The results from qRT-PCR and histopathology confirmed the toxicity level. Hence, this pharmacokinetic profile of the nanoformulation proved it has a unique synergistic hypolipidemic modality.A pharmaceutical formulation with favorable pharmacokinetic parameters is more likely to be efficacious and safe to overcome the failures of the drug resulting from lack of efficacy, poor bioavailability, and toxicity. In this view, we aimed to evaluate the pharmacokinetic functionalities and safety margin of an optimized CS-SS nanoformulation (F40) by in vitro/in vivo methods. The everted sac technique was used to evaluate the improved absorption of a simvastatin formulation. In vitro protein binding in bovine serum and mice plasma was performed. The formulation’s liver and intestinal CYP3A4 activity and metabolic pathways were investigated by the qRT-PCR technique. The excretion of cholesterol and bile acids was measured to demonstrate the formulation’s cholesterol depletion effect. Safety margins were determined by histopathology as well as fiber typing studies. In vitro protein binding results revealed the existence of a high percentage of free drugs  compared to the standard formulation. The controlled metabolism in the liver was demonstrated from CYP3A4 activity. The formulation showed enhanced PK parameters in rabbits such as a lower C This study describes various steps tangled for polymeric nanoparticles to reach systemic circulation after oral administration. Nanoparticles that permeate the gut wall can enter the systemic circulation and be distributed to target organs if they do not bind to plasma proteins. Still, plasma protein binding is vital for other pharmacokinetic parameters . Hence, It Is prudent to gain further understanding of in vitro/in vivo correlation and PK/PD reports at an early stage. Some marketed drugs approved by the FDA such as atorvastatin and simvastatin, with an absolute bioavailability of 14% and 5%, respectively ,3, have Hence, this research study aimed to focus on third-generation controlled drug delivery systems based on rectifying both formulation and biological barriers. In this study, nanotechnology and a pH-sensitive smart polymer were utilized to overcome the formulation barriers, which in turn were proposed to overcome the biological barriers for better pharmacological activities. Therefore, the optimized nanoformulation F40  is hypothesized to have improved physicochemical properties than conventional drugs and current research mainly focused on evaluating this optimized nanoformulation F40 in terms of its pharmacokinetic profile (ADME/PK) and its safety margins.The experiment performed by an everted intestinal sac method showed that absorption of simvastatin from formulation F40 was markedly elevated as compared to standard simvastatin as shown in The results of the BSA binding test showed that at a given concentration, the average protein binding of nanoformulation F40 was 77.02 ± 4.58% and that of pure simvastatin was 95.00 ± 3.1%. In addition, pure simvastatin resulted in a higher percentage of protein binding compared to nanoformulation F40 coated with chitosan. The free drug present in the supernatant of nanoformulation F40 was found to be 22.31 ± 3.1%, which was higher than pure simvastatin. In the experiment involving human plasma, the free drug available for nanoformulation F40 was found to be 18.20 ± 1.9% (for pure simvastatin 4.3 ± 1.0%), whereas, for mice plasma, it was found to be 16.9 ± 2.2% for formulation F40 and for pure simvastatin 4.0 ± 0.7%. As shown in The mRNA levels of CYP3A4 in the liver and intestines were measured using qRT-PCR to investigate whether there was any alteration in the pharmacokinetics of simvastatin in formulation F40. This was assumed by observing the modified metabolism of F40-treated mice in the liver as simvastatin is primarily eliminated in the liver via metabolism. Results showed that, as compared to the standard simvastatin-treated group, the formulation F40-treated group had less CYP3A4 expression in both the intestine and liver. Metabolism via the intestine was less as the expression of CYP3A4 mRNA was minor in the intestine as compared to the liver. The decreased expression of hepatic CYP3A4 mRNA (threefold) in the formulation F40-treated group as compared to the standard simvastatin group (fold expression—7.90) might be due to the size as it is a nanoformulation and formation of intermolecular bonding for the presence of novel excipients due to a controlled release of simvastatin.This study showed that fold expression for SREBP-2 was higher for standard simvastatin (5.02) than formulation F40 and transcription factor PPAR-γ fold expression (6.06) was greater for formulation F40 than for standard simvastatin as shown in Animals in all groups were healthy and active for up to ten weeks of study. At the end of this study, some of the group II animals were inactive and group III animals were observed to have some behavioral changes, which were suggested due to certain muscular dystrophy produced by standard simvastatin. All groups gained weight during the experimental period except the formulation F40-treated group as depicted in As shown in This study showed that only trace amounts of total cholesterol and bile acid were excreted in feces of control, high-fat diet, and standard simvastatin-treated animals during the whole experimental period, whereas certain amounts of fecal total cholesterol and bile acid were found in feces of formulation F40-treated animals.HPTLC revealed that cholesterol excretion in feces increased from 1.71 ± 0.28 to 2.89 ± 0.19 mg/day/animal in the formulation F40-treated group compared to other groups. These results were compared using HPLC peaks. The bile acid chenodeoxycholic acid increased from 0.19 ± 0.001 to 4.02 ± 0.21 mg/day/animal of fecal matter in the formulation F40 group compared to the control group as shown in The concentrations of simvastatin/simvastatin metabolite (18.98 ± 0.20 ng/mL/23.12 ± 1.3 ng/mL) were found to be higher in standard simvastatin-treated mice feces, as shown in The pharmacokinetic study conducted in rabbits was used to quantify SS and its active metabolite SSA. The mean plasma concentration profiles of SS and SSA as a function of time obtained after a 10 mg oral dose of both standard and test are shown in max F40  were significantly higher than for standard simvastatin . The Cmax was lower for formulation F40  than for standard simvastatin . The higher Tmax, AUC, and a lower Cmax indicate the sustained-release properties of formulation F40. Generally, the drug molecules are absorbed rapidly from GIT due to an improved dissolution rate by a reduced particle size, an increased surface area, and diffusional layer thickness (nanoparticle formation and intermolecular hydrogen bonding). The mucoadhesion and controlled delivery of the drug from formulation F40 were responsible for sustained release, leading to a low Cmax but a prolonged Tmax and AUC. Half-life and MRT were also higher for nanoformulation F40 than for standard drug  and KE and a lower Cl  for nanoformulation F40 , Cl, and MRT at p < 0.05. Relative bioavailability or bioequivalence is the most common measure for comparing the bioavailability of one formulation of the same drug to another. The mean responses such as the Cmax and the AUC were considered to determine relative bioavailability.Lower plasma levels of simvastatin and simvastatin acid were observed with formulation F40 than with standard drug after oral administration. The AUC of formulation F40  and the Tard drug . A highetion F40  than formax refers to the rate of bioavailability. The relative bioavailability of formulation F40 was 154%, and 209% compared to that of the pure drug simvastatin. There was only a little increase in the bioavailability of formulation F40. As simvastatin is a narrow therapeutic-indexed drug, an increase in drug concentration in the plasma might enhance toxicity. When the results of PD studies were compared with PK studies, a reduction in the TC level and an increase in relative bioavailability in the case of nanoformulation F40 were observed. Thus, the results obtained in the PK study are well supported by the PD studies, which showed the same hypolipidemic activity of nanoformulation F40 compared with standard drugs with reduced doses and negligible toxicity.The AUC refers to the extent of bioavailability, while the CIn the control/HFD group of mice, the incidence of fiber necrosis was not observed .The muscle fiber type of mammals is determined by the particular myosin heavy chain (MHC) isoform expressed. The limb muscles of the adult mice express one slow and three fast MHC isoforms . Most fiThis study showed that all the tested concentrations of formulation F40 neither showed hemolytic activity nor thrombus formation, making it a biocompatible systemic application. In addition to this, the group treated with SLS (positive control) showed 100% hemolysis marked by complete lysis of the red blood cells (RBCs). The PBS (negative control), drug, and nanoformulation F40 groups did not show any hemolysis or toxicity to the RBCs, revealing its possible biocompatibility. Hemolytic activity was further confirmed on a blood agar plate.Simvastatin is a medication with a significant problem of substantial first-pass metabolism, which results in a very low bioavailability of only 5%. The use of innovative medicine delivery techniques can boost this . PresentIn general, simvastatin is a P-gp inhibitor and due to this property, in the present study, the absorption of standard simvastatin was initially high, and it was decreased till the end of the experiment due to the efflux mechanism . HoweverThe metabolic pathway of nanoformulation F40 was observed to be increased more with the activity of PPAR-γ than the standard simvastatin group, which is contradictory to the previously observed downregulation of PPAR-γ expression in hypercholesterolemic animals . There wThe present study demonstrated a significant increase in the excretion of cholesterol (4-fold) and bile acids in feces. For the nanoformulation-treated group, the cholesterol concentration was found to be 0.49 ± 0.005 mg/day/animal, which was higher when compared to the standard-treated group . Nanoformulation F40 feeding has been reported to increase the fecal excretion of cholesterol, its non-polar derivatives and bile acids. The present study demonstrated two different mechanistic pathways for serum cholesterol reduction, the SREPB-2 pathway being followed by simvastatin and the PPAR-γ pathway being followed by chitosan. The higher efficacy of nanoformulation F40 in terms of fecal elimination of cholesterol and bile acids was due to the synergistic effect produced by simvastatin and chitosan. Chitosan is primarily responsible for the above effect as it upregulates fecal excretion upon hypercholesterolemia. Previous studies in animal models  as well In this study, using a low-molecular-weight, high-DD chitosan, nanoparticle size added value in high binding and excretion of lipids in feces. This study also indicated that chitosan lowered the plasma total cholesterol level by enhancement of the hepatic LDL receptor mRNA and chitosan in formulation F40 might have the potential to increase the excretion of fecal bile acids. The use of chitosan, PVA, and surfactant Tween 80 in the nanoformulation is responsible for the increased dissolution of simvastatin and influx against P-gp activity. The previous in vitro studies showed that the lactone ring in formulation F40 showed maximum stability in gastric pH, and no degradation of the