E. coli only and 4243 non-drug targets from E. coli to identify differences in their properties and to predict new potential drug targets.We analysed 48 non-redundant antibiotic target proteins from all bacteria, 22 antibiotic target proteins from E. coli proteins, allowing the assignment of any sequence to the drug target or non-target classes, with an accuracy in the training set of 94%. We identified 319 proteins (7%) in the non-target set that have target-like properties, many of which have unknown function. 63 of these proteins have significant and undesirable similarity to a human protein, leaving 256 target like proteins that are not present in humans.When compared to non-targets, bacterial antibiotic targets tend to be long, have high β-sheet and low α-helix contents, are polar, are found in the cytoplasm rather than in membranes, and are usually enzymes, with ligases particularly favoured. Sequence features were used to build a support vector machine model for We suggest that antibiotic discovery programs would be more likely to succeed if new targets are chosen from this set of target like proteins or their homologues. In particular, 64 are essential genes where the cell is not able to recover from a random insertion disruption. Staphylococcus aureus, Streptococcus pneumoniae and group A Streptococcus, and gram negative bacteria, such as E. coli and Helicobacter pylori. Antibacterial drugs are the major weapons to kill bacteria or suppress their activity. Due to the inevitable evolution of antibiotic resistance, the development of novel antibiotics is essential.Infectious and parasitic diseases caused by pathogenic microorganisms, including bacteria, viruses and fungi, are major threats to human health. In particular, diseases commonly result from exposure to gram positive bacteria, such as Antibiotics work either by stopping bacterial growth or by killing the bacteria, without harming the human host. The following are the most common modes-of-action of antibiotics:1) Inhibit synthesis of peptidoglycan. These antibiotics work by interfering with the synthesis of bacterial cell walls by either: blocking the transport of peptidoglycan monomers synthesized in the cytosol across the cytoplasmic membrane, inhibiting a transpeptidase and hence the formation of the peptide cross-links, or blocking both the transglycosidase and transpeptidase enzymes. The transglycosidases are essential for the formation of glycosidic bonds between sugars and transpeptidases are essential for the formation of peptide cross-links [ Inhibit 2) Alter the microbial cytoplasmic membrane. The polymixins are cationic peptides consisting of a cyclic peptide with a fatty acid chain. The interaction between the cationic peptide and the membrane causes disruption of the bacterial cell membrane and increases the permeability of cell components [ Alter th3) Alter translation. Many antibiotics work by binding to bacterial ribosomes. Examples of antibiotics that work by binding to the 30S ribosomal subunit are aminoglycosides and tetracyclines, which prevent the binding of tRNA  Alter tr. Other m(4) Inhibit nucleic acid replication by blocking topoisomerases that are essential for supercoiling, bacterial DNA replication and separation of circular bacterial DNA. The fluoroquinolone antibiotic class contains potent inhibitors for topoisomerases or DNA gyrase .(5) Inhibit transcription. Some antibiotics, such as rifampin or rifampicin, work by binding to RNA polymerase and inhibiting the transcription of DNA to mRNA .et al. addressed this problem by identifying essential bacterial genes  that have no human homologues[After the first widespread use of antibacterial drugs in the 1940s, bacterial pathogens started to develop resistance to existing drugs, particularly after excessive antibiotic use. The three basic mechanisms of bacterial resistance to antibiotics are: (1) Production of an enzyme to inactivate the antibiotic, such as a β-lactamase to hydrolyse penicillin. (2) Mutation in the target site receptor of the enzyme or the ribosomal subunit that leads to ineffective drug binding. (3) Alteration in transport proteins to prevent antibiotic entry or promote active efflux from the cell . There iHere, we determine key properties of antibiotic target proteins and use machine learning to identify new potential targets.E. coli respectively compared to 316 for non-targets. Bacterial targets show significant preferences for Pro and Val and disfavour Trp, Met, Leu and Gln. Secondary structure analysis shows that bacterial targets have less α-helix and more β-Sheet. The percentage of target helices is 32% compared to 42% for non-targets with significant p-values . The prediction of transmembrane helices shows that targets tend to have fewer helices  compared to 1.4 for non-targets. Antibiotic and E. coli targets are less hydrophobic  compared to non-targets (-21) with significant p-values . No statistically significant differences were observed for pI, SignalP, NetNGlyc and NetOGlyc and LCR between the bacterial or E. coli targets and non-targets. This indicates that these features do not contribute to the druggability of a bacterial protein. This is expected for glycosylation, as the glycosylation site prediction programs are optimised for use in metazoa; indeed the predicted frequencies for this property are low. Nevertheless, it is possible to use any sequence feature (including these) for machine learning.All the proteins in the targets and non-targets data sets were analysed for their primary sequence properties and post-translational modifications. As all features showed a non-normal distribution using the Kolmogorov-Smirnov test, p-values were calculated using the Mann-Whitney test. Table E. coli (14.3%) and in all targets (14%) compared to non-targets (12.5%) . The mean frequency of polar amino acids in E. coli (45.1%) and all targets (45.3%) was higher compared to non-targets (42.8%) with a significant p-value of 0.0251 for all targets; similarly, there was a higher proportion of non-polar amino acids in non-targets compared to all targets  with a p-value of 0.0281 for all targets. The frequencies of negative amino acids were greater for E. coli (11.9%) and all targets (11.9%) compared to non-targets (10.8%) .Targets were found to contain more polar, charged, basic and acidic amino acids. The mean frequency of positively charged amino acids was greater in E. coli and 71% (34/48) for bacterial targets. Figure E. coli (17%), compared to 5% for non-targets. Ligases are common antibacterial drug targets as they are involved in the formation of the cell wall. Lyases were not found in either the bacterial or E. coli target sets.The percentage of enzymes was found to be 33% 1420/4243) in non-targets compared to 81% (18/22) for 420/4243 E. coli targets and 47% (23/48) of the bacterial targets have subcellular location annotations in SwissProt. These percentages might be misleading due to bias of annotated sequences to a specific family or incomplete annotation. Figure membrane single-pass type II, peripheral membrane protein, peripheral membrane protein, multi-pass membrane, cell membrane and cell inner membrane . The next highest preferences for drug targets are in the cytoplasm and periplasm.43% (1829/4243) of the non-targets, 40% (9/22) of the E. coli targets and non-targets. Non-targets are localised in the cytoplasmic membrane more than the cytoplasm, and vice versa for E. coli targets. Unknown assignments, where the program is unable to make a prediction, are frequent for both E. coli targets (50%) and non-targets (65%).A prediction method (PSORTb)  was usedE. coli targets and non-targets.Additional file metabolic process and cellular process are found with similar percentages in bacterial targets, E. coli targets and non-targets. Response to antibiotic is unsurprisingly found in bacterial targets and E. coli targets, but not non-targets. Terms including localization and establishment of localization are common for non-targets, but they are not associated with bacterial or E. coli targets. The distribution of biological process at level 2  and in all bacterial targets (6%), but no proteins are associated with this process in the non-targets. Other processes favoured for bacterial and E. coli targets are biosynthetic process and cell wall organization. Transport proteins are more prevalent for non-targets.The distribution of biological process at level 1 . At level 2, more bacterial and E. coli targets are localised in the intracellular part compared to non-targets  for an incorrect prediction and the radial basis function parameter (γ) which controls how smooth the boundary is in hyperspace between the target and non-target areas. The accuracy is defined as the overall probability that the prediction is correct. A PERL program was coded to search for the optimal C and γ values by performing a coarse grid and a fine grid search. The optimal parameters for C and γ were found to be 0.9 and 0.1, respectively. This model had an accuracy of 94.2% using 32 features including 20 amino acids, length, hydrophobicity, signalP, netNglyc, netOlgyc-S, netOglyc-T, LCR, secondary structure (α and β), TMHMM and pI. This accuracy is based on 5-fold cross validation using 22 E. coli targets as the positive training dataset and 200 proteins for the E. coli non-targets. The area under curve in a ROC plot was 0.996, confirming the high accuracy of the model.The features described above were used with the support vector machine algorithm to develop a model that is able to assign any sequence to the E. coli non-target sequences. 319 of them were assigned as targets, suggesting that 7% of bacterial proteins have target-like properties  are annotated as uncharacterised proteins and 8% (28/319) as 50S or 30S ribosomal proteins. 8% (26/319) of the new predicted targets are transferases and 5% (16/319) are hydrolases, in broad agreement with annotation data  algorithm was used to classify 4243 a Figure . Other EE. coli target proteins were further analysed to check for similarity to human proteins. If any similarity is found, the predicted antibiotic targets may well be problematic, as their inhibition could lead to toxic effects in the patient. 63 proteins were found to have maximal sequence similarities to a human protein of 25% to 63% using BLAST  of the new predicted targets showed a high similarity to an essential gene where 11 proteins had a sequence identity of 50% to 70% and 53 proteins had a sequence identity > 70%. Additional File E. coli colonies in humans, so targeting E. coli may have unwanted effects.The list of 319 proteins was subjected to a BLAST search against the es. 20% 6/319 of tet al. recently performed a detailed and thorough analysis of orphan E. coli proteins, giving many new functional assignments [Hu ignments . In partE. coli targets and those from all bacteria. Targets are more likely to be enzymes than non-targets. Major antibiotic mechanisms include inhibiting enzymes responsible for translation, transcription, replication and bacterial cell wall biosynthesis. Inhibition of such mechanisms usually results in cell growth inhibition or bacterial death. The molecular function analyses of Gene Ontology and enzyme class data show a preference for ligases as targets, such as those involved in peptidoglycan biosynthesis. The biological process analysis shows that response to antibiotic, regulation of cell shape and cell wall organization terms are favoured for targets. The cellular components data showed a high preference for intracellular part targets, with the cytoplasm subdivision favoured in particular, perhaps because enzymes are abundant in the cytoplasm and despite the difficulty of a drug crossing the membrane. The membrane distribution for targets was lower that non-targets, supporting the predicted subcellular locations. While the annotated results had a higher preference for targets to be localised in the membrane, the predicted results had a higher preference for the cytoplasm. There are fewer predicted transmembrane helices in targets, supporting the subcellular location data.Even though we have only a fairly small number of distinct targets in our data set, we still find many properties that are significantly different between antibiotic targets and non-targets.  