Association between different metabolic obesity phenotypes and colorectal adenoma

Li Lin; Yuhan Ying; Long Shu; Xiaoling Lv; Qin Zhu

doi:10.1371/journal.pone.0343556

. 2026 Feb 23;21(2):e0343556. doi: 10.1371/journal.pone.0343556

Association between different metabolic obesity phenotypes and colorectal adenoma

Li Lin ¹, Yuhan Ying ¹, Long Shu ², Xiaoling Lv ³, Qin Zhu ^2,^4,^*

Editor: Muhammad Shahzad Aslam⁵

PMCID: PMC12928563 PMID: 41729988

Abstract

Background

Obesity and metabolic abnormalities are independently and interactively associated with colorectal adenoma (CRA). This study aimed to investigate the association between different metabolic obesity phenotypes and CRA, and to assess whether this relationship is influenced by stratification based on age and sex.

Patients and methods

A total of 2042 subjects were enrolled in this study. The patients were classified into four metabolic obesity phenotypes based on their body mass index (BMI) and metabolic status, including metabolically healthy non-obesity (MHNO), metabolically unhealthy non-obesity (MUNO), metabolically healthy obesity (MHO), and metabolically unhealthy obesity (MUO). Multiple logistic regression analysis and further subgroup analysis based on sex and age were performed to evaluate the association between the metabolic obesity phenotypes and the occurrence of CRA.

Results

In the overall population, CRA was detected at significantly higher rates in both the MUNO and MUO phenotypes compared to the MHNO and MHO phenotypes. After adjusting for confounders, in the overall population, the MUNO (OR = 1.353, 95% CI: 1.099–1.666, P < 0.05) and MUO (OR = 1.558, 95% CI: 1.111–2.187, P < 0.05) phenotypes were identified as risk factors for the development of CRA compared to the MHNO phenotype. Subgroup analysis based on sex revealed that in female subjects, the MUNO phenotype (OR 1.694; 95% CI 1.243–3.308, p < 0.01) exhibited significantly elevated risks of CRA compared to those in the MHNO phenotype, whereas the MHO phenotype showed no significant differences. Furthermore, in the male subjects, no statistically significant differences were observed among the four groups. Subgroup analysis based on age revealed that under 60 years with the MUNO (OR = 1.648, 95% CI: 1.252–2.169, P < 0.01) phenotype and over 60 years with MUO (OR = 2.301, 95% CI: 1.252–4.348, P < 0.01) phenotype, had significantly increased risks of CRA.

Conclusion

Metabolically unhealthy phenotypes are associated with a higher risk of CRA incidence. Clinical screening for CRA should focus on metabolically unhealthy subjects.

Introduction

Colorectal adenoma (CRA) is the most common benign tumor of the colorectum and has been recognized as a precancerous lesion of colorectal cancer (CRC) [1]. CRA accounts for about 70–80% of CRC cases, particularly in cases with advanced adenomas [2]. CRC is the second most common cause of cancer-related deaths worldwide and the third most common type of cancer overall [3]. Statistics have shown a growing incidence of CRC among younger age groups [4]. The annual rise in CRC cases in China has resulted in a significant financial burden for the country [5]. Additionally, due to the insidious onset of CRC, the disease is frequently diagnosed at an advanced stage. Among all early screening techniques, total colonoscopy remains the gold standard for CRC diagnosis [6], enabling prompt treatment of early lesions and direct access to pathologic samples. Following the advent of colonoscopic treatments, endoscopic CRA removal has significantly decreased the mortality rate of CRC [7]. Determining the risk factors for CRA and reliably identifying subjects at increased risk for CRA may improve the prevention and intervention strategies.

Studies have shown that age, sex, family history, weight, lifestyle, diet, and medications are known risk factors for CRA [8]. Obesity has emerged as a serious global public health concern, driven by changes in diet and routines and posing a significant risk for CRC [9]. A retrospective case-control study reported a significant association between overweight or obesity and advanced adenomas [10]. Obesity typically coexists with other metabolic problems, such as low levels of high-density lipoprotein cholesterol, excessive triglycerides, and insulinemia [11]. These metabolic diseases also affect the development of CRA via their association with obesity. Obese individuals may exhibit varying metabolic abnormalities. However, not all individuals with metabolic disorders are overweight or obese, and not all overweight or obese individuals suffer from metabolic diseases. Consequently, the concept of metabolically healthy obesity (MHO), which provides a comprehensive description of the body's metabolic processes and overall physical health, was originally put forth by Sims in 2001 [12]. A growing number of studies have shown that individuals classified as MHO may be exposed to a higher risk of cardiovascular diseases, prostate cancer, and erosive esophagitis compared to metabolically healthy non-obesity (MHNO) counterparts [13–15].

Previous studies have explored the association between various metabolic obesity phenotypes and gastrointestinal polys [16]. The complexity of the interaction between obesity and metabolic disorders and its impact on patients with CRA remains poorly understood. However, the differential role of obesity and metabolic health phenotypes on CRA in the Chinese population has not yet been fully evaluated. This cross-sectional cohort study of a Chinese population aimed to elucidate the relationship between several metabolic obesity phenotypes and the incidence of CRA. The findings could be used to identify individuals at high risk for CRA, enhance the understanding of the role of obesity phenotypes in CRA development, and lay a foundation for CRC prevention and clinical intervention.

Materials and methods

Data source

Participants in this retrospective analysis were those who had received a complete medical examination at Zhejiang Hospital. The study recruited 6689 participants between1/1/2023 to 31/8/2024, who had comprehensive medical examinations, including physicals, blood tests, and colonoscopies.Authors completed data collection between 1/10/2024 and 10/2/2025 after obtaining ethical approval from the Ethics Committee of Zhejiang Hospital, had no access to information that could identify individual participants during or after data collection.Exclusion criteria include: (1) Age under 18 years; (2) HIV infection, pregnancy, severe organ failure, and active gastrointestinal cancer;(3) absence of information on body mass index (BMI), systolic blood pressure (SBP),diastolic blood pressure (DBP), fasting blood glucose (FBG), triglyceride(TG),total cholesterol (TC),low-density lipoprotein cholesterol(LDL-C) or high-density lipoprotein cholesterol (HDL-C) at admission; (4) have inflammatory bowel illness (such as Crohn's disease and ulcerative colitis) or hereditary CRC syndrome (such as familial adenomatous polyposis).This study was conducted according to the Declaration of Helsinki as revised in 2013, and the protocol was approved by the Ethics Committee of Zhejiang Hospital (Approval NO: 2024 Clinical Trial Approval No.114K). The need for patient consent was waived due to the retrospective nature of the study.

Data collection and measurement

Eligible nurses followed predefined procedures to record demographic information such as age, gender, family history, smoking and alcohol usage, past medical history, and medications taken. All patients' blood pressure was recorded in the seated position using a digital sphygmomanometer after resting for at least ten minutes. The patients were fasted for at least ten hours, and blood samples were collected. The blood samples were analyzed at the Medical Laboratory Center of Zhejiang Hospital, including serum biochemical indices such as fasting blood glucose, total cholesterol, high-density lipoprotein, and triglycerides.

Colonoscopy

All patients received a liquid diet for 24 hours before the examination, and polyethylene glycol was used to prepare their bowels as part of the usual protocol. In our hospital, a specialized endoscopist performed a colonoscopy, and all lesions were endoscopically removed. The biopsy tissues were viewed under a microscope by a skilled pathologist to categorize polyps according to pathologic characteristics.

Definition of the metabolic obesity phenotypes

The metabolic profiles of the individuals were analyzed according to the Adult Treatment Panel III (ATP-III) criteria [17] and the China Guidelines for Type 2 Diabetes [18]. The parameters for determining metabolically unhealthy individuals were established based on the following criteria: (1) high blood pressure (SBP ≥ 130 mmHg or DBP ≥ 85 mmHg) or taking antihypertensive medical treatments; (2) hyperglycemia (FBG level ≥ 5.60 mmol/L) or taking antidiabetic medical treatments; (3) fasting TG ≥ 1.7 mmol/L or taking lipid-lowering medical treatments; (4) fasting HDL-C < 1.04 mmol/L or taking lipid-lowering medical treatments.

The BMIs of all the enrolled individuals were determined by dividing the weights of the individuals by the square of their heights (kg/m²). Obesity was defined as BMI ≥ 28 kg/m² based on the criteria developed by the Working Group on Obesity in China [19].

