Mineral content affects the biological processes underlying beef quality. Muscle mineral concentration depends not only on intake-outtake balance and muscle type, but also on age, environment, breed, and genetic factors. To unveil the genetic factors involved in muscle mineral concentration, we applied a pairwise differential gene expression analysis in groups of Nelore steers genetically divergent for nine different mineral concentrations. Here, based on significant expression differences between contrasting groups, we presented candidate genes for the genetic regulation of mineral concentration in muscle. Functional enrichment and protein-protein interaction network analyses were carried out to search for gene regulatory processes concerning each mineral. The core genetic regulation for all minerals studied, except Zn, seems to rest on interactions between components of the extracellular matrix. Regulation of adipogenesis-related pathways was also significant in our results. Antagonistic patterns of gene expression for fatty acid metabolism-related genes may explain the Cu and Zn antagonistic effect on fatty acid accumulation. Our results shed light on the role of these minerals on cell function. The second meat quality trait affected by minerals is beef tenderness since calcium related-proteases take part in the post-mortem degradation of myofibrillar proteins2. As minerals are essential for a variety of biological processes such as metabolism, homeostasis maintenance, growth, influencing cellular structural components, enzyme cofactors, regulation of cell replication, and differentiation3, they may affect other economically important traits in livestock production.The role of minerals in meat quality traits is perceived in the nutritional value of beef. For example, high iron content is a major player in the claims regarding nutritional value4, muscle type5, age, environment6, breed7, and other genetic factors8. Minerals can only perform their biological function in muscle cells if they are available in the right amount3. Their concentration is under strict control for homeostasis maintenance. The genetic control of mineral homeostasis is not fully understood, although there is evidence regarding specific genes and certain gene functions linked to mineral concentration in many species. Calcium concentration in humans depends upon a complex network of hormones9. Both serum and urinary calcium were deemed continuous heritable traits, in a study with twins9. Genes related to sodium and potassium homeostasis in humans were reviewed elsewere10. Magnesium absorption in bovine is breed dependent11. Zinc homeostasis is poorly understood, but in C. elegans there is a conserved motif called low zinc activation element in promoters that seems to be involved in the process12. Genes associated to copper transport in higher eukaryotes  were detected in a yeast functional screen that aimed to find genes linked to copper-dependent respiratory growth, which could be candidate markers to human mitochondrial diseases13. Genes related to iron concentration participate in lipid metabolism in Nelore cattle14. Still, information about genes associated with mineral composition in beef is scarce.Mineral concentration in mammalian muscles depends on animal intake-outtake imbalance4, Nelore15, Holstein, and Jersey16 cattle. These studies reported some overlapping QTLs and enriched functional processes among different minerals, indicating shared genetic regulation.Scientific evidence about genetic mechanisms associated with bovine mineral deposition regulation in muscle comes from a limited number of studies. Quantitative trait loci (QTLs) related to mineral concentration were described in Jersey x Limousin crosses17. Understanding the genetics and gene regulation associated with muscle minerals in cattle may also provide some evidence for how conserved these are across species. Given biochemical similarities across mammals, it is possible that increased knowledge from cattle studies might be generalized to humans.Once minerals participate in a variety of biochemical processes that might affect production traits, understanding the genetic and physiological processes underlying muscle mineral concentration might provide the basis for modulating these processes to the benefit of cattle production. Selective breeding could incorporate gene polymorphisms that influence mineral composition to improve the nutritional value of beefLongissimus thoracis muscle of contrasting mineral content Nelore steers, pinpointing genes, processes, and pathways related to mineral homeostasis. The minerals analyzed were Calcium (Ca), Copper (Cu), Potassium (K), Magnesium (Mg), Sodium (Na), Phosphorus (P), Sulfur (S), Selenium (Se), and Zinc (Zn).Herein, to characterize the biological pathways involved in muscle mineral deposition, we described a differential expression RNA-seq analysis from 15 and mineral concentration for contrasting groups confirmed they were significantly different and comparable, as shown in Table versus reads sequenced) from the samples assessed here are shown in Supplementary Fig. Each contrasting group for a specific mineral was called Low (L) or High (H) and differentiated by the corresponding mineral symbol. Due to the overlapping of samples among groups, 44 samples comprised our 18 groups. The average GEBV18 parameters were set to do not take into account genes with less than ten reads aligned, in both differential expression analysis and multiple test correction.We identified 29,312 transcripts but tested only 15,012 for differential expression due to their expression levels, since Cuffdiff v2.2.1COL11A1, COMP and TNMD genes, common to eight minerals . The minerals with more DEGs overlapping were Mg, Na, K, and P, with 25. In all expression comparisons between contrasting groups, upregulation means higher expression in H-groups than in L-groups. Conversely, downregulation means lower expression in H-groups than in L-groups. Unlike Zn, which had 50% of the DEGs downregulated, the remaining minerals presented at least 66% of the DEGs as downregulated.We identified 327 annotated DEGs considering all minerals. The number and annotation status of the DEGs were variable among the evaluated groups  between groups were TNMD, COMP, and COL11A1 for eight minerals (except for Zn); FBLN7 in seven of them (except for S and Zn), and CILP2 in six . Among DEGs with the lowest FC (downregulated in the H-group) for at least two minerals, we found PERP for Cu, K, Mg, P, and Se; TNC and THBS4 for Cu, K, Mg, and P; COL22A1 for Cu, Na, P, and Se; ADAM12 for Cu and Se; ACTC1 for K and P; CRABP2 and CRTAC1 for K, Mg, and P; KCNK2, MKX and MXRA5 for Ca and Cu.DEGs with the highest estimated FC ( > 1.9) between each contrasting mineral group were TF, HOXA9, MIR196B, and SYT4 genes as top downregulated in H-Ca group; ELOVL6, PTGIR, COL12A1, GAS2, POSTN, WISP1, MLLT11, and THRSP for Cu; PI1S for Na; MYLK3 for S; and RN5-8S1 for Zn. From Se and S analyses, RN5851 gene was upregulated in higher mineral concentration group.Regarding individual minerals, we found http://trinotate.sourceforge.net/) to identify possible biological functions of non-annotated DEGs. We retrieved possible functions for 31 transcripts. From these, 18 presented functions related to LINE-1 retrotransposable elements, retrovirus-related Pol polyprotein, and immune response related functions. We also recovered the function “similar to the protein SAMHD1”, a restriction nuclease that suppresses LINE-1 retrotransposition activity19 (Supplementary Table MRLN gene (91.30% of similarity). Another one was annotated as the Sentrin-specific protease 3, having homology with a mouse SENP3 gene (92.86% of similarity).We performed a functional annotation analysis applying the Trinotate pipeline  network analysis among DEGs for each mineral using STRING v.1.2.2 softwarei.e., they were either all upregulated or all downregulated  and two were exclusive to Cu concentration analysis (ELOVL5 and ACACA)  Fig. . From alCOL11A2 for K, Ca, Na, Mg and Cu; COL11A1 for P; CILP2 for K; CD44 for Na; and PTGIR for Cu.After filtering out DEGs that did not interact in our PPI network, 96 remained for Ca, 64 for Cu, 17 for K, 18 for Mg, 19 for Na, 20 for P, two for S, 11 for Se, and 10 for Zn. In total, Ca and Cu had more than 50% of their DEGs taking part in an interaction , P and Se had around 45% , K, Na, Mg, and Zn had about 35%  and S had the lowest rate of DEGs in interactions, 13.3%. From DEGs’ products that did not take part in protein-protein interactions, only five were part of a pathway: 15. Understanding the genetic component related to muscle mineral concentration might be useful to better comprehend mineral metabolism and metabolic diseases.Muscle mineral concentrations are moderately heritable traits. Estimates of heritability from our Nelore population ranged from 0.29 to 0.33As expected from the correlations among GEBVs for the minerals and from the biological interconnection among them, most contrasting groups for each mineral also differed concerning other mineral’s GEBVs, except for Cu and Se, even though they did not meet the criteria of representing both 5% extremes from the normal distribution. The most extreme example comes from the minerals Mg, Na, K, and P, presenting a correlation higher than 0.88 among their GEBVs. As a consequence, the same samples comprised the low-Mg and low-K, the high-Mg and high-Na and in the high-K and high-P groups. As the complementary contrasting group for each mineral had at least one different sample, the DEGs, functions, and pathways are not entirely the same among these minerals. Given this fact, one should consider that some correlated response regarding other than the mineral in discussion could exist within our results.15. Similarly, despite the correlations, there were no common QTLs among Mg, K, Na, and P15. Thus, although not fully explained by pairwise correlation, to some extent, the common DEGs, functions, and pathways among these four minerals can result from the high correlation and sample overlapping among them.Mg and K, which in our analysis showed a correlation of 0.97 and 46 DEGs in common, presented the same QTLs in previous experiments with this population, thus reinforcing the common genetic control for these minerals. However, Mg and P showed the same pairwise correlation and presented 38 common DEGs, but did not showed QTLs in common15, whose dataset included our samples, indicates conserved mechanisms affecting mineral concentration. The involvement of common DEGs in shared pathways among minerals reinforces that various genes affect these phenotypes.All the enriched gene functions convey to one or more enriched pathways. The previous detection of similar functional gene clusters in a GWAS for 14 minerals22. Our DEGs were not harbored in/or near QTL regions already reported15. However, the functional analyses of DEGs and QTLs pointed to similar gene functions. Due to this fact, we will focus our discussion on the genetic similarities among different mineral analyses.Differential expression and QTL analyses can produce similar functional annotation results, but different gene lists due to differences in the methodologiesMT2A and HSPA6 were the DEGs with the expression pattern presenting the highest FC contrariety and discrepancy among minerals. Both genes have a known relationship with heavy metals. The MT2A gene encodes a metallothionein protein that binds divalent heavy metals and participates in metal control and Zn homeostasis in the cell, affecting apoptotic and autophagy pathways23. MT2A was a DEG for almost all minerals in this study, except Ca and Se. It is downregulated in the H-Cu group and upregulated in the H-groups of Mg, P, Zn, Na, S, and K. From these, only Cu and Zn are divalent heavy metals. Different polymorphisms in MT2A or its promoter disturb Zn and Cd concentrations in human blood of healthy patients24 and carotid artery stenosis patients25. Our results suggest that, apart from the already described in the literature, this gene could also be related to the concentration of other non-heavy metal minerals like Mg, K, Na, P, and S.HSPA6 gene was upregulated in the H-Cu and H-S groups, while the opposite occurs for Zn and P. This gene product responds to stress, and its expression increases with the increase of heavy metal, like Cu, concentration26. This protein takes part in the fatty acid metabolism pathway (FAM), where Zn and Cu are essential. The relationship between HSPA6, P, and S is still unknown.The COMP, COL11A1, TNC, THBS4, and