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dietgene is a health and lifestyle app that provides guidelines and recommendations to help you on your journey towards a happy and healthier life. While encouraging your understanding of what nutrition and healthy habits can do for you, dietgen and its recommendations are provided for information purposes only. The app does not provide medical advice. The app does not claim to take any medications. Recommendations are not and should not be regarded as a substitute for medical, physical, nutritional, or other professional advice. Always consult with a doctor or healthcare professional for medical advice and treatments before making any medical decisions.


Sources of recommendations

The scientific studies used in this app are provided below and can be referenced at PubMed.  All of these papers were published in peer-reviewed journals. PubMed is a service managed by the National Institutes of Health (NIH), a part of the U.S. Department of Health and Human Services, and it tracks more than 19 million citations for biomedical articles and scientific research.

DNA based recommendations sources

All health and metabolic recommendations are based on the sources:

  1. Ordovas JM et al. Dietary Fat Intake Determines The Effect Of A Common Polymorphism In The Hepatic Lipase Gene Promoter On High-density Lipoprotein Metabolism: Evidence Of A Strong Dose Effect In This Gene-nutrient Interaction In The Framingham Study. Circulation 106, 2315-21 (2002).
  2. Junyent M et al. Novel Variants At KCTD10, MVK, And MMAB Genes Interact With Dietary Carbohydrates To Modulate HDL-cholesterol Concentrations In The Genetics Of Lipid Lowering Drugs And Diet Network Study. The American Journal Of Clinical Nutrition 90, 686-94 (2009).
  3. Sonestedt E et al. Fat And Carbohydrate Intake Modify The Association Between Genetic Variation In The FTO Genotype And Obesity. The American Journal Of Clinical Nutrition 90, 1418-25 (2009).
  4. Corella D et al. APOA2, Dietary Fat, And Body Mass Index: Replication Of A Gene-diet Interaction In 3 Independent Populations. Archives Of Internal Medicine 169, 1897-906 (2009).
  5. Warodomwichit D et al. ADIPOQ Polymorphisms, Monounsaturated Fatty Acids, And Obesity Risk: The GOLDN Study. Obesity (Silver Spring, Md.) 17, 510-7 (2009).
  6. Memisoglu A et al. Interaction Between A Peroxisome Proliferator-activated Receptor Gamma Gene Polymorphism And Dietary Fat Intake In Relation To Body Mass. Human Molecular Genetics 12, 2923-9 (2003).
  7. Kathiresan S et al. Common Variants At 30 Loci Contribute To Polygenic Dyslipidemia. Nature Genetics 41, 56-65 (2009).
  8. Dupuis J et al. New Genetic Loci Implicated In Fasting Glucose Homeostasis And Their Impact On Type 2 Diabetes Risk. Nature Genetics 42, 105-16 (2010).
  9. Glaser C et al. Genetic Variation In Polyunsaturated Fatty Acid Metabolism And Its Potential Relevance For Human Development And Health. Maternal & Child Nutrition 7 Suppl 2, 27-40 (2011).
  10. Simopoulos AP. The Importance Of The Omega-6/omega-3 Fatty Acid Ratio In Cardiovascular Disease And Other Chronic Diseases. Experimental Biology And Medicine (Maywood, N.J.) 233, 674-88 (2008).
  11. Tanaka T et al. Genome-wide Association Study Of Plasma Polyunsaturated Fatty Acids In The InCHIANTI Study. PLoS Genetics 5, e1000338 (2009).
  12. Lemaitre RN et al. Genetic Loci Associated With Plasma Phospholipid N-3 Fatty Acids: A Meta-analysis Of Genome- wide Association Studies From The CHARGE Consortium. PLoS Genetics 7, e1002193 (2011).
  13. Epstein LH et al. Food Reinforcement, The Dopamine D2 Receptor Genotype, And Energy Intake In Obese And Nonobese Humans. Behavioral Neuroscience 121, 877-86 (2007).
