Abstract
Background/Objectives
Colorectal cancer (CRC) is influenced by genetic, environmental, and dietary factors, with increasing evidence highlighting the role of the gut microbiota in its development. Probiotics, prebiotics, and fermented foods such as yogurt have been recognized for their ability to promote gut microbial balance and potentially reduce CRC risk. This study try to investigate the association between the consumption of these dietary components and CRC prevalence among adults aged 50 years and older.
Subjects/Methods
This study analyzed data from the National Health and Nutrition Examination Survey (NHANES) from 2001 to 2020. Dietary intake was assessed using the Food Frequency Questionnaire and the 30-Day Dietary Supplement Use Questionnaire, while CRC history was based on self-reported diagnoses. Multivariable logistic regressions were applied, adjusting for demographic characteristics (age, sex, race/ethnicity, poverty income ratio, education), lifestyle factors (smoking status, total energy intake, red meat intake, total dietary fiber intake), and clinical variables (BMI, cardiovascular disease, chronic kidney disease, fasting plasma glucose, and serum albumin).
Results
The final analytic sample included 9405 participants, representing an estimated 37 million U.S. adults. After adjustment, consumption of probiotics, prebiotics, or yogurt was associated with approximately 50% lower odds of CRC (adjusted odds ratio = 0.50; 95% CI: 0.29–0.88).
Conclusions
These findings indicate a potential protective association of these dietary components with CRC, likely mediated through modulation of the gut microbiota. While the cross-sectional design limits causal interpretation, the results support existing literature on the beneficial role of diet in cancer prevention. Further longitudinal studies are necessary to confirm these associations and inform public health strategies aimed at reducing CRC risk through targeted dietary interventions.
Subject terms: Cancer, Gastrointestinal diseases
Introduction
Colorectal cancer (CRC) is one of the leading global causes of cancer-related morbidity and mortality [1]. The incidence and prevalence of CRC are influenced by a complex interplay of genetic, environmental, and dietary factors [2]. While genetic susceptibility contributes to CRC risk, dietary components such as excessive red and processed meat intake have been consistently associated with increased CRC risk, whereas higher fiber intake has demonstrated protective effects [3, 4].
The gut microbiota, defined as the diverse community of microorganisms inhabiting the human gastrointestinal tract, plays a crucial role in maintaining intestinal homeostasis, nutrient metabolism, and immune regulation [5, 6]. Dysbiosis, referring to an imbalance in the microbial composition, may promote colorectal carcinogenesis by inducing inflammation and producing carcinogenic metabolites [7]. Conversely, maintaining a balanced microbiota is thought to exert protective effects against CRC by preserving epithelial integrity and regulating immune responses.
Diet is one of the primary modulators of the gut microbiota composition [8]. In particular, prebiotics, probiotics, and fermented foods such as yogurt have been recognized as important modulators of microbial balance and potential protective factors against CRC [9]. Probiotics, which are live microorganisms that confer health benefits when consumed in adequate amounts, have been shown to enhance gut barrier function, modulate immune responses, and produce short-chain fatty acids (SCFAs) through microbial fermentation [10]. Prebiotics, defined by the International Scientific Association for Probiotics and Prebiotics (ISAPP) as “a substrate that is selectively utilized by host microorganisms conferring a health benefit” [11], include naturally occurring dietary fibers found in foods such as onion, garlic, asparagus, barley, and banana [12–14]. Yogurt, a widely consumed fermented dairy product, contains beneficial bacterial strains (e.g., Lactobacillus, Bifidobacterium) that may improve gut health and reduce inflammation, thereby lowering CRC risk [15, 16].
Although the evidence suggesting a link between the consumption of probiotics, prebiotics, and yogurt and the prevention of CRC is compelling, population-based research, particularly from national-level datasets, remains insufficient. Therefore, the current study aimed to use data from the National Health and Nutrition Examination Survey (NHANES), a nationally representative dataset of adults in the United States (US), to examine the relations between consumption of probiotics, prebiotics, and yogurt and CRC. By leveraging this extensive dataset, our study aims to enrich the existing literature and solidify the evidence regarding dietary strategies and their impact on CRC risk.
