1 00:00:06,019 --> 00:00:08,699 Speaker 1: Hi, this is Kopi Time podcast series on markets and 2 00:00:08,699 --> 00:00:12,310 Speaker 1: economies from DBS Group Research. I'm Taiu Bei, chief economist, 3 00:00:12,449 --> 00:00:15,350 Speaker 1: welcoming you to our 149th episode. 4 00:00:16,110 --> 00:00:19,819 Speaker 1: Today we will discuss longevity with the foremost expert in 5 00:00:19,819 --> 00:00:23,559 Speaker 1: the field. Brian Kennedy is distinguished professor of biochemistry and 6 00:00:23,559 --> 00:00:27,180 Speaker 1: physiology at Yong Lu Lin School of Medicine at National 7 00:00:27,180 --> 00:00:30,170 Speaker 1: University of Singapore. He's also the director of the Center 8 00:00:30,170 --> 00:00:35,060 Speaker 1: for Healthy Longevity National University Health System. Professor Brian Kennedy, 9 00:00:35,220 --> 00:00:36,129 Speaker 1: welcome to Kobe time. 10 00:00:37,040 --> 00:00:37,958 Speaker 2: Thanks, pleasure to be 11 00:00:37,959 --> 00:00:38,430 Speaker 2: here. 12 00:00:38,799 --> 00:00:41,348 Speaker 1: It's great to have you. This is a subject close 13 00:00:41,348 --> 00:00:43,759 Speaker 1: to my heart. I was just telling you right before 14 00:00:43,759 --> 00:00:47,520 Speaker 1: this recording that all the 40-somethings and 50-somethings I know they're, 15 00:00:47,560 --> 00:00:49,799 Speaker 1: they're deep into this. So I will have a lot 16 00:00:49,799 --> 00:00:53,720 Speaker 1: of eager friends listening into this podcast. Uh prof, let's 17 00:00:53,720 --> 00:00:56,240 Speaker 1: begin by defining senescence. 18 00:00:57,860 --> 00:01:01,159 Speaker 2: Yeah, senescence is a, a term that gets used for 19 00:01:01,159 --> 00:01:04,260 Speaker 2: multiple meanings, and I think that's one of the reasons 20 00:01:04,260 --> 00:01:07,459 Speaker 2: that it's complicated for people to understand. So for instance, 21 00:01:07,540 --> 00:01:11,699 Speaker 2: when a uh salmon comes back into the fresh water 22 00:01:11,699 --> 00:01:16,139 Speaker 2: and dies, that's called senescence. Um, but I think most 23 00:01:16,139 --> 00:01:16,910 Speaker 2: people 24 00:01:17,589 --> 00:01:21,529 Speaker 2: In in today's discussion would refer to it as the 25 00:01:22,089 --> 00:01:27,000 Speaker 2: uh cessation of division of cells in your body, uh, so, uh, 26 00:01:27,010 --> 00:01:30,690 Speaker 2: cells can for a variety of different reasons, stop dividing 27 00:01:30,690 --> 00:01:34,599 Speaker 2: and enter into a senescent state. Uh, and in that 28 00:01:34,599 --> 00:01:39,250 Speaker 2: senescent state, they're no longer able to support regeneration of tissues, 29 00:01:39,330 --> 00:01:42,089 Speaker 2: but they still are live cells and they can secrete 30 00:01:42,089 --> 00:01:45,089 Speaker 2: factors that are pro-inflammatory. 31 00:01:46,050 --> 00:01:46,059 Speaker 2: No 32 00:01:47,889 --> 00:01:48,730 Speaker 2: Go ahead, please. No, 33 00:01:49,089 --> 00:01:50,730 Speaker 1: go ahead, no, no, I'm, I'm listening. Yeah, 34 00:01:50,889 --> 00:01:54,010 Speaker 2: so 11 thing senescence is therefore is to keep cells 35 00:01:54,010 --> 00:01:56,769 Speaker 2: from becoming cancer cells. So when something goes wrong in 36 00:01:56,769 --> 00:01:58,260 Speaker 2: a cell and it may be on its way to 37 00:01:58,260 --> 00:02:02,169 Speaker 2: being a tumor cell, the senescence pathway can be invoked 38 00:02:02,169 --> 00:02:04,610 Speaker 2: and that stops the cell from being able to divide. 39 00:02:04,650 --> 00:02:06,730 Speaker 2: So in a sense, it's anti-cancer. 40 00:02:07,500 --> 00:02:12,360 Speaker 2: On the other hand, these, these cells secrete pro-inflammatory factors 41 00:02:12,360 --> 00:02:15,820 Speaker 2: that influence cells around them and maybe even throughout the 42 00:02:15,820 --> 00:02:19,100 Speaker 2: body in some cases, if there's enough senescence and so 43 00:02:19,100 --> 00:02:21,859 Speaker 2: that can actually stimulate aging and cancer, so it's kind 44 00:02:21,860 --> 00:02:22,660 Speaker 2: of a trade-off. 45 00:02:23,889 --> 00:02:26,470 Speaker 1: Right, OK, so that's exactly where I was to jump into. 46 00:02:26,669 --> 00:02:28,809 Speaker 1: So the question I guess is, you know, so what 47 00:02:28,809 --> 00:02:32,500 Speaker 1: are the molecular hallmarks of aging? Is it, is it 48 00:02:32,500 --> 00:02:35,919 Speaker 1: the what you just described that the cells stop dividing 49 00:02:35,919 --> 00:02:39,820 Speaker 1: and then they may be secreting inflammatory material? 50 00:02:41,029 --> 00:02:44,130 Speaker 2: Yeah, I, I think we can list several different things 51 00:02:44,130 --> 00:02:46,889 Speaker 2: that are driving the aging process. Cells in essence is 52 00:02:46,889 --> 00:02:50,449 Speaker 2: certainly one of them. Um, the inability of your adult 53 00:02:50,449 --> 00:02:54,410 Speaker 2: stem cells to replenish their tissues, levels of inflammation in 54 00:02:54,410 --> 00:02:57,690 Speaker 2: the body, your telomeres, DNA damage, all of these things 55 00:02:57,690 --> 00:03:00,008 Speaker 2: probably contribute to the aging process. 56 00:03:00,508 --> 00:03:04,669 Speaker 2: Um, and, uh, it's still somewhat of a mystery to 57 00:03:05,199 --> 00:03:08,198 Speaker 2: put them in order and talk about how they contribute, 58 00:03:08,279 --> 00:03:11,038 Speaker 2: what percentage each of, each of them contributes. So I 59 00:03:11,038 --> 00:03:13,449 Speaker 2: think a better way of thinking about it is that 60 00:03:14,020 --> 00:03:16,549 Speaker 2: You have a a network in your body that maintains 61 00:03:16,550 --> 00:03:20,779 Speaker 2: health and it's kind of a homeostatic network, uh, and 62 00:03:21,029 --> 00:03:23,949 Speaker 2: this network can respond to things that happen in your body. You, 63 00:03:24,110 --> 00:03:25,710 Speaker 2: you know, you get out in the sun, you get 64 00:03:25,710 --> 00:03:29,339 Speaker 2: skin damage, you start eating a lot of fast food, 65 00:03:29,429 --> 00:03:31,989 Speaker 2: you know, your body adapts to that and it keeps 66 00:03:31,990 --> 00:03:35,020 Speaker 2: you healthy for the most part. But over time that 67 00:03:35,020 --> 00:03:37,179 Speaker 2: network breaks down, and when it breaks down, 68 00:03:37,750 --> 00:03:40,699 Speaker 2: These hallmarks of what you see, high levels of inflammation, 69 00:03:40,789 --> 00:03:42,630 Speaker 2: your stem cells don't work as well, so all of 70 00:03:42,630 --> 00:03:47,029 Speaker 2: a sudden the senescent burden goes way up. Um, and 71 00:03:47,029 --> 00:03:49,929 Speaker 2: so they're, they're telling you that aging is happening, but 72 00:03:49,929 --> 00:03:52,389 Speaker 2: I think preserving the network is really what we want 73 00:03:52,389 --> 00:03:55,270 Speaker 2: to do to keep people young, not, you know, treat 74 00:03:55,270 --> 00:03:58,229 Speaker 2: each hallmark separately. I think that that so far hasn't 75 00:03:58,229 --> 00:03:59,100 Speaker 2: worked very well. 76 00:04:00,250 --> 00:04:04,539 Speaker 1: And when does it begin? My son is 11 years old. 77 00:04:04,630 --> 00:04:07,509 Speaker 1: Clearly he is not going through the process of aging yet, 78 00:04:07,589 --> 00:04:09,990 Speaker 1: but would he at the age of 18, at 25, 79 00:04:10,070 --> 00:04:12,300 Speaker 1: is it hard to put a marker around that? 80 00:04:12,710 --> 00:04:15,270 Speaker 2: Well, I, I may even disagree with you. I think 81 00:04:15,270 --> 00:04:19,350 Speaker 2: we we recently did a biomarker study using DNA methylation 82 00:04:19,350 --> 00:04:21,059 Speaker 2: of 10,000 Singaporeans. 83 00:04:21,570 --> 00:04:24,380 Speaker 2: And we happened to have a birth cohort in that study, 84 00:04:24,540 --> 00:04:27,940 Speaker 2: so we were looking at newborn babies, and already we 85 00:04:27,940 --> 00:04:31,808 Speaker 2: can detect that baby boys were older than baby girls. Uh, 86 00:04:31,829 --> 00:04:34,859 Speaker 2: and so that suggests that maybe aging starts in the womb, 87 00:04:35,100 --> 00:04:38,380 Speaker 2: you know, and that already there's something happening that's different 88 00:04:38,380 --> 00:04:40,140 Speaker 2: between boys and girls. 89 00:04:40,630 --> 00:04:46,220 Speaker 2: Um, so, certainly during development, you don't see manifestations of aging, 90 00:04:47,149 --> 00:04:49,760 Speaker 2: you know, kids are getting stronger, they're getting smarter, their, 91 00:04:49,829 --> 00:04:53,720 Speaker 2: their brain is, is developing, um, and so, you know, 92 00:04:53,790 --> 00:04:56,869 Speaker 2: you begin to see the decline in aging in adulthood, 93 00:04:57,070 --> 00:04:57,980 Speaker 2: you know, maybe. 94 00:04:59,269 --> 00:05:02,269 Speaker 2: You know, starting around 30 maybe, but 95 00:05:03,109 --> 00:05:05,988 Speaker 2: And there may be parameters that are driving that aging 96 00:05:05,988 --> 00:05:09,070 Speaker 2: process that are not detectable, but happening throughout life. 97 00:05:10,220 --> 00:05:14,470 Speaker 1: And is the manifestation of aging first in cognition or 98 00:05:14,470 --> 00:05:18,708 Speaker 1: is it more physiological developments that you pick up as 99 00:05:18,709 --> 00:05:19,700 Speaker 1: signs of aging? 