  14. Doehring A et al. Genetic Diagnostics Of Functional Variants Of The Human Dopamine D2 Receptor Gene. Psychiatric Genetics 19, 259-68 (2009).
  15. Eny KM et al. Dopamine D2 Receptor Genotype (C957T) And Habitual Consumption Of Sugars In A Free-living Population Of Men And Women. Journal Of Nutrigenetics And Nutrigenomics 2, 235-42 (2009).
  16. de Krom M et al. Common Genetic Variations In CCK, Leptin, And Leptin Receptor Genes Are Associated With Specific Human Eating Patterns. Diabetes 56, 276-80 (2007).
  17. Bouchard L et al. Neuromedin Beta: A Strong Candidate Gene Linking Eating Behaviors And Susceptibility To Obesity. The American Journal Of Clinical Nutrition 80, 1478-86 (2004)
  1. Frayling TM et al. A Common Variant In The FTO Gene Is Associated With Body Mass Index And Predisposes To Childhood And Adult Obesity. Science (New York, N.Y.) 316, 889-94 (2007).
  2. Wardle J et al. Obesity Associated Genetic Variation In FTO Is Associated With Diminished Satiety. The Journal Of Clinical Endocrinology And Metabolism 93, 3640-3 (2008).
  3. den Hoed M et al. Postprandial Responses In Hunger And Satiety Are Associated With The Rs9939609 Single Nucleotide Polymorphism In FTO. The American Journal Of Clinical Nutrition 90, 1426-32 (2009).
  4. Dotson CD et al. Variation In The Gene TAS2R38 Is Associated With The Eating Behavior Disinhibition In Old Order Amish Women. Appetite 54, 93-9 (2010).
  5. Epstein LH et al. Food Reinforcement. Appetite 46, 22-5 (2006).
  6. Eny KM et al. Genetic Variant In The Glucose Transporter Type 2 Is Associated With Higher Intakes Of Sugars In TwoDistinct Populations. Physiological Genomics 33, 355-60 (2008).
  7. Cornelis MC et al. Coffee, Caffeine, And Coronary Heart Disease. Current Opinion In Clinical Nutrition And MetabolicCare 10, 745-51 (2007).
  8. Sachse C et al. Functional Significance Of A C–>A Polymorphism In Intron 1 Of The Cytochrome P450 CYP1A2 GeneTested With Caffeine. British Journal Of Clinical Pharmacology 47, 445-9 (1999).
  9. Djordjevic N et al. Induction Of CYP1A2 By Heavy Coffee Consumption Is Associated With The CYP1A2 -163C>APolymorphism. European Journal Of Clinical Pharmacology 66, 697-703 (2010).
  10. Gunes A et al. Variation In CYP1A2 Activity And Its Clinical Implications: Influence Of Environmental Factors AndGenetic Polymorphisms. Pharmacogenomics 9, 625-37 (2008).
  11. Zhou SF et al. Structure, Function, Regulation And Polymorphism And The Clinical Significance Of Human CytochromeP450 1A2. Drug Metabolism Reviews 42, 268-354 (2010).
  12. Kim UK et al. Positional Cloning Of The Human Quantitative Trait Locus Underlying Taste Sensitivity ToPhenylthiocarbamide. Science (New York, N.Y.) 299, 1221-5 (2003).
  13. Reed DR et al. The Perception Of Quinine Taste Intensity Is Associated With Common Genetic Variants In A BitterReceptor Cluster On Chromosome 12. Human Molecular Genetics 19, 4278-85 (2010).
  14. Hayes JE et al. Explaining Variability In Sodium Intake Through Oral Sensory Phenotype, Salt Sensation And Liking.Physiology & Behavior 100, 369-80 (2010).
  15. Fushan AA et al. Allelic Polymorphism Within The TAS1R3 Promoter Is Associated With Human Taste Sensitivity ToSucrose. Current Biology : CB 19, 1288-93 (2009).
  16. Fang L et al. The Human Lactase Persistence-associated SNP -13910*T Enables In Vivo Functional Persistence OfLactase Promoter-reporter Transgene Expression. Human Genetics 131, 1153-9 (2012).
  17. Olds LC et al. Lactase Persistence DNA Variant Enhances Lactase Promoter Activity In Vitro: Functional Role As A CisRegulatory Element. Human Molecular Genetics 12, 2333-40 (2003).
  18. Troelsen JT et al. An Upstream Polymorphism Associated With Lactase Persistence Has Increased Enhancer Activity.Gastroenterology 125, 1686-94 (2003).