COL22A. They were involved in common pathways for at least six minerals, which may indicate a potential common genetic regulation of mineral concentration or a possible role of mineral concentration in the control of these genes expression. They genes act in pathways such as ECM-receptor interaction, focal adhesion, PI3K-Akt signaling pathway, and protein digestion and absorption. The first three pathways are interconnected (http://www.genome.jp/kegg/pathway/hsa/hsa04510.html).Among the downregulated DEGs with the lowest FC accross minerals, lower than −1.9, we found COMP and COL11A1, common to eight minerals, are part of the ECM-receptor pathway. They encode cell membrane proteins that mediate the interaction between the cell and extracellular matrix28. Ligands such as COL11A1 and COMP are essential for the initial steps of the ECM-receptor interaction pathway. Integrins continue the pathway processes, culminating in different cell functions such as growth and regeneration29. Also, focal adhesion and PI3K-Akt signaling pathways specifically need the involvement of integrins to start their metabolic processes29.The DEGs ITGA10 was predicted to interact with COL11A1 and COMP. All analyses showing COL11A1, COMP, and ITGA10 also showed the TNMD gene. This gene possibly interacts with ITGA10 by the THBS4 gene, which is also part of the three connected pathways. Moreover, ITGA10 connects to TNC, involved in collagen formation. Thus, COL11A1, COMP, and TNMD take part in the three integrated pathways for K, P, Na, Mg, and Cu by its interaction with ITGA10. Their downregulation in H-groups suggests that a high concentration of these minerals suppresses these pathways.The integrin gene 30, which explains this pathway being found in muscle transcriptome. Simple diffusion of minerals can occur through pores in the tight junctions if the electrochemical gradient exists to push the ions through the pores31. The CD44 gene, a DEG for almost all minerals, has a possible role in tight junction regulation32. The relation of ECM-receptor interaction pathway to mineral concentration may be partially explained by the tight junctions’ role in mineral absorption.The ECM-receptor interaction pathway plays an essential role in skeletal muscle development6. These results indicate that ECM-interactions are related to mineral concentration regulation for most of the minerals in this study.The protein digestion and absorption pathway was significant for six minerals . The DEGs in this pathway encompass genes from the collagen family. Collagens are the most abundant protein in the ECM and take part in cell adhesion regulation, cell migration, and direct tissue development, the latest initiating after modifications in the ECM structure mediated by substratesACACA, FASN, SCD, ELOVL6 and ELOVL5. From these, FASN, SCD and ELOVL6 were the only genes for Zn content in the same pathway. They all showed interactions between their encoded proteins. All DEGs included in this pathway were downregulated in Cu and upregulated in Zn analyses.Fatty acid metabolism pathway (FAM) was enriched in Zn and Cu analyses. Cu analysis identified five DEGs in this pathway, 33, and Zn has an antagonistic relationship in this phenomenon34. The five FAM related genes involved in Cu analysis take part in the cytoplasmic portion of the pathway, in which fatty acids biosynthesis occurs by the addition of one or more acetyl-CoA molecules, doubling the number of carbons in the fatty acid molecule produced in each cycle, as per KEGG data (https://www.genome.jp/kegg-bin/show_pathway?map01212).Animals with clinical Cu deficiency tend to accumulate fat due to disturbances in FAMACACA, that catalyzes the carboxylation of acetyl-CoA to malonyl-CoA35. Subsequently, the product of FASN is responsible for the elongation of the fatty acid chains to precursors with 16 carbons. The elongation to 18 carbons requires the product of ELOVL635. After that, the Stearoyl-CoA desaturase enzyme, which is the product of SCD, catalyzes the synthesis of Oleic acid31. Cu is a cofactor of this enzyme36 and, in the presence of this mineral, the FAM progresses just until the production of fatty acids with 20 carbons by the product of ELOVL531, because it inhibits the production of Linoleic acid by increasing the Oleic acid synthesis36. The downregulation of ACACA, FASN, ELOVL6, SCD, and ELOVL5 in the H-Cu group can explain the inhibition of long-chain fatty acids and fat accumulation under low Cu.Fatty acid biosynthesis can start with the co-enzyme Acetyl-CoA carboxylase, the product of 35. In rabbits, copper supplementation in the diet decreased the intramuscular fat content by improving fatty acid uptake and fatty acid oxidation37. This switch depends on the regulation of malonyl-CoA. For example, in ketosis, ketonic bodies accumulate in the tissue, and the activation of malonyl-CoA activates AMPK. This activation breaks malonyl-CoA, stopping the biosynthesis and starting the oxidation of fatty acids35. The AMPK signaling pathway was enriched for Cu.A second hypothesis is that malonyl-CoA can also be the switch from fatty acids biosynthesis to fatty acids oxidation and energy production, which can lead to less fatty acid biosynthesisFASN, ACACA and SCD, belonging both to AMPK and FAM pathways, as well as ADIPOQ, PCK2 and LEP genes, which are exclusive from the AMPK pathway. Thus, animals with less Cu can have higher fat accumulation by biosynthesis (FAM)38 or oxidation 37; probably by both processes.The second hypothesis can be reinforced by the simultaneous presence among DEGs for Cu of the genes 38. In the Zn analysis, we did not identify the ACACA gene as a DEG. Therefore, we hypothesized that, in this case, the product of FASN does the first step of fatty acid synthesis. As already discussed, the pathway continues to the precursor of oleic acid. However, in the presence of high Zn, the pathway does not stop on fatty acids with 20 carbons29. Zn stimulates the linoleic acid desaturation and the production of long-chain fatty acids29.Zn is a known Cu antagonist in FAM, due to its role in the stimulation of linoleic acid desaturation39. This breed has more intramuscular fat than European cattle breeds. In our population, we did not identify a significant correlation between the GEBVs for oleic acid and the GEBVs for Zn and Cu concentration. We found a weak positive correlation (r = 0.23) between linoleic acid and Cu GEBVs (data not shown). The absence of higher correlations can be attributed to the little variation of these minerals15 and fat deposition in our samples40. The samples used in the two contrasting groups for Cu and Zn analyses did not present significant (p > 0.05) difference for seven fatty acids concentrations obtained elsewhere41 (data not shown). Also, our animals did not exhibit a clinical deficiency of these minerals. Thus, we can assume that, even if the difference in expression did not lead to a significant increase in fat, animals with low Cu concentration present modifications in FAM.In Japanese Black Cattle, there is a low negative correlation between Cu concentration and oleic acid (−0.15), between Cu and linoleic acid , and between Zn and linoleic acid (−0.05)42. Only PLIN1 gene was in the other fatty acid associated pathways. This gene was found downregulated in Cu, like all the other FAM related genes, and its product is involved directly in lipid metabolism43 and adipocyte differentiation44. LEP gene is also related to Cu and Zn and has an alleged role in the PPAR pathway regulation. It has a well-known relationship with obesity and stimulus for fatty acid oxidation45. As all the DEGs mentioned in FAM, LEP was upregulated in Zn and downregulated in Cu analyses and interacted with all DEG products in this pathway, when considering Cu, and with FASN and SCD, when considering Zn. FAM genes were already shown to be related to iron concentration in a differential expression analysis with samples from the same population used in this study14.PPAR signaling pathway, enriched in Cu analysis, was also identified and is related to FAM. PPAR is one of the significant adipogenesis activatorsSENP3 gene. This gene has high similarity to other SENP family protein gene, SENP246. Both encode proteases that release SUMO3 and SUMO2 monomers, involved in several biological processes46. Regarding fat deposition, overexpression of SENP2 increases fatty acid oxidation by upregulating the expression of enzymes linked to this process47. This non-annotated DEG can corroborate the hypothesis of the involvement of Cu concentration in fatty acid oxidation in cattle.We retrieved high similarity with known proteins for two non-annotated DEGs for Cu. One of them, downregulated for Cu, is similar to the mouse THRSP gene, identified as upregulated for Cu and Zn, encodes a nuclear protein involved in fatty acid synthesis48 interacting with FASN and ELOVL6. THRSP upregulation activates FASN49, being a candidate to the mechanism of FAM regulation by Zn.The PCK2 is a candidate for obesity50 and is part of AMPK and PPAR signaling pathways. This gene’s product interacts with ACACA. It has an impact in FAM by receptor interaction and changes in RBP4 gene, which plays a role in non-alcoholic fatty liver disease and can contribute to insulin resistance51. All these genes and pathways linking Cu and Zn to lipid metabolism can explain the genetic mechanisms underlying Cu associations to FAM and Zn antagonism in these processes.Among the other four genes downregulated for Cu, COMP, FCGR3A, BLA-DQB, and THBS4 genes are involved in the phagosome pathway, all downregulated for K. BLA-DQB encodes an antigen, and the other genes encode glycoproteins with already known roles in phagocytosis. Potassium channels are known to modulate changes in the membrane during phagocytosis52, which can explain the relationship between the expression of these genes and K concentration.C1QB, C1QC, C7, and NCAM1 were downregulated for Ca and partake in the prion disease pathway. The first two genes showed an interaction, and they encode proteins that form the complement component 1, involved in the immune complement system. These genes are linked to the LAPTM5 gene, which encodes a lysosomal transmembrane protein. C7 gene encodes a serum protein involved in the immune system and is connected to C1QTNF3, a gene that encodes another protein involved in the immune complement system. NCAM1 gene encodes a protein that is a cell adhesion linked to CD44, part of the ECM-receptor pathway, showing that all pathways detected in this study are linked.The genes in silico prediction.By comparing the expression of genes in muscle samples with contrasting mineral concentrations, we hypothesized that the genetic regulation core for all minerals studied, except Zn, resides in events of extracellular matrix interaction. ECM-receptor interaction, focal adhesion, and PI3k-Akt signaling pathways seem to be related to K, P, Na, Mg, Cu, and Ca content profiles in skeletal muscle. We also pointed out genes that may explain Cu and Zn association to adipogenesis-related pathways, as well as their antagonism on fat accumulation. Future studies can target our raised hypotheses and validate our DEGs to elucidate these biological mechanisms, since our main goal was 15, and RNA-Seq14. The entire sample group comes from a population of 373 Nelore steers fathered by 34 purebred Nelore sires in half-sibling families.All animal and experimental procedures were carried out following the guidelines provided by the Institutional Animal Care and Use Committee Guidelines of Embrapa Pecuária Sudeste ethics committee . The Ethical Committee of Embrapa Pecuária Sudeste  approved all experimental and animal protocols . A group of 133 Nelore steers composes our samples that previous projects already used to produce data for mineral concentrationad libitum feed access twice a day with 5% refusals, discarded daily. The diet contained 40% of dry matter constituted by corn silage, crude protein, ground corn, soybean meal, cottonseed, soybean hull, limestone, mineral mixture, urea and monensin (Rumensin®).The animals used in our work result from artificial insemination, were born in two different breeding seasons, in two different farms. Approximately at 21 months of age, all animals used in this research were transferred and maintained in a feedlot at Embrapa Pecuária Sudeste . After a 28 days adaptation period, they received food, water, and had a similar nutritional and sanitary management until the slaughter. The animals had 6 from Longissimus thoracis muscle steaks sampled between 