Supplemental Digital Content is available in the text. Early detection of adverse effects of novel therapies and understanding of their mechanisms could improve the safety and efficiency of drug development. We have retrospectively applied large-scale proteomics to blood samples from ILLUMINATE (Investigation of Lipid Level Management to Understand its Impact in Atherosclerotic Events), a trial of torcetrapib (a cholesterol ester transfer protein inhibitor), that involved 15 067 participants at high cardiovascular risk. ILLUMINATE was terminated at a median of 550 days because of significant absolute increases of 1.2% in cardiovascular events and 0.4% in mortality with torcetrapib. The aims of our analysis were to determine whether a proteomic analysis might reveal biological mechanisms responsible for these harmful effects and whether harmful effects of torcetrapib could have been detected early in the ILLUMINATE trial with proteomics.A nested case-control analysis of paired plasma samples at baseline and at 3 months was performed in 249 participants assigned to torcetrapib plus atorvastatin and 223 participants assigned to atorvastatin only. Within each treatment arm, cases with events were matched to controls 1:1. Main outcomes were a survey of 1129 proteins for discovery of biological pathways altered by torcetrapib and a 9-protein risk score validated to predict myocardial infarction, stroke, heart failure, or death.P=0.0004). Thirty-seven proteins changed in the direction of increased risk of 49 proteins previously associated with cardiovascular and mortality risk.Plasma concentrations of 200 proteins changed significantly with torcetrapib. Their pathway analysis revealed unexpected and widespread changes in immune and inflammatory functions, as well as changes in endocrine systems, including in aldosterone function and glycemic control. At baseline, 9-protein risk scores were similar in the 2 treatment arms and higher in participants with subsequent events. At 3 months, the absolute 9-protein derived risk increased in the torcetrapib plus atorvastatin arm compared with the atorvastatin-only arm by 1.08% (Heretofore unknown effects of torcetrapib were revealed in immune and inflammatory functions. A protein-based risk score predicted harm from torcetrapib within just 3 months. A protein-based risk assessment embedded within a large proteomic survey may prove to be useful in the evaluation of therapies to prevent harm to patients.https://www.clinicaltrials.gov. Unique identifier: NCT00134264.URL:  The trial was terminated because of increases in cardiovascular events and mortality.In a retrospective analysis of baseline and on-treatment blood samples from ILLUMINATE, a large-scale proteomic analysis detected harm from torcetrapib at 3 months of treatment, before it became apparent clinically.Proteomic analysis revealed unexpected alterations in inflammation and immunity by torcetrapib and explained the previously reported activation of aldosterone.A longitudinal survey of the proteome in blood samples can provide an early warning of unexpected harm from drug therapies and inform responsible mechanisms.Proteomics, a tool of precision medicine, may prove to be useful in improving the safety and efficiency of drug development.1 were described as a “nightmare created by unintended pharmacological effects” of torcetrapib.2 Torcetrapib, a cholesteryl ester transfer protein inhibitor, given alongside atorvastatin, raised high-density lipoprotein (HDL) cholesterol by a remarkable 72% and reduced low-density lipoprotein (LDL) cholesterol by 25%. Despite these favorable changes in lipid profile, the trial was terminated after a median follow-up of 550 days because of significant absolute increases of 1.2% in cardiovascular events and 0.4% in all-cause deaths in the torcetrapib plus atorvastatin arm compared with the atorvastatin-only arm.1 Aside from the harm to patients and the reported $1 billion cost of the failed torcetrapib development program,3 the misleading results from the previously trusted cardiovascular risk biomarkers LDL and HDL cholesterol have led to a view that these (and possibly other) biomarkers are not reliable surrogates for meaningful clinical outcomes.5 The consequences of this view are clinical drug development programs that are potentially harmful to patients, slow to complete, and expensive. Indeed, the absence of reliable biomarkers to predict adverse drug effects in a timely manner and before harm to study participants accrues is currently regarded by the US Food and Drug Administration as a key obstacle to the pace of innovation in drug development across all diseases.6In 2006, the results of the 15 067–patient ILLUMINATE trial (Investigation of Lipid Level Management to Understand its Impact in Atherosclerotic Events)8 The field of proteomics has matured over the past 20 years, and it is now poised to have a transformative impact on cardiovascular health and disease.9 Technologies have been developed that can readily measure the levels of hundreds or even thousands of proteins in a small sample of blood.9 The objectives of this study were to use one such technology, modified aptamers,13 to measure plasma proteins in a nested case-control subset of patients from ILLUMINATE to determine whether mechanistic insights into the harmful effects of torcetrapib could be discerned from an analysis of 1129 plasma proteins with the tools of pathway analysis and to determine whether a previously validated 9-protein cardiovascular risk score8 could have detected changes in cardiovascular and all-cause mortality risk over the first 3 months of assigned treatment with torcetrapib plus atorvastatin compared with atorvastatin only.Proteins are key regulators of biological processes and relate to the risk of diseases and their clinical outcomes.1 Men and women between the ages of 45 and 75 years were eligible to participate if they had a history of cardiovascular disease  or type 2 diabetes mellitus without overt cardiovascular disease. During a run-in period of 4 to 10 weeks, patients underwent lifestyle counseling and uptitration of atorvastatin, as needed, at 2-week intervals to achieve an LDL cholesterol level <100 mg/dL. The trial was terminated prematurely on December 2, 2006, because of significant absolute increases of 1.2% in cardiovascular events and 0.4% in mortality in patients receiving torcetrapib, with a median follow-up of 550 days and the longest follow-up of 880 days.1 An institutional review board at each ILLUMINATE center approved the study protocol, and patients provided written informed consent.1ILLUMINATE was a prospective, multicenter, placebo-controlled trial that randomized a total of 15 067 patients between August 23, 2004, and December 28, 2005.14 to maximize the power to detect an effect by including all the participants with outcome events and available plasma samples. Paired plasma samples obtained at baseline and at 3 months from a total of 494 study participants were used to determine within-participant changes in plasma proteins during the first 3 months of randomized treatment. All individuals selected as cases had a first event after the 3-month sample. Cases in this analysis were selected with the same definition of events used previously to derive and validate the 9-protein risk model .8 Although not identical to the primary outcome of ILLUMINATE, each of the end points reported in the present analysis was adjudicated by a committee of ILLUMINATE as a primary or a secondary end-point of the trial.1 For each case, a matched control participant was selected within each treatment arm on the basis of the following baseline characteristics: atorvastatin dose (after the run-in uptitration period), presence of known coronary heart disease, diabetes status, age, sex, and censoring time. The baseline plasma samples were obtained before the participant was assigned treatment, after the run-in atorvastatin uptitration. The study flowchart is shown in Figure Plasma (EDTA) samples from the study participants were biobanked by the ILLUMINATE study sponsor (Pfizer Inc.). We used a nested case-control design8 In brief, each of the 1129 individual proteins measured has its binding reagent made of chemically modified DNA, referred to as a modified aptamer. The assay version was nearly identical to that described previously to derive a 9-protein cardiovascular risk score.8 Each sample of plasma was incubated with the mixture of modified aptamers to generate modified aptamer-protein complexes under equilibrium conditions. Unbound modified aptamers and unbound or nonspecifically bound proteins were eliminated by 2 bead-based immobilization steps. After elution of the modified aptamers from the target protein, the fluorescently labeled modified aptamers were directly quantified on a hybridization array (Agilent Technologies). Calibrators were included so that the degree of fluorescence was a consistent reflection of protein concentration. The median intra-assay, interassay, and total coefficients of variation for the 1129 proteins are all <4%. The distribution of coefficients of variation across the 1129 proteins measured and coefficients of variation for each of the proteins within the 9-protein prognostic model8 are provided in Table I in the online-only Data Supplement. All of the 1129 protein targets are above the limit of detection as defined by the Clinical and Laboratory Standards Institute.15 Of the 1129 proteins measured, 143 did not pass the interrun calibration quality control metrics in at least 1 of the thirteen 96-well plates that were run and were thus excluded from further analysis (detailed information on exclusion of proteins is provided in Method I in the online-only Data Supplement). One sample of the 988 samples was not analyzed because of an error. Twenty-two samples did not pass the intrarun normalization quality control metrics and were excluded from analysis. In 2 samples, 10% of the protein measurements exceeded an outlier threshold defined as the median protein signal level±6 median absolute deviations and 5 times higher or lower than the median protein signal level. A further 19 samples were removed from the analysis because their pair was not run, did not pass the quality control threshold, or exceeded the aforementioned outlier threshold. Overall, the exclusions from the protein assay and sample quality control metrics resulted in an assessment of 986 of the 1129 assayed proteins, 249 pairs of samples in the torcetrapib plus atorvastatin arm, and 223 pairs in the atorvastatin-only arm. The flow diagram is shown in Figure In total, 988 plasma samples were analyzed by a modified aptamer assay.11 Assay personnel were unaware of the treatment or the clinical outcome. Because the assay version was slightly more recent compared