The joint association of obesity and physical activity on cognitive function in Down syndrome

Abstract

Background

Lifetime incidence of Alzheimer's disease (AD) in Down syndrome (DS) exceeds 90%. In adults with and without DS, low moderate-to-vigorous physical activity (MVPA) and obesity have independently been associated with AD. Research across other disease conditions indicates MVPA may attenuate some negative consequences of obesity.

Objective

To evaluate the potential joint association of obesity and MVPA on cognitive function in adults with DS.

Methods

Seventy-five adults with DS (age 39.1 years, 46.7% female) enrolled in the Alzheimer Biomarker Consortium-Down Syndrome (ABC-DS) study and completed a 7-day accelerometer protocol. Cognitive function was assessed using the ABC-DS cognitive battery. Participants were categorized with Obesity (body mass index (BMI) ≥ 30 kg/m²) or No Obesity (BMI < 30 kg/m²). A median split of MVPA was then used to create four groups: No Obesity/High PA (n = 18), No Obesity/Low PA (n = 16), Obesity/High PA (n = 20), and Obesity/Low PA (n = 21). Linear models were used to compare cognitive function across groups.

Results

The Obesity/High PA group performed better than the No Obesity/Low PA group for episodic memory (β = 4.86, p = 0.009), executive functioning (β = 3.07, p = 0.013), and dementia symptoms (β = −6.57, p = 0.022). The Obesity/High PA group also performed better than the Obesity/Low PA group for memory and orientation (β = 4.43, p = 0.012), social functioning (β = 4.52, p = 0.006), visuo-spatial processing (β = −2.39, p = 0.038), and overall dementia symptoms (β = −7.21, p = 0.016). There were no assessments for which either high PA group performed worse than either Low PA group.

Conclusions

Physical activity may benefit AD-related cognitive function in persons with DS, regardless of obesity status.

Keywords

Alzheimer's disease cognitive function Down syndrome obesity physical activity Trisomy 21

Introduction

Down syndrome (DS) or trisomy 21 is the most common chromosomal abnormality associated with intellectual disability (ID).¹ Advances in medical care for those with DS have resulted in an increase in median life expectancy from 10 years to approximately 60 years in the last 60 years.² With this increase in life expectancy, aging-related challenges for persons with DS have become evident. All adults with DS will develop Alzheimer's disease (AD) associated neuropathology^3,4 with AD being the leading cause of death in this population.^4,5 Research suggests that deposition of amyloid-β (Aβ) occurs decades earlier in adults with DS compared with typically developed adults^6,7 and lifetime incidence of dementia is 90% in DS.^4,5 However, a recent review found significant individual variability in onset of clinical AD⁵ and that ∼10% of individuals with DS over 60 years of age have Aβ pathology associated with AD but have not developed cognitive symptoms of dementia.⁵ Together, this suggests that even in the context of Aβ positivity, the risk of dementia is modifiable.

Obesity, defined as a body mass index (BMI) ≥ 30 kg/m², and physical inactivity are associated with cognitive decline and the development of AD in the general population.^8,9 The prevalence of both obesity and low physical activity (PA) are higher in adults with DS compared with their typically developed counterparts.^10,11 For example, the prevalence of obesity is twice as high in adults with DS (85%) compared to typically developing adults (42%).^12–14 Additionally, it is estimated that only 9% of adults with ID, including those with DS, achieve 150 min per week of moderate-to-vigorous physical activity (MVPA)¹⁰ as recommend by the Physical Activity Guidelines for Americans¹⁵ compared with ∼52% of typically developed adults.¹⁶ However, the information regarding the association of obesity and MVPA on cognitive function and the development of AD in adults with DS is limited and has shown inconsistent results.^17,18 In an analysis of 61 adults with DS and no dementia, Fleming et al.¹⁷ reported that a higher BMI was significantly associated with poorer episodic memory. In contrast, Dodd et al.¹⁸ analyzed 79 young adults with DS found no significant associations between BMI and cognitive function. Regarding physical activity, Fleming et al.¹⁷ reported that a higher percent of time spent in MVPA was significantly correlated with higher scores on 8/9 cognitive outcomes assessed. Similarly, Pape et al.¹⁹ observed that higher MVPA at baseline, was associated with a reduced risk for decline in memory and orientation across 12 months in a cohort of adults with DS (n = 214, age ∼46 years).

