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The many faces of obesity and its influence on breast cancer risk

Subject: Health & Medicine
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  • Published: January 4, 2022
  • Updated: January 4, 2022
  • University / College: London School of Hygiene & Tropical Medicine
  • Language: English
  • Downloads: 36

Introduction

Obesity, a state of increased adiposity, is categorized according to body mass index (BMI) as having a BMI > 30 kg/m 2 ( 1 , 2 ) and is now considered a chronic disease ( 3 ). The weight gain, along with associated metabolic disturbances, that characterizes obesity results from disruption of energy balance, causing tissue stress and dysfunction ( 4 , 5 ). The serious consequences of these physiological effects of obesity have evolved into major health concerns in recent years. Obesity is increasingly becoming a worldwide epidemic, with global obesity rates nearly tripling since 1975 ( 3 ). In 2015, the worldwide prevalence of obesity among adults reached 12%, with higher rates among women ( 2 , 6 ).

Epidemiology of Obesity and Breast Cancer Risk According to Life Stage and Race

High adiposity (BMI, adult weight gain, and abdominal obesity) is a risk factor for several types of cancer, including breast cancer ( 7 ). The association between overweight/obesity and breast cancer risk varies in relation to several factors including menopausal status and specific life stages. For postmenopausal women, several meta-analyses have consistently shown positive associations among high adiposity, adult weight gain, and risk of hormone receptor-positive (estrogen receptor-positive/ER+ and progesterone receptor-positive/PR+) breast cancer ( 6 , 8 – 12 ). Conversely, the epidemiologic literature supports an inverse association or no association between high BMI and premenopausal hormone receptor-positive breast cancer risk ( 13 – 15 ). Additionally, high BMI during childhood, adolescence, and early adulthood is associated with decreased risk of premenopausal breast cancer ( 12 , 16 , 17 ). However, the association between measures of adiposity and premenopausal breast cancer risk may vary by ethnicity. For example, a few studies suggest that high adiposity may confer greater risk for premenopausal breast cancer among Asian women ( 18 , 19 ). Other studies assessed abdominal, i. e., central, adiposity, and found a significantly positive association with both pre-and postmenopausal breast cancer risk ( 20 , 21 ). The association appears to be strongest with triple negative breast cancer (TNBC), which occurs most often in women under 40 years of age ( 22 ). Harris et al. ( 23 ) revealed that measures of abdominal obesity (e. g., waist circumference, waist-to-hip ratio) were associated with increased risk for premenopausal ER- breast cancer when examining the highest vs. the lowest quintile for each measurement. Similarly, Pierobon and Frankenfeld ( 24 ) demonstrated in a systematic review and meta-analysis that a significant association existed between TNBC and obesity, but when stratified by menopausal status the results were significant only among premenopausal women.

These obesity-breast cancer associations can also be addressed in relation to race or ethnicity. This approach is especially relevant given that the prevalence of obesity in the U. S. is higher among blacks than whites. In 2015–2016, the highest rates of obesity in the U. S. population was among black women (54. 8%) ( 10 ). This contrasts with an overall rate of 39. 8% in the general population. Furthermore, variation in body fat distribution among racial and ethnic groups may account for differences in breast cancer rates by menopausal status and breast cancer subtypes ( 25 – 27 ). However, clear patterns have not been identified. The AMBER Consortium, a collaboration of four studies, examined obesity and body fat distribution among black women ( 26 ). In this study, breast cancer subtypes were examined by menopausal status, BMI, and abdominal obesity. For postmenopausal black women, higher recent BMI (> 35 kg/m 2 ) was associated with ER+ breast cancer and decreased risk of TNBC. Among premenopausal black women, higher BMI (> 30 kg/m 2 ) was associated with decreased risk of ER+ breast cancer. When examining abdominal obesity, breast cancer risk also differed by menopausal status. For postmenopausal black women, a high waist-to-hip ratio (WHR) (> 0. 88 vs. ≤0. 64 cm) was associated with increased risk for each tumor subtype (ER-, ER+, PR-, PR+), and a higher risk for TNBC tumors. In contrast, among premenopausal black women, high WHR (> 0. 88 vs. ≤0. 64 cm) was only associated with increased risk of ER+ breast cancer ( 26 ). Other studies have also shown that regardless of menopausal status, abdominal obesity increases the risk for TNBC among black women; TNBC is a particularly aggressive phenotype ( 22 , 27 ); however, inconsistent results have been reported ( 28 ).

The Carolina Breast Cancer Study, which is contained within the AMBER Consortium, demonstrated an increased incidence of TNBC in premenopausal women. An association with obesity is suggested by the observation that women with a high compared to low WHR had a significantly higher risk of developing basal-type TNBC. This increased risk of TNBC in association with obesity applies to both pre- and postmenopausal black women ( 29 ), although the risk is highest in premenopausal women ( 22 , 29 ).

To summarize, the relationship between adiposity and breast cancer risk is complex and varies depending upon several factors. Increased breast cancer risk in postmenopausal women is especially notable among those who are obese ( 2 ), as demonstrated in large studies using different study designs ( 20 , 21 , 24 ).

On the one hand, early life obesity is protective against premenopausal breast cancer, whereas the scientific literature provides clear and consistent evidence linking high adult adiposity as a risk factor with postmenopausal breast cancer. Although the incidence of overall breast cancer is lower among black women compared to white women, black women have a higher incidence of ER- and TNBC tumors and their tumors tend to be of a higher grade than tumors in women from other racial and ethnic groups ( 30 ). The increased frequency of these tumors may be partially attributable to the higher abdominal adiposity rates in black populations.

Obesity, Socioeconomic Status, and Breast Cancer Risk

Obesity is associated with socioeconomic status (SES) in high-and-middle income countries ( 6 ). In high-income countries, the shift in the food supply created opportunities to consume inexpensive, energy-dense foods with low nutritional value, which is a major driver of the obesity epidemic, especially among low SES individuals ( 31 ). For example, a systematic review revealed that lower life course SES was associated with obesity risk (summary OR: 1. 35; 95% CI: 1. 04, 1. 76) and higher waist circumference (summary OR: 4. 67; 95% CI: 4. 15, 5. 20) ( 32 ). In women, the overall obesity prevalence was shown to decrease with increased income and educational attainment ( 33 ). SES is linked not only to obesity risk, but also to breast cancer incidence and mortality ( 34 ). Evidence also exists for a relationship between SES and breast cancer outcomes, with low SES being associated with advanced disease stage at the time of diagnosis, greater disease recurrence, and poorer survival in multiple studies ( 34 ). However, other studies suggest that the contribution of SES to racial and ethnic disparities in breast cancer is modest and varies by hormone receptor subtypes and stage at diagnosis ( 35 ). Thus, the relationship between SES and obesity may affect breast cancer risk and prognosis differently according to race and ethnicity. Limited research has been conducted to identify a direct association between SES and breast cancer risk ( 36 , 37 ). However, the indirect link via their mutual association with obesity emphasizes the importance of such investigations, especially in light of the current epidemic of obesity ( 31 ).

Obesity Prevention and Breast Cancer Risk

Intervention studies aimed at reducing the incidence of obesity can provide opportunities to decrease breast cancer risk, specifically post-menopausal breast cancer. The increase in obesity rates is associated with changes in the food and built environments which contribute to increased consumption of energy-dense foods and less physical activity. These changes result in a positive energy balance—the state in which energy intake exceeds energy expenditure—which, over time, can lead to obesity. Several studies have shown that reducing caloric intake and increasing physical activity may be protective against both pre- and post-menopausal breast cancer ( 38 , 39 ). As such, targeting modifiable risk factors of obesity such as diet and physical activity is one strategy to reduce breast cancer risk and improve survival.

The complex interplay of diet and physical activity, together with the role of adipose tissue, pose challenges to our understanding of the mechanisms by which obesity confers increased breast cancer risk. Furthermore, obesity is intertwined with social deprivation, environmental conditions, genetics, hormones, and epigenetic factors, all of which can impact breast cancer risk and the aggressiveness of breast cancer phenotypes. In this review we discuss obesity and diet-related biological mechanisms with the aim of identifying molecular and behavioral targets that can inform research into novel interventions to reduce breast cancer incidence and mortality. The focus of this review is on the relationship between obesity and postmenopausal breast cancer risk. Although it is an important topic, the interplay between adiposity and breast cancer survival is not addressed here.

Mechanistic Basis of Obesity and Its Impact on Breast Cancer Risk

Adipose Tissue as an Endocrine Organ, Regulating Metabolism and Immune Responses

The increased adipose tissue that characterizes the state of obesity is not merely a passive reservoir to store lipids and energy, as once thought. Adipose tissue is biologically active, and is now considered to be an “ endocrine organ,” given the multiple factors it produces that impact systemic energy metabolism, neuroendocrine function, and immune responses ( 40 ). These areas of adipose function can be broadly classified as protein products that affect the metabolism of distant cells/tissues and enzymes that are involved in steroid hormone metabolism.

Metabolic Dysregulation in Obesity

In obesity multiple metabolic changes are observed, including alterations in lipids, hyperglycemia and glucose intolerance, and insulin resistance/hyperinsulinemia ( 1 , 5 , 41 – 43 ). Dysregulated secretion of adipocyte-derived proteins (adipokines) which act both locally and systemically is also observed. These changes in secreted hormones and other factors include increased leptin, decreased adiponectin and resistin, retinol binding protein-4 (RBP4), tumor necrosis factor-α (TNF-α), interleukin-1β (Il-1β), and IL-6 ( 5 , 40 , 44 , 45 ). Leptin has been a focus of much early work on obesity. Although the primary function of the protein leptin has generally been viewed as promoting leanness, by signaling back to the CNS to decrease intake of food and increase energy expenditure to limit obesity, the overall role of leptin is far more complex and to date remains somewhat elusive ( 46 ). From an oncology perspective, high leptin levels appear to correlate with increased risk of certain cancers, including breast cancer ( 1 ).

Of note, all accumulations of adipose tissue, i. e., adipose depots, are not the same. The adipose depots that characterize obesity are complex and must be analyzed at a granular level in order to understand their effect on cancer risk. Excessive visceral deposits of adipose tissue, primarily in the abdomen, are considered to be the main culprits involved in disease causation ( 47 , 48 ). Specific abdominal organs such as the greater omentum (referred to as the “ abdominal policeman”) are preferred sites of this visceral adiposity tissue (VAT). In contrast, subcutaneous adipose tissue (SAT) is generally less active in the mechanisms implicated in these disruptions of biologic homeostasis. Excessive adipose tissue, especially VAT, is associated with the “ metabolic syndrome,” involving insulin resistance, hyperglycemia, dyslipidemia, and hypertension. Prothrombotic and proinflammatory states are also characteristic of VAT. Besides the adipocytes, which secrete endocrine hormones such as leptin and adiponectin, adipose tissue contains other types of cells that also secrete proteins. Examples include leukocytes and stromovascular cells which, along with adipocytes, express TNF-α, particularly in SAT ( 40 , 49 ). These dissimilar cell types function in an integrated manner, consistent with the view that adipose tissue is actually an entire endocrine organ ( 40 ). White adipose tissue (WAT), the subtype of adipose tissue whose main function is to store energy in the form of lipids and maintain energy homeostasis ( 50 – 52 ), functions as a complex secretory and endocrine organ. In the obese state adipocytes in WAT secrete a number of inflammatory cytokines, including TNF-α and IL-6 ( 51 ).