  19. Enattah NS et al. Identification Of A Variant Associated With Adult-type Hypolactasia. Nature Genetics 30, 233-7 (2002).
  20. Matsuo K et al. Alcohol Dehydrogenase 2 His47Arg Polymorphism Influences Drinking Habit Independently Of Aldehyde Dehydrogenase 2 Glu487Lys Polymorphism: Analysis Of 2,299 Japanese Subjects. Cancer Epidemiology, Biomarkers & Prevention : A Publication Of The American Association For Cancer Research, Cosponsored By The American Society Of Preventive Oncology 15, 1009-13 (2006).
  1. Tanaka F et al. Polymorphism Of Alcohol-metabolizing Genes Affects Drinking Behavior And Alcoholic Liver Disease In Japanese Men. Alcoholism, Clinical And Experimental Research 21, 596-601 (1997).
  2. Higuchi S et al. Influence Of Genetic Variations Of Ethanol-metabolizing Enzymes On Phenotypes Of Alcohol-related Disorders. Annals Of The New York Academy Of Sciences 1025, 472-80 (2004).
  3. Powers HJ. Riboflavin (vitamin B-2) And Health. The American Journal Of Clinical Nutrition 77, 1352-60 (2003).
  4. McNulty H et al. Homocysteine, B-vitamins And CVD. The Proceedings Of The Nutrition Society 67, 232-7 (2008).
  5. Hustad S et al. The Methylenetetrahydrofolate Reductase 677C–>T Polymorphism As A Modulator Of A B Vitamin Network With Major Effects On Homocysteine Metabolism. American Journal Of Human Genetics 80, 846-55 (2007).
  6. Yazdanpanah N et al. Low Dietary Riboflavin But Not Folate Predicts Increased Fracture Risk In Postmenopausal Women Homozygous For The MTHFR 677 T Allele. Journal Of Bone And Mineral Research : The Official Journal Of The American Society For Bone And Mineral Research 23, 86-94 (2008).
  7. Horigan G et al. Riboflavin Lowers Blood Pressure In Cardiovascular Disease Patients Homozygous For The 677C–>T Polymorphism In MTHFR. Journal Of Hypertension 28, 478-86 (2010).
  8. McNulty H et al. Riboflavin Lowers Homocysteine In Individuals Homozygous For The MTHFR 677C->T Polymorphism. Circulation 113, 74-80 (2006).
  9. Tanaka T et al. Genome-wide Association Study Of Vitamin B6, Vitamin B12, Folate, And Homocysteine Blood Concentrations. American Journal Of Human Genetics 84, 477-82 (2009).
  10. Hazra A et al. Genome-wide Significant Predictors Of Metabolites In The One-carbon Metabolism Pathway. Human Molecular Genetics 18, 4677-87 (2009).
  11. Zittoun J et al. Modern Clinical Testing Strategies In Cobalamin And Folate Deficiency. Seminars In Hematology 36, 35-46 (1999).
  12. Hazra A et al. Common Variants Of FUT2 Are Associated With Plasma Vitamin B12 Levels. Nature Genetics 40, 1160-2 (2008).
  13. Bailey LB et al. Folate Metabolism And Requirements. The Journal Of Nutrition 129, 779-82 (1999).
  14. Yang QH et al. Prevalence And Effects Of Gene-gene And Gene-nutrient Interactions On Serum Folate And Serum Total Homocysteine Concentrations In The United States: Findings From The Third National Health And Nutrition Examination Survey DNA Bank. The American Journal Of Clinical Nutrition 88, 232-46 (2008).
  15. Voutilainen S et al. Low Dietary Folate Intake Is Associated With An Excess Incidence Of Acute Coronary Events: The Kuopio Ischemic Heart Disease Risk Factor Study. Circulation 103, 2674-80 (2001).
  16. Gerster H. Vitamin A–functions, Dietary Requirements And Safety In Humans. International Journal For Vitamin And Nutrition Research. Internationale Zeitschrift Fur Vitamin- Und Ernahrungsforschung. Journal International De Vitaminologie Et De Nutrition 67, 71-90 (1997).
  17. Semba RD. The Role Of Vitamin A And Related Retinoids In Immune Function. Nutrition Reviews 56, S38-48 (1998).
  18. Dawson MI. The Importance Of Vitamin A In Nutrition. Current Pharmaceutical Design 6, 311-25 (2000).