Methods
Data source
The present study was a secondary analysis of data from the NHANES database, which is administered by the Centers for Disease Control and Prevention (CDC), National Center for Health Statistics (NCHS) in the US (http://www.cdc.gov/nchs/nhanes/). The survey is designed to evaluate the health and nutritional status of adults and children in the US. It uses a complex, multistage design to collect and analyze data representative of the national, non-institutionalized population of the US. The data are released for research purposes and permission to use the data is granted to researchers by the NCHS. Participants in NHANES complete a household interview and are invited for an extensive examination in a mobile examination center (MEC), which includes a physical examination, specialized measurements, and laboratory tests. Consequently, an evaluation of subjects within the NHANES database is reliable and multidimensional, and can be equated to a population-level assessment.
The NCHS Research Ethics Review Board reviewed and approved NHANES, and all survey participants provided written informed consent. Therefore, no further ethical approval or informed consent was required to perform the secondary analyses undertaken in this study. The NCHS Research Ethics Review Board approval is available on the NHANES website at (https://www.cdc.gov/nchs/nhanes/irba98.htm). Additionally, all NHANES data released by the NCHS are de-identified, and the data remain anonymous during data analysis.
Study population
The present study extracted data from the NHANES from 2001 to 2020. Participants aged 50 years or older were eligible for inclusion, consistent with CRC screening recommendations. Individuals were included if they had responded to the questionnaire regarding personal cancer history and had available fasting plasma glucose measurements. Participants were excluded if they had missing information on sex or sample weights, or if they reported a cancer type other than CRC.
Determination of CRC history
CRC was confirmed based on participants’ responses to the questionnaire regarding their cancer history. Participants first responded whether they had cancer in the past, followed by what kind of cancer was it. This approach aligned with previous NHANES studies [17, 18].
Determination of yogurt/prebiotics/probiotics consumption
Consumption of yogurt, prebiotics, and probiotics was determined using two dietary assessment tools from the NHANES database: The Food Frequency Questionnaire (FFQ) and the 30-Day Dietary Supplement Use Questionnaire (DSQ). For yogurt intake, participants who reported consuming yogurt within the FFQ during the dietary recall period were classified as yogurt consumers. For prebiotics and probiotics, a standardized text-mining approach was applied to the DSQ and prescription datasets to identify relevant supplement use. Specifically, the names, ingredients, and components of dietary supplements and medications were screened for keywords related to prebiotics (e.g., inulin, fructooligosaccharides, galactooligosaccharides) and probiotics (e.g., Lactobacillus, Bifidobacterium, Saccharomyces boulardii). This automated identification process followed the methodology of previous NHANES-based studies that examined prebiotic, probiotic, and yogurt consumption patterns [19, 20].
Covariates
Participant information, including age, sex, race/ethnicity, poverty income ratio, education level, cigarette smoking status, and alcohol consumption status were obtained by standard questionnaires through in-person home interviews conducted by trained interviewers using the Family and Sample Person Demographics questionnaires and the Computer-Assisted Personal Interviewing (CAPI) system (Confirmit Corp., New York, USA).
Body mass index (BMI) was obtained from the NHANES examination measurements, and calculated as body weight (kilograms) divided by height (meters squared). Dietary variables, including total energy intake, total dietary fiber intake, and red meat intake, were obtained from the NHANES dietary interview component. Physical activity was quantified using Metabolic Equivalent of Task (MET)-minutes per week. From 2007 onwards, MET-minutes were computed for activities like walking or bicycling for transportation, vigorous leisure-time activities, and moderate leisure-time activities, by multiplying the days per week, times per day, and a MET value specific to each activity’s intensity. Before 2007, MET scores were calculated by summing MET values, adjusted for each leisure activity’s duration and monthly frequency. Based on these MET-minutes, individuals are classified into 3 activity levels: optimal (1000 or more MET-minutes per week), active (500–1000), and inactive (<500).
