cause mortality rate - Nature

International Journal of Obesity (1999) 23, 603±611 ß 1999 Stockton Press All rights reserved 0307±0565/99 $12.00 http://www.stockton-press.co.uk/ijo

Weight loss increases and fat loss decreases allcause mortality rate: results from two independent cohort studies DB Allison1*, R Zannolli1,2, MS Faith1, M Heo1, A Pietrobelli1,3, TB VanItallie1, FX Pi-Sunyer1 and SB Heyms®eld1 1

Obesity Research Center, St Luke's=Roosevelt Hospital, Columbia University College of Physicians and Surgeons, New York, NY, USA; Department of Pediatrics, Policlinico LeScotte, University of Siena, Italy; and 3Department of Pediatrics, Scienti®c Institute H San Raffaele, University of Milan, Milan, Italy

2

OBJECTIVE: In epidemiological studies, weight loss is usually associated with increased mortality rate. Contrarily, among obese people, weight loss reduces other risk factors for disease and death. We hypothesised that this paradox could exist because weight is used as an implicit adiposity index. No study has considered the independent effects of weight loss and fat loss on mortality rate. We studied mortality rate as a function of weight loss and fat loss. DESIGN: Analysis of `time to death' in two prospective population-based cohort studies, the Tecumseh Community Health Study (1890 subjects; 321 deaths within 16 y of follow-up) and the Framingham Heart Study (2731 subjects; 507 deaths within 8 y of follow-up), in which weight and fat (via skinfolds) loss were assessable. RESULTS: In both studies, regardless of the statistical approach, weight loss was associated with an increased, and fat loss with a decreased, mortality rate (P < 0.05). Each standard deviation (s.d.) of weight loss (4.6 kg in Tecumseh, 6.7 kg in Framingham) was estimated to increase the hazard rate by 29% (95% con®dence interval CI), (14%, 47%, respectively) and 39% (95% CI, 25%, 54% respectively), in the two samples. Contrarily, each s.d. of fat loss (10.0 mm in Tecumseh, 4.8 mm in Framingham) was estimated to reduce the hazard rate 15% (95% CI, 4%, 25%) and 17% (95% CI, 8%, 25%) in Tecumseh and Framingham, respectively. Generalisability of these results to severely (that is, body mass index BMI) 34) obese individuals is unclear. CONCLUSIONS: Among individuals that are not severely obese, weight loss is associated with increased mortality rate and fat loss with decreased mortality rate. Keywords: body mass index; fat loss; weight loss; longevity; mortality; obesity

Introduction There is an apparent paradox in the obesity ®eld. Obesity is associated with numerous morbidities1 and even small weight losses can be associated with shortterm reductions in risk factors for disease.2,3 Obesity is also associated with an increased all-cause mortality rate.4 In contrast, the majority of studies show that weight loss is associated with an increased mortality rate.5 This paradox may be attributable to: a) not restricting studies of weight loss to obese people; b) not controlling for smoking status; c) not distinguishing among different methods and causes of weight loss; and d) not controlling for pre-existing disease.6,7 Only two epidemiological studies have addressed these concerns.8,9 Williamson et al 8 found that, among never-smoking obese women who were apparently free from wasting diseases and apparently lost weight intentionally, weight loss was associated with *Correspondence: Dr David B. Allison, Obesity Research Center, St Luke's=Roosevelt Hospital, Columbia University College of Physicians and Surgeons; 1090 Amsterdam Avenue, 14th ¯oor, New York, NY 10025, USA.. E-mail: [email protected]

a decreased mortality rate. However, several factors mitigate against immediate acceptance of the idea that intentional weight loss, among obese people, decreases the mortality rate. For example, there was no doseresponse relationship between amount of weight lost and reduction of mortality in this study. Moreover, the bene®cial effects of weight loss on mortality rate were almost exclusive to women with co-morbidities.8 Also noteworthy is a more recent study by Williamson et al 9 presenting similar analyses for men. They found no effect of apparently `intentional' weight loss on the allcause mortality rate. An additional complication when interpreting previous data is the fact that studies used weight and weight-based indices (for example, body mass index (BMI), kg=m2) as implicit indicators of body fatness. It is true that, especially after controlling for height, weight is highly correlated with body fat. However, weight is also highly correlated with the amount of lean mass individuals have,10,11 making interpretation of relations between weight and outcomes, such as mortality, dif®cult. Indeed, Allison et al 12 combined theoretical models and actual data to show that the Ushaped relationship frequently observed between BMI and mortality could be a function of increasing fat mass having a linear and increasing effect on mortality rate, but increasing lean mass having a linear and