Considerable evidence in both typically developed adults, and adults with ID demonstrates that regardless of obesity status, physical activity or fitness are associated with lower risk for mortality^20,21 and development of obesity-related conditions such as cardiovascular disease^22,23 or impaired glucose metabolism.^21,24 However, this potential joint association of obesity and physical activity on cognition in adults with DS has not been investigated. This purpose of this cross-sectional analysis was to evaluate the potential joint association of obesity, as measured by BMI, and MVPA, measured via accelerometry, on cognitive function in a sample of adults with DS. Given the available evidence, it is hypothesized that MVPA will be associated with better cognitive function regardless of obesity status.

Methods

Participants

Seventy-five adults with DS who were part of the Alzheimer Biomarker Consortium on Down Syndrome study completed an optional 7-day accelerometer protocol at either the University of Wisconsin-Madison or the University of Pittsburgh. Inclusion criteria for the ABC-DS study were being 25 years or older and having a estimated age equivalent IQ scores of ≥30 months, karyotype testing confirming DS, no conditions contraindicative for brain imaging scans (e.g., pregnant or breastfeeding, metal in the body, or a history of claustrophobia or other behavioral concerns), or medical or mental health conditions that impacted cognitive functioning. Internal Review Boards at sites reviewed and approved the study. Consent and/or assent was obtained from all participants. Assessments were completed across a 2-day period, with anthropometric, health history, and cognitive assessments being completed on day 1, and the accelerometer for physical activity assessment given to participants on day 2 to begin their 7-day protocol the following day.

Socio-demographics

Chronological age (in years) was calculated using the date of the study visit and date of birth. Biological sex and race were reported by the caregiver. Premorbid ID level was based on medical records or standardized intelligence quotient (IQ) tests conducted at baseline prior to any cognitive changes. IQ assessments included the Stanford-Binet Intelligence Scales, Fifth Edition²⁵ Abbreviated IQ and/or the Kaufman Brief Intelligence Test, Second Edition.²⁶ Level of ID was coded: mild (1), moderate (2), or severe/profound (3) based on IQ standard scores (mild: 50–69, moderate: 35–49, and severe/profound: <35) or estimated age equivalent scores (mild: 9–14 years, moderate: 4–8 years, and severe/profound: ≤ 3 years). BMI was calculated as weight in kilograms divided by height in meters squared. Blood-based genotyping was used to evaluate the presence of an APOE allele 4 (ε4). Blood pressure was collected following a 5-min period of sitting quietly, with legs uncrossed, using the non-dominant arm.

Health history and clinical AD status

Caregivers reported health history including presence of various conditions via a survey at the study visit. This included asking about current and past health conditions, such as depression, sleep apnea, obsessive compulsive disorder, hypertension, and presence of congenital heart disease. Clinical AD status was determined via case consensus process, including a team of a licensed psychologists, physicians, and other medical or clinically trained staff who were blind to imaging, biofluid and physical activity data. All available data on dementia symptoms, cognitive function, and adaptive behavior were considered and interpreted based on level of ID, medical changes, and recent life events. A full description of the case consensus process has been described previously.²⁷ Participants were characterized as cognitively stable (i.e., no evidence of cognitive or functional decline), mild cognitive impairment (MCI; i.e., evidence of cognitive and/or functional decline but limited in scope), or dementia (i.e., evidence of marked cognitive and functional decline). An “unable to be determine status” was used if cognitive or functional declines were present but changes in medical conditions or recent life events could not be ruled out as the cause.

Physical activity assessment

Each participant wore an ActiGraph GT9X Link accelerometer (ActiGraph LLC, Pensacola, FL) on the non-dominant wrist over 7 consecutive days to measure habitual physical activity in a free-living environment. Participants were instructed to always wear the monitor, except during water-based activities (e.g., swimming)—which were recorded in the daily records. All ActiGraphs were initialized at a sampling frequency of 30 Hz and the raw acceleration data were calibrated with GGIR²⁸ and summarized using Euclidean Norm Minus One (ENMO) over 1-s epochs to capture intermittent movement. Non-wear time was detected using the van Hees et al.²⁹ algorithm which applies non-wear to 60-min overlapping time windows with a standard deviation of <13 milli-gravitational (mg; 1 mg = 0.00981 m/s²) units and a value range <50 mg from at least 2 out of 3 axes. A day was considered valid if there were ≥23 h of observation and at least 2/3 of the waking hours were not classified as non-wear. Participants were included in the analysis if they had at least 4 valid days with 1 weekend day. The Hildebrand cut-points^30,31 were applied to non-sleep time to categorize activities into sedentary (<44.8 mg), light (44.8 to <100.6 mg), moderate (100.6 to <428.8 mg), and vigorous (≥428.8 mg) intensities.

