Next Article in Journal
Feel Good, Eat Better: The Role of Self-Compassion and Body Esteem in Mothers’ Healthy Eating Behaviours
Previous Article in Journal
LAT1 and SNAT2 Protein Expression and Membrane Localization of LAT1 Are Not Acutely Altered by Dietary Amino Acids or Resistance Exercise Nor Positively Associated with Leucine or Phenylalanine Incorporation in Human Skeletal Muscle
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Nutrients
Volume 13
Issue 11
10.3390/nu13113905
nutrients-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Is the Mediterranean Diet Pattern Associated with Weight Related Health Complications in Adults? A Cross-Sectional Study of Australian Health Survey

by
Canaan Negash Seifu
1,
Paul Patrick Fahey
1,2,
Kedir Yimam Ahmed
2 and
Evan Atlantis
1,2,3,*
1
School of Health Sciences, Western Sydney University, Penrith, NSW 2751, Australia
2
Translational Health Research Institute, Western Sydney University, Penrith, NSW 2751, Australia
3
Discipline of Medicine, Faculty of Medicine and Health, Nepean Clinical School, The University of Sydney, Sydney, NSW 2006, Australia
*
Author to whom correspondence should be addressed.
Nutrients 2021, 13(11), 3905; https://doi.org/10.3390/nu13113905
Submission received: 30 September 2021 / Revised: 26 October 2021 / Accepted: 27 October 2021 / Published: 30 October 2021
(This article belongs to the Section Nutrition and Obesity)
Versions Notes

Abstract

:
We hypothesized that unhealthy dietary pattern would be associated with weight related complications among overweight. We analysed data from the Australian Health Survey conducted from 2011 to 2013. A total of 5055 adults with at least overweight (body mass index ≥25 kg/m2) were analysed. We used logistic regression to assess the association between unhealthy dietary pattern, defined by low adherence to Mediterranean Diet Score (MDS), and weight related complications, defined by the Edmonton Obesity Staging System (EOSS). We repeated the logistic regression models by age and socio-economic disadvantage strata in sensitivity analyses. We also repeated the main analysis on a propensity score matched dataset (n = 3364). Complications by EOSS ≥2 was present in 3036 (60.1%) participants. There was no statistically significant association between unhealthy dietary pattern and weight related complication (odds ratio 0.98 (95%confidence interval: 0.85, 1.12)). The null association remained the same after repeating the analysis on three age and five socio-economic indexes for areas strata. The finding persisted after the analysis was repeated on a propensity score matched dataset. We found no evidence to support the hypothesis that unhealthy dietary pattern was associated with weight related complications in this cross-sectional study of the Australian population with overweight or obesity.

1. Introduction

Overweight and obesity is a major global health issue. It is estimated that more than 1.9 billion adults (aged 18 years and over) worldwide had overweight and obesity in 2016 [1]. Excess body weight, defined by Body Mass Index (BMI) of at least 25, is one of the leading factors believed to be causally associated with increased fatal and non-fatal disease outcomes globally [2]. For instance, there is an elevated risk for non-communicable disease risk such as type 2 diabetes, coronary heart disease, stroke, asthma, and several cancers [3,4,5]. Studies have shown that metabolically healthy obesity, especially when combined with a high level of fitness, is associated with lower disease risk [6]. However, longitudinal studies have showed that metabolically healthy individuals living with obesity can develop weight related complications [7,8].
The Edmonton Obesity Staging System (EOSS) [9] has become one of the most widely researched clinical obesity staging systems after it was introduced in 2009 [10]. It is a five-stage system that categorizes individuals with overweight and obesity according to the severity of weight related complications. The EOSS has been shown to be useful for predicting risk and benefits of surgical and non-surgical weight management treatments in clinical populations. It also differentiates mortality, health service use (including polypharmacy), poor work, and mortality outcomes [11]. Therefore, a better understanding of risk factors associated with weight related complications defined using the EOSS could help with the development of new public health prevention strategies.
Unhealthy dietary pattern may be a useful indicator of weight related complications, as it has been consistently associated with an increased prevalence of overweight and obesity in population-wide studies [12,13]. Further, evidence from a recently published umbrella review suggests that adherence to a healthy dietary pattern such as the Mediterranean diet is associated with a decreased risk of weight gain or obesity [14]. The Mediterranean diet pattern has been linked with health benefits [15] and is considered a healthy dietary pattern by dietary guidelines [16]. Only a few studies with inconsistent findings have investigated associations between diet/dietary patterns and weight related complications [17,18]. Therefore, we aimed to explore the association between an unhealthy dietary pattern (defined by low adherence to the Mediterranean diet pattern) and weight related complications (defined by the EOSS ≥ 2) in Australian adults with overweight or obesity.

2. Materials and Methods

We present this study according to the journal’s formatting requirements and the STROBE guideline for reporting cross-sectional studies [19].

2.1. Study Design, Setting and Participants

The Australian Bureau of Statistics (ABS) conducted the Australian Health Survey (AHS) in 2011–2013 that contained the National Nutrition and Physical Activity Survey (NNPAS), the National Health Survey (NHS), and the National Health Measures Survey (NHMS). Study participants who were in the NHS or NNPAS and gave consent to the NHMS provided urine and blood samples; the samples were then tested for a range of chronic disease biomarkers. We used data from the NNPAS and NHMS, with 9435 fully responding adults. For the present study, we selected participants who were adults (aged 18 years or over) with at least overweight (BMI ≥ 25 kg/m2) and not pregnant or breast feeding at the time of the survey (n = 5055). Since a comprehensive description of the methodology used in this study has already been published [20], we present a brief summary below.

