Health: Understanding the links between cardiovascular and psychiatric conditions
Individuals recently diagnosed with a cardiovascular disease are at higher risk of developing a mental illness, with mortality increasing when both conditions are present.
- Dalla Lana School of Public Health, Epidemiology Division, University of Toronto, Canada
- Centre for Global Health, Dalla Lana School of Public Health, University of Toronto, Canada
Cardiovascular diseases are the leading cause of mortality worldwide, accounting for approximately 32% of all deaths globally. Mental illnesses are similarly common, with approximately one in every eight individuals living with a mental health disorder in 2019 (World Health Organization, 2022). Given their high prevalence, these conditions are likely to exist alongside each other and this co-occurrence warrants rigorous scientific investigation.
The relationship between heart disease and mental illness is complex and bidirectional. For example, being diagnosed with heart failure can understandably cause stress and despair, and consequently elevate an individual’s risk of developing a major depressive disorder (Hare et al., 2014). Conversely, depressive disorders are known to manifest as sleep disturbances, reduced levels of physical activity and difficulty following health recommendations — all factors linked to an increased likelihood of developing cardiovascular conditions.
Evidence exists that the risks for mental and cardiovascular diseases increase in tandem (Schöttke and Giabbiconi, 2015; Ziegelstein, 2001). However, this body of work has important limitations that hinder drawing meaningful conclusions. For example, some studies only capture patient information at a single point in time, making it difficult to establish whether it was the cardiovascular or the psychiatric condition which appeared first in individuals with both illnesses (Almhdawi et al., 2021). In addition, research in this area has mainly focused on the relationship between cardiovascular health and depression or generalized anxiety disorder, with little attention paid to other psychiatric conditions such as psychosis and bipolar disorder. Lastly, no studies have so far adequately accounted for family-related mechanisms that may be driving any observed associations, such as certain genetic backgrounds or early childhood environments. Now, in eLife, Unnur Valdimarsdóttir, Qing Shen and colleagues report the results of a study designed to address some of these limitations (Shen et al., 2022).
The team (who are based in China, the United States, Iceland and Sweden) used the Swedish Patient Register to identify nearly 0.9 million individuals recently diagnosed with cardiovascular disease, and with no prior history of psychiatric disorders. Throughout the study period, these patients were then followed until they first received a mental health diagnosis within the study period. In addition, the study included a remarkable family-comparison design, whereby participants’ siblings who had no mental health or cardiovascular conditions at the time of the diagnosis were also tracked over time. The risk of developing any psychiatric condition in both patients and siblings could therefore be compared. This approach allowed Shen et al. to control for familial factors that are often difficult to measure and, if left unaccounted for in study design, could contribute to a spurious association between cardiovascular disorders and subsequent mental illness.
The results indicate that, compared to their unaffected siblings, study participants were 2.7 times more at risk of developing a psychiatric disorder within a year of having received their diagnoses of cardiovascular illness (even after accounting for familial factors, prior history of psychiatric illness and sociodemographic variables such as age, sex or socioeconomic status). Similar associations were observed when study participants were compared to non-sibling controls. In addition, individuals who developed a psychiatric disorder during that first year had a 55% increased risk of dying from a heart-related condition compared to patients who retained good mental health. In this cohort, the co-occurrence of any mental illness therefore negatively impacted the course of cardiovascular diseases.
Despite its strengths, this work also has some limitations. Notably, smoking behaviour and alcohol consumption were not adequately controlled for, despite being directly and independently associated with cardiovascular disease and mental illnesses (Dani and Harris, 2005; Mukamal, 2006). Not accounting for either of these lifestyle factors could overestimate the true relationship between these two conditions. In addition, various psychiatric subtypes with distinct phenotypes were combined — for example, all types of anxiety conditions, from generalized anxiety to post-traumatic stress disorder, were merged into a single mental health outcome. Each of these disorders is likely to have specific associations with cardiovascular health, which could not be captured by this experimental design.
The work by Shen et al. highlights how important it is to monitor psychiatric symptoms while treating cardiovascular diseases. Their findings should encourage the scientific community to fill existing knowledge gaps. In particular, it is becoming increasingly clear that evidence derived from high-income countries, where most research is conducted, cannot be directly translated to other settings. For instance, age-standardized mortality rates for cardiovascular disease are mostly decreasing in European and North American populations, while suicide mortality (as an indicator of mental health burdens) rises with age. By contrast, cardiac mortality rates are rising in certain low- and middle-income countries such as Mexico and India, with suicide mortality occurring at younger ages (Reynales-Shigematsu et al., 2018; Ke et al., 2018; World Health Organization, 2022; Phillips and Cheng, 2012). Context-specific data will therefore need to be collected for cardiovascular diseases to be appropriately managed across the world through integrated healthcare approaches.
