Metabolic variables are associated with colorectal neoplasia in an Australian outpatient population

doi:10.21203/rs.3.rs-3912231/v1

Background: Colorectal cancer (CRC) is a major cause of cancer-related deaths within the Australian population. Colonoscopy and polypectomy represent effective forms of prevention. Factors such as diabetes, hypertension and dyslipidaemia have been linked to adenoma development across a range of ethnicities, however there are limited data from the Australian population. This study investigates established and potential risk factors for early colorectal neoplasia in an Australian population.

Methods: This was a prospective, observational case-control study in subjects aged 20-85 years, referred for outpatient colonoscopy. Clinical, anthropometric, and biochemical variables were collected at baseline. Polyps were classified as conventional adenomas or sessile serrated lesions, and correlated with clinical and biochemical variables.

Results: The study included 357 subjects, median age 55 years (IQR: 43.0 – 64.0), and 52.9% were female. 41.7% had metabolic syndrome. Individuals with increased triglyceride (aOR: 2.23; 95%CI: 1.05 – 4.73) demonstrated a positive association with advanced adenoma. Multiple positive associations were observed in those over 40 years and with a BMI ≥ 25, including any polyp (aOR: 2.26; 95%CI: 1.22 – 4.18); adenoma (aOR: 2.64; 95%CI: 1.31 – 5.31); and, advanced adenoma (aOR: 3.30; 95%CI: 1.29 – 8.47).

Conclusions: Our study demonstrates that elevated BMI is an independent risk factor for colorectal neoplasia in Australians undergoing colonoscopy. Further efforts should be focused on both diet and weight optimization in the general population given these findings and the recent national statistics indicating that almost two-thirds of the population are either overweight or obese.

Metabolic Syndrome

colorectal neoplasia

adenoma

obesity

colonoscopy

Colorectal cancer remains the second most common malignancy in Australia, accounting for 11% of all cancer related deaths [1]. Whilst no randomised study has examined the effect of post-polypectomy surveillance on incidence or mortality of colorectal cancer, observational data have placed surveillance colonoscopy as the mainstay of colorectal cancer prevention in national guidelines [2]. These guidelines are grounded on established pathways placing adenomatous polyps as predictable precursor lesions to adenocarcinoma [3]. Currently the 10-year cumulative progression from adenoma to carcinoma is a 0.25% transition rate per year and it is ‘advanced adenomas’ that progress at up to 5% per year [4]. It is these advanced features of increased size, high-grade dysplasia and villosity as well as number of adenomas that increase the risk of progression to malignancy [2].

What continues to emerge is the role played by environmental factors such as diet and lifestyle in colorectal neoplasia development. As is reflected in our screening guidelines, age is a clear risk factor for adenoma development, in addition to number and advanced features. Growing colorectal cancer rates in increasingly westernized countries, suggests a substantial impact of environmental factors [5].

Diet and obesity are repeatedly noted for their contribution to the development of adenomas and subsequent progression to advanced lesions [6]. Gaining more recent attention is the contribution of other metabolic diseases such as diabetes and dyslipidaemia [7–9]. The impact of these individual diseases raises the question: is this part of a systemic metabolic process, such as the ‘metabolic syndrome’? Metabolic syndrome (MetS) has been identified as a major contributor to the detection and pathogenesis of both benign and advanced colonic polyps [10–14]. As a result, this has been identified as a consideration in current national polyp surveillance guidelines. What remains to be seen is how applicable the findings of these studies are to the Australian population, with many of these studies conducted in Asian countries.

This study examines the relationships between a range of metabolic variables and colorectal neoplastic lesions in a consecutive, real-life series of patients referred for an outpatient colonoscopy at a metropolitan Australian centre.

