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Effect of dietary patterns differing in carbohydrate and fat content on blood lipidand glucose profiles based on weight-loss success of breast-cancer survivors



Healthy body weight is an important factor for prevention of breast cancerrecurrence. Yet, weight loss and weight gain are not currently included inclinical-practice guidelines for posttreatment of breast cancer. The work reportedaddresses one of the questions that must be considered in recommending weight lossto patients: does it matter what diet plan is used, a question of particularimportance because breast cancer treatment can increase risk for cardiovasculardisease.


Women who completed treatment for breast cancer were enrolled in a nonrandomized,controlled study investigating effects of weight loss achieved by using twodietary patterns at the extremes of macronutrient composition, although both dietarms were equivalent in protein: high fat, low carbohydrate versus low fat, highcarbohydrate. A nonintervention group served as the control arm; women wereassigned to intervention arms based on dietary preferences. During the 6-monthweight-loss program, which was menu and recipe defined, participants had monthlyclinical visits at which anthropometric data were collected and fasting blood wasobtained for safety monitoring for plasma lipid profiles and fasting glucose.Results from 142 participants are reported.


Adverse effects on fasting blood lipids or glucose were not observed in eitherdietary arm. A decrease in fasting glucose was observed with progressive weightloss and was greater in participants who lost more weight, but the effect was notstatistically significant, even though it was observed across both diet groups(P = 0.21). Beneficial effects of weight loss on cholesterol (4.7%;P = 0.001), triglycerides (21.8%; P = 0.01), and low-densitylipoprotein (LDL) cholesterol (5.8%; P = 0.06) were observed in bothgroups. For cholesterol (P = 0.07) and LDL cholesterol (P =0.13), greater reduction trends were seen on the low-fat diet pattern; whereas,for triglycerides (P = 0.01) and high-density lipoprotein (HDL)cholesterol (P = 0.08), a decrease or increase, respectively, was greateron the low-carbohydrate diet pattern.


Because an individual's dietary preferences can affect dietary adherence andweight-loss success, the lack of evidence of a negative effect of dietary patternon biomarkers associated with cardiovascular risk is an important consideration inthe development of breast cancer practice guidelines for physicians who recommendthat their patients lose weight. Whether dietary pattern affects biomarkers thatpredict long-term survival is a primary question in this ongoing clinicaltrial.


A lifestyle change can halve the risk of breast cancer recurrence and reduce the risk ofbreast cancer-associated mortality by one third. However, many clinicians do notrecommend this strategy to their patients. The simple course of action, which generallyis not discussed, is promoting a healthy weight, and for many individuals, this meansweight loss. In this article, we begin to address this dormant opportunity in theclinical management of breast cancer with the goal of stimulating interest in generatingthe scientific evidence base required for considering weight management in clinicalpractice guidelines for the long-term survival of breast cancer patients.

A number of reports indicate that the prognosis for long-term survival after treatmentfor breast cancer is better in women who have a body weight for height, assessed by bodymass index (BMI, body weight (kg)/(height (m))2), that is considered to be inthe normal range (BMI, 18.5 to 24.9) versus women who are overweight (BMI, 25.0 to 29.9)or obese (BMI, ≥ 30.0) [111]. Consistent with those reports is the observation that weight gain afterdiagnosis increases risk for breast cancer recurrence, whereas weight loss in breastcancer survivors improves the chances of long-term survival [12, 13]. If one takes the available epidemiologic and clinical data at face value, itprompts the question, why is relatively little attention paid to weight control in theclinical management of breast cancer after treatment.

Overweight and obesity are common problems in the United States, and little evidenceindicates that prevalence is less in breast cancer survivors than in the population atlarge, which is estimated to be more than 60% [1416]. Thus, given that the majority of breast-cancer survivors have excess weightas a risk factor, the population at risk is large. However, a number of challenges arefaced by the physician. They include issues such as initiating a conversation aboutweight loss while recognizing the sensitivity of the subject and time constraints onoffice visits, which do not allow sufficient time to address the complexity ofweight-management issues for each patient, including the knowledge and behavioral gapsrelated to diet and weight loss. Moreover, doctors may hesitate to emphasize weightloss, given the recognized 95% long-term failure rates of most weight-control efforts,making this information less a priority during the office visit (15-18). Additionally,because of lack of knowledge about the subject matter, basic questions such as "howshould weight loss be achieved?" and "how much weight loss will provide benefit?" cannotbe answered with confidence.

Although many studies have been reported about differences in effectiveness amongvarious approaches to weight loss [1725], relatively few studies have been conducted in a free-living population ofbreast-cancer survivors in the private-practice setting. The focus of this article is onwhether diets that are the extremes of what most patients adopt for weight loss have anyobvious deleterious effects in this population. Although treatment for breast cancercontinues to improve, some first-line approaches still involve the use of agents withcardiotoxic potential [2628]. Consequently, concern exists about cardiovascular risk factors and survivalimplications after breast-cancer treatment. This situation provided the rationale forthe analysis of the blood-lipid profile, widely used for monitoring cardiovasculardisease risk, and fasting glucose as an early indicator of insulin resistance, whichwere collected as a safety component of the investigation of the effects of dietcomposition and weight loss on biomarkers of long-term survival in breast-cancerpatients after treatment.