Fat loss and mortality DB Allison et al

604

decreasing effect on mortality rate. This is consistent with other research implicating body composition, rather than body weight per se, in determining health risk. For example, Segal et al 13 compared obese men to a group of football players, wrestlers and power lifters who were matched for age and BMI, but had a very low amount of body fat. Among these subjects, the obese individuals had signi®cantly greater elevations in cardiovascular disease (CVD) risk factors (diastolic blood pressure (DBP), fasting plasma insulin, lower high density lipoprotein (HDL)cholesterol and greater low density lipoprotein (LDL)cholesterol) than did the BMI-matched but low body fat group. This thinking about differential effects of fat and lean mass on health from static models of body composition can also be applied to dynamic models. That is, greater clarity on the effects of weight changes on health risks and mortality may be produced by separately considering the differential effects of weight change per se vs changes in the degree of fatness. Van Itallie and Yang14 suggested that the degree of health bene®t achieved from weight loss is likely to be dependent on the degree to which the weight is lost as fat and lean mass is preserved. Given the above, we hypothesised that, conditional on fat loss, weight loss may be associated with increased mortality rate because it is accompanied by an undesirable loss of lean mass. Conversely, we hypothesised that, conditional on weight loss, fat loss will be associated with decreased mortality rate. We tested these hypotheses, using data from two prospective longitudinal population-based cohort studies: 1) the Tecumseh Community Health Study; and 2) the Framingham Heart Study.

Methods The Tecumseh sample

The Tecumseh Community Health Study is a prospective epidemiological study of residents from Table 1

Tecumseh, Southeast Michigan. Data collection began in 1957. Information was obtained from baseline medical history interviews, medical examinations, clinical measurements, laboratory work and electrocardiograms. Mortality follow-up data for the 8637 white respondents to the original data collection, was provided by the Tecumseh Mortality Follow-Up Study.15 In data analyzed herein, `Mortality of cohort members was monitored through local newspapers and from individual questioning of Tecumseh residents. Recontact with all eligible members of the cohort was attempted in 1977 ± 1979 with 99 percent of success rate. Death certi®cates were obtained for all deceased members of the cohort'.16 Time 1 corresponds to the baseline measurement. Time 2 was 4 y later. Follow-up data were available for 16 y after Time 2. During these 16 y, 321 deaths occurred (187 men; 134 women). Details on excluded and included subjects are given in Table 1. Subjects. Details of 1890 individuals (887 men and 1003 women) who had complete weight and skinfold data at Time 1 and Time 2 and complete mortality data are presented in Table 2. Variables. Weight-change was calculated as weight (kg) at Time 2 minus weight (kg) at Time 1 (positive values therefore denote weight gains and negative values weight loss). Two indicators of (subcutaneous) fatness were available at both Time 1 and Time 2; subscapular and triceps skinfold measurements. For details on the measurements of these skinfolds in the Tecumseh Community Health Study, see Ref. 17. Fatloss was calculated as skinfold thickness (triceps plus subscapular) at Time 2 minus skinfold thickness (triceps plus subscapular) at Time 1. An additional analysis (data not shown) which converted the two different skinfolds to a z-score, prior to summing, as recommended by Wainer,18 produced essentially identical results. `Time to death' was the time to death from any cause after Time 2.

Subjects excluded from included in the Tecumseh Community Health Study and Framingham Heart Study

Initial cohort Respondents remaining after eliminating those with incomplete mortality follow-up Respondents remaining after excluding those with missing BMI at Time 1 Respondents remaining after excluding those with missing weight loss informationa Respondents remaining after excluding those with missing fat loss information Respondents remaining after eliminating children (that is, age 18 y and under) at Time 1 Deaths among respondents included

Tecumseh study (n) (M=F)

Framingham study (n) (M=F)

8637 (4237=4400) 8601 (4220=4381) 8034 (3957=4077) 6027 (2937=3090) 3346 (1369=1707) 1890 (887=1003) 321 (187=134)

5209 (2336=2873) 5209 (2336=2873) 4405 (1941=2464) 3129 (1327=1802) 2731 (1160=1571) 2731 (1160=1571) 507 (290=217)

BMI body mass index. Many subjects with missing BMI data at Time 2 are those that died between Time 1 and Time 2.