The participants were classified into four groups based on these criteria: (1) MHO, people with BMI ≥ 28 kg/m² and one or no indicators of metabolically unhealthy; (2) MUO, people with BMI ≥ 28 kg/m² and two or more indicators of metabolically unhealthy; (3) MHNO, people with BMI < 28 kg/m² and one or no indicators of metabolically unhealthy; and (4) MUNO, people with BMI < 28 kg/m² and two or more indicators of metabolically unhealthy.

Statistical analysis

All of the statistical analyses were carried out by SPSS (version 23.0) and the R software (version 4.4.2). The Shapiro-Wilk test was conducted to assess whether continuous variables conformed to a normal distribution. Continuous data were presented as means ± standard deviations (SD) or median (interquartile range), and comparisons were made using one-way analysis of variance for normally distributed data and the Whitney U-tests for skewed distributions. Categorical variables were presented as frequencies and percentages, and differences between groups were evaluated using the chi-squared or Fisher's exact tests as appropriate. Comparisons across more than two groups were performed using one-way analysis of variance (ANOVA), followed by Tukey's test for between-group comparisons. The odds ratio (OR) and 95% confidence interval (CI) of the risk of CRA in each metabolic obesity phenotype group were calculated by logistic regression analysis. Given the multiple comparisons performed across different subgroups (sex, age), we applied a more stringent significance level (P < 0.01) for interpreting the results of subgroup analyses to reduce the risk of Type I error. The primary analysis in the overall population was interpreted at P < 0.05.

Results

Comparative analysis of baseline data between the non-adenomas and adenomas groups

The behavioral and biochemical characteristics of all 2042 subjects, comprising 1049 males and 993 females, were collected and analyzed, as shown in Table 1. In the overall population, subjects diagnosed with CRA exhibited significantly higher levels of BMI, SBP, DBP, TG, and FBG compared to those without CRA. In addition, a higher prevalence of diabetes and hypertension, elevated rates of smoking and alcohol consumption, and a greater risk of developing CRA were observed (all P < 0.05). Furthermore, adenoma patients demonstrated reduced HDL levels, though no significant differences were observed in LDL-C or TC between the two groups.

Table 1. Comparison of baseline characteristics of enrolled subjects with and without adenoma.

Variables	All subjects			Females			Males
Variables	No Adenoma	Adenoma	P-value	No Adenoma	Adenoma	P-value	No Adenoma	Adenoma	P-value
No. of cases	1166	876	–	686	307	–	480	569	–
Age, years	54.00 [46.00, 61.00]	59.00 [53.00, 67.00]	<0.001	55.00 [47.25, 62.00]	60.00 [54.00, 67.00]	<0.001	53.00 [45.00, 60.00]	59.00 [53.00, 67.00]	<0.001
Sex (male), n (%)	480 (41.2)	569 (65.0)	<0.001	–	–	–	–	–	–
Smoking, n (%)	189 (16.2)	257 (29.3)	<0.001	8 (1.2)	4 (1.3)	>0.050	181 (37.7)	253 (44.5)	0.032
Drinking, n (%)	188 (16.1)	244 (27.9)	<0.001	28 (4.1)	10 (3.3)	0.655	160 (33.3)	234 (41.1)	0.011
Diabetes, n (%)	89 (7.6)	126 (14.4)	<0.001	42 (6.1)	34 (11.1)	0.010	47 (9.8)	92 (16.2)	0.003
Hypertention, n (%)	301 (25.8)	354 (40.4)	<0.001	144 (21.0)	111 (36.2)	<0.001	157 (32.7)	243 (42.7)	0.001
BMI, kg/m²	23.44 [21.34, 25.69]	24.22 [22.29, 26.23]	<0.001	22.48 [20.70, 24.64]	23.62 [21.66, 25.07]	<0.001	24.64 [22.84, 26.96]	24.57 [22.66, 26.57]	0.316
SBP, mmHg	125.00 [115.00, 136.00]	130.00 [119.00, 141.00]	<0.001	123.00 [112.00, 135.00]	130.00 [119.00, 141.00]	<0.001	128.00 [118.75, 137.00]	130.00 [118.00, 141.00]	0.021
DBP, mmHg	79.00 [72.00, 86.00]	80.00 [72.75, 87.00]	0.011	76.00 [69.00, 83.75]	78.00 [71.00, 84.00]	0.056	82.50 [75.00, 90.00]	81.00 [74.00, 89.00]	0.149
TC, mmol/L	4.84 [4.23, 5.51]	4.84 [4.18, 5.44]	0.197	4.92 [4.29, 5.63]	5.12 [4.49, 5.74]	0.039	4.74 [4.15, 5.39]	4.70 [4.07, 5.29]	0.129
TG, mmol/L	1.31 [0.91, 1.97]	1.58 [1.07, 2.34]	<0.001	1.15 [0.83, 1.65]	1.51 [1.04, 1.93]	<0.001	1.62 [1.09, 2.49]	1.66 [1.12, 2.57]	0.770
HDL-C, mmol/L	1.23 [1.04, 1.48]	1.17 [0.98, 1.39]	<0.001	1.35 [1.16, 1.61]	1.31 [1.12, 1.52]	0.010	1.09 [0.93, 1.26]	1.10 [0.93, 1.28]	0.582
LDL-C, mmol/L	2.77 [2.24, 3.30]	2.72 [2.20, 3.22]	0.167	2.83 [2.26, 3.34]	2.93 [2.32, 3.42]	0.141	2.68 [2.19, 3.19]	2.63 [2.12, 3.12]	0.128
FBG, mmol/L	5.01 [4.68, 5.59]	5.26 [4.80, 6.12]	<0.001	4.97 [4.64, 5.43]	5.20 [4.76, 5.82]	<0.001	5.13 [4.74, 5.79]	5.29 [4.85, 6.31]	0.001

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The occurrence of adenomas was 54.2% and 30.9% among male and female subjects, respectively. Among female subjects, those with adenomas displayed elevated BMI, SBP, and TG levels, as well as lower HDL levels compared to non-adenoma counterparts (all P < 0.05). However, no significant differences in smoking or alcohol consumption behaviors were observed. In male subjects, significant differences were observed only in SBP and FBG levels, whereas other indicators showed no significant differences.

Comparative analysis of baseline data between different metabolic obesity cohorts

This study explored the baseline characteristics of female participants (n = 993) across distinct metabolic obesity phenotypes, as shown in Table 2. The female individuals enrolled in this study had an average age of 57 years. The MHNO, MHO, MUNO, and MUO cohorts comprised 582 (58.6%), 27 (2.7%), 339 (34.1%), and 45 (4.5%) of the female subjects, respectively. Moreover, the frequency of adenomas in these cohorts was 23.5%, 33.3%, 41.6%, and 44.4%, respectively (p < 0.05; Fig 1A). Compared with participants with the MHNO or MHO phenotypes, those classified as MUNO and MUO phenotypes displayed a significantly elevated occurrence of CRA (P < 0.05). Furthermore, CRA incidence increased progressively with higher proportions of metabolic risk factors among female participants (P < 0.05; Fig 1B). Metabolic parameters, such as SBP, FBG, and TG, were elevated in the metabolically unhealthy groups compared to the metabolically healthy groups; conversely, HDL-C and LDL-C exhibited an inverse association (all P < 0.05). BMI levels were significantly greater in the obesity groups (MHO and MUO) compared to the normal weight groups (MHNO and MUNO). The prevalence of metabolic disorders, such as diabetes and hypertension, was greater in the metabolically unhealthy groups compared to the metabolically healthy groups (all P < 0.05). Alcohol consumption and smoking exhibited no significant variations among the four cohorts of metabolic obesity.

Table 2. Traits of enrolled female subjects at baseline in the different cohorts of metabolic obesity.

