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Systematic Review

A Systematic Review of the Prognostic Significance of the Body Mass Index in Idiopathic Pulmonary Fibrosis

by
Angelo Zinellu
1,
Ciriaco Carru
1,2,
Pietro Pirina
3,4,
Alessandro G. Fois
3,4 and
Arduino A. Mangoni
5,6,*
1
Department of Biomedical Sciences, University of Sassari, 07100 Sassari, Italy
2
Quality Control Unit, University Hospital of Sassari (AOU), 07100 Sassari, Italy
3
Department of Medical, Surgical and Experimental Sciences, University of Sassari, 07100 Sassari, Italy
4
Clinical and Interventional Pneumology, University Hospital Sassari (AOU), 07100 Sassari, Italy
5
Discipline of Clinical Pharmacology, College of Medicine and Public Health, Flinders University, Bedford Park, SA 5042, Australia
6
Department of Clinical Pharmacology, Flinders Medical Centre, Southern Adelaide Local Health Network, Bedford Park, SA 5042, Australia
*
Author to whom correspondence should be addressed.
J. Clin. Med. 2023, 12(2), 498; https://doi.org/10.3390/jcm12020498
Submission received: 21 December 2022 / Revised: 3 January 2023 / Accepted: 5 January 2023 / Published: 7 January 2023
(This article belongs to the Special Issue An Update on the Diagnostic, Predictive and Prognostic Biomarkers of Respiratory Diseases)
Browse Figure
Versions Notes

Abstract

:
The identification of novel prognostic biomarkers might enhance individualized management strategies in patients with idiopathic pulmonary fibrosis (IPF). Although several patient characteristics are currently used to predict outcomes, the prognostic significance of the body mass index (BMI), a surrogate measure of excess fat mass, has not been specifically investigated until recently. We systematically searched PubMed, Web of Science, and Scopus, from inception to July 2022, for studies investigating associations between the BMI and clinical endpoints in IPF. The Joanna Briggs Institute Critical Appraisal Checklist was used to assess the risk of bias. The PRISMA 2020 statement on the reporting of systematic reviews was followed. Thirty-six studies were identified (9958 IPF patients, low risk of bias in 20), of which 26 were published over the last five years. Significant associations between lower BMI values and adverse outcomes were reported in 10 out of 21 studies on mortality, four out of six studies on disease progression or hospitalization, and two out of three studies on nintedanib tolerability. In contrast, 10 out of 11 studies did not report any significant association between the BMI and disease exacerbation. Our systematic review suggests that the BMI might be useful to predict mortality, disease progression, hospitalization, and treatment-related toxicity in IPF (PROSPERO registration number: CRD42022353363).

1. Introduction

Idiopathic pulmonary fibrosis (IPF) is clinically characterized by an insidious decline in lung function, which generally leads to respiratory failure and death within four years of diagnosis [1]. However, significant inter-individual variability exists in disease progression. This variability is at least partly related to the frequency of disease exacerbations and the presence of specific comorbid conditions [2,3,4,5]. Several patient characteristics, as well as measures of lung function, have also been shown to predict survival and other relevant outcomes, e.g., disease progression and exacerbation, in this patient group. In particular, advancing age, male sex, lower values of forced vital capacity (FVC) and diffusing capacity of carbon monoxide (DLCO) percentage predicted at baseline and during follow-up, severe dyspnea, supplemental oxygen requirement, lower distance walked during the six-minute walk test (6MWT), and greater fibrotic burden on high resolution computed tomography (HRCT) are currently used as prognostic markers in IPF. Their use is typically combined in validated clinical prediction models, such as the gender-age-physiology (GAP) model, the longitudinal GAP model, and the composite physiologic model [1,2,6,7,8,9,10]. However, the predictive capacity of available tools could be potentially improved following the identification of additional biomarkers.
There is an intense research focus on determining the prognostic role of several circulating biomarkers, e.g., small molecules and peptides, that are involved in pathways thought to play a critical pathophysiological role in IPF. However, the widespread clinical use of such biomarkers is likely to be curtailed by the highly specific and expensive analytical techniques and facilities often required for their determination, particularly in less developed countries [11,12,13,14]. An alternative approach in the quest for novel prognostic biomarkers consists of the identification of alternative clinical characteristics that are routinely assessed in patients with IPF. In this context, an increasing number of studies have investigated the prognostic role of the body mass index (BMI), a surrogate marker of body fatness routinely used in the risk stratification of patients with cardiovascular disease, diabetes, and other metabolic disorders [15,16,17,18], in IPF. Therefore, we sought to critically appraise the available evidence regarding the prognostic significance of the BMI in IPF by conducting a systematic review of studies reporting associations between baseline BMI values and their temporal changes, clinical outcomes, and other relevant clinical parameters in this patient group.

2. Materials and Methods

We systematically searched the electronic databases PubMed, Web of Science, and Scopus for articles published between inception and 15 July 2022, using the following terms and their combination: “BMI” or “body mass index” and “IPF” or “idiopathic pulmonary fibrosis”. Two investigators independently reviewed the abstracts and, if relevant, the full articles. The citation lists of these articles were also hand-searched to identify additional studies. The inclusion criteria for selection were: (a) description of associations between the BMI and clinical outcomes or other relevant clinical parameters in observational and intervention studies in patients with IPF; (b) full-text available, and (c) English language used. The following data were extracted from each study and transferred into an electronic spreadsheet: age, sex, year of publication, country where the study was conducted, study design (observational, prospective vs. retrospective, or randomized controlled study), sample size, criteria used for the diagnosis of IPF, pharmacological treatment for IPF, main comorbid conditions, clinical endpoints assessed, baseline BMI, whether the BMI was assessed as a continuous variable or using cut-off values, results of multivariate Cox regression with details of analyzed confounders, and other univariate associations between the BMI and relevant clinical variables. The Joanna Briggs Institute Critical Appraisal Checklist was used to assess the risk of bias [19]. The PRISMA 2020 statement on the reporting of systematic reviews was followed (Supplementary Tables S1 and S2) [20]. The protocol was registered in the International Prospective Register of Systematic Reviews (PROSPERO, CRD42022353363).

