Abstract
Objective
This study investigated the associations of changes in metabolic syndrome status and the number of metabolic syndrome risk factors after cancer diagnosis with the incidence of cardiovascular disease (CVD) events among cancer survivors.
Methods
Using nationwide health screening data, we identified 344,681 individuals diagnosed with cancer at age ≥19 years from 2012 to 2017, who survived for at least 3 years without CVD events. Participants were classified according to their metabolic syndrome status and risk factor count both before and after cancer diagnosis. A CVD event was defined as a composite of myocardial infarction, stroke, or cardiovascular death.
Results
During a median follow-up period of 5.0 years after surviving cancer for 3 years, 7,529 CVD events occurred. The cumulative incidence of CVD was highest among participants with persistent metabolic syndrome. Compared to participants consistently free of metabolic syndrome, the multivariable-adjusted hazard ratios (HRs) for CVD were 1.21 (95% confidence interval [CI], 1.13–1.30) for newly developed metabolic syndrome, 1.19 (95% CI, 1.10–1.27) for recovered metabolic syndrome, and 1.37 (95% CI, 1.30–1.45) for persistent metabolic syndrome. Participants who recovered from metabolic syndrome exhibited a lower risk compared to those with persistent metabolic syndrome (HR, 0.86; 95% CI, 0.80–0.93). Each +1 increase in risk factor count after cancer diagnosis was associated with increased CVD risk (HR, 1.09; 95% CI, 1.07–1.11).
Conclusion
Changes in metabolic syndrome status and metabolic syndrome risk factor count after cancer diagnosis were significantly associated with CVD risk among cancer survivors.
Keywords: Cancer survivors, Metabolic syndrome, Cardiovascular diseases
INTRODUCTION
The number of cancer survivors is rapidly increasing, with an estimated 53.5 million survivors worldwide in 2022.1,2 Although improvements in cancer care have significantly reduced cancer-related mortality, they have concurrently extended survival periods, exposing cancer survivors to a growing burden of non-cancer comorbidities.3,4 Among these comorbidities, cardiovascular disease (CVD) is the leading cause of non-cancer mortality.4 Furthermore, cancer survivors are at a higher risk of developing CVD due to shared risk factors and adverse effects associated with cancer treatments.5,6,7 Thus, managing CVD among cancer survivors has emerged as a critical public health priority.
Cancer diagnosis can induce substantial alterations in cardiovascular risk factors through various mechanisms. Inflammatory responses triggered by cancer can adversely impact lipid profiles and promote atherosclerosis.7,8 Additionally, elevated stress hormones, such as cortisol, may worsen insulin resistance.9,10 These post-cancer changes in cardiovascular risk factors may subsequently influence CVD risk.11 Metabolic syndrome, defined as a cluster of cardiovascular risk factors, serves as a comprehensive indicator for predicting CVD risk.12 Despite its well-established role as a predictor of CVD risk in the general population, little is known about how changes in this holistic measure affect CVD risk specifically among cancer survivors.
Utilizing a nationwide health claims database in Korea, we evaluated the association between changes in metabolic syndrome status after cancer diagnosis and the risk of CVD events among cancer survivors.
MATERIALS AND METHODS
1. Data source
We utilized a Korean nationwide database provided by the National Health Insurance Service (NHIS). The NHIS operates as a universal, single-payer health insurance system that covers approximately 97% of the Korean population. The NHIS database contains detailed sociodemographic data, insurance claims with diagnosis codes classified according to the International Classification of Disease, 10th revision (ICD-10), and death records.13 Additionally, the NHIS provides biennial health examinations for all Korean adults, collecting clinical, biochemical, and lifestyle-related data.14 Health examination centers undergo monitoring to ensure quality control, in accordance with relevant laws and regulations. Further details on the NHIS database have been documented in previous studies.13,14,15
The study protocol was approved by the Institutional Review Board of Severance Hospital, Seoul, Korea (4-2024-0498). Due to the anonymized nature of the database, the requirement for informed consent was waived.
2. Study population
Individuals diagnosed with cancer were identified using ICD-10 codes (Supplementary Table 1) and the rare incurable disease code for cancer (V193).16 A total of 1,208,036 adults aged 19 years or older were newly diagnosed with cancer from January 1, 2012, to December 31, 2017; among these, 892,782 (73.9%) survived for at least 3 years. Individuals who experienced myocardial infarction (MI) or stroke before the 3-year survival date (index date), those who did not undergo health examinations within 3 years before or after cancer diagnosis, and those with missing data for health examination variables were excluded. Consequently, the final study population comprised 344,681 participants (Supplementary Fig. 1).
3. Change in metabolic syndrome status after cancer diagnosis
Metabolic syndrome was defined according to the modified criteria of the National Cholesterol Education Program’s Adult Treatment Panel III, as having at least 3 of the following 5 cardiovascular risk factors: 1) waist circumference ≥90 cm (for males) or ≥85 cm (for females); 2) triglycerides ≥150 mg/dL or receiving specific treatment; 3) high-density lipoprotein cholesterol <40 mg/dL (for males) or <50 mg/dL (for females) or receiving specific treatment; 4) blood pressure ≥130/85 mmHg or receiving specific treatment; and 5) fasting glucose ≥100 mg/dL or receiving specific treatment.17 Waist circumference criteria were modified according to cutoffs recommended by the Korean Society for the Study of Obesity for Korean adults.18
Metabolic syndrome status was assessed using physical measurements and laboratory results from the last health examination conducted within 3 years before (exam 1) and after (exam 2) cancer diagnosis. Prescription records from claims data within the respective 3-year periods were also considered (Supplementary Fig. 2). Participants were classified into 4 distinct groups based on their metabolic syndrome status before and after cancer diagnosis (presence vs. absence).
