Abstract
Background
The impact of bacterial vaginosis on women’s health is an increasing concern; however, the effect of the obesity index on bacterial vaginosis is controversial. We investigated the association between body mass index and bacterial vaginosis in women in the United States.
Methods
This was a cross-sectional study which obtained the data from the National Health and Nutrition Examination Survey from 2001 to 2004, in which weighted multivariate regression and logistic regression analyses were performed to explore the independent relationship between body mass index and bacterial vaginosis. Subgroup analyses and smoothed curve fitting were also performed.
Results
A total of 5,428 participants were enrolled, and the findings show that the participants with higher body mass index tended to have a higher incidence of bacterial vaginosis. In the fully adjusted model, a positive association between bacterial vaginosis and body mass index was observed (Odd’s ratio = 1.03, 95% Confidence interval, 1.01–1.04). The subgroup analysis showed that this positive association was significant in non-Hispanic White individuals (Odd’s ratio = 1.0327, 95% Confidence interval, 1.0163, 1.0493).
Conclusion
Increased bacterial vaginosis positivity may be associated with an increased body mass index.
Material and methods
Data source
The data used in this analysis were obtained from the National Health and Nutrition Examination Survey (NHANES), an ongoing survey aimed at assessing the health and nutritional status of individuals in the United States. Specifically, we used nationally representative data from the 2001–2004 cycles of NHANES. It is important to note that data on bacterial vaginosis (BV) were available only for the 2001–2002 and 2003–2004 cycles. This study used a cross-sectional design.
Study population
Our study utilized population-based data from the NHANES. This survey included interviews and medical examinations focusing on various health and nutrition measurements. All participants provided written informed consent and the study was approved by the NCHS Research Ethics Review Board. The inclusion criteria were as follows: Women who participated in the NHANES 2001–2004 and had available Nugent score and body mass index (BMI) records (BMI <40 kg/m2). Exclusion criteria: Participants without BMI or Nugent score records. For more information, please refer to the official NHANES website (https://www.cdc.gov/nchs/nhanes/index.htm).
Variables
Bacterial vaginosis
The BV scores were calculated using Nugent’s method. The final BV scores were categorized as follows: scores of 0–3 indicating normal vaginal flora, 4–6 indicating intermediate flora, and 7–10 indicating bacterial vaginosis [1]. The outcomes were defined as BV-confirmed yes (positive and intermediate) or no (negative).
Body mass index
BMI is a measure of body weight relative to height and is calculated as weight (in kilograms) divided by the square of height (in meters). In our analysis, the BMI was treated as a continuous variable.
Social demographic data
Social demographic data included age (in years), race (categorized as 1: Non-Hispanic White, 2: Non-Hispanic Black, 3: Mexican American, and 4: other races), educational level (categorized as below high school, high school, and college degree or above), marital status (categorized as living with a partner [married] or living alone [widowed, divorced, or separated]), and household income to poverty ratio (PIR). Other health-related variables included smoking status (whether the participant had smoked at least 100 cigarettes in their lifetime) 、vaginal douching in the past six months (yes or no)、diabetes (The diagnostic criteria for diabetes are:1.doctor told you have diabetes, 2.glycohemoglobin HbA1c(%) > = 6.5, 3.fasting glucose (mmol/l) > = 7.0, 4.random blood glucose (mmol/l) > = 11.1, 5.Use of diabetes medication or insulin; Prediabetes: Hba1c: > = 5.7 and <6.5,FPG: 5.6–7.0), pregnant.
Statistical analysis
To ensure the accuracy of our analysis, the sampling weight of the NHANES data was incorporated into all statistical analyses. Specifically, multivariate logistic regression was used to assess the association between BV and BMI, while adjusting for covariates. Continuous variables are presented as mean ± standard deviation, and categorical variables are presented as percentages. A multivariate logistic regression model with NHANES sampling weights was used for the three models. No covariates were adjusted for in Model 1. In Model 2, adjustments were made for age and race. In Model 3, adjustments were made for age, race, education level, household income ratio, marital status, smoking status, and vaginal douching in the past 6 months. In the sensitivity analyses, we converted from a continuous variable to a categorical variable to assess its robustness. Additionally, we utilized a generalized additive model (GAM) and smoothed curve fitting to account for potential nonlinear relationships between BMI and BV. The statistical software packages R (http://www.R-project.org) and Empower Stats (http://www.empowerstats.com) were used for data analysis. Statistical significance was set at P < 0.05.
