The prevalence of obesity is increasing at an alarming rate, across both developed and developing countries(1,2) . Obesity increases risk of many chronic diseases including type 2 diabetes, CVD, liver and kidney diseases, neurodegenerative diseases and at least thirteen types of cancer, including more common ones such as colorectal and post-menopausal breast cancer(Reference Lauby-Secretan, Scoccianti and Loomis3). Central (abdominal) obesity is also strongly associated with morbidity and mortality(Reference Ross, Neeland and Yamashita4).
In Australia in 2017–2018, 67 % of adults were overweight or obese, 10 % higher than in 1995(5). In 2011–2012, obesity cost the Australian economy an estimated $8·6 billion, not including the cost of reduced well-being and foregone earnings(6). Further, it has been estimated that 14 % of the 2020 burden of disease due to overweight and obesity could have been avoided if the BMI of the population at risk in 2011 was reduced by 1 kg/m2(7).
The global obesity epidemic is understood to be a consequence of lifestyle changes, favouring high intakes of energy-dense food and less physical activity than in the past, and often interacting with genetic susceptibility(Reference Heymsfield and Wadden8). Lifestyle modification, predominantly through diet and exercise, is a frontline strategy to manage obesity and obesity-related conditions(6). The question remains – what diet composition is best to prevent weight gain or lead to weight loss? Early research focused on macronutrient composition(Reference Thom and Lean9). It is now well established that in terms of weight loss, it is energy deficit rather than diet composition that is most important(Reference Thom and Lean9,Reference Shai, Schwarzfuchs and Henkin10) . However, energy-dense foods tend to encourage overconsumption of energy, while less energy-dense foods high in fibre and non-starch polysaccharides such as whole grains, and in water such as fruit and vegetables, may help limit energy consumption(Reference Swinburn, Caterson and Seidell11).
As in other areas of nutritional epidemiology, recent studies of obesity have tended to focus on measures of overall diet rather than single foods or nutrients(Reference Asghari, Mirmiran and Yuzbashian12). There are many approaches to look at diet using scores. The Alternative Healthy Eating Index (AHEI)-2010 assesses adherence to the US dietary guidelines, which are based on minimising the risk of chronic disease(Reference Chiuve, Fung and Rimm13). The Dietary Inflammatory Index (DII®) is based on literature identifying foods and nutrients associated with pro-inflammatory biomarkers, which are then combined into an index of dietary inflammatory potential(Reference Shivappa, Steck and Hurley14). The Mediterranean Diet Score (MDS) was developed to assess adherence to the traditional diet of Crete which was identified as being associated with low CVD risk in the seven Countries Study(Reference Trichopoulou, Kouris-Blazos and Wahlqvist15). Thus, these three diet scores (AHEI, MDS and DII) have very different theoretical bases, but all tend to be consistent in favouring consumption of fruit, vegetables and whole grains over meat and saturated fats.
Studies of these diet indices in relation to obesity provide some support for AHEI being inversely associated with obesity over time(Reference Boggs, Rosenberg and Rodriguez-Bernal16), especially in sub-groups identified as genetically susceptible to obesity(Reference Ding, Ellervik and Huang17,Reference Wang, Heianza and Sun18) . In an 8-year follow-up of the Seguimiento University of Navarra (SUN) study cohort, a more pro-inflammatory diet, measured using the DII, was associated with greater yearly weight gain and increased risk of overweight and obesity(Reference Ramallal, Toledo and Martinez19). In cross-sectional analysis of the Prevención con Dieta Mediterránea Study, a higher DII was associated with obesity(Reference Ruiz-Canela, Zazpe and Shivappa20). A recent meta-analysis looking at DII and BMI or obesity also supported a positive association across twenty-two studies, including thirteen cohorts(Reference Varkaneh, Fatahi and Tajik21). In the European Prospective Investigation into Cancer and Nutrition–Physical Activity, Nutrition, Alcohol Consumption, Cessation of Smoking, Eating Out of Home, and Obesity (EPIC-PANACEA) study, higher adherence to a Mediterranean diet was associated with reduced risk of weight gain and of developing overweight or obesity(Reference Romaguera, Norat and Vergnaud22).
