Abstract
Background
Obesity and poor physical fitness in youth are established risk factors for future cardiovascular disease and cancer. However, their potential impact on the risk of severe bacterial infections later in life remains unclear.
Methods
This register‐based cohort study followed almost 1 million Swedish conscripts (mean age 18.3 years) over a period of three decades. Measured body mass index (BMI) and cardiorespiratory fitness (CRF) at conscription to military service served as exposure variables, whereas outcomes included morbidity and mortality attributed to bacterial pneumonia, sepsis, and infective endocarditis.
Results
High BMI and low CRF in late adolescence were strongly associated with an increased risk of bacterial pneumonia, sepsis, and infective endocarditis in adulthood. The highest risk was seen among the obese for sepsis (hazard ratio [HR] 3.1 (2.7–3.5)), with elevated hazards observed already at high‐normal BMI levels (22.5–25 kg/m2), compared with BMI 18.5–19.9. The risk of dying due to bacterial infections increased gradually with higher BMI. High, compared with low CRF, was associated with lower risk of contracting bacterial infections (0.78, 0.76–0.80), and dying from them (0.58, 0.51–0.65).
Conclusion
High BMI and low CRF in adolescence are associated with an increased risk of contracting and dying of severe bacterial infections later in life. Hence, addressing these preventable risk factors in youths may serve as an effective measure to improve their long‐term health.
Keywords: bacterial pneumonia, body mass index, cardiorespiratory fitness, endocarditis, obesity, sepsis
Introduction
Bacterial infections are common causes of morbidity and mortality, with a disproportionate amount occurring in low‐ and middle‐income countries [1]. Although mostly treatable, antibiotic resistance poses a significant threat to global health [2], preventive measures are accordingly of utmost importance. Obesity and poor physical fitness are modifiable factors with potential to alter the risk for infectious diseases. The World Health Organization (WHO) is estimating that one in eight people in the world are obese [3], and forecasting that a third of the world's children and adolescents will have overweight or obesity by 2050 [4]. At the same time, >80% of adolescents worldwide fail to meet the recommendations of 60 min of daily physical activity of moderate‐to‐vigorous intensity recommended by WHO [5].
Obesity is defined as an excessive fat accumulation (body mass index [BMI] ≥ 30 kg/m2) that may impair health in various ways, including increased risk of cancer and cardiovascular disease [6, 7]. Obesity also predisposes for a vast range of infectious diseases in adults, such as surgical‐site infections, skin infections, urinary tract infections, gastrointestinal tract infections, respiratory tract infections, and sepsis [8, 9, 10, 11, 12, 13]. Moreover, obese individuals are known to respond poorly to vaccines [14]. Still, overweight and moderate obesity are paradoxically associated with an improved morbidity and mortality attributed to bacterial infections in different clinical settings [15, 16]. This could, however, be related to reverse causality due to baseline comorbidities, particularly for those with low BMI. In young healthy individuals, reverse causality is, however, less of an issue. A study of young Israeli conscripts with few comorbidities demonstrated that overweight and obesity in adolescence, starting already at levels defined as high‐normal BMI, were strongly associated with risk for death attributed to infectious diseases in adulthood [17], but with no data on nonfatal outcomes. Moreover, our research group has shown an increased risk for severe COVID‐19 with higher BMI as a teenager [18]. There is extensive evidence that BMI tracks from adolescence to adulthood [19, 20, 21], and together, these findings suggest that long exposure to obesity may impair the resilience to infectious diseases.
Obesity and physical inactivity are often interrelated. Two large observational studies have reported an association between exercise and reduced risk of both contracting and dying of infection [22, 23]. Moreover, high cardiorespiratory fitness (CRF) has been associated with better resilience to severe COVID‐19 [24], and this has also been shown for high CRF in adolescence [25]. The association between CRF early in life and future risk of severe bacterial infections has not been studied.
Given that obesity and physical inactivity are steadily growing problems worldwide, not least among young people, and that data from large cohorts that also include nonfatal outcomes are lacking, we investigated whether BMI and CRF in late adolescence are associated with severe bacterial infections in adult life. To achieve this, we studied a cohort of almost one million young Swedish men enlisting for military service, followed for three decades.
Methods
Study design
This is a register‐based nationwide cohort study based on data from the Swedish Military Service Conscription Register (SMCR). Through the Swedish personal identification number, the SMCR was linked to the Swedish national patient register (NPR) and the Swedish cause of death register. Information on parental education was collected through linkage to the Longitudinal integrated database for health insurance and labor market studies (LISA) kept by Statistics Sweden.
Study population
The study cohort, derived from SMCR, contains conscription data on nearly two million young individuals who enlisted for military service in Sweden between 1968 and 2005 (born 1951–1987). During this period, conscription was mandatory by law for all male inhabitants, except for imprisoned individuals or those with severe chronic disorders or functional disabilities. The register has a population coverage of approximately 90% for men during this period, and encompasses data on for example anthropometry, CRF, and muscle strength [26].
In the present study, the following exclusion criteria were applied: age <17 or >25 years (n = 55,146); missing information regarding BMI or CRF (n = 595,160), muscle strength (n = 73,044), and/or parental education (n = 143,276); severe bacterial infection recorded within 4 years after military conscription (n = 16,375) (to minimize risk of reverse causation [27, 28]); and finally, women (n = 10,495) due to low numbers and self‐selection, as it was not mandatory for women to enlist at that time. The final study sample consisted of 995,091 young men with a mean age of 18.3 years (Fig. 1).
Fig. 1.
Flowchart depicting original study population, exclusion criteria with exclusion numbers, and final study cohort. BMI, body mass index; CRF, cardiorespiratory fitness.
Ethical approval
The Ethics Committee of the University of Gothenburg and Confidentiality Clearance at Statistics Sweden approved the study (EPN reference numbers: EPN Dnr 462‐14; T174‐15, T653‐17, T2019‐05875, T2020‐02420, and T2021‐03310). Because only pseudonymized data were analyzed, informed consent was not possible, nor required for this study.
Exposure variables
The main exposure variables for this study were BMI and CRF at conscription. BMI, calculated as weight (kg) divided by height (m2), was separated into six categories (BMI < 18.5, 18.5–19.9, 20–22.4, 22.5–24.9, 25–29.9, and ≥30) with BMI 18.5–19.9 as the reference. The reference was selected on the basis of previous studies using data from SMCR, where the lowest risks of several conditions were seen within this BMI interval [18, 29, 30]. CRF was evaluated by a maximal bicycle ergometry test, and nine categories were defined on the basis of the final work rate in relation to body weight [31]. For this study, the scores were collapsed into three categories corresponding to low (1–5), moderate (6–7), and high (8–9) CRF, with low as the reference. The asymmetrical allocation of CRF scores was prompted by the fact that the fitness scores in the lowest categories were omitted from the register after 1999, skewing the distribution toward higher scores.