4. Covariables
Lifestyle information, including tobacco smoking, alcohol consumption, and physical activity, was collected in exam 2. For alcohol consumption and physical activity, both frequency and amount were measured. Alcohol consumption amount was calculated by multiplying drinking frequency by the amount consumed per occasion. The amount per drinking occasion was estimated assuming that 1 drink contains 7 g of ethanol19 for health examinations conducted between 2012 and 2017, and using beverage-specific alcohol content and volume between 2018 and 2020. According to WHO guidelines, cut points of 40 g/day for male and 20 g/day for female were applied.20 Physical activity amount was assessed using a modified International Physical Activity Questionnaire.21 Weekly energy expenditure was estimated by multiplying activity frequency by session duration (30 minutes for moderate intensity, 20 minutes for vigorous intensity), weighted by standardized metabolic equivalent task (MET) values of 4.0 and 7.0, respectively.22,23 A cut point of 600 MET-min/week, consistent with the 2022 American Cancer Society guideline, was applied.24
The Charlson Comorbidity Index (CCI)25 was calculated using claims data from the 3-year period prior to the index date. Cancer-related information—including cancer type, presence of distant metastasis at initial diagnosis, and the use of chemotherapy and external beam radiotherapy—was also obtained from claims data.16
5. Outcomes
The primary outcome was a CVD event, defined as a composite of the first hospitalization for MI (ICD-10 codes: I21–I23), first hospitalization for stroke (ICD-10 codes: I60–I64), or cardiovascular death (ICD-10 codes: I00–I99). The accuracy of ICD-10 codes for MI and stroke has been validated previously.26 Secondary outcomes were predefined as individual components of the primary composite outcome. Participants were followed up from the index date until the occurrence of an outcome event, death, or December 31, 2022, whichever came first (Supplementary Fig. 2).
6. Statistical analysis
Baseline characteristics are presented as median (interquartile range) or number (%). Cumulative incidence of CVD events was estimated using the subdistribution cumulative incidence function, accounting for competing risks from non-cardiovascular death.27,28 Incidence rates were calculated as the number of events per 1,000 person-years of follow-up. Hazard ratios (HRs) and 95% confidence intervals (CIs) for primary and secondary outcomes were estimated using cause-specific Cox proportional hazards models, censoring participants at the time of competing death.28,29 The models were adjusted for age, sex, household income quartile, residential area, tobacco smoking, alcohol consumption frequency, physical activity frequency, CCI, cancer type, distant metastasis at initial diagnosis, use of chemotherapy and external beam radiotherapy, and year of cancer diagnosis.16 The proportional hazards assumption was confirmed visually using log-minus-log plots and Schoenfeld residuals.
In a secondary analysis, we examined the associations between changes in metabolic syndrome risk factor count after cancer diagnosis and CVD events. Participants were categorized based on their metabolic syndrome risk factor counts (0–1, 2, 3, 4–5) in exam 1 and exam 2. Additionally, trends were estimated by analyzing metabolic syndrome risk factor counts as continuous variables.
Cancer-specific analyses were conducted to investigate the association between changes in metabolic syndrome status and incident CVD among 8 major cancer types prevalent in Korea: upper gastrointestinal (GI), lower GI, hepatobiliary/pancreatic/other GI, lung, breast/female genital organs, male genital organs, urinary tract, and endocrine cancers. Subgroup analyses stratified by age, sex, body mass index (BMI), CCI, and year of cancer diagnosis were also performed. Sensitivity analyses involved excluding the first 2 years of follow-up to address potential reverse causality, and repeating the primary analysis in a cohort of 5-year cancer survivors to evaluate the robustness of findings among long-term survivors. All statistical analyses were conducted using SAS version 9.4 (SAS Institute Inc.) and R version 4.0.3 (R Foundation for Statistical Computing).
RESULTS
1. Baseline characteristics
Among the 344,681 cancer survivors included (median age, 59 years; 55.0% women), 212,891 (61.8%) participants were consistently free of metabolic syndrome, 38,089 (11.1%) participants newly developed metabolic syndrome, 34,950 (10.1%) recovered from metabolic syndrome, and 58,751 (17.0%) had persistent metabolic syndrome. Participants who had metabolic syndrome after cancer diagnosis were older, more likely to be male, more frequently resided in rural areas, were less physically active, and had higher CCI scores than those without metabolic syndrome after cancer diagnosis (Table 1). The most common cancer type was endocrine (26.5%), followed by upper GI (12.8%), lower GI (12.4%), and breast cancer (12.3%).
Table 1. Baseline characteristics of the participants.
