Actual blood tests to get: get wellnessfx.com, jinfiniti, opencures
Shop OYL - Own Your Labs gives you many results at a discount
BRAIN SCANS: Brain trust – our product review of BrainKey
https://agingbiotech.info/diagnostics/
20 Best Aging Biomarkers to Track for Longevity - Longevity Advice (and ray kurzweil’s fantastic voyage) give some pointers
ben greenfield fitness lists a lot (as does his book)
for fraility: balance test, gait strength
micronucleus frequency
urinary 1-hydroxypyrene (1-OHP)
https://www.gdx.net/product/organix-basic-profile-metabolic-function-test-urine
(from Neurotransmitters: The Inflamed Brain - YouTube )
Date: Sun, Aug 15, 2021 at 3:05 PM
also ION panel -
https://www.gdx.net/product/ion-profile-with-40-amino-acids-nutritional-test-blood
(from ION Panel: Organic Acids, Mental Health + Treatment Plans - YouTube )
ceramides - Could Ceramides Become the New Cholesterol?: Cell Metabolism
Postprandial blood samples were analysed for Nε-carboxymethyl-lysine (CML), appetite-regulating gut hormones, glucose, insulin, triacylglycerol, and markers of inflammation and endothelial activation. Subjective appetite ratings and subsequent food intake were also assessed, and urine was analysed for CML, methylglyoxal-derived hydroimidazolone (MG-H1), and F2-isoprostanes.
Glycomark | Blood Test - Life Extension - glycomark for measuring glucose spikes!
In 2006, 1,5-AG showed its most compelling clinical use when it was demonstrated that an assay (GlycoMark, developed by Nippon Kayaku, Inc.) for postprandial hyperglycemia was able to differentiate two patients who had similar, near goal, hemoglobin A1c values, yet very different glucose profiles as shown by continuous blood glucose monitoring - one of the patients having excessive glycemic variability.[7] In 2014, 1,5-AG in saliva was shown to mirror 1,5-AG in blood, indicating that it could be used as a noninvasive marker of short-term glycemic control.[8]
Underlying physiology
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1,5-AG is ingested from nearly all foods during the course of a regular diet and is nearly 100% non-metabolized. It is carried in the blood stream and filtered by the glomerulus, where it enters the kidney. Once in the kidney, 1,5-AG is re-absorbed back into the blood through the renal proximal tubule. A small amount, equal to the amount ingested, of 1,5-AG is released in the urine to maintain a constant amount in the blood and tissue.
Glucose is a competitive inhibitor of the re-absorption. If blood glucose values rise over 180 mg/dL for any period of time, the kidney cannot re-absorb all glucose back into the blood, leading to increased excretion in the urine (glucosuria). As a result, blood levels of 1,5-AG decrease immediately, and continue to decrease until glucose values go below 180 mg/dL. Once the hyperglycemia is corrected, 1,5-AG begins to be re-absorbed from the kidney back into the blood at a steady rate. If a person’s glucose levels remain below 180 mg/dL for approximately 4 weeks, 1,5-AG will return to its normal levels. As a result, measurement of the level of 1,5-AG in the blood is a test for a recent history of hyperglycemic episodes.
An imbalance between FA uptake and oxidation leads to accumulation of long chain FAs that are incorporated into triglyceride (TG) and phospholipids, as well as a multitude of other lipid subspecies. Although TG is the most easily detected, other lipids are more likely to be toxic. Diacylglycerols (DAGs) and ceramides are signaling lipids that are thought to be toxic when their intracellular concentrations are increased. Defective mitochondrial FA oxidation could lead to accumulation of medium chain acyl carnitines (Koves et al., 2008), another possible toxin. Finally, saturated long chain FAs, most notably palmitate, are associated with toxicity in cells either because of their direct actions or their incorporation into phospholipids
Lipid peroxides or TBARS test looking at the amount of oxidized or rancid fat. This should be
within normal limits of the test and indicates whether or not you have oxidized cholesterol
cfDNA - DNA Debris From Dying Fat Cells Causes Chronic Inflammation – Fight Aging!
seen in the SAA restriction videos
Let me give you an example. And let’s stick with diabetes, since that’s the time bomb. To diagnose diabetes, most doctors use a test called “fasting glucose.” It’s not very accurate. If you’ve got fancy health care, your physician would use a more expensive test for “glycated hemoglobin.” It’s a good test—it has some accuracy issues in edge cases. But if you want to detect diabetes before it happens—which is the goal here—you need these:
Fructosamine, Glycated albumin, 1,5-Anhydroglucitol, Adiponectin, Fetuin-A, alpha-ketobutyrate, L-alpha glycerylphosphorylcholine, Lipoprotein(a), Triglycerides, HDL-C, Ceramide, Ferritin, Transferrin, MBL-associated serine proteases, Thrombospondin 1, GPLD1, Acylcarnitine, miRNAs, CRP, IL-6, WBCs, Fibrinogen.
