Overall Adiposity, Adipose Tissue Distribution, and Endometriosis: A Systematic Review

Abstract

Background

Endometriosis has been associated with a lean body habitus. However, we do not understand whether endometriosis is also associated with other characteristics of adiposity, including adipose tissue distribution and amount of visceral adipose tissue (VAT; adipose tissue lining inner organs). Having these understandings may provide insights on how endometriosis develops—some of the physiologic actions of adipose tissue differ depending on tissue amount and location, and are related to proposed mechanisms of endometriosis development.

Objectives

To review the literature regarding overall adiposity, adipose tissue distribution and/or VAT, and endometriosis.

Methods

We reviewed and synthesized studies indexed in PubMed and/or Web of Science. We included studies that had one or more measures of overall adiposity, adipose tissue distribution, and/or VAT, and women with and without endometriosis for comparison. We summarized the findings and commented on the methods used and potential sources of bias.

Results

Out of 366 identified publications, 19 (5.2%) were eligible. Two additional publications were identified from reference lists. Current research included measures of overall adiposity (e.g., body figure drawings) or adipose tissue distribution (e.g., waist-to-hip ratio), but not VAT. The weight of evidence indicated that endometriosis was associated with low overall adiposity and with a preponderance of adipose tissue distributed below the waist (peripheral).

Discussion

Endometriosis may be associated with being lean or having peripherally distributed adipose tissue. Well-designed studies with various sampling frameworks and precise measures of adiposity and endometriosis are needed to confirm associations between adiposity measures and endometriosis, and delineate potential etiologic mechanisms underlying endometriosis.

Endometriosis is a chronic women’s health problem that affects at least 10%-11% of reproductive age women (Buck Louis et al., 2011; Olive & Schwartz, 1993). It is defined by the presence of uterine endometrial glands and stroma located outside of the uterine cavity (Burney & Giudice, 2012; Rogers et al., 2013). Endometriosis is diagnosed via surgical visualization (Scholefield, Sajjad, & Morgan, 2002) with or without histologic confirmation. Severity of disease is described as stages I (minimal), II (mild), III (moderate), and IV (severe) depending on the location, size, and depth of endometrial implants and related operative findings such as adhesions (Canis et al., 1997; Practice Committee of the American Society for Reproductive Medicine [ASRM], 2012). Compared to women without endometriosis, women with the disease experience a 20% reduction in quality-adjusted life years (Simoens et al., 2012) and have greater risk for certain cancers (Melin, Sparén, Persson, & Bergqvist, 2006; Pearce et al., 2012). The societal and economic costs of endometriosis are estimated to be about $70 billion annually for both in- and out-patient medical costs and indirect costs associated with reduced productivity.

The natural history of endometriosis remains unknown despite this being an active area of research. Historically, endometriosis was thought to arise after the onset of menses from retrograde menstruation. This is the theory that endometrial tissue spreads outside the uterus when menstrual fluid flows back through the fallopian tubes during menstruation (Sampson, 1927). There are no definitive data that retrograde menstruation occurs more in women with than without endometriosis (D’Hooghe & Debrock, 2002; Halme, Hammod, Hulka, Raj, & Talbert, 1984). Recently, researchers have posited that endometriosis may have fetal origins (Signorile et al., 2010), further complicating our understanding of the natural history of the disease. To better understand the etiology and risk factors for endometriosis, researchers are searching for endometriosis biomarkers —”objective, quantifiable characteristics of biological processes” (Strimbu & Tavel, 2010, p. 2). While this is an active area of research, investigators have yet to definitively identify endometriosis biomarkers (Borrelli, Abrão, & Mechsner, 2014; Fassbender et al., 2013; Fassbender, Burney, Dorien, D’Hooghe, & Giudice, 2015; May et al., 2010; May, Villar, Kirtley, Kennedy, & Becker, 2011).

Complicating understanding of endometriosis is its symptomatology. Key endometriosis symptoms include menstrual irregularities, chronic pelvic pain, dyspareunia, or dysmenorrhea difficulty conceiving (Burney & Giudice, 2012; Murphy, 2002; Practice Committee of the ASRM, 2012). However, many of these endometriosis symptoms are associated with other gynecologic disorders. Moreover, not all women with endometriosis are symptomatic. Although health practitioners often initiate diagnostic workups based on symptoms (Ballard, Lowton, & Wright, 2006), evidence does not support this practice (Eskenazi & Warner, 1997; Vercellini et al., 2007).

Some research findings suggest that endometriosis is associated with lean body habitus as measured by body mass index (BMI) (Viganò et al., 2012). However, BMI is a proxy of adiposity, reflecting overall body mass rather than adipose tissue mass. In addition, BMI does not reflect the distribution of body fat (Deurenberg, Yap, & van Staveren, 1998; Shah & Braverman, 2012). Better understanding of which aspects of adiposity may be associated with endometriosis are needed in order to identify etiologic or mediating pathways associated with adiposity and endometriosis. For instance, some research findings suggest that adipose tissue has immunologic properties (Rasouli & Kern, 2008; Schäffler, Schölmerich, & Salzberger, 2007; Waki & Tontonoz, 2007). The immunologic properties of adipose tissue are known to differ depending on tissue type and location (Ibrahim, 2010; Vatier et al., 2012). It is possible that the immunologic properties of adipose tissue may be involved in or impacted by the development of endometriosis. Endometriosis may arise due to altered immunologic functioning (Committee on Practice Bulletins—Gynecology, 2010; Practice Committee of the ASRM, 2012; Rocha, Reis, & Taylor, 2013). This altered functioning may include the promotion or inhibition of inflammation (Kobashi et al., 2005; Mandal, Pratt, Barnes, McMullen, & Nagy, 2011; Ouchi et al., 2000) and stimulation of angiogenesis (Adya, Tan, Chen, & Randeva, 2012; Cao, Brakenhielm, Wahlestedt, Thyberg, & Cao, 2001). One potential link among immunity, endometriosis, and adiposity is M2 phenotype macrophages. These macrophages, which are anti-inflammatory and promote tissue regeneration at injury sites (Murray & Wynn, 2011), have been implicated in the development of endometriosis (Smith, Pearson, Hachey, Xia, & Wachtman, 2012; Wang et al., 2014; Wang et al., 2015). These macrophages are also associated with leanness in mice (Lumeng, Bodzin, & Saltiel, 2007). If links exist among adiposity-related immunity, quantity and location of adipose tissue, and endometriosis, it could be possible to identify diagnostic markers of endometriosis that combine adiposity measures with biomarkers.