100 00:05:20,109 --> 00:05:20,190 Speaker 2: Well, 101 00:05:20,250 --> 00:05:22,229 Speaker 2: I, I think that a lot of people would say 102 00:05:22,230 --> 00:05:25,109 Speaker 2: that the ovarian aging is the first thing you really 103 00:05:25,109 --> 00:05:27,709 Speaker 2: see in women, so you, you know, you have a 104 00:05:27,709 --> 00:05:30,549 Speaker 2: decline in fertility starting in your 30s and 105 00:05:30,820 --> 00:05:35,519 Speaker 2: Uh, menopause somewhere in your 40s and 50s, um, that's 106 00:05:35,519 --> 00:05:39,510 Speaker 2: indicating that ovaries are, are aging, I think. Uh, also, 107 00:05:39,559 --> 00:05:44,399 Speaker 2: your thymus tends to undergo what's called involution, which leads 108 00:05:44,399 --> 00:05:48,220 Speaker 2: to a dramatic reduction in production of immune cells. So 109 00:05:48,480 --> 00:05:51,320 Speaker 2: those are two things that happen relatively early during the 110 00:05:51,320 --> 00:05:53,519 Speaker 2: aging process, um. 111 00:05:54,178 --> 00:05:57,380 Speaker 2: Muscle loss, you know, starts usually around 40 and people 112 00:05:57,380 --> 00:06:00,659 Speaker 2: say you, the average person loses about 1% muscle mass 113 00:06:00,660 --> 00:06:05,140 Speaker 2: a year after that. Um, cognitive decline, and, and actually 114 00:06:05,140 --> 00:06:07,988 Speaker 2: physical decline, it depends on how you measure it, you know, 115 00:06:08,100 --> 00:06:11,489 Speaker 2: you don't see a whole lot of 40 year old 116 00:06:11,488 --> 00:06:14,058 Speaker 2: athletes right at the top level. 117 00:06:14,510 --> 00:06:18,920 Speaker 2: Um, and already when you're measuring how fast like neurons 118 00:06:18,920 --> 00:06:21,600 Speaker 2: fire and connect, you know, it's already slowing down at 119 00:06:21,600 --> 00:06:24,880 Speaker 2: that point, but it's hard to measure because your body's, 120 00:06:25,079 --> 00:06:28,040 Speaker 2: like I said, is good at compensating, you know, so 121 00:06:28,040 --> 00:06:30,839 Speaker 2: it finds other ways to adapt to to to things 122 00:06:30,839 --> 00:06:32,440 Speaker 2: that are going a little bit slower and you don't 123 00:06:32,440 --> 00:06:35,269 Speaker 2: really notice that in your daily life. So, if you 124 00:06:35,269 --> 00:06:37,799 Speaker 2: really want to take really deep measures, you can already 125 00:06:37,799 --> 00:06:40,410 Speaker 2: start to see things in 30s and 40s, but 126 00:06:40,790 --> 00:06:44,149 Speaker 2: You know, in terms of actual functioning disease-free health, you know, 127 00:06:44,269 --> 00:06:48,070 Speaker 2: that's usually not until 50s and 60s where you begin 128 00:06:48,070 --> 00:06:49,630 Speaker 2: to see a significant decline. 129 00:06:50,500 --> 00:06:54,630 Speaker 1: I recall reading somewhere that in terms of physics and 130 00:06:54,630 --> 00:06:59,350 Speaker 1: pure mathematics, the pathbreaking discoveries come from scientists who are 131 00:06:59,350 --> 00:07:02,019 Speaker 1: in their late 20s, early 30s. They even the Nobel 132 00:07:02,019 --> 00:07:05,070 Speaker 1: laureates don't necessarily go through pathbreaking discoveries in the late 133 00:07:05,070 --> 00:07:10,070 Speaker 1: 30s and 40s. I've heard about musicians that the really 134 00:07:10,070 --> 00:07:12,470 Speaker 1: brilliant musicians are much younger than the older ones. 135 00:07:13,290 --> 00:07:16,309 Speaker 2: Yeah, I think that that's certainly, it's certainly been well 136 00:07:16,309 --> 00:07:20,709 Speaker 2: uh defined for math and physics. Um, you know, biology 137 00:07:20,709 --> 00:07:22,540 Speaker 2: research is a little bit different because I have a 138 00:07:22,540 --> 00:07:25,459 Speaker 2: whole team of people working with me and so it's not. 139 00:07:26,399 --> 00:07:28,429 Speaker 2: You know, we may have a great discovery, I hope 140 00:07:28,429 --> 00:07:30,440 Speaker 2: we do, but whether it comes from my brain or 141 00:07:30,440 --> 00:07:33,109 Speaker 2: one of the young people's brains is debatable. 142 00:07:34,799 --> 00:07:38,839 Speaker 1: Um, Proff Kennedy, I have heard you talk, ah, in, 143 00:07:38,880 --> 00:07:43,200 Speaker 1: in public forum in, in terms of the distinction between 144 00:07:43,200 --> 00:07:46,829 Speaker 1: lifespan and health span, and really that I've also heard 145 00:07:46,829 --> 00:07:50,200 Speaker 1: you talk about this phrase, uh, compressed morbidity. So could 146 00:07:50,200 --> 00:07:52,750 Speaker 1: you help us sort of walk through these three things? 147 00:07:53,579 --> 00:07:56,500 Speaker 2: So, so healthspan is, it's a little bit hard to define, 148 00:07:56,519 --> 00:07:58,760 Speaker 2: but I think you can understand that. It's the period 149 00:07:58,760 --> 00:08:02,480 Speaker 2: of time that you're largely disease-free and that you're highly 150 00:08:02,480 --> 00:08:03,929 Speaker 2: functional in life, um. 151 00:08:04,829 --> 00:08:08,760 Speaker 2: And you typically that if the average lifespan in Singapore 152 00:08:08,760 --> 00:08:13,579 Speaker 2: is 85, the average health span would be around low 70s. Um, 153 00:08:13,589 --> 00:08:16,260 Speaker 2: and so there's a period of time, maybe a decade 154 00:08:16,609 --> 00:08:20,029 Speaker 2: on average, where people are alive, but they're not in 155 00:08:20,029 --> 00:08:23,350 Speaker 2: good health, or they're not functioning well, uh, and that 156 00:08:23,350 --> 00:08:26,269 Speaker 2: would be, that's where all the healthcare costs are and 157 00:08:26,269 --> 00:08:29,059 Speaker 2: that's where quality of life diminishes dramatically. 158 00:08:29,510 --> 00:08:33,169 Speaker 2: So if you have an intervention that uh compresses morbidity, 159 00:08:33,210 --> 00:08:37,080 Speaker 2: and we've published that with alphakitic glittering in mice, uh, 160 00:08:37,159 --> 00:08:39,809 Speaker 2: what you see is that the health span goes up 161 00:08:39,809 --> 00:08:42,890 Speaker 2: more than the lifespan. So it's, it causes a small 162 00:08:42,890 --> 00:08:45,809 Speaker 2: increase in lifespan, but a big increase in health span. 163 00:08:46,169 --> 00:08:48,929 Speaker 2: And so that period of time when you're sick and 164 00:08:48,929 --> 00:08:52,289 Speaker 2: have multi morbidity gets diminished, and so that's what compressed 165 00:08:52,289 --> 00:08:55,569 Speaker 2: morbidity is. And that's really what we would like to see, right? 166 00:08:55,719 --> 00:08:56,659 Speaker 2: Are you, you. 167 00:08:57,039 --> 00:08:59,979 Speaker 2: You know, jokingly, you'd like to be totally healthy and 168 00:08:59,979 --> 00:09:02,090 Speaker 2: then eventually get hit by a bus. I mean, there's, 169 00:09:03,659 --> 00:09:06,169 Speaker 2: I mean, maybe not, but you get the idea. 170 00:09:07,140 --> 00:09:09,579 Speaker 1: Yeah, I think, uh, Atul Goanes being mortal in the 171 00:09:09,580 --> 00:09:12,340 Speaker 1: very first chapter has something about like that his grandfather, 172 00:09:12,460 --> 00:09:15,210 Speaker 1: who was a lawyer till his 90s, basically, you know, 173 00:09:15,460 --> 00:09:17,299 Speaker 1: was healthy till the day he died because he got 174 00:09:17,299 --> 00:09:18,299 Speaker 1: hit by something like that. 175 00:09:18,780 --> 00:09:24,190 Speaker 1: Um, but, uh, I, you know, in WHO they have this, uh, 176 00:09:24,200 --> 00:09:30,190 Speaker 1: life expectancy adjusted marker called HAL, HALE Health adjusted life expectancy. 177 00:09:30,320 --> 00:09:32,359 Speaker 1: So is that the way to look at it then 178 00:09:32,359 --> 00:09:33,500 Speaker 1: that instead of looking at 179 00:09:33,500 --> 00:09:36,159 Speaker 2: that's one parameter you can use to try to measure 180 00:09:36,159 --> 00:09:38,030 Speaker 2: health span. I think there are different ways to look 181 00:09:38,030 --> 00:09:40,880 Speaker 2: at it, but, um, and like I said, it's there 182 00:09:40,880 --> 00:09:43,390 Speaker 2: I don't know if there's consensus on how to measure it, 183 00:09:43,429 --> 00:09:44,848 Speaker 2: but um. 184 00:09:45,450 --> 00:09:47,840 Speaker 2: That would be one parameter you could think of, yes. 185 00:09:48,210 --> 00:09:50,369 Speaker 1: I, I just like the fact that Singapore ranks number 186 00:09:50,369 --> 00:09:52,849 Speaker 1: one in the health adjusted life expectancy metric. 187 00:09:53,859 --> 00:09:56,119 Speaker 2: Yeah, I mean, Singapore is doing quite well, and you know, 188 00:09:56,289 --> 00:09:58,569 Speaker 2: what I hope is that they really make a commitment 189 00:09:58,570 --> 00:10:02,289 Speaker 2: to healthy longevity because the the time is right to 190 00:10:02,289 --> 00:10:05,770 Speaker 2: do that and this island could be the, the model 191 00:10:05,770 --> 00:10:09,949 Speaker 2: case for really extending health span because uh there's a 192 00:10:09,950 --> 00:10:15,718 Speaker 2: lot of things going in advantage for Singapore, uh, it's small, manageable, um, 193 00:10:15,770 --> 00:10:17,319 Speaker 2: already have good healthcare system. 