| Variables | Total (n=344,681) | No MetS before cancer | MetS before cancer | |||
|---|---|---|---|---|---|---|
| No MetsS after cancer (n=212,891) | MetS after cancer (n=38,089) | No MetsS after cancer (n=34,950) | MetS after cancer (n=58,751) | |||
| Age (yr) | 59 (51–68) | 57 (48–65) | 61 (54–69) | 63 (55–70) | 64 (57–72) | |
| Sex | ||||||
| Male | 155,267 (45.0) | 85,085 (40.0) | 18,508 (48.6) | 19,933 (57.0) | 31,741 (54.0) | |
| Female | 189,414 (55.0) | 127,806 (60.0) | 19,581 (51.4) | 15,017 (43.0) | 27,010 (46.0) | |
| Household income* | ||||||
| Q4, highest | 132,977 (38.6) | 82,066 (38.5) | 14,426 (37.9) | 13,671 (39.1) | 22,814 (38.8) | |
| Q3 | 84,164 (24.4) | 51,789 (24.3) | 9,429 (24.8) | 8,479 (24.3) | 14,467 (24.6) | |
| Q2 | 60,654 (17.6) | 38,118 (17.9) | 6,640 (17.4) | 5,937 (17.0) | 9,959 (17.0) | |
| Q1, lowest | 66,886 (19.4) | 40,918 (19.2) | 7,594 (19.9) | 6,863 (19.6) | 11,511 (19.6) | |
| Residential area | ||||||
| Metropolitan | 153,737 (44.6) | 97,449 (45.8) | 16,461 (43.2) | 14,770 (42.3) | 25,057 (42.6) | |
| Urban | 157,911 (45.8) | 97,089 (45.6) | 17,492 (45.9) | 16,255 (46.5) | 27,075 (46.1) | |
| Rural | 33,033 (9.6) | 18,353 (8.6) | 4,136 (10.9) | 3,925 (11.2) | 6,619 (11.3) | |
| Tobacco smoking | ||||||
| Never | 234,938 (68.2) | 153,140 (71.9) | 24,703 (64.9) | 21,138 (60.5) | 35,957 (61.2) | |
| Past | 79,889 (23.2) | 43,925 (20.6) | 9,572 (25.1) | 10,252 (29.3) | 16,140 (27.5) | |
| Current | 29,854 (8.7) | 15,826 (7.4) | 3,814 (10.0) | 3,560 (10.2) | 6,654 (11.3) | |
| Alcohol consumption, frequency | ||||||
| None | 250,248 (72.6) | 155,584 (73.1) | 27,258 (71.6) | 25,691 (73.5) | 41,715 (71.0) | |
| 1–2 times/wk | 70,151 (20.4) | 44,615 (21.0) | 7,593 (19.9) | 6,485 (18.6) | 11,458 (19.5) | |
| ≥3 times/wk | 24,282 (7.0) | 12,692 (6.0) | 3,238 (8.5) | 2,774 (7.9) | 5,578 (9.5) | |
| Alcohol consumption, amount | ||||||
| None | 250,248 (72.6) | 155,584 (73.1) | 27,258 (71.6) | 25,691 (73.5) | 41,715 (71.0) | |
| <40 g/day (male), <20 g/day (female) | 88,291 (25.6) | 54,275 (25.5) | 9,953 (26.1) | 8,612 (24.6) | 15,451 (26.3) | |
| ≥40 g/day (male), ≥20 g/day (female) | 6,025 (1.7) | 2,956 (1.4) | 863 (2.3) | 639 (1.8) | 1,567 (2.7) | |
| Unknown | 117 (0.0) | 76 (0.0) | 15 (0.0) | 8 (0.0) | 18 (0.0) | |
| Physical activity, frequency | ||||||
| None | 140,449 (40.7) | 81,476 (38.3) | 16,912 (44.4) | 14,756 (42.2) | 27,305 (46.5) | |
| 1–2 times/wk | 78,755 (22.8) | 51,172 (24.0) | 8,373 (22.0) | 7,177 (20.5) | 12,033 (20.5) | |
| 3–4 times/wk | 68,055 (19.7) | 44,321 (20.8) | 6,951 (18.2) | 6,573 (18.8) | 10,210 (17.4) | |
| ≥5 times/wk | 57,422 (16.7) | 35,922 (16.9) | 5,853 (15.4) | 6,444 (18.4) | 9,203 (15.7) | |
| Physical activity, amount | ||||||
| None | 140,449 (40.7) | 81,476 (38.3) | 16,912 (44.4) | 14,756 (42.2) | 27,305 (46.5) | |
| <600 MET-min/week | 107,921 (31.3) | 69,837 (32.8) | 11,435 (30.0) | 9,981 (28.6) | 16,668 (28.4) | |
| ≥600 MET-min/week | 95,612 (27.7) | 61,127 (28.7) | 9,661 (25.4) | 10,136 (29.0) | 14,688 (25.0) | |
| Unknown | 699 (0.2) | 451 (0.2) | 81 (0.2) | 77 (0.2) | 90 (0.2) | |
| Body mass index | ||||||
| <18.5 kg/m2 | 11,463 (3.3) | 10,434 (4.9) | 277 (0.7) | 555 (1.6) | 197 (0.3) | |
| 18.5–22.9 kg/m2 | 129,863 (37.7) | 105,231 (49.4) | 7,381 (19.4) | 10,286 (29.4) | 6,965 (11.9) | |
| 23–24.9 kg/m2 | 85,977 (24.9) | 54,796 (25.7) | 9,730 (25.5) | 9,987 (28.6) | 11,464 (19.5) | |
| ≥25 kg/m2 | 117,378 (34.1) | 42,430 (19.9) | 20,701 (54.3) | 14,122 (40.4) | 40,125 (68.3) | |
| Systolic BP (mmHg) | 121 (112–132) | 119 (110–129) | 130 (120–137) | 124 (116–133) | 130 (120–139) | |
| Diastolic BP (mmHg) | 75 (70–80) | 73 (68–80) | 80 (72–85) | 77 (70–81) | 80 (72–85) | |
| Fasting glucose (mg/dL) | 97 (89–107) | 93 (87–100) | 104 (97–113) | 98 (91–110) | 110 (100–126) | |
| Total cholesterol (mg/dL) | 188 (164–214) | 189 (166–214) | 193 (167–220) | 184 (158–211) | 183 (156–211) | |
