What are the risk factors for Breast cancer?

['Yevelela emẽla eli']

Nye ci koka uveyi wo kanser yo vimo?

Kuli ovina vialua vi koka uveyi wo kanser yo vimo, vimue vi pondola oku pongoloka kuenda vikuavo sio.

Kuli ovina vimue vi koka ohele yoku kuatiwa luvei waco ndeci:

1. Oku Kuka: Uveyi wo kanser wo vimo u livokiya calua eci omunu a kuka.

2. Esapulo liepata: Oku kuata umue ukuepata ndeci ina, manjove ukãi ale omõla wove ukãi okuti o kuete uveyi wo kanser, ci vokiya ohele yoku kuatiwa luveyi waco.

3. Oku pongoluiwa kuovimatamata vimue viuyali ndeci: BRCA1 kuenda BRCA2, vi vokiya ohele yoku kuatiwa luvei wo kanser.

4. Oku cita omãla: Oku fetika kuosãi selo, oku liwekapo oku cita noke yoku tẽlisa 30 kanyamo, oku yuvula oku cita omãla, ale oku cita omãla noke yoku tẽlisa 30 kanyamo, ci vokiya ohele yoku kuatiwa luvei waco.

5. Oku sakuiwa lovihemba vikuavo vi kuatisa koku kula kuocimatamata cuyali: Oku sakuiwa lovihemba vikuavo vi kuatisa koku kula kuocimatamata cuyali noke yoku pita vanyamo oku pita ombuto yulume, ci vokiya vali ohele yoku kuatiwa luvei waco.

6. Oku nyua calua evi vi koluisa: Oku nyua calua evi vi koluisa ci vokiya ohele yoku kuatiwa luvei wo kanser.

7. Oku lẽla calua: Oku lẽla calua ku vokiya ohele yoku kuatiwa luvei waco, ca piãla enene eci ukãi a pita vanyamo a sulako oku pita volosãi.

8. Oku kuata ombili yoku linga olomapalo: Oku kuata owesi woku linga olomapalo, ci vokiya uvei waco.

9. Oku kapiwa vocinyi: Oku kapiwa vocinyi calua, ca piãla vali enene vutila, ci pondola oku vokiya ohele.

10. Oku lẽla calua: Akãi vana okuti va lẽla calua, va kuete ohele yalua yoku kuatiwa luvei wo kanser.

11. Oku pita kuosãi: Akãi vana va fetika oku pita olosãi osimbu handi ka va tẽlisile 12 kanyamo, ale vana va pita otembo yalua okuti ka va kuete vali osonde, (okuti va tẽlisa 55 kanyamo), va kuete ohele yalua yoku kuatiwa luvei waco.

12. Oku Nyisa Oñaña: Akãi vana okuti lalimue eteke va nyisile omãla vavo, va kuete vali ohele yalua yoku kuatiwa luvei wo kanser.

Ci kuete esilivilo oku limbuka okuti oku kuata cimue ale ovina vialua vi koka ohele ka ci lomboloka okuti omunu umue oka kuata uveyi wo kanser yo vimo, kuenda oku yuvula ovina viosi vi koka ohele ka ci lomboloka okuti omunu ka ka kuata uveyi wo kanser yo vimo.

Pole, oku kuata elomboloko liovina viaco, ci kuatisa omunu oku nõla onjila ya sunguluka poku sakuiwa kuenda oku yuvula ovitangi.

['Atosi a Velapo']

PubMed/Medline https://www.nlm.nih.gov/databases/download/pubmed_medline.html

RefinedWeb https://arxiv.org/abs/2306.01116

Wung SF, Hepworth JT, Sparenga D, Merkle CJ: Cardiovascular Disease Risk and Breast Cancer Outcomes: A Pilot Study. Oncol Nurs Forum. 2015, 42 (5): E330-8.

Frankl G: Risk factors in breast cancer: are they important, are they the same in pre- and post-menopausal breast cancer patients? Oncology. 1980, 37 (1): 41-5.

Ho PJ, Lau HSH, Ho WK, Wong FY, Yang Q, Tan KW, Tan MH, Chay WY, Chia KS, Hartman M, Li J: Incidence of breast cancer attributable to breast density, modifiable and non-modifiable breast cancer risk factors in Singapore. Sci Rep. 2020, 10 (1): 503.

Barnard ME, Boeke CE, Tamimi RM: Established breast cancer risk factors and risk of intrinsic tumor subtypes. Biochim Biophys Acta. 2015, 1856 (1): 73-85.

Hines LM, Risendal B, Slattery ML, Baumgartner KB, Giuliano AR, Sweeney C, Rollison DE, Byers T: Comparative analysis of breast cancer risk factors among Hispanic and non-Hispanic white women. Cancer. 2010, 116 (13): 3215-23.

Rosner B, Glynn RJ, Eliassen AH, Hankinson SE, Tamimi RM, Chen WY, Holmes MD, Mu Y, Peng C, Colditz GA, Willett WC, Tworoger SS: A Multi-State Survival Model for Time to Breast Cancer Mortality among a Cohort of Initially Disease-Free Women. Cancer Epidemiol Biomarkers Prev. 2022, 31 (8): 1582-1592.