11th and 13th ribs. Briefly, the samples were lyophilized and digested with microwave assistance using a closed-vessel microwave digestion system . The mineral quantification was obtained in the Vista Pro-CCD ICP-OES spectrometer with a radial view . Among measured minerals we selected Calcium (Ca), Copper (Cu), Potassium (K), Magnesium (Mg), Sodium (Na), Phosphorus (P), Sulfur (S), Selenium (Se), and Zinc (Zn) for our analyses because they have distinguished extreme animal groups.Mineral concentrations were measured as described elsewhere15 for 373 animals encompassing our samples using a Bayesian model implemented in GenSel software53. The model considered contemporary groups formed by birthplace, feedlot location, and breeding season as fixed effects and age at slaughter as a covariate. The GEBVs were used to select 12 animals for each mineral with extreme phenotypes .The genetic breeding values (GEBV) for all mineral’s concentration were estimated elsewhere14. Total RNA was extracted using TRIzol® . Its integrity was analyzed in a Bioanalyzer 2100® . Library preparation for RNA-Seq analysis was carried out using the TruSeq RNA Sample Preparation Kit . Sequencing was carried out in an Illumina HiSeq 2500®. All laboratory procedures were carried out in ESALQ Genomics Center .We used muscle samples from all animals in each contrasting group for RNA extraction and RNA-Seq analysis as described elsewhere14 with the insertion of StringTie v1.2.254 instead of Cufflinks18 in Tuxedo Suite pipeline18.RNA-Seq data obtained from muscle samples belonging to contrasting groups for a given mineral were used to determine DEGs for each mineral. The pipeline was as described inhttp://sourceforge.net/projects/seqclean/files/) was used to trim low-quality sequences and adapters. TopHat software v2.0.1118 was used to align reads to the reference bovine genome . After that, the StringTie v1.2.254 was used to assemble the transcripts and to estimate their expression levels, normalized as FPKM (fragments per kilobases per million). Cuffdiff v2.2.118 was then used to test for differential expression, calculating the average of each gene expression among the samples of the same contrasting group and calculating the FC. Only transcripts that passed the threshold of at least ten fragments aligned entered the differential expression test.SeqClean software (http://trinotate.sourceforge.net/) to identify possible functions of non-annotated and predicted differentially expressed proteins for the minerals.We performed a functional annotation analysis using Trinotate pipeline (We used a pairwise Pearson correlation analysis for the GEBVs of all minerals to quantify their dependency. Also, we performed a Pearson correlation analysis between GEBV and raw concentration measure for each mineral in order to convey the reliability of the GEBVs. A t-test was applied to verify if the mean GEBVs of the samples for all contrasting groups would also be statistically different for any other mineral.20 to discover biological processes in which the DEGs are acting. To access known protein-protein interaction regarding DEGs and pathways in which they may participate, we used STRING v10.5 software55.We performed enrichment analysis using DAVID v6.8 softwareSupplementary information


The 1-year and 5-year relative survival (RS) rates of patients with PRL were 78% and 64%. The RS of patients diagnosed between 2000 to 2013 was better than that of patients diagnosed between 1980–1999. A multivariate Cox hazards regression analysis revealed that older age, male gender, diagnosis before 2000, advanced stage, not receiving surgical treatment, and DLBCL or T/NK cell lymphoma type were independent predictors of unfavorable survival.Primary renal lymphoma (PRL) is a rare lymphoid malignancy with only a few cases reported in the literature. We performed a population-based study of PRL to determine its incidence, clinical characteristics and factors associated with survival using the Surveillance, Epidemiology, and End Results (SEER) database. We identified 723 patients with PRL. The most common histological subtype of PRL was diffuse large B-cell lymphoma (56.3%). The incidence and mortality rate of PRL was 0.053/100,000 person-years and 0.036/100,000 person-years, respectively. The incidence rate of PRL was increasing significantly with an annual percentage change (APC) of 3.45% ( It is defined as lymphoma involving the kidney without prior lymphatic disease beyond the kidney2. The etiology of PRL is not clear. As a normal kidney does not contain lymphoid tissue3, it has been postulated that PRL originates from the renal capsule, which is rich in lymphatic tissue and penetrates the renal parenchyma4. Another hypothesis is that lymphoid cells are attracted by the chronic inflammatory conditions of the kidney and develop lymphoma eventually6. Clinical presentation of PRL is nonspecific and includes such symptoms as gross hematuria, flank pain, loss of weight, fever, or acute/chronic kidney failure7. Renal biopsy is the gold standard for diagnosis8.Primary renal lymphoma (PRL) is a rare disease that comprises less than 1% of extranodal lymphomas9. Up to our knowledge, only 70 cases were reported as stated in a literature review conducted in 20162. Using data from the Surveillance, Epidemiology, and End Results (SEER) database, our study aimed to examine the incidence, clinical characteristics and survival of patients with PRL as well as to determine prognostic factors in a population-based cohort in the United States.As a result of the rarity of this malignancy, there are very limited data on its incidence, optimal management and clinical outcome, mainly from case reports or case series10.The SEER database is a program of the National Cancer Institute, representing nearly 30% of the US population from 18 registries currently. This population-based program collects information on patient demographics, morphology and site of primary tumor, follow-up and outcomes. Further information about SEER can be found on its web site11. We used International Classification of Diseases for Oncology, 3rd edition (ICD-O-3) histologic codes 9590–9595, 9650–9699 and 9702–9729 for lymphoma and primary site code C64.9 for the kidney to identify all patients with PRL diagnosed between 1980 and 2013  database released in April 2016 using SEER*Stat software 14.The following patient-specific information was extracted: age at diagnosis, gender, race, year of diagnosis, marital status, laterality, morphological subtype, cancer stage, therapy type, the length of survival and the cause of death as recorded in the database. Cancer stages reported were according to the Ann Arbor staging system of American Joint Committee on Cancer (AJCC) (7th edition)15. The significance level was calculated using Student’s t test16. We analyzed the overall survival (OS), disease-specific survival (DSS) and relative survival (RS) using the Kaplan-Meier method. OS rate is defined as the percentage of survivors  after a duration of follow-up time. DSS rate is defined as the percentage of patients who have not died from PRL (rather than from other causes) in a defined period of time17. RS is defined as the ratio of the proportion of observed survivors in a cohort of cancer patients to the proportion of expected survivors in a comparable set of cancer free individuals. The Ederer method was used to calculate RS18. The cases whose survival month was 0 or unknown were excluded in the survival analysis  between 1980 and 2013. The incidence rates were age-adjusted to the 2000 US standard population and expressed per 100,000 person-years. A log-linear model was used to calculate APCs of incidence and mortality rates. If there were no cases or deaths recorded in a certain year, the incidence or mortality rate in that year would be zero, which was replaced with a small positive constant (0.0001) for logarithmic transformationAll analyses were performed using SEER*Stat software , Joinpoint Regression Program , SPSS Statistics software  and STATA MP . Tests were two-tailed, with a p-value of less than 0.05 considered statistically significant.This article does not contain any studies with human participants or animals.P < 0.001)  Table . A totalp < 0.001) , and remained stable in the years between 1995 and 2013   and 0.036 per 100,000 person-years , respectively. Overall, the incidence of PRL increased from 0.020 per 100 000 person-years  in 1980 to 0.066 per 100 000 person-years  in 2013, generating an APC of 3.45% . Compared to female patients, male patients also had higher mortality rate of PRL .The incidence rate of PRL among male population  was higher than that of females , both of which were increasing significantly in the study period , male gender , diagnosis before 2000 , advanced stage , not receiving treatment with surgery  and a histologic subtype of DLBCL  were associated with unfavorable survival for patients with PRL (Table p < 0.001), male gender , diagnosis before 2000 , advanced stage , not receiving treatment with surgery  and the histologic subtype of DLBCL  or T/NK cell lymphoma  25. In our model, Ann Arbor stage was also a determinant.The median OS of patients with PRL that we calculated was 77 months, which is better than the OS found by a previous review of 49 patients29.An improvement in prognosis after the year 2000 was also noted. We observed increased 5-year RS and DSS rates since 2000. We also found that both the incidence and mortality rates elevated with time. However, the trends in incidence increased faster than that of mortality since 1995. Thus, the better survival of PRL observed in our study since 2000 may reflect improved diagnostics in the modern era. Besides, the introduction of rituximab may also account for this improvement in prognosis, as the outcomes of patients with CD20-positive B lymphoma were significantly improved by the targeted therapy of rituximab2. Here, we identified 7.9% of the cases with bilateral involvement with a slight predominance of left involvement. Previous studies suggested that PRL generally appeared to be bilateral in patients who were younger; these studies also showed a shorter survival time with bilateral PRL than with unilateral PRL2. In our study, patients younger than 18 also had a higher percentage of bilateral involvement. However, we did not observe a survival difference between unilateral and bilateral PRL.PRL is usually unilateral, whereas it can also be bilateralOur data suggested that NHL constituted the majority of PRL, and the most prevalent subtype was DLBCL. HL accounted for only 0.7% of all lymphomas in our study. As an indolent type, MZL was associated with a good prognosis with a 1-year RS rate of 94% and a 5-year RS rate of 87%, while T-cell or NK-cell lymphoma revealed particularly poor outcomes, with a 1-year RS of 51%. We did not find a significant difference in survival in patients with renal DLBCL compared with nodal DLBCL.31. Belbaraka R et al. recommended chemotherapy regimens of R-CHOP to manage PRL rather than surgery30. However, Chen X. et al. reported a better outcome for patients who received surgical treatment2. In the current study with a large population, we observed that surgical treatment was associated with a significant improvement in survival. The use of radiation in PRL is also poorly studied. Here, we did not find a survival advantage for patients who received radiotherapy.The role of surgery in PRL is controversial, and evidence is based only on case series or literature reviews2. To reduce the risk of including secondary renal involvement of terminal systemic or adjacent lymphoma, our study focused on a diagnosis of lymphoma and a primary location in the kidney with no history of prior cancer diagnosis, as defined by the ICD-O-3. Second, there is a lack of treatment information on chemotherapy from the SEER database. It is not possible to know the actual proportions of patients who received chemotherapy, and such data are important because chemotherapy plays an important role in the management of lymphoma. Third, the SEER database does not provide other objective prognostic factors  or other laboratory parameters in order to perform further analysis.Our study has several limitations. First, the diagnosis of PRL has been controversialNevertheless, based on a large population, the SEER database remains a valuable source for studying such rare kinds of cancers despite these limitations. This population-based approach can normalize the outcome analysis from potential selection biases, standardize both category and outcome definitions, and provide reasonable statistical utility to identify prognostic factors.In this study, we adopted a population-based dataset to examine the incidence, clinical characteristics and factors associated with survival of an extremely rare disease. The incidence rate of PRL is increasing over time. Older age, male gender, diagnosis before 2000, advanced stage, not receiving treatment with surgery, and DLBCL type or T/NK cell lymphoma type were associated with worse survival of patients with PRL in the multivariate analyses.Supplementary materials


