Sex differences in brain aging among adults with family history of Alzheimer’s disease and APOE4 genetic risk

Subramaniapillai, Sivaniya; Rajagopal, Sricharana; Snytte, Jamie; Otto, A. Ross; Einstein, Gillian; Rajah, M. Natasha

doi:10.1016/j.nicl.2021.102620

Cited by 27 publications

(26 citation statements)

References 83 publications

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“…This could reflect that lower BF% in older females may be an indicator of frailty and preclin-ical dementia, and/or indicate a protective role of certain sources of adipose fat in females at later ages [43,44,45,48]. While our results showed that earlier menopause transition was associated with greater BAG, we found no significant interactions between age at menopause and CMRs.…”

Section: Discussioncontrasting

confidence: 67%

“…Modality-specific BAG estimates are also relevant for identifying differences in brain tissue affected by a specific condition or disease [1,55,68,72]. For example, one of our previous studies found that BMI interacted with AD risk to influence grey-matter based BAG, such that females with greater AD risk benefited more from a higher BMI [45]. While the current study focused on WM measures given their susceptibility to CMRs, future studies may aim to include several brain modalities to directly compare sex-and age-specific effects.…”

Section: Discussionmentioning

confidence: 99%

“…For example, one study found that BMI had a positive association with dementia risk when measured >20 years before dementia diagnosis, and a negative association when measured <10 years before dementia diagnosis [42]. Low BMI at later life stages may indicate indicate frailty, sarcopenia (muscle loss), or preclinical dementia [43,44,45,46]. Hence, while high BMI in mid-adulthood may largely reflect obesity, higher BMI in senescence may reflect overall physical fitness or lack of degenerative diseases.…”

Section: Introductionmentioning

confidence: 99%

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Sex- and age-specific associations between cardiometabolic risk and white matter brain age in the UK Biobank cohort

Subramaniapillai¹,

Suri²,

Barth³

et al. 2021

Preprint

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Cardiometabolic risk factors (CMRs) are associated with accelerated brain aging and increased risk for sex-dimorphic illnesses such as Alzheimer’s Disease (AD). Yet, it is unknown how CMRs interact with sex and apolipoprotein E-ε4 (APOE4), a known genetic risk factor for AD, to influence brain age across different life stages. Using age prediction based on multi-shell diffusion-weighted imaging data in 21,308 UK Biobank participants, we investigated whether associations between white matter Brain Age Gap (BAG) and body mass index (BMI), waist-to-hip ratio (WHR), body fat percentage (BF%), and APOE4 status varied i) between males and females, ii) according to age at menopause in females, and iii) across different age groups in males and females. We report sex differences in associations between BAG and all three CMRs, with stronger positive associations among males com- pared to females. Higher BAG (older brain age relative to chronological age) was associated with greater BMI, WHR, and BF% in males, whereas in females, higher BAG was associated with greater WHR, but not BMI and BF%. These divergent associations were most prominent within the oldest group of females (66-81yrs), where higher BF% was linked to lower BAG (younger brain age relative to chronological age). Earlier menopause transition was associated with higher BAG, but no interactions were found with CMRs. APOE4 status was not significantly associated with BAG, and no significant interactions with CMRs were found. In conclusion, the findings point to sex- and age-specific associations between body fat composition and brain age. Incorporating sex as a factor of interest in studies addressing cardiometabolic risk may promote sex-specific precision medicine, consequently improving health care for both males and females.

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Section: Discussioncontrasting

confidence: 67%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Sex- and age-specific associations between cardiometabolic risk and white matter brain age in the UK Biobank cohort

Subramaniapillai¹,

Suri²,

Barth³

et al. 2021

Preprint

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“…Male mice presented significant brain alterations at older ages (12–15 months of age) compared to female mice (6–9 months of age). In Subramaniapillai et al ( 2021 ), among adult humans with family history of Alzheimer's disease and APOE4 genetic risk, women appear to have more advanced brain aging than men. Nevertheless, the DL-based method used here, allowed the detection of age-related changes in the retina of younger male mice, suggesting that our approach is a powerful tool in predicting age-associated effects at earlier time points.…”

Section: Discussionmentioning

confidence: 99%

Retinal Aging in 3× Tg-AD Mice Model of Alzheimer's Disease

Guimarães

Serranho²,

Martins³

et al. 2022

Front. Aging Neurosci.