Study Design and Population

This is a prospective case-control study of consecutive patients referred for a diagnostic or surveillance colonoscopy at the Royal Brisbane and Women’s Hospital (RBWH) between September 2013 and December 2016. Exclusion criteria included: patients with a diagnosis of Inflammatory Bowel Disease (IBD); those with a high risk of CRC (Polyposis syndrome, Lynch syndrome); unable to provide written informed consent; adults over 85 years of age at recruitment; current pregnancy. Patients were recruited prospectively from a weekly colonoscopy consent clinic within the Gastroenterology unit. Eligible patients were provided with a description of the aims, purpose and nature of the study and informed consent was obtained. At enrolment basic demographic and clinical data were obtained through a face-to-face interview conducted by the study’s research nurse. This included: details of the current referral, other past medical history, family history, medications, and previous colonoscopy. Standard observations such as blood pressure, height and weight and fasting blood samples were collected prior to colonoscopy including full blood count, biochemistry, and lipids. All data were entered into a dedicated database. Cases were defined as those who had a previous history of colorectal polyps, specifically any adenoma confirmed on historical records, and who were found to have colorectal adenoma(s) on the current colonoscopy. Controls were defined as those having their index colonoscopy with no polyps identified, or those with no history of polyps on previous colonoscopy records.

Patients underwent colonoscopy with high definition colonoscopes following standard bowel preparation with Glyco-prep and Go-Kit (bisacodyl/Magnesium citrate) as per an established unit protocol. Extent of examination, quality of bowel preparation and polyp size/location were documented via the shared endoscopic reporting system (Provation Medical Inc®). All resected tissue was submitted for histopathological interrogation by highly experienced and specialized gastrointestinal pathologists.

Metabolic Syndrome

The ‘metabolic syndrome’ was defined using modified NCEP ATP III criteria of three or more of: BMI ≥ 25, blood pressure equal to or greater than 130 mmHg for systolic and/or 85 mmHg for diastolic level, fasting triglyceride (TG) level ≥ 1.7 mmol/L, fasting blood sugar ≥ 5.5 mmol/L and HDL-C < 1mmol/L in men and < 1.3mmol/L in women [15]. While waist circumference is typically used as part of the above criteria, it was not included as a variable at the commencement of the study and hence body mass index (BMI) (≥ 25) was included as a substitute criterion. BMI was further characterized into underweight (BMI < 18.5), healthy weight (BMI 18.5–24.9), overweight (BMI 25.0–29.9) and obese (BMI ≥ 30). Due to the multi-ethnic background of the patients, we had also incorporated an alternative BMI criterion for Asian patients. We had assigned BMI 18.5–22.9 as healthy weight, BMI 23.0–24.9 as overweight and BMI ≥ 25 as obese [16]. If biochemical criteria were not met, any active treatment for dyslipidaemia, diabetes mellitus and/or hypertension was deemed acceptable substitutes for the relevant criterion.

Colonic Lesion Definitions

Presence of polyps was divided into adenomas and serrated polyps. Adenomas were further divided into advanced (AA) and non-advanced adenoma (nAA). Sessile Serrated Lesions consisted of those with hyperplastic polyps (HP) or serrated lesions and were divided into advanced (ASL) and non-advanced serrated lesions (nASL).

Advanced adenoma (AA) was defined as having any of: ≥ 3 adenomas, ≥ 10mm, high-grade dysplasia or villous features. Advanced serrated lesions included: >3 non-advanced serrated lesions, ≥ 10mm or high-grade dysplasia and/or HP in the proximal region of the colon, or traditional serrated adenomas. Non – advanced serrated lesions included the following: serrated lesions < 10mm, those without high grade dysplasia, and HP in the left colon. Advanced adenomas and advanced serrated polyps were grouped together as advanced polyps. Non-advanced polyps consisted of non-advanced adenoma and non-advanced serrated polyps.

Outcomes

The primary outcome measured was the association between the metabolic syndrome and all polyps (adenomas and serrated lesions).

Secondary outcome measures included the association of individual components of the metabolic syndrome and colonic polyps, and polyp subgroups, stratified by age.

Statistical methods

Statistical analysis was performed using R version 4.1.3 [17]. Descriptive analysis was expressed as either median and interquartile range or number and percentage. Differences in categorical variables were analyzed using Pearson’s Chi – squared test while Kruskal – Wallis test was used for continuous variables. A binary logistic regression model was used to estimate the association between polyps, metabolic syndrome parameters, anthropometric, lifestyle and medication by univariate analysis. Multinomial logistic regression was used to test for associations between metabolic parameters and the different classifications of colonic polyps. We divided our cohort into two age ranges, < 40 years and ≥ 40 years of age, and tested for associations between metabolic parameters and colonic polyps. For statistical purposes, previous and current smoking were grouped together and so dichotomized into any smokers versus never smokers. Alcohol consumption was similarly dichotomized into any drinkers versus non-drinkers. Adjustments were made for age, gender, aspirin use, smoking and alcohol use as they are associated with the progression of colorectal neoplasia. Results of binary logistic regressions are presented as odds ratios (OR) with a 95% confidence interval (CI). Two-sided P values of < 0.05 were considered significant.

