Tag Archives: Biohacking

Quantifying Biological Age: Blood Test #5 in 2022

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https://www.patreon.com/MichaelLustgartenPhD

TruDiagnostic Discount Link (Epigenetic Testing)
CONQUERAGING!
https://bit.ly/3Rken0n

Bristle Discount Link (Oral microbiome quantification):
ConquerAging15
https://www.bmq30trk.com/4FL3LK/GTSC3/

Cronometer Discount Link (Daily diet tracking):
https://shareasale.com/r.cfm?b=1390137&u=3266601&m=61121&urllink=&afftrack=

Support the channel with Buy Me A Coffee!
https://www.buymeacoffee.com/mlhnrca

Papers referenced in the video:
A new aging measure captures morbidity and mortality risk across diverse subpopulations from NHANES IV: A cohort study
https://pubmed.ncbi.nlm.nih.gov/30596641/

Underlying features of epigenetic aging clocks in vivo and in vitro
https://pubmed.ncbi.nlm.nih.gov/32930491/

Biological Aging Predicts Vulnerability to COVID-19 Severity in UK Biobank Participants
https://pubmed.ncbi.nlm.nih.gov/33684206/

Population Specific Biomarkers of Human Aging: A Big Data Study Using South Korean, Canadian, and Eastern European Patient Populations
https://pubmed.ncbi.nlm.nih.gov/29340580/

The Excel file to calculate Levine’s Biological Age is embedded in this link from my website:
https://michaellustgarten.com/2019/09/09/quantifying-biological-age/

Biohacking the Oral Microbiome

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Join us on Patreon! https://www.patreon.com/MichaelLustgartenPhD

Bristle Discount Link:
ConquerAging15 https://www.bmq30trk.com/4FL3LK/GTSC3/

Cronometer Discount Link:
https://shareasale.com/r.cfm?b=1390137&u=3266601&m=61121&urllink=&afftrack=

You can support the channel by buying me a coffee!
https://www.buymeacoffee.com/mlhnrca

Papers referenced in the video:
Interconnections Between the Oral and Gut Microbiomes: Reversal of Microbial Dysbiosis and the Balance Between Systemic Health and Disease https://pubmed.ncbi.nlm.nih.gov/33652903/

A Brief Introduction to Oral Diseases: Caries, Periodontal Disease, and Oral Cancer
https://tinyurl.com/z45d6djt

Xylitol, mutans streptococci, and dental plaque
https://pubmed.ncbi.nlm.nih.gov/19717413/

Growth Inhibition of Streptococcus mutans with Low Xylitol Concentrations
https://pubmed.ncbi.nlm.nih.gov/18176823/

The antimicrobial activity of essential oils and essential oil components towards oral bacteria https://pubmed.ncbi.nlm.nih.gov/7478759/

Nitrate as a potential prebiotic for the oral microbiome
https://pubmed.ncbi.nlm.nih.gov/32732931/

A Physiology Clock for Human Aging: BP and FEV1 As Top Predictors

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Papers referenced in the video:

A Physiology Clock for Human Aging (preprint) https://www.biorxiv.org/content/10.1101/2022.04.14.488358v1

Spirometry Reference Equations for Central European Populations from School Age to Old Age https://pubmed.ncbi.nlm.nih.gov/23320075/

An Integrated, Multimodal, Digital Health Solution for Chronic Obstructive Pulmonary Disease: Prospective Observational Pilot Study https://pubmed.ncbi.nlm.nih.gov/35142291/

Predicting Age by Mining Electronic Medical Records with Deep Learning Characterizes Differences between Chronological and Physiological Age https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5716867/

Methionine Restriction Extends Lifespan-What’s Optimal For Protein Intake? n=1 Analysis

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Papers referenced in the video:

Life-Span Extension in Mice by Preweaning Food Restriction and by Methionine Restriction in Middle Age https://pubmed.ncbi.nlm.nih.gov/19414512/

