Dhafer Malouche

Lack of association between the angiotensin-converting enzyme gene (I/D) polymorphism and diabetic nephropathy in Tunisian type 2 diabetic patients

Objective. The aim of the present study was to investigate whether the angiotensin-converting enzyme (ACE) insertion/deletion (I/D) polymorphism is associated with diabetic nephropathy and type 2 diabetes in the Tunisian population. Design. A case-control study was conducted among 141 unrelated type 2 diabetic patients with (90 patients) or without nephropathy (51 patients) and 103 non-diabetic controls with normal fasting blood glucose. Genotyping was performed using a nested polymerase chain reaction amplification in order to identify correctly heterozygous individuals. Results. The distribution of DD, ID and II genotypes did not significantly differ between type 2 diabetic patients with or without nephropathy (DD: 44%; ID: 46%; II: 10% vs. DD: 41%; ID: 47 %; II: 12%, respectively).There was also no significant statistical difference between the genotype distribution and allele frequencies of the (I/D) polymorphism in all type 2 diabetic subjects compared to non-diabetic controls with normal fasting blood glucose (DD: 43%; ID: 46%; II: 11% vs. DD: 37%; ID: 48% ;II: 15%, respectively). Conclusions. In the present preliminary study, the (I/D) polymorphis within the ACE gene is likely not associated with diabetic nephropathy nor with type 2 diabetes in the Tunisian studied population.

Familial aggregation and excess maternal transmission of type 2 diabetes in Tunisia

Aim: To evaluate the degree of familial aggregation of type 2 diabetes mellitus in Tunisia and to investigate transmission patterns of the disease and their relationships with patients’ clinical profiles. Methods: Family history of diabetes and clinical data were collected for 132 unrelated type 2 diabetic Tunisian patients. Diabetes status was recorded for first degree relatives (parents, siblings) and second degree relatives (aunts and uncles from both maternal and paternal sides). Information about family history of diabetes was gathered for a total of 1767 individuals. Results: Familial aggregation of type 2 diabetes was prominent and more important among first degree relatives than among second degree relatives (p = 0.01). Among studied subjects, 70% reported at least one relative with diabetes and 34% had at least one parent with diabetes. Diabetes was more frequent among mothers than fathers of probands (p = 0.03). This maternal effect extends to second degree relatives as diabetes was more common among maternal than paternal aunts and uncles (p = 0.01). There is no significant difference in clinical and metabolic profiles between patients according to transmission patterns of the disease. Conclusion: These results suggest familial aggregation and excess maternal transmission of type 2 diabetes in the Tunisian studied population.

Application of Multi-SOM clustering approach to macrophage gene expression analysis

The production of increasingly reliable and accessible gene expression data has stimulated the development of computational tools to interpret such data and to organize them efficiently. The clustering techniques are largely recognized as useful exploratory tools for gene expression data analysis. Genes that show similar expression patterns over a wide range of experimental conditions can be clustered together. This relies on the hypothesis that genes that belong to the same cluster are coregulated and involved in related functions. Nevertheless, clustering algorithms still show limits, particularly for the estimation of the number of clusters and the interpretation of hierarchical dendrogram, which may significantly influence the outputs of the analysis process. We propose here a multi level SOM based clustering algorithm named Multi-SOM. Through the use of clustering validity indices, Multi-SOM overcomes the problem of the estimation of clusters number. To test the validity of the proposed clustering algorithm, we first tested it on supervised training data sets. Results were evaluated by computing the number of misclassified samples. We have then used Multi-SOM for the analysis of macrophage gene expression data generated in vitro from the same individual blood infected with 5 different pathogens. This analysis led to the identification of sets of tightly coregulated genes across different pathogens. Gene Ontology tools were then used to estimate the biological significance of the clustering, which showed that the obtained clusters are coherent and biologically significant.

