Patterns of Aging Changes in Bodyweight May Predict Alzheimer’s Disease

INTRODUCTION

Lower bodyweight has been associated with Alzheimer’s disease (AD) and other dementias [1–5]. However, mechanisms behind such association remain poorly understood. The lower weight could be a consequence of already developing AD pathology in the body and a symptom of preclinical AD. Alternatively, it may be a risk factor for AD and a potential target for AD prevention. Despite a substantial amount of research connecting changes in weight at certain age intervals with risks of AD and other dementia’s [4–9], this direction remains underexplored. The age-trajectories of weight evaluated over longer periods of life may provide better insights into the cause-and-effect relationship between patterns of aging-related changes in weight and AD. The ɛ4 allele of APOE gene (a.k.a. APOE4), a major genetic risk factor for late-onset-AD, was also linked to lower weight/body mass index (BMI) in some studies [3, 10–13]; however, it is unclear how APOE4 influences long-term changes in weight over the life course and whether this influence plays a significant role in AD.

In this study, we explored the relationships between late-onset-AD and patterns of aging-related changes in bodyweight, as well as the impact of APOE4 on these patterns. We examined how long before AD onset individuals start to decline in weight compared to those who remain AD-free. We also investigated key characteristics of the non-monotonic trajectories of weight changes during aging, such as the age at reaching maximum and the rate of subsequent decline, and evaluated their associations with AD risk.

MATERIALS AND METHODS

Phenotypes

Alzheimer’s disease (AD). In FHS, a dementia review panel, which included a neurologist and a neuropsychologist, reviewed every case of cognitive decline and dementia. The diagnosis of AD was based on criteria for possible, probable, or definite AD from the Alzheimer’s Disease and Related Disorders Association and the National Institute of Neurological and Communicative Disorders and Stroke (ADRDA– NINCDS) [14, 15]. In HRS, AD onset was assessed using the survey data, based on HRS questionnaires. In Waves 4–9, questions about memory-related diseases were asked, and since Wave 10, the respondents were asked if they have been told by the doctor that they have AD.

Binary phenotypes characterizing patterns of aging changes in weight. Behavior of non-monotonic mean age-trajectories of weight may be described by such characteristics as age at reaching maximum weight (AgeMax), and rate of change in weight (Slope). We created respective binary phenotypes (1 versus 0) categorizing such behavior. For AgeMax, group (0) included individuals with younger AgeMax (<70 years), and group (1) included individuals with older AgeMax (≥70 years), with condition of survival to age older than 70 in both groups. Slope was calculated as relative change in weight between ages 65 and 85. Group (0) included individuals with Slope below median (steeper decline in weight), and group (1) included individuals with Slope above median (flatter decline or non-declining pattern), with condition of survival to age older than 75 in both groups. For both AgeMax and Slope phenotypes, our working hypothesis was that being in group (1) could be more beneficial for older people than being in group (0), assuming that the ability to postpone/decelerate the decline in weight at older ages may reflect a slower physical aging with later onset of frailty [16].

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Fig. 1

Mean age-trajectories of weight for women with and without history of AD, and by APOE4 carrier status. A) Age-patterns of weight for females with and without AD history. Dashed line – females with AD onset at ages 75+. Solid line - females without AD and with lifespan (LS) 75 years or older. FHS. B) Age-patterns of weight for females with and without AD history. Dashed line – females with AD onset at ages 75+. Solid line - females without AD and with lifespan (LS) 75 years or older. HRS. C) Age-patterns of weight for females with and without APOE ɛ4 allele. Dashed line – females, who are ɛ4 allele carriers. Solid line - females, who are ɛ4 allele non-carriers. FHS. D) Age-patterns of weight for females with and without APOE ɛ4 allele. Dashed line – females, who are ɛ4 allele carriers. Solid line - females, who are ɛ4 allele non-carriers. HRS. E) Age-patterns of weight for females without APOE ɛ4 allele, by AD history. Dashed line – females with AD onset at ages 75+. Solid line - females without AD and with lifespan (LS) 75 years or older. FHS. F) Age-patterns of weight for females without APOE ɛ4 allele, by AD history. Dashed line – females with AD onset at ages 75+. Solid line - females without AD and with lifespan (LS) 75 years or older. HRS. G) Age-patterns of weight for female APOE ɛ4 allele carriers, by AD history. Dashed line – females with AD onset at ages 75+. Solid line - females without AD and with lifespan (LS) 75 years or older. FHS. H) Age-patterns of weight for female APOE ɛ4 allele carriers, by AD history. Dashed line – females with AD onset at ages 75+. Solid line - females without AD and with lifespan (LS) 75 years or older. HRS.

RESULTS

We compared the patterns of aging-related changes in weight between individuals who developed late-onset-AD and those who did not, taking into account the APOE4 carrier status. We also evaluated the impact of age at reaching maximum weight, and the slope of weight decline, on the risk of AD. Results of our analyses are shown in Figs. 1 and 2, and Table 1. We found that women who developed AD at ages 75+ had, on average, lower body weight, starting as early as in their 40s, and a younger AgeMax, compared to women who did not have AD history (Fig. 1). No significant differences were found for males (trajectories not shown).

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Fig. 2

Trajectories of probability of staying AD-free for women with different ages at maximum weight, and different slopes of weight. A) Probability of staying free of AD at ages 80+ for women who reached maximum weight at ages 75 or older (solid line), compared to those who reached maximum weight at ages younger than 75 (dashed line). FHS. B) Probability of staying free of AD at ages 80+ for women who reached maximum weight at ages 75 or older (solid line), compared to those who reached maximum weight at ages younger than 75 (dashed line). HRS. C) Probability of staying free of AD at ages 80+ for women who reached maximum weight at ages 75–80 (solid line), compared to those who reached maximum weight at ages younger than 75 (dashed line). FHS. D) Probability of staying free of AD at ages 80+ for women who reached maximum weight at ages 75–80 (solid line), compared to those who reached maximum weight at ages younger than 75 (dashed line). HRS. E) Probability of staying free of AD at ages 80+ for women with slope of weight change above median (solid line), meaning slower or no decline in weight, compared to women with slope of weight change below median (dashed line), meaning faster decline in weight. FHS. F) Probability of staying free of AD at ages 80+ for women with slope of weight change above median (solid line), meaning slower or no decline in weight, compared to women with slope of weight change below median (dashed line), meaning faster decline in weight. HRS. The CoxPH P is p-value for testing the null hypothesis that the regression coefficient for respective variable is zero.

Similar relationships between AD and patterns of aging changes in weight were seen in carriers and non-carriers of APOE4, with older AgeMax seen in women without AD history (Fig. 1). Notably, in FHS with its longer follow up, a lower weight was observed in APOE4 non-carriers with AD up to four decades before AD onset (Fig. 1). The same was not evident for APOE4 carriers, though this might be because the latter group was relatively small in FHS.

Regardless of AD, APOE4 carriers compared to non-carriers had lower weight starting around age 65 (Fig. 1). Analysis of associations between APOE4 and binary phenotypes characterizing age-trajectories of weight, using logistic regression, found that carrying ɛ4 alleles was associated with younger AgeMax in HRS, and with steeper slopes of the weight decline in both HRS and FHS, particularly in females (Table 1). For males, results were not significant in FHS. They were also not significant in Black HRS participants (not shown).

The analysis of the probability of staying free of AD, stratified by AgeMax (<75 versus ≥75), as well as by Slope (above versus below median), revealed that AgeMax ≥75 and flatter Slope were both significant predictors of a lower risk of AD after age 80 in women, in both FHS and HRS data (Fig. 2).

DISCUSSION

Our study found that women who developed AD after age 75 had on average lower weight than AD-free women, seen as early as in their forties (Fig. 1). They also reached maximum values of weight about ten years earlier than AD-free women. This indicates that AD-relevant changes in the female body may start twenty, thirty or more years before clinical manifestation of AD. The weight loss could be an indicator of an underlying detrimental process (e.g., accelerated physical aging) which may contribute to AD risk. The early weight loss could also be a sign of already existing, hidden, AD. In the latter case, this would imply that the latent period between AD initiation and AD clinical manifestation could be very long, and that “late-onset” AD is actually disease that begins in early adulthood. The latter is not impossible and deserves further investigation. The lack of similar associations in men may point to female-specific hormonal changes during aging, such as menopause-related metabolic transitions, including decline in estrogen levels, among others, which might contribute to the higher female vulnerability to neurological diseases, including AD [17]. This possibility also deserves further investigation.

This study revealed that older AgeMax and flatter Slope are both significant predictors of lower risk of AD in women (Fig. 2). Respectively, younger age at reaching maximum weight, and faster decline in weight towards older ages were associated with higher AD risk. This points to a major role of accelerated physical aging in AD, which was also suggested by others [18]. A more detailed analysis of the interaction between AgeMax and weight groups (that is, whether the association between AgeMax and AD risk varies in normal weight and overweight/obese groups) would also be helpful in future studies.

The number of APOE ɛ4 alleles carried by an individual was associated with younger AgeMax and steeper Slope in our analysis (Table 1). This may point to a role of APOE4 in accelerated aging too, though the mechanism needs clarification. It might involve, e.g., impaired physical resilience of APOE4 carriers [19], among other factors. One should note, however, that female non-carriers of APOE4, who developed AD at ages 75+, had significantly lower weight and reached peak values of the weight at younger ages compared to non-carriers without AD history (Fig. 1). This means that the relationship between AD and lower weight, as well as aging changes in the weight, observed in this study, cannot be explained by the effects of APOE4. It remains to be determined whether or not APOE4 influences AD risk through the biological effects of Apolipoprotein E on age-trajectories of weight. This could be addressed by further exploration of these relationships using causal inference approaches.

Our findings are also broadly in line with recent research that explored connections between temporal changes in BMI and dementia. A study of FHS offspring cohort with 76 cases of various dementias found BMI trends with first increasing and then declining mid-life pattern being associated with higher risk of dementia in later life [20]. Also, a British cohort study of the changes in BMI during 28 years before dementia diagnosis, found that BMI in dementia cases became lower starting 8 years before the diagnosis [9]. That study, however, used a mixture of dementia diagnoses, while our study is focused on AD.

In conclusion, women with AD onset after age 75 had lower bodyweight (as compared to AD-free women) long before AD diagnosis, as early as in their forties. They also reached peak values of weight about ten years earlier than AD-free women. The observation that weight loss may precede clinical manifestation of AD by three or more decades is compatible with the idea that “late-onset” AD could be silently developing in the body since young adulthood and involve accelerated physical aging. In line, a higher age at maximum weight and a slower decline in weight were both significant predictors of lower risk of AD onset after age 80 in women. APOE4 is unlikely to be the main factor responsible for the association between lower weight and AD risk, as APOE4 non-carriers who developed AD had even lower weight than APOE4 carriers with AD (prior to AD onset). Using causal inference approaches may further clarify the relationships between APOE4, weight loss, and AD.