To our knowledge, none of the studies compared the three indices in the same cohort and looked, simultaneously, at both overall and central (abdominal) obesity. Hence, the aim of this study was to investigate which of the three diet scores – AHEI, DII and MDS – was a better predictor of obesity, assessed as BMI or waist-to-hip circumference ratio (WHR), using data from the Melbourne Collaborative Cohort Study (MCCS).
Methods
Study participants
The MCCS is a prospective cohort with 41 513 participants recruited in 1990–1994, via the electoral roll and direct approach through churches, clubs and ethnicity-specific mass media. A detailed description of the cohort, including recruitment, data collection and follow-up, has been presented elsewhere(Reference Milne, Fletcher and MacInnis23). At baseline, physical measurements were taken and other information was collected using interviewer-administered questionnaires. At follow-up in 2003–2007, physical measurements (not including height) were again taken and other information was collected using self-administered questionnaires.
We excluded participants who did not attend the second follow-up, those who had other chronic diseases at baseline or developed over follow-up conditions that may affect body size, including diabetes, asthma, cancer, stroke, angina, heart attack or had cardiac surgery (Fig. 1).
Participant flow chart
Dietary assessment
Information on dietary intake was collected using a 121-item self-administered FFQ(Reference Ireland, Jolley and Giles24). Sex-specific average portion sizes were assigned to each food item, and daily frequencies of some fruits were seasonally adjusted. Nutrient composition data were largely derived from the Australian food composition tables(Reference Lewis, Milligan and Hunt25). Because these tables do not include folate or vitamin E, we used British data for these(Reference Holland, Welch and Unwin26). Fatty acid data were sourced from Royal Melbourne Institute of Technology(27) and carotenoid data from the United States Department of Agriculture(28). Mean daily nutrient intakes were obtained by multiplying the daily frequency of each food item by the nutrient composition for an average sex-specific portion size. Comparisons of antioxidant and fatty acid intakes against plasma biomarkers for this FFQ have been described previously(Reference Hodge, Simpson and Fridman29,Reference Hodge, Simpson and Gibson30) .
Three diet scores were calculated from the estimated food and nutrient intakes. The DII is based on reviewing and scoring literature assessing the association between various dietary components including nutrients, spices and food items and six inflammatory biomarkers: IL-1β, IL-4, IL-6, IL-10, TNF-α and C-reactive protein, giving a single score assessing anti- or pro-inflammatory potential of the overall diet(Reference Shivappa, Steck and Hurley14). The DII has been validated in various populations(Reference Shivappa, Hebert and Rietzschel31–Reference Wirth, Shivappa and Davis33). DII values for MCCS participants were calculated using twenty-nine of forty-five possible foods and nutrients. The higher the score, the more pro-inflammatory the diet. The AHEI-2010 for the MCCS was calculated using the method described by Chiuve et al. (Reference Chiuve, Fung and Rimm13), which defines eleven dietary components with scores of 0–10 for each. A higher component score represents greater consumption of vegetables, fruit, whole grains, nuts and legumes, n-3 and PUFA, moderate alcohol intake, and a lower consumption of sugar-sweetened beverages and fruit juice, red and processed meat, trans fat and Na. The overall AHEI-2010 score is the sum of the eleven dietary component scores, ranging from 0 (minimum score) to 110 (maximum score). The MDS assesses how closely the diet adheres to the traditional Cretan diet. Intakes of cereals, legumes, fruits, vegetables, olive oil and fish above the sex-specific medians for the cohort were scored one, intakes below or equal to the medians scored zero. Intakes of dairy and meat above the sex-specific median scored zero, and below or equal to the median scored one. Alcohol intake within the range of 10 g/d–50 g/d for men and 5 g/d–25 g/d for women also scored one. Intakes outside these ranges scored zero. Although the original MDS included dietary monounsaturated to saturated fat ratio as the ninth component, we used intake of olive oil because in Australia, red meat is an important source of MUFA(Reference Baghurst, Record and Leppard34). The total score ranged from 0 to 9 with a higher score indicating stronger compliance with a traditional Mediterranean diet(Reference Trichopoulou, Kouris-Blazos and Wahlqvist15).