with that used to derive and validate the 9-protein model,8 60 bridging samples from the earlier analyses were used as protein standards to align the fluorescence readouts of the same proteins in the same samples across the different assay versions (Method II in the online-only Data Supplement).All samples were placed randomly on 96-well plates, run in a single batch, and normalized against protein calibrator samples included on each plate.P<0.05 were considered statistically significant for inclusion in the pathway analysis.For study objective 1, to discern the biological mechanisms of torcetrapib by assessing treatment-associated changes in plasma protein concentrations and organizing differentially expressed proteins according to biological pathways, differences between the baseline and 3-month samples were expressed as log2 ratios for each of the 986 proteins measured that passed quality control. Significance values were calculated within each treatment arm with the Wilcoxon signed-rank test for paired comparisons corrected for a false discovery rate in 986 measurements and across treatment arms with the Wilcoxon signed-rank test for unpaired comparisons. Proteins with a false discovery rate–adjusted value of www.ingenuity.com). A pathway analysis was not performed within the atorvastatin-only treatment arm because only a few proteins changed over 3 months. For those modified aptamers that had multiple Uniprot identifications associated with 1 result, only the first listed Uniprot identification was used in the pathway analysis. The Fisher right-tailed exact test was used to calculate a P value to determine the probability that the association of the differently expressed proteins in the measured data set, and the pathway is explained by chance alone. Of the 986 proteins measured, 980 were recognized by IPA and used as the background reference. The pathway analysis results obtained by IPA, a commercial system, were confirmed by 2 open-source pathway analysis systems, Reactome Pathway Database (http://reactome.org/) and Database for Annotation, Visualization and Integrated Discovery (DAVID) Bioinformatics resource (https://david.ncifcrf.gov/).Ingenuity Pathway Analysis (IPA) was used to cluster differentially expressed proteins at 3 months compared with baseline into pathways and functional groups in the torcetrapib plus atorvastatin treatment arm : ANGPT2 (angiopoietin-2), GDF11/8 (growth differentiation factor 11/8), C7 (complement 7), SERPINF2 (α2-antiplasmin), CCL18 (chemokine [C-C motif] ligand 18), ANGPTL4 (angiopoietin-related protein 4), SERPINA3 (α1-antichymotrypsin complex), MMP12 , and TNNI3 (troponin I).8 The prognostic risk score is as follows:For study objective 2, to determine whether a previously validated 9-protein cardiovascular risk score could detect changes in risk over the first 3 months of assigned treatment with torcetrapib plus atorvastatin compared with atorvastatin only, the 9-protein risk scorewhere the prognostic index (PI) combines the measurements of the 9 proteins as follows:8 For this application, an output of 550 days, the median duration of treatment for patients in ILLUMINATE,1 was selected so that the change in observed event rate in ILLUMINATE could be compared with the prediction of the 9-protein risk model.The published risk score allows the calculation of probabilities of events for any specified time horizon.P values were not corrected for multiple comparisons. The Ansari-Bradley test16 was applied to discern whether torcetrapib-associated changes in the 9-protein risk score represented a single distribution and thus whether torcetrapib affected all individuals exposed.For study objective 2, the analysis combined all participants with or without events within each of the 2 treatment arms. Changes in the 9-protein risk score between baseline and 3 months were compared within each treatment arm with the paired Wilcoxon signed-rank test for paired comparisons. Changes in risk score in cases compared with controls within each treatment arm were also evaluated with the unpaired Wilcoxon rank-sum test. 1 Because no differences in distributions of protein-based risk scores could be discerned between participants with coronary heart disease and the larger group of participants that also included diabetics without coronary heart disease , it was the larger group that was used in the analyses.Although most participants in the ILLUMINATE trial had known coronary heart disease and therefore matched the population on which the 9-protein model was derived, the study protocol also allowed enrollment of diabetics without known coronary heart disease, who represented 19% of the ILLUMINATE study population.1 balance out against improvements in HDL and LDL cholesterol levels for cardiovascular risk prediction, the Framingham secondary event risk prediction score was also calculated for the identical time horizon.17 This Framingham risk score is applicable to patients with established cardiovascular disease such as those enrolled in the ILLUMINATE trial.17For comparative purposes, to clarify how reported torcetrapib-associated increases in blood pressure in ILLUMINATEAll pathway analyses were performed at the University of California, San Francisco (by A.C.M. and P.G.). All other statistical analyses were performed initially at SomaLogic, Inc., (by S.J.W.) and replicated at University of California, San Francisco (by M.R.S.).All statistical computing was performed with the R Language for Statistical Computing, version 3.3.1 at SomaLogic, Inc., and version 3.2.1 at the University of California, San Francisco.1 the participants in this analysis were older and more likely to be male and diabetic (data not shown). These differences are expected because they reflect the nested case-control study design with half of the participants experiencing outcomes events (cases) to which the control participants were matched for high-risk cardiovascular characteristics, particularly age, sex, and diabetes status.The baseline characteristics of the study population after the atorvastatin uptitration run-in period are shown in Table Table II in the online-only Data Supplement). Of the 986 proteins entered into the IPA, Uniprot identification names of 980 were recognized by IPA and used as the total number of background proteins. A pathway analysis of the 200 proteins affected by torcetrapib showed significant enrichment for proteins involved in inflammation and immunity , and 2 to pancreatic β-cell function , insufficient for a pathway analysis. Twelve of these 18 proteins changed both in the torcetrapib plus atorvastatin and in the atorvastatin-only arm. Removing the 12 proteins shared by the 2 treatment arms from the 200 proteins in the torcetrapib plus atorvastatin arm had little effect on the IPA top canonical pathway analysis results (Table VI in the online-only Data Supplement).There were 18 proteins that changed significantly in the atorvastatin-only arm (P=0.16). Among participants assigned to torcetrapib plus atorvastatin, the baseline 9-protein risk scores were 10.5% and 6.5% for cases and controls, respectively (P=0.00046), and for participants assigned to atorvastatin only, the baseline risk scores were 8.44% and 6.0% for cases and controls, respectively (P=0.0066).At baseline, the 9-protein risk scores were similar among the 2 treatment arms  in all participants, 1.05% (P=0.006) in the cases, and 0.23% (P=0.10) in the controls. Compared across the 2 treatment arms from baseline to 3 months, the 9-protein risk score increased in the torcetrapib plus atorvastatin arm compared with the atorvastatin-only arm in all participants by 1.08% (P=0.0004), in participants with events by 1.55% (P=0.004), and in participants with no events by 0.52% (P=0.039).Table P=0.15), suggesting that torcetrapib affected all exposed individuals to some extent .The changes in 9-protein risk with torcetrapib for the entire population of cases and controls showed a single distribution with no discontinuity . From baseline to 3 months, the Framingham risk score within participants decreased (mean [SD]) from 6.1% (2.5%) to 4.6% (2.0%) in the torcetrapib plus atorvastatin arm (P=2.2e−16) and remained unchanged from 6.1% (2.9%) to 6.2% (2.9%) in the atorvastatin alone arm (P=0.16). Cross-treatment comparison of the within-participant changes revealed a highly significant reduction in absolute Framingham risk favoring torcetrapib plus atorvastatin over atorvastatin alone .Framingham secondary event risk scores8 In the present study, 49 of those 200 prognostic proteins changed significantly within the torcetrapib arm between baseline and 3 months . Among these proteins, 37 (76%) changed in the direction of increased risk and 12 proteins changed in the direction of reduced risk. Within the atorvastatin-only arm, only 5 of the 200 prognostic proteins changed significantly (Table VII in the online-only Data Supplement).We address here 200 proteins previously found to be prognostic of the same cardiovascular outcomes in a population of patients with coronary heart disease similar to that in the present study, measured by the same modified aptamer assay.1 a survey of 1129 proteins at baseline and at 3 months of treatment revealed heretofore unknown and widespread effects of torcetrapib on inflammation and immunity, with 8 of the top 10 canonical pathways affected by torcetrapib involved in inflammatory and immune functions. Pathway analysis also reinforced the previous finding of increased aldosterone levels with torcetrapib1 by pinpointing 8 plasma proteins altered by torcetrapib that relate to aldosterone synthesis or function. Pathway analysis also provided further explanation for the reported improvement in glycemic control with torcetrapib in the ILLUMINATE trial20 by identifying 9 proteins related to insulin sensitivity and 2 proteins related to pancreatic β-cell function that are affected by torcetrapib. In addition, a previously validated 9-protein cardiovascular risk score8 predicted the harm associated with the use of torcetrapib at just 3 months of treatment that had previously been observed by adverse cardiovascular and mortality outcomes at 550 days (≈18 months) of treatment when the study was halted.1 The results of this study suggest that a protein-based risk assessment embedded within a larger proteomic survey may prove to be useful in the clinical and mechanistic evaluation of therapies to prevent harm to patients.In this retrospective proteomic analysis of the ILLUMINATE trial,22 Then, the diabetes drug rosiglitazone (Avandia) was suspected of raising the risk of myocardial infarction, which also was not detected during its development.23 Finally, the ILLUMINATE trial results were even more concerning: There was a clear adverse effect of torcetrapib on cardiovascular and