dii-calculator 1.0.1

A Python implementation of the Dietary Inflammatory Index (DII) calculator for nutritional epidemiology research

DII Calculator

A Python implementation of the Dietary Inflammatory Index (DII) for nutritional epidemiology research

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Overview

The Dietary Inflammatory Index (DII) is a literature-derived, population-based index designed to assess the inflammatory potential of an individual's diet. This Python package provides a validated, efficient implementation for calculating DII scores from nutrient intake data.

Key Features:

• Vectorized calculations for high performance on large datasets
• Support for all 45 DII food parameters
• Graceful handling of missing nutrients
• Built-in visualization tools
• Command-line interface for quick analysis
• Bundled reference data (no external files required)
• Validated against 13,580 NHANES participants with perfect match to original calculations

Sample Output

DII score distribution from NHANES 2017-2018 data (n=13,580). Over half of U.S. adults have pro-inflammatory diets.

Interpretation

DII Score	Dietary Pattern
Negative (e.g., -4 to -1)	Anti-inflammatory diet
Near zero	Neutral inflammatory potential
Positive (e.g., +1 to +4)	Pro-inflammatory diet

Scores typically range from approximately -8 (maximally anti-inflammatory) to +8 (maximally pro-inflammatory).

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Installation

Prerequisites

• Python 3.8 or higher (Download Python)

Option 1: Quick Install from GitHub

pip install git+https://github.com/strathlab-data/DII.git

Option 2: Full Development Setup (Recommended for Researchers)

This creates an isolated environment that won't conflict with other Python projects.

Windows (PowerShell):

# Clone the repository
git clone https://github.com/strathlab-data/DII.git
cd dii-calculator

# Create virtual environment
python -m venv venv
.\venv\Scripts\Activate.ps1

# Install package and dependencies
pip install -e .

# Verify installation
python -c "from dii import calculate_dii; print('Installation successful!')"

macOS / Linux:

# Clone the repository
git clone https://github.com/strathlab-data/DII.git
cd dii-calculator

# Create virtual environment
python3 -m venv venv
source venv/bin/activate

# Install package and dependencies
pip install -e .

# Verify installation
python -c "from dii import calculate_dii; print('Installation successful!')"

Running Tests

# Make sure your virtual environment is activated, then:
pip install pytest
pytest tests/ -v

Dependencies

Automatically installed:

• pandas ≥ 1.3.0
• numpy ≥ 1.20.0
• scipy ≥ 1.7.0

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Quick Start

import pandas as pd
from dii import calculate_dii

# Load your nutrient intake data
nutrients = pd.read_csv("your_nutrient_data.csv")

# Calculate DII scores
results = calculate_dii(nutrients, id_column="participant_id")

print(results.head())
#    participant_id  DII_score
# 0               1      -1.23
# 1               2       0.45
# 2               3       2.87

Detailed Output

For transparency in the calculation, request detailed output:

detailed = calculate_dii(nutrients, id_column="participant_id", detailed=True)

# View contribution of each nutrient
print(detailed[["participant_id", "Fiber_contribution", "Alcohol_contribution", "DII_score"]])

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Input Data Format

Your input DataFrame should contain columns named to match the DII reference nutrients. The following 45 nutrients are supported:

Nutrient	Unit	Nutrient	Unit
Alcohol	g/day	Magnesium	mg/day
Beta-carotene	μg/day	MUFA	g/day
Caffeine	g/day	n-3 fatty acid	g/day
Carbohydrate	g/day	n-6 fatty acid	g/day
Cholesterol	mg/day	Niacin	mg/day
Energy	kcal/day	Onion	g/day
Eugenol	mg/day	Pepper	g/day
Fiber	g/day	Protein	g/day
Flavan-3-ol	mg/day	PUFA	g/day
Flavones	mg/day	Riboflavin	mg/day
Flavonols	mg/day	Rosemary	mg/day
Flavonones	mg/day	Saffron	mg/day
Folic acid	μg/day	Saturated fat	g/day
Garlic	g/day	Selenium	μg/day
Ginger	mg/day	Thiamin	mg/day
Green/black tea	g/day	Thyme/oregano	mg/day
Iron	mg/day	Total fat	g/day
Isoflavones	mg/day	Trans fat	g/day
Anthocyanidins	mg/day	Turmeric	mg/day
vitamin B6	mg/day	Vitamin A	RE/day
vitamin B12	μg/day	Vitamin C	mg/day
Zinc	mg/day	Vitamin D	μg/day
		Vitamin E	mg/day

Note: Column names must match exactly (case-sensitive). Nutrients not present in your data will be excluded from the calculation.

Example Input CSV

participant_id,Fiber,Alcohol,Vitamin C,Saturated fat,Energy
1,18.8,13.98,118.2,28.6,2056
2,25.0,0.0,150.0,22.0,1800
3,12.0,30.0,80.0,35.0,2400

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API Reference

calculate_dii()

calculate_dii(
    nutrient_data: pd.DataFrame,
    reference_df: pd.DataFrame = None,
    id_column: str = None,
    detailed: bool = False
) -> pd.DataFrame

Parameters:

• nutrient_data: DataFrame with nutrient intake values
• reference_df: Custom reference table (optional, uses bundled data by default)
• id_column: Name of participant identifier column
• detailed: If True, returns per-nutrient breakdown

Returns: DataFrame with DII scores

get_available_nutrients()

get_available_nutrients() -> List[str]

Returns a list of all 45 supported nutrient names.

load_reference_table()

load_reference_table(custom_path: str = None) -> pd.DataFrame

Load the DII reference table with inflammatory weights and global statistics.

