WEBVTT 00:00:00.706 --> 00:00:03.417 Some of my most wonderful memories of childhood 00:00:03.417 --> 00:00:06.968 are of spending time with my grandmother, Mamar, 00:00:06.968 --> 00:00:10.768 in our four-family home in Brooklyn, New York. 00:00:10.768 --> 00:00:13.843 Her apartment was an oasis. 00:00:13.843 --> 00:00:15.888 It was a place where I could sneak a cup of coffee, 00:00:15.888 --> 00:00:19.769 which was really warm milk with just a touch of caffeine. 00:00:19.769 --> 00:00:22.303 She loved life. 00:00:22.303 --> 00:00:24.798 And although she worked in a factory, 00:00:24.798 --> 00:00:27.970 she saved her pennies and she traveled to Europe. 00:00:27.970 --> 00:00:31.299 And I remember poring over those pictures with her 00:00:31.299 --> 00:00:35.411 and then dancing with her to her favorite music. NOTE Paragraph 00:00:35.411 --> 00:00:40.033 And then, when I was eight and she was 60, 00:00:40.033 --> 00:00:42.086 something changed. 00:00:42.086 --> 00:00:44.440 She no longer worked or traveled. 00:00:44.440 --> 00:00:46.131 She no longer danced. 00:00:46.131 --> 00:00:48.884 There were no more coffee times. 00:00:48.884 --> 00:00:51.068 My mother missed work and took her to doctors 00:00:51.068 --> 00:00:53.454 who couldn't make a diagnosis. 00:00:53.454 --> 00:00:58.791 And my father, who worked at night, would spend every afternoon with her, 00:00:58.791 --> 00:01:01.876 just to make sure she ate. NOTE Paragraph 00:01:01.876 --> 00:01:06.576 Her care became all-consuming for our family. 00:01:06.576 --> 00:01:08.223 And by the time a diagnosis was made, 00:01:08.223 --> 00:01:10.928 she was in a deep spiral. NOTE Paragraph 00:01:10.928 --> 00:01:14.489 Now many of you will recognize her symptoms. 00:01:14.489 --> 00:01:17.152 My grandmother had depression. 00:01:17.152 --> 00:01:20.472 A deep, life-altering depression, 00:01:20.472 --> 00:01:23.562 from which she never recovered. 00:01:23.562 --> 00:01:27.253 And back then, so little was known about depression. NOTE Paragraph 00:01:27.253 --> 00:01:30.658 But even today, 50 years later, 00:01:30.658 --> 00:01:33.932 there's still so much more to learn. 00:01:33.932 --> 00:01:38.840 Today, we know that women are 70 percent more likely 00:01:38.840 --> 00:01:41.831 to experience depression over their lifetimes 00:01:41.831 --> 00:01:44.112 compared with men. 00:01:44.112 --> 00:01:46.952 And even with this high prevalence, 00:01:46.952 --> 00:01:53.239 women are misdiagnosed between 30 and 50 percent of the time. NOTE Paragraph 00:01:53.239 --> 00:01:56.172 Now we know that women are more likely 00:01:56.172 --> 00:02:01.560 to experience the symptoms of fatigue, sleep disturbance, 00:02:01.560 --> 00:02:04.105 pain and anxiety compared with men. 00:02:04.105 --> 00:02:06.804 And these symptoms are often overlooked 00:02:06.804 --> 00:02:09.767 as symptoms of depression. NOTE Paragraph 00:02:09.767 --> 00:02:13.550 And it isn't only depression in which these sex differences occur, 00:02:13.550 --> 00:02:18.069 but they occur across so many diseases. NOTE Paragraph 00:02:18.069 --> 00:02:20.064 So it's my grandmother's struggles 00:02:20.064 --> 00:02:23.797 that have really led me on a lifelong quest. 00:02:23.797 --> 00:02:27.025 And today, I lead a center in which the mission 00:02:27.025 --> 00:02:30.804 is to discover why these sex differences occur 00:02:30.804 --> 00:02:32.906 and to use that knowledge 00:02:32.906 --> 00:02:35.865 to improve the health of women. NOTE Paragraph 00:02:35.865 --> 00:02:39.998 Today, we know that every cell has a sex. 00:02:39.998 --> 00:02:43.951 Now, that's a term coined by the Institute of Medicine. 00:02:43.951 --> 00:02:47.712 And what it means is that men and women are different 00:02:47.712 --> 00:02:52.154 down to the cellular and molecular levels. 