Immune Function of Adipose Tissue

These metabolic functions are intimately connected to the immune activities of adipose tissue ( 4 ). In addition to adipocytes and stromovascular cells, leukocytes, which include a variety of immune cells—macrophages, neutrophils, T cells, B cells and mast cells—are found in increased numbers in adipose tissue of obese individuals. In particular, macrophages, which make up 5-10% of cells in healthy adipose tissue, constitute 50% of all cell types in hypertrophic adipose tissue ( 4 , 49 ). The macrophages located within adipose deposits skew toward the M1 type, which secretes inflammatory cytokines, including TNF-α, IL-6, and IL-1β; this contrasts with M2 macrophages which have the antithetical effect of improving metabolic function and reducing adipose inflammation. The inflammatory macrophages are the primary cell type responsible for inflammation associated with obesity. As a result, in obesity the circulating levels of these macrophage-secreted factors are elevated, resulting in a chronic inflammatory state ( 52 ). Although self-limited inflammation in response to pathogens is a normal function of the innate immune system, including macrophages, individuals with obesity and metabolic syndrome experience chronic low-grade inflammation, which is associated with higher levels of inflammatory cytokines in both plasma and subcutaneous adipose tissue ( 4 , 53 ). Such impaired resolution of acute inflammation leads to metabolic tissue stress with tissue destruction and dysfunction ( 53 ), including insulin resistance and diabetes ( 5 , 45 , 54 ). Thus, the connection between obesity and metabolic dysfunction/insulin resistance is dependent at least in part on inflammation which is initiated by the innate immune system ( 54 ).

The dysfunctional milieu of obesity-associated adipose tissue has additional adverse immune effects, such as ectopic accumulation of lipids in non-adipose tissue, including tissues of the immune system: bone marrow and thymus ( 49 ). Obesity results in altered lymphocyte tissue architecture and integrity with shifts in populations of immune cells that lead to inflammatory phenotypes ( 4 ). Among these changes are increases in T helper type 1 (Th1) cells and cytotoxic CD8+ T cells, which produce cytokines [interferon-ɤ (IFN-ɤ), TNF, and IL-6] that induce M1 macrophages, which, in turn, secrete proinflammatory cytokines (TNF, IL-6, IL-1β, and others) ( 49 ). B cells are also increased in VAT, as shown in mice fed a high-fat diet ( 48 ). Total B cells, B-1a cells and B2 cells are all elevated in this setting. Increased abundance of mature B cells which had undergone class switching, including IgG+ cells which are involved in progressive immune activity, is observed. These mice exhibit increased serum concentrations of IgG2c, a pro-inflammatory isotype. B lymphocytes are therefore involved in the development of VAT inflammation, to which they contribute by activating CD8+ and Th1 cells as well as releasing pathogenic antibodies. The downstream metabolic effects of pro-inflammatory cytokine produced by the CD8+ and Th1 cells include insulin resistance and glucose intolerance, which ultimately are attributable to B cell activity.

Endocrine Function of Adipose Tissue in Obesity Increases Breast Cancer Risk

Immune System: Role in Breast Cancer Risk

The alterations in the immune system that are associated with obesity can predispose to development of 13 cancer types via a variety of mechanisms ( 2 , 53 , 55 ). The mechanistic underpinnings of the observed causal relationship of obesity with breast cancer exemplify the intertwining of the various adipose mechanisms described above. In one prospective population-based cohort of postmenopausal women followed from 1990 through 2005, 272 women were diagnosed with incident breast cancer. Among three markers altered by obesity [leptin, adiponectin and soluble TNF receptor 2 (sTNF-R2)], plasma levels of sTNF-R2 and leptin showed independent positive association with breast cancer risk ( 56 ). Given the known carcinogenic nature of the inflammatory cytokine TNF, derived from macrophages that infiltrate adipose tissue, these data are consistent with an immunologic mechanism linking obesity and breast cancer. In the setting of obesity, WAT becomes altered, manifesting changes in production of steroid hormones and adipokines as well as chronic subclinical inflammation, activities which predispose to cancer ( 50 ). M1 macrophages, the CD68 staining immune cells that secrete inflammatory cytokines—TNF-α, IL-6, and IL-1β–that are implicated in promoting obesity-associated inflammation ( 49 ), are abundant in breast WAT ( 50 , 52 ). These macrophages aggregate in histologically defined crown-like structures (CLS) in which they surround necrotic adipocytes, a histopathologic feature that is observed in mice and humans ( 41 , 47 , 57 ). Macrophage-based CLS formations are found in normal breast tissue, at a higher frequency in obese women ( 58 , 59 ). These breast CLS (CLS-B) serve as measures of breast inflammation, quantified as the CLS-B index ( 60 ).

Steroid Hormones: Role in Breast Cancer Risk

The increased incidence of estrogen-receptor-positive (ER+) breast cancer in obesity supports the role for estrogen, a steroid hormone, in breast carcinogenesis ( 61 ), bringing the endocrine function of adipose tissue into play. Key factors that are increased in breast tissue of obese women have been shown to play a role in stimulating expression of aromatase, the enzyme that carries out the rate-limiting step of estrogen biosynthesis ( 56 , 61 ). The mechanisms responsible for production of these factors rely on activation of the immune system, bridging the previously described immune and hormonal effects of obesity. For example, TNF produced by adipose-infiltrating macrophages stimulates expression of aromatase in adipose fibroblasts ( 56 , 61 ). Prostaglandin E 2 (PGE 2 ), an inflammatory factor, and hypoxia-inducible factor 1α (HIF-1 α) both participate in inducing aromatase production by adipose stromal cells (ASCs) ( 62 ). Elevated levels of aromatase are found in VAT and SAT as well as adipose tissue in the breast of obese postmenopausal women ( 63 ), including inflamed breast adipose tissue of obese women with breast cancer ( 64 ). This “ obesity-inflammation-aromatase axis” has been proposed to play an important role in increased risk of ER+ breast cancer in postmenopausal women, by elevating estrogen levels in the breasts of women in whom levels of estrogen in the general circulation are reduced ( 60 , 64 , 65 ).

Molecular Mechanisms Contributing to Obesity and Breast Cancer: Genetics, Epigenetics, and Microbiomics

At the molecular, mechanistic level, genetics, epigenetics, and microbiomics are likely involved in susceptibility to weight gain and obesity ( 66 ). These molecular factors may also interact to give rise to obese phenotypes. Furthermore, the interaction between these molecular factors with behavior and environmental factors likely add to the etiologic complexity and biological variation that is observed with weight gain and the obese state. Moreover, dysregulation of these molecular mechanisms may explain not only the link between obesity and breast cancer, but also the comorbid conditions associated with obesity.

Genetics

Many gene variants have been found to be associated with obesity. Recent reviews highlight both the candidate gene approach utility for identifying monogenic obesity genes as well as genetic variants identified through Genome Wide Association Studies (GWAS), which implicate genes from several biological pathways in polygenic obesity ( 66 – 68 ). These GWAS approaches have revealed that loci associated with obesity carry genes involved in pathways influencing neuro-circuits of appetite and satiety regulation ( BDNF, MC4R, NEGR, POMC ) ( 69 – 73 ), insulin secretion and action ( TCF7L2, IRS1 ) ( 69 , 74 ), adipogenesis ( 75 ) and energy and lipid metabolism [ FTO, RPTOR, MAP2K5 ( 69 , 74 , 76 )]. Using well-powered GWAS studies, more than 870 SNPs have been found to be associated with BMI ( 68 ). However, the findings also indicate that these loci only explain 5% of the variance of BMI ( 77 ). Although challenging, attempting to explain the remaining variability is a focus of obesity research. In this regard, the utilization of other omics, such as transcriptomics, proteomics, epigenomics, microbiomics, and metabolomics, may increase the phenotypic prediction of weight gain ( 66 , 78 ). Associations between obesity, genetics and breast cancer have been documented and more are emerging. One example concerns the fat mass and obesity associated ( FTO ) gene, which was the topic of a recent systematic review that promulgated FTO gene as a possible mediator for the association between obesity and breast cancer ( 79 ). The FTO gene encodes a dependent oxygenase related to 2-oxoglutarate that has a role in DNA demethylation but its molecular mechanism in obesity and metabolism has not been elucidated ( 80 ). In their systematic review, Akbari et al. ( 79 ) suggested that polymorphisms in the FTO gene may influence the risk of breast cancer as well as obesity through expression of the homeobox transcription factor iriquois 3 ( IRX3 ) gene. IRX3 is a developmental transcription factor that more recently has been implicated in regulating energy expenditure ( 81 ).

Epigenetics

With a great degree of complexity and flexibility, epigenetic mechanisms influence how genetic information is transcribed and translated into proteins, ultimately affecting health and disease, including the conditions of weight gain and obesity. In contrast to genetic modifications, which lead to a change in the base sequence of DNA, epigenetic changes are thought to be reversible and consist of chemical modifications to DNA (or DNA-associated chromosomal proteins called histones) that occur in the absence of a change in the DNA sequence ( 82 ). Epigenetic mechanisms include DNA methylation, histone modifications, and microRNA-mediated regulation, which can be passed on mitotically (through cell division) or meiotically (through generational inheritance) ( 83 ). Epigenetics has emerged as a significant link between genes and the environment, serving as a molecular mechanism to explain individual variation in biological response to environmental factors. Interestingly, recent evidence suggests an association between obesity and DNA methylation; but whether this is a cause or a consequence of the obese phenotype requires mechanistic examination ( 84 ). A brief discussion of the relationship between DNA methylation, obesity and breast cancer follows. The role of microRNA and histones in influencing obesity and their relationships to breast cancer are discussed elsewhere ( 83 , 85 – 87 ).

DNA Methylation

In mammals, the addition of methyl groups to DNA (methylation) occurs predominantly at cytosines adjacent to guanines (“ CpG” sites) through DNA methyltransferases. Promoter DNA methylation disrupts the binding of transcription factors and attracts methyl-binding proteins that typically initiate chromatin compaction and gene silencing ( 88 ). Promoter hypomethylation, on the other hand, is associated with activation of transcription. DNA methylation is the best studied and most stable epigenetic mechanism, and both candidate gene methylation and epigenome-wide methylation studies have been performed to understand connections with obesity ( 68 , 83 , 87 ). These have led to discovery of DNA methylation changes that are associated with many genes and pathways related to obesity and its comorbidities, including appetite control, insulin signaling, immunity, and inflammation. Interestingly, candidate genes implicated in monogenic obesity (e. g., POMC ) have also been found to be influenced by DNA methylation changes contributing to common obesity ( 89 ). With the use of genetic association analyses along with epigenome-wide association analyses, alterations in DNA methylation have been shown to be the result of obesity rather than the cause of obesity ( 90 ). This study suggested epigenetics as a mechanism by which some individuals with excess BMI move to the next step in the causal pathway to metabolic disease. Other evidence, however, is suggestive of a putative causal relationship for DNA methylation alterations in the onset of obesity and metabolic disease. Such is the case for evidence from the Dutch Winter Hunger cohort with inclusion of subjects that experienced famine early in life ( 91 ). Investigators recently performed a genome-wide analysis of differential DNA methylation in whole blood from this cohort ( 92 ). They show that the associations between exposure to an adverse environment during early development and health outcomes in adulthood are mediated by alterations in DNA methylation; interestingly, PIM3 methylation (cg09349128), a gene involved in energy metabolism, mediated approximately 13% of the association between famine exposure and BMI.