There was little difference between Bacterial targets tend to be larger and more polar than non-targets. Larger proteins have more potential surface of interaction between the drug and the target and may participate in more protein-protein interactions, thus having a larger effect when a drug binds. The presence of charged amino acids might facilitate the interaction between the drug and the target, as bonds to charged amino acids from target groups are common -14. PosiE. coli showed that 35% of metabolic enzymes, including amino acid, purine and pyrimidine biosynthesis, are non-essential genes for cell growth [Studies on l growth . This meAn ideal drug should have no similar protein in the host cell, as the drug might interact with it and lead to adverse effects in the patient. There exist some exceptions for this rule: Trimethoprim is such an example of an antibiotic used to treat urinary tract infections. It works on inhibiting dihydrofolate reductase, despite the presence of a close human homologue . On the Our previous work analysed desirable properties in human drug target proteins , so we cIn summary, the following seem to be desirable features for an antibiotic drug target:• Essential for the survival of the bacterial cell• No close human homologue• Must be present in a number of pathogens if broad-spectrum action is required. Narrow spectrum antibiotics target specific pathogens• The most targeted biological processes are peptidoglycan biosynthesis, cell wall synthesis, transcription and translation• Capable of binding to a small molecule, implying the presence of a binding site• Favoured to be localized in the cytoplasm and less frequent in membranes• Favoured to be ribosomal proteins• More likely to be enzymes, transferases, hydrolases or ligase, but not lyases. Although enzymes are highly targeted, metabolic enzymes are disfavoured for antibacterial drug targets.While many of the above properties have been reported in qualitative terms, they have not been previously quantified, to our knowledge. In addition, some of the preferences we find appear to be new, such as EC class, length, amino acid frequencies, post-translational modifications, secondary structure contents, pI, SignalP and transmembrane helices.E. coli protein to the target or non-target classes. The use of cross-validation and the generalisation parameter in the SVM ensures to a certain extent that the model can generalise to data not used in the training process. After classifying the entire E. coli proteome to identify novel target-like proteins and pruning it to remove those that are similar to a human protein, we find 256 proteins that may be potential antibiotic targets, especially those 64 proteins highly similar to essential genes. While 40 previously identified orphan genes can now be assigned to cell envelope biogenesis or translation, many are currently of unknown function and thus deserve further investigation. The rules listed above and our list of potential new antibiotic targets may help in the identification of new antibiotics drug target.The machine learning work can accurately assign an E. coli has allowed the identification of a number of properties that are desirable in drug targets. Even though the number of unique known targets is not large, statistically significant differences in property frequencies were found. Using sequence features for machine learning allows accurate identification of targets, as shown by a cross-validation accuracy of 94%. Applying the optimised support vector machine to the E. coli proteome identifies hundreds of proteins that have similar properties to known antibiotic drug targets. These proteins may therefore be considered as potential new targets for novel antibiotics.Comparison of antibiotic protein targets with non-target proteins from E. coli (strain K-12). This E. coli targets dataset consists of 22 proteins. The non-targets dataset was downloaded from the High-quality Automated and Manual Annotation of microbial Proteomes (HAMAP) at Expasy http://www.expasy.ch/sprot/hamap/. It is not obvious what a bacterial non-target protein data set should be, as it is always arbitrary which species are included. We therefore picked just one species, namely E. coli strain K-12, as this is the most common species for targets. As of 2006, the E. coli proteome contains highly accurate and complete sequences for the K-12 strain with 4338 entries. This set was culled with a 20% sequence identity cut-off to leave 4265 entries for the non-redundant dataset. Known antibiotic drug targets were removed from the set to give a non-redundant non-targets dataset of 4243 entries. Full lists of bacterial targets and non-targets data sets are found in Additional File The bacterial target dataset was downloaded from DrugBank  in FebruE. coli strain. Hence, we may be picking up differences in protein features arising from differences between species, rather than between targets and non-targets. Comparing targets with non-targets entirely within the E. coli K-12 strain avoids this problem, though the number of targets is considerably fewer. We therefore analysed three data sets, namely all antibiotic targets, E. coli K-12 strain targets and E. coli K-12 strain non-targets. The non-drug target dataset may contain proteins that could be drug targets, making it less well defined. However, as it is doubtful whether more than a small fraction of the bacterial proteome will ever be a target, this error should not be large.When comparing all targets to non-targets, differences between the sets may arise since the targets are taken from a wide range of bacteria and they are compared only to one Properties were tested for normality using the Kolmogorov-Smirnov test. As all the distributions were not normal, the Mann-Whitney U test was used to test for statistically significant differences between the sets using SPSS .SWISS-PROT annotations were used for some sequence properties, such as sub-cellular locations. We were concerned, however, that variations in the frequency of annotation may bias the results. For example, one would expect that drug target proteins are better studied than non-targets, leading to a possible increase in annotation of targets compared to non-targets. We therefore also often used predicted properties, as well as annotations, where possible.http://emboss.cbr.nrc.ca/cgi-bin/emboss/pepstats was used to output protein sequence information statistics including isoelectric points , and numbers of positively charged and negatively charged amino acids. A PERL script was written to extract protein sequence features and calculate the mean frequencies.The frequency of each amino acid in each protein is divided by protein length to give percentage frequencies. Amino acids were grouped as follows: non-polar ; polar ; positive  and negative . Hydrophobicity was calculated as the sum of hydrophobicity values using the Kyte & Doolittle index , dividedPrimary EC numbers were taken from the description line "DE", as this line contains the EC numbers corresponding to an entry, where primary EC numbers 1-6 are oxidoreductase, transferase, hydrolase, lyase, isomerase and ligase, respectively.http://www.geneontology.org/GO.downloads.shtml, dated September 2006. GO ID annotations are in SWISS-PROT in the database reference line "DR". A PERL program was coded to extract the Gene Ontology IDs for each datasets and to parse a GO file and write child/parent relationship as a full path for every GO term and to output the highest level and next highest level for every path as levels 1 and 2. If a GO term id was present more than once at level 1 or 2, it was only counted once at that level.Gene Ontology (GO) provides a controlled vocabulary to describe gene function , using thttp://www.cbs.dtu.dk/services/NetOGlyc/ was used for identification of O-glycosylation sites using neural networks [http://www.cbs.dtu.dk/services/NetNGlyc/ was used for identification of N-glycosylation sites [http://wolfpsort.seq.cbrc.jp/[http://www.cbs.dtu.dk/services/SignalP/ was used to perform signal peptide prediction [http://www.cbs.dtu.dk/services/TMHMM/[ftp://ftp.ncbi.nih.gov/pub/seg/seg/ to detect low complexity regions. It is used to mask composition-biased regions in the query, based on a statistical approach. The JPred program http://www.compbio.dundee.ac.uk/~www-jpred/[The NetOglyc program networks . The Neton sites . The WoL.cbrc.jp/ was usedediction . The TMHes/TMHMM/ was usedes/TMHMM/ was downww-jpred/ was usedE. coli targets from non-targets, using features that can be calculated from any protein sequence. The features we used were: amino acid compositions, length, hydrophobicity, SignalP, NetOglyc Ser, NetOglyc Thr, low complexity regions, α-helix, β-sheet, transmembrane helices and pI. A scaling scheme was used for every vector by restricting all entries to be between 0 and 1, by calculating for every feature (X - Min)/(Max - Min) where X is the feature score, and Min and Max are the minimum and maximum values of X in the set.Support vector machines , using aE. coli targets were used as the positive training dataset and 200 proteins were randomly selected from the E. coli non-targets dataset to represent the negative set. This model is therefore based on training E. coli targets versus non-targets, rather than training all targets versus non-targets because differences to all targets may reflect species differences instead of differences between targets and non-targets. As the target and non-target sets have different sizes, an error penalty parameter (C) is introduced to ensure generalization of the classifier. The ratio of the error penalty for targets:non-targets is set to 9:1, in proportion to the data set sizes of 200:22 and applied in the LIBSVM program using the weight parameter (wi) for both classes . The program (svm-predict-margins) was downloaded from http://www.work.caltech.edu/~htlin/program/libsvm/#margin[The SVM was assessed using 5-fold cross validation using the following training set splits: 22 m/#margin. This toE. coli essential genes were downloaded from the National Microbial Pathogen Data resource (NMPDR) http://www.nmpdr.org/FIG/wiki/view.cgi/Main/EssentialGenes. The database consists of 603 non-redundant putative essential genes, updated in September 2008. The list is checked for any redundancy using BLASTCLUST[Essential genes are those that play an important and essential functional role in a specific pathway. The presence of these genes is vital to the cell, as their inhibition would lead to cellular growth arrest or death of the pathogen. Essential genes are those that are not able to recover from a random insertion disruption . E. coliLASTCLUST, so thatTB participated in the design of the study, carried out the work and helped to draft the manuscript. AD conceived of the study, participated in its design and coordination and helped to draft the manuscript. Both authors read and approved the final manuscript.Gene Ontology Terms. Pie charts for frequencies of Gene Ontology terms for E. coli targets, bacterial targets and non-targets. Supplemental Figure 1a - Distribution of molecular functions at level 1 for E. coli targets. Supplemental Figure 1b - Distribution of molecular functions at level 1 for bacterial targets. Supplemental Figure 1c - Distribution of molecular functions at level 1 for non-targets. Supplemental Figure 1d - Distribution of molecular functions at level 2 for E. coli targets. Supplemental Figure 1e - Distribution of molecular functions at level 2 for bacterial targets. Supplemental Figure 1f - Distribution of molecular functions at level 2 for non-targets. Supplemental Figure 1g - Distribution of biological processes at level 1 for E. coli targets. Supplemental Figure 1h - Distribution of biological processes at level 1 for bacterial targets. Supplemental Figure 1i - Distribution of biological processes at level 1 for non-targets. Supplemental Figure 1j - Distribution of biological processes at level 2 for E. coli targets. Supplemental Figure 1k - Distribution of biological processes at level 2 for bacterial targets. Supplemental Figure 1l - Distribution of biological processes at level 2 for non-targets. Supplemental Figure 1m - Distribution of cellular components at level 2 for E. coli targets. Supplemental Figure 1n - Distribution of cellular components at level 2 for bacterial targets. Supplemental Figure 1o - Distribution of cellular components at level 2 for non-targets.Click here for fileNew predicted antibiotic targets. E. coli proteins with target-like properties. Results from applying SVM trained on distinguishing bacterial targets from non-targets.Click here for filePredicted bacterial protein targets with sequence similarity to human proteins. Predicted bacterial protein targets (Additional File Click here for fileNew predicted targets with high similarity to essential genes. Predicted bacterial protein targets was subjected to a BLAST search against the E. coli essential genes. The 64 targets that have a high similarity to an essential gene are listed here, along with their p-values, identities and BLAST scores.Click here for fileE. coliComparison to global functional atlas of . Hu et al. analysed E. coli orphan proteins, giving many new functional assignments [ignments . In partClick here for fileBacterial targets and non-targets data sets. Non-redundant bacterial targets and non-targets data sets.Click here for fileNon-redundant putative essential genes. Essential genes are those that are not able to recover from a random insertion disruption [http://www.nmpdr.org/FIG/wiki/view.cgi/Main/EssentialGenes.sruption . 603 nonClick here for file


