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Article

Cross-Sectional Associations between Dietary Fat-Related Behaviors and Continuous Metabolic Syndrome Score among Young Australian Adults

by
Yile Sun
1,
Costan G. Magnussen
1,2,
Terence Dwyer
1,3,
Wendy H. Oddy
1,
Alison J. Venn
1 and
Kylie J. Smith
1,*
1
Menzies Institute for Medical Research, University of Tasmania, Hobart, TAS 7000, Australia
2
Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, 20520 Turku, Finland
3
The George Institute for Global Health, University of Oxford, Oxford OX1 3QX, UK
*
Author to whom correspondence should be addressed.
Nutrients 2018, 10(8), 972; https://doi.org/10.3390/nu10080972
Submission received: 27 June 2018 / Revised: 24 July 2018 / Accepted: 25 July 2018 / Published: 26 July 2018
(This article belongs to the Special Issue Dietary Behaviours during Young Adulthood)
Versions Notes

Abstract

:
Dietary guidelines recommend removing visible fat from meat, choosing low-fat options and cooking with oil instead of butter. This study examined cross-sectional associations between fat-related eating behaviors and a continuous metabolic syndrome (cMetSyn) score among young adults. During 2004–2006, 2071 participants aged 26–36 years reported how often they trimmed fat from meat, consumed low-fat dairy products and used different types of fat for cooking. A fasting blood sample was collected. Blood pressure, weight and height were measured. To create the cMetSyn score, sex-specific principal component analysis was applied to normalized risk factors of the harmonized definition of metabolic syndrome. Higher score indicates higher risk. For each behavior, differences in mean cMetSyn score were calculated using linear regression adjusted for confounders. Analyses were stratified by weight status (Body mass index (BMI) < 25 kg/m2 or ≥ 25 kg/m2). Mean cMetSyn score was positively associated with consumption of low-fat oily dressing (PTrend = 0.013) among participants who were healthy weight and frequency of using canola/sunflower oil for cooking (PTrend = 0.008) among participants who were overweight/obese. Trimming fat from meat, cooking with olive oil, cooking with butter, and consuming low-fat dairy products were not associated with cMetSyn score. Among young adults, following fat-related dietary recommendations tended to not be associated with metabolic risk.

1. Introduction

Although the cardiovascular health of adults in Australia and other developed countries has improved greatly in the last decades, cardiovascular disease (CVD) is still a leading cause of premature death and morbidity [1]. Poor diet is a modifiable behavior that can lead to risk factors for CVD, such as obesity, hypertension, hyperlipidemia, and type 2 diabetes. Dietary guidelines for the prevention of CVD recommend limiting fat intake, especially saturated fat, to reduce blood cholesterol concentration and body weight [2,3,4].
Red meat is thought to be associated with the development of CVD due to the high proportion of saturated fat. However, after trimming visible fat, red meat such as beef and lamb contains up to 5% total fat [5], which is no more than that of some white meats [6]. Studies examining the association between red meat consumption and CVD risk have reported inconsistent results. The Nurses’ Health Study reported that red meat consumption was positively associated with an increased risk of nonfatal myocardial infarction or fatal coronary heart disease (CHD) [7] whereas a meta-analysis in 2010 found no association between consumption of red meat and incidence of CHD or diabetes risk [8].
Dairy products are also believed to increase CVD risk as the fat in dairy products is mainly saturated. However, different saturated fats have different effects on plasma lipids and lipoproteins and some may protect against CVD risk factors [9]. The food matrix may also modify the effect of dairy fat on CVD outcomes [10,11]. A recent review, funded by the dairy industry, found that most studies reported either an inverse association or no association between consumption of dairy products and CVD risk [12]. Studies comparing the effect of full fat and reduced fat dairy on CVD risk are limited [12].
Oils derived from plants have been promoted as being healthier than animal fat, with recommendations to replace animal fats with oil when cooking [13]. The composition of monounsaturated and polyunsaturated fatty acids varies among different vegetable oils. Replacing saturated fatty acids with polyunsaturated fatty acids, found in plant-based oils, can result in an increased risk of death from CVD if the polyunsaturated fats are not omega-3s [14]. This suggests that substitution of animal fat with vegetable oils may not necessarily reduce CVD risk.
Most studies have examined whether following advice for fat-related dietary behaviors is associated with lower risk of CVD among middle-aged to elderly individuals. It is important to determine whether the same associations exist among young adults. This study examined whether young adults who usually trimmed fat from meat, consumed low-fat products, and used oil for cooking had lower CVD risk than those who did not follow these practices. CVD risk was assessed using a continuous metabolic syndrome (cMetSyn) score.

2. Materials and Methods

2.1. Study Population

The Childhood Determinants of Adult Health (CDAH) Study is a follow-up of the 1985 Australian Schools Health and Fitness Survey, which included 8498 children aged 7–15 years [15]. After tracing participants during 2001–2002, 5170 were enrolled in the CDAH study. During 2004–2006, 2410 participants aged 26–36 years attended one of 34 clinics around Australia. This study was conducted according to the guidelines laid down in the Declaration of Helsinki and all procedures involving human participants were approved by the Southern Tasmania Health and Medical Ethics Committee (H6020). Written informed consent was obtained from all participants.

2.2. Dietary Assessment

Diet was assessed using a food frequency questionnaire and a food habits questionnaire [16]. Whether visible fat was removed from meat before eating was assessed using the question ‘how often is the meat you eat trimmed of fat either before or after cooking’, with response options: ‘never/rarely’, ‘sometimes’, ‘usually’, and ‘I don’t eat meat’. How often participants used specific types of fat for cooking was assessed using the question ‘when cooking, how often do you or the person who cooks your food use the following (olive oil, canola or sunflower oil, butter)?’ Response options were ‘never/rarely’, ‘sometimes’, ‘usually’, and ‘don’t know’. Consumption of low or reduced fat dairy products (cream, ice-cream, cheddar-type cheese), oily salad dressing and spreads was assessed using the question ‘when you eat the following products, how often do you eat a low/reduced fat variety?’ Response options were ‘never/rarely’, ‘sometimes’, ‘usually’, and ‘I don’t eat this food’.

