Victoria (Shu) Zhang, PhD Mark Olfson, MD, MPH Marissa King, PhD
Author Affiliations: School of Management, Yale University, New Haven, Connecticut (Zhang, King); New York State Psychiatric Institute, Department of Psychiatry, Vagelos College of Physicians and Surgeons, Columbia University, New York (Olfson).
Corresponding Author: Victoria (Shu) Zhang, PhD, School of Management, Yale University, 165 Whitney Ave, New Haven, CT 06510
(victoria.zhang@yale.edu).
Accepted for Publication: July 7, 2019.
Published Online: September 18, 2019. doi:10.1001/jamapsychiatry.2019.2563 Author Contributions: Dr Zhang had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Concept and design: All authors.
Acquisition, analysis, or interpretation of data: Zhang, King. Drafting of the manuscript: Zhang.
Critical revision of the manuscript for important intellectual content: All authors. Statistical analysis: Zhang.
Obtained funding: Zhang, King. Supervision: Olfson, King.
Conflict of Interest Disclosures: Dr Zhang reports grants from the National Institute on Drug Abuse during the conduct of the study. Dr Olfson reports grants from the National Institute on Drug Abuse during the conduct of the study; personal fees for serving on the advisory board of Lundbeck Pharmaceuticals in 2018 outside the submitted work. Dr King reports grants from the National Institute on Drug Abuse during the conduct of the study and outside the submitted work.
Funding/Support: This research was funded by the National Institutes of Health to Yale University (grant R01DA044981; Dr King).
Role of the Funder/Sponsor: The sponsor had no additional role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Disclaimer: The findings and opinions expressed in this article are the authors’ own and do not reflect the view of the National Institutes of Health. 1. HealthData.gov. Wide-ranging Online Data for Epidemiologic Research (WONDER). https://healthdata.gov/dataset/wide-ranging-online-data-epidemiologic-research-wonder. Accessed August 7, 2019.
2. Park TW, Saitz R, Ganoczy D, Ilgen MA, Bohnert ASB. Benzodiazepine prescribing patterns and deaths from drug overdose among US veterans receiving opioid analgesics: case-cohort study. BMJ. 2015;350:h2698. doi:10. 1136/bmj.h2698
3. Hwang CS, Kang EM, Kornegay CJ, Staffa JA, Jones CM, McAninch JK. Trends in the concomitant prescribing of opioids and benzodiazepines, 2002-2014. Am J Prev Med. 2016;51(2):151-160. doi:10.1016/j.amepre.2016.02.014 4. Bohnert ASB, Guy GP Jr, Losby JL. Opioid prescribing in the United States before and after the centers for disease control and prevention’s 2016 opioid guideline. Ann Intern Med. 2018;169(6):367-375. doi:10.7326/M18-1243 5. Centers for Disease Control and Prevention. U.S. Opioid Prescribing Rate Maps.https://www.cdc.gov/drugoverdose/maps/rxrate-maps.htmlUpdated October 3, 2018. Accessed August 7, 2019.
6. Dowell D, Haegerich TM, Chou R. CDC guideline for prescribing opioids for chronic pain: United States, 2016. MMWR Recomm Rep. 2016;65(1):1-49. doi:10. 15585/mmwr.rr6501e1
COMMENT & RESPONSE
Factors Associated With Brain Heterogeneity
in Schizophrenia
To the EditorThe article by Alnæs et al,1published in JAMA Psychiatry, addresses a topic of great interest. The study find-ings revealed higher heterogeneity of cortical thickness and area, cortical and ventricles volumes, and hippocampal sub-fields volume in individuals with schizophrenia. Moreover, higher polygenic risk score for schizophrenia was associated with thinner frontal and temporal cortices and smaller hippo-campal subfields but not with structural brain variability.