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The retina, as part of the central nervous system (CNS), can be the perfect target for in vivo, in situ, and noninvasive neuropathology diagnosis and assessment of therapeutic efficacy. It has long been established that several age-related brain changes are more pronounced in Alzheimer's disease (AD). Nevertheless, in the retina such link is still under-explored. This study investigates the differences in the aging of the CNS through the retina of 3× Tg-AD and wild-type mice. A dedicated optical coherence tomograph imaged mice's retinas for 16 months. Two neural networks were developed to model independently each group's ages and were then applied to an independent set containing images from both groups. Our analysis shows a mean absolute error of 0.875±1.1 × 10−2 and 1.112±1.4 × 10−2 months, depending on training group. Our deep learning approach appears to be a reliable retinal OCT aging marker. We show that retina aging is distinct in the two classes: the presence of the three mutated human genes in the mouse genome has an impact on the aging of the retina. For mice over 4 months-old, transgenic mice consistently present a negative retina age-gap when compared to wild-type mice, regardless of training set. This appears to contradict AD observations in the brain. However, the ‘black-box” nature of deep-learning implies that one cannot infer reasoning. We can only speculate that some healthy age-dependent neural adaptations may be altered in transgenic animals.

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“…For example, one study found that BMI had a positive association with dementia risk when measured >20 years before dementia diagnosis, and a negative association when measured <10 years before dementia diagnosis (Kivimäki et al, 2018 ). Low BMI at later life stages may indicate frailty, sarcopenia (muscle loss), or preclinical dementia (Buchman et al, 2005 ; Hassan et al, 2019 ; Johnson et al, 2006 ; Subramaniapillai et al, 2021 ). Hence, while high BMI in mid‐adulthood may largely reflect obesity, higher BMI in senescence may reflect overall physical fitness or lack of degenerative diseases.…”

Section: Introductionmentioning

confidence: 99%

Sex‐ and age‐specific associations between cardiometabolic risk and white matter brain age in the UK Biobank cohort

Subramaniapillai

Suri

Barth

et al. 2022

Human Brain Mapping

Self Cite

View full text Add to dashboard Cite

Cardiometabolic risk (CMR) factors are associated with accelerated brain aging and increased risk for sex‐dimorphic illnesses such as Alzheimer's disease (AD). Yet, it is unknown how CMRs interact with sex and apolipoprotein E‐ϵ4 (APOE4), a known genetic risk factor for AD, to influence brain age across different life stages. Using age prediction based on multi‐shell diffusion‐weighted imaging data in 21,308 UK Biobank participants, we investigated whether associations between white matter Brain Age Gap (BAG) and body mass index (BMI), waist‐to‐hip ratio (WHR), body fat percentage (BF%), and APOE4 status varied (i) between males and females, (ii) according to age at menopause in females, and (iii) across different age groups in males and females. We report sex differences in associations between BAG and all three CMRs, with stronger positive associations among males compared to females. Independent of APOE4 status, higher BAG (older brain age relative to chronological age) was associated with greater BMI, WHR, and BF% in males, whereas in females, higher BAG was associated with greater WHR, but not BMI and BF%. These divergent associations were most prominent within the oldest group of females (66–81 years), where greater BF% was linked to lower BAG. Earlier menopause transition was associated with higher BAG, but no interactions were found with CMRs. In conclusion, the findings point to sex‐ and age‐specific associations between CMRs and brain age. Incorporating sex as a factor of interest in studies addressing CMR may promote sex‐specific precision medicine, consequently improving health care for both males and females.

show abstract

Sex differences in brain aging among adults with family history of Alzheimer’s disease and APOE4 genetic risk

Cited by 27 publications

References 83 publications

Sex- and age-specific associations between cardiometabolic risk and white matter brain age in the UK Biobank cohort

Sex- and age-specific associations between cardiometabolic risk and white matter brain age in the UK Biobank cohort

Retinal Aging in 3× Tg-AD Mice Model of Alzheimer's Disease

Sex‐ and age‐specific associations between cardiometabolic risk and white matter brain age in the UK Biobank cohort

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