These findings expand our knowledge of metabolic changes in aortic aneurysms and provide insights into the pathophysiology of aortic aneurysms.Aortic aneurysms are associated with fatal aortic rupture. Current therapeutic approaches are limited to implantation of aortic prostheses and stent-grafts; no effective drugs are available because the pathogenic mechanisms of aortic aneurysms remain unclear. Here, we aimed to elucidate the molecular mechanisms of the initiation and progression of aortic aneurysm by lipidomics. We performed lipidomics analyses of lipids in the aortic media of normal, border, and aneurysm areas from patients with thoracic atherosclerotic aortic aneurysm (N = 30), thoracic nonatherosclerotic aortic aneurysm (N = 19), and abdominal atherosclerotic aortic aneurysm (N = 11) and from controls (N = 8) using liquid chromatography and mass spectrometry. Significant alterations were observed in the lipid profiles of patients with atherosclerotic aortic aneurysms and to a lesser extent in those with nonatherosclerotic aneurysms. Increased triacylglycerols (TGs) and decreased ether-type phosphatidylethanolamines (ePEs) were observed throughout the normal, border, and aneurysm areas of thoracic and abdominal atherosclerotic aortic aneurysms. Prostaglandin D Most aortic aneurysms are asymptomatic until rupture, and most patients with aortic aneurysms are diagnosed when undergoing diagnostic imaging for other suspected diseases or during voluntary medical examinations. Moreover, current therapeutic approaches are limited to implantation of aortic prostheses and stent-grafts, and no effective drugs have been developed. Therefore, identifying novel diagnostic biomarkers and appropriate molecular targets for drug development is essential. However, the molecular mechanisms initiating, promoting, and exacerbating aortic aneurysms are still poorly understood, although inflammation, oxidative stress, matrix degradation, thrombosis, and hemodynamic forces contribute to these processes5.Aortic aneurysm is a progressive disease with aortic dilation, mainly caused by remodelling of the aortic wall by atherosclerosis; morbidities associated with aortic aneurysm are increasing worldwide owing to lifestyle changes. Importantly, aortic aneurysm can lead to fatal consequences of aortic dissection and rupture7. The aortic media in the aortic wall is the focus of atherosclerotic remodelling; this tissue can be collected from the normal-to-aneurysm areas of surgically excised aortic aneurysm tissue, even if it has thinned in the advanced stages of the disease. To identify target molecules for the development of therapies and drugs, the aortic media of aortic aneurysms is a promising target tissue and can be subjected to Omics analyses to elucidate the pathophysiology of atherosclerotic aortic aneurysms.The aortic wall contains a concentric medial layer, the aortic media, which is made up of a relatively uniform lamellar tissue layer composed of vascular smooth muscle cells and extracellular matrices, such as elastin and collagen; this layer is sandwiched by intima and adventitia in the aortic wall and separated from the blood stream10. In addition, another class of lipids oxylipins, including prostaglandins and leukotrienes, play important roles in inflammation11. Therefore, profiling of the lipid status could provide novel mechanistic insights into the pathophysiology of diseases. Recently, a liquid chromatography/mass spectrometry (LC/MS)-based technique, called lipidomics, which simultaneously monitors alterations in a broad range of lipids13, was used to screen changes in lipid statuses in several diseases, address the pathophysiology of the diseases, and/or identify biomarkers. For example, phosphatidylethanolamine (PE) levels were found to be dramatically decreased in clear cell renal cell carcinoma by lipidomics, whilst consolidated analysis with transcriptome and cell based analysis showed that the PE synthetic pathway was suppressed in this form of carcinoma14. In addition, an increasing number of studies performing lipidomic analyses have demonstrated dysregulated lipid metabolism, such as excess glycosphingolipid levels, in the plasma and brain tissue of patients with Parkinson’s disease15. These lipidomics studies have highlighted the possibility that abnormal lipid composition due to dysregulated lipid metabolism is one of the main causes of the pathogenesis of these diseases. The major cause of aortic aneurysm is atherosclerosis, wherein pathological lipid accumulation typically results in atheroma formation and induces the remodelling of the aortic wall, predominantly in the aortic media. Thus, lipidomics of the aortic media of aortic aneurysms could also help to elucidate the fundamental lipid status of this fatal disease, leading to mechanistic insights into the pathophysiology of aortic aneurysms.Lipids, such as phosphoglycerolipids, sphingolipids, and neutral lipids, play key roles in a variety of physiological and biological processes as membrane constituents, energy sources, and second messengersIn the present study, we collected aortic media from patients with thoracic atherosclerotic aortic aneurysm (TAAA), thoracic nonatherosclerotic aortic aneurysm (TNAA), and abdominal atherosclerotic aortic aneurysm (AAAA) and used these samples to determine the lipidomic signatures of these conditions. For TAAA and TNAA, we also collected thoracic aorta from patients without any vascular disease as controls. Our results provided fundamental information for elucidating the molecular mechanisms of atherosclerotic aortic aneurysms and identifying potential drug targets for the treatment of this disease.p < 0.001 for TAAA, p = 0.004 for AAAA; Table p = 0.005) mainly because majority of the TNAA group had hereditary disorders. In all groups of aortic aneurysms, men were predominant (82%). The average maximum aneurysmal diameters of TAAA, TNAA, and AAAA, were 56.5 ± 1.2, 54.5 ± 1.5, and 57.0 ± 3.1 mm, respectively, and no differences were observed among their diameters. The differences between the greatest dimensions and the anastomotic site diameters of TAAA and TNAA were almost the same, indicating similarities in the dimensions of these tissues. In contrast, AAAA (32.6 ± 2.5 mm) showed significantly greater differences between maximal aneurysmal and anastomotic site diameters than either TAAA  or TNAA   (p < 0.001) and TAAA groups (p = 0.001), and higher low-density lipoprotein levels than the TAAA group (p = 0.018) and the control group (p = 0.001). The AAAA group showed higher total cholesterol levels than the control group (p = 0.031). Although some data, including the higher total cholesterol and low-density lipoprotein levels in the TNAA group, were unexpected, the high ratio of statin prescriptions to patients with hyperlipidaemia in the TAAA and AAAA groups may have reduced these two indices. C-reactive protein and creatinine levels were not dramatically different among the three aortic aneurysm groups.When analysing underlying diseases as risk factors of atherosclerosis, hypertension was commonly and dominantly observed in the three groups of aortic aneurysms (77%), and diabetes mellitus was present in less than 20% of patients in these groups. Hyperlipidaemia was more frequently observed in the TAAA group (57%). Nearly all patients with TAAA and AAAA who were diagnosed as having hyperlipidaemia received statins. In contrast, patients in the control group had no underlying diseases, except for one patient who had hypertension. In blood lipid tests, oxidised low-density lipoprotein levels did not differ among the three aortic aneurysm groups, and high-density lipoprotein levels did not differ among the aortic aneurysm groups and the control group. The TNAA group showed higher total cholesterol levels than the control  + 2O, were detected at a higher frequency in the border and aneurysm areas of TAAA and AAAA than in the normal areas of TAAA and AAAA and all areas of TNAA  and PC(36:3)a (p = 0.009), and eight SMs, including SM(34:0) (p = 0.021) and SM(42:2)b (p = 0.001), were increased in the aneurysm area. In addition, six cholesterols (Chs)/ChEs and all triacylglycerols (TGs), except for TG(50:0)b, also increased. In contrast, two ether-type phosphatidylethanolamines (ePEs), i.e., PE(38:5e)b (p = 0.021 for border and p < 0.001 for aneurysm) and PE(40:7e) (p = 0.007 for border and p < 0.001 for aneurysm), gradually decreased in the border and aneurysm areas of TAAA. In addition, total quantified ePE (p = 0.050 for border and p = 0.001 for aneurysm) also gradually decreased in the border and aneurysm areas. Compositional determination of the fatty acid side chains demonstrated that decreased ePEs were all plasmalogens; PE(38:5e)b was PE(18:0p/20:4) and PE(40:7e) was PE(20:2p/20:4)  Fig. . In cont2 (p = 0.001) and 15-lipoxygenase (LOX) metabolites (15-hydroxyeicosatetraenoic acid [15-HETE] (p = 0.005), 15-hydroxyeicosapentaenoic acid [15-HEPE] (p = 0.020), and 17-hydroxydocosapentaenoic acid [17-HDoHE] (p = 0.020)), increased in the aneurysm area. In contrast to the findings in TAAA, none of the lipid molecules except for 5,6-dihydroxyeicosatetraenoic acid (DiHETrE) changed in the normal, border, and aneurysm areas of TNAA.Next, we compared targeted lipid profiles of oxylipins in the aortic media among normal, border, and aneurysm areas obtained from patients with TAAA and TNAA Fig. . Processp = 0.043) and TG(50:0)b (p = 0.018), increased in the aneurysm area. In addition, total TGs also tended to increase, although the changes were not significant  and 14-HDoHE (p = 0.029), increased in the border area, and another 12-LOX metabolite, i.e., 12-HEPE (p = 0.042 for border and p = 0.038 for aneurysm), increased in both the border and normal areas. Two other DHA metabolites, i.e., 10-HDoHE (p = 0.037) and 20-HDoHE (p = 0.048), were also increased in the border area. In contrast, three ePEs, i.e., PE(36:5e) (p = 0.004), PE(38:5e)a (p = 0.007), and PE(38:6e) (p = 0.002), SM(34:1)a (p = 0.034), and SM(34:1) + O (p = 0.009) decreased in the aneurysm area of AAAA. In addition, total measurable ePE (p = 0.004) also decreased in the aneurysm area  and LPC(18:0) (p = 0.021 for TAAA and p = 0.039 for TNAA); glycosylceramides (GCers), such as G1Cer(42:1) (p = 0.020 for TAAA and p = 0.040 for TNAA) and G3Cer(34:1) (p = 0.018 for TAAA and p = 0.019 for TNAA); increased in both TAAA and TNAA. In addition, several oxylipins, such as PGD2 (p < 0.001 for TAAA and p = 0.016 for TNAA) and 5-LOX metabolites (5-HETE (p = 0.001 for TAAA and p = 0.048 for TNAA)), also increased in both conditions. Only SMs, such as SM(34:2) (p < 0.001) and SM(42:3) (p = 0.003), and ChEs, such as ChE(18:1) (p < 0.001) and ChE(18:2) (p < 0.001), increased specifically in TAAA. In contrast, ePEs, such as PE(38:6e)a (p = 0.008 for TAAA and p = 0.023 for TNAA), DG(38:4) (p = 0.011 for TAAA and p = 0.015 for TNAA), and TGs, such as TG(48:1) (p = 0.014 for TAAA and p = 0.009 for TNAA) and TG(52:2) (p = 0.013 for TAAA and p = 0.014 for TNAA), decreased