Introduction
The stability of vaginal microecology plays an important role in the health of the female reproductive tract [2]. In 2011, Ravel et al. performed a bacterial sequencing analysis of vaginal samples from 396 reproductive-aged women and revealed the existence of five dominant bacterial vaginal microbiota types [3]. Subsequently, they classified them into 13 types. Although racial differences exist in the vaginal microbiota, lactobacilli dominate the vaginal microbiota of healthy reproductive-aged women. A decrease in lactobacilli and an increase in vaginal microbial diversity have been linked to the acquisition and persistence of HPV as well as the development of cervical cancer [4, 5]. Furthermore, changes in the vaginal microecology can affect the endometrium [2].
Bacterial vaginosis is one of the most common types of vaginitis in women of reproductive age [6]. In BV, the dominant Lactobacilli is replaced by facultative anaerobes or anaerobic bacteria such as Gardnerella vaginalis and Atobola vaginalis in the vagina, resulting in an imbalance of normal flora and mixed infections (Klatt and Barnabei, 2010; Bradshaw and Sobel, 2016; Ranjit et al., 2018) [4, 7, 8]. If left untreated, BV can lead to complications, such as pelvic inflammatory disease, an increased risk of sexually transmitted infections, and preterm birth in pregnant women [9, 10].
Obesity is a global concern that poses significant health risks to individuals, and its impact on women should not be underestimated. Obesity can contribute to various gynecological complications, including irregular menstruation, polycystic ovary syndrome [11–13], infertility [14], hypertension during pregnancy [15], and gestational diabetes [16]. However, the effect of obesity on these outcomes is not fully understood. Although extensive research has been conducted on the effects of obesity on intestinal microecology, few studies have demonstrated its impact on vaginal microecology. Moreover, the association between obesity and BV remains controversial.
This study used the National Health and Nutrition Examination Survey (NHANES) database to evaluate the relationship between BV and BMI in American women.
Discussion
In this cross-sectional study involving 5,428 participants, a positive correlation was found between BV and BMI, particularly among individuals of the Non-Hispanic White race. This suggests that an increase in BMI may contribute to a higher incidence of BV.
Previous studies on the relationship between BV and obesity have reported conflicting results. For instance, a prospective cohort study focusing on female sex workers in Mombasa, Kenya, found that obese women had a nearly 20% lower risk of BV than normal weight [17]. However, it is important to note that the Mombasa study differed significantly in population characteristics from the current NHANES cross-sectional study, which included women of various ethnicities. These racial differences were significant mainly because of the higher prevalence of BV in black women than in white women, thus indicating the presence of a more diverse vaginal microbiota in different ethnicities.
In a retrospective analysis, the researchers investigated the relationship between obesity and BV in 106 women of reproductive age. They concluded the there was no significant relationship between BMI and BV [18]. It is worth noting that this retrospective study only included 106 participants and did not take race into account, which may be the main reason for the difference with the results of the NHANES cross-sectional study.
Another retrospective analysis conducted by Daubert et al. included 10,184 women infected with HIV or at risk of infection. The study showed that the rate of BV in women with obesity (BMI >30 kg/m2) was lower compared to the reference group (BMI 18.5–24.9 kg/m2) (adjusted OR, 0.87: 95% CI, 0.79–0.97: P = 0.009). It is worth considering that this study focused on HIV-positive women, the differences in sexual behavior between HIV-positive patients and non-infected women may have influenced the results [19]. The current NHANES cross-sectional study did not consider HIV infection as a factor. Si et al. investigated the genetic relationship between vaginal microbiota and obesity in 542 Korean women and demonstrated that obesity was associated with an increase in Prevotella levels and a decrease in the relative abundance of lactobacilli [20]. This finding was inconsistent with our conclusions. Similarly, Raglan et al. conducted a prospective study on 109 women and discovered that obese women had a lower prevalence of lactobacilli-dominated vaginal microbiota and a higher prevalence of high-diversity vaginal microbiota (depleted in Lactobacillus spp. and Gardnerella spp.) when compared to non-obese participants (P < 0.001) [21]. Brookheart et al. conducted a cross-sectional study on 5,918 participants and observed a higher incidence of BV in overweight and obese women compared to thin women [22].
