Obesity has reached epidemic proportions in the USA and affects one in three adult women aged 20 years or older(Reference Ogden, Carroll and Curtin1). Recent estimates indicate that 62 % of women are overweight or obese(Reference Ogden, Carroll and Curtin1) (BMI ≥ 25 kg/m2) and experts predict that obesity will soon surpass tobacco and become the leading cause of preventable death in the USA(Reference Mokdad, Marks and Stroup2). Women may be at greater risk of obesity than men. The sex difference is possibly related to differences in dietary patterns(Reference Millen, Quatromoni and Copenhafer3, Reference Millen, Quatromoni and Pencina4), as well as physiological (for example, decreased RMR) and behavioural (for example, reduced physical activity) changes associated with ageing(Reference Simkin-Silverman and Wing5), higher levels of body fat and the ability to store more fat(Reference Blaak6), fluctuations in sex hormone concentrations(Reference Lovejoy7), dysregulation of serotonin(Reference Roca, Proenza and Palou8) and higher leptin levels(Reference Hellstrom, Wahrenberg and Hruska9). More noteworthy is that women may also experience a larger burden of adverse health outcomes attributable to the disease, including CVD, hypertension, dyslipidaemia, respiratory and pulmonary disturbances, type 2 diabetes and osteoarthritis(Reference Patterson, Frank and Kristal10, Reference Muennig, Lubetkin and Jia11). The direct and indirect costs of obesity in the USA are estimated to be more than $117 billion annually(12).
At present, guidelines for obesity prevention and treatment exist(13–Reference Bantle, Wylie-Rosett and Albright17), but there is no consensus on an appropriate evidence-based preventive and treatment nutrition model or dietary interventions to sustain long-term weight loss (>1 year). It has been established that very-low-fat(Reference Ornish, Scherwitz and Billings18), low-fat(Reference Astrup, Grunwald and Melanson19) and low-carbohydrate(Reference Nordmann, Nordmann and Briel20) diets produce short-term weight loss, but evidence suggests that their long-term ineffectiveness may relate to their restrictive nature or deviation from the habitual, sustainable eating patterns of individuals(Reference Wierenga and Oldham21). Increasingly, experts are urging for the development of innovative nutrition intervention strategies that are guided by research which better characterises the links between the characteristics and quality of habitual long-term eating behaviours and obesity-related outcomes(12, Reference Katz22). Epidemiological investigations that utilise comprehensive measures of diet exposures, such as dietary patterns and diet quality indices, may offer new insights into nutrition intervention planning for obesity risk reduction. Most research on diet quality indices and weight-related outcomes to date is cross-sectional in nature(Reference Fitzgerald, Dewar and Veugelers23–Reference Schroder, Marrugat and Covas32). Of the two prospective studies that have been conducted, one did not assess obesity status(Reference Quatromoni, Pencina and Cobain33) and the other did not stratify obesity results by sex(Reference Gao, Beresford and Frank34).
The present study prospectively examined the relationship between diet quality and the development of overweight or obesity in women over a broad age range, a subject that is not adequately addressed in the literature. In the present investigation, we evaluated whether a previously validated, global diet index, the Framingham nutritional risk score (FNRS)(Reference Millen, Quatromoni and Copenhafer3), predicted the development of overweight or obesity over 16 years in healthy, normal-weight (BMI < 25 kg/m2) women.
Methods
Participants
The cohort for this analysis is women of the Framingham Offspring and Spouse Study (FOS). Detailed methods have been described previously(Reference Feinleib, Kannel and Garrison35). Briefly, in 1948 the Framingham Study was initiated to identify factors contributing to CVD development and to study the progression of CVD among residents from the town of Framingham (MA, USA). The original cohort consisted of 5209 men and women, aged 28–62 years, representing a two-thirds random sample of Framingham residents. The FOS began in 1971 and includes adult children and their spouses of the original Framingham Study cohort. This second-generation group of 5124 offspring and spouses are examined, on average, every 4 years. At each examination, following a standardised protocol, participants provide an updated, detailed medical history and undergo a complete physical examination with laboratory and non-invasive diagnostic testing. Of the 1956 women who attended the third examination (Exam 3; 1984–88), 1265 (65%) had completed a 3 d dietary record(3). Of those 1265 women, 590 were normal weight (BMI < 25 kg/m2), aged 25 to 71 years, without CVD (including CHD and stroke), cancer, or diabetes at baseline (Exam 3) and comprise the study sample for these analyses. Follow-up was assessed through to Exam 7 (1998–2001) for a total of 16 years.