lactone in 24 h was observed ,26.max and a higher AUC values could potentially reduce the incidence of such toxic events and could sustain the efficacy of SS at the same time. This nano-controlled drug delivery system resulted in a lower plasma concentration, but it provides a constant pharmacological availability of the drug which might reduce toxic side effects [However, in the present research, significantly higher MRT values of metabolite SSA from nanoformulation F40 in comparison to the standard drug are responsible for the prolonged residence of metabolite in rabbits. Thus, it might be expected that the enhanced residence would have a positive effect on the efficacy of the active metabolite. It appeared that nanoformulation F40 delivered SS in a more sustained fashion, providing smoother plasma concentration profiles and lower maximum plasma concentrations compared with those of standard simvastatin. Since increased peak concentrations of SS are related to the incidence of adverse events, the obtained smooth plasma concentration coupled with a lower C effects . MucoadhNanoformulation F40 was designed, formulated, and optimized in our laboratory and the results are published . Brieflyad libitum. The experiment was performed as per the reported method [Female albino mice (24–30 g) were used for this study and were obtained from King Institute, Chennai. The animals were kept in a controlled environment of 25 °C with a 12 h light/12 h dark cycle and all protocols and procedures used were approved by the IAEC PRIST University, Thanjavur (Project 1 Pon/Phar1/2015). The mice were fasted overnight before experimentation and had access to water d method ,29. BrieThe in vitro bovine serum albumin binding test was performed by the most popular method of equilibrium dialysis with the use of an activated 10–12 kDa molecular cut-off dialysis membrane . It was In vitro protein binding of nanoformulation F40 was calculated using the above equation . Mice plFor separation of m-RNA, the mice were anesthetized for collecting the blood and other organs were dissected and immediately frozen in liquid nitrogen.g for 15 min at 2–8 °C, which separated the mixture into three phases: a red organic phase (containing protein), an interphase (containing DNA), and a colorless upper aqueous phase (containing RNA). The upper aqueous phase was processed further and allowed to stand for 5–10 min at room temperature and centrifuged at 12,000× g for 10 min at 4 °C. The supernatant was carefully aspirated off the tube, leaving behind the precipitated RNA pellet on the sides of the tube, which was washed by adding a minimum of 1 mL of 75% ethanol per 1 mL of TRI reagent. The sample was then vortexed and then centrifuged at 7500× g for 5 min at 2–8 °C. The RNA pellet was air-dried and suspended in 20–30 μL of RNase-free water [For direct lysis of the cells, 1 mL of TRI reagent was added per 1000 mg of the tissue sample. The processed samples were centrifuged at 12,000× ee water .−ΔCt [qRT-PCR was used to measure mRNA levels of CYP3A4 in the liver and intestine by quantitative RT-PCR analysis using a cDNA input converted from 2 μg of total RNA. Primer sequences of mice mRNA for CYP3A4 are Forward 5′-CAGGAGGAAATTGATGCAGTTTT-3′; Reverse 5′ GTCAAGATACTCCATCTGTAGCACAGT-3′. After denaturing at 95 °C for 2 min, the amplification was obtained by 40 cycles of 95 °C for 5 s and 60 °C for 30 s. Melting curves were obtained to investigate the specificity of the PCR reaction. For normalization of the gene levels, β-actin (primer sequence—Forward 5′-CAGTCGGTTGGAGCGAGCAT-3′ Reverse 5′-GGACTTCCTGTAACAACGCATCT-3′) was used to correct minor variations in the input RNA amount or inefficiencies of the reverse transcription. The relative quantification (RQ) of each gene expression was calculated according to the comparative Ct method using the formula: RQ = 2−ΔCt .−ΔCt method [After extraction of RNA using TRI reagent as explained above, the mRNA expression for PPAR-γ, and SREPB2 was carried out in the liver using an RT-PCR kit from PROMEGA by using a standard protocol. The PCR reaction was performed in the thermal cycle with RT reaction at 40 °C for 30 min, initial PCR activation at 94 °C for 2 min, followed by 35 cycles of 94 °C (denaturation) for the 30 s, 58 °C  for 30 s, and 72 °C (extension) for 1 min. Finally, the reaction mixture was incubated at 72 °C for 10 min to extend any incomplete single strands. Optimal oligonucleotide primer pairs for RT-PCR were selected with the aid of the software Gene Runner. The primer sequence (5′ to 3′) for mice gene coding (+) strand was: PPARγ (Sequence ID: NM_001145366.1); Forward: 5′-GCCCTTTGGTGACTTTATGG-3′; Reverse: 5′CAGCAGGTTGTCTTGGATGT-3′; β-actin (Sequence ID: NM_031144); Forward: 5′-CACCCGCGAGTACAACCTTC-3′; Reverse: 5′-CCCATACCCACCATCACACC-3′; SREBP2; Forward primer 5′ to 3′ CCAAAGAAGGAGAGAGGCGG; Reverse primer 5′ to 3′ CGCCAGACTTGTGCATCTTG. Melt curve analyses were obtained with each series to confirm the specificity of the primers and products were amplified by heating the amplified products from 658 °C to 958 °C at 0.58 °C steps for 5 s. Relative quantification of mRNA expression was analyzed by the 2For the fecal matter evaluation, mice were individually housed in polypropylene cages at a controlled room temperature at 25 °C, under a 12 h light/12 h dark cycle and with free access to food and water. The mice were randomly divided into four experimental groups (n = 6). Group 1 was fed a standard laboratory diet (CD). Group 2 was fed a cholesterol-rich diet (HFD). Groups 3 and 4 received HFD with standard drug and formulation F40 (10 mg/kg). The duration of the treatment was 16 weeks. Each treatment group received a standard cholesterol diet daily orally in the morning throughout 16 weeks to induce hyperlipidemia except the control treatment group. A high-cholesterol diet was prepared by mixing cholesterol 2%, sodium cholate 1%, and coconut oil 2% with animal food. Throughout this study, various parameters were monitored and measured which includes bodyweight, food intake, fecal dry weight, total cholesterol concentration in feces , total bile acids in feces , simvastatin concentration in ng/mL, and simvastatin metabolite in ng/mL.3-H2SO4 reagent was added. The intensity of immense purple color formed was measured in a Shimadzu-UV spectrophotometer at 560 nm. The bile acids and total cholesterol were determined by an enzymatic method [Fecal total sterol and total fecal bile acids were extracted from dry feces and quantified enzymatically according to the procedures provided in the assay kit purchased from Sigma Aldrich. Neutral sterols (NSs) in feces were determined as previously described , with soc method . WorkingAUCt) was calculated by formula.lastC and eK are the last measurable concentration and the elimination rate constant, respectively [Albino rabbits of either sex weighing 1.5 to 3.0 kg were used for the estimation of pharmacokinetic parameters of nanoformulation F40 and standard simvastatin. The animal study protocol was approved by IAEC in presence of the CPCSEA nominee with Approval no. Project 1 Pon/Phar/2015 at PRIST University, Thanjavur. Pharmacokinetic studies were carried out based on a single dose complete cross-over method in twelve healthy albino rabbits. The rabbits were weighed and randomly divided into two groups, standard and test with six animals in each group. The data were acquired and calculated on Shimadzu controlled using a software analyst. The pharmacokinetic parameters for pure simvastatin and nanoparticles following oral administration were determined from plasma concentration data. The area under the concentration–time curve ectively . The eliThe animals were sacrificed as per CPCSEA protocol. The heart, kidney, liver, stomach, brain, and a range of muscle tissues were sampled for necropsy and histology. The muscle tissues sampled were biceps femoris, soleus, tibialis cranialis, vastus medialis from the left hind limb; biceps brachii from the left forelimb. Tissues were fixed in buffered 10% formalin, processed to wax blocks, and then sectioned and stained with hematoxylin and eosin for examination by light microscopy. During the histopathological examination under microscopy, the presence or absence of necrosis was observed for all four groups.In 95 mL of sterile nutrient agar placed in Petri dishes, 5.0 mL of human blood was added aseptically and allowed for solidification. Then, wells were cut into the agar plate using a corkscrew borer (8 mm diameter) and loaded with 50 μL (1 mg/mL) of samples. The plates were observed for hemolysis after overnight incubation at room temperature .p < 0.05.All the data were presented as the mean ± SD and analyzed by the statistical software package GraphPad Prism 5 version . The statistical analysis included a one-way analysis of variance (ANOVA) followed by the Tukey post hoc test. The difference between the two parameters was considered statistically significant for d, 378.90 ± 112.31 and 404 ± 134.98 for simvastatin and its acid in nanoformulation, respectively) was noticed. In addition, in plasma, controlled metabolism of simvastatin to simvastatin acid occurs. This all causes a higher Tmax of 10.00 ± 2.78 (simvastatin) and 14.56 ± 2.19 (simvastatin acid), and a lower Cmax. Drug elimination by biliary and feces was also altered due to increased drug transporter P-gp activity present in the intestine, liver, and kidney exerted by Tween-80. This causes decreases in the V area , clearance, and an increase in half-life (12.29 ± 4.57 for simvastatin and 16.87 ± 3.91 for simvastatin acid) on the whole representing a higher AUC and relative bioavailability. The reduced dose subsequently decreased muscle-related toxicity, which was confirmed by histopathological analysis. Finally, it can be concluded that this hypolipidemic action of the F40 CS-SS nanoformulation may be due to inhibition of the absorption of dietary cholesterol by the biliary secretion of cholesterol and cholesterol excretion in the feces caused by chitosan and its reduced production by the liver induced by simvastatin. Thus, the multifunctional properties of chitosan not only modify drug properties but also importantly synergize the hypolipidemic activity of simvastatin. Thus, the promising in vitro and In vivo results obtained in this research suggest pathways for future research.The present study represents an important contribution since it demonstrated a promising pharmacokinetic profile and ideal safety of nanoformulation F40 as a unique synergistic hypolipidemic modality. Pure simvastatin has less absorption due to dissolution-limited absorption, the P-gp efflux mechanism, gut/liver metabolism, being highly protein bound, and less elimination at half-life. Although its AUC is not particularly high, it produces toxicity in tissues through liver and muscle. In the present study, the use of a hydrophilic polymer in the nanoformulation improves the wettability of the drug and dissolution. Utilization of surfactant Tween 80 modulates the P-gp efflux mechanism, allowing penetration of more drug and a higher distribution volume. This increase in absorption and protective effect of chitosan decrease gut/liver metabolism. Therefore, a higher percentage of the drug is distributed in body fluids entering plasma. When a positive surface charge reduces protein binding, an increase in the volume of distribution (V


