Diet quality in late midlife is associated with faster walking speed in later life in women, but not men

Healthy diet has been linked to better age-related physical functioning, but evidence on the relationship of overall diet quality in late midlife and clinically relevant measures of physical functioning in later life is limited. Research on potential sex differences in this relationship is scarce. The aim was to investigate the prospective association between overall diet quality, as assessed by the Healthy Eating Index-2015 at age 60-64y and measures of walking speed seven years later, among men and women from the Insight46, a neuroscience sub-study of the Medical Research Council National Survey of Health and Development. Diet was assessed at age 60-64y using five-day food diaries, from which total HEI-2015 was calculated. At age 69-71y, walking speed was estimated during four 10-meter walks at self-selected pace, using inertial measurement units. Multivariable linear regression models with sex as modifier, controlling for age, follow-up, lifestyle, health, social variables and physical performance were used. The final sample was 164 women and 167 men (n=331). Women had higher HEI-2015 scores and slower walking speed than men. A 10 point increase in HEI-2015 was associated with faster walking speed seven years later among women (B: 0.024, 95% CI: 0.006, 0.043), but not men. The association remained significant in the multivariable model (B: 0.021, 95% CI: 0.003, 0.040). In women in late midlife higher diet quality is associated with faster walking speed. A healthy diet in late midlife is likely to contribute towards better age-related physical capability and sex differences are likely to affect this relationship

Collaborative and competitive strategies in the variability and resiliency of large-scale societies in Mesoamerica

Examinations of the variation and duration of past large-scale societies have long involved a conceptual struggle between efforts at generalization and the unraveling of specific trajectories. Although historical particulars are critical to understanding individual cases, there exist both scientific and policy rationales for drawing broader implications regarding the growing corpus of cross-cultural data germane to understanding variability in the constitution of human societies, past and present. Archaeologists have recently paid increased attention to successes and failures in communal-resource management over the long term, as articulated by the transdisciplinary theory on cooperation and collective action. In this article, we consider frameworks that have been traditionally employed in studies of the rise, diversity, and fall of large-scale preindustrial aggregations. We suggest that a comparative theoretical perspective that foregrounds collective-action problems, unaligned individual and group interests, and the social mechanisms that promote or hamper cooperation advances our understanding of variability in these early cooperative arrangements. We apply such a perspective to an examination of cities from pre-Columbian Mesoamerica to demonstrate tendencies for more collective systems to be larger and longer lasting than less collective ones, likely reflecting greater resiliency in the face of the ecological and cultural perturbations specific to the region and era

Understanding the effect of retirement on health: Mechanisms and heterogeneity

This paper investigates the mechanisms behind the health effects of retirement. Using a Regression Discontinuity Design to exploit financial incentives in the German pension system for identification, I find that retirement improves subjective health status and mental health, while also reducing outpatient care utilization. I explore a wide range of health behaviors, time use, and effect heterogeneity as potential mechanisms. Relief from work-related stress and strain, increased sleep duration as well as more frequent physical exercise seem to be key mechanisms through which retirement affects health.This is the preprint of an article published in Journal of Health Economics 43 (2015), pp. 1-12, available online at: http://dx.doi.org/10.1016/j.jhealeco.2015.05.00

Fluoxetine during Development Reverses the Effects of Prenatal Stress on Depressive-Like Behavior and Hippocampal Neurogenesis in Adolescence

Depression during pregnancy and the postpartum period is a growing health problem, which affects up to 20% of women. Currently, selective serotonin reuptake inhibitor (SSRIs) medications are commonly used for treatment of maternal depression. Unfortunately, there is very little research on the long-term effect of maternal depression and perinatal SSRI exposure on offspring development. Therefore, the aim of this study was to determine the role of exposure to fluoxetine during development on affective-like behaviors and hippocampal neurogenesis in adolescent offspring in a rodent model of maternal depression. To do this, gestationally stressed and non-stressed Sprague-Dawley rat dams were treated with either fluoxetine (5 mg/kg/day) or vehicle beginning on postnatal day 1 (P1). Adolescent male and female offspring were divided into 4 groups: 1) prenatal stress+fluoxetine exposure, 2) prenatal stress+vehicle, 3) fluoxetine exposure alone, and 4) vehicle alone. Adolescent offspring were assessed for anxiety-like behavior using the Open Field Test and depressive-like behavior using the Forced Swim Test. Brains were analyzed for endogenous markers of hippocampal neurogenesis via immunohistochemistry. Results demonstrate that maternal fluoxetine exposure reverses the reduction in immobility evident in prenatally stressed adolescent offspring. In addition, maternal fluoxetine exposure reverses the decrease in hippocampal cell proliferation and neurogenesis in maternally stressed adolescent offspring. This research provides important evidence on the long-term effect of fluoxetine exposure during development in a model of maternal adversity

Permutation and parametric tests for effect sizes in voxel-based morphometry of grey matter volume in brain structural MRI

