Noor neiu soovib alandada rasvaprotsenti

ROtter ei lingi kuigi asjalkke artikleid viimasel ajal ” srcset=”/uploads/emoticons/smile@2x.png 2x” width=”20″ height=”20″>

See Vomaxi “valguvajadus on ülehinnatud” ja Otsuse “valgul ja valgul pole eriti vahet ja sojavalk on sama hea” ignoreerivad hulka uuringutulemusi.

Vadakuvalk kusjuures on eriti alahinnatud ja peaks minu teooria kohaselt kaitsma isegi Alzheimeri eest. Suhkruhaigusest rääkimata.

Livestrongi artikkel oli asjalik.

Sa ei oska ka puzzlet iseseisvalt kokku panna ja õigeid asju välja lugeda.

Ülaltoodust võiks teha järgmised praktilised järeldused:

1. Liigse valgu tarbimine (s.o üle 2 g/kg) ei anna tulemusi, sellega toodame vaid energiat. Energia tootmiseks sobivad aga paremini süsivesikud.

2. Ühe toidukorraga üle 20-30 g valgu tarbimine ei anna samuti mingeid tulemusi, seda eriti kiirelt seeduvate (nt vadakuproteiin) valkude puhul.

3. Tuleb arvestada, et seedeprotsessi kiireks toimimiseks on vajalik ja oluline ka rasvade ja süsivesikute optimaalne tarbimine.

Teemaalgataja on tütarlaps, kaalub 50 kg, kui palju tal lihast on, siin soovitab mõni tal valgu norme, mis sobivad kulturistidele ja pealegi tegeleb tütarlaps kickpoksiga.

Veidi veel…

Eric Helms Menno,

First off I just want to say much respect for your willingness to talk about this all, I really enjoy discussions like this and I think everyone benefits ” srcset=”/uploads/emoticons/smile@2x.png 2x” width=”20″ height=”20″>

Wanted to address a couple things you directed to me.

I’m not sure we can ever eliminate the need for paying attention to context. A study can only tell you something if the researchers measured that variable. For example on the topic of body composition, if stats aren’t ran to find correlations between protein and body comp, a study can’t tell you this data. Also, a study can be only be inferred to the population it is targeting, and if certain aspects of that population aren’t taken into consideration the results will require the appreciation of context. For example, if you just studied protein intake in athletes, with no attention to type of sport, magnitude of calorie surplus/deficit, body composition etc. you might get some data that is being skewed by variables you didn’t think to account for.

To illustrate this point, and to address your comment that it is “practically impossible to lose weight and not LBM in resistance trained athletes”, check out these two studies done on the same group of subjects by Garthe et al in 2011:

http://www.ncbi.nlm….pubmed/21558571

http://www.ncbi.nlm….pubmed/21896944

The first study was done on elite athletes from a range of sports, all following a weight loss diet for 4-12 weeks. Protein was set to 1.6g/kg and then a deficit was created, primarily through fat reduction to maintain carbohydrate levels, and they divided the subjects into two groups determined by the target rate of weight loss; .5kg/week and 1kg/week. In the .5kg/week group, we actually see an increase in LBM. However in the 1kg/week group we see maintenance and loss of LBM. As you read further into the results and start looking at subject characteristics, you can actually see that the leaner subjects in the 1kg group lost LBM. In the second study (following up on the same subjects), more data is given and we see a discussion of training volume. For some folks, the level of resistance training they did in their sport was a lot. Others, not much. It was the folks who increased training volume, were higher in bodyfat percentage, and followed a less severe caloric restriction who gained LBM. While it was the experienced lifters, lower in body fat percentage, following a more aggressive caloric deficit that lost LBM. So context really does matter here, because the researchers weren’t even examining protein, and they weren’t examining body comp, yet the data is there for analysis. So really, we have to look at the broader context any time we evaluate a study, since no one study is going to draw correlations between every variable of interest, and no one study can examine every possible permutation of populations who might be consuming protein under different conditions.

As to experience level and protein intake, I think I was not clear. I don’t think more advanced athletes need more protein. I was suggesting that if training is not set up to create a high level of adaptive stimulus you won’t need as much protein, since protein turn over would not be as high. That was the intended point I wanted to make in the example I used. Alan brought this up in the roundtable discussion; if you have a study were you put novices and experienced lifters on the same training program, it very well might be enough to create an adaptive stimulus in novices but not nearly enough to do the same in the experienced lifters. So, a difference of protein turnover betwen them might not be indicative that experienced athletes need less protein, it just might be indicative that only a training program that elicits adaptation will require extra building material to support this, which is quite logical when you think about it.