Heart failure with preserved ejection fraction (HFpEF) is emerging as a widespread disease with global socioeconomic impact. Patients with HFpEF show a dramatically increased morbidity and mortality, and, unfortunately, specific treatment options are limited. This is due to the various etiologies that promote HFpEF development. Indeed, cluster analyses with common HFpEF comorbidities revealed the existence of several HFpEF phenotypes. One especially frequent, yet underappreciated, comorbidity is sleep-disordered breathing (SDB), which is closely intertwined with the development and progression of the “obese HFpEF phenotype”. The following review article aims to provide an overview of the common HFpEF etiologies and phenotypes, especially in the context of SDB. As general HFpEF therapies are often not successful, patient- and phenotype-individualized therapeutic strategies are warranted. Therefore, for the “obese HFpEF phenotype”, a better understanding of the mechanistic parallels between both HFpEF and SDB is required, which may help to identify potential phenotype-individualized therapeutic strategies. Novel technologies like single-cell transcriptomics or CRISPR-Cas9 gene editing further broaden the groundwork for deeper insights into pathomechanisms and precision medicine. Heart failure with preserved ejection fraction (HFpEF) is a widespread disease with a prevalence of approximately 1% in developed countries and it is expected to rise to more than 5% among elderly populations  . HFpEF sThese figures and trends are concerning because, in contrast to patients that have heart failure with reduced ejection fraction (HFrEF), currently only one class of pharmacological drugs has been shown to reduce morbidity in patients with HFpEF . TherefoOne such comorbidity is sleep-disordered breathing (SDB), which affects up to 58% (or up to 80% in certain cohorts) of HFpEF patients ,10,11. SThe aim of this review article is to summarize the up-to-date evidence on HFpEF and SDB to provide an overview of the intricate relationship between both diseases, as well as to identify important gaps in the knowledge and research needs, which might ultimately lead to improved and patient-individualized therapeutic strategies for HFpEF patients with SDB.Left ventricular diastolic dysfunction, which is a precursor to HFpEF , is highPatients with HFpEF have a high rate of recurrent hospitalizations. After an episode of acute HF, the 30 day all-cause readmission rate is up to 21% and the HF-specific readmission rate up to 10%, thus reflecting the large burden of comorbidities in HFpEF patients  or EUR 1Based on ejection fraction (EF), patients with chronic HF are classified as having HFpEF (EF ≥ 50%), heart failure with mildly reduced ejection fraction , or HfrEF (EF < 40%) according to current European guidelines and position papers ,23,24.HFpEF is a clinical syndrome composed of many different etiologies, which complicates the aim of establishing a definition of clear diagnostic criteria . The curAlso, there are empirically derived scoring systems for the diagnosis of HFpEF. Similar to their guideline criteria, the HFA-PEFF algorithm   and the There are two main SDB sub-types that are diagnosed by poly(somno)graphy: central sleep apnea (CSA) and obstructive sleep apnea (OSA) . MechaniSDB is a widespread disease, and it currently affects about one billion individuals worldwide, as well as up to 40% in patients with cardiovascular disease ,33. In pEver since HFpEF was acknowledged as a distinct clinical syndrome, it has challenged cardiologists with respect to successfully finding definitive diagnostic criteria and offering effective treatment strategies. The difficulties and failures at this task most likely stem from the diversity of underlying etiologies, pathomechanisms, and relevant comorbidities, which defy a diagnostic or therapeutic “one-size-fits-all” approach .Similar to SDB, HFpEF is strongly associated with several frequent diseases and pathologies such as metabolic syndrome , pulmonary diseases , coronary artery disease, and chronic kidney disease ,34. A seSince the first attempt to phenotype HFpEF, many of the different approaches that utilize machine learning have identified distinct HFpEF phenotypes . Based oThe review by Peters et al. summarizes three main phenotypes: the “older, vascular aging phenotype”, the “metabolic, obese phenotype”, and the “relatively young, natriuretic peptide deficiency phenotype”  41]. It. It41]. A cluster analysis of the TOPCAT study cohort also revealed three different phenotypes . HoweverWhile there have been major advances in the treatment of HFrEF, pharmacological treatment options for HFpEF remain limited ,24. The A novel and emerging type of therapy, especially for the “obese HFpEF phenotype”, may be treatments with glucagon-like peptide-1 (GLP-1) receptor agonists. These drugs have been shown to effectively reduce body weight and blood glucose levels ,57. As oAs many common comorbidities have reciprocal detrimental effects on HFpEF, the additional treatment of comorbidities and the optimization of risk factors is pivotal . Active Overall, effective pharmacological and non-pharmacological treatment options remain limited for patients with HFpEF. Many different etiologies, pathomechanisms, and comorbidities contribute to this syndrome in which an individualized treatment approach that targets personal risk factors and disease constellations, as well as a discussion of lifestyle optimization with the patient, are warranted.As specific therapeutic strategies for patients with HFpEF are scarce, the treatment of risk factors and comorbidities such as SDB becomes imperative, especially due to the fact that the “obese HFpEF phenotype” closely resembles typical OSA patients. However, phenotyping studies ,40,41, aApart from lifestyle changes , the treatment of SDB consists in delivering positive airway pressure (PAP) therapy. There are three modalities: continuous PAP (CPAP), bilevel PAP (BiPAP), and adaptive servo-ventilation (ASV) ,63,64. TApart from this small but informative study, a recent meta-analysis showed that PAP therapy moderately reduces arterial blood pressure, which is an important risk factor for HFpEF . HoweverSeveral similar pathological aberrations are found in SDB and HF, thereby suggesting a mechanistic overlap or even causal interaction between both disorders ,67. EvenIntermittent hypoxia/reoxygenation and arousals with sudden awakening are key features of SDB, thereby leading to an increased production of reactive oxygen species (ROS) and increased sympathetic activation, respectively . As is eIntermittent hypoxia/reoxygenation is also a strong inducer of systemic inflammatory signaling . In In addition, intermittent hypoxia/reoxygenation also increases the levels of angiotensin II, which is known to induce myocardial hypertrophy and fibrosis, and it is also associated with HFpEF development ,78. As d2+/calmodulin-dependent protein kinase II (CaMKII) is a central regulator of myocardial function and signaling, and it has also been implemented into the pathomechanisms of SDB [2+ cycling, and the cardiac transcriptome upon physiological homeostasis, overactivated CaMKII is also a key indicator and inducer of various cardiac diseases like HF [+ and Ca2+ homeostasis, transcriptional changes, inflammation, apoptosis, and fibrosis [Besides inflammation and structural remodeling, there is growing evidence that functional myocardial remodeling is also critical for HFpEF development ,80. Ca2+s of SDB ,82,83  Ca2+ leak [+ channel NaV1.8 [V1.8 expression in the atrial myocardium from patients with SDB, which further enhanced a late Na+ current, as well as subsequently increased the diastolic SR Ca2+ leak [Several clinical features of SDB have been shown to possibly promote CaMKII overactivation. Potential mechanisms are an increased cardiac wall stress , increased sympathetic activation  or oxidative stress  ,88,89. Ia2+ leak . This pra2+ leak . CaMKII-l NaV1.8 . Indeed,a2+ leak . All of Importantly, these CaMKII-dependent proarrhythmic aberrations in SDB could be blocked by inhibiting CaMKII, thus making this enzyme a promising therapeutic target in SDB ,90. UnfoSDB, especially OSA, is frequently associated with diabetes mellitus. The prevalence of OSA in patients with type 2 diabetes varies from 18% in primary care  to 58% iMechanistically, intermittent hypoxia/reoxygenation, as a key feature of SDB, seems to be critical. Intermittent hypoxia/reoxygenation for 12 weeks impaired glucose tolerance and increased fasting serum insulin levels in leptin-deficient obese mice . SimilarDiabetes mellitus is an important risk factor for numerous cardiovascular diseases, including heart failure (both HFpEF and HFrEF), coronary artery heart disease, kidney dysfunction, strokes, etc. . On a ce+ currents, and it subsequently impaired the diastolic function in mice [V1.8 expression was also increased in patients with cardiac hypertrophy with preserved ejection fraction, whereby HFpEF development was possibly favored by dysregulating cellular Na+ and Ca2+ homeostasis [The SDB-related mechanisms described above have also been linked to the development of HFpEF, thus indicating a close mechanistic interrelationship . An incr in mice . As obseeostasis ,110. Fureostasis ,112. In eostasis . Diabeteeostasis ,102,103.2+ homeostasis and proarrhythmic events in SDB mice [2+ alterations and arrythmias [However, detailed knowledge about the mechanistic interaction of SDB and HFpEF is still limited. Mechanistic models are difficult to fathom when the HFpEF syndrome is an amalgam of markedly different etiologies and phenotypes. Reductionist disease models are needed to investigate pathomechanisms. Therefore, our group has recently established a novel SDB mouse model by injecting polytetrafluoroethylene (PTFE) into the murine tongue . PTFE isSDB mice . Notablyrythmias . This SDSDB and HFpEF are often closely interwoven, and there might even be bidirectional associations that promote a vicious circle. It is important to acknowledge that HFpEF is not a singular syndrome, but that it comprises many different etiologies that result in different phenotypes. There are various underlying pathomechanisms requiring specific prevention and treatment strategies. SDB is a very prevalent disorder with profound implications on the development of cardiovascular disease, especially HFpEF. It is yet unknown whether the severity of SDB is correlated with the severity of diastolic dysfunction or HFpEF. Patients with HFpEF should be regularly evaluated for SDB, which includes the specific history taking regarding signs and symptoms of SDB. As the detection of SDB with polysomnography is cost- and time-intensive, a widespread screening for SDB is currently not feasible. However, the implementation of wearable and artificial intelligence-based devices for screening of SDB is an interesting and growing field in development and research ,115 thatCaMKII-edited human cardiomyocytes showed preserved diastolic Ca2+ levels following hypoxia/reoxygenation, which could possibly improve diastolic cardiac function [CaMKII editing, which attenuated a myocardial remodeling like hypertrophy and fibrosis, this strategy could be beneficial for the “obese HFpEF phenotype” with SDB; however, this remains to be tested [For patient-individualized therapies, a more detailed understanding of the various HFpEF entities is urgently needed. Only specific mechanistic insights into each etiology enable the development of targeted efficient therapies. Novel cutting-edge technologies like single-nucleus RNA sequencing allow for transcriptional analyses of individual cell types at a very high resolution, thereby providing detailed insights into the cellular mechanisms of SDB and HFpEF . Moreovefunction . As CaMKe tested ,106,113.


