PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 8993797

Multiple myeloma: clusters, clues, and dioxins.

G G Schwartz
View original source ↗Export metadata ↓

Abstract

Multiple myeloma (MM) is a B-cell neoplasm of unknown etiology. We searched for etiological clues by examining the literature on geographic clusters of MM. We searched the MEDLINE database from 1966 to 1996 for spatial occurrences of MM that were significantly greater than expected (spatial "clusters"). Eight clusters with verified diagnoses of MM were identified. All of the eight clusters occurred in locations that were proximate to a body of water. Six of these bodies of water are known to have been contaminated with dioxins. We hypothesize that the observed association between MM and proximity to bodies of water is caused by exposure to dioxins in individuals who consume local fish and seafood. This hypothesis is consistent with the significantly elevated risks for MM in groups with high consumption of dioxin-contaminated fish, e.g., Baltic Sea fishermen and Alaskan Indians, and among persons accidentally exposed to dioxins in Seveso, Italy. Dioxins are immunotoxic and inhibit the differentiation of B cells. Thus, dioxins are plausible myelomagens. A dioxin hypothesis could illuminate many epidemiological features of MM and may suggest new avenues for analytic research.

Explore related subjects

Cluster AnalysisDioxinsFood ContaminationHumansMultiple MyelomaPopulation SurveillanceRisk FactorsSeafoodWater Pollutants, ChemicalJournal ArticleResearch Support, Non-U.S. Gov'tReview

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

G G Schwartz. 1997. Multiple myeloma: clusters, clues, and dioxins.. https://pubmed.ncbi.nlm.nih.gov/8993797/

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2025

Clustering individuals using INMTD: a novel versatile multi-view embedding framework integrating omics and imaging data.

MOTIVATION: Combining omics and images can lead to a more comprehensive clustering of individuals than classic single-view approaches. Among the various approaches for multi-view clustering, nonnegative matrix tri-factorization (NMTF) and nonnegative Tucker decomposition (NTD) are advantageous in learning low-rank embeddings with promising interpretability. Besides, there is a need to handle unwanted drivers of clusterings (i.e. confounders). RESULTS: In this work, we introduce a novel multi-view clustering method based on NMTF and NTD, named INMTD, which integrates omics and 3D imaging data to derive unconfounded subgroups of individuals. According to the adjusted Rand index, INMTD outperformed other clustering methods on a synthetic dataset with known clusters. In the application to real-life facial-genomic data, INMTD generated biologically relevant embeddings for individuals, genetics, and facial morphology. By removing confounded embedding vectors, we derived an unconfounded clustering with better internal and external quality; the genetic and facial annotations of each derived subgroup highlighted distinctive characteristics. In conclusion, INMTD can effectively integrate omics data and 3D images for unconfounded clustering with biologically meaningful interpretation. AVAILABILITY AND IMPLEMENTATION: INMTD is freely available at https://github.com/ZuqiLi/INMTD.

Cluster Analysis↗
PubMed2007

Relative efficiency of unequal versus equal cluster sizes in cluster randomized and multicentre trials.

Cluster randomized and multicentre trials evaluate the effect of a treatment on persons nested within clusters, for instance, patients within clinics or pupils within schools. Optimal sample sizes at the cluster (centre) and person level have been derived under the restrictive assumption of equal sample sizes per cluster. This paper addresses the relative efficiency of unequal versus equal cluster sizes in case of cluster randomization and person randomization within clusters. Starting from maximum likelihood parameter estimation, the relative efficiency is investigated numerically for a range of cluster size distributions. An approximate formula is presented for computing the relative efficiency as a function of the mean and variance of cluster size and the intraclass correlation, which can be used for adjusting the sample size. The accuracy of this formula is checked against the numerical results and found to be quite good. It is concluded that the loss of efficiency due to variation of cluster sizes rarely exceeds 10 per cent and can be compensated by sampling 11 per cent more clusters.

Cluster Analysis↗
PubMed2007

A spatial scan statistic for ordinal data.

Spatial scan statistics are widely used for count data to detect geographical disease clusters of high or low incidence, mortality or prevalence and to evaluate their statistical significance. Some data are ordinal or continuous in nature, however, so that it is necessary to dichotomize the data to use a traditional scan statistic for count data. There is then a loss of information and the choice of cut-off point is often arbitrary. In this paper, we propose a spatial scan statistic for ordinal data, which allows us to analyse such data incorporating the ordinal structure without making any further assumptions. The test statistic is based on a likelihood ratio test and evaluated using Monte Carlo hypothesis testing. The proposed method is illustrated using prostate cancer grade and stage data from the Maryland Cancer Registry. The statistical power, sensitivity and positive predicted value of the test are examined through a simulation study.

Cluster Analysis↗