Therefore, the identification of biomarkers that differentiate these diseases is of importance for preventing misdiagnosis. In this regard, the present study aimed to identify the disorders at the early stages, based on lung transcriptomics data and drug-target interactions.To this end, the differentially expressed genes were found in each disease. Then, WGCNA was utilized to find specific and consensus gene modules among the three diseases. Finally, the disease-disease similarity was analyzed, followed by determining candidate drug-target interactions.The results confirmed that the asthma lung transcriptome was more similar to COPD than IPF. In addition, the biomarkers were found in each disease and thus were proposed for further clinical validations. These genes included RBM42, STX5, and TRIM41 in asthma, CYP27A1, GM2A, LGALS9, SPI1, and NLRC4 in COPD, ATF3, PPP1R15A, ZFP36, SOCS3, NAMPT, and GADD45B in IPF, LRRC48 and CETN2 in asthma-COPD, COL15A1, GIMAP6, and JAM2 in asthma-IPF and LMO7, TSPAN13, LAMA3, and ANXA3 in COPD-IPF. Finally, analyzing drug-target networks suggested anti-inflammatory candidate drugs for treating the above mentioned diseases.In general, the results revealed the unique and common biomarkers among three chronic lung diseases. Eventually, some drugs were suggested for treatment purposes. Lungs are considered as vital and vulnerable parts of the respiratory system and play critical roles in the body  are struggling with COPD. The incidence rate has indicated an increase of 44.2% from 1990 to 2015  were estimated based on healthy and patient states for the samples . The mean of expression was used for multiple probes mapping to the same gene  . The gene list of shared DEGs between the two diseases (three states) and among the three diseases was used for further investigations Fig. .Fig. 1WAccording to the study by Horvath and Dong  was calculated using the pickSoftThreshold function. Then, disease-specific adjacency matrices were generated utilizing the selected β threshold and were transformed to the Topological Overlap Matrix (TOM) using the TOMsimilarity function : The correlation between gene expression profile in the modules and the disease states, as well as the correlation between the MEs and the gene expression were referred to as Gene Significance (GS) and Module Membership (MM), respectively. Modules with a P < 0.05 were filtered in the MM-GS analysis for each disease.Analysis of determining the genes associated with the disease: The DisGeNET (version 5.0) online database was used to determine the number of genes associated with each disease within the significant modules that were obtained in previous steps (a and b)  is defined as the first principal component of the gene expression in each module. In the present study, the MEs were combined using the mergeCloseModules function based on their similarity in the profile of the gene expression. This function uses a cutHeight parameter to determine the correlation among the MEs. The parameter was set to 0.15 in this study, and modules with a correlation ≥0.85 (1-cutHeight) were merged accordingly. Finally, three subsequent analyses were used to identify the most important modules for each disease as follows   and pathway databases, including the Kyoto Encyclopedia of Genes and Genomes (KEGG), Biocarta, and Reactome via the Enrichr  was utilized to identify the candidate drugs in the studied lung diseases  and GSE23611 (GPL6480) were used in this study. After data normalization and quality control, 34 COPD samples  and 32 healthy controls, as well as 118 IPF samples and 86 healthy controls from the GSE47460 dataset were utilized to determine the DEGs. In addition, 26 samples from asthmatic patients and 7 healthy controls were selected from GSE23611. The results of differential analyses are displayed in Fig. The DEGs with a matching up/down-regulated pattern between two or among three diseases were nominated as the DEGs with Identical Pattern (DIPs) to assess disease-disease expression similarity. The results of the three pairwise comparisons revealed 616, 615, 1452, and 304 shared DEGs between asthma-COPD, asthma-IPF, COPD-IPF, and asthma-COPD-IPF, respectively. Table Based on the results, there are more DIPs between asthma and COPD compared to COPD and IPF, as well as asthma and IPF. However, there are more DIPs between COPD and IPF compared to asthma and COPD or asthma and IPF.Disease-specific DEGs  were used to construct three disease-specific co-expression networks. The optimal β parameter for obtaining a scale-free network was computed as 11, 15, and 21 for COPD, IPF, and asthma, respectively  were constructed, including asthma-COPD, COPD-IPF, and asthma-IPF. Each CN was used to extract the Consensus Modules (CMs). The plots of β parameter computation for the constructed CNs are provided in Additional file A CN including 304 shared DEGs was reconstructed to extract the CMs among the three diseases. However, no CM could be extracted using the WGCNA.Enrichment analysis was performed to functionally evaluate the gene sets that were involved in specific modules, CMs, and overlapping DEGs among three diseases as follows.Table Some of the enriched significant biological pathways and GO biological processes of the CMs are reported in Table Table The candidate drug-target interactions were investigated after identifying consensus and specific modules among three diseases. Figure Figure Figure Progressive chronic respiratory diseases are associated with a persistent and progressive decline in lung function and exacerbations, which require smart diagnosis and treatment. Decreasing the rate of progression, staging, and exacerbations in chronic respiratory diseases such as asthma, COPD, and IPF is critically vital in controlling mortality -beta signaling pathways. These pathways are involved in the inflammation of the respiratory tract, immune system, and chronic inflammatory diseases  were investigated after evaluating the disease-specific modules. Also, two modules (Turquoise and Pink) for asthma-COPD, one module (Purple) for asthma-IPF, and three modules  for COPD-IPF pairs were identified after the module-trait analysis. Biological pathways and GO analysis conducted for the genes, are involved in each significant consensus module.Based on the results of the biological process analysis for the asthma-COPD, the genes of the turquoise module were mainly enriched in cilium assembly, cilium organization, cilium movement, and plasma membrane-bounded cell projection assembly. These processes were due to the dysfunction of the bronchial epithelial and the disruption of cilia function as the important features of asthma and COPD diseases  were identified in asthma-IPF among ten hub DEGs. In the purple module, COL15A1 is involved in the smooth muscle cell (SMC) phenotype that is controlled by the epigenetic state, so that the hypomethylation of COL15A1 transpires during SMC proliferation and the consequent increased gene expression may affect the SMC phenotype (Connelly et al. Ca2+-dependent manner. It also plays a vital role as a mediator in cellular growth, signal transduction pathways, cancer proliferation, invasion, and migration (Wang et al. The results of the biological process analysis for COPD-IPF represented that the genes of the blue module are enriched in immune processes, inflammation, and remodeling. Nine novel hub DEGs were specified in COPD-IPF, including GPM6A, ARHGEF26, ANKRD29, LMO7, TSPAN13, LAMA3, ST6GALNAC5, ANXA3, and CLIC5. In the blue module, LMO7 is known as a negative feedback regulator of TGF-β signaling and extracellular matrix deposition (Xie et al. Previous studies have only evaluated the overlap mechanism between two diseases whereas the present study considered three diseases together and could identify the focal biological processes for all three diseases. These results were not observed due to the presence of other genes available in the top rank between two diseases, and the statistical analysis led to these processes that were not shared in the overlap of two diseases. Likewise, the processes observed in COPD and IPF confirmed the irreversibility of these diseases whereas a cure is suggested in asthma.Based on the data in Tables Drug-target identification for consensus and common DEGs in three diseases revealed that based on specific DEGs, tretinoin as a retinoid drug is recommended for all three diseases. Retinoids are involved in many essential functions of the body such as the immune system, cell proliferation, and differentiation, bone growth, the activation of tumor suppressor genes, as well as natural and malignant epithelial cell growth in various tissues (Buckley et al. Bevacizumab is one of the VEGF inhibitors that can be identified as a candidate for COPD (Papaioannou et al. Low-dose paclitaxel is one of the TGF-β1/Smad3 pathways suppressing drugs, and in this study, it was identified as a candidate drug for IPF. TGF-β1 is responsible for creating a cascade of incidents leading to fibrosis, and the abnormal activation of TGF-β1 plays a vital role in the pathogenesis of IPF (Wang et al. Likewise, sirolimus as an immunosuppressant drug is suggested as a candidate for the asthma-COPD therapy. This drug inhibits the activation of T and B-cells by inhibiting mTOR and plays an essential role in the biogenesis of the ribosome, cell cycle progression, lipid synthesis, mitochondrial biogenesis, and autophagy of COPD (Miłkowska-Dymanowska et al. Tacrolimus is one of the candidate medicines in asthma-IPF as well. This drug inhibits the transcription of IL-2 and several other cytokines, especially in T-helper lymphocytes (Horita et al. In addition, cyclophosphamide is regarded as one of the candidate drugs for COPD-IPF. The combination of this drug with corticosteroids is used to reduce the severity of IPF (Emura and Usuda The current study proposed unique and common biomarkers and drugs among the three diseases. Moreover, it was confirmed that asthma lung transcriptome is more similar to COPD than IPF. These findings could help the researchers to differentiate the mentioned diseases. However, this study has some limitations including ignoring the side effects of the drugs. Additionally, novel biomarkers were proposed computationally. Therefore, the clinical studies are recommended to confirm the validity and reliability of the results of this study, as well as those of similar studies in the future.The findings indicated that the proposed biomarkers in chronic lung diseases might serve as predictive targets among the three diseases. They include RBM42, STX5, and TRIM41 in asthma, CYP27A1, GM2A, LGALS9, SPI1, and NLRC4 in COPD, as well as ATF3, PPP1R15A, ZFP36, SOCS3, NAMPT, and GADD45B in IPF. In addition, other identified genes were LRRC48 and CETN2 in asthma-COPD, COL15A1, GIMAP6, and JAM2 in asthma-IPF, along with LMO7, TSPAN13, LAMA3, and ANXA3 in COPD-IPF. Finally, anti-inflammatory drugs such as corticosteroids were proposed for treating the three diseases.Additional file 1. The list of DIPs and shared DEGs among three diseases.Additional file 2 Selecting the optimal β parameter for disease-specific networks.Additional file 3. The list of all gene symbols in candidate disease-specific modules.Additional file 4. The list of selected modules for three diseases.Additional file 5 Selecting the optimal β parameter for consensus networks.Additional file 6. The list of all gene symbols in candidate consensus modules.Additional file 7. Significant biological pathways and GO Biological Processes in disease-specific modules.Additional file 8. Significant biological pathways and GO Biological Processes in consensus modules.Additional file 9. Significant GO biological processes of overlapping DEGs among three diseases.Additional file 10:. The list of candidate disease-specific drug-target interactions.Additional file 11. The list of candidate consensus drug-target interactions.Additional file 12. The list of candidates shared drug-target interactions among three diseases.


