diabetic-utils/README.md

Zero-bloat TypeScript utilities for diabetes data: glucose, A1C, conversions, time-in-range, and more.

# 🩸 Diabetic Utils

Built and maintained by Mark Learst.

**A TypeScript toolkit for diabetes analytics and health data.**

![Diabetic Utils Logo](https://raw.githubusercontent.com/marklearst/diabetic-utils/refs/heads/main/assets/dujs.png)

A modern, strictly-typed utility library for glucose, A1C, insulin, and diabetes metrics. Designed for reliability and transparency—no bloat, no guesswork, just robust utilities with referenced formulas from published guidelines.

> **Disclaimer**: This library is for **informational and educational purposes only**.
> It does not constitute medical advice, diagnosis, or treatment. Always consult
> a qualified healthcare provider for medical decisions.

> **v1.5.0** adds a full advanced CGM metrics suite, CGM vendor adapters, and health data interoperability (FHIR, Open mHealth).

---

## 📊 Status & Quality

![Status](https://img.shields.io/badge/status-stable-brightgreen)
[![codecov](https://codecov.io/gh/marklearst/diabetic-utils/branch/main/graph/badge.svg)](https://codecov.io/gh/marklearst/diabetic-utils)
![CI](https://github.com/marklearst/diabetic-utils/actions/workflows/ci-cd.yml/badge.svg)
![TypeScript](https://img.shields.io/badge/TypeScript-100%25_Strict-blue?logo=typescript)
![Coverage](https://img.shields.io/badge/coverage-100%25-success)
![npm](https://img.shields.io/npm/v/diabetic-utils)
![npm downloads](https://img.shields.io/npm/dm/diabetic-utils)
![License](https://img.shields.io/github/license/marklearst/diabetic-utils)

---

## 🚀 What's New in v1.5.0

### 📈 Advanced CGM Metrics Suite
A complete set of published CGM analytics, each with peer-reviewed references:
- **ADRR**: Average Daily Risk Range (Kovatchev 2006)
- **GRADE**: Glycemic Risk Assessment Diabetes Equation with hypo/eu/hyper partitioning (Hill 2007)
- **J-Index**: Composite mean + variability score (Wojcicki 1995)
- **CONGA**: Continuous Overall Net Glycemic Action for intra-day variability (McDonnell 2005)
- **Active Percent**: CGM wear-time tracking against clinical thresholds (Danne 2017)
- **AGP Aggregate**: Single-call `calculateAGPMetrics()` computes all Tier 1 metrics at once
- **LBGI / HBGI**: Low/High Blood Glucose Index (Kovatchev 2006)
- **GRI**: Glycemia Risk Index with zone A-E classification (Klonoff 2023)
- **MODD**: Mean of Daily Differences for day-to-day variability (Service 1980)

### 🔌 CGM Connector Adapters
Pure transformation helpers that normalize vendor payloads into a canonical `NormalizedCGMReading` type:
- **Dexcom Share** — normalize Dexcom Share API responses
- **Libre LinkUp** — normalize Libre LinkUp API responses
- **Nightscout** — normalize Nightscout SGV entries

### 🏥 Health Data Interoperability
Build standards-compliant payloads for health data exchange:
- **FHIR CGM IG** — HL7 FHIR-aligned CGM summary and sensor reading observations
- **Open mHealth** — OMH blood-glucose datapoints with full header support

### ✅ 337 Passing Tests
- 100% coverage across lines, branches, functions, and statements
- New edge-case coverage for out-of-order timestamps, mixed units, and cross-module interactions

---

## 📦 Installation

```bash
npm install diabetic-utils
# or
pnpm add diabetic-utils
# or
yarn add diabetic-utils
```

**Requirements:** TypeScript 4.5+ or JavaScript (ES2020+)

---

## ⚡ Quick Start

### Basic Conversions & Calculations

```typescript
import {
  mgDlToMmolL,
  mmolLToMgDl,
  estimateGMI,
  estimateA1CFromAverage
} from 'diabetic-utils'

// Glucose unit conversions
mgDlToMmolL(180)  // → 10.0
mmolLToMgDl(5.5)  // → 99

// GMI calculation (multiple input formats)
estimateGMI(100, 'mg/dL')              // → 5.4
estimateGMI('5.5 mmol/L')              // → 5.4
estimateGMI({ value: 100, unit: 'mg/dL' })  // → 5.4

// A1C estimation
estimateA1CFromAverage(154, 'mg/dL')  // → 7.0
```

