Evidence map›Paper›PMID 35410453›Full record

Observational studyTranslational psychiatry2022

Large-scale real-world data analysis identifies comorbidity patterns in schizophrenia.

Chenyue Lu, Di Jin, Nathan Palmer, Kathe Fox, Isaac S Kohane, Jordan W Smoller, Kun-Hsing Yu

Open access · goldAbstract readObservational Study
In one paragraph

Observational study in Translational psychiatry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
3.4field-weighted citation impact, top 7% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

19 citing papers in PubMed, 25 citations in OpenAlex.

  1. Article
  2. Real-world comprehensive care of people living with schizophrenia: recommendations across different settings and clinical stages.World psychiatry : official journal of the World Psychiatric Association (WPA) · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 2 institutions in 1 country.

Chenyue LuDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
Di JinComputer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
Nathan PalmerDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
Kathe FoxDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
Isaac S KohaneDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
Jordan W SmollerDepartment of Psychiatry, Massachusetts General Hospital, Boston, MA, USA.
Kun-Hsing YuDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. Kun-Hsing_Yu@hms.harvard.edu.ORCID http://orcid.org/0000-0001-9892-8218
Harvard University · USMassachusetts Institute of Technology · US

Funding

Research ProjectP50MH106933 · NIMH · HARVARD MEDICAL SCHOOL · PI KOHANE, ISAAC S. · 2014 to 2018
$17.0M
Robust, Generalizable, and Fair Machine Learning Models for BiomedicineR35GM142879 · NIGMS · HARVARD MEDICAL SCHOOL · PI YU, KUN-HSING · 2021 to 2025
$2.4M
Mitigating Hematologic Adverse Events in Patients with Myeloid Malignancies: A Novel Causal Artificial Intelligence ApproachR01HL174679 · NHLBI · HARVARD MEDICAL SCHOOL · PI Kun-Hsing Yu · 2024 to 2026
$2.3M
NHLBI NIH HHS R01 HL174679NIGMS NIH HHS R35 GM142879NIGMS NIH HHS R35GM142879NIMH NIH HHS P50 MH106933NIMH NIH HHS P50MH106933
6 · The paper itself

Abstract

Schizophrenia affects >3.2 million people in the USA. However, its comorbidity patterns have not been systematically characterized in real-world populations. To address this gap, we conducted an observational study using a cohort of 86 million patients in a nationwide health insurance dataset. We identified participants with schizophrenia and those without schizophrenia matched by age, sex, and the first three digits of zip code. For each phenotype encoded in phecodes, we compared their prevalence in schizophrenia patients and the matched non-schizophrenic participants, and we performed subgroup analyses stratified by age and sex. Results show that anxiety, posttraumatic stress disorder, and substance abuse commonly occur in adolescents and young adults prior to schizophrenia diagnoses. Patients aged 60 and above are at higher risks of developing delirium, alcoholism, dementia, pelvic fracture, and osteomyelitis than their matched controls. Type 2 diabetes, sleep apnea, and eating disorders were more prevalent in women prior to schizophrenia diagnosis, whereas acute renal failure, rhabdomyolysis, and developmental delays were found at higher rates in men. Anxiety and obesity are more commonly seen in patients with schizoaffective disorders compared to patients with other types of schizophrenia. Leveraging a large-scale insurance claims dataset, this study identified less-known comorbidity patterns of schizophrenia and confirmed known ones. These comorbidity profiles can guide clinicians and researchers to take heed of early signs of co-occurring diseases.

Indexed as

Diabetes Mellitus, Type 2Psychotic DisordersSchizophreniaAdolescentComorbidityData AnalysisFemaleHumans

Identifiers

PMID35410453
PMCPMC9001711
OpenAlexW4223520168

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.