194 00:10:17,650 --> 00:10:20,770 Speaker 2: Uh, and I think there's also a population that believes 195 00:10:20,770 --> 00:10:23,049 Speaker 2: in the government. So when the government takes the lead 196 00:10:23,049 --> 00:10:26,250 Speaker 2: on something, people will follow, you know, you, you could 197 00:10:26,250 --> 00:10:29,840 Speaker 2: say anything in the US you could say. 198 00:10:30,229 --> 00:10:32,679 Speaker 2: You know, drink 3 cups of water a day or 199 00:10:32,679 --> 00:10:35,080 Speaker 2: more and then half the population would disagree with you 200 00:10:35,080 --> 00:10:36,710 Speaker 2: on my definitions 201 00:10:37,929 --> 00:10:40,919 Speaker 1: for sure, absolutely, uh, I want to talk in greater 202 00:10:40,919 --> 00:10:44,440 Speaker 1: detail about Singapore uh later. I just want to cover 203 00:10:44,440 --> 00:10:46,760 Speaker 1: a couple of things first with you. Uh, when I 204 00:10:46,760 --> 00:10:48,000 Speaker 1: told my wife that, you know, I was gonna 205 00:10:48,114 --> 00:10:52,314 Speaker 1: Discuss with you this this health span lifespan issue. She 206 00:10:52,315 --> 00:10:55,924 Speaker 1: basically said, well, it's largely genetics. So is it largely 207 00:10:55,924 --> 00:10:59,455 Speaker 1: genetics or and she's a polysci PhD, so let's not 208 00:10:59,455 --> 00:11:02,034 Speaker 1: necessarily give her full credence. You, the expert, tell me 209 00:11:02,034 --> 00:11:05,864 Speaker 1: whether the genetics versus environment, how much do these things matter? 210 00:11:06,674 --> 00:11:08,395 Speaker 2: How much trouble am I going to get if I 211 00:11:08,395 --> 00:11:13,965 Speaker 2: disagree with her she has a very old you get. 212 00:11:16,049 --> 00:11:20,020 Speaker 2: Um, there's certainly a genetic component, but most research suggests 213 00:11:20,020 --> 00:11:25,580 Speaker 2: that it's relatively small, maybe 30% of the, of the equation, 214 00:11:25,710 --> 00:11:28,619 Speaker 2: and the rest of it is sort of lifestyle environment, 215 00:11:28,789 --> 00:11:30,340 Speaker 2: where you live, how you live. 216 00:11:30,710 --> 00:11:34,159 Speaker 2: Um, and that it is very important. Now, there's an 217 00:11:34,159 --> 00:11:36,840 Speaker 2: exception to that though. There are people that live to 218 00:11:36,840 --> 00:11:41,520 Speaker 2: be centenarians. Um, they chose, they got there by choosing 219 00:11:41,520 --> 00:11:45,200 Speaker 2: the right parents. Uh, that's very genetic when, so people 220 00:11:45,200 --> 00:11:49,119 Speaker 2: with exceptional longevity have the right genetics, but this is 221 00:11:49,119 --> 00:11:53,939 Speaker 2: extremely rare. The, the vast majority of us, um, uh, 222 00:11:53,950 --> 00:11:56,150 Speaker 2: environment and lifestyle are probably more important. 223 00:11:57,400 --> 00:11:57,840 Speaker 2: But this, 224 00:11:57,849 --> 00:12:01,619 Speaker 1: this genetic lottery fascinates me. I'll give you my personal example. 225 00:12:01,750 --> 00:12:05,189 Speaker 1: My mom, she's no longer with us. She had all 226 00:12:05,190 --> 00:12:08,030 Speaker 1: sorts of morbidities, even at a fairly young age, diabetes 227 00:12:08,030 --> 00:12:11,989 Speaker 1: related morbidity and so on in her 60s. My dad 228 00:12:11,989 --> 00:12:15,219 Speaker 1: still around 90+. We had his 90th birthday last year. 229 00:12:15,320 --> 00:12:18,669 Speaker 1: Fingers crossed, he lives longer, and he has never really 230 00:12:18,669 --> 00:12:21,119 Speaker 1: stressed out about, you know, the right diet or the 231 00:12:21,119 --> 00:12:24,270 Speaker 1: right pills, whereas my mom who did pass away, actually 232 00:12:24,270 --> 00:12:25,349 Speaker 1: was far more worried about her 233 00:12:25,349 --> 00:12:26,228 Speaker 2: health. Yeah. 234 00:12:26,729 --> 00:12:28,530 Speaker 2: Well, there's two things I would say to that. One 235 00:12:28,530 --> 00:12:32,210 Speaker 2: is that, uh, centenarians don't typically live well, that they 236 00:12:32,210 --> 00:12:35,130 Speaker 2: have the genetics, right? So they, if you look at them, 237 00:12:35,169 --> 00:12:37,250 Speaker 2: they were more likely to be a little bit overweight. Now, 238 00:12:37,289 --> 00:12:39,729 Speaker 2: they weren't like crazy obese, but they were more likely 239 00:12:39,729 --> 00:12:43,169 Speaker 2: to be a little bit overweight. Uh, and near Barzilai's 240 00:12:43,169 --> 00:12:46,569 Speaker 2: study of Ashkenazi Jews, they're slightly more likely to be 241 00:12:46,570 --> 00:12:50,000 Speaker 2: smoking than than the po you know, so they have 242 00:12:50,000 --> 00:12:52,320 Speaker 2: the right genetics, they're resistant, um. 243 00:12:53,000 --> 00:12:54,510 Speaker 2: I would also say that. 244 00:12:55,559 --> 00:12:59,209 Speaker 2: I, I think it's great to be uh very alert 245 00:12:59,210 --> 00:13:01,900 Speaker 2: to a healthy lifestyle and doing your best to live 246 00:13:01,900 --> 00:13:04,380 Speaker 2: a healthy life, but I don't think worrying about it 247 00:13:04,380 --> 00:13:06,940 Speaker 2: constantly is a good thing. And so, you know, I, 248 00:13:07,020 --> 00:13:10,539 Speaker 2: I think that sometimes people forget the, the, the level 249 00:13:10,539 --> 00:13:14,059 Speaker 2: that stress plays in their aging, uh, and trying to 250 00:13:14,059 --> 00:13:17,299 Speaker 2: manage your stress, um, is, is critical. It's hard for 251 00:13:17,299 --> 00:13:20,700 Speaker 2: us to study that in animals, but I'm convinced it's 252 00:13:20,700 --> 00:13:23,940 Speaker 2: a critical component of aging, the mindset, you know. 253 00:13:24,299 --> 00:13:26,929 Speaker 2: And so you said father, not worrying, and he seems 254 00:13:26,929 --> 00:13:29,330 Speaker 2: to like go through life and take things kind of 255 00:13:29,330 --> 00:13:32,369 Speaker 2: as they come, maybe yeah, your father's worried that could 256 00:13:32,369 --> 00:13:33,330 Speaker 2: have been a factor. 257 00:13:33,950 --> 00:13:34,210 Speaker 2: Right. 258 00:13:34,609 --> 00:13:36,549 Speaker 1: Um, OK, so let's talk about the things that we 259 00:13:36,549 --> 00:13:40,119 Speaker 1: can control, um, you know, diet, this issue has been 260 00:13:40,119 --> 00:13:42,340 Speaker 1: around for maybe half a century, and there is my mother, 261 00:13:42,349 --> 00:13:48,340 Speaker 1: so exactly. So since we can't control our father and mother, uh, 262 00:13:48,559 --> 00:13:50,119 Speaker 1: the things that we can, let's talk a little bit 263 00:13:50,119 --> 00:13:53,819 Speaker 1: about diet and exercise, the two things that, you know, 264 00:13:54,000 --> 00:13:57,039 Speaker 1: a lot of people talk about, where do we stand 265 00:13:57,039 --> 00:13:59,950 Speaker 1: in terms of scientific zeitgeist in these two areas? 266 00:14:00,440 --> 00:14:00,679 Speaker 2: Let's 267 00:14:00,679 --> 00:14:02,919 Speaker 2: start with exercise because I think it's a little bit 268 00:14:02,919 --> 00:14:04,280 Speaker 2: of an easier discussion. 269 00:14:04,700 --> 00:14:10,630 Speaker 2: Um, you do it. If some combination of resistance training 270 00:14:10,630 --> 00:14:15,030 Speaker 2: and cardiovascular training is probably good for everybody. Um, it's 271 00:14:15,030 --> 00:14:17,150 Speaker 2: important to try to do both, as as you get older, 272 00:14:17,190 --> 00:14:19,549 Speaker 2: you wanna try to maintain that muscle mass. So one 273 00:14:19,549 --> 00:14:21,349 Speaker 2: of the most important things you can do is keep 274 00:14:21,349 --> 00:14:26,109 Speaker 2: your muscle mass. Um, but, you know, cardiovascular health is 275 00:14:26,109 --> 00:14:28,979 Speaker 2: obviously good for your heart and your vascular system, and 276 00:14:29,260 --> 00:14:32,890 Speaker 2: And a range of other systems as well. So, uh, the, 277 00:14:32,929 --> 00:14:36,650 Speaker 2: the key to me is sustainability, you know, and trying 278 00:14:36,650 --> 00:14:40,570 Speaker 2: to find exercise programs that fit your needs. If you're 279 00:14:40,570 --> 00:14:43,010 Speaker 2: doing something and you don't like it, you're, you're gonna 280 00:14:43,010 --> 00:14:47,119 Speaker 2: stop doing it. I just be honest, you know. So, um, 281 00:14:47,609 --> 00:14:50,090 Speaker 2: for me, you know, I enjoy running. I, I kind 282 00:14:50,090 --> 00:14:52,059 Speaker 2: of get my mindfulness from running. 283 00:14:52,559 --> 00:14:56,419 Speaker 2: It's, it's meditative almost for me as well. Um, and 284 00:14:56,419 --> 00:14:58,659 Speaker 2: so it's very good for me, and then I for 285 00:14:58,659 --> 00:15:02,119 Speaker 2: I forced myself to go do weightlifting, and I've, I've 286 00:15:02,119 --> 00:15:03,900 Speaker 2: learned to live with it. I don't love it, but 287 00:15:03,900 --> 00:15:06,260 Speaker 2: I don't hate it either, and so, and I feel 288 00:15:06,260 --> 00:15:09,619 Speaker 2: good after I, I complete it, so, uh, and I 289 00:15:09,619 --> 00:15:12,940 Speaker 2: can see the benefit of doing it over the long term, so. 290 00:15:13,700 --> 00:15:16,090 Speaker 2: You know, that that for me is what works, but if, 291 00:15:16,099 --> 00:15:18,890 Speaker 2: if tennis works for you, that's great. The the key 292 00:15:18,890 --> 00:15:21,979 Speaker 2: is find something that's sustainable. 293 00:15:24,729 --> 00:15:27,809 Speaker 1: The um one point that I wanted, sorry, I, did 294 00:15:27,809 --> 00:15:30,929 Speaker 1: I lose you? you can hear me, right? Yeah, um, 295 00:15:31,169 --> 00:15:32,489 Speaker 1: we're going to get your diet for a second. I 296 00:15:32,489 --> 00:15:35,049 Speaker 1: just want to share with you my personal slightly off-track 297 00:15:35,049 --> 00:15:37,369 Speaker 1: observation on this issue. I also like to run. I 298 00:15:37,369 --> 00:15:39,789 Speaker 1: also like the fact that during running all sorts 299 00:15:39,840 --> 00:15:42,460 Speaker 1: A serendipitous thing can happen. I was in Dubai yesterday. 