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This article is part of the series Obesity: exploring the causes, consequences and solutions.

Open Access Highly Accessed Research article

Obesity survival paradox in pneumonia: a meta-analysis

Wei Nie1, Yi Zhang2, Sun Ha Jee3, Keum Ji Jung3, Bing Li1* and Qingyu Xiu1*

Author Affiliations

1 Department of Respiratory Medicine, Shanghai Changzheng Hospital, Second Military Medical University, 415 Fengyang Road, Shanghai 200003, China

2 Department of Clinical Nutrition, The 452nd Hospital of PLA, Chengdu 610000, Sichuan, China

3 Department of Epidemiology and Health Promotion, Graduate School of Public Health, Yonsei University, Seoul 120-749, Korea

For all author emails, please log on.

BMC Medicine 2014, 12:61  doi:10.1186/1741-7015-12-61

The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1741-7015/12/61


Received:26 November 2013
Accepted:13 March 2014
Published:10 April 2014

© 2014 Nie et al.; licensee BioMed Central Ltd.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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, I2 = 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, I2 = 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, I2 = 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, I2 = 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 [5-7]. 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 [8-28]. 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 [26-28]. 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.

Additional file 1. Search strategy.

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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.

Additional file 2. Check list for methodological quality assessment and quality scores of cohort studies using the Newcastle-Ottawa Scale.

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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 [32-34].

Statistical heterogeneity among studies was evaluated using the Q and I2 statistics. For the I2 metric, we considered low, moderate and high I2 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 [8-28]. 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 [8-28,35].

thumbnailFigure 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 [8-19], and ten studies (n = 1,375,482) assessed the association between BMI and pneumonia mortality [20-28,35]. There were seven retrospective cohort studies [11,15-18,25,26] and fifteen prospective cohort studies [8-10,12-14,19-24,27,28,35]. The durations of follow-up varied from 1 year to 15.8 years. Sixteen studies collected measured BMI [8,10-15,19,20,23-28,35]; 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

Additional file 3. PRISMA checklist.

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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, I2 = 87%). A small-study effect was demonstrated using a funnel plot [see Additional file 4]. Ten studies reported RRs for categorized BMI levels [9-17,19]. 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, I2 = 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].

thumbnailFigure 2. Relative risks of pneumonia risk per 5 kg/m2 increase in body mass index. CI: indicates confidence interval; and RR: risk ratio.

thumbnailFigure 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.

Additional file 4. Funnel plot of the association between obesity and pneumonia risk.

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Additional file 5. Funnel plot of the association between obesity and pneumonia risk in dose-response analysis.

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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.

Additional file 6. Subgroup analyses of pneumonia risk, overweight and obesity versus normal weight and dose–response analyses, respectively.

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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, I2 = 34%). A small-study effect was revealed by the funnel plot [see Additional file 7].

Additional file 7. Funnel plot of the association between obesity and pneumonia mortality risk.

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Six cohort studies were identified in the dose–response analysis [23,25-28,35]. The summary RR was 0.95 (95% CI 0.93 to 0.98, P < 0.01, I2 = 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].

thumbnailFigure 4. Relative risks of pneumonia mortality risk per 5 kg/m2increase in body mass index. CI: indicates confidence interval; and RR: risk ratio.

thumbnailFigure 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.

Additional file 8. Funnel plot of the association between obesity and pneumonia mortality risk in dose-response analysis.

Format: PPTX Size: 100KB Download fileOpen Data

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.

Additional file 9. Subgroup analyses of pneumonia mortality risk, overweight and obesity versus normal weight and dose–response analyses, respectively.

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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.

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.

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

WN searched the papers, created tables, and wrote the manuscript. YZ searched the papers, created tables, and wrote the manuscript. SHJ analyzed and provided data. KJJ analyzed and provided data. BL reviewed the study proposal and contributed to the manuscript writing. QX reviewed the study proposal and contributed to the manuscript writing. All authors read and approved the final manuscript.

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.

References

  1. Baskin ML, Ard J, Franklin F, Allison DB: Prevalence of obesity in the United States.

    Obes Rev 2005, 6:5-7. PubMed Abstract | Publisher Full Text OpenURL

  2. Berrington de Gonzalez A, Hartge P, Cerhan JR, Flint AJ, Hannan L, MacInnis RJ, Moore SC, Tobias GS, Anton-Culver H, Freeman LB, Beeson WL, Clipp SL, English DR, Folsom AR, Freedman DM, Giles G, Hakansson N, Henderson KD, Hoffman-Bolton J, Hoppin JA, Koenig KL, Lee IM, Linet MS, Park Y, Pocobelli G, Schatzkin A, Sesso HD, Weiderpass E, Willcox BJ, Wolk A, et al.: Body-mass index and mortality among 1.46 million white adults.