The present study was conducted according to the guidelines laid down in the Declaration of Helsinki and all procedures involving human subjects were approved by the Human Subjects Institutional Review Board of the Boston University Medical Campus and Boston Medical Center. Written informed consent was obtained from all subjects.
Diet assessment and nutritional risk score
Diet was assessed from 3 d dietary records completed according to standardised research protocols(Reference Posner, Martin-Munley and Smigelski36, Reference Posner, Smigelski and Duggal37). At the Exam 3 clinic visit, participants were instructed by a registered dietitian to record all foods consumed over 2 weekdays and 1 weekend day with no deviation in their current eating habits. To quantify portion sizes, participants were trained using a validated two-dimensional pictorial food portion model(Reference Posner, Smigelski and Duggal37). The dietary records were reviewed and coded by trained coders following formal protocols. Nutrient intake calculations were performed using the Minnesota Nutrition Data System software (NDS version 2.6, Food Database 6A, Nutrient Database 23; Nutrition Coordinating Center, University of Minnesota, Minneapolis, MN, USA). For each woman, 3 d mean nutrient intake estimates were determined. Diet exposure is considered Exam 3 for these analyses because this was the time point that the 3 d dietary records were collected.
The FNRS is a validated nutritional risk score to assess diet quality and is comprised of nineteen nutrients which include total energy, protein, total fat, monounsaturated, polyunsaturated and saturated fats, carbohydrate, fibre, alcohol, dietary cholesterol, Na, Ca, Se, vitamins C, B6, B12 and E, folate and β-carotene(Reference Millen, Quatromoni and Copenhafer3). Originally, these nutrients were selected for their relation to CVD risk following a review of the literature on diet and CVD. Macronutrients and alcohol are presented as a percentage of total energy, and fibre, dietary cholesterol and micronutrients are presented per 4184 kJ (1000 kcal). Although research has found monounsaturated fat to be beneficial(Reference Martinez-González and Bes-Rastrollo38), the rationale for ranking this fat subtype as a risk nutrient rather than a protective nutrient in the FNRS was done because the majority of monounsaturated fat consumed by FOS women in the mid-1980s was derived from animal rather than plant sources.
The FNRS is calculated using a ranking system of intakes of each nutrient. The ranking of individual nutrients in the score is based on the number of women in the sample (n 590) where each nutrient is ranked from 1 (low risk) to 590 (high risk) for each woman with completed 3 d dietary records. Ranks are assigned so that a woman with a desirable nutrient intake level (protective nutrients) receives a lower rank while a woman with an undesirable nutrient intake level (risk-promoting nutrients) receives a higher rank. Energy, protein, alcohol, total, saturated, and monounsaturated fats, dietary cholesterol, and Na intakes were ranked low to high, whereas polyunsaturated fat, carbohydrate, fibre, Ca, Se, vitamins C, B6, B12, and E, folate and β-carotene intakes were ranked high to low. The mean ranks of each individual nutrient are used to calculate the overall dietary risk score of each woman. These composite scores are then ranked and categorised into tertiles.
Outcome measure
Body weight was measured at clinic visits with a calibrated spring balance scale. Participants were weighed wearing lightweight hospital gowns. Standing height was measured using a stationary anthropometer with participants standing erect and head positioned in the Frankfurt plane. The main outcome was development of overweight or obesity at any time point during follow-up. To maintain statistical power, overweight and obesity were considered together. Overweight or obesity was defined according to the recommended BMI category (BMI ≥ 25 kg/m2)(14).
