Role of HOMA-IR on Breast Cancer Women
Ishita Saha1 and Rabindra Nath Das2*
1Department of Physiology, Calcutta Medical College and Hospital, Kolkata, W.B., India
2Department of Statistics, The University of Burdwan, Burdwan, West Bengal, India
Submission: August 23, 2021; Published: September 20, 2021
*Corresponding Address: Nath Das, Department of Statistics, The University of Burdwan, Burdwan, West Bengal, India
How to cite this article: Ishita S, Rabindra Nath D. Role of HOMA-IR on Breast Cancer Women. Canc Therapy & Oncol Int J. 2021; 19(5): 556021. DOI: 10.19080/CTOIJ.2021.19.556021
Editorial
Insulin resistance is suggested as a mediator of the onward cancer incidence and mortality related to obesity. Obesity is linked to increased breast cancer (BC) incidence, and high insulin circulating levels may inversely impact on cancer incidence. The association of insulin and homeostasis model assessment of insulin resistance (HOMA-IR) with BC and all-causes related to cancer mortality has been examined in many observational studies with mixed results [1-4]. TheroleHOMA-IR on BC biomarkers such as monocyte chemoattractant protein-1 (MCP-1), leptin, adiponectin, and resistin is little focused in the medical literature based on statistical modeling [5-7]. Relationship of HOMA-IR with BC has been reported in many articles [8, 9]. The following queries are investigated in the current editorial report.
Ø Are there any effects of HOMA-IR on BC biomarkers for BC patients?
Ø For the affirmative case, what are the probable relations of HOMA-IR with BC markers?
Ø What are the roles of HOMA-IR on BC biomarkers for BC patients?
The above-mentioned inquiries are studied in the current editorial report with the help of a real data set containing 116 BC patients & normal women with related 10 study characters. The data set can be viewed in the UCI Machine Learning Repository. A clear illustration of the data is given in [10,11]. For necessary use of the 10 study characters, they are reported as follows.
Ø Age (years),
Ø Body mass index (BMI) (kg/m2),
Ø HOMA-IR,
Ø Insulin (μU/mL),
Ø MCP-1 (pg/dL),
Ø Glucose (mg/ dL),
Ø Resistin (ng/mL),
Ø Leptin (ng/mL),
Ø Adiponectin (μg /mL),
Ø Study unit type (SUT) (1=Healthy controls; 2= BC women).
The above questions can only be properly studied based on a probabilistic model of HOMA-IR on the remaining explanatory variables. It is noted that HOMA-IR is a continuous heteroscedastic positive random dependent variable that can be modeled using joint generalized linear models (JGLMs) under both the lognormal and gamma distributions [12,13]. Very shortly the joint gamma model is displayed in a recent article by Das [14]. JGLMs of HOMA-IR under gamma distribution give better results than lognormal fit, therefore, only the gamma model fit findings of HOMA-IR are displayed in Table 1.
The data developed for the HOMA-IR gamma model fit is diagnosed by Figure 1. Figure 1(a) shows the absolute residuals plot against the HOMA-IR predicted values, which reveals that all absolute residuals are randomly located at a single point except only two residuals. The smooth fitted line is exactly a flat straight line, except the right end is decreasing as a lower residual is located at the right boundary. Figure 1(b) shows the mean normal probability plot of HOMA-IR in Table 1. Figures 1(a) & 1(b) do not show any lack of fit. So, the gamma JGLMs fitted HOMA-IR model (Table 1) is an approximate true model (Figure 1).
Gamma fitted mean & dispersion models of HOMA-IR are as follows.
Gamma fitted HOMA-IR mean () model (from Table 1) is
= exp(--3.18 + 0.02 BMI + 0.42 Insulin -- 0.05 Study Unit type – 0.01 Insulin*BMI + 0.02 Glucose – 0.01 Insulin*Glucose + 0.01 Leptin -- 0.01 Leptin*Insulin),
and the gamma fitted HOMA-IR variance () model (from Table 1) is
= exp(–15.79 – 0.01 Leptin + 0.13 Glucose – 0.46 Adiponectin + 0.01 Adiponectin*Glucose + 0.17 Age – 0.01 Glucose*Age + 0.24 Insulin + 0.01 Insulin*Age + 0.03 Resistin -- 0.01 Insulin*Glucose).
From the HOMA-IR fitted mean & dispersion models in Table 1, the following associations of HOMA-IR with BC markers can be noted.
- Mean HOMA-IR is inversely related to the study unit type (1=Healthy controls; 2= BC women) (P<0.01), interpreting that HOMA-IR is higher for healthy women than BC patients.
- Mean HOMA-IR is directly linked to BMI (P<0.01), implying that mean HOMA-IR rises as BMI increases.
- Mean HOMA-IR is directly linked to insulin level (P<0.001), concluding that HOMA-IR rises as insulin levels increase.
- Mean HOMA-IR is inversely connected to the interaction effect BMI*insulin (P<0.001), concluding that HOMA-IR rises as BMI*insulin decreases. Note that BMI and insulin levels are directly connected to HOMA-IR, while their joint interaction effect is inversely connected to it.
