Glucose Data Analysis Report

Published

January 30, 2026

Glucose Data Analysis Report

This comprehensive report contains glucose monitoring data analysis across multiple years using four different analysis approaches:

  • Basic Analysis (B1): Fundamental glucose pattern analysis and overview
  • Yearly Analysis (B2): Overall yearly trends and patterns
  • Subtype Analysis (C1): Analysis based on four Type 2 diabetes subtypes
  • AGP Report (D1): Ambulatory Glucose Profile standardized analysis

Years Included in This Report

  • 2022
  • 2023
  • 2024
  • 2025
  • 2026
  • Overall

2022 Analysis

Basic Analysis Report (B1)

This section provides fundamental glucose data analysis with essential metrics and visualizations.

Yearly Analysis Report (B2)

This section provides comprehensive yearly glucose data analysis with standard metrics and visualizations.

2022 01 Alldataovertime

Complete glucose readings plotted over time showing trends and patterns across the entire year.

2022 02 Dailyoverlay

All daily glucose profiles overlaid to identify typical patterns and variations throughout the day.

2022 03 Subtypemetrics

Analysis of glucose patterns categorized by the four Type 2 diabetes subtypes.

2022 04 Histogram

Distribution of glucose values showing frequency across different ranges.

2022 05 Summarystats

Statistical summary including mean, median, standard deviation, and percentile ranges.

Diabetes Subtype Analysis (C1)

This section analyzes glucose patterns based on the four Type 2 diabetes subtypes identified in Stanford research.

Year 2022 01 Alldataovertime

Yearly glucose data with subtype classification overlay showing pattern evolution.

Year 2022 02 Dailyoverlay

Daily profiles color-coded by diabetes subtype for pattern identification.

Year 2022 03 Subtypemetrics

Detailed metrics for each of the four Type 2 diabetes subtypes throughout the year.

Year 2022 04 Histogram

Glucose value distribution analysis with subtype-specific ranges highlighted.

Year 2022 05 Summarystats

Comprehensive statistics broken down by diabetes subtype categories.

Ambulatory Glucose Profile (D1)

This section presents standardized AGP analysis following international glucose monitoring guidelines.

2022 01 Agp Profile

Ambulatory Glucose Profile showing median glucose curve with percentile ranges (10th-90th).

2022 02 Daily Profiles

Individual daily glucose profiles for detailed day-by-day analysis.

2022 03 Tir Bar

Time in Range metrics showing percentage of time in target, above, and below range.

2022 04 Statistics

AGP statistical summary including GMI, CV, and glucose management indicators.

2023 Analysis

Basic Analysis Report (B1)

This section provides fundamental glucose data analysis with essential metrics and visualizations.

Yearly Analysis Report (B2)

This section provides comprehensive yearly glucose data analysis with standard metrics and visualizations.

2023 01 Alldataovertime

Complete glucose readings plotted over time showing trends and patterns across the entire year.

2023 02 Dailyoverlay

All daily glucose profiles overlaid to identify typical patterns and variations throughout the day.

2023 03 Subtypemetrics

Analysis of glucose patterns categorized by the four Type 2 diabetes subtypes.

2023 04 Histogram

Distribution of glucose values showing frequency across different ranges.

2023 05 Summarystats

Statistical summary including mean, median, standard deviation, and percentile ranges.

Diabetes Subtype Analysis (C1)

This section analyzes glucose patterns based on the four Type 2 diabetes subtypes identified in Stanford research.

Year 2023 01 Alldataovertime

Yearly glucose data with subtype classification overlay showing pattern evolution.

Year 2023 02 Dailyoverlay

Daily profiles color-coded by diabetes subtype for pattern identification.

Year 2023 03 Subtypemetrics

Detailed metrics for each of the four Type 2 diabetes subtypes throughout the year.

Year 2023 04 Histogram

Glucose value distribution analysis with subtype-specific ranges highlighted.

Year 2023 05 Summarystats

Comprehensive statistics broken down by diabetes subtype categories.

Ambulatory Glucose Profile (D1)

This section presents standardized AGP analysis following international glucose monitoring guidelines.

2023 01 Agp Profile

Ambulatory Glucose Profile showing median glucose curve with percentile ranges (10th-90th).

2023 02 Daily Profiles

Individual daily glucose profiles for detailed day-by-day analysis.

2023 03 Tir Bar

Time in Range metrics showing percentage of time in target, above, and below range.

2023 04 Statistics

AGP statistical summary including GMI, CV, and glucose management indicators.

2024 Analysis

Basic Analysis Report (B1)

This section provides fundamental glucose data analysis with essential metrics and visualizations.

Yearly Analysis Report (B2)

This section provides comprehensive yearly glucose data analysis with standard metrics and visualizations.

2024 01 Alldataovertime

Complete glucose readings plotted over time showing trends and patterns across the entire year.

2024 02 Dailyoverlay

All daily glucose profiles overlaid to identify typical patterns and variations throughout the day.

2024 03 Subtypemetrics

Analysis of glucose patterns categorized by the four Type 2 diabetes subtypes.

2024 04 Histogram

Distribution of glucose values showing frequency across different ranges.

2024 05 Summarystats

Statistical summary including mean, median, standard deviation, and percentile ranges.

