Can mean shift trackers perform better?

Many tracking algorithms have difficulties dealing with occlusions and background clutters, and consequently don't converge to an appropriate solution. Tracking based on the mean shift algorithm has shown robust performance in many circumstances but still fails e.g. when encountering dramatic intensity or colour changes in a pre-defined neighbourhood. In this paper, we present a robust tracking algorithm that integrates the advantages of mean shift tracking with those of tracking local invariant features. These features are integrated into the mean shift formulation so that tracking is performed based both on mean shift and feature probability distributions, coupled with an expectation maximisation scheme. Experimental results show robust tracking performance on a series of complicated real image sequences. © 2010 IEEE.

Association of haplotypes in the IL8 gene with susceptibility to chronic periodontitis in a Brazilian population

Background: Interleukin 8 (IL-8) is a chemokine related to the initiation and amplification of acute and chronic inflammatory processes. Polymorphisms in the IL8 gene have been associated with inflammatory diseases. We investigated whether the - 845(T/C) and - 738(T/A) single nucleotide polymorphisms (SNPs) in the IL8 gene, as well as the haplotypes they form together with the previously investigated -353(A/T), are associated with susceptibility to chronic periodontitis. Methods: DNA was extracted from buccal epithelial cells of 400 Brazilian individuals (control n =182, periodontitis n=218). SNPs were genotyped by the polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method. Disease associations were analyzed by the chi(2) test, Exact Fisher test and Clump program. Haplotypes were reconstructed using the expectation-maximization algorithm and differences in haplotype distribution between the groups were analyzed to estimate genetic susceptibility for chronic periodontitis development. Results: When analyzed individually, no SNPs showed different distributions between the control and chronic periodontitis groups. Although, nonsmokers carrying the TTA/CAT (OR = 2.35, 95% CI = 1.03-5.36) and TAT/CTA (OR= 6.05, 95% CI = 1.32-27.7) haplotypes were genetically susceptible to chronic periodontitis. The ITT/TAA haplotype was associated with protection against the development of periodontitis (for nonsmokers OR= 0.22, 95% CI = 0.10-0.46). Conclusion: Although none of the investigated SNPs in the IL8 gene was individually associated with periodontitis, some haplotypes showed significant association with susceptibility to, or protection against, chronic periodontitis in a Brazilian population. (C) 2010 Elsevier B.V. All rights reserved.

Fitting mixtures of Kent distributions to aid in joint set identification

When examining a rock mass, joint sets and their orientations can play a significant role with regard to how the rock mass will behave. To identify joint sets present in the rock mass, the orientation of individual fracture planer can be measured on exposed rock faces and the resulting data can be examined for heterogeneity. In this article, the expectation-maximization algorithm is used to lit mixtures of Kent component distributions to the fracture data to aid in the identification of joint sets. An additional uniform component is also included in the model to accommodate the noise present in the data.

Feature selection for clustering categorical data with an embedded modelling approach

Research on the problem of feature selection for clustering continues to develop. This is a challenging task, mainly due to the absence of class labels to guide the search for relevant features. Categorical feature selection for clustering has rarely been addressed in the literature, with most of the proposed approaches having focused on numerical data. In this work, we propose an approach to simultaneously cluster categorical data and select a subset of relevant features. Our approach is based on a modification of a finite mixture model (of multinomial distributions), where a set of latent variables indicate the relevance of each feature. To estimate the model parameters, we implement a variant of the expectation-maximization algorithm that simultaneously selects the subset of relevant features, using a minimum message length criterion. The proposed approach compares favourably with two baseline methods: a filter based on an entropy measure and a wrapper based on mutual information. The results obtained on synthetic data illustrate the ability of the proposed expectation-maximization method to recover ground truth. An application to real data, referred to official statistics, shows its usefulness.