Permutation testing has been widely implemented in voxel-based morphometry (VBM) tools. However, this type of non-parametric inference has yet to be thoroughly compared with traditional parametric inference in VBM studies of brain structure. Here we compare both types of inference and investigate what influence the number of permutations in permutation testing has on results in an exemplar study of how gray matter proportion changes with age in a group of working age adults. High resolution T1-weighted volume scans were acquired from 80 healthy adults aged 25–64 years. Using a validated VBM procedure and voxel-based permutation testing for Pearson product-moment coefficient, the effect sizes of changes in gray matter proportion with age were assessed using traditional parametric and permutation testing inference with 100, 500, 1000, 5000, 10000 and 20000 permutations. The statistical significance was set at P < 0.05 and false discovery rate (FDR) was used to correct for multiple comparisons. Clusters of voxels with statistically significant (PFDR < 0.05) declines in gray matter proportion with age identified with permutation testing inference (N ≈ 6000) were approximately twice the size of those identified with parametric inference (N = 3221 voxels). Permutation testing with 10000 (N = 6251 voxels) and 20000 (N = 6233 voxels) permutations produced clusters that were generally consistent with each other. However, with 1000 permutations there were approximately 20% more statistically significant voxels (N = 7117 voxels) than with ≥ 10000 permutations. Permutation testing inference may provide a more sensitive method than traditional parametric inference for identifying age-related differences in gray matter proportion. Based on the results reported here, at least 10000 permutations should be used in future univariate VBM studies investigating age related changes in gray matter to avoid potential false findings. Additional studies using permutation testing in large imaging databanks are required to address the impact of model complexity, multivariate analysis, number of observations, sampling bias and data quality on the accuracy with which subtle differences in brain structure associated with normal aging can be identified

Characterizing geomorphological change to support sustainable river restoration and management

The hydrology and geomorphology of most rivers has been fundamentally altered through a long history of human interventions including modification of river channels, floodplains, and wider changes in the landscape that affect water and sediment delivery to the river. Resultant alterations in fluvial forms and processes have negatively impacted river ecology via the loss of physical habitat, disruption to the longitudinal continuity of the river, and lateral disconnection between aquatic, wetland, and terrestrial ecosystems. Through a characterization of geomorphological change, it is possible to peel back the layers of time to investigate how and why a river has changed. Process rates can be assessed, the historical condition of rivers can be determined, the trajectories of past changes can be reconstructed, and the role of specific human interventions in these geomorphological changes can be assessed. To achieve this, hydrological, geomorphological, and riparian vegetation characteristics are investigated within a hierarchy of spatial scales using a range of data sources. A temporal analysis of fluvial geomorphology supports process-based management that targets underlying problems. In this way, effective, sustainable management and restoration solutions can be developed that recognize the underlying drivers of geomorphological change, the constraints imposed on current fluvial processes, and the possible evolutionary trajectories and timelines of change under different future management scenarios. Catchment/river basin planning, natural flood risk management, the identification and appraisal of pressures, and the assessment of restoration needs and objectives would all benefit from a thorough temporal analysis of fluvial geomorphology

Maternal hormonal milieu influence on fetal brain development

An adverse maternal hormonal environment during pregnancy can be associated with abnormal brain growth. Subtle changes in fetal brain development have been observed even for maternal hormone levels within the currently accepted physiologic ranges. In this review, we provide an update of the research data on maternal hormonal impact on fetal neurodevelopment, giving particular emphasis to thyroid hormones and glucocorticoids. Thyroid hormones are required for normal brain development. Despite serum TSH appearing to be the most accurate indicator of thyroid function in pregnancy, maternal serum free T4 levels in the first trimester of pregnancy are the major determinant of postnatal psychomotor development. Even a transient period of maternal hypothyroxinemia at the beginning of neurogenesis can confer a higher risk of expressive language and nonverbal cognitive delays in offspring. Nevertheless, most recent clinical guidelines advocate for targeted high-risk case finding during first trimester of pregnancy despite universal thyroid function screening. Corticosteroids are determinant in suppressing cell proliferation and stimulating terminal differentiation, a fundamental switch for the maturation of fetal organs. Not surprisingly, intrauterine exposure to stress or high levels of glucocorticoids, endogenous or synthetic, has a molecular and structural impact on brain development and appears to impair cognition and increase anxiety and reactivity to stress. Limbic regions, such as hippocampus and amygdala, are particularly sensitive. Repeated doses of prenatal corticosteroids seem to have short-term benefits of less respiratory distress and fewer serious health problems in offspring. Nevertheless, neurodevelopmental growth in later childhood and adulthood needs further clarification. Future studies should address the relevance of monitoring the level of thyroid hormones and corticosteroids during pregnancy in the risk stratification for impaired postnatal neurodevelopment.This work was supported by the grant "Doutoramento em Medicina Jose de Mello Saude 2014" by Jose de Mello Saude to AM

Multiple novel prostate cancer susceptibility signals identified by fine-mapping of known risk loci among Europeans

Genome-wide association studies (GWAS) have identified numerous common prostate cancer (PrCa) susceptibility loci. We have fine-mapped 64 GWAS regions known at the conclusion of the iCOGS study using large-scale genotyping and imputation in 25 723 PrCa cases and 26 274 controls of European ancestry. We detected evidence for multiple independent signals at 16 regions, 12 of which contained additional newly identified significant associations. A single signal comprising a spectrum of correlated variation was observed at 39 regions; 35 of which are now described by a novel more significantly associated lead SNP, while the originally reported variant remained as the lead SNP only in 4 regions. We also confirmed two association signals in Europeans that had been previously reported only in East-Asian GWAS. Based on statistical evidence and linkage disequilibrium (LD) structure, we have curated and narrowed down the list of the most likely candidate causal variants for each region. Functional annotation using data from ENCODE filtered for PrCa cell lines and eQTL analysis demonstrated significant enrichment for overlap with bio-features within this set. By incorporating the novel risk variants identified here alongside the refined data for existing association signals, we estimate that these loci now explain ∼38.9% of the familial relative risk of PrCa, an 8.9% improvement over the previously reported GWAS tag SNPs. This suggests that a significant fraction of the heritability of PrCa may have been hidden during the discovery phase of GWAS, in particular due to the presence of multiple independent signals within the same regio