Associations between circulating levels of obesity-related biomarkers and risk of esophageal adenocarcinoma and Barrett esophagus have been reported, but the results are inconsistent. A literature search until October 2018 in MEDLINE and EMBASE was performed. Pooled ORs with 95% confidence intervals (CI) were estimated for associations between 13 obesity-related inflammatory and metabolic biomarkers and risk of esophageal adenocarcinoma or Barrett esophagus using random effect meta-analyses. Among 7,641 studies, 19 were eligible for inclusion (12 cross-sectional, two nested case–control, and five cohort studies). Comparing the highest versus lowest categories of circulating biomarker levels, the pooled ORs were increased for leptin (OR, 1.68; 95% CI, 0.95–2.97 for Barrett esophagus), glucose (OR, 1.12; 95% CI, 1.03–1.22 for esophageal adenocarcinoma), insulin (OR, 1.47; 95% CI, 1.06–2.00 for Barrett esophagus), C-reactive protein (CRP; OR, 2.06; 95% CI, 1.28–3.31 for esophageal adenocarcinoma), IL6 (OR, 1.50; 95% CI, 1.03–2.19 for esophageal adenocarcinoma), and soluble TNF receptor 2 (sTNFR-2; OR, 3.16; 95% CI, 1.76–5.65 for esophageal adenocarcinoma). No associations were identified for adiponectin, ghrelin, insulin-like growth factor 1, insulin-like growth factor-binding protein 3, triglycerides, IL8, or TNFα. Higher circulating levels of leptin, glucose, insulin, CRP, IL6, and sTNFR-2 may be associated with an increased risk of esophageal adenocarcinoma or Barrett esophagus. More prospective studies are required to identify biomarkers that can help select high-risk individuals for targeted prevention and early detection.

The incidence of esophageal adenocarcinoma has rapidly increased in Western populations (1), although the increase seems to have slowed down during recent years in some countries, for example, in the United Kingdom and the Netherlands (2, 3). The prognosis of esophageal adenocarcinoma is poor with an overall 5-year survival <15% to 20% (1, 4). There is a striking male predominance in esophageal adenocarcinoma with the male-to-female incidence ratio of up to 9:1, for which the reasons remain unclear (1, 5). Barrett esophagus, a replacement of the native squamous lining of the esophagus with a specialized columnar epithelium (metaplasia), is the precursor of esophageal adenocarcinoma (1, 6).

Gastroesophageal reflux disease (GERD) and obesity are the main risk factors for esophageal adenocarcinoma and Barrett esophagus (1, 4, 6). GERD can damage the esophageal mucosa and lead to esophagitis, Barrett esophagus, and subsequently esophageal adenocarcinoma. Obesity, particularly central obesity (typical male fat distribution), may promote reflux through increased intragastric pressure and disruption of the gastroesophageal junction and the lower esophageal sphincter (7). Obesity is also a systemic disease that might increase esophageal adenocarcinoma risk through other mechanisms, including chronic inflammation and metabolic alterations (6, 8).

Despite these strong and readily assessable risk factors, it has been difficult to identify individuals with a high absolute risk of esophageal adenocarcinoma enough to advocate endoscopic screening or surveillance. Circulating biomarkers could be a useful addition in this respect. Some studies have investigated associations between circulating levels of inflammatory and metabolic biomarkers and the risk of esophageal adenocarcinoma or Barrett esophagus (9, 10). The findings from the individual studies are not consistent, however, and whether associations differ between the sexes and contribute to the strong male predominance is unclear.

To clarify the role of a range of circulating levels of obesity-related inflammatory and metabolic biomarkers in the development and prediction of esophageal adenocarcinoma and Barrett esophagus, we conducted a systematic review and meta-analysis.

Literature search

A systematic search for studies published in MEDLINE and EMBASE databases from inception through October 2018 was conducted with no language restriction. The search strategy is presented in detail in Supplementary Table S1. Briefly, we used a combination of keywords for inflammatory and metabolic biomarkers and those for the outcomes, esophageal adenocarcinoma and Barrett esophagus, to identify relevant publications. Candidate biomarkers were those that had been reported to be associated with obesity or cancer risk. The search terms for the biomarkers were predefined after a scoping search and referring to two previous systematic reviews on the topic (9, 11). We also reviewed the reference lists of eligible original articles and the two previous systematic reviews to identify additional studies.

Study selection

Studies meeting the following criteria were included: (i) cross-sectional, case–control, or cohort studies in humans and published as original articles, (ii) measuring the incidence of esophageal adenocarcinoma or Barrett esophagus (rather than mortality) as an outcome, (iii) examining associations between circulating levels of inflammatory or metabolic biomarkers and risk of esophageal adenocarcinoma or Barrett esophagus, and (iv) containing information necessary to estimate RR compared with a reference group and with a measure of precision [e.g. confidence interval (CI), SE, variance, χ2 and degree of freedom, or P value]. In case of multiple reports on the same biomarker from the same study population, only the most recent or informative ones were included. Case–control studies in which biomarker levels were measured at or after onset of the disease outcome were considered cross-sectional, because the temporal relation could not be determined.

Data extraction and study quality assessment

The following information was collected from the eligible studies into an electronic database by one researcher (S. Rabbani) and independently checked by a second researcher (S.-H. Xie): (i) study design and characteristics (first author, year of publication, study setting, follow-up period, number of participants by group, study population or comparison group, and verification of cases), (ii) participants' age and sex, (iii) examined biomarkers, (iv) statistical analysis strategy (statistical model, covariates matched or adjusted for, and any stratified or sensitivity analysis), and (v) main findings. The quality of the included studies was independently assessed by two researchers (S.-H. Xie and E. Ness-Jensen) and discrepancies were resolved by joint review of reports to reach consensus. The study quality was quantitatively scored according to the Newcastle–Ottawa scale for assessing the quality of nonrandomized studies in meta-analyses (12). This scale contains eight items, which are categorized into three domains, that is, selection, comparability, and assessment of exposure (case–control or cross-sectional studies) or outcome (cohort studies) of study participants, and the assessment provides a score ranging from 0 to 9, where higher scores indicate better quality.