For the analyses reported, magnitude of weight loss was dichotomized as being greater orless than the mean weight loss of the study population. This was done to address whetherall participants are affected similarly or if the blood-biomarker outcomes are dependenton the magnitude of weight loss.

Materials and methods


Women recruited for participation were from the same oncology practice and were atleast 4 months after chemotherapy, radiation, and surgical treatment for breastcancer and considered clinically free of cancer. Accrual occurred from 2008 to 2010.Participants were referred by their medical oncologist and had a BMI in theoverweight or obese class I range (BMI, 25 to 34.9 kg/m2).


To be eligible, participants did not anticipate surgery over the study durationperiod; did not follow a special diet excluding foods or food groups; had not lost 4or more pounds of body weight over the month preceding study initiation; did not takepharmaceuticals or supplements for weight management; were not being treated fordiabetes or blood-glucose control; had no history of eating disorders; did not havedigestive issues that might interfere with dietary intake, such as irritable bowelsyndrome, Crohn disease, or diverticulitis; never had surgery involving constrictionor removal of any portion of the gastrointestinal tract; had not been diagnosed withhepatitis B, C, or HIV; did not have implanted electronic devices such as apacemaker; and did not use tobacco products. Participants also had to be willing tofollow a dietary plan prescribed for the duration of the study; adhere to AmericanCancer Society alcohol guidelines (one or fewer standard drinks per day); maintain orincrease physical activity as prescribed to achieve negative energy balance requiredfor 0.5 to 1.0 kg weight loss per week; wear a pedometer and record daily activity;wear an accelerometer/heart-rate monitor for 2 weeks during the study; wear a body orswim suit and cap for body-composition testing; record food intake daily; attend upto 10 one-on-one clinic visits and five group visits over a 27-week period, andprovide seven fasting blood samples and 3-day pooled urine samples.

Study design

This study, referred to as CHOICE, compares the effects of opposing dietary patterns(carbohydrate and fat content at opposite extremes of popular weight-loss dietcomposition) on weight loss and body composition changes, as well as effects onbiomarkers of metabolic and hormonal processes known to affect breast carcinogenesisand that are predictive of long-term survival. The details of the CHOICE researchprotocol have been published[29]. The data reported herein were collected as part of safety monitoring in astudy designed as a nonrandomized, controlled trial. Assignment to treatment arm wasbased on dietary preferences because patient motivation is critical to successfulweight loss, and dietary preferences can be strong determinants of adherence to adietary plan. Participants were followed up for 6 months. During the course of thestudy, the withdrawal rate was 9.4% for the nonintervention control group, 12.9% forthe low-carbohydrate group, and 11.3% for the low-fat group.

Demographic data and usual food intake via food-frequency questionnaire (VioFFQ;Viocare, Princeton, NJ) were collected at baseline. At the initiation of the studyand at 4-week intervals thereafter, up to 6 months, anthropometry including weight,waist-to-hip ratio, BMI, body composition (BOD POD; Life Measurement, Inc., Concord,CA), and bioelectrical impedance (Tanita Corporation of America, Arlington Heights,IL) were obtained. Blood samples were obtained to assess cardiovascular-risk markers.These measures were assessed and compared at baseline and at 6 months in the controlgroup. The clinical protocol was approved by the Institutional Committee for theProtection of Human Subjects. Written consent was obtained before enrollingparticipants.

Dietary intervention


Individuals not interested in joining the weight-loss arms of the study but whowished to participate were assigned to the nonintervention control group and weregiven the same information currently provided to all breast-cancer patients aboutthe importance of avoiding posttreatment weight gain, and the health benefits ofhaving a BMI in the normal range.


Intervention participants follow a structured diet/physical activity programdesigned to create a weekly negative energy balance equivalent to 3, 500 kcal,after adjustments for metabolic adaptations that occur during extended periods ofweight loss. The intervention groups received the same physical-activity protocolpromoting the physical-activity guidelines and translated into steprecommendations, but one of two diets that reflect commonly used weight-lossapproaches that were identified in women attending a private oncology practice forlong-term breast-cancer follow-up.

The diet plan for each group comprised a 28-day cycle of menus and recipes. Theingredients for each day's diet plan were entered into ProNutra Diet Analysissoftware. The macronutrient composition of the 28-day menu plan is shown in Table1. The intended diet composition was derived from (a)identifying the most popular weight-loss programs undertaken by the clinicbreast-cancer population; (b) conducting a systematic review of the literature todefine the macronutrient composition of these diets and the actual intakes duringweight-loss studies to determine feasible upper and lower limits; (c) defining anacceptable overlap that ensured diet separation (< 5% for fat andcarbohydrate). The 28-day meal plans were designed for five calorie levels in eachdiet arm. The meal plans included interchangeable meal options (home-preparedrecipes and meal instructions; eating out; and convenience meal options),educational material and a program incorporating weight-loss strategies based on asystematic review of those that support successful weight loss and maintenance andpromote high levels of dietary adherence. The intervention was designed to reflecta feeding study in free-living individuals, where strict dietary structure ispresented in a format that also offers enough flexibility to be adopted into dailyliving and by the families and social-support networks of participants.

