Tracking Diet Progress Quantitatively in Excel

Weight loss time targets (or, "how am I gonna fit into my wedding dress?!")

Setting a target weight is the first part of any diet/exercise regime. Usually this is done by looking at BMI charts for your height:

BMI = [body mass (kg)]/[height in m]^2
Where a normal BMI is 20-25 for Caucasians (upper limit is 23 for Asians -- check data for your individual ethnicity).

So if you have a BMI of 30, you might make it your goal to reach BMI 25 in one year. Or six months. And you work out the number of kilograms that corresponds to, and maybe you figure out your average weekly weight loss target.
But inevitably you have to concede that maximal healthy weight loss is about 1-1.5kg/week, which limits how quickly you can fit into that wedding dress.

Most people tend to leave it there: a weight target and a vague time-based goal to reach that target. But how do you know if you're on track to meet your goal? And if your weight loss stagnates/plateaus (as it can do, for a multitude of reasons), how do you know whether you're still going to meet your long-term goal?

Mathematically-inclined people won't learn much from this post and can probably stop reading here.

But if you're not very good at Excel and want to lose weight efficiently, then I hope this post helps you out :)

The Basics

Dieting is a pretty simple idea that gets marketed to the confused masses in a variety of exciting way, to create a multibillion $/year industry containing a lot of quackery.

Ultimately it's an accounting problem. Since most people understand businesses/budgets vaguely, let's try this analogy:

• Energy (calorie) intake = Sales injecting revenue into company
• Energy expenditure = metabolic + exercise = Costs of company
• Fat on patient = Balance sheet

If your target is to shrink the balance sheet (here is probably where the analogy breaks down ;) ), you are just aim to persistently have a calorie deficit. Energy expenditure > Energy intake. That's all there is to it.

On the intake end: Eat fewer calories.

On the expenditure end: Eat foods that are less efficient to metabolise, and Exercise more.

So wait, what about Atkins?

In a previous post, I talk about the Atkins diet (in *mostly* positive terms) as a means to lose weight. If you're considering a fad diet, I recommend reading that post.

The Atkins diet doesn't explicitly stipulate a calorie limit. This is part of the reason it's a good diet for me - I'm bad at self control when it comes to eating, and with work commitments I don't get to do heaps of exercise.

Basically, the functions of Atkins are to 1) decrease caloric intake (due to protein-mediated appetite suppression) and 2) decrease metabolic efficiency (because you generate most of your blood glucose through gluconeogenesis, which is endothermic).

While this diet is convenient, it's not nutritionally complete. I recommend a multivitamin and fibre supplements.

Step 1: Collect some data

You need to start a weekly calorie deficit appropriate to the amount of fat you want to lose each week.

One kg of adipose tissue (body fat tissue) contains about 7000-7500 Calories. If you want to lose 1kg per week, you need to cut at least 1000 dietary Calories per day (=1000 kcal in SI units).

Set up your dietary and exercise regime. I'm not very inventive so I do the same thing every day to avoid hassles.

Now carry on with your regime for a month. In Excel, write the date in one column (A) and your weight in kg in the another column (B). On the dates column, select the entire column by right clicking on A and clicking "Format cells". Then select "Date" as the data type. Note that dates in Excel are actually stored as numbers (a number of days since some day in the 70's when Excel was invented) but can display as dates in the usual format. We use this property later.

Add a third column for BMI, and enter the formula "=B2/(1.x^2)" where you replace 1.x with your height in metres. (B2 assumes the first row is column titles eg Date, Weight etc). Put this formula in C2. Click on C2 and then click the bottom right corner, and drag it down the column to extrapolate the formula for all future values.

Now we have 4 records of date/weight/BMI (one for each week you've entered).

If you're interested, you can add a "weekly weight loss" column in column F (say), where the formula in F3 is "=(B2-B3)/(A2-A3)/7"

Step 2: What will be my weight in the future?

Select the date and weight columns, and go Insert>Chart. Follow the prompts to make a line graph. Then right click on the chart and go "Add trend line" and select linear. This is your approximate weight trajectory. A formula should pop up (y= a*x + b), which you can copy paste into a new column D (cell D2), replacing x with the value A2. Again, click and drag this formula down the rows and you will get an approximate weight for every date in the column A. If you drag beyond your entered data, you can add in notional future dates and it will work out your future weights for you.

