The Effect of Insulin Resistance on Breast Cancer Risk in Latinas of Mexican Origin

Abstract

Background:

Conclusive evidence has yet to emerge regarding the association between markers of hyperinsulinemia and breast cancer. We determined the effect of insulin resistance (IR) on breast cancer risk in Latinas of Mexican origin who did not have a direct family history of breast cancer and had not been previously diagnosed with prediabetes or diabetes.

Methods:

This was a case–control study in which a case (n=124) was defined as a patient with a recent histopathologic diagnosis of breast cancer and a control (n=197) was defined as a participant who had recently undergone a mammography and had either a Breast Imaging, Reporting & Data System (BI-RADS)-1 or a BI-RADS-2 score. Plasma glucose, insulin, and glycated hemoglobin (HbA1c) levels were measured. IR was determined by using the homeostasis model assessment (HOMA-IR) criterion. Odds ratios (OR) and 95% confidence intervals (CI) were determined using unconditional binary logistic regression analysis.

Results:

IR was detected in 33.9% of cases and 41.6% of controls, based on a HOMA-IR ≥3.5. Although multivariate analysis did not show any association between IR and breast cancer risk (OR 0.56, 95% CI 0.31–1.01), it showed that an HbA1c ≥5.7% increased the risk of breast cancer (OR 3.41, 95% CI 1.93–6.01), regardless of menopausal status.

Conclusions:

The findings suggest that IR had no effect on breast cancer risk; however HbA1c increased the risk in Latinas of Mexican origin who had not been diagnosed previously with prediabetes or diabetes and had no direct family history of breast cancer. Prospective studies are required to establish the impact of IR over time.

Introduction

Breast cancer is the most common malignant neoplasia in industrialized countries. The GLOBOCAN 2012 project reported a worldwide breast cancer incidence of 43.3 per 100,000 women and a breast cancer mortality rate of 14.7 per 100,000 women, positioning it as the most lethal cancer globally.¹ In Mexico, where the incidence of breast cancer is 35.3 per 100,000 women and the mortality rate is 9.7 per 100,000 women, breast cancer is the most common form of cancer and it is the malignancy with the highest number of associated deaths.¹ Insulin resistance (IR) is a genetic or acquired condition in which cells fail to respond to the actions of insulin, leading to inappropriate glucose binding, particularly by the liver, muscles, and adipose tissue. Over time, IR causes hyperglycemia and a compensatory state of hyperinsulinemia caused by the overproduction of insulin. For this reason, insulin levels, glucose levels, the glucose/insulin ratio, and the homeostasis model assessment (HOMA)-IR index, which comprises the product of the fasting plasma insulin and the fasting plasma glucose (FPG) concentrations, are considered to be surrogate IR markers.² In the United States, 24% of white people, 33% of African American people,³ and 39.1% of Hispanic people⁴ have IR. In Mexico, the prevalence of IR is reportedly 36.4%.⁵

Several biological mechanisms link insulin to breast cancer. Hyperinsulinemia increases ovarian estrogen and testosterone production while inhibiting the hepatic production of sex hormone–binding globulins, thereby generating high levels of free estradiol and testosterone in the blood. Estradiol and insulin-like growth factor-1 (IGF-1) act in conjunction and reciprocally as mammary cell mitogenic factors. Moreover, insulin itself is directly mitogenic within mammary tissues.^6
–8 Hyperglycemia also creates a favorable milieu for neoplastic cell proliferation, and a high rate of glucose metabolism is one of the main characteristics of malignant tissue. Importantly, if IR is overlooked, it progresses to type 2 diabetes mellitus (T2DM) when the production of insulin can no longer compensate for the IR among the tissues. Furthermore, other disorders, including dyslipidemia and hypertension, may develop, which together with central obesity comprise the so-called metabolic syndrome.⁹ The components of this syndrome influence breast cancer development through the impact of adiposity on the bioavailability of inflammatory cytokines, leptin, and adiponectin, which are factors that act on the intracellular messengers that are involved in the growth and survival of breast tissue neoplastic cells.^8,10