Lipoprotein lipase (Lpl) acts on triglyceride-rich lipoproteins in the peripheral circulation, liberating free fatty acids for energy metabolism or storage. This essential enzyme is synthesized in parenchymal cells of adipose tissue, heart, and skeletal muscle and migrates to the luminal side of the vascular endothelium where it acts upon circulating lipoproteins. Prior studies suggested that Lpl is immobilized by way of heparan sulfate proteoglycans on the endothelium, but genetically altering endothelial cell heparan sulfate had no effect on Lpl localization or lipolysis. The objective of this study was to determine if extracellular matrix proteoglycans affect Lpl distribution and triglyceride metabolism.We examined mutant mice defective in collagen XVIII (Col18), a heparan sulfate proteoglycan present in vascular basement membranes. Loss of Col18 reduces plasma levels of Lpl enzyme and activity, which results in mild fasting hypertriglyceridemia and diet-induced hyperchylomicronemia. Humans with Knobloch Syndrome caused by a null mutation in the vascular form of Col18 also present lower than normal plasma Lpl mass and activity and exhibit fasting hypertriglyceridemia.This is the first report demonstrating that Lpl presentation on the lumenal side of the endothelium depends on a basement membrane proteoglycan and demonstrates a previously unrecognized phenotype in patients lacking Col18. Col18a1−/−) are viable but exhibit major ocular defects including abnormal retinal vessel development and poor anchoring of vitreal collagen fibrils to the inner lining membrane of the retina Heparan sulfate proteoglycans present in basement membranes aid in the formation of tissue barriers and facilitate cell adhesion and migration during development Gpihbp1 reduces vascular presentation of Lpl and plasma levels of Lpl activity and mass, which causes striking hypertriglyceridemia. Injection of heparin releases Lpl into the circulation and rescues the hyperlipidemic phenotype in Gpihbp1-deficient mice; however, both Lpl release and the subsequent decline of plasma triglycerides occur more slowly in the mutant compared to wild-type animals Lipoprotein lipase (Lpl) hydrolyzes triglycerides in triglyceride-rich lipoproteins in the circulation, liberating free fatty acids and monoacylglycerol for storage and energy production. This essential enzyme is synthesized in parenchymal cells of adipose tissue, heart, and skeletal muscle and migrates to the luminal side of the vascular endothelium where it acts upon circulating lipoproteins Δ3/Δ3Hspg2) did not have mild hypertriglyceridemia. These findings are of clinical relevance because we show that patients lacking the primary vascular form of Col18  also exhibit fasting hypertriglyceridemia and decreased plasma Lpl, previously unrecognized phenotypes in these patients.Lpl binds to heparin, suggesting that it might associate with heparan sulfate that is covalently attached to extracellular matrix or cell surface proteoglycans −/−Col18a1 mice and Δ3/Δ3Hspg2 mutant mice that lack the heparan sulfate attachment sites in the basement membrane proteoglycan, perlecan −/−Col18a1 mice vs. 119±25 mg/dl in mutants, n = 10, P<0.0001). These values did not vary significantly according to the duration of fasting (4–8 hr), age of the animals (2–12 months) or their sex. Plasma cholesterol levels were slightly lower in mutants, but the difference did not achieve statistical significance . Col18a1+/− heterozygotes did not display any changes in plasma lipids compared to the wild-type controls (data not shown). In contrast to Col18a1−/− mice, Δ3/Δ3Hspg2 mice did not exhibit any change in fasting plasma triglycerides and cholesterol compared to wild type , consistent with the studies in Δ3/Δ3 apoE−/−Hspg2 mutants by Tran-Lundmark et al To investigate the possibility that sub-vascular heparan sulfate proteoglycans participate in lipoprotein metabolism, we examined ificance  . Further analysis of plasma lipoproteins 2 hrs after gavage showed that the majority of the triglycerides and [3H]retinol counts were recovered in the large buoyant lipoproteins of δ<1.006 g/ml (data not shown). These lipoproteins were larger  To determine if turnover of dietary lipids was affected, we examined the rate of lipoprotein clearance after oral gavage with corn oil containing [ 4 hours . Thereaf3H]triolein, which are proposed to resemble chylomicrons 2 = .96) and 2%±0.5/min (R2 = 0.88) for the mutant. In contrast to these findings, hepatic clearance appeared to be normal in Col18a1−/− mice based on clearance of intravenously injected human VLDL . Recent studies indicate that Lpl is localized to the vascular endothelium, bound to its receptor, Gpihbp1 Col18a1−/− mice and colocalized with the blood endothelial cell marker, CD31 . The time course of release of Lpl was also comparable . Since Lpl is rapidly cleared in the liver −/−Col18a1 mice compared to wild-type mice.Reduced presentation of Lpl at the lumenal side of capillaries could explain the phenotype of P = 0.006). Attempts to measure enzyme activity in the plasma were not successful because the signal-to-noise ratio for the assay was not sufficient, even after enrichment of plasma samples by heparin-Sepharose chromatography.Inspection of the curves in Col18a1−/− mice. However, transcript levels of Gpihbp1 were comparable in mutant and wild-type mice in adipose, heart, and muscle . This extremely rare disorder is characterized primarily by ocular defects, including myopia, vitreoretinal and macular degeneration, retinal detachments, and occipital encephalocele defects thought to arise from altered basement membrane structures Col18a1−/− mice The hypertriglyceridemia and reduction in plasma and endothelium-bound Lpl in COL18A1 common to vascular basement membranes, which is distinct from a long form splice variant expressed abundantly in the liver and heart and regulated by a different promoter P<0.05), but no significant difference was noted in heterozygotes compared to normal or affected subjects .A large consanguineous family of Knobloch patients was identified in a remote farming community in Bahia State, Brazil subjects . Ultracesubjects  was bordP<0.01; and 51.4±15 for normal family members, P<0.01). Unlike mouse plasma, human plasma had sufficient Lpl activity to measure reliably. Knobloch patients also exhibited reduced plasma Lpl activity . The extreme remote location of most family members and local dietary restraints precluded other postprandial studies and interventions.Finally, fasting plasma samples from Knobloch, heterozygous and normal control related family members were compared for differences in Lpl mass and activity levels. Results demonstrated that the plasma levels of LpL enzyme in Knobloch subjects were reduced compared to related heterozygous and normal control family members  How does a proteoglycan located in basement membranes affect presentation of Lpl in the plasma and at the endothelial surface? Lpl is secreted by the parenchymal cells of adipose, cardiac and skeletal muscle and migrates from its site of secretion through the interstitial space, across basement membranes and eventually the endothelium −/−Col18a1 mice resembles that seen in mutants that affect Lpl expression or presentation. For example, Lpl heterozygotes have reduced plasma levels of Lpl, mild hypertriglyceridemia in both the fasted and post-prandial states, and acute accumulation of plasma lipids after oral gavage Gpihbp1−/− mice differ significantly from Col18a1−/− mice in the extent of hypertriglyceridemia and localization of Lpl in tissues Col18a1−/− mice, Lpl colocalized with CD31+ endothelium , and perlecan (Δ3/Δ3Hspg2 ) were described previously ad libitum with water and standard rodent chow (Harlan-TekLad) unless indicated otherwise. Genotyping of Col18A1−/− was performed as described Mice deficient in Col18 . Pooled mouse plasma lipoproteins were also separated by gel filtration FPLC . Plasma samples were centrifuged for 20 hr at 45,000× rpm in a Beckman 50.3Ti rotor at δ = 1.006 g/ml to collect triglyceride-rich lipoproteins. Isolated lipoproteins were dialyzed against PBS and analyzed for lipid content.Lipoproteins of δ<1.006 g/ml were purified from pooled plasma obtained from fasted animals or after oral gavage of 0.2 ml corn oil (n = 3 or each genotype). To determine particle size, samples were coded, negatively stained with 2% potassium phosphotungstate (pH 7.6), and imaged by transmission electron microscopy . The diameters of particles in 8 fields were measured, and the data from two separate experiments were pooled.via the tail vein into mice fasted for 4 hr, and triglycerides were measured in plasma samples taken 30, 60, and 90 min post-injection. The secretion rate (mg/min) was calculated from the increment in triglyceride concentration per minute multiplied by the plasma volume of the mouse (estimated as 0.035% of body weight in grams). To determine the intestinal triglyceride secretion rate, Triton WR-1339 was injected into mice that had been given a bolus of corn oil by gavage (0.2 ml/mouse).Triglyceride secretion rates were determined by the method of Hirano et al. 3H]triolein-labeled Intralipid particles were made as described previously 5 cpm) were injected into the tail vein, blood samples were taken at the indicated times, and radioactivity was measured (10 µl of serum) by scintillation counting. Retinol excursion studies were done essentially as described 3H]retinol  in ethanol was mixed with 1 ml of corn oil (Sigma) and administered by oral gavage (200 µl/mouse). Blood was sampled at the times indicated by retro-orbital sinus bleed and radioactivity was measured in triplicate (10 µl of serum) by scintillation counting.Clearance of human VLDL was performed by measuring the level of human apoB-100 present in plasma at timed intervals following intravenous injection of VLDL purified from fasted, healthy human donors. Human apoB-100 was measured by ELISA using mAb MB47, which binds human but not murine apoB-100, exactly as described in Col18a1−/− mice . RNA was isolated , reverse transcribed  and amplified using gene specific intron spanning primers to Lpl or Gpihbp1. Primer sequences, Lpl, 5′-AGGTGGACATCGGAGAACTG and 3′- TTTGTCCAGTGTCAGCCAGA and, Gpihbp1, 5′- AACATGATCCCTGGAAGCAG and 3′- ACAGTGTGGACTGGCAACAG. Quantitation was done by the 2−ΔΔCt method using β-actin as a control RNA in a Stratagene MxP3500. Final numbers represent fold-expression compared to β-actin (100%). Ct values from triplicate assays were used to calculate fold-expression according to Stratagene manual. Results were verified in two independent assays.Lpl and Gpihbp1 mRNA expression were determined in RNA samples isolated from gonadal fat pad, heart and soleus muscle from wild-type and Intravenous injection of heparin (0.5 U/g body weight) or 20% Intralipid (7.5 ml/kg body weight) was used to release Lpl into the plasma. Blood samples were taken by retro-orbital bleeding prior to and at various times after injection. Lpl mass in plasma samples was measured by ELISA with a specific antibody as described previously 3H]olein, ∼0.5 mCi/ml; Perkin Elmer) and calculated as nmol triolein hydrolyzed per min (i.e. mU activity) for each sample Lpl activity in plasma or tissues was determined using sonicated radiolabeled triolein substrate -Mice were perfused through the left ventricle with ∼10 ml phosphate-buffered saline at a rate of 2–3 ml/min after cutting the inferior vena cava, then perfused with 10 ml 3% paraformaldehyde in PBS. Excised tissues were post-fixed in 3% paraformaldehyde for 1 hr at room temperature, placed in 30% sucrose dissolved in PBS overnight at 4°C and embedded the next day in OCT.To detect LPL in mouse tissues, 10 µm-thick frozen sections were prepared and incubated overnight with primary antibodies [1∶400 for the hamster anti-CD31 antibody , 1∶800 for the rat anti-GPIHBP1 antibody COLA18 and characterized previously at the Human Genome Center in São Paulo, SP, Brazil Knobloch patients were recruited from a large family diagnosed with a loss of function mutation affecting the expression of the vascular isoform of −/−Col18a1 mice were determined using an unpaired Student's (two-tailed) t test. Knobloch, heterozygote carriers and unaffected family members were compared by the non-parametric Kruskal-Wallis test with Dunns post-test comparisons between all pairs of genotypes. Significance was taken as P<0.05.Statistical analyses were performed using PRISM (GraphPad Software). All data are expressed as mean values ± SD unless otherwise indicated. Comparisons between wild-type and Table S1Knobloch patient data.(0.05 MB DOC)Click here for additional data file.