IMGT/StatClonotype for Pairwise Evaluation and Visualization of NGS IG and TR IMGT Clonotype (AA) Diversity or Expression from IMGT/HighV-QUEST

There is a huge need for standardized analysis and statistical procedures in order to compare the complex immune repertoires of antigen receptors immunoglobulins (IG) and T cell receptors (TR) obtained by next generation sequencing (NGS). NGS technologies generate millions of nucleotide sequences and have led to the development of new tools. The IMGT/HighV-QUEST, available since 2010, is the first global web portal for the analysis of IG and TR high throughput sequences. IMGT/HighV-QUEST provides standardized outputs for the characterization of the “IMGT clonotype (AA)” (AA for amino acids) and their comparison in up to one million sequences. Standardized statistical procedures for “IMGT clonotype (AA)” diversity or expression comparisons have recently been described, however, no tool was yet available. IMGT/StatClonotype, a new IMGT® tool, evaluates and visualizes statistical significance of pairwise comparisons of IMGT clonotype (AA) diversity or expression, per V (variable), D (diversity), and J (joining) gene of a given IG or TR group, from NGS IMGT/HighV-QUEST statistical output. IMGT/StatClonotype tool is incorporated in the R package “IMGTStatClonotype,” with a user-friendly interface. IMGT/StatClonotype is downloadable at IMGT®1 for users to evaluate pairwise comparison of IG and TR NGS statistical output from IMGT/HighV-QUEST and to visualize, on their web browser, the statistical significance of IMGT clonotype (AA) diversity or expression, per gene, the comparative analysis of CDR-IMGT and the V–D–J associations, in immunoprofiles from normal or pathological immune responses.

Forecasting Tunisian type 2 diabetes prevalence to 2027: validation of a simple model.

BackgroundMost projections of type 2 diabetes (T2D) prevalence are simply based on demographic change (i.e. ageing). We developed a model to predict future trends in T2D prevalence in Tunisia, explicitly taking into account trends in major risk factors (obesity and smoking). This could improve assessment of policy options for prevention and health service planning.MethodsThe IMPACT T2D model uses a Markov approach to integrate population, obesity and smoking trends to estimate future T2D prevalence. We developed a model for the Tunisian population from 1997 to 2027, and validated the model outputs by comparing with a subsequent T2D prevalence survey conducted in 2005.ResultsThe model estimated that the prevalence of T2D among Tunisians aged over 25 years was 12.0% in 1997 (95% confidence intervals 9.6%–14.4%), increasing to 15.1% (12.5%–17.4%) in 2005. Between 1997 and 2005, observed prevalence in men increased from 13.5% to 16.1% and in women from 12.9% to 14.1%. The model forecast for a dramatic rise in prevalence by 2027 (26.6% overall, 28.6% in men and 24.7% in women).However, if obesity prevalence declined by 20% in the 10 years from 2013, and if smoking decreased by 20% over 10 years from 2009, a 3.3% reduction in T2D prevalence could be achieved in 2027 (2.5% in men and 4.1% in women).ConclusionsThis innovative model provides a reasonably close estimate of T2D prevalence for Tunisia over the 1997–2027 period. Diabetes burden is now a significant public health challenge. Our model predicts that this burden will increase significantly in the next two decades. Tackling obesity, smoking and other T2D risk factors thus needs urgent action. Tunisian decision makers have therefore defined two strategies: obesity reduction and tobacco control. Responses will be evaluated in future population surveys.

