Fasting insulin test: what it shows and where it falls short

A fasting insulin test measures the concentration of insulin in serum or plasma after an overnight fast. Insulin is released by the pancreas into the portal vein, and a large share of it is cleared by the liver before it reaches the general circulation. A 2022 review in the journal Obesity puts hepatic uptake at roughly 60% to 70%, so the number on the report reflects both how much insulin is secreted and how quickly it is cleared.

Because of that, fasting insulin levels are an indirect signal. They reflect secretion and clearance together, and they do not measure insulin sensitivity directly.

Insulin resistance means tissues respond less to a given amount of insulin. The reference method for measuring it is the hyperinsulinemic euglycemic glucose clamp, which a review by Muniyappa and colleagues describes as labor and time intensive. That is why clinical practice relies on simpler surrogates.

Several surrogate indexes are built from fasting values: HOMA-IR, QUICKI and the simple reciprocal of fasting insulin among them. Others use insulin and glucose measured during an oral glucose tolerance test. Each has advantages and limits, and the right choice depends on the question being asked. Our page on HOMA-IR covers the most common fasting index.

Clinics that add the fasting insulin test to a fasting panel usually read it next to fasting glucose as one more metabolic signal. How much weight that signal can carry for a given patient depends heavily on how the insulin was measured.

This is the most important caveat for anyone reading fasting insulin levels. Unlike glucose, insulin is measured with immunoassays that are not standardized to a common reference.

Units add a second layer. Labs report insulin in µIU/mL or in pmol/L, and a 2019 commentary by Knopp and colleagues notes at least two accepted conversion factors between them, with the most widely used one underreporting insulin by about 15%. A value converted by hand can drift from the value the lab measured.

We found no major clinical guideline that sets a diagnostic cutoff for fasting insulin, and the ADA workgroup notes that the lack of assay standardization hinders consistent measures for treatment guidelines. The reference interval printed on a report is set by that lab for its own method, and laboratories differ on how to interpret the result.

Research cutoffs do exist. The Gastaldelli review, for example, uses a fasting insulin limit of 12.2 mU/L published by McAuley and colleagues to derive cutoffs for several surrogate indexes. Cutoffs like this come from specific study populations measured on specific assays, so they do not transfer cleanly to another lab's results.

• Confirm the sample was truly fasting. A non-fasting draw is not a fasting insulin result.
• Read insulin next to fasting glucose from the same draw, not on its own.
• Check the units and the lab before comparing with any earlier value.
• Look at the direction across several draws on one method rather than one number in isolation.

Some longevity clinics read fasting insulin against a tighter band than the lab's reference interval. That is a clinical decision, and with an unstandardized assay it needs to be written down carefully. A documented range should say which lab and method it applies to, which units it uses, what evidence the clinic relied on and when it will be reviewed.

Our guide to optimal vs reference ranges walks through how to set and document a clinic range. For fasting insulin in particular, tie the range to one assay. If your main lab changes method, revisit the range before the next result is graded.

LongevityEHR is built for the way longevity clinics follow markers like this one. Your clinic sets its own range for fasting insulin, split by sex if you choose, and every released value is graded against it and added to a trend across draws. Each value keeps a snapshot of the range that applied when it was recorded, so tightening a range later does not regrade old results. See biomarker tracking for how ranges and trends work.

Results arrive through AI lab extraction: upload the lab PDF or an image of it, and values are matched to your biomarkers. A unit that doesn't match the biomarker's configured unit is flagged for review and never silently converted, which matters for insulin given the conversion problem above. A provider reviews every result before it is released to the patient's portal.

LongevityEHR classifies values against your ranges. It does not interpret them. Your providers write every word of interpretation, and changes to the plan live in the patient's care plan.