1 00:00:00,080 --> 00:00:03,160 Speaker 1: Guess what, Mango? What's that? Well, you remember a few 2 00:00:03,240 --> 00:00:05,960 Speaker 1: years ago when the social media sites and apps, and 3 00:00:06,040 --> 00:00:10,480 Speaker 1: I'm thinking Facebook especially, they started using this facial recognition 4 00:00:10,520 --> 00:00:13,360 Speaker 1: programming and that was used to recognize all of us 5 00:00:13,360 --> 00:00:16,160 Speaker 1: and these photos that we were posting. And I remember 6 00:00:16,200 --> 00:00:19,400 Speaker 1: when it first started happening. I think this is honestly 7 00:00:19,440 --> 00:00:21,720 Speaker 1: just a little bit creepy, Like, how did you know 8 00:00:21,880 --> 00:00:24,119 Speaker 1: that was my friend Steve? I mean, the guy was 9 00:00:24,160 --> 00:00:26,440 Speaker 1: wearing a hat, he had on glasses, then he had 10 00:00:26,520 --> 00:00:28,880 Speaker 1: longer hair than he might have weighed five or six 11 00:00:28,920 --> 00:00:31,600 Speaker 1: more pounds, Like, how did you know that was Steve? 12 00:00:31,800 --> 00:00:35,320 Speaker 1: I know, it was so impressive, but also so creepy. 13 00:00:35,640 --> 00:00:37,760 Speaker 1: I felt the same way. I kind of had mixed 14 00:00:37,760 --> 00:00:40,040 Speaker 1: emotions about it. But you know, as much as the 15 00:00:40,080 --> 00:00:43,680 Speaker 1: world of artificial intelligence can sometimes weird us out and 16 00:00:44,040 --> 00:00:46,720 Speaker 1: leave us feeling a little bit violated, even you know, 17 00:00:46,760 --> 00:00:49,400 Speaker 1: the more I've read about its potential applications in the 18 00:00:49,400 --> 00:00:52,879 Speaker 1: world of medicine, the more I'm actually fascinated, you know, 19 00:00:52,920 --> 00:00:55,480 Speaker 1: by what it might be capable of in the decades ahead. 20 00:00:56,120 --> 00:00:58,640 Speaker 1: So how do you mean exactly, Well, I've been reading 21 00:00:58,760 --> 00:01:02,480 Speaker 1: some about how so many conditions and genetic ones in particular, 22 00:01:02,960 --> 00:01:05,000 Speaker 1: have what some in the medical field would call a 23 00:01:05,120 --> 00:01:08,360 Speaker 1: certain face, and that is a set of similar features 24 00:01:08,400 --> 00:01:12,600 Speaker 1: that might indicate whether someone might have a certain genetic syndrome. Now, 25 00:01:12,640 --> 00:01:15,360 Speaker 1: the advantage of technology is obviously that these machines can 26 00:01:15,400 --> 00:01:18,839 Speaker 1: process so many millions of pieces of data at one time, 27 00:01:19,280 --> 00:01:22,360 Speaker 1: and they'd be looking for these common features, even very 28 00:01:22,440 --> 00:01:25,400 Speaker 1: subtle ones. So some of those same programmers who were 29 00:01:25,400 --> 00:01:28,319 Speaker 1: helping places like Facebook, they're getting involved to help with 30 00:01:28,360 --> 00:01:32,319 Speaker 1: the diagnosis of Alzheimer's, something that's often incredibly difficult to 31 00:01:32,319 --> 00:01:35,320 Speaker 1: diagnose before significant damage has been done in the brain, 32 00:01:35,920 --> 00:01:38,720 Speaker 1: and they're often looking for these subtle speech delays or 33 00:01:38,760 --> 00:01:41,720 Speaker 1: slight hiccups as you might describe them, that a doctor 34 00:01:41,840 --> 00:01:44,440 Speaker 1: might not be able to see it first. And it's 35 00:01:44,440 --> 00:01:47,400 Speaker 1: still very early, but of course we all hope this 36 00:01:47,520 --> 00:01:51,240 Speaker 1: leads to better diagnosis and better treatment, and the progress 37 00:01:51,280 --> 00:01:54,080 Speaker 1: to date does give us some real hope. I feel 38 00:01:54,120 --> 00:01:56,680 Speaker 1: like most of us have been affected by Alzheimer's in 39 00:01:56,720 --> 00:01:59,240 Speaker 1: some way, knowing a loved one who's suffered from it. 40 00:01:59,480 --> 00:02:01,960 Speaker 1: I know both of us have. So you know, today 41 00:02:02,000 --> 00:02:04,160 Speaker 1: we wanted to talk a little bit about that, Like 42 00:02:04,200 --> 00:02:07,160 Speaker 1: what we know about Alzheimer's, what kind of progress we're 43 00:02:07,200 --> 00:02:09,919 Speaker 1: making and fighting it, and who seems to be doing 44 00:02:09,960 --> 00:02:13,160 Speaker 1: the best job of caring for those with Alzheimer's. So 45 00:02:13,280 --> 00:02:36,919 Speaker 1: let's get started, right hey, their podcast listeners, Welcome to 46 00:02:36,960 --> 00:02:39,440 Speaker 1: Part Time Genius. I'm Will Pearson and as always I'm 47 00:02:39,480 --> 00:02:41,760 Speaker 1: joined by my good friend man guest shot Ticketer and 48 00:02:41,800 --> 00:02:43,799 Speaker 1: on the other side of the soundproof glass, hunched over 49 00:02:43,880 --> 00:02:46,880 Speaker 1: his brand new copy of Mandarin for Dummies. It's been 50 00:02:46,919 --> 00:02:49,880 Speaker 1: a while since I've seen a dummies book, but anyway, 51 00:02:49,880 --> 00:02:52,880 Speaker 1: that that's our friend and producer Tristan McNeil, and of 52 00:02:52,919 --> 00:02:55,640 Speaker 1: course he's on theme as usual. You know, I was 53 00:02:55,639 --> 00:02:57,840 Speaker 1: reading just this week about how learning a new language 54 00:02:57,840 --> 00:03:01,079 Speaker 1: can help stave off Alzheimer's in some cases. And this 55 00:03:01,120 --> 00:03:03,640 Speaker 1: was from a study out of York University in Toronto 56 00:03:03,960 --> 00:03:07,280 Speaker 1: where they actually examined over a hundred long time bilingual 57 00:03:07,320 --> 00:03:10,919 Speaker 1: Alzheimer's patients along with over a hundred monolingual patients, and 58 00:03:11,000 --> 00:03:13,960 Speaker 1: it turned out that on average, the bilingual patients have 59 00:03:14,000 --> 00:03:16,880 Speaker 1: been diagnosed with Alzheimer's about four years later than the 60 00:03:16,919 --> 00:03:19,919 Speaker 1: monolingual patients. So the idea is that because learning a 61 00:03:19,960 --> 00:03:22,919 Speaker 1: second language gives the brains such a strong workout, it 62 00:03:22,960 --> 00:03:25,320 Speaker 1: can actually help keep the full effects of the disease 63 00:03:25,400 --> 00:03:29,200 Speaker 1: at bay longer, which is really interesting. But you know, 64 00:03:29,400 --> 00:03:33,160 Speaker 1: it's honestly sometimes tough to say for certain, because they're 65 00:03:33,160 --> 00:03:36,440 Speaker 1: all kinds of genetic and environmental factors that can increase 66 00:03:36,520 --> 00:03:40,520 Speaker 1: or decrease a person's chances of contracting Alzheimer's, and we'll 67 00:03:40,560 --> 00:03:43,600 Speaker 1: be talking about a few of those later on. But ultimately, 68 00:03:43,680 --> 00:03:46,760 Speaker 1: no one is immune to the disease, and the greatest 69 00:03:46,840 --> 00:03:49,520 Speaker 1: risk factor associated with it, of course, is is just 70 00:03:49,720 --> 00:03:53,040 Speaker 1: age itself. In fact, studies have clearly shown that the 71 00:03:53,120 --> 00:03:57,040 Speaker 1: number of people with Alzheimer's disease increases with age, so 72 00:03:57,200 --> 00:03:59,920 Speaker 1: much so that roughly one in five people now suffer 73 00:04:00,040 --> 00:04:02,560 Speaker 1: from it by the age of eight five. And I 74 00:04:02,600 --> 00:04:04,760 Speaker 1: know all of this is upsetting to think about, and 75 00:04:05,120 --> 00:04:07,680 Speaker 1: people who have Alzheimer's disease and their family are already 76 00:04:07,680 --> 00:04:10,160 Speaker 1: dealing with it on a daily basis, and those of 77 00:04:10,200 --> 00:04:12,040 Speaker 1: us who have been lucky enough not to have to 78 00:04:12,080 --> 00:04:15,120 Speaker 1: face it in some big way would probably rather focus 79 00:04:15,120 --> 00:04:17,760 Speaker 1: on just about anything else. But the truth is this 80 00:04:17,839 --> 00:04:20,840 Speaker 1: is something everybody needs to stay informed about, particularly if 81 00:04:20,880 --> 00:04:23,920 Speaker 1: you plan to live past the age of sixty. Another 82 00:04:23,960 --> 00:04:26,400 Speaker 1: good news is that scientists are already hard at work 83 00:04:26,440 --> 00:04:29,920 Speaker 1: on new research and new treatments, and that's what we'll 84 00:04:29,960 --> 00:04:32,800 Speaker 1: be focused on today. You know, all those promising approaches 85 00:04:32,839 --> 00:04:36,520 Speaker 1: to Alzheimer's disease that could hopefully lead to earlier detection 86 00:04:36,680 --> 00:04:39,839 Speaker 1: and improve symptom management and hopefully one day of course 87 00:04:39,839 --> 00:04:42,560 Speaker 1: secure that's right, But before we get to the more 88 00:04:42,600 --> 00:04:44,680 Speaker 1: hopeful side of the topic, we should probably do a 89 00:04:44,720 --> 00:04:47,599 Speaker 1: quick crash course on what the disease is exactly. So 90 00:04:48,000 --> 00:04:52,600 Speaker 1: for starters, Alzheimer's disease is a chronic, progressive neurodegenerative disease, 91 00:04:52,960 --> 00:04:55,120 Speaker 1: which means it's a form of dementia that leads to 92 00:04:55,200 --> 00:04:58,800 Speaker 1: severe cognitive loss and eventual death. And while there are 93 00:04:58,880 --> 00:05:02,120 Speaker 1: several different kinds of dementia, Alzheimer's is the most common, 94 00:05:02,120 --> 00:05:06,440 Speaker 1: accounting for somewhere between sixty eight percent of all dementia cases. 