Modeling zoonotic cutaneous leishmaniasis incidence in central Tunisia from 2009-2015: Forecasting models using climate variables as predictors

Transmission of zoonotic cutaneous leishmaniasis (ZCL) depends on the presence, density and distribution of Leishmania major rodent reservoir and the development of these rodents is known to have a significant dependence on environmental and climate factors. ZCL in Tunisia is one of the most common forms of leishmaniasis. The aim of this paper was to build a regression model of ZCL cases to identify the relationship between ZCL occurrence and possible risk factors, and to develop a predicting model for ZCLs control and prevention purposes. Monthly reported ZCL cases, environmental and bioclimatic data were collected over 6 years (2009–2015). Three rural areas in the governorate of Sidi Bouzid were selected as the study area. Cross-correlation analysis was used to identify the relevant lagged effects of possible risk factors, associated with ZCL cases. Non-parametric modeling techniques known as generalized additive model (GAM) and generalized additive mixed models (GAMM) were applied in this work. These techniques have the ability to approximate the relationship between the predictors (inputs) and the response variable (output), and express the relationship mathematically. The goodness-of-fit of the constructed model was determined by Generalized cross-validation (GCV) score and residual test. There were a total of 1019 notified ZCL cases from July 2009 to June 2015. The results showed seasonal distribution of reported ZCL cases from August to January. The model highlighted that rodent density, average temperature, cumulative rainfall and average relative humidity, with different time lags, all play role in sustaining and increasing the ZCL incidence. The GAMM model could be applied to predict the occurrence of ZCL in central Tunisia and could help for the establishment of an early warning system to control and prevent ZCL in central Tunisia.

Assessment of cardiovascular risk in Tunisia: applying the Framingham risk score to national survey data

Objective This paper aims to assess the socioeconomic determinants of a high 10 year cardiovascular risk in Tunisia. Setting We used a national population based cross sectional survey conducted in 2005 in Tunisia comprising 7780 subjects. We applied the non-laboratory version of the Framingham equation to estimate the 10 year cardiovascular risk. Participants 8007 participants, aged 35–74 years, were included in the sample but effective exclusion of individuals with cardiovascular diseases and cancer resulted in 7780 subjects (3326 men and 4454 women) included in the analysis. Results Mean age was 48.7 years. Women accounted for 50.5% of participants. According to the Framingham equation, 18.1% (17.25–18.9%) of the study population had a high risk (≥20% within 10 years). The gender difference was striking and statistically significant: 27.2% (25.7–28.7%) of men had a high risk, threefold higher than women (9.7%; 8.8–10.5%). A higher 10 year global cardiovascular risk was associated with social disadvantage in men and women; thus illiterate and divorced individuals, and adults without a professional activity had a significantly higher risk of developing a cardiovascular event in 10 years. Illiterate men were at higher risk than those with secondary and higher education (OR=7.01; 5.49 to 9.14). The risk in illiterate women was more elevated (OR=13.57; 7.58 to 24.31). Those living in an urban area had a higher risk (OR=1.45 (1.19 to 1.76) in men and OR=1.71 (1.35 to 2.18) in women). Conclusions The 10 year global cardiovascular risk in the Tunisian population is already substantially high, affecting almost a third of men and 1 in 10 women, and concentrated in those more socially disadvantaged.

Graphical interaction models to extract predictive risk factors of the cost of managing stroke in Tunisia

Managing stroke is a real public health problem. This study has mainly two purposes. First to evaluate the medical cost of managing this disease and to identify risk factors that influence its variation in Tunisia. We have then used a prospective study of 630 patients hospitalized for stroke in 2010 at the National Institute of Neurology of Tunis. We have assessed three different kinds of costs: in-hospital, post-hospitalization and annual costs. Afterward we have noticed huge variations in these different costs. We have then used an unsupervised clustering algorithm called the EM-algorithm to cluster the patients according to each kind of cost. We have obtained homogenous cost-clusters where each type of cost seems to be sampled from a normal distribution. Our second purpose was to identify the factors that make these costs high. We have then used a statistical technic called graphical interaction models. We mainly assume that the variables composing the data are jointly sampled from a conditional Gaussian distribution and where the interactions between the variables can be represented by an undirected graph where the vertices are the variables and where any separation statement implies a conditional independence between the concerned variables according to a specific protocol. Once these graphs are estimated we are able to determine direct and undirect factors that influence the increasing of the disease cost.