Other covariates
Because of potential confounding of the associations between the exposures (BMI/CRF) and outcomes (studied bacterial infections), we adjusted for muscle strength, maximal parental education, and asthma (diagnosed in the year of conscription or before) in the analyses. Isometric muscle strength was measured as a weighted sum of knee extension (weight 1.3), elbow flexion (weight 0.8), and handgrip (weight 1.7). This was used to calculate a standardized score from 1 to 9, which we collapsed into three categories: low (1–3), moderate (4–6), and high (7–9) muscle strength. We utilized maximal parental education (i.e., the parent with the highest education) as a proxy for socioeconomic status during childhood [32], as most conscripts were 18–19‐year old at the time of their conscription examinations and had not completed their own education. Using data from the LISA register, parental education was divided into three categories: low (compulsory school), intermediate (high school with <3 years at university), and high (≥3 years at university) education [33]. Asthma is associated with BMI [34] and fitness [35] and is also a risk factor for severe respiratory infections [36]. The following International Classification of Diseases (ICD)‐codes were used for asthma: 493 (ICD‐8 and 9), J45, and J46 (ICD‐10).
Outcomes
The outcomes were a diagnosis of or death due to bacterial pneumonia, sepsis or infective endocarditis identified from January 1, 1968, to December 31, 2019. Follow‐up ended in 2019 to avoid potential effects caused by the COVID‐19 pandemic. Data were retrieved from the cause of death register and the NPR, in which diagnostic codes according to the ICD are recorded in patients receiving health care within the publicly funded Swedish national health system. The NPR has been shown to have high validity [37]. The following ICD‐codes were used for the purpose of the present study: bacterial pneumonia: 481–486 (ICD‐8), 481–483, 485, and 486 (ICD‐9), J13–J16, and J18 (ICD‐10); sepsis: 038 (ICD‐8), 038 (ICD‐9), A40, and A41 (ICD‐10); and infective endocarditis: 421 (ICD‐8 and 9) and I33 (ICD‐10).
Statistical analyses
Cox proportional hazards models were used to explore the association between BMI and CRF in adolescence and subsequent risk of severe bacterial infection. The follow‐up started at conscription examination (baseline) and ended on the date of hospitalization, death, emigration, or end of follow‐up December 31, 2019, whichever came first. The assumption of proportional hazards for all predictors was tested with Schoenfeld residuals, as well as graphically. No evidence for violation of the proportionality assumption was seen. Confounders were chosen by careful selection, with the models built in a stepwise fashion, where Model 1 was adjusted for conscription center, examination year, age at conscription, and CRF for BMI calculations and vice versa. In Model 2, we further adjusted for muscle strength at conscription and maximal parental education (as a measure of socioeconomic status). For bacterial pneumonia, Model 3 was additionally adjusted for asthma at baseline. In analyses with rare outcomes (<1000 events), we used Firth's biased reduction method [38]. Results were presented as hazard ratios (HRs) with 95% confidence intervals (95% CI). Statistical significance was set at 0.05 (two‐sided tests). Statistical calculations were performed using SAS version 9.4 (SAS Institute, Cary, NC, USA).
Results
The study cohort consisted of 995,091 male conscripts with a mean age of 18.3 years. Mean BMI was 21.8 kg/m2 with 9.2% being overweight (BMI 25–29.9) and 1.5% with obesity (BMI ≥ 30). Men across all BMI categories had similar age and height. In the fitness test, 42.9% of the conscripts performed at a moderate level but those with underweight and obesity had lower CRF than those with normal weight. Obese conscripts were less likely to have parents with higher education. Cohort characteristics by BMI category are presented in Table 1. Mean follow‐up time in this study was 33.2 years.
Table 1.
Baseline demographics at conscription by body mass index (BMI) category.
| Characteristics | BMI < 18.5 (n = 72,786) | BMI 18.5–19.9 (n = 181,586) | BMI 20.0–22.4 (n = 414,628) | BMI 22.5–24.9 (n = 219,941) | BMI 25.0–29.9 (n = 91,217) | BMI ≥ 30.0 (n = 14,933) | Overall (n = 995,091) |
|---|---|---|---|---|---|---|---|
| Follow‐up time, mean (SD) (years) | 35.8 (9.1) | 34.6 (9.3) | 33.3 (9.5) | 32.0 (9.7) | 31.0 (9.7) | 30.0 (9.3) | 33.2 (9.6) |
| Age at conscription, mean (SD) (years) | 18.3 (0.6) | 18.3 (0.6) | 18.3 (0.6) | 18.3 (0.6) | 18.3 (0.7) | 18.3 (0.7) | 18.3 (0.6) |
| Height, mean (SD) (m) | 1.8 (0.07) | 1.8 (0.07) | 1.8 (0.06) | 1.8 (0.06) | 1.8 (0.07) | 1.8 (0.07) | 1.8 (0.07) |
| Weight, mean (SD) (kg) | 57.2 (4.8) | 62.3 (4.7) | 68.2 (5.4) | 75.4 (5.9) | 85.4 (7.6) | 104.3 (11.2) | 70.0 (10.1) |
| BMI, mean (SD) (kg/m2) | 17.7 (0.6) | 19.3 (0.4) | 21.2 (0.7) | 23.5 (0.7) | 26.6 (1.3) | 32.5 (2.6) | 21.8 (2.8) |
| Cardiorespiratory fitness, n (%) | |||||||
| Low (1–5) | 37,133 (51.0) | 63,024 (34.7) | 94,414 (22.8) | 48,709 (22.2) | 35,928 (39.4) | 10,042 (67.3) | 289,250 (29.1) |
| Moderate (6–7) | 30,369 (41.7) | 84,705 (46.7) | 181,632 (43.8) | 91,959 (41.8) | 34,853 (38.2) | 3194 (21.4) | 426,712 (42.9) |
| High (8–9) | 5284 (7.3) | 33,857 (18.7) | 138,582 (33.4) | 79,273 (36.0) | 20,436 (22.4) | 1697 (11.4) | 279,129 (28.1) |
| Muscle strength, n (%) | |||||||
| Low (1–3) | 27,425 (37.7) | 31,112 (17.1) | 29,911 (7.2) | 8179 (3.7) | 3058 (3.4) | 432 (2.9) | 100,117 (10.1) |
| Moderate (4–6) | 43,086 (59.2) | 129,383 (71.3) | 260,758 (62.9) | 102,690 (46.7) | 35,489 (38.9) | 5026 (33.7) | 576,432 (57.9) |
| High (7–9) | 2275 (3.1) | 21,091 (11.6) | 123,959 (29.9) | 109,072 (49.6) | 52,670 (57.7) | 9475 (63.5) | 318,542 (32.0) |
| Maximal parental education, n (%) | |||||||
| Compulsory school | 23,918 (32.9) | 54,493 (30.0) | 116,202 (28.0) | 60,724 (27.6) | 27,216 (29.8) | 4922 (33.0) | 287,475 (28.9) |
| High school to ≤2 years university | 38,494 (52.9) | 97,773 (53.8) | 227,846 (55.0) | 124,490 (56.6) | 52,651 (57.7) | 8690 (58.2) | 549,944 (55.3) |
| ≥3 years university | 10,374 (14.3) | 29,320 (16.2) | 70,580 (17.0) | 34,727 (15.8) | 11,350 (12.4) | 1321 (8.9) | 157,672 (15.8) |
| Asthma diagnosis at conscription, n (%) | 247 (0.3) | 756 (0.4) | 1738 (0.4) | 1067 (0.5) | 530 (0.6) | 96 (0.6) | 4434 (0.5) |
Moreover, we performed a sensitivity analysis comparing the final study sample with those who had been excluded due to missing data on BMI, CRF, muscle strength, and parental education, or experienced the events of interest before conscription or within 4 years thereafter. The analysis showed that excluded individuals were on average examined in later years, were slightly older, and were more likely to have low CRF and unhealthy BMI, that is, underweight, overweight, or obesity (Table S1).
