Heart failure (HF), the terminal state of different heart diseases, imposed a significant health care burden worldwide. It is the last battlefield in dealing with cardiovascular diseases. HF with preserved ejection fraction (HFpEF) is a type of HF in which the symptoms and signs of HF are mainly ascribed to diastolic dysfunction of left ventricle, whereas systolic function is normal or near-normal. Compared to HF with reduced ejection fraction (HFrEF), the diagnosis and treatment of HFpEF have made limited progress, partly due to the lack of suitable animal models for translational studies in the past. Given metabolic disturbance and inflammatory burden contribute to HFpEF pathogenesis, recent years have witnessed emerging studies focusing on construction of animal models with HFpEF phenotype by mimicking metabolic disorders. These models prefer to recapitulate the metabolic disorders and endothelial dysfunction, leading to the more detailed understanding of the entity. In this review, we summarize the currently available animal models of HFpEF with metabolic disorders, as well as their advantages and disadvantages as tools for translational studies. Heart failure (HF) refers to a complex clinical syndrome when cardiac output failed to meet the body’s need. The typical symptoms and signs include dyspnea, fatigue, lower limb edema and so on . At age The understandings of HF are dynamic and new challenges continue to emerge. Considering any cardiac structural and/or functional dysfunctions might contribute to HF, updated classification of HF is proposed according to left ventricular ejection fraction (LVEF) . The fouNotably, HFpEF and HFrEF are the two most common types of HF. HFrEF has an overt decreasing of myocardial systolic capacity, that is often caused by myocardial infarction, hypertension, heart valvular diseases and dilated cardiomyopathy. Due to the relatively clear etiology of HFrEF, many animal models have been established for HFrEF researches .The surgically induced HFrEF models are most widely used in mechanistic studies. For example, the hallmark murine model with thoracic aorta constriction (TAC) reported by Rockman , which b−/−)   showed HHFpEF accounts for about 50% of all cases with HF diagnosis. However, there is a scarcity of definite etiologies and unanimous clinical diagnostic criteria for HFpEF. Furthermore, eﬀective therapeutic alternatives, such as inhibitors of the renin-angiotensin system, beta blockers, aldosterone inhibitors and neprylisin inhibitors  for HFrE2+ handling. it might trigger mitochondrial permeability transition pore opening and reduced ATP production (2+ reuptake by sarcoplasmic reticulum in cardiomyocytes is primarily dependent on Ca2+ -ATPase (SERCA2a). Increased levels of reactive oxygen species (ROS) in cardiomyocytes impaired the phosphorylation of SERCA2a, and reduced the ability of the sarcoplasmic reuptake of Ca2+, and atherogenic dyslipidemia. These conditions are interrelated and share underlying mediators . Metaboloduction .In addite of Ca2+, leadingfunction .Insulin resistance also induces endothelial dysfunction. In obese rats with HFpEF, arginine-metabolizing enzymes were noticed to be increased in blood and heart, and nitric oxide (NO) turnover was reduced . PROMIS-Chronic low-level metabolic inflammation  is prediMachine learning and cluster analysis were applied to identify similar patients based on their combined features. In a previous study, female patients were divided into three groups. The first group had a low proportion of vascular risk factors. The second group had a higher proportion of hypertension and diabetes, but lower proportion of kidney disease and diastolic dysfunction. The third group had higher rates of atrial ﬁbrillation (AF) and chronic kidney disease and worst long-term outcomes. Similarly, three groups of male patients were identified by machine learning. The first group had a high proportion of coronary artery disease (CAD), dyslipidemia, higher serum creatinine, and diastolic dysfunction. The second group had highest BMI, and high proportion of CAD, obstructive sleep apnea, and poorly controlled diabetes. The third group had high rates of AF, elevated brain natriuretic peptide (BNP) and biventricular remodeling with a higher cardiovascular mortality . The higMice are the most commonly used animal models in HF studies because they share most of their genes with humans, and approximately 85% of the protein-coding regions of the human genome can be found in the mouse genome. Mice also have the advantages of short reproductive cycle, low feeding cost, and being ready to gene editing. At present, some rodent HFpEF models have been constructed through chemical agents, diet, surgical intervention, genome editing and so on.To build the animal models of HFpEF, tools to assess the atrial, ventricular or overall cardiac function of small animals should be established first. However, imaging or functional modalities are limited in small animals for estimating cardiac function. Up to date, aggressive cardiac pressure-volume loop analysis has been deemed as the “gold standard” in assessment of load-dependent and load-independent cardiac function. After the preparation of anesthesia and mechanical ventilation, conductance catheter had to be inserted through rodents’ apical stab or right carotid artery, which generally end up at the cost of termination of modeling. It was suggested that transient aortic occlusion can be used to alter afterload, whereas transient inferior Vena Cava occlusion can be used to alter preload. Hemodynamic parameters such as LV end-systolic pressure, LV end-diastolic pressure (LVEDP), peak positive LV dp/dt, peak negative LV dp/dt, time constant relaxation (tau), stroke volume, and cardiac output could be analyzed by pressure-volume conductance catheter technique .Transthoracic echocardiography (TTE) specific to small animals  were perIn addition to hemodynamic and imaging measurements, exercise stress and energy metabolism can be used to evaluate cardiovascular function when taking mouse physiology into account. Therefore, the mouse-graded maximal exercise testing (GXTm) was designed by staged increases in inclination as speed progress until exercise exhaustion. During exercise test, metabolic chamber encloses the treadmill that mice run on. Run time, maximum run speed, oxygen consumption (VO2), carbon dioxide expiration (VCO2), and VCO2/VO2 could be generated from the gas exchange occurring inside the metabolic chamber. Therefore, GXTm could be used as a functional assessment to determine the cardio-metabolic phenotype of various small animal models .Hypertension, the chronic pathological stress resulting in cardiac hypertrophy and fibrosis, as well as renal dysfunction, is an important metabolic risk factor for HFpEF incidence. Angiotensin II (Ang II), a vasoactive peptide, is known to regulate physiological blood pressure and is one of main players in hypertension development . Ang II Obesity induces metabolic and inflammatory disturbances and is linked to incidence of HFpEF . After 1l-arginine methyl ester hydrochloride (l-NAME) increased blood pressure and reduced glucose tolerance in C57BL/6N mice, leading to HFpEF phenotype such as poor left ventricular overall longitudinal strain % (GLS%), pulmonary edema, myocardial hypertrophy, cardiac fibrosis, increased aortic stiffness, decreased exercise endurance and the mitral flow deceleration time  C57BL/6J mice were administered with 60% HFD for 12 weeks and Ang II for the last 4 weeks . Metaboll-NAME, deoxycorticosterone acetate (DOCA) and desoxycorticosterone pivalate (DOCP) were used as agent for increasing blood pressure and constructing two-hit or multi-hit model of hypertension. An obese, senile, and hypertensive murine model induced by HFD for 13 months combined DOCP in the last month successfully produced HFpEF phenotypes  for 4 weeks displayed HFpEF phenotype assessed by ultrasound and Millar catheter transducer (Patients with chronic kidney diseases (CKD) and renal dysfunction represent a subpopulation of HFpEF with worse outcome . To studansducer . HFpEF mSome gene-edited mice were predisposed to develop HFpEF due to altered genotypes. Leptin resistant db/db mice constructed under the C57BL/6J background spontaneously developed severe obesity and diabetes . Alex et−/− mice was used in a model of human Alport syndrome with hypertension and kidney failure phenotype. Col4a3−/− mice manifested diastolic dysfunction, cardiac hypertrophy, fibrosis and pulmonary edema via upregulation of osteopontin (OPN). 2-Oxo-Glutarate Dehydrogenase-like (OGDHL), a mitochondrial protein involved in metabolic substrate fluxes and signaling, mediates OPN-induced mitochondrial dysfunction in promoting a HFpEF-like cardiac phenotype in this model  rats exhibit spontaneous type 2 diabetes, diabetic kidney disease, hypertension, hyperlipidemia, obesity and metabolic syndrome features . A numbeAlthough rodents are widely used as models to study the mechanisms of human heart diseases, they do not fully meet the needs of heart disease models due to limited information obtained by measurements of heart anatomic structures and function. The construction of large animal models using sheep, pigs or dogs provides approaches to solve this problem. But ethical concerns, cost, study duration and difficulties in gene editing limit large animal models of HFpEF.Similar to the evaluation of cardiac function of humans, clinical modalities, such as ECG-gated multidetector computed tomography (MDCT), cardiovascular magnetic resonance (CMR), echocardiography and cardiac catheter were used to assess functional, metabolic, and structural changes of myocardium in large animal models . Myocard2+ sensitivity (Hypertension-induced LV hypertrophy (OHT) was built by bilateral kidney ligation on elderly dogs that developed hypertension 8 weeks later. Compared with the control group, the OHT group showed HFpEF phenotype as increased left ventricular mass, myocardial hypertrophy, abnormal Tau index and BNP level. Mechanistically, functional impairment of sarcomeres may be an early event of HFpEF as this model suggested, mainly manifesting hypophosphorylation of myofilament proteins and increased Casitivity . HoweverUnilateral renal artery stenosis with renovascular hypertension (RVHT) was constructed by placing an irritant coil in the main renal artery under fluoroscopy, which leads to a gradual narrowing of the renal artery. In combination of HFD, this model displayed increased LV mass and diastolic dysfunction. In the HFD + RVHT pig model, the elevation in LV filling pressures with volume loading was mitigated by percutaneous approach using pericardial resection . MyocardThirty-nine-week-old female landrace pigs that were delivered with Ang II and DOCA, and fed with HFD exhibited a significant increase in the LV thickness compared with the Normal group. Total cholesterol (TC), triglyceride (TG) and low-density lipoprotein (LDL) were markedly increased in HFpEF group. As expected, the levels of IL-6 and TNF-α were elevated in the aortas of HFpEF pigs. And the plasma BNP, plasma epinephrine, norepinephrine and Ang II concentrations in HFpEF pigs were strongly increased when compared with the normal group. Invasive right cardiac catheterization was performed to show that PASP and PCWP were significantly higher in HFpEF pigs compared with the normal group, especially at the 18th week .HFpEF was induced in pigs by subcutaneous implantation of DOCA pellets along with a high-salt, high-sugar, high-potassium diet over 3 months. When compared with weight-matched normal pigs, cardiomyocyte changes were similar in subepicardial, midmyocardial and subendocardial regions but DOCA-induced changes in the interstitium appeared to be more pronounced in the subendocardial ventricular wall layers . The resIn this review, we summarized the published animal models of HFpEF and metabolic disorders, as well as the imaging and functional modalities for animal cardiac assessment . First a


