Blood biomarkers and prognosis of amyotrophic lateral sclerosis.

BACKGROUND AND PURPOSE: The objective was to assess the ability of eight commonly measured blood markers to serve as prognostic biomarkers in amyotrophic lateral sclerosis (ALS). METHODS: A cohort study was conducted of 399 individuals with newly diagnosed ALS between 2006 and 2011 in Stockholm, Sweden. Information on eight blood markers, including creatinine, albumin, haemoglobin, C-reactive protein (CRP), glucose, potassium, sodium and calcium, measured at or after the date of ALS diagnosis, was collected. The Cox regression model and joint model were used to explore the associations between biomarkers and risk of mortality. RESULTS: The mean age at ALS diagnosis was 66.25 years and 58% of the patients were male. A lower than median level of serum creatinine [hazard ratio (HR) 1.67; 95% confidence interval (CI) 1.31-2.12] or albumin (HR 1.49, 95% CI 1.13-1.96) whereas a higher than median level of log-transformed CRP (HR 1.33, 95% CI 1.04-1.71) or glucose (HR 1.34, 95% CI 1.01-1.78) at baseline was associated with a higher mortality risk. Taking all available measurements after ALS diagnosis into account, an association was found between per standard deviation (SD) decrease in serum creatinine (HR 2.23, 95% CI 1.81-2.75) or albumin (HR 1.83, 95% CI 1.43-2.36) as well as per SD increase of log(CRP) (HR 1.96, 95% CI 1.58-2.43) or glucose (HR 1.61, 95% CI 1.21-2.12) and a higher mortality risk. No clear association was found for haemoglobin, potassium, sodium or calcium. CONCLUSIONS: This study suggests that serum creatinine, albumin, CRP and glucose measured at the time of ALS diagnosis as well as their temporal changes after ALS diagnosis could serve as additional prognostic biomarkers for ALS. Their values in routine clinical practice and clinical trials of ALS need to be investigated further.

Antibiotics use and risk of amyotrophic lateral sclerosis in Sweden.

BACKGROUND AND PURPOSE: Previous animal studies have suggested a disrupted intestinal microbiome in amyotrophic lateral sclerosis (ALS). Due to the known effect of antibiotics on gut microflora, the potential role of antibiotics use on the risk of ALS deserves an investigation. METHODS: A nested case-control study was conducted using several Swedish national registers. In all, 2484 ALS patients diagnosed between 1 July 2006 and 31 December 2013 were included as cases, and five controls per case individually matched to the case by sex, birth year and area of residence were randomly selected from the general Swedish population. Information on antibiotics prescriptions before ALS diagnosis was extracted from the Prescribed Drug Register for both cases and controls. A conditional logistic regression model was used to calculate odds ratios (ORs) and 95% confidence intervals (CIs). RESULTS: After accounting for potential diagnostic delay in ALS by excluding all prescriptions within 1 year before diagnosis, any antibiotics use was associated with a higher risk of ALS. The ORs (95% CIs) were 1.06 (0.94-1.19), 1.13 (1.00-1.28) and 1.18 (1.03-1.35) when comparing 1, 2-3 and ≥4 prescriptions to no prescription (P for trend = 0.0069). Similar results were noted for antibiotics used for respiratory infections and urinary tract as well as skin and soft tissue infections. Amongst different individual antibiotics, the risk of ALS was especially increased in relation to more than two prescriptions of beta-lactamase sensitive penicillin (OR 1.28; 95% CI 1.10-1.50). CONCLUSIONS: Use of antibiotics, especially repeated, might be associated with a higher subsequent risk of ALS.

Bounds on causal interactions for binary outcomes.

A common goal of epidemiologic research is to study how two exposures interact in causing a binary outcome. Causal interaction is defined as the presence of subjects for which the causal effect of one exposure depends on the level of the other exposure. For binary exposures, it has previously been shown that the presence of causal interaction is testable through additive statistical interaction. However, it has also been shown that the magnitude of causal interaction, defined as the proportion of subjects for which there is causal interaction, is generally not identifiable. In this article, we derive bounds on causal interactions, which are applicable to binary outcomes and categorical exposures with arbitrarily many levels. These bounds can be used to assess the magnitude of causal interaction, and serve as an important complement to the statistical test that is frequently employed. The bounds are derived both without and with an assumption about monotone exposure effects. We present an application of the bounds to a study of gene-gene interaction in rheumatoid arthritis.