In total, 44,112 severe bacterial infections were recorded during follow‐up, including 2261 deaths attributed to these infections (5.1% of all severe bacterial infections) (Table 2). Mean age at diagnosis was 46.2 years. As expected, bacterial pneumonia was the most common infection with 36,626 cases (83.0%), followed by sepsis with 10,459 cases (23.7%), and infective endocarditis with 1308 cases (3.0%). The highest proportion of deaths was seen among patients with sepsis (8.3% vs. 5.5% for infective endocarditis and 4.1% for bacterial pneumonia). Incidence rates of bacterial infections among conscripts examined in different 10‐year intervals during the study period were compared. We found decreasing incidences, in spite of increasing prevalence of overweight and obesity, which is explained by the longer follow‐up for the earlier cohorts. However, if follow‐up was restricted to 10 years for all groups, there was an increase in incident bacterial infections over time (Table S2). Men with obesity had longer hospital stays during their first bacterial pneumonia and sepsis compared with their normal‐weight peers. For infective endocarditis, the number of men with obesity was too small to draw any conclusions (Table 2).
Table 2.
Total incidence of and death due to bacterial pneumonia, sepsis, and infective endocarditis by body mass index (BMI) category.
| Outcomes | BMI < 18.5 (n = 72,786) | BMI 18.5–19.9 (n = 181,586) | BMI 20.0–22.4 (n = 414,628) | BMI 22.5–24.9 (n = 219,941) | BMI 25.0–29.9 (n = 91,217) | BMI ≥ 30.0 (n = 14,933) | Overall (n = 995,091) |
|---|---|---|---|---|---|---|---|
| Bacterial pneumonia, n (%) | 3232 (4.4) | 6999 (3.9) | 14,622 (3.5) | 7439 (3.4) | 3548 (3.9) | 786 (5.3) | 36,626 (3.7) |
| Of which deaths, n (%) | 168 (5.2) | 322 (4.6) | 565 (3.9) | 259 (3.5) | 141 (4.0) | 34 (4.3) | 1489 (4.1) |
| Age at diagnosis, mean (SD) (years) | 47.4 (11.0) | 46.9 (10.9) | 45.8 (10.8) | 45.1 (10.8) | 44.3 (10.7) | 43.7 (10.0) | 45.8 (10.8) |
| Length of hospital stay a , mean (SD) (days) | 7.5 (42.3) | 6.2 (15.8) | 6.1 (11.3) | 6.4 (12.7) | 5.8 (8.9) | 7.0 (14.2) | 6.3 (17.5) |
| Sepsis, n (%) | 846 (1.2) | 1850 (1.0) | 4072 (1.0) | 2210 (1.0) | 1157 (1.3) | 324 (2.2) | 10,459 (1.1) |
| Of which deaths, n (%) | 74 (8.7) | 151 (8.2) | 326 (8.0) | 193 (8.7) | 90 (7.8) | 29 (9.0) | 863 (8.3) |
| Age at diagnosis, mean (SD) (years) | 50.0 (10.5) | 48.9 (11.1) | 48.7 (11.0) | 48.0 (10.9) | 48.6 (10.4) | 47.3 (9.7) | 48.6 (10.8) |
| Length of hospital stay a , mean (SD) (days) | 11.9 (19.9) | 10.9 (15.6) | 10.9 (15.2) | 11.8 (22.3) | 11.2 (16.3) | 12.0 (17.2) | 11.3 (17.6) |
| Infective endocarditis, n (%) | 109 (0.2) | 236 (0.1) | 510 (0.1) | 289 (0.1) | 137 (0.2) | 27 (0.2) | 1308 (0.1) |
| Of which deaths, n (%) | 10 (9.2) | 6 (2.5) | 25 (4.9) | 14 (4.8) | 14 (10.2) | 3 (11.1) | 72 (5.5) |
| Age at diagnosis, mean (SD) (years) | 47.5 (10.9) | 47.4 (10.3) | 46.6 (11.4) | 47.6 (10.7) | 46.7 (10.3) | 50.0 (9.9) | 47.1 (10.9) |
| Length of hospital stay a , mean (SD) (days) | 13.7 (12.1) | 16.1 (13.2) | 15.9 (14.2) | 15.7 (14.7) | 15.1 (12.0) | 11.7 (11.1) | 15.5 (13.7) |
| Any bacterial infection b , n (%) | 3783 (5.2) | 8349 (4.6) | 17,580 (4.2) | 9030 (4.1) | 4359 (4.8) | 1011 (6.8) | 44,112 (4.4) |
| Of which deaths, n (%) | 231 (6.1) | 453 (5.4) | 856 (4.9) | 429 (4.8) | 231 (5.3) | 61 (6.0) | 2261 (5.1) |
| Age at diagnosis, mean (SD) (years) | 47.6 (11.0) | 47.1 (11.0) | 46.2 (10.9) | 45.5 (10.9) | 45.0 (10.7) | 44.4 (10.1) | 46.2 (10.9) |
| Length of hospital stay a , mean (SD) (days) | 7.5 (14.0) | 7.3 (16.1) | 7.2 (12.4) | 7.8 (16.1) | 7.2 (11.1) | 8.4 (15.6) | 7.4 (14.1) |
Hospitalized patients only, first incident of hospital care.
Bacterial pneumonia, sepsis, and infective endocarditis are included in the present study.
Higher adolescent BMI was associated with elevated risk for bacterial pneumonia, sepsis, and infective endocarditis in adult life (Table 3). The risk increased gradually with increasing BMI, with the highest risk seen in men with obesity (HR 1.69 (95% CI 1.57–1.82) for bacterial pneumonia, 3.06 (2.71–3.46) for sepsis, and 1.80 (1.20–2.71) for infective endocarditis). Notably, even individuals with high‐normal BMI (22.5–24.9) had a significantly elevated risk of these infections compared with those with BMI 18.5–19.9.
Table 3.