Body size assessment
Standard protocols were used to measure height, weight, waist and hip circumferences at baseline(Reference Milne, Fletcher and MacInnis23) from which BMI and WHR were calculated and classified as follows: BMI ≥ 25 kg/m2 (overweight), BMI ≥ 30 kg/m2 (obese) and WHR ≥ 0·90 (men); ≥0·85 (women) raised WHR. At follow-up, these measures, except height, were repeated. Baseline height was used to calculate BMI at follow-up.
We chose to use WHR rather than waist circumference as our measure of central obesity as it is less closely correlated with BMI in the MCCS (BMI and waist r = 0·82, BMI and WHR r = 0·37) and may represent a more distinct measure of body fat distribution.
Confounders
Information on country of birth, age, smoking, alcohol consumption and physical activity was derived from questionnaires at baseline. Participants were divided according to country of origin as Australia/New Zealand/others, UK and Southern European. Socio-economic position was represented as quintiles of Socio-Economic Indexes for Areas Index of Relative Socio-economic Disadvantage based on postcode at baseline.
Self-reported health information covering diabetes, asthma, cancer, stroke, angina, heart attack and cardiac surgery was collected at both surveys and used to exclude participants with conditions that could impact on body size. Energy intake estimated from the FFQ was also included as a confounder.
Statistical analysis
Univariate associations of baseline characteristics and the dietary indices were evaluated using one-way ANOVA for continuous variables and χ 2 test for categorical variables. Assumptions of linear relationships between dietary indices and anthropometric indices were assessed by fitting both linear and quadratic curves; the results showed linear relationship between the dietary indices with BMI and WHR. Regression coefficients and 95 % CI for the association of each dietary score at baseline with BMI and WHR at follow-up 2 were generated using linear regression models adjusted for age, energy intake, BMI or WHR at baseline as continuous measure and sex (male, female), Socio-Economic Indexes for Areas (quintiles 1–5), smoking status (never, former and current), alcohol drinking status (never, former and current) and physical activity level (0, >0 and <4, ≥4 and <6, ≥6) as categorical.
To test linear trends, we assigned the median daily equivalent frequency to each of the categories of dietary indices and used this as a continuous variable in the linear regression models. Changes in the outcome (BMI and WHR) by one category increment in the dietary score and the P for trend were generated. We used the Akaike information criterion and Bayesian information criteria (BIC) to compare models including each of the diet indices and the grades of evidence proposed by Raftery(Reference Raftery35) to interpret these. Statistical analyses were performed using Stata/se release 15 (Stata Corp.).
Results
We excluded 13 679 participants with self-reported conditions at baseline, and six with missing dietary data, which left 27 834 subjects. Of those excluded, 2297 had diabetes, 7060 had asthma, 3734 had cancer, 492 had had a stroke, 845 had angina, 496 had had a heart attack and 98 had had heart bypass surgery (Fig. 1).
Median (IQR) follow-up duration of the participants included in the analysis was 11·6 (10·7–12·8) years. The characteristics of the study participants at baseline by quintiles of each dietary index are presented in Tables 1–3. In general, people with diets who were assessed as healthier were more likely to be older, female (except for DII where there was a higher proportion of women relative to men in the highest scoring (most inflammatory) group), less disadvantaged, Australian, New Zealander or of Northern European origin, non-smokers, more physically active and have WHR and BMI in the normal range.
Descriptive statistics by Dietary Inflammatory Index (DII) quintile
IQR, interquartile range; SEIFA, Socio-Economic Indexes for Areas; WHR, waist-to-hip circumference ratio.
* P for trend.
The least inflammatory DII group (Q1) had the highest energy intake; for AHEI, the healthiest diet (Q5) had the lowest energy intake, and for MDS, the healthiest diet (high score) had the highest energy intake.
The proportion consuming alcohol increased with healthier dietary scores except for DII, where an inverted ‘U’ shape pattern was observed.
At baseline, 10 539 people (37·9 %) had normal weight (BMI: <25 kg/m2), 11 991 (43·2 %) were overweight (BMI: 25–29·9 kg/m2) and 5258 (18·9 %) were obese (BMI: ≥30 kg/m2) (Tables 1–3). Among those who had normal weight at baseline, 27·4 % were overweight and 1·5 % were obese at follow-up. Among those who were overweight at baseline, 10·4 % were normal weight, 20·3 % were overweight at follow-up and the remainder were obese. Among those who were obese at baseline, 0·5 % were normal weight and 14·2 % were overweight at follow-up, while 85·3 % remained obese. The percentages were similar for both men and women.