Meta-analysis

Meta-analyses were performed for associations between circulating biomarker levels and the risk of esophageal adenocarcinoma, Barrett esophagus, and the combined outcome esophageal adenocarcinoma or Barrett esophagus (hereafter labeled as EAC/BE), whenever possible. Pooled ORs with 95% CIs were estimated using a random effect model. The following 13 biomarkers, for which data were available in at least two studies, were examined: (i) adiponectin, (ii) leptin, (iii) ghrelin, (iv) glucose, (v) insulin, (vi) insulin-like growth factor 1 (IGF-1), (vii) IGF-binding protein 3 (IGFBP-3), (viii) triglycerides, (ix) C-reactive protein (CRP), (x) IL6, (xi) IL8, (xii) TNFα, and (xiii) soluble TNF receptor 2 (sTNFR-2). HRs and risk ratios reported in cohort studies were used as proxies of OR, which was justified by the low incidence of esophageal adenocarcinoma and Barrett esophagus in the population. For all biomarkers, except for glucose and triglycerides, we transformed effect sizes into a common scale of comparison before conducting the meta-analysis, that is, comparing the highest versus lowest tertiles, under the assumption that the biomarker level is normally distributed and has a log-linear association with the outcome risk (13, 14). The logarithm of OR for the highest versus lowest tertiles was estimated to be 1.27 times that for the top versus bottom halves, 0.86 times the highest versus lowest quartiles, 0.78 times the highest versus lowest quintiles, and 2.18 times that for per SD increase. When studies reported risk estimates with different degrees of statistical adjustment for covariates, we used the fully adjusted estimates. If multiple measures of obesity were employed, we used the risk estimates adjusted for waist-to-hip ratio, over waist circumference, and over body mass index. Four studies compared biomarker levels in patients with Barrett esophagus separately with two control groups, that is, patients with GERD and another control group from the general population or patients undergoing colonoscopy screening (15–18). For these studies, we used the risk estimates calculated in comparison with patients with GERD in the main analyses. For the two most studied biomarkers, that is, adiponectin and leptin, we conducted separate meta-analyses of risk estimates using different comparators, that is, (i) patients with GERD (for esophageal adenocarcinoma or Barrett esophagus) or patients with Barrett esophagus (for esophageal adenocarcinoma) and (ii) general population or patients undergoing colonoscopy or upper endoscopy. We also conducted meta-analysis excluding one study that compared patients with esophageal adenocarcinoma with patients with Barrett esophagus (19), on the combined outcome EAC/BE for adiponectin. We stratified the analyses by sex for adiponectin and leptin, but not for other biomarkers because of the limited number of available studies.

Statistical heterogeneity across studies was assessed by the I2 statistic and Cochran Q test. The I2 statistics indicate the proportion of the total variance in risk estimates across studies that is due to heterogeneity rather than chance, and thus, the I2 lies between 0% and 100%, with larger values indicating more heterogeneity (20). A P < 0.10 in the Q test was considered statistically significant, as conventionally used (21). Publication bias was assessed by visual inspection of funnel plots and the Begg and Egger tests (22, 23).

The software Comprehensive Meta-Analysis version 3 (Biostat, Inc.) was used for all statistical analyses. All statistical tests were two-sided. We interpreted the results in terms of magnitudes of associations and precision of the risk estimates conveyed by 95% CIs, rather than using P values as measures of significance (24, 25).

Literature search and study characteristics

The literature search identified 7,641 studies. Among these, 19 studies fulfilled the inclusion criteria for meta-analysis, including 12 cross-sectional (15–18, 26–33), two nested case–control (10, 34), and five cohort studies (19, 35–38). The full selection procedure of eligible studies is shown in Supplementary Fig. S1. Fourteen of the eligible studies were conducted in the United States, two in Europe, one was conducted in Australia, and the remaining one study included participants from both the United States and Europe. Eleven of 12 cross-sectional studies measured circulating levels of biomarkers in patients with Barrett esophagus compared with patients with GERD, general population, or patients who had undergone endoscopy, while esophageal adenocarcinoma was the outcome in the remaining one cross-sectional study. All nested case–control and cohort studies investigated associations between biomarker levels and esophageal adenocarcinoma risk, either in the general population or in cohorts of patients with Barrett esophagus. Characteristics of the included cross-sectional and nested case–control studies are presented in Table 1 and the cohort studies in Table 2.

Table 1.

Characteristics of cross-sectional and nested case–control studies identified in a systematic search and included in the meta-analysis.

