These results add to the growing body of literature suggesting that the prevalence of overweight and obesity is increasing. The mean baseline BMI exceeded 25.0 for every age and gender category studied, ranging from 25.6 (women aged 35–44) to 27.7 (men aged 55–64). The mean change over 5 years does not appear large at face value, ranging from an increase of 1.07 for the youngest group of men to a drop of 0.96 for the oldest group of women. However, if one adds the mean change to the mean baseline value, all values remain greater than 25, and the values increase for all but the two oldest age groups, for both men and women. In view of the fact that the mean BMI exceeded 25 even in the youngest age groups in both men and women (Figure 1), it seems likely that excess weight gain has its inception before age 25.
Weight regulation depends on a balance between energy intake via food and energy expenditure. Evolving knowledge indicates that complex neuronal and hormonal input to the hypothalamus and brain stem controls food intake, and peripheral circulating adipostat factors and gut hormones are highly important in appetite control and food intake regulation [28]. The results presented in Tables 1 and 2 suggest that the number within the BMI categories remains relatively stable despite the trend towards small annual increases as seen in Figure 1, which is consistent with previous research [20, 21] and the well-developed homeostatic role for weight regulation [28]. The results in Tables 3 and 4 suggest that the highest percentage of those studied will remain within their original weight classification after 5 years. Only in men who were initially underweight was there a tendency to increase BMI into the normal range. This observation may be consistent with the thesis that the complex neurohormonal regulation of weight regulation may have evolved primarily to prevent starvation [28].
In considering the relative stability of the weight categories despite evidence of increasing weight over time, one must bear in mind that a person can stay within their weight category despite a significant weight gain. For example, a woman at the average Canadian height of 163 centimetres (cm) would weigh 49.1 kg for a BMI of 18.50 and 66.3 kg for a BMI of 25.0, representing an increase of 17.2 kg (37.9 pounds) to move into the overweight category if starting at the lowest point in the normal weight category. To do the same, a man at the average Canadian height of 178 cm would weigh 58.5 kg for a BMI of 18.50 and 79.1 kg for a BMI of 25.0, representing an increase of 20.6 kg (45.4 pounds). In these two examples, to move up by one BMI point, the woman would have to gain 2.6 kg (5.7 pounds), while the man would have to gain 3.1 kg (6.8 pounds). Given the results in Figure 1, this suggests that men under age 45 and women under age 55 may be putting on approximately 0.45 kg or one pound per year, which levels off in the middle-aged groups and begins to reverse in the oldest age groups.
The regression models suggest that there are factors associated with change in BMI, but much of the variance remains unexplained, and the models must be interpreted with caution due to the wide confidence intervals associated with many of the estimates. Nevertheless, the findings tend to be consistent with previous research, although there are some notable exceptions. For example, several studies have noted an association between increased weight and a variety of comorbid conditions [3, 11] as well as the number of comorbid conditions [11], which was not observed in the current study. This could in part be because comorbidities may have been less severe than is typical for many diseases, as very sick subjects tended not to participate or to drop out of the cohort. Moreover, previously noted associations between increased weight and lower income [10, 22] were not found. Activity restriction also had little effect, despite previous research to the contrary [22], but that may be because other variables such as change in sedentary hours and change in participation in regular activity are also in our models. Level of pain also produced little in the way of interesting results, other than for women who noted moderate to severe pain that prevented some activities.
Our data did suggest a moderate effect of region, with the central region most likely to show an increase in BMI. One previous Canadian study noted almost no effect of region [22], but a study of region in the USA did note higher rates of obesity in the South Central and Northeast Central regions as compared to New England, the Atlantic regions, Mountain and Pacific regions [17]. Younger participants gained more weight than older participants, and the impact of menopause was small but in the expected direction [24], with more weight gain in those who were already menopausal at baseline.
Current smokers had less gain than those who had never smoked or had recently quit, particularly for women, which is consistent with other research [5, 22], while the results for alcohol intake suggest that higher levels in intake are associated with small declines in weight, which differs from previous findings [22]. However, it should be noted that alcohol consumption in this sample was relatively low, with a median of 0.2 drinks per week for the women and 2.0 per week for the men.
All education levels below the University level were associated with more weight gain, which was more pronounced for women and consistent with past findings [5, 10, 18]. Number of children appeared to have little effect on weight change for women, which has also been noted by others [26], despite widely held beliefs to the contrary [25, 26]. However, this may be because many respondents had their children long before the baseline measurement, and parity may therefore have contributed to baseline weight rather than change in weight.
The ability to assess the effect of behaviour change, and its impact on change in weight, is one of the strengths of this study. Compared to those who did not participate in physical activity but do now, all other groups showed small BMI gains, supporting other research that demonstrated a healthy effect of increased activity on BMI [10, 29]. Moreover, compared to those who went from a high to a low number of sedentary hours, the other groups showed small gains in BMI. Those who indicated that their level of happiness had declined or stayed the same saw small decreases in BMI compared with those whose level of happiness had improved. Finally, a decrease in self-rated health, which has been associated with higher BMI in other research [30], was associated with increased BMI in women but not in men. For men, increase in BMI tended to be associated with a perception of improved general health.
One limitation to this study was the loss to follow-up, as only 90.7% of the baseline sample could be included even when using multiple imputation. Moreover, multiple imputation also has assumptions and limitations. For example, the technique assumes that the baseline data were sufficiently detailed to predict year 5 weights, and that, given the information available for predicting missing year 5 weights, those who participated are similar at baseline to those who did not participate [31]. It is possible that despite adjusting for baseline characteristics, the non-respondents at year 5 were different from those with complete data. However, inclusion of the imputed data was considered to be preferable to simply basing all estimates on those with complete data at both time points, as at least some bias adjustment is preferable to none.
Caution must be used when interpreting any results based on BMI data. While BMI is a commonly used indicator of weight category, it is a composite measure that is unable to distinguish between fat and lean tissue [20]. Moreover, the BMI cut-points for overweight and obese subjects may need to be adjusted for certain non-white people, as well as the elderly [2, 10]. Finally, waist circumference, which is also an important indication for assessing obesity-related health risk [2, 10, 32], was not measured in the CaMos cohort.
