Neuropattern: A new translational tool to detect and

Stress, September 2012; 15(5): 479–487 q Informa Healthcare USA, Inc. ISSN 1025-3890 print/ISSN 1607-8888 online DOI: 10.3109/10253890.2011.644604

Neuropattern: A new translational tool to detect and treat stress pathology I. Strategical consideration D. HELLHAMMER1, T. HERO1, F. GERHARDS1, & J. HELLHAMMER2 1

Center of Psychobiological and Psychosomatic Research, University of Trier, Trier, Germany, and 2Daacro, Science Park Trier, Trier, Germany

Stress Downloaded from informahealthcare.com by Universitaetsspital on 08/28/12 For personal use only.

(Received 13 May 2011; revised 1 September 2011; accepted 23 November 2011)

Abstract Translational research is most prominently represented by the search for biomarkers and preclinical research. Aside from generating such new measures and methodologies, translational research additionally refers to translation of integrated knowledge. This strategy involves synthesis, exchange, and dissemination of available knowledge, with the goal of improving health services and health care systems. For stress-related disorders, such as depression and anxiety disorders, this strategy meets numerous challenges, as the great majority of these patients are treated by family physicians. Here, we introduce Neuropattern, a new diagnostic tool, which allows translation of psychobiological knowledge to this stress “bedside.” Neuropatterns are conceptualized endophenotypes of the activity and reactivity status of neurobiological interfaces, which participate in the crosstalk between the brain and peripheral organs under stressful conditions. Neuropattern can easily be implemented in routine clinical work, and helps the physician to individualize those therapeutic interventions that are already available.

Keywords: Biomarker, endophenotypes, neuroendocrinology, neuropattern, stress pathology, translational stress medicine

Introduction Stress-related mental and physical disorders continuously increase and add a tremendous societal burden of intangible, direct, and indirect costs. In Germany, for example, annual reports of insurance companies and federal institutions show that mental disorders result in the highest rates of sick leave, longest hospital stays, absenteeism, and are the far most common cause of early retirement (Badura et al. 2010). This trend is reflected over the past decade in an average increase in prescriptions for antidepressants of 15% per year (Lohse and Mu¨ller-Oerlinghausen 2010). The World Health Organization considers depression as a silent epidemic, probably becoming the single biggest source of this burden of all health conditions in the next 20 years (Baumann et al. 2010). Depression, anxiety, pain, or cardiometabolic disorders are just some examples linking impaired physical and mental health to stress physiology. Three recently published textbooks, edited by Contrada and

Baum (2011), Steptoe (2010), and Koob et al. (2010), provide numerous impressive overviews, linking both etiopathogenetic determinants and treatment effects to stress response networks in the brain. Although countless research papers have been published on the psychobiology of stress-related disorders, most of this knowledge has not yet been shown to efficiently improve treatment for stress-related disorders. There are several reasons for this limited translational success. First, there is hope that new biomarkers may soon improve the ability of physicians to diagnose diseases and to individualize treatments. “However, research into biomarkers — disease-associated molecular changes in body tissues and fluids — hasn’t delivered on its promise. Technologies such as proteomics and DNA microarrays have contributed a voluminous literature of more than 150,000 papers documenting thousands of claimed biomarkers, but fewer than 100 have been validated for routine clinical practice” (Poste 2011, p. 156). In stress-related

Correspondence: D. H. Hellhammer, Center of Psychobiological and Psychosomatic Research, Trier University, Johanniterufer 15, D-54290, Trier, Germany. Tel: þ 49 0651 2012928. Fax: þ 49 0651 201 2934. E-mail: [email protected]


480 D. Hellhammer et al. disorders, most biomarkers relevant for depression and anxiety disorders (Filiou et al. 2011) still need to demonstrate smarter care, and that needs time. This calls for an additional strategy in translational research, e.g. translation of results from clinical studies into everyday clinical practice and health decision making, “ensuring that new treatments and research knowledge actually reach the patients or populations for whom they are intended” (Woolf 2008, p. 211). Contrary to preclinical translational research, this strategy has been termed applied clinical research (Fiscella et al. 2008). Both strategies, however, do not automatically bring profit for the patients. Notably, preclinical and applied clinical research can only be considered relevant once they are implemented into clinical routine. The end point is evidence-based data proving an (additional) reduction in intangible, direct, and/or indirect costs, as compared to the current gold standard. These requirements are not easily met. They constitute a significant hurdle for translational success. The reason is that the great majority of patients with stress-related disorders are treated by family physicians or general practitioners. For example, 70 –90% of all depressed patients are treated by family doctors; in about half of these patients the physicians initially do not diagnose depression or anxiety disorders, and only 10% are referred to psychiatrists (Kessler et al. 2002; Mu¨hlenfeld 2005). Thus, the practice of the family physician provides the natural primary target (bedside) for translational stress medicine. However, due to “high demands for clinical practice productivity, family physicians have drifted away from participation in scientific inquiry” (Khanna et al. 2009, p. 440). This and low funding priorities for family medicine (Lucan et al. 2008, 2009) currently hinder applied translational research in this important area. At this bedside, translational transfer is simply restricted by availability of time, cost, and available methodology (e.g. neuroimaging, fluid sampling, and analysis). In addition, the classifications, diagnoses, and treatments of stress-related disorders still refer to International Statistical Classification of Disease (ICD) or Diagnostic and Statistical Manual of Mental Disorders (DSM) criteria. Nowadays, such clinical phenotypes are considered a poor mirror of nature. Consequently, the National Institute of Mental Health recently called for a new brain-based classification of mental illness (Miller 2010). Adding biomarkers to such classifications refers to the definition and assessment of neuroendophenotypes, which represent the activity and reactivity status of discrete subsystems of the brain and related peripheral organs. This has a long way to go, as the current ICD and DSM criteria and phenotypes are well established and represent the international standards for health systems, statistical

