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More sugar research from the USA.
GaleHawkins
Posts: 8,159 Member
newsroom.ucla.edu/releases/fructose-alters-hundreds-of-brain-genes-which-can-lead-to-a-wide-range-of-diseases
Does anyone have any university level study links that support/counters these reported findings?
Does anyone have any university level study links that support/counters these reported findings?
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Replies
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"Americans get most of their fructose in foods that are sweetened with high-fructose corn syrup"
Trying to figure out what scientific study came up with that conclusion. Fructose comes in so many forms that I really doubt that this is even close to true. Fruit, regular sugar, etc all break down to fructose. Statements like that make me very wary of any article.2 -
makingmark wrote: »"Americans get most of their fructose in foods that are sweetened with high-fructose corn syrup"
Trying to figure out what scientific study came up with that conclusion. Fructose comes in so many forms that I really doubt that this is even close to true. Fruit, regular sugar, etc all break down to fructose. Statements like that make me very wary of any article.
If one considers the number of foods with this sweetener, it is hard to believe it's not true. I think they simply measure it by annual purchase patterns. It's in hot dogs, sausage, soda, pancake syrup, ketchup, barbecue sauce, fruit flavored beverages, etc...Among a ton of other things I am forgetting.
I don't have time to look it up at the moment; but I believe that this can be supported unless we believe people aren't eating what they are buying.
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makingmark wrote: »"Americans get most of their fructose in foods that are sweetened with high-fructose corn syrup"
Trying to figure out what scientific study came up with that conclusion. Fructose comes in so many forms that I really doubt that this is even close to true. Fruit, regular sugar, etc all break down to fructose. Statements like that make me very wary of any article.
If one considers the number of foods with this sweetener, it is hard to believe it's not true. I think they simply measure it by annual purchase patterns. It's in hot dogs, sausage, soda, pancake syrup, ketchup, barbecue sauce, fruit flavored beverages, etc...Among a ton of other things I am forgetting.
I don't have time to look it up at the moment; but I believe that this can be supported unless we believe people aren't eating what they are buying.
I was curious, and cane/beet sugar is something like 59% of added sugar, with HFCS only 29%. No idea how much sugar is non-added, but obviously some.
HFCS is about 55% fructose, and sucrose is 50% fructose.
So most? I'm skeptical.
Also, these stats are always problematic because on average doesn't mean much. My understanding is that a TON of the added sugar Americans (on average) eat comes from soda, and so that's HFCS. But heavy users of soda are very heavy uses and many or most Americans probably don't consume sugary soda at all (I managed to get fat without it, personally).* So is the average actually that useful if a segment of people is distorting it through extra high use? (I'm not sure if this is the case or not, but I'd like better numbers.)
This source says almost half of the added sugar is from sweetened/sugary drinks (https://cspinet.org/new/pdf/combined_infographic.pdf) although I've seen other numbers too, and imagine it depends on what's being included in the category and how they are measuring (reported intake vs. estimates from food production. etc.).
Anyway, of course there are lots of other products HFCS is in (enough to make up 29% of all added sugar when
*Oh, yay, dug up some stats: on a given day, half the people in the U.S. consume sugary drinks [this includes juice and energy and sports drinks and sweetened coffee and tea, as well, I believe]; 1 in 4 consume at least 200 calories from such drinks; and 5% drink at least 567 calories—equivalent to four cans of soda (http://www.hsph.harvard.edu/nutritionsource/healthy-drinks/sugary-drinks/)0 -
It is a recently published study on rats. It is certainly interesting, but I think we can wait to panic about eating a little ice cream or other sweets as part of a well designed diet. The study even suggests omega 3s counteract the negative effects of fructose, so even if it turns out to be applicable to humans it looks like moderate sugar consumption as part of a well designed diet may be fine. I don't eat much sugar so I'm not concerned in any event.1
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lemurcat12 wrote: »makingmark wrote: »"Americans get most of their fructose in foods that are sweetened with high-fructose corn syrup"
Trying to figure out what scientific study came up with that conclusion. Fructose comes in so many forms that I really doubt that this is even close to true. Fruit, regular sugar, etc all break down to fructose. Statements like that make me very wary of any article.
If one considers the number of foods with this sweetener, it is hard to believe it's not true. I think they simply measure it by annual purchase patterns. It's in hot dogs, sausage, soda, pancake syrup, ketchup, barbecue sauce, fruit flavored beverages, etc...Among a ton of other things I am forgetting.
I don't have time to look it up at the moment; but I believe that this can be supported unless we believe people aren't eating what they are buying.
I was curious, and cane/beet sugar is something like 59% of added sugar, with HFCS only 29%. No idea how much sugar is non-added, but obviously some.
