1 00:00:00,706 --> 00:00:03,417 Some of my most wonderful memories of childhood 2 00:00:03,417 --> 00:00:06,968 are of spending time with my grandmother, Mamar, 3 00:00:06,968 --> 00:00:10,768 in our four-family home in Brooklyn, New York. 4 00:00:10,768 --> 00:00:13,843 Her apartment was an oasis. 5 00:00:13,843 --> 00:00:15,888 It was a place where I could sneak a cup of coffee, 6 00:00:15,888 --> 00:00:19,769 which was really warm milk with just a touch of caffeine. 7 00:00:19,769 --> 00:00:22,303 She loved life. 8 00:00:22,303 --> 00:00:24,798 And although she worked in a factory, 9 00:00:24,798 --> 00:00:27,970 she saved her pennies and she traveled to Europe. 10 00:00:27,970 --> 00:00:31,299 And I remember pouring over those pictures with her 11 00:00:31,299 --> 00:00:35,411 and then dancing with her to her favorite music. 12 00:00:35,411 --> 00:00:40,033 And then, when I was eight and she was 60, 13 00:00:40,033 --> 00:00:42,086 something changed. 14 00:00:42,086 --> 00:00:44,440 She no longer worked or traveled. 15 00:00:44,440 --> 00:00:46,131 She no longer danced. 16 00:00:46,131 --> 00:00:48,884 There were no more coffee times. 17 00:00:48,884 --> 00:00:51,068 My mother missed work and took her to doctors 18 00:00:51,068 --> 00:00:53,454 who couldn't make a diagnosis. 19 00:00:53,454 --> 00:00:58,791 And my father, who worked at night, would spend every afternoon with her, 20 00:00:58,791 --> 00:01:01,876 just to make sure she ate. 21 00:01:01,876 --> 00:01:06,576 Her care became all-consuming for our family. 22 00:01:06,576 --> 00:01:08,223 And by the time a diagnosis was made, 23 00:01:08,223 --> 00:01:10,928 she was in a deep spiral. 24 00:01:10,928 --> 00:01:14,489 Now many of you will recognize her symptoms. 25 00:01:14,489 --> 00:01:17,152 My grandmother had depression. 26 00:01:17,152 --> 00:01:20,472 A deep, life-altering depression, 27 00:01:20,472 --> 00:01:23,562 from which she never recovered. 28 00:01:23,562 --> 00:01:27,253 And back then, so little was known about depression. 29 00:01:27,253 --> 00:01:30,658 But even today, 50 years later, 30 00:01:30,658 --> 00:01:33,932 there's still so much more to learn. 31 00:01:33,932 --> 00:01:38,840 Today, we know that women are 70 percent more likely 32 00:01:38,840 --> 00:01:41,831 to experience depression over their lifetimes 33 00:01:41,831 --> 00:01:44,112 compared with men. 34 00:01:44,112 --> 00:01:46,952 And even with this high prevalence, 35 00:01:46,952 --> 00:01:53,239 women are misdiagnosed between 30 and 50 percent of the time. 36 00:01:53,239 --> 00:01:56,172 Now we know that women are more likely 37 00:01:56,172 --> 00:02:01,560 to experience the symptoms of fatigue, sleep disturbance, 38 00:02:01,560 --> 00:02:04,105 pain and anxiety compared with men. 39 00:02:04,105 --> 00:02:06,804 And these symptoms are often overlooked 40 00:02:06,804 --> 00:02:09,767 as symptoms of depression. 41 00:02:09,767 --> 00:02:13,550 And it isn't only depression in which these sex differences occur, 42 00:02:13,550 --> 00:02:18,069 but they occur across so many diseases. 43 00:02:18,069 --> 00:02:20,064 So it's my grandmother's struggles 44 00:02:20,064 --> 00:02:23,797 that have really led me on a lifelong quest. 45 00:02:23,797 --> 00:02:27,025 And today, I lead a center in which the mission 46 00:02:27,025 --> 00:02:30,804 is to discover why these sex differences occur 47 00:02:30,804 --> 00:02:32,906 and to use that knowledge 48 00:02:32,906 --> 00:02:35,865 to improve the health of women. 49 00:02:35,865 --> 00:02:39,998 Today, we know that every cell has a sex. 50 00:02:39,998 --> 00:02:43,951 Now, that's a term coined by the Institute of Medicine. 51 00:02:43,951 --> 00:02:47,712 And what it means is that men and women are different 52 00:02:47,712 --> 00:02:52,154 down to the cellular and molecular levels. 53 00:02:52,154 --> 00:02:57,094 It means that we're different across all of our organs. 