tag:blogger.com,1999:blog-65771266912638907752024-03-13T12:19:58.690-07:00Low carb UKLow carb dieting experience in the UK.PhilThttp://www.blogger.com/profile/17675583272641426292noreply@blogger.comBlogger36125tag:blogger.com,1999:blog-6577126691263890775.post-65588856649028904882023-07-15T03:10:00.001-07:002023-07-15T03:10:33.965-07:00Abbott / Apple Librelink SNAFU<p> Abbott have updated their Librelink software for the Libre 2 sensor in the UK and the new version 2.10.x has the advantage of streaming glucose values every 60s to the app screen. It works well on Android. https://www.freestyle.abbott/uk-en/home.html<br /><br />Not much else changed, there's no watch integration or lock screen / notification area display of blood glucose data. But it does work quite well, and the values are pushed through to the Libreview.com cloud and hence on to LibreLinkUp followers continuously. <br /><br />Unfortunately there were many problems with iPhone / iOS users experiencing white screens when the app updated. This was solvable by deleting the app (and losing up to 90 days data off the phone) and reinstalling. The data is still held at Libreview.com. This problem led Abbott to pull the 2.10.x update from the app store, but not replace it with the old one, leaving no Librelink-GB app on the App Store.<br /><br />For some reason the Ireland app store still carries their updated version, perhaps the white screen problem didn't arise. https://apps.apple.com/ie/app/freestyle-librelink-ie/id1307010255 <br /><br />So many iPhone Libre users were suddenly left in the lurch with no working app, and no app in the App Store to download.<br /><br />Abbott released a video https://www.youtube.com/watch?v=LfIbDA8ciSg explaining how to delete the app and then recover it from "Purchased apps" in the user's App Store profile. This seemed to work for the majority, the loss of recent history on the phone was collateral damage. There is now a web page too https://www.freestyle.abbott/uk-en/iphone.html <br /><br />It seems strange that Abbott did not rewind the update process and just put v2.8.x back on the app store. This would have restored the status quo and given them time to resolve the issue. There are some questions to be answered about the testing regime as after all there's a very limited set of hardware using iOS and an even more limited OS/user interface - surely a beta test by 50 end users would have revealed this problem in advance of messing up thousands of users.<br /><br />The iOS version 2.10.x does work once installed, although some users were struggling to adapt to the movement of the "Scan" button to a more subtle RFID icon top right as shown below. With the new streaming data feature you only really need to scan to start the sensor and if it won't reconnect.<br /></p><p><img alt="" 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" /></p>PhilThttp://www.blogger.com/profile/17675583272641426292noreply@blogger.com0tag:blogger.com,1999:blog-6577126691263890775.post-24143726802354818882023-04-23T04:04:00.008-07:002023-04-24T06:21:13.490-07:00<p><u>Type 2 Diabetes Remission - UK Direct Trial update.<br /></u></p><p>Diabetes UK have been <a href="https://www.diabetes.org.uk/about_us/news/weight-loss-can-put-type-2-diabetes-remission-least-five-years-reveal-latest-findings">publicising</a> unpublished results from a 5 year follow-up of the <a href="https://www.directclinicaltrial.org.uk/">DiRECT trial</a> where a harsh calorie restriction diet ((825–853 kcal/day formula diet
for 3–5 months)) was used to attempt <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8929179/">remission</a> of Type 2 diabetes in obese and overweight adults. 85 subjects from the initial intervention group of 149 were included in the follow-up.<br /></p><p>After 5 years, 11 people were still in remission with a mean weight loss of 6.1 kg. This compares to an average 5-year weight loss of 4.6kg, with 3.4% in remission, for those in the control group. <br /><br />At the 3 year point 48 of the 85 had been in remission, so only 23% had sustained remission from years 2-5. The percentage in remission in this group fell from 56.5% to 13%.</p><p>After the <a href="https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(17)33102-1/fulltext">first year</a> of DiRECT, remission was seen in 68 out of 149 subjects, or 46%. Follow up after the <a href="http://eprints.gla.ac.uk/180894/">second year</a> reported that 53 of the original 149 were still in remission, or 36%.<br /><br /></p><p>This suggests that the 85 people in the extension study had a higher success rate at the outset than the whole intervention group. Even if we scale up the 13% in remission at year 5 from 85 to the original 149 it would suggest 19 long term successes in total.<br /><br /></p><p>The other primary goal of DiRECT was to achieve a 15 kg weight loss, not least because remission is much higher if this level of loss is achieved. Despite the severity of the calorie restriction and the amount of support provided only 36 (24%) of original participants achieved this at one year, falling to 17 (11%) after 2 years. The Diabetes UK press release does not appear to clarify how many of the 85 had sustained a 15 kg weight loss at year 5, but reports a mean weight loss in the group of 6.1 kg at year 5 compared to 10 kg in the whole intervention group at the end of the first year.<br /></p><p><br />Dr Nicola Guess has written <a href="https://link.springer.com/article/10.1007/s13300-022-01220-4">an article</a> suggesting that there is merit in looking at the macronutrient content of the diet beyond the energy balance, as <a href="https://link.springer.com/article/10.1007/s00125-019-4956-4">studies show</a> that liver and pancreas fat can be reduced by carbohydrate reduction without weight loss due to the change in fat use for energy and reducing denovo lipogenesis from high carbohydrate intake. This could improve the success rate and reduce the regression seen in DiRECT where weight loss proves hard to achieve and harder to sustain.<br /></p>PhilThttp://www.blogger.com/profile/17675583272641426292noreply@blogger.com0tag:blogger.com,1999:blog-6577126691263890775.post-8309555404440829092022-02-17T04:14:00.004-08:002023-04-23T11:00:30.063-07:00Testing Mosley's Fast 800 Keto diet<p>Dr Michael Mosley has published a Keto version of his "Fast" diet series in <a href="https://www.amazon.co.uk/Fast-800-Keto-manage-long-term/dp/B09DZ5Z2XF">book form</a>, serialised in the <a href="https://www.dailymail.co.uk/news/article-10429047/DR-MICHAEL-MOSLEY-Learn-eat-BURN-FAT-Fast-800-Keto-plan.html">Daily Mail</a> for the January "dieting season" and <a href="https://thefast800.com/how-to-do-the-fast-800-keto/">online</a> on his web site.<br /></p><p>I decided to give it a try, as it's written in British English with familiar foods and takes the interesting approach of targetting a calorie reduction explicitly, alongside carbohydrate restriction. In the first phase it presents meal plans of 2 or 3 meals per day broadly designed around keeping daily protein at 50g or more, carbs below 50g and calories around 800-1000. None of the meal plan days are actually as low as the stated 800 kcal/day. For our Transatlantic friends the 50g of carbs is digestible carbohydrate excluding fibre, what the US would call "net carbs".</p><p>After one week I am writing this and can report a weight loss of 4.6 lbs / 2.1 kg. Blood glucose this morning was 4.6 mmol/l (83 mg/dl) and ketones 1.5 mmol/litre, so the approach certainly works for me. My wife has a smaller weight loss of 3.8 lbs, eating approximately the same as me.</p><p>We have prepared and cooked all the food for the recipes and not used any of the shakes or soups that are available for the "Fast 800" series of diets. Generally speaking we eat the three meals between 9 or 10 am and try to finish by 6pm, in part to avoid any hunger issues around 5pm.</p><p>I have tracked the meals using <a href="https://cronometer.com/">Cronometer</a> to see how the macro and micronutrient intake looks on this restricted calorie intake. The macros are broadly in line with the recipe values in the boook, which are wriiten by Mosley's wife Dr Clare Bailey. There were a number of shortfalls in the micronutrients though, with tpyically 85% of daily targets being met. I shall discuss these individually below. At this stage I think some of it may be missing data, but some of it is a deficiency in the diet which would probably also apply to my standard diet. Here's what the Cronometer web site shows for one of the days (ignore the water intake, I don't reliably log water or black coffee intake) :-<br /><br /><img alt="" src="data:image/png;base64,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" /></p><p>Starting with the lipids, the Omega-3 value above includes a fish oil based supplement I take which has 360 mg of EPA and 240 mg of DHA. This accounts for ~38% of the omega 3 value and avoids a deficit.<br /><br />Omega-6 typically runs low on this diet as there is a little olive oil used but no sunflower or other high PUFA oils. I don't feel it necessary to increase the 3-6 g/day intake.<br /><br />Moving on to protein the list of amino acids is generally filled out to at least 100% with the inclusion of eggs, salmon, chicken and similar foods that contain high quality protein.</p><p>In the Vitamins we start to see some gaps occur regularly. Vitamins D & K are taken as a supplement and without that D would always be deficient and K often so. The B vitamins often show deficiency which I have chose to correct with a daily teaspoon of nutritional yeast flakes reinforced with B12. Vitamin E is persistently low, and not easy to find in foods. While some rapeseed or sunflower oils appear to contain an amount of E it is at a level that would involve too many calories or too much omega-6 intake. We have added some nuts and seeds to help with this, but it's a work in progress.<br /><br />When it comes to minerals the Calcium and Potassium are persistently low. As my wife has a broken wrist she is now supplementing with calcium to offset this. I am a little unsure if the actual intake is low, or if we have a data problem with minerals in the food databases. More investigation required. Marnesium would be low if I wasn't supplementing with 300 mg of magnesium as citrate. The sodium intake is good and I beleive this will help offset any potassium issues based on what Dr Stephen Phinney has said about the action of the kidneys on a keto diet.</p><p><br /></p><p>So overall we have had initial success with 3 meals a day of 1000 +/- 150 kcal/day following the book's eating plan. We haven't looked at switching to two (larger) meals per day and it is later in the diet that we may switch to 4 days of higher calorie intake and 3 days at the above restricted intake.<br /></p>PhilThttp://www.blogger.com/profile/17675583272641426292noreply@blogger.com0tag:blogger.com,1999:blog-6577126691263890775.post-10555407481617622872017-12-10T23:21:00.004-08:002017-12-10T23:21:27.869-08:00How much meat or fish per mealSome thoughts around the standard Atkins portion suggestion of 113g / 4 ozs of meat per meal: <br />
<br />
<br />
You're trying to achieve an overall balance of fat and protein, with negligible carbs.<br />
<br />
If you choose lean protein sources, rather than ones that fit the above objective, then in my experience it gets more difficult.<br />
<br />
115g of 20% fat beef mince for example is a good fit, as are some of the
oily fish, hard cheese and eggs. They come with the right balance of
fats and protein built in.<br />
<br />
<a name='more'></a><br />
If you choose 115g of a lean protein like turkey breast you'll get 33g
of protein and very little fat, If you then add 30g of fat to compensate
you end up with a lot more intake - 60% more - than if you chose 115g
of 20% fat beef mince.<br />
<br />
If you use lean protein sources aim for less than the standard portion to compensate.
PhilThttp://www.blogger.com/profile/17675583272641426292noreply@blogger.com0tag:blogger.com,1999:blog-6577126691263890775.post-14786823420546514972017-06-18T01:45:00.002-07:002017-11-25T23:43:51.741-08:00Atkins diet food listThis list is reproduced from the Atkins (UK) <a href="https://uk.atkins.com/why-atkins/the-phases/phase-1-induction.html" target="_blank">website</a> document "<a href="https://uk.atkins.com/static/default/files/documents/pdf/Atkins%20Food%20List.pdf" target="_blank">low carb foodlist</a>" (PDF download link), with some adjustments to language and presentation while retaining the lists themselves.<br />
<br />
The core of your food intake should be protein sources with ample inherent fat. Typically 4 to 6 ozs of meat/fish/poultry would be a portion size for protein in a meal. That's 115 - 175 grams of food, giving you 20 to 35 grams of actual protein content. The smaller weight is appropriate for women while the largest value is appropriate for men of average or above average height. Tall muscular men engaging in strenuous activity may merit an increase to 225 grams of meat etc per meal but Atkins is not a "maximum protein" diet. An alternative guideline is a piece of meat or fish about the size of the palm of your hand.<br />
<br />
<a name='more'></a><br />
<br />
Acceptable low carb fish, meat & poultry:<br />
(all uncoated except for herbs & spices and not in sauces)<br />
<br />
<h4>
<b>Fish</b></h4>
• Cod<br />
• Halibut<br />
• Herring<br />
• Salmon<br />
• Sardines<br />
• Sole<br />
• Trout<br />
• Tuna<br />
<h4>
Meat</h4>
• Bacon<br />
• Beef<br />
• Ham<br />
• Lamb<br />
• Pork<br />
• Veal<br />
• Venison<br />
<h4>
Poultry</h4>
• Chicken (including skin)<br />
• Cornish Hen<br />
• Duck<br />
• Goose<br />
• Pheasant<br />
• Quail<br />
• Turkey<br />
<h4>
Shellfish</h4>
• Clams<br />
• Crab<br />
• Lobster<br />
• Mussels<br />
• Oysters<br />
• Shrimp/Prawns<br />
• Squid<br />
<br />
If you find an item that isn't on the list then check the nutritional label - anything that is below 1g of carbohydrate per 100g is going to be fine. The objective is to get your protein without added carbs.<br />
<br />
<h4>
Eggs</h4>
<br />
Eggs are a good Atkins food - the right balance of protein and fat without carbs. The list suggests a limit of three a day. If you're having eggs for the protein source in one of your meals three would give you about 18-20g of protein, equivalent to about 110g of meat (depending on size, obviously).<br />
<br />
<h4>
Cheese </h4>
Hard cheese is also good like eggs, a guideline of 4 ozs / 115 grams per day is suggested. That's four individual wrapped portions or four cubes the size of a large dice. Might be worth measuring on the scale to get your eye in as it's easy to overeat. This limit covers the total cheese per day, whether snacks or added into meals.<br />
<br />
Cottage cheese and ricotta are not on the phase 1 list due to higher carb content. Also avoid low-fat cheeses, ‘diet’ cheese, ‘cheese products’, whey cheese or any cheese flavoured with fruit. As always, if in doubt look at the label for the carbohydrate content per 100g and aim for 1 or less.<br />
<br />
Common low carb cheeses :-<br />
<br />
• Blue cheese<br />
• Brie<br />
• Cheddar<br />
• Cream cheese<br />
• Emmental<br />
• Feta<br />
• Goat’s cheese<br />
• Gouda<br />
• Mozzarella, made with whole-milk<br />
• Parmesan<br />
• Romano<br />
<br />
<h4>
Dairy and substitutes</h4>
<br />
45ml (1½ oz) daily or a total of 2–3 tablespoons of<br />
<br />
• Sour cream<br />
• Single or double cream<br />
<br />
Avoid non-dairy creamers as these will contain carbohydrates. Ordinary milk is too high in lactose so this too should be avoided in Phase 1. Some almond, coconut or soya based milk alternatives are low carb and acceptable, but others are sweetened. Read the label carefully looking for added sugar, fruit juice etc and ensure the carbohydrate content is very low, preferably less than 1g per 100ml.<br />
<br />
A "milk alternative" to put in your tea or coffee (caffeine is fine on Atkins) can be made by diluting double cream with water in a 10:1 ratio to give a similar fat content to whole milk.<br />
<br />
<h4>
Dietary Fats</h4>
Most of your fat intake will come from choosing fattier meats and oily fish with a similar number of grams of fat and protein per 100g. Think in terms of 20% fat beef mince not the 5% ultra-lean version, mackerel not cod, and so on. Most "healthy" or "diet" choices are low fat, and Atkins is a diet high in healthy fats.<br />
<br />
Add-on fats used for cooking or dressing are fine, typically a tablespoon / 15 ml per serving.<br />
<br />
• Butter, hard or whipped (including butter/oil blends)<br />
• Oils including extra virgin/virgin olive oil, flaxseed oil, grapeseed oil, sesame oil etc.<br />
• Coconut oil<br />
• Full fat mayonnaise (check the label for carbs / sugars)<br />
<br />
Other high fat food items<br />
<br />
• ½ avocado<br />
• Black or green olives<br />
<br />
Phase 1 avoids nuts but later in the plan wthese are a good source of dietary fat too.<br />
<br />
<h4>
Vegetables and Salads</h4>
Phase 1 of Atkins aims to get 12-15 grams of carbohydrate per day from vegetables and salad. If you choose appropriate low carb veg that's 300 to 500 grams a day (about a pound) of veg per day, or in other words "Your 12–15g of carbs works out at about 175 g (6 oz) of salad leaves plus 200–300 g (7–11 oz) cooked vegetables."<br />
<br />
As this is a UK site this quantity is carbohydrates off the nutritional label and doesn't include fibre. You'll be getting fibre from the veg too, but that's accounted for separately on our labels.<br />
<br />
Acceptable low carb vegetables and salads:<br />
<br />
<h4>
Salad leaves</h4>
About 30 g (1 oz) or a big handful of each of raw salad leaves comes in at less than 1g of carbs:<br />
<br />
• Bok Choy<br />
• Chives<br />
• Cabbage<br />
• Endive<br />
• Lettuce, all types<br />
• Rocket<br />
• Spinach<br />
• Sprouts, all kinds<br />
• Watercress<br />
<br />
<h4>
Other Salad Vegetables</h4>
• Avocados<br />
• Bamboo shoots, tinned<br />
• Celery<br />
• Cucumber<br />
• Mushrooms<br />
• Olives, black or green<br />
• Onions<br />
• Gherkins, dill or sour<br />
• Radishes, Daikon<br />
• Spring onions<br />
• Sweet peppers, any colour<br />
• Tomatoes<br />
<br />
<h4>
Vegetables</h4>
• Asparagus<br />
• Aubergine<br />
• Beans, French,broad, green<br />
• Broccoli<br />
• Cauliflower<br />
• Swiss Chard<br />
• Courgette<br />
• Kale<br />
• Mushrooms<br />
• Okra<br />
• Sauerkraut<br />
• Spinach<br />
• Squash<br />
• Turnips<br />
<br />
<h4>
Condiments, dressings etc</h4>
• Celery salt<br />
• Chilli peppers<br />
• Garlic<br />
• Ginger root<br />
• Italian seasoning<br />
• Lemon or orange peel, grated<br />
<br />
• Blue cheese dressing<br />
• Caesar salad dressing<br />
