Protocol development in integrative medicine is not typically a simple process. Individuals require individualized care, and what works for one patient may not work for another.

To establish these protocols, we first developed a Rating Scale that could be used to discern the rigor of evidence supporting a specific nutrient’s therapeutic effect.

The following protocols were developed using only A through D-quality evidence.

Class
Qualifying studies
Minimum requirements
A
Systematic review or meta-analysis of human trials
 
B
RDBPC human trials
2+ studies and/or 1 study with 50 + subjects
C
RDBPC human trials
1 study
D
Non-RDBPC human or In-vivo animal trials
 

Introduction

What Is Cognitive Support for Health Aging?

Cognitive health encompasses the capacity to maintain attention, memory, and mental clarity by supporting the systems that influence brain function. These include mitochondrial efficiency, antioxidant capacity, inflammation balance, adequate nutrient status, and circadian rhythm alignment. (Jost 2025)(Melzer 2021)(Wright 2015) 

This clinical guide emphasizes proactive, measurable approaches that specifically target metabolic health to help sustain optimal brain performance and overall vitality across the aging process.

Why Metabolic Health Matters

Metabolic health is increasingly recognized as a critical determinant of cognitive function, given the brain’s high metabolic demands and dependence on tightly regulated glucose and insulin signaling. Insulin resistance, type 2 diabetes, obesity, and metabolic syndrome (MetS) are associated with neuroinflammation, oxidative stress, endothelial dysfunction, and impaired cerebral glucose metabolism, all of which contribute to cognitive decline. Insulin plays a direct role in synaptic plasticity, neurotransmitter regulation, and memory consolidation, and disruption of central insulin signaling has been implicated in the pathophysiology of Alzheimer’s disease (AD), sometimes described as “type 3 diabetes.” (Craft 2009)(Arnold 2018)(Ponce-Lopez 2025)(Yaffe 2004)(Steen 2025)

MetS—or having 1–2 components of MetS (i.e., blood sugar dysregulation, hypertension, dyslipidemia, or abdominal obesity)—increases the odds of poor memory performance. A 2023 meta-analysis of 122 studies found that diabetes mellitus increases relative risk for cognitive impairment and dementia by 1.25 to 1.91 times. Additionally, metabolic dysfunction increases the risk of cerebrovascular disease, increasing the risk of vascular cognitive impairment and mixed dementia. (Alsuwaidi 2023)(Sebastian 2023)(Arnold 2018)(Yaffe 2004)

Purpose of the Clinical Guide

The Metabolic Health as a Root Driver for Cognitive Health clinical guide was designed to:

    1. Simplify decision-making using standardized, evidence-rated nutrient interventions.
    2. Integrate laboratory and biomarker data to identify modifiable metabolic contributors to cognitive health.
    3. Complement the Cognitive Essentials for Healthy Aging clinical guide, enabling providers to integrate metabolic-focused strategies alongside foundational interventions when addressing cognitive health and function.

Labs

Fasting Blood Glucose

Fasting glucose levels provide valuable insights into the body’s ability to regulate blood sugar. They’re a crucial indicator of metabolic health and an essential tool for the early detection of metabolic disorders. A fasting blood glucose test result between 70–99 mg/dL is considered normal, levels between 100–125 mg/dL relate to prediabetes, and levels of 126 mg/dL or higher on two separate tests usually relate to diabetes. (Mathew 2023)(Nakrani 2023)

Insulin Resistance Panel

Insulin resistance panels can provide a comprehensive evaluation of insulin resistance by combining measurements of fasting insulin and C-peptide using advanced LC/MS/MS technology. Insulin resistance panels can offer a more accurate assessment of metabolic health than traditional glucose tests alone, as they can detect early signs of insulin resistance before glucose levels become elevated. (Cho 2022)(Nolan 2019)

Hemoglobin A1c (HbA1c)

HbA1c provides insights into the average blood glucose levels over the previous 2–3 months, making it a crucial biomarker for assessing long-term glycemic control and metabolic health. Beyond its role in diagnosing and monitoring diabetes, HbA1c has been shown to correlate with insulin resistance, cardiovascular risk, and other components of MetS, making it an important tool for evaluating overall metabolic health even in nondiabetic individuals. (Osei 2003)(Sherwani 2016)

Oral Glucose Tolerance Test (OGTT)