The past two decades have seen a rise in the number of investigations examining the health-related effects of religiously motivated fasts. Islamic Ramadan is a 28 - 30 day fast in which food and drink are prohibited during the daylight hours. The majority of health-specific findings related to Ramadan fasting are mixed. The likely causes for these heterogeneous findings are the differences between studies in the following: 1) the amount of daily fasting time; 2) the percentage of subjects who smoke, take oral medications, and/or receive intravenous fluids; and 3) the subjects' typical food choices and eating habits. Greek Orthodox Christians fast for a total of 180 - 200 days each year, and their main fasting periods are the Nativity Fast (40 days prior to Christmas), Lent (48 days prior to Easter), and the Assumption (15 days in August). The fasting periods are more similar than dissimilar, and they can each be described as a variant of vegetarianism. Some of the more favorable effects of these fasts include the lowering of body mass, total cholesterol, LDL-C, and the LDL-C/HDL-C ratio. The Biblical-based Daniel Fast prohibits the consumption of animal products, refined carbohydrates, food additives, preservatives, sweeteners, flavorings, caffeine, and alcohol. It is most commonly partaken for 21 days, although fasts of 10 and 40 days have been observed. Our initial investigation of the Daniel Fast noted favorable effects on several health-related outcomes, including: blood pressure, blood lipids, insulin sensitivity, and biomarkers of oxidative stress. This review summarizes the health-specific effects of these fasts and provides suggestions for future research. Fasting is defined as a partial or total abstention from all foods, or a select abstention from prohibited foods. As a potential non-pharmacological intervention for improving health and increasing longevity, fasting has been the subject of numerous scientific investigations. The three most commonly studied fasts are caloric restriction (CR), alternate-day fasting (ADF), and dietary restriction (DR). A summary of the main findings is presented below.ad libitum consumption. CR has been demonstrated to improve health and increase longevity in a diverse group of species, including: dog, fruit fly, nematode, rodent, rotifer, spider, non-human primate, and zebrafish [CR is the reduction of kilocalorie  intake by a certain percentage  of ebrafish . Additioebrafish . Regardiebrafish ,6.ad libitum; during the "fast period," food consumption is restricted or halted altogether. Water is allowed ad libitum during all times. Animal ADF trials have reported extended lifespan [ADF consists of alternating 24-hour periods: during the "feast period," fasters may consume food lifespan  as well lifespan . ADF haslifespan ,9. Regarlifespan . Furtherlifespan .DR is a reduction of one or more components of dietary intake  with minimal to no reduction in total kcal intake. Research suggests that neither carbohydrate restriction nor lipid restriction extend life . On the While religious fasts are partaken primarily for spiritual purposes, they also have the potential to greatly affect one's physical health. Accordingly, the health effects of religious fasting have recently been the subject of scientific inquiry, with most of the research being performed in the last two decades. The following religious fasting periods are featured in this review: 1) Islamic Ramadan; 2) the three principal fasting periods of Greek Orthodox Christianity ; and 3) the Biblical-based Daniel Fast. The reason for the inclusion of these specific forms of religious fasts and the exclusion of others is that, to our knowledge, these are the only fasts about which scholarly research has been performed that explicitly detailed the subjects' dietary intake and health-related outcomes. Specifically, this review will examine the effects of these fasts on energy and nutrient intake, anthropometry, hematometry, blood pressure, and other health-related biomarkers. This paper concludes with a summarization of the findings and suggestions for future research.Each year, millions of Muslims refrain from eating or drinking from sunrise (Sahur) to sunset (Iftar) during the holy month of Ramadan, which lasts between 28 and 30 days. Thus, Ramadan fasting is similar to ADF, because both fasts incorporate feast periods and fast periods. The feast periods and fast periods of Ramadan fasting are each 12 hours in length on average , which aThe common dietary practice of Ramadan fasting is to consume one large meal after sunset and one lighter meal before dawn , but somUpon reaching puberty, all healthy Muslims are required to partake in the fast. Individuals who are sick, traveling, pregnant, breast-feeding, menstruating, or debilitated are exempt from fasting ,19. HoweDue to the fact that the Islamic calendar (Hijra) is a lunar calendar, the first day of Ramadan advances 11 days each year in relation to the Gregorian calendar. Consequently, Ramadan falls on different parts of the seasonal year over a 33-year cycle. This seasonal shift dramatically impacts the amount of daily fasting time that occurs in any given location. Moreover, a location's latitudinal distance from the equator also substantially impacts daily fasting time. While the average fast period during Ramadan is 12 hours in length , it can The variability in daily fasting time is one of several confounding variables that influence the effect of Ramadan fasting on health-related biomarkers. Other confounding variables include smoking status, medication, diet, and cultural habits -24. SmokFew definitive conclusions can currently be made regarding the effects of Ramadan fasting on human health, because the collective body of research has noted mostly heterogeneous findings regarding both dietary intake and health-related outcomes. The above-listed confounding variables in studies of Ramadan fasting - particularly the dietary norms of the subjects - are likely the main causes of these heterogeneous findings.As mentioned above, the majority of findings related to energy and macronutrient intake during Ramadan fasting are mixed. A summary of available findings pertaining to daily energy intake as well as the percentage intake of carbohydrate, protein, and fat is presented in Table Aside from macronutrients, vitamins and minerals are generally consumed in similar amounts during Ramadan. Examples include: thiamin ; niacin Mixed findings exist regarding the intake of sodium and fiber during the fast. Regarding sodium, studies reported either decreased consumption  or no chCollectively, the literature indicates that dietary changes pertaining to kcal intake, as well as macro- and micro-nutrient intake, may or may not differ over the period of Ramadan. These differences, or lack thereof, may be responsible for the discrepancies in health-related outcomes across studies.Regarding anthropometric variables, body mass index (BMI) may or may not decrease in response to Ramadan fasting. See Table The majority of findings related to total cholesterol, low density lipoprotein cholesterol (LDL-C), and high density lipoprotein cholesterol (HDL-C) are mixed. See Table Heterogeneous findings have been noted regarding other biochemical outcomes during Ramadan fasting, including blood triglyceride and blood glucose levels. See Table Little consensus exists regarding blood count outcomes during Ramadan. Hematocrit has been reported to either decrease ,30, incrTwo studies to date have examined the effects of Ramadan fasting on oxidative stress and antioxidant status. Ibrahim and coworkers  observedThere are mixed results regarding the effects of Ramadan fasting on blood pressure. Studies have reported that neither resting systolic pressure nor resting diastolic pressure changes during the fast ,41, but To summarize, little consensus exists regarding the effects of Ramadan fasting on the majority of health-related outcomes. Many of the discrepancies regarding findings are likely due to differences between studies in daily fasting time, smoking status, medication consumption, and/or dietary norms. Although most manuscripts report daily fasting time, many do not report one or more of the other confounding variables. Future research should be designed so as to eliminate - or minimize the effect of - as many confounding variables as possible. For example, future studies should likely exclude smokers from the subject population so that the effects of daytime smoking cessation are not conflated with the effects of the fast itself. Alternatively, smokers should be allocated to a separate group of fasters, which may then be compared to non-smokers undergoing the fast. The same care should be taken to control for other potential confounding variables, as only through such careful control within the research design will reliable results pertaining to the health effects of Ramadan fasting be obtained.th and Palm Sunday. During the Assumption (15 days), fasters abstain from dairy products, eggs, and meat. Also, fasters abstain from olive oil on weekdays during this period and abstain from fish every day except for August 6th. In addition to these principal fasts, every Wednesday and Friday that falls outside of a principal fasting period calls for the proscription of cheese, eggs, fish, meat, milk, and olive oil. Exceptions to these proscriptions occur on the week following Christmas, Easter, and the Pentecost. Collectively, dietary consumption is restricted for 180 - 200 days each year. The Greek Orthodox Christian diet consists largely of bread, fruits, legumes, nuts, seafood, snails, and vegetables during fasting periods [There are three principal fasting periods for Greek Orthodox Christians. During the Nativity fast (40 days), fasters abstain from dairy products, eggs, and meat every day. Also, fasters abstain from fish and olive oil on Wednesdays and Fridays during this period. During Lent (48 days), fasters abstain from dairy products, eggs, and meat every day. Additionally, fasters abstain from olive oil on weekdays during this period and abstain from fish every day except for March 25 periods . This diDaily kcal intake may or may not decrease during the fasting periods -46. In t12 may or may not decrease during Greek Orthodox Christian fasting [In terms of vitamin and mineral intake, both riboflavin  and calc fasting ,44,46.In terms of anthropometric outcomes, BMI may or may not decrease during fasting periods ,46. AlsoRegarding biochemical outcomes, both total cholesterol and LDL-C levels decrease during fasting periods ,43,46. OSarri and coworkers compared other hematological changes in fasters and non-fasters during the Christmas fasting period : the autThere are conflicting findings on the effects of Greek Orthodox Christian fasting on blood pressure. One study found that systolic blood pressure increased during fasting periods , while aAlthough no study to date has examined oxidative stress in response to Greek Orthodox Christianity fasting, a recent study measured serum antioxidant levels before and after the Nativity fast . This inIn sum, Greek Orthodox Christian fasting appears to lower body mass. Carbohydrate intake appears to increase, while the intake of protein, total fat, saturated fat, and trans fatty acids decrease during fasting periods. Both total and LDL-C decrease, although the LDL-C/HDL-C ratio does not appear to change. Fiber intake increases during fasting periods, which may partly explain the change in serum lipids. The intake of most vitamins and minerals does not appear to change during these periods, although riboflavin and calcium intake each appear to decrease, and magnesium intake appears to increase. Also, one investigation reported increases in serum retinol and serum α-tocopherol. More research remains to be performed on hematological variables and blood pressure during fasting periods due to both the lack of previous research and the mixed