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INSIG2 Genotype

Explore whether an inherited variation in your body's fat-control gene nudges you toward weight gain.
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Should you take a INSIG2 test?

This test is most useful if any of these apply to you.

Family History of Obesity
You want to know whether an inherited variation in your body's fat-control gene may be quietly nudging your weight risk upward.
Building a Genetic Health Picture
You're piecing together your inherited metabolic tendencies and want one more data point alongside lipid and obesity gene variants.
Starting Antipsychotic Medication
You want to know whether you carry a genetic variant tied to higher weight gain risk on these medications.
Curious About Metabolic Genes
You read the science, want to understand your own version of a much-discussed obesity-related gene, and know how to handle nuance.

About INSIG2 Genotype

The INSIG2 (insulin-induced gene 2) gene helps control the brakes your body puts on cholesterol and fat production. A few common variations in this gene have been studied for their possible link to obesity, body fat distribution, and lipid levels, but the picture is far from settled.

This is a research-grade test. The effects tied to INSIG2 variations are small, inconsistent, and clearly shaped by diet, activity, and other genes. Think of your result as one tile in a larger mosaic of inherited metabolic tendencies, not a diagnosis or a destiny.

What This Gene Actually Does

INSIG2 makes a protein that sits inside a part of the cell called the endoplasmic reticulum (the cell's internal sorting and manufacturing area). That protein acts as a brake on a master switch called SREBP, which turns on the machinery for making cholesterol and fatty acids. When INSIG2 is doing its job well, your body keeps cholesterol and fat production in check. INSIG2 works alongside a sister gene, INSIG1, which appears to be the dominant brake in liver cells, while INSIG2 may matter more in fat cells.

Common variations in the DNA near this gene, such as the one called rs7566605, do not change the protein itself. They sit nearby and may subtly tune how much INSIG2 your cells make. That is why effects on weight and lipids, if they exist, tend to be modest rather than dramatic.

Body Weight and Obesity

The most studied INSIG2 variant, rs7566605, was first linked to higher body weight in 2006. Since then, results have been mixed. Large studies in European, Japanese, Danish, and Chinese populations found no clear association between this variant and body mass index or being overweight.

A pooled analysis combining 74,345 people found no overall link between rs7566605 and obesity. However, when researchers looked only at the most extreme obesity (body mass index of 37.5 or 40 and above compared with under 25), a modest signal appeared. An earlier multi-cohort analysis of 16,969 people saw the link in five of nine groups but not in the other four.

What this means for you: if you carry the risk version of this variant, do not expect a large, automatic effect on your weight. The signal is real in some studies but small, and many people with the risk genotype have entirely normal weight.

Body Fat Distribution

Where fat sits on your body matters more for health than the total amount. Some studies have linked INSIG2 variants to differences in fat measured directly by CT (computed tomography) scans, including the deeper fat that wraps around organs (visceral fat) and the fat just under the skin (subcutaneous fat). In one analysis, the minor C version of rs7566605 was tied to more subcutaneous fat.

These findings are suggestive rather than definitive. They have not been consistently reproduced, and they do not yet support using the genotype to predict where your body stores fat.

Lipids and Metabolic Traits

Several INSIG2 variants beyond rs7566605 have been tied to lipid levels in specific groups. A variant called rs9308762 was linked to poorer metabolic control and higher sensitivity of triglyceride levels to a modern dietary pattern in Samoans. In non-Hispanic white children, rs12464355 was linked to higher LDL (low-density lipoprotein) cholesterol, and rs17047757 was linked to being overweight. In a large U.S. cohort, the variants rs1352083 and rs10185316 were tied to how HDL (high-density lipoprotein) cholesterol changes with age in white participants.

These effects are population-specific and often modest. They reinforce the broader picture: INSIG2 nudges metabolism in small ways, and the nudge often depends on diet and other factors.

Diet and Exercise Interactions

The clearest practical takeaway from the INSIG2 research is that environment seems to matter more than genotype alone. In Samoans, the link between INSIG2 variants and triglycerides showed up specifically in people eating a modern dietary pattern. In the Danish population, the rs7566605 variant influenced body mass index only when combined with self-reported physical activity levels, with physically inactive CC carriers showing a body mass index about 0.53 kg/m² higher than physically inactive G-allele carriers.

In other words, even if you carry a variant tied to higher metabolic risk, lifestyle choices appear to shape whether that risk shows up in your actual health.