StudyCountryStudy periodNumber of cases/controlsAge, yearsSex, males %Source of controlsOutcomeBiomarkers
Rubenstein and colleagues, 2008 United States N.A. 50/50 18–79, mean, 60 Cases 80%, controls 54% Patients for upper endoscopy BE Adiponectin, CRP 
Kendall and colleagues, 2008 Australia 2003–2006 Pilot: 51/67; validation: 306/309 18–79 Pilot: cases 51%, controls 51%; validation: cases 68%, controls 67% General population BE Adiponectin, leptin 
Rubenstein and colleagues, 2009 United States 2002–2007 112/199 Mean (SD) cases: 57.4 (11.3); controls: 50.7 (13.3) Cases 65%, controls 35% GERD patients BE Adiponectin 
Thompson and colleagues, 2010 United States 1997–2000 177/173 20–80 Cases 60%, controls 64% General population BE Adiponectin, leptin 
Greer and colleagues, 2012 United States 2005–2009 135/932+135 Mean (SD) cases: 63.7 (11.2); colonoscopy screening controls: 54.5 (8.9); GERD controls: 56.4 (11.1) Cases 80%, colonoscopy screening controls 35%, GERD controls 60% Patients for colonoscopy screening, GERD patients BE Insulin, IGF-1, IGFBP1, IGFBP3 
Rubenstein and colleagues, 2013 United States N.A. 150/751 Mean (SD) cases: 61 (6.6); controls: 58.5 (6.7) All men Patients for colonoscopy screening BE Leptin, insulin, ghrelin 
Greer and colleagues, 2013 United States N.A. 10/65 Mean (SD), 64.7 (11.8) 80% BE patients EAC Insulin, IGF-1, IGFBP1, IGFBP3 
Garcia and colleagues, 2014 United States 2008–2011 141/139 Mean (SD) cases: 62.8 (6.7); controls: 61.2 (7.6) Cases 97%, controls 97% Patients for colonoscopy screening BE Adiponectin, leptin, insulin, 7 cytokines 
Almers and colleagues, 2015 United States 2002–2005 284/285+294 Mean (SD) cases: 62 (10.7); population controls: 62 (10.2); GERD controls: 62 (10.7) Cases 73%, population controls 68%, GERD controls 69% General population, GERD patients BE Adiponectin 
Greer and colleagues, 2015 United States 2005–2009 135/1,157+133 Mean (SD) cases: 63.7 (11); colonoscopy screening controls: 54.6 (8.8); GERD controls: 65.4 (11.1) Cases 80%, colonoscopy screening controls 35%, GERD controls 40% Patients for colonoscopy screening, GERD patients BE Adiponectin, leptin 
Thomas and colleagues, 2016 United States 2002–2005 300/290+296 Mean (SD) cases: 62 (11); population controls: 62 (10); GERD controls: 62 (11) Cases 73%, population controls 68%, GERD controls 69% General population, GERD patients BE Leptin, ghrelin 
Di Caro and colleagues, 2016 United Kingdom 2011–2013 250/224 Mean (SD) cases: 63.8 (12.4); controls: 52 (16.4) Cases 77%, controls 41% Patients for upper endoscopy BE Glucose, insulin, cholesterol, triglycerides, high-density lipoprotein 
Drahos and colleagues, 2017 United States 2003–2009 3,167/15,835 Mean (SD), 78.0 (6.5) 78% General population EAC Glucose, triglycerides 
Cook and colleagues, 2019 United States, 10 European countries N.A. 296/296 Mean age at baseline, 63.4 77% General population EAC 69 inflammation markers 
StudyCountryStudy periodNumber of cases/controlsAge, yearsSex, males %Source of controlsOutcomeBiomarkers
Rubenstein and colleagues, 2008 United States N.A. 50/50 18–79, mean, 60 Cases 80%, controls 54% Patients for upper endoscopy BE Adiponectin, CRP 
Kendall and colleagues, 2008 Australia 2003–2006 Pilot: 51/67; validation: 306/309 18–79 Pilot: cases 51%, controls 51%; validation: cases 68%, controls 67% General population BE Adiponectin, leptin 
Rubenstein and colleagues, 2009 United States 2002–2007 112/199 Mean (SD) cases: 57.4 (11.3); controls: 50.7 (13.3) Cases 65%, controls 35% GERD patients BE Adiponectin 
Thompson and colleagues, 2010 United States 1997–2000 177/173 20–80 Cases 60%, controls 64% General population BE Adiponectin, leptin 
Greer and colleagues, 2012 United States 2005–2009 135/932+135 Mean (SD) cases: 63.7 (11.2); colonoscopy screening controls: 54.5 (8.9); GERD controls: 56.4 (11.1) Cases 80%, colonoscopy screening controls 35%, GERD controls 60% Patients for colonoscopy screening, GERD patients BE Insulin, IGF-1, IGFBP1, IGFBP3 
Rubenstein and colleagues, 2013 United States N.A. 150/751 Mean (SD) cases: 61 (6.6); controls: 58.5 (6.7) All men Patients for colonoscopy screening BE Leptin, insulin, ghrelin 
Greer and colleagues, 2013 United States N.A. 10/65 Mean (SD), 64.7 (11.8) 80% BE patients EAC Insulin, IGF-1, IGFBP1, IGFBP3 
Garcia and colleagues, 2014 United States 2008–2011 141/139 Mean (SD) cases: 62.8 (6.7); controls: 61.2 (7.6) Cases 97%, controls 97% Patients for colonoscopy screening BE Adiponectin, leptin, insulin, 7 cytokines 
Almers and colleagues, 2015 United States 2002–2005 284/285+294 Mean (SD) cases: 62 (10.7); population controls: 62 (10.2); GERD controls: 62 (10.7) Cases 73%, population controls 68%, GERD controls 69% General population, GERD patients BE Adiponectin 
Greer and colleagues, 2015 United States 2005–2009 135/1,157+133 Mean (SD) cases: 63.7 (11); colonoscopy screening controls: 54.6 (8.8); GERD controls: 65.4 (11.1) Cases 80%, colonoscopy screening controls 35%, GERD controls 40% Patients for colonoscopy screening, GERD patients BE Adiponectin, leptin 
Thomas and colleagues, 2016 United States 2002–2005 300/290+296 Mean (SD) cases: 62 (11); population controls: 62 (10); GERD controls: 62 (11) Cases 73%, population controls 68%, GERD controls 69% General population, GERD patients BE Leptin, ghrelin 
Di Caro and colleagues, 2016 United Kingdom 2011–2013 250/224 Mean (SD) cases: 63.8 (12.4); controls: 52 (16.4) Cases 77%, controls 41% Patients for upper endoscopy BE Glucose, insulin, cholesterol, triglycerides, high-density lipoprotein 
Drahos and colleagues, 2017 United States 2003–2009 3,167/15,835 Mean (SD), 78.0 (6.5) 78% General population EAC Glucose, triglycerides 
Cook and colleagues, 2019 United States, 10 European countries N.A. 296/296 Mean age at baseline, 63.4 77% General population EAC 69 inflammation markers 

Abbreviations: BE, Barrett esophagus; CRP, C-reactive protein; EAC, esophageal adenocarcinoma; GERD, gastroesophageal reflux disease; IGF-1, insulin-like growth factor 1; IGFBP1, IGF-binding protein 1; IGFBP3, IGF-binding protein 3; N.A., not available; SD, standard deviation.

Table 2.

Characteristics of cohort studies assessing associations between circulating biomarkers and risk of esophageal adenocarcinoma.

StudyCountryStudy periodNumber of participantsFollow-up, yearsAge at entry, mean (SD)Sex, males %Study populationBiomarkers
Siahpush and colleagues, 2007 United States 1995–2003 344 Median 5.4 61.6 (11.7) 81% Barrett esophagus patients IGF-1, IGFBP3 
Duggan and colleagues, 2013 United States 1995–2009 392 Median 6.7 61 (11.5) 82% Barrett esophagus patients Adiponectin, leptin, glucose, triglycerides, high-density lipoprotein 
Lindkvist and colleagues, 2014 Austria, Norway, Sweden 1972–2006 578700 Mean 12 44 (11.7) 50% General population Glucose, cholesterol, triglycerides 
Hardikar and colleagues, 2014 United States 1995–2009 397 Median 6.14 61.2 81% Barrett esophagus patients CRP, IL6, sTNFRs, F2-isoprostanes 
Lin and colleagues, 2015 Norway 1994–2010 192903 Mean 10.6 49.5 (15.7) 48% General population Glucose, triglycerides, high-density lipoprotein 
StudyCountryStudy periodNumber of participantsFollow-up, yearsAge at entry, mean (SD)Sex, males %Study populationBiomarkers
Siahpush and colleagues, 2007 United States 1995–2003 344 Median 5.4 61.6 (11.7) 81% Barrett esophagus patients IGF-1, IGFBP3 
Duggan and colleagues, 2013 United States 1995–2009 392 Median 6.7 61 (11.5) 82% Barrett esophagus patients Adiponectin, leptin, glucose, triglycerides, high-density lipoprotein 
Lindkvist and colleagues, 2014 Austria, Norway, Sweden 1972–2006 578700 Mean 12 44 (11.7) 50% General population Glucose, cholesterol, triglycerides 
Hardikar and colleagues, 2014 United States 1995–2009 397 Median 6.14 61.2 81% Barrett esophagus patients CRP, IL6, sTNFRs, F2-isoprostanes 
Lin and colleagues, 2015 Norway 1994–2010 192903 Mean 10.6 49.5 (15.7) 48% General population Glucose, triglycerides, high-density lipoprotein 

Abbreviations: CRP, C-reactive protein; IGF-1, insulin-like growth factor 1; IGFBP3, IGF-binding protein 3; SD, standard deviation; sTNFR, soluble TNF receptor.