The OGTT provides insights into diabetes mellitus status, insulin resistance, and impaired pancreatic β-cell function. A dose of glucose is administered orally, and blood glucose levels are measured after two hours. (Zubair 2025)

Lipid Panel

A lipid panel evaluates a patient’s lipid metabolism and cardiovascular risk. It typically includes measurements of total cholesterol, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglycerides, with some panels also reporting non-HDL cholesterol or calculated ratios. Results are interpreted in the context of the patient’s overall clinical profile, including age, sex, comorbidities (such as diabetes or MetS), family history, and use of lipid-altering medications. (White-Al Habeeb 2023)

Advanced Lipid Markers

Advanced lipid markers may provide a clearer picture of a patient’s cardiovascular (CVD) risk. Apolipoprotein B is a strong indicator of coronary artery disease (CAD), and it can be elevated even when LDL cholesterol levels are normal, helping to identify risk in patients who might otherwise be considered low-risk based on cholesterol alone. Increased concentrations of Apolipoprotein A1 are associated with reduced risk of cardiovascular disease. Elevated concentrations of lipoprotein (a)—Lp(a)—are associated with increased risk of coronary artery disease. (Quest Diagnostics n.d.)

Comprehensive Metabolic Panel (CMP) – Aspartate Aminotransferase (AST) and Alanine Aminotransferase (ALT)

Higher AST levels and lower ALT levels have been associated with an increased risk of cognitive decline and dementia. Elevated AST and AST to ALT ratio have been correlated with poorer cognitive performance, while reduced ALT has been linked to lower cognitive scores and greater dementia risk. Liver enzyme imbalances have also been associated with structural brain changes in regions such as the hippocampus, amygdala, and thalamus, suggesting that liver dysfunction may contribute to neurodegeneration and cognitive impairment. (Gao 2024)

Gamma-Glutamyl Transferase (GGT)

Elevated serum GGT levels are associated with increased risk of dementia, including AD and vascular dementia, likely reflecting pro-oxidant pathways contributing to neurodegeneration. (Han 2020)

Ingredients

Protein Powder

Dosing: Start with a general dose of 10–20 g daily of plant-based or whey protein (although dosages are typically in the 20–40 g range)

Supporting evidence:

  • Healthy adults engaging in minimal physical activity should aim for at least 0.8 g of protein per kg of body weight per day to meet the recommended dietary allowance (RDA) of protein to avoid deficiency. This translates to about 10–15% of total daily energy expenditure.  
  • Daily protein intake should be adjusted depending on age, physical activity, and metabolic health goals. Studies suggest that a high-protein diet consisting of 1.07–1.6 g of protein per kg of body weight daily (27–35% of total daily energy expenditure) provides enhanced weight-loss effects while preserving fat-free mass. (Bray 2024)(Moon 2020)
  • GLP-1 secretion is enhanced by all forms of dietary proteins (from whole proteins to peptides and amino acids), each interacting with unique or unknown cellular mechanisms based on their structure. (Hira 2021)(Miguéns-Gómez 2021)(Volpi 2001)

Glutamine

Dosing: 15–30 g daily (Meek 2016)

Supporting evidence:

  • A review article summarizing findings from multiple animal, cell model, and human studies investigated the effects of various food factors, such as amino acids, including glutamine, on GLP-1 secretion and their potential impact on glucose metabolism. Glutamine, in particular, was shown to elevate cytosolic calcium and cell adenosine 3′,5′-cyclic monophosphate (cAMP) in enteroendocrine L cells, promoting GLP-1 secretion in experiments. This suggests that glutamine could play a role in improving glucose tolerance by stimulating GLP-1 secretion. However, the study also notes that effective doses of glutamine (15–30 g) are needed to achieve beneficial metabolic effects in humans, as lower doses have not consistently led to significant metabolic improvements. (Meek 2016)

Soluble Fiber 

Dosing: 5–10 g daily

Supporting evidence:

  • Soluble fibers, including guar gum and larch arabinogalactan, can support metabolic health by moderating energy intake, stabilizing postprandial blood glucose levels, and improving satiety, ultimately addressing risk factors for obesity, hyperglycemia, and hypercholesterolemia. These ingredients are resistant to digestion and promote beneficial gastrointestinal microflora and short-chain fatty acid (SCFA) production, supporting a healthy gut environment that favors comprehensive metabolic health. (den Besten 2015)(Dion 2016)(Kim 2002)(Wu 2023)

