Abstract
Asthma is associated with adverse cardiovascular outcomes, but little is known about its role in the development of cardiometabolic multimorbidity (CMM). We aimed to examine the associations of asthma with both incident and coexisting cardiometabolic diseases (CMDs), characterizing their patterns and transitions to CMM in men and women. This prospective cohort study, based on the UK Biobank, included 51,335 participants with asthma and 395,890 without asthma at baseline in 2006–2010. Participants were followed for the development of CMDs, including type 2 diabetes, coronary heart disease, and stroke, using primary care records, hospital admission and death register data, and self-reported medical information up to December 31, 2022. CMM was defined as the coexistence of two or more CMDs. We used Cox proportional hazards models and multi-state models to assess the associations of asthma with the incidence and transitions to CMDs and CMM among participants free of CMDs. During a median follow-up of 13.8 years, 60,033 participants (13.4%) developed CMD, of whom 7,048 (1.6%) progressed to CMM. Asthma was associated with increased risks of all incident CMDs and CMM (hazard ratio [HR] = 1.54, 95% confidence interval = 1.44–1.64), as well as CMD counts and CMM patterns (e.g., HR = 1.60 [1.50–1.71] for 2 CMDs, and HR = 1.70 [1.56–1.84] for comorbid type 2 diabetes and coronary heart disease). For the transitions from no CMD to first CMD, from first CMD to CMM, and from no CMDs to death, the hazard ratios were 1.29 (1.26–1.33), 1.20 (1.12–1.28), and 1.14 (1.09–1.18), respectively. All these associations were more pronounced in women. In summary, individuals with asthma were at increased risk of developing cardiometabolic diseases and progressing to cardiometabolic multimorbidity. Early prevention and management of asthma, with integration into cardiometabolic risk assessment, may be crucial for mitigating future cardiometabolic multimorbidity.
Subject terms: Cardiology, Diseases, Health care, Medical research, Risk factors
Introduction
Asthma is a lifelong chronic respiratory disease affecting people across all age groups, affecting approximately 7% of adults and 10% of children globally. Over the past few decades, the global prevalence of asthma has been steadily increasing, especially in Western developed countries1. According to the 2019 Global Burden of Disease report, asthma accounted for the second-largest proportion of respiratory disease-related disability-adjusted life years among middle-aged and older adults2. Asthma not only manifests as severe symptoms during attacks but also has lifelong impacts on patients’ health outcomes, highlighting the importance of addressing the adverse events of asthma across the life course1,3,4. Previous studies have demonstrated that asthma is associated with increased risk of multiple noncommunicable chronic diseases, including cardiometabolic diseases (CMDs)5–7.
CMDs, including type 2 diabetes mellitus (T2DM), coronary heart disease (CHD), and stroke, often co-occur in individuals8. Cardiometabolic multimorbidity (CMM), defined as the co-existence of two or more of these CMDs, has become a global public health challenge as its association with increased utilization of healthcare resources, reduced life expectancy, and increased risk of mortality8,9. Previous studies have explored the association between asthma and individual CMDs; however, the existing evidence is not comprehensive nor consistent10–15. A meta-analysis suggests that asthma is associated with an increased risk of developing T2DM11. Two cohort studies from the United States reported an association between asthma and the risk of CHD and stroke12,13. Contrary results were reported in a large Korean cohort study, which indicated no association between asthma and stroke14. A recent observational and Mendelian randomization study has contradicted previous findings, suggesting no association between asthma and CHD15. Additionally, a comprehensive review revealed sex differences in asthma susceptibility, prevalence, and severity, highlighting the need to consider sex differences when investigating the risk of future diseases related to asthma, a factor often overlooked in previous studies16. Furthermore, although the association between asthma and individual CMDs has been explored, little is known about its association with the risk of CMM.
Therefore, we aimed to examine the association of asthma with the risks of individual CMDs and the incidence and transitions to CMM, and potential sex differences in these associations.
