{"paper_id":"17814749-95e9-4a75-a385-b4aa8df99155","body_text":"Age-Related Changes in Cognition, Plasma Levels of Brain-Derived Neurotrophic Factors and Selected Indices of Inflammation in Adults at Different Decades of Life | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Age-Related Changes in Cognition, Plasma Levels of Brain-Derived Neurotrophic Factors and Selected Indices of Inflammation in Adults at Different Decades of Life Sheu Kadiri Rahamon, Abiodun Olaide Yusuff, Olatunde Olayinka Ayinde, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5194167/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Cognition, plasma brain derived neurotrophic (BDNF) levels and indices of inflammation were determined in 88 adults sub-divided into 4 groups; Group I (30–39 years), Group II (40–49 years), Group III (50–59 years) and Group IV (≥ 60 years) using standard procedures. There was a significant progressive reduction in cognitive score and plasma BDNF levels as the decades of life increased. The neurocognitive scores were significantly higher in Groups I, II and III than in Group IV. Similarly, the median plasma BDNF level was significantly higher in Group I than in Groups III and IV. Regression analysis revealed that age was negatively related to cognition (R 2 = 0.522, p = 0.000) and BDNF levels (R 2 = 0.095, p = 0.003). Plasma BDNF levels and cognitive scores progressively decrease with increasing age hence, plasma BDNF levels could predict susceptibility to neurocognitive dysfunction as aging progresses. Immunology Aging Brain-derived neurotrophic factor Immunosenescence Inflammation Neurocognition Figures Figure 1 Introduction Aging is a complex process that is associated with changes in biological, physiological, immunological, psychological, behavioral, and social processes. It is characterized by a progressive and irreversible decline in physical functions resulting from the gradual loss of certain bioprotective and regenerative capabilities (Hernandez-Segura et al., 2018 ). As aging progresses, vital organs begin to lose some functions, and all cells undergo changes, becoming larger and less able to divide and multiply (Dulken et al., 2019 ). Genetic and epigenetic factors play multifaceted roles in the process of aging and usually result in a reduction in endocrine, immunological, and cognitive functions, among others. It has been shown that gradual loss of cognitive capacity in elderly individuals is linked to changes in the cortex or hippocampal regions of the brain that are involved in learning and memory (Crook et al., 1987 ; Kwok, 2010 ). Some neuronal loss also occurs during normal aging but usually does not exceed 10% (Morrison and Baxter, 2012 ). Morphological changes in neurons, especially dendrites and axons, are involved in cognitive decline and behavioral changes (Dickstein et al., 2013 ). With increasing age, the dendritic trees undergo regression, while the dendritic shafts decrease in number, become shorter and less branched, and have fewer spines (Dickstein et al., 2013 ). A number of proteins, including brain-derived neurotrophic factor (BDNF), are involved in regulating the growth, survival, and differentiation of neurons. BDNF is a neuropeptide of the neurotrophin family that can bind to a specific receptor known as tropomyosin kinase receptor B, activating intracellular signaling pathways that result in the regulation of the growth, survival, and differentiation of neurons (Webster et al., 2006 ). It is involved in the modulation of synaptic plasticity, including the induction or enhancement of long-term potentiation (Dong et al., 2022 ), through the promotion of neurogenesis and/or dendritic outgrowth (Pizzorusso et al., 2000 ). BDNF is highly expressed in the hippocampus, amygdala, cerebellum and cerebral cortex in both rodents and humans, with the highest levels found in the hippocampus, an area that is involved in learning and memory (Hofer et al., 1990 ; Miranda et al., 2019 ). Reports abound indicating that BDNF plays a critical role in maintaining hippocampal volume in adults (Teixeira et al., 2010 ). It is hypothesized that BDNF influences age-related changes in hippocampal volume through several age-related alterations within the hippocampus. In the central nervous system, BDNF and downstream prosurvival pathways have been demonstrated to protect neurons from damage and enhance neuronal network reorganization after injury (Numakawa et al., 2010 ). It has also been reported that BDNF treatment can reduce the degree of microglial activation in certain brain injury models, although these responses are considered a consequence of reduced neuronal injury and death elicited by BDNF (Jiang et al., 2011 ). Mizoguchi et al. ( 2020 ) also reported that BDNF hypofunction is associated with age-related memory impairment. The aging process is accompanied by a state of low-grade nonresolving activation of inflammatory pathways, a phenomenon known as inflammaging (Bowirrat, 2022 ). This stage is usually associated with an increased number of inflammatory cells in the central nervous system (CNS), thus contributing to a greater degree of neuroinflammation (Stichel and Luebbert, 2007 ). Inflammatory pathways can be activated by self-debris and self-molecules that result from unhealthy or dead cells produced at a relatively high rate in aged tissues, the accumulation of senescent cells and their associated proinflammatory secretome (Garthwaite et al., 1988 ; Sama and Norris, 2013 ). Several age-related synaptic alterations in Ca 2+ homeostasis may be responsible for the elevation of intracellular Ca 2+ and neuroinflammation, as indicated by the production of proinflammatory cytokines, including interleukin-1 beta (IL-1β) and tumor necrosis factor alpha (TNF-α) (Lourenço et al., 2017 ). Cellular markers of inflammation, such as the neutrophil-lymphocyte ratio (NLR), positively correlate with age. Older people have been observed to have elevated NLRs compared with young adults (Li et al., 2015 ). Nitric oxide (NO) plays important roles in several neurobiological processes, ranging from the regulation of endothelium-dependent vasodilation to neurotransmission, and participates in host defense mechanisms (Boje, 2004 ). Because it has been described to be both neuroprotective and neurotoxic, it has been referred to as a Janus-faced molecule (Toledo and Augusto, 2012 ). It is a crucial component of the signal transduction pathways used for memory formation, sensory processing, and the regulation of cerebral blood flow (Bernard-Gauthier et al., 2013 ). NO signaling in the aging brain revealed that a small decrease in neuronal-derived NO in the aged hippocampus was accompanied by a more robust decrease in the cerebrovascular response produced upon stimulation of neuronal activity. It has been shown that there is decreased nitric oxide synthase (NOS) expression with age, both as constitutive and inducible isoforms (Lourenço et al., 2017 ). An increase in mediators of inflammation reduces BDNF expression, and BDNF may play an important negative regulatory role in inflammation within the brain (Schramm et al., 2002 ). Presently, there is a dearth of information on age-related changes in BDNF levels and inflammation in Nigerian adults. This study was thus designed to determine the relationship between cognition, plasma BDNF levels and selected indices of inflammation in adults of different decades of life to provide information that could be of therapeutic importance during aging. Materials and Methods Human ethics and consent to participate This study was carried out in accordance with the Declaration of Helsinki and approved by the University of Ibadan/University College Hospital Joint Ethics Committee. Additionally, written informed consent was obtained from each study participant. Study participants A