A detailed study quality assessment is shown in Supplementary Tables S2 and S3. Briefly, most studies (14/19) had overall quality scores ranging from 6 to 8, while two cross-sectional studies scored lower (4 or 5) and the remaining one cross-sectional study and two cohort studies scored higher (9). All included studies, except for the two cross-sectional studies with lower quality scores, performed well in terms of comparability, that is, regarding controlling for confounding from the major risk factors for esophageal adenocarcinoma and Barrett esophagus, that is, age, sex, GERD, obesity, and tobacco smoking.

Adipokines (adiponectin, leptin, and ghrelin)

Seven cross-sectional studies reported associations of circulating adiponectin levels with Barrett esophagus risk, and one nested case–control study and one cohort study reported associations with esophageal adenocarcinoma risk. Meta-analysis showed no associations between the highest versus lowest tertiles of adiponectin levels and risk of Barrett esophagus (pooled OR, 0.90; 95% CI, 0.59–1.37), esophageal adenocarcinoma (OR, 0.87; 95% CI, 0.60–1.25), or EAC/BE (OR, 0.88; 95% CI, 0.67–1.16; Figs. 1 and 2). Adiponectin levels were not associated with risk of EAC/BE in the meta-analysis when excluding the study comparing patients with esophageal adenocarcinoma with patients with Barrett esophagus (OR, 0.91; 95% CI, −0.65–1.27). There was a tendency of a decreased risk of EAC/BE associated with higher adiponectin levels in comparison with the general population or patients undergoing endoscopy (OR, 0.79; 95% CI, 0.55–1.12), but not in comparison with patients with GERD or Barrett esophagus (OR, 0.96; 95% CI, 0.56–1.55; Supplementary Fig. S2). Stratified analysis by sex showed no associations between adiponectin levels and risk of EAC/BE either in men or in women (Fig. 3).

Figure 1.

Associations between adipokine levels and risk of esophageal adenocarcinoma or Barrett esophagus. Forest plots for the associations between circulating adiponectin and leptin levels and risk of esophageal adenocarcinoma or Barrett esophagus, expressed as OR and 95% CI comparing the highest with the lowest tertiles. BE, Barrett esophagus; CI, confidence interval; EAC, esophageal adenocarcinoma.

Figure 1.

Associations between adipokine levels and risk of esophageal adenocarcinoma or Barrett esophagus. Forest plots for the associations between circulating adiponectin and leptin levels and risk of esophageal adenocarcinoma or Barrett esophagus, expressed as OR and 95% CI comparing the highest with the lowest tertiles. BE, Barrett esophagus; CI, confidence interval; EAC, esophageal adenocarcinoma.

Close modal
Figure 2.

Associations between biomarker levels and risk of esophageal adenocarcinoma or Barrett esophagus. Associations between circulating levels of inflammatory and metabolic biomarkers and risk of esophageal adenocarcinoma or Barrett esophagus, expressed as OR and 95% CI comparing the highest with the lowest tertiles. BE, Barrett esophagus; CI, confidence interval; CRP, C-reactive protein; EAC, esophageal adenocarcinoma; IGF-1, insulin-like growth factor 1; IGFBP-3, IGF-binding protein 3; sTNFR-2, soluble TNF receptor 2.

Figure 2.

Associations between biomarker levels and risk of esophageal adenocarcinoma or Barrett esophagus. Associations between circulating levels of inflammatory and metabolic biomarkers and risk of esophageal adenocarcinoma or Barrett esophagus, expressed as OR and 95% CI comparing the highest with the lowest tertiles. BE, Barrett esophagus; CI, confidence interval; CRP, C-reactive protein; EAC, esophageal adenocarcinoma; IGF-1, insulin-like growth factor 1; IGFBP-3, IGF-binding protein 3; sTNFR-2, soluble TNF receptor 2.

Close modal
Figure 3.

Sex-specific associations between adipokine levels and risk of esophageal adenocarcinoma or Barrett esophagus. Forest plots for the sex-specific associations between circulating adiponectin and leptin levels and risk of esophageal adenocarcinoma or Barrett esophagus, expressed as OR and 95% CI comparing the highest with the lowest tertiles. CI, confidence interval.

Figure 3.

Sex-specific associations between adipokine levels and risk of esophageal adenocarcinoma or Barrett esophagus. Forest plots for the sex-specific associations between circulating adiponectin and leptin levels and risk of esophageal adenocarcinoma or Barrett esophagus, expressed as OR and 95% CI comparing the highest with the lowest tertiles. CI, confidence interval.

Close modal

Meta-analysis of seven studies (including one pilot study and one validation study in Australian) found an increased risk of Barrett esophagus associated with higher leptin levels (OR, 1.68; 95% CI, 0.95–2.97, comparing the highest vs. lowest tertiles; Figs. 1 and 2). An increased esophageal adenocarcinoma risk was also reported in one cohort study (OR, 1.53; 95% CI, 0.58–4.05). Meta-analysis of these eight studies generated similar estimates for the combined outcome, EAC/BE (OR, 1.64; 95% CI, 1.01–2.68; Fig. 2). Stratified analysis by type of comparators showed that the association for leptin was restricted in comparison with the general population or patients undergoing colonoscopy screening (OR, 1.90; 95% CI, 1.29–2.81) rather than in comparison with patients with GERD or Barrett esophagus (OR, 0.97; 95% CI, 0.49–1.90; Supplementary Fig. S3). The association between leptin levels and risk of EAC/BE was not substantially stronger in men (OR, 2.00; 95% CI, 0.96–4.16) than in women (OR, 1.57; 95% CI, 0.29–8.42; Fig. 3).

Associations between ghrelin levels and Barrett esophagus risk were reported only in two studies, providing a pooled OR of 0.93 (95% CI, 0.33–2.63; Fig. 2; Supplementary Fig. S4).

Diabetes biomarkers (glucose, insulin, IGF-1, and IGFBP-3)

Meta-analysis of one nested case–control study and two cohort studies showed a slightly increased risk of esophageal adenocarcinoma associated with elevated glucose levels (OR, 1.12; 95% CI, 1.03–1.22; Fig. 2; Supplementary Fig. S5).

The pooled estimates of five cross-sectional studies showed that higher insulin levels were associated with an increased risk of Barrett esophagus (OR, 1.47; 95% CI, 1.08–2.00) and EAC/BE (OR, 1.42; 95% CI, 1.05–1.93, comparing the highest vs. lowest tertiles; Fig. 2; Supplementary Fig. S6).

