In this article, we’ll look at a large review showing how the Lipid Accumulation Product (LAP)—a simple combination of waist size and triglycerides—is linked with type 2 diabetes, hypertension, and mortality risk. I also included an LAP calculator in the article so you can quickly estimate your own LAP score.
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🇨🇳 中文（简体）
这篇文章将解读一项大型综述研究：脂质累积指数（LAP）——把腰围和甘油三酯结合起来——与2型糖尿病、高血压以及死亡风险有关。文章里还加入了一个 LAP 计算器，方便你快速算出自己的 LAP 分数。
请按下方的播放按钮收听。
🇪🇸 Spanish (Latinoamérica)
En este artículo revisamos un gran estudio que muestra cómo el Producto de Acumulación de Lípidos (LAP)—una combinación simple de circunferencia de cintura y triglicéridos—se relaciona con el riesgo de diabetes tipo 2, hipertensión y mortalidad. También incluí una calculadora de LAP en el artículo para que puedas estimar tu puntaje fácilmente.
Presiona el botón de reproducir para escuchar.
Introduction
Most people have been taught to judge health risk by weight and BMI. But BMI can miss a big problem: you can look “normal weight” and still carry high-risk belly fat, especially the kind that wraps around organs and quietly drives insulin resistance.
Now consider this question:
What if there were a simple number—built from two things many people already have, a tape measure and a fasting triglyceride level—that could help predict risk for type 2 diabetes, hypertension, and even mortality?
That’s the idea behind the Lipid Accumulation Product (LAP).
LAP combines waist circumference (a clue to visceral fat) with triglycerides (a clue to metabolic strain). In a large systematic review and meta-analysis, researchers looked across many studies and found that higher LAP levels were consistently linked with type 2 diabetes and high blood pressure, and in some groups, with a higher risk of death over time.
In this article, I’ll explain what LAP is, what the research found, and how to use it.
II. Why LAP Uses Waist Circumference + Triglycerides (and Why the Combo Makes Sense)
Most “obesity” markers measure size, not metabolic strain. That’s why the Lipid Accumulation Product (LAP) combines waist circumference (WC) with fasting triglycerides (TG)—it captures both an anatomic signal (where fat is stored) and a physiologic signal (how stressed your metabolism is).
Waist circumference is a practical proxy for central fat, which often tracks with visceral fat (the deeper fat around organs).
Visceral fat tends to be more metabolically harmful than the fat under the skin, partly because it’s linked with insulin resistance and releases inflammatory signals.
Triglycerides add something waist circumference alone cannot: they act like a “metabolic stress meter.” Two people can have the same waist size, but very different risk if one has high TG.
The authors explain that TG in the LAP formula is an independent risk factor for type 2 diabetes, and that LAP is tied to visceral adiposity and insulin resistance—a pathway that helps explain why LAP tracks with diabetes risk.
In plain terms, TG helps identify “spillover fat.” When the body can’t safely store excess energy, fat tends to accumulate in places it shouldn’t (like the liver, blood vessels, and even the heart). This “ectopic” fat is closely linked with insulin resistance—one of the core drivers of type 2 diabetes.
This also helps explain why LAP can track with hypertension risk: visceral fat and the metabolic changes that accompany high TG levels can influence blood vessels and endothelial function, and fat tissue releases signaling molecules (adipocytokines) that may raise blood pressure.
Bottom line: WC tells you how much central fat you’re carrying; TG tells you how metabolically “unhappy” that fat storage situation is. Putting them together gives a clearer picture than either one alone.
III: How This Review Was Done
This paper by Khanmohammadi and colleagues is a systematic review and meta-analysis, meaning the authors didn’t rely on a single study—they gathered all eligible studies, screened them using a structured method, and summarized what the body of evidence showed.
Where they searched how broad
The authors searched four major medical databases—Embase, Web of Science, PubMed, and Scopus—and identified 684 publications to start.
How they narrowed it down to the final studies
After removing duplicates, they screened 301 records by title/abstract, then reviewed 34 full-text papers. Five were excluded for clear reasons (like insufficient data, mixing diabetes and prediabetes together, or using a different LAP formula). In the end, 29 articles were included.
What kinds of studies were included
The 29 included studies were mostly observational:
- 18 cross-sectional studies (a snapshot in time)
- 12 cohort studies (people followed over time, prospectively or retrospectively)
Study sizes ranged from 264 to 215,651 participants, and cohort follow-up ranged from 5 to 18.1 years.
