Text box 1. Contributions to the literature

- Obesity has been suggested to be associated with COVID-19, but it was unclear whether obesity or metabolic abnormalities accompanied by obesity have a stronger association with COVID-19 outcomes.

- In this study, metabolic health was more significantly associated with COVID-19 mortality and severity than with obesity, particularly in adults aged < 70 years.

- Public health strategies that implement more intensive COVID-19 prevention and treatment for metabolically unhealthy adults, especially those under 70 years of age, are needed in the era of ongoing pandemics.

Background

In the era of the coronavirus disease 2019 (COVID-19) pandemic, obesity and metabolic health have gained interest because of their possible associations with COVID-19 infection or severity, as suggested in previous studies [1,2,3,4,5,6]. According to a meta-analysis including 220,000 subjects, obese subjects had a 1.5–fold higher risk of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) positivity, and COVID-19 patients with obesity had a 1.5–fold higher risk of hospitalization than non-obese patients [5]. Recent investigations have suggested that poor metabolic health might also increase the risk of severe COVID-19 [6]. Several studies have been conducted regarding the impact of obesity or metabolic health on COVID-19 following these results, and some have observed an additive effect of obesity and metabolic diseases [7]. However, few researchers have investigated whether obesity or metabolic health consequences associated with obesity more strongly affect COVID-19 prognosis.

Obesity is a risk factor for various metabolic diseases, including hypertension, type 2 diabetes mellitus, and hyperlipidemia [8]. Metabolic changes caused by abnormal fat accumulation and insulin resistance are thought to be critical links between obesity and cardiometabolic complications [9]. Considering that putative mechanisms of the obesity–COVID-19 relationship include cardiometabolic deterioration, the inflammatory role of adipocytes, and impaired vascular health [10, 11], it is possible that metabolic health is a crucial component of poor prognosis in obese COVID-19 patients.

Since not all obese individuals may have metabolic abnormalities, the concept of metabolic obesity phenotype, which classifies obesity into metabolically unhealthy obesity and healthy obesity, has been suggested [9]. Consensus on the criteria for metabolic health is still lacking and novel phenotypes have also been proposed [9, 12], but the definition based on metabolic syndrome is most commonly used [9, 13]. Although several researchers have observed that metabolic health is related to COVID-19 severity [6, 14,15,16], only a few studies have examined the effects of metabolic and obesity phenotypes on COVID-19 simultaneously [17, 18]. However, these studies were conducted early during the COVID-19 pandemic and included only a limited number of COVID-19 patients [17, 18]. Therefore, to clarify whether obesity or metabolic health has a stronger association with COVID-19 risk, we examined the relationship between metabolic obesity phenotypes and COVID-19 prevalence, mortality, severity, and cardiovascular complications using a large nationwide population-based dataset with an extended follow-up period. Additionally, a stratified analysis by age was performed because the infection rate and severity of COVID-19 vary with age.

Methods

Data source

This study used data from the Korean National Health Insurance Service (NHIS) combined with the COVID-19 database from the Korea Disease Control and Prevention Agency (KDCA). As the NHIS provides universal health coverage in Korea, nearly the entire population is included in the NHIS database [19]. The NHIS database provides information on the demographics, diagnostic records, healthcare utilization, and treatment histories of its beneficiaries. Furthermore, it includes data from a national general health screening, which is provided biennially to all NHIS beneficiaries aged over 40 years or who are employed [20]. These health screening data contain anthropometric measurements, laboratory tests, and lifestyle information. In addition, the COVID-19 database of KDCA provides information on the date of COVID-19 diagnosis and mortality of the entire population in Korea, which occurs between October 8, 2020, and December 31, 2021 (for diagnosis date) or March 31, 2022 (for mortality date).

Study population

From the NHIS database, subjects who had participated in a national general health screening at least once during the baseline period (2016–2019) and who were aged 20 years or older at the health screening date were initially included (n = 28,620,098). Next, subjects with missing data on metabolic obesity phenotypes, such as body mass index (BMI), systolic blood pressure (SBP), diastolic blood pressure (DBP), fasting plasma glucose (FPG), triglyceride (TG), high-density lipoprotein (HDL), and waist circumference, were excluded (n = 4,309,815). In the case of subjects who underwent health screening twice, the more recent health screening data were used for analysis. Finally, 24,310,283 participants were included in a study population.

The definition of metabolic obesity phenotypes

Obesity was defined as BMI ≥ 25 kg/m2, and normal weight was defined as BMI < 25 kg/m2, according to the Asia-Pacific criteria of the World Health Organization (WHO) [8, 21].