Data from ULTRA 2, GEMINI 1, VARSITY, and True North inpatients with ulcerative colitis indicate that induction adalimumab,vedolizumab, and ozanimod showed lower clinical remission rates after anti-TNFtherapy, while UNIFI, OCTAVE 1&2, and U-ACHIEVE/U-ACCOMPLISH showustekinumab, tofacitinib, and upadacitinib did not. In patients with Crohn’sdisease, endoscopic remission or mucosal healing after induction therapy ratherthan clinical remission as well as assessment of persistent endoscopic remissionare good measures of long-term disease outcomes. Considering the drugs for whichdata on endoscopic remission rates are available, EXTEND and GEMINI 2&3 showadalimumab and vedolizumab with persistently lower endoscopic remission ratesafter prior anti-TNF therapy, while IM-UNIFI, SEAVUE, and FORTIFY showustekinumab and risankizumab did not. Data from the multicenter retrospectiveEVOLVE study indicate that the effectiveness of anti-TNF therapy does not seemto be significantly impacted by prior vedolizumab therapy, and may furthersuggest the benefit of using vedolizumab as a first-line biologic. As adverseevent rates remain low across all treatments, the magnitude of harm fromuntreated or poorly treated disease far outweighs harm from any individualtherapy. Regardless of the treatment sequence, careful monitoring for earlysigns of treatment nonresponse and switching to another potentially highlyactive therapy are critical to effective management of IBD.Over the past two decades, 11 biologic agents have been approved for use in mostcountries for the treatment of moderate-to-severe inflammatory bowel disease(IBD). Antitumor necrosis factor α (anti-TNF) agents are commonly used as thefirst biologic in clinical practice, and nearly all pivotal studies of inductiontherapy enrolled patients with and without prior use of anti-TNF therapy. Thisnarrative review presents a reasonable approach to devising treatment sequences,examining the magnitude of benefit for each drug  Over the past two decades, 11 biologic agents have been approved for use in mostcountries for the treatment of moderate-to-severe inflammatory bowel disease(IBD).2 and adalimumab4  are used in both ulcerative colitis (UC) and Crohn’s disease (CD), asare the anti-α4β7-integrin vedolizumab6 and the anti-interleukin(IL)12/23 ustekinumab. The anti-TNF golimumab, the JAK inhibitors tofacitinib and upadacitinib, and the sphingosine 1-phosphate receptor modulator ozanimod are used in UC only, while the anti-TNF certolizumab pegol, the anti-integrin natalizumab, and the anti-IL23 risankizumab15 are used in CD only.The tumor necrosis factor (TNF)-α antagonists infliximab Whether the consequences of multiple years of ineffectual treatment can beavoided through better treatment sequencing is critical to consider.After induction therapy with each of these drugs, fewer than 20% of patients showclinical remission, and the rate improves to only half of all patients aftertreatment with maintenance therapy. This, in turn, leads to low rates of treatmentpersistence. Approximately one-third of patients switch to a second biologic after ayear, and 20% continue on to a third drug. while the American College of Gastroenterology recommends any anti-TNF or vedolizumab and the American Gastroenterological Association (AGA) recommends infliximab,adalimumab, or ustekinumab. For UC patients, AGA recommends using infliximab or vedolizumab first but ECCO does not overtly favor any individual treatment or class oftreatments over another as first-line therapy.Because of the long experience with anti-TNF agents, they are commonly used as thefirst biologic in clinical practice. However, management guidelines offerconflicting recommendations on whether that is best. For CD patients, the EuropeanCrohn’s and Colitis Organisation (ECCO) recommends using any anti-TNF therapy in thefirst line while the other ranked upadacitinib first with no distinction for prior treatment. One analysis in CD ranked infliximab or adalimumab first and adalimumab orrisankizumab second, while another ranked infliximab first overall, but ranked risankizumab firstwhen dividing patients into biologic-naïve and biologic-exposed cohorts. Putting these data together suggests that patients with UC who are naïve toanti-TNF therapy could start with infliximab, vedolizumab, or upadacitinib, and thenswitch to ustekinumab or tofacitinib after treatment failure, while TNF-naïvepatients with CD could start with infliximab or adalimumab, and then switch torisankizumab as second-line therapy.These conflicting recommendations largely stem from the difficulties associated withcomparing treatment effectiveness across trials, particularly as many have differentdesigns. In the past few years, at least five network meta-analyses attempted tocorrect for these differences by extracting data from randomized controlled trialsin IBD, indirectly comparing results, and rank-ordering the drugs according to theirlikelihood of achieving clinical remission with induction therapy. Yet, here, too,no consensus has emerged. In two analyses in UC, one ranked infliximab andvedolizumab first overall but ranked ustekinumab or tofacitinib first for patientspreviously treated with anti-TNF therapy, considered neither upadacitinib nor ozanimod because they were not yetavailable, while the other included the investigational etrolizumab, whose study in IBD has since beendiscontinued. Furthermore, the CD studies ranked the therapies only according totheir ability to achieve clinical remission, yet, as discussed below, endoscopicremission or mucosal healing may be more informative in predicting long-term diseaseoutcomes.27However, utility of these analyses may be limited. In the UC studies, one of the analysesversusplacebo or active comparator. Because nearly all pivotal studies of inductiontherapy enrolled patients with and without prior use of anti-TNF therapy, it istherefore possible to devise treatment sequences based on how that benefit changeswith prior anti-TNF treatment.This narrative review presents an alternative approach, considering each trial on itsown and examining the magnitude of benefit for the drug notused first, and finally, considerations are presented for potential treatmentsequencing.Although definitions of clinical and endoscopic remission vary somewhat betweenstudies, and one cannot derive statistically significant results from subgroup posthoc comparisons, review of available data can nevertheless offer some insight intowhether each agent might best be used first or second (or third) in a treatmentsequence in TNF-naïve and TNF-exposed patients. Thus, the sections to follow analyzestudies of adalimumab, vedolizumab, ustekinumab, tofacitinib, upadacitinib,ozanimod, and risankizumab in UC and/or CD using this methodology.  First, study dataon induction therapy in UC are considered for each biologic based on clinicalremission rates before and after anti-TNF therapy. A similar strategy is employedfor study data on endoscopic remission rates with maintenance therapy in CD. Next,efficacy of biologic therapy is considered when anti-TNF is Broadly speaking, in patients with UC, induction adalimumab, vedolizumab, andozanimod showed lower clinical remission rates after anti-TNF therapy, whileustekinumab, tofacitinib, and upadacitinib did not . compared adalimumab versus placebo in patients withmoderate-to-severe UC. At week 8, there was a 7.2 percentage-pointdifference in the rate of clinical remission for the entire cohort . Among those naïveto anti-TNF, the difference was even greater at 10.3 points (21.3%versus 11.0%). However, the advantage for adalimumabwas lost among the anti-TNF-exposed patients, where a difference of only2.3 percentage points was noted (9.2% versus 6.9%). Asimilar pattern was seen when comparing clinical response rates. Here, adifference in the rate between drug and placebo was 20.7 points foranti-TNF-naïve (59.3% versus 38.6%) but only 8.0 inTNF-exposed patients (36.7% versus 28.7). The importance ofconsidering prior anti-TNF use was particularly obvious when assessingmucosal healing rates: in TNF-naïve patients, there was a 14.1-pointdifference between adalimumab and placebo (49.3% versus35.2%) but only a 1.9-point difference in TNF-exposed patients (28.6%versus 26.7%).The ULTRA 2 study28 also showed lowerrates of efficacy in TNF-exposed than in TNF-naïve patients treated withinduction vedolizumab versus placebo. At week 8, thedifference in clinical response rates for the full cohort was26.1 percentage points between vedolizumab and placebo (47.1%versus 25.5%) and a difference of 11.5 points forclinical remission (16.9% versus 5.4%). Yet, when prioranti-TNF therapy use was considered, the difference in clinical responserate was greater in the TNF-naive group  than in the TNF-exposed group , and was considerably greaterwhen evaluating clinical remission rates in TNF-naïve  than TNF-exposed  groups. An even greater difference betweenTNF-naïve and TNF-exposed patients was seen in mucosal healing rates, with adifference of 24.2 percentage points in the TNF-naïve cohort (49.2%versus 25.0%) and only 9.9 points in the TNF-exposedcohort (30.5% versus 20.6%).Analysis of data from the GEMINI 1 study Results demonstrated superiority for vedolizumab over adalimumab inachieving clinical remission, with a difference of 8.8 percentage pointsbetween the two arms (31.2% versus 22.5%). But whenevaluating only patients with prior exposure to an anti-TNF therapy otherthan adalimumab, clinical remission rate with both drugs fell to 20.3% and16.0%, respectively. Similar outcomes were noted when evaluating thesecondary endpoint of endoscopic remission: in the overall population, therewas a 12-point difference between vedolizumab and adalimumab (39.7%versus 27.7%), but in the TNF-exposed group, rates werelower in both groups (26.6% versus 21.0%).The phase IIIb VARSITY study comparing adalimumab with vedolizumab inpatients with UC is one of the few prospective head-to-head studies in IBD. assessed clinical remission, clinical response, and endoscopicremission at 10 weeks in patients previously treated with an anti-TNF,vedolizumab, or tofacitinib, but subgroup analysis was only done forclinical remission in those with prior anti-TNF exposure. In the overallpopulation, there was a difference of 12.4 percentage points in the clinicalremission rate between ozanimod and placebo (18.4% versus6.0%), and the magnitude of benefit was even somewhat better in those whowere naïve to anti-TNF therapy . However, in those previously treated withanti-TNF therapy, clinical remission rates were considerably worse, with adifference of only 5.4 percentage points (10% versus 4.6%)between ozanimod and placebo, which was not statistically significant.The True North study of induction ozanimodDespite differences in the design of ULTRA 2, GEMINI 1, VARSITY, and TrueNorth studies, and despite the fact that each of the drugs studied has adifferent mechanism of action, the studies all showed that the use ofadalimumab, vedolizumab, and ozanimod yield lower rates of clinicalremission, clinical response, and/or endoscopic remission in UC patientspreviously treated with anti-TNF therapy. These data suggest thatvedolizumab and ozanimod would optimally be used earlier in the treatmentsequence in patients with UC, before initiating anti-TNF therapy, and thatin general switching to a second anti-TNF therapy after initial failure isnot an optimal strategy. prior treatment did not seem to have an effect on clinical remissionrate, although patients were stratified based on prior exposure to anybiologic therapy, including an anti-TNF or vedolizumab. At week 8, there wasa 10.2 percentage point difference between ustekinumab and placebo in theoverall cohort (15.5% versus 5.3%), an 8.5-point differencein biologic-naïve patients (18.4% versus 9.9%), and an11.5-point difference in biologic-exposed patients (12.7%versus 1.2%). Clinical response rates were also quitesimilar regardless of prior biologic use. In the overall cohort, there was a30.5 percentage point difference between ustekinumab and placebo (61.8%versus 31.3%), a 30.9-point difference in biologicnaïve patients (66.7% versus 35.8%), and a 29.9-pointdifference in biologic-exposed patients (57.2% versus27.3%). The difference between ustekinumab and placebo was similarly minimalwhen considering the endpoint of endoscopic improvement, with a 12.1-pointdifference in the biologic-naïve group (33.3% versus 21.2%)and a 14.3-point difference (21.1% versus 6.8%) in thebiologic-exposed group.In the ustekinumab UNIFI induction study in moderate-to-severe UC, prior exposure to anti-TNF therapy did not seem to alter outcomes.The difference in clinical remission rates at 8 weeks between tofacitiniband placebo in the overall group was 10.3 percentage points in OCTAVE 1(18.5% versus 8.2%) and 13 percentage points in OCTAVE 2(16.6% versus 3.6%). On subgroup analysis, TNF-naïve andTNF-exposed patients also showed similar outcomes. In OCTAVE 1, thedifference between tofacitinib and placebo in the TNF-naïve