Categorical data clustering using a minimum message length criterion

Research on cluster analysis for categorical data continues to develop, new clustering algorithms being proposed. However, in this context, the determination of the number of clusters is rarely addressed. We propose a new approach in which clustering and the estimation of the number of clusters is done simultaneously for categorical data. We assume that the data originate from a finite mixture of multinomial distributions and use a minimum message length criterion (MML) to select the number of clusters (Wallace and Bolton, 1986). For this purpose, we implement an EM-type algorithm (Silvestre et al., 2008) based on the (Figueiredo and Jain, 2002) approach. The novelty of the approach rests on the integration of the model estimation and selection of the number of clusters in a single algorithm, rather than selecting this number based on a set of pre-estimated candidate models. The performance of our approach is compared with the use of Bayesian Information Criterion (BIC) (Schwarz, 1978) and Integrated Completed Likelihood (ICL) (Biernacki et al., 2000) using synthetic data. The obtained results illustrate the capacity of the proposed algorithm to attain the true number of cluster while outperforming BIC and ICL since it is faster, which is especially relevant when dealing with large data sets.

Brownian motion with drift threshold model

In this thesis we implement estimating procedures in order to estimate threshold parameters for the continuous time threshold models driven by stochastic di®erential equations. The ¯rst procedure is based on the EM (expectation-maximization) algorithm applied to the threshold model built from the Brownian motion with drift process. The second procedure mimics one of the fundamental ideas in the estimation of the thresholds in time series context, that is, conditional least squares estimation. We implement this procedure not only for the threshold model built from the Brownian motion with drift process but also for more generic models as the ones built from the geometric Brownian motion or the Ornstein-Uhlenbeck process. Both procedures are implemented for simu- lated data and the least squares estimation procedure is also implemented for real data of daily prices from a set of international funds. The ¯rst fund is the PF-European Sus- tainable Equities-R fund from the Pictet Funds company and the second is the Parvest Europe Dynamic Growth fund from the BNP Paribas company. The data for both funds are daily prices from the year 2004. The last fund to be considered is the Converging Europe Bond fund from the Schroder company and the data are daily prices from the year 2005.

Hyperspectral unmixing based on mixtures of Dirichlet components

This paper introduces a new unsupervised hyperspectral unmixing method conceived to linear but highly mixed hyperspectral data sets, in which the simplex of minimum volume, usually estimated by the purely geometrically based algorithms, is far way from the true simplex associated with the endmembers. The proposed method, an extension of our previous studies, resorts to the statistical framework. The abundance fraction prior is a mixture of Dirichlet densities, thus automatically enforcing the constraints on the abundance fractions imposed by the acquisition process, namely, nonnegativity and sum-to-one. A cyclic minimization algorithm is developed where the following are observed: 1) The number of Dirichlet modes is inferred based on the minimum description length principle; 2) a generalized expectation maximization algorithm is derived to infer the model parameters; and 3) a sequence of augmented Lagrangian-based optimizations is used to compute the signatures of the endmembers. Experiments on simulated and real data are presented to show the effectiveness of the proposed algorithm in unmixing problems beyond the reach of the geometrically based state-of-the-art competitors.

Ankylosing spondylitis: genomic and functional characterization of candidate genes and their repercussion in clinical practice