• Italian dressing<br />
• Ranch dressing<br />
• Vinaigrette<br />
<br />
You can also have up to 2 tablespoons of lemon or lime juice a day.<br />
<br />
You can enjoy any prepared salad dressing that has no added sugar and no more than 3g of carbs per serving (1–2 tablespoons). A better lower-carb option is to make your own vinaigrette with olive oil<br />
plus either vinegar, lemon or lime juice.<br />
<br />
All fresh herbs are acceptable in Phase 1 and contain virtually no carbs. Small amounts of dried herbs, including basil, bay leaves, chives, coriander, cumin, oregano, rosemary, thyme, and others, plus salt and pepper are fine as are most spices and spice mixes such as chilli powder and<br />
curry powder. Beware of prepared coating mixes or spice pastes that may contain added sugar.<br />
<br />
<br />
<h4>
Non-caloric Sweeteners</h4>
You can add sweeteners to your low-carb recipes. Count each packet as 1g of carbs, and consume no<br />
more
than three per day. Although the sweeteners themselves contain no
carbs, the powdered agent that keeps them from clumping has a small
amount. Typically a gram of a granular sweetener is 0.9g or more of
carbohydrate/<br />
<br />
Acceptable low carb sweeteners:<br />
<br />
• Splenda or Sweetex (sucralose)<br />
• Truvia (a natural product made from stevia)<br />
• Canderel (a blend of aspartame, acesulfame-k, sucralose and stevia)<br />
• Sweet’n Low (saccharin)<br />
• Xylitol or Erythritol (available in health food shops, online and some supermarkets)<br />
<br />
<br />
<h3>
Drinks / beverages</h3>
• Coffee (caffeinated or decaffeinated, hot or iced) and espresso<br />
• Tea (caffeinated or decaffeinated)<br />
• Herbal teas and infusions without added sugar<br />
• Soda water<br />
• Diet fizzy drinks sweetened with noncaloric sweeteners, such as Diet Coke, Pepsi Max and Diet Lemonade<br />
• Sugar-free tonic water<br />
• Carbonated water – must say ‘no calories’<br />
• Unflavoured soy/almond milk<br />
• Cream – single or double<br />
<br />
<h3>
Thou Shalt Not.....</h3>
Here's a list of things you'll be avoiding eating on Atkins Phase 1 :-<br />
<br />
• Fruit (other than rhubarb, which is really a vegetable). <br />Avocados, olives, and tomatoes – all of which are actually fruit – are fine, but no sweet fruits.<br />
• Fruit juice (other than 2 tablespoons lemon and/or lime juice a day)<br />
• Caloric fizzy drinks/juice<br />
• Bread, pasta, muffins, tortillas, crisps and any other food made with flour or other grain products, with the exception of low-carb products with 3g of net carbs or less<br />
• Any foods made with added sugar of any sort, including but not limited to pastries, biscuits, cakes, and sweets<br />
• Alcohol in any form<br />
• Nuts and seeds, nut and seed butters, and nut flours or meals, with the exception of flax meal and coconut flour. (Nuts and seeds are okay after two weeks on Phase 1)<br />
• Grains, even wholegrains<br />
• Kidney beans, chickpeas, lentils, and other pulses<br />
• Starchy vegetables such as carrots, potatoes, sweet potatoes, and winter squash. Check the Atkins Carb Counter or Supermarket web site if you’re unsure<br />
• Dairy products other than cream, souredcream, single cream and aged cheeses. No cow’s or goat’s milk, yoghurt, cottage cheese,or ricotta for now<br />
• ‘Low-fat’ foods, which are usually higher in carbs<br />
• ‘Diet’ products, unless they specifically state ‘low carbohydrate’ and have no more than 3g of Net Carbs per serving<br />
• ‘Junk food’ in any form<br />
• Products such as chewing gum, breath mints, cough syrups and drops, or liquid vitamins, unless they’re sweetened with sorbitol or xylitol. You can have up to three a day of those. Count 1g of carbs per piece<br />
• Sauces which contain added carbs such as BBQ, cocktail, ketchup, pasta sauces etc.<br />
• Tomato sauce, tinned or stewed tomatoes, tomato purée and tomato paste are all<br />
acceptable in Phase 1, as long as they contain no added sugarPhilThttp://www.blogger.com/profile/17675583272641426292noreply@blogger.com9tag:blogger.com,1999:blog-6577126691263890775.post-67170075560701700492016-10-19T09:34:00.000-07:002016-10-19T09:34:41.154-07:00Epidemiology - the "science " of deceit.Today a link on Twitter pointed to a study that claimed to show that "<strong></strong>Eating breakfast was associated with significantly lower CHD risk in this cohort of male health professionals".<br />
<br />
So I took a quick look at <a href="http://circ.ahajournals.org/content/128/4/337" target="_blank">the study</a> and weeded out some of the raw data. It's an analysis of 27,000 ageing male healthcare professionals of whom about 3,400 said they skipped breakfast. This subgroup were generally a bit younger (5 year difference in average age) and three times more of them were smokers - 15% vs 5%.<br />
<a name='more'></a>Over the course of about 15 years there were a total of 1527 cases of diagnosed coronary heart disease, 171 of which were in the small breakfast skipping group :-<br /><br />
<br />
<br />
<table border="0" cellpadding="0" cellspacing="0" style="width: 265px;"><colgroup><col style="mso-width-alt: 4522; mso-width-source: userset; width: 95pt;" width="127"></col>
<col span="2" style="mso-width-alt: 2446; mso-width-source: userset; width: 52pt;" width="69"></col>
</colgroup><tbody>
<tr height="18" style="height: 13.2pt;">
<td height="18" style="height: 13.2pt; width: 95pt;" width="127">Cases</td>
<td align="right" style="width: 52pt;" width="69">1356</td>
<td align="right" style="width: 52pt;" width="69">171</td>
</tr>
<tr height="18" style="height: 13.2pt;">
<td height="18" style="height: 13.2pt;">N</td>
<td align="right">23616</td>
<td align="right">3386</td>
</tr>
<tr height="18" style="height: 13.2pt;">
<td height="18" style="height: 13.2pt;"><br /></td>
<td><br /></td>
<td><br /></td>
</tr>
<tr height="18" style="height: 13.2pt;">
<td height="18" style="height: 13.2pt;">Cases per 1k</td>
<td align="right" class="xl64">57.4</td>
<td align="right" class="xl64">50.5</td>
</tr>
<tr height="18" style="height: 13.2pt;">
<td height="18" style="height: 13.2pt;"><br /></td>
<td align="right" class="xl63">1.14</td>
<td align="right" class="xl63">1.00</td>
</tr>
<tr height="18" style="height: 13.2pt;">
<td height="18" style="height: 13.2pt;"><br /></td>
<td><br /></td>
<td><br /></td>
</tr>
<tr height="18" style="height: 13.2pt;">
<td height="18" style="height: 13.2pt;">Person years</td>
<td align="right">338074</td>
<td align="right">49880</td>
</tr>
<tr height="18" style="height: 13.2pt;">
<td height="18" style="height: 13.2pt;"><br /></td>
<td><br /></td>
<td><br /></td>
</tr>
<tr height="18" style="height: 13.2pt;">
<td height="18" style="height: 13.2pt;">Average years</td>
<td align="right" class="xl64">14.3</td>
<td align="right" class="xl64">14.7</td>
</tr>
<tr height="18" style="height: 13.2pt;">
<td height="18" style="height: 13.2pt;"><br /></td>
<td><br /></td>
<td><br /></td>
</tr>
<tr height="18" style="height: 13.2pt;">
<td height="18" style="height: 13.2pt;">Ratios</td>
<td align="right" class="xl64">7.9</td>
<td><br /></td>
</tr>
<tr height="18" style="height: 13.2pt;">
<td height="18" style="height: 13.2pt;"><br /></td>
<td align="right" class="xl64">7.0</td>
<td><br /></td>
</tr>
</tbody></table>
<br />
<br />
So there were 8 times more cases of CHD in 7 times the number of participants in the breakfast eating group. That's 14% more cases per 1,000 study participants amongs the breakfast <i>eaters</i>.<br />
<br />
The study goes on to "adjust" the data in line with invisble relationships until out of the other end of the computational sausage machine merges the conclusion that "Men who skipped breakfast had a 27% higher risk of CHD compared with men who did not".<br />
<br />
I don't know how this works, or what they assume, or how valid it is. If less people die or get ill by skipping breakfast and smoking more, as per the data, then I am disinclined to believe that skipping breakfast can be harmful. Obviously the age advantage helps, but it almost looks like skipping breakfast can counter the impact of smoking which ordinarily we would expect to increased CHD markedly.<br />
<br />
I am indebted to Prof Richard Feinman for his teachings on the importance of looking at the raw data.<br />
<table border="0" cellpadding="0" cellspacing="0" style="width: 265px;"><tbody></tbody></table>
<table border="0" cellpadding="0" cellspacing="0" style="width: 265px;"><tbody></tbody></table>
PhilThttp://www.blogger.com/profile/17675583272641426292noreply@blogger.com0tag:blogger.com,1999:blog-6577126691263890775.post-23250947082371398012016-08-08T06:20:00.001-07:002016-10-19T09:10:45.506-07:00Doubly Labelled Water - Gold Standard ? Doubly Labelled Water (DLW) is said to be the "gold standard" of energy measurements but in the world of nutritional research that probably means it's just the least worst option. Most of the materials I've read propose it as a good measure of energy <i>expenditure</i> and it is then combined with body weight and composition changes to determine what the energy <i>intake</i> "must have been" using the calorie hypothesis.<br />
<br />
DLW is in the news today as the UK Government's "Nudge Unit" - now operating independently as <a href="http://www.behaviouralinsights.co.uk/publications/counting-calories-how-under-reporting-can-explain-the-apparent-fall-in-calorie-intake/" target="_blank">Behavioural Insights</a> - has been looking at alleged under-reporting of calorie intake in official statistics.<br />
<br />
One aspect of accuracy is how repeatable a measure is - if you take the same person and repeat a DLW test in the same circumstances do you get the same answer ? This has been done :-<br />
<br />
<a name='more'></a><br /><br />
<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><img alt="" data-aria-label-part="" src="https://pbs.twimg.com/media/CpVcKruWgAAZMDD.jpg" style="margin-left: auto; margin-right: auto;" /></td></tr>
<tr><td class="tr-caption" style="text-align: center;">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3985832/</td><td class="tr-caption" style="text-align: center;"></td><td class="tr-caption" style="text-align: center;"></td></tr>
</tbody></table>
This apparently is viewed as a good result for reproducibility over a couple of years, but there are many subjects with a difference of about 100 kcal/day between the two measurments are evident. In the sphere of public obesity policy we're often arguing about differences smaller than that.<br />
<br />
My biggest query about DLW is whether it has been validated as a way to determine energy <i>intake</i> in a controlled situation where food eaten was independently verified. Most of the published work uses DLW to determine "calories out" and therefore points the finger of under-reporting at anyone reporting a smaller value for "calories in".<br />
<br />
The US CALERIE project rides to my rescue with a <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2662402/" target="_blank">7 day calorimeter study</a> snappily titled "Validation study of energy expenditure and
intake during calorie restriction using doubly labeled water and changes
in body composition". The methodology was to measure energy expenditure by DLW while subjects were contained in a metabolic chamber to provide another measure of energy expenditure. This came out fairly well :<br />
<blockquote class="tr_bq">
"We found close agreement (<i>R</i> = 0.88) between EE measured in the metabolic chamber and EE<sub>DLW</sub> during CR. Using the measured respiratory quotient, we found that the mean (±SD) EE<sub>DLW</sub> was 1934 ± 377 kcal/d and EE measured in the metabolic chamber was 1906 ± 327 kcal/d, ie, a 1.3 ± 8.9% overestimation."</blockquote>
So DLW reported slightly higher values than the chamber gas analysis but the difference was less than 30 kcal/day.<br />
<br />
The next step was to combine the DLW data with changes in body composition and weight, this didn't turn out so well :<br />
<blockquote class="tr_bq">
"EI calculated from EE<sub>DLW</sub> and from changes in ES was 8.7 ± 36.7% higher than the actual EI provided during the chamber stay (1596 ± 656 kcal/d)."</blockquote>
Oops. DLW based calculations estimate a food intake 8.7% higher than that delivered to subjects living in a metabolic chamber. Providing 1430 kcal of food on average led to calculated energy intakes of ~1600 kcal on average using DLW for energy expenditure and weight / DEXA composition to determine the change of energy stores in the body.<br />
<br />
So here we see a difference of 170 kcal/day under tightly controlled conditions. Did the subjects sneak in a candy bar each day ? Is the DEXA analysis suspect - it reported a fat-free mass loss of 0.7 kg from the 0.9 kg weight loss. Had all of the 0.9 kg been fat loss the change in energy stores would have risen from a loss of 337 kcal/day to over 1000 kcal/day so this is obviously a sensitive part of the analysis.<br />
<br />
So far I am seeing DLW as a method that might be 100 kcal/day off when estimating energy expenditure, and when combined with body weight and composition changes the error may increase when trying to use this as a measure of energy intake.<br />
<br />
A couple of other references for measured intake vs DLW - in <a href="http://www.ncbi.nlm.nih.gov/pubmed/1537719/" target="_blank">female athletes</a> the DLW + bodyweight change estimate was found to be "221 +/- 550 kcal/day in excess of those calculated from <span class="highlight">food</span> records (2,193 +/- 466 kcal/day)". Note that 221 +/- 550 means a range from +770 to -330 which is a considerable range of uncertainty.<br />
<br />
In another study <a href="http://www.ncbi.nlm.nih.gov/pubmed/2916443" target="_blank">Underweight adults</a> also failed to perform to expectation, overfeeding by 720 kcal/day for 8 weeks failed to increase measured energy expenditure by DLW and 1/3 of the extra energy went unaccounted for during the ~2 kg weight gain.<br />
<br />
So where does this leave us ? I don't believe doubly labelled water is sufficiently robust and proven by validation to allow it to be used to determine whether reported energy intake by other means is flawed.<br />
<br />
There seems to be too much blind faith in the calorie hypothesis at work - the population got fatter therefore their calorie intake must have gone up even though careful measuring efforts don't show this. Does the science of DLW and assumptions about calories and weight gain really trump detailed reporting of food intake ?PhilThttp://www.blogger.com/profile/17675583272641426292noreply@blogger.com0tag:blogger.com,1999:blog-6577126691263890775.post-69348675926701183622016-08-07T10:25:00.001-07:002016-08-07T10:26:59.303-07:00UK Retailer files low carb health claimUK retailer Marks & Spencer has filed a claim with the European regulator EFSA for their "Balanced for You" range of ready meals with a limited ratio of carbohydrate to protein.<br />
<a name='more'></a>Previously know as "<a href="http://www.marksandspencer.com/c/food-and-wine/balanced-for-you" target="_blank">Fuller Longer</a>" the range had to be renamed after a regulatory <a href="http://www.nutraingredients.com/Regulation-Policy/UK-retailer-files-carb-protein-ratio-health-claim" target="_blank">complaint</a> that there was no evidence to support that as a health claim. Developed in <a href="http://www.abdn.ac.uk/research/research-impact/fuller-longer-food-169.php" target="_blank">collaboration</a> with the University of Aberdeen's Rowett School of Nutrition and Health the range was very succesful with an <a href="http://impact.ref.ac.uk/CaseStudies/CaseStudy.aspx?Id=43262" target="_blank">impact study</a> citing the popularity and commerical success.<br />
<br />
M&S are <a href="http://www.nutraingredients.com/Regulation-Policy/UK-retailer-files-carb-protein-ratio-health-claim" target="_blank">reported</a> to have submitted a scientific claim that a carbohydrate / protein ratio of 1.8 or less will lead to weight loss in a diet of 2000 calories or less over a period of at least 12 weeks.<br />
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If successful they will be able to claim a health / weight loss benefit that is not currently available on EFSA's list of approved claims.<br />
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For low carb people this is interesting but not earth shattering. If I were to eat the <a href="https://www.efsa.europa.eu/en/press/news/120209" target="_blank">EFSA recommended</a> intake of protein at 0.83 grams per day per kg of body weight I would eat 67 g/day of protein and following the M&S idea I would limit my carbs to 120 grams. Not exactly Atkins but equally a big shift from UK standard dietary guidelines that would have me eating more than double that level of carbs.<br />
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The M&S range isn't high fat either, it's aiming at a <a href="https://health.marksandspencer.com/menuplanner/balanced-for-you.html" target="_blank">relatively low calorie intake</a> and hence is low in fat too to hit a 1200 calorie goal.<br />
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If I were to construct an 1800 calorie plan using the M&S ratio of 1.8 it might have 70 grams of protein, 120 grams of carbs with 58% of calories from 84 grams of fat. Maybe there is hope after all.PhilThttp://www.blogger.com/profile/17675583272641426292noreply@blogger.com0tag:blogger.com,1999:blog-6577126691263890775.post-80954439883888610762016-03-16T07:04:00.001-07:002016-03-16T07:34:19.378-07:00UK to tax production of sugary soft drinksIn the <a href="https://www.gov.uk/government/publications/budget-2016-documents/budget-2016" target="_blank">2016 Budget statement</a> the UK Chancellor (Finance Minister) announced that a levy will be charged on manufacture of sugar sweetened soft drinks. Fruit juice and milk products will be excluded. It is expected to raise £520m (US$730m, €660m) in the first year 2018-19 (UK tax years start on 6th April) declining annually thereafter (£500m, £445m....)<br />
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Budget 2016 announces a new soft drinks industry levy targeted at producers and importers of soft drinks that contain added sugar. The levy will be designed to encourage companies to reformulate by reducing the amount of added sugar in the drinks they sell, moving consumers towards lower sugar alternatives, and reducing portion sizes.</div>
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Under this levy, if producers change their behaviour, they will pay less tax. The levy is expected to raise £520 million in the first year. The <abbr style="border: none; cursor: help; margin: 0px; padding: 0px;" title="Office for Budget Responsibility">OBR</abbr> expect that this number will fall over time as the total consumption of soft drinks in scope of the levy drops, in part as a result of producers changing their behaviour and helping consumers to make healthier choices.</div>
