Modeling and predicting osteoarthritis progression: data from the osteoarthritis initiative.

OBJECTIVE: The goal of this study was to model the longitudinal progression of knee osteoarthritis (OA) and build a prognostic tool that uses data collected in 1 year to predict disease progression over 8 years. DESIGN: To model OA progression, we used a mixed-effects mixture model and 8-year data from the Osteoarthritis Initiative (OAI)-specifically, joint space width measurements from X-rays and pain scores from the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) questionnaire. We included 1243 subjects who at enrollment were classified as being at high risk of developing OA based on age, body mass index (BMI), and medical and occupational histories. After clustering subjects based on radiographic and pain progression, we used clinical variables collected within the first year to build least absolute shrinkage and selection (LASSO) regression models for predicting the probabilities of belonging to each cluster. Areas under the receiver operating characteristic curve (AUC) represent predictive performance on held-out data. RESULTS: Based on joint space narrowing, subjects clustered as progressing or non-progressing. Based on pain scores, they clustered as stable, improving, or worsening. Radiographic progression could be predicted with high accuracy (AUC = .86) using data from two visits spanning 1 year, whereas pain progression could be predicted with high accuracy (AUC = .95) using data from a single visit. Joint space narrowing and pain progression were not associated. CONCLUSION: Statistical models for characterizing and predicting OA progression promise to improve clinical trial design and OA prevention efforts in the future.

Physical activity is associated with changes in knee cartilage microstructure.

OBJECTIVE: The purpose of this study was to determine if there is an association between objectively measured physical activity and longitudinal changes in knee cartilage microstructure. METHODS: We used accelerometry and T2-weighted magnetic resonance imaging (MRI) data from the Osteoarthritis Initiative, restricting the analysis to men aged 45-60 years, with a body mass index (BMI) of 25-27 kg/m2 and no radiographic evidence of knee osteoarthritis. After computing 4-year changes in mean T2 relaxation time for six femoral cartilage regions and mean daily times spent in the sedentary, light, moderate, and vigorous activity ranges, we performed canonical correlation analysis (CCA) to find a linear combination of times spent in different activity intensity ranges (Activity Index) that was maximally correlated with a linear combination of regional changes in cartilage microstructure (Cartilage Microstructure Index). We used leave-one-out pre-validation to test the robustness of the model on new data. RESULTS: Nineteen subjects satisfied the inclusion criteria. CCA identified an Activity Index and a Cartilage Microstructure Index that were significantly correlated (r = .82, P < .0001 on test data). Higher levels of sedentary time and vigorous activity were associated with greater medial-lateral differences in longitudinal T2 changes, whereas light activity was associated with smaller differences. CONCLUSIONS: Physical activity is better associated with an index that contrasts microstructural changes in different cartilage regions than it is with univariate or cumulative changes, likely because this index separates the effect of activity, which is greater in the medial loadbearing region, from that of patient-specific natural aging.

Coronary risk assessment among intermediate risk patients using a clinical and biomarker based algorithm developed and validated in two population cohorts.

BACKGROUND: Many coronary heart disease (CHD) events occur in individuals classified as intermediate risk by commonly used assessment tools. Over half the individuals presenting with a severe cardiac event, such as myocardial infarction (MI), have at most one risk factor as included in the widely used Framingham risk assessment. Individuals classified as intermediate risk, who are actually at high risk, may not receive guideline recommended treatments. A clinically useful method for accurately predicting 5-year CHD risk among intermediate risk patients remains an unmet medical need. OBJECTIVE: This study sought to develop a CHD Risk Assessment (CHDRA) model that improves 5-year risk stratification among intermediate risk individuals. METHODS: Assay panels for biomarkers associated with atherosclerosis biology (inflammation, angiogenesis, apoptosis, chemotaxis, etc.) were optimized for measuring baseline serum samples from 1084 initially CHD-free Marshfield Clinic Personalized Medicine Research Project (PMRP) individuals. A multivariable Cox regression model was fit using the most powerful risk predictors within the clinical and protein variables identified by repeated cross-validation. The resulting CHDRA algorithm was validated in a Multiple-Ethnic Study of Atherosclerosis (MESA) case-cohort sample. RESULTS: A CHDRA algorithm of age, sex, diabetes, and family history of MI, combined with serum levels of seven biomarkers (CTACK, Eotaxin, Fas Ligand, HGF, IL-16, MCP-3, and sFas) yielded a clinical net reclassification index of 42.7% (p < 0.001) for MESA patients with a recalibrated Framingham 5-year intermediate risk level. Across all patients, the model predicted acute coronary events (hazard ratio = 2.17, p < 0.001), and remained an independent predictor after Framingham risk factor adjustments. LIMITATIONS: These include the slightly different event definition with the MESA samples and inability to include PMRP fatal CHD events. CONCLUSIONS: A novel risk score of serum protein levels plus clinical risk factors, developed and validated in independent cohorts, demonstrated clinical utility for assessing the true risk of CHD events in intermediate risk patients. Improved accuracy in cardiovascular risk classification could lead to improved preventive care and fewer deaths.

