Differential correlation network analysis identified novel metabolomics signatures for non-responders to total joint replacement in primary osteoarthritis patients.

INTRODUCTION: Up to one third of total joint replacement patients (TJR) experience poor surgical outcome. OBJECTIVES: To identify metabolomic signatures for non-responders to TJR in primary osteoarthritis (OA) patients. METHODS: A newly developed differential correlation network analysis method was applied to our previously published metabolomic dataset to identify metabolomic network signatures for non-responders to TJR. RESULTS: Differential correlation networks involving 12 metabolites and 23 metabolites were identified for pain non-responders and function non-responders, respectively. CONCLUSION: The differential networks suggest that inflammation, muscle breakdown, wound healing, and metabolic syndrome may all play roles in TJR response, warranting further investigation.

Estimating smooth distribution function in the presence of heteroscedastic measurement errors.

Measurement error occurs in many biomedical fields. The challenges arise when errors are heteroscedastic since we literally have only one observation for each error distribution. This paper concerns the estimation of smooth distribution function when data are contaminated with heteroscedastic errors. We study two types of methods to recover the unknown distribution function: a Fourier-type deconvolution method and a simulation extrapolation (SIMEX) method. The asymptotics of the two estimators are explored and the asymptotic pointwise confidence bands of the SIMEX estimator are obtained. The finite sample performances of the two estimators are evaluated through a simulation study. Finally, we illustrate the methods with medical rehabilitation data from a neuro-muscular electrical stimulation experiment.

Generalized linear mixed quantile regression with panel data.

A new generalized linear mixed quantile model for panel data is proposed. This proposed approach applies GEE with smoothed estimating functions, which leads to asymptotically equivalent estimation of the regression coefficients. Random effects are predicted by using the best linear unbiased predictors (BLUP) based on the Tweedie exponential dispersion distributions which cover a wide range of distributions, including those widely used ones, such as the normal distribution, Poisson distribution and gamma distribution. A Taylor expansion of the quantile estimating function is used to linearize the random effects in the quantile process. The parameter estimation is based on the Newton-Raphson iteration method. Our proposed quantile mixed model gives consistent estimates that have asymptotic normal distributions. Simulation studies are carried out to investigate the small sample performance of the proposed approach. As an illustration, the proposed method is applied to analyze the epilepsy data.

Metabolomics Signature for Non-Responders to Total Joint Replacement Surgery in Primary Osteoarthritis Patients: The Newfoundland Osteoarthritis Study.

Although total joint replacement (TJR) surgery is considered as the most effective treatment for advanced osteoarthritis (OA) patients, up to one-third of patients reported unfavorable long-term post-operative pain outcomes. We aimed to identify metabolic biomarkers to predict non-responders to TJR using a metabolomics approach. TJR patients were recruited and followed-up at least 1-year post-surgery; TJR outcomes were assessed by Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain and function subscales. Targeted metabolomic profiling was performed on plasma samples collected pre-surgery and pairwise metabolite ratios, as proxies for enzymatic reactions, were calculated. Association tests were performed between each metabolite ratio and non-responders. The metabolome-wide significance was defined as p < 2 × 10-5 . A total of 461 TJR patients due to primary OA were included in the analysis. Fifteen percent of patients were classified as pain non-responders; 16% were classified as function non-responders. Lower baseline WOMAC pain and function scores were significantly associated with pain and function non-responders, respectively (both p < 0.03). Two metabolite ratios were significantly associated with pain non-responders; acetylcarnitine (C2) to phosphatidylcholine acyl-alkyl C40:1 (PC ae C40:1) was five times higher in pain non-responders whereas phosphatidylcholine diacyl C36:4 (PC aa C36:4) to isoleucine was twenty one times lower in pain non-responders than responders (all p ≤ 1.93 × 10-5 ). One metabolite ratio, glutamine to isoleucine, was significantly lower in function non-responders than responders (eight times lower; p = 1.08 × 10-5 ). Three metabolite ratios (C2 to PC ae C40:1, PC aa C36:4, and glutamine to isoleucine) related to inflammation and muscle breakdown could be considered as novel plasma markers for predicting non-responders to TJR and warrant further investigation. © 2019 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 38:793-802, 2020.

Assessing time-dependent association between scalp EEG and muscle activation: A functional random-effects model approach.

