This page summarizes markers and tests you can use to track your progress on a low carb/ketogenic diet. Reference ranges given refer to optimal ranges, as found in studies, instead of the population averages generally given by labs.

There is also a Google Sheet template you can use to track results over time. It includes some reference and calculated markers. All input is welcome, as the research changes over time.

Blood lipids

The most common and commonly misunderstood markers available. You should test those before changing your diet to have a base line.

Total Cholesterol (TC)

Cholesterol is essential to the structure of animal cells and the precursor of all other steroids in the body, like bile acid or vitamin D. The body can synthesize cholesterol in most tissue. Part is also absorbed from animal foods.[1, 2]

Being insoluble in water, cholesterol is transported by lipoproteins.[1-p267] There are different kinds of lipoproteins who fulfill different logistical purposes. TC measures the cholesterol contained in all those lipoproteins combined.

For women and older (>40) men a higher TC is associated with lower mortality from cancer and infectious disease.[2] By itself the number is still not very meaningful and should be looked at in concert with TG, HDL-C and remnant cholesterol.

Triglycerides, Triacylglycerols (TG): < 100 mg/dl (1.1 mmol/l)

TG are the body's main mechanism to store fatty acids for later use.[3] Increased fasting TGs in your blood are a strong predictor of heart disease.[4, 5] In contrast to LDL-C they are strongly determined by lifestyle and diet.[6]

Low carb diets consistently reduce TGs[7, 8] and it's no uncommong to see TGs in the 50s or 60s. Anything less than 100 mg/dl will be on the safe side.[9] This marker is best looked at in combination with HDL-C (see below).

LDL-C

Measures the amount of cholesterol contained in low density lipoproteins (LDL). This number is often calculated from other numbers on a standard lipid panel using either the Friedwald equation or Iranian equation.[SOURCE]

There are different sizes of LDL particles and knowing the composition is more meaningful than knowing the total.

HDL-C: > 60 mg/dl (1.5 mmol/l)

As opposed to LDL, high density lipoprotein (HDL) collects collesterol and returns it to the liver. High HDL is associated with decreased atherosclerosis[1], longevity and mental health.[13] A level of > 60 mg/dl seems desirable.

TG/HDL ratio: < 1.3

A proxy of LDL particle size and good predictor for glucose tolerance[12], heart disease mortality and diabetes.[10] A ratio of < 1.3 means you likely have large LDL particles.[11]

Remnant Cholesterol: < 15 mg/dl (0.4 mmol/l)

RC is closely related to increased TG. They comprise small, leftover lipoproteins that are small enough to get stuck in arterial walls. They are calculated by subracting LDL and HDL from TC.[14] Increased RC also predicted diabetes.[15]

Atherogenic Index of Plasma (AIP): < 0

A slight differentiation from the previous TG/HDL ratio. Calculated as Log(TG/HDL-C). Also correlated well with lipoprotein particle size.[16] In another study subjects with pre-diabetes had a higher AIP and higher CIMT score (see below for this marker).[17]

Diabetes-related Markers

Fasting Glucose

The body regulates glucose within a very narrow range and increase levels are detrimental to many cells and organs.

Glucose levels vary widely throughout the day. Different foods can have a very different response in different people. Some big influencers are your microbiome[24], time of the day, meal timing and others.

A low fasting glucose level is likewise a bad indicator for the absence of insuling resistance (IR). More important is the body's reaction to glucose intake over time. In subjects with IR, blood glucose will go much higher after a meal and come down more slowly. Dr. Kraft's Oral Glucose Tolerance Test is a better way to assess insulin sensitivity.[25]

Fasting Insuline

HBA1c: <5%

Measures the percent of red blood cells who are glycenated. Since red blood cells stay around for about 90 days, this test is an approximation of the previous three month's glucose levels.

Oral Glucose Tolerance Test

HOMA-IR

Waist/Height Ratio: <0.5

Probably the cheapest marker. A large meta-study found it to be a better predictor of cardiometabolic risk factors, than e.g. BMI.[22] The accepted cut-off value for increased risk is 0.5.[23]

Liver-related Markers

ALT <25 and GGT <15 iU/l

Elevated liver enzyme – at the upper end of the normal range – is associated with non-alcoholic fatty liver disease (NAFLD).[20] Elevated ALT is also associated with increased insuline-resistance.

Uric Acid

Heart-related Markers

Coronary Artery Calcium (CAC) Score: =0

A test popularized by Ivor Cummins. Measures calcium deposits in coronary arteries.

