1 Overview

This report examines the state-space trajectory of Absolute Neutrophils and Absolute Lymphocytes. Together, these markers define the Neutrophil to Lymphocyte Ratio (NLR).

  • Absolute Neutrophils: Absolute Neutrophils (ANC) measure the primary ‘first responders’ of the innate immune system. Chronic elevation suggests ongoing systemic stress or subclinical inflammation.
  • Absolute Lymphocytes: Absolute Lymphocytes represent the adaptive immune reserve. Optimal levels are necessary for effective viral surveillance and immune regulation.
  • Neutrophil to Lymphocyte Ratio (NLR): The NLR is a measure of the balance between innate and adaptive immunity. A trajectory moving toward the top-left (low lymphocytes, high neutrophils) indicates a shift toward systemic inflammation and physiological stress.
Analysis Parameters and Logic
Component Units Logic Reference Optimal
Absolute Neutrophils 10^3/uL Raw Marker 1.5 - 7.5 1.8 - 4.5
Absolute Lymphocytes 10^3/uL Raw Marker 1 - 4 1.5 - 2.8
Neutrophil to Lymphocyte Ratio (NLR) Ratio \(NLR = \frac{Neutrophils_{abs}}{Lymphocytes_{abs}}\) 1 - 3 1.2 - 2

1.1 Methodology: State-Space Trajectory Analysis

This report utilizes State-Space Trajectory Analysis, a methodology first implemented by the author in 2015 to evaluate electrolyte flux and system stability over time (Seiter et al. 2015). By mapping the relationship between two dependent variables as well as a derived value, we move beyond the “Snapshot” model of medicine into a “2 Dimensional Temporal Visualization” of health. While contemporary research frameworks like STREAM (2026) have recently begun applying these principles to ICU monitoring (STREAM Research Group 2026), this report continues the author’s long-term development of trajectory-based clinical modeling.

2 Data Processing

3 Lab Results Listing

Historical Results for Absolute Neutrophils and Absolute Lymphocytes
Date Age Absolute Neutrophils (10^3/uL) Absolute Lymphocytes (10^3/uL) Neutrophil to Lymphocyte Ratio (NLR)
1998-05-12 32 3.19 1.35 2.36
2009-09-10 43 1.73 1.01 1.71
2009-12-09 44 1.20 0.95 1.26
2010-03-17 44 1.96 1.52 1.29
2010-07-21 44 1.56 1.22 1.28
2010-09-02 44 2.11 1.46 1.45
2010-10-25 44 1.55 1.40 1.11
2011-02-02 45 1.22 1.25 0.98
2011-06-28 45 1.37 1.44 0.95
2011-09-29 45 1.55 1.33 1.17
2013-04-18 47 2.45 1.54 1.59
2013-09-27 47 2.07 1.11 1.86
2016-05-28 50 3.02 1.64 1.84
2017-07-06 51 2.30 1.68 1.37
2017-11-17 51 2.59 1.54 1.68
2018-05-10 52 2.45 1.58 1.55
2018-11-09 52 2.30 1.60 1.44
2019-06-05 53 1.72 1.72 1.00
2020-04-09 54 1.86 1.96 0.95
2020-09-30 54 3.83 1.85 2.07
2021-07-23 55 2.06 1.91 1.08
2022-10-07 56 3.22 1.67 1.93
2023-08-10 57 2.26 1.73 1.31
2023-11-29 57 2.12 1.12 1.89
2024-03-05 58 2.76 1.70 1.62
2024-06-05 58 2.81 1.61 1.75
2025-08-06 59 4.18 2.16 1.94

4 Statistical Summary

Summary of historical distribution and the Z-score for the most recent result.

