1. Introduction
Major depressive disorder (MDD) is a mood disorder with a high lifetime prevalence, particularly in some European countries [1]. The pathophysiology of MDD has not yet been fully clarified, although various mechanisms such as altered neurotransmission, dysregulation of the hypothalamus–pituitary–adrenal axis, reduced hippocampal volume, increased systemic inflammation, and influence of genetic factors have been described [2,3]. Even though onset often begins in childhood, different pathophysiological mechanisms might be present in adolescents and adults with MDD [4]. One of the current research strategies is a robust identification of biomarkers useful for diagnosis, severity and prognosis determination, and antidepressant response monitoring. A recent systematic review on metabolomics in MDD and bipolar diseases (BD) identified three possible metabolic pathways involved in the pathophysiology of these diseases—energy/mitochondrial metabolism, glutamatergic metabolism, and neurotransmission [5]. Energy metabolism imbalances due to impaired oxidative phosphorylation have a role in the etiology of neurological diseases. Such alterations are linked with oxidative stress (OS) [6] and a positive association between OS and depressive disorder has been already discussed [7,8,9,10]. The oxidative stress hypothesis of depressive disorder was proposed suggesting volumetric reductions in specific brain areas, such as the prefrontal cortex (PFC) and hippocampus, could be the causes of elevated OS and thus increased free radical production in these regions [9,11].
One of the putative OS markers could be o,o′-dityrosine (di-Tyr). It is metabolically stable and excreted as non-changed in urine, enabling easy detection both in plasma and urine. In conditions of whole-body oxidative stress, such as atherosclerosis, acute inflammation, or systemic bacterial infections, di-Tyr has been shown to be a potential biomarker as its concentrations were significantly elevated compared to levels of healthy individuals. It is formed after oxidative stress in a protein leading to subsequent proteolysis. Hence, it is considered not only a marker for oxidative protein damage, but it may also serve as a “marking” step for protein degradation [12,13]. Myeloperoxidase is an enzyme that catalyses the production of free radical derivatives and contributes to the oxidation of L-Tyr to a stable di-Tyr. Apart from immune cells, it is also expressed in the brain, where its increased expression was related to neurodegeneration [14,15].
Energy metabolism and cellular reducing potential/status could be reflected by NAD(P)H to NAD(P)+ ratio. Nicotinamide adenine dinucleotide (NADH) acts as a carrier of electrons that can donate to the respiratory chain. Depressed patients were found to have reduced blood flow and glucose metabolism in the specific brain areas such as PFC [16]. Several metabolic processes could affect the cytosolic ratio of NADH/NAD+ and subsequently could exert their effect on glycolytic glucose flow and blood flow rate in the brain [17,18]. Nicotinamide adenine dinucleotide phosphate (NADPH) is another carrier of electrons with roles in cellular anabolic metabolism of lipids and antioxidative protection. It is an indispensable substrate for maintaining and regeneration of glutathione and cytoplasmic coenzyme Q10 and thus contributes to antioxidant defence [19,20]. The metabolism of NADPH is linked with several metabolic pathways, including energy metabolism [21].
3-hydroxybutyrate (3HB), also known as β-hydroxybutyrate, is one of the ketone bodies and supports mammalian survival by serving as an alternative source of ATP during conditions with energy deficits. Its higher levels were measured during starvation, caloric restriction, high-intensity exercise, and a low-carbohydrate ketogenic diet [22,23]. 3HB is an essential source of acetyl moieties for sustaining the cellular metabolism in the brain. In addition to its metabolic function, 3HB possesses the signalling and regulatory roles through receptors and histone methylation [24]. One of the 3HB receptors, hydroxycarboxylic acid receptor 2 (HCAR2), has been highly expressed in macrophages and microglia in the brain showing anti-inflammatory and neuroprotective effects [25,26]. 3HB itself has been proposed to control emotional systems in the brain through energy metabolism processes [27]. It has been shown that mild elevation of ketone bodies (3HB, acetoacetate) in the blood occurs physiologically during the process of aging [28]. Recently, serum 3HB concentrations have been proposed as markers of depression severity [27,29].
To evaluate oxidative damage, levels of oxidative stress products or levels of antioxidant enzymes and molecules might be estimated. This experimental study aimed to identify any abnormalities regarding energy metabolism and oxidative stress from peripheral blood in adolescent and adult patients with MDD and observe the effect of a short antidepressant treatment.
2. Materials and Methods
2.1. Population and Blood Samples
This study followed the Declaration of Helsinki, approval was gained by the Ethics Committee of the Jessenius Faculty of Medicine, Comenius University in Bratislava (No EK 1983/2017), and all study subjects signed the written consent. The consent of parents was given in the case of underage patients. Adolescent and adult inpatients diagnosed with MDD from the Psychiatric Clinic of Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, and University Hospital Martin, Slovakia, were included in the study. The diagnosis of MDD without psychotic symptoms was assessed by detailed clinical examination using unstructured diagnostic interview by either a child/adolescent psychiatrist or an adult psychiatrist according to the Diagnostic and Statistical Manual of Mental Disorders, DSM-5 [30]. The Clinical Global Impression rating scale was used for the assessment of depression severity [31].
During the hospital stay, adolescent patients were taking fluoxetine (15–40 mg) or vortioxetine (10–15 mg); adult patients were taking duloxetine (30–120 mg), escitalopram (10–20 mg), venlafaxine (225–300 mg), vortioxetine (20 mg), or a combination as directed by their doctor. Patients’ peripheral blood samples were collected at hospital admission (case1) and after a certain period of antidepressant treatment (case2; in adolescents on seventh day of hospitalisation and in adult patients at discharge day—10–27 days; on average, on the 21st day). The children’s first blood sample was collected before their meal and morning medication and the second sample two weeks under the same conditions. Adult samples were both withdrawn after night-fasting. Age-matched volunteers were included in the study as healthy controls and their fasting blood samples were withdrawn in the morning hours. All blood samples were collected in EDTA tubes and shortly after the withdrawal (within 2 h), the plasma was obtained by centrifugation (4 °C, 15 min, 590 g) and stored at −80 °C until the analysis.
