Disease trajectories and mortality among individuals diagnosed with depression: a community-based cohort study in UK Biobank

Depression increases the risk of gallstone: A cross-sectional study and Mendelian randomization analysis

BACKGROUND

Gallstone, a common digestive disorder, poses a significant public health burden. Concurrently, depression is acknowledged as a health risk. However, limited information exists on depression's impact on gallstone formation. This study investigates depression's causal effect on gallstone risk.

METHODS

Using National Health and Nutrition Examination Survey (NHANES) data, we conducted an observational study. The severity of depression was assessed using the Patient Health Questionnaire-9 (PHQ-9). Multivariable logistic regression and subgroup analyses explored the correlation between depression and gallstone risk. Mendelian Randomization (MR) analysis, leveraging Genome-Wide Association Studies (GWAS) data, reduced observational bias and elucidated causality. Inverse Variance Weighting (IVW) was the primary method, with sensitivity analyses validating results.

RESULTS

In the observational study (7707 participants), gallstone risk was elevated in mild (OR: 1.58, 95 % CI 1.31-1.90, P < 0.001), moderate (OR: 2.07, 95 % CI 1.59-2.67, P < 0.001), and severe (OR: 2.41, 95 % CI 1.70-3.34, P < 0.001) depression groups (P for trend <0.001). Subgroup analyses revealed a stronger association in those under 65, females, non-Hispanic Black, individuals with obesity, smokers, and those with college education or higher. Mendelian Randomization indicated a causal link between genetically predicted depression and higher cholelithiasis risk (OR: 2.06, 95 % CI 1.34-3.17, P = 0.001), validated through sensitivity analyses and multi-cohort verification.

CONCLUSION

Depression independently increases gallstone risk, particularly in those under 65, females, non-Hispanic Black, individuals with obesity, smokers, and those with college education or higher. Further validation is needed through multi-center, prospective cohort studies.

Associations between frailty, depression and risk of hospitalisation for infection: A large prospective cohort study

BACKGROUND

There is a considerable lack of epidemiological evidence on whether frailty, and frailty comorbid depression could increase the risk of infections in older adults. This study aimed to examine the prospective association between frailty, depression, and risk of infections.

METHODS

A total of 308,892 eligible participants were included. Linked hospital admission records (HES) were used to identify a primary or secondary diagnosis of depression, and infection. Frailty was assessed by Fried frailty phenotype indicators. Cox proportional hazard model was conducted to examine the associated risk between frailty, depression, comorbid frailty and depression and risk of incident infections. Results were stratified by age and gender.

RESULTS

During the follow-up, 74,749 (24.19 %) incident any infection cases were identified, the incidence density of any infection was 17.29/1000 person years. Frailty alone (HR = 1.38, 95 % CI: 1.33-1.43), depression alone (HR = 1.90, 95 % CI: 1.86-1.94), and comorbid frailty and depression (HR = 1.91, 95 % CI: 1.82-1.99) were associated with greater risks of any infections relative to participants with neither frailty nor depression. The associations between frailty alone, depression alone, comorbid frailty and depression, and any infections/most infection subtypes were significant for all age strata in both male and female.

LIMITATIONS

Frailty phenotype was assessed through the adapted Fried criteria, based on a mix of self-reported and objective measurements.

CONCLUSION

Frailty, depression, and comorbid frailty and depression were significantly associated with increased risk of incident infections.

The impact of anxiety on the risk of kidney stone disease: Insights into eGFR-mediated effects

BACKGROUND

Previous studies have linked kidney stone disease (KSD) with depression, but there are no reports on the relationship between anxiety and KSD, and the mechanism underlying the potential relationship remains unclear.

METHODS

Associations of anxiety and incident KSD were assessed in the National Health and Nutrition Examination Survey (NHENES) using multivariate logistic regression. Two-sample bidirectional Mendelian randomization studies and a two-step two-sample MR was used to estimate the mediating factors that influence KSD risk.

RESULTS

Examinations of NHANES data revealed that a rise in the frequency and intensity of anxiety were independently associated with incident KSD. In MR analysis, anxiety (uk-a-51 and uk-b-6519) were from the UK Biobank, with sample sizes of 328,717 and 450,765 respectively. KSD data were from the FinnGen, including 8597 cases and 333,128 controls. In the IVW analysis, genetically predicted anxieties (ukb-a-51 and ukb-b-6519) were found to be causally associated with a higher risk of KSD, with odds ratios of 6.18 (95 % CI 2.54-15.04) and 3.44 (95 % CI 1.67-7.08), respectively. There were no reverse causal effects. Further mediation analysis indicated that anxiety increases the risk of KSD by raising eGFR, through which 11.8 % of the effect of anxiety on KSD risk was mediated.

LIMITATIONS

The research was confined to individuals of European heritage, and there could be specific genetic variances among diverse ethnicities.

CONCLUSION

The current study suggests anxiety as an independent causal risk factor for KSD and unveils a new pathogenic mechanism, showing that anxiety raises eGFR, thereby increasing the risk of KSD.

Analysing disease trajectories in a cohort of 71,849 Patients: A visual analytics and statistical approach

BACKGROUND

Disease trajectories have become increasingly relevant within the context of an aging population and the rising prevalence of chronic illnesses. Understanding the temporal progression of diseases is crucial for enhancing patient care, preventive measures, and effective management.

OBJECTIVE

The objective of this study is to propose and validate a novel methodology for trajectory impact analysis and interactive visualization of disease trajectories over a cohort of 71,849 patients.

METHODS

This article introduces an innovative comprehensive approach for analysis and interactive visualization of disease trajectories. First, Risk Increase (RI) index is defined that assesses the impact of the initial disease diagnosis on the development of subsequent illnesses. Secondly, visual graphics methods are used to represent cohort trajectories, ensuring a clear and semantically rich presentation that facilitates easy data interpretation.

RESULTS

The proposed approach is demonstrated over the disease trajectories of a cohort comprising 71,849 patients from Tolosaldea, Spain. The study finds several clinically relevant trajectories in this cohort, such as that after suffering a cerebral ischemic stroke, the probability of suffering dementia increases 10.77 times. The clinical relevance of the study outcomes have been assessed by an in-depth analysis conducted by expert clinicians. The identified disease trajectories are in agreement with the latest advancements in the field.

CONCLUSION

The proposed approach for trajectory impact analysis and interactive visualization offers valuable graphs for the comprehensive study of disease trajectories for improved clinical decision-making. The simplicity and interpretability of our methods make them valuable approach for healthcare professionals.

