LabDiet, Your World of Nutritional Answers
J. Nutr. First published February 11, 2009; doi:10.3945/jn.108.096784
 Journal of Nutrition, doi:10.3945/jn.108.096784
Vol. 139, No. 4, 734-742, April 2009
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© 2009 American Society for Nutrition

Nutritional Epidemiology

Dietary Quality Is Associated with Diabetes and Cardio-Metabolic Risk Factors1–3,

Sarah A. McNaughton4,*, David W. Dunstan5, Kylie Ball4, Jonathan Shaw5 and David Crawford4

4 School of Exercise and Nutrition Sciences, Deakin University, Melbourne, 3125, Australia and 5 Baker IDI Heart and Diabetes Institute, Caulfield, Victoria, Australia 3162

* To whom correspondence should be addressed. E-mail: sarah.mcnaughton{at}deakin.edu.au.


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ABSTRACT
 
In this study, our aim was to investigate the associations between diet quality and newly diagnosed diabetes, prediabetes, and cardio-metabolic risk factors. The analysis was based on 7441 participants of the Australian Diabetes, Obesity and Lifestyle study, a cross-sectional study of adults aged ≥25 y involving a 75-g oral glucose tolerance test. Diet quality was assessed via a dietary guideline index and FFQ data. Associations between diet quality and diabetes, prediabetes (impaired fasting glycemia, impaired glucose tolerance), and cardiovascular risk factors were investigated using linear and logistic regression adjusted for age, education, smoking, physical activity, sedentary behavior, and BMI. Higher diet quality was significantly associated with lower systolic and diastolic blood pressure among men, lower fasting plasma glucose among men and women, and lower systolic blood pressure, fasting plasma insulin, and 2-h plasma glucose and greater insulin sensitivity among women. Diet quality was inversely associated with abdominal obesity [odds ratio (OR) for top quartile: 0.68, 0.48–0.96], hypertension (OR: 0.50, 0.31–0.81), and type 2 diabetes (OR: 0.38, 0.18–0.80) among men. Lack of compliance with established dietary guidelines was associated with type 2 diabetes and cardio-metabolic risk factors. Further work is required to determine whether this dietary index has predictive validity for health in longitudinal studies.


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Introduction
 
Recent evidence has identified alarming levels of type 2 diabetes, metabolic syndrome and prediabetes (impaired fasting glucose, impaired glucose tolerance) worldwide (1,2). People with type 2 diabetes and impaired fasting glucose have a greatly increased risk of death from cardiovascular disease than those without type 2 diabetes (3). Although there is convincing evidence for the role of obesity in development of type 2 diabetes, further research is required to identify optimal eating patterns for the prevention of type 2 diabetes and to provide the evidence base for dietary targets (4).

Research concerning the effect of diet in chronic disease has tended to focus on the role of individual nutrients and foods; however, there is increasing interest in the development of methods to assess the total diet or dietary patterns (5,6). Increasingly, dietary patterns or the overall diet are thought to be important determinants of chronic disease (7). Focusing on dietary patterns rather than specific dietary components recognizes that potential interactions occur between dietary constituents, that diverse aspects of the diet may play a role in disease development, and that the balance between protective and harmful components of the diet may be important.

Two approaches are used to describe dietary patterns: multivariate statistical techniques such as factor and cluster analysis (also known as data-driven approaches) (5), and dietary scores or indices (6). Diet indices are measures of "healthy" eating patterns or diet quality and utilize rating or scoring systems determined by a priori dietary recommendations. Advantages of dietary indices compared with data-driven approaches include the fact that they are based on existing knowledge of optimal dietary patterns and provide a clear nutritional benchmark (6). Multivariate statistical approaches to dietary pattern analysis can be difficult to interpret, because the results do not always correspond to established recommendations and the identified patterns may be difficult to compare across studies as the results are dependent on the specific population (8).