Variables	Total	MHNO	MHO	MUNO	MUO	P-value
No. of cases	993	582	27	339	45	–
Age, years	57.00 [50.00, 63.00]	54.00 [46.00, 60.00]	55.00 [49.50, 60.50]	61.00 [55.00, 68.00]	61.00 [55.00, 66.00]	<0.001
Smoking, n (%)	12 (1.2)	6 (1.0)	0 (0.0)	4 (1.2)	2 (4.4)	0.218
Drinking, n (%)	38 (3.8)	22 (3.8)	1 (3.7)	12 (3.5)	3 (6.7)	0.785
Diabetes, n (%)	76 (7.7)	9 (1.5)	0 (0.0)	56 (16.5)	11 (24.4)	<0.001
Hypertention, n (%)	255 (25.7)	79 (13.6)	6 (22.2)	143 (42.2)	27 (60.0)	<0.001
BMI, kg/m²	22.83 [20.94, 24.89]	22.00 [20.40, 23.78]	29.38 [28.74, 30.67]	23.71 [21.88, 24.97]	29.53 [28.58, 30.61]	<0.001
SBP, mmHg	125.00 [114.00, 137.00]	119.00 [110.00, 128.00]	131.00 [120.00, 143.00]	134.00 [124.00, 143.00]	133.00 [123.00, 149.00]	<0.001
DBP, mmHg	77.00 [70.00, 84.00]	75.00 [68.00, 81.00]	81.00 [73.50, 87.50]	80.00 [72.00, 86.00]	84.00 [77.00, 89.00]	<0.001
TC, mmol/L	5.00 [4.33, 5.66]	5.03 [4.38, 5.62]	5.47 [4.66, 6.22]	4.94 [4.28, 5.74]	4.76 [4.25, 5.36]	0.151
TG, mmol/L	1.25 [0.88, 1.75]	1.01 [0.76, 1.34]	1.29 [1.02, 1.56]	1.82 [1.28, 2.44]	1.74 [1.26, 2.56]	<0.001
HDL-C, mmol/L	1.34 [1.15, 1.57]	1.44 [1.24, 1.69]	1.32 [1.19, 1.48]	1.19 [1.00, 1.40]	1.20 [1.02, 1.33]	<0.001
LDL-C, mmol/L	2.86 [2.31, 3.36]	2.91 [2.40, 3.33]	3.44 [2.82, 3.76]	2.69 [2.12, 3.38]	2.80 [2.07, 3.28]	<0.001
FBG, mmol/L	5.03 [4.68, 5.59]	4.84 [4.59, 5.15]	5.09 [4.84, 5.31]	5.58 [4.99, 6.32]	5.65 [5.09, 6.46]	<0.001

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Abbreviations: BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; FBG, fasting blood glucose; TG, triglyceride; TG, total cholesterol; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol.MHNO, metabolically healthy non-obesity; MHO, metabolically healthy obesity; MUNO, metabolically unhealthy non-obesity; MUO, metabolically unhealthy obesity.

Frequency of occurrence of CRA in the Fig 1A different phenotypes of metabolic obesity, and Fig 1B according to the proportion of metabolic risk factors.

The baseline characteristics of male participants (n = 1049) across distinct metabolic obesity phenotypes are presented in Table 3. The male individuals enrolled in the study had an average age of 57 years. The MHNO, MHO, MUNO, and MUO cohorts comprised 339 (32.3%), 16 (1.5%), 551 (52.5%), and 143 (13.6%) males, respectively. The frequency of occurrence of adenomas in these cohorts was 49.9%, 43.8%, 57.4%, and 53.8%, respectively (P < 0.05; Fig 1A). Compared with participants with the MHNO and MHO phenotypes, those with the MUNO and MUO phenotypes displayed a significantly elevated occurrence of CRA (P < 0.05). However, the occurrence of CRA showed no difference in the male participants with the proportion of metabolic risk factors (Fig 1B). The metabolic parameters DBP, FBG, and TG were elevated in the metabolically unhealthy groups compared to the metabolically healthy groups, whereas HDL-C exhibited an inverse association (all P < 0.05). Moreover, BMI levels were significantly greater in the obesity groups (MHO and MUO) compared to the normal weight groups (MHNO and MUNO). Notably, the prevalence of metabolic disorders, such as diabetes and hypertension, was greater in the metabolically unhealthy groups compared to the metabolically healthy groups (all P < 0.05). A significant difference in cigarette consumption was observed between the metabolically unhealthy and healthy groups.

Table 3. Traits of enrolled male subjects at baseline in the different cohorts of metabolic obesity.

Variables	Total	MHNO	MHO	MUNO	MUO	P-value
No. of cases	1049	339	16	551	143	–
Age, years	57.00 [49.00, 64.00]	56.00 [47.00, 64.00]	52.50 [41.00, 64.00]	59.00 [51.00, 66.00]	53.00 [46.00, 59.00]	<0.001
Smoking, n (%)	434 (41.4)	120 (35.4)	8 (50.0)	254 (46.1)	52 (36.4)	0.007
Drinking, n (%)	394 (37.6)	111 (32.7)	5 (31.2)	219 (39.7)	59 (41.3)	0.134
Diabetes, n (%)	139 (13.3)	3 (0.9)	0 (0.0)	113 (20.5)	23 (16.1)	<0.001
Hypertention, n (%)	400 (38.1)	60 (17.7)	3 (18.8)	258 (46.8)	79 (55.2)	<0.001
BMI, kg/m²	24.61 [22.68, 26.73]	23.34 [21.53, 24.81]	29.24 [28.39, 30.65]	24.61 [23.04, 26.04]	29.39 [28.57, 30.47]	<0.001
SBP, mmHg	129.00 [118.00, 139.00]	124.00 [115.00, 132.00]	131.50 [121.25, 137.00]	131.00 [120.50, 140.00]	136.00 [123.50, 144.50]	<0.001
DBP, mmHg	82.00 [75.00, 89.00]	79.00 [72.00, 85.00]	81.00 [75.00, 89.25]	82.00 [75.00, 90.00]	88.00 [81.00, 94.00]	<0.001
TC, mmol/L	4.72 [4.11, 5.32]	4.72 [4.20, 5.27]	4.62 [4.12, 5.09]	4.71 [3.98, 5.36]	4.76 [4.14, 5.40]	0.664
TG, mmol/L	1.64 [1.10, 2.56]	1.13 [0.84, 1.46]	1.16 [0.90, 1.58]	2.03 [1.40, 2.93]	2.34 [1.63, 3.22]	<0.001
HDL-C, mmol/L	1.09 [0.93, 1.27]	1.23 [1.12, 1.44]	1.24 [1.15, 1.39]	1.00 [0.88, 1.19]	0.96 [0.85, 1.10]	<0.001
LDL-C, mmol/L	2.67 [2.17, 3.15]	2.79 [2.41, 3.17]	2.52 [2.32, 3.01]	2.54 [2.02, 3.13]	2.66 [2.12, 3.17]	<0.001
FBG, mmol/L	5.22 [4.78, 6.06]	4.93 [4.63, 5.28]	4.92 [4.58, 5.09]	5.57 [4.90, 6.52]	5.55 [4.99, 6.40]	<0.001

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Sex-based differences in metabolic obesity phenotypes and colorectal adenoma

Exploratory analyses were performed in subgroups stratified by sex. Fig 2 illustrates the logistic analysis of CRA prevalence across different obesity phenotypes subgrouped by gender. The findings demonstrated that, irrespective of sex, the individuals in the MUNO and MUO cohorts exhibited elevated risks of developing CRA compared to those in the MHNO cohort (P < 0.05). Nonetheless, the MHO cohort showed a similar rate of CRA occurrence as the MHNO group. After adjusted for age, sex, smoking and alcohol intake, the adjusted odds ratios (ORs) (95% CI) for the frequency of occurrence of CRA in the MUNO and MUO cohorts were determined to be 1,353 (1.099–1.666) and 1.558 (1.111–2.187) compared to the MHNO phenotype, respectively. After adjusting for age, smoking, and alcohol intake, the female participants with the MUNO phenotype (OR 1.694; 95% CI 1.243–3.308, p < 0.01) exhibited significantly elevated risks of CRA compared to those in the MHNO group. Among the male participants, after adjusting for age, smoking, and alcohol intake, no significant differences were found across the four groups of metabolic health phenotypes.

Age-based differences in metabolic obesity phenotypes and colorectal adenoma

Fig 3 presents the relationships between the different metabolic obesity phenotypes and the prevalence of CRA stratified by age. Regardless of age, the MUNO and MUO phenotypes were identified as risk factors for developing CRA, unlike the MHNO phenotype (p < 0.05). After adjusting for age, sex, smoking, and alcohol intake, the adjusted ORs (95% CI) for the prevalence of CRA in the MUNO and MUO cohorts were determined to be 1,353 (1.099–1.666) and 1.558 (1.111–2.187) compared to that of the MHNO phenotype, respectively. Among participants aged less than 60 years, after adjusting for sex, smoking, and alcohol consumption, the MUNO group (OR: 1.648; 95% CI: 1.252–2.169, p < 0.01) demonstrated significantly elevated risks of developing CRA compared to the MHNO cohorts. In contrast, MHO individuals and MUO individuals showed no significantly increased risk of CRA compared with MHNO individuals. Among the participants aged above 60 years, after adjusting for sex, smoking status, and alcohol consumption, the individuals in the MUO cohort exhibited the greatest OR of 2.301 (95% CI: 1.252–4.348, p < 0.01) among all the cohorts.

