1 00:00:06,360 --> 00:00:10,230 Welcome to Science with Sanjula where we talk about anything global health. 2 00:00:10,950 --> 00:00:14,940 My name is Sanjula Singh, and I'm a researcher at the University of Oxford. 3 00:00:15,570 --> 00:00:20,850 Join me as I speak to world leading scientists who tackle today's biggest challenges in health care. 4 00:00:22,690 --> 00:00:29,890 Today, we're joined in the studio by Dr. Keren Papier, a senior nutritional epidemiologist at the University of Oxford. 5 00:00:30,460 --> 00:00:34,810 We're going to talk meat, veganism, alcohol and much more. 6 00:00:35,530 --> 00:00:43,420 Thank you so much for joining us today, Keren. First of all, can you please tell me what made you so interested in what we eat? 7 00:00:43,930 --> 00:00:50,200 It started really as a child I was really interested in nutrition, and I was really fortunate. 8 00:00:50,200 --> 00:00:59,440 I got to live in different countries across the world and I got to see what people ate and how different it was and how lifestyles went around food. 9 00:00:59,860 --> 00:01:08,560 I just was really interested in it. I was really fortunate during my studies, so I studied public health, nutrition, and in my first year, 10 00:01:08,800 --> 00:01:16,930 really in my first semester, I had the topic called epidemiology, which is about people and diseases and patterns. 11 00:01:16,930 --> 00:01:18,820 And I just thought, this is amazing. 12 00:01:19,120 --> 00:01:26,530 What a way to tie in interest in nutrition and people and different cultures around the world and understanding disease and patterns. 13 00:01:26,920 --> 00:01:32,739 And I just fell in love with it. And through my degrees I got to do research over the world as well. 14 00:01:32,740 --> 00:01:40,540 So I was able to go to the Philippines and investigate malnutrition in children and schistosomiasis. 15 00:01:40,540 --> 00:01:45,580 So it really unfortunate worm that children are unfortunately dealing with. 16 00:01:45,700 --> 00:01:51,909 Just from from the lifestyles, from from farming and being in the water and being there was really interesting 17 00:01:51,910 --> 00:01:54,969 because I saw that there were a lot of children who were undernourished. 18 00:01:54,970 --> 00:01:59,020 So we see malnourished as in poor nutrition, but really not getting enough. 19 00:01:59,320 --> 00:02:02,139 But a lot of the adults actually had over nutrition. 20 00:02:02,140 --> 00:02:07,900 So they were actually larger overweight or obese, but not necessarily eating what they should be eating. 21 00:02:08,440 --> 00:02:17,380 And I found that really interesting. And that led me to doing my Ph.D. from Australia, but also through Thailand and looking at yeah, 22 00:02:17,410 --> 00:02:22,690 looking at how the changing lifestyles were changing diseases and how diabetes was becoming 23 00:02:22,690 --> 00:02:27,310 an issue that hadn't been an issue previously because of all the different changes going on. 24 00:02:27,850 --> 00:02:36,100 Then how did you move to Oxford? So at the end of my PhD, I was of course looking for opportunities and thinking about what the next stage would be. 25 00:02:36,100 --> 00:02:44,140 And having done a lot of work in global health and really in countries that were sort of becoming either upper middle income or not quite high income, 26 00:02:44,410 --> 00:02:48,340 but seeing that some of the issues were quite similar with what's happening in high income countries, 27 00:02:48,850 --> 00:02:55,150 I thought that I could easily translate that research and wanted to see, well, what else is happening in high income countries as well? 28 00:02:55,690 --> 00:02:58,989 What happens when we eat meat, for instance? What happens when we don't eat meat? 29 00:02:58,990 --> 00:03:04,240 What does that do to our health and our long term health? What are people that are eating meat doing? 30 00:03:04,240 --> 00:03:06,730 What are their lifestyles? Are there differences between them? 31 00:03:07,000 --> 00:03:12,360 And that's evolved into projects also on what happens to people who completely omit meat. 32 00:03:12,370 --> 00:03:14,170 So what we call vegetarians, for instance. 