  19. Ross AC et al. The Function Of Vitamin A In Cellular Growth And Differentiation, And Its Roles During Pregnancy And Lactation. Advances In Experimental Medicine And Biology 352, 187-200 (1994).
  20. Leung WC et al. Two Common Single Nucleotide Polymorphisms In The Gene Encoding Beta-carotene 15,15′- monoxygenase Alter Beta-carotene Metabolism In Female Volunteers. FASEB Journal : Official Publication Of The Federation Of American Societies For Experimental Biology 23, 1041-53 (2009).
  1. Witschi JC et al. Preformed Vitamin A, Carotene, And Total Vitamin A Activity In Usual Adult Diets. Journal Of The American Dietetic Association 57, 13-6 (1970).
  2. Solomons NW et al. Plant Sources Of Provitamin A And Human Nutriture. Nutrition Reviews 51, 199-204 (1993).
  3. Cahill LE et al. Vitamin C Transporter Gene Polymorphisms, Dietary Vitamin C And Serum Ascorbic Acid. Journal OfNutrigenetics And Nutrigenomics 2, 292-301 (2009).
  4. Timpson NJ et al. Genetic Variation At The SLC23A1 Locus Is Associated With Circulating Concentrations Of L-ascorbic Acid (vitamin C): Evidence From 5 Independent Studies With >15,000 Participants. The American Journal Of Clinical Nutrition 92, 375-82 (2010).
  5. Holick MF. Vitamin D And Bone Health. The Journal Of Nutrition 126, 1159S-64S (1996).
  6. Ahn J et al. Vitamin D-related Genes, Serum Vitamin D Concentrations And Prostate Cancer Risk. Carcinogenesis 30,769-76 (2009).
  7. Wang TJ et al. Common Genetic Determinants Of Vitamin D Insufficiency: A Genome-wide Association Study. Lancet(London, England) 376, 180-8 (2010).
  8. Beharka A et al. Vitamin E Status And Immune Function. Methods In Enzymology 282, 247-63 (1997).
  9. Morrissey PA et al. Optimal Nutrition: Vitamin E. The Proceedings Of The Nutrition Society 58, 459-68 (1999).
  10. Ferrucci L et al. Common Variation In The Beta-carotene 15,15′-monooxygenase 1 Gene Affects Circulating Levels Of Carotenoids: A Genome-wide Association Study. American Journal Of Human Genetics 84, 123-33 (2009).
  11. Bartali B et al. Serum Micronutrient Concentrations And Decline In Physical Function Among Older Persons. JAMA 299, 308-15 (2008).
  12. Maras JE et al. Intake Of Alpha-tocopherol Is Limited Among US Adults. Journal Of The American Dietetic Association 104, 567-75 (2004).
  13. Garenc C et al. Evidence Of LPL Gene-exercise Interaction For Body Fat And LPL Activity: The HERITAGE Family Study. Journal Of Applied Physiology (Bethesda, Md. : 1985) 91, 1334-40 (2001).
  14. Teran-Garcia M et al. Hepatic Lipase Gene Variant -514C>T Is Associated With Lipoprotein And Insulin Sensitivity Response To Regular Exercise: The HERITAGE Family Study. Diabetes 54, 2251-5 (2005).
  15. Hautala AJ et al. Peroxisome Proliferator-activated Receptor-delta Polymorphisms Are Associated With Physical Performance And Plasma Lipids: The HERITAGE Family Study. American Journal Of Physiology. Heart And Circulatory Physiology 292, H2498-505 (2007).
  16. Orkunoglu-Suer FE et al. INSIG2 Gene Polymorphism Is Associated With Increased Subcutaneous Fat In Women And Poor Response To Resistance Training In Men. BMC Medical Genetics 9, 117 (2008).
  17. Saltin B et al. Maximal Oxygen Uptake In Athletes. Journal Of Applied Physiology 23, 353-8 (1967).
  18. Lucia A et al. PPARGC1A Genotype (Gly482Ser) Predicts Exceptional Endurance Capacity In European Men. JournalOf Applied Physiology (Bethesda, Md. : 1985) 99, 344-8 (2005).
  19. Yang N et al. ACTN3 Genotype Is Associated With Human Elite Athletic Performance. American Journal Of HumanGenetics 73, 627-31 (2003).
  20. Druzhevskaya AM et al. Association Of The ACTN3 R577X Polymorphism With Power Athlete Status In Russians.European Journal Of Applied Physiology 103, 631-4 (2008).