Where the studies came from (multiple countries)
The studies covered 14 countries, with the largest share from China (12 studies), plus Iran (3), Korea (2), USA (2), Japan (2), and others including Brazil, Germany, Italy, Mongolia, Netherlands, Poland, Romania, Serbia, and Thailand.
What outcomes did they focus on
They specifically examined how LAP relates to three outcomes:
- Type 2 diabetes
- Hypertension
- All-cause mortality
Across studies, results were reported using common statistical measures like odds ratios (OR), hazard ratios (HR), relative risk (RR), and sometimes discrimination statistics like AUC.
How they judged study quality
They evaluated study quality using the National Institutes of Health (NIH) Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies (14 criteria). Two authors rated each study independently; disagreements were resolved by discussion or a third author, and quality ratings were used to clarify the strength of evidence (not to automatically exclude studies).
How they did the meta-analysis (what they pooled)
They performed a meta-analysis when two or more studies reported the same outcome measure. Because LAP differs by sex, they ran analyses separately for men and women.
For mortality, they pooled results using an inverse-variance random-effects model, assessed heterogeneity (I² and τ²), and used R (version 4.0.3) with the “meta” package.
IV. Key Findings
Across 29 studies, the authors found a consistent pattern: higher Lipid Accumulation Product (LAP) was directly linked with both type 2 diabetes and hypertension, and it also showed a relationship with all-cause mortality (stronger in women than men).
1) LAP and Type 2 Diabetes (T2D): consistently linked
Every study included in this review that evaluated diabetes found a direct association between higher LAP and T2D.
Several studies also showed a clear “dose-response” pattern: higher LAP categories (like quartiles) tended to show higher diabetes risk.
Importantly, many papers suggested that LAP often predicts diabetes better than BMI and sometimes better than waist circumference alone.
For example, one study summary table shows diabetes prediction (AUC) of 0.77 for LAP vs 0.66 for BMI.
2) LAP and Hypertension: consistently linked (all 10 studies)
Ten studies evaluated LAP and hypertension, and all found a positive, significant association.
In multiple studies, the highest LAP groups (like the top quartile) showed much higher odds of hypertension compared with the lowest group. The review highlights examples where the odds ratios were very large in the highest groups (these are study-specific, not universal numbers).
Several studies also compared LAP to other measures (BMI, waist, etc.) and found that LAP was often superior for predicting hypertension.
3) LAP and All-Cause Mortality: signal stronger in women; mixed in men
Only four studies looked at LAP and all-cause mortality, so this part of the evidence base is smaller than that of diabetes or hypertension.
When the authors pooled results (meta-analysis), they found:
- Women: Each 1 standard deviation increase in LAP was linked to HR 1.24 (95% CI 1.00–1.53).
- Men: The pooled estimate was HR 1.07, which was not statistically significant, and results varied widely among studies.
One included study also reported that LAP was a better predictor of mortality than BMI (8.2% vs 5.4% mortality at 6 years), and noted stronger findings in non-diabetic subgroups than diabetic groups.
4) Is LAP “better than BMI” overall?
The authors’ overall conclusion: most studies found that LAP outperformed conventional measures like BMI and waist circumference for predicting T2D and hypertension, and LAP may have higher prognostic significance in females.
They also explain why: BMI and waist circumference are “size-only” measures that don’t reliably distinguish visceral from subcutaneous fat, whereas LAP combines waist circumference with triglycerides, both independent risk factors, giving a better window into fat distribution and metabolic risk.
5) The major caution for readers
There is no single universal LAP cutoff because the included studies used different cut-off points, and that inconsistency is one reason the authors could not pool all diabetes and hypertension results into one meta-analysis.
Then you can calculate your own LAP score using the LAP calculator below.
V. The LAP Calculator
Before you use the LAP calculator: a quick note about “cutoffs”
Right now, there are no universally agreed-upon cutoff values for LAP. In the meta-analysis you’re reading about, the included studies used different LAP cutoffs, which makes it hard to give one single “high LAP” number that applies to everyone. Association of lipid accumulati…
Because the overwhelming majority of my readers are in the United States, we’ll use the metabolic syndrome thresholds from the NCEP ATP III / AHA–NHLBI criteria as practical guideposts. These are widely used clinical thresholds for “abdominal obesity” (waist) and high triglycerides.