300 00:15:42,669 --> 00:15:46,109 Speaker 1: I went running and I saw two peacocks flexing their 301 00:15:46,109 --> 00:15:50,109 Speaker 1: plumes in just all glory. If I were driving or biking, 302 00:15:50,150 --> 00:15:51,950 Speaker 1: I would not have seen that, and that that delight, 303 00:15:52,030 --> 00:15:55,739 Speaker 1: I think will sustain my running habit for years to come. Yeah, 304 00:15:55,869 --> 00:15:59,950 Speaker 2: I run outside instead of treadmills almost I only run 305 00:15:59,950 --> 00:16:02,150 Speaker 2: on a treadmill if there's no other choice because I 306 00:16:02,150 --> 00:16:04,849 Speaker 2: think you're right, you experience the world being outside running 307 00:16:04,849 --> 00:16:05,229 Speaker 2: and 308 00:16:05,640 --> 00:16:07,840 Speaker 2: Although I did try to run in Dubai last summer 309 00:16:07,840 --> 00:16:09,520 Speaker 2: and that was, that didn't go well. 310 00:16:12,010 --> 00:16:15,130 Speaker 1: December, January, February is what you want to do. Yeah, absolutely. Um, 311 00:16:15,320 --> 00:16:18,700 Speaker 1: you had mentioned about the muscle loss aspect, about 1% 312 00:16:18,700 --> 00:16:22,400 Speaker 1: muscle loss in your 40s onward, uh, so resistance training 313 00:16:22,400 --> 00:16:23,679 Speaker 1: is something that you would want. 314 00:16:23,765 --> 00:16:25,895 Speaker 1: People in their 40s onward to focus on as well, 315 00:16:26,034 --> 00:16:28,234 Speaker 2: yeah, for sure. And also it's good for your bones, 316 00:16:28,354 --> 00:16:30,635 Speaker 2: you know, one of the things that maintains bone density 317 00:16:30,635 --> 00:16:33,955 Speaker 2: is putting resistance on your bones, you know, putting load 318 00:16:33,955 --> 00:16:38,205 Speaker 2: on your bones. And so, um, when you're doing uh 319 00:16:38,395 --> 00:16:40,914 Speaker 2: resistance training, you're not only helping your muscle but your 320 00:16:40,914 --> 00:16:41,875 Speaker 2: bones as well. 321 00:16:42,950 --> 00:16:46,349 Speaker 1: Shouldn't dairy also help with respect to bone? Got lots 322 00:16:46,349 --> 00:16:47,059 Speaker 1: of calcium? 323 00:16:47,789 --> 00:16:51,429 Speaker 2: Uh, dairy is complicated. Um, you want to maintain the 324 00:16:51,429 --> 00:16:55,510 Speaker 2: appropriate calcium levels for sure, uh, as you age. Um, 325 00:16:55,669 --> 00:17:01,099 Speaker 2: a lot of studies suggest that very high dairy, accelerates aging. Um, 326 00:17:01,349 --> 00:17:04,390 Speaker 2: there are a lot of, uh, observational studies in, in 327 00:17:04,390 --> 00:17:05,819 Speaker 2: humans to suggest that. 328 00:17:06,189 --> 00:17:09,188 Speaker 2: Uh, so I tend to, I keep to a very 329 00:17:09,189 --> 00:17:12,069 Speaker 2: low dairy diet. I eat yogurt and I, I will 330 00:17:12,069 --> 00:17:17,629 Speaker 2: eat cheese sometimes, but I don't drink milk, uh, at all. So, but, 331 00:17:18,030 --> 00:17:19,829 Speaker 2: you know, how your body reacts to that and what 332 00:17:19,829 --> 00:17:22,420 Speaker 2: happens to your calcium levels is something you should be measuring, 333 00:17:22,430 --> 00:17:25,270 Speaker 2: and this is, this is one point I would like 334 00:17:25,270 --> 00:17:28,150 Speaker 2: to make is that people ask me about supplements and 335 00:17:28,150 --> 00:17:30,459 Speaker 2: vitamins and all of all of these things. 336 00:17:31,050 --> 00:17:33,290 Speaker 2: Well, you need to know where you are on this equation. 337 00:17:33,410 --> 00:17:34,810 Speaker 2: You need to be able to, you need to know 338 00:17:34,810 --> 00:17:38,489 Speaker 2: your vitamin levels. You need to know your meta metabolic levels, and, 339 00:17:38,810 --> 00:17:41,050 Speaker 2: you know, we, and now we have a clinical aging 340 00:17:41,050 --> 00:17:43,810 Speaker 2: clock where we can measure your biologic age based on 341 00:17:43,810 --> 00:17:48,040 Speaker 2: 50 clinical variables that everybody should be getting anyway. You know, 342 00:17:48,089 --> 00:17:52,290 Speaker 2: it's uh it's LDL and inflammatory factors and so if you're, 343 00:17:52,369 --> 00:17:55,000 Speaker 2: if you're one of these people that likes to do interventions, 344 00:17:55,089 --> 00:17:57,869 Speaker 2: whether it's lifestyle or supplements or drugs, a lot of 345 00:17:57,869 --> 00:17:59,280 Speaker 2: people are taking rapamycin. 346 00:17:59,550 --> 00:18:03,819 Speaker 2: You should be measuring outcomes as well and and trying 347 00:18:03,819 --> 00:18:06,649 Speaker 2: to find the strategy that works best for you, uh, 348 00:18:06,699 --> 00:18:09,520 Speaker 2: and I think a lot of people, uh, I think 349 00:18:09,520 --> 00:18:12,140 Speaker 2: I always actually I think my my God, I don't 350 00:18:12,140 --> 00:18:12,520 Speaker 2: know what your levels. 351 00:18:15,609 --> 00:18:16,010 Speaker 2: These are. 352 00:18:18,790 --> 00:18:21,959 Speaker 1: Um, prof, we sort of lost you in the last 353 00:18:21,959 --> 00:18:24,569 Speaker 1: 30 seconds. Just let's just uh go back to that 354 00:18:24,569 --> 00:18:27,170 Speaker 1: point of the, uh, after you talked about dairy, but 355 00:18:27,170 --> 00:18:29,689 Speaker 1: the 30, 40 things that you can do to test 356 00:18:29,689 --> 00:18:32,209 Speaker 1: your vitamin levels, that part, let's repeat that part. 357 00:18:33,579 --> 00:18:37,170 Speaker 2: Yeah, so we recently generated a clinical chemistry clock that 358 00:18:37,170 --> 00:18:40,709 Speaker 2: predicts your biologic age, and it does so from about 359 00:18:40,709 --> 00:18:44,599 Speaker 2: 40 or 50 analytes that you commonly get when you get, uh, 360 00:18:44,609 --> 00:18:47,649 Speaker 2: go to a doctor's office or hospital. And these are 361 00:18:47,650 --> 00:18:50,569 Speaker 2: things you should be doing all the time, measuring these factors. 362 00:18:50,930 --> 00:18:54,810 Speaker 2: These things like HbA1C, LDL, inflammatory factors. 363 00:18:55,170 --> 00:18:58,899 Speaker 2: Um, and you should know what those are, and if 364 00:18:58,900 --> 00:19:01,369 Speaker 2: you have them, we can also tell you your biologic age, 365 00:19:01,800 --> 00:19:04,859 Speaker 2: but you should also know your vitamin levels. Typically these 366 00:19:04,859 --> 00:19:08,500 Speaker 2: things are not measured, um, and people ask me, should 367 00:19:08,500 --> 00:19:12,198 Speaker 2: I take vitamin D or B complex, and I, I'm like, 368 00:19:12,260 --> 00:19:16,079 Speaker 2: I don't know, what are your levels? And they don't know. So, uh, 369 00:19:16,260 --> 00:19:19,140 Speaker 2: you should measure yourself and, and pay attention to how 370 00:19:19,140 --> 00:19:22,339 Speaker 2: you're doing, uh, before you just start taking a bunch 371 00:19:22,339 --> 00:19:23,329 Speaker 2: of different things. 372 00:19:24,050 --> 00:19:26,160 Speaker 1: All right, let's go to the broader issue of diet, 373 00:19:26,239 --> 00:19:28,719 Speaker 1: which you already have sort of foreshadowed that it's a 374 00:19:28,719 --> 00:19:31,599 Speaker 1: bit complex. um, what should one be eating and what 375 00:19:31,599 --> 00:19:32,599 Speaker 1: should one be avoiding? 376 00:19:33,260 --> 00:19:35,729 Speaker 2: Yeah, I mean, this is still highly debated. You can 377 00:19:35,729 --> 00:19:38,510 Speaker 2: find experts that will say just about anything. I think 378 00:19:38,510 --> 00:19:41,609 Speaker 2: the two common diets that people talk about for aging 379 00:19:41,609 --> 00:19:46,160 Speaker 2: are a vegetable rich diet, uh, that's low in, uh, 380 00:19:46,170 --> 00:19:51,939 Speaker 2: red meat and dairy, uh, kind of a Mediterranean diet, um, and, uh, 381 00:19:51,959 --> 00:19:54,250 Speaker 2: and that's the one I tend to believe in, uh, 382 00:19:54,339 --> 00:19:57,729 Speaker 2: and then there is another group that are very keto-oriented, 383 00:19:57,810 --> 00:20:01,410 Speaker 2: so they're like high protein, high fat, no carbs, um. 384 00:20:03,199 --> 00:20:07,170 Speaker 2: I think that that keto diet probably makes people lose weight, 385 00:20:08,060 --> 00:20:13,859 Speaker 2: but it also unbalances your uh uh nutrition such that 386 00:20:13,859 --> 00:20:18,260 Speaker 2: your body has to make relatively serious adaptation to what 387 00:20:18,260 --> 00:20:21,380 Speaker 2: it needs, and I think over the long term, most 388 00:20:21,380 --> 00:20:24,739 Speaker 2: of the evidence suggests that having high amino acid levels 389 00:20:24,739 --> 00:20:29,260 Speaker 2: accelerates aspects of aging, uh, whereas if you look at 390 00:20:29,260 --> 00:20:31,140 Speaker 2: the animal models where you keep 391 00:20:31,680 --> 00:20:35,599 Speaker 2: Uh, calorie and take uh isochlorine, so you're, we're not 392 00:20:35,599 --> 00:20:40,040 Speaker 2: changing total calories, and then you change macronutrients, it's actually 393 00:20:40,040 --> 00:20:43,520 Speaker 2: high carb, low protein that's associated with longer lifespan. 