Hazard ratios for body mass index (BMI) and cardiorespiratory fitness (CRF) at conscription and incidence of severe bacterial infections (n = 995,091).
| Hazard ratio (95% confidence interval) | |||
|---|---|---|---|
| Model 1 | Model 2 | Model 3 | |
| Bacterial pneumonia (events/population) | 36,626/995,091 | 36,626/995,091 | 36,626/995,091 |
| BMI | |||
| <18.5 | 1.05 (1.01–1.10) | 1.05 (1.00–1.09) | 1.05 (1.00–1.09) |
| 18.5–19.9 | Ref | Ref | Ref |
| 20.0–22.4 | 1.02 (0.99–1.05) | 1.02 (0.99–1.05) | 1.02 (0.99–1.05) |
| 22.5–24.9 | 1.05 (1.02–1.09) | 1.05 (1.01–1.09) | 1.05 (1.01–1.09) |
| 25.0–29.9 | 1.24 (1.19–1.29) | 1.23 (1.18–1.28) | 1.23 (1.18–1.28) |
| ≥30.0 | 1.71 (1.59–1.84) | 1.69 (1.57–1.82) | 1.69 (1.57–1.82) |
| CRF | |||
| Low (1–5) | Ref | Ref | Ref |
| Moderate (6–7) | 0.87 (0.85–0.89) | 0.88 (0.86–0.90) | 0.88 (0.86–0.90) |
| High (8–9) | 0.75 (0.73–0.77) | 0.76 (0.74–0.78) | 0.76 (0.74–0.79) |
| Sepsis (events/population) | 10,459/995,091 | 10,459/995,091 | |
| BMI | |||
| <18.5 | 1.01 (0.93–1.10) | 1.00 (0.92–1.09) | |
| 18.5–19.9 | Ref | Ref | |
| 20.0–22.4 | 1.11 (1.05–1.17) | 1.11 (1.05–1.17) | |
| 22.5–24.9 | 1.27 (1.19–1.35) | 1.26 (1.18–1.35) | |
| 25.0–29.9 | 1.69 (1.57–1.82) | 1.68 (1.55–1.81) | |
| ≥30.0 | 3.10 (2.75–3.49) | 3.06 (2.71–3.46) | |
| CRF | |||
| Low (1–5) | Ref | Ref | |
| Moderate (6–7) | 0.88 (0.84–0.92) | 0.88 (0.84–0.93) | |
| High (8–9) | 0.78 (0.74–0.82) | 0.79 (0.75–0.83) | |
| Infective endocarditis (events/population) | 1308/995,091 | 1308/995,091 | |
| BMI | |||
| <18.5 | 1.01 (0.81–1.27) | 1.00 (0.80–1.26) | |
| 18.5–19.9 | Ref | Ref | |
| 20.0–22.4 | 1.10 (0.94–1.28) | 1.09 (0.93–1.28) | |
| 22.5–24.9 | 1.31 (1.10–1.56) | 1.28 (1.07–1.53) | |
| 25.0–29.9 | 1.54 (1.24–1.90) | 1.48 (1.19–1.85) | |
| ≥30.0 | 1.90 (1.27–2.83) | 1.80 (1.20–2.71) | |
| CRF | |||
| Low (1–5) | Ref | Ref | |
| Moderate (6–7) | 0.83 (0.73–0.94) | 0.85 (0.75–0.96) | |
| High (8–9) | 0.67 (0.58–0.78) | 0.70 (0.61–0.82) | |
| Any severe bacterial infection a (events/population) | 44,112/995,091 |
44,112/995,091 |
|
| BMI | |||
| <18.5 | 1.03 (0.99–1.07) | 1.02 (0.98–1.06) | |
| 18.5–19.9 | Ref | Ref | |
| 20.0–22.4 | 1.03 (1.00–1.06) | 1.03 (1.00–1.05) | |
| 22.5–24.9 | 1.08 (1.05–1.11) | 1.08 (1.04–1.11) | |
| 25.0–29.9 | 1.30 (1.25–1.35) | 1.29 (1.24–1.34) | |
| ≥30.0 | 1.90 (1.78–2.03) | 1.87 (1.75–2.00) | |
| CRF | |||
| Low (1–5) | Ref | Ref | |
| Moderate (6–7) | 0.88 (0.86–0.90) | 0.88 (0.87–0.90) | |
| High (8–9) | 0.77 (0.75–0.79) | 0.78 (0.76–0.80) | |
Note: Ref = reference group. Model 1: Models including BMI and CRF are further adjusted for examination year, age at conscription, and conscription test center. Model 2: adjusted for 1 + muscle strength + maximal parental education. Model 3 (bacterial pneumonia): adjusted for 1 + 2 + asthma at baseline.
Bacterial pneumonia, sepsis, and infective endocarditis are included in the present study.
Moreover, the risk of dying due to bacterial infections also increased with higher BMI in a dose‐dependent manner (Table 4). For bacterial pneumonia, men with obesity had a more than two‐fold increased mortality risk compared with men with normal weight (2.29 (1.60–3.29)). The association was even stronger for sepsis and infective endocarditis, where men with obesity had more than 4‐ and 11‐fold increased risk of dying compared with those with normal weight (4.34 (2.89–6.50) and 11.5 (2.96–44.8)).
Table 4.
Hazard ratios for body mass index (BMI) and cardiorespiratory fitness (CRF) at conscription and death due to severe bacterial infections (n = 995,091).