all-cause mortality outcomes despite a remarkable improvement in lipid profile.1The need for improved assessment of cardiovascular safety reached a turning point in 2004 to 2007 with a series of unfortunate events. First, rofecoxib (Vioxx), a cyclooxygenase-2 inhibitor, was withdrawn from the market because of an adverse cardiovascular safety profile missed during its development.24 only proteomic analyses can pinpoint any unsuspected drug toxicities that are related to off-target effects of the drug molecule. Modified aptamers have advanced the field of proteomics by enabling rapid quantification of hundreds or even thousands of proteins in a small volume of blood.13 The technique is highly sensitive, with a median limit of detection ≈1 pg/mL, and precise, with median intra-assay and interassay coefficients of variation <4%. The binding of modified aptamers to their intended protein target is highly specific and quantitative, as shown by the use of mass spectrometry as an orthogonal technique.12The harmful effects of drugs could potentially be predicted early in clinical trials by the application of the modern tools of precision medicine. Measurement of proteins in particular is advantageous because they are the key regulators of biological processes in health and in diseases. Unlike genes, proteins can change over time to reflect alterations in disease risk. Although genetic mendelian randomization approaches can also predict the effects of drugs on the intended pharmacological targets,25 Unexpectedly, this process revealed that torcetrapib exerted biological effects that were widespread, altering the plasma concentrations of 200 of the 986 proteins (20.3%) successfully measured (Table II in the online-only Data Supplement). Furthermore, when organized according to biological pathways, this approach revealed that 8 of the top 10 canonical pathways affected by torcetrapib relate to inflammatory and immune functions , effects not previously associated with torcetrapib. These results obtained through IPA were reinforced with Reactome (Table III in the online-only Data Supplement) and DAVID (Table IV in the online-only Data Supplement), which similarly revealed significant enrichment in pathways involving inflammation and immunity. This finding not only has important implications for increased cardiovascular risk observed with torcetrapib in the ILLUMINATE trial, because inflammation and immunity are central to cardiovascular disease,26 but also supports the possible contribution of the immune surveillance and inflammatory axes to the excess noncardiovascular deaths caused by sepsis and cancer with torcetrapib reported in the ILLUMINATE trial.1 Whether these inflammatory and immune function activities of torcetrapib are related specifically to the molecule itself or reflect accumulation of proinflammatory HDL particles from the inhibition of cholesterol ester transfer protein27 can be resolved by proteomic investigation of trials that used other agents in the same class but with a different molecular structure. Some of the 200 proteins that were subjected to the IPA pathway analysis in the torcetrapib arm also changed in the atorvastatin-only arm. Excluding all 12 such proteins from the analysis did not materially alter our findings (Table VI in the online-only Data Supplement). Because atorvastatin inhibits cholesterol ester transfer protein to raise HDL cholesterol,28 some of these shared proteins may relate to inhibition of the same cholesterol ester transfer protein target by torcetrapib and atorvastatin. Many proteins that we measured in plasma are secretable, evidenced by their gene encoding for a signal sequence or by meeting other established criteria for secretion.29 Other proteins we measured are typically considered intracellular; the mechanisms by which they appear in plasma remain largely speculative but include release by cell necrosis,30 cell apoptosis,30 reversible membrane permeabilization,30 formation of cell membrane blebs,31 and extracellular vesicles.31The first objective in this study, enabled by a large-scale proteomics survey, was to derive a mechanistic understanding of the biological effects of torcetrapib. The aim was to identify those proteins with levels that were significantly altered by torcetrapib and to organize any such differentially expressed proteins into biological pathways.1 At the time of the original publication, the mechanisms by which torcetrapib raised aldosterone levels were unknown. In the present study, IPA identified significant enrichment in 8 proteins involved in aldosterone synthesis, concentration, release, and stimulation . We used the 9-protein risk score to predict the response of torcetrapib because this risk score was mathematically derived from 1130 protein biomarkers that we previously tested; it is externally validated8 and applies to a population of individuals with stable coronary heart disease similar to those in the ILLUMINATE study. We present the 9-protein score as a proof of principle, acknowledging that other protein-based risk scores might perform well. Furthermore, 49 of the 200 proteins that were previously shown to be prognostic of cardiovascular and mortality outcomes in patients with coronary heart disease8 were affected by torcetrapib. The observation that 37 of 49 prognostic proteins moved in the direction of adverse risk would create a significant concern for torcetrapib even in the absence of a formal 9-protein risk score. Twelve of 49 prognostic proteins changed in the direction of reduced risk, suggesting that torcetrapib may have had some beneficial effects (perhaps mediated by LDL cholesterol reduction or improved glycemic control) but that these effects were masked by the dominant harmful effects of the drug. Although these 37 prognostic proteins included 2 established cardiovascular biomarkers, notably troponin I (which is part of the 9-protein risk score) and brain (B-type) natriuretic peptide, C-reactive protein did not predict the harm from torcetrapib in this analysis. The observation that treatment-associated changes in C-reactive protein are strongly prognostic of the clinical benefit derived from the cholesterol-lowering agents statins35 and ezetimibe36 but not prognostic of the harm from torcetrapib points to the advantages of a large-scale proteomic screen because individual biomarkers cannot be reasonably expected to cover all relevant biological systems and will inevitably miss some adverse drug effects. The up-front choice of which biomarkers would be affected is a guessing game that is rendered unnecessary by measuring them all .The second objective in the present study was to evaluate whether a previously validated 9-protein cardiovascular risk score2 and one might reasonably question whether they should have raised greater concern. However, a formal calculation of the Framingham secondary risk score in the present study shows convincingly that the benefits of torcetrapib on steeply raising HDL cholesterol and lowering LDL cholesterol would be expected to outweigh the detriment in cardiovascular risk caused by the level of increased blood pressure observed. The improvement in Framingham risk score with torcetrapib clearly represents a false result and illustrates how misleading risk calculation based on traditional risk factors would be in this instance, in contrast to the correct risk prediction based on the plasma proteome. The likely flaw in the Framingham risk calculation is that it focuses on HDL cholesterol levels without accounting for HDL particle function.5The hypertensive effects of torcetrapib were known early in its development,P=0.056), when perhaps no effect would have been expected. This is likely a “spillover” effect from the atorvastatin dose uptitration during the run-in phase of the study just before treatment randomization.1 In this regard, it is known that higher doses of atorvastatin reduce cardiovascular risk more than lower doses.37 This potential spillover effect of atorvastatin uptitration would be expected to affect both treatment arms similarly.The atorvastatin-only treatment arm had a slight, borderline significant reduction in the 9-protein risk score at 3 months from baseline , and we did not examine how well our predictions may have performed in individual participants . Our results apply only to the population tested. It is not known how our findings would translate to cohorts with other characteristics.Another potential limitation of the study is the nested case-control design of the present study. Available information strongly supports the use of nested case-control designs for the evaluation of risk prediction measures in cardiovascular disease.Last, some proteins are shared among ≥2 biological pathways. For the purposes of this study, we assumed that a change in a protein concentration affects all the pathways that contain it. The validity of that assumption will be clarified by future studies.38 , or Merck’s anacetrapib. More broadly, our proteomic study provides evidence for the recent scientific statement from the American Heart Association that proteomics can have a “transformative impact for cardiovascular health and disease.”9The application of proteomics provides strong evidence and potential mechanistic explanations  for harmful biological effects of torcetrapib that could have alerted investigators early in the ILLUMINATE trial or could have been useful when making the decision to proceed to this phase III study. Admittedly, the results of our study are novel and thus will need further validation. For example, it would be interesting to compare these results with drugs of the same class that may not share molecule-specific off-target effects of torcetrapib such as Lilly’s evacetrapib, Roche’s dalcetrapibThe authors thank the Assay Execution Team at SomaLogic, Inc., for performing the SOMAscan assays; Christina Lee and Jennifer Hajj for providing logistical support; and Fraser Gaspar for statistical advice.The ILLUMINATE trial was supported by Pfizer Inc., and the proteomic analysis by SomaLogic, Inc. Dr Ganz’s proteomic research is supported by National Institutes of Health grants 1RO1HL129856, 1UO1DK108809, and 1R01AG052964.Dr Murthy reports no conflict. Dr Ganz serves on a medical advisory board to SomaLogic, Inc., for which he accepts no salary, honoraria, or any other financial incentives. Drs DeLisle, Ostroff, Weiss, and Williams are employees of SomaLogic, Inc. Drs Hyde and Malarstig are employees of Pfizer Inc.Pfizer Inc. and SomaLogic, Inc., had a role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, and approval of the manuscript. Dr Ganz had the ultimate responsibility for all aspects of this study. Pfizer Inc. and SomaLogic, Inc., had no veto rights concerning the decision to submit the manuscript for publication.