Overall study population

Study recruitment is outlined in Fig. 1. Of the 663 patients initially recruited over the study period from consent clinic, 306 (46%) either withdrew or did not fulfil all study criteria. Factors included a history of polyps on historical records in patients initially allocated to the control arm based upon self-reporting and delays in colonoscopy scheduling due to demand.

Of the 357 patients who fulfilled all study criteria, 189 (52.9%) were females. The median age of the participants was 55 years (IQR: 43.0–64.0) (Table 1). Metabolic syndrome was found in 149 participants (41.7%). The most common indication for colonoscopy referral was abdominal symptoms (54.1%) followed by polyp surveillance (26.1%).

Case-control analysis

Of the 357 patients with complete data, 126 were controls and 231 were cases. Compared to the control group, the case group was significantly older and more often males (Table 2). The case group also had higher rates of hypertension, consumption of blood pressure medications, alcohol consumption and MetS (Table 2).

Metabolic syndrome was identified in 108 (46.8%) cases and 82 (55%) of these cases were male. The median age of individuals with MetS was 61 years (IQR: 54.0–67.0). The case group also had more individuals with elevated triglycerides (30.1%), elevated glucose (20.3%), hypertension (61.4%) and overweight or obesity (70.2%). The control group had a slightly higher prevalence of reduced HDLC (48.4%) (Supp: Table 1). Univariable logistic regression analysis showed associations between polyp cases and the following factors, age (OR: 1.05; 95%CI: 1.03–1.07), male gender (OR: 2.05; 95%CI: 1.32–3.22), alcohol consumption (OR: 2.40; 95%CI: 1.23–4.75) and smoking (OR: 1.55; 95%CI: 1.00–2.41) (Supp: Table 2). Positive associations were also observed between polyp cases and the following metabolic parameters, hypertension (OR: 1.67; 95%CI: 1.08–2.60), overweight and obesity (OR: 1.70; 95%CI: 1.07–2.68), and metabolic syndrome (OR: 1.82; 95%CI: 1.16–2.88) (Supp: Table 2).

In multinomial logistic regression, adjusting for covariates (age, gender, aspirin use, smoking and alcohol use), we observed positive associations between increased triglyceride criteria and advanced adenoma (aOR: 2.23; 95%CI: 1.05–4.73) (Table 4). However, we did not observe any significant association between metabolic syndrome and polyp development (Table 3).

Next, we tested for associations between metabolic parameters and the different classifications of polyps in our ≥ 40 age cohort. We observed the following positive associations for increased BMI (BMI ≥ 25): any polyp (aOR: 2.26; 95%CI: 1.22–4.18); any adenoma (aOR: 2.64; 95%CI: 1.31–5.31); any advanced polyps (aOR: 2.38; 95%CI: 1.13–5.00); any non-advanced polyps (aOR: 2.18; 95%CI: 1.11–4.29); any non-advanced adenoma (aOR: 2.33; 95%CI: 1.07–5.05); and any advanced adenoma (aOR: 3.30; 95%CI: 1.29–8.47) (Table 5, Table 6).

Other findings

In our cohort we also observed a protective association between reduced HDLC and the presence of any polyp (aOR: 0.59; 95%CI: 0.35–0.98). We observed a similar association with non-advanced polyps (aOR: 0.52; 95%CI: 0.30–0.92) and non-advanced adenomas (aOR: .51; 95%CI: 0.26–0.99) (Table 4).

This study examined the relationships between colorectal polyps, metabolic syndrome and individual parameters for metabolic syndrome. We found that after adjusting for patient age, gender and lifestyle choices the prevalence of both conventional and sessile adenomas did not appear to relate to metabolic syndrome or individual criteria such as hypertension or raised triglycerides. However, when we examined for association between metabolic parameters and colorectal polyps with increased neoplastic potential, we observed a positive association between elevated triglyceride levels and advanced adenoma. We also found positive associations between elevated BMI and risk of adenomas (and risk of finding any polyps) in those individuals aged 40 years or more at the time of study recruitment.