Methods
Study design and participants
This study used data from the UK Biobank, a large-scale prospective cohort study that recruited over 500,000 participants aged 40–69 years across England, Scotland, and Wales in the UK between 2006 and 2010. At baseline, participants completed touchscreen questionnaires, which collected their sociodemographic characteristics, lifestyle behaviors, and medical information. Health-related outcomes were followed through linked data from national databases, including primary care systems, hospital admission records, and death registers. The UK Biobank has received approval from the North-west Multi-Centre Research Ethics Committee. All participants provided informed consent at the recruitment visit, and records of those who withdrew were removed. This cohort study adheres to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines.
We included all participants who completed the baseline assessment (n = 502,364), excluding those lost to follow-up (n = 1,297), those with incorrected event date for asthma (n = 19) or CMD (n = 7), and those with any CMDs at baseline (n = 53,816). In total, 447,225 participants were included in the analyses of the associations of asthma with incidence and transitions to CMM. Figure 1 displays the detailed study design.
Fig. 1. Study flowchart.
CMD, cardiometabolic disease; NHS, national health service.
Ascertainment of asthma
We ascertained asthma (ICD-10 code J45) using first-occurrence data derived from primary care records, inpatient hospital records, death register data, and self-reported medical conditions (Table S1). Based on the age at asthma diagnosis, we further classified asthma into childhood-onset asthma (COA, <18 years) and adulthood-onset asthma (AOA, ≥18 years). Due to limited records of early-life disease registries in the UK Biobank, asthma history was primarily determined based on self-reported information in the current study. Previous studies support the validity and reliability of self-reported asthma in the UK Biobank17.
Ascertainment of cardiometabolic multimorbidity and death
In line with previous studies18,19, CMM was defined as the co-occurring of two or more of the following CMDs: T2DM, CHD, and stroke. Information about CMD diagnosis was obtained from first occurrence fields and coded according to the ICD-10: T2DM (E11, E14), CHD (I20-I25), and stroke (I60-I64, I69) (Table S1). The incidence of CMM was defined as the onset of CMM for participants who were free of any CMD at baseline. We also defined three CMM incidence-derived outcomes: CMD counts (0, 1, 2, and 3 CMDs), CMM patterns (a. none; b. only T2DM; c. only CHD; d. only stroke; e. T2DM and CHD; f. CHD and stroke; g. T2DM and stroke; h. T2DM, CHD, and stroke), and transitional phases of CMM progression.
Death information was obtained from death certificates held within the National Health Service Information Centre (England and Wales) and the National Health Service Central Register (Scotland).
Covariates
Covariates assessed in this study included sociodemographic (age at baseline, ethnicity, recruitment center, education level, and Townsend deprivation index), lifestyle (smoking status, drinking status, physical activity, intake of fruits and vegetables, and sleep duration), and early-life factors (breastfed, part of multiple births, maternal smoking around birth, and family history of CMD). Responses of “don’t know” or “prefer not to answer” for these covariates were considered missing and classified into the “unknown” group. Inhaled corticosteroids (ICS) are the first-line treatment for asthma, but they are associated with adverse cardiometabolic outcomes. Therefore, we extracted self-reported information on the use of ICS at baseline. Further details of the measurement of covariates are provided in Table S1.
Statistical analysis
The baseline characteristics of the participants were summarized according to asthma and CMM. Continuous variables were summarized as mean ± standard deviation (SD), and categorical variables were summarized as frequency (percentage). Differences between groups were compared using the Student’s t-test or Chi-square test, as appropriate.
After we assessed the proportional hazards assumption, we used Cox proportional hazards models to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs) for the associations of asthma with CMD and CMM. Years from baseline to the onset of CMD, CMM, death, or the end of the follow-up (31st December 2022), whichever occurred first, were considered the time scale. Schoenfeld residuals indicated no violations of the proportionality assumption. We further used multi-state models to estimate the association between asthma and transitional phases of CMM progression. In line with the previous study20, five transition stages were constructed, including a. baseline to first CMD (FCMD); b. FCMD to CMM; c. baseline to death; d. FCMD to death; and e. CMM to death (transition pattern A, Fig. 2A). For participants entering different stages on the same date, we calculated the entering date of the theoretically prior state as the date of the latter state minus 0.5 day20. We also divided the FCMD into three individual diseases, constructing eleven transitions (transition pattern B, Fig. 3A). In this transition pattern, we excluded 1489 participants diagnosed with at least two CMDs on the same date, as we could not determine the temporal sequence of the CMDs. All models were adjusted for age, sex, ethnicity, region, education level, Townsend deprivation index, smoking status, drinking status, physical activity, intake of fruits and vegetables, breastfed, part of multiple births, maternal smoking around birth, and family history of CMD. We also did separate analyses for the male and female cohorts.