After more than 40 years of neuroimaging research in schizophrenia, the issue of variability of brain volumes has been claimed to represent a promising tool to disentangle the biological phenotypic complexity of the disease. However, it is quite surprising to note that none of the well-known major confounders of brain volume abnormalities in schizophrenia (ie, antipsychotic medication and cannabis use) were taken into account in the data analysis in the study by Alnæs at al.1 In particular, a large number of cross-sectional and longitudi-nal studies showed that gray matter volume decrease in patients with schizophrenia is correlated with higher cumu-lative exposure to antipsychotics over time and demon-strated a different and contrasting moderating role of first-generation vs second-first-generation antipsychotic medication intake on cortical gray matter changes.2
Thus, it could be pointed out that the variability of brain volumes observed by Alnæs et al1may represent a consequence of the impact of antipsychotic medication rather than a primary effect of heterogeneous pathological processes of schizophrenia. A 2019 meta-analysis on the same topic showed that increased cumulative antipsychotic dosage correlated with increased variability in brain structures volumes in patients with schizophrenia.3
Figure. Percentage Change in US Patients Prescribed Concurrent Opioids and Benzodiazepines, Opioids, and Benzodiazepines
5 –5 0 –10 –15 –20 –25 Change in Patients R e ceiving Prescriptions, % December 2017 January 2017 January 2016 January 2015
Opioid and benzodiazepine Opioid
Benzodiazepine
FDA boxed warning announced
The vertical dotted line represents the time when the US Food and Drug Administration (FDA) boxed warning was announced. The y-axis reports percentage changes, calculated as Δt= (nt− n1)/n1, where n1is the count in the first month (January 2016), and ntis the count in month t. Data are from IQVIA LRx. Patient counts were projected to reflect the overall US population. Adjustments implemented with the following steps: (1) We estimated the annual percentage coverages of IQVIA LRx. This was done by dividing the number of opioid prescriptions in IQVIA LRx by the total number of opioid prescriptions reported by Centers for Disease Control and Prevention. The coverages for IQVIA were 86.3%, 86.8%, and 90.5% for 2015, 2016, and 2017, respectively. (2) We also calculated the number of patients with coprescriptions from IQVIA LRx. (3) We projected counts from IQVIA LRx to national estimates by dividing by the percentage coverages.
Letters
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The current global picture deriving both from original studies or large database reviews of magnetic resonance imaging findings in individuals with schizophrenia seems to be somewhat unclear and often contradictory. In our opin-ion, there is the compelling need to set new methodological standards both for developing truly innovative neuroimag-ing research in schizophrenia and for reliably interpretneuroimag-ing magnetic resonance imaging databases. Otherwise, the sci-entific community will continue to deal with redundant or weak findings with high-potential risk of confusing or dissi-pating already well-grounded notions about the pathophysi-ology of the disease.
Luca De Peri, MD Antonio Vita, MD, PhD
Author Affiliations: Cantonal Psychiatric Clinic, Cantonal Socio-Psychiatric Association, Mendrisio, Switzerland (De Peri); Department of Mental Health, Spedali Civili Hospital, University of Brescia School of Medicine, Brescia, Italy (Vita).
Corresponding Author: Luca De Peri, MD, Cantonal Psychiatric Clinic, Cantonal Socio-Psychiatric Association, Via Maspoli 8, Mendrisio 6850, Switzerland (luca.deperi@ti.ch).
Published Online: July 31, 2019. doi:10.1001/jamapsychiatry.2019.1852 Conflict of Interest Disclosures: None reported.
1. Alnæs D, Kaufmann T, van der Meer D, et al; Karolinska Schizophrenia Project Consortium. Brain heterogeneity in schizophrenia and its association with polygenic risk [published online April 10, 2019]. JAMA Psychiatry. doi:10.1001/ jamapsychiatry.2019.0257
2. Vita A, De Peri L, Deste G, Barlati S, Sacchetti E. The effect of antipsychotic treatment on cortical gray matter changes in schizophrenia: does the class matter? a meta-analysis and meta-regression of longitudinal magnetic resonance imaging studies. Biol Psychiatry. 2015;78(6):403-412. doi:10.1016/j. biopsych.2015.02.008
3. Kuo SS, Pogue-Geile MF. Variation in fourteen brain structure volumes in schizophrenia: a comprehensive meta-analysis of 246 studies. Neurosci Biobehav Rev. 2019;98:85-94. doi:10.1016/j.neubiorev.2018.12.030
In ReplyWe thank De Peri and Vita for their thoughtful com-ments on our recent study of brain structure heterogeneity in patients with schizophrenia.1
They point out some method-ological aspects important for interpreting our results and dis-cuss important challenges facing clinical neuroimaging as the field moves toward big data and large-scale integration of cohorts.
De Peri and Vita propose that medication and cannabis use might be important for explaining our observations of higher brain structure heterogeneity among patients with schizo-phrenia compared with controls. As they point out, we did not investigate the role of these variables, mainly owing to lack of harmonized protocols for clinical phenotyping across samples in our multisite approach. In addition to the putative heter-ogenous primary disease processes acting on the brain, we concede that the large brain structure heterogeneity may also result from and interact with secondary factors related to life-style, education, work status, physical activity, social net-work and support, nutrition, smoking, drugs, and medica-tion, all of which are expected to influence brain development and maintenance during the course of the lifespan. All these variables, and indeed most constituents of life itself, are likely
to interact with myriad neurogenetic and neurodevelopmen-tal processes and experiences to sculpt the brain into its cur-rent form and shape.