Findings regarding associations between markers of hyperinsulinemia and breast cancer are inconclusive. In Kuwait, Al Awadhi et al.¹¹ reported an increased risk of breast cancer that was associated with the HOMA. In women from North America, Kabat et al.¹² documented a positive association between the risk of breast cancer and HOMA, but no association with FPG. Mink et al.¹³ did not find any association between the risk of breast cancer and either insulin or FPG. In Italy, Capasso et al.¹⁴ reported an increased risk of breast cancer in association with both HOMA and FPG. Muti et al.¹⁵ found an association between an increased risk of breast cancer and FPG, but only among premenopausal women. Other authors, Hirose et al.¹⁶ in Japan and Garmendia et al.¹⁷ in Chile, have identified an increased risk of breast cancer only among postmenopausal women. These discrepancies may be influenced in part by a history of diabetes and a family history of breast cancer, because few studies have excluded these confounding factors or controlled for them statistically.^11
–13 In addition, applying results obtained from North American, European, or Asian populations to Hispanic populations is rife with shortcomings, because the cultures differ in relation to their genetic compositions and their lifestyles, and these factors can affect hugely on the risk of developing breast cancer. Furthermore, conclusions from studies about risk factors for breast cancer carried out within the same country have been contradictory.^12

–15

This study aimed to determine the effect of IR on breast cancer risk among Latinas from Mexico who did not have direct family histories of breast cancer and had not been previously diagnosed with prediabetes or diabetes.

Materials and Methods

This case-controlled study was conducted between 2012 and 2013 in the northeastern region of Mexico, which is a predominantly industrial region with one of the highest levels of development in the country. The study took place at a referral center operated by the Instituto Mexicano del Seguro Social. The center is an obstetrics and gynecology hospital with numerous subspecialties—gynecological oncology, female pelvic medicine and reconstructive surgery, advanced laparoscopic surgery, maternal–fetal medicine, and reproductive endocrinology, among others. It provides care for patients referred by a family practitioner. A case (n=124) was defined as a patient with a histopathologic diagnosis of breast cancer during the surgery or within 6 weeks from the procedure, and that was at a clinical stage no higher than II-b to avoid the abnormal glucose and insulin levels that are caused by the overall health deterioration that is associated with advanced disease. A control (n=197) was defined as a patient who had just undergone mammography and received either a Breast Imaging, Reporting & Data System (BI-RADS)-1 (normal) or a BI-RADS-2 (benign) score. The following exclusion criteria were applied to all participants: (1) A family history of breast cancer in mothers, sisters, or daughters; (2) a history of prediabetes or diabetes; (3) treatment with glucocorticoids; (4) a history of ovarian, endometrial, or pancreatic cancers; (5) a history of Paget's disease, a phyllodes tumor, angiosarcoma, or primary lymphoma; (6) a history of significant thoracic radiation; and (7) being pregnant. Cases were screened consecutively as soon as they were admitted to the hospital for breast cancer surgery; approximately, 42% of the cases were excluded because of self-reported diabetes and 8% due to other selection criteria. Controls were recruited consecutively before mammogram screening, and the exclusion rate was approximately 30%. All patients who met the selection criteria accepted to participate in the study; they provided written, signed consent. The protocol was submitted to and approved by the institute's research and ethics committee.

Information about the patients' reproductive profiles was gathered during interviews and included data on nulliparity, low parity (≤2 full-term pregnancies), advanced maternal age (first pregnancy at an age of ≥30 years), breastfeeding history, menarche at an age of <12 years, menopausal status (perimenopause was defined as irregular bleeding over the previous year and the last menstruation occurring >6 months before the survey, and postmenopause was defined as being amenorrheic for >1 year or a having had a hysterectomy), the use of hormonal replacement therapy, and the use of birth control medication via the oral or parenteral routes.

We also recorded the participants' sociodemographic information, their alcohol intakes and smoking habits, and whether they had a history of gestational diabetes, fetal macrosomia (an infant weighing ≥4.000 kg at birth), or a first-degree relative with diabetes. Following the survey, the participants, dressed in lightweight clothing and, not wearing their shoes, were weighed; their heights were measured using a mechanical scale with a stadiometer. The results were recorded without rounding, and their nutritional status was categorized on the basis of their body mass index (BMI) values (low weight, BMI <18.5 kg/cm²; normal weight, BMI 18.5–24.9 kg/cm²; overweight, BMI 25.0–29.9 kg/cm²; and obese, BMI ≥30.0 kg/cm²). Waist circumferences (WC) were measured between the lower ribs and the umbilical area, and a WC >88 cm was defined as central obesity. A research intern and an epidemiology resident that had been trained and were subject to periodic appraisals collected the data. FPG, glycated hemoglobin (HbA1c), and insulin were measured in venous blood samples drawn from all subjects in the mornings after they had fasted for 12 hr. Aware that the stress of the surgery could affect real FPG and insulin levels, samples from cases were obtained the day before the breast cancer surgical procedure; samples from controls were drawn before mammogram screening. Glucose determinations were obtained using the glucose oxidase method, HbA1c was measured using a turbidimetric inhibition immunoassay, and insulin levels were measured in real time using an enzyme-linked immunosorbent assay (insulin ELISA de ALPCO Immunoassays; Salem, NH).

