Gopherus polyphemus. All tortoises produced antibodies against Mycoplasma agassizii  and, at the time of sampling, either had (n = 10) or lacked (n = 6) clinical signs.Infectious disease is the single greatest threat to taxa such as amphibians (chytrid fungus), bats (white nose syndrome), Tasmanian devils , and black-footed ferrets . Although understanding the genetic basis to disease susceptibility is important for the long-term persistence of these groups, most research has been limited to major-histocompatibility and Toll-like receptor genes. To better understand the genetic basis of infectious disease susceptibility in a species of conservation concern, we sequenced all known/predicted immune response genes  in 16 Florida gopher tortoises, Mycoplasma immunity. Thirty-five genes deviated from neutrality according to Tajima’s D. These genes were enriched in functions relating to macromolecule and protein modifications, which are vital to immune system functioning.We found several variants associated with URTD clinical status in complement and lectin genes, which may play a role in These results are suggestive of genetic differences that might contribute to disease severity, a finding that is consistent with other mycoplasmal diseases. This has implications for management because tortoises across their range may possess genetic variation associated with a more severe response to URTD. More generally: 1) this approach demonstrates that a broader consideration of immune genes is better able to identify important variants, and; 2) this data pipeline can be adopted to identify alleles associated with disease susceptibility or resistance in other taxa, and therefore provide information on a population’s risk of succumbing to disease, inform translocations to increase genetic variation for disease resistance, and help to identify potential treatments.The online version of this article (10.1186/s12864-018-4452-0) contains supplementary material, which is available to authorized users. Sarcophilus harrisii) to extinction at an alarming rate [Mustela nigripes) population crashed as a result of canine distemper disease. Disease and reintroduced populations are susceptible to plague [Several species of conservation concern are in direct danger of extinction from disease. For example, devil facial tumor disease, a contagious cancer, is driving Tasmanian devils , and the associated pathogens they vector, are expected to spread north as a consequence of climate change [Some diseases are projected to spread as a result of changing land use and climate as well as through anthropogenic spread . For exae change . Other ee change –9, see [e change  for reviInfectious disease susceptibility in plants and animals may have a genetic basis, as changes in the coding portions of immune response genes can affect protein conformation and alter pathogen recognition and binding . Most stGopherus polyphemus, is a threatened tortoise that can contract a chronic, and occasionally fatal, infectious upper respiratory tract disease (URTD) caused by Mycoplasma agassizii [The gopher tortoise, gassizii . There mgassizii , 23. Torgassizii .The gopher tortoise is federally listed as threatened in the portion of its range west of the Mobile and Tombigbee Rivers in Alabama, and the eastern tortoises are candidates for listing on the Endangered Species Act . As suchM. agassizii was extremely low, and mortality events and observed clinical disease were rare. However, in 2006, increases in seroprevalence, clinical signs, and mortality events were observed, indicating that the FC site had transitioned from an M. agassizii URTD-negative site to an acute epizootic site. Prior to 2003, the seroprevalence of M. agassizii at the CF site ranged from 0 to 20%. In 2003, dramatic increases were seen in seroprevalence (77% positive), tortoises exhibited a nasal discharge (33%), and tortoises (58%) positive by PCR for M. agassizii. Mortality events were observed, seroprevalence remained high, but clinical disease expression and severity decreased over time, suggesting that the CF site had transitioned from an acute epizootic site to an endemically stable site. The OLD site was endemically stable but had experienced a large-scale die-off circa 1996–1998 attributed to URTD [M. agassizii [Between 2002 and 2006, 11 populations in Florida were sampled annually as part of a long-term field study of gopher tortoise health . Serum a to URTD . In 2006gassizii . Clinicagassizii . HoweverChrysemys picta bellii (western painted turtle) genome [We used a next-generation sequencing approach and previously developed ribonucleic acid (RNA) baits , 29 from) genome  relating) genome , except https://sourceforge.net/projects/bbmap/), aligned merged and unpaired reads and paired reads separately to the C. p. bellii 3.0.3 genome [n = 16,169), tri- (n = 651), and tetra-allelic (n = 1) SNP loci. To determine what polymorphic variants were under selection, we used BayeScan v2.1 [FST outlier test, and used a false discovery rate [We used the same analysis pipeline of  to call 3 genome  using BW3 genome , then le3 genome , and fol3 genome  best pra3 genome  except wcan v2.1 , an FST ery rate  of 0.1.D [D based on the empirical P value calculated for each population and locus combination as follows: first we simulated extreme values of Tajima’s D using 1000 simulations for a population of five individuals with a single locus containing 1–138 segregating sites using the program ms [P value of the empirical Tajima’s D given the simulated extreme values for a particular number of segregating sites and retained only loci with a false discovery rate less than 0.05. We performed functional enrichment analysis using FatiGO [We calculated Tajima’s D  for eachD  for all ogram ms . Then weogram ms  code to g FatiGO  implemeng FatiGO  to deterWe labeled alternate SNPs and indels as synonymous or non-synonymous using snpEff v4.0e . We perfFrom two Illumina MiSeq runs using 75-bp paired end reads, we obtained 21.5 million reads that passed quality control, and each tortoise had 1.3 ± 0.2 (mean ± SD) million reads. Mean coverage was 47.5 ± 7.6 reads , 0.060 (CFxOLD), and − 0.003 (FCxOLD): low values were expected based on geographic proximities of the populations.SNP allelic richness was lowest in CF, intermediate in OLD, and highest in FC. Observed and expected heterozygosity was lowest in CF but equivalent in FC and OLD Table . PairwisK value of two based on STRUCTURE HARVESTER [Population admixture inferred with SNPs using principle component analysis  for 27 regions suggesting either population expansion or positive selection but positive (≥ 2.06) for 43 regions suggesting either population bottlenecks, balancing selection, or strong population structure. These regions represented 35 genes  but also shared by two non-clinical animals (CF69 and OLD77). The SNPs were found on the following National Center for Biotechnology Information (NCBI) contig NW_007281632.1 at positions 53,577 and 53,578, and the indel was found at position 53,579  estimate genetic diversity, differentiation, and admixture in each sampling location, and; 2) to identify SNPs putatively under selection, genes deviating from neutrality, and to better understand the genetic basis of URTD susceptibility, and; 3) to outline an approach that could be used to identify important genetic variants associated with diseases in other threatened species. We discuss each of these below.FST values showed genetic differentiation corresponded to geographic distance. Second, genetic admixture inferred by PCA and STRUCTURE methods was congruent and reasonable as FC and OLD are geographically close and cluster together whereas CF is farther away and clusters separately. Because the populations represented three transition states in URTD epidemiology, differences also could be reflective of changes in the population after transition from initial URTD acute outbreaks to endemically stable and/or recrudescent outbreaks rather than geography alone. Clinically ill tortoises at CF could represent a subpopulation of more susceptible animals, which might be predicted to be among those impacted most during an initial disease outbreak. The historic OLD die-off [Genetic differentiation and admixture were in line with expectations. First, pairwise  die-off  could haRanavirus as well as Herpesviruses affect G. polyphemus [G. polyphemus with Ranavirus examined by AJ Johnson, AP Pessier, JFX Wellehan, A Childress, TM Norton, NL Stedman, DC Bloom, W Belzer, VR Titus, R Wagner, et al. [Two SNPs appeared to be under selection. The first SNP, NW_007281406.1:2,298,828, was in the gene IFIT1-like; IFIT1 can bind to viral RNA with a triphosphate group on the 5′ end . Speciflyphemus . In fact, et al.  was founD values. We do not think this was due to population expansion, as G. polyphemus populations have declined across their range, especially during the past century [D values, which is unlikely to be due to population bottlenecks as estimates of genetic diversity are very high for the populations assessed in this study relative to other gopher tortoise populations [There were 27 gene regions that had negative Tajima’s  century . Therefo century . Forty-tulations . These pulations . It is iulations ).Functional enrichment analysis of the 35 gene regions that deviated from neutrality  encodes a protein component of Austrelaps superbus (lowland copperhead snake) venom that is structurally and functionally similar to the complement protein C3 [A. superbus venom factor 1 may function like its analog C3 in mycoplasmal infections. Specifically, mycoplasmas such as M. pulmonis activate C3 [Genes that contained SNPs and indels with the strongest associations to clinical status may influence key biological functions in otein C3 , which iotein C3 . A. supeivate C3 , which civate C3 .M. pulmonis have lower mycoplasmal load than mice with intact NK [LOC101950941 likely encodes a C-type lectin-like receptor found on natural killer cells (NK) based on a Coding Domain Search . The fulntact NK . Thus, NMycoplasma arthritidis and M. fermentans have membrane-associated lipoproteins that upregulate CD40 expression by dendritic cells and macrophages [M. agassizii could similarly impact CD40 upregulation. Therefore it is reasonable to suggest that there may be interactions between variation at the TNFRSF5-like gene and mycoplasma immunity in G. polyphemus.The strongest associated indels were present in the genes LOC101950941 and TNFRSF5-like. The indel at NW_007281632.1:53,579, which was contained within LOC101950941 was also found in our analysis of indels with non-synonymous alleles shared by tortoises with clinical signs. The TNFRSF5 gene  encodes a receptor on antigen-presenting cells of the immune system such as dendritic and B cells as well as macrophages. TNFRSF5 functions in a variety of immune and inflammatory responses including the survival and proliferation of B cells  and indurophages –67, and M. agassizii; 44 out of 191 (23%) presented mild to severe clinical signs of URTD; and 101 out of 366 (28%) had evidence of chronic URTD lesions [There are several conservation implications based on the associations found between genetic variation and URTD clinical status. First, previous immunome work by JP Elbers, RW Clostio and SS Taylor  showed t lesions . This suk allele can properly recognize cytomegalovirus-infected cells [More generally, understanding the genetic basis of disease susceptibility is critical for a variety of reasons. First it can provide information on populations’ risk to succumbing to disease. For example, mice with the H-2Ded cells , thus mied cells .Previous studies examining the genetic basis of disease resistance focus on a limited number and type of genes . HeAdditional file 1: Figure S1.Coverage plots for first eight samples showing number of sequencing reads at or above specified proportions. A value at 50 Depth and 0.5 fraction means 50% of bases were at or above 50X coverage. (TIFF 149 kb)Additional file 2: Figure S2.Coverage plots for last eight samples showing number of sequencing reads at or above specified proportions. (TIFF 124 kb)Additional file 3: Table S1.Genes with regions deviating from neutrality. (DOCX 17 kb)Additional file 4: Table S2.Gopherus polyphemus samples by clinical status and diploid genotypes at several bases along the A. superbus venom factor 1-like and TNFRSF5-like genes for NCBI contigs NC_024232.1 and NC_024234.1 respectively. Non-clin for tortoises that were never observed with nasal discharge during the duration of the field study and Clin for tortoises that had at least one incident of mild to severe nasal discharge. CF for Cecil Field, FC for Fort Cooper, and OLD for Oldenburg. (DOCX 17 kb)Description of