Purpose

The purpose of our systematic review is to summarize the literature describing observational studies in which researchers included women with—or at risk for—endometriosis and measures of endometriosis and adiposity inclusive of overall adiposity, adipose tissue distribution, and visceral adipose tissue (VAT). We defined overall adiposity as the total adipose tissue on people’s bodies, adipose tissue distribution as the location of adipose tissue across the body, and VAT as the amount of adipose tissue that lines the inner organs.

Methods

Study Selection

We deemed publications eligible for review if they had been published before January 22, 2014 and met our inclusion criteria: (a) reported findings from observational studies; (b) included one or more measures of adiposity; and (c) included both women with and without endometriosis. Endometriosis could have been diagnosed by physicians during the studies or reported by participants as being diagnosed with endometriosis by a physician. We retained studies in which (a) researchers recruited small samples of women, (b) adiposity measures were self-reported, and (c) women were assessed for endometriosis using methods other than the clinical gold standard of visualized disease (Practice Committee of the ASRM, 2012). We did this to minimize selection bias and provide a comprehensive summary of the current literature available to researchers and health practitioners.

We excluded experimental research studies because many participants would have been taking medications which may alter the amount and distribution of adipose tissue (Bruce et al., 1991). Publications in which authors only reported BMI without also reporting weights and heights were excluded because we did not have original data to review. In addition, we excluded studies in which researchers only assessed BMI as an adiposity measure; Viganò et al. (2012) recently completed a review on associations between BMI and endometriosis; to complement their work, we chose to review studies based on measures of adiposity other than BMI.

Search Strategy

We conducted searches in PubMed and Web of Science using a predetermined list of search terms (see Table, Supplemental Digital Content 1). We uploaded citations into Reference Manager (v. 11). After full-text review, we verified that the studies we planned to include did meet our eligibility criteria. We also checked reference lists to identify additional publications.

Reporting Procedure

We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Statement (prisma-statement.org) to guide our reporting. We abstracted the following data from each publication: design; sample; description of comparison group, criteria for assessing adiposity and diagnosing endometriosis; methods that researchers used to improve internal validity in their designs; and results. We identified limitations and (dis)similarities across studies we reviewed. We reported: (a) differences either in means for continuous variables or in proportions for ordinal variables from bivariate analyses; (b) odds ratios (ORs) and 95% confidence intervals (CIs) for findings from cross-sectional case control studies; and (c) rate ratios (RR) and 95% CIs for findings from prospective cohort studies. Because none of the studies that we identified included findings on VAT, we report findings based on measures of overall adiposity and/or adipose tissue.

Results

Eligible Studies

The complete process of identifying eligible publications is shown in a figure (see Supplemental Digital Content 2). After removing duplicates, we vetted 366 unique publications. We excluded 347 (94.8%) publications because they failed to meet inclusion criteria; 19 publications (5.2%) met these criteria. With two additional publications we found from reference lists, a total of 21 publications were available for this review. Of the 21 publications, 18 (85.7%) were described as case control studies. Three of the 21 were based on data from a single, prospective cohort study which was the Nurses’ Health Study II (NHSII) (see Tables 1 and 2 for references).

TABLE 1.

Case-Control Studies: Study Characteristics

		Setting		n				Age
Studya	Year	Location	Clinic type	Case	Control	Group	Inclusionb	M	(SD)	Parityc
Ayas	2012	Turkey	Hospital OB-GYN	36	37	Case	Laparotomy/laparoscopy	32.4	(5.7)	1.0 (1.3)
						Control	Same as cases	30.5	(5.8)	0.9 (1.1)
Bedaiwy	2006	USA	Tertiary hospital	60	48	Case	Laparoscopyd	33.0	(5.5)	0.6 (0.9)
						Control-1	Laparoscopyd,e	31.3	(3.6)	0.3 (0.6)
						Control-2	Laparoscopyd,f	33.6	(5.9)	1.6 (1.1)
Cramer	1986	USA, Canada	Infertility centers	268	3794	Case	Infertility evaluation	--m	--m	9.2
						Control	Delivery of live-born child, no infertility history	--m	--m	100.0
Darrow	1994	USA	Endometriosis center	104	198	Case	New endometriosis diagnosis, age 19–45 years, New York state residents, English speaking	--n	--n	--
						Control-1	Friend of case	--n	--n	--
						Control-2	Clinic patientg	--n	--n	--
Ferrero	2005	Italy	Hospital OB-GYN	366	248	Case	Laparoscopyh	33.3	(4.6)	24.9
						Control				34.7
Hediger	2005	USA	Hospitals	32	52	Case	Laparoscopyi, age 18–40 years	32.7	(4.4)	15.6
						Control		31.6	(5.0)	50.0
Kvaskoff	2009	France	n/a	4241	92,974	Case	Age 40–65 years, insured by national health plan	--	--	--
						Control		--	--	--
Pillet	2012	France	Hospital OB-GYN	238	238	Case	Laparotomy/laparoscopyh age <42 years	31.5	(5.3)	0.2 (0.6)
						Control		31.4	(5.2)	0.5 (1.0)
Matalliotakis	2008	USA	Hospital OB-GYN	535	200	Case	Laparotomy/laparoscopyk,l	34.3	(6.5)	35.3
						Control	Infertility, surgical evaluation	34.4	(4.4)	33.5
McCann	1993	USA	Endometriosis center	88	88	Case	Age 19–45 years, laparoscopy-diagnosed endometriosis within 1 year of interview, White, English speaking	29.4	(4.7)	--
						Control	Friends of cases, not biologically related, no endometriosis, within 2 years of case friend not patients at the center	29.4	(4.6)	--
Nagle	2009	Australia	n/aj	268	244	Case	Enrolled in Endogene Study, no family history of endometriosis, diagnosed with stage III/IV endometriosis	36.4	(7.1)	--
						Control	Enrolled on Australian Twin Registry	36.3	(7.6)	--
Pandey	2010	India	Hospital OB-GYN	15	35	Case	Laparoscopyl, age 18–45 years, infertility	29.3	(5.2)	--
						Control		30.1	(3.1)	--
Rico	1993		No description	28	33	Case	Laparoscopyl	31.9	(1.6)	--
						Control		32.7	(6.0)	--
Sahmani	2013	Iran	Surgical hospital	97	107	Case	Laparoscopyk,l, age 18–42 years, chronic pelvic pain or infertility	29.5	(5.5)	--
						Control	Laparoscopyk,l	29.8	(4.5)	--
Savaris	2011	Brazil	Endometriosis clinic	25	20	Case	Laparoscopy	27.1	(4.6)	--
						Control		28.9	(4.5)	--
Signorello	1997	USA	Surgical hospital	50	136	Case	Pelviscopy/laparoscopy, English speaking, age 23- 44 years, infertility or history of miscarriages	--o	--o	--
						Control-1		--o	--o	--
						Control-2	Laparoscopic tubal ligation	--m	--m	--
Vercellini	2013	Italy	Hospital OB-GYN	200	100	Case-1	Laparotomy/laparoscopyh	32.1	(4.0)	--
						Case-2		32.1	(3.8)	--
						Control		32.1	(3.8)	--
Yi	2010		Hospital OB-GYN	48	36	Case	Pelvic surgical procedureh,k, l	36.5	(1.0)	--
						Control		37.8	(1.0)	--

