Sex differences in predictors and regional patterns of brain age gap estimates

Sanford, Nicole; Ge, Ruiyang; Antoniades, Mathilde; Modabbernia, Amirhossein; Haas, Shalaila S.; Whalley, Heather; Galea, Liisa A.M.; Popescu, Ştefan; Cole, James R.; Frangou, Sophia

doi:10.1002/hbm.25983

Cited by 29 publications

(20 citation statements)

References 68 publications

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“…4,26,31 Again, sex-related differences in accelerated brain aging might only be observable at the local level, as recent reports suggest. 50,61 Contrary to a previous report by S ör ös and Bantel, 52 we did find a significant difference between all MSK pain participants and controls. Hung et al 35 had hypothesized that the lack of a significant MSK pain control difference reported by S ör ös and Bantel 52 owed to the fact that this difference differed among pain types, and thus, pain effects could not be detected by merging all MSK pain participants.…”

Section: Discussioncontrasting

confidence: 92%

“…Under the premise that different neurobiological mechanisms underlie OA and CBP, 3 we hypothesize that brain-PAD would also differ among both groups. Moreover, based on the well-known differences in mechanisms of chronic pain among sexes, 4,26 specifically in the brain, 31 as well as the observed sex differences in brain-PAD, 10,21,50 we hypothesized that these differences would be moderated by sex (ie, women with MSK pain have significantly "older-appearing" brains compared with male patients or controls).…”

Section: Introductionmentioning

confidence: 99%

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Brain-predicted age difference estimated using DeepBrainNet is significantly associated with pain and function—a multi-institutional and multiscanner study

Valdes-Hernandez,

Laffitte Nodarse,

Johnson

et al. 2023

Pain

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Brain age predicted differences (brain-PAD: predicted brain age minus chronological age) have been reported to be significantly larger for individuals with chronic pain compared with those without. However, a debate remains after one article showed no significant differences. Using Gaussian Process Regression, an article provides evidence that these negative results might owe to the use of mixed samples by reporting a differential effect of chronic pain on brain-PAD across pain types. However, some remaining methodological issues regarding training sample size and sex-specific effects should be tackled before settling this controversy. Here, we explored differences in brain-PAD between musculoskeletal pain types and controls using a novel convolutional neural network for predicting brain-PADs, ie, DeepBrainNet. Based on a very large, multi-institutional, and heterogeneous training sample and requiring less magnetic resonance imaging preprocessing than other methods for brain age prediction, DeepBrainNet offers robust and reproducible brain-PADs, possibly highly sensitive to neuropathology. Controlling for scanner-related variability, we used a large sample (n = 660) with different scanners, ages (19-83 years), and musculoskeletal pain types (chronic low back [CBP] and osteoarthritis [OA] pain). Irrespective of sex, brain-PAD of OA pain participants was ∼3 to 4.7 years higher than that of CBP and controls, whereas brain-PAD did not significantly differ among controls and CBP. Moreover, brain-PAD was significantly related to multiple variables underlying the multidimensional pain experience. This comprehensive work adds evidence of pain type–specific effects of chronic pain on brain age. This could help in the clarification of the debate around possible relationships between brain aging mechanisms and pain.

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Section: Discussioncontrasting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Brain-predicted age difference estimated using DeepBrainNet is significantly associated with pain and function—a multi-institutional and multiscanner study

Valdes-Hernandez,

Laffitte Nodarse,

Johnson

et al. 2023

Pain

Self Cite

View full text Add to dashboard Cite

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“…Previous study on brain aging reported on average younger brain in women throughout adulthood compared to men of the same age (Goyal et al, 2019). Predictors of brain age were also found to be sex-specific, highlighting the value of sex-specific analyses (Sanford et al, 2022). Further research is needed on examining sex-specific risk and protective factors that influence brain aging and disease accumulation.…”

Section: Discussionmentioning

confidence: 89%

Biological age and brain age in midlife: relationship to multimorbidity and mental health