We present the first database-wide study on the citation contexts of retracted papers, which covers 7,813 retracted papers indexed in PubMed, 169,434 citations collected from iCite, and 48,134 citation contexts identified from the XML version of the PubMed Central Open Access Subset. Compared with previous citation studies that focused on comparing citation counts using two time frames , our analyses show the longitudinal trends of citations to retracted papers in the past 60 years (1960–2020). Our temporal analyses show that retracted papers continued to be cited, but that old retracted papers stopped being cited as time progressed. Analysis of the text progression of pre- and postretraction citation contexts shows that retraction did not change the way the retracted papers were cited. Furthermore, among the 13,252 postretraction citation contexts, only 722 (5.4%) citation contexts acknowledged the retraction. In these 722 citation contexts, the retracted papers were most commonly cited as related work or as an example of problematic science. Our findings deepen the understanding of why retraction does not stop citation and demonstrate that the vast majority of postretraction citations in biomedicine do not document the retraction. Empirical studies have shown that papers continue to be cited after being retracted . For exaAlthough citations to retracted papers have been widely discussed in previous studies, the bulk of studies focused on a small number of retracted papers , our analyses show the longitudinal trends of citations to retracted papers in the past 60 years (1960–2020). In contrast to previous citation context analyses (which focused on the tones and the number of citation contexts acknowledging retraction), our analyses reveal the locations of the citation contexts mentioning retracted papers in full-text articles, contribute to the understanding of acknowledgment of retraction shown in citation contexts at scale, and indicate the purposes for intentionally citing retracted papers. In particular, we analyze citation contexts extracted from PubMed Central (PMC) open access articles that cited the retracted papers. The present work is the first database-wise study to examine citation contexts of retracted papers without time limitations.RQ1: How were PubMed-indexed retracted papers cited over time?RQ2: How were papers cited in full-text articles before and after their retractions?RQ3: How many postretraction citations acknowledged the retractions?RQ4: What were the purposes for intentionally citing retracted papers?In this study, we aim to answer the following research questions (RQs):We used two sets of data to answer the RQs. For RQ1, we investigated iCite citations to retracted papers indexed in PubMed. For RQ2, RQ3, and RQ4, we analyzed retracted papers in PubMed and citation contexts extracted from PMC open access articles. Our citation analyses deepen our understanding of the citation patterns of retracted biomedical research papers by tracing citations to retracted papers in the past 60 years. Our investigation of citation contexts to a vast collection of retracted papers reveals the locations where retracted papers were cited in full-text articles before and after their retractions and the purposes for intentionally citing retracted papers.Citations to retracted papers have been studied since the 1990s . HoweverIn contrast to large-scale studies, citation analyses of retracted papers in a particular field are abundant . For exaNature. Another set of citation analyses investigated retracted papers by one or a few authors , or a sishowing related work, use, comparison, and corroboration  to annotate 1,153 citations to Wakefield’s infamous Lancet paper connecting the MMR vaccine to autism  of each citation context . Bar-Ilao autism .By contrast, the present work focuses on the temporal trends of citations to retracted papers and examines citation contexts to a vast collection of retracted papers. We analyze how retracted papers in PubMed were cited over time and investigate the appearance of citation contexts mentioning these retracted papers in full-text articles. Different from previous citation studies comparing citation counts using two time frames , our citation analyses present the longitudinal trends of citations to retracted papers in the past 60 years (1960–2020). Going beyond the tones of citation contexts, we examine where retracted papers were mentioned in full-text articles and identify the purposes for intentionally citing retracted papers. Our approach of systematically identifying citations acknowledging retraction in PMC open access articles reveals acknowledgment of retraction shown in citation contexts at scale.The data for this study was collected from various sources. In particular, the retracted papers were collected from PubMed, the citations to the retracted papers were collected from iCite, and the citation contexts were identified from the PMCOA Citation Context Dataset , which cretracted publication” [PT] on August 20, 2020. The search yielded 7,813 results, including four retraction notices incorrectly indexed as retracted papers . We corrected our data set to use the retracted papers associated with these four retraction notices, based on their titles and PubMed’s retraction in links. Using PubMed’s retraction in information, the retraction years of 7,766/7,813 (99.40%) retracted papers were identified.Retracted papers in PubMed were searched using the query “Citations to the retracted papers were collected on August 20, 2020. The citation data were collected from iCiteNature Publishing Group and 139 days for BioMed Central journals . When there are multiple citation contexts, the acknowledgment of retraction may only appear in one of the citation contexts. We counted all the citation contexts in a paper as acknowledging the retraction if at least one did so explicitly. There were 611 postretraction citation contexts explicitly acknowledging the retraction  was used to aid in choosing the most appropriate citation purpose.Related work, Comparison, and Use), we also observed purposes relating to the unique nature of our data set: citations to retracted work. These particular purposes are Republication of retraction, Example of problematic science, and Notify retraction included.For annotating the citation purposes, we created a classification scheme consisting of 11 categories. before their publication year; these papers might have been retracted in the online-first stage. Also, 4,308 (55.47%) papers were retracted within 2 years after publication. This section analyzes citations to retracted papers , where the proportion of active nonretracted papers dropped from 60.28% to 26.49%. Together, the results show that the proportion of active papers has decreased in recent years, whether they were retracted or not.The decreasing trend in the proportion of d papers  in the factive retracted papers could be partly explained by the fact that old retractions became inactive as time progressed (see uncited and inactive retracted papers observed in the third phase (2013–2020) could be affected by the following reasons: For uncited retracted papers published in recent years, the time window might not have been long enough for receiving their first citations, or for inactive retracted papers published in recent years, there could be a gap between the year of last citation found in our data and future citations. In other words, uncited and inactive retracted papers could become active in the future.The decrease in ssed see . Anotheractive retracted papers among the papers retracted in a given year. Note that, in our data set, no paper was retracted in 1967, 1969, 1971–1974, 1976, 1978, or 1979. active retracted papers in the third phase , the shares of active retracted papers were less than or about 50%; notice the blue shading of the area highlighted in the yellow box in active retracted papers were less than or about 50% among the papers retracted before 2014; notice the blue shading of the area highlighted in the green box in main text of PMC open access articles , 3,983 pairs in the year of retraction (13.15%), and 9,405 postretraction pairs (31.06%). Of the 9,405 postretraction pairs, 422 pairs (4.49%) acknowledged retractions, and 8,983 pairs (95.51%) did not acknowledge retractions. In the following analyses, we omitted the pairs in the year of retraction for two reasons: The cited paper might have not been retracted when the citing author(s) submitted the manuscript; and The time frame might be too short to allow retraction notices to be apparent to the citing authors.Because a retracted paper can be mentioned multiple times in the full text of the citing paper, we further analyzed the number of mentions and the locations of citation contexts. Note that a paper may cite more than one retracted paper, and a retracted paper may be cited in multiple papers. Furthermore, each cited retracted paper may be mentioned one or multiple times in the citing paper’s full text. To capture how each citing paper mentions the cited retracted paper(s) in the full text, we pair citing papers and cited retracted papers as citing–cited pairs . We found 30,283 citing–cited pairs in the 46,069 citation contexts mentioning retracted papers found in the p < .001; postretraction pairs (retraction not acknowledged): coef = 0.29, p < .001; postretraction pairs (retraction acknowledged): coef = 0.47, p < .001). In other words, the higher the number of mentions, the more likely the multiple mentions would appear in more IMRaD sections.As for the retracted papers mentioned multiple times (hereafter referred to as multiple mentions), there were 5,006 preretraction pairs (29.63%) and 1,979 postretraction pairs (21.04%). Note that for a retracted paper with multiple mentions, all the mentions could appear in the same IMRaD section. As shown in As for postretraction pairs with acknowledgment of retraction, the trends are slightly different. When the multiple mentions appeared in different IMRaD sections, these multiple mentions were most commonly found in both the introduction/background section and the discussion/conclusion section, the same as the preretraction pairs and postretraction pairs without acknowledgment of retraction. However, when the multiple mentions were all in a single IMRaD section, the section was usually the introduction/background, and second most common was the discussion/conclusion section. Despite the slight differences, the postretraction pairs (regardless of whether the retraction was acknowledged or not) were most commonly observed in two sections: introduction/background and discussion/conclusion.To understand, at scale, whether postretraction citations acknowledge retractions, we used the methods described in Notify retraction included as Related work, and interpreted Example of problematic science differently than the first annotator.related work, accounted for 453 (62.74%) of the 722 citation contexts. This provides insight into why retraction did not stop citations: Findings reported in the retracted papers were still regarded as parts of the development of a particular research topic even though some of the retracted papers were mentioned negatively. The following examples illustrate this phenomenon.As shown in Another trial of a multivitamin and multimineral supplement in healthy elderly subjects reported beneficial effects after one year in six of seven tests [Retraction PMID: 11527656], though these findings have recently been retracted in the light of concerns about the veracity of the data and possible conflicting commercial interest [Retraction notice PMID: 11527656].An example of a negative mention (from PMID: 17474991):One of the first biomarkers proposed was serum IGF-I. Despite the retraction of one study suggesting that elevated pre-treatment free IGF-I levels were associated with NSCLC patient response to figitumumab ([Retraction PMID: 21102589]), additional evidence supporting these findings has been published.An example of a nonnegative mention (from PMID: 26029167):related work citations suggest that sometimes citations to retractions might be inevitable. In these citation contexts, retracted papers were cited because the reported findings caused an important event in the field, or the reported findings were once regarded as landmarks in the field. For example, Related work indicates that the retracted paper(s) was