Changing Comorbidity Clusters in Patients With Congenital Heart Disease Across the Lifespan

Abstract

Background

Benefiting from advances in medical care, patients with congenital heart disease (CHD) now survive to adulthood but face elevated risks of cardiac and noncardiac complications. Understanding how comorbidity evolves across the lifespan is the cornerstone to improving long‐term outcomes. We hypothesized that comorbidity occurs in clusters that change over time in patients with CHD.

Methods

Using the Quebec CHD database with 35 years of follow‐up (1983–2017), we designed a longitudinal cohort study evaluating 38 International Classification of Diseases, Ninth Revision/Tenth Revision (ICD‐9/10) coded comorbidities. Observed sequences of comorbidities were mapped using median ages of onset. Associations between disease pairs were quantified using hazard ratios from Cox proportional hazard models adjusting for age, sex, genetic syndrome, competing risks of death, and time‐varying predictor diseases.

Results

The cohort comprised 9764 individuals with severe CHD and 127 728 with nonsevere CHD. Patients with severe CHD demonstrated higher cumulative probabilities of developing comorbidities than those with nonsevere CHD from birth to older adulthood. By age 40 years, cumulative probabilities for developing cardiac, vascular, endocrine/metabolic, digestive, infectious, renal, and neurological disease were ≥2‐fold higher in severe versus nonsevere CHD. Median ages of onset for most comorbidities occurred below age 40 in severe CHD, 2 to 3 decades earlier than patients with nonsevere CHD where peak comorbidity onsets occurred between their 60s and 80s. Disease progression in severe CHD began with childhood cardiovascular diseases, progressing to early‐adulthood metabolic, hepatic, renal diseases, and later heart failure and dementia.

Conclusions

Distinct multimorbidity clusters were observed by CHD severity, with earlier clustering in severe CHD, highlighting the need for early risk characterization and preventive strategies.

Clinical Perspective.

What Is New?

• We delineated multimorbidity trajectories in patients with severe and nonsevere congenital heart disease (CHD) lesions using a large population‐based cohort with up to 35 years of follow‐up.

• Distinct comorbidity temporal orderings were observed by CHD lesion severity, and patients with severe CHD lesions showed earlier onset and accelerated development of both cardiovascular and noncardiovascular comorbidities than patients with nonsevere CHD lesion.

What Are the Clinical Implications?

• Identifying the observed temporal order and clustering of comorbidities among patients with CHD provides opportunities to optimize specialty referral and plan timely intervention.

The survival of patients with congenital heart disease (CHD) has improved dramatically over the past 5 decades, with >90% of children with CHD now surviving to adulthood. By 2004, the distribution of age at death for patients with CHD had begun to resemble that of the general population, reflecting a fundamental shift in long‐term outcomes. ¹ This success has unmasked new challenges: adults with CHD face higher risk of premature morbidity and mortality compared with the general population, driven not only by residual cardiac lesions but also by accelerated development of acquired cardiac and systemic comorbidities. ² , ³ , ⁴ Additionally, multimorbidity, the coexistence of ≥2 chronic conditions, affects >70% of adults with CHD by age 40 years, contributing to fragmented care, reduced quality of life, and higher hospitalization rates than age‐matched peers. ⁵ , ⁶

A growing body of evidence highlights the diverse spectrum of comorbidities afflicting adults with CHD. Compared with the general population, patients with CHD exhibited increased propensity of cardiovascular conditions, such as stroke, ⁷ heart failure, ⁸ , ⁹ myocardial infarction, ⁷ hypertension, ¹⁰ and diabetes. ¹⁰ , ¹¹ Beyond cardiovascular complications, adults with CHD faced a disproportionate burden of noncardiac comorbidities, including mental health disorders, ¹² liver disease, and renal dysfunction. ¹³ Several studies have identified various comorbidities as significant risk factors and predictors of conditions such as heart failure in adults with CHD. ¹⁴ , ¹⁵ However, these studies focused on a single end point and revealed significant knowledge gaps in providing a comprehensive overview of comorbidities and identifying evolving clusters of risk factors for various health complications in patients with CHD throughout their lifespan. This motivated the need to explore the evolving risk of comorbidities across the lifespan, recognizing that the specific type of CHD lesion acts as an effect modifier throughout all stages of life, shaping risk in the causal model of disease progression. ¹⁶

To address these knowledge gaps, we leveraged one of the largest databases of patients with CHD to map the temporal sequences and interconnectedness of multisystem comorbidities present in patients with CHD from birth to older adulthood. We hypothesized that comorbidities in patients with CHD occur in clusters that evolve over time and that the timing and patterns of acquiring these clusters differ based on the patient's initial CHD defect and condition. By identifying critical windows of vulnerability and high‐risk disease‐evolving pathways, we aimed to furnish a holistic understanding of multimorbidity trajectories in CHD.

