Abstract
Objective:
Obese and overweight body mass index (BMI) categories have been associated with increased immune-related adverse events (irAEs) in patients with cancer receiving immune checkpoint inhibitors (ICIs); however, the impact of being overweight in conjunction with related metabolic syndrome-associated factors on irAEs have not been investigated. We aimed to evaluate the impact of overweight and obese BMI according to metabolic disease burden on the development of irAEs.
Design and Methods:
We conducted a retrospective observational study of patients receiving ICIs at a cancer center. Our main study outcome was development of ≥grade 2 (moderate) irAEs. Our main predictor was weight/metabolic disease risk category: (1) normal weight (BMI 18.5-24.9 kg/m2)/low metabolic risk (<2 metabolic diseases [diabetes, dyslipidemia, hypertension]), (2) normal weight/high metabolic risk (≥2 metabolic diseases), (3) overweight (BMI ≥25 kg/m2)/low metabolic risk, and (4) overweight/high metabolic risk.
Results:
Of 411 patients in our cohort, 374 were eligible for analysis. Overall, 111 (30%) patients developed ≥grade 2 irAEs. In Cox analysis, overweight/low metabolic risk was significantly associated with ≥grade 2 irAEs (hazard ratio [HR]: 2.0, 95% confidence interval [95% CI]: 1.2-3.4) when compared to normal weight/low metabolic risk, while overweight/high metabolic risk (HR: 1.3, 95% CI: 0.7-2.2) and normal weight/high metabolic risk (HR: 1.5, 95% CI: 0.7-3.0) were not.
Conclusions:
Overweight patients with fewer metabolic comorbidities were at increased risk for irAEs. This study provides an important insight that BMI should be evaluated in the context of associated metabolic comorbidities in assessing risk of irAE development and ICI immune response.
Keywords: obesity, overweight, immune checkpoint inhibitor, immune related adverse events
Introduction
Immune checkpoint inhibitor (ICI) therapies have recently revolutionized the treatment of cancer and are increasingly used as therapy for many types of malignancies. ICIs, including anti-cytotoxic T-lymphocyte antigen 4 (CTLA-4) and anti-programmed cell death protein 1/programmed cell death ligand protein 1 (PD-1/PD-L1) monoclonal antibodies, treat cancer by inducing anti-tumor immune responses 1. This immune activation can lead to immune-related adverse events (irAEs) that affect multiple organ systems 2. With the increased use of ICIs to treat cancer, irAEs are becoming more frequently reported, with an incidence of up to 34-39% of all grade irAEs in real-world populations 3, 4. IrAEs often lead to treatment cessation and cause significant patient morbidity5. Furthermore, there is emerging evidence that irAEs may be a predictor for ICI response, particularly in patients receiving anti-PD-1 and anti-PD-L1 therapies 3, 6.
Recent literature suggests that overweight and obese body mass index (BMI) categories are associated with an increased incidence of irAEs, as well as improved tumor response to ICI therapy 4, 7–10. Excess adiposity has been shown to promote a chronic low-grade inflammatory state that is associated with metabolic comorbidities such as diabetes mellitus and dyslipidemia, as well as autoimmune diseases 11, 12. Excess adiposity may enhance ICI activity due to obesity-induced changes in the composition of the tumor immune microenvironment, such as increased presence of T-lymphocytes that express PD-1 13. This process may be mediated by increased leptin, which is known to increase inflammation via increased T helper type 1 (Th1) cell responses 13, 14.
While obesity is a potential risk factor for irAEs in patients receiving ICI therapy, little is known regarding the association of different subtypes of obesity (metabolically healthy versus unhealthy) and other metabolic diseases, such as diabetes, hypertension, and dyslipidemia, with irAE development. Metabolically healthy and metabolically unhealthy overweight and obese patients are known to have different risk profiles in regard to developing cancer and have been shown to have different inflammatory phenotypes 15–17. In addition to obesity, other metabolic diseases have been shown to influence key immune pathways, which may impact ICI toxicity 18–21. One recently published retrospective study in patients with cancer receiving ICIs showed that obesity and dyslipidemia improved overall survival, while hypertension worsened survival, but the authors did not specifically investigate immunotherapy-specific outcomes such as tumor progression or irAEs 22. In this study, we aim to evaluate the impact of overweight and obesity (as defined by BMI), with and without metabolic risk factors such as diabetes, dyslipidemia, and hypertension, on the development of irAEs in a cohort of real-world patients receiving ICI therapy.
