text:
Molecular Docking Analysis of Phytic Acid and 4-hydroxyisoleucine as Cyclooxygenase-2, Microsomal Prostaglandin E Synthase-2, Tyrosinase, Human Neutrophil Elastase, Matrix Metalloproteinase-2 and -9, Xanthine Oxidase, Squalene Synthase, Nitric Oxide Synthase, Human Aldose Reductase, and Lipoxygenase Inhibitors

Background: The phytoconstituents phytic acid and 4-hydroxyisoleucine have been reported to posses various biological properties. Objective: This prompted us to carry out the docking study on these two ligands (phytic acid & 4-hydroxyisoleucine) against eleven targeted enzymes. Materials and Methods: Phytic acid & 4-hydroxyisoleucine were evaluated on the docking behaviour of cyclooxygenase-2 (COX-2), microsomal prostaglandin E synthase-2 (mPGES-2), tyrosinase, human neutrophil elastase (HNE), matrix metalloproteinase (MMP 2 and 9), xanthine oxidase (XO), squalene synthase (SQS), nitric oxide synthase (NOS), human aldose reductase (HAR) and lipoxygenase (LOX) using Discovery Studio Version 3.1 (except for LOX, where Autodock 4.2 tool was used). Results: Docking and binding free energy analysis revealed that phytic acid exhibited the maximum binding energy for four target enzymes such as COX-2, mPGES-2, tyrosinase and HNE. Interestingly, we found that 4-hydroxyisoleucine has the potential to dock and bind with all of the eleven targeted enzymes. Conclusion: This present study has paved a new insight in understanding 4-hydroxyisoleucine as potential inhibitor against COX-2, mPGES-2, tyrosinase, HNE, MMP 2, MMP 9, XO, SQS, NOS, HAR and LOX. SUMMARY 4-hydroxyisoleucine has the potential to dock and bind with all 11targeted enzymes such as (cyclooxygenase-2 [COX-2], microsomal prostaglandin E synthase-2 [mPGES-2], tyrosinase, human neutrophil elastase [HNE], matrix metalloproteinase [MMP-2 and -9], xanthine oxidase, squalene synthase, nitric oxide synthase, human aldose reductase, and lipoxygenase) Moreover, docking studies and binding free energy calculations revealed that phytic acid exhibited the maximum binding energy for four target enzymes such as COX-2, mPGES-2, tyrosinase, and HNE; however, for other six target enzymes, it fails to dock. Abbreviations used: COX-2: Cyclooxygenase-2, mPGES-2: Microsomal prostaglandin E synthase-2, HNE: Human neutrophil elastase, MMP-2 and -9: Matrix metalloproteinase-2 and -9, XO: Xanthine oxidase, SQS: Squalene synthase, NOS: Nitric oxide synthase, HAR: Human aldose reductase, LOX: Lipoxygenase, ADME: Absorption, distribution, metabolism, and excretion, TOPKAT: Toxicity Prediction by Computer-assisted Technology.

MATERIALS AND METHODS
In this section, ligand preparation, target protein identification and preparation, molecular descriptors calculation, absorption, distribution, metabolism, and excretion (ADME), and Toxicity Prediction by Komputer-assisted Technology (TOPKAT) analysis were carried out according to the previously reported method [9] as briefly stated below.

Target protein identification and preparation
The three-dimensional protein structures of the COX-2 (PDB ID: 3 LN1 with resolution of 2.40 Å), mPGES-2 (PDB ID: 1Z9H with resolution of 2.60 Å), tyrosinase (PDB ID: 2Y9W with resolution of 2.30 Å), HNE (PDB ID: 1H1B with resolution of 2.00 Å), MMP-2 (PDB ID: 1QIB with resolution of 2.80 Å), MMP-9 (PDB ID: 4H1Q with resolution of 1.59 Å), XO (PDB ID: 3NRZ with resolution of 1.80 Å), SQS (PDB ID: 3ASX with resolution of 2.00 Å), NOS (PDB ID: 4NOS with resolution of 2.30 Å), HAR (PDB ID: 1US0 with resolution of 0.66 Å), and LOX (PDB ID: 1JNQ with resolution of 2.10 Å) were retrieved from the Research Collaboratory for Structural Bioinformatics Protein Data Bank (PDB) (Anonymous, www.rcsb.org). A chain of all proteins (except for XO and COX-2, where C chain; mPGES-2, where A, B, C, and D chains; and tyrosinase, where A and B chains) was preprocessed separately by deleting other chains (B, C, and D), ligand, as well as the crystallographically observed water molecules (water without hydrogen bonds). All the proteins above mentioned were prepared using UCSF Chimera software (www.cgi.ucsf.edu/chimera) for Autodock 4.2, whereas in the case of CDOCKER inbuild protein preparation protocol (Accelrys, San Diego, USA) was adopted.

Molecular descriptors calculation
Molinspiration online database was used for the two selected ligands to calculate thirteen descriptors (www.molinspiration.com) which are logP, polar surface area, molecular weight (MW), number of atoms, number of O or N, number of OH or NH, number of rotatable bonds, volume, drug-likeness including G-protein-coupled receptors ligand, ion channel modulator, kinase inhibitor, and nuclear receptor ligand, and the number of violations to Lipinski's rule.
Absorption, distribution, metabolism, and excretion and Toxicity Prediction by Komputer-assisted Technology analysis Both ADME and TOPKAT analyses were performed using Discovery Studio ® 3.1 (Accelrys, San Diego, USA). ADME analysis was performed using six descriptors such as human intestinal absorption, aqueous solubility, blood-brain barrier, cytochrome P450 2D6, plasma protein binding, and hepatotoxicity. As for the TOPKAT analysis, five descriptors were used which includes aerobic biodegradability (AB), Ames mutagenicity, ocular irritancy, skin irritancy, and skin sensitization.

Docking studies
Docking studies were performed on the protein crystal structures of COX-2, mPGES-2, tyrosinase, HNE, MMP-2, MMP-9, XO, SQS, NOS, and HAR obtained from PDB using the CDOCKER protocol under the protein-ligand interaction section in Discovery Studio ® 3.1 (Accelrys, San Diego, USA). In general, CDOCKER is a grid-based molecular docking method that employs CHARMM force fields. Protein was first held rigid while the ligands were allowed to flex during the refinement. Two hundred random ligand conformations were then generated from the initial ligand structure through high-temperature molecular dynamics followed by random rotations, refinement by grid-based (GRID I) simulated annealing, and a final grid-based or full force field minimization. [10] In this experiment, the ligand was heated to a temperature of 700 K in 2000 steps, and the cooling steps were set in 5000 steps to 300 K with the grid extension set to 10 Å. Hydrogen atoms were added to the structures, and all ionizable residues were set at their default protonation state at a neutral pH. For each ligand, top ten ligand binding poses were ranked according to their CDOCKER energies, and the predicted binding interactions were then analyzed, from which the best among the ten ligand binding poses were chosen and carried out in situ ligand minimization using a standard protocol. Docking was performed using Autodock 4.2 version, in which combined energy evaluation through precalculated grids of affinity potential employing various search algorithms to find the suitable binding position for a ligand on a given protein (LOX). All rotatable bonds in the ligands were kept free to allow flexible docking. Grid size was set to 60 × 60 × 60 grid points (x, y, and z), with spacing between grid points kept at 0.375 Å. The Lamarckian genetic algorithm was chosen to search for the best conformers. Standard docking protocol was applied. One hundred independent docking runs for each ligand were generated using genetic algorithm search. [11]

