Skip to main content
PMC home page
Obesity Facts logoLink to Obesity Facts
. 2024 Jan 24;17(2):169–182. doi: 10.1159/000536318

Analysis of Factors Associated with Constipation in the Population with Obesity: Evidence from the National Health and Nutrition Examination Survey

Yongping Hong a, Xingxing Chen b, Junping Liu a,✉
PMCID: PMC10987188  PMID: 38266495

Abstract

Introduction

Obesity and constipation are both global problems, but the factors associated with constipation in individuals with obesity are currently understudied. The aim of our study was to explore the factors associated with constipation in people with obesity.

Methods

From three cycles of the National Health and Nutrition Examination Survey (NHANES) 2005–2010, data from 14,048 persons aged ≥20 years were collected. Variables included demographics, lifestyle, comorbidities, and dietary data. Multiple logistic regression analysis was used to calculate adjusted prevalence odds ratio (OR) and assess the relationship between different variables and constipation in population with obesity.

Results

Using stool consistency definition, multivariate analysis revealed that education ≥12th grade (OR: 0.456; 95% CI: 0.300, 0.694; p = 0.00024), hypertension (OR: 0.505; 95% CI: 0.334, 0.763; p = 0.00119), polypharmacy (OR: 1.669; 95% CI: 1.104, 2.521; p = 0.01507), high cholesterol (OR: 0.400; 95% CI: 0.213, 0.750; p = 0.00430), and high dietary fiber (OR: 0.454; 95% CI: 0.245, 0.841; p = 0.01206) were substantially linked with constipation in the population with obesity. For constipation defined using stool frequency, multivariate regression analysis show constipation in people with obesity had a significant association with the female sex (OR: 2.684; 95% CI: 1.379, 5.223; p = 0.00366 multivariate), Mexican American (OR: 0.142; 95% CI, 0.033, 0.616; p = 0.00914 multivariate), hypertension (OR: 0.569; 95% CI: 0.324, 0.998; p = 0.04916), depression (OR: 2.280; 95% CI: 1.240, 4.195; p = 0.00803), occasional/often milk consumption (OR: 0.473; 95% CI: 0.286, 0.782; p = 0.00356), medium energy (OR: 0.318; 95% CI: 0.118, 0.856; p = 0.02338), polypharmacy (OR: 1.939; 95% CI: 1.115, 3.373; p = 0.01907), and medium moisture (OR: 0.534; 95% CI: 0.285, 0.999; p = 0.04959). In nonobese people, constipation was significantly associated with the female sex and high moisture but not with hypertension and polypharmacy.

Conclusion

This study suggests that the population with obesity has many factors that affect constipation such as hypertension, polypharmacy, cholesterol, dietary fiber, depression, and so on, of which hypertension and polypharmacy were significant associated with constipation, regardless of definitions of constipation. Notably, hypertension might be associated with a reduced risk of constipation in people with obesity.

Keywords: Constipation, Hypertension, Polypharmacy, Functional bowel disorders, Obesity

Introduction

Constipation is one of the most prevalent gastrointestinal conditions worldwide, with a previous study reporting that it affects up to 10.2% of females and 4% of males [1]. Physical functioning, mental health, and social functioning are all compromised in patients with the condition [2, 3]. Patients with constipation inevitably to seek medical assistance and the cost of medical treatment is relatively high. Nearly USD 230 million is spent on constipation treatment in the US each year [4] There are many factors associated with constipation, with a cross-sectional study conducted in Turkey revealing that physical inactivity, reduced fiber and water intake, aging, the female sex, and obesity were associated with an increased risk of constipation [5].

The global rate of obesity and obesity-related diseases continues to increase, with the number of individuals with excess body weight now exceeding 2 billion [6]. Obesity is associated with many diseases. It can increase the risk of peripheral vascular disease, type 2 diabetes, coronary heart disease, and tumors such as colon, stomach, breast, and kidney cancer [7–9]. Many studies have examined the relationship between obesity and constipation. One study found no statistically significant association between obesity or overweight and constipation in children [10], whereas others suggested that obesity might be a risk factor for constipation in adults [11]. However, little is known about the factors associated with constipation in people with obesity. A recent study on constipation reported a significant association between constipation and the concomitant use of at least five medications, younger age and former smoking in adults with class II and class III obesity [12]. There were 150 patients in the single-center study whose sample was not large enough. People with constipation who were not obese during the study period were excluded from the study, and there was no stratified comparison of relevant factors. Therefore, a more complete study of a larger population is required to identify the factors associated with constipation in people with obesity.

This study aimed to determine the various factors associated with constipation in population with obesity. Using data from the National Health and Nutrition Examination Survey (NHANES), we examined a number of variables, including demographic factors, lifestyle, comorbidities, and dietary data, to identify factors associated with constipation.

Materials and Methods

This study was conducted per relevant guidelines and regulations. NHANES is a long-running survey that began in the 1960s and continues today to examine the health and nutritional statuses of various groups in the USA. The National Center for Health Statistics of the US Centers for Disease Control and Prevention is in charge of the program. Each year the survey examines a nationally representative sample of people from cities across the country. The National Center for Health Statistics Ethics Review Board approved the NHANES, and all participants gave their written informed consent before taking the survey.

We used data from the NHANES 2005–2006, 2007–2008, and 2009–2010 cycles, with a total of 17,132 participants aged ≥20 years. A total of 2,401 participants lacking data on fecal concentration and frequency, 134 people lacking body measurement data and 409 pregnant women were excluded from the study. The final sample to be analyzed comprised 14,048 participants (Table 1).

Table 1.

Baseline characteristics of the study sample from NHANES 2005–2010 (using the Stool Consistency Definition of Constipation)