33 00:03:14,200 --> 00:03:21,130 Well, let's dive into a vegetarian diet and maybe could you maybe explain first what is a vegetarian diet and what is a vegan diet? 34 00:03:21,430 --> 00:03:25,000 It's a great question because we know what they are not. 35 00:03:25,480 --> 00:03:29,020 So we know that vegetarians do not eat meat and do not eat fish. 36 00:03:29,500 --> 00:03:33,880 We know that vegans don't eat meat, don't eat fish, don't eat eggs, don't eat dairy. 37 00:03:34,390 --> 00:03:40,660 But what they do eat could be completely different. So it's even a tough one to define, so to say. 38 00:03:42,150 --> 00:03:47,460 Absolutely. And of course, what does that mean for health? Because if someone is having, say, 39 00:03:48,090 --> 00:03:57,420 vegetables and fruit and legumes and pulses and nuts and really a variety diet versus someone who might just be having white pasta, 40 00:03:57,540 --> 00:04:02,770 maybe tomato sauce soda the whole day. And both could be vegan diets. 41 00:04:02,800 --> 00:04:07,050 Right. That's exactly right. So how do you go about and do research on that? 42 00:04:07,320 --> 00:04:14,250 We're very fortunate that people are really kind and are happy to take part in research and help us answer interesting questions. 43 00:04:14,730 --> 00:04:18,420 So a lot of the research we do is through cohort studies. 44 00:04:19,110 --> 00:04:25,229 So what that means is we'll ask people to take part in a study and the people that we asked to take part, 45 00:04:25,230 --> 00:04:26,970 they might have something interesting about them. 46 00:04:27,390 --> 00:04:33,120 So in the case, for instance, of vegetarians and vegans, we really target people who are omitting meat, 47 00:04:33,120 --> 00:04:37,410 are omitting fish, are omitting dairy, or eggs and say, can you please take part in our study? 48 00:04:38,010 --> 00:04:45,930 And we've done this over the years. And for instance, we have a study that's been running since the mid 1990s called the Epic Oxford Study, 49 00:04:46,470 --> 00:04:51,030 and it's got over 65,000 people who have said, Yep, I'll take part in your study. 50 00:04:51,540 --> 00:04:57,509 So then we sent them surveys. These are questionnaires and they ask about their diet, their lifestyle habits. 51 00:04:57,510 --> 00:05:00,990 So do you smoke? Do you drink alcohol? What do you eat? 52 00:05:01,560 --> 00:05:06,690 And looking at these questions and information, we then know who's following a vegetarian diet, 53 00:05:06,690 --> 00:05:11,010 who's eating a meat diet, for instance, and we can then follow them up over time. 54 00:05:11,370 --> 00:05:17,399 So people are very kind to also allow us to access their records, their NHS records. 55 00:05:17,400 --> 00:05:24,300 So National Health Service. So if someone's gone to hospital or had a cancer or passed away, we can see what's happened. 56 00:05:24,720 --> 00:05:28,440 So we can see did someone follow a vegetarian diet, say, in 2000? 57 00:05:29,010 --> 00:05:33,620 And then we could see over time we might ask them again repeatedly, Oh, what was your diet five years later? 58 00:05:33,630 --> 00:05:40,170 What was your diet ten years later? And we can use this information to look at diet over time with different health outcomes. 59 00:05:40,530 --> 00:05:45,810 So then what we're doing is really comparing. We see people who, say, developed ischaemic heart disease. 60 00:05:45,840 --> 00:05:52,530 So for instance, heart disease, what's different to the people who developed heart disease compared to those who didn't develop heart disease? 61 00:05:52,950 --> 00:05:56,340 Were they eating a different way? Did they smoke? Did they not smoke? 62 00:05:56,520 --> 00:06:00,360 What was their weight, for instance? So then we can compare and see. 63 00:06:00,540 --> 00:06:07,470 Right. Are the vegetarians and vegans having more or less of different disease outcomes, for instance, to people who are eating meat? 64 00:06:08,740 --> 00:06:12,130 You just some very interesting points. I'm just going to kind of pinpoint those. 65 00:06:12,520 --> 00:06:13,810 First of all, biomarkers. 66 00:06:13,840 --> 00:06:21,400 What are those? The biomarkers, unlike, for instance, with a questionnaire where you're asking questions maybe on a survey or in paper, 67 00:06:21,970 --> 00:06:26,170 biomarkers are actual measures of what's happening in the body. 68 00:06:26,560 --> 00:06:31,900 So, for instance, blood biomarkers is something that we might use a fair bit in in nutrition. 