in both TAAA and TNAA. In addition, 12-LOX and 15-LOX metabolites, i.e., 14-HDoHE (p = 0.031 for TAAA and p = 0.024 for TNAA), 17-HDoHE (p = 0.022 for TAAA and p = 0.022 for TNAA), and 15-HETE (p = 0.027 for TAAA and p = 0.029 for TNAA), also decreased in both TAAA and TNAA.Next, we compared lipid profiles in normal areas of TAAA and TNAA with CNTA from patients without vascular disease Fig. . Unlike Alterations in lipid profiles emerged as a distinctive consequence of pathophysiological alterations in many diseases. In the present study, we first focused on alterations in lipid profiles in the aortic media from normal, border, and aneurysm tissues in patients with TAAA, TNAA, and AAAA, and then compared normal areas of aortic aneurysm patients with those from patients without vascular disease.17, our findings clearly demonstrated ChE + O accumulation in both TAAA and AAAA. In addition, we found several notable features of TAAA, including decreased ePE; increased PC, SM, Ch/ChE, and TG; and increased PGD2 and 15-LOX metabolites. Moreover, we also found decreased ePE and increased 12-LOX metabolites in AAAA, despite limited sample numbers.Changes in lipid profiles were evident in normal, border, and aneurysm areas in TAAA but not TNAA. Lipid profiles also changed among these areas of AAAA. Consistent with previous findings in atherosclerotic lesions19. In contrast, we found that grade-associated decreases in ePE were common features of both TAAA and AAAA, suggesting that decreased ePE in the aortic media could play a pivotal role in atherosclerotic aortic aneurysm development. In addition, our results demonstrated that decreased ePEs were all plasmalogens. Although the role of ePE in the aortic media is unclear, the major class of ePEs, i.e., plasmalogens, has been proposed to act as free radical scavengers20. Thus, ePE levels may have been reduced by oxidative stress generated during atherosclerotic events. Alternatively, alterations in ePE levels may be associated with cell transitions22. The mesenchymal-to-epithelial transition and mesenchymal-to-endothelial transition are associated with increased ePE levels. Conversely, the epithelial-to-mesenchymal transition is associated with decreased ePE levels. Because the mesenchymal transition acquires migratory and invasive properties, decreased ePE levels may be associated with increased migratory and invasive properties. In atherosclerotic plaques, macrophages transform into foam cells (lipid-laden macrophages), and vascular smooth muscle cells (VSMCs) proliferate and migrate from media to intima24. In addition, decreased VSMC contents in the aortic media, i.e., thinning of the aortic media, is a key feature in advanced-stage atherosclerotic aortic aneurysm development6. Thus, it is also possible that decreased ePE in the aortic media during atherosclerotic aortic aneurysm development reflects increased migratory and invasive properties of VSMCs and decreased VSMC contents in the aortic media. In a similar manner, increased levels of TGs and Ch/ChEs in the aortic media during atherosclerotic aortic aneurysm development may also be associated with the transition of VSMCs to migratory and invasive cell types.Consistent with our findings, grade-associated increases in phospholipids, such as PCs and SMs, as well as Ch/ChEs and TGs in elastin from the aortic media have been characterised in atherosclerotic aortas2 synthase (L-PGDS), which acts downstream of cyclooxygenase to produce PGD2, is elevated in atherosclerotic plaques25. Consistent with this previous report, our current study demonstrated that PGD2 levels were elevated in the aortic media during TAAA development. Moreover, knockout of L-PGDS accelerates aortic lipid accumulation and development of atherosclerosis in mice26. Thus, increased PGD2 in the aortic media may counteract the TAAA development, although the role of PGD2 in TAAA development remains unclear. Notably, L-PGDS expression levels are inversely correlated with matrix metalloproteinase 9 (MMP-9) levels, which are elevated to promote degradation of elastin and collagen matrices during the development of atherosclerotic aortic aneurysms27 and in atherosclerotic plaques28. In addition, L-PGDS inhibits the migration of VSMCs obtained from diabetic rats29. Therefore, suppression of both MMP-9 activity and VSMC migration may be one of the downstream effects of PGD2 in TAAA development.Lipocalin-type PGD2 synthesis, synthesis of 5-LOX, 12-LOX, and 15-LOX metabolites has been reported to contribute to the development of atherosclerosis32. Thus, increased LOX metabolites also play important roles in the development of atherosclerotic aortic aneurysms. However, increased levels of 15-LOX metabolites  during aortic aneurysm development were only observed in TAAA, whereas increased levels of 12-LOX metabolites  during aortic aneurysm development were only observed in AAAA. Although the reason for this discordant upregulation of LOX metabolite levels between TAAA and AAAA remains to be elucidated; however, we hypothesise that these differences may be related to the different cell origins of VSMCs in the thoracic and abdominal aorta33. In addition, the different levels of LOX metabolites may contribute to differences in extracellular matrix density and responses of VSMCs to vasoactive growth factors34. The heterogeneity of VSMC origins may also explain the diverse production of LOX metabolites in the media of TAAA and AAAA.In addition to PGD2 and 5-LOX metabolites were also observed. In addition, increased GCers and LPCs and decreased TGs and 12- and 15-LOX metabolites were noted in the aortic media of thoracic aortic aneurysms. Extracellular signal-regulated kinase (ERK) has been shown to mediate platelet-derived growth factor-directed migration of VSMCs35. Moreover, GCers and LPCs can activate ERK signalling in VSMCs37. Thus, increased GCers and LPCs may represent the enhanced migratory activity of VSMCs by activating ERK signalling, which indicates the status of VSMCs in the aortic media leading to thoracic aortic aneurysm development. In the normal aortic media prior to thoracic aortic aneurysm development, 5-LOX metabolites increased, but 12- and 15-LOX metabolites decreased. Although the pathological implications of these lipid alterations have not been clarified, 5-HETE and 12- HETE/15-HETE have been shown to have opposite effects on insulin secretion in isolated pancreatic islets38. Thus, 12- and 15-HETE may also counteract 5-HETE in the initiation and development of thoracic aortic aneurysms, and imbalances may enhance the risk of initiation of thoracic aortic aneurysms.Along with alterations in the lipid profiles during aortic aneurysm development, we also demonstrated alterations in lipid profiles in normal areas of the aortic media in both TAAA and TNAA, when compared with CNTAs without vascular disease. These alterations were similar between TAAA and TNAA; therefore, these lipid alterations could represent risky transitional markers of the aortic media leading to thoracic aortic aneurysm, although we cannot rule out the possibility that these alterations may be attributed to age. During the development of thoracic aortic aneurysms, decreased ePE and increased PGDThere were several limitations in the present study. First, this study was performed with a limited number of patients; i.e., the patient numbers of TAAA, TNAA, AAAA and CNTA are 30, 19, 11 and 8, respectively. Especially, those of AAAA and CNTA are limited; therefore, only the comparisons of each lipid for TAAA provided false discovery rate (FDR) scores of less than 0.05. Second, there was a large age gap between patients with TAAA or AAAA and those with CNTA. This is mainly because of age limitation to those who can receive heart transplantation in Japan. We have once collected aortas from autopsy patients who had not been diagnosed as vascular diseases in the age range comparable to or more than TAAA and AAAA patients; their aortic tissues were all shown to have atherosclerotic denaturation by pathological examinations and not usable as normal control. There was also age difference between patients with TAAA or AAAA and those with TNAA. Third, there was personal heterogeneity of collected aortic tissue, and three areas  were not uniformly dissected and collected from aortic aneurysm tissues. Last, we performed lipidomic analysis of aortic media of aortic aneurysm, and other aortic wall components, such as intima and adventitia, and endothelial cells, have not been analysed. Therefore, we need to interpret and discuss the data under these limitations.In conclusion, our comprehensive lipidomics approach using the aortic media of TAAA, TNAA, and AAAA revealed changes in lipid profiles in aortic aneurysm development for the first time. Additionally, from analyses of lipid profiles of the aortic media from TAAA and TNAA against CNTAs, we demonstrated lipid alterations in the thoracic aortic media that may initiate phenotype changes in VSMCs, leading to the development of aortic aneurysms. Through elucidation of the molecular mechanisms of these changes in lipidomics, the pathophysiology of atherosclerotic aortic aneurysms could be understood in greater detail, leading to the development of novel drugs to prevent and treat atherosclerotic aortic aneurysms.This study followed the principles of the Declaration of Helsinki, and all patients provided written informed consent for tissue sampling and omics analyses, including lipidomics analyses. The protocol was approved by the Institutional Review Board of National Cerebral and Cardiovascular Center  and National Institute of Health Sciences , Japan. Aortic wall tissues were collected from patients undergoing elective surgery for aortic aneurysm, defined as TAAA (N = 30), TNAA , and AAAA (N = 11). CNTAs (N = 8) were collected from the root portions of aortas accompanied with hearts excised from patients who received heart transplantation and had no prominent symptoms of atherosclerosis and vascular disease. Details of patient characteristics are listed in Table Aortic walls were divided into three areas according to the degree of progression from the viewpoint of surgeons: aneurysm area, maximum diameter region of the aorta; border area, intermediate region between the aneurysm and normal areas; normal area, normal diameter and shape region of the aorta. Each tissue was further dissected into three layers, i.e., the intima, media, and adventitia. Aortic media used for lipidomic analysis were divided into pieces, weighed, and frozen in liquid nitrogen. Aortic tissues containing dissociations or calcifications were excluded to avoid problems associated with tissue heterogeneity. As controls, aortic media of root portions of aortas from recipient hearts were prepared using the same procedures. All samples were stored at −80 °C until lipidomic analysis.Portions of aortic tissues for pathological examinations and histological analyses (approximately 1 × 1 cm) were firstly separated, fixed overnight with 4% formalin , and embedded in paraffin without