METHODS

Data Availability

This study was approved by the McGill University Health Centre ethics board. The requirement for individual informed consent was waived because the study used nonidentifiable administrative health data. To minimize the possibility of unintentionally sharing information that can reidentify private information, an aggregated subset of the data is available from the corresponding authors upon reasonable request.

Data Sources and Study Population

In Quebec (Canada), each individual is assigned a unique Medicare number during infancy, which serves as an identifier for tracking all medical diagnoses and health care services provided. The electronic health records in Quebec include records from the physicians' services, medication claims, hospital discharge summaries, and the Quebec Health Insurance Board and Death Registry. ¹⁷ These electronic health records contain demographic and vital information and detailed diagnostic and procedural coding according to the International Classification of Diseases, Ninth Revision (ICD‐9), and Tenth Revision (ICD‐10, as of 2006), covering both inpatients and outpatients in Quebec starting from 1983. ¹⁸ Using the province‐wide electronic health records, we established the Quebec CHD Database, a longitudinal cohort of 137 492 patients with CHD, with continuous follow‐up from 1983 to 2017. This enabled tracking of morbidity trajectories from birth or diagnosis through adulthood, censoring only at death or study end. A flow diagram summarizing data linkage, cohort assembly, and stratification by CHD severity is provided in Figure S1.

To examine variations in multimorbidity trajectories for various severity of congenital heart defects, we stratified individuals into severe or nonsevere CHD lesion categories using a previously described and validated hierarchical algorithm. ¹⁷ Severe CHD was defined as lesions associated with a high probability of cyanosis or necessitating early surgical intervention. Specifically, severe lesions include endocardial cushion defect, tetralogy of Fallot, univentricular heart, transposition complex including complete and congenitally corrected truncus arteriosus and hypoplastic left heart syndrome. All remaining CHD lesions, classified as nonsevere, comprise conditions such as atrial septal defect, patent ductus arteriosus, and Ebstein's anomaly, representing relatively lower complexity and clinical acuity compared with severe CHD lesions. ¹⁷

Follow‐Up for Comorbidities of Interest

In the Quebec CHD database, the health records of individuals can be traced back to the year of birth or year 1983, whichever came last, and followed up to the end of the study (December 31, 2017) or death of the individual, whichever came first.

Selection of the comorbidities was done based on literature and clinical expertise of the study investigators. ² , ³ , ⁸ A total of 38 comorbidities were selected and classified into 10 categories: (1) cardiac morbidities—childhood heart failure, adulthood heart failure, acute myocardial infarction, atrial arrhythmia, ventricular arrhythmia, pulmonary hypertension, and infective endocarditis; (2) vascular morbidities—coronary artery disease, hypertension, peripheral vascular disorders, arterial thromboembolism, and venous thromboembolism; (3) mental disorders—alcohol abuse or drug abuse, anxiety, depression, and psychoses; (4) endocrine/metabolic morbidities—diabetes, obesity, hypothyroidism, dyslipidemia, fluid, and electrolyte disorders; (5) renal diseases—acute kidney disease and chronic kidney disease; (6) neurological morbidities—stroke, meningitis, cerebral palsy, seizure, epilepsy, dementia, and acute cerebrovascular disorder; (7) cancer—hematopoietic cancer and nonhematopoietic cancer; (8) infectious morbidities—influenza and sepsis; (9) digestive disease—chronic liver disease; and (10) respiratory morbidities—chronic obstructive pulmonary disease or chronic bronchitis, asthma, and obstructive sleep apnea.

Diagnosis for comorbidities was coded according to ICD‐9 and ICD‐10 codes from both inpatient and outpatient records. Data missingness is minimal in this administrative registry as diagnostic codes are mainly derived from billing records, which are well documented. To lower misclassification bias, we included physician specialty type into comorbidity identification to take into account the discrepancy in the accuracy of disease diagnoses across physicians with different specialties. Detailed information can be found in Table S1. Because the Quebec CHD database contained medical records since 1983, we applied a 3‐year washout period to patients who entered the study cohort after birth to minimize misclassification of prevalent conditions as incident diagnoses, excluding comorbidities recorded within 3 years of cohort entry (eg, diagnoses before age 10 years for a patient entering the database at age 7).