Vitamin D3+K2 

Dosing: 5,000 IUs (adjust dose based on testing) plus 25–95 mcg daily (depending on the dose of vitamin D)

Supporting evidence:

Beta‐Hydroxy‐Beta‐Methylbutyrate (HMB)

Dosing: 2,000 mg daily

Supporting evidence:

  • HMB is a metabolite of the essential amino acid leucine. It has been shown to effectively mitigate age-related declines in lean mass, while also enhancing muscle strength and functionality in older adults. These benefits may be enhanced with vitamin D3 supplementation. (Flakoll 2003)(Rathmacher 2020)(Wilson 2008)

Disclaimer

The Fullscript Integrative Medical Advisory team has developed or collected these protocols from practitioners and supplier partners to help health care practitioners make decisions when building treatment plans. By adding this protocol to your Fullscript template library, you understand and accept that the recommendations in the protocol are for initial guidance and may not be appropriate for every patient.

View protocol on Fullscript
References
  1. Alsuwaidi, H. N., Ahmed, A. I., Alkorbi, H. A., Ali, S. M., Altarawneh, L. N., Uddin, S. I., Roueentan, S. R., Alhitmi, A. A., Djouhri, L., & Chivese, T. (2023). Association between metabolic syndrome and decline in cognitive function: A cross-sectional study. Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy, 16, 849–859. https://doi.org/10.2147/DMSO.S393282
  2. Arnold, S. E., Arvanitakis, Z., Macauley-Rambach, S. L., Koenig, A. M., Wang, H. Y., Ahima, R. S., Craft, S., Gandy, S., Buettner, C., Stoeckel, L. E., Holtzman, D. M., & Nathan, D. M. (2018). Brain insulin resistance in type 2 diabetes and Alzheimer disease: Concepts and conundrums. Nature Reviews. Neurology, 14(3), 168–181. https://doi.org/10.1038/nrneurol.2017.185
  3. Bray, G. A., Qi, L., & Sacks, F. M. (2024). Is there an ideal diet? Some insights from the POUNDS Lost Study. Nutrients, 16(14), 2358. https://doi.org/10.3390/nu16142358 
  4. Cho, Y., & Lee, S. Y. (2022). Useful biomarkers of metabolic syndrome. International Journal of Environmental Research and Public Health, 19(22), 15003. https://doi.org/10.3390/ijerph192215003
  5. Craft S. (2009). The role of metabolic disorders in Alzheimer disease and vascular dementia: two roads converged. Archives of Neurology, 66(3), 300–305. https://doi.org/10.1001/archneurol.2009.27
  6. den Besten, G., Gerding, A., van Dijk, T. H., Ciapaite, J., Bleeker, A., van Eunen, K., Havinga, R., Groen, A. K., Reijngoud, D. J., & Bakker, B. M. (2015). Protection against the metabolic syndrome by guar gum-derived short-chain fatty acids depends on peroxisome proliferator-activated receptor γ and glucagon-like peptide-1. PloS One, 10(8), e0136364. https://doi.org/10.1371/journal.pone.0136364
  7. Dion, C., Chappuis, E., & Ripoll, C. (2016). Does larch arabinogalactan enhance immune function? A review of mechanistic and clinical trials. Nutrition & Metabolism, 13(1). https://doi.org/10.1186/s12986-016-0086-x
  8. Flakoll, P. J., Judy, T., Flinn, K., Carr, C., & Flinn, S. (2004). Postexercise protein supplementation improves health and muscle soreness during basic military training in Marine recruits. Journal of Applied Physiology (Bethesda, Md. : 1985), 96(3), 951–956. https://doi.org/10.1152/japplphysiol.00811.2003
  9. Gao, P. Y., Ou, Y. N., Wang, H. F., Wang, Z. B., Fu, Y., He, X. Y., Ma, Y. H., Feng, J. F., Cheng, W., Tan, L., & Yu, J. T. (2024). Associations of liver dysfunction with incident dementia, cognition, and brain structure: A prospective cohort study of 431 699 adults. Journal of Neurochemistry, 168(1), 26–38. https://doi.org/10.1111/jnc.15988