dissection into three layers. Sections (3 µm thick) were acquired using a sledge microtome, mounted on slides, and stained with hematoxylin and eosin, elastica van Gieson, and Masson’s trichrome to determine aortic wall morphology. CNTAs from patients without vascular disease were processed according to the same protocol.39. Briefly, lipids were extracted from 9 or 10 mg tissue by Bligh and Dyer’s method with a few modifications. The lower phase (corresponding to 20 µg of tissue) was used for measurement of phospholipids, sphingolipids, neutral lipids, and acylcarnitines. The upper phase (corresponding to 3 mg of tissue) was subjected to solid extraction and was used for measurement of oxylipins and their parental substrate polyunsaturated fatty acids (PUFAs). PC(16:0/16:0)-d6 , TG(16:0/16:0/16:0)-13C3 , and leukotriene B4-d4  were added as internal standards before extraction.Lipid extraction from tissues was performed as described previously40. Aortic samples of normal, border, and aneurysm areas were randomised across the run. Because of limitations in sample numbers within each run sequence, samples were divided into four series of run sequences. Raw data obtained by LC/TOFMS were processed using the 2DICAL software , which allowed detection and alignment of the ion peaks of each ionised biomolecule obtained at the specific m/z and column retention time (RT). The main parameters of 2DICAL were set as described previously, with a few modifications40. To extract the ion peaks of phospholipids  and sphingolipids , the RT range was from 2.0 to 38.0 min in the negative ion mode; whereas for ion peaks of neutral lipids , the RT range was from 2.0 to 60.0 min in the positive ion mode. Extracted ion peaks were subjected to identification of lipid molecules by comparison of the ion features, including RT, m/z, preferred adducts, and in-source fragments, of the experimental samples with those of our reference library of lipid molecule entries, as described previously40. Structural analysis of fatty acid side chains from selected ePEs was performed by LC/Fourier transform MS  as previously described40.Phospholipids, sphingolipids, and neutral lipids were quantified using LC/time-of-flight MS , as described previously40. The aortic samples were divided in the same manner as for nontargeted measurement and subjected to analyses. Targeted lipid molecules were annotated by comparison of RT, parent ions, and MS/MS ion fragments with standard lipid molecules using MultiQuant Software .Oxylipins and PUFAs were quantified by a targeted approach using LC/MS-MS , as described previously6 + HCOO− for phospholipids and sphingolipids, and TG[16:0/16:0/16:0]-13C3 + NH4+ for neutral lipids). The relative standard deviation of the internal standards , which were used to monitor experimental quality during extraction, measurement, and data processing, were 9.7%, and 15.5%, respectively. For samples with missing values for a lipid, the minimum relative level of each lipid in all measurable samples was used instead. All data obtained by nontargeted measurements were presented as normalised levels of ion peak intensity. Comparisons of the metabolite levels were performed by t-test analyses to assess statistical differences and FDRs were calculated using the Benjamini-Hochberg method. Due to limited sample numbers, in this study, p values of less than 0.05 without FDR consideration were regarded as showing statistical significance for lipidomic measurements. The processed data, average values, and standard deviations of the levels of lipid molecules as well as statistical values  are presented in Supplemental Tables The data cut off point for nontargeted measurements was set at 50 for negative ion mode and 100 for positive ion mode in height. Using our non-targeted lipidomics platform, the majority of ion peaks lower than these cut off values had vague peak shapes and/or were interrupted by noise signals and were therefore not suitable for quantitative analysis. Due to the limited number of subjects, this study spanned multiple run sequences and therefore required the variation resulting from instrument inter-run tuning differences to be corrected for quantitative analysis. In addition, due to limited tissue availability and heterogeneity, we were unable to use quality control samples or pooled samples. Therefore, we used the median value of each lipid from the aortic TAAA samples, which were the most abundant and were included in all run sequences, to normalise multiple run sequences for the levels of each lipid in every sample of each run sequence. In addition, we set measurable limits such that 50% of samples within each run sequence exceeded the data cut off point. Moreover, to correct variations in each sample run, the intensity of each extracted ion peak was normalised to that of the major adduct ion of internal standard (PC[16:0/16:0]-d4-d4-H+) to correct for variation in each sample run. The relative standard deviation of the internal standards (LTB4-d4), which were used to monitor experimental quality during extraction, measurement, and data processing, were 15.2%. For samples with missing values for a lipid, the minimum relative level for each lipid in all measurable samples was used instead. All data obtained by targeted measurements were presented as normalised values against the median values of all quantified samples. Comparisons of the metabolite levels were performed by t-test analyses to assess statistical differences and FDR were calculated using the Benjamini-Hochberg method. Due to limited sample numbers, in this study, p values of less than 0.05 without FDR consideration were regarded as showing statistical significance for lipidomic measurements. The processed data, average values, and standard deviations of the levels of lipid molecules as well as statistical values  are presented in Supplemental Tables The data cut off point for targeted measurements was set at a signal-to-noise ratio of 10. For samples with missing values for a metabolite, the minimum observed value of the metabolite among all samples was applied in targeted measurements. Data processing for quantitative analysis was conducted in the same manner as for the nontargeted lipidomics, except that the area of each ion peak from targeted lipid molecules was normalised to that of the major adduct ion the internal standard (LTBSupplementary FiguresSupplemental Tables


Tuberculosis and parasitosis are the widely distributed diseases in Ethiopia with the leading cause of mortality and morbidity, respectively. There has been no information on the status of co-infections of tuberculosis and parasitosis in Oromia Zone of Amhara Region and South Wollo, Ethiopia. Hence, this study primarily focuses on determining the status of tuberculosis and parasitosis co-infections and associated factors.The study was conducted in Oromia Special Zone of the Amhara Regional State and South Wollo Zone, northeastern Ethiopia from April 2015 to January 2017. Tuberculosis cases confirmed by health personnel at the health institutions were the source of the study sample. In a cross-sectional study 384 smear positive pulmonary and extra-pulmonary tuberculosis cases were recruited. Faecal specimens provided by the study participants were examined for parasitic co-infections using direct saline microscopic test, Kato-Katz and concentration techniques. Nutritional status was determined using body mass index and mid-upper arm circumferences. Data were analyzed using descriptive statistical methods and Pearson chi-square.Tuberculosis and parasitosis co-infection prevalence was 10.8%, and the proportion of intestinal helminths accounted for 9.7% while intestinal protozoa accounted for 1.9%. Cases with single parasitic infection was 89.3% among co-infected individuals. Co-infection of both disease was not significantly associated with gender and age (P > 0.05). The prevalence of undernutrition was 58.6% as determined using body mass index and 73.0% as determined using mid-upper arm circumference with no significant association with gender. Among all forms of tuberculosis cases (384) screened for the study, the bacterial positivity was relatively more common in males (55.5%) than females (44.5%). Tuberculosis lymphadenitis was found to be the most prevalent (85.9%) form of extra-pulmonary tuberculosis with cervical adenopathy (75.3%) being the commonly existing disease.The rate of helminthic co-infection is predominantly high than that of intestinal protozoa. Single parasitic co-infection was more common than double or multiple co-infections. Both body mass index and mid-upper arm circumference anthropometric parameters revealed greater risk of undernutrition in tuberculosis patients. Thus, screening and prompt treatment of parasites in tuberculosis patients and a support of nutritional supplementation for malnourished tuberculosis patients should be further studied which might enhance the disease treatment and minimize the risk of its complexity. Please see Additional file Mycobacterium tuberculosis (Mtb) having an infection rate of over one-third of the global population [Mtb complex like M. bovis, M. africanum and M. microti can also cause human TB in rare cases [Tuberculosis (TB) is the leading bacterial infectious diseases that cause profound mortality and morbidity throughout the world. It is a highly chronic unpardonable respiratory disease mainly affecting lungs and other body parts as extra-pulmonary tuberculosis (EPTB). The disease is primarily caused by pulation . Other mre cases . In 2017re cases .Mtb. Prevalence of intestinal parasitic infection is remarkably high in sub-Saharan African countries where TB is also common [It is also estimated that 3.5 billion of the global population are infected with intestinal parasites of whom 450 million are ill as a result of the infection . The paro common –10.Diabetes mellitus, malnutrition, and malignancies are also known to enhance the risk of TB infection [Helminth infections can reactivate latent TB and aggravate the disease expression . Limitednfection , 16.Identifying nutritional status of an individual could bring a substantial role in the prevention and control of the diseases. The relative risk of TB infection among persons in the lowest body mass index (BMI) category was more than five-fold higher compared to the group that fall in the highest BMI category . UndernuAs far as this study is concerned, parasitic co-infection and nutritional status of tuberculosis patients are not known in the study area. Thus, the study was primarily undertaken to determine the prevalence of parasitic co-infection and malnutrition among TB cases in Oromia Zone of Amhara Region, and South Wollo Zone. The findings are expected to contribute towards proper treatment and management of TB in the study area and beyond.The study was conducted in Oromia Special Zone of the Amhara Regional State and South Wollo Zone, northeastern Ethiopia. The area was selected purposely for this study that there was no any reported documents that shows the status