group was9.4 points (25.2% versus 15.8%) and in the TNF-exposed itwas 11.1 points (12.6% versus 1.5%), while in OCTAVE 2,TNF-naïve patients saw a 13.5-point difference (22.1%versus 8.5%), and TNF-exposed patients saw a 12-pointdifference (12.0% versus 0%). Mucosal healing rates werealso unaffected by prior anti-TNF use. In OCTAVE 1, there was a differenceof 15.7 percentage points (31.3% versus 15.6%) in theoverall population and 17.9 points (24% versus 6.2%) inTNF-exposed patients, while in OCTAVE 2 there was a difference of16.8 points in the overall population (28.4% versus 11.6%)and 15.6 points (21.8% versus 6.2%) in TNF-exposedpatients.In the OCTAVE 1 and OCTAVE 2 studies of induction tofacitinib,versus placebo was evaluated in apair of studies that showed largely similar results somewhat different magnitudes ofbenefit in biologic-naïve and biologic-exposed patients, but the differencewas fairly small and both groups in each study showed clinical remissionrates similar to that in the overall cohort. The clinical remission rate atweek 8 in the overall population in U-ACHIEVE was 26% with upadicitinibversus 5% with placebo, or a difference of21 percentage points, and was 33% versus 4%, or adifference of 29 points, in U-ACCOMPLISH. Among those with no prior biologicuse , the difference in clinicalremission rates was similar to the overall population in both U-ACHIEVE and U-ACCOMPLISH.Biologic-exposed patients in U-ACCOMPLISH  and in U-ACHIEVE showed generally similaroutcomes . Ofnote, differences in endoscopic remission were more noticeable, with adifference of 13 percentage points (14% versus 1%) in theoverall population in U-ACHIEVE and a similar magnitude of benefit of16 percentage points (18% versus 2%) in U-ACCOMPLISH. Thisdifference was unchanged or improved when restricting to only thebiologic-naïve cohort , and fellwhen restricting to the biologic-exposed cohort .Induction upadacitinib Data from UNIFI, OCTAVE 1&2, and U-ACHIEVE/U-ACCOMPLISH show a similarmagnitude of benefit in clinical remission rates when induction ustekinumab,tofacitinib, and upadicitinib are compared with placebo regardless oftreatment history, suggesting that saving these agents for a later line oftherapy might be a prudent approach. showed that upon assessment ⩾50 weeks after study onset, patients withmucosal healing at first endoscopic assessment were more likely to showsignificantly higher rates of clinical remission , as wellas a trend toward lower rates of freedom from CD-related surgery .As indicated above, in patients with CD, endoscopic remission or mucosal healingafter induction therapy rather than clinical remission is likely a better predictorof long-term disease outcomes, and therefore a better measure to use whenconsidering the magnitude of treatment benefit. Meta-analysis of 10 studies patients who showed any measure of mucosal healing at 48 weeks at study endshowed decreased risk of disease progression after a median of 3 years, but riskreduction was greatest in those who achieved deep remission, defined as Crohn’sDisease Activity Index < 150, Crohn’s Disease Endoscopic Index of Severity < 4with no deep ulcerations, and no steroids for ⩾8 weeks, with an adjusted HR of 0.19 compared with those not in remission. These data suggest thatassessment of persistent endoscopic remission is a good measure of long-term diseaseoutcomes.Importantly, the benefit from endoscopic remission persists. In long-term follow-upof the CALM study,Not all studies of biologic therapy in CD include endoscopic remission or mucosalhealing as an endpoint, potentially limiting its use in defining optimal treatmentsequences for patients with CD. Considering the drugs for which data on endoscopicremission rates are available, broadly speaking, only adalimumab and vedolizumabshowed persistently lower endoscopic remission rates after prior anti-TNF therapy,while ustekinumab and risankizumab did not . evaluated mucosal healing rates defined as the absence of mucosalulceration in patients started on 160 mg adalimumab at week 0, switched to80 mg at week 2, then randomized at week 4 to 40 mg or placebo. At week 12,there was a difference of 14 percentage points (27% versus13%) in the mucosal healing rate between those treated with continuousadalimumab versus those who switched to placebo. Themagnitude of benefit for continuous adalimumab was maintained in theTNF-exposed population (23.3% versus 14.3%) but wasconsiderably greater in the TNF-naïve population (31.3%versus 11.5%).The EXTEND study32 In the phase IIIbVERSIFY study, patients treated with vedolizumab were assessed for endoscopicremission, defined as Simplified Endoscopic Severity Crohn’s disease(SES-CD) score ⩽4, at 26 weeks and again 52 weeks. In the overall cohort,endoscopic remission was seen in 11.9% at 26 weeks and in 17.9% at 52 weeks.However, consistent with activity of vedolizumab in TNF-exposed patientswith UC, patients with CD treated with vedolizumab after anti-TNF therapyshowed considerably worse outcomes. At 26 weeks, 19.6% of TNF-naïve patientsachieved endoscopic remission compared with only 5.5% of TNF-exposedpatients, and a similar discrepancy of 25.0% versus 8.3%was seen at 52 weeks.In the pivotal GEMINI 2 and GEMINI 3 studies of vedolizumab, patients with CDwere assessed for clinical remission after 6 weeks or 10 weeks of inductiontherapy, respectively.These data clearly indicate that reserving vedolizumab for CD patients naïveto anti-TNF therapy is the best option for maximizing benefit from thisagent, and that switching to a second anti-TNF therapy after first-linefailure is not an optimal approach. Patients enrolled in the endoscopy substudy underwent assessment forSES-CD score at week 0 of induction, at week 8 of induction, which alsoserved as week 0 of maintenance, and at week 44 of maintenance. There was a difference of 17.8 percentage points in endoscopicimprovement (SES-CD ⩾3 points from baseline) in the overall population atweek 8 (47.7% versus 29.9%) and a 12-point difference atweek 44 (37% versus 25%). Rates with maintenance therapy atweek 44 stratified by prior treatment use were not described in IM-UNITI.Results of the phase IIIb SEAVUE study comparing adalimumab and ustekinumab do not suggest a particularadvantage of ustekinumab to achieve the desired outcome of endoscopicremission after 1 year of treatment. At 52 weeks, rates of endoscopicremission, defined as SES-CD ⩽3, were largely equivalent  regardless ofbaseline SES-CD score.Ustekinumab was studied in the UNITI-1 study of TNF-exposed patients as wellas in the UNITI-2 study of patients who were TNF-naïve or did not meetcriteria for having failed prior TNF; those who completed induction wereenrolled in the IM-UNITI maintenance ustekinumab study. The phase III FORTIFY study of maintenance risankizumab (180 mg or 360 mg) enrolled patients from ADVANCE and MOTIVATE who had achieved clinicalresponse at week 12 or 24, and assessed them at 52 weeks for both clinicalremission and endoscopic response defined as a decrease in SES-CD ⩾50% frombaseline. In the overall cohort, there was a difference of approximately25 percentage points in endoscopic response rates at 52 weeks between thetwo doses of risankizumab versus placebo . Among biologic-naïve patients,the magnitude of benefit was higher, with a difference of approximately32 percentage points between the risankizumab doses and placebo , but those with priorbiologic failure showed an approximately 22-point magnitude of benefit, similar tothat of the overall population.The ADVANCE study of induction risankizumab enrolling both biologic-naïve andbiologic-exposed patients, as well as MOTIVATE enrolling onlybiologic-exposed patients assessed clinical remission rates and endoscopicresponse (50% reduction from baseline SES-CD) at 12 weeks.Although IM-UNIFI, SEAVUE, and FORTIFY differed slightly in their definitionsof endoscopic endpoints, they all showed that maintenance therapy withustekinumab and risankizumab can yield reasonable rates of persistentendoscopic remission and/or mucosal healing regardless of anti-TNF treatmenthistory. This suggests that both of these drugs could be used as first-linetherapy in both TNF-naïve and TNF-exposed patients. However, as only FORTIFYincluded endoscopic response as a co primary endpoint, risankizumab has thehighest quality data examining this endpoint. Furthermore, considering thedata from VERSIFY showing lower rates with vedolizumab after anti-TNFtherapy, and the limited controlled data for ustekinumab after anti-TNFtherapy, at this time neither of these medications should be first choice inanti-TNF-exposed patients.Because the anti-TNF agents were developed first and are commonly used first inclinical practice, there are no prospective studies of anti-TNF therapies inpatients previously treated with other biologics. Nevertheless, retrospective datacan be highly useful in assessing outcomes in this patient population, particularlywhen considered alongside prospective data.  and 19.6%versus 14.7% (p = 0.69), respectively] butrates were higher in CD patients using anti-TNF in the second line at these sametimepoints [22.9% versus 49.2 (p = 0.02) and 36.2%versus 74.6% , butthe number of patients in the second line group was very small.EVOLVE was a multicenter retrospective study of 1095 patients with IBD, including 604with UC and 491 with CD, who were treated with vedolizumab or anti-TNF as a firstbiologic therapy and followed over 24 months . RatesoThus, if data from VARSITY, the GEMINI studies, and VERSIFY indicate that maximaleffect for vedolizumab is seen when it is used before anti-TNF therapy, data fromEVOLVE further indicate that effectiveness of anti-TNF therapy does not seem to besignificantly impacted by prior vedolizumab therapy, and may further suggest thebenefit of using vedolizumab as a first-line biologic.Weighing all of the data presented above, when considering clinical remission ratesin UC, anti-TNF therapy is likely not the best choice for first-line therapy inbiologic-naïve patients. Most non-TNF biologic therapies show worse results whenused after anti-TNF, while the reverse does not necessarily seem be true. Thus, asequence of a biologic other than anti-TNF followed by an anti-TNF is likely toallow for maximal benefit from both agents . Risankizumab, ustekinumab, vedolizumab, and adalimumab all induce somemeasure of endoscopic response or mucosal healing, and only vedolizumab shows pooreroutcomes after an anti-TNF therapy. Thus, a sequence of a biologic other than ananti-TNF followed by an anti-TNF is attractive for TNF-naïve patients, while onlyrisnakizumab or ustekinumab would be recommended in TNF-exposed patients  in clinical remission rate at 8 weeks and a16.3-point difference (42.2% versus 25.9%) at 52 weeks in favor of ustekinumab, while the other found a similar 16.1-point difference (54.4%versus 38.3%) at week 48 in favor of ustekinumab.There may also be a mechanistic reason that ustekinumab and risankizumab wouldspecifically benefit patients previously treated with an anti-TNF therapy.24 data from EVOLVE demonstrated a lower rate of serious infections in biologic-naïve patientsstarted on vedolizumab compared to anti-TNF, and ustekinumab in CD showed no new safety signals emerging over time. There are some suggestionsthat ustekinumab may be associated with a lower rate of serious infection inpatients with CD compared with both anti-TNF and vedolizumab, and, conversely, thatvedolizumab may be associated with a lower risk of serious infection in UC comparedwith anti-TNF therapy. Nevertheless, as rates remain low across all treatments, safety is not likelyto be a main driver of treatment choice. Indeed, as the magnitude of harm fromuntreated or poorly treated disease far outweighs harm from any individual therapy,the primary focus should be on maximizing benefit from each available biologic agentover time.Another consideration for treatment sequencing is safety. Network meta-analysesevaluating treatment efficacy show largely similar rates of adverse events acrossall drugs,Finally, and perhaps most important, regardless of study data, it is critical thatevery treatment choice be individualized to the patient. We cannot yet quantify therisk of disease progression and disease complications for any IBD phenotype, andrisk factors for rare side effects of biologic therapy must always be considered.The potential of a single biologic agent to treat both IBD and a concomitant immunemediated disorder might also drive selection. For example, risankizumab orustekinumab might be a good option for a patient with both CD and psoriasis, whilean anti-TNF might be optimally selected for a patient with IBD and concomitantuveitis. Availability of drug, cost, and patient preference for schedule/deliveryare also likely to affect treatment choice. Ultimately, regardless of the treatmentsequence selected, careful monitoring for early signs of treatment nonresponse and arecognition that nonresponse to one therapy still leaves the door open to othertherapies are key to effective management of IBD.