2.2. Enzymatic Estimation of 3-Hydroxybutyrate Level in Plasma
The concentration of 3-hydroxybutyrate in plasma was determined enzymatically by modifying the previously published method [32]. Briefly, 30 µL of plasma was mixed with 270 µL of the reaction mixture (0.2 M glycine/0.16 M hydrazine buffer pH 9.0 supplemented with 5 mM NAD+) in a 96-well plate. After reading the initial absorbance at 340 nm (A0) by SynergyTM plate reader (Bio Tek, Winooski, VT, USA), 3-hydroxybutyrate dehydrogenase was added to the final activity 50 mU/well. After incubation at 37 °C for 60 min, the end-point absorbance at 340 nm (AE) was measured. The concentration of 3-hydroxybutyrate in plasma was estimated based on the calculated amount of generated NADH, using the value of extinction coefficient for NADH equal to 6.22 mM−1·cm−1.
2.3. Estimation of Relative Levels of Dityrosine, NADH and Tryptophan in Plasma
The relative levels of dityrosine [33], NADH [34] and tryptophan [35] were determined by measuring their natural fluorescence. Plasma (5 µL) was mixed with 250 µL of glycine/hydrazine buffer pH 9.0 and the intensity of dityrosine, NADH and tryptophan fluorescence was measured by SynergyTM plate reader (Bio Tek, USA). The pairs of emission and extinction wavelength to estimate the fluorescence intensity of dityrosine, NADH or tryptophan were 325 nm/410 ± 20 nm, 340 nm/440 ± 20, or 280 nm/348 ± 9 nm, respectively. The intensity of natural fluorescence of dityrosine, NADH or tryptophan estimated in plasma obtained from healthy individuals was considered as 100%. Levels of tryptophan were measured only for protein level standardization.
2.4. Levels of Myeloperoxidase in Plasma
The plasmatic concentration of myeloperoxidase was measured using a commercial enzyme-linked immunosorbent assay (ELISA) kit (Abcam, Cambridge, UK) with the concentration range 312–20,000 pg/mL and sensitivity of 10 pg/mL. The pilot study was performed to estimate the dilution factor for plasma samples. The samples were run in singlets with no dilution. The analysis was performed according to the manufacturer´s protocol and the final absorbance was measured at 450 nm using multimode plate reader Varioskan (Thermo Fisher Scientific, Waltham, MA, USA). The concentration was calculated using four parametric logistic curves in an online calculator (MyAssays, Brighton, UK).
2.5. Statistical Methods
The statistical analyses were performed in GraphPad Prism 8.0.1 (GraphPad, San Diego, CA, USA). The data were tested for normality by a Shapiro–Wilk test, and outliers were identified and excluded using the ROUT test with Q = 1%. Accordingly, a parametric paired t-test (normally distributed data) or Wilcoxon matched test (not normally distributed) was used to compare levels in case1 and case2 groups, while parametric unpaired t-test (normally distributed data) or a Mann–Whitney unpaired test (not normally distributed) were used to compare patients (case1) and controls. A p-value < 0.05 was considered statistically significant. The impact of age and duration of hospitalisation was analysed using linear regression and correlation. The impact of gender was measured by comparing female and male groups with an unpaired t-test or nonparametric tests. Power analysis (effect size, power) was performed post-hoc using G*Power 3.1.9.4 (Dusseldorf University, Dusseldorf, Germany) with the following input parameters: difference between two (in)dependent means, two tails, α = 0.05, and sample size according to the real group size.
3. Results
In our study, there were 11 depressed adolescents (15–18 years old) with 20 age-matched controls (16–18 years old) and 21 depressed adult patients (52–75 years old) with 15 age-matched controls (50–77 years old). For the assessment of the influence of antidepressant therapy on the levels of observed parameters, we used 11 adolescent samples but only 15 elderly patients’ samples (case2). The basic demographic data are displayed in Table 1.
All of the results between depressed patients and controls and after a short antidepressant treatment during hospitalisation are shown in Table 2. Moreover, a comparison of 3HB levels in adolescents and adult subjects are in Figure 1 and Figure 2.
The levels of 3HB were elevated by 55% and 89% in adolescents and adults with MDD compared to healthy controls, respectively. Dityrosine levels were increased by 19% in adults with MDD compared to controls. The achieved power of the measurements with statistically significant differences was the following: in the adult group, 0.83 and 1.00 for di-Tyr and 3HB, respectively (effect sizes were 1.04 and 1.68); in the adolescent group, 0.85 for 3HB (effect size d = 1.17).
We have not found that age impacted the levels of measured parameters in adolescents. However, male adolescent controls had significantly higher levels of NADH (0.40 ± 0.05) compared to female controls (0.35 ± 0.04; p = 0.0279). Linear regression analysis showed the negative effect of age on 3HB levels in samples from adult patients. A low number of samples in the female adult control group limited the gender correlation within that group. We have not found any significant correlation between plasmatic levels of 3HB and duration of hospital stay in MDD adult patients (p = 0.3891).
4. Discussion
The present study assessed the selected parameters related to energy metabolism and oxidative stress in adolescent and adult patients with MDD. The parameters were chosen in line with the growing evidence of altered ATP production and electron transport chains, and mitochondrial dysfunction in MDD [36,37]. Along with these studies, even the effect of ketamine, a rapid antidepressant, was recently suggested to be mediated by mitochondrial energy metabolism and antioxidant defence system [38]. We aimed to measure these parameters in two different age groups—adolescents and older adults to compare the possible pathophysiological mechanism difference related to the age of MDD patients. We chose to quantify the level of myeloperoxidase, an enzyme involved in oxidative stress, and interrelated dityrosine levels. Moreover, by measuring NADH and 3HB in plasma, we could partially evaluate the peripheral conditions associated with redox potential and energy metabolism in MDD patients.
The concentration of 3HB was measured in all the study subjects higher than the reference range of 0.01–0.25 mmol/L in non-fasting plasma samples [39]. This might be explained by the overnight fasting of controls and patients at hospital admission. However, we found that the plasmatic levels of 3HB were more elevated in patients with MDD compared to healthy age-matched subjects, which was observed in both studied age groups of depressed individuals. We confirmed that increased 3HB levels are present independently of the age of the patients during an acute depressive episode and possibly also chronically, as we did not observe any decrease after proper antidepressant therapy during hospitalisation.