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Visualization

The package includes built-in visualization tools (requires matplotlib):

pip install matplotlib

from dii import calculate_dii, calculate_dii_detailed
from dii import plot_dii_distribution, plot_nutrient_contributions

# Calculate DII
results = calculate_dii(nutrients)
detailed = calculate_dii_detailed(nutrients)

# Generate plots
plot_dii_distribution(results, save_path="dii_distribution.png")
plot_nutrient_contributions(detailed, save_path="nutrient_contributions.png")

Nutrient Contributions Analysis

Average nutrient contributions to DII in NHANES data. Green bars indicate anti-inflammatory contributions; red bars indicate pro-inflammatory contributions.

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Command-Line Interface

Run DII calculations directly from the terminal:

# Calculate DII scores
python -m dii input_data.csv -o results.csv

# Include detailed breakdown
python -m dii input_data.csv -o results.csv --detailed

# List supported nutrients
python -m dii --nutrients

# Show help
python -m dii --help

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Methodology

The DII calculation follows the standardized approach from Shivappa et al. (2014):

1. Z-score standardization: For each nutrient, compute:

   z = (intake - global_mean) / global_sd
   

2. Centered percentile conversion: Transform z-score to a value between -1 and +1:

   percentile = 2 × Φ(z) - 1
   

   where Φ is the standard normal CDF.

3. Weighted summation: Multiply each percentile by the nutrient's inflammatory weight and sum:

   DII = Σ (percentile_i × weight_i)
   

Positive weights indicate pro-inflammatory effects; negative weights indicate anti-inflammatory effects.

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Validation

This implementation has been validated against:

• Synthetic test cases with mathematically verifiable results (SEQN 1, 2, 3)
• The dietaryindex R package methodology
• Original R implementation from PANDA-1 study statistician
• Independent verification by Jiyan Aslan Ceylan (University of Florida)

Validation result: 13,580 NHANES participants tested with perfect match to original calculations.

Run the test suite:

pytest tests/ -v

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Limitations

• Nutrient availability: DII accuracy depends on having data for as many of the 45 parameters as possible. Studies with limited nutrient data will have less precise scores.
• Reference population: Global means and SDs are derived from world literature circa 2014. Dietary patterns may have shifted.
• Food-level data: This package calculates DII from nutrient totals, not individual foods. Some DII parameters (e.g., garlic, turmeric) may be underestimated in datasets that don't capture specific foods.
• Supplement data: The DII was designed for dietary intake. Including supplement data may affect score interpretation.

For detailed information about data sources, see docs/DATA_SOURCES.md.

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Citation

If you use this package in your research, please cite:

@software{clark2025dii,
  author = {Clark, Ted and Strath, Larissa},
  title = {dii-calculator: Dietary Inflammatory Index Calculator for Python},
  year = {2025},
  url = {https://github.com/strathlab-data/DII}
}

Original DII Methodology

Please also cite the original DII development paper:

Shivappa N, Steck SE, Hurley TG, Hussey JR, Hébert JR. Designing and developing a literature-derived, population-based dietary inflammatory index. Public Health Nutr. 2014;17(8):1689-1696. doi:10.1017/S1368980013002115

dietaryindex R Package

This implementation was inspired by and validated against the dietaryindex R package. If you use this Python package, please also cite their excellent work:

Zhan JJ, Hodge RA, Dunlop AL, et al. Dietaryindex: a user-friendly and versatile R package for standardizing dietary pattern analysis in epidemiological and clinical studies. Am J Clin Nutr. 2024. doi:10.1016/j.ajcnut.2024.08.021

@article{zhan2024dietaryindex,
  author = {Zhan, Jiada James and Hodge, Rebecca A and Dunlop, Anne L and Lee, Matthew M and Bui, Linh and Liang, Donghai and Ferranti, Erin P},
  title = {Dietaryindex: a user-friendly and versatile R package for standardizing dietary pattern analysis in epidemiological and clinical studies},
  journal = {American Journal of Clinical Nutrition},
  year = {2024},
  doi = {10.1016/j.ajcnut.2024.08.021},
  url = {https://github.com/jamesjiadazhan/dietaryindex}
}

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Authors & Acknowledgments

Authors:

• Ted Clark — Data Analyst, University of Florida ([e-mail])
• Larissa Strath, PhD — Assistant Professor, Department of Health Outcomes and Biomedical Informatics, University of Florida ([e-mail])

Affiliation:
University of Florida, College of Medicine
Department of Health Outcomes and Biomedical Informatics

This package was developed as part of the PANDA-1 study at the University of Florida Pain Research & Intervention Center of Excellence.

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License

MIT License — see LICENSE for details.

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Contributing

Contributions are welcome! Please feel free to submit issues or pull requests.

1. Fork the repository
2. Create a feature branch (git checkout -b feature/improvement)
3. Commit your changes (git commit -am 'Add new feature')
4. Push to the branch (git push origin feature/improvement)
5. Open a Pull Request