Disease Trajectories from Healthcare Data: Methodologies, Key Results, and Future Perspectives

Disease trajectories, defined as sequential, directional disease associations, have become an intense research field driven by the availability of electronic population-wide healthcare data and sufficient computational power. Here, we provide an overview of disease trajectory studies with a focus on European work, including ontologies used as well as computational methodologies for the construction of disease trajectories. We also discuss different applications of disease trajectories from descriptive risk identification to disease progression, patient stratification, and personalized predictions using machine learning. We describe challenges and opportunities in the area that eventually will benefit from initiatives such as the European Health Data Space, which, with time, will make it possible to analyze data from cohorts comprising hundreds of millions of patients.

Distinct biological signature and modifiable risk factors underlie the comorbidity between major depressive disorder and cardiovascular disease

Major depressive disorder (MDD) and cardiovascular disease (CVD) are often comorbid, resulting in excess morbidity and mortality. Here we show that CVDs share most of their genetic risk factors with MDD. Multivariate genome-wide association analysis of shared genetic liability between MDD and atherosclerotic CVD revealed seven loci and distinct patterns of tissue and brain cell-type enrichments, suggesting the involvement of the thalamus. Part of the genetic overlap was explained by shared inflammatory, metabolic and psychosocial or lifestyle risk factors. Our data indicated causal effects of genetic liability to MDD on CVD risk, but not from most CVDs to MDD, and showed that the causal effects were partly explained by metabolic and psychosocial or lifestyle factors. The distinct signature of MDD-atherosclerotic CVD comorbidity suggests an immunometabolic subtype of MDD that is more strongly associated with CVD than overall MDD. In summary, we identified biological mechanisms underlying MDD-CVD comorbidity and modifiable risk factors for prevention of CVD in individuals with MDD.

MixEHR-SurG: A joint proportional hazard and guided topic model for inferring mortality-associated topics from electronic health records

Survival models can help medical practitioners to evaluate the prognostic importance of clinical variables to patient outcomes such as mortality or hospital readmission and subsequently design personalized treatment regimes. Electronic Health Records (EHRs) hold the promise for large-scale survival analysis based on systematically recorded clinical features for each patient. However, existing survival models either do not scale to high dimensional and multi-modal EHR data or are difficult to interpret. In this study, we present a supervised topic model called MixEHR-SurG to simultaneously integrate heterogeneous EHR data and model survival hazard. Our contributions are three-folds: (1) integrating EHR topic inference with Cox proportional hazards likelihood; (2) integrating patient-specific topic hyperparameters using the PheCode concepts such that each topic can be identified with exactly one PheCode-associated phenotype; (3) multi-modal survival topic inference. This leads to a highly interpretable survival topic model that can infer PheCode-specific phenotype topics associated with patient mortality. We evaluated MixEHR-SurG using a simulated dataset and two real-world EHR datasets: the Quebec Congenital Heart Disease (CHD) data consisting of 8211 subjects with 75,187 outpatient claim records of 1767 unique ICD codes; the MIMIC-III consisting of 1458 subjects with multi-modal EHR records. Compared to the baselines, MixEHR-SurG achieved a superior dynamic AUROC for mortality prediction, with a mean AUROC score of 0.89 in the simulation dataset and a mean AUROC of 0.645 on the CHD dataset. Qualitatively, MixEHR-SurG associates severe cardiac conditions with high mortality risk among the CHD patients after the first heart failure hospitalization and critical brain injuries with increased mortality among the MIMIC-III patients after their ICU discharge. Together, the integration of the Cox proportional hazards model and EHR topic inference in MixEHR-SurG not only leads to competitive mortality prediction but also meaningful phenotype topics for in-depth survival analysis. The software is available at GitHub: https://github.com/li-lab-mcgill/MixEHR-SurG.

Unraveling cradle-to-grave disease trajectories from multilayer comorbidity networks

We aim to comprehensively identify typical life-spanning trajectories and critical events that impact patients' hospital utilization and mortality. We use a unique dataset containing 44 million records of almost all inpatient stays from 2003 to 2014 in Austria to investigate disease trajectories. We develop a new, multilayer disease network approach to quantitatively analyze how cooccurrences of two or more diagnoses form and evolve over the life course of patients. Nodes represent diagnoses in age groups of ten years; each age group makes up a layer of the comorbidity multilayer network. Inter-layer links encode a significant correlation between diagnoses (p < 0.001, relative risk > 1.5), while intra-layers links encode correlations between diagnoses across different age groups. We use an unsupervised clustering algorithm for detecting typical disease trajectories as overlapping clusters in the multilayer comorbidity network. We identify critical events in a patient's career as points where initially overlapping trajectories start to diverge towards different states. We identified 1260 distinct disease trajectories (618 for females, 642 for males) that on average contain 9 (IQR 2-6) different diagnoses that cover over up to 70 years (mean 23 years). We found 70 pairs of diverging trajectories that share some diagnoses at younger ages but develop into markedly different groups of diagnoses at older ages. The disease trajectory framework can help us to identify critical events as specific combinations of risk factors that put patients at high risk for different diagnoses decades later. Our findings enable a data-driven integration of personalized life-course perspectives into clinical decision-making.

Observational and genetic analyses of the bidirectional relationship between depression and hypertension

BACKGROUND

While the association between depression and hypertension has been extensively investigated, the pattern and nature of such association remain inconclusive. We sought to investigate the bidirectional relationship between depression and hypertension and its causal.

METHODS

We first performed observational analyses using longitudinal data from the UK Biobank. We then performed genetic analyses leveraging summary statistics from large-scale genome-wide association studies (GWASs) conducted in European ancestry for depression and hypertension.

RESULTS

Observational analysis suggested a significant bidirectional phenotypic association between depression and hypertension (Depression → Hypertension: HR = 1.27, 95 % CI: 1.19, 1.36; Hypertension → Depression: HR = 1.65, 95 % CI: 1.58, 1.72). Linkage disequilibrium score regression demonstrated a positive genetic correlation between the two conditions (rg=0.15, P = 5.75 × 10-10). Bidirectional two-sample Mendelian randomization (MR) suggested that genetic liability to depression was significantly associated with an increased risk of hypertension (OR = 1.27, 95 % CI: 1.12, 1.43), while the genetic liability to hypertension was not associated with the risk of depression (OR = 1.01, 95 % CI: 0.99, 1.03). Multivariate MR, after adjusting for smoking, drinking, and body mass index, further supported an independent causal effect of genetic liability to depression on hypertension risk (OR = 1.10, 95 % CI: 1.02, 1.18).

LIMITATIONS

(1) interference of confounders, (2) absence of adequate statistical power, and (3) limitation to European populations.