Cognitive function assessments

As a part of the ABC-DS study, participants completed a battery of cognitive assessments. Select assessments were chosen from this ABC-DS battery, with this analysis only including assessments that have previously demonstrated to be significantly associated with BMI or MVPA.¹⁷

The modified Cued Recall test (mCRT)³² uses 12 items with unique category cues to assess episodic memory. The Free and Cued Recall scores were summed to create the mCRT total score (score range 0–36). The Total Recall and the Cued Recall intrusions (i.e., number of incorrectly identified items after being given category cue) have been shown to be sensitive to early AD pathology and early stages of AD-related cognitive decline.³³

The Stroop Dog and Cat Task³⁴ is a modified Stroop task consisting of a strip of 16 dog and cat pictures that assesses executive functioning. The Cat Dog Switch Error score is the number of errors made during the switch trial. In adults with DS, number of errors (i.e., calling dog “dog” and cat “cat”) on this task correlate with higher levels of biomarkers related to AD (PET Aβ and tau PET) prior to AD dementia diagnosis.³³

Visuospatial ability was assessed with the WICS-IV³⁵ Block Design and Haxby Extension,³⁶ where participants replicate patterns using red and white blocks and the Developmental Test of Visual-Motor Integration (VMI), 5th Edition,³⁷ has participants replicate various pictures to assess visuospatial ability. Both assessments are able to detect dementia symptoms in adults with DS.^38,39

Dementia symptoms were assessed using the Down Syndrome Mental Status Examination (DSMSE)³⁶ and the Dementia Questionnaire for People with Learning Disabilities (DLD).⁴⁰ The DSMSE is a direct measure of dementia symptoms in DS that assess ability to recall personal information, memory, apraxia, language, and visuospatial ability. This assessment has been shown to be sensitive to later AD symptoms in DS.⁴¹ The DLD assesses early dementia symptoms in adults with ID.⁴² The Sum of Cognitive Scores (DLD Cognitive) assesses short- and long- term memory and orientation. The Sum of Social Scores (DLD Social) assess speech, practical skills, mood, activity and interest, and behavioral disturbance.

Statistical analyses

Prior to analyses, data were examined for skewedness and outliers using descriptive statistics, histograms and scatterplots. Participants were categorized with obesity (BMI ≥ 30 kg/m²) or no obesity (BMI < 30 kg/m²) and a median split of MVPA (median MVPA: 48.9 min/day) was used to create four groups: No Obesity/High physical activity (PA) (n = 18), No Obesity/Low PA (n = 16), Obesity/High PA (n = 20), and Obesity/Low PA (n = 21). Kruskal-Wallis rank sum test, Fisher's exact test, and Pearson's Chi-squared tests were used to compare participant characteristics by Obesity/PA group. Linear regression was used to examine potential demographic predictors of BMI or MVPA. Scores on cognitive assessments were compared across groups using unadjusted linear mixed models and models adjusting for age, clinical AD status, sex, level of ID, APOE-4 status, and health history conditions that significantly differed across groups, with separate models run using the No Obesity/High PA as the reference group and Obesity/High PA as the reference group. In these adjusted models, age was included as a continuous variable with all other covariates included as categorical variables. Moderation models were employed to further investigate the potential interaction between BMI and MVPA on each cognitive outcome. BMI was kept as a continuous variable, and MVPA left as a categorical variable (above versus below median) and these models were adjusted for age, sex, clinical AD status, level of ID, and APOE-4 status. The interaction term between BMI and MVPA was included to examine whether MVPA moderates the effect of BMI on cognitive outcomes. In all models, site where data were collected was included as a random effect.

Results

Participants

Participant characteristics are presented in Table 1. The sample was 39.1 years of age, 46.7% female, with an average BMI of 32.4 kg/m², and had an average of 60.7 min of daily MVPA, with only 8 participants not meeting the 150 min/week physical activity guidelines. As anticipated, BMI was higher in the “Obesity” groups (High PA: 38.4 kg/m²; Low PA: 37.7 kg/m²) when compared with the “No Obesity” groups (High PA: 26.6 kg/m²; Low PA: 26.2 kg/m²; p < 0.001), and MVPA was higher in the “High PA” groups (Obesity: 92.4 min; No Obesity: 86.5 min) when compared with the “Low PA” groups (Obesity: 30.3 min; No Obesity: 31.7 min; p < 0.001). Most of the sample was of White race (97.3%) and 86.7% had no MCI or dementia. Obesity/PA groups did not differ in terms of age, race, ethnicity, sex, level of ID, clinical AD status, APOE-4 copies, or measures of blood pressure.