Levels of HbA1c, fasting plasma glucose, serum total cholesterol, triglycerides, high density lipoprotein-cholesterol (HDL-C), low density lipoprotein-cholesterol (LDL-C), albumin, and serum creatinine were obtained through NHANES laboratory data.
Medical conditions were identified through self-reported physician diagnoses of the condition along with laboratory data and/or physical examinations. Hypertension was identified if a person had been told on 2 or more different visits that they have hypertension, reported they were taking blood pressure-reducing medication, or had an average systolic blood pressure (SBP) of 140 mmHg or higher, or an average diastolic blood pressure (DBP) of 90 mmHg or higher from 3 readings. Diabetes was considered present if a person stated they had been diagnosed with diabetes by a doctor, was currently using insulin or oral anti-diabetic drugs, had a HbA1c level of 6.5% or higher, a fasting glucose level of 126 mg/dL or higher, or an oral glucose tolerance test result of 200 mg/dL or higher.
A diagnosis of cardiovascular disease (CVD) included a history of conditions such as coronary heart disease, angina pectoris, heart attack, stroke, or congestive heart failure. A self-reported history of any type of cancer was considered a history of cancer. The estimated glomerular filtration rate (eGFR) was calculated from a re-calibrated serum creatinine level using the 4-variable Modification of Diet in Renal Disease (MDRD) Study equation. The isotope dilution mass spectrometry (IDMS)-traceable MDRD Study equation using a standardized creatinine value is: eGFR = 175 × (standardized serum creatinine)−1.154 × (age)−0.203 × 0.742 (if the subject is a woman) × 1.212 (if the subject is black). Participants were considered to have chronic kidney disease (CKD) if their eGFR was <60 ml/min/1.73 m2.
Chronic respiratory disease includes conditions such as emphysema, chronic bronchitis, or asthma, based on specific survey responses. The number of comorbidities (including diabetes, hypertension, cardiovascular disease, chronic kidney disease, and chronic respiratory disease) was categorized from 0 to 5, based on the presence of these conditions in an individual.
Statistical analysis
Descriptive statistics were used to summarize patient demographic and clinical characteristics, with categorical variables reported as counts and weighted percentages, and continuous variables presented as means with standard errors (SE). Group comparisons for categorical variables were conducted using the Rao-Scott chi-square test, while weighted mean differences for continuous variables were analyzed using survey methods that account for stratification, clustering, and sampling weights, ensuring valid and robust statistical inferences within the context of complex survey designs. Univariate and multivariable logistic regression analyses were performed to calculate the odds ratios (ORs) and 95% confidence intervals (CIs) for the associations between CRC and the study variables. We constructed a series of progressively adjusted models: Model II: adjusted for age, sex, race/ethnicity, BMI, and Model III: furtherly added fiber, red meat intake, smoking, DM, CVD, and FPG. Model IV: adjusted for covariates with p < 0.05 in univariate analysis. A 2-sided p value of <0.05 was considered statistically significant. All analyses accounted for the NHANES’s complex survey design to ensure accurate national estimates. Statistical analyses were conducted using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA).
Results
Study population
Initially, 97,657 participants were identified in NHANES database 2001–2020. After applying the inclusion and exclusion criteria, a total of 9405 participants were included for the subsequent analyses. This sample represents a total of 37,022,487 US residents after weighting (Fig. 1).
Fig. 1. Flow diagram of patient selection.
Flow diagram of the study population selection process and the final analytic cohort included in the study.