a

Fat loss and mortality DB Allison et al Table 2

605

Characteristics of the included subjects from the Tecumseh Community Health Study

Time 1 Age (y) Smokers=Non-Smokers (n)b BMI (kg=m2) Height (cm) Weight (kg) Triceps skinfold (mm) Subscapular skinfold (mm) Time 2c Weight (kg) Triceps skinfold (mm) Subscapular skinfold (mm) Change scores Weight-change (kg) Fat-change (mm)

All (n 1890)

Men (n 887)

Women (n 1003)

40.3 14.1 (18.0, 91.0)a 1159=731 25.1 4.3 (15.0, 49.2) 167.0 9.2 (138.0, 194.0) 70.3 13.8 (39.0, 139.0) 17.1 7.3 (3.0, 47.0) 19.9 8.6 (4.0, 58)

40.6 13.5 (18.0, 89.0) 706=181 25.5 3.5 (16.3, 43.9) 174.1 6.6 (152.0, 194.0) 77.3 11.8 (42.0, 139.0) 13.01 5.6 (3.0, 38.0) 16.8 9.8 (5.0, 58.0)

39.9 14.6 (18.0, 91.0) 453=550 24.8 4.8 (15.1, 49.2) 160.7 6.1 (138.0, 180.0) 64.2 12.5 (39.0, 126.0) 20.7 6.8 (4.0, 47.0) 18.6 9.3 (4.0, 58.0)

71.0 14.1 (37.0, 140.0) 21.0 9.5 (3.0, 61.0) 18.9 9.9 (4.0, 68.0)

78.2 11.9 (44.0, 140.0) 15.6 6.2 (3.0, 50.0) 17.8 8.0 (4.0, 61.0)

64.5 12.9 (37.0, 132.0) 25.8 9.3 (4.0, 61.0) 19.9 11.3 (4.0, 68.0)

0.66 4.6 (ÿ22.0, 19.0) 5.1 10.0 (ÿ39.0, 53.0)

0.96 4.3 (ÿ21.0, 13.0) 6.4 11.0 (ÿ25.0, 53.0)

0.39 4.9 (ÿ22.0, 19.0) 3.7 8.4 (ÿ39.0, 32.0)

BMI body mass index. a All data are means s.d. (range). b Ever=never. c Time 2 is four years later than Time 1.

The Framingham sample

The Framingham Heart Study is a longitudinal, prospective cohort study initiated in 1948 to study 5209 residents in Framingham, Massachusets. Participants have undergone biennial examinations since the study's inception.19 These included measurements of height, weight and various risk factors. At the 5th and 12th biennial examinations, measurements of skinfolds were also made. Examination procedures and mortality follow-up information have been described in detail elsewhere.20 Time 1 corresponds to the measurement taken between 1954 and 1956. Time 2 was 14 y later. Follow-up data were available for eight years after Time 2. During these eight years, 507 deaths occurred (290 men; 217 women). Details on subjects excluded and included are given in Table 1. Subjects. Details of 2731 individuals (1160 men and 1571 women) who had complete weight and skinfold Table 3

data at Time 1 and Time 2 and complete mortality data, are presented in Table 3. Variables. Weight-change was calculated as weight (kg) at Time 2 minus weight (kg) at Time 1. In the Framingham Heart study there was only one indicator of fatness, that is, the subscapular skinfold. For details on the measurement of this skinfold in the Framingham Heart Study, see Cupples and D'Agostino.20 Fatchange was calculated as subscapular skinfold thickness at Time 2 minus the subscapular skinfold thickness at Time 1. Preliminary inspection of the data in categorical analyses, as recommended by Zhao and Kolonel,21 indicated that the linearity of the association between fat change and the mortality rate could be improved by taking a monotonic transformation of fat change. Thus, fat change was transformed by taking its natural logarithm (after adding a constant to insure all positive numbers) and then scaled to have

Characteristics of the included subjects from the Framingham Heart Study.

Time 1 Age (y) Smokers=Non-smokers (n)b BMI (kg=m2) Height (cm) Weight (kg) Subscapular skinfold (mm) Time 2c Weight (kg) Subscapular skinfold (mm) Change scores Weight-change (kg) Fat-change (mm) BMI body mass index. a Data are means s.d. (range). b Current=never former. c Time 2 is 14 y later than Time 1.