Example of weight vs time chart made in Excel, a line of best fit, and the generated formula

If you like, you can repeat the above process to make a BMI column, as well.

Step 3: When will I reach my target?

Now for the interesting part, we work out the date at which you reach a target kg (or BMI).

Just calculate the inverse of your kg function by swapping x and y in the formula, then solve for x. Or, just the Wolfram Alpha tool.

Then create a column with target weights you're interested in, and another column that computes the date for these weights next to it, using the formula you found.

Fairly easy to do the same thing for BMI, too!

Can I copy your Excel sheet?

Sure, I'll upload a user-friendly template later. Or comment below and I'll send it to you.

External links

• Diet, Issues, & Weight Management
• Physiology of diet
• Diet-related health problems

References

• http://www.mayoclinic.org/healthy-lifestyle/weight-loss/in-depth/weight-loss-plateau/art-20044615
• http://www.mayoclinic.org/healthy-lifestyle/weight-loss/in-depth/calories/art-20048065

Medical reading list (non-study)

This post is to collect and collaborate on a list of interesting medical reading (but not textbooks/study). Comment your favourite books below!

Front page of the New Yorker in 1969, drawn by Saul Steinberg, and used as the cover of Dennett's Sweet Dreams: Philosophical obstacles to a science of consciousness. The thought bubble represents the stream of consciousness

Neuroscience

• Oliver Sacks - case histories, interesting anecdotes (Migraine, Awakenings, A Leg to Stand On, The Man Who Mistook His Wife For a Hat, Seeing Voices, An Anthropologist on Mars, The Island of the Colorblind, Uncle Tungsten, Musicophilia, The Mind's Eye, Hallucinations)
• A. R. Luria - Higher Cortical Functions in Man
• S. Freud - Dream psychology
• David Marr - Vision
• D. Hofstadter - Godel Escher Bach
• N. Doidge - The mind that changes itself
• Kandel - Principles of neural science
• En Megrim
• Blumenfeld - Neuroanatomy through clinical cases
• (Springer Series in Computational Neuroscience) Antonio Di Ieva-The Fractal Geometry of the Brain
• D Dennett - Consciousness explained
• Daniel Kahnemann - Thinking fast and slow

Philosophy

• S. Freud - Civilisation and its discontents
• A. Verghese - When breath becomes air"
• M. Aurelius - Meditations

Surgery

• Bailey & Love - Physical signs in clinical surgery

The h-index in monetised academia

Academics live and die by their h-indices, which contributes to promotions, grant applications and professional reputation.
The key real-world value producing this derived metric is the citation count, which is highly sensitive to which journal you publish into. For example, equivalent articles in PloS One (free) and Nature (pay wall for both author and audience) would be expected receive vastly different citation counts.
Therefore the h-index is significantly dependent on whether you publish for free or via paywall, not just the scientific content of the paper.

h-index definition (from Wikipedia)

• The definition of the index is that a scholar with an index of h has published h papers each of which has been cited in other papers at least h times.
• This metric would be fine if it weren't true that citation count is confounded by publication destination.
• We could of course do something like plotting # citations vs impact factor of citing article. But this gives no value to the number of articles published by the proband (academic age)

Proposed impactdex

We introduce a "trickle down" effect of impact into the author's "impactdex", or modified h-index

Two different methods come immediately to mind

1. Distort the graph by a factor proportional to r=(impact factor of citing paper)/(impact factor of proband's paper), to shove points toward the f(i)=i diagonal (allowing bigger h)
2. Define the index by a maximal cube with third dimension being r=(impact factor of citing paper)/(impact factor of proband's paper)

The second approach is clearly more aesthetically pleasing, however the implicit meaning of the method is that all three variables (papers, citations, impact of citing work) are independent, while our motivation arises from the observation that this is false.

Method 1

We want to penalise (away from the diagonal) points with r<1, and reward points with r>1.

