The genomic landscape of 8-oxodG reveals enrichment at specific inherently fragile promoters.

8-Oxo-7,8-dihydro-2'-deoxyguanosine (8-oxodG) is the most common marker of oxidative stress and its accumulation within the genome has been associated with major human health issues such as cancer, aging, cardiovascular and neurodegenerative diseases. The characterization of the different genomic sites where 8-oxodG accumulates and the mechanisms underlying its formation are still poorly understood. Using OxiDIP-seq, we recently derived the genome-wide distribution of 8-oxodG in human non-tumorigenic epithelial breast cells (MCF10A). Here, we identify a subset of human promoters that accumulate 8-oxodG under steady-state condition. 8-oxodG nucleotides co-localize with double strand breaks (DSBs) at bidirectional and CG skewed promoters and their density correlate with RNA Polymerase II co-occupancy and transcription. Furthermore, by performing OxiDIP-seq in quiescent (G0) cells, we found a strong reduction of oxidatively-generated damage in the majority of 8-oxodG-positive promoters in the absence of DNA replication. Overall, our results suggest that the accumulation of 8-oxodG at gene promoters occurs through DNA replication-dependent or -independent mechanisms, with a possible contribution to the formation of cancer-associated translocation events.

Genome-wide mapping of 8-oxo-7,8-dihydro-2'-deoxyguanosine reveals accumulation of oxidatively-generated damage at DNA replication origins within transcribed long genes of mammalian cells.

8-Oxo-7,8-dihydro-2'-deoxyguanosine (8-oxodG) is one of the major DNA modifications and a potent pre-mutagenic lesion prone to mispair with 2'-deoxyadenosine (dA). Several thousand residues of 8-oxodG are constitutively generated in the genome of mammalian cells, but their genomic distribution has not yet been fully characterized. Here, by using OxiDIP-Seq, a highly sensitive methodology that uses immuno-precipitation with efficient anti-8-oxodG antibodies combined with high-throughput sequencing, we report the genome-wide distribution of 8-oxodG in human non-tumorigenic epithelial breast cells (MCF10A), and mouse embryonic fibroblasts (MEFs). OxiDIP-Seq revealed sites of 8-oxodG accumulation overlapping with γH2AX ChIP-Seq signals within the gene body of transcribed long genes, particularly at the DNA replication origins contained therein. We propose that the presence of persistent single-stranded DNA, as a consequence of transcription-replication clashes at these sites, determines local vulnerability to DNA oxidation and/or its slow repair. This oxidatively-generated damage, likely in combination with other kinds of lesion, might contribute to the formation of DNA double strand breaks and activation of DNA damage response.

Nucleotide distance influences co-methylation between nearby CpG sites.

The tendency of individual CpG sites to be methylated is distinctive, non-random and well-regulated throughout the genome. We investigated the structural and spatial factors influencing CpGs methylation by performing an ultra-deep targeted methylation analysis on human, mouse and zebrafish genes. We found that methylation is not a random process and that closer neighboring CpG sites are more likely to share the same methylation status. Moreover, if the distance between CpGs increases, the degree of co-methylation decreases. We set up a simulation model to analyze the contribution of both the intrinsic susceptibility and the distance effect on the probability of a CpG to be methylated. Our finding suggests that the establishment of a specific methylation pattern follows a universal rule that must take into account of the synergistic and dynamic interplay of these two main factors: the intrinsic methylation susceptibility of specific CpG and the nucleotide distance between two CpG sites.

DNA Methylation variability among individuals is related to CpGs cluster density and evolutionary signatures.

BACKGROUND: In recent years, epigenetics has gained a central role in the understanding of the process of natural selection. It is now clear how environmental impacts on the methylome could promote methylation variability with direct effects on disease etiology as well as phenotypic and genotypic variations in evolutionary processes. To identify possible factors influencing inter-individual methylation variability, we studied methylation values standard deviation of 166 healthy individuals searching for possible associations with genomic features and evolutionary signatures. RESULTS: We analyzed methylation variability values in relation to CpG cluster density and we found a strong association between them (p-value < 2.2 × 10- 16). Furthermore, we found that genes related to CpGs with high methylation variability values were enriched for immunological pathways; instead, those associated with low ones were enriched for pathways related to basic cellular functions. Finally, we found an association between methylation variability values and signals of both ancient (p-value < 2.2 × 10- 16) and recent selective pressure (p-value < 1 × 10- 4). CONCLUSION: Our results indicate the presence of an intricate interplay between genetics, epigenetic code and evolutionary constraints in humans.

ampliMethProfiler: a pipeline for the analysis of CpG methylation profiles of targeted deep bisulfite sequenced amplicons.