Historical Statistics Summary
Min Q1.25% Median Mean Q3.75% Max N SD Latest Latest Z
Absolute Neutrophils 1.20 1.73 2.12 2.28 2.67 4.18 27 0.75 4.18 2.53
Absolute Lymphocytes 0.95 1.34 1.54 1.52 1.69 2.16 27 0.29 2.16 2.19
Neutrophil to Lymphocyte Ratio (NLR) 0.95 1.21 1.45 1.50 1.79 2.36 27 0.38 1.94 1.16

5 Absolute Neutrophils-Absolute Lymphocytes State-Space Trajectory

  • Dots: Dated lab results colored by value of Neutrophil to Lymphocyte Ratio (NLR). Hover for more detailed test information.
  • Contours: Represent constant values of Neutrophil to Lymphocyte Ratio (NLR).
  • Grid Lines: Dotted/Red = Reference Range; Dashed/Green = Optimal Range.
  • Trajectory: Colored segments indicate the temporal path between samples.

6 Longitudinal History: The Linear Audit

The Longitudinal History provides a traditional time-series view of each component. While the 2D State-Space reveals the 2 Dimensional Trajectory of the system, these 1D facets identify which specific component is driving the systemic shift. A trajectory move toward the high-risk ridge in the state-space can be traced here to either a surge in Innate activity (Neutrophils) or a depletion of Adaptive reserve (Lymphocytes).

7 Clinical Significance & Literature Correlations

The Neutrophil to Lymphocyte Ratio (NLR) is increasingly recognized in clinical literature as a sensitive marker for systemic inflammation and physiological stress. While standard laboratory reference ranges provide broad bounds, specific healthy population distributions and risk quartiles offer a higher-resolution “Audit” of immune status.

7.1 Healthy Population Benchmarks

Current research indicates that in healthy, asymptomatic individuals, the mean NLR is approximately 1.65. This aligns closely with our established optimal “basin” of 1.2 – 2.0.

7.2 Autoimmune Risk & Inflammatory Quartiles

A 2026 study published in Nature utilizes a quartile-based approach to categorize inflammatory states. These thresholds provide a specific “Software” layer for interpreting where a trajectory sits on the metabolic landscape.

Quartile NLR Range Clinical Interpretation
Q1 < 1.65 Healthy/Normal Range
Q2 1.65 – 2.13 Upper End of Normal
Q3 2.13 – 2.74 Mild Subclinical Inflammation
Q4 > 2.74 Elevated Inflammatory State

Source: Nature (2026) Autoimmune-Risk Study - s41598-025-21188-y (Scientific Reports Research Group 2026)

7.3 Future Analysis Framework

To evolve this report from a “one-off” trajectory analysis into a comprehensive clinical tool, the following sub-sections are identified for future integration:

  1. Multi-Omics Correlations: Mapping NLR shifts against other inflammatory biomarkers such as IL-6 and TNF-α.
  2. Condition-Specific Literature: Curating correlations for cardiometabolic and autoimmune conditions.
  3. Therapeutic Impact: Tracking trajectory response to specific interventions, whether published or theoretical.
  4. AI Integration: Utilizing AI/Notebook tools to rapidly parse literature and identify novel correlations between 50-100 different biomarker state-spaces.

8 Studies Evaluating Quartiles of Neutrophil to Lymphocyte Ratio in the Context of Cardiovascular Disease

The NLR literature across cardiovascular disease contexts consistently shows quartile-based risk stratification, with the highest quartile (Q4) carrying significantly elevated mortality risk. Here is a synthesis of the key findings:

8.1 NLR Quartile Thresholds Across Studies

Different populations and disease contexts yield different quartile cutpoints, which is clinically important:

Study Population Q1 Q2 Q3 Q4
CVD with diabetes/pre-diabetes \[1\] ≤1.682 1.683–2.291 2.292–3.149 ≥3.150
Critical/severe coronary artery disease \[2\] Lowest — — NLR >5.54 (inflection point)
CAD with LDL-C <1.4 mmol/L \[3\] Reference — — Q4 elevated
Heart failure \[4\] Lowest neutrophil ≤5.93 — — Neutrophil ≥5.93