IR was categorized using the HOMA-IR index, hereafter referred to as HOMA-IR, by employing the following formula: Insulin (μU/mL)×FPG (mg/dL)/405. Two cutoff points were established. The first cutoff point was set at 3.5 as determined by the HOMA-IR 75^th percentile of the subjects in the study with FPG <100 mg/dL or HbA1c <5.7%. The second cutoff point was set at HOMA-IR ≥3.8, based on a pre-established value that identified IR with 77.8% specificity and 61.6% sensitivity in a Hispanic population comprising 90% Mexican individuals.⁴ The HOMA-β-cell index was also determined using the following formula: Insulin (μU/mL)×20/FPG (mmol/L)−3².

Statistical analysis

Categorical variables were compared using the chi-squared test, and continuous variables were compared using either the Student t-test or Mann–Whitney test, depending on the normality of the data distributions. The association between IR and the breast cancer risk was assessed using odds ratios (OR) and 95% confidence intervals (CI). Patients without IR formed the reference group. Multivariate analysis involved unconditional binary logistic regression on the basis of the forward stepwise likelihood ratio, and it was conducted using the premenopausal and postmenopausal statuses separately and in combination. For both cutoff points, HOMA-IR ≥3.5 and HOMA-IR ≥3.8. Variables that were significant in the univariate analysis (breastfeeding, birth control, central obesity, and age at menarche) were included in the multivariate analysis. Because the study design did not facilitate matching particular sociodemographic characteristics, the cases and controls differed in terms of their residential locations, and urbanization is associated with specific lifestyle characteristics that may affect the risk of breast cancer, we added the following variables to the multivariate model—advanced maternal age, use of replacement hormone therapy, low parity, and age.

Results

The mean ages of the cases and controls were similar (53.2±12.3 years vs. 55.4±10.5 years, respectively, P=0.08). Among the cases, the following variables predominated—a grade school education, no history of breastfeeding, use of birth control, and central obesity. The controls were more likely to live in urban areas, have experienced an early menarche, and have at least one first-degree relative with diabetes (Table 1). A history of gestational diabetes was apparent in 0.8% of the cases and in 1.6% of the controls (P=0.93), and a history of fetal macrosomia was evident in 20.5% of the cases and in 18.1% of the controls (P=0.72).

Table 1.

Reproductive and Sociodemographic Profile

	Cases (n=124)	Controls (n=197)	P value
Marital status, with partner	96 (77%)	153 (78%)	1.00
Occupation, economically active	45 (36%)	89 (45%)	0.145
Residence, urban	91 (79%)	170 (92%)	0.003
Schooling
Grade school	49 (40%)	59 (30%)	0.0001
Middle school	38 (31%)	35 (18%)
High school and above	37 (30%)	103 (52%)
Alcohol intake, every day or every weekend	1 (0.8%)	3 (1.5%)	0.66
Smoking, yes	10 (8.5%)	19 (10.6%)	0.115
No breastfeeding history	21 (17.2%)	16 (8.8%)	0.04
Nulliparity	6 (4.8%)	14 (7.1%)	0.56
Low parity, ≤2 full-term pregnancies	44 (35.5%)	65 (33.0%)	0.74
Advanced maternal age, first pregnancy at an age of ≥30 years	10 (8.5%)	27 (14.9%)	0.15
Menarche, <12 years	49 (13.8%)	16 (25.4%)	0.02
Birth control (oral or parenteral route)
Never	60 (48.4%)	103 (52.3%)	0.004
Yes, less than 5 years	25 (20.2%)	15 (7.6%)
Yes, 5 or more years	39 (31.5%)	79 (40.1%)
Use of hormonal replacement therapy
Never	93 (75.0%)	149 (75.6%)	0.92
Yes, less than 5 years	9 (7.3%)	12 (6.1%)
Yes, 5 or more years	22 (17.7%)	36 (18.3%)
Postmenopause	74 (61.2%)	127 (64.5%)	0.64
Central obesity, waist circumference >88 cm	103 (83.1%)	139 (70.6%)	0.03
Nutritional status
Normal weight/low weight, body mass index <25.0 kg/cm²	27 (22.0%)	41 (20.8%)	0.359
Overweight, body mass index 25.0–29.9 kg/cm²	46 (37.4%)	89 (45.2%)
Obesity, body mass index ≥30.0 kg/cm²	50 (40.7%)	67 (34.0%)
First-degree relative with diabetes	49 (39.8%)	103 (63.2%)	0.0001