Goal: Artificial intelligence applied to medical image analysis has been extensively used to develop non-invasive diagnostic and prognostic signatures. However, these imaging biomarkers should be largely validated on multi-center datasets to prove their robustness before they can be introduced into clinical practice. The main challenge is represented by the great and unavoidable image variability which is usually addressed using different pre-processing techniques including spatial, intensity and feature normalization. The purpose of this study is to systematically summarize normalization methods and to evaluate their correlation with the radiomics model performances through meta-analyses. This review is carried out according to the PRISMA statement: 4777 papers were collected, but only 74 were included. Two meta-analyses were carried out according to two clinical aims: characterization and prediction of response. Findings of this review demonstrated that there are some commonly used normalization approaches, but not a commonly agreed pipeline that can allow to improve performance and to bridge the gap between bench and bedside. I.Currently, the role of medical imaging is evolving from being mainly a diagnostic tool to gaining a central role in the context of personalized precision medicine Different standardization guidelines have been proposed to address the above-mentioned problem in the case of computer tomography (CT) and positron emission tomography (PET)/CT imaging, while they are not available for Magnetic Resonance Imaging (MRI) Currently, different pre-processing algorithms have been implemented to reduce the variability The aim of this study is to systematically review normalization approaches applied to multi-center abdominal MRI, to assess whether it is possible to provide guidelines or evidences about the performances of the most frequently used methods. Even if this topic was widely addressed for the brain II.A.This review was carried out according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. Relevant articles were identified by searching three databases: PubMed, Web of Science and Scopus. The query used for the literature search was “MRI” AND “multicenter” AND  AND .Literature searching, study identification, and data extraction from eligible studies were performed by one investigator with experience in abdominal radiomics field research (J.P.).B.Searched studies had to further come across the following eligibility criteria to be incorporated in the present review: (i) written in English; (ii) AI radiomics-based studies; (iii) published between January 2012  papers assessing image quality, (ii) papers describing only harmonization or standardization acquisition pipelines without using them for the development of a radiomics signature or AI model for detection/characterization/prognosis, (iii) papers describing challenges or online databases, and (iv) not clearly specifying the pre-processing step.D.For all included papers, the following information was collected : organ of interest, clinical aim, publication year, database details, normalization approach and method, radiomics features extracted, developed radiomics model, distinguishing between Machine and Deep Learning (ML and DL), and performances on the validation set. We grouped clinical aims in three categories: (i) detection as the capability to detect the pathologic tissue, i.e., its presence, and, if confirmed, its localization on the medical image; (ii) characterization as the ability to predict the clinical outcome; and (iii) prediction of therapy response, only appliable on pathologic tissues.E.We conducted different meta-analyses on the model performances, according to the aim, i.e., detection, characterization, response to therapy. We considered the area under the curve (AUC) and its Confidence Interval (CI) as the performance metric, which was the most frequently evaluated parameter among the studies. We excluded papers that did not report AUC and neither its standard deviation, standard error or CI values, and that weren't validated on an external dataset, since this does not determine model reproducibility and generalizability to new and different patients 2 statistic were calculated. Cochran's Q statistic tests the studies' heterogeneity, under the null hypothesis H0 that all studies are homogeneous . The I2 value was used to quantify heterogeneity, providing an estimate of the percentage of variability among included studies: values of 25% and less are usually considered to be low, 25%–50% moderate and above 75% are considered high. In case of detected heterogeneity, a moderator analysis was carried out by dividing studies into subgroups according to the organ, for each aim. Subgroups containing only one paper were excluded from the meta-analysis. The weight of each study was calculated with the inverse variance method, in which the weight given to each study is inverse of the variance of the effect estimate, minimizing the uncertainty of the pooled effect estimate metafor package The random-effects model was used to calculate the pooled AUC and to produce the forest plot. Cochran's Q and the IIII.A.During the identification step, 4777 papers were collected from three search databases. Among them 1574 were duplicate records, therefore they were excluded resulting in a total of 3203 papers. During the screening step, 3069 were excluded as they did not meet the eligibility criteria. Of the remaining 134 papers, 60 were eliminated after reviewing the study context and design. Finally, 74 papers were included in the final analysis. Fig. B.All included papers have been published since 2015, with an increase from 2019. The most studied organs are the prostate The turning year, in which the number of papers showed a big rise also in other abdominal organs, was 2020. This might be due to the increasing number of public databases C.Fig. Of note, since 2019, 15/74 papers include clinical features, available from routine practice, into their model in order to increase the predictive potential usefulness (S-Table I–V).1)•Spatial normalization: a process that changes the spatial characteristics of the image, e.g., the pixel's resolution, the Field of View (FOV), sequences orientation, etc. All methods apply geometrical transformations , which do not need normalization parameters. This approach allows obtaining images including almost the same anatomical regions, thus avoiding histogram mismatches due to different FOVs rather than signal intensity dissimilarities. However, spatial modifications may alter the organs' morphology, undermining the clinical significance.•Intensity normalization: a process that rescales the pixel intensity values to the same range. Most methods allow evaluating the normalization parameters independently on the sequences, except those which need a normalization reference, e.g., hist_normNyulUdupa•Feature normalization: a process that rescales values of the radiomics feature extracted to the same range. On one hand, all methods allow reducing the differences between the features, without altering the clinical significance. On the other hand, a training set is needed to evaluate the normalization parameters.The following normalization approaches were used on MRI datasets:Among all, intensity normalization is applied on 61/74 (82%) papers, being the most frequent, while the feature and spatial normalizations on 27/74 (36%) and 48/74 (65%) papers, respectively. In 52/74 studies the images underwent more than one normalization approach: 6/74 (8%) both intensity and feature, 34/74 (46%) spatial and intensity, 2/74 (3%) spatial and feature, and 10/74 (14%) all three approaches. Colored boxes in Fig. registration to align the sequences and reduce the motion artefacts, resampling to obtain the same resolution, and resizing to obtain the same size for all images. The most commonly applied method is resampling (31/48), which has been used individually in 12/16 studies on the prostate, 4/5 on the liver, and 6/9 in the miscellaneous group. Moreover, it has been applied in combination with at least another spatial normalization method in 14/48 papers, i.e., resampling and resizing on the female pelvis (4/9), and the rectum (3/9). Registration is the least frequently used method (10/48) among all organs. For intensity normalization, the two most used methods are the standardization (z-score) of the intensity distribution, which is applied on 21/61, and custom algorithms on 31/61, including normalization using pre-defined values, discretization of the distributions, the use of Advanced Normalization Tools (ANTs) or filters, the application of DL methods for harmonizing the image, or the combination of more than one method. The remaining 9/61 papers applied one of the following methods: min-max or 3sigma scaling and mean centering (mean-cent), and histogram normalization (hist-norm) which matches the original distribution to a histogram reference . Most papers related to the prostate (10/20) and miscellaneous (8/10) have developed custom methods for the intensity normalization, while the z-score is frequently applied to female pelvis (5/10) and rectal (7/16), and hist-norm on the liver (2/5). More details are in S-Table I–V. Regarding feature normalization, only three methods have been found in the included papers and are almost equally used: i.e., rescaling according to the minimum and maximum feature values (min-max) is applied on 6/27 papers, standardization (z-score) on 9/27, and harmonization using ComBatComBat method can only normalize features across different centers by using values computed on a subset of data from each center that will be normalized. Therefore, it requires available and labelled data from all centers/scanners included in the dataset, not allowing the direct application of previously determined normalization parameters on external validation cohorts ComBat and z-score. Observing the S-Tables I–V, the z-score is mostly applied on the rectum (3/6), the min-max on the prostate (3/5), while the ComBat on the female pelvis (4/7).In general, three methods are currently used for spatial normalization: D.The 22 papers included in the meta-analysis are reported in the supplementary tables in bold. We did not perform the meta-analysis on detection, since there were not enough studies that could be included for this purpose.p-value < 0.01), and differences are partially explainable using the organs as the moderator .Concerning the 16 papers addressing the characterization Fig. , the stup-value < 0.01), which differ in that one of them applies features normalization in addition to spatial and intensity normalizations. This behavior might suggest that in this case normalizing the features does not improve the performance (AUC = 0.60 vs AUC = 0.87), however, the sample size is very small to strongly assume this point. It is noteworthy that the papers related to the liver, rectal and prostate show very low heterogeneity (I2 = 0% for the three organs) even though they applied very different normalization approaches. This might suggest that there is no preferable normalization strategy when dealing with these organs. Considering average results, two organs reach an average AUC higher than the overall pooled AUC, i.e., female pelvis (0.88 vs 0.81) and prostate (0.82 vs 0.81). It is noteworthy that, in studies regarding the female pelvis papers, ComBat feature normalization seems to slightly increase performances (AUC = 0.86 and 0.91 vs 0.85). However, there is not a clear correlation between the used approach and performance, therefore it is not possible to define a pathway for MRI variability reduction.Considering each organ separately, only the kidney showed high significant heterogeneity between the two included studies , partially explainable using the organs as the moderator . In this case, the performances of the two groups were statistically different , and in particular results on female pelvis were higher than rectal cancer , but both groups showed a heterogeneity higher than 75%. Focusing on the rectal area, all papers applied both spatial and intensity methods, except Song et al. z-score method, in combination with either spatial or feature normalization. In particular, the latter approach allowed reaching higher results (AUC = 0.99 vs AUC = 0.92). However, since there were only two papers in this group, we cannot strongly assume that the combination of z-score and features normalization is more suitable for this organ.Concerning the 6 papers addressing the prediction of response to therapy (Fig. Considering all the above results, it is not possible to extract clear indications on the type of pre-processing for reducing abdominal MRI variability, mainly because we found a multiplicity of algorithms and pipelines applied. Moreover, no definite correlation between normalization and performance emerges from the meta-analyses, since most of the considered subgroups were very small. Both aspects could be partially associated to the quite