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CC BY-NC-ND 4.0 · South Asian J Cancer 2025; 14(01): 004-014
DOI: 10.1055/s-0043-1775805
Original Article
Breast Cancer

Usefulness of Indian Diabetes Risk Score in Predicting Treatment-Induced Hyperglycemia in Women Undergoing Adjuvant Chemotherapy for Breast Cancer

Krishna Prasad 
1   Department of Medical Oncology, Mangalore Institute of Oncology, Mangaluru, Karnataka, India
,
Sanath Hegde
2   Department of Radiation Oncology, Mangalore Institute of Oncology, Mangaluru, Karnataka, India
,
Suresh Rao
2   Department of Radiation Oncology, Mangalore Institute of Oncology, Mangaluru, Karnataka, India
,
Rhea Katherine D'souza
3   Department of Research, Research Unit, Mangalore Institute of Oncology, Mangaluru, Karnataka, India
,
Thomas George
3   Department of Research, Research Unit, Mangalore Institute of Oncology, Mangaluru, Karnataka, India
,
Sucharitha Suresh
4   Department of Community Medicine, Father Muller Medical College, Mangalore, Karnataka, India
,
Manjeshwar Shrinath Baliga 
3   Department of Research, Research Unit, Mangalore Institute of Oncology, Mangaluru, Karnataka, India
› Author Affiliations
Funding Mangalore Institute of Oncology funded the study.
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Abstract

In the curative treatment of cancer with adjuvant chemotherapy, antineoplastic drugs, along with glucocorticoids, can induce hyperglycemia. The objective of this study was to assess the utility of the Indian Diabetes Risk Score (IDRS) in predicting treatment-induced hyperglycemia in women who were nondiabetic and normoglycemic at the start of chemotherapy. This prospective study was conducted with nondiabetic women who required adjuvant chemotherapy. Participants voluntarily completed the IDRS, providing information on age, waist circumference, family history of diabetes, and physical activity. Chemotherapy-induced hyperglycemia was defined as fasting blood glucose levels ≥100 mg/dL or random blood glucose levels ≥140 mg/dL during treatment. Data were categorized into women who developed hyperglycemia and those who remained normoglycemic during treatment and were analyzed using Fisher's exact test. A significance level of p < 0.05 was applied. Receiver operating characteristic (ROC) curves were constructed to validate the IDRS for predicting hyperglycemia. A total of 208 women met the inclusion criteria and participated in the study. The results revealed that 38.93% (81/208) developed hyperglycemia by the end of chemotherapy, as observed during their first follow-up after treatment. Fisher's exact test demonstrated a significant difference in the total IDRS score and its domains, including family history, physical activity, and waist circumference (p = 0.017–< 0.001), but not age. ROC analysis indicated that an IDRS score above 60 increased the likelihood of developing hyperglycemia, with a sensitivity of 81.3%, specificity of 54.7%, and an area under the curve of 0.727. These findings suggest that the IDRS is a sensitive tool for predicting adjuvant chemotherapy-induced hyperglycemia in breast cancer patients without diabetes. To the best of the authors' knowledge, this is the first study to evaluate the utility of the IDRS in predicting treatment-induced hyperglycemia in women undergoing adjuvant chemotherapy for breast cancer. Ongoing efforts are focused on understanding the underlying mechanisms and strategies for mitigation.

Keywords

Breast Cancer - adjuvant chemotherapy - hyperglycemia - Indian Diabetes Risk Score - diabetes

Informed Consent

Informed consent was obtained from all participants included in the study.


Ethical Approval

This study was performed in accordance with the ethical standards of the institutional and national research committee, the 1964 Declaration of Helsinki and in accordance to the guidelines stipulated by Indian Council of Medical Research 2008 for research after obtaining permission from the hospital ethics committee (MIO/IEC/2018/02/07).




