Women_NN2 's_GE Health_NN1 Report_NN1 :_: Change_VV0 your_APPGE life_NN1 8_MC fresh_JJ facts_NN2 about_II women_NN2 's_GE health_NN1 ,_, explained_VVN by_II our_APPGE medical_JJ writer_NN1 ,_, Jean_NP1 Carper_NP1 I_PPIS1 've_VH0 been_VBN lucky_JJ so_RG far_RR ._. 
Although_CS I_PPIS1 recently_RR twisted_VVD my_APPGE knee_NN1 playing_VVG tennis_NN1 ,_, my_APPGE only_JJ similar_JJ injury_NN1 was_VBDZ a_AT1 sprained_JJ ankle_NN1 at_II age_NN1 19_MC ._. 
Tests_NN2 show_VV0 my_APPGE bones_NN2 are_VBR pretty_RG strong_JJ ._. 
I_PPIS1 do_VD0 yoga_NN1 and_CC work_VV0 out_RP at_II the_AT gym_NN1 to_TO keep_VVI them_PPHO2 that_DD1 way_NN1 ._. 
I_PPIS1 eat_VV0 lots_PN of_IO salmon_NN and_CC blueberries_NN2 to_TO keep_VVI my_APPGE brain_NN1 sharp_JJ ._. 
I_PPIS1 took_VVD a_AT1 quiz_NN1 to_TO see_VVI if_CS I_PPIS1 showed_VVD signs_NN2 of_IO Alzheimer_NP1 's_GE ._. 
I_PPIS1 did_VDD n't_XX ,_, but_CCB I_PPIS1 know_VV0 I_PPIS1 'm_VBM genetically_RR susceptible_JJ ._. 
If_CS you_PPY get_VV0 the_AT idea_NN1 that_CST I_PPIS1 spend_VV0 a_AT1 lot_NN1 of_IO time_NNT1 focusing_VVG on_II my_APPGE health_NN1 ,_, then_RT you_PPY 're_VBR right_JJ ._. 
The_AT first_MD thing_NN1 I_PPIS1 do_VD0 every_AT1 morning_NNT1 is_VBZ read_VVN all_DB my_APPGE e-mail_NN1 alerts_NN2 on_II new_JJ studies_NN2 about_II heart_NN1 disease_NN1 ,_, diet_NN1 ,_, diabetes_NN1 ,_, aging_VVG and_CC cancer_NN1 ,_, plus_II several_DA2 minor_JJ maladies_NN2 ._. 
I_PPIS1 'm_VBM particularly_RR intrigued_VVN by_II new_JJ genetic_JJ discoveries_NN2 that_CST promise_VV0 to_TO radically_RR individualize_VVI how_RRQ we_PPIS2 prevent_VV0 and_CC overcome_VV0 many_DA2 standard_JJ health_NN1 problems_NN2 ._. 
All_DB year_NNT1 long_RR ,_, I_PPIS1 've_VH0 been_VBN keeping_VVG track_NN1 of_IO the_AT happenings_NN2 in_II women_NN2 's_GE health_NN1 that_CST strike_VV0 me_PPIO1 as_II the_AT most_RGT fascinating_JJ ._. 
Here_RL are_VBR eight_MC that_CST I_PPIS1 hope_VV0 will_VM give_VVI you_PPY new_JJ ideas_NN2 for_IF boosting_VVG your_APPGE own_DA and_CC your_APPGE family_NN1 's_GE health_NN1 ._. 
Heart_NN1 Disease_NN1 :_: Look_VV0 into_II earlier_JJR ,_, easier_JJR scans_NN2 Heart_NN1 disease_NN1 is_VBZ underdiagnosed_JJ and_CC undertreated_JJ in_II women_NN2 ,_, making_VVG it_PPH1 the_AT No._NN1 1_MC1 cause_NN1 of_IO death_NN1 for_IF American_JJ women_NN2 ._. 
New_JJ non-invasive_JJ cardiac_JJ imaging_NN1 technology_NN1 can_VM determine_VVI whether_CSW you_PPY have_VH0 heart_NN1 disease_NN1 early_RR ,_, while_CS it_PPH1 's_VBZ most_RGT treatable_JJ ,_, says_VVZ the_AT Society_NN1 for_IF Women_NN2 's_GE Health_NN1 Research_NN1 ._. 
The_AT traditional_JJ way_NN1 to_TO detect_VVI a_AT1 blockage_NN1 in_II coronary_JJ arteries_NN2 is_VBZ via_II an_AT1 angiogram_NN1 ,_, in_II which_DDQ a_AT1 wire_NN1 and_CC catheter_NN1 are_VBR run_VVN from_II the_AT groin_NN1 up_RP through_II arteries_NN2 ._. 