Sensitivity analysis

To test the robustness of our findings, we redefined obesity using the World Health Organization (WHO) recommendation for Asian populations (BMI ≥ 25 kg/m²) instead of the Chinese criteria (BMI ≥ 28 kg/m²). All other statistical models remained identical to the primary analysis(S1 Table).The metabolically unhealthy groups (MUNO and MUO) also had the highest risk of CRA compared with the MHNO group.

Discussion

This cross-sectional study analyzed the association between different phenotypes of metabolic obesity and the incidence of CRA. The results revealed that the MUNO and MUO phenotypes were risk factors for CRA development compared to the MHNO phenotype. Collectively, the findings demonstrate that metabolically unhealthy status is more closely associated with CRA pathogenesis than BMI-defined obesity, which highlights the importance of focusing on metabolic health as a preventive measure against CRA. In the gender subgroup analysis, the occurrence of MUNO or MUO was associated with an elevated incidence of CRA in females. In contrast, no significant difference was found in the incidence of CRA among the four groups in males. The age subgroup analysis showed that the MUNO phenotype was a risk factor for CRA among participants under 60 years, while both the MUNO and MUO phenotypes were risk factors in participants over 60 years. It is important to note that while there is no universally accepted definition of “metabolic health”, the criteria used in this study (based on Adult Treatment Panel III criteria and the China Guidelines for Type 2 Diabetes) have been widely adopted in epidemiological research for their clinical practicality. This definition facilitates comparison with a substantial body of existing literature. However, we acknowledge that alternative definitions incorporating insulin resistance or waist circumference might capture different aspects of metabolic dysfunction, and findings may vary accordingly. However, the chosen definition was selected for its feasibility within large-scale health examination datasets.

This study conducted comparative analyses across multiple metabolic phenotypes, revealing statistically significant differences in adenoma detection rates (P < 0.05). Compared with the MHNO group, the MHO, MUNO, and MUO groups all demonstrated elevated adenoma detection rates. However, across the entire enrolled population, the results of our logistic analyses showed adjusted ORs greater than 1 in the MUO and MUNO groups compared to the MHNO group, which suggests that metabolic dysfunction may play a more important pathogenetic role in the formation of CRA compared to obesity. The precise mechanism underlying this link remains unknown. However, some studies have revealed that hyperglycemia, dyslipidemia, and hypertension are strongly associated with CRA, which is consistent with our findings.

A previous study investigated the connection between a high intake of simple sugars and the prevalence of CRA [20]. The results indicated an association between an elevated risk of rectal adenomas and a higher intake of simple sugars, indicating that tighter glycemic management should be given priority for younger populations [21]. The potential mechanisms are as follows. Firstly, excessive glucose concentrations directly damage human endothelial cells' DNA [20], which can result in alterations in intestinal mucosa permeability and compromise the integrity of the gut barrier [22]. Secondly, some intestinal microbial compounds, including lipopolysaccharides, can cross the intestinal barrier and induce inflammatory processes that promote cell survival and proliferation, resulting in CRA [23,24]. Thirdly, dysregulation of the insulin/IGF-1 signaling pathway may be involved. Insulin can act as an anabolic and oncogene, either directly or indirectly by regulating IGF-1 [25]. This may accelerate CRA growth and the spread of colorectal cancer cells. Fourthly, the Warburg effect: under persistent hyperglycemic conditions, cancer cells gain a metabolic advantage by preferentially utilizing glucose and transforming glycolytic intermediates into biosynthetic pathways [26].

As endogenous mediators, lipids play a crucial role in many physiological processes, including membrane trafficking, cell signaling, apoptosis, and cell proliferation [27]. Hence, dyslipidemia impacts the microenvironment, resulting in neovascularization, cell proliferation, and DNA damage [28]. The majority of studies have revealed that elevated HDL levels are inversely correlated with the formation of CRA, whereas higher blood TG levels are substantially linked to an increased risk of CRA [29,30]. Dyslipidemia may contribute to the development of insulin resistance and hyperinsulinemia, prevent apoptosis through its interaction with the insulin-like growth factor 1 (IGF-1) receptor, and encourage the growth of colorectal cells, which can result in the formation of CRA and the initiation of cancer [31]. Furthermore, lipid disorders have been shown to alter the bile acid cycle, which may raise gut bile acid levels [32] and and promote dysbiosis and carcinogen production, leading to an increased risk of CRA [33]. The release of inflammatory cytokines is also associated with lipid abnormalities. Conversely, the release of tumor necrosis factor and anti-inflammatory cytokines is associated with a reduction in the survival of tumor cells, which may facilitate the growth of CRA and cancerous cells [34,35].

The existing epidemiological evidence on the association between CRA and hypertension is inconclusive, with only a few studies reporting positive results. Kaneko et al. analyzed data from the National Health Claims Database and demonstrated that stage 2 hypertension and elevated systolic and diastolic blood pressure were associated with an increased risk of developing CRC [36]. In a prior study, hypertension model rats developed aberrant crypt foci (ACF) and colonic preneoplastic lesions by azoxymethane exposure more quickly than normotensive rats. Oxidative stress and exacerbation of colonic mucosal inflammation were observed in hypertensive rats. Furthermore, the administration of an angiotensin-converting enzyme inhibitor resulted in a notable reduction in the total number and size of ACF compared to the untreated control group. These findings suggest that reducing inflammation and oxidative stress may inhibit the development of ACF. The renin-angiotensin-aldosterone system may be a key factor in the development of CRA and CRC. Considering that angiotensin is a growth factor that encourages angiogenesis and cell proliferation, the renin-angiotensin system plays a significant role in controlling blood pressure and cell growth [37]. However, further research is required to fully elucidate the potential pathways and associations between hypertension and recurrent CRA.

Hyperglycemia, hypertension, and dyslipidemia share the pathogenic mechanisms of insulin resistance and inflammation and are closely related. Given the above results, a metabolically unhealthy phenotype could be a more significant contributing factor to the formation of CRA. Furthermore, the mechanisms linking metabolic abnormalities to CRA may exhibit distinct characteristics in Asian, particularly Chinese, which should be considered when interpreting our findings. Epidemiological and physiological studies indicate that Chinese individuals are susceptible to developing metabolic syndrome and significant visceral adiposity at lower BMI thresholds compared to Caucasian populations [38,39]. This “lean but metabolically unhealthy” phenotype may suggests that the pathogenic impact of dysmetabolism on the colorectum might be initiated earlier in the disease course and be less dependent on overall obesity as defined by BMI alone. Moreover, dietary patterns prevalent in China, characterized by high consumption of refined carbohydrates and low intake of dietary fiber [19], may exacerbate postprandial hyperglycemia, insulin resistance, and systemic inflammation—key drivers implicated in CRA pathogenesis. These dietary habits could potentiate the mechanisms described above, such as gut barrier dysfunction and dysbiosis, in a population-specific manner [40]. Additionally, genetic factors and environmental factors in different regions play distinct roles in colorectal cancer development [41]. Therefore, while common pathological mechanisms such as hyperglycemia, dyslipidemia, and hypertension are associated, their relative contributions, interactions, and clinical manifestations in colorectal cancer may differ among Chinese populations. Future additional research integrating population-specific biomarkers with diverse dietary pattern data is required to elucidate these relevant pathways and develop more precise and effective clinical prevention strategies.

Confounding factors were adjusted for, and no independent correlation was found between the MHO phenotype and CRA risk. Our findings corroborate the results of Kim et al. [42], which indicated that BMI-defined MHO individuals did not exhibit significantly increased CRA risk. Notably, longitudinal follow-up data from the Meigs [43] research team revealed no significant associations between the MHO phenotype and either cardiovascular endpoints or diabetes incidence. Importantly, 42.1% of baseline MHO individuals experienced deterioration in metabolic parameters during a 10-year follow-up period [44], ultimately progressing to the MUO phenotype. Collectively, this evidence reconceptualizes MHO as a dynamic phenotype in the metabolic compensation phase rather than a stable clinical subtype.The current conclusion that “no independent association between MHO phenotype and CRA risk” may be limited by the duration of phenotypic observation. Future prospective cohort studies are needed to track the long-term impact of phenotypic conversion on CRA occurrence, thereby avoiding conclusions biased by short-term observation. Consequently, implementing lifestyle interventions prior to phenotypic transition is recommended to reduce the incidence of metabolic abnormalities.