Publication History

Article published online:
13 October 2023

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  • References

  • 1 Huang J, Chan PS, Lok V. et al. Global incidence and mortality of breast cancer: a trend analysis. Aging (Albany NY) 2021; 13 (04) 5748-5803
    CrossrefPubMedSearch in Google Scholar
  • 2 Malvia S, Bagadi SA, Dubey US, Saxena S. Epidemiology of breast cancer in Indian women. Asia Pac J Clin Oncol 2017; 13 (04) 289-295
    Search in Google Scholar
  • 3 Ferlay J, Soerjomataram I, Dikshit R. et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer 2015; 136 (05) E359-E386
    CrossrefPubMedSearch in Google Scholar
  • 4 Lima SM, Kehm RD, Terry MB. Global breast cancer incidence and mortality trends by region, age-groups, and fertility patterns. EClinicalMedicine 2021; 38: 100985
    CrossrefPubMedSearch in Google Scholar
  • 5 Sopik V. International variation in breast cancer incidence and mortality in young women. Breast Cancer Res Treat 2021; 186 (02) 497-507
    CrossrefPubMedSearch in Google Scholar
  • 6 World Health Organization. Guidelines for Management of Breast Cancer. EMRO Technical Publications Series 31. Geneva, Switzerland: World Health Organization;; 2006
    Search in Google Scholar
  • 7 Wang L, Zhang S, Wang X. The metabolic mechanisms of breast cancer metastasis. Front Oncol 2021; 10: 602416
    CrossrefPubMedSearch in Google Scholar
  • 8 Riggio AI, Varley KE, Welm AL. The lingering mysteries of metastatic recurrence in breast cancer. Br J Cancer 2021; 124 (01) 13-26
    CrossrefPubMedSearch in Google Scholar
  • 9 Sakibuzzaman M, Mahmud S, Afroze T. et al. Pathology of breast cancer metastasis and a view of metastasis to the brain. Int J Neurosci 2021; •••: 1-18
    Search in Google Scholar
  • 10 Ocaña A, Cruz JJ, Pandiella A. Trastuzumab and antiestrogen therapy: focus on mechanisms of action and resistance. Am J Clin Oncol 2006; 29 (01) 90-95
    CrossrefPubMedSearch in Google Scholar
  • 11 Ioannidis JP, Pavlidis N. Levels of absolute survival benefit for systemic therapies of advanced cancer. a call for standards. Eur J Cancer 2003; 39 (09) 1194-1198
    CrossrefPubMedSearch in Google Scholar
  • 12 Tamez-Pérez HE, Quintanilla-Flores DL, Rodríguez-Gutiérrez R, González-González JG, Tamez-Peña AL. Steroid hyperglycemia: prevalence, early detection and therapeutic recommendations: a narrative review. World J Diabetes 2015; 6 (08) 1073-1081
    CrossrefPubMedSearch in Google Scholar
  • 13 Panigrahi G, Ambs S. How comorbidities shape cancer biology and survival. Trends Cancer 2021; 7 (06) 488-495
    CrossrefPubMedSearch in Google Scholar
  • 14 Jeong Y, Han HS, Lee HD. et al. A pilot study evaluating steroid-induced diabetes after antiemetic dexamethasone therapy in chemotherapy-treated cancer patients. Cancer Res Treat 2016; 48 (04) 1429-1437
    CrossrefPubMedSearch in Google Scholar
  • 15 Hickish T, Astras G, Thomas P. et al. Glucose intolerance during adjuvant chemotherapy for breast cancer. J Natl Cancer Inst 2009; 101 (07) 537
    CrossrefPubMedSearch in Google Scholar
  • 16 Agnoli C, Berrino F, Abagnato CA. et al. Metabolic syndrome and postmenopausal breast cancer in the ORDET cohort: a nested case-control study. Nutr Metab Cardiovasc Dis 2010; 20 (01) 41-48
    CrossrefPubMedSearch in Google Scholar
  • 17 Lu LJ, Wang RJ, Ran L. et al. On the status and comparison of glucose intolerance in female breast cancer patients at initial diagnosis and during chemotherapy through an oral glucose tolerance test. PLoS One 2014; 9 (04) e93630