An expression signature at diagnosis to estimate prostate cancer patients' overall survival.

BACKGROUND: This study aimed to identify biomarkers for estimating the overall and prostate cancer (PCa)-specific survival in PCa patients at diagnosis. METHODS: To explore the importance of embryonic stem cell (ESC) gene signatures, we identified 641 ESC gene predictors (ESCGPs) using published microarray data sets. ESCGPs were selected in a stepwise manner, and were combined with reported genes. Selected genes were analyzed by multiplex quantitative polymerase chain reaction using prostate fine-needle aspiration samples taken at diagnosis from a Swedish cohort of 189 PCa patients diagnosed between 1986 and 2001. Of these patients, there was overall and PCa-specific survival data available for 97.9%, and 77.9% were primarily treated by hormone therapy only. Univariate and multivariate Cox proportional hazard ratios and Kaplan-Meier plots were used for the survival analysis, and a k-nearest neighbor (kNN) algorithm for estimating overall survival. RESULTS: An expression signature of VGLL3, IGFBP3 and F3 was shown sufficient to categorize the patients into high-, intermediate- and low-risk subtypes. The median overall survival times of the subtypes were 3.23, 4.00 and 9.85 years, respectively. The difference corresponded to hazard ratios of 5.86 (95% confidence interval (CI): 2.91-11.78, P<0.001) for the high-risk subtype and 3.45 (95% CI: 1.79-6.66, P<0.001) for the intermediate-risk compared with the low-risk subtype. The kNN models that included the gene expression signature outperformed the one designed on clinical parameters alone. CONCLUSIONS: The expression signature can potentially be used to estimate overall survival time. When validated in future studies, it could be integrated in the routine clinical diagnostic and prognostic procedure of PCa for an optimal treatment decision based on the estimated survival benefit.

Low-dose aspirin use and cancer characteristics: a population-based cohort study.

BACKGROUND: Long-term daily use of aspirin has been associated with reduced cancer mortality. To explore this association, we compared tumour TNM characteristics among aspirin users with those among non-users. METHODS: From the Swedish Cancer Register, we identified patients diagnosed with colorectal, lung, prostate and breast cancers between 2006 and 2009 and matched them to the Swedish Prescribed Drug Register to obtain information on low-dose aspirin use prior to diagnosis. Contingency table and logistic regression analyses were used to test for association and obtain odds ratios (ORs). RESULTS: We identified 17,041 colorectal, 9766 lung, 29,770 prostate and 20,299 breast cancer patients. The proportion of low-dose aspirin users was ~26% among colorectal, lung and prostate cancer patients and ~14% among breast cancer patients. Adjusted for age, gender, education level and place of residence, low-dose aspirin use was associated with lower tumour extent (T) for colorectal and lung cancers (P<0.0001) but not for prostate and breast cancers. The adjusted OR of aspirin use for the T4 vs T1 categories was ~0.7 (95% confidence interval (CI) 0.6-0.8). For all cancers, we found no evidence of association of aspirin use with nodal involvement (N). Except for a borderline result in prostate cancer (OR ~0.9; 95% CI 0.8-1.0), aspirin use was associated with a lower rate of metastatic disease (ORs ~0.6-0.8). Among the histological subgroups of lung cancer, significant differences in tumour extent were observed most clearly within the adenocarcinoma subgroup (OR ~0.6, 95% CI 0.5-0.8), although numbers of other subtypes were more limited; and there was a significant reduction of ~20-30% in the odds of metastasis among the aspirin users across the subgroups. CONCLUSION: Use of low-dose aspirin in the year prior to diagnosis was found to be associated with lower tumour extent and fewer metastatic disease for colorectal and lung cancers. For these cancers, the benefit of aspirin use appears to be during both early and late cancer progression.

A new paradigm emerges from the study of de novo mutations in the context of neurodevelopmental disease.

