Skip to main content
Download PDF
Download ePub

Higher body mass index was associated with a lower mortality of idiopathic pulmonary fibrosis: a meta-analysis

Journal of Health, Population and Nutrition volume 43, Article number: 124 (2024) Cite this article

Abstract

Purpose

In the past few years, there has been a notable rise in the incidence and prevalence of idiopathic pulmonary fibrosis (IPF) on a global scale. A considerable body of research has highlighted the ‘obesity paradox,’ suggesting that a higher body mass index (BMI) can confer a protective effect against numerous chronic diseases. However, the relationship between BMI and the risk of mortality in IPF patients remains underexplored in the existing literature. We aim to shed light on this relationship and potentially offer novel insights into prevention strategies for IPF.

Methods

We conducted a systematic search of the PubMed, Embase, and Web of Science databases to collect all published studies examining the correlation between Body Mass Index (BMI) and the mortality risk in patients with IPF, up until February 14, 2023. For the synthesis of the findings, we employed random-effects models. The statistical significance of the association between BMI and the mortality risk in IPF patients was evaluated using the hazard ratio (HR), with the 95% Confidence Interval (CI) serving as the metric for effect size.

Results

A total of 14 data sets involving 2080 patients with IPF were included in the meta-analysis. The combined results of the random-effects models were suggestive of a significant association between lower BMI and a higher risk of death (HR = 0.94, 95% CI = 0.91–0.97, P < 0.001). For baseline BMI, the risk of death from IPF decreased by 6% for each unit increase. The results of the subgroup analysis suggest that geographic location (Asian subgroup: HR = 0.95, 95%CI = 0.93–0.98, P = 0.001; Western subgroup: HR = 0.91, 95%CI = 0.84–0.98, P = 0.014), study type (RCS subgroup: HR = 0.95, 95%CI = 0.92–0.98, P = 0.004; PCS subgroup: HR = 0.89, 95%CI = 0.84–0.94, P < 0.001), and sample size (< 100 groups: HR = 0.93, 95%CI = 0.87–1.01, P = 0.079; >100 groups: HR = 0.94, 95%CI = 0.91–0.97, P < 0.001 ) were not significant influences on heterogeneity. Of the included literature, those with confounding factors corrected and high NOS scores reduced heterogeneity (HR = 0.93, 95%CI = 0.90–0.96, P < 0.001). Sensitivity analyses showed that the combined results were stable and not significantly altered by individual studies (HR = 0.93 to 0.95, 95% CI = 0.90–0.96 to 0.92–0.98). Egger’s test suggested no significant publication bias in the included studies (P = 0.159).

Conclusions

Higher BMI (BMI ≥ 25 kg/m2) is negatively correlated to some extent with the risk of death in IPF patients, and BMI may become a clinical indicator for determining the prognosis of IPF patients.

Introduction

IPF is a chronic, progressive, fibrous interstitial pneumonia of unknown etiology. The currently recognized mechanism of IPF is that it is a chronic inflammatory and abnormal repair process. In persistent microinjury of the alveolar epithelium from multiple causes, abnormal activation of fibroblasts and excessive accumulation of extracellular matrix lead to an abnormal repair process of lung scar formation as the inflammatory response progresses [1,2,3]. In the United States, the median age of newly diagnosed patients is 62 years, 54% of whom are men [4]. The global incidence and prevalence of IPF is between 0.09 and 1.3 per 10,000 people with a rising trend year by year. Compared to other countries studied, the United States, Korea, and Canada have the highest incidence rates, with a median survival of less than 4 years and a worse prognosis than many common cancers [5, 6]. Although corresponding epidemiological data are lacking in China, clinical practice has revealed a significant trend of increasing IPF cases in recent years. The etiology of IPF remains unclear. Several studies have identified potential risk factors, including genetic alterations, viral infections, lifestyle habits, environmental influences, and occupational hazards. However, current evidence suggests that IPF is the result of a complex interaction between genetic and environmental factors. In addition, the occurrence of several diseases can increase the incidence of IPF, such as gastroesophageal reflux (GERD) [7, 8], diabetes (DM) [9], and obstructive sleep apnea (OSA) [10]. Two introduced antifibrotic drugs, pirfenidone and nintedanib, may significantly delay the decline in lung function and reduce the incidence and severity of associated complications [6]. However, they are not a cure for IPF. Therefore, the continuous exploration of new specific biomarkers and therapeutic targets will become a major trend in the future.