The explanation of elevated plasmatic ketone bodies in MDD might lie in a higher demand in energy utilization and/or insulin insensitivity in patients with major depression [40,41]. However, elevated 3HB levels were also found in the serum of patients with schizophrenia compared to healthy subjects [40,42], which suggests that this marker could generally reflect psychosomatic stress [29]. There was also a previously reported correlation between high 3HB levels in serum and effectiveness of antidepressant therapy with sertraline or venlafaxine where higher 3HB levels were associated with symptoms resolution [29]. In our study, the parameter reflecting effectiveness of antidepressant therapy was the duration of the hospitalisation of patients. However, the second blood samples from adolescents were withdrawn after too short of a time period (1 week), and in the group of adult inpatients, no significant correlation was discovered.
Stress itself creates the conditions for adipocyte lipolysis and consequential long-chain fatty acid degradation to ketone bodies. A study by Kubera et al. [43] showed that psychosocial stress elevated serum 3HB concentrations in normal-weight men implying extra energy demand to satisfy its increased needs. Considering insulin insensitivity, pro-inflammatory cytokines associated with desensitised glucocorticoid receptors are of great importance. Due to chronic stress, levels of glucocorticoids are increased and inhibit the expression of GLUT4 transporter. Moreover, the cortisol-induced release of fatty acids from lipoproteins further alters insulin receptor signalling and increases pro-inflammatory cytokines, e.g., tumour necrosis factor (TNF)-α contributes to insulin desensitisation [41,44,45].
Apart from 3HB’s role as a metabolism intermediate, it possesses regulatory functions influencing gene expression, neuronal protection, overall metabolic rate, and it is an important epigenetic regulator by inhibiting histone deacetylases [24]. A recent experimental study showed the anti-inflammatory effect of 3HB in hippocampus of rats exposed to acute and chronic stress. Hippocampal levels of TNFα and IL-1β were reduced after 3HB administration, which was likely mediated by the inhibition of the NLRP3 inflammasome [46]. Similar results were found in the levels of IL-1β and IL-18 in human monocytes [23].
Regarding the finding that levels of 3HB decreased with the age of adult patients, it could be caused by age-related reduced lipolytic activity or reduced ketogenic activity due to impaired mitochondrial function [47].
The biosynthesis of 3HB from acetoacetate is associated with NAD+ regeneration from reduced-form NADH [24]. Although NAD+ is predominantly an intracellular nucleotide, it also plays important extracellular roles [48,49]. The oxidized and reduced form NAD+/NADH ratio in plasma might provide essential information regarding redox metabolism disorders or cellular bioenergetics alterations. Physiologically, increased levels of reduced NAD+ are also reported in relation to aging [50], but we have not found any correlation.
In our study, we measured only levels of NADH in plasma. We have not observed any significant difference between the levels of depressed patients and healthy controls, nor after the short antidepressant treatment. To the best of our knowledge, the levels of NADH, NAD+, or their ratio in the plasma of MDD patients have not yet been characterized yet. Increased levels of NADH in plasma with decreased NAD+ and NAD+/NADH ratio were previously found in children with autism [51], in cerebrospinal fluid in neurodegenerative diseases were levels of NAD+ increased, but no change was observed in plasma [52]. Nonetheless, we found increased levels of NADH in female adolescents compared to male adolescents, but not in adults. The same was detected in a recent study suggesting the influence of gonadal hormones, pre- and postmenopausal phase of women’s life, and sex- and age-related differences of the intake of dietary NAD+ precursors [53].
To evaluate the peripheral oxidative stress in depressed adolescents and adults, we quantified levels of di-Tyr and myeloperoxidase in plasma. Myeloperoxidase was previously reported as an enzyme of inflammation important in the etiology of recurrent depressive disorders [54]. Upon the activation of neutrophils in peripheral blood and tissue, MPO is released to subcellular compartments as well as extracellular environments. While chronical overproduction of MPO can cause tissue damage, it can play an anti-inflammatory role in particular situations [55]. Correspondingly, the plasmatic levels of MPO were reported higher in various diseases such as cancer [56], or in patients after myocardial infarction [57].
In our study, we observed a strong variation within our results of this marker, so we cannot draw any relevant conclusion even though there was a paradoxical trend of higher levels in controls. However, the level of MPO activity seems to be a better diagnostic tool in inflammatory conditions associated with oxidative stress than the concentration of MPO itself. To date, consensus has not been reached for standardised MPO activity measurement. Several studies documented that naturally occurring compounds such as polyphenols, melatonin, flavonoids, but also anti-inflammatory drugs show inhibitory activities against MPO [58,59]. Furthermore, niacin possesses inhibitory effects against cellular ROS production and MPO release [60]. This vitamin from the group of B vitamins was found to ameliorate depressive symptoms in animal models [61], but also in clinical studies of patients with affective disorder [62]. Moreover, this vitamin activates the HCAR2 receptor as the 3HB does [63].
Although we did not observe any relevant result from MPO level measurement, levels of possibly MPO-generated stable di-Tyr were enhanced in MDD adults compared to controls. However, we did not observe the same in adolescents with MDD. Higher levels of dityrosine might reflect higher oxidative stress and higher protein oxidation damage. Increased levels of dityrosine in blood were also found in psychiatric inpatients with previous suicide attempts and there was a positive correlation between the number of suicide attempts during a lifetime and the level of di-Tyr [64]. Since MDD is a recurrent disorder [65], levels of di-Tyr could also correlate with the number of depressive episodes, which could explain our findings; however, bigger studies are warranted to confirm this hypothesis. A short period of antidepressant therapy, as well as small sample size, were limitations to relevantly interpret the results of measured parameters in the second patient blood samples, and thus evaluate the antidepressants’ effects.
In this study, we have not confirmed the effects of antidepressants on energy metabolism and oxidative stress parameters. However, our study was limited by small sample sizes and a short period of observation. Several in vitro studies [66] and in vivo studies [67,68,69,70] have previously found an association. Not only the disorder itself but also the effect of the antidepressant could be an important topic for future studies, considering that our patients were not drug-naïve at the time of hospital admission and their medication could have affected the measured parameters. Our findings should be considered as preliminary, and larger studies could reveal if the metabolic status of MDD patients after antidepressant therapy is of high importance. A slight difference in the study design in adolescents and adults with MDD might be another limitation of our study.
5. Conclusions
In this study, we were able to find a significant difference in the levels of dityrosine and 3HB in adults and significantly different levels of 3HB in adolescents with MDD compared to controls. These results were obtained with high effect sizes according to power analysis. Therefore, we can conclude that the alteration of energy metabolism might be present in patients with MDD independently of age. We also confirmed increased protein oxidation damage in adult patients diagnosed with MDD. More studies are necessary to verify this in depressed children and adolescent patients and the impact of antidepressant therapy, especially after a longer duration.