Zhang

Chang

Schaefer

et al. 2022

Preprint

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Multimorbidity, co-occurrence of two or more chronic conditions, is one of the top priorities in global health research and has emerged as the gold standard approach to study disease accumulation. As aging underlies the development of many chronic conditions, surrogate aging biomarkers are not disease-specific and capture health at the whole person level, having the potential to improve our understanding of multimorbidity. Biological age has been examined in recent years as a surrogate biomarker to capture the process of aging. However, relatively few studies have investigated the relationship between biological age and multimorbidity. More research is needed to quantify biological age using a broad range of biological markers and multimorbidity based on a comprehensive set of chronic conditions. Brain age estimated by neuroimaging data and machine learning models is another surrogate aging biomarker predictive of a wide range of health outcomes. Little is known about the relationship between brain age and multimorbidity. To answer these questions, our study investigates whether elevated biological age and accelerated brain age are associated with an increased risk of multimorbidity using a large dataset from the Midlife in the United States (MIDUS) Refresher study. Ensemble learning is utilized to combine multiple machine learning models to estimate biological age using a comprehensive set of biological markers. Brain age is obtained using convolutional neural networks and neuroimaging data. Our study is the first to examine the relationship between accelerated brain age and multimorbidity and presents the first effort to test whether sex moderates the relationship between these surrogate aging biomarkers and multimorbidity. Furthermore, it is the first attempt to explore how biological age and brain age are related to multimorbidity in mental health. Our findings hold the potential to advance the understanding of the accumulation of physical and mental health conditions, which may contribute to new strategies to improve the treatment of multimorbidity and detection of at-risk individuals.

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“…We also find that the effects of sex and the sex-age interaction were highly variable across diffusion models predicting brain age with sex and the sex-age interaction being mostly non-significant predictors across diffusion models ( Figure 4-7 ). Nevertheless, brain age does significantly differ between sexes (Sanford et al, 2022; Subramaniapillai et al, 2022), and we cannot exclude sex difference in WM microstructure. These relationships might also lead to differences in WM brain ages between sexes.…”

Section: Discussionmentioning

confidence: 94%

Bio-psycho-social factors’ associations with brain age: a large-scale UK Biobank diffusion study of 35,749 participants

Korbmacher

Gurholt

Lange

et al. 2022

Preprint

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Brain age refers to age predicted by brain features. Brain age has previously been associated with various health and disease outcomes and suggested as a potential biomarker of general health. Few previous studies have systematically assessed brain age variability derived from single and multi-shell diffusion magnetic resonance imaging data. Here, we present multivariate models of brain age derived from various diffusion approaches and how they relate to bio-psycho-social variables within the domains of sociodemographic, cognitive, life-satisfaction, as well as health and lifestyle factors in midlife to old age (N = 35,749, 44.6 to 82.8 years of age). Bio-psycho-social factors could uniquely explain only a small proportion of the brain age variance. We observed a similar pattern across diffusion approaches. We consistently identified bio-psycho-social associations of interest across models. Waist-to-hip ratio, diabetes, hypertension, smoking, matrix puzzles solving, and job and health satisfaction and perception were associated with brain age. At the same time, sex and ethnicity showed large variability in their predictor strengths. As expected, chronological age contributed the strongest to explaining variability in brain age. Our results show that brain age cannot be sufficiently explained by bio-psycho-social variables alone. However, the observed associations suggest to adjust for sex, ethnicity, cognitive factors, as well as health and lifestyle factors.

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Sex differences in predictors and regional patterns of brain age gap estimates

Cited by 29 publications

References 68 publications

Brain-predicted age difference estimated using DeepBrainNet is significantly associated with pain and function—a multi-institutional and multiscanner study

Brain-predicted age difference estimated using DeepBrainNet is significantly associated with pain and function—a multi-institutional and multiscanner study

Biological age and brain age in midlife: relationship to multimorbidity and mental health

Bio-psycho-social factors’ associations with brain age: a large-scale UK Biobank diffusion study of 35,749 participants

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