Obesity, Epigenetics, Breast Cancer

There is an emerging interest in interrogating DNA methylation as a possible mechanistic link between obesity and breast cancer. An example concerns estrogen receptor 1 ( ESR1 ) gene hypermethylation, which may be involved in the development of breast cancer. Investigators hypothesized that BMI and estrogen-related reproductive risk factors may influence the methylation status of the ESR1 CpG loci in the normal breasts of healthy women. They found that ESR1 promoter methylation in women with a BMI ≥30 kg/m 2 was higher than in the subgroups of women with BMI <25 kgm 2 or BMI 25–29 kg/m 2 and was also higher in postmenopausal women compared with that in premenopausal women ( 93 ). The finding provides possible clues to the relationship between epigenetic changes within the ESR1 gene CpG island and postmenopausal obesity and aging in cancer-free women, and merits additional study. In another example, investigators explored the association of adiposity-related CpG loci and subsequent risk of postmenopausal breast cancer, colorectal cancer and myocardial infarction ( 94 ). Using peripheral blood leucocytes from over 1900 individuals from four prospective European cohorts, these investigators measured the relationship between DNA methylation profiles and body mass index, waist circumference, waist-hip and waist-height ratio within a meta-analytical framework that also assessed the relationship of adiposity-related CpG to comorbidities. Among the 40 adiposity-related CpG loci identified, two loci in IL2RB and FGF18 and one CpG locus in an intergenic region of chromosome 1 were associated with colorectal cancer and myocardial infarction development ( 94 ). However, none of the adiposity-related CpG loci were associated with post-menopausal breast cancer following Bonferroni correction; the authors also noted that the number of post-menopausal breast cancer cases included in the study was relatively small.

DNA methylation has been suggested as a mechanism that could explain inter-individual variability in terms of weight loss response as well as the metabolic response to weight loss ( 95 ). In this regard, there is interest in examining whether weight loss might reverse abnormal DNA methylation changes observed in obesity and thereby reduce comorbidities. Rossi et al. identified several hypermethylated gene promoters in mice that were obese, compared to leaner controls ( 96 ). Interestingly, many of these genes showed intermediate methylation in formerly obese mice, suggesting that some obesity-associated epigenetic changes may be resistant to reprogramming after weight loss. These authors also found that weight loss in the formerly obese mice did not reduce proinflammatory cytokine gene expression nor the basal-like mammary tumor burden ( 96 ). The authors mention that weight loss in combination with epigenetic or anti-inflammatory interventions may be needed to disrupt the obesity–breast cancer link. Furthermore, examination of DNA methylation, in combination with genetic variants, gut microbiota and other molecular mechanisms, might be useful in understanding the relationship between obesity, weight loss and breast cancer.

Microbiomics

The collective genomes of the microbes (composed of bacteria, bacteriophage, fungi, protozoa, and viruses) that live inside and on the human body are referred to as the microbiome ( 97 ). Alterations of gut microbiota and its microbiome are associated with obesity and are responsive to weight loss ( 98 ). For example, transferring the luminal contents from the ceca of obese and lean mice to germ-free animal recipients resulted in more weight gain over a 2-week period in recipients receiving the microbes from obese animals compared to the recipients inoculated with the lean mouse microbes, despite equivalent food intake ( 99 ). Hints are also found from human studies, including a study in twins which found that obese individuals displayed reduced bacterial diversity, a depletion of Bacteroidetes as well as greater abundance of carbohydrate and lipid-utilizing microbial genes compared to lean individuals ( 100 ). Many mechanisms have been implicated in these associations such as increased dietary energy harvest, microbe-induced changes in host glucose and lipid metabolism, microbial signaling through host endocrine systems, and chronic low-grade inflammation leading to insulin resistance ( 98 ). Backhed et al. observed a direct link between the intestinal microbiome and increased adiposity when they inoculated germ-free mice with the cecal contents from conventional mice ( 101 ). These recipient mice gained weight despite calorie restriction; experiments revealed that weight gain was in part due to increased intestinal monosaccharide absorption and increased hepatic lipogenesis. Furthermore, the microbiome in these mice suppressed a host gene ( Fiaf or fasting-induced adipose factor) coding a circulating lipoprotein lipase inhibitor (Angptl4), which resulted in an increase in triglyceride deposition in adipose tissue ( 102 ). The magnitude of the contribution of the gut microbiota and its gene content to obesity and its related comorbidities is still uncertain ( 66 ). Perhaps a better understanding of host-microbe and microbe-microbe interactions may lead to the development of novel strategies for reversing obesity ( 103 ).

Microbial perturbations (dysbiosis) have been observed in breast cancer patients compared to healthy individuals ( 104 , 105 ). Here it is interesting to note that the gut microbiota may influence the production of estrogen metabolites and it has been hypothesized that alterations in the microbiota might lead to elevated levels of circulating estrogens and its metabolites, thus increasing the risk of breast cancer ( 105 ). Although an altered intestinal microbiome has been implicated in obesity and alterations of the microbiome (both distal and local) may influence breast cancer risk, little to no research has examined the mechanisms that may explain the association between obesity, microbiome and breast cancer.

Tackling Obesity: The Many Facets of Weight Loss

Obesity-Targeting Weight Loss Interventions—Addressing Above Mechanisms

Several observational studies found that adult weight loss was associated with decreased risk for postmenopausal breast cancer ( 106 – 109 ), although others did not find an association ( 110 , 111 ). A meta-analysis assessing the effect of weight loss on breast cancer incidence found that weight loss significantly reduces breast cancer risk in both pre- and post- menopausal women ( 112 ). In a recent study, investigators examined the effect of weight change on breast cancer incidence in 61, 335 postmenopausal women enrolled in the Women’s Health Initiative Observational Study ( 109 ). This study reported that women who lost weight (> 5% of body weight) compared to women with stable weight had a significantly lower breast cancer risk (HR, 0. 88, 95% CI, 0. 78–0. 98). Similar findings were found in the Nurses’ Health Study for weight loss and reduced breast cancer risk (HR, 0. 77, 95% CI = 0. 65–0. 91) ( 108 ). These results are also supported by bariatric surgery research revealing a reduction in the risk of breast cancer ( 113 ). Although the presented evidence that weight loss is associated with decreased breast cancer risk appears to be convincing, more rigorous data involving clinical trials and timing of weight loss are needed.

Weight loss, a state of negative energy balance, is believed to significantly influence postmenopausal breast cancer risk through alterations in several pathways including sex-steroid hormones, endocrine hormones, and inflammatory markers. Obesity-targeting weight loss interventions that include hypocaloric diets and/or exercise have been shown to significantly reduce total body weight, adipose tissue (visceral and subcutaneous) and biomarkers associated with breast cancer risk ( 114 ). Here we review how weight loss can modulate obesity-related mechanisms that favor decreased breast cancer risk. Randomized trials of weight loss as an intervention in cancer survivors has been reviewed elsewhere ( 115 , 116 ).

Weight-Loss and Sex-Steroid Hormones

As described above, excess adipose tissue modulates steroid aromatization, resulting in elevated levels of estrogen and, therefore, increased breast cancer risk. Weight loss interventions have been shown to have beneficial effects on estradiol, free estradiol, sex hormone binding globin (SHBG) and free testosterone concentrations ( 117 , 118 ). For example, the Nutrition and Exercise in Women (NEW) study revealed that participants in the diet plus exercise group had greater reductions in total body weight and waist circumference compared to diet-only and exercise-only groups (mean 8. 9, 7. 2, 2. 0 kg, respectively) ( 119 ). A dose-response relationship was also found, such that greater weight loss was associated with greater decreases in estrone, estradiol, free estradiol, and free testosterone, as well as a greater increase in SHBG ( 120 ). Another study found that overweight and obese postmenopausal women with > 10 vs. <10% weight loss, had significant changes in bioavailable estradiol ( p < 0. 001), testosterone ( p = 0. 033), and SHBG ( p < 0. 001) ( 121 ). Research studies and meta-analyses provide sufficient evidence that weight loss interventions, in the form of reduced caloric intake and exercise, are associated with significant reductions in sex-steroid hormones ( 39 , 118 ).

Weight-Loss and Endocrine Hormones (Insulin and IGF-1)

Abdominal obesity, specifically visceral fat, is associated with metabolic abnormalities such as hyperinsulinemia, insulin resistance and elevated IGF-1 concentrations, all of which are risk factors for breast cancer ( 122 , 123 ). Obesity-targeting weight loss interventions have produced favorable changes in fasting insulin, glucose and HOMA-IR concentrations ( 121 , 124 – 126 ). For example, weight losses > 10% were associated with a median absolute change in insulin concentrations (−3. 4 μIU/ml; p = 0. 018) among women at increased risk for breast cancer ( 121 ). Another study revealed that weight loss (subcutaneous and visceral fat) at 6 months was significantly associated with reductions in fasting insulin and HOMA concentrations, which remained significantly lower than baseline at 12 months, even after weight regain for women assigned to the diet group ( 124 ). However, the literature is somewhat contradictory as it relates to insulin-like growth factor-1 (IGF-1) concentrations. Several weight loss interventions have shown that weight loss is positively associated with IGF-1 concentrations and decreased IGFBP-1 & 3 ( 114 , 121 , 124 , 127 ). Mason et al. ( 128 ) found no significant changes in IGF-1 or IGFBP-3 by intervention arm, but did find that greater weight loss was associated with elevated IGF-1 and molar ratio of IGF-1: IGFBP-1 concentrations in obese postmenopausal women. However, a few interventions found either no significant change ( 125 ) or slight decreased serum IGF-1 and increased IGFBP-3 concentrations after the adoption of a very low-calorie diet ( 129 ). A multicenter trial examining caloric restriction of 25% over 2 years suggests that insignificant changes in IGF-1 and IGF-1: IGFBP-3 molar ratio concentrations may be related to chronic high protein intake ( 130 ). It is well-established that weight loss can reduce insulin, glucose, and measures of insulin resistance. However, large intervention studies are needed to better understand the effects of weight loss on IGF-1 concentrations.