Statistical Analysis

Baseline characteristics were described with median (interquartile range [IQR]) for continuous variables and frequency (%) for categorical variables. For each comorbidity, we calculated the median and IQR of age of onset. For each comorbidity category, cumulative comorbidity risks were estimated via Kaplan–Meier curves. All analyses were done among patients with severe and nonsevere CHD lesions separately. Comparisons between the severe and nonsevere CHD lesion groups were made using the Mann–Whitney U test for median follow‐up years and age at study entry and chi‐square test for categorical variables.

Temporal sequences of comorbidities were mapped by ordering conditions by their median age of onset, enabling visualization of lifespan trajectories of sequential comorbidities. Age of onset was coded at the first incident diagnosis. Median age of onset was adopted as the parameter for mapping the temporal ordering of the comorbidities across the lifespan, after assessing their age distribution. For comorbidity with a bimodal distribution indicating that the comorbidity likely contains 2 different risk groups, we separated it into 2 comorbidities. Heart failure was thus redefined as childhood heart failure and adulthood heart failures. Most of the comorbidities showed skewed age distribution and exhibited wide interquartile ranges (Table S2). Compared with mean which is much more influenced by distributional skewness and competing mortality, the median provides a robust measure of central tendency that better reflects the typical age of onset and is less influenced by extreme early or late diagnoses of comorbidities.

We employed Cox proportional hazard models to measure the associations between disease pairs; a disease with an earlier onset was treated as the predictor and a disease with a later onset as the outcome. To account for the variation in measuring age of onset for different comorbidities and to ensure biological plausibility, only disease pairs with an interval of their age onsets ≥5 years were considered. Genetic syndrome and sex were considered as confounders and were adjusted for in the models. Regardless of the age of diagnosis, genetic syndrome was treated as a disease condition at birth. For each patient, follow‐up began at the earlier of birth or cohort entry and continued until the onset of the outcome disease, death, or December 31, 2017, whichever occurred first. We treated the predictor diseases as time‐varying variables in our Cox regression analyses. This approach accounts for the timing of comorbidity and enables us to capture the dynamic nature of the comorbidities throughout the study period. ¹⁹ , ²⁰ , ²¹ , ²² Thus, if a patient developed the predictor disease at age 20 years, their status for the predictor disease was encoded as 0 (disease free) before age 20 and 1 (disease diagnosed) after age 20. If a patient developed the predictor disease after the outcome disease, their status for predictor disease throughout the entire follow‐up period was coded as 0. As individuals with CHD age with comorbid diseases, the competing risk of death is a factor that can influence the hazard ratio (HR) of outcome disease with respect to predictor disease. Traditional cause‐specific Cox models treat death as noninformative censoring, which may introduce bias in this context. To account for this, we employed the hazard of subdistribution approach of Fine and Gray, which is commonly used to address the informative censoring of death in time‐to‐event models to account for biases caused by death. ²³ The Fine and Gray subdistribution hazard models estimate subdistribution HRs while treating death as a competing event of subsequent comorbid outcomes. The Fine and Gray method models the cumulative incidence function in the presence of competing risks. It provides effect estimates that more accurately reflect real‐world risk in the presence of competing mortality. A detailed description of Cox proportional hazards models can be found in Data S1. Comorbidities with low occurrence (number of cases <200) were excluded to ensure the convergence of Cox regression models. The identification of associated comorbidities relied on the 95% CIs of HRs rather P values derived from Cox regression models. To address potential unmeasured confounding, a conservative approach was adopted by taking the lower limit of the 95% CI rather than the point estimate of HRs and a threshold of 2.0 to establish significant associations between disease pairs.

The comorbidities were coded as time‐varying variables. The proportional hazards assumption was evaluated using complementary cause‐specific Cox regression with an interaction between the comorbidity and the logarithm of follow‐up time. A statistically significant interaction (P<0.05) suggested that the association between the comorbidity and outcome changed over follow‐up. In these cases, the HR was calculated as a function of follow‐up time using both the coefficient of the comorbidity and the coefficient of the interaction term. Details are provided in Data S1. Influential observations were evaluated for every disease pair using influence diagnostics from the complementary cause‐specific Cox regression models. Specifically, DFBETAS (standardized changes in regression coefficients resulting from deletion of individual observations) were calculated to quantify the influence of each observation on the estimated regression coefficients, and subjects exceeding the predefined criterion were identified as influential. ²⁴ For regression models in which influential observations were identified, the analyses were repeated after excluding the influential subjects. Checking nonlinearity in the relationship between the log hazard and the covariates is an important approach in building Cox proportional hazard models. In this analysis, we did not conduct this checking as all the exposure variables were binary variables including sex, genetic syndrome status, and comorbidity status, with age treated as the underlying time indicator rather than modeled as a continuous variable.

