Last updated September 2, 2026
Key finding
No significant difference was found in the AUC of the PA and modified PA groups over the whole period (0-12 hours).
This study investigated the impact of additional insulin dosing for high fat/high energy density meals in adolescents with Type 1 Diabetes, finding no significant differences in glucose control between the algorithms used.
Quick read
The essential study design details in one scan.
EvidenceScore™
Moderate
Study type
RCTs
Follow-up
Short-Term (≤3 mo)
Risk of bias
Some Concerns
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Plain-language summary
A plain-language read of the study’s main message and where it applies.
Study focus
This study investigated the impact of additional insulin dosing for high fat/high energy density meals in adolescents with Type 1 Diabetes, finding no significant differences in glucose control between the algorithms used.
Understanding how to manage blood sugar levels effectively in adolescents with Type 1 Diabetes is crucial, especially after high fat meals. This study highlights that current algorithms may not significantly improve glucose control, which can inform dietary and treatment strategies for better diabetes management.
Limited sample size may affect generalizability. Short monitoring period may not capture long-term effects. Potential confounding factors not controlled for.
Published in
Publication details and source links for this paper.
Yasemin AA, Günay D, Hafize &, Samim &, Şükran D, Damla G. The Effect of Additional Insulin Dosing for High Fat/High Energy Density Meals in Adolescents with Type 1 Diabetes. Journal of Clinical Research in Pediatric Endocrinology. 2023;15(2):138-144. doi:10.4274/jcrpe.galenos.2022.2022-8-10
No significant difference in AUC for carbohydrate counting between PA and modified PA groups.
50% of patients experienced hypoglycemia using the PA algorithm postprandially.
No hypoglycemic events occurred in patients using the modified PA algorithm.
Evidence network
Understand where this research contributes within the broader evidence network.
This study contributes evidence to Carbohydrate Counting (CC), Modified Pańkowska Algorithm, Pańkowska Algorithm (PA) and Glucose iAUC (OGTT), Time in range, Number of hypoglycaemic episodes.
This study contributes evidence to
Primary intervention
Carbohydrate Counting (CC)
Primary outcomes
Evidence topics
Primary intervention
Primary outcomes
Intervention and outcome relationships this study adds to the evidence network.
Editorial context
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Evidence network role
This section describes how the study fits into the current evidence network. It does not determine whether an intervention works on its own.
3
Related topics
6
Evidence pairs
714
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Evidence topic
Contributes evidence
Evidence topic
Contributes evidence
Evidence topic
Contributes evidence
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Primary evidence
Evidence topic
matched_outcome
Related evidence
Evidence topic
Save evidence
Core evidence
The primary outcomes reported in this study.
Carbohydrate Counting (CC) → Glucose iAUC (OGTT)
Carbohydrate Counting (CC) → Glucose iAUC (OGTT)
Modified Pańkowska Algorithm → Glucose iAUC (OGTT)
Modified Pańkowska Algorithm → Glucose iAUC (OGTT)
Modified Pańkowska Algorithm → Number of hypoglycaemic episodes
Modified Pańkowska Algorithm → Number of hypoglycaemic episodes
Pańkowska Algorithm (PA) → Glucose iAUC (OGTT)
Pańkowska Algorithm (PA) → Glucose iAUC (OGTT)
Pańkowska Algorithm (PA) → Number of hypoglycaemic episodes
Pańkowska Algorithm (PA) → Number of hypoglycaemic episodes
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Relationships organized using the Dediabetes Evidence Intelligence™ framework.
This study contributes to evidence on Carbohydrate Counting (CC) and Postprandial and OGTT Glucose, Carbohydrate Counting (CC) and CGM Time in Range.
This study contributes to the evidence on the following intervention-outcome relationships.
Behavioral & Lifestyle
Behavioral & Lifestyle
Curated evidence collections and hubs this study is part of.
All studies on Carbohydrate Counting (CC)
Contributes to Carbohydrate Counting (CC) evidence base.
All studies measuring CGM Time in Range
Measures CGM Time in Range as a key outcome.
All studies measuring Postprandial and OGTT Glucose
Measures Postprandial and OGTT Glucose as a key outcome.
Jump to pre-filtered views in Evidence Explorer.
1 results
1 results
1 results
1 results
1 results
Generated from the study's connected evidence using Evidence Intelligence™.
Modified Pańkowska Algorithm appears to affect number of hypoglycaemic episodes.
ConsistencyScore™: Consistency cannot yet be determined from the available evidence.
Ranked evidence signals
Number of hypoglycaemic episodes
EvidenceScore™ Emerging | EvidenceScore™ 59.0 | strong positive | ConsistencyScore™ Unclear | 1 study
Why this answer: This answer is based on a single supporting study.
Limitations
Current evidence does not show a clear benefit of Carbohydrate Counting (CC) for glucose iauc (OGTT).
ConsistencyScore™: Consistency cannot yet be determined from the available evidence.
Ranked evidence signals
Glucose iAUC (OGTT)
EvidenceScore™ Emerging | EvidenceScore™ 59.0 | neutral | ConsistencyScore™ Unclear | 1 study
Why this answer: This answer is based on a single supporting study.
Limitations
Current evidence does not show a clear benefit of Carbohydrate Counting (CC) for time in range.
ConsistencyScore™: Consistency cannot yet be determined from the available evidence.
Ranked evidence signals
Time in range
EvidenceScore™ Emerging | EvidenceScore™ 59.0 | neutral | ConsistencyScore™ Unclear | 1 study
Why this answer: This answer is based on a single supporting study.
Limitations
Current evidence does not show a clear benefit of Modified Pańkowska Algorithm for glucose iauc (OGTT).
ConsistencyScore™: Consistency cannot yet be determined from the available evidence.
Ranked evidence signals
Glucose iAUC (OGTT)
EvidenceScore™ Emerging | EvidenceScore™ 59.0 | neutral | ConsistencyScore™ Unclear | 1 study
Why this answer: This answer is based on a single supporting study.
Limitations
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