Profiling classical neuropsychiatric biomarkers across biological fluids and following continuous lumbar puncture: A guide to sample type and time

Gallen Triana-Baltzer, Maarten Timmers, Peter De Boer, Manja Schoene, Maura Furey, Cathy Bleys, Isabeau Vrancken, Randy Slemmon, Marc Ceusters, Luc van Nueten, Hartmuth Kolb, Gallen Triana-Baltzer, Maarten Timmers, Peter De Boer, Manja Schoene, Maura Furey, Cathy Bleys, Isabeau Vrancken, Randy Slemmon, Marc Ceusters, Luc van Nueten, Hartmuth Kolb

Abstract

Identification of putative biomarkers for neuropsychiatric disorders has produced a diverse list of analytes involved in inflammation, hypothalamic-pituitary-adrenal axis (HPA) regulation, growth factor and metabolic pathways. However, translation of these findings to accurate and robust assays has been stalled, affecting objective diagnoses, tracking relapse/remission, and prediction/monitoring of drug affect. Two important factors to control are the sample matrix (e.g. serum, plasma, saliva, or cerebrospinal fluid) and time of sample collection. Additionally, sample collection procedures may affect analyte level. In this study, a panel of 14 core neuropsychiatric biomarkers was measured in serum, plasma, saliva, and cerebrospinal fluid (CSF), all collected from 8 healthy volunteers at the same time. In a second cohort of 7 healthy volunteers, 6 analytes were measured in serum and CSF collected at 13 timepoints over a 24-h period after catheter placement. We found that many of the analytes were quantifiable in all sample types examined, but often at quite different concentrations and without correlation between the sample types. After catheter placement, a diurnal pattern was observed for cortisol and interleukin-6 in serum, and transient spikes were observed in interleukin-1β. In CSF, a chronic elevation of several cytokines was observed instead, perhaps due to the continuous sampling procedure. These findings enable more informed decision-making around sample type and collection time, which can be implemented in future biomarker studies.

Clinicaltrialgov identifiers: NCT02933762, NCT02475148.

Keywords: Diurnal pattern; Neuropsychiatric biomarkers; Neuropsychiatric disorders.

Conflict of interest statement

GTB, MT, MS, CB, IV, RS, and KB are employees of Janssen Research & Development, LLC or Janssen Research and Development, a division of Janssen Pharmaceutica NV, and hold stock/stock options in the company (Johnson & Johnson). MF, MC, LvN were employees of Janssen at the time the study was conducted. LvN is retired; MC is currently employed by The Marc Ceusters Company, and MF is currently employed by Neurocrine Biosciences. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in the subject matter or materials discussed in the manuscript apart from those disclosed.

© 2022 Janssen Research & Development, LLC.