The ideal 

Define the impactdex such that:

• Map each point (i, f(i)) with gradient f(i)/i, to a point translated point based on r
• Gradient m from origin to translated point:
• If f(i)/i >1: m = f(i)/i-(1-1/r)*(f(i)/i-1) = 1+1/r*(f(i)/i)
• If f(i)/i <1: m = 1-1/r*(f(i)/i)
• Then our corrected index is given by the point whose distance is √[f(i)^2+i^2] from the origin at a gradient of m
• Translated point = (√[f(i)^2+i^2] cos (arctan(m)) , √[f(i)^2+i^2] sin (arctan(m))
• Then the definition of the index is that a scholar with a corrected index of i has published i "corrected papers"
• I.e. it is the h-index of the "corrected scholar"

Method 2

Define the impactdex such that:

• The definition of the index is that a scholar with an index of i has published i papers each of which has been cited in other papers at least i times with a calculated r of at least i
• Where r=(impact factor of citing paper)/(impact factor of proband's paper)
• We seek the maximal cube where one face fills in the h-index criteria like before and the other face fills in the derived r (axis representing David vs. Goliath)

Pitfalls

• Clunky definition, especially method 1
• Increased computation time
• Harder for Google Scholar etc to calculate it, when all it stores natively is citation count (not sure if it has impact factors now)
• People who publish in Nature etc can only do worse with this approach
• Axes in method 2 are not independent

Comments?

What do you think? I'm particularly looking for:

• Someone who could calculate these on a few interesting authors and compare to their h index
• If there's an error in the above, please let me know!

Atkins Diet (AKA: Paleo, Keto): Fad or Fact?

I.             Introduction

Type 2 diabetes mellitus (T2DM) is a highly lifestyle-dependent metabolic disease. Its global prevalence is predicted to reach 300 million by 2025, increasing concomitant with obesity and the global adoption of a sedentary lifestyle. This has worrying public health and economic implications (Hussain, Claussen, Ramachandran, & Williams, 2007).

Atkins’ Nutritional Approach (ANA) is a low-carbohydrate ketogenic diet (LCKD) for weight loss. Protein and fat make up most of the calories in ANA (Atkins, 2002) (see Appendix A).

This report describes T2DM, its conventional treatment, and discusses treatment using ANA. The author describes his experiences of ANA, and records progress statistics (body mass index (BMI) and waist circumference; see supporting document). The document concludes with statements of reflection on the diet and on the author’s learning experience.

II.          Type 2 Diabetes Mellitus (T2DM)

A.            Signs and symptoms

T2DM is characterised by hyperglycaemia, as diagnosed by elevated fasting plasma glucose (FPG) and oral glucose tolerance testing (OGTT). Glycosylated haemoglobin (HbA_1C) tests are also used (American Diabetes Association, 2011).

Symptoms of hyperglycaemia include polyuria, thirst, blurred vision, weight loss, dry skin and fatigue. If untreated, symptoms include ketoacidosis, coma and death (World Health Organisation, 1998).

T2DM complications produce major signs and symptoms (Figure 1).

B.            Pathogenesis

Hyperglycaemia is associated with insulin resistance, where body tissues produce a weaker-than-normal response to a given amount of insulin. This is accelerated by increased adiposity, causing increased insulin secretion from pancreatic beta cells, and poor glycaemic control. Long term, this can cause beta cell exhaustion, and relative insulin deficiency compared to demand (Kumar, Abbas, Fausto, & Aster, 2010).

A.            Risk factors

A primary risk factor for T2DM is obesity (BMI >30; vide supra). Genetic factors and family history also contribute, but the current epidemic is mostly caused by excessive energy intake versus expenditure (Sladek et al., 2007).

Other risk factors include age, hypertension, low HDL and high triglyceride concentrations, and polycystic ovarian syndrome or gestational diabetes (National Centre for Biotechnology Information, 2011).

B.            Epidemiology

T2DM contributes up to 95% of all diabetes mellitus (DM) cases. Between 2000 and 2010 the Australian DM prevalence increased 33% (1.0 to 1.3 million) (Zimmet, Alberti, & Shaw, 2001).

Aboriginal and Torres Strait Islander, Polynesian, Micronesian, Indian and Chinese are at increased risk. In developed countries, T2DM is more common in lower socioeconomic groups. Education is implicated in this discrepancy. The reverse is true for developing countries (Shaw & Chisholm, 2003).

C.            Complications

Hyperglycaemia causes T2DM complications, including nephropathy/renal failure, neuropathy, foot ulcers, neuropathic arthropathy, retinopathy, blindness and hypertension (Figure 1) (National Centre for Biotechnology Information, 2011). Cardiovascular disease (CVD) and stroke incidences are also increased (World Health Organisation, 1998).