95 00:05:06,800 --> 00:05:09,640 Speaker 1: So really, dementia is something of an epidemic at this point, 96 00:05:09,680 --> 00:05:11,359 Speaker 1: I would think, because you know, some of the stats 97 00:05:11,400 --> 00:05:15,800 Speaker 1: I found all researching, we're pretty jaw dropping. Apparently, Alzheimer's 98 00:05:15,880 --> 00:05:18,080 Speaker 1: is now the sixth leading cause of death in the 99 00:05:18,160 --> 00:05:21,680 Speaker 1: US and the seventh worldwide. And that really wasn't the 100 00:05:21,720 --> 00:05:26,280 Speaker 1: case even fifty years ago. Yeah, So Alzheimer's was formally 101 00:05:26,320 --> 00:05:29,240 Speaker 1: recognized as an epidemic back in the seventies, and sadly 102 00:05:29,320 --> 00:05:32,479 Speaker 1: that designation still stands today. In fact, that the world 103 00:05:32,560 --> 00:05:35,760 Speaker 1: has a larger aging population than ever before, which inevitably 104 00:05:35,800 --> 00:05:37,960 Speaker 1: means that the number of people who developed the disease 105 00:05:38,080 --> 00:05:41,360 Speaker 1: is only set to grow. For example, this organization called 106 00:05:41,400 --> 00:05:45,520 Speaker 1: Alzheimer's Disease International, they estimated that about five million people 107 00:05:45,520 --> 00:05:48,799 Speaker 1: in the US and nearly fifty million people worldwide currently 108 00:05:48,839 --> 00:05:52,160 Speaker 1: suffer from dimension one form or another, but by twenty 109 00:05:52,200 --> 00:05:55,279 Speaker 1: fifty the number is projected actually to reach one hundred 110 00:05:55,400 --> 00:05:59,400 Speaker 1: thirty one million. Wow. So it's obvious in light of 111 00:05:59,440 --> 00:06:01,359 Speaker 1: that why they such a big push to find a 112 00:06:01,400 --> 00:06:04,320 Speaker 1: cure as soon as possible. But you know, I'm curious 113 00:06:04,360 --> 00:06:06,920 Speaker 1: how long have we known about the problem, Like, is 114 00:06:06,960 --> 00:06:09,320 Speaker 1: Alzheimer's something we've been trying to get a grip on 115 00:06:09,360 --> 00:06:14,480 Speaker 1: for centuries or this a relatively new threat. Well, dementia 116 00:06:14,520 --> 00:06:16,840 Speaker 1: as a broad concept is popped up in medical text 117 00:06:16,920 --> 00:06:19,359 Speaker 1: going all the way back to ancient Greece, but the 118 00:06:19,520 --> 00:06:22,560 Speaker 1: first case study for Alzheimer's in particular was in nineteen 119 00:06:22,600 --> 00:06:26,640 Speaker 1: o one, and that's when this German neuropathologist named A. 120 00:06:26,760 --> 00:06:29,599 Speaker 1: Louis Alzheimer, you know who the disease is named after, 121 00:06:30,040 --> 00:06:33,240 Speaker 1: began treating a woman at an asylum in Frankfurt, and 122 00:06:33,360 --> 00:06:35,440 Speaker 1: the fifty one year old patients seemed to suffer from 123 00:06:35,520 --> 00:06:38,479 Speaker 1: some kind of psychosis. In addition to rapid memory loss, 124 00:06:38,520 --> 00:06:41,920 Speaker 1: she reported I guess uh, strange feelings of jealousy towards 125 00:06:41,960 --> 00:06:44,680 Speaker 1: her husband. She also had these odd behaviors, like she'd 126 00:06:44,720 --> 00:06:48,360 Speaker 1: dragged furniture to and fro She'd hied uh. Sometimes she'd 127 00:06:48,360 --> 00:06:50,520 Speaker 1: think people were out to kill her, so then she 128 00:06:50,560 --> 00:06:54,000 Speaker 1: would scream out loudly. It was pretty sad, and so 129 00:06:54,200 --> 00:06:57,320 Speaker 1: was Dr Alzheimer able to help her in anyway? No, 130 00:06:57,400 --> 00:06:59,960 Speaker 1: not really. But when the patient died five years later, 131 00:07:00,520 --> 00:07:03,919 Speaker 1: Alzheimer was able to dissect or brain, and his findings 132 00:07:03,920 --> 00:07:06,440 Speaker 1: formed the basis for the first formal description of pre 133 00:07:06,560 --> 00:07:11,600 Speaker 1: senile dementia. So what did he find when he looked? So? 134 00:07:11,760 --> 00:07:14,240 Speaker 1: Today we know that the main effects of Alzheimer's, like 135 00:07:14,360 --> 00:07:18,840 Speaker 1: memory laws, decreased thinking ability, Personality changes like these are 136 00:07:18,920 --> 00:07:21,680 Speaker 1: all the result of dyeing brain cells and the atrophy 137 00:07:21,680 --> 00:07:24,960 Speaker 1: of certain key regions of the brain. And the reason 138 00:07:25,080 --> 00:07:27,560 Speaker 1: these brain cells start to die off is because something 139 00:07:27,600 --> 00:07:30,360 Speaker 1: has disrupted the communication between the neurons and the brain. 140 00:07:30,960 --> 00:07:35,640 Speaker 1: So Alzheimer's biggest contribution was really identifying what that something was, 141 00:07:36,200 --> 00:07:39,480 Speaker 1: or at least most likely what it is, because when 142 00:07:39,480 --> 00:07:43,080 Speaker 1: he dissected that patient's brain and examined it under a microscope, 143 00:07:43,360 --> 00:07:47,120 Speaker 1: he identified these tiny tangles and plaques dotting the gray tissues. 144 00:07:48,200 --> 00:07:50,200 Speaker 1: And and these are the masses that that are some 145 00:07:50,280 --> 00:07:53,360 Speaker 1: kind of protein build up, right, yeah, there are. There 146 00:07:53,360 --> 00:07:55,240 Speaker 1: are these two types of proteins at work. It's the 147 00:07:55,280 --> 00:07:58,000 Speaker 1: amyloid and the tao. And while we don't know what 148 00:07:58,040 --> 00:08:01,520 Speaker 1: prompts the accumulation of these proteins are how exactly they interact, 149 00:08:01,760 --> 00:08:05,000 Speaker 1: their presence is usually a telltale sign of Alzheimer's. So 150 00:08:05,280 --> 00:08:08,920 Speaker 1: the amaloid is recognizable because it bunches together into sticky 151 00:08:08,960 --> 00:08:12,520 Speaker 1: clumps through these beta amaloid plaques. And the tow is 152 00:08:12,560 --> 00:08:15,000 Speaker 1: easy to spot because it accumulates as tangles of these 153 00:08:15,040 --> 00:08:18,480 Speaker 1: watered up protein strands. You know, even before we started 154 00:08:18,480 --> 00:08:21,440 Speaker 1: doing our research for today's episode, I'd always heard that 155 00:08:21,480 --> 00:08:24,840 Speaker 1: one of the biggest obstacles for treating Alzheimer's is that it's, 156 00:08:24,840 --> 00:08:28,280 Speaker 1: you know, notoriously difficult to diagnose. By the time the 157 00:08:28,360 --> 00:08:31,520 Speaker 1: symptoms manifest enough to be caught, much of that damage 158 00:08:31,560 --> 00:08:34,240 Speaker 1: has already been done. But why is that, like, if 159 00:08:34,280 --> 00:08:37,480 Speaker 1: these plaques and tangles are so noticeable. Yeah, it's a 160 00:08:37,480 --> 00:08:40,120 Speaker 1: good question, and the answer is that we actually haven't 161 00:08:40,160 --> 00:08:43,280 Speaker 1: found a reliable or accurate way to measure these protein 162 00:08:43,320 --> 00:08:46,120 Speaker 1: build ups, especially in living patients. So more than a 163 00:08:46,240 --> 00:08:49,120 Speaker 1: hundred years after that initial case in Germany, there's still 164 00:08:49,200 --> 00:08:52,880 Speaker 1: no definitive way to diagnose Alzheimer's without dissecting the brain 165 00:08:53,000 --> 00:08:56,000 Speaker 1: post mortem to look for these hallmark plaques and tangles. 166 00:08:56,040 --> 00:08:59,000 Speaker 1: And in recent years, some postmortems have given a reason 167 00:08:59,040 --> 00:09:01,200 Speaker 1: to question whether the presence of these build ups is 168 00:09:01,280 --> 00:09:04,679 Speaker 1: really telling the whole story anyway. Oh, so what do 169 00:09:04,760 --> 00:09:07,319 Speaker 1: you mean by that? Well, there have actually been some 170 00:09:07,360 --> 00:09:10,000 Speaker 1: cases where patients who were said to have Alzheimer's were 171 00:09:10,040 --> 00:09:13,040 Speaker 1: revealed to not have plaques and tangles in their brains. 172 00:09:13,080 --> 00:09:15,800 Speaker 1: And maybe even weirder, there are also cases where post 173 00:09:15,800 --> 00:09:18,160 Speaker 1: mortem exams show massive build ups in the brains of 174 00:09:18,200 --> 00:09:21,960 Speaker 1: people who had excellent memories. So while these plaques and 175 00:09:22,000 --> 00:09:24,240 Speaker 1: these tangles appear to have some part in the onset 176 00:09:24,240 --> 00:09:27,600 Speaker 1: of Alzheimer's, it isn't true in all cases, and as 177 00:09:27,600 --> 00:09:30,000 Speaker 1: a result, a growing number of researchers are starting to 178 00:09:30,040 --> 00:09:33,079 Speaker 1: think that chronic inflammation might actually play the larger role 179 00:09:33,120 --> 00:09:35,520 Speaker 1: in the disease than they initially thought. So here's how 180 00:09:35,559 --> 00:09:38,720 Speaker 1: Scientific American broke down the new hypothesis and article from 181 00:09:38,840 --> 00:09:43,719 Speaker 1: last November. In the brain tissue damaging long term inflammation 182 00:09:43,760 --> 00:09:45,880 Speaker 1: can also be caused by a build up of cells 183 00:09:45,920 --> 00:09:49,560 Speaker 1: known as microglia. In a healthy brain, these cells engulf 184 00:09:49,600 --> 00:09:52,959 Speaker 1: and destroy waste and toxins, but in Alzheimer's patients, the 185 00:09:53,000 --> 00:09:56,360 Speaker 1: microglia failed to clear away this debris, which can include 186 00:09:56,360 --> 00:10:00,480 Speaker 1: toxic tow tangles or amyloid plaques. The body then activates 187 00:10:00,520 --> 00:10:03,400 Speaker 1: more microglia to try to clear the waste, but this 188 00:10:03,480 --> 00:10:07,360 Speaker 1: in turn causes inflammation. Long term or chronic inflammation is 189 00:10:07,400 --> 00:10:10,680 Speaker 1: particularly damaging to brain cells and ultimately leads to brain 190 00:10:10,720 --> 00:10:15,559 Speaker 1: cell death. Okay, so in this case, the inflammation, which 191 00:10:15,600 --> 00:10:18,080 Speaker 1: could be worsened by the plaques and the tangles would 192 00:10:18,400 --> 00:10:21,680 Speaker 1: would actually be the real culprit behind the dying brain cell. 193 00:10:21,800 --> 00:10:24,800 Speaker 1: So how would just been thinking about this? How would 194 00:10:24,800 --> 00:10:27,560 Speaker 1: that impact the search for for treatments, though, I mean, 195 00:10:27,760 --> 00:10:30,120 Speaker 1: from everything I've read, it seems like most researchers have 196 00:10:30,160 --> 00:10:33,080 Speaker 1: been focusing on the other ways to prevent or destroy 197 00:10:33,200 --> 00:10:35,959 Speaker 1: these protein build ups. So I mean, I hate to 198 00:10:35,960 --> 00:10:37,600 Speaker 1: ask this, but have we been heading in the wrong 199 00:10:37,640 --> 00:10:41,200 Speaker 1: direction this whole time? Well it's too early to say 200 00:10:41,240 --> 00:10:43,680 Speaker 1: for sure, but I really hope