CONCLUSION

Our study indicates depression is a causal risk factor for hypertension, whereas the reverse maybe not. Findings support that prevention of depression might help in decreasing hypertension incidence.

Trajectories network analysis of chronic diseases among middle-aged and older adults: evidence from the China Health and Retirement Longitudinal Study (CHARLS)

BACKGROUND

Given the increased risk of chronic diseases and comorbidity among middle-aged and older adults in China, it is pivotal to identify the disease trajectory of developing chronic multimorbidity and address the temporal correlation among chronic diseases.

METHOD

The data of 15895 participants from the China Health and Retirement Longitudinal Study (CHARLS 2011 - 2018) were analyzed in the current study. Binomial tests and the conditional logistic regression model were conducted to estimate the associations among 14 chronic diseases, and the disease trajectory network analysis was adopted to visualize the relationships.

RESULTS

The analysis showed that hypertension is the most prevalent disease among the 14 chronic conditions, with the highest cumulative incidence among all chronic diseases. In the disease trajectory network, arthritis was found to be the starting point, and digestive diseases, hypertension, heart diseases, and dyslipidemia were at the center, while memory-related disease (MRD), stroke, and diabetes were at the periphery of the network.

CONCLUSIONS

With the chronic disease trajectory network analysis, we found that arthritis was prone to the occurrence and development of various other diseases. In addition, patients of heart diseases/hypertension/digestive disease/dyslipidemia were under higher risk of developing other chronic conditions. For patients with multimorbidity, early prevention can preclude them from developing into poorer conditions, such as stroke, MRD, and diabetes. By identifying the trajectory network of chronic disease, the results provided critical insights for developing early prevention and individualized support services to reduce disease burden and improve patients' quality of life.

Childhood maltreatment and risk of endocrine diseases: an exploration of mediating pathways using sequential mediation analysis

BACKGROUND

Adverse childhood experiences (ACEs), including childhood maltreatment, have been linked with increased risk of diabetes and obesity during adulthood. A comprehensive assessment on the associations between childhood maltreatment and all major endocrine diseases, as well as the relative importance of different proposed mechanistic pathways on these associations, is currently lacking.

METHODS

Based on the UK Biobank, we constructed a cohort including 151,659 participants with self-reported data on childhood maltreatment who were 30 years of age or older on/after January 1, 1985. All participants were followed from the index date (i.e., January 1, 1985, or their 30th birthday, whichever came later) until the first diagnosis of any or specific (12 individual diagnoses and 9 subtypes) endocrine diseases, death, or the end of follow-up (December 31, 2019), whichever occurred first. We used Cox models to examine the association of childhood maltreatment, treated as continuous (i.e., the cumulative number of experienced childhood maltreatment), ordinal (i.e., 0, 1 and ≥ 2), or binary (< 2 and ≥ 2) variable, with any and specific endocrine diseases, adjusted for multiple covariates. We further examined the risk of having multiple endocrine diseases using Linear or Logistic Regression models. Then, sequential mediation analyses were performed to assess the contribution of four possible mechanisms (i.e., suboptimal socioeconomic status (SES), psychological adversities, unfavorable lifestyle, and biological alterations) on the observed associations.

RESULTS

During an average follow-up of 30.8 years, 20,885 participants received a diagnosis of endocrine diseases. We observed an association between the cumulative number of experienced childhood maltreatment and increased risk of being diagnosed with any endocrine disease (adjusted hazard ratio (HR) = 1.10, 95% confidence interval 1.09-1.12). The HR was 1.26 (1.22-1.30) when comparing individuals ≥ 2 with those with < 2 experienced childhood maltreatment. We further noted the most pronounced associations for type 2 diabetes (1.40 (1.33-1.48)) and hypothalamic-pituitary-adrenal (HPA)-axis-related endocrine diseases (1.38 (1.17-1.62)), and the association was stronger for having multiple endocrine diseases, compared to having one (odds ratio (95% CI) = 1.24 (1.19-1.30), 1.35 (1.27-1.44), and 1.52 (1.52-1.53) for 1, 2, and ≥ 3, respectively). Sequential mediation analyses showed that the association between childhood maltreatment and endocrine diseases was consistently and most distinctly mediated by psychological adversities (15.38 ~ 44.97%), while unfavorable lifestyle (10.86 ~ 25.32%) was additionally noted for type 2 diabetes whereas suboptimal SES (14.42 ~ 39.33%) for HPA-axis-related endocrine diseases.

CONCLUSIONS

Our study demonstrates that adverse psychological sequel of childhood maltreatment constitutes the main pathway to multiple endocrine diseases, particularly type 2 diabetes and HPA-axis-related endocrine diseases. Therefore, increased access to evidence-based mental health services may also be pivotal in reducing the risk of endocrine diseases among childhood maltreatment-exposed individuals.

Disease clusters subsequent to anxiety and stress-related disorders and their genetic determinants

Anxiety/stress-related disorders have been associated with multiple diseases, whereas a comprehensive assessment of the structure and interplay of subsequent associated diseases and their genetic underpinnings is lacking. Here, we first identify 136, out of 454 tested, medical conditions associated with incident anxiety/stress-related disorders attended in specialized care using a population-based cohort from the nationwide Swedish Patient Register, comprising 70,026 patients with anxiety/stress-related disorders and 1:10 birth year- and sex-matched unaffected individuals. By combining findings from the comorbidity network and disease trajectory analyses, we identify five robust disease clusters to be associated with a prior diagnosis of anxiety/stress-related disorders, featured by predominance of psychiatric disorders, eye diseases, ear diseases, cardiovascular diseases, and skin and genitourinary diseases. These five clusters and their featured diseases are largely validated in the UK Biobank. GWAS analyses based on the UK Biobank identify 3, 33, 40, 4, and 16 significantly independent single nucleotide polymorphisms for the link to the five disease clusters, respectively, which are mapped to several distinct risk genes and biological pathways. These findings motivate further mechanistic explorations and aid early risk assessment for cluster-based disease prevention among patients with newly diagnosed anxiety/stress-related disorders in specialized care.