3. Results

3.1. Study Selection

A total of 1257 articles were initially identified. Among them, 1220 were excluded because they were either duplicates or irrelevant. After a full-text review of the remaining 37 articles, one was further excluded because it did not fulfill the inclusion criteria, thus leaving 36 studies (9958 IPF patients, 78% males) for final analysis [21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56] (Figure 1). Fourteen studies were conducted in Japan [22,27,29,30,32,33,34,35,36,37,43,45,50,51], nine in the USA [21,25,31,38,41,46,47,48,49], three in France [44,53,54], three in Italy [26,52,56], three in China [28,39,42], two in South Korea [24,55], one in Ireland [23], and one in Saudi Arabia [40]. The reported clinical endpoints included mortality in 21 studies [21,22,23,24,26,29,30,32,33,37,38,39,40,45,49,50,52,53,54,55,56], disease exacerbation in 11 [22,23,25,27,28,31,34,37,41,46,55], disease progression in five [42,43,44,47,54], hospitalization in three [48,53,54], tolerability to the antifibrotic agent nintedanib in three [35,36,51], and incident pneumothorax in one [32]. Ten studies were prospective [25,26,27,28,30,43,44,46,53,54], while the remaining 26 were retrospective [21,22,23,24,29,31,32,33,34,35,36,37,38,39,40,41,42,45,47,48,49,50,51,52,55,56]. The baseline mean/median BMI values in these studies ranged between 21 and 30 kg/m2. Twenty-nine studies assessed the BMI as a continuous variable [21,22,23,25,26,27,28,29,30,31,32,33,34,35,37,39,40,41,43,45,46,47,48,51,52,53,54,55,56], six used cut-off values [24,36,38,42,44,49], and one assessed both [50]. Twenty-six out of 36 studies were published over the last five years [31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56] (Table 1).

3.2. Risk of Bias

The risk of bias was assessed as low in 20 studies [21,22,24,29,32,33,35,36,38,39,40,43,45,48,50,51,52,53,55,56] and high in the remaining 16 studies [23,25,26,27,28,30,31,34,37,41,42,44,46,47,49,54] (Table 2).

3.3. Results of Individual Studies and Syntheses

3.3.1. Mortality

A significant association between the BMI and mortality was reported in 10 studies, including nine retrospective studies and nine with low risk of bias [21,24,29,38,49,50,52,53,55,56] (Table 1). Alakhras et al. were the first to report a significant relationship between the BMI and survival in 197 IPF patients categorized according to BMI tertiles (<25, 25–30, and >30 kg/m2). The bottom tertile (n = 46) had a median survival of 3.6 years [1-year survival rate, 76% (95% CI 65 to 90); 3-year survival rate, 54% (95% CI 41 to 70)]. The middle tertile (n = 85) had a median survival of 3.8 years [1-year survival rate, 84% (95% CI 76 to 92); 3-year survival rate, 58% (95% CI 48 to 70)]. The upper tertile (n = 66) had a median survival of 5.8 years (1-year survival rate, 91% (95% CI 84 to 98); 3-year survival rate, 69% (95% CI 58 to 81%)]. Proportional hazards regression showed a significant, independent, and negative association between the baseline BMI and mortality [21]. Kim et al. reported an independent association between baseline BMI values < 18.5 kg/m2 and increased 15-year mortality in 67 IPF patients [24]. Kishaba et al. investigated the impact of changes in BMI during the first year on 12-year mortality. In their analysis, the magnitude of BMI reduction was significantly associated with mortality after adjusting for several confounders, including hospitalization during the first year. Similar associations with 12-year mortality were observed with absolute values of baseline and one-year BMI [29]. Kulkarni et al. also investigated the association between BMI temporal trajectories and one-year transplant or mortality and post-transplant mortality in a discovery cohort (n = 131). The quartile with the greatest temporal BMI reduction (>0.68%/month) was independently associated with a higher risk of transplant or death. The association with mortality was maintained after excluding patients undergoing transplant (HR = 2.9, 95% CI 1.6 to 5.2, p = 0.0002). In further analysis, patients with temporal BMI reduction >0.68%/month in the year preceding the transplant also had a greater risk of mortality following surgery (HR = 4.6, 95% CI 1.7 to 12.6, p = 0.003). The same authors confirmed the presence of an independent association between temporal BMI reduction >0.68%/month and risk of transplant or death in a validation cohort (n = 148) [38]. Sangani et al. retrospectively investigated 138 IPF patients categorized as non-obese (BMI < 30 kg/m2) and obese (BMI ≥30 kg/m2). The usual interstitial pneumonia pattern was less prevalent in the obese group (69% vs. 85%, p = 0.007). Significantly lower mortality was observed in this group. A similar trend was also observed when BMI values were analyzed as tertiles (mortality of 20%, 47%, and 75% for BMI values of 25–29.9, 20–24.9, and <20 kg/m2, respectively, p < 0.001) [49]. Two cohorts receiving antifibrotic treatment with pirfenidone or nintedanib, for a total of 208 IPF patients, were investigated by Suzuki et al. A significant, negative, and independent association was observed with five-year mortality both when considering BMI values as a continuous variable and using a cut-off value of 24.1 kg/m2 [50]. Zinellu et al. reported a negative and independent association between the baseline BMI and four-year mortality in a cohort of 82 IPF patients, after adjusting for several confounders including the recently developed aggregate index of systemic inflammation [52,57,58,59,60]. In another prospective cohort study in 153 newly diagnosed IPF patients, Jouneau et al. reported that a lower baseline BMI was independently associated with one-year mortality in multivariate analysis, after adjusting for age, sex, GAP score, and self-evaluation of food intake [53]. Yoo et al. similarly reported that a lower baseline BMI was independently associated with higher three-year mortality in 445 patients with IPF, after adjusting for several confounders including the Charlson comorbidity index, disease progression, and acute exacerbation [55]. Finally, Zinellu et al. investigated 90 IPF patients and reported an independent association between the baseline BMI and four-year mortality, with an area under the curve (AUC) of 0.702 [56]. Incorporating the BMI into a four-domain predictive model (IC4) including the six-minute walking distance, FVC, and DLCO significantly increased the AUC to 0.859 (95% CI 0.770–0.924, p < 0.0001) [56].
In contrast, 11 studies, including eight retrospective studies and six with low risk of bias, failed to report a significant association between the BMI and mortality [22,23,26,30,32,33,37,39,40,45,54]. A non-significant association between the BMI and mortality was reported in multivariate analyses in six studies [26,32,33,39,40,45]. Four studies failed to demonstrate a significant association in univariate analysis [22,23,30,37], whereas the remaining study, a post-hoc analysis of five randomized placebo-controlled trials investigating the effects of pirfenidone, interferon-γ-1b, and the monoclonal antibody lebrikizumab, did not report a formal statistical analysis of the association between the BMI and one-year mortality [54].