    CrossrefPubMedSearch in Google Scholar
  • 18 Juanjuan L, Wen W, Zhongfen L. et al. Clinical pathological characteristics of breast cancer patients with secondary diabetes after systemic therapy: a retrospective multicenter study. Tumour Biol 2015; 36 (09) 6939-6947
    CrossrefPubMedSearch in Google Scholar
  • 19 Dieli-Conwright CM, Wong L, Waliany S, Bernstein L, Salehian B, Mortimer JE. An observational study to examine changes in metabolic syndrome components in patients with breast cancer receiving neoadjuvant or adjuvant chemotherapy. Cancer 2016; 122 (17) 2646-2653
    CrossrefPubMedSearch in Google Scholar
  • 20 Rao S, Prasad K, Abraham S, George T, Chandran SK, Baliga MS. Incidence of hyperglycemia/secondary diabetes in women who have undergone curative chemotherapy for breast cancer: first study from India. South Asian J Cancer 2020; 9 (03) 130-135
    Thieme ConnectPubMedSearch in Google Scholar
  • 21 Simmons LR, Molyneaux L, Yue DK, Chua EL. Steroid-induced diabetes: is it just unmasking of type 2 diabetes?. ISRN Endocrinol 2012; 2012: 910905
    CrossrefPubMedSearch in Google Scholar
  • 22 van Raalte DH, Ouwens DM, Diamant M. Novel insights into glucocorticoid-mediated diabetogenic effects: towards expansion of therapeutic options?. Eur J Clin Invest 2009; 39 (02) 81-93
    CrossrefPubMedSearch in Google Scholar
  • 23 Uzu T, Harada T, Sakaguchi M. et al. Glucocorticoid-induced diabetes mellitus: prevalence and risk factors in primary renal diseases. Nephron Clin Pract 2007; 105 (02) c54-c57
    CrossrefPubMedSearch in Google Scholar
  • 24 Geer EB, Islam J, Buettner C. Mechanisms of glucocorticoid-induced insulin resistance: focus on adipose tissue function and lipid metabolism. Endocrinol Metab Clin North Am 2014; 43 (01) 75-102
    CrossrefPubMedSearch in Google Scholar
  • 25 Brady VJ, Grimes D, Armstrong T, LoBiondo-Wood G. Management of steroid-induced hyperglycemia in hospitalized patients with cancer: a review. Oncol Nurs Forum 2014; 41 (06) E355-E365
    CrossrefPubMedSearch in Google Scholar
  • 26 Suh S, Park MK. Glucocorticoid-induced diabetes mellitus: an important but overlooked problem. Endocrinol Metab (Seoul) 2017; 32 (02) 180-189
    CrossrefPubMedSearch in Google Scholar
  • 27 Bonaventura A, Montecucco F. Steroid-induced hyperglycemia: an underdiagnosed problem or clinical inertia? A narrative review. Diabetes Res Clin Pract 2018; 139: 203-220
    CrossrefPubMedSearch in Google Scholar
  • 28 Hollingdal M, Juhl CB, Dall R. et al. Glucocorticoid induced insulin resistance impairs basal but not glucose entrained high-frequency insulin pulsatility in humans. Diabetologia 2002; 45 (01) 49-55
    CrossrefPubMedSearch in Google Scholar
  • 29 Dimitriadis G, Leighton B, Parry-Billings M. et al. Effects of glucocorticoid excess on the sensitivity of glucose transport and metabolism to insulin in rat skeletal muscle. Biochem J 1997; 321 (Pt 3): 707-712
    CrossrefPubMedSearch in Google Scholar
  • 30 Dimitriadis G, Parry-Billings M, Bevan S. et al. The effects of insulin on transport and metabolism of glucose in skeletal muscle from hyperthyroid and hypothyroid rats. Eur J Clin Invest 1997; 27 (06) 475-483
    CrossrefPubMedSearch in Google Scholar
  • 31 Saeedi P, Petersohn I, Salpea P. et al. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: results from the International Diabetes Federation Diabetes Atlas, 9th edition. Diabetes Res Clin Pract 2019; 157: 107843
    CrossrefPubMedSearch in Google Scholar