The mean HbA1c level was higher among the cases than the controls, regardless of menopausal status. Compared with the controls, the mean FPG level, insulin level, HOMA-IR index, and HOMA-β-cell index were lower, particularly in premenopausal women (Table 2). A HbA1c value ≥5.7% was detected in 55.7% of the cases and in 32.1% of the controls (P<0.001). The criteria for a diagnosis of diabetes (a HbA1c value ≥6.5% and/or FPG ≥126 mg/dL) were fulfilled in 12.9% of the cases and in 7.1% of the controls (P=0.12).

Table 2.

Anthropometric and Biochemical Profile According to Menopause Status (Mean±Standard Deviation)

	All		Premenopause		Postmenopause
	Cases (n=124)	Controls (n=197)	Cases (n=48)	Controls (n=70)	Cases (n=76)	Controls (n=127)
Body mass index (kg/cm²)	29.4±5.5	28.8±5.0	28.8±6.1	29.5±6.1	29.7±5.2	28.5±4.4
Waist circumference (cm)	100.8±13.1^*	94.7±12.9	96.5±12.8	94.6±13.3	103.2±12.9^*	94.7±12.7
Fasting plasma glucose (mg/dL)	100.7±12.4	103.5±15.7	96.7±10.8^*	102.6±12.9	102.9±12.2	103.9±17.1
Glycated hemoglobin (%)	5.7±0.6^*	5.4±0.7	5.6±0.5^*	5.3±0.6	5.7±0.6^*	5.4±0.7
Insulin (μU/mL)	11.6±8.2^*	14.2±8.6	9.6±5.7^*	12.7±6.3	12.8±9.4	15.0±9.5
Insulin-nl^a	2.1±0.6^*	2.5±0.6	2.1±0.6^*	2.4±0.6	2.4±0.6	2.5±0.6
HOMA-IR	2.9±2.6^*	3.7±2.8	2.3±1.5^*	3.3±1.9	3.4±3.0	3.9±3.1
HOMA-IR-nl^a	0.9±0.7^*	1.1±0.7	0.7±0.6^*	1.0±0.6	1.0±0.7	1.1±0.7
HOMA-β-cell	89.9±55.9^*	106.5±64.5	86.6±58.8^*	96.3±44.6	91.9±54.3	112.1±72.8
HOMA-β-cell-nl^a	4.3±0.6^*	4.5±0.6	4.3±0.6	4.4±0.6	4.4±0.5	4.5±0.6

Geometric mean.

P<0.01 is based on Mann–Whitney tests except for insulin-nl, HOMA-IR-nl, and HOMA-β-cell-nl, whose data distributions were normal and ^* P<0.01 is based on t-tests.

nl, natural logarithm; IR, insulin resistance; HOMA-IR, homeostasis model assessment of insulin resistance.

The prevalence of IR was 33.9% among the cases and 41.6% among the controls, based on a HOMA-IR value ≥3.5 (P=0.20), and 30.9% among the cases and 35% among the controls, based on a HOMA-IR value ≥3.8 (P=0.54). While univariate and multivariate analysis did not show any association between IR and breast cancer risk, they showed that an HbA1c value ≥5.7% increased the risk of breast cancer, regardless of menopausal status (Tables 3 and 4). Central obesity also increased the risk of breast cancer, particularly in postmenopausal women (Table 4).

Table 3.