As such, we agree with De Peri and Vita that a profound understanding of the sources of brain heterogeneity requires detailed mapping of a range of clinical and lifestyle factors, in-cluding use of psychoactive substances. We also concur with the need for new methodological standards to develop inno-vative and interpretable clinical neuroimaging research. Re-cent large-scale case-control studies have demonstrated re-markable consistency with respect to the regional patterns of mean differences between groups of patients and controls.2 However, novel approaches have demonstrated that the con-sistency in brain aberrations between patients is far less impressive,3and the value of increasing precision in estimat-ing the average patient’s brain aberrations rest on a debatable assumption that the current clinical diagnostic nosology com-plies with the underlying pathophysiology. As the field now moves into the era of big data and our capabilities of integrat-ing and dissectintegrat-ing complex data sets keep improvintegrat-ing, we are starting to uncover a multitude of associations, each with a low effect size, between clinical, genetic, behavioral, demo-graphic, and brain variables.4
Likewise, psychiatric genomics have revealed that mental disorders are complex traits asso-ciated with multiple common genetic variants with small ef-fect sizes, with substantial overlap between disorders as well as with normal traits.5
After more than 40 years of clinical neuroimaging, a rea-sonable conclusion is that detecting a single or a small num-ber of causal brain anum-berrations in any mental illness is highly unlikely. This is supported by our findings of substantial brain structure dispersion1
and evidence of largely nonover-lapping brain aberrations among individual patients with schizophrenia.3We would argue that instead of understand-ing the substantial heterogeneity as noise or confounds ham-pering the precise delineation of the average patient brain, we need to incorporate models that embrace and parse hetero-geneity across diagnostic boundaries and allow for inference at the individual level.6
Dag Alnæs, PhD Lars T. Westlye, PhD
Author Affiliations: Norwegian Centre for Mental Disorders Research, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway (Alnæs, Westlye); Department of Psychology, University of Oslo, Oslo, Norway (Westlye).
Corresponding Author: Dag Alnæs, PhD, Norwegian Centre for Mental Disorders Research, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, PO Box 4956 Nydalen, 0424 Oslo, Norway (dag.alnas@psykologi.uio.no).
Published Online: July 31, 2019. doi:10.1001/jamapsychiatry.2019.1855 Conflict of Interest Disclosures: Dr Alnæs reports a grant from the
South-Eastern Norway Regional Health Authority (2019107). Dr Westlye reports grants from the Research Council of Norway (204966, 249795) and the South-Eastern Norway Regional Health Authority (2014097, 2015073, 2016083).
1. Alnæs D, Kaufmann T, van der Meer D, et al; Karolinska Schizophrenia Project Consortium. Brain heterogeneity in schizophrenia and its association with
Letters
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polygenic risk [published online April 10, 2019]. JAMA Psychiatry. doi:10.1001/ jamapsychiatry.2019.0257
2. Kochunov P, Thompson PM, Hong LE. Toward high reproducibility and accountable heterogeneity in schizophrenia research [published online April 10, 2019]. JAMA Psychiatry. doi:10.1001/jamapsychiatry.2019.0208
3. Wolfers T, Doan NT, Kaufmann T, et al. Mapping the heterogeneous phenotype of schizophrenia and bipolar disorder using normative models. JAMA Psychiatry. 2018;75(11):1146-1155. doi:10.1001/jamapsychiatry.2018.2467 4. Paulus MP, Thompson WK. The challenges and opportunities of small effects: the new normal in academic psychiatry. JAMA Psychiatry. 2019;76(4): 353-354. doi:10.1001/jamapsychiatry.2018.4540
5. Sullivan PF, Agrawal A, Bulik CM, et al; Psychiatric Genomics Consortium. Psychiatric genomics: an update and an agenda. Am J Psychiatry. 2018;175(1): 15-27. doi:10.1176/appi.ajp.2017.17030283
6. Marquand AF, Kia SM, Zabihi M, Wolfers T, Buitelaar JK, Beckmann CF. Conceptualizing mental disorders as deviations from normative functioning. [published online June 14, 2019]. Mol Psychiatry. doi:10.1038/s41380-019-0441-1
Reconsidering the Association Between
Infection-Related Health Care Use and Occurrence
of Eating Disorders: Chicken or Egg?
To the EditorBreithaupt and colleagues1published epidemio-logical data from a population-based female Danish cohort and concluded that infections that require hospitalization and treat-ment with anti-infective agents in childhood are associated with an increased risk for an eating disorder. This risk was great-est in the first 3 months after hospitalization. Such epidemio-logical cohort studies may contribute important evidence to the identification of risk factors for mental disorders.