69 00:06:32,860 --> 00:06:40,510 We have different types of biomarkers. So we have what's called recovery biomarkers that are trying to reflect absolute intake. 70 00:06:40,520 --> 00:06:44,500 So, for instance, energy, how much energy did somebody consume? 71 00:06:45,280 --> 00:06:48,639 Now, this is great because it's very, very precise. 72 00:06:48,640 --> 00:06:51,940 In theory, you should be able to find out how much energy someone ate. 73 00:06:52,240 --> 00:06:56,860 But it's very expensive; you have to take it to a lab to process it. 74 00:06:57,220 --> 00:07:00,670 It's not always the most feasible thing to do. 75 00:07:01,450 --> 00:07:08,580 So we do have some other biomarkers in nutrition, but they need to be used in combination with dietary surveys. 76 00:07:09,000 --> 00:07:12,990 So for instance, you might want to know if somebody had fruit and vegetable intake. 77 00:07:13,290 --> 00:07:15,180 So you might ask them to tell you in a questionnaire. 78 00:07:15,780 --> 00:07:20,760 We can have a biomarker that tells you, well, what, someone's vitamin C in their blood right now. 79 00:07:21,300 --> 00:07:28,050 Now, that's helpful because you can try to compare them. And you can imagine that if I looked at your vitamin C in your blood, 80 00:07:28,230 --> 00:07:32,610 that doesn't require you having to remember if you ate fruits or vegetables yesterday. 81 00:07:32,970 --> 00:07:40,770 So it does get rid of that. But it's not always perfect because when I measure the biomarker might change completely. 82 00:07:41,190 --> 00:07:44,310 So you might change during the day between days. 83 00:07:44,640 --> 00:07:48,690 Also, your metabolism, your personality, the things that are about you 84 00:07:48,690 --> 00:07:52,740 for instance, your body size, how you metabolise that might affect it. 85 00:07:53,790 --> 00:07:57,329 So if you think of something like vitamin D, for instance, if we look at intake, 86 00:07:57,330 --> 00:08:02,100 that information is not really good because most of our vitamin D actually comes from the sun. 87 00:08:02,790 --> 00:08:07,350 So something like a biomarker might actually tell you that, whereas an intake might be a bit tricky. 88 00:08:07,980 --> 00:08:14,010 But of course, your body size, whether you smoke all these different factors, might affect the levels in your blood. 89 00:08:14,790 --> 00:08:20,710 Do you think in the future there may be more biomarkers that are going to adequately reflect our long term intake? 90 00:08:20,730 --> 00:08:26,370 Or do you think that something that may just not be possible for diet? I think it's hard to have sort of a one stop shop. 91 00:08:26,400 --> 00:08:31,680 I think the best we can do in research is really look at the evidence coming from different sources 92 00:08:31,680 --> 00:08:36,270 and seeing how it lines up and really try to understand why is this telling us something different. 93 00:08:36,750 --> 00:08:43,540 So for instance, with the vitamin D example, it makes sense that because it comes from the sun that looking at our intake might not be enough. 94 00:08:43,980 --> 00:08:51,480 So this area is developing and we have a lot of really clever techniques now to measure all these different types of biomarkers, 95 00:08:51,750 --> 00:08:55,260 and that is super informative and super helpful and it's a really exciting space. 96 00:08:55,830 --> 00:08:57,390 So that area is still developing, 97 00:08:57,600 --> 00:09:04,950 but I still think that it's best to do it in combination with the different types of data that are available, not the sort of the one solution. 98 00:09:05,910 --> 00:09:09,500 All right. So we need cohort studies. We need very good questionnaires. 99 00:09:09,510 --> 00:09:15,450 We need biomarkers. What else do we need? Well, genetics is an area that's of interest. 100 00:09:16,260 --> 00:09:24,870 It's been particularly useful for some areas, maybe not for all aspects of nutrition, but it depends how you define nutrition. 101 00:09:24,900 --> 00:09:31,230 So for instance, something like alcohol could be called a dietary factor, but it's really also a lifestyle factor. 