Getting enough sleep supports everything from energy levels to metabolism. But did you know that sleep may also influence your thyroid hormone levels?

New research suggests that the relationship between sleep and thyroid function is more complex than previously understood.

What does the latest research show?

A 2026 systematic review examined 25 studies investigating how sleep deprivation affects thyroid hormones.

Researchers found that insufficient sleep was associated with changes in thyroid-related blood markers, particularly TSH and Free T3.

However, the findings varied between studies, and more research is needed to understand how these changes affect long-term thyroid health.

Importantly, this does not mean that a few nights of poor sleep will cause thyroid disease.

Can thyroid function also affect your sleep?

Yes. Thyroid hormones help regulate your body’s activity and energy use.

When thyroid hormone levels are excessively high, some people experience difficulty sleeping, increased sweating, or restlessness.

Low thyroid hormone levels can also be associated with fatigue and certain sleep disturbances.

However, poor sleep and daytime tiredness are extremely common and can have many causes unrelated to thyroid function.

What does this mean for you?

Maintaining consistent, healthy sleep habits is an important part of overall wellness.

If you’re tracking your thyroid biomarkers, it may be helpful to consider your recent sleep patterns alongside other factors such as stress, medications, and general health.

Persistent sleep difficulties or concerning thyroid results can be discussed with your healthcare provider.

Key takeaway: Sleep and thyroid function may influence one another. While improving sleep isn’t a treatment for thyroid disease, understanding your sleep patterns can provide valuable context when reviewing your thyroid health.

References

1. Jain SP, et al. (2026). Sleep Deprivation as a Modulator of Thyroid Function: A Systematic Review and Meta-Analysis. Journal of Endocrinological Investigation.
2. Green ME, et al. (2021). Thyroid Dysfunction and Sleep Disorders. Frontiers in Endocrinology.

Your thyroid plays an important role in metabolism, helping regulate how your body uses energy throughout the day.

But if you’re experiencing low energy, difficulty managing your weight, or feeling unusually cold, does that automatically mean your thyroid is responsible?

Not necessarily.

Thyroid hormones influence your metabolic rate, which is the amount of energy your body uses to maintain essential functions. When thyroid hormone levels are too low, metabolism can slow down.

However, your weight and energy levels are influenced by many other factors, including nutrition, physical activity, sleep, and overall health.

Research also suggests that weight gain associated with an underactive thyroid is often more modest than people expect, with some of the increase related to fluid retention rather than body fat.

What does this mean for you?

Understanding your TSH and Free T4 levels can help provide a clearer picture of your thyroid function.

If you’re experiencing persistent changes in weight or energy, reviewing your thyroid results alongside your symptoms and lifestyle may help identify areas worth exploring.

Key takeaway: Your thyroid is an important part of your metabolism, but it’s not the whole story. Understanding your thyroid health alongside your daily habits can help you make more informed wellness decisions.

References:

• American Thyroid Association. Thyroid and Weight.
• American Thyroid Association. Hypothyroidism.

Your thyroid is part of your endocrine system, a network of glands that produce hormones to regulate important functions throughout your body.

Thyroid hormones influence how your body uses energy, maintains temperature, supports cardiovascular function and regulates many metabolic processes.

Understanding how these hormones are produced and controlled can help you make sense of your thyroid blood test results.

Step 1: Your brain signals your thyroid

Thyroid hormone regulation begins in your brain.

The hypothalamus releases a hormone called thyrotropin-releasing hormone (TRH).

TRH stimulates your pituitary gland to produce thyroid-stimulating hormone (TSH).

TSH then travels through your bloodstream to your thyroid, where it stimulates thyroid hormone production and release.

This communication system is known as the hypothalamic-pituitary-thyroid axis.

Step 2: Your thyroid produces T4 and T3

Your thyroid primarily produces two hormones:

Thyroxine (T4): The main hormone released by your thyroid. T4 acts largely as a precursor that can be converted into the more biologically active T3.

Triiodothyronine (T3): The hormone that directly activates many thyroid hormone receptors in your body’s tissues.

Most circulating T3 is produced when enzymes in tissues outside the thyroid convert T4 into T3.

This process helps regulate thyroid hormone availability in different tissues.

Step 3: Thyroid hormones influence your metabolism

Once thyroid hormones enter cells, they interact with specialized receptors that influence gene activity and cellular function.

These effects help regulate:

• Energy metabolism: How your body uses and expends energy.
• Body temperature: How much heat your tissues generate.
• Heart function: Heart rate, contraction and circulation.
• Digestion: The movement of food through your digestive system.
• Growth and development: Normal development of the brain and other organs.
• Lipid metabolism: How your body processes cholesterol and other fats.

This explains why thyroid dysfunction can produce symptoms across several organ systems rather than affecting only one part of the body.

Step 4: Your body adjusts thyroid hormone production

Your thyroid operates through a negative feedback system.

When circulating thyroid hormone levels are sufficient or elevated, your brain generally reduces TSH production.

When thyroid hormone availability falls, your pituitary gland typically increases TSH to encourage additional hormone production.

This feedback mechanism helps keep thyroid hormone levels relatively stable.

However, the system can be influenced by pregnancy, illness, medications and disorders affecting the pituitary gland.

Understanding TSH, Free T4 and Free T3

Each thyroid blood test provides different information.

TSH: Reflects the signal your pituitary gland sends to your thyroid. It is usually the preferred initial screening test for thyroid dysfunction.