of TB-parasitic co-infections and the associated factors. Both zones have administrative centers in Kemise and Dessie Towns. The former Town is the administrative center for the Oromia Special Zone since 1994 with a latitude of 10°43′27.4“N and longitude of 39°52’24.03”E. The town is found at an altitude of 1446.88 m and a distance of 325 km northeast of Addis Ababa. On the other hand, Dessie Town is the capital of South Wollo Zone having a north latitude and east longitude of 11°8′ and 39°38′, respectively. Dessie has an average altitude of 2475.1 m and located 401 km northeast of Addis Ababa .Administratively, the Oromia Special Zone has seven districts. Residents of the zone receive governmental health service from 27 health centers and one general hospital that partially began its function at Kemise Town in 2015. The two towns administration of Oromia Special Zone (Kemise and Bati) have one governmental health center each and were accessible for our pulmonary tuberculosis (PTB) sample collection. Similarly, South Wollo Zone has 18 districts and two town administration (Dessie and Kombolcha)  and those who fulfilled the inclusion criteria were included in the study. The samples  and stools) were collected on the spot from consenting participants until the expected sample size was achieved.In addition, non-active TB cases using clinical symptoms , apparently healthy individuals matched with TB cases by age (with a difference of + 5 year), sex and place of living were used as control to compare the nutritional status. The controls were not bacteriologically and parasitologically confirmed for TB and parasitosis, respectively. On the other hand, those individuals who came to the health facilities with the TB cases but didn’t fulfill the indicated criteria were excluded.All TB cases from April 2015 to January 2017 who were 18 years and older and willing to participate in the study based on their written voluntary consent were included. This is due to small proportion of TB cases younger than 18 years. Those with severe TB and unable to provide sputum, and those who gave their sputum for TB examination but couldn’t provide faecal specimens were excluded from the study.Preliminary survey was made for 3 months in all governmental health facilities of the seven districts in Oromia Special Zone. Based on the availability of samples (convenience sampling) and transportation access, the data were collected from Kemise and Bati Town health centers of the Special Zone. In addition, to get more TB cases Dessie Referral Hospital (DRH), Bikat Higher Diagnostic Laboratories (BHDL), Dessie Health Center (DHC) and Boru Meda Hospital (BMH) were also the sample collection sites from Dessie Town Fig. . ClinicaMtb and intestinal parasitic infections (IPIs) in northwest Ethiopia [Institution-based cross-sectional study design was performed. For determination of sample size, co-infection prevalence (33.3%) of Ethiopia , 95% conDry, translucent, leak-proof 50 ml capacities of falcon tubes were used to collect a minimum of 3–5 ml sputum sample from BMH where drug resistant TB cases were handled and treated. Similarly, a 30 ml sputum cup was used for sputum collection from the rest of the study sites. The data were safely recorded on the spot of sample collection to determine the patients’ and control group socio-demographic characteristics.Direct microscopic examination of the sputum was done by fluorescent microscope (FM) at a magnification of 200×. The bacterial grading scale was considered as negative when no AFB (acid fast bacillus)/in at least 30 fields, scanty (1–29 AFB/30 fields), 1+ (30–299 AFB/30 fields), 2++ (10–99 AFB/ in at least 15 fields) and 3 +++ (> 100 AFB/field in at least 6 fields). Positive specimens from patients were kept at a range of − 10 to − 20 °C refrigerator until being transported to Aklilu Lemma Institute of Pathobiology (ALIPB) for culturing following the protocol described elsewhere .FNA samples from patients suspected for EPTB was collected using a 21-gauge needle attached to a 10 ml syringe with maximum care and safety by an experienced pathologist . AfterwaMycobacterium culture based on the manufacturer’s instruction  and the standard operating procedure of TB laboratory at ALIPB. Glycerol (0.75%) and pyruvate (0.6%) was added to enrich the media for the cultivation of Mtb and M. bovis, respectively. The prepared media were left for 48 h to check for contamination before culturing the bacteria. Poor quality media were also identified and discarded. All the inoculated LJ slants in culture tubes were incubated aerobically at 37 °C in a slanted position and contamination was checked daily for the first week. The inoculated media were placed in an upright position starting from the second week and colony formation was checked for eight consecutive weeks. For weakly grown colonies, sub-culturing of the colony was done in another new medium [A stock of selective LJ media was used for the preparation of w medium .Faecal specimens were collected using stool cup with a tight fitting lid and two applicator sticks given to the TB patients to bring a sizable stool sample of his/her own. The slides were labeled with identification number and a drop of saline (0.9%) was placed at the middle for direct wet mount faecal examination. A small proportion (about a match stick size) of the specimen was picked up and mixed with the saline and covered with cover slip for entire microscopic observation under low power objective. A single Kato-Katz thick smear per stool sample was also prepared from the stool using a template delivering 41.7 mg of feces. A multiplication factor of 24 was used to convert egg count into eggs per gram of stool . A porti2), normal (18.5 kg/m2 < BMI ≤ 25 kg/m2), overweight (25 kg/m2 < BMI ≤ 30 kg/m2), and obese (BMI > 30 kg/m2). Weighing of the participants was made to the nearest 0.1 kg and the proportion of mildly (BMI 17.0–18.5 kg/m2), moderately (16.0 kg/m2–16.9 kg/m2) and severely (BMI < 16.0 kg/m2) undernourished participants identified [Age, sex, height, weight and mid-upper arm circumference (MUAC) were measured and recorded. The BMI of participants was calculated from individuals’ weight in kilograms and height measured in meters to one decimal place using calibrated measurements. In BHDL, measurements for BMI were also done using calibrated ultrasonic height and weight measuring machine. The print outs of the machine were collected and registered for each of the participants. The result was categorized as underweight  by an expert who was fluent in both languages to maintain its consistency. Training was given for data collectors and supervisors. Pre-testing of the questionnaire was made on 30 study participants for its clarity and appropriateness which was latter included in the study. Data collection process was strictly followed day to day by the supervisor and principal investigator. In addition, quality of reagents and instruments was checked by experienced laboratory technicians using negative and positive control slides. Each of the specimens was done by two experienced laboratory technicians. In cases where the results were discordant, a third reader was used whose result was considered as the final result. Continuous quality control of the health institutes was also made by Dessie regional laboratory experts as an external quality assurance. In addition to the trainings on BMI measurements, the instruments were also calibrated for the research work.P-value of < 0.05.The recorded data was checked for completeness and consistency, and then entered into Microsoft Excel 2007 spreadsheets. This data was then exported to IBM SPSS Statistics for Windows, Version 25.0.  program for analysis. Percentage and mean were used to descriptively summarize the data. Association of TB-parasitosis co-infection and malnutrition with gender and age was analyzed using chi-square. Differences were considered significant at a n = 247). Their age ranged from 18 to 75 years with a mean age of 33.7 (SD = 12) having no significant difference between males  and females . The average household size of the TB cases was 4.4. Pulmonary TB cases accounted for 74.5% (286/384), the overall prevalence of TB was highest (67.0%) in the 18–37 years age group and those with family size ranging from 3 to 5 were more affected  were involved in the study. Most of the cases were recruited from South Wollo Zone  were collected and examined. The remaining tuberculosis cases refused to give faecal specimens and some of them were also severely sick and did not provide specimen. From the study, comparable intestinal parasitic infection was found among pulmonary TB cases  and extra-pulmonary ones  . From the total co-infected cases, 89.3% (25/28) had single parasitic infection, while 7.1% (2/28) had double infection, and 3.6% (1/28) had four infections. The infection of intestinal helminths accounted for 9.7% and those of intestinal protozoa accounted for 1.9%. Although there was greater helminthic co-infection than protozoa co-infection among tuberculosis patients in the study area, the difference was not statistically significant .The overall Schistosoma mansoni infection was the most prevalent parasitic co-infection (4.25%), followed by Ascaris lumbricoides (2.32%). Trichuris trichiura, Enterobius vermicularis and Hymenolepis nana were the other helminthic co-infections whereas Entamoeba histolytica/dispar and Giardia lamblia cysts were the only detected protozoan parasitic co-infections . Similarly, significant difference of Mtb-parasitic co-infection was also not observed across different age groups .The association of TB-parasite co-infection between males  and females  was not statistically significant  had chronic energy deficiency, 34.6% (133/384) were normal and data was missing for 15.4% (59/384). Of the chronic energy deficient TB cases, 61.5% (118/192) were mildly undernourished, 21.9% (42/192) were moderately undernourished and 16.7% (32/192) were severely undernourished.The overall mean BMI of TB cases was 18.2 kg/mn = 139) were underweight and there was no significant difference (P = 0.821) in the mean body mass index of TB-parasitic co-infected cases  and TB positive cases without parasitic co-infection . The BMI of co-infected cases didn’t differ significantly from those of non-co-infected cases at different age groups  and sex .Of 237 TB cases whose faecal specimens were examined, 58.6% . When MUAC was used to determine nutritional status, 6.4% of the undernourished TB cases and 2.1% of the normal nourished TB cases had parasitic co-infection showing non-significant association . As a whole, significant difference was not observed between TB parasitosis co-infections and nutritional status of the TB cases  of them were undernourished and was not significantly associated with gender (P = 0. 029) as MUAC parameter was used. However, it is not statistically significant (P = 0. 