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According to the <a href="http://www.britishsoftdrinks.com/write/MediaUploads/Publications/BSDA_Annual_Report_2015.pdf" target="_blank">British Soft Drinks Association</a> some 45% of carbonated drinks are "regular" rather than "mid" or "no/low" calorie at 6 and 49% respectively. With a 2014 volume of 6380 million litres of total carbonates that makes 2870 m litres of regular sugar sweetened beverage so the proposed levy looks to cost 18 p/litre (25 US cents).<br />
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With the detail to be worked out and various exemptions for small producers etc the exact cost implication to the consumer remains to be seen. The sugar in a litre of 10% SSB probably costs ~4p so this levy is substantial compared to the manufacturer's costs.<br />
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There are two bands of levy proposed :-<br />
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<span style="background-color: white; font-family: "nta" , "arial" , sans-serif; font-size: 27px; line-height: 1.11111111;">Soft drinks industry levy</span><br />
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Soft drinks industry levy – The government will introduce a new soft drinks industry levy to be paid by producers and importers of soft drinks that contain added sugar. The levy will be charged on volumes according to total sugar content, with a main rate charge for drink above 5 grams of sugar per 100 millilitres and a higher rate for drinks with more than 8 grams of sugar per 100 millilitres. There will be an exclusion for small operators, and we will consult on the details over the summer, for legislation in Finance Bill 2017 and implementation from April 2018 onwards. (Finance Bill 2017)</div>
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PhilThttp://www.blogger.com/profile/17675583272641426292noreply@blogger.com0tag:blogger.com,1999:blog-6577126691263890775.post-43186473040613285532016-02-19T07:39:00.000-08:002016-02-19T07:40:36.796-08:00Sugar Tax & UK Obesity This morning the media are awash with PR from a <a href="http://www.cancerresearchuk.org/about-us/cancer-news/press-release/2016-02-19-sugar-tax-could-prevent-37-million-cases-of-obesity-over-next-decade" target="_blank">UK Cancer Charity</a> launching a study prepared by a coalition of charities that claims obesity can be reduced by 5% of the UK population by 2025.<br />
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I delved into the <a href="http://nhfshare.heartforum.org.uk/RMAssets/Modelling/CRUKSSB/Short%20and%20Sweet%20Tech%20Sum%20LIVE.pdf" target="_blank">technical summary</a> as I have an interest in these extrapolations. The claim is that a 20% excise tax levied on sweetened soft drinks could avoid 3.7 million people (about 5%) becoming obese (>=30 BMI) by 2025 and that without the tax obesity will rise from 29% in 2015 to 34% in 2025.<br />
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<a name='more'></a><br />
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I've had issues with the "inevitable upward trend" claims about obesity before, as the Foresight report of the Blair years drew an exponential growth curve (rising to 55% obesity by 2050) that hasn't really materialised :<br />
<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="https://3.bp.blogspot.com/-rIRQIIk-02s/VschWs6MeMI/AAAAAAAAKM8/uc3NBuOMJws/s1600/vid_21831_HSE_AdultObesityTrend.jpg" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" height="255" src="https://3.bp.blogspot.com/-rIRQIIk-02s/VschWs6MeMI/AAAAAAAAKM8/uc3NBuOMJws/s400/vid_21831_HSE_AdultObesityTrend.jpg" width="400" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">UK Actual obesity data (3y average, Public Health England)</td></tr>
</tbody></table>
But hold on, how did we get from 25% in 2014 to the 29% baseline claim ? This isn't stated in the reports published today and as far as I can see the last official data releases were nearly a year ago.<br />
<br />
10 years ago the chart above shows obesity at about 22.5%, lets' call it 22% which gives us a 10 year growth of 3% to 2014. This seems inconsistent with the claimed 5% in 10 years projection even if we gloss over the apparent problem with the baseline figure.<br />
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Does the analysis overstate the baseline prevalence of obesity as well as projected growth in order to deliver a greater forecast benefit from the proposed measure ?<br />
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Moving on from how fat we all are, who drinks this stuff anyway ? Our annual household consumption of sugar sweetened beverages (SSB) is probably less than 5 litres, if that. The technical summary reveals some concerns about the limitations due to lack of data of SSB stratified by BMI however there is published data from (for example) the <a href="http://www.diabetologia-journal.org/files/OConnor.pdf" target="_blank">EPIC-Norfolk study</a> which shows us that the consumers of SSBs had a mean BMI 1 unit lower than that of the consumers of diet equivalents - in other words the heavier people were perhaps inclined to select the diet option. This shouldn't be a surprise, we've all witnessed the "Big Mac and fries with a diet coke" mindset in action.<br />
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The EPIC-Norfolk data is also interesting in that of the 24,700 participants only 12,800 were consumers of SSBs with 5,600 consumers of diet equivalents. That's right, only about HALF of the population drink any SSBs at all in that dataset.<br />
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I do recall another dataset where the median consumption of SSBs in adults was zero - ie less than half of the population surveyed drank <i>any</i> "sugary fizzy drinks". From memory that was in Scotland.<br />
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As the <a href="http://www.ukhealthforum.org.uk/" target="_blank">UKHF</a> modelling used data from the UK's <a href="https://www.gov.uk/government/statistics/national-diet-and-nutrition-survey-headline-results-from-years-1-2-and-3-combined-of-the-rolling-programme-200809-201011" target="_blank">National Diet and Nutrition Survey</a>: (Headline Results from Years 1, 2 and 3 (combined) of the Rolling Programme 2008/09 – 2010/11) I had a dig into their SSB consumption data. Men were reported as consuming a mean average of 170 grams/day of SSBs with a standard deviation of 270 g/day whereas women consumped 109 g/day with an sd of 193 g/day. These large sds clearly point to a very non-normal (skewed) distribution of consumption.<br />
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Digging back to <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3328127/" target="_blank">earlier work</a> based on the NDNS data we can see that the 2008/09 <i>median</i> consumption of "Soft drinks, not low calorie" was 63 g/day in men with an interquartile range (IQR) of 0-248 and 22 g/day in women with an IQR of 0-149. From this we can deduce that 50% of women reported consuming 22g/day or less of SSBs while at least 25% of them consumed none.<br />
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In men 50% consumed 63 g/day or less and at least 25% consumed none. 25% of men, the upper quartile, were consuming at least 248 g/day ie at least one can or serving sized bottle.<br />
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It looks to me as if the target population of a sugar tax on sugar sweetened drinks is quite a small part of the whole, and as such it is difficult to see how the BMI of large swathes of the population will be reduced as claimed by the charities today.PhilThttp://www.blogger.com/profile/17675583272641426292noreply@blogger.com0tag:blogger.com,1999:blog-6577126691263890775.post-68256329040148096492016-01-30T09:57:00.001-08:002019-02-17T01:36:11.596-08:00Everyone's a fruit and nut caseEarlier in the week I awoke to the radio pronouncing that eating fruit was better than eating nuts, or some such. Google turned up a Swedish study "A Randomized Study of the Effects of Additional Fruit and Nuts Consumption on Hepatic Fat Content, Cardiovascular Risk Factors and Basal Metabolic Rate" by <a href="http://www.ncbi.nlm.nih.gov/pubmed/26788923" target="_blank">Christian Agebratt et al</a>.<br />
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This study set out to compare the effects of adding about 500 calories a day of either fruit or nut intake to the existing diet of some young lean active healthy Swedish students. The primary objective was to see if the sugar in the fruits had any adverse effect.<br />
<a name='more'></a>As is often the case in free living dietary studies the outcome was not exactly as intended. The fruit eaters cut back on the intake of other foods to such an extent that their (self reported) calorie intake was practically identical to baseline. Over 8 weeks there was no statistically significant increase in body weight.<br />
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The nut eaters were less frugal in their eating and did show an increase in body weight of 0.67 kg which was statistically significant (p=0.049) having reported a 244 kcal/day net increase in food intake. In the interests of full disclosure I should point out that the weight increase in the fruit group was numerically very similar, but not statistically significant.<br />
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So we have the fruit eaters with the same calories per day but a lot more fructose intake (3x), and the nut eaters with an increased calorie intake but reduced fructose intake by ~50%.<br />
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What else happened ? Scanning the results for statistical significance / trends I found<br />
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<ul>
<li>Fruit eaters tended to move around less than nut eaters according to their accelerometers.</li>
<li>Nut eaters showed an increase in BMR, although most of this was down to one subject.</li>
<li>Systolic blood pressure fell 5% in the fruit eaters.</li>
<li>Fasting insulin rose and blood HbA1c fell in the fruit eaters.</li>
<li>Triglycerides trended higher with fruit and lower with nuts (NS).</li>
<li>Fructose intake in the fruit group rose from 9 to 26 grams/day.</li>
<li>Fructose intake in the nut group fell from 12 to 6 grams/day.</li>
</ul>
<div>
No significant differences between groups were measured in the liver fat and various fat and muscle measurements, I suspect this is because the subjects failed to eat their free fruits and nuts as extra and self-regulated their intake - robbing the researchers of some expected fat deposition.</div>
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<br /></div>
<div>
In the text (but not the data table) it is stated that Levels of lipoprotein(a) (Lp(a)) tended to decrease in the nut group and to increase in the fruit group and the change in levels between groups was</div>
<div>
statistically significant (p = 0.047) which may be an argument in favour of nuts over fruit for health benefits.</div>
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<br /></div>
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Some of the statistics are difficult for me to understand, where near identical changes in means are significant in one group but not the other (despite similar variability). Perhaps this is an artefact of the modest sample size of 15 in each group and of the small changes seen in calorie intake.</div>
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<br /></div>
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The high fruit intake only amounted to 26 grams of fructose, which is barely enough to get even Robert Lustig excited. At 100 calories or <4% of calorie intake it's below even the most hair shirt recommendation on fructose or sugars intake.</div>
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<br /></div>
<div>
The subjects were pretty active at 12,000 steps a day so their carbohydrate oxidation rate would have easily absorbed the added sugars, although we do not know the total change in intake as ee were not given the total intake of monosaccharides, though apparently it correlated well with baseline liver fat in women but not men. The composition of the diet in terms of macronutrients is also not provided.</div>
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<br /></div>
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Another nagging doubt I have is to whether the reported fructose figure is the free fructose in the diet or includes the fructose element of sucrose in the baseline diet and the added fruit.</div>
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<u>Conclusion.</u></div>
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I think we can conclude that if there is a harmful effect of adding ~500 grams of fruit a day (25 grams/day of fructose intake in total) then it does not appear in a young healthy ideal weight active population who adapt by cutting their intake of other (unspecified) foods.</div>
PhilThttp://www.blogger.com/profile/17675583272641426292noreply@blogger.com0tag:blogger.com,1999:blog-6577126691263890775.post-13578479242543139392015-12-29T02:03:00.000-08:002016-02-06T06:25:05.622-08:00Chef succeeds on Atkins DietAnother <a href="http://uk.atkins.com/community/forums/thread/138924/a-new-me#dis-post-248400" target="_blank">personal testimony</a> from the Atkins UK forum :-<br />
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<span style="background-color: white; color: #444444; font-family: "arial" , "helvetica" , sans-serif; font-size: 15px; line-height: 21px;">Hi Folks,</span><br />
<br style="background-color: white; color: #444444; font-family: Arial, Helvetica, sans-serif; font-size: 15px; line-height: 21px;" />
<span style="background-color: white; color: #444444; font-family: "arial" , "helvetica" , sans-serif; font-size: 15px; line-height: 21px;">A have been following some of the threads on this forum for a couple of months now but have not posted until now, so this is my first post here. </span><br />
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<a name='more'></a><br style="background-color: white; color: #444444; font-family: Arial, Helvetica, sans-serif; font-size: 15px; line-height: 21px;" /><br />
<span style="background-color: white; color: #444444; font-family: "arial" , "helvetica" , sans-serif; font-size: 15px; line-height: 21px;">I am a 44 year old male. A little over 2 months ago I weighed in at 20st. 7lbs. I am a chef. So I am always surrounded by food. The jeans I used to wear had a 40' waist and basically for the last couple of years I felt that I wore a uniform. My wardrobe consisted of XXL black T-Shirts and my large waisted jeans. I was very unhappy with my weight. The struggle to bend over to put on socks and the belly squashing up and then getting out of breath just doing that. It made me sad. The colour in my face at rest was always a crimson flushed colour, which I presume was from high blood pressure. I also play music in a band at weekends and the uncomfortable feeling of being onstage in a total bog of sweat with the lights, just got me down. I mean being physically able to wring out my shirt and have the sweat drip off after the show. </span><br />
<br style="background-color: white; color: #444444; font-family: Arial, Helvetica, sans-serif; font-size: 15px; line-height: 21px;" />
<span style="background-color: white; color: #444444; font-family: "arial" , "helvetica" , sans-serif; font-size: 15px; line-height: 21px;">The turning point for me was going on holidays at the end of Sept and being in the sun, feeling incredibly hot, depressed that nothing fitted me. It strengthened my resolve though. As I lay on that sun-lounger on my holidays, I started to formulate my plan. I decided on the day that I would start Atkins. The day after returning from hols. We returned on a Saturday and then Sunday morning I got up and had eggs and bacon for breakfast. I went on the Kindle website and downloaded the Atkins book. I spent the afternoon reading the basics to get me started. I felt informed enough from that first skim of the book on that first day to set up the next couple of days. And when the shopping was done to prepare for what I was embarking on, I devoured the rest of the book cover to cover. I felt good, I mean really good. I was excited about what was in store for me. I was still huge (in my own head anyway), but I had made the decision. The decision to make some changes in my life.</span><br />
<br style="background-color: white; color: #444444; font-family: Arial, Helvetica, sans-serif; font-size: 15px; line-height: 21px;" />
<span style="background-color: white; color: #444444; font-family: "arial" , "helvetica" , sans-serif; font-size: 15px; line-height: 21px;">I have now been doing Atkins since the first week in October. I have been incredibly vigilant about what I'm eating and quite enjoy the meals. I love the fact that I'm eating so much more green veg than I used to and love to quickly stir-fry up my greens with my meal, be it fish or meat. Even in work with all the food that I prepare for my menus, I still stay true to the program. But this is the best bit. I have absolutely no carb cravings. I can make desserts with the lightest of pastries and have no desire to consume them. I do have to taste things, so I use the tip of a tea spoon and put a trace on my tongue. Then when I have assessed to food for taste, I have a glass of water and rinse my mouth. I make sure that I do not consume sugar. But that isn't even a worry, because the best bit is I don't desire to consume sugar. </span><br />
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<span style="background-color: white; color: #444444; font-family: "arial" , "helvetica" , sans-serif; font-size: 15px; line-height: 21px;">To cut a long story short, the tale of the tape. A little over 2 months and I now weigh 16st. 7lbs. I have lost 4st. in weight. I am wearing jeans with a 36' waist. I have gone from belt hole 2 to belt hole 8. When I play on stage I sweat a very modest amount. My faced is no longer flushed in colour. I have a normal colouring on my faced and my blood pressure is at normal levels. I can bend down and easily do things like put on my socks. And come back up again without getting dizzy or out of breath. I get a shock nearly everytime I have a shower and catch myself in the mirror. This body that I used to have for many years, and was very used to, has changed to a shape that I never had thought would be possible on me. I exercise a modest amount. I like to go for walks and the odd cycle. I would like to get myself fitter but I am taking that bit slowly. I am going to continue to lose some more weight. I want to get to 15st. on induction and then transfer to phase 2 to try and get myself to 14st. That is my target weight. At that stage, if I pull it off, I will have lost 6 and a half stone. Wouldn't that be something!!!</span><br />