HFCS is about 55% fructose, and sucrose is 50% fructose.
So most? I'm skeptical.
Also, these stats are always problematic because on average doesn't mean much. My understanding is that a TON of the added sugar Americans (on average) eat comes from soda, and so that's HFCS. But heavy users of soda are very heavy uses and many or most Americans probably don't consume sugary soda at all (I managed to get fat without it, personally).* So is the average actually that useful if a segment of people is distorting it through extra high use? (I'm not sure if this is the case or not, but I'd like better numbers.)
This source says almost half of the added sugar is from sweetened/sugary drinks (https://cspinet.org/new/pdf/combined_infographic.pdf) although I've seen other numbers too, and imagine it depends on what's being included in the category and how they are measuring (reported intake vs. estimates from food production. etc.).
Anyway, of course there are lots of other products HFCS is in (enough to make up 29% of all added sugar when
*Oh, yay, dug up some stats: on a given day, half the people in the U.S. consume sugary drinks [this includes juice and energy and sports drinks and sweetened coffee and tea, as well, I believe]; 1 in 4 consume at least 200 calories from such drinks; and 5% drink at least 567 calories—equivalent to four cans of soda (http://www.hsph.harvard.edu/nutritionsource/healthy-drinks/sugary-drinks/)
Your numbers are accurate. If course, I've seen no evidence that there is any different between HFCS and cane sugar anyways....
I consume relatively little added sugar; but I don't think it is a big deal at all in moderation. I use it when making Asian dishes.....1 -
Interesting stuff. Thanks for sharing it with us.
The study says the sugar consumption of the rats was roughly equivalent to 130g of sugar per day for a 60kg person. 130g is almost double what MFP suggests as a maximum daily sugar consumption level (68g for me). I don't often exceed my daily sugar goals while I'm actively dieting/logging, but during my weight gain periods, I would bet I exceeded 130g/day often.
Interesting that the addition of DHA mitigates many of the problems created by consuming excess sugar. I thought it was supposed to be the fiber in fruits that reduced problematic response of fructose consumption. Not sure if it was in the article you quoted, but the blood glucose levels in fructose+DHA were no different than fructose alone. No mitigating effects there.
The study is especially meaningful for me because I have a tendency to eat a lot of sweets when I am studying for something. Wondering how short term/long term effects differ.
The study itself, for anyone interested, is http://www.sciencedirect.com/science/article/pii/S2352396416301438
If there are criticisms of the way this study was conducted or conclusions drawn, I'd love to hear them. I know that I have seen posters on here dismiss rat studies as being unapplicable to humans. This study includes the sentence "To infer translatability to human pathophysiology, we assessed the intersection of the molecular signals from our rodent models with human genome-wide association studies (GWAS) of metabolic and brain disorders". Not sure I completely understand this sentence, but if you are arguing against "translatability" hopefully you can dissect this sentence further for me.2 -
Small sample size, high daily dose (which 60 kg person drinks 2 liters of soda every single day?), sprague dawley rats that get health problems if you so much as look at them funny.2
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stevencloser wrote: »Small sample size, high daily dose (which 60 kg person drinks 2 liters of soda every single day?), sprague dawley rats that get health problems if you so much as look at them funny.
WRT sample size, perhaps you or someone else could help me unravel the statement " Sample size was chosen to yield > 80% statistical power to detect 30% between-group difference with 10% within-group difference in a phenotype using two-sided Student's t-test. " Surely this doesn't mean that the authors of the study chose only the 8 rats (out of presumably more rats) that gave them the results that they had been seeking?
As to high daily dose, yes, it's high but far from outrageous. I have been at or near 60kg/132lbs most of my life and when I am in "weight gain mode" I'm certain I had 130g sugar/day. It's only double my current levels. Outrageous is when these rats are given 10x or 100x the equivalent typical consumption in humans (I'm looking at you, resveratrol studies).
As to your argument that Sprague Dawley rats are unusually sensitive, you have my interest. Wiki says "The researchers found that the incidence of tumors in Sprague-Dawley rats from different commercial sources varied as much from each other as from the other strains of rats. The authors of the study "stressed the need for extreme caution in evaluation of carcinogenicity studies conducted at different laboratories and/or on rats from different sources". I remember the big acrylamide scare some years ago that had me swearing off bread crust and French fries. I hate being duped or overly alarmed unnecessarily. But unless someone presents new info on these rats, I'd say that these rats are the same rats used in most studies and aren't reason enough to dismiss the findings.
The results don't support the idea that sugar is a problem in moderation. But for me at least, it presents new reasons not to binge on sugar. I just need to remember this next Halloween.
