Original Article
Variability of Body Weight and Health Outcomes in the Framingham Population
N Engl J Med 1991; 324:1839-1844June 27, 1991
- Abstract
- Abstract
Background.
Fluctuation in body weight is a common phenomenon, due in part to the high prevalence of dieting. In this study we examined the associations between variability in body weight and health end points in subjects participating in the Framingham Heart Study, which involves follow-up examinations every two years after entry.
Methods.
The degree of variability of body weight was expressed as the coefficient of variation of each subject's measured body-mass-index values at the first eight biennial examinations during the study and on their recalled weight at 25 years of age. Using the 32-year follow-up data, we analyzed total mortality, mortality from coronary heart disease, and morbidity due to coronary heart disease and cancer in relation to intraindividual variation in body weight, including only end points that occurred after the 10th biennial examination. We used age-adjusted proportional-hazards regression for the data analysis.
Results.
Subjects with highly variable body weights had increased total mortality (P = 0.005 for men, P = 0.01 for women), mortality from coronary heart disease (P = 0.009 for men, P = 0.009 for women), and morbidity due to coronary heart disease (P = 0.0009 for men, P = 0.006 for women). Using a multivariate analysis that also controlled for obesity, trends in weight over time, and five indicators of cardiovascular risk, we found that the positive associations between fluctuations in body weight and end points related to mortality and coronary heart disease could not be attributed to these potential confounding factors. The relative risks of these end points in subjects whose weight varied substantially, as compared with those whose weight was relatively stable, ranged from 1.27 to 1.93.
Conclusions.
Fluctuations in body weight may have negative health consequences, independent of obesity and the trend of body weight over time. (N Engl J Med 1991;324:1839–44.)
Media in This Article
Figure 1
Chronology and Schematic Representation of Body-Weight Variables for a Hypothetical Subject.Table 1
Characteristics of the Subjects during the Observation Period.*Article Activity
- Article
BODY weight can fluctuate for a variety of reasons, one of which is dieting with the goal of weight reduction and the subsequent regaining of the weight that was lost. Because repeated episodes of weight loss and gain are so common, it is important to examine the implications for health of this pattern, which we refer to as weight cycling. The results of a study of employees of the Western Electric Company indicated that a single cycle of weight gain and loss in men was a risk factor for death from coronary heart disease, but not for death from all causes.1 The Gothenburg Prospective Studies found that variability in body weight measured at three time points was a risk factor for subsequent coronary heart disease in men and for total mortality in both men and women.2 In contrast, no significant associations between fluctuations in body weight and mortality were detected in two other cohorts.3 , 4 Three of these studies1 2 3 were limited by the fact that only a single cycle of weight fluctuation was represented in the indicators of variability of body weight used by the investigators.
These studies demonstrate the importance of investigating further the associations between fluctuations in body weight and health, and they underscore the challenges of doing such research. In overweight persons, voluntary weight reduction is assumed to be beneficial to health, whereas involuntary weight loss often reflects serious disease. Conversely, weight gain may lead to obesity and its concomitant adverse effects, yet some increments in body weight occur normally in healthy persons as they age. Fluctuations in weight that precede disease must be distinguished from those caused by disease. We addressed these issues by studying the associations between the degree of variability in body weight and subsequent health outcomes in subjects participating in the Framingham Heart Study.