2.3. Anthropometric Measurements

All anthropometric measurements were made by trained staff using standardized techniques. Weight was measured without heavy clothing or shoes using a Heine portable scale (Heine, Dover, NH, USA) and recorded to the nearest 0.1 kg. Height was measured without shoes using a portable Leicester stadiometer (Invicta, Leicester, UK) and recorded to the nearest 0.5 cm. Waist circumference was measured using a non-stretch tape at the narrowest point between the lower costal border and the iliac crest. Participants were standing, and the measurement was taken at the end of gentle expiration. Waist circumference was measured three times, the mean of the three measurements was used. Body mass index (BMI, kg/m2) was calculated as weight (kg)/height (m2).

2.4. Blood Chemistry

Venous blood samples were taken from the antecubital vein of participants after an overnight fast. Triglycerides, total cholesterol, high density lipoprotein (HDL) cholesterol and blood glucose were determined by enzymatic procedures using an Olympus AU5400 automated analyzer (Olympus Optical, Tokyo, Japan) [17].

2.5. Blood Pressure

Blood pressure was measured from the right brachial artery three times with a 1-min interval between each measurement, using a digital automatic monitor (Omron HEM907, Omron Healthcare Inc., Kyoto, Japan). The mean of the three measurements was used. Mean arterial pressure was calculated as 1/3((2 × diastolic) + systolic).

2.6. Continuous Metabolic Syndrome (cMetSyn) Score

A cMetSyn score was created using the methods described by Wijndaele et al. [18,19]. A continuous score was used because cardiometabolic risk increases as the number of risk factors increases and it prevents the need to dichotomize continuous outcomes. All risk factors in the harmonized definition of the metabolic syndrome [20] were normalized and sex-specific principal component analysis with varimax rotation was applied. Two principal components with eigenvalue >1.0 were identified (fasting glucose and mean arterial pressure; waist circumference, triglycerides and HDL cholesterol). These two principal components explained 26% and 34% of the variance in men and 25% and 31% of the variance in women [21]. The cMetSyn score was calculated by summing the principal component scores and weighting them based on the relative proportion of variance explained. A higher score indicates higher risk. The score ranged from −2.6 to 3.1. To determine if the continuous score was appropriate at identifying those with higher metabolic risk, cMetSyn scores were compared between those with and without the harmonized definition of the metabolic syndrome. The mean (SD) cMetSyn scores were higher among participants classified as having metabolic syndrome than those without metabolic syndrome for both men (metabolic syndrome 1.19 (0.38), no metabolic syndrome −0.14 (0.60)), and women (metabolic syndrome 1.68 (0.55), no metabolic syndrome 0.06 (−0.63)).

2.7. Covariates

Participants reported their age, occupation, highest level of education, smoking status, and marital status. The food frequency questionnaire was used to estimate usual consumption of 127 food and beverages over the previous 12 months. For each item, participants chose one of nine response options ranging from ‘never or <1/month’ to ‘6+ times/day’. Daily equivalents were calculated for each item, assuming one serve was consumed at each eating occasion [16]. Usual consumption of hot takeaway food was reported [16]. Usual daily intake of fruit and vegetables were estimated using short questions [16,22]. The number of standard alcoholic drinks (10 g of alcohol) consumed per week was estimated from the frequency of nine alcohol beverages and their average alcohol concentration [16]. Diet quality was estimated using a Dietary Guideline Index, which assessed compliance with the Australian Dietary Guidelines [3]. The Dietary Guideline Index included 15 components, scored 0–10, with a higher score indicating greater compliance [23,24]. Scores for each component were summed to give the total Dietary Guideline Index score (potential range: 0–150) [25]. Participants reported if they were currently following a weight loss diet or a fat-modified diet. The long version of the International Physical Activity Questionnaire [26] was used to estimate leisure-time physical activity (minutes/week). As an objective measure of physical activity, pedometers (Yamax Digiwalker SW200) were worn for seven days to estimate daily steps.

2.8. Statistical Methods

Participant characteristics are presented as mean (SD) or median (25th, 75th percentiles) for continuous variables and n (%) for categorical variables. Associations between dietary behaviors and mean cMetSyn score were calculated using linear regression. The dependent variable was transformed where necessary to improve normality of residuals. To determine if the association varied by weight status (healthy weight BMI < 25 kg/m2 versus overweight or obese BMI ≥ 25/m2) an interaction term was added to each model and stratified by weight status when an interaction was found (p < 0.05). Men and women were analyzed together and sex interactions were examined. The results were stratified by sex when a sex interaction was found (p < 0.05). Model 1 adjusted for age and sex (if no sex interaction). Fat-related behaviors that were found to be associated (p < 0.1) with cMetSyn score in model 1, were adjusted for other covariates in model 2. Covariates were included in model 2 if they changed the coefficient of the predictor by at least 10%. The covariates considered were: marital status, education, occupation, smoking status, daily fruit and vegetable consumption, weekly alcohol consumption, leisure-time physical activity, and daily steps. When low-fat products were the predictor of interest, model 2 was also adjusted for intake (daily equivalent) of that item (e.g., consumption of low-fat cheese was adjusted for cheese intake). When the predictor was trimming fat from meat, model 2 was adjusted for intake of meats considered to be high in fat if the fat had not been removed [16]. When type of oil used for cooking was the predictor, model 2 was adjusted for takeaway food consumption as takeaway foods can contain large amounts of oil and the participant may not know what type of oil was used. In a sensitivity analysis, the Dietary Guideline Index was added to model 2 to determine if the association was mediated by diet quality. Additional sensitivity analyses adjusted for being on a weight loss or fat-modified diet. Participants were excluded from the analysis examining type of fat used for cooking if they did not know how often that fat was used (olive oil n = 11, canola oil n = 32, butter n = 8). All statistical analyses were conducted with STATA software (version 13.1, 2016, StataCorp., College Station, TX, USA).