BACKGROUND: CpG sites in an individual molecule may exist in a binary state (methylated or unmethylated) and each individual DNA molecule, containing a certain number of CpGs, is a combination of these states defining an epihaplotype. Classic quantification based approaches to study DNA methylation are intrinsically unable to fully represent the complexity of the underlying methylation substrate. Epihaplotype based approaches, on the other hand, allow methylation profiles of cell populations to be studied at the single molecule level. For such investigations, next-generation sequencing techniques can be used, both for quantitative and for epihaplotype analysis. Currently available tools for methylation analysis lack output formats that explicitly report CpG methylation profiles at the single molecule level and that have suited statistical tools for their interpretation. RESULTS: Here we present ampliMethProfiler, a python-based pipeline for the extraction and statistical epihaplotype analysis of amplicons from targeted deep bisulfite sequencing of multiple DNA regions. CONCLUSIONS: ampliMethProfiler tool provides an easy and user friendly way to extract and analyze the epihaplotype composition of reads from targeted bisulfite sequencing experiments. ampliMethProfiler is written in python language and requires a local installation of BLAST and (optionally) QIIME tools. It can be run on Linux and OS X platforms. The software is open source and freely available at http://amplimethprofiler.sourceforge.net .

The PPARγ2 Pro12Ala variant is protective against progression of nephropathy in people with type 2 diabetes.

OBJECTIVE: Cross-sectional studies suggest the association between diabetic nephropathy and the PPARγ2 Pro12Ala polymorphism of the peroxisome proliferator-activated receptor γ2 (PPARγ2). Prospective data are limited to microalbuminuria and no information on renal function is available to date. The present study evaluates the association between the Pro12Ala polymorphism of PPARγ2 and the progression of albuminuria and decay in glomerular filtration rate (GFR) in type 2 diabetes. PATIENTS AND MEASUREMENTS: We studied 256 patients with an average 5-year follow-up. Among others, urinary albumin excretion rate (UAER) was measured on spot sample, GFR was estimated with the CKD-EPI Equation. RESULTS: Baseline UAER and GFR were similar for carriers or non-carriers of the polymorphism. At follow-up no significant changes from baseline were observed for UAER or eGFR in carriers of the Pro12Ala polymorphism whereas a significant increase in UAER [17 (11.3-37.9) versus 24.5 (13.8-49.9) µg/mg, p < 0.006)] and a significant reduction in the eGFR (82.8 ± 14.5 versus 80.3 ± 17.3 ml/min/1.73, m(2) p = 0.02), were observed in non carriers of the Pro12Ala polymorphism. Progression of nephropathy - defined according to a combined end point of UAER and eGFR- i.e. doubling of baseline UAER to at least 100 µg/mg, or new onset microalbuminuria, or progression from micro to macroalbuminuria, or 25% reduction of eGFR, or annualized eGFR decline >3 ml/min/year - was significantly less frequent in Ala carriers than non carriers (11.4% vs 35.8%; p < 0.01); HR adjusted for baseline age, AER, eGFR, HbA1c, diabetes duration and blood pressure was 0.32 (0.12-0.80). CONCLUSIONS: This study found that among patients with type 2 diabetes, the PPARγ2 Pro12Ala polymorphism is protective against progression of nephropathy and decay of renal function independent of major confounders.

CpG islands under selective pressure are enriched with H3K4me3, H3K27ac and H3K36me3 histone modifications.

BACKGROUND: Histone modification is an epigenetic mechanism that influences gene regulation in eukaryotes. In particular, histone modifications in CpG islands (CGIs) are associated with different chromatin states and with transcription activity. Changes in gene expression play a crucial role in adaptation and evolution. RESULTS: In this paper, we have studied, using a computational biology approach, the relationship between histone modifications in CGIs and selective pressure in Homo sapiens. We considered three histone modifications: histone H3 lysine 4 trimethylation (H3K4me3), histone H3 lysine 27 acetylation (H3K27ac) and histone H3 lysine 36 trimethylation (H3K36me3), and we used the publicly available genomic-scale histone modification data of thirteen human cell lines. To define regions under selective pressure, we used three distinct signatures that mark selective events from different evolutionary periods. We found that CGIs under selective pressure showed significant enrichments for histone modifications. CONCLUSION: Our result suggests that, CGIs that have undergone selective events are characterized by epigenetic signatures, in particular, histone modifications that are distinct from CGIs with no evidence of selection.

