| Literature DB >> 31970058 |
Patrick J Smith1,2,3.
Abstract
Alzheimer's disease and related dementias (ADRD) represent an increasingly urgent public health concern, with an increasing number of baby boomers now at risk. Due to a lack of efficacious therapies among symptomatic older adults, an increasing emphasis has been placed on preventive measures that can curb or even prevent ADRD development among middle-aged adults. Lifestyle modification using aerobic exercise and dietary modification represents one of the primary treatment modalities used to mitigate ADRD risk, with an increasing number of trials demonstrating that exercise and dietary change, individually and together, improve neurocognitive performance among middle-aged and older adults. Despite several optimistic findings, examination of treatment changes across lifestyle interventions reveals a variable pattern of improvements, with large individual differences across trials. The present review attempts to synthesize available literature linking lifestyle modification to neurocognitive changes, outline putative mechanisms of treatment improvement, and discuss discrepant trial findings. In addition, previous mechanistic assumptions linking lifestyle to neurocognition are discussed, with a focus on potential solutions to improve our understanding of individual neurocognitive differences in response to lifestyle modification. Specific recommendations include integration of contemporary causal inference approaches for analyzing parallel mechanistic pathways and treatment-exposure interactions. Methodological recommendations include trial multiphase optimization strategy (MOST) design approaches that leverage individual differences for improved treatment outcomes.Entities:
Year: 2019 PMID: 31970058 PMCID: PMC6971820 DOI: 10.3233/BPL-190083
Source DB: PubMed Journal: Brain Plast ISSN: 2213-6304
Fig.1Directed acyclic graph linking lifestyle modification to neurocognitive outcomes. As shown, lifestyle modification likely has beneficial effects on structural markers of neuropathology, reserve capacities, and more directly on neurotransmitter systems, all of which contribute to neurocognitive function as observed through behavioral testing. The associations between markers of reserve capacity and/or scaffolding (i.e. blood brain barrier integrity, cerebrovascular reactivity, neurogenesis) likely impact neurocognition indirectly through their influence on structural markers and by potentiating or blunting neurotransmitter systems.
Fig.2Directed acyclic graph linking lifestyle to neurocognition among middle-aged adults through indirect, downstream pathways including metabolic function, inflammation, and neurotrophic factors. As shown, these factors likely impact neurocognitive function through their downstream impact on brain structure and reserve capacity, with additional, indirect influences through modulation of neurotransmitter systems (e.g. monoamines).
Example of a factorial design to determine optimal intervention components among exercise, diet, and cognitive training. Within the example below, main effect comparisons for factors would consist of 1) Exercise vs. No-Exercise (conditions 1, 2, 3, 4 vs. 5, 6, 7, 8); 2) Diet vs. No-Diet (conditions 1, 2, 5, 6 vs. 3, 4, 7, 8); and Cognitive training vs. No-cog training (conditions 1, 3, 5, 7 vs. 2, 4, 6, 8). Interactions between intervention factors can also be examined
| Experimental Condition | Exercise Factor | Diet Factor | Cognitive Training Factor |
| 1 | Exercise | Diet | Cog Training |
| 2 | Exercise | Diet | No-Cog Training |
| 3 | Exercise | No-Diet | Cog Training |
| 4 | Exercise | No-Diet | No-Cog Training |
| 5 | No-Exercise | Diet | Cog Training |
| 6 | No-Exercise | Diet | No-Cog Training |
| 7 | No-Exercise | No-Diet | Cog Training |
| 8 | No-Exercise | No-Diet | No-Cog Training |
Fig.3Example of a SMART-based design among older adults with metabolic syndrome. In this example, participants are initially randomized to either walking or a more metabolically intensive intervention using high intensity interval training (HIIT). The tailoring variable used to determine secondary randomization is stabilization of cortical atrophy. Secondary randomization among non-responders introduces an additional metabolic treatment, dietary modification, in order to determine if some individuals are able to achieve improved cognitive outcomes when metabolic parameters are targeted with additional treatment components.
Fig.4Example of a SMART-based design among older adults with mild cognitive impairment (MCI). In this example, participants are initially randomized to either cognitive training or a combined cognitive and physical activity program, that may augment neuroplasticity. The tailoring variable used to determine secondary randomization is improvements on a brief measure of processing speed, which has been associated with intervention responsivity in other trial settings. Secondary randomization among non-responders introduces an additional treatment augmenting metabolic and inflammatory function, dietary modification, in order to determine if some individuals are able to achieve improved cognitive outcomes with additional treatment.