RESUMO: Introdução: A espondilite anquilosante (EA) é uma doença inflamatória crónica caracterizada pela inflamação das articulações sacroilíacas e da coluna. A anquilose progressiva motiva uma deterioração gradual da função física e da qualidade de vida. O diagnóstico e o tratamento precoces podem contribuir para um melhor prognóstico. Neste contexto, a identificação de biomarcadores, assume-se como sendo muito útil para a prática clínica e representa hoje um grande desafio para a comunidade científica. Objetivos: Este estudo teve como objetivos: 1 - caracterizar a EA em Portugal; 2 - investigar possíveis associações entre genes, MHC e não-MHC, com a suscetibilidade e as características fenotípicas da EA; 3 - identificar genes candidatos associados a EA através da tecnologia de microarray. Material e Métodos: Foram recrutados doentes com EA, de acordo com os critérios modificados de Nova Iorque, nas consultas de Reumatologia dos diferentes hospitais participantes. Colecionaram-se dados demográficos, clínicos e radiológicos e colhidas amostras de sangue periférico. Selecionaram-se de forma aleatória, doentes HLA-B27 positivos, os quais foram tipados em termos de HLA classe I e II por PCR-rSSOP. Os haplótipos HLA estendidos foram estimados pelo algoritmo Expectation Maximization com recurso ao software Arlequin v3.11. As variantes alélicas dos genes IL23R, ERAP1 e ANKH foram estudadas através de ensaios de discriminação alélica TaqMan. A análise de associação foi realizada utilizando testes da Cochrane-Armitage e de regressão linear, tal como implementado pelo PLINK, para variáveis qualitativas e quantitativas, respetivamente. O estudo de expressão génica foi realizado por Illumina HT-12 Whole-Genome Expression BeadChips. Os genes candidatos foram validados usando qPCR-based TaqMan Low Density Arrays (TLDAs). Resultados: Foram incluídos 369 doentes (62,3% do sexo masculino, com idade média de 45,4 ± 13,2 anos, duração média da doença de 11,4 ± 10,5 anos). No momento da avaliação, 49,9% tinham doença axial, 2,4% periférica, 40,9% mista e 7,1% entesopática. A uveíte anterior aguda (33,6%) foi a manifestação extra-articular mais comum. Foram positivos para o HLA-B27, 80,3% dos doentes. Os haplótipo A*02/B*27/Cw*02/DRB1*01/DQB1*05 parece conferir suscetibilidade para a EA, e o A*02/B*27/Cw*01/DRB1*08/DQB1*04 parece conferir proteção em termos de atividade, repercussão funcional e radiológica da doença. Três variantes (2 para IL23R e 1 para ERAP1) mostraram significativa associação com a doença, confirmando a associação destes genes com a EA na população Portuguesa. O mesmo não se verificou com as variantes estudadas do ANKH. Não se verificou associação entre as variantes génicas não-MHC e as manifestações clínicas da EA. Foi identificado um perfil de expressão génica para a EA, tendo sido validados catorze genes - alguns têm um papel bem documentado em termos de inflamação, outros no metabolismo da cartilagem e do osso. Conclusões: Foi estabelecido um perfil demográfico e clínico dos doentes com EA em Portugal. A identificação de variantes génicas e de um perfil de expressão contribuem para uma melhor compreensão da sua fisiopatologia e podem ser úteis para estabelecer modelos com relevância em termos de diagnóstico, prognóstico e orientação terapêutica dos doentes. -----------ABSTRACT: Background: Ankylosing Spondylitis (AS) is a chronic inflammatory disorder characterized by inflammation in the spine and sacroiliac joints leading to progressive joint ankylosis and in progressive deterioration of physical function and quality of life. An early diagnosis and early therapy may contribute to a better prognosis. The identification of biomarkers would be helpful and represents a great challenge for the scientific community. Objectives: The present study had the following aims: 1- to characterize the pattern of AS in Portuguese patients; 2- to investigate MHC and non-MHC gene associations with susceptibility and phenotypic features of AS and; 3- to identify candidate genes associated with AS by means of whole-genome microarray. Material and Methods: AS was defined in accordance to the modified New York criteria and AS cases were recruited from hospital outcares patient clinics. Demographic and clinical data were recorded and blood samples collected. A random group of HLA-B27 positive patients and controls were selected and typed for HLA class I and II by PCR-rSSOP. The extended HLA haplotypes were estimated by Expectation Maximization Algorithm using Arlequin v3.11 software. Genotyping of IL23R, ERAP1 and ANKH allelic variants was carried out with TaqMan allelic discrimination assays. Association analysis was performed using the Cochrane-Armitage and linear regression tests as implemented in PLINK, for dichotomous and quantitative variables, respectively. Gene expression profile was carried out using Illumina HT-12 Whole-Genome Expression BeadChips and candidate genes were validated using qPCR-based TaqMan Low Density Arrays (TLDAs). Results: A total of 369 patients (62.3% male; mean age 45.4±13.2 years; mean disease duration 11.4±10.5 years), were included. Regarding clinical disease pattern, at the time of assessment, 49.9% had axial disease, 2.4% peripheral disease, 40.9% mixed disease and 7.1% isolated enthesopathic disease. Acute anterior uveitis (33.6%) was the most common extra-articular manifestation. 80.3% of AS patients were HLA-B27 positive. The haplotype A*02/B*27/Cw*02/DRB1*01/DQB1*05 seems to confer susceptibility to AS, whereas A*02/B*27/Cw*01/DRB1*08/DQB1*04 seems to provide protection in terms of disease activity, functional and radiological repercussion. Three markers (two for IL23R and one for ERAP1) showed significant single-locus disease associations. Association of these genes with AS in the Portuguese population was confirmed, whereas ANKH markers studied did not show an association with AS. No association was seen between non-MHC genes and clinical manifestations of AS. A gene expression signature for AS was established; among the fourteen validated genes, a number of them have a well-documented inflammatory role or in modulation of cartilage and bone metabolism. Conclusions: A demographic and clinical profile of patients with AS in Portugal was established. Identification of genetic variants of target genes as well as gene expression signatures could provide a better understanding of AS pathophysiology and could be useful to establish models with relevance in terms of susceptibility, prognosis, and potential therapeutic guidance.