3. Results

In total, 2084 participants answered the food habits questionnaire and had data required to calculate the cMetSyn score. Participants were excluded if they were pregnant (n = 7) or used lipid lowing medication (n = 6), leaving 2071 participants for the analysis.
The characteristics of the participants are presented in Table 1. Compared with the Australian population of 25–34-year-olds, the CDAH sample had a higher percentage of participants who were married or living as married (57% men, 64% women in the general population [27]) and employed as professionals or managers (40% men, 38% women in the general population [28]). The percentage of participants who were overweight or obese (BMI ≥ 25 kg/m2) was comparable to the Australian population of similar age (58% men, 35% women [29]). Mean (SD) cMetSyn score was −0.01 (−0.70) for men and −0.01 (−0.71) for women.
The analysis was stratified by weight status due to significant interactions between weight status and all fat-related behaviors. Most participants reported usually trimming fat from meat before or after cooking (Table 2, 63% healthy weight, 78% overweight/obese). Participants who were overweight or obese tended to consume low-fat products more frequently than those who were healthy weight. Most participants usually used olive oil for cooking (62% healthy weight, 60% overweight/obese) whereas the proportion of participants who usually used canola oil (10% healthy weight, 10% overweight or obese) or butter (8% healthy weight, 7% overweight or obese) was low. Compared to healthy weight participants (3.3%), a higher percentage of those who were overweight or obese (8.2%) reported they were on a weight loss diet, whereas the percentage on a fat-modified diet was similar (11.9% healthy weight, 10.6% overweight/obese).
Among participants who were healthy weight, frequency of using low-fat oily dressing was the only fat-related behavior that was significantly associated with cMetSyn (Table 3). Participants who never or rarely used low-fat oily dressing had lower cMetSyn scores than those who usually used it. The association remained significant after adjusting for education, daily fruit and vegetable consumption, weekly alcohol consumption, smoking status and intake of oil and creamy dressings. Further adjustment for diet quality, being on a weight loss or fat-modified diet did not attenuate the results.
Among participants who were overweight or obese, participants who usually used canola/sunflower oil for cooking had higher cMetSyn scores than those who never/rarely used it (Table 3). The association remained after adjusting for smoking status and takeaway food consumption, and after adjusting for diet quality, being on a weight loss or fat-modified diet in the sensitivity analyses. A significant sex interaction was found for cooking with butter among those who were overweight or obese, with a trend for a higher score among males who more frequently used butter for cooking. Participants who were overweight or obese and usually consumed whole milk had higher cMetSyn scores than those who usually consumed skim or low/reduced fat milk. However, the association was attenuated and no longer statistically significant after adjusting for education and occupation.