  10. Han, E., Lee, J. Y., Han, K. D., Cho, H., Kim, K. J., Lee, B. W., Kang, E. S., Cha, B. S., Younossi, Z. M., & Lee, Y. H. (2020). Gamma glutamyltransferase and risk of dementia in prediabetes and diabetes. Scientific Reports, 10, Article 6800. https://doi.org/10.1038/s41598-020-63803-0
  11. Hira, T., Trakooncharoenvit, A., Taguchi, H., & Hara, H. (2021). Improvement of glucose tolerance by food factors having glucagon-like peptide-1 releasing activity. International Journal of Molecular Sciences, 22(12), 6623. https://doi.org/10.3390/ijms22126623
  12. Jost, Z., & Kujach, S. (2025). Understanding cognitive decline in aging: Mechanisms and mitigation strategies – A narrative review. Clinical Interventions in Aging, 20, 459–469. https://doi.org/10.2147/cia.s510670
  13. Kim, L. S., Burkholder, P. M., & Waters, R. F. (2002). Effects of low-dose larch arabinogalactan from Larix occidentalis: A randomized, double-blind, placebo-controlled pilot study. Complementary Health Practice Review, 7(3), 221–229. https://doi.org/10.1177/153321010200700305
  14. Kuang, X., Liu, C., Guo, X., Li, K., Deng, Q., & Li, D. (2020). The combination effect of vitamin K and vitamin D on human bone quality: a meta-analysis of randomized controlled trials. Food & Function, 11(4), 3280–3297. https://doi.org/10.1039/c9fo03063h
  15. Mathew, T. K., Zubair, M., & Tadi, P. (2023, April 23). Blood glucose monitoring. StatPearls – NCBI Bookshelf. https://www.ncbi.nlm.nih.gov/books/NBK555976/
  16. Meek, C. L., Lewis, H. B., Vergese, B., Park, A., Reimann, F., & Gribble, F. (2016). The effect of encapsulated glutamine on gut peptide secretion in human volunteers. Peptides, 77, 38–46. https://doi.org/10.1016/j.peptides.2015.10.008
  17. Melzer, T. M., Manosso, L. M., Yau, S., Gil-Mohapel, J., & Brocardo, P. S. (2021). In pursuit of healthy aging: Effects of nutrition on brain function. International Journal of Molecular Sciences, 22(9), 5026. https://doi.org/10.3390/ijms22095026
  18. Miguéns-Gómez, A., Casanova-Martí, À., Blay, M. T., Terra, X., Beltrán-Debón, R., Rodríguez-Gallego, E., Ardévol, A., & Pinent, M. (2021). Glucagon-like peptide-1 regulation by food proteins and protein hydrolysates. Nutrition Research Reviews, 34(2), 259–275. https://doi.org/10.1017/s0954422421000019
  19. Moon, J., & Koh, G. (2020). Clinical evidence and mechanisms of high-protein diet-induced weight loss. Journal of Obesity & Metabolic Syndrome, 29(3), 166–173. https://doi.org/10.7570/jomes20028
  20. Nakrani, M.N., Wineland, R.H., Anjum, F. (2023, July 17). Physiology, glucose metabolism. StatPearls [Internet]. https://www.ncbi.nlm.nih.gov/books/NBK560599/ 
  21. Nolan, C. J., & Prentki, M. (2019). Insulin resistance and insulin hypersecretion in the metabolic syndrome and type 2 diabetes: Time for a conceptual framework shift. Diabetes and Vascular Disease Research, 16(2), 118–127. https://doi.org/10.1177/1479164119827611
  22. Osei, K., Rhinesmith, S., Gaillard, T., & Schuster, D. (2003). Is glycosylated hemoglobin A1c a surrogate for metabolic syndrome in nondiabetic, first-degree relatives of African-American patients with type 2 diabetes? The Journal of Clinical Endocrinology & Metabolism, 88(10), 4596–4601. https://doi.org/10.1210/jc.2003-030686
  23. Pazarci, Ö., Dogan, H. O., Kilinc, S., & Çamurcu, Y. (2019). Evaluation of serum glucagon-like peptide 1 and vitamin D levels in elderly patients with bone fractures. Medical Principles and Practice, 29(3), 219–224. https://doi.org/10.1159/000502132
  24. Ponce-Lopez T. (2025). Peripheral inflammation and insulin resistance: Their impact on blood-brain barrier integrity and glial activation in Alzheimer’s disease. International Journal of Molecular Sciences, 26(9), 4209. https://doi.org/10.3390/ijms26094209