cited to provide context about a background event.Furthermore, some of the example of problematic science, shows another reason why retraction did not stop citations: Retracted papers were cited to discuss problems in scientific research or scientific publishing. For instance, retracted papers were cited to provide examples of irreproducible research, scientific misconduct, and fraudulent science. We also observed cases in which retracted papers were cited to demonstrate that peer review could fail due to the fact that problematic research had been published.The second most prevalent motive, While examining the citation contexts, we observed certain types of purposes in different types of citing articles see . We idenRelated work was the most common purpose observed in research articles and review articles, but not in other types of articles. In systematic reviews and meta-analyses, exclusion rationale was the most common purpose. This type of citation is hard to avoid, because when researchers perform systematic reviews and meta-analyses, they need to perform thorough literature searches, report the search results, and provide explicit reasons for excluding papers from the analysis. In articles about retraction or scientific misconduct, subject of study was the most common purpose. This type of citation is also hard to avoid because retracted papers were the “data” in these articles.Different purposes for intentionally citing retracted papers were observed in different types of articles. active retracted papers in each retraction year across the citation years answers RQ1 and provides further insight into the postretraction decrease in citation counts: Old retracted papers have stopped being cited as time progressed.Our findings show that retracted papers in biomedicine were mostly retracted within 3 years after publication and continued to be cited after retraction. Although our findings cannot be projected into the future because journals and authors can reach back in time to retract articles , our findings are aligned with those of previous studies. To answer RQ2 and RQ3, we analyzed how retracted papers were cited in full-text articles. For RQ2, the similar distributions of text progression of preretraction and postretraction citation contexts imply that the purpose for citing retraction papers did not change much before or after retraction. Interestingly, Related work was the most common purpose observed. The prevalence of related work partly explains why retraction did not stop citation: Findings reported in retracted papers were still regarded as part of the development of a particular research topic and might be cited to provide background context. Note that we also observed citation purposes indicating that some citations to retracted work might be inevitable, such as exclusion rationale and subject of study. Moreover, the purposes notify retraction included and example of problematic science show that citations to retracted papers can also be used for pointing out problematic science.To answer RQ4, we analyzed the purposes for intentionally citing retracted papers. Our findings on the 722 citation contexts acknowledging retraction show that retracted papers were intentionally cited for various reasons. In summary, this study examined the postretraction citations to PubMed-indexed retracted papers. To the best of our knowledge, Our analysis of the purposes for intentionally citing retracted papers contributes to a deeper understanding of why retraction has not stopped citations. We are not against citing retracted papers if retracted papers are appropriately cited. The problem is that the vast majority (94.6%) of postretraction citations did not document the retraction. Previous studies  reportedReducing the Inadvertent Spread of Retracted Science: Shaping a Research and Implementation Agenda Project” MetaArXiv Preprints July 2021 (https://doi.org/10.31222/osf.io/ms579). iCite was used for public data.We thank Dr Yuanxi Fu and Malik Salami for helping us annotate the citation motives. Thanks to Dr Yuanxi Fu and Randi Proescholdt for collaborating on Tzu-Kun Hsiao: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Visualization, Writing—original draft, Writing—review & editing. Jodi Schneider: Conceptualization, Data curation, Funding acquisition, Methodology, Project administration, Supervision, Writing—review & editing.Tzu-Kun Hsiao has no competing interests. Jodi Schneider has been an invited speaker for the publisher organization CrossRef and has received data-in-kind from Retraction Watch and scite.Alfred P. Sloan Foundation G-2020-12623. NIH 5R01LM010817.https://doi.org/10.13012/B2IDB-8255619_V2.For the data used for this study, see the Illinois Data Bank: 1 Two lead researchers (Y. Sato and J. Iwamoto) in a research group in the osteoporosis field.2 https://icite.od.nih.gov/api3 The extraction of citation context is limited to PMC open access articles because this is the largest set of publicly available articles indexed in PubMed with standardized XML format that can be processed systematically.4 PubMed_retracted_publication_full_v3.tsv  by filt5 Citation contexts are found concentrated at the beginning of papers the most, followed by the end of papers, for nonretracted papers as well .Click here for additional data file.


A growing number of studies support a bidirectional relationship between inflammation and bipolar disorders. Tumor necrosis factor‐α (TNF‐α) inhibitors have recently attracted interest as potential therapeutic compounds for treating depressive symptoms, but the risk for triggering mood switches in patients with or without bipolar disorders remains controversial. Thus, we conducted a systematic review to study the anti‐TNF‐α medication‐induced manic or hypomanic episodes. PubMed, Scopus, Medline, and Embase databases were screened for a comprehensive literature search from inception until November 2020, using The Preferred Reporting Items for Systematic Reviews and Meta‐Analyses guidelines. Out of the initial 75 references, the screening resulted in the inclusion of four case reports (each describing one patient) and a cohort study (in which 40 patients out of 7600–0.53% – experienced elated mood episodes after infliximab administration). Of these 44 patients, 97.7% experienced a manic episode and 2.3% hypomania. 93.2% of patients had no history of psychiatric disorder or psychotropic treatment. Only 6.8% had a history of psychiatric disorders with the affective spectrum (4.6% dysthymia and 2.3% bipolar disorder). The time of onset of manic or hypomanic symptoms varied across TNF‐α inhibitors with an early onset for Infliximab and a later onset for Adalimumab and Etanercept. These findings suggest that medications targeting the TNF‐α pathway may trigger a manic episode in patients with or without affective disorders. However, prospective studies are needed to evaluate the relative risk of such side effects and identify the population susceptible to secondary mania. A growing number of studies supports a bidirectional relationship between inflammation and bipolar disorders (BDs)Moreover, lithium therapy – the mainstay treatment for BDs treatment – yields immunomodulatory effectsGiven the case for the role of inflammation and innate immunity in BDs pathophysiology, molecules targeting the TNF‐α pathway have recently attracted interest as potential therapeutic compounds. Disease‐modifying antirheumatic drugs (DMARDs), including TNF‐α inhibitors , have shown positive effects on the affective, cognitive, and somatic function of patients with inflammatory illnesses.https://osf.io/mt7jb/quickfiles) and adhered to the procedures of Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) statementThis systematic review followed a pre‐defined protocol available online  reported the use of TNF‐α inhibitors in patients with or without mood disorders according to the Diagnostic and Statistical Manual (DSM) or the International Classification of Diseases (ICD); (ii) a qualitative measure of manic or hypomanic episodes induced by TNF‐α inhibitors as side effects; (iii) were written in English. Commentaries, editorials, and reviews were excluded. All articles published until November 2020 were included, while no publication status restrictions were imposed.Every reference was screened by at least two researchers independently (A.M. and V.D.P.), any disagreement was discussed between the two, and whenever it was not possible to make a decision, a third researcher was involved in the discussion (F.S.). Once the full‐text articles were selected, the data retrieved have been entered into a spreadsheet. Sample size, demographics, previous treatment, treatment response, adverse effects, and follow‐up time were extracted. The analysis of the data was made by comparison. The heterogeneity of the results, such as the type of studies identified, did not allow us to perform a meta‐analysis. A narrative synthesis was considered the best approach to describe and analyze the results.The database search, after duplicates removal, brought a total of 71 records. Following the inclusion/exclusion criteria, the screening resulted in the inclusion of 5 full‐text articles Fig. 24. The To our knowledge, this is the first systematic review conducted to date assessing the available evidence about the role of TNF‐α antagonists in inducing secondary manic episodes or exacerbating a mood switch in patients with or without mood disorders.Hitherto, a cohort study,TNF‐α antagonists had recently attracted interest as potential therapeutic compounds for mood disorders.A recent systematic review and meta‐analysis finally confirmed altered peripheral markers in BD, according to which IL‐6 seems to be a trait marker for BDs, while CRP and TNF‐α could constitute state markers, as they are increased during mood episodes,Increased cytokine variability suggests that a subset but not all patients may exhibit cytokine elevations as part of a manic episode.TNF‐α is a major Th1‐class pro‐inflammatory cytokine, that can bind to TNFR1 and/or TNFR2, activating downstream signaling pathways that mediate a wide variety of biological responses, including apoptosis, cell differentiation, proliferation, survival, homeostatic synaptic plasticity and inflammationvia the tumor necrosis factor‐α pathway that are crucial factors in the pathogenesis of BDs.TNF‐α contributes to brain development, particularly by modulating hippocampal growth and function.et al. assessed infliximab safety and efficacy for patients with treatment‐resistant unipolar and bipolar depression. Although the overall antidepressant effect was negative, a significant antidepressant effect was observed in the subgroup with elevated serum C reactive protein levels.From the therapeutic point of view, given the role of the interaction between the Serotonin/Norepineprhin system and Th1/Th2 immune response balance,Some limitations to this systematic review have to be acknowledged. First, the available evidence is limited to four case reports and a cohort study, all characterized by small sample sizes. Second, given the lack of prospective evidence, we cannot draw firm conclusions on causality. Third, the population who experienced secondary mania is heterogeneous both in terms of diagnosis and treatment.In conclusion, these psychiatric adverse events' characterization is essential for adequately assessing the risk–benefit ratio and improving the management of these events when they occur. Prospective studies with a multidisciplinary approach, close psychiatric monitoring, and serial cytokine levels measures are warranted to demonstrate the relative risk of secondary mania after TNF‐α inhibitors administration, or abrupt discontinuation (as seldom reported for SSRIsThe authors declare no conflict of interest.A.M., V.D.P., and F.S. conducted the literature review. F.S., G.P., and M.S. guided the areas for discussion. A.M., V.D.P., N.M., and F.S. conducted the statistical analysis. A.M., V.D.P., and N.M. wrote the draft of the manuscript. All authors have equally contributed to the critical revision of the manuscript.The authors received no specific funding for this work.Appendix S1. Supporting information.Click here for additional data file.