I put the list in small print because it’s the sort of thing you shouldn’t actually read. Unless you’re a doctor. But doctors cannot detect diabetes before it happens, because they can’t measure all those biomarkers. You can get at those details only in the world’s best research labs, and it would cost thousands of dollars to run all the varied tests.
This field of high-resolution biomarker detection is called multiomics. It’s just starting to show some incredible promise. For example, to detect nonsmall cell lung cancer, in the very early stage, physicians were measuring a molecule called pro-surfactant protein B. But then they found they could improve accuracy dramatically by also looking at the metabolite N1, N12-diacetylspermine.
I’m hitting these examples pretty hard, and with detail, to make my original point more convincing: Vital signs can’t do it. Basic blood workups can’t do it. Artificial intelligence can’t do it. To fix health care, we need to be able to measure trace compounds that we’ve never measured before.
Diagnosing patients is hard. Symptoms are just the first clue. For instance, let’s say a doctor knows a patient has something wrong with her pancreas. But is it pancreatic cancer, or just chronic pancreatitis? To make this diagnosis, physicians rely on a test to look for a cancer antigen called CA19-9. To do it earlier, with accuracy, you need CA19-9 and these nine other analytes:
Proline, Sphingomyelin s17:1, Phosphatidylcholine, Isocitrate, Sphinganine-1-phosphate, Histidine, Pyruvate, Ceramide, and Sphingomyelin d18:2.
It would take thousands of dollars to run the multiple tests to capture all that data. Most tests just capture a dozen or so analytes. We created a company, with a scientist out of UCLA, that found a unique way to hack testing equipment—using a combination of engineering and physics and computation—to get thousands of biomarkers out of a single low-cost test.
It opens the door to the possibility of a new era of medicine. For a low annual cost, physicians could diagnose disorders before symptoms appear. This would drastically reduce health care costs, and vastly improve the health of everyone.
We created several companies that can spy on cell-to-cell communications. They can read small RNAs, they can stratify and count the 4 billion immune cells in your body, and they can detect autism in a newborn baby. They’re part of a fleet of many new companies that are finally going to revolutionize health care.
TORCH panel
To determine muscular oxidative capacity (the ability to use oxygen) after exercise, the researchers measured the recovery rate of phosphocreatine (kPCr). A higher oxidative capacity represents more favorable muscle health, and a primary function of phosphocreatine is to maintain adenosine triphosphate (ATP) levels after muscle use.
https://onlinelibrary.wiley.com/doi/10.1111/acel.13256
Table 2. Examples of common aging plasma proteins that can significantly extend life span in a vertebrate animal model when manipulated
| Protein | q-value, age coefficient | Life span effect |
|---|---|---|
| AKT2 | 1.61E−16, 1.04E−03 | Mice deficient in Akt2 display a 9.1% increase in median survival and an improvement in myocardial contractile function (Ren et al., 2017) |
| GDF11 | 1.92E−02, −7.20E−04 | In killifish, levels of gdf11 decrease with age and treating aged animals with recombinant gdf11 lengthens mean life span by 8.3% (Zhou et al., 2019) |
| GDF15 | 1.71E−249, 5.26E−03 | The overexpression of human GDF15 in female mice extends median life span (19.5% for transgenic line 1377 and 12.9% for transgenic line 1398) and protects against weight gain and insulin insensitivity (Wang et al., 2014) |
| GHR | 7.56E−24, −1.53E−03 | Ghr −/− mice live longer (8.7%–28.2% increase in median life span depending on the sex and mouse strain), weigh less, and exhibit reduced levels of fasting glucose and insulin (Coschigano et al., 2003) |
| NAMPT | 5.39E−04, 1.12E−03 | Wheel-running activity is enhanced and longevity is boosted (10.2% increase in median life span) in aged female mice treated with extracellular vesicles containing Nampt (Yoshida et al., 2019) |
| PAPPA | 9.29E−05, 8.09E−04 | The incidence of spontaneous tumors is reduced and life is prolonged (37.5% increase in mean life span) in mice lacking Pappa (Conover & Bale, 2007) |
| PLAU | 6.46E−11, 8.67E−04 | Overexpressing Plau in mice elongates median life span (36%, 16%, and 23% for 75th, 50th, and 25th percentile survivors, respectively), reduces food intake, and decreases body weight (Miskin & Masos, 1997) |
| PTEN | 2.41E−02, 4.06E−04 | Longevity is enhanced (12.4% increase in median life span), cancer incidence is decreased, and insulin sensitivity is improved in mice harboring additional copies of Pten (Ortega-Molina et al., 2012) |
| SHC1a | 7.18E−04, 8.53E−04 | Median life span is extended by 27.9% and oxidative stress resistance is enhanced in Shc1 −/− mice (Migliaccio et al., 1999) |
The skin features were measured using the VISIA® Complexion Analysis System. The photographic images were captured with standard, cross-polarized, parallel polarized, and ultraviolet light. Images were taken in two different close-up views (front and left lateral 37°) for each subject to quantify the scores for spots, wrinkles, pores, texture, and erythema.