The definition of metabolic unhealthiness varies according to literatures and might refer to the presence of any metabolic abnormality in some studies, but we adopted the most commonly used criteria based on metabolic syndrome, in order to imply high risks of adverse health outcomes [13, 22]. Metabolically unhealthy was defined as having three or more of the following five components based on the harmonized criteria of metabolic syndrome presented by major organizations in 2009 [21, 23]: (1) FPG ≥ 100 mg/dL, (2) SBP ≥ 130 mmHg or DBP ≥ 85 mmHg, (3) TG ≥ 150 mg/dL, (4) HDL < 40 mg/dL in men or < 50 mg/dL in women; and (5) waist circumference ≥ 90 cm in men or ≥ 85 cm in women. In contrast, metabolically healthy individuals were defined as those with two or fewer of these components.

Based on these definitions, study participants were classified into four groups: (1) metabolically healthy and normal weight (MHNW), (2) metabolically unhealthy and normal weight (MUNW), (3) metabolically healthy and obese (MHO), and (4) metabolically unhealthy and obese (MUO).

Study outcome

The primary outcome was COVID-19 mortality and severity. The secondary outcomes were COVID-19 prevalence, hospitalization, length of hospital stay, and major adverse cardiovascular events (MACE). Mortality was defined as all-cause death in COVID-19 patients within one month of medical facility usage with COVID-19 diagnosis. The severity of COVID-19 was classified into three groups (ambulatory state, hospitalized mild disease, or hospitalized severe disease), except for those who died, based on the WHO’s COVID-19 severity scale [24]. Hospitalization was defined as admission after COVID-19 diagnosis for one or more days in all patients, including those who died. The length of hospital stay was calculated in days.

MACE was analyzed to determine the cardiovascular risk after COVID-19 infection by metabolic obesity phenotypes. MACE was defined as myocardial infarction (I21-I23), stroke (I60-I64), heart failure (I11.0, I13.0, I13.2, I50), unstable angina (I20.0), cardiac death (death within one month after admission under the diagnostic code starting with I), and total MACE (composite outcome) [25] based on the International Classification of Diseases-10th Revision (ICD-10) codes. For this analysis, a cohort of subjects without prior MACE (from 2016 until the COVID-19 diagnosis date) and diagnosed with COVID-19 (n = 3,638,727) was constructed. The study cohort was followed from the date of COVID-19 diagnosis to the date of the MACE event, death, or March 31, 2022, whichever came first.

Covariates

Baseline age, sex, socioeconomic status, region, Charlson Comorbidity Index (CCI), and comorbidities were extracted from the NHIS claims database. Age groups were categorized as 20–50, 50–70, and ≥ 70 years [3]. Socioeconomic status (SES) was trichotomized based on insurance premiums. The region of residence was categorized into Seoul metropolitan area and other areas (Daegu and Gyeongsangbuk Province and other areas). CCI was calculated using claims data and classified into three groups (0, 1–2, ≥ 3) [26]. Comorbidities included hypertension (I10, I15), diabetes mellitus (E10, E118, E119, E13, E149), and dyslipidemia (E78) and were confirmed using ICD–10 codes. Information about lifestyle (smoking and drinking) based on questionnaires, anthropometric measurements, and blood tests were extracted from the national health screening database.

Statistical analysis

Subjects with missing data on metabolic-obesity phenotypes were excluded from the analysis. Descriptive statistics were used to examine the baseline characteristics of each metabolic-obesity phenotype. The COVID-19 prevalence was calculated as the number of patients per 100,000. Differences in prevalence according to metabolic obesity phenotypes in the total study population and each age–and sex–subgroup were examined. Among the subjects diagnosed with COVID-19, the COVID-19 mortality, severity, hospitalization rate, and length of hospital stay were also compared with the metabolic obesity phenotypes (in the total population and each age group). For comparisons, analysis of variance (ANOVA) was used for continuous variables, and the chi-squared or Fisher’s exact test was used for categorical variables. In addition, factors affecting the prevalence and mortality rate of COVID-19 were evaluated using multivariable logistic regression analysis with adjustment for metabolic obesity phenotypes, age, sex, region, SES, CCI, smoking, and drinking.

Cox proportional hazards analysis was performed to evaluate the associations between metabolic phenotypes and incident MACE following COVID-19 diagnosis. The proportional hazard assumption was evaluated using the Schönfeld test. Model 1 was unadjusted, Model 2 was adjusted for age and sex, and Model 3 was additionally adjusted for region, SES, CCI, smoking, and drinking.