### Enhanced Time-in-Range

```typescript
import { calculateEnhancedTIR } from 'diabetic-utils'
import type { GlucoseReading } from 'diabetic-utils'

const readings: GlucoseReading[] = [
  { value: 120, unit: 'mg/dL', timestamp: '2024-01-01T08:00:00Z' },
  { value: 95,  unit: 'mg/dL', timestamp: '2024-01-01T08:05:00Z' },
  { value: 180, unit: 'mg/dL', timestamp: '2024-01-01T08:10:00Z' },
  // ... more readings
]

const result = calculateEnhancedTIR(readings)

console.log(`TIR: ${result.inRange.percentage}%`)
// TIR: 72.5%

console.log(`Very Low: ${result.veryLow.percentage}%`)
// Very Low: 0.5%

console.log(`Assessment: ${result.meetsTargets.overallAssessment}`)
// Assessment: good

console.log(result.meetsTargets.recommendations)
// ['All metrics meet consensus targets.']
```

### Pregnancy TIR

```typescript
import { calculatePregnancyTIR } from 'diabetic-utils'

const result = calculatePregnancyTIR(readings)

console.log(`TIR (63-140 mg/dL): ${result.inRange.percentage}%`)
// TIR (63-140 mg/dL): 85.2%

console.log(`Meets pregnancy targets: ${result.meetsPregnancyTargets}`)
// Meets pregnancy targets: true

console.log(result.recommendations)
// ['All metrics meet pregnancy consensus targets.', ...]
```

### AGP Metrics (All-in-One)

```typescript
import { calculateAGPMetrics } from 'diabetic-utils'
import type { GlucoseReading } from 'diabetic-utils'

const readings: GlucoseReading[] = [
  { value: 120, unit: 'mg/dL', timestamp: '2024-01-01T08:00:00Z' },
  { value: 95,  unit: 'mg/dL', timestamp: '2024-01-01T08:05:00Z' },
  { value: 180, unit: 'mg/dL', timestamp: '2024-01-01T08:10:00Z' },
  // ... more readings across multiple days
]

const agp = calculateAGPMetrics(readings)

console.log(`Mean: ${agp.meanGlucose} mg/dL`)
console.log(`SD: ${agp.sd}, CV: ${agp.cv}%`)
console.log(`LBGI: ${agp.lbgi}, HBGI: ${agp.hbgi}`)
console.log(`ADRR: ${agp.adrr}`)
console.log(`GRADE: ${agp.grade.gradeScore}`)
console.log(`GRI: ${agp.gri.gri} (Zone ${agp.gri.zone})`)
console.log(`J-Index: ${agp.jIndex}`)
console.log(`MODD: ${agp.modd} mg/dL`)
console.log(`CONGA: ${agp.conga} mg/dL`)
console.log(`Active: ${agp.activePercent.activePercent}%`)
```

### CGM Connector Adapters

```typescript
import {
  normalizeDexcomEntries,
  normalizeLibreEntries,
  normalizeNightscoutEntries
} from 'diabetic-utils'

// Normalize vendor data into a canonical format
const dexcomReadings = normalizeDexcomEntries(dexcomShareResponse)
const libreReadings = normalizeLibreEntries(libreLinkUpResponse)
const nightscoutReadings = normalizeNightscoutEntries(nightscoutSGVEntries)

// All return NormalizedCGMReading[] with:
//   { value, unit, timestamp, trend, source }
// Ready to pass into any diabetic-utils analytics function
```

### FHIR & Open mHealth Export

```typescript
import {
  buildFHIRCGMSummary,
  buildFHIRSensorReading,
  buildOMHDataPoint
} from 'diabetic-utils'

// Build a FHIR CGM summary observation
const fhirSummary = buildFHIRCGMSummary(tirResult, {
  start: '2024-01-01',
  end: '2024-01-14'
})

// Build a FHIR sensor reading observation
const fhirReading = buildFHIRSensorReading({
  value: 120, unit: 'mg/dL', timestamp: '2024-01-01T08:00:00Z'
})

// Build an Open mHealth blood-glucose datapoint
const omhPoint = buildOMHDataPoint(
  { value: 120, unit: 'mg/dL', timestamp: '2024-01-01T08:00:00Z' },
  'reading-001'
)
```

