The Obesity-Breast Cancer Conundrum: An Analysis of the Issues

Breast cancer develops over a timeframe of 2–3 decades prior to clinical detection. Given this prolonged latency, it is somewhat unexpected from a biological perspective that obesity has no effect or reduces the risk for breast cancer in premenopausal women yet increases the risk for breast cancer in postmenopausal women. This conundrum is particularly striking in light of the generally negative effects of obesity on breast cancer outcomes, including larger tumor size at diagnosis and poorer prognosis in both pre- and postmenopausal women. This review and analysis identifies factors that may contribute to this apparent conundrum, issues that merit further investigation, and characteristics of preclinical models for breast cancer and obesity that should be considered if animal models are used to deconstruct the conundrum

Development of a core collection of <it>Triticum</it> and <it>Aegilops</it> species for improvement of wheat for activity against chronic diseases

Abstract Background The objective of this study was to develop a core collection of Triticum and Aegilops species as a resource for the identification and characterization of wheat lines with preventive activity against chronic diseases. Given that cancer is the leading cause of mortality in the world and shares risk factors with obesity, type-2 diabetes, and cardiovascular disease, and given that wheat has been reported to protect against these diseases, the core collection was developed based on cancer prevalence. Methods The Germplasm Resources Information Network (GRIN) database was used to identify Triticum and Aegilops species grown in regions of the world that vary in cancer prevalence based on the International Agency for Cancer Research GLOBOCAN world map of cancer statistics (2008). Cancer incidence data drove variety selection with secondary consideration of ploidy, center of origin, and climate. Results Analysis indicated that the geographic regions from which wheat is considered to have originated have a lower incidence of cancer than other geographic regions (P Conclusions A diverse core collection of wheat germplasm has been established from a range of regions worldwide. This core collection will be used to identify wheat lines with activity against chronic diseases using anticancer activity as a screening tool.</p

Premenopausal Obesity and Breast Cancer Growth Rates in a Rodent Model

Obese premenopausal women with breast cancer have poorer prognosis for long term survival, in part because their tumors are larger at the time of diagnosis than are found in normal weight women. Whether larger tumor mass is due to obesity-related barriers to detection or to effects on tumor biology is not known. This study used polygenic models for obesity and breast cancer to deconstruct this question with the objective of determining whether cell autonomous mechanisms contribute to the link between obesity and breast cancer burden. Assessment of the growth rates of 259 chemically induced mammary carcinomas from rats sensitive to dietary induced obesity (DS) and of 143 carcinomas from rats resistant (DR) to dietary induced obesity revealed that tumors in DS rats grew 1.8 times faster than in DR rats. This difference may be attributed to alterations in cell cycle machinery that permit more rapid tumor cell accumulation. DS tumors displayed protein expression patterns consistent with reduced G1/S checkpoint inhibition and a higher threshold of factors required for execution of the apoptotic cell death pathway. These mechanistic insights identify regulatory targets for life style modifications or pharmacological interventions designed to disrupt the linkage between obesity and tumor burden

Impact of Weight Loss on Plasma Leptin and Adiponectin in Overweight-to-Obese Post Menopausal Breast Cancer Survivors

Women who are obese at the time of breast cancer diagnosis have higher overall mortality than normal weight women and some evidence implicates adiponectin and leptin as contributing to prognostic disadvantage. While intentional weight loss is thought to improve prognosis, its impact on these adipokines is unclear. This study compared the pattern of change in plasma leptin and adiponectin in overweight-to-obese post-menopausal breast cancer survivors during weight loss. Given the controversies about what dietary pattern is most appropriate for breast cancer control and regulation of adipokine metabolism, the effect of a low fat versus a low carbohydrate pattern was evaluated using a non-randomized, controlled study design. Anthropometric data and fasted plasma were obtained monthly during the six-month weight loss intervention. While leptin was associated with fat mass, adiponectin was not, and the lack of correlation between leptin and adiponectin concentrations throughout weight loss implies independent mechanisms of regulation. The temporal pattern of change in leptin but not adiponectin was affected by magnitude of weight loss. Dietary pattern was without effect on either adipokine. Mechanisms not directly related to dietary pattern, weight loss, or fat mass appear to play dominant roles in the regulation of circulating levels of these adipokines

Metabolite Profiling of a Diverse Collection of Wheat Lines Using Ultraperformance Liquid Chromatography Coupled with Time-of-Flight Mass Spectrometry