Variable Constipation (n = 1,052), n (%) No constipation (n = 12,996), n (%) p value
Gender <0.001
 Male 341 (32.4) 6,731 (51.8)
 Female 711 (67.6) 6,265 (48.2)
Age, years 0.005
 <45 488 (46.4) 5,381 (41.4)
 ≥45, <65 320 (30.4) 4,455 (34.3)
 ≥65 244 (23.2) 3,160 (24.3)
Ethnicity <0.001
 Non-Hispanic white 456 (43.3) 6,481 (49.9)
 Non-Hispanic black 246 (23.4) 2,555 (19.7)
 Mexican American 195 (18.5) 2,350 (18.1)
 Other 155 (14.7) 1,610 (12.4)
BMI <0.001
 Normal 370 (35.17) 3,656 (28.13)
 Overweight 347 (32.98) 4,490 (34.55)
 Obesity 335 (31.84) 4,850 (37.32)
Education <0.001
 <12th grade 643 (61.3) 6,669 (51.4)
 ≥High school 406 (38.7) 6,316 (48.6)
Marital status <0.001
 Never married 279 (26.5) 3,110 (23.9)
 Widowed/divorced 499 (47.4) 6,965 (53.6)
 Separated 274 (26.0) 2,913 (22.4)
Income-poverty ratio <0.001
 <2 522 (53.8) 5,439 (45.1)
 ≥2 449 (46.2) 6,611 (54.9)
Physical activity 0.018
 <500 396 (42.7) 4,506 (38.7)
 ≥500 532 (57.3) 7,123 (61.3)
Drinking <0.001
 No 406 (38.7) 3,526 (27.1)
 Yes 644 (61.3) 9,462 (72.9)
Smoking <0.001
 Never 606 (57.6) 6,701 (51.6)
 Current 215 (20.4) 2,944 (22.7)
 Former 231 (22.0) 3,346 (25.8)
Diabetes 0.159
 No 910 (86.7) 11,028 (85.1)
 Yes 140 (13.3) 1,937 (14.9)
Depression <0.001
 No 915 (87.0) 11,867 (91.3)
 Yes 137 (13.0) 1,129 (8.7)
Hypertension 0.002
 No 568 (54.8) 6,421 (49.9)
 Yes 468 (45.2) 6,436 (50.1)
Asthma 0.061
 No 886 (84.2) 11,204 (86.3)
 Yes 166 (15.8) 1,779 (13.7)
COPD 0.337
 No 930 (93.5) 11,552 (94.2)
 Yes 65 (6.5) 710 (5.8)
Vitamin D deficiency 0.692
 No 601 (64.3) 7,743 (64.9)
 Yes 334 (35.7) 4,184 (35.1)
Milk 0.993
 Never/rarely 327 (31.1) 4,018 (30.9)
 Occasionally/often 721 (68.5) 8,929 (68.7)
Energy, kcal <0.001
 Low (0–1,596) 434 (41.8) 4,167 (32.6)
 Medium (1,596–2,257) 349 (33.6) 4,261 (33.3)
 High (>2,257) 256 (24.6) 4,353 (34.1)
Protein, g <0.001
 Low (0–61.3) 454 (43.7) 4,150 (32.5)
 Medium (61.3–88.4) 339 (32.6) 4,268 (33.4)
 High (>88.4) 246 (23.7) 4,363 (34.1)
Carbohydrate, g 0.003
 Low (0–195) 394 (37.9) 4,210 (32.9)
 Medium (195–277.5) 335 (32.2) 4,272 (33.4)
 High (>277.5) 310 (29.8) 4,299 (33.6)
Total sugars, g 0.448
 Low (0–78.4) 351 (33.8) 4,255 (33.3)
 Medium (78.4–126.9) 328 (31.6) 4,277 (33.5)
 High (>126.9) 359 (34.6) 4,248 (33.2)
Dietary fiber, g <0.001
 Low (0–11.7) 438 (42.2) 4,148 (32.5)
 Medium (11.7–18.2) 327 (31.5) 4,281 (33.5)
 High (>18.2) 273 (26.3) 4,347 (34.0)
Total fat, g <0.001
 Low (0–55.6) 430 (41.4) 4,173 (32.7)
 Medium (55.6–85.5) 353 (34.0) 4,254 (33.3)
 High (>85.5) 255 (24.6) 4,353 (34.1)
Cholesterol, mg <0.001
 Low (0–176.5) 426 (41.0) 4,176 (32.7)
 Medium (176.5–317.5) 334 (32.2) 4,263 (33.4)
 High (>317.5) 278 (26.8) 4,333 (33.9)
Moisture, g <0.001
 Low (0–2,101.7) 465 (44.8) 4,139 (32.4)
 Medium (2,101.7–3,042.7) 329 (31.7) 4,278 (33.5)
 High (>3,042.7) 245 (23.6) 4,364 (34.1)
Polypharmacy 0.315
 No 422 (68.3) 5,251 (70.2)
 Yes 196 (31.7) 2,228 (29.8)
Hypertension drugs 0.005
 No 368 (58.506) 4,080 (52.693)
 Yes 261 (41.494) 3,663 (47.307)
Open in a new tab

COPD, chronic obstructive pulmonary.

Significant values are shown in bold (p < 0.05).

Definition of Obesity and Constipation

Body measurements were taken at the Mobile Examination Center for NHANES by qualified health technicians (MEC). According to the body mass index (BMI, kg/m2), which was calculated from body measurements, participants were classified as either obese (BMI ≥30.0 kg/m2) or nonobese (BMI <30.0 kg/m2).

The bowel health questionnaire in NHANES is primarily a survey that records data on the frequency and consistency of participants’ bowel movements over the previous 30 days. The frequency of bowel movements was determined by asking participants how many times per week they had a bowel movement. Constipation was judged as <3 bowel movements (BMs) per week and non-constipation as ≥3. Stool consistency was determined by showing participants a colored card containing descriptions of the seven stool types using the Bristol Stool Form Scale [13] and asking them about the usual or most common type of stool. In line with earlier studies [1, 14, 15], constipation was characterized as BSFS type 1 (separate hard lumps, like nuts) or type 2 (sausage-like, but lumpy) stool type. In this study, we mainly used stool consistency to define constipation and defined constipation in terms of stool frequency as a complementary study.

Study Variables

Demographic data included data on age (divided into three groups: <45, 45–65, ≥65), sex (male, female), race/ethnicity (non-Hispanic white, non-Hispanic black, Hispanic American, and other race/ethnicity), education level (below high school, high school, and above), marital status (never married, widowed/divorced/separated, married), and household income to poverty ratio (divided into two groups <2, ≥2).

We derived the metabolic equivalent of task (MET) per hour (MET-h) index by multiplying the time spent in each activity per week by its MET value. In the post-2007 cycle, due to changes in the questionnaire, we multiplied the number of minutes of moderate and vigorous activity per week for participants by the corresponding met values to estimate the weekly MET-min. Participants with a MET-min week of 500 or more were classified physically active, whereas those with a MET-min week of less than 500 were labeled non-active, according to the US Department of Health and Human Services [16]. According to their smoking statuses, participants were categorized as never smokers (never smoked or smoked <100 cigarettes in their lives), current smokers (smoked ≥100 cigarettes in their lives and are still smoking), and former smokers (smoked ≥100 cigarettes in their lives but are no longer smoking). Drinkers were defined as those who consumed at least 12 alcoholic beverages per year.

Diabetes is defined as a condition diagnosed by a medical professional, or having a glycosylated hemoglobin level of ≥6.5%. Asthma and chronic obstructive pulmonary disease (COPD) were defined as having been diagnosed by a medical professional and yet not being cured. Patients with hypertension were classified as those who had been diagnosed by a medical professional or who were taking hypertension medication, or whose systolic blood pressure was greater than 130 mm Hg, or whose diastolic blood pressure was greater than 80 mm Hg. Determine whether to use hypertensive drugs based on each individual’s prescription drug information. According to a previous study, a score of ≥10 on the Patient Health Questionnaire (PHQ-9) was considered to indicate depression [17]. Vitamin D deficiency was defined as serum 25-hydroxyvitamin D levels of less than 50 nmol/L (20 ng/mL) [18]. Polypharmacy was defined as taking five or more drugs at the same time [19].

