.; Zhu, L.; Pencina, M.J.; Kimokoti, R.W.; Newby, P.K.; D’Agostino, R.B.; Millen, B.E. An obesity dietary quality index predicts abdominal obesity in women: Potential opportunity for new prevention and treatment paradigms. J. Obes. 2010, 2010, 945987. [Google Scholar] [CrossRef] [Green Version]
  18. Smee, D.; Pumpa, K.; Falchi, M.; Lithander, F.E. The Relationship between Diet Quality and Falls Risk, Physical Function and Body Composition in Older Adults. J. Nutr. Health Aging 2015, 19, 1037–1042. [Google Scholar] [CrossRef]
  19. Panizza, C.E.; Wong, M.C.; Kelly, N.; Liu, Y.E.; Shvetsov, Y.B.; Lowe, D.A.; Weiss, E.J.; Heymsfield, S.B.; Kennedy, S.; Boushey, C.J.; et al. Diet quality and visceral adiposity among a multiethnic population of young, middle and older aged adults. Curr. Dev. Nutr. 2020, 4, nzaa090. [Google Scholar] [CrossRef]
  20. Funtikova, A.N.; Benítez-Arciniega, A.A.; Gomez, S.F.; Fitó, M.; Elosua, R.; Schröder, H. Mediterranean diet impact on changes in abdominal fat and 10-year incidence of abdominal obesity in a Spanish population. Br. J. Nutr. 2014, 111, 1481–1487. [Google Scholar] [CrossRef] [Green Version]
  21. George, M.G.; Tong, X.; Kuklina, E.V.; Labarthe, D.R. Trends in stroke hospitalizations and associated risk factors among children and young adults, 1995–2008. Ann. Neurol. 2011, 70, 713–721. [Google Scholar] [CrossRef]
  22. Whitsel, E.A.; Nguyen, Q.C.; Suchindran, C.M.; Tabor, J.W.; Cuthbertson, C.C.; Wener, M.H.; Potter, A.J.; Killeya-Jones, L.; Hussey, J.M.; Halpern, C.T.; et al. Dried capillary whole blood spot-based hemoglobin A1c, fasting glucose, and diabetes prevalence in a nationally representative population of young U.S. adults: Add Health, wave IV. Circulation 2012, 125, AP010. [Google Scholar] [CrossRef]
  23. Pollack, L.M.; Wang, M.; Leung, M.Y.M.; Colditz, G.; Herrick, C.; Chang, S.-H. Obesity-related multimorbidity and risk of cardiovascular disease in the middle-aged population in the United States. Prev. Med. 2020, 139, 106225. [Google Scholar] [CrossRef] [PubMed]
  24. Akune, T.; Muraki, S.; Oka, H.; Tanaka, S.; Kawaguchi, H.; Nakamura, K.; Yoshimura, N. Exercise habits during middle age are associated with lower prevalence of sarcopenia: The ROAD study. Osteoporos. Int. 2014, 25, 1081–1088. [Google Scholar] [CrossRef] [PubMed]
  25. Centers for Disease Control and Prevention. National Health and Nutrition Examination Survey. Available online: https://wwwn.cdc.gov/nchs/nhanes (accessed on 12 October 2021).
  26. United States Department of Agriculture. Food Patterns Equivalents Database. Available online: https://www.ars.usda.gov/northeast-area/beltsville-md-bhnrc/beltsville-human-nutrition-research-center/food-surveys-research-group/docs/fped-databases/ (accessed on 12 October 2021).
  27. National Center for Health Statistics. Centers for Disease Control and Prevention National Health and Nutrition Examination Survey (NHANES) Questionnaire and Exam Protocol. Available online: http://www.cdc.gov/nchs/about/major/nhanes/questexam.htm (accessed on 11 October 2021).
  28. Shepherd, J.A.; Fan, B.; Lu, Y.; Wu, X.P.; Wacker, W.K.; Ergun, D.L.; Levine, M.A. A multinational study to develop universal standardization of whole-body bone density composition using GE healthcare lunar and Hologic DXA systems. J. Bone Miner. Res. 2012, 27, 2208–2216. [Google Scholar] [CrossRef] [PubMed]
  29. World Health Organization. Global Physical Activity Questionnaire (GPAQ) Analysis Guide. Available online: https://www.who.int/ncds/surveillance/steps/resources/GPAQ_Analysis_Guide.pdf (accessed on 11 October 2021).