All statistical tests were two-sided; a P value < 0.05 was considered statistically significant. All analyses were conducted using the SAS Enterprise Guide version 8.2.

Results

Baseline characteristics

The baseline characteristics of the study population are presented in Table 1. Among the study population (n = 24,310,283), 13,765,872 (56.6%), 1, 469, 604 (6.0%), 5, 364, 884 (22.1%), and 3, 709, 923 (15.3%) had MHNW, MUNW, MHO, and MUO, respectively. Metabolically unhealthy subjects (MUNW & MUO) were older, smoked more, had higher CCI, and had more comorbidities and prior MACE history compared with metabolically healthy subjects (MHNW & MHO) (all p < 0.001).

Table 1 Baselinea characteristics of the study population in Korea according to metabolic obesity phenotypes
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The prevalence of COVID-19

Overall, 3,956,807 of 24,310,283 subjects (16.3%) were diagnosed with COVID-19 (between October 8, 2020, and December 31, 2021) (Supplementary Table S1). The prevalence rate was highest among MHO (16,843 per 100,000) and was similar in MHNW (16,495), followed by MUO (15,537), and MUNW (14,027) (p < 0.0001).

When sub-classified by age group, the COVID-19 prevalence rate was higher in metabolically healthy subjects (MHNW & MHO) than in unhealthy subjects (MUO & MUNW) aged 20–50 years (p < 0.0001). Meanwhile, in older age groups (50–70 and ≥ 70 years), obese subjects (MHO & MUO) showed a higher prevalence rate than normal-weight subjects (MHNW & MUNW) (p < 0.0001). Overall, no evident trend was observed in COVID-19 prevalence according to metabolic obesity phenotypes.

The mortality, severity and hospitalization rate of COVID-19

Table 2 presents the results of the analyses of COVID-19 mortality, severity, and hospitalization rate among COVID-19 patients (n = 3,956,807). The mortality, hospitalized severe disease, and total hospitalization rates were 0.27% (n = 10,807), 0.29% (n = 11,460), and 7.59% (n = 300,400), respectively. When comparing metabolic obesity phenotypes, metabolically unhealthy subjects showed higher mortality rates than metabolically healthy subjects, with the highest rate in MUNW (0.81%), followed by MUO (0.40%), MHNW (0.23%), and MHO (0.19%). The rates of severe hospitalized disease were also higher in metabolically unhealthy subjects than in healthy subjects (0.59% for MUNW, 0.55% for MUO, 0.19% for MHNW, and 0.31% for MHO) (Fig. 1). The total hospitalization rate also showed a similar trend to the severity rate (10.61% in MUNW, 9.04% in MUO, 6.99% in MHNW, and 7.49% in MHO). The length of hospital stay was also longer in metabolically unhealthy subjects (average 1.09 in MUNW, 0.95 in MUO, 0.70 in MHNW, and 0.78 in MHO, respectively; all in days) (all p < 0.0001).

Table 2 COVID-19 mortality, severity, hospitalization rate, and length of hospital stay according to metabolic obesity phenotype in Korea, October 8, 2020–March 31, 2022
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Fig. 1
figure 1

COVID-19 mortality and hospitalized severe disease rates according to metabolic obesity phenotype in Korea, October 8, 2020–March 31, 2022 COVID-19, coronavirus disease 2019; MHNW, metabolically healthy & normal weight; MUNW, metabolically unhealthy & normal weight; MHO, metabolically healthy & obese; MUO, metabolically unhealthy & obese

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Supplementary Table S2 shows the results of the analyses of COVID-19 mortality, severity, and hospitalization rates in each age group. Mortality was higher in metabolically unhealthy subjects (MUNW & MUO) than in healthy subjects (MHNW & MHO) among subjects aged 20–50 and 50–70 years. Among subjects aged ≥ 70 years, normal-weight subjects (MUNW & MHNW) showed higher mortality than obese subjects (MUO & MHO). The rates of hospitalized severe disease and hospitalization were both consistently higher in metabolically unhealthy subjects (MUNW & MUO) than in healthy subjects (MHNW & MHO) among all age groups (all p < 0.0001).

Factors associated with the COVID-19 prevalence and mortality

In the multivariable logistic regression analysis, metabolic obesity phenotypes, sex, age, region, SES, CCI, smoking, and drinking were all associated with COVID-19 prevalence and mortality (Table 3). When comparing different metabolic obesity phenotypes, the adjusted odds ratio (OR) of mortality risk was highest in MUNW (OR 1.44, 95% confidence interval (CI) 1.36–1.52), followed by MUO (OR 1.06, 95% CI 1.01–1.11), and lowest in MHO (OR 0.77, 95% CI 0.73–0.82), using MHNW as the reference group.