4. Discussion

This study examined whether fat-related eating behaviors (trimming fat from meat, consuming low-fat dairy, type of fat used for cooking) were associated with a cMetSyn score among young Australian adults. Healthy fat-related dietary behaviors were reported by a higher percentage of participants who were overweight or obese than those who were healthy weight. This may be a result of reducing fat intake to try to lose weight or greater pressure to report socially desirable answers. The cMetSyn score was higher among healthy weight participants who reported more frequent use of low-fat oily dressings and among participants who were overweight or obese and used canola oil more frequently for cooking. The other fat-related dietary behaviors were not associated with cMetSyn.
Trimming fat from meat was not associated with the cMetSyn score. This findings is consistent with previous studies that have reported no significant relationship between intake of animal fat [30,31,32] or saturated fat [33] and the risk of CHD. In a meta-analysis of 17 cohort studies and three case-control studies, red meat intake was not associated with CHD or Type 2 diabetes [8]. It is worth noting in that study the effects of lean and non-lean cuts of meat were not examined separately. Other studies that investigated lean red meat also found no negative effects on lipid levels, thrombotic risk factors, markers of oxidative stress or blood pressure [34]. In contrast, a recent review of systematic reviews and meta-analyses recommended reducing meat-derived saturated fat for CVD health [4].
Among healthy weight participants, more frequent use of low-fat oily dressing was associated with a higher cMetSyn score. Oily dressings are usually high in vegetable oils, which are recommended for cardiometabolic health. Although low-fat dressings usually have a lower energy content, the healthy oils have been replaced with sugar, starch and salt, components which are detrimental to cardiometabolic health [35].
Our study found using olive oil for cooking was not associated with cMetSyn score. These results contrast those from studies of older participants that found higher olive oil to associate with better cardiometabolic health. In the PREvención con DIeta MEDiterránea (PREDIMED) Study (N = 7216, 55–80 years old), the risk of developing CVD during the 4.8 year follow-up was 35% lower among participants with the highest baseline olive oil consumption, compared to those in the lower third [36]. In a Spanish cohort of the European Prospective Investigation into Cancer and Nutrition (EPIC) study (N = 40,142, 29–69 years old) [37], olive oil, especially extra virgin olive oil, was related to a reduced risk of CHD events during a mean follow-up of 10.4 years. A meta-analysis of nine studies found an inverse relationship between olive oil consumption and stroke (and with stroke and CHD combined), but no association was found for CHD [38]. In a cross-sectional study of Spanish adults (N = 4572, 18 years and older), olive oil consumption was associated with a lower risk of obesity, impaired glucose regulation and hypertriglyceridemia than consumption of sunflower oil [39]. The younger age of our study participants and assessed use of olive oil for cooking, not overall intake, could explain the observed differences with previous findings.
Among participants who were overweight or obese, higher consumption of canola/sunflower oil was associated with higher mean cMetSyn score. These findings are in contrast with a review, funded by the Canola industry [40], in which most studies found canola oil was associated with beneficial effects on biomarkers of CVD risk (total cholesterol, LDL-cholesterol, and inflammatory biomarkers) compared with saturated fat. Sunflower oil appeared to have similar effects on plasma lipids as canola oil whereas olive oil was less protective [40]. One potential reason for the conflicting findings is that most of the studies included in that review used raw oil which restricts the interpretation of these studies, because canola oil is mostly used for frying, and heat can cause the loss of some of the beneficial components such as α-linolenic acid [41].
We found that using butter for cooking was not significantly associated with cMetSyn score. However, the number of participants who usually used butter for cooking was low and there was a trend for more frequent use of butter to be associated with a higher cMetSyn score among males who were overweight or obese. Previous studies have reported that butter consumption was not associated with Ischemic Heart Disease events [42], incident CVD [43] or stroke [44].
Consumption of low-fat dairy products was not associated with cMetSyn scores after adjusting for potential confounders, which is consistent with recent studies [45,46,47]. In a multicenter cross-sectional study of 4827 French adults aged over 35 years, no association was found between high-fat dairy intake and lipid profile or risk of mortality [45]. In a Netherlands cohort (N = 120,852, 55–69 years old), intake of full-fat or low-fat milk products did not predict the risk of Ischemic Heart Disease or stroke in men or women during the 10-year follow-up [46]. A meta-analysis assessing six prospective cohort studies [47] also indicated no association between total high-fat and low-fat dairy consumption and the risk of CHD. Among 2907 adults aged 65 years and older from the USA, serial measures of biomarkers for dairy fat were not significantly associated with incident CVD after 22 years of follow-up [48]. Higher circulating heptadecanoic acid was associated with lower risk of CVD and stroke mortality [48]. In the Nurses’ Health Study [49] (80,082 women, aged 34–59 years), no association was found between high-fat or low-fat dairy consumption and risk of major CHD events during the 14-year follow-up; however, a higher ratio of high-fat to low-fat dairy consumption was associated with greater CHD risk. Whole-fat milk was also found to be positively associated with CHD risk in that study.
This study had limitations. Self-reported dietary data may be subject to error due to recall or reporting of socially desirable answers. The present study made no distinctions between canola and sunflower oil, or different varieties of olive oil. Data on participants use of oil was only collected in relation to cooking, not other uses such as dressings. The cross-sectional design means the direction of the association cannot be determined. It is possible that participants who are concerned about their metabolic health (e.g., have been informed they have high cholesterol) or have a family history of CVD may have recently changed their behavior to improve their cardiometabolic health [50]. In addition, the study focused on components of the harmonized definition of the metabolic syndrome [20] and did not include other risk factors such as non-alcoholic fatty liver disease, which is strongly associated with cardiometabolic health [51]. Finally, data was only collected on the frequency of the eating behaviors rather than the amount consumed, therefore the overall fat composition of the diets, such as total, saturated and unsaturated fat intake is unknown. However, estimating portion sizes is difficult and prone to measurement error and the major variance of food intake is explained by frequency alone [52].
Strengths of the study include studying a relatively healthy population, which avoids possible treatment and disease effects on the outcomes of interest. Most existing studies have focused on middle-aged and elderly populations whereas this study examined associations between fat-related eating behaviors and CVD risk among young adults. In addition, the CDAH study is a multicenter study with a large sample size from across Australia, with generalizability to healthy young Australian adults. The analyses were adjusted for various lifestyle and dietary factors.

5. Conclusions

In this cross-sectional study of young Australian adults, a higher cMetSyn score was associated with more frequent consumption of low-fat oily dressings among participants who were healthy weight and frequent use of canola oil for cooking among those who were overweight or obese. Trimming fat from meat, cooking with olive oil and consumption of low-fat dairy products was not associated with metabolic risk.

Author Contributions

Conceptualization, T.D., A.J.V. and K.J.S.; Formal analysis, Y.S. and K.J.S.; Funding acquisition, T.D. and A.J.V.; Methodology, Y.S., C.G.M. and K.J.S.; Supervision, C.G.M. and K.J.S.; Writing—original draft, Y.S.; Writing—review and editing, C.G.M., W.H.O., T.D., A.J.V. and K.J.S. All authors provided critical revision of the manuscript and approved the final version.

Funding

This research was funded by grants from the National Health and Medical Research Council (211316); the National Heart Foundation (GOOH0578); the Tasmanian Community Fund and Veolia Environmental Services. The study was sponsored by Sanitarium, and Target Australia. CGM is supported by a National Heart Foundation of Australia Future Research Leader Fellowship (100849), AJV is supported by NHMRC Fellowship (APP1008299) and KJS by an NHMRC Early Career Research Fellowship (APP1072516).

Acknowledgments

We thank the participants for their on-going contribution to the CDAH Study. We gratefully acknowledge the contribution of Marita Dalton (study project manager) and all other CDAH Study staff.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