recent interest in multi-center studies concerning abdominal MRI, increased only from 2020.resampling for spatial and min-max for features normalizations. Conversely, intensity normalization was preferably applied using custom algorithms, tailored by each research group considering the characteristics of the organ, i.e., its heterogeneity and the presence of small lesion difficult to be detected even by experts, and clinical task. For these reasons, we strongly recommend to carefully analyze the available images and aim.The prostate was the only organ showing a low heterogeneity with a sufficiently high number of included papers: in this case, the most used methods for the characterization aim are resampling and resize have become a standard step since 2021. Moreover, the combination with z-score intensity normalization seems to lead to higher results for characterization and therapy response aims.Regarding both the rectal are and female pelvis, we noticed that In recent years, several efforts have been made toward the implementation of new approaches based on DL algorithms, i.e., transfer learning, data synthesis, thanks to their capability to learn from a given datasets This systematic review highlighted the shortage of evidences and guidelines on normalization approaches for abdominal MRI, differently from the agreed pipelines related to the brain Several insights emerge from our analyses. First, it could be useful to evaluate how the different normalizations really affect the model performances. In this review, it was not possible to carry out this evaluation per each study since most of them did not present the results without applying the normalization. Then, despite being able to identify the most commonly used normalization approaches, we could not provide precise explanations of the reason behind, since the majority of the collected papers did not justify the choice or provided evidences of pros and cons of different methodologies. Finally, the number of studies included in the two meta-analyses is lower than the reviewed articles, since not all of them evaluated the performances using the same metric and externally validated their models.Our preliminary findings should be further validated using a larger amount of paper. This could be achieved by including other normalization methods applied on different AI-based system development steps, such as feature selection and dimensionality reduction, IV.Recently the need to define a suitable and useful normalization method for the reduction of multi-center MRI database variability for all abdominal organs has increased. Thanks to the findings obtained by the systematic review and meta-analyses carried out on subgroups of papers, we observed that there are some commonly used approaches, but not clear guidelines on different methodologies. Therefore, the definition of an abdominal pre-processing pipeline is still ongoing research, and it is of crucial importance to keep working on defining a proper methodology to reduce the multi-center database variability. In conclusion, we suggest carefully selecting the proper normalization approach considering the MRI database provided, the clinical aim, and the radiomics model.


Epicardial adipose tissue (EAT) is an endocrine and paracrine organ constituted by a layer of adipose tissue directly located between the myocardium and visceral pericardium. Under physiological conditions, EAT exerts protective effects of brown-like fat characteristics, metabolizing excess fatty acids, and secreting anti-inflammatory and anti-fibrotic cytokines. In certain pathological conditions, EAT acquires a proatherogenic transcriptional profile resulting in increased synthesis of biologically active adipocytokines with proinflammatory properties, promoting oxidative stress, and finally causing endothelial damage. The role of EAT in heart failure (HF) has been mainly limited to HF with preserved ejection fraction (HFpEF) and related to the HFpEF obese phenotype. In HFpEF, EAT seems to acquire a proinflammatory profile and higher EAT values have been related to worse outcomes. Less data are available about the role of EAT in HF with reduced ejection fraction (HFrEF). Conversely, in HFrEF, EAT seems to play a nutritive role and lower values may correspond to the expression of a catabolic, adverse phenotype. As of now, there is evidence that the beneficial systemic cardiovascular effects of sodium-glucose cotransporter-2 receptors-inhibitors (SGLT2-i) might be partially mediated by inducing favorable modifications on EAT. As such, EAT may represent a promising target organ for the development of new drugs to improve cardiovascular prognosis. Thus, an approach based on detailed phenotyping of cardiac structural alterations and distinctive biomolecular pathways may change the current scenario, leading towards a precision medicine model with specific therapeutic targets considering different individual profiles. The aim of this review is to summarize the current knowledge about the biomolecular pathway of EAT in HF across the whole spectrum of ejection fraction, and to describe the potential of EAT as a therapeutic target in HF. Epicardial adipose tissue (EAT) is a peculiar fat depot located between the myocardium and the visceral layer of the epicardium. Interestingly, EAT evolved from brown adipose tissue and shares the same embryologic origin as omental and mesenteric fat .EAT works as a lipid-storing depot, but it has been also identified as an endocrine organ secreting hormones, part of the immune signalling network, and as an inflammatory tissue secreting cytokines and chemokines . NotablyFurthermore, EAT has been shown to be a highly active biological tissue secreting many adipokines that have the capacity to affect the adjacent myocardium . Among t+ effector memory T lymphocytes [Systemic inflammation and metabolic disorders cause EAT proliferation and deranged adipogenesis . As EAT phocytes . The estphocytes , responsphocytes ,28,29,30phocytes . Myocardphocytes .However, the available—although limited—literature suggests that the EAT may exert a different pathophysiological effect in HF in relation to the ejection fraction status. In this review, we summarize the currently available evidence regarding the role of EAT in the development of HF, highlighting the different pathophysiological pathways in HF with reduced (HFrEF), mildly reduced (HFmrEF), and preserved (HFpEF) ejection fraction.Transthoracic echocardiography is a simple, cost-effective, and readily available method which allows the measurement of EAT thickness. EAT thickness is measured as the echo-free space between the outer wall of the right ventricle and the visceral pericardium in the parasternal long-axis view at end-systole, perpendicularly to the aortic annulus. This point presents with the highest absolute EAT thickness ,33. AnotHFpEF is a heterogenous and incompletely understood syndrome with specific molecular, genetic, and metabolomic features, all of which reflect on vascular and myocardial cell adaptations . HFpEF eHigher EAT volumes might negatively affect the left ventricle (LV), impairing its distensibility and leading to the development of HFpEF . In a laThere is increasing evidence showing that patients with HFpEF have more EAT compared with healthy controls and patients with HFrEF . MoreoveObesity is a key component in HFpEF, favoring disease progression, and both increasing myocardial load and worsening HFpEF-related comorbidities, such as arterial hypertension . Lately,The relationship between obesity and HFpEF is complex. Increased pericardial fat may exert a direct compressive effect on the right ventricle, thus preventing its distension ,54. ObesThese findings point towards an obesity-related HFpEF phenotype as a separate form of cardiac failure with specific hemodynamic changes and characteristics, which should be specifically addressed.Clinical studies demonstrated conflicting results regarding the amount of EAT in men compared to women ,67. In pAn increased EAT volume has been associated with a markedly reduced exercise capacity , independent of BMI in HFpEF, but not in HfrEF ,56. SubcAlthough the evidence on differences concerning a reduced exercise capacity in HFpEF patients according to EAT volume are not concordant, different testing modalities and experimental setup might account for different results  .The expansion of EAT has been associated with both obesity and type 2 diabetes mellitus, two important risk factors for atrial fibrillation . In thesThe potential role of EAT in HfrEF has not been clearly understood yet and, up to now, only few studies have been conducted in this setting . The majSeveral observations showed that both EAT mass and EAT thickness are significantly reduced in HFrEF patients as compared to both healthy controls and HFpEF patients ,56,71. TNevertheless, other studies reported that EAT mass increases in patients with HFrEF ,71, but Thus, a reduced EAT thickness in HFrEF patients might, thus, be a sign of preclinical cardiac cachexia and its assessment might help in individuating those patients with worse cardiovascular prognosis. However, this conclusion is partially speculative and further studies addressing this question are warranted.2), regardless of BMI [Pugliese and colleagues were the first to describe EAT-related differences between HFrEF and HFpEF in terms of cardiometabolic profile, hemodynamics, and cardiovascular outcome . In HFrEs of BMI . Increass of BMI . In addis of BMI .Although almost all of the available studies investigated HF patients with a LVEF >40% as a single entity ,57,58, HThe role of inflammation in HF pathophysiology has been increasingly acknowledged .In this point of view, EAT might play a key role as pathophysiological mediator in the development of HF. In fact, in pathological conditions, EAT acquires a proatherogenic transcriptional profile resulting in the increased synthesis of biologically active adipocytokines with proinflammatory properties, promoting oxidative stress, and finally causing endothelial damage . As prevInhibitors of sodium-glucose cotransporter-2 receptors (SGLT-2i) represent a pillar of the modern HF therapy due to their beneficial effect on the reduction of adverse cardiovascular events in HF patients across the entire ejection fraction spectrum, and their broad eligibility ,90,91. ABesides the above-mentioned therapies, metformin and glucagon-like peptide-1 (GLP-1) receptor agonists have also been found to reduce EAT inflammation and EAT mass, respectively. However, randomized-controlled trials on the outcome-modifying effect of metformin in HF are lacking. Interestingly, GLP-1 receptor agonists such as liraglutide have been shown to worsen congestion and to increase cardiovascular adverse events in HFrEF independently from diabetes mellitus type 2 ,97. In pIn the recent future, adipose tissue could be a new target for gene therapy as well. The fibroblast growth factor 21 (FGF21) has been identified as a promising therapeutic agent for type 2 diabetes and other metabolic diseases. The overexpression of the FGF21 in the visceral adipose tissue was associated with an improvement in energy homeostasis. A low-dose intraperitoneal injection of engineered serotype adeno-associated viral vector-FGF21 resulted in reduced whole-body adiposity and inflammatory cytokine release, lower insulin resistance, and glycemic processing .Taken together, these studies raise the question of whether EAT volume alone is the right surrogate marker at all, as the metabolic activity of the EAT may be independent of its total volume. Overall, different interventions seem to lead to similar reductions in EAT in both HFpEF and HFrEF, but we need more clinical trials to investigate the different roles of EAT across the spectrum of LVEF and underlying etiologies.Epicardial adipose tissue represents an important determinant for altered cell signals, energetic substrate, and an excessive immune response. Moreover, it shows additional diagnostic and prognostic properties in the HFpEF population, aside from the common markers of inflammation, cardiovascular dysfunction, and fibrosis. On the contrary, EAT is reduced in HFrEF patients, in whom its role is not fully understood and is likely related to a nurturing function. The assessment of EAT in clinical practice could lead to a better understanding of the molecular pathways and biological mechanisms responsible for HF syndromes, where it might represent a promising therapeutic target. In particular, understanding the EAT transcriptional profile in physiological and pathological conditions might represent the keystone to developing disease-modifying therapies in HFpEF, and to deep-phenotyping this not yet fully understood, poly-etiologic clinical syndrome. Until then, EAT remains a risk factor for adverse outcomes and not a treatment target. As effective therapies to reduce the disease burden, particularly of HFpEF, are sparse, EAT may provide novel insights into treatment strategies in the future.