  • 32 Singla R, Bindra J, Singla A, Gupta Y, Kalra S. Drug prescription patterns and cost analysis of diabetes therapy in India: audit of an endocrine practice. Indian J Endocrinol Metab 2019; 23 (01) 40-45
    CrossrefPubMedSearch in Google Scholar
  • 33 Richardson LC, Pollack LA. Therapy insight: influence of type 2 diabetes on the development, treatment and outcomes of cancer. Nat Clin Pract Oncol 2005; 2 (01) 48-53
    CrossrefPubMedSearch in Google Scholar
  • 34 Montagnese C, Porciello G, Vitale S. et al. Quality of life in women diagnosed with breast cancer after a 12-month treatment of lifestyle modifications. Nutrients 2020; 13 (01) 136
    CrossrefPubMedSearch in Google Scholar
  • 35 Villarreal-Garza C, Shaw-Dulin R, Lara-Medina F. et al. Impact of diabetes and hyperglycemia on survival in advanced breast cancer patients. Exp Diabetes Res 2012; 2012: 732027
    CrossrefPubMedSearch in Google Scholar
  • 36 Ahn HR, Kang SY, Youn HJ, Jung SH. Hyperglycemia during adjuvant chemotherapy as a prognostic factor in breast cancer patients without diabetes. J Breast Cancer 2020; 23 (04) 398-409
    CrossrefPubMedSearch in Google Scholar
  • 37 Sharma KM, Ranjani H, Nguyen H. et al. Indian Diabetes Risk Score helps to distinguish type 2 from non-type 2 diabetes mellitus (GDRC-3). J Diabetes Sci Technol 2011; 5 (02) 419-425
    CrossrefPubMedSearch in Google Scholar
  • 38 Nugawela MD, Sivaprasad S, Mohan V, Rajalakshmi R, Netuveli G. Evaluating the performance of the Indian Diabetes Risk Score in different ethnic groups. Diabetes Technol Ther 2020; 22 (04) 285-300
    CrossrefPubMedSearch in Google Scholar
  • 39 Nagarathna R, Tyagi R, Battu P, Singh A, Anand A, Nagendra HR. Assessment of risk of diabetes by using Indian Diabetic Risk Score (IDRS) in Indian population. Diabetes Res Clin Pract 2020; 162: 108088
    CrossrefPubMedSearch in Google Scholar
  • 40 Lindström J, Tuomilehto J. The diabetes risk score: a practical tool to predict type 2 diabetes risk. Diabetes Care 2003; 26 (03) 725-731
    CrossrefPubMedSearch in Google Scholar
  • 41 Mohan V, Deepa R, Deepa M, Somannavar S, Datta M. A simplified Indian Diabetes Risk Score for screening for undiagnosed diabetic subjects. J Assoc Physicians India 2005; 53: 759-763
    PubMedSearch in Google Scholar
  • 42 Heikes KE, Eddy DM, Arondekar B, Schlessinger L. Diabetes risk calculator: a simple tool for detecting undiagnosed diabetes and pre-diabetes. Diabetes Care 2008; 31 (05) 1040-1045
    CrossrefPubMedSearch in Google Scholar
  • 43 Chen L, Magliano DJ, Balkau B. et al. AUSDRISK: an Australian Type 2 Diabetes Risk Assessment Tool based on demographic, lifestyle and simple anthropometric measures. Med J Aust 2010; 192 (04) 197-202
    CrossrefPubMedSearch in Google Scholar
  • 44 Robinson CA, Agarwal G, Nerenberg K. Validating the CANRISK prognostic model for assessing diabetes risk in Canada's multi-ethnic population. Chronic Dis Inj Can 2011; 32 (01) 19-31
    CrossrefPubMedSearch in Google Scholar
  • 45 Tappan SGAR, Dampil OAC, Machacon MSS, Yumul SE. Validation of the modified filipino version of the American diabetes association diabetes risk test and the St. Luke's internal medicine diabetes risk test to identify population at risk for type 2 diabetes mellitus among adults. Philipp J Intern Med 2018; 56: 234-246
    Search in Google Scholar
  • 46 Bala S, Pandve H, Kamala K, Dhanalakshmi A, Sarikonda H. Performance of Indian Diabetic Risk Score as a screening tool of diabetes among women of industrial urban area. J Family Med Prim Care 2019; 8 (11) 3569-3573