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goldthistime wrote: »stevencloser wrote: »Small sample size, high daily dose (which 60 kg person drinks 2 liters of soda every single day?), sprague dawley rats that get health problems if you so much as look at them funny.
WRT sample size, perhaps you or someone else could help me unravel the statement " Sample size was chosen to yield > 80% statistical power to detect 30% between-group difference with 10% within-group difference in a phenotype using two-sided Student's t-test. " Surely this doesn't mean that the authors of the study chose only the 8 rats (out of presumably more rats) that gave them the results that they had been seeking?
Statistical power is a measure of how well a trend can be detected in your data. There are methods to determine how many samples you need to obtain a pre-set level of statistical power. That does not mean that the authors rigged the study. It just means that they were careful in determining the minimum number of unbiased samples they set out to acquire based on their choices of how strong they wanted their results to be. The caveat is that statistical power has a lot to do with expected variance. For data that has high variance, more samples are required to tease out any possible non-random trend. It also depends on what level of significance you want to achieve. Statistical significance refers to how likely it is that your data is the result of a non-random process.
IMO, as a data scientist and mathematician, a sample size of 8 is grossly insufficient to determine, with any reasonable certainty, whether or not a non-random trend exists, much less is causative.3 -
SapiensPisces wrote: »goldthistime wrote: »stevencloser wrote: »Small sample size, high daily dose (which 60 kg person drinks 2 liters of soda every single day?), sprague dawley rats that get health problems if you so much as look at them funny.
WRT sample size, perhaps you or someone else could help me unravel the statement " Sample size was chosen to yield > 80% statistical power to detect 30% between-group difference with 10% within-group difference in a phenotype using two-sided Student's t-test. " Surely this doesn't mean that the authors of the study chose only the 8 rats (out of presumably more rats) that gave them the results that they had been seeking?
Statistical power is a measure of how well a trend can be detected in your data. There are methods to determine how many samples you need to obtain a pre-set level of statistical power. That does not mean that the authors rigged the study. It just means that they were careful in determining the minimum number of samples they set out to acquire based on their choices of how strong they wanted their results to be. The caveat is that statistical power has a lot to do with expected variance. For data that has high variance, more samples are required to tease out any possible non-random trend. It also depends on what level of significance you want to achieve. Statistical significance refers to how likely it is that your data is the result of a non-random process.
IMO, as a data scientist and mathematician, a sample size of 8 is grossly insufficient to determine, with any reasonable certainty, whether or not a non-random trend exists, much less is causative.
Thanks. I certainly get variance, p-values and the like, but am still a little baffled. Does this mean that they predicted very little variance and so could choose a relatively small sample size? Wouldn't this negate any dismissal of the results using the argument that it is too small a sample size?
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goldthistime wrote: »SapiensPisces wrote: »goldthistime wrote: »stevencloser wrote: »Small sample size, high daily dose (which 60 kg person drinks 2 liters of soda every single day?), sprague dawley rats that get health problems if you so much as look at them funny.
WRT sample size, perhaps you or someone else could help me unravel the statement " Sample size was chosen to yield > 80% statistical power to detect 30% between-group difference with 10% within-group difference in a phenotype using two-sided Student's t-test. " Surely this doesn't mean that the authors of the study chose only the 8 rats (out of presumably more rats) that gave them the results that they had been seeking?
Statistical power is a measure of how well a trend can be detected in your data. There are methods to determine how many samples you need to obtain a pre-set level of statistical power. That does not mean that the authors rigged the study. It just means that they were careful in determining the minimum number of samples they set out to acquire based on their choices of how strong they wanted their results to be. The caveat is that statistical power has a lot to do with expected variance. For data that has high variance, more samples are required to tease out any possible non-random trend. It also depends on what level of significance you want to achieve. Statistical significance refers to how likely it is that your data is the result of a non-random process.
IMO, as a data scientist and mathematician, a sample size of 8 is grossly insufficient to determine, with any reasonable certainty, whether or not a non-random trend exists, much less is causative.
Thanks. I certainly get variance, p-values and the like, but am still a little baffled. Does this mean that they predicted very little variance and so could choose a relatively small sample size? Wouldn't this negate any dismissal of the results using the argument that it is too small a sample size?
They probably calculated variance from their samples, which, being such a small number of them, means that they got a very weak estimation of the expected variance. It reinforces the idea of being highly skeptical of the results.
More importantly, if you look at the criterion they chose (> 80% statistical power to detect 30% between-group difference with 10% within-group difference), these are very weak criterion, since it means that they, for example, only require a sample size large enough to determine with ~80% certainty that they can detect a non-random trend that has at least a 10% difference in magnitude in their within-group data. That means that there is ~20% chance that they cannot determine, with any certainty, if a significant non-random trend of such magnitude exists.