3.3.2. Disease Exacerbation

Only one study reported significant associations between the baseline BMI and risk of disease exacerbation. Kondoh et al. observed an independent and positive association between the baseline BMI and risk of three-year exacerbations in 64 IPF patients [22]. In contrast, no significant associations were reported in the remaining 10 studies, including six retrospective studies and nine with a high risk of bias, all of which reported data from univariate analyses [23,25,27,28,31,34,37,41,46,55].

3.3.3. Disease Progression

Two studies reported a significant impact of the BMI on IPF progression. Fang et al. reported that patients exhibiting disease progression at one year had significantly lower baseline BMI values than those with stable disease. A significant association was also observed with the Kaplan-Meyer log-rank test using a cut-off of ≥25 kg/m2 [42]. Similarly, in a post-hoc analysis of a randomized placebo-controlled trial investigating pirfenidone, Ikeda et al. observed that a lower baseline BMI was independently associated with one-year progression. Notably, this association was observed both in the placebo and pirfenidone groups [43]. In contrast, two studies failed to report a significant association with disease progression in univariate analyses [44,47]. In one study, while a significantly greater decline in FVC was observed in patients with BMI < 27 kg/m2, no significant BMI-related differences were reported with temporal changes in FVC (% predicted) and St. George’s Respiratory Questionnaire [44]. In a further study, no formal statistical analysis was presented on the association between the baseline BMI and one-year disease progression [54].

3.3.4. Nintedanib Tolerance

Two Japanese studies investigated the potential influence of the BMI on the risk of early discontinuation of treatment with the antifibrotic drug nintedanib, with contrasting results. Ikeda et al. observed that lower baseline BMI values were significantly and independently associated with increased risk of discontinuation in 72 IPF patients [35]. In contrast, no significant association was observed between the baseline BMI and risk of early discontinuation after adjusting for FVC (% predicted) in the study by Uchida et al. involving 78 patients with IPF [51]. In another Japanese study, a BMI of <21.6 kg/m2 was independently associated with a tenfold increase in the risk of developing nausea and a threefold increase in the risk of developing diarrhea during nintedanib treatment [36].

3.3.5. Other Clinical Outcomes

Two studies reported a negative association between the BMI at baseline and the risk of hospitalization. Kim et al. observed that a lower BMI was significantly and independently associated with a higher rate of respiratory-related hospitalizations within two years in 1002 IPF patients [48]. Similarly, Jouneau et al. reported that a lower BMI was independently associated with one-year hospitalization in 153 patients with IPF [53]. In another study by Jouneau et al., the associations between BMI tertiles and all-cause hospitalization at one year were not statistically assessed [54]. Finally, Nishimoto et al. reported that lower BMI values at baseline were independently associated with a statistically higher risk of pneumothorax in a retrospective study of 71 IPF patients. In this study, incident pneumothorax was independently associated with increased mortality after adjusting for age, sex, and FVC (% predicted) [32].