| Hazard ratio (95% confidence interval) | |||
|---|---|---|---|
| Model 1 | Model 2 | Model 3 | |
| Bacterial pneumonia (events/population) | 1489/995,091 | 1489/995,091 | 1489/995,091 |
| BMI | |||
| <18.5 | 1.06 (0.88–1.28) | 1.00 (0.83–1.22) | 1.00 (0.83–1.22) |
| 18.5–19.9 | Ref | Ref | Ref |
| 20.0–22.4 | 0.97 (0.84–1.11) | 1.01 (0.88–1.16) | 1.01 (0.87–1.16) |
| 22.5–24.9 | 0.99 (0.84–1.17) | 1.06 (0.89–1.26) | 1.06 (0.89–1.26) |
| 25.0–29.9 | 1.36 (1.11–1.66) | 1.47 (1.19–1.80) | 1.47 (1.19–1.80) |
| ≥30.0 | 2.10 (1.47–2.99) | 2.29 (1.60–3.29) | 2.29 (1.60–3.29) |
| CRF | |||
| Low (1–5) | Ref | Ref | Ref |
| Moderate (6–7) | 0.71 (0.63–0.80) | 0.74 (0.66–0.84) | 0.74 (0.66–0.84) |
| High (8–9) | 0.51 (0.44–0.59) | 0.55 (0.48–0.64) | 0.55 (0.48–0.64) |
| Sepsis (events/population) | 863/995,091 | 863/995,091 | |
| BMI | |||
| <18.5 | 1.02 (0.77–1.34) | 1.00 (0.75–1.33) | |
| 18.5–19.9 | Ref | Ref | |
| 20.0–22.4 | 1.18 (0.97–1.43) | 1.19 (0.98–1.45) | |
| 22.5–24.9 | 1.57 (1.26–1.96) | 1.61 (1.29–2.01) | |
| 25.0–29.9 | 1.92 (1.47–2.50) | 1.97 (1.50–2.58) | |
| ≥30.0 | 4.22 (2.84–6.29) | 4.34 (2.89–6.50) | |
| CRF | |||
| Low (1–5) | Ref | Ref | |
| Moderate (6–7) | 0.80 (0.68–0.93) | 0.81 (0.69–0.95) | |
| High (8–9) | 0.61 (0.51–0.73) | 0.64 (0.53–0.77) | |
| Infective endocarditis (events/population) | 72/995,091 | 72/995,091 | |
| BMI | |||
| <18.5 | 3.55 (1.30–9.69) | 3.46 (1.25–9.53) | |
| 18.5–19.9 | Ref | Ref | |
| 20.0–22.4 | 2.00 (0.83–4.86) | 2.09 (0.86–5.09) | |
| 22.5–24.9 | 2.47 (0.95–6.43) | 2.69 (1.02–7.10) | |
| 25.0–29.9 | 6.39 (2.47–16.6) | 7.06 (2.67–18.7) | |
| ≥30.0 | 10.3 (2.71–39.0) | 11.5 (2.96–44.8) | |
| CRF | |||
| Low (1–5) | Ref | Ref | |
| Moderate (6–7) | 0.90 (0.52–1.55) | 0.91 (0.53–1.58) | |
| High (8–9) | 0.73 (0.39–1.36) | 0.76 (0.41–1.43) | |
| Any severe bacterial infection a (events/population) | 2261/995,091 | 2261/995,091 | |
| BMI | |||
| <18.5 | 1.04 (0.89–1.22) | 1.00 (0.85–1.18) | |
| 18.5–19.9 | Ref | Ref | |
| 20.0–22.4 | 1.04 (0.93–1.17) | 1.07 (0.95–1.20) | |
| 22.5–24.9 | 1.17 (1.02–1.34) | 1.23 (1.07–1.41) | |
| 25.0–29.9 | 1.60 (1.37–1.88) | 1.70 (1.44–2.01) | |
| ≥30.0 | 2.76 (2.11–3.61) | 2.96 (2.25–3.89) | |
| CRF | |||
| Low (1–5) | Ref | Ref | |
| Moderate (6–7) | 0.74 (0.67–0.82) | 0.77 (0.70–0.84) | |
| High (8–9) | 0.54 (0.48–0.60) | 0.58 (0.51–0.65) | |
Note: Ref = reference group. Model 1: Models including BMI and CRF are further adjusted for examination year, age at conscription, and conscription test center. Model 2: adjusted for 1 + muscle strength + maximal parental education. Model 3 (bacterial pneumonia): adjusted for 1 + 2 + asthma at baseline.
Bacterial pneumonia, sepsis, and infective endocarditis are included in the present study.
For CRF in adolescence, conscripts who performed on a moderate or high fitness level had reduced risk to contract bacterial pneumonia, sepsis, and infective endocarditis later in life, compared with those with low CRF (Table 3). The risk reductions displayed a dose‐response pattern with the lowest risk for infection seen among men with high adolescent CRF, such that adjusted HRs were 0.76 (0.74–0.79) for bacterial pneumonia, 0.79 (0.75–0.83) for sepsis, and 0.70 (0.61–0.82) for infective endocarditis. In addition, men with high CRF had reduced risk for death attributed to bacterial pneumonia and sepsis, but not for infective endocarditis (Table 4).
Taken together, high BMI in late adolescence was associated with increased risk of contracting and dying of severe bacterial infections in adult life, including bacterial pneumonia, sepsis, and infective endocarditis. Conversely, high adolescent CRF was associated with lower risk for those infections and death attributed to them. As we mutually adjusted for CRF and BMI in the models, the associations between BMI and the outcomes were independent of the associations between CRF and the outcomes, and vice versa. Moreover, adjustment for parental education and muscle strength in the analyses did not alter the risk estimates for any of the studied outcomes in any significant way, either for BMI or CRF. Similarly, asthma at baseline did not significantly influence the risk in the case of bacterial pneumonia.
Discussion
In this register‐based, nationwide cohort study, including nearly one million Swedish men, we found strong, dose‐dependent associations between higher adolescent BMI and increased hazard of bacterial pneumonia, sepsis, and infective endocarditis, and also of death attributed to these infections. The association was particularly strong for sepsis, with an increased risk that was more than three‐fold among men with obesity, but detectable already at BMI levels considered as high‐normal (BMI 22.5–24.9). Moreover, high CRF in adolescence was linked to a reduced risk for bacterial pneumonia, sepsis, and infective endocarditis. Thus, obesity and poor fitness in youth are not only risk factors for later cardiovascular disease and cancer development as previously shown, but also for severe bacterial infections in adult life.
As severe bacterial infections are common causes of morbidity and mortality worldwide [1], the need for studies on preventive measures is important. To our knowledge, the present study is the first to investigate the association between measured adolescent BMI and CRF and risk of contracting bacterial pneumonia, sepsis, and infective endocarditis later in life. In a study of Israeli conscripts, Twig et al. found an association between obesity in youth and dying of infectious diseases that was almost as strong as for cardiovascular death [17]. We confirm this increased mortality risk, but we also demonstrate an elevated risk of contracting severe bacterial infections. Interestingly, the strong dose‐dependent association that we found between BMI and severe bacterial infections started already at high‐normal BMI levels (22.5–24.9). This is in line with previous studies on risk for cardiovascular disease [30, 39] and mortality [40], as well as for some site‐specific cancers [29]. Taken together, the established BMI threshold of 25 indicating normal weight may not be applicable to young people when estimating future health risks. Our research group has previously shown that there is an increasing prevalence of overweight, obesity, and severe obesity among Swedish conscripts from 1968 to 2005 [33]. Combining those findings with our present results, it seems probable that the incidence of severe bacterial infections will increase henceforth. Potentially, recent advances in surgical and pharmacological treatments against obesity and related comorbidities may mitigate these negative effects of obesity. However, future studies are needed to determine this.
We have recently found that high adolescent CRF lowered the risk for severe COVID‐19 during the pandemic [25]; however, the present study is the first to report an association between high CRF in youth and reduced risk of severe bacterial infections in adulthood. Similar to obesity, poor CRF in adolescence has previously been linked to increased risk of cardiovascular disease [41, 42], cancer [43], and early death [44]. Conversely, high aerobic fitness has been shown to mitigate the increased mortality risk coupled to overweight (BMI ≥ 25–30 kg/m2) and obesity grade 1 (BMI ≥ 30–35 kg/m2), but not obesity grade 2–3 (BMI ≥ 35) [44]. In the current study, obesity was associated with greater risk increments for bacterial infections overall compared to low CRF. This is in line with the findings reported by Högström et al., showing that the risk of both myocardial infarction and early death was higher in fit individuals with obesity than in unfit normal‐weight individuals [44]. Thus, high CRF may not fully be able to compensate for the health risks associated with obesity.