The study of de novo point mutations (new germline mutations arising from the gametes of the parents) remained largely static until the arrival of next-generation sequencing technologies, which made both whole-exome sequencing (WES) and whole-genome sequencing (WGS) feasible in practical terms. Single nucleotide polymorphism genotyping arrays have been used to identify de novo copy-number variants in a number of common neurodevelopmental conditions such as schizophrenia and autism. By contrast, as point mutations and microlesions occurring de novo are refractory to analysis by these microarray-based methods, little was known about either their frequency or impact upon neurodevelopmental disease, until the advent of WES. De novo point mutations have recently been implicated in schizophrenia, autism and mental retardation through the WES of case-parent trios. Taken together, these findings strengthen the hypothesis that the occurrence of de novo mutations could account for the high prevalence of such diseases that are associated with a marked reduction in fecundity. De novo point mutations are also known to be responsible for many sporadic cases of rare dominant mendelian disorders such as Kabuki syndrome, Schinzel-Giedion syndrome and Bohring-Opitz syndrome. These disorders share a common feature in that they are all characterized by intellectual disability. In summary, recent WES studies of neurodevelopmental and neuropsychiatric disease have provided new insights into the role of de novo mutations in these disorders. Our knowledge of de novo mutations is likely to be further accelerated by WGS. However, the collection of case-parent trios will be a prerequisite for such studies. This review aims to discuss recent developments in the study of de novo mutations made possible by technological advances in DNA sequencing.

Re-expression of microRNA-375 reverses both tamoxifen resistance and accompanying EMT-like properties in breast cancer.

Epithelial-mesenchymal transition (EMT) is an initiating event in tumor cell invasion and metastasis. It has been shown to occur in resistance to a range of cancer therapies, including tamoxifen. MicroRNAs (miRNAs) have been associated with EMT as well as resistance to standard therapies. To investigate the role of miRNAs in the development of resistance to tamoxifen as well as accompanying EMT-like properties, we established a tamoxifen-resistant (TamR) model by continually exposing MCF-7 breast cancer cells to tamoxifen. In addition to the molecular changes known to be involved in acquired tamoxifen resistance, TamR cells displayed mesenchymal features and had increased invasiveness. Genome-wide miRNA microarray analysis revealed that miRNA-375 was among the top downregulated miRNAs in resistant cells. Re-expression of miR-375 was sufficient to sensitize TamR cells to tamoxifen and partly reversed EMT. A combination of mRNA profiling, bioinformatics analysis and experimental validation identified metadherin (MTDH) as a direct target of miR-375. Knockdown of MTDH partially phenocopied the effects of miR-375 on the sensitivity to tamoxifen and the reversal of EMT. We observed an inverse correlation between the expression of miR-375 and its target MTDH in primary breast cancer samples, implying the pathological relevance of targeting. Finally, tamoxifen-treated patients with higher expression of MTDH had a shorter disease-free survival and higher risk of relapse. As most cancer-related deaths occur because of resistance to standard therapies and metastasis, re-expression of miR-375 or targeting MTDH might serve as potential therapeutic approaches for the treatment of TamR breast cancer.

Estimating the number of true discoveries in genome-wide association studies.

Recent genome-wide association studies have reported the discoveries of genetic variants of small to moderate effects. However, most studies of complex diseases face a great challenge because the number of significant variants is less than what is required to explain the disease heritability. A new approach is needed to recognize all potential discoveries in the data. In this paper, we present a practical model-free procedure to estimate the number of true discoveries as a function of the number of top-ranking SNPs together with the confidence bounds. This approach allows a practical methodology of general utility and produces relevant statistical quantities with simple interpretation.

Time trends in risk and risk determinants of non-Hodgkin lymphoma in solid organ transplant recipients.