not. It would be 201 00:10:43,720 --> 00:10:46,240 Speaker 1: devastating to see such a colossal amount of effort and 202 00:10:46,280 --> 00:10:48,880 Speaker 1: money lead to a dead end. I mean, we hear 203 00:10:48,920 --> 00:10:52,200 Speaker 1: about new promising treatments all the time, but all too 204 00:10:52,280 --> 00:10:54,520 Speaker 1: many of them disappear from headlines a few months later 205 00:10:54,600 --> 00:10:58,360 Speaker 1: because they were later proven ineffective during trials. And in fact, 206 00:10:58,679 --> 00:11:01,480 Speaker 1: at this point, none of the hot twenty three experimental 207 00:11:01,640 --> 00:11:06,200 Speaker 1: Alzheimer's drugs developed between two thousand and fourteen have made 208 00:11:06,280 --> 00:11:09,760 Speaker 1: it past the later stages of clinical testing. And that's 209 00:11:09,800 --> 00:11:12,600 Speaker 1: obviously a concern for all sorts of reasons, you know, 210 00:11:12,679 --> 00:11:14,600 Speaker 1: not the least of which is just how much it 211 00:11:14,679 --> 00:11:17,800 Speaker 1: costs to develop even one new form of treatment. For example, 212 00:11:17,840 --> 00:11:20,640 Speaker 1: that there was this two thousand sixteen study from the 213 00:11:20,760 --> 00:11:24,640 Speaker 1: Journal of Health Economics, and it found the average research 214 00:11:24,679 --> 00:11:27,040 Speaker 1: and development cost for a new medicine are about two 215 00:11:27,040 --> 00:11:31,080 Speaker 1: point six billion dollars. Good lord, it's hard to even 216 00:11:31,200 --> 00:11:33,679 Speaker 1: wrap your head around that kind of money for something 217 00:11:33,760 --> 00:11:35,840 Speaker 1: like that. But you know the truth is that, you know, 218 00:11:35,880 --> 00:11:38,520 Speaker 1: we as a country still aren't spending as much on 219 00:11:38,559 --> 00:11:42,520 Speaker 1: Alzheimer's researches we likely should be. I mean, stimulate spending 220 00:11:42,520 --> 00:11:45,120 Speaker 1: for Alzheimer's research was increased to around I think it 221 00:11:45,160 --> 00:11:47,719 Speaker 1: was around one point eight billion dollars this year, and 222 00:11:48,120 --> 00:11:50,680 Speaker 1: that does get it pretty close to that two billion 223 00:11:50,679 --> 00:11:54,200 Speaker 1: dollar mark that the Alzheimer's Association has has long been 224 00:11:54,240 --> 00:11:58,120 Speaker 1: saying that researchers needed in order to treat the disease effectively. 225 00:11:58,760 --> 00:12:01,360 Speaker 1: But this funding has to be every year, so there's 226 00:12:01,400 --> 00:12:04,520 Speaker 1: no guarantee that that upper trend will continue long term. 227 00:12:04,600 --> 00:12:07,520 Speaker 1: And not to mention that's just funding for new research, 228 00:12:07,559 --> 00:12:09,880 Speaker 1: I mean that the actual cost of health care for 229 00:12:09,920 --> 00:12:13,920 Speaker 1: dementia already exceeds two hundred billion dollars each year in 230 00:12:13,960 --> 00:12:18,640 Speaker 1: the US. Yeah. But despite the uncertainties that still surround Alzheimer's, 231 00:12:18,679 --> 00:12:21,960 Speaker 1: like all that plaques and tangles, research we've been spending 232 00:12:21,960 --> 00:12:24,120 Speaker 1: so much money on will still be super useful no 233 00:12:24,240 --> 00:12:27,640 Speaker 1: matter what. And that's partly because many researchers are now 234 00:12:27,679 --> 00:12:30,880 Speaker 1: pushing for a more personalized approach to Alzheimer's treatment. It's 235 00:12:30,920 --> 00:12:33,160 Speaker 1: similar to the way we're dealing with cancer, like there's 236 00:12:33,160 --> 00:12:36,200 Speaker 1: no one treatment that's effective for every kind of cancer, 237 00:12:36,480 --> 00:12:38,560 Speaker 1: or even for every case of the same kind, and 238 00:12:38,720 --> 00:12:41,160 Speaker 1: it's the same way with Alzheimer's. So the idea you 239 00:12:41,200 --> 00:12:43,280 Speaker 1: had the new approach is to use multiple drugs to 240 00:12:43,320 --> 00:12:45,480 Speaker 1: target the many different changes in the brain that can 241 00:12:45,480 --> 00:12:49,079 Speaker 1: occur with Alzheimer's. Okay, I see. So for instance, you 242 00:12:49,160 --> 00:12:52,120 Speaker 1: might start a patient off with one drug if you're 243 00:12:52,120 --> 00:12:55,040 Speaker 1: trying to reduce that build up of amyloid plaques or 244 00:12:55,080 --> 00:12:57,560 Speaker 1: something like that, and then you might switch to another 245 00:12:57,720 --> 00:13:01,000 Speaker 1: if inflammation is the bigger issue, or or change it 246 00:13:01,040 --> 00:13:04,599 Speaker 1: up if it's different for another patient exactly. So, Alzheimer's 247 00:13:04,600 --> 00:13:07,240 Speaker 1: is obviously this complex disease and it doesn't manifest the 248 00:13:07,280 --> 00:13:09,840 Speaker 1: same across the board, so it's helpful to have as 249 00:13:09,880 --> 00:13:12,360 Speaker 1: many tools at our disposal as possible to treat it, 250 00:13:12,480 --> 00:13:15,000 Speaker 1: and that way we can mix and match medications to 251 00:13:15,160 --> 00:13:18,920 Speaker 1: create treatment plants tailor made for each patient's pelecular profile. 252 00:13:20,320 --> 00:13:22,599 Speaker 1: Al Right, well, that does sound like a more promising 253 00:13:22,600 --> 00:13:24,840 Speaker 1: approaches as well. As a nice reminder that it's worth 254 00:13:24,880 --> 00:13:28,000 Speaker 1: tackling this problem from as many different angles as we can. 255 00:13:28,200 --> 00:13:30,280 Speaker 1: I mean, you never know which one might lead to 256 00:13:30,920 --> 00:13:34,959 Speaker 1: that game changing solution that we're always looking for. Absolutely, 257 00:13:35,000 --> 00:13:37,160 Speaker 1: And in the meantime, there's still some effective ways to 258 00:13:37,200 --> 00:13:40,160 Speaker 1: reduce your chances of contracting Alzheimer's, and even a few 259 00:13:40,200 --> 00:13:43,920 Speaker 1: new ideas for how to detect and diagnose Alzheimer's without 260 00:13:43,960 --> 00:13:46,640 Speaker 1: having to wait for a postmortem. Yeah, well, I definitely 261 00:13:46,640 --> 00:13:48,360 Speaker 1: want to talk about those, but before we do, let's 262 00:13:48,400 --> 00:14:09,600 Speaker 1: take a quick break. You're listening to Part Time Genius 263 00:14:09,600 --> 00:14:11,920 Speaker 1: and we're talking about the most promising efforts to treat 264 00:14:11,960 --> 00:14:14,920 Speaker 1: Alzheimer's disease. All right, mango, So we talked about how, 265 00:14:14,960 --> 00:14:18,400 Speaker 1: for decades now, the prevailing theory has been that Alzheimer's 266 00:14:18,400 --> 00:14:20,960 Speaker 1: is caused by these cell killing protein build ups in 267 00:14:20,960 --> 00:14:24,240 Speaker 1: the brain. And you noted how chronic inflammation is also 268 00:14:24,280 --> 00:14:27,160 Speaker 1: thought to have some role in that degeneration. And what 269 00:14:27,320 --> 00:14:29,360 Speaker 1: strikes me about all this is it it seems like 270 00:14:29,400 --> 00:14:32,840 Speaker 1: we know where the accumulations and inflammation may come from, 271 00:14:32,840 --> 00:14:36,760 Speaker 1: but we don't really know why they occur. Yeah, that's true, 272 00:14:36,800 --> 00:14:39,720 Speaker 1: but but a person's genetics do provided least some clues 273 00:14:39,720 --> 00:14:41,840 Speaker 1: as to what's going on. For for example, we know 274 00:14:41,920 --> 00:14:44,240 Speaker 1: that the trigger for most cases of Alzheimer's is a 275 00:14:44,320 --> 00:14:48,120 Speaker 1: single mutated gene and it's on one of our chromosomes. 276 00:14:48,120 --> 00:14:50,920 Speaker 1: Then remember that we all have two copies of every gene, 277 00:14:51,120 --> 00:14:53,320 Speaker 1: one from each of our parents, so potentially someone could 278 00:14:53,360 --> 00:14:55,920 Speaker 1: have two copies of this mutated gene. Al right, So 279 00:14:55,960 --> 00:14:58,320 Speaker 1: how big a factor is this mutation on whether a 280 00:14:58,400 --> 00:15:02,000 Speaker 1: person develops Alzheimer's or not? I mean, it really depends 281 00:15:02,040 --> 00:15:04,080 Speaker 1: on how many copies of the muta genes someone has. 282 00:15:04,160 --> 00:15:06,880 Speaker 1: So if a person has one copy, then there's up 283 00:15:06,920 --> 00:15:10,200 Speaker 1: to chance of them developing the disease, And if they 284 00:15:10,240 --> 00:15:13,520 Speaker 1: have two copies, then the likelihood jumps to as high 285 00:15:13,520 --> 00:15:17,680 Speaker 1: as eight seven m alright, And so how common is 286 00:15:17,680 --> 00:15:21,400 Speaker 1: it to have one or two of these genes? So 287 00:15:21,640 --> 00:15:26,200 Speaker 1: apparently about of the population has one copy, but it's 288 00:15:26,200 --> 00:15:28,680 Speaker 1: only two percent that have two copies. And testing for 289 00:15:28,720 --> 00:15:31,120 Speaker 1: the genes is actually something you can do through something 290 00:15:31,120 --> 00:15:33,200 Speaker 1: like twenty three and ME or one of those other 291 00:15:33,280 --> 00:15:36,160 Speaker 1: personal genetics tests. You know, though, you know, whether you 292 00:15:36,200 --> 00:15:38,000 Speaker 1: want to stress yourself out with something like that is 293 00:15:38,000 --> 00:15:41,120 Speaker 1: another question entirely. Yeah, I can see. I'm you know, 294 00:15:41,160 --> 00:15:42,880 Speaker 1: a lot of people might feel like that level of 295 00:15:42,880 --> 00:15:46,200 Speaker 1: self knowledge is is something they're not quite prepared to handle, 296 00:15:46,240 --> 00:15:49,400 Speaker 1: and especially considering that many researchers say there's more to 297 00:15:49,480 --> 00:15:52,720 Speaker 1: the disease than just genetics. For example, I was reading 298 00:15:52,720 --> 00:15:55,560 Speaker 1: how some scientists think that the true causes of Alzheimer's 299 00:15:55,600 --> 00:15:59,000 Speaker 1: can be traced back to more environmental factors. That includes 300 00:15:59,000 --> 00:16:02,000 Speaker 1: everything from your diet to how many times you've been 301 00:16:02,040 --> 00:16:04,800 Speaker 1: you know, hitting the head in your lifetime too. Of course, yes, 302 00:16:04,880 --> 00:16:07,800 Speaker 1: you know how many languages you speak? Yeah, definitely, so 303 00:16:08,040 --> 00:16:10,000 Speaker 1: that language one is actually rooted in the fact that 304 00:16:10,040 --> 00:16:14,000 Speaker 1: people who stay mentally active experienced less cognitive decline as 305 00:16:14,000 --> 00:16:16,960 Speaker 1: they age, and amazingly, that remains the case even for 306 00:16:17,000 --> 00:16:19,200 Speaker 1: people whose brains were later found to be riddled with 307 00:16:19,320 --> 00:16:22,280 Speaker 1: familiar signs of Alzheimer's, like those plaques or tangles we've 308 00:16:22,280 --> 00:16:25,360 Speaker 1: been talking about. So even when the disease is already 309 00:16:25,360 --> 00:16:27,880 Speaker 1: set in, elderly patients who make it a habit to 310 00:16:27,960 --> 00:16:30,880 Speaker 1: read or write or play thinking games like chess can 311 00:16:31,000 --> 00:16:34,040 Speaker 1: sometimes stave off its symptoms for a lot longer. In fact, 312 00:16:34,120 --> 00:16:38,000 Speaker 1: one two thirteen study found that people who didn't routinely exercise, 313 00:16:38,040 --> 00:16:42,440 Speaker 1: their brains experienced cognitive decline full forty eight percent faster 314 00:16:42,680 --> 00:16:47,800 Speaker 1: than those dident Yeah, that's pretty pretty good endorsement for reading. 