  21. Raleigh SM et al. Variants Within The MMP3 Gene Are Associated With Achilles Tendinopathy: Possible Interaction With The COL5A1 Gene. British Journal Of Sports Medicine 43, 514-20 (2009).
  1. Li S et al. Cumulative Effects And Predictive Value Of Common Obesity-susceptibility Variants Identified By Genome- wide Association Studies. The American Journal Of Clinical Nutrition 91, 184-90 (2010).
  2. Vimaleswaran KS et al. Physical Activity Attenuates The Body Mass Index-increasing Influence Of Genetic Variation In The FTO Gene. The American Journal Of Clinical Nutrition 90, 425-8 (2009).
  3. Rankinen T et al. Effect Of Endothelin 1 Genotype On Blood Pressure Is Dependent On Physical Activity Or Fitness Levels. Hypertension (Dallas, Tex. : 1979) 50, 1120-5 (2007).
  4. O’Rahilly S et al. Human Obesity: A Heritable Neurobehavioral Disorder That Is Highly Sensitive To Environmental Conditions. Diabetes 57, 2905-10 (2008).
  5. Tao YX. The Melanocortin-4 Receptor: Physiology, Pharmacology, And Pathophysiology. Endocrine Reviews 31, 506-43 (2010).
  6. Fawcett KA et al. The Genetics Of Obesity: FTO Leads The Way. Trends In Genetics : TIG 26, 266-74 (2010).
  7. Loos RJ et al. Common Variants Near MC4R Are Associated With Fat Mass, Weight And Risk Of Obesity. NatureGenetics 40, 768-75 (2008).
  8. Willer CJ et al. Six New Loci Associated With Body Mass Index Highlight A Neuronal Influence On Body WeightRegulation. Nature Genetics 41, 25-34 (2009).
  9. Meyre D et al. Genome-wide Association Study For Early-onset And Morbid Adult Obesity Identifies Three New RiskLoci In European Populations. Nature Genetics 41, 157-9 (2009).
  10. Cho YS et al. A Large-scale Genome-wide Association Study Of Asian Populations Uncovers Genetic FactorsInfluencing Eight Quantitative Traits. Nature Genetics 41, 527-34 (2009).
  11. Leskinen T et al. Leisure-time Physical Activity And High-risk Fat: A Longitudinal Population-based Twin Study.International Journal Of Obesity (2005) 33, 1211-8 (2009).
  12. Swinburn BA et al. Diet, Nutrition And The Prevention Of Excess Weight Gain And Obesity. Public Health Nutrition 7,123-46 (2004).
  13. Goyenechea E et al. The – 11391 G/A Polymorphism Of The Adiponectin Gene Promoter Is Associated With Metabolic Syndrome Traits And The Outcome Of An Energy-restricted Diet In Obese Subjects. Hormone And Metabolic Research = Hormon- Und Stoffwechselforschung = Hormones Et Metabolisme 41, 55-61 (2009).
  14. Loos RJ et al. Polymorphisms In The Leptin And Leptin Receptor Genes In Relation To Resting Metabolic Rate And Respiratory Quotient In The Québec Family Study. International Journal Of Obesity (2005) 30, 183-90 (2006).
  15. Puglisi MJ et al. Modulation Of C-reactive Protein, Tumor Necrosis Factor-alpha, And Adiponectin By Diet, Exercise, And Weight Loss. The Journal Of Nutrition 138, 2293-6 (2008).
  16. Qi Y et al. Adiponectin Acts In The Brain To Decrease Body Weight. Nature Medicine 10, 524-9 (2004).
  17. Heid IM et al. Clear Detection Of ADIPOQ Locus As The Major Gene For Plasma Adiponectin: Results Of Genome-wideAssociation Analyses Including 4659 European Individuals. Atherosclerosis 208, 412-20 (2010).
  18. Natarajan P et al. High-density Lipoprotein And Coronary Heart Disease: Current And Future Therapies. Journal Of TheAmerican College Of Cardiology 55, 1283-99 (2010).
  19. Alwaili K et al. High-density Lipoproteins And Cardiovascular Disease: 2010 Update. Expert Review Of Cardiovascular Therapy 8, 413-23 (2010).
  20. Renström F et al. Genetic Predisposition To Long-term Nondiabetic Deteriorations In Glucose Homeostasis: Ten-year Follow-up Of The GLACIER Study. Diabetes 60, 345-54 (2011).