4. Discussion

In our systematic review, we identified 36 studies assessing the prognostic role of baseline and temporal changes in BMI values in IPF patients receiving a range of immunosuppressive and antifibrotic therapies. Whilst there is currently no evidence of a link between the BMI and a diagnosis of IPF, the available evidence suggests that this routinely assessed surrogate marker of body fatness is a promising predictor of mortality, disease progression, hospitalization, tolerability to specific antifibrotic treatments, and specific complications, i.e., pneumothorax, in this group. In particular, relatively low BMI values at baseline and/or greater temporal declines in BMI are associated with adverse clinical outcomes, barring the risk of disease exacerbation.
The BMI was first described by Quetelet, a Belgian scientist, as an anthropometric index in the nineteenth century under the denomination “social physics” [61]. Following the first publication under its current name in 1972 [62], the BMI has been extensively used in clinical practice and public health screening and intervention programs to categorize people as underweight (<18.5 kg/m2), normal weight (≥18.5 and <25.0 kg/m2), overweight (≥25.0 and <30.0 kg/m2), and obese (≥30.0 kg/m2). Although several experts have questioned the physiological significance of the BMI as a reliable indicator of adiposity and excess fat, its use has significantly contributed to the stratification of short- and long-term risks associated with key disease states, e.g., cardiovascular disease, diabetes, and several types of cancer, and to promote lifestyle interventions aimed at reducing this risk both individually and at the population level [15,16,17,18,63]. However, while the health risks associated with relatively higher BMI values are well established, an increasing number of studies over the last decade have reported that individuals with relatively higher BMI and specific overt disease states, e.g., heart failure and cancer, have a more favorable prognosis than those with lower BMI values [64,65]. This phenomenon, known as the “obesity paradox,” has also been described in respiratory conditions such as chronic obstructive pulmonary disease [66,67]. One possible explanation for the putative protective effects of higher BMI values in these conditions and IPF is related to the inherent limitations of this index as a reliable measure of excess fat mass and adiposity. The formula used for its calculation (body weight in kg divided by height in m2) does not take into consideration whether changes in body weight are secondary to changes of specific body composition compartments, e.g., fat mass vs. fat-free mass, and/or their distribution, e.g., visceral vs. subcutaneous adiposity [68,69]. Furthermore, a concomitant increase in fat mass (obesity) and a reduction in fat-free mass (sarcopenia) can occur in the same individual. This condition, also known as sarcopenic obesity, is associated with a worse prognosis in disease states such as heart failure and cancer [70,71,72]. Therefore, it is possible that a higher BMI in patients with IPF experiencing a more favorable prognosis is not primarily associated with an increase in fat mass, but rather with an increase in fat-free mass, e.g., muscle mass. This might lead to increased exercise tolerance and cardiorespiratory fitness through increased oxygen consumption via increased muscle diffusion, mitochondrial respiration capacity, and skeletal muscle strength, as already proposed in patients with heart failure [73,74]. This hypothesis is further supported by the results of studies reporting that lower skeletal muscle mass and strength are significantly associated with advanced disease and mortality in patients with IPF [75,76,77]. Furthermore, one study in our systematic review reported a significant and positive association between BMI and the cross-sectional area of elector spine muscles, an imaging parameter used to investigate sarcopenia and cachexia. However, no significant associations were reported with another parameter, muscle attenuation of elector spine muscles [33]. In another study, a significant and positive association was reported between the relative temporal decline in BMI and temporal reduction in the cross-sectional area of elector spine muscles in IPF patients [45]. Another possibility is that interplay between the BMI and clinical outcomes in patients with IPF is modulated by the coexistence of disease states, e.g., heart failure, where an inverse association between BMI values and adverse outcomes has been described [64]. However, this hypothesis requires further investigation as the presence of comorbidities was described in only nine of the studies identified in our systematic review [23,25,29,40,41,42,49,52,53].
It is important to emphasize that several studies failed to report significant associations between the BMI and mortality [22,23,26,30,32,33,37,39,40,45,54] or disease progression [44,47]. Possible reasons for such discrepancies include between study differences in baseline patient characteristics, including severity of IPF, comorbidity burden, ethnicity, and specific treatment received. However, as previously mentioned, information regarding comorbidities was provided in a limited number of studies [23,25,29,40,41,42,49,52,53]. More research is required to investigate possible differences in studies reporting negative findings and to determine whether the prognostic significance of the BMI varies across IPF subgroups.
Another intriguing observation is the possible reduced tolerance to the antifibrotic agent nintedanib in IPF patients with lower BMI reported in two of three studies [35,36,51]. This issue is clinically relevant as the early discontinuation of antifibrotic therapy is associated with worse outcomes in this group [78]. Nintedanib is a relatively fat-soluble drug with a large volume of distribution in humans [79,80]. Assuming that a lower BMI value is secondary, at least partly, to a reduced fat mass, the consequent reduction in the volume of distribution might theoretically lead to higher circulating concentrations of this agent. However, whether this phenomenon accounts for increased risk of toxicity and early treatment discontinuation deserves further study.
In order to establish the prognostic significance of the BMI in IPF, larger and appropriately designed prospective studies are warranted to confirm the findings of our review. Such trials should investigate the predictive capacity of the BMI, singly or in combination with other clinical characteristics and lung function parameters, in IPF patients with a wide range of clinical severity, comorbid status, sarcopenia, and immunosuppressive and antifibrotic treatments. The potential utility of combining the BMI with other parameters in prediction models was recently reported by Zinellu et al. in a study where the incorporation of BMI with 6MWD, FVC, and DLCO significantly increased the AUC for predicting four-year mortality [56].
The strengths of our systematic review include the assessment of a wide range of clinical endpoints as well as the association between the BMI and other relevant patient characteristics, including parameters of lung function and functional capacity. Furthermore, the selected studies investigated Asian, European, and North American patient populations, ensuring some degree of generalization of the findings, and the risk of bias was considered low in the majority of studies (20 out of 36). The limitations of our review include the lack of meta-analytical evaluation given the between study differences in the assessment of the BMI as a continuous variable or cut-off value, baseline variable vs. temporal changes, type of endpoint assessed, and the paucity of details regarding specific comorbidities, markers of muscle mass, and sarcopenia in most studies.

5. Conclusions

Our systematic review has shown that the BMI has the potential to be used as an easily measured and inexpensive predictive marker in IPF, particularly for mortality, disease progression, risk of hospitalization, and tolerability to specific therapies. However, prospective, accurately designed studies are warranted to convincingly demonstrate the prognostic utility of this anthropometric parameter and justify its widespread use in the routine management of patients with IPF.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/jcm12020498/s1, Table S1: PRISMA 2020 abstract checklist; Table S2: PRISMA 2020 manuscript checklist.

Author Contributions

A.Z. and A.A.M. generated the idea. A.Z. and A.A.M. conducted the literature search and collected the data. A.Z., C.C., P.P., A.G.F. and A.A.M. analyzed and interpreted the data. A.A.M. wrote the first draft of the manuscript. A.Z., C.C., P.P., A.G.F. and A.A.M. critically appraised subsequent manuscript drafts. A.Z., C.C., P.P., A.G.F. and A.A.M. reviewed and approved the final manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by grants from the “Sardinian Fondo di Sviluppo e Coesione” (FSC) 2014–2020, “Patto per lo Sviluppo della Regione Sardegna” LR7-2017 (RASSR82005), and “Fondo di Ateneo per la Ricerca–annualità 2020”.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The datasets used and/or analyzed in this study are available from the corresponding author on reasonable request.

Conflicts of Interest

The authors declare no conflict of interest.