The NHANES database collected information on participants’ dietary intake (including information on all foods and beverages) for the 2 days prior to the interview. The average dietary data for 2 days including energy, fiber, water, carbohydrates, sugar, protein, fat, and cholesterol were selected for this study. The dietary variables described above were divided into tertiles in this study. According to their milk consumption, the participants of this study were divided into two categories: never consume/rarely consume (less than once a week) and occasionally consume/often consume (once a week or more).

Statistical Analysis

We used weights in our analysis to better reflect the population representation of the NHANES database. All statistical analyses were performed using R version 3.4.3 (http://www.R-project.org/) and Empower Stats 2.0 (http://www.empowerstats.com). Categorical variables were presented as frequencies and percentages, and they were compared using χ2 tests. Continuous variables were transformed into categorical variables and analyzed as the latter. First, we used univariate logistic regression analyses to identify potential risk factors for constipation in obese and nonobese participants. Variables that were statistically significant in the univariate analysis were assessed using multivariate logistic regression models, with adjustments for other variables associated with constipation in the analysis. In this study, constipation was characterized by stool consistency or frequency. The p value of <0.05 was considered statistically significant.

Results

Study Sample

Out of the 31,034 people who participated in the three cycles, we included 17,132 persons aged ≥20 years in the study. Of these, 2,401 participants did not have data on stool consistency and frequency, which led to their exclusion from the study. 409 pregnant women and those who did not measure their BMIs (n = 134) were excluded from the study, reducing the final sample to 14,048 people (7,072 males and 6,976 females). Constipation (as determined based on stool consistency) was present in 7.5% of the study population. Using the frequency-based definition of constipation (<3 BMs/week), the prevalence of constipation was reduced to 3.4%. Table 1 shows the clinical characteristics of the study participants according to stool consistency definition. Constipation was significantly associated with gender, age, ethnicity, BMI, education, marriage, the income-poverty ratio, smoking, physical activity, drinking, depression, hypertension, energy, carbohydrate, dietary fiber, total fat, total saturated fatty acids, and moisture (p < 0.05).

Univariate and Multivariate Regression Analysis of Constipation in a Population with Obesity

Based on the different definitions of constipation, Tables 2 and 3 show the interaction between different factors and constipation in obese and nonobese participants. In the multivariate regression analysis, we adjusted for gender, age, ethnicity, BMI, education, marriage, income-poverty ratio, smoking, physical activity, drinking, depression, hypertension, diabetes, asthma, chronic obstructive pulmonary, energy, vitamin D deficiency, milk, polypharmacy, carbohydrate, total sugars, dietary fiber, total fat, total saturated fatty acids, and moisture. In multifactorial regression analyses based on the stool consistency-based definition of constipation, education ≥12th grade (odds ratio [OR]: 0.456; 95% CI: 0.300, 0.694; p = 0.00024), hypertension (OR: 0.505; 95% CI: 0.334, 0.763; p = 0.00119), polypharmacy(OR: 1.669; 95% CI: 1.104, 2.521; p = 0.01507), high cholesterol (OR: 0.400; 9.95% CI: 0.213, 0.750; p = 0.00430), and high dietary fiber (OR: 0.454; 95% CI: 0.245, 0.841; p = 0.01206) were significantly associated with constipation in the population with obesity, in line with the results of the univariate regression analysis. Similarly, in the nonobese participants, the female sex (OR: 2.373; 95% CI: 1.743, 3.231; p < 0.00001), education ≥12th grade (OR: 0.731; 95% CI: 0.555, 0.964; p = 0.02643), high moisture (OR: 0.572; 95% CI: 0.392, 0.836: p = 0.00387), high total sugars (OR: 1.812; 95% CI: 1.119, 2.933: p = 0.01564), and hypertensive drugs (OR: 0.680; 95% CI: 0.466, 0.994: p = 0.04664) were significantly associated with constipation. For constipation defined using the stool frequency, univariate and multivariate regression analyses demonstrated that constipation in people with obesity was significantly associated with the female sex (OR: 2.684; 95% CI: 1.379, 5.223; p = 0.00366), Mexican American ethnicity (OR: 0.142; 95% CI: 0.033, 0.616; p = 0.00914), hypertension (OR: 0.569; 95% CI: 0.324, 0.998; p = 0.04916), depression (OR: 2.280; 95% CI: 1.240, 4.195; p = 0.00803), occasional/frequent milk consumption (OR: 0.473; 95% CI: 0.286, 0.782; p = 0.00356), medium energy (OR: 0.318; 95% CI: 0.118, 0.856; p = 0.02338), polypharmacy (OR: 1.939; 95% CI: 1.115, 3.373; p = 0.01907), and medium moisture (OR: 0.534; 95% CI: 0.285, 0.999; p = 0.04959). For the nonobese population, the female sex, age ≥45, an income-poverty ratio of ≥2, and high moisture were significantly associated with constipation.

Table 2.

Logistic regression analysis of factors associated with constipation (consistency-based) in an obese subgroup