102 00:09:31,950 --> 00:09:39,510 We also have trials. So trials don't work for all aspects of nutrition because of course some diseases take so long to develop. 103 00:09:40,200 --> 00:09:44,490 Some things trials are quite useful for nutrition. For some things, genetics might be. 104 00:09:44,670 --> 00:09:50,190 For others, observational study biomarkers. So it's really this kind of mixed bag of where we're getting our evidence from. 105 00:09:51,700 --> 00:09:56,890 If you open up pretty much any magazine these days in the UK or anywhere in the world, 106 00:09:57,280 --> 00:10:02,530 you'll see a very large headline saying "this is a superfood, you should eat this" or "this is really bad for you". 107 00:10:03,070 --> 00:10:08,450 What do you think about that when you read such statements? One - we're popular. 108 00:10:10,700 --> 00:10:13,710 Everyone loves nutrition. Everybody eats all the time. 109 00:10:13,730 --> 00:10:21,200 It's not a drug. It's not a for instance, smoking, for instance, food affects everyone from the day they are born to the day you die. 110 00:10:21,230 --> 00:10:25,460 You're going to be eating. So people have a vested interest. It's going to catch headlines. 111 00:10:26,150 --> 00:10:29,480 But then for somebody on the other end, reading the headline, 112 00:10:30,200 --> 00:10:35,750 I guess a few things that would be amazing if people could consider is please don't hate the scientists right away. 113 00:10:36,350 --> 00:10:39,950 Often the things we put out are not exactly what's being picked up in the headline. 114 00:10:40,310 --> 00:10:45,770 And I think it's good to have like a toolkit of what do you do if you do read this headline that just says something, 115 00:10:46,370 --> 00:10:49,430 you know, "butter is good", "butter is bad". What do you do? 116 00:10:49,770 --> 00:10:53,930 Yeah. And I think that some things to sort of keep in mind are really what was the question? 117 00:10:54,320 --> 00:10:58,010 What were they asking? So, for instance, if they're asking, is butter good or bad, 118 00:10:58,520 --> 00:11:03,740 what are they comparing it to? Are they comparing eating butter versus just eating sugar all day? 119 00:11:04,490 --> 00:11:13,490 Because in that case, butter might look a little bit better than if you're comparing butter to maybe whole grains and different oils, for instance. 120 00:11:13,490 --> 00:11:17,120 So what's the actual question? Are you comparing something that makes sense? 121 00:11:17,930 --> 00:11:23,210 It needs to sort of be plausible. And when we're thinking about that, things that can actually maybe lead to something. 122 00:11:23,480 --> 00:11:27,680 So having more of this might lead to more of that, it's really does it make sense? 123 00:11:27,710 --> 00:11:32,240 Is it biologically plausible? Is this something that could have just been an artefact? 124 00:11:32,630 --> 00:11:36,950 We look at really large numbers. So sometimes you find things that you weren't looking for. 125 00:11:37,640 --> 00:11:41,810 So to think about what was the question at hand to think about, does it make sense? 126 00:11:42,260 --> 00:11:56,820 What does the evidence say overall? I am going to ask you to give a mini lecture on what you think, 127 00:11:56,830 --> 00:12:03,940 given all the current evidence out there, is the best diet for any person living on the planet right now. 128 00:12:05,300 --> 00:12:12,170 I think anybody who's got any sort of aunt or grandmother in their corner would have probably grown up with hearing. 129 00:12:12,290 --> 00:12:15,979 "it's all about balance", and "don't have too much of those, don't have too much of that". 130 00:12:15,980 --> 00:12:19,280 And I really think there's some really good advice there. 131 00:12:19,610 --> 00:12:24,380 In the end of the day, that it's really about staying within some form of balance. 132 00:12:24,800 --> 00:12:32,810 We have some good evidence around plant based foods so that consuming more fruits and vegetables and beans and legumes and 133 00:12:33,020 --> 00:12:40,849 maybe trying to limit the amount of red and processed meat that were eating every day that it seems to be health promoting. 