315 00:16:47,880 --> 00:16:49,800 Speaker 1: But you know, you know, for those times when your 316 00:16:49,840 --> 00:16:53,320 Speaker 1: brain just needs a break or you know, it does 317 00:16:53,400 --> 00:16:56,040 Speaker 1: look like there's some other ways, and and one of 318 00:16:56,080 --> 00:16:58,640 Speaker 1: these that many people might find of interest is by 319 00:16:58,680 --> 00:17:01,560 Speaker 1: knocking back a couple of of drinks. Or at least 320 00:17:01,600 --> 00:17:03,440 Speaker 1: that's the word from a study that came out earlier 321 00:17:03,480 --> 00:17:07,080 Speaker 1: this year in Scientific Reports. Honestly, that kind of sounds 322 00:17:07,119 --> 00:17:09,400 Speaker 1: like a made up name for a dun over like this. 323 00:17:09,400 --> 00:17:11,680 Speaker 1: This was in Scientific Reports, So I should have a 324 00:17:11,760 --> 00:17:15,520 Speaker 1: drink a lot, that's right. But the researchers found that 325 00:17:15,600 --> 00:17:18,480 Speaker 1: drinking just two glasses of wine daily is enough to 326 00:17:18,560 --> 00:17:21,680 Speaker 1: net you a positive effect, which it works due to 327 00:17:21,720 --> 00:17:25,160 Speaker 1: the way the alcohol interacts with the brain. So apparently 328 00:17:25,160 --> 00:17:28,040 Speaker 1: the wine enhances the brain's ability to remove those damaging 329 00:17:28,080 --> 00:17:31,200 Speaker 1: toxins that build up in the brain, which of course 330 00:17:31,240 --> 00:17:34,199 Speaker 1: includes those TAO and beta amyloid proteins that we've been 331 00:17:34,200 --> 00:17:37,520 Speaker 1: talking about. The brain typically does this by pumping in 332 00:17:37,640 --> 00:17:41,639 Speaker 1: some cerebral fluid to flush away those troublesome plaques and tangles. 333 00:17:41,640 --> 00:17:45,160 Speaker 1: But there's something about alcohol that gets the old cerebral 334 00:17:45,240 --> 00:17:48,919 Speaker 1: fluid flowing more efficiently. And in case you're not a 335 00:17:48,920 --> 00:17:51,240 Speaker 1: wine drinker, there is some evidence that other forms of 336 00:17:51,280 --> 00:17:54,479 Speaker 1: alcohol work too. For instance, there was another study from 337 00:17:54,480 --> 00:17:56,800 Speaker 1: a couple of years ago that found that beer drinkers 338 00:17:56,840 --> 00:18:00,880 Speaker 1: tend to have fewer ammoloid build ups than non beer drinkers. Now, 339 00:18:00,960 --> 00:18:03,440 Speaker 1: as always, I do need to pause here and say 340 00:18:03,760 --> 00:18:07,560 Speaker 1: the key part here is moderation. Excessive alcohol consumption is 341 00:18:07,600 --> 00:18:10,919 Speaker 1: obviously not a good thing for anybody. Yeah, I mean, 342 00:18:11,080 --> 00:18:13,560 Speaker 1: it's really wild to lay out all the environmental factors 343 00:18:13,600 --> 00:18:17,200 Speaker 1: potentially connected with Alzheimer's, Like I saw this one report 344 00:18:17,280 --> 00:18:20,199 Speaker 1: in the Journal of Neuroscience about how even something like 345 00:18:20,240 --> 00:18:23,000 Speaker 1: sleeping on your side can help delay the development of 346 00:18:23,040 --> 00:18:26,879 Speaker 1: both Alzheimer's and Parkinson's. And apparently it goes back to 347 00:18:26,920 --> 00:18:30,080 Speaker 1: the fluid flush routine our brains used to clear out waste. Like, 348 00:18:30,119 --> 00:18:33,160 Speaker 1: it turns out the fluid flows most effectively when we're 349 00:18:33,160 --> 00:18:35,720 Speaker 1: asleep and on our sides. All right, so it sounds 350 00:18:35,760 --> 00:18:38,680 Speaker 1: like we've got the solution here. We just need to 351 00:18:38,760 --> 00:18:41,440 Speaker 1: do some Sudoku puzzles all day long. Line on our 352 00:18:41,480 --> 00:18:44,440 Speaker 1: side and then drink ourselves into a wine comas that 353 00:18:44,440 --> 00:18:47,520 Speaker 1: that's the solution here, Yeah, and pick up cantonese. I 354 00:18:47,520 --> 00:18:49,600 Speaker 1: think it's not the part of right, right, Okay, that's right. 355 00:18:49,600 --> 00:18:52,600 Speaker 1: I forgot, But I mean, you know it's it's there's 356 00:18:52,640 --> 00:18:56,040 Speaker 1: no surefire way to hold back Alzheimer's either, you know that. 357 00:18:56,040 --> 00:18:58,320 Speaker 1: That's the thing about all these environmental risks we're talking about, 358 00:18:58,359 --> 00:19:01,480 Speaker 1: like they can only suggest probable connections between things that 359 00:19:01,520 --> 00:19:05,120 Speaker 1: have already happened. Like I've even seen studies that link 360 00:19:05,200 --> 00:19:09,080 Speaker 1: drinking soda, both regular and diet, to the onset of Alzheimer's. 361 00:19:09,080 --> 00:19:11,560 Speaker 1: And and while there might be a correlation between getting 362 00:19:11,560 --> 00:19:14,880 Speaker 1: Alzheimer's and drinking soda, that doesn't mean that one necessarily 363 00:19:14,960 --> 00:19:17,800 Speaker 1: caused the other to happen. Well, that's a good point, 364 00:19:17,840 --> 00:19:21,520 Speaker 1: And I mean these kinds of lifestyle considerations can be 365 00:19:21,640 --> 00:19:24,040 Speaker 1: kind of reassuring for us to think about. But the 366 00:19:24,080 --> 00:19:27,040 Speaker 1: research I do find most exciting is this stuff aimed 367 00:19:27,040 --> 00:19:30,680 Speaker 1: at finding new detection methods for Alzheimer's. After all, it's 368 00:19:30,680 --> 00:19:33,159 Speaker 1: two thousand and eighteen, and it feels like we need 369 00:19:33,200 --> 00:19:37,000 Speaker 1: a better system than having to dissect these brains post mortem. Yeah, 370 00:19:37,240 --> 00:19:39,240 Speaker 1: I agree, and that's why I was excited when I 371 00:19:39,280 --> 00:19:41,320 Speaker 1: first found out that we actually do have tests that 372 00:19:41,359 --> 00:19:44,440 Speaker 1: can identify those gene mutations I mentioned earlier, the ones 373 00:19:44,480 --> 00:19:47,720 Speaker 1: that make a person more likely to develop Alzheimer's. But 374 00:19:47,800 --> 00:19:50,160 Speaker 1: as it turns out, many people who develop Alzheimer's don't 375 00:19:50,160 --> 00:19:53,040 Speaker 1: actually carry that genetic marker. Well, many who do never 376 00:19:53,119 --> 00:19:56,400 Speaker 1: end up exhibiting Alzhemer's symptoms. Well, so this is kind 377 00:19:56,400 --> 00:19:58,800 Speaker 1: of like the plaques and tangles then, right, like that, 378 00:19:59,000 --> 00:20:03,080 Speaker 1: they're reliable markers that maybe point the way to Alzheimer's, 379 00:20:03,160 --> 00:20:05,639 Speaker 1: except for all of these cases where for some reason 380 00:20:05,720 --> 00:20:08,480 Speaker 1: they don't, yeah, exactly, And and that means that even 381 00:20:08,520 --> 00:20:11,720 Speaker 1: genetic testing can't provide any real degree of certainty one 382 00:20:11,720 --> 00:20:14,920 Speaker 1: way or another. And that's what makes it all so confusing, Like, 383 00:20:15,080 --> 00:20:17,600 Speaker 1: even if you don't have the genetic markers, you could 384 00:20:17,600 --> 00:20:22,080 Speaker 1: still develop the disease. Well, thankfully, some researchers have begun 385 00:20:22,119 --> 00:20:26,080 Speaker 1: branching out from genetic and environmental factors and they're concentrating 386 00:20:26,080 --> 00:20:29,840 Speaker 1: on these so called biomarkers instead. Now, the idea here 387 00:20:29,920 --> 00:20:32,840 Speaker 1: is that the body exhibits tell tale biological signs of 388 00:20:32,880 --> 00:20:35,720 Speaker 1: Alzheimer's that we don't necessarily have to look at the 389 00:20:35,760 --> 00:20:38,360 Speaker 1: brain to find so that we can look for these 390 00:20:38,359 --> 00:20:41,320 Speaker 1: clues in all kinds of places that might be in 391 00:20:41,359 --> 00:20:44,600 Speaker 1: the blood or in the cerebro spinal fluid we mentioned earlier, 392 00:20:45,000 --> 00:20:48,119 Speaker 1: or even in the eyes of all places, And honestly, 393 00:20:48,160 --> 00:20:50,720 Speaker 1: that last one is probably my favorite because all it 394 00:20:50,800 --> 00:20:54,199 Speaker 1: involves is administering these fancy eye drops, and that's just 395 00:20:54,280 --> 00:20:58,280 Speaker 1: so much less obtrusive than digging around in somebody's brain. Yeah, 396 00:20:58,280 --> 00:21:00,320 Speaker 1: I mean that that obviously sounds way better there to me, 397 00:21:00,600 --> 00:21:04,120 Speaker 1: But walk me through what makes these eye drops so fancy. Well, 398 00:21:04,160 --> 00:21:06,440 Speaker 1: even though we have things like pets cans to help 399 00:21:06,520 --> 00:21:08,840 