  25. Quest Diagnostics. (n.d.). Advanced lipid panel, Cardio IQ® [Test information]. Quest Diagnostics Test Directory. Retrieved January 16, 2026, from https://testdirectory.questdiagnostics.com/test/test-detail/92145/advanced-lipid-panel-cardio-iq?cc=MASTER
  26. Rathmacher, J. A., Pitchford, L. M., Khoo, P., Angus, H., Lang, J., Lowry, K., Ruby, C., Krajek, A. C., Fuller, J. C., & Sharp, R. L. (2020). Long-term effects of calcium Β-Hydroxy-Β-Methylbutyrate and vitamin D3 supplementation on muscular function in older adults with and without resistance training: A randomized, double-blind, controlled study. The Journals of Gerontology Series A, 75(11), 2089–2097. https://doi.org/10.1093/gerona/glaa218
  27. Sebastian, M. J., Khan, S. K., Pappachan, J. M., & Jeeyavudeen, M. S. (2023). Diabetes and cognitive function: An evidence-based current perspective. WorldJournal of Diabetes, 14(2), 92–109. https://doi.org/10.4239/wjd.v14.i2.92
  28. Sherwani, S. I., Khan, H. A., Ekhzaimy, A., Masood, A., & Sakharkar, M. K. (2016). Significance of HBA1C test in diagnosis and prognosis of diabetic patients. Biomarker Insights, 11, BMI.S38440. https://doi.org/10.4137/bmi.s38440
  29. Steen, E., Terry, B. M., Rivera, E. J., Cannon, J. L., Neely, T. R., Tavares, R., Xu, X. J., Wands, J. R., & de la Monte, S. M. (2005). Impaired insulin and insulin-like growth factor expression and signaling mechanisms in Alzheimer’s disease–is this type 3 diabetes?. Journal of Alzheimer’s Disease: JAD, 7(1), 63–80. https://doi.org/10.3233/jad-2005-7107
  30. Volpi, E., Sheffield-Moore, M., Rasmussen, B. B., & Wolfe, R. R. (2001). Basal muscle amino acid kinetics and protein synthesis in healthy young and older Men. JAMA, 286(10), 1206. https://doi.org/10.1001/jama.286.10.1206
  31. White-Al Habeeb, N. M. A., Higgins, V., Wolska, A., Delaney, S. R., Remaley, A. T., & Beriault, D. R. (2023). The present and future of lipid testing in cardiovascular risk assessment. Clinical Chemistry, 69(5), 456–469. https://doi.org/10.1093/clinchem/hvad012
  32. Wilson, G. J., Wilson, J. M., & Manninen, A. H. (2008). Effects of beta-hydroxy-beta-methylbutyrate (HMB) on exercise performance and body composition across varying levels of age, sex, and training experience: A review. Nutrition & Metabolism, 5(1). https://doi.org/10.1186/1743-7075-5-1
  33. Wright, K. P. Jr, Drake, A. L., Frey, D. J., Fleshner, M., Desouza, C. A., Gronfier, C., & Czeisler, C. A. (2015). Influence of sleep deprivation and circadian misalignment on cortisol, inflammatory markers, and cytokine balance. Brain, Behavior, and Immunity, 47, 24–34. https://doi.org/10.1016/j.bbi.2015.01.004
  34. Wu, S., Jia, W., He, H., Yin, J., Xu, H., He, C., Zhang, Q., Peng, Y., & Cheng, R. (2023). A new dietary fiber can enhance satiety and reduce postprandial blood glucose in healthy adults: A randomized cross-over trial. Nutrients, 15(21), 4569. https://doi.org/10.3390/nu15214569
  35. Yaffe, K., Kanaya, A., Lindquist, K., Simonsick, E. M., Harris, T., Shorr, R. I., Tylavsky, F. A., & Newman, A. B. (2004). The metabolic syndrome, inflammation, and risk of cognitive decline. JAMA, 292(18), 2237–2242. https://doi.org/10.1001/jama.292.18.2237
  36. Zhang, S., Miller, D. D., & Li, W. (2021). Non-Musculoskeletal Benefits of Vitamin D beyond the Musculoskeletal System. International Journal of Molecular Sciences, 22(4), 2128. https://doi.org/10.3390/ijms22042128
  37. Zubair, M., Launico, M.V. (2025, September 15). Glucose tolerance test. StatPearls [Internet]. https://www.ncbi.nlm.nih.gov/books/NBK532915/