<br style="background-color: white; color: #444444; font-family: Arial, Helvetica, sans-serif; font-size: 15px; line-height: 21px;" />
<span style="background-color: white; color: #444444; font-family: "arial" , "helvetica" , sans-serif; font-size: 15px; line-height: 21px;">My life has changed utterly now. And I love it. I am a New Me!!!! Thanks for reading this. I hope it helps someone who is thinking of making the decision to take action. The hardest part is making the decision. Once you have started, you won't believe how easy it is. If I can do it, surrounded by food in kitchens all day long then, really everyone can.</span><br />
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<span style="background-color: white; color: #444444; font-family: "arial" , "helvetica" , sans-serif; font-size: 15px; line-height: 21px;">Regards,</span><br />
<span style="background-color: white; color: #444444; font-family: "arial" , "helvetica" , sans-serif; font-size: 15px; line-height: 21px;">Dave</span></blockquote>
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PhilThttp://www.blogger.com/profile/17675583272641426292noreply@blogger.com0tag:blogger.com,1999:blog-6577126691263890775.post-49651302120168885072015-10-10T04:03:00.000-07:002015-10-10T04:04:54.476-07:00Atkins Diet success storyShirley, age 63 and with hypothyroidism, reports a weight loss of 6 stone / 84 lbs / 38 kg in 17 months through following the Atkins diet without exercise and with support from the <a href="http://uk.atkins.com/community/forums/thread/138002/not-sure-i-could-do-it-but-i-certainly-have---nearly-6-stone-off-and-at-goal#dis-post-241486" target="_blank">Atkins UK community forum</a>. That's an average loss of over 1.2 lbs per week.<br />
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Here's her story in her own words......<br />
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<span style="background-color: white; color: #444444; font-family: Arial, Helvetica, sans-serif; font-size: 15px; line-height: 21px;">Hi everyone - have not posted for a while (and when I do it tends to be a novel so prepare yourselves!) but wanted to share with you all that I have reached and even exceeded my goal weight by over 2lb! It has taken 17 months but I am beyond chuffed with myself. From a start weight of 15st 11lb and a size 22/24 to 9st 111/4lb and a size 12/14 as of today. That is nearly 6 st off and 52 ins in total from upper arms, bust, waist, hips and thighs and may even be more inches as I did not measure myself until 8 weeks in. My BMI would probably have me at still slightly overweight but I feel that the weight I am now is the right one for me. </span><br />
<br style="background-color: white; color: #444444; font-family: Arial, Helvetica, sans-serif; font-size: 15px; line-height: 21px;" />
<span style="background-color: white; color: #444444; font-family: Arial, Helvetica, sans-serif; font-size: 15px; line-height: 21px;">When I started I was not sure if Atkins would work being far less active than when I was working and having hypothyroidism but it certainly has. I do have a little loose skin now but hey ho I am 63, was overweight for many years and no doubt have less collagen than when I was younger so it is understandable. I also feel that this is a small price to pay for feeling great and not just psychologically but physically as well which I am convinced in the case of the latter is due to the low carb food I eat now.</span><br />
<br style="background-color: white; color: #444444; font-family: Arial, Helvetica, sans-serif; font-size: 15px; line-height: 21px;" />
<span style="background-color: white; color: #444444; font-family: Arial, Helvetica, sans-serif; font-size: 15px; line-height: 21px;">I am lucky in that I have had no major stalls and for the first year the weight and inches came off steadily. In the last few months there have been a few weeks where I may not have lost but I have been able to identify and rectify what has caused it which has usually been too much protein and/or not enough fat. Interestingly and especially of late I developed a pattern whereby I would go perhaps 2 weeks where I lost little weight but lost inches and then have a couple of weeks where I lost weight and little in the way of inches.I did not exercise apart from that gained from housework, walking etc. I am most definitely not a gym person and while I may have lost weight more quickly if I had been it confirms that exercise is not necessary in the long term if you cannot or do not want to go down that route.</span><br />
<br style="background-color: white; color: #444444; font-family: Arial, Helvetica, sans-serif; font-size: 15px; line-height: 21px;" />
<span style="background-color: white; color: #444444; font-family: Arial, Helvetica, sans-serif; font-size: 15px; line-height: 21px;">In the main I have adapted what I am preparing for others in the house to suit the way I eat. At other times I may make a different meal for myself but they always are quick to prepare. Yesterday I fancied something different to my other half so had crispy skinned salmon on a bed of sautéed (in butter) leeks, shallots and courgette noodles and it was delicious - prepared and ready in about 20 mins. While on the subject of courgette noodles no need to buy a spiralizer just keep a look out for a julienne peeler which does the job no problem. I got mine from a local market for £2. MIMS - love them! I make mine in the oven in batches and freeze them. For the savoury ones I use half milled flax and half ground almonds (very cheap at Lidl's) and flavour with parmesan cheese and salt and pepper and bake in a deep muffin tray. I also make a mean chocolate one topped with a small amount of coconut sprinkled on which toasts as they bake. When I first started making them I found them to be a bit dry so now add almond milk to slacken off the mixture and leave it to stand for about 5 mins before I bake them which works a treat and gives a lovely moist result.</span><br />
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<span style="background-color: white; color: #444444; font-family: Arial, Helvetica, sans-serif; font-size: 15px; line-height: 21px;">I have to admit to using Atkins bars but limit then to about two per week. For me they have been a bit of a 'treat' with a cup of coffee made with cream in the evenings and have not found that they adversely affected my weight loss or given me 'tummy' problems but I am aware that others may have had a different experience. However they do cost a lot if you pay full price so I wait until they are on special offer and stock up.</span><br />
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<span style="background-color: white; color: #444444; font-family: Arial, Helvetica, sans-serif; font-size: 15px; line-height: 21px;">Meals out in the UK have not been a problem as I am usually able to find something on a menu that suits Atkins and even over Christmas I stuck very much to plan. However my dilemmas of whether to eat off plan came on holidays. In the early days I kept as close to Atkins as I could throughout a stay in Venice and Med cruise and came home 2.5lbs lighter. On the next trip (an all inclusive) I again stuck as close as I could to Atkins food wise but allowed myself a few drinks of spirits and diet coke - I neither lost or gained on that one. The next two I decided I would have a few treats and of course gained weight. I did get right back on track as soon as I got home and lost what I put on within a week. Although this gave me confidence that when on holiday I can have a few treats and deal with the consequences I think it is up to the individual how they feel about and approach eating off plan at certain times. For me I think staying on plan as much as possible in the early days was the right </span><br />
<span style="background-color: white; color: #444444; font-family: Arial, Helvetica, sans-serif; font-size: 15px; line-height: 21px;">thing as gaining back lost weight may well have de-motivated me. We are off to the Far East in a few weeks and I fully intend to try some of the food on offer but will have, as Ellen has mentioned in her posts and which I have used before, a clear exit strategy which will be a return to induction the minute we touch down on British soil so that I can deal with the inevitable weight gain.</span><br />
<span style="background-color: white; color: #444444; font-family: Arial, Helvetica, sans-serif; font-size: 15px; line-height: 21px;">I really have to end this post as although I did warn you about my 'novels' and I am sure by now you are thoroughly bored. Before I sign off though I just wanted to say that although I post infrequently I do read the forum every day and have got a great of inspiration, humour, comfort, support, advice and guidance from Linda and the many people who contribute. I do not think I would have done it without you all and have no doubt that I will continue to visit often to check how you are all doing!</span></blockquote>
PhilThttp://www.blogger.com/profile/17675583272641426292noreply@blogger.com0tag:blogger.com,1999:blog-6577126691263890775.post-40550417563581508542015-08-23T05:49:00.000-07:002019-02-17T01:31:21.145-08:00Carbohydrate Restriction for Type 2 Diabetes.We are often told to look to the £m charity Diabetes UK for dietary and other advice on receiving a diagnosis of Diabetes. As a pre-diabetic (HbA1C 38 mmol/mol or 5.8 % in old money) I did this and prepared a short summary :-<br />
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Moving on, I looked for evidence based ways to reduce the symptoms and severity of the disease beyond the emphasis on weight loss. Weight loss may be helpful but it is somewhat long term and unreliable, so can we do anything useful with carbohydrate restriction alone ?<br />
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10 years ago a <a href="https://www.physiology.org/doi/10.1152/ajpendo.00069.2005" target="_blank">study was published</a> on the effect of a 5% carbohydrate / 35% protein / 60% fat weight maintenance diet (1.3 * Resting metabolic rate) in normal weight healthy subjects.The run-in diet was 60% carbohydrate/ 30% fat and a rapid reduction in the 24h insulin concentration was seen on switching to the low carb diet :-<br />
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OK, they weren't diabetic but the effect is quick and was achieved in a weight maintenance diet so no confusion from calorie restriction or weight loss. Tim Noakes, Jason Fung and <a href="http://www.thefatemperor.com/blog/2015/8/10/insulin-vs-glucose-the-mathematics-of-properly-diagnosing-diabetes-in-situ" target="_blank">Joseph Kraft</a> are all about the insulin being the problem, so this looks promising.<br />
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My next find was a <a href="http://www.nutritionandmetabolism.com/content/11/1/33" target="_blank">much more recent study</a> looking at the effect of moderate carbohydrate restriction in Japanese subjects who had a diagnosis of Type 2 diabetes for ~4 years on average. These people were also of a healthy weight but at ~60 were twice as old as the short term group above.<br />
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"Moderate carbohydrate restriction" involves omitting carbohydrates at both dinner and breakfast, at dinner alone, or halving the amount at dinner - three levels of restriction. Carbohydrate snacks were forbidden but protein and fat were acceptable, grams of nutrients were not specified. By assigning those with the highest HbA1c (>=9.0%) to the greatest level of carbohydrate restriction, and those with the lowest(<=7.4%) to the least, the changes achieved after 6 months were :-<br />
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The large reductions in HbA1c were mainly seen in the high group (red circles) with the low group (green triangles) having much less scope for improvement. 25 subjects reduced or eliminated diabetes medication and 12 started or increased. These are shown as blue and orange lines respectively :-<br />
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This graph also illustrates the wide range and generally high carbohydrate intake at baseline, and the relatively moderate intake adopted of 70 - 230 grams/day. On average the energy intake dropped 400 kcal/day leading to an average BMI reduction of 0.9 to 24.0. Reductions in HbA1c were not however correlated with weight loss (changes in BMI) :-<br />
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Weight loss is clearly not a prerequisite to improved blood glucose and insulin levels, as further demonstrated by <a href="http://www.nutritionandmetabolism.com/content/3/1/16" target="_blank">Gannon & Nuttall's</a> "diet only" approach to reducing sugars and starches in their Low Biologically Available Glucose (LoBAG) diet :-<br />
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Finally we turn to a <a href="http://www.ncbi.nlm.nih.gov/pubmed/16409560" target="_blank">study funded by Diabetes UK</a> but available behind a paywall :<br />
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The abstract gives nothing away about blood glucose control, bizarrely, but displays DBUK's obsession with weight loss and fat intake. The subjects here were obese diabetics with poor glucose control under supervision of hospital consultants, so in some respects more challenging cases. The "severe dietary carbohydrate restriction" adopted was advice to limit carbs to 70 g/day in the low carb arm, however the 12 week food diary revealed an intake mean of 109.5g of carbohydrate or 33.5% of the restricted energy intake of 1290 calories per day. The low fat arm was at 169g / 45% of energy.</div>
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33 vs 45% carbohydrate does not sound like a test of "severe" carbohydrate restriction, although the intended intake was about a third lower, and the non-significant difference in HbA1c reduction between the diets is perhaps predictable. Low carb reduced HbA1c by 0.55 average compared to 0.23 on low fat. Low carb did do significantly better in terms of weight loss and reducing Total/HDL cholesterol. In seeking to explain the lack of effect on glycaemic control the authors conducted a post hoc analysis of ~75% of subjects and revealed that 85% of LC subjects on insulin were able to reduce their dose.</div>
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Many other studies are summarised in the <a href="http://www.nutritionjrnl.com/article/S0899-9007(14)00332-3/abstract" target="_blank">paper by Feinman et al</a> -</div>
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"Dietary carbohydrate restriction as the first approach in diabetes management: Critical review and evidence base"</h1>
<br />PhilThttp://www.blogger.com/profile/17675583272641426292noreply@blogger.com0tag:blogger.com,1999:blog-6577126691263890775.post-3765939070287542802015-08-17T02:16:00.000-07:002016-02-06T06:30:07.046-08:00Hall shows greater weight loss on reduced carbohydrateData mining in Hall (2015) and ignoring the press and social media nonsense storm I find that in the (insulin sensitive) men the <i>measured fat loss</i> <i></i>using the gold standard (?) DEXA method was actually marginally <i>greater</i> <i></i>on the reduced carbohydrate diet :<br />
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I'm not sure how this slipped past the journalists who were too busy reformatting press releases for publication, but that's life. The data tables are available for download at <a href="http://www.cell.com/action/showImagesData?pii=S1550-4131%2815%2900350-2#excelTab" target="_blank">cell.com</a><br />
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<table border="1" cellpadding="0" cellspacing="0" dir="ltr" style="border-collapse: collapse; border: 1px solid #ccc; font-family: Calibri; font-size: 13px; table-layout: fixed;"><colgroup><col width="109"></col><col width="109"></col><col width="109"></col><col width="109"></col><col width="109"></col><col width="109"></col></colgroup><tbody>
<tr style="height: 21px;"><td data-sheets-value="[null,2,"Fat Mass (kg)"]" style="border-bottom-color: rgb(0, 0, 0); border-bottom-style: solid; border-bottom-width: 2px; border-left-color: rgb(0, 0, 0); border-left-style: solid; border-left-width: 2px; border-right-color: rgb(0, 0, 0); border-right-style: solid; border-right-width: 2px; font-family: 'times new roman', serif; font-size: 100%; font-weight: bold; padding: 0px 3px; vertical-align: middle; word-wrap: break-word;">Fat Mass (kg)</td><td data-sheets-value="[null,2,"-0.82\u00b10.15"]" style="border-bottom-color: rgb(0, 0, 0); border-bottom-style: solid; border-bottom-width: 2px; border-right-color: rgb(0, 0, 0); border-right-style: solid; border-right-width: 2px; font-family: 'times new roman', serif; font-size: 100%; padding: 0px 3px; text-align: center; vertical-align: middle; word-wrap: break-word;">-0.82±0.15</td><td data-sheets-value="[null,3,null,0.0016]" style="border-bottom-color: rgb(0, 0, 0); border-bottom-style: solid; border-bottom-width: 2px; border-right-color: rgb(0, 0, 0); border-right-style: solid; border-right-width: 2px; font-family: 'times new roman', serif; font-size: 100%; padding: 0px 3px; text-align: center; vertical-align: middle; word-wrap: break-word;">0.0016</td><td data-sheets-value="[null,2,"-0.784\u00b10.16"]" style="border-bottom-color: rgb(0, 0, 0); border-bottom-style: solid; border-bottom-width: 2px; border-right-color: rgb(0, 0, 0); border-right-style: solid; border-right-width: 2px; font-family: 'times new roman', serif; font-size: 100%; padding: 0px 3px; text-align: center; vertical-align: middle; word-wrap: break-word;">-0.784±0.16</td><td data-sheets-value="[null,3,null,0.0027]" style="border-bottom-color: rgb(0, 0, 0); border-bottom-style: solid; border-bottom-width: 2px; border-right-color: rgb(0, 0, 0); border-right-style: solid; border-right-width: 2px; font-family: 'times new roman', serif; font-size: 100%; padding: 0px 3px; text-align: center; vertical-align: middle; word-wrap: break-word;">0.0027</td><td data-sheets-value="[null,3,null,0.8]" style="border-bottom-color: rgb(0, 0, 0); border-bottom-style: solid; border-bottom-width: 2px; border-right-color: rgb(0, 0, 0); border-right-style: solid; border-right-width: 2px; font-family: 'times new roman', serif; font-size: 100%; padding: 0px 3px; text-align: center; vertical-align: middle; word-wrap: break-word;">0.8</td></tr>