total of 88 apparently healthy adults were enrolled in this cross-sectional study. The participants consisted of 20, 22, 22 and 24 adults within the age ranges of 30–39 years, 40–49 years, 50–59 years, and 60 years and above, respectively. The participants were enrolled using a convenient sampling method. Assessment of neurocognition Neurocognition was assessed using the Mini-Mental State Examination (MMSE), a neuropsychological screening test used to assess the speed of information processing, executive function, learning, memory, motor function, and verbal function (Folstein et al., 1975 ). The test yields a single composite score that reflects disease severity. Scores < 10, 10–20, 21–24 and ≥ 25 indicate severe impairment, moderate impairment, mild impairment and no impairment, respectively (Folstein et al., 1975 ). Exclusion criteria Patients with stroke, posttraumatic stress disorder (PTSD), learning disability, mental illness, substance abuse and/or dependence, metabolic and cardiovascular diseases or autoimmunity were excluded from the study. Additionally, patients with HIV or central nervous system (CNS) opportunistic infections such as toxoplasmosis and meningitis were excluded from the study. Furthermore, patients with a history of previous or present smoking and alcohol consumption and patients who recently used anti-inflammatory drugs and steroids were excluded from the study. Blood sample collection Venous blood (5 ml) was collected from each participant after at least 10 minutes of rest and dispensed into heparin-containing sample bottles. Twenty-five microliters (25 µL) of whole blood was taken for the nitroblue tetrazolium (NBT) assay. The remaining blood sample was centrifuged at 3000 rpm for 15 minutes, and the obtained plasma samples were stored at -20°C until analysis. Laboratory analysis The plasma BDNF level was determined using a sandwich ELISA following the manufacturer’s instructions (ElabScience Biotechnology, Inc., USA). The plasma level of NO was determined using the Griess reagent as described by Green et al. ( 1982 ), while the neutrophil phagocytic activity was determined using the modified semiquantitative NBT procedure as described by Edem and Arinola ( 2015 ). Complete blood analysis was performed using a 3-parameter Hematology Autoanalyzer (Mindray BC-5390, Shenzhen Mindray Bio-Medical Electronics Co., China). Thereafter, the neutrophil-to-lymphocyte ratio (NLR) was calculated as the ratio of the neutrophil count (%) to the lymphocyte count (%). Data analysis The Statistical Package for Social Sciences (SPSS), version 23.0, was used for data analysis. The data were assessed for a Gaussian distribution; thereafter, an appropriate statistical tool was applied. The Gaussian distribution of the data was assessed using a histogram with a normal curve. Mean differences between the groups (for variables with Gaussian distributions) were determined using ANOVA followed by post hoc tests. However, the Kruskal‒Wallis and Mann‒Whitney U tests were used to determine differences in median values between the groups (for variables with a non-Gaussian distribution). Correlations between the variables were determined using the Spearman rho correlation. Linear regression analysis was used to examine the influence of age (as an independent variable) on the cognitive score and plasma BDNF level (dependent variable). P values less than 0.05 were considered to indicate statistical significance. Results The age of the study participants ranged between 31 and 82 years, with a mean of 52.28 ± 13.63 years. As shown in Fig. 1, there was a significant progressive reduction in the neurocognitive score (p value = 0.000) and plasma BDNF level (p value = 0.009) as the age increased. The neurocognitive scores were significantly higher in Group I (29.35 ± 0.67), Group II (28.00 ± 1.72), and Group III (26.68 ± 2.32) than in Group IV (19.17 ± 8.48). Similarly, the median plasma BDNF level was significantly higher in Group I [8561.65 (5895-10310.90)] than in Groups III [5082.72 (3154.31-5360.06), p value = 0.007] and IV [4630.59 (1758.43-4630.59), p value = 0.002]. As shown in Table 1 , there were significant differences in the mean plasma levels of NO and mixed count among the four groups. The mean mixed count was significantly higher in Group IV than in Group I (p value = 0.003), while the mean plasma NO level was significantly higher in Group III than in Group I (p value = 0.003). Table 1 White blood cell count, neutrophil-to-lymphocyte ratio and plasma nitric oxide level in adults in different decades Parameters Group I (n = 20) Group II (n = 22) Group III (n = 22) Group IV (n = 24) P-value WBC count (CMM)) 6267 ± 2414 7547 ± 3501 819 5 ± 3465 6727 ± 1622 0.200 Neutrophil count (%) 44.42 ± 13.70 53.45 ± 13.87 53.31 ± 12.21 46.90 ± 11.85 0.088 Lymphocytes count (%) 40.26 ± 11.07 39.07 ± 14.29 37.28 ± 18.24 40.39 ± 9.38 0.898 Mixed count (%) 5.75 (3.63–9.78) a 8.35 (2.73–10.08) 8.60 (5.30–11.20) 12.10 (9.40–15.40) 0.021* NLR 1.11 (0.63–1.92) a 1.54 (0.97–1.86) 1.35 (0.81–2.19) 1.02 (0.81–1.60) 0.231 NO (µM) 0.85 (0.83–0.89) b 0.92 (0.84–1.09) 0.93 (0.88–0.98) 0.87 (0.85 - 0.92) 0.011* *Significant at P < 0.05, WBC = white blood cell, cmms = cubic millimeter, Mixed = other leucocytes, NLR = neutrophil–to-lymphocyte ratio, NO = nitric oxide. a Compared with Group IV, b Compared with Group III In Table 2 , correlations between the neurocognitive score and other parameters are shown. The median plasma level of BDNF level had significant positive correlation with the neurocognitive score in Group I. None of the other parameters were significantly correlated with the neurocognitive score. Table 2 Correlations between the neurocognitive score, neutrophil-to-lymphocyte ratio and plasma BDNF level concentration Parameters Group I (n = 20) Group II (n = 22) Group III (n = 22) Group IV (n = 24) MMSE r- value p value r- value p – value r- value p- value r- value P-value BDNF (pg/ml) 0.604 0.013* 0.402 0.098 0.233 0.368 0.315 0.294 NO (µM) -0.237 0.315 0.166 0.460 -0.399 0.066 0.033 0.878 NLR 0.318 0.198 -0.094 0.701 -0.121 0.613 -0.226 0.417 *Significant at P < 0.05; MMSE = Mini-Mental State Examination; WBC = white blood cell; cmm = cubic millimeter; Mixed = other leucocytes; NLR = neutrophil–to–lymphocyte ratio; BDNF = brain-derived neurotrophic factor; NO = nitric oxide. The influence of age on the cognitive score and plasma BDNF level is shown in Table 3 . Regression analysis revealed that age has a significant influence on cognition, as a significant negative relationship was found between age and cognitive score. The proportion of variance in cognitive score that could be explained by age was 52.2%. The contribution of age to the model was statistically significant according to the following regression equation: Cognitive score = 42.545 + [-0.325 (Age)] Similarly, as shown in Table 3 , regression analysis revealed that age has a significant influence on the plasma BDNF level, as a significant negative relationship was found between age and the plasma BDNF level. However, the proportion of variance in plasma BDNF levels that could be explained by age was low, at only 9.5%. The contribution of age to the model was statistically significant according to the following regression equation: Plasma BDNF level = 11875.107 + [-107.66 (Age)] Table 3 Linear regression analysis of age, cognitive score and plasma BDNF level in the study participants Independent parameter Dependent parameters Regression coefficient P-value Age R 2 = 0.522; F = 94.093; p = 0.000* Cognitive score -0.723 0.000* R 2 = 0.095; F = 9.031; p = 0.003* BDNF -0.308 0.003* *Significant at P < 0.05 Discussion It is well established that cognitive function is affected by aging, as approximately 40% of individuals aged 65 years and older suffer from some form of memory