Of the 2,322 Baltimore Longitudinal Study of Aging participants at risk, 187 developed AD after a median of 23.4 years of follow-up. Both BMI and waist circumference increased with age at population-average annual rates of 0.071 units and 0.35 cm, respectively, with wide interindividual variability. Our analysis of the longitudinal data on the association of AD risk with BMI and waist circumference at baseline and their dynamic changes during follow-up showed marked gender differences. Among men, weight gain was associated with a more than 3-fold increased risk of AD at ages between 30 and 50 years for any 5-year interval (HR = 3.70, 95% CI: 1.43, 9.56); and men who were underweight at age 30, 40, or 45 years had an increased likelihood of developing AD (HR = 5.76, 95% CI: 2.07, 16.00). Among women, being obese (BMI ≥30) at age 30, 40, or 45 years and jointly centrally obese (waist circumference ≥80th percentile) at age 30, 35, or 50 years increased the risk of AD 6.6-fold (HR = 6.57, 95% CI: 1.96, 22.02). To our surprise, women who experienced appreciable weight loss between ages 30 and 45 years were found to have increased risk of AD (HR = 2.02, 95% CI: 1.06, 3.85); and a notable longitudinal increase in waist circumference, as compared with normal fluctuations, was found to be protective among women (HRs = 0.49–0.50), though not among men. Further research is needed to confirm our findings among women.
Our results regarding the association between obesity and AD are consistent with those of a number of previous studies (3, 5–7, 24). For instance, in the Cache County Study (n
= 3,123; mean follow-up time = 3.2 years; 104 AD cases), Hayden et al. (24
) showed that obesity increased the risk of AD in women (adjusted HR = 2.23, 95% CI: 1.09, 4.30) but not in men (adjusted HR = 1.48, 95% CI: 0.41, 4.18). In a Kaiser Permanente study, the largest study carried out to date (n
= 10,136; follow-up time ≤36 years; 477 AD cases), Whitmer et al. (5
) found hazard ratios of 2.60 (95% CI: 1.44, 4.69) among men and 3.38 (95% CI: 2.20, 5.19) among women, indicating a stronger effect among women. However, they did not find an association between underweight and risk of AD (5
). Two other studies did not find statistically significant associations (6
), though they followed a similar trend.
Investigators have examined various other measures of adiposity, including waist circumference and subscapular and triceps skinfold thicknesses, and have obtained mixed findings (7
). In fact, while Yoshitake et al. (26
) did not find an association between BMI and subscapular:triceps skinfold thickness ratio on the one hand and dementia or AD risk on the other for men and women separately, Luchsinger et al. (7
) found that among both genders combined, having a waist circumference greater than 97 cm more than doubled the risk of vascular dementia (HR = 2.3, 95% CI: 1.0, 5.1) but not the risk of AD. Luchsinger et al. also found a positive association between vascular dementia and weight gain (7
). More recently, among 6,583 subjects in the Kaiser Permanente cohort, Whitmer et al. (22
) showed that in those with baseline central obesity (waist circumference measured between 1964 and 1973), the risk of developing dementia after an average of 36 years of follow-up was more than doubled when the uppermost quintile of waist circumference was compared with the lowest (HR = 2.72, 95% CI: 2.33, 3.33). Being defined as “obese” on the basis of both BMI and waist circumference concurrently increased the risk further (HR = 3.60, 95% CI: 2.85, 4.55) (22
). These findings were similar to ours in terms of the joint effect of weight status and central obesity on the risk of AD, though our results were confined to women.