This, combined with the very weak estimate of variance, leads to a lot of room for skepticism.0 -
SapiensPisces wrote: »goldthistime wrote: »SapiensPisces wrote: »goldthistime wrote: »stevencloser wrote: »Small sample size, high daily dose (which 60 kg person drinks 2 liters of soda every single day?), sprague dawley rats that get health problems if you so much as look at them funny.
WRT sample size, perhaps you or someone else could help me unravel the statement " Sample size was chosen to yield > 80% statistical power to detect 30% between-group difference with 10% within-group difference in a phenotype using two-sided Student's t-test. " Surely this doesn't mean that the authors of the study chose only the 8 rats (out of presumably more rats) that gave them the results that they had been seeking?
Statistical power is a measure of how well a trend can be detected in your data. There are methods to determine how many samples you need to obtain a pre-set level of statistical power. That does not mean that the authors rigged the study. It just means that they were careful in determining the minimum number of samples they set out to acquire based on their choices of how strong they wanted their results to be. The caveat is that statistical power has a lot to do with expected variance. For data that has high variance, more samples are required to tease out any possible non-random trend. It also depends on what level of significance you want to achieve. Statistical significance refers to how likely it is that your data is the result of a non-random process.
IMO, as a data scientist and mathematician, a sample size of 8 is grossly insufficient to determine, with any reasonable certainty, whether or not a non-random trend exists, much less is causative.
Thanks. I certainly get variance, p-values and the like, but am still a little baffled. Does this mean that they predicted very little variance and so could choose a relatively small sample size? Wouldn't this negate any dismissal of the results using the argument that it is too small a sample size?
They probably calculated variance from their samples, which, being such a small number of them, means that they got a very weak estimation of the expected variance. It reinforces the idea of being highly skeptical of the results.
More importantly, if you look at the criterion they chose (> 80% statistical power to detect 30% between-group difference with 10% within-group difference), these are very weak criterion, since it means that they, for example, only require a sample size large enough to determine with ~80% certainty that they can detect a non-random trend that has at least a 10% difference in magnitude in their within-group data. That means that there is ~20% chance that they cannot determine, with any certainty, if a significant non-random trend of such magnitude exists.
This, combined with the very weak estimate of variance, leads to a lot of room for skepticism.
Thank you for clearing that up!
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goldthistime wrote: »SapiensPisces wrote: »goldthistime wrote: »SapiensPisces wrote: »goldthistime wrote: »stevencloser wrote: »Small sample size, high daily dose (which 60 kg person drinks 2 liters of soda every single day?), sprague dawley rats that get health problems if you so much as look at them funny.
WRT sample size, perhaps you or someone else could help me unravel the statement " Sample size was chosen to yield > 80% statistical power to detect 30% between-group difference with 10% within-group difference in a phenotype using two-sided Student's t-test. " Surely this doesn't mean that the authors of the study chose only the 8 rats (out of presumably more rats) that gave them the results that they had been seeking?
Statistical power is a measure of how well a trend can be detected in your data. There are methods to determine how many samples you need to obtain a pre-set level of statistical power. That does not mean that the authors rigged the study. It just means that they were careful in determining the minimum number of samples they set out to acquire based on their choices of how strong they wanted their results to be. The caveat is that statistical power has a lot to do with expected variance. For data that has high variance, more samples are required to tease out any possible non-random trend. It also depends on what level of significance you want to achieve. Statistical significance refers to how likely it is that your data is the result of a non-random process.
IMO, as a data scientist and mathematician, a sample size of 8 is grossly insufficient to determine, with any reasonable certainty, whether or not a non-random trend exists, much less is causative.
Thanks. I certainly get variance, p-values and the like, but am still a little baffled. Does this mean that they predicted very little variance and so could choose a relatively small sample size? Wouldn't this negate any dismissal of the results using the argument that it is too small a sample size?
They probably calculated variance from their samples, which, being such a small number of them, means that they got a very weak estimation of the expected variance. It reinforces the idea of being highly skeptical of the results.
More importantly, if you look at the criterion they chose (> 80% statistical power to detect 30% between-group difference with 10% within-group difference), these are very weak criterion, since it means that they, for example, only require a sample size large enough to determine with ~80% certainty that they can detect a non-random trend that has at least a 10% difference in magnitude in their within-group data. That means that there is ~20% chance that they cannot determine, with any certainty, if a significant non-random trend of such magnitude exists.
This, combined with the very weak estimate of variance, leads to a lot of room for skepticism.
Thank you for clearing that up!