Conceptualization, M.K., E.G., R.M. and J.M.; methodology, E.G., R.M., M.K. and M.D.; software, E.G., M.D. and R.M.; validation, E.G., R.M., J.M. and M.K.; formal analysis, M.K., E.G. and R.M.; investigation, I.O., I.H., P.H., N.F., Z.V., M.K., E.G. and I.T.; resources, I.O., I.T., I.H., N.F., Z.V. and J.M.; data curation, M.K., E.G., M.D. and R.M.; writing—original draft preparation, M.K.; writing—review and editing, E.G., R.M., M.D., I.T., I.O. and J.M.; visualization, M.K. and E.G.; supervision, J.M., R.M., I.T. and I.O.; project administration, J.M.; funding acquisition, J.M. and I.T. All authors have read and agreed to the published version of the manuscript.
This research was funded by the grants VEGA 1/0255/18, VEGA 1/0255/20, VEGA 1/0093/22 and VEGA 1/0030/22.
The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethics Committee of the Jessenius Faculty of Medicine, Comenius University in Bratislava (No. EK 1983/2017).
Informed consent was obtained from all subjects involved in the study. A consent of parents was given in case of underage patients.
Data available upon request from the authors.
The authors declare no conflict of interest.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Enlarge this image.Figure 1. Levels of 3HB in adult patients and controls. CA1—case1, CA2—case2, CO—controls; *** p < 0.001.
Enlarge this image.Figure 2. Levels of 3HB in adolescent patients and controls. CA1—case1, CA2—case2, CO—controls; *** p < 0.001.
Basic demographic data of depressed patients and controls.
| Group | No | Age ± SD | Males/Females | |
|---|---|---|---|---|
| Adolescents | Patients | 11 | 16.0 ± 1.2 | 6/5 |
| Controls | 20 | 16.9 ± 0.7 | 10/10 | |
| Adults | Patients | 21 | 63.3 ± 5.8 | 8/13 |
| Controls | 15 | 63.3 ± 6.9 | 12/3 | |
Comparison of levels of measured parameters between case1, case2, and controls.
| Group | Parameter | CA1 ± SD | CA2 ± SD | CO ± SD | CA1 vs. CO | CA1 vs. CA2 |
|---|---|---|---|---|---|---|
| Adolescents | DITYR | 0.26 ± 0.05 | 0.28 ± 0.07 | 0.27 ± 0.03 | 0.4380 | 0.3345 |
| NADH | 0.36 ± 0.11 | 0.39 ± 0.11 | 0.38 ± 0.05 | 0.2116 | 0.7002 | |
| 3HB | 0.83 ± 0.35 | 0.90 ± 0.21 | 0.54 ± 0.09 | 0.0004 | 0.1016 | |
| MPO | 26 ± 19 | 25 ± 13 | 37 ± 31 | 0.5501 | 0.1934 | |
| Adults | DITYR | 0.40 ± 0.09 | 0.36 ± 0.05 | 0.34 ± 0.02 | 0.0092 | 0.0928 |
| NADH | 0.49 ± 0.10 | 0.49 ± 0.13 | 0.48 ± 0.06 | 0.6113 | 0.7197 | |
| 3HB | 1.39 ± 0.53 | 1.42 ± 0.67 | 0.74 ± 0.13 | <0.0001 | 0.6698 | |
| MPO | 19 ± 12 | 16 ± 14 | 48 ± 52 | 0.1292 | 0.0942 |
3HB—3-hydroxybutyrate, CA1—case1, CA2—case2, CO—controls, DITYR—dityrosine, MPO—myeloperoxidase, NADH—nicotinamide adenine dinucleotide. Levels of 3HB in mmol/l, MPO in ng/ml, NADH, and DITYR as ratios NADH/TRP (tryptophan) and DITYR/TRP.
References
1. Gutiérrez-Rojas, L.; Porras-Segovia, A.; Dunne, H.; Andrade-González, N.; Cervilla, J.A. Prevalence and Correlates of Major Depressive Disorder: A Systematic Review. Rev. Bras. Psiquiatr.; 2020; 42, pp. 657-672. [DOI: https://dx.doi.org/10.1590/1516-4446-2020-0650] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32756809]
2. de Menezes Galvão, A.C.; Almeida, R.N.; de Sousa, G.M.; Leocadio-Miguel, M.A.; Palhano-Fontes, F.; de Araujo, D.B.; Lobão-Soares, B.; Maia-de-Oliveira, J.P.; Nunes, E.A.; Hallak, J.E.C. et al. Pathophysiology of Major Depression by Clinical Stages. Front. Psychol.; 2021; 12, 2944. [DOI: https://dx.doi.org/10.3389/fpsyg.2021.641779] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34421705]
3. Hasler, G. Pathophysiology of Depression: Do We Have Any Solid Evidence of Interest to Clinicians?. World Psychiatry; 2010; 9, pp. 155-161. [DOI: https://dx.doi.org/10.1002/j.2051-5545.2010.tb00298.x] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/20975857]
4. Rice, F.; Riglin, L.; Lomax, T.; Souter, E.; Potter, R.; Smith, D.J.; Thapar, A.K.; Thapar, A. Adolescent and Adult Differences in Major Depression Symptom Profiles. J. Affect. Disord.; 2019; 243, pp. 175-181. [DOI: https://dx.doi.org/10.1016/j.jad.2018.09.015] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30243197]
5. MacDonald, K.; Krishnan, A.; Cervenka, E.; Hu, G.; Guadagno, E.; Trakadis, Y. Biomarkers for Major Depressive and Bipolar Disorders Using Metabolomics: A Systematic Review. Am. J. Med. Genet. Part B Neuropsychiatr. Genet.; 2019; 180, pp. 122-137. [DOI: https://dx.doi.org/10.1002/ajmg.b.32680]
6. Emerit, J.; Edeas, M.; Bricaire, F. Neurodegenerative Diseases and Oxidative Stress. Biomed. Pharmacother.; 2004; 58, pp. 39-46. [DOI: https://dx.doi.org/10.1016/j.biopha.2003.11.004]
7. Palta, P.; Samuel, L.J.; Miller, E.R.; Szanton, S.L. Depression and Oxidative Stress: Results from a Meta-Analysis of Observational Studies. Psychosom. Med.; 2014; 76, pp. 12-19. [DOI: https://dx.doi.org/10.1097/PSY.0000000000000009]
