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Obesity survival paradox in pneumonia: a meta-analysis

Contributed equally
BMC Medicine201412:61

DOI: 10.1186/1741-7015-12-61

Received: 26 November 2013

Accepted: 13 March 2014

Published: 10 April 2014

Abstract

Background

It is unclear whether an ‘obesity survival paradox’ exists for pneumonia. Therefore, we conducted a meta-analysis to assess the associations between increased body mass index (BMI), pneumonia risk, and mortality risk.

Methods

Cohort studies were identified from the PubMed and Embase databases. Summary relative risks (RRs) with their corresponding 95% confidence intervals (CIs) were calculated using a random effects model.

Results

Thirteen cohort studies on pneumonia risk (n = 1,536,623), and ten cohort studies on mortality (n = 1,375,482) were included. Overweight and obese individuals were significantly associated with an increased risk of pneumonia (RR = 1.33, 95% CI 1.04 to 1.71, P = 0.02, I 2  = 87%). In the dose–response analysis, the estimated summary RR of pneumonia per 5 kg/m2 increase in BMI was 1.04 (95% CI 1.01 to 1.07, P = 0.01, I 2  = 84%). Inversely, overweight and obese subjects were significantly associated with reduced risk of pneumonia mortality (RR = 0.83, 95% CI 0.77 to 0.91, P < 0.01, I 2  = 34%). The estimated summary RR of mortality per 5 kg/m2 increase in BMI was 0.95 (95% CI 0.93 to 0.98, P < 0.01, I 2  = 77%).

Conclusions

This meta-analysis suggests that an ‘obesity survival paradox’ exists for pneumonia. Because this meta-analysis is based on observational studies, more studies are required to confirm the results.

Keywords

Body mass index Obesity Pneumonia Dose–response relationship Meta-analysis

Background

The prevalence of obesity has dramatically increased in the last two decades [1]. The diagnosis of obesity is often based on body mass index (BMI), calculated as weight in kilograms divided by height in meters squared (kg/m2). The ideal BMI is between 18.5 and 24.9. Being overweight is considered as having a BMI between 25 and 29.9, and being classified as obese falls into a BMI of 30.0 or greater [2]. Obesity is associated with an increased risk of cardiovascular disease and type 2 diabetes [3, 4]. However, an inverse relationship between obesity and mortality has been described in patients with heart failure, coronary heart disease, and diabetes [57]. This phenomenon is known as the ‘obesity survival paradox.’

Pneumonia is one of the most common infectious diseases; however, there is uncertainty about the association between obesity and pneumonia risk or pneumonia mortality [828]. For example, Baik et al.[9] suggested that obesity was directly associated with the development of community-acquired pneumonia (CAP). However, Phung et al.[19] did not find that obesity was significantly associated with pneumonia risk. Takata et al.[24] indicated that mortality risk was not different between obese pneumonia patients and normal weight patients. However, other studies reported that obese subjects with pneumonia had lower mortality compared to normal weight subjects [2628]. Thus, whether the ‘obesity survival paradox’ exists in pneumonia is still unclear.

To date, no meta-analysis has shown whether an ‘obesity survival paradox’ exists for pneumonia. The aim of this meta-analysis was to investigate the relationships between elevated BMI, pneumonia risk, and mortality.

Methods

This meta-analysis was performed according to a predetermined protocol described in the following paragraphs, using standard systematic review techniques, as outlined by the Meta-Analysis of Observational Studies in Epidemiology (MOOSE) criteria [29].

Literature search

A literature search was performed (WN and YZ) using the PubMed search engine, with the database being last accessed on 15 June 2013. The Embase database was also searched for relevant studies published up to June 2013. References from relevant articles were manually checked for further studies. The detailed search strategy is presented in the Additional file 1.

Study selection

Two reviewers (WN and YZ) independently screened the abstracts of papers identified by the literature search, retrieved potentially relevant studies and determined study eligibility. Studies were included if: (1) the study design was a prospective or retrospective cohort study; (2) the exposure of interest was BMI; (3) they reported adjusted relative risks (RRs), hazard ratios (HRs) or odds ratios (ORs) with corresponding 95% confidence intervals (CIs), or provided a RR/HR/OR with corresponding 95% CI per unit increment in BMI; and (4) the outcome was pneumonia incidence or mortality. If the same cohort was used in more than one publication, we included the publication that reported the results in greater detail or, if similar, the one with the largest number of cases. Data published only in abstract form were excluded. Case reports, review articles and commentary articles were also excluded. Studies with pediatric participants or pregnant populations were not included.