394 00:20:44,390 --> 00:20:46,589 Speaker 2: But there are two caveats to that. One is that 395 00:20:46,589 --> 00:20:49,859 Speaker 2: it's complex carbs, not simple sugars. I think those are, 396 00:20:49,989 --> 00:20:53,708 Speaker 2: are not good for you. Um, the other is that 397 00:20:53,709 --> 00:20:56,989 Speaker 2: for humans, if you say you're eating a diet rich 398 00:20:56,989 --> 00:21:00,959 Speaker 2: in carbohydrates, you're probably getting way too many calories. Carbohydrates 399 00:21:00,959 --> 00:21:03,339 Speaker 2: is the fastest way to over nutrition, and I think 400 00:21:03,339 --> 00:21:06,550 Speaker 2: that's why people perceive it as bad. But if you're 401 00:21:06,550 --> 00:21:09,280 Speaker 2: eating complex carbs and you're not eating too many calories, 402 00:21:09,310 --> 00:21:10,979 Speaker 2: it's probably not bad for you. 403 00:21:13,489 --> 00:21:16,209 Speaker 1: But when I think of diet in this part of 404 00:21:16,209 --> 00:21:18,689 Speaker 1: the world, there's a lot of noodles or rice in 405 00:21:18,689 --> 00:21:21,910 Speaker 1: people's diet. Those are pretty simple carbs, right? Yeah, 406 00:21:22,780 --> 00:21:25,050 Speaker 2: well, especially white rice. That's why there's been a lot 407 00:21:25,050 --> 00:21:27,319 Speaker 2: of movement to have brown rice or other types of 408 00:21:27,319 --> 00:21:31,599 Speaker 2: rice that are maintaining some of their nutrient richness. 409 00:21:32,880 --> 00:21:34,810 Speaker 1: And I'm I'm, I'm I'm smiling a little bit because 410 00:21:34,810 --> 00:21:37,050 Speaker 1: as you said, that these days you can hear just 411 00:21:37,050 --> 00:21:37,869 Speaker 1: about every 412 00:21:38,064 --> 00:21:41,425 Speaker 1: Variety of arguments in this regard, so in uh social 413 00:21:41,425 --> 00:21:45,545 Speaker 1: media right now, there's a whole anti-brown rice movement about 414 00:21:45,545 --> 00:21:47,224 Speaker 1: how brown rice is bad and we should be eating 415 00:21:47,224 --> 00:21:50,344 Speaker 1: white rice, not really sure how scientific those observations are, 416 00:21:50,425 --> 00:21:51,744 Speaker 1: but to your point there. 417 00:21:51,844 --> 00:21:53,823 Speaker 2: I don't know the details of that argument, but like 418 00:21:53,824 --> 00:21:56,104 Speaker 2: I said, I, I'm sure we could find somebody that 419 00:21:56,104 --> 00:21:58,744 Speaker 2: advocates for the milkshake diet if we looked hard enough. 420 00:22:00,385 --> 00:22:02,665 Speaker 2: This field is, is, um. 421 00:22:03,319 --> 00:22:05,880 Speaker 2: Well, it's not, we have a rough idea of what 422 00:22:05,880 --> 00:22:08,209 Speaker 2: people should do for health, but you know, there's a 423 00:22:08,209 --> 00:22:10,879 Speaker 2: lot of details that we don't have good research on 424 00:22:10,880 --> 00:22:11,479 Speaker 2: and 425 00:22:12,030 --> 00:22:15,540 Speaker 2: A lot of serious scientists try to understand what's good 426 00:22:15,540 --> 00:22:17,199 Speaker 2: for you and what's bad for you, and then on 427 00:22:17,199 --> 00:22:19,000 Speaker 2: top of that, there's a lot of people trying to 428 00:22:19,000 --> 00:22:21,560 Speaker 2: make money that are advocating for things that may not 429 00:22:21,560 --> 00:22:24,319 Speaker 2: have a lot of science behind them. So, I, I, 430 00:22:24,400 --> 00:22:27,369 Speaker 2: I get it. I understand when people are trying to 431 00:22:27,369 --> 00:22:30,280 Speaker 2: do healthy longevity, it's not as easy as it sounds 432 00:22:30,280 --> 00:22:33,589 Speaker 2: because depending on who you read and, and, and what 433 00:22:33,589 --> 00:22:36,839 Speaker 2: nutrition religion you brought to you, you may be doing 434 00:22:36,839 --> 00:22:38,349 Speaker 2: completely different things, so. 435 00:22:39,270 --> 00:22:43,089 Speaker 1: So let's talk about the science of slowing or even 436 00:22:43,089 --> 00:22:48,689 Speaker 1: reversing aging. You run a lab. You have talked in 437 00:22:48,689 --> 00:22:52,229 Speaker 1: public forum a lot about alpha keto glutarates and also, 438 00:22:52,250 --> 00:22:56,010 Speaker 1: if I'm pronouncing it right, spermmis. Tell us a little 439 00:22:56,010 --> 00:22:57,199 Speaker 1: bit about these two molecules. 440 00:22:57,920 --> 00:23:00,369 Speaker 2: Yeah, so, and, and I think that's just two of 441 00:23:00,369 --> 00:23:02,929 Speaker 2: a bunch of different molecules. I'll start with AKG cause 442 00:23:02,930 --> 00:23:06,469 Speaker 2: we've done a lot of research on it. Um, and, 443 00:23:06,530 --> 00:23:08,729 Speaker 2: and actually we have a product on the market called 444 00:23:08,729 --> 00:23:12,669 Speaker 2: Rejuin that has ato glutarate, it's time release version, plus 445 00:23:12,670 --> 00:23:17,770 Speaker 2: some vitamins, uh, full disclosure, I'm involved with that company, um. 446 00:23:18,739 --> 00:23:23,180 Speaker 2: But, uh, what, this is a central metabolite in the body. 447 00:23:23,260 --> 00:23:26,140 Speaker 2: You may have heard of NAD, that's another central metabolite. 448 00:23:26,180 --> 00:23:30,409 Speaker 2: These things are important for hundreds of reactions in the cell. Basically, 449 00:23:30,500 --> 00:23:32,939 Speaker 2: they're important for metabolism and 450 00:23:33,880 --> 00:23:36,099 Speaker 2: Excuse me, the levels go down with aging? 451 00:23:36,939 --> 00:23:39,959 Speaker 2: Uh, and if you bring the levels back up, uh, 452 00:23:40,020 --> 00:23:45,448 Speaker 2: you can improve aspects of health span or life span. Uh, so, uh, 453 00:23:45,459 --> 00:23:48,139 Speaker 2: I think that there's pretty good evidence for both of 454 00:23:48,140 --> 00:23:53,020 Speaker 2: those molecules. Uh, it's hard with NAD back up because 455 00:23:53,020 --> 00:23:57,060 Speaker 2: you can't take pills with NAD, um, so that, so 456 00:23:57,060 --> 00:24:00,500 Speaker 2: people do precursors, uh, with NM or you know things 457 00:24:00,500 --> 00:24:02,010 Speaker 2: that are converted to NAD. 458 00:24:03,000 --> 00:24:07,229 Speaker 2: But even NMR don't get good bioavailability, so you, you 459 00:24:07,229 --> 00:24:09,420 Speaker 2: swallow a pill, very, not that much of it gets 460 00:24:09,420 --> 00:24:13,099 Speaker 2: in the bloodstream. Um, there are ways around it. Now 461 00:24:13,099 --> 00:24:16,060 Speaker 2: there's a company that I'm helping out called IX BioPharma 462 00:24:16,060 --> 00:24:19,300 Speaker 2: that has a sublingual NAD, so this is directly NAD 463 00:24:19,660 --> 00:24:23,849 Speaker 2: and it goes directly into the bloodstream that way. Uh, so, uh, 464 00:24:24,060 --> 00:24:28,500 Speaker 2: those two, those two, metabolites are probably very important for aging. 465 00:24:29,000 --> 00:24:33,310 Speaker 2: Uh, spermidine is a natural product, uh, and it activates 466 00:24:33,310 --> 00:24:37,938 Speaker 2: autophagy in itself. Uh, and so autophagy is kind of a, a, 467 00:24:37,969 --> 00:24:41,760 Speaker 2: a garbage collection service for your cells. It takes the 468 00:24:41,760 --> 00:24:47,359 Speaker 2: damaged proteins and organelles and recycles the recycles them into, uh, 469 00:24:47,369 --> 00:24:51,640 Speaker 2: primary ingredients that can make new proteins, uh, and it's 470 00:24:51,640 --> 00:24:53,958 Speaker 2: important to clear out the damage in your cells and 471 00:24:53,959 --> 00:24:56,050 Speaker 2: if you can enhance autophagy. 472 00:24:56,449 --> 00:24:59,389 Speaker 2: It's typically good for aging as well, spermidine is one 473 00:24:59,390 --> 00:25:00,849 Speaker 2: of the molecules that does that. 474 00:25:01,670 --> 00:25:03,890 Speaker 2: But there are a lot of other natural products that 475 00:25:03,890 --> 00:25:07,290 Speaker 2: I think are impacting aging, and we tend to uh 476 00:25:07,290 --> 00:25:10,920 Speaker 2: believe that a lot of people believe that supplements are 477 00:25:12,089 --> 00:25:15,010 Speaker 2: kind of all snake oil and uh again, it's a, 478 00:25:15,089 --> 00:25:19,060 Speaker 2: it's a very, it's a profit-driven market, so, you know, 479 00:25:19,130 --> 00:25:20,810 Speaker 2: it's kind of the wild west, but I think there 480 00:25:20,810 --> 00:25:23,719 Speaker 2: are a lot of good ingredients in there that help people. 481 00:25:24,119 --> 00:25:26,099 Speaker 2: The most important thing we need to do is figure 482 00:25:26,099 --> 00:25:29,930 Speaker 2: out which people respond to which supplements or drugs or 483 00:25:29,930 --> 00:25:33,639 Speaker 2: lifestyle changes, any of these things, and improve their aging, 484 00:25:33,650 --> 00:25:36,329 Speaker 2: and right now we're still trying to figure that out. 485 00:25:36,410 --> 00:25:40,040 Speaker 2: How do we personalize this? Um, you know, it's I, 486 00:25:40,290 --> 00:25:42,689 Speaker 2: instead of telling everybody to take 10 different things or 487 00:25:42,689 --> 00:25:45,410 Speaker 2: every everybody to take one thing, we, we really wanna 488 00:25:45,410 --> 00:25:49,310 Speaker 2: know which person needs which intervention and, and that, I 489 00:25:49,310 --> 00:25:50,879 Speaker 2: don't think we're there yet as a field. 490 00:25:51,859 --> 00:25:56,489 Speaker 1: And your scientific approach is to try these things on 491 00:25:56,489 --> 00:25:59,719 Speaker 1: mice and then try it on humans, or is there 492 00:25:59,719 --> 00:26:00,540 Speaker 1: something in between? 