220) when body mass index is considered with average mean (+ SD) of 18.2 (+ 1.95) and 20.7 (+ 2.73) for TB cases and the control groups, respectively.Considering our matched criteria set in the methodology part, 86 participants  were used as control groups. The difference between the nutritional status of smear positive TB cases  and the control groups  was statistically significant (n = 112) samples after culturing all the smear and GeneXpert positive samples on LJ media. Culture positivity was not significantly different as compared to bacterial load under laboratory examination of smear test in both fluorescent microscope  and GeneXpert (P = 0.427). Thirty six samples had missed grading scale (MGS) of the bacterial load  was the most prevalent 85.9% (73/85). Of these TBLNs cervical adenopathy 75.3% (55/73) was the most frequently occurring disease. Of the suspected smear positive EPTB samples, 18.4% were culture positive  is less than the recent co-infection rate (22%) reported from Addis Ababa [Mtb intestinal parasitic co-infection prevalence rate reported from other parts of Ethiopia in Gondar (33.3%) and Arba Minch (26.3%) [Prevalence of is Ababa . This is (26.3%) , 14. Sucn Gondar .3% and A (26.3%) .Mtb parasitic co-infection in northwestern Ethiopia also found 44.5 and 7.6% as the proportion of helminthic and protozoa infection, respectively [A. lumbericoides infection in the country, our finding showed S. mansoni to be the most widespread helminthic infections followed by A. lumbericoides. This is considered as due to high prevalence and endemicity of schistosomiasis in Oromia Special Zone specifically in Bati and Kemise areas of the study site [Similar to this study, findings in Arba Minch showed that the prevalence of intestinal helminth infections was greater (24.4%) than that of intestinal protozoa (6.1%) . Comparaectively . Althougudy site , 36.Although there was no significant difference in parasitic co-infections among male and female study participants, males were infected in a greater proportion than females similar to the former finding in northwest Ethiopia . By contDiabetes mellitus and co-infections are important risk factors that aggravate the progress of TB [Mtb parasitic co-infected patients. In all indicated studies, mild malnutrition was at its highest peak than moderate and severe malnutrition [Several conditions including undernutrition, smoking, ss of TB , 16. To ss of TB , 38. In utrition .Mtb parasitic co-infected cases in this study than parasite free TB cases (41.4%) could imply that co-infection had an impact on the nutritional status of TB cases. Measurements using MUAC also confirmed the findings of BMI in similar way that greater percentage of the TB cases with parasitic co-infection were undernourished than those of non-coinfected cases. Such greater rate of undernourishment in co-infected cases might be due to the parasitic impact. Similar finding of nutritional assessment using both BMI and MUAC parameter suggests co-infection have an impact on the nutritional status of TB cases [The higher percentage (58.6%) of undernutrition in TB cases , 41. In TB cases .The extent of EPTB in Ethiopia is much higher (33%) than the global average (15%) . This fin = 124) [The overall LJ culture positivity was confirmed in 29.2% (112/384) which is close to the study in Addis Ababa  . On the n = 124) –46. The Mtb in both pulmonary and EPTB cases in that order [Mycobacteria cellular infection could be paucibacillary which decrease the sensitivity of diagnostic test in EPTB.Regarding both pulmonary and EPTB culturing results, of 286 pulmonary and 98 extra-pulmonary smear positive specimens, 94 (32.8%) and 18 (18.4%) were culture positive, respectively. This proportion is less than that of a study in India who found 329 (61.5%) and 19 (29.2%) as culture positive for at order . A studyat order . Similarat order , 49. In The EPTB has different manifestations based on the organs to be attacked and its intent of dissemination in the body. Similar to other relevant studies in the country, this finding also revealed that lymph nodes as the leading organs affected. In fact, the percentage of their infection rate differs as cervical, auxiliary, inguinal, supra-clavicular, sub-mandibular and anterior neck lymph nodes , 49. ThiCervical lymph nodes were found as the most frequently infected anatomical site similar to other report from Ethiopia . Such hiIn the present study, there is relatively higher helminthic-TB co-infection than those of protozoa parasites. Most of the TB cases in the study were found as undernourished. The prevalence of extra-pulmonary tuberculosis was found as in a greater proportion with cervical lymphadenitis as the principal infection site. Thus, parasitic co-infections and nutritional status of tuberculosis cases should be further studied by treating the parasites and improving nutritional status of the co-infected patients.Additional file 1. Multilingual abstracts in the five official working languages of the United Nations


Gestational diabetes mellitus (GDM) is associated with adverse perinatal outcomes. Screening for GDM and applying adequate interventions may reduce the risk of adverse outcomes. However, the diagnosis of GDM depends largely on tests performed in late second trimester. The aim of the present study was to bulid a simple model to predict GDM in early pregnancy in Chinese women using biochemical markers and machine learning algorithm.Data on a total of 4771 pregnant women in early gestation were used to fit the GDM risk-prediction model. Predictive maternal factors were selected through Bayesian adaptive sampling. Selected maternal factors were incorporated into a multivariate Bayesian logistic regression using Markov Chain Monte Carlo simulation. The area under receiver operating characteristic curve (AUC) was used to assess discrimination.The prevalence of GDM was 12.8%. From 8th to 20th week of gestation fasting plasma glucose (FPG) levels decreased slightly and triglyceride (TG) levels increased slightly. These levels were correlated with those of other lipid metabolites. The risk of GDM could be predicted with maternal age, prepregnancy body mass index (BMI), FPG and TG with a predictive accuracy of 0.64 and an AUC of 0.766 .This GDM prediction model is simple and potentially applicable in Chinese women. Further validation is necessary.The online version of this article (10.1186/s12884-019-2374-8) contains supplementary material, which is available to authorized users. The prevalence of diabetes in pregnancy, the majority of which is gestational diabetes mellitus (GDM), has been increasing in many jurisdictions . GDM is The changes of energy and nutrient metabolism during pregnancy have been postulated to follow a biphasic pattern, predominantly anabolic in early pregnancy and mainly catabolic in later pregnancy . These dIt has been reported that the model combining maternal factors including history of GDM, family history of diabetes, ethnicity, parity, BMI, mean arterial pressure (MAP), uterine artery pulsatility index (UtA PI) and pregnancy associated plasma protein A (PAPP-A) in the first trimester predicted GDM with an area under the receiver operating characteristic curve (AUC) of 0.90 (95% CI 0.87–0.92) . AnotherFurthermore, a growing body of evidence suggests that hypertriglyceridaemia is associated with GDM , 26. HypThis study was conducted in Xinhua Hospital between Jan 2015 and Dec 2017. Xinhua Hospital is a referral medical center with 3500–4000 deliveries annually. Study participants were recruited in two ways. First, there was retrospective recruitment of consecutive pregnant women delivered from Jan 2015 to Jun 2016, with data obtained solely from medical records. Women were asked before delivery for consent to obtain prenatal clinical records (XHEC-C-2013-001); a total of 2343 women consented and were included in the final analysis. Information abstracted from medical records included pregnancy complications, parity, gestational age at delivery and results of biological indicator, such as FPG, TG, TC, HDL, vitamin D3. Second, women from a prospective hospital-based birth cohort, the Early Life Plan (ELP) study, between Jun 2016 and Dec 2017  was measured immediately in the hospital laboratory . Coefficients of variance were all less than 5% for the clinic quality assurance.A five milliliter of fasting venous blood sample was taken from pregnant woman on the next day of their first hospital visit. Participants had to be fasting for at least 8 h prior to blood draw. FPG, TC, TG, high density lipoprotein (HDL), LDL, vitamin DFPG is routinely measured during early pregnancy to detect pre-existing diabetes (FPG ≥ 7 mmol/L). Intermediate level of hyperglycemia is defined as FPG in the range 5.1 to 6.9 mmol/L. The two-step approach of testing for GDM in this study included an initial screening at 22–24 weeks by administering a 50-g oral glucose solution followed by a 1-h venous glucose test. Women whose glucose levels met or exceeded 7.2 mmol/L underwent a diagnostic 2-h 75-g oral glucose tolerance test (OGTT) between 24 and 26 weeks. The diagnosis of GDM was established when any single glucose concentration met or exceeded a  by IADPSG criteria 2010 .Gestational age was determined by ultrasound within 3 months of pregnancy confirmation. Preterm delivery was defined as delivery before 37 weeks of gestation. Sex-specific small for gestational age (SGA) and large for gestational age (LGA) were defined according to the 10th and 90th percentiles of gestational age-specific birth weight in the Chinese population . Recurrehttps://cran.r-project.org/web/packages/ppcor/ppcor.pdf). Odds ratio of individual maternal factor as a potential predictor for GDM was estimated through logistic regression. Continuous potential predictors were categorized as the “U-type” response is common in biological systems. Such predictors were included in the model as categorical variables when the association between continuous variables and GDM are not linear. As a sensitivity analysis, we examined AUC for the model including these predictors as continuous variables.Standard descriptive statistics were used to summarize continuous data  and categorical data (numbers and percentages). Student’s t test and the chi-square test were used for comparisons between groups. A partial correlation analysis was performed for maternal factors (using the “ppcor” package implemented in R) (https://cran.r-project.org/web/packages/BAS/BAS.pdf). The selected potentially predictive maternal factors were incorporated into the multivariate Bayesian logistic regression using Markov Chain Monte Carlo simulation  and the Metropolis-Hastings algorithm (using the “MCMC” package implemented in R) (https://cran.r-project.org/web/packages/mcmc/mcmc.pdf). An AUC curve was calculated to estimate the predictive probability and to compare the performance of models. The AUC and optimal cut-off value of the predicted probability of GDM were analyzed (using the “pROC” package) (https://cran.r-project.org/web/packages/pROC/pROC.pdf). To assess the effect of substituting continuous variables for categorical variables in the sensitivity