RESULTS AND DISCUSSION
Phytic acid and 4-hydroxyisoleucine were the two ligands selected for the present study; it could be beneficial to know the physicochemical and drug-likeness properties of these ligands before carry out docking studies. Lipinski's rule of five was applied to know the above said properties and further helps to determine whether a lead compound having a certain pharmacological or biological activity could be made into an orally active drug for human. [12] Violation of the Lipinski's rule of five is when logP >5, MW >500, number of N, O (hydrogen bond acceptor) >10, number of OH and NH (hydrogen bond donor) >5, and number of rotatable bond (rotb) >15. In the present study, 4-hydroxyisoleucine showed no violation with respect to thumb rule of five (Lipinski's rule of five). On the other hand, phytic acid showed three violations as given in Table 1. With regard to drug-likeness score, if the score is >0 is active, −5.0-0.0 is moderate active, and <−5.0 is inactive. [13] Phytic acid showed active bioactivity score (>0) toward all descriptors, whereas 4-hydroxyisoleucine has shown active bioactivity score toward two descriptors as shown in Table 2. ADME prediction is also required before carry out docking studies and which is now commonly acceptable in early stage of drug discovery, drug screening, and drug design, owing to its unique characteristic nature. [14] Table 3 shows the ADME profile of the two selected ligands, wherein phytic acid was predicted to have very poor intestinal absorption and hepatotoxic effect. On the other hand, 4-hydroxyisoleucine was predicted to have good intestinal absorption. The toxicity profile of the two ligands as depicted in Table 4 shows that phytic acid was nondegradable toward AB nature, and both the ligands were predicted to have ocular/eye irritancy effect in humans. COX is the key enzyme which catalyzes the conversion of arachidonic acid (AA) to prostaglandins. In human, COX exists in two isoforms; COX-1 is a constitutive enzyme, whereas COX-2 is an inducible enzyme. Cytokines and growth factors increase the expression of COX-2 mainly at inflammatory sites. [15] The docking studies and binding free energy reported in Table 5 show that phytic acid had the highest interaction energy (−42.20 kcal/mol) with COX-2 and both ligands exhibited interaction with Glu539 amino acid residue of COX-2 as shown in Table 5. Phytic acid has been reported to suppress the COX-2 expression in azoxymethane -induced colon cancer cells. [16] Similarly, in the present study, phytic acid exhibited interaction with Glu350, Trp531, and Asn546 amino acid residues of COX-2; this finding was in good agreement with Khokra et al. report. [17] In the case of 4-hydroxyisoleucine, there is no available reported investigation for their COX-2 inhibitory activity. mPGES-2 has exhibited board substrate specificity and also expressed constitutively in a variety of human tissues. Interestingly, it is not induced by pro-inflammatory signals like that of mPGES-1. [18] Recently, mPGES-2 has been reported to play a protective role against different types of liver injury. [19] As for the docking studies and binding free energy calculations with mPGES-2, phytic acid exhibited the highest interaction energy (−56.21 kcal/mol), and both ligands showed interaction with Ser295 amino acid residue of mPGES-2 as shown in Table 6. Interestingly, phytic acid interacts with all the four chains (A, B, C, and D) of mPGES-2. In the present study, both ligands had potential to dock with mPGES-2; however, until now, there is no report available with regard to their docking studies.   False False *0 -good, 1 -moderate, 2 -poor and 3 -very poor; **0 -extremely low, 1 -very low, 2 -low, 3 -good, 4 -optimal, 5 -too soluble, and 6 -warning; ***0 -very high penetrate, 1 -high, 2 -medium, 3 -low, and 4 -undefined. HIA: Human intestinal absorption; AS: Aqueous solubility; BBB: Blood-brain barrier; PPB: Plasma protein binding; CYP2D6: Cytochrome P450 2D6; HT: Hepatotoxicity; PSA: Polar surface area

S515
Tyrosinase is the key regulatory enzyme in melanin biosynthesis pathway that too particularly in the first two steps such as (i) tyrosine hydroxylation to 3, 4-dihydroxyphenylalanine (DOPA) and (ii) the oxidation of DOPA to dopaquinone. [20] The maximum interaction energy in the docking studies and binding free energy calculations with that of tyrosinase was exhibited by phytic acid (−33.26 kcal/mol), and 4-hydroxyisoleucine showed interaction with Pro14 amino acid residue of tyrosinase as shown in Table 7. Graf et al. have reported that phytic acid as inhibitor of mushroom tyrosinase, [21] this was good in agreement with the present finding. Previously, we reported L-cysteine as tyrosinase inhibitor using molecular docking; [22] similarly, in the present study, we again revealed another amino acid (4-hydroxyisoleucine) as tyrosinase inhibitor. HNE is key enzyme which plays a major role in degenerative and inflammatory diseases, through proteolysis and extracellular matrix (ECM) components. [23] HNE is another target protein/enzyme which its docking studies and binding free energy calculations showed phytic acid having the maximum interaction energy (−47.59 kcal/mol). Interestingly, both ligands exhibited interaction with Phe192 and Ser195 amino acid residues of elastase as shown in Table 8. In the present study, both ligands showed interaction with Ser195 amino acid residue of HNE; this was good in agreement with previous reports. [23,24] MMPs are a group of zinc-dependent endopeptidase which is capable of degrading ECM components, and among the MMPs family, MMP-2 and -9 were reported to be elevated in the pathological conditions such as inflammation, wound healing, cancer, and aging. [24] The docking studies and binding free energy reported in Table 9 show that 4-hydroxyisoleucine had the highest interaction energy (−43.47 kcal/mol) with that of MMP-2, but phytic acid fails to dock with the MMP-2. 4-hydroxyisoleucine showed interaction with Ala165, His201, and Glu202 amino acid residues of MMP-2 as shown in Table 9. As for the docking studies and binding free energy calculations with MMP-9, 4-hydroxyisoleucine exhibited the highest interaction energy (−38.60 kcal/mol), but phytic acid fails to dock with the same. 4-hydroxyisoleucine exhibited interaction with Ala189 and Gln227 amino acid residues of MMP-9 as shown in Table 10. In the present study, 4-hydroxyisoleucine exhibited interaction with Ala165, His201, and Glu202 amino acid residues of MMP-2 and with Ala189 and Gln227 amino acid residues of MMP-9, respectively, this finding was in good agreement with previous reports. [9,23] However, phytic acid fails to dock with the MMP-2 and -9 might due to the general poor binding phenomenon as reported by Akdogan et al. [25] XO is the key enzyme which catalyzes the oxidation of hypoxanthine to xanthine and then to uric acid. [9] XO is another target protein/ enzyme which its docking studies and binding free energy calculations showed 4-hydroxyisoleucine having the maximum interaction energy (−29.23 kcal/mol), but phytic acid fails to dock with the XO. Interestingly, 4-hydroxyisoleucine showed interaction with     Table 11. In the present study, 4-hydroxyisoleucine showed interaction with Arg880, Thr1010, and MOS1328 amino acid residues of XO; this was in good agreement with the previous report. [9] However, phytic acid fails to dock with the XO might due to the general poor binding phenomenon as reported by Akdogan et al. [25] SQS is the key enzyme involves in cholesterol biosynthesis pathway, and it is inhibition leads to direct decrease in cholesterol biosynthesis resulted in reduction of plasma cholesterol level. [9] The maximum interaction energy in the docking studies and binding free energy calculations with that of SQS was exhibited by 4-hydroxyisoleucine (−22.59 kcal/mol), but phytic acid fails to dock with the same. 4-hydroxyisoleucine exhibited interaction with Val175 and Gln212 amino acid residues of SQS as shown in Table 12. In the present study, 4-hydroxyisoleucine exhibited interaction with Val175 and Gln212 amino acid residues of SQS; this finding was in good agreement with the previous report. [9] However, phytic acid fails to dock with the SQS might due to the general poor binding phenomenon as reported by Akdogan et al. [25] NOS is a family of enzymes that catalyze the production of nitric oxide from L-arginine. Nitric oxide is a key cellular signaling molecule which plays a vital role in various cellular processes. [9] The docking studies and binding free energy reported in Table 13 show that 4-hydroxyisoleucine had the highest interaction energy (−24.66 kcal/mol) with that of NOS, but phytic acid fails to dock with the NOS. Interestingly, 4-hydroxyisoleucine does not interact with any amino acid residue of NOS. In the present study, phytic acid fails to dock with the NOS might due to the general poor binding phenomenon as reported by Akdogan et al. [25] HAR is the key enzyme which plays a major role in the development of secondary diabetic complications through polyol pathway, which follows the entry of excess glucose into the body. [26] HAR is another target protein/enzyme which its docking studies and binding free energy calculations showed 4-hydroxyisoleucine had the maximum interaction energy (−34.50 kcal/mol), but phytic acid fails to dock with the same. Interestingly, 4-hydroxyisoleucine showed interaction with six amino acid residues (Thr19, Trp20, Lys21, Asp43, Ser210, and Ile260) of HAR as shown in Table 14. Thus, the present finding was in good agreement with Umamaheswari et al. report. [27] However, phytic acid fails to dock with the HAR might due to the general poor binding phenomenon as reported by Akdogan et al. [25] LOXs are the class of oxidative enzymes, which catalyze the formation of hydroperoxy eicosatetraenoic acids (HPETEs) from AA. Moreover, these HPETEs are further reduced and transformed to form so-called eicosanoids. [28] The docking studies and binding free energy reported in Table 15 show that 4-hydroxyisoleucine with the lowest binding energy (−5.15 kcal/mol) using Autodock 4.2, and interestingly, 4-hydroxyisoleucine exhibited interaction with three amino acid residues (Ser510, His513, and Gln716) of LOX. The present finding was in good agreement with the previous report. [29] Phytic acid showed very least binding energy (+42.57 kcal/mol) with LOX, which might due to unfavorable interactions phenomenon as reported by Castro et al. [30]