2.2. Ethics

The AHS was approved by all relevant ethics committee(s) and was conducted according to Australian law (Census and Statistics Act 1905) [21] and the principles expressed in the Declaration of Helsinki. Also, a written informed consent was sought from study participants [20].

2.3. Data Sources/Measurement

The NNPAS was conducted from May 2011 to June 2012 [20]. Trained ABS interviewers used a Computer Assisted Personal Interview (CAPI) methodology to collect self-reported data. Dietary data was collected using the 24-h recall methodology. The interviewers used the Automated Multiple-Pass Method (AMPM) developed by the Agricultural Research Service of the United States Department of Agriculture [22]. It was adapted by the ABS and the Food Standards Australia New Zealand to reflect the Australian food supply. The AMPM has five phases: quick list, forgotten foods, time and occasion, detail cycle, and final probe that encourage participants to think about foods and beverages they have consumed in the previous 24 h from different perspectives. Interviewers also used food model booklets to further assist participants describe their consumption. For anthropometry data collection, participants were encouraged to remove shoes and heavy clothing before taking weight and height measurements. Interviewers used digital scales to measure weight (maximum 150 kg) and a stadiometer to measure height (maximum 210 cm). The NHMS was conducted from March 2011 to September 2012. The biomedical data collection involved blood and urine samples collected at collection clinics or via home visits using standard procedures [20].

2.4. Variables

All survey questions are described in the AHS User Guide [23].

2.5. Dependent Variable

The EOSS variable was created using chronic disease biomarkers (for diabetes, cholesterol, triglycerides, kidney disease, and liver enzymes), measured blood pressure, and self-reported long-term health conditions. Specific criteria and thresholds were used to classify study participants in one of the EOSS categories (0–4) (Table S1). Study participants who did not meet at least one of the EOSS 1 to 4 criteria were classified into the EOSS 0 category. When participants fulfilled more than one of the EOSS 1 to 4 criterion, the highest score was used to classify their final EOSS stage.

2.6. Independent Variable

We created our Mediterranean Diet Score (MDS) variable according to one of the most common methods used to assess adherence to Mediterranean diet pattern [14,24]. The methodology used herein has been previously published in detail elsewhere [25]. Briefly, to create the MDS variable, we first calculated sex-specific medians for each of the nine components of MDS for the 5055 study participants. Second, we assigned a value of 0 or 1 to each observation i.e., for healthy components a consumption equal to or above the median got a value of 1 and otherwise 0 and for unhealthy components, a consumption below the median got a value of 1 otherwise 0. Lastly, the values of each component were summed to create final MDS scores which ranged from 0 to 9. The methodology used herein has been published in detail elsewhere [26]. In the present study, only dietary data from day one of the 24-h recalls was used.

2.7. Covariates

We considered the following range of demographic and behavioural covariates in our statistical analyses. Demographic variables included sex, age, country of birth, marital status, highest year of school (determined by asking participants “What is the highest year of primary or secondary school completed?”), Socio-Economic Indexes For Areas (SEIFA) (a measure of socio-economic advantage and disadvantage created from other variables on income, housing, and education according to the residential postcodes of participants) [27], and hours usually worked each week (determined by asking participants “How many hours usually do you work each week?”). Behavioural variables included whether exercise last week met 150 min recommended guidelines according to standard National Physical Activity Guidelines for Australian adults (determined by asking participants questions such as: “In the last week, how many times did you do any other more moderate physical activity that you have not already mentioned? (e.g., gentle swimming, social tennis, golf)” and “In the last week, how many times did you do any vigorous physical activity which made [you/him/her] breathe harder or puff and pant? (e.g., jogging, cycling, aerobics, competitive tennis)”); smoking status, (determined by asking participants questions such as: “Do you currently smoke?” and “Have you ever smoked regularly, that is, at least once a day?”), and dieting, (determined by asking participants if they were currently on any kind of diet to lose weight or for some other health related reason) [20].

2.8. Sample Size Considerations

Studies with the same sample size as ours (comparing groups of size 2019 and 3036) have 90% power to detect a difference in proportions from 0.50 in group 1 to 0.55 in group 2 and statistically significant at the 0.05 significance level. We conclude that our study is well powered to detect any clinically meaningful differences in EOSS risk category associated with poor diet.

2.9. Bias

Out of the 12,366 dwellings in the actual sample, 9519 adequately or fully responded to the first interview of the NNPAS, with an estimated response rate of 77%. As BMI was calculated from objectively measured weight and height, with scales and stadiometers, selection of the overweight sample was error free.

2.10. Statistical Analysis

We presented the characteristics of study participants by EOSS categories 0–1 vs. 2–4 as counts and percentages and tested for differences between these two groups using Chi-square tests. We used logistic regression to assess the association between low adherence to MDS (scores 0–4) (unhealthy dietary pattern exposure) vs. high adherence to MDS (scores 5–9) (healthy dietary pattern reference) associated with EOSS 2–4 (dependent variable). We fitted five models to assess the association: unadjusted (Model 1), adjusted for SEIFA, sex, age, country of birth, marital status, hours usually worked each week, and level of highest education (Model 2), adjusted for whether exercise last week met 150 min recommended guidelines and smoking status (Model 3), adjusted for dieting (Model 4), and adjusted for energy (Model 5).