This study investigates time-dependent associations between source strength estimated from high-density scalp electroencephalogram (EEG) and force of voluntary handgrip contraction at different intensity levels. We first estimate source strength from raw EEG signals collected during voluntary muscle contractions at different levels and then propose a functional random-effects model approach in which both functional fixed effects and functional random-effects are considered for the data. Two estimation procedures for the functional model are discussed. The first estimation procedure is a two-step method which involves no iterations. It can flexibly use different smoothing methods and smoothing parameters. The second estimation procedure benefits from the connection between linear mixed models and regression splines and can be fitted using existing software. Functional ANOVA is then suggested to assess the experimental effects from the functional point of view. The statistical analysis shows that the time-dependent source strength function exhibits a nonlinear feature, where a bump is detected around the force onset time. However, there is the lack of significant variations in source strength on different force levels and different cortical areas. The proposed functional random-effects model procedure can be applied to other types of functional data in neuroscience.

Differential metabolomics networks analysis of menopausal status.

Menopause is an endocrine-related transition that induces a number of physiological and potentially pathological changes in middle-aged and elderly women. The intention of this research was to investigate the influence of menopause on the intricate relationships between major biochemical metabolites. The study involved metabolic profiling of 186 metabolic markers measured in blood plasma collected from 120 healthy female participants. We developed a method of network analysis using differential correlation that enabled us to detect and characterize differences in metabolites and changes in inter-relationships in pre- and post-menopausal women. A topological analysis was performed on the differential network that uncovered metabolite differences in pre-and post-menopausal women. In this analysis, our method identified two key metabolites, sphingomyelins and phosphatidylcholines, which may be useful in directing further studies into menopause-specific differences in the metabolome, and how these differences may underlie the body's response to stress and disease following the transition from pre- to post-menopausal status for women.

Differential metabolomics analysis allows characterization of diversity of metabolite networks between males and females.

Females and males are known to have different abilities to cope with stress and disease. This study was designed to investigate the effect of sex on properties of a complex interlinked network constructed of central biochemical metabolites. The study involved the blood collection and analysis of a large set of blood metabolic markers from a total of 236 healthy participants, which included 140 females and 96 males. Metabolic profiling yielded concentrations of 168 metabolites for each subject. A differential correlation network analysis approach was developed for this study that allowed detection and characterization of interconnection differences in metabolites in males and females. Through topological analysis of the differential network that depicted metabolite differences in the sexes, we identified metabolites with high centralities in this network. These key metabolites were identified as 10 phosphatidylcholines (PCaaC34:4, PCaaC36:6, PCaaC34:3, PCaaC42:2, PCaeC38:1, PCaeC38:2, PCaaC40:1, PCaeC34:1, PC aa C32:1 and PC aa C40:6) and 4 acylcarnitines (C3-OH, C7-DC, C3 and C0). Identification of these metabolites may help further studies of sex-specific differences in the metabolome that may underlie different responses to stress and disease in males and females.

METABOLOMICS DIFFERENTIAL CORRELATION NETWORK ANALYSIS OF OSTEOARTHRITIS.

Osteoarthritis (OA) significantly compromises the life quality of affected individuals and imposes a substantial economic burden on our society. Unfortunately the pathogenesis of the disease is till poorly understood and no effective medications have been developed. OA is a complex disease that involves both genetic and environmental influences. To elucidate the complex interlinked structure of metabolic processes associated with OA, we developed a differential correlation network approach to detecting the interconnection of metabolite pairs whose relationships are significantly altered due to the diseased process. Through topological analysis of such a differential network, we identified key metabolites that played an important role in governing the connectivity and information flow of the network. Identification of these key metabolites suggests the association of their underlying cellular processes with OA and may help elucidate the pathogenesis of the disease and the development of novel targeted therapies.

Buckley-James estimator of AFT models with auxiliary covariates.

In this paper we study the Buckley-James estimator of accelerated failure time models with auxiliary covariates. Instead of postulating distributional assumptions on the auxiliary covariates, we use a local polynomial approximation method to accommodate them into the Buckley-James estimating equations. The regression parameters are obtained iteratively by minimizing a consecutive distance of the estimates. Asymptotic properties of the proposed estimator are investigated. Simulation studies show that the efficiency gain of using auxiliary information is remarkable when compared to just using the validation sample. The method is applied to the PBC data from the Mayo Clinic trial in primary biliary cirrhosis as an illustration.