Covariates
Anthropometric and metabolic measurements are routinely collected at clinic visits according to validated, published methods(Reference Cupples, D'Agostino, Kannel, Wolf and Garrison39, Reference Meigs, D'Agostino and Wilson40): waist circumference at Exam 4, 1988–92, systolic and diastolic blood pressure (duplicate measurements), fasting lipids (total cholesterol, HDL, LDL, TAG) and fasting glucose.
Sociodemographic and behavioural characteristics are also assessed at clinic visits. Self-reported dietary behaviour was evaluated using the Framingham food habit questionnaire and included adherence to a modified diet (currently ‘on a diet’) and usual weight pattern described as stable ( ± 2·7 kg (5 pounds)) or fluctuating ( ± 4·5 kg (10 pounds)) body weight. Other self-reported characteristics included age, smoking status, parity, menopausal status, use of hormone replacement therapy, use of hypertension or lipid medications and physical activity.
Physical activity was assessed using a standardised questionnaire(Reference Kannel and Sorlie41) at Exam 2 (1979–83) and not at Exam 3; these values were used in the analyses consistent with published Framingham protocols(Reference Quatromoni, Pencina and Cobain33).
Statistical analysis
Age-adjusted mean levels of baseline characteristics and nutrient intakes were computed for each nutritional risk score tertile. The general linear model (GLM) procedure in SAS (analysis of covariance; SAS Institute, Cary, NC, USA) was used to compute the age-adjusted means for continuous variables. Logistic regression (LOGISTIC procedure in SAS; SAS Institute, Inc.) was used to compute age-adjusted proportions for dichotomous variables. Bonferroni's correction was used to adjust for multiple comparisons in analyses of baseline characteristics and nutrient intakes. The primary research goal was to examine the association between diet quality, assessed by the FNRS, and the development of overweight or obesity. Stepwise multiple logistic regression was used to evaluate variables that were related to overweight or obesity. Covariates that were considered include age, physical activity, menopausal status, smoking status and parity. Metabolic, anthropometric and demographic variables that did not differ at baseline according to nutritional risk score tertile were not identified to be confounders and were thus not added to the model. The final model adjusted for age (continuous), physical activity (continuous) and smoking status (never, former or current smokers). The main analyses are not adjusted for total energy because it is a component of the nutritional risk score and all nutrients are energy-adjusted in the index using the nutrient-density method. OR were calculated for each nutritional risk score tertile of the FNRS, with the lowest tertile as the referent group. The P value for trend was determined using the tertile groups of the FNRS in a continuous form. For main outcome statistical testing, α was set at 0·05.
Secondary analyses consistent with our primary diet quality model which adjusted for age, physical activity and smoking status were conducted to determine the relationship of individual risk score components to overweight or obesity. Intakes of each index component were ranked low to high and categorised into tertiles and OR were calculated for each intake tertile, with the lowest tertile as the referent group. The P value for trend was determined using the tertile groups of intake for each individual nutrient in a continuous form. For these post hoc analyses, α was set at 0·05.
All analyses were performed using SAS software (version 9.1, 2003; SAS Institute, Inc.).
Results
Women did not differ across tertiles in most baseline characteristics (Table 1). Women with higher diet quality (i.e. lowest nutritional risk tertile) were significantly older and smoked less currently and during their lifetimes. Those with the poorest diet quality scores (i.e. highest nutritional risk tertile) had lower intakes of energy, carbohydrate, fibre and all micronutrients (except vitamin B12) and higher intakes from alcohol and total, saturated and monounsaturated fats (Table 2).
(Least squares (LS) means and 95 % confidence intervals or percentages)
a,b Values within a row with unlike superscript letters were significantly different (P < 0·05).
* The general linear model (GLM) procedure in SAS (analysis of covariance) was used to obtain age-adjusted means for continuous variables and to identify subgroups that differed significantly. Logistic regression (SAS procedure LOGISTIC) was used to obtain age-adjusted proportions for dichotomous variables and to identify subgroups that differed significantly. Both sets of analyses used Bonferroni's correction for each variable.