Discovery of molecular subtypes in leiomyosarcoma through integrative molecular profiling.

Leiomyosarcoma (LMS) is a soft tissue tumor with a significant degree of morphologic and molecular heterogeneity. We used integrative molecular profiling to discover and characterize molecular subtypes of LMS. Gene expression profiling was performed on 51 LMS samples. Unsupervised clustering showed three reproducible LMS clusters. Array comparative genomic hybridization (aCGH) was performed on 20 LMS samples and showed that the molecular subtypes defined by gene expression showed distinct genomic changes. Tumors from the 'muscle-enriched' cluster showed significantly increased copy number changes (P=0.04). A majority of the muscle-enriched cases showed loss at 16q24, which contains Fanconi anemia, complementation group A, known to have an important role in DNA repair, and loss at 1p36, which contains PRDM16, of which loss promotes muscle differentiation. Immunohistochemistry (IHC) was performed on LMS tissue microarrays (n=377) for five markers with high levels of messenger RNA in the muscle-enriched cluster (ACTG2, CASQ2, SLMAP, CFL2 and MYLK) and showed significantly correlated expression of the five proteins (all pairwise P<0.005). Expression of the five markers was associated with improved disease-specific survival in a multivariate Cox regression analysis (P<0.04). In this analysis that combined gene expression profiling, aCGH and IHC, we characterized distinct molecular LMS subtypes, provided insight into their pathogenesis, and identified prognostic biomarkers.

A working guide to boosted regression trees.

1. Ecologists use statistical models for both explanation and prediction, and need techniques that are flexible enough to express typical features of their data, such as nonlinearities and interactions. 2. This study provides a working guide to boosted regression trees (BRT), an ensemble method for fitting statistical models that differs fundamentally from conventional techniques that aim to fit a single parsimonious model. Boosted regression trees combine the strengths of two algorithms: regression trees (models that relate a response to their predictors by recursive binary splits) and boosting (an adaptive method for combining many simple models to give improved predictive performance). The final BRT model can be understood as an additive regression model in which individual terms are simple trees, fitted in a forward, stagewise fashion. 3. Boosted regression trees incorporate important advantages of tree-based methods, handling different types of predictor variables and accommodating missing data. They have no need for prior data transformation or elimination of outliers, can fit complex nonlinear relationships, and automatically handle interaction effects between predictors. Fitting multiple trees in BRT overcomes the biggest drawback of single tree models: their relatively poor predictive performance. Although BRT models are complex, they can be summarized in ways that give powerful ecological insight, and their predictive performance is superior to most traditional modelling methods. 4. The unique features of BRT raise a number of practical issues in model fitting. We demonstrate the practicalities and advantages of using BRT through a distributional analysis of the short-finned eel (Anguilla australis Richardson), a native freshwater fish of New Zealand. We use a data set of over 13 000 sites to illustrate effects of several settings, and then fit and interpret a model using a subset of the data. We provide code and a tutorial to enable the wider use of BRT by ecologists.

New cutpoints to identify increased HER2 copy number: analysis of a large, population-based cohort with long-term follow-up.