  30. US Department of Health and Human Services. Physical Activity Guidelines for Americans, 2nd ed.; U.S. Department of Health and Human Services: Washington, DC, USA, 2018.
  31. National Cancer Institute—Division of Cancer Control & Population Sciences. The Healthy Eating Index. Available online: https://epi.grants.cancer.gov/hei/sas-code.html (accessed on 11 October 2021).
  32. Hu, F.B. Dietary pattern analysis: A new direction in nutritional epidemiology. Curr. Opin. Lipidol. 2002, 13, 3–9. [Google Scholar] [CrossRef]
  33. Comee, L.; Taylor, C.A.; Nahikian-Nelms, M.; Ganesan, L.P.; Krok-Schoen, J.L. Dietary patterns and nutrient intake of individuals with rheumatoid arthritis and osteoarthritis in the United States. Nutrition 2019, 67, 110533. [Google Scholar] [CrossRef] [Green Version]
  34. Xu, F.; Cohen, S.A.; Lofgren, I.E.; Greene, G.W.; Delmonico, M.J.; Greaney, M.L. Relationship between Diet Quality, Physical Activity and Health-Related Quality of Life in Older Adults: Findings from 2007–2014 National Health and Nutrition Examination Survey. J. Nutr. Health Aging 2018, 22, 1072–1079. [Google Scholar] [CrossRef] [Green Version]
  35. Centers for Disease Control and Prevention. About Adult BMI. Available online: https://www.cdc.gov/healthyweight/assessing/bmi/adult_bmi/index.html (accessed on 12 October 2021).
  36. Mogensen, C.S.; Færch, K.; Bruhn, L.; Amadid, H.; Tetens, I.; Quist, J.S.; Clemmensen, K.K.B. Timing and Frequency of Daily Energy Intake in Adults with Prediabetes and Overweight or Obesity and Their Associations with Body Fat. Nutrients 2020, 12, 3484. [Google Scholar] [CrossRef]
  37. National Center for Health Statistics. NHANES Survey Methods and Analytic Guidelines. Available online: https://wwwn.cdc.gov/nchs/nhanes/analyticguidelines.aspx (accessed on 12 October 2021).
  38. Belsley, D.A. A Guide to using the collinearity diagnostics. Comput. Sci. Econ. Manag. 1991, 4, 33–50. [Google Scholar]
  39. Ainsworth, B.E.; Haskell, W.L.; Herrmann, S.D.; Meckes, N.; Bassett, D.R.; Tudor-Locke, C.; Greer, J.L.; Vezina, J.; Whitt-Glover, M.C.; Leon, A.S. Compendium of Physical Activities: A second update of codes and MET values. Med. Sci. Sports Exerc. 2011, 43, 1575–1581. [Google Scholar] [CrossRef] [Green Version]
  40. Krebs-Smith, S.M.; Pannucci, T.E.; Subar, A.F.; Kirkpatrick, S.I.; Lerman, J.L.; Tooze, J.A.; Wilson, M.M.; Reedy, J. Update of the Healthy Eating Index: HEI-2015. J. Acad. Nutr. Diet. 2018, 118, 1591–1602. [Google Scholar] [CrossRef] [Green Version]
Figure 1. Study flow chart. Note: NHANES = National Health and Nutrition Examination Survey, DXA = dual-energy X-ray absorptiometry.
Figure 1. Study flow chart. Note: NHANES = National Health and Nutrition Examination Survey, DXA = dual-energy X-ray absorptiometry.
Nutrients 14 02804 g001
Table 1. Respondents’ characteristics stratified by sex, NHANES 2011–2018.
Table 1. Respondents’ characteristics stratified by sex, NHANES 2011–2018.