54 00:02:57,094 --> 00:03:02,589 From our brains to our hearts, our lungs, our joints. 55 00:03:02,589 --> 00:03:06,473 Now, it was only 20 years ago 56 00:03:06,473 --> 00:03:10,300 that we hardly had any data on women's health 57 00:03:10,300 --> 00:03:13,183 beyond our reproductive functions. 58 00:03:13,183 --> 00:03:16,084 But then in 1993, 59 00:03:16,084 --> 00:03:20,696 the NIH Revitalization Act was signed into law. 60 00:03:20,696 --> 00:03:23,299 And what this law did was it mandated 61 00:03:23,299 --> 00:03:27,772 that women and minorities be included in clinical trials 62 00:03:27,772 --> 00:03:31,823 that were funded by the National Institutes of Health. 63 00:03:31,823 --> 00:03:34,750 And in many ways, the law has worked. 64 00:03:34,750 --> 00:03:38,420 Women are now routinely included in clinical studies, 65 00:03:38,420 --> 00:03:40,671 and we've learned that there are major differences 66 00:03:40,671 --> 00:03:42,764 in the ways that women and men 67 00:03:42,764 --> 00:03:45,300 experience disease. 68 00:03:45,300 --> 00:03:47,465 But remarkably, 69 00:03:47,465 --> 00:03:52,559 what we have learned about these differences is often overlooked. 70 00:03:52,559 --> 00:03:56,384 So, we have to ask ourselves the question: 71 00:03:56,384 --> 00:04:00,897 Why leave women's health to chance? 72 00:04:00,897 --> 00:04:03,540 And we're leaving it to chance in two ways. 73 00:04:03,540 --> 00:04:07,237 The first is that there is so much more to learn 74 00:04:07,237 --> 00:04:09,646 and we're not making the investment 75 00:04:09,646 --> 00:04:13,814 in fully understanding the extent of these sex differences. 76 00:04:13,814 --> 00:04:18,536 And the second is that we aren't taking what we have learned, 77 00:04:18,536 --> 00:04:22,240 and routinely applying it in clinical care. 78 00:04:22,240 --> 00:04:26,229 We are just not doing enough. 79 00:04:26,229 --> 00:04:28,488 So, I'm going to share with you three examples 80 00:04:28,488 --> 00:04:32,194 of where sex differences have impacted the health of women, 81 00:04:32,194 --> 00:04:34,864 and where we need to do more. 82 00:04:34,864 --> 00:04:36,876 Let's start with heart disease. 83 00:04:36,876 --> 00:04:42,078 It's the number one killer of women in the United States today. 84 00:04:42,078 --> 00:04:44,974 This is the face of heart disease. 85 00:04:44,974 --> 00:04:47,255 Linda is a middle-aged woman, 86 00:04:47,255 --> 00:04:49,840 who had a stent placed in one of the arteries 87 00:04:49,840 --> 00:04:51,940 going to her heart. 88 00:04:51,940 --> 00:04:55,191 When she had recurring symptoms she went back to her doctor. 89 00:04:55,191 --> 00:04:57,829 Her doctor did the gold standard test: 90 00:04:57,829 --> 00:05:00,116 a cardiac catheterization. 91 00:05:00,116 --> 00:05:02,533 It showed no blockages. 92 00:05:02,533 --> 00:05:04,605 Linda's symptoms continued. 93 00:05:04,605 --> 00:05:07,078 She had to stop working. 94 00:05:07,078 --> 00:05:09,830 And that's when she found us. 95 00:05:09,830 --> 00:05:13,978 When Linda came to us, we did another cardiac catheterization 96 00:05:13,978 --> 00:05:17,395 and this time, we found clues. 97 00:05:17,395 --> 00:05:19,869 But we needed another test 98 00:05:19,869 --> 00:05:22,463 to make the diagnosis. 99 00:05:22,463 --> 00:05:27,043 So we did a test called an intracoronary ultrasound, 100 00:05:27,043 --> 00:05:29,348 where you use soundwaves to look at the artery 101 00:05:29,348 --> 00:05:32,079 from the inside out. 102 00:05:32,079 --> 00:05:34,017 And what we found 103 00:05:34,017 --> 00:05:36,468 was that Linda's disease didn't look like 104 00:05:36,468 --> 00:05:39,538 the typical male disease. 105 00:05:39,538 --> 00:05:42,734 The typical male disease looks like this. 106 00:05:42,734 --> 00:05:46,046 There's a discrete blockage or stenosis. 