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PubMed2000

Analysis strategies for longitudinal attachment loss data.

The purpose of this invited review is to describe and discuss methods currently in use to quantify the progression of attachment loss in epidemiological studies of periodontal disease, and to make recommendations for specific analytic methods based upon the particular design of the study and structure of the data. The review concentrates on the definition of incident attachment loss (ALOSS) and its component parts; measurement issues including thresholds and regression to the mean; methods of accounting for longitudinal change, including changes in means, changes in proportions of affected sites, incidence density, the effect of tooth loss and reversals, and repeated events; statistical models of longitudinal change, including the incorporation of the time element, use of linear, logistic or Poisson regression or survival analysis, and statistical tests; site vs person level of analysis, including statistical adjustment for correlated data; the strengths and limitations of ALOSS data. Examples from the Piedmont 65+ Dental Study are used to illustrate specific concepts. We conclude that incidence density is the preferred methodology to use for periodontal studies with more than one period of follow-up and that the use of studies not employing methods for dealing with complex samples, correlated data, and repeated measures does not take advantage of our current understanding of the site- and person-level variables important in periodontal disease and may generate biased results.

Disease Progression↗
PubMed1991

5-Hydroxytryptamine (5-HT)-induced shape change in human platelets determined by computerized data acquisition: correlation with [125I]-iodoLSD binding at 5-HT2 receptors.

The 5-HT-induced shape change and subsequent aggregation of platelets provides a functional assay for 5-HT2 receptors. In the present study we describe a method to increase aggregometer sensitivity by digital conversion of the voltage produced by changes in light transmittance through a platelet suspension, thereby allowing accurate analyses of the primary shape-change response. The pharmacology of 5-HT-induced shape change was then compared with that of [125I]-iodoLSD binding in human platelets. 5-HT caused a dose-dependent change in platelet shape (maximum response 5 x 10(-6) M, EC50 10(-6) M). Furthermore, there was a significant correlation across a selection of drugs between IC50 values for inhibition of 5-HT-induced shape change and for inhibition of platelet binding of the 5-HT2 receptor ligand [125I]-iodoLSD. These results support the hypothesis that 5-HT-induced shape change and [125I]-iodoLSD binding in human platelets are mediated through the same receptor, and validate the methods of data acquisition described.

Blood Platelets↗
PubMed1990

Physicochemical factors associated with binding and retention of compounds in ocular melanin of rats: correlations using data from whole-body autoradiography and molecular modeling for multiple linear regression analyses.

The relationship between the physicochemical characteristics of 27 new drug candidates and their distribution into the melanin-containing structure of the rat eye, the uveal tract, was examined. Tissue distribution data were obtained from whole-body autoradiograms of pigmented Long-Evans rats sacrificed at 5 min and 96 hr after dosing. The physicochemical parameters considered include molecular weight, pKa, degree of ionization, octanol/water partition coefficient (log Po/w), drug-melanin binding energy, and acid/base status of the functional groups within the molecule. Multiple linear regression analysis was used to describe the best model correlating physicochemical and/or biological characteristics of these compounds to their initial distribution at 5 min and to the retention of residual radioactivity in ocular melanin at 96 hr post-injection. The early distribution was a function primarily of acid/base status, pKa, binding energy, and log P(o/w), whereas uveal tract retention in rats was a function of volume of distribution (V1), log P(o/w), pKa, and binding energy. Further, there was a relationship between the initial distribution of a compound into the uveal tract and its retention 96 hr later. More specifically, the structures most likely to be distributed and ultimately retained at high concentrations were those containing strongly basic functionalities, such as piperidine or piperazine moieties and other amines. Further, the more lipophilic and, hence, widely distributed the basic compound, the greater the likelihood that it interacts with ocular melanin. In summary, the use of multiple linear regression analysis was useful in distinguishing which physicochemical characteristics of a compound or group of compounds contributed to melanin binding in pigmented rats in vivo.

Animals↗
PubMed2004

Functional genomics approaches in arthritis.