Imputation in data fusion of heterogeneous data sets a model-based numerical experiment

Given the very large amount of data obtained everyday through population surveys, much of the new research again could use this information instead of collecting new samples. Unfortunately, relevant data are often disseminated into different files obtained through different sampling designs. Data fusion is a set of methods used to combine information from different sources into a single dataset. In this article, we are interested in a specific problem: the fusion of two data files, one of which being quite small. We propose a model-based procedure combining a logistic regression with an Expectation-Maximization algorithm. Results show that despite the lack of data, this procedure can perform better than standard matching procedures.

A pipeline approach with spatial information for segmenting multiple sclerosis lesions on brain magnetic resonance imaging

Background: Conventional magnetic resonance imaging (MRI) techniques are highly sensitive to detect multiple sclerosis (MS) plaques, enabling a quantitative assessment of inflammatory activity and lesion load. In quantitative analyses of focal lesions, manual or semi-automated segmentations have been widely used to compute the total number of lesions and the total lesion volume. These techniques, however, are both challenging and time-consuming, being also prone to intra-observer and inter-observer variability.Aim: To develop an automated approach to segment brain tissues and MS lesions from brain MRI images. The goal is to reduce the user interaction and to provide an objective tool that eliminates the inter- and intra-observer variability.Methods: Based on the recent methods developed by Souplet et al. and de Boer et al., we propose a novel pipeline which includes the following steps: bias correction, skull stripping, atlas registration, tissue classification, and lesion segmentation. After the initial pre-processing steps, a MRI scan is automatically segmented into 4 classes: white matter (WM), grey matter (GM), cerebrospinal fluid (CSF) and partial volume. An expectation maximisation method which fits a multivariate Gaussian mixture model to T1-w, T2-w and PD-w images is used for this purpose. Based on the obtained tissue masks and using the estimated GM mean and variance, we apply an intensity threshold to the FLAIR image, which provides the lesion segmentation. With the aim of improving this initial result, spatial information coming from the neighbouring tissue labels is used to refine the final lesion segmentation.Results:The experimental evaluation was performed using real data sets of 1.5T and the corresponding ground truth annotations provided by expert radiologists. The following values were obtained: 64% of true positive (TP) fraction, 80% of false positive (FP) fraction, and an average surface distance of 7.89 mm. The results of our approach were quantitatively compared to our implementations of the works of Souplet et al. and de Boer et al., obtaining higher TP and lower FP values.Conclusion: Promising MS lesion segmentation results have been obtained in terms of TP. However, the high number of FP which is still a well-known problem of all the automated MS lesion segmentation approaches has to be improved in order to use them for the standard clinical practice. Our future work will focus on tackling this issue.

Polymorphisms in Toll-like receptor 9 influence the clinical course of HIV-1 infection.