findings. Moreover, given that each fasting period has unique durations and dietary precepts, future research should examine these fasting periods both separately and collectively.ad libitum intake of specific foods, but the food choices are restricted to fruits, vegetables, whole grains, legumes, nuts, seeds, and oil. This plan resembles a vegan diet, which has been reported to yield health-enhancing properties [A popular fast practiced by Christians, the Daniel Fast derives from the Biblical story of Daniel (1:8-14 NIV), in which Daniel resolved not to defile himself with the royal food and wine and requested permission to consume nothing but vegetables (pulse) and water for 10 days. Later in the same book (Daniel 10:2-3 NIV), Daniel again followed a 21 day period of fasting, during which time he ate no choice food (meat or wine). Based on these two passages, a modern day Daniel Fast involves operties ,49.Like Greek Orthodox Christian fasting, the Daniel Fast may be viewed as a form of DR. However, unlike the Greek Orthodox Christian fasts, or a simple vegetarian diet, a Daniel Fast is much more stringent; this is because refined foods, white flour, preservatives, additives, sweeteners, flavorings, caffeine, and alcohol are each forbidden. Nonetheless, because individuals traditionally follow the Daniel Fast for strict religious purposes in an attempt to become closer to God during a time of extended prayer, anecdotal reports and preliminary scientific study have indicated excellent compliance. The Daniel Fast is most commonly partaken for 21 days, although longer  and shorter  fasts have been observed. While the Daniel Fast can be partaken at any time of the year, it is often practiced during the month of January in order to begin the New Year through fasting and prayer.To test the health benefits of the Daniel Fast within a laboratory-based protocol, we recently enrolled men and women of varying age (20-62 yrs) into a 21-day Daniel Fast . Pre andWe noted excellent compliance to the fast (> 98%), as well as both excellent results for overall mood and satiety . As expected, a reduction was noted in the following variables from the seven days prior to starting the fast to the final seven days of the fast: total kcal, protein, total fat, saturated fat, trans fat, and cholesterol. Also, an increase was noted in the following: carbohydrate, fiber, and vitamin C.-1). Although body weight and body fat were reduced slightly, no significant difference was noted. In regards to measures of blood antioxidant status and oxidative stress, a significant increase was noted in Trolox Equivalent Antioxidant Capacity and nitrate/nitrite (a surrogate marker of nitric oxide), while a significant decrease was noted in MDA (manuscript in progress).The following variables related to cardiovascular disease risk were significantly lower following the fast as compared to before the fast: total cholesterol, LDL-C, SBP, and DBP. Insulin, HOMA-IR, and C-reactive protein were all lowered to a clinically meaningful extent, but this decrease failed to reach statistical significance. Due to the drastic decrease in total cholesterol (~19%), HDL-C was lower after the fast as compared to before the fast  increase, while no changes occur regarding the intake of monounsaturated fat as well as most vitamins and minerals. To date, only a few investigations have examined the health-related effects of these fasting periods. Clearly, more work remains to be performed, particularly regarding hematological and blood pressure variables. Also, each principal fasting period should be studied both separately and aggregately due to their differences in food proscriptions and durations.Only one study to date has examined the health-related effects of a Daniel Fast. This study reported meaningful improvements in blood pressure, blood lipids, and biomarkers of oxidative stress. Moreover, insulin, HOMA-IR, and C-reactive protein were all lowered to a clinically meaningful extent. However, the study also noted an undesirable lowering of HDL-C. Future work should modify food choices in an effort to maintain HDL-C. Also, future research should examine the tolerability and health-related effects of partaking in a Daniel Fast for longer durations.quality dietary intake in eliciting favorable effects on health. Both the Daniel Fast and the Greek Orthodox Christian fasts are associated with many favorable health outcomes despite the fact that fasters can eat as much as they desire. On the other hand, whether or not Ramadan fasting elicits favorable health outcomes appears to greatly depend on the food choices of the fasters. This is not to suggest that the quantity of food intake has no bearing on health; hundreds of studies have demonstrated the virtue of restricting kcal consumption [Collectively, the religious fasts featured in this review emphasize the importance of sumption . Rather,Diet plays an integral role in the religious customs of a variety of faiths. For many religions, this role is manifested in the form of specialized fasting periods. Although religious fasting is often a time of great spiritual growth, it can also be a time of great improvement to one's physical health. This review highlights the potential of different religious fasts as forms of dietary modification. It is our hope that the information provided within this review will initiate the design and performance of future investigations focused on the health benefits of religious fasting.The authors declare that they have no competing interests relative to the material presented within this article.JFT was chiefly responsible for reviewing the relevant literature and writing this article. RJB provided the article outline and assisted with the writing and editing of this article. Both authors read and approved the final manuscript.


After simple geometric and atmospheric correction, we found a significant relationship  between in situ measured TSM concentrations and MODIS band normalization difference of band 3 and band 1. From this, we retrieved TSM concentrations in eight regions of Lake Taihu in 2007 and analyzed the characteristic distribution and variation of TSM. Synoptic maps of model-estimated TSM of 2007 showed clear geographical and seasonal variations. TSM in Central Lake and Southern Lakeshore were consistently higher than in other regions, while TSM in East Taihu was generally the lowest among the regions throughout the year. Furthermore, a wide range of TSM concentrations appeared from winter to summer. TSM in winter could be several times that in summer.Although there has been considerable effort to use remotely sensed images to provide synoptic maps of total suspended matter (TSM), there are limited studies on universal TSM retrieval models. In this paper, we have developed a TSM retrieval model for Lake Taihu using TSM concentrations measured  It determines the transparency of water and can also serve as a transport medium for various pollutants , e.g., hWith the development of remote sensing technology, satellite estimates of water quality significantly complement conventional monitoring techniques and have found widespread applications. Remotely sensed monitoring of TSM started in 1974 using ERTSA data . Since tFor water quality management of large lakes like Lake Taihu, a severely eutrophic lake, it is essential to quickly and accurately understand the dynamic variations in water quality. TSM monitoring from remote sensing in Lake Taihu has been studied for many years, and the methodologies used can be divided into three types: (1) analytical methods, also referred to as bio-optical algorithms, in which reflectance spectra are simulated by theoretical formulas based on the specific inherent optical properties of substances in the water ,9; (2) eIn this paper, Lake Taihu was selected as the study area, and a common yearly-scaled TSM retrieval model is developed. Synoptic maps of TSM in 2007 are obtained using this model, and the characteristic temporal and spatial variability of TSM is analyzed. In addition, considering that atmospheric correction is essential, especially for multi-temporal MODIS images, a simple but effective image-based relative radiometric normalization method is presented. The significance of this study is that it is the first attempt to establish a common TSM retrieval model for practical application in Lake Taihu.2.2. It is a shallow lake, with mean depth 1.9 m, and maximum depth 2.6 m at a water surface elevation of 3.0 m above sea level. The Lake Taihu drainage basin (Microcystis spp.) have occurred frequently, which badly affects the function of the lake as a drinking water supply. More efficient water quality monitoring of the lake is urgently needed [Lake Taihu is located in eastern China between 30° 55′ 40″ N and 31° 32′ 58″ N, and 119° 52′ 32″ E and 120° 36′ 10″ E. It is the third largest freshwater lake in China, with a total water area of about 2,338.11 kmge basin  is the my needed .3.3.1.in situ measurements were carried out once a month in 2005 by the Taihu Ecosystem Research and Field Observation Station, Chinese Academy of Sciences . Twenty-eight sampling sites were selected, which were considered representative of surface distributions of TSM encompassing the whole of Lake Taihu. February, May, August and November water samples were collected at all 28 sampling sites, while in the other eight months the field survey was only conducted at 13 sites, mainly located in the north part of Lake Taihu. The distribution of the 28 sampling stations in Lake Taihu is shown in TSM TSM concentrations were measured by filtering the water samples (taken from 0.5 m below the water surface) on Whatman Grade GF/F filters, which were dried and weighed on an electronic balance.3.2.MODIS images provide high radiometric sensitivity (12 bit) in 36 spectral bands from 0.4 μm to 14.4 μm in wavelength. The spatial resolution is 250 m for bands 1–2, 500 m for bands 3–7, and 1,000 m for bands 8–36. As mentioned above, numerous studies have demonstrated that MODIS has the potential for use in water quality monitoring. In particular, the MODIS 250 m and 500 m bands, centered at 469 nm (b3), 555 nm (b4), 645 nm (b1) and 858 nm (b2), can provide sufficient sensitivity for water applications [MODIS-Terra level 1B calibrated radiance products, with spatial resolutions of 250 m and 500 m, covering the region of interest in the middle November of 2003 and the whole of 2005 and 2007 were downloaded via the web interface of the Level 1 and Atmosphere Archive and Distribution System (LAADS) of the National Aeronautics and Space Administration (NASA). MODIS images from 2005 were obtained for days corresponding to field measurements of TSM, and the time difference between the field measurements and MODIS images was no more than three days. Because of cloud coverage over the target no appropriate images were available for March and June of 2005. Two cloud-free MODIS images from every month (except June) in 2007, were downloaded to analyze the distribution variation of TSM in Lake Taihu.(1) Geometric correctionEach image was geo-referenced based on the WGS 84 UTM zone 51 North coordinate system. The 500 m resolution images were resized to 250 m using the nearest neighbor resampling method during geo-referencing.