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Learn more: PMC Disclaimer | PMC Copyright Notice BMC Psychiatry . 2026 Mar 10;26:324. doi: 10.1186/s12888-026-07936-y Search in PMC Search in PubMed View in NLM Catalog Add to search Social Isolation and Aging health in Schizophrenia spectrum disorders (SIAS): study protocol for a multinational longitudinal study Merel Koster Merel Koster 1 Department of Psychiatry, Amsterdam UMC Location University of Amsterdam, Amsterdam, The Netherlands 2 Amsterdam Neuroscience, Amsterdam, The Netherlands Find articles by Merel Koster 1, 2 , Marieke van der Pluijm Marieke van der Pluijm 1 Department of Psychiatry, Amsterdam UMC Location University of Amsterdam, Amsterdam, The Netherlands 2 Amsterdam Neuroscience, Amsterdam, The Netherlands Find articles by Marieke van der Pluijm 1, 2 , Helen Baldwin Helen Baldwin 3 Department of Health Service & Population Research, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK Find articles by Helen Baldwin 3 , Anna Greenburgh Anna Greenburgh 3 Department of Health Service & Population Research, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK Find articles by Anna Greenburgh 3 , Diana King Diana King 4 Olin Neuropsychiatry Research Center, Institute of Living, Hartford Hospital, Hartford, CT USA Find articles by Diana King 4 , Cole Arnold Cole Arnold 4 Olin Neuropsychiatry Research Center, Institute of Living, Hartford Hospital, Hartford, CT USA Find articles by Cole Arnold 4 , Javier-David Lopez-Morinigo Javier-David Lopez-Morinigo 5 Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, IiSGM, CIBERSAM, ISCIII, Madrid, Spain 6 Southeast University Hospital, Arganda del Rey, Madrid, Spain 7 Universidad Internacional de la Rioja (UNIR), Logroño, Spain Find articles by Javier-David Lopez-Morinigo 5, 6, 7 , Slayton Underwood Slayton Underwood 8 Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY USA Find articles by Slayton Underwood 8 , Carlos A Larrauri Carlos A Larrauri 9 Harvard T. H. Chan School of Public Health, Boston, MA USA Find articles by Carlos A Larrauri 9 , Matthew Racher Matthew Racher 10 National Alliance on Mental Illness, Arlington, VA USA Find articles by Matthew Racher 10 , David C Glahn David C Glahn 4 Olin Neuropsychiatry Research Center, Institute of Living, Hartford Hospital, Hartford, CT USA 11 Department of Psychiatry, Yale University School of Medicine, New Haven, CT USA 12 Department of Psychiatry, Harvard Medical School, Boston, MA USA 13 Department of Psychiatry and Behavioral Sciences, Boston Children’s Hospital, Boston, MA USA Find articles by David C Glahn 4, 11, 12, 13 , Philip D Harvey Philip D Harvey 14 Department of Psychiatry and Behavioral Sciences, University of Miami Miller School of Medicine, Miami, FL USA Find articles by Philip D Harvey 14 , Sven Sandin Sven Sandin 8 Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY USA 15 Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden Find articles by Sven Sandin 8, 15 , Michael Stevens Michael Stevens 4 Olin Neuropsychiatry Research Center, Institute of Living, Hartford Hospital, Hartford, CT USA 11 Department of Psychiatry, Yale University School of Medicine, New Haven, CT USA Find articles by Michael Stevens 4, 11 , Godfrey Pearlson Godfrey Pearlson 4 Olin Neuropsychiatry Research Center, Institute of Living, Hartford Hospital, Hartford, CT USA 11 Department of Psychiatry, Yale University School of Medicine, New Haven, CT USA 16 Department of Neuroscience, Yale University School of Medicine, New Haven, CT USA Find articles by Godfrey Pearlson 4, 11, 16 , Celso Arango Celso Arango 17 Hospital Universitario La Paz, IdiPAZ, School of Medicine, Universidad Autónoma de Madrid, CIBERSAM, Madrid, Spain Find articles by Celso Arango 17 , Paola Dazzan Paola Dazzan 18 Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK Find articles by Paola Dazzan 18 , Craig Morgan Craig Morgan 19 ESRC Centre for Society and Mental Health & Department of Health Service and Population Research, Institute of Psychiatry, Psychology & Neuroscience (IoPPN), King’s College London, London, UK Find articles by Craig Morgan 19 , Lieuwe de Haan Lieuwe de Haan 1 Department of Psychiatry, Amsterdam UMC Location University of Amsterdam, Amsterdam, The Netherlands 2 Amsterdam Neuroscience, Amsterdam, The Netherlands Find articles by Lieuwe de Haan 1, 2 , Abraham Reichenberg Abraham Reichenberg 8 Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY USA Find articles by Abraham Reichenberg 8 , Eva Velthorst Eva Velthorst 1 Department of Psychiatry, Amsterdam UMC Location University of Amsterdam, Amsterdam, The Netherlands 20 Department of Research, GGZ Noord- Holland-Noord, Heerhugowaard, The Netherlands Find articles by Eva Velthorst 1, 20, ✉ Author information Article notes Copyright and License information 1 Department of Psychiatry, Amsterdam UMC Location University of Amsterdam, Amsterdam, The Netherlands 2 Amsterdam Neuroscience, Amsterdam, The Netherlands 3 Department of Health Service & Population Research, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK 4 Olin Neuropsychiatry Research Center, Institute of Living, Hartford Hospital, Hartford, CT USA 5 Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, IiSGM, CIBERSAM, ISCIII, Madrid, Spain 6 Southeast University Hospital, Arganda del Rey, Madrid, Spain 7 Universidad Internacional de la Rioja (UNIR), Logroño, Spain 8 Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY USA 9 Harvard T. H. Chan School of Public Health, Boston, MA USA 10 National Alliance on Mental Illness, Arlington, VA USA 11 Department of Psychiatry, Yale University School of Medicine, New Haven, CT USA 12 Department of Psychiatry, Harvard Medical School, Boston, MA USA 13 Department of Psychiatry and Behavioral Sciences, Boston Children’s Hospital, Boston, MA