Free T4: Measures the unbound fraction of T4 circulating in your bloodstream. It helps assess thyroid hormone availability and is particularly useful alongside TSH.

Free T3: Measures the unbound fraction of T3. It may provide additional information in certain situations, particularly when hyperthyroidism is suspected.

Importantly, a Free T3 result alone cannot determine how effectively every tissue in your body converts or uses thyroid hormone.

What can interfere with thyroid hormone regulation or testing?

Several factors can influence thyroid hormone levels or the way they are measured.

For example, severe illness can temporarily reduce circulating T3, while certain medications can change hormone production, metabolism or pituitary signaling.

High-dose biotin supplements can also interfere with some laboratory methods used to measure thyroid hormones, potentially producing misleading results.

This is one reason thyroid blood tests should be interpreted together with your medical history, symptoms, medications and supplements.

How strong is the evidence?

Evidence strength: High for established thyroid physiology.

The hormonal feedback system linking your hypothalamus, pituitary gland and thyroid is supported by extensive physiological and clinical research.

However, predicting how an individual will feel based on a small change in one thyroid hormone measurement is less precise.

This is why thyroid test results are most useful when interpreted as part of a broader clinical assessment.

Key takeaway: Your thyroid works through a carefully regulated communication system between your brain, thyroid gland and body tissues. Understanding TSH, Free T4 and Free T3 together provides a more complete picture of thyroid function than relying on a single marker.

References

1. Mariotti S, Beck-Peccoz P. Physiology of the Hypothalamic-Pituitary-Thyroid Axis. Endotext, NCBI Bookshelf.
2. Peeters RP, Visser TJ. Metabolism of Thyroid Hormone. Endotext, NCBI Bookshelf.
3. American Thyroid Association. Thyroid Function Tests.
4. Zhang Y, et al. (2021). Biotin Interference in Assays for Thyroid Hormones, Thyrotropin and Thyroglobulin. Thyroid.

Your thyroid is a small, butterfly-shaped gland located at the front of your neck, just below your voice box (larynx).

It sits in front of your windpipe (trachea) and is surrounded by muscles, blood vessels, and other important structures.

In most adults, the thyroid weighs approximately 15–25 grams, although its size and shape can vary naturally between individuals.

What does your thyroid look like?

Your thyroid has three main structural components:

• Right and left lobes: The two larger, wing-shaped sections on either side of your windpipe.
• Isthmus: A narrow bridge of tissue connecting the two lobes.
• Outer capsule: A thin layer of connective tissue surrounding the gland and helping maintain its structure.

Some people also have a small additional extension called a pyramidal lobe. This is a normal anatomical variation.

What’s inside your thyroid?

Although your thyroid appears to be a solid gland, it contains thousands of tiny, rounded structures called thyroid follicles.

Each follicle is lined with specialized thyroid cells and contains a gel-like substance called colloid.

The gland also has an extensive network of small blood vessels and connective tissue supporting its internal structure.

These microscopic features give the thyroid its distinctive appearance under a microscope.

What are the parathyroid glands?

Located behind or near your thyroid are several much smaller glands called the parathyroid glands.

Most people have four, although the number and exact location can vary.

Despite their similar names, the thyroid and parathyroid glands are separate structures with different functions.

Can the structure of your thyroid change?

Like other organs, your thyroid can develop changes in its size or appearance.

Some common structural findings include:

• Thyroid nodules: Small lumps or growths within the gland. Most are benign.
• Goiter: An enlargement of the thyroid gland.
• Thyroiditis: Inflammation that can sometimes cause swelling or changes in the gland’s appearance.

A thyroid ultrasound can help healthcare providers examine the gland’s size, shape, and internal structure.

Importantly, structural changes can occur even when thyroid blood test results are within normal ranges.

Why understanding thyroid anatomy matters

Knowing where your thyroid is located and how it is structured can help you better understand thyroid examinations, ultrasound findings, and common anatomical changes.

While blood tests provide information about thyroid function, imaging techniques such as ultrasound help evaluate its physical structure.

Key takeaway: Your thyroid may be small, but it has a surprisingly complex structure. Understanding its anatomy is a useful first step toward learning more about thyroid health.

References

1. Rousset B, et al. Thyroid Hormone Synthesis and Secretion. Endotext.
2. American Thyroid Association. Thyroid Nodules.
3. American Thyroid Association. Goiter.

At SiPhox Health, we use the EasyDraw arm collector to collect capillary blood from the upper arm. EasyDraw was validated in an internal 470-subject comparative study in which capillary samples were collected at the same time as venous samples and the results were compared directly. SiPhox has also validated its 50+ supported biomarkers, including hormone, metabolic, chemistry, lipid, and related health markers, using this capillary collection workflow.

That means EasyDraw capillary testing is as good as venous testing under proper collection, handling, and processing conditions. A paired study of 480 adults comparing fingertip and upper-arm capillary collection against matched venous samples on a single integrated analyzer platform found capillary and venous results clinically comparable for 16 of 18 routine analytes once sample-quality cutoffs were applied. [1] Similar equivalence has been demonstrated for other upper-arm collection devices, with 17 of 19 analytes falling within CLIA acceptance limits against matched venous samples. [2]

At the same time, traditional venous collection often requires far more blood than the analyzer itself needs. Modern chemistry analyzers require under 50 µL of plasma for a routine comprehensive metabolic panel, and hospital laboratories have been shown to collect roughly 8.5 times the required analytical volume for a complete blood count and about 12 times the required volume for a basic metabolic panel, with much of the sample discarded. [3,4] Large reference labs may request multiple tubes because their workflows are built around high-throughput routing, tube-type requirements, volume checks, and separate lab stations. That does not mean every assay requires that much blood. It often means the lab is collecting enough blood to make processing easier across a large, divided system.