records, drug admission, and records of insurance companies. In summary, there is a big gap between the treasury of knowledge from psychobiological stress research and its translation into clinical practice. In the long term, there is surely no alternative to translational stress medicine, but the respective strategies mentioned above may all have their own hassles that impede success. Here, we present a modification of the applied clinical translational research strategy, which may help particularly to improve practice at the bedside. The neuropattern approach Over the past decade, we have developed Neuropattern, a new diagnostic tool, which can easily be implemented into the clinical routine of general practitioners and specialists. Our new approach can best be termed “integrated knowledge translation,” as defined by the Canadian Institutes of Health Research (CIHR): “Knowledge translation is a dynamic and iterative process that includes synthesis, dissemination, exchange and ethically sound application of knowledge to improve the health of Canadians, provide more effective health services and products and strengthen the health care system. . . . Integrated knowledge translation is a different way of doing research that meaningfully engages knowledge users in the research process” (Graham and Tetroe 2008, p. 2149). Our approach with respect to the criteria addressed by the CIHR is illustrated below. Knowledge synthesis As early as 1895, the young physiologist Sigmund Freud was one of the first who tried to conceptualize and translate brain physiology into clinical neurology and psychotherapy (Pribram and Gill 1976). In the 20th century, the rapid increase in knowledge in brain research stepwise elucidated the mechanisms behind stress pathology (Weiner 1992). In parallel, there were several attempts to conceptualize available physiological knowledge to improve diagnostic and therapeutic interventions in stress-related disorders. Such early concepts referred to specific pathological mechanisms (Weiner 1977), conceptual nervous subsystems (Hebb 1949; Gray 1973; Hellhammer 1983), or a comprehensive biopsychosocial approach (Engel 1977; McEwen 1998). They all aimed to use interdisciplinary knowledge to improve our ability to assess and treat stress pathology. Norman et al. (2001, p. 631) have recently addressed this kind of strategy as follows: “The ultimate goal of multilevel research is to promote meaningful reductionism and extensionism so that knowledge and constructs at multiple levels of organization and analysis can mutually inform,


Neuropattern I — strategic aspects elucidate, and constrain theory and research at other levels.” With respect to multilevel analyses of stress, the authors emphasized the bidirectional crosstalk between the brain and the peripheral organs via the autonomic nervous system and the hypothalamus – pituitary– adrenal (HPA) axis, as both these systems play a prominent role in stress and stress-related disorders. An early approach to conceptualize the functional role of the diencephalon and the autonomic nervous systems was made by the Swiss physiologists and Nobel laureate Walter Rudolf Hess (Hess 1924, 1925, 1961). Beyond Cannon’s (1914) conceptualization of the role of the sympathetic nervous system (SNS) in the fight –flight response, Hess distinguished between ergotropic and trophotropic functions. Ergotropy encompasses sympathetic functions associated with arousal, mental or physical work, and alertness, whereas trophotropy mainly refers to parasympathetic functions, regeneration, recovery, and protection against stress overload. Selye’s (1936, 1956) model of the general adaptation syndrome further included the HPA axis as another major stress response system. Given the tremendous complexity of mechanisms in stress-related disorders, we decided to focus solely on measures of interfaces, which participate in the crosstalk between the brain and the peripheral body under stressful conditions, e.g. the HPA axis, the autonomic nervous system, and selected components of the central nervous system, namely the locus coeruleus – noradrenergic (norepinephrinergic, NE) and the dorsal raphe –serotonergic (5-hydroxytryptaminergic, HT) systems. The latter two systems were chosen, because they are (a) clinically important modulators of the stress response, (b) still the most relevant targets for current psychopharmacological treatments, and (c) fulfill criteria for central ergotropic and trophotropic functions (Hellhammer and Klingmann 2008; Klingmann and Hellhammer 2008). In our Neuropattern approach, we termed the endocrine functions “glandotropic,” referring to the activity status of the different central and peripheral components of the HPA axis, associated with mobilization of energy resources, prevention of an overshooting stress response, and psychological characteristics, such as anticipation, worrying, lack of control, and ego involvement (Hellhammer 2008). Next, we conceptualized (neuro-)endophenotypes, which represent estimated states of activity and reactivity of these interfaces, each identified by combined constellations of specific psychological, biological, and symptom measures. Based on extensive analyses of the literature and our own research studies in probands, patients, and animals, we tried to identify those psychological, biological, and symptom variables that characterize states of hyperactivity and hypoactivity and reactivity of each of these interfaces (Hellhammer and Hellhammer 2008). Once a subject