Table 1 Dietary composition by diet group for a 28-day menu cycle (1, 200kcal/day)

Laboratory measurements

Laboratory analyses were performed by Quest Diagnostics Inc. Fasting glucose wasmeasured by using the hexokinase/glucose-6-phosphate dehydrogenase method withspectrophotometry [30]. Total cholesterol, HDL cholesterol, and triglyceride in plasma weredetermined enzymatically. For HDL, serum was combined with the 20% wt/volpolyethylene glycol in glycine buffer at pH 10.0 (25°C). All β-lipoproteins(LDL and VLDL) were precipitated. The HDL fraction (α-fraction) remained in thesupernatant. The supernatant was then treated as a sample and assayed for cholesterolby an enzymatic method to determine HDL cholesterol value. Plasma LDL cholesterol wascalculated by using following formula: LDL cholesterol = total cholesterol - (HDLcholesterol + (triglyceride/5)) [3034].

Statistical methods

Cohort characteristics at baseline were described as mean ± SD, and differencesacross diet groups were evaluated by using the global F test in a one-wayanalysis of variance. Maximum likelihood (ML) estimates of a repeated-measures model [35] by using complete cases were developed to assess the change over time inlipid measures; in other words, these were not intent-to-treat analyses. Separateslopes were estimated for diet group, and successful weight loss, defined as above orbelow the overall average weight loss in the two diet groups; that is, five slopeswere present: control, high carbohydrate with weight loss greater than average, highcarbohydrate with weight loss less than average, high fat with weight loss greaterthan average, and high fat with weight loss less than average. Linear contrasts wereused to evaluate differences between selected slopes. Because visits were scheduledat roughly 1-month intervals, the slopes can be interpreted as the observed averagechange in a given measure for 1 month on treatment; the 6-month change can beestimated by multiplication. SAS version 9.2 (SAS Institute Inc., Cary, NC) was usedfor all statistical analyses. The Hochberg step-up procedure was use to adjust formultiple comparisons within each marker [36]. The algorithm is sort the P values from largest to smallest [37]: (k) , P(k-1) , ...,P(1)

p ̃ ( k ) = p ( k ) p ̃ ( k - 1 ) = min ( p ̃ ( k ) , 2 p ( k - 1 ) ) . p ̃ ( 1 ) = min ( p ̃ ( 2 ) , k p ( 1 ) ) .

The adjustments are valid whether test statistics are independent or positivelycorrelated.

Results and Discussion

The effect of dietary pattern on blood-lipid and -glucose profiles was evaluated in 142study participants. Data at baseline for the participants are shown in Table 2. No differences among groups were found in age, BMI, body weight,fat mass, disease stage, type of chemotherapy or hormonal therapy received, use ofstatins, or among the blood chemistries that served as end points in this investigation.To determine whether magnitude of weight loss had a moderating effect on the blood-lipidprofile in the diet, the active treatment arms were subdivided into two groupscorresponding to patients above or below the mean weight loss, which was 10 kg duringthe 6-month program. In brief, the range in weight loss over a 6-month period for thelow-fat dietary group was 3.5 to 18.9 kg, and the range in body-fat loss was 3.4 to 19.3kg. For the low-carbohydrate dietary group, the range in weight loss was 2.1 to 17.2 kg,and the range in body-fat loss was 1.2 to 18.5 kg. Based on self-reported pedometercounts, participants following the low-fat diet plan recorded 9, 661 ± 162 stepsper day versus 8, 741 ± 170 steps per day for participants following thelow-carbohydrate diet plan (mean ± SEM, P < 0.05).

Table 2 Baseline data profile by diet group

The research protocol involved monthly clinical visits during which fasting blood wasdrawn, and anthropometric data were collected. Blood was sent to the clinical laboratoryroutinely used by the office practice in which the weight management facility is locatedto base analysis and interpretation of results on the same source of data routinely usedby the attending physicians. The detailed data from each clinic visit throughout thecourse of the study for each subgroup are shown in Table 3.Although inspection of those data is useful, it was decided that data interpretationwould be facilitated by performing regression analyses of the entire set of data foreach participant for each variable assessed. For ease of understanding, a representativeregression is shown in Figure 1, with a detailed explanation of the data resulting from the regression analysis provided in the figure legend. The regression coefficients for all plasma analytes evaluated are summarized in Table 4, which also contains the statistical results.