USA 14 Department of Psychiatry and Behavioral Sciences, University of Miami Miller School of Medicine, Miami, FL USA 15 Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden 16 Department of Neuroscience, Yale University School of Medicine, New Haven, CT USA 17 Hospital Universitario La Paz, IdiPAZ, School of Medicine, Universidad Autónoma de Madrid, CIBERSAM, Madrid, Spain 18 Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK 19 ESRC Centre for Society and Mental Health & Department of Health Service and Population Research, Institute of Psychiatry, Psychology & Neuroscience (IoPPN), King’s College London, London, UK 20 Department of Research, GGZ Noord- Holland-Noord, Heerhugowaard, The Netherlands ✉ Corresponding author. Received 2025 Nov 20; Accepted 2026 Feb 24; Collection date 2026. © The Author(s) 2026 Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ . PMC Copyright notice PMCID: PMC13085470 PMID: 41808086 Abstract Background Individuals diagnosed with schizophrenia spectrum disorder (SSD) face an elevated risk of premature mortality, with life expectancy reduced by 10 to 20 years compared with the general population. This excess mortality is largely due to the early onset of medical conditions typically associated with older age, a process known as accelerated aging. While unhealthy lifestyle behaviors, such as smoking and poor diet, contribute to this risk, they do not fully account for the excess of physical comorbidities. Social isolation, which is associated with numerous health conditions in the general population and is a common, persistent characteristic among people with SSD, has not been comprehensively investigated as a contributing factor. Methods The Social Isolation and Aging in Schizophrenia spectrum disorders (SIAS) study establishes a longitudinal database of 650 participants initially recruited to studies in the Netherlands, UK, Spain, and US, including 500 individuals with SSD and 150 unaffected first-degree relatives. An accelerated longitudinal design is employed, combining prior research data collected when participants were aged 20–55 with new follow-up assessments now that they are aged 40–70, allowing to study exposures and outcomes through the entire age-range of the sample (20–70). Clinical and digital phenotype data are being collected. The primary objective is to determine the association between social isolation and adverse health outcomes in SSD. Additionally, the study examines the directionality of this relationship, moderating factors, and the impact of the COVID-19 pandemic. Discussion This is the first large-scale follow-up study examining the long-term health impact of social isolation in individuals with SSD. Key strengths include the integration of data across a five-decade age span, the cross-national design, the inclusion of unaffected first-degree relatives to assess familial factors, and the integration of digital phenotypic and clinical assessments. Findings from SIAS will provide critical insights into prevention targets for reducing premature mortality and improving overall health in SSD. Clinical trial number This study was registered at ClinicalTrials.gov with ID number NCT07419321 . Keywords: Schizophrenia, Psychosis, Social isolation, Physical health, Longitudinal trajectory Introduction Individuals diagnosed with a schizophrenia spectrum disorder (SSD) exhibit an increased risk of premature mortality, with a notably shorter lifespan of approximately 15–20 years relative to the general population [ 1 , 2 ]. Despite continued improvements in physical health interventions, recent research shows that individuals with SSD do not demonstrate the same increase in life expectancy as their unaffected peers, widening the already huge mortality gap [ 1 ]. Death due to suicide is a known contributing factor to the premature mortality rates in SZ [ 3 , 4 ], but research shows that at up to 85% of the increased death rates are attributed to earlier manifestation of medical conditions that normally occur later in life [ 2 , 5 – 7 ], a process known as accelerated aging [ 8 , 9 ]. It is well established that unhealthy lifestyle behaviors, such as smoking, poor diet, and physical inactivity account, in part, for accelerated aging in schizophrenia [ 10 ]. However, a growing number of studies suggest that the excess of physical comorbidities cannot be primarily attributed to these factors [ 10 – 12 ]. For example, even after controlling for high Body Mass Index (BMI) that could result from poor diet or inactivity, individuals with SSD are still twice as likely to have a metabolic syndrome compared with the general population [ 13 ]. Similarly, in a large United Kingdom (UK) cohort study, smoking accounted for only 20% of the increased risk for coronary heart-related mortality in schizophrenia [ 14 ]. Consequently, there is an urgent need to identify other factors that may contribute to accelerated aging and to understand their underlying mechanisms. In the general population, reduced social connectedness, whether through objective social isolation or subjective feelings of loneliness, has consistently been linked to numerous health conditions, including cardiovascular disease [ 15 , 16 ], diabetes [ 17 ], and cognitive decline [ 18 , 19 ]. Converging evidence underscores the impact of social connection on physical health outcomes [ 20 – 22 ], with evidence that health risks linked to social isolation are comparable to the well-established detrimental effects of smoking and obesity [ 23 ]. One theory is that social isolation co-varies with sedentary behaviors, which could mediate some of the observed adverse health outcomes [ 24 ]. In people with mental disorders, diminished social connections are associated with poorer physical health [ 25 ] and higher mortality rates [ 26 ], emphasizing the critical