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expresses an a priori defined number of measures in each category, we hypothetically assume a reasonable likelihood that the respective state exists, which may be of clinical relevance for a given subject. In the first step, we evaluated hypothetically 27 patterns in a series of studies in 1524 probands and patients. Each pattern was conceptualized to represent different components of ergotropic, trophotropic, and glandotropic systems, which can be considered to act as a kind of interface in the crosstalk between the brain and the peripheral organs. Thirteen of these patterns delivered robust data and could be appropriately assessed and termed (1) corticotropin-releasing factor (CRF) hyperactivity, (2) CRF hyperreactivity, (3) CRF hypoactivity, (4) cortisol hyperactivity, (5) cortisol hypoactivity, (6) glucocorticoid receptor (GR) resistance, (7) NE hyperactivity, (8) NE hyperreactivity, (9) NE hypoactivity, (10) SNS hyperactivity, (11) SNS hyperreactivity, (12) serotonin (5-HT) hyperreactivity, and (13) serotonin hypoactivity. Our tool additionally controls for clinical criteria of hypochondria and masked depression, as well as for states of vagal and sympathetic hypoactivity, as estimated from measures of heart rate variability (HRV). In addition, we assessed the possible impact of early adversity. The glandotropic patterns associated with enhanced HPA activity and reactivity (1,2,4) were described by Pu¨tz (2008), those associated with lowered HPA activity and reactivity (3,5,6) by Fries (2008). The ergotropic patterns (7 – 11) were illustrated by Klingmann and Hellhammer (2008) and the trophotropic patterns (12,13) by Hellhammer and Klingmann (2008). The possible interactions among these patterns are visualized by a “stress triangle.” The stress triangle is used as a visual aid for the physician (Figure 1). In a non-stressed individual, the stress triangle connects the middle of each of three parallel virtual pillars, representing a balanced status of the glandotropic, ergotropic, and trophotropic systems. In a stressed individual, each of the points of the triangle may be positioned above or below the respective pillar middles. The position of the triangle illustrates the diagnostic results of the Neuropattern analyses. In most cases, the points of the triangle deviate from the middle. However, it is also possible that all three points are equally balanced above or below the middles, representing a kind of hyper- and hypobalanced mode. In summary, the stress triangle represents the diagnostic end point of our integrated knowledge synthesis, from which personalized indications for treatment can be derived. The individual positions on the three pillars are semi-quantitatively determined, once the patient fulfills the quantitative criteria for one or more Neuropatterns. The Neuropatterns CRF hyperactivity, CRF hyper-reactivity, and cortisol hyperactivity are considered to represent a hyperactive glandotropic


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Figure 1. The “stress triangle” is a simplified illustration of the diagnostic results with respect to the assumed relationship between the ergotropic, trophotropic, and glandotropic status. The bold triangle shows the results (above) and the therapeutic goals (below) for a given patient. In this example, the patient qualified for three patterns [serotonin hypoactivity (trophotropic column), CRF hyperreactivity (glandotropic column), and NE hyperactivity (ergotropic column)]. The therapeutic goal is to strengthen the serotonergic effects by selective serotonin reuptake inhibitor (SSRI) and relaxation training (black arrow). This is expected to indirectly dampen CRF- and NE-mediated psychobiological symptoms (gray arrows) and achieve a balanced mode among the three systems.

mode, while CRF hypoactivity, cortisol hypoactivity, and GR resistancy are related to a hypoactive glandotropic mode. The Neuropatterns NE hyperactivity, NE hyperreactivity, SNS hyperactivity, and SNS hyperreactivity are seen to reflect a hyperactive ergotropic mode, while NE hypoactivity represents ergotropic hypoactivity. A hyperactive trophotropic mode is represented by 5-HT hyperactivity, while 5-HT hypoactivity represents trophotropic hypoactivity. On each pillar of the triangle, the position for a hyperactive, normoactive, or hypoactive state represents the cumulative number of biological, psychological, and symptom variables of the respective Neuropatterns, expressed in percentiles. Thus, the Neuropatterns do not add up to gradually modify the position on a pillar in one or the other direction. This makes it easier for the physician to explain the results to the patients. Practical ontologies In the first issue of a new journal Translational Behavioral Medicine, Young and Borland (2011) have recently addressed the conceptual challenges in the translation of research into practice. They argue that

basic researchers and clinicians have different ontologies. Although the basic researcher is interested to detect cause – effect relationships and statistical significance, the bedside researcher is interested to demonstrate evidence-based effectiveness of interventions and clinical significance. Both use different goals and strategies: the basic researcher tries to understand causalities independent of individual differences; he/she looks from the particular to the general. The bedside researchers understand causalities as unambiguously personal and interpersonal; they look from the general to the particular. The authors conclude that research syntheses have to be framed to meet practitioners’ needs. They propose to use “generalization gradients” to help practitioners apply general research conclusions to their particular situation and researchers to identify the relevance of their work. Our strategy aims to use such generalization gradients, by introducing the stress triangle with its three glandotropic, ergotropic, and trophotropic systems. Each Neuropattern is a composition of biological, psychological, and symptom characteristics. For example, the conceptualization of “CRF hyperactivity” used numerous measures that have been shown to be associated with elevated CRF activity in the hypothalamic paraventricular nucleus (Pu¨tz 2008). However, if a patient meets a sufficient number of each of these measures, it does definitively not imply the verification of a cause – effect relationship among these criteria in a given patient. Due to a considerable complexity of intervening variables, the missing covariance between stressinduced CRF activation and the associated measures of cortisol, psychological variables, and symptoms will be too small to confirm causalities (Hellhammer et al. 2009). Rather, the pattern suggests a general possibility of a CRF-mediated activation of the HPA axis. This is reflected by the generalization to the term “glandotropic” hyperactivity. Consequently, Neuropattern can only be accredited by randomized control trials demonstrating an improved effectiveness of clinical interventions in stress-related disorders, once Neuropattern is used in practice. However, the data collected from the bedside can become valuable to test hypotheses from the benchside. These findings may then become accredited by publications of original research data.