Table 3 Mean levels of biomarkers over time by diet group
Table 4 Estimated slope from repeated-measures models of biomarkers by time and dietgroup
Figure 1
figure 1

Effect of weight loss and diet on plasma triglycerides. Estimated slopesfor predicted triglycerides (TGs) based on a repeated-measures model by group andwhether weight loss was below or above the mean for the study population. Slope isthe estimated monthly change in TGs. All slopes are significantly different from0, with the exception of the control group. The Hochberg step-up procedure wasused to adjust P values for multiple comparisons within each marker.Averaging over diet, the high- versus low-weight-loss slopes are different fromeach other (P = 0.01); with averaging over weight loss, the low-fatversus low-carbohydrate slopes are different from each other (P = 0.01),and within the low-carbohydrate diet, the high-weight-loss slope is significantlydifferent from the low-weight-loss slope (P = 0.01). LFLWL, Low-fat lowweight loss; LFHWL, low-fat high weight loss; LCLWL, low-carbohydrate low weightloss; LCHWL, low-carbohydrate high weight loss.

Fasting blood glucose was assessed because elevations in this parameter can be an earlyindicator of developing insulin resistance, which is a risk factor for cardiovasculardisease [38, 39]; data on insulin, which are necessary for the computation of HOMA-IR, werenot collected as part of safety monitoring, and therefore, further assessment is notpossible at this time. The regression coefficients are negative for all weight-losssubgroups investigated. This means that fasting glucose decreased with progressiveweight loss. The effect of weight loss was more pronounced than the effect of diet,although neither was statistically significant. The magnitude of the decrease in fastingglucose over time (slope of the line) was somewhat greater in participants who lost moreweight when data were collapsed across diet groups: the difference in slopes was -0.46± 0.24 (P = 0.21). Similarly, the magnitude of the decrease in fastingglucose over time was somewhat greater in the low-fat arm than in the low-carbohydratearm when data were collapsed across weight-loss groups: the difference in slopes was-0.31 ± 0.24 (P = 0.21). These findings are notable for several reasons.First, widespread debate is ongoing about the potential for high-fat diets to promoteatherogenesis through the induction of insulin resistance, which can lead to elevatedfasting glucose [25]. Second, relative to creating a microenvironment conducive to tumor growth, arepeated emergence of attention is noted in the metabolic re-programming thataccompanies the development of cancer and recognition of the preference of manycarcinomas for glucose or glutamine, which is actively taken up from the vascular system [4044]. Hence, concern exists that diets rich in carbohydrates with a high glycemicload would stimulate tumor growth [45, 46]. In the context of weight loss, no evidence was obtained to support eitherconcern, as reflected by fasting glucose determined monthly over a period of 6 months;however, the differences between the slopes on diet averaged over weight were smaller(P = 0.06) than the differences between weight-loss groups averaged overdiet (P = 0.21). This finding is consistent with the importance of weight lossto attain a body weight in the target range for height, which is generally stated as abody mass index (BMI, body weight in kilograms/height in square meters) between 18.5 and24.9, although the target range can vary based on race [47].

Table 4 also shows the regression coefficients (estimated slopes)for plasma cholesterol, triglycerides, HDL cholesterol, and LDL cholesterol. These lipidand lipoprotein analytes are routinely used to monitor cardiovascular disease risk, butemerging evidence also indicates their potential relevance to tumor growth andprogression [48]. A beneficial change in cholesterol, triglyceride, or LDL cholesterol isindicated by a negative regression coefficient, whereas higher levels of HDLcholesterol, indicated by a positive regression coefficient, are desirable forcardiovascular disease risk, although this may not be the case for cancer. The data inTable 4 show beneficial effects of weight loss on all four lipidanalytes, as well as the ratio of cholesterol to HDL cholesterol. The degree of benefitwas significantly greater for cholesterol (P = 0.001), triglycerides (P = 0.01), and LDL cholesterol (P = 0.06) in individuals who lost more than10 kg versus those individuals who were less successful. A more-detailed inspection ofthe regression coefficients reveals that overall (irrespective of whether weight losswas below or above the mean), differential effects on the lipid analytes were found,depending on dietary assignment. For cholesterol (P = 0.07) and LDL cholesterol(P = 0.13), greater reductions appeared on the high-carbohydrate dietpattern; whereas, for triglycerides (P = 0.01) and HDL cholesterol (P = 0.08), changes in the beneficial direction were greater on the high-fat dietarypattern. Similar effects have been reported in a non-cancer survivor population [49].

Breast-cancer patients have elevated cardiovascular disease risk depending on theirtreatment; in patients who receive anthrocyclins, a well-known potential exists toinduce cardiomyopathies with associated problems [28]. Because it has been reported that high dietary concentrations of lipidpromote the metabolic processes that predispose to atherogenesis [25], safety monitoring focused on circulating lipids that are recognizedindicators of cardiovascular disease risk is indicated. From the observed changes inlipid profiles, two findings are particularly noteworthy: (a) weight loss resulted inprotective changes in the blood-lipid profiles, and (b) the beneficial changes occurredirrespective of dietary pattern. Because patient motivation is critical to successfulweight loss, and dietary preferences can be strong determinants of adherence to adietary plan, these findings indicate that from a safety perspective relative tocardiovascular disease risk, determined by the type of blood chemistries that attendingphysicians routinely have at their disposal, it is acceptable for patients to follow adietary plan that meets their personal preferences during weight loss, provided that itis nutritionally balanced. Deeper inspection of the data shown in Table 4 indicates more-subtle differences of dietary pattern on specific lipidindicators; effects were associated with classification by whether weight loss was belowor above the mean.