The meta-analysis of two cross-sectional studies and one cohort study showed a possibly decreased risk of EAC/BE associated with higher levels of IGF-1 (OR, 0.60; 95% CI, 0.31–1.16, comparing the highest vs. lowest tertiles), while no such association was found for IGFBP-3 (Fig. 2; Supplementary Fig. S6).

Triglycerides

Pooling of three cohort studies showed no association between triglycerides levels and esophageal adenocarcinoma risk (OR, 0.95; 95% CI, 0.88–1.03, comparing the highest vs. lowest categories; Fig. 2; Supplementary Fig. S7).

Inflammatory biomarkers (CRP, IL6, IL8, TNFα, and sTNFR-2)

Meta-analysis of two studies showed an increased risk of esophageal adenocarcinoma associated with higher CRP levels (OR, 2.06; 95% CI, 1.28–3.30, comparing the highest vs. lowest tertiles), while no association between CRP levels and Barrett esophagus risk was found in the only identified study. An increased OR remained for the combined outcome, EAC/BE (OR, 1.43; 95% CI, 1.02–2.01; Fig. 2; Supplementary Fig. S8).

Two studies reported an increased risk of esophageal adenocarcinoma associated with higher levels of IL6 (pooled OR, 1.50; 95% CI, 1.03–2.19, comparing the highest vs. lowest tertiles), while no associations were found for IL8 or TNFα (Fig. 2; Supplementary Figs. S8 and S9). Meta-analysis combining three studies showed an increased risk of EAC/BE associated with higher IL6 levels (OR, 1.58; 95% CI, 1.12–2.22; Fig. 2; Supplementary Fig. S8).

Meta-analysis of one nested case–control study and one cohort study showed that higher prediagnostic sTNFR-2 levels were associated with an increased risk of esophageal adenocarcinoma (OR, 3.16; 95% CI, 1.76–5.65; Fig. 2; Supplementary Fig. S9).

Other biomarkers

Seven of the included studies also measured serum levels of some biomarkers except for the 13 biomarkers presented above. No meta-analysis was performed for these biomarkers because data were available in only one study for each of these. No associations were found for total cholesterol or high-density lipoprotein cholesterol (35, 38). A cross-sectional study comparing 141 patients with Barrett esophagus with 139 patients undergoing colonoscopy screening reported an increased risk of Barrett esophagus associated with higher levels of IL-12p70 and lower levels of IL10 and IL1β, while no associations were found for IFNγ (27). A cohort study of 397 patients with Barrett esophagus found no associations between plasma levels of sTNFR-1 or F2-isoprostanes and risk of esophageal adenocarcinoma (37). A recent case–control study nested in seven cohorts quantitated 69 circulating inflammation markers (using Luminex-based multiplex assays) in 296 patients with esophageal adenocarcinoma and an equal number of control participants. This study suggested an increased risk of esophageal adenocarcinoma associated with higher levels of soluble IL6 receptor, soluble VEGFR-3, lipocalin-2, resistin, and serum amyloid A, and with lower levels of IL3 and IL17A (10).

Heterogeneity and publication bias

Measurements of heterogeneity across studies and publication bias for each biomarker are presented in Table 3. The heterogeneity tests suggested moderate to high heterogeneity across studies on adiponectin (I2 = 45%; P = 0.070) and leptin (I2 = 70%; P = 0.002). No evident publication bias was detected by the funnel plots (Supplementary Fig. S10) or the Begg and Egger tests for these studies.

Table 3.

Measurements of heterogeneity across studies and publication bias.

HeterogeneityPublication biasa
BiomarkerNumber of studiesI2Q testBegg testEgger test
Adiponectin 45 P = 0.070 P = 0.677 P = 0.292 
Leptin 70 P = 0.002 P = 0.216 P = 0.163 
Ghrelin 82 P = 0.010 — — 
Glucose P = 0.979 P = 0.602 P = 0.599 
Insulin P = 0.597 P = 0.327 P = 0.159 
IGF1 35 P = 0.213 P = 0.602 P = 0.867 
IGFBP3 P = 0.390 P = 0.602 P = 0.492 
Triglycerides P = 0.579 P = 0.602 P = 0.343 
CRP 61 P = 0.080 P = 0.118 P = 0.063 
IL6 P = 0.551 P = 0.118 P = 0.038 
IL8 85 P = 0.010 — — 
TNFα P = 0.650 — — 
sTNFR-2 P = 0.330 — — 
HeterogeneityPublication biasa
BiomarkerNumber of studiesI2Q testBegg testEgger test
Adiponectin 45 P = 0.070 P = 0.677 P = 0.292 
Leptin 70 P = 0.002 P = 0.216 P = 0.163 
Ghrelin 82 P = 0.010 — — 
Glucose P = 0.979 P = 0.602 P = 0.599 
Insulin P = 0.597 P = 0.327 P = 0.159 
IGF1 35 P = 0.213 P = 0.602 P = 0.867 
IGFBP3 P = 0.390 P = 0.602 P = 0.492 
Triglycerides P = 0.579 P = 0.602 P = 0.343 
CRP 61 P = 0.080 P = 0.118 P = 0.063 
IL6 P = 0.551 P = 0.118 P = 0.038 
IL8 85 P = 0.010 — — 
TNFα P = 0.650 — — 
sTNFR-2 P = 0.330 — — 

Abbreviations: CRP, C-reactive protein; IGF1, insulin-like growth factor 1; IGFBP3, IGF-binding protein 3; sTNFR-2, soluble TNF receptor 2.

aPublication bias was assessed for meta-analyses of at least three studies only.

This study indicates an increased risk of esophageal adenocarcinoma or Barrett esophagus associated with higher circulating levels of some inflammatory and metabolic biomarkers, that is, leptin, glucose, insulin, CRP, IL6, and sTNFR-2. No associations were found for adiponectin, ghrelin, IGF-1, IGFBP-3, triglycerides, IL8, or TNFα.

Among strengths of the study, first is the extensive search strategy adopted to identify all relevant publications covering a wide range of inflammatory and metabolic biomarkers. Second, the harmonization of the reported associations on different scales of comparison into a common form enabled comparison of magnitudes of the associations for most of the studied biomarkers, and also more accurate assessment of heterogeneity and publication bias. There are also limitations. First, most of the included studies were cross-sectional in design, where biomarker levels were measured after the disease onset. Thus, reverse causality could not be ruled out. However, this should not be an issue for the findings for glucose, triglycerides, and sTNFR-2 because they were based on prospective studies. Second, no more than five studies were identified for most biomarkers, except for adiponectin and leptin. Third, substantial heterogeneity across studies was observed, probably due to the combination of different outcomes and types of comparators. We conducted meta-analyses for the three different outcomes, that is, esophageal adenocarcinoma, Barrett esophagus, and the combined outcome EAC/BE, whenever possible. We also stratified the analyses for adiponectin and leptin by type of comparators and sex, but this was not possible for other biomarkers because of the limited number of available studies. Finally, the measurement of biomarker levels was based on a single sample only, even in the prospective studies, making it impossible to assess any influence of longitudinal changes of biomarker levels on their associations with esophageal adenocarcinoma or Barrett esophagus risk.