Data collection and methodological quality assessment

From each study, two reviewers (WN and YZ) independently extracted the first author, publication year, study design, location where the study was performed, number of cases and cohort size, gender and age of study participants, follow-up duration, method for assessing height and weight, ascertainment of pneumonia, type of pneumonia, BMI category, adjusted RR/HR/OR and the corresponding 95% CI, and covariates controlled for multivariable analysis. The authors of the relevant studies were contacted by Email if more information was needed.

Two independent reviewers (WN and YZ) completed the quality assessment. The Newcastle–Ottawa Scale (NOS) was used to evaluate the methodological quality, which scored studies by the selection of the study groups, the comparability of the groups and the ascertainment of the outcome of interest [30]. Discrepancies were resolved by consensus and discussion. The detailed criteria of the methodological quality assessment are in Additional file 2.

Statistical analysis

For pneumonia risk and pneumonia mortality risk, we calculated summary RRs and 95% CIs for overweight and obesity versus normal weight. The random effects model was utilized. HRs and ORs were regarded as equivalent to RRs in cohort studies. If a study reported results specifically for men and women, respectively, we combined the sex-specific RR estimates using a fixed-effects model before combining with other studies.

In dose–response analysis, we calculated the RR per 5-unit increase in the BMI levels for each study. The average of the natural logarithm of the RRs was estimated and the RR from each study was weighted by the inverse of its variance. A two-tailed P < 0.05 was considered statistically significant. We also combined the sex-specific estimates using a fixed-effects model to generate an estimate for both genders combined. The method described by Greenland and Longnecker [31] was used for the dose–response analysis and study-specific slopes (linear trends) and 95% CIs were computed from the natural logs of the RRs and CIs across categories of BMI. This method requires the distribution of case and person-years and the median level of BMI in each category to the corresponding RR for each study (the RRs with estimates for at least three quantitative exposure categories are known). The midpoint between the upper and lower boundary for each BMI category was assigned to the corresponding RR estimate. For studies with an open-ended highest or lowest BMI category, we assumed that the amplitude was the same as the closest adjacent category. Random effects models were used to pool the respective results. The dose–response results in the forest plot were presented for a 5 kg/m2 BMI increment.

Nonlinear dose–response curves were plotted using restricted cubic splines for each study, using knots fixed at percentiles 10%, 50% and 90% through the distribution; then these were combined using multivariate meta-analysis [3234].

Statistical heterogeneity among studies was evaluated using the Q and I 2 statistics. For the I 2 metric, we considered low, moderate and high I 2 values to be 25%, 50% and 75%, respectively. We examined the role of several potential sources of heterogeneity by subgroup analyses according to study design, gender, ascertainment of case, pneumonia type, assessment of anthropometry, and duration of follow-up. Meta-regression was also performed to find the sources of heterogeneity. Sensitivity analysis was conducted by excluding one study at a time to explore whether the results were driven by one large study or by a study with an extreme result. Potential small study effects, such as publication bias, were investigated with funnel plots.

All statistical analyses were performed with the Stata software (version 12.0, Stata Corporation, College Station, Texas). A threshold of P < 0.1 was used to decide whether heterogeneity was present. In other cases, P values were two sided with a significance level of 0.05.

Results

Literature search

The process of identifying relevant studies is shown in Figure 1. The initial search produced 1,035 studies from the PubMed and Embase databases. After exclusion of duplicates and irrelevant studies, 115 potentially eligible studies were selected. After detailed evaluations, 21 studies were selected for final meta-analysis [828]. A manual search of reference lists from these studies did not yield any new eligible study. Several studies investigated the association between BMI and mortality. We contacted these authors to get additional data on pneumonia mortality. Dr. Sun Ha Jee and colleague shared their data [35]. Finally, 22 studies were included in this meta-analysis [828, 35].
https://static-content.springer.com/image/art%3A10.1186%2F1741-7015-12-61/MediaObjects/12916_2013_Article_947_Fig1_HTML.jpg
Figure 1

Flow of study identification, inclusion and exclusion.