So the real comparison is not “small sample versus large sample.” The better comparison is validated EasyDraw capillary collection versus traditional venous collection. EasyDraw capillary testing delivers venous-level performance while using substantially less blood.

EasyDraw samples are tested on validated lab equipment

EasyDraw collection happens at home, but the testing itself happens in a laboratory setting.

SiPhox works with CLIA-certified and CAP-accredited laboratories, and samples are processed using professional laboratory analyzers. The same analyzer runs both venous and EasyDraw capillary samples, which means the assay volume requirements are the same once the sample is ready to be tested.

The analyzer needs the correct sample type, enough usable volume, and proper processing conditions. It does not need a large venous tube. What it actually requires is the per-assay volume plus the instrument’s dead volume, which varies by platform and container. [3,5]

EasyDraw capillary testing is validated marker by marker

EasyDraw isn’t assumed to match venous testing because it’s convenient. Each biomarker is evaluated against defined acceptance criteria, including coefficient of variability expectations, some of which differ by gender, such as CRP and C-peptide. Results outside the expected two-deviation acceptance window are not considered viable.

That marker-level validation is what supports the conclusion, for the biomarkers SiPhox supports, when samples are collected and processed properly.

Venous draws often collect more blood than the assays actually need

Traditional venous collection is designed for large-scale lab operations. It is not always designed around the minimum blood volume required by the analyzer.

For example, a large reference lab may ask for several vacutainers for a panel that, in a more optimized workflow, uses a much smaller amount of blood. This is because reference labs often collect extra blood to support operational efficiency. Conventional large-volume collection tubes have been shown to collect blood far in excess of analyzer requirements, and low-volume alternatives can match analytical performance at a fraction of the draw. [6]

There are several reasons for this:

• Different lab departments or stations may process different tests.
• Each station may prefer or require its own tube.
• Some tests require different tube types.
• Some specialty markers require special handling, such as tinted or light-protected tubes.
• Automated systems may require extra volume for checks, transfers, and processing.
• Extra blood reduces the chance that a high-throughput lab needs to request a redraw.

This does not mean the extra blood makes the test more accurate. It means the lab’s workflow is designed to make processing easier and more reliable at scale.

Why multiple venous tubes do not mean better testing

A venous blood draw can require multiple tubes even when the actual analytical volume used by the instruments is much smaller.

The Labcorp AccuDraw example above shows a panel requiring three serum gel tubes and one lavender-top EDTA tube. The listed panel is the SiPhox longevity panel that includes markers such as Lipoprotein(a), C-reactive protein, Apolipoprotein B, a lipid panel, vitamin D, HbA1c, Apolipoprotein A-1, TSH, FSH, LH, estradiol, C-peptide, cortisol, ferritin, and DHEA-S.

The collection requirement is three serum gel tubes at 8.5 mL each plus one lavender-top EDTA tube at 4.0 mL.

SiPhox runs the same panel from one microtainer: about 0.5 mL. Instead of giving each lab station its own full tube, the sample is aliquoted and routed based on what the panel actually needs. SiPhox also designs its panels around validated markers, which avoids specialty tests that carry their own tube or handling requirements.

The bottom line

Under proper conditions, EasyDraw capillary testing delivers venous-level results while requiring substantially less blood.

Those conditions matter: the collection method must be validated, the biomarkers must be validated, the sample must be collected and handled correctly and processed on appropriate laboratory equipment, and there must be enough usable volume for each assay.

Traditional venous collection uses more blood not because every test analytically requires it, but because large reference labs are built around routing, separate stations, volume checks, and specialized tube requirements.

References

1. Venous–capillary blood agreement for routine chemistry analytes: effects of collection site and sample matrix. Clin Chem Lab Med (2026). https://pubmed.ncbi.nlm.nih.gov/42446434/
2. Evaluation of a Simplified Upper Arm Device for Vacuum-Assisted Collection of Capillary Blood Specimens. Diagnostics 15(15):1935 (2025). https://doi.org/10.3390/diagnostics15151935
3. Listening Closely When the Volume Is Turned Down. Clinical Laboratory News / ADLM. https://myadlm.org/cln/articles/2016/may/listening-closely-when-the-volume-is-turned-down
4. Reducing Blood Loss by Changing to Small Volume Tubes for Laboratory Testing. https://pmc.ncbi.nlm.nih.gov/articles/PMC7930757/
5. Reducing neonatal phlebotomy blood losses through the accurate calculation of minimum test volume requirements. Ann Clin Biochem (2021). https://journals.sagepub.com/doi/full/10.1177/00045632211030953
6. Performance evaluation of low-volume blood collection tubes for coagulation tests (2026). https://pubmed.ncbi.nlm.nih.gov/42414931/

IGF-1 (Insulin-like Growth Factor 1) is a hormone produced mainly by the liver in response to growth hormone signaling from the pituitary gland. Once released into the bloodstream, it acts on nearly every tissue in the body, driving cell growth, tissue repair, and metabolic regulation. It’s especially important for muscle protein synthesis, bone density, and the general maintenance of lean tissue as we age.