Weight Loss and Inflammatory Markers

White adipose tissue is metabolically active and is a major contributor to the release of cytokines and adipokines in the bloodstream ( 131 ). Weight loss interventions have shown reductions in systemic markers of chronic inflammation ( 121 , 124 , 132 – 134 ). A study in obese postmenopausal women found that those randomized to the diet plus exercise group and the diet only group experienced the greatest amount of weight loss, which, in turn, was associated with significant increases in adiponectin (+11. 7 % and 18. 5% in each group, respectively) as well as reductions in leptin ( p -trend < 0. 001), compared to the control group ( 135 ). Another study found that obese postmenopausal women assigned to a hypocaloric diet plus aerobic exercise condition vs. a diet-only condition lost more weight, particularly abdominal fat, and had significantly greater reductions in C-reactive protein (CRP), IL-6, sIL-6R, and TNFR1 concentrations ( 136 ). In this study, reductions in abdominal fat stimulated lipolysis, which correlated with reductions in plasma IL-6 and TNFR1 ( 136 ). Other studies in obese postmenopausal women reported that > 10% total weight loss and reductions in waist circumference produced favorable changes in CRP, adiponectin, leptin, and the molar ratio of adiponectin: leptin concentrations at 12 weeks and at 1- year follow-up ( 121 ). A systematic review and meta-analysis found that diet-induced weight loss was associated with reductions in adiponectin concentrations ( 137 ). Similar findings have shown reductions in several systemic concentrations of acute phase reactants and pro-inflammatory cytokines after weight loss intervention ( 124 , 138 , 139 ). Nicklas et al. ( 140 ) observed that the strongest correlations with change in CRP was a change in weight, waist circumference, insulin and HOMA. Overall, obesity-targeting weight loss interventions have shown reductions in most inflammatory markers, especially for CRP.

Weight-Loss and Macronutrient Composition

There may be differential amounts of weight loss in response to specific dietary macronutrient (e. g., protein, fat, carbohydrate) composition. Several meta-analyses of weight loss randomized controlled trials (RCTs) examined the efficacy of low-carbohydrate (LC) vs. low-fat (LF) diets on weight change ( 141 – 143 ). One study found non-significant differences for macronutrient composition on the amount of weight loss at 12 months ( 144 ); whereas, the other meta-analyses found that LC diets rather than LF diets led to significantly greater weight loss at 12 months, but the weight loss differences between diets were small ( 141 – 143 ). Additionally, two large RCTs did not observe differential effects of macronutrient intakes on the amount of weight loss ( 145 , 146 ). Specifically, the POUNDS LOST trial did not find differences in 4 diets that varied in macronutrient composition on changes in body composition, abdominal fat, or hepatic fat ( 145 ). The DIETFIT study examined the effects of a healthy LF vs. a healthy LC diet on weight change in 609 overweight participants. There were no significant differences between the two diets in terms of weight loss (−5. 3 kg HLF and −6. 0 kg HLC) nor were there between-group differences for BMI, body fat percent, or waist circumference at 12 months ( 146 ). It appears that a reduction in total energy intake may be more important for weight loss rather than manipulating the macronutrient content of the diet. However, the literature is mixed, and further study is required.

Weight-Loss, Macronutrient Composition, and Biomarkers

Fasting glucose and insulin may impact response to weight loss diets with different macronutrient composition. Researchers suggest that a LC diet may provide greater weight loss in overweight and obese women who are insulin resistant ( 147 , 148 ); in contrast, normoglycemic participants lose more weight on an LF diet ( 149 ). A recent study found that overweight/obese participants who were insulin resistant (HOMA-IR > 4) lost significantly more weight on a high-fat (HF) high-protein (HP) diet; however, it should be noted the diet was also very low in carbohydrates (40% fat, 25% protein, 35% carbohydrates) compared to a HF-average protein diet (40% fat, 15% protein, 45% carbohydrate) ( 149 ). Rock et al. ( 133 ) found that women who were insulin sensitive lost greater weight at 12 months in the LF vs. LC diet group. However, a large RCT did not reveal differential effects for the LF vs. LC diets on weight loss by baseline insulin status ( 146 , 150 ). Our understanding of macronutrient composition on weight loss in obese insulin-sensitive and insulin resistant individuals requires further study.

Furthermore, it is possible that there is a differential weight loss response to diet composition and that biomarkers associated with breast cancer risk may mediate this association. For example, a LC vs. LF weight loss diet was associated with increased adiponectin concentrations in obese women; however, there were no correlations between weight loss and increased adiponectin ( 151 ). Other studies did not find significant differences by intervention arm (caloric-restricted LF vs. LC diet) on favorable changes in adipokine and leptin concentrations at study completion, although leptin concentrations decreased with both diets ( 152 ). Weight loss induced by overall caloric restriction rather than the macronutrient content of the diet appears to be more effective in reducing chronic systemic inflammation ( 140 , 153 – 155 ) and endocrine markers such as insulin and HOMA ( 156 ). Research is needed using large RCTs to understand whether differential weight loss response to macronutrient composition is influenced by biomarkers of breast cancer risk.

Pharmacological Approaches to Obesity and Weight Loss

Although our emphasis has been on weight loss as a remedy to obesity, other approaches are being tried. As previously discussed, increased physical activity has potential to decrease breast cancer risk, at least in part by reducing obesity ( 39 ). However, targeting physical activity as an isolated behavioral change whose increase might facilitate decreased obesity is complicated by the interplay between this approach, caloric reduction and their effects on energy balance. Alternatively, pharmacological approaches to weight, and hence obesity, reduction have been considered. Metformin, the first-line treatment for type II diabetes, which has been extensively studied regarding its cancer preventive activity, including breast cancer ( 157 ), has exhibited efficacy in reducing weight in a number of studies. Weight reduction is expected to disrupt the association between obesity and cancer, suggesting a possible mechanistic basis for the anti-cancer effect of metformin ( 158 ). In a study of 154 consecutive non-diabetic, overweight/obese individuals, metformin-treated patients had a mean weight loss of 5. 8 ± 7. 0 kg in contrast to a loss of 0. 8 ± 3. 5 kg in an untreated group ( 159 ). A meta-analysis of 13 studies addressing the effects of metformin on simple obesity showed that metformin is effective in reducing body weight in this population, without inducing hypoglycemia ( 160 ). The Diabetes Prevention Program is a clinical trial that randomized 3234 participants with elevated glucose and overweight/obesity, to metformin, intensive lifestyle intervention (ILS), or placebo. Whereas, at 1-year follow-up, only 28. 5% of participants in the metformin arm had lost at least 5% of their weight, 62. 6% in the ILS group and 13. 4% in the placebo group had achieved this goal ( 161 ). In contrast, between years 6 and 15, after unmasking, maintenance of mean weight loss was 6. 2% with metformin, 3. 7% with ILS, and 2. 8% with placebo, suggesting a benefit to metformin with respect to a long-term weight loss endpoint. Although much remains to be investigated, metformin has exhibited potential to induce weight loss in both diabetic and non-diabetic individuals.

Another agent showing benefits for weight management is liraglutide, a glucagon like peptide-1 (GLP-1) receptor agonist that is approved by the Food and Drug Administration (FDA) as an adjunct to diet and exercise for management of type 2 diabetes. A review of five randomized clinical trials showed that compared to placebo, liraglutide was associated with a higher proportion of patients achieving at least a 5-10% weight loss ( 162 ). The main drawbacks to its use are gastrointestinal side effects and the need for injection. In addition, pharmacologic agents that have been investigated for treatment of eating disorders also offer possible interventions to induce weight loss in obese patients ( 163 ). One such agent, lisdexamfetamine, a central nervous system amphetamine, has been used in children with severe obesity, although long-term use is discouraged, given its high potential for abuse ( 164 ). The state of pharmacologic interventions to induce weight loss thus remains in flux as studies aimed at identifying an improved balance between efficacy and side effects continue.

Conclusions and Future Directions

Obesity has reached epidemic proportions in the United States and increasingly around the world. Undesirable health-related sequelae are expected to follow as the obese state is increasingly being observed in children and young adults. Obesity is physiologically complex, however, and we have discussed only a few of the endocrine, immunologic and molecular abnormalities that characterize this state. In addressing the need for reducing obesity we have concentrated on evidence derived from weight loss initiatives. However, other approaches are currently being undertaken. For example, physical activity as a major intervention, with or without accompanying diet directives, has potential to improve obesity-related metabolic parameters. Intermittent fasting approaches, including time-restricted feeding, are emerging weight loss strategies, which may also improve metabolic parameters. Bioactive food components such as omega-3-fatty acids are being studied as interventions to facilitate loss of weight. Finally, pharmacologic approaches, including agents such as metformin, need to be investigated in relation to their weight reducing efficacy.

Breast cancer, the most common cancer in postmenopausal women in the U. S., is one of the malignant outcomes associated with chronic obesity. Thus, efforts to improve interventions to prevent breast cancer, along with other serious obesity-associated diseases, require a deeper understanding of the physiological basis of obesity as well as the development of interventions to reduce this high-risk state in the population.

Despite the extensive research that has been ongoing into the multiple facets of obesity on general health and cancer in particular, huge gaps remain in our understanding of mechanisms and associations. Of immediate interest is the disconnect between obesity’s positive association with postmenopausal ER-positive breast cancer and its inverse association with premenopausal ER-positive disease; what is the mechanistic basis for this difference? How do the duration and timing in the life cycle influence the chronic nature of obesity that appears to be linked to breast cancer? Additional gaps address the complex molecular mechanisms at the genetic and epigenetic levels which control expression of proteins that contribute to obesity. The integration of various omics data, including transcriptomics, proteomics, epigenomics, microbiomics, and metabolomics, may also assist in elucidating the link between obesity and cancer.

The majority of the epidemiologic studies linking obesity to breast cancer used self-reported anthropometric measures (e. g., BMI, waist circumference) to assess risk. However, more meaningful assessments of body composition compartments (e. g., VAT and SAT), which capture known physiological and metabolic changes associated with breast cancer risk, need to be used in future studies. Also, one must not ignore the enormous effect the obesity epidemic is having on low SES populations, which in the future may potentially lead to associated chronic diseases, including cancer. Lastly, since the majority of the studies were conducted among Caucasian women, research is needed to understand the association between body fat distribution and specific breast cancer subtypes across various racial and ethnic groups.

Author Contributions

TA-C, SR, and BD each wrote sections of the manuscript and each reviewed and edited the manuscript for content and cohesion.

Conflict of Interest Statement

TA-C, SR, and BD are employees of the National Cancer Institute, National Institutes of Health in Rockville, MD.

References

1. Goodwin PJ, Stambolic V. Impact of the obesity epidemic on cancer. Annu Rev Med . (2015) 66: 281–96. doi: 10. 1146/annurev-med-051613-012328

||

2. Sung H, Siegel RL, Torre LA, Pearson-Stuttard J, Islami F, Fedewa SA, et al. Global patterns in excess body weight and the associated cancer burden. CA Cancer J Clin . (2019) 69: 88-112. doi: 10. 3322/caac. 21499

||

3. WHO. World Health Organization. Obesity and Overweight.(2018). Available online at:(accessed February 16, 2018).

4. Andersen CJ, Murphy KE, Fernandez ML. Impact of obesity and metabolic syndrome on immunity. Adv Nutr . (2016) 7: 66–75. doi: 10. 3945/an. 115. 010207

||

5. Guilherme A, Virbasius JV, Puri V, Czech MP. Adipocyte dysfunctions linking obesity to insulin resistance and type 2 diabetes. Nat Rev Mol Cell Biol . (2008) 9: 367–77. doi: 10. 1038/nrm2391

||

6. Afshin A, Reitsma MB, Murray CJL. Health effects of overweight and obesity in 195 countries. N Engl J Med . (2017) 377: 1496–7. doi: 10. 1056/NEJMc1710026

|

7. 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: 794–8. doi: 10. 1056/NEJMsr1606602

||

8. Liu K, Zhang W, Dai Z, Wang M, Tian T, Liu X, et al. Association between body mass index and breast cancer risk: evidence based on a dose-response meta-analysis. Cancer Manag Res . (2018) 10: 143–51. doi: 10. 2147/CMAR. S144619

||

9. Xia X, Chen W, Li J, Chen X, Rui R, Liu C, et al. Body mass index and risk of breast cancer: a nonlinear dose-response meta-analysis of prospective studies. Sci Rep . (2014) 4: 7480. doi: 10. 1038/srep07480

||

10. Hales CM, Carroll MD, Fryar CD, Ogden CL. Prevalence of obesity among adults and youth: United States, 2015-2016. NCHS Data Brief . (2017)288: 1–8.