All (n 2731)

Men (n 1160)

Women (n 1571)

50.0 7.9 (37.0, 70.0)a 1468=1263 25.7 3.9 (15.5, 45.7) 164.8 9.2 (138.4, 193.7) 69.9 13.0 (36.7, 125.1) 14.7 4.5 (3.0, 33.0)

49.8 7.8 (37.0, 70.0) 812=348 26.2 3.3 (15.5, 36.9) 172.3 7.0 (149.9, 193.7) 77.7 11.3 (40.8, 122.5) 14.3 4.1 (5.0, 33.0)

50.2 7.9 (37.0, 70.0) 657=914 25.3 4.3 (16.3, 45.7) 159.2 6.2 (138.4, 179.1) 64.1 11.1 (36.7, 125.2) 14.9 4.8 (3.0, 32.0)

70.3 13.2 (32.2, 128.4) 16.2 6.1 (3.0, 42.0)

77.7 11.8 (34.5, 125.2) 16.6 6.1 (4.0, 42.0)

64.9 11.6 (32.2, 128.4) 15.9 6.1 (3.0, 41.0)

0.46 6.7 (ÿ35.8, 27.2) 1.6 4.8 (ÿ15.0, 22.0)

0.03 6.6 (ÿ26.3, 22.7) 2.4 4.8 (ÿ14.0, 20.0)

0.82 6.8 (ÿ35.8, 27.2) 1.0 4.8 (ÿ15.0, 22.0)


606

its original mean and s.d. for ease of interpretation `Time to death' was the time to death from any cause after Time 2.

Statistical analysis

For both studies, we conducted a `primary' analysis and a set of secondary=sensitivity analyses. The primary analysis consisted of Cox regression in which time to death was used as the dependent variable, weight change and fat change were used as independent variables, and height at Time 1, age, gender, and smoking status were used as covariates. In addition, we repeated this primary analysis after ®rst residualising fat change for weight change to reduce collinearity as suggested by a reviewer. Results were not meaningfully different. In the secondary=sensitivity analyses, we evaluated whether the results remained generally consistent when we: 1) treated weight change and fat change as categorical rather than continuous variables, by dividing the distribution along quintile lines; 2) used logistic rather than Cox regression, to avoid the assumption of proportional hazards; 3) controlled for baseline values of weight and fat; 4) allowed for interactions between baseline BMI (kg=m2) (or a categorical dummy code for obesity status) and weight change and fat change; 5) allowed for interactions between gender and weight change and fat change; 6) incorporated information about change in smoking status between Time 1 and Time 2; 7) restricted the analysis to never smokers (Tecumseh sample) or to never plus former smokers (Framingham sample) at Time 2; and 8) to evaluate whether the effects on weight change and fat change differed by age, we added interaction terms between weight change and fat change and age into the model, and tested these terms for signi®cance. We did not exclude subjects who died during the ®rst few years of followup, for reasons described elsewhere.22 Because our thesis is that fat change and weight change each confound control the other's association with mortality, we also examined the coef®cients for weight change and fat change in separate reduced models,

to evaluate the effects of failing to control for one, while estimating the effect of the other. The two data sets were not pooled for the following reasons. The data sets, though remarkably similar in many ways, contained a number of important differences that we believe could have affected the outcome and therefore militated against raw data pooling. These differences included: 1) the lengths of followup available after the second measurements; 2) the intervals between the ®rst and second measurements; 3) the average age at baseline; 4) the Tecumseh data allowed categorization of subjects into never vs ever smokers, whereas Framingham only allowed categorization into current vs former plus never smokers, and 5) the number of skinfold measures available. Statistical analyses were conducted using the SPSS statistical package.23 Figures were produced by the SPLUS software.24

Results The Tecumseh sample

Primary analysis. Using Cox proportional hazards regression,25 the log of the hazard ratio was regressed on fat change and weight change while controlling for age, gender, smoking status (ever smoker vs never smoker) and height. The coef®cients for this estimated model and the associated inferential statistics are displayed in Table 4. Of primary note is that both the coef®cients for weight change (P 0.0001) and fat change (P 0.0112) were statistically signi®cant. As hypothesized, the coef®cient for weight change was negative in sign (b bÃ 0.0559; 95% con®dence intervals (CI) ÿ0.0839, ÿ0.0279) and the coef®cient for fat change was positive in sign (b bÃ ÿ0.0163; 95% CI ÿ0.0038, ÿ0.0288). This indicates that weight loss is associated with increased mortality rate and fat loss with decreased mortality rate. Expressed in standard deviation units (s.d.), each 4.6 kg (that is, 1 s.d.) of weight loss resulted in a 29% (95% CI 14%, 47%) increase in the hazard for