Diet sources – Genetic References

Professional scientists and nutritionists help us to make dietgene not only scientifically based but also experience-based platform for a healthy and happy life. dietgene supports a lot of different diets. The diet choice is based on the DNA phenotypes that are responsible for the cholesterol, lipids, sugar, triglycerides, and some other metabolic predispositions. In general, the app supports three main diet directions – balanced, low fat and low carb diets. Clarification of each diet branch depends on the user DNA, preferences and blood test results.

I Low Fat Diets

The app supports the Low Fat Diet types daily calorie distributions:

  • Baseline Low Fat (proteins 25%, carbs 55%, fats 20%)
  • Low Fat – Dietary Approaches to Stop Hypertension (DASH diet) (proteins 15%, carbs 55%, fats 30%)
  • Low Fat – Ornish diet (proteins 20%, carbs 55%, fats 25%)
  • Low Fat – Therapeutic Lifestyle Changes (TLC) Diet (proteins 20%, carbs 50%, fats 30%)
  • Low Fat – T2D Diet (proteins 30%, carbs 45%, fats 25%)
  • Low Fat – High Performance (proteins 20%, carbs 60%, fats 20%)


  1. Sonestedt, E. et al. Fat and carbohydrate intake modify the association between genetic variation in the FTO genotype and obesity. Am J Clin Nutr 90, 1418-25 (2009).
  2. Lappalainen, T. et al. Association of the fat mass and obesity-associated (FTO) gene variant (rs9939609) with dietary intake in the Finnish Diabetes Prevention Study. Br J Nutr 108, 1859-65 (2012).
  3. Rudkowska, I. et al. Gene-diet interactions on plasma lipid levels in the Inuit population. Br J Nutr 109, 953-61 (2013).Smith, C.E. et al. Dietary fat modulation of hepatic lipase variant -514 C/T for lipids: a crossover randomized dietary intervention trial in Caribbean Hispanics. Physiol Genomics 49, 592-600 (2017).
  4. Challa, H.J. & Uppaluri, K.R. DASH Diet (Dietary Approaches to Stop Hypertension). in StatPearls (Treasure Island (FL), 2018).
  5. Siervo, M. et al. Effects of the Dietary Approach to Stop Hypertension (DASH) diet on cardiovascular risk factors: a systematic review and meta-analysis. Br J Nutr 113, 1-15 (2015).
  6. Blumenthal, J.A. et al. Effects of the DASH diet alone and in combination with exercise and weight loss on blood pressure and cardiovascular biomarkers in men and women with high blood pressure: the ENCORE study. Arch Intern Med 170, 126-35 (2010).
  7. Ooi, E.M. et al. Effects of Therapeutic Lifestyle Change diets high and low in dietary fish-derived FAs on lipoprotein metabolism in middle-aged and elderly subjects. J Lipid Res 53, 1958-67 (2012).
  8. Lichtenstein, A.H. et al. Efficacy of a Therapeutic Lifestyle Change/Step 2 diet in moderately hypercholesterolemic middle-aged and elderly female and male subjects. J Lipid Res 43, 264-73 (2002).
  9. Li, Z. et al. Men and women differ in lipoprotein response to dietary saturated fat and cholesterol restriction. J Nutr 133, 3428-33 (2003).


II Low Carb Diets

The app supports the Low Carb Diet types daily calorie distributions:

  • Baseline Low Carb Diet (proteins 30%, carbs 35%, fats 35%)
  • Low Carb – Intermittent fasting diet (proteins 30%, carbs 30%, fats 40%)
  • Low Carb – Paleo diet (proteins 25%, carbs 35%, fats 40%)
  • Very Low Carb Diet – Diabetes and blood sugar levels (proteins 35%, carbs 20%, fats 45%)
  • Low Carb Keto Diet (proteins 10%, carbs 20%, fats 70%)
  • Low Carb – High Performance Diet (proteins 30%, carbs 45%, fats 25%)


  1. Noakes, T.D. & Windt, J. Evidence that supports the prescription of low-carbohydrate high-fat diets: a narrative review. Br J Sports Med 51, 133-139 (2017).
  2. Valenzuela Mencia, J. et al. Diets low in carbohydrates for type 2 diabetics. Systematic review. Nutr Hosp 34, 224-234 (2017).
  3. Junyent, M. et al. Novel variants at KCTD10, MVK, and MMAB genes interact with dietary carbohydrates to modulate HDL-cholesterol concentrations in the Genetics of Lipid Lowering Drugs and Diet Network Study. Am J Clin Nutr 90, 686-94 (2009).
  4. Noakes, T.D. & Windt, J. Evidence that supports the prescription of low-carbohydrate high-fat diets: a narrative review. Br J Sports Med 51, 133-139 (2017).
  5. Meng, Y. et al. Efficacy of low carbohydrate diet for type 2 diabetes mellitus management: A systematic review and meta-analysis of randomized controlled trials. Diabetes Res Clin Pract 131, 124-131 (2017).
  6. Feinman, R.D. et al. Dietary carbohydrate restriction as the first approach in diabetes management: critical review and evidence base. Nutrition 31, 1-13 (2015).