Distinct genomic signatures and modifiable risk factors underly the comorbidity between major depressive disorder and cardiovascular disease

Major depressive disorder (MDD) and cardiovascular disease (CVD) are often comorbid, resulting in excess morbidity and mortality. Using genomic data, this study elucidates biological mechanisms, key risk factors, and causal pathways underlying their comorbidity. We show that CVDs share a large proportion of their genetic risk factors with MDD. Multivariate genome-wide association analysis of the shared genetic liability between MDD and atherosclerotic CVD (ASCVD) revealed seven novel loci and distinct patterns of tissue and brain cell-type enrichments, suggesting a role for the thalamus. Part of the genetic overlap was explained by shared inflammatory, metabolic, and psychosocial/lifestyle risk factors. Finally, we found support for causal effects of genetic liability to MDD on CVD risk, but not from most CVDs to MDD, and demonstrated that the causal effects were partly explained by metabolic and psychosocial/lifestyle factors. The distinct signature of MDD-ASCVD comorbidity aligns with the idea of an immunometabolic sub-type of MDD more strongly associated with CVD than overall MDD. In summary, we identify plausible biological mechanisms underlying MDD-CVD comorbidity, as well as key modifiable risk factors for prevention of CVD in individuals with MDD.

Progression and trajectory network of age-related functional impairments and their combined associations with mortality

Age-related functional impairments (ARFIs) contribute to the loss of independence in older adults, but their progressions, interrelations, and combined relations with mortality are largely unknown. We conducted a prospective study among 17,914 participants in the Health and Retirement Study (2000-2020). The incidence rates of visual impairment, hearing impairment, physical frailty, and cognitive impairment increased exponentially with age, while those of restless sleep and depression increased relatively slowly. These ARFIs were associated with each other in temporal sequence and constituted a hazard network. We observed a dose-response relationship between the number of ARFIs and mortality risk, and the dyads involving physical frailty demonstrated the strongest associations with mortality. Our findings may assist in the identification of individuals at higher mortality risk and highlight the potential for future investigations to explore the impact of multiple ARFIs in aging.

A Population-Based Outcome-Wide Association Study of the Comorbidities and Sequelae Following COVID-19 Infection

BACKGROUND

Immense attention has been given to the outcome of COVID-19 infection. However, comprehensive studies based on large populational cohort with long-term follow-up are still lacking. This study aimed to investigate the risk of various short-term comorbidities (within one month) and long-term sequelae (above one month) after COVID-19 infection.

METHODS

In this large prospective cohort study with 14 months follow-up information based on UK biobank, we included 16,776 COVID-19-positive participants and 58,281 COVID-19-negative participants matched for comparison. The risk of each comorbidity and sequela was evaluated by multivariable logistic regression analysis and presented as hazard ratio (HR) and 95% confidence interval (95% CI).

RESULTS

COVID-19-positive individuals had a higher risk of 47 types of comorbidities within one month following COVID-19 infection, especially those who were older, male, overweight/obese, ever-smoked, with more pre-existing comorbidities and hospitalized. About 70.37% of COVID-19 patients with comorbidities had more than one co-occurring comorbidities. Additionally, only 6 high-risk sequelae were observed after one month of COVID-19 infection, and the incidence was relatively low (< 1%).

CONCLUSION

In addition to long-term sequelae following COVID-19 infection, plenty of comorbidities were observed, especially in patients with older age, male gender, overweight/obese, more pre-existing comorbidities and severe COVID-19, indicating that more attention should be given to these susceptible persons within this period.

Multi-system diseases and death trajectory of metabolic dysfunction-associated fatty liver disease: findings from the UK Biobank

BACKGROUND

Metabolic dysfunction-associated fatty liver disease (MAFLD) is a newly defined condition encompassing hepatic steatosis and metabolic dysfunction. However, the relationship between MAFLD and multi-system diseases remains unclear, and the time-dependent sequence of these diseases requires further clarification.

METHODS

After propensity score matching, 163,303 MAFLD subjects and 163,303 matched subjects were included in the community-based UK Biobank study. The International Classification of Diseases, Tenth Revision (ICD-10), was used to reclassify medical conditions into 490 and 16 specific causes of death. We conducted a disease trajectory analysis to map the key pathways linking MAFLD to various health conditions, providing an overview of their interconnections.

RESULTS

Participants aged 59 (51-64) years, predominantly males (62.5%), were included in the study. During the 12.9-year follow-up period, MAFLD participants were found to have a higher risk of 113 medical conditions and eight causes of death, determined through phenome-wide association analysis using Cox regression models. Temporal disease trajectories of MAFLD were established using disease pairing, revealing intermediary diseases such as asthma, diabetes, hypertension, hypothyroid conditions, tobacco abuse, diverticulosis, chronic ischemic heart disease, obesity, benign tumors, and inflammatory arthritis. These trajectories primarily resulted in acute myocardial infarction, disorders of fluid, electrolyte, and acid-base balance, infectious gastroenteritis and colitis, and functional intestinal disorders. Regarding death trajectories of MAFLD, malignant neoplasms, cardiovascular diseases, and respiratory system deaths were the main causes, and organ failure, infective disease, and internal environment disorder were the primary end-stage conditions. Disease trajectory analysis based on the level of genetic susceptibility to MAFLD yielded consistent results.

CONCLUSIONS

Individuals with MAFLD have a risk of a number of different medical conditions and causes of death. Notably, these diseases and potential causes of death constitute many pathways that may be promising targets for preventing general health decline in patients with MAFLD.

Epigenetic regulation in major depression and other stress-related disorders: molecular mechanisms, clinical relevance and therapeutic potential

Major depressive disorder (MDD) is a chronic, generally episodic and debilitating disease that affects an estimated 300 million people worldwide, but its pathogenesis is poorly understood. The heritability estimate of MDD is 30-40%, suggesting that genetics alone do not account for most of the risk of major depression. Another factor known to associate with MDD involves environmental stressors such as childhood adversity and recent life stress. Recent studies have emerged to show that the biological impact of environmental factors in MDD and other stress-related disorders is mediated by a variety of epigenetic modifications. These epigenetic modification alterations contribute to abnormal neuroendocrine responses, neuroplasticity impairment, neurotransmission and neuroglia dysfunction, which are involved in the pathophysiology of MDD. Furthermore, epigenetic marks have been associated with the diagnosis and treatment of MDD. The evaluation of epigenetic modifications holds promise for further understanding of the heterogeneous etiology and complex phenotypes of MDD, and may identify new therapeutic targets. Here, we review preclinical and clinical epigenetic findings, including DNA methylation, histone modification, noncoding RNA, RNA modification, and chromatin remodeling factor in MDD. In addition, we elaborate on the contribution of these epigenetic mechanisms to the pathological trait variability in depression and discuss how such mechanisms can be exploited for therapeutic purposes.

Progression to myocardial infarction short-term death based on interval sequential pattern mining

BACKGROUND

Myocardial infarction (MI) is one of the significant cardiovascular diseases (CVDs). According to Taiwanese health record analysis, the hazard rate reaches a peak in the initial year after diagnosis of MI, drops to a relatively low value, and maintains stable for the following years. Therefore, identifying suspicious comorbidity patterns of short-term death before the diagnosis may help achieve prolonged survival for MI patients.