107 00:05:46,046 --> 00:05:50,347 Linda's disease, like the disease of so many women, 108 00:05:50,347 --> 00:05:52,189 looks like this. 109 00:05:52,189 --> 00:05:55,726 The plaque is laid down more evenly, more diffusely 110 00:05:55,726 --> 00:05:59,698 along the artery, and it's harder to see. 111 00:05:59,698 --> 00:06:03,104 So for Linda, and for so many women, 112 00:06:03,104 --> 00:06:06,796 the gold standard test wasn't gold. 113 00:06:06,796 --> 00:06:09,783 Now, Linda received the right treatment. 114 00:06:09,783 --> 00:06:11,961 She went back to her life and, fortunately, today 115 00:06:11,961 --> 00:06:13,713 she is doing well. 116 00:06:13,713 --> 00:06:15,319 But Linda was lucky. 117 00:06:15,319 --> 00:06:17,945 She found us, we found her disease. 118 00:06:17,945 --> 00:06:20,856 But for too many women, that's not the case. 119 00:06:20,856 --> 00:06:23,171 We have the tools. 120 00:06:23,171 --> 00:06:26,852 We have the technology to make the diagnosis. 121 00:06:26,852 --> 00:06:30,249 But it's all too often that these sex diffferences 122 00:06:30,249 --> 00:06:32,553 are overlooked. 123 00:06:32,553 --> 00:06:34,726 So what about treatment? 124 00:06:34,726 --> 00:06:37,436 A landmark study that was published two years ago 125 00:06:37,436 --> 00:06:39,858 asked the very important question: 126 00:06:39,858 --> 00:06:44,913 What are the most effective treatments for heart disease in women? 127 00:06:44,913 --> 00:06:48,572 The authors looked at papers written over a 10-year period, 128 00:06:48,572 --> 00:06:51,050 and hundreds had to be thrown out. 129 00:06:51,050 --> 00:06:55,823 And what they found out was that of those that were tossed out, 130 00:06:55,823 --> 00:06:59,477 65 percent were excluded 131 00:06:59,477 --> 00:07:03,841 because even though women were included in the studies, 132 00:07:03,841 --> 00:07:10,197 the analysis didn't differentiate between women and men. 133 00:07:10,197 --> 00:07:13,418 What a lost opportunity. 134 00:07:13,418 --> 00:07:15,449 The money had been spent 135 00:07:15,449 --> 00:07:17,596 and we didn't learn how women fared. 136 00:07:17,596 --> 00:07:20,397 And these studies could not contribute one iota 137 00:07:20,397 --> 00:07:22,814 to the very, very important question, 138 00:07:22,814 --> 00:07:25,205 what are the most effective treatments 139 00:07:25,205 --> 00:07:28,100 for heart disease in women? 140 00:07:28,100 --> 00:07:33,592 I want to introduce you to Hortense, my godmother, 141 00:07:33,592 --> 00:07:37,035 Hung Wei, a relative of a colleague, 142 00:07:37,035 --> 00:07:39,226 and somebody you may recognize -- 143 00:07:39,226 --> 00:07:42,873 Dana, Christopher Reeve's wife. 144 00:07:42,873 --> 00:07:47,233 All three women have something very important in common. 145 00:07:47,233 --> 00:07:50,808 All three were diagnosed with lung cancer, 146 00:07:50,808 --> 00:07:53,737 the number one cancer killer of women 147 00:07:53,737 --> 00:07:56,438 in the United States today. 148 00:07:56,438 --> 00:08:00,314 All three were nonsmokers. 149 00:08:00,314 --> 00:08:05,480 Sadly, Dana and Hung Wei died of their disease. 150 00:08:05,480 --> 00:08:11,620 Today, what we know is that women who are nonsmokers are three times more likely 151 00:08:11,620 --> 00:08:14,357 to be diagnosed with lung cancer than are men 152 00:08:14,357 --> 00:08:16,343 who are nonsmokers. 153 00:08:16,343 --> 00:08:20,033 Now interestingly, when women are diagnosed with lung cancer, 154 00:08:20,033 --> 00:08:23,212 their survival tends to be better than that of men. 155 00:08:23,212 --> 00:08:25,087 Now, here are some clues. 156 00:08:25,087 --> 00:08:27,486 Our investigators have found that there are 157 00:08:27,486 --> 00:08:32,158 certain genes in the lung tumor cells of both women and men. 158 00:08:32,158 --> 00:08:34,350 And these genes are activated 159 00:08:34,350 --> 00:08:36,440 mainly by estrogen. 