role of social support in this population. Unlike most other psychiatric disorders where social functioning tends to fluctuate and is strongly associated with symptom severity [ 27 , 28 ], social isolation in schizophrenia can be pervasive, chronic and present across the manifestations of other symptoms [ 29 , 30 ]. Therefore, it is conceivable that social isolation may contribute to the premature manifestation of health conditions in SSD. However, the long-term health-related consequences of social isolation in SSD have remained largely unexplored. Therefore, the overall objective of this project is to investigate whether, and how, social isolation impacts the health outcomes among individuals with SSD as they age. Here, we present the research protocol for the Social Isolation and Aging in Schizophrenia spectrum disorders (SIAS) study, initiated by the Icahn School of Medicine at Mount Sinai and currently conducted across four countries: The Netherlands, Spain, the United States of America (USA), and the United Kingdom (UK). Using an accelerated longitudinal design, we leverage data from a large sample across a five-decade age span, covering the period from midlife to later adulthood. We aim to: (i) examine the age-related association between social isolation and adverse health outcomes in individuals with SSD; (ii) assess the directionality of this relationship, identify potential moderating factors, and determine whether the association is driven by illness-specific factors or shared familial influences, by including data from both patients and unaffected first-degree relatives; and finally, and (iii) assess the extent to which the global COVID-19 pandemic, both through its direct impact on health and resulting imposed lockdowns that prohibited in-person social activities, impacted the social isolation-health relationship. Our central hypothesis is that social isolation contributes to the excess of physical comorbidities in SSD. Ultimately, the expected findings may demonstrate that promoting social engagement in midlife can reduce the risk of physical ill-health and inform the development of targeted prevention strategies to reduce premature mortality in SSD. Methods Study objectives The overarching goal of the SIAS study is to determine whether and how social isolation contributes to the health of patients with schizophrenia as they age. The primary aim of the study is to determine the association between social isolation and adverse health outcomes in schizophrenia, including whether the association is confounded by age and/or is familial. Secondary aims will examine the directionality, temporal dynamics and moderating factors of this association, and explore whether the COVID-19 pandemic modified associations. Design and setting The SIAS study uses an accelerated longitudinal design by re-assessing and extracting (medical record) data on individuals with SSD and unaffected first-degree relatives who previously participated in research studies between the ages of 20 and 55 (Timepoint 1), and who are now aged 40 to 70 (Timepoint 2), a period when many medical conditions tend to manifest. This design enables the investigation of participants across a five-decade span within a single project. At Timepoint 1, we draw on data collected from participants that were enrolled in one of three large studies: the B-SNIP [ 31 ] at the Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT, the Genetic Risk and Outcome of Psychosis (GROUP) study [ 32 ] in the Netherlands; and the EUropean network of national schizophrenia networks studying Gene-Environment Interactions (EU-GEI) [ 33 , 34 ]. For EU-GEI, we include data from sites in Spain, the Netherlands, and the UK. Data are supplemented by comparable data from the UK-based Aetiology and Ethnicity in Schizophrenia and Other Psychoses (AESOP) study [ 35 ] and the Clinical Record Interactive Search–First Episode Psychosis (CRIS-FEP) cohort [ 36 ], both conducted in the UK. Across these studies, comprehensive data were collected on social isolation, health, health-related risks factors, and cognitive functioning, all of which are utilized in the SIAS study. For the new data collection (Timepoint 2), participants from the GROUP, EU-GEI and Olin studies that participated in the first study at least 5 years ago, are being re-contacted for a follow-up assessment. Data are collected at six different sites across four countries: Amsterdam University Medical Center (The Netherlands), three CIBERSAM-related sites (Madrid, Oviedo and Santiago de Compostela in Spain), Hartfort Hospital (USA), and King’s College London (UK). Each participant completes a single in-person or virtual assessment, during which data on social isolation, health, health-related risk factors and cognitive functioning are collected. In addition, participants who provide additional consent complete Ecological Momentary Assessment (EMA) surveys three times per day for 14 consecutive days using the “EMA-wellness” smartphone application. This approach provides ecologically valid measures of participants’ daily activities, symptoms, stress levels, and social interactions. GPS tracking via the app will provide complementary behavioral data on mobility and time spent away from home. Additional health information will be derived from participants’ medical and general practitioner records. Data collected at Timepoint 1 will be linked with the new data collection at Timepoint 2 to form a comprehensive longitudinal dataset. The study protocol was subject to formal peer review through the National Institute of Mental Health (NIMH) grant review process, where it was evaluated by independent expert reviewers for scientific merit, methodological rigor, and feasibility. Further, the study received ethical approval from the Institutional Review Board (IRB) of