Now_RT ,_, experts_NN2 say_VV0 a_AT1 new_JJ scan_NN1 called_VVN "_" coronary_JJ computed_JJ tomography_NN1 angiography_NN1 ,_, "_" or_CC CCTA_NP1 ,_, is_VBZ an_AT1 accurate_JJ alternative_NN1 ._. 
I_PPIS1 've_VH0 had_VHN several_DA2 types_NN2 of_IO scans_NN2 ,_, including_II an_AT1 echocardiogram_NN1 to_TO look_VVI for_IF signs_NN2 of_IO clogged_JJ neck_NN1 arteries_NN2 ._. 
Ask_VV0 your_APPGE doctor_NN1 if_CS you_PPY could_VM benefit_VVI from_II a_AT1 CCTA_NN1 ._. 
Breast_NN1 Cancer_NN1 :_: Let_VV0 your_APPGE genes_NN2 decide_VVI on_II treatment_NN1 New_JJ tests_NN2 ,_, such_II21 as_II22 the_AT Breast_NN1 Bioclassifier_NN1 ,_, predict_VV0 how_RRQ each_DD1 woman_NN1 with_IW breast_NN1 cancer_NN1 will_VM respond_VVI to_II treatment_NN1 ,_, based_VVN on_II her_APPGE genetic_JJ makeup_NN1 ._. 
Result_VV0 :_: Doctors_NN2 can_VM tailor_VVI treatment_NN1 to_II a_AT1 woman_NN1 's_GE genes_NN2 ,_, dramatically_RR increasing_VVG her_APPGE chance_NN1 of_IO success_NN1 ._. 
"_" If_CS chemo_NN1 is_VBZ n't_XX going_VVGK to_TO be_VBI beneficial_JJ ,_, we_PPIS2 should_VM n't_XX be_VBI giving_VVG it_PPH1 ,_, "_" says_VVZ Philip_NP1 Bernard_NP1 ,_, a_AT1 test_NN1 developer_NN1 at_II the_AT University_NN1 of_IO Utah_NP1 School_NN1 of_IO Medicine_NN1 ._. 
Expect_VV0 other_JJ personalized_JJ tests_NN2 that_CST promise_VV0 to_TO reduce_VVI the_AT use_NN1 of_IO potentially_RR hazardous_JJ treatments_NN2 or_CC to_TO identify_VVI women_NN2 who_PNQS need_VV0 aggressive_JJ therapy_NN1 ._. 
Pregnancy_NN1 :_: Do_VD0 n't_XX take_VVI childbirth_NN1 lying_VVG down_RP Sure_JJ ,_, it_PPH1 's_VBZ traditional_JJ to_TO lie_VVI in_II bed_NN1 when_CS you_PPY 're_VBR in_II labor_NN1 ._. 
But_CCB a_AT1 Yale_NP1 expert_NN1 suggests_VVZ that_CST staying_VVG out_II21 of_II22 bed_NN1 during_II early_JJ labor_NN1 may_VM shorten_VVI labor_NN1 and_CC reduce_VVI pain_NN1 ._. 
This_DD1 recommendation_NN1 stems_VVZ from_II a_AT1 new_JJ Australian_JJ analysis_NN1 of_IO many_DA2 past_JJ studies_NN2 on_II the_AT topic_NN1 ._. 
Unfortunately_RR ,_, medical_JJ professionals_NN2 still_JJ pressure_NN1 women_NN2 to_TO stay_VVI in_II bed_NN1 during_II labor_NN1 because_CS it_PPH1 's_VBZ more_RGR convenient_JJ for_IF nurses_NN2 and_CC doctors_NN2 ,_, says_VVZ Teri_NP1 Stone-Godena_NP1 ,_, director_NN1 of_IO nurse-midwifery_NN1 at_II Yale_NP1 University_NN1 School_NN1 of_IO Nursing_NN1 ._. 
She_PPHS1 has_VHZ said_VVN the_AT analysis_NN1 clearly_RR shows_VVZ no_AT advantages_NN2 to_II staying_VVG in_II bed_NN1 --_NN1 unless_CS that_DD1 's_VBZ where_RRQ you_PPY want_VV0 to_TO be_VBI ._. 
Beauty_NN1 :_: Check_VV0 out_RP a_AT1 catalog_NN1 of_IO wrinkle_NN1 cures_NN2 If_CS you_PPY get_VV0 injections_NN2 to_TO help_VVI erase_VVI wrinkles_NN2 ,_, or_CC you_PPY 're_VBR thinking_VVG about_II it_PPH1 ,_, check_VV0 out_RP injectablesafety.org_NNU ._. 