Materials and Methods
Patients eligible for our analysis received ICIs (nivolumab, pembrolizumab, atezolizumab, ipilimumab, and tremilimumab) between 2011 and 2017 at a National Cancer Institute-designated Cancer Center. We identified our eligible patient population through our institution’s electronic medical record (EMR) data warehouse by querying for patients who had been documented to receive ICIs at our institution within the given timeframe. We included patients older than 18 years with documented solid (e.g. melanoma, lung cancer, renal cell carcinoma, hepatocellular carcinoma) and hematologic malignancies (e.g. multiple myeloma, lymphoma). We excluded patients without height and weight data. We also excluded patients with underweight BMI (<18.5 kg/m2), as defined by World Health Organization (WHO) criteria 23, as the intent of our study was to compare normal weight and overweight groups.
Our primary outcome of interest was the development of grade 2 or above (moderate) irAEs. We selected this as our primary outcome, as suspension or interruption of treatment is recommended for most irAEs grade 2 or above 24. We defined and graded irAEs according to the Common Toxicity Criteria for Adverse Events (CTCAE) version 5. The CTCAE are standardized criteria to classify adverse events during the course of cancer treatment published by the National Cancer Institute 25. IrAEs were ascertained by review of the electronic medical record diagnosis codes and reviewing clinician notes, laboratory values, and imaging studies. IrAEs were defined as inflammatory or autoimmune adverse events that occurred after ICI initiation to 1 year after cessation of ICI therapy, including worsening of existing autoimmune diseases (e.g., hypothyroidism) and could occur in multiple organ systems. We chose this timeframe as most irAEs can occur up to a year after cessation of ICI treatment administration 26. IrAE type (classified by organ system), date, and grade were recorded. Due to known variation and subjective interpretation in the documentation and assessment of irAEs, two physicians determined irAEs 27 based on the CTCAE criteria described above. Agreement was assessed by Cohen’s kappa. Discrepancies in irAE determination were mediated by a third physician.
Our main exposure of interest was BMI category according to metabolic disease burden (diabetes, dyslipidemia, and hypertension). There is no uniform definition of metabolically healthy vs unhealthy obese/overweight individuals, but previous studies have made this distinction by the absence/presence of metabolic comorbidity burden 28, 29. We categorized patients according to BMI as “normal weight” (18.5-24.9 kg/m2 by WHO criteria) and “overweight” (≥25 kg/m2 ), which included overweight and obese individuals with by WHO BMI criteria 23. Metabolic risk categorization was based on the presence of less than 2 (low metabolic risk) or ≥2 (high metabolic risk) of the following three comorbidities: diabetes, dyslipidemia and hypertension (i.e. proxy for metabolically healthy vs. unhealthy) 28, 29. We then generated four groups: (1) normal weight/low metabolic risk, (2) normal weight/high metabolic risk, (3) overweight/low metabolic risk, and (4) overweight/high metabolic risk. We also assessed the impact of metabolic comorbidities and medications individually on irAE development: BMI category on its own and diagnoses of diabetes, dyslipidemia, and hypertension. BMI was recorded at time of ICI initiation. Diabetes, dyslipidemia (including hyperlipidemia), and hypertension diagnoses were determined by EMR review (diagnosis codes and if diagnoses were listed in the medical chart notes). Metformin and statin use at the time of ICI initiation were also determined by EMR review.
Additional baseline patient characteristics were also collected from the EMR, including sociodemographic variables, malignancy characteristics, and ICI treatment information. Sociodemographic variables included age, sex, and self-reported race (with “other” race signifying race marked as “other” in the EMR and “unknown” race signifying no race was indicated in the EMR). We collected information on preexisting autoimmune disease as there has been a reported association with irAEs 30. Malignancy type and clinical stage were recorded. Immune checkpoint inhibitor type (anti-CTLA-4, anti-PD-1/PD-L1, sequential therapy, combined anti-CTLA-4 and anti-PD-1/PD-L1 therapy) and dosing information including whether or not dosing was weight-based were also documented.