Obesity is a growing global health problem that affects multiple organ systems and is a potentially modifiable factor in many diseases. Studies have shown that overweight or obesity also increases the risk of a number of diseases, such as: hypertension, dyslipidemia, type 2 diabetes mellitus, metabolic syndrome, chronic kidney disease, coronary heart disease, cerebral vascular lesions, gallbladder stones, arthropathies, polycystic ovary syndrome, sleep apnea syndrome, and a number of tumors [11]. Current research suggests that the mechanisms of action of obesity in IPF may be related to factors such as chronic inflammation, oxidative stress, and insulin resistance, all of which may be associated with obesity-induced ectopic fat deposition. Obesity-induced low-grade aseptic inflammation and over-infiltration of immune cells in adipose tissue is accompanied by a decreased ability of adipose tissue to store lipids, leading to ectopic fat deposition, which accelerates the onset and progression of pulmonary fibrosis [12, 13]. However, multiple studies have reported that adipose tissue plays a protective role, which is called the “obesity paradox” [14]. A French study showed that nearly one-third of IPF patients were malnourished, and the worse the nutritional status of the patients, the worse their prognosis [15]. Due to the ease of measurement and relatively high acceptance, BMI has been adopted by most research institutes as an indicator of obesity and health status. It has been found that higher BMI increases the risk of IPF, but the degree of pulmonary fibrosis and the risk of death are low relative to patients with lower BMI, and more refined subgroups have been found to be associated with a higher risk of death with lower BMI [14, 16]. In addition, in antifibrotic therapy, higher BMI patients also have relatively better treatment outcomes [17]. However, the existence of an “obesity paradox” in IPF patients is controversial, with some studies finding no correlation between BMI and mortality [18].

Currently, many research studies have focused on the diagnosis and treatment modalities of IPF, while other risk factors influencing disease pathogenesis and disease prognosis have received less attention. The available studies suggest that BMI and the risk of death from IPF are uncertain [19,20,21,22].

Therefore, this study systematically reviewed the relevant literature and defined the relationship between BMI and the mortality of IPF, aiming to provide basis for evaluating the prognosis of IPF patients.

Materials and methods

This meta-analysis was planned and conducted according to the Preferred Reporting Items for Systematic Evaluation and Meta-Analysis (PRISMA) list [23]. In addition, since the included studies were observational in design, we also followed the Meta-analysis of Observational Studies in Epidemiology (MOOSE) guidelines [24].

Literature search strategy

In this article, a literature search was conducted through PubMed, Embase, and Web of Science databases according to a pre-defined search strategy. The search terms included: “idiopathic pulmonary fibrosis”, “body mass index”, and “BMI”. Subject terms were searched in combination with free terms, adapting the search formula to the characteristics of the database (see Supplementary Tables 1–3 for specific search steps of the database). The search was conducted up to February 14, 2023, with no language restrictions. In addition, this study screened references of relevant reviews and included literature in the hope of obtaining more studies that could be used for meta-analysis.

Inclusion and exclusion criteria

The following were eligible for inclusion in this study: (1) the study population was patients diagnosed with IPF; (2) the exposure factor was BMI at baseline; (3) the study reported a hazard ratio (HR) (95% CI) for change in risk of death with a 1 kg/m2 increase in BMI; and (4) the study type was a prospective or retrospective cohort study.

Meanwhile, we excluded the following: (1) non-thesis studies such as reviews, conference abstracts, and commentaries; (2) studies with acute exacerbation of IPF; (3) for duplicate publications or the same data used in multiple articles, only the one with the most complete study information was included, and the rest were excluded. Two of the authors independently searched all references, and any discrepancies were resolved by all authors on a voting basis.

Data extraction

Two investigators (Xiaomin Su and Dengyun Pan) independently completed the literature screening according to the inclusion and exclusion criteria described above. After identifying the literature to be included in the analysis, they carried out the data extraction exercise independently according to a pre-designed form. For each study, the following information was collected: name of the first author, publication year, region where the study was conducted, study type, basic characteristics of the study population (sample size, sex, age), follow-up time, drugs used to treat IPF, study outcome and correction factors. After both of them finished the above data extraction work, they exchanged audit extraction forms and discussed and resolved any inconsistencies.

Quality assessment

Quality assessment of eligible studies was performed by two researchers (Xiaomin Su and Bingdi Yan) using the Newcastle-Ottawa Quality Assessment Scale (NOS), which is a validated scale for use in non-randomized observational studies [25]. The evaluation included three aspects of study subject selection, comparability, and exposure. Scores ranged from 0 to 9 points. Depending on the score, they can be classified as low (0–3 points), medium (4–6 points) and high-quality studies (7–9 points).

Statistical analysis

HR and 95% CI were used as effect sizes to assess whether the association between BMI and the risk of death in patients with IPF was statistically significant. Heterogeneity was tested using Cochran’ s Q test and I2 test. For the Q statistic, P < 0.05, or I2 > 50%, indicated significant heterogeneity between studies, and a random-effects model was used for meta-analysis; if P ≥ 0.05 and I2 ≤ 50%, heterogeneity was not significant and a fixed-effects model was used [26].

Subgroup analysis was performed based on geographic location, study type, sample size, whether to correct for confounders, and methodological quality grouping. The effect of individual included studies on the results of the meta-analysis was evaluated for significance using the one-by-one exclusion test. Egger’s test was used to assess whether there was significant publication bias in the included studies [27]. If significant publication bias existed, the stability of the combined results was assessed using the cut-and-patch method [28].

The above statistical analysis was done using Stata 12.0 software.

Results

Literature search

A total of 1270 publications (163 in PubMed, 904 in Embase, and 203 in Wen of Science) were searched in electronic databases for this meta-analysis. Two hundred ninety-two duplicates were excluded, and 957 papers that clearly did not meet the inclusion criteria were excluded after browsing the titles and abstracts. Finally, 21 publications were excluded after full-text reading of 8, and the remaining 13 were included in the meta-analysis. The details of literature search are shown in Fig. 1.