VariablesTotalMaleFemalep Value
n = 10,014n = 4962 (51.3%)n = 5052 (48.7%)
Age, n (weighted %)
      18–39 yrs5326 (52.1)2730 (54.3)2596 (49.8)0.002 *
      40–59 yrs4688 (47.9)2232 (45.7)2456 (50.2)0.002 *
Race/ethnicity, n (weighted %)
      White3552 (60.7)1786 (60.7)1766 (60.6)0.921
      Black2286 (12.0)1089 (11.5)1197 (12.6)0.007 *
      Hispanic2425 (17.4)1150 (17.7)1275 (17.1)0.363
      Others1751 (9.9)937 (10.1)814 (9.7)0.491
Education, n (weighted %)
      High school or less3457 (32.9)1876 (36.0)1581 (29.7)<0.001 *
      Some college or more5758 (67.1)2687 (64.0)3071 (70.3)<0.001 *
Ratio of family income to poverty, n (weighted %)
      <1.02086 (15.9)945 (14.4)1141 (17.5)<0.001 *
      ≥1.07166 (84.1)3623 (85.6)3543 (82.5)<0.001 *
Body Mass Index (kg/m2)28.91 ± 0.1528.72 ± 0.1729.11 ± 0.200.079
Weight status, n (weighted %)
      Underweight196 (1.6)86 (1.1)110 (2.0)0.008 *
      Normal2927 (29.1)1373 (26.2)1554 (32.1)<0.001 *
      Overweight3026 (31.5)1734 (36.0)1292 (26.7)<0.001 *
      Obese3812 (37.3)1743 (36.2)2069 (38.4)0.17
Daily energy intake (kcal/d)2158.07 ± 13.802478.55 ± 18.051820.78 ± 16.94<0.001 *
Android percent fat34.91 ± 0.1931.58 ± 0.2238.41 ± 0.28<0.001 *
Gynoid percent fat35.38 ± 0.1528.82 ± 0.1442.29 ± 0.15<0.001 *
Android to Gynoid ratio1.00 ± 0.001.09 ± 0.010.90 ± 0.00<0.001 *
Dietary quality (HEI-2015)
      Total dietary quality score52.67 ± 0.3351.30 ± 0.3554.11 ± 0.43<0.001 *
      1st tertile (10 ≤ HEI ≤ 45.8), n (weighted %)3337 (32.8)1796 (36.2)1541 (29.3)<0.001 *
      2nd tertile (45.8 < HEI ≤ 58.1), n (weighted %)3338 (33.2)1670 (33.5)1668 (32.9)0.67
      3rd tertile (58.1 < HEI ≤ 95.8), n (weighted %)3339 (34.0)1496 (30.3)1843 (37.8)<0.001 *
Physical Activity (MET-minutes/week)
Total Physical Activity 3777.41 ± 106.594917.00 ± 178.132578.03 ± 88.64<0.001 *
      Insufficiently active (<600)3387 (31.0)1288 (24.7)2099 (37.6)<0.001 *
      Active (600–1200)1356 (13.3)577 (11.0)779 (15.6)<0.001 *
      Highly active (>1200)5271 (55.8)3097 (64.3)2174 (46.8)<0.001 *
Met PA recommendation (active +highly active), n (weighted %)6627 (69.0)3674 (75.3)2953 (62.4)<0.001 *
PA + Diet, n (weighted %)
      Did not meet PA recommendation + lower dietary quality2354 (22.0)917 (17.4)1437 (26.9)<0.001 *
      Did not meet PA recommendation + higher dietary quality1033 (9.0)371 (7.3)662 (10.7)<0.001 *
      Met PA recommendation + lower dietary quality4321 (44.0)2549 (52.3)1772 (35.3)<0.001 *
      Met PA recommendation + higher dietary quality2306 (25.0)1125 (23.0)1181 (27.1)0.002 *
Note: Results expressed as weighted means ± standard errors or count (percentage); NHANES = National Health and Nutrition Examination Survey; PA = physical activity; HEI = Healthy Eating Index; meeting PA recommendation = 600 MET-minutes physical activity time or more each week; Higher dietary quality = 3rd tertile, 58.1 < HEI ≤ 95.8; lower dietary quality = 1st and 2nd tertiles, 10 ≤ HEI ≤ 58.1. * Symbols indicate statistical significance.