the Icahn School of Medicine at Mount Sinai (IRB study number: 22–00730) and by the Medical Ethics Committees of all participating sites, and conducted in accordance with the Declaration of Helsinki. Two individuals with lived experience of schizophrenia were consulted regarding the study’s design and feasibility and provided feedback to the research team. See Fig. 1 for an overview of the study design. Fig. 1. Open in a new tab SIAS study design. Participants were first assessed between 2004 and 2015 at one of the study sites in Spain, the Netherlands, the UK, or the USA, when they were aged 20–55. In the current follow-up, participants (now aged 40–70) complete questionnaires and EMA through a smartphone application. We aim to include 500 patients and 150 unaffected first-degree relatives. Data are collected on three domains: social isolation (beige), health-related outcomes (purple), and health-related risk factors such as BMI, smoking and stress (green). Daily symptom data via EMA covers all three domains. Further, cognitive functioning is re-assessed. Abbreviations: EMA, ecological momentary assessments; GPS, global Positioning System; SSD, schizophrenia spectrum disorders Study population and eligibility criteria We aim to ascertain 650 participants in total, comprising of 500 individuals with SSD and 150 unaffected first-degree relatives. Participants who enrolled in previous research when they were 20–55 years old and are now 40–70 years old are eligible for inclusion. At Timepoint 1, patients were required to have a DSM diagnosis of an SSD, whilst unaffected first-degree relatives were only included if they did not have such diagnosis. Diagnoses were determined by licensed professionals using DSM-IV criteria, and confirmed with the Operational Criteria Checklist for Psychotic Illness and Affective Illness (OPCRIT; in EUGEI), the Comprehensive Assessment of Symptoms and History (CASH) [ 37 ] or Schedules for Clinical Assessment for Neuropsychiatry (SCAN 2.1) [ 38 ] (GROUP), and the Structured Clinical Interview for DSM-IV Axis I Disorders [ 39 ] (Olin). Inclusion criteria for all participants reassessed at Timepoint 2 were: (1) sufficient proficiency in the spoken language of the participating country to enable completion of the assessments, and (2) clinical stability, defined as no inpatient hospitalizations within the three months prior to enrollment and no changes in psychotropic medication during the four weeks preceding enrollment. Individuals were excluded if they had a documented history of intellectual disability (intelligence quotient [IQ] < 70) or developmental disability, based on medical chart review. Measures Primary outcome measures Social isolation See Table 1 for an overview of all measurements and questionnaires for every cohort at both timepoints. Social isolation at Timepoint 1 was measured using different instruments across cohorts. In the EU-GEI and GROUP cohorts, the ‘diminished social drive’ domain of the Schedule for Deficit Syndrome (SDS) [ 40 ] was applied. For unaffected first-degree relatives, the ‘Social Isolation’ subscale of the Structured Interview for Schizotypy-Revised (SIS-R) [ 41 ] was used. In the Olin cohort, social isolation was measured using the Birchwood social functioning scale (SFS) [ 42 ]. Across all cohorts, these data were complemented with information from the ‘social withdrawal’ items of the Positive And Negative Syndrome Scale (PANSS; patients only) [ 43 ], as well as with information on living arrangements, marital status, and employment. All measures will be harmonized to allow for cross-cohort comparisons (see data collection management section). Additionally, premorbid social isolation for EU-GEI and GROUP participants (patients only) was assessed using data from the childhood and adolescent ‘social withdrawal’ domains of the Premorbid Adjustment Scale (PAS) [ 44 ]. Table 1. Summary of measurements at all timepoints Measure Timepoint 1 Timepoint 2 1–3 Social isolation Current SDS 1,2 , SIS-R 1,2 , Birchwood SFS 3 , PANSS 1–3 SDS, Birchwood SFS, Lubben SNS, Social disconnectedness scale, COVID-19 questionnaires + EMA Premorbid PAS 1,2 PAS 3 Health Medical conditions & health problems Medical history questionnaire 1–3 Medical & medication history (self-report, GP and hospital) Self-perceived health - SF-36 + EMA Other health risks Unhealthy lifestyle Diet, alcohol, drugs, BMI, and exercise Lifestyle questionnaire 1–3 Lifestyle questionnaire Smoking CIDI 1 , CEQ 2 , FTND 3 Lifestyle questionnaire Loneliness SDS 1,2 , CECA (premorbid) 1 SDS, R-UCLA Loneliness scale + EMA Depression/sleep OPCRIT 2 , CASH/SCAN 1 , MADRS 3 Lifestyle questionnaire Antipsychotic use Medication list questionnaire 1–3 Medication history Cognitive functioning WAIS-III 2 , BACS 1 , variety of tests 3 Symbol coding, Information, Matrix reasoning Illness severity OPCRIT 2 , CASH/SCAN 1 , PANSS 1–3 , SCID 3 , GAF 1–3 EMA Stress - EMA Social class Hollingshead Index 1–3 Hollingshead Index Life events - LTE Access to care - BACE Open in a new tab Abbreviations: BACE, Barriers to access to care evaluation scale; BACS, Brief Assessment of Cognition in Schizophrenia battery; BMI, Body mass index; CASH, Comprehensive Assessment of Symptoms and History; CECA, Childhood Experience of Care and Abuse-Interview; CEQ, Cannabis Experience Questionnaire (adapted slightly for EU-GEI); EMA, Ecological momentary assessment; FTND, Fagerström Test for Nicotine Dependence; GAF, Global Assessment of Functioning scale; GP, General practitioner; LTE, List of threatening experience; MADRS, Montgomery-Åsberg Depression Rating Scale; OPCRIT, Operational Criteria Checklist for Psychotic Illness and Affective Illness; PANSS, Positive And Negative Syndrome Scale; SCAN, Schedules for Clinical Assessment for Neuropsychiatry; SCID, Structured