### Glucose Labeling & Validation

```typescript
import {
  getGlucoseLabel,
  isHypo,
  isHyper,
  isValidGlucoseValue
} from 'diabetic-utils'

// Label glucose values
getGlucoseLabel(60, 'mg/dL')   // → 'low'
getGlucoseLabel(5.5, 'mmol/L') // → 'normal'
getGlucoseLabel(200, 'mg/dL')  // → 'high'

// Threshold checks
isHypo(65, 'mg/dL')   // → true
isHyper(180, 'mg/dL') // → false

// Validation
isValidGlucoseValue(120, 'mg/dL')  // → true
isValidGlucoseValue(-10, 'mg/dL')  // → false
```

### Variability Analytics

```typescript
import {
  glucoseStandardDeviation,
  glucoseCoefficientOfVariation,
  glucosePercentiles,
  glucoseMAGE
} from 'diabetic-utils'

const data = [90, 100, 110, 120, 130, 140, 150, 160, 170, 180]

// Standard deviation (unbiased sample SD, n-1)
glucoseStandardDeviation(data)  // → 30.28

// Coefficient of variation (CV%)
glucoseCoefficientOfVariation(data)  // → 22.43

// Percentiles (nearest-rank method)
glucosePercentiles(data, [10, 50, 90])
// → { 10: 90, 50: 130, 90: 170 }

// MAGE (Mean Amplitude of Glycemic Excursions)
const mage = glucoseMAGE([100, 120, 80, 160, 90, 140, 70, 180])
console.log(`MAGE: ${mage} mg/dL`)
```

### Custom Thresholds

```typescript
import { getGlucoseLabel, isHypo, getA1CCategory } from 'diabetic-utils'

// Custom hypoglycemia threshold
isHypo(75, 'mg/dL', { mgdl: 80 })  // → true

// Custom hyperglycemia threshold
isHyper(9.0, 'mmol/L', { mmoll: 8.5 })  // → true

// Custom glucose label thresholds
getGlucoseLabel(75, 'mg/dL', {
  hypo: { mgdl: 80 },
  hyper: { mgdl: 160 }
})  // → 'low'

// Custom A1C category cutoffs
getA1CCategory(6.5, {
  normalMax: 6.0,
  prediabetesMax: 7.0
})  // → 'prediabetes'
```

---

## 🌟 Features

### Core Utilities
- ✅ **Glucose Conversions**: mg/dL ⇄ mmol/L
- ✅ **A1C Calculations**: GMI, eAG, A1C estimation
- ✅ **Time-in-Range**: Enhanced TIR (5 ranges), Pregnancy TIR
- ✅ **HOMA-IR**: Insulin resistance calculation
- ✅ **Variability Metrics**: SD, CV, MAGE, percentiles
- ✅ **Validation**: Input guards, string parsing
- ✅ **Labeling**: Glucose status (low/normal/high)

### Advanced CGM Metrics
- ✅ **LBGI / HBGI**: Low/High Blood Glucose Index (Kovatchev 2006)
- ✅ **GRI**: Glycemia Risk Index with zone A-E classification (Klonoff 2023)
- ✅ **MODD**: Mean of Daily Differences for day-to-day variability (Service 1980)
- ✅ **ADRR**: Average Daily Risk Range (Kovatchev 2006)
- ✅ **GRADE**: Glycemic Risk Assessment Diabetes Equation with partitioning (Hill 2007)
- ✅ **J-Index**: Composite mean + variability score (Wojcicki 1995)
- ✅ **CONGA**: Continuous Overall Net Glycemic Action (McDonnell 2005)
- ✅ **Active Percent**: CGM wear-time tracking (Danne 2017)
- ✅ **AGP Aggregate**: All Tier 1 metrics in a single call

### CGM Connector Adapters
- ✅ **Dexcom Share**: Normalize Dexcom Share API responses
- ✅ **Libre LinkUp**: Normalize Libre LinkUp API responses
- ✅ **Nightscout**: Normalize Nightscout SGV entries
- ✅ **Canonical Type**: `NormalizedCGMReading` with trend + source metadata

### Interoperability
- ✅ **FHIR CGM IG**: Build HL7 FHIR-aligned CGM summary and sensor reading payloads
- ✅ **Open mHealth**: Build OMH blood-glucose datapoints

### Quality & DX
- ✅ **TypeScript-First**: 100% strict mode, zero `any` types
- ✅ **100% Test Coverage**: 337 tests, all edge cases covered
- ✅ **Zero Dependencies**: No bloat, tree-shakable
- ✅ **Published References**: ADA, CDC, ISPAD, PubMed citations
- ✅ **TSDoc**: Complete API documentation
- ✅ **ESM + CJS**: Works everywhere
- ✅ **Type Predicates**: Better type narrowing
- ✅ **Named Constants**: Self-documenting formulas