analysis, we compared the difference in AUC using Delong’s ROC curve test [www.R-project.org). For frequentist analyses, the p < 0.05 threshold was regarded as statistically significant unless indicated otherwise.Because our analysis included multiple biomarkers that might be correlated, and correlated variables in multivariate regression model may cause problems in estimation of coefficients, we investigated the correlation between FPG, Hb and TG using partial correlation analysis. As we found significant correlations among all participants (further details in section 3.2), we decided to use multivariable logistic regression through Bayesian inference. Thus, the predictive maternal factors were further selected through Bayesian adaptive sampling by multivariate logistic regression (using the “BAS” package implemented in R) (rve test . All then = 605) or second (n = 4653) trimester of pregnancy. All participants were Asian. The prevalence of hyperglycemia was 12.7% (743/5868). The number of women who met the inclusion criteria and remained in the study up to the time of GDM screening was 4907; the incidence of GDM diagnosed between 24 and 28 weeks of gestation was 12.8% (630/4907). Among 4907 women, 63 women delivered earlier than 34 weeks and 73 delivered in other hospitals, resulting in 4771 in the final analysis. Just over half (52.8%) of the women were nulliparous and around 0.4% had a history of recurrent spontaneous abortion. Compared with women who did not have GDM, women with GDM were older (mean age 1.8 years higher), had higher prepregnancy BMI and level of FPG, TG, TC and lower HDL values . Compared women without GDM, women with GDM had higher proportion of LGA, length of hospital stay and proportion of deliveries by caesarean section were increased in GDM women, but had lower neonatal height and lower prevalence of neonatal jaundice  and 0.0444 (p < 0.0001), respectively. Among all participants, after adjusting for maternal age, prepregnancy BMI and gestational age at first visit, FPG was correlated with Hb, vitamin D3 and lipids except apolipoprotein B and lipoprotein A. The correlation patterns differed between women with or without GDM. Among those with GDM, FPG was only correlated with Hb and HDL  of adjusted odds ratio (OR) that did not include 1.0 Table . In thisp = 0.49) . In the sensitivity analysis including these risk factors as continuous rather than categorical variables in the model, the AUC was 0.766 . These two models were not significantly different (Delong’s ROC curve test 49) Fig. .Next, we estimated sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) of the final model. When median specificity was held at 0.80 the sensitivity was at 0.60~0.61 and the cut-off value of probability was 0.160~0.161 using metric variables of maternal age, prepregnancy BMI, FPG and TG in model. The sensitivity of the model with categorical variables was 0.59 and cut-off probability was 0.130~0.131 Fig. . We thenProbability of GDM = 1/(1 + 1/exp. (Age*0.0601 + BMI*0.03405 + FPG*2.46427 + TG*0.30648–16.87528)).A reference probability table of various sensitivities and specificities is also provided for future validation  was lower than the overall frequency (17.8%) reported in HAPO study . The pre% was lowWomen with GDM also had elevated FPG and TG levels in the early stage of pregnancy, and as a consequence had an increased risk of adverse perinatal outcomes such as high birth weight  and caesarean section , 33. In Risk factors for GDM included a history of hyperglycemia, having a first degree relative with diabetes mellitus, a history of macrosomia, polycystic ovarian syndrome, age > 40 years, pre-pregnancy BMI > 30 kg/m , and takp = 0.316). The categorical variables did not show significant interaction effects. Fasting glucose and triglycerides may increase with gestation weeks. The original full model also included a history of abnormal gravidity . Following variable selection the best model incorporated four factors including maternal age, prepregnancy BMI, FPG and TG. The pseudo-R-squared was 0.165 for the best fit model, suggesting that the potential risk factors included could accounted only for a small fraction of the variance of GDM. Further studies on GDM risk factors and prediction models should pay more attention to understanding the interactions among the risk factors.We also classified the continuous variables such as maternal age, pre-pregnancy BMI, FPG, TG, TC, HDL, LDL etc. into categories according to clinical reference intervals for healthy adults. In terms of predicted risk probability and AUC, the model based on categorical variables did not show higher predictive performance than the model based on continuous variables . This formula can be applied even if only a calculator is available, such as in less-developed parts of China. The limitations are that we were not able to incorporate all known potential risk factors in modeling, that the participants came from a single medical center, and that we have not yet conducted external validation. Due to intrinsic nature of observational data in this cohort study, the internal validity was low to moderate.From 8th to 20th gestational week, the average FPG level indicated a slight decreasing trend while, in line with previous studies, TG levels displayed a slight increasing trend. The metabolites were intercorrelated during this period of pregnancy. We developed a prediction model in Chinese women, which addressed the correlation of predictors and incorporated maternal age, prepregnancy BMI, FPG and TG, with a predictive accuracy of 0.64 and an AUC of 0.766 . Thus, a simple model, which can be applied using a hand calculator, may predict the risk of GDM and be used in less economically developed parts of China.Additional file 1:Figure S1. Trace and density of estimated coefficients of final multivariate logistic models obtained using Markov Chain Monte Carlo simulation. The Markov Chain Monte Carlo logistic regression was conducted using the Metropolis iteration  with Burn-in = 3000 . Left column panel displays the variation in parameters of the included variables while the right column panel displays summaries of the distribution of the parameters (PDF 585 kb)Additional file 2:Table S1. Thresholds of predicted GDM probability and corresponding specificity and sensitivity. Probability of GDM = 1/(1 + 1/exp. (Age*0.0601 + BMI*0.03405 + FPG*2.46427 + TG*0.30648–16.87528)) (PDF 1779 kb)Additional file 3:Table S2. Odds ratios of GDM associated with risk factors selected for inclusion in model, cutoff probability at maximal AUC, AUC, sensitivity and specificity estimated using frequentist method and Bayesian inference (PDF 1436 kb)


Several studies have shown positive associations between higher WBC count and deaths from all-causes, CHD, stroke and cancer among occidental populations or developed countries of Asia. No study on the association of WBC count with all-cause and cause-specific mortality in Chinese populations was reported. We studied this using prospective data from a large Chinese cohort.We used prospective data from the Guangzhou Biobank Cohort Study (GBCS), a total of 29,925 participants in present study. A Cox proportional hazards regression model was used to estimate the hazard ratios (HR) and 95% confidence interval (CI).9/L; men > 8.8 × 109/L) was 1.83  1.54, 2.17), 3.02  and 2.52 , respectively, after adjusting for multiple potential confounders. The associations were similar when deaths during the first 2 years of follow-up were excluded. After further adjusting for pulmonary function, the highest decile of WBC count was associated with 90% higher risk of respiratory disease mortality . No evidence for an association between higher WBC count and cancer mortality was found. Sub-type analysis showed that only granulocyte count remained significantly predictive of all-cause, CHD, and respiratory disease mortality.The hazard ratios (HR) for all-cause, CHD, and respiratory disease mortality for the highest decile of WBC count  contains supplementary material, which is available to authorized users. In clinical practice, the circulating white blood cell (WBC) count is readily available and widely used as an indicator of systemic inflammation. Numerous studies have shown positive associations between higher WBC count and deaths from all-causes –4, coronAs previous studies suggested an adverse association between pulmonary function and WBC count , 10, we Participants of the Guangzhou Biobank Cohort Study (GBCS) were recruited from September 2003 to January 2008. Details of GBCS were described elsewhere . BrieflyThe GBCS was approved by Guangzhou Medical Ethics Committee of the Chinese Medical Association. All participants provided written, informed consent before participation.The baseline examination included an interview about demographic characteristics, lifestyle , family and personal medical history and assessment of weight, height, blood pressure, fasting plasma glucose, lipids and inflammatory markers. Body mass index was calculated by body weight (kg) divided by height (m) squared. Physical activity was assessed by the Chinese version of International Physical Activity Questionnaire (IPAQ) and was classified into inactive, moderately active and physically active. Reliability and validity of the questionnaire was tested by recalling 200 randomly selected subjects for re-interview. The Kappa values for the following selected questions were: self-reported vascular disease (0.66), smoking (0.96 and 0.88 for the two questions on smoking status), drinking (0.60), education (0.90), and occupation (0.80) , 13. Forn = 6).Information on underlying causes of deaths up to January 2016 was mostly obtained via record linkage with the Guangzhou Center for Disease Control and Prevention (GZCDC). Causes of death were coded according to the 10th revisions of the International Classification of Diseases (ICD) by trained nosologists in each hospital. Cancer types included: breast, liver, stomach, colorectal and lung cancer. When the death certificates were not issued by medical institutions , the causes of death were verified by GZCDC as part of their quality assurance programme by cross-checking past medical history and conducting verbal autopsy. In addition, from August 2015 to May 2016, ten verbal autopsy meetings were conducted in the Guangzhou 12th Hospital to further clarify the deaths with unclear causes. A physician panel including 5 chief physicians from various disciplines reviewed all available medical records of the same individuals and assigned in a standard manner a cause of death, with assistance of an epidemiologist for unsettled cases. When participants were confirmed dead but the exact dates of death were not available, the mid-point between the date of recruitment and 31 January 2016 was used (2), diabetes (yes/no), triglycerides and high-density lipoprotein cholesterol (mmol/l), platelet count (109/l) and haemoglobin (g/l).Chi-square tests or analysis of variance was used to compare participants’ baseline characteristics