All tests were performed 2 sided with a P value <0.05 considered as statistically significant. Cumulative probability curves were generated in R (version 4.5.0) using the survival and survminer packages. Cox proportional hazards models were conducted using SAS version 9.4 (SAS Institute Inc, Cary, NC).

RESULTS

Characteristics of the Study Population

Out of the 137 492 patients with CHD in the Quebec CHD database, there were 9764 patients with severe and 127 728 with nonsevere CHD lesions. The median follow‐up time was 25.3 years for patients with severe CHD with an IQR of 11.3 to 30.8 years and 19.7 years for patients with nonsevere CHD with an IQR of 9.6 to 30.8 years. A total of 7817 (80.0%) patients with severe CHD and 92 942 (72.8%) patients with nonsevere CHD presented at least 1 comorbidity across their lifespan. In addition, 14.1% of patients with severe CHD and 3.2% of patients with nonsevere CHD had a genetic syndrome. More detailed information about characteristics of the study population is presented in the TableTable1.

Table 1.

1Baseline Characteristics of the Study Population

	Severe CHD (n=9764)	Nonsevere CHD (n=127 728)	P value
Male sex, n (%)	4697 (48.1)	63 817 (50.0)	<0.001*
Genetic syndrome, n (%)	1381 (14.1)	4036 (3.2)	<0.001*
At least one comorbidity, n (%)	7817 (80.0)	92 942 (72.8)	<0.001*
Follow‐up years, median (IQR)	25.3 (11.3, 30.8)	21.7 (9.6, 30.8)	<0.001 †

CHD indicates congenital heart disease; and IQR, interquartile range.

^*From chi‐square test.

^† From Mann–Whitney U test.

Cumulative Probabilities of Developing Comorbidities Across the Lifespan

The cumulative probability of developing at least 1 cardiac, vascular, psychiatric, endocrine/metabolic, respiratory, digestive, infectious, renal, neurological disease, and cancer respectively, across the lifespan was shown in Figure 1. Patients with severe CHD faced significantly higher risks of developing all the 10 categories of comorbidities throughout their lifespan. By age 40 years, the probabilities in the group with severe CHD were ≥2‐fold of the probabilities in the group with nonsevere CHD for all these comorbidities except mental disorders, respiratory disease, and cancer.

Figure 1. Cumulative probability of developing comorbidites across the lifespan for (A) cardiac diseases, (B) vascular diseases, (C) mental disorders, (D) endocrine/metabolic diseases, (E) respiratory diseases, (F) renal diseases, (G) digestive diseases, (H) infectious diseases, (I) neurological diseases, and (J) cancer.

CHD indicates congenital heart disease.

Temporal Comorbidity Clusters

To ensure robust analysis, comorbidities were selected based on number of events (≥200 cases per CHD severity subgroup) and prior evidence linking them to CHD in the literature. This resulted in 31 comorbidities analyzed for patients with severe and 32 for nonsevere CHD. Aligning comorbidities along the x axis (from birth to age 80 years) according to their median age at onset and connecting those disease pairs that were significantly associated with each other by solid lines and arrows from the predictor disease to the outcome disease, a sequential cascade of the comorbidities was constructed in both groups with severe and nonsevere CHD (Figure 2A and 2B).

Figure 2. Multimorbidity trajectories across the lifespan in patients with severe (A) and nonsevere (B) congenital heart disease.

Comorbidities along the x axis are in order of increasing median age of onset. The size of bubbles for comorbidities is proportional to the frequency of comorbidities. Arrows connect disease pairs with the lower limits of the 95% CI of the hazard ratios ≥2.0. aHF indicates adult Heart Failure; AKD, acute kidney disease; AMI, acute myocardial infarction; ARR, atrial arrhythmia; CAD, coronary artery disease; cHF, childhood heart failure; CKD, chronic kidney disease; CLD, chronic liver disease; COPD/CB, chronic obstructive pulmonary disease/chronic bronchitis; HC, hematopoietic cancer; HTN, hypertension; IE, infective endocarditis; NHC, nonhematopoietic cancer; alcohol/drug, alcohol abuse/drug abuse; OSA, obstructive sleep apnea; PH, pulmonary hypertension; PVD, peripheral vascular disorders; VARR, ventricular arrhythmia; and VTE, venous thromboembolism.