Speaker 1: us take a closer look at living brains, it can 400 00:21:08,880 --> 00:21:12,240 Speaker 1: still be incredibly tough to identify those beta em aloid 401 00:21:12,320 --> 00:21:15,800 Speaker 1: build ups that often point to Alzheimer's. And that's largely 402 00:21:15,840 --> 00:21:18,920 Speaker 1: because the betas are just one of many kinds of ammolloids, 403 00:21:19,359 --> 00:21:22,160 Speaker 1: and they're all kinds of hard to tell apart. Plus, 404 00:21:22,160 --> 00:21:24,679 Speaker 1: there are many different neurological disorders that are linked to 405 00:21:24,760 --> 00:21:28,040 Speaker 1: specific ammolloids, So if you can't tell which protein you're 406 00:21:28,080 --> 00:21:30,960 Speaker 1: looking at, then you really can't determine which disorder it's 407 00:21:30,960 --> 00:21:33,920 Speaker 1: pointing to. I see, So how did the eye drops 408 00:21:33,920 --> 00:21:36,120 Speaker 1: help with that? All right? Well, this is where things 409 00:21:36,160 --> 00:21:38,480 Speaker 1: get really cool. So you know how the eyes are 410 00:21:38,520 --> 00:21:42,439 Speaker 1: closely connected to the brain, right, Well, the connection is 411 00:21:42,520 --> 00:21:45,680 Speaker 1: so close that amyloids actually accumulate in our eyes too, 412 00:21:45,880 --> 00:21:49,000 Speaker 1: not just the brains. So the researchers are hoping that 413 00:21:49,040 --> 00:21:52,359 Speaker 1: by adding these fluorescent markers to eye drops, they'll be 414 00:21:52,400 --> 00:21:54,639 Speaker 1: able to light up the ammoloids in the eyes and 415 00:21:54,840 --> 00:21:58,119 Speaker 1: different colors, and of course each color would correspond to 416 00:21:58,280 --> 00:22:00,840 Speaker 1: a different ammoloid and bike stay chin from that to 417 00:22:00,880 --> 00:22:04,800 Speaker 1: the disorder it's associated with. So in theory, you could 418 00:22:04,800 --> 00:22:08,359 Speaker 1: have a doctor diagnosing a patient's condition just by looking 419 00:22:08,440 --> 00:22:11,280 Speaker 1: them in the eye. Isn't that incredible? Yeah, it really is. 420 00:22:11,400 --> 00:22:13,639 Speaker 1: And it actually reminds me of this other new Alzheimer's 421 00:22:13,640 --> 00:22:16,199 Speaker 1: tests I've read about. It's called the Opposite or the 422 00:22:16,280 --> 00:22:20,240 Speaker 1: University of Pennsylvania Smell Identification Test, and it's basically one 423 00:22:20,240 --> 00:22:23,240 Speaker 1: of those scratch and sniff cards with like forty different 424 00:22:23,240 --> 00:22:26,639 Speaker 1: odors on it, which is really clever because you know, 425 00:22:26,720 --> 00:22:29,640 Speaker 1: we've known for a while now that people with neurological diseases, 426 00:22:29,680 --> 00:22:32,960 Speaker 1: including Alzheimer's, often lose some or all of their sense 427 00:22:33,000 --> 00:22:35,879 Speaker 1: of smell. So if a patient nails the opposite, you 428 00:22:35,960 --> 00:22:38,360 Speaker 1: pretty much know that they're not going to have Alzheimer's, 429 00:22:38,440 --> 00:22:40,919 Speaker 1: at least for the next few years. And I know 430 00:22:40,960 --> 00:22:42,800 Speaker 1: there are plenty of other reasons why people lose their 431 00:22:42,800 --> 00:22:44,760 Speaker 1: ability to smell, So you know, it's not like it's 432 00:22:44,760 --> 00:22:48,200 Speaker 1: just this comprehensive test for Alzheimer's or anything. But as 433 00:22:48,280 --> 00:22:51,400 Speaker 1: Kate Horowitz from Mental Fauce puts it, the results are 434 00:22:51,400 --> 00:22:56,160 Speaker 1: instantaneous and at it's a far cheaper starting point than 435 00:22:56,240 --> 00:23:00,200 Speaker 1: other brain scans. Huh, well, that's that's true. But all well, 436 00:23:00,200 --> 00:23:04,080 Speaker 1: we've talked about ways to potentially delay the onset of Alzheimer's, 437 00:23:04,160 --> 00:23:07,120 Speaker 1: as well as some new ideas for how to detect 438 00:23:07,200 --> 00:23:10,159 Speaker 1: and even diagnose it earlier. So now why don't we 439 00:23:10,200 --> 00:23:12,040 Speaker 1: take a look at a few of them may maybe 440 00:23:12,040 --> 00:23:15,040 Speaker 1: more out of the box approaches to Alzheimer's treatment and 441 00:23:15,480 --> 00:23:17,480 Speaker 1: how they hope to make a difference in the lives 442 00:23:17,480 --> 00:23:20,400 Speaker 1: of those with Alzheimer's. That sounds great, but first let's 443 00:23:20,440 --> 00:23:39,119 Speaker 1: take another quick break. Okay, well, so what kinds of 444 00:23:39,240 --> 00:23:42,959 Speaker 1: unorthodox approaches to Alzheimer's treatments are you excited about? All right, 445 00:23:43,000 --> 00:23:46,000 Speaker 1: so I came across two rest examples that really stood 446 00:23:46,000 --> 00:23:48,440 Speaker 1: out to me, and they're pretty different because one highlights 447 00:23:48,480 --> 00:23:51,280 Speaker 1: the power of the individual in this ongoing fight, and 448 00:23:51,760 --> 00:23:55,040 Speaker 1: one showcases what a motivated community can give back to 449 00:23:55,119 --> 00:23:58,119 Speaker 1: those who are suffering from Alzheimer's. So I'll start with 450 00:23:58,160 --> 00:24:00,640 Speaker 1: the first one, which is this may a new app 451 00:24:00,640 --> 00:24:03,520 Speaker 1: and it's called Timeless and it's being developed by a 452 00:24:03,600 --> 00:24:06,840 Speaker 1: fourteen year old girl named Emma Yang. And yes, you 453 00:24:06,920 --> 00:24:10,679 Speaker 1: heard that right. Emma is indeed a teenage program prodigy. 454 00:24:11,359 --> 00:24:13,840 Speaker 1: According to an article in Fast Company, Emma code of 455 00:24:13,880 --> 00:24:15,760 Speaker 1: the app herself and the hope that it will one 456 00:24:15,840 --> 00:24:19,480 Speaker 1: day help her grandmother, who has Alzheimer's, to remember her 457 00:24:19,520 --> 00:24:23,360 Speaker 1: loved ones. I mean, first off, that is so inspiring 458 00:24:23,520 --> 00:24:27,200 Speaker 1: and also heartbreaking, but it also sounds like a really 459 00:24:27,200 --> 00:24:30,280 Speaker 1: great concept. So how does it work well. The idea 460 00:24:30,440 --> 00:24:33,280 Speaker 1: is to use facial recognition as a way to remind 461 00:24:33,359 --> 00:24:36,520 Speaker 1: the user who a certain person is and also what 462 00:24:36,560 --> 00:24:39,680 Speaker 1: their relationship is to them. So once the Timeless app 463 00:24:39,760 --> 00:24:42,960 Speaker 1: is loaded up with all of this information and Alzheimer's 464 00:24:42,960 --> 00:24:46,119 Speaker 1: patient can refer to it anytime they have trouble recognizing 465 00:24:46,280 --> 00:24:48,680 Speaker 1: someone's face, and all they have to do is take 466 00:24:48,720 --> 00:24:51,000 Speaker 1: a quick photo of the person, and the app will 467 00:24:51,040 --> 00:24:53,680 Speaker 1: pull up all of their information. So the hope is 468 00:24:53,720 --> 00:24:57,040 Speaker 1: that once a patient refers to the same entries enough times, 469 00:24:57,400 --> 00:25:00,840 Speaker 1: they'll start to better retain that information. And that's not 470 00:25:00,880 --> 00:25:03,119 Speaker 1: the only thing it does either, So Emma has also 471 00:25:03,200 --> 00:25:06,880 Speaker 1: programmed the app to provide appointment reminders and even help 472 00:25:06,960 --> 00:25:09,639 Speaker 1: users recognize when they might be repeating a task that 473 00:25:09,680 --> 00:25:12,920 Speaker 1: they've already completed. So, for instance, if a patient calls 474 00:25:12,960 --> 00:25:15,280 Speaker 1: a friend for a second time because they, you know, 475 00:25:15,320 --> 00:25:17,920 Speaker 1: forgot about the first call they made, the app will 476 00:25:17,960 --> 00:25:20,359 Speaker 1: notify them that this is the second call to that 477 00:25:20,480 --> 00:25:23,679 Speaker 1: number in that short period of time. So while this 478 00:25:23,720 --> 00:25:25,919 Speaker 1: isn't something that would be useful in the later stages 479 00:25:25,920 --> 00:25:27,960 Speaker 1: of Alzheimer's, it could be a way to help prolong 480 00:25:28,000 --> 00:25:30,639 Speaker 1: those family interactions in the meantime. I mean that that 481 00:25:30,800 --> 00:25:33,199 Speaker 1: is really clever. I'm guessing the app hasn't hit the 482 00:25:33,200 --> 00:25:36,040 Speaker 1: market though yet. Right now, m is still working on 483 00:25:36,080 --> 00:25:38,199 Speaker 1: securing all the funding that she needs to get it 484 00:25:38,200 --> 00:25:40,240 Speaker 1: out the door, but it sounds like the plan is 485 00:25:40,280 --> 00:25:43,080 Speaker 1: to release it within the next couple of years. Well, 486 00:25:43,119 --> 00:25:46,280 Speaker 1: I mean, I'm obviously rooting for her, but I'm curious, like, 487 00:25:46,520 --> 00:25:49,160 Speaker 1: what's the more community driven program that caught your eye. 488 00:25:49,480 --> 00:25:51,560 Speaker 1: So this one's part of a cultural program that was 489 00:25:51,600 --> 00:25:53,760 Speaker 1: started a couple of years ago by a handful of 490 00:25:53,840 --> 00:25:57,800 Speaker 1: museums that are in Wisconsin and Minnesota, and it's called 491 00:25:57,840 --> 00:26:01,480 Speaker 1: Spark and it works in partnership with Alzheimer's Association. Is 492 00:26:01,480 --> 00:26:05,800 Speaker 1: a way to help stimulate patients memories. So according to Smithsonian, 493 00:26:05,840 --> 00:26:09,280 Speaker 1: the program's main goal is to quote use artwork and 494 00:26:09,359 --> 00:26:12,920 Speaker 1: other sensory input to help stimulate long term memory retention 495 00:26:13,040 --> 00:26:15,919 Speaker 1: among patrons. And so some of these tours even have 496 00:26:15,960 --> 00:26:18,960 Speaker 1: the patients interact with sensory items along the way you 497 00:26:19,119 --> 00:26:22,199 Speaker 1: think about like scented candles or a piece of textured cloth, 498 00:26:22,720 --> 00:26:24,879 Speaker 1: and the ideas that all of this can help spark 499 00:26:24,960 --> 00:26:28,200 Speaker 1: their memories. So, for example, if a group of patients 500 