References

  1. Waters, A.M.; Trinh, L.; Chau, T.; Bourchier, M.; Moon, L. Latest statistics on cardiovascular disease in Australia. Clin. Exp. Pharmacol. Physiol. 2013, 40, 347–356. [Google Scholar] [CrossRef] [PubMed]
  2. National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) final report. Circulation 2002, 106, 3143–3421. [Google Scholar]
  3. National Health and Medical Research Council. Australian Dietary Guidelines; National Health and Medical Research Council: Canberra, Australia, 2013.
  4. Clifton, P.; Keogh, J. Dietary Fats and Cardiovascular Disease: An Evidence Check Rapid Review Brokered by the Sax Institute for the National Heart Foundation of Australia; The National Heart Foundation of Australia: Adelaide, Australia, 2017. [Google Scholar]
  5. Enser, M.; Hallett, K.G.; Hewett, B.; Fursey, G.A.; Wood, J.D.; Harrington, G. Fatty acid content and composition of UK beef and lamb muscle in relation to production system and implications for human nutrition. Meat Sci. 1998, 49, 329–341. [Google Scholar] [CrossRef]
  6. Chan, W.; Brown, J.; Church, S.; Buss, D. Meat Products and Dishes. Sixth Supplement to the Fifth Edition of McCance & Widdowson’s the Composition of Foods; The Royal Society of Chemistry and Ministry of Agriculture, Fisheries and Food: London, UK, 1996. [Google Scholar]
  7. Bernstein, A.M.; Sun, Q.; Hu, F.B.; Stampfer, M.J.; Manson, J.E.; Willett, W.C. Major dietary protein sources and risk of coronary heart disease in women. Circulation 2010, 122, 876–883. [Google Scholar] [CrossRef] [PubMed]
  8. Micha, R.; Wallace, S.K.; Mozaffarian, D. Red and processed meat consumption and risk of incident coronary heart disease, stroke, and diabetes mellitus: A systematic review and meta-analysis. Circulation 2010, 121, 2271–2283. [Google Scholar] [CrossRef] [PubMed]
  9. Kratz, M.; Baars, T.; Guyenet, S. The relationship between high-fat dairy consumption and obesity, cardiovascular, and metabolic disease. Eur. J. Nutr. 2013, 52, 1–24. [Google Scholar] [CrossRef] [PubMed]
  10. Lorenzen, J.K.; Astrup, A. Dairy calcium intake modifies responsiveness of fat metabolism and blood lipids to a high-fat diet. Br. J. Nutr. 2011, 105, 1823–1831. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  11. Nestel, P.J.; Chronopulos, A.; Cehun, M. Dairy fat in cheese raises LDL cholesterol less than that in butter in mildly hypercholesterolaemic subjects. Eur. J. Clin. Nutr. 2005, 59, 1059–1063. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  12. Lovegrove, J.A.; Givens, D.I. Dairy food products: Good or bad for cardiometabolic disease? Nutr. Res. Rev. 2016, 29, 249–267. [Google Scholar] [CrossRef] [PubMed]
  13. Willett, W.C.; Stampfer, M.J. Current evidence on healthy eating. Annu. Rev. Public Health 2013, 34, 77–95. [Google Scholar] [CrossRef] [PubMed]
  14. Astrup, A. Yogurt and dairy product consumption to prevent cardiometabolic diseases: Epidemiologic and experimental studies. Am. J. Clin. Nutr. 2014, 99, 1235s–1242s. [Google Scholar] [CrossRef] [PubMed]
  15. Dwyer, T.; Gibbons, L.E. The Australian Schools Health and Fitness Survey. Physical fitness related to blood pressure but not lipoproteins. Circulation 1994, 89, 1539–1544. [Google Scholar] [CrossRef] [PubMed]
  16. Smith, K.J.; McNaughton, S.A.; Gall, S.L.; Blizzard, L.; Dwyer, T.; Venn, A.J. Takeaway food consumption and its associations with diet quality and abdominal obesity: A cross-sectional study of young adults. Int. J. Behav. Nutr. Phys. Act. 2009, 6, 29. [Google Scholar] [CrossRef] [PubMed]
  17. Friedewald, W.T.; Levy, R.I.; Fredrickson, D.S. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin. Chem. 1972, 18, 499–502. [Google Scholar] [PubMed]
  18. Wijndaele, K.; Beunen, G.; Duvigneaud, N.; Matton, L.; Duquet, W.; Thomis, M.; Lefevre, J.; Philippaerts, R.M. A continuous metabolic syndrome risk score: Utility for epidemiological analyses. Diabetes Care 2006, 29, 2329. [Google Scholar] [CrossRef] [PubMed]
  19. Smith, K.J.; Blizzard, L.; McNaughton, S.A.; Gall, S.L.; Dwyer, T.; Venn, A.J. Takeaway food consumption and cardio-metabolic risk factors in young adults. Eur. J. Clin. Nutr. 2012, 66, 577–584. [Google Scholar] [CrossRef] [PubMed]
  20. Alberti, K.G.; Eckel, R.H.; Grundy, S.M.; Zimmet, P.Z.; Cleeman, J.I.; Donato, K.A. Harmonizing the metabolic syndrome: A joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation 2009, 120, 1640–1645. [Google Scholar] [PubMed]
  21. Smith, K.J.; Gall, S.L.; McNaughton, S.A.; Blizzard, L.; Dwyer, T.; Venn, A.J. Skipping breakfast: Longitudinal associations with cardiometabolic risk factors in the Childhood Determinants of Adult Health Study. Am. J. Clin. Nutr. 2010, 92, 1316–1325. [Google Scholar] [CrossRef] [PubMed]
  22. Coyne, T.; Ibiebele, T.I.; McNaughton, S.; Rutishauser, I.H.; O’dea, K.; Hodge, A.M.; McClintock, C.; Findlay, M.G.; Lee, A. Evaluation of brief dietary questions to estimate vegetable and fruit consumption—Using serum carotenoids and red-cell folate. Public Health Nutr. 2005, 8, 298–308. [Google Scholar] [CrossRef] [PubMed]