At baseline, 18 520 people (66·7 %) had WHR within normal limits and 9264 people (33·3 %) were centrally obese, with high WHR (Tables 1–3). Around one-third (32·5 %) of those with normal WHR at baseline were obese at follow-up and 86·8 % of those with high WHR remained centrally obese at follow-up.
Descriptive statistics by Alternative Healthy Eating Index (AHEI) quintile
IQR, interquartile range; SEIFA, Socio-Economic Indexes for Areas; WHR, waist-to-hip circumference ratio.
* P for trend.
Descriptive statistics by Mediterranean Diet Score (MDS) categories
IQR, interquartile range; SEIFA, Socio-Economic Indexes for Areas; WHR, waist-to-hip circumference ratio.
* P for trend.
Table 4 presents the association of baseline dietary indices with follow-up BMI in multivariable adjusted models.
Association of dietary indices with BMI adjusting for plausible confounders and baseline BMI level
AIC, Akaike information criterion; BIC, Bayesian information criteria; DII, Dietary Inflammatory Index; AHEI, Alternative Healthy Eating Index; SEIFA, Socio-Economic Indexes for Areas; MDS, Mediterranean Diet Score.
* Adjusted for age, sex, SEIFA, smoking status, alcohol drinking status, physical activity level and BMI and average energy intake at baseline and country of birth.
Baseline DII was associated with follow-up BMI in the multivariable model. Follow-up BMI increased across DII quintiles (from most least inflammatory to most inflammatory). Change in BMI for one category increment in the DII score was 0·085 (0·045, 0·125) (P-trend <0·001). Follow-up BMI decreased across increasing AHEI quintiles (from least healthy to most healthy). Change in BMI for one category increment in the AHEI score was −0·016 (−0·022, −0·01) (P-trend <0·001). No association was found between baseline MDS score and follow-up BMI (P-trend 0·250).
Table 5 presents the association of dietary indices with follow-up WHR in multivariable models. Follow-up WHR increased across increasing DII quintiles (from least inflammatory to most inflammatory). Change in WHR for one category increment in the DII score was 0·002 (0·001, 0·003); (P-trend <0·001). Follow-up WHR decreased across increasing AHEI quintiles (from least healthy to most healthy). Change in WHR for one category increment in the AHEI score was −0·00035 (−0·00044, −0·00026) (P-trend <0·001). Baseline MDS score also showed a significant inverse association with follow-up WHR. Change in WHR for one category increment in the MSD score was −0·0009 (−0·0014, −0·00036) (P-trend = 0·001).
Association of dietary indices with waist-to-hip circumference ratio (WHR) adjusting for plausible confounders and baseline WHR level
AIC, Akaike information criterion; BIC, Bayesian information criteria; DII, Dietary Inflammatory Index; AHEI, Alternative Healthy Eating Index; SEIFA, Socio-Economic Indexes for Areas; MDS, Mediterranean Diet Score.
* Adjusted for age, sex, SEIFA, smoking status, alcohol drinking status, physical activity level and WHR and average energy intake at baseline and country of birth.
To assess the model fit for the different dietary scores, Akaike information criterion and BIC are also presented in Tables 4 and 5. These values provide evidence that AHEI was a better predictor of both BMI and WHR at follow-up than the other diet scores, with differences for BIC AHEI relative to BIC MDS and BIC DII being around 20 and 23, respectively, for BMI and 31 and 27 for WHR.
Discussion
We describe for the first time the associations between three theoretically different dietary indices and two measures of body size in the same population to assess whether one particular diet stands out as being associated with body size, and whether overall or central obesity is differently associated with dietary patterns. After adjustment for potential confounders and baseline body size measurements, DII and AHEI were associated with BMI and WHR at follow-up, while MDS was associated with WHR but not with BMI at follow-up. The associations were positive for DII, indicating that a more inflammatory diet at baseline was associated with a higher BMI or WHR at follow-up, while the associations for AHEI and MDS were negative, indicating that diets more closely adhering to the US dietary guidelines or the traditional Cretan diet were associated with lower body size at follow-up. Poorer quality or more inflammatory diets predicted larger increases in markers of overall and central obesity. The AHEI appeared to be a stronger predictor than the other scores for markers of both central and overall obesity.