Sure! Sorry if I came across as condescending in my first reply. I never know how much background others have and try to err on the side of full explanation whenever possible.
ETA: In studies like this, it's not uncommon to see very small sample sizes being used, simply because getting reliable unbiased data on biological processes, even in a laboratory, is really expensive and difficult. High levels of uncertainty and variance make detecting meaningful trends much more difficult than in the physical sciences. So, I'm not saying that the paper is bad outright, because it's probably fairly common to see this problem, but I'm still really skeptical.1 -
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goldthistime wrote: »stevencloser wrote: »Small sample size, high daily dose (which 60 kg person drinks 2 liters of soda every single day?), sprague dawley rats that get health problems if you so much as look at them funny.
WRT sample size, perhaps you or someone else could help me unravel the statement " Sample size was chosen to yield > 80% statistical power to detect 30% between-group difference with 10% within-group difference in a phenotype using two-sided Student's t-test. " Surely this doesn't mean that the authors of the study chose only the 8 rats (out of presumably more rats) that gave them the results that they had been seeking?
As to high daily dose, yes, it's high but far from outrageous. I have been at or near 60kg/132lbs most of my life and when I am in "weight gain mode" I'm certain I had 130g sugar/day. It's only double my current levels. Outrageous is when these rats are given 10x or 100x the equivalent typical consumption in humans (I'm looking at you, resveratrol studies).
As to your argument that Sprague Dawley rats are unusually sensitive, you have my interest. Wiki says "The researchers found that the incidence of tumors in Sprague-Dawley rats from different commercial sources varied as much from each other as from the other strains of rats. The authors of the study "stressed the need for extreme caution in evaluation of carcinogenicity studies conducted at different laboratories and/or on rats from different sources". I remember the big acrylamide scare some years ago that had me swearing off bread crust and French fries. I hate being duped or overly alarmed unnecessarily. But unless someone presents new info on these rats, I'd say that these rats are the same rats used in most studies and aren't reason enough to dismiss the findings.
The results don't support the idea that sugar is a problem in moderation. But for me at least, it presents new reasons not to binge on sugar. I just need to remember this next Halloween.
Sprague Dawley rats are commonly used. The thing you're quoting more or less says that variances in tumor occurence between two SD rats as big as between a SD rat and other types of rats (which do not get cancer at an average of 40% of cases or so in females). Highly variable rates of medical issue occurence (even when just left to live their lives with no additional contamination to test for) + only a handful of rats that get tested = Possibly less than reliable results.
Most studies on SD rats I've seen had sample sizes in the hundreds per group.1 -
stevencloser wrote: »goldthistime wrote: »stevencloser wrote: »Small sample size, high daily dose (which 60 kg person drinks 2 liters of soda every single day?), sprague dawley rats that get health problems if you so much as look at them funny.
WRT sample size, perhaps you or someone else could help me unravel the statement " Sample size was chosen to yield > 80% statistical power to detect 30% between-group difference with 10% within-group difference in a phenotype using two-sided Student's t-test. " Surely this doesn't mean that the authors of the study chose only the 8 rats (out of presumably more rats) that gave them the results that they had been seeking?
As to high daily dose, yes, it's high but far from outrageous. I have been at or near 60kg/132lbs most of my life and when I am in "weight gain mode" I'm certain I had 130g sugar/day. It's only double my current levels. Outrageous is when these rats are given 10x or 100x the equivalent typical consumption in humans (I'm looking at you, resveratrol studies).
As to your argument that Sprague Dawley rats are unusually sensitive, you have my interest. Wiki says "The researchers found that the incidence of tumors in Sprague-Dawley rats from different commercial sources varied as much from each other as from the other strains of rats. The authors of the study "stressed the need for extreme caution in evaluation of carcinogenicity studies conducted at different laboratories and/or on rats from different sources". I remember the big acrylamide scare some years ago that had me swearing off bread crust and French fries. I hate being duped or overly alarmed unnecessarily. But unless someone presents new info on these rats, I'd say that these rats are the same rats used in most studies and aren't reason enough to dismiss the findings.
The results don't support the idea that sugar is a problem in moderation. But for me at least, it presents new reasons not to binge on sugar. I just need to remember this next Halloween.
Sprague Dawley rats are commonly used. The thing you're quoting more or less says that variances in tumor occurence between two SD rats as big as between a SD rat and other types of rats (which do not get cancer at an average of 40% of cases or so in females). Highly variable rates of medical issue occurence (even when just left to live their lives with no additional contamination to test for) + only a handful of rats that get tested = Possibly less than reliable results.
Most studies on SD rats I've seen had sample sizes in the hundreds per group.
Thanks.