Characteristics of the participants
The mean age of participants was 62.8 years. Slightly more than half of the participants were female (53.4%), and 73.7% were non-Hispanic White. The occurrence rate of CRC was 1.5% and patients consuming yogurt, prebiotics, or probiotics had lower percentage of CRC compared to those who did not (1.2% vs 2.1%, p = 0.023).
A significant difference in racial composition was observed (p < 0.001), with a higher proportion of non-Hispanic White individuals consuming these products compared to those who did not (78.2% vs 65.8%, p < 0.001).
A higher proportion of individuals in the consumption group were “not poor” (92.0% vs. 82.9%, p < 0.001), with normal BMI (26.0% vs 21.5%, p = 0.009) and had completed high school (84.9% vs. 75.7%, p < 0.001). The consumption group also had a lower proportion of current smokers (14.6% vs. 21.1%, p < 0.001) and higher intake of dietary fiber, energy, and red meat (mean: 17.7 g vs. 14.8 g, 2039.8 kcal vs 1869.1 kcal, and 72.3% vs 66.3%, respectively, all p < 0.001). Furthermore, the consumption group had a lower frequency of DM (23.5% vs. 28.8%, p < 0.001), chronic respiratory disease (12.1% vs. 15.1%, p = 0.041). Significant differences between the 2 groups were found in laboratory measures, including HDL-C, albumin, HbA1c, and fasting glucose (Table 1).
Table 1.
Characteristics of the study participants categorized by yogurt, prebiotic, and probiotic consumption.
| All (n = 9405) | Yogurt/Prebiotics/ Probiotics use | p value | ||
|---|---|---|---|---|
| Yes (n = 5540) | No (n = 3865) | |||
| CRC cases | 151 (1.5) | 81 (1.2) | 70 (2.1) | 0.023 |
| Age, years | 62.8 ± 0.1 | 62.9 ± 0.2 | 62.4 ± 0.2 | 0.096 |
| 50–59 | 3221 (44.3) | 1873 (43.4) | 1348 (45.8) | 0.364 |
| 60–69 | 3241 (31.1) | 1830 (31.3) | 1411 (30.7) | |
| 70–79 | 1868 (16.7) | 1133 (17.2) | 735 (16.0) | |
| ≥80 | 1075 (7.9) | 704 (8.1) | 371 (7.5) | |
| Sex | 0.409 | |||
| Male | 4624 (46.6) | 2618 (46.1) | 2006 (47.4) | |
| Female | 4781 (53.4) | 2922 (53.9) | 1859 (52.6) | |
| Race/ethnicity | <0.001 | |||
| Non-Hispanic White | 4315 (73.7) | 2884 (78.2) | 1431 (65.8) | |
| Non-Hispanic Black | 2010 (10.0) | 1035 (8.1) | 975 (13.4) | |
| Mexican American | 1414 (5.2) | 712 (3.9) | 702 (7.4) | |
| Other Hispanic | 922 (4.8) | 466 (3.8) | 456 (6.7) | |
| Other/unknown | 744 (6.3) | 443 (6.0) | 301 (6.8) | |
| Poverty income ratio | <0.001 | |||
| Not poor (>1) | 6938 (88.8) | 4336 (92.0) | 2602 (82.9) | |
| Poor (≤1) | 1554 (11.2) | 719 (8.0) | 835 (17.1) | |
| Education | <0.001 | |||
| High school and above | 6557 (81.6) | 4130 (84.9) | 2427 (75.7) | |
| Never attend high school | 2834 (18.4) | 1400 (15.1) | 1434 (24.3) | |