Fig. 1
figure 1

Literature search results and flowchart of the literature screening process

Full size image

Study characteristics and quality assessment

The characteristics of the participating studies and patients are shown in Table 1. As of February 14, 2023, a total of 13 papers were included in this meta-analysis. Ten of them were retrospective cohort studies [9, 19, 29,30,31,32,33,34,35,36] and three were prospective cohort studies [20, 37, 38]. Study publication years range from 2007 to 2022. Nine of these studies were conducted in Asia [9, 29,30,31,32,33,34,35, 38], including Saudi Arabia (n = 1), China (n = 1), Japan (n = 6) and Korea (n = 1); Five were conducted in Europe [20, 30, 36, 37], which includes France (n = 1), Italy (n = 2) and the UK (n = 1); One was conducted in the United States (n = 1), located in North America. The baseline mean/median BMI values in these studies ranged between 21 and 30 kg/m2. Twelve studies assessed the BMI as a continuous variable [9, 19, 20, 29, 31,32,33, 35,36,37,38], and one as both a continuous variable and a cut-off value [34]. The 14 data sets included in this study involve a combined total of 2,080 individuals (1657 males and 423 females). The sample size ranged from 44 to 445 cases. Baseline BMI of study subjects was obtained from medical records, and survival status was confirmed by medical records or telephone callbacks. Information on age, treatment protocol study outcomes, and correction factors are shown in Table 1.

The NOS scores of the included studies ranged from 4 to 8 points. Thus, nine of the included studies were of high-quality [9, 19, 20, 29, 31, 33,34,35,36] and the others were of moderate methodological quality [30, 32, 37, 38]. The main types of bias in the included studies were recall bias and confounding bias.

Table 1 Characteristics of 13 included studies in this meta-analysis
Full size table

Meta regression

Since the study by Nakatsuka et al. [30]. had data from two cohorts of the study, there were 14 data sets. A forest plot of the association analysis between BMI and the risk of death from IPF is shown in Fig. 2. The results of the heterogeneity test of the included studies suggested a statistically significant heterogeneity (I2 = 46.3%, P = 0.029). And the random effects model was applied, the summary HR for 14 data sets showed that the association between BMI and the risk of death in IPF patients is significant (HR = 0.94, 95%CI = 0.91–0.97, P < 0.001). For baseline BMI, the risk of death from IPF decreased by 6% for each unit increase.

Fig. 2
figure 2

Forest plot of BMI and risk of death in patients with IPF

Full size image

Subgroup analysis

The results of the subgroup analysis are shown in Table 2, based on geographic location, study type, sample size, whether or not to correct for confounding factors, and methodological quality grouping.

Table 2 Subgroup analyses of the relationship between BMI and mortality
Full size table

The combined results of Asian subgroup (HR = 0.95, 95%CI = 0.93–0.98, P = 0.001) and Western subgroup (HR = 0.91, 95%CI = 0.84–0.98, P = 0.014) were statistically significant (Fig. 3A). Similarly, according to the study type grouping (Fig. 3B), the combined results of the two subgroups were significantly (RCS subgroup: HR = 0.95, 95%CI = 0.92–0.98, P = 0.004; PCS subgroup: HR = 0.89, 95%CI = 0.84–0.94, P < 0.001). However, subgroup analysis of sample size (Fig. 3C), for < 100 groups, combined results were not meaningful (HR = 0.93, 95%CI = 0.87–1.01, P = 0.079). In addition, geographic location, study type, and sample size were not influential factors on heterogeneity in any significant way. The grouping scheme of whether to correct for confounding factors and methodological quality grouping was consistent, and the within-group heterogeneity of subgroups was not at a significant level (Fig. 3D and E), which indicates that these two factors are sources of heterogeneity. Studies with high multifactor correction or methodological quality had statistically significant combined results (HR = 0.93, 95%CI = 0.90–0.96, P < 0.001). Furthermore, considering the significant role of Forced Vital Capacity (FVC) in IPF patients, we conducted a subgroup analysis correcting for FVC. The results indicate that the pooled outcomes are statistically significant, regardless of whether FVC adjustments were applied or not (Fig. 3F).

Fig. 3
figure 3

Subgroup analysis: geographic location (A), types of studies (B), sample size (C), correcting for confounders and methodological quality(D), quality of studies (E), correcting for FVC(F). RCS, Retrospective cohort study; PCS, Prospective cohort study

Full size image

Sensitivity analysis and publication Bias test

Sensitivity analysis showed that excluding the literature one by one, the value of the change in the combined results was HR (95% CI): 0.93 (0.90, 0.96) to 0.95 (0.92, 0.98), and excluding any one of the studies, the combined results of the remaining studies had a P < 0.05. Therefore, the combined results were stable and did not change significantly by individual studies (Fig. 4).

Fig. 4
figure 4

sensitivity analysis

Full size image

Egger’s test was used to assess whether there was significant publication bias between studies, and the results showed P = 0.159 (Fig. 5), suggesting that there was no significant publication bias in the included studies.