Table 2. Android fat composition and distribution by physical activity and/or dietary quality levels, NHANES 2011–2018 (n = 10,014).
Table 2. Android fat composition and distribution by physical activity and/or dietary quality levels, NHANES 2011–2018 (n = 10,014).
VariableAndroid Percent FatAndroid to Gynoid Ratio
Weighted Mean ± SEWeighted Mean ± SE
Total34.91 ± 0.191.00 ± 0.00
Physical activity
      Insufficiently active37.91 ± 0.281.01 ± 0.01
      Active35.48 ± 0.410.99 ± 0.01
      Highly active33.11 ± 0.231.00 ± 0.01
Dietary quality (HEI-2015)
      1st tertile35.48 ± 0.251.02 ± 0.01
      2nd tertile35.24 ± 0.261.01 ± 0.00
      3rd tertile34.03 ± 0.280.97 ± 0.01
PA + Diet
      Did not meet PA recommendation + lower dietary quality38.35 ± 0.301.01 ± 0.01
      Did not meet PA recommendation + higher dietary quality36.83 ± 0.581.00 ± 0.01
      Met PA recommendation + lower dietary quality33.87 ± 0.221.01 ± 0.00
      Met PA recommendation + higher dietary quality33.02 ± 0.290.96 ± 0.01
Male31.58 ± 0.221.09 ± 0.01
Physical activity
      Insufficiently active33.77 ± 0.371.12 ± 0.01
      Active32.44 ± 0.601.11 ± 0.02
      Highly active30.60 ± 0.261.08 ± 0.01
Dietary quality (HEI-2015)
      1st tertile32.09 ± 0.291.09 ± 0.01
      2nd tertile31.44 ± 0.351.09 ± 0.01
      3rd tertile31.13 ± 0.411.09 ± 0.01
PA + Diet
      Did not meet PA recommendation + lower dietary quality34.05 ± 0.351.11 ± 0.01
      Did not meet PA recommendation + higher dietary quality33.09 ± 0.941.13 ± 0.02
      Met PA recommendation + lower dietary quality31.03 ± 0.281.08 ± 0.01
      Met PA recommendation + higher dietary quality30.50 ± 0.401.08 ± 0.01
Female38.41 ± 0.280.90 ± 0.00
Physical activity
      Insufficiently active40.77 ± 0.300.93 ± 0.01
      Active37.74 ± 0.440.90 ± 0.01
      Highly active36.74 ± 0.390.88 ± 0.01
Dietary quality (HEI-2015)
      1st tertile39.89 ± 0.400.93 ± 0.01
      2nd tertile39.32 ± 0.380.92 ± 0.01
      3rd tertile36.48 ± 0.350.87 ± 0.01
PA + Diet
      Did not meet PA recommendation + lower dietary quality41.27 ± 0.360.94 ± 0.01
      Did not meet PA recommendation + higher dietary quality39.51 ± 0.580.91 ± 0.01
      Met PA recommendation + lower dietary quality38.30 ± 0.390.91 ± 0.01
      Met PA recommendation + higher dietary quality35.28 ± 0.350.86 ± 0.01
Note: Results expressed as weighted means ± standard errors (SE) or count (percentage); NHANES = National Health and Nutrition Examination Survey; PA = physical activity; HEI = Healthy Eating Index; meeting PA recommendation = 600 MET-minutes physical activity time or more each week; Higher dietary quality = 3rd tertile, 58.1 < HEI ≤ 95.8; lower dietary quality = 1st and 2nd tertiles, 10 ≤ HEI ≤ 58.1.
Table 3. The relationship of physical activity and/or dietary quality with android fat composition and distribution, NHANES 2011–2018 (n = 10,014).
Table 3. The relationship of physical activity and/or dietary quality with android fat composition and distribution, NHANES 2011–2018 (n = 10,014).