Fig. 2. Association between asthma and transitional phases of CMM progression (pattern A).
A. Numbers (percentages) of participants in transition pattern A; B. Forest plot for the association between asthma and five transitional phases. FCMD, first cardiometabolic disease; CMM, cardiometabolic multimorbidity; HR, hazard ratio; CI, confidence interval.
Fig. 3. Association between asthma and transitional phases of CMM progression (pattern B).
A. Numbers (percentages) of participants in transition pattern A; B. Forest plot for the association between asthma and eleven transitional phases. T2DM, type 2 diabetes mellitus; CHD, coronary heart disease; CMM, cardiometabolic multimorbidity; HR, hazard ratio; CI, confidence interval.
We conducted subgroup analyses to assess the association between asthma and CMM stratified by age (<60 and ≥60), recruitment center (England, Scotland, and Wales), ethnicity (White and non-White), TDI (quintile 1 to quintile 5), and education level (college or university degree and other qualifications). The interaction effects were evaluated by performing analyses of variance comparing regression models with and without interaction terms21.
We also conducted several sensitivity analyses to assess the robustness of the longitudinal analyses. First, we excluded participants who developed CMM within 2 or 4 years to reduce potential reverse causation. Second, we conducted competing risk models, wherein death before CMM was set as the competing event. Third, to test the influence of missing values, we excluded participants with missing covariates. Fourth, we assumed that missing data were missing at random and imputed missing covariates using multiple imputation, generating five complete datasets, with results combined according to Rubin’s rules. Table S2 presents the frequency of participants by the number of missing covariates. Fifth, we only included participants who did not use ICS. Sixth, we redefined T2DM using ICD-10 code E11 as the criterion instead of E11 and E14. Finally, we restricted the events of interest to those identified solely from hospital diagnosis records.
All analyses were performed using R software (version 4.4.0). A two-sided P < 0.05 was considered statistically significant.
Ethics approval and informed consent
All participants in the UK Biobank provided informed consent. The UK Biobank has ethical approval from the North-West Multicenter Research Ethics Committee. All methods were performed in accordance with the relevant guidelines and regulations.
Results
Baseline characteristics
We included 447,225 participants without CMDs to investigate the associations of asthma with the incidence and transitions to CMM, among whom 51,335 (11.5%) had asthma. The baseline characteristics of participants according to asthma and CMM are summarized in Table 1 and Table S3. The mean (SD) age of participants was 56.04 (8.10), and 252,988 (56.6) were women (Table 1). Participants with asthma were likely to be female, Welsh, highly educated, highly deprived, former drinkers, low physical activity participants, have shorter sleep duration, not breastfed, born in multiple births, exposed to maternal smoking around birth, and with a family history of CMD.
Table 1.
Baseline characteristics of participants by asthma.