Right-sided colon cancer (RCC) and left-sided colon cancer (LCC) differ with respect to their biology and genomic patterns, but inflammatory index variation did not fully investigate. This study aimed to examine the difference of inflammatory indexes and its value between RCC and LCC.The differences of common clinicopathologic factors, inflammatory indexes including PLR (Platelet lymphocyte ratio) between LCC and RCC were analyzed in the training cohort with logistic regression model, subsequently, confirmed in validation cohort. Kaplan-Meier analysis was applied for the analysis of the survival difference distinguished by the PLR and the Nonparametric Test was adopted to demonstrate the difference of PLR variation with the standard TNM classification between RCC and LCC.P = 0.002) and was elevated with the TNM stage in the LCC patients (P < 0.001), however, the two relationships did not sustain in RCC patients.A total of 1846 CRC patients entered the study, 744 (40.3%) patients were RCC, 1102 (59.7%) were LCC. The patients’ number in both cohorts was 923. It was found that LCC patients in the training cohort significantly to be with higher CEA, adenocarcinoma, early UICC/AJCC stage, p-MMR (mismatch-repair proficient), and lower PLR, and the later four features were confirm in validation cohort. Higher PLR, the unique inflammatory index, was significantly associated with poorer OS in LCC cohort  in the LCC and more common in advanced TNM stage.The online version of this article (10.1186/s12885-017-3862-8) contains supplementary material, which is available to authorized users. Bufill et al. firstly observed the clinical characteristics difference between right-sided colon cancer (RCC) and left-sided colon cancer (LCC) [Gibbs et al. has reported that in the patients received the Bevacizumab, the RCC patients has the most obvious PFS  benefit [Boisen revealed that there exists the apparent survival advantage in the LCC when combined the chemotherapy with the Bevacizumab [Colon cancer (CRC) has always be viewed as two different subtype since er (LCC) .Since ther (LCC) , 3. Rece benefit . Howeveracizumab . Furtheracizumab , 7.Asaf et al. has found that Ly6G + neutrophils suppress intraluminal natural killer cell (NK)-mediated tumor cell clearance and facilitate extravasation of carcinoma cells [In the exploratory classification system of consensus molecular subtypes (CMS), CRC can be divided into four types –10: CMS1ma cells , it indima cells –20. The ma cells .https://www.nccn.org/. Simply, stage I colon cancer receive radical surgery and colon cancer patients with low-risk stage II disease can be enrolled in a clinical trial, observed without adjuvant therapy, or considered for capecitabine or 5-FU/leucovorin(LV). For patients with high-risk stage II disease, they can be considered for adjuvant chemotherapy with 5-FU/LV (5-Fluorouracil/Leucovorin), capecitabine, FOLFOX , CapeOX , FLOX, or observation. Radiotherapy, chemotherapy and surgery were combined for the treatment of the stage III and stage IV colon cancer. In the present study, intensity-modulated radiation therapy (IMRT) was performed with 6–8 MV X-ray. The adjuvant chemotherapy was either one of FOLFOX, XELOX or Capecitabine alone with median cycles of 2  pathological evidence of adenocarcinoma of CRC; (ii) complete baseline clinical information and laboratory data; and (iii) complete follow-up data. Treatment regimen is implemented based on the NCCN guidelines 6 cycles .Patients with rectal cancer, as well as patients with the ascertained MSI status were excluded. The whole cohort was divided into two cohorts, with 923 patients in the training cohort from the January of 2004 to the November of 2013 and the other 923 patients in the validation cohort from December 2013 to the August 2015. Ethical approval was obtained from the institutions through the respective institutional review boards. The study protocol was designed in accordance with the guidelines outlined in the Declaration of Helsinki and was approved by the Ethics Committee of Sun Yat-sen University Cancer Center.A standardized data collection form was designed to retrieve all relevant sociodemographic data ; preoperative baseline laboratory data: carcino-embryonic antigen (CEA), Carbohydrate antigen (CA199), albumin (ALB), C-reactive protein (CRP), etc.; staging data. All patients had received standard chemotherapies of FOLFIRI19 (47.2%), FOLFOX20 (33.5%), or XELOX21 (19.3%), and/or in combination with bevacizumab every 3 weeks.Colon cancers were identified by ICD-O-3 site codes. If the cancer located in cecum, ascending colon, hepatic flexure of colon, and transverse colon, it would be defined as RCC, while those located in splenic flexure of colon, descending colon, sigmoid colon, and rectosigmoid were defined as LCC , 23–28. All samples were collected before any treatment and were tested within 24 h after collection. The supernatants were processed for analyzing CEA, CA199 on UniCelDxI 800 immunoassay system .Plasma CRP was measured using a high sensitivity assay  as described previously .Immunohistochemistry was performed to examine the four most common mismatch repair proteins under the standard Envision two-step procedure. In brief, the slides were backed at 60°Cfor 2 h, cleared through xylene, rehydrated, then pre-treated in EDTA antigen retrieval buffer, treated with 3% hydrogen for 20 min to block endogenous peroxidase activities and then incubated with 10% normal goat serum at room temperature to block non-specific activity. Then, the slides were incubated overnight at 4°Cusing the following polyclonal antibodies, MLH1 , PMS2 , MSH2  and MSH6 . After washing, the tissues were incubated with a secondary antibody  for 1 h at room temperature. Finally, the sections were counterstained with 10% Mayer’s hematoxylin, dehydrated and mounted in Crystal Mount. Non-neoplastic colonic mucosa, stromal cells, infiltrating lymphocytes or the centers of lymphoid follicles were accepted as internal positive control and the known MMR deficient colorectal carcinomas used as external negative controls. Immunostaining was scored by two experienced pathologists and without any prior knowledge of the patients’ clinical data. Nuclear staining within tumor cells was defined as the normal expression, while complete absence of nuclear staining within tumor cells with concurrent internal positive controls was illustrated as negative protein expression. MLH1/PMS2/MSH2/MSH6 protein expression negative was defined as tumor with loss of MLH1/PMS2/MSH2/MSH6 protein visualized by light microscopy. Whatever one of these MLH1/PMS2/MSH2/MSH6 protein expressions is negative; it was defined as DMMR cohort. If the all the four protein is positive, the specimen then will de classified to the PMMR cohort.P < 0.05 were entered into multivariate logistic regression analyses via stepwise procedures. Statistical data analyses were performed using SPSS 22.0 .Continuous variables were expressed as mean ± standard deviation, median and range, and were transformed into dichotomous variables at median value. The threshold of CEA and C19–9 were established at 5 ng/ml and 37 U/ml as commonly suggested , 31. Com3). The optimal cutoff level for the neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), CAR (C-reactive/Albumin Ratio)and PNI was determined using the median value [The PNI was calculated as10 × serum albumin value (g/dl) + 0.005 × peripheral lymphocyte count .P = 0.003, HR = 1.475, 95% CI, 1.137–1.914), which is consistent with the previous study [A total of 1846 patients were included in the analyses for the analysis, with 744 patients in the RCC cohort and 1102 patients in the LCC. MSI status was successfully determined in 1846 patients. One thousand ninety-nine patients had received the chemotherapy and 378 patients had received radiotherapy. Patients in the training cohort were 923 patients and the other 923 patients were included in the validation cohort. The median follow-up time was for OS was 37 months (range: 4–138 months) in the whole cohort. Five-year OS was 86%in the whole cohort, 85.9% in LCC cohort and 88.7% RCC cohort, with the apparent poorer survival in the LCC (us study –37. The P < 0.001), PLT (P = 0.004), CEA (P < 0.001), PLR (P = 0.011), TNM stage (P = 0.001) retained independent prognostic significance for the location of CRC. Detailed summaries of the multivariate analyses are shown in Tables P < 0.001), age (P = 0.007), ALB (P < 0.001), PLR (P = 0.022) and TNM stage (P = 0.011) were proven to be independent different prognostic factors , higher tumor marker CEA, higher ALB and higher probability of microsatelite stability in the univariate analysis. All significant variables were entered into multivariate logistic regression; MMR status   but not the RCC (P = 0.869) in the early-staged TNM staging  Fig. . HoweverP < 0.001, Fig. P = 0.174, Fig. The variation trend between systemic inflammatory factors and the tumor staging was shown in Fig. Currently, most studies had focused on the biology, microenvironment and survival difference in RCC and LCC, however, there is still no report regarding the inflammatory distinction between them –41.To ouAs shown in Table Gervaz et al. reported that CRC is a heterogeneous disease and could be differentiated into two anatomical and functional entities [The PLR has been demonstrated as a prognostic factor in several malignant tumors, including colorectal cancer, gastric cancer, esophageal carcinoma, esophageal squamous cell carcinoma (ESCC), small cell lung cancer –52. The entities .Guinney’s (14) research, LCC related to CMS2, which characterized as epithelial, chromosomally unstable, marked WNT and MYC signaling activation; RCC related to CMS1, which characterized as hypermutated, microsatellite unstable, strong immune activation. Obviously, LCC related to inflammation, but RCC not, so, it is consistent with the opinion that the PLR reflect inflammation too. Chapman et al. has demonstrated that platelets present antigen to T cells in a platelet MHCI (major histocompatibility complex I) dependent manner, which indicate platelets not only support and promote acquired immune responses, but may also directly participate in the initiation of acquired immune responses. Liang et al. has revealed that the over-activation of platelets enhances survival of tumor cells in circulation by the CD62P ligand [Interestingly, the difference of PLR in our study has significantly translated into OS difference only in LCC rather than RCC, and PLR changes with TNM stage only in LCC. So, what is the inner link between PLR and LCC? According to P ligand . It has P ligand . PlateleP ligand , 57. TheP ligand , 59. PlaP ligand –62. BeyoP ligand , 64.Finally, with the increase of TNM stage, PLR significantly increased either in LCC rather than RCC in our study. The LCC exhibit the characteristics of higher rates of microsatellite stability (MSS)  and a noThe study was conducted retrospectively and selection bias may exist. However, we included a relatively large cohort to assess the difference of PLR in LCC and RCC in independent training cohort and validation cohort. Of course, additional validation of the PLR is necessary in prospective datasets. In summary, this study suggests that the prognostic value of the PLR, a continuous variable, may help to stratify LCC and RCC patient and guide treatment especially in the LCC.This is the first study that regarding the inflammatory status between the LCC and the RCC and we found the PLR was the merely different inflammatory parameter between the LCC and the RCC. Additionally, the PLR variation trend with the tumor staging was shown in only in the LCC.