Obesity (univariate) Non-obese Obesity (multivariate) Non-obese
odds ratio p value odds ratio p value odds ratio p value odds ratio p value
Gender
 Male 1 1 1 1
 Female 2.081 (1.637, 2.646) <0.001 2.376 (2.023, 2.792) <0.001 1.496 (0.958, 2.336) 0.076 2.373 (1.743, 3.231) <0.001
Age, years
 <45 1 1 1 1
 ≥45, <65 0.785 (0.610, 1.009) 0.059 0.819 (0.683, 0.982) 0.031 1.039 (0.630, 1.713) 0.881 0.754 (0.526, 1.081) 0.125
 ≥65 0.837 (0.625, 1.121) 0.233 0.858 (0.709, 1.039) 0.117 0.779 (0.420, 1.442) 0.426 0.710 (0.467, 1.079) 0.109
Ethnicity
 Non-Hispanic white 1 1 1 1
 Non-Hispanic black 1.767 (1.353, 2.307) 0.003 1.231 (0.998, 1.517) 0.052 1.584 (0.986, 2.544) 0.057 1.079 (0.722, 1.611) 0.712
 Mexican American 1.242 (0.904, 1.706) 0.181 1.180 (0.957, 1.454) 0.122 0.760 (0.399, 1.445) 0.402 0.852 (0.541, 1.341) 0.488
 Other 1.463 (1.017, 2.105) 0.040 1.328 (1.062, 1.660) 0.013 1.071 (0.569, 2.016) 0.832 1.187 (0.793, 1.777) 0.404
Education
 <12th grade 1 1 1 1
 ≥High school 0.565 (0.447, 0.714) <0.001 0.710 (0.608, 0.829) <0.001 0.456 (0.300, 0.694) <0.001 0.731 (0.555, 0.964) 0.026
Marital status
 Never married 1 1 1 1
 Widowed/divorced 0.674 (0.514, 0.884) 0.004 0.876 (0.728, 1.053) 0.158 0.829 (0.488, 1.407) 0.487 0.986 (0.683, 1.425) 0.942
 Separated 0.949 (0.701, 1.285) 0.735 1.116 (0.902, 1.381) 0.312 0.856 (0.478, 1.533) 0.600 1.056 (0.694, 1.607) 0.799
Income-poverty ratio
 <2 1 1 1 1
 ≥2 0.544 (0.429, 0.689) <0.001 0.792 (0.676, 0.929) 0.004 0.842 (0.558, 1.271) 0.413 0.975 (0.738, 1.289) 0.859
Physical activity
 <500 1 1 1 1
 ≥500 0.822 (0.647, 1.045) 0.109 0.848 (0.719, 0.999) 0.049 1.079 (0.740, 1.572) 0.694 0.995 (0.766, 1.292) 0.968
Drinking
 No 1 1 1 1
 Yes 0.604 (0.482, 0.757) 0.001 0.565 (0.481, 0.663) <0.001 0.850 (0.573, 1.260) 0.418 0.855 (0.642, 1.139) 0.284
Smoking
 Never 1 1 1 1
 Current 0.856 (0.639, 1.146) 0.296 0.781 (0.643, 0.948) 0.012 0.733 (0.439, 1.222) 0.233 1.026 (0.735, 1.432) 0.881
 Former 0.949 (0.728, 1.235) 0.695 0.683 (0.562, 0.832) <0.001 0.971 (0.633, 1.488) 0.892 0.833 (0.610, 1.137) 0.249
Hypertension
 No 1 1 1 1
 Yes 0.702 (0.561, 0.879) 0.002 0.939 (0.803, 1.098) 0.429 0.505 (0.334, 0.763) 0.001 0.924 (0.690, 1.238) 0.598
Depression
 No 1 1 1 1
 Yes 1.344 (0.969, 1.865) 0.077 1.765 (1.398, 2.230) <0.001 1.070 (0.619, 1.849) 0.809 1.390 (0.933, 2.072) 0.106
Diabetes
 No 1 1 1 1
 Yes 0.958 (0.728, 1.262) 0.760 0.878 (0.682, 1.131) 0.313 0.925 (0.594, 1.441) 0.730 0.807 (0.542, 1.199) 0.288
Asthma
 No 1 1 1 1
 Yes 1.108 (0.826, 1.485) 0.495 1.253 (1.011, 1.554) 0.039 0.889 (0.552, 1.431) 0.628 1.371 (0.976, 1.925) 0.069
COPD
 No 1 1 1 1
 Yes 1.127 (0.738, 1.721) 0.581 1.180 (0.844, 1.650) 0.334 0.932 (0.483, 1.797) 0.834 0.794 (0.474, 1.331) 0.381
Vitamin D deficiency
 No 1 1 1 1
 Yes 1.168 (0.925, 1.476) 0.192 1.011 (0.847, 1.207) 0.905 0.738 (0.489, 1.114) 0.148 0.902 (0.652, 1.248) 0.535
Milk
 Never/rarely 1 1 1 1
 Occasionally/often 0.874 (0.691, 1.104) 0.258 1.055 (0.892, 1.246) 0.532 1.365 (0.891, 2.089) 0.153 1.154 (0.864, 1.541) 0.331
Energy, kcal
 Low (0–1,596) 1 1 1 1
 Medium (1,596–2,257) 0.702 (0.540, 0.912) 0.008 0.817 (0.683, 0.976) 0.026 1.065 (0.511, 2.220) 0.866 0.689 (0.433, 1.097) 0.116
 High (>2,257) 0.561 (0.424, 0.742) <0.001 0.559 (0.460, 0.680) <0.001 1.071 (0.339, 3.386) 0.907 0.699 (0.333, 1.469) 0.345
Protein, g
 Low (0–61.3) 1 1 1 1
 Medium (61.3–88.4) 0.632 (0.488, 0.820) <0.001 0.775 (0.648, 0.926) 0.005 1.321 (0.784, 2.226) 0.296 1.077 (0.761, 1.525) 0.674
 High (>88.4) 0.461 (0.347, 0.612) <0.001 0.544 (0.447, 0.661) <0.001 1.169 (0.540, 2.532) 0.692 1.119 (0.673, 1.862) 0.665
Carbohydrate, g
 Low (0–195) 1 1 1 1
 Medium (195–277.5) 0.750 (0.573, 0.982) 0.036 0.866 (0.720, 1.042) 0.127 1.613 (0.818, 3.180) 0.167 1.177 (0.762, 1.819) 0.462
 High (>277.5) 0.808 (0.618, 1.057) 0.119 0.739 (0.611, 0.893) 0.002 1.747 (0.626, 4.881) 0.287 1.431 (0.752, 2.722) 0.275
Total sugars, g
 Low (0–78.4) 1 1 1 1
 Medium (78.4–126.9) 0.759 (0.577, 0.999) 0.049 1.012 (0.835, 1.226) 0.903 0.667 (0.381, 1.168) 0.157 1.180 (0.815, 1.707) 0.380
 High (>126.9) 0.939 (0.721, 1.223) 0.642 1.054 (0.873, 1.273) 0.585 1.008 (0.485, 2.098) 0.982 1.812 (1.119, 2.933) 0.015
Dietary fiber, g
 Low (0–11.7) 1 1 1 1
 Medium (11.7–18.2) 0.641 (0.496, 0.828) <0.001 0.768 (0.639, 0.924) 0.005 0.716 (0.459, 1.116) 0.139 0.855 (0.619, 1.182) 0.342
 High (>18.2) 0.462 (0.344, 0.621) <0.001 0.647 (0.536, 0.781) <0.001 0.454 (0.245, 0.841) 0.012 0.842 (0.568, 1.249) 0.393
Total fat, g
 Low (0–55.6) 1 1 1 1
 Medium (55.6–85.5) 0.655 (0.502, 0.854) 0.002 0.878 (0.735, 1.049) 0.152 0.618 (0.347, 1.099) 0.101 1.111 (0.770, 1.605) 0.573
 High (>85.5) 0.534 (0.405, 0.703) <0.001 0.588 (0.483, 0.717) <0.001 0.972 (0.447, 2.115) 0.944 0.872 (0.507, 1.502) 0.622
Cholesterol, mg
 Low (0–176.5) 1 1 1 1
 Medium (176.5–317.5) 0.744 (0.572, 0.967) 0.027 0.786 (0.655, 0.943) 0.009 0.735 (0.461, 1.172) 0.196 1.206 (0.878, 1.658) 0.248
 High (>317.5) 0.564 (0.427, 0.745) 0.005 0.672 (0.555, 0.813) 0.004 0.400 (0.213, 0.750) 0.004 1.134 (0.760, 1.692) 0.538
Moisture, g
 Low (0–2,101.7) 1 1 1 1
 Medium (2,101.7–3,042.7) 0.610 (0.467, 0.796) <0.001 0.728 (0.609, 0.869) <0.001 0.868 (0.550, 1.369) 0.542 0.889 (0.657, 1.202) 0.443
 High (>3,042.7) 0.530 (0.403, 0.697) <0.001 0.489 (0.401, 0.596) <0.001 0.842 (0.500, 1.420) 0.519 0.572 (0.392, 0.836) 0.004
Polypharmacy
 No 1 1 1 1
 Yes 1.334 (1.007, 1.766) 0.044 1.043 (0.826, 1.319) 0.722 1.669 (1.104, 2.521) 0.015 1.212 (0.879, 1.671) 0.239
Hypertension drugs
 No 1 1 1 1
 Yes 0.920 (0.696, 1.217) 0.559 0.791 (0.641, 0.977) 0.029 0.800 (0.449, 1.424) 0.447 0.680 (0.466, 0.994) 0.046
Open in a new tab

Adjusted for gender, age, ethnicity, education, marital status, income-poverty ratio, physical activity, drinking, smoking, hypertension, depression, diabetes, asthma, COPD, vitamin D deficiency, milk, energy, protein, carbohydrate, total sugars, dietary fiber, total fat, cholesterol, moisture, and polypharmacy.