CONCLUSION
In the present study, it was found that 4-hydroxyisoleucine has the potential to dock and bind with all of the 11 targeted enzymes, whereas phytic acid failed to dock and bind with six enzymes except COX-2, mPGES-2, tyrosinase, HNE, and LOX. Hence, it is strongly suggested that the results of this study have paved better understanding of 4-hydroxyisoleucine as potential COX-2, mPGES-2, tyrosinase, HNE, MMP-2, MMP-9, XO, SQS, NOS, HAR, and LOX inhibitor in


extfieldsofstudy: Medicine Psychology

s2fieldsofstudy: Biology Psychology

text:
Radiological biomarkers for diagnosis in PSP: Where are we and where do we need to be?

PSP is a pathologically defined neurodegenerative tauopathy with a variety of clinical presentations including typical Richardson's syndrome and other variant PSP syndromes. A large body of neuroimaging research has been conducted over the past two decades, with many studies proposing different structural MRI and molecular PET/SPECT biomarkers for PSP. These include measures of brainstem, cortical and striatal atrophy, diffusion weighted and diffusion tensor imaging abnormalities, [18F] fluorodeoxyglucose PET hypometabolism, reductions in striatal dopamine imaging and, most recently, PET imaging with ligands that bind to tau. Our aim was to critically evaluate the degree to which structural and molecular neuroimaging metrics fulfill criteria for diagnostic biomarkers of PSP. We queried the PubMed, Cochrane, Medline, and PSYCInfo databases for original research articles published in English over the past 20 years using postmortem diagnosis or the NINDS‐SPSP criteria as the diagnostic standard from 1996 to 2016. We define a five‐level theoretical construct for the utility of neuroimaging biomarkers in PSP, with level 1 representing group‐level findings, level 2 representing biomarkers with demonstrable individual‐level diagnostic utility, level 3 representing biomarkers for early disease, level 4 representing surrogate biomarkers of PSP pathology, and level 5 representing definitive PSP biomarkers of PSP pathology. We discuss the degree to which each of the currently available biomarkers fit into this theoretical construct, consider the role of biomarkers in the diagnosis of Richardson's syndrome, variant PSP syndromes and autopsy confirmed PSP, and emphasize current shortfalls in the field. © 2017 International Parkinson and Movement Disorder Society.

senting biomarkers for early disease, level 4 representing surrogate biomarkers of PSP pathology, and level 5 representing definitive PSP biomarkers of PSP pathology. We discuss the degree to which each of the currently available biomarkers fit into this theoretical construct, consider the role of biomarkers in the diagnosis of Richardson's syndrome, variant PSP syndromes and autopsy confirmed PSP, and emphasize current shortfalls in the field. V C 2017 International Parkinson and Movement Disorder Society.
K e y W ord s: progressive supranuclear palsy; diagnosis; magnetic resonance imaging; positron emission tomography; single-photon emission computed tomography Progressive supranuclear palsy (PSP) is a pathologic diagnosis with neurodegeneration characterized by abnormal tau pathology in the form of globose neurofibrillary tangles, tufted astrocytes, coiled bodies, and threads, 1 with a predominance of 4-repeat (4R) tau isoforms. 2 Tau pathology is typically observed in the brain stem, basal ganglia, diencephalon, and temporal, motor, and premotor cortices, 1,2 although distribution can vary. 3,4 The most commonly recognized clinical presentation of PSP is Richardson's syndrome (PSP-RS), in which patients have early and notable gait and postural instability, frequent falls, and abnormal vertical eye movements (supranuclear gaze palsy). 5,6 However, a number of other clinical presentations of PSP have been increasingly recognized, including but not limited to PSP with predominant parkinsonism (PSP-P), 6 PSP with progressive gait freezing (PSP-PGF), 7 PSP with predominant frontal presentation (PSP-F), 8 PSP with a predominant speech/language disorder (PSP-SL), 9 and PSP with predominant corticobasal syndrome (PSP-CBS). 10 We have recently developed the Movement Disorder Society-endorsed PSP clinical diagnostic criteria that recognize this heterogeneity and provide criteria for the different clinical variants of PSP. 11 A major challenge faced during the revision of the diagnostic criteria was to determine whether there was enough evidence to support the inclusion of neuroimaging biomarkers in the diagnosis of PSP-RS, the other variant syndromes of PSP (vPSP), or in the diagnosis of pathological PSP, and what role they should play in the diagnostic criteria. Table 1 provides a theoretical construct to judge the utility of diagnostic neuroimaging biomarkers in PSP. The first step is to demonstrate abnormalities in the group of interest compared with matched healthy controls and other clinically overlapping disease groups (level 1). In the context of PSP, this typically means demonstrating abnormalities in PSP-RS compared with other parkinsonian disorders, such as Parkinson's disease (PD), multiple system atrophy with predominant parkinsonism (MSA-P), and CBS. However, if one wishes to ultimately develop a diagnostic biomarker for PSP pathology, it is also important not to ignore vPSP, for which neuroimaging signatures may differ from PSP-RS. A biomarker differentiating PSP-F, PSP-SL, and PSP-CBS from other frontotemporal lobar degeneration spectrum disorders may also be valuable. For these group-level findings to translate into useful biomarkers, the next step is to demonstrate useful sensitivity and specificity (>80%) for the clinical diagnosis at the individual patient level (level 2). Biomarkers that perform well at this level could be valuable to support the clinical diagnosis. However, because these analyses are based on comparison with clinical diagnosis rather than the gold standard of neuropathology, there is still no evidence at this point that the biomarker adds anything to clinical diagnosis, other than to increase confidence. A biomarker could surpass clinical diagnosis if one can demonstrate utility for early clinical diagnosis, when patients have mild or nonspecific symptoms and signs before they meet clinical criteria for the disease (level 3), or if one can demonstrate that a biomarker has a strong relationship with the presence of PSP pathology regardless of clinical phenotype (level 4). The latter will ideally require the demonstration that a biomarker is highly associated with PSP pathology, not only in patients diagnosed with PSP-RS but also in vPSP, thus representing utility for the entire clinical spectrum of PSP. However, neuroimaging biomarkers that satisfy level 4 may still be considered only a surrogate marker of pathology, meaning that they correlate well with pathology but do not directly measure pathology. Thus, the holy grail in neuroimaging is to identify a biomarker that directly measures underlying pathology and hence could be considered a definitive pathological biomarker (level 5). We are getting closer to this goal with the development of PET ligands that can bind to abnormal tau in the brain, and current knowledge of these biomarkers will be discussed. At levels 4 and 5, the ideal biomarker would be one that is specific to PSP pathology, although biomarkers that could identify a 4R tauopathy could also be diagnostically useful. Another issue to consider when assessing the value of neuroimaging biomarkers is how well the proposed measures would translate into clinical practice; ideally they should be relatively inexpensive, convenient, safe, widely available, and comparable across different centers.
This review will utilize the theoretical construct outlined in Table 1 to evaluate the degree to which different proposed structural and functional neuroimaging metrics fulfill criteria as diagnostic biomarkers in PSP. As part of our efforts to develop the new diagnostic criteria, a detailed literature search and content evaluation was performed that formed the basis of this review (Supplemental Data).