Comparable estimates of health spending are crucial for the assessment of health systems and to optimally deploy health resources. The methods used to track health spending continue to evolve, but little is known about the distribution of spending across diseases. We developed improved estimates of health spending by source, including development assistance for health, and, for the first time, estimated HIV/AIDS spending on prevention and treatment and by source of funding, for 188 countries.We collected published data on domestic health spending, from 1995 to 2015, from a diverse set of international agencies. We tracked development assistance for health from 1990 to 2017. We also extracted 5385 datapoints about HIV/AIDS spending, between 2000 and 2015, from online databases, country reports, and proposals submitted to multilateral organisations. We used spatiotemporal Gaussian process regression to generate complete and comparable estimates for health and HIV/AIDS spending. We report most estimates in 2017 purchasing-power parity-adjusted dollars and adjust all estimates for the effect of inflation.Between 1995 and 2015, global health spending per capita grew at an annualised rate of 3·1% , with growth being largest in upper-middle-income countries (5·4% per capita [UI 5·3–5·5]) and lower-middle-income countries (4·2% per capita [4·2–4·3]). In 2015, $9·7 trillion (9·7 trillion to 9·8 trillion) was spent on health worldwide. High-income countries spent $6·5 trillion (6·4 trillion to 6·5 trillion) or 66·3% (66·0 to 66·5) of the total in 2015, whereas low-income countries spent $70·3 billion (69·3 billion to 71·3 billion) or 0·7% (0·7 to 0·7). Between 1990 and 2017, development assistance for health increased by 394·7% ($29·9 billion), with an estimated $37·4 billion of development assistance being disbursed for health in 2017, of which $9·1 billion (24·2%) targeted HIV/AIDS. Between 2000 and 2015, $562·6 billion (531·1 billion to 621·9 billion) was spent on HIV/AIDS worldwide. Governments financed 57·6% (52·0 to 60·8) of that total. Global HIV/AIDS spending peaked at 49·7 billion (46·2–54·7) in 2013, decreasing to $48·9 billion (45·2 billion to 54·2 billion) in 2015. That year, low-income and lower-middle-income countries represented 74·6% of all HIV/AIDS disability-adjusted life-years, but just 36·6% (34·4 to 38·7) of total HIV/AIDS spending. In 2015, $9·3 billion (8·5 billion to 10·4 billion) or 19·0% (17·6 to 20·6) of HIV/AIDS financing was spent on prevention, and $27·3 billion (24·5 billion to 31·1 billion) or 55·8% (53·3 to 57·9) was dedicated to care and treatment.From 1995 to 2015, total health spending increased worldwide, with the fastest per capita growth in middle-income countries. While these national disparities are relatively well known, low-income countries spent less per person on health and HIV/AIDS than did high-income and middle-income countries. Furthermore, declines in development assistance for health continue, including for HIV/AIDS. Additional cuts to development assistance could hasten this decline, and risk slowing progress towards global and national goals.The Bill & Melinda Gates Foundation. For more than 50 years, health spending has increased steadily, in many cases outpacing economic growth.In many low-income countries, health spending amounts to less than $100 (2017 purchasing-power parity-adjusted dollars) per person annually, whereas in many high-income countries, health spending exceeds $5000 per person.Evidence before this studyPrevious evidence about health financing, including global resource tracking efforts produced by WHO and the Global Burden of Disease (GBD) Health Financing Collaborator Network, has been published for diverse topics. In 2017, WHO updated estimates of health spending disaggregated by source, using System of Health Accounts (2011) classifications. Other research has captured health spending for subsets of countries and spending types. This study builds on those data and methods used previously by the GBD Health Financing Collaborator Network in 2017. The 2017 study used these data to illustrate health financing patterns and changes in health spending as countries develop. However, previous work by WHO and the Collaborator Network did not estimate uncertainty, did not estimate spending for all countries, and did not estimate disease-specific spending.Previous evidence about HIV/AIDS spending includes National AIDS Spending Assessments, National Health Accounts, and other sources, which captured HIV/AIDS financing for short timespans and subsets of countries. To our knowledge, no study has collated these HIV/AIDS data and estimated spending for all countries across time. Furthermore, no comprehensive global estimates of total spending by disease or disaggregated by source or type of care exist to date for all countries, building from a broad dataset.Added value of this studyThis study improved estimates of health spending published in 2017, and expanded the scope to capture disease-specific spending. We added four more countries and estimated a complete series of health spending, disaggregated by source, for 188 countries, alongside 95% uncertainty intervals that quantify which estimates are most and least certain. We included recently published data from WHO that is based on the System of Health Accounts 2011, and built models favouring datapoints drawn from an identified source or clear estimation methods. We estimated development assistance for health disaggregated across health focus areas, expanded to include pandemic preparedness. Finally, we collected and harmonised 5385 datapoints to estimate HIV/AIDS spending across 188 countries. We report estimates of domestic government and private contributions to HIV/AIDS funding and spending on HIV/AIDS treatment and prevention.Implications of all the available evidenceTotal health spending continues to outpace economic growth in many contexts, but development assistance is levelling off. Global HIV/AIDS spending amounted to $562·6 billion from 2000 to 2015, although the amount of HIV/AIDS spending varies substantially across countries, income, and prevalence groups. The substantial share of spending sourced externally in countries with high HIV/AIDS prevalence highlights the risk posed by future reductions in development assistance for HIV/AIDS and the vigilance required to ensure that global goals, such as the UNAIDS Fast-Track Targets and Sustainable Development Goal target 3.3, are achieved. Comparable and comprehensive estimates of health spending and disease-specific spending are crucial for assessing the allocation of resources worldwide and pinpointing important gaps in spending. Paired with GBD estimates, these data make possible a diverse set of comparisons, including analyses across different periods, countries, income groups, regions, and funding sources.top global health priority was solidified in Millennium Development Goal 6 and Sustainable Development Goal (SDG) 3.Our study begins to fill this gap by tracking health spending on HIV/AIDS for 188 countries from 2000 to 2015. Quantifying HIV/AIDS spending is an initial step in conducting global disease-specific spending assessments and is a crucial priority for the international community and countries. The position of HIV/AIDS as a We aimed to investigate three features of global health spending to characterise the variation in health spending across health systems and to characterise how international and domestic partners have responded to recent global health crises, such as Ebola and HIV/AIDS. First, we estimated health spending worldwide, tracking how much was spent on health in 188 countries. Second, we tracked development assistance for health, tracing disbursements from their source, through intermediary channels, to recipients in low-income and middle-income countries. We aimed to quantify how much of that financing focuses on core health focus areas, including HIV/AIDS, pandemic preparedness, and other primary areas of health financing. Finally, we tracked international and domestic spending on HIV/AIDS, with funds disaggregated across four financing sources and broken down by spending on prevention and treatment. This evidence will be particularly important to both donors and countries as they coordinate responses to future global health challenges, move to adopt sustainable health financing polices that reduce reliance on development assistance for health, and chart courses towards the achievement of the SDGs.Each health financing component we tracked required unique input data and, consequently, estimation focused on different time periods. We tracked health spending by source from 1995 to 2015, development assistance for health from 1990 to 2017, and HIV/AIDS spending from 2000 through to the end of 2015. Most spending estimates reported in this paper are reported using 2017 purchasing-power parity-adjusted dollars to adjust for inflation and to reflect the country-specific purchasing power of the resources. Development assistance for health estimates that are stratified by source, channel, or health focus area are tracked using 2017 US$ to reflect the quantity of development assistance for health provided by donors, using an internationally recognisable currency . Development assistance for health estimates stratified by recipient country are converted into 2017 purchasing-power parity-adjusted dollars based on the country to which the resources were provided.We extracted data about transfers from government domestic revenue , social insurance contributions, compulsory prepayment, voluntary prepayment, other domestic revenue from households, corporations, and non-profit institutions serving the household, and gross domestic product (GDP), each measured in local currency, from the WHO Global Health Expenditure Database.Many of the extracted data are not tied to an underlying data source and are estimated. Although more information is available in recent iterations,Development assistance for health includes the financial and in-kind resources provided by development agencies to low-income and middle-income countries, with the primary objective of maintaining or improving health. We estimated development assistance for health using project records, annual reports, budgets, and financial statements from international organisations. We relied on commitment and budget data to generate estimates for the most recent years when disbursement data were not available.Our estimates of development assistance for health tracked disbursements from the originating source through the disbursing agency, called the channel, to the recipient country and targeted health focus area or programme area. We used disbursement and income data to remove resources that were passed between development agencies before being disbursed to prevent double counting. We also accounted for the administrative expenses incurred by estimating in-kind expenses.We disaggregated development assistance for health disbursements into nine health focus areas: HIV/AIDS, tuberculosis, malaria, maternal health, newborn and child health, other infectious diseases, non-communicable diseases, sector-wide approaches and health system strengthening, and other. The other category captured all projects that did not align with any of the other health focus areas. We further disaggregated these health focus areas by programme area, which are spending categories that represent programmatic aims or implementation approaches within the broader health focus areas. For example, we disaggregated development assistance for HIV/AIDS into treatment, diagnosis, care and support, counselling and testing, orphan and vulnerable children, prevention of mother-to-child transmission, and HIV/AIDS system support. Additionally, we tracked development assistance for pandemic preparedness as a programme area within sector-wide approaches and health system strengthening, and treatment and diagnosis as separate programme areas under tuberculosis. We used keywords from project titles, descriptions, and budgets to determine the targeted health focus and programme areas