The article "Genetics of Perceived Family Interaction From 12 to 17 Years of Age", written by Karri Silventoinen, Jinni Su, Lea Pulkkinen, Peter Barr, Richard J. Rose, Danielle M. Dick, Jaakko Kaprio, was originally published electronically on the publisher’s internet portal (currently SpringerLink) on 24 May 2019 without open access.http://creativecommons.org/licenses/by/4.0/), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.With the author(s)’ decision to opt for Open Choice the copyright of the article changed on 13 June 2019 to © The Author(s) 2019 and the article is forthwith distributed under the terms of the Creative Commons Attribution 4.0 International License (The original article has been corrected.


Staphylococcus aureus, a common cause of hospital and community outbreaks. Among the rich diversity of mobile genetic elements regularly occurring in S. aureus such as phages, pathogenicity islands, and staphylococcal cassette chromosomes, plasmids are the major mean for dissemination of resistance determinants and virulence factors. Unfortunately, a vast number of whole-genome sequencing projects does not aim for complete sequence determination, which results in a disproportionately low number of known complete plasmid sequences. To address this problem we determined complete plasmid sequences derived from 18 poultry S. aureus strains and analyzed the prevalence of antibiotic and heavy metal resistance determinants, genes of virulence factors, as well as genetic elements relevant for their maintenance. Some of the plasmids have been reported before and are being found in clinical isolates of strains typical for humans or human ones of livestock origin. This shows that livestock-associated staphylococci are a significant reservoir of resistance determinants and virulence factors. Nevertheless, nearly half of the plasmids were unknown to date. In this group we found a potentially mobilizable plasmid pPA3 being a unique example of accumulation of resistance determinants and virulence factors likely stabilized by a presence of a toxin–antitoxin system.The use of antibiotics on a mass scale, particularly in farming, and their release into the environment has led to a rapid emergence of resistant bacteria. Once emerged, resistance determinants are spread by horizontal gene transfer among strains of the same as well as disparate bacterial species. Their accumulation in free-living as well as livestock and community-associated strains results in the widespread multiple-drug resistance among clinically relevant species posing an increasingly pressing problem in healthcare. One of these clinically relevant species is  Staphylococcus aureus. As an opportunistic pathogen S. aureus can colonize healthy individuals and reside asymptomatically as a commensal. The estimated carriage rate in communities reaches between 25 and 30% . Each of them poses a serious threat to people’s health , resistant to an array of β-lactams, as well as vancomycin intermediate/resistant S. aureus (VISA and VRSA), resistant to one of last resort drugs, vancomycin. However, the spread of other antibiotic resistance determinants is not to be underestimated.Growing antibiotic resistance among clinically relevant bacteria is currently becoming a grave concern of global health. The shrinking array of effective antibacterial drugs elevates mortality and increases frequency and scale of outbreaks . One of  and 30% . Regardl setting . Communi strains . For alls health . With reContinuing emergence of resistance determinants against novel drugs seems to be only a smaller part of a bigger picture. In fact, their rapid spread is what truly aggravates the problem in healthcare. When speaking about dissemination of resistance determinants, the role of mobile genetic elements (MGEs) and mechanisms determining their mobility should be considered. In staphylococci these include predominantly: transposons, insertion sequences, and integrons , which sApart from antibiotic resistance determinants, MGEs facilitate spread of heavy metal resistance and genes encoding virulence factors. The latter are a diverse group of mostly proteins and peptides that facilitate colonization and infection progress. These include for instance: cell-wall-anchored surface proteins interacting with host proteins, extracellular proteases, lipases, nucleases, super-antigens or cytotoxic proteins, and peptides . AcquisiNonetheless, emergence and dissemination of either antibiotic resistance determinants or virulence factors’ genes do not comprise the full picture. The last piece, out of the crucial ones, seems to be their stable maintenance. This is of particular importance to those encoded on low-copy-number plasmids . Once acS. aureus strains are reported along with a detailed analysis of distribution of antibiotic and heavy metal resistance determinants as well as other relevant factors among them. Moreover, the physiological effect of such determinants’ presence is carefully examined. Finally, a unique example is presented of plasmids’ shuffling and fragment exchange leading to accumulation of drug and heavy metal resistance determinants along with a TA operon in a likely mobilizable plasmid pPA3.In this study, complete sequences of 16 different plasmids, including 7 unknown to date, derived from 18 poultry S. aureus used in this study have been described previously elsewhere  using the Illumina MiSeq system. The obtained data were processed in CLC Genomics Workbench, version 8.5.1 (Qiagen/CLC Bio). Reads were analyzed, trimmed, and filtered using Trim Sequences tool from the NGS Core Tools package, at default parameter values, and subsequently assembled into contigs using on 2.3.0 , and ordoriT mimics were searched using the core sequence of those from conjugative plasmids , yes; estimate topology, yes; estimate gamma distribution parameter, yes; perform bootstrap analysis, yes; and replicates, 100.The detection of pAvY, pAvY-B1, and pAvY-B2 plasmids was carried out by PCR reaction using primers listed in A few bacterial colonies were sampled from an overnight agar plate, suspended in 0.9% NaCl solution and diluted to 0.5 density in McFarland scale using DEN-1B McFarland Densitometer (Biosan). Suspensions were streaked on Mueller Hinton Agar (MHA) plates (Oxoid) using Eddy Jet 2 automatic spiral plater (IUL) in the Lawn 3000 mode. Plates were dried shortly and MIC Test Strips (Liofilchem) were carefully placed on them. The incubation conditions and measurement were carried out according to the manufacturer’s protocol and the interpretation of the results according to EUCAST 2015 (European Committee for Antibiotic Susceptibility Testing) and CLSI 2015  recommendations, which in most cases meant that the results were interpreted after 24 h incubation in 37°C. The analyzed strains always fell into the sensitive or resistant group regardless of whether CLSI or EUCAST interpretation was followed. For the lack of CLSI or EUCAST interpretation for streptomycin, a previous report was used to interpret the results .2, Na2HAsO4) solutions in TSB. Twofold serial dilutions in the concentration range from 10 to 160 μM in case of CdCl2 and 10-fold dilutions in range of 20 μM to 20 mM in case of Na2HAsO4 were used. Subsequently plates were incubated at 37°C for 20 h and the minimal inhibitory concentration (MIC) was determined as the lowest heavy metal compound concentration where no bacterial growth was observed. S. aureus RN4220 was used as a reference non-resistant to heavy metals. The results were interpreted according to previous reports  plasmids entirely unknown to date and characterized in this study  poultry-associated plasmids; (II) plasmids of previously known sequences occurring in is study .The first group contains known plasmids characteristic for poultry strains. These are pAvX and pAvY  as well pemIK-Sa1, which is the most frequently occurring in staphylococcal plasmids TA system of mazEF/pemIK family , these plasmids are frequent carriers of antibiotic resistance determinants against drugs from groups of aminoglycosides , β-lactams , macrolides , and tetracyclines , next to resistance determinants to cadmium (pLUH02 and pMW2) or putatively to arsenic (pRIVM1295-2). Except the strain ch3, for the reasons discussed further, all the remaining carrier strains displayed corresponding resistant phenotypes , S2.oriT, which potentially renders them mobilizable.Regarding genes of known virulence factors, only pLUH02 carries a cluster of genes of enterotoxins D, J, and R. In pMW2, however, there is a gene of a yet uncharacterized lactococcin 972 family bacteriocin. Bacteriocins facilitate the producer host or environment colonization by eliminating other bacteria. Nevertheless, some bacteriocins exhibit properties of virulence factors . The neiThe last group consists of plasmids that have not as yet been reported. Prevalence of these plasmids among the analyzed strains reaching 28% is the lowest when compared to the previous groups. However, these are as frequent carriers of genes and determinants, whose prevalence is analyzed in this study, as the second group. Their sequences have been entirely unknown to date with the exception of pPA3. The sequence of pPA3 is, to a considerable extent, a mosaic of sequences derived from other plasmids, each of them provides a unique element and each is flanked by shuffle-inducing sequences such as genes of transposases or invertases .As regards resistance determinants, pPA3 carries two of such, against erythromycin and tetracycline. The erythromycin resistance determinant is located in a fragment originating from another unknown to date and reported in this study plasmid, namely pPH2, whereas the tetracycline resistance determinant originates from pBC16, a broad-host plasmid. Both, pPA3 and pPH2, contain determinants of cadmium resistance. Cadmium resistance determinants, together with one of resistance against arsenic, are also located in pPH1-2.Concerning genes of possible virulence factors, pCH11 carries an immunity determinant against a bacteriocin. However, the authors were unable to find any gene coding for a bacteriocin in this plasmid, which would mean the existing gene provides immunity to one or more bacteriocins produced by other strains. Apart from this gene, pCH11, pPA3, and pPH1-1 possess one encoding lysophospholipase of very high similarity to the one encoded in pAvX.pemIK-Sa1 localized in a