The relationship between the metabolic syndrome and development of colonic neoplasia has been gaining more attention in recent years as metabolic disorders have attained global significance. Epidemiological studies have identified associations between obesity, metabolic risk factors and colorectal cancer risk [18, 19]. However, there are limited studies that prospectively investigate the relationship between metabolic risk factors and precursors of colorectal cancer. The growing burden of this malignancy observed in developing countries is thought to be attributed at least in part to rapid industrialization and changes in lifestyle practices, including diet and exercise. Bishehsari et al., highlighted this growing trend, exploring multiple contributors such as lifestyle and diet with a possible impact on the intestinal microbiome [5]. However, as reflected in current surveillance guidelines, the key risk factors for polyp development and metachronous neoplasia remain age, number of polyps and features of neoplasia such as high-grade dysplasia, villosity and increased polyp size [2]. Such is the evidence behind these characteristics that they remain the key components dictating current surveillance practices.

There appears to be a relationship between metabolic disease and polyp development as individual components of the metabolic syndrome have frequently been investigated. Kim et al., found that after adjusting for caloric intake and fat intake as well as known risk factors for colonic polyps such as age, abdominal obesity was significantly associated with increased risk of colonic adenoma (OR 2.74) 95%CI 1.66–4.51) [20]. Seo et al., examined the contribution of visceral fat via computed tomography and found in multivariate analysis that colorectal adenoma was significantly associated with visceral fat, particularly in male patients [20]. Yu et al., conducted a meta-analysis of 17 studies which identified Type 2 Diabetes as a risk factor for colorectal adenomas (RR: 1.52; 95% CI: 1.29–1.80) [7]. This paper also recognized that the findings could be confounded by similar risk factors such as physical inactivity, obesity, and unhealthy diet with a large number of papers being centred in Asia or Europe. Other work by Suh et al., found polyps in diabetic participants to be larger and more numerous than non-diabetic subjects despite the two groups being matched well for gender, age and BMI [8]. Xie et al., found LDL-C was significantly higher in patients with advanced adenoma compared to controls while obesity, age, and increased triglycerides were independent risk factors for any colorectal polyp [9].

Since it is suggested that the individual components of metabolic syndrome appear to correlate with colorectal polyps, this points to potentially a larger metabolic profile or ‘metabolic syndrome’ that is influencing the pathogenesis of these lesions. Kim et al., examined 62,171 asymptomatic patients and assessed risk of adenoma recurrence with hazards modelling. Metabolic syndrome was identified as a risk factor for adenoma occurrence at follow up colonoscopy (aHR, 1.28; 95% CI, 1.09–1.51) [12]. Fliss-Isakov et al., found abdominal obesity, hypertension and high glycosylated haemoglobin were individually associated with colorectal polyps [10] as was metabolic syndrome. Taniguchi et al., identified the three factors of age, BMI and fasting blood glucose influenced the recurrence rate of colorectal adenoma [14].

Current national polyp surveillance guidelines reflect these findings, incorporating the presence of metabolic syndrome as a factor to consider in terms of surveillance interval. A large number of referenced studies have originated from Asian countries, with differing metabolic profiles, and it is yet to be seen if these findings replicate in an Australian population. Jih et al., illustrates that despite a normal BMI, Asian Americans suffer from a disproportionate burden of type 2 diabetes and associated metabolic abnormalities. In addition for the same amount of body fat Asians have a consistently lower BMI [21]. Other limitations include the definition of metabolic syndrome. Whilst some studies would use strict criteria for metabolic syndrome, others only used diagnoses or medication for such conditions. Obesity varied between waist circumference and BMI and whilst some studies used 3/5 criteria for metabolic syndrome, others looked at individual components only.

Two recent studies have provided data from Caucasian populations. Milano et al., demonstrated a prevalence of Metabolic syndrome at 41.6% with a correlation with adenomas identified (OR 1.76, 95% CI 1.54–2.00) [22]. Shapero et al., using exclusively participants undergoing screening colonoscopy, only found obesity to correlate with an increased risk of colonic adenomas. However, medical records alone were used to define the presence or absence of hypertension, diabetes or dyslipidaemia with no prospective evaluation of participants as a method of categorisation [23].