Significant values are shown in bold (p < 0.05).

Table 3.

Logistic regression analysis of factors associated with constipation (frequency-based) in an obese subgroup

Obesity (univariate) Non-obese Obesity (multivariate) Non-obese
odds ratio p value odds ratio p value odds ratio p value odds ratio p value
Gender
 Male 1 1 1 1
 Female 3.830 (2.547, 5.758) <0.001 3.437 (2.666, 4.431) <0.001 2.684 (1.379, 5.223) 0.004 2.133 (1.308, 3.476) 0.002
Age, years
 <45 1 1 1 1
 ≥45, <65 0.676 (0.470, 0.973) 0.034 0.677 (0.520, 0.880) 0.003 0.877 (0.453, 1.698) 0.696 0.504 (0.291, 0.873) 0.014
 ≥65 0.693 (0.450, 1.067) 0.095 0.586 (0.436, 0.788) <0.001 0.751 (0.328, 1.721) 0.498 0.314 (0.164, 0.603) <0.001
Ethnicity
 Non-Hispanic white 1 1 1 1
 Non-Hispanic black 1.648 (1.166, 2.330) 0.005 2.225 (1.717, 2.883) <0.001 1.031 (0.561, 1.898) 0.921 1.399 (0.807, 2.426) 0.231
 Mexican American 0.333 (0.171, 0.648) 0.001 0.795 (0.557, 1.133) 0.204 0.142 (0.033, 0.616) 0.009 0.792 (0.382, 1.639) 0.529
 Other 0.568 (0.291, 1.107) 0.097 0.922 (0.634, 1.341) 0.672 0.443 (0.163, 1.203) 0.110 0.702 (0.342, 1.442) 0.335
Education
 <12th grade 1 1 1 1
 ≥High school 0.593 (0.423, 0.833) 0.002 0.782 (0.625, 0.978) 0.031 0.824 (0.483, 1.406) 0.477 0.854 (0.554, 1.318) 0.477
Marital status
 Never married 1 1 1 1
 Widowed/divorced 0.516 (0.353, 0.755) <0.001 0.664 (0.511, 0.862) 0.002 0.818 (0.412, 1.623) 0.565 1.148 (0.652, 2.022) 0.633
 Separated 0.781 (0.512, 1.192) 0.252 0.969 (0.720, 1.302) 0.833 0.802 (0.380, 1.697) 0.564 1.368 (0.729, 2.567) 0.329
Income-poverty ratio
 <2 1 1 1 1
 ≥2 0.413 (0.290, 0.587) <0.001 0.565 (0.446, 0.715) <0.001 0.636 (0.362, 1.116) 0.114 0.646 (0.420, 0.995) 0.047
Physical activity
 <500 1 1 1 1
 ≥500 0.756 (0.537, 1.064) 0.108 0.801 (0.630, 1.019) 0.070 0.984 (0.594, 1.631) 0.951 0.792 (0.531, 1.181) 0.251
Drinking
 No 1 1 1 1
 Yes 0.546 (0.396, 0.754) <0.001 0.643 (0.508, 0.813) <0.001 0.808 (0.478, 1.368) 0.428 1.055 (0.674, 1.649) 0.815
Smoking
 Never 1 1 1 1
 Current 1.320 (0.905, 1.925) 0.149 1.150 (0.885, 1.494) 0.296 1.120 (0.607, 2.065) 0.716 1.079 (0.660, 1.764) 0.761
 Former 0.771 (0.506, 1.173) 0.223 0.746 (0.556, 1.000) 0.049 0.726 (0.387, 1.361) 0.317 0.734 (0.441, 1.220) 0.232
Hypertension
 No 1 1 1 1
 Yes 0.684 (0.494, 0.946) 0.021 1.006 (0.803, 1.261) 0.956 0.569 (0.324, 0.998) 0.049 1.537 (0.962, 2.458) 0.072
Depression
 No 1 1 1 1
 Yes 1.932 (1.270, 2.939) 0.002 2.658 (1.983, 3.563) <0.001 2.280 (1.240, 4.195) 0.008 1.666 (0.963, 2.882) 0.067
Diabetes
 No 1 1 1 1
 Yes 1.118 (0.762, 1.641) 0.566 0.899 (0.623, 1.297) 0.570 1.238 (0.695, 2.205) 0.468 0.774 (0.428, 1.402) 0.398
Asthma
 No 1 1 1 1
 Yes 1.569 (1.068, 2.305) 0.021 1.472 (1.096, 1.977) 0.010 1.626 (0.926, 2.858) 0.091 1.150 (0.673, 1.963) 0.608
COPD
 No 1 1 1 1
 Yes 1.432 (0.816, 2.512) 0.211 1.543 (0.997, 2.389) 0.051 0.546 (0.218, 1.367) 0.196 1.027 (0.506, 2.086) 0.940
Vitamin D deficiency
 No 1 1 1 1
 Yes 1.745 (1.248, 2.441) 0.001 1.514 (1.190, 1.926) <0.001 0.957 (0.556, 1.646) 0.873 0.821 (0.508, 1.328) 0.422
Milk
 Never/rarely 1 1 1 1
 Occasionally/often 0.643 (0.464, 0.893) 0.008 0.717 (0.570, 0.901) 0.004 0.473 (0.286, 0.782) 0.003 0.818 (0.539, 1.241) 0.343
Energy, kcal
 Low (0–1,596) 1 1 1 1
 Medium (1,596–2,257) 0.664 (0.450, 0.980) 0.039 0.778 (0.603, 1.004) 0.053 0.318 (0.118, 0.856) 0.023 1.482 (0.745, 2.946) 0.262
 High (>2,257) 0.681 (0.463, 1.003) 0.051 0.470 (0.351, 0.631) <0.001 0.458 (0.101, 2.067) 0.309 0.932 (0.278, 3.128) 0.909
Protein, g
 Low (0–61.3) 1 1 1 1
 Medium (61.3–88.4) 0.510 (0.346, 0.752) <0.001 0.663 (0.515, 0.854) 0.001 0.954 (0.453, 2.011) 0.901 0.602 (0.352, 1.030) 0.064
 High (>88.4) 0.470 (0.315, 0.699) <0.001 0.373 (0.276, 0.505) <0.001 1.389 (0.485, 3.977) 0.539 0.787 (0.348, 1.777) 0.564
Carbohydrate, g
 Low (0–195) 1 1 1 1
 Medium (195–277.5) 1.256 (0.859, 1.838) 0.239 0.883 (0.685, 1.139) 0.339 2.593 (1.030, 6.531) 0.043 1.300 (0.692, 2.443) 0.414
 High (>277.5) 0.994 (0.661, 1.494) 0.976 0.502 (0.374, 0.674) <0.001 2.408 (0.574, 10.106) 0.229 1.037 (0.376, 2.861) 0.943
Total sugars, g
 Low (0–78.4) 1 1 1 1
 Medium (78.4–126.9) 0.802 (0.528, 1.217) 0.299 0.905 (0.689, 1.188) 0.471 0.489 (0.227, 1.058) 0.069 0.968 (0.571, 1.642) 0.905
 High (>126.9) 1.350 (0.930, 1.960) 0.113 0.867 (0.659, 1.139) 0.304 1.175 (0.451, 3.063) 0.741 0.986 (0.476, 2.046) 0.970
Dietary fiber, g
 Low (0–11.7) 1 1 1 1
 Medium (11.7–18.2) 0.918 (0.647, 1.302) 0.631 0.519 (0.400, 0.672) <0.001 0.898 (0.486, 1.661) 0.732 0.657 (0.404, 1.068) 0.089
 High (>18.2) 0.386 (0.239, 0.621) <0.001 0.288 (0.212, 0.390) <0.001 0.689 (0.287, 1.654) 0.404 0.615 (0.328, 1.150) 0.127
Total fat, g
 Low (0–55.6) 1 1 1 1
 Medium (55.6–85.5) 0.802 (0.549, 1.171) 0.252 0.951 (0.736, 1.228) 0.698 1.815 (0.874, 3.770) 0.109 1.184 (0.681, 2.058) 0.550
 High (>85.5) 0.620 (0.417, 0.923) 0.018 0.613 (0.459, 0.820) <0.001 2.158 (0.707, 6.590) 0.176 1.475 (0.651, 3.345) 0.351
Cholesterol, mg
 Low (0–176.5) 1 1 1 1
 Medium (176.5–317.5) 0.607 (0.414, 0.892) 0.011 0.749 (0.577, 0.972) 0.029 0.596 (0.307, 1.160) 0.127 0.971 (0.600, 1.573) 0.904
 High (>317.5) 0.531 (0.358, 0.787) 0.001 0.593 (0.447, 0.785) <0.001 0.510 (0.220, 1.183) 0.116 0.762 (0.398, 1.458) 0.411
Moisture, g
 Low (0–2,101.7) 1 1 1 1
 Medium (2,101.7–3,042.7) 0.501 (0.342, 0.735) <0.001 0.529 (0.409, 0.685) <0.001 0.534 (0.285, 0.999) 0.049 0.669 (0.423, 1.058) 0.085
 High (>3,042.7) 0.413 (0.276, 0.617) <0.001 0.302 (0.220, 0.413) <0.001 0.591 (0.297, 1.173) 0.132 0.296 (0.152, 0.579) <0.001
Polypharmacy
 No 1 1 1 1
 Yes 1.609 (1.094, 2.366) 0.015 1.404 (1.008, 1.955) 0.044 1.939 (1.115, 3.373) 0.019 1.579 (0.976, 2.554) 0.062
Hypertension drugs
 No 1 1 1 1
 Yes 0.865 (0.591, 1.266) 0.454 0.839 (0.615, 1.144) 0.266 0.721 (0.339, 1.535) 0.396 0.632 (0.364, 1.098) 0.103
Open in a new tab