134 00:12:40,850 --> 00:12:43,309 And this might be from various types of diets, 135 00:12:43,310 --> 00:12:50,390 but these are the components that we know more about and that our guidelines are supporting. In terms of what we eat in our diet 136 00:12:50,750 --> 00:12:53,899 it's so cultural and it's so personal and specific. 137 00:12:53,900 --> 00:13:02,000 And for instance, when I was doing my research in the Philippines, rice was so important and it was really sort of the central meal of the day, 138 00:13:02,000 --> 00:13:05,630 whereas in other parts of the world, maybe bread is the centre of the meal. 139 00:13:05,870 --> 00:13:10,429 So what goes into your meal is going to be so dependent on what you like, what makes you feel good, 140 00:13:10,430 --> 00:13:16,070 what you can afford, what you know how to cook, what you enjoy, what maybe you've been handed down from your family. 141 00:13:16,700 --> 00:13:23,480 But I think also when we're thinking about diet, we do know that we have more evidence for consuming more plant based foods. 142 00:13:24,050 --> 00:13:27,450 But it's not just about our health. There's other aspects. 143 00:13:27,450 --> 00:13:32,479 So besides maybe religion and culture and also preference, taste time what 144 00:13:32,480 --> 00:13:34,670 you know how to make- environment. 145 00:13:35,210 --> 00:13:42,320 We have good evidence that reducing our red and processed meat, it is helpful and it does help reduce impact on the environment. 146 00:13:42,890 --> 00:13:46,219 So I think when we think about our diet, there's so much to think about. 147 00:13:46,220 --> 00:13:51,860 But in the end of the day, it's what's going to fit in with your lifestyle and you might be someone who doesn't consume meat at all. 148 00:13:52,250 --> 00:13:55,010 So what you're eating in the day might be completely different. 149 00:13:55,520 --> 00:14:00,349 And we've learned from looking at vegetarians and vegans, for instance, in our cohort in Epic Oxford, 150 00:14:00,350 --> 00:14:06,200 that it's not just about removing the steak from the plate and putting tofu on there, but the whole day is different. 151 00:14:06,410 --> 00:14:09,800 More vegetables, more grains, more cereal. Everything changes throughout the day. 152 00:14:10,400 --> 00:14:17,030 So to answer your question, I think the day could be completely different within those sort of boundaries and guidelines 153 00:14:17,030 --> 00:14:20,929 and really also thinking about not just health but our environment and other things that are 154 00:14:20,930 --> 00:14:25,459 important to us and trying to limit some of the things that we think could be worth limiting, 155 00:14:25,460 --> 00:14:37,640 like salt and sugar and saturated fat. But what can you tell us about alcohol? 156 00:14:37,670 --> 00:14:42,230 Because some people would say, well, one glass of wine per day is actually good for you. 157 00:14:42,240 --> 00:14:45,080 Other people, they would say you should not drink at all. 158 00:14:45,710 --> 00:14:50,420 Other people may say, well, maybe a moderate amount of alcohol is actually good for your mental health, for example. 159 00:14:51,890 --> 00:14:55,610 Is there any evidence on that or what is the evidence like? 160 00:14:56,510 --> 00:14:58,240 Alcohol is so interesting. 161 00:14:58,250 --> 00:15:05,330 Again, it's both could be considered diet, but it can also be considered lifestyle and and living in the UK seeing how embedded, 162 00:15:05,690 --> 00:15:11,360 makes you think about well what happens if someone who's never having alcohol? They're probably going to be different, 163 00:15:11,720 --> 00:15:17,880 right? What does that say about them? It's probably not random if you're not having alcohol, especially somewhere like the UK. 164 00:15:18,230 --> 00:15:24,920 Maybe you're sick, maybe religious purposes, maybe for some different reason you're not having alcohol. 165 00:15:24,920 --> 00:15:28,879 So it's good to keep in mind when we're thinking about alcohol that comparing anything, 166 00:15:28,880 --> 00:15:33,830 any intake to someone who never drinks, the never drinkers are a little bit different. 167 00:15:33,830 --> 00:15:39,950 They're not at random. There's probably something a bit special about them. Now in terms of the research about 168 00:15:39,950 --> 00:15:44,870 what we know about consuming alcohol in different health outcomes when it comes to cancer, 169 00:15:45,320 --> 00:15:48,050 more alcohol, pretty much more of many cancers. 