Study characteristics

Twelve cohort studies (n = 1,536,623) investigated the association between BMI and pneumonia risk [819], and ten studies (n = 1,375,482) assessed the association between BMI and pneumonia mortality [2028, 35]. There were seven retrospective cohort studies [11, 1518, 25, 26] and fifteen prospective cohort studies [810, 1214, 1924, 27, 28, 35]. The durations of follow-up varied from 1 year to 15.8 years. Sixteen studies collected measured BMI [8, 1015, 19, 20, 2328, 35]; three studies collected self-reported data [9, 17, 21]. The characteristics of each study are presented in Table 1. The methodological quality assessment is provided in Additional file 2. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist for meta-analysis is provided in Additional file 3.
Table 1

Characteristics of included cohort studies

Author

Study design

Year

Location

Gender

Age (y)

Years of follow-up (y)

Assessment of weight and height

Sample size

Cases ascertainment

No. of cases

Type

BMI category (kg/m2)

Adjustment for covariates

Incidence

             

Delgado-Rodriguez

PC

1997

Spain

Mixed

53

2

Measured

1,483

Physician-diagnosed

19

HAP

<33.75, ≥33.75

Mechanical ventilation, upper abdominal surgery, COPD, NNIS index

Baik

PC

2000

USA

Mixed

56.3 (men) 36.4 (women)

6

Self-reported

104,491

Physician-diagnosed

595

CAP

<21, 21–22.9, 23–24.9, 25–26.9, 27–29.9, ≥30

Age, smoking status, physical activity, alcohol intake

Newell

PC

2007

USA

Mixed

45

5

Measured

1,543

NA

315

HAP

18.5–24.9, 25.0–29.9, 30.0–39.9, ≥40

Age, gender, injury severity score, revised trauma score

Yap

RC

2007

Australia

Mixed

66.4

6

Measured

3,968

Physician-diagnosed

174

HAP

<20, 20–30, 30–40, ≥40

Age, gender, diabetes, hypercholesterolemia, renal impairment, preoperative dialysis, hypertension, cerebrovascular disease, peripheral vascular disease, lung disease, NYHA class IV, severe LV impairment, mean PA pressure, emergency status and total bypass time

Dossett

PC

2008

USA

Mixed

45

NA

Measured

1,406

Physician-diagnosed

446

HAP

<18.5, 18.5–24.9, 25.0–29.9, 30.0–39.9, ≥40

Age, gender, TRISS, AIS head score

Mannino

PC

2008

USA

Mixed

>45

3

Measured

20,375

ICD-9 codes 480-486

214

CAP

<20, 20–24, 25–29, ≥30

Age, gender, race, smoking status, education level, diabetes status, cardiovascular disease status

Kornum

PC

2010

Denmark

Mixed

50-64

11.8

Measured

48,551

ICD-10 codes J12.x–J18.x, ICD-10codes A709.x, ICD-10 codes A481.x

2,112

CAP

<22.5, 22.5–24.9, 25.0–29.9,30.0–34.9, ≥35

Age, smoking status, alcohol intake, schooling, educational level

Morgan

RC

2010

USA

Mixed

>20

1

Measured

196,684

Physician-diagnosed

134

CAP

<18.5, 18.5–24.9, 25.0–29.9, 30-39.9, ≥40

Age, gender, chronic disease

Blumentals

RC

2011

UK

Mixed

>18

7

NA

1,074,315

NA

877

CAP

<18.5, 18.5–24.9, 25.0–29.9, ≥30

Age, gender, type 2 diabetes, hypertension, statin use, antibiotic use, cigarette smoking status, influenza vaccination status, and year of index date

Kwong

RC

2011

Canada

Mixed

>18

12

Self-reported

82,545

ICD-9 codes 480–486, ICD-10-CM codes J10-J18

228

CAP

<18.5, 18.5–24.9, 25.0–29.9, ≥30

Age, gender, influenza vaccination status, rural residence, income quintile, smoking status, previous hospitalizations, previous outpatient visits, and the presence of chronic disease

Viasus

RC

2011

Spain

Mixed

39

NA

NA

585

Physician-diagnosed

11

CAP

<30, 30–39.9, ≥40

Age, gender, smoking, alcohol drinking, comorbid condition, influenza vaccine, early antiviral treatment, concomitant and/or secondary bacterial co-infection

Phung

PC

2013

Australia

Mixed

33

15.6

Measured

677

ICD-9 codes 480–486, ICD-10 codes J12-J18

141

CAP

<18.5, 18.5–24.9, 25.0–29.9, ≥30

Age, smoking, alcohol consumption status

Mortality

             

