Capturing functional connectomics using Riemannian partial least squares.

For neurological disorders and diseases, functional and anatomical connectomes of the human brain can be used to better inform targeted interventions and treatment strategies. Functional magnetic resonance imaging (fMRI) is a non-invasive neuroimaging technique that captures spatio-temporal brain function through change in blood-oxygen-level-dependent (BOLD) signals over time. FMRI can be used to study the functional connectome through the functional connectivity matrix; that is, Pearson's correlation matrix between time series from the regions of interest of an fMRI image. One approach to analysing functional connectivity is using partial least squares (PLS), a multivariate regression technique designed for high-dimensional predictor data. However, analysing functional connectivity with PLS ignores a key property of the functional connectivity matrix; namely, these matrices are positive definite. To account for this, we introduce a generalisation of PLS to Riemannian manifolds, called R-PLS, and apply it to symmetric positive definite matrices with the affine invariant geometry. We apply R-PLS to two functional imaging datasets: COBRE, which investigates functional differences between schizophrenic patients and healthy controls, and; ABIDE, which compares people with autism spectrum disorder and neurotypical controls. Using the variable importance in the projection statistic on the results of R-PLS, we identify key functional connections in each dataset that are well represented in the literature. Given the generality of R-PLS, this method has the potential to investigate new functional connectomes in the brain, and with future application to structural data can open up further avenues of research in multi-modal imaging analysis.

A comparison of survival models for prediction of eight-year revision risk following total knee and hip arthroplasty.

BACKGROUND: There is increasing interest in the development and use of clinical prediction models, but a lack of evidence-supported guidance on the merits of different modelling approaches. This is especially true for time-to-event outcomes, where limited studies have compared the vast number of modelling approaches available. This study compares prediction accuracy and variable importance measures for four modelling approaches in prediction of time-to-revision surgery following total knee arthroplasty (TKA) and total hip arthroplasty (THA). METHODS: The study included 321,945 TKA and 151,113 THA procedures performed between 1 January 2003 and 31 December 2017. Accuracy of the Cox model, Weibull parametric model, flexible parametric model, and random survival forest were compared, with patient age, sex, comorbidities, and prosthesis characteristics considered as predictors. Prediction accuracy was assessed using the Index of Prediction Accuracy (IPA), c-index, and smoothed calibration curves. Variable importance rankings from the Cox model and random survival forest were also compared. RESULTS: Overall, the Cox and flexible parametric survival models performed best for prediction of both TKA (integrated IPA 0.056 (95% CI [0.054, 0.057]) compared to 0.054 (95% CI [0.053, 0.056]) for the Weibull parametric model), and THA revision. (0.029 95% CI [0.027, 0.030] compared to 0.027 (95% CI [0.025, 0.028]) for the random survival forest). The c-index showed broadly similar discrimination between all modelling approaches. Models were generally well calibrated, but random survival forest underfitted the predicted risk of TKA revision compared to regression approaches. The most important predictors of revision were similar in the Cox model and random survival forest for TKA (age, opioid use, and patella resurfacing) and THA (femoral cement, depression, and opioid use). CONCLUSION: The Cox and flexible parametric models had superior overall performance, although all approaches performed similarly. Notably, this study showed no benefit of a tuned random survival forest over regression models in this setting.

What Is the Effect of Using a Competing-risks Estimator when Predicting Survivorship After Joint Arthroplasty: A Comparison of Approaches to Survivorship Estimation in a Large Registry.