To investigate the impact of gender on the relationship between CRA and the metabolic obesity phenotype, the participants were stratified by sex. Our analysis revealed that among females, metabolically unhealthy individuals also appeared to have a greater propensity for CRA than those with healthy metabolism. Obesity status showed minimal to no influence on adenoma occurrence. Subsequent multiple logistic analyses using the adjusted model revealed that females with MUNO phenotypes had significantly elevated adjusted OR for CRA.Interestingly, we did not observe a significant association between metabolic phenotypes and CRA in the male subgroup. This could suggest a genuine biological difference; however, it is also important to note that the sample sizes for some phenotypes (particularly MHO and MUO) in the male subgroup were relatively small, which may have limited our statistical power to detect existing associations, leaving the possibility of false negatives. Therefore, the conclusion that no significant differences were found among male subgroups should be interpreted with caution and requires further validation in larger-scale studies to explore the biological mechanisms underlying gender differences. A previous study investigated a colon cancer mouse model; male mice exhibited a significantly higher incidence of CRC than female mice [45]. Additionally, administration of 17 β-estradiol (E2) during the inflammatory phase was shown to inhibit CRC development, highlighting the protective effect of estrogen in inflammatory responses [46]. Moreover, estrogen plays a regulatory role in insulin sensitivity and lipid metabolism [47]. Furthermore, females generally adopt healthier lifestyles and dietary practices compared to males [47]. These findings suggest that gender disparities in CRA may arise from a combination of biological and behavioral factors. The observed gender differences in CRA incidence highlight the importance of considering gender-specific approaches in prevention, screening, and treatment strategies to improve outcomes. Cheng's study [16] indicated an increased incidence of colorectal polyps in postmenopausal women, which may be attributed to lower estrogen levels. Future studies should increase the sample size and perform stratified analyses based on menopausal status and estrogen levels to explore the correlation between metabolic obesity phenotype, estrogen levels, and CRA.

Furthermore, this study investigated the relationship between metabolic obesity phenotypes and the occurrence of CRA in different age groups. Our analysis revealed that among participants under 60 years, individuals with the MUNO phenotype exhibited higher risks of developing CRA compared to the individuals with the MHNO phenotype. Among individuals over 60 years, the MUO phenotype was identified as risk factors for CRA. These findings indicate that age affects the relationship between metabolic health status and colorectal adenoma risk, with metabolic abnormalities alone raising risk in younger populations, while obesity and metabolic abnormalities in combination may increase risk in older populations. This research emphasizes the necessity of optimizing colonoscopy screening indications in high-risk groups while developing age-specific metabolic management methods in clinical practice.

Nevertheless, the limitations of the present study should be acknowledged. First, The cross-sectional study design of this research can not determine the causal relationship between the metabolic obesity phenotype and CRA. The exposure and outcome were assessed simultaneously, making it impossible to determine temporality. Furthermore, MHO is recognized as a potentially transient state, with a significant proportion of individuals progressing to MUO over time. Our study provides only a snapshot in time, and we could not ascertain the duration for which individuals maintained their MHO phenotype or their potential for future transition. Therefore, the lack of association observed for MHO in our study might reflect insufficient duration of exposure rather than a true absence of risk. Future prospective cohort studies are needed to track the long-term impact of phenotypic conversion on CRA risk, thereby avoiding conclusions biased by short-term observation. Second, obesity is defined only by BMI, Supplementary indicators reflecting central obesity, such as waist circumference and body fat percentage, were not included.However, some studies have also explored the association between central obesity and metabolic abnormalities with intestinal lesions. Future research should integrate body composition analysis to refine the relationship between different obesity subtypes and CRA. Third, this study only accounted for smoking and alcohol consumption as confounding factors, key lifestyle factors such as dietary patterns and physical activity were not considered, and these confounding factors were not accounted for. These factors may have influenced the risk assessment results of this study. Moreover, adenoma detection rates may also have varied throughout the study period due to differences in skills between gastroenterologists.

Conclusion

This is the first study in China to investigate the correlation between metabolic health phenotypes and CRA. The present study confirmed that MUO and MUNO are risk factors for CRA compared to MHNO. This seems to prove that metabolism may have a greater impact on CRA than obesity, which serves as a reminder to focus on people with metabolic disorders during clinical screening. Second, our investigation demonstrated gender-specific and age-specific disparities in the linkage between metabolic obesity phenotypes and CRA development.

Supporting information

S1 Table. Sensitivity analysis.

Notes: Model 1: not adjusted. Model 2: adjustment for age, and sex. Model 3: adjustment for age, sex, smoking, and drinking.

(DOC)

pone.0343556.s001.doc^{(24.5KB, doc)}

S2 Data. Raw data underlying the analyses presented in this study.

(DOCX)

pone.0343556.s002.docx^{(36.5KB, docx)}

Acknowledgments

The authors thank all patients and research staffs who participated in this study, and also gratefully thank the nurses for their help in the process of data collection.

Data Availability

All relevant data are within the manuscript and its Supporting information files.

Funding Statement

This work was supported by the National Natural Science Foundation of China (No.82004040).Qin Zhu(No.82004040) participated in methodology and supervision of this work.

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Reviewer #1: This study identified metabolically unhealthy status (irrespective of obesity) as a significant risk factor for CRA, offering valuable clinical insights. It refines the conventional broad concept of "obesity" into four distinct phenotypes, underscoring that metabolic health is more critical than body mass index (BMI) alone. The conclusions suggest that clinical screening for CRA should prioritize metabolically unhealthy individuals and emphasize the importance of managing metabolic syndrome (e.g., hypertension, hyperglycemia, dyslipidemia), which may directly benefit reducing colorectal cancer risk, beyond preventing cardiovascular diseases.

My comments as follows:

1.The study used a cross-sectional design, measuring both exposure (metabolic obesity phenotype) and outcome (presence of CRA) at a single time point. This approach cannot establish temporal causality. MHO is known to be a transient state, with many individuals progressing to MUO over time. Since the study only captured a snapshot, it remains unknown how long the MHO individuals had maintained this phenotype or whether they might transition in the future. Thus, the conclusion that "MHO is not associated with CRA risk" may be short-sighted, potentially reflecting insufficient exposure duration rather than a true lack of association. A prospective cohort study would be the ideal design to address this, though this remains an irreparable limitation of the current study. This point should be discussed more thoroughly in the manuscript.

2.There is no universally accepted definition for "metabolically healthy" and "metabolically unhealthy" (e.g., some use ATP-III metabolic syndrome criteria, others use HOMA-IR for insulin resistance). Varying definitions can significantly alter the classification of MHO/MUNO individuals, directly affecting the stability and comparability of the results. The manuscript must transparently report the specific criteria used (including BMI cutoffs, metabolic indicators, and their thresholds). Where possible, sensitivity analyses using multiple established definitions should be conducted to test the robustness of the findings.

3.In the male subgroup, no significant differences were observed among the four phenotypes, contradicting the results in the overall and female populations, may be due to: (a) a genuine biological effect—metabolic abnormalities may influence CRA risk differently in men (e.g., via interaction with sex hormones); or (b) insufficient sample size—particularly in the MHO and MUO male subgroups, leading to low statistical power and an inability to detect actual differences (i.e., a false negative). Therefore, it is crucial to report the exact sample sizes (n) for each subgroup. If samples are small, the authors should explicitly state that the negative results may be due to low power and interpret them cautiously. Larger studies are needed to validate these sex-specific differences.

4.The study conducted numerous statistical comparisons (across four phenotypes in the overall population, males, females, and age groups <60/≥60 years), but did not mention whether correction for multiple testing (e.g., Bonferroni correction) was applied. When performing extensive subgroup analyses, a stricter significance level (e.g., p < 0.01) or appropriate multiplicity adjustment should be used, and uncorrected results must be interpreted with caution.

**********

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Reviewer #1: No

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PLoS One. 2026 Feb 23;21(2):e0343556. doi: 10.1371/journal.pone.0343556.r002

Author response to Decision Letter 1

2 Dec 2025

Dear Reviewer,

We appreciate the opportunity to revise our manuscript titled “Association between different metabolic obesity phenotypes and colorectal adenoma” and are grateful for the insightful comments provided by the peer-reviewer. Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our researches. We have tried our best to make all the revisions clear, and we hope that the revised manuscript meets the requirements for publication. In the following, we have provided a point-by-point response addressing each issue raised in the peer-review report:

Comment 1: The study used a cross-sectional design, measuring both exposure (metabolic obesity phenotype) and outcome (presence of CRA) at a single time point. This approach cannot establish temporal causality. MHO is known to be a transient state, with many individuals progressing to MUO over time. Since the study only captured a snapshot, it remains unknown how long the MHO individuals had maintained this phenotype or whether they might transition in the future. Thus, the conclusion that "MHO is not associated with CRA risk" may be short-sighted, potentially reflecting insufficient exposure duration rather than a true lack of association. A prospective cohort study would be the ideal design to address this, though this remains an irreparable limitation of the current study. This point should be discussed more thoroughly in the manuscript.