for projects.We report development assistance for health estimates in 2017 US$, but converted disbursements from 2017 US$ to 2017 purchasing-power parity-adjusted dollars to add them to domestic spending estimates. We did this by first deflating disbursements to current US$, exchanging disbursements to the current national currency units of the recipient country, deflating to constant 2017 local currency, and then exchanging to 2017 purchasing-power parity-adjusted dollars. Detailed explanations of the methods used to track development assistance for health, including how disbursements for cross-cutting areas are allocated, are included in the We estimated HIV/AIDS spending measures by financing source  and three HIV/AIDS spending categories .AIDS data hub. We converted all reported spending measures to 2017 purchasing power parity.We extracted HIV/AIDS spending data from five data sources. First, we used the spending data in the AIDSinfo database.We aimed to adhere to the definition and boundaries of health spending as defined by the System of Health Accounts 2011 framework. This approach required us to harmonise the extracted data to correct for known definitional differences between data sources and observed biases within the data. The National AIDS Spending Assessment's definition of HIV/AIDS spending included spending on non-health related categories such as spending on orphan and vulnerable children, enabling environment, and social protection. To correct for this, we extracted data from these three non-health-related spending categories and subtracted their values from all National AIDS Spending Assessment-reported spending by financing source. This correction probably accounted for most definitional biases between National AIDS Spending Assessments and National Health Accounts, but the granularity with which the data were reported limited further efforts to harmonise these two data sources. Similarly, we removed orphan and vulnerable children disbursements from our development assistance for health data.Not all data sources reported spending as granularly as we required. For example, some data sources only reported total domestic spending  or reported only private spending (sum of out-of-pocket and prepaid private). Although these spending measures did not match our measures of interest, they still provided valuable information. To use all available data, we estimated a total of five HIV/AIDS financing by source models . To ensure internal consistency across all models, we developed a sophisticated aggregating procedure that included information about the number of underlying datapoints each series had, and how the estimated series related to each other. More information is provided in the n where n is the number of extracted datapoints.We used a spatiotemporal Gaussian process regression model to model each HIV/AIDS financing source and spending category model. For all HIV/AIDS spending variables, the model consisted of a mixed-effect model with random effects on GBD super-region, region, and country, as well as covariates ranging from antiretroviral therapy coverage to the natural log of lag distributed GDP per capita, natural log of HIV prevalence, natural log of HIV incidence, natural log of HIV mortality rate, and, the natural log of antiretroviral therapy prices. We determined the exact specifications of each model through out-of-sample prediction tests . We sourWe reported health spending for each country, income group, and geographical region. We used 2017 World Bank income groups and GBD Study 2016 regions to categorise all years of data.The funder of this study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. All authors had full access to all the data in the study, and JLD and CJLM had final responsibility for the decision to submit for publication.In 2015, $9·7 trillion  was spent globally on health. Spending per capita varied widely across countries, spanning from less than $100 per capita per year on health  to more than $5000 per capita  between 1995 and 2015. Growth was largest in upper-middle-income countries, where health spending per capita grew by 5·4% (5·3–5·5) and in lower-middle-income countries, where it grew by 4·2% (4·2–4·3). Spending in low-income countries increased at a rate of 1·9% (1·7–2·0) annually and was the lowest rate of growth observed among income groups, whereas in high-income countries, which generally aim to slow health spending growth, per capita growth was 3·0% (3·0–3·1).In addition to spending more on health, wealthy nations also tended to finance more health spending from public sources of funds. Panels C and D of Despite clear patterns connecting total health spending and national income, country-level spending varied substantially, even within income groups and geographical regions . Across More development assistance for health was targeted at HIV/AIDS than at any other health focus area, with an estimated $9·1 billion spent in 2017 . This isDevelopment assistance for health that targeted other health focus areas was also substantial. We estimated that, in 2017, $7·7 billion of development assistance for health targeted newborn and child health, and $4·2 billion targeted sector-wide approaches and health system strengthening. There was substantially less development assistance targeting pandemic preparedness in 2017. We estimated this amount to be $204·2 million, with 82·6% ($168·7 million) of these funds channelled through WHO .We estimated that between 2000 and 2015, $562·6 billion (531·1 billion to 621·9 billion) was spent on HIV/AIDS. Global HIV/AIDS spending peaked at 49·7 billion (95% UI 46·2 billion to 54·7 billion) in 2013, but decreased slightly to $48·9 billion (45·2 billion to 54·2 billion) in 2015. Still, the 2015 total remains nearly three times that of spending in 2000 ($16·4 billion [14·6 billion to 19·3 billion]).Most spending on HIV/AIDS occurs in high-income and upper-middle-income countries , table 2For each dollar of development assistance for health for HIV/AIDS, in countries that receive it, there is $2·1 (95% UI 1·9–2·3) in government health spending for HIV/AIDS. In 2015, low-income, lower-middle-income, and upper-middle-income countries with low (<1%) HIV/AIDS prevalence received just $4·1 billion or 26·0% (22·8–28·5) of their HIV/AIDS spending as development assistance for health, whereas high prevalence (1–5%) countries received $6·0 billion or 76·5% (68·7–81·5) of total HIV/AIDS spending as development for health, and extremely high prevalence countries (>5%) received $4·4 billion or 50·5% (39·1–59·6) of total HIV/AIDS spending as development assistance for health. In 2015, Haiti received 97·1% (95·9–98·0) and Guinea-Bissau received 89·9% (83·0–94·2) of their HIV/AIDS spending as development assistance for health; both are categorised as high prevalence countries. Similarly, of the extremely high prevalence countries, Mozambique received 94·0% (90·8–96·1) and Zambia received 86·6% (77·2–92·7) of their HIV/AIDS spending as development assistance for health.Among low-income countries with high or extremely high prevalence of HIV/AIDS, $5·3 billion or 85·4% (79·8–88·6) of HIV/AIDS spending was sourced externally. However, during the past 5 years, the share of HIV/AIDS spending that is development assistance has been decreasing in countries of high and extremely high prevalence.The ratio of development assistance for health to government spending, in 2015, was 1·5 in low-income countries and 0·1 in lower-middle-income countries, although the ratio of development assistance for HIV/AIDS to government HIV/AIDS spending was 7·7 for low-income countries and 1·9 for lower-middle-income countries.From 1995 to 2015, global per capita health spending increased substantially. Health spending is on the verge of surpassing $10 trillion and accounts for 10% of the world's total economy. The USA alone accounted for almost a third of the world's health spending in 2015—slightly less than what was spent by all low-income and middle-income countries combined. Between 1995 and 2015, per capita health spending grew the fastest in middle-income countries, despite the declines in development assistance for health that occurred from 2010 to 2015. More broadly, development assistance for health remained relatively flat since 2013, although disbursements to some health focus areas, such as HIV/AIDS, have declined.In recognition of the growing threat of infectious disease outbreaks and epidemics in an increasingly interconnected world, we tracked development assistance for pandemic preparedness for the first time. We expect our estimates of development assistance targeted towards pandemic preparedness to improve over time as global initiatives that address these threats, such as the Global Health Security Agenda, become well established. According to the World Bank, the estimate of the economic and fiscal costs of the Ebola crises in the three affected countries—Guinea, Liberia, and Sierra Leone—was approximately $2·8 billion.In 1995, 18 million people were living with HIV/AIDS globally and 3 million additional cases emerged each year. At its peak in 2005, nearly 2 million people died from HIV/AIDS each year. Since 2005, the number of new HIV/AIDS cases each year have decreased by 23%, the global HIV/AIDS death rate declined by 52%, and 20·9 million people are now on antiretroviral therapy. These historic gains were made partly because of the extraordinary amount of resources made available to fight HIV/AIDS. Between 2000 and 2015, $562·6 billion (531·1 billion to 621·9 billion) was spent to combat the disease, with annual growth in HIV/AIDS spending surpassing 10% for many years. International support played a key part in the escalation of funding.Domestic governments also played an important role in the fight against HIV/AIDS, constituting the majority of worldwide spending on HIV/AIDS between 2000 and 2015. Government resources have continued to grow in all income groups while development assistance for health for HIV/AIDS declined. Governments played an integral part in the development and advancement of key prevention and treatment programmes. Still, in low-income countries and countries with extremely high prevalence, development assistance for health constituted most HIV/AIDS spending, even in 2015.Unique to HIV/AIDS is the small share spent out-of-pocket. Domestic HIV/AIDS spending is drawn predominately from government financing rather than being out-of-pocket, the latter of which leaves people susceptible to financial instability and impoverishment. In 2015, out-of-pocket spending accounted for less than 10% of HIV spending, smaller than overall out-of-pocket spending, which comprised nearly a quarter of total health spending. Our analysis suggests international and domestic government efforts surrounding HIV/AIDS have a major role in mitigating the financial hardship associated with HIV/AIDS.Despite the considerable domestic response to HIV/AIDS, many low-income and middle-income countries remain dependent on development assistance for health to fund HIV/AIDS programmes. Development assistance for health made up most of the total spending on HIV/AIDS in high prevalence (1–5%) countries in 2015. In extremely high prevalence (>5%) countries, development assistance for health comprises half of HIV/AIDS spending. Low-income countries make up half of high-prevalence countries and a third of extremely high-prevalence countries.High-prevalence countries reliant on development assistance for health must plan strategically so that decreases in external financing do not alter trajectories towards ending the transmission of HIV and sustaining HIV-positive populations with antiretroviral therapy. Ageing HIV-positive populations will continue to need antiretroviral therapy to live healthy and productive lives. Potential ways to offset declines in external HIV/AIDS funding include reallocating more government resources to the health sector, reallocating more government health resources to HIV/AIDS, or reallocating government HIV/AIDS resources to focus on the most effective approaches to HIV/AIDS prevention or