| BMI (kg/m2) | 0.009 | |||
| Normal: 18.5–25 | 2209 (24.4) | 1349 (26.0) | 860 (21.5) | |
| Underweight: <18.5 | 98 (1.1) | 58 (1.0) | 40 (1.3) | |
| Overweight: 25–29 | 3313 (36.2) | 1939 (35.8) | 1374 (36.8) | |
| Obese: ≥30 | 3606 (38.3) | 2081 (37.1) | 1525 (40.4) | |
| Total dietary fiber, g/day | 16.6 ± 0.2 | 17.7 ± 0.2 | 14.8 ± 0.3 | <0.001 |
| Total energy intake, kcal/day | 1979.0 ± 11.8 | 2039.8 ± 15.5 | 1869.1 ± 20.6 | <0.001 |
| Red meat intake | 6540 (70.1) | 3987 (72.3) | 2553 (66.3) | <0.001 |
| Total red meat intake, g/day | 208.4 ± 3.3 | 204.3 ± 4.2 | 216.4 ± 5.7 | 0.098 |
| Cigarette smoking | <0.001 | |||
| Never | 4580 (47.7) | 2808 (49.4) | 1772 (44.7) | |
| Former | 3219 (35.4) | 1925 (36.0) | 1294 (34.2) | |
| Current | 1594 (16.9) | 802 (14.6) | 792 (21.1) | |
| Physical activity | 0.721 | |||
| Optimal >=1000 | 2074 (45.0) | 1298 (45.6) | 776 (43.8) | |
| Active 500–1000 | 1176 (24.4) | 729 (24.0) | 447 (25.2) | |
| Inactive <500 | 1555 (30.6) | 976 (30.5) | 579 (31.0) | |
| Medical history/comorbidity | ||||
| DM | 3005 (25.4) | 1681 (23.5) | 1324 (28.8) | <0.001 |
| HTN | 5711 (55.3) | 3393 (55.3) | 2318 (55.2) | 0.975 |
| CVD | 1819 (17.0) | 1040 (16.7) | 779 (17.4) | 0.557 |
| CKD | 1546 (15.2) | 930 (15.1) | 616 (15.5) | 0.716 |
| Chronic respiratory disease | 1240 (13.2) | 682 (12.1) | 558 (15.1) | 0.041 |
| Number of comorbidities | 0.040 | |||
| 0 | 2267 (29.5) | 1386 (30.6) | 881 (27.4) | |
| 1 | 3137 (34.1) | 1833 (34.5) | 1304 (33.5) | |
| 2 | 2345 (21.9) | 1367 (21.0) | 978 (23.6) | |
| 3 | 1181 (10.4) | 680 (9.9) | 501 (11.4) | |
| 4 | 424 (3.6) | 251 (3.6) | 173 (3.6) | |
| 5 | 51 (0.4) | 23 (0.3) | 28 (0.5) | |
| Laboratory measures | ||||
| Total cholesterol, mg/dL | 201.2 ± 0.8 | 201.0 ± 1.0 | 201.5 ± 1.2 | 0.752 |
| Triglyceride, mg/dL | 136.9 ± 1.6 | 136.6 ± 2.1 | 137.5 ± 2.7 | 0.794 |
| HDL-c, mg/dL | 55.9 ± 0.3 | 56.5 ± 0.4 | 54.6 ± 0.5 | <0.001 |
| LDL-c, mg/dL | 117.9 ± 0.7 | 117.1 ± 0.8 | 119.3 ± 1.0 | 0.068 |
| Albumin, g/dL | 4.2 ± 0.006 | 4.2 ± 0.007 | 4.1 ± 0.009 | <0.001 |
| HbA1c, % | 5.9 ± 0.02 | 5.8 ± 0.02 | 6.0 ± 0.03 | <0.001 |
| FPG, mg/dL | 113.3 ± 0.6 | 111.7 ± 0.7 | 116.1 ± 0.9 | <0.001 |
Continuous variables are presented as mean ± SE. Categorical variables are presented as unweighted counts (weighted percentages).
Missing values for different variables: poverty income ratio, n = 913; education, n = 14; BMI, n = 179; total dietary fiber/ energy intake, n = 1834; cigarette smoking, n = 12; physical activity, n = 4600; CKD, n = 152; total cholesterol, n = 153; triglycerides, n = 185; HDL-C, n = 117; LDL-C, n = 411; albumin, n = 150; HbA1c, n = 15.