Table 4 Results of the primary analysis of the Tecumseh Community Health Study and the Framingham Heart Study. Time of Death (Log of the hazard ratio) regressed (via Cox proportional hazards regression) on weight change and fat change, while controlling for Ã for weight-change (negative) and fatage, gender, smoking status and height. Note the opposite signs of regression coef®cients (b b) change (positive) Tecumseh

Age Height Male Smokers=Non smokersa,b Weight-change Fat-change a

bª

s.e.

P

(exp b) bª

bª

s.e.

P

(exp b) bª

0.0966 ÿ0.0026 0.6561 0.3436 ÿ0.0559 0.0163

0.0045 0.0093 0.1764 0.1295 0.0143 0.0064

< 0.0001 0.7769 0.0002 0.0080 0.0001 0.0112

1.1015 0.9974 1.9237 1.4100 0.9456 1.0164

0.0929 ÿ0.0058 ÿ0.6365 0.3623 ÿ0.0495 0.0393

0.0061 0.0069 0.1289 0.0984 0.0080 0.0107

< 0.0001 0.4010 < 0.0001 0.0002 < 0.0001 0.0002

1.0973 0.9942 0.5292 1.4366 0.9517 1.0401

Ever=never for the Tecumseh Community Health Study. Current=never former for the Framingham Heart Study.

b

Framingham


mortality whereas a 1 s.d. (10.0 mm) increase in fat loss resulted in a 15% (95% CI 4%, 25%) decrease in the hazard for mortality. Secondary analyses. Each secondary analysis con®rmed the general pattern observed in the primary analysis. That is, whether we treated weight loss and fat loss as categorical rather than continuous variables, used logistic rather than Cox regression, controlled for baseline values of weight and fat, restricted the analysis to never smokers, or incorporated information about smoking status at Time 2 to account for changes in smoking status, weight loss was associated with an increased mortality rate and fat loss was associated with a decreased mortality rate. The stability of the estimates from analysis to analysis was quite high with the hazard ratios never changing more than 10% (see Table 5). Moreover, in every case, with two exceptions, the coef®cients for the effects of weight loss and fat loss were statistically signi®cant (P < 0.05). One of these exceptions was observed from the analysis restricted to `never' smokers, in which only 731 subjects were available in the analysis. In this analysis, given the small sample size, it is not surprising that not all effects were statistically signi®cant. However, the coef®cients for both weight loss and fat loss remained in their predicted direction (Weight change bbÃ ÿ0.0578; P 0.0080; Fat change bÃ 0.0132; P 0.1508). The second exception occurred in the categorical analysis. Categorisation of weight and fat losses, for example, by quintiles, is known to decrease statistical power.26 Nevertheless, some (though not all) of the higher categories of weight loss had statistically signi®cant increases in mortality rate, relative to the lowest category of weight loss, and similarly, some of the categories of fat loss had statistically signi®cantly higher mortality rates than the lower categories

of fat loss. Moreover, there was a clear trend toward increasing mortality rate with higher categories of weight loss and decreasing mortality rate with higher categories of fat loss. Coef®cients from the categorical models are displayed graphically in Figure 1. Interactions' between gender and weight loss (P 0.6549) and fat loss (P 0.2430) were not signi®cant. Interactions between age and weight loss (P 0.9040), and age and fat loss (P 0.5980), were also not signi®cant. When interaction terms between baseline BMI and weight loss and fat loss were added to the model, only the interaction term for

Figure 1 Tecumseh Community Health Study sample, secondary analysis. Estimated hazard ratios (to the last category) of individuals in the corresponding categories of weight change (WC) and fat change (FC), and their standard errors (s.e.). The numbers in parenthesis represent the minimum and maximum values for the corresponding quintile-de®ned categories. For both WC (kg) and FC (change in skinfolds thickness, mm), the positive and negative values represent gains and losses, respectively. There is a clear trend toward a decreasing mortality rate with higher categories of WC (d) and an increasing mortality rate with higher categories of FC (j).