| BP-lowering drug use | 119,824 (34.8) | 43,600 (20.5) | 18,354 (48.2) | 16,123 (46.1) | 41,747 (71.1) | |
| Glucose-lowering drug use | 47,920 (13.9) | 11,314 (5.3) | 6,260 (16.4) | 6,751 (19.3) | 23,595 (40.2) | |
| Lipid-lowering drug use | 109,284 (31.7) | 45,319 (21.3) | 15,861 (41.6) | 13,746 (39.3) | 34,358 (58.5) | |
| CCI† | ||||||
| 0 | 82,454 (23.9) | 58,277 (27.4) | 7,557 (19.8) | 7,340 (21.0) | 9,280 (15.8) | |
| 1 | 81,497 (23.6) | 54,476 (25.6) | 8,236 (21.6) | 7,717 (22.1) | 11,068 (18.8) | |
| 2 | 64,174 (18.6) | 37,964 (17.8) | 7,255 (19.0) | 6,858 (19.6) | 12,097 (20.6) | |
| ≥3 | 116,556 (33.8) | 62,174 (29.2) | 15,041 (39.5) | 13,035 (37.3) | 26,306 (44.8) | |
| Cancer type‡ | ||||||
| Head/neck | 4,136 (1.2) | 2,498 (1.2) | 423 (1.1) | 542 (1.6) | 673 (1.1) | |
| Upper GI | 44,225 (12.8) | 26,931 (12.7) | 3,244 (8.5) | 7,446 (21.3) | 6,604 (11.2) | |
| Lower GI | 42,888 (12.4) | 23,408 (11.0) | 5,194 (13.6) | 5,103 (14.6) | 9,183 (15.6) | |
| HBP/other GI | 22,549 (6.5) | 12,544 (5.9) | 2,758 (7.2) | 2,681 (7.7) | 4,566 (7.8) | |
| Lung | 15,091 (4.4) | 8,527 (4.0) | 1,985 (5.2) | 1,604 (4.6) | 2,975 (5.1) | |
| Other resp./intrathoracic | 3,398 (1.0) | 1,952 (0.9) | 416 (1.1) | 379 (1.1) | 651 (1.1) | |
| Bone/cartilage | 956 (0.3) | 595 (0.3) | 115 (0.3) | 86 (0.2) | 160 (0.3) | |
| Skin | 9,337 (2.7) | 5,144 (2.4) | 1,215 (3.2) | 953 (2.7) | 2,025 (3.4) | |
| Mesothelium/soft tissue | 2,517 (0.7) | 1,546 (0.7) | 301 (0.8) | 229 (0.7) | 441 (0.8) | |
| Breast | 42,436 (12.3) | 30,382 (14.3) | 4,257 (11.2) | 2,769 (7.9) | 5,028 (8.6) | |
| Female genital organs | 17,230 (5.0) | 11,695 (5.5) | 1,934 (5.1) | 1,196 (3.4) | 2,405 (4.1) | |
| Male genital organs | 25,225 (7.3) | 13,290 (6.2) | 3,456 (9.1) | 2,848 (8.1) | 5,631 (9.6) | |
| Urinary tract | 17,643 (5.1) | 8,888 (4.2) | 2,411 (6.3) | 1,909 (5.5) | 4,435 (7.5) | |
| Central nervous system | 3,264 (0.9) | 2,001 (0.9) | 410 (1.1) | 305 (0.9) | 548 (0.9) | |
| Endocrine | 91,185 (26.5) | 61,771 (29.0) | 9,641 (25.3) | 6,752 (19.3) | 13,021 (22.2) | |
| Hodgkin lymphoma | 323 (0.1) | 185 (0.1) | 53 (0.1) | 23 (0.1) | 62 (0.1) | |
| Non-Hodgkin lymphoma | 5,212 (1.5) | 3,172 (1.5) | 620 (1.6) | 494 (1.4) | 926 (1.6) | |
| Leukemia | 2,186 (0.6) | 1,214 (0.6) | 291 (0.8) | 272 (0.8) | 409 (0.7) | |
| Other hematologic | 3,354 (1.0) | 2,046 (1.0) | 388 (1.0) | 322 (0.9) | 598 (1.0) | |
| Unspecified | 15,653 (4.5) | 9,943 (4.7) | 1,753 (4.6) | 1,505 (4.3) | 2,452 (4.2) | |
| Chemotherapy | ||||||
| Alkylating agents | 29,992 (8.7) | 20,325 (9.5) | 3,397 (8.9) | 2,275 (6.5) | 3,995 (6.8) | |
| Antimetabolites | 37,809 (11.0) | 22,573 (10.6) | 4,070 (10.7) | 4,873 (13.9) | 6,293 (10.7) | |
| Antitumor antibiotics | 33,021 (9.6) | 21,586 (10.1) | 3,883 (10.2) | 2,782 (8.0) | 4,770 (8.1) | |
| Biologic agents | 17,675 (5.1) | 10,476 (4.9) | 2,331 (6.1) | 1,663 (4.8) | 3,205 (5.5) | |
| Hormonal agents | 65,890 (19.1) | 44,311 (20.8) | 7,353 (19.3) | 5,064 (14.5) | 9,162 (15.6) | |
| Molecular targeted agents | 5,237 (1.5) | 2,962 (1.4) | 662 (1.7) | 651 (1.9) | 962 (1.6) | |
| Microtubule inhibitors | 24,395 (7.1) | 15,774 (7.4) | 3,011 (7.9) | 2,028 (5.8) | 3,582 (6.1) | |
| Platinum-based agents | 26,265 (7.6) | 15,462 (7.3) | 3,058 (8.0) | 3,124 (8.9) | 4,621 (7.9) | |
| Topoisomerase inhibitors | 4,391 (1.3) | 2,513 (1.2) | 595 (1.6) | 482 (1.4) | 801 (1.4) | |
| Other chemotherapeutics | 1,370 (0.4) | 781 (0.4) | 194 (0.5) | 124 (0.4) | 271 (0.5) | |
| External beam radiotherapy | 42,634 (12.4) | 28,182 (13.2) | 4,632 (12.2) | 3,607 (10.3) | 6,213 (10.6) | |
| Distant metastasis | 6,630 (1.9) | 3,922 (1.8) | 842 (2.2) | 711 (2.0) | 1,155 (2.0) | |
| Year of cancer diagnosis | 2014 (2013–2016) | 2014 (2013–2016) | 2014 (2013–2016) | 2014 (2013–2016) | 2014 (2013–2016) | |
Values as median (interquartile range) or number (%).