170 00:15:48,200 --> 00:15:53,929 So if you think about it, it makes sense through the mouth, the pharynx, this sort of area really through the GI. 171 00:15:53,930 --> 00:15:57,020 that all going down to the liver, of course, right, 172 00:15:57,020 --> 00:16:02,849 your breast, colorectal cancer, there are several cancers that more alcohol, more risk of these cancers. 173 00:16:02,850 --> 00:16:06,080 So not drinking at all would mean the lowest risk of those cancers? 174 00:16:06,110 --> 00:16:12,889 That's right. So it's really sort of this dose response where more alcohol is more of these cancers. With cardiovascular disease risk 175 00:16:12,890 --> 00:16:15,740 there have been a bit of a mixed bag in the literature. 176 00:16:16,040 --> 00:16:23,600 So we used to see, and we still see, this situation where some studies show what's called a j-shaped association, 177 00:16:23,780 --> 00:16:31,339 which means that no alcohol looked like it had it looked worse than some alcohol. 178 00:16:31,340 --> 00:16:34,700 Exactly. Actually, one or two glasses per wine is a for example. Exactly. 179 00:16:34,790 --> 00:16:41,839 Now why that's happening. So again, if you keep in mind, if we're comparing to people who have no alcohol, they might be different. 180 00:16:41,840 --> 00:16:45,410 So again, are they people who had an illness who are no longer drinking because of their illness? 181 00:16:45,890 --> 00:16:51,470 It might not be the best comparison group. So more and more we're moving towards, instead of comparing it to non-drinkers, 182 00:16:51,620 --> 00:16:57,980 to maybe people who drink a little bit because they might be a little bit more or less outstanding than the non-drinkers. 183 00:16:58,220 --> 00:17:01,130 In terms of the evidence we do have, it's mostly observational. 184 00:17:01,700 --> 00:17:07,550 Now, if you're trying to report alcohol, that is really tricky because it can come with judgement. 185 00:17:08,150 --> 00:17:12,590 Also, if you're trying to remember if it's wine versus if it's beer, if, if you're say out, 186 00:17:12,650 --> 00:17:16,370 you might not really know the differences or maybe you're having a drink that mix with them all together. 187 00:17:16,970 --> 00:17:23,600 Now also, okay, let's say you remembered in your report and you put it in the survey and you know the amount and you're okay with all that. 188 00:17:23,960 --> 00:17:25,630 The type of drink might just depend. 189 00:17:25,640 --> 00:17:32,600 So people who are drinking beer might not be the same as people who are drinking wine and champagne in terms of maybe your ethnicity, 190 00:17:32,600 --> 00:17:36,650 in terms of how much you can handle having different alcohol types or if you're a man versus a woman. 191 00:17:37,250 --> 00:17:41,989 So there's trickiness around reporting how much we're consuming, what we're consuming, 192 00:17:41,990 --> 00:17:45,110 the types we're consuming, who's drinking the different types of drink. 193 00:17:45,350 --> 00:17:51,560 So all these factors muddle up a little bit when you're trying to then look at alcohol, which makes it so tricky. 194 00:17:52,070 --> 00:17:56,180 So these associations we were seeing with cardiovascular disease risk, 195 00:17:56,660 --> 00:18:00,140 we didn't know if to take them at face value or if this is really what's happening. 196 00:18:00,620 --> 00:18:05,150 And more recently, we've been able to have some genetic evidence with regards to alcohol. 197 00:18:05,630 --> 00:18:14,120 And in East Asians particularly is is a really interesting group to look at because they have a genetic variant. 198 00:18:14,120 --> 00:18:22,130 So it means that they have something different about their genetic predisposition to how they metabolise alcohol and they don't do it as quickly, 199 00:18:22,370 --> 00:18:26,030 which means that they're probably not going to feel very good if they drink too much. 200 00:18:26,330 --> 00:18:32,659 So having this genetic predisposition to metabolising alcohol slowly is sort 201 00:18:32,660 --> 00:18:39,080 of a predictor of the fact that you're probably not going to drink very much. So using that evidence, using genetic evidence, 202 00:18:39,410 --> 00:18:46,220 there's been research showing that when you don't look at the observational, so when you're not looking at people self-reporting in questionnaires, 203 00:18:46,490 --> 00:18:52,070 but rather looking with the genetic evidence, this j-shaped association not as clear. 