Table 1.

Participant characteristics.

	Total (n = 75)	No Obesity/High PA (n = 18)	No Obesity/Low PA (n = 16)	Obesity/High PA (n = 20)	Obesity/Low PA (n = 21)
Weight (kg), M ± SD	76.3 ± 18.6*	61.5 ± 10.0	64.9 ± 7.7	86.0 ± 17.4	88.3 ± 17.9
BMI (kg/m²), M ± SD	32.4 ± 7.4*	26.6 ± 2.6	26.2 ± 3.0	38.4 ± 6.9	37.7 ± 5.8
Age (y), M ± SD	39.1 ± 8.4	36.4 ± 10.9	41.0 ± 7.0	38.4 ± .7.8	40.5 ± 7.3
Female, n (%)	35 (46.7)	6 (33.3)	9 (56.3)	11 (55.0)	9 (44.2)
Race
White, n (%)	73 (97.3)	16 (88.9)	16 (100.0)	20 (100.0)	21 (100.0)
Unknown, n (%)	2 (2.7)	2 (11.1)	0 (0.0)	0 (0.0)	0 (0.0)
Hispanic or Latino, n (%)	5 (6.7)	1 (5.6)	2 (12.5)	1 (5.0)	1 (4.8)
Daily MVPA Minutes, M ± SD*	60.7 ± 40.5	86.5 ± 33.3	31.7 ± 16.2	92.4 ± 7.8	30.3 ± 9.5
Meets MVPA Guidelines, n (%)*	67 (89.3)	18 (100.0)	11 (68.8)	20 (100.0)	18 (85.7)
Obstructive Sleep Apnea, n (%)*	36 (48.0)	4 (22.2)	8 (50.0)	9 (45.0)	16 (76.2)
Depression, n (%)*	11 (14.7)	2 (11.1)	1 (6.3)	1 (5.0)	7 (33.3)
Hyperlipidemia, n (%)	12 (16.0)	3 (16.7)	1 (6.3)	3 (15.0)	5 (23.8)
Hypothyroidism, n (%)	45 (60.0)	10 (55.6)	8 (50.5)	13 (65.0)	14 (66.7)
Congenital Heart Disease, n (%)	35 (46.7)	6 (33.3)	9 (56.3)	11 (55.5)	9 (42.9)
OCD, n (%)	20 (27.0)	4 (22.2)	4 (25.0)	3 (15.0)	9 (45.0)
Systolic BP (mmHg), M ± SD	110 ± 13.4	110.5 ± 14.6	109.0 ± 14.4	118.0 ± 11.5	109.0 ± 3.8
Diastolic BP (mmHg), M ± SD	70.0 ± 11.4	70.0 ± 7.4	73.0 ± 13.8	67.0 ± 12.1	68.0 ± 12.0
APOE4 Copies, n (%)
0	52 (69.3)	12 (66.7)	10 (62.5)	14 (70.0)	16 (76.2)
1	18 (24.0)	4 (22.2)	5 (31.3)	5 (25.0)	4 (19.0)
2	1 (1.3)	0 (0.0)	1 (6.3)	0 (0.0)	0 (0.0)
Unknown	4 (5.3)	2 (11.1)	0 (0.0)	1 (5.0)	1 (4.8)
Clinical AD Status, n (%)
No MCI	65 (86.7)	15 (83.3)	13 (81.3)	19 (95.0)	18 (85.7)
MCI	7 (9.3)	2 (11.0)	2 (12.5)	0 (0.0)	3 (14.3)
Dementia	3 (4.0)	1 (5.6)	1 (6.3)	1 (5.0)	0 (0.0)
Level of ID, n (%)
Mild	35 (46.7)	10 (55.6)	7 (43.8)	9 (45.0)	9 (42.9)
Moderate	30 (40.0)	8 (44.4)	5 (31.3)	9 (45.0)	8 (38.1)
Severe	10 (13.3)	0 (0.0)	4 (25.0)	2 (10.0)	4 (19.0)

Characteristics were compared across groups using Kruskal-Wallis rank sum test, Fisher's exact test, and Pearson's Chi-squared test.

*Indicates significant differences within groups.

AD: Alzheimer's disease; APOE: Apolipoprotein E; BMI: body mass index; BP: blood pressure; ID: intellectual disability; kg: kilograms; M: mean; m: meter; mm: millimeter; MCI: mild cognitive impairment; OCD: obsessive-compulsive disorder; PA: physical activity; SD: standard deviation; y: years.