Distinct patterns of comorbidity clustering were observed between patients with severe and nonsevere CHD, both in terms of timing and conditions involved (Figure 2). Among patients with severe CHD the cascade started with childhood diseases such as childhood heart failure, seizure, and sepsis, progressing to early adulthood comorbidities such as diabetes, hypertension, chronic liver disease, and acute kidney diseases and followed by a clustering of end‐stage disease like adulthood heart failure and dementia. Notably, mental disorders including psychoses and anxiety manifested during early adulthood, before the cardiovascular comorbidity cluster, and were associated with subsequent acute and chronic kidney diseases, as well as dementia. Additionally, early adulthood cardiovascular comorbidities, such as hypertension, coronary artery disease, acute myocardial infarction, and atrial arrhythmia, were associated with subsequent renal diseases, that is, acute or chronic kidney diseases. In patients with nonsevere CHD, early childhood diseases such as childhood heart failure, influenza, and seizure typically occurred before the age of 10 years and were associated with late adulthood or older adulthood diseases such as ventricular arrythmia, pulmonary hypertension, acute kidney disease, obstructive sleep apnea, and sepsis.

Patients with severe CHD developed clusters of acquired cardiovascular and systemic comorbidities nearly 3 decades earlier than those with nonsevere CHD, with onset occurring in early adulthood rather than later in life. Of the 31 comorbidities in the severe group, 9 appeared before age 18, 17 between ages 18 and 40, 5 between 40 and 60, and none after 60. In contrast, among the 32 comorbidities in the group with nonsevere, only 5 occurred before age 18, whereas 14 had a median onset after age 60 years. Comorbidities including venous thromboembolism, pulmonary hypertension, hematopoietic cancer, and chronic obstructive pulmonary disease/chronic bronchitis were all labeled as childhood comorbidities (median age of onset ≤ age 18) in the group with severe CHD, in contrast to the group with nonsevere CHD where their incidence was in adulthood or even older adulthood (median age of onset ≥age 18 or ≥age 65, respectively). In addition, many early adulthood comorbidities (median age of onset between age 18 and 40) for the group with severe CHD such as obesity, diabetes, hypertension, coronary artery disease, stroke, mental disorders, acute kidney disease, acute myocardial infarction, and atrial and ventricular arrhythmia occurred in late adulthood or older adulthood disease for the group with nonsevere CHD. Detailed information regarding the median and IQR of age of onset for each comorbidity is shown in Table S3. The lower limits of the 95% CIs for the HRs assessing the associations between pairs of diseases in both patients with severe and nonsevere CHD are presented in Tables S4 and S5, respectively. The estimated HRs remained unchanged after removing influential subjects, indicating that the reported associations were robust.

Several sequential patterns of comorbidities were found in both groups with severe and nonsevere CHD (Figure 2). Specifically, a progression from obesity to diabetes, followed by acute or chronic kidney disease, was observed. Specifically, a progression from obesity to diabetes as well as a progression from asthma to obstructive sleep apnea, followed by acute or chronic kidney disease, were observed. Psychiatric disorders were associated with subsequent dementia. A few cardiovascular comorbidities including childhood heart failure, hypertension, and ventricular arrhythmia were identified as risk factors for the development of acute kidney diseases. These sequential associations were consistently observed across the lifespan among patients, regardless of CHD severity. We observed that several comorbidities may serve as “intermediary” conditions. For example, anxiety manifested in early adulthood and was linked to subsequent systemic complications in patients with severe CHD lesions. In patients with nonsevere CHD, psychiatric conditions including alcohol/drug abuse were linked to subsequent chronic liver disease. In both patients with severe and nonsevere CHD, a similar trajectory was observed indicating a progression from obstructive sleep apnea to cardiometabolic conditions including kidney disease. In patients with severe CHD, obstructive sleep apnea was also temporally associated with subsequent dementia.

DISCUSSION

This study serves as the first data‐driven exploration of multimorbidity clusters across the lifespan in patients with CHD. The findings revealed that patients with severe CHD had earlier onset of subsequent morbidity clusters, faster accumulation of comorbidity clusters, and showed distinct patterns in morbidity development compared with patients with nonsevere CHD. Specifically, patients with severe CHD experienced most comorbidities by early adulthood and presented significantly higher risk of developing cardiac, vascular, psychiatric, respiratory, renal, digestive, neurological, infectious, and endocrine/metabolic diseases and cancer across the lifespan than patients with nonsevere CHD. Among patients with severe CHD, the multimorbidity trajectory started earlier than nonsevere CHD with the cascade started with childhood disease clusters such as childhood heart failure and seizure, progressing to early adulthood comorbidities such as diabetes, hypertension, chronic liver disease, and acute/chronic kidney diseases and followed by adulthood heart failure and dementia. This acceleration of disease clustering beginning in early adulthood represented a key inflection point in severe CHD morbidity. In contrast, patients with nonsevere CHD exhibited delayed comorbidity clusters at late adulthood or older adulthood. These findings suggested that patients with severe and nonsevere CHD endured different accumulative adverse effects from earlier co‐occurring morbid conditions and had difference in pathways of multimorbidity development. Notably, several multimorbidity trajectories were present irrespective of CHD severity, including the progression from obesity to diabetes and kidney disease, from psychiatric disorders to dementia, and from cardiac complications to acute kidney disease. These shared pathways suggest that multimorbidity in CHD is not solely determined by CHD severity, but also influenced by modifiable cardiometabolic and neuropsychiatric risk factors.