Response: We agree with the reviewer that the cross-sectional design is a limitation for establishing causality and that the transient nature of the MHO phenotype is an important consideration. As suggested, we have now added a detailed discussion on this limitation in the Discussion section of the manuscript(Line 385 to Line 396).

Comment 2: There is no universally accepted definition for "metabolically healthy" and "metabolically unhealthy" (e.g., some use ATP-III metabolic syndrome criteria, others use HOMA-IR for insulin resistance). Varying definitions can significantly alter the classification of MHO/MUNO individuals, directly affecting the stability and comparability of the results. The manuscript must transparently report the specific criteria used (including BMI cutoffs, metabolic indicators, and their thresholds). Where possible, sensitivity analyses using multiple established definitions should be conducted to test the robustness of the findings.

Response: We sincerely thank the reviewer for this critical and constructive comment. We agree that the lack of a universal definition is a key consideration. We have now explicitly and transparently detailed the specific criteria used to define metabolic health status and obesity in the Methods section(Line 121 to Line 132). This includes the exact components, cut-off values, and the reference standards we adopted; While we did not measure leptin or fasting insulin levels, which precluded a sensitivity analysis using HOMA-IR, we fully recognized the importance of testing the robustness of our findings. Therefore, we conducted an alternative sensitivity analyses using the data available to us: We altered the definition of obesity. In addition to the primary definition of BMI ≥ 28 kg/m² (Chinese criteria), we tested the World Health Organization (WHO) standard for Asian populations (BMI ≥ 25 kg/m²) to define obesity(Line 247 to Line 253).

We are pleased to report that the results of both sensitivity analyses were consistent with our primary findings. The core conclusion—that metabolically unhealthy phenotypes, irrespective of obesity status, are associated with higher odds of CRA—remained robust. These new results have been added to the Results sections of the revised manuscript (see Supplementary Tables S1).

Comment 3: In the male subgroup, no significant differences were observed among the four phenotypes, contradicting the results in the overall and female populations, may be due to: (a) a genuine biological effect—metabolic abnormalities may influence CRA risk differently in men (e.g., via interaction with sex hormones); or (b) insufficient sample size—particularly in the MHO and MUO male subgroups, leading to low statistical power and an inability to detect actual differences (i.e., a false negative). Therefore, it is crucial to report the exact sample sizes (n) for each subgroup. If samples are small, the authors should explicitly state that the negative results may be due to low power and interpret them cautiously. Larger studies are needed to validate these sex-specific differences.

Response: We agree that the sample size in subgroups is a key issue. We have now: First, clearly reported the exact sample sizes (n) for each phenotype within the male and female subgroups in the corresponding Figures(Figure 2 and Figure 3). Second, added a statement in the Results and Discussion sections to caution the interpretation of the non-significant finding in the male subgroup, explicitly acknowledging the possibility of a false negative due to limited sample size and lower statistical power(Line 351 to Line 357).

Comment 4: The study conducted numerous statistical comparisons (across four phenotypes in the overall population, males, females, and age groups <60/≥60 years), but did not mention whether correction for multiple testing (e.g., Bonferroni correction) was applied. When performing extensive subgroup analyses, a stricter significance level (e.g., p < 0.01) or appropriate multiplicity adjustment should be used, and uncorrected results must be interpreted with caution.

Response: We sincerely thank the reviewer for raising this crucial methodological point. We agree that appropriate control for multiple testing is essential. Therefore, in the revised manuscript, the primary analysis in the overall population, which tests our main hypothesis, is interpreted at the conventional significance level of P < 0.05. For the extensive subgroup analyses (by sex and age), we applied a more stringent significance level of P < 0.01 for all tests within these subgroups, as detailed in the 'Statistical Analysis' section of the Methods(Line 151 to Line 155). This approach balances the need to mitigate the inflation of Type I error while maintaining reasonable power to detect potential effects in these secondary analyses.

We have explicitly stated this strategy in the methods, noted it in the results (including table footnotes) and discussed the cautious interpretation of subgroup findings in the context of multiple testing in the Discussion section(Line 351 to Line 357). We believe this significantly strengthens the statistical rigor and interpretation of our findings.

We appreciate your time and consideration, and we anticipate your favorable response at your earliest convenience.

Sincerely yours,

Dr. Qin Zhu

Professor

Department of Gastroenterology,

Zhejiang Hospital

HangZhou 310013, P. R. China

Tel: (86)-17706413537

Email: zhuqin-1@163.com

Attachment

Submitted filename: Response to Reviewers.docx

pone.0343556.s004.docx^{(14.3KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0343556.r003

Decision Letter 1

Muhammad Shahzad Aslam

2 Feb 2026

Dear Dr. Zhu,

The study clinically relevant, methodologically sound, and within the scope of the journal, but requests clearer discussion of limitations, subgroup interpretation, and conceptual definitions.

The required revisions are primarily related to strengthening the Discussion section and improving transparency of subgroup reporting, and do not affect the validity of the main findings. The authors are therefore invited to revise the manuscript accordingly.

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We look forward to receiving your revised manuscript.

Kind regards,

Muhammad Shahzad Aslam, Ph.D.,M.Phil., Pharm-D

Academic Editor

PLOS One

Journal Requirements:

If the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise.

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments:

Study Design and Limitations

Emphasize more clearly in the Discussion that the cross-sectional design does not allow causal inference.

Note that the metabolically healthy obesity (MHO) phenotype may be transient and that long-term cohort studies are needed to evaluate phenotype transitions and CRA risk.

Acknowledge the limitation of defining obesity using BMI alone, and highlight the absence of central obesity indicators (e.g., waist circumference, body fat percentage). Indicate that future studies should integrate body composition measures.

Subgroup Analyses

Clearly report subgroup sample sizes (especially in male phenotypes) in the Results tables.

In the Discussion, interpret non-significant findings in males cautiously and acknowledge the possibility of limited statistical power and potential false-negative results.

Confounding Factors

Expand the limitations to mention unmeasured lifestyle confounders such as diet and physical activity, and discuss their potential impact on the observed associations.

Mechanistic Discussion

Strengthen the mechanism section by briefly discussing metabolic features that may be particularly relevant in the Chinese population, supported by existing literature.

Definition of Metabolic Health

Add a short comparison between your definition of metabolic health and other commonly used definitions.

Justify the rationale for selecting the current classification to enhance interpretability and comparability with other studies.

These revisions are mainly explanatory and conceptual and do not require additional analyses. Please revise accordingly and provide a point-by-point response.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #1: All comments have been addressed

Reviewer #2: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions??>

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #1: Yes

Reviewer #2: Yes

**********

Reviewer #1: This is my second review of the article and all the comments I previously made have been addressed by the authors.

No additional issues have been identified. The revisions made by the authors are greatly appreciated.

In particular, the author analyzed the limitations of the current study in the discussion section and expressed the hope that further research will be conducted to address the existing issues.

Reviewer #2: Dear author:

This study focuses on the association between metabolic obesity phenotypes and colorectal adenoma (CRA), a topic closely aligned with clinical needs. As the main precancerous lesion of colorectal cancer (CRC), identifying the risk phenotypes of CRA is of great guiding value for early screening. The study enrolled 2042 Chinese participants, verified the robustness of results through stratified analyses (by sex and age) and sensitivity analysis, and featured a clear design logic with sufficient data support. The core conclusion (metabolically unhealthy phenotypes are risk factors for CRA) holds clinical reference significance, and the overall work complies with the publication scope and academic standards of PLOS ONE.

Main Strengths

1. Practical research perspective: Breaks through the traditional research on the association between simple obesity and CRA, refining into four metabolic obesity phenotypes. It reveals that "metabolic health status" has a more significant impact on CRA risk than BMI, providing a new target for clinical screening.

2. Comprehensive analysis dimensions: Incorporates stratified analyses by sex and age, clarifying risk differences among different populations, making the conclusions more targeted.

3. Relatively rigorous methodology: Adopts multivariate logistic regression to adjust for confounding factors, and verifies the robustness of core conclusions through sensitivity analysis using two BMI standards.