BACKGROUND: There is increasing interest in the development of statistical models that can be used to estimate risk of adverse patient outcomes after joint arthroplasty. Competing risk approaches have been recommended to estimate risk of longer-term revision, which is often likely to be precluded by the competing risk of death. However, a common approach is to ignore the competing risk by treating death as a censoring event and using standard survival models such as Cox regression. It is well-known that this approach can overestimate the event risk for population-level estimates, but the impact on the estimation of a patient's individualized risk after joint arthroplasty has not been explored. QUESTIONS/PURPOSES: We performed this study to (1) determine whether using a competing risk or noncompeting risk method affects the accuracy of predictive models for joint arthroplasty revision and (2) determine the magnitude of difference that using a competing risks versus noncompeting risks approach will make to predicted risks for individual patients. METHODS: The predictive performance of a standard Cox model, with competing risks treated as censoring events, was compared with the performance of two competing risks approaches, the cause-specific Cox model and Fine-Gray model. Models were trained and tested using data pertaining to 531,304 TKAs and 274,618 THAs recorded in the Australian Orthopaedic Association National Joint Replacement Registry between January 1, 2003 and December 31, 2017. The registry is a large database with near-complete capture and follow-up of all hip and knee joint arthroplasty in Australia from 2003 onwards, making it an ideal setting for this study. The performance of the three modeling approaches was compared in two different prediction settings: prediction of the 10-year risk of all-cause revision after TKA and prediction of revision for periprosthetic fracture after THA. The calibration and discrimination of each approach were compared using the concordance index, integrated Brier scores, and calibration plots. Calibration of 10-year risk estimates was further assessed within subgroups of age by comparing the observed and predicted proportion of events. Estimated 10-year risks from each model were also compared in three hypothetical patients with different risk profiles to determine whether differences in population-level performance metrics would translate into a meaningful difference for individual patient predictions. RESULTS: The standard Cox and two competing risks models showed near-identical ability to distinguish between high-risk and low-risk patients (c-index 0.64 [95% CI, 0.64 to 0.64] for all three modeling approaches for TKAs and 0.66 [95% CI 0.66 to 0.66] for THA). All models performed similarly in patients younger than 75 years, but for patients aged 75 years and older, the standard Cox model overestimated the risk of revision more than the cause-specific Cox and Fine-Gray model did. These results were echoed when predictions were made for hypothetical individual patients. For patients with a low competing risk of mortality, the 10-year predicted risks from the standard Cox, cause-specific Cox, and Fine-Gray models were similar for TKAs and THAs. However, a larger difference was observed for hypothetical 89-year-old patients with increased mortality risk. In TKAs, the revision risk for an 89-year-old patient was so low that this difference was negligible (0.83% from the cause-specific Cox model versus 1.1% from the standard Cox model). However, for THAs, where older age is a risk factor for both death and revision for periprosthetic fracture, a larger difference was observed in the 10-year predicted risks for a hypothetical 89-year-old patient (3.4% from the cause-specific Cox model versus 5.2% from the standard Cox model). CONCLUSION: When developing models to predict longer-term revision of joint arthroplasty, failing to use a competing risks modeling approach will result in overestimating the revision risk for patients with a high risk of mortality during the surveillance period. However, even in an extreme instance, where both the frequency of the event of interest and the competing risk of death are high, the largest absolute difference in predicted 10-year risk for an individual patient was just 1.8%, which may not be of consequence to an individual. Despite these findings, when developing or using risk prediction models, researchers and clinicians should be aware of how competing risks were handled in the modeling process, particularly if the model is intended for use populations where the mortality risk is high. LEVEL OF EVIDENCE: Level III, therapeutic study.

Performance of methods for estimating the effect of covariates on group membership probabilities in group-based trajectory models.

One purpose of a longitudinal study is to gain insight of how characteristics at earlier points in time can impact on subsequent outcomes. Typically, the outcome variable varies over time and the data for each individual can be used to form a discrete path of measurements, that is a trajectory. Group-based trajectory modelling methods seek to identify subgroups of individuals within a population with trajectories that are more similar to each other than to trajectories in distinct groups. An approach to modelling the influence of covariates measured at earlier time points in the group-based setting is to consider models wherein these covariates affect the group membership probabilities. Models in which prior covariates impact the trajectories directly are also possible but are not considered here. In the present study, we compared six different methods for estimating the effect of covariates on the group membership probabilities, which have different approaches to account for the uncertainty in the group membership assignment. We found that when investigating the effect of one or several covariates on a group-based trajectory model, the full likelihood approach minimized the bias in the estimate of the covariate effect. In this '1-step' approach, the estimation of the effect of covariates and the trajectory model are carried out simultaneously. Of the '3-step' approaches, where the effect of the covariates is assessed subsequent to the estimation of the group-based trajectory model, only Vermunt's improved 3 step resulted in bias estimates similar in size to the full likelihood approach. The remaining methods considered resulted in considerably higher bias in the covariate effect estimates and should not be used. In addition to the bias empirically demonstrated for the probability regression approach, we have shown analytically that it is biased in general.

The impact of covariance misspecification in group-based trajectory models for longitudinal data with non-stationary covariance structure.

One purpose of a longitudinal study is to gain a better understanding of how an outcome of interest changes among a given population over time. In what follows, a trajectory will be taken to mean the series of measurements of the outcome variable for an individual. Group-based trajectory modelling methods seek to identify subgroups of trajectories within a population, such that trajectories that are grouped together are more similar to each other than to trajectories in distinct groups. Group-based trajectory models generally assume a certain structure in the covariances between measurements, for example conditional independence, homogeneous variance between groups or stationary variance over time. Violations of these assumptions could be expected to result in poor model performance. We used simulation to investigate the effect of covariance misspecification on misclassification of trajectories in commonly used models under a range of scenarios. To do this we defined a measure of performance relative to the ideal Bayesian correct classification rate. We found that the more complex models generally performed better over a range of scenarios. In particular, incorrectly specified covariance matrices could significantly bias the results but using models with a correct but more complicated than necessary covariance matrix incurred little cost.