MetS, metabolic syndrome; Q, quartile; MET, metabolic equivalent of task; BP, blood pressure; CCI, Charlson Comorbidity Index; GI, gastrointestinal.
*Categorized based on quartiles among the entire Korean population.
†Excluding diabetes and cancer.
‡The sum exceeds 100% due to patients with multiple cancer diagnoses.
2. Primary analyses
During a median follow-up of 5.0 years, 7,529 incident CVD events were recorded. The cumulative incidence of CVD was highest among participants with persistent metabolic syndrome and lowest among participants who were consistently free of metabolic syndrome (Fig. 1). Compared to participants consistently free of metabolic syndrome, the multivariable-adjusted HRs (95% CIs) for CVD events were 1.21 (1.13–1.30) among participants who newly developed metabolic syndrome, 1.19 (1.10–1.27) among those who recovered from metabolic syndrome, and 1.37 (1.30–1.45) among those with persistent metabolic syndrome (Fig. 2 and Supplementary Table 2). Participants who recovered from metabolic syndrome exhibited a significantly lower risk of CVD compared to those with persistent metabolic syndrome (HR, 0.86; 95% CI, 0.80–0.93) (Supplementary Table 2). Similar trends were observed for secondary outcomes, including MI, stroke, and cardiovascular death (Supplementary Table 3).
Fig. 1. Cumulative incidence of cardiovascular disease according to changes in MetS after cancer diagnosis.
MetS, metabolic syndrome.
Fig. 2. Risk of cardiovascular disease according to changes in MetS after cancer diagnosis.
HRs were adjusted for age, sex, household income, residential area, tobacco smoking, alcohol consumption frequency, physical activity frequency, Charlson Comorbidity Index, cancer type, presence of distant metastasis, year of cancer diagnosis, and chemotherapy and external beam radiotherapy use.
MetS, metabolic syndrome; HR, hazard ratio; CI, confidence interval.
*Incidence rate per 1,000 person-years.
3. Secondary analyses
A consistent, gradual increase in CVD risk was observed with increasing numbers of metabolic syndrome risk factors, both before and after cancer diagnosis. Specifically, multivariable-adjusted HRs (95% CIs) for CVD were 1.06 (1.04–1.09) per +1 metabolic syndrome risk factor count before cancer diagnosis, and 1.09 (1.07–1.11) per +1 metabolic syndrome risk factor count after cancer diagnosis (Fig. 3).
Fig. 3. Risk of cardiovascular disease according to changes in MetS risk factor count after cancer diagnosis.
HRs (95% confidence intervals) were reported. HRs were adjusted for age, sex, household income, residential area, tobacco smoking, alcohol consumption frequency, physical activity frequency, Charlson Comorbidity Index, cancer type, presence of distant metastasis, year of cancer diagnosis, and chemotherapy and external beam radiotherapy use. For groups with fewer than 1% of the study population, data were combined with the adjacent group.
HR, hazard ratio; MetS, metabolic syndrome.
4. Cancer-specific analyses
Participants who newly developed metabolic syndrome after cancer diagnosis had higher CVD risk compared to those consistently free of metabolic syndrome among survivors of upper GI (HR, 1.24; 95% CI, 1.02–1.50), lower GI (HR, 1.52; 95% CI, 1.30–1.79), and endocrine cancers (HR, 1.30; 95% CI, 1.05–1.61) (Supplementary Table 4). Additionally, participants who recovered from metabolic syndrome had lower CVD risk compared to those with persistent metabolic syndrome among survivors of upper GI (HR, 0.80; 95% CI, 0.67–0.95), lower GI (HR, 0.82; 95% CI, 0.68–0.98), and lung cancers (HR, 0.71; 95% CI, 0.52–0.97).
5. Subgroup and sensitivity analyses
Subgroup analyses demonstrated consistent findings across various subgroups defined by age, sex, BMI, CCI, and year of cancer diagnosis (Supplementary Table 5). Sensitivity analyses further confirmed the robustness of results, with findings remaining consistent after excluding the first 2 years of follow-up, and upon repeating the primary analyses in a cohort of 5-year cancer survivors (Supplementary Figs. 3 and 4).
DISCUSSION
In this Korean nationwide study of cancer survivors, changes in metabolic syndrome status after cancer diagnosis were significantly associated with subsequent CVD risk. Individuals who newly developed metabolic syndrome after cancer diagnosis had a 21% higher risk of CVD compared to those consistently free of metabolic syndrome. Conversely, participants who recovered from metabolic syndrome exhibited a 14% lower CVD risk compared to those with persistent metabolic syndrome. Additionally, our secondary analyses showed that a higher count of metabolic syndrome risk factors, both before and after cancer diagnosis, was progressively associated with an increased risk of CVD. These associations were consistent across subgroup analyses and remained robust in sensitivity analyses.
CVD among cancer survivors is increasingly recognized as an important public health concern. However, current cardio-oncology guidelines primarily emphasize treatment-related cardiotoxicity, such as cardiotoxic chemotherapy agents and chest radiotherapy, with limited attention given to comprehensive preventive strategies for CVD among cancer survivors as a whole.30 Specifically, it remains unclear whether established preventive measures for CVD in the general population can also be effectively applied to cancer survivors. Clarifying this point is crucial for systematically incorporating such strategies into cardio-oncology guidelines. Building upon prior research that demonstrated associations between changes in metabolic syndrome status and CVD risk in the general population,31,32 our study extends these observations to cancer survivors. Thus, our findings emphasize the importance of managing metabolic health as a critical component of CVD prevention among cancer survivors.