Statistical Analysis
Baseline patient characteristics were detailed and compared between patients who did not vs. those who did experience grade 2 or above irAEs using the Fisher’s exact test for categorical variables and the Kruskal-Wallis test for continuous variables. We then analyzed the irAE development as a time-dependent variable, as we wanted to take into account variable patient follow-up time inherent to a retrospective data cohort and the presumption that patients with lower BMI may have shortened follow-up time due to worsened survival and shorter duration of ICI treatment 4. Time to irAE development was defined as the period from ICI initiation to the date of the first grade 2 or greater irAE. Patients without irAEs were censored at 1 year after last ICI treatment or death, or date of last assessment in our EMR system if these events occurred prior to 1 year.
We conducted univariate and adjusted analyses with Cox proportional hazards regression models. Variables known to have a potential relationship with irAE development (such as age, sex, race, malignancy stage, malignancy type, and ICI type) were included in the models 26, 31–35. We performed additional adjusted analyses that incorporated metformin and statin use to account for the potential influence of these drugs. The product limit of the Kaplan-Meier was used to estimate time-to-event curves for development of grade 2 or above irAEs across BMI/metabolic risk categories and the log-rank test was used to compare irAE development across BMI/metabolic risk categories.
Statistical analyses were conducted with STATA v14 (Statacorp, 2018). A p-value of <0.05 was considered statistically significant. The study was conducted in accordance with the principles set out by the Declaration of Helsinki. This study received approval from the Icahn School of Medicine at Mount Sinai Institutional Review Board (IRB-17-01894). Waiver of consent was obtained based on Code for Federal Regulations Title 45 Part 46.116.
Results
Of 411 patients treated with ICIs, 13 were excluded because they did not have BMI data and 24 were excluded for underweight BMI, leaving a study cohort of 374 patients. Mean follow-up was 8.8 months (range 1 day to 68.3 months). Cohen’s kappa of agreement between two physician irAE reviewers was 0.64 (substantial) for irAE type and grade. Overall, 111 (29.7%) patients had a grade 2 (moderate) or greater irAE; 10.7% of patients had grade 3 or 4 (severe) irAEs. The greatest documented irAE grade was grade 2 for 71 patients (19.0%), grade 3 for 34 patients (9.1%) and grade 4 for 7 patients (1.6%). Of 149 total grade 2 or above irAEs that occurred in these 111 patients, the most common irAE categories were endocrine (34.2%), gastrointestinal (22.1%) and dermatologic (18.8%) (Table 1).
Table 1:
Description of grade 2 or greater adverse events in patient cohort
| n (%) | |||
|---|---|---|---|
| Total patients with irAE* | 111 (29.7) | ||
| Highest Grade | |||
| Grade 2 | 71 (19.0) | ||
| Grade 3 | 34 (9.1) | ||
| Grade 4 | 6 (1.6) | ||
| Total irAEs† | 149 (100) | ||
| Organ System | |||
| Gastrointestinal | 33 (22.1) | ||
| Colitis | 18 | ||
| Hepatitis | 12 | ||
| Pancreatitis | 1 | ||
| Hyperbilirubinemia | 2 | ||
| Endocrine | 51 (34.2) | ||
| Hypophysitis | 8 | ||
| Thyroid disease | 36 | ||
| Adrenal insufficiency | 6 | ||
| Diabetes | 1 | ||
| Dermatologic | 28 (18.8) | ||
| Rash | 21 | ||
| Pruritus | 2 | ||
| Mucositis | 4 | ||
| Other | 1 | ||
| Ophthalmologic | 2 (1.3) | ||
| Conjuctivitis | 1 | ||
| Blepharitis | 1 | ||
| Neurologic | 4 (2.7) | ||
| Encephalopathy | 1 | ||
| Neuropathy | 3 | ||
| Hematologic | 1 (0.7) | ||
| Anemia | 1 | ||
| Renal | 2 (1.3) | ||
| Acute kidney injury | 2 | ||
| Respiratory | 13 (8.7) | ||
