Glycaemic variability, assessed with continuous glucose monitors, is associated with diet, lifestyle and health in people without diabetes
Background: Continuous glucose monitors (CGMs) provide high-frequency information regarding daily glucose variation and are recognised as effective for improving glycaemic control in individuals living with diabetes. Despite increased use in individuals with non-diabetic blood glucose concentrations (euglycemia), their utility as a health tool in this population remains unclear.
Objectives: To characterise variation in time in range (TIR) and glycaemic variability in large populations without diabetes or impaired glucose tolerance; describe associations between CGM-derived glycaemic metrics and metabolic and cardiometabolic health traits; identify key diet and lifestyle factors associated with TIR and glycaemic variability.
Design: Glycaemic variability (coefficient of variation) and time spent in both the ADA secondary target range (TIRADA; 3.9-7.8 mmol/L) and a more stringent range (TIR3.9-5.6; 3.9-5.6 mmol/L) were calculated during free-living in PREDICT 1, PREDICT 2, and PREDICT 3 euglycaemic community-based volunteer cohorts. Associations between CGM derived glycaemic metrics, markers of cardiometabolic health, diet (food frequency questionnaire and logged diet records), diet-habits, and lifestyle were explored.
Results: Data from N=4135 participants (Mean SD; Age: 47 12 y; Sex: 83% Female, BMI: 27 6 kg/m2). Median glycaemic variability was 14.8% (IQR 12.6-17.6%), median TIRADA was 95.8% (IQR 89.6-98.6%) and TIR3.9-5.6 was 75.0% (IQR 64.6-82.8%). Greater TIR3.9-5.6 was associated with lower HbA1c, ASCVD 10y risk and HOMA-IR (all p < 0.05). Lower glycaemic variability was associated with lower % energy derived from carbohydrate (rs: 0.17, p < 0.01), ultra-processed foods (NOVA 4, % EI; rs: 0.12, p = 0.01) and a longer overnight fasting duration (rs: -0.10, p = 0.01).
Conclusions: A stringent TIR target provides sensitivity to detect changes in HOMA-IR, ASCVD 10 y risk and HbA1c that were not detected using ADA secondary targets. Associations among TIR, glycaemic variability, dietary intake (e.g. carbohydrate and protein) and habits (e.g. nocturnal fasting duration) highlight potential strategic targets to improve glycaemic metrics derived from continuous glucose monitors.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - year:2023 |
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| Enthalten in: |
Research square - (2023) vom: 30. Okt. |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Bermingham, Kate M [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Revised 10.04.2024 published: Electronic Citation Status PubMed-not-MEDLINE |
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| doi: |
10.21203/rs.3.rs-3469475/v1 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM364530596 |
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| 100 | 1 | |a Bermingham, Kate M |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Glycaemic variability, assessed with continuous glucose monitors, is associated with diet, lifestyle and health in people without diabetes |
| 264 | 1 | |c 2023 | |
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| 500 | |a Date Revised 10.04.2024 | ||
| 500 | |a published: Electronic | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a Background: Continuous glucose monitors (CGMs) provide high-frequency information regarding daily glucose variation and are recognised as effective for improving glycaemic control in individuals living with diabetes. Despite increased use in individuals with non-diabetic blood glucose concentrations (euglycemia), their utility as a health tool in this population remains unclear | ||
| 520 | |a Objectives: To characterise variation in time in range (TIR) and glycaemic variability in large populations without diabetes or impaired glucose tolerance; describe associations between CGM-derived glycaemic metrics and metabolic and cardiometabolic health traits; identify key diet and lifestyle factors associated with TIR and glycaemic variability | ||
| 520 | |a Design: Glycaemic variability (coefficient of variation) and time spent in both the ADA secondary target range (TIRADA; 3.9-7.8 mmol/L) and a more stringent range (TIR3.9-5.6; 3.9-5.6 mmol/L) were calculated during free-living in PREDICT 1, PREDICT 2, and PREDICT 3 euglycaemic community-based volunteer cohorts. Associations between CGM derived glycaemic metrics, markers of cardiometabolic health, diet (food frequency questionnaire and logged diet records), diet-habits, and lifestyle were explored | ||
| 520 | |a Results: Data from N=4135 participants (Mean SD; Age: 47 12 y; Sex: 83% Female, BMI: 27 6 kg/m2). Median glycaemic variability was 14.8% (IQR 12.6-17.6%), median TIRADA was 95.8% (IQR 89.6-98.6%) and TIR3.9-5.6 was 75.0% (IQR 64.6-82.8%). Greater TIR3.9-5.6 was associated with lower HbA1c, ASCVD 10y risk and HOMA-IR (all p < 0.05). Lower glycaemic variability was associated with lower % energy derived from carbohydrate (rs: 0.17, p < 0.01), ultra-processed foods (NOVA 4, % EI; rs: 0.12, p = 0.01) and a longer overnight fasting duration (rs: -0.10, p = 0.01) | ||
| 520 | |a Conclusions: A stringent TIR target provides sensitivity to detect changes in HOMA-IR, ASCVD 10 y risk and HbA1c that were not detected using ADA secondary targets. Associations among TIR, glycaemic variability, dietary intake (e.g. carbohydrate and protein) and habits (e.g. nocturnal fasting duration) highlight potential strategic targets to improve glycaemic metrics derived from continuous glucose monitors | ||
| 650 | 4 | |a Preprint | |
| 700 | 1 | |a Smith, Harry A |e verfasserin |4 aut | |
| 700 | 1 | |a Gonzalez, Javier T |e verfasserin |4 aut | |
| 700 | 1 | |a Duncan, Emma L |e verfasserin |4 aut | |
| 700 | 1 | |a Valdes, Ana M |e verfasserin |4 aut | |
| 700 | 1 | |a Franks, Paul W |e verfasserin |4 aut | |
| 700 | 1 | |a Delahanty, Linda |e verfasserin |4 aut | |
| 700 | 1 | |a Dashti, Hassan S |e verfasserin |4 aut | |
| 700 | 1 | |a Davies, Richard |e verfasserin |4 aut | |
| 700 | 1 | |a Hadjigeorgiou, George |e verfasserin |4 aut | |
| 700 | 1 | |a Wolf, Jonathan |e verfasserin |4 aut | |
| 700 | 1 | |a Chan, Andrew T |e verfasserin |4 aut | |
| 700 | 1 | |a Spector, Tim D |e verfasserin |4 aut | |
| 700 | 1 | |a Berry, Sarah E |e verfasserin |4 aut | |
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