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Jcm 12 00498 g001 550
Figure 1. PRISMA 2020 flow diagram.
Figure 1. PRISMA 2020 flow diagram.
Jcm 12 00498 g001
Table
Table 1. Characteristics of the studies investigating the association between body mass index and adverse outcomes in idiopathic pulmonary fibrosis.
Table 1. Characteristics of the studies investigating the association between body mass index and adverse outcomes in idiopathic pulmonary fibrosis.
First Author, Year, Country (Ref)Study DesignSample Size
Age (Years)
M/F		Diagnosis
Treatment
Endpoint(s)		Baseline BMI (kg/m2)
BMI Assessment in Cox Model
Main Comorbidities		Results of Multivariate Cox Regression
Confounders in the Model		Additional Findings
Alakhras M, 2007, USA [21]R197
71
137/60		ATS/ERS
Colchicine, prednisolone
3-year mortality		28
Continuous variable
NR		HR = 0.86, 95% CI 0.79 to 0.94, p < 0.001
Sex, diagnosis by open lung biopsy, FVC (% predicted), DLCO (% predicted), O2 saturation with exercise		No significant differences between BMI tertiles (<25, ≥25 and <30, and ≥30) in age, sex, smoking status, baseline pulmonary function tests, or recommended treatment at the index visit
Kondoh Y, 2010, Japan [22]R74
64
61/13		ATS/ERS
Prednisone, cyclophosphamide, azathioprine, cyclosporin
3-year acute exacerbation, 3-year mortality		23
Continuous variable
NR		Acute exacerbation
HR = 1.20, 95% CI 1.03 to 1.40, p < 0.001
mMRC scale (2 and above), 10% decline in FVC at 6 months		No significant association between BMI and mortality in univariate Cox regression (HR = 0.97, 95% CI 0.88 to 1.07, p = 0.590)
Judge EP, 2012, Ireland [23]R55
60
41/14		NR
NR
Acute exacerbation, 5-year mortality		26
Continuous variable
PH		NRNo significant association between BMI and acute exacerbation in univariate Cox regression (HR = 1.043, 95% CI 0.939 to 1.159, p = 0.437)
No significant association between BMI and mortality in univariate Cox regression (HR = 0.984, 95% CI 0.886 to 1.092, p = 0.758)
Kim JH, 2012, South Korea [24]R67
70
43/24		ATS/ERS/JRS/ALAT
Steroids
15-year mortality		23
BMI < 18.5
NR		HR = 12.085, 95% CI 1.277 to 114.331, p = 0.030
Age, sex, FVC <70% predicted, respiratory symptoms at diagnosis, disease progression on CT before 36 months		NR
Lee JS, 2012, USA [25]P54
65
42/12		ATS
Steroids
Acute exacerbation		25
Continuous variable
CAD, GERD, OSA, PH		NRNo significant association between BMI and acute exacerbation in univariate Cox regression (OR = 1.04, 95% CI 0.91 to 1.20, p = 0.55)
Mura M, 2012, Italy [26]P70
67
57/13		ATS
NR
3-year mortality		27
Continuous variable
NR		No significant associations between BMI and mortality in multivariate analysis (data not reported),
mMRC, 6MWD, desaturation during 6MWT, alveolar-arterial O2 tension, FVC (% predicted), DLCO (% predicted), HRCT fibrosis score, bronchoalveolar lavage total cell count, concomitant emphysema, CPI 		Significant association between BMI and mortality in univariate Cox regression (HR = 0.89, 95% CI 0.80 to 0.98, p = 0.01)
Kondoh Y, 2015, Japan [27]P267
65
213/54		JRS
Pirfenidone
Acute exacerbation		24
Continuous variable
NR		NRNo significant association between BMI and acute exacerbation in univariate Cox regression (HR = 0.935, 95% CI 0.782 to 1.118, p = 0.46)
Cao M, 2016, China [28]P62
66
51/11		ATS/ERS/JRS/ALAT
NR
Acute exacerbation		24
Continuous variable
NR		NRNo significant difference in BMI between patients with and without exacerbation (24.1 ± 2.9 vs. 24.6 ± 2.7, p = 0.679)
Kishaba T, 2016, Japan [29]R65
72
41/24		ATS/ERS/JRS/ALAT
Prednisolone, cyclosporin, pirfenidone, nintedanib
12-year mortality		25
Continuous variable (BMI changes during the first year)
DM, HT		HR = 1.324, 95% CI 1.045 to 1.676, p = 0.02
FVC (% predicted) changes during the first year, hospitalization during the first year		Significant associations between mortality and baseline (HR = 7.708, 95% CI 2.669 to 12.748, p = 0.008) and 1-year BMI (HR = 9.058, 95% CI 2.925 to 15.192, p = 0.009) in univariate Cox regression
Nishiyama O, 2017, Japan [30]P44
72
35/9		ATS/ERS/JRS/ALAT
No treatment
4-year mortality		23
Continuous variable
NR		NRNo significant association between BMI and mortality in univariate Cox regression (HR = 0.88, 95% CI 0.76 to 1.02, p = 0.09).
Significant associations between BMI and age (r = −0.33, p = 0.03), DLCO (r = 0.50, p = 0.0005), 6MWT (r = 0.35, p = 0.02), and GAP index (r = −0.42, p = 0.003)
Dotan Y, 2018, USA [31]R89
66
64/25		ATS/ERS
Pirfenidone, nintedanib
Acute exacerbation		27
Continuous variable
NR		NRNo significant difference in BMI between patients with and without exacerbation (27 ± 5 vs. 28 ± 4, p = 0.26)
Nishimoto K, 2018, Japan [32]R84
71
74/10		ATS/ERS/JRS/ALAT
Prednisolone, cyclophosphamide, cyclosporin, tacrolimus, pirfenidone, nintedanib
Pneumothorax, 12-year mortality		22
Continuous variable
NR		Pneumothorax