(2) Atmospheric correctionBesides the changes in target reflectance, changes in satellite sensor calibration, differences in illumination and observation angles, and variations in atmospheric effects can induce variation in a sensor’s response to a given target over time. Therefore, to detect the actual changes in ground target from the total signal measured at the spacecraft sensor, atmospheric correction is necessary. Two types of atmospheric corrections, absolute correction and relative correction, are commonly employed to normalize remotely sensed images for time-series inter-comparison –17. Absoet al. [band1R and band2R are the radiance of band 1 and band 2 respectively. These PIFs are mainly situated in the new industrial zone around Taihu Lake, such as Changzhou National Hi-tech District, Wuxi Economic District and Suzhou National Hi-tech District. The other set of PIFs is located in clean sea, where water quality is good and little influenced by TSM. Following the normalization procedure of Du, these two screened PIFs sets were achieved. After identifying the PIFs centers in the density map, the normalization regression line is obtained as that intersecting these two PIFs centers. The last step is to recalculate the target image pixel values based on the normalization regression function. Now, the target image has been normalized to the scale of the base image [A variety of relative radiometric correction techniques have been developed , most ofet al.  developeet al.  developeet al.  proposedet al. , and theet al. . Assumptet al. ,24. Ther3.3.in situ measured TSM concentration data in 2005. All 166 data pairs were used for the development of the TSM retrieval model, and another 16 data pairs from 2003 were used for model validation.In total, there were 166 pairs of radiometric normalized MODIS radiance data and −1) to atmospherically corrected MODIS band data. This included single band (band 1–4) and band combinations . The best fitting model was selected to map the temporal and spatial variability of TSM in 2007.Instead of using a sophisticated optical model, simple linear and non-linear models  were used to correlate TSM concentration (mg L4.−1) and MODIS single band, band difference, band ratio and band normalized difference, showing that the last two models performed better than the first two models in terms of R and standard error. Comparison between in situ measured TSM and model-estimated TSM from the last two models using both the 166 data pairs for model development and the 16 data pairs for model validation is shown in Finally, the synoptic maps of TSM concentrations in 2007 were obtained see , which r−1; Meiliang Bay, Western and Eastern Lakeshore also show relatively high TSM concentrations; while there is good water quality in East Taihu and Zhushan Bay, with the maximum TSM concentrations being <100 mg L−1. Moreover, the variation ranges of TSM concentration in the open areas are much larger than in the bays, and the minimum variation range is located in East Taihu due to the sheltered environment and dense hydrophytes. Furthermore, obvious seasonal variations in TSM are also characterized in the lake. The water body of Lake Taihu is divided into eight water regions. These include Meiliang Bay (MB), Zhushan Bay (ZB), Gonghu Bay (GB), Central Lake (CL), Western Lakeshore (WL), Southern Lakeshore (SL), East Taihu (ET) and Eastern Lakeshore (EL) . Figure 5.5.1.−1) and relatively corrected MODIS Terra band 1 data used in this study  and TSM [a concentration is >10 μg L−1, there are an absorption peak and a reflectance peak in 672 nm and 695 nm respectively [a strongly absorb spectrum in the blue region (440 nm). Hence MODIS band 3 data (blue band reflectance) could assist band 1 data (red band reflectance) to weaken the influence from chlorophyll a and CDOM.A significant  relationship was observed between TSM concentration  measured in situ and relatively corrected MODIS Terra band 1 data, which is consistent with the conclusions of other studies. The model performance analysis indicated that this empirical model could map TSM concentrations successfully except when TSM concentration is higher than 200 mg L−1. The temporal and spatial distribution of TSM concentrations in Lake Taihu from January to December 2007 revealed that Central Lake and Southern Lakeshore are characterized by extremely high TSM concentrations, and there is good water quality in East Taihu. The variation range of TSM concentration in the open area is much larger than that in the bays, affected by higher wind velocities in the open area. The minimum variation range is located in East Taihu due to the sheltered environment and dense hydrophytes. On the whole maximum TSM concentrations appear in winter and the minimum appear in summer. This pattern is notable in the Central Lake, but very weak in East Taihu.The common model retrieving TSM concentration showed that a significant  relationship was observed between TSM concentrations (mg Li.e., the slope of the regression line, It should be noted that the approach developed in this study could be applied to other inland regions, but the specific relationship between TSM concentration and MODIS data (


The metabolic syndrome (MetS), in addition to its lipid, metabolic, and anthropomorphic characteristics, is associated with a prothrombotic and the proinflammatory state. However, the relationship of inflammatory biomarkers to MetS is not clear.To study the association between a group of thrombotic and inflammatory biomarkers and the MetS.Ten conventional MetS risk variables and ten biomarkers were analyzed. Correlations, factor analysis, hexagonal binning, and regression of each biomarker with the National Cholesterol Education Program (NCEP) MetS categories were performed in the Family Heart Study .Subjects in the top 75% quartile for plasminogen activator inhibitor-1 (PAI1) had a 6.9 CI95 [4.2–11.2] greater odds (p < 0.0001) of being classified with the NCEP MetS. Significant associations of the corresponding top 75% quartile to MetS were identified for monocyte chemotactic protein 1 , C-reactive protein , interleukin-6 , sICAM1 (OR = 1.61), and fibrinogen (OR = 1.86). PAI1 correlated significantly with all obesity and dyslipidemia variables. CRP had a high correlation with serum amyloid A (0.6) and IL6 (0.51), and a significant correlation with fibrinogen (0.46). Ten conventional quantitative risk factors were utilized to perform multivariate factor analysis. Individual inclusion, in this analysis of each biomarker, showed that, PAI1, CRP, IL6, and fibrinogen were the most important biomarkers that clustered with the MetS latent factors.PAI1 is an important risk factor for MetS. It correlates significantly with most of the variables studied, clusters in two latent factors related to obesity and lipids, and demonstrates the greatest relative odds of the 10 biomarkers studied with respect to the MetS. Three other biomarkers, CRP, IL6, and fibrinogen associate also importantly with the MetS cluster. These 4 biomarkers can contribute in the MetS risk assessment. Metabolic syndrome (MetS) is a cluster of physiologic markers that includes obesity, insulin resistance, dyslipidemia, and hypertension. The National Cholesterol Education Program ATP III (NCEP) considers a proinflammatory and prothrombotic state as characteristic of metabolic syndrome (MetS) . A groupInflammation and thrombosis are considered to influence the pathogenesis of coronary heart disease. Inflammation in general is associated with increased levels of leucocytes, fibrinogen, and C reactive protein (CRP) as well as other biomarkers. For example, Interleukin-6 (IL6) is considered an important inducer of the hepatic secretion of CRP. Plasma levels of IL6 and CRP have been reported to predict type 2 diabetes in humans . It is aet al.[et al.[et al.[et al.[et al.[Although several studies have used multivariate factor analysis as a tool for characterizing MetS clusters of risk variables -11, onlyet al. studied et al. commentel.[et al. showed tl.[et al. . Their combinations in a multivariate factor analysis generated factors of MetS. A subgroup of the conventional variables in our study represented the set of the variables utilized to qualitatively characterize MetS . Consequently, the 10 conventional variables served as a representative construct for MetS. Adding ten prothrombotic and proinflammatory biomarkers in the study provided an ample vision to assess the role of ten biomarkers in the MetS cluster.We studied ten conventional MetS risk variables in the NHLBI Family Heart Study  was recruited through 2002–2003 at five centers. Details of this study are reported elsewhere [The NHLBI Family Heart Study (FHS-Time 2) is a family-based study that investigates genetic and non-genetic causes of coronary calcification, atherosclerosis, and cardiovascular risk factors during a 2lsewhere ,17. The 2), waist circumference , waist to hip ratio (WHR), percent body fat (PBF), fasting (≥ 12 hours) glucose , high density lipoprotein cholesterol , tryglicerides , low density lipoprotein cholesterol , and systolic  and diastolic blood pressure . PBF was measured as the bioelectric impedance of the body and was calculated based on the Lukaski formula [Ten MetS conventional risk variables were studied: body mass index , fibrinogen, interleukin-2 soluble receptor alpha (IL2SR), IL6, monocyte chemotactic protein 1 (MCP1), matrix metalloproteinase (MMP3), PAI1, serum amyloid A (SAA), and soluble intercellular adhesion molecule 1 (sICAM1) (Table CRP (mg/l) was measured using the BNII nephelometer from Dade Behring utilizing a particle enhanced immunonepholometric assay. Intra-assay coefficient of variation (IACV) ranged from 2.3 – 4.4% and inter-assay coefficient of variation (IRCV) ranged from 2.1 – 5.7%.DDIMER (ug/ml) was measured using an immuno-turbidimetric assay  on the Sta-R analyzer (Stago) [ (Stago) .Fibrinogen (mg/dl) concentrations were quantified by the STAR automated coagulation analyzer (Diagnostica Stago), and the clotting method of Clauss [f Clauss . In thisIL2SR (pg/ml) was measured by ultra-sensitive ELISA  [lis, MN) . IACV waIL6 (pg/ml) a pro-inflammatory cytokine was measured by ultra-sensitive ELISA  [lis, MN) . The IRCMCP1 (pg/ml) was measured using an ultra-sensitive ELISA assay . The sample type used was citrated plasma. The IACV and IRCV ranged 4.7–7.8% and 4.6–6.7% respectively.MMP3 (ng/ml) was measured by an ultra-sensitive, solid-phase sandwich ELISA using a polyclonal antibody specific for both the pro- and active forms of MMP-3  Immunoassay; R&D Systems, Minneapolis, MN). The IACV and IRCV ranged from 5.7–6.4% and 7.0–8.6%, respectively.PAI1 (ng/ml) assay is sensitive to free PAI1 (both latent and active), but not PAI1 in complex with t-PA. It is done as a two-site ELISA. The analytical CV for this assay was 3.47% [as 3.47% -25.SAA (mg/l) was measured with the BNII nephelometer from Dade Behring utilizing a particle enhanced immunonepholometric assay . IACV ranged from 4.3–6.2% and IACV ranged from 2.8–4.7%.sICAM1 (ng/ml) was measured by an ELISA assay . The laboratory CV was 5.0% [was 5.0% ,27.MetS categories beyond the NCEP defined thresholds in a subject, was considered as a clinical expression of the NCEP MetS: Abdominal obesity, given as large WAIST for men >102 cm and > 88 cm for women; high fasting GLUC ≥ 110 mg/dl or medication for diabetes; high TG ≥ 150 mg/dl; low HDLC < 40 mg/dl in men or < 50 mg/dl in women; high blood pressure (SBP/DBP) ≥ 130/≥ 85 mm Hg or antihypertensive medication use [A grouping of at least any 3 of the 5 following Variables of interest were expressed as mean ± SD Table . In addiFACTANAL function, with the maximum likelihood estimate option and the "Varimax" rotation, in Splus version 6.2 of Insightful Corp., Seattle, WA. This method was performed first on 10 conventional MetS risk variables. After, one biomarker was added in the analysis, subject to the sample sizes available, to discover any cluster of each biomarker onto the identified latent factors. The acceptance of an extra factor in the model was made by applying two general rules, the p-value of the model had to be < 0.05, and the last factor had to have at least two contributing risk variables (with a contributing coefficient about 0.4 or above). We stopped beyond the 5-th factor because the additional factors represented only single variables. The contributing coefficients for the standardized variables correspond to correlation coefficients of the original risk variables to the latent factors. A contributing coefficient value in the tables greater/equal to 