11. Renehan AG, Tyson M, Egger M, Heller RF, Zwahlen M. Body-mass index and incidence of cancer: a systematic review and meta-analysis of prospective observational studies. Lancet . (2008) 371: 569–78. doi: 10. 1016/S0140-6736(08)60269-X

||

12. Horn-Ross PL, Canchola AJ, Bernstein L, Neuhausen SL, Nelson DO, Reynolds P. Lifetime body size and estrogen-receptor-positive breast cancer risk in the California Teachers Study cohort. Breast Cancer Res . (2016) 18: 132. doi: 10. 1186/s13058-016-0790-5

||

13. Suzuki R, Rylander-Rudqvist T, Ye W, Saji S, Wolk A. Body weight and postmenopausal breast cancer risk defined by estrogen and progesterone receptor status among Swedish women: a prospective cohort study. Int J Cancer . (2006) 119: 1683–9. doi: 10. 1002/ijc. 22034

||

14. Tehard B, Clavel-Chapelon F. Several anthropometric measurements and breast cancer risk: results of the E3N cohort study. Int J Obes . (2006) 30: 156–63. doi: 10. 1038/sj. ijo. 0803133

||

15. Chen Y, Liu L, Zhou Q, Imam MU, Cai J, Wang Y, et al. Body mass index had different effects on premenopausal and postmenopausal breast cancer risks: a dose-response meta-analysis with 3, 318, 796 subjects from 31 cohort studies. BMC Public Health . (2017) 17: 936. doi: 10. 1186/s12889-017-4953-9

||

16. Baer HJ, Tworoger SS, Hankinson SE, Willett WC. Body fatness at young ages and risk of breast cancer throughout life. Am J Epidemiol . (2010) 171: 1183–94. doi: 10. 1093/aje/kwq045

||

17. Premenopausal Breast Cancer Collaborative G, Schoemaker MJ, Nichols HB, Wright LB, Brook MN, Jones ME, et al. Association of body mass index and age with subsequent breast cancer risk in premenopausal women. JAMA Oncol . (2018) 4: e181771. doi: 10. 1001/jamaoncol. 2018. 1771

||

18. Lee KR, Hwang IC, Han KD, Jung J, Seo MH. Waist circumference and risk of breast cancer in Korean women: a nationwide cohort study. Int J Cancer . (2018) 142: 1554–9. doi: 10. 1002/ijc. 31180

||

19. Wang F, Liu L, Cui S, Tian F, Fan Z, Geng C, et al. Distinct effects of body mass index and waist/hip ratio on risk of breast cancer by joint estrogen and progestogen receptor status: results from a case-control study in Northern and Eastern china and implications for chemoprevention. Oncologist . (2017) 22: 1431–43. doi: 10. 1634/theoncologist. 2017-0148

||

20. Chen GC, Chen SJ, Zhang R, Hidayat K, Qin JB, Zhang YS, et al. Central obesity and risks of pre- and postmenopausal breast cancer: a dose-response meta-analysis of prospective studies. Obes Rev . (2016) 17: 1167–77. doi: 10. 1111/obr. 12443

||

21. Connolly BS, Barnett C, Vogt KN, Li T, Stone J, Boyd NF. A meta-analysis of published literature on waist-to-hip ratio and risk of breast cancer. Nutr Cancer . (2002) 44: 127–38. doi: 10. 1207/S15327914NC4402_02

||

22. Millikan RC, Newman B, Tse CK, Moorman PG, Conway K, Smith LV, et al. Epidemiology of basal-like breast cancer. Breast Cancer Res Treat . (2008) 109: 123–39. doi: 10. 1007/s10549-007-9790-6

||

23. Harris HR, Willett WC, Terry KL, Michels KB. Body fat distribution and risk of premenopausal breast cancer in the Nurses’ Health Study II. J Natl Cancer Inst . (2011) 103: 273–8. doi: 10. 1093/jnci/djq500

||

24. Pierobon M, Frankenfeld CL. Obesity as a risk factor for triple-negative breast cancers: a systematic review and meta-analysis. Breast Cancer Res Treat . (2013) 137: 307–14. doi: 10. 1007/s10549-012-2339-3

||

25. Slattery ML, Sweeney C, Edwards S, Herrick J, Baumgartner K, Wolff R, et al. Body size, weight change, fat distribution and breast cancer risk in Hispanic and non-Hispanic white women. Breast Cancer Res Treat . (2007) 102: 85–101. doi: 10. 1007/s10549-006-9292-y

||

26. Bandera EV, Chandran U, Zirpoli G, Gong Z, McCann SE, Hong CC, et al. Body fatness and breast cancer risk in women of African ancestry. BMC Cancer . (2013) 13: 475. doi: 10. 1186/1471-2407-13-475

||

27. Amadou A, Ferrari P, Muwonge R, Moskal A, Biessy C, Romieu I, et al. Overweight, obesity and risk of premenopausal breast cancer according to ethnicity: a systematic review and dose-response meta-analysis. Obes Rev . (2013) 14: 665–78. doi: 10. 1111/obr. 12028

||

28. Phipps AI, Chlebowski RT, Prentice R, McTiernan A, Stefanick ML, Wactawski-Wende J, et al. Body size, physical activity, and risk of triple-negative and estrogen receptor-positive breast cancer. Cancer Epidemiol Biomarkers Prev . (2011) 20: 454–63. doi: 10. 1158/1055-9965. EPI-10-0974

||

29. Dietze EC, Chavez TA, Seewaldt VL. Obesity and triple-negative breast cancer: disparities, controversies, and biology. Am J Pathol . (2018) 188: 280–90. doi: 10. 1016/j. ajpath. 2017. 09. 018

||

30. Chlebowski RT, Chen Z, Anderson GL, Rohan T, Aragaki A, Lane D, et al. Ethnicity and breast cancer: factors influencing differences in incidence and outcome. J Natl Cancer Inst . (2005) 97: 439–48. doi: 10. 1093/jnci/dji064

||

31. Zobel EH, Hansen TW, Rossing P, von Scholten BJ. Global changes in food supply and the obesity epidemic. Curr Obes Rep . (2016) 5: 449–55. doi: 10. 1007/s13679-016-0233-8

||

32. Newton S, Braithwaite D, Akinyemiju TF. Socio-economic status over the life course and obesity: Systematic review and meta-analysis. PLoS ONE . (2017) 12: e0177151. doi: 10. 1371/journal. pone. 0177151

||

33. Ogden CL, Fakhouri TH, Carroll MD, Hales CM, Fryar CD, Li X, et al. Prevalence of obesity among adults, by household income and education – United States, 2011-2014. MMWR Morb Mortal Wkly Rep . (2017) 66: 1369–73. doi: 10. 15585/mmwr. mm6650a1

||

34. Vona-Davis L, Rose DP. The influence of socioeconomic disparities on breast cancer tumor biology and prognosis: a review. J Womens Health . (2009) 18: 883–93. doi: 10. 1089/jwh. 2008. 1127

||

35. Parise CA, Caggiano V. The influence of socioeconomic status on racial/ethnic disparities among the ER/PR/HER2 Breast cancer subtypes. J Cancer Epidemiol . (2015) 2015: 813456. doi: 10. 1155/2015/813456

||

36. Yost K, Perkins C, Cohen R, Morris C, Wright W. Socioeconomic status and breast cancer incidence in California for different race/ethnic groups. Cancer Causes Control . (2001) 12: 703–11. doi: 10. 1023/A: 1011240019516

||

37. Dunn BK, Agurs-Collins T, Browne D, Lubet R, Johnson KA. Health disparities in breast cancer: biology meets socioeconomic status. Breast Cancer Res Treat . (2010) 121: 281–92. doi: 10. 1007/s10549-010-0827-x

||

38. Lope V, Martin M, Castello A, Ruiz A, Casas AM, Baena-Canada JM, et al. Overeating, caloric restriction and breast cancer risk by pathologic subtype: the EPIGEICAM study. Sci Rep . (2019) 9: 3904. doi: 10. 1038/s41598-019-39346-4

||

39. de Roon M, May AM, McTiernan A, Scholten R, Peeters PHM, Friedenreich CM, et al. Effect of exercise and/or reduced calorie dietary interventions on breast cancer-related endogenous sex hormones in healthy postmenopausal women. Breast Cancer Res . (2018) 20: 81. doi: 10. 1186/s13058-018-1009-8

||

40. Kershaw EE, Flier JS. Adipose tissue as an endocrine organ. J Clin Endocrinol Metab . (2004) 89: 2548–56. doi: 10. 1210/jc. 2004-0395

||

41. Alkhouri N, Gornicka A, Berk MP, Thapaliya S, Dixon LJ, Kashyap S, et al. Adipocyte apoptosis, a link between obesity, insulin resistance, and hepatic steatosis. J Biol Chem . (2010) 285: 3428–38. doi: 10. 1074/jbc. M109. 074252

||

42. Park J, Morley TS, Kim M, Clegg DJ, Scherer PE. Obesity and cancer–mechanisms underlying tumour progression and recurrence. Nat Rev Endocrinol . (2014) 10: 455–65. doi: 10. 1038/nrendo. 2014. 94

||

43. Goodwin PJ. Obesity, insulin resistance and breast cancer outcomes. Breast . (2015) 24 (Suppl 2): S56–9. doi: 10. 1016/j. breast. 2015. 07. 014

||

44. Ahima RS, Flier JS. Adipose tissue as an endocrine organ. Trends Endocrinol Metab . (2000) 11: 327–32. doi: 10. 1016/S1043-2760(00)00301-5

||

45. Galic S, Oakhill JS, Steinberg GR. Adipose tissue as an endocrine organ. Mol Cell Endocrinol . (2010) 316: 129–39. doi: 10. 1016/j. mce. 2009. 08. 018

||

46. Flier JS, Maratos-Flier E. Leptin’s physiologic role: does the emperor of energy balance have no clothes? Cell Metab . (2017) 26: 24–6. doi: 10. 1016/j. cmet. 2017. 05. 013

||

47. West-Eberhard MJ. Nutrition, the visceral immune system, and the evolutionary origins of pathogenic obesity. Proc Natl Acad Sci USA . (2019) 116: 723–31. doi: 10. 1073/pnas. 1809046116

