Welcome—today we’ll discuss how AIP (log TG/HDL) can help predict heart attack risk in younger adults with premature coronary artery disease, based on recent research. This article also includes an AIP calculator so you can calculate your number and track it over time.
🎧 ▶️ Press the play button below to listen in English.
🇨🇳 中文（简体）
欢迎收听——今天我们用通俗的话讲解动脉粥样硬化指数（AIP，log TG/HDL）如何帮助评估年轻人早发冠心病的心梗风险，并结合最新研究做解读。本文还提供AIP 计算器，方便你计算自己的数值并长期追踪变化。
请按下方的播放按钮收听。
🇪🇸 Spanish (Latinoamérica)
Bienvenido—hoy hablaremos de cómo el AIP (log TG/HDL) puede ayudar a predecir el riesgo de infarto en adultos jóvenes con enfermedad coronaria prematura, según estudios recientes. Este artículo también incluye una calculadora de AIP para que puedas obtener tu número y seguir tu progreso.
Presiona el botón de reproducir para escuchar.
I. Introduction
What if there is a tool that can help predict if you will have a heart attack at an early age? Would you be interested?
That question matters because heart disease remains the #1 killer in the United States. CDC
One promising tool is the Atherogenic Index of Plasma (AIP)—a simple number calculated from two standard lipid-panel values: triglycerides (TG) and HDL cholesterol (HDL-C).
In the study we’ll focus on here, Ran et al. (2025) calculated AIP as log[TG/HDL-C] and tested whether this simple index could predict future major events in people with premature coronary artery disease.
Why does this matter? Because when coronary artery disease shows up early, it often means the arteries have been under metabolic stress (like insulin resistance) for years, and the person may face more years of risk ahead.
Ran et al. point out that CAD is becoming more common in younger individuals, making early risk detection even more important.
In plain terms, this article answers one question: Can a high AIP identify younger CAD patients who are more likely to run into serious cardiovascular trouble later on?
Ran et al. followed patients for a median of 52 months and tracked MACE (major adverse cardiovascular events), using AIP as the key predictor.
II. What is “Premature” Coronary Artery Disease?
Premature coronary artery disease (PCAD) is coronary artery disease that happens earlier than expected.
Ran et al. (2025) define PCAD as coronary disease occurring in:
- Men <45 years
- Women <55 years
Why this definition matters
When CAD appears in these younger age groups, it’s not just “the same problem, earlier.” It often signals:
- a heavier burden of risk factors earlier in life, and
- a longer runway for plaque progression and repeat events.
Ran et al. emphasize that CAD incidence has increased rapidly among young individuals in recent years, which is one reason PCAD has become such an important prevention target.
Why PCAD matters for preventing ACS (heart attacks and unstable angina)
Acute coronary syndromes (ACS) usually occur when coronary plaques become unstable and trigger a clot. With PCAD, the stakes are high because people may face decades of exposure to that risk unless the underlying drivers are addressed.
Ran et al. cite prior evidence that PCAD can carry a poor long-term outlook, noting that in a prospective cohort, one-third of PCAD patients experienced an ischemic event and 36% had at least a second recurrent event—underscoring why better risk markers and earlier intervention matter.
III. Incidence and why PCAD matters for ACS prevention
Premature CAD and early heart attacks are not rare
Premature coronary artery disease (PCAD) is a smaller slice of all coronary disease, but it’s clinically outsized because it happens earlier and can repeat for decades.
- In a JACC commentary, Michos et al. (2019) note that about 4% to 10% of acute myocardial infarction (AMI) events occur in young adults.
- In a large U.S. hospitalization analysis, Arora et al. (2019) found that the proportion of AMI hospitalizations attributable to young patients increased from 1995 to 2014, especially among women.
- In the PCAD study we’re focusing on, Ran et al. (2025) emphasize that coronary disease has been increasing among younger individuals in recent years, which is exactly why early risk signals matter. Association between the atherog…
Even if PCAD is a “minority” subgroup, the trend and the lifetime consequences make it a major prevention target.
Why PCAD matters so much for preventing ACS
ACS (acute coronary syndrome) includes heart attacks and unstable angina—events that usually happen when a coronary plaque becomes unstable and triggers a clot.
With PCAD, the problem isn’t only the first event. It’s the long runway for repeat events.
- Collet et al. (2019) reported that over long-term follow-up, about one-third of patients with premature CAD experienced ischemic events, and 36% had at least a second recurrence—showing how “event-prone” premature CAD can be over time.