Knowledge users In stress medicine, the primary knowledge user is the family doctor. Thus, we have to ask ourselves, how we can improve the ability of the physician to diagnose and treat stress-related disorders. In addition, we should help the patient to understand the biological, psychological, and social determinants of his/her disease, thus facilitating treatment success.


Neuropattern I — strategic aspects We assume that it is not always easy for the family doctor to reliably diagnose a stress-related disorder. This is particularly true for stress-related physical disorders. Although perceived stress can be reported by the patient in personal conversation, physiological stress responses are difficult to assess, and most physicians are not equipped to assess stress states. Interestingly, the psychological and the peripheral physiological stress response show either no, weak, or inconsistent correlations, if data are collected under such conditions. The biological stress response is processed on different levels, each adding to the variance. Moreover, not all of these processes are conscious, thus limiting perception of physiological responses. In consequence, the doctor may wrongly interpret the impact of stress in a given patient, and finally assign an inappropriate treatment. Although missing covariance of the psychological and biological stress response is well known (Hellhammer and Hellhammer 2008), its relevance is often underestimated in clinical research and practice. In addition, the etiology and pathogenesis of stressrelated disorders usually reflect a complex interplay of multiple biological, social, and psychological determinants. Thus, patients sharing similar ICD or DSM diagnoses may show quite different constellations of such determinants, for example, genetic dispositions, which affect the ability to adapt to stress; epigenetic determinants, which program gene expression, particularly in pre- and postnatal life; or socioeconomic conditions and learning history, which impact on our ability to cope with stress (Contrada and Baum 2011). All these conditions affect the adaptability of the central nervous system, the autonomic nervous system, the endocrine system, the immune system, and peripheral organ functions, and finally determine the efficacy of mechanisms participating in stress vulnerability and resilience (Feder et al. 2009; Leyro et al. 2010) In summary, stress-related disorders are multi-causal, and we cannot expect to find simple mono-causal mechanisms to target efficient and highly personalized treatment strategies. Practically, it will simply be impossible for a practitioner to oversee and assess the variety of all these factors. Even if the physician could individualize treatments, he/she is restricted to a rather rigid treatment repertoire, which mainly consists of antidepressants and anxiolytics. On-label prescriptions of these drugs refer to ICD diagnoses and the respective phenotypes, but not to distinct neuroendophenotypes of the stress response network. Thus, one has to enable the physician to make the optimal choice of a drug. This choice has to fulfill three requirements: it has to relate (a) to pathological relevant neuroendophenotypes like neuropatterns, (b) to the diagnosis (ICD/DSM) for on-label prescription of the selected drug, and (c) to specific characteristics of the phenotype (symptoms).

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The “Detect Study” revealed that a family physician in Germany sees an average of 58 patients per day, and spends about 3 min with each of them (Wittchen and Pieper 2006). Thus, family physicians probably do not have the time to sufficiently analyze the biopsychosocial determinants of the respective disorders. Hence, a translational diagnostic tool will not be accepted, if it takes additional time of the physician. Finally, the family physician will not have his/her own access to sophisticated technologies such as brain imaging, and it is unlikely that they will refer most of their patients to specialists. This means that the patient has to collect as much data as possible at home. The technologies have to meet a compromise between applicability and optimal scientific standards. Preferably, the data should apply both activity and reactivity measures. In any case, a translational tool simply cannot become successful if all of these requirements cannot be sufficiently met. Knowledge application Given these practical and conceptual limitations, knowledge application is confined by measures that can routinely be taken by the physician and the patient at home. In our current version, we use the patient health questionnaire (PHQ), the Neuropatterne questionnaires (NPQs) and two biological measures: PHQ. The PHQ is a self-administered questionnaire that screens for ICD diagnoses of depressive disorders, somatoform disorders, eating disorders, anxiety disorders, and alcohol abuse. In addition, the PHQ addresses psychosocial functioning, stress, life events as well as menstruation, pregnancy, and birth (Spitzer et al. 1999, 2000). NPQ. The NPQ consists of four questionnaires: the NPQ-A is an anamnestic questionnaire for the physician, while the other three self-administered questionnaires address psychological and symptom variables (NPQ-P), stress reactivity (NPQ-S), and pre-/postnatal adversity (NPQ-PSQ). The 77 scales (based on 195 items) of the NPQ-P were derived from extensive reviews of the literature and knowledge integration from several research projects. The scales were constructed with assistance from psychologists, physicians, affected persons, and non-professionals. The first versions of all questionnaires were psychometrically evaluated in over 1500 subjects. After checking for item distributions, every item was analyzed and selected according to the classical test theory. Items with either high or low difficulty levels were rephrased or deleted. Low inter-correlations between item and scale also led to the exclusion of items. Since deletion of items caused lowered measurement accuracy in some scales, items have been reformulated or added and subsequently reevaluated. In 2008, a second revised version of the NPQ-P, NPQ-S, and NPQ-PSQ was scaled on over