493 00:26:01,300 --> 00:26:03,439 Speaker 2: Well, we use a whole range of bottles, so we 494 00:26:03,439 --> 00:26:08,000 Speaker 2: use uh uh yeast, worms, flies, killifish, which are these 495 00:26:08,000 --> 00:26:12,400 Speaker 2: rapidly aging fish from Africa and, and mice. Uh, we 496 00:26:12,400 --> 00:26:16,540 Speaker 2: don't use primates, uh, primates live a long time, so it, 497 00:26:16,560 --> 00:26:18,680 Speaker 2: it kind of makes the studies hard and they also 498 00:26:18,680 --> 00:26:22,880 Speaker 2: cost a fortune. Um, and so we, we don't do that. 499 00:26:22,920 --> 00:26:24,920 Speaker 2: We do some stem cell models in the lab as 500 00:26:24,920 --> 00:26:29,119 Speaker 2: well in in vitro organoid models, uh, but I think 501 00:26:29,119 --> 00:26:29,589 Speaker 2: that 502 00:26:30,209 --> 00:26:31,688 Speaker 2: My beliefs is that. 503 00:26:32,459 --> 00:26:35,300 Speaker 2: There's gonna be a lot of conservation of, of aging 504 00:26:35,300 --> 00:26:40,339 Speaker 2: pathways between animals and humans and other animals. Uh, the 505 00:26:40,339 --> 00:26:43,849 Speaker 2: reason I say that is that, you know, we found 506 00:26:43,849 --> 00:26:48,139 Speaker 2: the sirtuin pathway affects aging by studying single-celled yeast, and 507 00:26:48,140 --> 00:26:50,500 Speaker 2: it turns out to be relevant to human aging. We 508 00:26:50,500 --> 00:26:54,459 Speaker 2: found the MTOR pathway that way. The insulin IGF pathway 509 00:26:54,459 --> 00:26:55,969 Speaker 2: comes from worms and flies. 510 00:26:56,680 --> 00:26:59,760 Speaker 2: It seems like for whatever reason, the pathways that govern 511 00:26:59,760 --> 00:27:03,599 Speaker 2: aging are highly conserved between species. And so I think 512 00:27:03,599 --> 00:27:06,800 Speaker 2: if something is working in a mouse, there's a pretty 513 00:27:06,800 --> 00:27:10,040 Speaker 2: high probability that it's gonna work in a human, and 514 00:27:10,040 --> 00:27:14,030 Speaker 2: that's different from disease because the actual diseases humans get 515 00:27:14,030 --> 00:27:16,239 Speaker 2: are different than the ones mice get. And so you 516 00:27:16,239 --> 00:27:19,160 Speaker 2: can sort of engineer a human disease in a mouse, 517 00:27:19,599 --> 00:27:22,000 Speaker 2: but that's not the same thing as having the disease 518 00:27:22,000 --> 00:27:23,609 Speaker 2: developed naturally in a human. 519 00:27:24,010 --> 00:27:26,199 Speaker 2: And so I think the disease models in mice are 520 00:27:26,199 --> 00:27:28,000 Speaker 2: not as good as the aging model 521 00:27:28,000 --> 00:27:28,439 Speaker 2: will be. 522 00:27:29,790 --> 00:27:32,219 Speaker 1: OK, I'm absolutely fascinated by that point. I think David 523 00:27:32,219 --> 00:27:34,489 Speaker 1: Sinclair in his book sort of talks about it that, 524 00:27:34,500 --> 00:27:37,339 Speaker 1: you know, you don't have to wait for the final 525 00:27:37,339 --> 00:27:39,339 Speaker 1: trial on humans. As long as you know these are 526 00:27:39,339 --> 00:27:42,420 Speaker 1: not doing harm. If I show you some good results 527 00:27:42,420 --> 00:27:45,500 Speaker 1: out of mice, you can probably go ahead and take it. 528 00:27:45,540 --> 00:27:47,619 Speaker 1: I think that's David's argument to some extent. 529 00:27:48,239 --> 00:27:50,930 Speaker 2: Yeah, I, I, in principle, I agree with that. I mean, 530 00:27:51,189 --> 00:27:53,689 Speaker 2: you never for sure know about safety, so, you know, 531 00:27:53,910 --> 00:27:56,869 Speaker 2: the question is, you know, where do you want to 532 00:27:56,869 --> 00:28:01,579 Speaker 2: be on the, the, the risk reward spectrum for healthy longevity? Uh, 533 00:28:01,589 --> 00:28:03,829 Speaker 2: if you want to be an early adopter, you know, 534 00:28:03,989 --> 00:28:06,659 Speaker 2: and you know the safety profile and and the and 535 00:28:06,670 --> 00:28:08,979 Speaker 2: and the potential efficacy of something. 536 00:28:09,390 --> 00:28:11,958 Speaker 2: Uh, and you want to try it. I, I support that. I, 537 00:28:11,989 --> 00:28:14,430 Speaker 2: I think one of the problems we have in medicine 538 00:28:14,430 --> 00:28:20,619 Speaker 2: is that we don't have, we don't empower patients or clients, uh, and, um, 539 00:28:20,959 --> 00:28:22,819 Speaker 2: they go in, they have 6 minutes with the doctor, 540 00:28:22,949 --> 00:28:24,790 Speaker 2: the doctor looks at some charts and says you have 541 00:28:24,790 --> 00:28:26,229 Speaker 2: to do this and you should do that and you 542 00:28:26,229 --> 00:28:30,319 Speaker 2: should do that. And the patient's not invested in doing it. 543 00:28:30,550 --> 00:28:33,229 Speaker 2: And so even when you give them statins, they tend 544 00:28:33,229 --> 00:28:35,119 Speaker 2: not to take it, you know, so. 545 00:28:35,400 --> 00:28:38,439 Speaker 2: Uh, compliance is very low. Uh, I think that we 546 00:28:38,439 --> 00:28:41,630 Speaker 2: need to work on empowering people to make their own decisions, 547 00:28:41,640 --> 00:28:44,439 Speaker 2: and if they wanna be like me, I try a 548 00:28:44,439 --> 00:28:46,800 Speaker 2: lot of different things. If they wanna be, you know, 549 00:28:46,920 --> 00:28:49,239 Speaker 2: on the edge and try things, I think it's OK 550 00:28:49,239 --> 00:28:50,479 Speaker 2: as long as they're educated. 551 00:28:51,300 --> 00:28:52,699 Speaker 1: OK to try metformin. 552 00:28:54,140 --> 00:28:57,660 Speaker 2: Um, metformin is not my favorite intervention, but I, you know, it's, 553 00:28:57,719 --> 00:29:00,949 Speaker 2: it's a very safe one. So there, there are very 554 00:29:00,949 --> 00:29:05,160 Speaker 2: rare side effects that can happen. Uh, and so that, 555 00:29:05,270 --> 00:29:09,469 Speaker 2: that should be kept in mind. Uh, I, I would, 556 00:29:09,599 --> 00:29:14,109 Speaker 2: my personal recommendation on that is if you have, um, 557 00:29:14,390 --> 00:29:17,359 Speaker 2: you should take metformin or, or one of the newer 558 00:29:17,359 --> 00:29:20,229 Speaker 2: drugs in the market, a GOPonister. 559 00:29:20,680 --> 00:29:25,170 Speaker 2: Uh, SCLLT inhibitors, but, um, it's still unclear whether those 560 00:29:25,170 --> 00:29:28,800 Speaker 2: drugs for somebody with normal weight and normal glucose, whether 561 00:29:28,800 --> 00:29:33,449 Speaker 2: those drugs are gonna enhance longevity. Uh, and there's evidence 562 00:29:33,449 --> 00:29:38,050 Speaker 2: for the GLP agonist and also metformin that they take 563 00:29:38,050 --> 00:29:40,530 Speaker 2: the wrong way, they can reduce muscle mass, which is 564 00:29:40,530 --> 00:29:43,290 Speaker 2: not good. Now, my guess is that can be highly 565 00:29:43,290 --> 00:29:44,959 Speaker 2: managed and anybody that 566 00:29:45,479 --> 00:29:48,910 Speaker 2: is obese should be on one of these drugs cause the, the, 567 00:29:48,920 --> 00:29:53,310 Speaker 2: the accelerated aging you get from obesity is extreme. Um, 568 00:29:53,319 --> 00:29:57,060 Speaker 2: but I don't know what to recommend for people that 569 00:29:57,060 --> 00:30:01,640 Speaker 2: are in relatively optimal, uh, weight at the moment. I 570 00:30:01,640 --> 00:30:03,069 Speaker 2: think that's still an unknown. 571 00:30:03,560 --> 00:30:04,040 Speaker 2: OK. 572 00:30:04,680 --> 00:30:08,040 Speaker 1: Um, I was listening to one of your conversations recently 573 00:30:08,040 --> 00:30:11,609 Speaker 1: where you were alluding to the next brave frontier, gene 574 00:30:11,609 --> 00:30:13,849 Speaker 1: therapy and stem cell therapy. 575 00:30:15,170 --> 00:30:18,920 Speaker 2: Yeah, I'm, I'm really excited about gene therapy because, you know, 576 00:30:19,270 --> 00:30:23,810 Speaker 2: before we were identifying drugs that extend lifespan in different animals, 577 00:30:23,829 --> 00:30:27,199 Speaker 2: we were doing a lot of genetic mutations. So, you know, 578 00:30:27,390 --> 00:30:30,310 Speaker 2: we have hundreds of genes that affect aging in yeast 579 00:30:30,310 --> 00:30:34,550 Speaker 2: and worms and maybe uh almost 100 in mice, I 580 00:30:34,550 --> 00:30:39,900 Speaker 2: would guess. Uh, the what if you can effectively manipulate 581 00:30:39,900 --> 00:30:43,699 Speaker 2: the genome, it gives you a much wider range of possibility. 582 00:30:44,079 --> 00:30:47,439 Speaker 2: Uh, for changing aging, I believe, and it looks like 583 00:30:47,439 --> 00:30:52,680 Speaker 2: gene therapy is finally, uh, reaching the, the, the forefront, 584 00:30:52,760 --> 00:30:56,680 Speaker 2: you know, it's being used to treat rare childhood diseases, um, 585 00:30:56,719 --> 00:30:58,719 Speaker 2: and there are already clinics that you can get gene 586 00:30:58,719 --> 00:31:01,760 Speaker 2: therapy done in humans. It may be possible to do 587 00:31:01,760 --> 00:31:04,520 Speaker 2: it in, in ways that you don't actually integrate the 588 00:31:04,520 --> 00:31:06,949 Speaker 2: gene into the genome, and so you don't, you take 589 00:31:06,949 --> 00:31:10,390 Speaker 2: less risk of making mutations in your genome with gene therapy. 590 00:31:10,819 --> 00:31:13,640 Speaker 2: So I, I think it offers great promise. I'm not 591 00:31:13,640 --> 00:31:19,939 Speaker 2: sure whether it's, it's, we're at the point where the, the, 592 00:31:19,949 --> 00:31:23,520 Speaker 2: the rewards outweigh the risk for aging yet, but I 593 00:31:23,520 --> 00:31:25,319 Speaker 2: think we'll get there and I think it'll open up 594 00:31:25,319 --> 00:31:26,910 Speaker 2: a range of possibilities. 595 00:31:28,040 --> 00:31:32,119 Speaker 1: I recently read Walter Isaacson's biography of Jennifer Doudna, the 596 00:31:32,119 --> 00:31:35,900 Speaker 1: Code Breaker book, and the work is absolutely fascinating and 597 00:31:35,900 --> 00:31:38,719 Speaker 1: the acceleration and innovation in that area in the last 598 00:31:38,719 --> 00:31:40,829 Speaker 1: 1520 years is pretty astounding. 