HR = 0.80, 95% CI 0.67 to 0.94, p = 0.008
Extent of reticular abnormalities on HRCT (grade ≥2)
Mortality
HR = 1.01, 95% CI 0.88 to 1.15, p = 0.894
Age, sex, FVC (% predicted), pneumothorax, extent of reticular abnormalities (grade ≥2), acute exacerbation		NR
Suzuki Y, 2018, Japan [33]R131
69
117/14		ATS/ERS/JRS/ALAT
NR
20-year mortality		23
Continuous variable
NR		HR = 1.009, 95% CI 0.892 to 1.141, p = 0.89
Age, sex, ESMCSA, ESMMA, FVC (% predicted), FEV1/FVC, DLCO (% predicted)		Significant association between BMI and ESMCSA (r = 0.500, p < 0.0001).
No significant association between BMI and ESMMA (r = 0.01, p = 0.90)
Hanaka T, 2019, Japan [34]R89
72
74/15		ATS/ERS/JRS/ALAT
NR
Acute exacerbation		23
Continuous variable
NR		NRNo significant difference in median BMI between patients with and without exacerbation (22.9, IQR 21.1–25.8 vs. 22.9, IQR 20.7–24.7, p = 0.785)
Ikeda S, 2019, Japan [35]R30
72
24/6		ATS/ERS/JRS/ALAT
Nintedanib
Early nintedanib termination		21
Continuous variable
NR		HR = 0.487, 95% CI 0.280 to 0.849, p = 0.01
Surfactant protein D, weight loss (grade ≥2) during prior treatment with pirfenidone		Median BMI significantly lower in patients switched from pirfenidone to nintedanib than in patients naïve to pirfenidone (21.0, IQR 19.0–23.6 vs. 23.9, IQR 20.7–26.2, p = 0.001)
Kato M, 2019, Japan [36]R77
72
65/12		ATS/ERS/JRS/ALAT
Nintedanib, prednisolone
Nintedanib-induced nausea and diarrhea		23
BMI < 21.6
NR		Nausea
HR = 10.841, 95% CI 2.644 to 44.448, p = 0.001
Performance status, mMRC, GAP index, co-treatment with prednisolone, nintedanib dose
Diarrhea
HR = 3.460, 95% CI 1.044 to 11.467, p = 0.04
Performance status, mMRC, GAP index, co-treatment with prednisolone, nintedanib dose		BMI AUC for nausea (0.873, 95% CI 0.784 to 0.962, p = 0.001)
BMI AUC for diarrhea (0.668, 95% CI 0.502 to 0.834, p = 0.036)
Kono M, 2019, Japan [37]R96
72
77/19		ATS/ERS/JRS/ALAT
Pirfenidone, prednisolone, immunosuppressants
Acute exacerbation, 4-year mortality		22
Continuous variable
NR		NRNo significant association between BMI and acute exacerbation in univariate Cox regression (HR = 1.096, 95% CI 0.989 to 1.912, p = 0.08).
No significant association between BMI and mortality in univariate Cox regression (HR = 0.610, 95% CI 0.107 to 3.173, p = 0.56)
Kulkarni T (a), 2019, USA [38]R131
69
101/30		ATS/ERS/JRS/ALAT
NR
1-year transplant or death, mortality post-transplant		30
BMI reduction >0.68%/month
NR		1-year transplant or death
HR = 1.8, 95% CI 1.1 to 3.2, p = 0.038
Age, pulmonary function, baseline BMI		Significant association between BMI reduction >0.68%/month pre-transplant and post-transplant mortality in univariate Cox regression (HR = 4.6, 95% CI 1.7 to 12.6, p = 0.003).
Significant correlation between changes in BMI and changes in serum leptin (r = 0.43, p < 0.01) and serum adiponectin (r = −0.33, p = 0.04)
Lower CD28% in patients with BMI reduction >0.68%/month (p = 0.018)
Kulkarni T (b), 2019, USA [38]R148
65
100/48		ATS/ERS/JRS/ALAT
NR
1-year transplant or death		30
BMI reduction >0.68%/month
NR		1-year transplant or death
HR = 2.5, 95% CI 1.2 to 5.2, p = 0.02
Age, pulmonary function, baseline BMI		NR
Li B, 2019, China [39]R148
65
108/40		ATS/ERS/JRS/ALAT
NR
6-year mortality		24
Continuous variable
NR		HR = 0.97, 95% CI 0.89–1.04, p = 0.374
FVC (% predicted), serum albumin, serum globulin, serum prealbumin		No significant difference in median BMI between patients with serum prealbumin concentrations <0.2 and ≥0.2 mg/L (24.4, IQR 21.9–26.9 vs. 23.7, IQR 25.4–27.5, p = 0.063)
Alhamad EH, 2020, Saudi Arabia [40]R212
66
150/62		ATS/ERS/JRS/ALAT
Pirfenidone, nintedanib
10-year mortality		27
Continuous variable
PH, DM, HT, OP, GORD, CAD 		HR = 0.948, 95% CI 0.896–1.003, p = 0.06
Acute exacerbation, 6MWT final SpO2 <85%, antifibrotic therapy, 6MWTD <300 m, TLC (% predicted), FVC (% predicted)		NR
Dotan Y, 2020, USA [41]R89
66
61/28		ATS/ERS/JRS/ALAT
NR
Acute exacerbation		28
Continuous variable
DM, HT, CAD		NRNo significant difference in BMI between patients with and without exacerbation (28 ± 4 vs. 28 ± 4, p = 0.28)
Fang C, 2020, China [42]R117
64
110/7
ATS/ERS
Pirfenidone, prednisone, cyclophosphamide, azathioprine, methotrexate, tacrolimus
1-year disease progression		24
BMI < 25
DM, HT, CAD		NRSignificant difference in BMI between patients with stable disease and those with progressive disease (24.8 ± 2.7 vs. 22.9 ± 3.0, p = 0.005).
Kaplan-Meyer log-rank test for progression-free survival with BMI ≥ 25 (HR = 2.81, 95% CI 1.03 to 7.68, p = 0.044)
Ikeda K, 2020, Japan [43]P267
65
213/54
ATS/ERS
Pirfenidone, placebo
1-year disease progression		24
Continuous variable
NR		Placebo group
HR = 0.833, 95% CI 0.704 to 0.985, p = 0.03
Lowest SpO2 during 6MWT, FVC (% predicted)
Pirfenidone group
HR = 0.849, 95% CI 0.723 to 0.998, p = 0.046
Smoking status, alveolar-arterial O2 difference, FVC (% predicted), surfactant protein D		NR