BACKGROUND: HER2 gene amplification and/or protein overexpression in breast cancer is associated with a poor prognosis and predicts response to anti-HER2 therapy. We examine the natural history of breast cancers in relationship to increased HER2 copy numbers in a large population-based study. PATIENTS AND METHODS: HER2 status was measured by fluorescence in situ hybridization (FISH) and immunohistochemistry (IHC) in approximately 1,400 breast cancer cases with greater than 15 years of follow-up. Protein expression was evaluated with two different commercially-available antibodies. RESULTS: We looked for subgroups of breast cancer with different clinical outcomes, based on HER2 FISH amplification ratio. The current HER2 ratio cut point for classifying HER2 positive and negative cases is 2.2. However, we found an increased risk of disease-specific death associated with FISH ratios of >1.5. An 'intermediate' group of cases with HER2 ratios between 1.5 and 2.2 was found to have a significantly better outcome than the conventional 'amplified' group (HER2 ratio >2.2) but a significantly worse outcome than groups with FISH ratios less than 1.5. CONCLUSION: Breast cancers with increased HER2 copy numbers (low level HER2 amplification), below the currently accepted positive threshold ratio of 2.2, showed a distinct, intermediate outcome when compared to HER2 unamplified tumors and tumors with HER2 ratios greater than 2.2. These findings suggest that a new cut point to determine HER2 positivity, at a ratio of 1.5 (well below the current recommended cut point of 2.2), should be evaluated.

Cortisol and behavior in fragile X syndrome.

OBJECTIVE: The purpose of this study was to determine if children with fragile X syndrome, who typically demonstrate a neurobehavioral phenotype that includes social anxiety, withdrawal, and hyper-arousal, have increased levels of cortisol, a hormone associated with stress. The relevance of adrenocortical activity to the fragile X phenotype also was examined. METHOD: One hundred and nine children with the fragile X full mutation (70 males and 39 females) and their unaffected siblings (51 males and 58 females) completed an in-home evaluation including a cognitive assessment and a structured social challenge task. Multiple samples of salivary cortisol were collected throughout the evaluation day and on two typical non-school days. Measures of the fragile X mental retardation (FMR1) gene, child intelligence, the quality of the home environment, parental psychopathology, and the effectiveness of educational and therapeutic services also were collected. Linear mixed-effects analyses were used to examine differences in cortisol associated with the fragile X diagnosis and gender (fixed effects) and to estimate individual subject and familial variation (random effects) in cortisol hormone levels. Hierarchical multiple regression analyses were conducted to determine whether adrenocortical activity is associated with behavior problems after controlling for significant genetic and environmental factors. RESULTS: Results showed that children with fragile X, especially males, had higher levels of salivary cortisol on typical days and during the evaluation. Highly significant family effects on salivary cortisol were detected, consistent with previous work documenting genetic and environmental influences on adrenocortical activity. Increased cortisol was significantly associated with behavior problems in boys and girls with fragile X but not in their unaffected siblings. CONCLUSIONS: These results provide evidence that the function of the hypothalamic-pituitary-adrenal axis may have an independent association with behavioral problems in children with fragile X syndrome.

Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications.

The purpose of this study was to classify breast carcinomas based on variations in gene expression patterns derived from cDNA microarrays and to correlate tumor characteristics to clinical outcome. A total of 85 cDNA microarray experiments representing 78 cancers, three fibroadenomas, and four normal breast tissues were analyzed by hierarchical clustering. As reported previously, the cancers could be classified into a basal epithelial-like group, an ERBB2-overexpressing group and a normal breast-like group based on variations in gene expression. A novel finding was that the previously characterized luminal epithelial/estrogen receptor-positive group could be divided into at least two subgroups, each with a distinctive expression profile. These subtypes proved to be reasonably robust by clustering using two different gene sets: first, a set of 456 cDNA clones previously selected to reflect intrinsic properties of the tumors and, second, a gene set that highly correlated with patient outcome. Survival analyses on a subcohort of patients with locally advanced breast cancer uniformly treated in a prospective study showed significantly different outcomes for the patients belonging to the various groups, including a poor prognosis for the basal-like subtype and a significant difference in outcome for the two estrogen receptor-positive groups.

Brain anatomy, gender and IQ in children and adolescents with fragile X syndrome.