I'm still further motivated to watch my sugar loving tendencies, but do recognize that this study isn't conclusive. As to what I am guessing is the OP's unstated argument, that this study argues for a LCHF diet, my counter would be the old adage "the dose makes the poison". Excess is different than moderate.
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lemurcat12 wrote: »makingmark wrote: »"Americans get most of their fructose in foods that are sweetened with high-fructose corn syrup"
Trying to figure out what scientific study came up with that conclusion. Fructose comes in so many forms that I really doubt that this is even close to true. Fruit, regular sugar, etc all break down to fructose. Statements like that make me very wary of any article.
If one considers the number of foods with this sweetener, it is hard to believe it's not true. I think they simply measure it by annual purchase patterns. It's in hot dogs, sausage, soda, pancake syrup, ketchup, barbecue sauce, fruit flavored beverages, etc...Among a ton of other things I am forgetting.
I don't have time to look it up at the moment; but I believe that this can be supported unless we believe people aren't eating what they are buying.
I was curious, and cane/beet sugar is something like 59% of added sugar, with HFCS only 29%. No idea how much sugar is non-added, but obviously some.
HFCS is about 55% fructose, and sucrose is 50% fructose.
So most? I'm skeptical.
Also, these stats are always problematic because on average doesn't mean much. My understanding is that a TON of the added sugar Americans (on average) eat comes from soda, and so that's HFCS. But heavy users of soda are very heavy uses and many or most Americans probably don't consume sugary soda at all (I managed to get fat without it, personally).* So is the average actually that useful if a segment of people is distorting it through extra high use? (I'm not sure if this is the case or not, but I'd like better numbers.)
This source says almost half of the added sugar is from sweetened/sugary drinks (https://cspinet.org/new/pdf/combined_infographic.pdf) although I've seen other numbers too, and imagine it depends on what's being included in the category and how they are measuring (reported intake vs. estimates from food production. etc.).
Anyway, of course there are lots of other products HFCS is in (enough to make up 29% of all added sugar when
*Oh, yay, dug up some stats: on a given day, half the people in the U.S. consume sugary drinks [this includes juice and energy and sports drinks and sweetened coffee and tea, as well, I believe]; 1 in 4 consume at least 200 calories from such drinks; and 5% drink at least 567 calories—equivalent to four cans of soda (http://www.hsph.harvard.edu/nutritionsource/healthy-drinks/sugary-drinks/)
Your numbers are accurate. If course, I've seen no evidence that there is any different between HFCS and cane sugar anyways....
I consume relatively little added sugar; but I don't think it is a big deal at all in moderation. I use it when making Asian dishes.....
Yeah, I don't eat much HFCS, since I don't really eat packaged foods that contain added sugar (just because of how I eat, not specifically avoiding them) but for a few kinds of ice cream that have cane sugar, but I also am not convinced it's any different than cane/beet sugar in its effects.
I also don't eat all that much added sugar and don't see it as a big deal in moderation (and think it's going to be in moderation if someone eats a sensible balanced diet). I'm also not afraid of fruit.0 -
vinegar_husbands wrote: »makingmark wrote: »"Americans get most of their fructose in foods that are sweetened with high-fructose corn syrup"
Trying to figure out what scientific study came up with that conclusion. Fructose comes in so many forms that I really doubt that this is even close to true. Fruit, regular sugar, etc all break down to fructose. Statements like that make me very wary of any article.
HFCS isn't even that high in fructose, anyway. I think read in Salt Sugar Fat that it was higher when it first came around, but not now (50-55%).
Yeah -- sucrose is 50% fructose; HFCS is 55%.1 -
goldthistime wrote: »Interesting stuff. Thanks for sharing it with us.
The study says the sugar consumption of the rats was roughly equivalent to 130g of sugar per day for a 60kg person. 130g is almost double what MFP suggests as a maximum daily sugar consumption level (68g for me). I don't often exceed my daily sugar goals while I'm actively dieting/logging, but during my weight gain periods, I would bet I exceeded 130g/day often.
Interesting that the addition of DHA mitigates many of the problems created by consuming excess sugar. I thought it was supposed to be the fiber in fruits that reduced problematic response of fructose consumption. Not sure if it was in the article you quoted, but the blood glucose levels in fructose+DHA were no different than fructose alone. No mitigating effects there.
The study is especially meaningful for me because I have a tendency to eat a lot of sweets when I am studying for something. Wondering how short term/long term effects differ.