Methods
Data Collection
The Framingham Heart Study offers a valuable opportunity for the analysis of fluctuations in body weight in relation to the subsequent incidence of chronic disease. This prospective study contains more data on measured body weight and uses longer follow-up periods than much of the previous work on weight fluctuations and health. Since 1948, the health status of 5127 male and female residents of Framingham, Massachusetts, who were initially free of coronary heart disease has been monitored. At entry the subjects were between 30 and 62 years of age. Body weight was measured at entry and every two years thereafter at regular follow-up examinations; body weight at 25 years of age as recalled by the subjects was also recorded. The details of sampling, response rates, methods, and follow-up have been described in detail,5 and the results of the 24-year follow-up have been reported in a comprehensive review.6 This new analysis is based on data from 32 years of follow-up. Other investigators have previously used these data to address questions on the relation of obesity, weight loss, and weight gain to various health end points.7 , 8
Chronology
In order to study the relations between fluctuations in weight and chronic disease, it is important to understand the temporal sequence of weight change and onset of disease. The design for this analysis entailed a chronologic separation of weight change and health end points (Fig. 1Figure 1
Chronology and Schematic Representation of Body-Weight Variables for a Hypothetical Subject.), by specifying the lengths of time required between determinations of body weight and end points included in the analyses. All measurements of body weight during the first 14 years of the Framingham Heart Study (eight follow-up examinations), along with each subject's recalled body weight at the age of 25, were used to construct independent variables representing body weight and its fluctuation. The subjects included in this analysis were examined at all of the first eight scheduled follow-up visits; those who missed any of these examinations were excluded. The dependent variables in our analyses were various end points occurring during a follow-up period that began at visit 10 — that is, at least four years after the last body-weight measurement was analyzed. The purpose of excluding earlier end points was to eliminate variability in body weight that might be attributed to antecedent disease. We also analyzed body weight in relation to the same end points after increasing the length of time required from the eighth follow-up examination to the first end points included in the analysis from four to six years.Weight Cycling and Major Covariates
Weight cycling was defined on the basis of variability in each subject's body-mass index (the weight in kilograms divided by the square of the height in meters) during the period from the age of 25 (recalled weight) through the eighth biennial follow-up examination. A coefficient of variation for each subject's body-mass-index values was calculated at nine time points from the age of 25 through the eighth examination (Fig. 1). This value, which will be referred to as variability in weight, was calculated as the standard deviation of each subject's nine body-mass-index values divided by the average body-mass index for that subject. This variable reflects the extent to which a subject's body-mass index fluctuated around his or her own mean value. A high degree of variability in weight indicates many changes in weight or large changes, whereas a low value indicates stability of the body-weight values.
The two key covariates in this analysis were also within-subject variables. The first was a subject's mean body-mass index over the same nine time points. We call this a body-mass-index "level" to emphasize the fact that a value for this variable exists for each subject. The second covariate indicates the direction and magnitude of the change in a subject's body-mass index over the same period and is calculated as the change in the body-mass index per year ("slope"). A positive value indicates weight gain over time, whereas a negative value indicates weight loss.
Descriptive statistics for the key independent variables are shown in Table 1Table 1
Characteristics of the Subjects during the Observation Period.*. For men and women, respectively, the average age at entry was 42.8 and 43.5 years. The average body-mass index (the mean of the individual means) was 25.9 for the men and 24.9 for the women, values that are based on mean body weights of 77.0 and 63.2 kg, respectively (data not shown). The mean values for the slope of the body-mass index indicate that both the men and the women gained weight at an average rate of 0.11 kg per square meter per year. Finally, the mean coefficients of variation suggest that women's weights tend to vary more than men's. A value of 0.06 indicates an average 6 percent deviation about a subject's mean body-mass index.We had several reasons for including the level and slope of the body-mass index as covariates in this analysis. Because we included the slope of the body-mass index over time as an independent variable, it was possible to distinguish the effects of systematic change in body weight from the effects of random or periodic fluctuation. This distinction was necessary because the coefficient of variation of the body-mass index and the slope of the body-mass index are correlated (Table 1). Similarly, correcting for the level of the body-mass index enabled us to separate any effects of obesity from the effects of weight cycling. This was important because obese subjects are likely to have more fluctuation in weight induced by dieting than subjects who are not obese.
End Points
The four end points evaluated in relation to the degree of variability in body weight were total mortality, morbidity due to coronary heart disease, mortality from coronary heart disease, and morbidity due to cancer. Total mortality was determined and validated by a review of death certificates and other pertinent data from hospital records. Morbidity due to coronary heart disease was defined as angina pectoris, coronary insufficiency, myocardial infarction, sudden death, or death as a result of any other coronary heart disease identified on the basis of a review of the records by a panel of three physicians.5 Clinical, electrocardiographic, and enzymatic data were used to establish the diagnosis of myocardial infarction. Mortality from coronary heart disease was a subgroup of total morbidity due to coronary heart disease, as described above. Both categories included sudden death from coronary heart disease, which was defined as death in a subject who had apparently been well who died within 60 minutes of the onset of symptoms in the absence of any other cause. Morbidity due to cancer included all malignant neoplasms. The cases were documented by reviewing the records of all subjects suspected or known to have had cancer. The dates of diagnosis were ascertained and all pertinent data reviewed. Pathological or cytologic reports were available for all but a few potential cases. A supplemented format from the International Classification of Diseases for Oncology was used to encode topographic and morphologic features.9 , 10 Because of the relatively small number of cancers at specific sites, this group included all cases of cancer. The exclusion of subjects with skin cancer from this group did not alter the results.
