Measured weight loss as a precursor to cancer diagnosis: retrospective cohort analysis of 43 302 primary care patients

Abstract

Background Unexpected weight loss is a presenting feature of cancer in primary care. Data from primary care are lacking to quantify how much weight loss over what period should trigger further investigation for cancer. This research aimed to quantify cancer diagnosis rates associated with measured weight change in people attending primary care. Methods Retrospective cohort study of primary care electronic health records data linked to the Surveillance, Epidemiology, and End Results cancer registry (Integrated healthcare delivery system in Washington State, United States). Multivariable Cox regression incorporating time varying covariates using splines to model non-linear associations (age, percentage weight change, and weight change interval). Fifty thousand randomly selected patients aged 40 years and over followed for up to 9 years (1 January 2006 to 31 December 2014). Outcome measures are hazard ratios (95% confidence intervals) to quantify the association between percentage weight change and cancer diagnosis for all cancers combined, individual cancer sites and stages; percentage risk of cancer diagnosis within 6 months of the end of each weight change episode; and the positive predictive value for cancer diagnosis. Results There were 43 302 included in the analysis after exclusions. Over 287 858 patient-years of follow-up, including 24 272 (56.1%) females, 23 980 (55.4%) aged 40 to 59 years, 15 113 (34.9%) 60 to 79 years, and 4209 (9.7%) aged 80 years and over. Adjusted hazard ratios (95% confidence interval) for cancer diagnosis in a 60 years old ranged from 1.04 (1.02 to 1.05, P < 0.001) for 1% weight loss to 1.44 (1.23 to 1.68, P < 0.001) for 10%. An independent linear association was observed between percentage weight loss and increasing cancer risk. The absolute risk of cancer diagnosis increased with increasing age (up to 85 years) and as the weight change measurement interval decreased (<1 year). The positive predictive value for a cancer diagnosis within 1 year of ≥5% measured weight loss in a 60 to 69 years old was 3.41% (1.57% to 6.37%) in men and 3.47% (1.68% to 6.29%) in women. The risk of cancer diagnosis was significantly increased for pancreatic, myeloma, gastro-oesophageal, colorectal, breast, stage II and IV cancers. Conclusions Weight loss is a sign of undiagnosed cancer regardless of the interval over which it occurs. Guidelines should resist giving an arbitrary cut-off for the interval of weight loss and focus on the percentage of weight loss and the patient's age. Future studies should focus on the association between diagnostic evaluation of weight change and risk of cancer mortality.

Is the association of overweight and obesity with colorectal cancer underestimated? An umbrella review of systematic reviews and meta-analyses

“…Sojourn time for CRC in the preclinical state has consistently been estimated to be about 3–6 years [ 10 – 12 ]. This prediagnostic period often goes along with weight loss [ 7 , 55 , 56 ]. Since most of the case–control studies ascertained BMI close to the time of diagnosis, such studies, and systematic reviews and meta-analyses that included them, are likely to have underestimated the relationship between overweight and obesity and CRC.…”

“…However, a large 2017 meta-analysis reported even stronger associations with colorectal adenomas, the precursors of most CRCs, with a risk increase by more than 40% for both overweight and obesity [ 6 ]. Furthermore, there is evidence that CRC patients may experience substantial weight loss in the preclinical phase prior to diagnosis [ 7 ], suggesting that the strength of the association of overweight and obesity with CRC risk may have been underestimated in epidemiological studies due to prediagnostic weight loss. In case–control studies, BMI is often reported for the time close to or shortly before diagnosis among cases.…”

Relationship between preoperative malnutrition, frailty, sarcopenia, body composition, and anthropometry in elderly patients undergoing major pancreatic and biliary surgery

“…Table 1 displays the comparison of FFP, WGS, NRS2002, and GLIM, which reflect the commonality and individuality of the tools. Weight loss was the only criterion shared by the four tools, which is not only for nutrition assessment but also a sensitive precursor for tumor diagnosis, especially for pancreatic cancer ( 23 ). Besides weight loss, NRS2002 and GLIM contain age, BMI, intake reduction, and assessment of disease (inflammation burden), which are relatively more comprehensive to assess the nutrition status.…”

Using Mendelian Randomization to model the causal effect of cancer on health economic outcomes and to simulate the cost-effectiveness of anti-cancer interventions

“…For example, risk factors such as cigarette smoking or adverse weight profiles may co-occur, are known to independently and jointly influence risk for many cancers (17)(18)(19)(20)(21), and have quantitatively important causal influences on health economic outcomes (22)(23)(24)(25). Cohort studies (for example (26)) that attribute costs to cancer patients may account for direct costs following a diagnosis of cancer, but cannot reflect all causal impacts associated with liability to cancer or cancer diagnoses because in general it is not possible to identify all confounding variables, to assess their contribution without measurement error, to condition away their influence in quantitative analysis, or to include all downstream causal impacts.…”