00:26:28,240 --> 00:26:31,000 Speaker 1: was looking at a sculpture while a scented candle was burning, 501 00:26:31,359 --> 00:26:33,560 Speaker 1: and then you lit that same candle a few hours 502 00:26:33,640 --> 00:26:37,000 Speaker 1: later and ask the patients about the sculpture, the scent 503 00:26:37,160 --> 00:26:40,400 Speaker 1: might help them recall more about that piece and their 504 00:26:40,440 --> 00:26:43,119 Speaker 1: perceptions of it, and you know, they would have a 505 00:26:43,160 --> 00:26:46,960 Speaker 1: better memory of it than without that sensory input. I mean, 506 00:26:47,200 --> 00:26:49,399 Speaker 1: that's really awesome. So do you have any sense of 507 00:26:49,480 --> 00:26:52,760 Speaker 1: like how many museums offer programs like this, Well, so far, 508 00:26:52,920 --> 00:26:55,560 Speaker 1: I think there are fourteen of them that are part 509 00:26:55,600 --> 00:26:58,959 Speaker 1: of the Spark program and they hold art tours and 510 00:26:59,000 --> 00:27:02,280 Speaker 1: painting classes, as even dances, and these are all for 511 00:27:02,320 --> 00:27:06,240 Speaker 1: Alzheimer's patients and their caregivers. But these programs are actually 512 00:27:06,240 --> 00:27:08,879 Speaker 1: modeled after a similar initiative that was at MoMA and 513 00:27:08,920 --> 00:27:12,440 Speaker 1: that started a little over a decade ago. And because 514 00:27:12,480 --> 00:27:14,720 Speaker 1: of the success of these kinds of programs, you've got 515 00:27:14,800 --> 00:27:17,720 Speaker 1: museums all over the US that have started investing in 516 00:27:17,760 --> 00:27:20,359 Speaker 1: their own ways, and all of these are efforts to 517 00:27:20,359 --> 00:27:22,720 Speaker 1: help them, you know, those with memory laws re engage 518 00:27:22,760 --> 00:27:25,040 Speaker 1: with art. That is such a great idea. And and 519 00:27:25,080 --> 00:27:28,800 Speaker 1: since we're talking about ingenious, community driven Alzheimer's programs, I 520 00:27:28,880 --> 00:27:30,600 Speaker 1: do want to talk just a little bit about this 521 00:27:30,720 --> 00:27:34,439 Speaker 1: government funded nursing care company outside of Amsterdam that runs 522 00:27:34,480 --> 00:27:37,280 Speaker 1: what it refers to as a dementia village. So I'm 523 00:27:37,320 --> 00:27:39,760 Speaker 1: sure you've heard about this, but it's an entire model 524 00:27:39,840 --> 00:27:42,840 Speaker 1: town known as hoke Awake, and there are more than 525 00:27:42,880 --> 00:27:46,000 Speaker 1: a hundred fifty resident patients living together in groups there. 526 00:27:46,280 --> 00:27:48,159 Speaker 1: It opened in two thousand nine as a way to 527 00:27:48,320 --> 00:27:51,400 Speaker 1: combat the social isolation and lack of activity that people 528 00:27:51,400 --> 00:27:54,480 Speaker 1: with dementia often deal with, and instead of feeling cooped 529 00:27:54,560 --> 00:27:57,320 Speaker 1: up with nothing to do, patients are encouraged to carry 530 00:27:57,320 --> 00:28:00,720 Speaker 1: out everyday activities in this controlled environment. And it's built 531 00:28:00,760 --> 00:28:04,400 Speaker 1: like a functioning tiny town, so they can go grocery shopping, 532 00:28:04,560 --> 00:28:06,840 Speaker 1: they can visit the hair salon, catch a movie at 533 00:28:06,840 --> 00:28:09,159 Speaker 1: the theater, or even grab a cup of coffee at 534 00:28:09,160 --> 00:28:13,160 Speaker 1: the cafe. All of these storefronts are staffed by caretakers, 535 00:28:13,480 --> 00:28:15,600 Speaker 1: and they also populated the town and help look out 536 00:28:15,600 --> 00:28:19,000 Speaker 1: for patients safety. It's really incredible. Yeah, and I love 537 00:28:19,040 --> 00:28:21,560 Speaker 1: this concept and you're thinking about it. It gives these 538 00:28:21,560 --> 00:28:25,400 Speaker 1: Alzheimer's patients a chance to socialize more and and even 539 00:28:25,440 --> 00:28:28,480 Speaker 1: regain some of that independence that they had before. And 540 00:28:28,880 --> 00:28:31,160 Speaker 1: imagine it must have such a big impact on their 541 00:28:31,240 --> 00:28:34,160 Speaker 1: quality of life overall. It actually reminds me a lot 542 00:28:34,200 --> 00:28:37,000 Speaker 1: of that town in Belgium where there's like this seven 543 00:28:37,080 --> 00:28:40,240 Speaker 1: hundred year old tradition of residents taking in patients with 544 00:28:40,360 --> 00:28:44,400 Speaker 1: neurological disease or mental illnesses. So the towns people just 545 00:28:44,480 --> 00:28:47,640 Speaker 1: kind of adopt these patients. Into their families and look 546 00:28:47,680 --> 00:28:50,440 Speaker 1: after them like their own And it's that same kind 547 00:28:50,440 --> 00:28:52,560 Speaker 1: of all for one mentality that I get from that, 548 00:28:52,640 --> 00:28:56,240 Speaker 1: you know, that hoc Awake approach. Yeah. Plus this prop 549 00:28:56,320 --> 00:28:58,560 Speaker 1: town set up in general is just kind of awesome, right, 550 00:28:58,560 --> 00:29:01,680 Speaker 1: Like it's like Truman Show, but without any of that 551 00:29:02,240 --> 00:29:05,600 Speaker 1: exploitation aspect to it. What's the fun and that no, 552 00:29:05,760 --> 00:29:08,640 Speaker 1: but you're you're right well, And it's also cool to 553 00:29:08,640 --> 00:29:11,280 Speaker 1: see this immersive approach to dementia care catching on in 554 00:29:11,360 --> 00:29:14,880 Speaker 1: other places, like I read there's a similar facility in Ontario. 555 00:29:15,000 --> 00:29:18,280 Speaker 1: Now there's actually a daytime care center in San Diego 556 00:29:18,440 --> 00:29:20,880 Speaker 1: and it's set to open I think later this year, 557 00:29:20,920 --> 00:29:24,080 Speaker 1: and that one was partly inspired by hok Awake. So 558 00:29:24,240 --> 00:29:26,440 Speaker 1: at a daytime center, and I guessink the patients don't 559 00:29:26,440 --> 00:29:29,720 Speaker 1: actually live there full time. Yeah. So the project is 560 00:29:29,840 --> 00:29:32,960 Speaker 1: largely the brainchild of Scott Tardey, who's the CEO of 561 00:29:32,960 --> 00:29:37,240 Speaker 1: a nonprofit called Glenner Alzheimer's Family Care Centers, and his 562 00:29:37,320 --> 00:29:40,560 Speaker 1: idea was to use set design to craft this immersive 563 00:29:40,640 --> 00:29:45,240 Speaker 1: environment for Alzheimer's patients to visit and reminisce during the day. 564 00:29:45,480 --> 00:29:48,240 Speaker 1: And reminiscence is really the key word here, because the 565 00:29:48,360 --> 00:29:51,000 Speaker 1: unique aspect of this project is that it's patterned after 566 00:29:51,080 --> 00:29:54,440 Speaker 1: a clinical practice called reminiscence therapy. Now, this is when 567 00:29:54,480 --> 00:29:58,600 Speaker 1: facilitators use prompts like photographs and music to help patients 568 00:29:58,640 --> 00:30:01,880 Speaker 1: recall events and fee feelings from earlier in their life, 569 00:30:01,920 --> 00:30:05,280 Speaker 1: and it works really well. There's all kinds of studies 570 00:30:05,320 --> 00:30:07,840 Speaker 1: to show that this form of treatment actually helps boost 571 00:30:07,920 --> 00:30:11,920 Speaker 1: both cognitive function and quality of life. So the idea 572 00:30:12,000 --> 00:30:14,160 Speaker 1: here was that if you can build an entire town 573 00:30:14,240 --> 00:30:17,440 Speaker 1: designed to seem familiar, the effects of this kind of 574 00:30:17,480 --> 00:30:20,280 Speaker 1: therapy might be even greater. So I'm curious, how do 575 00:30:20,320 --> 00:30:22,200 Speaker 1: you make a place feel familiar to a whole bunch 576 00:30:22,200 --> 00:30:26,240 Speaker 1: of different people. Well, the nonprofit did something really smart here. 577 00:30:26,400 --> 00:30:29,120 Speaker 1: They designed the whole town, which is called Glenner Town 578 00:30:29,200 --> 00:30:31,440 Speaker 1: Square by the way, to look like it's from the 579 00:30:31,480 --> 00:30:35,720 Speaker 1: period between nineteen fifty three and nineteen sixty one. And 580 00:30:35,760 --> 00:30:37,960 Speaker 1: they did this because that's the period when most of 581 00:30:38,000 --> 00:30:41,560 Speaker 1: today's dementia patients were young adults. So, for example, if 582 00:30:41,600 --> 00:30:44,720 Speaker 1: a patient is in their early eighties and two thousand eighteen, 583 00:30:45,160 --> 00:30:47,760 Speaker 1: that means they were born in the mid nineteen thirties 584 00:30:47,760 --> 00:30:51,080 Speaker 1: and would have been you know, eighteen nineteen twenties, somewhere 585 00:30:51,080 --> 00:30:54,200 Speaker 1: around that in the mid fifties, right, So that's interesting, 586 00:30:54,200 --> 00:30:56,320 Speaker 1: But like why focus on when they were young adults 587 00:30:56,320 --> 00:30:58,520 Speaker 1: over when they were kids or adults or some other 588 00:30:58,560 --> 00:31:02,280 Speaker 1: period of their lives. Well, apparently the memories from the 589 00:31:02,320 --> 00:31:05,440 Speaker 1: person's twenties and thirties are the ones that stick around 590 00:31:05,480 --> 00:31:09,160 Speaker 1: the longest. It Scott tart To explained, quote graduation from 591 00:31:09,200 --> 00:31:13,719 Speaker 1: high school, college, first jobs, marriage, perhaps children, These are 592 00:31:13,720 --> 00:31:17,440 Speaker 1: the milestones typically in somebody's life. So that twenty year 593 00:31:17,520 --> 00:31:21,080 Speaker 1: period seems to be where memories are the strongest. M 594 00:31:21,800 --> 00:31:24,920 Speaker 1: I mean, it's really interesting, and I think it's curious 595 00:31:24,960 --> 00:31:28,640 Speaker 1: that like this example is more pleasant filled than Truman Show, 596 00:31:28,760 --> 00:31:31,640 Speaker 1: but it's it really is stunning to see how people 597 00:31:31,640 --> 00:31:33,880 Speaker 1: are thinking outside the box and coming up with new 598 00:31:33,880 --> 00:31:36,960 Speaker 1: approaches to deal with Alzheimer's. But you know that there's 599 00:31:37,040 --> 00:31:39,520 Speaker 1: one group of people affected by Alzheimer's