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This article is from Journal of the International AIDS Society , volume 16 . Abstract Introduction: Research investigating HIV, neurocognition and ageing is well developed using neuropsychometric or other quantitative approaches; however, little is known about individuals’ subjective experiences. The purpose of this article is to explore the experiences of men aged 50 and older who self-identify as having HIV-associated neurocognitive challenges. In particular, this study uses the Episodic Disability Framework (EDF) to explore participants’ perceptions regarding: 1) symptoms/impairments, difficulties with day-to-day activities, challenges with social inclusion and uncertainty; 2) ageing as related to their HIV-associated neurocognitive challenges, and 3) the episodic nature of their HIV-associated neurocognitive challenges. Methods: This qualitative, interpretive study involved in-depth, semi-structured interviews with 12 men aged 50 years and older who self-identified as having HIV-associated neurocognitive challenges. Participants were recruited from a neurobehavioural research unit (NBRU) at a large hospital in Toronto, Canada. Data were analyzed thematically and with reference to the EDF. Results: Participants’ experiences reflected all concepts within the EDF to some extent. Difficulties with daily activities were diverse but were addressed using similar living strategies. Participants described challenges with work and social relationships resulting from neurocognitive challenges. Participants downplayed the significance of uncertainty in their lives, which they attributed to effective living strategies. Most men reported confusion regarding the link between their neurocognitive challenges and ageing. Others discussed ageing as an asset that helped with coping. Conclusions: This is the first study to use a disability framework to examine the subjective experiences of men ageing with HIV-associated neurocognitive challenges. Findings reframe the episodic disability experienced by these individuals as being predictably linked to certain triggers. As such, support for managing neurocognitive challenges could focus on triggers that exacerbate the condition in addition to the impairments themselves. The study also describes ageing as not only a source of problems but also as an asset among men growing older with HIV.

Statistics published by the Health Foundation indicate that between 2021 and 2046, the proportion of the population in England aged over 85 is predicted to increase two-fold to around 2.6 million people (Raymond et al., 2021), highlighting the need for healthier ageing strategies. Mental and physical illness are often comorbid, for example cardiovascular disease and depression (You et al., 2022). In an elderly population, research has found that long-term physical disease can often lead to the onset of mental illness, and vice versa (Chen et al., 2017). Brain health, which is linked to mental health (Ibanez and Zimmer, 2023), encompasses both physiological and cognitive health (Peters, 2006) and is a useful neuroscientific indicator of healthy brain ageing (Turrini et al., 2023). Despite this known link between brain health (including mental health) and physical health, the degree to which they are connected has not been fully elucidated. To explore this, we propose using a mouse model of Grb10 as it provides a way to alter metabolic pathways either in the brain, to impact brain health, or in peripheral tissues, to impact physical health, to investigate whether these metabolic pathways have differential effects on brain health during ageing. The gene Grb10 encodes growth factor receptor bound protein 10 (Grb10), an adaptor protein that binds to receptor tyrosine kinases such as the insulin receptor. Molecules binding to these tyrosine kinases activate pathways which influence metabolic and mitogenic processes (Wang, J. et al., 1999; Lim, Riedel and Liu, 2004; Wang, L. et al., 2007). Grb10 is also a target for phosphorylation by the mTORC1 complex, providing a further link to nutrient sensing, energy metabolism and growth regulation (Oldham and Hafen, 2003; Hsu et al., 2011; Yu et al., 2011). Deregulation of these metabolic pathways has been associated with ageing (López-Otín et al., 2023), with experiments showing that a decrease in signalling through the insulin signalling pathway, such as through caloric restriction (Weindruch, 1996; Dorling, Martin and Redman, 2020), mutations in metabolically active genes (Arum et al., 2014) and drugs such as metformin (Soukas, Hao and Wu, 2019), is linked to decelerated ageing and longevity. However, removal of Grb10 from these pathways is expected to increase flux through the insulin signalling pathway leading to increased metabolic dysfunction and accelerated ageing. Importantly, Grb10 is also a unique imprinted gene, where depending on the parental origin, differential gene expression patterns are displayed. Maternally inherited Grb10 is predominantly expressed in peripheral tissue whereas paternally inherited Grb10 is almost exclusively expressed in brain tissue (Garfield et al., 2011). Additionally, maternally and paternally expressed Grb10 were found to have distinctive functions. Maternally inherited Grb10 functions as a growth repressor (Garfield et al., 2011), and in vivo studies found that it is involved in peripheral insulin metabolism. In adult mice with disrupted maternal Grb10 expression, a change in body composition and increased insulin receptor signalling were found (Smith et al., 2007; Wang, L. et al., 2007; Holt et al., 2009), linking back to the metabolic phenotype mentioned previously. In contrast, paternally inherited Grb10 was found to influence behaviour such as social dominance (Garfield et al., 2011) and impulsivity towards choice making (Dent et al., 2018). An in vivo study using a mouse model with Grb10 knocked out solely in hypothalamic neurons showed that the protein is also involved in leptin signalling, influencing changes in energy regulation and appetite. This neuron specific Grb10 knockout led to an increase in mouse weight (Liu et al., 2023), linking Grb10 to metabolic dysfunction further. To investigate whether peripheral and brain health can be differentiated during ageing, we will use this tissue-specific mouse model of Grb10 to examine its impact on biological brain age, as a measure of brain health. Biological brain age is the estimated age of a brain, predicted from (structural) magnetic resonance imaging data using machine learning algorithms (Cole and Franke, 2017). The differences between brain age and chronological age of the organism can indicate whether the brain is older or younger than expected, giving insight into whether a brain is ageing healthily (Elliott et al., 2021). In our study, the predicted brain age difference (the difference between chronological age and predicted brain age) will be calculated in maternally inherited Grb10 knockout (Grb10m/+) and paternally inherited Grb10 knockout (Grb10+/p) mice and compared to wildtype control mice in two different age groups (either 10 months or 20 months of age). This will indicate whether there are differences in brain age between the genotype groups at different ages.