- Ran et al. (2025) cite this same reality to underscore that premature CAD carries a poor long-term prognosis, even with contemporary prevention strategies. Association between the atherog…
If someone develops plaque early, they may face decades of exposure to plaque progression, inflammation, and repeat ACS risk—unless the underlying drivers are aggressively improved.
The Practical Prevention Takeaway
PCAD prevention is really two prevention goals:
- Prevent the first ACS (especially in people with insulin resistance, smoking, family history, or metabolic syndrome).
- Prevent the next ACS (because premature CAD patients have more time to accumulate recurrent events).
That’s why markers like AIP are being studied in PCAD: they may help identify residual risk that isn’t captured by LDL alone—so interventions can start earlier and be tracked over time.
The Hidden cost: Income Loss + Higher Expenses
Premature CAD hits at a uniquely difficult time: people are still working, raising families, and building income. That’s why its impact isn’t only medical—it’s financial.
- Lost productivity is part of the national burden. The CDC estimates heart disease costs about $417.9 billion (2020–2021), including health care services, medicines, and lost productivity due to death.
- But the “living costs” can be even bigger. Luo et al. (2023) estimated U.S. labor income losses due to morbidity (being alive but less able to work) at $203.3 billion for heart disease and $63.6 billion for stroke—and they note these morbidity-related income losses can exceed losses from premature death.
When CAD starts early, the person may face:
- more years of medications, follow-up visits, and procedures,
- more time away from work (or reduced work capacity),
- higher insurance and out-of-pocket costs,
- and a longer period of financial stress.
That’s one more reason prevention matters: preventing ACS isn’t only about living longer—it’s also about protecting income, independence, and family stability.
IV. What is AIP?
AIP (the Atherogenic Index of Plasma) is a simple number derived from two routine lipid-panel values: triglycerides (TG) and HDL cholesterol (HDL-C).
In Ran et al. (2025), AIP is calculated as log[TG/HDL-C] and specifically described as the log of the ratio of TG (mmol/L) to HDL-C (mmol/L).
Why AIP matters:
- AIP tends to rise when TG is high and/or HDL is low—a common metabolic pattern seen with insulin resistance and inactivity.
- Ran et al. note that AIP reflects dyslipidemia and has been shown to predict CAD/atherosclerosis risk in prior studies.
- They also point out that AIP is strongly linked with the development of insulin resistance, prediabetes, and type 2 diabetes in other research.
Bottom line: AIP is a quick way to “summarize” a TG/HDL pattern that often accompanies metabolic risk—especially relevant for people who develop CAD early.
V. Study Spotlight: Ran et al. (2025) — AIP and outcomes in premature CAD
Study question
Does baseline AIP predict future major adverse cardiovascular events (MACE) in people with premature coronary artery disease (PCAD)?
Study design (what it is, and why it still matters)
- Retrospective cohort study (stated by the authors).
Plain meaning: they used real-world hospital data, calculated AIP at baseline, then tracked what happened later. - Not prospective, but appropriate for prognosis research because they used time-to-event analysis (Cox regression).
Who was included (and how PCAD was confirmed)
- Started with 1517 young patients suspected of CAD (men <45, women <55) at Qilu Hospital (Jan 2013–Jul 2020).
- All underwent coronary angiography to confirm CAD.
- Final analysis: 721 patients with PCAD confirmed by angiography.
Why it matters: angiography confirmation strengthens the diagnosis compared with symptom-based definitions.
Follow-up and sample size (is it adequate?)
- Median follow-up: 52 months (~4.3 years).
- 138 MACE events (19.4%).
Interpretation: 721 patients with 138 events is solid for modeling overall MACE risk, but rare components (stroke, CV death) are harder to analyze because they occurred infrequently.
How AIP groups were defined (tertiles)
- T1: AIP < 0.045
- T2: 0.045–0.255
- T3: > 0.255
What outcome they tracked (MACE)
MACE included: CV death, coronary revascularization, non-fatal MI, and non-fatal stroke.
Why researchers use MACE: it captures the big outcomes people care about and improves statistical power by combining serious events.
Headline results
Event rates increased across AIP tertiles:
- 11.8% (lowest AIP)
- 19.8% (middle AIP)
- 26.6% (highest AIP)
Adjusted risk stayed higher even after accounting for many factors:
Highest vs lowest tertile: HR 2.27 (95% CI 1.30–3.94)— meaning the highest AIP tertile was about 2.27× more likely to experience MACE over the follow-up period than the lowest tertile.
Per 1 SD higher AIP: HR 1.33 (95% CI 1.06–1.67)— meaning that for each typical step upward in AIP, the risk of MACE over time was about 33% higher.
What drove most events?
