Obesity and lung cancer—a narrative review

Introduction

Cancer is the second leading cause of death worldwide, with an estimated 14.1 million incident cases and 8.2 million deaths (1). Lung cancer in particular, is the leading cause of global cancer incidence and mortality (2). Besides well-established risk factors such as genetic predisposition, radiation, tobacco use and other environmental exposures, obesity has been linked to the development of many cancers (3). This is quite concerning as the global prevalence of overweight and obesity has increased by 27% in adulthood and 47% in childhood during the last few decades (4). Excess adiposity has been hypothesized to act as a carcinogen by increasing inflammation, metastatic potential, angiogenesis, and evasion of apoptosis (5). Obesity has also been linked with increased overall cancer mortality in many cancers such as colorectal, liver, gallbladder, pancreatic, and kidney cancer (6). A nuanced relationship exists, however, between obesity and lung cancer risk/prognosis, termed the ‘obesity paradox’. Multiple meta-analyses have shown that a high body mass index (BMI) is an independent predictor of lower lung cancer risk, better treatment outcomes, and longer overall survival (OS) (7-9). This goes against conventional understanding of the deleterious biological effects of excess fat.

Many researchers have further investigated to find out the underpinnings of the obesity paradox. One proposed major cause is the improper classification and quantification of obesity. The World Health Organization (WHO) considers overweight or obesity as abnormal or excess accumulation of fat in the body that poses a risk to health (10). The commonly used metric for identifying such unhealthy fat accumulation is BMI, the ratio of body weight to the square of its height (11). BMI between 18.5 and 24.99 kg/m² is considered normal, while BMI between 25 and 29.99 kg/m² is considered overweight and BMI ≥30 kg/m² a considered overweight and obese, respectively. A major drawback of BMI as an indicator of health is that it is agnostic of sex, ethnicity, age, and physiological status (12-14). BMI also does not differentiate between distinct types of adipose tissue with respect to metabolic activity and distribution in different anatomic locations. This is important because different adiposity patterns are associated with different biological effects. For example, visceral fat is more biologically active and is associated with poorer outcomes when compared to subcutaneous fat (15). Additionally, BMI is known to overestimate obesity when there is excess muscle mass and underestimate obesity in cancer patients (14).

Besides BMI, other anthropometric measures of obesity include skin fold thickness, waist circumference (WC), hip circumference (HC), and the waist-hip ratio (WHR). These are easy to measure and validate. Unlike BMI, WC and WHR attempt to assess obesity at the abdominal level and are thought to be better than BMI in identifying the more harmful central adiposity (12,16). Direct and non-invasive assessment of body fat content and distribution is possible with methods such as dual energy X-ray absorptiometry (DEXA), computerized tomography (CT), and magnetic resonance imaging (MRI) (12,13,17,18). DEXA and MRI are both highly accurate measures of obesity but cannot be feasibly performed in all patients due to their high cost (3). Bioelectrical impedance analysis (BIA) is another technique to measure obesity and is convenient, fast, and inexpensive (19). However, it is limited by inaccuracy introduced during illness, dehydration, weight loss, and a BMI of 35 or higher (19). Quantifying obesity using CT allows for assessing body composition as it not only differentiates VAT (visceral adipose tissue), SAT (subcutaneous adipose tissue), and skeletal muscle mass but also identifies fat infiltration into muscle and various organs (12). Measurement of obesity using CT images is quick, highly accurate and can be done retrospectively, allowing for collection of data that can subsequently be analyzed. In all, more than three dozen measures of obesity have been used in research studies, though only very few of them, like BMI, have widely accepted cutoffs (20). A lack of standardization leads to large variations in the correlation between these measures and makes it difficult to synthesize a coherent picture of obesity and its relationship with lung cancer.

Assessing the impact of obesity on lung cancer prognosis is further complicated by treatment modality. Some studies have suggested improved survival in obese patients with a higher BMI to immune checkpoint inhibitor (ICI) therapy (21-24). This maybe because the tumor microenvironment is known to over express programmed cell death protein 1 (PD-1), and programmed death ligand 1 (PD-L1) receptors which in turn causes immune exhaustion (25). Lung cancer patients also tend to lose weight due to disease progression, which if not accounted for as reverse causation, may explain the inverse relationship between BMI and lung cancer prognosis (26). Presence of confounding factors such as tobacco, statin, and metformin use also need to be accounted for when describing the obesity paradox.