The study itself, for anyone interested, is http://www.sciencedirect.com/science/article/pii/S2352396416301438
If there are criticisms of the way this study was conducted or conclusions drawn, I'd love to hear them. I know that I have seen posters on here dismiss rat studies as being unapplicable to humans. This study includes the sentence "To infer translatability to human pathophysiology, we assessed the intersection of the molecular signals from our rodent models with human genome-wide association studies (GWAS) of metabolic and brain disorders". Not sure I completely understand this sentence, but if you are arguing against "translatability" hopefully you can dissect this sentence further for me.
What that sentence is saying is that they- created a set of genes involved in the molecular signaling they found significant in rats
- created a curated human GWAS set of genes associated with various metabolic and brain disorders in other human studies
- translated one of the two sets into it's homolog and/or ortholog in the other species. Normally, you'd translate the rat gene set into the set of human homologs, but I've seen it done the other way in an attempt to get more hits in the set (naughty, naughty)
- (assuming done as usual) intersect* the human homolog set with the curated human GWAS set and look for commonalities
The idea is that if you get a large intersection, the effects may be analogous. And that is true. They may be.
The reason you can't extrapolate past 'this is something interesting to investigate in humans' is that there are rather large signaling networks that are well conserved between human and rat as far as homologs/orthologs go, but in reality operate differently. Sometimes it's one or two totally different genes in the pathway that make the difference. Sometimes it's the homolog/ortholog in the human that doesn't behave entirely like its rat counterpart. Sometimes it's another network entirely that interacts with the one of interest that causes the difference. Anyway, you get the idea.
* ETA: calculation may be more complicated than intersection depending on the group, but looking for commonalities is the general idea2 -
SapiensPisces wrote: »goldthistime wrote: »SapiensPisces wrote: »goldthistime wrote: »SapiensPisces wrote: »goldthistime wrote: »stevencloser wrote: »Small sample size, high daily dose (which 60 kg person drinks 2 liters of soda every single day?), sprague dawley rats that get health problems if you so much as look at them funny.
WRT sample size, perhaps you or someone else could help me unravel the statement " Sample size was chosen to yield > 80% statistical power to detect 30% between-group difference with 10% within-group difference in a phenotype using two-sided Student's t-test. " Surely this doesn't mean that the authors of the study chose only the 8 rats (out of presumably more rats) that gave them the results that they had been seeking?
Statistical power is a measure of how well a trend can be detected in your data. There are methods to determine how many samples you need to obtain a pre-set level of statistical power. That does not mean that the authors rigged the study. It just means that they were careful in determining the minimum number of samples they set out to acquire based on their choices of how strong they wanted their results to be. The caveat is that statistical power has a lot to do with expected variance. For data that has high variance, more samples are required to tease out any possible non-random trend. It also depends on what level of significance you want to achieve. Statistical significance refers to how likely it is that your data is the result of a non-random process.
IMO, as a data scientist and mathematician, a sample size of 8 is grossly insufficient to determine, with any reasonable certainty, whether or not a non-random trend exists, much less is causative.
Thanks. I certainly get variance, p-values and the like, but am still a little baffled. Does this mean that they predicted very little variance and so could choose a relatively small sample size? Wouldn't this negate any dismissal of the results using the argument that it is too small a sample size?
They probably calculated variance from their samples, which, being such a small number of them, means that they got a very weak estimation of the expected variance. It reinforces the idea of being highly skeptical of the results.
More importantly, if you look at the criterion they chose (> 80% statistical power to detect 30% between-group difference with 10% within-group difference), these are very weak criterion, since it means that they, for example, only require a sample size large enough to determine with ~80% certainty that they can detect a non-random trend that has at least a 10% difference in magnitude in their within-group data. That means that there is ~20% chance that they cannot determine, with any certainty, if a significant non-random trend of such magnitude exists.
This, combined with the very weak estimate of variance, leads to a lot of room for skepticism.
Thank you for clearing that up!
Sure! Sorry if I came across as condescending in my first reply. I never know how much background others have and try to err on the side of full explanation whenever possible.
ETA: In studies like this, it's not uncommon to see very small sample sizes being used, simply because getting reliable unbiased data on biological processes, even in a laboratory, is really expensive and difficult. High levels of uncertainty and variance make detecting meaningful trends much more difficult than in the physical sciences. So, I'm not saying that the paper is bad outright, because it's probably fairly common to see this problem, but I'm still really skeptical.
+1
Part of my job is helping with study design. There's a not very funny and unfortunately often true joke that when an Investigator asks us how many samples he needs for his particular study, the answer is: take the money you have and divide by the experiment cost per sample. That's sufficient power.
5 -
SapiensPisces wrote: »goldthistime wrote: »SapiensPisces wrote: »goldthistime wrote: »SapiensPisces wrote: »goldthistime wrote: »stevencloser wrote: »Small sample size, high daily dose (which 60 kg person drinks 2 liters of soda every single day?), sprague dawley rats that get health problems if you so much as look at them funny.