Diabetes Subtype Analysis (C1)

This section analyzes glucose patterns based on the four Type 2 diabetes subtypes identified in Stanford research.

Year 2024 01 Alldataovertime

Yearly glucose data with subtype classification overlay showing pattern evolution.

Year 2024 02 Dailyoverlay

Daily profiles color-coded by diabetes subtype for pattern identification.

Year 2024 03 Subtypemetrics

Detailed metrics for each of the four Type 2 diabetes subtypes throughout the year.

Year 2024 04 Histogram

Glucose value distribution analysis with subtype-specific ranges highlighted.

Year 2024 05 Summarystats

Comprehensive statistics broken down by diabetes subtype categories.

Ambulatory Glucose Profile (D1)

This section presents standardized AGP analysis following international glucose monitoring guidelines.

2024 01 Agp Profile

Ambulatory Glucose Profile showing median glucose curve with percentile ranges (10th-90th).

2024 02 Daily Profiles

Individual daily glucose profiles for detailed day-by-day analysis.

2024 03 Tir Bar

Time in Range metrics showing percentage of time in target, above, and below range.

2024 04 Statistics

AGP statistical summary including GMI, CV, and glucose management indicators.

2025 Analysis

Basic Analysis Report (B1)

This section provides fundamental glucose data analysis with essential metrics and visualizations.

Yearly Analysis Report (B2)

This section provides comprehensive yearly glucose data analysis with standard metrics and visualizations.

2025 01 Alldataovertime

Complete glucose readings plotted over time showing trends and patterns across the entire year.

2025 02 Dailyoverlay

All daily glucose profiles overlaid to identify typical patterns and variations throughout the day.

2025 03 Subtypemetrics

Analysis of glucose patterns categorized by the four Type 2 diabetes subtypes.

2025 04 Histogram

Distribution of glucose values showing frequency across different ranges.

2025 05 Summarystats

Statistical summary including mean, median, standard deviation, and percentile ranges.

Diabetes Subtype Analysis (C1)

This section analyzes glucose patterns based on the four Type 2 diabetes subtypes identified in Stanford research.

Year 2025 01 Alldataovertime

Yearly glucose data with subtype classification overlay showing pattern evolution.

Year 2025 02 Dailyoverlay

Daily profiles color-coded by diabetes subtype for pattern identification.

Year 2025 03 Subtypemetrics

Detailed metrics for each of the four Type 2 diabetes subtypes throughout the year.

Year 2025 04 Histogram

Glucose value distribution analysis with subtype-specific ranges highlighted.

Year 2025 05 Summarystats

Comprehensive statistics broken down by diabetes subtype categories.

Ambulatory Glucose Profile (D1)

This section presents standardized AGP analysis following international glucose monitoring guidelines.

2025 01 Agp Profile

Ambulatory Glucose Profile showing median glucose curve with percentile ranges (10th-90th).

2025 02 Daily Profiles

Individual daily glucose profiles for detailed day-by-day analysis.

2025 03 Tir Bar

Time in Range metrics showing percentage of time in target, above, and below range.

2025 04 Statistics

AGP statistical summary including GMI, CV, and glucose management indicators.

2026 Analysis

Basic Analysis Report (B1)

This section provides fundamental glucose data analysis with essential metrics and visualizations.

Yearly Analysis Report (B2)

This section provides comprehensive yearly glucose data analysis with standard metrics and visualizations.

2026 01 Alldataovertime

Complete glucose readings plotted over time showing trends and patterns across the entire year.

2026 02 Dailyoverlay

All daily glucose profiles overlaid to identify typical patterns and variations throughout the day.

2026 03 Subtypemetrics

Analysis of glucose patterns categorized by the four Type 2 diabetes subtypes.

2026 04 Histogram

Distribution of glucose values showing frequency across different ranges.

2026 05 Summarystats

Statistical summary including mean, median, standard deviation, and percentile ranges.

Diabetes Subtype Analysis (C1)

This section analyzes glucose patterns based on the four Type 2 diabetes subtypes identified in Stanford research.

Year 2026 01 Alldataovertime

Yearly glucose data with subtype classification overlay showing pattern evolution.

Year 2026 02 Dailyoverlay

Daily profiles color-coded by diabetes subtype for pattern identification.

Year 2026 03 Subtypemetrics

Detailed metrics for each of the four Type 2 diabetes subtypes throughout the year.

Year 2026 04 Histogram

Glucose value distribution analysis with subtype-specific ranges highlighted.

Year 2026 05 Summarystats

Comprehensive statistics broken down by diabetes subtype categories.

Ambulatory Glucose Profile (D1)

This section presents standardized AGP analysis following international glucose monitoring guidelines.

2026 01 Agp Profile

Ambulatory Glucose Profile showing median glucose curve with percentile ranges (10th-90th).

2026 02 Daily Profiles

Individual daily glucose profiles for detailed day-by-day analysis.

2026 03 Tir Bar

Time in Range metrics showing percentage of time in target, above, and below range.

2026 04 Statistics

AGP statistical summary including GMI, CV, and glucose management indicators.

Overall Analysis

Basic Analysis Report (B1)

This section provides fundamental glucose data analysis with essential metrics and visualizations.