We present this article in hopes of clarifying the relationship between various measures of obesity, lung cancer risk and lung cancer prognosis. We present this article in accordance with the Narrative Review reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-22-1835/rc).

Methods

The PubMed medical literature database and search engine of the United States National Library of Medicine at the National Institutes of Health were used on 10 August 2022 to identify published research studies. Literature published in English between 2018 and 2022 which included all study designs that were possibly relevant to this review were included. Only full manuscripts were considered. Reviews of existing literature, abstracts, preprints, and letters to editors were excluded. The search string, designed to cover the primary themes of the review and optimized for high sensitivity, was: (airway[Title/Abstract] OR lung[Title/Abstract] OR nscl*[Title/Abstract] OR pulmonary[Title/Abstract]) AND (cancer[Title/Abstract] OR carcino*[Title/Abstract] OR oncol*[Title/Abstract]) AND (adipo*[Title] OR BMI[Title] OR “mass index”[Title] OR “body composition”[Title] OR fat[Title] OR fatness[Title] OR obes*[Title] OR overweight[Title] OR “waist”) NOT embolism NOT stroke NOT infection NOT review[Publication Type]. Titles of the 304 publications that resulted with the search were read by two authors to identify 159 publications that were relevant to the topic of obesity’s influence on lung cancer. Following a reading of the abstracts of these publications, 69 publications were considered relevant and their full text studied to collate information for this review. The search strategy is summarized in Table 1.

Obesity and risk of lung cancer

Obesity has been associated with increased risk of developing various cancers (27). The obesity paradox, however, puts forth the notion that obesity may be protective against the development of lung cancer. This is backed by the fact that most authors have found that a higher BMI is protective against lung cancer (8,26,28-36) (Table 2). Some, however, found that this association varies by race and gender. According to Zhu et al., a significant inverse association between lung cancer risk and BMI only existed in women, but not in men (8). Similarly, Zhao et al. found that overweight and obese Caucasians had a decreased risk of lung cancer, but not African Americans (35). These differences can be explained by the heterogeneity of fat deposition across gender and race being inadequately captured by BMI (3). Most studies have used BMI as a static measure, however, the use of BMI as a dynamic measure to explore trends in change of BMI has been suggested. Accordingly, Wu et al. found that a major change in pre-diagnosis BMI was associated with increased lung cancer incidence (7).

Confounding by smoking and reverse causation due to pretreatment weight change are frequently used explanations to explain the obesity paradox (8,26,28). Lung cancer patients tend to lose weight on disease progression. Mohan et al. found that non-small cell lung cancer (NSCLC) patients had significant alteration in their body composition with decreased BMI, fat mass (FM), fat free mass (FFM), fat%, total body weight and functional status compared to healthy age matched controls (38). Thus, low pre-diagnosis BMI may be associated with increased risk of lung cancer if pre-clinical disease is not accounted for. Smoking greatly increases the risk of developing lung cancer (39). Cross sectional nationwide studies in UK and Japan have suggested that current smokers were less likely to be obese than former and never smokers (40,41). The lower rates of obesity amongst current smokers are a widely proposed confounder in the association between lung cancer and obesity. However, studies and meta-analyses found that the inverse association between BMI and risk of lung cancer existed even after accounting for smoking and reverse causation (8,26,28,30-32,34,35) (Table 2). Similarly a meta-analysis by Zhu et al., found an inverse association between BMI and lung cancer risk in a cohort of 15,000,000 never smokers (8). Whereas, in a meta-analysis of 28 cohort studies, Gao et al. found no statistically significant inverse association between BMI and lung cancer risk after stratifying for smoking status and excluding effects of pre-clinical cancer (37).

In contrast with studies using BMI, increased risk of lung cancer was found when measures of central obesity such as WC, WHR and A Body Shape Index (ABSI) were used as metrics of obesity (28,34,37). A combination of general and central obesity indicators maybe a better indicator of risk; Yu et al. found that low BMI and high WC/WHR was associated with a 40% increased risk of lung cancer compared to a high BMI and normal or moderate WC/WHR (34). Similarly, body composition analysis using DEXA showed that total body fat mass (BFM) was not associated with risk of lung cancer (29). However, a decrease in lean body mass (LBM) was predictive of lung cancer risk, implying that body composition is a more accurate measure of obesity than overall FM.