treatment.Apart from the novel tracking of HIV/AIDS spending, our analysis provides further evidence of the wide variation in health spending, as well as the disconnect between health spending and health burden. Although these differences are stark, many factors affect how much is spent on health and which diseases are prioritised for spending in each country. Factors such as access to and price of health care, and efficiency of health systems, probably explain some variation in spending and prioritisation. Additionally, political and social preferences, and the availability of cost-effective interventions, govern how many resources are directed to the health sector, as well as to which patients and diseases. Although we would not expect spending levels or health system foci to be commensurate across income groups or to merely reflect health burden, we believe these discrepancies highlight potential gaps and places where more attention should be given to determine if health need is being met. We hope this exercise and future disease tracking spending studies could help parse out the factors associated with disease spending and identify how donors and governments can reduce financial barriers impeding progress towards important health-related goals.This research takes an initial step towards global disease-specific resource tracking, which is essential for several reasons. Disease-specific spending estimates make a host of new, policy-relevant analyses possible, including decomposing the drivers of health spending growth,Although increasingly granular tracking of health spending is advantageous for many reasons, it is not without challenges and limitations. The estimates for development assistance for health do not capture transfers of assistance among middle-income and low-income countries, largely because of the requirement that we capture a complete time series of disbursements for each agency we track, and that these data are comparable with all other data sources. Publicly available data that meet these requirements from low-income and middle-income countries are sparse, which is a gap we aim to fill in the future. Moreover, some of the input data used for parts of this study were not precise and required modelling. The input data for total health spending and HIV/AIDS spending were, in some cases, contradictory, had incomplete underlying documentation, and included many gaps. It is difficult in some cases for health accountants and financing experts to disentangle HIV/AIDS and tuberculosis funding, which could affect the underlying data used. A greater push and adherence to an agreed-upon set of spending definitions and methods to track resources would help produce more precise and comparable estimates of health spending. It is our hope that these estimates help demonstrate the use of disease-specific resource tracking studies, and can be a catalyst for more investment in global resource tracking for health. That approach includes necessary investment in low-income and middle-income countries, as well as in high-income countries, where the internationally consistent tracking for HIV/AIDS spending was weakest. The wide UIs surrounding our estimates should be a recurring reminder of the need for sustained investment in health systems capable of disease-specific resource tracking. In addition to tracking health spending with more rigour and precision, we urge that investments be made in tracking spending subnationally to assess, with more accuracy and consistency, within-country spending disparities across important socioeconomic and geographical stratifiers. Many country-specific studies have shown that within-country health spending varies as substantially as cross-country estimates, with equally as poignant conclusions.Even as development assistance for health levels off, health spending continues to increase, outpacing economic growth in many contexts. With growth steady or accelerating, it is more important than ever to understand where resources for health go and how they align with health needs, particularly because major variation in spending persists across countries. Estimates of spending on HIV/AIDS are a step toward better understanding this variation. The noteworthy increases in spending on HIV/AIDS has mitigated, at least at present, a major global health crisis. Prevention efforts will remain essential in all contexts. However, the vulnerability of low-income and high-burden countries to reductions in development assistance for health is also a crucial finding, capturing the risk posed by future reductions in development assistance for health for HIV/AIDS and the vigilance required to ensure UNAIDS Fast-Track Targets and SDG target 3.3 are achieved.dieleman@uw.eduCorrespondence to: Dr Joseph L Dieleman, Institute for Health Metrics and Evaluation, Seattle, WA 98121, USA


Chronic obstructive pulmonary disease (COPD) is a significant cause of morbidity in persons living with HIV (PLWH) and HIV appears to uniquely cause COPD, independent of smoking. The mechanisms by which HIV leads to COPD are not clear. The objective of this study was to identify metabolomic biomarkers and potential mechanistic pathways of HIV-associated COPD (HIV-COPD).We performed case–control metabolite profiling via mass spectrometry in plasma from 38 individuals with HIV-COPD (cases), comparing to matched controls with/without HIV and with/without COPD. Untargeted metabolites of interest were identified with liquid chromatography with mass spectrometry (LC-MS/mass spectrometry (MS)), and targeted metabolomics for tryptophan (Trp) and kynurenine (Kyn) were measured by selective reaction monitoring (SRM) with LC-MS/MS. We used mixed-effects models to compare metabolite concentrations in cases compared with controls while controlling for relevant biological variables.We identified 1689 analytes associated with HIV-COPD at a false discovery rate (FDR) of 10%. In PLWH, we identified 263 analytes (10% FDR) between those with and without COPD. LC MS/MS identified Trp and 17 lipids, including sphingolipids and diacylglycerol. After adjusting for relevant covariates, the Kyn/Trp ratio measured by SRM was significantly higher in PLWH (p=0.022), but was not associated with COPD status (p=0.95).There is a unique metabolite profile in HIV-COPD that includes sphingolipids. Trp metabolism is increased in HIV, but does not appear to independently contribute to HIV-COPD.NCT01810289, NCT01797367, NCT00608764. HIV-associated chronic obstructive pulmonary disease (HIV-COPD) has a unique plasma metabolomic profile that includes sphingolipids and fatty acids.Tryptophan catabolism is increased in PLWH, but does not correlate with COPD status.Additional studies are needed to determine how such metabolic pathways contribute to HIV-COPD and whether therapeutic interventions to alter sphingolipid expression can reduce the risk of COPD in persons living with HIV.Combination antiretroviral therapy (ART) has markedly improved the survival of individuals living with HIV-1. However, improved survival has led to a higher prevalence of several chronic illnesses including chronic obstructive pulmonary disease (COPD), which affects an estimated 3–23% of persons living with HIV (PLWH).15Recent developments in metabolomics and metabolic profiling have enabled the measurements of thousands of metabolites simultaneously in biological samples leading to new discoveries of biomarkers of disease.We performed a cross-sectional, matched case–control study using plasma samples from two large cohort studies.3. From this cohort, we selected 38 participants with HIV-COPD (defined as forced expiratory volume in 1 s (FEV1)/forced vital capacity (FVC)< lower limits of normal and FEV1<80% predicted at baseline pulmonary function testing, 1/FVC>lower limits of normal and FEV1>80% predicted) matched on age, sex, region and smoking status. HIV-negative controls were identified in the Genetic Epidemiology of Chronic Obstructive Pulmonary Disease (COPDGene) study, an observational study that enrolled current or former smokers. COPDGene controls were matched on lung function, age, sex and race. These consisted of 17 individuals without COPD and 20 individuals with COPD  used in this study was of high pressure liquid chromatography (HPLC) grade and purchased from Fisher Scientific . HPLC grade methanol (MeOH) was acquired from Sigma-Aldrich  and analytical grade formic acid from Fluka . The mass spectrometer lock-spray solution, leucine encephaline, was purchased from Waters . Ultra high purity bottled water was procured from Invitrogen .Aliquots of 100 µL of plasma had a heavy standard of 3 µL of 100 µM of kynurenine (Kyn)-D6 and 3 µL of 1 mM of tryptophan (Trp)-C11For analyte profiling and quantitation, 10 µL of the undiluted sample was injected into Thermo Q-exactive LC-MS  with Acquity ultra performance liquid chromatography (UPLC) C18 2.1×100 mm, 1.7 µm column  at 40˚C. All of the samples were subjected to a gradient going from buffer A to B over 15 min then flushing for 5 min. Buffer A consisted of 99.9% water with 0.1% formic acid, while buffer B was 99.9% ACN with 0.1% formic acid. To avoid batch effects, all samples were run in one continuous MS run. For quality control, internal standards consisted of blank samples spiked with four metabolites  and were placed at random intervals in the MS run. For quantitation, the individual samples were spiked with the same four metabolites for normalisation of the peak intensities. For metabolite identification, two samples from each group  were run on the Thermo Q-exactive LC-MS in MS/MS data-independent acquisition mode with a 25 min of gradient A to B followed by 5 min of flushing.http://proteowizard.sourceforge.net/tools.shtml), and processed using the XCMS algorithm as implemented in the xcms package for R using the recommended pipeline (the symmetric family was used for retention time (RT) correction). Data from each sample were normalised to the mean of the heavy standards we introduced for Trp (mass/charge (m/z)=215.1231 and RT=1.841226) and Kyn (m/z=216.1318 and RT=1.941770), so that on the log scale the means of these two analytes were zero for all individuals.The .RAW files from the Q-Exactive were converted into mzXML using MSconvert  were subjected to injection using an Agilent autosampler LC-MS/MS with an analytical Waters Symmetry C18, 3.5 µm column connected to the 5500 iontrap  fitted with a turbo V electrospray source. The samples were subjected to a linear gradient of 2% ACN, 0.1% formic acid to 98% ACN 0.1% formic acid for 10 min at a column flow rate of 250 µL/min. Transitions monitored are in online 10.1136/bmjresp-2017-000180.supp1supplementary table 11 per cent predicted, race and smoking status and a random effect for match group. All matched groups were matched on age and sex. For analysis of individual metabolites, a logarithmic transformation was employed prior to model fitting, whereas the component of the targeted analysis which focused on the ratio of Kyn to Trp did not employ such a transformation. Otherwise, the same approach was employed for the analysis of the metabolites identified in the untargeted analysis as for the targeted analysis, except that we report an unadjusted p value for the targeted analysis, whereas we use the q-value approach to control the false discovery rate (FDR) at 10% for the untargeted analysis. V.3.2.3 of the statistical software package R was used for all statistical analyses.To test for differences in metabolite concentrations, mixed-effects models were used that included fixed effects for HIV status, COPD status, the interaction between HIV and COPD status, FEVhttps://metlin.scripps.edu/index.php). A degree of restraint, within 10% of the RT, was used when matching to feasible options based on RT of the analyte. Analytes illuted in the first 30s and after 15 min were eliminated.MS/MS spectra .Raw files were observed using Thermo Xcalibur Qual Browser. MS/MS spectra for each analyte of interest were matched to metabolite spectra through the Metlin metabolite database from Scripps  controls were significantly older than the HIV-positive