Fig. 5
figure 5

Egger’s test for detecting publication bias in included studies

Full size image

Discussion

IPF is the most common type of (Idiopathic interstitial pneumonias, IIPs) and has a high degree of individual variability in prognosis. IPF is common in patients over 60 years of age, especially in men who smoke. According to epidemiologic data, the prevalence of IPF is increasing year by year, and the prognosis of IPF worsens with age. Currently, the diagnosis of IPF is mainly based on HRCT imaging and histopathological examination. IPF is incurable, and the main clinical methods are to slow down the progression of the disease, improve the quality of life and prolong the survival period through antifibrotic drug treatment and rehabilitation training. Therefore, the search for markers for the diagnosis and treatment of IPF and for predicting prognosis is crucial.

Studies have found that the 6-minute walk test (6MWT) and its alterations were found to be highly correlated with the prognosis of IPF. 1-year mortality was 2.65 times higher in patients with a 6MWT < 250 m than in patients with a 6MWT ≥ 350 m, and 1-year mortality in patients with a 6-month alteration of > 50 m was 4 times higher in patients with a 6-month alteration ≤ 25 m [39]. Lung function can also play a role in the prognosis of IPF, with the rate of decline in forced vital capacity of percentage prediction (FVC%pred) being more indicative of disease progression and a better predictor of patient prognosis [40]. In addition, Ley et al [41] proposed a four-component gender, age, FVC%pred and Diffusing capacity of the lung for carbon monoxide of percentage prediction (DLCO%pred) gender- age-lung function model and a composite physiological index reflecting the degree of pulmonary fibrosis proposed by Wells et al [42] are also good predictors of IPF prognosis. Currently, the biomarkers identified are Krebs von den lungen 6 (KL-6), Matrix metalloproteinase 7 (MMP-7), chemokines and their ligands, Surfactant associated protein A (SP-A), SP-D and other markers [43]. However, biomarkers are still relatively unspecific and do not predict disease progression and regression.

Higher BMI has been shown to be a risk factor in IPF and may increase the incidence of IPF [44]. Alakhras et al [19] first reported that there was a negative correlation between BMI and mortality, with relatively lower mortality in patients with higher BMI. This was validated by the results of several subsequent studies. However, some studies have also suggested that higher BMI increases their mortality, especially in IPF patients with lung transplantation. Therefore, this view is still highly controversial.

In this study, the exposure levels of BMI were consistent, and meta-analysis indicated that there was a significant linear association between baseline BMI and the risk of death in patients with IPF. The results of the heterogeneity test for the included studies showed that there was statistically significant heterogeneity. Nevertheless, subgroup analyses found that heterogeneity could be avoided by studies that were corrected for confounders or used high methodological quality. The results by the one-by-one exclusion method suggested that the combined results were not dramatically changed by individual studies, and the results were stable. Moreover, the studies we included had no significant publication bias test and were highly credible.

Over the past few decades, BMI has been shown to be a risk factor for a number of diseases, including cardiovascular disease, diabetes, subarachnoid haemorrhage, cerebral haemorrhage, and some cancers [45]. This has led to active lifestyle interventions aimed at reducing this risk for individuals and populations. However, in recent years, the newly proposed idea of the “obesity paradox” has caused great controversy. This refers to the fact that individuals with relatively higher values of BMI have a better prognosis than those with lower values of BMI, which has been validated in COPD, cardiovascular disease, certain cancers and other diseases [46].

Alakhras et al. [19]. were the first to report a significant relationship between BMI and survival in 197 patients with IPF. Three groups (< 25, 25–30, and > 30 kg/m2) were categorized according to BMI, and median survival was 3.6, 3.8, and 5.8 years, respectively. Proportional risk regression showed a significant, independent negative association between baseline BMI and mortality. Yoo et al. [35]. similarly reported that lower baseline BMI was independently associated with higher three-year mortality among 445 patients with IPF after adjusting for several confounders including the Charlson Comorbidity Index, disease progression, and acute exacerbations. Suzuki et al. [34]. conducted a cohort study of 208 patients with IPF in two groups receiving antifibrotic therapy with pirfenidone or nintedanib. A significant, negative and independent association with five-year mortality was observed when BMI was considered as a continuous variable and a threshold value of 24.1 kg/m2 was used. Similarly, Jouneau et al. [20]. found that patients with a baseline BMI < 25 kg/m2, or an annual weight loss of > 0–5% or ≥ 5% may have a poorer prognosis at 1 year than patients with a BMI ≥ 25 kg/m2 or no weight loss. This negative correlation was also found in a study by Mura et al [37] (HR = 0.89, 95% CI = 0.80–0.98, p = 0.0155), but was not corrected for other confounders.

This is consistent with the results of our analysis. BMI and risk of death from IPF were significantly associated. We also found that for baseline BMI, the risk of death from IPF decreased by 6% for each unit increase. This is also consistent with the findings of Alakhras et al. [19]. (HR = 0.93, 95%CI = 0.89–0.97, P = 0.002 per 1-U increase in BMI).