Clinical Interview for DSM-IV Axis I Disorders ; SDS, Schedule for deficit syndrome; SFS, Social functioning scale; SF-36, 36-Item Short-form health survey; SIS-R, Structured Interview for Schizotypy-Revised; SNS, Social Network Scale, WAIS-III, Wechsler Adult Intelligence Scale-III 1 used in GROUP 2 used in EU-GEI 3 used in Olin At Timepoint 2, self-report measures of social isolation will include the ‘diminished social drive’ domain of the SDS and Birchwood SFS, supplemented with the Lubben Social Network Scale [ 45 ] and the Social Disconnectedness Scale [ 46 ]. The inclusion of the ‘diminished social drive’ domain of the SDS and Birchwood SFS at both timepoints allows for direct comparisons over time. Similarly, for unaffected first-degree relatives in the GROUP and EU-GEI cohorts, social isolation will again be assessed with the ‘Social Isolation’ subscale of the SIS-R. Information on current living situation, employment, and marital status will also be collected. For participants in the Olin cohort, the PAS will be administered at Timepoint 2 to retrospectively assess premorbid social isolation, as this data was not collected at Timepoint 1. Health At Timepoint 1, medical conditions and health problems in EU-GEI and GROUP were assessed with a ‘Medical History’ questionnaire that was specifically designed for use in these studies. The assessment includes questions relating to reasons for general practitioner visits over the last 2 years, surgeries, serious illnesses, and the presence of any physical problems. Participants were also asked about the use and dose of any non-psychotropic and psychotropic medications with a separate ‘Medication list.’ All patients at Olin completed a detailed health survey that included information on current and past medical conditions, and type and dose of any psychotropic and non-psychotropic (e.g., respiratory, anti-diabetic) medication. Information on health status in all study cohorts will be supplemented with data from participants’ medical records after consent. Primary adverse health outcomes at Timepoint 2 of interest are: (1) physician diagnosed medical conditions, (2) total number of self-perceived health problems, (3) high cholesterol (defined as low-density lipoprotein ≥ 160 mg/dL or total cholesterol ≥ 240 mg/dL), (4) high blood pressure (defined as systolic ≥ 130 mm Hg or diastolic ≥ 80 mm Hg). Medical records from hospitals and general practitioners will be reviewed to identify the occurrence and timing of any newly diagnosed conditions (e.g., cardiovascular disease, COPD, diabetes), and the most recent available data on blood pressure and cholesterol. In addition, self-perceived physical health will be assessed using the five physical domains of the 36-Item Short Form Health Survey (SF-36) [ 47 ], which includes five physical domains: physical functioning, role limitations due to physical problems, bodily pain, general health, and vitality. Finally, the Barriers to Access to Care Evaluation scale (BACE) [ 48 ] will be used to establish any problems related to access to healthcare over the years. Digital phenotyping of social isolation and health To complement questionnaire data on social isolation and health at Timepoint 2, we will use the “EMA-wellness” application to measure participants’ daily activities, symptoms, stress, and social interactions. In short, participants will receive prompts for 2-minute EMA surveys 3 times a day for 14 days. Data is collected on participants’ current location, social context (who they are with) and current activities. Participants will also be asked about how they are feeling (including perceived stress), mental health symptoms (0–7, with higher scores reflecting higher symptom severity) and whether they are experiencing any physical health issues (0–7, with higher scores reflecting worse physical well-being). To complement this active data, GPS location is tracked as additional behavioral “passive” data. Secondary outcome measures Other health risk factors To determine the unique contribution of social isolation on health, we also collect information on other health risk factors at both Timepoints 1 and 2 including the timing of occurrence. These include lifestyle factors (i.e., drug use, smoking, physical activity, BMI, and diet), feelings of loneliness, stressful life events, depression/sleep, social class, illness severity, and psychotropic medication use. A detailed overview of the measures used to establish health risk factors across cohorts and timepoints is provided in Table 1 . For the new data collection (Timepoint 2), drug use and smoking will, in line with Timepoint 1, be categorized as never, former, or current. Further, dietary intake is assessed using a 24-hour recall with standardized food models, focusing on nutritional value (calories, protein, carbohydrate, fat, cholesterol, fiber, caffeine) and polyunsaturated fat intake [ 49 ]. Illness severity between assessments is estimated by the number of re-hospitalizations for schizophrenia, defined as psychiatric or medical hospital stays > 24 h for a psychotic episode. Potentially stressful life events will be assessed using the List of Threatening Experiences questionnaire [ 50 ], which covers twelve categories of common stressful life events, such as job loss. Social class at both timepoints will be determined in all cohorts at using the Hollingshead Index for the “family of origin head of household,” which categorizes social class as upper, upper-middle, middle, working, or lower class. Cognitive functioning At Timepoint 1 an abbreviation of the Wechsler Adult Intelligence Scale-III (WAIS-III) [ 51 ] was used to cognitive measure performance in participants from the EU-GEI cohort. In Olin, the Brief Assessment of Cognition in Schizophrenia (BACS) battery [ 52 ] was used. In GROUP, a variety of