    N Engl J Med 2010, 363:2211-2219. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  3. Lavie CJ, Milani RV, Ventura HO: Obesity and cardiovascular disease: risk factor, paradox, and impact of weight loss.

    J Am Coll Cardiol 2009, 53:1925-1932. PubMed Abstract | Publisher Full Text OpenURL

  4. Narayan KM, Boyle JP, Thompson TJ, Gregg EW, Williamson DF: Effect of BMI on lifetime risk for diabetes in the U.S.

    Diabetes Care 2007, 30:1562-1566. PubMed Abstract | Publisher Full Text OpenURL

  5. Lavie CJ, Alpert MA, Arena R, Mehra MR, Milani RV: Impact of obesity and the obesity paradox on prevalence and prognosis in heart failure.

    JACC Heart Failure 2013, 1:93-102. PubMed Abstract | Publisher Full Text OpenURL

  6. De Schutter A, Lavie CJ, Milani RV: The impact of obesity on risk factors and prevalence and prognosis of coronary heart disease—the obesity paradox.

    Prog Cardiovasc Dis 2014, 56:401-408. PubMed Abstract | Publisher Full Text OpenURL

  7. Carnethon MR, De Chavez PJ, Biggs ML, Lewis CE, Pankow JS, Bertoni AG, Golden SH, Liu K, Mukamal KJ, Campbell-Jenkins B, Dyer AR: Association of weight status with mortality in adults with incident diabetes.

    JAMA 2012, 308:581-590. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  8. Delgado-Rodríguez M, Medina-Cuadros M, Martínez-Gallego G, Sillero-Arenas M: Usefulness of intrinsic surgical wound infection risk indices as predictors of postoperative pneumonia risk.

    J Hosp Infect 1997, 35:269-276. PubMed Abstract | Publisher Full Text OpenURL

  9. Baik I, Curhan GC, Rimm EB, Bendich A, Willett WC, Fawzi WW: A prospective study of age and lifestyle factors in relation to community-acquired pneumonia in US men and women.

    Arch Intern Med 2000, 160:3082-3088. PubMed Abstract | Publisher Full Text OpenURL

  10. Newell MA, Bard MR, Goettler CE, Toschlog EA, Schenarts PJ, Sagraves SG, Holbert D, Pories WJ, Rotondo MF: Body mass index and outcomes in critically injured blunt trauma patients: weighing the impact.

    J Am Coll Surg 2007, 204:1056-1061. PubMed Abstract | Publisher Full Text OpenURL

  11. Yap CH, Zimmet A, Mohajeri M, Yii M: Effect of obesity on early morbidity and mortality following cardiac surgery.

    Heart Lung Circ 2007, 16:31-36. PubMed Abstract | Publisher Full Text OpenURL

  12. Dossett LA, Heffernan D, Lightfoot M, Collier B, Diaz JJ, Sawyer RG, May AK: Obesity and pulmonary complications in critically injured adults.

    Chest 2008, 134:974-980. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  13. Mannino DM, Davis KJ, Kiri VA: Chronic obstructive pulmonary disease and hospitalizations for pneumonia in a US cohort.

    Respir Med 2009, 103:224-229. PubMed Abstract | Publisher Full Text OpenURL

  14. Kornum JB, Nørgaard M, Dethlefsen C, Due KM, Thomsen RW, Tjønneland A, Sørensen HT, Overvad K: Obesity and risk of subsequent hospitalisation with pneumonia.

    Eur Respir J 2010, 36:1330-1336. PubMed Abstract | Publisher Full Text OpenURL

  15. Morgan OW, Bramley A, Fowlkes A, Freedman DS, Taylor TH, Gargiullo P, Belay B, Jain S, Cox C, Kamimoto L, Fiore A, Finelli L, Olsen SJ, Fry AM: Morbid obesity as a risk factor for hospitalization and death due to 2009 pandemic influenza A(H1N1) disease.

    PLoS One 2010, 5:e9694. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  16. Blumentals WA, Nevitt A, Peng MM, Toovey S: Body mass index and the incidence of influenza-associated pneumonia in a UK primary care cohort.

    Influenza Other Respir Viruses 2012, 6:28-36. PubMed Abstract | Publisher Full Text OpenURL

  17. Kwong JC, Campitelli MA, Rosella LC: Obesity and respiratory hospitalizations during influenza seasons in Ontario, Canada: a cohort study.