484 D. Hellhammer et al. 1000 healthy subjects and normal values for age and sex groups were generated. Data are only used for analyses if they are in the upper quartile (. 75%) of each measure. The mean Cronbach’s a for the 77 scales was 0.73, with a 95% confidence interval of 0.69 –0.76, meeting the expected range (Peterson 1994). In more detail, Neuropattern collects the following information: NPQ-A. The NPQ-A is an anamnestic questionnaire for the physician, which summarizes the medical history (e.g. diseases, previous therapies, and medication), data on current drug intake as well as anthropometric and vital parameters from the patient. NPQ-P. This self-administered questionnaire takes psychological (e.g. drivenness and inability to relax) and symptom measures (e.g. fatigue and gastrointestinal symptoms), associated with stress that have been conceptually assigned to the status of the single interfaces. NPQ-S. The NPQ-S addresses perceived stress responses (e.g. exhaustion, anxiousness, anger, and helplessness) as experienced during the past month. Additionally, the patient has to report the impact of recently experienced life events (e.g. physical abuse and emotional neglect), which he/she considers relevant for the onset of his/her complaints. NPQ-Pre-/postnatal-Stress-Questionaire (PSQ). The NPQ-PSQ retrospectively records relevant pre-, peri-, and postnatal adverse events. The patient is asked to fill in the questionnaire with the aid of parents, relatives, and birth records. Salivary cortisol measures and the low-dose dexamethasone suppression test. Subjects collect saliva at home on three consecutive weekdays upon morning awakening plus 30, 45, and 60 min afterwards. Cortisol concentrations increase after awakening and this is interpreted as a measure of reactivity to awakening (Pruessner et al. 1997; Clow et al. 2010). During the first 2 days, subjects take additional samples at 15:00 and 20:00 h, which, together with cortisol concentrations at awakening, reflect basal measures. In the evening (23:00 h) of the second day, each patient takes 0.25 mg of dexamethasone to assess feedback sensitivity of the HPA axis on the following day. Reference data were obtained from 470 healthy subjects. This protocol allows the collection of reliable data under ecological conditions (Hellhammer et al. 2007). HRV. Each subject receives a small portable electrocardiogram (ECG; Autonomic-NervousSystem (ANS)-recorder pico; Neurocor Ltd & Co. KG, Trier, Germany) with five electrodes, which he/she has to attach according to instructions for an overnight long-term ECG monitoring. The standardized protocol contains a baseline measure under calm sitting conditions directly before bedtime, an overnight measure, and a final measure immediately after awakening, again under calm sitting conditions. The

HRV measure after awakening is considered a response measure, as compared to the evening measure. Variability of inter-beat intervals is analyzed to estimate sympathetic and parasympathetic activities. HRV data were scaled on 1860 healthy subjects and corrected for effects of sex, age, and body mass index. Values in the lower and upper quartiles, as well as deviations of 20 percentiles between evening and morning measures are included for analysis. The ECG data are read with the software “Neurocor ANSExplorer” (Neurocor Ltd & Co. KG). After a spectral analysis, the following parameters are incorporated for Neuropattern analyses: high-frequency power (HFms 2), low-frequency power (LFms 2), total power (TPms2), LFms2/HFms2, and beats per minute, Standard Deviation (SD) of R-R intervals (indicator of autonomic control) and root mean square successive differences (measure of vagusmediated HRV) in heart rate. After all measures are completed, the patient sends the package to a central laboratory. All diagnostic data are analyzed and undergo an integrated pattern evaluation. This analysis first compares the scores of each of the biological, psychological, and symptom variables with the cut-off values of the control population. Thereafter, only those variables are further included that score in the upper quartile (of the control population). Next, the criteria for each Neuropattern are checked. For CRF hyperactivity, for example, four biological, five psychological, and two symptom criteria have to be fulfilled. Some variables are considered obligatory. Among the 36 variables are high cortisol concentrations after awakening, a large area under the cortisol curve, elevated concentrations of cortisol after dexamethasone, worrying, anticipatory ruminations, memory impairments, irritable bowel syndrome, depression, and amyasthenia. Only such variables are included that are supported by the literature or own laboratory observations. For example, worrying has been shown to be associated with such cortisol criteria (Schlotz et al. 2004; Zoccola et al. 2011). Based on this evaluation, a diagnostic report is generated that first summarizes NPQ-A data, such as previous and current symptoms, ICD diagnoses, type and efficacy of former and current treatments, and family history. If the patient fulfills criteria for one or more neuropatterns, the report next describes these findings, and addresses likely interactions among the respective patterns, the potential impact of pre- and postnatal adversity, and the possible pathogenetic impact of reported recent life events. Finally, the report makes the physician aware about individualized pharmacological and psychotherapeutic treatment options. Each diagnostic report is generated by a board certified physician or clinical psychologist.