    CrossrefPubMedSearch in Google Scholar
  • 47 Muthuvelraj SB, Maiya GR. Screening for the risk of diabetes among people aged 31 to 40 years using Indian' Diabetic Risk Score among people attending medicine out-patient department of a tertiary care hospital in Chennai. J Family Med Prim Care 2021; 10 (01) 213-217
    CrossrefPubMedSearch in Google Scholar
  • 48 Khan MM, Sonkar GK, Alam R. et al. Validity of Indian Diabetes Risk Score and its association with body mass index and glycosylated hemoglobin for screening of diabetes in and around areas of Lucknow. J Family Med Prim Care 2017; 6 (02) 366-373
    CrossrefPubMedSearch in Google Scholar
  • 49 Kapoor N, Lotfaliany M, Sathish T. et al. Prevalence of normal weight obesity and its associated cardio-metabolic risk factors - results from the baseline data of the Kerala Diabetes Prevention Program (KDPP). PLoS One 2020; 15 (08) e0237974
    CrossrefPubMedSearch in Google Scholar
  • 50 Rajput M, Garg D, Rajput R. Validation of simplified Indian Diabetes Risk Score for screening undiagnosed diabetes in an urban setting of Haryana. Diabetes Metab Syndr 2017; 11 (2, suppl 2): S539-S542
    CrossrefPubMedSearch in Google Scholar
  • 51 Acharya AS, Singh A, Dhiman B. Assessment of diabetes risk in an adult population using Indian Diabetes Risk Score in an urban resettlement colony of Delhi. J Assoc Physicians India 2017; 65 (03) 46-51
    PubMedSearch in Google Scholar
  • 52 Kumar S, Anand A, Nagarathna R. et al. Prevalence of prediabetes, and diabetes in Chandigarh and Panchkula region based on glycated haemoglobin and Indian Diabetes Risk Score. Endocrinol Diabetes Metab 2020; 4 (01) e00162
    CrossrefPubMedSearch in Google Scholar
  • 53 Pawar SD, Naik JD, Prabhu P, Jatti GM, Jadhav SB, Radhe BK. Comparative evaluation of Indian Diabetes Risk Score and Finnish Diabetes Risk Score for predicting risk of diabetes mellitus type II: a teaching hospital-based survey in Maharashtra. J Family Med Prim Care 2017; 6 (01) 120-125
    CrossrefPubMedSearch in Google Scholar
  • 54 Chaturvedi M, Pandey A, Javed M, Baiswar R. Validity of Indian Diabetes Risk Score (IDRS) in population in and around Agra. J Assoc Physicians India 2018; 66 (10) 33-35
    PubMedSearch in Google Scholar
  • 55 Adhikari P, Pathak R, Kotian S. Validation of the MDRF-Indian Diabetes Risk Score (IDRS) in another south Indian population through the Boloor Diabetes Study (BDS). J Assoc Physicians India 2010; 58: 434-436
    PubMedSearch in Google Scholar
  • 56 Vijayakumar V, Balakundi M, Metri KG. Challenges faced in diabetes risk prediction among an indigenous South Asian population in India using the Indian Diabetes Risk Score. Public Health 2019; 176: 114-117
    CrossrefPubMedSearch in Google Scholar
  • 57 Sathish T, Aziz Z, Absetz P. et al. Participant recruitment into a community-based diabetes prevention trial in India: learnings from the Kerala Diabetes Prevention Program. Contemp Clin Trials Commun 2019; 15: 100382
    CrossrefPubMedSearch in Google Scholar
  • 58 Thankappan KR, Sathish T, Tapp RJ. et al. A peer-support lifestyle intervention for preventing type 2 diabetes in India: a cluster-randomized controlled trial of the Kerala Diabetes Prevention Program. PLoS Med 2018; 15 (06) e1002575
    CrossrefPubMedSearch in Google Scholar
  • 59 Sengupta B, Bhattacharjya H. Validation of Indian Diabetes Risk Score for screening prediabetes in West Tripura District of India. Indian J Community Med 2021; 46 (01) 30-34
    CrossrefPubMedSearch in Google Scholar