---

## 🏆 Why Choose Diabetic Utils?

### Referenced Formulas
Every formula, threshold, and calculation references published guidelines:
- **International Consensus on Time in Range (2019)** - TIR calculations
- **ADA Standards of Care (2024)** - Pregnancy targets, A1C guidelines
- **ISPAD Guidelines (2018)** - Glucose variability metrics
- **NIH/NIDDK** - HOMA-IR, eAG formulas
- **Kovatchev et al. (2006)** - LBGI, HBGI, ADRR
- **Hill et al. (2007)** - GRADE
- **Klonoff et al. (2023)** - GRI
- **Wojcicki (1995)** - J-Index
- **McDonnell et al. (2005)** - CONGA
- **Danne et al. (2017)** - Active Percent

### Production-Ready
- **100% Test Coverage** - Every line tested
- **Type-Safe** - Catch errors at compile time
- **Zero Dependencies** - Small bundle, no supply chain risk
- **Modern ESM** - Tree-shakable, works with Vite, Next.js, etc.

### Developer-Friendly
- **Clear API** - Predictable function signatures
- **Great DX** - Autocomplete with literal types
- **Working Examples** - Copy-paste ready code
- **Test Helpers** - Utilities for your own tests

### Unique Features
**Only TypeScript/JavaScript library with:**
- Full AGP metrics suite in a single call
- Enhanced TIR (5-range breakdown) and Pregnancy TIR
- MAGE calculation (Service 1970)
- ADRR, GRADE, J-Index, CONGA, and Active Percent
- CGM vendor adapters (Dexcom, Libre, Nightscout)
- FHIR CGM IG-aligned export utilities
- LBGI/HBGI, GRI, and MODD metrics
- Type predicates for validation

---

## 📚 Full API Reference

### Glucose Conversions
- `mgDlToMmolL(value)` - Convert mg/dL to mmol/L
- `mmolLToMgDl(value)` - Convert mmol/L to mg/dL
- `convertGlucoseUnit({ value, unit })` - Generic unit conversion

### A1C & GMI
- `estimateA1CFromAverage(glucose, unit)` - A1C from average glucose
- `estimateGMI(input, unit?)` - GMI from average glucose
- `a1cToGMI(a1c)` - Convert A1C to GMI
- `estimateAvgGlucoseFromA1C(a1c)` - A1C to estimated average glucose (mg/dL)

### Time-in-Range
- `calculateTimeInRange(readings, low, high)` - Basic TIR
- `calculateEnhancedTIR(readings, options?)` - 5-range TIR
- `calculatePregnancyTIR(readings, options?)` - Pregnancy TIR

### Glucose Analysis
- `getGlucoseLabel(value, unit, thresholds?)` - Label as low/normal/high
- `isHypo(value, unit, threshold?)` - Check hypoglycemia
- `isHyper(value, unit, threshold?)` - Check hyperglycemia
- `isValidGlucoseValue(value, unit)` - Validate glucose value

### A1C Analysis
- `getA1CCategory(a1c, cutoffs?)` - Categorize A1C
- `isA1CInTarget(a1c, target?)` - Check if A1C meets target

### Variability Metrics
- `glucoseStandardDeviation(readings)` - SD (unbiased)
- `glucoseCoefficientOfVariation(readings)` - CV%
- `glucosePercentiles(readings, percentiles)` - Percentile ranks
- `glucoseMAGE(readings, options?)` - Mean Amplitude of Glycemic Excursions

### Insulin Metrics
- `calculateHOMAIR(glucose, insulin, unit)` - HOMA-IR
- `isValidInsulin(value)` - Validate insulin value

### Advanced CGM Metrics
- `calculateAGPMetrics(readings, options?)` - All Tier 1 metrics in a single call
- `glucoseLBGI(readings)` - Low Blood Glucose Index (Kovatchev 2006)
- `glucoseHBGI(readings)` - High Blood Glucose Index (Kovatchev 2006)
- `calculateADRR(readings)` - Average Daily Risk Range (Kovatchev 2006)
- `calculateGRADE(readings)` - Glycemic Risk Assessment Diabetes Equation (Hill 2007)
- `calculateGRI(input)` - Glycemia Risk Index with zone A-E (Klonoff 2023)
- `calculateJIndex(readings)` - J-Index composite score (Wojcicki 1995)
- `calculateMODD(readings, options?)` - Mean of Daily Differences (Service 1980)
- `calculateCONGA(readings, options?)` - Continuous Overall Net Glycemic Action (McDonnell 2005)
- `calculateActivePercent(readings, options?)` - CGM wear-time percentage (Danne 2017)