† The risk score was calculated from the consumption of nineteen nutrients (protein, carbohydrate, fibre, Ca, alcohol, total fat, polyunsaturated fat, monounsaturated fat, saturated fat, cholesterol, Na, Se, vitamin C, vitamin B6, vitamin B12, folate, vitamin E, β-carotene, total energy), which were ranked for each woman in the sample.
‡ To convert kg to pounds, divide by 0·454.
§ Physical activity index scores range from 24 (total bed rest) up to 120.
∥ To convert mmol/l cholesterol to mg/l, divide by 0·00259.
¶ To convert mmol/l TAG to mg/l, divide by 0·00113.
** To convert mmol/l glucose to mg/l, divide by 0·00555.
(Least squares (LS) means and 95 % confidence intervals)
a,b,c Mean values within a row with unlike superscript letters were significantly different (P < 0·05).
* The general linear model (GLM) procedure in SAS (analysis of covariance) was used to obtain age-adjusted means for continuous variables and to identify subgroups that differed significantly. This set of analyses used Bonferroni's correction for each variable.
† The risk score was calculated from the consumption of nineteen nutrients (protein, carbohydrate, fibre, Ca, alcohol, total fat, polyunsaturated fat, monounsaturated fat, saturated fat, cholesterol, Na, Se, vitamin C, vitamin B6, vitamin B12, folate, vitamin E, β-carotene, total energy), which were ranked for each woman in the sample.
‡ To convert kJ to kcal, divide by 4·184.
The overall incidence of overweight or obesity over 16 years was 44 % (n 258; Table 3). In multiple logistic regression analyses adjusted for age, physical activity and smoking status, women in the highest nutritional risk tertile were 1·76 (95 % CI 1·16, 2·69) times more likely to become overweight or obese compared with those in the lowest (referent) nutritional risk tertile (P for trend = 0·009). We also explored associations with quartiles and results were similar (data not shown). In additional analyses, including total energy intake in the regression model attenuated the findings (data not shown).
Development of overweight or obesity over 16 years in 590 healthy women in the Framingham Offspring and Spouse Study according to diet quality
* The risk score was calculated from the consumption of nineteen nutrients (protein, carbohydrate, fibre, Ca, alcohol, total fat, polyunsaturated fat, monounsaturated fat, saturated fat, cholesterol, Na, Se, vitamin C, vitamin B6, vitamin B12, folate, vitamin E, β carotene, total energy), which were ranked for each woman in the sample.
† The P value for trend was determined using the tertile groups of the Framingham nutritional risk score in a continuous form. Significance testing was at P < 0·05.
‡ Overweight defined as BMI 25–29·9 kg/m2; obesity defined as BMI ≥ 30 kg/m2.
§ Stepwise multiple logistic regression model adjusted for age, smoking and physical activity.
In post hoc analyses, energy, fibre, alcohol and vitamin E were inversely associated, while protein was positively associated, with development of overweight or obesity (P for trend < 0·05). In multiple logistic regression analyses adjusted for age, smoking status and physical activity, compared with the lowest tertile of intake, the odds of becoming overweight or obese were lower in the highest tertile of energy intake (OR 0·56; 95 % CI 0·37, 0·84), fibre intake (OR 0·51; 95 % CI 0·34, 0·78), alcohol intake (OR 0·67; 95 % CI 0·46, 0·99) and vitamin E intake (OR 0·64; 95 % CI 0·42, 0·95) and higher in the highest tertile of protein intake (OR 1·89; 95 % CI 1·25, 2·86).
Discussion
Diet quality, assessed using the validated nineteen-nutrient Framingham composite nutritional risk score, predicted the development of overweight or obesity over 16 years in disease-free FOS women, aged 25–71 years, at baseline. Women with the poorest diet quality (i.e. highest nutritional risk) consumed diets that were lower in energy, carbohydrate and micronutrients and higher in total fat, particularly saturated fat, and alcohol. These findings suggest that overall diet quality appears to be an important component of the diet–obesity relationship and provide potential new insights for use in future translational research on developing preventive nutrition strategies.