This_DD1 website_NN1 was_VBDZ set_VVN up_RP by_II the_AT Physicians_NN2 Coalition_NN1 for_IF Injectable_JJ Safety_NN1 ,_, a_AT1 group_NN1 of_IO plastic_NN1 surgery_NN1 organizations_NN2 ,_, to_TO give_VVI consumers_NN2 the_AT latest_JJT unbiased_JJ info_NN1 on_II dermal_JJ fillers_NN2 ._. 
The_AT site_NN1 lists_VVZ 17_MC brands_NN2 of_IO injectable_JJ cosmetic_JJ treatments_NN2 --_NN1 including_II Sculptra_NP1 ,_, which_DDQ is_VBZ newly_RR approved_VVN by_II the_AT Food_NN1 and_CC Drug_NN1 Administration_NN1 --_NN1 and_CC explains_VVZ their_APPGE purpose_NN1 ,_, cost_NN1 ,_, duration_NN1 ,_, side_NN1 effects_NN2 and_CC complications_NN2 ._. 
For_IF the_AT best_JJT results_NN2 with_IW the_AT least_RRT risk_VV0 ,_, choose_VV0 a_AT1 board-certified_JJ plastic_NN1 surgeon_NN1 or_CC dermatologist_NN1 who_PNQS personally_RR administers_VVZ the_AT injections_NN2 in_II a_AT1 medically_RR equipped_VVN office_NN1 ._. 
And_CC be_VB0 sure_JJ the_AT injectable_JJ is_VBZ an_AT1 FDA-approved_JJ brand_NN1 name_NN1 ,_, not_XX a_AT1 generic_JJ ._. 
Stress_NN1 :_: Get_VV0 help_NN1 for_IF anxiety_NN1 disorder_NN1 Women_NN2 are_VBR twice_RR as_RG likely_JJ to_TO suffer_VVI from_II anxiety_NN1 as_CSA men_NN2 are_VBR ._. 
In_II fact_NN1 ,_, it_PPH1 has_VHZ become_VVN a_AT1 way_NN1 of_IO life_NN1 for_IF many_DA2 women_NN2 ._. 
I_PPIS1 recently_RR reviewed_VVD a_AT1 book_NN1 on_II the_AT subject_NN1 by_II Jerilyn_NP1 Ross_NP1 ,_, president_NN1 and_CC CEO_NN1 of_IO the_AT Anxiety_NN1 Disorders_NN2 Association_NN1 of_IO America_NP1 ,_, called_VVD "_" One_MC1 Less_DAR Thing_NN1 to_TO Worry_VV0 About_RP ._. "_" 
It_PPH1 's_VBZ a_AT1 good_JJ read_NN1 --_NN1 especially_RR riveting_JJ are_VBR Ross_NP1 '_GE personal_JJ stories_NN2 of_IO how_RRQ she_PPHS1 handled_VVD episodes_NN2 of_IO anxiety_NN1 --_NN1 and_CC has_VHZ multiple_JJ questionnaires_NN2 to_TO help_VVI you_PPY discover_VVI whether_CSW your_APPGE anxiety_NN1 is_VBZ normal_JJ or_CC could_VM be_VBI a_AT1 disorder_NN1 that_CST requires_VVZ medical_JJ treatment_NN1 ._. 
Exercise_VV0 :_: Try_VV0 tai_NN121 chi_NN122 to_TO slow_VVI aging_JJ Tai_NN121 chi_NN122 often_RR is_VBZ described_VVN as_II "_" meditation_NN1 in_II motion_NN1 ,_, "_" but_CCB "_" Harvard_NP1 Women_NN2 's_GE Health_NN1 Watch_NN1 "_" says_VVZ this_DD1 low-impact_JJ exercise_NN1 could_VM be_VBI called_VVN "_" medicine_NN1 in_II motion_NN1 ._. "_" 
Compelling_JJ evidence_NN1 shows_VVZ it_PPH1 prevents_VVZ and_CC treats_VVZ --_JJ often_RR better_RRR than_CSN standard_JJ therapies_NN2 --_NN1 an_AT1 array_NN1 of_IO age-related_JJ health_NN1 conditions_NN2 ._. 
It_PPH1 stabilizes_VVZ bone_NN1 density_NN1 ,_, lowers_VVZ blood_NN1 pressure_NN1 and_CC cholesterol_NN1 and_CC improves_VVZ Parkinson_NP1 's_GE patients_NN2 '_GE well-being_NN1 ._. 
Even_CS21 if_CS22 you_PPY do_VD0 well_RR on_II typical_JJ treatments_NN2 ,_, adding_VVG tai_NN121 chi_NN122 can_VM improve_VVI quality_NN1 of_IO life_NN1 ,_, says_VVZ Peter_NP1 Wayne_NP1 ,_, assistant_JJ professor_NN1 at_II Harvard_NP1 Medical_JJ School_NN1 ._. 