BACKGROUND: The clinical course of HIV-1 infection is highly variable among individuals, at least in part as a result of genetic polymorphisms in the host. Toll-like receptors (TLRs) have a key role in innate immunity and mutations in the genes encoding these receptors have been associated with increased or decreased susceptibility to infections. OBJECTIVES: To determine whether single-nucleotide polymorphisms (SNPs) in TLR2-4 and TLR7-9 influenced the natural course of HIV-1 infection. METHODS: Twenty-eight SNPs in TLRs were analysed in HAART-naive HIV-positive patients from the Swiss HIV Cohort Study. The SNPs were detected using Sequenom technology. Haplotypes were inferred using an expectation-maximization algorithm. The CD4 T cell decline was calculated using a least-squares regression. Patients with a rapid CD4 cell decline, less than the 15th percentile, were defined as rapid progressors. The risk of rapid progression associated with SNPs was estimated using a logistic regression model. Other candidate risk factors included age, sex and risk groups (heterosexual, homosexual and intravenous drug use). RESULTS: Two SNPs in TLR9 (1635A/G and +1174G/A) in linkage disequilibrium were associated with the rapid progressor phenotype: for 1635A/G, odds ratio (OR), 3.9 [95% confidence interval (CI),1.7-9.2] for GA versus AA and OR, 4.7 (95% CI,1.9-12.0) for GG versus AA (P = 0.0008). CONCLUSION: Rapid progression of HIV-1 infection was associated with TLR9 polymorphisms. Because of its potential implications for intervention strategies and vaccine developments, additional epidemiological and experimental studies are needed to confirm this association.

Estimation du modèle GARCH à changement de régimes et son utilité pour quantifier le risque de modèle dans les applications financières en actuariat

Le modèle GARCH à changement de régimes est le fondement de cette thèse. Ce modèle offre de riches dynamiques pour modéliser les données financières en combinant une structure GARCH avec des paramètres qui varient dans le temps. Cette flexibilité donne malheureusement lieu à un problème de path dependence, qui a empêché l'estimation du modèle par le maximum de vraisemblance depuis son introduction, il y a déjà près de 20 ans. La première moitié de cette thèse procure une solution à ce problème en développant deux méthodologies permettant de calculer l'estimateur du maximum de vraisemblance du modèle GARCH à changement de régimes. La première technique d'estimation proposée est basée sur l'algorithme Monte Carlo EM et sur l'échantillonnage préférentiel, tandis que la deuxième consiste en la généralisation des approximations du modèle introduites dans les deux dernières décennies, connues sous le nom de collapsing procedures. Cette généralisation permet d'établir un lien méthodologique entre ces approximations et le filtre particulaire. La découverte de cette relation est importante, car elle permet de justifier la validité de l'approche dite par collapsing pour estimer le modèle GARCH à changement de régimes. La deuxième moitié de cette thèse tire sa motivation de la crise financière de la fin des années 2000 pendant laquelle une mauvaise évaluation des risques au sein de plusieurs compagnies financières a entraîné de nombreux échecs institutionnels. À l'aide d'un large éventail de 78 modèles économétriques, dont plusieurs généralisations du modèle GARCH à changement de régimes, il est démontré que le risque de modèle joue un rôle très important dans l'évaluation et la gestion du risque d'investissement à long terme dans le cadre des fonds distincts. Bien que la littérature financière a dévoué beaucoup de recherche pour faire progresser les modèles économétriques dans le but d'améliorer la tarification et la couverture des produits financiers, les approches permettant de mesurer l'efficacité d'une stratégie de couverture dynamique ont peu évolué. Cette thèse offre une contribution méthodologique dans ce domaine en proposant un cadre statistique, basé sur la régression, permettant de mieux mesurer cette efficacité.

Les algorithmes de haute résolution en tomographie d'émission par positrons : développement et accélération sur les cartes graphiques