METHODS

Interval sequential pattern mining was applied with odds ratio to the hospitalization records from the Taiwan National Health Insurance Research Database to evaluate the disease progression and identify potential subjects at the earliest possible stage.

RESULTS

Our analysis resulted in five disease pathways, including "diabetes mellitus," "other disorders of the urethra and urinary tract," "essential hypertension," "hypertensive heart disease," and "other forms of chronic ischemic heart disease" that led to short-term death after MI diagnosis, and these pathways covered half of the cohort.

CONCLUSION

We explored the possibility of establishing trajectory patterns to identify the high-risk population of early mortality after MI.

Spatiotemporal evolution and characteristics of worldwide life expectancy

Exploring global differences in life expectancy can facilitate the development of strategies to narrow regional disparities. However, few researchers have systematically examined patterns in the evolution of worldwide life expectancy over a long time period. Spatial differences among 181 countries in 4 types of worldwide life expectancy patterns from 1990 to 2019 were investigated via geographic information system (GIS) analysis. The aggregation characteristics of the spatiotemporal evolution of life expectancy were revealed by local indicators of spatial association. The analysis employed spatiotemporal sequence-based kernel density estimation and explored the differences in life expectancy among regions with the Theil index. We found that the global life expectancy progress rate shows upward then downward patterns over the last 30 years. Female have higher rates of spatiotemporal progression in life expectancy than male, with less internal variation and a wider spatial aggregation. The global spatial and temporal autocorrelation of life expectancy shows a weakening trend. The difference in life expectancy between male and female is reflected in both intrinsic causes of biological differences and extrinsic causes such as environment and lifestyle habits. Investment in education pulls apart differences in life expectancy over long time series. These results provide scientific guidelines for obtaining the highest possible level of health in countries around the world.

Psychiatric disorders and subsequent risk of cardiovascular disease: a longitudinal matched cohort study across three countries

Background

Several psychiatric disorders have been associated with increased risk of cardiovascular disease (CVD), however, the role of familial factors and the main disease trajectories remain unknown.

Methods

In this longitudinal cohort study, we identified a cohort of 900,240 patients newly diagnosed with psychiatric disorders during January 1, 1987 and December 31, 2016, their 1,002,888 unaffected full siblings, and 1:10 age- and sex-matched reference population from nationwide medical records in Sweden, who had no prior diagnosis of CVD at enrolment. We used flexible parametric models to determine the time-varying association between first-onset psychiatric disorders and incident CVD and CVD death, comparing rates of CVD among patients with psychiatric disorders to the rates of unaffected siblings and matched reference population. We also used disease trajectory analysis to identify main disease trajectories linking psychiatric disorders to CVD. Identified associations and disease trajectories of the Swedish cohort were validated in a similar cohort from nationwide medical records in Denmark (N = 875,634 patients, same criteria during January 1, 1969 and December 31, 2016) and in Estonian cohorts from the Estonian Biobank (N = 30,656 patients, same criteria during January 1, 2006 and December 31, 2020), respectively.

Findings

During up to 30 years of follow-up of the Swedish cohort, the crude incidence rate of CVD was 9.7, 7.4 and 7.0 per 1000 person-years among patients with psychiatric disorders, their unaffected siblings, and the matched reference population. Compared with their siblings, patients with psychiatric disorders experienced higher rates of CVD during the first year after diagnosis (hazard ratio [HR], 1.88; 95% confidence interval [CI], 1.79-1.98) and thereafter (1.37; 95% CI, 1.34-1.39). Similar rate increases were noted when comparing with the matched reference population. These results were replicated in the Danish cohort. We identified several disease trajectories linking psychiatric disorders to CVD in the Swedish cohort, with or without mediating medical conditions, including a direct link between psychiatric disorders and hypertensive disorder, ischemic heart disease, venous thromboembolism, angina pectoris, and stroke. These trajectories were validated in the Estonian Biobank cohort.

Interpretation

Independent of familial factors, patients with psychiatric disorders are at an elevated risk of subsequent CVD, particularly during first year after diagnosis. Increased surveillance and treatment of CVDs and CVD risk factors should be considered as an integral part of clinical management, in order to reduce risk of CVD among patients with psychiatric disorders.

Funding

This research was supported by EU Horizon 2020 Research and Innovation Action Grant, European Research Council Consolidator grant, Icelandic Research fund, Swedish Research Council, US NIMH, the Outstanding Clinical Discipline Project of Shanghai Pudong, the Fundamental Research Funds for the Central Universities, and the European Union through the European Regional Development Fund; the Research Council of Norway; the South-East Regional Health Authority, the Stiftelsen Kristian Gerhard Jebsen, and the EEA-RO-NO-2018-0535.

Association Between Depression and Physical Conditions Requiring Hospitalization

Importance

Depression is associated with an increased risk of physical illness, but the most common causes of hospitalization among people with depression are unclear.

Objective

To examine the association of depression with an array of physical conditions requiring hospital treatment.

Design, Setting, and Participants

In this outcomewide prospective multicohort study, primary analysis was based on data from the UK Biobank, a population-based study in the United Kingdom. Analyses were repeated in an independent data set of 2 cohorts in Finland, a population-based study and an occupational cohort. Data analysis was conducted between April and September 2022.

Exposures

Self-reported depression, recurrent severe major depression, recurrent moderate major depression, and a single major depressive episode.

Main Outcomes and Measures

A total of 77 common health conditions ascertained from linkage data to national hospital and mortality registries.

Results

The analytical sample of UK Biobank participants consisted of 130 652 individuals (71 565 women [54.8%]; 59 087 men [45.2%]; mean [SD] age at baseline, 63.3 [7.8] years). The pooled data from the Finnish replication cohorts included 109 781 participants (82 921 women [78.6%]; 26 860 men [21.4%]; mean [SD] age, 42 [10.8] years). In the main analysis, severe/moderately severe depression was associated with the incidence of 29 nonoverlapping conditions requiring hospital treatment during a 5-year follow-up. Twenty-five of these associations remained after adjustment for confounders and multiple testing (adjusted hazard ratio [HR] range, 1.52-23.03) and were confirmed in the analysis of the Finnish cohorts. These included sleep disorders (HR, 5.97; 95% CI, 3.27-10.89), diabetes (HR, 5.15; 95% CI, 2.52-10.50), ischemic heart disease (HR, 1.76; 95% CI, 1.36-2.29), chronic obstructive bronchitis (HR, 4.11; 95% CI, 2.56-6.60), bacterial infections (HR, 2.52; 95% CI, 1.99-3.19), back pain (HR, 3.99; 95% CI, 2.96-5.38), and osteoarthritis (HR, 1.80; 95% CI, 1.46-2.20). The highest cumulative incidence was observed for endocrine and related internal organ diseases (245 per 1000 persons with depression; risk difference relative to unaffected individuals: 9.8%), musculoskeletal diseases (91 per 1000 persons; risk difference, 3.7%), and diseases of the circulatory system and blood (86 per 1000 persons; risk difference, 3.9%). The cumulative incidence was lower for hospital-treated mental, behavioral, and neurological disorders (20 in 1000 persons; risk difference, 1.7%). Depression was also associated with disease progression in people with prevalent heart disease or diabetes, and for 12 conditions, there was evidence of a bidirectional relationship.