LaCROIX

PC

1989

USA

Mixed

65.8

12

Measured

5474

ICD-9 codes 480-486

76

CAP

Lowest quartile, Second quartile,Third quartile, Highest quartile

Age

Salive

PC

1993

USA

Mixed

74

6

Self-reported

10,269

ICD-9 codes 480-486

403

CAP

Lowest quartile, Second quartile,Third quartile, Highest quartile

Age, race, education, co-morbidities, smoking, peak expiratory flow, emphysema, exercise

Lange

PC

1995

Denmark

Mixed

30-70

12

NA

13,423

ICD-8 codes 480-486

260

CAP

<20, 20–29, ≥30

Age, predicted forced expiratory volume in one second

Jee

PC

2006

Korea

Mixed

46

15

Measured

1,213,829

ICD-10

962

CAP

<18.5, 18.5–19.9, 20.0–21.4, 21.5–22.9, 23.0–24.9, 25.0–26.4,26.5–27.9, 28.0–29.9, 30.0–31.9

Age, smoking status, alcohol intake, physical exercise, physical activity

Inoue

PC

2007

Japan

Mixed

57.6

13

Measured

110,792

ICD-9 codes 480-486 ICD-10 codes J12-J18

1,082

CAP

10.0–17.9, 18.0–22.9, 23.0–24.9,25.0–32.9

Age, diabetes mellitus

Takata

PC

2007

Japan

Mixed

80

4

Measured

697

ICD-10

19

CAP

<18.5, 18.5–24.9, ≥25

Gender, tobacco use, alcohol use, weight loss, current outpatient, systolic blood pressure, physical activity, functional status, marital status, and levels of total serum cholesterol and glucose

Corrales-Medina

RC

2011

USA

Mixed

65.5

7

Measured

266

Physician-diagnosed

31

CAP

<18.5, 18.5–24.9, 25.0–29.9, >30

Age, race, cancer, CURB-65

King

RC

2012

USA

Mixed

67.5

7

Measured

18,746

ICD-9 codes 480-487

3,340

CAP

<18.5, 18.5–24.9, 25.0–29.9, 30-39.9, >40

Age, gender, marital status, race, count of current medications, medical and psychiatric comorbid conditions, alcohol abuse, tobacco use, and drug abuse

Kahlon

PC

2012

Canada

Mixed

68

2

Measured

907

Physician-diagnosed

79

CAP

<18.5, 18.5–24.9, 25.0–29.9, >30

Age, functional status, prior pneumococcal vaccination, chest radiograph confirmation, PSI

Singanayagam

PC

2013

UK

Mixed

50-78

NA

Measured

1,079

Physician-diagnosed

103

CAP

<18.5, 18.5–24.9, 25.0–29.9,30.0–34.9

Age, gender, co-morbidities, current smoking, functional status, nursing home residence, PSI

AIS, abbreviated injury score; BMI, body mass index; CAP, community-acquired pneumonia; COPD, chronic obstructive pulmonary diseases, CURB-65, confusion, urea >7 mmol/L, respiratory rate ≥30 breaths/minute, low blood pressure and age ≥65 years; F, female; HAP, hospital-acquired pneumonia; ICD, International Classification of Diseases; LV, left ventricle; M, male; NA, not available; NNIS, National Nosocomial Infection Surveillance; NYHA, New York Heart Association; PC, prospective cohort; PSI, Pneumonia Severity Index; RC, retrospective cohort; TRISS, trauma-related injury severity score.

Quantitative data synthesis

Pneumonia risk (overweight and obesity versus normal weight)

Compared with normal weight individuals, overweight and obese individuals were associated with a significantly increased risk of pneumonia (RR = 1.33, 95% CI 1.04 to 1.71, P = 0.02, I 2  = 87%). A small-study effect was demonstrated using a funnel plot [see Additional file 4]. Ten studies reported RRs for categorized BMI levels [917, 19]. Thus, we included these studies for the dose–response analysis. The summary RR was 1.04 (95% CI 1.01 to 1.07, P = 0.01, I 2  = 84%; Figure 2). A potentially nonlinear dose–response relationship was not detected (P > 0.05; Figure 3). We found evidence of a small-study effect as assessed by funnel plot [see Additional file 5].
https://static-content.springer.com/image/art%3A10.1186%2F1741-7015-12-61/MediaObjects/12916_2013_Article_947_Fig2_HTML.jpg
Figure 2