It is well-established that receptor activator of NF-κB ligand (RANKL) is the inducer of physiological osteoclast differentiation. However, the specific drivers and mechanisms driving inflammatory osteoclast differentiation under pathological conditions remain obscure. This is especially true given that inflammatory cytokines such as tumor necrosis factor (TNF) demonstrate little to no ability to directly drive osteoclast differentiation. Here, we found that transforming growth factor β (TGFβ) priming enables TNF to effectively induce osteoclastogenesis, independently of the canonical RANKL pathway. Lack of TGFβ signaling in macrophages suppresses inflammatory, but not basal, osteoclastogenesis and bone resorption in vivo. Mechanistically, TGFβ priming reprograms the macrophage response to TNF by remodeling chromatin accessibility and histone modifications, and enables TNF to induce a previously unrecognized non-canonical osteoclastogenic program, which includes suppression of the TNF-induced IRF1-IFNβ-IFN-stimulated-gene axis, IRF8 degradation and B-Myb induction. These mechanisms are active in rheumatoid arthritis, in which TGFβ level is elevated and correlates with osteoclast activity. Our findings identify a TGFβ/TNF-driven inflammatory osteoclastogenic program, and may lead to development of selective treatments for inflammatory osteolysis. Mechanisms for inflammatory osteolysis are unclear. Here, Xia et al. find that TGFβ shifts the inflammatory action of TNF on macrophages to a potent osteoclastogenic function and identify a novel TGFβ/TNF-mediated non-canonical osteoclastogenic program. Osteoclast cells derive from monocyte/macrophage lineage, and are the sole effective bone-resorbing cells. Osteoclasts play an important role not only in physiological bone development and remodeling, but also function actively to directly drive musculoskeletal tissue damage and accelerate the pathogenesis of diseases characterized by inflammatory osteolysis, such as RA7. Receptor activator of nuclear factor-kappa-Β ligand (RANKL) is the only known physiological inducer of osteoclastogenesis. RANKL-induced osteoclastogenesis is important for maintaining normal bone remodeling and healthy bone mass in physiological/basal conditions. The mechanisms of RANKL-induced osteoclastogenesis have been extensively investigated and well defined. However, the mechanisms driving osteoclastogenesis under inflammatory conditions are complex and remain poorly understood, especially due to the fact that inflammatory cytokines such as tumor necrosis factor (TNF) display little to no direct ability to drive the differentiation of osteoclasts.Inflammatory bone loss by osteoclasts is a signature feature and severe consequence of many inflammatory disorders, such as rheumatoid arthritis (RA), periodontitis, and psoriatic arthritis. Bone destruction is also a major contributor to morbidity and disability in inflammatory arthritis patients9. TNF stimulates bone erosion in many inflammatory diseases, such as in RA10. TNF blockade therapy has demonstrated the success in the inhibition of arthritic bone erosion14. However, how TNF drives its effects on bone resorption is a longstanding enigma given that TNF has only very modest direct osteoclastogenic effects on macrophages or osteoclast precursors, and needs to synergize with RANKL to promote osteoclast differentiation19.TNF is a crucial cytokine involved in immunity and inflammation, and plays a key role in driving chronic inflammation in a number of inflammatory and autoimmune diseases21. Discontinuation of denosumab, a RANKL inhibitor, is associated with rapidly rebound bone resorption and fracture risk22. TNF inhibitors have been utilized to treat RA-associated inflammation and joint erosion, but long-term usage has immunosuppressive side effects, such as common and opportunistic infections, and reactivation of latent tuberculosis9. Moreover, inflammatory bone erosion is resistant to standard anti-resorptive therapies29. This suggests that alternative molecular pathways for osteoclast formation and function may be active under inflammatory conditions, and such pathways need to be identified to develop anti-resorptive approaches that are effective in inflammatory diseases. Therefore, there is an important medical unmet need to discover these unknown pathways and mechanisms that mediate inflammatory osteoclastogenesis. These findings can provide attractive therapeutic developments to suppress inflammatory bone loss, whilst eliminating or minimizing negative effects on bone remodeling or immune response in physiological or disease settings. These pathways and mechanisms are currently poorly understood.RANK receptor blockers and anti-RANKL neutralizing antibodies are recently available forms of treatment for excessive bone resorption, such as that occurring in osteoporosis, by inhibiting osteoclast formation. However, these treatments strongly inhibit osteoclast formation and could result in long-term side effects. Blocking RANKL signaling can lead to defective and dysregulated bone remodeling and bone repair, and risks of atypical femoral fractures and osteonecrosis of the jawTgfbr2) and type I (Tgfbr1) and plays important roles in bone mass maintenance30. TGFβ is a multifunctional cytokine, which often interacts with other cytokines or growth factors to play diverse roles in different cells and settings32. The interaction between TGFβ and TNF is underappreciated. Despite a lot of effort having been put forth to investigate the effect of TGFβ on RANKL-induced osteoclastogenesis, the results in the literature are controversial and often seemingly contradictory38. For example, TGFβ was reported to play a positive role in RANKL-induced osteoclastogenesis35. Other studies, however, found the inhibitory or dual (both inhibitory and stimulatory) effects of TGFβ on RANKL-induced osteoclastogenesis38. These studies do not have genetic evidence to support their results except that one group used the knockout mice of smad436, which does not represent TGFβ effect. In this study, we generated TGFβ receptor 2 conditional knockouts (KO) mice  and show that deficiency of TGFβ signaling pathway in macrophages/osteoclast precursors does not affect RANKL-induced osteoclastogenesis in vitro and in vivo, or bone mass, providing clear evidence that TGFβ signaling is dispensable for RANKL-induced osteoclastogenesis under physiological conditions. In contrast, we found that TGFβ priming renders TNF sufficient to initiate a non-canonical, RANKL-independent pathway for osteoclast differentiation. This finding introduces a second molecular pathway for osteoclast generation distinct from the classical RANKL/RANK pathway. TGFβ shifts the pro-inflammatory action of TNF on macrophages to a highly efficient osteoclastogenic function by creating a favorable chromatin environment for osteoclastic gene expression. In mouse inflammatory models, blockade of TGFβ signaling protects mice from TNF-induced inflammatory bone erosion. As evidence of the potential human relevance of this pathway, TGFβ levels are significantly elevated in RA patients44, and we found that TGFβ signaling pathway and osteoclastic gene expression are highly enriched and correlated in peripheral blood monocytes (PBMCs) isolated from RA patients. TGFβ and TNF-mediated osteoclastogenic mechanisms are active in RA, indicating a clinical relevance and presence of TGFβ and TNF-dependent inflammatory osteoclastogenic pathways in RA. This previously unrecognized non-canonical pathway for osteoclast generation mediated by TGFβ and TNF opens therapeutic avenues to treat inflammatory bone loss.There is an abundant amount of transforming growth factor β (TGFβ) in bone tissue. TGFβ binds to its receptor composed of TGFβ receptor type II  peripheral blood monocyte (PBMC) derived macrophages, which are not only macrophages but are also circulating quiescent osteoclast precursors that migrate and attach to bone surface and differentiate into osteoclasts in response to RANKL46. The human system offers advantages of using cells directly relevant for human diseases and enabling future comparisons with patient cells. In this human system that mimics in vivo osteoclastogenesis, CD14(+) PBMCs are cultured with M-CSF for 3 days to induce macrophage differentiation  and NFATC1 (encoding NFATc1), and osteoclast marker genes ITGB3 (encoding β3 integrin), ACP5 (encoding TRAP), CTSK (encoding cathepsin K) and CALCR  was significantly enhanced by TGFβ priming  controls (hereafter referred to as WT). There is a basal level of TGFβ signaling in the physiological condition in WT mice, reflected by TGFβ target gene expression  mice, in which TGFβ receptor 2 is specifically deleted in myeloid lineage macrophages/osteoclast precursors by crossing NKL Fig. . Consisttes Fig. . These dion Fig. . To testsis Fig. . Tgfbr2 ace Fig. . We furt49, which resembles RA and therefore has been widely used to study inflammation-induced peri-articular bone erosion by inflammatory cytokines, including TNF. This model allows the investigation of bone resorption during the effector phase of inflammatory arthritis. Deficiency of basal TGFβ signaling (non-priming) in Tgfbr2∆M mice resulted in significant suppression of peri-articular bone erosion, and reduction of osteoclast numbers and surface in resorption sites  from the human culture system to identify transcriptomic changes by TGFβ priming. Consistent with prior literatured1, Fig. , and a sd6, Fig. . Gene sed6, Fig. . These dAT1 Fig. , and cheL10 Fig. , which w53. Indeed, H3K27ac signals were detected around the ATAC peaks. At non-priming gene loci, including ISGs, H3K27ac signals were present at baseline and increased after TNF stimulation . TGFβ priming enriched distinct transcription factor binding sites for the priming genes, which include FOS, E2F, SMAD2/3/4, MYBL2, and MITF , AP-1, NF-κB, and IRF1 Fig. 50,51. TG50, we next examined the effect of TGFβ priming on IFNβ expression. Similar to the effects seen at ISG loci, TGFβ priming substantially reduced chromatin accessibility and the levels of H3K4me3/H3K27ac at the IFNB1 promoter and 3′ downstream regulatory regions  induced by either TGFβ priming/TNF or RANKL stimulation  and examined the effects of myeloid cell-specific Mybl2 deletion on TNF-induced inflammatory osteoclastogenesis and bone resorption in vivo. Compared to the WT control mice, Mybl2 deficiency significantly suppressed TNF-induced calvarial bone erosions  OC gene loci in macrophages, on the contrary, exhibit a generally repressed state with compacted chromatin and H3K27me3 mark. Interestingly, the promoters and enhancers of NFATC1 show bivalent domains marked by both H3K4me3 and