Structural MRI Brain Stem Measures
Striking midbrain atrophy is typically observed in PSP-RS, and a number of midbrain metrics have been proposed as potential biomarkers. These metrics include visual assessment of midbrain atrophy, midbrain profile or the presence of specific morphological markers such as the "hummingbird" sign (atrophy of dorsal midbrain resembles hummingbird's head and bill in midsagittal plane 12 ), "Mickey Mouse" sign (rounded rather than rectangular midbrain peduncles in axial planes), 13 and "morning glory" sign (concavity of the lateral margin of the midbrain tegmentum in axial planes 14 ); see Figure 1. Quantitative measures of midbrain anterior-posterior diameter and midsagittal area or volume have also been assessed. Studies are in general agreement that midbrain measurements are smaller in PSP-RS compared with MSA and PD, 14-31 although overlap can occur at the individual level, particularly between PSP-RS and MSA. 15,16,28,30 Diagnostic sensitivity and specificity values ( Table 2) are typically high (>90%) for differentiating PSP-RS from controls and from MSA and PD using midbrain area, 15-17,32 although midbrain diameter 15,18-20,22,23,32 and volume 15,24 and visual assessments 13,14,20,21,25,33,34 have been more variable, not always meeting the 80% cut point required for a level 2 biomarker. Visual assessments of the midbrain can be particularly problematic because they are not quantitative, lack objectivity, and can be highly dependent on image acquisition and patient positioning. 35,36 A ratio of midbrain to pons area ( Fig. 1) in the midsagittal plane has been proposed as a biomarker to differentiate PSP-RS from MSA-P, given that MSA-P is associated with atrophy of the pons and sparing of the midbrain, the opposite pattern to PSP-RS. 15 Some studies have found high sensitivity and specificity for the midbrain-pons area ratio in differentiating PSP-RS from MSA-P and from PD, 15 Table 2. A number of studies have found that the MRPI was superior or equivalent to the midbrain-pons ratio in differentiating PSP-RS from MSA-P and PD 17,37,38,40,41 (Table 2). Fewer data are available to assess how well midbrain measures could differentiate PSP-RS from CBS. 13,45 Therefore, there is plenty of evidence to support brain stem measurements as level 2 diagnostic biomarkers in PSP-RS (Table 3). However, proposing one specific measure for the purposes of diagnostic criteria is challenging because centers differ in how they perform these measurements, and specific cut points vary and will likely be cohort-and acquisition-specific. The MRPI appears to be less affected by aging compared with the midbrain-pons ratio 46 but requires detailed measurement of a number of structures that may be difficult to standardize. Indeed, 1 multicenter study found that the MRPI did not perform as well as the midbrain-to-pons ratio in differentiating PSP-RS from PD and MSA-P. 32 However, another multicenter study showed high sensitivity/specificity for the MRPI in differentiating PSP-RS and PD and showed that an automated MRPI measurement that does not rely on rater reliability performs as well as a manual MRPI measurement. 43 Automated methods for measuring midbrain volume are also now available 47 and may improve standardization. There is evidence that these biomarkers could reach level 3 and show diagnostic value in early PSP-RS (Table 3). Abnormal MRPI and midbrain-pons ratios have been shown to predate and predict the development of PSP-RS in patients with clinically unclassifiable parkinsonism at baseline in a retrospective 23 and prospective study, 48 with abnormalities detected 15 months before patients fulfill criteria for PSP-RS in the retrospective study. 23 Given that the clinical diagnosis of PSP-RS has high sensitivity and specificity for pathological PSP, 13,49,50 the midbrain-based measures discussed above also tend to perform well in autopsy-confirmed studies. 19,29 However, it is less clear whether these measures add anything to the clinical diagnosis of PSP-RS in predicting pathology and hence could be level 4 biomarkers. 13 Group-level studies have failed to find midbrain atrophy in patients with PSP pathology who presented with clinical syndromes other than PSP-RS, 51 including patients presenting with CBS. 52 Conversely, reduced midbrain areas were identified in PSP-RS that had underlying corticobasal degeneration pathology. 51 It therefore appears that in many instances midbrain atrophy is related to the PSP-RS clinical was only present in 68.4%, even after a disease duration of 4.8 years. 13 However, midbrain atrophy has been observed in speech and language disorders that are confirmed or suspected of having PSP pathology, 53-56 as well as in PSP-F 8 and PSP-P. 39,57-59 Midbrain atrophy in vPSP is typically less severe than in PSP-RS, 39,56-58 although there is some suggestion that abnormalities on the MRPI could be an early feature in PSP-P 59 and have some value as a level 3 biomarker.

Cortical Measures
A number of group-level studies have demonstrated cortical atrophy in PSP-RS, typically involving the frontal lobes. 33,60-74 The focus of atrophy appears to be the premotor cortex, but atrophy also spreads into the prefrontal cortex. Studies have demonstrated that whole-brain and frontal atrophy are greater in PSP-RS than in PD 24,64,67,72,75 and MSA-P, 72 although visual assessment of frontal atrophy had poor sensitivity (17% and 57%) and moderate specificity (75% and 83%) in differentiating PSP-RS from MSA 13,20 in 2 studies, reflecting the fact that discernible frontal atrophy is only present in approximately 60% of PSP-RS patients. 13 Frontal atrophy may be more useful if considered in addition to brain stem regions. One study found that adding frontal, third ventricle, and wholebrain volumes to midbrain and SCP volumes improved the differentiation of PSP-RS from PD and MSA (sensitivity, 88.9%; specificity, 97.3%). 24 Another showed that combining frontal, ventricular, and whole-brain volumes could differentiate PSP-RS from PD and controls with 95.2% sensitivity and 90.9% specificity. 64 One caveat to consider, however, is that frontal atrophy is unlikely to differentiate PSP-RS from CBS, given that CBS shows striking frontal atrophy. 60,62,68,76 Quantitative methods for assessing frontal volume or thickness also vary widely across studies and may influence diagnostic utility.
Frontal atrophy also occurs in vPSP, particularly in PSP-F, 8 PSP-SL, 9,54,55 and PSP-CBS 52,77 and can be greater than in PSP-RS, 62 likely reflecting a shift in PSP pathological burden from brain stem to cortex. 78 The degree of frontal atrophy is similar in both PSP-PGF 79 and PSP-P 57 compared with PSP-RS. Although no diagnostic data are available on the value of frontal atrophy in vPSP, the presence of frontal atrophy would be consistent with these diagnoses. Data are needed to determine whether cortical measures could help to differentiate vPSP from other frontotemporal lobar degeneration disorders that are primarily characterized by frontal atrophy.