A distinct group of CpG islands shows differential DNA methylation between replicas of the same cell line in vitro.

BACKGROUND: CpG dinucleotide-rich genomic DNA regions, known as CpG islands (CGIs), can be methylated at their cytosine residues as an epigenetic mark that is stably inherited during cell mitosis. Differentially methylated regions (DMRs) are genomic regions showing different degrees of DNA methylation in multiple samples. In this study, we focused our attention on CGIs showing different DNA methylation between two culture replicas of the same cell line. RESULTS: We used methylation data of 35 cell lines from the Encyclopedia of DNA Elements (ENCODE) consortium to identify CpG islands that were differentially methylated between replicas of the same cell line and denoted them Inter Replicas Differentially Methylated CpG islands (IRDM-CGIs). We identified a group of IRDM-CGIs that was consistently shared by different cell lines, and denoted it common IRDM-CGIs. X chromosome CGIs were overrepresented among common IRDM-CGIs. Autosomal IRDM-CGIs were preferentially located in gene bodies and intergenic regions had a lower G + C content, a smaller mean length, and a reduced CpG percentage. Functional analysis of the genes associated with autosomal IRDM-CGIs showed that many of them are involved in DNA binding and development. CONCLUSIONS: Our results show that several specific functional and structural features characterize common IRDM-CGIs. They may represent a specific subset of CGIs that are more prone to being differentially methylated for their intrinsic characteristics.

Simulating gene-gene and gene-environment interactions in complex diseases: Gene-Environment iNteraction Simulator 2.

BACKGROUND: The analysis of complex diseases is an important problem in human genetics. Because multifactoriality is expected to play a pivotal role, many studies are currently focused on collecting information on the genetic and environmental factors that potentially influence these diseases. However, there is still a lack of efficient and thoroughly tested statistical models that can be used to identify implicated features and their interactions. Simulations using large biologically realistic data sets with known gene-gene and gene-environment interactions that influence the risk of a complex disease are a convenient and useful way to assess the performance of statistical methods. RESULTS: The Gene-Environment iNteraction Simulator 2 (GENS2) simulates interactions among two genetic and one environmental factor and also allows for epistatic interactions. GENS2 is based on data with realistic patterns of linkage disequilibrium, and imposes no limitations either on the number of individuals to be simulated or on number of non-predisposing genetic/environmental factors to be considered. The GENS2 tool is able to simulate gene-environment and gene-gene interactions. To make the Simulator more intuitive, the input parameters are expressed as standard epidemiological quantities. GENS2 is written in Python language and takes advantage of operators and modules provided by the simuPOP simulation environment. It can be used through a graphical or a command-line interface and is freely available from http://sourceforge.net/projects/gensim. The software is released under the GNU General Public License version 3.0. CONCLUSIONS: Data produced by GENS2 can be used as a benchmark for evaluating statistical tools designed for the identification of gene-gene and gene-environment interactions.

A novel workflow for the qualitative analysis of DNA methylation data.

DNA methylation is an epigenetic modification that plays a pivotal role in major biological mechanisms, such as gene regulation, genomic imprinting, and genome stability. Different combinations of methylated cytosines for a given DNA locus generate different epialleles and alterations of these latter have been associated with several pathological conditions. Existing computational methods and statistical tests relevant to DNA methylation analysis are mostly based on the comparison of average CpG sites methylation levels and they often neglect non-CG methylation. Here, we present EpiStatProfiler, an R package that allows the analysis of CpG and non-CpG based epialleles starting from bisulfite sequencing data through a collection of dedicated extraction functions and statistical tests. EpiStatProfiler is provided with a set of useful auxiliary features, such as customizable genomic ranges, strand-specific epialleles analysis, locus annotation and gene set enrichment analysis. We showcase the package functionalities on two public datasets by identifying putative relevant loci in mice harboring the Huntington's disease-causing Htt gene mutation and in Ctcf +/- mice compared to their wild-type counterparts. To our knowledge, EpiStatProfiler is the first package providing functionalities dedicated to the analysis of epialleles composition derived from any kind of bisulfite sequencing experiment.