0.4 was highlighted in bold and is considered as having a substantial influence on the factor. In advance to factor analysis, distributions of all risk variables were checked for their fit to a normal distribution. Variables BMI, WHR, HDLC, TG, CRP, DDIMER, fibrinogen, IL2SR, IL6, MCP1, MMP3, PAI1, SAA, and SICAM1, were natural log transformed. GLUC was expressed as the inverse of a power transformation (1/GLUC2). Risk variables went also through covariate adjustments, in a stepwise regression in SAS v. 9.1.3 for Linux OS, for age, age2, age3 and recruitment center, within gender. The residuals were standardized to mean 0 and variance 1.First, the multivariate factor analysis was performed with the Second, Pearson correlations were produced on the above preprocessed data, by using the PROC CORR of SAS Table .Third, a binary association between the PRESENCE/ABSENCE of a participant in the top 75% quartile, as well as in the 50% of the data for each biomarker and if CLASSIFIED/NOT with MetS was evaluated through logistic regression (PROC LOGISTIC of SAS).Appendix.In addition, a graphical technique named "hexagonal binning" was implemented . For its25) and 75% quartile (q75) , IL6 (0.49), dyslipidemia, even with SBP and DBP, reaching almost the lowest but still significant correlation with fibrinogen Table . CRP hadThe above correlation matrix reflected in the results of factor analysis. The factor analysis with "Varimax" rotation on the 10 conventional risk variables produced 4 latent factors. The first factor was a contribution of BMI, WAIST, PBF, and more modest contributions of WHR and GLUC. The second factor was mostly SBP and DBP contributions. The third factor was contributed by TG, HDLC, with less contribution of GLUC, LDLC, and PBF. The last factor represented a contribution of WHR and WAIST. The MetS cluster of latent factors explained about 60% of the total variance of the conventional risk variables.Figure Statistical analysis in the Methods section). A red color annotates PAI1 values above the 75th PAI1 percentile; a green color annotates PAI1 values below the 25th percentile; and a black color annotates the 50% central data of PAI1 distribution. The locations of these points in Figure Figure Appendix, and Figures 2 or greater). Of them, from the BMI top 75% percentile only a subset, 16.3% classified with MetS. Another 18% of the data, located in the BMI central 50%, classified with MetS. PAI1 showed a similar pattern as BMI. PAI1 fitted regression line intersected with the 75% quartile line at the graphical point of 3 MetS categories, confining 17.1% of the sample with MetS, and an additional 15.7% with MetS between q25 and q75 lines. Differently, the CRP fitted regression line intersected with the CRP top 75% quartile line at the graphical point of 1 MetS NCEP category. At CRP values 4.2 mg/l or greater, 11.8% of the sample were classified with MetS. A subsample (20.7%) classified with MetS was represented by participants having CRP values between q25 and q75 lines. IL6 and fibrinogen respectively confined 13.2% and 11.6% of the sample, with MetS and placed at the top biomarkers' 75% percentiles. In contrast, DDIMER and MMP3 had flat regression lines.The other graphical technique employed by us to better understand relationships of MetS NCEP categories and biomarkers is hexagonal binning. This technique helps visually to better investigate the predictive evidence of biomarkers to the NCEP MetS categories  and comparable with BMI odds  when participants were in the top 75% quartile and classified as having the NCEP MetS. Other biomarkers MCP1 , CRP , IL6 , sICAM1 , and fibrinogen  had also significant odds for participants being in the top 75% quartile of the biomarker distribution and classified as having the NCEP MetS. The association of biomarker values per participant within the corresponding central 50% of the data and being classified with MetS, was significant only for CRP .et al.[et al.[et al.[et al.[et al.[Inflammation, procoagulation and fibrinolysis biomarkers have been recently hypothesized to be associated with MetS  and the et al. and in tl.[et al., and in l.[et al.. PAI1 wal.[et al.. PAI1 prl.[et al., and of l.[et al.. Similarl.[et al.. PAI1 isl.[et al.-37. Tranl.[et al.. Transgel.[et al.. Smith el.[et al. concludel.[et al.. Skurk al.[et al. reviewedl.[et al. studyinget al.[et al.[et al.[et al.[et al.[et al.[A large number of studies have scrutinized the role of CRP. Bassuk et al., Ridker l.[et al., and Bial.[et al. summarizl.[et al. argued tl.[et al. who founl.[et al.) and 0.5l.[et al.) with fiAnother interesting biomarker with significant findings in our data was IL6. On severe exercise, IL6 is released in large quantities from the skeletal muscle . IL6 hassICAM1 is an important adhesion molecule and is implicated to mediate the firm adhesion of leukocytes into endothelium . sICAM1 et al.[It has been hypothesized that a chronic, mild inflammatory state is the ground of MetS. Besides, Dancan and Schmidt  suggesteet al. showed tet al..To our surprise, LDLC was not a good measure of the relationships with the biomarkers studied. Its correlation coefficients with biomarkers were practically insignificant. Therefore, we would suggest that future studies of MetS use HDLC and TG as more reliable measures for the lipids component.Our study has also limitations. We studied only 10 biomarkers. Their sample size depended on the cost of the biochemical assays. We have previously reported that about 25% of the FHS Time 2 participants studied used anti-hyperlipidemics, and that these medications were a confounder when studying the MetS prevalence . It is kIn conclusions, PAI1 significantly correlated with most of the MetS conventional variables. PAI1 clustered in obesity and lipids factors, and participants in the PAI1 top 75% percentile had higher odds, than other 9 biomarkers, to be classified with MetS. PAI1 is an important risk factor for MetS. Three other biomarkers, CRP, IL6, and fibrinogen associate importantly with MetS cluster. In contrast DDIMER, SSA, and MMP3 did not associate directly with MetS cluster.All authors contributed equally.Metabolic syndrome paragraph in the Methods section). In the hexagonal binning graphs  were classified with MetS. Although a small group of subjects were with BMI less than 25 (q25), they also were classified with MetS. This shows that MetS is not only result of obesity in combination with other components of MetS, but it can also be a hypertension-dyslipidemic-hyperglycemic combination. For the PAI1 biomarker,  were predominantly classified with MetS. In conclusion, hexagonal binning was a spatial graphical attempt to better characterize and understand the contributions of each biomarker studied in relation to MetS NCEP categories.To the best of our knowledge, hexagonal binning is a technique that is not implemented in the epidemiological studies including MetS studies. It is applied in other areas of research, for example, earthquake graphical pattern detections . Its mai


The metabolic syndrome (MetS) is a cluster of risk factors associated with morbidity from cardiovascular disease (CVD) and associated mortality. Russia has one of the highest CVD mortality rates in the world. However, the prevalence of MetS in Russia remains largely unknown. The aim of this study is to estimate the prevalence of MetS and its components in an urban Russian setting.Altogether, 3705 Russian adults aged 18-90 years were enrolled in a cross-sectional study in Arkhangelsk (Northwest Russia). All subjects completed a questionnaire and underwent a physical examination. Blood samples were taken and analyzed in TromsØ, Norway. Three separate modified definitions of MetS were used, namely, the National Education Cholesterol Education Program Adult Treatment Panel III (NCEP), the American Heart Association/National Heart, Lung and Blood Institute (AHA/NHLBI) and the International Diabetes Federation (IDF). To ensure comparability of the findings, the prevalence data were standardized using world and European standard populations and Russian population.The age-standardized (Segi's world standard population) prevalence rates of the MetS among women were 19.8% (95% CI: 18.1-21.5), 20.6% (95% CI: 18.9-22.3) and 23.1% (95% CI: 21.3-24.9) by the NCEP, AHA/NHLBI and IDF criteria, respectively. The corresponding rates for men were 11.5% (95% CI: 10.1-12.9), 13.7% (95% CI: 12.2-15.2) and 11.0% (95% CI: 9.7-12.4). Among subjects with MetS, central obesity was more common among women, while elevated triglycerides and blood glucose were more common among men. Almost perfect agreement was found between the NCEP and AHA/NHLBI criteria (κ = 0.94). There was less agreement between the used definitions of MetS in men than in women.While the prevalence of MetS among Russian women is comparable to the data for Europe and the U.S., the prevalence among Russian men is considerably lower than among their European and North-American counterparts. Our results suggest that MetS is unlikely to be a major contributor to the high cardiovascular mortality among Russian men. Further studies of MetS determinants and associated cardiovascular risk are needed for a better understanding of the mechanisms leading to the exceptionally high cardiovascular mortality in Russia. MetS is an unfavourable cluster of factors that increases the risk of CVD and type-2 diabetes -3. MetS Internationally, there is no uniform accepted definition of MetS. Altogether, six sets of diagnostic criteria have been proposed by different expert groups. Despite considerable similarity among the definitions, the prevalence of the MetS in the same population may vary dramatically depending on the specific diagnostic criteria considered . This coWhile cardiovascular mortality in Western Europe and the U.S. has decreased during recent decades, the opposite trend has been observed in Russia where it has increased from 412 per 100,000 in 1970 to 927 per 100,000 in 2003. Mortality from cardiovascular diseases in Russia is currently the highest in Europe. In 2003, CVDs accounted for about 56% of all deaths . CoronarGiven that MetS is a strong predictor of cardiovascular mortality and morbidity -6, one mThe aim of the present study is to estimate the prevalence of MetS and its components in an urban Russian setting using several international definitions and reference populations to ensure comparability of the findings.The survey was conducted in 2000 in Arkhangelsk, the capital of the Arkhangelsk Region of Russia. The population of Arkhangelsk prior to the study's initiation was approximately 170,000 men and 197,000 women. The city is ethnically homogenous: 95% of inhabitants are registered as Russians and most of the reminder are ethnically and culturally close to Russians (e.g. 2% of the population are registered as Ukrainians and 1% as Byelorussians).No population register for medical research exists in Arkhangelsk. Primary health care departments provide medical services to the general population within the regional general health and occupational health network. People are registered at polyclinics according to their home address and/or place of work. All study participants when registered at the same out-patient clinic in Arkhangelsk. Of those who were invited, only 40 persons (1.1 %) refused to participate with "lack of time" as the primary reason given. Individuals coming for their annual medical check-up at the out-patient clinic were recruited consecutively to avoid the "healthy volunteer effect". Workers and students were similarly invited either through the obligatory annual medical examination or through their places of work or study. Pensioners were recruited through the clinic's register. About 90% of males and 70% of females were recruited through an annual medical examination consisting mainly of working people but also students, pensioners and unemployed individuals. Other subjects were invited to the study. Students