References
-
Post-stroke depression, anxiety, and stress symptoms and their associated factors: a cross-sectional studyNeuropsychological Rehabilitation 31:1091–1104.https://doi.org/10.1080/09602011.2020.1760893
-
Nicotine addiction and comorbidity with alcohol abuse and mental illnessNature Neuroscience 8:1465–1470.https://doi.org/10.1038/nn1580
-
Depression and cardiovascular disease: a clinical reviewEuropean Heart Journal 35:1365–1372.https://doi.org/10.1093/eurheartj/eht462
-
The effects of smoking and drinking on cardiovascular disease and risk factorsAlcohol Research & Health 29:199–202.
-
The changing global face of suicideLancet 379:2318–2319.https://doi.org/10.1016/S0140-6736(12)60913-1
-
Post-stroke depression and post-stroke anxiety: prevalence and predictorsInternational Psychogeriatrics 27:1805–1812.https://doi.org/10.1017/S1041610215000988
-
Depression after myocardial infarctionCardiology in Review 9:45–51.https://doi.org/10.1097/00045415-200101000-00009
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Sonali Amarasekera
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- Version of Record published: December 2, 2022 (version 1)
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© 2022, Amarasekera and Jha
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Health: Understanding the links between cardiovascular and psychiatric conditions
eLife 11:e84524.
https://doi.org/10.7554/eLife.84524
Further reading
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- Computational and Systems Biology
- Epidemiology and Global Health
Background: While biological age in adults is often understood as representing general health and resilience, the conceptual interpretation of accelerated biological age in children and its relationship to development remains unclear. We aimed to clarify the relationship of accelerated biological age, assessed through two established biological age indicators, telomere length and DNA methylation age, and two novel candidate biological age indicators , to child developmental outcomes, including growth and adiposity, cognition, behaviour, lung function and onset of puberty, among European school-age children participating in the HELIX exposome cohort.
Methods: The study population included up to 1,173 children, aged between 5 and 12 years, from study centres in the UK, France, Spain, Norway, Lithuania, and Greece. Telomere length was measured through qPCR, blood DNA methylation and gene expression was measured using microarray, and proteins and metabolites were measured by a range of targeted assays. DNA methylation age was assessed using Horvath's skin and blood clock, while novel blood transcriptome and 'immunometabolic' (based on plasma protein and urinary and serum metabolite data) clocks were derived and tested in a subset of children assessed six months after the main follow-up visit. Associations between biological age indicators with child developmental measures as well as health risk factors were estimated using linear regression, adjusted for chronological age, sex, ethnicity and study centre. The clock derived markers were expressed as Δ age (i.e., predicted minus chronological age).
Results: Transcriptome and immunometabolic clocks predicted chronological age well in the test set (r= 0.93 and r= 0.84 respectively). Generally, weak correlations were observed, after adjustment for chronological age, between the biological age indicators. Among associations with health risk factors, higher birthweight was associated with greater immunometabolic Δ age, smoke exposure with greater DNA methylation Δ age and high family affluence with longer telomere length. Among associations with child developmental measures, all biological age markers were associated with greater BMI and fat mass, and all markers except telomere length were associated with greater height, at least at nominal significance (p<0.05). Immunometabolic Δ age was associated with better working memory (p = 4e -3) and reduced inattentiveness (p= 4e -4), while DNA methylation Δ age was associated with greater inattentiveness (p=0.03) and poorer externalizing behaviours (p= 0.01). Shorter telomere length was also associated with poorer externalizing behaviours (p=0.03).
Conclusions: In children, as in adults, biological ageing appears to be a multi-faceted process and adiposity is an important correlate of accelerated biological ageing. Patterns of associations suggested that accelerated immunometabolic age may be beneficial for some aspects of child development while accelerated DNA methylation age and telomere attrition may reflect early detrimental aspects of biological ageing, apparent even in children.
Funding: UK Research and Innovation (MR/S03532X/1); European Commission (grant agreement numbers: 308333; 874583).
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- Epidemiology and Global Health
Background:
Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive performance of such forecasts if multiple models are combined into an ensemble. Here, we report on the performance of ensembles in predicting COVID-19 cases and deaths across Europe between 08 March 2021 and 07 March 2022.
Methods:
We used open-source tools to develop a public European COVID-19 Forecast Hub. We invited groups globally to contribute weekly forecasts for COVID-19 cases and deaths reported by a standardised source for 32 countries over the next 1–4 weeks. Teams submitted forecasts from March 2021 using standardised quantiles of the predictive distribution. Each week we created an ensemble forecast, where each predictive quantile was calculated as the equally-weighted average (initially the mean and then from 26th July the median) of all individual models’ predictive quantiles. We measured the performance of each model using the relative Weighted Interval Score (WIS), comparing models’ forecast accuracy relative to all other models. We retrospectively explored alternative methods for ensemble forecasts, including weighted averages based on models’ past predictive performance.