BMI body mass index, CRC colorectal cancer, DM diabetes mellitus, HTN hypertension, CKD chronic kidney disease, CVD cardiovascular disease, HDL-C high-density lipoprotein cholesterol, LDL-C low-density lipoprotein cholesterol, HbA1c glycated hemoglobin, FPG fasting plasma glucose.
p values < 0.05 are shown in bold.
Association between probiotic, prebiotic, and yogurt consumption and CRC
The association between probiotic/prebiotic/yogurt consumption and CRC is documented in Table 2. In the unadjusted model (Model I), the crude OR for CRC among consumers versus non-consumers was 0.56 (95% CI: 0.33–0.94, p = 0.027), indicating a significant difference between the groups. Subsequent models were adjusted for additional variables and in the model adjusted for all key confounders, the aOR indicated that consumers of prebiotics/probiotics/yogurt had a significantly lowered likelihood of CRC (Model II, III, IV: aOR = 0.48, 0.47, and 0.50 with p = 0.011, 0.027, and 0.016, respectively) (Table 2). Detailed univariate and multivariable analytic results are presented in Supplementary Tables S1 and S2, respectively.
Table 2.
Association between prebiotic, probiotic, and yogurt consumption (vs. no consumption) and CRC.
| Model | aOR (95% CI) | p value |
|---|---|---|
| I | 0.56 (0.33, 0.94) | 0.027 |
| II | 0.48 (0.28, 0.84) | 0.011 |
| III | 0.47 (0.24, 0.92) | 0.027 |
| IV | 0.50 (0.29, 0.88) | 0.016 |
Model I: crude model without adjusting for any variables.
Model II: adjusted for age, sex, race/ethnicity, BMI.
Model III: adjusted for age, sex, race/ethnicity, BMI, fiber, red meat intake, smoking, DM, CVD, and FPG .
Model IV: adjusted for related factors (p < 0.05 in univariate analysis), including age, fiber, HTN, CVD, CKD, albumin, and FPG, except HbA1c and number of comorbidities because the collinearly of HbA1c & FPG (correlation r = 0.822 > 0.8) and comorbidities and their number.
aOR adjusted odds ratio, CI confidence interval.
p values < 0.05 are shown in bold.
Discussion
The present study examined the association between the consumption of probiotics, prebiotics, and yogurt and CRC prevalence among U.S. adults using nationally representative NHANES data from 2001 to 2020. After adjustment for demographic, lifestyle, and clinical factors, higher consumption of these dietary components was associated with approximately 50% lower odds of CRC, indicating a significant inverse association. These findings suggest that intake of probiotics, prebiotics, or yogurt may be linked to a lower CRC prevalence, potentially through mechanisms related to gut microbiota composition and systemic metabolic health. However, given the observational and cross-sectional nature of NHANES, causal relationships cannot be established. Nonetheless, the nationally representative sampling framework of NHANES supports the generalizability of these associations at the population level.
A great amount of current research has been devoted to studying the gut microbiota, the particular species and numbers colonizing the gut [21, 22]. The studies have found that colonization of the gut by microorganism not normally found in the gut, referred to as dysbiosis, is associated with a large number of diseases (e.g., cardiovascular disease, autoimmune diseases, malignancies), and that restoration of the normal gut microbiota is associated with improved overall health and a decrease or elimination of disease symptoms [21–23]. One of the primary methods of restoring the gut microbiota to a healthy state is the ingestion of live microorganisms that normally colonize the gut (probiotics), and variations of probiotics such as prebiotics, postbiotics, and synbiotics [24, 25].