loss (Aigbogun et al., 2017 ). Although a number of factors have been identified, the mechanisms through which the identified factors result in cognitive dysfunction are poorly understood. In this study, a significant progressive decrease in cognitive score was observed as the duration of life increased. This was further supported by the regression analysis showing that age was negatively related to cognition. These observations corroborate the findings of Park et al. ( 2002 ), who showed that there are gradual age-related declines in the cognitive mechanisms of speed, working memory, and long-term memory beginning in young adulthood. The observations from this study could be due to a decrease in neuronal activation and alterations in several regions of the brain as aging progresses. Brain activity in regions such as the cortex, hippocampus and cerebellum is altered during aging (Calautti et al., 2001 ; Brito et al., 2020 ). This results in a decrease in the speed of processing, working memory, inhibitory function, and long-term memory (Park and Reuter-Lorenz, 2009 ; Randhawa and Varghese, 2024 ). BDNF is involved in plasticity, neuronal survival, formation of new synapses, dendritic branching, and modulation of excitatory and inhibitory neurotransmitter profiles (Edelmann et al., 2014 ). A decrease in its level has been associated with age-related memory impairment (Panja et al., 2014 ). In this study, there was a progressive decrease in the plasma BDNF concentration as the patients aged. The plasma BDNF level were significantly lower in participants aged 50 years and older than in those who were younger. Additionally, a negative relationship was observed between age and BDNF level. These observations are in line with the findings of Mizoguchi et al. ( 2020 ), who showed that low serum BDNF levels are associated with age-related memory impairment in elderly individuals. Although a reduction in BDNF expression has been associated with chronic inflammation, which is a common feature in elderly individuals (2011), the findings of this study might not be inflammation related, as no significant difference in the NLR was observed when all the groups were compared. Our observed reduced plasma BDNF could therefore be due to decreased physical activity, which is common in elderly people. BDNF levels have been shown to be activity dependent (Gorgoulis et al., 2019 ). Exercise is an inducer of neuronal plasticity, neurogenesis and survival (Huang et al., 2014 ). Furthermore, the observed low level of BDNF in aged individuals could be diet dependent, as poor appetite is a common problem in older people (Pilgrim et al., 2015 ). A study by Gorgoulis et al. ( 2019 ) showed that diet can provoke changes in BDNF levels under physiological conditions. Low BDNF levels have been reported in adults consuming high sugar and fat-containing diets (Mizoguchi et al. ( 2020 ), while diets rich in omega-3 fatty acids have been shown to induce an increase in BDNF levels and a reduction in cognitive impairment (Wu et al., 2004 ; Mizoguchi et al., 2020 ). The observed significant positive correlation between BDNF level and cognitive score indicated that an increase in BDNF level may be associated with improved cognition. This observation is not surprising, as the interplay between BDNF and cognition is well established. BDNF could therefore be considered a potential therapy for brain pathologies because it can directly or indirectly modulate changes within the brain (Nagahara and Tuszynski, 2011 ; Gorgoulis et al., 2019 ). Nagahara and Tuszynski ( 2011 ) reported that BDNF has a potential role in the pathogenesis and treatment of both neurological and psychiatric disorders. Chronic inflammation is a common feature of aging. Cellular markers of inflammation, such as the neutrophil-to-lymphocyte ratio (NLR), have been shown to correlate positively with age. In this study, no significant difference in the NLR was observed between the groups. This observation contradicts the findings of Li et al . (Li et al., 2015 ), who showed that older people have a higher NLR than young adults. These differences could be due to variations in the selection of study participants, as participants in this study were grouped according to decade of life, and age-associated chronic inflammation might not be linear. Nitric oxide is a mediator and regulator of inflammatory responses (Sharma et al., 2007 ). The function of NO in the hypothalamus has largely been implicated in learning processes and in memory formation. In this study, the mean plasma NO concentration was significantly higher in participants aged 40–49 years (Group II) or 50–59 years (Group III) than in participants aged 30–39 years (Group I). This contradicts the findings of Van der Loo et al. ( 2000 ), who showed that the bioavailability or generation of NOS-derived NO decreases with age. However, Siervo et al. ( 2018 ) reported that age-related changes in serum NO levels peaked between 50 and 59 years in both sexes and declined thereafter. This finding is similar to what was observed in this study, in which the NO concentration decreased in participants aged 60 years and older. Although this observation could not be fully explained at present, it might indicate that the serum NO concentration reflects the well-established immunosenescence pattern. The small sample size was a limitation in this study. Additionally, the use of only the MMSE to assess cognitive function was a limitation. The MMSE has been reported to have low sensitivity for detecting mild cognitive disorders, and it is largely a screening tool It could be concluded from this study that there is a progressive reduction in the plasma BDNF concentration and cognitive score with increasing decade of life. This may indicate that the plasma BDNF concentration could predict susceptibility to cognitive impairment as aging progresses and could be further explored as a potential neuroprotective and functional restorative factor in patients with cognitive dysfunction. However, large population studies are suggested to confirm the findings of this study. Similarly, a randomized blinded clinical trial is suggested to evaluate the therapeutic effect of BDNF administration on improving cognition and preventing dementia in elderly people. 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Gene Expr Patterns 6(8):941–951. 10.1016/j.modgep.2006.03.009 Wu A, Ying Z, Gomez-Pinilla F (2004) Dietary omega-3 fatty acids normalize bdnf levels, reduce oxidative damage, and counteract learning disability after traumatic brain injury in rats. J Neurotrauma 21(10):1457–1467. 10.1089/neu.2004.21.1457 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-5194167\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":361593582,\"identity\":\"baf95b07-3801-4b94-bbb8-6954f817ae0f\",\"order_by\":0,\"name\":\"Sheu Kadiri Rahamon\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYDACHiBmbACxmA8wJMDYRGphSyBZC48BAwMxWuR7DrB9+LnDLppfuuebxAMGG9kNB3gPv8CnxeBsA/PM3jPJuTPnnN0mkcCQZrzhAF+aBV4t/AzMDLxtzLkbbuSCtBxO3HCAx8wAr8P6GZgZ/7bVA7XkPANq+U9YCwPQYcy8bYdBWtiAWg6AtBg/wOuwMwebmWXbjufOnJFmbJFgkGw88zCPGV5L5HuSDzO+bavO7ZdIfnjzR4WdbN/xHuMPePWgRgTIE8wMbBL4tWABzARsGQWjYBSMghEGAAG6SweXT09lAAAAAElFTkSuQmCC\",\"orcid\":\"https://orcid.org/0000-0003-0305-5026\",\"institution\":\"University of Ibadan\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Sheu\",\"middleName\":\"Kadiri\",\"lastName\":\"Rahamon\",\"suffix\":\"\"},{\"id\":361593583,\"identity\":\"3804089e-e981-4002-ae46-21a652002579\",\"order_by\":1,\"name\":\"Abiodun Olaide Yusuff\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of Ibadan\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Abiodun\",\"middleName\":\"Olaide\",\"lastName\":\"Yusuff\",\"suffix\":\"\"},{\"id\":361593584,\"identity\":\"eb533c03-cc4a-4820-8ab9-296e35710aa9\",\"order_by\":2,\"name\":\"Olatunde