Neuropattern I — strategic aspects


Knowledge exchange Knowledge exchange takes place once the physician and the patient receive the diagnostic results. For the patient, the physician receives an access code to a personal website that he/she can hand over. The patient receives his/her individual diagnostic information, including introductory information, which is visualized by a cartoon figure, the weight lifter “Sam.” Sam has to lift barbells, which represent the burden of stress. The right arm represents the noradrenergic system and the right leg the sympathetic system. Both ergotropic systems help Sam to mobilize the necessary power to lift the barbells. His left arm represents the serotonergic system, which together with his left leg, the parasympathetic system, helps to keep the balance by adjusting trophotropic functions, once the barbells have to be lifted. The degree of energy mobilization to support this process is illustrated by the level of cortisol in the trunk, representing pituitary –adrenal (glandotropic) functions. The patient can compare a picture of a “healthy” Sam with his personal Sam. The personal Sam reflects the individual diagnostic results. For example, if there is evidence for serotonin hypoactivity, Sam is unable to lift his left arm to balance the barbells. In this case, the patient understands the possible indication for a serotonin reuptake inhibitor and of relaxation techniques, which may both strengthen trophotropic functions. We have gained the impression that the patient’s compliance and active contribution to treatment implies his/her acceptance of the therapeutic strategies, as derived from such an illustration. Knowledge exchange further includes a research perspective. Neuropattern can be compared to a ferry, continuously traveling between bench and bedside. It delivers in- and outpatient data from hospitals and medical practices. In some studies, we currently add genetic analyses to further personalize diagnostic and therapeutic treatment. Once Neuropattern is implemented in clinical routine, the search for new biomarkers is logistically and scientifically facilitated, as such measures are easy to add and relatable to specific (elements of) endophenotypes. We consider that information from practitioners at the bedside will be most valuable and fruitful to stimulate both preclinical and applied translational research.

Knowledge dissemination Knowledge dissemination requires implementation in health care. This implies that a translational diagnostic tool like Neuropattern has to prove its clinical and cost efficacy in a series of evidence-based clinical trials, before being accepted by the respective health authorities and insurance companies. Notably, these institutions will only accept clinical significance as verified by an improvement of therapeutic success.

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Thus, we are currently running a series of proof of concept studies, comparing Neuropattern with current gold standards (for a first example, see Hero et al. 2011). These studies investigate effects of Neuropattern on intangible, direct, and indirect costs. Implementation of Neuropattern in health care is likely, once the benefits exceed such costs. Conclusions Psychobiological research in stress-related disorders has a long and successful tradition (Nemeroff and Loosen 1987; Wolkowitz and Rothschild 2003; McEwen 2007). In extension of other approaches, Neuropattern has been developed as a practical diagnostic tool, which can be implemented in routine clinical work. Thus, the underlying strategy targets improved pharmacological and psychological treatment, by allowing a personalized selection of available drugs and psychotherapeutic methods. Our strategy is based on integrated knowledge translation, as conceptualized by 13 endophenotypes of ergotropic, trophotropic, and glandotropic states. Integrated knowledge translation, as illustrated here by the Neuropattern strategy, may help to facilitate both preclinical and applied clinical research in translational stress medicine. From a bench researcher’s viewpoint, however, it may be an irritation that the proof of concept will mainly be obtained by evidencebased clinical studies, e.g. randomized control trials. At first glance, this implies that the underlying psychobiological conceptualizations of our endophenotypes are clinically but not scientifically validated. However, in the past decade, we have performed multiple studies to develop and validate single patterns in basic research studies. As mentioned above, Neuropattern facilitates the exchange between bench and bedside, and continuously delivers valuable feedback on the usefulness of measures. One important limitation is the control of the patient’s compliance with the measures taken at home. This is particularly true for salivary cortisol measures. Schweisthal (2007) addressed this issue in his dissertation and found that the means of measurements on two consecutive days represent well the means of 10 consecutive days. Thus, we decided to take the measures on 2 days. However, Dockray et al. (2008) showed that the latency between awakening and saliva collection affects the awakening response. The authors used wrist actigraphs to control for awakening times. This and other technologies may help to improve such measures in future. We consider that Neuropattern in its present form represents a first prototype. Its efficacy can be continuously controlled and improved by data mining from bench and bedside. Thus, our endophenotypes are under continuous development, and may be considered “intermediate endophenotypes” (Insel and Cuthbert


486 D. Hellhammer et al. 2009). In addition, it will likely become a useful tool for preclinical translational research. Clearly, there are numerous limitations with respect to the conceptualization and operationalization of our neuropatterns. For example, our conceptualization does not yet include other important transmitter systems, receptors, and important electrical synapses (Dolgin 2010; Bissiere et al. 2011). Furthermore, pathways for symptom expression which are not related to stress are currently not considered (Dantzer et al. 2011). Furthermore, stress-induced pathology may exert effects by itself and individuals’ responses vary with adapting to stress and socioeconomic conditions (McEwen and Gianaros 2010; Piazza et al. 2010). Our reductionism, the information derived, and the illustrations using the stress triangle and Sam are highly simplified, but are intended to be helpful to communicate the results and treatment goals in clinical routine. Good communication with the patient is essential for his/her compliance and, consequently, for the treatment success. It seems to be necessary that translational psychiatry has to compromise with clinical reality, where the family physician treats most of the patients with stressrelated disorders. Preclinical research may lead to personalized treatments of higher therapeutic efficacy, but this strategy is challenged by the extreme heterogeneity and complexity of etiopathogenetic causalities in stress-related disorders. While preclinical research adds new biomarkers and treatments, Neuropattern helps the physician to individualize therapeutic interventions, which are already available. From a patient’s perspective, translational researchers should do their best to improve therapeutic success. As described here, integrated knowledge translation seems to be a useful harmonizing strategy to reach this goal.

Declaration of interest: This study was supported by a grant of the Rhineland-Palatinate foundation for innovations. The research grant awarded to DH implicates the goal to make Neuropattern commercially available to physicians and participants in future. Neuropattern is a registered trademark. DH holds the copyright on all questionaires (except the PHQ). The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper.