Alternative healthy eating index
Boggs et al. (2013) reported that for young African American, women who had a normal BMI (18·5–24·9 kg/m2) at baseline and a high AHEI score, reflecting a higher quality diet in both 1995 and 2001, the risk of incident obesity between 2001 and 2011 was less than for women with a lower AHEI score(Reference Boggs, Rosenberg and Rodriguez-Bernal16). The association was not seen for women who were overweight at baseline. Two other studies(Reference Ding, Ellervik and Huang17,Reference Wang, Heianza and Sun18) looked at dietary quality scores, including AHEI, in relation to obesity and interactions with genetic susceptibility to obesity. Ding et al. (Reference Ding, Ellervik and Huang17) analysed BMI after 2–3 years in three cohorts: Nurse’s Health Study, Health Professional’s Follow-up Study and the Women’s Genetic Health Study, and used a genetic risk score for obesity based on ninety-seven SNP. Wang et al. (Reference Wang, Heianza and Sun18) looked at BMI change over 20 years in the Nurse’s Health Study and Health Professional’s Follow-up Study with a seventy-seven SNP genetic risk score. In both analyses, a higher quality diet measured as AHEI tended to attenuate the effect of genetic risk on obesity and higher quality diet was most beneficial for people at higher genetic risk. These studies are consistent with the MCCS in showing benefits of a higher AHEI for minimising weight gain or obesity. The MCCS participants were predominantly non-obese at baseline, both by BMI (81·1 % with BMI < 30 kg/m2) and WHR (66·7 % with normal WHR) which is consistent with the observations of Boggs et al. (Reference Boggs, Rosenberg and Rodriguez-Bernal16), but no information was available to assess genetic risk of obesity.
Dietary inflammatory Index
A cross-sectional analysis of Prevención con Dieta Mediterránea study data found that a higher DII was associated with a higher BMI, waist circumference and waist-to-height ratio, despite an inverse association between DII and energy intake(Reference Ruiz-Canela, Zazpe and Shivappa20). The association tended to be stronger for waist circumference than the other anthropometric measures. Despite the cross-sectional nature of the study, the authors concluded that the results support the hypothesis that diet has an impact on obesity via inflammatory modulatory mechanisms. Similar findings of an association of DII with WHR but not BMI were reported from cross-sectional analyses in the Polish–Norwegian Study(Reference Sokol, Wirth and Manczuk36). In another cross-sectional analysis, black South African women were divided according to positive or negative DII scores and various body composition measures compared. Waist circumference, WHR and visceral adipose area were higher for those women with a positive (more inflammatory) DII, but there was no difference between groups for BMI, body weight or body fat % by DXA(Reference Mtintsilana, Micklesfield and Chorell37). In one of the few longitudinal analyses of the DII and weight gain, over 7000 healthy-weight university graduates in the SUN study were followed over 8 years. A higher (more pro-inflammatory) DII score was associated with a greater risk of developing obesity or gaining weight than a diet with a lower DII(Reference Ramallal, Toledo and Martinez19). This is consistent with our findings, but the SUN study did not look specifically at abdominal obesity, which appeared to be more strongly associated with DII in cross-sectional analyses(Reference Wang, Heianza and Sun18,Reference Baghurst, Record and Leppard34,Reference Raftery35) . While it is generally accepted that obesity drives inflammation, the SUN study authors also considered their findings to support the idea that inflammation may contribute to obesity and cited other studies where inflammatory biomarkers were associated with weight gain and the development of obesity(Reference Engstrom, Hedblad and Stavenow38). In the MCCS, we found that a higher DII was associated with larger increases in BMI and WHR, but we did not specifically assess whether the association differed according to the outcome.