| Pneumonitis | 13 | ||
| Cardiac | 3 (2.0) | ||
| Myocarditis | 3 | ||
| Rheumatologic | 12 (8.1) | ||
| Arthralgia | 5 | ||
| Myalgia | 4 | ||
| Other | 3 | ||
irAE, immune-mediated adverse event;
percent of total patients is given in parenthesis;
number in parenthesis indicates percent of irAEs
Baseline patient characteristics according to the presence or absence of a grade 2 or greater irAE are shown in Table 2. A higher percentage of patients in the group that developed grade 2 or greater irAEs were in the overweight/low metabolic risk category (39.6%) than in the group without irAEs (26.2%, p<0.01). Patients who experienced grade 2 or greater irAEs were more likely to be overweight (38.7% vs 30.8%) and obese (27.0% vs 16.4%) (p-value <0.01) than those who did not develop grade 2 or above irAEs. Patients with grade 2 or higher irAEs were also more likely to have dyslipidemia (39.6% vs 30.4%, p=0.05). Patients with and without grade 2 or higher irAEs had similar rates of diabetes (22.5% vs 23.9%, p=0.43) and hypertension (55.0% vs 56.7%, p=0.51). There was a higher proportion of patients with grade 2 or above irAEs who had preexisting autoimmune disease (including lupus, rheumatoid arthritis, autoimmune thyroid disease, inflammatory bowel disease) versus those who did not develop grade 2 or greater irAEs (20.0% vs 7.6%, p <0.01). Patients with grade 2 or greater irAEs had different distribution of malignancy types (p<0.01). They were more likely to have received anti-CTLA-4 ICIs, combination anti-CTLA-4 and anti-PD-1/PD-L1 therapy, or sequential combination therapy, and less likely to have received weight-based anti-PD-1/PD-L1 treatment (p<0.01).
Table 2.
Baseline Characteristics of ICI Treated Patients with and without ≥ Grade 2 Adverse Events. Data are presented as n (%).
| Characteristics | All patients | No irAE | IrAE | P-value* | |
|---|---|---|---|---|---|
| n | 374 | 263 | 111 | ||
| Age, years median (range) | 65.7 (21-96) | 65.3 (31-93) | 66.4 (21-96) | 0.29 | |
| Male | 237 (63.4) | 169 (64.3) | 68 (61.3) | 0.33 | |
| Race | 0.05 | ||||
| White | 208 (55.6) | 133 (50.6) | 75 (67.6) | ||
| Black | 36 (9.6) | 26 (9.9) | 10 (9.0) | ||
| Hispanic | 38 (10.2) | 30 (11.4) | 8 (7.2) | ||
| Asian | 27 (7.2) | 22 (8.4) | 5 (4.5) | ||
| Other/Unknown | 65 (17.4) | 52 (19.8) | 13 (11.7) | ||
| BMI, kg/m2 | <0.01 | ||||
| 18.5-24.9 (Normal) | 177 (47.3) | 139 (42.9) | 38 (34.2) | ||
| 25-29.9 (Overweight) | 124 (33.2) | 81 (30.8) | 43 (38.7) | ||
| ≥30 (Obese) | 73 (19.5) | 43 (16.4) | 30 (27.0) | ||
| Diabetes | 88 (23.5) | 63 (23.9) | 25 (22.5) | 0.43 | |
| Hypertension | 210 (56.2) | 149 (56.7) | 61 (55.0) | 0.51 | |
| Dyslipidemia | 124 (33.1) | 80 (30.4) | 44 (39.6) | 0.05 | |
| Metformin | 39 (10.4) | 30 (11.4) | 9 (8.1) | 0.46 | |
| Statin | 105 (28.1) | 68 (25.9) | 37 (33.3) | 0.17 | |
| Weight/metabolic risk categorya | <0.01 | ||||
| Normal/Low | 129 (34.5) | 104 (39.5) | 25 (22.5) | ||
| Normal/High | 48 (12.8) | 35 (13.3) | 13 (11.7) | ||
| Overweight/Low | 113 (30.2) | 69 (26.2) | 44 (39.6) | ||
| Overweight/High | 84 (22.5) | 55 (20.9) | 29 (26.1) | ||
| Preexisting autoimmune disease | 43 (11.5) | 20 (7.6) | 23 (20.7) | <0.01 | |
| Malignancy type | <0.01 | ||||
| Melanoma | 74 (19.8) | 39 (14.8) | 35 (31.5) | ||
| Non-small cell lung cancer | 82 (21.9) | 63 (24.0) | 19 (17.1) | ||
| Urothelial | 49 (13.1) | 35 (13.3) | 14 (12.6) | ||
| Hepatocellular carcinoma | 55 (14.7) | 39 (14.8) | 16 (14.4) | ||
| Multiple Myeloma | 21 (5.6) | 19 (7.2) | 2 (1.8) | ||
| Other | 93 (24.9) | 68 (25.9) | 25 (22.5) | ||
| Clinical stage | 0.24 | ||||
| Clinically localized | 32 (8.6) | 21 (8.0) | 11 (9.9) | ||
| Regionally advanced | 32 (8.6) | 23 (8.8) | 9 (8.1) | ||