III Balanced Diets / Mediterranean

The app supports the Balanced Diet types daily calorie distributions:

  • Baseline Balanced Diet (proteins 20%, carbs 45%, fats 35%)
  • Balanced – Flexitarian Diet (proteins 25%, carbs 45%, fats 30%)
  • Balanced  – T2D (proteins 20%, carbs 40%, fats 40%)
  • Balanced – Flexitarian Diet (proteins 25%, carbs 45%, fats 30%)
  • Balanced – High Performance diet (proteins 25%, carbs 50%, fats 25%)
  • Anti-inflammatory Diets (proteins 30%, carbs 40%, fats 30%)
  • Balanced – DASH Intervention for Neurodegenerative Delay (MIND) Diet (proteins 20%, carbs 45%, fats 35%)


  1. Razquin, C. et al. The Mediterranean diet protects against waist circumference enlargement in 12Ala carriers for the PPARgamma gene: 2 years’ follow-up of 774 subjects at high cardiovascular risk. Br J Nutr 102, 672-9 (2009).
  2. Garaulet, M., Smith, C.E., Hernandez-Gonzalez, T., Lee, Y.C. & Ordovas, J.M. PPARgamma Pro12Ala interacts with fat intake for obesity and weight loss in a behavioural treatment based on the Mediterranean diet.
  3. Forsyth, C. et al. The effects of the Mediterranean diet on rheumatoid arthritis prevention and treatment: a systematic review of human prospective studies. Rheumatol Int 38, 737-747 (2018).
  4. Oliviero, F. et al. How the Mediterranean diet and some of its components modulate inflammatory pathways in arthritis. Swiss Med Wkly 145, w14190 (2015).
  5. Bonaccio, M., Cerletti, C., Iacoviello, L. & de Gaetano, G. Mediterranean diet and low-grade subclinical inflammation: the Moli-sani study. Endocr Metab Immune Disord DrugTargets 15, 18-24 (2015).
  6. Casas, R., Sacanella, E. & Estruch, R. The immune protective effect of the Mediterranean diet against chronic low-grade inflammatory diseases. Endocr Metab ImmuneDisord Drug Targets 14, 245-54 (2014).
  7. Olendzki, B.C. et al. An anti-inflammatory diet as treatment for inflammatory bowel disease: a case series report. Nutr J 13, 5 (2014).
  8. Knight-Sepulveda, K., Kais, S., Santaolalla, R. & Abreu, M.T. Diet and Inflammatory Bowel Disease. Gastroenterol Hepatol (N Y) 11, 511-20 (2015).
  9. Khanna, S., Jaiswal, K.S. & Gupta, B. Managing Rheumatoid Arthritis with Dietary Interventions. Front Nutr 4, 52 (2017).
  10. Shivappa, N., Hebert, J.R., Behrooz, M. & Rashidkhani, B. Dietary Inflammatory Index and Risk of Multiple Sclerosis in a Case-Control Study from Iran. Neuroepidemiology 47, 26-31 (2016).
  11. Lourida, I. et al. Mediterranean diet, cognitive function, and dementia: a systematic review. Epidemiology 24, 479-89 (2013).
  12. Loughrey, D.G., Lavecchia, S., Brennan, S., Lawlor, B.A. & Kelly, M.E. The Impact of the Mediterranean Diet on the Cognitive Functioning of Healthy Older Adults: A Systematic Review and Meta-Analysis. Adv Nutr 8, 571-586 (2017).


Life score

Vitamins intake recommendations

  1. Dietary Reference Intakes for Calcium, Phosphorous, Magnesium, Vitamin D, and Fluoride (1997);
  2. Dietary Reference Intakes for Thiamin, Riboflavin, Niacin, Vitamin B6, Folate, Vitamin B12, Pantothenic Acid, Biotin, and Choline (1998);
  3. Dietary Reference Intakes for Vitamin C, Vitamin E, Selenium, and Carotenoids (2000);
  4. Dietary Reference Intakes for Vitamin A, Vitamin K, Arsenic, Boron, Chromium, Copper, Iodine, Iron, Manganese, Molybdenum, Nickel, Silicon, Vanadium, and Zinc (2001);
  5. Dietary Reference Intakes for Energy, Carbohydrate, Fiber, Fat, Fatty Acids, Cholesterol, Protein, and Amino Acids (2002/2005);
  6. Dietary Reference Intakes for Calcium and Vitamin D (2011).

These reports may be accessed via National academy of Science Engineering Medicine.

Blood panel recommendations

The reference ranges in dietgene in Blood test feature are provided for illustration only and are not intended to be comprehensive or definitive. Each laboratory and health organization determine its own values, and reference ranges are highly method dependent. Reference values given in the app are for adults only.