tests were used [ 32 ]. Assessments in all three cohorts cover similar domains, including verbal knowledge, working memory, reasoning, and processing speed. At Timepoint 2, tests assessing the same domains as those assessed earlier using the WAIS and BACS scales will be administered digitally through Gorilla Experiment Builder ( www.gorilla.sc ) [ 53 ] to measure cognitive functioning in mid-late adulthood. COVID-specific measures At Timepoint 2, data on changes in social isolation related to the COVID lockdown are acquired through additional items incorporated into the social isolation questionnaires. These items assess the frequency of social contact and feelings of loneliness during the lockdown periods. Participants will also be asked about whether they experienced COVID-19 symptoms, when these symptoms occurred, their severity, and whether they tested positive for the SARS-CoV-2 virus. This information is collected using an NIH recommended COPDGene COVID-19 Survey [ 54 ]. In addition, information on perceived changes in health during and after the COVID-19 pandemic are collected. Mortality data For individuals who have died (as determined by health records or death registers), we will explore underlying causes of death and incorporate this information into our analyses. For example, for individuals who died from cardiovascular disease, this condition will be treated as the primary outcome. If the cause of death is unknown, the outcome will be recorded as missing. Data supplementation from CRIS-FEP and AESOP cohorts Data will be supplemented with data from CRIS-FEP cohort ( n = 558) [ 36 ] and the AESOP London-based sample ( n = 330) [ 35 ]. Supplementing our data with these cohorts will further increase the robustness of our findings. The CRIS-FEP cohort comprises individuals who presented with a first-episode psychotic disorder to South London and Maudsley NHS Foundation Trust (SLaM) between 2010 and 2012, while residing in the London boroughs of Lambeth and Southwark, and thus represents approximately 15 years of follow-up. We will use the SLaM Biomedical Research Centre (BRC) Clinical Record Interactive Search (CRIS) system to extract follow-up data from the de-identified electronic health records [ 55 , 56 ] alongside linkage to Hospital Episode Statistics data. Using clinical information from both structured fields (e.g., demographics, diagnoses, hospitalizations) and unstructured free-text fields (e.g., clinical notes and correspondence), we will apply Natural Language Processing applications to derive variables reflecting social isolation, physical health, and health-related risk factors, aligning them as closely as possible with the data collected in SIAS. Similarly, an approximately 25-year follow-up of the AESOP London-based sample ( n = 330) [ 35 ] will be conducted via linkage to electronic medical records. The AESOP cohort comprises individuals who presented with a FEP to secondary and tertiary services in South-East London between September 1997 and August 2000. Clinical, cognitive, sociodemographic, psychosocial, and biological data were collected via in-person assessments at baseline and a 10-year follow-up (AESOP-10). We now aim to link these data to local electronic medical records to determine long-term outcomes for this cohort. Data collection management All data from both timepoints will be entered into REDCap, a secure web application for building and managing online surveys and databases. New questionnaire data will be collected via web-based forms administered through REDCap. The primary data analyses will be conducted by a biostatistician at the Karolinska Institutet, Stockholm, Sweden. Coded data will be transferred to a secure server at the Karolinska Institute. Fully anonymized (aggregated) data will be transferred to the NIMH Data Archive (NDA) based in the USA. Data transfers will comply with local data security standards and industry best practices. Analyses within the validation samples will be conducted locally in London, UK. Although the measures used in GROUP, EU-GEI and Olin largely assess similar constructs (e.g., for social isolation, all questionnaires prompt for the amount of time spent in contact with other people), some scales used in mid-adulthood (Timepoint 1) vary slightly. To address this, data harmonization will be performed using a modified version of Asparouhov and Muthén’s alignment method. This approach was previously applied successfully by the Whole Genome Sequencing in Psychiatric Disorders consortium, a multinational project that also integrated diverse clinical instruments, much like the current study [ 57 ]. The alignment method enables the integration of data across disparate item sets and response formats by accommodating differences in item content and response category structures. It has shown to be a successful tool to harmonize the highly complex and sparse data structure consisting of phenotype measures [ 57 ]. Data analysis To determine the association between social isolation and adverse health outcomes in schizophrenia (Aim 1), we will test the association between levels of social isolation at age 20–55 and adverse health outcomes (physician diagnosed medical condition, self-perceived health problems, high cholesterol, and high blood pressure) at age 40–70. Further, we will determine whether the association between social isolation and health is familial by leveraging data from unaffected first-degree relatives. To this end, we will fit log-binomial regression models and calculate relative risks. For each of the outcome measures we will first fit a crude model including social isolation and adjusting for site, age at entry and sex. Subsequent models will control for potential confounders including social, lifestyle, SSD-related, and general health factors. We hypothesize that