The post-genomic era of functional genomics and target validation will allow us to narrow the bridge between clinically correlative data and causative data for complex diseases, such as arthritis, for which the etiological agent remains elusive. The availability of human and other annotated genome sequences, and parallel developments of new technologies that allow analysis of minute amounts of human and animal cells (peripheral blood cells and infiltrating cells) and tissues (synovium and cartilage) under different pathophysiological conditions, has facilitated high-throughput gene mining approaches that can generate vast amounts of clinically correlative data. Characterizing some of the correlative/causative genes will require reverting to the hypothesis-driven, low throughput method of complementary experimental biology using genomic approaches as a tool. This will include in silico gene expression arrays, genome-wide scans, comparative genomics using various animal models (such as rodents and zebrafish), bioinformatics and a team of well trained translational scientists and physicians. For the first time, the "genomic tools" will allow us to analyze small amounts of surgical samples (such as needle biopsies) and clinical samples in the context of the whole genome. Preliminary genomic analysis in osteoarthritis has already resurrected the debate on the semantic issues in the definition of inflammation. Further analyses will not only facilitate the development of unbiased hypotheses at the molecular level, but also assist us in the identification and characterization of novel targets and disease markers for pharmacological intervention, gene therapy, and diagnosis.

Animals↗
PubMed2026

Investigating the causal role of smoking in gout: A triangulation approach combining NHANES data, genetic correlation, and Mendelian randomization.

The relationship between smoking and the development of gout is not well understood. To address this, we adopted a triangulation framework that integrates observational analysis, genetic correlation estimation, and two-sample Mendelian randomization (MR) to examine whether smoking confers a causal risk for gout. We first performed a cross-sectional analysis using information for 13,626 participants from the National Health and Nutrition Examination Survey between 2013 and 2018. The association of smoking with gout was subsequently assessed through logistic regression models. We next investigated the extent of shared genetic factors between smoking phenotypes and gout. We were able to demonstrate this using the linkage disequilibrium score regression applied to genome-wide association study data of European ancestry. Finally, to verify the causality of our relationship, we carried out a two-sample MR analysis. We selected the inverse-variance weighted (IVW) method and confirmed the consistency of using the IVW method with other statistical methods, including weighted median, weighted mode, and simple mode, as well as MR-Egger regression. We performed sensitivity analyses to investigate the heterogeneity of the hypothesis and stability of the data. Our findings based on National Health and Nutrition Examination Survey data reveal that there is a strong positive association between smoking and the risk of gout (odds ratio [OR]&#x2005;=&#x2005;1.94, 95% confidence interval [CI]&#x2005;=&#x2005;1.48-2.55, P&#x2005;<&#x2005;.001). This association persisted after confounding adjustments (OR&#x2005;=&#x2005;1.41, 95% CI&#x2005;=&#x2005;1.04-1.91, P&#x2005;=&#x2005;.027). In the subgroup analyses, former smokers and current smokers of 10 to 20 cigarettes per day had a substantially increased risk. Post-linkage disequilibrium score regression analysis revealed that the significantly positive genetic correlations of smoking initiation and lifetime smoking index with gout risk were both significantly positive. Additional evidence for causality is presented by MR. Genetic prediction of smoking initiation statistically increases gout risk (IVW OR&#x2005;=&#x2005;1.55, 95% CI&#x2005;=&#x2005;1.26-1.90, P&#x2005;=&#x2005;3.17&#x2005;&#xd7;&#x2005;10-5). A much stronger association is evident for lifetime smoking index (IVW OR&#x2005;=&#x2005;1.99, 95% CI&#x2005;=&#x2005;1.44-2.76, P&#x2005;=&#x2005;3.24&#x2005;&#xd7;&#x2005;10-5). These findings are the same with or without heterogeneity by sensitivity analysis. In light of our integrated analysis, smoking is a causative factor for gout. This suggests that public health interventions like anti-smoking campaigns might reduce gout incidence.