Levels of IGF-1 rise through childhood and adolescence, peak in early adulthood, and then decline steadily from there. That decline is a normal part of aging, but the rate and degree vary a lot from person to person, shaped by nutrition, sleep, exercise, and body composition. Abnormally low levels can point to growth hormone deficiency, malnutrition, or liver dysfunction, since the liver is where most of the production happens. Abnormally high levels can be associated with growth hormone excess or, in some research contexts, have been studied in relation to other long-term health considerations. A single number rarely tells the full story on its own, which is why we treat IGF-1 as one input among many rather than a standalone verdict.

Why it’s especially relevant right now

IGF-1 has become a more interesting marker to watch as GLP adoption has climbed. Rapid weight loss changes the hormonal environment in ways that don’t show up on a scale. Appetite suppression can mean lower overall caloric and protein intake, and lower protein intake is one of the biggest levers affecting both IGF-1 production and muscle retention. That connection matters because muscle isn’t just about strength. Lean mass plays a direct role in metabolic rate, glucose regulation, and how well the body holds onto its progress.

This ties directly into one of the most consistent findings from recent industry research on GLP users: most people losing weight on these medications are losing some amount of muscle along with fat, and most aren’t tracking it. Cardio activity tends to increase, but strength training, the single best lever for preserving lean mass, is often the most neglected part of the routine. IGF-1 sits right at the intersection of that story. It’s a hormonal signal that reflects whether the body is in a state that supports muscle maintenance or one that’s working against it.

What this means in practice

Tracking IGF-1 alongside body composition gives a more complete picture than either one alone. Body composition scanning shows what’s changing (the ratio of fat loss to muscle loss). IGF-1 offers a window into part of why that’s happening on a hormonal level, and whether protein intake and resistance training are keeping pace with the rate of weight loss. Watched over time, a downward trend paired with disproportionate muscle loss is a signal worth acting on, whether that means adjusting protein intake, adding resistance training, or flagging it for a conversation with a prescriber.

That’s the same logic behind pairing IGF-1 with metabolic, thyroid, and nutritional markers in a single panel rather than testing it in isolation. Hormones don’t operate independently, and neither does weight loss. A marker like IGF-1 is far more useful in context than as a standalone data point.

Getting the full picture

We offer IGF-1 as an optional upgrade alongside our core panels, collected through our EasyDraw arm-based draw and processed at a CLIA-certified, CAP-accredited lab, so it’s easy to add to your existing testing routine without a separate blood draw or lab visit.

Want to see where your IGF-1 stands? Add it to your next panel and track it alongside your other biomarkers over time.

References

1. MedlinePlus. “IGF-1 (Insulin-like Growth Factor 1) Test.” National Library of Medicine. https://medlineplus.gov/lab-tests/igf-1-insulin-like-growth-factor-1-test/
2. Kouvari M, et al. “About 31% of weight lost by adults during GLP-1 therapy comes from lean mass.” Healio, reporting on a systematic review and meta-analysis of 9 RCTs. https://www.healio.com/news/endocrinology/20250313/about-31-of-weight-lost-by-adults-during-glp1-therapy-comes-from-lean-mass
3. “Greater lean-body-mass decline with tirzepatide than semaglutide in routine care, revealed by body-composition digital phenotyping.” medRxiv preprint, nference, 2026. https://www.medrxiv.org/content/10.64898/2026.04.11.26350687.full.pdf

If you have both results in your dashboard, you may notice they don’t always agree. That is expected, and it is not a sign that one of them is wrong. They are built to answer two different questions.

Biological Age asks: which age does my biomarker pattern most resemble?

Advanced Biological Age asks: my predicted long-term disease risk matches the average person of what age?

Those sound similar. The difference in how each number is built is what makes them useful together.

What Biological Age measures

Biological Age is a machine learning model trained on publicly available CDC data paired with internal SiPhox Health data to recognize how biomarker patterns shift with chronological age across large populations. When your results come in, the model compares your pattern to the patterns it learned and estimates the age your profile most closely resembles.

Key characteristics:

• One whole-body number. It summarizes the combined shape of your biomarker profile into a single trackable figure.
• Broad marker set. It draws on a wide panel including metabolic, lipid, liver, kidney, inflammatory, nutrient, and hormonal markers, with sex-specific inputs. At least about half of the eligible markers are needed to generate a result.
• Trained on age itself. The target the model learned to predict is chronological age.

Biological Age is best used as a general wellness summary and a trend line. It answers “is my overall biomarker picture drifting older or younger over time?”

What Advanced Biological Age measures

Advanced Biological Age comes from our partnership with VOLO Health, whose modeling platform we use to generate the Advanced BioAge Report. The underlying models were trained on long-term health outcome data from large longitudinal cohorts, including the UK Biobank and a large body of US clinical records. Rather than learning to predict age, the models learned which biomarker patterns are associated with the development of specific conditions over a multi-year observation window, using standardized diagnostic code definitions for each condition.

The result is a predicted long-term risk estimate, compared against people of your same age and sex. That estimate is then translated back into an age frame: if your profile carries the predicted risk typical of someone four years younger, your delta reads as four years younger.