Although this study identifies strong associations, it is important to emphasize that observational data cannot establish causality. The associations between adolescent obesity and poor physical fitness, and severe bacterial infections later in life may reflect direct biological effects, shared underlying risk factors, or unmeasured confounding. Nevertheless, the observed relationships are biologically plausible. Several potential pathophysiological mechanisms may explain the link between obesity and poor fitness in adolescence and unfavorable outcomes in infectious diseases later in life. Both obesity and physical activity are known to cause alterations of the immune system [45, 46]. Obesity is characterized by an abnormal metabolic state with disturbed levels of metabolic hormones, as well as altered immunomodulatory mediators secreted by the adipose tissue, fat infiltration of lymphoid organs, and shifts in leukocyte population toward more proinflammatory phenotypes [45, 47]. Through a complex interplay, only partially understood, these alterations interact with both the innate and adaptive immune system [47], giving rise to a state of low‐grade, chronic inflammation [48], and an impaired immune function [45, 47, 49]. Obesity has also been linked to dysbiosis [50], which, in turn, has been associated with an increased risk of infections [51]. Besides reduced immunocompetence, increasing BMI causes a dampened response to certain vaccines [14, 52, 53, 54]. A previous study showed lower uptake for vaccination against human papillomavirus type 4 with increased body weight [55]. However, the vaccines targeting bacterial infections relevant for this study (Streptococcus pneumoniae and Hemophilus influenzae) were adopted in the Swedish national vaccination program for children in 1993 and 2009, respectively. As the youngest participants in the present study were born in 1987, this will have had no impact on the results. Regarding physical exercise and the immune system, the immunomodulatory effects are both acute and chronic, depending on the intensity, duration, and regularity with which the activity is performed [56]. Exercise has been reported to alter stress hormones, cellular immune response, humoral immune response, and systemic inflammation. The acute effects are transient, but regular exercise of moderate intensity leads to improved immunocompetence and reduced inflammation [56, 57]. To conclude, obesity and physical activity may affect our defense against infectious diseases in several ways. Regardless of the exact mechanisms, long‐term exposure to obesity and poor fitness appears to cause considerably increased risks of both contracting and dying from severe bacterial infections.
Strengths and limitations
This study has several strengths. First, utilizing data from the Swedish Military SMCR provided us with objective measures of BMI and CRF. Measured values are favorable as the accuracy of self‐reported BMI is known to decline with increasing BMI [58], and men tend to overestimate physical fitness when asked to rate their own fitness level [59]. Second, our population‐based approach with large sample size, long follow‐up period, and prospectively registered data with high validity generates results with high generalizability [26, 37]. Third, we minimized the risk for reverse causality by excluding individuals with an infectious disease diagnosis within 4 years from the BMI and CRF registration at conscription. Collectively, these strengths enabled us to draw conclusions about BMI and CRF in adolescence and risks over the life course for bacterial pneumonia, sepsis, and infective endocarditis.
There are also several limitations with the present study. First, no women were included and accordingly the data may not be applicable to women. Previous publications have found significant gender differences on obesity and bacterial infections regarding death due to infectious diseases [17], as well as risk for specific bacterial infections, including sepsis [13]. Second, the absence of follow‐up data on BMI and CRF in adulthood limits our ability to determine whether the observed associations are driven by obesity and poor fitness during adolescence, adulthood, or both, but both are traits that are likely to track into adulthood. Third, during follow‐up we lack data on lifestyle risk factors, such as smoking, alcohol, diet, as well as hypertension. Fourth, 31% of the original study population were excluded due to missing values for BMI and/or CRF, inferring a risk of potential selection bias. To address this, we performed a sensitivity analysis comparing excluded individuals with the final sample. The differences we found (see Table S1) are mostly due to a change of policy during the most recent years of mandatory conscription, when conscripts with unhealthy body weight were no longer asked to perform an exercise test. Hence, most missing data are on CRF. As CRF and overweight/obesity are strong risk factors for bacterial infections, we conclude that rates of infection on the basis of the final data set are probably underestimated. The present risk factor associations may thus be even stronger. A fifth limitation is that the diagnostic criteria for sepsis have changed over time and the diagnosis is known to be underreported [60]. Infective endocarditis is a heterogenous infectious entity, including both acute disease most commonly caused by Staphylococcus aureus and subacute disease caused mainly by non‐hemolytic streptococci. Unfortunately, we lack information regarding the course of disease, as well as causative bacteria. Lastly, BMI is a proxy for adiposity status with known limitations [61], but other anthropometric variables, such as waist circumference and waist–hip ratio, were not available in our data set. For CRF, we divided the conscripts into three groups (low, moderate, and high) as in our previous published works. Using more subgroups would potentially reveal risk differences also within these groups. However, we believe that dividing fitness into only three groups facilitates the interpretation of our data.
In conclusion, we found strong associations between late adolescent BMI and CRF, and future risk of morbidity and mortality caused by bacterial infections. Our findings indicate that obesity and poor physical fitness are factors that if prevented may attenuate the risk not only for cardiovascular disease and cancer, but also for severe bacterial infections. As these risk factors are known to be established early in life, targeting unhealthy lifestyles in youths may be crucial to improve their future health.
Author contributions
Maria Åberg collected data from Statistics Sweden and the Swedish National Board of Health and Welfare. Magnus Gisslén, Birger Sourander, and Josefina Robertson came up with the idea for the study and initiated the project. Birger Sourander and Josefina Robertson were responsible for the conception and design of the study, as well as for analysis of data. Kirsten Mehlig performed the statistical analyses. All authors took part in drafting the manuscript and approved the final version.
Funding information
This work was supported by grants from the Swedish state under an agreement between the Swedish government and the county councils, the ALF agreement (ALFGBG‐942707 (JR), ALFGBG‐971608 (ML), ALFGBG‐1006439 (AR), ALFGBG‐1005368 (MÅ), ALFGBG‐1005848 (MG)), the Swedish Heart and Lung Foundation 2024‐0678 (AR), and the Swedish Research Council 2023‐02144 (AR).
Conflict of interest statement
The authors declare no conflicts of interest.
Supporting information
Table S1: Sensitivity analysis comparing excluded conscripts with the final study sample.
Table S2: Secular trends in overweight, obesity, and incident bacterial infections.
Sourander B, Mehlig K, Olsen F, Lindgren M, Snygg‐Martin U, Gisslén M, et al. High BMI and low cardiorespiratory fitness in adolescence are associated with increased risk of severe bacterial infections in adulthood. J Intern Med. 2026;299:158–171.
Data availability statement
The raw data for our analyses are potentially identifiable. Access to individual‐level data is possible but requires permission through the Swedish Ethical Review Authority and from the primary data providers: the Swedish National Board of Health and Welfare and Statistics Sweden.
