Cross-sectional associations between dietary antioxidant vitamins C, E and carotenoid intakes and sarcopenic indices in women aged 18–79 years

The prevalence of sarcopenia is increasing in aging populations, so prevention is critical. Vitamins (A, C, E and carotenoids) modify skeletal muscle via protein and collagen synthesis and anti-inflammatory activities. Previous studies have not investigated intake of these vitamins in relation to sarcopenic indices in both younger and older-aged women. Indices of skeletal muscle mass (as fat-free mass (FFM) relative to body size) were measured using DXA and leg explosive power (LEP) using the Nottingham Power Rig in 2570 women aged 18-79 years. Adjusted measures of skeletal muscle were calculated according to quintiles of vitamin C, E, retinol and carotenoid intake, derived from Food Frequency Questionnaires, after stratification by age. Higher vitamin C intake was associated with significantly higher indices of FFM and LEP, (Q5-Q1 = 2.0-12.8%, P < 0.01-0.02). Intakes of total and individual carotenoids were significantly associated with indices of FFM and LEP (Q5-Q1 = 1.0-7.5%). Vitamin E was significantly associated with FFM% and FFM BMI only. In mutually adjusted analysis with vitamin C, total carotene, vitamin E and protein in the model, the strongest associations were with vitamin C. These associations were stronger in younger women (< 65 years). For the first time, our research shows higher dietary intakes of antioxidant vitamins, particularly vitamin C, is associated with higher skeletal muscle mass and power in free-living women. These findings have relevance for the treatment and prevention of frailty and sarcopenia throughout adulthood

The relationship between weight-related indicators and depressive symptoms during adolescence and adulthood: results from two twin studies

Background: The association between weight and depressive symptoms is well established, but the direction of effects remains unclear. Most studies rely on body mass index (BMI) as the sole weight indicator, with few examining the aetiology of the association between weight indicators and depressive symptoms. // Methods: We analysed data from the Twins Early Development Study (TEDS) and UK Adult Twin Registry (TwinsUK) (7658 and 2775 twin pairs, respectively). A phenotypic cross-lagged panel model assessed the directionality between BMI and depressive symptoms at ages 12, 16, and 21 years in TEDS. Bivariate correlations tested the phenotypic association between a range of weight indicators and depressive symptoms in TwinsUK. In both samples, structural equation modelling of twin data investigated genetic and environmental influences between weight indicators and depression. Sensitivity analyses included two-wave phenotypic cross-lagged panel models and the exclusion of those with a BMI <18.5. // Results: Within TEDS, the relationship between BMI and depression was bidirectional between ages 12 and 16 with a stronger influence of earlier BMI on later depression. The associations were unidirectional thereafter with depression at 16 influencing BMI at 21. Small genetic correlations were found between BMI and depression at ages 16 and 21, but not at 12. Within TwinsUK, depression was weakly correlated with weight indicators; therefore, it was not possible to generate precise estimates of genetic or environmental correlations. // Conclusions: The directionality of the relationship between BMI and depression appears to be developmentally sensitive. Further research with larger genetically informative samples is needed to estimate the aetiological influence on these associations

Brain-age is associated with progression to dementia in memory clinic patients

Background: Biomarkers for the early detection of dementia risk hold promise for better disease monitoring and targeted interventions. However, most biomarker studies, particularly in neuroimaging, have analysed artificially ‘clean’ research groups, free from comorbidities, erroneous referrals, contraindications and from a narrow sociodemographic pool. Such biases mean that neuroimaging samples are often unrepresentative of the target population for dementia risk (e.g., people referred to a memory clinic), limiting the generalisation of these studies to real-world clinical settings. To facilitate better translation from research to the clinic, datasets that are more representative of dementia patient groups are warranted. Methods: We analysed T1-weighted MRI scans from a real-world setting of patients referred to UK memory clinic services (n = 1140; 60.2 % female and mean [SD] age of 70.0[10.8] years) to derive ‘brain-age’. Brain-age is an index of age-related brain health based on quantitative analysis of structural neuroimaging, largely reflecting brain atrophy. Brain-predicted age difference (brain-PAD) was calculated as brain-age minus chronological age. We determined which patients went on to develop dementia between three months and 7.8 years after neuroimaging assessment (n = 476) using linkage to electronic health records. Results: Survival analysis, using Cox regression, indicated a 3 % increased risk of dementia per brain-PAD year (hazard ratio [95 % CI] = 1.03 [1.02,1.04], p < 0.0001), adjusted for baseline age, age2, sex, Mini Mental State Examination (MMSE) score and normalised brain volume. In sensitivity analyses, brain-PAD remained significant when time-to-dementia was at least 3 years (hazard ratio [95 % CI] = 1.06 [1.02, 1.09], p = 0.0006), or when baseline MMSE score ≥ 27 (hazard ratio [95 % CI] = 1.03 [1.01, 1.05], p = 0.0006). Conclusions: Memory clinic patients with older‐appearing brains are more likely to receive a subsequent dementia diagnosis. Potentially, brain-age could aid decision-making during initial memory clinic assessment to improve early detection of dementia. Even when neuroimaging assessment was more than 3 years prior to diagnosis and when cognitive functioning was not clearly impaired, brain-age still proved informative. These real-world results support the use of quantitative neuroimaging biomarkers like brain-age in memory clinics

Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker.