Adjusted for gender, age, ethnicity, education, marital status, Income-poverty ratio, physical activity, drinking, smoking, hypertension, depression, diabetes, asthma, COPD, vitamin D deficiency, milk, energy, protein, carbohydrate, total sugars, dietary fiber, total fat, cholesterol, moisture, and polypharmacy.

Significant values are shown in bold (p < 0.05).

Discussion

In this study, we mainly explored the factors associated with constipation in the population with obesity by marshaling several variables from the NHANES database. Simultaneously, we also analyzed the factors associated with constipation in nonobese participants. For the difference in the definition of constipation, we analyzed the data related to the two definitions of constipation and found not exactly the same results. Hypertension and polypharmacy showed significant associations with constipation populations with obesity across two different definitions, whereas demographic and dietary factors did not have the same variables associated with the two constipation types.

An interesting finding of our study was that people with both obesity and hypertension were less predisposed to constipation (OR: 0.505; 95% CI: 0.334, 0.763; p = 0.00119 consistency-related constipation). To the best of our knowledge, only a few studies have examined the relationship between hypertension and constipation. One such study [20] identified hypertension as a possible risk factor for constipation in older patients, which is contrary to our results. However, the specific diagnostic criteria for hypertension were not mentioned in the literature, so no comparisons could be made. Our diagnostic criteria for hypertension were based on the 2017 American College of Cardiology/American Heart Association 2017 (ACC/AHA 2017) guidelines that reduce the diagnostic threshold to ≥130/80 mm Hg. The prevalence of hypertension in our study population was 49.7%, with an increase in prevalence due to the lower diagnostic threshold value. In the nonobese population, hypertension did not show a significant association with constipation (OR: 0.924; 95% CI: 0.690, 1.238; p = 0.59790). Hypertension might be associated with a reduced risk of constipation in people with obesity. To verify whether the above results were due to the use of hypertensive medication, we analyzed the hypertensive medication variable and found no correlation between the use of hypertensive medication and constipation. Studies have suggested that the ratio of Firmicutes to Bacteroidetes in the intestine of a hypertensive patient is significantly increased, which is a manifestation of intestinal dysbiosis [21–25]. Hypertension may interact with intestinal dysbiosis through intestinal pathology, intestinal permeability, and the gut-brain axis. Several studies have shown that obesity is associated with microbial imbalances and endotoxemia [26–29]. Dysbiosis is an imbalance in the microbiological environment of the gut with an overgrowth of harmful bacteria that can lead to a variety of diseases such as ulcerative colitis and Crohn’s disease, which are characterized by diarrhea and increased frequency of bowel movements [30, 31]. Therefore, we consider that hypertension may exacerbate dysbiosis in population with obesity, causing intestinal tract inflammation, which may increase the frequency of BMs and change the stool consistency to some extent, ultimately reducing the incidence of constipation. Therefore, for people with obesity and hypertension, even if they do not have symptoms of constipation, there may be a dysbiosis in the gut. In stool consistency-related constipation, high cholesterol intake is associated with a reduced incidence of constipation in population with obesity. Long-term cholesterol diet consumption alters the intestinal flora composition, intestinal barrier function, and immune system [32]. High cholesterol intake may also alter stool consistency through intestinal dysfunction caused by bacterial imbalance.