Note. hx = history; OB-GYN = obstetrics-gynecology; SD = standard deviation; US = United States.

^aFirst author last name only.

^bInclusion criteria as described in the publications.

^cMean (SD) or percent parous.

^dFor chronic pelvic pain, infertility, tubal ligation, or sterilization reversal.

^e10 controls with idiopathic infertility.

^f38 controls undergoing tubal ligation or reanastomosis.

^gCondition other than endometriosis.

^hFor benign gynecological conditions.

ⁱFor diagnosis or tubal sterilization.

^jMost participants were recruited from the general population and a smaller number were referred from endometriosis associations and collaborating gynecologists (Treloar et al., 2002).

^kFor pelvic pain.

^lFor infertility.

^mMajority of cases and controls age 30–34 years.

ⁿMajority of cases, friend controls, and medical controls age <30 years.

^oMajority of cases and infertile controls age 30–34 years and fertile controls age 35–39 years.

TABLE 2.

Nurses’ Health Study II Publications: Study Characteristics^a

		Sample
Studyb	Year	Incident cases	N	Characteristics
Missmer	2004	1,721c	90,065	• None provided
Shah	2013	5,504d	101,074	• Majority White (>91%)
				• Age: mean in the 30s
Vitonis	2010	1,817c	87,603	• Majority parous
				• Age: mean early–mid 30s
				•Parity: mean about 2

^aNoteResearchers used data from the Nurses’ Health Study II, a prospective cohort study (www.channing.harvard.edu/nhs/).

^bFirst author last name shown.

^cNo infertility history or evaluated for infertility at time of laparoscopy.

^dFertile or infertile.

Study Characteristics

Participants in case control studies were recruited predominantly from clinical sites. Participants in the NHSII study were recruited from the general population across 14 states and included nurses 25–42 years of age. Information about case control and prospective cohort study participants is provided in Tables 1 and 2, respectively.

Researchers used a variety of measures to assess overall adiposity and/or adipose tissue distribution (Tables 3 and 4). In 16 of 21 publications (76.2%), researchers included studies that included overall adiposity measures such as: weight; percentage body fat; body silhouettes, which included eight visual representations of overall adiposity from lowest amount (score = 1) to greatest amount (score = 9) across the life course; and Stunkard body figures (Stunkard, Sørenson, & Schulsinger, 1983), which are nine graded visual representations of overall adiposity from underweight to obese. In eight of the 21 publications (38.1%), researchers described studies that included measures of adipose tissue distribution. These measures included skinfold thicknesses, body circumferences, or circumference-based ratios (e.g., waist-to-hip).

TABLE 3.

Case-Control Studies: Measurement of Adiposity and Endometriosis

			Adiposity		Endometriosis
Studya	Year	Type	Measures	Procedure	Cases	Controls
Ayas	2012	Overall	Weight Percentage body fat	TANITA analyzerb	Laparotomy/laparoscopy	Same as cases
Bedaiwy	2006	Overall	Weight	No description	Laparotomy	Same as cases
Cramer	1986	Overall	Weight	No description	Laparotomy/laparoscopy	Unclear
Darrow	1994	Overall	Weight: usual adult Weight: maximum ever	Self-report	Laparoscopy	Unclear
		Distribution	Waistc Hipc Thighc	No description
Ferrero	2005	Overall	Weight	Chart extractiond	Laparoscopye	Same as cases
Hediger	2005	Overall	Weight Adult weight (minimum) Adult weight (maximum) SBFS	Self-report	Laparoscopy	Same as cases
Kvaskoff	2009	Overall	Body silhouettef	Self-reportg	Self-reporth	Same as cases
Pillet	2012	Overall	Weight	Self-reporti	Laparotomy/laparoscopyj	Same as cases
Matalliotakis	2008	Overall	Weight	Chart extractiond	Laparoscopye	Same as cases
McCann	1993	Overall	Weight	Self-report	Laparoscopy	Unclear
		Distribution	Waist circumference Hip circumference Left thigh circumference Waist-to-hip ratio Waist-to-thigh ratio	Investigator-using calibrated tape measure
Nagle	2009	Overall	Relative weightk	Self-report	Surgically confirmedl	Unclear
Pandey	2010	Overall	Weight	No description	Laparoscopyj	Same as cases
Rico	1993	Overall	Weight	No description	Clinical data Laparoscopic data	Unclear
Sahmani	2013	Distribution	Waist circumference	No description	Laparoscopy/laparotomy	Same as cases
Savaris	2011	Overall	Body densitym Fat percentageo	No description	Videolaparoscopyj,n	Unclear
		Distribution	Skinfoldsp	Harpenden adipometer
Signorello	1997	Overall	Weight	Self-reportd	Surgical confirmationl	Same as cases
Vercellini	2013	Distribution	Waist-to-hip ratio Breast-to-underbreast ratio	Trained physician- measured before surgery	Pelvic visualization at surgery	Pelvic visualization at surgery/histology
Yi	2010	Overall	Weight	No description	Laparoscopy or histology	Same as cases
		Distribution	Waist circumference Hip circumference Waist-to-hip ratio	Waist: at minimal girth Hip: at maximum of buttocks