  • 60 Gupta SK, Singh Z, Purty AJ, Vishwanathan M. Diabetes prevalence and its risk factors in urban Pondicherry. Int J Diabetes Dev Ctries 2009; 29 (04) 166-169
    CrossrefPubMedSearch in Google Scholar
  • 61 Vardhan A, Prabha M R A, Shashidhar M K, Shankar N, Gupta S, Tripathy A. Value of Indian Diabetes Risk Score among medical students and its correlation with fasting plasma glucose, blood pressure and lipid profile. J Clin Diagn Res 2012; 6 (09) 1528-1530
    PubMedSearch in Google Scholar
  • 62 Tripathy JP. Burden and risk factors of diabetes and hyperglycemia in India: findings from the Global Burden of Disease Study 2016. Diabetes Metab Syndr Obes 2018; 11: 381-387
    CrossrefPubMedSearch in Google Scholar
  • 63 Roopa M, Deepa M, Indulekha K, Mohan V. Prevalence of sleep abnormalities and their association with metabolic syndrome among Asian Indians: Chennai Urban Rural Epidemiology Study (CURES-67). J Diabetes Sci Technol 2010; 4 (06) 1524-1531
    CrossrefPubMedSearch in Google Scholar
  • 64 Mohan V, Anbalagan VP. Expanding role of the Madras Diabetes Research Foundation - Indian Diabetes Risk Score in clinical practice. Indian J Endocrinol Metab 2013; 17 (01) 31-36
    CrossrefPubMedSearch in Google Scholar
  • 65 Anbalagan VP, Venkataraman V, Vamsi M, Deepa M, Mohan V. A simple Indian Diabetes Risk Score could help identify nondiabetic individuals at high risk of non-alcoholic fatty liver disease (CURES-117). J Diabetes Sci Technol 2012; 6 (06) 1429-1435
    CrossrefPubMedSearch in Google Scholar
  • 66 Mohan V, Deepa M, Farooq S, Narayan KM, Datta M, Deepa R. Anthropometric cut points for identification of cardiometabolic risk factors in an urban Asian Indian population. Metabolism 2007; 56 (07) 961-968
    CrossrefPubMedSearch in Google Scholar
  • 67 Awasthi A, Rao CR, Hegde DS, Rao N K. Association between type 2 diabetes mellitus and anthropometric measurements - a case control study in South India. J Prev Med Hyg 2017; 58 (01) E56-E62
    PubMedSearch in Google Scholar
  • 68 Misra A, Khurana L. Obesity-related non-communicable diseases: South Asians vs white Caucasians. Int J Obes 2011; 35 (02) 167-187
    CrossrefPubMedSearch in Google Scholar
  • 69 Venkatrao M, Nagarathna R, Patil SS, Singh A, Rajesh SK, Nagendra H. A composite of BMI and waist circumference may be a better obesity metric in Indians with high risk for type 2 diabetes: an analysis of NMB-2017, a nationwide cross-sectional study. Diabetes Res Clin Pract 2020; 161: 108037
    CrossrefPubMedSearch in Google Scholar
  • 70 Das M, Pal S, Ghosh A. Family history of type 2 diabetes and prevalence of metabolic syndrome in adult Asian Indians. J Cardiovasc Dis Res 2012; 3 (02) 104-108
    CrossrefPubMedSearch in Google Scholar
  • 71 Jali MV, Kambar S, Jali SM, Gowda S. Familial early onset of type-2 diabetes mellitus and its complications. N Am J Med Sci 2009; 1 (07) 377-380
    PubMedSearch in Google Scholar
  • 72 Geetha A, Gopalakrishnan S, Umadevi R. Study on the impact of family history of diabetes among type 2 diabetes mellitus patients in an urban area of Kancheepuram district, Tamil Nadu. Int J Community Med Public Health 2017; 4: 4151-4156
    CrossrefSearch in Google Scholar
  • 73 Hariri S, Yoon PW, Qureshi N, Valdez R, Scheuner MT, Khoury MJ. Family history of type 2 diabetes: a population-based screening tool for prevention?. Genet Med 2006; 8 (02) 102-108
    CrossrefPubMedSearch in Google Scholar