160 00:08:36,440 --> 00:08:39,258 And when these genes are over-expressed, 161 00:08:39,258 --> 00:08:41,989 it's associated with improved survival 162 00:08:41,989 --> 00:08:44,656 only in young women. 163 00:08:44,656 --> 00:08:46,330 Now this is a very early finding 164 00:08:46,330 --> 00:08:49,973 and we don't yet know whether it has relevance 165 00:08:49,973 --> 00:08:52,414 to clinical care. 166 00:08:52,414 --> 00:08:56,273 But it's findings like this that may provide hope 167 00:08:56,273 --> 00:08:59,040 and may provide an opportunity to save lives 168 00:08:59,040 --> 00:09:01,587 of both women and men. 169 00:09:01,587 --> 00:09:02,968 Now, let me share with you an example 170 00:09:02,968 --> 00:09:06,946 of when we do consider sex differences, it can drive the science. 171 00:09:06,946 --> 00:09:09,274 Several years ago a new lung cancer drug 172 00:09:09,274 --> 00:09:10,920 was being evaluated, 173 00:09:10,920 --> 00:09:15,292 and when the authors looked at whose tumors shrank, 174 00:09:15,292 --> 00:09:18,725 they found that 82 percent were women. 175 00:09:18,725 --> 00:09:21,688 This led them to ask the question: Well, why? 176 00:09:21,688 --> 00:09:23,243 And what they found 177 00:09:23,243 --> 00:09:26,723 was that the genetic mutations that the drug targeted 178 00:09:26,723 --> 00:09:29,450 were far more common in women. 179 00:09:29,450 --> 00:09:31,149 And what this has led to 180 00:09:31,149 --> 00:09:33,331 is a more personalized approach 181 00:09:33,331 --> 00:09:37,485 to the treatment of lung cancer that also includes sex. 182 00:09:37,485 --> 00:09:39,580 This is what we can accomplish 183 00:09:39,580 --> 00:09:43,406 when we don't leave women's health to chance. 184 00:09:43,406 --> 00:09:46,614 We know that when you invest in research, 185 00:09:46,614 --> 00:09:48,140 you get results. 186 00:09:48,140 --> 00:09:52,648 Take a look at the death rate from breast cancer over time. 187 00:09:52,648 --> 00:09:54,573 And now take a look at the death rates 188 00:09:54,573 --> 00:09:57,905 from lung cancer in women over time. 189 00:09:57,905 --> 00:10:01,515 Now let's look at the dollars invested in breast cancer -- 190 00:10:01,515 --> 00:10:04,484 these are the dollars invested per death -- 191 00:10:04,484 --> 00:10:08,712 and the dollars invested in lung cancer. 192 00:10:08,712 --> 00:10:13,633 Now, it's clear that our investment in breast cancer 193 00:10:13,633 --> 00:10:15,422 has produced results. 194 00:10:15,422 --> 00:10:17,755 They may not be fast enough, 195 00:10:17,755 --> 00:10:19,981 but it has produced results. 196 00:10:19,981 --> 00:10:21,785 We can do the same 197 00:10:21,785 --> 00:10:26,776 for lung cancer and for every other disease. 198 00:10:26,776 --> 00:10:30,102 So let's go back to depression. 199 00:10:30,102 --> 00:10:32,412 Depression is the number one cause 200 00:10:32,412 --> 00:10:37,004 of disability in women in the world today. 201 00:10:37,004 --> 00:10:39,139 Our investigators have found 202 00:10:39,139 --> 00:10:40,955 that there are differences in the brains 203 00:10:40,955 --> 00:10:42,432 of women and men 204 00:10:42,432 --> 00:10:45,551 in the areas that are connected with mood. 205 00:10:45,551 --> 00:10:47,624 And when you put men and women 206 00:10:47,624 --> 00:10:49,499 in a functional MRI scanner -- 207 00:10:49,499 --> 00:10:54,046 that's the kind of scanner that shows how the brain is functioning when it's activated -- 208 00:10:54,046 --> 00:10:58,089 so you put them in the scanner and you expose them to stress. 209 00:10:58,089 --> 00:11:01,976 You can actually see the difference. 210 00:11:01,976 --> 00:11:04,505 And it's findings like this 211 00:11:04,505 --> 00:11:07,486 that we believe hold some of the clues 212 00:11:07,486 --> 00:11:11,239 for why we see these very significant sex differences 213 00:11:11,239 --> 00:11:13,493 in depression. 