References Badura B, Schro¨der H, Klose J, Macco K. 2010. Fehlzeiten-Report 2010: Schwerpunktthema: Vielfalt managen: Gesundheit fo¨rdern – Potenziale nutzen. Berlin, Heidelberg: Springer. Baumann A, Muijen M, Gaebel W. 2010. Mental health and wellbeing at the workplace—protection and inclusion in challenging times. Copenhagen: World Health Organization.

Bissiere S, Zelikowsky M, Ponnusamy R, Jacobs NS, Blair HT, Fanselow MS. 2011. Electrical synapses control hippocampal contributions to fear learning and memory. Science 331:87–91. Cannon WB. 1914. The emergency function of the adrenal medulla in pain and the major emotions. Am J Physiol 33:356–372. Clow A, Hucklebridge F, Thorn L. 2010. The cortisol awakening response in context. Int Rev Neurobiol 93:153– 175. Contrada RJ, Baum A. 2011. The handbook of stress science: Biology, psychology and health. New York: Springer. Dantzer R, O’Connor JC, Lawson MA, Kelley KW. 2011. Inflammation-associated depression: From serotonin to kynurenine. Psychoneuroendocrinology 36:426–436. Dockray S, Bhattacharyya MR, Molloy GJ, Steptoe A. 2008. The cortisol awakening response in relation to objective and subjective measures of waking in the morning. Psychoneuroendocrinology 33:77–82. Dolgin E. 2010. Newer antidepressants go beyond serotonin-and the synapse. Nat Med 16:1345. Engel GL. 1977. The need for a new medical model: A challenge for biomedicine. Science 196:129 –136. Feder A, Nestler EJ, Charney DS. 2009. Psychobiology and molecular genetics of resilience. Nat Rev Neurosci 10:446–457. Filiou MD, Turck CW, Martins-de-Souza D. 2011. Quantitative proteomics for investigating psychiatric disorders. Proteomics Clin Appl 5:38–49. Fiscella K, Bennett NM, Szilagyi PG. 2008. Nomenclature in translational research. JAMA 299:2148–2149; author reply 2149-2150. Fries E. 2008. Hypocortisolemic disorders. In: Hellhammer DH, Hellhammer J, editors. Stress: The mind–body connection. Basel: Karger. p 60–77. Graham ID, Tetroe J. 2008. Nomenclature in translational research. JAMA 299:2149; author reply 2149-2150. Gray JA. 1973. Causal theories of personality and how to test them. In: Royce JR, editor. Multivariate analysis and psychological theory. London: Academic Press. Hebb DO. 1949. The organization of behavior: A neuropsychological theory. New York: Wiley-Interscience. Hellhammer D. 1983. Gehirn und Verhalten, Eine anwendungsorientierte Einfu¨hrung in die Psychobiologie Muenster: Aschendorff. Hellhammer DH, Hellhammer J, editors. 2008. Stress: The brain– body connection, key issues in mental health. Vol. 174. Basel: Karger. Hellhammer DH, Klingmann P. 2008. Serotonergic and parasympathetic disorders. In: Hellhammer DH, Hellhammer J, editors. Stress: The mind–body connection. Basel: Karger. p 91–105. Hellhammer DH. 2008. Principles of the crosstalk between brain and body — glandotropy, ergotropy, and trophotropy. In: Hellhammer DH, Hellhammer J, editors. Stress: The mind– body connection. Basel: Karger. p 21–28. Hellhammer J, Fries E, Schweisthal OW, Schlotz W, Stone AA, Hagemann D. 2007. Several daily measurements are necessary to reliably assess the cortisol rise after awakening: State- and trait components. Psychoneuroendocrinology 32:80–86. Hellhammer DH, Wu¨st S, Kudielka BM. 2009. Salivary cortisol as a biomarker in stress research. Psychoneuroendocrinology 34: 163 –171. Hero T, Gerhards F, Thiart H, Hellhammer DH, Linden M. (2011). Neuropattern: A new translational tool to detect and treat stress pathology. II. The Teltow Study. Stress: The International Journal on the Biology of Stress. Stress, in press. Hess WR. 1924. Ueber die Wechselbeziehungen zwischen psychischen und vegetativen Funktionen. Schweiz Arch Neurol Psychiatr 15:260–277. Hess WR. 1925. Ueber die Wechselbeziehungen zwischen psychischen und vegetativen Funktionen. Schweiz Arch Neurol Psychiatr 16:36–55.