Organ transplantation increases risk of non-Hodgkin lymphoma (NHL), but long-term risk and time trends have seldom been evaluated. Immunosuppressive drug load is an important risk determinant, but the details are unclear. We studied NHL risk in a nationwide Swedish cohort of 11 081 graft recipients transplanted 1970-2008. Relative risks (RRs) were estimated within the cohort and versus the general population by age, sex, follow-up time and calendar period. NHL risk was also assessed by cumulative and average doses of immunosuppressive treatments in a nested case-control design throughout 1997 using conditional logistic regression. We observed 153 NHL cases during 97 853 years of follow-up. Compared with the general population, NHL risk was eightfold increased (RR 7.9; 95% confidence interval [CI] 6.6-9.4), and increased risks persisted after ≥15 years of follow-up among kidney (6.1; 95% CI 3.5-10) and nonkidney recipients (44; 14-103). Among nonkidney recipients, NHL risk was lower in the 2000s compared with the 1990s (0.5; 95% CI 0.3-1.0; p = 0.04). A high average dose of antithymocyte immunoglobulin (ATG) conferred an eightfold increased risk of NHL (OR 8.5; 95% CI 1.9-38). To conclude, posttransplant NHL risk decreased during the last decade among nonkidney recipients, possibly because of a more careful use of ATG, the introduction of new drugs, or both.

TMPRSS2:ERG gene fusion associated with lethal prostate cancer in a watchful waiting cohort.

The identification of the TMPRSS2:ERG fusion in prostate cancer suggests that distinct molecular subtypes may define risk for disease progression. In surgical series, TMPRSS2:ERG fusion was identified in 50% of the tumors. Here, we report on a population-based cohort of men with localized prostate cancers followed by expectant (watchful waiting) therapy with 15% (17/111) TMPRSS2:ERG fusion. We identified a statistically significant association between TMPRSS2:ERG fusion and prostate cancer specific death (cumulative incidence ratio=2.7, P<0.01, 95% confidence interval=1.3-5.8). Quantitative reverse-transcription-polymerase chain reaction demonstrated high ets-related [corrected] gene (ERG) expression to be associated with TMPRSS2:ERG fusion (P<0.005). These data suggest that TMPRSS2:ERG fusion prostate cancers may have a more aggressive phenotype, possibly mediated through increased ERG expression.

Estimation of genetic and environmental factors for melanoma onset using population-based family data.

Estimation of genetic and environmental contributions to cancers falls in the framework of generalized linear mixed modelling with several random effect components. Computational challenges remain, however, in dealing with binary or survival phenotypes. In this paper, we consider the analysis of melanoma onset in a population of 2.6 million nuclear families in Sweden, for which none of the current survival-based methodologies is feasible. We treat the disease outcome as a binary phenotype, so that the standard proportional hazard model leads to a generalized linear model with the complementary-log link function. For rare diseases this link is very close to the probit link, and thus allows the use of marginal likelihood for the estimation of the variance components. We correct for the survival length bias by censoring the parent generation within each family at the time they attain the same cumulative hazard as the child generation, thus improving the validity of the estimates. Our finding that childhood shared environment in addition to genetic factors had a considerable effect on the development of melanoma is consistent with epidemiological studies.

Multicomponent variance estimation for binary traits in family-based studies.

In biometrical genetic analyses of binary traits, the use of family data overcomes some limitations of twin studies, particularly in terms of sample size and types of genetic or environmental factors that can be estimated. However, because of computational problems, recent methods in the application of generalized linear mixed models for family data structure have limited the ability to handle large data sets with general covariates. In this paper, we investigate the use of the hierarchical likelihood approach to the analysis of binary traits from family data. In a simulation study, the method is shown to be highly accurate for the estimation of both the variance components and fixed regression parameters, even for small family sizes. For illustration, we analyze a real data set of familial aggregation of preeclampsia, a pregnancy-induced hypertension. When possible, the analysis is compared with the exact maximum likelihood approach.

Children and adults with acute lymphoblastic leukaemia have similar gene expression profiles.

OBJECTIVES: To compare the gene expression pattern in children and adults with acute lymphoblastic leukaemia (ALL) in order to improve our understanding of the difference in disease biology and prognosis. METHODS: The gene expression profiles in diagnostic samples from 29 children and 15 adults with ALL were analysed using the oligonucleotide chip Hu95ver2a, produced by Affymetrix. RESULTS: Unsupervised hierarchical cluster analysis revealed that, in spite of differences in outcome, patients clustered irrespective of age, first by T-cell or B-precursor immunophenotype, and second by cytogenetic changes within the B-precursor group. The expression pattern analysis allowed the reclassification of some samples into the proper cytogenetic group. We also showed that separate clustering of samples with the BCR/ABL translocation could be explained by different breakpoint regions in the BCR. No significant difference in gene expression was observed between samples with and without CDKN2A deletion within the B-precursor group. Analysis of different age groups revealed a similarity in expression profiles when infants with the MLL translocation and adults over 40 yr of age were compared irrespective of karyotype. CONCLUSIONS: In spite of the difference in clinical outcome, the gene expression pattern in children and adults with ALL is very similar and is primarily dependent on immunophenotype and cytogenetic aberrations. However, when age groups are compared, the expression patterns of infants and adults over 40 show a remarkable similarity.