Key characteristics:

• Eleven results, not one. You get an Overall Advanced Biological Age plus a separate age for each of ten body systems: cardiovascular, metabolic, respiratory, brain, liver, kidney, bone, thyroid, blood, and inflammation.
• Trained on outcomes, not on age. The age number is the final translation step, not the modeling target.
• Peer-relative by design. Every result is calibrated to your age and sex, and each system also comes with a peer group ranking.
• Attribution built in. Each system shows which of your biomarkers are pushing that age up or down.

Advanced Biological Age is best used to find out where to focus. Because each system is modeled independently, you can have a strong cardiovascular result and a middling metabolic one, and that pattern tells you something a single number cannot.

Side by side

Why your two results might not match

4. They were trained on different targets. One model learned age. The other learned disease outcomes and converted to age afterward. A biomarker can be strongly age-associated without being strongly outcome-associated, and the reverse is also true. The two models will weight that marker very differently.
5. They use different inputs. The marker sets overlap but are not identical. Biological Age leans more heavily on hormonal and nutrient markers. The Advanced models lean more heavily on markers with long-run outcome evidence behind them. A result that moves one number may barely register in the other.
6. They use different reference points. Advanced Biological Age is always calibrated against people of your exact age and sex. Biological Age compares your pattern against age patterns across the whole population. Being average for your age and being average overall are not the same thing.
7. One is a summary, the other is a set. Advanced Biological Age reports eleven results. Your Overall Advanced Biological Age is its own independently modeled result and is not the average of the ten system ages. Comparing a single whole-body number to one system’s age, or to an overall figure built a different way, will rarely produce a clean match.

If the two numbers differ by a few years, that is normal. If they differ substantially and consistently across several tests, the more useful next step is to look at your contributing factors in each system and bring both results to your healthcare provider.

Which one should I pay attention to?

Both, for different jobs.

• Tracking progress over time: Biological Age is a clean single line to watch, especially if you test consistently under similar conditions.
• Deciding what to work on: Advanced Biological Age is more actionable, because it separates your body into systems and shows which markers are driving each one.
• Talking to your doctor: Bring the contributing factors, not just the ages. The underlying biomarker values are what a clinician can act on.

What neither number is

Both results are wellness and educational tools. Specifically:

• Neither is a diagnosis, and neither should be used on its own to diagnose or treat any condition.
• Neither is a literal measurement of how old your cells or organs are. Both are comparisons expressed in years because years are intuitive.
• The Advanced models have not been reviewed or cleared by the FDA and are not intended as clinical decision support.
• Neither captures everything that shapes aging, including genetics, fitness, body composition, sleep quality, imaging findings, and medical history.

The bottom line

Biological Age gives you one number that tracks your overall biomarker aging signal over time. Advanced Biological Age gives you eleven outcome-anchored, peer-calibrated numbers that tell you which systems are strong and which deserve attention, plus the specific markers behind each. They are complementary views, and the most useful thing you can do with either is retest and watch the direction of travel.

References and further reading

On biological age models built from standard blood biomarkers

1. Levine ME, Lu AT, Quach A, et al. An epigenetic biomarker of aging for lifespan and healthspan. Aging (Albany NY). 2018;10(4):573-591. doi:10.18632/aging.101414
2. Liu Z, Kuo PL, Horvath S, Crimmins E, Ferrucci L, Levine M. A new aging measure captures morbidity and mortality risk across diverse subpopulations from NHANES IV: a cohort study. PLoS Med. 2018;15(12):e1002718. doi:10.1371/journal.pmed.1002718
3. Rutledge J, Oh H, Wyss-Coray T. Measuring biological age using omics data. Nat Rev Genet. 2022;23:715-727. doi:10.1038/s41576-022-00511-7

On organ systems aging at different rates within the same person

1. Oh HSH, Rutledge J, Nachun D, et al. Organ aging signatures in the plasma proteome track health and disease. Nature. 2023;624(7990):164-172. doi:10.1038/s41586-023-06802-1
2. Oh HSH, Le Guen Y, Rappoport N, et al. Plasma proteomics links brain and immune system aging with healthspan and longevity. Nat Med. 2025;31:2703-2711. doi:10.1038/s41591-025-03798-1

On the outcome data behind the Advanced models

1. Sudlow C, Gallacher J, Allen N, et al. UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 2015;12(3):e1001779. doi:10.1371/journal.pmed.1001779
2. Khan SS, Matsushita K, Sang Y, et al. Development and validation of the American Heart Association’s PREVENT equations. Circulation. 2024;149(6):430-449. doi:10.1161/CIRCULATIONAHA.123.067626

Your blood work is processed in a CLIA-certified, CAP-accredited laboratory. The Advanced BioAge Report is generated using VOLO Health’s modeling platform and is provided for educational and informational purposes only. It is not intended to diagnose, cure, mitigate, treat, or prevent any disease or medical condition and does not constitute medical advice. Statistical associations described here are population-level findings and are not evidence of causation at the individual level. Always consult a qualified healthcare professional with questions regarding your health.

Your biological age reveals how your body is actually aging, not just how many birthdays you have had. The Advanced BioAge Report translates your blood biomarker results into an age for each major organ system, so you can see where your health is thriving and where it needs attention.

This guide walks through every part of the report and what each piece means.

What’s in your report

• Your Overall Advanced Biological Age
• Ten system-specific biological ages
• A peer group ranking for your overall result and for each system
• Contributing factors showing which biomarkers are most influencing each system

Your Overall Advanced Biological Age

At the top of your report you will see two numbers: your chronological age, and your Overall Advanced Biological Age. The difference between them is your delta, shown as something like “2 years younger.”