The prevalence of obstructive sleep apnea varied significantly across groups, with the lowest prevalence in the No Obesity/High PA group (22.2%) and highest prevalence in the Obesity/Low PA group, at 76.2% (p = 0.003). Prevalence of obstructive sleep apnea in the No Obesity/Low PA (50.0%) or Obesity/High PA (45.0%) groups did not differ between any other group. Prevalence of depression was highest in the Obesity/Low PA group (33.3%) which was significantly greater than in the Obesity/High PA group (p = 0.04) and approached significance when compared to the No Obesity/High PA group (p = 0.09). There were no differences across groups for other health measures, APOE-4 status, clinical AD status, or level of ID.

Table 2 displays the results of the linear models used to evaluate different demographic characteristics and their association with MVPA and BMI respectively. For MVPA, older age (β = −1.57, p = 0.023) and “Severe” level of ID (β = −30.21, p = 0.035) were associated with less MVPA. There were no significant associations between MVPA and sex or clinical AD status. None of the demographic characteristics were associated with BMI.

Table 2.

Linear regression displaying associations between demographic characteristic and MVPA and BMI.

	β	SE	p
MVPA (min/day)
Chronological Age	−1.57	0.68	0.023
Level of ID
Moderate	−3.64	10.12	0.720
Severe	−30.21	14.01	0.035
Male Biological Sex	−11.36	9.16	0.220
Clinical AD Status
MCI	−4.11	26.80	0.878
Dementia	−2.25	25.28	0.929
BMI (kg/m²)
Chronological Age	0.14	0.13	0.286
Level of ID
Moderate	1.16	1.98	0.558
Severe	3.47	2.74	0.210
Male Biological Sex	−0.95	1.79	0.597
Clinical AD Status
MCI	2.63	5.24	0.618
Dementia	6.91	4.94	0.166

AD: Alzheimer's disease; β: beta-estimate of linear regression model; BMI: body mass index; ID: intellectual disability; kg: kilogram; m: meter, MCI: mild cognitive impairment; min: minutes; MVPA: moderate-to-vigorous physical activity; SE: standard error of beta-estimate.

Raw scores on each cognitive assessment by group are presented in Figure 1, which also shows unadjusted p values from linear models. Table 3 shows results from the adjusted linear mixed models comparing the No Obesity/High PA group with all other groups. There were no statistically significant differences when comparing the No Obesity/Low PA group with the No Obesity/High PA group, however the No Obesity/High PA group was just above the threshold of significance for better performance on the mCRT (β = −3.79, p = 0.055) and Cat and Dog Stroop Errors (β = 2.40, p = 0.075). The No Obesity/High PA group had more mCRT Intrusions when compared with the Obesity/High PA group (β = 3.30, p = 0.031). There were no differences in any other cognitive assessments between the High PA groups. Finally, when compared with the Obesity/Low PA group, the No Obesity/High PA group had better scores on the DLD Cognitive (β = 3.87, p = 0.048) and DLD Social assessments (β = 3.92, p = 0.033), with no differences between these groups on other assessments.

Figure 1.

Raw scores on cognitive assessments by obesity/PA group. Lower scores for mCRT intrusion, DLD Cognitive, DLD Social, and Stroop Errors represent better cognitive function, whereas higher scores on the mCRT, DSMSE, BMI, and Block Design represent superior performance. p values presented from unadjusted linear models. DLD: Dementia Questionnaire for People with Learning Disabilities; DSMSE: Down Syndrome Mental Status Examination; mCRT: Modified Cued Recall Test; PA: physical activity; VMI: Visual-Motor Integration.

Table 3.

Linear mixed effects models comparing cognitive assessments with no obesity/high PA reference group.

	No Obesity/Low PA (n = 16)			Obesity/High PA (n = 20)			Obesity/Low PA (n = 21)
	β	SE	p	β	SE	p	β	SE	p
mCRT Total Score	−3.79	1.9	0.055	1.08	1.8	0.555	−2.17	1.9	0.298
mCRT Intrusions	−0.56	1.6	0.731	−3.30	1.5	0.031	−1.85	1.7	0.280
DLD Cognitive	2.25	1.8	0.212	−0.55	1.7	0.739	3.87	1.9	0.048
DLD Social	1.03	1.7	0.540	−0.63	1.6	0.703	3.92	1.8	0.033
Cat and Dog Stroop Errors	2.40	1.3	0.075	−0.66	1.2	0.583	−0.24	1.4	0.865
DSMSE	−4.12	3.2	0.205	3.04	2.9	0.291	−3.52	3.0	0.245
VMI	−0.88	1.2	0.452	0.53	1.1	0.637	−1.87	1.3	0.146
Block Design Total Score	−0.32	3.6	0.928	6.10	3.3	0.069	2.57	3.7	0.494

Β: beta-estimate of linear model; DLD: Dementia Questionnaire for People with Learning Disabilities; DSMSE: Down Syndrome Mental Status Examination; mCRT: Modified Cued Recall Test; PA: physical activity; SE: standard error; VMI: Visual-Motor Integration.