||

48. Winer DA, Winer S, Shen L, Wadia PP, Yantha J, Paltser G, et al. B cells promote insulin resistance through modulation of T cells and production of pathogenic IgG antibodies. Nat Med . (2011) 17: 610–7. doi: 10. 1038/nm. 2353

||

49. Kanneganti TD, Dixit VD. Immunological complications of obesity. Nat Immunol . (2012) 13: 707–12. doi: 10. 1038/ni. 2343

||

50. Iyengar NM, Hudis CA, Dannenberg AJ. Obesity and cancer: local and systemic mechanisms. Annu Rev Med . (2015) 66: 297–309. doi: 10. 1146/annurev-med-050913-022228

||

51. Trayhurn P, Wood IS. Adipokines: inflammation and the pleiotropic role of white adipose tissue. Br J Nutr . (2004) 92: 347–55. doi: 10. 1079/BJN20041213

||

52. Iyengar NM, Zhou XK, Gucalp A, Morris PG, Howe LR, Giri DD, et al. Systemic correlates of white adipose tissue inflammation in early-stage breast cancer. Clin Cancer Res . (2016) 22: 2283–9. doi: 10. 1158/1078-0432. CCR-15-2239

||

53. Dranoff G. Cytokines in cancer pathogenesis and cancer therapy. Nat Rev Cancer . (2004) 4: 11-22. doi: 10. 1038/nrc1252

||

54. Shi H, Kokoeva MV, Inouye K, Tzameli I, Yin H, Flier JS. TLR4 links innate immunity and fatty acid-induced insulin resistance. J Clin Invest . (2006) 116: 3015–25. doi: 10. 1172/JCI28898

||

55. Howe LR, Subbaramaiah K, Hudis CA, Dannenberg AJ. Molecular pathways: adipose inflammation as a mediator of obesity-associated cancer. Clin Cancer Res . (2013) 19: 6074–83. doi: 10. 1158/1078-0432. CCR-12-2603

||

56. Gross AL, Newschaffer CJ, Hoffman-Bolton J, Rifai N, Visvanathan K. Adipocytokines, inflammation, and breast cancer risk in postmenopausal women: a prospective study. Cancer Epidemiol Biomarkers Prev . (2013) 22: 1319–24. doi: 10. 1158/1055-9965. EPI-12-1444

||

57. Subbaramaiah K, Howe LR, Bhardwaj P, Du B, Gravaghi C, Yantiss RK, et al. Obesity is associated with inflammation and elevated aromatase expression in the mouse mammary gland. Cancer Prev Res . (2011) 4: 329–46. doi: 10. 1158/1940-6207. CAPR-10-0381

||

58. Sun X, Casbas-Hernandez P, Bigelow C, Makowski L, Joseph Jerry D, Smith Schneider S, et al. Normal breast tissue of obese women is enriched for macrophage markers and macrophage-associated gene expression. Breast Cancer Res Treat . (2012) 131: 1003–12. doi: 10. 1007/s10549-011-1789-3

||

59. Mullooly M, Yang HP, Falk RT, Nyante SJ, Cora R, Pfeiffer RM, et al. Relationship between crown-like structures and sex-steroid hormones in breast adipose tissue and serum among postmenopausal breast cancer patients. Breast Cancer Res . (2017) 19: 8. doi: 10. 1186/s13058-016-0791-4

||

60. Subbaramaiah K, Morris PG, Zhou XK, Morrow M, Du B, Giri D, et al. Increased levels of COX-2 and prostaglandin E2 contribute to elevated aromatase expression in inflamed breast tissue of obese women. Cancer Discov . (2012) 2: 356–65. doi: 10. 1158/2159-8290. CD-11-0241

||

61. Bulun SE, Chen D, Moy I, Brooks DC, Zhao H. Aromatase, breast cancer and obesity: a complex interaction. Trends Endocrinol Metab . (2012) 23: 83–9. doi: 10. 1016/j. tem. 2011. 10. 003

||

62. Subbaramaiah K, Iyengar NM, Morrow M, Elemento O, Zhou XK, Dannenberg AJ. Prostaglandin E2 down-regulates sirtuin 1 (SIRT1), leading to elevated levels of aromatase, providing insights into the obesity-breast cancer connection. J Biol Chem . (2019) 294: 361–71. doi: 10. 1074/jbc. RA118. 005866

||

63. Zahid H, Simpson ER, Brown KA. Inflammation, dysregulated metabolism and aromatase in obesity and breast cancer. Curr Opin Pharmacol . (2016) 31: 90–6. doi: 10. 1016/j. coph. 2016. 11. 003

||

64. Morris PG, Hudis CA, Giri D, Morrow M, Falcone DJ, Zhou XK, et al. Inflammation and increased aromatase expression occur in the breast tissue of obese women with breast cancer. Cancer Prev Res . (2011) 4: 1021–9. doi: 10. 1158/1940-6207. CAPR-11-0110

||

65. Iyengar NM, Morris PG, Zhou XK, Gucalp A, Giri D, Harbus MD, et al. Menopause is a determinant of breast adipose inflammation. Cancer Prev Res . (2015) 8: 349–58. doi: 10. 1158/1940-6207. CAPR-14-0243

||

66. Pigeyre M, Yazdi FT, Kaur Y, Meyre D. Recent progress in genetics, epigenetics and metagenomics unveils the pathophysiology of human obesity. Clin Sci . (2016) 130: 943–86. doi: 10. 1042/CS20160136

||

67. Speakman JR, Loos RJF, O’Rahilly S, Hirschhorn JN, Allison DB. GWAS for BMI: a treasure trove of fundamental insights into the genetic basis of obesity. Int J Obes. (Lond). (2018) 42: 1524–31. doi: 10. 1038/s41366-018-0147-5

||

68. Rohde K, Keller M, la Cour Poulsen L, Bluher M, Kovacs P, Bottcher Y. Genetics and epigenetics in obesity. Metabolism . (2019) 92: 37–50. doi: 10. 1016/j. metabol. 2018. 10. 007

||

69. Locke AE, Kahali B, Berndt SI, Justice AE, Pers TH, Day FR, et al. Genetic studies of body mass index yield new insights for obesity biology. Nature . (2015) 518: 197–206. doi: 10. 1038/nature14177

||

70. Ho EV, Klenotich SJ, McMurray MS, Dulawa SC. Activity-based anorexia alters the expression of BDNF transcripts in the mesocorticolimbic reward circuit. PLoS ONE . (2016) 11: e0166756. doi: 10. 1371/journal. pone. 0166756

||

71. Horstmann A, Kovacs P, Kabisch S, Boettcher Y, Schloegl H, Tonjes A, et al. Common genetic variation near MC4R has a sex-specific impact on human brain structure and eating behavior. PLoS ONE . (2013) 8: e74362. doi: 10. 1371/journal. pone. 0074362

||

72. Boender AJ, van Rozen AJ, Adan RA. Nutritional state affects the expression of the obesity-associated genes Etv5, Faim2, Fto, and Negr1. Obesity . (2012) 20: 2420–5. doi: 10. 1038/oby. 2012. 128

||

73. Millington GW. The role of proopiomelanocortin (POMC) neurones in feeding behaviour. Nutr Metab . (2007) 4: 18. doi: 10. 1186/1743-7075-4-18

||

74. Kilpelainen TO, Zillikens MC, Stancakova A, Finucane FM, Ried JS, Langenberg C, et al. Genetic variation near IRS1 associates with reduced adiposity and an impaired metabolic profile. Nature genetics . (2011) 43: 753–60. doi: 10. 1038/ng. 866

||

75. Shungin D, Winkler TW, Croteau-Chonka DC, Ferreira T, Locke AE, Magi R, et al. New genetic loci link adipose and insulin biology to body fat distribution. Nature . (2015) 518: 187–96. doi: 10. 1038/nature14132

||

76. Frayling TM, Timpson NJ, Weedon MN, Zeggini E, Freathy RM, Lindgren CM, et al. A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. Science . (2007) 316: 889–94. doi: 10. 1126/science. 1141634

||

77. Yengo L, Sidorenko J, Kemper KE, Zheng Z, Wood AR, Weedon MN, et al. Meta-analysis of genome-wide association studies for height and body mass index in approximately 700000 individuals of European ancestry. Hum Mol Genet . (2018) 27: 3641–9. doi: 10. 1093/hmg/ddy271

||

78. Shah S, Bonder MJ, Marioni RE, Zhu Z, McRae AF, Zhernakova A, et al. Improving phenotypic prediction by combining genetic and epigenetic associations. Am J Hum Genet . (2015) 97: 75–85. doi: 10. 1016/j. ajhg. 2015. 05. 014

||

79. Akbari ME, Gholamalizadeh M, Doaei S, Mirsafa F. FTO gene affects obesity and breast cancer through similar mechanisms: a new insight into the molecular therapeutic targets. Nutr Cancer . (2018) 70: 30–6. doi: 10. 1080/01635581. 2018. 1397709

||

80. Gerken T, Girard CA, Tung YC, Webby CJ, Saudek V, Hewitson KS, et al. The obesity-associated FTO gene encodes a 2-oxoglutarate-dependent nucleic acid demethylase. Science . (2007) 318: 1469–72. doi: 10. 1126/science. 1151710

||

81. Smemo S, Tena JJ, Kim KH, Gamazon ER, Sakabe NJ, Gomez-Marin C, et al. Obesity-associated variants within FTO form long-range functional connections with IRX3. Nature . (2014) 507: 371–5. doi: 10. 1038/nature13138

||

82. Waterland RA, Michels KB. Epigenetic epidemiology of the developmental origins hypothesis. Annu Rev Nutr . (2007) 27: 363–88. doi: 10. 1146/annurev. nutr. 27. 061406. 093705

||

83. Thaker VV. Genetic and epigenetic causes of obesity. Adolesc Med State Art Rev . (2017) 28: 379–405.

|

84. van Dijk SJ, Molloy PL, Varinli H, Morrison JL, Muhlhausler BS, Members of Epi S. Epigenetics and human obesity. Int J Obes . (2015) 39: 85–97. doi: 10. 1038/ijo. 2014. 34

||

85. Lorente-Cebrian S, Gonzalez-Muniesa P, Milagro FI, Martinez JA. MicroRNAs and other non-coding RNAs in adipose tissue and obesity: emerging roles as biomarkers and therapeutic targets. Clin Sci . (2019) 133: 23–40. doi: 10. 1042/CS20180890

||

86. Kasiappan R, Rajarajan D. Role of MicroRNA regulation in obesity-associated breast cancer: nutritional perspectives. Adv Nutr . (2017) 8: 868–88. doi: 10. 3945/an. 117. 015800

||

87. Ling C, Ronn T. Epigenetics in human obesity and Type 2 diabetes. Cell Metab . (2019) 29: 1028–44. doi: 10. 1016/j. cmet. 2019. 03. 009

||

88. Jaenisch R, Bird A. Epigenetic regulation of gene expression: how the genome integrates intrinsic and environmental signals. Nat Genet . (2003) 33 (Suppl): 245–54. doi: 10. 1038/ng1089

||

89. Kuhnen P, Handke D, Waterland RA, Hennig BJ, Silver M, Fulford AJ, et al. Interindividual variation in DNA methylation at a putative POMC metastable epiallele is associated with obesity. Cell Metab . (2016) 24: 502–9. doi: 10. 1016/j. cmet. 2016. 08. 001