  

D.            Prognosis

Complications are common in unmanaged diabetics. The mortality rate of DM patients is three times that of other Australians, and T2DM is a leading cause of death in Australia (Australian Institute of Health and Welfare, 2009; Australian Bureau of Statistics, 2006). Diabetics have poor quality of life (Figure 2).

E.             Treatment

Diet and exercise are important T2DM treatments, reducing incidence, complications and mortality (Pate et al., 1995).

Pharmaceuticals are available (e.g. metformin, ascarbose and exogenous insulin) and most effective when combined with lifestyle changes (American Diabetes Association, 2010).

Care also involves counselling and teaching the patient to monitor blood glucose, administer insulin, and maintain healthy weight (Figure 3).

Figure 3: Management of T2DM (Ripsin, Kang, & Urban, 2009)

III.       Atkins’ Nutritional Approach

A.            Mechanism

The principle of ANA (see Appendix A) is that limiting dietary carbohydrates induces ketosis, similar to prolonged starvation (Figure 4).

Limited dietary carbohydrate lowers blood glucose, stimulating glucagon and suppressing insulin secretion. This activates glycogenolysis in liver and skeletal muscle, maintaining blood glucose until glycogen stores deplete. Suppressed insulin secretion inhibits triacylglycerol (fat) deposition and stimulates lipolysis. Since animals cannot synthesise glucose from fatty acids, ketone bodies become the primary blood “transport molecule” for acetyl groups from lipolysis. This state is called ketosis. Gluconeogenesis synthesises glucose from glycerol/lactate/amino acids in the liver. This glucose complements ketones in fuelling tissues (e.g. brain) that cannot oxidise fat, and maintains blood glucose upon glycogen depletion. Erythrocytes depend completely upon glucose (Westman, Mavropoulos, Yancy, & Volek, 2003).

Insulin and blood glucose “spikes” are associated with high-carbohydrate meals, not high-fat/protein meals, so LCKDs may improve glycaemic control (Hall, 2011). In hyperglycaemia, glucose reacts with endogenous proteins, impairing their function. This glycosylation is implicated in the pathogenesis of T2DM complications, and may be reduced by lowered blood glucose in LCKDs (Kumar et al., 2010).

Ketosis involves lipolysis for energy production, and gluconeogenesis, an inefficient endothermic process, to maintain blood glucose (Hall, 2011). These processes accelerate weight loss, important in T2DM treatment. The inefficiency of gluconeogenesis can allow weight loss despite excess caloric intake. Patients don’t feel “constantly hungry” since calories aren’t restricted. Unlike starvation, loss of muscle tissue is avoided by dietary protein and fat intake. Ketones suppress appetite, so ANA also improves satiety (Westman et al., 2003).

B.            Discussion and effectiveness

Weight loss improves the prognoses of T2DM patients, mainly through reducing insulin resistance, but which diet to recommend is controversial (Davis, Forbes, & Wylie-Rosett, 2009).

Conventional low glycaemic index dietary treatments for T2DM comprise whole grains, fruit and vegetables, complemented with omega-3 fatty acids and unsaturated fats (Hu & Willett, 2002; Mann, 2002).

ANA involves unrestricted fat and protein intake, and small amounts of vegetables, fruits and whole grains once healthy weight is achieved (see Figures 4 & 5) (Atkins, 2002).

Figure 6: A "food pyramid" summarising scientifically accepted nutritional guidelines (Harvard University School of Public Health, 2011)

American Diabetes Association (2007) does not recommend LCKDs in T2DM management. However, LCKDs are effective in weight loss and glycaemic control, increasing insulin sensitivity and improving lipid profiles (HDL/LDL ratios increased, decreased total cholesterol and triglyceride) for up to two years (Shai et al., 2008; A. J. Nordmann et al., 2006). Yancy, Foy, Chalecki, Vernon, & Westman (2005) showed that a 16-week LCKD significantly decreased glycosylated haemoglobin and fasting blood glucose in T2DM patients (Figures 6 & 7).

Limited studies exist on long-term safety of ANA. Ketogenic diets used to treat epilepsy reveal increased kidney disease rates (Kossoff et al., 2002). Brenner, Meyer, & Hostetter (1982) implicated increased protein intake in nephropathy. However, ANA may alleviate some nephropathy by treating T2DM.