fragment originating from pAvX. Strikingly, pPH1-1 possesses operons of two unrelated TA system. These are yefM/yoeB-Sa2, which is also class II TA system with the toxin being an RNase  systems , pPA3 coan RNase , and a hanscript . Noteworplasmids , yefM/yo-located  and sprFsistance . PlasmidoriT or its mimic. Plasmid pPA3 also contains a gene of mobilization protein. Notably, in case of pPA3, oriT mimic is located within a fragment originating from pPH2 but the gene of mobilization protein comes from pBC16  remains of a particular interest as it is a community-associated MRSA (CA-MRSA) responsible for invasive infections across the United States  isolates from a clinical outbreak in Sweden in 2009 , ph1 (ST1347), and tu1 (ST1). Its exact copy (GenBank accession GQ900417) was reported originally in 2010 in 0 strain . S. aured States . The seqd States . In bothisolates . Neverthisolates , and in isolates . Here weisolates . The nexisolates . A homol in 2009 , 2012. D in 2009 . A serie in 2009 . Worth m in 2009 . NeverthS. aureus strains. Among 16 different plasmid sequences reported here, 7 are novel. Among these sequences, there are a few of particular interest. The first one is the newly established group of pAvY plasmids, which replaces what was reported before as one pAvY plasmid , also known as nicking relaxase, is also present. The gene is provided either in cis, as the oriT mimic in pPA3, or in trans in co-existing plasmids, as in the pair of pMW2/pRIVM4390 or among pSAP060B/pPH1-1/pPH1-3 and pPH1-2 plasmids. However, alongside oriT and a mobilization protein the presence of a conjugative plasmid carrying accessory genes, as one for a coupling protein, and an essential, large gene array encoding a type IV secretion system (T4SS) responsible for creating the mating pore and the physical transfer, is also required (Regarding the mechanism of antibiotic resistance and other determinants dissemination, strikingly, a considerable number of plasmids reported in this study, i.e., 6 out of 16, that belong to the group II and III, carry an required . Neverthrequired .pemIK-Sa1 TA system. Another uncovered in this study group III plasmid pPH2 donates erythromycin and cadmium resistance determinants as well as an oriT mimic, whereas pBC16 is a source of a determinant of tetracycline resistance and a gene of mobilization protein. Strikingly, pBC16, which does not occur autonomously in the analyzed strains, is a broad-host plasmid that has been recorded so far in genera of Bacillus, Enterococcus, Staphylococcus, and Streptococcus. Hence, the observed sequence of pPA3 is a prime example of resistance determinants accumulation by interspecies transfer and DNA shuffling. Markedly, pAvX-derived pemIK-Sa1 TA system likely stabilizes the maintenance of pPA3 together with the resistance determinants it carries and may contribute to their dissemination in bacterial populations, similarly as yefM/yoeB-Sa2 and sprF3/sprG3 in pPH-1. Such a process might be further accelerated by the presence of mob from pBC16 and the oriT mimic from pPH2, which may render pPA3 mobilizable. Mimics of oriT are sequences that apart from the core fragment differ from the original oriT sequences derived from conjugative plasmids. However, in a form of single-stranded DNA they assume a structure mimicking the structure of original oriT sequences. Hence, it is suggested that their presence renders a far greater number of plasmids mobilizable than had initially been expected based on the prevalence of oriT sequences from conjugative plasmids (oriT mimic is reported in the shared fragment of unknown to date staphylococcal pPH2 and pPA3 plasmids (Among group III plasmids, which are the newly uncovered in this study, pPA3 is exceptionally compelling. In-depth analysis of its sequence demonstrates the major mechanisms that underpin resistance determinants and virulence factors dissemination brought up in this work. The fragment of pAvX, the plasmid of group I common among poultry strains, provides the gene of lysophospholipase and the operon of plasmids . Here a plasmids .S. aureus strains of poultry origin. The results show that most of plasmids occurring in the analyzed strains and reported beforehand are being found in clinical isolates of strains typical for humans or human ones of livestock origin. This shows that livestock-associated staphylococci are a significant reservoir of resistance determinants and virulence factors which are spread to human strains. Strikingly, nearly half of the plasmids presented here were unknown to date. In this group we found a plasmid named here as pPA3, which is a unique example of accumulation of resistance determinants and virulence factors likely stabilized by the presence of a TA system. These facts clearly demonstrate that there is a pressing need for studies aimed at complete plasmid sequence determination.The role of plasmids in dissemination of antibiotic and heavy metal resistance as well as host-specific virulence factors is commonly recognized. Unfortunately, a vast number of sequencing projects do not aim for complete sequence determination, which frequently requires a considerable amount of extra workload. In consequence, the number of known complete plasmid sequences is disproportionately low. To address this problem, we undertook an effort to determine complete plasmids sequences from 18 The datasets generated for this study can be found in GenBank.de novo assembly, and determined the final sequence of pPA3 plasmid. MB and RZ-P performed de novo assembly and determined the final sequences of the remaining plasmids. MB prepared annotations and carried out comparative sequence analyses. AM did antibiotic and heavy metal resistance screening. All authors analyzed and interpreted the results. MB prepared figures and tables. MB and BW wrote the manuscript. All authors revised the manuscript and agreed to be accountable for all aspects of the presented work.MB and BW designed the study. MH grew bacterial cultures and isolated plasmid DNA. RP and MB analyzed the NGS data, performed The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Torture and other forms of ill treatment have been reported in at least 141 countries, exposing a global crisis. Survivors face multiple physical, psychological, and social difficulties. Psychological consequences for survivors are varied, and evidence on treatment is mixed. We conducted a systematic review and meta-analysis to estimate the benefits and harms of psychological, social, and welfare interventions for torture survivors.p = 0.005), but PTSD symptoms at follow-up were not significantly reduced . No significant improvement was found for PTSD caseness at the end of treatment, and there was possible worsening at follow-up from one study (n = 28). Interventions showed no benefits for depression symptoms at end of treatment  or follow-up . A significant improvement in functioning for psychological interventions compared to control was found at end of treatment  but not at follow-up from only one study. No significant improvement emerged for quality of life at end of treatment  with no data available at follow-up. The main study limitations were the difficulty in this field of being certain of capturing all eligible studies, the lack of modelling of maintenance of treatment gains, and the low precision of most SMDs making findings liable to change with the addition of further studies as they are published.We updated a 2014 review with published randomised controlled trials (RCTs) for adult survivors of torture comparing any psychological, social, or welfare intervention against treatment as usual or active control from 1 January 2014 through 22 June 2019. Primary outcome was post-traumatic stress disorder (PTSD) symptoms or caseness, and secondary outcomes were depression symptoms, functioning, quality of life, and adverse effects, after treatment and at follow-up of at least 3 months. Standardised mean differences (SMDs) and odds ratios were estimated using meta-analysis with random effects. The Cochrane tool was used to derive risk of bias. Fifteen RCTs were included, with data from 1,373 participants  in 10 countries . No trials of social or welfare interventions were found. Compared to mostly inactive (waiting list) controls, psychological interventions reduced PTSD symptoms by the end of treatment —all of psychological interventions, including 1,373 participants across 10 countries—were systematically reviewed and analysed.Compared to control conditions, psychological interventions significantly reduced symptoms of post-traumatic stress disorder (PTSD) and improved functioning at the end of treatment, but not at follow-up.Psychological interventions did not significantly improve depression symptoms or quality of life.Psychological interventions did not significantly reduce the incidence of PTSD diagnosis, and one study, with 28 participants, showed an increase of PTSD diagnosis at follow-up compared to control conditions.More effective interventions are needed with this population.Future interventions for torture survivors should be more collaborative and should address broader psychological problems beyond PTSD as well as social and welfare needs that maintain psychological problems. Despite 156 countries having signed the United Nations Convention Against Torture and Other Cruel, Inhuman or Degrading Treatment and Punishment . Long-stTorture has psychological, physical, social, and spiritual impacts that interact in diverse ways. Psychological effects are well documented; predominantly post-traumatic stress, depression, anxiety, and phobias ,5. PhysiTorture often occurs against a backdrop of national and international power imbalances, war, civil unrest, and the destruction or erosion of medical and other welfare services. Arguably, treatment needs to incorporate wider conceptualisations of damage and distress than are represented in standard Western psychological treatments for psychological trauma ,11. A reA Cochrane systematic review and meta-analysis  aimed toMore recently, a meta-analysis of 18 pre-post studies of interventions for survivors of mass violence in low- and middle-income countries showed a large improvement in PTSD and depression across treatment  but smalA systematic review was performed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement , which iWe extracted RCTs from searches of PsycINFO, MEDLINE, EMBASE, Web