One thing worth knowing up front: your overall result is not the average of your ten system ages. It is modeled separately, using biomarker patterns associated with whole-body resilience and long-term susceptibility across multiple systems at once. So it is possible for your overall result to look better or worse than a simple average of the systems below it. That is not an error. It is a different calculation answering a broader question.

The ten body systems

Each system gets its own age, modeled independently. Because each one is calculated the same way, they are directly comparable to each other.

Cardiovascular reflects vascular and cardiac risk physiology, drawing on markers associated with atherosclerosis, endothelial stress, inflammation, and cardiometabolic load.

Metabolic reflects glucose regulation and insulin sensitivity, and how flexible your metabolism looks versus showing signs of strain.

Respiratory reflects patterns in your blood associated with reduced respiratory reserve and chronic airway inflammation.

Brain reflects biomarker patterns associated with brain aging and cognitive resilience, often vascular, metabolic, or inflammatory in nature.

Liver reflects hepatocellular integrity, metabolic liver load, and detoxification-related stress.

Kidney reflects filtration efficiency and renal reserve.

Bone reflects bone remodeling balance and mineral metabolism.

Thyroid reflects thyroid axis physiology and the downstream metabolic signaling it drives.

Blood reflects oxygen-carrying capacity, red cell and iron dynamics, and overall blood system resilience.

Inflammation reflects chronic inflammatory load, sometimes described as inflammaging, which cuts across many age-related conditions.

A note on systems we infer rather than measure directly

Respiratory, bone, and brain deserve a specific caveat. We are not performing a lung function test, a bone density scan, or a cognitive assessment. These ages are derived entirely from patterns in your blood biomarkers that the underlying models found to be associated with long-term outcomes in those areas across large populations.

That association is real and it is useful. It is not the same as a direct measurement of the organ. If one of these systems comes back with an unfavorable result, the appropriate next step is a conversation with your provider about whether direct testing makes sense for you, not a conclusion about your lungs or bones.

What your delta actually means

If your cardiovascular age reads four years younger than your chronological age, it means your cardiovascular biomarker profile looks like the average profile of someone four years younger than you.

What it does not mean:

• It is not a prediction that you will live four extra years.
• It is not a claim that your heart is literally four years younger.
• It is not a diagnosis, in either direction.

It is a comparison, expressed in years because years are the frame most people find intuitive.

Your deltas will often differ across systems, and that variation is the most useful thing in the report. A strong cardiovascular result alongside an average metabolic one tells you something specific about where to focus. A single number would hide that.

Your peer group ranking

Alongside each age, you will see a ranking such as “your cardiovascular health is in the top 14% for your peer group.” Your peer group is people of your same age and sex.

A ranking in the top 14% means roughly 14% of your peers have a result at least as favorable as yours, so you are ahead of about 86% of them. Lower percentages are better.

Why your ranking and your delta can tell different stories. You may see similar deltas across several systems while the rankings vary widely. This happens most often for younger customers, and it is expected. The models have less room to spread results apart at younger ages, when very few people in the reference data have developed the conditions being modeled. The age deltas compress toward a narrow band, while the peer ranking still has plenty of room to differentiate.

If that describes your report, read the ranking as your primary signal and the delta as secondary. As you get older, or as you retest over time, the deltas become more expressive.

Contributing factors

For each system, you can open the contributing factors view to see which of your biomarkers are influencing that age and in which direction. This is the part of the report you can actually act on.

Two things to keep in mind:

The same marker can matter differently in different systems. A given result might be a major driver of one system’s age and barely register in another. That is because the models learned, from outcome data, which markers carry weight for which outcomes.

A result can be inside the standard lab range and still pull your age up. Standard reference ranges were built to flag clinical outliers in a general population. These models are looking for the values associated with the most favorable long-term outcomes for someone your age and sex, which is a narrower target. A value that is clinically normal but not at that optimum can still show up as a contributing factor. That is a signal for proactive attention, not a cause for alarm.

What to do with your results

1. Start with the systems that ranked least favorably, not the ones with the biggest headline deltas.
2. Open the contributing factors for those systems and note which markers appear.
3. Cross-reference those markers with your individual biomarker insights in your dashboard for context on what each one reflects.
4. Retest. A single report is a snapshot. The direction of travel across two or three reports carries far more signal than any one result, and normal variation between tests is expected. 
5. Bring the report to your healthcare provider, especially the contributing factors rather than just the ages.

An unfavorable result in any system is not a diagnosis. It is a prompt for attention, and a starting point for a better conversation with your provider.