4. Transparent data reporting: Detailedly discloses the definition standards of metabolic phenotypes, statistical methods, and subgroup sample sizes, facilitating readers' reproduction and verification.

Issues to Be Improved and Suggestions

1 In-depth Discussion on Limitations of Study Design

1. The cross-sectional design cannot establish a temporal causal relationship between metabolic obesity phenotypes and CRA. Additionally, the MHO phenotype is transient, and the existing conclusion that "MHO is not associated with CRA risk" may be limited by the duration of phenotype observation. It is recommended to further clarify in the discussion that prospective cohort studies are needed in the future to track the long-term impact of phenotype transition on CRA occurrence, avoiding conclusion bias caused by short-term observation.

2. Obesity is only defined by BMI, without including complementary indicators reflecting central obesity such as waist circumference and body fat percentage. However, central obesity is more closely associated with metabolic abnormalities and intestinal lesions. It is suggested to supplement the discussion on this limitation and point out that future studies should integrate body composition analysis to refine the impact of different obesity subtypes.

2 Supplementary Explanations for Subgroup Analyses

1. The sample sizes of some phenotypes in the male subgroup are relatively small, leading to insufficient statistical power which may mask true associations. It is recommended to clearly mark the sample size of each subgroup in the result tables, and emphasize in the discussion that the "no significant difference in males" should be interpreted with caution. The false negative result cannot be excluded due to limited sample size, and larger-scale studies are required to verify the biological mechanisms underlying sex differences.

3 Improvement of Confounding Factor Control and Mechanism Discussion

1. The current confounding factors only include smoking and alcohol consumption, without incorporating key lifestyle factors such as dietary structure and physical activity, which may simultaneously affect metabolic status and CRA occurrence. It is recommended to supplement the discussion on the potential impact of this omission on the results or explain it in the limitations section.

2. The mechanism section only generally describes the role of hyperglycemia and dyslipidemia, lacking targeted analysis of the metabolic characteristics of the Chinese population. It is suggested to briefly supplement the specificity of relevant mechanisms in the Chinese population by referencing existing literature to enhance the depth of the discussion.

4 Supplementary Explanations for Definitions and Classifications

Although the definition standards of metabolic health have been clarified, there is no universal standard for "metabolic health". It is recommended to compare the definition used in this study with other mainstream definitions in the discussion, explain the rationality of the selected standard, and enhance the comparability of results.

Conclusion and Publication Recommendation

This study has clear clinical value and academic significance, with reliable core conclusions and basically rigorous methodology. The aforementioned issues are mostly related to in-depth discussion of limitations and supplementary explanations, which do not affect the validity of the core conclusions.

Recommendation: Accept after minor revisions. Please the authors supplement and improve the discussion section in response to the above suggestions, clearly mark subgroup sample sizes, refine limitations and future research directions, and further enhance the completeness and academic rigor of the manuscript.

**********

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Reviewer #1: No

Reviewer #2: No

**********

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PLoS One. 2026 Feb 23;21(2):e0343556. doi: 10.1371/journal.pone.0343556.r004

Author response to Decision Letter 2

4 Feb 2026

Dear Editor and Reviewers,

Thank you very much for your comments regarding our manuscript entitled “Association between different metabolic obesity phenotypes and colorectal adenoma” We are truly grateful to your valuable comments and thoughtful suggestions on how to improve our manuscript. The valuable comments have provided important guidance for our research and the revision of our manuscript. Based on these comments and suggestions, we have made appropriate modifications on the original manuscript, we hope that the revised manuscript meets the requirements for publication. In the following, we have provided a point-by-point response addressing each issue raised by the academic editor and reviewer:

Responds to academic editor’ comments:

Comment 1: Emphasize more clearly in the Discussion that the cross-sectional design does not allow causal inference.

Response: We agree with the editor that the cross-sectional design is a limitation for establishing causality. As suggested, we have now explicitly stated that due to the cross-sectional nature of our study, the observed associations cannot be interpreted as causal relationships in the Limitation section of the manuscript(Line 462 to Line 464).

Comment 2: Note that the metabolically healthy obesity (MHO) phenotype may be transient and that long-term cohort studies are needed to evaluate phenotype transitions and CRA risk.

Response: We sincerely thank the editor for this critical and constructive comment. We added a paragraph in the Discussion section noting that the MHO phenotype is potentially dynamic and may transition to metabolically unhealthy states over time. We now emphasize that prospective cohort studies are required to track these transitions and their longitudinal relationship with CRA risk(Line 413 to Line 417). And in the Limitation section, we also noted that this study have conclusion bias due to its short observation period, and emphasized the need for future long-term prospective research(Line 471 to Line 473).

Comment 3: Acknowledge the limitation of defining obesity using BMI alone, and highlight the absence of central obesity indicators (e.g., waist circumference, body fat percentage). Indicate that future studies should integrate body composition measures.

Response: We agree that defining obesity using BMI alone is a key issue. We expanded the Limitations subsection to acknowledge that using BMI as the sole criterion for obesity is a limitation, as it does not capture fat distribution or body composition. We specifically mention the lack of central obesity measures (e.g., waist circumference) and body fat percentage data. We have added a sentence suggesting that future studies should incorporate these measures to provide a more comprehensive assessment(Line 474 to Line 478).

Comment 4: Clearly report subgroup sample sizes (especially in male phenotypes) in the Results tables.

Response: We agree that clearly report subgroup sample sizes is essential. Therefore, in the revised manuscript, The sample sizes for all subgroups (with particular attention to male phenotype categories) have been explicitly specified by adding a separate row in the relevant results tables (Tables 2 and 3).

Comment 5: In the Discussion, interpret non-significant findings in males cautiously and acknowledge the possibility of limited statistical power and potential false-negative results.

Response: In the Discussion section, when interpreting the results for males, we now explicitly state that the non-significant findings should be interpreted with caution. We acknowledge that these analyses, particularly within certain phenotype strata, may have been underpowered, increasing the possibility of false-negative results(Line 431 to Line 434).

Comment 6: Expand the limitations to mention unmeasured lifestyle confounders such as diet and physical activity, and discuss their potential impact on the observed associations.

Response: We agree with the editor that we did not fully control for confounding factors.We have expanded the limitations section of the discussion as suggested. We acknowledge that unmeasured or inadequately measured potential confounders, such as dietary patterns and physical activity, may have influenced the findings presented in this paper(Line 478 to Line 482).

Comment 7: Strengthen the mechanism section by briefly discussing metabolic features that may be particularly relevant in the Chinese population, supported by existing literature.

Response: We agree it is necessary to briefly discuss the mechanisms of metabolic profile intensification that are particularly relevant to the Chinese population. We strengthened the discussion at the mechanistic level by integrating research evidence from studies conducted in Chinese and East Asian populations. We briefly explore population-specific metabolic and dietary characteristics, such as the tendency toward visceral fat accumulation at lower BMI thresholds and a predominantly high-carbohydrate diet, and how these factors differentially influence obesity tolerance phenotypes and their association with colorectal adenoma risk(Line 383 to Line 403). To support this context-specific discussion, we have supplemented relevant literature citations(Line 630 to Line 648).

Comment 8: Add a short comparison between your definition of metabolic health and other commonly used definitions.

Response: We agree with the editor for the useful suggestion.We have added a section to our discussion briefly comparing our definition with other methods (which may include waist circumference and insulin resistance). Our definition is both suitable for comparison with a large-scale of existing literature and clinically practical(Line 311 to Line 319).

Comment 9: Justify the rationale for selecting the current classification to enhance interpretability and comparability with other studies.

Response: We clearly justify our choice by stating that this definition was selected to balance clinical relevance with the available data in our study, and to enhance comparability with a significant body of existing literature that uses similar criteria, thereby facilitating the interpretation of our findings within the broader research context.(Line 311 to Line 319).

Responds to reviewer’s comments:

Comment 1: The cross-sectional design cannot establish a temporal causal relationship between metabolic obesity phenotypes and CRA. Additionally, the MHO phenotype is transient, and the existing conclusion that "MHO is not associated with CRA risk" may be limited by the duration of phenotype observation. It is recommended to further clarify in the discussion that prospective cohort studies are needed in the future to track the long-term impact of phenotype transition on CRA occurrence, avoiding conclusion bias caused by short-term observation.

Response:We fully agree with this important point. In the revised Discussion section, we have:

Clearly stated in a dedicated limitations paragraph that due to the cross-sectional design, our findings represent associations and cannot be interpreted as evidence of a temporal or causal relationship between metabolic phenotypes and CRA(Line 462 to Line 464).