Jouneau S, 2020, France [44]P1,061
68
841/220		NR
Pirfenidone, prednisone, azathioprine, cyclophosphamide, cyclosporine, N-acetylcysteine
1-year disease progression		28
BMI < 27
NR		NRPatients with BMI < 27 had a greater median annual rate of decline in FVC vs. placebo compared to those with BMI ≥ 27 (158, IQR 109–206 vs. 65, IQR 18–113, p = 0.007)
No significant differences between patients with BMI <27 and ≥27 in absolute change in FVC (% predicted) vs. placebo (4.3, IQR 2.6–6.0 vs. 1.8, IQR 0.4–3.2, p = 0.37), absolute change in SGRQ (−2.6, IQR −5.7–0.6 vs. −0.4, IQR −3.2–2.3, p = 0.80), ≥1 acute exacerbation (HR = 0.65, 95% CI 0.34 to 1.26 vs. 0.65, 0.31 to 1.40, p = 0.96), and mortality (HR = 0.46, 95% CI 0.24 to 0.92 vs. 1.07, 0.52 to 2.19, p = 0.11)
Nakano A, 2020, Japan [45]R119
67
98/21		ATS/ERS/JRS/ALAT
Pirfenidone, corticosteroids
7-year mortality		23
Relative decline in BMI in the first 6 months (%)
NR		HR = 1.036, 95% CI 0.896–1.088, p = 0.163
Relative decline in FCV (% predicted), relative decline in ESMCSA 		Significant correlation between relative decline in BMI and relative decline in ESMCSA (r = 0.394, p < 0.001)
Tang F, 2020, USA [46]P1,061
68
841/220		NR
Pirfenidone, prednisone, azathioprine, cyclophosphamide, cyclosporine, N-acetylcysteine
1-year acute exacerbation		28
Continuous variable
NR		NRNo significant association between BMI and acute exacerbation in univariate Cox regression (HR = 0.958, 95% CI 0.906 to 1.010, p-value NR)
Zaman T, 2020, USA [47]R1,263
68
901/362		ATS/ERS/JRS/ALAT
NR
Disease progression over 3 years		29
BMI increase by a factor of 5
NR		NRNo significant association between BMI and progression in univariate Cox regression in the whole population (HR = 0.942, 95% CI 0.675 to 1.321, p-value NR) males (HR = 1.213, 95% CI 0.704 to 2.113, p-value NR) and females (HR = 0.821, 95% CI 0.538 to 1.242, p-value NR)
Kim HJ, 2021, USA [48]R1,002
70
747/255		ATS/ERS/JRS/ALAT
NR
Respiratory-related hospitalization within 2 years		29
Continuous variable
NR		HR = 0.96, 95% CI 0.93 to 0.98, p < 0.001
Age, FVC (% predicted), O2 use at rest, pulmonary hypertension		NR
Sangani RG, 2021, USA [49]R138
76
83/55		ATS/ERS/JRS/ALAT
Pirfenidone, nintedanib
Mortality		NR
BMI < 30
HT, HL, GORD, COPD, HF, OSA, DM, PH		NRMortality significantly higher in patients with BMI < 30 than in those with BMI ≥30 (34.8% vs. 20.4%, p = 0.018)
Suzuki Y, 2021, Japan [50]R208
72
176/32		ATS/ERS/JRS/ALAT
Pirfenidone, nintedanib, immunosuppressants, N-acetylcysteine
5-year mortality		23
Continuous variable or BMI < 24.1
NR		Continuous variable
HR = 0.920, 95% CI 0.847 to 0.996, p = 0.04
Age, sex, ESMCSA, FVC (% predicted), DLCO (% predicted)
BMI < 24.1
HR = 1.673, 95% CI 1.063 to 2.709, p = 0.03
Age, sex, ESMCSA, FVC (% predicted)		NR
Uchida Y, 2021, Japan [51]R71
78
52/19		ATS/ERS/JRS/ALAT
Nintedanib, prednisolone, tacrolimus
Early nintedanib termination (at 6 months)		21
Continuous variable
NR		HR = 0.862, 95% CI 0.715 to 1.040, p = 0.12
FVC (% predicted)		NR
Zinellu A, 2021, Italy [52]R82
72
73/9		ATS/ERS
Pirfenidone, nintedanib
4-year mortality		27
Continuous variable
HT, CAD, GORD, PH, COPD, OSA, AF		HR = 0.859, 95% CI 0.768 to 0.960, p = 0.007
Age, sex, smoking status, disease stage, antifibrotic drugs, aggregate index of systemic inflammation		NR
Jouneau S, 2022, France [53]P153
72
119/34		ATS/ERS/JRS/ALAT
Pirfenidone, nintedanib, corticosteroids
1-year all-cause hospitalization, 1-year mortality		27
Continuous variable
HT, CAD, CVA, AF		Hospitalization
HR = 0.89, 95% CI 0.83 to 0.96, p = 0.003
GAP score, simple evaluation of food intake
Mortality
HR = 0.89, 95% CI 0.82 to 0.96, p = 0.003
GAP score, simple evaluation of food intake		Patients with BMI < 21 had a higher rate of acute exacerbation compared to those with BMI > 21 (73.1% vs. 41.7%, p = value NR)
Jouneau S, 2022, France [54]P1,604
67
1,374/230		ATS/ERS/JRS/ALAT
Pirfenidone, interferon-γ-1b, lebrikizumab
1-year disease progression, 1-year, hospitalization, and 1-year mortality		30
Continuous variable
NR		NRPatients with baseline BMI < 25, 25–30, or ≥30 kg/m2 showed annualized change in (p-values NR):
FVC (% predicted) of −6.6, −5.4, and −4.1, respectively
DLCO (% predicted) of −5.5, −5.0, and −4.0, respectively
6MWTD of −42.8, −32.5, and −30.5 m, respectively
SGRQ total score of 5.8, 5.2, and 3.1, respectively
and:
Relative decline in percent predicted FVC ≥10% or death in 19%, 15.1% and 9.4%, respectively
Any all-cause hospitalization in 23.8%, 25.4%, and 24.5%, respectively
All-cause mortality in 6.7%, 7.9%, and 6.2%, respectively
Any treatment-emergent serious adverse effect in 26.7%, 30.6%, and 27.0%, respectively
Yoo JW, 2022, South Korea [55]R445
67
335/110		ATS/ERS/JRS/ALAT
Steroid, azathioprine, mycophenolate mofetil, cyclosporine
3-year acute exacerbation, 3-year mortality		24