Humans↗
PubMed2005

A hierarchical Binomial-Poisson model for the analysis of a crossover design for correlated binary data when the number of trials is dose-dependent.

The differential reinforcement of a low-rate 72-seconds schedule (DRL-72) is a standard behavioral test procedure for screening a potential antidepressant compound. The data analyzed in the article are binary outcomes from a crossover design for such an experiment. Recently, Shkedy et al. (2004) proposed to estimate the treatments effect using either generalized linear mixed models (GLMM) or generalized estimating equations (GEE) for clustered binary data. The models proposed by Shkedy et al. (2004) assumed the number of responses at each binomial observation is fixed. This might be an unrealistic assumption for a behavioral experiment such as the DRL-72 because the number of responses (the number of trials in each binomial observation) is expected to be influenced by the administered dose level. In this article, we extend the model proposed by Shkedy et al. (2004) and propose a hierarchical Bayesian binomial-Poisson model, which assumes the number of responses to be a Poisson random variable. The results obtained from the GLMM and the binomial-Poisson models are comparable. However, the latter model allows estimating the correlation between the number of successes and number of trials.

Algorithms↗
PubMed2003

A need for a 'whole-istic functional genomics' approach in complex human diseases: arthritis.

'Genomic tools', such as gene/protein chips, single nucleotide polymorphism and haplotype analyses, are empowering us to generate staggering amounts of correlative data, from human/animal genetics and from normal and disease-affected tissues obtained from complex diseases such as arthritis. These tools are transforming molecular biology into a 'data rich' science, with subjects with an '-omic' suffix. These disciplines have to converge and integrate at a systemic level to examine the structure and dynamics of cellular and organismal function ('functionomics') simultaneously, using a multidimensional approach for cells, tissues, organs, rodents and Zebra fish models, which intertwines various approaches and readouts to study the development and homeostasis of a system. In summary, the postgenomic era of functionomics will facilitate narrowing the bridge between correlative data and causative data, thus integrating 'intercoms' of interacting and interdependent disciplines and forming a unified whole.

Arthritis↗
PubMed1998

Influence of anthropometric parameters and biochemical markers of bone metabolism on quantitative ultrasound of bone in the institutionalized elderly.

The assessment of bone quality by quantitative ultrasound (QUS), a transportable and relatively cheap method, shows some correlations with bone mineral density (BMD) as measured by dual-energy X-ray absorptiometry (DXA) and with fracture risk. To examine its correlation with bone metabolism in a population of institutionalized elderly people known to be at high risk for vitamin D deficiency and secondary hyperparathyroidism, QUS of the calcaneus and biochemical parameters were measured in 264 women aged 85 +/- 7 (SD) years and in 103 men aged 81 +/- 8 years living in 19 nursing homes. Vitamin D deficiency was frequent in this population: 41.9% of the women and 31.4% of the men had a serum 25-hydroxyvitamin (25OHD) level below the 2.5th percentile level of 3276 normal Swiss adults (6.2 micrograms/l or 15.5 mmol/l). Hyperparathyroidism was less frequent: serum parathyroid hormone (PTH) levels were above the 97.5th percentile level of normal adults (70 pg/l) in 18.9% of women and 9.8% of men. In women, QUS data correlated significantly with age (r = -0.297), body mass index (BMI) (r = 0.403), calcium (r = 0.220), PTH (r = -0.296), 25OHD (r = 0.298) and alkaline phosphatase (AP) (r = -0.170) for broadband ultrasound attenuation (BUA), and with age (r = -0.195), BMI (r = 0.208), PTH (r = -0.174), 25OHD (r = 0.140) and AP (r = -0.130) for speed of sound (SOS). In men, ultrasound data correlated with BMI (r = 0.326), calcium (r = 0.199), 25OHD (r = 0.258) and AP (r = -0.311) for BUA, and with AP (r = -0.196) for SOS. In women, but not in men because of their smaller number, a multivariate analysis was performed to examine relationships between age, BMI, biochemical markers and QUS. Age, BMI, PTH and phosphate explained 30% of the variance of BUA and 10% for SOS. In conclusion, QUS of bone evaluates characteristics of bone that are influenced, at least partially, by age, BMI and the secondary hyperparathyroidism due to vitamin D deficiency.