However, in this case, we would like to challenge the contiguous interpretation of the data and propose that the putative association may at least in parts be vice versa: indi-viduals who have (or are about to develop) an eating disorder often present with symptoms that may be misclassified as inflammation-associated symptoms in an early stage of the eating disorder. We believe that this hypothesis is supported by several aspects of the study design and data. First and most importantly, inflammatory processes in the cohort patients were not validated, but the exposure to inflamma-tion was defined only based on hospitalizainflamma-tion and antibiot-ics prescription data. Both can only be considered an indirect proxy for actual infections, especially given the well-known overprescription of antibiotics. Therefore, we believe that it would be more precise to talk about putative infections. Second, the authors specifically performed secondary analy-ses on gastrointestinal infections. It has recently been emphasized that clinical symptoms of an eating disorder can mimic those of gastrointestinal disorders2; this makes it additionally hard to disentangle what was there first. Third, incidence of anorexia nervosa in the Danish cohort was nearly 3 times as high as the incidence of bulimia nervosa, which is surprising as anorexia nervosa generally is rarer than bulimia nervosa.3Anorexia nervosa is often ego-syntonic in nature,4and mental disorders are associated with (self-)stigmatization; hence, it is often not the patient, but significant others, who consider eating disorder symp-toms as alarming. Therefore, patients who are just about to develop anorexia nervosa and their families might rather present with somatic (eg, gastrointestinal symptoms)
pre-dominantly caused by rapid weight loss, undernutrition, and inadequate compensatory behaviors.5
Taken together, we argue that the data might at least in parts mirror a misclassification of (gastrointestinal) toms as being caused by inflammation, while these symp-toms evolved in an early eating disorder stage, resulting in an overdiagnosis of infections and an underdiagnosis of eating disorder. This emphasizes the necessity for a consistent psy-chosomatic approach in medicine that differentiates psycho-logical and somatic contributions and their interactions to symptoms and their underlying processes.
Katrin E. Giel, PhD Florian Junne, MD Stephan Zipfel, MD
Author Affiliations: Department of Psychosomatic Medicine and Psychotherapy, Medical University Hospital Tübingen, Tübingen, Germany. Corresponding Author: Katrin E. Giel, PhD, Department of Psychosomatic Medicine and Psychotherapy, Medical University Hospital Tübingen, Osianderstr 5, 72076 Tübingen, Germany (katrin.giel@med.uni-tuebingen.de).
Published Online: August 14, 2019. doi:10.1001/jamapsychiatry.2019.2186 Conflict of Interest Disclosures: None reported.
1. Breithaupt L, Köhler-Forsberg O, Larsen JT, et al. Association of exposure to infections in childhood with risk of eating disorders in adolescent girls [published online April 24, 2019]. JAMA Psychiatry. doi:10.1001/jamapsychiatry. 2019.0297
2. Avila JT, Park KT, Golden NH. Eating disorders in adolescents with chronic gastrointestinal and endocrine diseases. Lancet Child Adolesc Health. 2019;3(3): 181-189. doi:10.1016/S2352-4642(18)30386-9
3. Smink FR, van Hoeken D, Donker GA, Susser ES, Oldehinkel AJ, Hoek HW. Three decades of eating disorders in Dutch primary care: decreasing incidence of bulimia nervosa but not of anorexia nervosa. Psychol Med. 2016;46(6):1189-1196. doi:10.1017/S003329171500272X
4. Zipfel S, Giel KE, Bulik CM, Hay P, Schmidt U. Anorexia nervosa: aetiology, assessment, and treatment. Lancet Psychiatry. 2015;2(12):1099-1111. doi:10. 1016/S2215-0366(15)00356-9
5. Hetterich L, Mack I, Giel KE, Zipfel S, Stengel A. An update on gastrointestinal disturbances in eating disorders [published online October 22, 2018]. Mol Cell Endocrinol. doi:10.1016/j.mce.2018.10.016
In ReplyWe thank Giel et al for reviewing our work1and shar-ing their concerns about possible misclassification of infec-tions as eating disorder symptoms. We fully acknowledge that register-based data cannot confirm nor deny the presence of inflammation/infections, that hospitalizations and prescrip-tions for antibiotics in the current study are used as a proxy for the presence of inflammation, and unmeasured confound-ing cannot be eliminated in observational studies. However, routine hospitalization protocols in Denmark include mea-surement of C-reactive protein levels in blood or urine to con-firm infection diagnosis. Furthermore, it is also unlikely that anti-infective agents are prescribed without the presence of infection symptoms, although we cannot determine the type or severity of the infection nor the compliance to the anti-infective treatment.
Giel et al suggest that early clinical symptoms of an eat-ing disorder may mimic those of gastrointestinal (GI) infec-tions. Although some overlap in symptoms can occur be-tween eating disorder and GI infection (eg, loss of appetite, early satiety, weight loss) several lines of evidence argue against their Letters
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