High fat and protein intake may cause CVD (particularly atherosclerosis) long-term (Kavey et al., 2003). One systematic review of LCKDs revealed no increase in CVD risk factors (HDL/LDL) over one year (Dena M. Bravata et al., 2003). Other studies showed improvement (Shai et al., 2008; A. J. Nordmann et al., 2006). In contrast, overweight/obesity and CVD risk correlate strongly (Figure 8). Longer-term studies are required.

Figure 9: Population attributable risk percentage effects for overweight and obesity on CVD risk factors and events in Framingham men and women followed up for 44 years (Wilson, D’Agostino, Sullivan, Parise, & Kannel, 2002)

Despite unknown harms, physicians and patients may try ANA because of rapid (and motivating) weight loss with increased satiety and unrestricted caloric intake, and optimistic short-term trials.

Caution should be exercised. Previous diabetes treatments (e.g. Avandia) were associated with increased risk of CVD events once on the market, although, like ANA, short-term trials were optimistic (Wooltorton, 2002). LCKDs can lower blood glucose, so patients require supervision to prevent hypoglycaemia (Westman, et al., 2003).

IV.             Conclusion

ANA is a controversial dietary therapy that may be used to effectively treat T2DM and its complications. However, its potential risks do not warrant its use over current conservative therapies.

Lifestyle interventions for T2DM require ongoing care and support. The dieting process is physically and emotionally draining, and information/support services would likely improve patient outcomes.

V.          References

American Diabetes Association. (2011). Diagnosis and Classification of Diabetes Mellitus. Diabetes Care, 34(Supplement 1), S70-S74.

American Diabetes, A. (2007). Nutrition Recommendations and Interventions for Diabetes. Diabetes Care., 30.

Atkins, R. C. (2002). Dr. Atkinsʼ new diet revolution. New York: M. Evans and Company, Inc.

Atkins Nutritionals. (2011). Atkins - Sweet. Sexy. Science. Retrieved July 31, 2011, from http://au.atkins.com/

Australian Bureau of Statistics. (2006). Diabetes in Australia: A Snapshot, 2004-05. Retrieved from http://www.abs.gov.au/ausstats/abs@.nsf/mf/4820.0.55.001#1.%20The%20AIHW%20National%20Diabetes%20Re

Australian Institute of Health and Welfare. (2009). Insulin-treated diabetes in Australia 2000-2007. Retrieved from http://www.aihw.gov.au/publication-detail/?id=6442468275

Berg, J. M., Tymoczko, J. L., & Stryer, L. (2002). Biochemistry. Retrieved from http://www.ncbi.nlm.nih.gov/books/NBK21154/

Bravata, Dena M., Sanders, L., Huang, J., Krumholz, H. M., Olkin, I., Gardner, C. D., & Bravata, Dawn M. (2003). Efficacy and Safety of Low-Carbohydrate Diets. JAMA: The Journal of the American Medical Association, 289(14), 1837 -1850. doi:10.1001/jama.289.14.1837

Davis, N., Forbes, B., & Wylie-Rosett, J. (2009). Nutritional strategies in type 2 diabetes mellitus. The Mount Sinai Journal of Medicine, New York, 76(3), 257-268. doi:10.1002/msj.20118

Garrouste-Orgeas, M., Troché, G., Azoulay, E., Caubel, A., de Lassence, A., Cheval, C., Montesino, L., et al. (2004). Body mass index. Intensive Care Medicine, 30(3), 437-443. doi:10.1007/s00134-003-2095-2

Hall, J. E. (2011). Guyton and Hall: Textbook of Medical Physiology (12th ed.).

Harris, S. (1924). Hyperinsulinism and dysinsulinism. Journal of the American Medical Association, 83(10), 729 -733. doi:10.1001/jama.1924.02660100003002

Harvard University School of Public Health. (2011). Food Pyramids: What should you really eat? Retrieved July 31, 2011, from http://www.hsph.harvard.edu/nutritionsource/what-should-you-eat/pyramid-full-story/index.html

Hu, F. B., & Willett, W. C. (2002). Optimal diets for prevention of coronary heart disease. JAMA: The Journal of the American Medical Association, 288(20), 2569-2578.

Hussain, A., Claussen, B., Ramachandran, A., & Williams, R. (2007). Prevention of type 2 diabetes: a review. Diabetes Research & Clinical Practice, 76(3), 317-26.