Obesity is characterized by a low inflammatory state, and the mechanisms that lead to an increased incidence of BV are likely multifaceted [23, 24]. First, obesity can contribute to gynecological diseases, such as menstrual irregularities and polycystic ovary syndrome [25]. Menstrual blood has a significant effect on vaginal microecology [25–27]. Second, obesity can affect personal hygiene habits and excess adipose tissue increases vaginal moisture, which may change the vaginal environment. Obesity can also affect glycogen levels in the vaginal epithelium, as it decreases estrogen levels rather than increasing them, resulting in a decrease in the abundance of Lactobacillus bacteria and an increase in the concentration of certain anaerobic bacteria [28]. Additionally, changes in intestinal microecology can influence vaginal microecology and dietary habits, as well as the intestinal microecology of obese individuals, which may contribute to synergistic changes in vaginal microecology [29].
This study has several strengths. First, the study was based on a large population from the NHANES database, which provided a more representative sample. This study also considered confounding factors to enhance the credibility of the results. However, this study has some limitations. As this was a cross-sectional study, a clear causality cannot be established. Furthermore, the collected data may not have been recent. Additionally, important comorbidities such as polycystic ovary syndrome was not included in the data collection, which limited the ability to control for confounding factors.
In conclusion, this study suggests that increased BMI is associated with a higher incidence of bacterial vaginosis. This positive correlation was particularly noticeable among Non-Hispanic White individuals. However, further large-scale prospective studies are needed to validate these findings.
Results
In total, there were 5,428 women enrolled in the current study, including 3,106 BV-positive women (age: 32.44±9.35, BMI: 26.16±5.28) and 2,322 BV-negative women (age: 32.34±9.57, BMI: 25.20±5.28). There were significant differences in BMI, household income, race, education, smoking, vaginal douching within 6 months, diabetes and pregnant. BV-positive individuals had a greater BMI when compared to that of BV-negative individuals (Table 1). The association between BMI and BV is shown in Table 2. Our results showed that a higher BMI was associated with BV positivity. In the fully adjusted model, a positive association was observed between BMI and BV positivity (OR = 1.03, 95% CI: 1.01–1.04), which suggests that for every unit increase in BMI, the incidence of BV increased by 2.03%. BMI was further converted from a continuous variable to a categorical variable for the sensitivity analysis. The increase in BV positivity was not very significant compared to that in the group with a BMI of less than 18.5 (Table 2). As shown in Table 3, the positive correlation between BV and BMI may have been influenced by age and educational attainment.
Table 1. Baseline characteristics of participant.
| Characteristic | BV | ||
|---|---|---|---|
| negative | positive | P value | |
| Age, mean(sd),years | 32.3050 ± 9.5692 | 32.4444 ± 9.3505 | 0.588099 |
| BMI,mean(sd),kg/m2 | 25.1969 ± 5.0009 | 26.1618 ± 5.2770 | <0.000001 |
| Poverty-to-income ratio, mean(sd) | 3.0935 ± 1.6818 | 2.8234 ± 1.6604 | <0.000001 |
| Race/Ethnicity (%) | <0.000001 | ||
| Non-Hispanic White | 80.8439 | 68.8613 | |
| Non-Hispanic Black | 16.0260 | 20.7384 | |
| Mexican American | 65.1974 | 61.8961 | |
| Other race/ethnicity | 8.6818 | 6.9757 | |
| Education level (%) | 0.000029 | ||
| Less than high school | 10.0948 | 10.3898 | |
| High school | 16.0260 | 20.7384 | |
| More than high school | 65.1974 | 61.8961 | |
| others | 8.6818 | 6.9757 | |
| Marital status (%) | 0.072891 | ||
| Married and living with partner | 57.4710 | 55.0497 | |
| Living alone | 42.5290 | 44.9503 | |
| Smoking (%) | 0.000311 | ||
| Yes | 35.6371 | 40.5489 | |
| No | 55.6812 | 52.4755 | |
| NA | 8.6818 | 6.9757 | |
| Whether there has been vaginal douching in the past 6 months | <0.000001 | ||
| Yes | 12.8783 | 20.5623 | |
| No | 82.9727 | 71.6656 | |
| NA | 4.1489 | 7.7721 | |
| Diabetes | 0.002 | ||
| No | 87.08 | 96.281 | |
| Pre-Diabetes | 0.89 | 0.965 | |
| Yes | 2.029 | 2.507 | |
| Pregnant | <0.001 | ||
| No | 87.037 | 90.019 | |
| Yes | 12.963 | 9.981 | |
Data in the table
For continuous variables: P-value was by survey-weighted linear regression。 For categorical variables: P-value was by survey-weighted Chi-square test。BMI, body mass index; NHANES, National Health, and Nutrition Examination Survey; SD, standard deviation. BV, bacterial vaginosis
Table 2. The association between BMI and BV.