Diabetes is a serious global health problem. A simple and effective screening tool should have substantial public health benefit. We investigated the performance of the latest American Diabetes Association diabetes screening methods in our aging Chinese population.Subjects without diabetes who returned for the 4th Hong Kong Cardiovascular Risk Factors Prevalence Study in 2010–2012 were evaluated for the probability of having diabetes with reference to the age- and body mass index-based screening criteria (screening criteria) and the diabetes risk test (risk test), and the conclusion drawn was compared to their measured glycaemic status. Diabetes was defined by fasting glucose ≥ 7 mmol/L or 2-hour post oral glucose tolerance test glucose ≥ 11.1 mmol/L.1415 subjects, aged 58.1±10.2, were evaluated. 95 (6.7%) had diabetes. The risk test showed good accuracy (area under the receiver operating curve 0.725) in screening for diabetes with an optimal cut-off score of five. Compared to the screening criteria, the risk test had significantly better specificity , positive predictive value  and positive diagnostic likelihood ratio . To diagnose one case of diabetes, fewer subjects (11 vs. 18) needed to be tested for blood glucose if the risk test was adopted.The risk test appears to be a more effective screening tool in our population. It is simple to use and can be adopted as a public health strategy for identifying people with undiagnosed diabetes for early intervention. Diabetes is a serious global health problem, with the worldwide prevalence of diabetes having more than doubled in the past two decades . Many ofThe list of diabetes risk factors is lengthy. It is important that the general public, healthcare professionals and policy makers are informed of the relative strength and the way to make use of the individual risk factors. Screening for diabetes through an assessment of risk factors with regard to an age- and body mass index (BMI)-based criteria (screening criteria); or with the American Diabetes Association (ADA) diabetes risk test (risk test) is recommended by the ADA to guide healthcare providers on whether or not a diagnostic test, i.e. blood glucose or HbA1c measurement, is necessary. The screening criteria suggested that adults aged 45 and older should be tested for diabetes every three years and testing should start earlier than 45 in overweight individuals with one or more of the risk factors for diabetes . In Hongth Hong Kong Cardiovascular Risk Factors Prevalence Study (CRISPS) in 2010–2012. Our study provides insight on the performance of the two well validated, easily accessible and commonly used ADA diabetes screening recommendations, which involve non-invasive and easily measurable parameters, for diabetes screening in the Chinese population.Here we evaluated the screening criteria and risk test using data from 1415 Hong Kong Chinese subjects who had no diabetes at prior assessments and returned for the 4Subjects were recruited from the CRISPS, a long-term, population-based, prospective study on the development of cardiovascular risk factors in Hong Kong. In 1995–1996 (CRISPS1), 2,895 unrelated Chinese subjects were invited randomly by their telephone numbers to undergo a detailed assessment , 11. TheAll subjects with no diabetes at prior assessments (550 or 38.9% were known to have impaired glucose tolerance and/or impaired fasting glucose) who returned for follow-up at CRISPS4 were evaluated for the probability of having diabetes with reference to the ADA screening criteria and risk test, and the conclusion drawn was compared to their measured glycaemic status at the CRISPS4 assessment . The ADAAll analyses were performed with SPSS version 23 software . Results were presented as mean ± SD or median with interquartile range (IQR) as appropriate. For data that were not normally distributed, natural logarithmic transformation was applied before analyses. In univariate analyses, variables were compared between groups by one-way ANOVA for continuous data and Chi-square test for categorical data as appropriate. The area under the receiver operating characteristic (ROC) curve (AUROC) of the risk score was calculated to assess the performance in identification of diabetes subjects. The optimal cut-off for the risk score was determined by Youden’s index  and compAt CRISPS4, 1415 subjects (age: 58.1±10.2) with no diabetes at the last preceding assessment returned for follow up and 95 (6.7%) were diagnosed to have diabetes . Table 1The AUROC of the risk test for identification of subject with diabetes was 0.725 with cut-off score being optimal at five, which was identical to the suggested cut-off by ADA . The staIn this study, we showed that the suggested recommendations by ADA were effective in screening for undiagnosed cases of diabetes in our population. The ADA diabetes risk test has a higher specificity, positive predictive value and positive likelihood ratio, but lower NNT comparing to the screening criteria with similar sensitivity, NPV and NDRL. The risk test appears to be attractive as a non-invasive means to be used in the Chinese population as this approach has high NPV, which is important as diabetes can be ruled out with high confidence, and the NNT is low.2 [Both strategies include common conventional risk factors for diabetes but the number of risk factors involved and their application are different. The risk test is based on the scores calculated from seven health-related questions. The performance of its original model has been robustly validated in different populations . While t2 , 20 as r2 . The sco2 , 22, was2 . Age is 2 . Our finOther health related questions asked in the risk test also include sex, history of gestational diabetes in woman, physical inactivity, family history of diabetes and history of hypertension, which are conventional risk factors for diabetes without involvement of measurements or invasive tests. The former three risk factors, however, did not show statistically significant difference in the diabetes subjects when compared with those without diabetes in our population . Each ofThe definition of diabetes used in development of the ADA diabetes risk test was based on fasting plasma glucose value. In this CRISPS cohort, subjects were also considered to have diabetes if they fulfilled either the fasting glucose or 2-hG criteria. We did not use HbA1c as a diagnostic criterion for diabetes because the diagnostic criteria have changed over time with HbA1c being adopted from 2011 onwards . The HbAThe risk test also has the advantage of being simple and applicable in various community or clinical settings. It can be quickly calculated even manually. The time required is minimal and the use of calculator or computer is not essential. Apart from being user-friendly, the risk test has the advantage of better accuracy than existing scores from various populations with an AUROC of 0.79 from the original 6 risk factors model . When apIn interpreting our findings, we took into account several limitations. First, at CRISPS4, the average age of the studied subjects was 58, with 43.9% of the subjects aged 45 or above . This waIn conclusion, we recommend using the ADA diabetes risk test for identification of individuals with increased risk of diabetes in the Chinese population. The test involves only personal medical information and simple non-invasive measurements which should be acceptable by healthcare providers as well as individuals with different education levels. It can also be easily adopted as a public health policy for identifying people with undiagnosed diabetes for early and appropriate treatment to prevent the long-term diabetic complications . WhetherS1 FileScreening criteria, the age- and body mass index-based screening criteria; risk test, the ADA diabetes risk test.(XLSX)Click here for additional data file.


Adolescents experience disproportionately high rates of poor ART outcomes compared to adults despite prolonged use of antiretroviral therapy in Southern African treatment programs, presenting a significant challenge to national attempts to meet the UNAIDS 90-90-90 targets for 2020. This cohort study among adolescents aged 12–20 years accessing ART care at two urban public-sector clinics in Johannesburg between September and November 2013 aimed to identify factors potentially associated with poor attendance at clinic visits. Patients were followed up through routine medical records to identify missed visits (failing to attend clinic within 30 days of scheduled visit date) up to 2 years after enrolment. We enrolled 126 adolescents on ART for a median of 6.3 years (IQR: 2.7–8.4). A total of 47 (38%) adolescents missed a scheduled visit within 24 months of enrolment. Older adolescents (18–20 years) were more likely to miss a visit compared to adolescents aged 12–14 years (risk ratio (RR) = 1.72; 95% CI: 1.00–2.95). Those who were identified to have difficulty in taking medication  as a barrier to care were more likely to miss a visit compared to adolescents who did not. Awareness of treatment fatigue, challenges to taking ART, and caregiver difficulties is important when considering interventions to improve treatment outcomes among adolescents. Despite the incredible success of the large-scale public-sector provision of antiretroviral therapy (ART) to HIV-infected people in South Africa, failure to retain these patients on long-term treatment threatens to undermine the massive gains made since 2004 and remains one of the most critical obstacles to achieving the UNAIDS 90-90-90 targets for 2020 which are intended to be a key milestone towards ending the HIV epidemic by 2030 . AdolescAdolescence is a stage of transition from childhood to adulthood, associated with specific challenges (including puberty) and vulnerability . In addition, coping with the clinical and psychosocial impacts of HIV imposes a substantial burden on this vulnerable group –11. HIV-Scale-up of ART in South Africa over the past ten years has been impressive with an estimated 3.4 million HIV-infected individuals on ART by 2015 , 19. HowDespite this, there are no national level estimates of adolescent retention in care or transfer across clinics and it remains unclear as to which adolescents are most likely to drop out of care in South Africa. Recent work using routine laboratory data to create a national HIV cohort has demonstrated the emergence of a youth treatment bulge, increasing numbers of adolescents aged 10 years and older accessing ART . Given tµL. Adolescents >15 years were eligible for treatment with (1) a CD4 count <200 irrespective of clinical stage, (2) a CD4 count <350 cells/mm3 (in the presence of TB or pregnancy), or (3) a WHO stage 4 condition or MDR/XDR TB irrespective of CD4 count.This prospective cohort study of HIV-infected adolescents accessing ART care was conducted at two public-sector antiretroviral treatment facilities in Johannesburg, South Africa. Both sites provide care and treatment according to the South African Department of Health National Antiretroviral Therapy Guidelines . Under tAt the time of the study, the Empilweni Clinic , 30, a pEligible patients included HIV-infected adolescents aged between 12 and 20 years of age who were aware of their status and accessing ART treatment at either Empilweni Clinic or Themba Lethu Clinic regardless of duration of ART use. Identification of potentially eligible participants occurred during routine HIV care visits between September and November 2013 at specific clinic locations in which patients wait for relevant services . Eligible and interested participants were provided with more detailed information about the study in a separate counseling room and individual consent  was obtained. This recruitment strategy proceeded in a consecutive manner until the end of the enrolment period. All study participants received a small financial compensation in the form of a food voucher and parents or legal guardians who returned to complete a scheduled study appointment with an adolescent were also reimbursed for transport money.Trained interviewers administered a structured questionnaire to each enrolled study participant. Though most questions were closed-ended, the questionnaire also included pairwise ranking and some open-ended questions. Participant data from the questionnaire was entered into a study database using CSPro software by trained data capturers. Adolescents were asked whether they considered several factors  as potential barriers to care during the interview. Routinely collected medical record data  were extracted from electronic medical records maintained by each