Dietary pattern research to date has focused predominantly on cardiovascular disease and cancer (5–7,9), with fewer studies investigating type 2 diabetes and its precursors (10–14). Only 1 published study to date by Fung et al. (15) has investigated measures of diet quality as a predictor of type 2 diabetes and this study relied on self-report measures of diabetes and included only women, although Fogli-Cawley et al. (16,17) also investigated associations among diet quality, insulin resistance, and metabolic syndrome. Given the increasing prevalence of diabetes (1,2), further research is required to investigate the impact of diet quality on risk of type 2 diabetes and its precursors.

We have developed a food-based dietary index based on the Dietary Guidelines for Australian Adults (18) and the Australia Guide to Healthy Eating (19) to reflect current Australian guidelines for optimal eating patterns and we previously showed this index to be a valid measure of diet quality (20). The aim of this study was to investigate the associations between diet quality and newly diagnosed diabetes, prediabetes, and cardio-metabolic risk factors.


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Materials and Methods
 
    Study population. The Australian Diabetes, Obesity and Lifestyle (AusDiab)6 study is a national, population-based study examining diabetes, prediabetes, cardiovascular disease and its risk factors, and kidney disease. The study was conducted using a stratified sample from 42 randomly selected, urban and nonurban areas across the 6 states and Northern Territory of Australia. In 1999–2000, participants aged ≥25 y were invited to participate in the study, which involved a household interview followed by a physical examination conducted at a local examination site. A total of 11,247 participants completed the interview and physical examination. The study was approved by the Ethics Committee of the International Diabetes Institute and written informed consent was obtained from all participants. Full details of the methods are described elsewhere (21,22).

    Type 2 diabetes and cardio-metabolic risk factors. All participants were invited to attend a physical examination where, following an overnight fast (≥8 h), a blood sample was collected for measures of glucose metabolism and lipids. All participants, except those who were taking oral hypoglycemic agents or insulin, then had a 75-g oral glucose tolerance test (OGTT). Weight, height, and waist circumference were measured with standardized protocols and BMI was calculated as weight divided by height squared (kg/m2). Abdominal overweight and obesity were defined as ≥94 cm for men and ≥80 cm for women (23).

    Glucose metabolism. Plasma glucose levels were measured enzymatically using the Olympus AU600 automated analyzer (Olympus Optical). Fasting insulin was assayed by RIA (Linco Research). Insulin assays were conducted only for participants aged >35 y due to budgetary constraints. The homeostasis model assessment of insulin sensitivity (HOMA-%S) was calculated using fasting insulin and glucose concentrations based on the computer model (24) where 100% represents normal insulin sensitivity. Total glycated hemoglobin was analyzed by HPLC (Bio-Rad Variant Hemoglobin Testing System) with standardized conversion to HbA1C values.

    Type 2 diabetes and prediabetes. Known diabetes was based on self-reported physician diagnosis of diabetes confirmed either by self-reported use of hypoglycemic drugs or results from the OGTT. Prediabetes included participants with impaired glucose tolerance and impaired fasting glucose. Newly diagnosed diabetes and prediabetes were based on the values for venous plasma glucose concentration (fasting and 2-h measurements) using the criteria of the WHO report on the diagnosis and classification of diabetes mellitus (25).

    Hypertension. Blood pressure was measured in a seated position after the participant had rested for at least 5 min. Mean blood pressure was calculated from 2 readings unless the difference between those readings was >10 mm Hg, in which case a 3rd measurement was taken and the mean of the 2 closest of 3 measurements was used. Hypertension was defined as a mean systolic reading >140 mm Hg or a mean diastolic reading >90 mm Hg.

    Dyslipidemia. Plasma total cholesterol, HDL-cholesterol, and triglycerides were measured by enzymatic methods using an Olympus AU600 analyzer (Olympus Optical). Dyslipidemia was defined as plasma HDL cholesterol <1.0 mmol/L or triglycerides >2.0 mmol/L (26).

    Dietary intake. Dietary intake was assessed using a 74-item FFQ developed for use in Australian adults (27,28). Participants were asked to report their usual intake over the previous 12 mo of each item with 10 frequency response options ranging from "never" to "3 or more times per day." The FFQ also included 10 short questions concerning the consumption of fruit, vegetables, sugar, eggs, and the amount and type of milk, cheese, bread, and fat spreads. Frequencies were converted to daily equivalents for statistical analyses. The FFQ also contained questions and photographs regarding portion size, which were used to generate sex-specific portion sizes required for the calculation of nutrient intakes. Nutrient intakes were calculated using Australian food composition data (29). The FFQ was previously validated using 7-d food diaries (27). Correlation coefficients for energy-adjusted nutrient intakes ranged from 0.28 (vitamin A) to 0.78 (carbohydrate).