||

90. Wahl S, Drong A, Lehne B, Loh M, Scott WR, Kunze S, et al. Epigenome-wide association study of body mass index, and the adverse outcomes of adiposity. Nature.(2017) 541: 81–6. doi: 10. 1038/nature20784

||

91. Ravelli GP, Stein ZA, Susser MW. Obesity in young men after famine exposure in utero and early infancy. N Engl J Med . (1976) 295: 349–53. doi: 10. 1056/NEJM197608122950701

||

92. Tobi EW, Slieker RC, Luijk R, Dekkers KF, Stein AD, Xu KM, et al. DNA methylation as a mediator of the association between prenatal adversity and risk factors for metabolic disease in adulthood. Sci Adv . (2018) 4: eaao4364. doi: 10. 1126/sciadv. aao4364

||

93. Daraei A, Izadi P, Khorasani G, Nafissi N, Naghizadeh MM, Younosi N, et al. Epigenetic changes of the ESR1 gene in breast tissue of healthy women: a missing link with breast cancer risk factors? Genet Test Mol Biomark . (2017) 21: 464–70. doi: 10. 1089/gtmb. 2017. 0028

||

94. Campanella G, Gunter MJ, Polidoro S, Krogh V, Palli D, Panico S, et al. Epigenome-wide association study of adiposity and future risk of obesity-related diseases. Int J Obes . (2018) 42: 2022–35. doi: 10. 1038/s41366-018-0064-7

||

95. Samblas M, Milagro FI, Martinez A. DNA methylation markers in obesity, metabolic syndrome, and weight loss. Epigenetics . (2019) 14: 421–44. doi: 10. 1080/15592294. 2019. 1595297

||

96. Rossi EL, de Angel RE, Bowers LW, Khatib SA, Smith LA, Van Buren E, et al. Obesity-associated alterations in inflammation, epigenetics, and mammary tumor growth persist in formerly obese mice. Cancer Prev Res . (2016) 9: 339–48. doi: 10. 1158/1940-6207. CAPR-15-0348

||

97. Schlaeppi K, Bulgarelli D. The plant microbiome at work. Mol Plant-Micro Interact . (2015) 28: 212–7. doi: 10. 1094/MPMI-10-14-0334-FI

||

98. Ley RE. Obesity and the human microbiome. Curr Opin Gastroenterol . (2010) 26: 5–11. doi: 10. 1097/MOG. 0b013e328333d751

||

99. Turnbaugh PJ, Ley RE, Mahowald MA, Magrini V, Mardis ER, Gordon JI. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature . (2006) 444: 1027–31. doi: 10. 1038/nature05414

||

100. Turnbaugh PJ, Hamady M, Yatsunenko T, Cantarel BL, Duncan A, Ley RE, et al. A core gut microbiome in obese and lean twins. Nature . (2009) 457: 480–4. doi: 10. 1038/nature07540

||

101. Backhed F, Ding H, Wang T, Hooper LV, Koh GY, Nagy A, et al. The gut microbiota as an environmental factor that regulates fat storage. Proc Nat Acad Sci USA . (2004) 101: 15718–23. doi: 10. 1073/pnas. 0407076101

|

102. Backhed F, Manchester JK, Semenkovich CF, Gordon JI. Mechanisms underlying the resistance to diet-induced obesity in germ-free mice. Proc Nat Acad Sci USA . (2007) 104: 979–84. doi: 10. 1073/pnas. 0605374104

|

103. Barko PC, McMichael MA, Swanson KS, Williams DA. The Gastrointestinal microbiome: a review. J Vet Int Med . (2018) 32: 9–25. doi: 10. 1111/jvim. 14875

||

104. Parida S, Sharma D. The power of small changes: comprehensive analyses of microbial dysbiosis in breast cancer. Biochim et Biophy Acta Rev Cancer . (2019) 1871: 392–405. doi: 10. 1016/j. bbcan. 2019. 04. 001

||

105. Fernandez MF, Reina-Perez I, Astorga JM, Rodriguez-Carrillo A, Plaza-Diaz J, Fontana L. Breast cancer and its relationship with the microbiota. Int J Environ Res Pub Health . (2018) 15: E1747. doi: 10. 3390/ijerph15081747

||

106. Eliassen AH, Colditz GA, Rosner B, Willett WC, Hankinson SE. Adult weight change and risk of postmenopausal breast cancer. JAMA . (2006) 296: 193–201. doi: 10. 1001/jama. 296. 2. 193

||

107. Parker ED, Folsom AR. Intentional weight loss and incidence of obesity-related cancers: the Iowa Women’s Health Study. Int J Obes Relat Metab Disord . (2003) 27: 1447–52. doi: 10. 1038/sj. ijo. 0802437

||

108. Rosner B, Eliassen AH, Toriola AT, Chen WY, Hankinson SE, Willett WC, et al. Weight and weight changes in early adulthood and later breast cancer risk. Int J Cancer . (2017) 140: 2003–14. doi: 10. 1002/ijc. 30627

||

109. Chlebowski RT, Luo J, Anderson GL, Barrington W, Reding K, Simon MS, et al. Weight loss and breast cancer incidence in postmenopausal women. Cancer . (2019) 125: 205–12. doi: 10. 1002/cncr. 31687

||

110. Neuhouser ML, Aragaki AK, Prentice RL, Manson JE, Chlebowski R, Carty CL, et al. Overweight, obesity, and postmenopausal invasive breast cancer risk: a secondary analysis of the Women’s health initiative randomized clinical trials. JAMA Oncol . (2015) 1: 611–21. doi: 10. 1001/jamaoncol. 2015. 1546

||

111. Emaus MJ, van Gils CH, Bakker MF, Bisschop CN, Monninkhof EM, Bueno-de-Mesquita HB, et al. Weight change in middle adulthood and breast cancer risk in the EPIC-PANACEA study. Int J Cancer . (2014) 135: 2887–99. doi: 10. 1002/ijc. 28926

||

112. Hardefeldt PJ, Penninkilampi R, Edirimanne S, Eslick GD. Physical activity and weight loss reduce the risk of breast cancer: a meta-analysis of 139 prospective and retrospective studies. Clin Breast Cancer . (2018) 18: e601–e12. doi: 10. 1016/j. clbc. 2017. 10. 010

||

113. McCawley GM, Ferriss JS, Geffel D, Northup CJ, Modesitt SC. Cancer in obese women: potential protective impact of bariatric surgery. J Am Coll Surg . (2009) 208: 1093–8. doi: 10. 1016/j. jamcollsurg. 2009. 01. 045

||

114. Telgenkamp I, Kusters Y, Schalkwijk CG, Houben A, Kooi ME, Lindeboom L, et al. Contribution of liver fat to weight loss-induced changes in serum hepatokines: a randomized-controlled trial. J Clin Endocrinol Metab . (2019) 104: 2719–27. doi: 10. 1210/jc. 2018-02378

||

115. Chlebowski RT, Reeves MM. Weight loss randomized intervention trials in female cancer survivors. J Clin Oncol . (2016) 34: 4238–48. doi: 10. 1200/JCO. 2016. 69. 4026

||

116. Playdon M, Thomas G, Sanft T, Harrigan M, Ligibel J, Irwin M. Weight loss intervention for breast cancer survivors: a systematic review. Curr Breast Cancer Rep . (2013) 5: 222–46. doi: 10. 1007/s12609-013-0113-0

||

117. van Gemert WA, Schuit AJ, van der Palen J, May AM, Iestra JA, Wittink H, et al. Effect of weight loss, with or without exercise, on body composition and sex hormones in postmenopausal women: the SHAPE-2 trial. Breast Cancer Res . (2015) 17: 120. doi: 10. 1186/s13058-015-0633-9

|

118. Stone SA, Han CJ, Senn T, Korde LA, Allott K, Reding S, et al. Sex hormones in women with elevated breast cancer risk undergoing weight loss. West J Nurs Res . (2019). doi: 10. 1177/0193945918820672. [Epub ahead of print].

||

119. Foster-Schubert KE, Alfano CM, Duggan CR, Xiao L, Campbell KL, Kong A, et al. Effect of diet and exercise, alone or combined, on weight and body composition in overweight-to-obese postmenopausal women. Obesity . (2012) 20: 1628–38. doi: 10. 1038/oby. 2011. 76

|

120. Campbell KL, Foster-Schubert KE, Alfano CM, Wang CC, Wang CY, Duggan CR, et al. Reduced-calorie dietary weight loss, exercise, and sex hormones in postmenopausal women: randomized controlled trial. J Clin Oncol . (2012) 30: 2314–26. doi: 10. 1200/JCO. 2011. 37. 9792

||

121. Fabian CJ, Kimler BF, Donnelly JE, Sullivan DK, Klemp JR, Petroff BK, et al. Favorable modulation of benign breast tissue and serum risk biomarkers is associated with > 10 % weight loss in postmenopausal women. Breast Cancer Res Treat . (2013) 142: 119–32. doi: 10. 1007/s10549-013-2730-8

|| 122. Chen Y, Wen YY, Li ZR, Luo DL, Zhang XH. The molecular mechanisms between metabolic syndrome and breast cancer. Biochem Biophys Res Commun . (2016) 471: 391–5. doi: 10. 1016/j. bbrc. 2016. 02. 034

||

123. Doyle SL, Donohoe CL, Lysaght J, Reynolds JV. Visceral obesity, metabolic syndrome, insulin resistance and cancer. Proc Nutr Soc . (2012) 71: 181–9. doi: 10. 1017/S002966511100320X

||

124. Lien LF, Haqq AM, Arlotto M, Slentz CA, Muehlbauer MJ, McMahon RL, et al. The STEDMAN project: biophysical, biochemical and metabolic effects of a behavioral weight loss intervention during weight loss, maintenance, and regain. Omics.(2009) 13: 21–35. doi: 10. 1089/omi. 2008. 0035

||

125. Beeken RJ, Croker H, Heinrich M, Obichere A, Finer N, Murphy N, et al. The impact of diet-induced weight loss on biomarkers for colorectal cancer: an exploratory study (INTERCEPT). Obesity . (2017) 25 (Suppl 2): S95–S101. doi: 10. 1002/oby. 21984

||

126. Diabetes Prevention Program Research G. The diabetes prevention program (DPP): description of lifestyle intervention. Diabet Care . (2002) 25: 2165–71. doi: 10. 2337/diacare. 25. 12. 2165

|

127. Brekke HK, Bertz F, Rasmussen KM, Bosaeus I, Ellegard L, Winkvist A. Diet and exercise interventions among overweight and obese lactating women: randomized trial of effects on cardiovascular risk factors. PLoS ONE . (2014) 9: e88250. doi: 10. 1371/journal. pone. 0088250

||

128. Mason C, Xiao L, Duggan C, Imayama I, Foster-Schubert KE, Kong A, et al. Effects of dietary weight loss and exercise on insulin-like growth factor-I and insulin-like growth factor-binding protein-3 in postmenopausal women: a randomized controlled trial. Cancer Epidemiol Biomarkers Prev . (2013) 22: 1457–63. doi: 10. 1158/1055-9965. EPI-13-0337