Firms that stand to gain from the institutional framework of a free trade zone (FTZ) usually opt for locations in the FTZ region where they could expect higher returns on investment. This concentration of industries in FTZs can result in a reallocation of productivity, potentially leading to the “hollowing out” effect in existing industries, which can have a beggar-thy-neighbour effect on regional growth. Over the long term, the outcome, whether it leads to prosperity or detriment, hinges on the delicate balance between the immediate static loss associated with resource reallocation and the dynamic gains based on traditional manufacturing sector’s growth along the evolving FTZ environment. The nationwide success of economic zones in China has given rise to significant implications for protectionist policies, indicating a shift in policymakers’ strategies towards incorporating regional industrial policies into broader economy-wide competition policies. Unlike the trend towards political centralisation, during much of the reform period, China’s local governments enjoyed substantial autonomy in shaping economic policies, contributing to the development of many thriving regional economies.The Chinese Communist Party’s longstanding tradition of using growth-based criteria, such as local GDP growth, to evaluate and promote officials has led to a phenomenon akin to a “promotion competition” –3 among hereafter FTZ). Some of today’s FTZs are geographically located within earlier-established economic zones, such as Shenzhen and Hainan. In essence, n for some FTZs, they are categorised as a type of an economic zone within an economic zone, wielding the highest level of decision-making power among all types of zones in China. When superimposed on land and population size, the quasi-industrial-policy effect becomes especially pronounced in FTZs.This institutional autonomy of economic zones has continued to expand, most notably within the latest iteration of these zones, known as the Pilot Free Trade Zone  and geographic measures to investigate regional industrial agglomeration and neighbourhood effects.Methodologically, this study employs an integrated approach that combines total factor productivity . This model takes into account firms’ input-output efficiencies.First, d Petrin   are employed to identify varying industry responses to FTZ policies [Finally, finite mixture models  for firms maintaining Hicks neutrality [In terms of changes in productivity  are statistically significant at the 99% confidence level, indicating a general lower productivity level for firms located in the FTZs. In columns 3 and 4, when distance to the FTZ, cash payment to employees, and payroll payable are introduced as additional factors, the direction of coefficients remains unchanged for both firms in and outside the FTZs. However, the statistical significance changes: the presence of the FTZ institution is no longer statistically significant for productivity changes among firms within the zone; while for firms outside the FTZ, the farther they are from the FTZ, the higher their productivity tends to be. This suggests that firms experience increased productivity when situated close to conventional industrial centres but away from FTZs. Consequently, et al. [It is worth noting that listed firms often undergo changes in location and primary business activities over time. Therefore, the influence of FTZ institutions on these firms may not occur randomly at the time of change. To better capture the immediate response of firms to the benefits of FTZs, et al. , by incoAs is shown, the coefficients associated with the “one year prior to” and “the year of” a firm’s entry into the FTZ have shifted from being positive (as seen in columns 1–2) to becoming negatively significant (in columns 3–4). Conversely, the coefficients related to the “one year after” joining the FTZ have shown an opposite trend. In practical terms, firms that have recently relocated to or newly registered in FTZs often benefit from favourable zone policies, resulting in an immediate improvement in their short-term accounting performance.It appears that An implicit assumption made in the preceding analysis was that the influence of zone institutions on individual firms was identical. However, in practice, the impact of these institutions on output varies significantly across different industries, leading to divergent outcomes even when subjected to the same set of zone institutions. There is a possibility that FTZ institutions may elicit multi-directional responses from various industries, which could elucidate the limited positive observations across FTZs as a whole.FMM to examine the unobservable heterogeneity among firms:ρ represents the parameter of the mixture model, X stands for the explanatory variables, k denotes the number of classes, πk signifies the probability for the kth class, and ℵk represents the parameter specific to each class.In light of this, we have opted for the k = 3, the Bayesian Information Criteria is minimised, indicating the presence of three distinct pathways through which FTZ institutions can influence firms’ productivity. When we set e1.095) higher than firms outside, signifying a significant productivity boost attributed to the presence of FTZ institutions. Conversely, an opposing scenario emerges for intra-zone firms in Class 2, where the productivity of firms inside FTZs is only half (1/e0.658) that of firms located outside. This discrepancy becomes apparent when we account for geographic variations through city of registration. It becomes evident that the disparities between Classes 2 and 3 stem from technical differences in industry clustering. Notably, as none of the covariates are industry-dependent, the industry heterogeneity presented in In Class 3 of Considering the inherent diversity in economic performance among industries, the growth experienced by FTZs may largely be a result of their capacity to attract specific industries, thus driving accelerated growth within the zone. In practice, FTZs appear to exert a notable inhibitory effect on the productivity of manufacturing industries (as indicated by a coefficient of –0.522 in column 3). Conversely, they stimulate growth in the real estate and leasing industries (with coefficients of 1.711 and 1.837 respectively). This phenomenon can be attributed to the growth in housing and related financial industries, particularly after 2008, where these sectors exhibited significantly higher growth rates compared to the broader industry. For instance, between 2009 and 2022, the housing sector’s growth rate reached an impressive 393.29%, approximately one-third higher than that of the industry as a whole. This surge further solidifies its industry concentration in FTZs.Today’s FTZs have a propensity to attract firms from high-growth industries; however, this does not necessarily imply a direct causal impact on individual firm growth. Similar to the case of Guangdong, there is a noteworthy overrepresentation of real estate-related industries within FTZs. This concentration of non-productive industries may lead to a permanent alteration in the regional factor distribution, thereby affecting productivity disparities both within and outside the zones. Consequently, unlike previous experiences, it becomes apparent that the most crucial lesson from today’s FTZs and their impact on regional growth is that the intricate relationship between industry clustering inside the zones and productivity changes in the surrounding areas is not always a “win-win” scenario.There is a widely held belief that the concentration of industries plays a pivotal role in stimulating regional growth, making economic zones a key strategy adopted by local officials in their “promotion competition”. Present-day FTZs have evolved from the foundation laid by special economic zones. Possessing the utmost economic autonomy, local governments are dedicated to promoting and streamlining investments in industries that yield higher GDP output and/or generate greater economic rents.In theory, firms that can benefit from the institutional framework of FTZs tend to prefer locating within these zones, where capital investments offer better returns, thus fostering faster growth in the zone area. However, in reality, the continuous reduction of transaction cost (i.e. tax and tariff reduction), deregulation, and financial incentives (i.e. quota and subsidies) have resulted in uneven industry concentration within FTZs.From a broader perspective, basic economic theory suggests that factors of production become indifferent to agglomerating when institutional constraints are removed. In the context of spatial growth, the regional distribution of factor ratio can be permanently altered by industry clustering, leading to ongoing disparities in factor returns and productivity between zones and their surrounding areas. However, it is crucial to note that firms within FTZs are not necessarily more advanced in terms of productivity. If the policy-induced clustering of firms fails to enhance returns to inputs, it essentially amounts to a mere reallocation of productivity–a “siphon effect”.When local governments focus their support on a single industry to drive GDP growth, they may inadvertently stifle potential innovations that other industries or locations could have promoted. The agglomeration resulting from favourable quasi-industrial policies may lead to the reallocation of productivity to FTZs or potentially result in the hollowing out of existing industries, ultimately having an adverse impact on regional growth–a “beggar-thy-neighbour” effect. In the long term, whether this effect is one of prosperity or detriment hinges on the interplay between the static loss stemming from resources reallocation and the dynamic gains associated with economies of scale during FTZ development.