Note. SBFS = Stunkard Body Figure Scale (9 figures).

^aFirst author last name.

^bMeasurements taken around surgery, between day 4 and 10 of women’s menstrual cycle; women assessed with empty bladders after 8 hours fasting; foot-to-foot TANITA body composition analyzer BC-420MA (Japan) was used.

^cWhat the measures were e.g., circumference) was not described.

^dNo information about how or when weight and height were gathered.

^eChart extraction of findings.

^fAt puberty, ages 20–25 and 35–40 years.

^gQuestionnaire included body figure drawings from 1 = leanest to 8 = largest.

^hSelf-report of treatment for or diagnosis of endometriosis.

ⁱWeight obtained via interview conducted by surgeon one month before surgery.

^jHistologically confirmed.

^kUnder-weight, average weight, or over-weight at age 10 and 16 years.

^lDetail not provided.

^mPetroski equation for women 18–51 years.

ⁿVisualization of implants.

^oSiri formula.

^pTricipital, subscapular, suprailiac, and calf.

TABLE 4.

Nurses’ Health Study II Publications: Measures of Adiposity and Endometriosis^a

Studyb	Year	Adiposity	Endometriosis
Missmer	2004	Distribution Waist circumferencec Hip circumferencec	Laparoscopically- confirmed
Shah	2013	Distribution Waist circumferencec Hip circumferencec	Physician- diagnosed
Vitonis	2010	Overall Body sized	Laparoscopically- confirmed

^aNoteResearchers used data from the Nurses’ Health Study II, a prospective cohort study (www.channing.harvard.edu/nhs/).

^bFirst author last name shown.

^cSelf-measurements obtained using an investigator-provided tape measure and instructions.

^dQuestionnaire with Stunkard figures for ages 5, 10, and 20 years.

Study Findings

In 13 of the 21 publications, researchers reported bivariate analyses between one or more measures of overall adiposity and endometriosis. In five of the 13 publications (38.5%), researchers found inverse associations between overall adiposity and endometriosis. In nine of the 13 publications (69.2%), researchers did not observe any such association (Table 5). In five of the 21 publications, researchers reported bivariate analyses between one or more measures of peripheral adipose tissue distribution and endometriosis. In two of the five publications (40.0%), researchers found a positive association between peripheral adiposity measures and endometriosis. In all five publications, researchers found no such association. In five publications of the 21 publications, researchers controlled for potentially confounding variables when assessing associations between endometriosis and overall adiposity. (See Table 6 for findings from case control studies. See Table 7 for findings from NHSII publications.) In three of these five publications (60.0%), researchers reported inverse relationships between endometriosis and at least one overall adiposity measure. In one of the five publications (20.0%), researchers reported a positive relationship, while in two publications (40.0%) researchers reported no relationship. In three of the 21 publications, researchers controlled for potential confounders when assessing associations between endometriosis and one or more measures of peripheral adipose tissue distribution (e.g., low waist-to-hip ratio). In two of the three publications (66.7%), researchers found association between endometriosis and adipose tissue distribution. In all three publications, researchers reported no association between other adipose distribution measures. Among Nurses’ Health Study II participants, there was an inverse relationship between adjusted risk of endometriosis and body size perception at age 20 (but not ages 5 or 10) among nulliparous women, who had a greater risk compared to parous women (Vitonis, Baer, Hankinson, Laufer, & Missmer, 2010).

TABLE 5.

Case-Control Studies: Association of Adiposity with Endometriosis from Bivariate Analyses