| Variables | Total (n = 447,225) |
Non-asthma (n = 395,890) |
Asthma (n = 51,335) |
Pa |
|---|---|---|---|---|
| Age at baseline, mean (SD) | 56.04 (8.10) | 56.14 (8.08) | 55.29 (8.26) | <0.001 |
| Sex, n (%) | <0.001 | |||
| Female | 252,988 (56.6) | 222,666 (56.2) | 30,322 (59.1) | |
| Male | 194,237 (43.4) | 173,224 (43.8) | 21,013 (40.9) | |
| Region, n (%) | <0.001 | |||
| England | 396,975 (88.8) | 351,457 (88.8) | 45,518 (88.7) | |
| Scotland | 31,800 (7.1) | 28,501 (7.2) | 3299 (6.4) | |
| Wales | 18,450 (4.1) | 15,932 (4.0) | 2518 (4.9) | |
| Ethnicity, n (%) | 0.009 | |||
| White | 404,953 (90.5) | 358,313 (90.5) | 46,640 (90.9) | |
| Non-white | 39,931 (8.9) | 35,470 (9.0) | 4461 (8.7) | |
| Unknown | 2341 (0.5) | 2107 (0.5) | 234 (0.5) | |
| Education level, n (%) | <0.001 | |||
| College or university degree | 148,681 (33.2) | 130,783 (33.0) | 17,898 (34.9) | |
| Other qualifications | 289,988 (64.8) | 257,495 (65.0) | 32,493 (63.3) | |
| Unknown | 8556 (1.9) | 7612 (1.9) | 944 (1.8) | |
| Townsend deprivation index, n (%) | <0.001 | |||
| Quintile 1 (least deprived) | 89336 (20.0) | 79425 (20.1) | 9911 (19.3) | |
| Quintile 2 | 89335 (20.0) | 79541 (20.1) | 9794 (19.1) | |
| Quintile 3 | 89335 (20.0) | 79434 (20.1) | 9901 (19.3) | |
| Quintile 4 | 89335 (20.0) | 78829 (19.9) | 10506 (20.5) | |
| Quintile 5 (most deprived) | 89335 (20.0) | 78181 (19.7) | 11154 (21.7) | |
| Unknown | 549 (0.1) | 480 (0.1) | 69 (0.1) | |
| Smoking status, n (%) | <0.001 | |||
| Never | 250,008 (55.9) | 221,448 (55.9) | 28,560 (55.6) | |
| Previous | 148,510 (33.2) | 130,880 (33.1) | 17,630 (34.3) | |
| Current | 46,319 (10.4) | 41,423 (10.5) | 4896 (9.5) | |
| Unknown | 2388 (0.5) | 2139 (0.5) | 249 (0.5) | |
| Drinking status, n (%) | <0.001 | |||
| Never | 18,486 (4.1) | 16,256 (4.1) | 2230 (4.3) | |
| Previous | 14,458 (3.2) | 12,410 (3.1) | 2048 (4.0) | |
| Current | 412,919 (92.3) | 365,989 (92.4) | 46,930 (91.4) | |
| Unknown | 1362 (0.3) | 1235 (0.3) | 127 (0.2) | |
| Physical activity, n (%) | <0.001 | |||
| Low | 61,822 (13.8) | 53,848 (13.6) | 7974 (15.5) | |
| Moderate | 140,601 (31.4) | 124,741 (31.5) | 15,860 (30.9) | |
| High | 142,731 (31.9) | 127,000 (32.1) | 15,731 (30.6) | |
| Unknown | 102,071 (22.8) | 90,301 (22.8) | 11,770 (22.9) | |
| Intake of fruits, n (%) | 0.307 | |||
| < 2.0 servings/day | 121,830 (27.2) | 107,755 (27.2) | 14,075 (27.4) | |
| 2.0–2.9 servings/day | 110,466 (24.7) | 97,826 (24.7) | 12,640 (24.6) | |
| 3.0–3.9 servings/day | 82,924 (18.5) | 73,558 (18.6) | 9366 (18.2) | |
| ≥ 4.0 servings/day | 81,224 (18.2) | 71,877 (18.2) | 9347 (18.2) | |
| Unknown | 50,781 (11.4) | 44,874 (11.3) | 5907 (11.5) | |
| Sleep duration | <0.001 | |||
| Short | 107900 (24.1) | 94250 (23.8) | 13650 (26.6) | |