of the study sites and linked to participant questionnaire data.The primary outcome was defined as missing a scheduled ART drug collection visit by more than 30 days. Several factors were investigated as potential predictors of poor adherence. These included the following: (1) demographic characteristics of the participant, (2) socioeconomic features of the participant's household, (3) caregiver issues, and (4) problems with service delivery at the treatment facility.The characteristics of the study population were summarized with descriptive statistics including simple proportions for categorical variables and medians with interquartile ranges for continuous variables. The overall frequency of each of the factors reported during the interview as being considered by the adolescent as a potential barrier to care is presented. In addition, we present these frequencies stratified by treatment facility and, by proxy, model of care (pediatric-based compared to adult-based treatment sites). Next, associations between each of the potential predictors and missing a clinic visit are estimated with crude risk ratios and 95% confidence intervals. Estimates were also adjusted for age, gender, mother as caregiver, and time on ART.In secondary analysis, we defined prevalent unsuppressed viral load at the time of study enrolment as having a VL > 400 copies/mL within 3 months prior to and 3 months after the interview date. A log-binomial regression model was fitted in order to determine risk factors associated with prevalent unsuppressed viral load.Previous work has demonstrated heterogeneity within age substrata of adolescent populations in terms of treatment outcomes , 25. In In total, 206 adolescents were screened for potential enrolment into the study. Of these, a total of 126 adolescents (61%) were enrolled in the study with a median of 6.3 years on ART (IQR 2.7–8.4): 19 adolescents from the adult-based facility (Themba Lethu clinic) and 107 from the pediatric-based facility (Empilweni Clinic). Of the remaining 80 not enrolled, 41 (51%) did not meet eligibility criteria and 18 (23%) were unable to consent, as their legal guardian was not present. Further 16 (8%) refused to participate  and the remaining 5 were not enrolled for unknown reasons. Similar overall proportions were enrolled at the pediatric-based (27%) and adult-based (24%) facilities. The demographic characteristics of the enrolled study participants are summarized in Overall, the factor most frequently reported during the interview as being considered a potential barrier to care was long travelling distance to the clinic , followed by the possibility that the adolescent's attendance at clinic visits would be noticed by friends or members of the school (33%), having an elderly caregiver (32%), high transport cost of the trip to the clinic visit  and long queues to wait in at the clinic (31%) .Though the factors most frequently reported to be potential barriers to care were the same for both the adult-based site and pediatric-based site, the relative importance differed slightly; having an elderly caregiver was reported as a potential barrier to care as frequently as long travelling distances to the clinic among predominantly older adolescents at the adult-based site, while it was the least frequently reported of the five among those attending care at the pediatric facility. Very few of the interviewed adolescents agreed that being disinterested in care or not believing that ART helps would be a barrier to care or that unfriendly health care workers presented problems in accessing and remaining in care.n = 47) of the study participants missed a scheduled clinic visit by 7 days or more. The median time to first missed visit was 6.7 months (IQR 3.0–11.1) and 46.8% (n = 22) of visits were missed within 6 months and further 34.0% (n = 16) within a year after study enrolment.Adolescents completing the questionnaire were then followed up passively through routine medical record data for 24 months after study enrolment. Of the 126 adolescents enrolled, 2 did not return to their treatment clinic at all after the study interview. Among the 124 that did attend at least one clinic visit after study enrolment, 92 (74%) remained in care through to the end of 24 months of follow-up, while 11 (9%) had been lost from care and 21 (17%) had transferred to another heath care facility. Overall, during the course of the 24 months of follow-up, 38% , and, of these, 32 (31%) had a VL > 400 copies/mL. The frequency of prevalent unsuppressed viral load differed by several demographic factors . A highehttp://dx.doi.org/10.1155/2016/4161738) and older adolescents aged 18–20 (Supplementary Table B). In addition, having their mother as their primary caregiver appeared to decrease the likelihood of missing a clinic visit after study enrolment (27% versus 42% for 12–17 years old and 39% versus 55% for 18–20 years old). Also greater proportions of adolescents who felt that unfriendly health care workers, having problems in taking the medication, and treatment fatigue were barriers to care missed a subsequent visit compared to those who did not agree with those statements.As noted earlier, older adolescents were more likely to miss a subsequent clinic visit after study enrolment than either of the other age categories . To deteLack of services at health care facilities was also noted by both age groups but the service of importance differed; younger adolescents were more impacted by lack of peer support and counseling (57% versus 31% missed visit), while older adolescents agreeing that lack of sexual health services could pose a barrier to care were more likely to miss a subsequent visit (67% versus 47%). Certain potential barriers only appeared to impact adherence to visits in one group and not the other. Among younger adolescents, visits to the clinic being noticed by friends or individuals in the school community were associated with a higher proportion of subsequent missed visits compared to adolescents not perceiving that as a barrier to care (46% versus 27%), as was agreeing that difficulty finding time to attend visits and expensive transport fees. For older adolescents, however, agreeing with the statement that having an ill caregiver who requires care (67% versus 47%) or a caregiver with financial difficulty (71% versus 43%) was barriers to care was more common among those who subsequently missed a clinic visit compared to those who did not agree with those factors as potential barriers.As pediatric ART has scaled up, HIV care services, mostly delivered in secondary-level health care facilities, are becoming heavily overburdened, while increasing numbers of children are transitioning to adolescence . This isFew studies have assessed treatment outcomes in older children and/or adolescents in this region, and those that have report disproportionately poor ART outcomes in this group compared to adults. A study investigating treatment outcomes in adolescents (9–19 years) accessing care from a community-based ART program within a periurban township in Cape Town, South Africa, showed that adolescents had significantly lower rates of virologic suppression (<400 copies/mL) (27.3%) compared to young adults (63.1%) . High raWe interviewed a group of 126 adolescents and aimed to uncover what factors adolescents perceived to be barriers to accessing and remaining in care. Further, we estimated associations between agreeing that a factor was a barrier to care and subsequently missing a scheduled HIV clinic visit and whether these differed between older and younger adolescents. We found that the most frequently reported perceived barriers to care were related to logistics of the actual clinic visit: time and cost involved in getting to the visit as well as duration of time spent waiting in queues. Treatment programs requiring patients to return to the clinic more frequently or travel longer distances to receive necessary drugs increase the economic burden of transport costs and the possibility of lost wages for patients, both of which are well known barriers of adherence to HIV care and treatment , 14. ThoWe also found that older adolescents appear to be a key subgroup of this already vulnerable population; older adolescents were at increased risk for missing a clinic visit and were also more likely to have experienced prevalent virologic failure at the time of study enrolment compared to younger age categories. This has been demonstrated elsewhere , 38, 39 Having problems taking ART (most frequently reported as forgetting to take the medication) was identified as one of the treatment-related barriers to care by both age groups of adolescents and was associated with an increased risk of poor adherence to visits. Several interventions have been tested for feasibility and efficacy in terms of reminders to take tablets and may present an opportunity to improve a relatively easily modifiable risk factor for poor adherence among adolescents. These include mobile phone text message reminders and electronic medication monitoring devices , 41. ThoDisclosure of HIV status is an important issue in adolescent populations: not only disclosure of status to the vertically infected child but also disclosure by the adolescents themselves to family, friends, and members of their school and community. Among the group of adolescents interviewed, >95% agreed that disclosing status to family would facilitate access to care, but only a third agreed that disclosing to school and friends would be of assistance. In fact, one of the most frequently perceived barriers to care was the fact that the frequency of HIV clinic visits would be noticed by friends or members of the school community and among the group aged 12–17; perceiving this factor as a barrier to care was associated with a 70% increased risk of subsequently missing a clinic visit, underlining the social challenges and stigma that school-going adolescents face while in ART care programs.Our findings must be considered in light of some limitations. First, we acknowledge that, by definition, all the participants interviewed were still accessing care at the time of the study and the factors identified by this group as important determinants of accessing and staying in care may differ from those that would potentially be identified by adolescents who have already dropped out of care. Second, the small numbers enrolled limited the statistical power to detect small differences and precision of our estimates. However, the analysis presented here aimed to explore possible associations between the factors investigated and adherence to ART treatment rather than establishing causal associations and should be interpreted as such. Third, we were unable to enroll some participants <18 years of age  due to the fact that a legal guardian was not able to be present to sign consent. If those who were not enrolled differed systematically from those <18 years that consented, this could result in selection bias. In addition, bias may arise if the need for caregiver consent to participate in the study influenced the responses provided by the adolescents interviewed. Though we cannot rule this possibility out completely, several steps were taken during the consenting and interviewing process to prevent this, including an explanation of the procedures undertaken to ensure confidentiality and conducting the interview in a private space. Finally, we did not measure depression or stigma directly as potential barriers to care and these may contribute significantly to the perception of other factors.Despite these limitations, our study results are important. First, we demonstrate the increased risk of poor adherence to care for older adolescents, highlighting this group as a key population for intervention if 90-90-90 targets are to be met. In addition, we report on several modifiable factors that may be barriers to accessing and remaining in care among adolescents and show that these differ in importance and impact between older and younger adolescents. Several different interventions to improve retention in care and adherence to treatment have been proposed including financial incentives and behavioral and facility level interventions such as peer social support and adherence clubs , 42–46. The supplementary material presents the analysis of risk factors associated with having at least one late or missed visit stratified by age category. First, we estimate factors associated with the primary outcome for younger adolescents aged 12-17 years (Supplementary Table A) and second, for older adolescents aged 18-20 years (Supplementary Table B).