https://www.cell.com/cell-reports/fulltext/S2211-1247(22)00186-3
| Biomarker of aging | How it is currently being measured |
|---|---|
| DNA damage increases with age | Measure phosphorylated H2A.X – critical protein for DNA damage repair – in circulating cells (red blood cells). Cost and labour intensive. |
| Senescent cell burden increases with age | Senescence Associated Betagalactosidase in circulating cells (T lymphocytes), skin and fat cells. Because of senescent cell heterogeneity, there is yet to be a single, effective marker of senescence. Other markers include secreted proteins, lipids and miRNAs, many of which make up the senescence associated secretory profile. |
| Mitochondrial health declines with age Oxidative stress increases with age | Measure oxidative damage to macromolecules in blood (specific nucleic acids, proteins and lipids), antioxidant capacity, markers of mitochondrial physiology and function (mtDNA copy number, mtDNA mutation, amount of mitochondria with muscle biopsy. However, these require extremely careful standardisation. In particular, blood measurements may be affected by changes in circulating cells and high levels of mtDNA copy number (or other markers) can also indicate chronic tissue hypoxia and are inversely correlated to age in disease contexts. |
| Proteostasis (autophagy and proteasome) declines with aging | Can measure LC3 (marker of lysosomal health and autophagy) using a fluorescent dye that marks particular chemical structure (cysteine thiols) of misfolded proteins. |
| microRNAs (miRNAs) | Extracted from lipoproteins or extracellular vesicles. Changes in specific miRNas are correlated with inflammation, senescence burden and systemic aging (such as miR-34a, miR-21, miR151a-3p) |
| Stem cell exhaustion | Stem cells can be extracted and cellular health assayed, but they are not easily accessible. Further data on changes with human aging in stem cell numbers, characteristics and replication potential are still limited. |
| Advanced glycation end (AGE) products | Originates from glycation of proteins, lipids, nucleic acids which leads to inflammation, oxidative stress, cellular senescence and cell death. Can be measured via cellular autofluorescence (directly correlated with AGE accumulation) in the skin or other tissues. |
| Progernoic factors increase with age and youth factors decrease with age | Factors secreted by cells, typically into the circulatory system, have been observed to increase (progeronic factors: IL-6, TNF-alpha, B2M, damage associated molecular patterns) or decrease (youth factors: NAD, GDF-11, oxytocin, estrogen) with age. Can measure with mass spectrometry and other plasma analytic techniques. |
| Rate of translation (elongation phase) declines with age | No good measurement metric in humans to date. Extremely chemically unstable process to dissect and analyse. Data is conflicted because different cell types have variable rates of translation. Further, the rate of translation is extremely susceptible to a wide variety of stressors. |
“For the total lipids in chylomicrons and extremely large VLDL and small high-density lipoprotein (HDL), the mean diameter for VLDL particles, the ratio of polyunsaturated fatty acids to total fatty acids, and the concentrations of histidine, leucine, valine, and albumin a higher level is associated with decreased mortality, while for the concentrations of glucose, lactate, isoleucine, phenylalanine, acetoacetate, and GlycA the opposite applies.”
Here’s a link to the full text of the paper:
A metabolic profile of all-cause mortality risk identified in an observational study of 44,168 individuals
https://agelesspartners.com/research/ageless-multiomics/ has SOME more
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8833109/ (didnt do grimage) “is a good paper”. Look at extreme correlation of Mega Omics (one based on all the omics assays considered simultaneously, which we term Mega-omics) => is most complicated. Proximity Extension Assay (PEA) proteomics does best
Glycanage correlates strongly wth CRP
I mean STNAFORD PPL do have multiomics studies
Protein signatures of centenarians and their offspring suggest centenarians age slower than other humans - PMC => 50-protein signature of oldest-old. Look at Paola Sebastiani papers