| Distant metastasis | 290 (77.5) | 201 (76.4) | 89 (80.2) | ||
| Other (hematologic) | 20 (5.4) | 18 (6.8) | 2 (1.8) | ||
| Immune checkpoint type | <0.01 | ||||
| CTLA-4 (weight-based) | 45 (12.0) | 24 (9.1) | 21 (18.9) | ||
| PD1/PDL1-not weight based | 143 (38.2) | 110 (41.8) | 33 (29.7) | ||
| PD1/PDL1-weight based | 148 (39.6) | 112 (42.8) | 33 (29.7) | ||
| Combination CTLA4 and PD1/PDL1 | 15 (4.0) | 6 (2.3) | 9 (8.1) | ||
| Sequential therapy | 23 (6.2) | 11 (4.2) | 12 (10.8) | ||
Kruskwal-Wallis for continuous variables, Fisher exact test for categorical
Normal BMI= BMI 18.5-24.9, High BMI= BMI ≥25, Low metabolic disease burden= <2 metabolic diseases (hypertension, diabetes, dyslipidemia), High metabolic disease burden= ≥2 metabolic diseases
Regarding analysis of individual metabolic parameters (Table 3), on univariate analysis, obese BMI (≥30 kg/m2) was significantly associated with grade 2 or above irAE development, while overweight BMI (25-29.9 kg/m2), diabetes, hypertension dyslipidemia were not. However, in an adjusted Cox regression model, development of grade 2 or above irAEs did not significantly differ among BMI categories when considered apart from metabolic disease (overweight BMI hazard ratio [HR] 1.48, 95% confidence interval [95%CI] 0.93-2.34), obese BMI (HR 1.40, 95% CI 1.42 0.84-2.34)). In multivariate analyses, diabetes, dyslipidemia, and hypertension diagnoses remained not associated with grade 2 or above irAE development.
Table 3:
Unadjusted and Adjusted Hazard Ratios (HR) for metabolic diseases for grade 2 or above irAE development
| Category | Unadjusted HR (95% CI) | Adjusted HR (95% CI) | |
|---|---|---|---|
| BMI category | |||
| 18.5-24.9 kg/m2 (Normal) | REF | REF | |
| 25-29.9 kg/m2 (Overweight) | 1.54 (0.99-2.38) | 1.48 (0.93-2.34)* | |
| ≥30 kg/m2 (Obese) | 1.72 (1.06-2.77) | 1.40 (0.84-2.34)* | |
| Diabetes | 0.87 (0.56-1.37) | 0.80 (0.50-1.27)* | |
| Hypertension | 0.86 (0.59-1.25) | 0.86 (0.58-1.29)* | |
| Dyslipidemia | 1.22 (0.83-1.78) | 1.21 (0.80-1.81)* | |
| Metformin use | 0.77 (0.39-1.52) | 0.80 (0.38-1.68)# | |
| Statin use | 1.24 (0.84-1.85) | 1.10 (0.69-1.60)# | |
adjusting for age, sex, malignancy type, malignancy stage, ICI type, preexisting autoimmune disease
adjusting for BMI, metabolic diseases, age, sex, malignancy type, malignancy stage, ICI type, preexisting autoimmune disease
Kaplan Meier time-to-event curves were plotted for development of a grade 2 or above irAE according to weight/metabolic disease category with no significant difference in irAE development across all weight/metabolic disease categories taken together in an unadjusted log rank test (p=0.06) (Figure 1). Unadjusted and adjusted Cox regression analyses are shown in Table 4. Regarding weight/metabolic disease category, in unadjusted Cox regression analysis, overweight/low metabolic risk was associated with an increased risk of developing a grade 2 or greater irAE when compared to normal weight/low metabolic risk (HR 1.91, 95%CI 1.17-3.11) while normal weight/high metabolic risk (HR 1.30, 95%CI 0.66-2.53) and overweight/high metabolic risk (HR 1.54, 95%CI 0.90-2.64) was not. Adjusted analyses showed that overweight/low metabolic risk category remained a statistically significant predictor of developing grade 2 or greater irAEs when compared to normal weight/low metabolic risk (HR:2.00, 95% CI: 1.19-3.36). Overweight/high metabolic risk (HR: 1.28, 95% CI: 0.73-2.22) and normal weight/high metabolic risk (HR: 1.47, 95% CI: 0.73-2.97) were not more likely to be associated with the development of grade 2 or greater irAEs. Additionally, in this multivariate analysis, preexisting autoimmune disease was significantly correlated with developing grade 2 or above irAEs.