Studya	Year	Adiposity measure (units)	Cases	Controls	p< .05b
Ayas	2012	Weight (kg), M (SD)	29.95 (8.02)	26.87 (7.55)	No
		Percent body fat (%),M (SD)	20.78 (9.19)	17.35 (7.1)	No
Bedaiwy	2006	Weight (kg), M (SD)	65.2 (12.6)	G1: 83.9 (40.2) G2: 72.1 (14.7)	No No
Ferrero	2005	Weight (kg), M (SD)	57.2 (8.0)	58.8 (10.0)	Yes
Hediger	2005	Weight (lb), M (SD)
		At time of study	143.4 (28.8)	158.9 (38.6)	Yes
		Minimum adult weight	119.9 (17.8)	125.3 (26.3)	No
		Maximum adult weight	157.8 (36.8)	167.3 (41.4)	No
Kvaskoff	2009	Body silhouette, n (%)
		At puberty
		1	991 (23.4)	19 715 (21.2)	Yes
		2	1511 (35.6)	33 084 (35.6)	No
		3	937 (22.1)	21 261 (22.9)	No
		4	594 (14.0)	13 920 (15.0)	No
		≥5	208 (4.9)	4994 (5.3)	No
		At 20–25 years old
		1	477 (11.3)	9225 (9.9)	Yes
		2	1906 (44.9)	40 756 (43.8)	No
		3	1357 (32.0)	30 184 (32.5)	No
		4	391 (9.2)	9958 (10.7)	No
		≥5	110 (2.6)	2851 (3.1)	No
		At 35–40 years old
		1	149 (3.5)	3428 (3.7)	No
		2	1203 (28.4)	25 112 (27)	No
		3	1889 (44.5)	41 265 (44.4)	No
		4	740 (17.5)	17 112 (18.4)	No
		≥5	260 (6.1)	6057 (6.5)	No
Pillet	2012	Weight (kg), M (SD)	59.1 (9.8)	63.6 (11.3)	Yes
Matalliotakis	2008	Weight (kg), M (SD)	66.5 (14.0)	70.9 (17.9)	Yes
McCann	1993	Weight (kg), M (SD)
		All participants	58.9 (8.3)	59.1 (9.0)	No
		< 30 years old	57.5 (7.8)	59.0 (10.7)	No
		≥ 30 years old	60.4 (9.6)	59.2 (6.8)	No
		Waist circumference (cm), M (SD)
		All participants	72.2 (8.0)	73.4 (9.1)	No
		< 30 years old	70.5 (7.8)	74.0 (10.3)	No
		≥ 30 years old	74.0 (7.8)	72.8 (7.7)	No
		Hip circumference (cm), M (SD)
		All participants	100.3 (8.4)	98.6 (10.7)	No
		< 30 years old	98.5 (7.3)	98.3 (12.1)	No
		≥ 30 years old	102.3 (9.1)	98.9 (9.0)	No
		Thigh circumference (cm), M (SD)
		All participants	57.2 (5.2)	57.2 (6.2)	No
		30 years old	55.9 (4.0)	57.1 (7.0)	No
		≥ 30 years old	58.5 (6.1)	57.4 (5.3)	No
		Waist/hip ratio, M (SD)
		All participants	0.72 (0.04)	0.75 (0.06)	Yes
		< 30 years old	0.72 (0.04)	0.76 (0.06)	Yes
		≥ 30 years old	0.72 (0.04)	0.74 (0.06)	No
		Waist/thigh ratio, M (SD)
		All participants	1.26 (0.09)	1.29 (0.12)	No
		< 30 years old	1.26 (0.10)	1.30 (0.11)	No
		≥ 30 years old	1.27 (0.07)	1.27 (0.13)	No
Pandey	2010	Weight (kg), M (SD)	55.3 (4.4)	57.6 (7.6)	No
Rico	1993	Weight (kg), M (SD)	57.8 (7.1)	58.3 (7.5)	No
Sahmani	2013	Waist circumference (cm), M (SD)	81.2 (9.7)	80.6 (9.1)	No
Savaris	2011	Body fat percent > 23% (%)	(84)	(65)	No
Vercellini	2013	Waist/hip ratio (tertiles), n (%)
		<0.7	RV: 19 (19) P/O: 16 (16)	12 (12)	No No
		0.7–0.8	RV: 53 (53) P/O: 56 (56)	51 (51)	No No
		>0.8	RV: 28 (28) P/O: 28 (28)	37 (37)	No No
		Breast/underbreast ratio (tertiles), n(%)
		< 1.1	RV: 16 (16) P/O: 24 (24)	21 (21)	Yes
		1.1–1.2	RV: 60 (60) P/O: 66 (66)	71 (71)	No No
		> 1.2	RV: 24 (24) P/O: 10 (10)	8 (8)	No No
Yi	2010	Weight (kg), M (SD)	55.3 (1.0)	59.6 (1.4)	Yes
		Waist circumference (cm), M (SD)	75.5 (1.0)	79.1 (1.2)	Yes
		Hip circumference (cm), M (SD)	90.9 (0.8)	93.0 (0.9)	No
		Waist/hip ratio, M (SD)	0.83 (0.007)	0.85 (0.01)	Yes

Note. CI = confidence interval; P/O = peritoneal or ovarian endometriosis; RV = rectovaginal endometriosis; SD = standard deviation.

^aFirst author last name.

^bOr indicated as significant by authors.

TABLE 6.

Case Control Studies: Adiposity and Endometriosis Diagnosis Using Logistic Regression

Studya	Year	Adiposity measure	OR	95% CI	Trend	AOR	95% CI	Trend
Cramerb	1986	Weight (kg)
		≤56	1	referent		1	referent
		56–63	1.4	NR		1.2	[0.8–1.6]
		≥64	1.1	NR		0.8	[0.5, 1.1]
Hedigerc	2005	Perceived figure BMI	0.9	[0.8, 1.0]		0.9	[0.75, 0.99]
McCannd	1993	Waist/hip ratio
		< 30 years old			.001			.006
		0.61–0.72	6.2	[2.0, 19.0]		6.2	[1.4, 26.7]
		0.72–0.76	1.4	[0.4, 4.4]		3.2	[0.6, 16.0]
		0.76–1.01	1	referent		1	referent
		≥ 30 years old			.20			.11
		0.61–0.72	1.3	[0.4, 4.3]		1.6	[0.4, 6.7]
		0.72–0.76	1.1	[0.3–3.8]		1.4	[0.3, 6.7]
		0.76–1.01	1	referent		1	referent
		Waist/thigh ratio
		< 30 years old			.08			.19
		1.03–1.22	3.6	[1.2, 10.8]		2.4	[0.6, 9.3]
		1.22–1.31	1.7	[0.6, 4.7]		2.7	[0.7, 9.9]
		1.31–1.75	1	referent		1	referent
		≥ 30 years old			.79			.76
		1.03–1.22	0.6	[0.2, 1.9]		0.6	[0.1, 2.2]
		1.22–1.31	1.2	[0.4, 3.7]		1.3	[0.4, 4.7]
		1.31–1.75	1	referent		1	referent
Naglee	2009	RW (age 10)
		Self-report
		Underweight	NR	NR		0.9	[0.5, 1.7]
		Average	NR	NR		1.0	referent
		Overweight	NR	NR		2.8	[1.1, 7.5]
		Mothers’ report
		Underweight	NR	NR		1.2	[0.5, 2.9]
		Average	NR	NR		1.0	referent
		Overweight	NR	NR		2.1	[0.4, 11.0]
		RW (age 16)
		Self-report
		Underweight	NR	NR		1.4	[0.7, 2.7]
		Average	NR	NR		1	referent
		Overweight	NR	NR		0.5	[0.3, 1.0]
		Mothers’ report
		Underweight	NR	NR		5.9	[1.6, 20.9]
		Average	NR	NR		1	referent
		Overweight	NR	NR		1.0	[0.3, 3.6]
Signorellof	1997	Weight/10 kg; FCs	0.9	NR		0.7
		Weight/10 kg; IFCs	0.9	NR		0.8	[0.6, 1.2]