Non-Gaussian smoothing of low-count transmission scans for PET whole-body studies.

A non-Gaussian smoothing (NGS) technique is developed for filtering low count transmission (TR) data to be used for attenuation correction (AC) of positron emission tomography (PET) studies. The method is based on a statistical technique known as the generalized linear mixed model that allows an inverse link function that avoids the inversion of the observed transmission data. The NGS technique has been implemented in the sinogram domain in one-dimensional mode as angle-by-angle computation. To make it adaptive as a function of the TR count statistics we also develop and validate an objective procedure to choose an optimal smoothing parameter. The technique is assessed using experimental phantoms, simulating PET whole-body studies, and applied to real patient data. Different experimental conditions, in terms of TR scan time (from 1 h to 1 min), covering a wide range of TR counting statistic are considered. The method is evaluated, in terms of mean squared error (MSE), by comparing pixel by pixel the distribution for high counts statistics TR scan (1 h) with the corresponding counts distribution for low count statistics TR scans (e.g., 1 min). The smoothing parameter selection is shown to have high efficiency, meaning that it tends to choose values close to the unknown best value. Furthermore, the counts distribution of emission (EM) images, reconstructed with AC generated using low count TR data (1 min), are within 5% of the corresponding EM images reconstructed with AC generated using the high count statistics TR data (1 h). An application to a real patient whole-body PET study shows the promise of the technique for routine use.

Gene expression profiling for prognosis using Cox regression.

Given the promise of rich biological information in microarray data we will expect an increasing demand for a robust, practical and well-tested methodology to provide patient prognosis based on gene expression data. In standard settings, with few clinical predictors, such a methodology has been provided by the Cox proportional hazard model, but no corresponding methodology is available to deal with the full set of genes in microarray data. Furthermore, we want the procedure to be able to deal with the general survival data that include censored information. Conceptually such a procedure can be constructed quite easily, but its implementation will never be straightforward due to computational problems. We have developed an approach that relies on an extension of the Cox proportional likelihood that allows random effects parameters. In this approach, we use the full set of genes in the analysis and deal with survival data in the most general way. We describe the development of the model and the steps in the implementation, including a fast computational formula based on a subsampling of the risk set and the singular value decomposition. Finally, we illustrate the methodology using a data set obtained from a cohort of breast cancer patients.

Estimation of genetic and environmental factors for binary traits using family data.

While the family-based analysis of genetic and environmental contributions to continuous or Gaussian traits is now straightforward using the linear mixed models approach, the corresponding analysis of complex binary traits is still rather limited. In the latter we usually rely on twin studies or pairs of relatives, but these studies often have limited sample size or have difficulties in dealing with the dependence between the pairs. Direct analysis of extended family data can potentially overcome these limitations. In this paper, we will describe various genetic models that can be analysed using an extended family structure. We use the generalized linear mixed model to deal with the family structure and likelihood-based methodology for parameter inference. The method is completely general, accommodating arbitrary family structures and incomplete data. We illustrate the methodology in great detail using the Swedish birth registry data on pre-eclampsia, a hypertensive condition induced by pregnancy. The statistical challenges include the specification of sensible models that contain a relatively large number of variance components compared to standard mixed models. In our illustration the models will account for maternal or foetal genetic effects, environmental effects, or a combination of these and we show how these effects can be readily estimated using family data.

Association between ease of suppression of ventricular arrhythmia and survival.