Lower age at menarche affects survival in older Australian women: results from the Australian Longitudinal Study of Ageing.

BACKGROUND: While menarche indicates the beginning of a woman's reproductive life, relatively little is known about the association between age at menarche and subsequent morbidity and mortality. We aimed to examine the effect of lower age at menarche on all-cause mortality in older Australian women over 15 years of follow-up. METHODS: Data were drawn from the Australian Longitudinal Study of Ageing (n = 1,031 women aged 65-103 years). We estimated the hazard ratio (HR) associated with lower age at menarche using Cox proportional hazards models, and adjusted for a broad range of reproductive, demographic, health and lifestyle covariates. RESULTS: During the follow-up period, 673 women (65%) died (average 7.3 years (SD 4.1) of follow-up for decedents). Women with menses onset < 12 years of age (10.7%; n = 106) had an increased hazard of death over the follow-up period (adjusted HR 1.28; 95%CI 0.99-1.65) compared with women who began menstruating aged >or= 12 years (89.3%; n = 883). However, when age at menarche was considered as a continuous variable, the adjusted HRs associated with the linear and quadratic terms for age at menarche were not statistically significant at a 5% level of significance (linear HR 0.76; 95%CI 0.56 - 1.04; quadratic HR 1.01; 95%CI 1.00-1.02). CONCLUSION: Women with lower age at menarche may have reduced survival into old age. These results lend support to the known associations between earlier menarche and risk of metabolic disease in early adulthood. Strategies to minimise earlier menarche, such as promoting healthy weights and minimising family dysfunction during childhood, may also have positive longer-term effects on survival in later life.

Genome-wide identification of human FOXP3 target genes in natural regulatory T cells.

The transcription factor FOXP3 is essential for the formation and function of regulatory T cells (Tregs), and Tregs are essential for maintaining immune homeostasis and tolerance. This is demonstrated by a lethal autoimmune defect in mice lacking Foxp3 and in immunodysregulation polyendocrinopathy enteropathy X-linked syndrome patients. However, little is known about the molecular basis of human FOXP3 function or the relationship between direct and indirect targets of FOXP3 in human Tregs. To investigate this, we have performed a comprehensive genome-wide analysis for human FOXP3 target genes from cord blood Tregs using chromatin immunoprecipitation array profiling and expression profiling. We have identified 5579 human FOXP3 target genes and derived a core Treg gene signature conserved across species using mouse chromatin immunoprecipitation data sets. A total of 739 of the 5579 FOXP3 target genes were differentially regulated in Tregs compared with Th cells, thus allowing the identification of a number of pathways and biological functions overrepresented in Tregs. We have identified gene families including cell surface molecules and microRNAs that are differentially expressed in FOXP3(+) Tregs. In particular, we have identified a novel role for peptidase inhibitor 16, which is expressed on the cell surface of >80% of resting human CD25(+)FOXP3(+) Tregs, suggesting that in conjunction with CD25 peptidase inhibitor 16 may be a surrogate surface marker for Tregs with potential clinical application.

Do social networks affect the use of residential aged care among older Australians?

BACKGROUND: Older people's social networks with family and friends can affect residential aged care use. It remains unclear if there are differences in the effects of specific (with children, other relatives, friends and confidants) and total social networks upon use of low-level residential care and nursing homes. METHODS: Data were drawn from the Australian Longitudinal Study of Ageing. Six waves of data from 1477 people aged > or = 70 collected over nine years of follow-up were used. Multinomial logistic regressions of the effects of specific and total social networks on residential care use were carried out. Propensity scores were used in the analyses to adjust for differences in participant's health, demographic and lifestyle characteristics with respect to social networks. RESULTS: Higher scores for confidant networks were protective against nursing home use (odds ratio [OR] upper versus lower tertile of confidant networks = 0.50; 95%CI 0.33-0.75). Similarly, a significant effect of upper versus lower total network tertile on nursing home use was observed (OR = 0.62; 95%CI 0.43-0.90). Evidence of an effect of children networks on nursing home use was equivocal. Nursing home use was not predicted by other relatives or friends social networks. Use of lower-level residential care was unrelated to social networks of any type. Social networks of any type did not have a significant effect upon low-level residential care use. DISCUSSION: Better confidant and total social networks predict nursing home use in a large cohort of older Australians. Policy needs to reflect the importance of these particular relationships in considering where older people want to live in the later years of life.