  • 74 Bener A, Darwish S, Al-Hamaq AO, Yousafzai MT, Nasralla EA. The potential impact of family history of metabolic syndrome and risk of type 2 diabetes mellitus: in a highly endogamous population. Indian J Endocrinol Metab 2014; 18 (02) 202-209
    CrossrefPubMedSearch in Google Scholar
  • 75 Tan JT, Tan LS, Chia KS, Chew SK, Tai ES. A family history of type 2 diabetes is associated with glucose intolerance and obesity-related traits with evidence of excess maternal transmission for obesity-related traits in a South East Asian population. Diabetes Res Clin Pract 2008; 82 (02) 268-275
    CrossrefPubMedSearch in Google Scholar
  • 76 Booth FW, Roberts CK, Laye MJ. Lack of exercise is a major cause of chronic diseases. Compr Physiol 2012; 2 (02) 1143-1211
    CrossrefPubMedSearch in Google Scholar
  • 77 Carbone S, Del Buono MG, Ozemek C, Lavie CJ. Obesity, risk of diabetes and role of physical activity, exercise training and cardiorespiratory fitness. Prog Cardiovasc Dis 2019; 62 (04) 327-333
    CrossrefPubMedSearch in Google Scholar
  • 78 Jayamani V, Gopichandran V, Lee P, Alexander G, Christopher S, Prasad JH. Diet and physical activity among women in urban and rural areas in South India: a community based comparative survey. J Family Med Prim Care 2013; 2 (04) 334-338
    CrossrefPubMedSearch in Google Scholar
  • 79 Padmanabha UR, Nalam U, Badiger S, Nagarajaiah P. Prevalence and risk factors of type 2 diabetes mellitus in the rural population of Mangalore, South India. Natl J Community Med 2017; 8 (08) 456-461
    Search in Google Scholar
  • 80 Rao CR, Kamath VG, Shetty A, Kamath A. A study on the prevalence of type 2 diabetes in coastal Karnataka. Int J Diabetes Dev Ctries 2010; 30 (02) 80-85
    CrossrefPubMedSearch in Google Scholar
  • 81 Dasappa H, Fathima FN, Prabhakar R, Sarin S. Prevalence of diabetes and pre-diabetes and assessments of their risk factors in urban slums of Bangalore. J Family Med Prim Care 2015; 4 (03) 399-404
    CrossrefPubMedSearch in Google Scholar
  • 82 Bhagyalaxmi A, Atul T, Shikha J. Prevalence of risk factors of non-communicable diseases in a District of Gujarat, India. J Health Popul Nutr 2013; 31 (01) 78-85
    CrossrefPubMedSearch in Google Scholar
  • 83 D'Souza M, D'Souza C, Tauro LF, Naik BM. Demographic profile of breast cancer at a tertiary care centre over a three year period. F Sch J App Med Sci 2018; 6 (11) 4422-4427
    Search in Google Scholar
  • 84 Rao C, Shetty J, Kishan Prasad HL. Morphological profile and receptor status in breast carcinoma: an institutional study. J Cancer Res Ther 2013; 9 (01) 44-49
    CrossrefPubMedSearch in Google Scholar
  • 85 Adnan M, Saldanha E, Palatty PL. et al. Incidence of breast cancer in women from agriculture based areas of costal Karnataka, India: A twenty year observations from a tertiary care hospital. Clin Oncol 2018; 3: 1437
    Search in Google Scholar
  • 86 Nagrani RT, Budukh A, Koyande S, Panse NS, Mhatre SS, Badwe R. Rural urban differences in breast cancer in India. Indian J Cancer 2014; 51 (03) 277-281
    CrossrefPubMedSearch in Google Scholar
  • 87 Nesan GS, Kundapur R. Assessment of physical activity and dietary pattern among adults in rural Mangalore. Indian J Public Health Res Dev 2018; 9 (05) 71-76
    CrossrefSearch in Google Scholar
  • 88 Duan W, Shen X, Lei J. et al. Hyperglycemia, a neglected factor during cancer progression. BioMed Res Int 2014; 2014: 461917
    CrossrefPubMedSearch in Google Scholar
  • 89 Wang M, Yang Y, Liao Z. Diabetes and cancer: epidemiological and biological links. World J Diabetes 2020; 11 (06) 227-238
    CrossrefPubMedSearch in Google Scholar