Figure 1:
Kaplan-Meier Curves: Immune-related adverse event occurrence across weight and metabolic disease risk categories
Table 4:
Univariate and Adjusted Cox regression analysis: Weight/Metabolic Risk Category and Risk of Developing Grade 2 or above iRAEs
| Variables | Unadjusted HR (95% CI) | Adjusted HR (95% CI)* | Adjusted HR (95% CI)# | |
|---|---|---|---|---|
| Weight/metabolic risk categorya | ||||
| Normal/Low | REF | REF | REF | |
| Normal/High | 1.30 (0.66-2.53) | 1.47 (0.73-2.97) | 1.43 (0.69-2.98) | |
| Overweight/Low | 1.91 (1.17-3.11) | 2.00 (1.19-3.36) | 1.98 (1.17-3.35) | |
| Overweight/High | 1.54 (0.90-2.64) | 1.28 (0.73-2.22) | 1.30 (0.70-2.40) | |
| Age | 1.01 (0.99-1.02) | 1.01 (0.99-1.03) | 1.01 (0.99-1.03) | |
| Sex | ||||
| Female | REF | REF | REF | |
| Male | 0.87 (0.60-1.28) | 1.06 (0.70-1.61) | 1.05 (0.69-1.60) | |
| Race | ||||
| White | REF | REF | REF | |
| Black | 0.83 (0.43-1.62) | 0.98 (0.48-2.01) | 0.98 (0.48-2.01) | |
| Hispanic | 0.53 (0.25-1.10) | 0.40 (0.18-0.90) | 0.41 (0.18-0.92) | |
| Asian | 0.52 (0.21-1.30) | 0.58 (0.21-1.56) | 0.57 (0.21-1.57) | |
| Other/Unknown | 0.55 (0.31-1.00) | 0.52 (0.28-0.97) | 0.53 (0.28-0.99) | |
| Malignancy type | ||||
| Melanoma | REF | REF | REF | |
| Non-small cell lung cancer | 0.50 (0.29-0.87) | 1.91 (0.81-4.47) | 1.89 (0.80-4.45) | |
| Urothelial | 0.66 (0.36-1.23) | 1.80 (0.83-3.89) | 1.78 (0.82-3.85) | |
| Hepatocellular carcinoma | 0.64 (0.35-1.16) | 3.19 (1.21-8.38) | 3.21 (1.22-8.46) | |
| Multiple Myeloma | 0.14 (0.03-0.59) | 0.45 (0.04-4.88) | 0.47 (0.04-5.04) | |
| Other | 0.61 (0.36-1.01) | 1.82 (0.82-4.05) | 1.81 (0.81-4.05) | |
| Clinical stage | ||||
| Clinically localized | REF | REF | REF | |
| Regionally advanced | 0.83 (0.34-2.00) | 0.55 (0.21-1.38) | 0.54 (0.21-1.38) | |
| Distant metastasis | 0.99 (0.53-1.86) | 0.75 (0.39-1.46) | 0.74 (0.38-1.44) | |
| Other | 0.22 (0.05-1.01) | 0.60 (0.06-6.34) | 0.55 (0.05-5.78) | |
| Immune checkpoint type | ||||
| CTLA-4 (weightbased) | REF | REF | REF | |
| PD-1/PD-L1 (not weightbased) | 0.40 (0.23-0.70) | 0.24 (0.11-0.56) | 0.25 (0.11-0.58) | |
| PD-1/PD-L1 (weightbased) | 0.43 (0.25-0.74) | 0.32 (0.14-0.75) | 0.33 (0.14-0.78) | |
| Combination CTLA-4 and PD-1/PDL-1 | 1.38 (0.62-3.04) | 2.18 (0.94-5.05) | 2.12 (0.91-4.94) | |
| Sequential therapy | 0.70 (0.34-1.43) | 0.55 (0.26-1.19) | 0.55 (0.25-1.19) | |
| Preexisting autoimmune disease | 2.65 (1.69-4.20) | 3.18 (1.90-5.33) | 3.16 (1.88-5.30) | |
| Metformin use | 0.77 (0.39-1.53) | N/A | 0.80 (0.38-1.67) | |
| Statin use | 1.24 (0.83-1.85) | N/A | 1.11 (0.70-1.76) | |
adjusting for age, sex, malignancy type, malignancy stage, ICI type, preexisting autoimmune disease
adjusting for age, sex, malignancy type, malignancy stage, ICI type, preexisting autoimmune disease, metformin use and statin use
Normal BMI= BMI 18.5-24.9, High BMI= BMI ≥25, Low metabolic disease burden: <2 metabolic diseases (hypertension, diabetes, dyslipidemia), High metabolic disease burden: ≥2 metabolic diseases
CI, confidence interval; N/A, not available
As metformin and statin therapy have previously been reported to have anti-inflammatory effects 36, 37, we performed an additional multivariate analysis incorporating metformin and statin use. These medications were not associated with grade 2 or above irAE development in the unadjusted model. PD-1/PD-L1 treatment compared to CTLA-4 treatment had a statistically significant negative association with developing a grade 2 or above irAE.