The findings on lipid metabolites also have implications related to mechanisms of tumorprogression [5054]. Although controversial, it has been reported in both epidemiologic studiesand laboratory investigations that circulating levels of cholesterol, LDL-cholesterol,and HDL-cholesterol play a role in tumor development, tumor growth, and/or tumoraggressiveness, as summarized in [48]. In this regard, if causality is ultimately demonstrated, activities thatlimit availability of cholesterol and reduce the activity associated with cholesterollipoprotein function would be considered beneficial. Hence, in view of the data shown inTable 4, our findings provide yet another mechanistic lead aboutthe role of weight loss in promoting long-term survival after treatment for breastcancer.


Participants in this study were breast-cancer survivors from one clinical practice,which may limit the generalizability of the findings. We caution about theoverinterpretation of these findings because the data were not the primary measuresfor the trial; we elected to control the type I error rate for the four comparisonsdone within each marker but not across markers. Another potential limitation of thestudy was that assignment to dietary arm was not randomized; however, this is likelyto have translational value because individuals generally self-select dietaryapproaches that they prefer by which to lose weight.


The work reported herein is a component of a systematic effort to increase awarenessabout the important contribution that weight loss and weight maintenance in the healthyrange can offer to promote the long-term survival of breast-cancer patients. Given theprevalence of overweight and obesity, not only in the U.S. population as a whole, butalso globally, and that a majority of women who have undergone treatment for breastcancer are overweight or obese, the importance of this issue is emphasized. The resultsof this investigation address a safety aspect of a question commonly asked of physiciansby their patients: does it matter what dietary plan I choose to lose weight? Because anindividual's dietary preferences can affect dietary adherence and weight-loss success,the lack of evidence of a negative effect of dietary pattern on cardiovascular risk isan important consideration in the development of clinical practice guidelines forphysicians who recommend that their patients lose weight. Once weight is lost, questionssimilar to those being asked in this study must be addressed in the context of long-termweight maintenance.



Body mass index


high-density lipoprotein


homeostasis modelassessment of insulin resistance


maximal likelihood.


  1. Ballard-Barbash R: Anthropometry and breast cancer, body size: a moving target. Cancer. 1994, 74: 1090-100. 10.1002/1097-0142(19940801)74:3+<1090::AID-CNCR2820741518>3.0.CO;2-X.

    Article  CAS  PubMed  Google Scholar 

  2. Daling JR, Malone KE, Doody DR, Johnson LG, Gralow JR, Porter PL: Relation of body mass index to tumor markers and survival among young women withinvasive ductal breast carcinoma. Cancer. 2001, 92: 720-9. 10.1002/1097-0142(20010815)92:4<720::AID-CNCR1375>3.0.CO;2-T.

    Article  CAS  PubMed  Google Scholar 

  3. Goodwin PJ, Boyd NF: Body size and breast cancer prognosis: a critical review of the evidence. Breast Cancer Res Treat. 1990, 16: 205-14. 10.1007/BF01806329.

    Article  CAS  PubMed  Google Scholar 

  4. Huang Z, Willett WC, Colditz GA, Hunter DJ, Manson JE, Rosner B, Speizer FE, Hankinson SE: Waist circumference, waist:hip ratio, and risk of breast cancer in the Nurses'Health Study. Am J Epidemiol. 1999, 150: 1316-24. 10.1093/oxfordjournals.aje.a009963.

    Article  CAS  PubMed  Google Scholar 

  5. La Vecchia C, Negri E, Franceschi S, Talamini R, Bruzzi P, Palli D, Decarli A: Body mass index and post-menopausal breast cancer: an age-specific analysis. Br J Cancer. 1997, 75: 441-4. 10.1038/bjc.1997.73.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. McTiernan A: Obesity and cancer: the risks, science, and potential management strategies. Oncology (Williston Park). 2005, 19: 871-81.

    Google Scholar 

  7. McTiernan A, Irwin M, Vongruenigen V: Weight, physical activity, diet, and prognosis in breast and gynecologiccancers. J Clin Oncol. 2010, 28: 4074-80. 10.1200/JCO.2010.27.9752.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Petrelli JM, Calle EE, Rodriguez C, Thun MJ: Body mass index, height, and postmenopausal breast cancer mortality in aprospective cohort of US women. Cancer Causes Control. 2002, 13: 325-32. 10.1023/A:1015288615472.

    Article  PubMed  Google Scholar 

  9. Radimer KL, Ballard-Barbash R, Miller JS, Fay MP, Schatzkin A, Troiano R, Kreger BE, Splansky GL: Weight change and the risk of late-onset breast cancer in the original Framinghamcohort. Nutr Cancer. 2004, 49: 7-13. 10.1207/s15327914nc4901_2.