### CGM Connector Adapters
- `normalizeDexcomEntries(entries)` - Dexcom Share → NormalizedCGMReading[]
- `normalizeLibreEntries(entries)` - Libre LinkUp → NormalizedCGMReading[]
- `normalizeNightscoutEntries(entries)` - Nightscout SGV → NormalizedCGMReading[]

### Interoperability
- `buildFHIRCGMSummary(tir, period, options?)` - FHIR CGM summary observation
- `buildFHIRSensorReading(reading)` - FHIR sensor reading observation
- `buildFHIRSensorReadings(readings)` - FHIR sensor reading observations from a list of readings
- `buildOMHBloodGlucose(reading)` - Open mHealth blood-glucose body
- `buildOMHBloodGlucoseList(readings)` - Open mHealth blood-glucose bodies from a list of readings
- `buildOMHDataPoint(reading, id)` - Full OMH datapoint with header

### Utilities
- `parseGlucoseString(str)` - Parse "120 mg/dL" → { value, unit }
- `formatGlucose(value, unit)` - Format glucose with unit
- `isValidGlucoseString(str)` - Validate glucose string

**[View Complete API Docs →](https://marklearst.github.io/diabetic-utils/)**

---

## 🧪 Test Helpers

The repository includes test utilities in `tests/test-helpers.ts` for contributors and downstream developers:

```typescript
// In your test files (not published to npm — copy as needed)
import {
  createGlucoseReadings,
  COMMON_TEST_VALUES,
  TEST_TIMESTAMP_BASE
} from './tests/test-helpers'

// Create test data easily
const readings = createGlucoseReadings([100, 110, 120], 'mg/dL', 5)
// → 3 readings at 5-minute intervals

// Use common test values
const { NORMAL_GLUCOSE_MGDL, HYPO_GLUCOSE_MGDL } = COMMON_TEST_VALUES
```

---

## 🔬 References

All calculations reference peer-reviewed published sources:

- **Time-in-Range**: [International Consensus (2019)](https://diabetesjournals.org/care/article/42/8/1593)
- **Pregnancy TIR**: [ADA Standards of Care (2024)](https://diabetesjournals.org/care/article/47/Supplement_1/S282)
- **ADA 2026 Standards**: [ADA Standards of Care (2026)](https://diabetesjournals.org/care/issue/49/Supplement_1)
- **A1C/eAG**: [Nathan et al. (2008)](https://diabetesjournals.org/care/article/31/8/1473)
- **HOMA-IR**: [Matthews et al. (1985)](https://diabetesjournals.org/diabetes/article/34/12/1212)
- **MAGE**: [Service et al. (1970)](https://diabetesjournals.org/diabetes/article/19/9/644)
- **LBGI/HBGI/ADRR**: [Kovatchev et al. (2006)](https://doi.org/10.2337/dc06-1085)
- **GRI**: [Klonoff et al. (2023)](https://doi.org/10.1177/19322968221085273)
- **MODD**: [Service & Nelson (1980)](https://doi.org/10.2337/diacare.3.1.58)
- **GRADE**: [Hill et al. (2007)](https://doi.org/10.1111/j.1464-5491.2007.02119.x)
- **J-Index**: [Wojcicki (1995)](https://doi.org/10.1055/s-2007-979906)
- **CONGA**: [McDonnell et al. (2005)](https://doi.org/10.1089/dia.2005.7.253)
- **Active Percent**: [Danne et al. (2017)](https://doi.org/10.2337/dc17-1600)
- **Variability**: [ISPAD Guidelines (2018)](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7445493/)
- **FHIR CGM IG**: [HL7 CGM IG v1.0.0](https://build.fhir.org/ig/HL7/cgm/index.html)