2.11. Sensitivity Analyses

We fitted models using MDS tertiles (Table S2) and quartiles (Table S3) to explore the association between the MDS and EOSS. We also repeated the main analysis using the mean of two dietary recalls using the data from participants who had intakes of both days (n = 3438) (Table S4). We further explored the association across age and SEIFA strata and tested for potential confounding. To further determine whether other potential confounders have influenced our result, we used Propensity Score Matching (PSM) to minimize confounding due to selection bias [28]. We used logistic regression to calculate the propensity scores, checked the balance of the scores across low adherence MDS (scores 0–4) and high adherence MDS (scores 5–9), examined the propensity scores graph, checked for covariate balance, and matched the data using the nearest neighbour 1:1 matching with a 0.1 caliper. The PSM excluded observations out of the common support area (Figure S1). We repeated the main analysis on the matched dataset (n = 3364) (Table S5). We performed the analyses using IBM SPSS Statistics 25 [29] and ‘Pscore’ and ‘psmatch2′ in STATA 15 [30] to conduct the PSM.

3. Results

3.1. Descriptive characteristics

The study population included 5055 adults with at least overweight (BMI ≥25 kg/m2) of whom 3036 (60.1%) were classified into the EOSS 2–4 group indicating clinically significant weight related complications (Table 1). The percentage distributions for MDS (e.g., poor MDS score 0–4: 67.0% vs. 65.2%) and sex (e.g., male: 51.9% vs. 54.1%) were similar across the two EOSS groups (EOSS 0–1 vs. 2–4) but statistically significantly different for SEIFA (e.g., SEIFA most disadvantaged: 17.4% vs. 21.4%), country of birth (e.g., other countries: 13.4% vs. 15.6%), and marital status (e.g., married/de facto: 53.4% vs. 57.1%).

3.2. Main Results

There were no statistically significant associations between adherence to the MDS and EOSS category in the unadjusted analysis in Model 1 or after adjustment for all other covariates in separate Models 2 to 5 (Table 2).

3.3. Sensitivity Analyses

We conducted sensitivity analyses to further explore the association between poor diet measured by the adherence to MDS and weight related complications measured by EOSS. After using tertiles (Table S2) and quartiles (Table S3) of the MDS, there was a reverse association in unadjusted models but after controlling for the covariates the significance was lost. Also, the results were not changed after the main analyses were repeated using mean dietary intake of two days (Table S4). Furthermore, the null association between low adherence MDS of 0–4 and EOSS ≥2 remained unchanged after we repeated the analysis on three age strata (Table 3) and five SEIFA strata (Table 4). Furthermore, the null finding persisted even after we tested for associations in the PSM dataset (n = 3364) (Table S5).

4. Discussion

To our knowledge, this is the first study to comprehensively examine the association between unhealthy dietary pattern and weight related complications. In this nationally representative subsample of the Australian population with overweight and obesity, we found no evidence of an association between unhealthy dietary pattern (low adherence MDS) and weight related complications (EOSS ≥ 2) in the unadjusted analysis and after controlling for covariates. Moreover, this null finding persisted after sensitivity analyses with mean dietary intake of two 24 h dietary recalls, in stratified models by age and SEIFA categories, and after our application of the PSM technique. Based on the results of our cross-sectional study, unhealthy dietary pattern was not found to be a useful indicator of weight related complications in the overweight Australian population.
Our null association is consistent with another cross-sectional study of the AHS data set which reported no association of the MDS, using the same definition as ours, with the presence of diabetes and cardiovascular disease after controlling for covariates [31] and another study which showed inconclusive results for poor diet (defined by lower diet quality scores) across EOSS stages [18]. In contrast, one study found that adequate fruit and vegetable intake (defined as “men >35 servings per week; women >25 servings per week”) was associated with a decreased risk of developing weight related complications (EOSS 2 or 3) [17]. Thus, the limited body of inconsistent evidence indicates that population-wide screening for unhealthy dietary patterns as a strategy for identifying risk of weight related complications in people with overweight and obesity is not yet warranted.
The null association reported herein was unexpected given that there is strong expert opinion favouring the Mediterranean diet pattern for better cardiovascular health outcomes [32,33] and evidence from systematic reviews of randomized controlled trials showing small but significant reductions in blood pressure [34,35]. Furthermore, the Mediterranean diet pattern has been associated with healthy weight outcomes [14] and cardio-vascular benefit [36] in both the Mediterranean and non-Mediterranean regions. In contrast, a recent systematic review of randomized controlled trials concluded that there is genuine uncertainty regarding the effect of the Mediterranean diet pattern on cardiovascular disease outcomes, as the quality of evidence is low to moderate [37]. Moreover, another systematic review reported the evidence on the Mediterranean diet intervention lacks robustness due to insignificant results for primary outcomes or as significant results were fragile (a small number of events needed to change the result from significant to non-significant) [38]. This conflicting evidence requires further examination and theoretical explanation.