La tomographie d’émission par positrons (TEP) est une modalité d’imagerie moléculaire utilisant des radiotraceurs marqués par des isotopes émetteurs de positrons permettant de quantifier et de sonder des processus biologiques et physiologiques. Cette modalité est surtout utilisée actuellement en oncologie, mais elle est aussi utilisée de plus en plus en cardiologie, en neurologie et en pharmacologie. En fait, c’est une modalité qui est intrinsèquement capable d’offrir avec une meilleure sensibilité des informations fonctionnelles sur le métabolisme cellulaire. Les limites de cette modalité sont surtout la faible résolution spatiale et le manque d’exactitude de la quantification. Par ailleurs, afin de dépasser ces limites qui constituent un obstacle pour élargir le champ des applications cliniques de la TEP, les nouveaux systèmes d’acquisition sont équipés d’un grand nombre de petits détecteurs ayant des meilleures performances de détection. La reconstruction de l’image se fait en utilisant les algorithmes stochastiques itératifs mieux adaptés aux acquisitions à faibles statistiques. De ce fait, le temps de reconstruction est devenu trop long pour une utilisation en milieu clinique. Ainsi, pour réduire ce temps, on les données d’acquisition sont compressées et des versions accélérées d’algorithmes stochastiques itératifs qui sont généralement moins exactes sont utilisées. Les performances améliorées par l’augmentation de nombre des détecteurs sont donc limitées par les contraintes de temps de calcul. Afin de sortir de cette boucle et permettre l’utilisation des algorithmes de reconstruction robustes, de nombreux travaux ont été effectués pour accélérer ces algorithmes sur les dispositifs GPU (Graphics Processing Units) de calcul haute performance. Dans ce travail, nous avons rejoint cet effort de la communauté scientifique pour développer et introduire en clinique l’utilisation des algorithmes de reconstruction puissants qui améliorent la résolution spatiale et l’exactitude de la quantification en TEP. Nous avons d’abord travaillé sur le développement des stratégies pour accélérer sur les dispositifs GPU la reconstruction des images TEP à partir des données d’acquisition en mode liste. En fait, le mode liste offre de nombreux avantages par rapport à la reconstruction à partir des sinogrammes, entre autres : il permet d’implanter facilement et avec précision la correction du mouvement et le temps de vol (TOF : Time-Of Flight) pour améliorer l’exactitude de la quantification. Il permet aussi d’utiliser les fonctions de bases spatio-temporelles pour effectuer la reconstruction 4D afin d’estimer les paramètres cinétiques des métabolismes avec exactitude. Cependant, d’une part, l’utilisation de ce mode est très limitée en clinique, et d’autre part, il est surtout utilisé pour estimer la valeur normalisée de captation SUV qui est une grandeur semi-quantitative limitant le caractère fonctionnel de la TEP. Nos contributions sont les suivantes : - Le développement d’une nouvelle stratégie visant à accélérer sur les dispositifs GPU l’algorithme 3D LM-OSEM (List Mode Ordered-Subset Expectation-Maximization), y compris le calcul de la matrice de sensibilité intégrant les facteurs d’atténuation du patient et les coefficients de normalisation des détecteurs. Le temps de calcul obtenu est non seulement compatible avec une utilisation clinique des algorithmes 3D LM-OSEM, mais il permet également d’envisager des reconstructions rapides pour les applications TEP avancées telles que les études dynamiques en temps réel et des reconstructions d’images paramétriques à partir des données d’acquisitions directement. - Le développement et l’implantation sur GPU de l’approche Multigrilles/Multitrames pour accélérer l’algorithme LMEM (List-Mode Expectation-Maximization). L’objectif est de développer une nouvelle stratégie pour accélérer l’algorithme de référence LMEM qui est un algorithme convergent et puissant, mais qui a l’inconvénient de converger très lentement. Les résultats obtenus permettent d’entrevoir des reconstructions en temps quasi-réel que ce soit pour les examens utilisant un grand nombre de données d’acquisition aussi bien que pour les acquisitions dynamiques synchronisées. Par ailleurs, en clinique, la quantification est souvent faite à partir de données d’acquisition en sinogrammes généralement compressés. Mais des travaux antérieurs ont montré que cette approche pour accélérer la reconstruction diminue l’exactitude de la quantification et dégrade la résolution spatiale. Pour cette raison, nous avons parallélisé et implémenté sur GPU l’algorithme AW-LOR-OSEM (Attenuation-Weighted Line-of-Response-OSEM) ; une version de l’algorithme 3D OSEM qui effectue la reconstruction à partir de sinogrammes sans compression de données en intégrant les corrections de l’atténuation et de la normalisation dans les matrices de sensibilité. Nous avons comparé deux approches d’implantation : dans la première, la matrice système (MS) est calculée en temps réel au cours de la reconstruction, tandis que la seconde implantation utilise une MS pré- calculée avec une meilleure exactitude. Les résultats montrent que la première implantation offre une efficacité de calcul environ deux fois meilleure que celle obtenue dans la deuxième implantation. Les temps de reconstruction rapportés sont compatibles avec une utilisation clinique de ces deux stratégies.

