Associations between abdominal adipose tissue, reproductive span, and brain characteristics in post-menopausal women

Schindler, Louise S.; Subramaniapillai, Sivaniya; Barth, Cláudia; Meer, Dennis van der; Pedersen, Mads Lund; Kaufmann, Tobias; Maximov, Ivan I.; Linge, Jennifer; Leinhard, Olof Dahlqvist; Beck, Dani; Gurholt, Tiril P.; Voldsbekk, Irene; Suri, Sana; Ebmeier, K P; Draganski, Bogdan; Andreassen, Ole A.; Lange, Ann-Marie G. de

doi:10.1016/j.nicl.2022.103239

Cited by 7 publications

(15 citation statements)

References 191 publications

(237 reference statements)

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“…The age prediction accuracy largely corresponded to our previous UK Biobank studies in overlapping samples 21, 56 , as shown in Table S2 . Figure 1 shows the correlations between GM BAG, WM BAG, left and right hippocampus volume, and WMH volume.…”

Section: Resultssupporting

confidence: 80%

“…In line with our previous studies 54, 56 , tissue-specific age prediction models in females only were run using XGBoost regression, which is based on a decision-tree ensemble algorithm (https://github.com/dmlc/xgboos). Hyper-parameters were tuned in nested cross-validations using 5 inner folds for randomized search, and 10 outer folds for model validation.…”

Section: Methodsmentioning

confidence: 99%

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Menopausal hormone therapy and the female brain: leveraging neuroimaging and prescription registry data from the UK Biobank cohort

Barth,

Galea,

Jacobs

et al. 2024

Preprint

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Background and ObjectivesMenopausal hormone therapy (MHT) is generally thought to be neuroprotective, yet results have been inconsistent. Here, we present a comprehensive study of MHT use and brain characteristics in middle- to older aged females from the UK Biobank, assessing detailed MHT data, APOE ε4 genotype, and tissue-specific gray (GM) and white matter (WM) brain age gap (BAG), as well as hippocampal and white matter hyperintensity (WMH) volumes.MethodsA total of 19,846 females with magnetic resonance imaging data were included (current-users = 1,153, 60.1 ± 6.8 years; past-users = 6,681, 67.5 ± 6.2 years; never-users = 12,012, mean age 61.6 ± 7.1 years;). For a sub-sample (n = 538), MHT prescription data was extracted from primary care records. Brain measures were derived from T1-, T2- and diffusion-weighted images. We fitted regression models to test for associations between the brain measures and MHT variables including user status, age at initiation, dosage and duration, formulation, route of administration, and type (i.e., bioidentical vs synthetic), as well as active ingredient (e.g., estradiol hemihydrate). We further tested for differences in brain measures among MHT users with and without a history of hysterectomy ± bilateral oophorectomy and examined associations by APOE ε4 status.ResultsWe found significantly higher GM and WM BAG (i.e., older brain age relative to chronological age) as well as smaller left and right hippocampus volumes in current MHT users, not past users, compared to never-users. Effects were modest, with the largest effect size indicating a group difference of 0.77 years (∼9 months) for GM BAG. Among MHT users, we found no significant associations between age at MHT initiation and brain measures. Longer duration of use and older age at last use post menopause was associated with higher GM and WM BAG, larger WMH volume, and smaller left and right hippocampal volumes. MHT users with a history of hysterectomy ± bilateral oophorectomy showedlowerGM BAG relative to MHT users without such history. Although we found smaller hippocampus volumes in carriers of two APOE ε4 alleles compared to non-carriers, we found no interactions with MHT variables. In the sub-sample with prescription data, we found no significant associations between detailed MHT variables and brain measures after adjusting for multiple comparisons.DiscussionOur results indicate that population-level associations between MHT use, and female brain health might vary depending on duration of use and past surgical history. Future research is crucial to establish causality, dissect interactions between menopause-related neurological changes and MHT use, and determine individual-level implications to advance precision medicine in female health care.

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Section: Resultssupporting

confidence: 80%

Section: Methodsmentioning

confidence: 99%

Menopausal hormone therapy and the female brain: leveraging neuroimaging and prescription registry data from the UK Biobank cohort

Barth,

Galea,

Jacobs

et al. 2024

Preprint

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“…Although Previous studies have demonstrated that variation in predicted brain age is partly explained by individual differences in body composition and health traits, including abdominal fat Schindler et al, 2022;Subramaniapillai et al, 2022), muscle-fat infiltration , hand-grip strength (Cole et al, 2018;Sanders et al, 2021) and muscle volume .…”

Section: Discussionmentioning

confidence: 99%

Dissecting unique and common variance across body and brain health indicators using age prediction