<div><p>Genetic differences among major types of wheat are well characterized; however, little is known about how these distinctions affect the small molecule profile of the wheat seed. Ethanol/water (65% v/v) extracts of seed from 45 wheat lines representing 3 genetically distinct classes, tetraploid durum (<em>Triticum turgidum</em> subspecies <em>durum</em>) (DW) and hexaploid hard and soft bread wheat (<em>T. aestivum</em> subspecies <em>aestivum</em>) (BW) were subjected to ultraperformance liquid chromatography coupled with time-of-flight mass spectrometry (UPLC-TOF-MS). Discriminant analyses distinguished DW from BW with 100% accuracy due to differences in expression of nonpolar and polar ions, with differences attributed to sterol lipids/fatty acids and phospholipids/glycerolipids, respectively. Hard versus soft BW was distinguished with 100% accuracy by polar ions, with differences attributed to heterocyclic amines and polyketides versus phospholipid ions, respectively. This work provides a foundation for identification of metabolite profiles associated with desirable agronomic and human health traits and for assessing how environmental factors impact these characteristics.</p> </div

Metabolite profiling distinguishes between genetically distinct wheat classes with 100% accuracy.

<p>Multivariate discriminant analysis of the high-quality ion list, consisting of 935 ions in 45 wheat lines, was used to distinguish between wheat classes of differing ploidy levels: tetraploid durum wheat (DW) vs. hexaploid bread wheat, which comprises hard (HBW) and soft (SBW) bread wheat. Each point represents a single observation (e.g. each wheat line). <b>(Panel 1A)</b> To visualize inherent clustering patterns, the scatter plot represents unsupervised analysis through the PCA 3-class model. Separation of DW lines from HBW and SBW lines is observed. Model fit: R2X(cum) = 68.6%, with 7 components, and Q2(cum) = 38.9%. <b>(Panel 1B)</b> To determine contributing sources of variation, the scatter plot represents supervised analysis of the 3-class OPLS-DA model, which rotates the model plane to maximize separation due to class assignment. Near-complete separation of the 3 classes was observed. Model fit: R2Y(cum) = 93.2%, Q2Y(cum) = 71.0%. <b>(Panel 1C-Inset)</b> The misclassification table for the 3-class OPLS-DA model indicates that 100% of wheat lines (45 of 45 lines) were correctly classified, with low probability (p = 3.10E−17) of random table generation as assessed by Fisher’s Exact Probability. <b>(Panel 1C)</b> To visualize the misclassification rate, the dendrogram depicts hierarchical clustering patterns among major wheat classes using single linkage and size. Two main clusters comprise 1) DW lines and 2) all BW lines, with cluster 2 branching into 2A, comprising SBW lines, and 2B, comprising HBW lines. Node height of cluster 1 from 0 confirms the high degree of chemical distinctness seen within the DW lines evaluated in this study compared to node height of cluster 2.</p

Pedigree information for 45 wheat lines evaluated.

<p>Table columns: Numbers = identifiers used within manuscript to visualize chemical separations in scatter plots and dendrograms; Wheat Line = common field identifier; Source = geographical location where grown; Class = 1 of 3 market classes: durum (DW), hard bread wheat (HBW), or soft bread wheat (SBW); Subclass = subclass within bread wheat market classes based on seed coat color and growth habit (HWW = hard white winter; HWS = hard white spring; HRW = hard red winter; HRS = hard red spring; SWW = soft white winter; SWS = soft white spring; SRW = soft red winter); Pedigree = wheat line development and breeding.</p

Metabolite profiling distinguishes between HBW subclasses with >62% accuracy.

<p>Multivariate discriminant analysis of the high-quality ion list, consisting of 935 ions in 27 wheat lines, was used to distinguish between subclasses of hard bread wheat (HBW) comprising hard white winter (HWW), hard white spring (HWS), hard red winter (HRW), and hard red spring (HRS). <b>(Panel 2A)</b> To visualize inherent clustering patterns, the scatter plot depicts unsupervised analysis through the PCA model. Model fit: R2X(cum) = 40.3%, with 3 components, and Q2(cum) = 10.8%. <b>(Panel 2B)</b> To determine contributing sources of variation, the scatter plot represents supervised analysis of the OPLS-DA model. Near-complete separation of subclasses was observed. Model fit: R2Y(cum) = 36.6%, Q2Y(cum) = 17.3%. <b>(Panel 2C-Inset)</b> The misclassification table for the OPLS-DA model indicates that approximately 63% of wheat lines (17 out of 27 lines) were correctly classified, with low probability (p = 1.40E−05) of random table generation as assessed by Fisher’s Exact Probability. <b>(Panel 2C)</b> To visualize the misclassification rate, the dendrogram was constructed using single linkage hierarchical clustering and sorted by size. Two main clusters comprise 1) HRS and 2) the other 3 subclasses, which do not cluster by subclass, indicating a high degree of chemical homogeneity and therefore resistance to clustering by hierarchical methods between HBW subclasses.</p