Dietary misreporting was evaluated using the ratio of total energy intake (EI):estimated energy expenditure (EE) (30). The ratio, EI:EE, is an indicator of the level of dietary misreporting, because EI:EE will be 1 if there is no dietary misreporting and <1 if there is underreporting (30,31). EE was calculated based on standard equations, with physical activity level determined based on occupation category and leisure time physical activity levels reported on the Active Australia physical activity questionnaire (see below) (32–35). We used the ratio EI:EE as a continuous variable in regression analysis to adjust estimates of risk for dietary misreporting.

    Diet quality. We measured diet quality using a previously described dietary guideline index (DGI) (20) (Supplemental Table 1). The DGI was developed to reflect Dietary Guidelines for Australian Adults and consisted of 15 items, including dietary indicators of vegetables and legumes, fruit, total cereals, meat and alternatives, total dairy, fluids, salt, saturated fat, alcoholic beverages, added sugars, and "extra foods." Diet quality was incorporated through items relating to whole-grain cereals, lean protein, reduced-/low-fat dairy, and dietary variety (20). The indicators used were based on the dietary guidelines, cut-points, and food groupings guided by the Australian Guide to Healthy Eating (AGHE), which provides age- and sex-specific recommendations for the consumption of 5 core food groups (vegetables, fruits, cereals, meat and alternatives, and dairy) and "extra foods" (19). According to the AGHE, extra foods are defined as foods that are not essential to provide nutrient requirements and contain too much fat, sugar, and salt (19). Existing national recommendations for nutrition indicators for saturated fat and added sugars were also utilized (36). Although fruit and vegetables were covered in 1 dietary guideline statement, we chose to separate these items into 2 index components to reflect the AGHE and recent recommendations on dietary index methodology (6). The DGI was adapted for use in this study, because appropriate measures of salt use or fluid intake were not available in this study population and therefore these indicators were excluded from the calculation of the DGI.

Each component of the DGI was scored from 0 to 10, where 10 indicated that a participant was meeting the recommendation or had an optimal intake. For example, with respect to fruit intake, 2 servings/d (recommended amount) scored 10 points, 1 serving/d scored 5 points, and no fruit consumption scored zero points. This proportionate scoring method has been recently recommended as the method of choice in dietary indices (6). The total score was the sum of 13 items so that the diet score had a possible range of 0–130, with higher score reflecting increased compliance with the dietary guidelines. The DGI was used both as a continuous variable and a categorical variable for which participants were grouped according to quartile cut-points. The DGI has been evaluated among the Australian population and has been shown to be associated with sociodemographic factors, health behaviors, self-assessed health, and intakes of key nutrients (20).

    Covariates. Demographic and lifestyle variables were collected by using standardized interviewer-administered questionnaires. Participants reported their family history of diabetes (mother or father diagnosed with diabetes), smoking status (current smoker, ex-smoker, never smoked), and highest educational level attained (secondary school or less, trade or certificate training, and degree/diploma or above). Women reported their menopausal status (postmenopausal, perimenopausal, or premenopausal). These variables were used as categorical variables in the regression analysis.

The validated Active Australia Survey questionnaire (35) was used to ascertain participants' frequency and duration of leisure time physical activity during the previous week. This included items about walking for recreation or transport, other moderate activity, and vigorous activity. Total leisure time physical activity time was calculated as the sum of the time spent walking (if continuous and ≥10 min), the time spent doing other moderate-intensity activities, plus double the time spent participating in vigorous physical activity. The Active Australia method accounts for the higher volume of EE per unit time associated with vigorous activities (35). TV viewing time was based on self-report. Participants reported total time spent watching TV or videos in the previous week (min/wk) using a validated instrument (37). Total leisure time physical activity time and TV viewing were used as continuous variables in the regression analysis.