This study utilized MRI data to describe neuroanatomical morphology in children and adolescents with fragile X syndrome, the most common inherited cause of developmental disability. The syndrome provides a model for understanding how specific genetic factors can influence both neuroanatomy and cognitive capacity. Thirty-seven children and adolescents with fragile X syndrome received an MRI scan and cognitive testing. Scanning procedures and analytical strategies were identical to those reported in an earlier study of 85 typically developing children, permitting a comparison with a previously published template of normal brain development. Regression analyses indicated that there was a normative age-related decrease in grey matter and an increase in white matter. However, caudate and ventricular CSF volumes were significantly enlarged, and caudate volumes decreased with age. Rates of reduction of cortical grey matter were different for males and females. IQ scores were not significantly correlated with volumes of cortical and subcortical grey matter, and these relationships were statistically different from the correlational patterns observed in typically developing children. Children with fragile X syndrome exhibited several typical neurodevelopmental patterns. Aberrations in volumes of subcortical nuclei, gender differences in rates of cortical grey matter reduction and an absence of correlation between grey matter and cognitive performance provided indices of the deleterious effects of the fragile X mutation on the brain's structural organization.

Missing value estimation methods for DNA microarrays.

MOTIVATION: Gene expression microarray experiments can generate data sets with multiple missing expression values. Unfortunately, many algorithms for gene expression analysis require a complete matrix of gene array values as input. For example, methods such as hierarchical clustering and K-means clustering are not robust to missing data, and may lose effectiveness even with a few missing values. Methods for imputing missing data are needed, therefore, to minimize the effect of incomplete data sets on analyses, and to increase the range of data sets to which these algorithms can be applied. In this report, we investigate automated methods for estimating missing data. RESULTS: We present a comparative study of several methods for the estimation of missing values in gene microarray data. We implemented and evaluated three methods: a Singular Value Decomposition (SVD) based method (SVDimpute), weighted K-nearest neighbors (KNNimpute), and row average. We evaluated the methods using a variety of parameter settings and over different real data sets, and assessed the robustness of the imputation methods to the amount of missing data over the range of 1--20% missing values. We show that KNNimpute appears to provide a more robust and sensitive method for missing value estimation than SVDimpute, and both SVDimpute and KNNimpute surpass the commonly used row average method (as well as filling missing values with zeros). We report results of the comparative experiments and provide recommendations and tools for accurate estimation of missing microarray data under a variety of conditions.

Supervised harvesting of expression trees.

BACKGROUND: We propose a new method for supervised learning from gene expression data. We call it 'tree harvesting'. This technique starts with a hierarchical clustering of genes, then models the outcome variable as a sum of the average expression profiles of chosen clusters and their products. It can be applied to many different kinds of outcome measures such as censored survival times, or a response falling in two or more classes (for example, cancer classes). The method can discover genes that have strong effects on their own, and genes that interact with other genes. RESULTS: We illustrate the method on data from a lymphoma study, and on a dataset containing samples from eight different cancers. It identified some potentially interesting gene clusters. In simulation studies we found that the procedure may require a large number of experimental samples to successfully discover interactions. CONCLUSIONS: Tree harvesting is a potentially useful tool for exploration of gene expression data and identification of interesting clusters of genes worthy of further investigation.

Quantitative determination of age-related geometric changes in the normal abdominal aorta.

PURPOSE: We conducted a novel quantitative three-dimensional analysis of computed tomography (CT) angiograms to establish the relationship between aortic geometry and age, sex, and body surface area in healthy subjects. METHODS: Abdominal helical CT angiograms from 77 healthy potential renal donors (33 men/44 women; mean age, 44 years; age range, 19-67 years) were selected. In each dataset, orthonormal cross-sectional area and diameter measurements were obtained at 1-mm intervals along the automatically calculated central axis of the abdominal aorta. The aorta was subdivided into six consecutive anatomic segments (supraceliac, supramesenteric, suprarenal, inter-renal, proximal infrarenal, and distal infrarenal). The interrelated effects of anatomic segment, age, sex, and body surface area on cross-sectional dimensions were analyzed with linear mixed-effects and varying-coefficient statistical models. RESULTS: We found that significant effects of sex and of body surface area on aortic diameters were similar at all anatomic levels. The effect of age, however, was interrelated with anatomic position, and gradually decreasing slopes of significant diameter-versus-age relationships along the aorta, which ranged from 0.14 mm/y (P <.0001) proximally to 0.03 mm/y (P =.013) distally in the abdominal aorta, were shown. CONCLUSION: The abdominal aorta undergoes considerable geometric changes when a patient is between 19 and 67 years of age, leading to an increase of aortic taper with time. The hemodynamic consequences of this geometric evolution for the development of aortic disease still need to be established.