Eric Helms As far as differences in body composition effecting protein metabolism I think that is quite well established, both more globally, and more mechanistically. Besides the two studies I posted above by Garthe et al 2011a, 2011b, some studies of bodybuilders during competition also show greater lean body mass losses as prep continues. Granted you can’t just attribute this to body composition as often training volume increases and food intake decreases with shifts in macronutrient composition. But there is also the papers by Forbes and Hall; “Forbes theory”, based on weight loss studies, being that the lower your body composition the greater the percentage of LBM loss is incurred during weight loss. The question posed is: can a higher protein intake offset this and how does it scale with body composition? We don’t have an answer to the second part of the question, but the sports nutrition community is more and more in agreement about protein needs being increased during a caloric deficit, with a recent review paper by Phillips et al 2011 (probably the most prolific protein researcher) stating that 1.8-2.7g/kg is likely appropriate during a caloric deficit.

More mechanistically, check out Elia et al 1999, it showed that during periods of fasting, protein losses and the percent of energy derived from protein are two to threefold less in obese subjects than in lean subjects. The results also show that urinary nitrogen excretion is twofold less in obese subjects, and lean subjects contribute twice as much protein to glucose production compared to their obese counterparts. Furthermore, in the first few days of starvation, leucine oxidation increases in lean individuals but not in obese individuals.

So clearly there is a difference caused by body composition, how much is certainly up for debate.

As to your comment about statistics. I really don’t think we have crappy journals in the fitness industry per say, but we do have low impact journals. That being said, impact factor is not a measure of quality but rather a measure of importance. I think it’s a given that a niche science like exercise science would have much lower impact factors than something like environmental science or biology. But that is not a reflection of quality but rather impact. In fact sports medicine, only has an impact factor of 5.2, but it is the highest impact factor among all the fitness industry related journals (and happens to be extremely high quality as well). Oddly enough, sports statistician Will Hopkins, who is currently pioneering magnitude based stats in the field of sports science (for the reasons I stated before) is a reviewer for Sports Medicine. I think this is interesting based on your evaluation of the statistical approach as low quality, not something tha meshes with Sports Med being the best journal in the field.

I think this approach is particularly appropriate for practioners, since a study with this statistical analysis tells them something they can use. “Statistically significant” doesn’t tell a strength and conditioning coach how useful an intervention is nor the chances of it being harmful or beneficial to his athletes. The approach to stats I am talking about does.

Also, I don’t know if the characterization is accurate that magnitude based inferences are only trotted out when traditional stats don’t show an effect as a post hoc analysis. Here is an exmaple of study published purely (and from the start) using this form of statistical analysis:

http://www.ncbi.nlm….pubmed/20397095

And that is one among many. In fact there are a good number of studies that don’t even use p values and go right to establishing a magnitude based effect, giving chances that it could be beneficial, trivial or harmful and provide that based on confidence intervals. In fact, I am guessing from your critique that you didn’t actually read the link I provided to sportscience.org? Because I felt a little straw manned by your response, so I assume that was just because you weren’t 100% clear on what I was referring to. So please do check out the link, it can only broaden your perspective.

Also I’m not sure if it was mentioned here, but the limitations of NBAL really should be made clear. In Wahlberg et all 1988 (where they compared 1.6 to 1.0 g/kg protein in lean bbers), even though the 1.6g/kg group lost LBM, Nitrogen Balance was positive. Also, in Pikosky 2010, they took folks, put them on 1.8g/kg and had them get into a deficit purely from activity, cardio, and they achieved positive nitrogen balance, but lost LBM as well. So if we are inferring information from NBAL and trying to make distinctions between 1.8 and 2.2, I really don’t think the measure utilized is sensitive enough, nor does it target skeletal muscle, or tell you anything about optimal intake, rather it provides a functional minimum to avoid losses in total body protein (nitrogen). And I don’t think functional minimum to prevent LBM loss=optimal ntake for adaptation.

Ma ise mõtlen umbes samamoodi. Eks muidugi paar korda nädalas kick-poksiga treeniv tütarlaps ei pea raudkindalt sööma 2.5g kehakilo kohta valke. Aga IMO on selline lähenemine nii mõneski mõttes soovitatav. Tead kindlalt, et valguvajadused on Dieedi ajal kaetud, näljatunne kontrolli all ning ega siis valgud mööda külgi energia mõttes maha ei jookse. ” srcset=”/uploads/emoticons/smile@2x.png 2x” width=”20″ height=”20″>

Seega pigem veidi üle, kui alla. ” srcset=”/uploads/emoticons/smile@2x.png 2x” width=”20″ height=”20″>

Kaloreid alla ajades tasub igaühel valke tõsta. Ja minu loetud artiklite põhjal on valguvajadus pigem just alahinnatud, eriti aeroobsetel aladel nagu kasvõi jalgrattasport ” srcset=”/uploads/emoticons/smile@2x.png 2x” width=”20″ height=”20″>