Constipation is a common side effect of medication. Many drugs such as furosemide, levothyroxine sodium, opioids, and ibuprofen can cause constipation [33, 34]. Polypharmacy can amplify this side effect. Our study showed that polypharmacy increased the risk of constipation in the population with obesity, but not in the nonobese participants.

Evidence from many previous studies suggests that higher dietary fiber intake may reduce the risk of functional constipation [35–37]. In stool consistency-based constipation, high dietary fiber intake is associated with a low risk of constipation in the people with obesity. This is consistent with the findings of a study that concluded that dietary fiber intake primarily relieves consistency-based constipation [38]. Another meta-analysis reported that dietary fiber intake contributed to an increase in stool frequency but did not improve stool consistency, which was contrary to our findings. Our results showed no correlation between dietary fiber and frequency-based constipation.

Many health experts recommend lifestyle changes and increased water intake to relieve constipation [39, 40]. The relationship between water and constipation has also been reported in studies conducted in different countries worldwide. A study of factors associated with functional constipation in young Japanese women showed that low magnesium intake and low dietary water intake were independent risk factors for constipation in these young women [41]. According to a study conducted in Korea, the nonconsumption of meat and drinking fewer than 500 mL of water per day were both substantial predictors of constipation in young children [42]. Our results suggest that high moisture levels in nonobese people can reduce the risk of constipation regardless of the definition of constipation. In people with obesity, the effect of water on constipation was not as significant, and only in frequency-related constipation was medium water consumption associated with constipation.

Among other factors, the female sex significantly increased the risk of stool frequency-based constipation in population with obesity and was also significantly associated with constipation in the nonobese population. Mexican Americans are the only ethnic group whose people with obesity were found to be at a lower risk of frequency-based constipation. Depression, one of the most widespread psychiatric disorders, was significantly associated with constipation in the above-mentioned groups. In terms of diet, our results indicate that occasional/often milk consumption and medium energy could reduce the risk of constipation in people with obesity.

In conclusion, our study shows a significant difference in the factors associated with constipation in the obese and nonobese participants. The population with obesity has specific factors that affect constipation such as hypertension, polypharmacy, cholesterol, dietary fiber, depression, and so on (of which hypertension and polypharmacy were significantly associated with constipation), regardless of the definition of constipation that was employed. We also used two different definitions of constipation, which makes the results more comprehensive. Further research is needed to verify the accuracy of the results.

Nevertheless, this study had some limitations. First, only dietary data for the 2 days preceding the interview (which might not represent long-term dietary habits) were available. Second, this was a cross-sectional study based on the NHANES database; therefore, it is not possible to determine the causal relationship between various factors and constipation. Third, the NHANES survey also used interviews and questionnaires to obtain self-reported information, which may involve recall bias.

Statement of Ethics

The survey protocol was reviewed and approved by the National Center for Health Statistics Ethics Review Board, approval number (2005-06). NHANES data is de-identified, so data analysis does not require approval from an Institutional Review Board or written informed consent from study participants.

Conflict of Interest Statement

No potential conflict of interest was reported by the author(s).

Funding Sources

No funding was received.

Author Contributions

The idea for the article was conceived by Junping Liu. The manuscript was drafted by Yongping Hong. Statistical analyses were performed by Xingxing Chen. The manuscript was critically reviewed by Junping Liu and Yongping Hong. All authors reviewed and approved the final version of the manuscript.

Funding Statement

No funding was received.

Data Availability Statement

The data that support the findings of this study are available from NHANES and do not require any permissions. Data sharing is not applicable to this article as no new data were created or analyzed in this study.