It is recognized that our overall findings differ to the current literature. Factors that may explain this include sample size, study design (inclusion and exclusion criteria), and the evolving nature of this field with Caucasian populations relatively understudied to date. Our study was conducted in a population otherwise reflective of modern practice. Specifically, rates of adenoma detection and metabolic syndrome were comparable to those in the current literature (20–53%) as did metabolic syndrome (46.8%) when compared to figures from the AusDiab Study (31.0%) [24]. In addition, our procedural quality exceeded that quoted in national guidelines with 3% of patients having poor preparation, compared to the nationally accepted standard of 10–15%. Caecal intubation rate was 95% in keeping with current GESA recertification guidelines [25] and our age-unadjusted adenoma detection rate at 31.84% compared favourably with the commonly quoted benchmark of 25% [26]. The majority of the current patient population with metabolic syndrome were also under treatment for their metabolic risk factors. Treatment effects on both development and progression of colorectal neoplasia may have narrowed any differences in the case-control analysis and is currently the subject of further work. We also recognise that the inclusion of a substantial number of patients undergoing surveillance colonoscopy may modify any associations with metabolic syndrome identified in an exclusively screening population.

Additional strengths of our study included the broad range of procedural indications, reflective of real-life practice with an unbiased selection process. The location in a combined secondary and tertiary centre provided a large number of high-quality endoscopic procedures with adenoma detection rates as discussed. Limitations of this study include definitions used for metabolic syndrome and the relatively small sample size. Unfortunately, waist circumference was not measured at time of enrolment and as a consequence BMI was used as a surrogate. Despite this factor our rates of metabolic syndrome were still higher than that quoted in the literature. This may relate to referral bias given the inherent referral of more co-morbid patients to a large centre that provides both secondary and tertiary care. Attempts were made to account for this by recruiting all-comers in our standard outpatient setting.

This study has not found a relationship between the metabolic syndrome and both conventional and advanced colonic adenomas after adjusting for traditional risk factors such as age and gender. This is the first study to examine this issue within the Australian population and raises the question as to whether this relationship is as strong as that found in other populations. Larger cohort size and analysis of screening populations separately from diagnostic and surveillance populations in future studies can examine this relationship further. Depending on the results, additional research objectives may include the development of a risk matrix for metabolic criteria and whether effective treatment of these risk factors can reduce the incidence of adenomatous polyps in those continuing colonoscopic surveillance.

Author contributions:

All authors contributed towards the conceptualisation and design of the study. Material preparation and data collection were performed by Allison Brown, Lisa Simms, Rina Kumar and Timothy O’Sullivan. Patient consent and recruitment were performed by Allison Brown. Data analysis was done by Rina Kumar, Satomi Okano, Gunter Hartel and Anton Lord. The first draft of the manuscript was written by Graham Radford-Smith, Timothy O’Sullivan and Rina Kumar. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Data Availability Statement:

The data used and analyzed for this study are available from the corresponding author upon reasonable request. Consent from participants and ethics approval for uploading of data to a publically available database was not obtained at the time of this study.

Funding Statement:

Funding for this study was provided by a Rio Tinto Ride to Conquer Cancer Research Grant and a Royal Brisbane and Women’s Hospital Project Grant.

Conflict of Interest Disclosure:

No potential conflict of interest was reported by the authors.

Ethics Approval Statement:

Ethical approval was obtained from the Human Research Ethics Committee from Metro North Hospital and Health Service at The Prince Charles Hospital (HREC/ 13/ QPCH/ 47).

Patient Consent Statement:

The patients/ participants provided written informed consent to participate in the study.

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Table 1

Population Characteristics
	Population (n = 357)
Demographic parameters
Age (y) (IQR)	55 (43.0–64.0)
Gender (%)
Male	47.1 (n = 168)
Female	52.9 (n = 189)
Anthropometric and metabolic parameters
BMI (kg/m²) (IQR)	27.0 (23.8–31.0)
Glucose (mmol/L) (IQR)	4.6 (4.1–501)
Triglycerides (mmol/L) (IQR)	1.1 (0.7–1.8)
HDL (mmol/L) (IQR)
Men	1.1 (0.9–1.4)
Women	1.4 (1.1–1.7)
Hypertension (%)	56.9
Metabolic Syndrome (%)	41.7
BMI classification (%)
Underweight (< 18.5)	1.7
Healthy weight (18.5–24.9)	32.1
Overweight (25.0–29.9)	32.4
Obese (≥ 30.0)	33.8
Medications (%)
Statin	21.7
Other cholesterol medications	2.3
Sartan	13
Other blood pressure medications	23.9
Oral hypoglycemic medication	9.1
Insulin IM	2.3
Aspirin	23.5
Lifestyle Parameters (%)
Smoking
Never	45.7
Current	20.7
Previous	33.5
Alcohol Consumption
Never	11.3
Current	76.2
Previous	12.5

Values represent: median and interquartile range (IQR) for continuous variables and percentages for categorical variables.