Incidence, impact on the family and cost of gastroenteritis among 4 to 6-year-old children in South Australia.

BACKGROUND: Gastroenteritis is an important cause of morbidity in both adults and children worldwide. Although the burden of morbidity and mortality is highest in developing countries, gastroenteritis is still a significant cause of morbidity in Australia, particularly in young children. The aims of the present study were to determine the incidence of gastroenteritis among 4-6-year-old children in South Australia, to describe the impact on daily activities of the child and caregiver and estimate the cost of gastroenteritis in this population. METHODS: A diary study of drinking water consumption and gastrointestinal and respiratory symptoms was undertaken among 965 rural children in South Australia over a 6-week period during February-May 1999. Data on the response to gastrointestinal episodes were collected by telephone surveys midway and at the end of the 6-week period. Costing was based on the methods of Hellard et al. RESULTS: The incidence of community gastroenteritis among 4-6-year-old children was 5.53 episodes per child-year (95% confidence interval: 4.87-5.80). The costs associated with childhood gastroenteritis were estimated to be between 16,858,360 AU dollars and 22,477,814 AU dollars per annum. CONCLUSIONS: The incidence of community gastroenteritis among the 4-6-year-olds in the South Australian population is high and it costs the community a significant amount of money in terms of direct medical costs and time lost by parents and carers looking after their ill children.

Genetic regulators of myelopoiesis and leukemic signaling identified by gene profiling and linear modeling.

Mechanisms controlling the balance between proliferation and self-renewal versus growth suppression and differentiation during normal and leukemic myelopoiesis are not understood. We have used the bi-potent FDB1 myeloid cell line model, which is responsive to myelopoietic cytokines and activated mutants of the granulocyte macrophage-colony stimulating factor (GM-CSF) receptor, having differential signaling and leukemogenic activity. This model is suited to large-scale gene-profiling, and we have used a factorial time-course design to generate a substantial and powerful data set. Linear modeling was used to identify gene-expression changes associated with continued proliferation, differentiation, or leukemic receptor signaling. We focused on the changing transcription factor profile, defined a set of novel genes with potential to regulate myeloid growth and differentiation, and demonstrated that the FDB1 cell line model is responsive to forced expression of oncogenes identified in this study. We also identified gene-expression changes associated specifically with the leukemic GM-CSF receptor mutant, V449E. Signaling from this receptor mutant down-regulates CCAAT/enhancer-binding protein alpha (C/EBPalpha) target genes and generates changes characteristic of a specific acute myeloid leukemia signature, defined previously by gene-expression profiling and associated with C/EBPalpha mutations.

Effect of social networks on 10 year survival in very old Australians: the Australian longitudinal study of aging.

STUDY OBJECTIVES: To examine if social networks with children, relatives, friends, and confidants predict survival in older Australians over 10 years after controlling for a range of demographic, health, and lifestyle variables. DESIGN: Prospective longitudinal cohort study (the Australian longitudinal study of aging) SETTING: Adelaide, South Australia. PARTICIPANTS: 1477 persons aged 70 years or more living in the community and residential care facilities. MAIN RESULTS: After controlling for a range of demographic, health, and lifestyle variables, greater networks with friends were protective against mortality in the 10 year follow up period. The hazard ratio for participants in the highest tertile of friends networks compared with participants in the lowest group was 0.78 (95%CI 0.65 to 0.92). A smaller effect of greater networks with confidants (hazard ratio = 0.84; 95%CI = 0.71 to 0.98) was seen. The effects of social networks with children and relatives were not significant with respect to survival over the following decade. CONCLUSIONS: Survival time may be enhanced by strong social networks. Among older Australians, these may be important in lengthening survival.

The effects of social networks on disability in older Australians.

OBJECTIVE: To investigate the effects of total social networks and specific social networks with children, relatives, friends, and confidants on disability in mobility and Nagi functional tasks. METHODS: Six waves of data from the Australian Longitudinal Study of Ageing were used. Data came from 1,477 participants aged 70 years or older. The effects of total social networks and those with children, relatives, friends, and confidants on transitions in disability status were analyzed using binary and multinomial logistic regression. RESULTS: After controlling for a range of health, environmental, and personal factors, social networks with relatives were protective against developing mobility disability (OR = 0.89; 95% CI = 0.79 to 1.00) and Nagi disability (OR = 0.85; 95% CI = 0.74 to 0.96). Other social subnetworks did not have a consistent effect on the development of disability. DISCUSSION: The effects of social relationships extend beyond disability in activities of daily living. Networks with relatives protect against disability in mobility and Nagi tasks.