Internationally we regard a BMI ≥ 30 kg/m2 as obese, but the use of this value alone as a criterion to define obesity in many studies is controversial. Body composition variability in patients with the same BMI, such as the co-occurrence of low muscle and high adipose tissue may occur in different subgroups of BMI. The BMI formula does not take into account body composition (muscle mass vs. adipose tissue content) and distribution (visceral fat vs. subcutaneous fat). Other tests such as dual-energy X-ray absorptiometry (DEXA), computed tomography (CT) and MRI are more expressive of obesity typing. But their application is limited by their cost, radiation and the technical expertise required. Therefore, BMI is more widely used and accepted than other tests [47].

As has been suggested in patients with heart failure, higher BMI in patients with IPF may not be primarily associated with increased fat mass, but rather with increased fat-free mass (muscle mass). This may increase oxygen consumption through increased muscle diffusion, mitochondrial respiratory capacity, and skeletal muscle strength, thereby improving exercise tolerance and cardiorespiratory fitness [48,49,50,51]. Suzuki et al. [34]. showed that BMI was remarkably associated with cross-sectional area of elector spine muscles (ESMCSA) (r = 0.500, P < 0.0001). In COPD this is also known as the muscle mass hypothesis, which states that obese patients are better able to adapt to acute exacerbations due to increased reserves from greater muscle mass [52]. In addition, respiratory failure is one of the leading causes of death in patients with IPF. This theory of muscle mass loss could also explain the poor prognosis of patients with IPF disease, as patients with better tolerance to muscle mass depletion have a higher chance of survival. Based on this hypothesis, we can assume that metabolically healthy overweight and obese patients with higher muscle mass have a higher chance of recovery than the non-obese population [49].

Another theory is that of brown adipose tissue and its anti-inflammatory effects on the body. Brown adipose tissue has properties similar to lean body mass, as it decreases the levels of lipopolysaccharides in the body, which stimulate pro-inflammatory cytokines, thus effectively reducing systemic inflammation levels. White adipose tissue functions in the opposite way and can have deleterious effects on the body. A shift from white adipose tissue to brown fat occurs in some obese individuals [52].

In addition, it may also be a possibility that the interaction between BMI and clinical symptoms in patients with IPF is influenced by disease states, such as heart failure, in which a negative correlation between BMI and poor outcomes has been described [53].

However, there are still some shortcomings in our study. The sample sizes of the included studies were relatively small, and we need more high-quality, large-sample studies to validate the extrapolation of the results. The included studies were all observational and had many confounders. Although most studies performed multifactorial corrections, the corrections were also not uniform and may have exaggerated the strength of the association between BMI and risk of death. Due to the lack of sufficient original studies, this meta-analysis was unable to quantitatively group the associations between different BMI classes, levels of BMI change, and the risk of death, and it is hoped that subsequent studies will focus on these two parts of the results.

Conclusions

Higher BMI (BMI ≥ 25 kg/m2) is negatively correlated to some extent with the risk of death in IPF patients, and BMI may become a clinical indicator for determining the prognosis of IPF patients.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

IPF:

Idiopathic pulmonary fibrosis

BMI:

Body mass index

HR:

Hazard ratio

GERD:

Gastroesophageal reflux

DM:

Diabetes

OSA:

Obstructive sleep apnea

PRISMA:

Preferred reporting items for systematic evaluation and meta-analysis

BMI:

Body mass index

HR:

Hazard ratio

GERD:

Gastroesophageal reflux

DM:

Diabetes

OSA:

Obstructive sleep apnea

PRISMA:

Preferred reporting items for systematic evaluation and meta-analysis

MOOSE:

Meta-analysis of observational studies in epidemiology

NOS:

Newcastle-ottawa quality assessment scale

DEXA:

Dual-energy X-ray absorptiometry

CT:

Computed tomography

TLC:

Total lung capacity

FVC:

Forced vital capacity

Dlco:

Diffusing capacity of the lung for carbon monoxide

GAP:

Gender-age-physiology

RCS:

Retrospective cohort study

PCS:

Prospective cohort study

6MWT:

6-min walk test

PPI:

Proton pump inhibitor

USA:

United States of America

UK:

The United Kingdom of Great Britain and Northern Ireland

ESMCSA:

Cross-sectional area of elector spine muscles

ESMMA:

Muscle attenuation of elector spine muscles

IM:

Azathioprine, Mycophenolate mofetil, Cyclosporine

AISI:

Aggregate systemic inflammation index

NR:

Not reported

M:

Male

F:

Female

IIPs:

Idiopathic interstitial pneumonias

KL-6:

Krebs von den lungen 6

MMP-7:

Matrix metalloproteinase 7

SP-A:

Surfactant associated protein A

FVC%pred:

Forced vital capacity of percentage prediction

DLCO%pred:

Diffusing capacity of the lung for carbon monoxide of percentage prediction

References

  1. Glassberg MK. Overview of idiopathic pulmonary fibrosis, evidence-based guidelines, and recent developments in the treatment landscape. Am J Manag Care. 2019;25(11 Suppl):S195–203.

    PubMed  Google Scholar 

  2. Spagnolo P, Kropski JA, Jones MG, Lee JS, Rossi G, Karampitsakos T, et al. Idiopathic pulmonary fibrosis: disease mechanisms and drug development. Pharmacol Ther. 2021;222:107798.