Machine learning analysis of neuroimaging data can accurately predict chronological age in healthy people. Deviations from healthy brain ageing have been associated with cognitive impairment and disease. Here we sought to further establish the credentials of 'brain-predicted age' as a biomarker of individual differences in the brain ageing process, using a predictive modelling approach based on deep learning, and specifically convolutional neural networks (CNN), and applied to both pre-processed and raw T1-weighted MRI data. Firstly, we aimed to demonstrate the accuracy of CNN brain-predicted age using a large dataset of healthy adults (N = 2001). Next, we sought to establish the heritability of brain-predicted age using a sample of monozygotic and dizygotic female twins (N = 62). Thirdly, we examined the test-retest and multi-centre reliability of brain-predicted age using two samples (within-scanner N = 20; between-scanner N = 11). CNN brain-predicted ages were generated and compared to a Gaussian Process Regression (GPR) approach, on all datasets. Input data were grey matter (GM) or white matter (WM) volumetric maps generated by Statistical Parametric Mapping (SPM) or raw data. CNN accurately predicted chronological age using GM (correlation between brain-predicted age and chronological age r = 0.96, mean absolute error [MAE] = 4.16 years) and raw (r = 0.94, MAE = 4.65 years) data. This was comparable to GPR brain-predicted age using GM data (r = 0.95, MAE = 4.66 years). Brain-predicted age was a heritable phenotype for all models and input data (h(2) ≥ 0.5). Brain-predicted age showed high test-retest reliability (intraclass correlation coefficient [ICC] = 0.90-0.99). Multi-centre reliability was more variable within high ICCs for GM (0.83-0.96) and poor-moderate levels for WM and raw data (0.51-0.77). Brain-predicted age represents an accurate, highly reliable and genetically-influenced phenotype, that has potential to be used as a biomarker of brain ageing. Moreover, age predictions can be accurately generated on raw T1-MRI data, substantially reducing computation time for novel data, bringing the process closer to giving real-time information on brain health in clinical settings

Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker

Machine learning analysis of neuroimaging data can accurately predict chronological age in healthy people. Deviations from healthy brain ageing have been associated with cognitive impairment and disease. Here we sought to further establish the credentials of ‘brain-predicted age’ as a biomarker of individual differences in the brain ageing process, using a predictive modelling approach based on deep learning, and specifically convolutional neural networks (CNN), and applied to both pre-processed and raw T1-weighted MRI data. Firstly, we aimed to demonstrate the accuracy of CNN brain-predicted age using a large dataset of healthy adults (N = 2001). Next, we sought to establish the heritability of brain-predicted age using a sample of monozygotic and dizygotic female twins (N = 62). Thirdly, we examined the test-retest and multi-centre reliability of brain-predicted age using two samples (within-scanner N = 20; between-scanner N = 11). CNN brain-predicted ages were generated and compared to a Gaussian Process Regression (GPR) approach, on all datasets. Input data were grey matter (GM) or white matter (WM) volumetric maps generated by Statistical Parametric Mapping (SPM) or raw data. CNN accurately predicted chronological age using GM (correlation between brain-predicted age and chronological age r = 0.96, mean absolute error [MAE] = 4.16 years) and raw (r = 0.94, MAE = 4.65 years) data. This was comparable to GPR brain-predicted age using GM data (r = 0.95, MAE = 4.66 years). Brain-predicted age was a heritable phenotype for all models and input data (h2 ≥ 0.5). Brain-predicted age showed high test-retest reliability (intraclass correlation coefficient [ICC] = 0.90–0.99). Multi-centre reliability was more variable within high ICCs for GM (0.83–0.96) and poor-moderate levels for WM and raw data (0.51–0.77). Brain-predicted age represents an accurate, highly reliable and genetically-influenced phenotype, that has potential to be used as a biomarker of brain ageing. Moreover, age predictions can be accurately generated on raw T1-MRI data, substantially reducing computation time for novel data, bringing the process closer to giving real-time information on brain health in clinical settings