To preserve the four-year intervals between the measurements of weight and health-outcome end points, any subject with a specific end point before the 10th examination was eliminated from the analysis of data on that particular outcome. As a result of these exclusions, the sample in this analysis was smaller than that of the original Framingham cohort.
Statistical Analysis
Proportional-hazards regression was used to analyze the data.11 Separate analyses were conducted for men and women, and all analyses were stratified to correct for age group at entry (30 through 44, 45 through 54, and 55 through 62 years). In the first analysis (Model A), each of the four outcome variables was modeled as a function of the variability of weight, with adjustment for age group. Next, the level of the body-mass index and the slope of the body-mass index over the same period were included with variability of weight in an age-adjusted multivariate model predicting each of the four outcomes (Model B). Finally, in Model C, a set of standard cardiovascular risk factors measured early in the study was added to the variables in the previous model. These were the number of cigarettes smoked daily, physical-activity level, serum cholesterol concentration, results of glucose-tolerance tests, and systolic blood pressure. Physical activity was assessed at the fourth examination; the other risk factors were measured in all subjects at the time of the first or second examination (or both), and the earliest available value for each subject was used.
The results for the three regression models are presented separately. Estimates of relative risk and the results of analyses in which we used an increasing interval between measurements of weight variability and end points, excluded recalled weight at 25 years of age, and stratified the data according to age are described separately. Two-tailed tests were used, and probability levels of less than 0.05 were considered to indicate statistical significance.
Results
Model A
In the first regression analysis (Model A), each of the four end points was modeled as a function of body-weight variability. As shown in Table 2Table 2
Regression Coefficients and Significance Levels for Variability of Weight in Relation to Outcome, According to Models A and B.*, all end points except cancer were significantly related to the variability of body weight. All the significant regression coefficients were positive, indicating that a high degree of variability in body weight was associated with an increased risk of death from all causes, coronary heart disease, and death from coronary heart disease in both men and women.Model B
In the next analysis (Model B), we added two covariates to the model that already included variability of weight in order to determine whether any of the significant effects of variability of weight could be accounted for by the average level or slope of a subject's body-mass index over time. The results of this analysis were largely consistent with those of the Model A analysis; in both men and women, variability of weight was significantly and positively associated with total mortality and mortality from coronary heart disease, but not with cancer (Table 2). Variation in weight remained a strong independent risk factor for total coronary heart disease in men. For the mortality and coronary heart disease end points, the regression coefficients of the level of the body-mass index were positive, and those of the slope of the body-mass index were negative (data not shown).
Model C
In Model C we added a set of standard cardiovascular risk factors to the independent variables included in Model B, in order to determine whether the results were confounded by cigarette smoking, physical-activity level, serum cholesterol level, systolic blood pressure, or glucose tolerance. After correction for the slope and level of the body-mass index, along with these five cardiovascular-disease risk factors, all the end points that were correlated with variability of weight in the previous analysis remained correlated with it (Table 3Table 3
Partial Regression Coefficients (β) and Significance Levels of Selected Independent Variables, According to Multivariate Analysis in Model C.*). Among men, death from all causes, coronary heart disease, and death from coronary heart disease all remained significantly associated with variability of weight. In women, significant positive associations remained for death from all causes and death from coronary heart disease.One of these risk factors, cigarette smoking, was examined in greater detail to determine whether the associations between fluctuation in weight and the health end points were dependent on smoking. We found, first, that the results from Model B were unaffected by the inclusion of cigarette smoking as the only additional covariate, indicating that cigarette smoking was not a significant confounding factor in this analysis. Second, we found no significant or suggestive statistical interactions between variability of weight and smoking in the prediction of coronary heart disease, death from coronary heart disease, or death from all causes, indicating that base-line smoking status did not modify the observed associations.