---

## 🏗️ Architecture

```
diabetic-utils/
├── src/
│   ├── index.ts              # Main exports
│   ├── constants.ts          # Clinical thresholds & formulas
│   ├── types.ts              # TypeScript types
│   ├── conversions.ts        # Glucose unit conversions
│   ├── a1c.ts               # A1C & GMI calculations
│   ├── tir.ts               # Basic time-in-range
│   ├── tir-enhanced.ts      # Enhanced & pregnancy TIR
│   ├── glucose.ts           # Glucose utilities
│   ├── alignment.ts         # HOMA-IR
│   ├── variability.ts       # SD, CV, percentiles
│   ├── mage.ts              # MAGE calculation
│   ├── formatters.ts        # String formatting
│   ├── guards.ts            # Type guards
│   ├── validators.ts        # Input validation
│   ├── connectors/          # CGM vendor adapters
│   │   ├── dexcom.ts        # Dexcom Share normalization
│   │   ├── libre.ts         # Libre LinkUp normalization
│   │   ├── nightscout.ts    # Nightscout SGV normalization
│   │   └── types.ts         # Vendor & canonical types
│   ├── interop/             # Health data interoperability
│   │   ├── fhir.ts          # FHIR CGM IG payload builders
│   │   ├── openmhealth.ts   # Open mHealth payload builders
│   │   └── types.ts         # Interop payload types
│   └── metrics/             # Advanced CGM metrics
│       ├── agp.ts           # Aggregate AGP metrics
│       ├── bgi.ts           # LBGI / HBGI
│       ├── adrr.ts          # Average Daily Risk Range
│       ├── grade.ts         # GRADE score
│       ├── gri.ts           # Glycemia Risk Index
│       ├── jindex.ts        # J-Index
│       ├── modd.ts          # Mean of Daily Differences
│       ├── conga.ts         # CONGA
│       └── active-percent.ts # CGM wear time
├── tests/
│   ├── test-helpers.ts      # Shared test utilities
│   └── *.test.ts            # 100% coverage tests (337 tests)
└── dist/                    # Built output (ESM + CJS)
```

**Key Principles:**
- ✅ Zero dependencies
- ✅ Tree-shakable modules
- ✅ Strict TypeScript
- ✅ 100% test coverage
- ✅ Published references in TSDoc

---

## 🤝 Contributing

Contributions are welcome! Please follow these steps:

1. **Fork** the repository
2. **Create** your feature branch: `git checkout -b feat/my-feature`
3. **Add tests** for any new functionality
4. **Ensure** 100% coverage: `pnpm test:coverage`
5. **Commit** with [conventional commits](https://www.conventionalcommits.org/): `git commit -m "feat: add new feature"`
6. **Push** to your branch: `git push origin feat/my-feature`
7. **Open** a pull request

### Development Commands

```bash
# Install dependencies
pnpm install

# Run tests
pnpm test

# Run tests with coverage
pnpm test:coverage

# Build library
pnpm build
```

---

## 📝 Changelog

See [CHANGELOG.md](CHANGELOG.md) for detailed release notes and version history.

---

## 📄 License

This project is licensed under the **MIT License**. See the [LICENSE](LICENSE) file for details.

© 2024–2026 [Mark Learst](https://marklearst.com)

Use it, fork it, build something that matters.

---

## 🔗 Links

- 📦 [NPM Package](https://www.npmjs.com/package/diabetic-utils)
- 📚 [API Documentation](https://marklearst.github.io/diabetic-utils/)
- 🐙 [GitHub Repository](https://github.com/marklearst/diabetic-utils)
- 🌐 [Website](https://diabeticutils.com) _(coming soon)_

---

## 🙋‍♂️ Author

**Mark Learst**
Full-stack developer, diabetes advocate, and open source contributor.

- 🐦 X (Twitter): [@marklearst](https://x.com/marklearst)
- 💼 LinkedIn: [Mark Learst](https://linkedin.com/in/marklearst)
- 🌐 Portfolio: [marklearst.com](https://marklearst.com)

> 💬 Using diabetic-utils in your project? [Let me know](https://x.com/marklearst)—I'd love to feature it!
> ⭐ Star the repo and help us build the best diabetes toolkit together!

---

## 💬 Support

- 🐛 **Bug Reports**: [Open an issue](https://github.com/marklearst/diabetic-utils/issues)
- 💡 **Feature Requests**: [Start a discussion](https://github.com/marklearst/diabetic-utils/discussions)
- 📧 **Email**: mark@marklearst.com

---

## 📝 A Personal Note

I built diabetic-utils because I believe in the power of data-driven diabetes management. As someone who's lived with diabetes, I know how hard it can be to make sense of the numbers.

That's why I've poured my heart into creating a library that's both **accurate** and **easy to use**. Whether you're building an app, working on research, or just trying to understand your own data, I hope diabetic-utils can help.

Let's work together to make diabetes management better, one data point at a time. 🩸

---

**Built with ❤️ by the diabetes community, for the diabetes community.**