Low thrust propulsion in a coplanar circular restricted four body problem

This paper formulates a circular restricted four body problem (CRFBP), where the three primaries are set in the stable Lagrangian equilateral triangle configuration and the fourth body is massless. The analysis of this autonomous coplanar CRFBP is undertaken, which identies eight natural equilibria; four of which are close to the smaller body, two stable and two unstable, when considering the primaries to be the Sun and two smaller bodies of the solar system. Following this, the model incorporates `near term' low-thrust propulsion capabilities to generate surfaces of articial equilibrium points close to the smaller primary, both in and out of the plane containing the celestial bodies. A stability analysis of these points is carried out and a stable subset of them is identied. Throughout the analysis the Sun-Jupiter-Asteroid-Spacecraft system is used, for conceivable masses of a hypothetical asteroid set at the libration point L4. It is shown that eight bounded orbits exist, which can be maintained with a constant thrust less than 1:5 10&#x100000;4N for a 1000kg spacecraft. This illustrates that, by exploiting low-thrust technologies, it would be possible to maintain an observation point more than 66% closer to the asteroid than that of a stable natural equilibrium point. The analysis then focusses on a major Jupiter Trojan: the 624-Hektor asteroid. The thrust required to enable close asteroid observation is determined in the simplied CRFBP model. Finally, a numerical simulation of the real Sun-Jupiter-624 Hektor-Spacecraft is undertaken, which tests the validity of the stability analysis of the simplied model

Detection of stable community structures within gut microbiota co-occurrence networks from different human populations

Microbes in the gut microbiome form sub-communities based on shared niche specialisations and specific interactions between individual taxa. The inter-microbial relationships that define these communities can be inferred from the co-occurrence of taxa across multiple samples. Here, we present an approach to identify comparable communities within different gut microbiota co-occurrence networks, and demonstrate its use by comparing the gut microbiota community structures of three geographically diverse populations. We combine gut microbiota profiles from 2,764 British, 1,023 Dutch, and 639 Israeli individuals, derive co-occurrence networks between their operational taxonomic units, and detect comparable communities within them. Comparing populations we find that community structure is significantly more similar between datasets than expected by chance. Mapping communities across the datasets, we also show that communities can have similar associations to host phenotypes in different populations. This study shows that the community structure within the gut microbiota is stable across populations, and describes a novel approach that facilitates comparative community-centric microbiome analyses

Gut microbiome diversity and high-fibre intake are related to lower long-term weight gain

BACKGROUND: Cross-sectional studies suggest that the microbes in the human gut have a role in obesity by influencing thehuman body’s ability to extract and store calories. The aim of this study was to assess if there is a correlation between change inbody weight over time and gut microbiome composition.METHODS: We analysed 16S ribosomal RNA gene sequence data derived from the faecal samples of 1632 healthy females fromTwinsUK to investigate the association between gut microbiome measured cross-sectionally and longitudinal weight gain (adjustedfor caloric intake and baseline body mass index). Dietary fibre intake was investigated as a possible modifier.RESULTS: Less than half of the variation in long-term weight change was found to be heritable (h2 = 0.41 (0.31, 0.47)). Gutmicrobiota diversity was negatively associated with long-term weight gain, whereas it was positively correlated with fibre intake.Nine bacterial operational taxonomic units (OTUs) were significantly associated with weight gain after adjusting for covariates,family relatedness and multiple testing (false discovery rate o0.05). OTUs associated with lower long-term weight gain includedthose assigned to Ruminococcaceae (associated in mice with improved energy metabolism) and Lachnospiraceae. A Bacterioidesspecies OTU was associated with increased risk of weight gain but this appears to be driven by its correlation with lower levels ofdiversity.CONCLUSIONS: High gut microbiome diversity, high-fibre intake and OTUs implicated in animal models of improved energymetabolism are all correlated with lower term weight gain in humans independently of calorie intake and other confounders

Host genetic and environmental factors shape the human gut resistome

BACKGROUND: Understanding and controlling the spread of antimicrobial resistance is one of the greatest challenges of modern medicine. To this end many efforts focus on characterising the human resistome or the set of antibiotic resistance determinants within the microbiome of an individual. Aside from antibiotic use, other host environmental and genetic factors that may shape the resistome remain relatively underexplored. METHODS: Using gut metagenome data from 250 TwinsUK female twins, we quantified known antibiotic resistance genes to estimate gut microbiome antibiotic resistance potential for 41 types of antibiotics and resistance mechanisms. Using heritability modelling, we assessed the influence of host genetic and environmental factors on the gut resistome. We then explored links between gut resistome, host health and specific environmental exposures using linear mixed effect models adjusted for age, BMI, alpha diversity and family structure. RESULTS: We considered gut microbiome antibiotic resistance to 21 classes of antibiotics, for which resistance genes were detected in over 90% of our population sample. Using twin modelling, we estimated that on average about 25% of resistome variability could be attributed to host genetic influences. Greatest heritability estimates were observed for resistance potential to acriflavine (70%), dalfopristin (51%), clindamycin (48%), aminocoumarin (48%) and the total score summing across all antibiotic resistance genes (38%). As expected, the majority of resistome variability was attributed to host environmental factors specific to an individual. We compared antibiotic resistance profiles to multiple environmental exposures, lifestyle and health factors. The strongest associations were observed with alcohol and vegetable consumption, followed by high cholesterol medication and antibiotic usage. Overall, inter-individual variation in host environment showed modest associations with antibiotic resistance profiles, and host health status had relatively minor signals. CONCLUSION: Our results identify host genetic and environmental influences on the human gut resistome. The findings improve our knowledge of human factors that influence the spread of antibiotic resistance genes and may contribute towards helping to attenuate it