H3K27me322. Thus, there is an unmet medical need to identify alternative strategies to predominantly control inflammatory bone erosion, with minimal side effects on underlying bone remodeling. The mechanisms that selectively regulate inflammatory osteoclastogenesis have remained obscure. In this study, we identified several unique mechanisms specifically involved in TGFβ and TNF co-mediated osteoclastogenesis, without affecting RANKL-induced osteoclastogenesis in physiological conditions. Belonging to the same TNF superfamily, TNF and RANKL share many major signaling pathways. TGFβ switches TNF to induce macrophages to differentiate from osteoclasts. In this process, despite the that TNF induces many of the same classic osteoclastogenic transcription factors and osteoclast marker genes as those induced by RANKL, there exist regulators that are specifically induced by TNF, such as B-Myb, which is a previously unrecognized positive regulator that exclusively promotes TNF-mediated inflammatory, but not RANKL-induced, osteoclastogenesis. The other unique mechanism is IRF1, which functions as an inhibitor of TNF-mediated osteoclastogenesis. Findings from our group and others70 show that IRF1 is dispensable for RANKL-induced osteoclast differentiation. This is not surprising after we found that RANKL does not induce IRF1 expression, whereas TNF induces a large amount of IRF1. Both the positive regulator B-Myb and the inhibitor IRF1 are selectively involved in TNF-mediated inflammatory osteoclast differentiation. Furthermore, the expression level of B-Myb is significantly elevated while IRF1 is downregulated in RA PBMCs, supporting their pathogenic roles in inflammatory diseases associated with bone loss. Because they do not significantly influence RANKL-induced osteoclastogenesis, B-Myb and IRF1 are promising therapeutic targets for inflammatory bone resorption. In addition, we found a drastic downregulation of IRF8 by TNF in the TGFβ priming condition. IRF8 is a well-known inhibitor for RANKL-induced osteoclastogenesis. Downregulation of IRF8 is essential for the differentiation process. However, the mechanisms of IRF8 downregulation between RANKL and TGFβ/TNF stimulations are distinct. RANKL inhibits IRF8 expression mainly through transcriptional suppression72. TGFβ/TNF promotes ubiquitination of IRF8 and subsequently its protein degradation. The underlying mechanisms of IRF8 protein degradation may provide unique therapeutic targets that are not included in RANKL signaling. Thus, B-Myb induction, IRF1 decrease, and IRF8 protein degradation are recognized as TNF-specific induced osteoclastogenic mechanisms, which form an inflammatory osteoclastogenic program. Targeting the mechanisms in this specific program has therapeutic implications in the selective treatment of inflammatory bone destruction, minimizing side effects on bone remodeling.In the past two decades, a great focus has been placed on RANKL-induced osteoclast differentiation, which includes various signaling pathways and a classic group of osteoclastogenic transcription factors, such as NFATc1, c-Fos, and Blimp1. As the physiological inducer of osteoclastogenesis, RANKL plays a key role in normal bone remodeling and bone homeostatic maintenance. Blockade of RANKL signaling by RANKL or RANK antibodies effectively suppresses osteoclast formation and bone erosion. However, long-term blockade of RANKL signaling can affect bone remodeling and suppress new bone formation for healing73. Thus, a certain serum TGFβ level  is maintained in healthy people to achieve the homeostatic function of TGFβ65. In disease settings, serum TGFβ levels are often altered, such as those elevated in RA patients but reduced in SLE patients65. The culture medium contains a low level of TGFβ (1–2 ng/ml) similarly to physiological level. We found that TGFβ levels lower than this physiological range in culture medium do not affect TNF function in macrophages  with the mice with a lysozyme M promoter-driven Cre transgene on the C57BL/6 background . Gender- and age-matched Tgfbr2flox/flox;LysMcre(+) mice (referred to as Tgfbr2ΔM) and their littermates with Tgfbr2+/+;LysMcre(+) genotype as WT controls (hereafter referred to as WT) were used for experiments. Irf1 knockout mice (referred to as Irf1−/−) were obtained from the Jackson Laboratory (Stock No: 002762). The bone marrow isolated from gender- and age-matched Irf1−/− and WT (Irf1+/+) were used in the experiments. We generated mice with myeloid/macrophage-specific deletion of Mybl2 by crossing the Mybl2flox/flox mice59 with the LysMcre mice. Gender- and age-matched Mybl2flox/flox;LysMcre(+) mice (referred to as Mybl2ΔM) and their littermates with Mybl2+/+;LysMcre(+) genotype as WT controls (hereafter referred to as WT) were used for experiments.We generated mice with myeloid/macrophage-specific deletion of 48. TNFα was administrated daily at the dose of 75 μg/kg to the calvarial periosteum of age- and gender-matched mice for 5 consecutive days or 14 consecutive days before the mice were sacrificed. The calvarial, femoral, and L5 vertebral bones were fixed and subjected to micro-computed tomography (µCT) analysis, sectioning, TRAP staining, and histological analysis.For inflammatory osteolysis experiments, we used the established TNF-induced supracalvarial osteolysis mouse model with minor modifications49. K/BxN serum pools were prepared, and arthritis was induced by intraperitoneal injection of 100 μl of K/BxN serum to the male mice on days 0 and 2. The development of arthritis was monitored by measuring the thickness of wrist and ankle joints with digital slide caliper (Bel-Art Products). For each animal, the joint thickness was calculated as the sum of the measurements of both wrists and ankles. Joint thickness was represented as the average for each group. Mice were sacrificed on day 14 and serum and paws were collected. Hind paws were subjected to μCT analysis, sectioning, TRAP staining and histological analysis. µCT analysis of femoral trabecular bones, cortical midshaft, and L5 vertebral trabecular bones was conducted to evaluate the bone volume and 3D bone architecture using a Scanco µCT-35 scanner  according to the manufacturer’s instructions and the American Society of Bone and Mineral Research (ASBMR) guidelines75. All mice were housed in a 12-h light cycle at room temperature and had dry laboratory food and water ad libitum. All animal procedures were approved by the Hospital for Special Surgery Institutional Animal Care and Use Committee (IACUC), and Weill Cornell Medical College IACUC.For Inflammatory arthritis experiments, we used K/BxN Serum Transfer-Induced Arthritis modelMurine or human M-CSF, murine or human TNFα, human TGFβ1, and soluble human RANKL were purchased from PeproTech. Murine TGFβ1 was purchased from R&D systems. MG132, and Cycloheximide (CHX) were purchased from Millipore.4/cm2 in α-MEM medium (Thermo Fisher Scientific) containing 10% FBS , glutamine , and Penicillin–Streptomycin (Thermo Fisher Scientific) in the presence of M-CSF  with or without human TGFβ1  for 3 days to induce macrophages with or without TGFβ priming, respectively. The cells were then washed with neat α-MEM medium to remove TGFβ, and further cultured with TNFα (40 ng/ml) and M-CSF (20 ng/ml) in the α-MEM medium for different times indicated in figure legends.For human cell cultures, de-identified blood buffy coats (blood leukocyte preparations) were purchased from the New York Blood Center using a protocol approved by the Hospital for Special Surgery (HSS) Institutional Review Board. The blood buffy coats were anonymous without any identifiable private information. As per Human Subjects Research in PHS SF424 (R&R) Application Guide, studies using purchased de-identified blood samples do not constitute human subject research; informed consent was not obtained at HSS. PBMCs from the buffy coats were isolated by density gradient centrifugation using Ficoll , and CD14(+) cells were purified from fresh PBMCs using anti-CD14 magnetic beads  according to the manufacturer’s instructions. Human CD14(+) monocytes were cultured at a density of 15.6 × 104/cm2, and cultured in α-MEM medium with 10% FBS, 1% glutamine and the condition medium for overnight. Except where stated, the cells were then treated without or with optimized concentrations of TNFα (40 ng/ml) in the presence of the condition medium for times indicated in the figure legends. Culture media were exchanged every 3 days.For mouse cell cultures, mouse bone marrow cells were harvested from the tibiae and femora of the age- and gender-matched mutant and control mice and cultured for 3 days in α-MEM medium containing 10% fetal bovine serum (FBS), glutamine , Penicillin–Streptomycin (Thermo Fisher Scientific) and CMG14–12 supernatant  with or without mouse TGFβ1 . The attached BMMs were scraped, seeded at a density of 4.5 × 10TRAP staining was performed with an acid phosphatase leukocyte diagnostic kit (Sigma-Aldrich) in accordance with the manufacturer’s instructions. TRAP-positive cells show red staining.4/cm2 in Osteo Assay Surface Plate and cultured in the presence of M-CSF with or without TGFβ for 3 days, then TGFβ was washed away, and the cells were further cultured with TNF (40 ng/ml) for 10 days. Cells were then removed twice with 10% bleach solution for 5 min at room temperature (RT), followed by washing with distilled water. The minerals were stained with Von Kossa to visualize the formation of resorptive pits. The resorptive area was analyzed using ImageJ .The mineral resorption activity of osteoclasts was examined using 96-well Corning Osteo Assay Surface Plates (Sigma-Aldrich). Human CD14(+) monocytes were seeded at a density of 15.6 × 10S. cerevisiae) BioParticles™, Alexa Fluor™ 488 conjugate (Thermo Fisher Scientific Z23373). Zymosan was incubated in cell cultures at a final concentration of 2.5 µg/ml for 20 min at 37 °C. The cells were fixed with 4% PFA for 30 min at RT, permeabilized with 0.5% TritonX-100/PBS for 5 min at RT, and then stained with DAPI (1:1000) and Alexa Fluor® 555 Phalloidin . Images were obtained by ZEISS Axio Observer 7 (Zeiss) and analyzed by ImageJ.Phagocytosis assay was performed by using Zymosan A (MYBL2 and non-targeting control LNAs were from Qiagen and were transfected into human CD14(+) monocytes derived macrophages at concentrations of 40 nM using TransIT-TKO transfection reagent (Mirus) in accordance with the manufacturer’s instructions.Antisense inhibition using locked nucleic acid (LNA) technology from Qiagen was applied to silence gene expression in vitro. LNA oligonucleotides specifically targeting 76. mRNA amounts were normalized relative to glyceraldehyde-3-phosphate dehydrogenase (GAPDH) mRNA. The primers for real-time PCR were as follows: Nfatc1: 5′-CCCGTCACATTCTGGTCCAT-3′ and 5′-CAAGTAACCGTGTAGCTCCACAA-3′; Prdm1: 5′-TTCTTGTGTGGTATTGTCGGGACTT-3′ and 5′-TTGGGGACACTCTTTGGGTAGAGTT-3′; Acp5: 5′-ACGGCTACTTGCGGTTTC-3′ and 5′-TCCTTGGGAGGCTGGTC-3′; Ctsk: 5′-AAGATATTGGTGGCTTTGG-3′ and 5′-ATCGCTGCGTCCCTCT-3′; Itgb3: 5′-CCGGGGGACTTAATGAGACCACTT-3′ and 5′-ACGCCCCAAATCCCACCCATACA-3′; Dcstamp: 5′-TTTGCCGCTGTGGACTATCTGC-3′ and 5′-AGACGTGGTTTAGGAATGCAGCTC-3′; Fos: 5′-AGACCAGAGCGCCCCATCCTTACG-3′ and 5′-GCTCTGCGCTCTGCCTCCTGACA-3′; Mx1: 5′-GGCAGACACCACATACAACC-3′ and 5′-CCTCAGGCTAGATGGCAAG-3′; Ifit1: 5′-CTCCACTTTCAGAGCCTTCG-3′ and 5′-TGCTGAGATGGACTGTGAGG-3′; Ifit2: 5′-AAATGTCATGGGTACTGGAGTT-3′ and 5′-ATGGCAATTATCAAGTTTGTGG-3′; Gapdh: 5′-ATCAAGAAGGTGGTGAAGCA-3′ and 5′-AGACAACCTGGTCCTCAGTGT-3′; NFATC1: 5′-AAAGACGCAGAAACGACG-3′ and 5′-TCTCACTAACGGGACATCAC-3′; CALCR: 5′-CTGAAGCTTGAGCGCCTGAGTC-3′ and 5′-TGGGGTTGGGTGATTTAGAAGAAG-3′; ITGB3: 5′-GGAAGAACGCGCCAGAGCAAAATG-3′ and 5′-CCCCAAATCCCTCCCCACAAATAC-3′; FOS: 5′-GCAAGGTGGAACAGGAGACA-3′ and 5′-CAGATCAAGGGAAGCCACAG-3′; ACP5: 5′-TGGCTTTGCCTATGTGGA-3′ and 5′-CCTGGTCTTAAAGAGGGACTT-3′; CTSK: 5′-CTCTTCCATTTCTTCCACGAT-3′ and 5′-ACACCAACTCCCTTCCAAAG-3′; IL1B: 5′-TTCGACACATGGGATAACGAGG-3′ and 5′-TTTTTGCTGTGAGTCCCGGAG-3′; IL6: 5′-TAATGGGCATTCCTTCTTCT-3′ and 5′-TGTCCTAACGCTCATACTTTT-3′; IRF1: 5′-CAAATCCCGGGGCTCATCTG-3′ and 5′-CTGGCTCCTTTTCCCCTGCTTTCT-3′; IRF8: 5′-TGCGCTCCAAACTCATTCTC-3′ and 5′-TGGAAACATCCGGAAGACCTG-3′; IFIT1: 5′-TTCGGAGAAAGGCATTAGA-3′ and 5′-TCCAGGGCTTCATTCATAT-3′; IFIT2: 5′-CGCAGTGCAGCCAAGTTTTATC-3′ and 5′-GCAGGTAGGCATTGTTTGGTAT-3′; MX1: 5′-AGCCACTGGACTGACGACTT-3′ and 5′-ACCACGGCTAACGGATAAG-3′; STAT1: 5′-CAGCTTGACTCAAAATTCCTGGA-3′ and 5′-TGAAGATTACGCTTGCTTTTCCT-3′; IFNB1: 5′-AGAAGCTCCTGTGGCAATTG-3′ and 5′- ACTGCTGCAGCTGCTTAATC-3′; CCL5: 5′-GAGGCTTCCCCTCACTATCC-3′ and 5′-CTCAAGTGATCCACCCACCT-3′; CXCL9: 5′-CTGTTCCTGCATCAGCACCAAC-3′ and 5′-TGAACTCCATTCTTCAGTGTAGCA-3′; CXCL10: 5′-ATTTGCTGCCTTATCTTTCTG-3′ and 5′-TCTCACCCTTCTTTTTCATTGTAG-3′; MYBL2: 5′-ACAGGTGGCTGAGAGTTTTG-3′ and 5′-TTCAGGTGCTTGGCAATCAG-3′; GAPDH: 5′-ATCAAGAAGGTGGTGAAGCA-3′ and 5′-GTCGCTGTTGAAGTCAGAGGA-3′.DNA-free RNA was extracted from cells using the RNeasy Mini Kit (Qiagen) with DNase treatment. Reverse transcription was performed using 1 μg of total RNA with random hexamers and MMLV-Reverse Transcriptase (Thermo Fisher Scientific) according to the manufacturer’s instructions. Real-time PCR was performed in triplicate with the QuantStudio 5 Real-time PCR system and Fast SYBR® Green Master Mix (Thermo Fisher Scientific) with 500 nM primersTotal cellular extracts were obtained using lysis buffer containing 150 mM Tris-HCl (pH 6.8), 6% SDS, 30% glycerol, and 0.03% Bromophenol Blue; 10% 2-ME was added immediately before harvesting cells. Cell lysates were fractionated on 7.5% SDS-PAGE, transferred to Immobilon-P membranes (Millipore), and incubated with specific antibodies. Western Lightning plus-ECL (PerkinElmer) was used for detection. NFATc1 antibody  was from BD Biosciences; Blimp1 , c-Fos , IRF8 , and p38α , B-Myb  antibodies were from Santa Cruz Biotechnology; IRF1  antibody was obtained from Cell Signaling Technology.Mouse serum IFNβ and culture medium IFNβ were measured by using VeriKine-HS Mouse Interferon Beta Serum ELISA Kit (PBL Assay Science) according to the manufacturer’s instruction.Cells  were washed with ice-cold PBS and lysed for 15 min on ice with lysis buffer  containing protease inhibitors (1× Complete™ Protease Inhibitor Cocktail (Roche) and 10 mM phenylmethylsulfonyl fluoride (PMSF)). Cell lysates were centrifuged at 14,000 rpm for 15 min at 4 °C. 5% supernatant was used as input. The leftover supernatant was incubated with anti-IRF8 antibody  overnight at 4 °C and then incubated with Dynabeads™ Protein G  for 4 hr at 4 °C for precipitation. The beads were washed five times with the lysis buffer and subjected to immunoblotting with anti-Ubiquitin antibody .77. Reads were counted by HTseq-count78 and edgeR79 was used to estimate the transcript abundances as counts per million (CPM) values and calculate adjusted p value  and log2 fold-change (Log2FC). Genes with low expression levels (<1 CPM) in all conditions were filtered from downstream analyses. Benjamini-Hochberg false discovery rate (FDR) procedure was used to correct for multiple testing. Genes with adjusted p value <0.05 and fold-change of at least 1.5 were identified as DEG between conditions using the edgeR analysis of three RNA-seq biological replicates from different donors. Fastq files of the RNA-seq data using cultured primary human macrophages stimulated with RANKL for two days to induce osteoclastogenesis were extracted from GSE17154280. The reads were aligned to the human genome (GRCh38) using HISAT2 with default parameters. HTseq-count was subsequently used to count reads and then edgeR was used to estimate the transcript abundances as CPM (counts per million) values and calculate adj.p value and Log2FC. Genes with low expression levels (<1 CPM) in all conditions were filtered from downstream analyses. Genes with adjusted p value <0.001 and log2(fold-change) of at least 2 were identified as DEGs between RANKL and TGFβ priming/TNF conditions in Fig. 81. Gene Set Enrichment Analysis  input with the DEGs was performed according to the program’s instructions82. p and FDR values were calculated following the program’s instructions.Total RNA was extracted from cultured primary human macrophages using RNeasy Mini Kit (QIAGEN) following the manufacturer’s instructions. NEBNext Ultra II RNA Library Prep Kit for Illumina (NEB) was used to purify poly-A + transcripts and generate libraries with multiplexed barcode adaptors following the manufacturer’s instructions. All samples passed quality control analysis using a Bioanalyzer 2100 (Agilent). High-throughput sequencing  was performed using the Illumina Hiseq 4000 in the Weill Cornell Medicine Genomics Resources Core Facility with a sequencing depth between 30 to 50 million reads per sample. RNA-seq reads were aligned to the human genome (GRCh38) using HISAT2 with default parameters83. In all, 50,000 cells were collected and washed with cold ATAC-Resuspension Buffer (RSB) containing 0.1% NP-40, 0.1% Tween-20, and 0.01% Digitonin. Cells were lysed with cold ATAC-RSB containing 0.1% Tween-20. Pelleted nuclei were incubated with transposition mix (25 µl 2× TD buffer (Illumina), 2.5 µl transposase (Illumina), 16.5 µl PBS, 0.5 µl 1% digitonin, 0.5 µl 10% Tween-20, 5 µl nuclease-free water) for 30 min at 37 °C in a thermomixer at 1000 rpm. Transposed DNA was purified using DNA Clean & Concentrator (Zymo Reserach). We amplified library fragments using previously published barcoded primers84, with the following PCR conditions: 72 °C for 5 min; 98 °C for 30 s; and thermocycling at 98 °C for 10 s, 63 °C for 30 s, and 72 °C for 1 min for a total of 10–13 cycles. The libraries were purified using DNA Clean & Concentrator. Library quality and quantification were assessed with an Agilent Bioanalyzer at the Weill Cornell Medicine Genomics Resources Core Facility. Barcoded sample libraries were pooled for a final concentration of 4 nM. Sequencing was performed on Illumina Hiseq4000  at the Weill Cornell Genomics Resources Core Facility. Sequenced reads were aligned to reference the human genome (GRCh38) using Bowtie2 with the default parameter. The read depth was 80 to 90 million reads for each sample. The total number of mapped reads in each sample was normalized to one million mapped reads. Peak calling was performed using MACS285 with a q value cutoff of 0.01. Differential accessibility analysis of peaks was performed with Diffbind86. Differentially accessible peaks were defined as false discovery rate (FDR) < 0.01 and fold-change of at least 2. The peaks were assigned to each gene locus, including 20 kb upstream of the transcription start site, gene body and 5 kb downstream of transcription termination site. Volcano plot was generated by ggplot2 package in R. For integrative analysis of RNA-seq and ATAC-seq, DEGs from RNA-seq or ATAC-peaks-associated genes were combined from all time points in priming or non-priming condition. The overlapped genes between DEGs and ATAC-peaks-associated genes in priming or non-priming condition were used for downstream analyses in Fig. 87, normalized using the Counts Per Million mapped reads (CPM) method, and then the peaks were visualized in Integrative Genomics Viewer (IGV)79. Tag density was generated by using annotatePeaks.pl in HOMER package88. Peak density heatmaps and peak profiles were generated by using deeptools87. Boxplots were generated by Prism 8 (GraphPad).ATAC-seq was performed according to the Omni-ATAC protocol89. In all, 500,000 cells were used per condition. Cells were washed by wash buffer , then mixed with Concanavalin A beads and permeabilized with Cell Permeabilization Buffer (wash buffer containing 0.01% Digitonin). The cells were then incubated with the primary antibodies  for 16 hr at 4 °C, followed by incubation with protein A-MNase  for 1 hr at 4 °C. MNase was activated with CaCl2 for 2 hr at 4 °C. After adding stop buffer , samples were incubated for 30 min at 37 °C to release chromatin from cells. Fragmented chromatins were collected and purified with DNA Clean & Concentrator (Zymo Research). Library preparation was performed using NEBNext® Ultra™ II DNA Library Prep Kit according to the manufacturer’s instructions. Sequencing was performed on the Illumina NextSeq 500 . The read depth was 8 to 10 million reads for each condition. Then, sequenced reads were aligned to reference the human genome (GRCh38) using Bowtie2 with the parameter,–end-to-end–very-sensitive–no-mixed–dovetail–no-discordant–phred33 -I 10 -X 700. Peak calling was performed using MACS285 with a q value cutoff of 0.01. Tag density was generated by using annotatePeaks.pl in HOMER package88. Peak density heatmaps and peak profiles were generated by using deeptools87. For visualizing the peaks of histone modifications, bigwig files were created from bam files with deeptools, normalized using the CPM method, and then the peaks were visualized in IGV79. Boxplots were generated by Prism 8 (GraphPad).Cut & Run-seq was performed according to a published protocolfindMotifsGenome.pl in HOMER package, on ±300 bp centered on the ATAC-seq peak. Peak sequences were compared to random genomic fragments of the same size and normalized G + C content to identify motifs enriched in the targeted sequences.For de novo motif analysis, finding enriched motifs of transcription factors was performed with 90. Chromatin accessibility is displayed relative to total input. Primers are listed in supplementary table 1,000,000 