MACE was largely driven by revascularization (repeat procedures like a heart bypass or stent to restore coronary blood flow):
- Whole cohort event breakdown: 89 revascularizations, 38 non-fatal MIs, 7 CV deaths, 4 strokes.
One-line takeaway
In people who develop CAD early, a higher AIP identified a group with substantially higher risk of major events over ~4 years, even after adjusting for other risk factors.
Why AIP might still matter even when LDL is treated
Ran et al. explicitly discuss the idea of residual cardiovascular risk even when LDL is controlled, pointing to the atherogenic impact of other lipid components (TG and HDL-C).
AIP may capture part of the “TG/HDL metabolic risk” that LDL alone can miss.
Alignment with Plaque Instability Studies
The Ran et al. (2025) study fits into a larger pattern observed in imaging studies: high AIP tends to be associated with more “vulnerable” (unstable) plaques, not just more plaque.
For example, Wu et al. (2024) used Optical Coherence Tomography which is like a high-resolution “inside the artery-camera” in ACS patients.
They found that higher AIP quartiles had much higher rates of thin-cap fibroatheroma (TCFA) and plaque rupture, and the highest AIP group had markedly higher odds of TCFA (OR 11.13) and plaque rupture (OR 5.33) compared with the lowest group.
Another study (angiography-based) also reported AIP as an independent predictor of plaque instability in fully adjusted models (OR 15), supporting the idea that AIP may reflect a lipid environment that promotes plaques that are more likely to trigger ACS.
Plaque instability is a big deal because most acute coronary syndromes (ACS) don’t happen just from a slowly growing “stable” blockage—they happen when an unstable plaque becomes inflamed, develops a fragile surface (thin cap), and then ruptures or erodes.
When that surface breaks, the body reacts as if there’s an injury and forms a blood clot on top of the plaque. That clot can suddenly block blood flow in a coronary artery, triggering unstable angina or a heart attack, often with little warning.
VI. Is this a good study? (Strengths + limitations you can trust)
Strengths (why it’s worth paying attention to)
- Clear definition + confirmed disease: PCAD patients were diagnosed by coronary angiography, not just symptoms or a questionnaire.
- Meaningful follow-up: median 52 months, long enough for clinically important outcomes to show up.
- Hard clinical endpoint: MACE includes outcomes people actually care about (death, MI, stroke, and repeat procedures).
- Serious adjustment for confounders: their fully adjusted model included a long list (age/sex, BMI, LVEF, smoking, diabetes, hypertension, LDL-C, kidney function, and medications, etc.).
- Robustness checks: sensitivity analyses still showed the association after excluding (1) those with prior lipid-lowering use and (2) those who had events within the first 2 years.
Meaning: It’s a solid “real-world” prognostic study—useful for risk stratification.
Limitations (what it cannot prove)
The authors are very direct about limitations:
- Single-center, retrospective design → uncontrolled confounding and limited generalizability.
- Relatively small sample for rare outcomes: CV death and non-fatal stroke were low, making those components hard to interpret.
- Only baseline AIP was used (no analysis of whether improving AIP over time lowers risk).
- Telephone follow-up may introduce selection/recall bias.
- They explicitly call for large prospective studies to confirm the findings and best clinical use.
This study supports AIP as a risk marker, not proof of cause-and-effect. It tells you “who is higher risk,” not “what intervention will definitely prevent events.”
This study doesn’t prove that a high AIP causes premature coronary events—but it strongly suggests that a higher AIP helps identify higher-risk people, even after adjusting for many other factors (Ran et al., 2025).
That’s exactly why AIP is useful in real life: it’s built from two routine lab values (TG and HDL), so you can calculate it today, then re-check it after lifestyle changes to see if you’re moving in the right direction.
AIP Guideposts
In general, many papers use these guideposts:
- AIP < 0.11 is usually considered more favorable
- 0.11–0.21 intermediate, and
- > 0.21 higher/unfavorable.
But don’t obsess over a single number—your trend is the goal: if your AIP is steadily moving down over time, you’re usually reversing the TG/HDL pattern that drives risk.
Use the calculator below to get your baseline AIP, then head into the action plan to start improving insulin resistance and building the exercise habits that push AIP in the right direction.
Atherogenic Index (AIP) Calculator
Calculates AIP = log10(Triglycerides ÷ HDL) using mmol/L internally.
VII. What this means for Acute Coronary Syndrome Prevention (and why AIP is practical)
If premature CAD is “heart disease that starts early,” then ACS prevention becomes a long game—because you’re trying to prevent not only the first major event, but the next one, and the one after that.
Ran et al. (2025) showed a clear pattern: people with higher AIP had more MACE over about four years of follow-up, even after adjusting for many risk factors.
That makes AIP useful as a risk flag—a way to identify who may have more “residual risk” even when they’re already being treated for CAD.
Why AIP matters even when LDL looks “good”
LDL cholesterol is important, but it’s not the whole story. Ran et al. emphasize that cardiovascular risk can remain even when LDL is controlled, and they highlight the role of other lipid components—especially triglycerides and HDL-C—in ongoing atherosclerotic risk.
Plain meaning: You can have an LDL number that looks decent and still carry risk if your metabolic pattern is TG high and HDL low.
Why AIP fits premature CAD specifically
Premature CAD often travels with metabolic drivers that start years earlier—things like insulin resistance, weight gain around the waist, inactivity, and a high-glycemic diet. Ran et al. note that AIP is strongly linked to the development of insulin resistance, prediabetes, and type 2 diabetes in other research.
So AIP isn’t just a “heart number.” It’s also a metabolic number—and metabolic improvement is one of the most modifiable paths to lowering long-term ACS risk.
The most practical takeaway: use AIP as a “feedback marker”
AIP has two features that make it useful for prevention:
- It’s easy to get (TG and HDL are already on most lipid panels).
- It’s repeatable, so you can track whether lifestyle and metabolic changes are working.
That’s why the next step is not just understanding the study—it’s doing something with it:
- Calculate your AIP (using the calculator in this article).
- Start the action plan aimed at the two biggest levers:
- lower TG by improving insulin resistance, and
- support HDL through regular exercise.
- Recheck and track your trend, because the direction over time is the point.
Bottom line: Ran et al. (2025) support AIP as a simple marker that can help flag higher-risk premature CAD patients—and, just as importantly, it gives you a number you can work on and measure again as you build a plan to prevent future ACS.
VIII. Take-home summary
- What PCAD is: Premature coronary artery disease means CAD happening early—men <45 and women <55 in Ran et al. (2025).
- What AIP is: AIP is a simple lipid index from your lab values—AIP = log(TG/HDL-C) (using molar units in the study).
- What Ran et al. found: Over a median 52 months, MACE was 26.6% in the highest AIP tertile vs 11.8% in the lowest; adjusted risk was HR 2.27 (highest vs lowest) and HR 1.33 per 1 SD higher AIP.
- What it means: AIP can help with risk stratification—it may identify residual risk (TG/HDL metabolic risk) that LDL alone can miss.
- What to do: Treat AIP as a trend marker—work on the TG/HDL drivers by improving insulin resistance (diet/waist control) and exercising consistently, then recheck AIP to confirm it’s moving down.
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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References:
- Ran, Xiangzhen, et al. “Association between the Atherogenic Index of Plasma and Major Adverse Cardiovascular Events in Patients with Premature Coronary Artery Disease.” European Journal of Medical Research, vol. 30, 2025, article 511. Springer, https://doi.org/10.1186/s40001-025-02796-w.
- Collet, Jean-Philippe, et al. “Long-Term Evolution of Premature Coronary Artery Disease.” Journal of the American College of Cardiology, 2019. PubMed, https://pubmed.ncbi.nlm.nih.gov/31601367/.
- Arora, Sandeep, et al. “Twenty-Year Trends and Sex Differences in Young Adults Hospitalized With Acute Myocardial Infarction.” Circulation, 2019. American Heart Association, https://www.ahajournals.org/doi/10.1161/CIRCULATIONAHA.118.037137.
- Michos, Erin D., and colleagues. “Coronary Artery Disease in Young Adults…” Journal of the American College of Cardiology, 2019. (Notes the proportion of AMI events in young adults.) https://www.jacc.org/doi/10.1016/j.jacc.2019.08.1023.
- Huo G, Tang Y, Zhou D. Nonlinear Association Between Atherogenic Index of Plasma and Unstable Carotid Plaque: A Single-Center Retrospective Study. J Cardiovasc Dev Dis. 2025 Nov 11;12(11):443. doi: 10.3390/jcdd12110443. PMID: 41295369; PMCID: PMC12653747. https://pmc.ncbi.nlm.nih.gov/articles/PMC12653747/
- Wu S, Gao Y, Liu W, Wang R, Ma Q, Sun J, Han W, Jia S, Du Y, Zhao Z, Liu Y, Wang Z, Zhou Y. The relationship between atherogenic index of plasma and plaque vulnerabilities: an optical coherence tomography study. Cardiovasc Diabetol. 2024 Dec 18;23(1):442. doi: 10.1186/s12933-024-02532-4. PMID: 39696478; PMCID: PMC11656644. https://pmc.ncbi.nlm.nih.gov/articles/PMC11656644/
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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