Estimates of Relative Risk
For purposes of illustration and to assist in the interpretation of the regression coefficients presented in Tables 2 and 3, we calculated the relative risk of each end point among the subjects whose weights were most variable (upper third), as compared with those whose weights varied least (lower third). This was done by dividing the group of subjects into thirds according to weight variability, calculating the mean values for the high-variability and low-variability groups, and applying proportional-hazards regression analysis to the distance between these points. As shown in Table 4Table 4
Age-Adjusted Relative Risk of Each Outcome for the Subjects with the Highest Degree of Variability of Weight, as Compared with Those with the Least Variable Weights.*, the relative-risk estimates for total mortality and both coronary heart disease end points were between 1.27 and 2.00 and tended to be higher for men than for women. However, the lower 95 percent confidence limit was less than 1 in the multivariate model describing coronary heart disease in women and that describing cancer in men and women; these findings are in agreement with the results of the analyses in which weight variability was treated as a continuous variable.Six-Year Intervals
The Model B analysis was repeated after we increased the length of time required between the eighth examination and the first occurrence in the study of death, coronary heart disease, or death from coronary heart disease. The number of end points included in this analysis was smaller than before, but the likelihood that the end points were determinants rather than consequences of the fluctuation in weight was also smaller. When the interval required for the inclusion of end points was increased from four to six years, the regression coefficients were similar; the average deviation from the original values was 11.5 percent. All previously significant outcomes remained significant after the extension of this interval except death from coronary heart disease among women (P = 0.07). Therefore, the associations became statistically weaker but did not decrease appreciably in magnitude when we used a more conservative chronology for independent and dependent variables.
Recalled Weight at 25 Years of Age
We included the recalled weight at the age of 25 in the calculation of body-weight variability for several reasons. The first was that subjects' reports of their body weight are fairly accurate.12 More important, changes in body weight during young adulthood may have important implications for health in later life and may be captured, at least partially, by using the weight at 25 years of age. Nonetheless, we repeated the Model B analysis after excluding the weight at the age of 25 from the calculation of variability of weight. The association with total mortality remained statistically significant for both men (P = 0.0003) and women (P = 0.0008), as did that with death from coronary heart disease among women (P = 0.0002). Although the other coronary heart disease end points were no longer significantly related to variability of weight, the direction of the associations was the same. These results suggest that weight at 25 years of age makes an important contribution to the total variance of individual subjects' weight, which is likely to reflect true change in addition to inaccuracies in self-reporting.
Results According to Age Groups
Although the associations already described were found in analyses in which the three defined age groups were pooled, the results were also considered for each age group individually. The strongest and most consistent associations between weight variability and the health end points were found in the youngest age groups (men and women 30 through 44 years of age).
Discussion
These results indicate that persons whose body weight fluctuates often or greatly have a higher risk of coronary heart disease and death than do persons with relatively stable body weights. The associations observed were generally independent of both weight for height and temporal trends in weight, as well as of a number of cardiovascular risk factors. Therefore, it appears that body-weight variability, together with obesity, the overall trend in body weight, and other known risk factors for disease, has value in predicting the risk of mortality and coronary heart disease.
The relative risks attributable to fluctuation in weight were comparable in magnitude to the risks attributable to being overweight for total mortality,13 cardiovascular disease,8 and coronary heart disease.6 Recalled body weight at the age of 25 did not affect the associations between fluctuation in weight and mortality from all causes, but it was important in some of the associations with coronary heart disease, suggesting that weight changes in early adulthood may have different health implications from subsequent weight changes. Although not central to this analysis, systematic weight change (as measured by the slope of the body-mass index) was inversely associated with many of the end points examined here. Further research is needed to examine the independent effects of systematic weight change and variability of weight.