    Analysis. In this analysis, the following participants were excluded: pregnant women; participants with incomplete FFQ (>10% of items with missing responses) or who reported EI outside plausible ranges according to established criteria (i.e. EI >16,800 kJ/d and <3360 kJ/d for men and >14,700 kJ/d and <2100 kJ/d for women) (38); and participants with missing covariates. Participants with known diabetes and participants who reported taking medication for blood pressure or dyslipidemia were also excluded. Therefore, in this analysis, cases of type 2 diabetes, hypertension, and dyslipidemia represented newly diagnosed cases to avoid any changes in behavior that may have resulted from previous diagnosis. The final sample for analysis comprised 3300 men and 4141 women (65 and 67% of the initial sample, respectively).

All analyses were performed separately for men and women. Associations between the DGI score and the covariates were investigated using chi-square analyses (categorical variables) and linear regression (continuous variables). Where possible, cardio-metabolic outcomes were investigated as both continuous and categorical variables, because the use of cut-points can result in a loss of information. Associations between the DGI score and the outcomes were assessed using linear and logistic regression.

Multivariable regression models were adjusted for age, education, energy misreporting, and menopausal status among women (model 1) and further adjustments were made for lifestyle behaviors (smoking, physical activity, TV viewing time; model 2). Because obesity may attenuate associations between diet quality and the health outcomes, the regression models were examined both with and without obesity (BMI) included as a covariate (model 3). Adjustment for waist circumference rather than BMI was also investigated. Models for prediabetes and diabetes were also adjusted for family history of diabetes.

Fasting plasma triglycerides, fasting plasma glucose, fasting plasma insulin, OGTT 2-h plasma glucose, HbA1C, and HOMA-%S were log-transformed before analysis due to skewed distributions. Analysis was conducted using Stata version 9.0 using the survey commands for analyzing complex survey data to account for clustering and stratification in the survey design. P < 0.05 was considered significant.


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Results
 
Among men and women, those having higher DGI scores were more likely to be older, nonsmokers, and have higher levels of physical activity and education (Table 1). Among women, those having higher diet quality were more likely to be postmenopausal. Among men, higher diet quality was significantly associated with lower systolic and diastolic blood pressure, lower plasma total and HDL cholesterol, and lower fasting plasma glucose after adjustment for age, education, energy misreporting, lifestyle behaviors (smoking, physical activity, TV viewing time), and BMI (Table 2). Among women, higher diet quality was significantly associated with lower systolic blood pressure, lower plasma total and HDL cholesterol, lower fasting plasma glucose, lower plasma 2-h glucose, and higher plasma insulin sensitivity after adjustment for age, education, energy misreporting, menopausal status, lifestyle behaviors (smoking, physical activity, TV viewing time), and BMI.


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  		TABLE 1 Participant characteristics according to DGI quartile in adults aged 25 y or older (AusDiab survey, 1999–2000)1


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  		TABLE 2 Crude and multivariable-adjusted regression coefficients and 95% CI per 10-unit increase in DGI score for each risk factor in adults aged 25 y or older (AusDiab survey, 1999–2000)1

Men with higher diet quality were less likely to have abdominal obesity [model 2; odds ratio (OR) for top quartile: 0.68,0.48–0.96), hypertension (model 3; OR: 0.50,0.31–0.81), and type 2 diabetes (model 3; OR: 0.38,0.18–0.80) after adjustment for age, education, energy misreporting, health behaviors (smoking, physical activity, TV viewing time), and BMI (Table 3). Among women, higher diet quality was inversely associated with risk of prediabetes (model 3; OR: 0.66,0.48–0.91) after adjustment for confounders, although the linear trend was not significant (P = 0.07). All regression models were also investigated adjusting for all confounders, but using waist circumference rather than BMI, and results were unaltered (data not shown).