social isolation at age 20–55 is associated with adverse health outcomes at age 50–65, such that the magnitude of association is lower for younger age groups, and that the association is not due to familial confounding. To test the directionality, temporal dynamics, and moderating factors of the association between social isolation and poor health outcomes (Aim 2), we will utilize digital phenotyping data and analyze daily-life data using time-lagged models to capture moment-to-moment relationships between social isolation and health variables. We will explore reverse associations, and examine moderation by mood-related variables such as loneliness, stress, and depression. Additionally, we will assess differences in the association across environmentally defined subgroups (e.g., smokers, those with sleep problems) through interaction models. We will also categorize patients by the persistence of social isolation over time to evaluate whether prolonged exposure increases risk. These analyses will be adjusted for relevant covariates including sex, ethnicity, age, country, and social class. We hypothesize that social isolation precedes increases in perceived health problems more strongly than the reverse, that this association is stronger in those with higher levels of loneliness, anxiety, stress or sleep problems, problems accessing care and with lifetime stressors, and that the effect size of association is greater among patients with persistent social isolation. To assess whether the COVID-19 pandemic altered the relationship between social isolation and health outcomes (Aim 3), we will test whether post-pandemic associations are affected by changes in social isolation experienced during government-imposed lockdowns. Furthermore, we will examine differences in health outcomes between individuals who did and did not experience COVID-19 symptoms and explore if these differences are modified by the level of social isolation. We will apply the same models as in Aim 1, and first fit crude models with COVID symptoms (yes/no) as a primary exposure, and then adjust for potential confounding. In the next step we will add the covariate for social isolation before the pandemic and examine if the association between COVID symptoms and later health outcomes can be explained by confounding through social isolation. We hypothesize that [ 1 ] participants with high pre-pandemic levels of social isolation have increased health risks, and [ 2 ] those with increased isolation during government-imposed lockdowns are at greater risk for new adverse health outcomes. Power estimations The sample size calculation was based on the power to detect an increased risk of cardiovascular disease (CVD) among individuals with SSD experiencing high levels of social isolation. CVD has been reported in approximately 12% of individuals with schizophrenia at a mean age of 50 [ 58 ]. Given the reported age-related difference in diabetes among the schizophrenia population [ 59 ], for which the prevalence increased from 6% at age 35–44 to 25% at age 44–65, it is estimated that the CVD prevalence in our target population (aged 40–70 years) is at least 15%. Previous studies have shown that chronic social isolation is associated with approximately a 2.5-fold increased risk of CVD [ 12 ]. Conservatively assuming a 2-fold increased CVD risk among individuals with schizophrenia in the highest social isolation tertile compared to the lowest, and with at least 159 participants in both the highest and lowest tertiles, the study has > 90% power to detect this difference using a two-sided chi-square test (α = 0.05). Power is expected to be even higher when using adjusted log-binomial regression models. Discussion The SIAS study investigates whether and how social isolation contributes to the health challenges faced by individuals with SSD as they age. This objective will be addressed by examining the age-related association between social isolation and adverse health outcomes in SSD, assessing familial and directional aspects of this relationship, identifying potential moderating factors, and evaluating the impact of the COVID-19 pandemic and related lockdown measures on the social isolation-health association. This ongoing work will be, to our knowledge, the first follow-up study to examine the long-term health impact of social isolation in SSD, following a large cohort from midlife to later adulthood. Leveraging a unique accelerated longitudinal design, we will be able to include a large, multi-national sample of individuals with schizophrenia and their unaffected first-degree relatives, capturing data across a five-decade age span within a single study. In addition, the project is among the first to explore cross-national variation in social isolation within this population. Furthermore, the study incorporates reliable digital assessments of social isolation and cognitive function to supplement and validate clinical measures. Lastly, by including unaffected first-degree relatives in the analysis, the study can help determine whether observed associations between social isolation and adverse health outcomes are specific to SSD or potentially attributable to broader familial or environmental influences. Unaffected first-degree relatives provide a valuable comparison group, as they share genetic and early-life environmental factors but are not subject to illness-related confounders such as antipsychotic medication, psychotic episodes, or reduced care-seeking behavior linked to symptom severity. This approach strengthens the interpretation of any detected associations. Several limitations of this study should be acknowledged. First, follow-up assessments may be influenced by participation bias, since only individuals who are alive and can be reached are able to take part. Moreover, it is anticipated