Other Subcortical Measures
Atrophy of subcortical structures, including the caudate nucleus, putamen, globus pallidus, subthalamus, and thalamus, has also been observed in group-level studies of PSP-RS either using visual assessment or volumetric measurements. 13,28,62,63,74,80-82 There is evidence that volumes of putamen, thalamus, and globus pallidus are smaller in PSP-RS than in PD, 82 with thalamus volumes also being smaller than in MSA-P. 28 However, studies have found that visual assessments of putamen and globus pallidus atrophy are not diagnostically useful in differentiating PSP-RS from MSA or PD. 13,20 The caudate nucleus, putamen, and thalamus have also been reported to be atrophic in CBS 13,62,83 and are unlikely to be diagnostically useful in differentiating PSP-RS and CBS. Basal ganglia structures have been reported to be atrophic in patients with PSP-P, 57 PSP-CBS, 52,77 and PSP-SL, 9 with thalamic atrophy reported in PSP-PGF. 79 However, the diagnostic value of these findings is unclear and limited to level 1 (

Pattern Approaches to Diagnosis
A number of studies have proposed that the assessment of multiple regions of the brain will optimize sensitivity and specificity for PSP-RS. These studies typically develop optimal prediction models 93 or use automated machine-learning techniques to identify diagnostic patterns. 94-100 A number of these studies have found that assessment of multiple regions including the midbrain, basal ganglia, 95,97,98,100 cerebellum, 98,100 or thalamus 99 provided excellent sensitivity and specificity to differentiate PSP-RS from PD and MSA-P. One study found that a prediction model using midbrain, putamen, and cerebellar gray-matter volumes could differentiate PSP-RS from MSA and PD with 90% sensitivity and 100% specificity in an early stage of the disease when not all patients had yet fulfilled clinical diagnostic criteria for these diseases. 100 It has also been suggested that volumetric white-matter measurements may show greater diagnostic utility than gray-matter measures. 94,96 There is also some evidence that a pattern-based approach using brain stem and cortical gray-and white-matter measures could be used in the differential diagnosis of autopsyconfirmed PSP and CBD. 93 Generally, assessing the pattern of atrophy, rather than focusing on specific regions, appears to be a sensible and sensitive and specific approach to differential diagnosis, although there is currently a lack of agreement across studies on which specific regions should be used, and further validation of these results in independent cohorts is necessary. In addition, no data are yet available on how well these approaches perform in vPSP. Further work is needed before these approaches can be incorporated into clinical criteria.

Diffusion Imaging
Measurements of microstructural damage using diffusion-weighted imaging (DWI) show some promise as biomarkers of PSP-RS. Apparent diffusion coefficient (ADC) measurements from DWI have been assessed in gray-and white-matter structures in PSP-RS, showing elevated ADC values in putamen, caudate, globus pallidus, midbrain, SCP, and prefrontal and precentral white matter. 101-107 PSP-RS patients typically show higher ADC values in the putamen, caudate nucleus, globus pallidus, SCP, and midbrain compared with PD, 102,106-108 with 1 study obtaining high sensitivity (90%) and specificity (100%) to differentiate PSP-RS from PD using values from the putamen 107 and another obtaining 100% sensitivity and specificity using the SCP. 103 Compared with MSA-P, PSP-RS has higher ADC values in the caudate nucleus 106 and SCP 103,106 but lower values in the MCP, 105,109 cerebellum, 110 and putamen. 101 Sensitivity and specificity values for differentiating PSP-RS from MSA-P are high using DWI of the SCP (sensitivity, 96.4%; specificity, 93.3% 103 ). Therefore, the diagnostic performance of DWI measurements is excellent, supporting these measurements as level 2 biomarkers (Table 3). There is no consensus regarding the best structure to assess, although the SCP appears promising.
Diffusion tensor imaging (DTI) allows for the assessment of directional water diffusion and the interrogation of specific white-matter tracts. White-matter tract degeneration has been demonstrated to be a striking feature of PSP-RS, with abnormalities observed predominantly in the SCP, cerebellum, body of the corpus callosum, cingulum, white-matter laminar of the thalamus, and premotor aspects of the superior longitudinal fasciculus. 63, [111][112][113][114][115][116][117][118][119][120][121][122][123][124] The majority of these white-matter tracts show greater degeneration in PSP-RS compared with PD 112,118,122,[125][126][127] and MSA-P. 72,118,126 Little data are currently available on the diagnostic utility of DTI measures, although the corpus callosum 113 and SCP 125 show high sensitivity and specificity in differentiating PSP-RS and PD. There is also evidence that adding DTI measures to the MRPI may help in the differentiation of PSP-RS from controls. 128 The diagnostic value of DTI measures to differentiate PSP-RS and MSA-P is unclear. It is also unclear whether DTI measures could differentiate PSP-RS and CBS, particularly given that patterns of DTI abnormalities overlap to a large degree between these 2 syndromes. 112,[129][130][131] A few studies have assessed DTI measures in PSP-P, which appears to show similar although slightly less severe patterns of tract abnormalities compared with PSP-RS. 128 Some studies have found regions with greater abnormalities in PSP-P compared with PSP-RS, although the results have not been consistent across studies. 117,120,128 In summary, DTI abnormalities are striking in PSP-RS and have the potential to be useful diagnostic biomarkers (Table 3). However, data are needed on the utility of both DWI and DTI measures in vPSP and autopsy-confirmed PSP. The issue of whether DWI and DTI measurements can be translated into clinical practice is also unclear, because there is little standardization of methods across studies and no established diagnostic cut points for these measurements.

PET/SPECT [ 18 F]FDG-PET
Studies of [ 18 F]-fluorodeoxyglucose PET (FDG-PET) have shown hypometabolism in the midbrain, basal ganglia, thalamus, and frontal lobes in PSP-RS, [132][133][134][135][136][137][138][139][140][141][142][143][144][145] with frontal involvement particularly targeting premotor, precentral, and prefrontal regions 134 and anterior cingulate 146 (Fig. 2A). In an autopsy cohort including 7 PSP patients (all PSP-RS), the most common FDG-PET findings were hypometabolism of the thalamus (100%), caudate (86%), midbrain (86%), and frontal lobes (71%). 145 PSP-RS tends to show greater frontal hypometabolism than PD and MSA, 146 with visual assessments of frontal hypometabolism producing good sensitivity (76%) and specificity (98%) for PSP-RS in 1 study. 147 Visual assessments of midbrain hypometabolism have performed modestly, with 1 study finding 79% sensitivity and 69% specificity in differentiating PSP-RS from MSA and CBS. 144 Consideration of the pattern of hypometabolism may hold more diagnostic promise. Visual assessment of the pattern of hypometabolism associated with PSP-RS (eg, anterior cingulate, midbrain, basal ganglia) gave 93% sensitivity and 90% specificity to differentiate PSP-RS from PD, MSA, and CBS in 1 study. 147 Automated pattern detection techniques have given mixed results. [148][149][150][151][152] Differentiating PSP-RS from CBS can be challenging, given that patterns of hypometabolism overlap between these 2 syndromes to a large degree 138,145,152 although there is some suggestion that PSP-RS may have greater hypometabolism in midbrain and thalamus, 136,153 and CBS patients have greater hypometabolism in parietal lobes. 135,138,153 The presence of hemispheric asymmetry in CBS may further help to differentiate it from PSP-RS. 145,152 Therefore, current evidence provides some support for frontal and midbrain hypometabolism or the combination of both as potential level 2 biomarkers of PSP-RS (Table 3). There is some evidence that hypometabolism in the striatum and cortex can be present before the development of clinical PSP-RS (level 3 biomarker), although this has only been observed in familial PSP. 154 Some FDG-PET findings have been reported in vPSP. One study found that PSP-P was associated with slightly greater hypometabolism of the putamen than PSP-RS, with less severe involvement of the thalamus, and that a putamen-to-thalamus ratio differentiated PSP-RS from PSP-P and PD with 100% sensitivity and 75% specificity. 155 The PSP-P patients in that study did not show much frontal hypometabolism. 155 Frontal hypometabolism has also not been observed in PSP-PGF, with midbrain hypometabolism only observed in 25% of patients. 156 Patients with PSP-SL have shown frontal, basal ganglia, and midbrain hypometabolism, 157,158 although these studies did not have autopsy confirmation. Taken together, these studies show that neither frontal nor midbrain hypometabolism is present consistently across the vPSP syndromes. Therefore, the presence of these features could be supportive of PSP, but the absence does not preclude underlying PSP.
However, there is a lack of standardization in the quantitative methods used across FDG-PET studies, particularly in regard to the choice of reference regions used to standardize regional uptake values, which vary across studies, including cerebellum, pons, cortical regions, or global mean values, each of which may have different limitations in PSP.