||

129. De Pergola G, Zamboni M, Pannacciulli N, Turcato E, Giorgino F, Armellini F, et al. Divergent effects of short-term, very-low-calorie diet on insulin-like growth factor-I and insulin-like growth factor binding protein-3 serum concentrations in premenopausal women with obesity. Obes Res . (1998) 6: 408–15. doi: 10. 1002/j. 1550-8528. 1998. tb00372. x

||

130. Fontana L, Villareal DT, Das SK, Smith SR, Meydani SN, Pittas AG, et al. Effects of 2-year calorie restriction on circulating levels of IGF-1, IGF-binding proteins and cortisol in nonobese men and women: a randomized clinical trial. Aging Cell . (2016) 15: 22–7. doi: 10. 1111/acel. 12400

||

131. Proenca AR, Sertie RA, Oliveira AC, Campana AB, Caminhotto RO, Chimin P, et al. New concepts in white adipose tissue physiology. Braz J Med Biol Res . (2014) 47: 192–205. doi: 10. 1590/1414-431X20132911

||

132. Miller GD, Isom S, Morgan TM, Vitolins MZ, Blackwell C, Brosnihan KB, et al. Effects of a community-based weight loss intervention on adipose tissue circulating factors. Diab Metab Syndr . (2014) 8: 205–11. doi: 10. 1016/j. dsx. 2014. 09. 003

||

133. Rock CL, Flatt SW, Pakiz B, Quintana EL, Heath DD, Rana BK, et al. Effects of diet composition on weight loss, metabolic factors and biomarkers in a 1-year weight loss intervention in obese women examined by baseline insulin resistance status. Metabolism . (2016) 65: 1605–13. doi: 10. 1016/j. metabol. 2016. 07. 008

||

134. Merra G, Gratteri S, De Lorenzo A, Barrucco S, Perrone MA, Avolio E, et al. Effects of very-low-calorie diet on body composition, metabolic state, and genes expression: a randomized double-blind placebo-controlled trial. Eur Rev Med Pharmacol Sci . (2017) 21: 329–45.

|

135. Abbenhardt C, McTiernan A, Alfano CM, Wener MH, Campbell KL, Duggan C, et al. Effects of individual and combined dietary weight loss and exercise interventions in postmenopausal women on adiponectin and leptin levels. J Intern Med . (2013) 274: 163–75. doi: 10. 1111/joim. 12062

||

136. You T, Berman DM, Ryan AS, Nicklas BJ. Effects of hypocaloric diet and exercise training on inflammation and adipocyte lipolysis in obese postmenopausal women. J Clin Endocrinol Metab . (2004) 89: 1739–46. doi: 10. 1210/jc. 2003-031310

||

137. Salehi-Abargouei A, Izadi V, Azadbakht L. The effect of low calorie diet on adiponectin concentration: a systematic review and meta-analysis. Horm Metab Res . (2015) 47: 549–55. doi: 10. 1055/s-0035-1549878

||

138. Salas-Salvado J, Bullo M, Garcia-Lorda P, Figueredo R, Del Castillo D, Bonada A, et al. Subcutaneous adipose tissue cytokine production is not responsible for the restoration of systemic inflammation markers during weight loss. Int J Obes . (2006) 30: 1714–20. doi: 10. 1038/sj. ijo. 0803348

|

139. Bianchi VE. Weight loss is a critical factor to reduce inflammation. Clin Nutr ESPEN . (2018) 28: 21–35. doi: 10. 1016/j. clnesp. 2018. 08. 007

||

140. Nicklas JM, Sacks FM, Smith SR, LeBoff MS, Rood JC, Bray GA, et al. Effect of dietary composition of weight loss diets on high-sensitivity c-reactive protein: the Randomized POUNDS LOST trial. Obesity . (2013) 21: 681–9. doi: 10. 1002/oby. 20072

||

141. Sackner-Bernstein J, Kanter D, Kaul S. Dietary intervention for overweight and obese adults: comparison of low-carbohydrate and low-fat diets. a meta-analysis. PLoS ONE . (2015) 10: e0139817. doi: 10. 1371/journal. pone. 0139817

||

142. Tobias DK, Chen M, Manson JE, Ludwig DS, Willett W, Hu FB. Effect of low-fat diet interventions versus other diet interventions on long-term weight change in adults: a systematic review and meta-analysis. Lancet Diabetes Endocrinol . (2015) 3: 968–79. doi: 10. 1016/S2213-8587(15)00367-8

||

143. Johnston BC, Kanters S, Bandayrel K, Wu P, Naji F, Siemieniuk RA, et al. Comparison of weight loss among named diet programs in overweight and obese adults: a meta-analysis. JAMA . (2014) 312: 923–33. doi: 10. 1001/jama. 2014. 10397

||

144. Nordmann AJ, Nordmann A, Briel M, Keller U, Yancy WS Jr, Brehm BJ, et al. Effects of low-carbohydrate vs low-fat diets on weight loss and cardiovascular risk factors: a meta-analysis of randomized controlled trials. Arch Intern Med . (2006) 166: 285–93. doi: 10. 1001/archinte. 166. 3. 285

||

145. de Souza RJ, Bray GA, Carey VJ, Hall KD, LeBoff MS, Loria CM, et al. Effects of 4 weight-loss diets differing in fat, protein, and carbohydrate on fat mass, lean mass, visceral adipose tissue, and hepatic fat: results from the POUNDS LOST trial. Am J Clin Nutr . (2012) 95: 614–25. doi: 10. 3945/ajcn. 111. 026328

||

146. Gardner CD, Trepanowski JF, Del Gobbo LC, Hauser ME, Rigdon J, Ioannidis JPA, et al. Effect of low-fat vs low-carbohydrate diet on 12-month weight loss in overweight adults and the association with genotype pattern or insulin secretion: The DIETFITS randomized clinical trial. JAMA . (2018) 319: 667–79. doi: 10. 1001/jama. 2018. 0245

||

147. Cornier MA, Donahoo WT, Pereira R, Gurevich I, Westergren R, Enerback S, et al. Insulin sensitivity determines the effectiveness of dietary macronutrient composition on weight loss in obese women. Obes Res . (2005) 13: 703–9. doi: 10. 1038/oby. 2005. 79

||

148. Pittas AG, Das SK, Hajduk CL, Golden J, Saltzman E, Stark PC, et al. A low-glycemic load diet facilitates greater weight loss in overweight adults with high insulin secretion but not in overweight adults with low insulin secretion in the CALERIE Trial. Diabetes Care . (2005) 28: 2939–41. doi: 10. 2337/diacare. 28. 12. 2939

||

149. Hjorth MF, Bray GA, Zohar Y, Urban L, Miketinas DC, Williamson DA, et al. Pretreatment fasting glucose and insulin as determinants of weight loss on diets varying in macronutrients and dietary fibers-The POUNDS LOST Study. Nutrients . (2019) 11: E586. doi: 10. 3390/nu11030586

||

150. Gardner CD, Offringa LC, Hartle JC, Kapphahn K, Cherin R. Weight loss on low-fat vs. low-carbohydrate diets by insulin resistance status among overweight adults and adults with obesity: a randomized pilot trial. Obesity . (2016) 24: 79–86. doi: 10. 1002/oby. 21331

||

151. Summer SS, Brehm BJ, Benoit SC, D’Alessio DA. Adiponectin changes in relation to the macronutrient composition of a weight-loss diet. Obesity . (2011) 19: 2198–204. doi: 10. 1038/oby. 2011. 60

||

152. Llanos AA, Krok JL, Peng J, Pennell ML, Olivo-Marston S, Vitolins MZ, et al. Favorable effects of low-fat and low-carbohydrate dietary patterns on serum leptin, but not adiponectin, among overweight and obese premenopausal women: a randomized trial. Springerplus . (2014) 3: 175. doi: 10. 1186/2193-1801-3-175

|

153. Rock CL, Flatt SW, Byers TE, Colditz GA, Demark-Wahnefried W, Ganz PA, et al. Results of the exercise and nutrition to enhance recovery and good health for you (ENERGY) trial: a behavioral weight loss intervention in overweight or obese breast cancer survivors. J Clin Oncol . (2015) 33: 3169–76. doi: 10. 1200/JCO. 2015. 61. 1095

|

154. Song X, Kestin M, Schwarz Y, Yang P, Hu X, Lampe JW, et al. A low-fat high-carbohydrate diet reduces plasma total adiponectin concentrations compared to a moderate-fat diet with no impact on biomarkers of systemic inflammation in a randomized controlled feeding study. Eur J Nutr . (2016) 55: 237–46. doi: 10. 1007/s00394-015-0841-1

||

155. van Bussel BC, Henry RM, Ferreira I, van Greevenbroek MM, van der Kallen CJ, Twisk JW, et al. A healthy diet is associated with less endothelial dysfunction and less low-grade inflammation over a 7-year period in adults at risk of cardiovascular disease. J Nutr . (2015) 145: 532–40. doi: 10. 3945/jn. 114. 201236

||

156. Veum VL, Laupsa-Borge J, Eng O, Rostrup E, Larsen TH, Nordrehaug JE, et al. Visceral adiposity and metabolic syndrome after very high-fat and low-fat isocaloric diets: a randomized controlled trial. Am J Clin Nutr . (2017) 105: 85–99. doi: 10. 3945/ajcn. 115. 123463

||

157. Heckman-Stoddard BM, Gandini S, Puntoni M, Dunn BK, DeCensi A, Szabo E. Repurposing old drugs to chemoprevention: the case of metformin. Semin Oncol . (2016) 43: 123–33. doi: 10. 1053/j. seminoncol. 2015. 09. 009

||

158. Chan AT. Metformin for cancer prevention: a reason for optimism. Lancet Oncol . (2016) 17: 407–9. doi: 10. 1016/S1470-2045(16)00006-1

||

159. Seifarth C, Schehler B, Schneider HJ. Effectiveness of metformin on weight loss in non-diabetic individuals with obesity. Exp Clin Endocrinol Diab . (2013) 121: 27-31. doi: 10. 1055/s-0032-1327734

||

160. Ning HH, Le J, Wang Q, Young CA, Deng B, Gao PX, et al. The effects of metformin on simple obesity: a meta-analysis. Endocrine . (2018) 62: 528–34. doi: 10. 1007/s12020-018-1717-y

||

161. Apolzan JW, Venditti EM, Edelstein SL, Knowler WC, Dabelea D, Boyko EJ, et al. Long-term weight loss with metformin or lifestyle intervention in the diabetes prevention program outcomes study. Ann Intern Med . (2019). doi: 10. 7326/M18-1605

|

162. Mehta A, Marso SP, Neeland IJ. Liraglutide for weight management: a critical review of the evidence. Obes Sci Pract . (2017) 3: 3-14. doi: 10. 1002/osp4. 84

||

163. Crow SJ. Pharmacologic treatment of eating disorders. Psychiatr Clin North Am . (2019) 42: 253–62. doi: 10. 1016/j. psc. 2019. 01. 007

||

164. Srivastava G, O’Hara V, Browne N. Use of lisdexamfetamine to treat obesity in an adolescent with severe obesity and binge eating. Children . (2019) 6: E22. doi: 10. 3390/children6020022

||

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