Yearly Analysis Report (B2)

This section provides comprehensive yearly glucose data analysis with standard metrics and visualizations.

Overall 01 Alldataovertime

Complete glucose readings plotted over time showing trends and patterns across the entire year.

Overall 02 Dailyoverlay

All daily glucose profiles overlaid to identify typical patterns and variations throughout the day.

Overall 03 Subtypemetrics

Analysis of glucose patterns categorized by the four Type 2 diabetes subtypes.

Overall 04 Histogram

Distribution of glucose values showing frequency across different ranges.

Overall 05 Summarystats

Statistical summary including mean, median, standard deviation, and percentile ranges.

Diabetes Subtype Analysis (C1)

This section analyzes glucose patterns based on the four Type 2 diabetes subtypes identified in Stanford research.

Ambulatory Glucose Profile (D1)

This section presents standardized AGP analysis following international glucose monitoring guidelines.

Overall 01 Agp Profile

Ambulatory Glucose Profile showing median glucose curve with percentile ranges (10th-90th).

Overall 02 Daily Profiles

Individual daily glucose profiles for detailed day-by-day analysis.

Overall 03 Tir Bar

Time in Range metrics showing percentage of time in target, above, and below range.

Overall 04 Statistics

AGP statistical summary including GMI, CV, and glucose management indicators.

Summary and Conclusions

This report provides a comprehensive view of glucose monitoring data across multiple years using three complementary analysis approaches:

  1. Yearly Analysis provides overall trends and standard metrics
  2. Subtype Analysis identifies patterns specific to different diabetes phenotypes
  3. AGP Reports offer standardized clinical assessment tools

Together, these analyses enable comprehensive glucose pattern recognition and informed diabetes management decisions.

Meal for Diabetic Type 2 Subtype

Since Michael Snyder’s research suggests that each of the four subtypes of Type 2 diabetes (or prediabetes) may benefit from tailored dietary strategies. While research on exact food prescriptions by subtype is still emerging, we can draw on known physiology of each category and existing nutritional science.

Here’s a practical breakdown:

1. Muscle Insulin Resistance

Problem: Muscles don’t efficiently take in glucose in response to insulin. Goal: Improve muscle sensitivity to insulin and support glucose uptake.

Best foods:

  • Lean protein + low-glycemic carbs → e.g., salmon with quinoa and leafy greens (protein helps muscles respond better, and slow carbs reduce spikes).

  • Magnesium-rich foods (linked to insulin sensitivity) → spinach, pumpkin seeds, almonds.

  • Berries & citrus (rich in antioxidants) → counter oxidative stress in muscle tissue.

2. Hepatic (Liver) Insulin Resistance

Problem: The liver continues to release glucose even when it shouldn’t. Goal: Reduce liver fat and stabilize glucose output.

Best foods:

  • Fatty fish (omega-3s) → salmon, sardines, mackerel (reduce liver inflammation and fat buildup).
  • High-fiber foods → oats, lentils, beans, chia seeds (improve hepatic glucose regulation).
  • Coffee & green tea (without added sugar) → associated with lower risk of fatty liver.
  • Leafy greens & cruciferous vegetables → kale, broccoli, Brussels sprouts (support liver detox pathways).

3. β-Cell Dysfunction

Problem: Pancreatic β-cells can’t produce enough insulin. Goal: Reduce insulin demand and protect β-cell health.

Best foods:

  • Low-carb or low-glycemic foods → cauliflower rice, zucchini noodles, legumes.
  • Nuts and seeds → walnuts, flaxseed, chia (healthy fats reduce insulin spikes).
  • Polyphenol-rich foods → blueberries, green tea, dark chocolate (≥70% cocoa) — shown to support β-cell survival in some studies.
  • Olive oil & avocados → healthy monounsaturated fats that keep blood sugar stable.

4. Impaired In-cretin Action

Problem: Gut hormones (GLP-1, GIP) don’t work well to trigger insulin release after meals. Goal: Support gut health and boost natural in-cretin function.

Best foods:

  • Fermented foods → yogurt with live cultures, kefir, kimchi, sauerkraut (improve gut microbiome, linked to in-cretin response).
  • High-fiber foods → apples, beans, lentils (slow carb absorption, improve GLP-1 response).
  • Resistant starch (feeds good gut bacteria) → cooked then cooled potatoes, green bananas.
  • Protein with meals → chicken, tofu, eggs (boosts post-meal GLP-1 and satiety).

✅ General across all subtypes:

  • Avoid ultra-processed foods, sugary drinks, and refined carbs.
  • Emphasize whole foods, balanced macronutrients, and portion control.
  • Physical activity (especially resistance training) amplifies the effect of diet in all groups.

How Snyder’s team did it

They compared oral glucose tolerance tests (OGTT) and CGM glucose curves. Using machine learning, they identified “signatures” in the glucose response curves that match each subtype.

Example:

  • Muscle IR → slow uptake of glucose into muscles, so your glucose curve stays higher for longer after meals.

  • Liver IR → elevated fasting glucose (liver keeps making sugar overnight or between meals).

  • β-cell dysfunction → delayed/weak insulin response, so glucose spikes very high right after meals.