Mediterranean dietary score
In a follow-up of almost 400 000 participants across ten countries in Europe participating in the EPIC-PANACEA study, the relative MDS (which is adjusted for energy intake) was inversely associated with weight gain and the risk of overweight or obesity(Reference Romaguera, Norat and Vergnaud22). The studies by Ding(Reference Ding, Ellervik and Huang17) and Wang(Reference Wang, Heianza and Sun18) both used the Alternative MDS which, unlike the AHEI-2010 and DASH diet score, was not associated with weight gain. In the EPIC-PANACEA study, the associations for three centres with relatively low adherence to a Mediterranean style diet in the UK, Netherlands and Sweden were not consistent with those for the other centres. In the American studies(Reference Ding, Ellervik and Huang17,Reference Wang, Heianza and Sun18) , variation in adherence to a Mediterranean diet may not have been sufficient to see an association. A review by Bendall et al. (Reference Bendall, Mayr and Opie39) of Mediterranean Diet interventions and central obesity identified eighteen studies with waist circumference, WHR or visceral fat as outcomes. Thirteen studies found some association, but seven of these included energy restriction and only three reported significant effects(Reference Bendall, Mayr and Opie39). The studies reporting significant effects were those without a control group comparison, so overall the evidence from this review is not strong. The inclusion of southern European migrants who tend to follow a Mediterranean diet in the MCCS may have contributed to the associations we saw for MDS and WHR; it is not known why an association was not seen for BMI and MDS.
In a 3-year follow-up of 67 000 post-menopausal women from the Women’s Health Initiative Observational Study, four different dietary patterns Healthy Eating Index-2010, AHEI-2010, DASH and Alternative MDS were assessed, along with weight and waist circumference. A 10 % increment in each diet score at baseline was associated with a smaller increase in waist circumference over 3 years, even accounting for weight change(Reference Cespedes Feliciano, Tinker and Manson40). The authors noted that maintaining or improving diet quality may be beneficial for reducing chronic disease via central adiposity(Reference Cespedes Feliciano, Tinker and Manson40). These authors did not assess whether any one diet pattern was more closely associated with waist circumference than the others, but did report that Alternative MDS was less strongly associated with change in waist circumference than were the other scores, and they noted the consistency of healthy diet components, irrespective of the score used(Reference Cespedes Feliciano, Tinker and Manson40).
Wirth et al. have previously shown that DII is inversely associated with the Healthy Eating Index, AHEI-2010 and the DASH diet score(Reference Wirth, Hebert and Shivappa41), confirming that a less inflammatory diet would also rate as healthier on these other scores. This is consistent with our findings that DII, AHEI and MDS were all associated with follow-up WHR, and AHEI and DII with follow-up BMI. Similar to other healthy diets, we have shown that in the MCCS, a lower DII, that is, a less inflammatory diet, contains more olive oil, more wholemeal bread, less white bread, more fruit, vegetables and legumes, and less red meat than a high DII diet(Reference Hodge, Bassett and Dugue42). We also reported a moderate inverse correlation between the DII and MDS (Spearman’s rho = −0·45)(Reference Hodge, Bassett and Shivappa43). These findings suggest that many versions of ‘healthy’ diets could be beneficial for reducing the risk of overall and abdominal obesity.
Energy intake is a major driver of weight gain, and the dietary scores we have examined had different associations with energy intake due to the way components were scored. Adjusting models for total energy intake attempts to account for this, but may be over-adjusting, as energy intake could be a mechanism through which different dietary patterns are associated with weight gain. Diet scores which adjust for energy as part of the scoring algorithm, such as the relative MDS(Reference Romaguera, Norat and Vergnaud22) and the energy-adjusted DII(Reference Hebert, Shivappa and Wirth44), which are likely not associated with energy intake, may give a better understanding of how diet composition is associated with weight, but here we have chosen to assess the more commonly used unadjusted scores and include energy in our models.
Our work is the first to attempt to answer the question of whether one healthy diet is likely to be better for avoiding increases in overall and abdominal obesity, and we have shown that models using AHEI have better fit than those with DII or the MDS. The AHEI may also be easier to use for advising the public as it is based on existing guidelines, reflects mostly food groups rather than single items or nutrients and gives recommended quantities of items to be consumed. The MDS on the other hand, as used here, is based on intake relative to the population mean(Reference Trichopoulou, Kouris-Blazos and Wahlqvist15), and the DII is based on mostly nutrients with a few herbs and does not provide any information on how much of anything should be consumed(Reference Shivappa, Steck and Hurley14), though interventions have been created around DII-based recommendations(Reference Turner-McGrievy, Wirth and Shivappa45).