8.2 All-Cause Mortality by Quartile

In CVD patients with diabetes or pre-diabetes followed across quartiles, all-cause mortality increased in a clear stepwise fashion: Q1=28.77%, Q2=33.10%, Q3=36.58%, Q4=50.98%. The hazard trend across quartiles was highly significant (P for trend <0.001), meaning even moving from Q1 to Q2 carries measurable additional risk.\[1\]

8.3 Cardiovascular-Specific Mortality

Cardiovascular mortality also rose progressively across quartiles — Q1=8.74%, Q2=11.41%, Q3=12.87%, Q4=18.28%. The relationship between NLR and cardiovascular mortality was roughly linear, with each 1-unit increase in NLR associated with a 19% increase in cardiovascular mortality risk (HR 1.17, 95% CI 1.10–1.25). In CAD patients with low LDL-C, Q4 NLR was associated with an HR of 2.18 for cardiovascular death.\[3\]\[1\]

8.4 Heart Failure Context

In heart failure specifically, the highest NLR quartile was associated with an HR of 1.77 for long-term all-cause mortality compared to the lowest quartile (95% CI 1.38–2.26, p<0.001). Patients in the highest quartile of neutrophil count (≥5.93 × 10⁹/L) had a 2-year mortality rate of 30% versus 14% in the lowest quartile.\[5\]\[4\]

8.5 Critical CAD: High NLR Threshold

In critical coronary artery disease (ICU-level), the inflection point in restricted cubic spline analysis was NLR >5.54, with Q4 patients showing 30-day mortality HR of 3.99 and 365-day mortality HR of 5.72. RCS analysis consistently reveals a U-shaped relationship between NLR and all-cause mortality across studies, with an inflection point around NLR of 1.776 in mixed CVD populations — meaning NLR below ~1.8 may also carry excess risk, possibly reflecting immunosuppression or severe cachexia.\[2\]\[1\]

8.6 Mechanistic Basis

The NLR quartile risk gradient reflects two converging processes: neutrophils in Q4 release myeloperoxidase (MPO), superoxide radicals, and NETs that increase atherosclerotic plaque vulnerability, while the reciprocally low lymphocyte count reflects impaired adaptive immune regulation and stress-related lymphocyte apoptosis. This dual-arm dysregulation makes NLR a more powerful prognostic signal than either cell count alone.\[6\]\[2\]

(Should really consolidate these with knitr references, but leave like this for now)

  1. https://www.nature.com/articles/s41598-024-74642-8

  2. https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2025.1502964/full

  3. https://pubmed.ncbi.nlm.nih.gov/40268567/

  4. https://onlinelibrary.wiley.com/doi/full/10.1002/ehf2.15240

  5. https://pmc.ncbi.nlm.nih.gov/articles/PMC10931846/

  6. https://pmc.ncbi.nlm.nih.gov/articles/PMC11355618/

  7. https://www.sciencedirect.com/science/article/pii/S2772963X25007926

  8. https://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0324849

9 File History

File Initially created: Saturday, April 11, 2026
File Updated for Neutrophil to Lymphocyte Ratio (NLR) Fine-Tuning: Sunday, April 26, 2026
File Updated for Visual Refinement: Sunday, April 26, 2026
File knitted: Tue Apr 28 10:17:49 2026

10 Bibliography

Forget, Patrice, Khalifa Khalifa, Jean-Pierre Cheron, et al. 2017. “What Is the Normal Value of the Neutrophil-to-Lymphocyte Ratio?” BMC Cancer 17 (1): 12. https://doi.org/10.1186/s12885-016-2993-y.
Scientific Reports Research Group. 2026. “Neutrophil-to-Lymphocyte Ratio as a Predictor of Autoimmune Disease Risk.” Scientific Reports. https://doi.org/10.1038/s41598-025-21188-y.
Seiter, Richard, Michael McEvoy, Robert F. Kidd MD, and Lynne August. 2015. “Phase Space for the Nernst Equation.” https://rpubs.com/rseiter/44006.
STREAM Research Group. 2026. “STREAM: State Trajectory Representation & Evolution-Aware Monitoring.” https://www.medrxiv.org/content/10.64898/2026.02.03.26345478v1.