VariableAndroid Percent Fat Android to Gynoid Ratio
Adj. β (95% CI)p-ValueR-SquareAdj. β (95% CI)p-ValueR-Square
PA total-per 100-point increase−0.0103 (−0.0140, −0.0066)<0.001 *0.638−0.0001 (−0.0002, 0.0000)0.0860.498
      Insufficiently activeRef-0.652Ref-0.502
      Active−0.38 (−0.85, 0.10)0.120.01 (−0.00, 0.03)0.06
      Highly active−1.64 (−2.05, −1.23)<0.001 *−0.01 (−0.02, 0.01)0.349
HEI total-per 10-point increase−0.34 (−0.49, −0.19)<0.001 *0.637−0.01 (−0.01, −0.00)0.014 *0.498
      1st tertileRef-0.648Ref-0.501
      2nd tertile−0.19 (−0.53, 0.15)0.257−0.01 (−0.01, 0.01)0.917
      3rd tertile−1.02 (−1.50, −0.54)<0.001 *−0.01 (−0.02, 0.00)0.068
PA + Diet
      Did not meet PA recommendation + lower dietary qualityRef-0.652Ref-0.502
      Did not meet PA recommendation + higher dietary quality−0.67 (−1.58, 0.24)0.145−0.01 (−0.02, 0.02)0.868
      Met PA recommendation + lower dietary quality−1.25 (−1.64, −0.85)<0.001 *0.01 (−0.01, 0.01)0.532
      Met PA recommendation + higher dietary quality−2.12 (−2.57, −1.68)<0.001 *−0.01 (−0.02, 0.00)0.117
Note: Adjusted β (95% CI), p-values and R-square were obtained by performing multiple linear regression (PROC SURVEYREG procedure in SAS), adjusted for sex, race/ethnicity, age, education level, family income to poverty ratio, body mass index and daily energy intake (kcal). NHANES = National Health and Nutrition Examination Survey; PA = physical activity, Ref = the reference group to which other groups compare themselves, HEI = Healthy Eating Index, meeting PA recommendation = 600 MET-minutes physical activity time or more each week; Higher dietary quality = 3rd tertile, 58.1 < HEI ≤ 95.8; lower dietary quality = 1st and 2nd tertiles, 10 ≤ HEI ≤ 58.1. * Symbols indicate statistical significance.
Table 4. The sex specific relationship of physical activity and/or dietary quality and android fat composition and distribution, NHANES 2011–2018 (n = 10,014).
Table 4. The sex specific relationship of physical activity and/or dietary quality and android fat composition and distribution, NHANES 2011–2018 (n = 10,014).
VariableAndroid Percent Fat Android to Gynoid Ratio
Adj. β (95% CI)p-ValueR-SquareAdj. β (95% CI)p-ValueR-Square
Male
PA total-per 100-point increase−0.0097 (−0.0138, −0.0056)<0.001 *0.588−0.0001 (−0.0002, 0.0000)0.0810.326
      Insufficiently activeRef 0.614Ref 0.334
      Active−0.24 (−0.95, 0.46)0.490.02 (−0.01, 0.05)0.164
      Highly active−1.68 (−2.26, −1.09)<0.001 *−0.01 (−0.02, 0.01)0.425
HEI total-per 10-point increase−0.29 (−0.52, −0.06)0.013 *0.584−0.01 (−0.01, 0.00)0.1610.325
      1st tertileRef 0.607Ref 0.332
      2nd tertile−0.83 (−1.35, −0.31)0.002 *−0.01 (−0.03, 0.01)0.433
      3rd tertile−0.96 (−1.73, −0.19)0.015 *−0.01 (−0.03, 0.01)0.363
PA + Diet
      Did not meet PA recommendation + lower dietary qualityRef 0.612Ref 0.332
      Did not meet PA recommendation + higher dietary quality−0.68 (−2.18, 0.82)0.3710.01 (−0.03, 0.05)0.669
      Met PA recommendation + lower dietary quality−1.51 (−1.99, −1.03)<0.001 *0.01 (−0.02, 0.02)0.788
      Met PA recommendation + higher dietary quality−1.98 (−2.74, −1.22)<0.001 *−0.01 (−0.03, 0.02)0.554
Female
PA total-per 100-point increase−0.0115 (−0.0169, −0.0060)<0.001 *0.59−0.0001 (−0.0001, 0.0001)0.910.398
      Insufficiently activeRef 0.599Ref 0.401