Metabolic syndrome cutoffs we’ll use as reference points
Triglycerides (all groups):
- TG ≥ 150 mg/dL (which equals 1.7 mmol/L)
Waist circumference (NCEP ATP III / AHA–NHLBI standard U.S. thresholds—commonly applied to White/Europid populations):
- Men: ≥ 102 cm (40 in)
- Women: ≥ 88 cm (35 in)
Asian Americans (AHA guidance recognizes lower waist cutpoints may be appropriate):
- Men: ≥ 90 cm (35 in)
- Women: ≥ 80 cm (31 in)
Why use metabolic syndrome cutoffs to interpret LAP?
LAP combines waist (a marker of central/visceral fat burden) with triglycerides (a marker of metabolic strain). In the absence of validated LAP cutoffs, using metabolic syndrome thresholds gives us a sensible, familiar “reference line”:
- If your LAP is below the MetS-based reference, you’re below the point where both waist and triglycerides meet metabolic syndrome criteria.
- If your LAP is at or above the reference, it suggests you meet both MetS components (waist + TG)—a pattern that often tracks with higher cardiometabolic risk in research—but it does not diagnose metabolic syndrome by itself.
This approach is a transparent workaround, not a claim that these LAP values are “official” disease cutoffs.
The LAP “reference numbers” that come out of those Metabolic Syndrome cutoffs (so you can compare your result)
Metabolic Syndrome-based LAP reference points (U.S. units + metric equivalents)
| Group | Waist cutpoint (in) | Waist cutpoint (cm) | TG cutpoint (mg/dL) | TG cutpoint (mmol/L) | MetS-equivalent LAP reference value* |
|---|---|---|---|---|---|
| White/Europid man (NCEP/AHA U.S. standard) | 40 in | 102 cm | 150 | 1.7 | 62.9 |
| White/Europid woman (NCEP/AHA U.S. standard) | 35 in | 88 cm | 150 | 1.7 | 51.0 |
| Asian man (AHA lower waist option) | 35 in | 90 cm | 150 | 1.7 | 42.5 |
| Asian woman (AHA lower waist option) | 31 in | 80 cm | 150 | 1.7 | 37.4 |
Short note on units: You’ll notice there is only one “MetS-equivalent LAP reference value” column. That’s because LAP is always calculated using waist in centimeters (cm) and triglycerides in mmol/L.
Even if you enter your waist in inches and triglycerides in mg/dL, the calculator converts them into cm and mmol/L first. So the final LAP number is the same either way—once the units are converted, the LAP result (and the reference values) do not change.
(These are reference points, not universal cutoffs.)
Note on the LAP calculator below: LAP is calculated using waist circumference (WC) and fasting triglycerides (TG): Men: (WC in cm − 65) × TG (mmol/L) and Women: (WC in cm − 58) × TG (mmol/L).
Our LAP calculator uses this same formula—you just enter your sex, waist circumference, and triglycerides, and the calculator automatically converts units (if needed) and automatically performs the (WC − 65) or (WC − 58) step for you.
Lipid Accumulation Product (LAP) Calculator
LAP uses waist circumference + triglycerides. It is not a diagnosis—just a screening-style risk marker.
Show formula + unit conversions
- Men: (Waist in cm − 65) × TG in mmol/L
- Women: (Waist in cm − 58) × TG in mmol/L
- Inches → cm: inches × 2.54
- Triglycerides mg/dL → mmol/L: mg/dL ÷ 88.57
Educational use only. If you have symptoms, very high readings, or medical concerns, discuss with your clinician.
There is no universally agreed “normal” cutoff for Lipid Accumulation Product (LAP). Most research interprets LAP using population percentiles or study-specific thresholds. In U.S. adults (NHANES III), the LAP 75th percentile is about 37.4 for men and 30.3 for women, while the 25th percentile is about 19.1 for men and 15.6 for women. These are reference percentiles, not diagnostic cutoffs—so use LAP as a risk marker and trend tool, not a diagnosis.
Reference:
- Grundy, Scott M., et al. “Diagnosis and Management of the Metabolic Syndrome: An American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement.” Circulation, 2005. https://www.ahajournals.org/doi/10.1161/circulationaha.105.169405
VI. How a reader could use this info
If you already have a waist measurement and a fasting triglyceride (TG) value, LAP gives you a simple way to combine them into one number that may reflect metabolic risk better than BMI alone. The authors note that LAP can be an inexpensive screening-style tool for risk related to type 2 diabetes, hypertension, and even mortality.
Here’s a practical way to use it:
- Use LAP as a “risk signal,” not a diagnosis. A higher LAP generally means a stronger signal of visceral fat + metabolic strain (waist + TG), which is why it tends to outperform BMI and waist alone in many studies.