References

  • 1. Markland AD, Palsson O, Goode PS, Burgio KL, Busby-Whitehead J, Whitehead WE. Association of low dietary intake of fiber and liquids with constipation: evidence from the National Health and Nutrition Examination Survey. Am J Gastroenterol. 2013;108(5):796–803. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2. Belsey J, Greenfield S, Candy D, Geraint M. Systematic review: impact of constipation on quality of life in adults and children. Aliment Pharmacol Ther. 2010;31(9):938–49. [DOI] [PubMed] [Google Scholar]
  • 3. Heidelbaugh JJ, Stelwagon M, Miller SA, Shea EP, Chey WD. The spectrum of constipation-predominant irritable bowel syndrome and chronic idiopathic constipation: US survey assessing symptoms, care seeking, and disease burden. Am J Gastroenterol. 2015;110(4):580–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4. Martin B, Barghout V, Cerulli A. Direct medical costs of constipation in the United States. Manag Care Interface. 2006;19(12):43–9. [PubMed] [Google Scholar]
  • 5. Yurtdas G, Acar-Tek N, Akbulut G, Cemali O, Arslan N, Beyaz Coskun A, et al. Risk factors for constipation in adults: a cross-sectional study. J Am Coll Nutr. 2020;39(8):713–9. [DOI] [PubMed] [Google Scholar]
  • 6. GBD 2015 Obesity Collaborators; Afshin A, Forouzanfar MH, Reitsma MB, Sur P, Estep K, et al. Health effects of overweight and obesity in 195 countries over 25 years. N Engl J Med. 2017;377(1):13–27. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7. Wilson PWF, D’Agostino RB, Sullivan L, Parise H, Kannel WB. Overweight and obesity as determinants of cardiovascular risk: the framingham experience. Arch Intern Med. 2002;162(16):1867–72. [DOI] [PubMed] [Google Scholar]
  • 8. Peeters A, Barendregt J, Willekens F, Mackenbach J, Al Mamun A, Bonneux L, et al. Obesity in adulthood and its consequences for life expectancy: a life-table analysis. Ann Intern Med. 2003;138(1):24–32. [DOI] [PubMed] [Google Scholar]
  • 9. Lauby-Secretan B, Scoccianti C, Loomis D, Grosse Y, Bianchini F, Straif K, et al. Body fatness and cancer — viewpoint of the IARC working group. N Engl J Med. 2016;375(8):794–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10. Koppen I, Velasco-Benítez C, Benninga M, Di Lorenzo C, Saps M. Is there an association between functional constipation and excessive bodyweight in children? J Pediatr. 2016;171:178–82.e1. [DOI] [PubMed] [Google Scholar]
  • 11. Pourhoseingholi M, Kaboli S, Pourhoseingholi A, Moghimi-Dehkordi B, Safaee A, Mansoori B, et al. Obesity and functional constipation; a community-based study in Iran. J Gastrointestin Liver Dis. 2009;18(2):151–5. [PubMed] [Google Scholar]
  • 12. Silveira EA, Santos A, Ribeiro JN, Noll M, Dos Santos Rodrigues AP, de Oliveira C. Prevalence of constipation in adults with obesity class II and III and associated factors. BMC Gastroenterol. 2021;21(1):217. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13. Lewis S, Heaton K. Stool form scale as a useful guide to intestinal transit time. Scand J Gastroenterol. 1997;32(9):920–4. [DOI] [PubMed] [Google Scholar]
  • 14. Singh P, Mitsuhashi S, Ballou S, Rangan V, Sommers T, Cheng V, et al. Demographic and dietary associations of chronic diarrhea in a representative sample of adults in the United States. Am J Gastroenterol. 2018;113(4):593–600. [DOI] [PubMed] [Google Scholar]
  • 15. Sommers T, Mitsuhashi S, Singh P, Hirsch W, Katon J, Ballou S, et al. Prevalence of chronic constipation and chronic diarrhea in diabetic individuals in the United States. Am J Gastroenterol. 2019;114(1):135–42. [DOI] [PubMed] [Google Scholar]
  • 16. Fazio VW. Complex anal fistulae. Gastroenterol Clin North Am. 1987;16(1):93–114. [PubMed] [Google Scholar]
  • 17. Kroenke K, Spitzer RL, Williams JBW. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001;16(9):606–13. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18. Forrest K, Stuhldreher W. Prevalence and correlates of vitamin D deficiency in US adults. Nutr Res. 2011;31(1):48–54. [DOI] [PubMed] [Google Scholar]
  • 19. Masnoon N, Shakib S, Kalisch-Ellett L, Caughey GE. What is polypharmacy? A systematic review of definitions. BMC Geriatr. 2017;17(1):230. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20. Abdul Wahab P, Mohd Yusoff D, Abdul Kadir A, Ali S, Yeong Yeh L. Prevalence, symptoms, and associated factors of chronic constipation among older adults in north-east of peninsular Malaysia. Clin Nurs Res. 2022;31(2):348–55. [DOI] [PubMed] [Google Scholar]
  • 21. Yang T, Santisteban MM, Rodriguez V, Li E, Ahmari N, Carvajal JM, et al. Gut dysbiosis is linked to hypertension. Hypertension. 2015;65(6):1331–40. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22. Mushtaq N, Hussain S, Zhang S, Yuan L, Li H, Ullah S, et al. Molecular characterization of alterations in the intestinal microbiota of patients with grade 3 hypertension. Int J Mol Med. 2019;44(2):513–22. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23. Yan Q, Gu Y, Li X, Yang W, Jia L, Chen C, et al. Alterations of the gut microbiome in hypertension. Front Cell Infect Microbiol. 2017;7:381. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24. Dong S, Liu Q, Zhou X, Zhao Y, Yang K, Li L, et al. Effects of losartan, atorvastatin, and aspirin on blood pressure and gut microbiota in spontaneously hypertensive rats. Molecules. 2023;28(2):612. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25. Li HB, Yang T, Richards EM, Pepine CJ, Raizada MK. Maternal treatment with captopril persistently alters gut-brain communication and attenuates hypertension of male offspring. Hypertension. 2020;75(5):1315–24. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26. Cani P, Amar J, Iglesias M, Poggi M, Knauf C, Bastelica D, et al. Metabolic endotoxemia initiates obesity and insulin resistance. Diabetes. 2007;56(7):1761–72. [DOI] [PubMed] [Google Scholar]
  • 27. Moreno-Navarrete J, Sabater M, Ortega F, Ricart W, Fernández-Real J. Circulating zonulin, a marker of intestinal permeability, is increased in association with obesity-associated insulin resistance. PLoS One. 2012;7(5):e37160. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28. Castaner O, Goday A, Park YM, Lee SH, Magkos F, Shiow SATE, et al. The gut microbiome profile in obesity: a systematic review. Int J Endocrinol. 2018;2018:4095789. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29. Boutagy N, McMillan R, Frisard M, Hulver M. Metabolic endotoxemia with obesity: is it real and is it relevant? Biochimie. 2016;124:11–20. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30. Wlodarska M, Kostic A, Xavier R. An integrative view of microbiome-host interactions in inflammatory bowel diseases. Cell Host Microbe. 2015;17(5):577–91. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31. Tarracchini C, Milani C, Longhi G, Fontana F, Mancabelli L, Pintus R, et al. Unraveling the microbiome of necrotizing enterocolitis: insights in novel microbial and metabolomic biomarkers. Microbiol Spectr. 2021;9(2):e0117621. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32. Rinninella E, Cintoni M, Raoul P, Lopetuso LR, Scaldaferri F, Pulcini G, et al. Food components and dietary habits: keys for a healthy gut microbiota composition. Nutrients. 2019;11(10):2393. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33. Lugoboni F, Hall G, Banerji V. Impact of opioid-induced constipation on opioid substitution therapy management: the patient perspective. Drugs Context. 2021;10:1–10. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34. Fosnes G, Lydersen S, Farup P. Constipation and diarrhoea: common adverse drug reactions? A cross sectional study in the general population. BMC Clin Pharmacol. 2011;11:2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35. Lee WT, Ip KS, Chan JS, Lui NW, Young BW. Increased prevalence of constipation in pre-school children is attributable to under-consumption of plant foods: a community-based study. J Paediatr Child Health. 2008;44(4):170–5. [DOI] [PubMed] [Google Scholar]
  • 36. Jennings A, Davies GJ, Costarelli V, Dettmar PW. Dietary fibre, fluids and physical activity in relation to constipation symptoms in pre-adolescent children. J Child Health Care. 2009;13(2):116–27. [DOI] [PubMed] [Google Scholar]
  • 37. Dukas L, Willett W, Giovannucci EL, Giovannucci EL. Association between physical activity, fiber intake, and other lifestyle variables and constipation in a study of women. Am J Gastroenterol. 2003;98(8):1790–6. [DOI] [PubMed] [Google Scholar]
  • 38. Zhang X, Tian H, Gu L, Nie Y, Ding C, Ge X, et al. Long-term follow-up of the effects of fecal microbiota transplantation in combination with soluble dietary fiber as a therapeutic regimen in slow transit constipation. Sci China Life Sci. 2018;61(7):779–86. [DOI] [PubMed] [Google Scholar]
  • 39. Dionne J, Ford AC, Yuan Y, Chey WD, Lacy BE, Saito YA, et al. A systematic review and meta-analysis evaluating the efficacy of a gluten-free diet and a low FODMAPS diet in treating symptoms of irritable bowel syndrome. Am J Gastroenterol. 2018;113(9):1290–300. [DOI] [PubMed] [Google Scholar]
  • 40. Bellini M, Tonarelli S, Nagy A, Pancetti A, Costa F, Ricchiuti A, et al. Low FODMAP diet: evidence, doubts, and hopes. Nutrients. 2020;12(1):148. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41. Murakami K, Sasaki S, Okubo H, Takahashi Y, Hosoi Y, Itabashi M, et al. Association between dietary fiber, water and magnesium intake and functional constipation among young Japanese women. Eur J Clin Nutr. 2007;61(5):616–22. [DOI] [PubMed] [Google Scholar]
  • 42. Park M, Bang YG, Cho KY. Risk factors for functional constipation in young children attending daycare centers. J Korean Med Sci. 2016;31(8):1262–5. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data that support the findings of this study are available from NHANES and do not require any permissions. Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Articles from Obesity Facts are provided here courtesy of Karger Publishers

RESOURCES