Age Factors↗
PubMed2002

"A system biology" approach to bioinformatics and functional genomics in complex human diseases: arthritis.

Human and other annotated genome sequences have facilitated generation of vast amounts of correlative data, from human/animal genetics, normal and disease-affected tissues from complex diseases such as arthritis using gene/protein chips and SNP analysis. These data sets include genes/proteins whose functions are partially known at the cellular level or may be completely unknown (e.g. ESTs). Thus, genomic research has transformed molecular biology from "data poor" to "data rich" science, allowing further division into subpopulations of subcellular fractions, which are often given an "-omic" suffix. These disciplines have to converge at a systemic level to examine the structure and dynamics of cellular and organismal function. The challenge of characterizing ESTs linked to complex diseases is like interpreting sharp images on a blurred background and therefore requires a multidimensional screen for functional genomics ("functionomics") in tissues, mice and zebra fish model, which intertwines various approaches and readouts to study development and homeostasis of a system. In summary, the post-genomic era of functionomics will facilitate to narrow the bridge between correlative data and causative data by quaint hypothesis-driven research using a system approach integrating "intercoms" of interacting and interdependent disciplines forming a unified whole as described in this review for Arthritis.

Animals↗
PubMed1996

The molecular biology of hormone and growth factor receptors in meningiomas.

Expression of a number of steroid hormone and growth factor receptors is characteristic of meningiomas. This paper reviews the analysis of receptors for progesterone, estrogen, androgen and platelet derived growth factor (PDGF) in human meningioma tissue specimens. Progesterone receptor was assessed by Northern blot analysis and immunohistochemistry in meningioma tissue specimens. Progesterone receptor mRNA was expressed in 64% of the meningiomas examined. Immunohistochemical data correlated well with the Northern blot analysis. The staining was clearly nuclear. Expression was more common in meningioma tissue from women than from men. Analysis of receptor expression in tissue culture derived from meningioma specimens demonstrated the loss of progesterone receptor within one to two passages. It was shown that the progesterone receptor mRNA expression which is present in meningiomas is functional by transfection techniques. The estrogen receptor was undetectable by Northern blot analysis; a small amount could be detected in meningioma tissue specimens by polymerase chain reaction (PCR). The androgen receptor was found in 67% of the specimens examined. Like the progesterone receptor, it was more common in women than in men (69% vs. 31%). The immunohistochemical data correlated well with the Northern blot analysis, with the receptor predominantly found in the nucleus. Unlike progesterone receptor, androgen receptor expression was not lost in cell culture. The subunits for PDGF were expressed in various quantities in meningiomas. Only the PDGF beta-receptor (PDGFR-beta) not alpha-receptor, was found in meningioma tissue specimens. In contrast, the ligands PDGF A and PDGF B were expressed in all tumors. The functionality of the PDGF beta-R was determined by examining the induction of the protooncogene C-fos by PDGF BB in meningioma cell cultures. A significant increase in C-fos protein was observed with the addition of PDGF BB to meningioma cultures.

Adult↗
PubMed2003

Frailty modeling for spatially correlated survival data, with application to infant mortality in Minnesota.