Table 5 Secondary analyses of the Tecumseh Community Health Study and the Framingham Heart Study. Evaluation of consistency of results in response to variations of analytic technique Weight change Statistical model Tecumseh Basicb Basic weight1c, fat1d Basic for never smokers only Basic smoking status at Time 2 Basic with logistic regression2 Framingham Basic Basic Weight1c, Fat1d Basic for never and former smokers only Basic smoking status at Time 2 Basic with logistic regression

Fat change

HR a

P

HR a

P

0.78 0.78 0.77 0.78 0.75

0.0001 0.0007 0.0080 0.0001 0.0010

1.18 1.18 1.14 1.18 1.25

0.0112 0.0311 0.1508 0.0099 0.0086

0.72 0.72 0.79 0.74 0.68

< 0.0001 < 0.0001 0.0039 < 0.0001 < 0.0001

1.20 1.20 1.19 1.20 1.23

0.0002 0.0004 0.0228 0.0002 0.0008

HR hazaro ratio. hazard ratio per 1.0 standard deviation increase in weight change or fat change. In the basic model, the effect of weight loss and fat loss have been adjusted for the following covariates measured at Time 1: age, gender, height and smoking status. c Weight1 is weight (kg) at Time 1. d Fat1 is tricep and subscapular skinfolds measurement (mm) at Time 1. e In the logistic model, the coef®cient labelled HR is actually an odds ratio (OR). a

b

607


608

weight loss and BMI was signi®cant (P 0.0022). The sign of the weight loss by BMI interaction coef®cient was negative indicating that, as baseline BMI increased, the apparently deleterious effect of weight loss decreased. That is, weight loss appeared more benign among heavier than among lighter people. It was observed from the Cox model, including interactions of BMI and weight loss and fat loss, that weight loss was estimated to be bene®cial (that is, reduce mortality rate) for individuals with initial BMI 34. However, the BMI by fat loss interaction term was not signi®cant (P 0.6342), suggesting that the bene®cial effects of losing fat were not only limited to those people who had high BMIs at the beginning of the study. This was consistent with an analysis treating obesity status as a categorical variable (that is, BMI < 28 0; BMI 28 1). This analysis showed a signi®cant interaction between obesity status and weight loss (P 0.0079), but not fat loss (P 0.3854), and indicated that weight loss did not appear deleterious among obese individuals. Finally, in analyses examining the effects of weight change and fat change separately that is, not controlling for the other, the absolute values of the coef®cients were substantially reduced (29% for weight change and 69% for fat change). This indicates that consistent with our thesis failing to adjust total weight change for fat change and vice versa causes a bias in the estimation of effects. The Framingham sample

Primary analysis. Again the log of the hazard ratio was regressed (via Cox proportional hazards regression) on fat change and weight change while controlling for age, gender, smoking status (current smoker or never smoker plus former) and height. The coef®cients for this estimated model and associated inferential statistics are shown in Table 4. Of primary note, is that both the coef®cients for weight change and fat change (P < 0.0001, P 0.0002, respectively) were statistically signi®cant. Moreover, as hypothesized, the coef®cient for weight change was negative in sign (b bÃ ÿ0.0495; 95% CI: ÿ0.0652, ÿ0.0338) and the coef®cient for fat change was positive in sign (b bÃ 0.0393; 95% CI: ÿ0.0183, ÿ0.0603). Thus, weight loss was associated with increased mortality rate and fat loss with decreased mortality rate. Expressed in s.d., each 6.7 kg (that is, 1 s.d.) of weight loss, would result in a 39% (95% CI, 25%, 54%) increase in the hazard ratio for mortality, whereas a 1 s.d. (4.8 mm) increase in fat loss would result in a 17% (95% CI, 8%, 25%) decrease in the hazard ratio for mortality. Secondary analyses. As in the Tecumseh data analysis and as highlighted in Table 5, each of the secondary analyses con®rmed the general pattern

Figure 2 Framingham Heart Study sample, secondary analysis. Estimated hazard ratios (to the last category) of individuals in the corresponding categories of weight change (WC) and fat change (FC), and their standard errors (s.e.). The numbers in parenthesis represent the minimum and maximum values for the corresponding quintile-de®ned categories. For both WC (kg) and FC (change in skinfold thickness, mm), the positive and negative values represent gains and losses, respectively. There is a clear trend toward a decreasing mortality rate with higher categories of weight change (d) and an increasing mortality rate with higher categories of fat change (u).