Relative risks of pneumonia risk per 5 kg/m2 increase in body mass index. CI: indicates confidence interval; and RR: risk ratio.

https://static-content.springer.com/image/art%3A10.1186%2F1741-7015-12-61/MediaObjects/12916_2013_Article_947_Fig3_HTML.jpg
Figure 3

Dose–response relationship between body mass index and relative risk of pneumonia. Body mass index was modeled with a nonlinear trend (black continuous line) in a random effects meta-regression model. Long-dashed black lines represent 95% confidence intervals. Short-dashed black lines represent the linear trend. The vertical axes are on a log scale.

We conducted a sensitivity analysis by omitting one study at a time and calculating the pooled RRs for the remainder of the studies. This sensitivity analysis showed that the results were not changed (data not shown). The potential sources of heterogeneity were explored by stratifying analyses. Studies comparing overweight and obese subjects with normal weight subjects, and studies assessing dose–response associations were explored, respectively. The positive relationship between BMI and risk of pneumonia was significant in subgroups by ascertainment of case and assessment of weight and height [see Additional file 6]. However, in subgroups of retrospective cohort studies, male population, female population, hospital-acquired pneumonia, and longer follow-up duration, the positive relationships were not statistically significant [see Additional file 6]. A meta-regression found that assessment of weight and height might be the source of the high heterogeneity.

Pneumonia mortality risk (overweight and obese versus normal weight)

Overweight and obese individuals were associated with decreased mortality risk (RR = 0.83, 95% CI 0.77 to 0.91, P < 0.01, I 2  = 34%). A small-study effect was revealed by the funnel plot [see Additional file 7].

Six cohort studies were identified in the dose–response analysis [23, 2528, 35]. The summary RR was 0.95 (95% CI 0.93 to 0.98, P < 0.01, I 2  = 77%; Figure 4). There was no evidence of a nonlinear relationship with BMI (P = 0.44; Figure 5). The shape of the funnel plot was asymmetrical, suggesting that there was a small-study effect [see Additional file 8].
https://static-content.springer.com/image/art%3A10.1186%2F1741-7015-12-61/MediaObjects/12916_2013_Article_947_Fig4_HTML.jpg
Figure 4

Relative risks of pneumonia mortality risk per 5 kg/m 2 increase in body mass index. CI: indicates confidence interval; and RR: risk ratio.

https://static-content.springer.com/image/art%3A10.1186%2F1741-7015-12-61/MediaObjects/12916_2013_Article_947_Fig5_HTML.jpg
Figure 5

Dose–response relationship between body mass index and relative risk of pneumonia mortality. Body mass index was modeled with a nonlinear trend (black continuous line) in a random effects meta-regression model. Long-dashed black lines represent 95% confidence intervals. Short-dashed black lines represent the linear trend. The vertical axes are on a log scale.

In sensitivity analyses, no result was significantly altered when excluding studies one by one (data not shown). Stratified analyses were defined by study design, gender, assessment of weight and height, and follow-up duration. Studies comparing overweight and obese subjects with normal weight subjects, and studies assessing dose–response associations were explored, respectively. The inverse relationship between BMI and risk of mortality was significant in subgroups that were defined by ascertainment of case and study design. However, in the female subgroup or studies with shorter follow-up duration, the inverse relationship was not statistically significant [see Additional file 9]. Study design was found to be the major source of heterogeneity by meta-regression.

Discussion

In this meta-analysis, we showed that an ‘obesity survival paradox’ might exist for pneumonia. On the one hand, there was a positive association of obesity with pneumonia risk. On the other hand, pneumonia mortality was lower for patients with high BMI compared to normal BMI.

In a recent meta-analysis, Phung and coworkers found a J-shaped relationship between BMI and risk of CAP and a U-shaped relationship between BMI and risk of influenza-related pneumonia [36]. Our results were partly similar to this previous report. In the current meta-analysis, we only investigated the association between higher BMI and pneumonia risk, but did not assess the association between subnormal BMI and pneumonia risk. Thus, the shape of our dose–response relationship deviated from J-shaped or U-shaped.

