Association of Bariatric Surgery and Risk of Cancer in Patients With Morbid Obesity

Syed I. Khalid, MD; Samantha Maasarani, MD, MPH; Julia Wiegmann, MSCR; Aaron L. Wiegmann, MD, MS; Adan Z. Becerra, PhD; Philip Omotosho, MD; Alfonso Torquati, MD


Annals of Surgery. 2022;275(1):1-6. 

In This Article


Data Collection

The all-Payer Claims Database, Mariner, containing >15 million patients' medical and surgical claims from January 1st, 2010 through June 30th, 2018 was retrospectively analyzed. This database is derived from provider networks and includes claims billed to all payer types, including commercial insurance, Medicare, Medicaid, and self-pay.

International Classification of Diseases (ICD)-9 and 10 diagnostic codes were utilized to identify bariatric-eligible patients based upon the following criteria by Centers for Medicare & Medicaid Services: BMI of ≥40 kg/m2 or BMI of ≥35 kg/m2 and at least ≥1 obesity-related comorbidities [such as type 2 diabetes mellitus (T2DM), hypertension (HTN), obstructive sleep apnea (OSA), nonalcoholic fatty liver disease, osteoarthritis, lipid abnormalities, gastrointestinal disorders, or heart disease].[39] Patients with a history of transplant, end-stage renal disease, gastric banding procedures, and procedure for repair of perforated gastric ulcer were excluded.

Patient Cohorts

Bariatric-eligible patients were identified as previously mentioned above. These patients were further divided into the following 3 cohorts: RYGB, VSG, and no surgical intervention. Patients undergoing RYGB or VSG were identified by querying the database with the ICD-9 and 10 procedure and Current Procedural Terminology codes as seen in Supplemental Table 1, Patients with any indication that this was not a first bariatric procedure or with evidence that the procedure was linked to GI malignancy rather than weight loss indications were excluded from our study (Supplemental Table 1, Additionally, bariatric surgery patients and bariatric-eligible no surgery


Demographic data for aggregate records included age and sex. ICD-9 and ICD-10 diagnostic codes were used to identify comorbidities as previously described and listed in Supplemental Table 2, Comorbidities were noted as follows: hypertension, coronary artery disease, congestive heart failure, T2DM, chronic kidney disease, non-alcoholic fatty liver disease, osteoarthritis, obstructive sleep apnea, chronic obstructive pulmonary disease, and smoking.


The primary aim of this study was to assess the association between bariatric surgical procedures and the odds of various cancer types. Secondarily, we sought to analyze if the type of surgical approach utilized impacted these same outcomes. Cohorts were queried to identify patients who have been diagnosed within 5 years postoperatively with various cancer types as defined by ICD-9 and ICD-10 diagnostic codes as seen in Supplemental Table 3, The following cancer types were included in our study: breast cancer, colorectal cancer, esophageal adenocarcinoma, gallbladder cancer, gastric cancer, liver cancer, lung cancer, meningioma, multiple myeloma, ovarian cancer, pancreatic cancer, prostate cancer, renal cancer, thyroid cancer, and uterine cancer. Analyses were done for any cancer as well as individual cancers. Furthermore, our cancer outcomes were stratified into groups obesity-related versus non–obesity related based on the classification system described by the Centers for Disease Control and Prevention.[40]

Statistical Analysis

Descriptive statistics were calculated and compared between the three cohorts of VSG, RYGB, and no surgery for age, sex, and comorbidities. These categorical variables were presented as percentages and compared using chi-squared tests.

1 to 1 to 1 exact matching was performed to restrict our analysis to the most optimally balanced patient groups. Patients were matched according to the following potential confounders: age, sex, and history of hypertension, coronary artery disease, congestive heart failure, T2DM, chronic kidney disease, nonalcoholic fatty liver disease, osteoarthritis, obstructive sleep apnea, chronic obstructive pulmonary disease, or smoking. A multivariable logistic regression model was used. Adjusted odds ratios and 95% confidence intervals were calculated to compare future cancer events based on whether or not the patient underwent VSG, RYGB, or neither. Bonferroni-corrected P values were calculated to account for multiple testing. Results were considered statistically significant at a P < .05 levels. The data were analyzed utilizing R statistical software (version 4.0, 2020).