'Gene shaving' as a method for identifying distinct sets of genes with similar expression patterns.

BACKGROUND: Large gene expression studies, such as those conducted using DNA arrays, often provide millions of different pieces of data. To address the problem of analyzing such data, we describe a statistical method, which we have called 'gene shaving'. The method identifies subsets of genes with coherent expression patterns and large variation across conditions. Gene shaving differs from hierarchical clustering and other widely used methods for analyzing gene expression studies in that genes may belong to more than one cluster, and the clustering may be supervised by an outcome measure. The technique can be 'unsupervised', that is, the genes and samples are treated as unlabeled, or partially or fully supervised by using known properties of the genes or samples to assist in finding meaningful groupings. RESULTS: We illustrate the use of the gene shaving method to analyze gene expression measurements made on samples from patients with diffuse large B-cell lymphoma. The method identifies a small cluster of genes whose expression is highly predictive of survival. CONCLUSIONS: The gene shaving method is a potentially useful tool for exploration of gene expression data and identification of interesting clusters of genes worth further investigation.

Bone mineral acquisition in healthy Asian, Hispanic, black, and Caucasian youth: a longitudinal study.

Ethnic and gender differences in bone mineral acquisition were examined in a longitudinal study of 423 healthy Asian, black, Hispanic, and white males and females (aged 9-25 yr). Bone mass of the spine, femoral neck, total hip, and whole body was measured annually for up to 4 yr by dual energy x-ray absorptiometry. Age-adjusted mean bone mineral curves for areal (BMD) and volumetric (BMAD) bone mineral density were compared for the 4 ethnic groups. Consistent differences in areal and volumetric bone density were observed only between black and nonblack subjects. Among females, blacks had greater mean levels of BMD and BMAD at all skeletal sites. Differences among Asians, Hispanics, and white females were significant for femoral neck BMD, whole body BMD, and whole body bone mineral content/height ratio, for which Asians had significantly lower values; femoral neck BMAD in Asian and white females was lower than that in Hispanics. Like the females, black males had consistently greater mean values than nonblacks for all BMD and BMAD measurements. A few differences were also observed among nonblack male subjects. Whites had greater mean total hip BMD, whole body BMD, and whole body bone mineral content/height ratio than Asian and Hispanic males; Hispanics had lower spine BMD than white and Asian males. The tempo of gains in BMD varied by gender and skeletal site. In females, total hip, spine, and whole body BMD reached a plateau at 14.1, 15.7, and 16.4 yr, respectively. For males, gains in BMD leveled off at 15.7 yr for total hip and at age 17.6 yr for spine and whole body. Black and Asian females and Asian males tended to reach a plateau in BMD earlier than the other ethnic groups. The use of gender- and ethnic-specific standards is recommended when interpreting pediatric bone densitometry data.

Meta-analysis of trials comparing beta-blockers, calcium antagonists, and nitrates for stable angina.

CONTEXT: Which drug is most effective as a first-line treatment for stable angina is not known. OBJECTIVE: To compare the relative efficacy and tolerability of treatment with beta-blockers, calcium antagonists, and long-acting nitrates for patients who have stable angina. DATA SOURCES: We identified English-language studies published between 1966 and 1997 by searching the MEDLINE and EMBASE databases and reviewing the bibliographies of identified articles to locate additional relevant studies. STUDY SELECTION: Randomized or crossover studies comparing antianginal drugs from 2 or 3 different classes (beta-blockers, calcium antagonists, and long-acting nitrates) lasting at least 1 week were reviewed. Studies were selected if they reported at least 1 of the following outcomes: cardiac death, myocardial infarction, study withdrawal due to adverse events, angina frequency, nitroglycerin use, or exercise duration. Ninety (63%) of 143 identified studies met the inclusion criteria. DATA EXTRACTION: Two independent reviewers extracted data from selected articles, settling any differences by consensus. Outcome data were extracted a third time by 1 of the investigators. We combined results using odds ratios (ORs) for discrete data and mean differences for continuous data. Studies of calcium antagonists were grouped by duration and type of drug (nifedipine vs nonnifedipine). DATA SYNTHESIS: Rates of cardiac death and myocardial infarction were not significantly different for treatment with beta-blockers vs calcium antagonists (OR, 0.97; 95% confidence interval [CI], 0.67-1.38; P = .79). There were 0.31 (95% CI, 0.00-0.62; P = .05) fewer episodes of angina per week with beta-blockers than with calcium antagonists. beta-Blockers were discontinued because of adverse events less often than were calcium antagonists (OR, 0.72; 95% CI, 0.60-0.86; P<.001). The differences between beta-blockers and calcium antagonists were most striking for nifedipine (OR for adverse events with beta-blockers vs nifedipine, 0.60; 95% CI, 0.47-0.77). Too few trials compared nitrates with calcium antagonists or beta-blockers to draw firm conclusions about relative efficacy. CONCLUSIONS: beta-Blockers provide similar clinical outcomes and are associated with fewer adverse events than calcium antagonists in randomized trials of patients who have stable angina.