participants.Cases consisted of 38 individuals with HIV-COPD . HIV(+) 1 per cent predicted, race, sex and smoking status, we identified 880 analytes at 10% FDR that were attributable to the main effect of HIV status, that is, differences due to HIV averaging over COPD status; we identified no analytes that differed at 10% FDR for the main effect of COPD. Tests of the statistical interaction between HIV status and COPD status identified 263 analytes that displayed differences of interest . Of these 263 analytes, 232 (88%) also had significant main effects for HIV status. On examination of the estimated regression coefficients for HIV status and the interaction between HIV and COPD status of the 263 analytes, it was found that differences between HIV(+) and HIV(−) participants are, on average, smaller in COPD(+) participants compared with COPD(−) participants. This is reflected in MS analysis detected 1689 plasma analyte signals, defined as a discreet m/z and RT, that correlated with a yet unknown metabolite. Since there is no universally accepted methodology to quantify analytes detected by mass spectrometry, we chose relative abundance as the sum of all peak intensities detected by the mass spectrometer that associated with the given analyte. Using a mixed-effects model with fixed effects for COPD status, HIV status, their interaction, FEVTo identify some of the significant analytes of interest, we performed LC-MS/MS targeting the 172 analytes we identified that differed in HIV-COPD. We were able to attribute 17 of these analyte spectra to lipids, including sphingolipids, diacylglycerol and fatty acids . Ceramid10.1136/bmjresp-2017-000180.supp2supplementary table 2−) participants as previously reported,One of the analytes differentially expressed in HIV+ participants was consistent with the essential amino acid Trp (m/z 205.097). Since Trp catabolism has been associated with both HIV infection and COPD,A major gap in knowledge is the limited understanding of why PLWH are more susceptible to developing COPD independent of smoking status.et alet al, where they used both targeted and untargeted metabolomic platforms that identified 23 sphingolipids common to both platforms in COPD. Some of these lipids correlated to COPD phenotype with five associating with emphysema and seven associating with COPD exacerbations.We performed LC-MS/MS to identify metabolites of interest and found that 17 of these metabolites were lipids. Among these lipids were diacylglycerol and several sphingolipids. Sphingolipids serve as a structural component of the plasma membrane lipid bilayer and also participate in cell recognition and signalling. In individuals with COPD, increased concentrations of sphingolipids have been measured in sputum. Telenga et alet alAmong the lipids, we also identified ceramide. Although ceramide concentrations were higher in HIV-COPD, in this relatively small exploratory study, the difference did not quite meet statistical significance . Ceramide consists of a sphingolipid backbone (sphingosine) plus a fatty acid. It has been implicated in the pathogenesis of COPD. Petrache et alet alLipid dysregulation is a known complication of AIDS and is a well-known side effect of protease inhibitors. Chetwynd In addition to lipids, we identified an analyte consistent with Trp that trended towards an association with HIV-COPD in our metabolite profiling. Trp and its main catabolic enzyme, IDO, have been associated with both COPD and HIV infection.There are several limitations to this study. While HIV-positive controls were matched on age, sex, race, region and smoking status to our cases, our non-HIV controls were difficult to match on age and race as our HIV-COPD cases were much younger and over half were black. In this study, we found that metabolites associated with HIV-COPD were not associated with COPD in HIV-negative controls. This may be due to confounders, such as age and race; however, COPD in HIV-negative individuals is most likely a very different phenotype and disease. Therefore, diagnosis and treatment may need to be very different for HIV-COPD and these metabolites have the potential to identify those with HIV who are at risk of developing COPD. This study was relatively small and may have been underpowered to determine all significant biomarkers and metabolomics pathways of HIV-COPD. Future studies would benefit from larger sample sizes and longitudinal study designs such as that recently completed and now possible in the START Pulmonary Substudy.HIV-COPD has a unique plasma metabolomic profile that includes sphingolipids and fatty acids. Additional studies are needed to determine how such metabolic pathways contribute to HIV-COPD and whether therapeutic interventions to alter sphingolipid expression can reduce the risk of COPD in PLWH.


Blood product transfusion occurs in a significant percentage of intensive care unit (ICU) patients. Pulmonary complications, such as acute respiratory distress syndrome (ARDS), occurring in the setting of transfusion, are associated with increased morbidity and mortality. Contrary to the ICU setting, there is little evidence describing the epidemiology of transfusion in the emergency department (ED) or its potential impact on outcome. The objectives of this study were to: (1) characterize transfusion practices in the ED with respect to patient characteristics and pre-transfusion laboratory values; and (2) investigate the effect of ED blood product transfusion on the incidence of pulmonary complications after admission. We hypothesized that blood product transfusion would increase the event rate for pulmonary complications, and have a negative impact on other clinically significant outcomes.This was a retrospective case-control study with one-one matching of 204 transfused ED patients to 204 non-transfused controls. The primary outcome was a composite pulmonary outcome that included: acute respiratory failure, new need for ICU admission, and ARDS. Multivariable logistic regression was used to evaluate the primary outcome as a function of transfusion.P = 0.77]. The mortality rate was 10.8% in the cases and 8.8% in the controls, P = 0.51.One-hundred twenty four (60.8%) patients were transfused packed red blood cells (PRBC) in the ED. The mean pre-transfusion hemoglobin level was 8.5 g/dl. There were 73 patients with a hemoglobin value ≥10 g/dl; 19 (26.0%) received a PRBC transfusion. A total of 54 (26.5%) patients were transfused platelets. The main indications were thrombocytopenia (27.8%) and neurologic injury (24.1%). Ten patients had a platelet level <10,000 (guideline recommended threshold for transfusion to prevent spontaneous hemorrhage). The mean platelet count for neurologic injury patients was 197,000 prior to transfusion. The primary outcome occurred in 26 control patients (12.7%), as compared with 28 cases (13.7%). In multivariable logistic regression analysis, ED transfusion was not associated with an increased odds of primary outcome [adjusted OR 0.91 (0.48–1.72), A significant percentage of ED blood product transfusions are discordant with guideline recommendations. However, there was no association with ED transfusion and worse clinical outcome.The online version of this article (doi:10.1186/s12245-017-0133-z) contains supplementary material, which is available to authorized users. In a general intensive care unit (ICU) population, 20–50% of patients are transfused blood products during their ICU stay . The preBlood product transfusion is associated with well-documented risks. From a pulmonary perspective, this includes an association with acute respiratory distress syndrome (ARDS), transfusion-related acute lung injury (TRALI), and transfusion-associated circulatory overload (TACO) –10. DeveThe objectives of this study were to: (1) characterize the transfusion practices in the ED with respect to patient characteristics and pre-transfusion laboratory values; and (2) investigate the effect of ED blood product transfusion on the incidence of pulmonary complications after admission. We hypothesized that blood product transfusion would increase the event rate for pulmonary complications, and have a negative impact on other clinically significant clinical outcomes.This was a retrospective case-control study and is reported in accordance with The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement: Guidelines for Reporting Observational Studies . The funThis study was conducted at a university-affiliated, urban teaching hospital (1250 beds), with an annual ED census of >95,000 patients. Over a 4-year period (June 2009 to May 2013), adult patients (age ≥18 years) presenting to the ED were electronically screened for inclusion. Exclusion criteria were: (1) multi-system trauma; (2) discharge from the ED; (3) hemoglobin <7 g/dl; (4) transfer to another hospital from the ED; and (5) death in the ED. The study population was restricted to those patients with a hemoglobin level ≥7 g/dl, as guideline recommendations suggest considering a transfusion below this threshold. Multi-system trauma patients were also excluded, as little controversy exists regarding transfusion in the setting of major hemorrhage.All adult patients admitted to the ED were identified as having received blood product transfusion (cases) in the ED by electronic registry query and verified by review of the medical record. Blood products were defined as packed red blood cells (PRBCs), fresh frozen plasma (FFP), or platelets. Using the same exclusion criteria, along with an electronic screen to identify patients with similar presenting diagnoses, non-transfused patients (controls) that were admitted to the ED over the same time period were identified. The a priori matching strategy was designed based on the assumption that the decision to transfuse blood products in the ED would be based upon the presence of a bleeding condition, laboratory values , and age. Therefore, patients were matched one-to-one for key indicators for transfusion: ED diagnosis, hemoglobin value, age, and gender. The matching criteria were: diagnosis (same), hemoglobin (±1 g/dl), age (±5 years), and gender (same) in this order. Non-matched cases were discarded.Baseline demographics, comorbid conditions, vital signs at ED presentation, laboratory values, illness severity, ED length of stay, and ED diagnosis were collected from the electronic medical record. Definitions of comorbid conditions are provided in Additional file After admission, blood products transfused during the first 24 h were collected. Fluid balance was recorded daily over the first 3 days. Patients were followed until hospital discharge or death.The primary outcome was a composite pulmonary outcome that included: acute respiratory failure, new need for ICU admission, and the presence of ARDS. This outcome was chosen a priori as it accounted for potential complications and clinical deterioration associated with transfusion occurring both in patients not initially requiring ICU admission, and those critically ill during ED presentation. The primary outcome was restricted to the first 3 days to better examine a temporal link between ED transfusion and complications, including lung injury at 72 h  . Acute rSecondary outcomes included ventilator-, hospital-, and ICU-free days, the need for renal replacement therapy (RRT), as well as hospital mortality.t test, Mann-Whitney U test, Chi-square test, or Fisher’s exact test, as appropriate. To assess predictors of the primary outcome, covariates associated with the outcome at P < 0.10 were candidates for inclusion in a bidirectional stepwise, multivariable logistic regression analysis. The stepwise regression method selected variables for inclusion or exclusion from the model in a sequential fashion based on the significance level of 0.10 for entry and 0.10 for removal. Collinearity was assessed, and the model used variables that contributed information that was statistically independent of the other variables in the model. The Hosmer-Lemeshow test, along with the examination of residuals, was used to assess model goodness of fit. Adjusted odds ratios (aORs) and corresponding 95% confidence