What Health Metrics Actually Predict Future Disease?

What Decades of Science Tell Us About Real Risk in Preventive Health

Introduction

We track steps. We watch calories. We glance at lab results labeled “normal.” Yet, most chronic diseases don’t appear suddenly. They develop silently over years, sometimes decades. The real question in preventive health isn’t, “What do my numbers look like today?” but rather:

Which health metrics reliably predict future disease?

Decades of peer-reviewed research provide clear answers. These insights are more actionable than most people realize.

1. Blood Pressure: One of the Strongest Predictors of Future Disease

Blood pressure is not just a snapshot; it’s a long-term signal. Large prospective cohort studies have consistently shown that:

• Higher systolic and diastolic blood pressure predict future heart attacks, strokes, heart failure, and kidney disease.

• Risk begins well below traditional hypertension cutoffs.

  
  

Even “high-normal” blood pressure is associated with a higher lifetime cardiovascular risk.

Why It Matters

Blood pressure integrates vascular health, metabolic health, stress physiology, and aging. This makes it one of the most powerful single predictors we have.

2. Cholesterol and Lipids: Risk Accumulates Over Time

Long-term studies, including multi-decade population cohorts, demonstrate that:

• Elevated LDL cholesterol strongly predicts future coronary heart disease.

• Cumulative exposure, or “cholesterol years,” matters more than a single value.

  
  

Importantly, cholesterol predicts risk even in people without symptoms and decades before the first event.

Key Insight

Waiting until cholesterol is “very high” misses years of preventable vascular damage.

3. Blood Glucose & Insulin Resistance: Predicting More Than Diabetes

Metrics such as:

• Fasting glucose

• HbA1c

• Markers of insulin resistance

  
  

These predict not only future diabetes but also:

• Cardiovascular disease

• Kidney disease

• Cognitive decline

  
  

Many people who develop diabetes had abnormal metabolic markers 10–15 years earlier, often still labeled “normal.”

4. Inflammation: The Silent Risk Multiplier

High-sensitivity C-reactive protein (hs-CRP) is a validated inflammatory marker. Prospective studies show:

• Elevated hs-CRP predicts future heart attacks and strokes.

• Risk is independent of cholesterol levels.

  
  

Inflammation acts as a risk amplifier, accelerating disease when combined with metabolic or vascular dysfunction.

5. Cardiac Biomarkers (Even in the “Normal” Range)

Emerging evidence shows that low-level elevations of:

• High-sensitivity troponin

• NT-proBNP

  
  

These predict future cardiovascular events and mortality, even in people without known heart disease. These markers reflect subclinical organ stress, long before symptoms appear.

6. Composite Risk Scores: The Most Accurate Predictors

Single numbers are helpful, but combinations are better. Validated tools such as:

• AHA Life’s Simple 7

• QRISK

• Other multivariable risk scores

  
  

These consistently outperform individual metrics in predicting:

• Cardiovascular disease

• Stroke

• All-cause mortality

  
  

People with optimal composite scores have:

• ~80% lower cardiovascular risk.

• Significantly lower cancer and mortality risk.

  
  

7. Fitness & Physical Activity: Often Undermeasured, Highly Predictive

Cardiorespiratory fitness is one of the strongest predictors of:

• Longevity

• Cardiovascular disease

• Metabolic disease

  
  

Yet it’s rarely measured directly in clinical care. Wearable-derived metrics and functional capacity measures are increasingly recognized as powerful risk indicators.

What Matters Most (And What Doesn’t)

Strong Predictors

• Blood pressure trends

• Lipids over time

• Glucose regulation

• Inflammation

• Fitness

• Composite risk scores

  
  

Weaker Predictors Alone

• Single “normal” lab values

• Genetics without clinical context

• One-time measurements

  
  

The Preventiononly Perspective

Prevention isn’t about chasing perfect numbers. It’s about:

• Identifying which metrics truly predict disease.

• Tracking them over time.

• Acting early—before symptoms begin.

  
  

The science is clear. The opportunity is earlier than most people think.

Empower Your Health Journey

By understanding these metrics, you can take proactive steps to manage your health. This knowledge empowers you to make informed decisions. Remember, the goal is prevention. You can be at the forefront of your health journey by monitoring these key indicators.

References (Peer-Reviewed)

Prospective Cohort and Meta-Analysis Data on Cardiovascular Health Metrics and Mortality

• PMID: 37445451 - Meta-analysis of 22 prospective studies (3,240,660 adults) demonstrating that higher numbers of ideal cardiovascular health metrics were associated with lower risk for composite CVD, stroke, and cardiovascular mortality.[1]

• PMID: 29278429 - Meta-analysis of 13 prospective studies (193,126 participants) showing that ideal cardiovascular health status resulted in substantial reductions in risk of CVD, stroke, and mortality, with a linear dose-response relationship.[2]

  
  

Long-Term Lipid and Blood Pressure Outcome Studies

• PMID: 26780010 - Review of long-term follow-up from lipid-lowering trials demonstrating legacy benefits including improved survival, decreased cardiovascular death rates, and lower hospitalization rates for cardiovascular disease.[3]

• PMID: 39126771 - Meta-analysis of 60 randomized controlled trials (408,959 participants) examining the course of LDL-cholesterol reduction effects on cardiovascular risk over time.[4]

  
  