Our results are consistent with previous studies and further expand upon their implications. Earlier studies found that cancer survivors with metabolic syndrome had a higher risk of CVD than those without metabolic syndrome. However, these studies typically assessed metabolic syndrome status at a single time point post-cancer diagnosis, without capturing temporal changes.33 Additionally, prior research showed that changes in individual cardiovascular risk factors—including smoking,34 physical activity,23 and BMI11—after cancer diagnosis influenced CVD risk among survivors. While these studies addressed isolated risk factors, they did not evaluate the combined impact of multiple risk factors on CVD risk. By examining associations between changes in overall metabolic syndrome status after cancer diagnosis and subsequent CVD risk among cancer survivors, our study addresses these gaps, offering a more comprehensive understanding of metabolic health’s impact on CVD in cancer survivors.
Furthermore, changes in the count of metabolic syndrome risk factors after cancer diagnosis were significantly linked to subsequent CVD risk. Previous studies have indicated that risk factor count and continuous severity score of metabolic syndrome are related to increased CVD risk, underscoring the importance of assessing metabolic syndrome beyond a simple binary classification.35,36 Additionally, earlier research has demonstrated cumulative effects of metabolic syndrome risk factor counts on CVD outcomes.36 Extending these findings, our study highlights the importance of closely monitoring metabolic syndrome severity and its longitudinal trajectory among cancer survivors. Regular health screening programs could be critical for tracking these trajectories, thus enabling timely interventions and mitigating long-term cardiovascular risks in this population.
Our findings suggest that interpreting the impact of metabolic syndrome changes on CVD risk among cancer survivors is complex, given that cancer type and treatment intensity might differentially influence metabolic risk factors. For example, recovery from metabolic syndrome might not always represent true metabolic improvement, as cancer-related weight loss and cachexia, which are factors independently associated with increased CVD risk,11,37 could lead to a misclassification of individuals as "recovered." This issue is particularly relevant for upper GI, lower GI, and lung cancers, where weight loss is more common.38 Despite this potential bias, our results still demonstrated a significantly reduced CVD risk among participants classified as metabolically recovered, suggesting that the actual protective effect may be even more pronounced. Similarly, the absence of significant findings in other cancer types may reflect an underestimation of true effects due to such misclassification. Therefore, further research is necessary to better delineate how cancer type and treatment intensity influence metabolic syndrome recovery and its implications for CVD risk.
This study has several strengths. First, to our knowledge, this is the first study evaluating associations between changes in metabolic syndrome status after cancer diagnosis and subsequent CVD risk in cancer survivors. Second, utilizing a nationwide claims database from a single-payer health insurance system allowed us to identify nearly all cancer survivors in Korea comprehensively. Third, the large sample size facilitated detailed subgroup analyses, enhancing the robustness and generalizability of the findings within this population.
Nevertheless, several limitations should be acknowledged. First, the observational nature of the study restricts causal inference. Although extensive covariate adjustment was performed, residual confounding cannot be entirely excluded. Second, detailed cancer-related clinical information, such as cancer stage and pathological subtype, was unavailable in our database. However, including data on distant metastasis at initial diagnosis, chemotherapy, and external beam radiotherapy in our models likely partially captures cancer severity. Third, the generalizability of findings might be limited by the exclusion of individuals who did not participate in health examinations or who had missing data on health examination variables. Nevertheless, baseline characteristics of participants included in the analysis were generally similar to those excluded due to non-participation or missing data in health examinations (Supplementary Table 6). Finally, as the data were derived exclusively from the Korean population, caution is necessary when generalizing the findings to other ethnicities.
In conclusion, our study demonstrated that changes in metabolic syndrome status and the number of metabolic syndrome risk factors following cancer diagnosis are significantly associated with CVD risk among cancer survivors. Regular monitoring and proactive management of metabolic syndrome could help mitigate the burden of subsequent CVD in this growing population.
ACKNOWLEDGEMENTS
This study used the National Health Insurance Service database (NHIS-2024-10-1-020).
Footnotes
Funding: This study was supported by a research grant from the Korean Society of Lipid and Atherosclerosis (KSOLA2023-03-002). The funder had no role in the design, collection, analysis, or interpretation of data; in the writing of the manuscript; or in the decision to submit the manuscript for publication.
Conflict of Interest: The authors have no conflicts of interest to declare.
Data Availability Statement: The datasets analyzed during the current study are not publicly available due to the sensitive nature of the database. Requests from qualified researchers to access the data may be sent to the National Health Insurance Service at https://nhiss.nhis.or.kr.
- Conceptualization: Lee HH, Lee H.
- Data curation: Lee J, Lee HH, Kim EJ, Kim HC, Lee H.
- Formal analysis: Lee J.
- Writing – original draft: Lee J.
- Writing – review & editing: Lee HH, Kim EJ, Kim HC, Lee H.
SUPPLEMENTARY MATERIALS
Classification of cancer types and corresponding ICD-10 codes
Crude, age-/sex-adjusted, and multivariable-adjusted risk of cardiovascular disease according to changes in MetS after cancer diagnosis
Risk of secondary outcomes according to changes in MetS after cancer diagnosis
Cancer-specific risk of cardiovascular disease according to changes in MetS after cancer diagnosis
Risk of cardiovascular disease according to changes in MetS after cancer diagnosis in subgroups
Baseline characteristics of the participants and individuals excluded due to non-participation or missing data in health examinations
Study flowchart.
Timeline for MetS assessment and outcome ascertainment. Change in MetS between the last exam within 3 years before the cancer diagnosis and the last exam within 3 years after the cancer diagnosis was used as the exposure. Follow-up started 3 years after cancer diagnosis.
Risk of cardiovascular disease according to changes in MetS after cancer diagnosis after excluding the first 2 years of follow-up.
Risk of cardiovascular disease according to changes in MetS after cancer diagnosis in 5-year cancer survivors.