Carotid Intima-Media Thickness (CIMT): <0.8mm

The thickness of the upper layer in your neck's main blood vessel.[needs source]

Vitamins and Micronutrients

Vitamin D (25OHD): 30 to 50 ng/mL[19]

Vitamin D protects agains a wide range of disease, from cancer to stroke. Cultures in the far north seem to get most Vitamin D from the diet, while those near the equator get it from the sun. Anyone in the middle or mostly indoors is often deficient.[18]

References:

• 1: Rodwell, V. W., Bender, D. A., Botham, K. M., Kennelly, P. J., & Weil, P. A. (n.d.). Harper’s illustrated biochemistry.
• 2: Okuyama, H., Hamazaki, T., Hama, R., Ogushi, Y., Kobayashi, T., Ohara, N., & Uchino, H. (2018). A Critical Review of the Consensus Statement from the European Atherosclerosis Society Consensus Panel 2017. Pharmacology, 101(3–4), 184–218. https://doi.org/10.1159/000486374
• 3: Gurr, M. I. (Michael I., Harwood, J. L., & Frayn, K. N. (Keith N. . (2002). Lipid biochemistry. Blackwell Science.
• 4: Hokanson, J. E., & Austin, M. A. (1996). Plasma Triglyceride Level is a Risk Factor for Cardiovascular Disease Independent of High-Density Lipoprotein Cholesterol Level: A Metaanalysis of Population-Based Prospective Studies. European Journal of Cardiovascular Prevention & Rehabilitation, 3(2), 213–219. https://doi.org/10.1177/174182679600300214
• 5: Manninen, V., Tenkanen, L., Koskinen, P., Huttunen, J. K., Mänttäri, M., Heinonen, O. P., & Frick, M. H. (1992). Joint effects of serum triglyceride and LDL cholesterol and HDL cholesterol concentrations on coronary heart disease risk in the Helsinki Heart Study. Implications for treatment. Circulation, 85(1), 37–45. https://doi.org/10.1161/01.CIR.85.1.37
• 6: de Vries, J. K., Balder, J. W., Pena, M. J., Denig, P., & Smit, A. J. (2018). Non-LDL dyslipidemia is prevalent in the young and determined by lifestyle factors and age: The LifeLines cohort. Atherosclerosis, 274, 191–198. https://doi.org/10.1016/j.atherosclerosis.2018.05.016
• 7: Hu, T., & Bazzano, L. A. (2014). The low-carbohydrate diet and cardiovascular risk factors: evidence from epidemiologic studies. Nutrition, Metabolism, and Cardiovascular Diseases : NMCD, 24(4), 337–343. https://doi.org/10.1016/j.numecd.2013.12.008
• 8: Hallberg, S. J., McKenzie, A. L., Williams, P. T., Bhanpuri, N. H., Peters, A. L., Campbell, W. W., … Volek, J. S. (2018). Effectiveness and Safety of a Novel Care Model for the Management of Type 2 Diabetes at 1 Year: An Open-Label, Non-Randomized, Controlled Study. Diabetes Therapy, 9(2), 583–612. https://doi.org/10.1007/s13300-018-0373-9
• 9: Jeppesen, J., Hein, H. O., Suadicani, P., & Gyntelberg, F. (2001). Low Triglycerides–High High-Density Lipoprotein Cholesterol and Risk of Ischemic Heart Disease. Archives of Internal Medicine, 161(3), 361. https://doi.org/10.1001/archinte.161.3.361
• 10: Vega, G. L., Barlow, C. E., Grundy, S. M., Leonard, D., & DeFina, L. F. (2014). Triglyceride-to-high-density-lipoprotein-cholesterol ratio is an index of heart disease mortality and of incidence of type 2 diabetes mellitus in men. Journal of Investigative Medicine : The Official Publication of the American Federation for Clinical Research, 62(2), 345–349. https://doi.org/10.2310/JIM.0000000000000044
• 11: Boizel, R., Benhamou, P. Y., Lardy, B., Laporte, F., Foulon, T., & Halimi, S. (2000). Ratio of triglycerides to HDL cholesterol is an indicator of LDL particle size in patients with type 2 diabetes and normal HDL cholesterol levels. Diabetes Care, 23(11), 1679–1685. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/11092292