Studies understanding the relationship between obesity, immune system, and clinical markers of inflammation found that obesity was associated with neutrophil to lymphocyte ratio (NLR), systemic immune inflammatory index (SII) (42,43). A study exploring the risk of 17 cancers in 440,000 participants in the UK Biobank found that NLR, and SII were positively associated with risk of seven cancers including lung cancer (44). Some authors have used indices to measure metabolic dysfunction as a proxy measure of central obesity. Patients with a low BMI and a high metabolic score (calculated using blood pressure, glucose, and triglycerides) were 50% more likely to develop lung cancer than those with a high BMI and a low metabolic score (31). When non-alcoholic fatty liver disease (NAFLD) was used as a sign of metabolic dysfunction, NAFLD patients had higher odds of having lung adenocarcinoma (45). These clinical markers of inflammation and metabolic dysfunction can function as additional biomarkers to better quantify lung cancer risk in high-risk patient populations.

The increased understanding of genetics in the recent past has opened a new avenue to study the associations between obesity, risk of lung cancer development, and confounders such as smoking. A Mendelian randomization (MR) study by Zhou et al. found that while high BMI increased the risk of small cell lung cancer (SCLC), and it decreased the risk of adenocarcinoma when adjusted for smoking (26). In another MR study, BMI change from normal weight to overweight/obese was associated with protective effects against NSCLC development after accounting for smoking (34).

Obesity and survival outcomes of lung cancer

Obesity has been shown to be associated with worse survival in other cancers like colorectal, hepatocellular, oropharyngeal, and breast cancer (46-49). However, in lung cancer, the obesity paradox applies not only to risk of lung cancer development, but also to lung cancer survival times. Many authors have found that higher BMI is associated with increased OS in lung cancer (9,50-53) (Table 3). Though most studies found a higher BMI to be protective, some authors identified that the relationship between obesity and lung cancer risk is non-linear (52,61). These investigators found a U-shaped hazard ratio relationship between lung cancer OS and BMI on a spline plot. Underweight and morbidly obese (BMI ≥35 kg/m²) patients had worse survival outcomes. According to Jiang et al., the relationship between BMI and lung cancer survival is affected by race, gender, and smoking status (50). A large meta-analysis of 3,152,552 lung cancer patients found an increase in BMI decreased lung-cancer specific as well as all-cause mortality in Asians (9). Jiang et al. showed that the protective effect of being overweight/obese was seen best in African Americans, but not Caucasians; and being underweight was associated with poor survival only among Caucasians (50). A study exploring the interaction between gender, smoking, obesity, and lung cancer found that female ever-smokers at the extremes of BMI had worse OS when compared to male ever-smokers (50).

In the above studies, a high BMI serves as a positive prognostic factor for OS only in some groups of patients. However, other studies have reported no effect of BMI on lung cancer survival after performing multi-variable analysis to eliminate confounders (53-56,58) (Table 3). Due to the conflicting nature of the results obtained when BMI is used as the measure for obesity, authors have proposed a shift of focus to WC, WHR and imaging studies to better characterize obesity and body composition.

Leitzmann et al., analyzed the National Institutes of Health and formerly the American Association of Retired Persons (NIH-AARP) Diet and Health study dataset with 225,712 patients and found that patients with a higher WC had a higher lung cancer-specific mortality (62). In a study conducted by our group, VFI (visceral fat index) was calculated as the proportion of total fat area which is truly visceral using CT scans. VFI was found to be an independent negative prognostic factor for recurrence free (RFS), OS and diseases specific survival (DSS) in a cohort of 513 stage I/II NSCLC patients (54). Central obesity (high VFI) was associated not only with shorter survival times, but also with accelerated tumor growth, sharply contrasting the obesity paradox (54). In similar image-based studies, a higher SAT volume was associated with better progression free survival (PFS) and BFM ratio >22% was predictive of longer OS (55,58). Sakai et al. reported increased length of hospital stay with increased skeletal muscle fat fraction and sarcopenia (59).

Another added advantage of using image-based studies to define body composition is the ability to identify obese cancer patients with sarcopenia. Sarcopenic obesity is a condition of combined skeletal muscle depletion in obese patients (63). While obesity is defined by WHO as accumulation of fat that negatively affects health, there is very little agreement on the definition of sarcopenia making the diagnosis of sarcopenic obesity even more challenging. Using the widely accepted sarcopenia cut-off suggested by Prado et al., Baracos et al. suggested a mean overall rate of 9.3% of sarcopenic obesity amongst all cancer patients and a rate of 17.9% in patients with a BMI >25 kg/m² (64,65). A meta-analysis of 14 studies exploring the implications of sarcopenic obesity on clinical outcomes in cancer patients found that sarcopenic obesity was significantly associated with dose limiting toxicity, surgical complications, and survival (63). In an observational study of 175 NSCLC patients receiving chemotherapy Gonzalez et al. found that sarcopenic obesity was a negative prognostic factor for survival (66).