Discussion
In this study, we analyzed the relationship between metabolic diseases and irAEs in a real-world cohort of patients with cancer receiving ICI therapy. In time-dependent adjusted analyses, having a BMI ≥25kg/m2 in the setting of <2 metabolic risk factors was significantly associated with irAE development, but BMI <25kg/m2 in the setting of ≥2 metabolic risk factors was not. To our knowledge, this is the first study to assess the impact of metabolic comorbidities beyond overweight and obese BMI on irAE development. This study has important clinical implications, as it highlights that assessing the burden of metabolic comorbidities combined with BMI, may be an important consideration for assessing patients’ risks for developing irAEs. Identifying predictors of irAEs is crucial, as patients at higher risk for developing irAEs can be more closely monitored while on ICIs and potentially receive more prompt management of irAEs to potentially avoid or shorten ICI treatment interruptions. These findings can potentially contribute to predictive nomograms to identify patients at high risk for developing irAEs, particularly those with complex comorbidities.
While elevated BMI in the context of <2 metabolic comorbidities was correlated with irAEs, our study did not show a correlation between BMI alone and irAEs on multivariate analysis. These results differ from recent reports that show a correlation between higher BMI and irAEs in patients taking ICIs 38. In our study, 53% of patients were overweight or obese, 30% of patients had grade 2 or above irAEs, and 11% had grade 3 or 4 irAEs, which is within the wide range of reported irAE incidences in previously published studies 8, 39. In contrast to these other studies, our study analyzed irAE development as a time-dependent variable. In another study assessing the impact of obesity on NSCLC survival and irAEs in patients taking ICI, irAEs were also assessed as a time-dependent variable and no correlation was found, with the exception of dermatologic irAEs 40. Particularly in retrospective studies, a time-dependent analysis is important to minimize the bias from unbalanced follow-up times 41.
Mechanisms underlying the association between overweight and obese BMI and irAEs have been explored in pre-clinical studies. Mice with diet-induced obesity have increased PD-1 expression on T lymphocytes and T cell dysfunction 13, 42. Additionally, diet-induced obesity in mice has led to a more pronounced psoriaform skin reaction to anti-PD1 monoclonal antibody therapy 42. This exaggerated response was hypothesized to be due to the increased PD-1 expression on T cells. In human studies, obese individuals (defined by BMI ≥30kg/m2) have increased PD-1 expression on circulating T cells compared with non-obese individuals 13. A number of autoimmune conditions, including psoriasis and autoimmune thyroid disease are more prevalent in obese individuals in the absence of ICI therapies 11, 43. Therefore, obesity may promote an immune environment that is primed to develop a more pronounced response to ICI therapy in terms of irAEs.