Table 4 shows results from the adjusted linear mixed models comparing the Obesity/High PA group with the two Low PA groups. The Obesity/High PA group scored significantly better than the No Obesity/Low PA group on the mCRT total score (β = −4.86, p = 0.009), Cat and Dog Stroop Errors (β = 3.07, p = 0.013), and the DSMSE (β = −6.57, p = 0.022). This group also scored better than the No Obesity/Low PA group for mCRT Intrusions (β = 2.75, p = 0.066), DLD Cognitive (β = 2.81, p = 0.093), and Block Design Total Score (β = −6.42, p = 0.061), however these were just above the threshold for significance. There were no differences between these groups on the DLD Social or VMI assessments. When compared with the Obesity/Low PA group, the Obesity/High PA group had better scores for DLD Cognitive (β = 4.43, p = 0.012), DLD Social (β = 4.52, p = 0.006), DSMSE (β = −7.21, p = 0.016) and VMI assessments (β = −2.39, p = 0.038). The Obesity/High PA group also scored better on the mCRT; however, this difference was just above the threshold for significance (β = −3.26, p = 0.083).

Table 4.

Linear mixed effects models comparing cognitive assessments with obesity/high PA reference group.

	No Obesity/Low PA (n = 16)			Obesity/Low PA (n = 20)
	β	SE	p	β	SE	p
mCRT Total Score	−4.86	1.8	0.009	−3.26	1.8	0.083
Cued Recall Intrusions	2.75	1.9	0.066	1.44	1.5	0.343
DLD Cognitive	2.81	1.6	0.093	4.43	1.7	0.012
DLD Social	1.64	1.5	0.294	4.52	1.6	0.006
Cat and Dog Stroop Errors	3.07	1.2	0.013	0.42	1.2	0.731
DSMSE	−6.57	2.8	0.022	−7.21	2.9	0.016
VMI	−1.40	1.1	0.203	−2.39	1.1	0.038
Block Design Total Score	−6.42	3.4	0.061	−3.52	3.4	0.301

Moderation models were used to further explore the potential interaction between BMI and MVPA on these health and cognitive outcomes. None of the interactions between BMI and MVPA for health outcomes reached the threshold of statistical significance. However, interaction term for BMI and MVPA was significant for DLD Social Scores (Figure 2; β = 0.43, p = 0.001), and was approaching significance for mCRT Intrusions (β = 0.249, p = 0.094) and Block Design Score (β = −0.594, p = 0.099) indicating that a negative effect of BMI on these cognitive assessments may be rescued by MVPA. No other cognitive assessments approached significance in the moderation models.

Figure 2.

Interaction between obesity and physical activity category on DLD social score. Linear model adjusted for age, sex, level of intellectual disability, and clinical AD status. A higher score on this assessment represents a worse outcome. BMI: body mass index; DLD: Dementia Questionnaire for People with Learning Disabilities; MVPA: moderate-to-vigorous physical activity.

Discussion

This study aimed to explore the potential joint association between obesity and physical activity on cognition in a sample of adults with DS. We hypothesized that MVPA would be associated with better cognitive function regardless of obesity status. Results from this analysis supported that hypothesis, showing that in general both “High PA” groups performed better on several measures of cognitive function. Additionally, those when compared to the Obesity/Low PA group, those with “High PA” had lower incidence of chronic conditions such as sleep apnea and depression, showing other potential effects of MVPA on health.