Low BMI is associated with a higher tumor-node-metastasis (TNM) stage, increased metastasis, ECOG-PS (European Cooperative Oncology Group-Performance Status), and a low FFM which in turn are associated with poor survival (55,57,67,68). Patients presenting with a combination of a higher ECOG-PS and a lower BMI had worse OS compared to a lower ECOG-PS and a higher BMI (51). Some authors have suggested using dynamic measures of weight/BMI change rather than static measurements to better characterize obesity (52,53,61). Decrease in BMI or weight loss at the time of lung cancer diagnosis relative to early adulthood was associated with poor OS (53). Post-diagnosis weight loss (representing cachexia) was also associated with worse OS (52,61). Obesity also potentially modifies the disease progression of lung cancer. Patients with low SAT volume have the highest rate of lung cancer progression and NSCLC patients with low BMI are at a higher risk of brain metastasis than those with high BMI (55,57).

Obesity and therapeutic survival of lung cancer

The complex relationship between obesity and lung cancer outcomes has been further characterized by investigating the effects of increased adiposity on various therapeutic modalities.

Obesity is known to induce a state of ‘meta-inflammation’ by increasing the secretion of inflammatory cytokines (IL-6, TNF-α) through disruption of the endocrine system (increased insulin resistance, leptin, estrogen levels) (54). Elevated levels of leptin have been shown to increase the expression of PD-1, causing T-cell exhaustion and therefore a reduced immune response in the tumor microenvironment (21,23,54,60,69,70). Thus, the outcomes of ICI therapy are invariably affected by obesity. In a cohort of 61 squamous cell carcinoma patients, Wang et al. found a positive linear relationship between pre-treatment BMI and post-treatment blood immune cells, indicating a better response to immunotherapy in individuals with higher BMI (71). Better treatment response also correlated with better survival outcomes (71). Many studies have found that higher BMI was significantly associated with better OS and PFS in patients treated with ICI (22,24,69,71-76) (Table 4). This association was strengthened for tumors expressing higher levels of PD-1/PD-L1 within the tumor microenvironment (21,22,69,71,89). In addition to immunotherapy, combination of chemotherapy with immunotherapy was associated with better outcomes compared to chemotherapy alone only in patients with a higher BMI (21,23). Some authors have disputed the association between high BMI and improved immunotherapy outcomes. Liu et al. found that the effect of BMI on survival times was only significant in univariate analysis, but not multivariable analysis (83). Others found no association between BMI and response to immunotherapy (73,78,84,85,88) (Table 4). Authors have proposed using CT based measures of obesity to better explore this discrepancy. Popinat et al. showed that increased subcutaneous fat mass (SCFM) was an independent negative prognostic factor for stage IV NSCLC treated by nivolumab (87). Similarly, Minami et al. found that low intra-muscular adipose content was predictive of longer OS (85). Some studies have shown that clinical indicators of inflammation such as blood albumin levels, neutrophil counts, C-reactive protein (CRP) and NLR are better prognostic factors than CT based body composition analysis for ICI therapy (78,80,81,84).

Like BMI and weight change, authors have proposed to use the change in body composition as a better prognostic indicator of ICI therapy. Nishioka et al. found that a decrease in the total adipose tissue index after ICI therapy was predictive of better overall response rate and PFS time in patients without cachexia (86). Pre-treatment weight loss or decrease in BMI, especially skeletal muscle related protein loss leading to sarcopenia or cachexia was associated with poor ORR, disease control rate (DCR), PFS, and OS (79,80,84,90) (Table 4). Body composition has been suggested to affect the pharmacokinetics of ICIs, which in turn may affect the number of immune related adverse events (70,82). Patients with sarcopenia or high BMI reported more adverse events, even though they developed these toxicities later when compared to normal or underweight patients (22,24,70,72,82).