To the best of our knowledge, this study is the first to systematically summarize evidence for the other examined biomarkers in relation to the risk of esophageal adenocarcinoma or Barrett esophagus, except for the biomarkers leptin, insulin, and adiponectin. The findings of an increased Barrett esophagus risk associated with higher levels of leptin and insulin and no association with adiponectin, are consistent with a previous meta-analysis (9). However, two more recent publications were added in this analysis, and the earlier meta-analysis did not assess the associations of these three biomarkers with esophageal adenocarcinoma risk. Specifically, for the association between leptin levels and Barrett esophagus risk, the newly added study in this updated meta-analysis found a decreased Barrett esophagus risk associated with higher leptin levels (17), which differed from the earlier studies. Such inconsistency might be due to heterogeneity across studies in characteristics of study population (e.g., sex composition), type of comparators, quantitative assay for leptin levels, ascertainment of patients with Barrett esophagus, and adjustment for confounders, or chance. However, the limited number of existing studies precluded exploring potential sources of heterogeneity, for example, using a meta-regression approach. More large-scale studies are needed to examine whether the association between leptin levels and Barrett esophagus risk varies across populations and strata of other factors, including sex, reflux, and obesity.

Leptin is an adipokine secreted by adipose tissue and is involved in the regulation of energy balance, suppressing food intake, and thereby inducing weight loss (39). The circulating levels of leptin positively correlate with the amount of body fat and are also influenced by sex hormones and some other inflammatory cytokines (40). Higher leptin levels have been linked with increased risk of several cancers, including breast, endometrial, colorectal, and prostate cancers. The carcinogenic mechanisms associated with elevated leptin levels may include enhanced cell proliferation, changes in the regulation of certain cell signaling pathways, and promotion of inflammation and angiogenesis (41–43). Our meta-analysis of three prospective studies found that elevated glucose levels were associated with a modestly (12%) increased risk of esophageal adenocarcinoma, which was in line with a recent pooled study of 13 population-based studies showing a 30% increased risk of esophageal adenocarcinoma or esophagogastric junctional adenocarcinoma in patients with diabetes (44). A stronger association between insulin levels and Barrett esophagus risk was indicated, but the evidence was mainly based on cross-sectional studies and the association was attenuated after adjustment for leptin (26).

The associations between higher levels of CRP, IL6, and sTNFR-2 and risk of esophageal adenocarcinoma or Barrett esophagus support a role of systemic inflammation in the development of esophageal adenocarcinoma, which may be a mechanism underlying the associations of obesity and tobacco smoking with esophageal adenocarcinoma. A mediation analysis indicated that sTNFR-2 accounted for 33% of the association between central obesity (measured by waist circumference) and esophageal adenocarcinoma risk (10). Taken together, the available evidence suggests that systemic inflammation and metabolic disorders may be pathways in the etiology of esophageal adenocarcinoma.

The striking male predominance in esophageal adenocarcinoma seems not to be explained by the two major risk factors, that is, GERD and general obesity, given the similar exposure prevalence and strengths of associations of these factors with esophageal adenocarcinoma risk between the sexes (1, 5). Abdominal obesity, however, which is more common in men than in women, may contribute to the sex difference in esophageal adenocarcinoma. Our findings in this study did not support stronger associations with adiponectin or leptin levels in men than in women, but large prospective investigations including more participants of both sexes are needed to clarify this question. Sex hormonal factors may play a role in the etiology of esophageal adenocarcinoma and Barrett esophagus (45, 46), but the existing evidence is limited (1, 5). If the role of sex hormones in the etiology of esophageal adenocarcinoma is confirmed, an underlying mechanism may be the influence of sex hormones on inflammation, for example, the anti- or proinflammatory effects of certain sex hormones depending on the biological microenvironment, which may subsequently lead to altered cancer risk.

Despite decades of efforts to develop the treatment, the overall prognosis in esophageal adenocarcinoma remains poor, mainly because most patients are diagnosed at an advanced tumor stage (1, 4). Earlier tumor detection, particularly among individuals at high absolute risk, has the potential to reduce the mortality from this cancer. A few risk stratification models have been developed for esophageal adenocarcinoma and Barrett esophagus, showing promising performance (47–51). These models mainly combine clinical and lifestyle risk factors, which are easily captured through questionnaires or medical records, while genetic biomarkers have thus far not improved the identification of high-risk individuals (50). Whether inclusion of a panel of inflammatory and metabolic biomarkers increases the accuracy of risk stratification models needs to be evaluated. However, because most of the identified biomarkers in this study showed modest associations with esophageal adenocarcinoma or Barrett esophagus risk and are also associated with the major risk factors for esophageal adenocarcinoma and Barrett esophagus, the addition of these biomarkers may improve the model performance, but only to a limited extent. Interestingly, this study found a more than 3-fold increased risk of esophageal adenocarcinoma or Barrett esophagus associated with higher prediagnostic sTNFR-2 levels, which particularly warrants further investigations. Nevertheless, any use of circulating biomarkers to identify high-risk individuals who may benefit from screening or surveillance should be scientifically justified before they may be tested in routine clinical practice.

In summary, this systematic review and meta-analysis suggests an increased risk of esophageal adenocarcinoma or Barrett esophagus associated with higher circulating levels of some inflammatory and metabolic biomarkers, that is, leptin, glucose, insulin, CRP, IL6, and sTNFR-2. The available studies were too few to examine sex-specific associations for these biomarkers. More prospective studies are required to identify biomarkers that can help select individuals at high absolute risk of esophageal adenocarcinoma for targeted prevention and early detection.

No potential conflicts of interest were disclosed.

The funding bodies had no role in the study design; the collection, analysis, and interpretation of data; or the writing of the article and the decision to submit it for publication.

This study was supported by the Swedish Research Council (grant no. 340-2013-5478, to J. Lagergren) and the Swedish Cancer Society (grant no. 190043, to S.-H. Xie; grant no. 180684, to J. Lagergren).