Olayinka Ayinde\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of Ibadan\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Olatunde\",\"middleName\":\"Olayinka\",\"lastName\":\"Ayinde\",\"suffix\":\"\"},{\"id\":361593585,\"identity\":\"92e7751b-f3f2-4601-9807-71560af56286\",\"order_by\":3,\"name\":\"Funmilola Taiwo\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of Ibadan\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Funmilola\",\"middleName\":\"\",\"lastName\":\"Taiwo\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2024-10-02 16:47:45\",\"currentVersionCode\":1,\"declarations\":{\"humanSubjects\":true,\"vertebrateSubjects\":false,\"conflictsOfInterestStatement\":false,\"humanSubjectEthicalGuidelines\":true,\"humanSubjectConsent\":true,\"humanSubjectClinicalTrial\":false,\"humanSubjectCaseReport\":false,\"vertebrateSubjectEthicalGuidelines\":false},\"doi\":\"10.21203/rs.3.rs-5194167/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-5194167/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":65879776,\"identity\":\"4cc7e3ec-2b62-4b71-9190-670b8fb04c9f\",\"added_by\":\"auto\",\"created_at\":\"2024-10-04 01:35:09\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":48989,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eNeurocognitive score and plasma BDNF levels in adults during different decades\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5194167/v1/dceb176798e45165b3d35568.png\"},{\"id\":65879780,\"identity\":\"b7682cd8-542b-4bb7-ad93-67167d5a3dd4\",\"added_by\":\"auto\",\"created_at\":\"2024-10-04 01:35:14\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":487483,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5194167/v1/93117948-09f2-4289-9fc9-ad0de5786f2a.pdf\"}],\"financialInterests\":\"The authors declare no competing interests.\",\"formattedTitle\":\"\\u003cp\\u003e\\u003cstrong\\u003eAge-Related Changes in Cognition, Plasma Levels of Brain-Derived Neurotrophic Factors and Selected Indices of Inflammation in Adults at Different Decades of Life\\u003c/strong\\u003e\\u003c/p\\u003e\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eAging is a complex process that is associated with changes in biological, physiological, immunological, psychological, behavioral, and social processes. It is characterized by a progressive and irreversible decline in physical functions resulting from the gradual loss of certain bioprotective and regenerative capabilities (Hernandez-Segura et al., \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). As aging progresses, vital organs begin to lose some functions, and all cells undergo changes, becoming larger and less able to divide and multiply (Dulken et al., \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eGenetic and epigenetic factors play multifaceted roles in the process of aging and usually result in a reduction in endocrine, immunological, and cognitive functions, among others. It has been shown that gradual loss of cognitive capacity in elderly individuals is linked to changes in the cortex or hippocampal regions of the brain that are involved in learning and memory (Crook et al., \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e1987\\u003c/span\\u003e; Kwok, \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eSome neuronal loss also occurs during normal aging but usually does not exceed 10% (Morrison and Baxter, \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e). Morphological changes in neurons, especially dendrites and axons, are involved in cognitive decline and behavioral changes (Dickstein et al., \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e). With increasing age, the dendritic trees undergo regression, while the dendritic shafts decrease in number, become shorter and less branched, and have fewer spines (Dickstein et al., \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eA number of proteins, including brain-derived neurotrophic factor (BDNF), are involved in regulating the growth, survival, and differentiation of neurons. BDNF is a neuropeptide of the neurotrophin family that can bind to a specific receptor known as tropomyosin kinase receptor B, activating intracellular signaling pathways that result in the regulation of the growth, survival, and differentiation of neurons (Webster et al., \\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e2006\\u003c/span\\u003e). It is involved in the modulation of synaptic plasticity, including the induction or enhancement of long-term potentiation (Dong et al., \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e), through the promotion of neurogenesis and/or dendritic outgrowth (Pizzorusso et al., \\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e2000\\u003c/span\\u003e). BDNF is highly expressed in the hippocampus, amygdala, cerebellum and cerebral cortex in both rodents and humans, with the highest levels found in the hippocampus, an area that is involved in learning and memory (Hofer et al., \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e1990\\u003c/span\\u003e; Miranda et al., \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). Reports abound indicating that BDNF plays a critical role in maintaining hippocampal volume in adults (Teixeira et al., \\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e). It is hypothesized that BDNF influences age-related changes in hippocampal volume through several age-related alterations within the hippocampus. In the central nervous system, BDNF and downstream prosurvival pathways have been demonstrated to protect neurons from damage and enhance neuronal network reorganization after injury (Numakawa et al., \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e). It has also been reported that BDNF treatment can reduce the degree of microglial activation in certain brain injury models, although these responses are considered a consequence of reduced neuronal injury and death elicited by BDNF (Jiang et al., \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e). Mizoguchi et al. (\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e) also reported that BDNF hypofunction is associated with age-related memory impairment.\\u003c/p\\u003e \\u003cp\\u003eThe aging process is accompanied by a state of low-grade nonresolving activation of inflammatory pathways, a phenomenon known as inflammaging (Bowirrat, \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). This stage is usually associated with an increased number of inflammatory cells in the central nervous system (CNS), thus contributing to a greater degree of neuroinflammation (Stichel and Luebbert, \\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e2007\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eInflammatory pathways can be activated by self-debris and self-molecules that result from unhealthy or dead cells produced at a relatively high rate in aged tissues, the accumulation of senescent cells and their associated proinflammatory secretome (Garthwaite et al., \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e1988\\u003c/span\\u003e; Sama and Norris, \\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e). Several age-related synaptic alterations in Ca\\u003csup\\u003e2+\\u003c/sup\\u003e homeostasis may be responsible for the elevation of intracellular Ca\\u003csup\\u003e2+\\u003c/sup\\u003e and neuroinflammation, as indicated by the production of proinflammatory cytokines, including interleukin-1 beta (IL-1β) and tumor necrosis factor alpha (TNF-α) (Louren\\u0026ccedil;o et al., \\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e). Cellular markers of inflammation, such as the neutrophil-lymphocyte ratio (NLR), positively correlate with age. Older people have been observed to have elevated NLRs compared with young adults (Li et