Metabolic pathways/mTOR, Research, Clinical decisions, Genomics, mTOR

Strengthen Your Ability to Predict If an Integrative Therapy Will Help Your Patient,

Part 2: Caveat “mTOR”

Ingrid Kohlstadt, MD

Caveat emptor, the Latin phrase for buyer beware, has typically been used to describe used cars and imported appliances. Now the phrase seems apt to US healthcare with its deluged marketplace and overwhelmed Federal Trade Commission. Some products make inaccurate claims as addressed in Part 1, BLUF (bottom line up front). Other products are therapeutic for only a few people and potentially unsafe for the rest of the population. Given that health and safety may be at stake, it’s wise for patients to seek their doctors’ guidance. “Which treatment is right for me?” This column proposes a way we might be able to change “beware” to “physician-informed.”

The desire to know the right treatment for each patient compels me into J.K. Rowling’s imaginative world of Harry Potter. Suppose the practice of medicine had what J.K. Rowling calls a Sorting Hat? Wear the hat, and you’ll be quickly placed in the right treatment group. The following steps bring this inventive concept into the medical treatment realm.

Step 1 – Split More and Lump Less

Most medical studies such as clinical trials and meta-analyses aggregate data. They apply a therapy to large studies to measure treatment effect. The larger the study size, the more power it has to detect a benefit. This study approach is sometimes called “lumping” because it combines those who benefit together with those who don’t.

Lumping has a downside, which is not considered as often as it might be. Lumping uses the law of averages, a one-size-fits-all approach. That means that lumped data doesn’t guide clinicians as to which patients will benefit from therapy and which patients will experience adverse effects such as therapeutic failure.

Lumped studies have a work-around, but it’s not adequate. Separating the participants by age, gender, and one or two medical variables is an attempt to use the research to guide clinical decision-making. However, in metabolic therapies these common strata seldom split the study participants into clinically meaningful groups. Rapidly emerging science from the microbiome research and the fields of science called the ‘omics are revealing many flaws in our assumptions about stratification. Science needs more studies to split the lumps.

Well-designed simple physiologic studies split data into biologically meaningful data. Step 1, therefore, is to find studies to help us split the data, to help us personalize our patient advice and sew our Harry Potter-style Sorting Hat.

Step 2 – Use Genomics

Genomic testing is a giant advance. Unlike epidemiologic data, which excels at lumping, metabologenomic data excels at splitting. It categorizes patients based on genetic variants called single nucleotide polymorphisms (SNPs).

The SNPs are already used clinically. For example, the field of pharmacogenomics tests patients for a handful of SNPs in order to select and dose pain medications, anesthesia, and blood thinners. The US Department of Defense is using SNPs to understand illness in returning soldiers and to prevent illness by strategically assigning soldiers to duty stations fitting their genetic susceptibilities. Personalized medicine has led the adoption of genomics into integrative therapies.

Genomics gives us the ability to split data in the powerful way folkloric Paul Bunyan splits wood. So, what’s holding us back? Many of genomics findings are poised to unseat current medical practices. So, resistance to change is probably the biggest roadblock. But there’s another reason, one feasible for us to address. Even those who are early adopters of integrative therapies think the voluminous ‘omics data requires artificial intelligence (AI) tools. They are awaiting “Dr. Bot.” AI may be tomorrow’s answer, but today’s patients can benefit from our existing understanding of metabolic pathways and the genomic road signs unique to each of us.

Step 3 – Sort Patients by Metabolic Differences such as mTOR

‘Omics data informs us on how patients differ in the metabolic pathways influencing the mammalian target of rapamycin (mTOR). The long and short of it is as follows.

The long: mTOR is a kinase that in humans is encoded by the mTOR gene. mTOR links with other proteins and regulates many cellular processes. mTOR regulates cell growth, cell proliferation, cell motility, cell survival, protein synthesis, and transcription. It also functions as a tyrosine protein kinase that promotes the activation of insulin receptors and insulin-like growth factor 1 receptors. mTOR influences autophagy in a way that when mTOR is active, autophagy is suppressed.

The short: mTOR predicts longevity! Help patients keep their mTOR low, and your medical guidance is life-extending.

Figure 1 depicts mTOR with its metabolic influencers. Figure 1 is reproduced with permission from the NutriGenetic Research Institute. Another reason mTOR is important is that it blocks autophagy, the process by which the body clears infections and cellular debris. How autophagy fits into the mTOR pathway is depicted in Figure 1.

Figure 1. Metabolic Pathways Influencing mTOR

Step 4 – Assign Patients to Treatment Categories

Modern living revs mTOR production in all of us. To get a sense for the pervasiveness of the impact consider a breakfast with coffee and toast. Today the breakfast is enjoyed while reading something electronic rather than the newspaper. The bread is iron- and folate-enriched. The jam has added fructose. The coffee, unless organic, contains more pesticides, and plastics were used in its preparation. EMFs, iron, folate, pesticides, fructose, and xenoestrogens all promote mTOR. It’s no longer feasible to avoid the myriad factors raising mTOR. What is possible is recognizing and avoiding the exposures to which each of us is most sensitive.

I therefore use Figure 1 to help my patients identify what they might do to strategically lower mTOR. Addressing genetic issues can further guide treatment.

Iron accumulation. People with HFE variants want to avoid iron from fortified foods. Some practitioners recommend the herb skullcap. Women with HFE variants may become more sensitive to iron after menopause and may benefit from donating blood. Inadequately researched is how magnetic iron increases susceptibility to EMFs. Also little-understood is how supplemental chromium may reduce iron absorption since they compete for the same transporter in the gut lumen.

EMF susceptibility. Anyone who perceives physical effects of EMFs should avoid them. So should others. Some individuals have generic variants in their calcium voltage genes that may make them more susceptible to electromagnetic fields from electrical devices and cell towers. EMFs raise insulin levels based on new research, so lowering EMF exposure to the extent possible is important in the setting of insulin resistance. A tip for patients is to avoid EMF exposure before their next blood test.

Protein drinks and amino acid supplements can be pro-inflammatory and raise mTOR. Therefore, I recommend non-caloric amino acid products and supplemental resveratrol or turmeric. Good Idea® is a non-caloric beverage that combines five amino acids, chromium, and natural flavors.

The amino acid glutamine has been shown to raise mTOR the most in people with variants in GAD and DAO genes. These patients are likely to find benefit from avoiding monosodium glutamate (MSG) and excess glutamine. A magnolia bark supplement may add support.

Pesticides are more harmful to people with PON1 variants. People with this genetic susceptibility may want to prioritize pesticide avoidance, organic foods, and supplementing with polyphenols, such as quercetin, and carotenoids such as astaxanthin and zeaxanthin.

Weakened autophagy. Those with genetic issues in the ULK1, ULK2, and ATG13 genes may already be predisposed to weakened autophagy. Supplemental berberine and lithium are thought to exert their desired effects by raising AMPK in support of autophagy. Eating less, intermittent fasting, and adhering to a modified ketogenic diet also support autophagy.

Summary

If only doctors’ bags came equipped with a Harry-Potter-style Sorting Hat, right? In a way, they do. Understanding genomic variation in the metabolic pathways such as mTOR can help doctors and their patients personalize therapies. Metabolic pathways such as Figure 1 serve as roadmaps for today’s patients. Eventually these pathways will come down from the clouds as AI. Whoever is already using the metabolic pathways for clinical decision-making will be at an advantage. The AI will merely be the systems engineers’ way of making our Harry-Potter-style Sorting Hats more efficient.