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

1.
Coleman
HG
,
Xie
SH
,
Lagergren
J
. 
The epidemiology of esophageal adenocarcinoma
.
Gastroenterology
2018
;
154
:
390
405
.
2.
Masclee
GM
,
Coloma
PM
,
de Wilde
M
,
Kuipers
EJ
,
Sturkenboom
MC
. 
The incidence of Barrett's oesophagus and oesophageal adenocarcinoma in the United Kingdom and the Netherlands is levelling off
.
Aliment Pharmacol Ther
2014
;
39
:
1321
30
.
3.
Offman
J
,
Pesola
F
,
Sasieni
P
. 
Trends and projections in adenocarcinoma and squamous cell carcinoma of the oesophagus in England from 1971 to 2037
.
Br J Cancer
2018
;
118
:
1391
8
.
4.
Lagergren
J
,
Smyth
E
,
Cunningham
D
,
Lagergren
P
. 
Oesophageal cancer
.
Lancet
2017
;
390
:
2383
96
.
5.
Xie
SH
,
Lagergren
J
. 
The male predominance in rsophageal adenocarcinoma
.
Clin Gastroenterol Hepatol
2016
;
14
:
338
47
.
6.
Inadomi
J
,
Alastal
H
,
Bonavina
L
,
Gross
S
,
Hunt
RH
,
Mashimo
H
, et al
Recent advances in Barrett's esophagus
.
Ann N Y Acad Sci
2018
;
1434
:
227
38
.
7.
Chandar
AK
,
Iyer
PG
. 
Role of obesity in the pathogenesis and progression of Barrett's esophagus
.
Gastroenterol Clin North Am
2015
;
44
:
249
64
.
8.
Lagergren
J
. 
Influence of obesity on the risk of esophageal disorders
.
Nat Rev Gastroenterol Hepatol
2011
;
8
:
340
7
.
9.
Chandar
AK
,
Devanna
S
,
Lu
C
,
Singh
S
,
Greer
K
,
Chak
A
, et al
Association of serum levels of adipokines and insulin with risk of Barrett's esophagus: a systematic review and meta-analysis
.
Clin Gastroenterol Hepatol
2015
;
13
:
2241
55
.
10.
Cook
MB
,
Barnett
MJ
,
Bock
CH
,
Cross
AJ
,
Goodman
PJ
,
Goodman
GE
, et al
Prediagnostic circulating markers of inflammation and risk of oesophageal adenocarcinoma: a study within the National Cancer Institute Cohort Consortium
.
Gut
2019
;
68
:
960
8
.
11.
Kunzmann
AT
,
McMenamin
UC
,
Spence
AD
,
Gray
RT
,
Murray
LJ
,
Turkington
RC
, et al
Blood biomarkers for early diagnosis of oesophageal cancer: a systematic review
.
Eur J Gastroenterol Hepatol
2018
;
30
:
263
73
.
12.
Wells
GA
,
Shea
B
,
O'Connell
D
,
Peterson
J
,
Welch
V
,
Losos
M
, et al
The Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses
. 2000 Jan. Available from: http://www.ohri.ca/programs/clinical_epidemiology/oxford.asp.
13.
Chene
G
,
Thompson
SG
. 
Methods for summarizing the risk associations of quantitative variables in epidemiologic studies in a consistent form
.
Am J Epidemiol
1996
;
144
:
610
21
.
14.
Zhang
X
,
Zhao
Q
,
Zhu
W
,
Liu
T
,
Xie
SH
,
Zhong
LX
, et al
The association of telomere length in peripheral blood cells with cancer risk: a systematic review and meta-analysis of prospective studies
.
Cancer Epidemiol Biomarkers Prev
2017
;
26
:
1381
90
.
15.
Almers
LM
,
Graham
JE
,
Havel
PJ
,
Corley
DA
. 
Adiponectin may modify the risk of Barrett's esophagus in patients with gastroesophageal reflux disease
.
Clin Gastroenterol Hepatol
2015
;
13
:
2256
64
.
16.
Greer
KB
,
Falk
GW
,
Bednarchik
B
,
Li
L
,
Chak
A
. 
Associations of serum adiponectin and leptin with Barrett's esophagus
.
Clin Gastroenterol Hepatol
2015
;
13
:
2265
72
.
17.
Thomas
SJ
,
Almers
L
,
Schneider
J
,
Graham
JE
,
Havel
PJ
,
Corley
DA
. 
Ghrelin and leptin have a complex relationship with risk of Barrett's esophagus
.
Dig Dis Sci
2016
;
61
:
70
9
.
18.
Greer
KB
,
Thompson
CL
,
Brenner
L
,
Bednarchik
B
,
Dawson
D
,
Willis
J
, et al
Association of insulin and insulin-like growth factors with Barrett's oesophagus
.
Gut
2012
;
61
:
665
72
.
19.
Duggan
C
,
Onstad
L
,
Hardikar
S
,
Blount
PL
,
Reid
BJ
,
Vaughan
TL
. 
Association between markers of obesity and progression from Barrett's esophagus to esophageal adenocarcinoma
.
Clin Gastroenterol Hepatol
2013
;
11
:
934
43
.
20.
Higgins
JP
,
Thompson
SG
. 
Quantifying heterogeneity in a meta-analysis
.
Stat Med
2002
;
21
:
1539
58
.
21.
Fletcher
J
. 
What is heterogeneity and is it important?
BMJ
2007
;
334
:
94
6
.
22.
Begg
CB
,
Mazumdar
M
. 
Operating characteristics of a rank correlation test for publication bias
.
Biometrics
1994
;
50
:
1088
101
.
23.
Egger
M
,
Davey Smith
G
,
Schneider
M
,
Minder
C
. 
Bias in meta-analysis detected by a simple, graphical test
.
BMJ
1997
;
315
:
629
34
.
24.
Greenland
S
,
Senn
SJ
,
Rothman
KJ
,
Carlin
JB
,
Poole
C
,
Goodman
SN
, et al
Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations
.
Eur J Epidemiol
2016
;
31
:
337
50
.
25.
Wasserstein
RL
,
Lazar
NA
. 
The ASA's statement on p-values: context, process, and purpose
.
Am Stat
2016
;
70
:
129
31
.
26.
Rubenstein
JH
,
Morgenstern
H
,
McConell
D
,
Scheiman
JM
,
Schoenfeld
P
,
Appelman
H
, et al
Associations of diabetes mellitus, insulin, leptin, and ghrelin with gastroesophageal reflux and Barrett's esophagus
.
Gastroenterology
2013
;
145
:
1237
44
.
27.
Garcia
JM
,
Splenser
AE
,
Kramer
J
,
Alsarraj
A
,
Fitzgerald
S
,
Ramsey
D
, et al
Circulating inflammatory cytokines and adipokines are associated with increased risk of Barrett's esophagus: a case-control study
.
Clin Gastroenterol Hepatol
2014
;
12
:
229
38
.
28.
Di Caro
S
,
Cheung
WH
,
Fini
L
,
Keane
MG
,
Theis
B