8. Black, C.N.; Bot, M.; Scheffer, P.G.; Cuijpers, P.; Penninx, B.W.J.H. Is Depression Associated with Increased Oxidative Stress? A Systematic Review and Meta-Analysis. Psychoneuroendocrinology; 2015; 51, pp. 164-175. [DOI: https://dx.doi.org/10.1016/j.psyneuen.2014.09.025]
9. Michel, T.M.; Frangou, S.; Thiemeyer, D.; Camara, S.; Jecel, J.; Nara, K.; Brunklaus, A.; Zoechling, R.; Riederer, P. Evidence for Oxidative Stress in the Frontal Cortex in Patients with Recurrent Depressive Disorder—A Postmortem Study. Psychiatry Res.; 2007; 151, pp. 145-150. [DOI: https://dx.doi.org/10.1016/j.psychres.2006.04.013]
10. Moylan, S.; Berk, M.; Dean, O.M.; Samuni, Y.; Williams, L.J.; O’Neil, A.; Hayley, A.C.; Pasco, J.A.; Anderson, G.; Jacka, F.N. et al. Oxidative & Nitrosative Stress in Depression: Why so Much Stress?. Neurosci. Biobehav. Rev.; 2014; 45, pp. 46-62. [DOI: https://dx.doi.org/10.1016/j.neubiorev.2014.05.007]
11. Michel, T.M.; Pulschen, D.; Thome, J. The Role of Oxidative Stress in Depressive Disorders. Curr. Pharm. Des.; 2012; 18, pp. 5890-5899. [DOI: https://dx.doi.org/10.2174/138161212803523554] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/22681168]
12. Giulivi, C.; Traaseth, N.J.; Davies, K.J.A. Tyrosine Oxidation Products: Analysis and Biological Relevance. Amino Acids; 2003; 25, pp. 227-232. [DOI: https://dx.doi.org/10.1007/s00726-003-0013-0] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/14661086]
13. Dalle-Donne, I.; Rossi, R.; Colombo, R.; Giustarini, D.; Milzani, A. Biomarkers of Oxidative Damage in Human Disease. Clin. Chem.; 2006; 52, pp. 601-623. [DOI: https://dx.doi.org/10.1373/clinchem.2005.061408] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/16484333]
14. Heinecke, J.W. Tyrosyl Radical Production by Myeloperoxidase: A Phagocyte Pathway for Lipid Peroxidation and Dityrosine Cross-Linking of Proteins. Toxicology; 2002; 177, pp. 11-22. [DOI: https://dx.doi.org/10.1016/S0300-483X(02)00192-0]
15. Green, P.S.; Mendez, A.J.; Jacob, J.S.; Crowley, J.R.; Growdon, W.; Hyman, B.T.; Heinecke, J.W. Neuronal Expression of Myeloperoxidase Is Increased in Alzheimer’s Disease. J. Neurochem.; 2004; 90, pp. 724-733. [DOI: https://dx.doi.org/10.1111/j.1471-4159.2004.02527.x]
16. Videbech, P. PET Measurements of Brain Glucose Metabolism and Blood Flow in Major Depressive Disorder: A Critical Review. Acta Psychiatr. Scand.; 2000; 101, pp. 11-20. [DOI: https://dx.doi.org/10.1034/j.1600-0447.2000.101001011.x]
17. Ido, Y.; Chang, K.; Williamson, J.R. NADH Augments Blood Flow in Physiologically Activated Retina and Visual Cortex. Proc. Natl. Acad. Sci. USA; 2004; 101, pp. 653-658. [DOI: https://dx.doi.org/10.1073/pnas.0307458100]
18. Vlassenko, A.G.; Rundle, M.M.; Raichle, M.E.; Mintun, M.A. Regulation of Blood Flow in Activated Human Brain by Cytosolic NADH/NAD+ Ratio. Proc. Natl. Acad. Sci. USA; 2006; 103, pp. 1964-1969. [DOI: https://dx.doi.org/10.1073/pnas.0510632103]
19. Takahashi, T.; Okuno, M.; Okamoto, T.; Kishi, T. NADPH-Dependent Coenzyme Q Reductase Is the Main Enzyme Responsible for the Reduction of Non-Mitochondrial CoQ in Cells. BioFactors; 2008; 32, pp. 59-70. [DOI: https://dx.doi.org/10.1002/biof.5520320108]
20. Bradshaw, P.C. Cytoplasmic and Mitochondrial NADPH-Coupled Redox Systems in the Regulation of Aging. Nutrients; 2019; 11, 504. [DOI: https://dx.doi.org/10.3390/nu11030504]
21. Nickel, A.G.; von Hardenberg, A.; Hohl, M.; Löffler, J.R.; Kohlhaas, M.; Becker, J.; Reil, J.C.; Kazakov, A.; Bonnekoh, J.; Stadelmaier, M. et al. Reversal of Mitochondrial Transhydrogenase Causes Oxidative Stress in Heart Failure. Cell Metab.; 2015; 22, pp. 472-484. [DOI: https://dx.doi.org/10.1016/j.cmet.2015.07.008] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/26256392]
22. Cotter, D.G.; Schugar, R.C.; Crawford, P.A. Ketone Body Metabolism and Cardiovascular Disease. Am. J. Physiol. Heart Circ. Physiol.; 2013; 304, pp. H1060-H1076. [DOI: https://dx.doi.org/10.1152/ajpheart.00646.2012] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/23396451]
23. Youm, Y.H.; Nguyen, K.Y.; Grant, R.W.; Goldberg, E.L.; Bodogai, M.; Kim, D.; D’Agostino, D.; Planavsky, N.; Lupfer, C.; Kanneganti, T.D. et al. The Ketone Metabolite β-Hydroxybutyrate Blocks NLRP3 Inflammasome–Mediated Inflammatory Disease. Nat. Med.; 2015; 21, pp. 263-269. [DOI: https://dx.doi.org/10.1038/nm.3804] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/25686106]
24. Mierziak, J.; Burgberger, M.; Wojtasik, W. 3-Hydroxybutyrate as a Metabolite and a Signal Molecule Regulating Processes of Living Organisms. Biomolecules; 2021; 11, 402. [DOI: https://dx.doi.org/10.3390/biom11030402]