cells were used per condition. Cells were treated with 1% formaldehyde for 5 min to crosslink chromatin. Crosslink was quenched by the addition of 0.125 M glycine for 5 min. The cells were washed with cold phosphate-buffered saline and collected. Fixed cells were lysed using buffer LB1  for 10 min. Pelleted nuclei were resuspended in buffer LB2  and incubated for 10 min on ice. The nuclei were pelleted and lysed in buffer LB3 . A total of 10% of nuclear lysates were saved as input. Chromatin was sheared using a Bioruptor Pico device (Diagenode). Free chromosomal DNAs were extracted from nuclear lysates by phenol-chloroform and then de-crosslinked for qPCR analysis using specific primers2O2 in MetOH for 20 min. After washing sections with PBS, sections were treated with 0.1% trypsin for 30 min at 37 °C. Then, sections were blocked with MOM blocking reagents (Vector Laboratories). Subsequently, sections were incubated with anti-B-Myb antibody  or normal mouse IgG  at 4 °C overnight followed with biotinylated anti-mouse IgG antibody  at room temperature for 1 hr. The sections were then incubated with VECTASTAIN ABC Reagent (Vector Laboratories) at room temperature for 30 min, and subsequently incubated with diaminobenzidine . Sections were then counter-stained with hematoxylin for nuclei staining and were mounted with cover slip using Clear-Mount (Electron Microscopy Sciences). IgG was used as a negative control for B-Myb staining. Nuclei stained with hematoxylin show blue.Calvarial bones from 12-week-old-male WT mice administrated with TNF (75ug/kg) via calvarial periosteum daily for 5 days were collected and immediately fixed in 4% paraformaldehyde solution overnight. Bones were decalcified with 0.5 M EDTA (pH 7.5) at 4 °C for 2 days. All samples were embedded in paraffin and sliced into 5-um-thick sections. Sections were deparaffinized and hydrated. Endogenous peroxidase activity of the tissues was quenched by incubating the sections with 3% H60. We analyzed the microarray data using the affy and limma package in R92. Genes with adjusted p value <0.05 and FC > 1.1 were identified as DEGs between RA and SLE. Integrated pathway analysis was performed using KEGG and Wikipathways in IMPaLA81 with gene ratio and p value. Combined score was calculated by Enrichr. The dot plots of enriched pathways and volcano plot were generated by ggplot2 package in R. Heatmaps were generated by the pheatmap package in R. Gene Set Enrichment Analysis  input with the DEGs was performed according to the program’s instructions. p and FDR values were calculated following the program’s instructions.Microarray raw data were extracted from GSE110169t test or Welch’s t test was applied when there were only two groups of samples. In the case of more than two groups of samples, one-way analysis of variance (ANOVA) was used with one condition, and two-way ANOVA was used with more than two conditions. ANOVA analysis was followed by post hoc Bonferroni’s correction for multiple comparisons. p < 0.05 was taken as statistically significant. Data are presented as the mean ± SD or ± SEM as indicated in the figure legends.Statistical analysis was performed using Graphpad Prism® software. Two-tailed Student’s Supplementary informationPeer Review File


None of the conventional echocardiographic parameters alone predict increased NTproBNP level and symptoms, making diagnosis of heart failure with preserved ejection fraction (HFpEF) very difficult in some cases, in resting condition. We evaluated LA functions by 2D speckle tracking echocardiography (STE) on top of conventional parameters in HFpEF and preHF patients with diastolic dysfunction (DD), in order to establish the added value of the LA deformation parameters in the diagnosis of HFpEF.We prospectively enrolled 125 patients, 88 with HFpEF (68±9 yrs), and 37 asymptomatic with similar risk factors with DD (preHF) (61±8 yrs). We evaluated them by NTproBNP, conventional DD parameters, and STE. Global longitudinal strain (GS) was added. LA reservoir (R), conduit (C), and pump function (CT) were assessed both by volumetric and STE. 2 reservoir strain (S) derived indices were also measured, stiffness (SI) and distensibility index (DI).2 = 0.506, p<0.001). Moreover, SR_CT, DI, and sPAP registered significant correlation with NTproBNP level.LA R and CT functions were significantly reduced in HFpEF compared to preHF group , whereas conduit was similarly in both groups. SI was increased, whereas DI was reduced in HFpEF group (p<0.001). By adding LA strain analysis, from all echocardiographic parameters, SR_CT<-1.66/s and DI<0.57  demonstrated the highest accuracy to identify HFpEF diagnosis. However, by multivariate logistic regression, the model that best identifies HFpEF included only SR_CT, GS and sPAP (RBy adding LA functional analysis, we might improve the HFpEF diagnosis accuracy, compared to present guidelines. LA pump function is the only one able to differentiates preHF from HFpEF patients at rest. A value of SR_CT < -1.66/s outperformed conventional parameters from the scoring system, reservoir strain, and LA overload indices in HFpEF diagnosis. We suggest that LA function by STE could be incorporated in the current protocol for HFpEF diagnosis at rest as a major functional criterion, in order to improve diagnostic algorithm, and also in the follow-up of patients with risk factors and DD, as a prognostic marker. Future studies are needed to validate our findings. Heart failure (HF) with preserved ejection fraction (HFpEF) has a growing prevalence, accounting for approximately 50% of patients with clinical HF syndrome –3. HowevSince 2D echocardiography (2DE) in resting conditions is the most important piece in the diagnosis of HFpEF, we need to better understand and define the transition from asymptomatic DD stage (pre-HFpEF) to symptomatic stage (HFpEF), in terms of echocardiographic parameters , 10, 11.In the earlier stage, LV filling pressure (LVFP) is normal at rest, but markedly increases during exercise, while in an advanced stage it increases continuously even at rest . ConsequPrevious studies reported preclinical atrial dysfunction, characterized by reduced reservoir and conduit function, with preserved contractile function in stage A of American College of Cardiology/American Heart Association (ACC/AHA) HF stages, prior to overt LA enlargement , 20. ProTherefore, we conducted this study to evaluate the added value of the LA functions by STE in diagnosis of HFpEF, on top of conventional parameters used in HFpEF diagnosis.We prospectively enrolled consecutive ambulatory patients with cardiovascular risk factors, signs and symptoms of HF, from January 2018 to January 2020. All patients were referred to our dedicated excellence HF centre to establish if they have HFpEF, according to guidelines . In ordeInclusion criteria for HFpEF group were: age > 18 years, sinus rhythm, stable patient with clinical, biological, and echocardiographic criteria suggestive for HFpEF, according to 2016 guidelines , informeDemographic and clinical data were collected before echo protocol . Treatment with all potentially cardiovascular active drugs was recorded on enrolment. After complete evaluation of the patients, the HFA-PEFF score was calculated, according to the current consensus .Conventional echocardiography was performed using a Vivid E9 echocardiographic ultrasound system  with a 3.5 MHz transducer. The electrocardiographic tracing was adjusted to show a well-defined P wave. Standard images were acquired and digitally stored for offline analysis using a vendor specific software Echo PAC PC, version BT12. Echocardiographic protocol included three apical views  optimized for LV, followed by dedicated apical four-chamber and two-chamber views for LA, avoiding foreshortening of the LV and LA, during acquisition. For each view, three consecutive heart cycles were recorded with a frame rate ranging between 50 and 80 frames/sec. A good quality electrocardiogram (ECG) trace with well visible P was recorded. All images were digitally stored and exported to a separate workstation for offline analysis. The operators performing the echocardiographic analysis were blinded to the patient’s clinical and lab details. The quantification of the cardiac chamber size and function was performed in agreement with the current guidelines [idelines , 24. LV idelines .LAVi max was calculated using the biplane disk summation technique and indexed to BSA, as a mean from the apical 4- and 2-chamber views . TransmiLV global longitudinal strain (GS) was measured from apical 2D views , by manually tracing the endocardial border of the LV at the end of systole, at the smallest LV chamber size. GS was calculated as the average strain values of all 18 LV segments, according to the guidelines [idelines .LA analysis was performed by two experienced operators, experts in STE analysis (RCR and SMB). LA volumetric assessment was done from 4C and 2C dedicated views, and reported as a mean value and indexed by BSA , as follLA maximal volume (LAVi max)—volume at the LV end-systole, before the mitral valve opening.LA pre-A volume (LAVi pre-A)—volume before the onset of the P-wave on the ECG tracing.LA minimal volume (LAVi min)—volume at the LV end-diastole, after mitral valve closure.LA phasic functions were generated by using published formulas, based on LA volumes [ volumes :Reservoir function:Conduit function: LA passive EF = (LAV max—LAV preA)/LAV max x 100Pump function: LA active EF = (LAV preA–LAV min)/LAV preA x100A detailed LA longitudinal deformation evaluation by 2DSTE was published previously by our echocardiography lab , 27. In LA reservoir function: LA reservoir strain (S_R) deformation between MVC to MVO , and LA reservoir strain rate (SR_R) during systole, defined as peak positive strain rate from de strain rate curve (te curve .LA conduit function: LA conduit strain (S_CD) as peak positive strain  as peak negative strain (e strain  and strae strain .LA overload indices. We defined two non-invasive indices as markers of LA overload:stiffness index (SI), estimated by the ratio between E/E’ ratio and S_R [ and S_R , as a madistensibility index (DI), estimated by the ratio between S_R and LAVi max, as a marker of volume overload, representing the amount of deformation for any volume change of LA, knowing that the excursion is lower for an already dilated LA [lated LA . We alsoIntra- and inter-observer variability from our echo lab for all conventional echocardiographic and TDI parameters, and also for STE parameters were already reported elsewhere , 31–33. U Test. A p value of <0.05 was considered significant. Categorical variables were expressed as percentages and compared with Chi-square test. Correlation between continuous variables was performed using Pearson’s or Spearman’s correlation coefficient as appropriate. Multivariate logistic regression was used to identify predictors of HFpEF and to calculate the corresponding odds ratios. The receiver operating characteristic (ROC) curve was used to identify prediction of HFpEF diagnosis for each echocardiographic parameter and to determine cut-off values. Sensitivity (Se) and specificity (Sp) were calculated. ICCs and their 95% confident intervals were calculated usingSPSS statistical package, based on an absolute-agreement, 2-way mixed-effects model. Statistical analysis was performed using SPSS version 21 .All continuous variables were assessed for the normal distribution by Kolmogorov–Smirnov test. Normally distributed continuous variables were reported as mean ± SD and compared for statistical significance with Independent Samples T Test. Non-normally distributed continuous variables were presented as the median and interquartile range (IQR) and compared using the Mann Whitney We screened 100 symptomatic patients potentially having HFpEF, and 50 asymptomatic patients with cardiovascular risk factors and DD (preHF). We enrolled prospectively 125 patients, 88 patients with stable HFpEF (HFpEF group), and 37 asymptomatic patients, with cardiovascular risk factors and LVDD (preHF group). 