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  		TABLE 3 Crude and multivariable-adjusted OR and 95% CI according to quartile of DGI score in adults aged 25 y or older (AusDiab survey, 1999–2000)1–4


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Discussion
 
The aim of this study was to investigate the associations between an index of diet quality (20) and newly diagnosed diabetes, prediabetes, and cardio-metabolic risk factors in a nationally representative population-based study. We showed that higher diet quality was significantly associated with lower systolic and diastolic blood pressure, total cholesterol, and fasting plasma glucose among men and lower total cholesterol, fasting plasma glucose, fasting plasma insulin, and plasma 2-h glucose and higher insulin sensitivity among women. Furthermore, high diet quality was inversely associated with prevalence of type 2 diabetes among men and with prediabetes among women.

Our results are comparable to the prospective cohort study of Fung et al. (15), which showed inverse associations between diet quality and risk of type 2 diabetes among women. However, our results showed this association only among men. We did show associations with OGTT 2-h plasma glucose and one possible explanation for the lack of association between diet quality and diabetes among women may be the smaller number of new cases of diabetes among women in our study. Consistent with our results, Fogli-Cawley et al. (17) identified inverse associations with blood pressure and waist circumference among adult males and females. However, Sodjinou et al. (39) showed no associations between their nutrient-based diet score and a range of cardio-metabolic risk factors (BMI, waist circumference, triglycerides, blood pressure, and fasting plasma glucose), which suggests that diet quality measures incorporating food-based indicators may be more useful for predicting health outcomes.

We found an inverse association between diet quality and total cholesterol and, unexpectedly, HDL cholesterol. Evidence of associations between lipids and diet quality indices has been mixed, with some studies showing inverse associations (17,40) whereas others showed no associations (41–43). It is possible that existing measures of diet quality, including the present measure, do not adequately discriminate intakes associated with blood lipids.

Although the comparisons are not straightforward due to differences in methodology, our results are also consistent with other dietary pattern research using multivariate statistical techniques such as factor analysis and cluster analysis. Dietary patterns rich in vegetables, fruit, and whole-grain cereals have been shown to be inversely associated with risk of type 2 diabetes (10,11,13,44,45). Similar dietary patterns have also been shown to be associated with prediabetes (44), fasting plasma glucose (46), fasting plasma insulin (47), and insulin resistance (HOMA-IR) (46); however, not all studies have identified associations between "healthy" dietary patterns and diabetes or markers of altered glucose metabolism (48–50).

Although the results were not significant, we identified among women a direct association between the DGI and risk of overweight and obesity. These analyses were adjusted for energy misreporting, which should account for differential reporting by weight status; however, residual confounding may occur, because the DGI reflects a range of dietary behaviors, not solely EI. It may also result from the cross-sectional study design and reverse causality (i.e. overweight people adopting a healthier diet to mange their weight) (51), as evidence suggests that dieting practices are more common among women than men (52,53). This finding is not unusual in cross-sectional studies of obesity with similar results observed in other cross-sectional studies of diet quality (6,40,54), and studies investigating dietary patterns using data-driven approaches have also shown mixed results in relation to their associations with obesity (55).

Strengths of this study include the large sample size and the rigorous methods of disease ascertainment. Type 2 diabetes was diagnosed using the OGTT. Previous studies using indices of diet quality and many of the studies of dietary patterns using factor and cluster analysis have relied on self-reported diabetes status, which may be subject to under-reporting (10,11,13,15). A range of sociodemographic factors (age, sex, ethnicity, employment grade), lifestyle behaviors (smoking, physical activity), and other risk factors (family history, BMI, waist circumference, and menopausal status in women) were investigated as confounders. However, it is possible that residual confounding remained as a result of unmeasured confounders or the presence of measurement error in those confounders included in the models. A further limitation of this analysis is the cross-sectional design. To reduce the impact of possible changes in dietary behavior due to existing disease, those with known hypertension, dyslipidemia, and type 2 diabetes (self-reported medication use plus doctor's diagnosis for type 2 diabetes) were excluded from the analysis. Finally, the small number of cases of type 2 diabetes is a potential limitation of this study.