Low methionine ingestion by rats extends life span https://pubmed.ncbi.nlm.nih.gov/8429371/

Fasting glucose level and all-cause or cause-specific mortality in Korean adults: a nationwide cohort study https://pubmed.ncbi.nlm.nih.gov/32623847/

Total plasma homocysteine and cardiovascular risk profile. The Hordaland Homocysteine Study https://pubmed.ncbi.nlm.nih.gov/7474221/

Predicting Age by Mining Electronic Medical Records with Deep Learning Characterizes Differences between Chronological and Physiological Age https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5716867/

Association between low-density lipoprotein cholesterol and cardiovascular mortality in statin non-users: a prospective cohort study in 14.9 million Korean adults https://pubmed.ncbi.nlm.nih.gov/35218344/

Blood counts in adult and elderly individuals: defining the norms over eight decades of life https://pubmed.ncbi.nlm.nih.gov/32030733/

How Much Oxalate Is Too Much? n=1 Analysis

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Papers referenced in the video:

Dietary oxalate to calcium ratio and incident cardiovascular events: a 10-year follow-up among an Asian population https://pubmed.ncbi.nlm.nih.gov/35346210/

Predicting Age by Mining Electronic Medical Records with Deep Learning Characterizes Differences between Chronological and Physiological Age https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5716867/

Association between low-density lipoprotein cholesterol and cardiovascular mortality in statin non-users: a prospective cohort study in 14.9 million Korean adults https://pubmed.ncbi.nlm.nih.gov/35218344/

Joint distribution of lipoprotein cholesterol classes. The Framingham study) AND abbott lipoproteins 1983 https://pubmed.ncbi.nlm.nih.gov/6573877/

Incidental lymphopenia and mortality: a prospective cohort study https://pubmed.ncbi.nlm.nih.gov/31932337/

Blood counts in adult and elderly individuals: defining the norms over eight decades of life https://pubmed.ncbi.nlm.nih.gov/32030733/

Effect of Aging on Serum Uric Acid Levels: Longitudinal Changes in a Large Japanese Population Group https://pubmed.ncbi.nlm.nih.gov/12242321/

Liver enzymes and risk of all-cause mortality in general populations: a systematic review and meta-analysis. https://pubmed.ncbi.nlm.nih.gov/24585856/

Increased red blood cell distribution width (RDW) is associated with higher glycosylated hemoglobin (HbA1c) in the elderly https://pubmed.ncbi.nlm.nih.gov/25651746/

Centenarian Blood Test Analysis (n=1,754)

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Papers referenced in the video:

Risk Factors For Hyperuricemia In Chinese Centenarians And Near-Centenarians https://pubmed.ncbi.nlm.nih.gov/31908434/

Association between fasting glucose and all-cause mortality according to sex and age: a prospective cohort study https://pubmed.ncbi.nlm.nih.gov/28811570/

Predicting age by mining electronic medical records with deep learning characterizes differences between chronological and physiological age https://pubmed.ncbi.nlm.nih.gov/29113935/

Age and sex variation in serum albumin concentration: an observational study https://pubmed.ncbi.nlm.nih.gov/26071488/

Commonly used clinical chemistry tests as mortality predictors: Results from two large cohort studies https://pubmed.ncbi.nlm.nih.gov/33152050/

The gamma gap predicts 4-year all-cause mortality among nonagenarians and centenarians https://pubmed.ncbi.nlm.nih.gov/29348636/

Triglyceride centenarian studies are referenced in this post from my website: https://michaellustgarten.com/2020/02/19/blood-testing-whats-an-optimal-value-for-triglycerides-2/

Effects of blood triglycerides on cardiovascular and all-cause mortality: a systematic review and meta-analysis of 61 prospective studies https://pubmed.ncbi.nlm.nih.gov/24164719/

High-density lipoprotein cholesterol and all-cause mortality by sex and age: a prospective cohort study among 15.8 million adults https://pubmed.ncbi.nlm.nih.gov/33313654/