    Clin Infect Dis 2011, 53:413-421. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  18. Viasus D, Paño-Pardo JR, Pachón J, Campins A, López-Medrano F, Villoslada A, Fariñas MC, Moreno A, Rodríguez-Baño J, Oteo JA, Martínez-Montauti J, Torre-Cisneros J, Segura F, Gudiol F, Carratalà J, Novel Influenza A (H1N1) Study Group of the Spanish Network for Research in Infectious Diseases (REIPI): Factors associated with severe disease in hospitalized adults with pandemic (H1N1) 2009 in Spain.

    Clin Microbiol Infect 2011, 17:738-746. PubMed Abstract | Publisher Full Text OpenURL

  19. Phung D, Wang Z: Risk of pneumonia in relation to body mass index in Australian Aboriginal people.

    Epidemiol Infect 2013, 141:2497-2502. PubMed Abstract | Publisher Full Text OpenURL

  20. LaCroix AZ, Lipson S, Miles TP, White L: Prospective study of pneumonia hospitalizations and mortality of U.S. older people: the role of chronic conditions, health behaviors, and nutritional status.

    Public Health Rep 1989, 104:350-360. PubMed Abstract | PubMed Central Full Text OpenURL

  21. Salive ME, Satterfield S, Ostfeld AM, Wallace RB, Havlik RJ: Disability and cognitive impairment are risk factors for pneumonia-related mortality in older adults.

    Public Health Rep 1993, 108:314-322. PubMed Abstract | PubMed Central Full Text OpenURL

  22. Lange P, Vestbo J, Nyboe J: Risk factors for death and hospitalization from pneumonia. A prospective study of a general population.

    Eur Respir J 1995, 8:1694-1698. PubMed Abstract | Publisher Full Text OpenURL

  23. Inoue Y, Koizumi A, Wada Y, Iso H, Watanabe Y, Date C, Yamamoto A, Kikuchi S, Inaba Y, Toyoshima H, Tamakoshi A: Risk and protective factors related to mortality from pneumonia among middleaged and elderly community residents: the JACC study.

    J Epidemiol 2007, 17:194-202. PubMed Abstract | Publisher Full Text OpenURL

  24. Takata Y, Ansai T, Soh I, Akifusa S, Sonoki K, Fujisawa K, Awano S, Kagiyama S, Hamasaki T, Nakamichi I, Yoshida A, Takehara T: Association between body mass index and mortality in an 80-year-old population.

    J Am Geriatr Soc 2007, 55:913-917. PubMed Abstract | Publisher Full Text OpenURL

  25. 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-e57. PubMed Abstract | Publisher Full Text OpenURL

  26. King P, Mortensen EM, Bollinger M, Restrepo MI, Copeland LA, Pugh MJ, Nakashima B, Anzueto A, Hitchcock NP: Impact of obesity on outcomes for patients hospitalised with pneumonia.

    Eur Respir J 2013, 41:929-934. PubMed Abstract | Publisher Full Text OpenURL

  27. Kahlon S, Eurich DT, Padwal RS, Malhotra A, Minhas-Sandhu JK, Marrie TJ, Majumdar SR: Obesity and outcomes in patients hospitalized with pneumonia.

    Clin Microbiol Infect 2013, 19:709-716. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  28. Singanayagam A, Singanayagam A, Chalmers JD: Obesity is associated with improved survival in community-acquired pneumonia.

    Eur Respir J 2013, 42:180-187. PubMed Abstract | Publisher Full Text OpenURL

  29. Stroup DF, Berlin JA, Morton SC, Olkin I, Williamson GD, Rennie D, Moher D, Becker BJ, Sipe TA, Thacker SB: Meta-analysis of observational studies in epidemiology: a proposal for reporting. Meta-analysis Of Observational Studies in Epidemiology (MOOSE) group.

    JAMA 2000, 283:2008-2012. PubMed Abstract | Publisher Full Text OpenURL

  30. Wells GA, Shea B, O'Connell D, Peterson J, Welch V, Losos M, Tugwell P: The Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses. Available at: http://www.ohri.ca/programs/clinical_epidemiology/oxford.asp webcite. (9 February 2014, date last accessed)

  31. Greenland S, Longnecker MP: Methods for trend estimation from summarized dose–response data, with applications to meta-analysis.

    Am J Epidemiol 1992, 135:1301-1309. PubMed Abstract | Publisher Full Text OpenURL

  32. Orsini N, Greenland S: A procedure to tabulate and plot results after flexible modeling of a quantitative covariate.

    Stata J 2011, 11:1-29. OpenURL

  33. White IR: Multivariate random-effects meta-analysis.

    Stata J 2009, 9:40-56. OpenURL

  34. Orsini N, Li R, Wolk A, Khudyakov P, Spiegelman D: Meta-analysis for linear and nonlinear dose–response relations: examples, an evaluation of approximations, and software.