| Referent | 322635 (72.1) | 287054 (72.5) | 35581 (69.3) | |
| Long | 13265 (3.0) | 11575 (2.9) | 1690 (3.3) | |
| Unknown | 3425 (0.8) | 3011 (0.8) | 414 (0.8) | |
| Intake of vegetables, n (%) | <0.001 | |||
| < 2.0 servings/day | 43,363 (9.7) | 38,298 (9.7) | 5065 (9.9) | |
| 2.0–2.9 servings/day | 119,570 (26.7) | 106,075 (26.8) | 13,495 (26.3) | |
| 3.0–3.9 servings/day | 120,524 (26.9) | 106,734 (27.0) | 13,790 (26.9) | |
| ≥ 4.0 servings/day | 129,085 (28.9) | 113,872 (28.8) | 15,213 (29.6) | |
| Unknown | 34,683 (7.8) | 30,911 (7.8) | 3,772 (7.3) | |
| Breastfed, n (%) | <0.001 | |||
| No | 97,102 (21.7) | 84,961 (21.5) | 12,141 (23.7) | |
| Yes | 248,128 (55.5) | 220,233 (55.6) | 27,895 (54.3) | |
| Unknown | 101,995 (22.8) | 90,696 (22.9) | 11,299 (22.0) | |
| Part of multiple births, n (%) | <0.001 | |||
| No | 429,499 (96.0) | 380,348 (96.1) | 49,151 (95.7) | |
| Yes | 9951 (2.2) | 8796 (2.2) | 1155 (2.2) | |
| Unknown | 7775 (1.7) | 6746 (1.7) | 1029 (2.0) | |
| Maternal smoking around birth, n (%) | <0.001 | |||
| No | 274,065 (61.3) | 243,533 (61.5) | 30,532 (59.5) | |
| Yes | 112,018 (25.0) | 98,067 (24.8) | 13,951 (27.2) | |
| Unknown | 61,142 (13.7) | 54,290 (13.7) | 6,852 (13.3) | |
| Family history of CMD, n (%) | 0.001 | |||
| No | 1497,05 (33.5) | 132,819 (33.5) | 16,886 (32.9) | |
| Yes | 263,937 (59.0) | 233,244 (58.9) | 30,693 (59.8) | |
| Unknown | 33,583 (7.5) | 29,827 (7.5) | 3756 (7.3) |
aP values were calculated by t-tests or chi-square test when appropriate;
CMD, cardiometabolic disease.
Incident cardiometabolic diseases and multimorbidity
During a median follow-up of 13.8 years, 1109 (2.2%) participants with asthma and 5939 (1.5%) without asthma developed CMM. Asthma was associated with higher risks of T2DM (HR: 1.40, 95% CI: 1.35–1.46) and CHD (HR: 1.32, 95% CI: 1.28–1.36), although the association between asthma and stroke was only observed in women (HR: 1.16, 95% CI: 1.07–1.25) (Table 2). Participants with asthma had an increased risk of incident CMM (HR: 1.54, 95% CI: 1.44–1.64), especially in women (HR: 1.35, 95% CI: 1.23–1.47 for men, and HR: 1.81, 95% CI: 1.64–1.99 for women, Table 2). The association was stronger in AOA patients than in COA patients (Table S4). In addition, in women asthma was associated with an elevated risk of developing more CMDs (HR [95% CI]: 1.40 [1.35–1.45] for 1 CMD; HR: 1.86 [1.69–2.05] for 2 CMDs; and HR: 3.05 [2.04–4.57] for 3 CMDs; Table 2). Analysis of CMM patterns showed that asthma was associated with specific combinations of CMDs, including e.g., coexisting T2DM and CHD (HR: 1.70 [1.56–1.84]), T2DM and stroke (HR: 1.43 [1.17–1.74]) and CHD and stroke (HR: 1.47 [1.28–1.69]).
Table 2.
Associations of asthma with cardiometabolic diseases and cardiometabolic multimorbidity in all participants and by sex.