The associations reported here may be interpreted in a number of different ways. One possibility is that many of the subjects who died or were given diagnoses of disease during follow-up were already ill when their body weight was measured. In this case, the fluctuations in weight could be the consequence, and not the cause, of the health end points. However, the intervals between each subject's last body-weight measurement and any end point included in our analyses were intended to exclude serious preexisting illnesses that might have affected weight. Furthermore, we controlled for unidirectional trends in body weight in order to diminish the effect of systematic weight loss (presumably associated with chronic illness) on the associations observed. Certain diseases, such as gastrointestinal disorders and alcoholism, may be associated with periodic fluctuations in weight rather than with systematic losses or gains, but this pattern is probably less characteristic of most illnesses than is weight loss alone. These observations decrease the plausibility of the explanation that the subjects' weight fluctuated because they were already seriously ill.
An alternative possibility is that subjects with risk factors for coronary heart disease are the most likely to be put on weight-loss regimens (and thereby to have weight loss or loss-regain patterns), but that these changes would not reverse the health risks they already face. We attempted to minimize this possibility by including the earliest available values for five risk factors for cardiovascular disease in the multivariate analysis. Furthermore, the absence of a significant interaction between variability of weight and smoking in the prediction of the health end points suggests that these associations do not vary significantly between smokers and nonsmokers.
Finally, these results may reflect an adverse effect of voluntary weight loss and subsequent relapse. Although adherence to weight-reduction diets was not routinely documented at the Framingham examinations, another study indicated that body-weight variability was significantly correlated with a history of dieting.2 In addition, our review of selected medical records from the Framingham Heart Study indicated that dieting was common among subjects whose body weights varied. Specifically, in a review of the medical charts of the 40 subjects with the greatest variability in body weight (corrected for the level and slope of the body-mass index), we found that weight-reduction dieting was documented in 50 percent of these subjects before the eighth examination. This rate of dieting is likely to underestimate the true rate, since dieting was not recorded consistently. Therefore, dieting cannot be ruled out as an explanation of these findings, although its specific role can be elucidated only by prospective studies designed to collect this type of information.
If dieting emerges as a major factor in body-weight fluctuation, it may be important to evaluate further the public health implications of current weight-loss practices. Approximately 50 percent of American women and 25 percent of American men are dieting at any time,13 and many diets are unsuccessful. In view of the high rates of dieting, we wish to highlight two aspects of our results. First, weight fluctuation was most strongly associated with adverse health outcomes in the youngest cohort (age, 30 through 44 years). In addition to being the age category in which the results were least distorted by competing causes of illness, this is the age group in which dieting is likely to be most common. Second, because the relative risks associated with variation in weight were similar to those attributed to obesity, the risks due to excess weight may not outweigh the risks due to weight fluctuation. Although it would be premature to make clinical recommendations on the basis of our findings, these results do suggest that overweight persons should be taught skills to maintain weight loss and that the prevention of relapse should become a more central focus of weight-loss programs.14 , 15
These results underscore the difficulties of using epidemiologic data to study the effects of weight change on health and longevity. Despite uncertainties in interpreting the associations described here, our findings are strengthened by their consistency with those of two earlier studies involving body-weight variability and health.1 , 2 Together, these results raise the possibility that weight cycling by dietary means may have a role in the development of chronic disease.
Supported by grants from the MacArthur Foundation, the Dana Foundation, and the National Institutes of Health (MH00319, NOl-HC-38038, S-232CA09430, 236–86-C-033, and l-R01-HL-40423–01).
We are indebted to Drs. David Kritchevsky and George Blackburn for their helpful comments at various stages of this project.
Source Information
From the Department of Primary Health Care, Redbergsvägen #6, Göteborg, Sweden (L.L.); the Department of Mathematics, Bryant College, Smithfield, R.I. (P.M.O.); the Department of Mathematics, Boston University, Boston (R.B.D., A.J.B.); the Department of Preventive Medicine and Epidemiology, Boston University Medical Center, Boston (J.S., B.E.K.); and the Department of Psychology, Yale University, New Haven, Conn. (K.D.B.). Address reprint requests to Dr. Brownell at the Department of Psychology, Yale University, 2 Hillhouse Ave., Box 11A Yale Sta., New Haven, CT 06520.
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