Univariate Analysis: Effect of Insulin Resistance on Breast Cancer According to Menopause Status

Marker	Cases (n=124)	Controls (n=197)	OR (95%CI)
HOMA-IR ≥3.5 (upper quartile)	42 (33.9%)	82 (41.6%)	0.72 (IC95% 0.45–1.15)
HOMA-IR ≥3.8^a	38 (30.9%)	64 (35.0%)	0.83 (IC95% 0.51–1.35)
Fasting plasma glucose≥100 mg/dL	60 (48.4%)	107 (54.3%)	0.79 (IC95% 0.50–1.24)
Glycated hemoglobin≥5.7%	64 (55.7%)	63 (32.1%)	2.65 (IC95% 1.65–4.26)

	Premenopause
	Cases (n=48)	Controls (n=70)	OR (95% CI)
HOMA-IR ≥3.5 (upper quartile)	10 (21.3%)	26 (37.1%)	0.46 (IC95% 0.20–1.07)
HOMA-IR ≥3.8 ^a	10 (21.3%)	23 (33.3%)	0.54 (IC95% 0.23–1.28)
Fasting plasma glucose ≥100 mg/dL	17 (36.2%)	39 (55.7%)	0.45 (IC95% 0.21–0.96)
Glycated hemoglobin ≥5.7%	20 (45.5%)	13 (18.6%)	3.65 (IC95% 1.57–8.51)

	Postmenopause
	Cases (n=76)	Controls (n=127)	OR (95% CI)
HOMA-IR ≥3.5(upper quartile)	30 (40.5%)	56 (44.1%)	0.86 (IC95% 0.48–1.55)
HOMA-IR ≥3.8 ^a	26 (35.6%)	41 (36.0%)	0.99 (IC95% 0.53–1.82)
Fasting plasma glucose ≥100 mg/dL	42 (56.8%)	68 (53.5%)	1.14 (IC95% 0.64–2.03)
Glycated hemoglobin ≥5.7%	41 (60.3%)	50 (39.7%)	2.31 (IC95% 1.26–4.21)

Referred as the best cutoff point to identify IR in a Hispanic population comprising 90% Mexican individuals.⁴

OR, odds ratio; CI, confidence interval; HOMA-IR, homeostasis model assessment of insulin resistance.

Table 4.

Effect of Insulin Resistance and Variables That Remained Significant for Breast Cancer Risk After Using Forward Stepwise Logistic Regression Analysis According to HOMA-IR ≥3.5 Cutoff Point and HOMA-IR ≥3.8 Cutoff Point, Both Stratified by Menopausal Status

HOMA-IR ≥3.5 cutoff point		HOMA-IR ≥3.8 cutoff point
Variable	Adjusted OR (95% CI)^a	Variable	Adjusted OR (95% CI)^a
HOMA-IR ≥3.5 (upper quartile)	0.56 (0.31–1.01)	HOMA-IR ≥3.8^b	0.65 (0.35–1.21)
Glycated hemoglobin≥5.7%	3.41 (1.93–6.01)	Glycated hemoglobin ≥5.7%	2.82 (1.61–4.92)
Central obesity^c	2.82 (1.43–5.59)	Central obesity^c	2.51 (1.27–4.94)
	Premenopause
HOMA-IR ≥3.5 (upper quartile)	0.24 (0.07–1.29)	HOMA-IR ≥3.8^b	0.36 (0.10–1.30)
Glycated hemoglobin≥5.7%	4.87 (1.73–13.72)	Glycated hemoglobin ≥5.7%	4.02 (1.40–11.60)
	Postmenopause
HOMA-IR ≥3.5 (upper quartile)	0.69 (0.34–1.39)	HOMA-IR≥3.8^b	0.76 (0.36–1.60)
Glycated hemoglobin≥5.7%	2.45 (1.24–4.84)	Glycated hemoglobin ≥5.7%	2.17 (1.08–4.37)
Central obesity^c	4.06 (1.56–10.57)	Central obesity^c	3.89 (1.47–10.30)

Adjusted for age, low parity, advanced maternal age, use of hormonal replacement therapy, birth control, age at menarche, and breastfeeding history.

Referred as the best cutoff point to identify IR in a Hispanic population comprising 90% Mexican individuals.⁴

Waist circumference>88 cm.

OR, odds ratio; CI, confidence interval; HOMA-IR, homeostasis model assessment of insulin resistance.