WRT sample size, perhaps you or someone else could help me unravel the statement " Sample size was chosen to yield > 80% statistical power to detect 30% between-group difference with 10% within-group difference in a phenotype using two-sided Student's t-test. " Surely this doesn't mean that the authors of the study chose only the 8 rats (out of presumably more rats) that gave them the results that they had been seeking?
Statistical power is a measure of how well a trend can be detected in your data. There are methods to determine how many samples you need to obtain a pre-set level of statistical power. That does not mean that the authors rigged the study. It just means that they were careful in determining the minimum number of samples they set out to acquire based on their choices of how strong they wanted their results to be. The caveat is that statistical power has a lot to do with expected variance. For data that has high variance, more samples are required to tease out any possible non-random trend. It also depends on what level of significance you want to achieve. Statistical significance refers to how likely it is that your data is the result of a non-random process.
IMO, as a data scientist and mathematician, a sample size of 8 is grossly insufficient to determine, with any reasonable certainty, whether or not a non-random trend exists, much less is causative.
Thanks. I certainly get variance, p-values and the like, but am still a little baffled. Does this mean that they predicted very little variance and so could choose a relatively small sample size? Wouldn't this negate any dismissal of the results using the argument that it is too small a sample size?
They probably calculated variance from their samples, which, being such a small number of them, means that they got a very weak estimation of the expected variance. It reinforces the idea of being highly skeptical of the results.
More importantly, if you look at the criterion they chose (> 80% statistical power to detect 30% between-group difference with 10% within-group difference), these are very weak criterion, since it means that they, for example, only require a sample size large enough to determine with ~80% certainty that they can detect a non-random trend that has at least a 10% difference in magnitude in their within-group data. That means that there is ~20% chance that they cannot determine, with any certainty, if a significant non-random trend of such magnitude exists.
This, combined with the very weak estimate of variance, leads to a lot of room for skepticism.
Thank you for clearing that up!
Sure! Sorry if I came across as condescending in my first reply. I never know how much background others have and try to err on the side of full explanation whenever possible.
ETA: In studies like this, it's not uncommon to see very small sample sizes being used, simply because getting reliable unbiased data on biological processes, even in a laboratory, is really expensive and difficult. High levels of uncertainty and variance make detecting meaningful trends much more difficult than in the physical sciences. So, I'm not saying that the paper is bad outright, because it's probably fairly common to see this problem, but I'm still really skeptical.
+1
Part of my job is helping with study design. There's a not very funny and unfortunately often true joke that when an Investigator asks us how many samples he needs for his particular study, the answer is: take the money you have and divide by the experiment cost per sample. That's sufficient power.
LOL. And thanks for the explanation above. Posts like these remind me why I love MFP so much.0 -
lemurcat12 wrote: »vinegar_husbands wrote: »makingmark wrote: »"Americans get most of their fructose in foods that are sweetened with high-fructose corn syrup"
Trying to figure out what scientific study came up with that conclusion. Fructose comes in so many forms that I really doubt that this is even close to true. Fruit, regular sugar, etc all break down to fructose. Statements like that make me very wary of any article.
HFCS isn't even that high in fructose, anyway. I think read in Salt Sugar Fat that it was higher when it first came around, but not now (50-55%).
Yeah -- sucrose is 50% fructose; HFCS is 55%.
HFCS is typically 55% by mass fructose, although 42% is also a commercial product.
Sucrose has molecular weight 342 and hydrolyses to two monosaccharides glucose and fructose each of MWt 180 so it's probably more accurate to say sucrose is 52.6% fructose.
HFCS consumption in the US is 1.4m metric tonnes, beet & cane sugars about 11m tonnes. https://www.census.gov/history/pdf/8-2015sugarforecast.pdf0 -
As one of limited background additional information is good. thank you.
[/quote]
Sure! Sorry if I came across as condescending in my first reply. I never know how much background others have and try to err on the side of full explanation whenever possible.
0 -
What that sentence is saying is that they
- created a set of genes involved in the molecular signaling they found significant in rats
- created a curated human GWAS set of genes associated with various metabolic and brain disorders in other human studies
- translated one of the two sets into it's homolog and/or ortholog in the other species. Normally, you'd translate the rat gene set into the set of human homologs, but I've seen it done the other way in an attempt to get more hits in the set (naughty, naughty)
- (assuming done as usual) intersect* the human homolog set with the curated human GWAS set and look for commonalities
This totally made my day. I did not expect to see a discussion involving homologs and orthologs here. A large part of my dissertation research involved inter-species comparisons of proteins in glycolysis. So your summary warmed my heart.
1
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