Low-molecular-weight heparins compared with unfractionated heparin for treatment of acute deep venous thrombosis. A meta-analysis of randomized, controlled trials.

BACKGROUND: Low-molecular-weight heparins may simplify the management of deep venous thrombosis. A critical clinical issue is whether this more convenient therapy is as safe and effective as treatment with unfractionated heparin. PURPOSE: To compare the safety and efficacy of low-molecular-weight heparins with those of unfractionated heparin for treatment of acute deep venous thrombosis. DATA SOURCES: Reviewers identified studies by searching MEDLINE, reviewing references from retrieved articles, scanning abstracts from conference proceedings, and contacting investigators and pharmaceutical companies. STUDY SELECTION: Randomized, controlled trials that compared a low-molecular-weight heparin preparation with unfractionated heparin for treatment of acute deep venous thrombosis. DATA EXTRACTION: Two reviewers extracted data independently. Reviewers evaluated study quality using a validated four-item instrument. DATA SYNTHESIS: Eleven of 37 studies met inclusion criteria for three major outcomes. Most studies used proper randomization procedures, but only one was double-blinded. Compared with unfractionated heparin, low-molecular-weight heparins reduced mortality rates over 3 to 6 months of patient follow-up (odds ratio, 0.71 [95% CI, 0.53 to 0.94]; P = 0.02). For major bleeding complications, the odds ratio favored low-molecular-weight heparins (0.57 [CI, 0.33 to 0.99]; P = 0.047), but the absolute risk reduction was small and not statistically significant (0.61% [CI, -0.04% to 1.26%]; P = 0.07). For preventing thromboembolic recurrences, low-molecular-weight heparins seemed as effective as unfractionated heparin (odds ratio, 0.85 [CI, 0.63 to 1.14]; P > 0.2). CONCLUSIONS: Low-molecular-weight heparin treatment reduces mortality rates after acute deep venous thrombosis. These drugs seem to be as safe as unfractionated heparin with respect to major bleeding complications and appear to be as effective in preventing thromboembolic recurrences.

Regression analysis of multiple protein structures.

A general framework is presented for analyzing multiple protein structures using statistical regression methods. The regression approach can superimpose protein structures rigidly or with shear. Also, this approach can superimpose multiple structures explicitly, without resorting to pairwise superpositions. The algorithm alternates between matching corresponding landmarks among the protein structures and superimposing these landmarks. Matching is performed using a robust dynamic programming technique that uses gap penalties that adapt to the given data. Superposition is performed using either orthogonal transformations, which impose the rigid-body assumption, or affine transformations, which allow shear. The resulting regression model of a protein family measures the amount of structural variability at each landmark. A variation of our algorithm permits a separate weight for each landmark, thereby allowing one to emphasize particular segments of a protein structure or to compensate for variances that differ at various positions in a structure. In addition, a method is introduced for finding an initial correspondence, by measuring the discrete curvature along each protein backbone. Discrete curvature also characterizes the secondary structure of a protein backbone, distinguishing among helical, strand, and loop regions. An example is presented involving a set of seven globin structures. Regression analysis, using both affine and orthogonal transformations, reveals that globins are most strongly conserved structurally in helical regions, particularly in the mid-regions of the E, F, and G helices.