Beck,

de Lange,

Gurholt

et al. 2024

Human Brain Mapping

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Ageing is a heterogeneous multisystem process involving different rates of decline in physiological integrity across biological systems. The current study dissects the unique and common variance across body and brain health indicators and parses inter‐individual heterogeneity in the multisystem ageing process. Using machine‐learning regression models on the UK Biobank data set (N = 32,593, age range 44.6–82.3, mean age 64.1 years), we first estimated tissue‐specific brain age for white and gray matter based on diffusion and T1‐weighted magnetic resonance imaging (MRI) data, respectively. Next, bodily health traits, including cardiometabolic, anthropometric, and body composition measures of adipose and muscle tissue from bioimpedance and body MRI, were combined to predict ‘body age’. The results showed that the body age model demonstrated comparable age prediction accuracy to models trained solely on brain MRI data. The correlation between body age and brain age predictions was 0.62 for the T1 and 0.64 for the diffusion‐based model, indicating a degree of unique variance in brain and bodily ageing processes. Bayesian multilevel modelling carried out to quantify the associations between health traits and predicted age discrepancies showed that higher systolic blood pressure and higher muscle‐fat infiltration were related to older‐appearing body age compared to brain age. Conversely, higher hand‐grip strength and muscle volume were related to a younger‐appearing body age. Our findings corroborate the common notion of a close connection between somatic and brain health. However, they also suggest that health traits may differentially influence age predictions beyond what is captured by the brain imaging data, potentially contributing to heterogeneous ageing rates across biological systems and individuals.

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“…Furthermore, individual differences in cognitive reserve (Stern, 2009(Stern, , 2012, or resilience to neuropathological changes typically associated with ageing, could influence differences in age-prediction scores. Future studies might therefore aim to investigate these difference scores in the context of cognitive functions known to change with age, such as memory and reaction time (Grady, 2012), as well as reserve-related mechanisms including education, socioeconomic status, and lifestyle (Anatürk et al, 2021) Previous studies have demonstrated that variation in predicted brain age is partly explained by individual differences in body composition and health traits, including abdominal fat (Beck, de Lange, Pedersen, et al, 2022;Schindler et al, 2022;Subramaniapillai et al, 2022), muscle-fat infiltration (Beck, de Lange, Alnaes, et al, 2022), hand-grip strength (Cole et al, 2018;Sanders et al, 2021) and muscle volume (Beck, de Lange, Alnaes, et al, 2022). Our findings support these previous reports, but also suggest that health traits may differentially influence age predictions beyond what is captured by the brain imaging measures.…”

Section: Discussionmentioning

confidence: 99%

“…Previous studies have demonstrated that variation in predicted brain age is partly explained by individual differences in body composition and health traits, including abdominal fat (Beck, de Lange, Pedersen, et al, 2022; Schindler et al, 2022; Subramaniapillai et al, 2022), muscle-fat infiltration (Beck, de Lange, Alnæs, et al, 2022), hand-grip strength (Cole et al, 2018; Sanders et al, 2021) and muscle volume (Beck, de Lange, Alnæs, et al, 2022). Our findings support these previous reports, but also suggest that health traits may differentially influence age predictions beyond what is captured by the brain imaging measures.…”

Section: Discussionmentioning

confidence: 99%

Dissecting unique and common variance across body and brain health indicators using age prediction

Beck,

de Lange,

Gurholt

et al. 2023

Preprint

Self Cite

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Ageing is a heterogeneous multisystem process involving different rates of decline in physiological integrity across biological systems. The current study dissects the unique and common variance across body and brain health indicators and parses inter-individual heterogeneity in the multisystem ageing process. Using machine-learning regression models on the UK Biobank dataset (N = 32,593, age range 44.6-82.3, mean age 64.1 years), we first estimated tissue-specific brain age for white and gray matter based on diffusion and T1-weighted magnetic resonance imaging (MRI) data respectively. Next, bodily health traits including cardiometabolic, anthropometric and body composition measures of adipose and muscle tissue from bioimpedance and body MRI were combined with brain MRI data to predict age for a body-brain age model. The results showed that the models including bodily health traits showed significantly higher age prediction accuracy than models trained solely on brain MRI data. Bayesian multilevel modelling carried out to quantify the associations between health traits and predicted age discrepancies showed that relative to the predictions based only on brain MRI, higher systolic blood pressure and muscle-fat infiltration levels contributed to an increase in predicted age, suggesting a link between poor health markers and older-apparent physiological age. In contrast, higher muscle volume and hand-grip strength contributed to a decrease in predicted age. While these findings corroborate the common notion of a close connection between somatic and brain health, the use of age prediction models allows for an individual-level dissection of the health-related factors that affect the estimated age of an individual. In conclusion, the results suggest that bodily health traits, together with brain imaging features, can provide a more comprehensive and individualised understanding of the ageing processes.

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Associations between abdominal adipose tissue, reproductive span, and brain characteristics in post-menopausal women

Cited by 7 publications

References 191 publications

Menopausal hormone therapy and the female brain: leveraging neuroimaging and prescription registry data from the UK Biobank cohort

Menopausal hormone therapy and the female brain: leveraging neuroimaging and prescription registry data from the UK Biobank cohort

Dissecting unique and common variance across body and brain health indicators using age prediction

Dissecting unique and common variance across body and brain health indicators using age prediction

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