Limitations

There are several plausible explanations for the apparent lack of consistent findings on unhealthy dietary pattern and having or developing weight related complications. First, several limitations in the accuracy and reliability of dietary assessment methods in research are generally well known [39,40]. For instance, although 24-h recall provides detailed intake data, it is a subjective method of assessing diet prone to recall bias [41,42]. Furthermore, the 24-h recall method is unable to capture the day to day or seasonal variation in food intake and reflects the average usual intake of a group [41,43]. Under-reporting is another common source of error in nutrition surveys, as individuals could change or mistakenly misrepresent their diet pattern [44]. More importantly, studies have shown that participants with overweight or obesity could omit some food groups during diet assessment, especially for total energy intake and/or unhealthy foods [20,45,46]. It is estimated in the NNPAS that low energy reporting was more common among those with obesity compared to normal weight individuals (males: 34% vs. 10%; females 37% vs. 16%) [20]. Second, the effects of unhealthy diet on weight related complications may be too small to detect, especially in populations with excess weight. For instance, one randomized controlled trial showed a very small and clinically meaningless (–1.1 mm Hg) decrease in systolic blood pressure and no change in diastolic blood pressure after six months of a Mediterranean diet intervention compared with usual diet controls [47]. Third, the null association may have been confounded by reverse causation. It is plausible to hypothesize that some individuals may have changed their dietary pattern or reporting thereof after a new diagnosis or medical event at the time of the survey. For instance, cohort studies have shown that higher adherence to the MDS was protective of cardiovascular diseases [48,49] while a cross-sectional study, similar to our finding, reported no association [31]. Furthermore, a study has shown that individuals may change their diet after having affected well-being (e.g., due to increase in weight) or learning a health problem [50]. Finally, the AHS was conducted using a multi-stage sampling and experienced some non-response. We did not use the weights provided by the AHS, so our sample and results may vary slightly from what would have been obtained with a completely random sample from the Australian population. But it is hard to see how this could have led to any systematic bias in the analyses. This potential source of bias is most likely to occur in cross-sectional studies such as ours as dietary pattern and weight related complications were measured at one point in time.

5. Conclusions

We found no evidence to support the hypothesis that unhealthy dietary pattern (defined by low adherence to the Mediterranean diet pattern), was associated with an increased likelihood of having weight related complications among individuals with overweight and obesity in Australia. Although the null association reported herein is consistent with a growing body of conflicting evidence on the health benefits of the Mediterranean diet, future studies should seek to improve the accuracy and frequency of dietary assessments to reduce several plausible sources of bias.

Supplementary Materials

The following are available online at https://www.mdpi.com/article/10.3390/nu13113905/s1, Figure S1. Propensity scores across the Mediterranean diet before and after matching, Table S1. Specific criteria and thresholds for variables used to classify study participants in one of the EOSS categories (0–4), Table S2. Unadjusted and multivariable adjusted associations between low adherence MDS (scores lowest tertile vs. highest tertile) and weight related complications (EOSS 2–4 vs. 0–1) category in the AHS 2011 to 2012 (n = 5055), Table S3. Unadjusted and multivariable adjusted associations between low adherence MDS (scores lowest quintile vs. highest quintile) and weight related complications (EOSS 2–4 vs. 0–1) category in the AHS 2011 to 2012 (n = 5055), Table S4. Unadjusted and multivariable adjusted associations between low adherence MDS (scores 0–4 vs. 5–9) and weight related complications (EOSS 2–4 vs. 0–1) category in the AHS 2011 to 2012 (n = 3438), Table S5. Unadjusted and multivariable adjusted associations between low adherence MDS (scores 0–4 vs. 5–9) and weight related complications (EOSS 2–4 vs. 0–1) category in the AHS 2011 to 2012 (n = 3364) (matched data).

Author Contributions

Conceptualization, C.N.S. and E.A.; formal analysis, C.N.S., P.P.F., K.Y.A. and E.A.; writing—original draft preparation, C.N.S., P.P.F. and E.A.; writing—review and editing, C.N.S., P.P.F. and E.A. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Ethical review and approval were waived for this study because it involves the use of existing collections of data only and the data used is non-identifiable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Publicly available datasets were analyzed in this study. This data can be found here subject to the ABS requirements- https://www.abs.gov.au/websitedbs/D3310114.nsf/home/MicrodataDownload, accessed on 23 August 2019.

Acknowledgments

The authors would like to thank the study participants.

Conflicts of Interest

E.A. was the Founding President, and now serves as the Secretary, of the National Association of Clinical Obesity Services. He has received honoraria from Novo Nordisk for speaking and participating at meetings. He has received unrestricted research funding from Novo Nordisk and iNova on behalf of National Association of Clinical Obesity Services. E.A. is the Guest Editor for the current Special Issue.