al., \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eNitric oxide (NO) plays important roles in several neurobiological processes, ranging from the regulation of endothelium-dependent vasodilation to neurotransmission, and participates in host defense mechanisms (Boje, \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e2004\\u003c/span\\u003e). Because it has been described to be both neuroprotective and neurotoxic, it has been referred to as a Janus-faced molecule (Toledo and Augusto, \\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e). It is a crucial component of the signal transduction pathways used for memory formation, sensory processing, and the regulation of cerebral blood flow (Bernard-Gauthier et al., \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e). NO signaling in the aging brain revealed that a small decrease in neuronal-derived NO in the aged hippocampus was accompanied by a more robust decrease in the cerebrovascular response produced upon stimulation of neuronal activity. It has been shown that there is decreased nitric oxide synthase (NOS) expression with age, both as constitutive and inducible isoforms (Louren\\u0026ccedil;o et al., \\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eAn increase in mediators of inflammation reduces BDNF expression, and BDNF may play an important negative regulatory role in inflammation within the brain (Schramm et al., \\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e2002\\u003c/span\\u003e). Presently, there is a dearth of information on age-related changes in BDNF levels and inflammation in Nigerian adults. This study was thus designed to determine the relationship between cognition, plasma BDNF levels and selected indices of inflammation in adults of different decades of life to provide information that could be of therapeutic importance during aging.\\u003c/p\\u003e\"},{\"header\":\"Materials and Methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eHuman ethics and consent to participate\\u003c/h2\\u003e \\u003cp\\u003e This study was carried out in accordance with the Declaration of Helsinki and approved by the University of Ibadan/University College Hospital Joint Ethics Committee. Additionally, written informed consent was obtained from each study participant.\\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eStudy participants\\u003c/h3\\u003e\\n\\u003cp\\u003eA total of 88 apparently healthy adults were enrolled in this cross-sectional study. The participants consisted of 20, 22, 22 and 24 adults within the age ranges of 30\\u0026ndash;39 years, 40\\u0026ndash;49 years, 50\\u0026ndash;59 years, and 60 years and above, respectively. The participants were enrolled using a convenient sampling method.\\u003c/p\\u003e\\n\\u003ch3\\u003eAssessment of neurocognition\\u003c/h3\\u003e\\n\\u003cp\\u003eNeurocognition was assessed using the Mini-Mental State Examination (MMSE), a neuropsychological screening test used to assess the speed of information processing, executive function, learning, memory, motor function, and verbal function (Folstein et al., \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e1975\\u003c/span\\u003e). The test yields a single composite score that reflects disease severity. Scores\\u0026thinsp;\\u0026lt;\\u0026thinsp;10, 10\\u0026ndash;20, 21\\u0026ndash;24 and \\u0026ge;\\u0026thinsp;25 indicate severe impairment, moderate impairment, mild impairment and no impairment, respectively (Folstein et al., \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e1975\\u003c/span\\u003e).\\u003c/p\\u003e\\n\\u003ch3\\u003eExclusion criteria\\u003c/h3\\u003e\\n\\u003cp\\u003ePatients with stroke, posttraumatic stress disorder (PTSD), learning disability, mental illness, substance abuse and/or dependence, metabolic and cardiovascular diseases or autoimmunity were excluded from the study. Additionally, patients with HIV or central nervous system (CNS) opportunistic infections such as toxoplasmosis and meningitis were excluded from the study. Furthermore, patients with a history of previous or present smoking and alcohol consumption and patients who recently used anti-inflammatory drugs and steroids were excluded from the study.\\u003c/p\\u003e\\n\\u003ch3\\u003eBlood sample collection\\u003c/h3\\u003e\\n\\u003cp\\u003eVenous blood (5 ml) was collected from each participant after at least 10 minutes of rest and dispensed into heparin-containing sample bottles. Twenty-five microliters (25 \\u0026micro;L) of whole blood was taken for the nitroblue tetrazolium (NBT) assay. The remaining blood sample was centrifuged at 3000 rpm for 15 minutes, and the obtained plasma samples were stored at -20\\u0026deg;C until analysis.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eLaboratory analysis\\u003c/h2\\u003e \\u003cp\\u003eThe plasma BDNF level was determined using a sandwich ELISA following the manufacturer\\u0026rsquo;s instructions (ElabScience Biotechnology, Inc., USA). The plasma level of NO was determined using the Griess reagent as described by Green et al. (\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e1982\\u003c/span\\u003e), while the neutrophil phagocytic activity was determined using the modified semiquantitative NBT procedure as described by Edem and Arinola (\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e). Complete blood analysis was performed using a 3-parameter Hematology Autoanalyzer (Mindray BC-5390, Shenzhen Mindray Bio-Medical Electronics Co., China). Thereafter, the neutrophil-to-lymphocyte ratio (NLR) was calculated as the ratio of the neutrophil count (%) to the lymphocyte count (%).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec9\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eData analysis\\u003c/h2\\u003e \\u003cp\\u003eThe Statistical Package for Social Sciences (SPSS), version 23.0, was used for data analysis. The data were assessed for a Gaussian distribution; thereafter, an appropriate statistical tool was applied. The Gaussian distribution of the data was assessed using a histogram with a normal curve. Mean differences between the groups (for variables with Gaussian distributions) were determined using ANOVA followed by post hoc tests. However, the Kruskal‒Wallis and Mann‒Whitney \\u003cem\\u003eU\\u003c/em\\u003e tests were used to determine differences in median values between the groups (for variables with a non-Gaussian distribution). Correlations between the variables were determined using the Spearman rho correlation. Linear regression analysis was used to examine the influence of age (as an independent variable) on the cognitive score and plasma BDNF level (dependent variable). \\u003cem\\u003eP\\u003c/em\\u003e values less than 0.05 were considered to indicate statistical significance.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003eThe age of the study participants ranged between 31 and 82 years, with a mean of 52.28\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;13.63 years. As shown in Fig.\\u0026nbsp;1, there was a significant progressive reduction in the neurocognitive score (p value\\u0026thinsp;=\\u0026thinsp;0.000) and plasma BDNF level (p value\\u0026thinsp;=\\u0026thinsp;0.009) as the age increased. The neurocognitive scores were significantly higher in Group I (29.35\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.67), Group II (28.00\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.72), and Group III (26.68\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.32) than in Group IV (19.17\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;8.48). Similarly, the median plasma BDNF level was significantly higher in Group I [8561.65 (5895-10310.90)] than in Groups III [5082.72 (3154.31-5360.06), p value\\u0026thinsp;=\\u0026thinsp;0.007] and IV [4630.59 (1758.43-4630.59), p value\\u0026thinsp;=\\u0026thinsp;0.002].