observed in the primary analysis. Speci®cally, the same secondary analyses that were applied to the Tecumseh data produced remarkably similar results with respect to the Framingham data. For example, there was, again, a clear trend toward increasing mortality rate with greater weight loss and decreasing mortality rate with greater fat loss. These coef®cients from the categorical models are displayed graphically in Figure 2. However, unlike in Tecumseh, when interaction terms between baseline BMI and weight change (P 0.1397) and fat change (P 0.5973) were added to the model, none were signi®cant (although the direction of effect was the same as that observed in Tecumseh), nor were those between a dichotomous indication of obesity and weight change (P 0.3272) and fat change (P 0.6852). Interactions between age and weight change (P 0.6980), and age and fat change (P 0.4035), were also not signi®cant. Thus, in contrast to the Tecumseh study, the apparently deleterious effects of weight loss and the bene®cial effects of fat loss were independent of baseline BMI. Interaction terms between gender and weight change (P 0.3582) and fat change (P 0.0889) were also not signi®cant. Finally, consistent with the Tecumseh results in analyses examining the effects of weight change and fat change separately (that is, not controlling for the others), the absolute values of the coef®cients were substantially reduced (31% for weight change and 99% for fat change), indicating the strong bias resulting from considering weight change without considering fat change and vice versa.


Discussion Using two population-based, prospective cohort studies, the Tecumseh Community Health Study and the Framingham Heart Study, we analysed time to death as a function of both weight loss (that is, loss of kilograms) and fat loss (that is, reduction in skinfold thickness), while controlling for some possible confounding factors: age, gender, smoking status and height. Results were consistent with the stated hypothesis that weight loss increases whereas fat loss decreases the all-cause mortality rate. The deleterious effects of weight loss and the bene®cial effects of fat loss were independent of baseline BMI in the Framingham study. In the Tecumseh study where weight loss was observed to be more benign among heavier than among lighter people, the bene®cial effects of weight loss did not appear until a BMI 34 (that is, in severely obese people). It is noteworthy that results were con®rmed in two completely independent cohorts. Although these two cohort studies had much in common (for example, similar sample sizes and measurements) there were also important differences (for example, the interval between the two weight and fat measurements and the length of follow-up). Such replication strengthens con®dence that the ®ndings were not likely to be due to chance or some `quirk' of an individual data set, and suggests a robust phenomenon. In interpreting these data, it is important to realize that weight change and fat change are positively correlated. That is, in general, when individuals lose weight, they lose fat and vice versa. In the studies analyzed herein, the correlations between weight loss and fat loss were between 0.50 ± 0.60. By analysing weight change and fat change simultaneously in the statistical model, we are able to look at their independent effects. Because weight loss is essentially equal to loss of body fat mass plus loss of lean body mass, when weight loss and fat loss are analysed simultaneously in the same statistical model, weight loss essentially becomes an indicator of lean body mass loss. Therefore, it may be reasonable to interpret these results as indicating that fat mass loss is bene®cial whereas lean body mass loss is deleterious and that the extent to which weight loss is bene®cial or deleterious will depend on the composition of that weight loss. It is also noteworthy that after controlling for the linear effects of weight loss and fat loss, there were no signi®cant quadratic effects (data not shown), indicating that there is no statistically signi®cant evidence for a non-monotonic association between our indicators of fat loss and weight loss and mortality rate. This implies that where we have written `weight loss is associated with increased mortality rate and fat loss with decreased mortality rate', the reader could as appropriately read, `weight gain is associated with decreased mortality rate and fat gain is associated with increased mortality rate.' Nevertheless, inspection of