References

  1. World Health Organization (WHO). Fact Sheet on Obesity and Overweight. Available online: http://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight (accessed on 8 August 2021).
  2. Afshin, A. Health Effects of Overweight and Obesity in 195 Countries over 25 Years. N. Engl. J. Med. 2017, 377, 13–27. [Google Scholar] [CrossRef] [PubMed]
  3. Poirier, P.; Giles, T.D.; Bray, G.A.; Hong, Y.; Stern, J.S.; Pi-Sunyer, F.X.; Eckel, R.H. Obesity and Cardiovascular Disease: Pathophysiology, Evaluation, and Effect of Weight Loss. Circulation 2006, 113, 898–918. [Google Scholar] [CrossRef] [Green Version]
  4. Guh, D.P.; Zhang, W.; Bansback, N.; Amarsi, Z.; Birmingham, C.L.; Anis, A.H. The incidence of co-morbidities related to obesity and overweight: A systematic review and meta-analysis. BMC Public Health 2009, 9, 88. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  5. Lauby-Secretan, B.; Scoccianti, C.; Loomis, D.; Grosse, Y.; Bianchini, F.; Straif, K. Body Fatness and Cancer—Viewpoint of the IARC Working Group. N. Engl. J. Med. 2016, 375, 794–798. [Google Scholar] [CrossRef] [Green Version]
  6. Lavie, C.J.; De Schutter, A.; Milani, R.V. Healthy obese versus unhealthy lean: The obesity paradox. Nat. Rev. Endocrinol. 2014, 11, 55–62. [Google Scholar] [CrossRef] [PubMed]
  7. Lassale, C.; Tzoulaki, I.; Moons, K.G.M.; Sweeting, M.; Boer, J.; Johnson, L.; Huerta, J.M.; Agnoli, C.; Freisling, H.; Weiderpass, E.; et al. Separate and combined associations of obesity and metabolic health with coronary heart disease: A pan-European case-cohort analysis. Eur. Hear. J. 2017, 39, 397–406. [Google Scholar] [CrossRef] [PubMed]
  8. Bell, J.A.; Hamer, M.; Batty, G.D.; Singh-Manoux, A.; Sabia, S.; Kivimäki, M. Incidence of Metabolic Risk Factors Among Healthy Obese Adults. J. Am. Coll. Cardiol. 2015, 66, 871–873. [Google Scholar] [CrossRef] [Green Version]
  9. Sharma, A.M.; Kushner, R.F. A proposed clinical staging system for obesity. Int. J. Obes. 2009, 33, 289–295. [Google Scholar] [CrossRef] [Green Version]
  10. Pérez, J.A.C.; Quesada, C.F.; Marco, M.D.V.G.; González, I.A.; Benavides, F.C.; Ponce, J.; Velasco, P.D.P.; Marchena-Gomez, J. Obesity Surgery Score (OSS) for Prioritization in the Bariatric Surgery Waiting List: A Need of Public Health Systems and a Literature Review. Obes. Surg. 2018, 28, 1175–1184. [Google Scholar] [CrossRef]
  11. Canning, K.L.; Brown, R.E.; Wharton, S.; Sharma, A.M.; Kuk, J.L. Edmonton Obesity Staging System Prevalence and Association with Weight Loss in a Publicly Funded Referral-Based Obesity Clinic. J. Obes. 2015, 1–7. [Google Scholar] [CrossRef] [Green Version]
  12. Roman, G. Dietary Patterns and their Association with Obesity: A Cross-Sectional Study. Acta Endocrinol. (Bucharest) 2019, 15, 86–95. [Google Scholar] [CrossRef] [PubMed]
  13. Sarkhosh-Khorasani, S.; Mozaffari-Khosravi, H.; Mirzaei, M.; Nadjarzadeh, A.; Hosseinzadeh, M. Empirically derived dietary patterns and obesity among Iranian Adults: Yazd Health Study-TAMYZ and Shahedieh cohort study. Food Sci. Nutr. 2020, 8, 2478–2489. [Google Scholar] [CrossRef] [Green Version]
  14. Seifu, C.N.; Fahey, P.P.; Hailemariam, T.G.; A Frost, S.; Atlantis, E. Dietary patterns associated with obesity outcomes in adults: An umbrella review of systematic reviews. Public Health Nutr. 2021, 1–25. [Google Scholar] [CrossRef]
  15. Crous-Bou, M.; Fung, T.T.; Prescott, J.; Julin, B.; Du, M.; Sun, Q.; Rexrode, K.; Hu, F.B.; De Vivo, I. Mediterranean diet and telomere length in Nurses’ Health Study: Population based cohort study. BMJ 2014, 349, g6674. [Google Scholar] [CrossRef] [Green Version]
  16. U.S. Department of Agriculture; U.S. Department of Health and Human Services. Dietary Guidelines for Americans 2020–2025, 9th ed.; U.S. Department of Agriculture: Washington, DC, USA, 2020.
  17. Kuk, J.L.; Ardern, C.I.; Church, T.S.; Sharma, A.M.; Padwal, R.; Sui, X.; Blair, S.N. Edmonton Obesity Staging System: Association with weight history and mortality risk. Appl. Physiol. Nutr. Metab. 2011, 36, 570–576. [Google Scholar] [CrossRef] [PubMed]
  18. Cha, E.; Mph, M.K.A.; Kim, K.H.; Dawkins, C.R.; Bs, H.M.L.; E Umpierrez, G.; Dunbar, S.B. Lifestyle habits and obesity progression in overweight and obese American young adults: Lessons for promoting cardiometabolic health. Nurs. Health Sci. 2015, 17, 467–475. [Google Scholar] [CrossRef] [Green Version]