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yes folks, a slightly <i>greater</i> fat loss on low carb although the p=0.8 shows that the difference between the two diets was not statistically significant and we should say "the measured fat loss was the same in the carbohydrate and fat reduction diet interventions".</div>
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I wonder how Cell Metabolism can justify the title -</div>
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Calorie for Calorie, Dietary Fat Restriction Results in More Body Fat Loss than Carbohydrate Restriction in People with Obesity</h1>
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when the fat loss was statistically the same but the energy deficit was not - it was greater in the reduced fat diet (p=0.014) :-</div>
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<tr style="height: 35px;"><td data-sheets-value="[null,2,"Energy Balance (kcal/d)"]" style="border-bottom-color: rgb(0, 0, 0); border-bottom-style: solid; border-bottom-width: 2px; border-left-color: rgb(0, 0, 0); border-left-style: solid; border-left-width: 2px; border-right-color: rgb(0, 0, 0); border-right-style: solid; border-right-width: 2px; font-family: 'times new roman', serif; font-size: 100%; font-weight: bold; padding: 0px 3px; vertical-align: middle; word-wrap: break-word;">Energy Balance (kcal/d)</td><td data-sheets-value="[null,2,"-748\u00b154.2"]" style="border-bottom-color: rgb(0, 0, 0); border-bottom-style: solid; border-bottom-width: 2px; border-right-color: rgb(0, 0, 0); border-right-style: solid; border-right-width: 2px; font-family: 'times new roman', serif; font-size: 100%; padding: 0px 3px; text-align: center; vertical-align: middle; word-wrap: break-word;">-748±54.2</td><td data-sheets-value="[null,2,"<.0001"]" style="border-bottom-color: rgb(0, 0, 0); border-bottom-style: solid; border-bottom-width: 2px; border-right-color: rgb(0, 0, 0); border-right-style: solid; border-right-width: 2px; font-family: 'times new roman', serif; font-size: 100%; padding: 0px 3px; text-align: center; vertical-align: middle; word-wrap: break-word;"><.0001</td><td data-sheets-value="[null,2,"-906\u00b156.9"]" style="border-bottom-color: rgb(0, 0, 0); border-bottom-style: solid; border-bottom-width: 2px; border-right-color: rgb(0, 0, 0); border-right-style: solid; border-right-width: 2px; font-family: 'times new roman', serif; font-size: 100%; padding: 0px 3px; text-align: center; vertical-align: middle; word-wrap: break-word;">-906±56.9</td><td data-sheets-value="[null,2,"<.0001"]" style="border-bottom-color: rgb(0, 0, 0); border-bottom-style: solid; border-bottom-width: 2px; border-right-color: rgb(0, 0, 0); border-right-style: solid; border-right-width: 2px; font-family: 'times new roman', serif; font-size: 100%; padding: 0px 3px; text-align: center; vertical-align: middle; word-wrap: break-word;"><.0001</td><td data-sheets-value="[null,3,null,0.014]" style="border-bottom-color: rgb(0, 0, 0); border-bottom-style: solid; border-bottom-width: 2px; border-right-color: rgb(0, 0, 0); border-right-style: solid; border-right-width: 2px; font-family: 'times new roman', serif; font-size: 100%; padding: 0px 3px; text-align: center; vertical-align: middle; word-wrap: break-word;">0.014</td></tr>
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OK so I chose to look at the men only. I did this because the women did not achieve a statistically significant fat loss on either diet :-<br />
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<tr style="height: 21px;"><td data-sheets-value="[null,2,"Fat Mass (kg)*"]" style="border-bottom-color: rgb(0, 0, 0); border-bottom-style: solid; border-bottom-width: 2px; border-left-color: rgb(0, 0, 0); border-left-style: solid; border-left-width: 2px; border-right-color: rgb(0, 0, 0); border-right-style: solid; border-right-width: 2px; font-family: 'times new roman', serif; font-size: 100%; font-weight: bold; padding: 0px 3px; vertical-align: middle; word-wrap: break-word;">Fat Mass (kg)*</td><td data-sheets-value="[null,2,"-0.265\u00b10.25"]" style="border-bottom-color: rgb(0, 0, 0); border-bottom-style: solid; border-bottom-width: 2px; border-right-color: rgb(0, 0, 0); border-right-style: solid; border-right-width: 2px; font-family: 'times new roman', serif; font-size: 100%; padding: 0px 3px; text-align: center; vertical-align: middle; word-wrap: break-word;">-0.265±0.25</td><td data-sheets-value="[null,3,null,0.32]" style="border-bottom-color: rgb(0, 0, 0); border-bottom-style: solid; border-bottom-width: 2px; border-right-color: rgb(0, 0, 0); border-right-style: solid; border-right-width: 2px; font-family: 'times new roman', serif; font-size: 100%; padding: 0px 3px; text-align: center; vertical-align: middle; word-wrap: break-word;">0.32</td><td data-sheets-value="[null,2,"-0.453\u00b10.25"]" style="border-bottom-color: rgb(0, 0, 0); border-bottom-style: solid; border-bottom-width: 2px; border-right-color: rgb(0, 0, 0); border-right-style: solid; border-right-width: 2px; font-family: 'times new roman', serif; font-size: 100%; padding: 0px 3px; text-align: center; vertical-align: middle; word-wrap: break-word;">-0.453±0.25</td><td data-sheets-value="[null,3,null,0.12]" style="border-bottom-color: rgb(0, 0, 0); border-bottom-style: solid; border-bottom-width: 2px; border-right-color: rgb(0, 0, 0); border-right-style: solid; border-right-width: 2px; font-family: 'times new roman', serif; font-size: 100%; padding: 0px 3px; text-align: center; vertical-align: middle; word-wrap: break-word;">0.12</td><td data-sheets-value="[null,3,null,0.67]" style="border-bottom-color: rgb(0, 0, 0); border-bottom-style: solid; border-bottom-width: 2px; border-right-color: rgb(0, 0, 0); border-right-style: solid; border-right-width: 2px; font-family: 'times new roman', serif; font-size: 100%; padding: 0px 3px; text-align: center; vertical-align: middle; word-wrap: break-word;">0.67</td></tr>
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Table 1 in the study also shows the men and women to be statistically different in all parameters other than Age, Body Weight and Respiratory RQ so I feel justified in treating the groups separately.<br />
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The depletion of glycogen reserves is illustrated in the paper :<br />
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and I had a shot at extrapolating and quantifying this<br />
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<img 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" /><br />
with some linear regression this looks to be approximately :<br />
<img src="data:image/png;base64,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" /><br />
and days 1-6 add up to about 1690 kcal of glycogen depletion displacing fat oxidation. 1690/6 = 281 kcal/day of glycogen, so the actual calorie deficit supplied by fat in the men is<br />
<br />
748 - 281 = 476 kcal/day or 51.5% of the deficit seen in the reduced fat diet.<br />
<br />
In fairness there was also an increased CHO oxidation in the RF diet, again showing depletion of glycogen during the reduced calorie intake. This was 176 kcal/day.<br />
<br />
So the "net deficit" driving fat loss was 476 kcal/day in RC and (906-176) = 730 kcal/day in RF. Despite this, the measured fat loss was the same.<br />
<br />
It is a great shame that the study's data collection didn't commence after 9 days on the diet, when the glycogen depletion would have tapered off to a low level.</div>
PhilThttp://www.blogger.com/profile/17675583272641426292noreply@blogger.com0tag:blogger.com,1999:blog-6577126691263890775.post-30013173996601180882015-08-15T03:58:00.002-07:002015-08-15T03:58:29.458-07:00Short term weight loss in insulin sensitive menDigging further into <a href="http://lowcarbuk.blogspot.co.uk/2015/08/we-need-to-talk-about-kevin-hall.html" target="_blank">Kevin Hall's study</a> it is apparent that the men and women were substantially different in terms of Insulin Resistance, Insulin and Leptin levels. The men average a HOMA-IR of 2.2 but the women average 3.3 putting the two genders either side of the 2.7 Insulin Resistance cut-off used in Brazil. Fasting insulin was 50% higher in the ladies and fasting leptin 2.5 times higher, but fasting blood glucose was the same in both at 88 mg/dl / 4.9 mmol/l.<br />
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The men tended to be older (38 vs 33) and less obese (34 vs 38 BMI) with p-value 0.15 or lower but not reaching statistical significance (9 women and 10 men is a modest sample size).<br />
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<br />
<a name='more'></a><br />As the women did not show a statistically significant measured weight loss I'm going to be overtly sexist and concetrate on the men. This isn't just self interest as they're a lot younger than me, much more insulin sensitive and less diabetic and currently I am on the verge of 25 BMI.<br />
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What results did the men achieve ? A measured weight loss of 2.1 kg on restricted carbohydrate (RC) was better than the 1.25 kg recorded on restricted fat (RF), p=0.014. Measured fat loss (DEXA) was not statistically different (p=0.8) between the diets with RC losing 0.82 kg and RF 0.78 kg.<br />
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This suggests weight loss was 39% fat on RC and 63% fat on RF, as glycogen / water was being depleted on RC. Another way of looking at this is that the calorie restriction required for 1 lb of weight loss was 975 on RC and 1975 on RF. I calculated this from the total weight loss and daily calorie deficit reported (-748 vs -906 respectively) and it suggest glycogen depletion is at work in both cases - the figure 3B below shows the results of CHO balance calculation for men & women combined :-<br />
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<a href="http://1.bp.blogspot.com/-Oa0Lfc7yx6E/Vc8AINOgBlI/AAAAAAAAI54/MthV6p70AVM/s1600/hallfig3b.PNG" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="249" src="http://1.bp.blogspot.com/-Oa0Lfc7yx6E/Vc8AINOgBlI/AAAAAAAAI54/MthV6p70AVM/s400/hallfig3b.PNG" width="400" /> </a></div>
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The red dots show carbohydrate depletion in restricted fat well below the red line of the model prediction and the blue dots of restricted carbs point to a large but declining carbohydrate depletion, again below the blue line prediction.</div>
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Looking through the supplementary data tables the men on restricted carbohydrate had a significant change in total daily energy expenditure (TDEE) by 139 calories (+/- 36 SEM). Much of this is from a reduction in Sleeping metabolic rate (SMR) of 98 calories (+/- 46, p=0.07).</div>
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<br />Digging more into the substrate balance, the supplementary data reveals that average fat oxidation increased by 393 cals/day on restricted carbohydrate. This is only half the 800 calorie reduction in carb intake :</div>
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" 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Here the "CHO oxidation" is the difference between the recorded fall in oxidation and the reduced CHO intake - we may have expected an 800 calorie reduction in CHO oxidation to match the intake but only saw a reduction of 605 due to depletion and oxidation of carbohydrate reserves. This effectively suppresses fat oxidation, as does the reduction in energy expenditure.</div>
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A longer term experiment, or one with a 2-3 week run-in on restricted carbs, is likely to show greater fat oxidation as the CHO reserves stabilise and possibly a restoration of TDEE as the body adapts to its new fuelling regime.</div>
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Other significant changes (P<0.10) in the restricted carb diet were in marginally increased ketones (B-OHB), reduced total and HDL cholesterol, reduced triglycerides, reduced leptin and ghrelin.</div>
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The restricted fat diet recorded a reduction in fasting blood glucose & insulin, reduced HOMA-IR, lower leptin and reductions in both HDL & total cholesterol. The HDL reduction of RF was double that of RC. Comparison of the two diets showed significant differences (P<0.05) only in ketones, HDL and ghrelin.</div>
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<u>The Insulin Hypothesis.</u></div>
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One of the studies goals was to explore the "Insulin Hypothesis" of Gary Taubes et al. Clearly there was weight loss in the fat reduced diet where carbohydrate intake was maintained. In the men the fasting insulin fell on the RF diet by 2.6 pg/ml, p=0.033, from a baseline of 10. The insulin secretion measured by 24h C-peptide is reported to have decreased by 22% (p=0.001) in the case of the RC diet which is perhaps an illustration of how reduced insulin secretion is associated with increased fat oxidation - the RC diet oxidised 400 kcal/day more fat where the RF diet saw a very small NS increase. Less carbs (A), less insulin (B), more fat oxidation (G) - in line with what Taubes proposes :-</div>
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" 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" /><span style="background-color: white;"><span><span style="background-color: white;"> </span></span></span></div>
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<span style="background-color: white;"><span><span style="background-color: white;"> If the alternative to the Insulin Hypothesis is "a calorie is a calorie" then the latter hypothesis didn't come out to well in this study either. If the effect of calories is uniform regardless of composition how does one explain Fig 2D & 2G where an identical change in energy intake produces a radically different fuel oxidation pattern ?</span></span></span></div>
PhilThttp://www.blogger.com/profile/17675583272641426292noreply@blogger.com1tag:blogger.com,1999:blog-6577126691263890775.post-81003536373429453782015-08-14T14:11:00.000-07:002015-08-14T14:44:56.592-07:00We need to talk about Kevin (Hall)A few months ago I <a href="http://lowcarbuk.blogspot.co.uk/2015/03/does-low-fat-dieting-give-faster-fat.html" target="_blank">chewed over</a> a poster presentation by Kevin Hall and associates which presented the findings of a detailed metabolic comparison over six days of an 800 calorie intake reduction of either carbohydrate or fat alone in a crossover design. I was concerned about an apparent imbalance in calories / carbohydrate balance that suggested the reduced carbohydrate (RC) phase was depleting glycogen reserves and hence the study was not of a steady state condition.<br />
<br />
I general I like the work done by Hall as I have a similar modelling background. There are however remaining concerns now that the <a href="http://www.cell.com/cell-metabolism/fulltext/S1550-4131%2815%2900350-2" target="_blank">full version</a> of the paper has been published in Cell Metabolism.<br />
<br />
Firstly I would like to address the "pictorial abstract" :<br />
<br />
<div class="separator" style="clear: both; text-align: center;">
<a href="http://www.cell.com/cms/attachment/2035293868/2050720873/fx1.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="http://www.cell.com/cms/attachment/2035293868/2050720873/fx1.jpg" height="320" width="320" /></a></div>
On the left we can presume that the ~800 kcal/day reduction in dietary fat intake was replaced by fat from body stores, as everything else is shown as the same. At 9.4 kcal/g that's a fat loss of 85 grams/day which is in line with the stated "89 ± 6 g/day of fat loss" for the RF diet (at 9 kcal/g it's a near exact match). So far so good.<br />
<br />
On the right life gets complicated. The reduction in carbohydrates allows fat oxidation to flourish to the tune of +400 kcal/day - the reason people like me favour carbohydrate restriction. So the subjects are burning 400 more of fat but 500 less of carbs and hence energy consumption has dropped by about 100 kcal/day. Although burning 500 kcal less of carbs the intake of CHO has been reduced by 800 kcal - so 300 kcal ( 81 grams/day) has appeared out of the woodwork, or more likely out of the body's glycogen stores.<br />
<br />
With 300 kcal of bonus fuel entering the mix the fat loss of the restricted carb diet is impaired - with the same fat intake it relies on increased oxidation and this only rose by ~400 kcal/d which is indeed only half of the reduction of intake seen in the fat restricted phase.<br />
<br />
The question remains as to how long this condition could be sustained before the additional carbohydrate reserve dried up and forced a further increase in oxidation of fat. A study for 6 days isn't even half of the Atkins induction phase and the carbohydrate intake here was ~140 g/day which is well north of any "Low carbohydrate" diet let alone anything ketogenic.<br />
<br />
A secondary question arises from the fact that supplementary table S3 shows that there was <i>no significant fat loss</i> in the female subjects on either diet - in fact their % body fat went up (NS). I shall be returning to this study to focus on the men who did at least lose weight and reduce their fat mass significantly. I always prefer single gender studies as so much variability is added and significance lost by combining men and women.<br />
<br />PhilThttp://www.blogger.com/profile/17675583272641426292noreply@blogger.com0tag:blogger.com,1999:blog-6577126691263890775.post-49080379682783724912015-08-03T10:07:00.000-07:002015-08-03T10:11:15.134-07:00Why do the obese overeat ?Another post inspired by a coincidence of two events - watching <a href="http://www.channel5.com/shows/supersized/episodes/65-stone-and-housebound" target="_blank">"65 Stone and Trapped In My House"</a> and reading a couple of blogs after seeing Twitter references to them.<br />
<br />
The TV programme was disturbing, to the extent where I nearly turned it off a couple of times. It centred on an obese man who had grown from 30 to 65 stone ( 910 lbs / 413 kg ) and become confined to one room in his own house and all but unable to move. Sadly the subject of the documentary died shortly after filming aged 33, from heart failure which was predicted by a bariatric surgeon who visited him for an assessment.<br />
<br />
We saw the poor guy drinking full sugar pepsi while wallowing in his bed, and when his friends came round for dinner he wanted to eat the whole dish that they had made for sharing amongst five people.<br />