Figure 1 and Figure 2 suggests that, although not statistically signi®cant, there may be some departures from linearity, such that the effects of weight gain and fat gain may not simply be the opposite of the effects of weight loss and fat loss. Future research in larger samples may be necessary to systematically determine the extent to which the results observed are due to fat loss and weight loss per se vs fat gain and weight gain, or both. The idea that lean body mass loss is deleterious is consistent with clinical research on diseased subjects. For example, Tellado et al 27 studied mortality rate as a function of several biomedical markers and weight loss in a sample of 73 patients with sepsis and malnutrition. To measure nutritional status, they used the ratio of exchangeable sodium to exchangeable potassium (the Nae=Ke ratio), that is the extracellular mass as a function of body cell mass.28 Results indicated that a high Nae=Ke ratio was a powerful predictor of mortality rate in this sample. Comparable results were reported by Kotler et al,29 who studied survival time among AIDS patients. They found that the lowest total potassium values (indicative of low lean body cell mass) were associated with the earliest death. This was observed even though low body cell mass was associated with a wide range of fat mass values. The implications of this study, if con®rmed in additional studies, are clear and profound. For example, weight loss might only be advisable under conditions promoting a suf®cient proportion of the lost weight as fat. When prospective cohort studies are conducted using direct measurements of total fat mass, via more sophisticated body composition measurements, it will then be possible to calculate directly the proportion of weight loss as fat necessary to achieve a net reduction in mortality rate. Given that the present study measured relative fatness, rather than fat mass per se, we can only say that a higher proportion of weight lost as fat is desirable. We cannot specify the `minimal' desirable proportion. Lean body mass is positively related to the body fat over a wide range of body weights.30 From this relationship, Forbes30 predicted that induced fat loss would be accompanied by a loss of lean body mass inversely related to the initial body fat content. That is, given a ®xed level of caloric restriction, individuals with a relatively greater amount of total body fat were expected to lose relatively smaller proportions of their weight as lean mass.30 Thus, clinical prescriptions for weight loss may need to consider patients' body composition at baseline and hence their projected lean mass loss for a ®xed level of weight loss. The samples studied herein consist of adults of European ancestry living in the United States under, presumably, reasonable conditions of health and nutrition. Moreover, although there was certainly a large number of obese individuals, where obesity is de®ned as, for example, a BMI > 28, there were few severely obese individuals. Thus, it is not clear that these

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results are generalisable to children, individuals of other ethnic backgrounds, individuals living under other circumstances, or severely obese or severely underweight individuals. To some extent, this is supported by a signi®cant interaction term between baseline BMI and weight loss in the Tecumseh study, which suggested that among severely obese individuals weight loss might be bene®cial regardless of its composition. Clearly more research is needed in this area. One avenue for future research is to replicate these ®ndings in a study in which body composition is measured by more sophisticated methods. At present, we are aware of no such studies. However, in the third National Health and Nutrition Examination Survey (NHANES III study),31 body composition measurements were taken on all individuals, via bioimpedance analysis. Moreover, in the currently planned NHANES IV study, it is anticipated that body fat will be measured by dual energy X-ray absorptiometry (DEXA). Hopefully, follow-up studies of these two cohorts would be conducted allowing replication of the present ®ndings. Moreover, by comparing the effects of fat mass loss and weight loss in a common metric (that is, kilograms), one can determine the proportion of weight that must be lost as fat to achieve a net reduction in mortality risk. In this regard, it must also be pointed out that the measures of fatness used herein consisted of triceps and subscapular skinfolds. It is conceivable, although perhaps unlikely, that an individual could lose fat from these two regions of the body, but simultaneously gain an equal or greater amount of fat in other parts of the body such that, despite a reduction in skinfold thickness, the individual may not have reduced their fatness overall. This can only be rigorously evaluated in future research in which direct estimates of total fat mass are obtained. A more speculative direction for future research concerns the development of treatments that preferentially promote fat loss relative to lean loss. Presently, most treatments for obesity are geared toward producing reduced caloric intake and, to some extent, increased caloric expenditure. This results in weight loss, but such methods usually do not preferentially promote fat loss, per se. In contrast, in some animal studies, certain newer compounds under investigation (for example, ciliary neurotrophic factor and leptin)32 appear to preferentially produce fat loss. It therefore may be possible that investigators will be able to develop therapeutics aimed speci®cally at fat loss per se. A second approach might be to put more emphasis on exercise, which tends to preserve lean mass.33 Finally, additional research might evaluate the current obesity treatments in terms of their effects on body composition. Virtually all studies of obesity treatment measure weight loss, but only a minority measure fat loss. Moreover, there has been relatively little exploration of the effects of various currently existing treatments, conditions of treatment, and rates

of weight loss, in terms of their differential effects on degree of body fat loss. Such research might help us select the treatments and conditions that would be expected to produce the greatest bene®ts for obese patients. Acknowledgements

This research was funded by NIH grants R29DK47256, R01DK51716, TD32DK37352, and P30DK26687. This paper uses data supplied by the Inter-University Consortium on Political and Social Research (ICPSR) from the Tecumseh Heart Study and the National Heart, Lung and Blood Institute, NIH, DHHS from the Framingham Heart Study. The views expressed in this paper are those of the authors and do not necessarily re¯ect the views of ICPSR, the Tecumseh Study, the National Heart, Lung and Blood Institute or the Framingham Study. References

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