Johnstone, A. M., Horgan, G. W., Murison, S. D., Bremner, D. M., & Lobley, G. E. (2008). Effects of a high-protein ketogenic diet on hunger, appetite, and weight loss in obese men feeding ad libitum. The American Journal of Clinical Nutrition, 87(1), 44-55.

Katyal, N. G., Koehler, A. N., McGhee, B., Foley, C. M., & Crumrine, P. K. (2000). The Ketogenic Diet in Refractory Epilepsy: The Experience of Childrenʼs Hospital of Pittsburgh. Clinical Pediatrics, 39(3), 153 -159. doi:10.1177/000992280003900303

Kavey, R.-E. W., Daniels, S. R., Lauer, R. M., Atkins, D. L., Hayman, L. L., & Taubert, K. (2003). American Heart Association Guidelines for Primary Prevention of Atherosclerotic Cardiovascular Disease Beginning in Childhood. Circulation, 107(11), 1562 -1566. doi:10.1161/01.CIR.0000061521.15730.6E

Kekwick, A., & Pawan, G. L. S. (1956). Calorie intake in relation to body-weight changes in the obese. Lancet, 271(6935), 155-161.

Kekwick, A., & Pawan, G. L. S. (1964). The effect of high fat and high carbohydrate diets on rates of weight loss in mice. Metabolism, 13(1), 87-97. doi:16/S0026-0495(64)80014-7

Kossoff, E. H., Pyzik, P. L., Furth, S. L., Hladky, H. D., Freeman, J. M., & Vining, E. P. G. (2002). Kidney Stones, Carbonic Anhydrase Inhibitors, and the Ketogenic Diet. Epilepsia, 43(10), 1168–1171. doi:10.1046/j.1528-1157.2002.11302.x

Kumar, V., Abbas, A. K., Fausto, N., & Aster, J. C. (2010). Robbins and Cotran Pathologic Basis of Disease (8th ed.). Philadelphia: Saunders Elsevier.

Mann, J. (2002). Diet and risk of coronary heart disease and type 2 diabetes. The Lancet, 360(9335), 783-789. doi:16/S0140-6736(02)09901-4

Meyer, K. A., Kushi, L. H., Jacobs, D. R., Slavin, J., Sellers, T. A., & Folsom, A. R. (2000). Carbohydrates, dietary fiber, and incident type 2 diabetes in older women. The American Journal of Clinical Nutrition, 71(4), 921 -930.

National Centre for Biotechnology Information. (2011). Type 2 diabetes. Text, . Retrieved July 31, 2011, from http://www.ncbi.nlm.nih.gov/pubmedhealth/PMH0001356/

Nordmann, A. J., Nordmann, A., Briel, M., Keller, U., Yancy, W. S., Brehm, B. J., & Bucher, H. C. (2006). Effects of Low-Carbohydrate vs Low-Fat Diets on Weight Loss and Cardiovascular Risk Factors: A Meta-analysis of Randomized Controlled Trials. Arch Intern Med, 166(3), 285-293. doi:10.1001/archinte.166.3.285

Pate, R. R., Pratt, M., Blair, S. N., Haskell, W. L., Macera, C. A., Bouchard, C., Buchner, D., et al. (1995). Physical Activity and Public Health. JAMA: The Journal of the American Medical Association, 273(5), 402 -407. doi:10.1001/jama.1995.03520290054029

Wooltorton, E. (2002). Rosiglitazone (Avandia) and pioglitazone (Actos) and heart failure. Canadian Medical Association Journal, 166(2), 219.

World Health Organisation. (1998). Definition, diagnosis and classification of diabetes mellitus and its complications. Diagnosis and classification of diabetes mellitus, 99(2). Retrieved from http://www.staff.ncl.ac.uk/philip.home/who_dmg.pdf

Ripsin, C. M., Kang, H., & Urban, R. J. (2009). Management of blood glucose in type 2 diabetes mellitus. American Family Physician, 79(1), 29-36.