25. Rahman, M.; Muhammad, S.; Khan, M.A.; Chen, H.; Ridder, D.A.; Müller-Fielitz, H.; Pokorná, B.; Vollbrandt, T.; Stölting, I.; Nadrowitz, R. et al. The β-Hydroxybutyrate Receptor HCA2 Activates a Neuroprotective Subset of Macrophages. Nat. Commun.; 2014; 5, 3944. [DOI: https://dx.doi.org/10.1038/ncomms4944]
26. Graff, E.C.; Fang, H.; Wanders, D.; Judd, R.L. Anti-Inflammatory Effects of the Hydroxycarboxylic Acid Receptor 2. Metab. Clin. Exp.; 2016; 65, pp. 102-113. [DOI: https://dx.doi.org/10.1016/j.metabol.2015.10.001]
27. Setoyama, D.; Kato, T.A.; Hashimoto, R.; Kunugi, H.; Hattori, K.; Hayakawa, K.; Sato-Kasai, M.; Shimokawa, N.; Kaneko, S.; Yoshida, S. et al. Plasma Metabolites Predict Severity of Depression and Suicidal Ideation in Psychiatric Patients-A Multicenter Pilot Analysis. PLoS ONE; 2016; 11, e0165267. [DOI: https://dx.doi.org/10.1371/journal.pone.0165267]
28. Sengupta, S.; Peterson, T.R.; Laplante, M.; Oh, S.; Sabatini, D.M. MTORC1 Controls Fasting-Induced Ketogenesis and Its Modulation by Ageing. Nature; 2010; 468, pp. 1100-1106. [DOI: https://dx.doi.org/10.1038/nature09584]
29. Saito, N.; Itoga, M.; Minakawa, S.; Kayaba, H. Serum 3-Hydroxybutyrate in Patients with Psychogenic Somatoform Symptoms May Be a Predictor of the Effectiveness of Sertraline and Venlafaxine. Int. J. Gen. Med.; 2021; 14, pp. 1785-1795. [DOI: https://dx.doi.org/10.2147/IJGM.S300517]
30. APA. Diagnostic and Statistical Manual of Mental Disorders; 5th ed. APA: Washington, DC, USA, 2013; Available online: https://www.psychiatry.org/psychiatrists/practice/dsm (accessed on 8 December 2021).
31. Busner, J.; Targum, S.D. The Clinical Global Impressions Scale: Applying a Research Tool in Clinical Practice. Psychiatry; 2007; 4, pp. 28-37.
32. McMurray, C.H.; Blanchflower, W.J.; Rice, D.A. Automated Kinetic Method for D-3-Hydroxybutyrate in Plasma or Serum. Clin Chem.; 1984; 30, pp. 421-425. [DOI: https://dx.doi.org/10.1093/clinchem/30.3.421] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/6697489]
33. Heinecke, J.W.; Li, W.; Francis, G.A.; Goldstein, J.A. Tyrosyl Radical Generated by Myeloperoxidase Catalyzes the Oxidative Cross-Linking of Proteins. J. Clin. Investig.; 1993; 91, pp. 2866-2872. [DOI: https://dx.doi.org/10.1172/JCI116531] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/8390491]
34. Lakowicz, J.R.; Szmacinski, H.; Nowaczyk, K.; Johnson, M.L. Fluorescence Lifetime Imaging of Free and Protein-Bound NADH. Proc. Natl. Acad. Sci. USA; 1992; 89, 1271. [DOI: https://dx.doi.org/10.1073/pnas.89.4.1271] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/1741380]
35. Ghisaidoobe, A.B.T.; Chung, S.J. Intrinsic Tryptophan Fluorescence in the Detection and Analysis of Proteins: A Focus on Förster Resonance Energy Transfer Techniques. Int. J. Mol. Sci.; 2014; 15, pp. 22518-22538. [DOI: https://dx.doi.org/10.3390/ijms151222518]
36. Gardner, A.; Boles, R.G. Beyond the Serotonin Hypothesis: Mitochondria, Inflammation and Neurodegeneration in Major Depression and Affective Spectrum Disorders. Prog. Neuro-Psychopharmacol. Biol. Psychiatry; 2011; 35, pp. 730-743. [DOI: https://dx.doi.org/10.1016/j.pnpbp.2010.07.030] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/20691744]
37. Kahl, K.G.; Stapel, B.; Frieling, H. Link between Depression and Cardiovascular Diseases Due to Epigenomics and Proteomics: Focus on Energy Metabolism. Prog. Neuro-Psychopharmacol. Biol. Psychiatry; 2019; 89, pp. 146-157. [DOI: https://dx.doi.org/10.1016/j.pnpbp.2018.09.004] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30194950]
38. Weckmann, K.; Deery, M.J.; Howard, J.A.; Feret, R.; Asara, J.M.; Dethloff, F.; Filiou, M.D.; Iannace, J.; Labermaier, C.; Maccarrone, G. et al. Ketamine’s Antidepressant Effect Is Mediated by Energy Metabolism and Antioxidant Defense System. Sci. Rep.; 2017; 7, 15788. [DOI: https://dx.doi.org/10.1038/s41598-017-16183-x]
39. Kraus, F.B.; Kocijancic, M.; Kluttig, A.; Ludwig-Kraus, B. Test Validation, Method Comparison and Reference Range for the Measurement of β-Hydroxybutyrate in Peripheral Blood Samples. Biochem. Med.; 2020; 30, pp. 118-127. [DOI: https://dx.doi.org/10.11613/BM.2020.010707]
40. Huang, Y.C.; Lin, P.Y.; Lee, Y.; Wu, C.C.; Hsu, S.T.; Hung, C.F.; Chen, C.C.; Chong, M.Y.; Lin, C.H.; Wang, L.J. β-Hydroxybutyrate, Pyruvate and Metabolic Profiles in Patients with Schizophrenia: A Case Control Study. Psychoneuroendocrinology; 2016; 73, pp. 1-8. [DOI: https://dx.doi.org/10.1016/j.psyneuen.2016.07.209]
41. Leonard, B.E.; Wegener, G. Inflammation, Insulin Resistance and Neuroprogression in Depression. Acta Neuropsychiatr.; 2020; 32, pp. 1-9. [DOI: https://dx.doi.org/10.1017/neu.2019.17]
42. Yang, J.; Chen, T.; Sun, L.; Zhao, Z.; Qi, X.; Zhou, K.; Cao, Y.; Wang, X.; Qiu, Y.; Su, M. et al. Potential Metabolite Markers of Schizophrenia. Mol. Psychiatry; 2013; 18, pp. 67-78. [DOI: https://dx.doi.org/10.1038/mp.2011.131] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/22024767]