Note. Values were rounded to the nearest tenth. Trend lists the p-value for the trend in ORs or AORs. AOR = adjusted odds ratio; CI = confidence interval; FC = fertile control; IFC = infertile control; NR = not reported; OR = odds ratio; RW = relative weight.

^aFirst author last name.

^bAdjusted for: Center, age at interview, difference between age at menarche and index date, education, religion, menstrual pain, and cycle length.

^cAdjusted for: Age, being ≥ 68 inches tall, parity.

^dAdjusted for: Age, BMI, whether ever pregnant, age at menarche, intensity of menstrual flow, any change in symptoms over time.

^eAnalyses for weight at age 10 adjusted for: Age at menarche; state of residence, relative weight at age 16 years. Analyses for weight at age 16 were adjusted for: Age at menarche; state of residence, relative weight at age 10 years.

^fAdjusted for: Age, highest educational level attained, regularity of menstrual cycle, exercise, height.

TABLE 7.

Nurses’ Health Study II Publications: Rate Ratios With Multivariate Adjustment, Stratified by Fertility Status

			All		No infertility		Infertility history
Studya	Year	Adiposity	RR	95% CI	RR	95% CI	RR	95% CI	pb
Missmer	2004c	Waist/hip ratio							.36
		< 0.70	0.9	[0.6, 1.4]	0.8	[0.5, 1.3]	1.5	[0.7, 3.1]
		0.70–0.79	1.0	referent	1.0	referent	1.0	referent
		0.80–0.89	1.0	[0.8, 1.2]	0.9	[0.7, 1.2]	1.2	[0.7, 2.2]
		> 0.89	1.0	[0.6, 1.5]	0.9	[0.5, 1.4]	1.6	[0.6, 4.2]
		ptrend	.87		.82		.79
Shah	2013d	Waist (cm)							<.001
		22.0–29.9	1.1	[1.0, 1.2]	1.1	[0.8, 1.3]	1.1	[0.9, 1.2]
		30.0–32.9	1.0	referent	1.0	referent	1.0	referent
		33.0–37.9	1.0	[0.9, 1.1]	0.9	[0.6, 1.2]	1.1	[0.9, 1.2]
		38.0–65.0	1.0	[0.8, 1.1]	0.6	[0.4, 0.8]	1.1	[0.9, 1.3]
		ptrend	.19		.0003		.60
		Waist/hip ratio							.16
		< 0.60	2.8	[1.4, 5.6]	2.9	[1.4, 6.2]	4.0	[0.5, 33.0]
		0.60–0.69	1.0	[0.9, 1.2]	0.9	[0.8, 1.1]	1.2	[0.9, 1.71]
		0.70–0.79	1.0	referent	1.0	referent	1.0	referent
		0.80–0.89	1.0	[0.9, 1.0]	0.9	[0.8, 1.0]	1.1	[0.9, 1.3]
		≥ 0.90	1.0	[0.9, 1.2]	1.1	[0.9, 1.3]	0.7	[0.5, 1.1]
		ptrend	.41		.76		.13
Vitonis	2010e	Figure (age 5)							.84
		1	1.2	[1.1, 1.4]	1.3	[1.1, 1.5]	1.1	[0.8, 1.5]
		2	1.0	[0.9, 1.1]	1.0	[0.9, 1.2]	0.9	[0.7, 1.2]
		3	1.0	referent	1.0	referent	1.0	referent
		4	0.9	[0.7, 1.0]	0.9	[0.7, 1.1]	0.8	[0.5, 1.2])
		≥ 5	0.9	[0.7, 1.1]	0.9	[0.7, 1.1]	0.9	[0.6, 1.4]
		ptrend	< .001		< .001		.24
		Figure (age 10)							.77
		1	1.1	[1.0, 1.3]	1.2	[1.0, 1.4]	1.1	[0.8, 1.5]
		2	1.0	[0.9, 1.2]	1.0	[0.9, 1.2]	1.0	[0.8, 1.4]
		3	1.0	referent	1.0	referent	1.0	referent
		4	0.9	[0.7, 1.0]	0.9	[0.7, 1.0]	0.8	[0.6, 1.2]
		≥ 5	0.9	[0.7, 1.0]	0.8	[0.7, 1.0]	1.0	[0.7, 1.5]
		ptrend	<.001		.001		.30
		Figure (age 20)
		1	1.3	[1.1, 1.7]	1.3	[1.0, 1.7]	1.6	[0.9, 2.6]	.36
		2	1.0	[0.9, 1.2]	1.0	[0.9, 1.1]	1.2	[0.9, 1.2]
		3	1.0	referent	1.0	referent	1.0	referent
		4	1.1	[0.9, 1.2]	1.0	[0.9, 1.2]	1.3	[1.0, 1.7]
		≥ 5	0.9	[0.7, 1.0]	0.9	[0.8, 1.1]	0.9	[0.6, 1.3]
		ptrend	.04		.21		.20

Note. Values for RRs and CI upper and lower limits were rounded to the nearest tenth. CI = confidence interval; RR = rate ratio.

^aFirst author last name.

^bp-value for test for heterogeneity comparing effects among women having no past or current infertility with those having concurrent infertility.

^cAdjusted for: Age, race, calendar time, parity, BMI at 18 years old.