References
- 1. GBDAR Collaborators . Global mortality associated with 33 bacterial pathogens in 2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2022;400:2221–48. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Antimicrobial Resistance Collaborators . Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. Lancet. 2022;399:629–55. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. WHO . Obesity and overweight. Geneva: WHO; 2024. https://www.who.int/news‐room/fact‐sheets/detail/obesity‐and‐overweight. [Google Scholar]
- 4. GBDAB Collaborators . Global, regional, and national prevalence of child and adolescent overweight and obesity, 1990–2021, with forecasts to 2050: a forecasting study for the Global Burden of Disease Study 2021. Lancet. 2025;405:785–812. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Guthold R, Stevens GA, Riley LM, Bull FC. Global trends in insufficient physical activity among adolescents: a pooled analysis of 298 population‐based surveys with 1.6 million participants. Lancet Child Adolesc Health. 2020;4:23–35. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Ungefroren H, Gieseler F, Fliedner S, Lehnert H. Obesity and cancer. Horm Mol Biol Clin Investig. 2015;21:5–15. [DOI] [PubMed] [Google Scholar]
- 7. Ortega FB, Lavie CJ, Blair SN. Obesity and cardiovascular disease. Circ Res. 2016;118:1752–70. [DOI] [PubMed] [Google Scholar]
- 8. Pugliese G, Liccardi A, Graziadio C, Barrea L, Muscogiuri G, Colao A. Obesity and infectious diseases: pathophysiology and epidemiology of a double pandemic condition. Int J Obes (Lond). 2022;46:449–65. [DOI] [PubMed] [Google Scholar]
- 9. Falagas ME, Kompoti M. Obesity and infection. Lancet Infect Dis. 2006;6:438–46. [DOI] [PubMed] [Google Scholar]
- 10. Kaspersen KA, Pedersen OB, Petersen MS, Hjalgrim H, Rostgaard K, Møller BK, et al. Obesity and risk of infection: results from the Danish Blood Donor Study. Epidemiology. 2015;26:580–9. [DOI] [PubMed] [Google Scholar]
- 11. Harpsøe MC, Nielsen NM, Friis‐Møller N, Andersson M, Wohlfahrt J, Linneberg A, et al. Body mass index and risk of infections among women in the Danish National Birth Cohort. Am J Epidemiol. 2016;183:1008–17. [DOI] [PubMed] [Google Scholar]
- 12. Kivimäki M, Strandberg T, Pentti J, Nyberg ST, Frank P, Jokela M, et al. Body‐mass index and risk of obesity‐related complex multimorbidity: an observational multicohort study. Lancet Diabetes Endocrinol. 2022;10:253–63. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Ghilotti F, Bellocco R, Ye W, Adami HO, Trolle Lagerros Y. Obesity and risk of infections: results from men and women in the Swedish National March Cohort. Int J Epidemiol. 2019;48:1783–94. [DOI] [PubMed] [Google Scholar]
- 14. Painter SD, Ovsyannikova IG, Poland GA. The weight of obesity on the human immune response to vaccination. Vaccine. 2015;33:4422–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Alsiö Å, Nasic S, Ljungström L, Jacobsson G. Impact of obesity on outcome of severe bacterial infections. PLoS One. 2021;16:e0251887. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Corrales‐Medina VF, Valayam J, Serpa JA, Rueda AM, Musher DM. The obesity paradox in community‐acquired bacterial pneumonia. Int J Infect Dis. 2011;15:e54–7. [DOI] [PubMed] [Google Scholar]
- 17. Twig G, Geva N, Levine H, Derazne E, Goldberger N, Haklai Z, et al. Body mass index and infectious disease mortality in midlife in a cohort of 2.3 million adolescents. Int J Obes (Lond). 2018;42:801–7. [DOI] [PubMed] [Google Scholar]
- 18. Robertson J, Adiels M, Lissner L, Mehlig K, af Geijerstam A, Lindgren M, et al. BMI in early adulthood is associated with severe COVID‐19 later in life: a prospective cohort study of 1.5 million Swedish men. Obesity (Silver Spring). 2022;30:779–87. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Gray L. Body weight in early and mid‐adulthood in relation to subsequent coronary heart disease mortality: 80‐year follow‐up in the Harvard Alumni Study. Arch Intern Med. 2011;171:1768–70. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20. Jeffreys M, McCarron P, Gunnell D, McEwen J, Smith GD. Body mass index in early and mid‐adulthood, and subsequent mortality: a historical cohort study. Int J Obes Relat Metab Disord. 2003;27:1391–7. [DOI] [PubMed] [Google Scholar]
- 21. Tirosh A, Shai I, Afek A, Dubnov‐Raz G, Ayalon N, Gordon B, et al. Adolescent BMI trajectory and risk of diabetes versus coronary disease. N Engl J Med. 2011;364:1315–25. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Stattin K, Eriksson M, Frithiof R, Kawati R, Hultström M, Lipcsey M. Physical activity is associated with a lower risk of contracting and dying in infection and sepsis: a Swedish population‐based cohort study. Crit Care. 2024;28:98. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Hamer M, O'Donovan G, Stamatakis E. Lifestyle risk factors, obesity and infectious disease mortality in the general population: linkage study of 97,844 adults from England and Scotland. Prev Med. 2019;123:65–70. [DOI] [PubMed] [Google Scholar]
- 24. Ekblom‐Bak E, Väisänen D, Ekblom B, Blom V, Kallings LV, Hemmingsson E, et al. Cardiorespiratory fitness and lifestyle on severe COVID‐19 risk in 279,455 adults: a case control study. Int J Behav Nutr Phys Act. 2021;18:135. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. af Geijerstam A, Mehlig K, Börjesson M, Robertson J, Nyberg J, Adiels M, et al. Fitness, strength and severity of COVID‐19: a prospective register study of 1 559 187 Swedish conscripts. BMJ Open. 2021;11:e051316. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26. Ludvigsson JF, Berglind D, Sundquist K, Sundström J, Tynelius P, Neovius M. The Swedish military conscription register: opportunities for its use in medical research. Eur J Epidemiol. 2022;37:767–77. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27. Strain T, Wijndaele K, Sharp SJ, Dempsey PC, Wareham N, Brage S. Impact of follow‐up time and analytical approaches to account for reverse causality on the association between physical activity and health outcomes in UK Biobank. Int J Epidemiol. 2020;49:162–72. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28. Tarp J, Hansen BH, Fagerland MW, Steene‐Johannessen J, Anderssen SA, Ekelund U. Accelerometer‐measured physical activity and sedentary time in a cohort of US adults followed for up to 13 years: the influence of removing early follow‐up on associations with mortality. Int J Behav Nutr Phys Act. 2020;17:39. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29. Onerup A, Mehlig K, af Geijerstam A, Ekblom‐Bak E, Kuhn H‐G, Lissner L, et al. Associations between BMI in youth and site‐specific cancer in men‐A cohort study with register linkage. Obesity (Silver Spring). 2024;32:376–89. [DOI] [PubMed] [Google Scholar]
- 30. Rosengren A, Aberg M, Robertson J, Waern, M , Schaufelberger, M , Kuhn, G , et al. Body weight in adolescence and long‐term risk of early heart failure in adulthood among men in Sweden. Eur Heart J. 2017;38:1926–33. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31. Nordesjo LSR. Validity of an ergometer cycle test and measures of isometric muscle strength when prediction some aspects of military performance. Swedish J Defence Med. 1974;10:10–23. [Google Scholar]