43. Kubera, B.; Hubold, C.; Wischnath, H.; Zug, S.; Peters, A. Rise of Ketone Bodies with Psychosocial Stress in Normal Weight Men. Psychoneuroendocrinology; 2014; 45, pp. 43-48. [DOI: https://dx.doi.org/10.1016/j.psyneuen.2014.03.008] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/24845175]
44. Solomon, S.S.; Odunusi, O.; Carrigan, D.; Majumdar, G.; Kakoola, D.; Lenchik, N.I.; Gerling, I.C. TNF-Alpha Inhibits Insulin Action in Liver and Adipose Tissue: A Model of Metabolic Syndrome. Horm. Metab. Res.; 2010; 42, pp. 115-121. [DOI: https://dx.doi.org/10.1055/s-0029-1241834] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/19960405]
45. Weinstein, S.P.; Paquin, T.; Pritsker, A.; Haber, R.S. Glucocorticoid-Induced Insulin Resistance: Dexamethasone Inhibits the Activation of Glucose Transport in Rat Skeletal Muscle by Both Insulin- and Non-Insulin-Related Stimuli. Diabetes; 1995; 44, pp. 441-445. [DOI: https://dx.doi.org/10.2337/diab.44.4.441]
46. Yamanashi, T.; Iwata, M.; Kamiya, N.; Tsunetomi, K.; Kajitani, N.; Wada, N.; Iitsuka, T.; Yamauchi, T.; Miura, A.; Pu, S. et al. Beta-Hydroxybutyrate, an Endogenic NLRP3 Inflammasome Inhibitor, Attenuates Stress-Induced Behavioral and Inflammatory Responses. Sci. Rep.; 2017; 7, 7677. [DOI: https://dx.doi.org/10.1038/s41598-017-08055-1]
47. Nakayama, H.; Tokubuchi, I.; Wada, N.; Tsuruta, M.; Ohki, T.; Oshige, T.; Sasaki, Y.; Iwata, S.; Kato, N.; Ohtsuka, Y. et al. Age-Related Changes in the Diurnal Variation of Ketogenesis in Patients with Type 2 Diabetes and Relevance to Hypoglycemic Medications. Endocr. J.; 2015; 62, pp. 235-241. [DOI: https://dx.doi.org/10.1507/endocrj.EJ14-0431]
48. Hubert, S.; Rissiek, B.; Klages, K.; Huehn, J.; Sparwasser, T.; Haag, F.; Koch-Nolte, F.; Boyer, O.; Seman, M.; Adriouch, S. Extracellular NAD+ Shapes the Foxp3+ Regulatory T Cell Compartment through the ART2-P2X7 Pathway. J. Exp. Med.; 2010; 207, pp. 2561-2568. [DOI: https://dx.doi.org/10.1084/jem.20091154]
49. Elliott, M.R.; Chekeni, F.B.; Trampont, P.C.; Lazarowski, E.R.; Kadl, A.; Walk, S.F.; Park, D.; Woodson, R.I.; Ostankovich, M.; Sharma, P. et al. Nucleotides Released by Apoptotic Cells Act as a Find-Me Signal to Promote Phagocytic Clearance. Nature; 2009; 461, pp. 282-286. [DOI: https://dx.doi.org/10.1038/nature08296]
50. Clement, J.; Wong, M.; Poljak, A.; Sachdev, P.; Braidy, N. The Plasma NAD + Metabolome Is Dysregulated in “Normal” Aging. Rejuvenation Res.; 2019; 22, pp. 121-130. [DOI: https://dx.doi.org/10.1089/rej.2018.2077]
51. Essa, M.M.; Subash, S.; Braidy, N.; Al-Adawi, S.; Lim, C.K.; Manivasagam, T.; Guillemin, G.J. Role of NAD+, Oxidative Stress, and Tryptophan Metabolism in Autism Spectrum Disorders. Int. J. Tryptophan Res.; 2013; 6, (Suppl. 1), 15. [DOI: https://dx.doi.org/10.4137/IJTR.S11355]
52. McGarry, A.; Gaughan, J.; Hackmyer, C.; Lovett, J.; Khadeer, M.; Shaikh, H.; Pradhan, B.; Ferraro, T.N.; Wainer, I.W.; Moaddel, R. Cross-Sectional Analysis of Plasma and CSF Metabolomic Markers in Huntington’s Disease for Participants of Varying Functional Disability: A Pilot Study. Sci. Rep.; 2020; 10, 20490. [DOI: https://dx.doi.org/10.1038/s41598-020-77526-9] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33235276]
53. Schwarzmann, L.; Pliquett, R.U.; Simm, A.; Bartling, B. Sex-Related Differences in Human Plasma NAD+/NADH Levels Depend on Age. Biosci. Rep.; 2021; 41, 20200340. [DOI: https://dx.doi.org/10.1042/BSR20200340] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33393613]
54. Talarowska, M.; Bobińska, K.; Zajaczkowska, M.; Su, K.P.; Maes, M.; Gałecki, P. Impact of Oxidative/Nitrosative Stress and Inflammation on Cognitive Functions in Patients with Recurrent Depressive Disorders. Med. Sci. Monit.; 2014; 20, pp. 110-115. [DOI: https://dx.doi.org/10.12659/msm.889853] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/24457625]
55. Aratani, Y. Myeloperoxidase: Its Role for Host Defense, Inflammation, and Neutrophil Function. Arch. Biochem. Biophys.; 2018; 640, pp. 47-52. [DOI: https://dx.doi.org/10.1016/j.abb.2018.01.004] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/29336940]
56. Jilani, I.; Vincenti, T.; Faraji, H.; Giles, F.J.; Estey, E.; Kantarjian, H.M.; Albitar, M. Clinical Relevance of Circulating Myeloperoxidase (MPO) in Acute Myeloid Leukemia (AML) and Myelodysplastic Syndrome (MDS). Blood; 2004; 104, 1073. [DOI: https://dx.doi.org/10.1182/blood.V104.11.1073.1073]
57. Mocatta, T.J.; Pilbrow, A.P.; Cameron, V.A.; Senthilmohan, R.; Frampton, C.M.; Richards, A.M.; Winterbourn, C.C. Plasma Concentrations of Myeloperoxidase Predict Mortality after Myocardial Infarction. J. Am. Coll. Cardiol.; 2007; 49, pp. 1993-2000. [DOI: https://dx.doi.org/10.1016/j.jacc.2007.02.040] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/17512353]