^dAdjusted for: Age, calendar time, infertility status, parity.

^dAdjusted for: Age, birth weight, calendar time, age at menarche, parity, oral contraceptive use, adult BMI.

^eAdjusted for: Adjusted for age, calendar time, birth weight, age at menarche, parity, oral contraceptive use, adult BMI.

Methodological Quality

The methodological quality of the studies was varied. Quality varied with regard to small sample sizes, recruitment of women from clinical sites, and inclusion of few measures of adiposity or its distribution. These issues could have increased the likelihood of measurement error and bias that impact generalizability of findings. Many limitations stemmed from the use of observational cross-sectional designs. The use of such designs to assess associations between endometriosis and adiposity is understandable because screening tools for endometriosis are lacking, the symptomatology of the disease is complicated, and the disease is difficult to diagnose.

Sample size was small (< 50 women with and without endometriosis) in seven of the total 21 publications (33.3%). The limited sample sizes can impact statistical power and Type II error rates. Power calculations were provided in only two of the 21 (9.5%) publications. In most publications (n = 13; 61.9%), women were recruited from clinical sites where women were undergoing gynecologic surgery (Table 1). The findings from these studies should be interpreted carefully because of the potential for centripetal (filter) bias, which can occur when women with endometriosis are overrepresented at clinics that provide gynecologic surgery relative to women in the general population. This bias may be compounded by referral bias, in which participants with endometriosis may be referred from primary care to specialty care sites and, therefore, overrepresented at these specialty sites relative to the general population. In addition, findings may be complicated by lead time bias that arises from a shorter time between the emergence of a disease to diagnosis among women with access to and receiving health care relative to women without access or not engaging in healthcare (Szklo & Nieto, 2004). In studies based on NHSII data, only registered nurses were included as participants, limiting the generalizability of the findings.

Choices of comparison groups are a key consideration when synthesizing research findings. Current endometriosis research has included different types of control groups, such as friends of case participants and women undergoing surgical evaluation for gynecologic pathologies (Table 1). Because women within a social network may have similar adiposity (Christakis & Fowler, 2007), it may be more to difficult to find differences in adiposity measures between women with and without endometriosis.

Adiposity has been measured in a limited number of ways. In most publications (n = 11; 52.4%) researchers reported use of a single measure of adiposity (Table 1). In nine publications (42.9%), women self-reported adiposity on questionnaires, but self-reported adiposity may not be accurate (Nyholm et al., 2007). To minimize such error, the NHSII protocol asked participants to read standardized instructions for measuring body circumferences, but participant measurements may not be as precise as those obtained by trained individuals (Rimm et al., 1990).

The operational definition of endometriosis is another consideration: in most studies, endometriosis was assessed via laparoscopy and/or laparotomy (Tables 1 and 2). This was consistent with the clinical gold standard in the United States which requires visualized disease, at a minimum (Practice Committee of the ASRM, 2012). In two of the 21 publications (9.5%), diagnoses were extracted from medical charts, but information about how clinicians had made the original diagnoses was not provided. The three NHSII-based publications relied on participant self-reports of endometriosis diagnosis or treatment. Validation studies showed that not all women who reported being diagnosed with endometriosis had had a diagnosis in their medical records (Kvaskoff et al., 2009; Missmer et al., 2004). They did not validate women’s self-reports of never having been diagnosed with endometriosis against women’s medical records.

Discussion

Our systematic review revealed that research on the relationships between measures of adiposity and endometriosis is limited. Among the publications we reviewed, most included only measures of overall adiposity (e.g., body silhouette figures). Few publications described studies in which researchers measured adipose tissue distribution (e.g., waist and hip circumferences). VAT was not measured in any of the studies. Therefore, the types of adiposity measures that researchers used were limited.

Overall findings suggested that lean body habitus, particularly low amounts of adipose tissue and adipose tissue below the waist, may be associated with endometriosis (Figure 1). The inverse relationship between overall adiposity and endometriosis is congruent with hypotheses and data about how adipose tissue may be involved in the development of endometriosis through immunological pathways. For example, being lean is associated with a predominance of the M2 macrophages, whereas being larger is associated with a predominance of M1 macrophages (Lumeng et al., 2007). (In general, M1 macrophages promote inflammation and inhibit angiogenesis and tissue remodeling; in contrast M2 macrophages are anti-inflammatory and promote angiogenesis and tissue remodeling; Brown et al., 2012; Jetten et al., 2014; Ruffell et al., 2009).

FIGURE 1.

Visual summary of findings from the systematic review. WTH = waist-to-hip.

Researchers have found some evidence that M2 macrophages may be involved in the development of endometriosis potentially through the actions of M2 including angiogenesis and tissue remodeling (Smith et al., 2012; Wang et al., 2014; Wang et al., 2015). Findings that adipose tissue below the waist is associated with endometriosis are generally consistent with the current hypotheses that estrogen influences the amount and distribution of adipose tissue (Jasieńska, Ziomkiewicz, Ellison, Lipson, & Thune, 2004; Ley, Lees, & Stevenson, 1992) and endometriosis is an estrogen-dependent disease (Kitawaki et al., 2002). Thus, when researchers observed that being lean and having adipose tissue predominantly below the waist were associated with endometriosis, their findings were consistent some proposed biological mechanisms of endometriosis.

We also found no publications that reported associations between VAT and endometriosis. Because we know that VAT is biologically active relative to subcutaneous fat (Ibrahim, 2010), researchers can design future studies to investigate relationships between VAT and endometriosis that may help identify possible etiologic mechanisms of the disease. These researchers could sample sufficient numbers of participants to power their planned analyses, recruit from community-dwelling female populations to reduce centripetal bias, and operationalize endometriosis with physicians’ reports of visualized disease.