High cholesterol levels and risk of prevalence of diabetes have been known to be associated with obesity (3). Thus, obese patients are more likely to use metformin and statins regularly. According to Patnaik et al., statin use is associated with improved RFS (HR =0.46, P=0.002) in patients with a high BMI only. On tumor transcriptome profiling using RNA sequencing, high expression of tumoricidal genes and statin use were positively correlated. Endoplasmic reticulum stress, caspase induction, NF-kB blockade, and mTOR inhibition are potential routes through which statins influence tumor cell (91). Metformin has also been shown to decrease tumor proliferation and growth in preclinical studies via inhibition of the Krebs cycle and lipid synthesis (92,93). BMI modulates the protective effect of metformin; with the greatest effect seen in patients who are overweight or obese (77,94,95). Metformin use in pts with a BMI >25 is known to significantly improve OS and DSS (60).

Treatment of locally advanced NSCLC (stage III) includes platinum-based chemoradiotherapy (CRT). Success rates of CRT are dependent upon body composition of the patient during and before the start of treatment (96). In contrast to immunotherapy, loss of both fat and FFM was associated with a poor OS (96). Patients with low pre-treatment FFM and a weak handgrip (a clinical indicator of muscle mass) were found to have worse survival, especially in patients with normal BMI and a good functional status (96-98) (Table 5). Using malnutrition universal screening tool (MUST), modified Glasgow prognostic score (mGPS) and tumor lesional glycolysis as clinical indicators of malnutrition and inflammation, studies have shown that poor nutrition and systemic inflammation were associated with worse survival outcomes following CRT (99,100) (Table 5).

When undergoing surgery for lung cancer, it is expected that obese patients will experience more complications due to having lower lung volumes, less diaphragm excursion and being relatively immobile (101). Numerous studies, however, have shown that overweight and obese BMIs were not predictive of peri- or postoperative complications or length of post-operative hospital stay (102-107). Guerrera et al. found that only morbidly obese (BMI ≥40 kg/m²) patients had increased postoperative morbidity (108). In 433 stage 1 NSCLC patients after surgical resection, BMI was predictive of home oxygen use but not acute post-operative morbidity or mortality (109). Contrary to conventional thought, multiple studies have found that increased pre-surgery BMI predicted better OS (104,106,110-112) (Table 6). In addition, other studies showed that underweight BMI was associated with compromised post-operative outcomes, with these patients experiencing more surgical and infectious complications (106). They also had poorer survival outcomes when compared to normal/overweight/obese individuals (104,110,112,113) (Table 6). Using abdominal fat measurements from pre-operative positron emission tomography (PET)/CT scans, Choi et al. found that adipopenia was associated with reduced OS in stage I lung cancer in post-lobectomy patients (103). Decreased muscle mass and FFM were also independently associated with increased risk of postoperative complications (prolonged air leak, pneumonia) and prolonged length of stay (102,114). When pericardial fat volume was used as a measure of obesity, higher fat volumes were associated with higher BMI and better RFS and OS in NSCLC patients undergoing resection (115). Using metabolic score as a measure of inflammation, Yuan et al. found that a combination of BMI and metabolic score was predictive of readmission in NSCLC patients after surgery (116).

A limitation of this review is that is that only English articles in the PubMed database were included. A more comprehensive search strategy including other languages and databases could have yielded more studies to incorporate.

Conclusions

The relationship between obesity and lung cancer is nuanced. The association between obesity or body composition and lung cancer risk and outcomes varies not only depending on age, gender, and race but also the metric used to define them. Obesity defined using BMI is associated with a decreased risk of lung cancer incidence and mortality, thus giving rise to the term ‘obesity paradox’. When assessing the ‘obesity paradox’ care must be taken to account for confounding factors such as smoking and reverse causation. In addition, the inability of BMI to differentiate between different patterns of fat distribution has also been well documented. The use of CT and MRI based techniques have been recently proposed as a gold standard to define body composition. Although the use of obesity metrics based on the above imaging modalities have been shown to be feasible and practical, a lack of standardization has led to difficulty interpreting study results. Further research must be done to understand the association between these obesity metrics and lung cancer. Clarifying the association between obesity and lung cancer is important for the development of novel preventive, and possibly therapeutic strategies.

Acknowledgments

Funding: This work was supported by National Institutes of Health, USA research grants (Nos. R01CA255515-01A1 and UG1CA242643 to SY).

Footnote

Reporting Checklist: The authors have completed the Narrative Review reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-22-1835/rc

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-22-1835/coif). SY serves as an unpaid editorial board member of Journal of Thoracic Disease from February 2023 to January 2025. SY reports that this work was supported by National Institutes of Health, USA research grants (Nos. R01CA255515-01A1 and UG1CA242643). The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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