MC profiling: a novel approach to analyze DNA methylation heterogeneity in genome-wide bisulfite sequencing data.

DNA methylation is an epigenetic mark implicated in crucial biological processes. Most of the knowledge about DNA methylation is based on bulk experiments, in which DNA methylation of genomic regions is reported as average methylation. However, average methylation does not inform on how methylated cytosines are distributed in each single DNA molecule. Here, we propose Methylation Class (MC) profiling as a genome-wide approach to the study of DNA methylation heterogeneity from bulk bisulfite sequencing experiments. The proposed approach is built on the concept of MCs, groups of DNA molecules sharing the same number of methylated cytosines. The relative abundances of MCs from sequencing reads incorporates the information on the average methylation, and directly informs on the methylation level of each molecule. By applying our approach to publicly available bisulfite-sequencing datasets, we individuated cell-to-cell differences as the prevalent contributor to methylation heterogeneity. Moreover, we individuated signatures of loci undergoing imprinting and X-inactivation, and highlighted differences between the two processes. When applying MC profiling to compare different conditions, we identified methylation changes occurring in regions with almost constant average methylation. Altogether, our results indicate that MC profiling can provide useful insights on the epigenetic status and its evolution at multiple genomic regions.

Relationship between COVID-19 Mortality, Hospital Beds, and Primary Care by Italian Regions: A Lesson for the Future.

One of the characteristics of the SARS-CoV-2 infection in Italy is the significant regional difference in terms of lethality and mortality. These geographical variances were clear in the first wave and confirmed in the second one as well. The study aimed to analyze the correlation between regional differences in COVID-19 mortality and different regional care models, by retrospectively analyzing the association between the Italian COVID-19 deaths and the number of hospital beds, long-term care facilities, general practitioners (GPs), and the health expenditure per capita. The period considered was from 1 March 2020 to 1 March 2021. The number of hospital beds (p < 0.0001) and the number of GPs (p = 0.0094) significantly predicted the COVID-19 death rate. The Italian regions with a higher number of hospital beds and a lower number of GPs showed a higher number of deaths. Multivariate analyses confirmed the results. The Italian regions with a higher amount of centralized healthcare, as represented by the number of hospital beds, experienced a higher number of deaths, while the regions with greater community support, as exemplified by the number of the GPs, faced higher survival. These results suggest the need for a change in the current healthcare system organization.

Epigenetic remodelling of Fxyd1 promoters in developing heart and brain tissues.

FXYD1 is a key protein controlling ion channel transport. FXYD1 exerts its function by regulating Na+/K+-ATPase activity, mainly in brain and cardiac tissues. Alterations of the expression level of the FXYD1 protein cause diastolic dysfunction and arrhythmias in heart and decreased neuronal dendritic tree and spine formation in brain. Moreover, FXYD1, a target of MeCP2, plays a crucial role in the pathogenesis of the Rett syndrome, a neurodevelopmental disorder. Thus, the amount of FXYD1 must be strictly controlled in a tissue specific manner and, likely, during development. Epigenetic modifications, particularly DNA methylation, represent the major candidate mechanism that may regulate Fxyd1 expression. In the present study, we performed a comprehensive DNA methylation analysis and mRNA expression level measurement of the two Fxyd1 transcripts, Fxyd1a and Fxyd1b, in brain and heart tissues during mouse development. We found that DNA methylation at Fxyd1a increased during brain development and decreased during heart development along with coherent changes in mRNA expression levels. We also applied ultra-deep methylation analysis to detect cell to cell methylation differences and to identify possible distinct methylation profile (epialleles) distribution between heart and brain and in different developmental stages. Our data indicate that the expression of Fxyd1 transcript isoforms inversely correlates with DNA methylation in developing brain and cardiac tissues suggesting the existence of a temporal-specific epigenetic program. Moreover, we identified a clear remodeling of epiallele profiles which were distinctive for single developmental stage both in brain and heart tissues.

Functional genomic analysis of frataxin deficiency reveals tissue-specific alterations and identifies the PPARgamma pathway as a therapeutic target in Friedreich's ataxia.