<br />
Why does a man with at least 300 kg of fat stores feel the need to eat and drink 10,000 calories a day? When a dietitian suggested he would maintain at 7,000 and then should reduce by 1,000 per day in weekly steps he could not contemplate this and flat out refused - this was "too fast".<br />
<br />
300 kg of fat stores is about 2.3 million calories so enough for 330 days energy supply at 7,000 a day. This is reminiscent of the infamous Scotsman "Mr AB" who did a water only fast for 342 days when faced with a similar dilemma - described <a href="http://www.abc.net.au/science/articles/2012/07/24/3549931.htm" target="_blank">here</a> (including audio) and published by <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2495396/?page=1" target="_blank">Stewart and Fleming</a>. Unlike the unfortunate Carl Thompson (RIP) Mr AB lost 276 pounds, reaching his target weight of 180 pounds
and maintaining the bulk of his weight loss. Over the five following
years of observation, AB regained just sixteen pounds.<br />
<br />
So what can we learn from a guy who on the night he died <a href="http://www.dailymail.co.uk/news/article-3141122/A-pizza-man-key-Carers-brought-junk-food-1-000-month-benefits-65st-Karl-allowed-eat-death-taxpayer.html" target="_blank">phoned a takeaway</a> at 11pm to order apple crumble and two servings of ice cream, no doubt opening the conversation with his familiar "It's Carl and I'm starving". Was he suffering from persistent high levels of insulin, brought on by eating mountains of carbohydrate food, leading to much of his food intake being shuffled into fat stores leaving his metabolism still hungry ? This is the general statement of Gary Taubes' "Insulin Hypothesis" and further illustrated in Bob Briggs' Blog <a href="http://www.buttermakesyourpantsfalloff.com/why-hungry/" target="_blank">"Why are fat people hungry"</a>.<br />
<br />
Bob quotes from the work of <a href="http://www.thefatemperor.com/blog/2015/5/10/lchf-the-genius-of-dr-joseph-r-kraft-exposing-the-true-extent-of-diabetes" target="_blank">Dr Joseph Kraft</a> who studies insulin levels and identified 4 patterns of insulin response to eating (the 5th pattern is minimal insulin response / Type 1 Diabetes) :<br />
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<a href="http://i0.wp.com/www.buttermakesyourpantsfalloff.com/wp-content/uploads/2015/07/jkraftall.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="http://i0.wp.com/www.buttermakesyourpantsfalloff.com/wp-content/uploads/2015/07/jkraftall.jpg" height="243" width="400" /></a></div>
We <a href="http://diabetes.diabetesjournals.org/content/60/10/2441/F3.expansion.html" target="_blank">know</a> that fatter people generally have higher insulin levels and consequently release less fatty acids from storage into the bloodstream to use as metabolic fuel :<br />
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<a href="http://diabetes.diabetesjournals.org/content/60/10/2441/F3.large.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="http://diabetes.diabetesjournals.org/content/60/10/2441/F3.large.jpg" height="151" width="400" /></a></div>
Also higher insulin levels reduce free fatty acid flux and concentration :<br />
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<a href="http://hyper.ahajournals.org/content/28/1/120/F1.large.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="http://hyper.ahajournals.org/content/28/1/120/F1.large.jpg" height="196" width="400" /></a></div>
So the obese person with elevated insulin is denied access to the massive calorie reserves they are carrying and consequently live in a state of frequent hunger and consequent overeating with a proportion of what they eat being stored away as the carbohydrate portion further inhibits lipid oxidation.<br />
<br />
The solution to unlocking the fat stores and reducing hungers lies in reducing insulin, through carbohydrate restriction and / or intermittent fasting. Even the Pattern 4 insulin response (green in the chart above) eventually falls after eating so a 16h fasting / 8h eating window approach may be beneficial.<br />
<br />
The ketogenic diet, which is high in fat and low in carbohydrates,
mimics the metabolic state of starvation, forcing the body to utilize
fat as its primary source of energy. This also results in reduction in insulin levels. Once adapted to such a diet the consumers of an LCHF diet with moderate protein are, in general, not hungry, and frequently under-eat in clinical studies.<br />
<br />
Evidence that carbohydrate restriction mimics total fasting has been provided by <a href="http://ajcn.nutrition.org/content/62/4/757.abstract" target="_blank">clinical studies</a> where infusing lipids into the bloodstream at a rate equivalent to the Basal Metabolic Rate have little effect on outcomes - "carbohydrate restriction, not total energy restriction, is responsible
for the increase in lipolytic sensitivity observed during fasting". S Klein and RR Wolfe have published several studies looking at lipolysis rates and the influence of various parameters on those rates.<br />
<br />
From this and other reading I am drawn to conclude that :<br />
<ol>
<li>Lack of fat release from storage leaves the fat person needing to eat for energy supply. Persistent hunger is a powerful motivator especially in the lonely or depressed.</li>
<li>Eating carbohydrates elevates insulin and reduces fat oxidation, reducing use of fats as well as their availability. </li>
<li>Elevated insulin responsible for locking up the fat also stores further dietary fat on the body. Eating carbs and fat together maximises this effect.</li>
<li>Carbohydrate restriction or fasting allows insulin levels to fall and unlock the fat reserves.</li>
<li>People on total fasts or 500-800 calorie modified fasts, or carbohydrate restriction to ketogenic levels, are not hungry and therefore do not typically overeat.</li>
</ol>
PhilThttp://www.blogger.com/profile/17675583272641426292noreply@blogger.com1tag:blogger.com,1999:blog-6577126691263890775.post-67667511259440462182015-07-29T03:48:00.002-07:002015-07-29T03:48:31.975-07:00Type 2 Diabetes - dietary advice from Diabetes UKA couple of weeks ago I received an e-newsletter "Type 2 Together" about peer support for Type 2 diabetics arranged by the charity Diabetes UK. It featured a recipe :<br />
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<a href="http://4.bp.blogspot.com/-YZm6GeFGgpw/Vbir3bmSy6I/AAAAAAAAIwI/P_5Pa3FAEzc/s1600/t2togetherrecipe.PNG" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="278" src="http://4.bp.blogspot.com/-YZm6GeFGgpw/Vbir3bmSy6I/AAAAAAAAIwI/P_5Pa3FAEzc/s400/t2togetherrecipe.PNG" width="400" /></a></div>
<br />
One slice of this contains at least 4 times the amount of glucose in my bloodstream, so I am somewhat confused as to why this would be a good recipe for Type 2 diabetics. Perhaps it's because it is low in fat. That's right, patients with an excess of carbohydrate in their bloodstream and a problem managing it are apparently supposed to be primarily concerned about fat, so at least there's only 1g per slice.<br />
<br />
The ingredients aren't exactly inspiring - mixed fruit, wholemeal flour and fructose - or sugar, sugar and sugar for simplicity.<br />
<br />
Around the same time Diabetes UK were running a PR campaign about the number of foot amputations occurring in the UK diabetic population. As I understand it the foot amputations result from diabetic neuropathy, which is caused by elevated blood glucose, which in turn is caused by digesting carbohydrates.<br />
<br />
<span style="font-size: large;">If we're worried about foot amputations, why not suggest eating less carbohydrate !</span><br />
<br />
The hand wringing about fat arises from increased heart disease in diabetics. I would imagine this is entirely due to the effects of elevated blood sugar on the cardiovascular system, so once again carbohydrate restriction would appear to have some merit.<br />
<br />
I used the <a href="https://www.dtu.ox.ac.uk/riskengine/download.php" target="_blank">UKPDS risk calculator</a> to estimate the effect of HbA1c on my personal heart disease risk over 10 years. With all other variables constant the risk rises with HbA1c :-<br />
<br />
HbA1c % 10 year risk %<br />
4 4.7<br />
5 5.5<br />
6 6.5<br />
7 7.6<br />
8 9.0<br />
<br />
So reducing HbA1c from 8 to 4 brings a corresponding halving of the heart disease risk. Once again, does this not suggest less blood glucose is good for the heart ? Especially when low carb diets also improve the CVD risk profile by increasing HDL cholesterol and reducing triglycerides.<br />
<br />
Whichever way I look at it the Diabetes UK cake recipe and their dietary advice in general is a recipe for heart disease and amputations.PhilThttp://www.blogger.com/profile/17675583272641426292noreply@blogger.com1tag:blogger.com,1999:blog-6577126691263890775.post-40725111106996908922015-07-21T02:28:00.001-07:002017-06-18T01:04:41.318-07:00Ironic Nutritional KetosisA couple of years ago I had an enjoyable 1 week holiday at Jason Vale's mountain retreat in Turkey aka "<a href="http://www.juicymountain.com/" target="_blank">Juicy Mountain</a>". Jason is a "juicing guru" who advocates freshly juiced fruits and vegetables for health and combines this with vigorous exercise programmes and yoga in a stunning setting to give a cleanse of mind, body and soul. To be honest I was somewhat skeptical as I am no spiritualist or gym bunny and after 3 years of low carb eating I thought I may need counselling to go on a diet that was substantially carbohydrate based.<br />
<br />
I took along my blood glucose and ketone testing kit but left most everything else electronic behind, in order to de-stress and "be present" as <a href="http://www.juicymountain.com/the-team/" target="_blank">Becky</a> the retreat manager requested we should be. We ate practically no food during the week but started each day with yoga followed by a small juice shot and then a long walk or similar. On returning there may be a gym or rebounding (trampoline) session before the first real juice of the day at 10am. After that came more exercise in the gym with the brilliant <a href="http://www.pursuitfitness.co.uk/" target="_blank">Tim Britton</a> as our trainer and other less formal exercise like volleyball, swimming or borrowing the mountain bikes. Another juice at about 1pm preceded the relaxing afternoon in the 30+ degree C heat (90 F) before we were back in the gym or on the trampolines followed by "tea" at around 5pm and a final juice watching a DVD or similar at about 8pm. Yoga was led by <a href="https://kenryan.wordpress.com/" target="_blank">Ken Ryan</a>, a brilliant Irishman with a level of quirky charisma that only the Irish can aspire to, as demonstrated by him living in an actual cave on the mountainside.<br />
<br />
Looking back I can identify several elements that contributed to my 7 pound / 3.2 kg weight loss during the week. Firstly we had a sort of intermittent fasting regime where we had virtually no calorie intake between 8pm and 10am the following day. In the morning we had yoga and exercise in a fasted state, walking for an hour up and down hills or doing a 5km run down and back up the mountain. Overall the calorie expenditure was high with several hours of physical activity per day and there was presumably a fairly low calorie intake from drinking about 1.2 - 1.5 litres of juices and blended smoothies per day as our sole "food" intake.<br />
<br />
I estimate I was taking in around 1,000 calories a day (+/- 20%) with probably 80% from carbohydrate and when I used my Polar heart rate monitor I estimate my daily exercise was at least 2000 calories, most of which was in temperatures above 30 degrees C.<br />
<br />
So to the numbers, below. I took a couple of baseline readings at home, then after flying to Turkey and arriving late at night a couple of readings on our first day in the retreat. The second reading was just before the evening juice and I followed that up with 3 half hourly glucose tests to see the postprandial effect of the juice - which was less alarming than I expected.<br />
<br />
<table border="1" cellpadding="0" cellspacing="0" dir="ltr" style="border-collapse: collapse; border: 1px solid #ccc; font-family: arial,sans,sans-serif; font-size: 13px; table-layout: fixed;"><colgroup><col width="120"></col><col width="71"></col><col width="71"></col><col width="71"></col><col width="71"></col><col width="71"></col><col width="71"></col><col width="71"></col><col width="71"></col></colgroup><tbody>
<tr style="height: 17px;"><td data-sheets-value="[null,2,"Date"]" style="padding: 0px 3px 0px 3px; vertical-align: bottom;">Date</td><td data-sheets-numberformat="[null,5]" data-sheets-value="[null,3,null,41514]" style="padding: 0px 3px 0px 3px; text-align: right; vertical-align: bottom;">28/08/2013</td><td data-sheets-numberformat="[null,5]" data-sheets-value="[null,3,null,41515]" style="padding: 0px 3px 0px 3px; text-align: right; vertical-align: bottom;">29/08/2013</td><td data-sheets-numberformat="[null,5]" data-sheets-value="[null,3,null,41517]" style="padding: 0px 3px 0px 3px; text-align: right; vertical-align: bottom;">31/08/2013</td><td data-sheets-numberformat="[null,5]" data-sheets-value="[null,3,null,41517]" style="padding: 0px 3px 0px 3px; text-align: right; vertical-align: bottom;">31/08/2013</td><td data-sheets-numberformat="[null,5]" data-sheets-value="[null,3,null,41517]" style="padding: 0px 3px 0px 3px; text-align: right; vertical-align: bottom;">31/08/2013</td><td data-sheets-numberformat="[null,5]" data-sheets-value="[null,3,null,41517]" style="padding: 0px 3px 0px 3px; text-align: right; vertical-align: bottom;">31/08/2013</td><td data-sheets-numberformat="[null,5]" data-sheets-value="[null,3,null,41517]" style="padding: 0px 3px 0px 3px; text-align: right; vertical-align: bottom;">31/08/2013</td><td data-sheets-numberformat="[null,5]" data-sheets-value="[null,3,null,41517]" style="padding: 0px 3px 0px 3px; text-align: right; vertical-align: bottom;">31/08/2013</td></tr>
<tr style="height: 17px;"><td data-sheets-numberformat="[null,7]" data-sheets-value="[null,2,"Time"]" style="padding: 0px 3px 0px 3px; vertical-align: bottom;">Time</td><td data-sheets-numberformat="[null,7,"HH:mm",1]" data-sheets-value="[null,3,null,0.75]" style="padding: 0px 3px 0px 3px; text-align: right; vertical-align: bottom;">18:00</td><td data-sheets-numberformat="[null,7,"HH:mm",1]" data-sheets-value="[null,3,null,0.7083333333333334]" style="padding: 0px 3px 0px 3px; text-align: right; vertical-align: bottom;">17:00</td><td data-sheets-numberformat="[null,7,"HH:mm",1]" data-sheets-value="[null,3,null,0.3125]" style="padding: 0px 3px 0px 3px; text-align: right; vertical-align: bottom;">07:30</td><td data-sheets-numberformat="[null,7,"HH:mm",1]" data-sheets-value="[null,3,null,0.7222222222222222]" style="padding: 0px 3px 0px 3px; text-align: right; vertical-align: bottom;">17:20</td><td data-sheets-numberformat="[null,7,"HH:mm",1]" data-sheets-value="[null,3,null,0.7402777777777778]" style="padding: 0px 3px 0px 3px; text-align: right; vertical-align: bottom;">17:46</td><td data-sheets-numberformat="[null,7,"HH:mm",1]" data-sheets-value="[null,3,null,0.7625]" style="padding: 0px 3px 0px 3px; text-align: right; vertical-align: bottom;">18:18</td><td data-sheets-numberformat="[null,7,"HH:mm",1]" data-sheets-value="[null,3,null,0.7833333333333333]" style="padding: 0px 3px 0px 3px; text-align: right; vertical-align: bottom;">18:48</td><td data-sheets-numberformat="[null,7,"HH:mm",1]" data-sheets-value="[null,3,null,0.8041666666666667]" style="padding: 0px 3px 0px 3px; text-align: right; vertical-align: bottom;">19:18</td></tr>
<tr style="height: 17px;"><td data-sheets-numberformat="[null,2,"#,##0.0",1,1]" data-sheets-value="[null,2,"Glucose mmol/l"]" style="padding: 0px 3px 0px 3px; vertical-align: bottom;">Glucose mmol/l</td><td data-sheets-numberformat="[null,2,"#,##0.0",1,1]" data-sheets-value="[null,3,null,5.4]" style="padding: 0px 3px 0px 3px; text-align: right; vertical-align: bottom;">5.4</td><td data-sheets-numberformat="[null,2,"#,##0.0",1,1]" data-sheets-value="[null,3,null,4.4]" style="padding: 0px 3px 0px 3px; text-align: right; vertical-align: bottom;">4.4</td><td data-sheets-numberformat="[null,2,"#,##0.0",1,1]" data-sheets-value="[null,3,null,5.9]" style="padding: 0px 3px 0px 3px; text-align: right; vertical-align: bottom;">5.9</td><td data-sheets-numberformat="[null,2,"#,##0.0",1,1]" data-sheets-value="[null,3,null,4]" style="padding: 0px 3px 0px 3px; text-align: right; vertical-align: bottom;">4.0</td><td data-sheets-numberformat="[null,2,"#,##0.0",1,1]" data-sheets-value="[null,3,null,4.6]" style="padding: 0px 3px 0px 3px; text-align: right; vertical-align: bottom;">4.6</td><td data-sheets-numberformat="[null,2,"#,##0.0",1,1]" data-sheets-value="[null,3,null,6.5]" style="padding: 0px 3px 0px 3px; text-align: right; vertical-align: bottom;">6.5</td><td data-sheets-numberformat="[null,2,"#,##0.0",1,1]" data-sheets-value="[null,3,null,5.9]" style="padding: 0px 3px 0px 3px; text-align: right; vertical-align: bottom;">5.9</td><td data-sheets-numberformat="[null,2,"#,##0.0",1,1]" data-sheets-value="[null,3,null,6.3]" style="padding: 0px 3px 0px 3px; text-align: right; vertical-align: bottom;">6.3</td></tr>
<tr style="height: 17px;"><td data-sheets-numberformat="[null,2,"#,##0.0",1,1]" data-sheets-value="[null,2,"Ketones mmol/l"]" style="padding: 0px 3px 0px 3px; vertical-align: bottom;">Ketones mmol/l</td><td data-sheets-numberformat="[null,2,"#,##0.0",1,1]" data-sheets-value="[null,3,null,0.8]" style="padding: 0px 3px 0px 3px; text-align: right; vertical-align: bottom;">0.8</td><td data-sheets-numberformat="[null,2,"#,##0.0",1,1]" data-sheets-value="[null,3,null,0.3]" style="padding: 0px 3px 0px 3px; text-align: right; vertical-align: bottom;">0.3</td><td data-sheets-numberformat="[null,2,"#,##0.0",1,1]" data-sheets-value="[null,3,null,0.8]" style="padding: 0px 3px 0px 3px; text-align: right; vertical-align: bottom;">0.8</td><td data-sheets-numberformat="[null,2,"#,##0.0",1,1]" data-sheets-value="[null,3,null,0.4]" style="padding: 0px 3px 0px 3px; text-align: right; vertical-align: bottom;">0.4</td><td data-sheets-numberformat="[null,2,"#,##0.0",1,1]" style="padding: 0px 3px 0px 3px; vertical-align: bottom;"></td><td data-sheets-numberformat="[null,2,"#,##0.0",1,1]" style="padding: 0px 3px 0px 3px; vertical-align: bottom;"></td><td data-sheets-numberformat="[null,2,"#,##0.0",1,1]" style="padding: 0px 3px 0px 3px; vertical-align: bottom;"></td><td data-sheets-numberformat="[null,2,"#,##0.0",1,1]" style="padding: 0px 3px 0px 3px; vertical-align: bottom;"></td></tr>