  19. von Elm, E.; Altman, D.G.; Egger, M.; Pocock, S.J.; Gøtzsche, P.C.; Vandenbroucke, J.P. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: Guidelines for reporting observational studies. J. Clin. Epidemiol. 2008, 61, 344–349. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  20. Australian Bureau of Statistics. Australian Health Survey: Users’ Guide, 2011–2013. 2014. Available online: https://www.abs.gov.au/ausstats/[email protected]/Lookup/4363.0.55.001Chapter6502011-13 (accessed on 8 August 2021).
  21. Australian Bureau of Statistics. The Australian Health Survey. Available online: www.abs.gov.au/australianhealthsurvey (accessed on 8 August 2021).
  22. Bliss, R.M. Researchers produce innovation in dietary recall. Agric. Res. 2004, 52, 10–12. [Google Scholar]
  23. Australian Bureau of Statistics. National Nutrition and Physical Activity sSurvey 2011-12: Questionnaire. Available online: https://www.abs.gov.au/Ausstats/[email protected]/0/CA5C45B822514D94CA257C44001902AF?OpenDocument (accessed on 8 August 2021).
  24. Trichopoulou, A.; Costacou, T.; Bamia, C.; Trichopoulos, D. Adherence to a Mediterranean Diet and Survival in a Greek Population. N. Engl. J. Med. 2003, 348, 2599–2608. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  25. Atlantis, E.; Fahey, P.; Williams, K.; Edwards, S.; Samaras, K.; Dugdale, P.; Shi, Z.; Sharma, A.M. Comparing the predictive ability of the Edmonton Obesity Staging System with the body mass index for use of health services and pharmacotherapies in Australian adults: A nationally representative cross-sectional study. Clin. Obes. 2020, 10, e12368. [Google Scholar] [CrossRef] [PubMed]
  26. Seifu, C.; Fahey, P.; Atlantis, E. Unhealthy Diet Pattern Mediates the Disproportionate Prevalence of Obesity among Adults with Socio-Economic Disadvantage: An Australian Representative Cross-Sectional Study. Nutrients 2021, 13, 1363. [Google Scholar] [CrossRef]
  27. Australian Bureau of Statistics. Socio-Economic Indexes for Areas (SEIFA). In Socio-Economic Indexes for Areas; Australian Bureau of Statistics: Canberra, Australia, 2011; pp. 14–19. [Google Scholar]
  28. Garrido, M.; Kelley, A.S.; Paris, J.; Roza, K.; Meier, D.E.; Morrison, R.S.; Aldridge, M.D. Methods for Constructing and Assessing Propensity Scores. Heal. Serv. Res. 2014, 49, 1701–1720. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  29. IBMCorp. Statistics for Windows, Version 25.0; IBM Corporation: Armonk, NY, USA, 2017.
  30. StataCorp. Stata Statistical Software: Release 15; StataCorp: College Station, TX, USA, 2017. [Google Scholar]
  31. Aridi, Y.S.; Walker, J.L.; Roura, E.; Wright, O.R.L. Adherence to the Mediterranean Diet and Chronic Disease in Australia: National Nutrition and Physical Activity Survey Analysis. Nutrients 2020, 12, 1251. [Google Scholar] [CrossRef] [PubMed]
  32. Martínez-González, M.A.; Gea, A.; Ruiz-Canela, M. The Mediterranean Diet and Cardiovascular Health. Circ. Res. 2019, 124, 779–798. [Google Scholar] [CrossRef] [PubMed]
  33. Martínez-González, M.A.; Salas-Salvadó, J.; Estruch, R.; Corella, D.; Fitó, M.; Ros, E. Benefits of the Mediterranean Diet: Insights From the PREDIMED Study. Prog. Cardiovasc. Dis. 2015, 58, 50–60. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  34. Filippou, C.D.; Thomopoulos, C.G.; Kouremeti, M.M.; Sotiropoulou, L.I.; Nihoyannopoulos, P.I.; Tousoulis, D.M.; Tsioufis, C.P. Mediterranean diet and blood pressure reduction in adults with and without hypertension: A systematic review and meta-analysis of randomized controlled trials. Clin. Nutr. 2021, 40, 3191–3200. [Google Scholar] [CrossRef] [PubMed]
  35. Cowell, O.R.; Mistry, N.; Deighton, K.; Matu, J.; Griffiths, A.; Minihane, A.M.; Mathers, J.C.; Shannon, O.M.; Siervo, M. Effects of a Mediterranean diet on blood pressure: A systematic review and meta-analysis of randomized controlled trials and observational studies. J. Hypertens. 2020, 39, 729–739. [Google Scholar] [CrossRef] [PubMed]
  36. Koloverou, E.; Esposito, K.; Giugliano, D.; Panagiotakos, D. The effect of Mediterranean diet on the development of type 2 diabetes mellitus: A meta-analysis of 10 prospective studies and 136,846 participants. Metabolism 2014, 63, 903–911. [Google Scholar] [CrossRef]
  37. Rees, K.; Takeda, A.; Martin, N.; Ellis, L.; Wijesekara, D.; Vepa, A.; Das, A.; Hartley, L.; Stranges, S. Mediterranean-style diet for the primary and secondary prevention of cardiovascular disease. Cochrane Database Syst. Rev. 2019, 2019, CD009825. [Google Scholar] [CrossRef] [Green Version]