High-Sensitivity C-Reactive Protein and Inflammatory Risk Prediction Studies

• PMID: 36362325 - Systematic review and meta-analysis demonstrating that hs-CRP predicts incident CVD among asymptomatic middle-aged adults (HR 1.19, 95% CI: 1.09-1.30).[7]

• PMID: 41378999 - Population-based study of 448,653 UK Biobank participants showing that individuals with hsCRP >3 mg/L had 34% higher risk of major adverse cardiovascular events compared to those with hsCRP.[8]

  
  

Cardiac Biomarker Prognostic Studies

• PMID: 33225348 - Review summarizing evidence on cardiac troponins and natriuretic peptides for risk prediction at different stages of CVD development, including growth differentiation factor-15, soluble ST2, and galectin-3.[12]

  
  

Composite Cardiovascular Risk Score Validation Studies

• PMID: 37793746 - Validation study examining the performance of the ACC/AHA Pooled Cohort Equations in real-world clinical practice.[13]

• PMID: 34167645 - MESA study review including validation of existing cardiovascular risk scores and derivation of novel risk scores, with the MESA CHD Risk Score achieving C-statistic of 0.80 when incorporating coronary artery calcium.[14]

  
  

1. Ideal Cardiovascular Health and Risk of Cardiovascular Events or Mortality: A Systematic Review and Meta-Analysis of Prospective Studies. Radovanovic M, Jankovic J, Mandic-Rajcevic S, et al. Journal of Clinical Medicine. 2023;12(13):4417. doi:10.3390/jcm12134417.

2. Association Between Ideal Cardiovascular Health Metrics and Risk of Cardiovascular Events or Mortality: A Meta-Analysis of Prospective Studies. Guo L, Zhang S. Clinical Cardiology. 2017;40(12):1339-1346. doi:10.1002/clc.22836.

3. Long-Term Follow-Up of Lipid-Lowering Trials. Packard CJ, Ford I. Current Opinion in Lipidology. 2015;26(6):572-9. doi:10.1097/MOL.0000000000000230.

4. Course of the Effects of LDL-cholesterol Reduction on Cardiovascular Risk Over Time: A Meta-Analysis of 60 Randomized Controlled Trials. Burger PM, Dorresteijn JAN, Koudstaal S, et al. Atherosclerosis. 2024;396:118540. doi:10.1016/j.atherosclerosis.2024.118540.

5. Longitudinal Blood Pressure Patterns and Cardiovascular Disease Risk. Nuotio J, Suvila K, Cheng S, Langén V, Niiranen T. Annals of Medicine. 2020 May - Jun;52(3-4):43-54. doi:10.1080/07853890.2020.1733648.

6. Benefit-Harm Trade-Offs of Intensive Blood Pressure Control Versus Standard Blood Pressure Control on Cardiovascular and Renal Outcomes: An Individual Participant Data Analysis of Randomised Controlled Trials. Guo X, Sun G, Xu Y, et al. Lancet (London, England). 2025;406(10507):1009-1019. doi:10.1016/S0140-6736(25)01391-1.

7. A Systematic Review and Meta-Analysis of Advanced Biomarkers for Predicting Incident Cardiovascular Disease Among Asymptomatic Middle-Aged Adults. Romero-Cabrera JL, Ankeny J, Fernández-Montero A, Kales SN, Smith DL. International Journal of Molecular Sciences. 2022;23(21):13540. doi:10.3390/ijms232113540.

8. C-Reactive Protein and Cardiovascular Risk in the General Population. Kurt B, Reugels M, Schneider KM, et al. European Heart Journal. 2025;:ehaf937. doi:10.1093/eurheartj/ehaf937.

9. A Test in Context: High-Sensitivity C-Reactive Protein. Ridker PM. Journal of the American College of Cardiology. 2016;67(6):712-723. doi:10.1016/j.jacc.2015.11.037.

10. Inflammation in Atherosclerosis: From Pathophysiology to Practice. Libby P, Ridker PM, Hansson GK, Leducq Transatlantic Network on Atherothrombosis. Journal of the American College of Cardiology. 2009;54(23):2129-38. doi:10.1016/j.jacc.2009.09.009.

11. Inflammation and Cholesterol as Predictors of Cardiovascular Events Among Patients Receiving Statin Therapy: A Collaborative Analysis of Three Randomised Trials. Ridker PM, Bhatt DL, Pradhan AD, et al. Lancet (London, England). 2023;401(10384):1293-1301. doi:10.1016/S0140-6736(23)00215-5.

12. Role of Cardiac Biomarkers in Epidemiology and Risk Outcomes. Haller PM, Beer BN, Tonkin AM, Blankenberg S, Neumann JT. Clinical Chemistry. 2021;67(1):96-106. doi:10.1093/clinchem/hvaa228.

13. Performance of the ACC/AHA Pooled Cohort Cardiovascular Risk Equations in Clinical Practice. Medina-Inojosa JR, Somers VK, Garcia M, et al. Journal of the American College of Cardiology. 2023;82(15):1499-1508. doi:10.1016/j.jacc.2023.07.018.

14. Multi-Ethnic Study of Atherosclerosis (MESA): JACC Focus Seminar 5/8. Blaha MJ, DeFilippis AP. Journal of the American College of Cardiology. 2021;77(25):3195-3216. doi:10.1016/j.jacc.2021.05.006.

15. Prognostic Value of Cardiovascular Biomarkers in the Population. Neumann JT, Twerenbold R, Weimann J, et al. JAMA. 2024;331(22):1898-1909. doi:10.1001/jama.2024.5596.