00:02:52.154 --> 00:02:57.094 It means that we're different across all of our organs. 00:02:57.094 --> 00:03:02.589 From our brains to our hearts, our lungs, our joints. NOTE Paragraph 00:03:02.589 --> 00:03:06.473 Now, it was only 20 years ago 00:03:06.473 --> 00:03:10.300 that we hardly had any data on women's health 00:03:10.300 --> 00:03:13.183 beyond our reproductive functions. 00:03:13.183 --> 00:03:16.084 But then in 1993, 00:03:16.084 --> 00:03:20.696 the NIH Revitalization Act was signed into law. 00:03:20.696 --> 00:03:23.299 And what this law did was it mandated 00:03:23.299 --> 00:03:27.772 that women and minorities be included in clinical trials 00:03:27.772 --> 00:03:31.823 that were funded by the National Institutes of Health. 00:03:31.823 --> 00:03:34.750 And in many ways, the law has worked. 00:03:34.750 --> 00:03:38.420 Women are now routinely included in clinical studies, 00:03:38.420 --> 00:03:40.671 and we've learned that there are major differences 00:03:40.671 --> 00:03:42.764 in the ways that women and men 00:03:42.764 --> 00:03:45.300 experience disease. 00:03:45.300 --> 00:03:47.465 But remarkably, 00:03:47.465 --> 00:03:52.559 what we have learned about these differences is often overlooked. NOTE Paragraph 00:03:52.559 --> 00:03:56.384 So, we have to ask ourselves the question: 00:03:56.384 --> 00:04:00.897 Why leave women's health to chance? 00:04:00.897 --> 00:04:03.540 And we're leaving it to chance in two ways. 00:04:03.540 --> 00:04:07.237 The first is that there is so much more to learn 00:04:07.237 --> 00:04:09.646 and we're not making the investment 00:04:09.646 --> 00:04:13.814 in fully understanding the extent of these sex differences. 00:04:13.814 --> 00:04:18.536 And the second is that we aren't taking what we have learned, 00:04:18.536 --> 00:04:22.240 and routinely applying it in clinical care. 00:04:22.240 --> 00:04:26.229 We are just not doing enough. NOTE Paragraph 00:04:26.229 --> 00:04:28.488 So, I'm going to share with you three examples 00:04:28.488 --> 00:04:32.194 of where sex differences have impacted the health of women, 00:04:32.194 --> 00:04:34.864 and where we need to do more. NOTE Paragraph 00:04:34.864 --> 00:04:36.876 Let's start with heart disease. 00:04:36.876 --> 00:04:42.078 It's the number one killer of women in the United States today. 00:04:42.078 --> 00:04:44.974 This is the face of heart disease. 00:04:44.974 --> 00:04:47.255 Linda is a middle-aged woman, 00:04:47.255 --> 00:04:49.840 who had a stent placed in one of the arteries 00:04:49.840 --> 00:04:51.940 going to her heart. 00:04:51.940 --> 00:04:55.191 When she had recurring symptoms she went back to her doctor. 00:04:55.191 --> 00:04:57.829 Her doctor did the gold standard test: 00:04:57.829 --> 00:05:00.116 a cardiac catheterization. 00:05:00.116 --> 00:05:02.533 It showed no blockages. 00:05:02.533 --> 00:05:04.605 Linda's symptoms continued. 00:05:04.605 --> 00:05:07.078 She had to stop working. 00:05:07.078 --> 00:05:09.830 And that's when she found us. 00:05:09.830 --> 00:05:13.978 When Linda came to us, we did another cardiac catheterization 00:05:13.978 --> 00:05:17.395 and this time, we found clues. 00:05:17.395 --> 00:05:19.869 But we needed another test 00:05:19.869 --> 00:05:22.463 to make the diagnosis. 00:05:22.463 --> 00:05:27.043 So we did a test called an intracoronary ultrasound, 00:05:27.043 --> 00:05:29.348 where you use soundwaves to look at the artery 00:05:29.348 --> 00:05:32.079 from the inside out. NOTE Paragraph 00:05:32.079 --> 00:05:34.017 And what we found 00:05:34.017 --> 00:05:36.468 was that Linda's disease didn't look like 00:05:36.468 --> 00:05:39.538 the typical male disease. 