  • Impaired incretin → similar to β-cell dysfunction, but tends to show erratic or prolonged spikes after meals because gut hormones aren’t helping insulin secretion properly.

🍴 Foods for Four Diabetic Subtypes — Grouped by Category

Legend: ✅ = recommended for that subtype.
GI values are approximate (whole foods; plain/unsweetened preparations).

Proteins (Animal & Dairy)

Food GI Muscle IR Hepatic IR β-Cell Dysf. Impaired Incretin
Chicken breast 0 ✅
Cottage cheese (unsweetened, low-fat) 0 ✅
Eggs 0 ✅
Greek yogurt (unsweetened) 0 ✅
Mackerel 0 ✅
Salmon 0 ✅ ✅
Sardines 0 ✅
Tempeh 0 ✅
Tofu 0 ✅
Trout 0 ✅
Tuna (fresh) 0 ✅
Turkey breast 0 ✅

Legumes & Pulses

Food GI Muscle IR Hepatic IR β-Cell Dysf. Impaired Incretin
Black beans 30 ✅ ✅ ✅ ✅
Chickpeas 28 ✅ ✅ ✅ ✅
Edamame 15 ✅
Kidney beans 29 ✅ ✅ ✅ ✅
Lentils 32 ✅ ✅ ✅ ✅

Grains, Pasta & Starches (Low-GI Emphasis)

Food GI Muscle IR Hepatic IR β-Cell Dysf. Impaired Incretin
Barley 28 ✅ ✅
Brown rice 50 ✅
Oats (steel-cut) 42 ✅ ✅
Quinoa 53 ✅ ✅
Sweet potato (boiled) 44 ✅
Whole-wheat pasta (al dente) 40–45 ✅

Leafy Greens & Cruciferous Veg

Food GI Muscle IR Hepatic IR β-Cell Dysf. Impaired Incretin
Arugula 15 ✅ ✅
Broccoli 10 ✅ ✅ ✅ ✅
Brussels sprouts 15 ✅
Cabbage 10 ✅ ✅ ✅
Cauliflower 10 ✅ ✅ ✅ ✅
Kale 15 ✅ ✅
Spinach 15 ✅ ✅ ✅ ✅

Other Non-Starchy Veg & Low-Carb Swaps

Food GI Muscle IR Hepatic IR β-Cell Dysf. Impaired Incretin
Cauliflower rice 10 ✅ ✅
Eggplant 15 ✅ ✅
Zucchini noodles 15 ✅ ✅

Fruits (Low to Moderate GI)

Food GI Muscle IR Hepatic IR β-Cell Dysf. Impaired Incretin
Apple 36 ✅
Blackberries 25 ✅ ✅ ✅
Blueberries 53 ✅ ✅ ✅
Cherries 22 ✅
Grapefruit 25 ✅
Kiwi 50 ✅
Orange 43 ✅
Peaches 42 ✅
Pears 38 ✅
Plums 24 ✅
Pomegranate 35 ✅
Raspberries 32 ✅ ✅ ✅
Strawberries 41 ✅ ✅ ✅

Nuts & Seeds

Food GI Muscle IR Hepatic IR β-Cell Dysf. Impaired Incretin
Almonds 10 ✅ ✅ ✅
Brazil nuts 10 ✅
Cashews 25 ✅ ✅ ✅
Chia seeds 1 ✅ ✅ ✅
Flaxseeds 10 ✅ ✅ ✅ ✅
Hemp seeds 5 ✅ ✅
Macadamia nuts 10 ✅ ✅
Pumpkin seeds 10 ✅ ✅ ✅
Sesame seeds 35 ✅ ✅
Sunflower seeds 20 ✅ ✅ ✅
Walnuts 15 ✅ ✅ ✅

Beverages (Unsweetened)

Food / Drink GI Muscle IR Hepatic IR β-Cell Dysf. Impaired Incretin
Black tea 0 ✅ ✅ ✅
Coffee (black) 0 ✅
Green tea / Matcha 0 ✅ ✅ ✅
Herbal teas (e.g., chamomile, peppermint) 0 ✅
Yerba mate 0 ✅

Oils & Healthy Fats

Food GI Muscle IR Hepatic IR β-Cell Dysf. Impaired Incretin
Avocado 15 ✅ ✅ ✅
Olive oil 0 ✅ ✅

Other

Food GI Muscle IR Hepatic IR β-Cell Dysf. Impaired Incretin
Dark chocolate (≥70% cocoa) 25 ✅ ✅

Notes

  • Muscle IR: emphasize lean proteins, low-GI carbs, magnesium-rich foods, and antioxidant-rich fruits.
  • Hepatic IR: emphasize omega-3 fish, unsweetened coffee/green tea, leafy & cruciferous veg, and soluble-fiber foods (oats, barley, legumes, flax/chia).
  • β-Cell Dysfunction: emphasize low-carb/low-GI choices, nuts/seeds, polyphenol-rich foods, olive oil & avocado.
  • Impaired Incretin Action: similar to β-cell focus—low-carb/low-GI, nuts/seeds, polyphenols, healthy fats.