Table 2

Polyp characteristics and comparison between cases and controls
	Controls (n = 126)	All polyps (n = 231)	Serrated Polyps (n = 104)	Adenoma (n = 127)
Demographic parameters
Age (y) (IQR)	47.5 (38 .0–57.0)	58.0 (49.5–66.5) **	55.0 (43.2–63.0)	61 (52–67.0)**
Gender (%)
Male	35.7 (n = 45)	53.2 (n = 123) **	48.1 (n = 50)	57.5 (n = 73)*
Female	64.3 (n = 81)	46.8 (n = 108)	51.9 (n = 54)	42.5 (n = 54)
Anthropometric and metabolic parameters
BMI (kg/m²) (IQR)	26.0 (22.9–30.8)	27.5 (24.5–31.1)	26.8 (24.1–30.9)	28.7 (24.7–31.4)*
Glucose (mmol/L) (IQR)	4.5 (4.1–5.0)	4.7 (4.2–5.2)	4.6 (4.2–4.8)	4.8 (4.2–5.3)
Triglycerides (mmol/L) (IQR)	1.1 (0.7–1.6)	1.2 (0.7–1.8)	1.0 (0.7–1.6)	1.3 (0.8–1.9)*
HDL (mmol/L) (IQR)
Men	1.1 (1.0–1.3)	1.2 (0.9–1.4)	1.2 (1–1.5)	1.1 (0.9–2.2)
Women	1.3 (1.1–1.6)	1.4 (1.1–1.7)	1.4 (1.1–2.6)	1.3 (1.1–1.6)
Hypertension (%)^a	48.8	61.4*	54.4	67.2**
Metabolic Syndrome (%)^b	32.5	46.8*	40.4	52**
BMI classification (%)
Underweight (< 18.5)	3.3	0.9	1	0.8
Healthy weight (18.5–24.9)	37.5	29.2	34.3	25
Overweight (25.0–29.9)	28.3	34.5	34.3	33.9
Obese (≥ 30.0)	30.8	35.4	30.4	40.3*
Medications (%)
Statin	15.9	24.9	20.3	28.6*
Other cholesterol medications	1.6	2.6	2.9	2.4
Sartan	13.5	12.7	10.8	14.2
Other blood pressure medications	16.7	27.9*	22.5	32.3**
Oral hypoglycemic medication	7.2	10.1	5.9	13.5*
Insulin IM	2.4	2.3	3.1	1.6
Aspirin	18.3	26.4	23.1	29.1
Lifestyle Parameters (%)
Smoking (ever)	47.2	58.1	52.0	63.0*
Alcohol Consumption (ever)	82.6	92.0*	91.0	92.7

Values represent: median and interquartile range (IQR) for continuous variables and percentages for categorical variables.

*P < 0.05, **P < 0.001, ***P < 0.0001

^a Hypertension: systolic blood pressure ≥ 130mm Hg or diastolic pressure ≥ 85mm Hg or use of hypotensive medications.

^b Metabolic Syndrome: the presence of 3 or more NCEP ATP III criteria.

Table 3

Adjusted association between metabolic parameters and cases of colorectal polyp types
	Polyps (n = 231) aOR (95% CI)	Adenoma (n = 127) aOR (95% CI)	Serrated Lesions (n = 104) aOR (95% CI)
Metabolic Syndrome^a	1.03 (0.59–1.78)	1.06 (0.57–1.97)	0.99 (0.53–1.86)
Elevated blood glucose level^b	0.71 (0.36–1.38)	0.83 (0.40–1.72)	0.55 (0.24–1.23)
Elevated triglyceride level^c	1.11 (0.64–1.93)	1.37 (0.74–2.53)	0.89 (0.46–1.71)
Low HDL cholesterol^d	0.59 (0.35–0.98) *	0.58 (0.32–1.04)	0.59 (0.33–1.06)
Overweight and Obesity^e	1.41 (0.85–2.35)	1.70 (0.93–3.10)	1.23 (0.69–2.19)
Hypertension^f	0.83 (0.48–1.45)	0.92 (0.48–1.76)	0.76 (0.40–1.43)

The reference group for Odds ratio (OR) is control group. All associations are adjusted for age, gender, aspirin, smoking and alcohol.