This study used a previously developed dietary index, based on recommendations for healthy eating in Australia, which has been shown to be a valid indicator of diet quality reflecting intakes of key nutrients such as total fat, saturated fat, fiber, β-carotene, vitamin C, folate, calcium, and iron (20). The use of food-based dietary indicators is a strength of the DGI (6) consistent with a move toward food-based dietary guidelines (56) and this approach is most similar to other dietary pattern methodology (5). A recent review of diet index methodology has recommended that diet indices should be food based (6). The concept of diet quality extends beyond a quantitative assessment of macro- and micronutrients (57) to whole foods and types of foods and dietary variety, and measures of these are incorporated into the DGI. In addition, this dietary index is an improvement on previous food-based scores, because it includes measures of overconsumption. In addition, we used age- and sex-specific cutoffs where they are available to incorporate variations in requirements (19).

Measures of total diet or dietary patterns are increasingly being used in epidemiology to capture the complexity of food intake, and the nutrient and nonnutrient components of the diet (9). Although the use of diet quality indices to assess diet-disease relationships has several advantages over data-driven measures of dietary patterns, one of the limitations is that a low proportion of participants consuming a diet consistent with the dietary recommendations can result in a lack of variation in the population and an inability to detect associations with health (6). Previous work has shown that compliance with many of the dietary guidelines is poor among the Australian population (20). However, in the current study, we were still able to detect significant associations in the hypothesized direction, suggesting that sufficient variation was present for many factors.

This study has shown that the DGI, which assesses compliance with established dietary guidelines, was associated with type 2 diabetes and cardio-metabolic risk factors. To our knowledge, this is one of the first studies internationally to investigate associations between diet quality and type 2 diabetes and its precursors using the OGTT to determine diabetes status. Given the limitations associated with cross-sectional studies, further work is required to determine whether this index has predictive validity with respect to health in longitudinal studies.


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ACKNOWLEDGMENTS
 
We thank A. Allman, B. Atkins, S. Bennett, S. Chadban, S. Colagiuri, M. de Courten, M. Dalton, M. D'Embden, T. Dwyer, D. Jolley, I. Kemp, P. Magnus, J. Mathews, D. McCarty, A. Meehan, K. O'Dea, P. Phillips, P. Popplewell, C. Reid, A. Stewart, R. Tapp, H. Taylor, T. Welborn, and F. Wilson for their invaluable contribution to the setup and field activities of AusDiab.


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FOOTNOTES
 
1 Supported by the following for the Australian Diabetes, Obesity and Lifestyle study: The Commonwealth Department of Health and Aged Care, Abbott Australasia, Alphapharm, Aventis Pharmaceutical, AstraZeneca, Bristol-Myers Squibb Pharmaceuticals, Eli Lilly (Australia), GlaxoSmithKline, Janssen-Cilag (Australia), Merck Lipha, Merck Sharp & Dohme (Australia), Novartis Pharmaceutical (Australia), Novo Nordisk Pharmaceutical, Pharmacia and Upjohn, Pfizer, Roche Diagnostics, Sanofi Synthelabo (Australia), Servier Laboratories (Australia), BioRad Laboratories, HITECH Pathology, the Australian Kidney Foundation, Diabetes Australia, Diabetes Australia (Northern Territory), Queensland Health, South Australian Department of Human Services, Tasmanian Department of Human Services, Victorian Department of Human Services and Health Department of Western Australia. S. A. M. is supported by a National Health & Medical Research Council (NHMRC) Public Health Postdoctoral Fellowship. D. D. is supported by a Victorian Health Promotion Foundation Public Health Research Fellowship. K. B. is supported by a National Health & Medical Research Council Senior Research Fellowship. D. C. is supported by a Victorian Health Promotion Foundation Senior Research Fellowship. Back

2 Author disclosures: S. A. McNaughton, D. W. Dunstan, K. Ball, J. Shaw, and D. Crawford, no conflicts of interest. Back

3 Supplemental Table 1 is available with the online posting of this paper at jn.nutrition.org. Back

6 Abbreviations used: AGHE, Australian Guide to Healthy Eating; AusDiab, Australian Diabetes, Obesity and Lifestyle study; DGI, dietary guideline index; EE, energy expenditure; EI, energy intake; HOMA-%S, homeostasis model assessment of insulin sensitivity; OGTT, oral glucose tolerance test. Back

Manuscript received 24 July 2008. Initial review completed 21 August 2008. Revision accepted 12 January 2009.


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