Appendix I: Suggested Food

1. Muscle Insulin Resistance

1.1 Lean Protein + Low-Glycemic Carbs

Proteins (GI = 0 unless mixed with sauces/carbs):

  • Salmon
  • Chicken breast
  • Turkey breast
  • Eggs
  • Tofu
  • Tempeh
  • Greek yogurt (unsweetened)
  • Cottage cheese (unsweetened, low-fat)

Low-Glycemic Carbs (GI ≤ 55):

  • Quinoa → GI ≈ 53
  • Barley → GI ≈ 28
  • Lentils → GI ≈ 32
  • Chickpeas → GI ≈ 28
  • Black beans → GI ≈ 30
  • Kidney beans → GI ≈ 29
  • Sweet potato (boiled) → GI ≈ 44
  • Steel-cut oats → GI ≈ 42
  • Whole wheat pasta (al dente) → GI ≈ 40–45
  • Brown rice → GI ≈ 50
  • Leafy greens (spinach, kale, arugula, lettuce) → GI ≈ 15
  • Broccoli, cauliflower, zucchini, cucumber → GI ≈ 10–20

1.2. Magnesium-Rich Foods

(Magnesium supports insulin sensitivity & muscle function)

  • Spinach → GI ≈ 15
  • Pumpkin seeds → GI ≈ 10
  • Almonds → GI ≈ 10
  • Cashews → GI ≈ 25
  • Walnuts → GI ≈ 15
  • Brazil nuts → GI ≈ 10
  • Flaxseeds → GI ≈ 10
  • Sunflower seeds → GI ≈ 20
  • Black beans → GI ≈ 30
  • Edamame → GI ≈ 15
  • Avocado → GI ≈ 15

1.3. Antioxidant-Rich Fruits

(Rich in vitamin C, polyphenols, and flavonoids—combat oxidative stress)

Berries:
- Blueberries → GI ≈ 53
- Strawberries → GI ≈ 41
- Raspberries → GI ≈ 32
- Blackberries → GI ≈ 25

Citrus:
- Orange → GI ≈ 43
- Grapefruit → GI ≈ 25
- Lemon → GI ≈ 20
- Lime → GI ≈ 20

Other fruits:
- Apples → GI ≈ 36
- Pears → GI ≈ 38
- Cherries → GI ≈ 22
- Plums → GI ≈ 24
- Peaches → GI ≈ 42
- Kiwi → GI ≈ 50
- Pomegranate → GI ≈ 35

✅ Summary Table (Expanded)

Category Food GI
Protein Salmon, chicken, turkey, eggs, tofu, Greek yogurt 0
Low-glycemic carbs Quinoa (53), lentils (32), chickpeas (28), black beans (30), barley (28), sweet potato (44), oats (42), brown rice (50), leafy greens (~15) ≤55
Magnesium-rich Spinach (15), pumpkin seeds (10), almonds (10), cashews (25), walnuts (15), flaxseeds (10), avocado (15), sunflower seeds (20), edamame (15) Low
Antioxidant-rich fruits Berries (25–53), citrus (20–43), apples (36), pears (38), cherries (22), plums (24), kiwi (50), pomegranate (35) Low–moderate

2. Hepatic (Liver) Insulin Resistance

2.1 Fatty Fish (Omega-3s)

Examples:

  • Salmon → GI = 0 (pure protein/fat, no carbs)
  • Sardines → GI = 0
  • Mackerel → GI = 0

Similar foods (omega-3 rich, low GI):

  • Trout
  • Herring
  • Anchovies
  • Tuna (especially fresh, not canned in oil)

2.2 Coffee & Green Tea (unsweetened)

Examples:

  • Black coffee → GI = 0
  • Green tea → GI = 0

Similar foods (low/zero-GI beverages with beneficial compounds):

  • Black tea
  • Herbal teas (chamomile, peppermint)
  • Matcha tea
  • Yerba mate

2.3 Leafy Greens & Cruciferous Vegetables

Examples:

  • Kale → GI ≈ 15
  • Broccoli → GI ≈ 10
  • Brussels sprouts → GI ≈ 15

Similar foods (low-GI vegetables, detox support):

  • Spinach → GI ≈ 15
  • Cabbage → GI ≈ 10
  • Cauliflower → GI ≈ 10
  • Arugula → GI ≈ 15

✅ Summary Table

Category Food GI Similar Foods
Fatty fish (Omega-3s) Salmon, sardines, mackerel 0 Trout, herring, anchovies, tuna
High-fiber foods Oats (42), lentils (32), beans (28–30), chia seeds (1) Low Barley, quinoa, flaxseeds, psyllium husk
Coffee & green tea Coffee (0), green tea (0) 0 Black tea, herbal teas, matcha, yerba mate
Leafy greens & cruciferous Kale (15), broccoli (10), Brussels sprouts (15) Low Spinach, cabbage, cauliflower, arugula

3. β-Cell Dysfunction

3.1 Low-Carb or Low-Glycemic Foods

Examples:

  • Cauliflower rice → GI ≈ 10
  • Zucchini noodles → GI ≈ 15
  • Legumes → Lentils (32), Chickpeas (28), Black beans (30), Kidney beans (29)

Similar foods (low-carb/low-GI):