There were several potential explanations for why obese individuals may have higher risk of pneumonia. First, obesity is often accompanied by co-morbid conditions, such as gastroesophageal reflux disease [37]. When reflux of gastric fluid occurs, the fluid can be aspirated into the respiratory tract resulting in pneumonia [37]. Second, obesity has been shown to be an independent predictor of diabetes and asthma. These two diseases are also important risk factors for pneumonia [38, 39]. Third, a recent study reported that a higher BMI led to lower 25(OH)D3 levels [40]. More recently, Aregbesola and colleagues found that subjects with a lower serum 25(OH)D3 concentration had a higher risk of pneumonia [41]. Fourth, leptin (ob/ob) and leptin receptor (db/db) deficient mice showed severe immune abnormalities and greater susceptibility to viral and bacterial infection [42]. Individuals with this leptin defect also exhibited greater susceptibility to respiratory infections [43]. Therefore, leptin plays an important role in the human immune response to infectious disease. Although there were elevated leptin levels in obese subjects, leptin resistance often coexisted with these persons [44]. Taken together, these results suggest that individuals with high BMI might have an increased risk of pneumonia compared to subjects with normal BMI.

This present meta-analysis suggested a survival advantage for obese patients with pneumonia. A study by LaCroix et al. showed that the risk of pneumonia mortality was 2.6 times higher in men of the lowest BMI quartile compared to the highest quartile [20]. Another study by Salive et al. indicated that the highest two quartiles of BMI had a significantly reduced risk of mortality compared with the lowest quartile [21]. We propose three explanations for the inverse relationship between obesity and the risk of pneumonia mortality. First, obese individuals have a higher risk of developing coronary heart disease, type II diabetes, and heart failure [45]. Thus, obese patients with pneumonia may receive optimal medical treatment or aggressive treatment. This may lead to a reduction in mortality. Second, tumor necrosis factor-alpha (TNF-α) is a potential proinflammatory cytokine that plays a critical role in inflammatory and immune responses. Puren et al.[46] indicated that the plasma level of TNF-α is a marker of pneumonia severity. Adipose tissue is known to produce soluble TNF-α receptors [47]. Additionally, recent studies showed that obese patients with pneumonia had lower pneumonia severity index scores and plasma levels of C-reactive protein [27, 28]. Third, as with other diseases, patients with pneumonia who are at a normal weight may not have enough metabolic reserve to counteract the increased catabolic stress. These patients may be particularly vulnerable to the adverse pathophysiologic consequences of a limited metabolic reserve.

There are several limitations of the current study. First, a meta-analysis of observational studies inherits the limitation of the original studies. Although most studies adjusted for potential confounders, such as age, gender, smoking and underlying diseases, the possibility of residual confounding cannot be ruled out. Because this meta-analysis investigated only BMI, we cannot exclude the possibility that the observed associations may be confounded by other lifestyle factors, such as lower physical activity or dietary factors. Second, the number of available studies that were included in this meta-analysis was moderate. Therefore, the results could be influenced by some factors,such as random error. Third, most of the studies used International Classification of Diseases (ICD)-9 or ICD-10 codes to classify pneumonia. van de Garde and coworkers suggested that ICD-9 codes showed modest sensitivity for detecting CAP, leaving at least one quarter of pneumonia cases undetected [48]. Fourth, statistical heterogeneity was detected when quantitative pooling was performed. In addition, small-study effects were detected in this study. Thus, caution with interpretation of the results is necessary, and these results should be confirmed by future studies.

Conclusions

This meta-analysis suggests that obese individuals may be at higher risk for pneumonia, but they might have a lower mortality risk. Additional prospective studies with adjustment for more confounding factors are warranted before a conclusion can be drawn.

Notes

Abbreviations

BMI: 

body mass index

CAP: 

community-acquired pneumonia

CI: 

confidence interval

HAP: 

hospital-acquired pneumonia

ICD: 

International Classification of Diseases

NOS: 

Newcastle–Ottawa Scale

RR: 

risk ratio

TNF-α: 

tumor necrosis factor-alpha.

Declarations

Acknowledgements

This study was supported by grants No. 81170025 from National Natural Science Foundation of China and projects of “Major New Drugs Innovation and Development” from the National Ministry of Science and Technology (No. 2011ZX09302-003-001). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Authors’ Affiliations

(1)
Department of Respiratory Medicine, Shanghai Changzheng Hospital, Second Military Medical University
(2)
Department of Clinical Nutrition
(3)
Department of Epidemiology and Health Promotion, Graduate School of Public Health, Yonsei University

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