Results:
Over 52 weeks, we collected forecasts from 48 unique models. We evaluated 29 models’ forecast scores in comparison to the ensemble model. We found a weekly ensemble had a consistently strong performance across countries over time. Across all horizons and locations, the ensemble performed better on relative WIS than 83% of participating models’ forecasts of incident cases (with a total N=886 predictions from 23 unique models), and 91% of participating models’ forecasts of deaths (N=763 predictions from 20 models). Across a 1–4 week time horizon, ensemble performance declined with longer forecast periods when forecasting cases, but remained stable over 4 weeks for incident death forecasts. In every forecast across 32 countries, the ensemble outperformed most contributing models when forecasting either cases or deaths, frequently outperforming all of its individual component models. Among several choices of ensemble methods we found that the most influential and best choice was to use a median average of models instead of using the mean, regardless of methods of weighting component forecast models.
Conclusions:
Our results support the use of combining forecasts from individual models into an ensemble in order to improve predictive performance across epidemiological targets and populations during infectious disease epidemics. Our findings further suggest that median ensemble methods yield better predictive performance more than ones based on means. Our findings also highlight that forecast consumers should place more weight on incident death forecasts than incident case forecasts at forecast horizons greater than 2 weeks.
Funding:
AA, BH, BL, LWa, MMa, PP, SV funded by National Institutes of Health (NIH) Grant 1R01GM109718, NSF BIG DATA Grant IIS-1633028, NSF Grant No.: OAC-1916805, NSF Expeditions in Computing Grant CCF-1918656, CCF-1917819, NSF RAPID CNS-2028004, NSF RAPID OAC-2027541, US Centers for Disease Control and Prevention 75D30119C05935, a grant from Google, University of Virginia Strategic Investment Fund award number SIF160, Defense Threat Reduction Agency (DTRA) under Contract No. HDTRA1-19-D-0007, and respectively Virginia Dept of Health Grant VDH-21-501-0141, VDH-21-501-0143, VDH-21-501-0147, VDH-21-501-0145, VDH-21-501-0146, VDH-21-501-0142, VDH-21-501-0148. AF, AMa, GL funded by SMIGE - Modelli statistici inferenziali per governare l'epidemia, FISR 2020-Covid-19 I Fase, FISR2020IP-00156, Codice Progetto: PRJ-0695. AM, BK, FD, FR, JK, JN, JZ, KN, MG, MR, MS, RB funded by Ministry of Science and Higher Education of Poland with grant 28/WFSN/2021 to the University of Warsaw. BRe, CPe, JLAz funded by Ministerio de Sanidad/ISCIII. BT, PG funded by PERISCOPE European H2020 project, contract number 101016233. CP, DL, EA, MC, SA funded by European Commission - Directorate-General for Communications Networks, Content and Technology through the contract LC-01485746, and Ministerio de Ciencia, Innovacion y Universidades and FEDER, with the project PGC2018-095456-B-I00. DE., MGu funded by Spanish Ministry of Health / REACT-UE (FEDER). DO, GF, IMi, LC funded by Laboratory Directed Research and Development program of Los Alamos National Laboratory (LANL) under project number 20200700ER. DS, ELR, GG, NGR, NW, YW funded by National Institutes of General Medical Sciences (R35GM119582; the content is solely the responsibility of the authors and does not necessarily represent the official views of NIGMS or the National Institutes of Health). FB, FP funded by InPresa, Lombardy Region, Italy. HG, KS funded by European Centre for Disease Prevention and Control. IV funded by Agencia de Qualitat i Avaluacio Sanitaries de Catalunya (AQuAS) through contract 2021-021OE. JDe, SMo, VP funded by Netzwerk Universitatsmedizin (NUM) project egePan (01KX2021). JPB, SH, TH funded by Federal Ministry of Education and Research (BMBF; grant 05M18SIA). KH, MSc, YKh funded by Project SaxoCOV, funded by the German Free State of Saxony. Presentation of data, model results and simulations also funded by the NFDI4Health Task Force COVID-19 (https://www.nfdi4health.de/task-force-covid-19-2) within the framework of a DFG-project (LO-342/17-1). LP, VE funded by Mathematical and Statistical modelling project (MUNI/A/1615/2020), Online platform for real-time monitoring, analysis and management of epidemic situations (MUNI/11/02202001/2020); VE also supported by RECETOX research infrastructure (Ministry of Education, Youth and Sports of the Czech Republic: LM2018121), the CETOCOEN EXCELLENCE (CZ.02.1.01/0.0/0.0/17-043/0009632), RECETOX RI project (CZ.02.1.01/0.0/0.0/16-013/0001761). NIB funded by Health Protection Research Unit (grant code NIHR200908). SAb, SF funded by Wellcome Trust (210758/Z/18/Z).