  38. Grammatikopoulou, M.G.; Nigdelis, M.P.; Theodoridis, X.; Gkiouras, K.; Tranidou, A.; Papamitsou, T.; Bogdanos, D.P.; Goulis, D.G. How fragile are Mediterranean diet interventions? A research-on-research study of randomised controlled trials. BMJ Nutr. Prev. Health 2021, 4, 115–131. [Google Scholar] [CrossRef]
  39. Paeratakul, S.; Popkin, B.M.; Kohlmeier, L.; Hertz-Picciotto, I.; Guo, X.; Edwards, L. Measurement error in dietary data: Implications for the epidemiologic study of the diet–disease relationship. Eur. J. Clin. Nutr. 1998, 52, 722–727. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  40. Prentice, R.L. Dietary Assessment and the Reliability of Nutritional Epidemiology Research Reports. J. Natl. Cancer Inst. 2010, 102, 583–585. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  41. Gibson, R.S. Principles of Nutritional Assessment, 2nd ed.; Oxford University Press: Oxford, NY, USA, 2005. [Google Scholar]
  42. Shim, J.-S.; Oh, K.; Kim, H.C. Dietary assessment methods in epidemiologic studies. Epidemiol. Health 2014, 36, e2014009. [Google Scholar] [CrossRef] [PubMed]
  43. Cade, J.E.; Consortium, O.B.O.T.D.; Warthon-Medina, M.; Albar, S.; Alwan, N.A.; Ness, A.; Roe, M.; Wark, P.A.; Greathead, K.; Burley, V.J.; et al. DIET@NET: Best Practice Guidelines for dietary assessment in health research. BMC Med. 2017, 15, 1–15. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  44. MacDiarmid, J.; Blundell, J. Assessing dietary intake: Who, what and why of under-reporting. Nutr. Res. Rev. 1998, 11, 231–253. [Google Scholar] [CrossRef] [Green Version]
  45. Wehling, H.; Lusher, J. People with a body mass index ≥30 under-report their dietary intake: A systematic review. J. Health Psychol. 2017, 24, 2042–2059. [Google Scholar] [CrossRef] [PubMed]
  46. Shaneshin, M.; Jessri, M.; Rashidkhani, B. Validity of Energy Intake Reports in Relation to Dietary Patterns. J. Health Popul. Nutr. 2014, 32, 36–45. [Google Scholar]
  47. Davis, C.R.; Hodgson, J.M.; Woodman, R.; Bryan, J.; Wilson, C.; Murphy, K. A Mediterranean diet lowers blood pressure and improves endothelial function: Results from the MedLey randomized intervention trial. Am. J. Clin. Nutr. 2017, 105, 1305–1313. [Google Scholar] [CrossRef] [Green Version]
  48. Fung, T.T.; Rexrode, K.; Mantzoros, C.S.; Manson, J.E.; Willett, W.C.; Hu, F.B. Mediterranean Diet and Incidence of and Mortality From Coronary Heart Disease and Stroke in Women. Circulation 2009, 119, 1093–1100. [Google Scholar] [CrossRef] [Green Version]
  49. Reedy, J.; Krebs-Smith, S.M.; Miller, P.E.; Liese, A.D.; Kahle, L.L.; Park, Y.; Subar, A.F. Higher Diet Quality Is Associated with Decreased Risk of All-Cause, Cardiovascular Disease, and Cancer Mortality among Older Adults. J. Nutr. 2014, 144, 881–889. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  50. Chapman, K.; Ogden, J. How Do People Change Their Diet? J. Health Psychol. 2009, 14, 1229–1242. [Google Scholar] [CrossRef] [PubMed]
Table
Table 1. Participant characteristics by EOSS category in the AHS 2011 to 2012 (n = 5055).
Table 1. Participant characteristics by EOSS category in the AHS 2011 to 2012 (n = 5055).
EOSS Categoriesp-Value
Number (percent)
EOSS 0–1
n = 2019		EOSS 2–4
n = 3036
MDS 1 0–41353 (67.0)1980 (65.2)0.193
5–9666 (33.0)1056 (34.8)
SEIFA 2Most disadvantaged352 (17.4)649 (21.4)<0.001
Second quintile403 (20.0)693 (22.8)
Third quintile418 (20.7)581 (19.1)
Fourth quintile394 (19.5)495 (16.3)
Least disadvantaged452 (22.4)618 (20.4)
Sex Male1048 (51.9)1641 (54.1)0.135
Female971 (48.1)1395 (45.9)
Country of birthAustralia1519 (75.2)2140 (70.5)0.001
Other English-speaking countries230 (11.4)423 (13.9)
Other countries270 (13.4)473 (15.6)
Marital statusMarried/de facto1079 (53.4)1733 (57.1)0.011
Not married940 (46.6)1303 (42.9)
Highest year of school completedYear 12 or equivalent1170 (57.9)1218 (40.1)<0.001
Year 11 or equivalent230 (11.4)323 (10.6)
Year 10 or equivalent449 (22.2)831 (27.4)
Year 9 or equivalent96 (4.8)308 (10.1)
Year 8 or below 74 (3.7)356 (11.7)
Age 18–34 years730 (36.2)270 (8.9)<0.001
35–54 years930 (46.1)1001 (33.0)
≥55 years359 (17.8)1765 (58.1)
Hours usually worked each weekNot in workforce/unemployed426 (21.1)1357 (44.7)<0.001
1–24 h253 (12.5)305 (10.0)
25–39 h477 (23.6)523 (17.2)
40 h and more863 (42.7)851 (28.0)
Whether exercise last week, met 150 min recommended guidelinesMet recommended guidelines1081 (53.5)1348 (44.4)<0.001
Did not meet or don’t know938 (46.5)1688 (55.6)
Smoking statusCurrent smoker424 (21.0)495 (16.3)<0.001
Ex-smoker620 (30.7)1,221 (40.2)
Never smoked975 (48.3)1320 (43.5)
DietingOn diet to lose weight 225 (11.1)193 (6.4)<0.001