The associations we observed were seen after adjusting for energy intake, suggesting that other mechanisms were involved. However, it should be noted that in many populations, the energy-adjusted DII does a better job of predicting outcomes than does the DII(Reference Hebert, Shivappa and Wirth44). Diets can modify gut microbiota, and specific phyla have been associated with obesity in animal models and humans(Reference Bajzer and Seeley46). Microbiota from obese mice can make gnotobiotic mice obese, and it is understood that healthy plant-based diets including plentiful fibre are more likely to be associated with a lean microbiome(Reference Turnbaugh47). Dysbiosis is associated with low-grade inflammation which may be a mechanism relating diet with obesity and poor health outcomes beyond the effect of energy intake(Reference Turnbaugh47,Reference Santos-Marcos, Perez-Jimenez and Camargo48) .
Strength and weakness
A major strength of our study is the use of a large data set and a prospective study design with adjustment for many plausible confounders. Our study used three diet scores with different theoretical bases and reached similar conclusions for each, although the MDS was not a significant predictor of BMI. Also, we had anthropometric measurements rather than relying on self-reported measures often used in similar large data sets.
One limitation of our study was that we used self-reported dietary data, which can be particularly problematic for energy calculation and may be associated with biases, particularly in energy-dense foods(Reference Hebert, Clemow and Pbert49,Reference Hebert, Ma and Clemow50) . Only twenty-nine of forty-five components of DII were included for this study, which may limit comparison of the findings with other similar studies using different dietary variables. Physical activity data were self-reported, and the questions were not as detailed as in instruments commonly used today, such as IPAQ(Reference Craig, Marshall and Sjostrom51).
Conclusion
Poor quality, pro-inflammatory diets predicted overall and abdominal obesity, consistent with other studies that have reported increases in weight or BMI associated with low diet quality diets. However, we were the first to identify AHEI among the three diet scores we studied, as being the best predictor of both overall and abdominal obesity. Given the generally similar items that contribute to more healthy diets as assessed by the three indices, it is reasonable in practice to promote diets high in fruit, vegetables, whole grains, legumes, using unsaturated fat with minimal meat and processed meat and moderate dairy foods, as recommended by dietary guidelines. The AHEI may provide the best way to operationalise this and may best capture subtle differences between the scores.
Acknowledgements
Acknowledgements: Cases and their vital status were ascertained through the Victorian Cancer Registry and the Australian Institute of Health and Welfare, including the National Death Index and the Australian Cancer Database. Financial support: The Melbourne Collaborative Cohort Study (MCCS) cohort recruitment was funded by VicHealth and Cancer Council Victoria. The MCCS was further augmented by Australian National Health and Medical Research Council grants 209057, 396414 and 1074383 and by infrastructure provided by Cancer Council Victoria. B.d.C. is supported by a Royal Australasian College of Physicians Fellows Career Development Fellowship. Conflict of interest: A.M.H., N.K., R.L.M. and B.d.C. have no conflicts of interest to declare. Dr. J.R.H. owns controlling interest in Connecting Health Innovations LLC (CHI), a company that has licenced the right to his invention of the dietary inflammatory index (DII®) from the University of South Carolina in order to develop computer and smart phone applications for patient counselling and dietary intervention in clinical settings. Dr. N.S. is an employee of CHI. Authorship: A.M.H., N.K. and B.d.C. planned the analysis, N.K. conducted the statistical analysis, A.M.H., N.K. and B.d.C. interpreted the results, A.M.H. wrote the first draft of manuscript, A.M.H. had primary responsibility for final content. All authors read and approve the final manuscript. Ethics of human subject participation: This study was conducted according to the guidelines laid down in the Declaration of Helsinki, and all procedures involving research participants were approved by the Cancer Council Victoria Human Research Ethics Committee. Written informed consent was obtained from all participants. The current study received approval from the Monash University Human Research Ethics Committee.