\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eAs shown in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e, there were significant differences in the mean plasma levels of NO and mixed count among the four groups. The mean mixed count was significantly higher in Group IV than in Group I (p value\\u0026thinsp;=\\u0026thinsp;0.003), while the mean plasma NO level was significantly higher in Group III than in Group I (p value\\u0026thinsp;=\\u0026thinsp;0.003).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eWhite blood cell count, neutrophil-to-lymphocyte ratio and plasma nitric oxide level in adults in different decades\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"6\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eParameters\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGroup I\\u003c/p\\u003e \\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;20)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eGroup II\\u003c/p\\u003e \\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;22)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eGroup III\\u003c/p\\u003e \\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;22)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eGroup IV\\u003c/p\\u003e \\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;24)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eP-value\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eWBC count (CMM))\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e6267\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2414\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e7547\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3501\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e819 5\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3465\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e6727\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1622\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.200\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNeutrophil count (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e44.42\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;13.70\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e53.45\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;13.87\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e53.31\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;12.21\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e46.90\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;11.85\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.088\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLymphocytes count (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e40.26\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;11.07\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e39.07\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;14.29\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e37.28\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;18.24\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e40.39\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;9.38\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.898\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMixed count (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5.75\\u003c/p\\u003e \\u003cp\\u003e(3.63\\u0026ndash;9.78)\\u003csup\\u003ea\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e8.35\\u003c/p\\u003e \\u003cp\\u003e(2.73\\u0026ndash;10.08)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e8.60\\u003c/p\\u003e \\u003cp\\u003e(5.30\\u0026ndash;11.20)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e12.10\\u003c/p\\u003e \\u003cp\\u003e(9.40\\u0026ndash;15.40)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.021*\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNLR\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.11\\u003c/p\\u003e \\u003cp\\u003e(0.63\\u0026ndash;1.92)\\u003csup\\u003ea\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.54\\u003c/p\\u003e \\u003cp\\u003e(0.97\\u0026ndash;1.86)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.35\\u003c/p\\u003e \\u003cp\\u003e(0.81\\u0026ndash;2.19)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.02\\u003c/p\\u003e \\u003cp\\u003e(0.81\\u0026ndash;1.60)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.231\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNO (\\u0026micro;M)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.85\\u003c/p\\u003e \\u003cp\\u003e(0.83\\u0026ndash;0.89)\\u003csup\\u003eb\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.92\\u003c/p\\u003e \\u003cp\\u003e(0.84\\u0026ndash;1.09)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.93\\u003c/p\\u003e \\u003cp\\u003e(0.88\\u0026ndash;0.98)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.87\\u003c/p\\u003e \\u003cp\\u003e(0.85 - 0.92)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.011*\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"6\\\"\\u003e*Significant at \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05, WBC\\u0026thinsp;=\\u0026thinsp;white blood cell, cmms\\u0026thinsp;=\\u0026thinsp;cubic millimeter, Mixed\\u0026thinsp;=\\u0026thinsp;other leucocytes, NLR\\u0026thinsp;=\\u0026thinsp;neutrophil\\u0026ndash;to-lymphocyte ratio, NO\\u0026thinsp;=\\u0026thinsp;nitric oxide. \\u003csup\\u003ea\\u003c/sup\\u003eCompared with Group IV, \\u003csup\\u003eb\\u003c/sup\\u003eCompared with Group III\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eIn Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e, correlations between the neurocognitive score and other parameters are shown. The median plasma level of BDNF level had significant positive correlation with the neurocognitive score in Group I. None of the other parameters were significantly correlated with the neurocognitive score.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eCorrelations between the neurocognitive score, neutrophil-to-lymphocyte ratio and plasma BDNF level concentration\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"9\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c9\\\" colnum=\\\"9\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eParameters\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003eGroup I\\u003c/p\\u003e \\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;20)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c5\\\" namest=\\\"c4\\\"\\u003e \\u003cp\\u003eGroup II\\u003c/p\\u003e \\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;22)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c7\\\" namest=\\\"c6\\\"\\u003e \\u003cp\\u003eGroup III\\u003c/p\\u003e \\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;22)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c9\\\" namest=\\\"c8\\\"\\u003e \\u003cp\\u003eGroup IV\\u003c/p\\u003e \\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;24)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"8\\\" nameend=\\\"c8\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eMMSE\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"1\\\" nameend=\\\"c9\\\" namest=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003er- value\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ep value\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003er- value\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003ep \\u0026ndash; value\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003er- value\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003ep- value\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003er- value\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eP-value\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"1\\\" nameend=\\\"c9\\\" namest=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBDNF (pg/ml)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.604\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.013*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.402\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.098\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.233\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.368\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.315\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0.294\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNO (\\u0026micro;M)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-0.237\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.315\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.166\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.460\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.399\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.066\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.033\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0.878\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNLR\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.318\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.198\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.094\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.701\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.121\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.613\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e-0.226\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0.417\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e*Significant at \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05; MMSE\\u0026thinsp;=\\u0026thinsp;Mini-Mental State Examination; WBC\\u0026thinsp;=\\u0026thinsp;white blood cell; cmm\\u0026thinsp;=\\u0026thinsp;cubic millimeter; Mixed\\u0026thinsp;=\\u0026thinsp;other leucocytes; NLR\\u0026thinsp;=\\u0026thinsp;neutrophil\\u0026ndash;to\\u0026ndash;lymphocyte ratio; BDNF\\u0026thinsp;=\\u0026thinsp;brain-derived neurotrophic factor; NO\\u0026thinsp;=\\u0026thinsp;nitric oxide.\\u003c/p\\u003e \\u003cp\\u003eThe influence of age on the cognitive score and plasma BDNF level is shown in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e. Regression analysis revealed that age has a significant influence on cognition, as a significant negative relationship was found between age and cognitive score. The proportion of variance in cognitive score that could be explained by age was 52.2%. The contribution of age to the model was statistically significant according to the following regression equation:\\u003c/p\\u003e \\u003cp\\u003eCognitive score\\u0026thinsp;=\\u0026thinsp;42.545 + [-0.325 (Age)]\\u003c/p\\u003e \\u003cp\\u003eSimilarly, as shown in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e, regression analysis revealed that age has a significant influence on the plasma BDNF level, as a significant negative relationship was found between age and the plasma BDNF level. However, the proportion of variance in plasma BDNF levels that could be explained by age was low, at only 9.5%. The contribution of age to the model was statistically significant according to the following regression equation:\\u003c/p\\u003e \\u003cp\\u003ePlasma BDNF level\\u0026thinsp;=\\u0026thinsp;11875.107 + [-107.66 (Age)]\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eLinear regression analysis of age, cognitive score and plasma BDNF level in the study participants\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"4\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eIndependent parameter\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eDependent parameters\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eRegression coefficient\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eP-value\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAge\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eR\\u003csup\\u003e2\\u003c/sup\\u003e\\u0026thinsp;=\\u0026thinsp;0.522; F\\u0026thinsp;=\\u0026thinsp;94.093; p\\u0026thinsp;=\\u0026thinsp;0.000*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCognitive score\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-0.723\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.000*\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eR\\u003csup\\u003e2\\u003c/sup\\u003e\\u0026thinsp;=\\u0026thinsp;0.095; F\\u0026thinsp;=\\u0026thinsp;9.031; p\\u0026thinsp;=\\u0026thinsp;0.003*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eBDNF\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-0.308\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.003*\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"4\\\"\\u003e*Significant at \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eIt is well established that cognitive function is affected by aging, as approximately 40% of individuals aged 65 years and older suffer from some form of memory loss (Aigbogun et al., \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e). Although a number of factors have been identified, the mechanisms through which the identified factors result in cognitive dysfunction are poorly understood.\\u003c/p\\u003e \\u003cp\\u003eIn this study, a significant progressive decrease in cognitive score was observed as the duration of life increased. This was further supported by the regression analysis showing that age was negatively related to cognition. These observations corroborate the findings of Park et al. (\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e2002\\u003c/span\\u003e), who showed that there are gradual age-related declines in the cognitive mechanisms of speed, working memory, and long-term memory beginning in young adulthood. The observations from this study could be due to a decrease in neuronal activation and alterations in several regions of the brain as aging progresses. Brain activity in regions such as the cortex, hippocampus and cerebellum is altered during aging (Calautti et al., \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e2001\\u003c/span\\u003e; Brito et al., \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). This results in a decrease in the speed of processing, working memory, inhibitory function, and long-term memory (Park and Reuter-Lorenz, \\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e; Randhawa and Varghese, \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eBDNF is involved in plasticity, neuronal survival, formation of new synapses, dendritic branching, and modulation of excitatory and inhibitory neurotransmitter profiles (Edelmann et al., \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e). A decrease in its level has been associated with age-related memory impairment (Panja et al., \\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e). In this study, there was a progressive decrease in the plasma BDNF concentration as the patients aged. The plasma BDNF level were significantly lower in participants aged 50 years and older than in those who were younger. Additionally, a negative relationship was observed between age and BDNF level. These observations are in line with the findings of Mizoguchi et al. (\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e), who showed that low serum BDNF levels are associated with age-related memory impairment in elderly individuals. Although a reduction in BDNF expression has been associated with chronic inflammation, which is a common feature in elderly individuals (2011), the findings of this study might not be inflammation related, as no significant difference in the NLR was observed when all the groups were compared. Our observed reduced plasma BDNF could therefore be due to decreased physical activity, which is common in elderly people. BDNF levels have been shown to be activity dependent (Gorgoulis et al., \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). Exercise