Conclusions and Relevance

In this study, the most common causes of hospitalization in people with depression were endocrine, musculoskeletal, and vascular diseases, not psychiatric disorders. These findings suggest that depression should be considered as a target for the prevention of physical and mental disease.

Dissecting Relations between Depression Severity, Antidepressant Use, and Metabolic Syndrome Components in the NHANES 2005-2020

We aimed to dissect the complex relations between depressive symptoms, antidepressant use, and constituent metabolic syndrome (MetS) components in a representative U.S. population sample. A total of 15,315 eligible participants were included from 2005 to March 2020. MetS components were defined as hypertension, elevated triglycerides, reduced high-density lipoprotein cholesterol, central obesity, and elevated blood glucose. Depressive symptoms were classified as mild, moderate, or severe. Logistic regression was used to evaluate the relationship between depression severity, antidepressant use, individual MetS components and their degree of clustering. Severe depression was associated with the number of MetS components in a graded fashion. ORs for severe depression ranged from 2.08 [95%CI, 1.29-3.37] to 3.35 [95%CI, 1.57-7.14] for one to five clustered components. Moderate depression was associated with hypertension, central obesity, raised triglyceride, and elevated blood glucose (OR = 1.37 [95%CI, 1.09-1.72], 1.82 [95%CI, 1.21-2.74], 1.63 [95%CI, 1.25-2.14], and 1.37 [95%CI, 1.05-1.79], respectively). Antidepressant use was associated with hypertension (OR = 1.40, 95%CI [1.14-1.72]), raised triglyceride (OR = 1.43, 95%CI [1.17-1.74]), and the presence of five MetS components (OR = 1.74, 95%CI [1.13-2.68]) after adjusting for depressive symptoms. The depression severity and antidepressant use were associated with individual MetS components and their graded clustering. Metabolic abnormalities in patients with depression need to be recognized and treated.

Prevalence of depressive symptoms and its association with social support among older adults: The Brazilian National Health Survey

BACKGROUND

The study aimed to assess the association between depressive symptoms and social support measures among non-institutionalized Brazilian older adults.

METHODS

A cross-sectional study was carried out with data from the last National Health Survey, conducted in 2019. A descriptive analysis of the study variables was performed, followed by a multiple regression analysis to teste the association between the dependent variable (depressive symptoms, assessed by the Patient Health Questionnaire (PHQ-9) and independent variables (household arrangement, marital relationship, leisure activities, voluntary activity, religious activity, activities in associations/leaderships, work and receiving social support (friends and family).

RESULTS

The sample comprised 22,723 older adults and 13.1 % had depressive symptoms. There was a positive relationship between the number of people in the household and the prevalence of depressive symptoms. Individuals with support from family members only (PR 0.69; 95 % CI 0.51; 0.92) and those with support from family and friends (PR 0.53 95 % CI 0.40; 0.70) had a lower prevalence of depressive symptoms than those without support. Participation in leisure and religious activities was inversely associated with depressive symptoms. A negative association was found between work and the presence of depressive symptoms (PR 0.83; 95 % CI 0.70; 0.99).

LIMITATIONS

The cross-sectional design limits causal inferences and the survey did not include institutionalized individuals.

CONCLUSIONS

The lower prevalence of the outcome among older adults with social support highlights the importance of evaluating these measures and practicing activities that allow personal and social interactions among this age group.

Barriers in psychiatrists' mind to active smoking cessation promotion in severe psychiatric disorders

BACKGROUND

Promoting the cessation of smoking in mental healthcare is a priority of international health organizations as it is the most cost-effective intervention in psychiatry.

AIM

To explore the representations of psychiatrists on their role in active smoking cessation prevention in severe psychiatric disorders.

METHODS

Psychiatrists and residents in psychiatry were recruited at a national level by professional mailings.

RESULTS

One thousand four hundred and sixty participants were included in the study, and only 46% reported actively promoting smoking cessation. In multivariate analyses, participants aged<35years were more likely to promote cessation of tobacco smoking, as well as the two thirds who believe that psychiatry is a systemic discipline with complex interactions between brain, body and mind. Almost two thirds of those promoting tobacco cessation reported lacking time to combine psychiatric and physical examination during one session. The psychiatrists who reported not promoting tobacco smoking cessation also reported never dealing with physical health in case of the absence of a general practitioner and thinking that physical examination may have a negative impact on the therapeutic relationship. Almost all (96%) reported promoting the need for a general practitioner for their patients. We found no significant difference between the public and private sectors (P>0.05).

INTERPRETATION

Young psychiatrists are more prone than their elders to promote smoking cessation but report lacking time to include it in their daily practice. Promotion of tobacco smoking cessation should be included in the components for quality evaluation for mental health services and specific sessions dedicated to this intervention.

Air pollution, genetic factors and the risk of depression

Both genetics and ambient air pollutants contribute to depression, but the degree to which genetic susceptibility modifies the effect of air pollution on depression remains unknown. We aimed to investigate the effect of the modification of genetic susceptibility on depression. Notably, 490,780 participants who were free of depression at baseline in the UK Biobank study were recruited from 2006 to 2010. A land use regression (LUR) model was performed to estimate the concentrations of particulate matter with diameters ranging from ≤2.5-≤10 μm (PM_2.5, PM_2.5-10 and PM₁₀), nitrogen dioxide (NO₂), and nitrogen oxides (NO_x). The International Classification of Diseases 10th Revision (ICD-10) code was used to identify depression cases. Cox proportional hazard models adjusted for covariates were used to investigate the association between ambient air pollutants and depression. Moreover, the polygenic risk score (PRS) was calculated to evaluate cumulative genetic effects, and additive interaction models were established to explore whether genetic susceptibility modified the effects of air pollutants on depression. PM_2.5, PM₁₀, NO₂ and NO_x exposure were significantly positively associated with the risk of depression, and the hazard ratios and 95 % confidence intervals for a 10-μg/m³ increase in PM_2.5, PM₁₀, NO₂ and NO_x concentrations were 2.12 (1.82, 2.47), 1.12 (1.03, 1.23), 1.07 (1.05, 1.10) and 1.04 (1.03, 1.05), respectively. Air pollutants and genetic variants exerted significant additive effects on the risk of depression (relative excess risk due to the interaction [RERI]: 0.15 for PM_2.5, 0.12 for PM₁₀, 0.10 for NO₂, and 0.12 for NO_x; attributable proportion due to the interaction [AP]: 0.12 for PM_2.5, 0.10 for PM₁₀, 0.08 for NO₂, and 0.09 for NO_x). Air pollution exposure was significantly associated with the risk of depression, and participants with a higher genetic risk were more likely to develop depression when exposed to high levels of air pollution.