58. van Antwerpen, P.; Dufrasne, F.; Lequeux, M.; Boudjeltia, K.Z.; Lessgyer, I.; Babar, S.; Moreau, P.; Moguilevsky, N.; Vanhaeverbeek, M.; Ducobu, J. et al. Inhibition of the Myeloperoxidase Chlorinating Activity by Non-Steroidal Anti-Inflammatory Drugs: Flufenamic Acid and Its 5-Chloro-Derivative Directly Interact with a Recombinant Human Myeloperoxidase to Inhibit the Synthesis of Hypochlorous Acid. Eur. J. Pharmacol.; 2007; 570, pp. 235-243. [DOI: https://dx.doi.org/10.1016/j.ejphar.2007.05.057]
59. Bensalem, S.; Soubhye, J.; Aldib, I.; Bournine, L.; Nguyen, A.T.; Vanhaeverbeek, M.; Rousseau, A.; Boudjeltia, K.Z.; Sarakbi, A.; Kauffmann, J.M. et al. Inhibition of Myeloperoxidase Activity by the Alkaloids of Peganum harmala L. (Zygophyllaceae). J. Ethnopharmacol.; 2014; 154, pp. 361-369. [DOI: https://dx.doi.org/10.1016/j.jep.2014.03.070]
60. Ganji, S.H.; Qin, S.; Zhang, L.; Kamanna, V.S.; Kashyap, M.L. Niacin Inhibits Vascular Oxidative Stress, Redox-Sensitive Genes, and Monocyte Adhesion to Human Aortic Endothelial Cells. Atherosclerosis; 2009; 202, pp. 68-75. [DOI: https://dx.doi.org/10.1016/j.atherosclerosis.2008.04.044]
61. Liu, Z.; Li, C.; Fan, X.; Kuang, Y.; Zhang, X.; Chen, L.; Song, J.; Zhou, Y.; Takahashi, E.; He, G. et al. Nicotinamide, a Vitamin B3 Ameliorates Depressive Behaviors Independent of SIRT1 Activity in Mice. Mol. Brain; 2020; 13, 162. [DOI: https://dx.doi.org/10.1186/s13041-020-00703-4]
62. Jonsson, B.H. Nicotinic Acid Long-Term Effectiveness in a Patient with Bipolar Type II Disorder: A Case of Vitamin Dependency. Nutrients; 2018; 10, 134. [DOI: https://dx.doi.org/10.3390/nu10020134] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/29382049]
63. Tuteja, S. Activation of HCAR2 by Niacin: Benefits beyond Lipid Lowering. Pharmacogenomics; 2019; 20, pp. 1143-1150. [DOI: https://dx.doi.org/10.2217/pgs-2019-0092] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31617441]
64. Koweszko, T.; Gierus, J.; Zalewska, A.; Maciejczyk, M.; Waszkiewicz, N.; Szulc, A. The Relationship between Suicide and Oxidative Stress in a Group of Psychiatric Inpatients. J. Clin. Med.; 2020; 9, 3462. [DOI: https://dx.doi.org/10.3390/jcm9113462]
65. Burcusa, S.L.; Iacono, W.G. Risk for Recurrence in Depression. Clin. Psychol. Rev.; 2007; 27, 959. [DOI: https://dx.doi.org/10.1016/j.cpr.2007.02.005]
66. Cikánková, T.; Fišar, Z.; Hroudová, J. In Vitro Effects of Antidepressants and Mood-Stabilizing Drugs on Cell Energy Metabolism. Naunyn-Schmiedebergs Arch. Pharmacol.; 2020; 393, pp. 797-811. [DOI: https://dx.doi.org/10.1007/s00210-019-01791-3] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31858154]
67. Villa, R.F.; Ferrari, F.; Gorini, A.; Brunello, N.; Tascedda, F. Effect of Desipramine and Fluoxetine on Energy Metabolism of Cerebral Mitochondria. Neuroscience; 2016; 330, pp. 326-334. [DOI: https://dx.doi.org/10.1016/j.neuroscience.2016.05.051] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/27268280]
68. Park, D.I.; Novak, B.; Yan, Y.; Kaya, M.E.; Turck, C.W. Paroxetine Binding and Activation of Phosphofructokinase Implicates Energy Metabolism in Antidepressant Mode of Action. J. Psychiatr. Res.; 2020; 129, pp. 8-14. [DOI: https://dx.doi.org/10.1016/j.jpsychires.2020.05.033]
69. de Mello, A.H.; Souza, L.D.R.; Cereja, A.C.M.; Schraiber, R.D.B.; Florentino, D.; Martins, M.M.; Petronilho, F.; Quevedo, J.; Rezin, G.T. Effect of Subchronic Administration of Agomelatine on Brain Energy Metabolism and Oxidative Stress Parameters in Rats. Psychiatry Clin. Neurosci.; 2016; 70, pp. 159-166. [DOI: https://dx.doi.org/10.1111/pcn.12371]
70. Scabia, G.; Barone, I.; Mainardi, M.; Ceccarini, G.; Scali, M.; Buzzigoli, E.; Dattilo, A.; Vitti, P.; Gastaldelli, A.; Santini, F. et al. The Antidepressant Fluoxetine Acts on Energy Balance and Leptin Sensitivity via BDNF. Sci. Rep.; 2018; 8, 1781. [DOI: https://dx.doi.org/10.1038/s41598-018-19886-x]
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Details
; Matus Dohal 3
; Ondrejka, Igor 4 ; Tonhajzerova, Ingrid 5
; Hutka, Peter 4 ; Ferencova, Nikola 1 ; Visnovcova, Zuzana 1 ; Hrtanek, Igor 4 ; Mokry, Juraj 3
1 Biomedical Centre Martin, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, 03601 Martin, Slovakia;
2 Department of Medical Biochemistry, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, 03601 Martin, Slovakia;
3 Department of Pharmacology, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, 03601 Martin, Slovakia;
4 Psychiatric Clinic, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, University Hospital Martin, 03659 Martin, Slovakia;
5 Department of Physiology, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, 03601 Martin, Slovakia;