Friedreich's ataxia (FRDA), the most common inherited ataxia, is characterized by focal neurodegeneration, diabetes mellitus and life-threatening cardiomyopathy. Frataxin, which is significantly reduced in patients with this recessive disorder, is a mitochondrial iron-binding protein, but how its deficiency leads to neurodegeneration and metabolic derangements is not known. We performed microarray analysis of heart and skeletal muscle in a mouse model of frataxin deficiency, and found molecular evidence of increased lipogenesis in skeletal muscle, and alteration of fiber-type composition in heart, consistent with insulin resistance and cardiomyopathy, respectively. Since the peroxisome proliferator-activated receptor gamma (PPARgamma) pathway is known to regulate both processes, we hypothesized that dysregulation of this pathway could play a key role in frataxin deficiency. We confirmed this by showing a coordinate dysregulation of the PPARgamma coactivator Pgc1a and transcription factor Srebp1 in cellular and animal models of frataxin deficiency, and in cells from FRDA patients, who have marked insulin resistance. Finally, we show that genetic modulation of the PPARgamma pathway affects frataxin levels in vitro, supporting PPARgamma as a novel therapeutic target in FRDA.

Epoetin alfa increases frataxin production in Friedreich's ataxia without affecting hematocrit.

Objective of the study was to test the efficacy, safety, and tolerability of two single doses of Epoetin alfa in patients with Friedreich's ataxia. Ten patients were treated subcutaneously with 600 IU/kg for the first dose, and 3 months later with 1200 IU/kg. Epoetin alfa had no acute effect on frataxin, whereas a delayed and sustained increase in frataxin was evident at 3 months after the first dose (+35%; P < 0.05), and up to 6 months after the second dose (+54%; P < 0.001). The treatment was well tolerated and did not affect hematocrit, cardiac function, and neurological scale. Single high dose of Epoetin alfa can produce a considerably larger and sustained effect when compared with low doses and repeated administration schemes previously adopted. In addition, no hemoglobin increase was observed, and none of our patients required phlebotomy, indicating lack of erythropoietic effect of single high dose of erythropoietin.

Artificial Intelligence for Epigenetics: Towards Personalized Medicine.

Epigenetics is a field of biological sciences focused on the study of reversible, heritable changes in gene function, not due to modifications of the genomic sequence. These changes are the result of a complex cross-talk between several molecular mechanisms that is in turn orchestrated by genetic and environmental factors. The epigenetic profile captures the unique regulatory landscape and the exposure to environmental stimuli of an individual. It thus constitutes a valuable reservoir of information for personalized medicine, which is aimed at customizing health-care interventions based on the unique characteristics of each individual. Nowadays, the complex milieu of epigenomic marks can be studied at the genome-wide level thanks to massive, high-throughput technologies. This new experimental approach is opening up new and interesting knowledge perspectives. However, the analysis of these complex omic data requires to face important analytic issues. Artificial Intelligence, and in particular Machine Learning, are emerging as powerful resources to decipher epigenomic data. In this review, we will first describe the most used ML approaches in epigenomics. We then will recapitulate some of the recent applications of ML to epigenomic analysis. Finally, we will provide some examples of how the ML approach to epigenetic data can be useful for personalized medicine.

DNA Methylation Profiles of Tph1A and BDNF in Gut and Brain of L. Rhamnosus-Treated Zebrafish.

The bidirectional microbiota-gut-brain axis has raised increasing interest over the past years in the context of health and disease, but there is a lack of information on molecular mechanisms underlying this connection. We hypothesized that change in microbiota composition may affect brain epigenetics leading to long-lasting effects on specific brain gene regulation. To test this hypothesis, we used Zebrafish (Danio Rerio) as a model system. As previously shown, treatment with high doses of probiotics can modulate behavior in Zebrafish, causing significant changes in the expression of some brain-relevant genes, such as BDNF and Tph1A. Using an ultra-deep targeted analysis, we investigated the methylation state of the BDNF and Tph1A promoter region in the brain and gut of probiotic-treated and untreated Zebrafishes. Thanks to the high resolution power of our analysis, we evaluated cell-to-cell methylation differences. At this resolution level, we found slight DNA methylation changes in probiotic-treated samples, likely related to a subgroup of brain and gut cells, and that specific DNA methylation signatures significantly correlated with specific behavioral scores.

A novel approach to simulate gene-environment interactions in complex diseases.