    Article  PubMed  Google Scholar 

  10. Reeves GK, Pirie K, Beral V, Green J, Spencer E, Bull D: Cancer incidence and mortality in relation to body mass index in the Million WomenStudy: cohort study. BMJ. 2007, 335: 1134-10.1136/bmj.39367.495995.AE.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Renehan AG, Tyson M, Egger M, Heller RF, Zwahlen M: Body-mass index and incidence of cancer: a systematic review and meta-analysis ofprospective observational studies. Lancet. 2008, 371: 569-78. 10.1016/S0140-6736(08)60269-X.

    Article  PubMed  Google Scholar 

  12. Harvie M, Howell A, Vierkant RA, Kumar N, Cerhan JR, Kelemen LE, Folsom AR, Sellers TA: Association of gain and loss of weight before and after menopause with risk ofpostmenopausal breast cancer in the Iowa Women's Health Study. Cancer Epidemiol Biomarkers Prev. 2005, 14: 656-61. 10.1158/1055-9965.EPI-04-0001.

    Article  PubMed  Google Scholar 

  13. Eliassen AH, Colditz GA, Rosner B, Willett WC, Hankinson SE: Adult weight change and risk of postmenopausal breast cancer. JAMA. 2006, 296: 193-201. 10.1001/jama.296.2.193.

    Article  CAS  PubMed  Google Scholar 

  14. Flegal KM, Carroll MD, Ogden CL, Curtin LR: Prevalence and trends in obesity among US adults, 1999-2008. JAMA. 2010, 303: 235-41. 10.1001/jama.2009.2014.

    Article  CAS  PubMed  Google Scholar 

  15. Rinaldi S, Key TJ, Peeters PH, Lahmann PH, Lukanova A, Dossus L, Biessy C, Vineis P, Sacerdote C, Berrino F, Panico S, Tumino R, Palli D, Nagel G, Linseisen J, Boeing H, Roddam A, Bingham S, Khaw KT, Chloptios J, Trichopoulou A, Trichopoulos D, Tehard B, Clavel-Chapelon F, Gonzalez CA, Larrañaga N, Barricarte A, Quirós JR, Chirlaque MD, Martinez C, et al: Anthropometric measures, endogenous sex steroids and breast cancer risk inpostmenopausal women: a study within the EPIC cohort. Int J Cancer. 2006, 118: 2832-9. 10.1002/ijc.21730.

    Article  CAS  PubMed  Google Scholar 

  16. Irwin ML, Aiello EJ, McTiernan A, Bernstein L, Gilliland FD, Baumgartner RN, Baumgartner KB, Ballard-Barbash R: Physical activity, body mass index, and mammographic density in postmenopausalbreast cancer survivors. J Clin Oncol. 2007, 25: 1061-6. 10.1200/JCO.2006.07.3965.

    Article  PubMed  Google Scholar 

  17. American Dietetic Association: Position of the American Dietetic Association: weight management. J Am Diet Assoc. 1997, 97: 71-4. 10.1016/S0002-8223(97)00024-2.

    Article  Google Scholar 

  18. Alhassan S, Kim S, Bersamin A, King AC, Gardner CD: Dietary adherence and weight loss success among overweight women: results from theA TO Z weight loss study. Int J Obes (Lond). 2008, 32: 985-91. 10.1038/ijo.2008.8.

    Article  CAS  Google Scholar 

  19. Sacks FM, Bray GA, Carey VJ, Smith SR, Ryan DH, Anton SD, McManus K, Champagne CM, Bishop LM, Laranjo N, Leboff MS, Rood JC, de Jonge L, Greenway FL, Loria CM, Obarzanek E, Williamson DA: Comparison of weight-loss diets with different compositions of fat, protein, andcarbohydrates. N Engl J Med. 2009, 360: 859-73. 10.1056/NEJMoa0804748.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Bravata DM, Sanders L, Huang J, Krumholz HM, Olkin I, Gardner CD, Bravata DM: Efficacy and safety of low-carbohydrate diets: a systematic review. JAMA. 2003, 289: 1837-50. 10.1001/jama.289.14.1837.

    Article  CAS  PubMed  Google Scholar 

  21. Greene LF, Malpede CZ, Henson CS, Hubbert KA, Heimburger DC, Ard JD: Weight maintenance 2 years after participation in a weight loss program promotinglow-energy density foods. Obesity (Silver Spring). 2006, 14: 1795-801. 10.1038/oby.2006.207.

    Article  Google Scholar 

  22. Brehm BJ, Seeley RJ, Daniels SR, D'Alessio DA: A randomized trial comparing a very low carbohydrate diet and a calorie-restrictedlow fat diet on body weight and cardiovascular risk factors in healthy women. J Clin Endocrinol Metab. 2003, 88: 1617-23. 10.1210/jc.2002-021480.

    Article  CAS  PubMed  Google Scholar 

  23. Grieb P, Kłapcińska B, Smol E, Pilis T, Pilis W, Sadowska-Krepa E, Sobczak A, Bartoszewicz Z, Nauman J, Stańczak K, Langfort J: Long-term consumption of a carbohydrate-restricted diet does not inducedeleterious metabolic effects. Nutr Res. 2008, 28: 825-33. 10.1016/j.nutres.2008.09.011.