References and further reading

On different organ systems aging at different rates

1. Oh HSH, Rutledge J, Nachun D, et al. Organ aging signatures in the plasma proteome track health and disease. Nature. 2023;624(7990):164-172. doi:10.1038/s41586-023-06802-1
2. Oh HSH, Le Guen Y, Rappoport N, et al. Plasma proteomics links brain and immune system aging with healthspan and longevity. Nat Med. 2025;31:2703-2711. doi:10.1038/s41591-025-03798-1

On the Inflammation domain

1. Franceschi C, Campisi J. Chronic inflammation (inflammaging) and its potential contribution to age-associated diseases. J Gerontol A Biol Sci Med Sci. 2014;69(Suppl 1):S4-S9. doi:10.1093/gerona/glu057
2. Franceschi C, Bonafè M, Valensin S, et al. Inflamm-aging: an evolutionary perspective on immunosenescence. Ann N Y Acad Sci. 2000;908:244-254. doi:10.1111/j.1749-6632.2000.tb06651.x

On the Kidney domain

1. Shlipak MG, Matsushita K, Ärnlöv J, et al. Cystatin C versus creatinine in determining risk based on kidney function. N Engl J Med. 2013;369(10):932-943. doi:10.1056/NEJMoa1214234

On the Brain domain

1. Livingston G, Huntley J, Liu KY, et al. Dementia prevention, intervention, and care: 2024 report of the Lancet standing Commission. Lancet. 2024;404(10452):572-628. doi:10.1016/S0140-6736(24)01296-0

On the Respiratory domain

1. Ellingsen J, Janson C, Bröms K, et al. CRP, fibrinogen, white blood cells, and blood cell indices as prognostic biomarkers of future COPD exacerbation frequency: the TIE cohort study. J Clin Med. 2024;13(13):3855. doi:10.3390/jcm13133855

On the Thyroid and Bone domains

1. Rodondi N, den Elzen WPJ, Bauer DC, et al. Subclinical hypothyroidism and the risk of coronary heart disease and mortality. JAMA. 2010;304(12):1365-1374. doi:10.1001/jama.2010.1361
2. Blum MR, Bauer DC, Collet TH, et al. Subclinical thyroid dysfunction and fracture risk: a meta-analysis. JAMA. 2015;313(20):2055-2065. doi:10.1001/jama.2015.5161

Your blood work is processed in a CLIA-certified, CAP-accredited laboratory. The Advanced BioAge Report is generated using VOLO Health’s modeling platform and is provided for educational and informational purposes only. It is not intended to diagnose, cure, mitigate, treat, or prevent any disease or medical condition and does not constitute medical advice. It has not been reviewed or cleared by the FDA. Statistical associations described here are population-level findings and are not evidence of causation at the individual level. Always consult a qualified healthcare professional with questions regarding your health.

There is no single lab test that directly measures biological age. Any biological age result is a model, and models differ enormously in how they are built. This guide explains how the Advanced BioAge Report is calculated, and what that method can and cannot tell you.

The Advanced BioAge Report is powered by VOLO Health’s modeling platform. We collect and process your blood work, and VOLO’s models turn those results into system-by-system age estimates.

We chose this platform for one reason above all others: the models are anchored to real health outcomes rather than to expert consensus about what “healthy” should look like. The model was never told which values are good. It learned that from data on what actually happened to hundreds of thousands of people over many years.

Step 1: Your blood work

Everything starts with measurement. Your sample is processed in a CLIA-certified, CAP-accredited laboratory, and the resulting biomarker values are the only inputs to your report.

The models draw on markers across several categories, including metabolic and glycemic markers, a full lipid picture, liver and kidney function, inflammatory markers, and blood count markers. Report completeness scales with panel breadth: the more of these markers your panel includes, the more confident each system result becomes.

Step 2: Removing the effect of age and sex

Age is by far the strongest predictor of most chronic disease, and sex matters substantially too. If a model does not handle this carefully, it mostly ends up rediscovering that older people get sick more often, which tells you nothing about you.

So the first modeling step builds a baseline expectation of disease prevalence as a function of age and sex, separately for each condition. Every individual in the training data is then measured against their own demographic baseline rather than against everyone else. What comes out is a relative signal: how does this person’s risk compare to what would be expected for someone of their age and sex?

This is why your report is explicitly peer-calibrated. Two people with identical lab values but different ages will receive meaningfully different reports, because the question being asked is always relative to your own demographic group.

Step 3: Learning from outcomes, not from age

The models were trained on two large bodies of data: a major long-term population health study following roughly half a million participants with linked medical records, plus a much larger set of US clinical records used for external validation and calibration.

For each health system, the outcomes being modeled are defined using standardized diagnostic code sets applied to those linked medical records, over a multi-year observation window following the original blood draw. In other words, the model looked at people’s biomarkers at one point in time, then looked at what conditions they went on to develop years later, and learned which biomarker patterns preceded which outcomes.

This is the single most important thing to understand about the method. The model was not trained to guess your age. It was trained to estimate long-term disease risk. The age you see in your report is the final translation step, not the modeling target.

Step 4: Capturing how markers interact

The models used are gradient-boosting machines, chosen specifically because they handle two things that simpler models struggle with:

Nonlinear relationships. Risk does not always rise in a straight line as a marker rises. Some markers carry risk at both ends of their range. Some have a threshold effect. The model can represent those shapes rather than forcing a straight line through them.

Interactions between markers. This is the part that a marker-by-marker reading of your labs cannot give you. Certain combinations carry more risk together than the sum of their individual contributions. Two people can have similar-looking individual results and meaningfully different combined risk pictures. Because the models were trained on the whole panel at once, those combinations are captured.

Step 5: Modeling each system separately

Every system in your report is modeled independently, using the same underlying data and the same set of biomarker inputs. No single condition gets privileged treatment.

This matters more than it sounds. Because the systems are modeled simultaneously on the same population, the models can capture trade-offs where a biomarker value that looks favorable for one outcome is less favorable for another. A single-condition model would miss that entirely.

It also means your ten system results are directly comparable to each other. A given ranking in the kidney system means the same thing, relative to your peers, as the same ranking in the brain system.