BACKGROUND: We tested the hypothesis that patients whose ventricular arrhythmias are easy to suppress have a lower rate of arrhythmic death, defined as arrhythmic death and nonfatal cardiac arrest, the primary end point in the Cardiac Arrhythmia Suppression Trials (CAST-I and CAST-II), than patients whose ventricular arrhythmias are hard to suppress. In addition, we evaluated the association between ease of suppression of ventricular arrhythmias and mortality of all causes. METHODS AND RESULTS: CAST-I investigated the effect on arrhythmic death of ventricular premature depolarization (VPD) suppression achieved by three drugs, encainide, flecainide, and moricizine, at two different dose levels; CAST-II investigated the same effect, using moricizine alone at three dose levels. If suppression was achieved, patients were randomized to the effective active drug or corresponding placebo. To examine the independence of easily suppressed ventricular arrhythmias as a predictor of arrhythmic death, we adjusted statistically for other variables that were related both to ease of suppression and arrhythmic death. Patients with ventricular arrhythmias (n = 1778) that were easy to suppress had fewer arrhythmic deaths during follow-up than those with ventricular arrhythmias that were hard to suppress (n = 1173) (relative risk, .59; P = .003). Patients whose VPDs were easily suppressed were older and had a lower frequency of prior history of heart failure and myocardial infarction. They also had a higher incidence of anterior myocardial infarction, VPD frequency, and average ejection fraction. After adjusting for these variables, we found that easily suppressed ventricular arrhythmias were still significant predictors of arrhythmic death (relative risk, .66; P = .013). CONCLUSIONS: This study shows that the ease of VPD suppression identifies a subgroup of postmyocardial infarction patients who have low risk of arrhythmic death.

Identification of a secondary peak in myocardial infarction onset 11 to 12 hours after awakening: the Cardiac Arrhythmia Suppression Trial (CAST) experience.

OBJECTIVES: The purpose of this study was to assess the relation between the time of awakening and the time of onset of acute myocardial infarction. BACKGROUND: Previous investigation has shown the onset of symptoms of acute myocardial infarction to have a primary peak 1 to 2 h after awakening. In studies not corrected for time of awakening, there appears to be a late afternoon/early evening peak, but data correlating the onset of symptoms with awakening have been limited by small numbers of patients, perhaps precluding identification of a secondary peak. METHODS: In the Cardiac Arrhythmia Suppression Trial (CAST), 3,549 patients had a documented myocardial infarction and entered antiarrhythmic drug titration. Of these, 3,309 had data on the onset of symptoms relative to the time of awakening and form the basis of this report. RESULTS: A total of 870 patients (26.3%) were awakened by symptoms. Of the remaining 2,439 patients who were not awakened by symptoms, 798 (32.7%) experienced the onset of symptoms in the 1st 4 h after awakening (with the highest number in the 1st h), after which the incidence of symptom onset decreased in a linear fashion, with a secondary peak 11 to 12 h after awakening. Both peaks are statistically significant. A similar pattern was seen in most of the subgroups examined (based on age, gender and various other demographic characteristics). CONCLUSIONS: Analysis of the very large CAST data base confirms the relation between awakening and onset of symptoms of myocardial infarction, suggesting involvement of the morning catecholamine surge. A secondary peak in symptom onset, occurring 11 to 12 h after awakening, is a new observation and may relate to ingestion of the evening meal or other trigger factors concentrated in those hours.

Psychosocial predictors of mortality in the Cardiac Arrhythmia Suppression Trial-1 (CAST-1).

Psychosocial variables predict the recurrence of clinical events in symptomatic patients, controlling for measures of disease severity. The Cardiac Arrhythmia Suppression Trial-1, a pharmacologic test of the arrhythmia suppression and mortality hypothesis among postmyocardial infarction patients, allowed a prospective test of the relationship of distress, perceived support, social interaction, life stress, and other variables, to mortality, adjusting statistically for ejection fraction, arrhythmia rates, and other known risk factors for coronary heart disease. Results indicated that the treatment medications, encainide and flecainide, were powerful predictors of mortality. Although the psychosocial variables were significant as univariate predictors, these variables were not significant as predictors in a multivariate model that included drug treatment. When the data analysis was restricted to patients randomized to placebo, thereby eliminating the antiarrhythmic drug effect, the level of perceived social support was a significant multivariate predictor of mortality, adjusting for measures of disease severity. The adjusted hazards ratio for a 1-point decrease in the perceived support score is equal to 1.46, based on the multivariate model.