The optoelectronic sensor OPT101 have merits in advanced optoelectronic response characteristics at wavelength range for medical near-infrared spectroscopy and small-size chip design with build-in trans-impedance amplifier. Our lab is devoted to developing a series of portable near-infrared spectroscopy (NIRS) devices embedded with OPT101 for applications in intensive care unit clinics, based on NIRS principle. Here we review the characteristics and advantages of OPT101 relative to clinical NIRS instrumentation, and the most recent achievements, including early-diagnosis and therapeutic effect evaluation of thrombus, noninvasive monitoring of patients' shock severity, and fatigue evaluation. The future prospect on OPT101 improvements in noninvasive clinical applications is also discussed. Currently, optoelectronic sensors have been applied in fruit or stem growth monitoring in agriculture , nitric 2) ) and Hb ([Hb]) are calculated by the following equations  and [Hb] relative to the initial values (∆[HbO2] and ∆[Hb] respectively) are given below  and ∆[Hb] could reflect the occurrence or state of thrombus.The OPT101 embedded NIRS device is shown in 2]/∆[Hb] for statistical comparisons between both populations. Thrombolytic therapy was taken on patients every other day. We also used our device to collect ∆[HbO2] from one day before and during the treatment days to analyze the therapeutic effects between thrombus and healthy legs.We collected data from nine patients with thrombosis during and after therapy and from seven healthy subjects for contrast. We analyzed these data and drew the scatterplot histograms of ∆ and [Hb] as follows:In this application, we recommended the tissue blood oxygen saturation  based on the neurovascular coupling theory. The data of variations of [HbO2] and [Hb] recorded in the attention test was used to test whether it could be used for fatigue evaluation. As shown in 2] and ∆[Hb]. The doctors and nurses usually work long hours in the ICUs, accordingly fatigue becomes a severe problem requiring quantitative management and proper intervention. Here we focused on the fatigue induced by prolonged work or operation. A computer game playing paradigm was used to induce the ICU clinicians’ working fatigue. We recruited 10 healthy subjects to play computer games for 7 h with a realistic driving simulator platform as shown in During NIRS data recording, we also recorded the response of subjects to the attention stimuli on keyboard and reaction time. For data analysis, we composited the mean behavioral factor from 10 subjects at every attention test as accuracy/RT to quantify the mental fatigue of subjects. Then we analyzed the relationship between the hemodynamic parameters and the behavioral data accuracy/RT following with the fatigue increment induced by computer game playing. We also computed the prefrontal activation map with fatigue degree increase for intuitively visualizing fatigue adjustment on prefrontal activity.2]/∆[Hb] between the normal subjects and thrombus patients. In general, the value of ∆[HbO2]/∆[Hb] of the normal subjects (11.0 ± 0.95) are higher than those of the thrombus patients (9.6 ± 1.10). Student t-test also shows that the above measurements are dramatically different between normal subjects and thrombus patients (p < 0.001). On the other side, 2] in thrombus legs decreases after therapy, and increases gradually during the recovery phase and finally reaches a significantly higher value after two days post treatment. For a healthy leg, the value of ∆[HbO2] recovers to the initial state in one day and then remains stable after the therapy. Accordingly, the hemodynamic response monitored by our noninvasive NIRS thrombus monitor is shown to be capable of differentiating thrombus region from health region of human body and evaluating the therapeutic effect, either from the composed hemodynamic parameter (∆[HbO2]/∆[Hb]) or the relatively more sensitive indicator (∆[HbO2]).2 data was also collected by the conventional blood drop sampling and blood-gas analysis for each subject during the experiment. ScvO2 was employed as a contrast to test whether StO2 measured by NIRS shock monitor was reliable and sensitive to monitor shock severity. 2 and ScvO2. The correlation coefficient between the results of StO2 and ScvO2 is high . As shown in 2. However, those three data points are quite close to the 95% confidence interval. 2 and ScvO2, which clearly illustrates that there exists a fairly good agreement between these two parameters (the mean difference is 4.16%). It indicates that StO2 measured in the ScvO2 origin site of the human body by NIRS has the potential to substitute the current golden standard parameter ScvO2.As a gold standard indicator for prediction of shock severity, ScvO2] measured by NIRS imager and the behavioral performance factor accuracy/RT varied nonlinearly along with the time duration of playing the computer game. 2] of every subject, we plot its variation with duration time statistically, as well as the behavioral performance. We find that the fatigue-induced ∆[HbO2] rise up while fatigue-induced behavioral performance fall down in a similar opposite tendency .As for fatigue evaluation, tendency b. IntereWith the optoelectronic detector OPT101, we innovatively developed a series of NIRS devices for ICU, including the common health scanner, the thrombus monitor, monitor for patients with shock, and the imager for fatigue evaluation. All of these devices were noninvasive, portable, compact, and real-time. These devices surpassed the current methods or conventional techniques used in those severe diseases in integrated advantages of noninvasive, nonionizing, and continuous measurements. The experimental studies of each device displayed good reliability and sensitivity. The whole study fully demonstrated the medical and life science application of OPT101 in near-infrared spectroscopy, and indicated the great potential of OPT101, as well as the feasibility of OPT101 embedded portable NIRS in ICU and multifunctional home health care.2]/∆[Hb] played a fairly sensitive role in diagnosis of thrombus, as well as the therapeutic effect tracking and evaluation. Of note, the monitor probe was applicable to the early thrombosis tissues, such as legs, which indicated the home care use of this device in early detection and development of thrombosis. In the application of monitoring patients with shock, we found that the StO2 measured in the tissue surrounding the central jugular vein is quite consistent with the golden standard indicator ScvO2, which was measured by blood drop sampling and blood-gas analysis. Apparently, the ScvO2 could not be measured continuously and in real time, which means that it was actually not possible to monitor shock and to predict the time window for rescuing the patients. In contrast, StO2 measured by NIRS shock monitoring was quite appropriate for monitoring the onset of the shock and severity change of the shock. For fatigue evaluation, the variation ∆[HbO2] measured by the NIRS fatigue imager increased with the working time and were highly negatively correlated with fatigue-adjusted behavioral performance score . This finding strongly supported the quantitative evaluation of fatigue in a noninvasive and in-situation way by NIRS.Our study on thrombus monitor showed that NIRS monitoring of ∆[HbOIn the future, we would like to expand the range of OPT101 embedded NIRS for more applications in life science. The sensitivity of OPT101 is expected to be closer and closer to the expensive sensors in other kinds of NIRS instruments, which may make the NIRSs compact, low-cost, and close-to-translational as well. In addition, according to Additionally, even OPT101 is small enough at the current stage, it is also expected to be in patch type design in the near future, which would allow the flexible and wearable design of the above NIRS devices for smarter healthcare.


In addition, we noted that STAT3 transcript was reduced in PTSD and this was possibly responsible for reduced AGO2 and DCR1, which in turn affected miRNA synthesis. Furthermore, we observed that activation of CD4+ T cells or monocytes led to reduced mature miRNA availability. Finally, the inflammation seen in PTSD was associated with downregulated miRNA profile. Altogether, the current study demonstrates that the chronic inflammation seen in PTSD may be a result of dysregulated miRNA biogenesis pathway due to diminished expression of the key molecules like AGO2, DCR1 and STAT3.Chronic inflammation is a characteristic of post-traumatic stress disorder (PTSD). The initiation of inflammation and molecules involved are not yet clearly understood. Here, we provide compelling evidence that the inflammation seen in PTSD may result from the dysregulated miRNA processing pathway. Using microarray analysis with a discovery group of peripheral blood mononuclear cell (PBMC) samples from War Veterans with PTSD, we found 183 significantly downregulated miRNAs, several of which target numerous genes categorized to be pro-inflammatory in nature. This observation was further confirmed in a replicate group by including more samples. Furthermore, employing RNA-sequencing, quantitative real time PCR (qRT-PCR) and  Following combat, the rate increases significantly to ~20% in U.S. military service personnel,3 leading to an estimated annual healthcare cost of around 180 million USD.4 The quality of life of the patient is significantly affected as the symptom of PTSD may be prolonged. As per DSMV-IV classification, symptoms of PTSD include hyperarousal, intrusive thoughts, flashbacks, nightmares, numbing of feelings, insomnia, fear, avoidance of reminders, irritability, hypervigilance, heightened startle response and distress when exposed to reminders.5 It is now clear that PTSD is a serious psychiatric disorder associated with a dysregulated immune system. Differential expression of several pro-inflammatory genes and dysregulated networks has already been reported in PTSD in the recent past.10 Moreover, in a recent review, after analyzing published literature on immune status, the authors concluded that PTSD patients exhibit an excessive inflammatory state.11 Above all, PTSD has also been associated with other clinical disorders such as cardiovascular disease, diabetes, gastrointestinal disease, fibromyalgia, chronic fatigue syndrome, musculoskeletal disorders and autoimmune diseases,13 all of which have an inflammatory component.Exposure to a traumatic event such as military combat, violence and natural disasters, can lead to the development of post-traumatic stress disorder (PTSD).et al.7 reported that pro-inflammatory cytokines such as interferon gamma (IFNG) and interleukin (IL) 17 were significantly elevated in PTSD and their expression was inversely related to miR-125a. In another study from our lab, Bam et al.8 reported that the level of IL12 was elevated in PTSD due to decreased expression of miR-193a-5p. These reports suggested that in PTSD, there was dysregulation in expression of miRNAs that control the expression of pro-inflammatory cytokines. When considering dysregulation of miRNAs, involvement of a faulty miRNA biogenesis mechanism must be considered. In this context, deficiency in DCR1, the enzyme responsible for the generation of mature miRNA by cleaving pre-miRNA to ~22 nucleotide mature miRNAs, is known to result in deficient miRNA biogenesis and can lead to lowered abundance of mature miRNAs.16 Remarkably, a recent report showed that the expression of Dicer1 was decreased in PTSD, and this was linked to