    Article  CAS  PubMed  Google Scholar 

  24. Adam-Perrot A, Clifton P, Brouns F: Low-carbohydrate diets: nutritional and physiological aspects. Obes Rev. 2006, 7: 49-58. 10.1111/j.1467-789X.2006.00222.x.

    Article  CAS  PubMed  Google Scholar 

  25. Nordmann AJ, Nordmann A, Briel M, Keller U, Yancy WS, Brehm BJ, Bucher HC: Effects of low-carbohydrate vs low-fat diets on weight loss and cardiovascularrisk factors: a meta-analysis of randomized controlled trials. Arch Intern Med. 2006, 166: 285-93. 10.1001/archinte.166.3.285.

    Article  CAS  PubMed  Google Scholar 

  26. Hooning MJ, Botma A, Aleman BM, Baaijens MH, Bartelink H, Klijn JG, Taylor CW, van Leeuwen FE: Long-term risk of cardiovascular disease in 10-year survivors of breast cancer. J Natl Cancer Inst. 2007, 99: 365-75. 10.1093/jnci/djk064.

    Article  PubMed  Google Scholar 

  27. Hooning MJ, Aleman BM, van Rosmalen AJ, Kuenen MA, Klijn JG, van Leeuwen FE: Cause-specific mortality in long-term survivors of breast cancer: a 25-yearfollow-up study. Int J Radiat Oncol Biol Phys. 2006, 64: 1081-91. 10.1016/j.ijrobp.2005.10.022.

    Article  PubMed  Google Scholar 

  28. Smith LA, Cornelius VR, Plummer CJ, Levitt G, Verrill M, Canney P, Jones A: Cardiotoxicity of anthracycline agents for the treatment of cancer: systematicreview and meta-analysis of randomised controlled trials. BMC Cancer. 2010, 10: 337-10.1186/1471-2407-10-337.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Sedlacek SM, Playdon MC, Wolfe P, McGinley JN, Wisthoff MR, Daeninck EA, Jiang W, Zhu Z, Thompson HJ: Effect of a low fat versus a low carbohydrate weight loss dietary intervention onbiomarkers of long term survival in breast cancer patients (CHOICE): StudyProtocol. BMC Cancer. 2011, 11: 287-10.1186/1471-2407-11-287.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Fischbach FT, Dunning MB: Manual of Laboratory and Diagnostic Tests. 2009, Philadelphia: Lippincott Williams & Wilkins

    Google Scholar 

  31. Grundy SM: Guidelines for cholesterol management: recommendations of the National CholesterolEducation Program's Adult Treatment Panel, II. Heart Dis Stroke. 1994, 3: 123-7.

    CAS  PubMed  Google Scholar 

  32. Grundy SM, Cleeman JI, Merz CN, Brewer HB, Clark LT, Hunninghake DB, Pasternak RC, Smith SC, Stone NJ, Coordinating Committee of the National Cholesterol Education Program: Implications of recent clinical trials for the National Cholesterol EducationProgram Adult Treatment Panel III guidelines. Circulation. 2004, 110: 227-39. 10.1161/01.CIR.0000133317.49796.0E.

    Article  PubMed  Google Scholar 

  33. Grundy S: Third Report of the National Cholesterol Education Program (NCEP) Expert Panel onDetection, Evaluation, and Treatment of High Blood Cholesterol in Adults (AdultTreatment Panel III). 2002, Bethesda:National Institutes of Health,

    Google Scholar 

  34. Balal M, Paydas S, Inal T, Demir E, Kurt C, Sertdemir Y: Validation of the Friedewald formula for the determination of low-densitylipoprotein cholesterol in renal transplant recipients. Ren Fail. 2010, 32: 455-8. 10.3109/08860221003658266.

    Article  CAS  PubMed  Google Scholar 

  35. Verbeke G, Molenberghs G: Linear Mixed Models in Practice: An SAS Oriented Approach. 1997, New York: Springer

    Chapter  Google Scholar 

  36. Hochberg Y: A sharper Bonferroni procedure for multiple tests of significance. Biometrika. 1988, 75: 800-10.1093/biomet/75.4.800.

    Article  Google Scholar 

  37. Westfall PH, Tobias RD, Rom D, Wolfinger RD, Hochberg Y: Multiple Comparisons and Multiple Tests using the SAS System. 1999, Cary, NC: SAS Institute Inc.

    Google Scholar 

  38. O'Malley G, Santoro N, Northrup V, D'Adamo E, Shaw M, Eldrich S, et al: High normal fasting glucose level in obese youth: a marker for insulin resistanceand beta cell dysregulation. Diabetologia. 2010, 53: 1199-209. 10.1007/s00125-010-1693-0.

    Article  PubMed  Google Scholar 

  39. Piche ME, Lemieux S, Perusse L, Weisnagel SJ: High normal 2-hour plasma glucose is associated with insulin sensitivity andsecretion that may predispose to type 2 diabetes. Diabetologia. 2005, 48: 732-40. 10.1007/s00125-005-1701-y.