We performed independent single-nutrient analyses of FNRS index components which, for comparison purposes, controlled for covariates consistent with our primary model. All results were found to be in agreement with those of the overall composite FNRS score. Higher energy, fibre and vitamin E intakes were associated with a lower risk of developing overweight or obesity. Higher protein consumption was associated with a higher risk of becoming overweight or obese in this sample of women who also consumed a higher-carbohydrate, higher-fat diet. While it is difficult to draw conclusions regarding alcohol, as consumption levels were low ( < 5 % of energy intake in all tertiles), it is noteworthy that alcohol appeared to have a protective effect in the single-nutrient analysis; yet when combined with the set of nutrients in the composite FNRS, women with lower overall diet quality consumed more alcohol and were at greater risk of becoming overweight or obese. Thus, it is important to emphasise that single-nutrient comparisons may not reflect the true magnitude or direction of risk due to food and nutrient collinearity, nutrient synergy and other complex metabolic and biochemical interactions.
To our knowledge, this is one of the first long-term prospective investigations of overweight or obesity status specifically in women in relation to diet quality. As the FNRS contains both macro- and micronutrients, it may better represent total diet quality and its reflection of the nutrient density of a diet may explain its ability to predict development of overweight or obesity. This finding is consistent with an earlier prospective investigation that demonstrated a direct relationship with a five-point diet quality index and weight gain(Reference Quatromoni, Pencina and Cobain33). This index included five nutrients (total and saturated fat, dietary cholesterol, Na and carbohydrate) and a score of 1 was given for each nutrient if mean intake levels met the 2000 Dietary Guidelines for Americans (42). Adults with the highest scores gained less weight over 8 years than those with lower scores. Average weight gain was about 1·4 kg (3 pounds) for those with higher-quality diets (i.e. higher scores) compared with 2·3–3·6 kg (5–8 pounds) for those with poorer-quality diets (i.e. lower scores) (P < 0·05). A recent longitudinal investigation(Reference Gao, Beresford and Frank34) conducted over 18 months demonstrated that for every 1-unit increase on the original 1990 healthy eating index(Reference Kennedy, Ohls and Carlson43) or a modified healthy eating index, based upon the 2005 Dietary Guidelines for Americans (13), the odds of obesity decreased by 3 % in a combined sample of white men and women in multinomial logistic regression analysis.
The cross-sectional literature using a priori diet quality indices or scores as correlates of obesity are also consistent with our findings(Reference Guo, Warden and Paeratakul31, Reference Schroder, Marrugat and Covas32). For example, in multivariate-adjusted analyses, Guo et al. (Reference Guo, Warden and Paeratakul31) found that American women with the lowest diet quality measured using the healthy eating index were 1·7 (95 % CI 1·2, 2·6) times more likely to be obese than women with higher-quality diets. In Spanish adults, Schroder et al. (Reference Schroder, Marrugat and Covas32) demonstrated a significant decrease in obesity risk for those in the top quartile of healthy eating index adherence (OR 0·68; 95 % CI 0·52, 0·89) compared with those in the first quartile in multivariate-adjusted analyses. In this same investigation(Reference Schroder, Marrugat and Covas32), men and women in the top Mediterranean diet score adherence quartile were 0·68 (95 % CI 0·52, 0·89) times less likely to be obese compared with those in the first quartile. The FNRS also may have been predictive of overweight or obesity, as it may better capture dietary variety as reflected in a broad range of macro- and micronutrients. This is in agreement with cross-sectional data from Kennedy et al. (Reference Kennedy, Bowman and Spence44) who found that adults consuming a diet with more variety had significantly lower BMI and were less likely to have BMI>25 kg/m2.