Table 3 Multivariable logistic regression analyses of factors associated with COVID-19 prevalence and mortality in Korea, October 8, 2020–December 31, 2021 (for prevalence) or March 31, 2022 (for mortality)
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Incidence of MACE after the COVID-19 infection

Over an average of 1.1 months during the follow-up period, 17, 990 of 3,638,727 patients (0.49%) developed incident MACE. The incidence of total MACE was highest in the MUNW group, with a slight, insignificant increase in the MUO group (hazard ratio (HR), 95% CI: 1.31, 1.16–1.47 in MUNW, 1.09, 0.99–1.19 in MUO, and 1.03, 0.94–1.12 in MHO after total adjustment, when using MHNW as a reference) (Supplementary Table S3). A similar pattern was observed regarding the incidence of myocardial infarction, stroke, and cardiac causes of death. As for unstable angina and heart failure, the MUO group showed the highest incidence in the fully adjusted model (HR, 95% CI: 1.40, 1.18–1.65, and 1.35, 1.28–1.42, respectively).

Discussion

Our large population-based dataset of approximately four million COVID-19 cases has shown that metabolic health is more strongly associated with mortality and severity of COVID-19 than obesity. While the prevalence of COVID-19 was higher in metabolically healthy subjects than in metabolically unhealthy subjects, COVID-19 mortality, the rate of hospitalized severe disease, and total hospitalization rate were all higher in metabolically unhealthy subjects, regardless of obesity, particularly in subjects aged < 70 years.

In our study, 5,364,888 (59.1%) of 9,070,807 obese subjects were metabolically healthy, showing that obesity does not necessarily induce significant metabolic abnormalities in all individuals, which is consistent with previous studies [9, 27]. Obesity is defined as an excessive accumulation of fat that causes chronic health risks, and it is usually diagnosed using BMI because it correlates with the body fat percentage observed in population studies [21, 28]. However, considerable evidence suggests that excess visceral fat and ectopic fat, rather than subcutaneous or total fat, are significant determinants of obesity-associated metabolic changes [29, 30]. Obese individuals with an accumulation of mostly subcutaneous fat might be at a similar cardiometabolic risk as normal-weight individuals. Therefore, metabolic health, as defined by markers of visceral or ectopic fat, such as waist circumference and TG level [29], could be a better predictor of health risks than obesity as defined by BMI. Novel findings from several small studies showing that high visceral adiposity independently associates with COVID-19 severity seem to support this hypothesis [6].

Indeed, metabolically unhealthy patients (MUO & MUNW) showed a worse outcome of COVID-19 infection, including mortality, severity, and hospitalization rate, than metabolically healthy patients (MHNW & MHO). In multivariable logistic regression, metabolic obesity phenotypes were significantly associated with COVID-19 mortality after adjustment for other relevant factors. Additionally, the ORs were higher in metabolically unhealthy subjects than in healthy subjects. Similar results were observed in a Korean retrospective cohort with a much smaller sample size than ours, including 4,069 COVID-19 cases [17]; the rate of composite critical COVID-19 outcomes increased in metabolically unhealthy individuals, with incidence rates (per 100 person-months) of 3.37 in MUO, 3.37 in MUNW, 1.64 in MHO, and 0.90 in MHNW. Furthermore, the hazard ratios of mortality were also higher in the metabolically unhealthy groups in both the unadjusted and adjusted models (2.22 in MUO, 1.90, MUNW, and 1.44 in MHO, compared with MHNW, in the adjusted model). In a study using the UK Biobank database with 3,502 hospitalized COVID-19 cases [18], the association between obesity and severe COVID-19 was attenuated but still significant after adjustment for metabolic status. However, as the definition of metabolically unhealthy status used in this study (having at least one metabolic disorder) was different from that of our study, the impact of metabolic unhealthiness might have been underestimated. Therefore, our analysis, based on a far larger sample size and a definition of metabolic unhealthiness by the presence of metabolic syndrome, has confirmed the stronger relationship between COVID-19 outcomes and metabolic health rather than obesity.

Another aspect to consider when interpreting these results is a difference in ethnic or geographical background. The prevalence of obesity in East Asia would be much lower than that in the West if Western criteria (BMI ≥ 30 kg/m2) is applied, but Asian populations are known to have higher visceral adiposity and diabetes risk than other populations with the same BMI range [21, 31,32,33]. A large number of diabetic patients in East Asia, including Korea, are observed to be in normal weight status, and it is possible that Asians with low BMI might have reduced pancreatic β-cell mass and subsequent lower insulin secretion function [31, 34]. The degree of discrepancy between obesity defined by BMI and metabolic health seems to vary across populations, and therefore associations between metabolic obesity phenotypes and COVID-19 might also differ. Future research is warranted regarding the association between metabolic health based on more rigorous definition with COVID-19 outcomes using population-based datasets from various regions.