Discussion

In this study, an association between IR and breast cancer risk was not apparent among the Latinas of Mexican origin who had no direct family history of breast cancer and had no previous diagnoses of prediabetes or diabetes, on the basis of the HOMA-IR as a marker of hyperinsulinemia. HbA1c and central obesity increased the risk of breast cancer within the population studied.

The IR prevalence was higher than the one reported by the National Health and Nutrition Examination Survey, carried out from 2001 to 2006, which stated that 23.8% of women in North America had IR.³ In the current study, the absence of an association between IR and the risk of breast cancer is similar to the results reported by Muti et al.,¹⁵ who found no association between an increased risk of breast cancer and insulin, and Mink et al.,¹³ who found no association between the risk of breast cancer and either insulin or FPG. Gunter et al.¹⁸ compared the highest and the lowest HOMA-IR quartiles and did not find an increased risk of breast cancer, but insulin showed a positive association with the risk of breast cancer [adjusted hazard ratio (HR) 1.46, 95% CI 1.00–2.13].

Several authors have reported an increased risk of breast cancer in association with the HOMA-IR, including Kabat et al.,¹² who reported an adjusted relative risk (RR) of 2.99 (95% CI 1.56–5.73), Al Awadhi et al.,¹⁷ who reported an adjusted OR of 2.0 (95% CI 1.1–3.4), and Capasso et al.,¹⁴ who reported a crude OR of 1.86 and did not report the 95% CI values. Menopause, which involves specific hormonal changes, can affect the association between IR and breast cancer risk. In this study, the absence of an association between IR and breast cancer risk was independent of menopausal status, a finding that concurs with work by Cust et al.,¹⁹ who investigated the risk in relation to C-peptide levels. Other authors have reported a positive association between IR and breast cancer risk, but in postmenopausal women only.^16,17,20

The association between glucose and breast cancer is supported by biological evidence.⁹ In our study, the mean FPG level was lower in premenopausal cases, which may have been associated with the lower number of cases who had first-degree relatives with diabetes; however, this association was not upheld following multivariate analysis. Results from studies investigating the link between FPG and breast cancer risk in premenopausal women are inconsistent.^12,13,15 HbA1c, a parameter commonly used to measure glucose metabolism, is a marker of chronic hyperinsulinemia and, unlike FPG, it reflects average pre- and postprandial glucose levels from the previous 6–8 weeks, at least. Lin et al.²¹ demonstrated an inverse relationship between HbA1c and the risk of breast cancer in postmenopausal women, after a mean follow-up period of 10 years (adjusted OR 0.73, 95% CI 0.54–0.98). Cust et al.¹⁹ reported similar findings from women aged ≥55 years (adjusted OR 0.61, 95% CI 0.40–0.94). However, HbA1c increased the risk of breast cancer two-fold in obese women with diagnosis of breast cancer that was at a clinical stage higher than II (OR 1.92, 95% CI 1.00–3.68), indicating the possibility of an interaction between obesity and HbA1c.

In the current study, HbA1c more than doubled the risk of breast cancer in postmenopausal women, and it increased the risk of breast cancer four-fold in premenopausal women, despite the presence of confounding factors. HbA1c is widely used to diagnose prediabetes and diabetes, and studies have been reported that both increase the risk of breast cancer, especially in postmenopausal women.^22

–25 Central obesity also more than doubled the risk of breast cancer in postmenopausal women in the current study. Folsom et al.²⁶ reported an adjusted RR of 1.7 (95% CI 1.4–2.0), whereas Krebs et al.²⁷ and Al Awadhi et al.¹¹ reported greater mean WC values in cases compared with controls in their studies investigating adiposity as a risk factor for breast cancer. Conversely, other studies have reported no association between central obesity and breast cancer.^12,17,28 Central obesity is an established clinical marker for IR,²⁹ and biological mechanisms that connect it to breast cancer are proven. These include the aromatization of androgens in the adipose tissue that increases plasma estrogen levels, thereby exposing the breast tissue to excessive levels of this hormone, particularly in postmenopausal women.^7,9

This study was subject to several limitations. First, we could not use the gold standard for measuring IR. While the euglycemic hyperinsulinemic clamp and the intravenous insulin tolerance test are the ideal methods for measuring IR, they are impractical and difficult to perform in population studies. However, the HOMA-IR has been widely used as an approximation of IR.² Second, the small sample size in this study may have led to the association between IR and breast cancer risk not being realized. On the basis of the frequency of IR among all participant controls, the sample size was adequate for the estimation of at least a two-fold increased risk. In other words, a larger sample size was necessary to detect a greater risk of lesser magnitude. However, a previously reported study did not show a positive association between IR and breast cancer risk, despite the sample size comprising 841 cases,¹⁸ and two other studies, one with 144 cases¹¹ and the other one with 190 cases,¹² showed an increased risk of breast cancer in association with IR.