  • Broccoli → GI ≈ 10
  • Cabbage → GI ≈ 10
  • Eggplant → GI ≈ 15
  • Spinach → GI ≈ 15

3.2 Nuts and Seeds

Examples:

  • Walnuts → GI ≈ 15
  • Flaxseed → GI ≈ 10
  • Chia seeds → GI ≈ 1

Similar foods (healthy fats, low GI):

  • Almonds → GI ≈ 10
  • Pumpkin seeds → GI ≈ 10
  • Sunflower seeds → GI ≈ 20
  • Cashews → GI ≈ 25

3.3 Polyphenol-Rich Foods

Examples:

  • Blueberries → GI ≈ 53
  • Green tea → GI = 0
  • Dark chocolate (≥70% cocoa) → GI ≈ 25

Similar foods (polyphenol-rich, low GI):

  • Strawberries → GI ≈ 41
  • Raspberries → GI ≈ 32
  • Blackberries → GI ≈ 25
  • Black tea → GI = 0

3.4 Olive Oil & Avocados

Examples:

  • Olive oil → GI = 0
  • Avocado → GI ≈ 15

Similar foods (healthy monounsaturated fats, low GI):

  • Almonds → GI ≈ 10
  • Macadamia nuts → GI ≈ 10
  • Sesame seeds → GI ≈ 35
  • Hemp seeds → GI ≈ 5

✅ Summary Table

Category Food GI Similar Foods
Low-carb/low-glycemic Cauliflower rice (10), zucchini noodles (15), legumes 28–32 Low Broccoli, cabbage, eggplant, spinach
Nuts & seeds Walnuts (15), flaxseed (10), chia (1) Very Low Almonds, pumpkin seeds, sunflower seeds, cashews
Polyphenol-rich Blueberries (53), green tea (0), dark chocolate ≥70% (25) Low–Moderate Strawberries, raspberries, blackberries, black tea
Healthy fats (mono-unsat) Olive oil (0), avocado (15) Very Low Almonds, macadamia nuts, sesame seeds, hemp seeds

4. Impaired In-cretin Action

4.1 Low-Carb or Low-Glycemic Foods

Examples:

  • Cauliflower rice → GI ≈ 10
  • Zucchini noodles → GI ≈ 15
  • Legumes → Lentils (32), Chickpeas (28), Black beans (30), Kidney beans (29)

Similar foods (low-carb/low-GI):

  • Broccoli → GI ≈ 10
  • Cabbage → GI ≈ 10
  • Eggplant → GI ≈ 15
  • Spinach → GI ≈ 15

4.2 Nuts and Seeds

Examples:

  • Walnuts → GI ≈ 15
  • Flaxseed → GI ≈ 10
  • Chia seeds → GI ≈ 1

Similar foods (healthy fats, low GI):

  • Almonds → GI ≈ 10
  • Pumpkin seeds → GI ≈ 10
  • Sunflower seeds → GI ≈ 20
  • Cashews → GI ≈ 25

4.3 Polyphenol-Rich Foods

Examples:

  • Blueberries → GI ≈ 53
  • Green tea → GI = 0
  • Dark chocolate (≥70% cocoa) → GI ≈ 25

Similar foods (polyphenol-rich, low GI):

  • Strawberries → GI ≈ 41
  • Raspberries → GI ≈ 32
  • Blackberries → GI ≈ 25
  • Black tea → GI = 0

4.4 Olive Oil & Avocados

Examples:

  • Olive oil → GI = 0
  • Avocado → GI ≈ 15

Similar foods (healthy monounsaturated fats, low GI):

  • Almonds → GI ≈ 10
  • Macadamia nuts → GI ≈ 10
  • Sesame seeds → GI ≈ 35
  • Hemp seeds → GI ≈ 5

✅ Summary Table

Category Food GI Similar Foods
Low-carb/low-glycemic Cauliflower rice (10), zucchini noodles (15), legumes 28–32 Low Broccoli, cabbage, eggplant, spinach
Nuts & seeds Walnuts (15), flaxseed (10), chia (1) Very Low Almonds, pumpkin seeds, sunflower seeds, cashews
Polyphenol-rich Blueberries (53), green tea (0), dark chocolate ≥70% (25) Low–Moderate Strawberries, raspberries, blackberries, black tea
Healthy fats (mono-unsat) Olive oil (0), avocado (15) Very Low Almonds, macadamia nuts, sesame seeds, hemp seeds

🍴 Foods for Four Diabetic Subtypes — Grouped by Category

Legend: ✅ = recommended for that subtype.
GI values are approximate (whole foods; plain/unsweetened preparations).