| All participants (n = 447,225) | Male (n = 194,237) | Female (n = 252,988) | |||
|---|---|---|---|---|---|
| Case / 100-person year | HR (95% CI) a | HR (95% CI) a | HR (95% CI) a | ||
| Non-asthma | Asthma | ||||
| Specific CMD | |||||
| T2DM vs Non-T2DM | 18231/52641.98 | 3235/6756.21 | 1.40 (1.35–1.46) | 1.24 (1.18–1.31) | 1.56 (1.48–1.65) |
| CHD vs Non-CHD | 30054/51971.58 | 4733/6683.14 | 1.32 (1.28–1.36) | 1.18 (1.13–1.23) | 1.50 (1.44–1.57) |
| Stroke vs Non-stroke | 9900/53220.20 | 1295/6888.29 | 1.09 (1.03–1.15) | 1.02 (0.94–1.11) | 1.16 (1.07–1.25) |
| CMM status | |||||
| Non-CMM | Ref. | Ref. | Ref. | ||
| CMM | 5939/53429.27 | 1109/6903.36 | 1.54 (1.44–1.64) | 1.35 (1.23–1.47) | 1.81 (1.64–1.99) |
| CMD counts | |||||
| 0 | Ref. | Ref. | Ref. | ||
| 1 | 45994/50387.04 | 6991/6413.86 | 1.26 (1.23–1.30) | 1.14 (1.10–1.18) | 1.40 (1.35–1.45) |
| 2 | 5626/47356.55 | 1055/5978.70 | 1.60 (1.50–1.71) | 1.42 (1.29–1.55) | 1.86 (1.69–2.05) |
| 3 | 313/46864.12 | 54/5887.01 | 1.52 (1.14–2.03) | 0.87 (0.55–1.36) | 3.05 (2.04–4.57) |
| CMM patterns | |||||
| None | Ref. | Ref. | Ref. | ||
| T2DM only | 13771/47915.34 | 2370/6061.83 | 1.38 (1.32–1.45) | 1.21 (1.13–1.29) | 1.54 (1.46–1.64) |
| CHD only | 24805/48692.40 | 3739/6161.74 | 1.29 (1.24–1.33) | 1.15 (1.10–1.21) | 1.47 (1.39–1.54) |
| Stroke only | 7418/47442.83 | 882/5953.11 | 1.03 (0.96–1.10) | 1.00 (0.90–1.11) | 1.06 (0.96–1.16) |
| T2DM and CHD | 3457/47150.06 | 696/5945.05 | 1.70 (1.56–1.84) | 1.52 (1.36–1.69) | 1.95 (1.72–2.20) |
| T2DM and stroke | 690/46899.07 | 115/5892.67 | 1.43 (1.17–1.74) | 1.27 (0.96–1.68) | 1.62 (1.23–2.14) |
| CHD and stroke | 1479/46970.95 | 244/5903.80 | 1.47 (1.28–1.69) | 1.25 (1.04–1.51) | 1.80 (1.47–2.19) |
| T2DM, CHD, and stroke | 313/46864.12 | 54/5887.01 | 1.52 (1.14–2.03) | 0.87 (0.55–1.36) | 3.05 (2.04–4.57) |
aAdjusted for age, sex, ethnicity, region, education level, Townsend deprivation index, smoking status, drinking status, physical activity, intake of fruits and vegetables, sleep duration, breastfed, part of multiple births, maternal smoking around birth, and family history of CMD.
CMM, cardiometabolic multimorbidity; CMD, cardiometabolic disease; T2DM, type 2 diabetes mellitus; CHD, coronary heart disease; HR, hazard ratio; CI, confidence interval.
Bold values indicate statistically significant.
There were no statistically significant differences in the associations between asthma and incident CMM across different sub-populations (Figures S1 and S2). Sensitivity analyses supported the robustness of our findings. Similarly, the findings remained consistent when restricted to participants who had never used ICS (Table S5).
Transitions to CMM and death
During a median follow-up of 13.8 years, 60,033 (13.4%) participants developed at least one CMD, of whom 7,048 (11.7%) further progressed to CMM. A total of 32,923 deaths were identified during follow-up. Among these cases, 9520 (28.9%) died with FCMD, and 1,875 (5.7%) died after experiencing CMM (Fig. 2A). When further dividing FCMD into individual CMDs, 18,185 (31.1%) participants’ FCMDs were T2DM, 31,104 (53.1%) were CHD, and 9254 (15.8%) were stroke. Participants with stroke were more likely to die compared to T2DM and CHD cases (Fig. 3A).
Asthma was associated with increased risks of transition from baseline to FCMD (HR: 1.29, 95% CI: 1.26–1.33) and subsequent CMM (HR: 1.20, 95% CI: 1.12–1.28, Fig. 2B). For transition to death, asthma was associated with mortality from baseline (HR: 1.14, 95% CI: 1.09–1.18, Fig. 2B) but not from FCMD and CMM. After dividing FCMDs into four specific CMDs, asthma had the strongest association with T2DM (HR: 1.42, 95% CI: 1.36–1.47) but a null association with stroke (Fig. 3B). Asthma was associated with the transition of any specific CMD to CMM (HR: 1.30 [1.16–1.46] for T2DM followed by CMM; HR: 1.18 [1.06–1.31] for CHD followed by CMM; and 1.30 [1.08–1.57] for stroke followed by CMM, Fig. 3B). We also found that asthma was associated with death from CHD (HR: 1.16 [1.07–1.25], Fig. 3B). Similar findings were observed in separate analyses for male and female cohorts (Figures S3–S6).