that a tiny proportion of eligible candidates, although willing to participate, may not be able to do so due to serious neurological conditions, living in residential care/supported accommodation, mobility problems or lack of mental capacity to give a valid informed consent. Mortality data will be collected and included in the statistical analyses to address this potential source of bias. Second, differences exist between the cohorts in terms of clinical measures and study design. All measures used have demonstrated strong psychometric properties and effectively capture the primary outcome. The variation in study design is also considered a strength, as consistent findings across different methodologies will enhance the validity of the study hypotheses. Lastly, although during the previous studies (Timepoint 1), efforts were made to improve generalizability by including ethnically diverse American and European cohorts, a substantial portion of the sample is white. Therefore, it will be important to replicate analyses in ethnically and racially diverse follow-up studies. In summary, this is the first study to comprehensively investigate the health impact of social isolation in individuals with SSD. The findings may indicate that increased social engagement during midlife reduces the risk of adverse health outcomes and supports the development of preventive, targeted interventions aimed at reducing premature mortality in this population. These results are expected to offer valuable insights into the relationship between social isolation and health in individuals with SSD and inform future research on underlying mechanisms and prevention strategies. Acknowledgements We would like to express our sincere gratitude to the study participants, whose extraordinary commitment over more than 15 years has made this research possible. Abbreviations AESOP Aetiology and Ethnicity in Schizophrenia and Other Psychoses BACS Brief Assessment of Cognition in Schizophrenia BMI Body Mass Index CASH Comprehensive Assessment of Symptoms and History CECA Childhood Experience of Care and Abuse-Interview CEQ Cannabis Experience Questionnaire CRIS-FEP Clinical Record Interactive Search–First Episode Psychosis CVD Cardiovascular disease EMA Ecological momentary assessments EU-GEI EUropean network of national schizophrenia networks studying Gene-Environment Interactions GPS Global Positioning System GROUP Genetic Risk and Outcome of Psychosis IQ Intelligence quotient MADRS Montgomery-Åsberg Depression Rating Scale OPCRIT Operational Criteria Checklist for Psychotic Illness and Affective Illness PANSS Positive And Negative Syndrome Scale PAS Premorbid Adjustment Scale SCAN Schedules for Clinical Assessment for Neuropsychiatry SCID Structured Clinical Interview for DSM-IV Axis I Disorders SDS Schedule for Deficit Syndrome SFS Birchwood social functioning scale SIS-R Structured Interview for Schizotypy-Revised SIAS Social isolation and aging in schizophrenia spectrum disorders SNS Social Network Scale SLaM South London and Maudsley NHS Foundation Trust UK United Kingdom USA United States of America WAIS-III Wechsler Adult Intelligence Scale-III SF-36 36-Item Short Form Health Survey Author contributions Conceptualization of study: EV and AR. Design and methodology of the study: CL, MR, DG, PH, SS, MS, GP, CeA, PD, CM, LdH, AR, and EV. Carrying out the study: MK, MvdP, HB, AG, DK, CoA, JL, SU. Supervision: MS, GP, CeA, PD, CM, LdH, AR, and EV. Original draft: MK, MvdP, HB and EV. All authors read and approved the final manuscript. Funding This study is supported by grant R01 MH128971 from the National Institutes of Mental Health. The National Institutes of Mental Health had no further role in study design; in the collection, analysis and interpretation of data; in the writing of the report; or in the decision to submit the paper for publication. Dr. C. Arango was supported by the Spanish Ministry of Science and Innovation, Instituto de Salud Carlos III (ISCIII), co-financed by the European Union, ERDF Funds from the European Commission, “A way of making Europe”, financed by the European Union – NextGenerationEU (PMP21/00051), PI19/01024. PI22/01824 CIBERSAM, Madrid Regional Government (B2017/BMD-3740 AGES-CM-2), European Union Structural Funds, European Union Seventh Framework Program, European Union H2020 Program under the Innovative Medicines Initiative 2 Joint Undertaking: Project PRISM-2 (Grant agreement No.101034377), Project AIMS-2-TRIALS (Grant agreement No 777394), Horizon Europe, the National Institute of Mental Health of the National Institutes of Health under Award Number 1U01MH124639-01 (Project ProNET) and Award Number 5P50MH115846-03 (project FEP-CAUSAL), Fundación Familia Alonso, and Fundación Alicia Koplowitz. Dr. G. Pearlson was supported by grant R01 MH077945 from the NIMH for the Bipolar & Schizophrenia Consortium for Parsing Endophenotypes (BSNIP-1 study). Data availability Anonymized (aggregated) data from the study will be submitted to the National Institute of Mental Health (NIMH) Data Archive (NDA) based in the USA and will be available through the NDA following the standard application and approval process. Declarations Ethics approval and consent to participate This study was approved by the Institutional Review Board of the Icahn School of Medicine at Mount Sinai and the Medical Ethics Committees of all participating sites, and conducted in accordance with the Declaration of Helsinki. All participants provided informed consent before study participation. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Footnotes Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. References 1. Laursen TM, Nordentoft M, Mortensen PB. Excess early mortality in schizophrenia. Ann Rev Clin Psychol. 2014;10:425–48. 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