Uncoupling protein 2 and 3 gene polymorphisms and their association with type 2 diabetes in asian indians

BACKGROUND: this study examined the association of -866G/A, Ala55Val, 45bpI/D, and -55C/T polymorphisms at the uncoupling protein (UCP) 3-2 loci with type 2 diabetes in Asian Indians. METHODS: a case-control study was performed among 1,406 unrelated subjects (487 with type 2 diabetes and 919 normal glucose-tolerant [NGT]), chosen from the Chennai Urban Rural Epidemiology Study, an ongoing population-based study in Southern India. The polymorphisms were genotyped using polymerase chain reaction-restriction fragment length polymorphism and direct sequencing. Haplotype frequencies were estimated using an expectation-maximization algorithm. Linkage disequilibrium was estimated from the estimates of haplotypic frequencies. RESULTS: the genotype (P = 0.00006) and the allele (P = 0.00007) frequencies of Ala55Val of the UCP2 gene showed a significant protective effect against the development of type 2 diabetes. The odds ratios (adjusted for age, sex, and body mass index) for diabetes for individuals carrying Ala/Val was 0.72, and that for individuals carrying Val/Val was 0.37. Homeostasis insulin resistance model assessment and 2-h plasma glucose were significantly lower among Val-allele carriers compared to the Ala/Ala genotype within the NGT group. The genotype (P = 0.02) and the allele (P = 0.002) frequencies of -55C/T of the UCP3 gene showed a significant protective effect against the development of diabetes. The odds ratio for diabetes for individuals carrying CT was 0.79, and that for individuals carrying TT was 0.61. The haplotype analyses further confirmed the association of Ala55Val with diabetes, where the haplotypes carrying the Ala allele were significantly higher in the cases compared to controls. CONCLUSIONS: Ala55Val and -55C/T polymorphisms at the UCP3-2 loci are associated with a significantly reduced risk of developing type 2 diabetes in Asian Indians.

Effect of polymorphisms in the PPARGC1A gene on body fat in Asian Indians

OBJECTIVE: To evaluate whether polymorphisms in the peroxisome proliferator-activated receptor-gamma coactivator-1 alpha (PPARGC1A) gene were related to body fat in Asian Indians. METHODS: Three polymorphisms of PPARGC1A gene, the Thr394Thr, Gly482Ser and +A2962G, were genotyped on 82 type 2 diabetic and 82 normal glucose tolerant (NGT) subjects randomly chosen from the Chennai Urban Rural Epidemiology Study using PCR-RFLP, and the nature of the variants were confirmed using direct sequencing. Linkage disequilibrium (LD) was estimated from the estimates of haplotypic frequencies using an expectation-maximization algorithm. Visceral, subcutaneous and total abdominal fat were measured using computed tomography, whereas dual X-ray absorptiometry was used to measure central abdominal and total body fat. RESULTS: None of the three polymorphisms studied were in LD. The genotype (0.59 vs 0.32, P=0.001) and allele (0.30 vs 0.17, P=0.007) frequencies of Thr394Thr polymorphism were significantly higher in type 2 diabetic subjects compared to those in NGT subjects. The odds ratio for diabetes (adjusted for age, sex and body mass index) for the susceptible genotype, XA (GA+AA) of Thr394Thr polymorphism, was 2.53 (95% confidence intervals: 1.30-5.04, P=0.009). Visceral and subcutaneous fat were significantly higher in NGT subjects with XA genotype of the Thr394Thr polymorphism compared to those with GG genotype (visceral fat: XA 148.2+/-46.9 vs GG 106.5+/-51.9 cm(2), P=0.001; subcutaneous fat: XA 271.8+/-167.1 vs GG 181.5+/-78.5 cm(2), P=0.001). Abdominal (XA 4521.9+/-1749.6 vs GG 3445.2+/-1443.4 g, P=0.004), central abdominal (XA 1689.0+/-524.0 vs GG 1228.5+/-438.7 g, P<0.0001) and non-abdominal fat (XA 18763.8+/-8789.4 vs GG 13160.4+/-4255.3 g, P<0.0001) were also significantly higher in the NGT subjects with XA genotype compared to those with GG genotype. The Gly482Ser and +A2962G polymorphisms were not associated with any of the body fat measures. CONCLUSION: Among Asian Indians, the Thr394Thr (G --> A) polymorphism is associated with increased total, visceral and subcutaneous body fat.