    Article  CAS  PubMed  Google Scholar 

  40. Shaw RJ: Glucose metabolism and cancer. Curr Opin Cell Biol. 2006, 18: 598-608. 10.1016/

    Article  CAS  PubMed  Google Scholar 

  41. DeBerardinis RJ, Sayed N, Ditsworth D, Thompson CB: Brick by brick: metabolism and tumor cell growth. Curr Opin Genet Dev. 2008, 18: 54-61. 10.1016/j.gde.2008.02.003.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. DeBerardinis RJ, Lum JJ, Hatzivassiliou G, Thompson CB: The biology of cancer: metabolic reprogramming fuels cell growth andproliferation. Cell Metab. 2008, 7: 11-20. 10.1016/j.cmet.2007.10.002.

    Article  CAS  PubMed  Google Scholar 

  43. Thompson CB: Attacking cancer at its root. Cell. 2009, 138: 1051-4. 10.1016/j.cell.2009.09.002.

    Article  CAS  PubMed  Google Scholar 

  44. Wise DR, Thompson CB: Glutamine addiction: a new therapeutic target in cancer. Trends Biochem Sci. 2010, 35: 427-33. 10.1016/j.tibs.2010.05.003.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Dong JY, Qin LQ: Dietary glycemic index, glycemic load, and risk of breast cancer: meta-analysis ofprospective cohort studies. Breast Cancer Res Treat. 2011, 126: 287-94. 10.1007/s10549-011-1343-3.

    Article  CAS  PubMed  Google Scholar 

  46. Belle FN, Kampman E, McTiernan A, Bernstein L, Baumgartner KB, Baumgartner R, et al: Dietary fiber, carbohydrates, glycemic index and glycemic load in relation tobreast cancer prognosis in the HEAL Cohort. Cancer Epidemiol Biomarkers Prev. 2011, 20: 890-899. 10.1158/1055-9965.EPI-10-1278.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. WHO Expert Consultation: Appropriate body-mass index for Asian populations and its implications for policyand intervention strategies. Lancet. 2010, 363: 157-63.

    Google Scholar 

  48. Llaverias G, Danilo C, Mercier I, Daumer K, Capozza F, Williams TM, Sotgia F, Lisanti MP, Frank PG: Role of cholesterol in the development and progression of breast cancer. Am J Pathol. 2011, 178: 402-12. 10.1016/j.ajpath.2010.11.005.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Foster GD, Wyatt HR, Hill JO, Makris AP, Rosenbaum DL, Brill C, Stein RI, Mohammed BS, Miller B, Rader DJ, Zemel B, Wadden TA, Tenhave T, Newcomb CW, Klein S: Weight and metabolic outcomes after 2 years on a low-carbohydrate versus low-fatdiet: a randomized trial. Ann Intern Med. 2010, 153: 147-57. 10.7326/0003-4819-153-3-201008030-00005.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Cantafora A, Blotta I: Neutral lipids production, transport, utilization. Anticancer Res. 1996, 16: 1441-9.

    CAS  PubMed  Google Scholar 

  51. Tomiki Y, Suda S, Tanaka M, Okuzawa A, Matsuda M, Ishibiki Y, Sakamoto K, Kamano T, Tsurumaru M, Watanabe Y: Reduced low-density-lipoprotein cholesterol causing low serum cholesterol levelsin gastrointestinal cancer: a case control study. J Exp Clin Cancer Res. 2004, 23: 233-40.

    CAS  PubMed  Google Scholar 

  52. Chang SJ, Hou MF, Tsai SM, Wu SH, Hou LA, Ma H, Shann TY, Wu SH, Tsai LY: The association between lipid profiles and breast cancer among Taiwanese women. Clin Chem Lab Med. 2007, 45: 1219-23. 10.1515/CCLM.2007.263.

    Article  CAS  PubMed  Google Scholar 

  53. Kucharska-Newton AM, Rosamond WD, Mink PJ, Alberg AJ, Shahar E, Folsom AR: HDL-cholesterol and incidence of breast cancer in the ARIC cohort study. Ann Epidemiol. 2008, 18: 671-7. 10.1016/j.annepidem.2008.06.006.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Kim Y, Park SK, Han W, Kim DH, Hong YC, Ha EH, Ahn SH, Noh DY, Kang D, Yoo KY: Serum high-density lipoprotein cholesterol and breast cancer risk by menopausalstatus, body mass index, and hormonal receptor in Korea. Cancer Epidemiol Biomarkers Prev. 2009, 18: 508-15. 10.1158/1055-9965.EPI-08-0133.

    Article  CAS  PubMed  Google Scholar 

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United States Public Health Service Grant CA126704 from the National Cancer Institutesupported this work.

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Correspondence to Henry J Thompson.

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HJT, SMS, PW, JNM, and MRW participated in the design and implementation of the study.DP, MCP, EAD, and SNB participated in the implementation of the study. All authorsparticipated in the preparation of the manuscript.

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Thompson, H.J., Sedlacek, S.M., Paul, D. et al. Effect of dietary patterns differing in carbohydrate and fat content on blood lipidand glucose profiles based on weight-loss success of breast-cancer survivors. Breast Cancer Res 14, R1 (2012).

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