Continuous variable
NR		Acute exacerbation
NR
Mortality
HR = 0.944, 95% CI 0.894 to 0.997, p = 0.037
Age, Charlson comorbidity index, FVC (% predicted), DLCO (% predicted), 6MWT distance, 6MWT resting and lowest SpO2, disease progression, acute exacerbation		No significant association between BMI and acute exacerbation in univariate Cox regression (HR = 0.973, 95% CI 0.902 to 1.049, p = 0.470)
Zinellu A, 2022, Italy [56]R90
70
79/11		ATS/ERS
Pirfenidone, nintedanib
4-year mortality		26
Continuous variable
NR		HR = 0.82, 95% CI 0.71 to 0.95, p = 0.008
Age, sex, smoking status, treatment		AUC for BMI to predict mortality (0.702, 95% CI 0.596 to 0.794, p = 0.0001)
Legend: AF, atrial fibrillation; ALAT, Asociación Latinoamericana de Tórax; ATS, American Thoracic Society; AUC, area under the curve; BMI, body mass index; CAD, coronary artery disease; COPD, chronic obstructive pulmonary disease; CPI, composite physiologic index; CT, computed tomography; CVA, cerebrovascular disease; DLCO, diffusion capacity for carbon monoxide; DM, diabetes mellitus; ERS, European Respiratory Society; ESMCSA, cross-sectional area of elector spine muscles; ESMMA, muscle attenuation of elector spine muscles; F, female; FEV1, forced expiratory volume in the 1st second; FVC: forced vital capacity; GAP, gender age physiology; GORD, gastroesophageal reflux disease; HF, heart failure; HL, hyperlipidemia; HR, hazard ratio; HRCT, high-resolution computed tomography; HT, hypertension; IQR, interquartile range; JRS, Japanese Respiratory Society; M, male; mMRC, modified Medical Research Council dyspnea scale; NR, not reported; OP, osteoporosis; OSA, obstructive sleep apnea; P, prospective; PH, pulmonary hypertension; R, retrospective; SGRQ, St. George’s Respiratory Questionnaire; TLC, total lung capacity; 6MWT, six-minute walking test; 6MWTD, six-minute walking test distance.
Table
Table 2. The Joanna Briggs Institute critical appraisal checklist.
Table 2. The Joanna Briggs Institute critical appraisal checklist.
StudyWere the Groups Comparable Other than the BMI?Were the Same Criteria Used to Identify Cases and Controls? Was Exposure Measured in a Valid and Reliable Way?Was Exposure Similarly Measured in Cases and Controls?Were Confounding Factors Identified?Were Strategies to Deal with Confounders Stated?Were Outcomes Assessed in a Valid, and Reliable Way for Cases and Controls?Was the Exposure Period of Interest Long Enough to Be Meaningful?Was Appropriate Statistical Analysis Used?Risk of Bias
Alakhras M [21]NoYesYesYesYesYesYesYesYesLow
Kondoh Y [22]NoYesYesYesYesYesYesYesYesLow
Judge EP [23]NoNRNRNRNoNoYesYesYesHigh
Kim JH [24]NoYesYesYesYesYesYesYesYesLow
Lee JS [25]NoYesYesYesNoNoYesYesYesHigh
Mura M [26]NoYesYesYesNoNoYesYesYesHigh
Kondoh Y [27]NoYesYesYesNoNoYesYesYesHigh
Cao M [28]NoYesYesYesNoNoYesYesYesHigh
Kishaba T [29]NoYesYesYesYesYesYesYesYesLow
Nishiyama O [30]NoYesYesYesNoNoYesYesYesHigh
Dotan Y [31]NoYesYesYesNoNoYesYesYesHigh
Nishimoto K [32]NoYesYesYesYesYesYesYesYesLow
Suzuki Y [33]NoYesYesYesYesYesYesYesYesLow
Hanaka T [34]NoYesYesYesNoNoYesYesYesHigh
Ikeda S [35]NoYesYesYesYesYesYesYesYesLow
Kato M [36]NoYesYesYesYesYesYesYesYesLow
Kono M [37]NoYesYesYesNoNoYesYesYesHigh
Kulkarni T [38]NoYesYesYesYesYesYesYesYesLow
Li B [39]NoYesYesYesYesYesYesYesYesLow
Alhamad EH [40]NoYesYesYesYesYesYesYesYesLow
Dotan Y [41]NoYesYesYesNoNoYesYesYesHigh
Fang C [42]NoYesYesYesNoNoYesYesYesHigh
Ikeda K [43]NoYesYesYesYesYesYesYesYesLow
Jouneau S [44]NoNRNRNRNoNoYesYesYesHigh
Nakano A [45]NoYesYesYesYesYesYesYesYesLow
Tang F [46]NoNRNRNRNoNoYesYesYesHigh
Zaman T [47]NoYesYesYesNoNoYesYesYesHigh
Kim HJ [48]NoYesYesYesYesYesYesYesYesLow
Sangani RG [49]NoYesYesYesNoNoYesYesYesHigh
Suzuki Y [50]NoYesYesYesYesYesYesYesYesLow
Uchida Y [51]NoYesYesYesYesYesYesYesYesLow
Zinellu A [52]NoYesYesYesYesYesYesYesYesLow
Jouneau S [53]NoYesYesYesYesYesYesYesYesLow
Jouneau S [54]NoYesYesYesNoNoYesYesYesHigh
Yoo JW [55]NoYesYesYesYesYesYesYesYesLow
Zinellu A [56]NoYesYesYesYesYesYesYesYesLow
Legend: NR, not reported.
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MDPI and ACS Style

Zinellu, A.; Carru, C.; Pirina, P.; Fois, A.G.; Mangoni, A.A. A Systematic Review of the Prognostic Significance of the Body Mass Index in Idiopathic Pulmonary Fibrosis. J. Clin. Med. 2023, 12, 498. https://doi.org/10.3390/jcm12020498

AMA Style

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. Journal of Clinical Medicine. 2023; 12(2):498. https://doi.org/10.3390/jcm12020498

Chicago/Turabian Style

Zinellu, Angelo, Ciriaco Carru, Pietro Pirina, Alessandro G. Fois, and Arduino A. Mangoni. 2023. "A Systematic Review of the Prognostic Significance of the Body Mass Index in Idiopathic Pulmonary Fibrosis" Journal of Clinical Medicine 12, no. 2: 498. https://doi.org/10.3390/jcm12020498

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