*P < 0.05

^a Metabolic Syndrome: the presence of 3 or more NCEP ATP III criteria.

^b Glucose: fasting glucose ≥ 5.5 mmol/L or hypoglycemic medications

^c Elevated triglyceride: fasting TG ≥ 1.7 mmol/L or medication

^d Low HDL cholesterol: HDL < 1 / 1.3 mmol/L for men/ women respectively

^e Overweight & Obesity: BMI ≥ 25

^f Hypertension: systolic blood pressure ≥ 130mm Hg or diastolic pressure ≥ 85mm Hg or use of hypotensive medications.

Table 4

Adjusted association between metabolic parameters and cases of colorectal polyp types and neoplastic potential
	Polyps aOR (95% CI)		Adenoma aOR (95% CI)		Serrated Lesions aOR (95% CI)
	Advanced Polyps (n = 93)	Non - advanced polyps (n = 138)	Advanced adenoma (n = 51)	Non-advanced adenoma (n = 76)	Advanced Serrated Lesions (n = 42)	Non-advanced serrated lesions (n = 62)
Metabolic Syndrome^a	1.16 (0.61–2.22)	0.94 (0.52–1.72)	1.19 (0.54–2.60)	0.98 (0.49–1.96)	1.14 (0.50–2.59)	0.90(0.43–1.88)
Elevated fasting glucose level^b	0.67 (0.30–1.48)	0.74 (0.36–1.53)	0.74 (0.30–1.87)	0.88 (0.39–1.97)	0.56 (0.19–1.62)	0.54 (0.21–1.41)
Elevated fasting triglyceride level^c	1.47 (0.78–2.78)	0.90 (0.49–1.66)	2.23 (1.05–4.73) *	0.96 (0.47–1.96)	0.86 (0.36–2.06)	0.92 (0.43–1.96)
Low HDL cholesterol^d	0.70 (0.38–1.29)	0.52 (0.30–0.92) *	0.70 (0.33–1.50)	0.51 (0.26–0.99) *	0.69 (0.32–1.49)	0.53 (0.27–1.06)
Overweight and Obesity^e	1.58 (0.84–2.96)	1.31 (0.75–2.31)	2.21 (0.96–5.09)	1.46 (0.74–2.87)	1.20 (0.56–2.60)	1.25 (0.63–2.48)
Hypertension^f	0.85 (0.43–1.68)	0.82 (0.44–1.51)	0.94 (0.40–2.21)	0.90 (0.43–1.88)	0.78 (0.34–1.80)	0.75 (0.36–1.57)

The reference group for Odds ratio is control group. All associations are adjusted for age, gender, aspirin, smoking and alcohol.

*P < 0.05

^a Metabolic Syndrome: the presence of 3 or more NCEP ATP III criteria.

^b Glucose: fasting glucose ≥ 5.5 mmol/L or hypoglycemic medications

^c Elevated triglyceride: fasting TG ≥ 1.7 mmol/L or medication

^d Low HDL cholesterol: HDL < 1 / 1.3 mmol/L for men/ women respectively

^e Overweight & Obesity: BMI ≥ 25

^f Hypertension: systolic blood pressure ≥ 130mm Hg or diastolic pressure ≥ 85mm Hg or use of hypotensive medications.

Table 5

Adjusted association between metabolic parameters and colorectal polyps in patients aged 40 years and above.
	Polyps (n = 200) aOR (95% CI)	Adenoma (n = 117) aOR (95% CI)	Serrated Lesions (n = 83) aOR (95% CI)
Metabolic Syndrome^a	1.28 (0.70–2.36)	1.30 (0.67–2.53)	1.26 (0.63–2.55)
Elevated glucose level^b	0.76 (0.36–1.60)	0.91 (0.41–2.00)	0.57 (0.24–1.39)
Elevated triglyceride level^c	1.32 (0.71–2.46)	1.47 (0.75–2.89)	1.15 (0.56–2.38)
Low HDL cholesterol^d	0.63 (0.35–1.14)	0.62 (0.32–1.20)	0.64 (0.32–1.27)
Overweight and Obesity^e	2.26 (1.22–4.18) *	2.64 (1.31–5.31) *	1.89 (0.93–3.84)
Hypertension^f	0.86 (0.46–1.61)	0.92 (0.45–1.85)	0.80 (0.39–1.66)

The reference group for Odds ratio is control group. All associations are adjusted for age, gender, aspirin, smoking and alcohol.