Proteins (Animal & Dairy)

Food GI Muscle IR Hepatic IR β-Cell Dysf. Impaired Incretin
Chicken breast 0 ✅
Cottage cheese (unsweetened, low-fat) 0 ✅
Eggs 0 ✅
Greek yogurt (unsweetened) 0 ✅
Mackerel 0 ✅
Salmon 0 ✅ ✅
Sardines 0 ✅
Tempeh 0 ✅
Tofu 0 ✅
Trout 0 ✅
Tuna (fresh) 0 ✅
Turkey breast 0 ✅

Legumes & Pulses

Food GI Muscle IR Hepatic IR β-Cell Dysf. Impaired Incretin
Black beans 30 ✅ ✅ ✅ ✅
Chickpeas 28 ✅ ✅ ✅ ✅
Edamame 15 ✅
Kidney beans 29 ✅ ✅ ✅ ✅
Lentils 32 ✅ ✅ ✅ ✅

Grains, Pasta & Starches (Low-GI Emphasis)

Food GI Muscle IR Hepatic IR β-Cell Dysf. Impaired Incretin
Barley 28 ✅ ✅
Brown rice 50 ✅
Oats (steel-cut) 42 ✅ ✅
Quinoa 53 ✅ ✅
Sweet potato (boiled) 44 ✅
Whole-wheat pasta (al dente) 40–45 ✅

Leafy Greens & Cruciferous Veg

Food GI Muscle IR Hepatic IR β-Cell Dysf. Impaired Incretin
Arugula 15 ✅ ✅
Broccoli 10 ✅ ✅ ✅ ✅
Brussels sprouts 15 ✅
Cabbage 10 ✅ ✅ ✅
Cauliflower 10 ✅ ✅ ✅ ✅
Kale 15 ✅ ✅
Spinach 15 ✅ ✅ ✅ ✅

Other Non-Starchy Veg & Low-Carb Swaps

Food GI Muscle IR Hepatic IR β-Cell Dysf. Impaired Incretin
Cauliflower rice 10 ✅ ✅
Eggplant 15 ✅ ✅
Zucchini noodles 15 ✅ ✅

Fruits (Low to Moderate GI)

Food GI Muscle IR Hepatic IR β-Cell Dysf. Impaired Incretin
Apple 36 ✅
Blackberries 25 ✅ ✅ ✅
Blueberries 53 ✅ ✅ ✅
Cherries 22 ✅
Grapefruit 25 ✅
Kiwi 50 ✅
Orange 43 ✅
Peaches 42 ✅
Pears 38 ✅
Plums 24 ✅
Pomegranate 35 ✅
Raspberries 32 ✅ ✅ ✅
Strawberries 41 ✅ ✅ ✅

Nuts & Seeds

Food GI Muscle IR Hepatic IR β-Cell Dysf. Impaired Incretin
Almonds 10 ✅ ✅ ✅
Brazil nuts 10 ✅
Cashews 25 ✅ ✅ ✅
Chia seeds 1 ✅ ✅ ✅
Flaxseeds 10 ✅ ✅ ✅ ✅
Hemp seeds 5 ✅ ✅
Macadamia nuts 10 ✅ ✅
Pumpkin seeds 10 ✅ ✅ ✅
Sesame seeds 35 ✅ ✅
Sunflower seeds 20 ✅ ✅ ✅
Walnuts 15 ✅ ✅ ✅

Beverages (Unsweetened)

Food / Drink GI Muscle IR Hepatic IR β-Cell Dysf. Impaired Incretin
Black tea 0 ✅ ✅ ✅
Coffee (black) 0 ✅
Green tea / Matcha 0 ✅ ✅ ✅
Herbal teas (e.g., chamomile, peppermint) 0 ✅
Yerba mate 0 ✅

Oils & Healthy Fats

Food GI Muscle IR Hepatic IR β-Cell Dysf. Impaired Incretin
Avocado 15 ✅ ✅ ✅
Olive oil 0 ✅ ✅

Other

Food GI Muscle IR Hepatic IR β-Cell Dysf. Impaired Incretin
Dark chocolate (≥70% cocoa) 25 ✅ ✅

Notes

  • Muscle IR: emphasize lean proteins, low-GI carbs, magnesium-rich foods, and antioxidant-rich fruits.
  • Hepatic IR: emphasize omega-3 fish, unsweetened coffee/green tea, leafy & cruciferous veg, and soluble-fiber foods (oats, barley, legumes, flax/chia).
  • β-Cell Dysfunction: emphasize low-carb/low-GI choices, nuts/seeds, polyphenol-rich foods, olive oil & avocado.
  • Impaired Incretin Action: similar to β-cell focus—low-carb/low-GI, nuts/seeds, polyphenols, healthy fats.

Appendix II: Detection Algorithm

How the Program Identifies 4 Subtypes of Type 2 Diabetes (Heuristic)

This program analyzes CGM (Continuous Glucose Monitoring) data to infer which of four physiological patterns your glucose responses most resemble:

  1. Muscle insulin resistance (Muscle IR)
  2. Hepatic (liver) insulin resistance (Hepatic IR)
  3. β-cell dysfunction
  4. Impaired incretin action

Inputs & Assumptions

  • CSV columns (required):
    • timestamp – ISO-like datetime string (local or UTC; script localizes to a chosen TZ).
    • glucose_mg_dL – glucose in mg/dL (if mmol/L, convert ×18 or run script with mmol option in extended versions).
  • Sampling: Any cadence is accepted; data are resampled to 5-minute intervals with light interpolation.
  • Meals: If no meal markers exist, meals are auto-detected from rapid rises in CGM.