by deciles of baseline WBC count. We used Cox proportional hazards models to assess the association of deciles of baseline WBC count with all-cause and cause-specific mortality. We checked the Cox proportional assumption using Schoenfeld residuals by “stphtest” command in Stata. As we found no evidence for the violation of the proportional hazard assumption, the Cox proportional hazards model was used, giving hazard ratios (HRs) and 95% confidence intervals (CI). To enable comparison with previous studies , 15, we P values for interaction from 0.34 to 0.99). However, as smoking plays a significant role in respiratory disease mortality and mortality due to many other causes, and in other underlying illnesses which can lead to some inflammation, we conducted sensitivity analyses in never or ever- (including former and current) smokers to minimize potential confounding. Furthermore, as a WBC count of less than 4 × 109/L may clinically be an indicator of leucopenia and a WBC count of greater than 10 × 109/L may indicate an elevated inflammatory response, we also conducted sensitivity analyses including participants with a WBC count within the normal range only, as per the internationally recognized criteria for abnormal WBC count , to minimize the confounding effect due to these underlying diseases. We also did similar analysis for WBC subtypes. All statistical analysis was performed using Stata/IC 14.0.Sensitivity analysis was performed by excluding deaths occurred within the first 2 years. For the association of WBC count with respiratory disease mortality, because the association might be confounded by poor lung function, we further adjusted for pulmonary function, as indicated by FVC and FEV1, and self-reported respiratory disease including COPD, chronic bronchitis, emphysema, asthma, tuberculosis, pneumonia. We also tested for an interaction between smoking status and WBC counts using likelihood ratio test to test for the fitness of models with or without the interaction terms. Models with a lower Akaike information criterion (AIC) or Bayesian information criterion (BIC) value indicate better fitness. We found no evidence of interaction between smoking status and WBC count for the risk of all-cause and cause-specific mortality  in the present analysis. The mean age at baseline was 62 (standard deviation (SD) 7.1) years. During the average follow-up of 9.3 (SD 1.9) years, 2979 deaths (women 1581 (7.3%); men 1398 (16.9%)) were recorded.Table 9/L in women and 5.8–6.0 × 109/L in men) had the lowest mortality rate. Hence, the 4th decile group was used as the reference. Table 9/L; men > 8.8 × 109/L; HR 1.83  and 3.02 , respectively).For both all-cause and CHD mortality, the 4th decile group , which was used as the reference group in the subsequent analyses. Table For cancer mortality and respiratory disease mortality, the lowest mortality rate was observed in the 3rd decile group  were associated with 45%, 69% and 1.6-fold higher risk of mortality from all-cause, CHD and respiratory disease, respectively, but not associated with cancer mortality , indicating the association between WBC count and the risk of all-cause and cause-specific mortality did not vary by sex. Moreover, sex was also adjusted in all models to minimize its potential confounding effect. Thus, the unbalanced sex ratio might not be a major concern in the current study. Finally, as all participants in the GBCS were older Chinese from southern China, generalizability may be a concern, although within sex and age group, the participants had fairly similar prevalence of major chronic diseases  to nationally representative samples of urban Chinese [The strengths of this study included the large sample size and comprehensive measurement and adjustment of potential confounding factors. However, there were some limitations. First, as noted above, the number of deaths from cause-specific mortality  was relatively small. More detailed analyses of specific types of cardiovascular or respiratory disease could not be performed. Moreover, participants in the lower decile groups appeared to have a higher risk of death from all-cause, CHD and respiratory disease, although the results were not statistically significant. As the number of deaths in the lower decile groups were small, further studies are warranted to clarify. Second, as baseline WBC count was measured only at one-time point, the WBC counts might not accurately indicate a lifelong exposure. Repeated WBC measurements may provide a more reliable measurement of WBC count and minimize potential misclassification of exposure. However, in a recent study in women, similar results were observed using a single baseline measurement and using the average of the measurements at baseline and at the third year . Thus, t Chinese .In conclusion, our results provide robust prospective evidence supporting that WBC and specifically granulocyte count is associated with the risk of all-cause, CHD and respiratory mortality. Further investigation is warranted to clarify whether decreases in inflammation would have effects on WBC count associated mortality.Additional file 1:Table S1. Association between the baseline white blood cell count within normal range (4 × 109/L < WBC count < 10 × 109/L) and total mortality and coronary heart disease (CHD) mortality in the Guangzhou Biobank Cohort Study, 2003–2016; Table S2. Association between the baseline white blood cell count within normal range (4 × 109/L < WBC count < 10 × 109/L) and cancer and respiratory disease mortality in the Guangzhou Biobank Cohort Study, 2003–2016; Table S3. Association between the baseline white blood cell count within normal range (4 × 109/L < WBC count < 10 × 109/L) and respiratory disease mortality in the Guangzhou Biobank Cohort Study, 2003–2016, excluding deaths within 2 years; Table S4. Association between the baseline white blood cell count and respiratory disease mortality in current smokers in the Guangzhou Biobank Cohort Study, 2003–2016; Table S5. Association between the baseline granulocyte count and total mortality and coronary heart disease (CHD) mortality in the Guangzhou Biobank Cohort Study, 2003–2016; Table S6. Association between the baseline granulocyte count and cancer and respiratory disease mortality in the Guangzhou Biobank Cohort Study, 2003–2016; Table S7. Association between the baseline lymphocyte count and total mortality and coronary heart disease (CHD) mortality in the Guangzhou Biobank Cohort Study, 2003–2016; Table S8. Association between the baseline lymphocyte count and cancer and respiratory disease mortality in the Guangzhou Biobank Cohort Study, 2003–2016. (DOCX 41 kb)


Staphylococcus aureus and beta-hemolytic streptococci (BHS) diseases disproportionately affect populations in middle/low-income countries. To assess if this disparity is reflected in colonization by these organisms, we compared their colonization frequency among children from different socioeconomic status (SES) communities in a city with high income inequality.S. aureus and BHS colonization among children who attended private and public pediatric clinics. Patients were classified as high SES, middle/low SES, and slum residents. We investigated the antimicrobial resistance profile, the SCCmec types and the presence of PVL genes among methicillin-resistant S. aureus (MRSA). We also examined the antimicrobial resistance profile and serogroups of BHS.Between May–August 2014, we collected nasal and throat swabs to investigate S. aureus, of which 49 (22%) were MRSA. MRSA colonization was higher in middle/low SES  compared with high SES  and slum  residents (p = 0.01). All MRSA strains were susceptible to clindamycin, nitrofurantoin, and rifampin. The highest non-susceptibility frequency (42.9%) was observed to erythromycin. SCCmec type V was only found in isolates from high SES children; types I and II were found only in middle/low SES children. Ten (20%) MRSA isolates carried PVL genes. Twenty-four (4%) children were BHS carriers. All BHS (n = 8) found in high SES children and six (67%) isolates from slum patients belonged to group A. All group B streptococci were from middle/low SES children, corresponding to five (71%) of the seven BHS isolated in this group. BHS isolates were susceptible to all drugs tested.Of 598 children, 221 (37%) were colonized with Children from different SES communities had distinct bacterial colonization profiles, including MRSA carriage. Public health officials/researchers should consider SES when assessing disease transmission and control measures. Staphylococcus aureus and beta-hemolytic streptococci (BHS), including Streptococcus pyogenes  and Streptococcus agalactiae , are Gram-positive bacteria that cause a range of infections, from mild cutaneous and upper-respiratory infections to invasive bloodstream and respiratory infections [S. aureus and S. pyogenes, can also asymptomatically colonize the human upper respiratory tract and the skin, making their spread difficult to control in a population. In a susceptible host, colonization can be a major risk factor for disease [fections –3. They fections . These b disease .S. aureus is one of the organisms most frequently isolated from patients with healthcare-associated infections, causing not only skin and soft tissue infections, but also invasive infections, such as bacteremia, osteomyelitis and septic arthritis [S. aureus (MRSA) infection, there seems to be an increasing trend in the incidence of MRSA infections in such children [In children, rthritis . Despitechildren . Also, Mchildren .mec (SCCmec). To date, 13 SCCmec types (I-XIII) have already been described [Methicillin resistance is encoded by genes organized in the staphylococcal cassette chromosome escribed . In addiescribed .The prevalence of GAS infections is higher among children, which is considered as a ‘hazard’ in school-aged children . In addiWhile these pathogens represent a major public health burden among pediatric populations globally, diseases caused by these pathogens disproportionately affect populations in middle- and low-income countries . ResourcS. aureus and BHS colonization patterns among children living in high and middle/low SES, and slum communities. We compared risk factors for colonization and drug resistance of the isolates by SES group in the same city.While the incidence of diseases caused by these Gram-positive bacteria differs between high-income vs. middle and low-income populations, colonization frequency and strain characteristics by socioeconomic status (SES) in children residing in the same city is not well characterized. As varying living conditions among communities may result in different disease prevalence and transmission pathways, we investigated Sistema Único de Saúde or SUS). The clinic is part of a primary care facility, located on the border between middle/low SES and slum neighborhoods in the eastern section of the city. The private clinics are 1.7 km apart from each other and approximately 8 km from the public clinic.For this cross-sectional study, we recruited