- Mean HOMA-IR is directly connected to glucose levels (P<0.01), indicating that mean HOMA-IR increases as glucose levels rise.
- Mean HOMA-IR is directly related to insulin and glucose levels, while it is inversely related to their joint interaction effect insulin*glucose (P<0.01), concluding that mean HOMA-IR rises as the interaction effect insulin*glucose decreases.
- Mean HOMA-IR is directly related to leptin (P=0.07), concluding that mean HOMA-IR rises as leptin levels increase.
- Mean HOMA-IR is directly related to leptin and insulin, while it is inversely related to their interaction effect leptin*insulin (P<0.01), implying that HOMA-IR rises as the interaction effect leptin*insulin decreases.
- Variance of HOMA-IR is directly related with glucose (P<0.01), insulin (P=0.04) and age (P=0.01), while their interaction effect insulin*age (P<0.01) is directly, and glucose*age (P<0.01) & insulin*glucose (P<0.01) are inversely related to the variance of HOMA-IR. Therefore, insulin, glucose and age have a very complex association with the variance of HOMA-IR.
- Variance of HOMA-IR is directly related with glucose (P<0.01), and inversely related with adiponectin (P=0.04), while it is directly linked to their joint interaction effect glucose*adiponectin (P=0.03). In addition, it is directly related to resistin (P=0.08).
All the above interpretations are derived from the developed HOMA-IR gamma fitted model in Table 1, while the standard errors of all the estimates of HOMA-IR (Table 1) are very small, concluding that estimates are stable. From Table 1, it is noted that mean and variance of HOMA-IR are highly linked to many BC biomarkers and their joint interaction effects. Mean HOMA-IR is directly connected with BMI, insulin, glucose, leptin, while it is inversely related with insulin*BMI, insulin*glucose and insulin*leptin. It is concluded here that HOMA-IR is higher for normal women than BC women, and it increases as BMI, or glucose, or insulin increases, or insulin*BMI, or insulin*glucose, or insulin*leptin decreases. BC women, medical researchers & practitioners will be benefited from the report. HOMA-IR, glucose, insulin, leptin levels along with BMI are regularly examined for BC women.
Conflict of Interest
The authors confirm that this article content has no conflict of interest.
References
- Kim LD, Jean RH, Janice YB, Nathan D K, Karen MW, et al. (2018) Impact of a behaviorally based weight loss intervention on parameters of insulin resistance in breast cancer survivors. BMC Cancer 18(1): 351.
- Dawood S, Broglio K, Gonzalez-Angulo AM, Kau SW, Islam R, et al. (2008) Prognostic value of body mass index in locally advanced breast cancer. Clin Cancer Res 14(6): 1718–1725.
- Protani M, Coory M, Martin JH (2010) Effect of obesity on survival of women with breast cancer: systematic review and meta-analysis. Breast Cancer Res Treat 123(3): 627–635.
- Chan DS, Vieira AR, Aune D, Bandera EV, Greenwood DC, et al. (2014) Body mass index and survival in women with breast cancer-systematic literature review and metaanalysis of 82 follow-up studies. Ann Oncol 25(10): 1901–1914.
- Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, et al. (1985) Homeostasis model assessment: insulin resistance and β-cell function from fasting glucose and insulin concentrations in man. Diabetologia 28(7): 412–419.
- Nichols HB, Trentham-Dietz A, Egan KM, Titus-Ernstoff L, Holmes MD, et al. (2009) Body mass index before and after breast cancer diagnosis: associations with all-cause, breast cancer, and cardiovascular disease mortality. Cancer Epidemiol Biomarkers Prev 18(5): 1403–1409.
- Goodwin PJ, Ennis M, Pritchard KI, Trudeau ME, Koo J, et al. (2002) Fasting insulin and outcome in early-stage breast cancer: results of a prospective cohort study. J Clin Oncol 20(1): 42–51.
- Gallagher EJ, LeRoith D (2010) Insulin, insulin resistance, obesity, and cancer. Curr Diab Rep 10(2): 93–100.
- Das RN, Lee Y, Mukherjee S, Oh S (2019) Relationship of body mass index with diabetes & breast cancer biomarkers. J Diabetes and Management 9(1): 163-168.
- Crisostomo J, Matafome P, Santos-Silva D, Gomes AL, Gomes M, et al. (2016) Hyperresistinemia and metabolic dysregulation: a risky crosstalk in obese breast cancer. Endocrine 53(2): 433-442.
- Patrício M, Pereira J, Crisóstomo J, Matafome P, Gomes M, et al. (2018) Using Resistin, glucose, age and BMI to predict the presence of breast cancer. BMC Cancer 18(1): 18-29.
- Lee, Y, Nelder JA, PawitanY (2017) Generalized Linear Models with Random Effects (Unified Analysis via H–likelihood) (second edition). Chapman & Hall, London, UK.
- Das RN, Lee Y (2009) Log-normal versus gamma models for analyzing data from quality-improvement experiments. Quality Engineering 21(1): 79-87.
- Das M (2021) Induced Abortion Trends for Very Young Girls in New Zealand for the Period 2000--2019. Journal of Medicine: Study and Research 4: 019.