204 00:18:52,430 --> 00:18:53,389 So, for instance, 205 00:18:53,390 --> 00:19:00,290 if some of the observational evidence showed that there might be this sort of protective effect of some alcohol for heart disease and for stroke, 206 00:19:00,680 --> 00:19:06,620 some of the genetic evidence isn't actually showing this and is actually showing that more alcohol, more stroke. 207 00:19:14,640 --> 00:19:23,400 What about red or processed meat? How far are we in figuring out how bad that actually is for you? 208 00:19:23,400 --> 00:19:28,800 Because I think there are some people who still don't believe that eating meat is bad for you. 209 00:19:28,830 --> 00:19:33,540 And they may use, I don't know, personal arguments that they just like eating meat, for example, 210 00:19:33,540 --> 00:19:36,570 or they'll say that people have been eating meat for centuries now. 211 00:19:37,620 --> 00:19:43,970 How do you feel about that? Great question. Something that takes up a lot of my thoughts during the day, I'll be honest with you. 212 00:19:45,570 --> 00:19:50,940 Processed meat and red meat. Not the same thing. Can you please explain what is the difference? 213 00:19:51,000 --> 00:19:56,250 By definition, processed meat should be something that has enhanced shelf life. 214 00:19:56,520 --> 00:20:01,889 It's been cooked or smoked or cured. It's had something added to it preserved. 215 00:20:01,890 --> 00:20:06,420 So like bacon that's been cured, for instance. So it's something a little bit different about it. 216 00:20:06,450 --> 00:20:09,599 Things have been added to it. A lot of salt has been added to it potentially, right? 217 00:20:09,600 --> 00:20:14,729 Which you then start thinking, right, a lot of salt, maybe blood pressure, maybe stroke and heart disease. 218 00:20:14,730 --> 00:20:21,060 But processed meat is still a little bit of a grey area because in terms of risks. 219 00:20:21,330 --> 00:20:24,559 In terms of the evidence, 220 00:20:24,560 --> 00:20:31,310 for instance, processed meat in colorectal cancer is our best evidence when it comes to what we're most confident about for processed meat, 221 00:20:31,700 --> 00:20:35,360 with higher processed meat intake, increasing risk of colorectal cancer. 222 00:20:35,810 --> 00:20:40,310 And some of the reasons we think is maybe because of the additives or what's been added to the processed meat. 223 00:20:41,180 --> 00:20:44,390 But processed meat could also be a burger. 224 00:20:44,630 --> 00:20:50,390 And if you made the burger at home by mincing it, basically that's unprocessed red meat ground up, 225 00:20:50,390 --> 00:20:56,719 whereas if you're getting it from an outlet, so a takeaway place where it's been processed differently, it comes a little bit different. 226 00:20:56,720 --> 00:21:01,129 So processed meat is different to unprocessed red meat, 227 00:21:01,130 --> 00:21:06,470 which in theory shouldn't have undergone the curing or the preservatives or the colours being added to it. 228 00:21:07,190 --> 00:21:13,820 Finding people who eat just one is a little bit tricky. So if you're studying people in the population, these are real people. 229 00:21:14,420 --> 00:21:17,720 If you're getting someone to fill in your questionnaire and say, Oh, do you eat bacon? Yes. 230 00:21:17,870 --> 00:21:21,020 Do you eat steak? No, no, no. I'm bacon only, right? 231 00:21:21,490 --> 00:21:26,960 Yeah. People tend to eat both of them. So then when we're studying it, sometimes it's tricky to pull apart. 232 00:21:27,200 --> 00:21:29,569 So some things I think are a little clearer. 233 00:21:29,570 --> 00:21:35,810 So I think if you think of maybe bacon or if you're thinking of salami, you might think of those as processed meat. 234 00:21:36,140 --> 00:21:42,770 But maybe some of the foods in the middle, maybe some of the pies and the burgers, maybe kebab, those sort of things. 235 00:21:42,770 --> 00:21:47,510 You might not know where you kind of sit. And then, of course, health implications. 236 00:21:48,380 --> 00:21:52,220 So the evidence we have is with processed meat, with colorectal cancer. 