Dopamine Imaging
Striatal presynaptic dopamine binding, measured using dopamine active transporter (DAT) imaging using [123I]-FP-CIT SPECT or [18F]FP-CIT-PET, is consistently decreased in PSP-RS compared with controls 159 (Fig. 2B). However, decreased DAT binding has also been observed in PD, MSA-P, and CBS, [160][161][162][163][164] without differences in the degree of general striatal binding observed across groups. 160,162,165 However, studies have found that the caudate nucleus is affected to a greater degree in PSP-RS than in PD 161,163,166,167 and that regional patterns of binding, such as ratio of caudate to ventral striatum (sensitivity, 94%; specificity, 92%), 163 ratio of caudate to putamen, 166 or ratio of anterior-posterior putamen, 167 could help to differentiate PSP-RS from other parkinsonian disorders; however, diagnostic performance has not always been consistent with these measures. 164,167 It has also been shown that PSP-RS shows more symmetric striatal binding than PD, 168 although the diagnostic value of this finding is unclear. Overall the finding of reduced striatal DAT binding is highly supportive and sensitive for a diagnosis of PSP-RS, but heterogeneity across studies and lack of diagnostic data limit its value in differentiating across parkinsonian disorders (Table 3). Midbrain DAT binding is also decreased in PSP-RS, with lower binding than in PD but a degree of binding similar to in MSA. 160,169 Brain stem DAT levels could differentiate PSP-RS and MSA from PD with 89.7% sensitivity and 94.1% specificity in 1 study. 169 Little is currently known about the diagnostic utility of DAT findings in vPSP, although there is evidence from a few studies that both PSP-PGF and PSP-P are associated with striatal DAT reductions similar to those in PSP-RS, 156,[170][171][172] with similar putamen-to-caudate ratios. 171 Imaging using D2 receptor ligands, most commonly [123I]-IZBM SPECT, to assess postsynaptic dopaminergic function also appears to be sensitive in PSP-RS, with the majority of patients showing striatal reductions. [172][173][174][175][176] However, the value of D2 receptor ligand imaging in the differential diagnosis from other parkinsonian disorders is unclear. 162,175,176 In addition, there is some evidence that striatal uptake may not be reduced in PSP-P. 172

Tau-PET Imaging
The development of PET ligands that can bind to aggregated tau inclusions in the brain has been an exciting recent advance in the field with the potential of becoming a biomarker of tau pathology. A number of tau-PET ligands have been developed, 177 but the [ 18 F]AV-1451 (previously known as T807) ligand 178,179 has been the most widely used to date. Studies have demonstrated relatively consistent patterns of increased [ 18 F]AV-1451 uptake in PSP-RS compared with controls in the globus pallidus, putamen, caudate nucleus, thalamus, subthalamic nucleus, midbrain, and dentate nucleus of the cerebellum 180-184 ( Fig. 2C and E). The cortex has typically shown less striking uptake in PSP-RS, 183 with measures from subcortical structures showing the most promise as potential diagnostic biomarkers. 180,182,183 Quantification of globus pallidus retention provided sensitivity and specificity of 93% in differentiating PSP-RS from controls and 93% sensitivity and 100% specificity in differentiating PSP-RS and PD in 1 study, 180 although the thalamus provided the best separation between PSP-RS and controls in another. 182 There is also evidence that the pattern of uptake in PSP-RS differs from that in Alzheimer's disease (AD), with many of the PSP-RSrelated regions showing greater uptake in PSP-RS than in AD despite AD showing greater cortical [ 18 F]AV-1451 uptake. 183,184 Therefore, there is some evidence to support [ 18 F]AV-1451 as a level 2 biomarker of PSP-RS. A caveat is that overlap in the [ 18 F]AV-1451 signal is observed between PSP-RS and controls, 182 with 1 study failing to observe differences between PSP-RS and controls. 185 Standardization of methods will also be required, including optimizing scan time and quantitative outcomes. Current studies have analyzed standard uptake value ratio (SUVR) values referenced to cerebellar gray-matter 180,182,183 or binding potentials. 184 Although early studies are certainly encouraging, several limitations of [ 18 F]AV-1451 need to be considered. One caveat is that regions that show [ 18 F]AV-1451 uptake in PSP, including the basal ganglia, thalamus, midbrain, and dorsal cerebellum, also show some degree of "off-target" binding in normal subjects, which increases with age. 186,187 The nature of this binding is unclear. Although age correction in quantitative studies may go some ways to correct for this off-target binding, it will likely limit the value of [ 18 F]AV-1451 in the differential diagnosis of individual patients. Furthermore, it is unknown whether the off-target signal may also be altered by the disease in PSP, confounding any potential true signal of tau. Another caveat comes from an apparent disconnect between in-vivo and ex vivo studies. Although regions that show elevated binding typically show tau deposition at autopsy, autoradiographic studies have found little or no binding of [ 18 F]AV-1451 to tau in autopsied brains of PSP patients, 182,[187][188][189][190][191][192] casting doubt on whether the signal identified by [ 18 F]AV-1451 reflects tau pathology and whether it could be considered a level 5 biomarker of tau. This kind of disconnect is not uncommon for PET tracers, and the utility of such in vitro studies has been questioned. 193 However, a recent article found that tau pathology discovered postmortem in a patient with PSP correlated with antemortem FDG-PET but not with [ 18 F]AV-1451 signal. 190 Another caveat is that elevated [ 18 F]AV-1451 uptake has also been observed in nontau diseases, 187,194 which again questions the specificity of the ligand to 4R tau. Another chemically distinct tau PET ligand, THK-5351, 195 was found to have high affinity for PSP tau lesions in an autoradiographic study 196 and has shown uptake in the globus pallidus and midbrain 196 in patients with PSP-RS ( Fig. 2D and  F). However, the degree of off-target THK-5351 binding in PSP-related regions is at least as high, if not higher, than that observed with [ 18 F]AV-1451. 197 Overall, much more work needs to be done to evaluate these PET tracers. It is likely that different tau-PET ligands may bind to tau conformers with differing sensitivity and specificity and show different off-target binding, and hence head-to-head and indirect comparisons of the currently available tau imaging agents are needed.

Other Biomarkers
There are a number of other neuroimaging biomarkers that have been assessed in PSP-RS with fewer data available to assess diagnostic value. MR modalities that demonstrate abnormalities in PSP-RS include magnetic resonance spectroscopy and magnetization transfer imaging, 73,[198][199][200][201][202][203][204][205] although the ability of these modalities to differentiate PSP-RS from other parkinsonian disorders is unclear. 181,184,185,205 Resting-state (task-free) functional MRI has also been used to demonstrate abnormalities in functional connectivity in PSP-RS across the network of PSP-RS-associated regions, 63,206,207 but the loss of cortical connectivity is not specific to PSP-RS versus PD. 208 Longitudinal MR studies have shown increased rates of whole-brain, cortical, and midbrain atrophy and SCP diffusivity in PSP-RS compared with controls, [209][210][211][212][213][214][215][216][217][218] with some evidence for greater rates than in PD, but similar rates of whole-brain and midbrain atrophy as in MSA-P. 212,215 However, cortical and whole-brain rates of atrophy are greater in CBS than in PSP-RS. 209,213 Cerebral blood flow single-photon emission computed tomography studies have demonstrated frontal [219][220][221][222][223][224][225] and, less commonly, thalamic 220 and striatal 222 hypoperfusion in PSP-RS. 221,226 Findings concerning differentiating PSP-RS from other parkinsonian disorders are lacking here, although PSP-RS may show greater frontal hypoperfusion than PD. 224,227 Abnormalities in other neurotransmitter systems, such as the cholinergic [228][229][230] and serotoninergic 231 systems, have also been demonstrated in PSP-RS.