intervals (CI) are reported for variables in the multivariable model, adjusted for all variables in the model.Descriptive statistics, including mean [standard deviation (SD)], median [interquartile range (IQR)], and frequency distributions were used to assess the characteristics of the patient cohort. Continuous and categorical variables were compared using an unpaired The expected event rate for the primary outcome in the cases was 20% , 18–20. A total of 2257 transfused patients were assessed for inclusion. A total of 204 matched pairs were included in the final study population Fig. .Fig. 1FlP < 0.001), had a higher incidence of central venous catheter use in the ED , and were admitted directly to the ICU from the ED more frequently .Baseline characteristics of the study population are shown in Table A total of 124 (60.8%) patients were transfused PRBCs in the ED (Table P < 0.001); there was a higher incidence of FFP transfusion among the cases (11.8 vs. 5.4% P = 0.02). There was no difference in the incidence of platelet transfusion between the cases and controls  after admission.During the first 24 h after ED admission, cases were transfused PRBC less frequently compared to controls  vs. 2.1 l (±4.5) in the cases, P = 0.77; Table The primary outcome occurred in 26 control patients (12.7%), as compared with 28 cases 13.7%). In multivariable logistic regression analysis, adjusting for body mass index, sepsis, shock , ED mechanical ventilation, and fluid balance, ED transfusion was not associated with an increased odds of primary outcome . There was no mortality difference, 14.3 vs. 8.9%, P = 0.17.There were 70 patients transfused in both the ED and during the first 24 h after admission. The primary outcome occurred in 16 (22.9%) of these patients, as compared with 38 (11.2%) patients that did not receive blood product at both time points [OR 2.34 (1.22–4.49), On the strength of a number of randomized trials and observational studies in the critical care and peri-operative setting, several guidelines regarding blood product transfusion have been published , 21, 22.The most common transfusion indication for PRBCs was hemorrhage  and infection. The mean hemoglobin level of 8.5 mg/dl is fairly consistent with multicenter observational studies in general ICU patients, however a 26% transfusion rate for patients with a hemoglobin ≥10 g/dl suggests discordance between ED transfusion practices and guideline recommendations , 24.With respect to clinical outcomes, there was no significant difference between the two groups, contrary to both our a priori hypothesis and the majority of previous data showing an association of harm with transfusion in the critically ill patient. There are several possible explanations. Transfusion therapy is likely safer, owing to improved blood preparation and leukocyte depletion. Our results are congruent with a more recent observational study that not only failed to show harm in transfusion, but showed greater survival in a propensity-matched analysis . An updaThis study has several important limitations. Our analysis did not include all patients transfused in the ED, and was restricted to the number needed based upon the sample size calculation. The results, especially descriptive data regarding transfusion practices, could have been different had the entire transfused sample been included. This highlights the need for further observational and epidemiological data in this domain. The study cohort was not restricted to ICU patients, and our event rate for complications could have been higher had we limited the analysis to ICU admissions. However, our current approach better describes the majority of ED transfusions and is therefore more generalizable. Patients with a hemoglobin <7 g/dl were also excluded. As there is less controversy regarding the risk: benefit of transfusion in this cohort, we wanted to restrict the analysis to patients in whom a transfusion could have potentially been avoided. While we excluded patients with traumatic hemorrhage, those with hemorrhage  from a gastrointestinal source were included. This provides valuable descriptive data, but could have further confounded results by including a patient group with a clear indication for transfusion. The cases and controls were well-matched with respect to the a priori matching strategy. However, the cases were potentially a sicker cohort, as demonstrated by a higher ICU admittance rate and greater fluid administration. However, this should have biased our results toward the primary hypothesis, which was not the case.In this case-control study, a significant percentage of ED blood product transfusions were discordant with current guideline recommendations. However, there was no association with ED transfusion and worse clinical outcome.


Trial Registration. ClinicalTrials.gov NCT03029494. Registered on 2017-01-19.The present study evaluated diurnal variations and day-to-day fluctuations of salivary oxidative stress (OS) markers in healthy adult individuals. Whole unstimulated saliva was collected at 2 time intervals over 3 consecutive days. Glutathione peroxidase (GPX), superoxide dismutase (SOD), total antioxidant capacity (TAC), and uric acid (UA) were analyzed using spectrophotometric methods, while 8-hydroxydeoxyguanosine (8-OHdG) and malondialdehyde (MDA) were determined using immunoassays. No significant differences for salivary OS markers between men and women were observed. For all examined OS markers, no significant day-to-day variations were demonstrated. Significant diurnal variations were found in salivary GPX, TAC and MDA levels. For SOD, TAC, GPX, and UA, good-to-moderate intraindividual coefficients of variations (CVs) were observed in more than 75% of the subjects. For MDA and 8-OHdG, intraindividual CVs > 35% were observed in 60% and 40% of the subjects, respectively. Between-subject variance was wide for all examined OS markers (CV% 30.08%–85.70%). Due to high intraindividual variability in the salivary concentrations of MDA and 8-OHdG, those markers cannot be reliably verified based on single measurements and multiple measurements over several days would provide more reliable information. Salivary SOD, TAC, GPX, and UA proved stable across three days of measurement.  It has been suggested that oxidative stress is a result of the imbalance of oxidant/antioxidant status in biological systems, due to either an excessive production of free radicals or a reduction in the effectiveness of the antioxidant system. Although recent evidence has shown that a certain degree of oxidative stress is a necessary component of intracellular signaling , excessiAntioxidant defense systems can be divided into two categories: enzymatic and nonenzymatic. The nonenzymatic antioxidants include vitamins C and E, uric acid (UA), and albumin, while the major enzymatic antioxidants are glutathione peroxidase (GPX), superoxide dismutase (SOD), and catalase. Although the concentrations of individual antioxidants can be determined separately, the more conventional approach is to measure total antioxidant capacity (TAC), the cumulative effect of all antioxidants present in biological samples .A number of biomarkers are routinely used as a measure of oxidative stress, including malondialdehyde (MDA), a product of lipid peroxidation , 5, and It is believed that oxidative stress plays a significant role in the development of many diseases –11. VariRegardless of this finding, there is little data available that measures the level of oxidative stress markers in human saliva in a healthy population. Most studies have used a case-control design, comparing patients with healthy controls, and little information regarding standardization and validation of method can be found. In other words, the results of those studies could be due to possible random variation in measurements.A recent study by Kamodyová et al. , conductThe purpose of our pilot study is to evaluate the diurnal variability of salivary GPX, SOD, TAC, UA, 8-OHdG, and MDA in healthy adult individuals over three consecutive days. An understanding of the interindividual, intraindividual diurnal, and day-to-day variability of oxidative stress markers will allow further experiments to be planned. If high repeatability is observed, our results will serve as a standardized reference, to which future comparisons can be made. Furthermore, if high repeatability is established, we will not need to perform a series of measurements within each subject.Our central hypothesis is that we will not observe intraindividual differences if sampling is performed at different times of the day or repeatedly on different days. We also hypothesize that the tested parameters will not vary substantially between healthy individuals.The study was approved by the Ethics Committee of the School of Dental Medicine, University of Zagreb . Written informed consent was obtained from all subjects after a detailed explanation of the study. All experimental procedures were conducted in accordance with the ethical standards of the Helsinki Declaration. Recruitment and sample collection were performed between April 2016 and October 2016.N = 15 for the within-group comparison, and 6 measurements per individual would be necessary in order to obtain reliable data.Power analysis, performed to estimate sample size, was based on Kato et al.'s blood biomarkers variability , with a 2. All subjects had normal systolic and diastolic blood pressures, and their resting heart rates were in the lower normal limit for healthy subjects.Fifteen healthy adult volunteers, nine females and six males (mean age 38.73 ± 5.18 years), were recruited for this study. Their body mass index ranged from 18.5 to 23.5 kg/mAll subjects were nontobacco users who had abstained from any dietary aids that might affect the outcome results. They did not take medications or supplements that could influence oxidative stress measures nor did they have any metabolic, heart, or muscle abnormalities. In addition, all maintained good oral hygiene and had good oral health with no apparent dental cavities, gingival, and periodontal diseases or mucosal lesions. All had their natural teeth.Whole unstimulated saliva samples were collected at 2 time intervals, 7 AM and 5 PM, on 3 consecutive days. In total, 90 samples from 15 subjects were collected and analyzed.Subjects were instructed to fast before morning saliva collection, and not to eat, drink anything except water, or use chewing gum for 2 hours before afternoon saliva sampling. Tooth brushing was also forbidden prior to collection in order to avoid contamination of saliva samples with blood. All saliva samples were collected after rinsing the mouth for 30 seconds with clean water. During collection, subjects were asked not to talk or think about food and to attempt not to generate saliva.Five milliliters of whole unstimulated saliva was collected into a graduated tube . The time needed to collect the sample was used to calculate salivary flow rate (mL/min). Mean flow rate was 0.41 mL/min (range 0.11–0.96 mL/min) for morning and 0.53 mL/min (range 0.12–0.77 mL/min) for afternoon collection.Saliva aliquots (1 mL) were stored at −80°C until analyzed. Saliva samples were thawed and centrifuged prior to analysis .MDA levels were measured using an MDA adduct competitive enzyme-linked immunosorbent assay (ELISA) kit . The test involves the addition of unknown samples to an MDA conjugate precoated ELISA plate, followed by the addition of an anti-MDA polyclonal antibody and a horseradish peroxidase (HRP) conjugate secondary antibody. The content of MDA protein adducts in saliva samples was determined by measuring the absorbance of an enzyme conversion product after the addition of an enzyme substrate at a specified wavelength (450 nm). MDA adduct level reflects the quantity of MDA that combines with proteins in the process of lipid peroxidation. The range of the assay is 6–1500 pm/mL; thus, the minimum detectable concentration of MDA adducts less than the lowest standard is reported as <6 pm/mL The amount of MDA adduct in the used standards was predetermined by the manufacturer