In our study, the relationship between overweight and obese BMI and irAE development was stronger in patients with fewer metabolic comorbidities. Similar to our study, a previous report demonstrated that overweight patients (BMI ≥25kg/m2) without metabolic diseases had increased prothrombotic response, a sign of inflammation . Diabetes, hypertension, and dyslipidemia are all known to affect the immune system in non-cancer settings, but why they appear to reduce the risk of developing irAEs to ICIs in the setting of obesity is unknown 18–21. Different metabolic derangements may have opposing effects on PD-1 expression in T cells, which complicates how concomitant metabolic disorders may contribute to increased or decreased risk of irAEs in response to ICI therapies. In contrast to the studies on PD-1 expression in obese individuals, a recent study in individuals with and without type 2 diabetes found decreased expression of PD-1 on peripheral T cells from individuals with type 2 diabetes 45. The mechanism through which PD-1 expression was decreased in that study was not explored. Further complicating how metabolic comorbidities may influence irAE incidence are the use of medications commonly used to treat metabolic conditions, such as metformin and statins. These medications have known anti-inflammatory properties that may have the potential to influence irAEs of ICIs 36, 37. In our study, the use of metformin and statins was not associated with irAE development. However, our numbers were limited and the influence of these medications on irAEs is worth further investigation in larger cohorts of individuals treated with ICIs.
Strengths of our study include the time-dependent analysis and a diverse patient population relative to clinical trials. However, our report has limitations that must be noted. As our study was a retrospective analysis of EMR data as part of routine oncologic medical care, irAE and metabolic diagnoses were not recorded in a systematic or uniform manner. Additionally, as waist circumference, duration of overweight, sarcopenia, and measures of insulin resistance are not evaluated as part of routine oncologic care, we were unable to obtain information on these metabolic parameters in our assessment of metabolic health. We also may not have had sufficient power in some subgroups to detect significant differences. Additionally, some documented AEs may not have been due to ICI use, but we tried to minimize this limitation by documenting known irAEs and having multiple physician reviewers. Our study included a heterogeneous patient population with many types of malignancies and variable treatment courses with different dose and duration of ICIs which may have differentially impacted irAE incidence. However, we adjusted for many of these factors in our model to attenuate their impact and may make our results more generalizable to a general oncology clinic population receiving ICIs.
In conclusion, in a time-dependent adjusted analysis, we showed that obese or overweight status with less than 2 metabolic risk factors was linked to grade 2 or above irAE development, suggesting that obesity-related comorbidities may influence the relationship between obesity and irAE development. Identifying predictors for irAE development is important for risk stratifying patients prior to ICI treatment. This study provides the important insight that BMI should likely be evaluated in the context of associated metabolic comorbidities when identifying patients at higher risk of developing irAEs. Future prospective studies should be done to further characterize how both metabolic diseases and body composition (in addition to BMI) impact the ICI irAEs and outcomes.
Funding:
The Tisch Cancer Institute is supported by NCI Cancer Center Support Grant P30CA196521. AL is supported by NCI/NIH T32CA225617. EJG is supported by NCI/NIH K08CA190770.
Declaration of Interest
AL, EC, DB, JBS, and EE have nothing to disclose. JPW reports consulting honoraria from GSK, Sanofi, and Banook, and a research grant from Sanofi. MG reports ownership interests in Rappta Therapeutics, research funding from Janssen Oncology, Dendreon, Novartis, Bristol-Myers Squibb, Merck, AstraZeneca, Genentech/Roche and consultancy/advisory roles for BioMotiv, Merck, Dendreon, Janssen, GlaxoSmithKline, Lilly, Estellas Pharma, Genentech, Bristol-Myers Squibb, Novartis, Pfizer, EMD Serono, AstraZeneca, Seattle Genetics, Incyte, Aileron Therapeutics, Inovio Pharmaceuticals, NuMab. EJG reports consultancy/advisory role for Novartis and Seattle Genetics, and research funding from Alkeon Capital Management.
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