00:05:39.538 --> 00:05:42.734 The typical male disease looks like this. 00:05:42.734 --> 00:05:46.046 There's a discrete blockage or stenosis. 00:05:46.046 --> 00:05:50.347 Linda's disease, like the disease of so many women, 00:05:50.347 --> 00:05:52.189 looks like this. 00:05:52.189 --> 00:05:55.726 The plaque is laid down more evenly, more diffusely 00:05:55.726 --> 00:05:59.698 along the artery, and it's harder to see. 00:05:59.698 --> 00:06:03.104 So for Linda, and for so many women, 00:06:03.104 --> 00:06:06.796 the gold standard test wasn't gold. NOTE Paragraph 00:06:06.796 --> 00:06:09.783 Now, Linda received the right treatment. 00:06:09.783 --> 00:06:11.961 She went back to her life and, fortunately, today 00:06:11.961 --> 00:06:13.713 she is doing well. 00:06:13.713 --> 00:06:15.319 But Linda was lucky. 00:06:15.319 --> 00:06:17.945 She found us, we found her disease. NOTE Paragraph 00:06:17.945 --> 00:06:20.856 But for too many women, that's not the case. 00:06:20.856 --> 00:06:23.171 We have the tools. 00:06:23.171 --> 00:06:26.852 We have the technology to make the diagnosis. 00:06:26.852 --> 00:06:30.249 But it's all too often that these sex diffferences 00:06:30.249 --> 00:06:32.553 are overlooked. NOTE Paragraph 00:06:32.553 --> 00:06:34.726 So what about treatment? 00:06:34.726 --> 00:06:37.436 A landmark study that was published two years ago 00:06:37.436 --> 00:06:39.858 asked the very important question: 00:06:39.858 --> 00:06:44.913 What are the most effective treatments for heart disease in women? 00:06:44.913 --> 00:06:48.572 The authors looked at papers written over a 10-year period, 00:06:48.572 --> 00:06:51.050 and hundreds had to be thrown out. 00:06:51.050 --> 00:06:55.823 And what they found out was that of those that were tossed out, 00:06:55.823 --> 00:06:59.477 65 percent were excluded 00:06:59.477 --> 00:07:03.841 because even though women were included in the studies, 00:07:03.841 --> 00:07:10.197 the analysis didn't differentiate between women and men. 00:07:10.197 --> 00:07:13.418 What a lost opportunity. 00:07:13.418 --> 00:07:15.449 The money had been spent 00:07:15.449 --> 00:07:17.596 and we didn't learn how women fared. 00:07:17.596 --> 00:07:20.397 And these studies could not contribute one iota 00:07:20.397 --> 00:07:22.814 to the very, very important question, 00:07:22.814 --> 00:07:25.205 what are the most effective treatments 00:07:25.205 --> 00:07:28.100 for heart disease in women? NOTE Paragraph 00:07:28.100 --> 00:07:33.592 I want to introduce you to Hortense, my godmother, 00:07:33.592 --> 00:07:37.035 Hung Wei, a relative of a colleague, 00:07:37.035 --> 00:07:39.226 and somebody you may recognize -- 00:07:39.226 --> 00:07:42.873 Dana, Christopher Reeve's wife. 00:07:42.873 --> 00:07:47.233 All three women have something very important in common. 00:07:47.233 --> 00:07:50.808 All three were diagnosed with lung cancer, 00:07:50.808 --> 00:07:53.737 the number one cancer killer of women 00:07:53.737 --> 00:07:56.438 in the United States today. 00:07:56.438 --> 00:08:00.314 All three were nonsmokers. 00:08:00.314 --> 00:08:05.480 Sadly, Dana and Hung Wei died of their disease. 00:08:05.480 --> 00:08:11.620 Today, what we know is that women who are nonsmokers are three times more likely 00:08:11.620 --> 00:08:14.357 to be diagnosed with lung cancer than are men 00:08:14.357 --> 00:08:16.343 who are nonsmokers. 00:08:16.343 --> 00:08:20.033 Now interestingly, when women are diagnosed with lung cancer, 00:08:20.033 --> 00:08:23.212 their survival tends to be better than that of men. 00:08:23.212 --> 00:08:25.087 Now, here are some clues. 00:08:25.087 --> 00:08:27.486 Our investigators have found that there are 00:08:27.486 --> 00:08:32.158 certain genes in the lung tumor cells of both women and men. 00:08:32.158 --> 00:08:34.350 And these genes are activated 00:08:34.350 --> 00:08:36.440 mainly by estrogen. 