of a TBARS assay kit; results obtained with this kit can be used to compare findings with those from other studies analyzing MDA. Within-laboratory CV for the MDA adduct assay was determined by 10 replicate measurements of one saliva sample, and the CV (%) obtained was 16.6% at 134 pmol/mL concentration.8-OHdG levels were measured in a similar manner, using a highly sensitive ELISA competitive kit . This kit uses an 8-OHdG monoclonal antibody to bind, in a competitive manner, 8-OHdG in the analyzed samples or 8-OHdG prebound to the wells of the microtiter plate. Immobilized 8-OHdG is detected with an HRP conjugate secondary antibody and tetramethylbenzidine as a chromogenic substrate, causing color development measured at 450 nm. Intra-assay variation was determined by measuring one saliva sample 10 times in a single batch, and the obtained CV (%) of the 8-OHdG assay was 13.9% at 1.19 ng/mL concentration.SOD, GPX, and TAC were measured utilizing commercial colorimetric reagent kits RANSOD, RANSEL, and TAS , respectively, and applied on a Cobas c501 biochemistry autoanalyzer .The RANSOD test measures SOD levels by employing xanthine and xanthine oxidase (XOD) to generate superoxide radicals, which subsequently react with 2-(4-iodophenyl)-3-(4-nitrophenol)-5-phenyltetrazolium chloride (INT), forming a red formazan dye. Because the role of SOD is to enhance the dismutation of superoxide radicals, its activity is easily measured by the degree of inhibition of this reaction. One unit of SOD corresponds to a 50% inhibition. The intra- and interassay variabilities for the RANSOD test were 4.6% and 7.1%, respectively, as declared by the manufacturer.The RANSEL test measures GPX levels by determining the decrease in absorbance at 340 nm of NADPH, which is converted to NADP+ in the oxidation reaction of glutathione catalyzed by glutathione peroxidase. The intra- and interassay variabilities for the RANSEL test were 4.9% and 7.3%, respectively, as determined by the manufacturer.A TAS kit measures the TAC of the samples in a reaction catalyzed by peroxidase, producing a radical cation, ABTS∗+, whose absorbance is consequently measured at 600 nm. Antioxidant capacity is determined by the capability of antioxidants present in the analyzed sample to suppress this reaction and subsequent color development. The intra-assay variability of this assay was 2.8% at 1.5 mmol/L TAC and was determined by repeated measurements of saliva samples.Uric acid was measured by the enzymatic uricase method, utilizing commercially available Roche Diagnostics reagents applied to a Cobas c501 Biochemistry analyzer . Within-run and between-run coefficients of variation (CV%) of the uric acid assay were 1.4% and 1.8%, respectively, and were determined by repeated measurements of commercial control samples during method validation.The amount of total proteins in saliva samples was determined by a commercially available Roche Diagnostics automated turbidimetric urinary and cerebrospinal fluid (CSF) protein assay whose measuring range (0.02–2 g/L) and lower detection limit (0.02 g/L) covered the expected values and had satisfactory sensitivity for saliva samples. Within-run and between-run CVs of the assay were 0.9% and 1.0%, respectively. Analysis was performed on a Cobas c501 biochemistry analyzer . All parameter values were normalized to total protein concentration.All measurements were performed using one reagent kit for each parameter.Laboratory measurements were performed at the Department of Laboratory Diagnostics, University Hospital Centre Zagreb.p < 0.05. The distributions of data were tested for normality using the Shapiro-Wilk test. Before performing statistical analysis, a log transformation was used for those biomarkers that were nonnormally distributed.Data analyses were performed using SPSS 17.0  with alpha set at Repeated measure ANOVA was used, with the time of day (morning/afternoon) and day (1/2/3) as within-factors and gender as between-factor.Within-subject reliability over 3 consecutive days was calculated at two time intervals (7 AM and 5 PM) using intraclass correlation coefficients (ICCs) (two-way mixed model effects) for salivary GPX, SOD, TAC, UA, 8-OHdG, and MDA. The single measure ICC was reported along with the associated 95% confidence interval. ICC values were interpreted as follows: ≥0.75 indicated excellent reproducibility, 0.35–0.75 indicated moderate reproducibility, and <0.35 indicated poor reproducibility. Between-day variance for each OS marker at both time intervals was expressed as coefficient of variation percentage (CV%) (between-day SD/between-day mean) × 100.Group means and standard deviations for each biomarker at both time intervals over the 3 days were calculated. Between-subject variability was expressed as group coefficient of variation percentage (CV%) ((group SD/group mean) × 100). CV was regarded as very good if CV ≤ 10%, good if 10% < CV ≤ 25%, moderate if 25% < CV ≤ 35%, and poor if CV > 35%.p > 0.05). None of the participants withdrew from the study.Fifteen participants completed all three testing sessions. No significant difference in age between genders was found (p > 0.05). Repeated measure ANOVA was used to evaluate diurnal variations and day-to-day fluctuations of oxidative stress marker levels. For all examined salivary oxidative stress markers, no significant day-to-day variations were found (p > 0.05). Significant diurnal variations were observed in salivary GPX levels  and TAC levels , with significantly lower values in the morning than in the afternoon, which suggests that diurnal rhythm is affected by these markers. Salivary UA concentrations were slightly higher in the afternoon, but the differences between morning and afternoon concentrations were not statistically significant. Significant diurnal variations were also observed in salivary MDA levels ; concentrations were lower in the morning and significantly higher in the afternoon. The levels of salivary SOD and 8-OHdG did not differ significantly during the day (p > 0.05).Mean salivary levels of GPX, SOD, TAC, UA, 8-OHdG, and MDA Figures  did not Since no gender differences were found, male and female data were combined for further analyses.Intraindividual variability for all oxidative stress markers was calculated. For salivary GPX, interday CV < 35% was observed in 75% of the subjects . Interdar = 0.76; ICC for 8-OHdG r = 0.82) and for afternoon  measurements. Between-day variance across the three test days ranged from 4.22% to 7.6% for SOD and from 6.68% to 14.5% for 8-OHdG. Salivary GPX showed greater reliability while tested in the morning (ICC r = 0.86) compared to the afternoon (ICC r = 0.61), although between-day variance was low at both times of the day . Salivary TAC showed greater reliability while tested in the afternoon (ICC r = 0.81) compared to that in the morning (ICC r = 0.76), with between-day variance of 4.06% for morning and 6.83% for afternoon measurements. Lower reproducibility for afternoon salivary UA (ICC r = 0.51) compared to that for morning was observed (ICC r = 0.72), with between-day CV% less than 10%. Salivary MDA showed higher reliability when tested in the afternoon (ICC r = 0.86) compared with morning measurements (ICC r = 0.34).Within-subject reliability  across tBetween-subject variability was the highest for 8-OHdG and MDA, for both morning  and afternoon measurements . Other oxidative stress markers also showed wide between-subject variability, with CV% between 30.08% and 49.66% for morning and 30.28% and 57.24% for afternoon measurements .Numerous studies have examined the relationship between oxidative stress and pathophysiological mechanisms of various diseases. Among other biological fluids, saliva is attracting interest from researchers because its collection represents a noninvasive method to evaluate oxidative changes. Despite a relatively large number of studies reporting the association of oral diseases with changed oxidative stress markers in saliva, reliable data on salivary oxidative stress markers in healthy individuals are lacking.It has been shown that blood oxidative stress markers can vary over time depending on season and location , 28. HowDue to the paucity of research on salivary oxidative stress levels, our aim was to evaluate diurnal variations and day-to-day fluctuations of salivary oxidative stress markers in healthy adult individuals. An understanding of the day-to-day variability of these markers could serve as standardized reference data, allowing researchers to better interpret future results and make future comparisons.It should be noted that in our study, salivary biomarker levels were reported as normalized for saliva total protein concentration. We corrected for total protein concentration in order to overcome variability associated with sample dilution. The technical repeatability of the used tests showed satisfactory analytical precision to be used clinically.Studies that analyzed the effect of gender on oxidative stress markers mostly report significant gender-related differences. In the study by Lettrichová et al. , all meaWhen evaluating intraindividual differences, salivary MDA showed the largest variability. This is in accordance with the results of the study by Behuliak et al.  who repoFor all examined salivary oxidative stress markers, no significant day-to-day variations were detected. However, a significant diurnal effect for GPX, TAC, and MDA, with significantly lower values in the morning than in the afternoon, was observed. In view of this effect on these markers, consideration should be given to the time of the day samples are collected. No such effect was observed for salivary SOD, UA, or 8-OHdG. Circadian variations in salivary concentrations of OS markers were examined by Kamodyová et al. , who notOur study demonstrated that reliability across the three days varied depending on the marker. For GPX, reliability was higher if sampling was performed in the morning, whereas afternoon sampling showed greater reliability for MDA and TAC. For other OS markers, similar reliability was observed for morning and afternoon testing. Between-day variances over the three test days were less than 10% for GPX, SOD, UA, and TAC, but we observed relatively high between-day variance (CV > 20%) for salivary MDA. Wide discrepancies in salivary MDA concentrations of healthy subjects have been recorded previously, with MDA values varying from 27 to 680 to 900 nmol/L –49, whicAll OS markers showed wide between-subject variability. The group CV% ranged from 30.08% to 75% for morning and 30.28% to 85.70% for afternoon measurements. High between-subject variability of salivary oxidative stress markers was also reported in other studies , 26. TheThe present pilot study is observational, describing the variability of oxidative stress markers in healthy individuals. The biggest limitation of the study, which reduces the strength of our conclusions, is its small sample size. Studies that involve larger populations are needed to address the issues; this will be the focus of our future investigations. Another limitation that could have affected our results is the single measurement of the parameters. However, all measurements were performed in an accredited laboratory, by a single analyst who applied all principles of good laboratory practice.Due to large intraindividual variability in the salivary concentrations of MDA and 8-OHdG, we conclude that the levels of these markers cannot be reliably determined based on single measurements and that multiple measurements taken over several days would generate information that is more accurate and reliable. Salivary SOD, TAC, GPX, and UA proved to be stable across the three days of measurement; therefore, single sampling might adequately reflect the level of these markers. These findings are of utmost importance for objectively assessing the results that have been and will be obtained by studies investigating salivary oxidative stress markers in various conditions. Without adequate consideration of these variations and reliability results, any study on this topic will lack proper interpretation.