The fat-brain axis (36
) and the hypothalamic-pituitary-adipose tissue axis (37
) have been implicated in the biologic mechanisms that link adiposity to cognitive performance. In fact, compounds secreted by adipose tissue, such as leptin and adiponectin, have been shown to regulate energy expenditure and hyperphagic responses by interacting with the hypothalamus (38
). In addition, direct administration of leptin to the hippocampus in mice was shown to improve memory processing and to shape the hypothalamus during the earliest stages (39
). Recently, Fewlass et al. (40
) found that leptin may contribute to amyloid beta deposition, while the results of another population-based study (41
) suggested that a low serum leptin level is associated with cognitive decline even after adjustment for BMI. In addition, excess adiposity was shown to be linked with increased inflammation, particularly the release of cytokines such as interleukin-6 and C-reactive protein (42
), which in turn was implicated in the cognitive decline process (43
Declining BMI may indicate pathologic processes that contribute to the subsequent development of AD (9
). Some suggested mechanisms include increased energy expenditure, biologic disturbances, dysfunction in body weight regulation, mesial cortex temporal atrophy (16
), and dysphagia (13
). Studies also indicate that there may be complex relations between apolipoprotein E ϵ4, increased cerebrospinal fluid levels of cortisol, weight loss, and hippocampal atrophy, particularly among women, increasing the risk of AD (45
). In addition, in a study by Mayeux et al. (47
), a lower BMI was associated with elevated plasma levels of amyloid β42, a possible risk factor for AD. As we showed in our recent review (28
), at least 3 other prospective cohort studies indicated that weight loss or underweight status at the mild cognitive impairment stage or earlier was associated with increased incidence of AD (8
). A general framework was recently proposed in which brain injury promoted by genetic and metabolic factors causes brain pathology and altered brain function, which in turn triggers changes in cognition, behavior, and appetite, promoting inadequate caloric intake, insufficient energy, impaired neuronal transport, and the stress response. These in turn may cause further increases in levels of free radicals, amyloid β, phosphorylated τ, cortisol, and cytokines and increase inflammation, which promotes further brain pathology and altered brain function (15
On the other hand, it is well-established that obesity in general and central obesity in particular is only 1 component of an etiologic cluster known as the metabolic syndrome. Prior research showed positive associations of hypertension (50
) and type 2 diabetes mellitus (53
) with risk of dementia and cognitive decline. However, the influence of plasma lipid level (a third component of the metabolic syndrome) remains unclear. Cholesterol alters the degradation of the amyloid precursor protein, a major player in the pathogenesis of AD (56
). Moreover, cerebrovascular disease that is associated with dyslipidemia may be related to the risk of AD (57
). Conflicting results have also been noted in studies relating levels of total cholesterol (58
), high density lipoprotein cholesterol (60
), and low density lipoprotein cholesterol (58
) to AD. It was recently found that among 1,616 elders, the risk of developing cognitive impairment over a period of 4 years was increased significantly among those with the metabolic syndrome (63
). In other recent studies linking the metabolic syndrome to incident or prevalent dementia, AD, and vascular dementia, researchers came to similar conclusions, though various measures of the metabolic syndrome were used (27
), while Muller et al. (66
) did not find an association between the metabolic syndrome and dementia risk. However, in the Kaiser Permanente study, adjustment for all other components of the metabolic syndrome did not attenuate the positive effect of mid-adulthood obesity (5
), indicating that the effect of adiposity on the risk of AD may follow an independent pathway among both men and women.
Our study had several strengths. First, it was based on a large cohort of men and women who were followed for a relatively long period of time (median follow-up time: 23.4 years after age 50). Second, it was one of the few studies to examine the effects of central obesity (measured by waist circumference) and dynamic BMI and waist circumference changes during early to mid-adulthood on the incidence of AD. Finally, we used advanced statistical techniques, including mixed-effects regression models, to predict BMI and waist circumference in an efficient manner over time. However, our study also had limitations, including a lack of complete measurements for certain variables, including depressive symptoms and physical activity, at each age; this impeded our ability to adjust for these variables in a suitable manner, particularly when our exposure (BMI/waist circumference status and change) was age-dependent. In addition, the Baltimore Longitudinal Study of Aging was a sample of convenience; the cohort was not fixed, and recruitment and dropout were continuous throughout follow-up. We did not adjust for components of the metabolic syndrome, as we felt that they are potentially in the causal pathway and hence may act as mediators or effect modifiers. Finally, some of our results, particularly hazard ratios, were indicative of poor precision due to lack of statistical power. However, a sensitivity analysis that set the hazard ratio to be detected as the observed one and fixed the sample size of failures as well as the distribution of the main adiposity exposure (67
) indicated that in fact power ranged between 0.80 and 1.00 for most of our analyses.
In conclusion, obesity, central obesity, and weight loss among women seem to play a role in the etiology of AD, while underweight and weight gain among men increase the risk. In future studies, investigators should address optimal age- and gender-specific healthy weight and weight loss strategies for prevention of AD. They should also suggest potential mechanisms for an effect of obesity or central obesity on AD, either through components of the metabolic syndrome or through other independent factors related to adiposity. Alternative pathways explaining the relation between weight loss and AD should also be investigated.