Several explanations exist regarding the link between metabolic abnormalities and severe COVID-19 outcomes. For instance, hyperinsulinemia, insulin resistance, and subsequent hypertrophy of adipocytes can result in excessive cytokine production, chronic inflammation, and endothelial dysfunction [11, 35]. Excess visceral fat might induce the overproduction of angiotensin II and derangement of the renin-angiotensin-aldosterone system after SARS-CoV-2 infection, thereby exacerbating inflammatory responses [35, 36]. Hepatic steatosis and fibrosis can contribute to severe COVID-19 [37]; Dysregulated or increased release of hepatokines like transforming growth factor-β is suggested to associate with systematic inflammation and decreased natural killer cell function [37, 38]. Dyslipidemia may also contribute to severe outcomes because cholesterol can promote viral replication [39]. In addition, as cardiac injury and thrombosis are common in COVID-19, underlying metabolic diseases might increase the risks of cardiovascular events in combination with SARS-CoV-2 infection, resulting in poor clinical outcomes [11, 40]. Similarly, the incidence of total MACE was increased in metabolically unhealthy subjects (MUNW & MUO) in our analysis, while the difference was insignificant in the MUO group after full adjustment. Close monitoring and management of cardiovascular events might be helpful in the improvement of the COVID-19 prognosis of metabolically unhealthy patients.

The relationship between metabolic obesity phenotypes and COVID-19 mortality differed according to age in stratified analyses. While metabolic health showed a stronger association than obesity among subjects aged < 70 years, normal-weight subjects (MUNW & MHNW) showed higher mortality than obese subjects (MUHO & MHO) among subjects aged ≥ 70 years. The exact reason is unclear, but it is possible that competing mortality risks from other conditions related to lower BMI in the elderly, such as nutritional deficiency or unintentional weight loss, outweigh the risks from metabolic health [41, 42]. Similarly, in a study of BMI and COVID-19 outcomes in hospitalized patients [3], the relationship between BMI and death or mechanical ventilation was strongest in subjects aged ≤ 50 years and weakest in those aged > 70 years, and severe obesity was associated with death only in adults aged ≤ 50 years.

In our analysis, metabolically healthy subjects (MHNW & MHO) had a higher prevalence of COVID-19 than unhealthy subjects (MUO & MUNW). As metabolically healthy subjects had lower CCI and fewer comorbidities than metabolically unhealthy subjects at baseline, it is possible that these subjects were in a better general health condition and therefore were more socially active, resulting in a higher COVID-19 infection rate. Along with metabolic obesity phenotypes, sex, age, SES, region, CCI, smoking, and drinking were all associated with COVID-19 prevalence, and all these factors have also been associated with COVID-19 mortality, which is consistent with previous reports [43, 44]. The relationship between metabolic obesity phenotypes and COVID-19 mortality remained significant even after adjusting for these factors.

There were several limitations to our study. First, we could not obtain information on several inflammatory markers that may be associated with metabolic abnormalities and COVID-19 outcomes, such as cytokine levels on admission, because we used administrative data [45]. Vaccination status was not also considered, while the vaccination rate against COVID-19 was reported to be almost 90% in Korea [46]. In addition, information on metabolic health was collected from the baseline health examination results, so it might not precisely reflect the metabolic status of patients at the time of COVID-19 infection. However, most previous studies also had similar limitations [17, 18]. Also, our result might not be generalizable to other populations from different ethnic or geographic backgrounds, but we believe that our investigation provides population-level evidence on Asian ethnicity, for whom existing data is limited. The most specific strength of our study is its large population-based sample size and long follow-up period. Our large-scale data enabled us to investigate differences in associations according to age groups and to examine diverse health outcomes, including MACE. In addition, we had detailed information on anthropometric measurements and laboratory tests; therefore, we determined metabolic health following a standardized definition. Finally, our dataset also included information on various demographic and lifestyle factors not investigated in previous research.

Conclusions

In conclusion, metabolic health was more significantly associated with COVID-19 mortality and severity than with obesity, particularly in adults aged < 70 years. Therefore, more intensive prevention and treatment strategies should be considered in metabolically unhealthy adults regarding COVID-19 in the era of ongoing pandemics.