*P < 0.05

^a Metabolic Syndrome: the presence of 3 or more NCEP ATP III criteria.

^b Glucose: fasting glucose ≥ 5.5 mmol/L or hypoglycemic medications

^c Elevated triglyceride: fasting TG ≥ 1.7 mmol/L or medication

^d Low HDL cholesterol: HDL < 1 / 1.3 mmol/L for men/ women respectively

^e Overweight & Obesity: BMI ≥ 25

^f Hypertension: systolic blood pressure ≥ 130mm Hg or diastolic pressure ≥ 85mm Hg or use of hypotensive medications.

Table 6

Adjusted association between metabolic parameters and colorectal polyps of neoplastic potential in patients aged 40 years and above.
	Polyps aOR (95% CI)		Adenoma aOR (95% CI)		Serrated Lesions aOR (95% CI)
	Advanced Polyps (n = 81)	Non - advanced polyps (n = 119)	Advanced adenoma (n = 47)	Non-advanced adenoma (n = 70)	Advanced Serrated Lesions (n = 34)	Non-advanced serrated lesions (n = 49)
Metabolic Syndrome^a	1.37 (0.68–2.78)	1.22 (0.63–2.37)	1.41 (0.61–3.23)	1.23 (0.59–2.58)	1.33 (0.55–3.25)	1.21 (0.53–2.75)
Elevated fasting glucose level^b	0.72 (0.31–1.71)	0.79 (0.35–1.75)	0.83 (0.31–2.19)	0.95 (0.40–2.24)	0.57 (0.18–1.79)	0.56 (0.20–1.60)
Elevated fasting triglyceride level^c	1.66 (0.81–3.37)	1.11 (0.56–2.20)	2.11 (0.93–4.79)	1.15 (0.54–2.46)	1.17 (0.46–2.99)	1.14 (0.49–2.67)
Low HDL cholesterol^d	0.70 (0.35–1.41)	0.58 (0.30–1.11)	0.72 (0.32–1.64)	0.57 (0.27–1.18)	0.68 (0.28–1.65)	0.61 (0.27–1.37)
Overweight and Obesity^e	2.38 (1.13–5.00) *	2.18 (1.11–4.29) *	3.30 (1.29–8.47) *	2.33 (1.07–5.05) *	1.66 (0.66–4.18)	2.06 (0.89–4.79)
Hypertension^f	0.85 (0.40–1.80)	0.87 (0.44–1.73)	0.98 (0.39–2.44)	0.88 (0.40–1.95)	0.72 (0.28–1.83)	0.87 (0.37–2.04)

The reference group for Odds ratio is control group. All associations are adjusted for age, gender, aspirin, smoking and alcohol.

*P < 0.05

^a Metabolic Syndrome: the presence of 3 or more NCEP ATP III criteria.

^b Glucose: fasting glucose ≥ 5.5 mmol/L or hypoglycemic medications

^c Elevated triglyceride: fasting TG ≥ 1.7 mmol/L or medication

^d Low HDL cholesterol: HDL < 1 / 1.3 mmol/L for men/ women respectively

^e Overweight & Obesity: BMI ≥ 25

^f Hypertension: systolic blood pressure ≥ 130mm Hg or diastolic pressure ≥ 85mm Hg or use of hypotensive medications.

No competing interests reported.

Supplementary.docx

Metabolic variables are associated with colorectal neoplasia in an Australian outpatient population

Status:

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Abstract

Figures

Introduction

Methods

Study Design and Population

Metabolic Syndrome

Colonic Lesion Definitions

Outcomes

Statistical methods

Results

Overall study population

Case-control analysis

Other findings

Discussion

Conclusion

Declarations

References

Tables

Additional Declarations

Supplementary Files

Status:

Version 1