['Oku yuvula oku litenga vovitangi:']

['O Web site eyi, ya sandekiwila lika oku longisa omanu kuenda oku va ĩha olonumbi.']

['Nda o yongola oku sakuiwa, sandiliya ondotolo yi kuete uloño woku ci linga.']

['Kũlĩhĩsa okuti, uloño woku konomuisa atambululo apulilo, ka u pondola oku eca atambululo a suapo, ca piãla enene nda etendelo liaco ka lia suilepo.']

['Olonjanja viosi sandiliya ekuatiso liondotolo yove ale liondotolo yikuavo ya loñoloha kueci catiamẽla kocitangi cimue cuhayele. Lalimue eteke ku ka sepule ekuatiso liondotolo ale oku livala koku sandiliya omo liocina cimue wa tanga vo website yilo. Nda o sima okuti o kuete ocitangi cimue cuhayele, vilikiya lonjanga o 911 ale enda lonjanga kosipitali. Ka kuli ukamba umue pokati kondotolo lukuenje wa vela omo lio website yilo ale oku talavaya layo.']

['Oku liyelisa: omoko yoku soneha']

['O Digital Millennium Copyright Act yo 1998, 17 U.S.C. § 512 (o DMCA) yeca epondolo kolonalavayi vi kuete omoko yoku linga eci va sima okuti ovina vi sangiwa vo Internet vi nyõla omoko yavo yi sangiwa vocihandeleko co Estados Unidos. ']

['Nda ove o tava okuti ovina vimue via kapiwa vo Internet ale vovipama vikuavo vi kasi vonumbi yetu vi nyõla omoko yove yoku panga alivulu, ove (ale omunu wa ku nõla) o pondola oku tu tumisa esapulo limue oco tu piñale ovina viaco ale oku vi tateka.']

['Asapulo te a tumiwa loku soneha kuenda vo email (tala vokakasia "Kontato" oco o sange onumbi yo email).']

['DMCA yi kisika okuti esapulo liove lioku lavisa ovihandeleko viovihandeleko viupange mua kongela asapulo akuãimo: (1) elomboluilo liupange u kuete ovihandeleko viupange okuti owo wa lavisiwa; (2) elomboluilo liupange u kuete ovihandeleko viupange kuenda asapulo a tẽla oku ecelela oku sanga upange waco; (3) asapulo oku ku sandiliya, oku kongelamo onumbi yove, etendelo liotelefone kuenda onumbi yo email; (4) esapulo liove okuti ove o kuete ekolelo liwa okuti upange waco ka wa taviwile la muẽliaco ovihandeleko viupange, ale onumiwa yaye, ale oku kuama ovihandeleko; ']

['(5) ukanda umue wa sinaliwa love okuti u lekisa okuti esapulo liaco liocili kuenda o kuete omoko yoku teyuila omoko yove yoku soneha.']

['Kuenda (6) ondimbukiso yocikuata ale yo komputador yomunu ukuete omoko yoku linga upange waco ale yomunu umue o kuete omoko yoku linga upange waco vonduko yomunu ukuete omoko yaco. ']

['Nda kua kongelele ulandu wosi wa tukuiwa ndeti, ci tava okuti o livala poku tetulula ocitangi cove.']

['Oku Sapela']

['Tu tumise o email lapulilo ale ovisimĩlo viove.']

What are the risk factors for breast cancer?

There are several risk factors for breast cancer, some of which are modifiable and others that are not.

Here are some of the most common risk factors:

1. Age: The risk of developing breast cancer increases with age, with most cases occurring in women over the age of 50.

2. Family history: Having a close relative, such as a mother, sister, or daughter, with breast cancer increases your risk.

3. Genetic mutations: Certain inherited gene mutations, such as BRCA1 and BRCA2, increase the risk of developing breast cancer.

4. Reproductive history: Early menarche (start of menstruation), late menopause, and having no children or having the first child after age 30 can increase the risk.

5. Hormone replacement therapy: Long-term use of combined estrogen and progesterone hormone therapy after menopause can increase the risk.

6. Alcohol consumption: Drinking alcohol increases the risk of breast cancer.

7. Obesity: Being overweight or obese increases the risk, especially after menopause.

8. Physical activity: Lack of physical activity can increase the risk.

9. Radiation exposure: Exposure to high doses of radiation, particularly during childhood, can increase the risk.

10. Breast density: Women with dense breasts have a higher risk of developing breast cancer.

11. Menstrual history: Women who started menstruating early (before age 12) or went through menopause late (after age 55) have a slightly higher risk.

12. Breastfeeding: Women who have never breastfed have a slightly higher risk of developing breast cancer.

It is important to note that having one or more risk factors does not mean that a person will definitely develop breast cancer, and not having any risk factors does not guarantee that a person will not develop breast cancer.

However, understanding these risk factors can help individuals make informed decisions about their health and potentially reduce their risk.

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Please note the neural net that generates answers to the questions, is specially inaccurate when it comes to numeric content. For example, the number of people diagnosed with a specific disease.

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