| Exposure | Non-adjusted model OR,95%CI | Minimally-adjusted model OR,95%CI | Fully-adjusted model OR,95%CI |
|---|---|---|---|
| BMI | 1.03 (1.02, 1.04) | 1.02 (1.01, 1.03) | 1.03 (1.01, 1.04) |
| BMI | |||
| <18.5 | Ref | Ref | Ref |
| > = 18.5, <25 | 0.87 (0.65, 1.15) | 0.86 (0.64, 1.14) | 0.83 (0.59, 1.16) |
| > = 25, <30 | 1.12 (0.84, 1.50) | 1.05 (0.78, 1.42) | 1.03 (0.72, 1.47) |
| > = 30 | 1.28 (0.95, 1.72) | 1.13 (0.83, 1.53) | 1.15 (0.80, 1.64) |
| P for trend | <0.0001 | 0.0004 | 0.003 |
Non-adjusted model: no covariates were adjusted for.
Minimally-adjusted model: we only adjusted for age and race.
Fully-adjusted model: we adjusted for all covariates presented in Table 1. Abbreviation: BMI, body mass index; BV, bacterial vaginosis
Table 3. Subgroup analysis of the association between BMI and BV.
| Subgroup | OR (95%CI) | P for interaction |
|---|---|---|
| Age | <0.0001 | |
| <20 | 1.1018 (1.0724, 1.1320) | |
| 20–40 | 1.0117 (0.9955, 1.0282) | |
| ≥40 | 0.9940 (0.9681, 1.0206) | |
| Race/ethnicity | 0.2497 | |
| Non-Hispanic White | 1.0327 (1.0163, 1.0493) | |
| Non-Hispanic Black | 1.0095 (0.9823, 1.0374) | |
| Mexican American | 1.0224 (0.9931, 1.0525) | |
| Other race/multiracial | 1.0612 (1.0142, 1.1104) | |
| Education level | <0.0001 | |
| Less than high school | 0.9880 (0.9481, 1.0297) | |
| High school | 0.9777 (0.9483, 1.0081) | |
| More than high school | 1.0188 (1.0018, 1.0362) | |
| others | 1.1020 (1.0726, 1.1322) |
Age, race, BMI, Marital status, Poverty-to-income ratio, Whether there has been vaginal douching
in the past 6 months, smoking were adjusted. In the subgroup analyses, the model is not adjusted for the stratification variable itself.
Abbreviation: BMI, body mass index; BV, bacterial vaginosis
The current results suggest that the association between BV and BMI is not consistent across all races, and the authors observed a positive association between BV and BMI in whites stratified by race. Its curvilinear relationship was also positive, whereas this positive association was not particularly evident among Blacks and Mexicans (Table 3, Fig 1B). Smoothed splines were used to demonstrate the relationship between BMI and BV (Fig 1A).
Fig 1. The association between BMI and BV.
A) Solid line plot of curve fitting with BV and bmi as variables. The red line indicates the smooth curve fit between the variables. The 95% confidence interval of the fit is shown by the blue bar. (B) Curve-fit solid line plot of the relationship between BV and BMI analysed stratified by race.1: Non-Hispanic White,2: Non-Hispanic Black,3: Mexican American,4: Other race/ethnicity.
Acknowledgments
We would like to thank Editage (www.editage.cn) for English language editing.
Data Availability
The datasets generated and analysed during the current study are available in the (https://www.cdc.gov/nchs/nhanes/).
Funding Statement
The author received no specific funding for this work.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets generated and analysed during the current study are available in the (https://www.cdc.gov/nchs/nhanes/).