,
Haidry
R
, et al
Role of body composition and metabolic profile in Barrett's oesophagus and progression to cancer
.
Eur J Gastroenterol Hepatol
2016
;
28
:
251
60
.
29.
Kendall
BJ
,
Macdonald
GA
,
Hayward
NK
,
Prins
JB
,
Brown
I
,
Walker
N
, et al
Leptin and the risk of Barrett's oesophagus
.
Gut
2008
;
57
:
448
54
.
30.
Rubenstein
JH
,
Dahlkemper
A
,
Kao
JY
,
Zhang
M
,
Morgenstern
H
,
McMahon
L
, et al
A pilot study of the association of low plasma adiponectin and Barrett's esophagus
.
Am J Gastroenterol
2008
;
103
:
1358
64
.
31.
Rubenstein
JH
,
Kao
JY
,
Madanick
RD
,
Zhang
M
,
Wang
M
,
Spacek
MB
, et al
Association of adiponectin multimers with Barrett's oesophagus
.
Gut
2009
;
58
:
1583
9
.
32.
Thompson
OM
,
Beresford
SA
,
Kirk
EA
,
Bronner
MP
,
Vaughan
TL
. 
Serum leptin and adiponectin levels and risk of Barrett's esophagus and intestinal metaplasia of the gastroesophageal junction
.
Obesity
2010
;
18
:
2204
11
.
33.
Greer
KB
,
Kresak
A
,
Bednarchik
B
,
Dawson
D
,
Li
L
,
Chak
A
, et al
Insulin/insulin-like growth factor-1 pathway in Barrett's carcinogenesis
.
Clin Transl Gastroenterol
2013
;
4
:
e31
.
34.
Drahos
J
,
Ricker
W
,
Pfeiffer
RM
,
Cook
MB
. 
Metabolic syndrome and risk of esophageal adenocarcinoma in elderly patients in the United States: an analysis of SEER-Medicare data
.
Cancer
2017
;
123
:
657
65
.
35.
Lin
Y
,
Ness-Jensen
E
,
Hveem
K
,
Lagergren
J
,
Lu
Y
. 
Metabolic syndrome and esophageal and gastric cancer
.
Cancer Causes Control
2015
;
26
:
1825
34
.
36.
Siahpush
SH
,
Vaughan
TL
,
Lampe
JN
,
Freeman
R
,
Lewis
S
,
Odze
RD
, et al
Longitudinal study of insulin-like growth factor, insulin-like growth factor binding protein-3, and their polymorphisms: risk of neoplastic progression in Barrett's esophagus
.
Cancer Epidemiol Biomarkers Prev
2007
;
16
:
2387
95
.
37.
Hardikar
S
,
Onstad
L
,
Song
X
,
Wilson
AM
,
Montine
TJ
,
Kratz
M
, et al
Inflammation and oxidative stress markers and esophageal adenocarcinoma incidence in a Barrett's esophagus cohort
.
Cancer Epidemiol Biomarkers Prev
2014
;
23
:
2393
403
.
38.
Lindkvist
B
,
Johansen
D
,
Stocks
T
,
Concin
H
,
Bjorge
T
,
Almquist
M
, et al
Metabolic risk factors for esophageal squamous cell carcinoma and adenocarcinoma: a prospective study of 580,000 subjects within the Me-Can project
.
BMC Cancer
2014
;
14
:
103
.
39.
Klok
MD
,
Jakobsdottir
S
,
Drent
ML
. 
The role of leptin and ghrelin in the regulation of food intake and body weight in humans: a review
.
Obes Rev
2007
;
8
:
21
34
.
40.
Kelesidis
T
,
Kelesidis
I
,
Chou
S
,
Mantzoros
CS
. 
Narrative review: the role of leptin in human physiology: emerging clinical applications
.
Ann Intern Med
2010
;
152
:
93
100
.
41.
Khandekar
MJ
,
Cohen
P
,
Spiegelman
BM
. 
Molecular mechanisms of cancer development in obesity
.
Nat Rev Cancer
2011
;
11
:
886
95
.
42.
Park
J
,
Scherer
PE
. 
Leptin and cancer: from cancer stem cells to metastasis
.
Endocr Relat Cancer
2011
;
18
:
C25
9
.
43.
Ando
S
,
Gelsomino
L
,
Panza
S
,
Giordano
C
,
Bonofiglio
D
,
Barone
I
, et al
Obesity, leptin and breast cancer: epidemiological evidence and proposed mechanisms
.
Cancers
2019
;
11
:
62
.
44.
Petrick
JL
,
Li
N
,
Anderson
LA
,
Bernstein
L
,
Corley
DA
,
El Serag
HB
, et al
Diabetes in relation to Barrett's esophagus and adenocarcinomas of the esophagus: a pooled study from the International Barrett's and Esophageal Adenocarcinoma Consortium
.
Cancer
2019
:
125
:
4210
23
.
45.
Derakhshan
MH
,
Liptrot
S
,
Paul
J
,
Brown
IL
,
Morrison
D
,
McColl
KE
. 
Oesophageal and gastric intestinal-type adenocarcinomas show the same male predominance due to a 17 year delayed development in females
.
Gut
2009
;
58
:
16
23
.
46.
Cronin-Fenton
DP
,
Murray
LJ
,
Whiteman
DC
,
Cardwell
C
,
Webb
PM
,
Jordan
SJ
, et al
Reproductive and sex hormonal factors and oesophageal and gastric junction adenocarcinoma: a pooled analysis
.
Eur J Cancer
2010
;
46
:
2067
76
.
47.
Thrift
AP
,
Kendall
BJ
,
Pandeya
N
,
Whiteman
DC
. 
A model to determine absolute risk for esophageal adenocarcinoma
.
Clin Gastroenterol Hepatol
2013
;
11
:
138
44
.
48.
Xie
SH
,
Lagergren
J
. 
A model for predicting individuals' absolute risk of esophageal adenocarcinoma: moving toward tailored screening and prevention
.
Int J Cancer
2016
;
138
:
2813
9
.
49.
Xie
SH
,
Ness-Jensen
E
,
Medefelt
N
,
Lagergren
J
. 
Assessing the feasibility of targeted screening for esophageal adenocarcinoma based on individual risk assessment in a population-based cohort study in Norway (the HUNT Study)
.
Am J Gastroenterol
2018
;
113
:
829
35
.
50.
Kunzmann
AT
,
Canadas Garre
M
,
Thrift
AP
,
McMenamin
UC
,
Johnston
BT
,
Cardwell
CR
, et al
Information on genetic variants does not increase identification of individuals at risk of esophageal adenocarcinoma compared to clinical risk factors
.
Gastroenterology
2019
;
156
:
43
5
.
51.
Kunzmann
AT
,
Thrift
AP
,
Cardwell
CR
,
Lagergren
J
,
Xie
S
,
Johnston
BT
, et al
Model for identifying individuals at risk for esophageal adenocarcinoma
.
Clin Gastroenterol Hepatol
2018
;
16
:
1229
36
.

Supplementary data