Neuropattern I — strategic aspects Hess WR. 1961. Die physiologischen Grundlagen der Psychosomatik. Deut Med Wochenschr 86:3–8. Insel TR, Cuthbert BN. 2009. Endophenotypes: Bridging genomic complexity and disorder heterogeneity. Biol Psychiatry 66: 988– 989. Kessler D, Bennewith O, Lewis G, Sharp D. 2002. Detection of depression and anxiety in primary care: Follow up study. BMJ 325:1016–1017. Khanna N, Nesbitt L, Roghmann MC, Tacket C. 2009. Translation of clinical research into practice: Defining the clinician scientist. Fam Med 41:440–443. Klingmann P, Hellhammer D. 2008. Noradrenergic and sympathetic disorders. In: Hellhammer DH, Hellhammer J, editors. Stress: The mind–body connection. Basel: Karger. p 78–90. Koob G, Le Moal M, Thompson RF. 2010. Encyclopedia of behavioral neuroscience. Oxford: Academic Press. Leyro TM, Zvolensky MJ, Bernstein A. 2010. Distress tolerance and psychopathological symptoms and disorders: A review of the empirical literature among adults. Psychol Bull 136:576– 600. Lohse MJ, Mu¨ller-Oerlinghausen B. 2010. Psychopharmaka. In: Schwabe U, Paffrath D, editors. Arzneiverordnungsreport 2010. Berlin: Springer. p 797–842. Lucan SC, Barg FK, Bazemore AW, Phillips RL, Jr. 2009. Family medicine, the NIH, and the medical-research roadmap: Perspectives from inside the NIH. Fam Med 41:188–196. Lucan SC, Phillips RL, Jr, Bazemore AW. 2008. Off the roadmap? Family medicine’s grant funding and committee representation at NIH. Ann Fam Med 6:534– 542. McEwen B. 2007. Physiology and neurobiology of stress and adaptation: Central role of the brain. Physiol Rev. 87:873–904. McEwen BS, Gianaros PJ. 2010. Central role of the brain in stress and adaptation: Links to socioeconomic status, health, and disease. Ann N Y Acad Sci 1186:190–222. McEwen BS. 1998. Protective and damaging effects of stress mediators. N Engl J Med 338:171–179. Miller G. 2010. Psychiatry. Beyond DSM: Seeking a brain-based classification of mental illness. Science 327:1437. Mu¨hlenfeld HM. 2005. Wann und wie sollten wir intervenieren? Der nervenarzt 5:48–51. Nemeroff CB, Loosen PT, editors. 1987. Handbook of clinical psychoneuroendocrinology. New York: The Guilford Press. Norman GJ, DeVries AC, Cacioppo JT, Berntson GB. 2001. Multilevel analysis of stress. In: Contrada RJ, Baum A, editors. Handbook of stress science: Psychology, biology, and health. New York: Springer. p 619–634. Peterson RA. 1994. A meta-analysis of Cronbach’s coefficient alpha. J Cons Res 21:381– 391. Piazza JR, Almeida DM, Dmitrieva NO, Klein LC. 2010. Frontiers in the use of biomarkers of health in research on stress and aging. J Gerontol B Psychol Sci Soc Sci 65:513–525. Poste G. 2011. Bring on the biomarkers. Nature 469:156 –157.

487

Pribram KH, Gill MM. 1976. Freud’s project reassessed. New York: Basic Books. Pruessner JC, Wolf OT, Hellhammer DH, Buske-Kirschbaum A, von Auer K, Jobst S, Kaspers F, Kirschbaum C. 1997. Free cortisol levels after awakening: A reliable biological marker for the assessment of adrenocortical activity. Life Sci 61: 2539–2549. Pu¨tz P. 2008. Hypercortisolemic disorders. In: Hellhammer DH, Hellhammer J, editors. Stress: The mind–body connection. Basel: Karger. p 39–59. Schlotz W, Hellhammer J, Schulz P, Stone AA. 2004. Perceived work overload and chronic worrying predict weekend-weekday differences in the cortisol awakening response. Psychosom Med 66:207–214. Schweisthal OW. 2007. Der Cortisol Awakening Response in der Diagnostik stressbezogener Erkrankungen, Dissertation. Universita¨t Trier, Trier. Selye H. 1936. A syndrome produced by diverse nocuous agents. Nature 138:32. Selye H. 1956. Endocrine reactions during stress. Curr Res Anesth Analg 35:182–193. Spitzer RL, Kroenke K, Williams JB. 1999. Validation and utility of a self-report version of PRIME-MD: The PHQ primary care study. Primary care evaluation of mental disorders. Patient health questionnaire. JAMA 282:1737–1744. Spitzer RL, Williams JB, Kroenke K, Hornyak R, McMurray J. 2000. Validity and utility of the PRIME-MD patient health questionnaire in assessment of 3000 obstetric-gynecologic patients: The PRIME-MD patient health questionnaire obstetrics-gynecology study. Am J Obstet Gynecol 183:759–769. Steptoe A. 2010. Handbook of behavioral medicine: Methods and applications. NewYork: Springer. Weiner H. 1977. Psychobiology and human disease. New York: Elsevier. Weiner H. 1992. Perturbing the organism: The biology of stressful experience. Chicago: The University of Chicago Press. Wittchen H-U, Pieper L. 2006. Schwerpunkt: Diabetes und HerzKreislauferkrankungen beim Hausarzt – Die Detect-Studie. Die Ersatzkasse 7:260–263. Wolkowitz OM, Rothschild AJ, editors. 2003. Psychoneuroendocrinology: The scientific basis of clinical practice. Washington DC: American Psychiatric Publishing Inc. Woolf SH. 2008. The meaning of translational research and why it matters. JAMA 299:211–213. Young D, Borland R. 2011. Conceptual challenges in the translation of research into practice: It’s not just a matter of “communication. Behav Med Pract Policy Res 1:256–269. Zoccola PM, Dickerson SS, Yim IS. 2011. Trait and state perseverative cognition and the cortisol awakening response. Psychoneuroendocrinology 36:592–595.

Neuropattern: A new translational tool to detect and

Neuropattern: A new translational tool to detect and

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