599 00:31:41,079 --> 00:31:43,959 Speaker 2: Yeah, like CRISPR cusine is really a revolution, you know, 600 00:31:44,119 --> 00:31:47,040 Speaker 2: and we, we could always make the genetic changes we 601 00:31:47,040 --> 00:31:49,239 Speaker 2: wanted in yeast, and that's one of the reasons it 602 00:31:49,239 --> 00:31:52,640 Speaker 2: was such a powerful organism, uh, but now you can 603 00:31:52,640 --> 00:31:55,520 Speaker 2: effectively do that in cell culture and gene therapy is 604 00:31:55,520 --> 00:31:57,160 Speaker 2: starting to be able to do it in people. 605 00:31:57,550 --> 00:32:00,060 Speaker 2: Uh, it's, it's exciting for sure. 606 00:32:00,800 --> 00:32:04,560 Speaker 1: Uh, how computationally intensive is some of this research, and 607 00:32:04,560 --> 00:32:06,520 Speaker 1: the reason I asked you is because, you know, artificial 608 00:32:06,520 --> 00:32:10,160 Speaker 1: intelligence and all the rage and, you know, large language 609 00:32:10,160 --> 00:32:13,849 Speaker 1: models and so on. Um, are any of those recent 610 00:32:13,849 --> 00:32:17,719 Speaker 1: developments in computer science helpful for the kind of research 611 00:32:17,719 --> 00:32:19,359 Speaker 1: that we're talking about here? Yeah, 612 00:32:19,640 --> 00:32:22,349 Speaker 2: it's dramatically changing the field. Half my labs doing AI 613 00:32:22,349 --> 00:32:26,280 Speaker 2: and I never thought I would say that, um, and, um. 614 00:32:26,869 --> 00:32:28,719 Speaker 2: You know, more and more people are coming to work 615 00:32:28,719 --> 00:32:31,319 Speaker 2: every day on that topic, and it's, it's being used 616 00:32:31,319 --> 00:32:34,609 Speaker 2: for a wide range of things. For one, we're using 617 00:32:34,609 --> 00:32:38,829 Speaker 2: them to make biologic aging clocks to measure how you age, um, 618 00:32:38,880 --> 00:32:41,640 Speaker 2: but we're also using it to select drugs that might 619 00:32:41,640 --> 00:32:47,239 Speaker 2: be better at, uh, some disease or even extending longevity, um, we, 620 00:32:47,410 --> 00:32:49,630 Speaker 2: we recently published a paper. 621 00:32:50,020 --> 00:32:53,939 Speaker 2: Um, with careful and, uh, looking at how to query 622 00:32:54,270 --> 00:32:58,800 Speaker 2: large language models to get, uh, appropriate answers for longevity. 623 00:32:58,910 --> 00:33:00,869 Speaker 2: So like an example would be like, should I take 624 00:33:00,869 --> 00:33:05,819 Speaker 2: rapamycin and 52 and, you know, I have diabetes, uh, and, 625 00:33:05,949 --> 00:33:08,390 Speaker 2: you know, the people are doing this now, a lot 626 00:33:08,390 --> 00:33:11,030 Speaker 2: of doctors will say you shouldn't ask large language models 627 00:33:11,030 --> 00:33:14,339 Speaker 2: those questions, but people are asking them every day, it's happening. 628 00:33:14,910 --> 00:33:17,630 Speaker 2: And so we're trying to figure out how to improve 629 00:33:17,630 --> 00:33:19,589 Speaker 2: the responses of the LLMs. 630 00:33:19,949 --> 00:33:23,550 Speaker 2: In a way that gives good medical advice to people. 631 00:33:23,800 --> 00:33:26,319 Speaker 2: So I think that's just 3 examples. There's so many 632 00:33:26,319 --> 00:33:29,569 Speaker 2: different ways that AI is being used now that it's, it, 633 00:33:29,599 --> 00:33:30,839 Speaker 2: it's really quite amazing. 634 00:33:31,939 --> 00:33:34,630 Speaker 1: Um, a few years ago I had Professor Dean Ho 635 00:33:34,630 --> 00:33:37,569 Speaker 1: on my podcast. I'm sure you know Dean, and he 636 00:33:37,569 --> 00:33:41,130 Speaker 1: was talking about in the context of personalized recommendation that 637 00:33:41,130 --> 00:33:43,959 Speaker 1: AI can take in all the data from a patient 638 00:33:43,959 --> 00:33:47,810 Speaker 1: overnight and then give you very specific recommendations of the 639 00:33:47,810 --> 00:33:50,739 Speaker 1: dosage for the following day. At that time, I had, 640 00:33:50,890 --> 00:33:52,489 Speaker 1: you know, of course, you know, it was years before 641 00:33:52,489 --> 00:33:56,010 Speaker 1: large language models entered my consciousness, but I suppose this 642 00:33:56,010 --> 00:33:57,719 Speaker 1: is one way of thinking about it as well. 643 00:33:58,839 --> 00:34:01,699 Speaker 2: Yeah, it's getting there. I mean, you know, you can also. 644 00:34:03,219 --> 00:34:05,439 Speaker 2: Take it down directions where you get the wrong answers, 645 00:34:05,449 --> 00:34:09,129 Speaker 2: and so I think that the value of elements for 646 00:34:09,129 --> 00:34:14,639 Speaker 2: medicine is, is, is gonna continue to, to grow, uh, and, uh, 647 00:34:14,649 --> 00:34:17,280 Speaker 2: already I think you can get really meaningful information back, 648 00:34:17,370 --> 00:34:19,399 Speaker 2: but with a note of caution perhaps. 649 00:34:19,979 --> 00:34:22,100 Speaker 1: Right, I mean, for my personal work in the world 650 00:34:22,100 --> 00:34:24,580 Speaker 1: of finance and economics, times I find it most useful 651 00:34:24,580 --> 00:34:27,500 Speaker 1: is when I control the information that is ingesting. So 652 00:34:27,500 --> 00:34:29,379 Speaker 1: when I upload a document and ask it to do 653 00:34:29,379 --> 00:34:31,979 Speaker 1: some analysis on that, that's where the real value added 654 00:34:31,979 --> 00:34:34,979 Speaker 1: comes to me. I don't necessarily trust it to tap 655 00:34:34,979 --> 00:34:36,929 Speaker 1: the entire internet and give me the right answers. 656 00:34:37,810 --> 00:34:40,290 Speaker 2: Yeah, yeah, but it's, it's changing so fast, you know, 657 00:34:40,620 --> 00:34:43,040 Speaker 2: I'm starting to wonder when I'm gonna be obsolete. 658 00:34:44,929 --> 00:34:49,229 Speaker 1: Um, uh, let's talk about Singapore. Uh, I know you're 659 00:34:49,229 --> 00:34:52,750 Speaker 1: deeply involved in the research scene here. Give me a 660 00:34:52,750 --> 00:34:56,070 Speaker 1: sense of how is the state of affairs on longevity 661 00:34:56,070 --> 00:34:58,750 Speaker 1: related research in Singapore beyond your lab. 662 00:34:59,600 --> 00:35:04,510 Speaker 2: Well, I, I think there's quite a, a field growing here. It's, it's, uh, 663 00:35:04,520 --> 00:35:07,360 Speaker 2: not much was happening when it came 7.5 years ago. 664 00:35:07,399 --> 00:35:10,600 Speaker 2: I mean, there was certainly, if you think of aging 665 00:35:10,600 --> 00:35:13,510 Speaker 2: in a broader sense, there was good geriatric medicine here, 666 00:35:13,520 --> 00:35:15,350 Speaker 2: there's geriatric research. 667 00:35:16,020 --> 00:35:19,310 Speaker 2: You know, I jokingly say that geriatricians focus on aging. 668 00:35:19,379 --> 00:35:22,479 Speaker 2: My job is to focus on non-aging. So, you know, 669 00:35:22,780 --> 00:35:26,899 Speaker 2: we're typically looking at middle-aged people trying to prevent aging, uh, 670 00:35:27,020 --> 00:35:28,600 Speaker 2: so it's a little bit of a different approach, but 671 00:35:28,600 --> 00:35:33,339 Speaker 2: geriatrics is essential. You need geriatricians nowhere in the world 672 00:35:33,340 --> 00:35:36,299 Speaker 2: has enough of them, and, uh, there's some very good 673 00:35:36,300 --> 00:35:40,820 Speaker 2: ones here. And also there are programs focused on social 674 00:35:40,820 --> 00:35:43,340 Speaker 2: aging that are very successful on. 675 00:35:43,810 --> 00:35:49,009 Speaker 2: Um, uh, and, and other aspects of aging, financial aspects, and, and, 676 00:35:49,050 --> 00:35:52,270 Speaker 2: and some health aspects of aging. Uh, we kind of 677 00:35:52,270 --> 00:35:54,989 Speaker 2: brought this sort of molecular biology approach and to slow 678 00:35:54,989 --> 00:35:59,139 Speaker 2: the aging process, and I, I think we're over the 679 00:35:59,139 --> 00:36:01,750 Speaker 2: time we've built an ecosystem now of people that are 680 00:36:01,750 --> 00:36:05,739 Speaker 2: thinking about this. I would also say that governments really, uh, 681 00:36:05,750 --> 00:36:07,819 Speaker 2: even by the time we got here, they already knew 682 00:36:07,820 --> 00:36:10,379 Speaker 2: how much of a problem they had with the aging population. 