    Am J Epidemiol 2012, 175:66-73. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  35. Jee SH, Sull JW, Park J, Lee SY, Ohrr H, Guallar E, Samet JM: Body-mass index and mortality in Korean men and women.

    N Engl J Med 2006, 355:779-787. PubMed Abstract | Publisher Full Text OpenURL

  36. Phung DT, Wang Z, Rutherford S, Huang C, Chu C: Body mass index and risk of pneumonia: a systematic review and meta-analysis.

    Obes Rev 2013, 14:839-857. PubMed Abstract | Publisher Full Text OpenURL

  37. Lagergren J: Influence of obesity on the risk of esophageal disorders.

    Nat Rev Gastroenterol Hepatol 2011, 8:340-347. PubMed Abstract | Publisher Full Text OpenURL

  38. Almirall J, Bolíbar I, Serra-Prat M, Roig J, Hospital I, Carandell E, Agustí M, Ayuso P, Estela A, Torres A, Community-Acquired Pneumonia in Catalan Countries (PACAP) Study Group: New evidence of risk factors for community-acquired pneumonia: a population-based study.

    Eur Respir J 2008, 31:1274-1284. PubMed Abstract | Publisher Full Text OpenURL

  39. Kornum JB, Thomsen RW, Riis A, Lervang HH, Schønheyder HC, Sørensen HT: Diabetes, glycemic control, and risk of hospitalization with pneumonia:a population-based case–control study.

    Diabetes Care 2008, 31:1541-1545. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  40. Vimaleswaran KS, Berry DJ, Lu C, Tikkanen E, Pilz S, Hiraki LT, Cooper JD, Dastani Z, Li R, Houston DK, Wood AR, Michaëlsson K, Vandenput L, Zgaga L, Yerges-Armstrong LM, McCarthy MI, Dupuis J, Kaakinen M, Kleber ME, Jameson K, Arden N, Raitakari O, Viikari J, Lohman KK, Ferrucci L, Melhus H, Ingelsson E, Byberg L, Lind L, Lorentzon M, et al.: Causal relationship between obesity and vitamin D status: bi-directional Mendelian randomization analysis of multiple cohorts.

    PLoS Med 2013, 10:e1001383. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  41. Aregbesola A, Voutilainen S, Nurmi T, Virtanen JK, Ronkainen K, Tuomainen TP: Serum 25-hydroxyvitamin D3 and the risk of pneumonia in an ageing general population.

    J Epidemiol Community Health 2013, 67:533-536. PubMed Abstract | Publisher Full Text OpenURL

  42. Mancuso P: Obesity and respiratory infections: does excess adiposity weigh down host defense?

    Pulm Pharmacol Ther 2013, 26:412-419. PubMed Abstract | Publisher Full Text OpenURL

  43. Ozata M, Ozdemir IC, Licinio J: Human leptin deficiency caused by a missense mutation: multiple endocrine defects, decreased sympathetic tone, and immune system dysfunction indicate new targets for leptin action, greater central than peripheral resistance to the effects of leptin, and spontaneous correction of leptin-mediated defects.

    J Clin Endocrinol Metab 1999, 84:3686-3695. PubMed Abstract | Publisher Full Text OpenURL

  44. Myers MG Jr, Leibel RL, Seeley RJ, Schwartz MW: Obesity and leptin resistance: distinguishing cause from effect.

    Trends Endocrinol Metab 2010, 21:643-651. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  45. Goel K, Lopez-Jimenez F, De Schutter A, Coutinho T, Lavie CJ: Obesity paradox in different populations: evidence and controversies.

    Future Cardiol 2014, 10:81-91. PubMed Abstract | Publisher Full Text OpenURL

  46. Puren AJ, Feldman C, Savage N, Becker PJ, Smith C: Patterns of cytokine expression in community-acquired pneumonia.

    Chest 1995, 107:1342-1349. PubMed Abstract | Publisher Full Text OpenURL

  47. Mohamed-Ali V, Goodrick S, Bulmer K, Holly JM, Yudkin JS, Coppack SW: Production of soluble tumor necrosis factor receptors by human subcutaneous adipose tissue in vivo.

    Am J Physiol 1999, 277:E971-E975. PubMed Abstract | Publisher Full Text OpenURL

  48. van de Garde EM, Oosterheert JJ, Bonten M, Kaplan RC, Leufkens HG: International classification of diseases codes showed modest sensitivity for detecting community-acquired pneumonia.

    J Clin Epidemiol 2007, 60:834-838. PubMed Abstract | Publisher Full Text OpenURL

Pre-publication history

The pre-publication history for this paper can be accessed here:

http://www.biomedcentral.com/1741-7015/12/61/prepub