214 00:11:13,493 --> 00:11:15,051 But even though we know 215 00:11:15,051 --> 00:11:17,918 that these differences occur, 216 00:11:17,918 --> 00:11:20,571 66 percent 217 00:11:20,571 --> 00:11:24,567 of the brain research that begins in animals 218 00:11:24,567 --> 00:11:26,893 is done in either male animals 219 00:11:26,893 --> 00:11:31,240 or animals in whom the sex is not identified. 220 00:11:31,240 --> 00:11:34,859 So, I think we have to ask again the question: 221 00:11:34,859 --> 00:11:39,661 Why leave women's health to chance? 222 00:11:39,661 --> 00:11:42,489 And this is a question that haunts those of us 223 00:11:42,489 --> 00:11:44,300 in science and medicine 224 00:11:44,300 --> 00:11:50,659 who believe that we are on the verge of being able to dramatically improve 225 00:11:50,659 --> 00:11:52,335 the health of women. 226 00:11:52,335 --> 00:11:54,821 We know that every cell has a sex. 227 00:11:54,821 --> 00:11:57,763 We know that these differences are often overlooked. 228 00:11:57,763 --> 00:12:02,051 And therefore we know that women are not getting the full benefit 229 00:12:02,051 --> 00:12:05,950 of modern science and medicine today. 230 00:12:05,950 --> 00:12:07,601 We have the tools 231 00:12:07,601 --> 00:12:11,483 but we lack the collective will and momentum. 232 00:12:11,483 --> 00:12:14,227 Women's health is an equal rights issue 233 00:12:14,227 --> 00:12:17,781 as important as equal pay. 234 00:12:17,781 --> 00:12:19,965 And it's an issue of the quality 235 00:12:19,965 --> 00:12:23,120 and the integrity of science and medicine. 236 00:12:23,120 --> 00:12:30,583 (Applause) 237 00:12:30,583 --> 00:12:35,128 So imagine the momentum we could achieve 238 00:12:35,128 --> 00:12:37,239 in advancing the health of women 239 00:12:37,239 --> 00:12:40,098 if we considered whether these sex differences were present 240 00:12:40,098 --> 00:12:43,687 at the very beginning of designing research. 241 00:12:43,687 --> 00:12:47,566 Or if we analyzed our data by sex. 242 00:12:47,566 --> 00:12:49,514 So, people often ask me: 243 00:12:49,514 --> 00:12:51,285 What can I do? 244 00:12:51,285 --> 00:12:53,619 And here's what I suggest: 245 00:12:53,619 --> 00:12:57,792 First, I suggest that you think about women's health 246 00:12:57,792 --> 00:12:59,535 in the same way 247 00:12:59,535 --> 00:13:05,580 that you think and care about other causes that are important to you. 248 00:13:05,580 --> 00:13:08,762 And second, and equally as important, 249 00:13:08,762 --> 00:13:10,974 that as a woman, 250 00:13:10,974 --> 00:13:13,615 you have to ask your doctor 251 00:13:13,615 --> 00:13:18,027 and the doctors who are caring for those who you love: 252 00:13:18,027 --> 00:13:22,903 Is this disease or treatment different in women? 253 00:13:22,903 --> 00:13:26,491 Now, this is a profound question because the answer is likely yes, 254 00:13:26,491 --> 00:13:30,188 but your doctor may not know the answer, at least not yet. 255 00:13:30,188 --> 00:13:34,564 But if you ask the question, your doctor will very likely 256 00:13:34,564 --> 00:13:36,846 go looking for the answer. 257 00:13:36,846 --> 00:13:39,183 And this is so important, 258 00:13:39,183 --> 00:13:41,345 not only for ourselves, 259 00:13:41,345 --> 00:13:43,998 but for all of those whom we love. 260 00:13:43,998 --> 00:13:48,482 Whether it be a mother, a daughter, a sister, 261 00:13:48,482 --> 00:13:52,179 a friend or a grandmother. 262 00:13:52,179 --> 00:13:54,322 It was my grandmother's suffering 263 00:13:54,322 --> 00:13:56,255 that inspired my work 264 00:13:56,255 --> 00:13:59,240 to improve the health of women. 265 00:13:59,240 --> 00:14:01,590 That's her legacy. 266 00:14:01,590 --> 00:14:06,259 Our legacy can be to improve the health of women 267 00:14:06,259 --> 00:14:08,398 for this generation 268 00:14:08,398 --> 00:14:11,420 and for generations to come. 269 00:14:11,420 --> 00:14:13,569 Thank you. 270 00:14:13,569 --> 00:14:16,851 (Applause)