00:08:36.440 --> 00:08:39.258 And when these genes are over-expressed, 00:08:39.258 --> 00:08:41.989 it's associated with improved survival 00:08:41.989 --> 00:08:44.656 only in young women. 00:08:44.656 --> 00:08:46.330 Now this is a very early finding 00:08:46.330 --> 00:08:49.973 and we don't yet know whether it has relevance 00:08:49.973 --> 00:08:52.414 to clinical care. 00:08:52.414 --> 00:08:56.273 But it's findings like this that may provide hope 00:08:56.273 --> 00:08:59.040 and may provide an opportunity to save lives 00:08:59.040 --> 00:09:01.587 of both women and men. NOTE Paragraph 00:09:01.587 --> 00:09:02.968 Now, let me share with you an example 00:09:02.968 --> 00:09:06.946 of when we do consider sex differences, it can drive the science. 00:09:06.946 --> 00:09:09.274 Several years ago a new lung cancer drug 00:09:09.274 --> 00:09:10.920 was being evaluated, 00:09:10.920 --> 00:09:15.292 and when the authors looked at whose tumors shrank, 00:09:15.292 --> 00:09:18.725 they found that 82 percent were women. 00:09:18.725 --> 00:09:21.688 This led them to ask the question: Well, why? 00:09:21.688 --> 00:09:23.243 And what they found 00:09:23.243 --> 00:09:26.723 was that the genetic mutations that the drug targeted 00:09:26.723 --> 00:09:29.450 were far more common in women. 00:09:29.450 --> 00:09:31.149 And what this has led to 00:09:31.149 --> 00:09:33.331 is a more personalized approach 00:09:33.331 --> 00:09:37.485 to the treatment of lung cancer that also includes sex. NOTE Paragraph 00:09:37.485 --> 00:09:39.580 This is what we can accomplish 00:09:39.580 --> 00:09:43.406 when we don't leave women's health to chance. 00:09:43.406 --> 00:09:46.614 We know that when you invest in research, 00:09:46.614 --> 00:09:48.140 you get results. 00:09:48.140 --> 00:09:52.648 Take a look at the death rate from breast cancer over time. 00:09:52.648 --> 00:09:54.573 And now take a look at the death rates 00:09:54.573 --> 00:09:57.905 from lung cancer in women over time. 00:09:57.905 --> 00:10:01.515 Now let's look at the dollars invested in breast cancer -- 00:10:01.515 --> 00:10:04.484 these are the dollars invested per death -- 00:10:04.484 --> 00:10:08.712 and the dollars invested in lung cancer. 00:10:08.712 --> 00:10:13.633 Now, it's clear that our investment in breast cancer 00:10:13.633 --> 00:10:15.422 has produced results. 00:10:15.422 --> 00:10:17.755 They may not be fast enough, 00:10:17.755 --> 00:10:19.981 but it has produced results. 00:10:19.981 --> 00:10:21.785 We can do the same 00:10:21.785 --> 00:10:26.776 for lung cancer and for every other disease. NOTE Paragraph 00:10:26.776 --> 00:10:30.102 So let's go back to depression. 00:10:30.102 --> 00:10:32.412 Depression is the number one cause 00:10:32.412 --> 00:10:37.004 of disability in women in the world today. 00:10:37.004 --> 00:10:39.139 Our investigators have found 00:10:39.139 --> 00:10:40.955 that there are differences in the brains 00:10:40.955 --> 00:10:42.432 of women and men 00:10:42.432 --> 00:10:45.551 in the areas that are connected with mood. 00:10:45.551 --> 00:10:47.624 And when you put men and women 00:10:47.624 --> 00:10:49.499 in a functional MRI scanner -- 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 -- 00:10:54.046 --> 00:10:58.089 so you put them in the scanner and you expose them to stress. 00:10:58.089 --> 00:11:01.976 You can actually see the difference. 