Medical conditions associated with coffee consumption: Disease-trajectory and comorbidity network analyses of a prospective cohort study in UK Biobank

BACKGROUND

Habitual coffee consumption has been associated with multiple health benefits. A comprehensive analysis of disease trajectory and comorbidity networks in relation to coffee consumption is, however, currently lacking.

OBJECTIVES

We aimed to comprehensively examine the health outcomes associated with habitual coffee consumption, through clarifying its disease trajectory and comorbidity networks.

METHODS

Based on the UK Biobank cohort, we included 395,539 individuals with available information on coffee intake collected at recruitment between 2006 and 2010. These individuals were categorized as having low (<1 cup per day), moderate (1-3 cups), and high (≥4 cups) levels of coffee intake, and were followed through 2020 to ascertain 496 medical conditions. Cox regression was used to assess the associations between high-level coffee intake and the risk of medical conditions with a prevalence ≥0.5% in the study population, after adjusting for multiple confounders, using low-level coffee intake as the reference. Disease-trajectory and comorbidity network analyses were then applied to visualize the temporal and nontemporal relationships between the medical conditions that had an inverse association with high-level coffee intake.

RESULTS

During a median follow-up of 11.8 years, 31 medical conditions were found to be associated with high-level coffee intake, among which 30 showed an inverse association (HRs ranged from 0.61 to 0.94). The inverse associations were more pronounced for women, compared with men. Disease-trajectory and comorbidity network analyses of these 30 conditions identified 4 major clusters of medical conditions, mainly in the cardiometabolic and gastrointestinal systems, among both men and women; 1 cluster of medical conditions following alcohol-related disorders, primarily among men; as well as a cluster of estrogen-related conditions among women.

CONCLUSIONS

Habitual coffee consumption was associated with lower risks of many medical conditions, especially those in the cardiometabolic and gastrointestinal systems and those related to alcohol use and estrogen regulation.

Depression increases the risk of kidney stone: Results from the National Health and Nutrition Examination Survey 2007-2018 and Mendelian randomization analysis

BACKGROUND

To investigate whether depression increases the kidney stone risk.

METHODS

First, we performed an observational study in the National Health and Nutrition Examination Survey (NHANES) 2007-2018. Depression severity was evaluated using the Patient Health Questionnaire-9 (PHQ-9) and classified into no, mild, moderate, and severe depression groups. Multivariable-adjusted logistic regression was used to assess the correlation between depression severity and kidney stone risk. Second, Mendelian randomization (MR) was applied to decrease the bias and avoid the reverse causality in the observational study. Genetic instruments were obtained from a large genome-wide association study (GWAS) meta-analysis of depression involved 246,363 cases and 561,190 controls. We obtained summary data for kidney stone from another large GWAS, which integrates data from 6536 stone formers and 388,508 controls. Inverse variance weighted (IVW) was the primary analytical method.

RESULTS

In the observational study, a total of 24,892 individuals were enrolled. Individuals with moderate (OR 1.38, 95 % CI 1.05-1.83, P = 0.022) and severe (OR 1.56, 95 % CI 1.02-2.40, P = 0.040) depression had a higher risk of kidney stone (P for trend = 0.006) compared with the control. For the MR, results also showed that genetically predicted depression was causally associated with a higher risk of kidney stone disease (OR 1.26, 95 % CI 1.04-1.53, P = 0.017) in IVW.

CONCLUSIONS

Depression might be associated with kidney stone risk. This finding is needed to be verified in further prospective cohort studies with a large sample size and enough follow-up time.

Temporal trajectories of important diseases in the life course and premature mortality in the UK Biobank

BACKGROUND

Little is known regarding life-course trajectories of important diseases. We aimed to identify diseases that were strongly associated with mortality and test temporal trajectories of these diseases before mortality.

METHODS

Our analysis was based on UK Biobank. Diseases were identified using questionnaires, nurses' interviews, or inpatient data. Mortality register data were used to identify mortality up to January 2021. The association between 60 individual diseases at baseline and in the life course and incident mortality was examined using Cox proportional regression models. Those diseases with great contribution to mortality were identified and disease trajectories in life course were then derived.

RESULTS

During a median follow-up of 11.8 years, 31,373 individuals (median age at death (interquartile range): 70.7 (65.3-74.8) years, 59.4% male) died of all-cause mortality (with complete data on diagnosis date of disease), with 16,237 dying with cancer and 6702 with cardiovascular disease (CVD). We identified 37 diseases including cancers and heart diseases that were associated with an increased risk of mortality independent of other diseases (hazard ratio ranged from 1.09 to 7.77). Among those who died during follow-up, 2.2% did not have a diagnosis of any disease of interest and 90.1% were diagnosed with two or more diseases in their life course. Individuals who were diagnosed with more diseases in their life course were more likely to have longer longevity. Cancer was more likely to be diagnosed following hypertension, hypercholesterolemia, CVD, or digestive disorders and more likely to be diagnosed ahead of CVD, chronic kidney disease (CKD), or digestive disorders. CVD was more likely to be diagnosed following hypertension, hypercholesterolemia, or digestive disorders and more likely to be diagnosed ahead of cancer or CKD. Hypertension was more likely to precede other diseases, and CKD was more likely to be diagnosed as the last disease before more mortality.

CONCLUSIONS

There are significant interplays between cancer and CVD for mortality. Cancer and CVD were frequently clustered with hypertension, CKD, and digestive disorders with CKD highly being diagnosed as the last disease in the life course. Our findings underline the importance of health checks among middle-aged adults for the prevention of premature mortality.