Conclusions
Neuroimaging research over the last several decades has improved our understanding of the neurobiology of PSP but has not yielded many confirmed diagnostic biomarkers ( Table 3). The most mature research area is the assessment of midbrain measurements, which has yielded a number of measures that have good sensitivity and specificity for PSP-RS versus other parkinsonian disorders, such as midbrain-pons area and the MRPI, which appear to be the most reliable biomarkers for the diagnosis of PSP-RS. The presence of frontal atrophy and hypometabolism are also prominent features of PSP-RS and may improve diagnosis when considered together with midbrain atrophy. It is clear that PSP-RS is associated with striking damage to the white matter, with DWI measures of the SCP providing good sensitivity and specificity for PSP-RS diagnosis, although data supporting this measure come from only a couple of studies. DTI measures could prove to be very valuable, although more work is needed to provide and validate standardized measures of the kind that could be used in diagnostic criteria. Measures of dopamine function are highly sensitive to PSP-RS and many of the vPSP syndromes, but specificity is low, and thus they are less useful in ruling out other parkinsonian syndromes. Data so far only support neuroimaging biomarkers as level 2 biomarkers for PSP-RS. Only a handful of studies have assessed patients early in the disease course to suggest level 3 biomarkers. More work is needed to assess the value of these measures in vPSP and in autopsyconfirmed cases to determine whether they could be useful level 4 biomarkers. Capturing the disease in its earliest phase will also be critical for developing wellvalidated level 3 biomarkers. Last, tau-PET imaging techniques are exciting, but more work is needed to truly understand the biological underpinnings of the tau-PET signal in PSP. However, these are early days in tau-PET imaging, and we expect our understanding of these biomarkers to increase exponentially over the coming years.


s2fieldsofstudy: Biology Medicine

text:
High levels of Nesfatin-1 in relation to the dysfunction of the hypothalamic–pituitary–adrenal and hypothalamus–pituitary–thyroid axes in depressed patients with subclinical hypothyroidism

Despite the increasing amount of evidence suggesting a relationship between depression and subclinical hypothyroidism (SCH), the exact mechanism underlying this relationship remains unclear. The main purpose of this study was to investigate the roles of plasma Nesfatin-1 levels and dysfunction of the hypothalamic–pituitary–adrenal (HPA) and hypothalamus–pituitary–thyroid (HPT) axes in the comorbidity of depression and SCH. Dysfunctions of the HPA and HPT axes were detected by measuring plasma corticosterone and thyroid-stimulating hormone (TSH) concentrations, respectively. Subjects in the patient group were selected from patients hospitalized at the Anhui Mental Health Center, and subjects in the control group were recruited from healthy volunteers. Healthy control subjects were matched to the patients in terms of weight and body mass index. The Hamilton Depression Rating Scale was administered to both the groups. The enzyme-linked immunosorbent assay method was used to measure plasma Nesfatin-1, corticosterone, and TSH levels. A radioimmunoassay kit was used for the measurement of the plasma-free triiodothyronine and plasma-free thyroxine. The results showed that the Hamilton Depression Rating Scale scores and average Nesfatin-1, corticosterone, and TSH levels were significantly higher in depressed patients with SCH than in the control group. Moreover, positive relationships were observed between Nesfatin-1 levels and the concentrations of corticosterone (r=0.626, P<0.001) and TSH (r=0.229, P=0.036) in depressed patients with SCH. These findings indicate that Nesfatin-1 is involved in the comorbidity of depression and SCH, and the mechanism underlying this involvement might be related to the dysfunction of the HPA and HPT axes.

Introduction
Depression is the most common mood disorder and is estimated to affect 350 million people worldwide according to the World Health Organization. Although several behavioral and biologic factors may be involved in precipitating and perpetuating the symptoms of depression, 1-3 the principal causes are not clear. A correlation between thyroid dysfunction and depression has been consistently reported in recent literature. 4,5 Clinical hypothyroidism (also known as overt hypothyroidism) has traditionally been recognized as an important cause of depression, 6 and thyroid hormones have been used as accelerators and augmenters of an antidepressant response for several decades. 7,8 Moreover, dysfunctions of the hypothalamus-pituitary-thyroid (HPT) axis have been documented in patients with depression. 9 submit your manuscript | www.dovepress.com Dovepress Dovepress 1648 Xu et al Subclinical hypothyroidism (SCH) is characterized by elevation of the plasma thyroid-stimulating hormone (TSH) concentration above the statistically defined upper limit of the reference range when plasma-free triiodothyronine (fT3) and plasma-free thyroxine (fT4) concentrations are within their reference ranges, and it is a common thyroid dysfunction that occurs in 4%-20% of the adult population. 10 Although the relationship between depression and SCH remains controversial, an increasing amount of evidence suggests that SCH is associated with neuropsychiatric disorders such as cognitive dysfunction 11 and depression. 12 Depression is reportedly observed more frequently among individuals with SCH than among those with overt hypothyroidism, 13 and patients with SCH exhibit a twofold higher prevalence of depression-like symptoms relative to healthy individuals. 14 Therefore, the guidelines of the American Association of Clinical Endocrinologists recommended that the diagnosis of SCH must be considered in every patient with depression. 15 Nesfatin-1, a novel satiety factor, plays a role in integrating feeding, glucose homeostasis, and energy expenditure. 16,17 Apart from the findings that Nesfatin-1 is distributed in stress-related brain regions, including the hypothalamus, and colocalized with stress-related substances, 18,19 intracerebroventricular administration of Nesfatin-1 has been reported to be able to activate the hypothalamic-pituitary-adrenal (HPA) axis, the hyperactivity of which is proposed to be among the causal factors for triggering depressive episodes. 20 Similarly, the results of our previous study confirmed that intraperitoneal administration of Nesfatin-1 in a single dose or chronically resulted in depression-like behavior and HPA axis hyperactivity in rats. 21 Furthermore, an increased plasma concentration of Nesfatin-1 and an increased hypothalamic mRNA expression of Nesfatin-1 have been observed in animal models of depression. 22 Consistent with the animal results, clinical studies have reported that the plasma Nesfatin-1 level is significantly higher in patients with major depressive disorder 23 than in healthy controls and is associated with elevated scores of anxiety and depression. 23,24 Given the roles of the HPA axis and Nesfatin-1 in the pathophysiology of depression, the plasma concentrations of Nesfatin-1 and corticosterone were further evaluated in depressed patients with SCH in the present study, and the relationship between Nesfatin-1 and corticosterone concentrations was also investigated.
Recent evidence has indicated a close relationship between Nesfatin-1 and thyroid function. 25 The level of Nesfatin-1 has been found to be low in children with hyperthyroidism. 26 In another study, the concentration of Nesfatin-1 was shown to be higher in hyperthyroid patients than in controls, and a negative correlation was observed between Nesfatin-1 and TSH concentrations. 27 A similar negative correlation was reported in patients with type 2 diabetes in a recent study. 25 However, to the best of our knowledge, little is known about the role of the plasma Nesfatin-1 levels in depressed patients with SCH. Considering the elevated plasma TSH concentration in depressed patients with SCH, another objective of this study was to investigate the association between Nesfatin-1 and TSH concentrations in depressed patients with SCH.
Given the dysfunction of the HPT and HPA axes, together with the important role of Nesfatin-1 in the pathophysiology of depression and SCH, we hypothesized that the plasma Nesfatin-1 levels should be increased in depressed patients with SCH and that this may be associated with the hyperactivity of the HPA axis and the overproduction of TSH. To test this hypothesis, subjects in the patient group were selected from patients hospitalized at the Anhui Mental Health Center, and subjects in the control group were recruited from healthy volunteers. The plasma Nesfatin-1, corticosterone, and TSH levels were measured using the enzyme-linked immunosorbent assay, and the plasma fT3 and fT4 levels were measured using a radioimmunoassay kit. Correlations were evaluated using a Pearson correlation test.