Processing Pipeline (High-Level)

  1. Load & normalize
    • Parse timestamps; localize/convert timezone.
    • Ensure glucose is in mg/dL.
    • Resample to 5-minute grid; fill small gaps.
  2. Meal detection (heuristic)
    • Flag a meal if there’s a rise ≥ 30 mg/dL within ~45 minutes or an average slope ≥ 0.35 mg/dL/min, enforcing ≥90 minutes between detected meals.
  3. Per-meal metrics For each detected (or provided) meal, compute:
    • Baseline (median in the 30 min pre-meal window)
    • Peak and Δ = peak − baseline
    • Time-to-peak (minutes from meal to peak)
    • Time above 140 mg/dL and time above 180 mg/dL
    • Time to return to (baseline + 10 mg/dL)
    • AUC(0–180 min) above baseline
  4. Overnight fasting metrics
    • Median and mean between 03:00–06:00 local time (proxy for hepatic output).
  5. Subtype scoring (heuristics)
    • Compute four scores (0–1), then normalize them to sum ≈ 1.
    • The top score is reported as the primary pattern; overlapping patterns are common.

Heuristic Criteria (What the Program Looks For)

Thresholds below are rules of thumb chosen to match typical physiology and to be robust to noisy consumer CGM data. They can be tuned.

1) Muscle Insulin Resistance (Muscle IR)

Idea: Glucose disposal by muscle is sluggish → late peaks and slow clearance.

  • Signals used - Share of meals with time-to-peak > 60 min
  • Share with return to baseline+10 > 180 min
  • Share with time > 140 mg/dL > 120 min - Score contribution
  • 0.4(late_peak_share) + 0.4(slow_return_share) + 0.2*(long_140_share)

2) Hepatic (Liver) Insulin Resistance

Idea: Liver continues to release glucose (gluconeogenesis) → elevated fasting and pre-meal baselines. - Signals used

  • Overnight fasting median: - ≥130 → +1.00 - 120–129 → +0.75 - 110–119 → +0.50 - 100–109 → +0.25
  • Share of meals with baseline ≥ 110 mg/dL → +0.6*(high_baseline_share)
  • Score contribution - Sum of fasting bump + baseline bump

3) β-Cell Dysfunction

Idea: Insulin secretion is insufficient/slow → very tall, early spikes.

  • Signals used - Share with Δ ≥ 80 mg/dL (large excursion)
  • Share with time-to-peak ≤ 45 min (early sharp rise)
  • Share with peak ≥ 200 mg/dL
  • Score contribution
  • 0.5*(large_spike_share) + 0.3*(early_peak_share) + 0.2*(very_high_peak_share)

4) Impaired Incretin Action

Idea: Gut hormones (GLP-1/GIP) aren’t amplifying post-meal insulin → prolonged, lingering spikes despite moderate peaks.

  • Signals used - Share with time > 180 mg/dL > 45 min
  • Share with return to baseline+10 > 150 min
  • Share with AUC(0–180) ≥ 8000 mg·dL·min - Score contribution
  • 0.4*(long_180_share) + 0.4*(long_return_share) + 0.2*(high_auc_share)

Normalization: After computing all contributions, scores are normalized to sum ≈ 1. If all contributions are zero, each subtype defaults to 0.25.

Outputs

  • cgm_meal_metrics.csv – one row per meal with all metrics above.
  • cgm_subtype_summary.json – fasting metrics, normalized subtype scores, and suggested top pattern.
  • cgm_subtype_report.html – visual report (overall trace, meal markers, bar chart of scores).

The program also names the output folder as

CGM-Report-<CSVName>-<yyyy-mm-dd--hh-mm> and opens the HTML report automatically.

Interpreting the Results

  • Scores reflect patterns, not diagnoses. It’s common to see mixed features (e.g., Muscle IR and β-cell).
  • High fasting/pre-meal → likely Hepatic IR.
  • Tall, early peaks → likely β-cell dysfunction.
  • Late peaks & long return → likely Muscle IR.
  • Spikes that linger (high AUC) → likely Incretin impairment.

Use these insights to tailor lifestyle & diet strategies, and discuss with a healthcare professional.

Limitations & Caveats

  • Heuristic thresholds: chosen to be practical; feel free to tune for your cohort.
  • Meal detection noise: auto-detection can mislabel snacks/graze patterns; providing real meal markers improves accuracy.
  • Sensor artifacts: compression lows, missed scans, and calibration shifts can distort metrics.
  • Context matters: meds (e.g., GLP-1 RAs), sleep, stress, and activity strongly influence curves.

Quick Pseudocode


if no meal flags: meals := rising_edge_detector(glucose, rise≥30 within \~45 min OR slope≥0.35) enforce 90 min between meals

for meal in meals: baseline := median(glucose in \[−30, 0\] min) peak := max(glucose in \[0, +240\] min) delta := peak − baseline t_peak := minutes to peak t\>140, t\>180 := durations above thresholds t_return := minutes until glucose ≤ baseline+10 auc0_180 := AUC(glucose − baseline, 0..180) with negatives clipped to 0

fasting := median/mean(glucose between 03:00–06:00)

scores := 0 for each subtype scores += rules for hepatic (fasting + high baseline share) scores += rules for muscle (late peak, slow return, long 140) scores += rules for beta-cell (large delta, early peak, high peak) scores += rules for incretin (long 180, long return, high AUC) normalize scores to sum ≈ 1