Step 6: Translating into an age

Once your relative risk estimate exists for a system, it is converted into an age by working backwards through the reference curve for people of your sex. The model asks: at what age would the average person in the reference population carry this same level of predicted risk?

If the answer is four years below your chronological age, your delta reads as four years younger. That is the whole of what the number means.

Why this differs from your standard lab reference range

The reference range printed next to a lab value is designed to flag clinical outliers in a general population. It is typically the central range of a healthy reference group, sometimes adjusted for perceived healthy limits.

These models are looking for something different: the biomarker values associated with the most favorable long-term outcomes across multiple systems at once, for someone of your specific age and sex. That target range is usually narrower than the standard lab range and can sit in a different place within it.

The consequence is that a value can be entirely normal on your lab report and still appear as a contributing factor pulling a system’s age upward. Both things are true at once. The lab range is telling you there is no immediate clinical concern. The model is telling you the value is not at the modeled optimum for your profile. Only the second one is a target for proactive attention, and neither is a diagnosis.

Honest limitations

• This is a model, not a measurement. Every result is an estimate built on population-level associations, and population associations are not evidence of causation for any individual.
• Blood biomarkers cannot see everything. Genetics, fitness, body composition, sleep quality, imaging findings, and medical history all shape aging and none of them are inputs here.
• Some systems are inferred, not measured. The respiratory, bone, and brain results are derived from blood patterns, not from lung function testing, bone density scanning, or cognitive assessment.
• Single results are snapshots. Lab values move with hydration, fasting state, time of day, sleep, illness, medication, and training load. The trend across multiple reports is more informative than any single one.

References and further reading

1. Sudlow C, Gallacher J, Allen N, et al. UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 2015;12(3):e1001779. doi:10.1371/journal.pmed.1001779
2. Levine ME, Lu AT, Quach A, et al. An epigenetic biomarker of aging for lifespan and healthspan. Aging (Albany NY). 2018;10(4):573-591. doi:10.18632/aging.101414
3. Rutledge J, Oh H, Wyss-Coray T. Measuring biological age using omics data. Nat Rev Genet. 2022;23:715-727. doi:10.1038/s41576-022-00511-7
4. Oh HSH, Rutledge J, Nachun D, et al. Organ aging signatures in the plasma proteome track health and disease. Nature. 2023;624(7990):164-172. doi:10.1038/s41586-023-06802-1
5. Oh HSH, Le Guen Y, Rappoport N, et al. Plasma proteomics links brain and immune system aging with healthspan and longevity. Nat Med. 2025;31:2703-2711. doi:10.1038/s41591-025-03798-1

Your blood work is processed in a CLIA-certified, CAP-accredited laboratory. The Advanced BioAge Report is generated using VOLO Health’s modeling platform and is provided for educational and informational purposes only. It is not intended to diagnose, cure, mitigate, treat, or prevent any disease or medical condition and does not constitute medical advice. Statistical associations described here are population-level findings and are not evidence of causation at the individual level. Always consult a qualified healthcare professional with questions regarding your health.

Symptoms and biomarkers work together

Thyroid symptoms are famously nonspecific, and no two people have exactly the same set. That is precisely why lab tests sit at the center of your report: the biomarkers show what your thyroid is doing, and your symptoms add the human context of how it is affecting you. Read together, they are far more informative than either alone.

When your thyroid slows down (hypothyroid)

An underactive thyroid slows your metabolism, so the symptoms tend to be ones of “slowing down”:

• Fatigue and sluggishness
• Feeling cold when others don’t
• Weight gain or fluid retention
• Constipation
• Dry skin and thinning hair
• Low mood, brain fog, or poor memory

When your thyroid speeds up (hyperthyroid)

An overactive thyroid does the opposite, revving your system up:

• Feeling hot, sweaty, or restless
• Weight loss despite a normal or bigger appetite
• A racing or irregular heartbeat
• Anxiety, nervousness, or irritability
• Trembling hands
• Trouble sleeping

Specific vs non-specific symptoms

Some symptoms are non-specific: tiredness, low mood, or weight change could stem from many causes and don’t point to the thyroid on their own. They gain meaning when they line up with your labs. Others are more specific (for example, a racing heartbeat with heat intolerance, or neck fullness) and point more directly toward a particular thyroid pattern. Your report groups them this way so the more telling signals carry appropriate weight.

How your report uses them

Your symptoms are scored for severity and combined with your biomarker results to build your overall picture: whether things look balanced, underactive, or overactive, and how strongly the signals agree. When your symptoms and your labs point the same way, that adds confidence; when they disagree, that is useful too, and worth discussing with your doctor.

What this means for you

If your labs are normal but you still feel off, that doesn’t mean your experience isn’t real: it means the answer may lie elsewhere, or may be worth rechecking over time. And if your labs are abnormal but you feel fine, your report helps flag it early. Either way, the goal is to bring the two together into one clear, personalized read.

Key takeaway

Hypothyroid symptoms are about slowing down; hyperthyroid symptoms are about speeding up. Neither is diagnosed on feeling alone. Your report pairs how you feel with what your biomarkers show to give you the fullest picture.

Where to read more

• American Thyroid Association: Hypothyroidism, Hyperthyroidism
• Mayo Clinic: Hypothyroidism, Hyperthyroidism.

This article is for general wellness and educational purposes and is not medical advice. For anything specific to your health, please speak with your doctor.