- Compare your LAP to the MetS-based guidepost we’re using (U.S.-friendly). Because there are no agreed-upon LAP cutoffs, we’re using metabolic syndrome waist + TG thresholds as a reasonable reference line (explained in the calculator section). The review itself emphasizes that studies used different LAP cutoffs.
- Track your trend over time. LAP is especially useful when you repeat it (for example every 3–6 months):
- If your waist decreases, LAP usually falls.
- If your TG improves, LAP usually falls.
- If both improve, LAP drops even more—because it’s a combined measure.
- Use it to focus your lifestyle targets. LAP basically points your attention to two levers:
- Waist reduction (central fat)
- Lower TG (metabolic stress) Association of lipid accumulati…
If your LAP is high, it’s a cue to take a closer look at blood pressure, A1C/fasting glucose, and the lifestyle drivers that influence TG and abdominal fat.
Good Use Case
LAP can be especially useful when your BMI looks “normal,” but your waist size and/or triglycerides are elevated—because BMI alone can miss hidden metabolic risk.
VII. Limitations and what researchers say is next
This meta-analysis is strong in scope, but the authors are clear about several limitations:
Different LAP cutoffs across studies. Because studies used different cut-off values, the authors could not perform a pooled meta-analysis for type 2 diabetes and hypertension outcomes.
Different adjustment models. Studies adjusted for different confounders, which makes “apples-to-apples” pooling harder.
Variable study quality and potential confounding bias. Some included studies were poorer quality, and confounding or methodology issues can’t be excluded.
Limited generalizability in some populations. Some studies focused on specific groups (example: post-menopausal women), so applying results broadly requires caution.
Often only a single LAP measurement. Many studies measured LAP once, even in long follow-up periods, which can miss how risk changes over time.
Not all studies reported results by sex, even though sex differences may matter.
What’s next? The paper strongly suggests the field needs more standardized cutoffs (by sex, age, and population) and more consistent reporting so results can be pooled and applied more confidently across groups. This is implied directly by their point that cutoff variation and inconsistent models blocked pooled analysis.
VIII. Take-home message
LAP is a simple number (waist + triglycerides) that consistently tracks with risk for type 2 diabetes and hypertension across many studies, and it may relate to mortality risk—especially in women—but cutoffs are not standardized.
Use LAP as a practical risk marker and trend tool:
- If your LAP is high, treat it as a signal to pay attention (BP, glucose/A1C, lifestyle).
- If your LAP improves over time, that usually means your waist and/or triglycerides are improving—two meaningful drivers of metabolic risk.
Don’t Get Sick!
About Dr. Jesse Santiano, MD
Dr. Santiano is a retired internist and emergency physician with extensive clinical experience in metabolic health, cardiovascular prevention, and lifestyle medicine. He reviews all medical content on this site to ensure accuracy, clarity, and safe application for readers. This article is for educational purposes and is not a substitute for personal medical care.
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Related:
References:
- Khanmohammadi, Shaghayegh, et al. “Association of Lipid Accumulation Product with Type 2 Diabetes Mellitus, Hypertension, and Mortality: A Systematic Review and Meta-Analysis.” Journal of Diabetes & Metabolic Disorders, vol. 21, 2022, pp. 1943–1973. doi: 10.1007/s40200-022-01114-z. https://pubmed.ncbi.nlm.nih.gov/36404835/
- Kahn, Henry S. “The ‘Lipid Accumulation Product’ Performs Better than the Body Mass Index for Recognizing Cardiovascular Risk: A Population-Based Comparison.” BMC Cardiovascular Disorders, vol. 5, 2005, article 26. https://pmc.ncbi.nlm.nih.gov/articles/PMC1236917/
- Kaneva, A. M., et al. “Age-Adjusted Cut-Off Values of Lipid Accumulation Product (LAP) for Hypertension Risk Prediction.” Scientific Reports, 2021. https://www.nature.com/articles/s41598-021-90648-y
- Yang, S. H., et al. “Lipid Accumulation Product Index Predicts New-Onset Type 2 Diabetes…” 2022. https://pmc.ncbi.nlm.nih.gov/articles/PMC9719681/
- Wakabayashi, Ichiro, and Tomoya Daimon. “A Strong Association between Lipid Accumulation Product and Diabetes Mellitus in Japanese Women and Men.” Journal of Atherosclerosis and Thrombosis, 2014. https://pubmed.ncbi.nlm.nih.gov/24304961/
Disclaimer:
This article is for educational purposes and is not a substitute for professional medical advice, diagnosis, or treatment. Always consult your physician before making health decisions based on the TyG Index or other biomarkers.
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