Current research in populations without DS demonstrate the potential for MVPA to attenuate AD-related outcomes even in the presence of obesity. In one study, Tolppanen et al.⁴³ found that participants (n = 1511, average age 78.8 years) with overweight or obesity who maintained a high level of PA had lower risk for dementia when compared to those with lower levels of PA. In the present study, participants in the Obesity/High PA group scored significantly higher than the No Obesity/Low PA group on measures of episodic memory, executive functioning, and overall dementia symptoms. Similarly, when compared to the Obesity/Low PA group, the Obesity/High PA group demonstrated superior performance in episodic memory, executive functioning, social functioning, and overall dementia symptoms. Importantly, the Obesity/High PA group performed as well as the No Obesity/High PA group on all assessments, showing no areas of inferior performance. However, it is important to note that due to the cross-sectional nature of this study the direction of effects between obesity and physical activity on cognition cannot be established. It is possible that lower cognitive functioning could lead to obesity or physical inactivity or that these are influenced by another mechanism not captured in the current analysis. Still, the results from this analysis highlight the potential significance of the combined effects of obesity and physical activity when considering cognitive function in individuals with DS.

The Obesity/Low PA group had a higher prevalence of obstructive sleep apnea and depression when compared to all other Obesity/PA groups. Thus, these results support the potential for MVPA to rescue other negative obesity-related health outcomes in populations with DS apart from AD. In the general population, risk for obesity-related conditions such as cardiovascular disease^22,23 and impaired glucose metabolism^21,24 and all-cause mortality,^20,21 is consistently demonstrated to be lower among those with obesity who have high versus low levels of cardiorespiratory fitness. We are aware of only one study investigating the potential joint association of MVPA and obesity in populations with ID. Oppewal and Hilgenkamp²⁰ included 874 adults with ID (n = 122 with DS, 14.0%), and compared 5-year survival using BMI and gait speed to assess fitness. This study concluded that those who were unfit were 3.6–4.6 times more likely to die within 5-years, regardless of obesity status.²⁰ Together, these findings highlight the potential of MVPA to mitigate disease risk even in the context of obesity. As with the cognitive findings, these results should be interpreted in light of the study's cross-sectional design. Rigorous longitudinal and interventional trials are needed to establish whether increasing MVPA, independent of obesity status, can meaningfully improve these comorbidities in adults with DS.

Results from linear models examining potential predictors of MVPA showed that those with older age and “severe” level of ID were associated with less MVPA. This highlights a potential important targets for future work and interventions. Research in the general population without DS has consistently shown that adults participate in less MVPA as they age,^44,45 which is consistent with these findings in adults with DS. The finding that those with more severe levels of ID and older age reporting less MVPA is also consistent with literature evaluating the predictors of MVPA in people with ID. Phillips et al. assessed various predictors of MVPA in a sample of people with DS (n = 152, age range 12–70) and found that MVPA decreased with age and severity of ID.⁴⁶ However, this study also found males reported higher MVPA, which was not significant in our analyses. Additionally, our analyses found no associations between any of the demographic characteristics and BMI, which should be further evaluated in a larger sample of adults with DS.

This study should be interpreted in the context of its limitations. Firstly, given the cross-sectional nature of this study, causality cannot be determined. It is possible that lower cognitive scores cause lower PA participation as opposed to the lower PA participation influencing the cognitive scores. A limitation of this study regarding the accelerometer was the use of wrist-worn devices, which demonstrate an over-estimate of physical activity.⁴⁷ The level of physical activity in this sample is much higher than what is typically reported in persons with DS,¹⁰ potentially reflecting this caveat. Additionally, the accelerometer-reported PA during this week of observation is not representative of lifetime PA participation, nor is current BMI representative of lifetime obesity status. Future work should use longitudinal observations of PA, obesity status, and cognitive function to better understand the potential joint associations. Other limitations include a small sample size and use of BMI as a measure of obesity as opposed to body composition to measure adiposity. The total sample for this study was 75, which is comparable to other similar studies evaluating the influence of lifestyle factors on cognition in DS,^17,18 however after categorizing participants into their respective Obesity/PA groups, each group was limited to only about 20 participants. Future work could consider categorizing this variable based on whether or not participants meet guidelines for physical activity of 150 min per week of MVPA.¹⁵ The small sample size precluded stratification by age group. Determining whether the joint association of BMI and physical activity varies across age, and how this relationship influences cognition after amyloid positivity, remains a critical priority for future research. This sample used in this analysis was also primarily white race (97.3%), and non-Hispanic ethnicity (93.3%), which limits the generalizability of the findings. In this sample, only 8 participants out of 75 did not meet those physical activity guidelines, which created groups that were too unbalanced for analysis, thus median split was used. Finally, due to the exploratory nature of this analysis, being the first to our knowledge to evaluate the joint association of MVPA and BMI on cognitive function in DS, we did not adjust for multiple comparisons in our analyses. Thus, there were no corrections for multiple comparisons which could increase the risk of Type I Error.