237 00:21:52,220 --> 00:21:59,090 That's our strongest evidence and probably for unprocessed red meat. With regards to heart disease, 238 00:21:59,420 --> 00:22:06,560 we think there's some good evidence there as well that consuming more red and processed meat might increase the risk of heart disease. 239 00:22:07,100 --> 00:22:13,160 And again, when we're thinking about diet or any risk factor, it needs to make sense and what could it be about it? 240 00:22:13,460 --> 00:22:16,700 And red and processed meat often have a lot of saturated fat. 241 00:22:17,360 --> 00:22:22,849 And saturated fat can increase LDL, which is your low cholesterol. 242 00:22:22,850 --> 00:22:25,580 That's considered the less favourable cholesterol, 243 00:22:25,730 --> 00:22:29,990 which has been associated with a higher risk of ischaemic heart disease or heart disease, for instance. 244 00:22:30,590 --> 00:22:34,549 So it could make sense that there's something but again 245 00:22:34,550 --> 00:22:39,500 I think it's interesting to really dive into separating out some of the different meats separately and getting 246 00:22:39,500 --> 00:22:44,420 more information from people who are eating sort of just this or just that and also how much they're eating. 247 00:22:44,700 --> 00:22:52,280 Are they eating just a rasher of bacon a day or are they eating really large amounts? because how much you eat might affect. 248 00:22:52,700 --> 00:22:57,560 So we're still figuring it out as well. There's no definite answer, but there are some very strong trends. 249 00:22:57,650 --> 00:23:01,549 Yeah. And so I would say for colorectal cancer and potentially for heart disease, 250 00:23:01,550 --> 00:23:06,470 those are the outcomes that we are more confident about with regards to red and processed meat. 251 00:23:08,310 --> 00:23:12,690 And you've mentioned salts already, but could you elaborate a little bit more on that, please? 252 00:23:13,500 --> 00:23:17,540 Salt does make our food tastes great. And it's everywhere. 253 00:23:17,550 --> 00:23:21,330 Like, how can we eliminate salt? I think it's almost impossible. 254 00:23:21,480 --> 00:23:25,180 Yeah, it's a really interesting point and also how it's consumed. 255 00:23:25,200 --> 00:23:31,439 So, for instance, in a place like the U.K., a lot of salt would be consumed in our pre-packaged foods where, 256 00:23:31,440 --> 00:23:36,030 for instance, maybe in some other countries, a lot of salt would be from home making it. 257 00:23:36,030 --> 00:23:40,920 So you're adding it yourself so that in terms of reducing it, if you're adding it when you're cooking at home, 258 00:23:41,250 --> 00:23:45,540 it's going to be different to if it's already in our products. So here, because it's already in our products, 259 00:23:45,540 --> 00:23:52,440 it kind of needs to be at the higher level in terms of reducing the amount of salt or maybe substituting the type of salt that's being used. 260 00:23:59,730 --> 00:24:06,000 I think my last question is always what would your personal and professional advice be for young professionals? 261 00:24:06,600 --> 00:24:13,079 I would say on a personal note, to stay curious, not to be afraid of asking questions, 262 00:24:13,080 --> 00:24:17,730 to keep learning, to just keep getting better and to just keep growing. 263 00:24:18,240 --> 00:24:21,510 And on a professional level? Do what you love. 264 00:24:22,910 --> 00:24:24,110 That's not always possible. 265 00:24:24,470 --> 00:24:31,550 Sometimes people can't afford to work in jobs that allow them to do exactly what they love, but just hang on to it in your mind. 266 00:24:31,790 --> 00:24:36,259 And down the track, there might be an opportunity to be able to pursue that path. 267 00:24:36,260 --> 00:24:42,530 Or if anyone asks for ideas, you're sort of ready to go. So you do what you enjoy and it'll make everything so much better. 268 00:24:44,810 --> 00:24:49,640 It was wonderful to have you in the studio with us today, Keren. Thank you so much for joining. 269 00:24:50,630 --> 00:24:54,470 Next week, we'll be joined by Professor Sir Rory Collins, 270 00:24:54,920 --> 00:25:01,880 who is one of my mentors and who is the head of the Nuffield Department of Population Health at the University of Oxford.