<tr style="height: 17px;"><td data-sheets-value="[null,2,"Ratio G/K"]" style="padding: 0px 3px 0px 3px; vertical-align: bottom;">Ratio G/K</td><td data-sheets-formula="=R[-2]C[0]/R[-1]C[0]" data-sheets-numberformat="[null,2,"0.0",1]" data-sheets-value="[null,3,null,6.75]" style="padding: 0px 3px 0px 3px; text-align: right; vertical-align: bottom;">6.8</td><td data-sheets-formula="=R[-2]C[0]/R[-1]C[0]" data-sheets-numberformat="[null,2,"0.0",1]" data-sheets-value="[null,3,null,14.666666666666668]" style="padding: 0px 3px 0px 3px; text-align: right; vertical-align: bottom;">14.7</td><td data-sheets-formula="=R[-2]C[0]/R[-1]C[0]" data-sheets-numberformat="[null,2,"0.0",1]" data-sheets-value="[null,3,null,7.375]" style="padding: 0px 3px 0px 3px; text-align: right; vertical-align: bottom;">7.4</td><td data-sheets-formula="=R[-2]C[0]/R[-1]C[0]" data-sheets-numberformat="[null,2,"0.0",1]" data-sheets-value="[null,3,null,10]" style="padding: 0px 3px 0px 3px; text-align: right; vertical-align: bottom;">10.0</td><td></td><td></td><td></td><td></td></tr>
<tr style="height: 17px;"><td style="padding: 0px 3px 0px 3px; vertical-align: bottom;"></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr>
<tr style="height: 17px;"><td data-sheets-value="[null,2,"Notes"]" style="padding: 0px 3px 0px 3px; vertical-align: bottom;">Notes</td><td data-sheets-value="[null,2,"UK"]" style="padding: 0px 3px 0px 3px; text-align: center; vertical-align: bottom; vertical-align: bottom;">UK</td><td data-sheets-value="[null,2,"UK"]" style="padding: 0px 3px 0px 3px; text-align: center; vertical-align: bottom; vertical-align: bottom;">UK</td><td data-sheets-value="[null,2,"Turkey"]" style="padding: 0px 3px 0px 3px; text-align: center; vertical-align: bottom; vertical-align: bottom;">Turkey</td><td data-sheets-value="[null,2,"End of day"]" style="padding: 0px 3px 0px 3px; vertical-align: bottom;">End of day</td><td data-sheets-value="[null,2,"Juice time"]" style="padding: 0px 3px 0px 3px; vertical-align: bottom;">Juice time</td><td colspan="3" data-sheets-value="[null,2," ------ post juice drink testing ------"]" rowspan="1" style="padding: 0px 3px 0px 3px; text-align: center; vertical-align: bottom; vertical-align: bottom;">------ post juice drink testing ------</td></tr>
<tr style="height: 17px;"><td data-sheets-value="[null,2,"UK time off meter"]" style="padding: 0px 3px 0px 3px; vertical-align: bottom;">UK time off meter</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr>
<tr style="height: 17px;"><td style="padding: 0px 3px 0px 3px; vertical-align: bottom;"></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr>
<tr style="height: 17px;"><td style="padding: 0px 3px 0px 3px; vertical-align: bottom;"></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr>
<tr style="height: 17px;"><td style="padding: 0px 3px 0px 3px; vertical-align: bottom;"></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr>
<tr style="height: 17px;"><td data-sheets-value="[null,2,"Date"]" style="padding: 0px 3px 0px 3px; vertical-align: bottom;">Date</td><td data-sheets-numberformat="[null,5]" data-sheets-value="[null,3,null,41518]" style="padding: 0px 3px 0px 3px; text-align: right; vertical-align: bottom;">01/09/2013</td><td data-sheets-numberformat="[null,5]" data-sheets-value="[null,3,null,41518]" style="padding: 0px 3px 0px 3px; text-align: right; vertical-align: bottom;">01/09/2013</td><td data-sheets-numberformat="[null,5]" data-sheets-value="[null,3,null,41519]" style="padding: 0px 3px 0px 3px; text-align: right; vertical-align: bottom;">02/09/2013</td><td data-sheets-numberformat="[null,5]" data-sheets-value="[null,3,null,41520]" style="padding: 0px 3px 0px 3px; text-align: right; vertical-align: bottom;">03/09/2013</td><td data-sheets-numberformat="[null,5]" data-sheets-value="[null,3,null,41521]" style="padding: 0px 3px 0px 3px; text-align: right; vertical-align: bottom;">04/09/2013</td><td data-sheets-numberformat="[null,5]" data-sheets-value="[null,3,null,41522]" style="padding: 0px 3px 0px 3px; text-align: right; vertical-align: bottom;">05/09/2013</td><td data-sheets-numberformat="[null,5]" data-sheets-value="[null,3,null,41522]" style="padding: 0px 3px 0px 3px; text-align: right; vertical-align: bottom;">05/09/2013</td><td data-sheets-numberformat="[null,5]" data-sheets-value="[null,3,null,41523]" style="padding: 0px 3px 0px 3px; text-align: right; vertical-align: bottom;">06/09/2013</td></tr>
<tr style="height: 17px;"><td data-sheets-numberformat="[null,7]" data-sheets-value="[null,2,"Time"]" style="padding: 0px 3px 0px 3px; vertical-align: bottom;">Time</td><td data-sheets-numberformat="[null,7,"HH:mm",1]" data-sheets-value="[null,3,null,0.19930555555555557]" style="padding: 0px 3px 0px 3px; text-align: right; vertical-align: bottom;">04:47</td><td data-sheets-numberformat="[null,7,"HH:mm",1]" data-sheets-value="[null,3,null,0.4597222222222222]" style="padding: 0px 3px 0px 3px; text-align: right; vertical-align: bottom;">11:02</td><td data-sheets-numberformat="[null,7,"HH:mm",1]" data-sheets-value="[null,3,null,0.21875]" style="padding: 0px 3px 0px 3px; text-align: right; vertical-align: bottom;">05:15</td><td data-sheets-numberformat="[null,7,"HH:mm",1]" data-sheets-value="[null,3,null,0.2298611111111111]" style="padding: 0px 3px 0px 3px; text-align: right; vertical-align: bottom;">05:31</td><td data-sheets-numberformat="[null,7,"HH:mm",1]" data-sheets-value="[null,3,null,0.17291666666666666]" style="padding: 0px 3px 0px 3px; text-align: right; vertical-align: bottom;">04:09</td><td data-sheets-numberformat="[null,7,"HH:mm",1]" data-sheets-value="[null,3,null,0.20833333333333334]" style="padding: 0px 3px 0px 3px; text-align: right; vertical-align: bottom;">05:00</td><td data-sheets-numberformat="[null,7,"HH:mm",1]" data-sheets-value="[null,3,null,0.2777777777777778]" style="padding: 0px 3px 0px 3px; text-align: right; vertical-align: bottom;">06:40</td><td data-sheets-numberformat="[null,7,"HH:mm",1]" data-sheets-value="[null,3,null,0.17708333333333334]" style="padding: 0px 3px 0px 3px; text-align: right; vertical-align: bottom;">04:15</td></tr>
<tr style="height: 17px;"><td data-sheets-numberformat="[null,2,"#,##0.0",1,1]" data-sheets-value="[null,2,"Glucose mmol/l"]" style="padding: 0px 3px 0px 3px; vertical-align: bottom;">Glucose mmol/l</td><td data-sheets-value="[null,3,null,6.3]" style="padding: 0px 3px 0px 3px; text-align: right; vertical-align: bottom;">6.3</td><td></td><td data-sheets-value="[null,3,null,5.8]" style="padding: 0px 3px 0px 3px; text-align: right; vertical-align: bottom;">5.8</td><td></td><td data-sheets-value="[null,3,null,5.3]" style="padding: 0px 3px 0px 3px; text-align: right; vertical-align: bottom;">5.3</td><td data-sheets-value="[null,3,null,5.8]" style="padding: 0px 3px 0px 3px; text-align: right; vertical-align: bottom;">5.8</td><td></td><td data-sheets-value="[null,3,null,4.9]" style="padding: 0px 3px 0px 3px; text-align: right; vertical-align: bottom;">4.9</td></tr>
<tr style="height: 17px;"><td data-sheets-numberformat="[null,2,"#,##0.0",1,1]" data-sheets-value="[null,2,"Ketones mmol/l"]" style="padding: 0px 3px 0px 3px; vertical-align: bottom;">Ketones mmol/l</td><td data-sheets-value="[null,3,null,1.6]" style="padding: 0px 3px 0px 3px; text-align: right; vertical-align: bottom;">1.6</td><td data-sheets-value="[null,3,null,2.3]" style="padding: 0px 3px 0px 3px; text-align: right; vertical-align: bottom;">2.3</td><td data-sheets-value="[null,3,null,2.8]" style="padding: 0px 3px 0px 3px; text-align: right; vertical-align: bottom;">2.8</td><td data-sheets-value="[null,3,null,2.4]" style="padding: 0px 3px 0px 3px; text-align: right; vertical-align: bottom;">2.4</td><td data-sheets-value="[null,3,null,2.9]" style="padding: 0px 3px 0px 3px; text-align: right; vertical-align: bottom;">2.9</td><td data-sheets-value="[null,3,null,1.4]" style="padding: 0px 3px 0px 3px; text-align: right; vertical-align: bottom;">1.4</td><td data-sheets-value="[null,3,null,0.6]" style="padding: 0px 3px 0px 3px; text-align: right; vertical-align: bottom;">0.6</td><td data-sheets-value="[null,3,null,3.4]" style="padding: 0px 3px 0px 3px; text-align: right; vertical-align: bottom;">3.4</td></tr>
<tr style="height: 17px;"><td data-sheets-value="[null,2,"Ratio G/K"]" style="padding: 0px 3px 0px 3px; vertical-align: bottom;">Ratio G/K</td><td data-sheets-formula="=R[-2]C[0]/R[-1]C[0]" data-sheets-numberformat="[null,2,"0.0",1]" data-sheets-value="[null,3,null,3.9374999999999996]" style="padding: 0px 3px 0px 3px; text-align: right; vertical-align: bottom;">3.9</td><td></td><td data-sheets-formula="=R[-2]C[0]/R[-1]C[0]" data-sheets-numberformat="[null,2,"0.0",1]" data-sheets-value="[null,3,null,2.0714285714285716]" style="padding: 0px 3px 0px 3px; text-align: right; vertical-align: bottom;">2.1</td><td></td><td data-sheets-formula="=R[-2]C[0]/R[-1]C[0]" data-sheets-numberformat="[null,2,"0.0",1]" data-sheets-value="[null,3,null,1.8275862068965518]" style="padding: 0px 3px 0px 3px; text-align: right; vertical-align: bottom;">1.8</td><td data-sheets-formula="=R[-2]C[0]/R[-1]C[0]" data-sheets-numberformat="[null,2,"0.0",1]" data-sheets-value="[null,3,null,4.142857142857143]" style="padding: 0px 3px 0px 3px; text-align: right; vertical-align: bottom;">4.1</td><td></td><td data-sheets-formula="=R[-2]C[0]/R[-1]C[0]" data-sheets-numberformat="[null,2,"0.0",1]" data-sheets-value="[null,3,null,1.4411764705882355]" style="padding: 0px 3px 0px 3px; text-align: right; vertical-align: bottom;">1.4</td></tr>
<tr style="height: 17px;"><td style="padding: 0px 3px 0px 3px; vertical-align: bottom;"></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr>
<tr style="height: 17px;"><td data-sheets-value="[null,2,"Notes"]" style="padding: 0px 3px 0px 3px; vertical-align: bottom;">Notes</td><td></td><td></td><td></td><td></td><td data-sheets-value="[null,2,"Boat trip"]" style="padding: 0px 3px 0px 3px; vertical-align: bottom;">Boat trip</td><td></td><td data-sheets-value="[null,2,"after run"]" style="padding: 0px 3px 0px 3px; vertical-align: bottom;">after run</td><td></td></tr>
<tr style="height: 17px;"><td style="padding: 0px 3px 0px 3px; vertical-align: bottom;"></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td><br /></td></tr>
</tbody></table>
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The days in September I mainly took a fasted reading on waking at dawn - we were "sleeping" in a very warm tent so tended to be up very early. The ketone levels I recorded were typically over 2.0 which is quite unusual for me, hence the 11:00 check on the second day. <a href="https://docs.google.com/spreadsheets/d/1OwLqScIkqCLZnagNIcihlme-_Hdl5k-xznNo_Z4xe0A/edit?usp=sharing" target="_blank">Google sheets link to data</a>.</div>
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On the Thursday we had a "day off" with an excursion down to Gocek -<br />
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and a boat trip where the enthusiastic captains of our boats provided us with quite a lot of fruit to eat and tomatoes with salt which proved very popular indeed - the group nicknamed themselves "Salty Tomatoes" after the primal behaviour displayed getting to the salt from the guests who were going through "keto flu" symptoms as they adapted (or failed to adapt) to the reduced carbohydrate and low calorie intake.</div>
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<div>
My ketone levels halved after the boat trip which I thought at the time was due to the high sugar intake from the fruits, although the reduced activity level may also have been a factor. Our juices at the retreat had a high vegetable content and blended avocado so I think the boat trip food was probably a bad idea in the middle of a week of otherwise controlled intake. I suspect the trip and fruit is a necessary part of the retreat programme in order to stop people fleeing to buy food or to escape from the isolated mountain location.</div>
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The day after the boat trip we started our morning with a 5km run down about 500' of fairly rough mountain tracks and back up again. This was a repeat of a run done at the beginning of the week to assess our progress. My ketone levels after the run were low, perhaps because I had been using them or perhaps a continuation of the alleged effect of the boat trip. Either way I was back up over 3 the next and final morning before departure.</div>
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So that's the long overdue story of how a low carb eater lived the juicy life for a week and saw <i>improved</i> levels of blood ketones despite a predominantly carbohydrate diet at restricted calorie intake. I lost 7 pounds and fell below 12 stone (168 lbs / 76.3 kg - I am 5'-10" / 1.80m) for the first time I can recall as an adult, I did exercise beyond any previous experience and had a really good time thanks to the excellent team in place at the resort and my fellow travellers.</div>
PhilThttp://www.blogger.com/profile/17675583272641426292noreply@blogger.com0tag:blogger.com,1999:blog-6577126691263890775.post-13276267026678658142015-07-09T02:13:00.001-07:002015-07-09T02:16:31.925-07:00BBC Inside Health - Gestational Diabetes, Low Carb Dieting.A recent edition of BBC Radio 4's "Inside Health" programme covered gestational diabetes and (separately) the experiences of a listener who lost 4 stone (56 lbs, 25 kg) on a low carbohydrate diet.<br />
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The programme web site is <a href="http://www.bbc.co.uk/programmes/b019dl1b" target="_blank">here</a>, and the mp3 audio file should be available to download via <a href="http://open.live.bbc.co.uk/mediaselector/5/redir/version/2.0/mediaset/audio-nondrm-download/proto/http/vpid/p02wjm4w.mp3" target="_blank">this link</a> however BBC media can be time or location limited so I apologise if it isn't available to you. I do have a copy of the mp3 so get in touch if you need one.<br />
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The gestational diabetes (GDM) item was based on the Rosie Hospital in Cambridge where they have recently <a href="http://www.medschl.cam.ac.uk/diagnosis-gestational-diabetes-mellitus-falling-net/" target="_blank">published work</a> on the sensitivity of different diagnostic criteria for GDM. The article in Diabetologia identifies patients with GDM who fall between the UK's NICE guidance on diagnosis and other criteria and looks at the risks of complications in that group. <a href="http://www.diabetologia-journal.org/files/Meek.pdf" target="_blank">Full paper here</a>.<br />
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Of interest to low carbers was the clear description by a dietitian on air as to how all carbohydrates including starch end up as blood sugar. She went on to say that this meant the hospital encouraged mothers-to-be to change from white bread to wholemeal as the lower GI reduced the blood sugar impact, which was a bit of a let down after a good start. Dr Mark Porter, who presents the program, said on Twitter that the Rosie Hospital's GDM program used diet and carbohydrate restriction, along with exercise, as its primary treatment method. Unfortunately this didn't come over in the audio, perhaps due to editing, so the message was more about changing the colour of your bread rather than eating a lot less of it in the first place.<br />
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The second item of interest concerned a listener Mark Robbins who called in to enthusiastically report his weight loss on a low carb diet. Dr Mark discussed this with regular sidekick Dr Margaret McCartney (a GP from Glasgow) and the eminent Professor Susan Jebb OBE. There was a general consensus that low carb dieting does work and that Drs Mark and Margaret see this in their practice patients. It was also agreed that diet trials of up to a year - said to be "short term" - support low carb dieting. Unfortunately nobody much does long term studies, perhaps due to cost or the difficulty of keeping subjects motivated and under control for so long.<br />
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Dr Mark asked (at 20m48) if there was a scientific reason / rational explanation of why low carb diets might make weight loss easy and Prof Jebb replied to the effect that "it is just about calories". Perhaps she needs to sit in on Metabolism or Biochemistry 101 classes and remind herself how chronic carbohydrate consumption elevates insulin which impairs the release and oxidation of fat from storage. To lose 25kg of fat in a year requires a fat oxidation rate of 2,9 grams per hour average above the dietary intake (or bodily production) of fat - that's about 25 calories an hour or 600 calories a day and probably 25% of the calorie burn of the listener. Clearly anything that increases fat release and fat oxidation rates is a good thing when you want to achieve this - carbohydrate ingestion does the opposite.<br />
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Speaking of Dr Jebb, she appears on the author list of a <a href="http://www.ncbi.nlm.nih.gov/pubmed/25865622" target="_blank">recent study</a> "Dietary patterns, cardiometabolic risk factors, and the incidence of cardiovascular disease in severe obesity" which looked at 2000 Swedish subjects over 10 years. After much complex maths they concluded "An energy-dense, high-saturated-fat, and low-fiber DP was longitudinally associated with increases in cardiometabolic risk factors in severe obesity <b>but not with CVD incidence</b>" (my emphasis) so once again the "saturated fat is bad" mantra did not stand up to analysis, neither did the "high fibre" approach that Prof Jebb is so keen on. She will never be in favour of low carb diets while she's locked into a high fibre from whole grains and low saturated fat dogma that is increasingly looking "so last century".<br />
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Who eats whole grains ? Only pigeons, as far as I can see. Sorry Susan.PhilThttp://www.blogger.com/profile/17675583272641426292noreply@blogger.com0tag:blogger.com,1999:blog-6577126691263890775.post-43324329719859412602015-07-09T01:37:00.004-07:002015-07-09T01:37:59.121-07:00Atkins - PRE-MAINTENANCE FOR VEGETARIANS AND VEGANS<h1 class="program_mainContentTitle" style="background-color: #fffdf9; color: #e41b23; font-family: Arial; font-size: 25px; font-weight: normal; margin: 20px 0px 6px; text-transform: uppercase;">
OBJECTIVES OF PRE-MAINTENANCE (FINE-TUNING)</h1>
<h2 class="program_mainContentSubTitle" style="background-color: #fffdf9; background-image: url(http://web.archive.org/web/20140108103606/http://atkins.vo.llnwd.net/e1//AtkinsDotCom/media/Master/programs/BGSubTitleProgram.gif); background-position: 13px 0px; background-repeat: no-repeat; color: #fba825; font-family: Arial; font-size: 20px; margin: -3px 18px 22px; overflow: hidden; padding-left: 27px; padding-top: 2px;">