Materials and methods subjects
A total of 1,234 subjects with depression, according to the Diagnostic and Statistical Manual for Psychiatric Disorders -Fourth Version, who were hospitalized at the Anhui Mental Health Center from February 2016 to February 2017 were scanned in the study, and 83 depressed patients with a comorbidity of SCH were selected from the above patients on the basis of the following biochemical evidence: a TSH concentration greater than the reference range (TSH .4.50 mIU/L), with fT3 and fT4 within the reference range (fT3 between 1.7 and 3.7 pg/mL, fT4 between 0.8 and 2 ng/dL). The inclusion criteria were as follows: 1) all newly diagnosed patients with a Diagnostic and Statistical Manual for Psychiatric Disorders -Fourth Version diagnosis of major depressive disorder, minor depressive disorder, dysthymia, or adjustment disorder (with depressed mood); 2) depressed patients with comorbid SCH (defined by elevated plasma TSH levels and normal plasma fT3 and fT4 levels); and 3) patients who were not receiving an antidepressant, electroconvulsive therapy, or antithyroid drugs. The exclusion criteria included the following: 1) patients with a psychotic illness other than depressive disorders, 2) patients with thyroid diseases other Neuropsychiatric Disease and Treatment 2017:13 submit your manuscript | www.dovepress.com Dovepress Dovepress 1649 role of Nesfatin-1 and dysfunction of the hPa and hPT axes than SCH, and 3) patients who were already on treatment for a depressive illness or thyroid dysfunction. In contrast, 60 healthy volunteers, selected on the basis of the biochemical evidence of normal thyroid function (TSH, fT3, and fT4 levels within the reference range), formed the control group. Figure 1 summarizes the recruitment process. In accordance with the principles of the Declaration of Helsinki, all subjects gave written informed consent prior to participation. This study was approved by the Ethics Committee of the Anhui Mental Health Center.

Measurements
Weight and height measurements and the body mass index (BMI) calculation were carried out meticulously for all subjects. Study nurses administered questionnaires through face-to-face interviews. After an overnight fast, blood samples were drawn in the morning at approximately 8 a.m. from a forearm vein of the participants. Tubes that had a 5-mL capacity and contained EDTA were used for collecting blood. After centrifugation of the blood at 3,000× g for 5 min at 4°C, the plasma was obtained. The separated plasma was stored in a -80°C freezer until the time of assay.
The concentrations of Nesfatin-1, corticosterone, and TSH were measured using commercially available enzyme-linked immunosorbent assay kits (Nesfatin-1: Cusabio Biotech. Co., Ltd, Wuhan, Hubei, People's Republic of China; corticosterone: Enzo Life Sciences, Inc., New York, NY, USA; TSH: Cusabio Biotech. Co., Ltd) according to the manufacturer's instructions. The plasma fT3 and fT4 levels were measured using a radioimmunoassay kit (North Institute of Biological Technology, Beijing, People's Republic of China) and apparatus from USTC Zonkia (USTC Zonkia Scientific Instruments Co., Ltd, Anhui, People's Republic of China).

statistical analysis
All of the statistical analyses were performed using Statistical Package for the Social Sciences version 12.0.1 (SPSS Inc., Chicago, IL, USA). Data are expressed as the mean ± standard error of the mean, and P,0.05 was considered statistically significant. A one-sample Kolmogorov-Smirnov test showed a normal distribution of the continuous variables (age, BMI, Hamilton Depression Rating Scale [HAM-D] scores, and the concentrations of Nesfatin-1, corticosterone, TSH, fT3, and fT4) in both the patient group and the control group. A Student's t-test was used to evaluate differences between the groups (age, BMI, HAM-D scores, and the concentrations of Nesfatin-1, corticosterone, TSH, fT3, and fT4). To analyze the sex difference between groups, the χ 2 test was used. The correlation analyses were performed using a Pearson correlation test.

Results
The demographic variables did not show any statistically significant difference between the groups. As shown in Table 1, differences between the groups were not statistically  Table 1).
The results of the Pearson correlation analysis showed that the plasma Nesfatin-1 concentrations were positively correlated with corticosterone concentrations among the depressed patients with SCH (r=0.626, P,0.001, Figure 2A). However, no such correlation was observed in the control group (r=0.012, P=0.926, Figure 2B).
Similarly, a positive correlation was found between the plasma Nesfatin-1 and TSH levels in the patient group (r=0.229, P=0.036, Figure 3A), but not in the control group (r=0.038, P=0.773, Figure 3B).

Discussion
In the present study, we demonstrated that the HAM-D scores and mean Nesfatin-1, corticosterone, and TSH concentrations were significantly higher in depressed patients with SCH than in healthy controls. Moreover, the plasma Nesfatin-1 concentrations were found to be positively correlated with corticosterone and TSH concentrations in depressed patients with SCH.
Accumulating evidence supports an association between depression and SCH. 28 SCH has been reported to markedly increase the risk of depression, 13 and the prevalence of depressive symptoms in the SCH population has been reported to be 63.5%. 29 Consistent with this, in the present study, the prevalence of SCH in depressed patients was 6.7% (83/1,234), whereas the prevalence of SCH in healthy subjects was 0.0% (0/60) according to the results of the blood tests. These findings highlight the importance of a depressive evaluation in the management of patients with SCH and suggest that SCH might be considered as a possible risk factor for depression.
Nesfatin-1, a novel satiety molecule, is involved in various affective disorders, including anxiety disorder and depression. 30,31 The results of clinical studies have demonstrated that the average Nesfatin-1 level is significantly higher in patients with major depressive disorder than in healthy controls. 23 Similarly, the results of animal studies have indicated that water-avoidance stress, which is a model of acute depression, could increase the plasma Nesfatin-1 levels. 22 Consistent with these findings, our results showed Notes: a χ 2 score. **P,0.001 was considered statistically significant. Abbreviations: BMi, body mass index; haM-D, hamilton Depression rating scale; seM, standard error of the mean.  20,21 suggesting that Nesfatin-1 is involved in the regulation of depression, and the underlying mechanism may relate to the relationship between Nesfatin-1 and hyperactivity of the HPA axis. 33 Interestingly, the results of our study confirmed that plasma Nesfatin-1 levels were positively correlated with corticosterone levels in depressed patients with SCH. Given that the central administration of Nesfatin-1 infusion has been shown to increase corticotropin-releasing hormone levels in the hypothalamus, 33 it is logical to hypothesize that the dysfunction of the HPA axis in depressed patients with SCH might be due to increased plasma Nesfatin-1 levels.
Recent studies corroborate the suggestion that Nesfatin-1 may have a close relationship with thyroid function. Correlations between Nesfatin-1 and TSH concentrations have been documented in several thyroid disorders. 25,27 In our study, we found that there was a positive relation between Nesfatin-1 and TSH concentrations in depressed patients with SCH; however, the underlying mechanisms remain unclear. Recent studies have shown that Nesfatin-1 is colocalized with thyrotropin-releasing hormone (TRH) and affects the membrane potential of TRH neurons in the paraventricular

1652
Xu et al nucleus, 34,35 which is known to be involved in the regulation of thyroid function. Moreover, a central Nesfatin-1 infusion has been shown to increase the TRH levels in the paraventricular nucleus of the hypothalamus. 33 In contrast, the central administration of anti-TRH has been shown to attenuate the Nesfatin-1-induced reduction in food intake, suggesting that TRH mediates the anorectic effect of Nesfatin-1 in the hypothalamus. 33 Together with the observation that the plasma level of Nesfatin-1, which crosses the blood-brain barrier without saturation, 36,37 is correlated with the level of Nesfatin-1 in the cerebrospinal fluid, 38 we speculated that the increased plasma Nesfatin-1 levels might regulate thyroid function by adjusting TRH section in the hypothalamus, thereby resulting in SCH in depressed patients. Further research is required to explore this hypothesis.
Several limitations in this study should also be acknowledged. First, the study's findings should be interpreted with caution until they have been replicated in larger patient groups as the sample size was small. Second, due to the crosssectional study design, no causality can be determined. Third, although all of the patients were drug free for at least 2 weeks, we were unable to take into account the probable long-term effects of previous drugs on their plasma Nesfatin-1 levels.

Conclusion
In conclusion, this study is the first to reveal that Nesfatin-1 levels are significantly higher in depressed patients with SCH than in healthy controls, which might be related to dysfunction of the HPA and HPT axes. However, the roles of neuropeptides such as Nesfatin-1 in depression and SCH need to be investigated in further studies.