    Article  CAS  PubMed  Google Scholar 

  3. Moss BJ, Ryter SW, Rosas IO. Pathogenic mechanisms underlying idiopathic pulmonary fibrosis. Annu Rev Pathol. 2022;17:515–46.

    Article  CAS  PubMed  Google Scholar 

  4. Mortimer KM, Bartels DB, Hartmann N, Capapey J, Yang J, Gately R, et al. Characterizing health outcomes in idiopathic pulmonary fibrosis using US health claims data. Respiration. 2020;99(2):108–18.

    Article  PubMed  Google Scholar 

  5. Maher TM, Bendstrup E, Dron L, Langley J, Smith G, Khalid JM, et al. Global incidence and prevalence of idiopathic pulmonary fibrosis. Resp Res. 2021;22(1):197.

    Article  Google Scholar 

  6. Mei Q, Liu Z, Zuo H, Yang Z, Qu J. Idiopathic pulmonary fibrosis: an update on pathogenesis. Front Pharmacol. 2022;12.

  7. Allaix ME, Rebecchi F, Morino M, Schlottmann F, Patti MG. Gastroesophageal reflux and idiopathic pulmonary fibrosis. World J Surg. 2017;41(7):1691–97.

    Article  PubMed  Google Scholar 

  8. Reynolds CJ, Del Greco MF, Allen RJ, Flores C, Jenkins RG, Maher TM et al. The causal relationship between gastro-oesophageal reflux disease and idiopathic pulmonary fibrosis: a bidirectional two-sample mendelian randomisation study. Eur Respir J 2023;61(5).

  9. Alhamad EH, Cal JG, Alrajhi NN, Aharbi WM, AlRikabi AC, AlBoukai AA. Clinical characteristics, comorbidities, and outcomes in patients with idiopathic pulmonary fibrosis. Ann Thorac Med. 2020;15(4):208–14.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Xu L, Lee JH, Jang JH, Park JH, Lee S, Kim JY et al. Prevalence and clinical impacts of obstructive sleep apnea in patients with idiopathic pulmonary fibrosis: a single-center, retrospective study. PLoS ONE. 2023;18(9).

  11. Lin X, Li H. Obesity: epidemiology, pathophysiology, and therapeutics. Front Endocrinol (Lausanne). 2021;12:706978.

    Article  PubMed  Google Scholar 

  12. Cheng X, Jiang S, Pan B, Xie W, Meng J. Ectopic and visceral fat deposition in aging, obesity, and idiopathic pulmonary fibrosis: an interconnected role. Lipids Health Dis. 2023;22(1):201.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Ma Y, Feng C, Tang H, Deng P, Li Y, Wang J, et al. Management of BMI is a potential new approach for the prevention of idiopathic pulmonary fibrosis. Front Genet. 2022;13:821029.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Awano N, Jo T, Yasunaga H, Inomata M, Kuse N, Tone M et al. Body mass index and in-hospital mortality in patients with acute exacerbation of idiopathic pulmonary fibrosis. Erj Open Res. 2021;7(2).

  15. Jouneau S, Kerjouan M, Rousseau C, Lederlin M, Llamas-Guttierez F, De Latour B, et al. What are the best indicators to assess malnutrition in idiopathic pulmonary fibrosis patients? A cross-sectional study in a referral center. Nutrition. 2019;62:115–21.

    Article  PubMed  Google Scholar 

  16. Sangani RG, Ghio AJ, Mujahid H, Patel Z, Catherman K, Wen S, et al. Outcomes of idiopathic pulmonary fibrosis improve with obesity: a rural appalachian experience. South Med J. 2021;114(7):424–31.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Jouneau S, Crestani B, Thibault R, Lederlin M, Vernhet L, Yang M, et al. Post hoc analysis of clinical outcomes in placebo- and pirfenidone-treated patients with IPF stratified by BMI and weight loss. Respiration. 2022;101(2):142–54.

    Article  CAS  PubMed  Google Scholar 

  18. Lee JS, Martin-Schwarze A, Freiheit E, Trzaskoma B, Burg C. Real-world clinical outcomes based on body mass index and annualized weight change in patients with idiopathic pulmonary fibrosis. Adv Ther. 2023;40(2):691–704.

    Article  PubMed  Google Scholar 

  19. Alakhras M, Decker PA, Nadrous HF, Collazo-Clavell M, Ryu JH. Body mass index and mortality in patients with idiopathic pulmonary fibrosis. Chest. 2007;131(5):1448–53.

    Article  PubMed  Google Scholar 

  20. Jouneau S, Rousseau C, Lederlin M, Lescoat A, Kerjouan M, Chauvin P, et al. Malnutrition and decreased food intake at diagnosis are associated with hospitalization and mortality of idiopathic pulmonary fibrosis patients. Clin Nutr. 2022;41(6):1335–42.