00:11:01.976 --> 00:11:04.505 And it's findings like this 00:11:04.505 --> 00:11:07.486 that we believe hold some of the clues 00:11:07.486 --> 00:11:11.239 for why we see these very significant sex differences 00:11:11.239 --> 00:11:13.493 in depression. NOTE Paragraph 00:11:13.493 --> 00:11:15.051 But even though we know 00:11:15.051 --> 00:11:17.918 that these differences occur, 00:11:17.918 --> 00:11:20.571 66 percent 00:11:20.571 --> 00:11:24.567 of the brain research that begins in animals 00:11:24.567 --> 00:11:26.893 is done in either male animals 00:11:26.893 --> 00:11:31.240 or animals in whom the sex is not identified. NOTE Paragraph 00:11:31.240 --> 00:11:34.859 So, I think we have to ask again the question: 00:11:34.859 --> 00:11:39.661 Why leave women's health to chance? 00:11:39.661 --> 00:11:42.489 And this is a question that haunts those of us 00:11:42.489 --> 00:11:44.300 in science and medicine 00:11:44.300 --> 00:11:50.659 who believe that we are on the verge of being able to dramatically improve 00:11:50.659 --> 00:11:52.335 the health of women. 00:11:52.335 --> 00:11:54.821 We know that every cell has a sex. 00:11:54.821 --> 00:11:57.763 We know that these differences are often overlooked. 00:11:57.763 --> 00:12:02.051 And therefore we know that women are not getting the full benefit 00:12:02.051 --> 00:12:05.950 of modern science and medicine today. 00:12:05.950 --> 00:12:07.601 We have the tools 00:12:07.601 --> 00:12:11.483 but we lack the collective will and momentum. NOTE Paragraph 00:12:11.483 --> 00:12:14.227 Women's health is an equal rights issue 00:12:14.227 --> 00:12:17.781 as important as equal pay. 00:12:17.781 --> 00:12:19.965 And it's an issue of the quality 00:12:19.965 --> 00:12:23.120 and the integrity of science and medicine. 00:12:23.120 --> 00:12:30.583 (Applause) 00:12:30.583 --> 00:12:35.128 So imagine the momentum we could achieve 00:12:35.128 --> 00:12:37.239 in advancing the health of women 00:12:37.239 --> 00:12:40.098 if we considered whether these sex differences were present 00:12:40.098 --> 00:12:43.687 at the very beginning of designing research. 00:12:43.687 --> 00:12:47.566 Or if we analyzed our data by sex. NOTE Paragraph 00:12:47.566 --> 00:12:49.514 So, people often ask me: 00:12:49.514 --> 00:12:51.285 What can I do? 00:12:51.285 --> 00:12:53.619 And here's what I suggest: 00:12:53.619 --> 00:12:57.792 First, I suggest that you think about women's health 00:12:57.792 --> 00:12:59.535 in the same way 00:12:59.535 --> 00:13:05.580 that you think and care about other causes that are important to you. 00:13:05.580 --> 00:13:08.762 And second, and equally as important, 00:13:08.762 --> 00:13:10.974 that as a woman, 00:13:10.974 --> 00:13:13.615 you have to ask your doctor 00:13:13.615 --> 00:13:18.027 and the doctors who are caring for those who you love: 00:13:18.027 --> 00:13:22.903 Is this disease or treatment different in women? 00:13:22.903 --> 00:13:26.491 Now, this is a profound question because the answer is likely yes, 00:13:26.491 --> 00:13:30.188 but your doctor may not know the answer, at least not yet. 00:13:30.188 --> 00:13:34.564 But if you ask the question, your doctor will very likely 00:13:34.564 --> 00:13:36.846 go looking for the answer. 00:13:36.846 --> 00:13:39.183 And this is so important, 00:13:39.183 --> 00:13:41.345 not only for ourselves, 00:13:41.345 --> 00:13:43.998 but for all of those whom we love. 00:13:43.998 --> 00:13:48.482 Whether it be a mother, a daughter, a sister, 00:13:48.482 --> 00:13:52.179 a friend or a grandmother. NOTE Paragraph 00:13:52.179 --> 00:13:54.322 It was my grandmother's suffering 00:13:54.322 --> 00:13:56.255 that inspired my work 00:13:56.255 --> 00:13:59.240 to improve the health of women. 00:13:59.240 --> 00:14:01.590 That's her legacy. 00:14:01.590 --> 00:14:06.259 Our legacy can be to improve the health of women 00:14:06.259 --> 00:14:08.398 for this generation 00:14:08.398 --> 00:14:11.420 and for generations to come. NOTE Paragraph 00:14:11.420 --> 00:14:13.569 Thank you. 00:14:13.569 --> 00:14:16.851 (Applause)