Added a specific discussion on the dynamic nature of the MHO phenotype. We now explicitly note that as our study captured metabolic health at a single time point, we cannot account for potential transitions from MHO to metabolically unhealthy states over time. We have revised the text to clarify that our conclusion regarding MHO and CRA risk is necessarily constrained by this snapshot assessment. Furthermore, we strongly emphasize that future longitudinal cohort studies are essential to track these phenotype transitions and to definitively evaluate their long-term impact on CRA development, thereby avoiding bias from short-term observation. (Line 413 to Line 417).

Comment 2: Obesity is only defined by BMI, without including complementary indicators reflecting central obesity such as waist circumference and body fat percentage. However, central obesity is more closely associated with metabolic abnormalities and intestinal lesions. It is suggested to supplement the discussion on this limitation and point out that future studies should integrate body composition analysis to refine the impact of different obesity subtypes.

Response: We thank the reviewer for highlighting this key limitation. We have expanded the Limitations subsection in the Discussion to explicitly acknowledge that using BMI as the sole measure is a significant constraint. We note that BMI does not distinguish fat distribution or differentiate between fat and lean mass. We specifically mention the absence of central obesity indicators like waist circumference or body fat percentage, which are more strongly correlated with visceral adiposity, metabolic dysfunction, and, as the reviewer notes, intestinal pathology. To address this, we have added a sentence recommending that future research should integrate detailed body composition analysis to refine the understanding of how different obesity subtypes influence CRA risk(Line 474 to Line 478).

Comment 3: The sample sizes of some phenotypes in the male subgroup are relatively small, leading to insufficient statistical power which may mask true associations. It is recommended to clearly mark the sample size of each subgroup in the result tables, and emphasize in the discussion that the "no significant difference in males" should be interpreted with caution. The false negative result cannot be excluded due to limited sample size, and larger-scale studies are required to verify the biological mechanisms underlying sex differences.

Response: We appreciate the reviewer's careful attention to this detail. We have taken the following actions:

The sample size (n) for each phenotype subgroup within the male and female cohorts is now clearly presented in the separate row in the relevant results tables (Tables 2 and 3).

In the Discussion section, when interpreting the sex-specific results, we have added a clear cautionary statement. We now explicitly state that the lack of statistically significant associations observed among males should be interpreted with caution due to the relatively smaller sample sizes within some phenotype strata, which may have resulted in limited statistical power. We acknowledge that this increases the possibility of Type II error (false-negative results) and that larger-scale studies are required to confirm or refute these findings and to robustly explore the biological mechanisms underlying the apparent sex differences(Line 431 to Line 434).

Comment 4: The current confounding factors only include smoking and alcohol consumption, without incorporating key lifestyle factors such as dietary structure and physical activity, which may simultaneously affect metabolic status and CRA occurrence. It is recommended to supplement the discussion on the potential impact of this omission on the results or explain it in the limitations section.

Response: This is a valid and important point. We have added a statement to the Limitations paragraph of the Discussion. We now explicitly state that although we adjusted for several important factors, we did not have comprehensive data on other potential confounders, most notably detailed dietary habits and objective measures of physical activity. We acknowledge that unmeasured or inadequately measured potential confounders, such as dietary patterns and physical activity, may have influenced the findings presented in this paper(Line 478 to Line 482).

Comment 5: The mechanism section only generally describes the role of hyperglycemia and dyslipidemia, lacking targeted analysis of the metabolic characteristics of the Chinese population. It is suggested to briefly supplement the specificity of relevant mechanisms in the Chinese population by referencing existing literature to enhance the depth of the discussion.

Respond�We thank the reviewer for this suggestion to enhance the depth of our discussion. In the revised Mechanism section of the Discussion, we have incorporated a brief, targeted discussion supported by relevant literature. We strengthened the discussion at the mechanistic level by integrating research evidence from studies conducted in Chinese and East Asian populations. We briefly explore population-specific metabolic and dietary characteristics, such as the tendency toward visceral fat accumulation at lower BMI thresholds and a predominantly high-carbohydrate diet, and how these factors differentially influence obesity tolerance phenotypes and their association with colorectal adenoma risk(Line 383 to Line 403). To support this context-specific discussion, we have supplemented relevant literature citations(Line 630 to Line 648).

Comment 6: Supplementary Explanations for Definitions and Classifications. Although the definition standards of metabolic health have been clarified, there is no universal standard for "metabolic health". It is recommended to compare the definition used in this study with other mainstream definitions in the discussion, explain the rationality of the selected standard, and enhance the comparability of results.

Respond�We have addressed this point by adding a new paragraph in the discussion section. In this paragraph: We briefly compare our definition with other commonly used criteria in the literature (which may include waist circumference and insulin resistance).We justify our selection by explaining that this definition was chosen for its clinical practicality, alignment with several major epidemiological studies, and suitability for our available data. We state that this approach was taken to enhance the interpretability and comparability of our results with a substantial body of existing research(Line 311 to Line 319).

We appreciate your time and consideration, and we anticipate your favorable response at your earliest convenience.

Sincerely yours,

Dr. Qin Zhu

Professor

Department of Gastroenterology,

Zhejiang Hospital

HangZhou 310013, P. R. China

Tel: (86)-17706413537

Email: zhuqin-1@163.com

Attachment

Submitted filename: Response_to_Reviewers_auresp_2.docx

pone.0343556.s005.docx^{(17.9KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0343556.r005

Decision Letter 2

Muhammad Shahzad Aslam

8 Feb 2026

Association between different metabolic obesity phenotypes and colorectal adenoma

PONE-D-25-42576R2

Dear Dr. Zhu,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Muhammad Shahzad Aslam, Ph.D.,M.Phil., Pharm-D

Academic Editor

PLOS One

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions??>

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #2: Yes

**********

Reviewer #2: (No Response)

**********

what does this mean? ). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy

Reviewer #2: No

**********

PLoS One. doi: 10.1371/journal.pone.0343556.r006

Acceptance letter

Muhammad Shahzad Aslam

PONE-D-25-42576R2

PLOS One

Dear Dr. Zhu,

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS One. Congratulations! Your manuscript is now being handed over to our production team.

At this stage, our production department will prepare your paper for publication. This includes ensuring the following:

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Supplementary Materials

S1 Table. Sensitivity analysis.

Notes: Model 1: not adjusted. Model 2: adjustment for age, and sex. Model 3: adjustment for age, sex, smoking, and drinking.

(DOC)

pone.0343556.s001.doc^{(24.5KB, doc)}

S2 Data. Raw data underlying the analyses presented in this study.

(DOCX)

pone.0343556.s002.docx^{(36.5KB, docx)}

Attachment

Submitted filename: Response to Reviewers.docx

pone.0343556.s004.docx^{(14.3KB, docx)}

Attachment

Submitted filename: Response_to_Reviewers_auresp_2.docx

pone.0343556.s005.docx^{(17.9KB, docx)}

Data Availability Statement

All relevant data are within the manuscript and its Supporting information files.

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PERMALINK

Association between different metabolic obesity phenotypes and colorectal adenoma

Li Lin

Yuhan Ying

Long Shu

Xiaoling Lv

Qin Zhu

Roles

Abstract

Background

Patients and methods

Results

Conclusion

Introduction

Materials and methods

Data source

Data collection and measurement

Colonoscopy

Definition of the metabolic obesity phenotypes

Statistical analysis

Results

Comparative analysis of baseline data between the non-adenomas and adenomas groups

Table 1. Comparison of baseline characteristics of enrolled subjects with and without adenoma.

Comparative analysis of baseline data between different metabolic obesity cohorts

Table 2. Traits of enrolled female subjects at baseline in the different cohorts of metabolic obesity.

Fig 1. Frequency of occurrence of CRA in the different cohorts of metabolic obesity according to the sex.

Table 3. Traits of enrolled male subjects at baseline in the different cohorts of metabolic obesity.

Sex-based differences in metabolic obesity phenotypes and colorectal adenoma

Fig 2. Association between the metabolic obesity phenotypes and risks of developing CRA based on the sex.

Age-based differences in metabolic obesity phenotypes and colorectal adenoma

Fig 3. Association between the metabolic obesity phenotypes and risks of developing CRA based on the age.

Sensitivity analysis

Discussion

Conclusion

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Miquel Vall-llosera Camps

Roles

Author response to Decision Letter 1

Decision Letter 1

Muhammad Shahzad Aslam

Roles

Author response to Decision Letter 2

Decision Letter 2

Muhammad Shahzad Aslam

Roles

Acceptance letter

Muhammad Shahzad Aslam

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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