Statistics published by the Health Foundation indicate that between 2021 and 2046, the proportion of the population in England aged over 85 is predicted to increase two-fold to around 2.6 million people (Raymond et al., 2021), highlighting the need for healthier ageing strategies. Mental and physical illness are often comorbid, for example cardiovascular disease and depression (You et al., 2022). In an elderly population, research has found that long-term physical disease can often lead to the onset of mental illness, and vice versa (Chen et al., 2017). Brain health, which is linked to mental health (Ibanez and Zimmer, 2023), encompasses both physiological and cognitive health (Peters, 2006) and is a useful neuroscientific indicator of healthy brain ageing (Turrini et al., 2023). Despite this known link between brain health (including mental health) and physical health, the degree to which they are connected has not been fully elucidated. To explore this, we propose using a mouse model of Grb10 as it provides a way to alter metabolic pathways either in the brain, to impact brain health, or in peripheral tissues, to impact physical health, to investigate whether these metabolic pathways have differential effects on brain health during ageing. The gene Grb10 encodes growth factor receptor bound protein 10 (Grb10), an adaptor protein that binds to receptor tyrosine kinases such as the insulin receptor. Molecules binding to these tyrosine kinases activate pathways which influence metabolic and mitogenic processes (Wang, J. et al., 1999; Lim, Riedel and Liu, 2004; Wang, L. et al., 2007). Grb10 is also a target for phosphorylation by the mTORC1 complex, providing a further link to nutrient sensing, energy metabolism and growth regulation (Oldham and Hafen, 2003; Hsu et al., 2011; Yu et al., 2011). Deregulation of these metabolic pathways has been associated with ageing (López-Otín et al., 2023), with experiments showing that a decrease in signalling through the insulin signalling pathway, such as through caloric restriction (Weindruch, 1996; Dorling, Martin and Redman, 2020), mutations in metabolically active genes (Arum et al., 2014) and drugs such as metformin (Soukas, Hao and Wu, 2019), is linked to decelerated ageing and longevity. However, removal of Grb10 from these pathways is expected to increase flux through the insulin signalling pathway leading to increased metabolic dysfunction and accelerated ageing. Importantly, Grb10 is also a unique imprinted gene, where depending on the parental origin, differential gene expression patterns are displayed. Maternally inherited Grb10 is predominantly expressed in peripheral tissue whereas paternally inherited Grb10 is almost exclusively expressed in brain tissue (Garfield et al., 2011). Additionally, maternally and paternally expressed Grb10 were found to have distinctive functions. Maternally inherited Grb10 functions as a growth repressor (Garfield et al., 2011), and in vivo studies found that it is involved in peripheral insulin metabolism. In adult mice with disrupted maternal Grb10 expression, a change in body composition and increased insulin receptor signalling were found (Smith et al., 2007; Wang, L. et al., 2007; Holt et al., 2009), linking back to the metabolic phenotype mentioned previously. In contrast, paternally inherited Grb10 was found to influence behaviour such as social dominance (Garfield et al., 2011) and impulsivity towards choice making (Dent et al., 2018). An in vivo study using a mouse model with Grb10 knocked out solely in hypothalamic neurons showed that the protein is also involved in leptin signalling, influencing changes in energy regulation and appetite. This neuron specific Grb10 knockout led to an increase in mouse weight (Liu et al., 2023), linking Grb10 to metabolic dysfunction further. To investigate whether peripheral and brain health can be differentiated during ageing, we will use this tissue-specific mouse model of Grb10 to examine its impact on biological brain age, as a measure of brain health. Biological brain age is the estimated age of a brain, predicted from (structural) magnetic resonance imaging data using machine learning algorithms (Cole and Franke, 2017). The differences between brain age and chronological age of the organism can indicate whether the brain is older or younger than expected, giving insight into whether a brain is ageing healthily (Elliott et al., 2021). In our study, the predicted brain age difference (the difference between chronological age and predicted brain age) will be calculated in maternally inherited Grb10 knockout (Grb10m/+) and paternally inherited Grb10 knockout (Grb10+/p) mice and compared to wildtype control mice in two different age groups (either 10 months or 20 months of age). This will indicate whether there are differences in brain age between the genotype groups at different ages.

With the aim of characterising the covariance pattern between lifestyle measures and indices of brain structure in older adults, this project uses MRI and behavioural data from two cohorts of older adults. Canonical correlation analyses have been conducted on one dataset. The obtained model will then be validated on a second dataset. Please see our “Lifestyle_BrainStructure_Analyses.docx” file for a description of our aims, methods, and results from the first dataset.

With the aim of characterising the covariance pattern between lifestyle measures and indices of brain structure in older adults, this project uses MRI and behavioural data from two cohorts of older adults. Canonical correlation analyses have been conducted on one dataset. The obtained model will then be validated on a second dataset. Please see our “Lifestyle_BrainStructure_Analyses.docx” file for a description of our aims, methods, and results from the first dataset.