      Active−0.57 (−1.29, 0.16)0.1230.01 (−0.01, 0.02)0.55
      Highly active−1.60 (−2.08, −1.13)<0.001 *−0.01 (−0.02, 0.01)0.579
HEI total-per 10-point increase−0.41 (−0.58, −0.25)<0.001 *0.591−0.01 (−0.01, −0.00)0.003 *0.4
      1st tertileRef-0.598Ref-0.404
      2nd tertile0.49 (−0.08, 1.07)0.0930.01 (−0.01, 0.02)0.458
      3rd tertile−1.08 (−1.61, −0.56)<0.001 *−0.02 (−0.03, 0.00)0.026 *
PA + Diet
      Did not meet PA recommendation + lower dietary qualityRef 0.602Ref 0.404
      Did not meet PA recommendation + higher dietary quality−0.76 (−1.60, 0.07)0.073−0.01 (−0.03, 0.01)0.209
      Met PA recommendation + lower dietary quality−0.96 (−1.53, −0.38)0.002 *0.01 (−0.01, 0.02)0.371
      Met PA recommendation + higher dietary quality−2.39 (−2.91, −1.87)<0.001 *−0.02 (−0.03, −0.00)0.022 *
Interaction term (sex × independent variable)
PA total-per 100-point increase−0.0013 (−0.0071, 0.0046)0.659-−0.0002 (−0.0004, −0.0001)0.003 *-
      Insufficiently activeRef -Ref -
      Active0.19 (−0.90, 1.27)0.7330.01 (−0.03, 0.04)0.651
      Highly active−0.35 (−1.01, 0.30)0.284−0.02 (−0.04, 0.00)0.114
HEI total-per 10-point increase0.15 (−0.10, 0.39)0.231-0.01 (−0.00, 0.01)0.073-
      1st tertileRef--Ref--
      2nd tertile−1.26 (−2.17, −0.35)0.008 *−0.01 (−0.03, 0.02)0.603
      3rd tertile0.26 (−0.61, 1.12)0.5570.02 (−0.00, 0.04)0.106
PA + Diet
      Did not meet PA recommendation + lower dietary qualityRef -Ref -
      Did not meet PA recommendation + higher dietary quality0.10 (−1.48, 1.69)0.8990.03 (−0.01, 0.07)0.172
      Met PA recommendation + lower dietary quality−0.86 (−1.58, −0.15)0.019 *−0.02 (−0.04, 0.01)0.18
      Met PA recommendation + higher dietary quality0.21 (−0.68, 1.09)0.6420.01 (−0.02, 0.03)0.592
Note: Adjusted β (95% CI), p-values and R-square were obtained by performing multiple linear regression (PROC SURVEYREG procedure in SAS), adjusted for sex, race/ethnicity, age, education level, family income to poverty ratio, body mass index and daily energy intake (kcal). NHANES = National Health and Nutrition Examination Survey; PA = physical activity, Ref = the reference group to which other groups compare themselves, HEI = Healthy Eating Index, meeting PA recommendation = 600 MET-minutes physical activity time or more each week; Higher dietary quality = 3rd tertile, 58.1 < HEI ≤ 95.8; lower dietary quality = 1st and 2nd tertiles, 10 ≤ HEI ≤ 58.1. * Symbols indicate statistical significance.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Xu, F.; Earp, J.E.; LoBuono, D.L.; Greene, G.W. The Relationship of Physical Activity and Dietary Quality with Android Fat Composition and Distribution in US Adults. Nutrients 2022, 14, 2804. https://doi.org/10.3390/nu14142804

AMA Style

Xu F, Earp JE, LoBuono DL, Greene GW. The Relationship of Physical Activity and Dietary Quality with Android Fat Composition and Distribution in US Adults. Nutrients. 2022; 14(14):2804. https://doi.org/10.3390/nu14142804

Chicago/Turabian Style

Xu, Furong, Jacob E. Earp, Dara L. LoBuono, and Geoffrey W. Greene. 2022. "The Relationship of Physical Activity and Dietary Quality with Android Fat Composition and Distribution in US Adults" Nutrients 14, no. 14: 2804. https://doi.org/10.3390/nu14142804

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop