Abstract
Background
Even small improvements in modifiable Alzheimer’s disease and related dementias (ADRD) risk factors could lead to a substantial reduction of dementia cases.
Aims
To determine if self-reported functional limitation associates with ADRD symptoms 4–18 years later.
Methods
We conducted a prospective longitudinal study using the Health and Retirement Study of adults aged 51–59 years in 1998 without symptoms of ADRD by 2002 and followed them to 2016. Main exposure variables were difficulty with activities of daily living, mobility, large muscle strength, gross motor and upper limb activities. The outcome was incident ADRD identified by the Lange-Weir algorithm, death, or alive without ADRD. We fit two GEE multinomial models for each measure: (1) baseline measure of function and (2) change in function over time.
Results
In the model with baseline only and outcome, only difficulty with mobility associated with future ADRD across levels of difficulty with near dose–response effect (risk ratios (RR) difficulty with 1–5 functions respectively, compared with no difficulty: 1.82; 2.70; 1.73 2.81; 4.03). Mobility also significantly associated with ADRD when allowing for change over time among those with 3, 4 or 5 versus no mobility limitations (RR 1.76; 2.36; 2.37).
Discussion
The results infer that an adult in midlife reporting difficulty with mobility as well as those with no mobility limitations in midlife but who later report severe limitations may be at increased risk of incident ADRD.
Conclusions
Self-reported measures of mobility limitation may be early indicators of ADRD and may be useful for public health planning.
Keywords: Physical function limitation, ADRD, Late midlife, Health and retirement study
Introduction
The societal and financial burden of Alzheimer’s disease and related dementias (ADRD) is increasing [1, 2] and by 2050, 13 million Americans may be living with ADRD which will cost $777 billion annually [3]. Even small improvements in modifiable dementia risk factors could lead to a substantial reduction of dementia cases [4]. Thus, identifying people who could benefit from early intervention is crucial. Experimental and observational studies have reported interventions such as physical activity and vitamin B12 plus folic acid could prevent ADRD [5]. However, evidence from prevention trials remains inconclusive, partially due to inclusion of subjects too old to benefit from intervention and relatively short periods of follow-up [6]. Several chronic diseases as well as cognitive, genetic, and lifestyle risk factors appear to act both independently and influence each other during the long preclinical ADRD state [7] before symptoms appear, typically after age 60 [8]. In general, Alzheimer’s is a progressive, degenerative brain disease in which changes in the brain may begin a decade or more before memory and other cognitive or functional problems (e.g., difficulty walking) appear. During this preclinical stage of Alzheimer’s disease, people seem to be symptom-free, but studies have identified early indicators of ADRD four or more years prior to onset. For example, performance-based measures of physical function, such as grip strength [9] and gait speed, [10] among adults aged 60 years and older were associated with ADRD at least 4 years prior to onset. In short, targeting adults with functional limitations during the preclinical phase of Alzheimer’s may allow for intervention. A systematic review of interventions for the prevention of ADRD found that while the majority of results showed no significant difference, the pattern of results across very different types of physical activity interventions provided an indication of effectiveness of physical activity [5]. If self-reported measures of functional limitation establish risk for ADRD earlier, that information could help identify individuals appropriate for targeted prevention. Key advantages of self-reported measures are that they are much easier and less expensive to obtain. Thus, this study aimed to determine if any of five measures of functional limitation among adults in their fifties associates with ADRD symptoms reported 4–18 years later.
Methods
Data source and study design
We analyzed data from the Health and Retirement Study (HRS), a population-based, prospective longitudinal interview survey of a cohort of adults aged 51 years and older in the United States [11, 12]. The survey data were collected every 2 years from 1998 to 2016. Our study population is designed and weighted to represent adults aged 51–59 years in the United States during 1998, followed to 2016. Thus, inclusion criteria included adults aged 51–59 who participated in the 1998 survey and had no symptoms of ADRD within 4 years of baseline or prior to the 2002 HRS interview. Participants were excluded who reported having a stroke prior to or at baseline (n = 147). If a study participant entered a nursing home and could not participate in the interview, then a proxy for the person was interviewed. Participants were followed until they were lost to follow-up or died. Deaths were confirmed using the National Death Index and the Social Security Death Index. To obtain data on the respondent prior to death, proxies for the respondents were interviewed. The HRS is sponsored by the National Institute on Aging and performed by the Institute for Social Research at the University of Michigan.
Outcome
The outcome of our analysis was incident ADRD. The HRS ADRD classification was validated with the Aging, Demographics, and Memory Study (ADAMS), a nationally representative survey of cognitive health which is a sub-sample of the HRS. We used the Langa-Weir algorithm which, in a validation study, the ADRD classification correctly classified 74% of prevalent ADRD cases as having dementia or not [13]. To our knowledge, although validation studies have identified algorithms to identify prevalent cases, [14, 15] no studies have effectively identified incident ADRD cases. In the absence of diagnoses, we used reported symptoms of ADRD to identify incident cases.
Main exposure
We assessed five exposure variables which are measures of function or disability: (1) activities of daily living (ADL, 0–5); (2) mobility (0–5); (3) large muscle strength (0–4); (4) gross motor activities (0–4); and 5) upper limb activities (0–4). Each measure has a score consisting of the number of individual functions. For example, if a subject has no difficulty with ADLs their score would be 0; if they had difficulty with one of the functions, their score would be 1, and so forth. (Table 1) Briefly, respondents were asked a series of questions related to physical function that were hierarchical scales to differentiate among adults with no problems to severe problems. For example, if a respondent reported they could not walk one block they were not asked if could not walk several blocks because it was assumed they could not; thus, there were skip patterns. Also, if they had no difficulty with certain functions, they were not asked the ADL questions because they were assumed to have no difficulty with any of them. The ADL, mobility, and large muscle strength summary measures were identified as domains from a previous HRS study using exploratory factor analysis [16]. All the summary measures were compiled by RAND, Corporation [17] for HRS users. We modified their previously identified ‘Fine Motor Skills’ summary measure which includes eating, dressing, and picking up a dime to also include pushing/pulling an object and label it ‘Upper Limb Activities’ to better represent fine motor skills leading to impairment in adults aged 50–59 years.
Table 1.
Functional limitation measures
| Functions (difficulty with:) | Activities of daily living (0–5) | Mobility (0–5) | Large muscle strength (0–4) | Gross motor activities (0–4) | Upper limb activities (0–4) |
|---|---|---|---|---|---|
|
| |||||
| Bathing | X | X | |||
| Eating | X | X | |||
| Dressing | X | X | |||
| Walking several blocks | X | ||||
| Walking one block | X | X | |||
| Walking across room | X | X | X | ||
| Climbing several flights stairs | X | ||||
| Climbing one flight stairs | X | X | |||
| Sitting for 2 h | X | ||||
| Getting up from chair | X | ||||
| Getting in/out of bed | X | ||||
| Stooping, kneeling, crouching | X | ||||
| Push/pull large object | X | X | |||
| Picking up a dime | X | ||||
Because of small numbers of respondents with high levels of functional limitation (e.g., 4 or 5) in some measures, we also conducted analyses re-categorizing measures with scores up to four or five as greater than or equal to three.
Covariates
We controlled for several covariates that potentially confound the relationship between physical function and ADRD. Time-invariant covariates included respondents’ level of education, sex, race (White, Black, Other), and Hispanic ethnicity. Time-variant covariates included age, marital status, smoking, physical activity, census region of residence, body mass index (kg/m2), diabetes, hypertension, depression, stroke, and heart disease.
Statistical analyses
We used STATA version 16 (College Station, TX) to fit generalized estimating equation (GEE) multinomial logit models that account for the complex survey design as well as the correlation from repeated measures within an individual over time, allowing for time-varying independent variables. We modeled each measure of function individually to determine if it was independently associated with incident ADRD. Two models were fit for each measure (exposure): 1) assessing the baseline measure in 1998 with the final outcome by end of study: incident ADRD, death, or neither; and 2) longitudinally including all time points, allowing for change in function and/or confounders until censorship due to incident ADRD, death, or end of study. If a respondent developed ADRD symptoms and later died, they were censored after onset of ADRD.
Using a SAS macro specifically designed to calculate population attributable fraction (PAF) in a cohort study design, [18] we calculated the PAF to quantify the impact of the various measures of function on incident ADRD at the population level. The resulting PAF estimate indicated the proportion of ADRD attributable to difficulty with function that would not have occurred if no one had functional limitation at baseline, whether or not they had difficulty with those functions during the study. Both the risk ratio, which indicates the strength of the association between difficulty with function and ADRD, and the presence of other risk factors were considered in the calculation of the PAF. Also, the estimation of PAF was for the time interval 1998–2016 and was adjusted for potential confounding factors and death as a competing risk.
Estimates presented are weighted to represent adults aged 51–59 years in the United States during 1998, followed to 2016. HRS data are public use with no identifiers and have been approved by the University of Michigan’s Institutional Review Board.
Results
At baseline, in the unadjusted analyses the majority of respondents were female (52.9%), White race (86.5%), aged 51–56 years (73.1%), and married (71.9%). Less than one-third were overweight (18.9%) or obese (9.4%), depressed (13.9%), currently smokers (24.4%), hypertensive (30.5%), diagnosed with diabetes (8.6%), or diagnosed with heart disease (8.5%). (Table 2) By 2016, 408 (6.2% (95%CI: 5.5, 6.9)) respondents reported ADRD symptoms and 1,045 (17.8% (95%CI: 16.7, 18.9)) died. Few (8.4%) respondents were censored due to loss to follow-up. The proportion of adults with incident ADRD symptoms differed statistically significantly (p < 0.05) from those without across all socio-demographic characteristics and chronic conditions assessed at baseline. For example, although having less than a high school education was the least frequently reported, persons with this level of education were 3.4 times as likely to develop ADRD symptoms than those with high school education (18.5% vs. 5.5%) and more than 5 times as likely as those with some college education (18.5% vs 3.6%). Black adults aged 51–59 years were more than 3 times as likely to develop ADRD symptoms than their White counterparts (16.2% vs 4.9%).
Table 2.
Unadjusted descriptive baseline characteristics overall and incident ADRD by 2016, US adults aged 51–59 years in 1998
| Characteristics | Overall population |
Incident ADRD |
||||||
|---|---|---|---|---|---|---|---|---|
| n | wtd% | Low CI | Up CI | n | wtd% | Low CI | Up CI | |
|
| ||||||||
| Total | 5415 | 100.0 | 408 | 6.2 | 5.5 | 6.9 | ||
| Socio-demographic characteristics | ||||||||
| Sex | ||||||||
| Male | 2294 | 47.1 | 46.0 | 48.2 | 175 | 6.3 | 5.2 | 7.3 |
| Female | 3121 | 52.9 | 51.8 | 54.0 | 233 | 6.1 | 5.3 | 6.9 |
| Age group | ||||||||
| 51–53 | 1402 | 37.4 | 35.9 | 38.8 | 71 | 4.8 | 3.9 | 5.8 |
| 54–56 | 1742 | 35.7 | 34.4 | 37.1 | 115 | 5.5 | 4.5 | 6.5 |
| 57–59 | 2271 | 26.9 | 25.9 | 27.9 | 222 | 8.9 | 7.3 | 10.4 |
| Race | ||||||||
| White | 4413 | 86.5 | 84.7 | 88.3 | 262 | 4.9 | 4.3 | 5.6 |
| Black | 778 | 9.2 | 8.0 | 10.4 | 124 | 16.2 | 12.9 | 19.6 |
| Other | 222 | 4.3 | 3.2 | 5.4 | 22 | 9.2 | 5.3 | 13.1 |
| Hispanic ethnicity | ||||||||
| Yes | 287 | 4.5 | 2.1 | 6.9 | 43 | 13.4 | 9.3 | 17.4 |
| No | 5128 | 95.5 | 93.1 | 97.9 | 365 | 5.8 | 5.1 | 6.5 |
| Education level | ||||||||
| No degree | 963 | 14.8 | 13.0 | 16.6 | 195 | 18.5 | 15.1 | 21.9 |
| High school/GED | 1991 | 35.9 | 34.1 | 37.8 | 125 | 5.5 | 4.5 | 6.5 |
| Some college | 1275 | 24.4 | 22.8 | 26.1 | 53 | 3.6 | 2.6 | 4.5 |
| College degree | 1185 | 24.9 | 22.3 | 27.4 | 35 | 2.4 | 1.5 | 3.2 |
| Married | ||||||||
| Yes | 4063 | 71.9 | 70.2 | 73.5 | 275 | 5.4 | 4.6 | 6.3 |
| No | 1348 | 28.1 | 26.5 | 29.8 | 132 | 8.0 | 6.4 | 9.5 |
| Census region | ||||||||
| Northeast | 881 | 17.4 | 13.7 | 21.2 | 59 | 5.5 | 4.5 | 6.5 |
| Midwest | 1362 | 25.4 | 21.9 | 28.8 | 97 | 6.5 | 5.0 | 7.9 |
| South | 2271 | 39.1 | 35.4 | 42.8 | 192 | 7.1 | 5.7 | 8.5 |
| West | 896 | 18.1 | 14.3 | 21.9 | 59 | 4.4 | 3.0 | 5.7 |
| Chronic conditions | ||||||||
| Body Mass Index (kg/m2) | ||||||||
| Underweight (< 18.5) | 1649 | 31.5 | 30.1 | 32.9 | 106 | 5.3 | 4.3 | 6.3 |
| Normal (18.5–24.9) | 2134 | 40.2 | 38.8 | 41.5 | 159 | 6.1 | 4.9 | 7.3 |
| Overweight (25–29.9) | 1047 | 18.9 | 17.8 | 20.0 | 86 | 6.8 | 5.2 | 8.5 |
| Obese (≥ 30) | 508 | 9.4 | 8.4 | 10.5 | 51 | 7.7 | 5.5 | 10.0 |
| Hypertension | ||||||||
| Yes | 1824 | 30.5 | 28.9 | 32.1 | 167 | 7.6 | 6.4 | 8.9 |
| No | 3587 | 69.5 | 67.9 | 71.1 | 241 | 5.5 | 4.7 | 6.3 |
| Diabetes | ||||||||
| Yes | 545 | 8.6 | 7.6 | 9.6 | 76 | 11.9 | 9.4 | 14.4 |
| No | 4867 | 91.4 | 90.4 | 92.4 | 332 | 5.6 | 4.9 | 6.4 |
| Heart disease | ||||||||
| Yes | 525 | 8.5 | 7.7 | 9.2 | 52 | 7.8 | 5.3 | 10.3 |
| No | 4888 | 91.5 | 90.8 | 92.3 | 355 | 6.0 | 5.2 | 6.8 |
| Difficulty with function by measure | ||||||||
| Activities of daily living (number of functions having difficulty with) | ||||||||
| 0 | 4904 | 91.3 | 90.4 | 92.2 | 331 | 5.5 | 4.8 | 6.2 |
| 1 | 265 | 4.5 | 3.9 | 5.1 | 37 | 12.4 | 8.3 | 16.4 |
| 2 | 129 | 2.3 | 1.9 | 2.7 | 15 | 11.4 | 4.8 | 18.1 |
| 3 | 62 | 1.2 | 0.8 | 1.4 | 12 | 14.7 | 7.0 | 22.4 |
| 4 | 36 | 0.6 | 0.3 | 0.8 | – | – | – | – |
| 5 | 11 | 0.2 | 0.1 | 0.3 | – | – | – | – |
| Mobility (number of functions having difficulty with) | ||||||||
| 0 | 3546 | 68.2 | 66.7 | 69.7 | 199 | 4.4 | 3.6 | 5.2 |
| 1 | 954 | 16.5 | 15.2 | 17.7 | 81 | 7.6 | 6.0 | 9.3 |
| 2 | 406 | 6.7 | 5.8 | 7.5 | 59 | 12.1 | 8.8 | 15.4 |
| 3 | 223 | 3.8 | 3.2 | 4.4 | 23 | 8.7 | 5.2 | 12.2 |
| 4 | 181 | 3.2 | 2.6 | 3.8 | 29 | 14.2 | 9.0 | 19.5 |
| 5 | 97 | 1.7 | 1.3 | 2.1 | 14 | 15.4 | 6.3 | 24.4 |
| Large muscle (number of functions having difficulty with) | ||||||||
| 0 | 2901 | 56.0 | 54.3 | 57.8 | 161 | 4.4 | 3.7 | 5.1 |
| 1 | 950 | 16.9 | 15.9 | 18.0 | 84 | 7.2 | 5.7 | 8.7 |
| 2 | 706 | 12.7 | 11.5 | 13.9 | 53 | 6.3 | 4.0 | 8.6 |
| 3 | 493 | 8.4 | 7.6 | 9.3 | 61 | 11.8 | 7.8 | 15.8 |
| 4 | 354 | 5.9 | 5.2 | 6.7 | 46 | 11.0 | 7.8 | 14.2 |
| Gross motor skills (number of functions having difficulty with) | ||||||||
| 0 | 4658 | 87.3 | 86.2 | 88.4 | 306 | 5.4 | 4.6 | 6.1 |
| 1 | 368 | 6.2 | 5.3 | 7.0 | 41 | 8.4 | 5.7 | 11.1 |
| 2 | 182 | 3.1 | 2.5 | 3.6 | 29 | 14.7 | 9.0 | 20.5 |
| 3 | 111 | 1.9 | 1.5 | 2.4 | 16 | 15.1 | 7.2 | 23.0 |
| 4 | 56 | 1.0 | 0.8 | 1.3 | – | – | – | – |
| 5 | 32 | 0.5 | 0.3 | 0.8 | – | – | – | – |
| Upper limb (number of functions having difficulty with) | ||||||||
| 0 | 4355 | 79.7 | 78.3 | 81.2 | 278 | 5.1 | 4.3 | 5.9 |
| 1 | 879 | 15.0 | 13.8 | 16.2 | 104 | 10.8 | 8.5 | 13.2 |
| 2 | 241 | 4.0 | 3.3 | 4.7 | 35 | 10.7 | 6.4 | 15.0 |
| 3 | 64 | 1.0 | 0.7 | 1.2 | – | – | – | – |
| 4 | 16 | 0.3 | 0.1 | 0.5 | – | – | – | – |
| Behavioral characteristics | ||||||||
| Depressive symptoms | ||||||||
| Yes (CES-D ≥ 4) | 756 | 13.9 | 12.9 | 15.0 | 115 | 13.2 | 10.2 | 16.2 |
| No (CES-D < 3) | 4319 | 86.1 | 85.0 | 87.1 | 251 | 4.7 | 4.1 | 5.2 |
| Physical activity | ||||||||
| Active (≥ 2 times weekly) | 2674 | 50.5 | 48.7 | 52.2 | 196 | 6.0 | 5.1 | 6.9 |
| Inactive (≤ once weekly) | 2735 | 49.5 | 47.8 | 51.3 | 212 | 6.3 | 5.2 | 7.5 |
| Smoking status | ||||||||
| Current | 1280 | 24.4 | 22.6 | 26.3 | 131 | 8.8 | 6.9 | 10.6 |
| Former | 2014 | 37.1 | 35.2 | 39.1 | 140 | 5.3 | 4.1 | 6.6 |
| Never | 2121 | 38.5 | 36.5 | 40.4 | 137 | 5.3 | 4.2 | 6.4 |
Those with previous stroke at baseline or before and those with ADRD symptoms prior to 2002 were excluded. CI: confidence interval; wtd%: weighted to represent US adults aged 51–59 years in 1998 followed to 2016
CES-D Center for Epidemiologic Studies Depression Scale, ‘–’ denotes n < 10
In the unadjusted analyses, at baseline for all five of the measures of function, adults in their 50’s with no difficulties were significantly less likely to develop ADRD symptoms compared with those who had difficulty with one or more components (p < 0.05). (Table 3) However, in the adjusted analyses only difficulty with mobility associated with future ADRD in near dose–response for difficulty with 1, 2, 4, 5 vs no difficulty; risk ratio (RR) 1.82 (95%CI 1.22, 2.70, RR: 2.45 (95%CI 1.59, 3.77), RR: 2.81 (1.45, 5.47), RR: 4.03 (95%CI 1.50, 10.85), respectively) when assessing function at baseline only; the association for difficulty with 3 measures was not statistically significant (RR: 1.73 (0.87, 3.46). For the other summary measures, only certain levels (number of measures with difficulty) were statistically significantly associated with incident ADRD symptoms. For ADLs, only difficulty with 1 vs 0 was significant [RR: 2.11 (95%CI 1.31, 3.41)]; for large muscle, difficulty with 1 vs 0 [RR: 1.52 (95%CI 1.06, 2.18)], 3 vs 0 [RR: 2.32 (95%CI 1.47, 3.65)] and 4 vs 0 [RR: 1.65 (95%CI 1.03, 2.64)] were significant; for gross motor activities, only 2 vs 0 [RR: 2.06 (95%CI 1.08, 3.95)] was significant; and for upper limb activities, difficulty with 1 was significantly different from no difficulty [RR: 1.99 (95%CI 1.41, 2.81)].
Table 3.
Unadjusted and adjusted risk of incident ADRD by function, US adults aged 51–59 years in 1998 (ADRD assessed two points in time, baseline and up to 2016)
| Characteristics | Unadjusted |
Adjusted |
||||
|---|---|---|---|---|---|---|
| Risk Ratio | Low CI | Up CI | Risk Ratio | Low CI | Up CI | |
|
| ||||||
| Activities of daily living (number of functions having difficulty with) | ||||||
| 0 | Ref | Ref | ||||
| 1 | 3.22 | 2.13 | 4.87 | 2.11 | 1.31 | 3.41 |
| 2 | 2.96 | 1.50 | 5.87 | 1.70 | 0.70 | 4.18 |
| 3 | 3.89 | 1.98 | 7.63 | 1.09 | 0.50 | 2.40 |
| 4 | – | – | – | – | – | – |
| 5 | – | – | – | – | – | – |
| Mobility (number of functions having difficulty with) | ||||||
| 0 | Ref | Ref | ||||
| 1 | 1.90 | 1.39 | 2.60 | 1.82 | 1.22 | 2.70 |
| 2 | 3.69 | 2.57 | 5.29 | 2.45 | 1.59 | 3.77 |
| 3 | 3.04 | 1.77 | 5.22 | 1.73 | 0.87 | 3.46 |
| 4 | 5.60 | 3.43 | 9.16 | 2.81 | 1.45 | 5.47 |
| 5 | 7.28 | 3.35 | 15.81 | 4.03 | 1.50 | 10.85 |
| Large muscle (number of functions having difficulty with) | ||||||
| 0 | Ref | Ref | ||||
| 1 | 1.79 | 1.39 | 2.30 | 1.52 | 1.06 | 2.18 |
| 2 | 1.63 | 1.06 | 2.52 | 1.37 | 0.81 | 2.33 |
| 3 | 3.55 | 2.32 | 5.44 | 2.32 | 1.47 | 3.65 |
| 4 | 3.45 | 2.30 | 5.17 | 1.65 | 1.03 | 2.64 |
| Gross motor skills (number of functions having difficulty with) | ||||||
| 0 | Ref | Ref | ||||
| 1 | 1.96 | 1.27 | 3.00 | 1.27 | 0.82 | 1.96 |
| 2 | 4.43 | 2.73 | 7.20 | 2.06 | 1.08 | 3.95 |
| 3 | 4.82 | 2.40 | 9.66 | 2.34 | 0.86 | 6.35 |
| 4 | – | – | – | – | – | – |
| 5 | – | – | – | – | – | – |
| Upper Limb Activities (number of functions having difficulty with) | ||||||
| 0 | Ref | Ref | ||||
| 1 | 2.75 | 2.01 | 3.77 | 1.99 | 1.41 | 2.81 |
| 2 | 2.92 | 1.81 | 4.73 | 1.29 | 0.73 | 2.30 |
| 3 | – | – | – | – | – | – |
| 4 | ||||||
Those with previous stroke at baseline or before and those with ADRD symptoms prior to 2002 were excluded. Adjusted estimates accounted for the complex survey and the following covariates: sex, race/ethnicity, age, level of education, census region of residence, marital status, body mass index, smoking, physical activity, depression, high blood pressure, diabetes, stroke, and heart disease. ‘–’ denotes incident ADRD < 10
In unadjusted analyses, when allowing for changes over time and controlling for confounders, all self-reported measures of function were statistically significantly associated with future ADRD. (Table 4) In adjusted analyses, for ADLs, difficulty with 2 vs 0 [RR: 1.95 (95%CI 1.03, 3.69)] and 3 vs 0 [RR: 3.01 (95%CI 1.58, 5.74)] were significant; mobility associated with future ADRD in dose–response for difficulty with 3, 4, 5 vs no difficulty: [RR: 1.76 (95%CI 1.07, 2.89, RR: 2.36 (95%CI 1.37, 4.08), RR: 2.37 (1.17, 4.79), respectively]; for large muscle, no levels of difficulty were significantly different from no difficulty; for gross motor activities, only 2 vs 0 [RR: 2.49 (95%CI 1.50, 4.12)] was significant; and upper limb activities associated with future ADRD for difficulty with 2 vs 0 [RR: 1.64 (95%CI 1.06, 2.53)].
Table 4.
Unadjusted and adjusted risk of incident ADRD by function, US adults aged 51–59 years in 1998 followed through 2016 (allowing for changes over time)
| Characteristics | Unadjusted |
Adjusted |
||||
|---|---|---|---|---|---|---|
| Risk Ratio | Low CI | Up CI | Risk Ratio | Low CI | Up CI | |
|
| ||||||
| Activities of daily living (number of functions having difficulty with) | ||||||
| 0 | Ref | Ref | ||||
| 1 | 2.64 | 1.86 | 3.74 | 1.44 | 0.98 | 2.12 |
| 2 | 5.06 | 3.29 | 7.79 | 1.95 | 1.03 | 3.69 |
| 3 | 8.81 | 5.39 | 14.41 | 3.01 | 1.58 | 5.74 |
| 4 | – | – | – | – | – | – |
| 5 | – | – | – | – | – | – |
| Mobility (number of functions having difficulty with) | ||||||
| 0 | Ref | Ref | ||||
| 1 | 1.56 | 1.07 | 2.29 | 1.14 | 0.70 | 1.85 |
| 2 | 2.25 | 1.47 | 3.46 | 0.94 | 0.62 | 1.42 |
| 3 | 3.75 | 2.41 | 5.83 | 1.76 | 1.07 | 2.89 |
| 4 | 6.32 | 4.18 | 9.55 | 2.36 | 1.37 | 4.08 |
| 5 | 12.43 | 8.60 | 17.98 | 2.37 | 1.17 | 4.79 |
| Large muscle (number of functions having difficulty with) | ||||||
| 0 | Ref | Ref | ||||
| 1 | 1.77 | 1.20 | 2.59 | 1.26 | 0.84 | 1.90 |
| 2 | 2.02 | 1.42 | 2.88 | 1.09 | 0.72 | 1.66 |
| 3 | 4.11 | 2.78 | 6.09 | 1.51 | 0.94 | 2.42 |
| 4 | 4.39 | 3.07 | 6.27 | 1.13 | 0.75 | 1.70 |
| Gross motor skills (number of functions having difficulty with) | ||||||
| 0 | Ref | Ref | ||||
| 1 | 2.26 | 1.56 | 3.25 | 1.36 | 0.95 | 1.93 |
| 2 | 5.23 | 3.50 | 7.84 | 2.49 | 1.50 | 4.12 |
| 3 | 4.13 | 2.57 | 6.64 | 1.45 | 0.81 | 2.61 |
| 4 | – | – | – | – | – | – |
| 5 | – | – | – | – | – | – |
| Upper limb activities (number of functions having difficulty with) | ||||||
| 0 | Ref | Ref | ||||
| 1 | 1.94 | 1.40 | 2.67 | 1.12 | 0.77 | 1.61 |
| 2 | 4.60 | 3.45 | 6.15 | 1.64 | 1.06 | 2.53 |
| 3 | – | – | – | – | – | – |
| 4 | – | – | – | – | – | – |
Those with previous stroke at baseline or before and those with ADRD symptoms prior to 2002 were excluded. Adjusted estimates accounted for the complex survey design and repeated measurements on individuals and the following covariates: sex, race/ethnicity, age, level of education, census region of residence, marital status, body mass index, smoking, physical activity, depression, high blood pressure, diabetes, and heart disease. ‘–’denotes incident ADRD < 10
In adjusted analyses re-categorizing measures with scores up to four or five as greater than or equal to three, and allowing for changes over time, none of the measures had significant associations for all levels with a dose–response relationship.
In assessing the population attributable fraction for difficulty with the different functional limitation measures separately: (1) difficulty with three or more ADLs accounted for 2.9% of the ADRD cases over 18 years; (2) difficulty with three or more mobility functions contributed to 5.2% of the ADRD cases, (3) difficulty with any large muscle functions did not contribute to ADRD cases during the study, (4) difficulty with one to five gross motor skills contributed to 2.2% of ADRD cases, and (5) difficulty with two to four upper limb activities accounted for 4.5% of ADRD cases.
Discussion
Overall, in adjusted analyses mobility limitation at baseline associated with future ADRD with near dose–response by level of difficulty, without considering changes over time, and when assessing for changes in mobility difficulty over time, difficulty with 3 or more components associated with ADRD. The results of the baseline assessment infer that an adult in midlife having difficulty with mobility may be at increased risk of incident ADRD. The results of the model allowing for change over time infer that an adult in midlife with no mobility limitations but who later develops severe limitations (i.e., 3 or more involving walking one block, several blocks, across the room, or climbing one flight or more of stairs) may be at increased risk of incident ADRD. Mobility limitation in midlife affects participation in physical activities, which likely affects cerebral blood flow [19] and plasticity, [20] hastening the onset of cognition and dementia by decreasing blood flow to the brain. Functional limitation and disease diagnosis often trek together, with experiencing difficulty serving as the impetus for a medical visit where upon a diagnosis (e.g., diabetes) may occur. A likely mechanism linking mobility limitation to future ADRD is that mobility limitation often is a consequence of several health conditions known to impact cognition, such as peripheral artery disease, [21] pulmonary disease, [22] and congestive heart failure [23]. However, mobility limitations have also been linked to onset of diabetes [24] and hypertension, [25] both of which increase the risk of damage to blood vessels which increases the risk of damage to blood vessels which affects the brain’s white matter and leads to vascular dementia. Diabetes is also a risk factor for Alzheimer’s disease through the cerebrovascular disease that diabetes causes [26]. Our study now suggests that self-reported mobility limitation in midlife may be associated with future ADRD. Previously, only performance-based measures have been identified as early indicators of ADRD [9, 10]. This is important because it is less time-consuming for a healthcare provider to screen a patient for self-reported difficultly with mobility rather than conducting performance-based testing across the board. Such screening may lead to more formal testing but it can be cost-saving initially. In addition, it would not be a deleterious false-positive as some biomarkers may be in identifying those at early risk of ADRD. Indeed, identifying those who would benefit from physical therapy or other interventions that may improve functional limitations would likely improve the individual’s health in myriad ways.
Use of self-reported measures of mobility limitation among adults in their fifties may be useful for targeted prevention interventions. Currently, there is little evidence to support a cause effect relationship between any preventive measure and the development of dementia [27]. However, individually several factors have been associated with improved cognition, such as physical exercise, healthy diet, stress reduction, vascular risk reduction and treatment for major depressive disorder. Moreover, because of the multifactorial etiology of ADRD, interventions that target several risk factors simultaneously might have the best preventive effect [28]. One large, long-term, randomized controlled trial found that a multidomain intervention could improve or maintain cognitive functioning in at-risk adults aged 60–77 years from the general population [29]. Of studies conducted to assess prevention or delay of cognitive decline, findings were most promising for physical activity, though those studies were done among adults aged 60 and older [5]. Thus, among adults aged 50–59, in addition to physical exercise, healthy diet, stress reduction, vascular risk reduction and treatment for major depressive disorder, physical therapy for those having difficulty with physical function should be considered for its potential as a preventative therapy.
It is also important to note that difficulty with upper limb activities over time associated with onset of ADRD. This result is not surprising given that clinically, Alzheimer’s disease both affects motor function later in the course of the illness (unlike vascular, Lewy Body, and other common forms of dementia), though it also affects the brain globally. So, perhaps it is no surprise that Alzheimer’s disease would therefore subtly have a global impact, not just on ADLs but also upper limb function, and likely other cerebral-dependent function.
One limitation of our study is that the cause(s) of difficulty with performing activities may vary and differ according to risk of ADRD, and such information was not available. In addition, we could not exclude those with orthopedic disorders or chronic alcohol consumption at baseline because such information was not available. Also, the lack of statistically significant effect modification of race on mobility limitation with respect to incident ADRD may be due to the small sample of Black respondents having difficulty with three or more mobility functions. Another limitation is that the 1998 cohort may not be representative of later birth cohorts. In addition, the lack of a dose–response effect in several of the measures may be due to the small sample reporting difficulty with multiple functions in the age group being studied.
Although the risk ratios for ADLs on ADRD were stronger than the risk ratios for difficulty with mobility in the models allowing for change over time, the PAF for ADRD was higher overall over time for difficulty with mobility. Thus, the significant baseline risk of ADRD by difficulty with mobility that was not seen with ADLs played a role. It is important to note that ADL difficulty may be an early manifestation of ADRD and not a remediable cause.
Our study is a contribution to the field in that it is the first to find self-reported difficulty with physical function to be associated with incident ADRD 4–16 years later. One study reported that approximately 910,000 age 65 and older developed ADRD in 2011 [30]. Approximately 47,320 (5.2%) of those ADRD cases in 2011 may have been attributed to potentially modifiable difficulty with mobility. With the annual number of new ADRD cases expected to double by 2050 and the annual cost valued at nearly $244 billion, [31] further research is needed to determine if interventions to improve difficulty with physical function lessen the risk of incident ADRD. Notably, self-reported measures of physical function, which are easier to administer than performance-based measures, may be useful to policy-makers and public health officials in terms of planning by means of information on changes in the population at risk (e.g., national prevalence and incidence of physical limitations as early indicators of ADRD).
Funding
This research was supported, in part, by NIH R03AG070668–01. Data from the Health and Retirement Survey were collected by the University of Michigan through funding from the National Institute on Aging (NIA U01AG009740) and the Social Security Administration. BHB, LR, EJ, and SG designed the study and edited the manuscript, BHB conducted the analyses, drafted the manuscript, and obtained funding for the project.
Footnotes
Conflict of interest Authors have no conflicts to report.
Declarations
Statement of human and animal rights This article does not contain any experimental study with humans or animals performed by the authors.
Informed consent Data from the Health and Retirement Study are publicly available, so informed consent was not applicable for the authors. Data were collected by the University of Michigan who obtained informed consent from study participants, per the University of Michigan’s IRB.
References
- 1.Patterson C (2018) World alzheimer report 2018. The state of the art of dementia research. New Frontiers, London [Google Scholar]
- 2.Corriveau RA, Koroshetz WJ, Gladman JT et al. (2017) Alzheimer’s disease-related dementias summit 2016: national research priorities. Neurology 89:2381–2391 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Alzheimer’s Impact Movement (2020) Fact sheet: costs of Alzheimer’s to medicare and medicaid. https://act.alz.org/site/DocServer/2012_Costs_Fact_Sheet_version_2.pdf?docID=7161. Accessed 21 June 2021
- 4.Rose G (1985) Sick individuals and sick populations. Int J Epidemiol 14:32–38 [DOI] [PubMed] [Google Scholar]
- 5.Kane RL, Butler M, Fink HA et al. (2017) Interventions to prevent age-related cognitive decline, mild cognitive impairment, and clinical Alzheimer’s-type dementia. AHRQ Comparative Effectiveness Reviews, Rockville [PubMed] [Google Scholar]
- 6.Eggink E, Moll van Charante EP, van Gool WA et al. (2019) A population perspective on prevention of dementia. J Clin Med 8:834. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Livingston G, Sommerlad A, Orgeta V et al. (2017) Dementia prevention, intervention, and care. Lancet 390:2673–2734 [DOI] [PubMed] [Google Scholar]
- 8.Centers for Disease Control and Prevention (2018) Alzheimer’s disease promoting health and independence for an aging population at a glance. https://www.cdc.gov/chronicdisease/resources/publications/factsheets/promoting-health-for-older-adults.htm?CDC_AA_refVal=https%3A%2F%2Fwww.cdc.gov%2Fchronicdisease%2Fresources%2Fpublications%2Faag%2Falzheimers.htm. Accessed 11 May 2021
- 9.Buchman AS, Wilson RS, Boyle PA et al. (2007) Grip strength and the risk of incident Alzheimer’s disease. Neuroepidemiology 29:66–73 [DOI] [PubMed] [Google Scholar]
- 10.Dumurgier J, Artaud F, Touraine C et al. (2017) gait speed and decline in gait speed as predictors of incident dementia. J Gerontol A Biol Sci Med Sci 72:655–661 [DOI] [PubMed] [Google Scholar]
- 11.Juster F, Suzman R (1995) An overview of the HRS. J Hum Resour 30:50 [Google Scholar]
- 12.Soldo BJ, Hurd MD, Rodgers WL et al. (1997) Asset and Health Dynamics Among the Oldest Old: an overview of the AHEAD Study. J Gerontol Ser B 52:1–20 [DOI] [PubMed] [Google Scholar]
- 13.Crimmins EM, Kim JK, Langa KM et al. (2011) Assessment of cognition using surveys and neuropsychological assessment: the health and retirement study and the aging, demographics, and memory study. J Gerontol Ser B 66:162–171 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Grodstein F, Chang CH, Capuano AW et al. (2021) Identification of dementia in recent medicare claims data, compared to rigorous clinical assessments. J Gerontol A Biol Sci Med Sci. 10.1093/gerona/glab377 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.McCarthy EP, Chang CH, Tilton N et al. (2021) Validation of claims algorithms to identify Alzheimer’s disease and related dementias. J Gerontol A Biol Sci Med Sci. 10.1093/gerona/glab373 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Wallace RB, Herzog AR (1995) Overview of the health measures in the health and retirement study. J Hum Resour 30:S84–S107 [Google Scholar]
- 17.RAND (2018) Data products of the center for the study of aging. https://www.rand.org/labor/aging/dataprod.html. Accessed 17 June 2021
- 18.Laaksonen MA, Virtala E, Knekt P et al. (2011) SAS macros for calculation of population attributable fraction in a cohort study design. J Stat Softw 43:1–25 [Google Scholar]
- 19.Rogers RL, Meyer JS, Mortel KF (1990) After reaching retirement age physical activity sustains cerebral perfusion and cognition. J Am Geriatr Soc 38:123–128 [DOI] [PubMed] [Google Scholar]
- 20.Colcombe SJ, Kramer AF, Erickson KI et al. (2004) Cardiovascular fitness, cortical plasticity, and aging. Proc Natl Acad Sci USA 101:3316–3321 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Hammond MM, Tian L, Zhao L et al. (2021) One-year change in walking performance and subsequent mobility loss and mortality rates in peripheral artery disease: longitudinal data from the WALCS. J Am Heart Assoc 10:e021917. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Grimmer M, Riener R, Walsh CJ et al. (2019) Mobility related physical and functional losses due to aging and disease: a motivation for lower limb exoskeletons. J Neuroeng Rehabil 16:2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Fried LP, Guralnik JM (1997) Disability in older adults: evidence regarding significance, etiology, and risk. J Am Geriatr Soc 45:92–100 [DOI] [PubMed] [Google Scholar]
- 24.Bardenheier BH, Gregg EW, Zhuo X et al. (2014) Association of functional decline with subsequent diabetes incidence in U.S. adults aged 51 years and older: the Health and Retirement Study 1998–2010. Diabetes Care 37:1032–1038 [DOI] [PubMed] [Google Scholar]
- 25.Faraco G, Iadecola C (2013) Hypertension: a harbinger of stroke and dementia. Hypertension 62:810–817 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Messier C (2003) Diabetes, Alzheimer’s disease and apolipoprotein genotype. Exp Gerontol 38:941–946 [DOI] [PubMed] [Google Scholar]
- 27.Rakesh G, Szabo ST, Alexopoulos GS et al. (2017) Strategies for dementia prevention: latest evidence and implications. Ther Adv Chronic Dis 8:121–136 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Kivipelto M, Mangialasche F, Ngandu T (2018) Lifestyle interventions to prevent cognitive impairment, dementia and Alzheimer disease. Nat Rev Neurol 14:653–666 [DOI] [PubMed] [Google Scholar]
- 29.Ngandu T, Lehtisalo J, Solomon A et al. (2015) A 2 year multidomain intervention of diet, exercise, cognitive training, and vascular risk monitoring versus control to prevent cognitive decline in at-risk elderly people (FINGER): a randomised controlled trial. Lancet 385:2255–2263 [DOI] [PubMed] [Google Scholar]
- 30.Rajan KB, Weuve J, Barnes LL et al. (2019) Prevalence and incidence of clinically diagnosed Alzheimer’s disease dementia from 1994 to 2012 in a population study. Alzheimers Dement 15:1–7 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.The Alzheimer’s Association (2020) 2020 Alzheimer’s disease facts and figures. Alzheimers Dement 16:391–460 [DOI] [PubMed] [Google Scholar]