Look_VV0 for_IF classes_NN2 at_II your_APPGE local_JJ YWCA_NP1 ,_, senior_JJ center_NN1 or_CC community_NN1 center_NN1 ._. 
Sex_NN1 :_: Find_VV0 help_NN1 from_II women_NN2 's_GE groups_NN2 It_PPH1 's_VBZ a_AT1 myth_NN1 that_CST sex_NN1 is_VBZ not_XX important_JJ to_II women_NN2 ._. 
In_II fact_NN1 ,_, a_AT1 healthy_JJ sex_NN1 life_NN1 is_VBZ a_AT1 higher_JJR priority_NN1 for_IF many_DA2 women_NN2 than_CSN career_NN1 satisfaction_NN1 ,_, home_NN1 ownership_NN1 ,_, traveling_VVG and_CC an_AT1 active_JJ social_JJ life_NN1 ,_, finds_VVZ a_AT1 2009_MC survey_NN1 of_IO women_NN2 ages_VVZ 18_MC to_II 50_MC ._. 
Still_RR ,_, 70%_NNU of_IO the_AT women_NN2 reported_VVD having_VHG a_AT1 distressing_JJ sexual_JJ health_NN1 issue_NN1 ,_, including_II a_AT1 lack_NN1 of_IO desire_NN1 ,_, pain_NN1 during_II intercourse_NN1 or_CC excessive_JJ desire_NN1 for_IF sexual_JJ activity_NN1 --_NN1 but_CCB fewer_DAR than_CSN a_AT1 fifth_MD of_IO the_AT women_NN2 sought_VVD professional_JJ help_NN1 for_IF their_APPGE sexual_JJ worries_NN2 ._. 
"_" Sex_NN1 and_CC a_AT1 Healthier_JJR You_PPY ,_, "_" a_AT1 campaign_NN1 launched_VVN by_II two_MC women_NN2 's_GE health_NN1 groups_NN2 ,_, hopes_VVZ to_TO "_" raise_VVI women_NN2 's_GE awareness_NN1 about_II sexual_JJ function_NN1 as_II a_AT1 natural_JJ and_CC valued_JJ aspect_NN1 of_IO women_NN2 's_GE lives_NN2 "_" and_CC offer_VV0 unbiased_JJ information_NN1 and_CC support_NN1 ._. 
For_IF advice_NN1 ,_, go_VV0 online_RR to_II SexandaHealthierYou.org_NP1 ._. 
Migraines_NP1 :_: Learn_VV0 about_II links_NN2 I_PPIS1 'm_VBM glad_JJ to_TO see_VVI researchers_NN2 focus_VVI on_II migraines_NN2 ,_, especially_RR in_II women_NN2 ,_, who_PNQS make_VV0 up_RP about_II three-fourths_MF of_IO all_DB migraine_VV0 sufferers_NN2 ._. 
I_PPIS1 am_VBM one_PN1 ,_, although_CS my_APPGE headaches_NN2 are_VBR mild_JJ and_CC infrequent_JJ these_DD2 days_NNT2 ._. 
New_JJ studies_NN2 bring_VV0 good_JJ news_NN1 and_CC bad_JJ news_NN1 :_: Migraine_NP1 victims_NN2 are_VBR less_RGR likely_JJ to_TO have_VHI breast_NN1 cancer_NN1 ,_, but_CCB we_PPIS2 're_VBR more_RGR likely_JJ to_TO have_VHI strokes_NN2 or_CC heart_NN1 disease_NN1 if_CS we_PPIS2 have_VH0 a_AT1 specific_JJ genotype_NN1 and_CC our_APPGE headaches_NN2 come_VV0 with_IW "_" auras_NN2 "_" (_( typically_RR a_AT1 visual_JJ disturbance_NN1 )_) ._. 
And_CC here_RL 's_VBZ a_AT1 surprising_JJ finding_NN1 from_II Drexel_NP1 University_NN1 in_II Philadelphia_NP1 :_: Women_NN2 ages_VVZ 20_MC to_II 55_MC who_PNQS have_VH0 large_JJ waists_NN2 are_VBR more_RGR prone_JJ to_II migraines_NN2 ._. 
So_RR slimming_VVG your_APPGE midriff_NN1 may_VM prevent_VVI migraines_NN2 ,_, researchers_NN2 say_VV0 ._. 
Oddly_RR ,_, migraine_VV0 risk_NN1 dropped_VVD slightly_RR in_II women_NN2 older_JJR than_CSN 55_MC who_PNQS had_VHD big_JJ waistlines_NN2 ._. 