683 00:36:10,469 --> 00:36:11,419 Speaker 2: I mean they have 684 00:36:11,939 --> 00:36:16,319 Speaker 2: Um, major challenges because extremely low birth rate, people are 685 00:36:16,320 --> 00:36:20,370 Speaker 2: living a long time, and immigration is a challenge on 686 00:36:20,370 --> 00:36:24,810 Speaker 2: a small island like this. So, um, they'd already recognized 687 00:36:24,810 --> 00:36:28,129 Speaker 2: this as a challenge. I think they've been taking their 688 00:36:28,129 --> 00:36:31,120 Speaker 2: time trying to figure out where to invest in mitigating 689 00:36:31,120 --> 00:36:34,689 Speaker 2: the problem, but, uh, I'm, I'm excited to hear that 690 00:36:34,689 --> 00:36:37,370 Speaker 2: there's a lot of interest in focusing on healthy longevity 691 00:36:37,370 --> 00:36:38,729 Speaker 2: going forward and 692 00:36:39,379 --> 00:36:42,379 Speaker 2: You know, I, I think that it's really changing the 693 00:36:42,379 --> 00:36:44,090 Speaker 2: mindset to say that, you know, 694 00:36:45,189 --> 00:36:48,350 Speaker 2: Putting up better signs to keep people with Alzheimer's from 695 00:36:48,350 --> 00:36:51,969 Speaker 2: getting lost is fine, uh, but it's not solving the 696 00:36:51,969 --> 00:36:54,939 Speaker 2: economic challenge of aging. The way to solve that is 697 00:36:54,939 --> 00:36:58,669 Speaker 2: to keep people healthy, keep them working, keep them raising 698 00:36:58,669 --> 00:37:01,709 Speaker 2: their grandkids, keep them spending money in the economy and 699 00:37:01,709 --> 00:37:04,638 Speaker 2: traveling and doing all the things that keep the economy moving, 700 00:37:04,709 --> 00:37:07,270 Speaker 2: and the only way to get to there is by 701 00:37:07,270 --> 00:37:09,989 Speaker 2: keeping them healthy, not waiting till they get sick and 702 00:37:10,570 --> 00:37:12,638 Speaker 2: Throwing a lot of money and trying to treat them. 703 00:37:12,969 --> 00:37:15,049 Speaker 2: So I think there's a recognition of that now and 704 00:37:15,050 --> 00:37:18,408 Speaker 2: a belief that that's possible. So I think it's it's 705 00:37:18,409 --> 00:37:20,129 Speaker 2: even gonna go faster as we go 706 00:37:20,129 --> 00:37:20,620 Speaker 2: forward. 707 00:37:21,770 --> 00:37:25,290 Speaker 1: And speaking of sort of the public sector of Singapore 708 00:37:25,290 --> 00:37:28,929 Speaker 1: and its intervention in the day to day lives of Singaporeans, 709 00:37:28,979 --> 00:37:32,179 Speaker 1: I mean, are you an advocate of, for example, subsidizing 710 00:37:32,179 --> 00:37:35,530 Speaker 1: healthy diet or giving people exercise vouchers, or you think 711 00:37:35,530 --> 00:37:38,139 Speaker 1: that there has to be a smarter ground up way 712 00:37:38,139 --> 00:37:40,770 Speaker 1: of doing it as opposed to a top down manner 713 00:37:40,770 --> 00:37:41,879 Speaker 1: from heaven from the government? 714 00:37:42,550 --> 00:37:46,620 Speaker 2: Yeah, I don't think any government's figured this out yet, and, uh, 715 00:37:47,139 --> 00:37:49,500 Speaker 2: you know, so this comes into play in a whole 716 00:37:49,500 --> 00:37:52,780 Speaker 2: range of ideas. You can, you can do carrots like that. 717 00:37:53,300 --> 00:37:55,179 Speaker 2: You know, you can also have an approach where you 718 00:37:55,179 --> 00:37:57,408 Speaker 2: have to pay a little bit of your healthcare costs, 719 00:37:57,419 --> 00:37:59,500 Speaker 2: where the government takes care of most of it, but 720 00:37:59,500 --> 00:38:01,610 Speaker 2: you're on the hook for a little bit of it 721 00:38:01,610 --> 00:38:04,669 Speaker 2: to try to motivate you to stay healthy, um. 722 00:38:05,989 --> 00:38:10,859 Speaker 2: Different countries are trying different uh approaches right now, and 723 00:38:11,510 --> 00:38:14,270 Speaker 2: in the first country that finds a strategy to really 724 00:38:14,270 --> 00:38:18,270 Speaker 2: increase healthy lifestyle is gonna benefit tremendously. I guess the 725 00:38:18,270 --> 00:38:22,020 Speaker 2: countries that are closest to that are probably Northern European countries, so. 726 00:38:22,479 --> 00:38:26,080 Speaker 2: And Denmark, places like that, people are very active, they're 727 00:38:26,580 --> 00:38:30,138 Speaker 2: less obese people and uh they're benefiting from a more 728 00:38:30,139 --> 00:38:33,969 Speaker 2: healthy lifestyle. Uh, Singapore is not bad, but I think 729 00:38:33,969 --> 00:38:39,560 Speaker 2: people don't exercise enough here, and it's partly climate. Uh, also, the, 730 00:38:39,659 --> 00:38:44,570 Speaker 2: the food is relatively healthy, certainly it's healthy relative to America, but, uh, 731 00:38:44,580 --> 00:38:47,060 Speaker 2: at the same time, you know, you go to the 732 00:38:47,060 --> 00:38:48,189 Speaker 2: store and 733 00:38:48,729 --> 00:38:51,340 Speaker 2: You know, you buy these cold teas in the store 734 00:38:51,340 --> 00:38:55,699 Speaker 2: and they're like full of sugar. There's still some simple 735 00:38:55,699 --> 00:38:58,090 Speaker 2: things that could be done that could really help out. 736 00:38:58,179 --> 00:39:01,000 Speaker 2: And uh if I'm doing a 20k run, one of 737 00:39:01,000 --> 00:39:03,899 Speaker 2: the piece full of sugar is really nice, but uh 738 00:39:03,899 --> 00:39:06,530 Speaker 2: having it every day would be a disaster. I mean, we, 739 00:39:06,540 --> 00:39:07,679 Speaker 2: we need to like 740 00:39:08,659 --> 00:39:12,290 Speaker 2: Make simple changes, continue to make simple changes that promote 741 00:39:12,290 --> 00:39:13,580 Speaker 2: the health of the population. 742 00:39:14,129 --> 00:39:18,209 Speaker 1: Well, one thing I saw in Singapore 15 years ago 743 00:39:18,209 --> 00:39:21,399 Speaker 1: when I moved here was the absence of covered sidewalks, 744 00:39:21,409 --> 00:39:24,209 Speaker 1: and today it's a sea change. There's so many sidewalks 745 00:39:24,209 --> 00:39:26,889 Speaker 1: that are covered and it makes a huge difference in 746 00:39:26,889 --> 00:39:28,729 Speaker 1: my ability to run through those areas. 747 00:39:29,050 --> 00:39:31,689 Speaker 2: Yeah, me too. Uh, I, I agree with that. Uh, 748 00:39:31,889 --> 00:39:34,479 Speaker 2: little things like that help for sure, but they're not 749 00:39:34,479 --> 00:39:37,340 Speaker 2: the solution, you know, the solution is also to just 750 00:39:37,340 --> 00:39:40,020 Speaker 2: go after the molecular biology and slow aging. 751 00:39:40,469 --> 00:39:43,520 Speaker 2: Um, I, I think we need to, those sort of 752 00:39:43,520 --> 00:39:46,719 Speaker 2: environmental changes are good, but they don't, they stand alone. 753 00:39:46,800 --> 00:39:48,590 Speaker 2: I don't think they're going to solve the problem. 754 00:39:49,040 --> 00:39:52,000 Speaker 1: Sure, I have saved the hardest question for you at 755 00:39:52,000 --> 00:39:55,760 Speaker 1: the end, which is, is aging a disease and if 756 00:39:55,760 --> 00:39:58,229 Speaker 1: it's a disease, should it be covered by health insurance, 757 00:39:58,360 --> 00:39:59,949 Speaker 1: any aging related treatment? 758 00:40:00,679 --> 00:40:03,479 Speaker 2: I'll I'll turn the question around and say that it 759 00:40:03,479 --> 00:40:05,790 Speaker 2: should be covered by health insurance, whether it's a disease 760 00:40:05,790 --> 00:40:06,428 Speaker 2: or not. 761 00:40:06,989 --> 00:40:09,780 Speaker 2: You know, we can get into semantic arguments about whether 762 00:40:09,780 --> 00:40:11,909 Speaker 2: it's a disease, but if it's, if you don't wanna 763 00:40:11,909 --> 00:40:14,229 Speaker 2: call it a disease, you have to at least acknowledge 764 00:40:14,229 --> 00:40:18,489 Speaker 2: it's the biggest risk factor for every disease we're afraid of. Um, 765 00:40:18,709 --> 00:40:20,989 Speaker 2: and when you think of the most successful drugs on 766 00:40:20,989 --> 00:40:24,870 Speaker 2: the market, they're not treating overt diseases. They're treating high 767 00:40:24,870 --> 00:40:26,949 Speaker 2: blood pressure. Why do you treat high blood pressure? Cause 768 00:40:26,949 --> 00:40:29,029 Speaker 2: you don't want a heart attack. You, you don't want 769 00:40:29,030 --> 00:40:33,310 Speaker 2: cardiovascular disease in the future. They're treating high glucose, uh, 770 00:40:33,389 --> 00:40:34,949 Speaker 2: they're treating high cholesterol. 771 00:40:35,360 --> 00:40:40,040 Speaker 2: Um, these are the drugs that work. They're, and they're reimbursed. 772 00:40:40,409 --> 00:40:43,209 Speaker 2: And so if we acknowledge these things are risk factors 773 00:40:43,209 --> 00:40:45,929 Speaker 2: for disease and we should treat them, we should also 774 00:40:45,929 --> 00:40:49,049 Speaker 2: acknowledge that aging is a 100 times bigger risk factor 775 00:40:49,050 --> 00:40:52,290 Speaker 2: than any of those. So we should reimburse treatment for 776 00:40:52,290 --> 00:40:54,729 Speaker 2: that too. I, I honestly, I think I would call 777 00:40:54,729 --> 00:40:57,729 Speaker 2: it a disease, but I, as long as I can 778 00:40:57,729 --> 00:41:00,770 Speaker 2: treat it and it gets reimbursed, you can call it 779 00:41:00,770 --> 00:41:01,479 Speaker 2: anything you want. 780 00:41:03,989 --> 00:41:06,879 Speaker 1: Uh, well, that's, that's a great note too, and uh 781 00:41:06,879 --> 00:41:08,909 Speaker 1: Professor Brian Kennedy, thank you so much for your time 782 00:41:08,909 --> 00:41:09,560 Speaker 1: and insights. 783 00:41:10,350 --> 00:41:11,179 Speaker 2: Anytime. Thanks 784 00:41:11,179 --> 00:41:15,139 Speaker 1: a lot. A pleasure. Thank you to our listeners as well. Uh, 785 00:41:15,219 --> 00:41:17,899 Speaker 1: Kobe Time was produced by Ken Delbridge at Spy Studios. 786 00:41:18,030 --> 00:41:21,229 Speaker 1: Violet Lee and Daisy Sharma provided additional assistance. It is 787 00:41:21,229 --> 00:41:24,360 Speaker 1: for information only and does not represent any trade recommendations 788 00:41:24,360 --> 00:41:25,629 Speaker 1: or drug recommendation. 789 00:41:26,120 --> 00:41:29,509 Speaker 1: All 149 episodes of COI Time are available on YouTube 790 00:41:29,510 --> 00:41:33,949 Speaker 1: and on all major podcast platforms, including Apple, Google, and Spotify. 791 00:41:34,199 --> 00:41:37,199 Speaker 1: As for our research publications, webinars, and live streams, you 792 00:41:37,199 --> 00:41:40,780 Speaker 1: can find them all by Googling DBS Research Library. Have 793 00:41:40,780 --> 00:41:41,510 Speaker 1: a great day.