Antipsychotic Weight Gain

One area where INSIG2 may have a more practical signal is medication-related weight gain. In a prospective study of people starting antipsychotic medications, the rs7566605 variant was associated with gaining 7% or more of body weight. A systematic review and meta-analysis also identified INSIG2 among the genes significantly associated with antipsychotic-related weight gain. That said, replication has been inconsistent: at least two studies in European-ancestry samples failed to confirm an INSIG2 effect on antipsychotic weight gain, so this signal should be treated as suggestive rather than settled.

If you are starting or already taking these medications, knowing your INSIG2 status may add a small piece of context, though it does not change which medication is appropriate for you.

Cervical Cancer in One Population

A separate INSIG2 variant, rs6726538, was linked to higher cervical cancer risk in Bangladeshi women in a single case-control study. The T allele and AT/TT genotype combinations were more common in cases than controls across most genetic models. This is one study in one population, and it should not be generalized to other groups without replication. Larger genome-wide studies of cervical cancer have pointed to other regions (such as the HLA region, PAX8, and CLPTM1L) as more robust susceptibility loci, not INSIG2.

What an Unexpected Result Should Make You Do

If your INSIG2 result flags a variant associated with weight or lipid effects, the action is not to repeat the genetic test. The genotype does not change. Instead, the result is best used to motivate closer attention to the things that actually move your numbers: lipid panels, fasting glucose and HbA1c (hemoglobin A1c, a three-month average of blood sugar), waist circumference, and body composition.

Pair this genotype with a standard lipid panel and a fasting metabolic workup. If you are considering antipsychotic medication, share the result with your prescribing clinician so they can factor it into shared decisions about weight monitoring. There is no specialist referral specific to INSIG2 itself.

What This Test Does Not Tell You

INSIG2 genotyping is not a clinically established diagnostic test. It does not predict obesity reliably, it does not stand in for a lipid panel or glucose testing, and it does not capture the dozens of other genetic and lifestyle factors that shape metabolic health. A single SNP (single nucleotide polymorphism, the technical term for a single-letter DNA variant) in a complex pathway can only carry so much information.

Treat the result as one input into a broader picture, alongside family history, standard labs, and your own measurements over time.

One-Time Result, Lifelong Use

Your INSIG2 genotype is fixed from birth. You do not need to retest it. The value of the result comes from how you use it over years to inform decisions about diet, activity, lipid monitoring, and weight tracking. The companion tests that benefit from ongoing tracking are the standard metabolic and lipid panels, not the gene itself.

When Results Can Be Misleading

Genetic tests have their own kinds of confounders that are different from the day-to-day issues that affect blood tests. The most important things to know:

  • Variant panel coverage: this test detects specific variants in the INSIG2 gene. A result that does not flag a known risk variant does not rule out other rare changes in the gene that the panel was not designed to find.
  • Population-specific allele frequencies: the risk versions of INSIG2 variants are more common in some ancestries than others, and the clinical meaning of a result depends partly on the population you most closely resemble genetically.
  • Variants of uncertain significance: occasionally a panel will report a DNA change whose meaning is not yet clear. These results require careful interpretation rather than action.
  • Clinical-grade vs direct-to-consumer differences: a result from a clinical genetics lab and a result from a consumer-grade test for the same variant should generally agree, but assay quality and reporting can differ.

A Note on Inherited Risk

Carrying a risk variant in INSIG2 does not mean you will become obese or develop metabolic disease. Many people with the risk genotype are lean and metabolically healthy. The effects of this gene are small, often only show up under certain dietary or activity conditions, and depend on your broader genetic and lifestyle context.

Frequently Asked Questions

References

22 studies
  1. Vimaleswaran KS, Franks P, Brage S, Sardinha L, Andersen L, Wareham N, Ekelund U, Loos RObesity2009
  2. Tabara Y, Kawamoto R, Osawa H, Nakura J, Makino H, Miki T, Kohara KObesity2008
  3. Boes E, Kollerits B, Heid I, Hunt S, Pichler M, Paulweber B, Coassin S, Adams T, Hopkins P, Lingenhel a, Wagner SA, Kronenberg FObesity2008
  4. Andreasen C, Mogensen M, Borch-johnsen K, Sandbæk a, Lauritzen T, Sørensen T, Hansen L, Almind K, Jørgensen T, Pedersen O, Hansen TPLoS ONE2008
  5. Liem E, Vonk J, Sauer P, Van Der Steege G, Oosterom E, Stolk R, Snieder HThe American Journal of Clinical Nutrition2010