Results indicate the potential for MVPA to benefit cognitive functioning for persons with DS, regardless of obesity status. Given the high risk of AD in persons with DS, longitudinal interventions examining this joint association of MVPA and obesity on AD in persons with DS are warranted. If future work replicates these findings, social policy, and interventions to promote MVPA may benefit this population.

Footnotes

Acknowledgements

The authors are grateful to the ABC-DS study participants, their families and care providers, and the ABC-DS research and support staff for their contributions to this study. This manuscript has been reviewed by ABC-DS investigators for scientific content and consistency of data interpretation with previous ABC-DS study publications. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH, the CPFT, the NIHR or the UK Department of Health and Social Care

ORCID iDs

Julianne G Clina

Brian C Helsel

Victoria L Fleming-Batayneh

Ethical considerations

Internal Review Boards at sites reviewed and approved the study.

Consent to participate

Consent and/or assent was obtained from all participants prior to the collection of any data.

Consent for publication

Not applicable.

Author contribution(s)

Julianne G Clina: Formal analysis; Investigation; Methodology; Visualization; Writing – original draft.

Brian C Helsel: Formal analysis; Investigation; Methodology; Visualization; Writing – review & editing.

Sigan L Hartley: Conceptualization; Data curation; Funding acquisition; Investigation; Methodology; Resources; Writing – review & editing.

Benjamin Handen: Conceptualization; Funding acquisition; Investigation; Methodology; Writing – review & editing.

Bradley Christian: Conceptualization; Funding acquisition; Investigation; Writing – review & editing.

Elizabeth Head: Conceptualization; Funding acquisition; Investigation; Methodology; Writing – review & editing.

Mark Mapstone: Conceptualization; Funding acquisition; Investigation; Methodology; Writing – review & editing.

Christy L Hom: Conceptualization; Funding acquisition; Investigation; Methodology; Writing – review & editing.

Jeffrey Burns: Conceptualization; Funding acquisition; Investigation; Methodology; Writing – review & editing.

Beau Ances: Conceptualization; Funding acquisition; Investigation; Methodology; Writing – review & editing.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The Alzheimer Biomarker Consortium–Down Syndrome (ABC-DS) is funded by the National Institute on Aging and the National Institute for Child Health and Human Development (U01 AG051406, U01 AG051412, U19 AG068054). The work contained in this publication was also supported through the following National Institutes of Health Programs: The Alzheimer's Disease Research Centers Program (P50 AG008702, P30 AG062421, P50 AG16537, P50 AG005133, P50 AG005681, P30 AG062715, and P30 AG066519), the Eunice Kennedy Shriver Intellectual and Developmental Disabilities Research Centers Program (U54 HD090256, U54 HD087011, and P50 HD105353), the National Center for Advancing Translational Sciences (UL1 TR001873, UL1 TR002373, UL1 TR001414, UL1 TR001857, UL1 TR002345), the National Centralized Repository for Alzheimer Disease and Related Dementias (U24 AG21886), and DS-Connect^® (The Down Syndrome Registry) supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD). In Cambridge, UK this research was supported by the NIHR Cambridge Biomedical Research Centre and the Windsor Research Unit, CPFT, Fulbourn Hospital Cambridge, UK. This work was also supported by The National Institute on Aging (NIA R01 AG070028), which provided funding for the physical activity data collection. Postdoctoral training for Julianne G. Clina was supported by the Alzheimer's Disease Research Center P30 AG072973 and T32 AG078114. Additionally, Brian C. Helsel was supported by a Clinical and Translational Science Award (CTSA) from National Center for Advancing Translational Sciences (NCATS) awarded to the University of Kansas for Frontiers: University of Kansas Clinical and Translational Science Institute (grant no.TL1TR002368). Additionally, Brian C. Helsel was supported by the National Institute on Aging and NIH INCLUDE Project (K01 AG083130). Predoctoral training for Victoria L. Fleming was supported by the National Institute on Aging (F31AG085730).

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data availability statement

The data supporting the findings of this study are available in The Alzheimer's Biomarker Consortium – Down Syndrome (ABC-DS) Laboratory of Neuro Imaging (LONI) database at . Qualified investigators can submit requests for access to data and samples, and all requests will be reviewed by ABC-DS investigators and NIH staff. Upon approval, access will be provided to current ABC-DS data on the LONI website, and available biospecimen samples (e.g., DNA, plasma, and serum) will be distributed by NCRAD; the ABC-DS biospecimen bank will distribute CSF.

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