12 patients were excluded from HFpEF group . Patients excluded based on the echocardiographic evaluation were because of the inability to obtain optimal images, either for LV or for LA analysis, an essential part of the STE evaluation.We excluded 13 patients from asymptomatic patients with RF because they do not have DD, according to 2016 guideline. Patients’ characteristics are summarized in LV dimensions, volumes, LVMI, and LVEF were similar in HFpEF and DD groups, but GS was impaired in HFpEF as we expected . HFpEF gLAVi max, preA, and min were significantly higher in HFpEF by comparison to preHF group (p<0.001). Reservoir function was significantly impaired in HFpEF compared to preHF . LA condAll parameters correlated to NT-proBNP are reported in Since from all LA deformation parameters the higher correlation of NT-proBNP was with SR_CT, we evaluated separately the additional correlations of this parameter. The best correlations were with DI, LAVi min and max, and S_CT . LAVi miFrom all parameters included in the current HFpEF algorithm (6), with their cut-off values specified in the guidelines, sPAP >35 mmHg demonstrated the highest accuracy for HFpEF diagnosis, although with limited sensibility and specificity , Fig 3. 2, able to detect HFpEF diagnosis with higher specificity by comparison to the conventional cut-off value (>34 ml/m2) (Sp = 75.7% vs 16.2%) (p<0.001)  , Fig 3. mely low . Howevermely low . GS <-20mely low , Fig 3.From all strain derived parameters, DI and SR_CT were the best parameters able to identify HFpEF, with a cut-off value of <0.57 and < -1.66 /s, respectively (p<0.001). S_R<25% demonstrated lower accuracy for HFpEF diagnosis, by comparison with these two parameters , Table 6When compared the AUC for all newly defined cut-off values, DI and SR_CT were superior to all echocardiographic parameters used for diagnosing HFpEF in scoring system .1/s, sPAP >33.5 mmHg, and GS< - 20%   . Neitherp<0,001) .Using parameters with the highest AUC, such as SR_CT, sPAP, GS, DI, LAVi min, with their cutt-off values, we create a combined model (1point for each parameters), in order to see if a combination of parameters might identify HFpEF better than conventional and new parameters alone. A combined index of 3 from 5 parameters identified better HFpEF than SR_CT, DI, sPAP, GS, and than guidelines combined index  . By usinIntra- and inter-observer reproducibility were good to excelent for all LA strain and strain rate parameters .In our prospective study we enrolled 125 patients, 88 with stable HFpEF, and 37 asymptomatic LVDD patients without HF, analysed by conventional 2DE and STE, to establish the added value of the LA function in the diagnosis of HFpEF, on top of conventional parameters from the scoring system.To the best of our knowledge, this is the first study that showed that LA pump function and distensibility index, evaluated by STE, are able to detect better HFpEF than any other structural or functional parameter from the 2019 scoring system. A value of SR_CT < -1.66/s and DI <0.57 outperformed conventional parameters, reservoir strain, and GS in HFpEF diagnosis. However, when clinical parameters, standard echocardiographic parameters, LA strain parameters, and GS were included in a multivariate logistic regression, the model that best predicted HFpEF, included only SR_CT, GS, and sPAP, SR_CT having the highest OR in this model. Our study also confirms significant impairment of the reservoir function, with similar conduit function in HFpEF compared to preHF group. Of note all LA strain and strain rate parameters had very good reproducibility in our study, similar to the most recent publications , 34.By comparison to our study, previous studies have shown contradictory data on potential predictors of HFpEF. However, they compared patients with cardiac vs. non-cardiac dyspnoea, regardless of the presence or absence of DD, with other possible confounding factors in the control group –37. SancOn the contrary, Reddy et al. , found tCompared to these two studies, in which in the control group GS was reduced, in our study, preHF patients had preserved GS, despite increased LAVi max and impaired LA phasic function. In this light, our findings confirm the hypothesis of an earlier LA failure in preHF patients .Telles et al. demonstrated that HFpEF patients compared to patients with non-cardiac dyspnea (NCD), have decreased S_R (24.3±9.6 vs. 36.7±8.4%), SR_R (0.9±0.3 vs. 1.2±0.3/s), S_CT (-11.5±3.2 vs.-17.0±3.4%) and SR_CT , with similar conduit function . Our datIn another study, LVH magnitude and atrial dilation/failure provided the best separation between preHF and HFpEF group in hypertensive patients. Their results suggested that atrial function may be a key compensatory mechanism countering evolution of HFpEF, and they highlight the diagnostic utility of atrial failure as a marker of the disease, too .2 can predict HFpEF with significant increased specificity when compared to the guideline value (>34 ml/m2). However, when LAVimax was included in the regression analysis, it did not predict HFpEF. Moreover, we found a better correlation of LAVi min, with SR_CT compared to LAVimax. Previous studies suggested that LAVimin, is a marker of atrial afterload and is more closely related to LVFP, being strongly associated with NT-proBNP than LAVimax [In our study, all LA volumes were significantly increased in HFpEF patients compared to preHF patients. LAVimax >43 ml/m LAVimax –42. Russ LAVimax  found thIn our study, sPAP and E/E`ratio, considered old surrogates for increased LVFP, demonstrated reduced diagnostic performance for HFpEF diagnosis, when compared to SR_CT and LAVimax. However, when included in a multivariate logistic regression, together with all the classical diastolic and strain parameters, only sPAP was incorporated in the model that best identified HFpEF, with p<0.001. Furthermore, we suggested new cut-off values to increase sensitivity for HFpEF diagnosis, for both sPAP (>33.5 mmHg) and E/E`ratio (>12), by comparison with values suggested by the scoring system.E/E′ has been the main non-invasive surrogate parameter to predict LVFP. However, many studies demonstrated that E/E′ ratio has a weak correlation with pulmonary capillary wedge pressure (PCWP) in patients with HFpEF –48. Over2 = 0.60, p < 0.001). Relationship was even stronger in patients with enlarged LA [Lindqvist et al. , in a rearged LA .As 2019 scoring system suggested a multiparametric approach for HFpEF diagnosis, we also evaluated a combined parameter, composed by SR_CT, DI, sPAP, GS, LAVi min, these parameters exceeding the accuracy of conventional parameters. A combination of three from five parameters, was able to predict with the highest accuracy HFpEF diagnosis, by comparison with each parameter alone and by comparison with combined index from the scoring system. In these light, we suggest that these parameters should be validated in larger studies, in order to incorporate them in the scoring system, as functional and structural criteria. We also suggest that functional criteria might have higher contribution than structural ones, in a future alghorithm for HFpEF diagnosis, since than all functional parameters registered a higher accuracy than structural parameters. These parameters could be of particular interest to follow-up the preHFpEF patients to determine whether they progress to HFpEF.Several limitations of our study should be acknowledged. The main limitation was the lack of invasive hemodynamic data. However, invasive assessment in our patients was not clinically indicated. Also, exercise stress echo was not performed in our study because we selected patients with a clear diagnosis of HFpEF. Another limitation was that it was a single centre and single vendor experience (GE). However, our subjects were rigorously selected, representing a reasonable sample. Finally, in terms ofapplicability in clinical practice, it is hard to be confident with strain rate values, since changes in the range of subunit are hard to manage. Most of HFpEF patients were in NYHA functional class II, with grade I and II of LVDD. Hence, further studies, with larger numbers of patients in NYHA functional class III or IV and grade III of LVDD are needed to validate the findings from our study.Our study demonstrated that by adding LA functional analysis, we might improve the HFpEF diagnosis accuracy, compared to present scoring system. Booster pump function is the only function that differentiates preHF from HFpEF patients. A value of SR_CT < -1.66/s outperformed conventional parameters from the scoring system, reservoir strain, and LA overload indices in HFpEF prediction. We also confirmed that none of the conventional parameters predict alone HFpEF diagnosis, making differentiation between HFpEF and preHF by echocardiography in resting condition impossible. The value of diagnosing HFpEF without the need for stress echo and invasive hemodynamic data is important, since not all patients are able to perform it in real world scenario. We suggest that functional LA analysis by STE could be used to improve diagnostic algorithms of HFpEF, and in the follow-up of HFpEF patients. Further studies are needed to validate our findings in a large population evaluated for HFpEF diagnosis.S1 File(DOCX)Click here for additional data file.