The cases in the current study were characterized by higher HbA1c values that were compatible with the diagnoses of prediabetes or diabetes. This suggests that these cases were insulin resistant and had defects in insulin secretion, and this was reflected in the lower insulin levels and in the HOMA-IR and HOMA-β-cell indices, which led to the inability to maintain pre- and postprandial glucose levels within the normal limits. Even though higher HbA1c values were registered, the FPG mean was similar between cases and controls. Furthermore, in premenopausal cases, the FPG mean was lower than in premenopausal controls. It is possible that younger women's concerns over the imminent breast cancer diagnosis might have led to the loss of appetite prior to surgery. The literature explains that unlike HbA1c, blood glucose levels are influenced by external factors, such as caloric intake and fasting.^30,31 However, further analysis and research is needed to clarify the herein results.

Conclusions

Despite biological evidence that links IR with breast cancer, this relationship remains controversial in the clinical literature. The findings from our study suggest that IR has no effect on breast cancer risk in Latinas of Mexican origin who had not been diagnosed previously with prediabetes or diabetes and had no direct family history of breast cancer; however, HbA1c and central obesity affected the risk of breast cancer. Prospective studies are required to establish the impact of IR over time.

Footnotes

Acknowledgments

We gratefully acknowledge the unconditional collaboration of Dr. Raúl Cortés-Flores, Chemist Mireya Adriana Villanueva-Cuellar, and Dr. Javier Vargas-Villarreal, whose support was essential to the development of this study. We also thank Dr. José M. Ramírez-Aranda, Dr. Georgina M. Núñez-Rocha, Dr. Dionicio A. Galarza-Delgado and Dr. Cecilia Guadalupe Lezama Torres.

Author Disclosure Statement

No competing financial interests exist. All the authors approved the final version of the article.

References

Ferlay

, Soerjomataram

, Ervik

, et al. GLOBOCAN 2012 v1.0, Cancer Incidence and Mortality Worldwide: IARC Cancer Base No. 11 [Internet]. Lyon, France: International Agency for Research on Cancer; 2013. Available at http://globocan.iarc.fr Accessed on January 26, 2014 .

Singh

, Saxena

. Surrogate markers of insulin resistance: A review. World J Diabetes, 2010; 1:36–47.

Williams

, Fiscella

, Winters

, et al. Association of racial disparities in the prevalence of insulin resistance with racial disparities in vitamin D levels: National Health and Nutrition Examination Survey (2001–2006). Nutr Res, 2013; 33:266–271.

, Li

, Rentfro

, et al. The definition of insulin resistance using HOMA-IR for Americans of Mexican descent using machine learning. PLoS One, 2011; 6:e21041.

Munguía-Miranda

, Sánchez-Barrera

, Hernández-Saavedra

, et al. Prevalencia de dislipidemias en una población de sujetos en apariencia sanos y su relación con la resistencia a la insulina. Salud Publica Mex, 2008; 50:375–382.

Vigneri

, Frasca

, Sciacca

, et al. Diabetes and cancer. Endocrine-Related Cancer, 2009; 16:1103–1123.

Pichard

, Plu-Bureau

, Neves-E Castro

, et al. Insulin resistance, obesity and breast cancer risk. Maturitas, 2008; 60:19–30.

Macció

, Madeddu

. Obesity, inflammation, and postmenopausal breast cancer: Therapeutic implications. ScientificWorldJournal, 2011; 11:2020–2036.

Xue Xue

, Michels

. Diabetes, metabolic syndrome, and breast cancer: A review of the current evidence. Am J Clin Nutr, 2007; 86(Suppl):823S–835S.

10.

Vona-Davis

, Howard-McNatt

, Rose

. Adiposity, type 2 diabetes and the metabolic syndrome in breast cancer. Obes Rev, 2007; 8:395–408.

11.

Al Awadhi

, Al Khaldi

, Al Rammah

, et al. Associations of adipokines & insulin resistance with sex steroids in patients with breast cancer. Indian J Med Res, 2012; 135:500–505.