References
- 1.International Agency for Research on Cancer. Cancer today: prevalence, both sexes, in 2022, all cancers [Internet] International Agency for Research on Cancer; 2024. [cited 2024 Dec 22]. Available from: https://gco.iarc.fr/today/en/dataviz/tables-prevalence?mode=population&include_nmsc=1&types=2. [Google Scholar]
- 2.Shapiro CL. Cancer survivorship. N Engl J Med. 2018;379:2438–2450. doi: 10.1056/NEJMra1712502. [DOI] [PubMed] [Google Scholar]
- 3.Santucci C, Carioli G, Bertuccio P, Malvezzi M, Pastorino U, Boffetta P, et al. Progress in cancer mortality, incidence, and survival: a global overview. Eur J Cancer Prev. 2020;29:367–381. doi: 10.1097/CEJ.0000000000000594. [DOI] [PubMed] [Google Scholar]
- 4.Zaorsky NG, Churilla TM, Egleston BL, Fisher SG, Ridge JA, Horwitz EM, et al. Causes of death among cancer patients. Ann Oncol. 2017;28:400–407. doi: 10.1093/annonc/mdw604. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Singh J, Blaes A. Shared modifiable risk factors between cancer and CVD. Am Coll Cardiol. Forthcoming 2017. [Google Scholar]
- 6.Yeh ET, Bickford CL. Cardiovascular complications of cancer therapy: incidence, pathogenesis, diagnosis, and management. J Am Coll Cardiol. 2009;53:2231–2247. doi: 10.1016/j.jacc.2009.02.050. [DOI] [PubMed] [Google Scholar]
- 7.Wilcox NS, Amit U, Reibel JB, Berlin E, Howell K, Ky B. Cardiovascular disease and cancer: shared risk factors and mechanisms. Nat Rev Cardiol. 2024;21:617–631. doi: 10.1038/s41569-024-01017-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Giza DE, Iliescu G, Hassan S, Marmagkiolis K, Iliescu C. Cancer as a risk factor for cardiovascular disease. Curr Oncol Rep. 2017;19:39. doi: 10.1007/s11912-017-0601-x. [DOI] [PubMed] [Google Scholar]
- 9.Vignjević Petrinović S, Milošević MS, Marković D, Momčilović S. Interplay between stress and cancer-a focus on inflammation. Front Physiol. 2023;14:1119095. doi: 10.3389/fphys.2023.1119095. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Schernthaner-Reiter MH, Wolf P, Vila G, Luger A. The interaction of insulin and pituitary hormone syndromes. Front Endocrinol (Lausanne) 2021;12:626427. doi: 10.3389/fendo.2021.626427. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Ueno K, Kaneko H, Suzuki Y, Okada A, Fujiu K, Jo T, et al. Change in body mass index and cardiovascular outcomes in patients with cancer. Mayo Clin Proc. 2024;99:891–901. doi: 10.1016/j.mayocp.2024.02.003. [DOI] [PubMed] [Google Scholar]
- 12.Gami AS, Witt BJ, Howard DE, Erwin PJ, Gami LA, Somers VK, et al. Metabolic syndrome and risk of incident cardiovascular events and death: a systematic review and meta-analysis of longitudinal studies. J Am Coll Cardiol. 2007;49:403–414. doi: 10.1016/j.jacc.2006.09.032. [DOI] [PubMed] [Google Scholar]
- 13.Cheol Seong S, Kim YY, Khang YH, Park JH, Kang HJ, Lee H, et al. Data resource profile: the national health information database of the National Health Insurance Service in South Korea. Int J Epidemiol. 2017;46:799–800. doi: 10.1093/ije/dyw253. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Shin DW, Cho J, Park JH, Cho B. National General Health Screening Program in Korea: history, current status, and future direction. Precision and Future Medicine. 2022;6:9–31. [Google Scholar]
- 15.Seong SC, Kim YY, Park SK, Khang YH, Kim HC, Park JH, et al. Cohort profile: the national health insurance service-national health screening cohort (NHIS-HEALS) in Korea. BMJ Open. 2017;7:e016640. doi: 10.1136/bmjopen-2017-016640. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Lee HH, Ahn J, Jiang C, Lee YG, Kim HC, Lee H. Post-diagnosis smoking habit change and incident dementia in cancer survivors. Alzheimers Dement. 2024;20:7013–7023. doi: 10.1002/alz.14180. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Grundy SM, Cleeman JI, Daniels SR, Donato KA, Eckel RH, Franklin BA, et al. Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute scientific statement. Circulation. 2005;112:2735–2752. doi: 10.1161/CIRCULATIONAHA.105.169404. [DOI] [PubMed] [Google Scholar]
- 18.Kim BY, Kang SM, Kang JH, Kang SY, Kim KK, Kim KB, et al. 2020 Korean Society for the Study of Obesity guidelines for the management of obesity in Korea. J Obes Metab Syndr. 2021;30:81–92. doi: 10.7570/jomes21022. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Ministry of Health and Welfare. National action plan for alcohol harm prevention. Ministry of Health and Welfare; 2018. [Google Scholar]
- 20.World Health Organization. International guide for monitoring alcohol consumption and related harm. World Health Organization; 2000. [Google Scholar]
- 21.Craig CL, Marshall AL, Sjöström M, Bauman AE, Booth ML, Ainsworth BE, et al. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc. 2003;35:1381–1395. doi: 10.1249/01.MSS.0000078924.61453.FB. [DOI] [PubMed] [Google Scholar]
- 22.Kim SR, Choi S, Kim K, Chang J, Kim SM, Cho Y, et al. Association of the combined effects of air pollution and changes in physical activity with cardiovascular disease in young adults. Eur Heart J. 2021;42:2487–2497. doi: 10.1093/eurheartj/ehab139. [DOI] [PubMed] [Google Scholar]