Our findings not only align with prior studies documenting elevated cardiovascular risks in CHD, but extend them by quantifying severity‐specific disparities across all 10 categories, driven by multifactorial biological, lifestyle, and systemic factors. For example, although many studies highlight increased renal dysfunction in CHD, ²⁵ our study reveals that patients with severe CHD develop chronic kidney disease 20 years earlier than those with nonsevere CHD (53.9 versus 74.8 years). Mechanistically, this disparity may stem from chronic hypoxia and neurohormonal activation ²⁶ in severe CHD lesions (eg, cyanotic defects), which promote oxidative stress ²⁷ and glomerulosclerosis, in contrast to the metabolic‐driven renal decline in nonsevere CHD, linked to obesity and diabetes ²⁸ (Figure 2B). Similarly, early‐onset dementia in severe CHD (59.3 years versus 79.3 years in nonsevere CHD) may reflect cerebral hypoperfusion from lifelong cardiac dysfunction, compounded by surgical burden (eg, cardiopulmonary bypass), whereas dementia in nonsevere CHD aligns with vascular aging pathways. ²⁹ Mental health trajectories further differentiated the groups. Early‐onset depression in severe CHD (median age 30.3 years) correlates with potential hypothalamic–pituitary–adrenal axis dysregulation due to chronic stress and childhood surgeries, ³⁰ whereas later psychiatric burdens in nonsevere CHD reflect lifestyle‐mediated risks (eg, alcohol use). This aligns with recent studies highlighting the bidirectional relationship between mental disorders and chronic medical conditions. For example, our analysis revealed the significant association between mental health and the development of acute and chronic kidney diseases for both patients with severe and nonsevere CHD. This finding aligns with a systematic review that demonstrated a higher risk of chronic kidney disease in individuals with severe mental illness. ³¹ Additionally, studies have shown that patients with CHD display a higher risk of psychiatric morbidity, such as anxiety and depression, ³² , ³³ and in turn, the presence of mental illnesses can increase the likelihood of developing physical diseases due to reduced self‐care and engagement in highly risky behaviors. ³⁴ Moreover, the significant link between mental health and dementia identified in our analysis was consistent with a 30‐year longitudinal study of >1.7 million individuals in New Zealand, which found that mental disorders were associated with an increased risk of developing dementia. ³⁵ Furthermore, with a >50% of prevalence in populations with CHD, ³⁶ obesity has long been recognized as being associated with increased risk of most cardiovascular diseases, such as heart failure, atrial fibrillation, hypertension, ³⁷ , ³⁸ , ³⁹ and metabolic syndrome. ³⁹ Common pathways involved in the pathogenesis of obesity and cardiovascular diseases are multifaceted such as insulin resistance and low‐grade inflammation. ⁴⁰ Our study further confirmed the well‐established obesity–diabetes–kidney disease association, aligning with emerging evidence on the interconnectedness of metabolic disorders and renal complications. ³⁹ , ⁴¹ , ⁴² The findings of several comorbidities serving as the “intermediary” conditions provide new data for understanding disease progression mechanisms in patients with CHD. The suggested role of psychiatric conditions as intermediary component within multimorbidity trajectories, linked early neuropsychiatric to later cardiometabolic disturbances. Similarly, obstructive sleep apnea demonstrated multiple associations within the multimorbidity network, particularly in relation to cardiometabolic and neuropsychiatric conditions, further underscoring the complex interdependence of multiple risk factors in conferring systemic complications of CHD. Collectively, these results highlight renal disease as a recurrent downstream outcome across severity groups, albeit through distinct biological and cardiometabolic pathways. The findings further provide new opportunities for risk stratification and strategic time windows for intervention. Identifying patients at intermediary state may allow clinicians to stratify high‐risk patients for early prevention including targeted screenings and lifestyle interventions. This may mitigate progression before advanced deterioration or irreversibility.