children who attended two private pediatric clinics and one public pediatric clinic for primary care or routine follow-up. Study clinics were located in the city of Niterói, in Rio de Janeiro metropolitan area, Rio de Janeiro state, Brazil. In 2014, the city had a population of approximately 488,000  and a GiAll children less than 18 years of age were consecutively recruited from May 12th to August 12th, 2014. Each of the clinics were sampled alternately for three half-day recruitment sessions per week. The number of recruited children was monitored weekly to assure corresponding sample size recruitment in each clinic per week. Age-appropriate written assent was obtained for children 7 years of age or older and verbal assent for children under 7 years of age. Following legal guardians’ written consent, one nasal swab and one throat swab were obtained from each child. Swabs were immediately placed into a tube containing Stuart medium until transported to the laboratory on the same day. A questionnaire for clinical and socioeconomic data was completed by child’s legal guardian using the mobile data collection application Magpi .S. aureus using catalase and coagulase tests. Staphylococcal isolates were tested for antimicrobial resistance against 30-μg cefoxitin and 1-μg oxacillin by the disk-diffusion method to identify MRSA strains [mecA gene via polymerase chain reaction (PCR) [mec type for MRSA isolates [We used the mannitol salt agar to isolate staphylococcal colonies from nasal swabs and we identified  strains . Resistaon (PCR) . Multiplisolates . Presencisolates . BHS colisolates . Antimicisolates . The antWe estimated that 600 children would be needed to detect a difference of 10% in MRSA and BHS colonization between private and public clinics, with a two-tailed α of 0.05 and a (1-β) of 0.80, for a comparison of two independent proportions. Final estimate accounts for a conservative 20% potential missing data/sample rate. This difference is based on previous studies on MRSA in the same city  and GAS aglomerados subnormais (AGSN). An AGSN has at least 51 housing units on illegally occupied land with non-municipal approved construction or insecure access to essential public services [For this analysis, we classified pediatric patients into three SES groups based on clinic attended and self-reported slum residence: high SES children were those who attended the private clinic and self-reported non-slum residence; middle/low SES were those children who attended the public clinic and self-reported non-slum residence; and slum children were those who attended either a public or private clinic and self-reported slum residence. To support the use of this SES classification, patient addresses were also geocoded with Google Maps API v3 and mapped in ArcGIS 10 . Patient addresses were then overlaid with census tracts to determine proximity to or residence inside of a slum, defined by the Brazilian government as services .Household salary is presented in USD based on the average USD:BRL exchange rate during the study period of 1:2.24 and categorized as multiples of the Brazilian monthly minimum wage in 2014 (USD $324).p < 0.10, were included in the regression analysis. Factors significant in the bivariate logistic regression or suspected to be potential confounding factors were included by step-wise variable selection to build the multivariate regression model. Lack of fit of the regression model was assessed by failure to reject the Hosmer and Lemeshow Goodness-of-Fit Test. Statistical analyses were carried out in SAS University Edition .We used Chi-squared tests, or Fisher’s exact tests (two-tailed) when appropriate, to assess associations between categorical variables and the outcomes of interest. We also used t-tests, or ANOVA tests when appropriate, to assess associations between continuous variables and the outcomes of interest. Variables found to have a statistically important association, defined as Of 682 pediatric patients invited to participate, 598 (88%) consented and were enrolled, including 298 children from private clinics and 300 children from public clinic. Non-participation frequency was 10% (33/332) in the private clinics and 14% (50/350) in the public clinic. Most frequent reasons for non-participation included children who were vomiting or too fatigued to participate at recruitment, not enough time, and legal guardian under the age of 18 years.p < 0.01). Mean monthly income of slum residents was $669, compared to $1005 for middle/low-income and $3500 for high-income residents (p < 0.01). Slum residents were more likely to be geocoded as living in a slum census tract (AGSN) compared to other groups (p < 0.01).Patient demographic characteristics were consistent with patient SES group, with high SES, middle/low SES, and slum groups, being significantly different across all demographic characteristics investigated except gender Table . The meaS. aureus, of which 49 (22%) were colonized with MRSA. All MRSA strains were susceptible to clindamycin, nitrofurantoin, and rifampin. Lower resistance frequencies among MRSA were observed for ciprofloxacin, chloramphenicol (2% each), gentamicin, trimethoprim-sulfamethoxazole (6.1% each), and tetracycline (12.2%). Non-susceptibility to erythromycin made up 42.9%, comprising two (4.1%) intermediate and 19 (38.8%) resistant isolates. Multidrug resistance was observed in eight (16.3%) MRSA strains, which presented resistance to beta-lactams and to other two or three classes of antimicrobial agents. BHS colonization was observed in 24 (4%) of the 598 patients. All BHS isolates were susceptible to all antimicrobial agents tested.Of the 598 children included in this study, 221 (37%) were colonized with mec type distribution. BHS colonization did not differ significantly between SES (p = 0.30). However, BHS serogroup differed significantly among the three SES groups (p < 0.01). GAS was the most frequent BHS found among the population investigated, comprising 15 (62.5%) of the 24 isolates.Table S. aureus varied between 32 and 42% among SES groups, but it was not statistically significant (p = 0.09). However, colonization with MRSA was more common among middle/low-SES patients (p = 0.01). MRSA SCCmec type IV was the most frequent type across all three SES and all 10 (20.4%) isolates that had the PVL genes carried this type. All the SCCmec types found among the isolates, but type III, were associated with multidrug-resistant (MDR) strains. Resistant to gentamicin was only observed among the three MDR strains that carried the PVL genes.Colonization with p = 0.03), income stability (p = 0.01), and number of household (p = 0.03) were variables associated with MRSA colonization. Age (p = 0.05) and receiving government financial assistance (p = 0.05) were also included in the multivariable logistic regression analysis as potential confounding factors. In the bivariate regression analysis for all patients, those attending the public clinic and living in a household with an unstable income had significantly higher odds of MRSA colonization  . In the stratified logistic regression analysis by clinic, no risk factor for MRSA colonization was significant among patients examined at the private clinic (data not shown). For patients examined at the public clinic, living in a household with a monthly income of $971–$1938 continued to be a significant risk factor for MRSA colonization in the adjusted model . Slum residence was found to be a protective factor among children who visited the public clinic .p < 0.05). Among clinical characteristics for all patients, attending the clinic due to symptoms/illness (p = 0.01), having an earache (p = 0.03), sore throat (p < 0.01), upper respiratory tract infection (p = 0.04) or rhinitis (p = 0.01) were associated with the presence of BHS (data not shown). Being 5 years of age or older and attending school were found to be significant risk factors for BHS colonization in the multivariate regression analysis  . Here, S. aureus colonization prevalence was 32% among high-income SES children. This frequency is similar to that found in healthy or outpatient children in high-income countries, such as USA (35–37%) [S. aureus colonization frequency [Differences in (35–37%) , 25, Can(35–37%) , Taiwan (35–37%) , China ((35–37%) , as well(35–37%) , Botswan(35–37%) , Iran (2(35–37%) , Gambia (35–37%) . However(35–37%) , 34. A srequency .MRSA colonization among all SES groups (8.1%) in Niterói was higher than frequencies observed in high-income populations (≤ 1%), such as in USA, Canada, northern Europe, and Hong Kong . Howevermec types IV and V are frequently found in CA-MRSA [In general, MRSA strains had low frequency of resistance to other antimicrobial agents, except to erythromycin. SCC CA-MRSA  and, in  CA-MRSA . In thisBHS colonization frequency was low (4%), similar to another study conducted in southern Brazil (5.6%) . HoweverA major limitation of this study is that some children with symptoms may have the disease caused by the bacteria investigated, especially BHS and sore throat. However, colonization is usually the first step of the infectious process and viruses cause most of the upper respiratory tract diseases. If we excluded symptomatic children from the analysis, we would exclude patients with viral infections. Also, the prevalence of children with symptoms did not differ between subpopulations and, therefore, does not affect the main conclusions of this report. In addition, parents may be more likely to take their children in for routine care when the child has an acute illness, resulting in a higher likelihood of bacterial colonization among our study population. However, these factors are likely to occur in both private and public pediatric populations and we would not expect them to decrease the association with SES found in the present study. Another important limitation was the difference in age between the private and public clinics, given older children are more likely to be colonized with BHS . This waS. aureus, MRSA, and BHS. We also observed variation among BHS serogroups and MRSA SCCmec types. While living conditions in slum communities would suggest they are at higher risk, residents who live on the periphery of slums or living at the intersection of extreme poverty and high income were shown to be at higher risk for both bacterial colonization and drug resistance. This study also supports the importance of including a representative sample of different SES subpopulations for research on disease transmission or prevalence studies in cities with high income inequality. Public health officials also should consider differences in transmission across SES subpopulations when developing public health interventions and conducting outbreak investigations for bacterial infections.Our study demonstrates that subpopulations from different SES strata within the same city may have different exposures or risk factors for bacterial transmission and drug resistance. Children living in middle/low SES communities were more likely to be colonized with