  23. McNaughton, S.A.; Ball, K.; Crawford, D.; Mishra, G.D. An index of diet and eating patterns is a valid measure of diet quality in an Australian population. J. Nutr. 2008, 138, 86–93. [Google Scholar] [CrossRef] [PubMed]
  24. McNaughton, S.A.; Dunstan, D.W.; Ball, K.; Shaw, J.; Crawford, D. Dietary quality is associated with diabetes and cardio-metabolic risk factors. J. Nutr. 2009, 139, 734–742. [Google Scholar] [CrossRef] [PubMed]
  25. Smith, K.J.; Sanderson, K.; McNaughton, S.A.; Gall, S.L.; Dwyer, T.; Venn, A.J. Longitudinal associations between fish consumption and depression in young adults. Am. J. Epidemiol. 2014, 179, 1228–1235. [Google Scholar] [CrossRef] [PubMed]
  26. Craig, C.L.; Marshall, A.L.; Sjorstrom, M.; Bauman, A.E.; Booth, M.L.; Ainsworth, B.E.; Pratt, M.; Ekelund, U.L.F.; Yngve, A.; Sallis, J.F.; et al. International Physical Activity Questionnaire: 12-country reliability and validity. Med. Sci. Sports Exerc. 2003, 35, 1381–1395. [Google Scholar] [CrossRef] [PubMed]
  27. Australian Bureau of Statistics. Census of Population and Housing Table: Social Marital Status by Age by Sex for Time Series, Australia. Catalog No. 20680; Australian Bureau of Statistics (ABS): Canberra, Australia, 2006.
  28. Australian Bureau of Statistics. Census of Population and Housing Table: Occupation by Sex, Australia. Catalogue No. 20680; Australian Bureau of Statistics (ABS): Canberra, Australia, 2001.
  29. Australian Bureau of Statistics. National Health Survey: Summary of Results 2004–2005. Catalogue No. 4364.0; Australian Bureau of Statistics (ABS): Canberra, Australia, 2006.
  30. Hu, F.B.; Stampfer, M.J.; Manson, J.E.; Rimm, E.; Colditz, G.A.; Rosner, B.A.; Hennekens, C.H.; Willett, W.C. Dietary fat intake and the risk of coronary heart disease in women. N. Engl. J. Med. 1997, 337, 1491–1499. [Google Scholar] [CrossRef] [PubMed]
  31. Halton, T.L.; Willett, W.C.; Liu, S.; Manson, J.E.; Albert, C.M.; Rexrode, K.; Hu, F.B. Low-carbohydrate-diet score and the risk of coronary heart disease in women. N. Engl. J. Med. 2006, 355, 1991–2002. [Google Scholar] [CrossRef] [PubMed]
  32. Fehily, A.M.; Yarnell, J.W.; Sweetnam, P.M.; Elwood, P.C. Diet and incident ischaemic heart disease: The Caerphilly Study. Br. J. Nutr. 1993, 69, 303–314. [Google Scholar] [CrossRef] [PubMed]
  33. Schmid, A. The role of meat fat in the human diet. Crit. Rev. Food Sci. Nutr. 2011, 51, 50–66. [Google Scholar] [CrossRef] [PubMed]
  34. McAfee, A.J.; McSorley, E.M.; Cuskelly, G.J.; Moss, B.W.; Wallace, J.M.; Bonham, M.P.; Fearon, A.M. Red meat consumption: An overview of the risks and benefits. Meat Sci. 2010, 84, 1–13. [Google Scholar] [CrossRef] [PubMed]
  35. Mozaffarian, D. Dietary and Policy Priorities for Cardiovascular Disease, Diabetes, and Obesity: A Comprehensive Review. Circulation 2016, 133, 187–225. [Google Scholar] [CrossRef] [PubMed]
  36. Guasch-Ferré, M.; Hu, F.B.; Martínez-González, M.A.; Fitó, M.; Bulló, M.; Estruch, R.; Ros, E.; Corella, D.; Recondo, J.; Gómez-Gracia, E.; et al. Olive oil intake and risk of cardiovascular disease and mortality in the PREDIMED Study. BMC Med. 2014, 12, 78. [Google Scholar]
  37. Buckland, G.; Travier, N.; Barricarte, A.; Ardanaz, E.; Moreno-Iribas, C.; Sánchez, M.J.; Molina-Montes, E.; Chirlaque, M.D.; Huerta, J.M.; Navarro, C.; et al. Olive oil intake and CHD in the European Prospective Investigation into Cancer and Nutrition Spanish cohort. Br. J. Nutr. 2012, 108, 2075–2082. [Google Scholar] [CrossRef] [PubMed]
  38. Martinez-Gonzalez, M.A.; Dominguez, L.J.; Delgado-Rodriguez, M. Olive oil consumption and risk of CHD and/or stroke: A meta-analysis of case-control, cohort and intervention studies. Br. J. Nutr. 2014, 112, 248–259. [Google Scholar] [CrossRef] [PubMed]
  39. Soriguer, F.; Rojo-Martínez, G.; Goday, A.; Bosch-Comas, A.; Bordiú, E.; Caballero-Díaz, F.; Calle-Pascual, A.; Carmena, R.; Casamitjana, R.; Castaño, L.; et al. Olive oil has a beneficial effect on impaired glucose regulation and other cardiometabolic risk factors. Di@betes study. Eur. J. Clin. Nutr. 2013, 67, 911–916. [Google Scholar] [CrossRef] [PubMed]
  40. Lin, L.; Allemekinders, H.; Dansby, A.; Campbell, L.; Durance-Tod, S.; Berger, A.; Jones, P.J. Evidence of health benefits of canola oil. Nutr. Rev. 2013, 71, 370–385. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  41. Hoffman, R.; Gerber, M. Can rapeseed oil replace olive oil as part of a Mediterranean-style diet? Br. J. Nutr. 2014, 112, 1882–1895. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  42. Shaper, A.G.; Wannamethee, G.; Walker, M. Milk, butter, and heart disease. BMJ 1991, 302, 785–786. [Google Scholar] [CrossRef] [PubMed]
  43. Sonestedt, E.; Wirfalt, E.; Wallstrom, P.; Gullberg, B.; Orho-Melander, M.; Hedblad, B. Dairy products and its association with incidence of cardiovascular disease: The Malmo diet and cancer cohort. Eur. J. Epidemiol. 2011, 26, 609–618. [Google Scholar] [CrossRef] [PubMed]