    Article  CAS  PubMed  Google Scholar 

  21. Sangani RG, Ghio AJ, Mujahid H, Patel Z, Catherman K, Wen SJ, et al. Outcomes of idiopathic pulmonary fibrosis improve with obesity: a rural appalachian experience. South Med J. 2021;114(7):424–31.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Comes A, Wong AW, Fisher JH, Morisset J, Johannson KA, Farrand E, et al. Association of BMI and change in weight with mortality in patients with fibrotic interstitial lung disease. Chest. 2022;161(5):1320–29.

    Article  PubMed  Google Scholar 

  23. Shamseer L, Moher D, Clarke M, Ghersi D, Liberati A, Petticrew M, et al. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015: elaboration and explanation. Bmj-British Med J. 2015;349:g7647.

    Article  Google Scholar 

  24. Stroup DF, Berlin JA, Morton SC, Olkin I, Williamson GD, Rennie D, et al. Meta-analysis of observational studies in epidemiology - A proposal for reporting. Jama-Journal Am Med Association. 2000;283(15):2008–12.

    Article  CAS  Google Scholar 

  25. Stang A. Critical evaluation of the Newcastle-Ottawa scale for the assessment of the quality of nonrandomized studies in meta-analyses. Eur J Epidemiol. 2010;25(9):603–05.

    Article  PubMed  Google Scholar 

  26. Higgins JPT, Altman DG, Gotzsche PC, Jüni P, Moher D, Oxman AD, et al. The Cochrane collaboration’s tool for assessing risk of bias in randomised trials. Bmj-British Med J. 2011;343:d5928.

    Article  Google Scholar 

  27. Egger M, Smith GD, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ. 1997;315:629–34.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Duval S, Tweedie R. Trim and fill: a simple funnel-plot–based method of testing and adjusting for publication bias in meta‐analysis. Biometrics. 2004;56(2):455–63.

    Article  Google Scholar 

  29. Li B, Zhang X, Xu G, Zhang S, Song H, Yang K, et al. Serum prealbumin is a prognostic indicator in idiopathic pulmonary fibrosis. Clin Respir J. 2019;13(8):493–98.

    Article  CAS  PubMed  Google Scholar 

  30. Nakatsuka Y, Handa T, Kokosi M, Tanizawa K, Puglisi S, Jacob J, et al. The clinical significance of body weight loss in idiopathic pulmonary fibrosis patients. Respiration. 2018;96(4):338–47.

    Article  PubMed  Google Scholar 

  31. Suzuki Y, Yoshimura K, Enomoto Y, Yasui H, Hozumi H, Karayama M, et al. Distinct profile and prognostic impact of body composition changes in idiopathic pulmonary fibrosis and idiopathic pleuroparenchymal fibroelastosis. Sci Rep-uk. 2018;8:14074.

    Article  Google Scholar 

  32. Kono M, Nakamura Y, Enomoto N, Saito G, Koyanagi Y, Miyashita K, et al. Prognostic impact of an early marginal decline in forced vital capacity in idiopathic pulmonary fibrosis patients treated with pirfenidone. Respiratory Invest. 2019;57(6):552–60.

    Article  Google Scholar 

  33. Nishimoto K, Fujisawa T, Yoshimura K, Enomoto Y, Enomoto N, Nakamura Y, et al. The prognostic significance of pneumothorax in patients with idiopathic pulmonary fibrosis. Respirology. 2018;23(5):519–25.

    Article  PubMed  Google Scholar 

  34. Suzuki Y, Aono Y, Kono M, Hasegawa H, Yokomura K, Naoi H, et al. Cause of mortality and sarcopenia in patients with idiopathic pulmonary fibrosis receiving antifibrotic therapy. Respirology. 2021;26(2):171–79.

    Article  PubMed  Google Scholar 

  35. Yoo JW, Kim J, Song JW. Impact of the revised definition on incidence and outcomes of acute exacerbation of idiopathic pulmonary fibrosis. Sci Rep-uk. 2022;12(1):8817.

    Article  CAS  Google Scholar 

  36. Zinellu A, Collu C, Nasser M, Paliogiannis P, Mellino S, Zinellu E, et al. The Aggregate Index of systemic inflammation (AISI): a novel prognostic biomarker in idiopathic pulmonary fibrosis. J Clin Med. 2021;10:18.

    Article  Google Scholar 

  37. Mura M, Porretta MA, Bargagli E, Sergiacomi G, Zompatori M, Sverzellati N, et al. Predicting survival in newly diagnosed idiopathic pulmonary fibrosis: a 3-year prospective study. Eur Respir J. 2012;40(1):101–09.

    Article  PubMed  Google Scholar 

  38. Nishiyama O, Yamazaki R, Sano H, Iwanaga T, Higashimoto Y, Kume H, et al. Fat-free mass index predicts survival in patients with idiopathic pulmonary fibrosis. Respirology. 2016;22(3):480–85.

    Article  PubMed  Google Scholar 

  39. Du Bois RM, Weycker D, Albera C, Bradford WZ, Costabel U, Kartashov A, et al. Six-minute-walk test in idiopathic pulmonary fibrosis: test validation and minimal clinically important difference. Am J Respir Crit Care Med. 2011;183(9):1231–7.