of Science, the Cumulative Index to Nursing and Allied Health Literature, Cochrane Central Register of Controlled Trials, the WHO International Clinical Trials Registry Platform, Clinical Trials.Gov, PTSDpubs, and the online library of Danish Institute Against Torture (DIGNITY) databases from 1 January 2014  through 22 June 2019 using key search terms including combinations of “torture,” “randomised,” “trial,” and “intervention” with Boolean operators . There wWe initially screened titles and abstracts against the inclusion criteria, with the aim of identifying potentially eligible studies for which the full paper was obtained. One author (AH) initially screened titles and abstracts to select full papers; another author (AW) checked a subsample of the excluded papers and agreed with all exclusions. Full papers were screened and selected for inclusion by 2 authors independently and agreed upon after discussion (AH and AW).Descriptive data, including participant characteristics, treatment mode, and setting, were collected. The primary area of interest for this review was outcomes in the domains of PTSD symptoms and caseness, psychological distress, functioning, and quality of life. PTSD symptoms were defined as the primary outcome given that the majority of identified reviews measured this. Psychological distress was measured as a secondary outcome, in the form of depression symptoms. Depression was chosen to define psychological distress because it is more distinct from PTSD than alternative scaled constructs of psychological distress, particularly anxiety. As in Patel and colleagues’ review , functioStudies in which a psychological, social, or welfare intervention was an active treatment of primary interest were investigated. When studies included more than one arm within a trial, it was decided that—where both arms represented the same content of intervention—data from those arms were combined. The respective control arms associated with these intervention arms were also combined, given that the main area of interest of this research is the impact of intervention relative to control. In studies in which both adjusted and unadjusted treatment effects for specific covariates were reported, the adjusted treatment effects were used.2, so a random-effects model was applied.Due to varying data collection and reporting methods, this review included both continuous and dichotomous scales. Meta-analyses were conducted using Review Manager (RevMan version 5.3) software . It was For continuous scales, treatment effects were estimated using standardised mean differences (SMDs). This requires the extraction of mean scores, standard deviations, and sample sizes for each arm. When standard deviations required for the analyses were not available, they were calculated from confidence intervals (CIs), as suggested in the Cochrane handbook . For dicThe newly included studies were added to the 9 previous studies in each analysis. Analyses were run for end of treatment and follow-up when available. End of treatment was defined as data collected within 3 months or less from the end of treatment; follow-up was defined as more than 3 months after the end of treatment.The risks of bias were assessed using the Cochrane guidance . Each stFrom an initial screen of 1,805 abstracts and titles, 6 RCTs since 2014 met our inclusion criteria –22 and wAccording to Cochrane risk of bias assessment , one stuz = 2.79, p = 0.005). Between-study heterogeneity, I2, was 55% (95% CI 0.38–0.68), indicating substantial heterogeneity [Twelve trials, with a total of 1,086 participants, reported data for PTSD symptoms no more than 3 months after the end of treatment –21,24–31ogeneity . The conogeneity .Seven trials, with 569 participants, reported data for PTSD symptoms more than 3 months after the end of treatment –24,26,27z = 1.68, p = 0.09) in PTSD symptoms at follow-up. Given the large CI, the precision of estimate was low, and all but one study [There was no difference between the intervention group and the control group . A heterogeneity of I2 = 0% was noted for this comparison (95% CI 0–0.61), and a number of sources of bias in methodology were observed.Four trials with 82 total participants, classifying participants using caseness as meeting criteria for PTSD no more than 3 months after the end of intervention ,23,24,30z = 2.15, p = 0.03).Only one trial compared PTSD caseness in intervention and control groups, at 6-month follow-up for 28 participants . Casenesz = 1.71, p = 0.09) with a substantial heterogeneity of I2 = 68% (95% CI 0.56–0.77).Ten trials reported data for psychological distress, measured as depression, no more than 3 months after the end of treatment, with 988 participants ,25,27,30z = 0.96, p = 0.34), and heterogeneity was considerable .Seven trials reported data for psychological distress, measured as depression using the Hopkins Symptom Checklist-25 (HSCL-25), more than 3 months after the end of treatment, with a total of 569 participants –24,26,27z = 3.72, p = 0.0002). A heterogeneity of I2 = 15% was observed (95% CI 0–0.73).Three trials reported data for functioning at the end of treatment, for 584 participants ,18,21, az = 1.62, p = −0.11).Only one study (28 participants) provided analysable data showing effects at 6-month follow-up  and no sp = 0.26) with a low precision of estimate. No study assessed quality of life at follow-up.Two trials ,30, withTwo studies reported on adverse effects of treatment. Weiss and colleagues  reportedAll but 2 trials ,29 reporCalculation of the SMD assumes that differences in standard deviations among studies reflect differences in assessment scales and not real differences in variability among study populations . We chosThis systematic review and meta-analysis of 15 studies of interventions for torture survivors included 1,373 participants from 10 countries. Six of the 15 studies were published since the previous review, but the sample size increased 3-fold. The range of treatments was somewhat wider, but treatments were still most often compared with inactive controls rather than with other treatment. The problems of torture survivors were largely conceptualised in terms of PTSD symptoms that constituted the focus of treatment and, often, the primary outcome. Meta-analysis demonstrated few benefits of treatment: a statistically significant but clinically small decrease in PTSD symptoms at the end of treatment—from varied psychological interventions compared to mostly inactive controls—not found at follow-up. Other outcomes—PTSD caseness, psychological distress, usually depression and often of clinical severity—were not significantly different either at the end of treatment or at follow-up, with the exception of a worsening of PTSD caseness at follow-up, a poorer outcome than in the previous review  and clinOutcomes representing broader health and participation in society were neglected, as was the context of social, economic, and political uncertainties survivors face; threats to civil and legal status, accommodation, safety, connections with family and friends, and other assaults on well-being ,33,34. BAlthough it should be interpreted with caution, the finding of worsening at follow-up in the study by Wang and colleagues , using COther reviews of psychological treatments for torture survivors ,38 or foMethodological quality of the included studies was largely similar to that in our previous review. Apart from the absence of blinding of therapists or patients to treatment allocation, rarely possible in trials of psychological treatment, bias arose mainly from incomplete reporting of outcomes, dropping noncompleters from outcome analysis, and uncertainty about whether the intended treatment had been delivered as designed, mainly because of lack of therapist qualifications to deliver it. Whether training volunteer therapists, with no existing clinical competences, in the specific therapeutic techniques for the trial is adequate to produce treatment fidelity is an open question and should be addressed within trials. The same comment applies to cultural adaptation of treatment that originated in Western healthcare. Studies gave little detail of what it was they meant by ‘cultural adaptation’, beyond translation of outcome scales and treatment materials, but effective cultural adaptation involves extensive work between people from all the main cultures represented in a study, who understand the context and content of treatment. Similar methods are required for true validation of translated scales in the languages of the cultures in which they will be used . Even whThe review has some limitations that potentially affect conclusions. Our search could have been widened by including the grey literature, but a zero yield from around 1,500 chapters, reports, and other articles accessed for the previous review decided us against it. It is possible that in the grey literature, or even in the peer-reviewed literature, our relatively broad search nevertheless missed a trial labelled in a way we did not anticipate, since the nomenclature is not well standardised. While we did not exclude studies in other languages, the majority of the databases searched have shown varied and incomplete coverage of non-English material  particul2) that made estimates of effect sensitive to inclusion or exclusion of single studies. Given the weakness and lack of precision of the I2 statistical test [2 = 0% for PTSD caseness at end of treatment and I2 = 15% for functioning at end of treatment, wide CIs were produced ranging from 0% to 61% and 73%, respectively, indicating caution in inferring heterogeneity in these cases. We did not anticipate having the power available for subanalyses, but these could be planned in a further update, to investigate each source of heterogeneity. Although widening our scope to refugee studies would have included some family and community interventions, heterogeneity would likely have been even greater, exacerbating problems of interpretation.Heterogeneity among studies was substantial and arose from multiple sources: participants, therapists, therapeutic methods, outcomes, delivery, and setting. This produced generally high levels of between-study heterogeneity Click here for additional data file.S1 Text(DOCX)Click here for additional data file.S1 TableCMHW, community mental health worker; NET, narrative exposure therapy.(DOCX)Click here for additional data file.