Figures

Fig. 1
Fig. 1
Comparison of analyte levels across sample types. Adiponectin, mature BDNF, leptin, cortisol, CRP, GP130, IL-6-R, IL-6, TNFα, and IL-10 (panels, A-J, respectively) were measured in matching serum, plasma, AM saliva, PM saliva, and CSF in 8 healthy volunteer donors (cohort 1). Concentrations are shown in the data tables and compared to serum levels in the graphs. Pearson's correlations (r2) are shown in each graph. Assay linear range is noted below each table and sample measurements outside of linear range are noted in italics. ND = Not Detected (i.e. signal is below limit of detection). BDNF= brain-derived neurotrophic factor; CSF= cerebrospinal fluid; CRP=C-reactive protein; LLOQ= lower limit of quantitation; ULOQ= upper limit of quantitation; TNFα=tumor necrosis factor alpha.
Fig. 1
Fig. 1
Comparison of analyte levels across sample types. Adiponectin, mature BDNF, leptin, cortisol, CRP, GP130, IL-6-R, IL-6, TNFα, and IL-10 (panels, A-J, respectively) were measured in matching serum, plasma, AM saliva, PM saliva, and CSF in 8 healthy volunteer donors (cohort 1). Concentrations are shown in the data tables and compared to serum levels in the graphs. Pearson's correlations (r2) are shown in each graph. Assay linear range is noted below each table and sample measurements outside of linear range are noted in italics. ND = Not Detected (i.e. signal is below limit of detection). BDNF= brain-derived neurotrophic factor; CSF= cerebrospinal fluid; CRP=C-reactive protein; LLOQ= lower limit of quantitation; ULOQ= upper limit of quantitation; TNFα=tumor necrosis factor alpha.
Fig. 1
Fig. 1
Comparison of analyte levels across sample types. Adiponectin, mature BDNF, leptin, cortisol, CRP, GP130, IL-6-R, IL-6, TNFα, and IL-10 (panels, A-J, respectively) were measured in matching serum, plasma, AM saliva, PM saliva, and CSF in 8 healthy volunteer donors (cohort 1). Concentrations are shown in the data tables and compared to serum levels in the graphs. Pearson's correlations (r2) are shown in each graph. Assay linear range is noted below each table and sample measurements outside of linear range are noted in italics. ND = Not Detected (i.e. signal is below limit of detection). BDNF= brain-derived neurotrophic factor; CSF= cerebrospinal fluid; CRP=C-reactive protein; LLOQ= lower limit of quantitation; ULOQ= upper limit of quantitation; TNFα=tumor necrosis factor alpha.
Fig. 1
Fig. 1
Comparison of analyte levels across sample types. Adiponectin, mature BDNF, leptin, cortisol, CRP, GP130, IL-6-R, IL-6, TNFα, and IL-10 (panels, A-J, respectively) were measured in matching serum, plasma, AM saliva, PM saliva, and CSF in 8 healthy volunteer donors (cohort 1). Concentrations are shown in the data tables and compared to serum levels in the graphs. Pearson's correlations (r2) are shown in each graph. Assay linear range is noted below each table and sample measurements outside of linear range are noted in italics. ND = Not Detected (i.e. signal is below limit of detection). BDNF= brain-derived neurotrophic factor; CSF= cerebrospinal fluid; CRP=C-reactive protein; LLOQ= lower limit of quantitation; ULOQ= upper limit of quantitation; TNFα=tumor necrosis factor alpha.
Fig. 1
Fig. 1
Comparison of analyte levels across sample types. Adiponectin, mature BDNF, leptin, cortisol, CRP, GP130, IL-6-R, IL-6, TNFα, and IL-10 (panels, A-J, respectively) were measured in matching serum, plasma, AM saliva, PM saliva, and CSF in 8 healthy volunteer donors (cohort 1). Concentrations are shown in the data tables and compared to serum levels in the graphs. Pearson's correlations (r2) are shown in each graph. Assay linear range is noted below each table and sample measurements outside of linear range are noted in italics. ND = Not Detected (i.e. signal is below limit of detection). BDNF= brain-derived neurotrophic factor; CSF= cerebrospinal fluid; CRP=C-reactive protein; LLOQ= lower limit of quantitation; ULOQ= upper limit of quantitation; TNFα=tumor necrosis factor alpha.
Fig. 2
Fig. 2
Diurnal rhythm of cortisol and IL-6 in serum. Cortisol (A) and IL-6 (B) were measured in serum from 7 healthy volunteers over a 24-h period (cohort 2). Data are shown as % of baseline (mean± SEM of the 7 volunteers). Dashed line indicates midnight, blue bar indicates typical nighttime. IL- interleukin -6. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 3
Fig. 3
Stable pattern of TNFα and TGFβ in serum. TNFα (A), IL-10 (B) and TGFβ (C) were measured in serum from 7 healthy volunteers over a 24-h period (cohort 2). Data are shown as % of baseline (mean± SEM of the 7 volunteers). Dashed line indicates midnight, blue bar indicates typical nighttime. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 4
Fig. 4
Spiking levels of IL-1β in serum. IL-1β was measured in serum from 7 healthy volunteers over a 24-h period (cohort 2). Data are shown as % of baseline (mean± SEM of the 7 volunteers). Dashed line indicates midnight, blue bar indicates typical nighttime. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 5
Fig. 5
Lumbar collection impact on levels of IL-6, TNFα, IL-10, and IL-1β in CSF. IL-6 (A), TNFα (B) and IL-10 (C), and IL-1β were measured in CSF from 7 healthy volunteers over a 24-h period (cohort 2). Data are shown as % of baseline (mean± SEM of the 7 volunteers). Dashed line indicates midnight, blue bar indicates typical nighttime. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 6
Fig. 6
Stable pattern of TGFβ in CSF. TGFβ was measured in CSF from 7 healthy volunteers over a 24-h period (cohort 2). Data are shown as % of baseline (mean± SEM of the 7 volunteers). Dashed line indicates midnight, blue bar indicates typical nighttime. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