- 32. Lundborg P, Nilsson A, Rooth DO. Parental education and offspring outcomes: evidence from the Swedish compulsory school reform. Am Econ J Appl Econ. 2014;6:253–78. [Google Scholar]
- 33. Lissner L, Mehlig K, Rosengren A, Toren K, Åberg M. A Growing social divide in body mass index, strength, and fitness of Swedish male conscripts. J Adolesc Health. 2019;65:232–8. [DOI] [PubMed] [Google Scholar]
- 34. Peters U, Dixon AE, Forno E. Obesity and asthma. J Allergy Clin Immunol. 2018;141:1169–79. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35. Urban N, Boivin MR, Cowan DN. Fitness, obesity and risk of asthma among Army trainees. Occup Med (Lond). 2016;66:551–7. [DOI] [PubMed] [Google Scholar]
- 36. Torres A, Blasi F, Dartois N, Akova M. Which individuals are at increased risk of pneumococcal disease and why? Impact of COPD, asthma, smoking, diabetes, and/or chronic heart disease on community‐acquired pneumonia and invasive pneumococcal disease. Thorax. 2015;70:984–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37. Ludvigsson JF, Andersson E, Ekbom A, Feychting M, Kim J‐L, Reuterwall C, et al. External review and validation of the Swedish National Inpatient Register. BMC Public Health. 2011;11:450. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38. Firth D. Bias reduction of maximum likelihood estimates. Biometrika. 1993;80:27–38. [Google Scholar]
- 39. Åberg M, Robertson J, Djekic D, Rosengren A, Schaufelberger M, Kuhn G, et al. Body weight in adolescent men in Sweden and risk of an early acute coronary event: a prospective population‐based study. J Am Heart Assoc. 2023;12:e029336. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40. Furer A, Afek A, Orr O, Gershovitz L, Landau Rabbi M, Derazne E, et al. Sex‐specific associations between adolescent categories of BMI with cardiovascular and non‐cardiovascular mortality in midlife. Cardiovasc Diabetol. 2018;17:80. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41. Högström G, Nordström A, Nordström P. High aerobic fitness in late adolescence is associated with a reduced risk of myocardial infarction later in life: a nationwide cohort study in men. Eur Heart J. 2014;35:3133–40. [DOI] [PubMed] [Google Scholar]
- 42. Högström G, Nordström A, Eriksson M, Nordström P. Risk factors assessed in adolescence and the later risk of stroke in men: a 33‐year follow‐up study. Cerebrovasc Dis. 2015;39:63–71. [DOI] [PubMed] [Google Scholar]
- 43. Högström G, Ohlsson H, Crump C, Sundquist J, Sundquist K Aerobic fitness in late adolescence and the risk of cancer and cancer‐associated mortality in adulthood: a prospective nationwide study of 1.2 million Swedish men. Cancer Epidemiol. 2019;59:58–63. [DOI] [PubMed] [Google Scholar]
- 44. Högström G, Nordström A, Nordström P. Aerobic fitness in late adolescence and the risk of early death: a prospective cohort study of 1.3 million Swedish men. Int J Epidemiol. 2016;45:1159–68. [DOI] [PubMed] [Google Scholar]
- 45. Andersen CJ, Murphy KE, Fernandez ML. Impact of obesity and metabolic syndrome on immunity. Adv Nutr. 2016;7:66–75. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46. Sharif K, Watad A, Bragazzi NL, Lichtbroun M, Amital H, Shoenfeld Y. Physical activity and autoimmune diseases: get moving and manage the disease. Autoimmun Rev. 2018;17:53–72. [DOI] [PubMed] [Google Scholar]
- 47. Milner JJ, Beck MA. The impact of obesity on the immune response to infection. Proc Nutr Soc. 2012;71:298–306. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48. Karczewski J, Sledzinska E, Baturo A, Jończyk, I , Maleszko, A , Samborski, P , et al. Obesity and inflammation. Eur Cytokine Netw. 2018;29:83–94. [DOI] [PubMed] [Google Scholar]
- 49. de Heredia FP, Gómez‐Martínez S, Marcos A. Obesity, inflammation and the immune system. Proc Nutr Soc. 2012;71:332–8. [DOI] [PubMed] [Google Scholar]
- 50. Klancic T, Reimer RA. Gut microbiota and obesity: impact of antibiotics and prebiotics and potential for musculoskeletal health. J Sport Health Sci. 2020;9:110–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 51. Schlechte J, Zucoloto AZ, Yu IL, Doig CJ, Dunbar MJ, McCoy KD, et al. Dysbiosis of a microbiota‐immune metasystem in critical illness is associated with nosocomial infections. Nat Med. 2023;29:1017–27. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 52. Young KM, Gray CM, Bekker LG. Is obesity a risk factor for vaccine non‐responsiveness? PLoS One. 2013;8:e82779. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 53. Eliakim A, Swindt C, Zaldivar F, Casali P, Cooper DM. Reduced tetanus antibody titers in overweight children. Autoimmunity. 2006;39:137–41. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 54. Sheridan PA, Paich HA, Handy J, Karlsson EA, Hudgens MG, Sammon AB, et al. Obesity is associated with impaired immune response to influenza vaccination in humans. Int J Obes (Lond). 2012;36:1072–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 55. Harper DM, Else BM, Bartley MJ, Arey AM, Barnett AL, Rosemergey BE, et al. In a safety net population HPV4 vaccine adherence worsens as BMI increases. PLoS One. 2014;9:e103172. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 56. Nieman DC, Wentz LM. The compelling link between physical activity and the body's defense system. J Sport Health Sci. 2019;8:201–17. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 57. Kurowski M, Seys S, Bonini M, Del Giacco S, Delgado L, Diamant Z, et al. Physical exercise, immune response, and susceptibility to infections‐current knowledge and growing research areas. Allergy. 2022;77:2653–64. [DOI] [PubMed] [Google Scholar]
- 58. Ekström S, Kull I, Nilsson S, Bergström A. Web‐based self‐reported height, weight, and body mass index among Swedish adolescents: a validation study. J Med Internet Res. 2015;17:e73. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 59. Obling KH, Hansen AL, Overgaard K, Normann K, Sandbaek A, Maindal HT. Association between self‐reported and objectively measured physical fitness level in a middle‐aged population in primary care. Prev Med Rep. 2015;2:462–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 60. Lengquist M, Lundberg OHM, Spångfors M, Annborn M, Levin H, Friberg H, et al. Sepsis is underreported in Swedish intensive care units: a retrospective observational multicentre study. Acta Anaesthesiol Scand. 2020;64:1167–76. [DOI] [PubMed] [Google Scholar]
- 61. Frankenfield DC, Rowe WA, Cooney RN, Smith JS, Becker D. Limits of body mass index to detect obesity and predict body composition. Nutrition. 2001;17:26–30. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Table S1: Sensitivity analysis comparing excluded conscripts with the final study sample.
Table S2: Secular trends in overweight, obesity, and incident bacterial infections.
Data Availability Statement
The raw data for our analyses are potentially identifiable. Access to individual‐level data is possible but requires permission through the Swedish Ethical Review Authority and from the primary data providers: the Swedish National Board of Health and Welfare and Statistics Sweden.