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56 Chronic Musculoskeletal Pain, Biobehavioral and Psychosocial Resilience Index, and Brain Age Gap
- Udell Holmes III, Jared Tanner, Brittany Addison, Kenia Rangel, Angela M Mickle, Cynthia S Garvan, Emily J Bartley, Amber K Brooks, Lai Song, Roland Staud, Burel Goodin, Roger B Fillingim, Catherine C Price, Kimberly T Sibille
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- Journal:
- Journal of the International Neuropsychological Society / Volume 29 / Issue s1 / November 2023
- Published online by Cambridge University Press:
- 21 December 2023, p. 465
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- Article
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Objective:
Chronic musculoskeletal pain is associated with neurobiological, physiological, and cellular measures. Importantly, we have previously demonstrated that a biobehavioral and psychosocial resilience index appears to have a protective relationship on the same biomarkers. Less is known regarding the relationships between chronic musculoskeletal pain, protective factors, and brain aging. This study investigates the relationships between clinical pain, a resilience index, and brain age. We hypothesized that higher reported chronic pain would correlate with older appearing brains, and the resilience index will attenuate the strength of the relationship between chronic pain and brain age.
Participants and Methods:Participants were drawn from an ongoing observational multisite study and included adults with chronic pain who also reported knee pain (N = 135; age = 58.3 ± 8.1; 64% female; 49% non-Hispanic Black, 51% non-Hispanic White; education Mdn = some college; income level Mdn = $30,000 - $40,000; MoCA M = 24.27 ± 3.49). Measures included the Graded Chronic Pain Scale (GCPS), characteristic pain intensity (CPI) and disability, total pain body sites; and a cognitive screening (MoCA). The resilience index consisted of validated biobehavioral (e.g., smoking, waist/hip ratio, and active coping) and psychosocial measures (e.g., optimism, positive affect, negative affect, perceived stress, and social support). T1-weighted MRI data were obtained. Surface area metrics were calculated in FreeSurfer using the Human Connectome Project's multi-modal cortical parcellation scheme. We calculated brain age in R using previously validated and trained machine learning models. Chronological age was subtracted from predicted brain age to generate a brain age gap (BAG). With higher scores of BAG indicating predicated age is older than chronological age. Three parallel hierarchical regression models (each containing one of three pain measures) with three blocks were performed to assess the relationships between chronic pain and the resilience index in relation to BAG, adjusting for covariates. For each model, Block 1 entered the covariates, Block 2 entered a pain score, and Block 3 entered the resilience index.
Results:GCPS CPI (R2 change = .033, p = .027) and GCPS disability (R2 change = 0.038, p = 0.017) significantly predicted BAG beyond the effects of the covariates, but total pain sites (p = 0.865) did not. The resilience index was negatively correlated and a significant predictor of BAG in all three models (p < .05). With the resilience index added in Block 3, both GCPS CPI (p = .067) and GCPS disability (p = .066) measures were no longer significant in their respective models. Additionally, higher education/income (p = 0.016) and study site (p = 0.031) were also significant predictors of BAG.
Conclusions:In this sample, higher reported chronic pain correlated with older appearing brains, and higher resilience attenuated this relationship. The biobehavioral and psychosocial resilience index was associated with younger appearing brains. While our data is cross-sectional, findings are encouraging that interventions targeting both chronic pain and biobehavioral and psychosocial factors (e.g., coping strategies, positive and negative affect, smoking, and social support) might buffer brain aging. Future directions include assessing if chronic pain and resilience factors can predict brain aging over time.
Alexander (‘Sandy’) Grant: Views from Lancaster and Beyond
- Edited by Steven Boardman, University of Edinburgh , David Ditchburn, Trinity College Dublin
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- Book:
- Kingship, Lordship and Sanctity in Medieval Britain
- Published by:
- Boydell & Brewer
- Published online:
- 15 September 2022
- Print publication:
- 10 June 2022, pp xxi-xxxii
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Summary
THE year 1979 saw a series of momentous events whose consequences still reverberate today. Margaret Thatcher became prime minister of the United Kingdom, Saddam Hussein took over in Iraq, Rhodesia was supplanted by Zimbabwe, the U.S.S.R. invaded Afghanistan – and Sandy Grant arrived to take up a lectureship in history at Lancaster University. He came from Queen's University, Belfast, and was naturally highly recommended by, among others, Lewis Warren – though if memory serves it was stressed that allowances would have to be made for his fervent support of clubs competing in the Scottish football league. For the next thirty-five years, until Sandy's retirement in September 2014, Lancaster's history department was his professional home and it is on his role as a contributor to departmental life that this appreciation begins. Sandy replaced Anthony Tuck, who left for the mastership of Collingwood College, Durham, and Sandy made an immediate impact, not least by strengthening the department's ability to offer medieval and renaissance courses in genuinely British history at a time when such were far from fashionable. He progressed to senior lecturer in 1992 – in an era when such appointments were exceptionally difficult to obtain – and he was awarded a very well-merited readership in 1995. Never a shrinking violet, Sandy also established himself as a lively and vociferous – as well as, in some guises, a challenging and dissenting – colleague, one who was and remained very much part of the lifeblood of the department. What marked out his many contributions to the department's affairs can be summed up in two words: passion and commitment.
Only the most introverted of Sandy's colleagues remained unaware of his impressive range of academic contacts beyond Lancaster, especially north of the border; none failed to appreciate his burgeoning scholarly reputation, particularly (but by no means exclusively) in the field of medieval Scottish studies. We shall say more about his research and publications shortly but it was characteristic of Sandy's loyalty to the department that as a co-editor and/or contributor he was involved in the production of as many as seven books that bore something of a distinctive ‘Lancaster stamp’.
2 - Modeling individual and average human growth data from childhood to adulthood
- Edited by David Magnusson, Stockholms Universitet, Lars R. Bergman, Stockholms Universitet, Georg Rudinger, Rheinische Friedrich-Wilhelms-Universität Bonn
- With Bertil Torestad, Stockholms Universitet
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- Book:
- Problems and Methods in Longitudinal Research
- Published online:
- 27 April 2010
- Print publication:
- 12 December 1991, pp 28-46
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Summary
INTRODUCTION
The first longitudinal growth study dates back to 1759 when Count de Montbeillard measured the body length of his son from birth to 18 years (Scammon, 1927; Tanner, 1962). Actually, when studying growth, there are two basically different approaches: longitudinal and crosssectional studies. In longitudinal growth studies, we measure the same children over several years at regular intervals (as was done by de Montbeillard) in order to be able to establish individual growth patterns. In cross-sectional growth studies, we measure children of different ages only once. A plot of the average height obtained at each age (or age group) depicts the average growth pattern in the sample. One should realize that the shape of the curve seen in an average growth pattern is different from the shape of individual growth curves (Hauspie, 1989). The information provided by the longitudinal and cross-sectional approaches is quite different. Both methods have their advantages and limitations. Whether the data concerns individual or average growth patterns, we are dealing with a series of measures of size (height or average height, for example) at particular ages, either precise chronological ages (in case of longitudinal studies) or mid-points of age classes (in case of cross-sectional studies). However, the researcher is quite often interested in determining the underlying continuous process of growth, from which he wants to derive certain characteristics, such as the age at maximum velocity at adolescence, for example.