Discussion
Using extensive longitudinal individual-level data from the UK Biobank, we found that participants who experienced asthma had a 1.54-fold increased risk of CMM, with the most common disease combination being co-existing type 2 diabetes and coronary heart disease. Although these associations were observed in both men and women, they were more pronounced in women. Furthermore, asthma appeared to play a role at multiple stages of CMM progression, including transitions from a CMD-free state to first CMD, from first CMD to CMM, and from a CMD-free state or prevalent CMD to death. Among the asthma-related CMD-specific transitions, the strongest was observed between asthma and developing type 2 diabetes.
Our study examined the associations of asthma with CMDs in a greater detail than previously, including the incidence of CMM and each transitional phase from a state free of CMD to first CMD, progression to CMM, and death. Our results are partially consistent with previous evidence on individual CMDs. A previous study using the UK Biobank data found that asthma was associated with an increased risk of cardiovascular disease22. Our study further distinguished the associations of asthma with the risks of CHD and stroke, and additionally provided evidence on the risk of coexisting CHD and stroke, as well as on the association between asthma and CMM. The results from cohort studies in the United States, Finland, and Singapore also support the association of asthma and T2DM with CHD5,13,23. In addition, a prospective analysis from the Framingham Offspring Study with more than 35 years of follow-up shows that asthma is a risk factor for cardiovascular disease24. Although a Mendelian randomization study suggested discrepancies in the association between asthma and CHD, the heterogeneity of asthma and the pleiotropic relationships between asthma and other metabolic conditions were not fully accounted for in that study15,25,26.
Our study also revealed significant differences in the association between asthma and CMM by sex and age at asthma onset. Compared to men, women with asthma have a higher risk of individual CMDs, CMM, and certain combinations and transition stages of CMM. A higher risk of CMM was also observed in participants with AOA versus those with COA. Two previous cohort studies on asthma and individual CMDs suggested that women have a higher risk of developing T2DM, CHD, and stroke23,27. The two studies also found that the associations were stronger for participants with AOA, supporting the potential differences by age at asthma onset in the association between asthma and CMM23,27. In addition, we found that the association between asthma and CMM was attenuated among individuals who did not use ICS. A previous cohort study reported similar findings, showing a lower risk of cardiovascular diseases among individuals not taking asthma medications12.
Several potential mechanisms may explain our findings. First, chronic inflammation is a shared risk factor for asthma and CMDs28. Mechanistic studies revealed the role of systemic inflammation in linking asthma to pro-inflammatory diseases (e.g., T2DM and CHD), which may be dependent on or independent of adiposity29–31. Second, insulin resistance, obesity, and other metabolic abnormalities caused by asthma and their complex interactions are all common risk factors for CMDs and CMDs1,32–34. Third, the remission of asthma may be a potential mechanism underlying the differences in the association between asthma and CMM by sex and age at asthma onset. Previous studies have shown that individuals in asthma remission have a lower risk of future adverse outcomes, including CMDs3,6,35. The sex differences in asthma remission have been widely studied, with consistent findings showing that women have more difficulty achieving asthma remission36,37. In addition, although the prevalence of COA is higher than that of AOA, the remission rate for AOA is only 5–15%3,4,37. Fourth, sex hormones may also contribute to the increased risk of CMD among women with asthma. Previous studies have suggested that sex hormones, such as estradiol, may mediate the association between asthma and CMD16,38. Finally, the use of ICS and unhealthy lifestyle factors, such as low physical activity, seem to be inevitable experiences for participants diagnosed with asthma and would serve as independent risk factors for CMD beyond pathological mechanisms20,26,39,40.