that we haven't 600 00:31:39,520 --> 00:31:41,440 Speaker 1: talked much about today, and and that's the tens of 601 00:31:41,480 --> 00:31:44,760 Speaker 1: millions of people who watched their loved ones struggle with Alzheimer's. 602 00:31:45,040 --> 00:31:47,400 Speaker 1: And what I find so inspiring about the ordeal is 603 00:31:47,440 --> 00:31:50,280 Speaker 1: that despite all the pain, they go through while caring 604 00:31:50,280 --> 00:31:52,720 Speaker 1: for friends and family members, they remain eager to find 605 00:31:52,760 --> 00:31:55,520 Speaker 1: the disease in any way they can. Yeah, that's very true. 606 00:31:55,520 --> 00:31:58,400 Speaker 1: And actually I found this Wired article from last September 607 00:31:58,440 --> 00:32:01,800 Speaker 1: that talks about a new way everyday people are helping 608 00:32:01,840 --> 00:32:04,920 Speaker 1: researchers just get a little bit closer to that cure 609 00:32:04,960 --> 00:32:08,400 Speaker 1: for Alzheimer's. And this program is called Stall Catchers, and 610 00:32:08,520 --> 00:32:11,480 Speaker 1: it's actually a video game where players examine images of 611 00:32:11,560 --> 00:32:15,440 Speaker 1: mouse brains and they try to spot any clogged blood vessels, 612 00:32:15,480 --> 00:32:18,440 Speaker 1: which they call stalls in this case, and and those 613 00:32:18,480 --> 00:32:21,880 Speaker 1: could be obstructing blood flow in the brain. This kind 614 00:32:21,880 --> 00:32:25,040 Speaker 1: of reduced cerebral blood flow has been linked to Alzheimer's, 615 00:32:25,040 --> 00:32:27,760 Speaker 1: So if you can treat this, there's a chance that 616 00:32:27,840 --> 00:32:31,040 Speaker 1: memory loss could actually be reversed. And the method has 617 00:32:31,080 --> 00:32:35,240 Speaker 1: already proven effective and lab mice. But there's one important caveat, 618 00:32:35,600 --> 00:32:37,800 Speaker 1: and that's that all the drugs tested so far to 619 00:32:37,880 --> 00:32:41,400 Speaker 1: improve blood flow have also destroyed the mice's ability to 620 00:32:41,480 --> 00:32:45,880 Speaker 1: ward off infections, which is obviously a deal breaker for humans. 621 00:32:46,280 --> 00:32:48,760 Speaker 1: And so as a result of this, researchers continue to 622 00:32:48,840 --> 00:32:50,920 Speaker 1: test new drugs in search of one that will leave 623 00:32:50,920 --> 00:32:55,120 Speaker 1: patients immune systems intact. But here's the problem. You know, 624 00:32:55,240 --> 00:32:57,760 Speaker 1: every time they test the new drug, they then have 625 00:32:57,880 --> 00:33:00,240 Speaker 1: to check images of the mice brains for stall halls 626 00:33:00,280 --> 00:33:03,200 Speaker 1: in order to see if that medicine is or isn't working. 627 00:33:03,680 --> 00:33:07,080 Speaker 1: And since each new drug brings about thirty thousand images, 628 00:33:07,120 --> 00:33:09,880 Speaker 1: to sort through the results of each trial can take 629 00:33:09,960 --> 00:33:13,000 Speaker 1: up to a year just a process. Wow. So so 630 00:33:13,040 --> 00:33:15,960 Speaker 1: I'm guessing that's where the game comes in. Yeah, that's 631 00:33:15,960 --> 00:33:18,640 Speaker 1: exactly where it comes in. So the researchers have teamed 632 00:33:18,720 --> 00:33:22,640 Speaker 1: up with this nonprofit innovator called the Human Computation Institute, 633 00:33:23,080 --> 00:33:25,640 Speaker 1: and so together they've developed this online game to help 634 00:33:25,720 --> 00:33:29,440 Speaker 1: crowdsource the data that they need. Players will sift through 635 00:33:29,560 --> 00:33:33,040 Speaker 1: thousands of grainy, black and white slides of these mice brains, 636 00:33:33,080 --> 00:33:36,240 Speaker 1: and they're hunting for any sign of stalls, which show 637 00:33:36,320 --> 00:33:39,560 Speaker 1: up as these small black spots in the images. So far, 638 00:33:39,600 --> 00:33:43,440 Speaker 1: more than six thousand people have logged onto play Stall Catchers, 639 00:33:43,440 --> 00:33:47,240 Speaker 1: and that's helped speed up the researchers work tremendously. In fact, 640 00:33:47,280 --> 00:33:50,480 Speaker 1: when game activity is at its peak, the players effectively 641 00:33:50,520 --> 00:33:53,720 Speaker 1: complete a week's worth of research in a single hour. 642 00:33:54,360 --> 00:33:57,120 Speaker 1: So process that you know would otherwise take decades, might 643 00:33:57,160 --> 00:33:59,680 Speaker 1: just take a few years instead, which could really mean 644 00:33:59,800 --> 00:34:02,960 Speaker 1: all all the difference for patients currently dealing with Alzheimer's. 645 00:34:03,320 --> 00:34:05,760 Speaker 1: I mean that that really is incredible. So do we 646 00:34:05,800 --> 00:34:09,000 Speaker 1: have any info on who the user basis for Stallcatchers, like? 647 00:34:09,320 --> 00:34:11,279 Speaker 1: Is it mostly people whose friends and family have been 648 00:34:11,280 --> 00:34:14,800 Speaker 1: affected by Alzheimer's? Yeah, I mean that's really the heart 649 00:34:14,840 --> 00:34:17,080 Speaker 1: of the game's community, and and that, to me is 650 00:34:17,120 --> 00:34:20,000 Speaker 1: such a powerful reminder of why it's important to not 651 00:34:20,120 --> 00:34:24,080 Speaker 1: be discouraged when the latest promising treatment for Alzheimer's falls through. 652 00:34:24,680 --> 00:34:27,200 Speaker 1: And this is something Miranda Cats actually touches on in 653 00:34:27,280 --> 00:34:30,359 Speaker 1: her piece for Wired. She's describing how the friends and 654 00:34:30,400 --> 00:34:34,280 Speaker 1: family members of Alzheimer's patients have responded to stall Catchers, 655 00:34:34,719 --> 00:34:37,560 Speaker 1: and this is how she puts it, Frustrated by high 656 00:34:37,680 --> 00:34:41,839 Speaker 1: nonprofit overheads and the glacial pace of research, they've left 657 00:34:41,840 --> 00:34:44,760 Speaker 1: at an opportunity to take things into their own hands. 658 00:34:45,360 --> 00:34:48,080 Speaker 1: And though a true cure for Alzheimer's is still distant, 659 00:34:48,400 --> 00:34:51,560 Speaker 1: Stallcatchers has already proven an effective treatment for one of 660 00:34:51,560 --> 00:34:56,400 Speaker 1: the diseases most insidious symptoms, helplessness. Yeah, I mean that 661 00:34:56,520 --> 00:34:59,000 Speaker 1: that really is so important to keep in mind. You know, 662 00:34:59,200 --> 00:35:02,120 Speaker 1: according to the All Cemer's Association, more than of what 663 00:35:02,160 --> 00:35:04,080 Speaker 1: we know about the disease was only discovered in the 664 00:35:04,120 --> 00:35:07,399 Speaker 1: last twenty years. So while progress might seem slow when 665 00:35:07,400 --> 00:35:10,879 Speaker 1: you consider what we've known about Alzheimer's for over a century, now, 666 00:35:11,320 --> 00:35:13,719 Speaker 1: at least we're now at a point where researchers are 667 00:35:13,760 --> 00:35:17,120 Speaker 1: covering a massive amount of ground fairly quickly. And I 668 00:35:17,200 --> 00:35:20,080 Speaker 1: know it sounds cliche, but we really are closer to 669 00:35:20,120 --> 00:35:23,040 Speaker 1: a cure than ever before, even if that happy outcomes 670 00:35:23,040 --> 00:35:25,719 Speaker 1: still has many years left to go. Yeah, that's true. 671 00:35:25,760 --> 00:35:28,520 Speaker 1: And you know, in the meantime, you've got these ridiculously 672 00:35:28,640 --> 00:35:31,239 Speaker 1: smart people that will continue coming up with new and 673 00:35:31,320 --> 00:35:34,440 Speaker 1: clever ways to manage symptoms in Alzheimer's patients and to 674 00:35:34,520 --> 00:35:37,920 Speaker 1: keep hope alive in their loved ones. Yeah. So with 675 00:35:37,960 --> 00:35:39,440 Speaker 1: that in mind, what do you say we dive into 676 00:35:39,480 --> 00:35:42,120 Speaker 1: the fact off and check out a few more promising approaches. 677 00:35:42,840 --> 00:35:53,239 Speaker 1: All right, let's do it, h Okay, So I want 678 00:35:53,280 --> 00:35:55,640 Speaker 1: to talk about a couple of very different groups that 679 00:35:55,680 --> 00:35:58,960 Speaker 1: have made for very interesting studies. And the first is 680 00:35:58,960 --> 00:36:01,840 Speaker 1: in a village in the mountains of northwestern Colombia, and 681 00:36:01,840 --> 00:36:04,799 Speaker 1: it's called Antioquia, which happens to be home to the 682 00:36:04,800 --> 00:36:09,080 Speaker 1: world's largest concentration of Alzheimer's suffers, And to make matters 683 00:36:09,120 --> 00:36:14,080 Speaker 1: more difficult, they're primarily people dealing with early onset Alzheimer's. Now, 684 00:36:14,120 --> 00:36:16,080 Speaker 1: this is because of a gene that's been passed down 685 00:36:16,080 --> 00:36:19,640 Speaker 1: through the generations and has unfortunately stayed in the population 686 00:36:19,760 --> 00:36:23,080 Speaker 1: because of inbreeding, and sadly, what it often means is 687 00:36:23,120 --> 00:36:26,120 Speaker 1: that rather than children eventually taking care of their parents 688 00:36:26,280 --> 00:36:30,200 Speaker 1: as those parents age, the reverse often happens, where the 689 00:36:30,280 --> 00:36:33,080 Speaker 1: elderly population is having to care for their children in 690 00:36:33,120 --> 00:36:37,120 Speaker 1: their forties and fifties. Oh man, that's tragic. So in 691 00:36:37,280 --> 00:36:39,520 Speaker 1: our research for today's episode, I was reading about how 692 00:36:39,560 --> 00:36:42,000 Speaker 1: scientists have been able to study the gradual impact of 693 00:36:42,000 --> 00:36:45,719 Speaker 1: Alzheimer's on writers as they battled the disease. And one 694 00:36:45,719 --> 00:36:48,160 Speaker 1: of these was Iris Murdoch, who is this philosopher and 695 00:36:48,200 --> 00:36:51,080 Speaker 1: writer who is eventually unable to write because her demension 696 00:36:51,120 --> 00:36:53,720 Speaker 1: has just got so bad. But because she wrote twenty 697 00:36:53,760 --> 00:36:56,480 Speaker 1: six novels, researchers were able to look at her writing 698 00:36:56,520 --> 00:36:58,440 Speaker 1: over