- 23.Lee HH, Lee H, Bhatt DL, Kang D, Youn JC, Shin DW, et al. Changes in physical activity and incident cardiovascular events in cancer survivors. Eur Heart J. 2023;44:4997–5000. doi: 10.1093/eurheartj/ehad677. [DOI] [PubMed] [Google Scholar]
- 24.Rock CL, Thomson CA, Sullivan KR, Howe CL, Kushi LH, Caan BJ, et al. American Cancer Society nutrition and physical activity guideline for cancer survivors. CA Cancer J Clin. 2022;72:230–262. doi: 10.3322/caac.21719. [DOI] [PubMed] [Google Scholar]
- 25.Quan H, Li B, Couris CM, Fushimi K, Graham P, Hider P, et al. Updating and validating the Charlson comorbidity index and score for risk adjustment in hospital discharge abstracts using data from 6 countries. Am J Epidemiol. 2011;173:676–682. doi: 10.1093/aje/kwq433. [DOI] [PubMed] [Google Scholar]
- 26.Park J, Kwon S, Choi EK, Choi Y, Lee E, Choe W, et al. Validation of diagnostic codes of major clinical outcomes in a National Health Insurance database. Int J Arrhythm. 2019;20:1–7. [Google Scholar]
- 27.Gray RJ. A class of K-sample tests for comparing the cumulative incidence of a competing risk. Ann Stat. 1988;16:1141–1154. [Google Scholar]
- 28.Austin PC, Lee DS, Fine JP. Introduction to the analysis of survival data in the presence of competing risks. Circulation. 2016;133:601–609. doi: 10.1161/CIRCULATIONAHA.115.017719. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Lau B, Cole SR, Gange SJ. Competing risk regression models for epidemiologic data. Am J Epidemiol. 2009;170:244–256. doi: 10.1093/aje/kwp107. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Lyon AR, López-Fernández T, Couch LS, Asteggiano R, Aznar MC, Bergler-Klein J, et al. 2022 ESC guidelines on cardio-oncology developed in collaboration with the European Hematology Association (EHA), the European Society for Therapeutic Radiology and Oncology (ESTRO) and the International Cardio-Oncology Society (IC-OS) Eur Heart J. 2022;43:4229–4361. doi: 10.1093/eurheartj/ehac244. [DOI] [PubMed] [Google Scholar]
- 31.Park S, Lee S, Kim Y, Lee Y, Kang MW, Han K, et al. Altered risk for cardiovascular events with changes in the metabolic syndrome status: a nationwide population-based study of approximately 10 million persons. Ann Intern Med. 2019;171:875–884. doi: 10.7326/M19-0563. [DOI] [PubMed] [Google Scholar]
- 32.He D, Zhang X, Chen S, Dai C, Wu Q, Zhou Y, et al. Dynamic changes of metabolic syndrome alter the risks of cardiovascular diseases and all-cause mortality: evidence from a prospective cohort study. Front Cardiovasc Med. 2021;8:706999. doi: 10.3389/fcvm.2021.706999. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Ueno K, Kaneko H, Suzuki Y, Okada A, Matsuoka S, Fujiu K, et al. Metabolic syndrome and cardiovascular disease in cancer survivors. J Cachexia Sarcopenia Muscle. 2024;15:1062–1071. doi: 10.1002/jcsm.13443. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Lee HH, Lee H, Bhatt DL, Lee GB, Han J, Shin DW, et al. Smoking habit change after cancer diagnosis: effect on cardiovascular risk. Eur Heart J. 2024;45:132–135. doi: 10.1093/eurheartj/ehad199. [DOI] [PubMed] [Google Scholar]
- 35.Jang YN, Lee JH, Moon JS, Kang DR, Park SY, Cho J, et al. Metabolic syndrome severity score for predicting cardiovascular events: a nationwide population-based study from Korea. Diabetes Metab J. 2021;45:569–577. doi: 10.4093/dmj.2020.0103. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Lee EY, Han K, Kim DH, Park YM, Kwon HS, Yoon KH, et al. Exposure-weighted scoring for metabolic syndrome and the risk of myocardial infarction and stroke: a nationwide population-based study. Cardiovasc Diabetol. 2020;19:153. doi: 10.1186/s12933-020-01129-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Lee SJ, Park YJ, Cartmell KB. Sarcopenia in cancer survivors is associated with increased cardiovascular disease risk. Support Care Cancer. 2018;26:2313–2321. doi: 10.1007/s00520-018-4083-7. [DOI] [PubMed] [Google Scholar]
- 38.Baracos VE, Martin L, Korc M, Guttridge DC, Fearon KCH. Cancer-associated cachexia. Nat Rev Dis Primers. 2018;4:17105. doi: 10.1038/nrdp.2017.105. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Classification of cancer types and corresponding ICD-10 codes
Crude, age-/sex-adjusted, and multivariable-adjusted risk of cardiovascular disease according to changes in MetS after cancer diagnosis
Risk of secondary outcomes according to changes in MetS after cancer diagnosis
Cancer-specific risk of cardiovascular disease according to changes in MetS after cancer diagnosis
Risk of cardiovascular disease according to changes in MetS after cancer diagnosis in subgroups
Baseline characteristics of the participants and individuals excluded due to non-participation or missing data in health examinations
Study flowchart.
Timeline for MetS assessment and outcome ascertainment. Change in MetS between the last exam within 3 years before the cancer diagnosis and the last exam within 3 years after the cancer diagnosis was used as the exposure. Follow-up started 3 years after cancer diagnosis.
Risk of cardiovascular disease according to changes in MetS after cancer diagnosis after excluding the first 2 years of follow-up.
Risk of cardiovascular disease according to changes in MetS after cancer diagnosis in 5-year cancer survivors.