References

    1. Brand S.J., Moller M., Harvey B.H. A review of biomarkers in mood and psychotic disorders: a dissection of clinical vs. Preclinical correlates. Curr. Neuropharmacol. 2015;13:324–368.
    1. Chen J., Hu S. Individualized treatment strategy for depressive disorder. Adv. Exp. Med. Biol. 2019;1180:219–232.
    1. Kraus C., Kadriu B., Lanzenberger R., Zarate C.A., Jr., Kasper S. Prognosis and improved outcomes in major depression: a review. Transl. Psychiatry. 2019;9:127.
    1. Strawbridge R., Young A.H., Cleare A.J. Biomarkers for depression: recent insights, current challenges and future prospects. Neuropsychiatric Dis. Treat. 2017;13:1245–1262.
    1. Haack M., Kraus T., Schuld A., Dalal M., Koethe D., Pollmacher T. Diurnal variations of interleukin-6 plasma levels are confounded by blood drawing procedures. Psychoneuroendocrinology. 2002;27:921–931.
    1. Ellervik C., Vaught J. Preanalytical variables affecting the integrity of human biospecimens in biobanking. Clin. Chem. 2015;61:914–934.
    1. Karege F., Bondolfi G., Gervasoni N., Schwald M., Aubry J.M., Bertschy G. Low brain-derived neurotrophic factor (BDNF) levels in serum of depressed patients probably results from lowered platelet BDNF release unrelated to platelet reactivity. Biol. Psychiatr. 2005;57:1068–1072.
    1. Otali D., Al Diffalha S., Grizzle W.E. Biological, medical, and other tissue variables affecting biospecimen utilization. Biopreserv. Biobanking. 2019;17:258–263.
    1. Polyakova M., Stuke K., Schuemberg K., Mueller K., Schoenknecht P., Schroeter M.L. BDNF as a biomarker for successful treatment of mood disorders: a systematic & quantitative meta-analysis. J. Affect. Disord. 2015;174:432–440.
    1. Agorastos A., Hauger R.L., Barkauskas D.A., Moeller-Bertram T., Clopton P.L., Haji U., Lohr J.B., Geracioti T.D., Jr., Patel P.M., Chrousos G.P., Baker D.G. Circadian rhythmicity, variability and correlation of interleukin-6 levels in plasma and cerebrospinal fluid of healthy men. Psychoneuroendocrinology. 2014;44:71–82.
    1. Klein A.B., Williamson R., Santini M.A., Clemmensen C., Ettrup A., Rios M., Knudsen G.M., Aznar S. Blood BDNF concentrations reflect brain-tissue BDNF levels across species. Int. J. Neuropsychopharmacol. 2011;14:347–353.
    1. Pan W., Banks W.A., Fasold M.B., Bluth J., Kastin A.J. Transport of brain-derived neurotrophic factor across the blood-brain barrier. Neuropharmacology. 1998;37:1553–1561.
    1. Panigrahi S.K., Toedesbusch C.D., McLeland J.S., Lucey B.P., Wardlaw S.L. Diurnal patterns for cortisol, cortisone and agouti-related protein in human cerebrospinal fluid and blood. J. Clin. Endocrinol. Metabol. 2019
    1. Dorn L.D., Lucke J.F., Loucks T.L., Berga S.L. Salivary cortisol reflects serum cortisol: analysis of circadian profiles. Ann. Clin. Biochem. 2007;44(pt3):281–284.
    1. Vining R.F., McGinley R.A., Symons R.G. Hormones in saliva: mode of entry and consequent implications for clinical interpretation. Clin. Chem. 1983;29(10):1752–1756.
    1. Patton D.F., Mistlberger R.E. Circadian adaptations to meal timing: neuroendocrine mechanisms. Front. Neurosci. 2013;7:185.
    1. Ketter T.A. Strategies for monitoring outcomes in patients with bipolar disorder. Prim. Care Companion J. Clin. Psychiatry. 2010;12:10–16.
    1. McNamara R.K., Lotrich F.E. Elevated immune-inflammatory signaling in mood disorders: a new therapeutic target? Expert Rev. Neurother. 2012;12:1143–1161.
    1. Fujimura H., Altar C.A., Chen R., Nakamura T., Nakahashi T., Kambayashi J., Sun B., Tandon N.N. Brain-derived neurotrophic factor is stored in human platelets and released by agonist stimulation. Thromb. Haemostasis. 2002;87:728–734.