We investigated the association regarding severe hypoglycemia episodes with cardiovascular disease risk and all-cause mortality in patients with type 2 diabetes.Baseline and follow-up data  from patients with type 2 diabetes were retrieved from the National Health Insurance System database (covering the entire Korean population). Type 2 diabetes, severe hypoglycemia, and major comorbidities were identified using International Classification of Diseases 10 codes and medication information. Individuals who were classified as type 2 diabetes in the year of 2009 were screened, and we counted severe hypoglycemia episodes from 2007 to 2009. The primary outcome was newly developed myocardial infarction (MI), stroke, heart failure, or all-cause mortality. Participants were followed from the baseline index date to the date of death or until December 31, 2015.P for trend < 0.001. Similar findings were noted regarding the relationship of severe hypoglycemia episodes with stroke, heart failure, and all-cause mortality. Risks for all outcomes were highest within 1 year from the index date and showed decreasing trends with follow-up. Sensitivity analyses of the data from the subgroup population and 797,544 subjects who received a national health examination did not change the significance of the main findings.In total, 19,660 (1.2%) patients developed at least one severe hypoglycemia event during the period from 2007 to 2009. Mean follow-up was 5.7 years. After adjustment for confounding factors, the hazard ratio (HR) of MI significantly and sequentially increased: 0 vs. 1 episode, HR 1.56, 95% CI 1.46–1.64; 0 vs. 2 episodes, HR 1.86, 95% CI 1.61–2.15; 0 vs. 3 or more episodes, HR 1.86, 95% CI 1.48–2.35, Among adult Korean patients with type 2 diabetes, a severe hypoglycemia episode is associated with increased risk for cardiovascular outcomes and all-cause mortality. Significant results from dose–response, temporal, and sensitivity analyses may suggest the possibility of direct causality between severe hypoglycemia and cardiovascular outcomes and mortality.The online version of this article (10.1186/s12933-019-0909-y) contains supplementary material, which is available to authorized users. A recent report from the International Diabetes Federation suggested the global prevalence of diabetes mellitus in adults is 1 in 11; this indicates that diabetes has become a silent epidemic on a global scale and poses a serious health concern , 3. ThusSH, which is defined as a hypoglycemic episode requiring aid to treat, is associated with a wide range of adverse outcomes . SeveralTo address this, our study aim was to evaluate the association between SH and myocardial infarction, stroke, heart failure, and all-cause mortality in type 2 diabetes using the National Health Insurance Service (NHIS) database, which covers the entire Korean population through a social insurance benefit system . We alsoWe performed a nationwide retrospective study using the Korean National Health Insurance (NHI) Claims Database maintained by the NHIS, a government-affiliated agency that administers the medical service system in Korea. This database includes general information on specifications, drug prescription information, consultation statement, and diagnosis statement using International Classification of Disease, 10th revision (ICD-10) classifications . In addiAmong all NHI beneficiaries, participants aged 30 years and older and who were diagnosed with type 2 diabetes according to ICD-10 codes (E11–E14) in the year of 2009 were included at baseline. Considering the characteristics of the Korean NHIS database, an operational definition of diabetes described in detail elsewhere was applied in this study . These pDue to the limitation of claims data, we collected all episodes reporting SH, which was defined as the diagnostic clinical codes for hypoglycemia  from the inpatient or emergency room claims dataset, and we counted the number of SH events of enrolled participants between January 1, 2007 and December 31, 2009. The index date was defined as January 1, 2010. The primary endpoint of this study was newly first diagnosed myocardial infarction (I21-I22), stroke (I63-I64), and heart failure (I50), based on codes from the ICD-10, along with subsequent hospitalization during the follow-up period. Claims data and national mortality data from the National Statistical Office were merged for analysis of death.2). Estimated glomerular filtration rate (eGFR) was calculated from serum creatinine using the Modification of Diet in Renal Disease Study Group equation (MDRD). Chronic kidney disease was defined as an eGFR < 60 mL/min/1.73 m2. Hypertension was defined by claim codes . Dyslipidemia was defined by claim codes . The covariates and baseline characteristics of regular medical checkup data were defined or measured from the results of the National Health Examination Program in the 2008–2009 year.The criteria for the socioeconomic status were based on the average monthly insurance payments imposed by the Korean National Health Insurance Corporation, which are determined based on annual income level in Korea. Socioeconomic status was categorized into three groups  based on income levels. Anti-diabetic drugs included seven classes  dispensed at the baseline period in Korea. Hypertension was defined using the ICD-10 code  and prescription of anti-hypertensive drugs. The subjects’ medical history, including chronic obstructive pulmonary disease , all types of cancer (C00–C97), liver cirrhosis , and end-stage renal disease , was identified using the ICD-10 codes. These covariates and baseline characteristics were defined using the data from the year 2009. In the sub-cohort for sensitivity analysis that consisted of the participants who received regular medical check-ups, information concerning smoking status , drinking habit , and physical activity  was obtained using a standard questionnaire during the health examination. Body mass index (BMI) was calculated as weight in kilograms divided by the square of height in meters  episodes with cardiovascular outcomes and all-cause mortality using Cox proportional hazard regression analysis. We also adjusted the models for potential confounders. Model 1 was a crude analysis, Model 2 was a sex- and age-adjusted analysis, and Model 3 was a further multivariable analysis adjusted for the following variables: age, sex, living place , income level, anti-diabetic drugs, the presence of hypertension, dyslipidemia, and major comorbidities . We performed a comparison of baseline and hazard ratio values using a trend analysis. The trends of values across the increasing outcomes were estimated using a multiple linear regression analysis for continuous variables and a Chi square test for categorical variables. Age was adjusted as a continuous variable. Given that mortality could compete with cardiovascular outcomes of interest, we performed a competing risk analysis using a sub-distribution hazards model .P value, P for trend, and P for interaction < 0.05 were considered statistically significant.Hazard ratios (HR) and 95% confidence intervals (CI) for the Cox proportional hazard model were used to evaluate the association between SH episodes and main outcomes. To confirm the time effect  of SH events on the risk of main outcomes, we compared the results by dividing the follow-up period into three categories 12 months or less, 13–24 months, and over 24 months from the index date. A sensitivity analysis was performed for the sub-cohort, which included only those who received the standardized medical checkup. Variables that could only be obtained from the medical health check-up database, such as smoking status, alcohol consumption, income level by standardized self-reporting questionnaires, BMI, blood pressure, and laboratory test results, were included in the sensitivity analysis. Forest plots for incident main outcomes in subgroups were evaluated. All statistical analyses were performed using Statistical Package for SAS 9.2. Of the 2,287,430 participants who had type 2 diabetes in the year of 2009, a total of 1,568,097 participants were followed from baseline to the end of the study period, with a mean follow-up duration of 5.7 years  experienced a myocardial infarction event, and the incidence of myocardial infarction was 6.34 per 1000 patient-years. In addition, the number of stroke events was 60,176 (3.8%), and the number of heart failure events was 54,330 (3.5%). The incidence of stroke and heart failure was 7.21 and 6.60 per 1000 patient-years, respectively. A total of 153,036 participants (9.8%) died during about the 6-year follow-up period, representing a mortality incidence ratio of 17.27 per 1000 patient-years. The incidence of all outcomes tended to be higher in the group that experienced SH episodes at baseline  cohort study showed a higher risk of CVD along with an increasing number of SH events for the outcomes of coronary heart disease, cardiovascular mortality, and all-cause mortality, not in the outcome of stroke. They explained the negative results of stroke and heart failure by the possibility of a small number of SH events or competing risks for mortality. Hypoglycemia also has a significant effect on the cerebral vascular system affecting systemic cardiovascular system by promoting inflammation, hypocoagulability, endothelial dysfunction, and oxidative stress . ChanginThere is controversy whether hypoglycemia-associated autonomic failure (HAAF) induced by recurrent episodes of hypoglycemia is an adaptive pathway or a maladaptive pathway for CVD . PreviouAlthough it is generally accepted that hypoglycemia acutely affects CVD, there is limited evidence on the long-term effect of SH on the development of CVD. Results from the ADVANCE and the Examination of Cardiovascular Outcomes with Alogliptin versus Standard of Care (EXAMINE) trials showed no significant temporal response in the relationship between SH and CVD . In contThe causality between SH and CVD has practical implications for selecting anti-diabetic agents. If there is a direct causality in the relationship between SH and CVD, anti-diabetic agents with a low risk for SH should be recommended preferentially to reduce the risk of CVD in patients with type 2 diabetes. However, there is still yet insufficient evidence to support this recommendation. A network meta-analysis of clinical trials suggested that the ranking of anti-diabetic agents for major adverse cardiovascular events was well correlated linearly with those for SH . AnotherThere are some considerations in interpreting clinical and observational studies about SH. In the previous clinical trials, the interpretation of the association between hypoglycemia and CVD was limited, as they were not primarily designed to analyze the effect of hypoglycemia. In addition, the subjects of these clinical trials were recruited from high-risk CVD populations and were thus less representative of the general population. Moreover, the trials had a non-blind, randomized design, and subjects self-reported their hypoglycemic episodes; accordingly, the reporting may have differed in each group. Thus, it is feasible that there was more underreporting of hypoglycemia in the standard treatment group than in the intensive treatment group during the long-term trial. Lastly, the small number of SH events in each clinical trial is also a limitation in terms of the effect of SH. On the other hand, the epidemiologic investigations of SH also have several weaknesses, including relying on claim data for identifying SH events, missing some important variables related to SH or outcomes. Our study has also several limitations, similar to other epidemiologic studies. First, this study was based on a medical claims database. Therefore, the definition of hypoglycemia was not based on an independent measure of glucose; thus, misclassification may be possible. Second, our study relied on the medical claims and National Health database, which lack data on important variables for CVD in diabetes, such as the duration of disease; level of HbA1c; glycemic variability; dosage, type, and exposure duration of antidiabetic medications; cardiac autonomic neuropathy; or baseline electrocardiogram status, which may explain the increased risk of CVD outcomes and mortality in patients with SH events. Therefore, additional community- or hospital-based cohort studies that have standardized, high-quality data on clinical characteristics are needed. Nevertheless, the present study was conducted in a large nationwide population, and we were able to analyze a sufficient number of SH events and include patients with low CVD risk factors. The strength of our study is that all relevant medical data for the Korean population were included in the analyses. Furthermore, to the best of our knowledge, this is the largest population-based analysis to date regarding the association between recurrent SH and CVD.This study highlights the prognostic importance of prior history of SH on cardiovascular events and mortality. SH was strongly and positively associated with and exhibited a dose-dependent and temporal relationship with subsequent macrovascular morbidity and all-cause mortality. In patients who experienced SH, preventing the risk of CVD and mortality should be carefully considered in patients who are at a greater risk of hypoglycemia. In addition, the study illustrates the need for careful management and frequent monitoring of all individuals with type 2 diabetes to minimize the risk of hypoglycemia. However, more studies are needed to clarify whether prior SH is itself a real risk factor for CVD and mortality or whether it is a marker for another factor, such as cardiac autonomic neuropathy, which may be associated with increased mortality.Additional file 1: Table S1. Baseline characteristics according to the number of severe hypoglycemia events of sensitivity analysis from the sub-cohort which included only the participants who had regular standardized medical checkups. Table S2. Crude and adjusted HRs of cardiovascular outcomes and all-cause mortality from the sub-cohort which included only the participants who had regular standardized medical checkup. Table S3. Crude and adjusted HRs of cardiovascular outcomes and all-cause mortality from the sub-cohort with exclusion of patients who experienced SH at least two years before the occurrence of main outcomes. Table S4. Competing risk analysis including mortality as a competing risk.