These findings have significant implications for clinical practice and health care policy. Understanding the temporal sequences and clustering of comorbidities in CHD provides insight into periods of heightened vulnerability across the lifespan. By identifying high‐risk periods and progression of certain morbidities, clinicians can implement targeted screening, surveillance, and preventive measures to mitigate risks and optimize patient outcomes. ⁴³ , ⁴⁴ Moreover, identifying typical disease trajectories, clusters, and interconnections between comorbidities can facilitate the early detection of complications and prompt intervention, thereby reducing the burden of morbidity and mortality associated with CHD. ¹⁰ , ⁴⁵ In this study, we identified a critical transitional period in patients with severe CHD during early adulthood (around age 20–40 years), marked by the onset of renal, hepatic, endocrine, and mental health comorbidity clusters. These high‐risk periods represent important time windows for intensified surveillance and earlier risk factor stratification. For instance, although endocrine disorders such as diabetes are classically associated with kidney disease prompting regular screening in these patients, our findings suggest that other comorbidities less traditionally linked with renal outcomes, such as mental health disorders, chronic liver disease, and obesity, should also prompt early serial evaluation for kidney dysfunction in patients with severe CHD. Recognizing these comorbidities as key stages in the progression toward chronic kidney disease may help inform the development of multidisciplinary care strategies that integrate medical, psychological, and dietary interventions tailored to both disease severity and stage of life. The findings also support proactive preclinical surveillance and emphasize the importance of early integration of noncardiovascular specialists into the care team, with the goal of reducing fragmented care and preventing the full development of multimorbidity clusters. The profound implications of interconnectivity between various disease groups suggest the necessity for multidisciplinary care teams that integrate cardiology, endocrinology, psychiatry, and primary care to optimally manage the diverse and interconnected health needs of patients with CHD. ⁴⁶ , ⁴⁷ For instance, the observed sequence from childhood heart failure to adulthood heart failure and dementia shows the long‐term implications of CHD on neurological health, suggesting the need for integrated management strategies addressing both cardiovascular and cognitive outcomes. This trajectory supports the recommendation that patients with early markers of cardiac dysfunction may benefit from standardized longitudinal neurocognitive surveillance to identify and manage potential cognitive decline, ⁴⁸ particularly in the population with severe CHD where dementia onset occurred nearly 2 decades earlier than in nonsevere cases. Additionally, we found elevated noncardiovascular comorbidities risk for all patients with CHD >40 years of age, with the risks being higher among patients with severe CHD lesions. Integrating geriatric and neurocognitive expertise into the care teams for adult patients with CHD may help mitigate these risks. Although geriatric multidisciplinary evaluations are traditionally reserved for the general population >65 years of age, our findings suggest that patients with severe CHD lesions may benefit from these comprehensive assessments at a significantly younger age due to their accelerated accumulation of multi‐system comorbidities. Recent evidence also suggests that early detection and management of CHD‐related complications as well as referral to specialized adult CHD care could extend life expectancy and improve quality of life, which underscores the importance of regular monitoring and holistic care approaches. This integration is critical not only for improving survival rates but also for reducing health care costs over the long term. ⁴⁹ Another example is the early integration of standardized mental health screening tools in CHD care, which may help identify individuals at increased risk of psychiatric disorders and substance use. Prior studies have demonstrated low rates of mental health follow‐up among patients with CHD with positive screening results, highlighting a potential gap in care that if addressed, may affect downstream comorbidities. ⁵⁰