  44. Larsson, S.C.; Mannisto, S.; Virtanen, M.J.; Kontto, J.; Albanes, D.; Virtamo, J. Dairy foods and risk of stroke. Epidemiology 2009, 20, 355–360. [Google Scholar] [CrossRef] [PubMed]
  45. Kai, S.H.Y.; Bongard, V.; Simon, C.; Ruidavets, J.B.; Arveiler, D.; Dallongeville, J.; Wagner, A.; Amouyel, P.; Ferrières, J. Low-fat and high-fat dairy products are differently related to blood lipids and cardiovascular risk score. Eur. J. Prev. Cardiol. 2014, 21, 1557–1567. [Google Scholar] [CrossRef] [PubMed]
  46. Goldbohm, R.A.; Chorus, A.M.; Galindo Garre, F.; Schouten, L.J.; van den Brandt, P.A. Dairy consumption and 10-year total and cardiovascular mortality: A prospective cohort study in The Netherlands. Am. J. Clin. Nutr. 2011, 93, 615–627. [Google Scholar] [CrossRef] [PubMed]
  47. Soedamah-Muthu, S.S.; Ding, E.L.; Al-Delaimy, W.K.; Hu, F.B.; Engberink, M.F.; Willett, W.C.; Geleijnse, J.M. Milk and dairy consumption and incidence of cardiovascular diseases and all-cause mortality: Dose-response meta-analysis of prospective cohort studies. Am. J. Clin. Nutr. 2011, 93, 158–171. [Google Scholar] [CrossRef] [PubMed]
  48. De Oliveira Otto, M.; Lemaitre, R.; Song, X.; King, I.; Siscovick, D.; Mozaffarian, D. Serial measures of circulating biomarkers of dairy fat and total and cause-specific mortality in older adults: The Cardiovascular Health Study. Am. J. Clin. Nutr. 2018. [Google Scholar] [CrossRef]
  49. Hu, F.B.; Stampfer, M.J.; Manson, J.E.; Ascherio, A.; Colditz, G.A.; Speizer, F.E.; Hennekens, C.H.; Willett, W.C. Dietary saturated fats and their food sources in relation to the risk of coronary heart disease in women. Am. J. Clin. Nutr. 1999, 70, 1001–1008. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  50. Juonala, M.; Viikari, J.; Räsänen, L.; Helenius, H.; Pietikäinen, M.; Raitakari, O. Young adults with family history of coronary heart disease have increased arterial vulnerability to metabolic risk factors: The Cardiovascular Risk in Young Finns Study. Arterioscler. Thromb. Vasc. Biol. 2006, 26, 1376–1382. [Google Scholar] [CrossRef] [PubMed]
  51. Lonardo, A.; Nascimbeni, F.; Mantovani, A.; Targher, G. Hypertension, diabetes, atherosclerosis and NASH: Cause or consequence? J. Hepatol. 2018, 68, 335–352. [Google Scholar] [CrossRef] [PubMed]
  52. Noethlings, U.; Hoffmann, K.; Bergmann, M.M.; Boeing, H. Portion size adds limited information on variance in food intake of participants in the EPIC-Potsdam study. J. Nutr. 2003, 133, 510–515. [Google Scholar] [CrossRef] [PubMed]
Table
Table 1. Sociodemographic, lifestyle and cardiometabolic characteristics of participants from the Childhood Determinants of Adult Health study, aged 26–36 years old.
Table 1. Sociodemographic, lifestyle and cardiometabolic characteristics of participants from the Childhood Determinants of Adult Health study, aged 26–36 years old.
CharacteristicMen (N = 1021) aWomen (N = 1050) a
n%n%
Education
University39138.550748.3
Vocational36736.125824.6
School only25925.528427.1
Marital status
Single 33032.333431.8
Married/living as married69167.771668.2
Occupation
Profession/Manager58658.453952.0
White collar787.828127.1
Blue collar31030.9525.0
Not in labor force303.016515.9
Smoking status
Never58457.458455.8
Former18318.024223.1
Current25124.722121.1
Weight status
Healthy weight (<25 kg/m2)39238.466263.1
Overweight (25 kg/m2–<30 kg/m2)47046.024523.3
Obese (≥30 kg/m2)15915.614313.6
MeanSDMeanSD
Age, years31.62.631.42.6
Average daily steps9221353289783041
Leisure-time physical activity (minutes/week) 1200–25012020–230
Cardiometabolic risk factors
Waist circumference (cm)89.310.377.911.1
Fasting glucose (mmol/L)5.20.44.80.4
HDL cholesterol (mmol/L)1.30.31.50.3
Systolic blood pressure (mmHg)1251111110
Diastolic blood pressure (mmHg)759709
cMetSyn b−0.01−0.70−0.01−0.71
MedianIQR cMedianIQR c
Triglycerides (mmol/L)1.00.7–1.60.80.6–1.1
a Numbers do not always equal the total due to missing data. b Continuous metabolic syndrome score, a higher score indicates higher cardiometabolic risk. c IQR, interquartile range (25th, 75th percentiles).
Table
Table 2. Distribution of fat-related eating behaviors, stratified by weight status, from the Childhood Determinants of Adult Health study.
Table 2. Distribution of fat-related eating behaviors, stratified by weight status, from the Childhood Determinants of Adult Health study.
Fat-Related Eating BehaviorBMI < 25 kg/m2 (N = 1054) aBMI ≥ 25 kg/m2 (N = 1017) a
n%n%
Trim fat from meat
Don’t eat this food565.4181.8
Usually74671.569969.5
Sometimes14914.319419.3
Rarely/never928.8959.4
Low-fat cream
Don’t eat this food17716.815815.6
Usually 27626.230630.2
Sometimes17416.520820.5
Rarely/never42540.434133.7
Low-fat ice-cream
Don’t eat this food858.1717.0
Usually 21520.523723.4
Sometimes21420.424624.2
Rarely/never53651.146145.4
Low-fat cheddar-type cheese