Ritz, E., & Orth, S. R. (1999). Nephropathy in Patients with Type 2 Diabetes Mellitus. New England Journal of Medicine, 341(15), 1127-1133. doi:doi: 10.1056/NEJM199910073411506

Samaha, F. F., Iqbal, N., Seshadri, P., Chicano, K. L., Daily, D. A., McGrory, J., Williams, T., et al. (2003). A Low-Carbohydrate as Compared with a Low-Fat Diet in Severe Obesity. New England Journal of Medicine, 348(21), 2074-2081. doi:10.1056/NEJMoa022637

Sampath, A., Kossoff, E. H., Furth, S. L., Pyzik, P. L., & Vining, E. P. G. (2007). Kidney Stones and the Ketogenic Diet: Risk Factors and Prevention. Journal of Child Neurology, 22(4), 375 -378. doi:10.1177/0883073807301926

Shaw, J. E., & Chisholm, D. J. (2003). 1: Epidemiology and Prevention of Type 2 Diabetes and the Metabolic Syndrome. The Medical Journal of Australia, 179(7), 379-383.

Sladek, R., Rocheleau, G., Rung, J., Dina, C., Shen, L., Serre, D., Boutin, P., et al. (2007). A genome-wide association study identifies novel risk loci for type 2 diabetes. Nature, 445(7130), 881-885.

Westman, E. C., Mavropoulos, J., Yancy, W. S., & Volek, J. S. (2003). A review of low-carbohydrate ketogenic diets. Current Atherosclerosis Reports, 5(6), 476-483. doi:10.1007/s11883-003-0038-6

Wilson, P. W. F., DʼAgostino, R. B., Sullivan, L., Parise, H., & Kannel, W. B. (2002). Overweight and Obesity as Determinants of Cardiovascular Risk: The Framingham Experience. Arch Intern Med, 162(16), 1867-1872. doi:10.1001/archinte.162.16.1867

Yancy, W. S., Foy, M., Chalecki, A. M., Vernon, M. C., & Westman, E. C. (2005). A low-carbohydrate, ketogenic diet to treat type 2 diabetes. Nutr Metab, 2(34), 1743-1775.

Zimmet, P., Alberti, K., & Shaw, J. (2001). Global and societal implications of the diabetes epidemic. Nature, 414(6865), 782-787.

VI.       Appendix A: Summary of Atkins’ Nutritional Approach

Below is a summary of the phases of ANA, as described in Atkins (2002). Note: 1) that the author’s diet was that described in “Phase 1”, since this represents the beginning of the programme and 2) that Atkins (2002) encourages regular exercise and vitamin/mineral supplements to complement the ANA

Phase 1 (“Induction”; at least two weeks – length depends on personal preference):

-       Less than 20g of daily carbohydrate, chosen from vegetables and green salad (e.g. lettuce, small amounts of broccoli; Atkins (2002) provides a list of allowed vegetables).

-       Liberal fat and protein intake (e.g. fish, red meat, chicken, eggs). Cheeses are also allowed.

-       Recommended drink is water. Caffeine should be avoided but is allowed.

-       Alcoholic beverages are prohibited.

-       Some artificial sweeteners are allowed: sucralose is preferred.

-       Avocadoes, olives, sour cream and full cream (unsweetened) are allowed in moderation, though may curtail weight loss

Phase 2 (“Ongoing Weight Loss”; begins once approaching a comfortable weight – designed to slow weight loss to allow more food options):

-       Add 5g of daily carbohydrate per week until no longer losing weight. The daily carbohydrate intake at this point is the “Critical Carbohydrate Level for Losing” (CCLL)

-       A list (“The Carbohydrate Ladder”) of carbohydrate-containing foods in the order in which Dr Atkins advises they be introduced into the diet is provided in Atkins (2002). The list is in order of increasing carbohydrate content

-        

Phase 3 (“Pre-Maintenance”; begins once within 5-10 pounds (~0.5-1.0 kg) of desired weight):

-       Add 10g of daily carbohydrate per week until weight gain begins, then cut back 10g. This is the “Critical Carbohydrate Level for Maintenance” (CCLM)

-       Any added food that promotes weight gain/cravings should be avoided

Phase 4 (“Life Maintenance”; begins once desired weight is reached and CCLM is achieved):

-       Continue eating at the CCLM, but try a variety of foods (in moderation) from the allowed list in Atkins (2002). (Sugar is never allowed).

-       If weekly weigh-ins reveal that weight is more than 5 pounds (~0.5 kg) above the desired weight, Phase 1 is re-entered, followed by return to Phase 4 once desired weight is returned.