the lowered expression of miR-3130-5p.17 Interestingly, the authors reported only miR-3130-5p as significantly downregulated but did not show any link between the downregulated miRNA and any physiological changes in PTSD. Recently, the AGO2-dependent pathway, which is independent of the DCR1-dependent pathway, for miRNA biosynthesis has been documented in the mammalian system.21 As an example, miR-451, which controls erythropoiesis, was shown to be exclusively processed through the AGO2-dependent miRNA biogenesis pathway.20We have reported elevated expression of pro-inflammatory cytokines in PTSD and provided evidence linking this with dysregulated expression of miRNAs and other epigenetic marks. Zhou 24 In response to signal from cytokines and growth factors, specific signal transducer and activator of transcription (STAT) protein members are phosphorylated and translocated to the nucleus where they activate target gene transcription.27 As analyzed by Genomatix Pathway System  available at www.Illumina.com), STAT3 was predicted to influence the transcription of AGO2 and DCR1, apart from several other TFs. However, it is not yet known whether STAT3 interacts directly with the promoter of AGO2 and DCR1 or, the influence on transcription is mediated indirectly.Gene expression can be regulated at various stages including transcription, post transcription, translation and post-translation.AGO2 and DCR1, were downregulated along with the expression of a large number of miRNAs in the peripheral blood mononuclear cells (PBMCs) of War Veterans suffering from PTSD. On the basis of our data, we suggest that the inflammation seen during PTSD is probably initiated due to lowered expression of the molecules that regulate mature miRNA generation.In the current study, we investigated miRNA biogenesis and found that miRNA biogenesis key molecules, 6 All of the donors were first clinically assessed by professionals for PTSD. Participants were evaluated by the psychometric properties of the PTSD Checklist (PCL)28 and the PTSD diagnosis was validated by the Clinician Administered PTSD Scale29 and the Diagnostic and Statistical Manual of Mental Disorder (DSM-V).4 PTSD patients with current alcohol and other substance abuse, undergoing immunosuppressive drug treatment or having immunosuppressive disease, were excluded. For normal controls, age-matched healthy volunteers were included. Individuals who did had symptoms of active infection or history of immune compromise such as HIV, cancer, pregnancy or were on chronic steroid therapy, were not included for the study.Informed and written consent was obtained from all participants included in the study. PTSD patients were Veterans of either the 1991 Persian Gulf war, or of the recent Iraq or Afghanistan wars, recruited from William Jennings Bryan Dorn Veterans Medical Center, as described earlier.et al.10We included (a) 4 controls vs 8 PTSD patients in the discovery group and 7 controls vs 3 PTSD in the replication group for the miRNA array analysis; (b) 5 controls and 5 PTSD patients for the RNA-sequencing (RNA-seq) analysis and (c) 22 controls and 18 PTSD patients for all the quantitative real time PCR (qRT-PCR) validation studies. The demographic of the samples included for microarray and RNA-Seq technologies are provided in Bam Peripheral blood samples (10–20 ml) were collected in EDTA coated collection tubes  and PBMCs were isolated using Ficoll-Paque  within 1 h of sample collection. PBMC viability was determined by trypan-blue exclusion. Using a universal kit  recommended for simultaneous isolation of high quality DNA and total RNA including miRNAs, all of the entities were isolated from the same ~10 million PBMCs and immediately frozen at −80 °C until use. All our PBMC samples were de-identified/blinded to us.P-value (<0.05) calculated using student’s t-test, were included for the analysis. For analyzing the miRNAs in the replication group, we combined all the data from both discovery and replication group. The miRNA array data are available in ArrayExpress .Microarray for the miRNAs was performed at Johns Hopkins Memorial Institute . Total RNA, including mRNA, miRNA and other small RNA molecules, were isolated from PBMC samples as described above. Next, total RNA samples were used in the analysis of miRNA expression level by miRNA array hybridization assay using the Affymetrix miRNA-v1 gene chip . Linear fold-change in miRNA was calculated to compare the differences of all the miRNAs expressed between PTSD patients and controls. A linear fold-change of at least ±1.5 was used as a cutoff value for the inclusion of a miRNA. Moreover, only the miRNAs which were significant on the basis of 30 The accepted hits were used for assembling transcripts and estimating their abundance using Cufflinks. The differentially expressed gene, promoter usage and splicing form were determined by Cuffdiff and Cuffcompare.31 The heat maps and links were generated using Circos software.32 Our data are available in NCBI’s GEO database (Accession #GSE83601).RNA-Seq libraries were constructed using Illumina TruSeq RNA Sample Preparation kit . Briefly, total RNA was purified from PBMCs using the Qiagen RNA easy kit. The oligo-dT beads were added to 1 μg of total RNA to isolate mRNA. The purified mRNA was fragmented to 200–400 bases. The RNA fragments were then reverse transcribed into double stranded cDNA fragments. The DNA fragments were repaired to generate blunt ends using T4 DNA polymerase, Klenow polymerase and T4 polynucleotide kinase. After DNA fragments were purified using Qiagen PCR purification kit (Qiagen #28004), an ‘A’ base was added to the 3′ end of the blunt DNA fragment by Klenow fragment. Sequencing adapters were ligated to the ends of DNA fragments using DNA ligase. The libraries were then amplified by limited PCR (15 cycles) using primers provided by the kit. The PCR products were then separated by 2% agarose gel electrophoresis and fragments with sizes ranging from 250 to 400 bp were excised and purified using the QIAquick Gel Extraction Kit (Qiagen #28704). The concentration and distribution of the library were determined by a NanoDrop spectrophotometer . The library was sequenced by Illumina HiSeq 2000 at Tufts University Genomic core facility. Raw sequencing reads (50 bp single-end) were mapped to human genome build hg19 using Tophat 2.33 We compared the expression of precursor (pri- and pre-miRNAs) miRNAs between controls and PTSD patients by looking into the NGS data. Because precursors are longer in length, these molecules are detected by RNASequencing. We compared the precursor levels of only the miRNAs listed in Figure 2e. The difference in the expression of the precursors was expressed as fold-change. The fold-change and the statistical significance values were obtained after analyzing the RNA-Seq data in Cufflink and Cuffdiff.The expression level of miRNA precursors can be detected by performing comparison of microarray data, next generation sequencing (NGS) data or by employing qRT-PCR analysis for individual precursors.All qRT-PCR reactions were performed in an Applied Biosystem ViiA 7 Real-Time PCR system . From all the samples, cDNA was synthesized after taking 1 μg each of total RNA in a 20 μl system using miScript II RT kit from Qiagen (Qiagen) following manufacturer instructions. For all the samples, the quantity and purity of total RNA was analyzed by Nanodrop (Thermo Scientific) equipment. For the detection of gene transcripts, 5–10 ng of cDNA per well in a 10 μl system of a 384 well were used and amplified with iQ universal SYBR Green supermix . As an internal control, 18S rRNA and GAPDH message was quantified along with the genes. The expression level of the genes was expressed as the relative abundance (RE) value to the internal control. For the detection of miRNAs, cDNA was synthesized as mentioned above and 4ng was used in each well of a 384 well plate in a 10 μl per well system. We used miScript SYBR Green PCR Kit (Qiagen) and miScript Primer Assay (Qiagen) for the qRT-PCR detection of the respective miRNAs. Furthermore, we selected eight genes  predicted or known to be targeted by miRNAs found downregulated in our data set. The primer sequences are provided in 5 cells in complete RPMI 1640 (with β-mercaptoethanol) 24 h prior to transfection in 24 well plates. For the transfection, a final amount of 15 pmol of siRNA per well was used and transfection performed using Lipofectamine RNAi MAX  following the instructions from the manufacturer. In the cultures with a time period exceeding 48 h, 350 μl medium was replaced at 48 h. All the cell types were cultured with 5% CO2 at 37 °C. After the indicated culture time, cells were harvested and total RNA isolated using the AllPrep DNA/RNA/miRNA Universal Kit (Qiagen). RNA samples were stored at −80 °C for future use.siRNA for respective targets were designed using the BLOCK-iT software (ThermoFisher Scientific) and the oligos were procured from Integrated DNA Technologies . Sequences of the siRNAs are provided in 6 cells were taken in 1000 μl of complete RPMI medium in a 24 well plate and 25 μl Dynabeads Human T-Activator CD3/CD28 (1:1 bead-to-cell ratio) , 30 U human rIL-2  and cultured the cells in 5% CO2 at 37 °C for 48 h. After the culture periods, cells were collected and total RNA was isolated.CD4+ T cells were isolated from PBMCs collected fresh from healthy individuals using EasySep kit  and PE-conjugated mAb  against CD4+ cells following the manufacturer’s instructions. The purity of the isolated CD4+ T cells was confirmed by running an aliquot in a Flow Cytometer . For the activation of the CD4+ T cells, 1x10E. coli at 1 μg ml−1 and recombinant human IFNG @ 100ng ml−1 was used with 1x106 THP-1 cells in complete RPMI medium and cultured for 24 and 48 h in a 5% CO2 at 37 °C. After the indicated culture periods, cells were collected and total RNA isolated as described earlier. Our cell line was obtained from ATCC and was authenticated recently at University of Arizona Genetics Core.To stimulate THP-1 cells, purified lipopolysaccharide, from Ingenuity Pathway Analysis  has the ability to provide information on targets of miRNAs for a data set. We used this tool to predict the targets of the differentially expressed miRNAs in PTSD. Toward this end, we used the 190 differentially expressed miRNAs and included some pro-inflammatory genes, as shown in the interactive network, and connected them by the IPA algorithm. IPA will connect the miRNA and the target gene only if they are experimentally shown and reported or predicted on the basis of computational analysis.We identified the probable genes that could be a binding site for STAT3 by using the Genomatix Pathway System tool available with Illumina. This tool identifies genes with promoter having a binding sequence of a transcription factor, which is based on the available evidence from published literatures.P-values, we performed the Student’s two tailed t-tests and Wilcoxon and Mann–Whitney tests. All the in vitro experimental results are from multiple (three or more) replicates.For obtaining the t-test. Thus, the power was adequate when it was combined with the replication data.According to the previous study, we assumed the group standard deviation as 0.4. Then, with a sample size of 4 controls and 8 patients for the microarray, it is adequate to obtain at least 80% power to detect around 100 genes with a true difference in expression of at least 1.5 with a false discovery rate (FDR) of 0.05 using a two-sided two-sample P-value <0.05 were considered for further analysis. Using