These findings have important public health implications. Asthma and CMDs impose a significant economic burden globally and are included in the World Health Organization’s Global Action Plan for the prevention and control of non-communicable diseases41. The economic cost of asthma extends beyond direct healthcare expenses; its indirect costs, including potential future adverse outcomes, are also substantial42. CMDs and CMM represent some of the most burdensome outcomes of asthma. In the UK, patients with diagnosed asthma receive annual primary care reviews, but cardiometabolic assessments are not routinely required43. The Global Strategy for Asthma Management and Prevention recommends cardiometabolic risk monitoring for individuals using corticosteroids44. Therefore, it was suggested to consider implementing such reviews for patients with asthma, particularly those on long-term corticosteroid therapy or with poor asthma control. Reports indicate that by age 60, individuals with a history of all three CMDs may experience a 15-year reduction in life expectancy9. Our study investigated the associations of asthma with the progression and incidence of CMD and CMM, providing a more comprehensive perspective on the role of asthma in the development of CMDs.
Our study also provides insights into both individual and clinical aspects of CMM in those with asthma. Asthma management has shifted from treating acute episodes to long-term control and prevention of future risks45. Additionally, distinguishing asthma severity, avoiding medication overuse, and enhancing patient self-care awareness is crucial in asthma management and its future CMM risk3,26,42.
Our study has several strengths. The prospective study design, long follow-up, and a large sample enabled a step-by-step observation of the associations between asthma and the development of distinct forms of CMM, adding to previous evidence focused on individual CMDs. In addition, we examined the associations of asthma with different combinations and numbers of CMDs, enabling us to explore the role of asthma in the development of CMDs in terms of both specific disease combinations and overall disease burden. A series of sensitivity analyses also supported the robustness of our findings.
Some limitations to our study are noteworthy. First, although we adjusted for several potential confounding factors, residual confounding due to unmeasured or unknown factors cannot be ruled out. Second, although detection bias may affect the observed associations, the increased general practitioner visits among patients with asthma may result from annual reviews, which do not include cardiometabolic assessments. Thus, the extent of potential overestimation remains uncertain. Third, as early-life disease information was not available in the medical databases, we relied on self-reports, which may be affected by recall and misclassification bias. Fourth, we had no information on the severity, remission status, or recurrence of asthma, which limited further investigation of the role of asthma progression in the CMM risk. Fifth, although the overall sample size was large, the number of cases in some groups remained limited due to the few cases of specific CMM patterns, resulting in wide 95% CIs for the estimates from these comparisons. Finally, UK Biobank participants may not represent the general UK population due to healthy volunteer selection bias46. Further studies are warranted to validate such associations in larger populations.
Conclusions
Our study revealed that in addition to individual cardiometabolic diseases, asthma is associated with increased risks of the transitions to cardiometabolic multimorbidities, as well as several specific transitions from a CMD-free state to incident and comorbid cardiometabolic diseases, and death, underscoring its important role in long-term cardiometabolic health. These findings strengthen the evidence base emphasizing the importance of effective asthma management, and suggest that health professionals should consider asthma in CMM risk assessment.
Supplementary information
Acknowledgements
We thank all the volunteers who participated in the study, and all the researchers and staff who contributed to the UK Biobank. X.X was supported by Zhejiang University, the Fundamental Research Funds for the Central Universities, the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, the China Medical Board (No. 21-416), and the National Natural Science Foundation of China (No. 72474197). C.X was support by Scientific Research Foundation for Scholars of Hangzhou Normal University (No. 4265C50221204119).
Author contributions
Conceptualization: Junjie Lin, Yangyang Cheng, Xiaolin Xu. Funding acquisition: Xiaolin Xu, Chenjie Xu. Data analysis: Junjie Lin, Yangyang Cheng. Data curation: Xiaolin Xu, Chenjie Xu. Drafting and revision: Junjie Lin, Yangyang Cheng, Yue Zhang, Mika Kivimäki, Rodrigo M Carrillo-Larco, Chenjie Xu, Xiaolin Xu. All authors read and approved the final manuscript.
Data availability
UK Biobank data are available online at https://www.ukbiobank.ac.uk. This research has been conducted using the UK Biobank Resource under Application Number 79095.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
These authors contributed equally: Junjie Lin, Yangyang Cheng.
Supplementary information
The online version contains supplementary material available at 10.1038/s41533-025-00474-2.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
UK Biobank data are available online at https://www.ukbiobank.ac.uk. This research has been conducted using the UK Biobank Resource under Application Number 79095.