Don’t eat this food313.0333.3
Usually 30529.135835.3
Sometimes24923.726926.5
Rarely/never46544.335535.0
Low-fat oily dressing
Don’t eat this food18818.319419.1
Usually 32231.435835.3
Sometimes20720.219419.1
Rarely/never30830.126926.5
Low-fat spread
Don’t eat this food474.5353.5
Usually 40138.145645.0
Sometimes23622.424223.9
Rarely/never36835.028027.6
Olive oil for cooking
Usually 64562.055154.7
Sometimes28327.232432.2
Rarely/never11310.913213.1
Canola/sunflower oil for cooking
Usually 999.710210.4
Sometimes32531.831231.7
Rarely/never59758.556957.9
Butter for cooking
Usually 777.5676.8
Sometimes48246.643243.6
Rarely/never47545.949149.6
Milk usually consumed
Skim milk13412.915315.7
Low/reduced fat51249.444946.0
Whole milk39137.737538.4
a Totals may not add to 100% due to rounding.
Table
Table 3. Adjusted mean (SE) continuous metabolic syndrome score by fat-related eating behaviors, stratified by weight status, for participants from the Childhood Determinants of Adult Health study.
Table 3. Adjusted mean (SE) continuous metabolic syndrome score by fat-related eating behaviors, stratified by weight status, for participants from the Childhood Determinants of Adult Health study.
Fat-Related Eating BehaviorI Don’t Eat This FoodUsuallySometimesNever/Rarelyp-Trend
nMean a(SE)nMean a(SE)nMean a(SE)nMean a(SE)Model 1 bModel 2
BMI < 25 kg/m2
Trim fat from meat56−0.54(0.07)746−0.46(0.03)149−0.42(0.05)92−0.50(0.06)0.690---- c
Low-fat cream177−0.48(0.04)276−0.44(0.04)174−0.48(0.04)425−0.45(0.03)0.771---- c
Low-fat ice-cream85−0.47(0.06)215−0.44(0.04)214−0.41(0.04)536−0.49(0.03)0.353---- c
Low-fat cheese31−0.51(0.09)305−0.43(0.04)249−0.40(0.04)465−0.50(0.03)0.184---- c
Low-fat oily dressing188−0.43(0.04)322−0.42(0.04)207−0.47(0.04)308−0.53(0.03)0.0100.013 d
Low-fat spread
-Males17−0.58(0.12)122−0.41(0.05)104−0.41(0.05)149−0.54(0.04)0.125---- c
-Females30−0.49(0.10)279−0.28(0.03)132−0.29(0.05)219−0.33(0.04)0.836---- c
Olive oil for cooking--- e--- e--- e645−0.48(0.03)283−0.42(0.04)113−0.45(0.05)0.209---- c
Canola/sunflower oil for cooking--- e--- e--- e99−0.44(0.06)325−0.44(0.03)597−0.49(0.03)0.223---- c
Butter for cooking--- e--- e--- e77−0.49(0.06)482−0.48(0.03)475−0.45(0.03)0.401---- c
Skim milkLow/reduced fat milkWhole milk
Milk usually consumed--- e--- e--- e134−0.46(0.05)512−0.46(0.03)391−0.46(0.03)0.929---- c
BMI ≥ 25 kg/m2
Trim fat from meat180.003(0.15)6990.25(0.03)1940.28(0.05)950.26(0.07)0.394---- c
Low-fat cream1580.20(0.05)3060.26(0.04)2080.23(0.05)3410.28(0.04)0.356---- c
Low-fat ice-cream710.22(0.08)2370.26(0.05)2460.27(0.04)4610.25(0.03)0.944---- c
Low-fat cheese330.38(0.12)3580.25(0.04)2690.29(0.04)3550.22(0.04)0.384---- c
Low-fat oily dressing1940.26(0.05)3580.22(0.04)1940.24(0.05)2690.29(0.04)0.489---- c
Low-fat spread350.13(0.11)4560.24(0.04)2420.29(0.04)2800.25(0.04)0.416---- c
Olive oil for cooking--- e--- e--- e5510.22(0.03)3240.30(0.04)1320.29(0.06)0.101---- c
Canola/sunflower oil for cooking--- e--- e--- e1020.35(0.07)3120.30(0.04)5690.20(0.03)0.0070.008 f
Butter for cooking
-Males--- e--- e--- e490.38(0.09)2720.28(0.04)2870.21(0.04)0.0600.077 g
-Females--- e--- e--- e180.29(0.15)1600.57(0.05)2040.48(0.05)0.792 ---- c
Skim milkLow/reduced fat milkWhole milk
Milk usually consumed--- e--- e--- e1530.17(0.06)4490.22(0.04)3750.29(0.04)0.0420.295 h
a Mean and SE are reported for model 1. Higher score indicates higher cardiometabolic risk. b Model 1 adjusted for age and sex, except when there was a sex interaction, then the analysis was stratified by sex. c no additional adjustments were made for model 2 as p-value for model 1 was not <0.1. Model 2 made additional adjustments for d education status, daily fruit and vegetable consumption, e the response option “I don’t eat this food” was not available for this variable; weekly alcohol consumption, smoking status, intake of oily and creamy dressings; f smoking status, takeaway food consumption; g occupation; h education, occupation.

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Sun, Y.; Magnussen, C.G.; Dwyer, T.; Oddy, W.H.; Venn, A.J.; Smith, K.J. Cross-Sectional Associations between Dietary Fat-Related Behaviors and Continuous Metabolic Syndrome Score among Young Australian Adults. Nutrients 2018, 10, 972. https://doi.org/10.3390/nu10080972

AMA Style

Sun Y, Magnussen CG, Dwyer T, Oddy WH, Venn AJ, Smith KJ. Cross-Sectional Associations between Dietary Fat-Related Behaviors and Continuous Metabolic Syndrome Score among Young Australian Adults. Nutrients. 2018; 10(8):972. https://doi.org/10.3390/nu10080972

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Sun, Yile, Costan G. Magnussen, Terence Dwyer, Wendy H. Oddy, Alison J. Venn, and Kylie J. Smith. 2018. "Cross-Sectional Associations between Dietary Fat-Related Behaviors and Continuous Metabolic Syndrome Score among Young Australian Adults" Nutrients 10, no. 8: 972. https://doi.org/10.3390/nu10080972

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