12.

Kabat

, Kim

, Caan

, et al. Repeated measures of serum glucose and insulin in relation to postmenopausal breast cancer. Int J Cancer, 2009; 125:2704–2710.

13.

Mink

, Shahar

, Rosamond

, et al. Serum insulin and glucose levels and breast cancer incidence. Am J Epidemiol, 2002; 156:349–352.

14.

Capasso

, Esposito

, Pentimally

, et al. Homestasis model assessment to detect insulin resistance and identify patients at high risk of breast cancer development: National Cancer Institute of Naples Experience. J Exp Clin Cancer Res, 2013; 32:14:1–6.

15.

Muti

, Quattrin

, Grant

, et al. Fasting glucose is a risk factor for breast cancer: A prospective study. Cancer Epidemiol Biomarkers Prev, 2002; 11:1361–1368.

16.

Hirose

, Toyama

, Iwata

, et al. Insulin, insulin-like growth factor-I and breast cancer risk in Japanese women. Asian Pacific J Cancer Prev, 2003; 4:239–246.

17.

Garmendia

, Pereira

, Alvarado

, et al. Relation between insulin resistance and breast cancer among Chilean women. Ann Epidemiol, 2007; 17:403–409.

18.

Gunter

, Hoover

, Yu

, et al. Insulin, insulin-like growth factor-I, and risk of breast cancer in postmenopausal women. J Natl Cancer Inst, 2009; 101:48–60.

19.

Cust

, Stocks

, Lukanova

, et al. The influence of overweight and insulin resistance on breast cancer risk and tumour stage at diagnosis: A prospective study. Breast Cancer Res Treat, 2009; 113:567–576.

20.

Yang

, Lu

, Jin

, et al. Population-based, case-control study of blood C-peptide level and breast cancer risk. Cancer Epidemiol Biomarkers Prev, 2001; 10:1207–1211.

21.

Lin

, Ridker

, Rifai

, et al. A prospective study of hemoglobin A1c concentrations and risk of breast cancer in women. Cancer Res, 2006; 66:2869–2875.

22.

Onitilo

, Stankowski

, Berg

, et al. Breast cancer incidence before and after diagnosis of type 2 diabetes mellitus in women: Increased risk in the prediabetes phase. Eur J Cancer Prev, 2014; 23:76–83.

23.

Lipscombe

, Goodwin

, Zinman

, et al. Increased prevalence of prior breast cancer in women with newly diagnosed diabetes. Breast Cancer Res Treat, 2006; 98:303–309.

24.

Larsson

, Mantzoros

ChS

, Wolk

. Diabetes mellitus and risk for breast cancer: A meta-analysis. Int J Cancer, 2007; 121:856–862.

25.

Ronco

, De Stefani

, Deneo-Pellegrini

, et al. Diabetes, overweight and risk of postmenopausal breast cancer: A case-control study in Uruguay. Asian Pac J Cancer Prev, 2012; 13:139–146.

26.

Folsom

, Kushi

, Anderson

, et al. Associations of general and abdominal obesity with multiple health outcomes in older women: The Iowa Women's Health Study. Arch Intern Med, 2000; 160:2117–2128.

27.

Krebs

, Taylor

, Cauley

, et al. Measures of adiposity and risk of breast cancer in older postmenopausal women. J Am Geriatr Soc, 2006; 54:63–69.

28.

Tehard

, Clavel-Chapelon

. Severe anthropometric measurements and breast cancer risk: Results of the E3N cohort study. Int J Obes (Lond), 2006; 30:156–163.

29.

Bonneau

, Pedrozo

, Berg

. Adiponectin and waist circumference as predictors of insulin-resistance in women. Diabetes Metab Syndr, 2014; 8:3–7.

30.

Merl

, Peters

, Oltmanns

, Kern

, Hubold

, Hallschmid

, Born

, Fehm

, Schultes

. Preserved circadian rhythm of serum insulin concentration at low plasma glucose during fasting in lean and overweight humans. Metabolism, 2004; 53:1449–1453.

31.

Hulmán

, Færch

, Vistisen

, Karsai

, Nyári

, Tabák

, Brunner

, Kivimäki

, Witte

. Effect of time of day and fasting duration on measures of glycaemia: Analysis from the Whitehall II Study. Diabetologia, 2013; 56:294–297.