time and found that while the structure of her 699 00:36:58,480 --> 00:37:01,320 Speaker 1: novels pretty much stayed the same, in her later works, 700 00:37:01,320 --> 00:37:04,400 Speaker 1: the vocabulary was far more limited, and there was actually 701 00:37:04,400 --> 00:37:07,239 Speaker 1: a similar study downe on Agatha Christie's work and they 702 00:37:07,239 --> 00:37:10,239 Speaker 1: found a twenty decrease in her vocabulary later in her 703 00:37:10,280 --> 00:37:13,000 Speaker 1: writing career. In fact, you know, it's kind of strange, 704 00:37:13,000 --> 00:37:16,320 Speaker 1: but one of her last novels, Elephants Can Remember, actually 705 00:37:16,360 --> 00:37:19,080 Speaker 1: involves the writer dealing with memory issues. But you know, 706 00:37:19,680 --> 00:37:21,759 Speaker 1: the whole thing really is fascinating that if you have 707 00:37:21,840 --> 00:37:25,000 Speaker 1: this legacy of written work, you know, it's not just 708 00:37:25,160 --> 00:37:29,480 Speaker 1: literature departments but scientists who can actually analyze it for humanity. Yeah, 709 00:37:29,560 --> 00:37:31,759 Speaker 1: it's definitely interesting. They're they're taking a look at that. 710 00:37:32,640 --> 00:37:35,120 Speaker 1: So I mentioned earlier there was another group that also 711 00:37:35,200 --> 00:37:38,200 Speaker 1: made for very interesting studies. And the other one I 712 00:37:38,239 --> 00:37:41,080 Speaker 1: wanted to talk about is this multi decade study. It's 713 00:37:41,080 --> 00:37:43,920 Speaker 1: called the nun Study, and it's actually one of the 714 00:37:44,000 --> 00:37:47,920 Speaker 1: largest Alzheimer's studies ever conducted. There was a researcher from 715 00:37:47,920 --> 00:37:50,759 Speaker 1: the University of Minnesota named David Snowden that got the 716 00:37:50,800 --> 00:37:53,400 Speaker 1: study going in the mid nineteen eighties, and it was 717 00:37:53,440 --> 00:37:55,959 Speaker 1: with a group called the school Sisters of Notre Dame 718 00:37:56,280 --> 00:37:58,759 Speaker 1: who agreed not only to have these certain evaluations done 719 00:37:58,880 --> 00:38:01,960 Speaker 1: during their lives. It also to have their brain studied 720 00:38:02,040 --> 00:38:05,040 Speaker 1: after they passed away. Now, what makes this group so 721 00:38:05,120 --> 00:38:08,200 Speaker 1: helpful to study is that it's a relatively homogeneous group 722 00:38:08,239 --> 00:38:11,799 Speaker 1: that lives pretty similar lifestyles. So it's a good bit 723 00:38:11,800 --> 00:38:14,040 Speaker 1: more control than what you could do with with most 724 00:38:14,120 --> 00:38:17,840 Speaker 1: other groups. The study is actually still going, but they've 725 00:38:17,880 --> 00:38:21,400 Speaker 1: had some very interesting findings over the years, and including 726 00:38:21,440 --> 00:38:23,360 Speaker 1: the fact that researchers have been able to look at 727 00:38:23,400 --> 00:38:27,600 Speaker 1: autobiographical essays that these sisters wrote when they joined the sisterhood, 728 00:38:27,960 --> 00:38:30,520 Speaker 1: not on average that would have been in their early twenties, 729 00:38:31,160 --> 00:38:33,360 Speaker 1: and even from those essays, they are able to predict 730 00:38:33,480 --> 00:38:36,680 Speaker 1: those who are more likely to develop Alzheimer's. So those 731 00:38:36,719 --> 00:38:39,120 Speaker 1: who wrote the more complex essays were found to be 732 00:38:39,200 --> 00:38:42,279 Speaker 1: less likely to develop the disease. It's just it's just 733 00:38:42,360 --> 00:38:44,600 Speaker 1: wild to me that they could find predictors like that 734 00:38:44,760 --> 00:38:48,200 Speaker 1: so early in these women's lives. Oh man, that is fascinating. 735 00:38:48,800 --> 00:38:50,759 Speaker 1: So here's something I think about a lot, right there. 736 00:38:50,760 --> 00:38:53,040 Speaker 1: All these studies out there that tell us one week 737 00:38:53,080 --> 00:38:54,879 Speaker 1: that something is bad for us, and then the next 738 00:38:54,920 --> 00:38:57,239 Speaker 1: week it's good for us. And here's another one of 739 00:38:57,280 --> 00:39:00,400 Speaker 1: those studies, and it's about cell phone use. Well, I mean, 740 00:39:00,400 --> 00:39:03,000 Speaker 1: I understand that that it might not be as harmful 741 00:39:03,080 --> 00:39:05,719 Speaker 1: as we once feared, but how in the world could 742 00:39:05,719 --> 00:39:08,880 Speaker 1: this be a good thing. Yeah, so I'm not suggesting 743 00:39:08,880 --> 00:39:11,720 Speaker 1: anyone start increasing their cell phone use, but a study 744 00:39:11,719 --> 00:39:14,239 Speaker 1: out of South Florida did find the exposing mice to 745 00:39:14,320 --> 00:39:17,680 Speaker 1: microwave radiation from cell phones both seemed to protect them 746 00:39:17,680 --> 00:39:20,320 Speaker 1: from Alzheimer's and in some case even seemed to reverse 747 00:39:20,360 --> 00:39:23,120 Speaker 1: the effects. So, as one of the study leads, this 748 00:39:23,200 --> 00:39:26,920 Speaker 1: guy wants Sanchez Ramo said, quote, it's such dramatic and 749 00:39:26,960 --> 00:39:30,120 Speaker 1: counterintuitive effect. I joked that the animals must have been 750 00:39:30,160 --> 00:39:33,400 Speaker 1: mislabeled or that the power wasn't switched on. And you 751 00:39:33,400 --> 00:39:35,560 Speaker 1: know that this effect was there for both the mice 752 00:39:35,600 --> 00:39:38,439 Speaker 1: who were exposed to the radiation before they showed signs 753 00:39:38,440 --> 00:39:41,160 Speaker 1: of Alzheimer's and for those who were exposed after they 754 00:39:41,200 --> 00:39:44,520 Speaker 1: started showing signs. And again, you know, we're not recommending 755 00:39:44,520 --> 00:39:47,880 Speaker 1: anyone start using their phones more because of this interesting study. 756 00:39:47,920 --> 00:39:50,800 Speaker 1: It's just that it's fascinating to think that there's studies 757 00:39:50,840 --> 00:39:53,120 Speaker 1: that you don't expect the results to come out the 758 00:39:53,120 --> 00:39:56,399 Speaker 1: way they do. You know, I've actually got another one 759 00:39:56,680 --> 00:39:59,120 Speaker 1: like that where you never know if the effect of 760 00:39:59,160 --> 00:40:01,600 Speaker 1: something is going to be positive or negative. And this 761 00:40:01,640 --> 00:40:04,680 Speaker 1: one involves caffeine, and of course we bring the mice 762 00:40:04,719 --> 00:40:06,680 Speaker 1: in again for a study, and it's a study led 763 00:40:06,680 --> 00:40:09,800 Speaker 1: by researcher Gary Aaron Dash and his team found that 764 00:40:09,840 --> 00:40:11,839 Speaker 1: a group of mice who were bred to end up 765 00:40:11,880 --> 00:40:15,160 Speaker 1: with Alzheimer's, if one group was given regular water to 766 00:40:15,239 --> 00:40:18,560 Speaker 1: drink and the other was given water with caffeine infused 767 00:40:18,560 --> 00:40:21,319 Speaker 1: in it, those who had receive the caffeine actually had 768 00:40:21,360 --> 00:40:25,320 Speaker 1: a fifty percent decrease in their beta amyloid levels. No, 769 00:40:25,440 --> 00:40:27,759 Speaker 1: those were, of course those tangled proteins that we talked 770 00:40:27,800 --> 00:40:30,440 Speaker 1: about earlier, And the amount of caffeine was roughly the 771 00:40:30,440 --> 00:40:33,279 Speaker 1: equivalent of giving a person five cups of coffee and 772 00:40:33,360 --> 00:40:35,840 Speaker 1: a day. And while this was a promising finding that 773 00:40:35,880 --> 00:40:38,359 Speaker 1: perhaps caffeine could be introduced in some way to help 774 00:40:38,400 --> 00:40:41,920 Speaker 1: those who have already developed Alzheimer's, it's of course still early, 775 00:40:42,000 --> 00:40:44,840 Speaker 1: And as with the study you last mentioned, there's definitely 776 00:40:44,920 --> 00:40:47,080 Speaker 1: not a recommendation here that people go out and start 777 00:40:47,120 --> 00:40:50,040 Speaker 1: consuming lots of caffeine. So, you know, I do think 778 00:40:50,040 --> 00:40:52,240 Speaker 1: it's good to end on a couple of hopeful facts, 779 00:40:52,280 --> 00:40:53,880 Speaker 1: even if there's still a long way to go with 780 00:40:53,920 --> 00:40:56,920 Speaker 1: all these studies. And I actually say we should dedicate 781 00:40:56,960 --> 00:41:00,640 Speaker 1: today's trophy to the many brilliant researchers, Kara loved Ones 782 00:41:00,719 --> 00:41:03,680 Speaker 1: and millions of brave patients who remained determined to fight 783 00:41:03,760 --> 00:41:07,760 Speaker 1: this disease. I second that. So here's the continued progress 784 00:41:07,760 --> 00:41:10,720 Speaker 1: and the fight against Alzheimer's. Thanks so much for listening, 785 00:41:25,120 --> 00:41:27,640 Speaker 1: Thanks again for listening. Part Time Genius is a production 786 00:41:27,640 --> 00:41:30,080 Speaker 1: of how stuff works and wouldn't be possible without several 787 00:41:30,120 --> 00:41:32,640 Speaker 1: brilliant people who do the important things we couldn't even 788 00:41:32,680 --> 00:41:36,000 Speaker 1: begin to understand. Tristan McNeil does the editing thing. Noel 789 00:41:36,040 --> 00:41:38,040 Speaker 1: Brown made the theme song and does the mixy mixy 790 00:41:38,120 --> 00:41:41,640 Speaker 1: sound thing. Jerry Rowland does the exact producer thing. Gay 791 00:41:41,680 --> 00:41:43,840 Speaker 1: blues Yer is our lead researcher, with support from the 792 00:41:43,880 --> 00:41:47,280 Speaker 1: research army including Austin Thompson, Nolan Brown and Lucas Adams 793 00:41:47,320 --> 00:41:49,200 Speaker 1: and Eve Jeff Cook gets the show to your ears. 794 00:41:49,280 --> 00:41:51,239 Speaker 1: Good job, Eves. If you like what you heard, we 795 00:41:51,280 --> 00:41:53,400 Speaker 1: hope you'll subscribe. And if you really really like what 796 00:41:53,440 --> 00:41:55,520 Speaker 1: you've heard, maybe you could leave a good review for us. 797 00:41:55,680 --> 00:42:02,759 Speaker 1: Do we do We forget Jason Jason, who did the 798 00:42:02,840 --> 00:42:03,400 Speaker 1: property bu