• 12: Abbasi, F., & Reaven, G. M. (2011). Comparison of two methods using plasma triglyceride concentration as a surrogate estimate of insulin action in nondiabetic subjects: triglycerides × glucose versus triglyceride/high-density lipoprotein cholesterol. Metabolism, 60(12), 1673–1676. https://doi.org/10.1016/J.METABOL.2011.04.006
• 13: Atzmon, G., Gabriely, I., Greiner, W., Davidson, D., Schechter, C., & Barzilai, N. (2002). Plasma HDL Levels Highly Correlate With Cognitive Function in Exceptional Longevity. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 57(11), M712–M715. https://doi.org/10.1093/gerona/57.11.M712
• 14: Varbo, A., Benn, M., & Nordestgaard, B. G. (2014). Remnant cholesterol as a cause of ischemic heart disease: Evidence, definition, measurement, atherogenicity, high risk patients, and present and future treatment. Pharmacology & Therapeutics, 141(3), 358–367. https://doi.org/10.1016/J.PHARMTHERA.2013.11.008
• 15: Saely, C., Rein, P., Leiherer, A., Vonbank, A., Zanolin, D., Schwerzler, P., … Drexel, H. (2017). Remnant cholesterol predicts the development of type 2 diabetes mellitus in patients with established coronary artery disease. Atherosclerosis, 263, e256. https://doi.org/10.1016/j.atherosclerosis.2017.06.830
• 16: Dobiás̆ová, M., & Frohlich, J. (2001). The plasma parameter log (TG/HDL-C) as an atherogenic index: correlation with lipoprotein particle size and esterification rate inapob-lipoprotein-depleted plasma (FERHDL). Clinical Biochemistry, 34(7), 583–588. https://doi.org/10.1016/S0009-9120(01)00263-6
• 17: Mahat, R. K., Singh, N., Rathore, V., Gupta, A., & Shah, R. K. (2018). Relationship between Atherogenic Indices and Carotid Intima-Media Thickness in Prediabetes: A Cross-Sectional Study from Central India. Medical Sciences, 6(3), 55. https://doi.org/10.3390/medsci6030055
• 18: Sunlight and Vitamin D. (2013). Dermato-Endocrinology, 5(1), 51–108. https://doi.org/10.4161/derm.24494
• 19: Vitamin D deficiency: a worldwide problem with health consequences. (2008). The American Journal of Clinical Nutrition, 87(4), 1080S–1086S. Retrieved from http://ajcn.nutrition.org/content/87/4/1080S
• 20: Sanyal, D., Mukherjee, P., Raychaudhuri, M., Ghosh, S., Mukherjee, S., & Chowdhury, S. (2015). Profile of liver enzymes in non-alcoholic fatty liver disease in patients with impaired glucose tolerance and newly detected untreated type 2 diabetes. Indian Journal of Endocrinology and Metabolism, 19(5), 597–601. https://doi.org/10.4103/2230-8210.163172
• 21: Sheng, X., Che, H., Ji, Q., Yang, F., Lv, J., Wang, Y., … Wang, L. (2018). The Relationship Between Liver Enzymes and Insulin Resistance in Type 2 Diabetes Patients with Nonalcoholic Fatty Liver Disease. Hormone and Metabolic Research. https://doi.org/10.1055/a-0603-7899
• 22: Waist-to-height ratio is a better screening tool than waist circumference and BMI for adult cardiometabolic risk factors: systematic review and meta-analysis. (2012). Obesity Reviews, 13(3), 275–286. https://doi.org/10.1111/j.1467-789X.2011.00952.x
• 23: Six reasons why the waist-to-height ratio is a rapid and effective global indicator for health risks of obesity and how its use could simplify the international public health message on obesity. (2005). International Journal of Food Sciences and Nutrition, 56(5), 303–307. https://doi.org/10.1080/09637480500195066
• 24: Zeevi, D., Korem, T., Zmora, N., Israeli, D., Rothschild, D., Weinberger, A., … Segal, E. (2015). Personalized Nutrition by Prediction of Glycemic Responses. Cell, 163(5), 1079–1094. https://doi.org/10.1016/j.cell.2015.11.001
• 25: DiNicolantonio, J. J., Bhutani, J., OKeefe, J. H., & Crofts, C. (2017). Postprandial insulin assay as the earliest biomarker for diagnosing pre-diabetes, type 2 diabetes and increased cardiovascular risk. Open Heart, 4(2), e000656. https://doi.org/10.1136/openhrt-2017-000656