constituted approximately 12%, pensioners 19%, unemployed 3% and working subjects 66 % of the study population.Altogether, 1968 men and 1737 women aged 18-90 years participated in the study. It involved a physical examination, completion of a comprehensive questionnaire and donating blood for tests. Data were collected by trained medical personnel. The study was approved by the Regional Ethics Committee, TromsØ, Norway. Verbal informed consent was obtained from all participants.Anthropometric measurements included weight, height, waist and hip circumference. Body mass index (BMI) and waist-to-hip ratio (WHR) were calculated. Blood pressure and heart rate were measured (upper right arm) in a sitting position three times at two minute intervals using a semiautomatic electronic device . Averages of the second and third systolic and diastolic blood pressure readings were used in the analysis. In addition, all subjects completed a six-page questionnaire on socio-demographic characteristics, medicines used (including regular intake of antihypertensive and anti-diabetic medications), smoking habit, alcohol consumption, diet and level of physical activity during leisure time and at work. History of cardiovascular diseases and diabetes were assessed by additional questions preceded by "do you now have or have you ever had" angina pectoris (AP), myocardial infarction (MI), stroke or diabetes mellitus (DM). Only basic socio-demographic data and self-reported diseases are presented for descriptive purposes in this paper. Age was categorized into five groups: 18-29, 30-39, 40-49, 50-59 and 60+ years. Education was classified as secondary or lower, vocational, incomplete higher, or higher. Income level was very difficult to determine during the year 2000 owing to high inflation and a collapsing economy, with about 30% of the population  having incomes below the survival minimum. ConsequVenous blood samples were drawn in the morning and centrifuged within 15-25 minutes in the laboratory at the study site in Arkhangelsk. Because subjects generally fast in preparation for these annual medical check-ups, since screening for diabetes and impaired glucose tolerance is part of the examination, we assume that most of the study participants indeed fasted. Nevertheless, none of them was directly asked by the study team to fast prior to the medical examination. The serum samples were stored at -20°C and then transported frozen to Norway where they were kept at -80°C pending analysis. Total serum cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), serum glucose (SG) and glycosylated hemoglobin (HBA1c) were measured. All laboratory analyses were carried out at the Department of Clinical Chemistry, University Hospital of Northern Norway (UNN) in Tromsø.Enzymatic colorimetric tests were used to measure TC  and TG . HDL-C was measured by a homogenous enzymatic colorimetric test . The coefficients of variation (CV) were, respectively, 5%, 2% and 3% for the TC, TG and HDL-C determinations. All biochemical analyses of serum lipids were performed using a Hitachi 737 analyzer. SG was measured by the hexokinase method using a Hitachi 917 analyzer (CV = 2%). HBA1c concentration was determined using the Bio-Rad Variant II HPLC system with reagents from Bio-Rad Laboratories , with CV <5%. The laboratory routinely participates in formal quality assurance exercises.MetS was defined according to the National Cholesterol Education Program Adult Treatment Panel III (NCEP) , the Ame2 and ≥30 kg/m2, a receiver operating characteristic (ROC) analysis was carried out. Agreement between different diagnostic criteria for MetS was assessed by Cohen's kappa statistic. All analyses were performed using SPSS version 14 .The prevalence estimates were standardized by age using Segi's world standard population, European standard population and Russian population  . The folAmong the 3705 study participants, 150 (4.0%) had missing data on one or more variables and were therefore excluded. The final study group included 1918 men and 1637 women, corresponding to 95% of all those invited.The women were slightly older and were better educated, but had much lower income and a higher prevalence of self-reported diseases than men. The men smoked more and took alcohol more frequently Table .The prevalence of abnormally high components of MetS as well as BMI and WHR increased with age in both genders Table , as did 2 with sensitivity (Se) of 0.35 and specificity (Sp) of 0.99. The WC cut-off ≥102 cm identified men having BMI ≥30 kg/m2 with a Se of 0.41 and Sp of 0.99. In men of our study setting, the BMI cut-offs of ≥25 kg/m2 and ≥30 kg/m2 corresponded best to WC of ≥84 cm  and 92 cm , respectively. The standard WC cut-offs of ≥80 cm and ≥88 cm applied in women, which corresponded respectively to BMIs of ≥25 kg/m2 and ≥30 kg/m2, were originally characterized by good test properties .The prevalence of obesity Table  varied sThe overall prevalence of the MetS varied with the definition and reference population used Table . StandarUsing the NCEP definition of MetS, hypertension was the most frequent element in both sexes, followed by dyslipidemia Table . The preAlmost three quarters of the men and more than two thirds of the women in the total study sample had at least one MetS component Table . AltogetTo our knowledge, this is the first relatively large study addressing the prevalence of MetS and its components in Russia. While the prevalence of MetS among Russian women is comparable to the European and the USA data, the prevalence among Russian men is considerably lower than among their European and North-American counterparts. The low prevalence rates of MetS combined with the high cardiovascular mortality among Russian men need to be explored in further studies.Assessment of the MetS' burden is the first step towards monitoring the occurrence of the syndrome and developing effective preventive measures for this condition in Russia. The use of different internationally accepted diagnostic criteria and different standardization procedures provide a unique opportunity for comparison with both international and Russian studies. However, the results should be interpreted with caution, taking into account several limitations of the study.The method used to recruit the study population might to a certain degree have resulted in a residual "healthy worker effect". Unemployed and marginalized subsets of the population such as alcohol or drug abusers and the homeless were underrepresented. Exclusion of 150 individuals from the sample because of missing values might represent another weakness. However, the prevalence of individual metabolic components in this group did not differ significantly from the group included into analyses.ad hoc definitions of hyperglycemia was relatively fair (kappa = 0.68). However when comparing the MetS rates defined by the NCEP criteria based on these two ad hoc definitions of hyperglycemia, corresponding agreement was very good (kappa = 0.97). Similar results were seen for other definitions . This might be rationalized by the cluster nature of MetS and by the fact that hyperglycemia was the least prevalent metabolic abnormality in both genders. The impact of the latter on the probability of having MetS was minimal.There was a potential for clinical-chemical measurement errors in the study. Glucose was measured in serum, not in plasma. Because serum has a higher content of water, the cut-off point for defining hyperglycemia should be slightly higher. Moreover, we assumed that all blood samples were fasting but this was not ensured. Thus, the estimate of the prevalence of MetS may be inaccurate. To address these methodological problems, we also calculated the prevalence of MetS using two alternative criteria for hyperglycemia. The first criterion involved accepting one of the following: HBA1c ≥ 6.1 %, or self-reported DM, or receiving treatment for high blood sugar. The HBA1c marker reflects the average level of glycemia over the preceding 2'3 months and does not depend on fasting. In this study, HBA1c was measured using a precise and reliable method certified by the US National Glycohemoglobin Standardization Program . An earlThe new estimates obtained by applying these modified criteria were slightly lower than previously described (data not shown), but the agreements between all three estimates were ≥0.95, suggesting that the degree to which the prevalence of MetS was overestimated in this study is small.Our findings on the prevalence of MetS among Russian women are comparable to corresponding studies from Europe -11 and tOnly one research publication was found that examined the prevalence of MetS among Russians . It was A population-based study of men living in Kuopio in Eastern Finland  reportedThe prevalence of MetS in the Arkhangelsk study increased progressively with age. This has also been observed in other studies, but to a lesser extent -11. In o2 and 30 kg/m2. The cut-offs for obesity using WC depend on ethnicity [2 and ≥30 kg/m2 were about 10 cm lower  than the original one  suggesting a lower tendency for central adiposity at a given BMI among the men in our study setting. Similar results were reported from a study of middle-aged eastern Finnish men in the late 1980th [2 and 30 kg/m2. Therefore, the original cut-offs used for WC in NCEP and IDF definitions of MetS may be inappropriate for men living in Northwest Russia. This is an important finding that might largely explain the unequal distribution of MetS by sex. The finding needs further verification.The method used to define obesity  strikingly affected the reported prevalence of this condition in both genders. The variation was particularly striking among men, ranging from 6% to 26% using the WC and WHR definitions, respectively. By contrast, among women, the frequency was highest when obesity was defined by the WC-criteria. The original sex-specific thresholds for WC were originally (at least partly) established in cross-sectional studies from Holland and the UK ,34, usinthnicity . Accordie 1980th . On the The most reasonable explanation for our main findings is the difference in life-style between Russian men and women: women smoked less, had lower alcohol consumption and, what is more important, lower levels of physical activity. In general, Russian women have occupations involving low levels of physical activity at work , whereas men have higher levels of work-related physical activity.Our study did not support the hypothesis that the high burden of cardiovascular morbidity and mortality among Russian men could be attributed to a high prevalence of MetS. A high prevalence of smoking and life-style associated with excessive alcohol consumption and specific drinking patterns may be other contributors to the high cardiovascular morbidity and mortality among Russian men. Alcohol consumption was high in our study population , especiaDifferences in life expectancy between Russian men and women  may contribute to an explanation of the observed differences between genders in the prevalence of MetS , suggestWhile the prevalence of MetS among Russian women is comparable to the data for Europe and the USA, the prevalence among Russian men is considerably lower than among their European and North-American counterparts. Our results suggest that MetS is unlikely to be a major contributor to high mortality from cardiovascular diseases among Russian men. Further studies of determinants for MetS and its components, and MetS and cardiovascular risk are needed for a better understanding of the mechanisms leading to the exceptionally high cardiovascular mortality in Russia.The authors declare that they have no competing interests.OS, AMG and ON drafted the manuscript. ON, TB, VA and SM planned the study and collected the data. OS and TB analyzed the data. All authors read and approved the final version of the manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2458/10/23/prepub