The use of survival models involving a random effect or 'frailty' term is becoming more common. Usually the random effects are assumed to represent different clusters, and clusters are assumed to be independent. In this paper, we consider random effects corresponding to clusters that are spatially arranged, such as clinical sites or geographical regions. That is, we might suspect that random effects corresponding to strata in closer proximity to each other might also be similar in magnitude. Such spatial arrangement of the strata can be modeled in several ways, but we group these ways into two general settings: geostatistical approaches, where we use the exact geographic locations (e.g. latitude and longitude) of the strata, and lattice approaches, where we use only the positions of the strata relative to each other (e.g. which counties neighbor which others). We compare our approaches in the context of a dataset on infant mortality in Minnesota counties between 1992 and 1996. Our main substantive goal here is to explain the pattern of infant mortality using important covariates (sex, race, birth weight, age of mother, etc.) while accounting for possible (spatially correlated) differences in hazard among the counties. We use the GIS ArcView to map resulting fitted hazard rates, to help search for possible lingering spatial correlation. The DIC criterion (Spiegelhalter et al., Journal of the Royal Statistical Society, Series B 2002, to appear) is used to choose among various competing models. We investigate the quality of fit of our chosen model, and compare its results when used to investigate neonatal versus post-neonatal mortality. We also compare use of our time-to-event outcome survival model with the simpler dichotomous outcome logistic model. Finally, we summarize our findings and suggest directions for future research.

Adult↗
PubMed1997

The association between arthritis and the weather.

Despite the prevasiveness of the idea that arthritis is influenced by the weather, scientific evidence on the matter is sparse and non-conclusive. This study, conducted in the Australian inland city of Bendigo, sought to establish a possible relationship between the pain and rigidity of arthritis and the weather variables of temperature, relative humidity, barometric pressure, wind speed and precipitation. Pain and rigidity levels were scored by 25 participants with osteoarthritis and/or rheumatoid arthritis four times per day for 1 month from each season. Mean pain and rigidity scores for each time of each day were found to be correlated with the meteorological data. Correlations between mean symptoms and temperature and relative humidity were significant (P < 0.001). Time of day was included in the analysis. Stepwise multiple regression analysis indicated that meteorological variables and time of day accounted for 38% of the variance in mean pain and 20% of the variance in mean rigidity when data of all months were considered. A post-study telephone questionnaire indicated 92% of participants perceived their symptoms to be influenced by the weather, while 48% claimed to be able to predict the weather according to their symptoms. Hence, the results suggest (1) decreased temperature is associated with both increased pain and increased rigidity and (2) increased relative humidity is associated with increased pain and rigidity in arthritis sufferers.

Aged↗
PubMed2003

Generalized common spatial factor model.

There are often two types of correlations in multivariate spatial data: correlations between variables measured at the same locations, and correlations of each variable across the locations. We hypothesize that these two types of correlations are caused by a common spatially correlated underlying factor. Under this hypothesis, we propose a generalized common spatial factor model. The parameters are estimated using the Bayesian method and a Markov chain Monte Carlo computing technique. Our main goals are to determine which observed variables share a common underlying spatial factor and also to predict the common spatial factor. The model is applied to county-level cancer mortality data in Minnesota to find whether there exists a common spatial factor underlying the cancer mortality throughout the state.

Bayes Theorem↗
PubMed2005

Behavioral testing of antidepressant compounds: an analysis of crossover design for correlated binary data.

The differential reinforcement of low-rate 72 seconds schedule (DRL-72) is a standard behavioral test procedure for screening potential antidepressant compounds. The protocol for the DRL-72 experiment, proposed by Evenden et al. (1993), consists of using a crossover design for the experiment and one-way ANOVA for the statistical analysis. In this paper we discuss the choice of several crossover designs for the DRL-72 experiment and propose to estimate the treatment effects using either generalized linear mixed models (GLMM) or generalized estimating equation (GEE) models for clustered binary data.

Algorithms↗
PubMed2005

Semiparametric proportional odds models for spatially correlated survival data.

The last decade has witnessed major developments in Geographical Information Systems (GIS) technology resulting in the need for statisticians to develop models that account for spatial clustering and variation. In public health settings, epidemiologists and health-care professionals are interested in discerning spatial patterns in survival data that might exist among the counties. This paper develops a Bayesian hierarchical model for capturing spatial heterogeneity within the framework of proportional odds. This is deemed more appropriate when a substantial percentage of subjects enjoy prolonged survival. We discuss the implementation issues of our models, perform comparisons among competing models and illustrate with data from the SEER (Surveillance Epidemiology and End Results) database of the National Cancer Institute, paying particular attention to the underlying spatial story.

Bayes Theorem↗