Our study examined the effects of probiotic consumption on CRC, and adds to the current body of literature which suggests probiotics can reduce the risk of CRC as well as be used as a complementary treatment for CRC [26–29]. With respect to the mechanistic pathways, dysbiosis is believed to promote intestinal inflammation which results in a microenvironment for tumor formation, and bacterial species that may lead to dysbiosis and increased CRC risk include Fusobacterium nucleatum, Enterotoxigenic Bacteroides fragilis, and pks+ E. coli [27, 28].
Dietary factors have long been known to influence CRC risk: higher intakes of dietary fiber, dietary calcium, and yogurt are associated with a lower risk of CRC, and higher intake of red meats and alcohol are associated with increased CRC risk [30]. A meta-analysis of epidemiologic studies published in 2019 found that high consumption of total dairy products and total milk was associated with a lower risk of CRC at any anatomical location, and cheese consumption was associated with a lower risk of proximal colon cancer [31]. Interestingly, a recently published Mendelian randomization study using data from the UK Biobank (a European population) found no causal associations between dietary factors such as alcohol, fruit, meat, full cream milk, sweets, and tea and CRC risk [32].
While the cross-sectional design of our study limits its ability to establish causality and places it at a lower level of evidence, it has the distinct advantage of utilizing NHANES survey data, which enables comprehensive adjustment for demographic, lifestyle, and health-related factors, and ensures the findings are nationally generalizable. Studies have explored the use of probiotics both as a preventive factor for CRC and as a treatment, but the study focus and findings remain inconsistent. For example, a review and meta-analysis of 16 observational studies by Sun et al. [12] reported that higher yogurt consumption lowered CRC risk in the overall comparison (OR = 0.87) and in case-control studies only (OR = 0.75). In a study published 2024, Kan et al. [33] performed a systematic review and meta-analysis of the effect of probiotics in preventing CRC and precancerous lesions in high-risk populations. Nine RCTs were included and the results showed that probiotic supplementation (1) significantly reduced adenoma incidence; (2) had no significant benefit with respect to CRC incidence; and (3) probiotics modulated gut microbiota and inflammatory biomarkers.
Moreover, published in 2024, Melo Moreira et al. [34] reviewed 24 RCTs that examined the impact of pre-, pro- and synbiotic supplementation in the treatment of CRC. The results showed that the administration of pre-, pro-, or synbiotics reduced the incidence of infectious and non-infectious postoperative complications, restored normal gut function, and reduced hospital length of stay and antibiotic use. Supplementation also alleviated diarrhea from chemotherapy and radiotherapy. A similar meta-analysis that included 13 studies examining the effect of probiotics on postsurgical complications in patients with CRC reported that probiotics significantly reduced the incidence of ileus, diarrhea, sepsis, pneumonia, and surgical site infections [35].
Although the mechanisms by which probiotics reduce the risk and complications of CRC have not been completely revealed, some studies provide potential mechanisms. Alamdary et al. [36] performed a systematic review of 33 studies that examined probiotics in animal models of CRC. The authors reported that based on the 33 studies, probiotics/synbiotics could significantly attenuate aberrant crypt foci (ACF) formation, restore beneficial bacteria in the microbiota population, increase short-chain fatty acids (SCFAs), and alter inflammatory marker expression. Although the systematic review of clinical trials only included 6 studies with 457 patients, Wierzbicka et al. [37] reported that administration of probiotics/synbiotics to patients with CRC improved the gut microbiota by increasing the abundance of beneficial bacteria such as Lactobacillus, Eubacterium, Peptostreptococcus, Bacillus and Bifidobacterium, and decreased the abundance of potentially harmful bacteria such as Fusobacterium, Porhyromonas, Pseudomonas and Enterococcus. The author also reported that probiotics/synbiotics improved intestinal permeability and tight junction function.