    Article  PubMed  Google Scholar 

  40. Paterniti MO, Bi Y, Rekic D, Wang Y, Karimi-Shah BA, Chowdhury BA. Acute exacerbation and decline in forced vital capacity are associated with increased mortality in idiopathic pulmonary fibrosis. Ann Am Thorac Soc. 2017;14(9):1395–402.

    Article  PubMed  Google Scholar 

  41. Ley B, Ryerson CJ, Vittinghoff E, Ryu JH, Tomassetti S, Lee JS, et al. A multidimensional index and staging system for idiopathic pulmonary fibrosis. Ann Intern Med. 2012;156(10):684–91.

    Article  PubMed  Google Scholar 

  42. Wells AU, Desai SR, Rubens MB, Goh NS, Cramer D, Nicholson AG, et al. Idiopathic pulmonary fibrosis: a composite physiologic index derived from disease extent observed by computed tomography. Am J Respir Crit Care Med. 2003;167(7):962–9.

    Article  PubMed  Google Scholar 

  43. Xiaoli O, Xiang P. Factors affecting the prognosis of idiopathic pulmonary fibrosis. Chin J Respiratory Crit Care Med. 2021;20(11):824–30.

    Google Scholar 

  44. Wu W, Li C, Zhu X, Liu X, Li P, Wan R, et al. Genetic association of telomere length, obesity and tobacoo smoking with idiopathic pulmonary fibrosis risk. BMC Public Health. 2023;23(1):868.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Stanaway JD, Afshin A, Gakidou E, Lim SS, Abate D, Abate KH, et al. Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990–2017: a systematic analysis for the global burden of Disease Study 2017. Lancet. 2018;392(10159):1923–94.

    Article  Google Scholar 

  46. Kramer AA. A different type of obesity paradox. Crit Care Med. 2019;47(2):300–01.

    Article  PubMed  Google Scholar 

  47. Prado CM, Gonzalez MC, Heymsfield SB. Body composition phenotypes and obesity paradox. Curr Opin Clin Nutr Metab Care. 2015;18(6):535–51.

    Article  CAS  PubMed  Google Scholar 

  48. Horwich TB, Fonarow GC, Clark AL. Obesity and the obesity paradox in heart failure. Prog Cardiovasc Dis. 2018;61(2):151–56.

    Article  PubMed  Google Scholar 

  49. Carbone S, Lavie CJ, Arena R. Obesity and Heart Failure: Focus on the Obesity Paradox. Mayo Clinic Proceedings. 2017;92(2):266 – 79.

  50. Houstis NE, Eisman AS, Pappagianopoulos PP, Wooster L, Bailey CS, Wagner PD, et al. Exercise intolerance in heart failure with preserved ejection fraction: diagnosing and ranking its causes using personalized O2 pathway analysis. Circulation. 2018;137(2):148–61.

    Article  PubMed  Google Scholar 

  51. Ortega FB, Silventoinen K, Tynelius P, Rasmussen F. Muscular strength in male adolescents and premature death: cohort study of one million participants. BMJ. 2012;345:e7279.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Giri Ravindran S, Saha D, Iqbal I, Jhaveri S, Avanthika C, Naagendran MS, et al. The obesity paradox in chronic heart disease and chronic obstructive pulmonary disease. Cureus. 2022;14(6):e25674.

    PubMed  PubMed Central  Google Scholar 

  53. Zinellu A, Carru C, Pirina P, Fois AG, Mangoni AA. A systematic review of the prognostic significance of the body mass index in idiopathic pulmonary fibrosis. J Clin Med. 2023;12(2).

Download references

Acknowledgements

Not applicable.

Funding

This work was supported by National Natural Science Foundation of China (Grant No. 82100317), Norman Bethune Program of Jilin University (Grant No. 2022B14), “13th five-year plan” Science and Technology Research Project of Jilin Provincial Department of Education (Grant No. JJKH20190048KJ), the Youth Science and Technology Backbone Training Program of Health Commission of Jilin Province (Grant No. 2019Q003), as well as the Medical and Health Talent Special Project of Jilin Province - National Natural Science Foundation of China Cultivation Project (Grant No. 2019SRCJ013).

Author information

Author notes

  1. Dengyun Pan and Qi Wang contributed equally as the first authors to this work.

Authors and Affiliations

  1. Department of Respiratory Medicine, The Second Hospital of Jilin University, 218 Ziqiang Street, Changchun, 130041, China

    Dengyun Pan, Qi Wang, Bingdi Yan & Xiaomin Su

Authors
  1. Dengyun Pan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qi Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bingdi Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiaomin Su
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Xiaomin Su designed the project, retrieved and analyzed the data, revised manuscript. Dengyun Pan retrieved and analyzed the data, wrote the manuscript. Qi Wang wrote the manuscript. Bingdi Yan revised the manuscript. All authors contributed and approved the final version of the manuscript.

Corresponding author

Correspondence to Xiaomin Su.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pan, D., Wang, Q., Yan, B. et al. Higher body mass index was associated with a lower mortality of idiopathic pulmonary fibrosis: a meta-analysis. J Health Popul Nutr 43, 124 (2024). https://doi.org/10.1186/s41043-024-00620-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s41043-024-00620-5

Keywords

Journal of Health, Population and Nutrition

ISSN: 2072-1315

Contact us