Despite the insights gained from our study, several limitations should be acknowledged. Our results might be subjective to surveillance bias as the analysis was based on data obtained from administrative databases. To address this, we used data that are less sensitive to surveillance bias such as hospitalization records and diagnoses from specialists in comorbidity identification (Table S1). In addition, because all patients with CHD are supposed to have regular checkups with their physicians, the findings from comparing patients with severe and nonsevere CHD lesions are less likely to be affected by surveillance bias. Even so, we calculated the yearly rate of seeking health care for key age groups and found similar results between patients with severe and nonsevere CHD lesions. From age 10 to 20, the average yearly rates of seeing a pediatrician and being hospitalized were 0.79 and 0.16 for patients with severe CHD and 0.64 and 0.10 for patients with nonsevere CHD. From age 30 to 40, the average yearly rates of seeing primary physicians (including general practitioners, family physicians, and internists) and being hospitalized were 3.21 and 0.21 for patients with severe CHD and 2.80 and 0.16 for patients with nonsevere CHD. Patients with severe CHD had slightly higher health care use than those with nonsevere CHD; however, the absolute differences were modest. Because use is closely related to lesion severity, adjusting for it could partially remove severity‐related differences. To avoid overadjustment that could distort underlying associations, we evaluated uses descriptively rather than adjusting for them in our analyses. Our data sources lack key clinical variables such as symptom severity, patient adherence to treatment, and lifestyle factors as well as referrals to tertiary or specialized care. These missing elements could affect the interpretation of the relationship between CHD and comorbidities and may introduce potential misclassification biases. ⁵¹ , ⁵² Although efforts were made to address these issues through rigorous data preprocessing and validation procedures, the inherent limitations of administrative data sets might have influenced our findings. Our study relied on inpatient and outpatient records to identify comorbidities and determine the age of onset. This method might not capture the true age of onset as accurately as clinical data. Also, using median age of onset to determine the observed sequence of disease development might ignore the variation in disease presentation and diagnosis. In addition, the current analysis focused on the observed temporal sequences derived from the median age of onset. Disease pairs for association analyses were also identified based on the observed order of median age of onset. This approach is limited as it does not take into consideration the intrinsic causal pathway among diseases. Although the data‐driven analysis identified several key associations in this study that corroborate findings from prior causal investigations, causality cannot be inferred from the present observational design alone. In addition to including sex and genetic syndrome as covariates in this study to control for confounding factors, we applied a threshold of 2.0 to the lower estimates of the 95% CIs for screening significant associations to further minimize the impact of unmeasured confounding. Similar results have been reported in literature that took into the consideration of confounders like age, sex, and CHD lesion type. ⁵³ , ⁵⁴ These limitations underscore the need for further research in this area, such as incorporating clinical, genetic, and environmental factors into analysis to better understand the biological pathways and genetic contribution to the observed multimorbidity patterns in patients with CHD. Another limitation pertains to the generalizability of our findings to broader populations beyond Quebec, such as Canada region. Although the large sample size and long‐term follow‐up period enhance the robustness of our analyses, there might be disparities in the demographic and health care characteristics of patients with CHD in other regions, potentially limiting the generalizability of our findings. Future research endeavors are needed to replicate our analyses using data from diverse populations to test the observed multimorbidity trajectories. Future studies are also needed to examine the role of lifestyle modifications on multimorbidity in nonsevere CHD, as well as investigate biomarkers markers and oxidative stress in severe CHD as drivers of long‐term comorbidities like chronic kidney disease.

Notwithstanding these limitations, our study represents a significant contribution to the field as the first large‐scale analysis of trajectories of changing comorbidity clusters in patients with CHD across their lifespan. The main strengths of our study include the unique large sample size, long‐term follow‐up, and the visualization of the highly likely trajectories among patients by CHD severity. The characteristics of patients with various CHD severity were studied, providing more clinically meaningful guidelines for research and public health policy. Moreover, the available longitudinal data enable us to track the morbidity progression of individuals across the lifespan. Through the integration of expert domain knowledge and advanced statistical methodologies, our investigation emphasizes the comprehensive quantification of lifelong associations and interconnectedness of health complications in patients with CHD. This holistic approach not only offers a more detailed understanding of the disease dynamics but also supports the development of personalized medicine and targeted therapeutic strategies. Our findings also facilitate the identification of critical periods for opportunities for preventative strategies that could defer or prevent the onset of severe comorbidities. A substantial sample size, longitudinal cohort study design and long follow‐up duration collectively enhanced the robustness of our findings.

Conclusions

In conclusion, this large‐scale, longitudinal analysis provides novel insights into the evolution and clustering of comorbidities in patients with CHD across their lifespan. By mapping temporal multimorbidity trajectories in a population‐based cohort of >137 000 individuals, we demonstrated that the burden of multimorbidity was both early and profound in individuals with severe CHD lesion, whereas those with nonsevere lesions experienced a delayed but still significant accumulation of comorbid conditions. These findings highlight the urgent need for proactive, lifespan‐oriented, and multidisciplinary care strategies tailored to the unique risk profiles of patients with CHD. Integrating early surveillance, mental health support, and coordinated specialty care can disrupt adverse comorbidity pathways and improve long‐term outcomes in this vulnerable population.

Sources of Funding

Chao Li was funded for this work by Fonds de recherche du Québec – Santé Master's Training Award and Canada Graduate Scholarships. Dr Yue Li is supported by a Natural Sciences and Engineering Research Council of Canada Discovery Grant (RGPIN‐2019‐06216) and a Canada Research Chair (Tier 2) in Machine Learning for Genomic and Healthcare. This work was supported by Dr Ariane Marelli's Heart and Stroke Foundation John Day, Excellence Award and the Canadian Institutes of Health Research Foundation Grant (#148462). Dr Marelli is supported by the Fonds de recherche du Québec – Santé Merit Scholar Award.

Disclosures

None.

Supporting information

Data S1 Supplemental Methods

STROBE Statement