Additionally, results of other studies have indicated that probiotics have anti-tumor properties due to their ability to modulate gut microbiota composition, promote apoptosis, enhance immunity, increase the production of anti-inflammatory cytokines and activate T cell-mediated immune responses [38]. Kvakova et al. [39] reported that restoration of the gut microbiota in cancer patients can stabilize and enhance intestinal barrier function, as well as promote anticarcinogenic, anti-inflammatory, antimutagenic, and other important biochemical pathways that show high specificity towards tumor cells. Notably, the results of an RCT by Huang et al. [40] reported that probiotics administration effectively reduce chemotherapy-induced gastrointestinal complications, particularly in diarrhea in patients with CRC, and restored the normal gut microbiota from the dysbiosis caused by chemotherapy.
Lastly, the positive effects of probiotics on CRC, as well as in other diseases, could potentially influence public health campaigns that emphasize the potential benefits of incorporating probiotics, prebiotics, and yogurt into daily diets as part of a broader strategy to improve gut health and potentially reduce CRC risk [41, 42]. National dietary guidelines could be revised to include specific recommendations for the consumption of probiotic- and prebiotic-rich foods and fermented products like yogurt, particularly for populations at higher risk of CRC [41, 42]. Policies could be developed to make probiotic and prebiotic-rich foods more affordable and accessible, especially in underserved communities, to reduce disparities in CRC prevention as well as integrate their consumption into programs that also address other lifestyle factors such as smoking cessation, physical activity, and weight management.
Strengths and limitations
The main strength of this study is the use of data from NHANES, a nationally representative survey with rigorous sampling methods and standardized data collection. This provides reliable estimates that are generalizable to the U.S. adult population. The availability of detailed demographic, lifestyle, and health-related information also allowed comprehensive adjustment for potential confounders, reducing residual confounding compared to previous studies.
However, several limitations should be acknowledged. First, the cross-sectional design precludes causal inference between yogurt, prebiotic, or probiotic consumption and CRC. Second, dietary exposure was assessed through self-reported questionnaires, which may be influenced by recall bias and misclassification. Third, prebiotic and probiotic intake was limited to supplement data due to the lack of detailed information on food-based sources in NHANES, potentially underestimating total consumption. Fourth, CRC was identified by self-report rather than medical verification, which may introduce reporting bias. Lastly, information on the dose, duration, and strain of probiotics or prebiotics was unavailable, limiting mechanistic interpretation.
Conclusions
This study highlights a potential link between the consumption of probiotics, prebiotics, and yogurt and a reduced risk of CRC after accounting for significant confounders. Using the broad and diverse NHANES dataset, our findings add a unique perspective to the existing body of research, including longitudinal studies, by providing real-world evidence from a nationally representative US sample. Although similar associations have been explored in other research, the advantage of this study lies in its use of national survey data, which offers a comprehensive view that is often lacking in more narrowly focused studies. While the results are promising, the cross-sectional design introduces limitations in establishing causality. Future research should continue to use longitudinal designs and include more detailed dietary assessments to confirm these associations and effectively translate them into public health recommendations.
Supplementary information
Author contributions
Conceptualization: C-WT. Data curation: H-LW. Formal Analysis: H-LW. Funding acquisition: H-LW. Investigation: C-WT. Methodology: C-WT. Project administration: C-WT. Resources: H-LW. Software: C-WT. Supervision: H-LW. Validation: C-WT. Visualization: H-LW. Writing—original draft: C-WT, H-LW. Writing—review & editing: C-WT, H-LW.
Funding
This study was sponsored by the Chung Shan Medical University Hospital Research Program(CSH-2019-A-021).
Data availability
The data that support the findings of this study are available from the corresponding author upon reasonable request.
Conflict of interest
The authors declare no competing interests.
Ethics approval
The study protocol followed the guideline of Declaration of Helsinki. The NCHS Research Ethics Review Board reviewed and approved NHANES, and all survey participants provided written informed consent. Therefore, no further ethical approval or informed consent was required to perform the secondary analyses undertaken in this study.
Footnotes
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary information
The online version contains supplementary material available at https://doi.org/10.1038/s41387-026-00432-y.
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Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.