Weighing the findings we reviewed, we note important methodological considerations from observational studies on endometriosis risk factors. First, these studies may contain threats to internal validity. These threats stem from recruiting small numbers of participants (e.g., < 50 participants) and including only women who sought care from specialty clinical sites. Small samples and samples consisting only of patients from specialty clinical sites limit researchers’ abilities to detect differences between women with and without endometriosis in the general population. Findings from such studies limit scientists in generalizing findings to women who either have no signs or symptoms of the disease or women who do not seek care (e.g., lack access to healthcare). Researchers might be able to detect differences between women with and without endometriosis if they were to recruit larger numbers of women and recruit women from varying sites and the general population.

Second, many studies were limited in how researchers assessed adiposity. In several publications, researchers did not include a variety of measures to capture various characteristics of adiposity, including adipose tissue distribution and visceral adiposity. Without adequate measures, researchers could miss detecting more nuanced relationships between adiposity characteristics (e.g., amount, location) and endometriosis that could identify potential disease mechanisms. Researchers can include varied measures of adiposity into their future studies.

Third, many studies were limited in the timing of the measures of adiposity. Most researchers assessed participants’ adiposity at the time of their endometriosis diagnosis. This practice may result in an incomplete picture of adiposity characteristics and the onset and progression of the disease. Endometriosis has been found among adolescents and young adults (Dun et al., 2015), yet many women who may have had endometriosis when they were younger are not diagnosed until they are later in adulthood. Researchers would likely gain rich information about the natural history and biological mechanisms of endometriosis by assessing adiposity and endometriosis from childhood through women’s’ reproductive years. This research may provide insights regarding the natural history and biological mechanisms of the disease.

Finally, several studies were limited in how endometriosis was assessed. In these studies, researchers did not apply the gold standard criteria for diagnosing endometriosis, which is a surgically visualized disease. Instead, these researchers included women’s reports of having received an endometriosis diagnoses from a physician. Because endometriosis may exist in women who have no symptoms, such women with endometriosis may have been excluded from case groups. While some researchers assessed the validity of women’s self-reported diagnosis of endometriosis, they did not assess the validity of women’s self-reported lack of having received endometriosis diagnoses. Researchers could benefit from assessing endometriosis with the clinical gold standard in clinical samples. For studies in which participants are recruited from the general population, researchers could use emerging radiologic imaging that can indicate endometriosis (e.g., transvaginal ultrasound or magnetic resonance imaging) (Exacoustos et al., 2014; Guerriero et al., 2015; Noventa et al., 2015; Thomeer et al., 2014). When researchers decide to assess endometriosis with women’s reports of having an endometriosis diagnosis, researchers could evaluate the validity of their measures. Researchers could compare women’s self-reports of never being diagnosed to corresponding medical records.

Implications

Findings in our review suggest that there may be an association between endometriosis and either overall adiposity or adipose tissue distribution. However, we found gaps in the literature. Many studies included (a) small numbers of participants recruited from specialty clinics; (b) one or a few number of adiposity measures; and (c) adiposity assessments at the time when endometriosis was diagnosed. Several studies included women’s reports of being diagnosed with endometriosis. To address these gaps, researchers can design longitudinal studies to investigate associations between adiposity and endometriosis by including (a) adequately sized samples of women randomly selected from a variety of sites, including the general population, in order to capture women who are asymptomatic and/or do not access healthcare; (b) rigorously measured adiposity, including measures of overall adiposity, distribution, and VAT; (c) measurements of adiposity at various points throughout the lifespan; and (d) physician-visualized endometriosis via surgery among women undergoing the procedure or via imaging technologies among women who do not have indications for surgery.

Researchers could investigate potential biological mechanisms involved in the development of endometriosis that are proposed to be related to overall adiposity, adipose tissue distribution, and/or VAT. Researchers can test current hypotheses about how adiposity may be linked to endometriosis as mediated by the immune system, including the activation and actions of M2 macrophages. To understand these links, researchers could continue to investigate peripheral biomarkers of endometriosis that are products of adipose tissue, such as leptin and adiponectin (Pandey, Kriplani, Yadav, Lyngdoh, & Mahapatra, 2010). Researchers could also investigate peripheral adipokines implicated in other gynecologic diseases but not yet studied with relation to endometriosis, such as visfatin (Tian et al., 2013). To provide insights regarding underlying pathways that contribute to the development of the disease, such as immune-related pathways, researchers can collect adipose tissue and endometriotic lesion biospecimens to quantify adipocytes or other adiposity- and endometriosis-related biomarkers.

Limitations

Despite a systematic search and consultation with a librarian, we may not have included all relevant articles. We decided a priori to exclude publications in which the only adiposity measure was BMI. We were not able to conduct a meta-analysis because the number of empirically-based studies with comparable adiposity measures was limited (Egger, Smith, & Phillips, 1997).

Conclusions

In this systematic review, 21 publications in which associations between endometriosis and either overall adiposity or adipose tissue distribution were assessed. The studies varied by design (case control, prospective cohort), sampling frameworks (recruited women undergoing surgery, recruited nurses across 14 states), adiposity measures (e.g., self-reported weight, researcher-measured waist circumference), and endometriosis assessments (surgically visualized disease, self-reported physician-diagnosed disease). Collectively, these data suggest that lower overall adiposity and adipose tissues concentrated below—rather than above—the waist are each associated with endometriosis. To identify disease mechanisms, studies can be designed that recruit both women seeking care from clinical sites and women randomly sampled from the general population. Relationships can be assessed between different characteristics of adiposity over time relative to the natural history of endometriosis and its clinical stages. To assess mechanisms and pathways of the disease, peripheral adiposity biomarkers associated with endometriosis can be assessed, as can presence of adipokines or endometriosis-related biomarkers in endometriotic lesions. Findings from future research could fill critical gaps regarding whether and how adiposity may be associated with endometriosis.

Supplementary Material

Supplemental Data File _doc_ pdf_ etc.__1

Supplemental Digital Content 1. Table that includes searches in PubMed and Web of Science using a predetermined list of search terms. .Doc

Supplemental Data File _doc_ pdf_ etc.__2

Supplemental Digital Content 2. Figure illustrating the complete process of identifying eligible publications. .TIFF