On diet for health reasons50 (2.5)184 (6.1)
On diet for both reasons69 (3.4)153 (5.0)
Not currently on a diet1675 (83.0)2506 (82.5)
Energy mean (SD) 8793.21 (3637.32)8398.91 (3408.24)<0.001
1 Mediterranean diet score; 2 Index of Relative Socio-Economic Disadvantage—2011—Quintiles—National; SD, Standard deviation; Energy (kilojoules).
Table
Table 2. Unadjusted and multivariable adjusted associations between low adherence MDS (scores 0–4 vs. 5–9) and weight related complications (EOSS 2–4 vs. 0–1) in the AHS 2011 to 2012 (n = 5055).
Table 2. Unadjusted and multivariable adjusted associations between low adherence MDS (scores 0–4 vs. 5–9) and weight related complications (EOSS 2–4 vs. 0–1) in the AHS 2011 to 2012 (n = 5055).
Model 1P-ValueModel 2p-ValueModel 3p-ValueModel 4p-ValueModel 5p-Value
MDSOR (95%CI) OR (95%CI) OR (95%CI) OR (95%CI) OR (95%CI)
0–40.92 (0.82, 1.04)0.190.96 (0.84, 1.10)0.550.95 (0.83, 1.09)0.440.99 (0.85, 1.12)0.760.98 (0.85, 1.12)0.74
5–9Reference Reference Reference Reference Reference
Notes: MDS, Mediterranean Diet Score 0–4 (low adherence, exposure) vs. 5–9 (high adherence, healthy pattern reference), Model 1, unadjusted; Model 2, adjusted for SEIFA, sex, age, country of birth, marital status, hours usually worked each week, and level of highest education; Model 3, adjusted for whether exercise last week met 150 min recommended guidelines and smoking status; Model 4, adjusted for dieting; Model 5, adjusted for energy.
Table
Table 3. Multivariable adjusted associations between low adherence MDS (scores 0–4 vs. 5–9) and weight related complications (EOSS 2–4 vs. 0–1) by age strata in the AHS 2011 to 2012 (n = 5055).
Table 3. Multivariable adjusted associations between low adherence MDS (scores 0–4 vs. 5–9) and weight related complications (EOSS 2–4 vs. 0–1) by age strata in the AHS 2011 to 2012 (n = 5055).
Age (18–34)
(n = 1000)		p-ValueAge (35–54)
(n = 1931)		p-ValueAge (≥55)
(n = 2124)		p-Value
MDSOR (95%CI) OR (95%CI) OR (95%CI)
0–40.88 (0.64, 1.20)0.401.01 (0.83, 1.23)0.940.93 (0.72, 1.18)0.50
5–9Reference Reference Reference
Notes: MDS, Mediterranean Diet Score 0–4 (low adherence, exposure) vs. 5–9 (high adherence, healthy pattern reference), the model is adjusted for SEIFA, sex, country of birth, marital status, hours usually worked each week, level of highest education, whether exercise last week met 150 min recommended guidelines, smoking status, dieting, and energy.
Table
Table 4. Multivariable adjusted associations between low adherence MDS (scores 0–4 vs. 5–9) and weight related complications (EOSS 2–4 vs. 0–1) by SEIFA strata in the AHS 2011 to 2012 (n = 5055).
Table 4. Multivariable adjusted associations between low adherence MDS (scores 0–4 vs. 5–9) and weight related complications (EOSS 2–4 vs. 0–1) by SEIFA strata in the AHS 2011 to 2012 (n = 5055).
Most Disadvantaged
(n = 1001)		p-ValueSecond Quintile
(n = 1096) 		p-ValueThird Quintile
(n = 999) 		p-ValueFourth Quintile
(n = 889) 		p-ValueLeast Disadvantaged
(n = 1070) 		p-Value
MDSOR (95%CI) OR (95%CI) OR (95%CI) OR (95%CI) OR (95%CI)
0–40.88(0.64, 1.22)0.430.87(0.63, 1.20)0.411.15(0.84, 1.56)0.380.81(0.59, 1.12)0.201.13(0.84, 1.51)0.43
5–9Reference Reference Reference Reference Reference
Notes: MDS, Mediterranean Diet Score 0–4 (low adherence, exposure) vs. 5–9 (high adherence healthy pattern reference), the model is adjusted for sex, age, country of birth, marital status, hours usually worked each week, level of highest education, whether exercise last week met 150 min recommended guidelines, smoking status, dieting, and energy.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Seifu, C.N.; Fahey, P.P.; Ahmed, K.Y.; Atlantis, E. Is the Mediterranean Diet Pattern Associated with Weight Related Health Complications in Adults? A Cross-Sectional Study of Australian Health Survey. Nutrients 2021, 13, 3905. https://doi.org/10.3390/nu13113905

AMA Style

Seifu CN, Fahey PP, Ahmed KY, Atlantis E. Is the Mediterranean Diet Pattern Associated with Weight Related Health Complications in Adults? A Cross-Sectional Study of Australian Health Survey. Nutrients. 2021; 13(11):3905. https://doi.org/10.3390/nu13113905

Chicago/Turabian Style

Seifu, Canaan Negash, Paul Patrick Fahey, Kedir Yimam Ahmed, and Evan Atlantis. 2021. "Is the Mediterranean Diet Pattern Associated with Weight Related Health Complications in Adults? A Cross-Sectional Study of Australian Health Survey" Nutrients 13, no. 11: 3905. https://doi.org/10.3390/nu13113905

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