    1. Pang P.T., Teng H.K., Zaitsev E., Woo N.T., Sakata K., Zhen S., Teng K.K., Yung W.H., Hempstead B.L., Lu B. Cleavage of proBDNF by tPA/plasmin is essential for long-term hippocampal plasticity. Science (New York, N.Y.) 2004;306:487–491.
    1. Nakahashi T., Fujimura H., Altar C.A., Li J., Kambayashi J., Tandon N.N., Sun B. Vascular endothelial cells synthesize and secrete brain-derived neurotrophic factor. FEBS Lett. 2000;470:113–117.
    1. Frommberger U.H., Bauer J., Haselbauer P., Fraulin A., Riemann D., Berger M. Interleukin-6-(IL-6) plasma levels in depression and schizophrenia: comparison between the acute state and after remission. Eur. Arch. Psychiatr. Clin. Neurosci. 1997;247:228–233.
    1. Kim Y.K., Jung H.G., Myint A.M., Kim H., Park S.H. Imbalance between pro-inflammatory and anti-inflammatory cytokines in bipolar disorder. J. Affect. Disord. 2007;104:91–95.
    1. Kohler C.A., Freitas T.H., Maes M., de Andrade N.Q., Liu C.S., Fernandes B.S., Stubbs B., Solmi M., Veronese N., Herrmann N., Raison C.L., Miller B.J., Lanctot K.L., Carvalho A.F. Peripheral cytokine and chemokine alterations in depression: a meta-analysis of 82 studies. Acta Psychiatr. Scand. 2017;135:373–387.
    1. Lichtblau N., Schmidt F.M., Schumann R., Kirkby K.C., Himmerich H. Cytokines as biomarkers in depressive disorder: current standing and prospects. Int. Rev. Psychiatr. 2013;25:592–603.
    1. Sothern R.B., Roitman-Johnson B., Kanabrocki E.L., Yager J.G., Fuerstenberg R.K., Weatherbee J.A., Young M.R., Nemchausky B.M., Scheving L.E. Circadian characteristics of interleukin-6 in blood and urine of clinically healthy men. vivo (Athens, Greece) 1995;9:331–339.
    1. Allijn I.E., Vaessen S.F., Quarles van Ufford L.C., Beukelman K.J., de Winther M.P., Storm G., Schiffelers R.M. Head-to-Head comparison of anti-inflammatory performance of known natural products in vitro. PLoS One. 2016;11
    1. Goldstein S.L., Leung J.C., Silverstein D.M. Pro- and anti-inflammatory cytokines in chronic pediatric dialysis patients: effect of aspirin. Clin. J. Am. Soc. Nephrol. : CJASN. 2006;1:979–986.
    1. Gump B.S., McMullan D.R., Cauthon D.J., Whitt J.A., Del Mundo J.D., Letham T., Kim P.J., Friedlander G.N., Pingel J., Langberg H., Carroll C.C. Short-term acetaminophen consumption enhances the exercise-induced increase in Achilles peritendinous IL-6 in humans. J. Appl. Physiol. 2013;115:929–936. Bethesda, Md: 1985.
    1. Paolucci E.M., Loukov D., Bowdish D.M.E., Heisz J.J. Exercise reduces depression and inflammation but intensity matters. Biol. Psychol. 2018;133:79–84.
    1. Petersen A.M., Pedersen B.K. The anti-inflammatory effect of exercise. J. Appl. Physiol. 2005;98:1154–1162. Bethesda, Md: 1985.
    1. Duffy D., Rouilly V., Braudeau C., Corbiere V., Djebali R., Ungeheuer M.N., Josien R., LaBrie S.T., Lantz O., Louis D., Martinez-Caceres E., Mascart F., Ruiz de Morales J.G., Ottone C., Redjah L., Guen N.S., Savenay A., Schmolz M., Toubert A., Albert M.L., Multinational F.C.o.E. Standardized whole blood stimulation improves immunomonitoring of induced immune responses in multi-center study. Clin. Immunol. 2017;183:325–335.
    1. Mueller S.C., Marz R., Schmolz M., Drewelow B. Intraindividual long term stability and response corridors of cytokines in healthy volunteers detected by a standardized whole-blood culture system for bed-side application. BMC Med. Res. Methodol. 2012;12:112.
    1. Bateman R.J., Wen G., Morris J.C., Holtzman D.M. Fluctuations of CSF amyloid-beta levels: implications for a diagnostic and therapeutic biomarker. Neurology. 2007;68:666–669.
    1. Van Broeck B., Timmers M., Ramael S., Bogert J., Shaw L.M., Mercken M., Slemmon J., Van Nueten L., Engelborghs S., Streffer J.R. Impact of frequent cerebrospinal fluid sampling on Abeta levels: systematic approach to elucidate influencing factors. Alzheimer's Res. Ther. 2016;8:21.

Source: PubMed

3
購読する