Biomarkers and Disease Trajectories Influencing Women's Health: Results from the UK Biobank Cohort

Women's health is important for society. Despite the known biological and sex-related factors influencing the risk of diseases among women, the network of the full spectrum of diseases in women is underexplored. This study aimed to systematically examine the women-specific temporal pattern (trajectory) of the disease network, including the role of baseline physical examination indexes, and blood and urine biomarkers. In the UK Biobank study, 502,650 participants entered the cohort from 2006 to 2010, and were followed up until 2019 to identify disease incidence via linkage to the patient registers. For those diseases with increased risk among women, conditional logistic regression models were used to estimate odds ratios (ORs), and the binomial test of direction was further used to build disease trajectories. Among 301 diseases, 82 diseases in women had ORs > 1.2 and p < 0.00017 when compared to men, involving mainly diseases in the endocrine, skeletal and digestive systems. Diseases with the highest ORs included breast diseases, osteoporosis, hyperthyroidism, and deformity of the toes. The biomarker and disease trajectories suggested estradiol as a risk predictor for breast cancer, while a high percentage of reticulocyte, body mass index and waist circumference were associated with an increased risk of upper-limb neuropathy. In addition, the risk of cholelithiasis was increased in women diagnosed with dyspepsia and diaphragmatic hernia. In conclusion, women are at an increased risk of endocrine, skeletal and digestive diseases. The biomarker and disease trajectories in women suggested key pathways to a range of adverse outcomes downstream, which may shed light on promising targets for early detection and prevention of these diseases.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43657-022-00054-1.

Trajectories: a framework for detecting temporal clinical event sequences from health data standardized to the Observational Medical Outcomes Partnership (OMOP) Common Data Model

Objective

To develop a framework for identifying temporal clinical event trajectories from Observational Medical Outcomes Partnership-formatted observational healthcare data.

Materials and Methods

A 4-step framework based on significant temporal event pair detection is described and implemented as an open-source R package. It is used on a population-based Estonian dataset to first replicate a large Danish population-based study and second, to conduct a disease trajectory detection study for type 2 diabetes patients in the Estonian and Dutch databases as an example.

Results

As a proof of concept, we apply the methods in the Estonian database and provide a detailed breakdown of our findings. All Estonian population-based event pairs are shown. We compare the event pairs identified from Estonia to Danish and Dutch data and discuss the causes of the differences. The overlap in the results was only 2.4%, which highlights the need for running similar studies in different populations.

Conclusions

For the first time, there is a complete software package for detecting disease trajectories in health data.

Recommendations of the treatment-resistant depression expert center network for promoting tobacco smoking cessation based on the results from the real-world FACE-TRD national cohort

BACKGROUND

Tobacco smoking has been associated with suicide, impulsivity and depression in non-clinical populations with differences across sexes.

OBJECTIVE

To determine the role of tobacco smoking in Treatment-Resistant Depression (TRD) according to sex in a precision-medicine approach.

METHOD

The FACE-TRD cohort is a national cohort of TRD patients recruited in 13 resistant depression expert centers between 2014 and 2021 and followed-up at 6 months. A standardized one-day long comprehensive battery was carried out, including trained-clinician and patient-reported outcomes, and patients were reevaluated at 6 months on their smoking and psychiatric hospitalization outcomes.

RESULTS

355 TRD participants were included (222 women). The smoking rate was much higher in TRD women compared to the French general population (34% vs 24%) while it was comparable for men (approximately 29%). In multivariate analyses, compared to non-smoking women, female smokers had significantly increased number of lifetime psychiatric hospitalizations (standardized beta B = 0.232, p = 0.014) and electro-convulsive therapy (adjusted odds ratio (aOR) = 2.748, p = 0.005), increased suicidal ideations (aOR = 4.047, p = 0.031), history of suicide attempt (aOR = 1.994, p = 0.033), and increased impulsivity (B = 0.210, p = 0.006) and were more frequently treated by benzodiazepines (aOR = 1.848, p = 0.035) and third- or fourth-line TRD treatments (antipsychotics aOR = 2.270, p = 0.006, mood stabilizers aOR = 2.067 p = 0.044). Tobacco smoking at baseline was predictive of psychiatric hospitalization within 6 months in persistent smoking women (aOR = 2.636, p = 0.031). These results were not replicated in men, for whom tobacco smoking was only associated with increased clinician-rated and self-reported depressive symptoms (respectively B = 0.207, p = 0.022 and B = 0.184, p = 0.048). The smoking cessation rate at 6 months was higher in women than in men (12% vs. 7%). No patient was administered nicotine substitute or varenicline at the two timepoints.

INTERPRETATION

Combining these results and those of the literature, we recommend that active tobacco cessation should be promoted in TRD to improve depression, suicide and impulsivity especially in women. Female smokers appear as a specific population with heavier mental health outcomes that should be specifically addressed.

Age- and Sex-Specific Differences in Multimorbidity Patterns and Temporal Trends on Assessing Hospital Discharge Records in Southwest China: Network-Based Study

Background

Multimorbidity represents a global health challenge, which requires a more global understanding of multimorbidity patterns and trends. However, the majority of studies completed to date have often relied on self-reported conditions, and a simultaneous assessment of the entire spectrum of chronic disease co-occurrence, especially in developing regions, has not yet been performed.

Objective

We attempted to provide a multidimensional approach to understand the full spectrum of chronic disease co-occurrence among general inpatients in southwest China, in order to investigate multimorbidity patterns and temporal trends, and assess their age and sex differences.

Methods

We conducted a retrospective cohort analysis based on 8.8 million hospital discharge records of about 5.0 million individuals of all ages from 2015 to 2019 in a megacity in southwest China. We examined all chronic diagnoses using the ICD-10 (International Classification of Diseases, 10th revision) codes at 3 digits and focused on chronic diseases with ≥1% prevalence for each of the age and sex strata, which resulted in a total of 149 and 145 chronic diseases in males and females, respectively. We constructed multimorbidity networks in the general population based on sex and age, and used the cosine index to measure the co-occurrence of chronic diseases. Then, we divided the networks into communities and assessed their temporal trends.

Results

The results showed complex interactions among chronic diseases, with more intensive connections among males and inpatients ≥40 years old. A total of 9 chronic diseases were simultaneously classified as central diseases, hubs, and bursts in the multimorbidity networks. Among them, 5 diseases were common to both males and females, including hypertension, chronic ischemic heart disease, cerebral infarction, other cerebrovascular diseases, and atherosclerosis. The earliest leaps (degree leaps ≥6) appeared at a disorder of glycoprotein metabolism that happened at 25-29 years in males, about 15 years earlier than in females. The number of chronic diseases in the community increased over time, but the new entrants did not replace the root of the community.

Conclusions

Our multimorbidity network analysis identified specific differences in the co-occurrence of chronic diagnoses by sex and age, which could help in the design of clinical interventions for inpatient multimorbidity.