BACKGROUND: Complex diseases are multifactorial traits caused by both genetic and environmental factors. They represent the major part of human diseases and include those with largest prevalence and mortality (cancer, heart disease, obesity, etc.). Despite a large amount of information that has been collected about both genetic and environmental risk factors, there are few examples of studies on their interactions in epidemiological literature. One reason can be the incomplete knowledge of the power of statistical methods designed to search for risk factors and their interactions in these data sets. An improvement in this direction would lead to a better understanding and description of gene-environment interactions. To this aim, a possible strategy is to challenge the different statistical methods against data sets where the underlying phenomenon is completely known and fully controllable, for example simulated ones. RESULTS: We present a mathematical approach that models gene-environment interactions. By this method it is possible to generate simulated populations having gene-environment interactions of any form, involving any number of genetic and environmental factors and also allowing non-linear interactions as epistasis. In particular, we implemented a simple version of this model in a Gene-Environment iNteraction Simulator (GENS), a tool designed to simulate case-control data sets where a one gene-one environment interaction influences the disease risk. The main aim has been to allow the input of population characteristics by using standard epidemiological measures and to implement constraints to make the simulator behaviour biologically meaningful. CONCLUSIONS: By the multi-logistic model implemented in GENS it is possible to simulate case-control samples of complex disease where gene-environment interactions influence the disease risk. The user has full control of the main characteristics of the simulated population and a Monte Carlo process allows random variability. A knowledge-based approach reduces the complexity of the mathematical model by using reasonable biological constraints and makes the simulation more understandable in biological terms. Simulated data sets can be used for the assessment of novel statistical methods or for the evaluation of the statistical power when designing a study.

Schizophrenia and vitamin D related genes could have been subject to latitude-driven adaptation.

BACKGROUND: Many natural phenomena are directly or indirectly related to latitude. Living at different latitudes, indeed, has its consequences with being exposed to different climates, diets, light/dark cycles, etc. In humans, one of the best known examples of genetic traits following a latitudinal gradient is skin pigmentation. Nevertheless, also several diseases show latitudinal clinals such as hypertension, cancer, dismetabolic conditions, schizophrenia, Parkinson's disease and many more. RESULTS: We investigated, for the first time on a wide genomic scale, the latitude-driven adaptation phenomena. In particular, we selected a set of genes showing signs of latitude-dependent population differentiation. The biological characterization of these genes showed enrichment for neural-related processes. In light of this, we investigated whether genes associated to neuropsychiatric diseases were enriched by Latitude-Related Genes (LRGs). We found a strong enrichment of LRGs in the set of genes associated to schizophrenia. In an attempt to try to explain this possible link between latitude and schizophrenia, we investigated their associations with vitamin D. We found in a set of vitamin D related genes a significant enrichment of both LRGs and of genes involved in schizophrenia. CONCLUSIONS: Our results suggest a latitude-driven adaptation for both schizophrenia and vitamin D related genes. In addition we confirm, at a molecular level, the link between schizophrenia and vitamin D. Finally, we discuss a model in which schizophrenia is, at least partly, a maladaptive by-product of latitude dependent adaptive changes in vitamin D metabolism.

Uncoupling protein 2 G(-866)A polymorphism: a new gene polymorphism associated with C-reactive protein in type 2 diabetic patients.

BACKGROUND: This study evaluated the relationship between the G(-866)A polymorphism of the uncoupling protein 2 (UCP2) gene and high-sensitivity C reactive protein (hs-CRP) plasma levels in diabetic patients. METHODS: We studied 383 unrelated people with type 2 diabetes aged 40-70 years. Anthropometry, fasting lipids, glucose, HbA1c, and hs-CRP were measured. Participants were genotyped for the G (-866)A polymorphism of the uncoupling protein 2 gene. RESULTS: Hs-CRP (mg/L) increased progressively across the three genotype groups AA, AG, or GG, being respectively 3.0 ± 3.2, 3.6 ± 5.0, and 4.8 ± 5.3 (p for trend = 0.03). Since hs-CRP values were not significantly different between AA and AG genotype, these two groups were pooled for further analyses. Compared to participants with the AA/AG genotypes, homozygotes for the G allele (GG genotype) had significantly higher hs-CRP levels (4.8 ± 5.3 vs 3.5 ± 4.7 mg/L, p = 0.01) and a larger proportion (53.9% vs 46.1%, p = 0.013) of elevated hs-CRP (> 2 mg/L). This was not explained by major confounders such as age, gender, BMI, waist circumference, HbA1c, smoking, or medications use which were comparable in the two genotype groups. CONCLUSIONS: The study shows for the first time, in type 2 diabetic patients, a significant association of hs-CRP levels with the G(-866)A polymorphism of UCP2 beyond the effect of major confounders.