is an inducer of neuronal plasticity, neurogenesis and survival (Huang et al., \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e). Furthermore, the observed low level of BDNF in aged individuals could be diet dependent, as poor appetite is a common problem in older people (Pilgrim et al., \\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e). A study by Gorgoulis et al. (\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e) showed that diet can provoke changes in BDNF levels under physiological conditions. Low BDNF levels have been reported in adults consuming high sugar and fat-containing diets (Mizoguchi et al. (\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e), while diets rich in omega-3 fatty acids have been shown to induce an increase in BDNF levels and a reduction in cognitive impairment (Wu et al., \\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e2004\\u003c/span\\u003e; Mizoguchi et al., \\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThe observed significant positive correlation between BDNF level and cognitive score indicated that an increase in BDNF level may be associated with improved cognition. This observation is not surprising, as the interplay between BDNF and cognition is well established. BDNF could therefore be considered a potential therapy for brain pathologies because it can directly or indirectly modulate changes within the brain (Nagahara and Tuszynski, \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e; Gorgoulis et al., \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). Nagahara and Tuszynski (\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e) reported that BDNF has a potential role in the pathogenesis and treatment of both neurological and psychiatric disorders.\\u003c/p\\u003e \\u003cp\\u003eChronic inflammation is a common feature of aging. Cellular markers of inflammation, such as the neutrophil-to-lymphocyte ratio (NLR), have been shown to correlate positively with age. In this study, no significant difference in the NLR was observed between the groups. This observation contradicts the findings of Li \\u003cem\\u003eet al\\u003c/em\\u003e. (Li et al., \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e), who showed that older people have a higher NLR than young adults. These differences could be due to variations in the selection of study participants, as participants in this study were grouped according to decade of life, and age-associated chronic inflammation might not be linear.\\u003c/p\\u003e \\u003cp\\u003eNitric oxide is a mediator and regulator of inflammatory responses (Sharma et al., \\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e2007\\u003c/span\\u003e). The function of NO in the hypothalamus has largely been implicated in learning processes and in memory formation. In this study, the mean plasma NO concentration was significantly higher in participants aged 40\\u0026ndash;49 years (Group II) or 50\\u0026ndash;59 years (Group III) than in participants aged 30\\u0026ndash;39 years (Group I). This contradicts the findings of Van der Loo et al. (\\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e2000\\u003c/span\\u003e), who showed that the bioavailability or generation of NOS-derived NO decreases with age. However, Siervo et al. (\\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e) reported that age-related changes in serum NO levels peaked between 50 and 59 years in both sexes and declined thereafter. This finding is similar to what was observed in this study, in which the NO concentration decreased in participants aged 60 years and older. Although this observation could not be fully explained at present, it might indicate that the serum NO concentration reflects the well-established immunosenescence pattern.\\u003c/p\\u003e \\u003cp\\u003eThe small sample size was a limitation in this study. Additionally, the use of only the MMSE to assess cognitive function was a limitation. The MMSE has been reported to have low sensitivity for detecting mild cognitive disorders, and it is largely a screening tool\\u003c/p\\u003e \\u003cp\\u003eIt could be concluded from this study that there is a progressive reduction in the plasma BDNF concentration and cognitive score with increasing decade of life. This may indicate that the plasma BDNF concentration could predict susceptibility to cognitive impairment as aging progresses and could be further explored as a potential neuroprotective and functional restorative factor in patients with cognitive dysfunction. However, large population studies are suggested to confirm the findings of this study. Similarly, a randomized blinded clinical trial is suggested to evaluate the therapeutic effect of BDNF administration on improving cognition and preventing dementia in elderly people.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eAigbogun MS, Stellhorn R, Krasa H, Kostic D (2017) Severity of memory impairment in the elderly: Association with health care resource use and functional limitations in the united states. 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J Neurotrauma 21(10):1457\\u0026ndash;1467. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1089/neu.2004.21.1457\\u003c/span\\u003e\\u003cspan address=\\\"10.1089/neu.2004.21.1457\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":true,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"University of Ibadan\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Aging, Brain-derived neurotrophic factor, Immunosenescence, Inflammation, Neurocognition\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-5194167/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-5194167/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eCognition, plasma brain derived neurotrophic (BDNF) levels and indices of inflammation were determined in 88 adults sub-divided into 4 groups; Group I (30\\u0026ndash;39 years), Group II (40\\u0026ndash;49 years), Group III (50\\u0026ndash;59 years) and Group IV (\\u0026ge;\\u0026thinsp;60 years) using standard procedures. There was a significant progressive reduction in cognitive score and plasma BDNF levels as the decades of life increased. The neurocognitive scores were significantly higher in Groups I, II and III than in Group IV. Similarly, the median plasma BDNF level was significantly higher in Group I than in Groups III and IV. Regression analysis revealed that age was negatively related to cognition (R\\u003csup\\u003e2\\u003c/sup\\u003e\\u0026thinsp;=\\u0026thinsp;0.522, p\\u0026thinsp;=\\u0026thinsp;0.000) and BDNF levels (R\\u003csup\\u003e2\\u003c/sup\\u003e\\u0026thinsp;=\\u0026thinsp;0.095, p\\u0026thinsp;=\\u0026thinsp;0.003). Plasma BDNF levels and cognitive scores progressively decrease with increasing age hence, plasma BDNF levels could predict susceptibility to neurocognitive dysfunction as aging progresses.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Age-Related Changes in Cognition, Plasma Levels of Brain-Derived Neurotrophic Factors and Selected Indices of Inflammation in Adults at Different Decades of Life\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2024-10-04 01:35:05\",\"doi\":\"10.21203/rs.3.rs-5194167/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"bfc1257b-e011-4aea-9ff6-b319fcea37df\",\"owner\":[],\"postedDate\":\"October 4th, 2024\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[{\"id\":38469939,\"name\":\"Immunology\"}],\"tags\":[],\"updatedAt\":\"2024-10-04T01:35:05+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2024-10-04 01:35:05\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-5194167\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-5194167\",\"identity\":\"rs-5194167\",\"version\":[\"v1\"]},\"buildId\":\"qtupq5eGEP_6zYnWcrvyt\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}