these criteria, a total of 190 mature miRNAs were obtained, of which 183 were downregulated and only 7 were upregulated  and NR_037194/ LINC00664 (−3.7 log2 fold-change), respectively. Realizing the interesting potential role played by long non-coding RNAs (lncRNAs), we studied the expression pattern of lncRNAs in our RNA-Seq data. We found 39 lncRNAs that were significantly altered in their expression levels in PTSD compared with the controls. Of the 39 significant lncRNAs, 25 (64%) were downregulated and rest upregulated in PTSD patients. The most highly up- and downregulated ones were NR_001434/HLA-H lncRNA  observed in PTSD samples and found their predicted or known target genes. We used the Qiagen miRNA-target interactive network designer tool within IPA to obtain the predicted or known targets of the dysregulated miRNAs in our data set. The claim made on the basis of this analysis is strongly supported by our previous reports on elevated pro-inflammatory cytokines in PTSD as a result of lowered miRNA expression targeting specific genes recognized as pro-inflammatory in nature. AGO2 and DCR1, which in turn downregulates the expression of a large number of miRNAs in PTSD. This observation provides a clear indication of a possible deficiency in the mature miRNA biogenesis pathway. DCR1 is the main catalytic enzyme that cleaves the pre-miRNA to generate the mature miRNA.35 Thus, it is expected that decreased expression of DCR1 would lead to decreased generation of mature miRNAs and thereby cause lowered abundance of miRNAs as seen in this study with PTSD.PTSD is a psychiatric disorder associated with a chronic inflammatory condition. The precise mechanisms that trigger inflammation during PTSD remain unclear. In this study, we provide new and interesting data which demonstrate a global defect consisting of decreased expression of 21 it can be suggested that lowered AGO2 expression can also result in decreased abundance of mature miRNAs. However, there is no distinction of miRNAs based on whether they are generated through DCR1 or AGO2-dependent pathways, except for a few such as miR-451.20 Our observation is consistent with this study demonstrating that miR-451 was significantly downregulated in the PBMCs of PTSD patients. However, besides miR-451, we do not know which of the differentially expressed miRNAs from our data set are selectively processed through the AGO2-dependent pathway. To prove that lowered AGO2 affects miRNA biogenesis, we knocked down AGO2 and observed a decrease in expression of some of the miRNAs we selected to study. We believe these miRNAs could be processed by the AGO2-dependent pathway, further implying that lowered AGO2 can affect the biosynthesis of miRNAs. We also observed that the abundance of some of the miRNAs were similar to that of control indicating that AGO2 was not the only miRNA biogenesis pathway involved.On the basis of the previous reports on AGO2-dependent, DCR1-independent, mature miRNA generation,To rule out that the observed difference in mature miRNA level in PTSD was not because of lowered transcription levels of the miRNA genes, we compared the expression levels of the pri- and pre-miRNAs. The pri- and pre-miRNAs are the two preceding stages of a mature miRNA in the miRNA biogenesis pathway. Considering that the transcription of the miRNA genes are equal in controls and PTSD, a bias in the processing of the precursors can lead to differential levels of the mature miRNAs. Our NGS data indicated that the level of precursors is more or less equal in controls and PTSD patients. This is indicative of an equal degree of transcription of miRNA genes in both controls and PTSD. However, as we have mentioned earlier, the altered levels of the corresponding mature miRNA in PTSD would imply that there is indeed a bias in the processing of the miRNA precursors after its gene is transcribed at an equal degree. Thus, this observation could be further linked to a biased pri-/pre-miRNA processing machinery in PTSD leading to an overall decreased level of mature miRNAs. Our knockdown experiments also indicated that lowering of either AGO2 or DCR1 can lead to a lowered level of the mature miRNA.AGO2 and DCR1. To that end, we used the Genomatix Pathway System tool and searched for transcription factors that could interact with both AGO2 and DCR1 promoters, and identified STAT3 to potentially interact with the promoters of both AGO2 and DCR1. When the level of STAT3 was knocked down, both the expression of AGO2 and DCR1 were also decreased thereby implying that their expression was regulated probably at the transcription level, by STAT3. Corroborating this observation, STAT3 was significantly downregulated in the PBMCs of PTSD patients clearly indicating that the downregulation of AGO2 and DCR1 could be the result of reduced expression of STAT3. We do not know why STAT3 is downregulated in the PTSD PBMCs, but we hypothesize that this in turn causes decreased transcription of both AGO2 and DCR1, which directly diminish the generation of mature miRNAs and the resultant effect is observed as a global downregulation of miRNAs in PTSD PBMCs. In support of this, many of the miRNAs selected for analysis were also reduced in their abundance after in vitro knockdown of STAT3. Thus, in the light of the recent report by Wingo et al.,36 we believe that indeed DCR1 and AGO2 are downregulated in the PBMCs of PTSD patients, which possibly results from decreased STAT3 expression, and eventually culminate into a lowered miRNA expression. Thus, although our findings related to STAT3 expression indicated that it is having a pro-inflammatory role in PTSD, it should be noted that STAT3 can have an anti-inflammatory role as well depending on the signaling molecule. It is known that in the presence of signaling from IL10/IL10R, the downstream signaling via STAT3 results in an anti-inflammatory state.37 Because in our present study, we do not know the status of IL10, it is not possible to make a concluding remark. Moreover, the very few available reports on IL10 level in PTSD is not convincing.43 Nevertheless, it appears that IL10 expression level is more in individuals who are resilient to PTSD development.44 Therefore, it further supports our hypothesis that STAT3 is probably having a pro-inflammatory role in PTSD.The next question we addressed was whether there was a common factor that regulates the expression of STAT3, AGO2 and DCR1 were also lowered upon stimulation of THP-1 cells. This observation implies that mature miRNA abundance could also be possibly lowered since the key molecules involved in their synthesis are lowered. Furthermore, it also implies that, at least in THP-1 cells (may be applicable to monocytes as well), stimulation can lead to lowering of miRNA biosynthesis. However, as only some of the miRNAs were downregulated upon activation/stimulation, it will be interesting to test whether the reduction of miRNAs is the cause for the activation of the cells leading to a pro-inflammatory state or vice versa in PTSD patients.As our previously published reports suggested activated state of T cells and monocytes/macrophages in PTSD, we wanted to see whether there is any link between the activated state of T cells and monocytes and diminished miRNA expression. We found that the abundance of some of the miRNAs is lowered in activated CD4+ T and THP-1 cells. This observation indicates that activated state of CD4+ T cells or monocytes might be possibly linked to lowered miRNA expression in the PBMCs of PTSD patients. Moreover, we found that 46 It is important to note that the interaction between miRNA and mRNA leads to destabilization/degradation of the mRNA in 66 to >90% of the instances.47 Therefore, we hypothesize that as a result of lowered abundance of miRNAs, the regulation of target genes at the post-transcriptional level by miRNAs is inefficient, which leads to an elevated expression of pro-inflammatory genes resulting in the observed inflammation in PTSD patients.We have previously reported the link between lowered miRNA level and elevated pro-inflammatory cytokines in PTSD. The bioinformatics tool-based analysis clearly indicated that numerous significantly downregulated miRNAs in PTSD target classical pro-inflammatory cytokines and transcription factors. These observations imply that regulation of the target genes at the post-transcriptional stage could be inefficient. In such a state, there will be more target genes (mRNA) available for translation resulting in elevated level of the proteins. Furthermore, in the present study, we found that many of the pro-inflammatory genes like JAK2, STAT1, CXCL3, IL23A and so on, were found to be significantly upregulated in PTSD patients. This observation further hints that indeed there is a deficiency in the regulation of the expression of genes in PTSD. In our study, it appears that the downregulated miRNA expression leads to increased level of target gene transcripts. Thus, it is possible that the inflammation seen during PTSD is a result of differentially regulated pro-inflammatory genes due to a failure in the gene expression regulation involving miRNAs, apart from other regulatory mechanisms like DNA methylation and histone modifications.51 In the recent past, it has been demonstrated that lncRNAs are critical regulators of gene expression, including immune system-related genes.56 However, because there is no significant literature available yet to support the role of these lncRNAs in our data set, we have not discussed all of these. Nevertheless, a few from our list of dysregulated lncRNAs are reported in literature. For example, LUCAT1, LINC01002 and MIR3945HG  were shown to be dysregulated and believed to have a role in lung cancer.61 As another example, TPT1-AS1 (upregulated in our data set) was reported to be associated with the prognosis of anaplastic gliomas.62 On a different note, interestingly, we observed that the expression pattern of the significantly dysregulated lncRNAs also had a similar pattern as the miRNA expression in PTSD. We saw that there was more downregulated lncRNAs in PTSD compared with controls. We do not know whether there is any significance attached to this observation. However, we believe that this aspect of the observation is definitely a promising area to look into in the future.lncRNAs are gaining significant attention recently due to their not yet clearly understood ways of functioning in the control of gene expression. The lncRNAs are considered a new class of gene regulators with various unknown mechanisms of action. These are a class of non-protein coding RNA molecules found to be expressed in different cell types.AGO2 and DCR1 resulting in inefficient biosynthesis of mature miRNAs that target pro-inflammatory genes. Furthermore, the lowered expression of AGO2 and DCR1 is probably due to reduced expression of STAT3 in PBMCs of PTSD patients. However, it is not clear from our studies whether miRNA downregulation is the result of activated state of the cells or downregulation of miRNAs leads to activated state of the cells of the immune system. A deeper study in the miRNA biosynthesis pathway in a similar setting as ours can definitely lead to a better understanding of the precise role of the key molecules that can be used in the development of effective therapeutic and/or management strategies for PTSD in War Veterans and the general population.In summary, our present data demonstrate that the inflammatory status seen in PTSD is due at least in part to the lowered abundance of miRNAs, which may result from reduced levels of