  1. Iverson C, Christiansen S, Flanagin A, et al. AMA Manual of Style: A Guide for Authors and Editors. 10th ed. New York, NY: Oxford University Press; 2007. © American Medical Association, p.798-815.
  2. Kratz A, Ferraro M, Sluss PM, Lewandrowski KB. Laboratory reference values. N Engl J Med. 2004;351(15):1548-1563;
  3. Young DS, Huth EJ. SI Units for Clinical Measurement. Philadelphia, PA: American College of Physicians; 1998;
  4. Henry JB, ed. Clinical Diagnosis and Management by Laboratory Methods. 20th ed. Philadelphia, PA: WB Saunders; 2001;
  5. Kasper DL, Braunwald E, Fauci AS, et al, eds. Harrison’s Principles of Internal Medicine, 16th ed. New York, NY: McGraw Hill; 2004;
  6. Goldman L, Ausiello D. Cecil Textbook of Medicine. 22nd ed. Philadelphia, PA: WB Saunders; 2004.


Calorie recommendations

The calorie recommendation is based on the variation of the Mifflin-St Jeor formula which gives the user a BMR (Basal metabolic rate). Based on the BMR, activity level and goal we recommend the user a daily calorie intake amount.

  • Daily budget – MALE = ( 10 x WEIGHT(kg) + 6.25 x HEIGH(cm) – 5 x AGE(y) + 5 ) x ACTIVITY COEFFICIENT
  • Daily budget – FEMALE = ( 10 x WEIGHT(kg) + 6.25 x HEIGH(cm) – 5 x AGE(y) – 161 ) x ACTIVITY COEFFICIENT

The recommended pace of losing weight depends on individual energy requirements. For weight loss, we recommend a slow and steady pace that works over a longer period with a maximum of 1 kg / 2.2 lb per week. The least possible goal weight is limited by 18.5 BMI. The least possible daily budget is limited by the budget required for 18.5 BMI.

  1. Frankenfield D, Roth-Yousey L, Compher C. Comparison of predictive equations for resting metabolic rate in healthy nonobese and obese adults: a systematic review. J Am Diet Assoc. 2005 May;105(5):775-89. doi: 10.1016/j.jada.2005.02.005. PMID: 15883556.
  2. Amirkalali B, Hosseini S, Heshmat R, Larijani B. Comparison of Harris Benedict and Mifflin-ST Jeor equations with indirect calorimetry in evaluating resting energy expenditure. Indian J Med Sci. 2008 Jul;62(7):283-90. doi: 10.4103/0019-5359.42024. PMID: 18688113.

For breastfeeding mothers, the extra 450 calories are used. That is based on the recommendations of the Center for disease control and prevention (CDC).

Physical activities recommendations

To calculate the calorie number during workouts, the MET coefficient approach is used.

For activities recommendations, World Heath Organization recommendations are used.

In some cases, when we believe it is necessary, we recalculate the logged steps into calories. We use the next approach for that:

  •  Calories burned per minute = ( 0.035 x WEIGHT(kg) ) + ( (Velocity^2) / Height ) ) x (0.029) x WEIGHT(kg)

Activity coefficient is recalculated and actualised based on the activity history. The more active you are, the higher activity budget for the next day will be.


  1.  Ludlow, L. W., & Weyand, P. G. (2016). Energy expenditure during level human walking: seeking a simple and accurate predictive solution. Journal of Applied Physiology, 120(5), 481-494.
  2. Ainsworth BE, Haskell WL, Herrmann SD, Meckes N, Bassett DR Jr, Tudor-Locke C, Greer JL, Vezina J, Whitt-Glover MC, Leon AS. 2011 Compendium of Physical Activities: a second update of codes and MET values. Med Sci Sports Exerc. 2011 Aug;43(8):1575-81. doi: 10.1249/MSS.0b013e31821ece12. PMID: 21681120.
  3. Bassett, D., and C. Tudor-Locke. How many steps/day are enough? Preliminary pedometer indices for public health. Sports Med 34 (2004): 1-8.


Individuals should not change their diet, physical activity, or any medical treatments they are currently using based on genetic testing results without consulting their personal health care provider.

Individuals may find that their experience is not consistent with OmeCare’s selected peer-reviewed scientific research findings of relative improvement for the study groups. The science in this area is still developing and many personal health factors affect diet and health. Since subjects in the scientific studies referenced in this report may have had personal health and other factors different from those of tested individuals, results from these studies may not be representative of the results experienced by tested individuals. Further, some recommendations may or may not be attainable, depending on the tested individual’s physical ability or other personal health factors. A limitation of this testing is that most scientific studies have been performed in Caucasian populations only. The interpretations and recommendations are done in the context of Caucasian studies, but the results may or may not be relevant to tested individuals of different or mixed ethnicities.

The association between genetic mutations and the information within this app is an active area of scientific research, and future scientific discoveries might alter our understanding of how this information is related to your diet, nutrition, and exercise.