Our research is a step towards moving diet quality index research into the translational phase of designing dietary interventions and informing current guidelines for obesity prevention and risk reduction. This is essential, as the prevalence of obesity in adult women increases substantially each decade beginning in their twenties and does not decline until they reach their seventies(Reference Flegal, Carroll and Ogden45); also, long-term weight loss in women is disappointing, with estimates that only 5 % of initially overweight or obese women successfully maintain at least 5 % of their lost weight at 9 years(Reference Sarlio-Lahteenkorva, Rissanen and Kaprio46). The FNRS captures nutrient intake levels in an overall pattern that poses the greatest risk, as well as the greatest benefit(13). As demonstrated here, a higher-quality diet appears to be less obesity promoting and provides support for national nutrition guidelines, which emphasise a balanced eating plan. While the FNRS cannot be applied directly ‘as is’ to design targeted nutrition interventions, we have previously linked the FNRS with food-related dietary patterns, derived using cluster analysis, in FOS women(Reference Millen, Quatromoni and Copenhafer3). Since the clusters are based on foods and food groups, this previous research provided increased interpretability, as well as a framework for application of this score in conjunction with food-based approaches to dietary assessment and intervention. Translational research has not identified an optimal composite diet quality index method for obesity risk assessment. Therefore, as translational research moves forward we would advocate that preventive nutrition interventions be guided by the combination of composite nutrient index (diet quality) and dietary pattern approaches.
The major strengths of the present investigation are the longitudinal design with a long duration of follow-up, inclusion of a relatively large sample of disease-free normal-weight women over a broad age range who had otherwise varying risk factor status and direct measurement of anthropometric outcome variables (as opposed to self-report). Additionally, the FNRS is nutrient-based, which is consistent with the foundation of the Dietary Guidelines for Americans (13) for determining adequacy of intake and overall diet quality. Other strengths include consideration of potential confounders (age, physical activity, smoking, parity, menopausal status) and calculation of nutritional risk (diet quality) using energy-adjusted index components. In contrast, our reliance on 3 d dietary records may introduce error into estimates of nutrient intake and result in a lower estimate of diet–obesity relationships, as 3 d dietary records may not fully capture usual diet, which would be more strongly connected with weight. While it is also true that we cannot assess nutritional risk over time, intakes in the FOS cohort have been shown to be stable(Reference Millen, Pencina and Kimokoti47). Lastly, 98 % of FOS women are white, which would limit the generalisability of our findings, since food and nutrient intake patterns have been shown to differ by ethnicity(Reference Wirfalt and Jeffery48).
In conclusion, we identified a link between diet quality, assessed using the FNRS, and risk of becoming overweight or obese in women. This finding has significant implications for obesity prevention and treatment, as it suggests the importance of comprehensively assessing nutritional risk, particularly at the time of initiating weight-gain prevention recommendations and weight-loss interventions. Fundamental to obesity management is identification of the behaviour to target in interventions. With respect to diet, focusing on food intake behaviour is essential. A nutritional risk score that is comprehensive and nutrient-based, like the FNRS, allows assessment of overall nutrient intake patterns of individuals to identify areas for translation to dietary changes in food patterns, rather than using a more uniform set of intervention guidelines that are potentially irrelevant or more restrictive than appropriate to the individual. These findings suggest that obesity risk reduction may be possible through strategies which optimise the nutrient quality of the individual's habitual eating behaviour. They also suggest that diverse dietary patterns in a population may be possible to maintain, as long as their nutrient composition and quality are enhanced by the use of nutrient-dense foods and suitable recipe modifications. Using this approach in future research would require the assessment of the individual's (or population's) habitual dietary patterns and their nutritional risk profiles and the development of options for dietary intervention that achieve an optimal balance between the individual's (or population's) food preferences, dietary patterns and nutrient-dense foods.
Acknowledgements
The present study was supported by grants R01-HL-60700 and R01-HL-54776 from the National Heart, Lung and Blood Institute (NHLBI) and funding from the Department of Family Medicine and the Division of Graduate Medical Sciences, Boston University School of Medicine. The Framingham Study is supported by NIH/NHLBI N01-HC-25195.
D. M. W. was responsible for the concept and design of the experiment and prepared the manuscript. B. E. M. provided overall direction to the research and overall critical review of reported intellectual content, and contributed to writing of the manuscript. L. Z. carried out the statistical analyses and critically reviewed the statistical content reported. M. J. P. and R. B. D. oversaw the statistical analyses and contributed to the interpretation of the data and critical review of the statistical content reported. P. K. N. contributed to the statistical design and writing of the manuscript. R. W. K. contributed to interpretation of the data and writing of the manuscript.
None of the authors had personal or financial conflicts of interest.