PRE-MAINTENANCE FOR VEGETARIANS AND VEGANS</h2>
<span style="background-color: #fffdf9; color: #736d62; font-family: Arial; font-size: 12px;">If you’re a vegetarian or vegan, whole grains and starchy vegetables may have traditionally been important components of your meals. These are the foods that you’ll reintroduce in Phase 3, Pre-Maintenance. However, they’re also some of the very foods that may have gotten you in trouble in the past. You may find that over time you can tolerate larger portions as long as you steer clear of refined grains and most processed foods.</span><br />
<h3 style="background-color: #fffdf9; border-bottom-color: rgb(228, 27, 35); border-bottom-style: solid; border-bottom-width: 1px; border-top-color: rgb(228, 27, 35); border-top-style: solid; border-top-width: 1px; color: #e41b23; font-family: Arial; font-size: 12px; margin: 8px 0px; overflow: hidden; padding: 2px 0px;">
Advice for Vegetarians and Vegans</h3>
<ul style="background-color: #fffdf9; color: #736d62; font-family: Arial; font-size: 12px; overflow: hidden;">
<li style="background: url(http://web.archive.org/web/20140108103606/http://atkins.vo.llnwd.net/e1//AtkinsDotCom/media/Master/NewBook/bullet.png) 0% 6px no-repeat scroll transparent; border: 0px none; line-height: 17px; list-style-type: none; margin-left: 0px; padding-left: 11px;">Follow the general guidelines for reintroducing foods at the top of the <a href="http://web.archive.org/web/20140111093319/http://www.atkins.com/Program/Overview/How-and-Why-Atkins-Work/Reach-Your-Goal-by-Climbing-the-Carb-Ladder.aspx" style="color: #185197; text-decoration: none;"><strong>Carb Ladder</strong></a>.</li>
<li style="background: url(http://web.archive.org/web/20140108103606/http://atkins.vo.llnwd.net/e1//AtkinsDotCom/media/Master/NewBook/bullet.png) 0% 6px no-repeat scroll transparent; border: 0px none; line-height: 17px; list-style-type: none; margin-left: 0px; padding-left: 11px;">Follow the basics for <a href="http://web.archive.org/web/20140111093319/http://www.atkins.com/Program/Phase-3/Objectives-of-Pre-Maintenance/THE-BASICS-OF-PRE-MAINTENANCE.aspx" style="color: #185197; text-decoration: none;"><strong>Pre-Maintenance</strong></a> and for doing Atkins as a <a href="http://web.archive.org/web/20140111093319/http://www.atkins.com/Program/Overview/Ways-to-Customize-Atkins,-Your-Diet-Plan/Atkins-for-Vegetarian.aspx" style="color: #185197; text-decoration: none;"><strong>vegetarian</strong></a> or a<a href="http://web.archive.org/web/20140111093319/http://www.atkins.com/Program/Overview/Ways-to-Customize-Atkins,-Your-Diet-Plan/Atkins-for-Vegans.aspx" style="color: #185197; text-decoration: none;"><strong>vegan</strong></a></li>
<li style="background: url(http://web.archive.org/web/20140108103606/http://atkins.vo.llnwd.net/e1//AtkinsDotCom/media/Master/NewBook/bullet.png) 0% 6px no-repeat scroll transparent; border: 0px none; line-height: 17px; list-style-type: none; margin-left: 0px; padding-left: 11px;">Always eat carbohydrates with fat and/or protein.</li>
<li style="background: url(http://web.archive.org/web/20140108103606/http://atkins.vo.llnwd.net/e1//AtkinsDotCom/media/Master/NewBook/bullet.png) 0% 6px no-repeat scroll transparent; border: 0px none; line-height: 17px; list-style-type: none; margin-left: 0px; padding-left: 11px;">Add back starchy vegetables followed by whole grains before higher-carb fruits (other than the berries and melon acceptable in OWL). </li>
<li style="background: url(http://web.archive.org/web/20140108103606/http://atkins.vo.llnwd.net/e1//AtkinsDotCom/media/Master/NewBook/bullet.png) 0% 6px no-repeat scroll transparent; border: 0px none; line-height: 17px; list-style-type: none; margin-left: 0px; padding-left: 11px;">Continue to focus on <a href="http://web.archive.org/web/20140111093319/http://www.atkins.com/Program/Phase-1/How-to-Do-Induction-Right/What-Are-Foundation-Vegetables-.aspx" style="color: #185197; text-decoration: none;"><strong>foundation vegetables</strong></a>.</li>
<li style="background: url(http://web.archive.org/web/20140108103606/http://atkins.vo.llnwd.net/e1//AtkinsDotCom/media/Master/NewBook/bullet.png) 0% 6px no-repeat scroll transparent; border: 0px none; line-height: 17px; list-style-type: none; margin-left: 0px; padding-left: 11px;">Regard starchy vegetables and whole grains (and legumes) as side dishes, rather than the mainstays of a meal.</li>
<li style="background: url(http://web.archive.org/web/20140108103606/http://atkins.vo.llnwd.net/e1//AtkinsDotCom/media/Master/NewBook/bullet.png) 0% 6px no-repeat scroll transparent; border: 0px none; line-height: 17px; list-style-type: none; margin-left: 0px; padding-left: 11px;">Avoid white rice, white flour and other refined grains.</li>
<li style="background: url(http://web.archive.org/web/20140108103606/http://atkins.vo.llnwd.net/e1//AtkinsDotCom/media/Master/NewBook/bullet.png) 0% 6px no-repeat scroll transparent; border: 0px none; line-height: 17px; list-style-type: none; margin-left: 0px; padding-left: 11px;">Use <a href="http://web.archive.org/web/20140111093319/http://www.atkins.com/Products/Cuisine/Penne.aspx" style="color: #185197; text-decoration: none;"><strong>Atkins Cuisine pasta</strong></a>, Shirataki pasta or whole-grain pasta instead of conventional pasta made from white flour.</li>
<li style="background: url(http://web.archive.org/web/20140108103606/http://atkins.vo.llnwd.net/e1//AtkinsDotCom/media/Master/NewBook/bullet.png) 0% 6px no-repeat scroll transparent; border: 0px none; line-height: 17px; list-style-type: none; margin-left: 0px; padding-left: 11px;">Bake with <a href="http://web.archive.org/web/20140111093319/http://www.atkins.com/Products/Cuisine/All-Purpose-Baking-Mix.aspx" style="color: #185197; text-decoration: none;"><strong>Atkins Cuisine All Purpose Bake Mix</strong></a> or soy flour instead of white flour.</li>
<li style="background: url(http://web.archive.org/web/20140108103606/http://atkins.vo.llnwd.net/e1//AtkinsDotCom/media/Master/NewBook/bullet.png) 0% 6px no-repeat scroll transparent; border: 0px none; line-height: 17px; list-style-type: none; margin-left: 0px; padding-left: 11px;">Continue to use <a href="http://web.archive.org/web/20140111093319/http://www.atkins.com/Program/Phase-2/What-You-Can-Eat-in-this-Phase/LOW-CARB-PRODUCTS-SUITABLE-FOR-OWL.aspx" style="color: #185197; text-decoration: none;"><strong>low-carb products</strong></a>suitable for Ongoing Weight Loss.</li>
</ul>
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<span style="color: #736d62; font-family: Arial;"><span style="font-size: 12px; line-height: 17px;">[ Reproduced without permission from now-defunct page on Atkins.com - links may not work ]</span></span></div>
PhilThttp://www.blogger.com/profile/17675583272641426292noreply@blogger.com0tag:blogger.com,1999:blog-6577126691263890775.post-57917075860178132572015-07-09T01:36:00.000-07:002015-07-09T01:36:17.069-07:00Atkins for Vegans<h2 class="program_mainContentSubTitle" style="background-color: #fffdf9; background-image: url(http://web.archive.org/web/20141002080413/http://atkins.vo.llnwd.net/e1//AtkinsDotCom/media/Master/programs/BGSubTitleProgram.gif); background-position: 13px 0px; background-repeat: no-repeat; color: #fba825; font-family: Arial; font-size: 20px; margin: -3px 18px 22px; overflow: hidden; padding-left: 27px; padding-top: 2px;">
Atkins for Vegans</h2>
<span style="background-color: #fffdf9; color: #736d62; font-family: Arial; font-size: 12px;">It’s challenging for vegans, who don’t eat eggs and dairy products, to do Atkins, but not impossible. The trick is to get sufficient protein from seeds, nuts, soy products, soy and rice cheeses, seitan, legumes and high-protein grains such as quinoa. Weight loss may proceed more slowly because of the higher carb intake than that of those following the standard Atkins program. Vegans should make the following modifications:</span><br />
<ol style="background-color: #fffdf9; color: #736d62; font-family: Arial; font-size: 12px; overflow: hidden;">
<li>Start in <a href="http://web.archive.org/web/20141216204344/http://www.atkins.com/Program/Phase-2/Objectives-of-OWL/Ongoing-Weight-Loss-for-Vegans.aspx" style="color: #185197; text-decoration: none;"><strong>Ongoing Weight Loss</strong></a> at 50 grams of Net Carbs so that you can have nuts, seeds and their butters, plus legumes, from the start.</li>
<li>If you don’t have much weight to lose, start in Pre-Maintenance at 60 grams of <a href="http://web.archive.org/web/20141216204344/http://www.atkins.com/Program/Overview/How-and-Why-Atkins-Work/What-Are-Net-Carbs-.aspx" style="color: #185197; text-decoration: none;"><strong>Net Carbs</strong></a>, in order to include small amounts of whole grains and starchy vegetables from the start.</li>
<li>Make sure you’re getting sufficient protein in plant sources.</li>
<li>In order not to interfere with fat metabolism, add extra flaxseed, olive, canola, walnut and other oils to salads and vegetables to make up for the smaller amount of fat in most of your protein sources.</li>
<li>Shakes made with plain unsweetened soymilk (or almond milk), soy (or hemp) protein, berries, and a little sweetener can make a tasty breakfast. Add some coconut milk to up the fat content and make the shake creamer.</li>
<li>You can also make shakes with silken tofu. Try it puréed with peanut or almond butter for added protein and fat.</li>
<li>Sauté silken tofu with onions and other vegetables to stand in for scrambled eggs.</li>
<li>Mayonnaise made with soy instead of eggs, mixed with crumbled tofu, chopped celery and onions, and a little curry powder makes a tasty eggless salad.</li>
<li>Silken tofu and soy creamer can be used in desserts, as can agar-agar in jellied desserts. </li>
</ol>
<br style="background-color: #fffdf9; color: #736d62; font-family: Arial; font-size: 12px;" /><br style="background-color: #fffdf9; color: #736d62; font-family: Arial; font-size: 12px;" /><span style="background-color: #fffdf9; color: #736d62; font-family: Arial; font-size: 12px;">When you move to Pre-Maintenance, follow the general guidelines for reintroduction and think of these foods, as well as legumes, as side dishes, rather than the mainstays of a meal. You may find that over time you can tolerate larger portions as long as you steer clear of refined grains and most processed foods. Add back starchy vegetables followed by whole grains before higher-carb fruits (other than the berries and melon acceptable in OWL). Continue to stay away from conventional pasta and other products made with white flour and other refined grains.</span><br />
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<span style="background-color: #fffdf9; color: #736d62; font-family: Arial; font-size: 12px;">[ Reproduced without permission from now defunct Atkins.com web page ]</span>PhilThttp://www.blogger.com/profile/17675583272641426292noreply@blogger.com0tag:blogger.com,1999:blog-6577126691263890775.post-59200578509671427452015-07-09T01:34:00.002-07:002015-07-09T01:34:57.578-07:00Atkins for Vegetarians<h1 class="program_mainContentTitle">
<span style="font-size: small;"><span style="font-weight: normal;">This is a copy of the content of a now-defunct page from the atkins.com web site, the links may not work but it is reproduced here for information.</span></span></h1>
<h1 class="program_mainContentTitle">
The Program: Ways to Create a Custom Diet Plan, With Atkins</h1>
<h2 class="program_mainContentSubTitle">
Atkins for Vegetarian</h2>
It’s perfectly possible to be a vegetarian—or simply minimize your intake of animal protein, add variety to your meals and trim your food budget—and still do Atkins. The typical American vegetarian often consumes far too many carbohydrates in the form of pasta and other refined grains. As long as you have at least two varieties of plant protein each day, you can get a balance of essential amino acids (the building blocks of protein). Which leads to the second challenge. Plant proteins are “packaged” with carbohydrate. Your objective is to consume enough protein without simultaneously getting so much carbohydrate that it interferes with weight loss or weight maintenance.<br />
<h3>
To adapt Atkins to your needs as a ovo-lacto vegetarian:</h3>
<ol>
<li>Start in Phase 2, <a href="http://web.archive.org/Program/Phase-2/Objectives-of-OWL/Ongoing-Weight-Loss-for-Vegans.aspx"><strong>Ongoing Weight Loss</strong></a> (OWL), at 30 grams of Net Carbs and introduce nuts and seeds and all unsweetened dairy products except milk (whether whole, skim, low fat, or no fat) and buttermilk before berries.</li>
<li>Or, if you have no more than 20 pounds to shed and are willing to swap slower weight loss for more food variety, you may start in Phase 3, Pre-Maintenance, at 50 grams of Net Carbs.</li>
<li>Make sure to get sufficient protein in eggs, cheese and soy products. Aim for no more than 6 grams of Net Carbs per serving of protein foods in OWL.</li>
<li>Meat substitutes may be made from textured vegetable protein (TVP), soy protein (tofu and tempeh), wheat gluten (seitan), and even fungi (Quorn) among other ingredients. See <a href="http://web.archive.org/Program/Phase-1/What-You-Can-Eat-in-this-Phase.aspx"><strong>Acceptable Induction Foods: Soy and Vegetarian Products</strong></a> for a more comprehensive list. Some of these products contain added sugars and starches and some are breaded, so read the list of ingredients carefully.</li>
<li>Get most of your carbs from foundation vegetables.</li>
<li>Most nonanimal protein sources (except for tofu and nut butters) are low in fat. Continue to get enough healthy oils in other dishes by dressing vegetables and salads with olive oil, canola oil, high-oleic safflower, walnut, flaxseed and other oils so as not to interfere with fat metabolism Also enjoy high-fat snacks such as half a Haas avocado or some olives.</li>
<li>Add back nuts and seeds before berries. Nuts and seeds contain fat and protein that will make Atkins easier to do and more effective.</li>
<li>Or, if you choose, add back legumes before other OWL-acceptable foods. But do so in extreme moderation (2-tablespoon servings), using them as garnishes on soups or salads.</li>
<li>Tempeh, made with fermented soybeans, is higher in protein than tofu and more flavorful. Sauté tempeh with veggies in a stir-fry, crumble it into chili, soup, or sauces or marinate and grill it. Avoid tempeh products that include rice or another grain until you’re in Pre-Maintenance.</li>
<li>Shakes made with plain unsweetened soymilk (or almond milk), soy (or hemp) protein, berries and a little sweetener can make a tasty breakfast.</li>
<li>Purée silken tofu with berries and other fruit in shakes, adding peanut or almond butter for added protein; or sauté firm tofu with vegetables for lunch or dinner.</li>
</ol>
<h3>
If you eat no eggs and dairy:</h3>
<ol>
<li>Substitute crumbled silken tofu for scrambled eggs—a pinch of turmeric provides an appealing yellow hue. For baking, use an egg substitute product.</li>
<li>Even some vegetarian products, such as Quorn, as well as shakes, may contain eggs or whey. Read labels carefully.</li>
<li>Mayonnaise made with soy instead of eggs, mixed with crumbled tofu, chopped celery and onions, and a little curry powder makes a tasty eggless salad.</li>
<li>Silken tofu and soy creamer can be used in desserts, as can agar-agar in jellied desserts.</li>
</ol>
For more ideas, see <a href="http://web.archive.org/Program/Overview/Ways-to-Customize-Atkins,-Your-Diet-Plan/Atkins-for-Vegans.aspx"><strong>Atkins for Vegans</strong>.</a><br />
<h3>
Pre-Maintenance and Beyond</h3>
Whole grains usually loom large for vegetarians, and starchy vegetables are often important components of meals. However, they’re among the very foods that may have gotten you in trouble in the past. Follow the general guidelines for reintroduction and think of these foods, as well as legumes, as side dishes, rather than the mainstays of a meal. You may find that over time you can tolerate larger portions as long as you steer clear of refined grains and most processed foods. Add back starchy vegetables, followed by whole grains, before higher-carb fruits (other than the berries and melon acceptable in OWL).PhilThttp://www.blogger.com/profile/17675583272641426292noreply@blogger.com0tag:blogger.com,1999:blog-6577126691263890775.post-60664281252271906202015-03-10T11:27:00.000-07:002015-03-10T11:27:05.631-07:00Book review may signal change is in the airA <a href="http://www.nationalobesityforum.org.uk/index.php/publications/707-book-review-big-fat-surprise-by-nina-teicholz.html" target="_blank">book review</a> may not be a policy statement, but it may reveal some of the thinking and views of the reviewers. The review in question is that of Nina Teicholz's journalistic tome "Big Fat Surprise: Why Butter, Meat & Cheese Belong in a Healthy Diet" which explores and exposes the background and evolution of current dietary guidelines. Nina herself <a href="http://www.thebigfatsurprise.com/my-cholesterol-test-for-nightline-vs-one-from-1998/" target="_blank">halved her blood triglycerides</a> by switching to a diet of "60% fat (plenty of it saturated), 25% protein and 15% carbohydrates" however it should be noted that she is an investigative writer rather than a research scientist or n=1 experimenter.<br />
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The authors of the review are Debbie Cook and David Haslam of the UK's "National Obesity Forum" - "Debbie Cook is the Vice Chair at National Obesity Forum, and a Nurse Practitioner and Clinical Nurse Manager" and Prof David Haslam is the Chair of the National Obesity Forum and the eminent <a href="http://www.nice.org.uk/about/who-we-are/board" target="_blank">non-executive Chair</a> of the UK's National Institute for Health and Care Excellence (NICE).<br />
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It seems likely, or at least possible, that the review will disappear in a storm of controversy, but some of the interesting extracts are :-<br />
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<blockquote class="tr_bq">
The UK is ready for the revelation that sugar is toxic, and that refined carbohydrates and fruit juice are detrimental to health, and has taken it fairly well. But the next big shock wave – that another macronutrient is an important, healthy and necessary part of the diet: namely saturated fat – may take some swallowing. </blockquote>
<blockquote class="tr_bq">
In a meticulously well researched book, we are informed and educated about why the modern world faces an epidemic of obesity, why generations of Americans religiously followed the nutritional dogma fed to them by researchers of questionable integrity.</blockquote>
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PhilThttp://www.blogger.com/profile/17675583272641426292noreply@blogger.com0