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A Transparent Rule-Based Decision-Support Prototype for Diabetic Menu Planning and Meal-Combination Optimization: Development and Computational Evaluation.

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Τίτλος: A Transparent Rule-Based Decision-Support Prototype for Diabetic Menu Planning and Meal-Combination Optimization: Development and Computational Evaluation.
Συγγραφείς: Marić M; Nutricionističko Savjetovalište za Prehranu dr. sc. Mirela Marić, Petrova 47, 10000 Zagreb, Croatia., Knights VA; Faculty of Technology and Technical Sciences, University 'St. Kliment Ohridski'-Bitola, Dimitar Vlahov 57, 1400 Veles, North Macedonia., Bytyqi D; Faculty of Technology and Technical Sciences, University 'St. Kliment Ohridski'-Bitola, Dimitar Vlahov 57, 1400 Veles, North Macedonia., Jankuloska V; Faculty of Technology and Technical Sciences, University 'St. Kliment Ohridski'-Bitola, Dimitar Vlahov 57, 1400 Veles, North Macedonia., Gajdoš Kljusurić J; Faculty of Food Technology and Biotechnology, University of Zagreb, Pierottijeva 6, 10000 Zagreb, Croatia.
Πηγή: Nutrients [Nutrients] 2026 Sep 23; Vol. 18 (19). Date of Electronic Publication: 2026 Sep 23.
Τύπος έκδοσης: Journal Article
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: MDPI Publishing Country of Publication: Switzerland NLM ID: 101521595 Publication Model: Electronic Cited Medium: Internet ISSN: 2072-6643 (Electronic) Linking ISSN: 20726643 NLM ISO Abbreviation: Nutrients Subsets: MEDLINE
Imprint Name(s): Original Publication: Basel, Switzerland : MDPI Publishing
Ιατρικοί όροι (MeSH): Menu Planning*/methods , Diabetes Mellitus*/diet therapy , Diet, Diabetic*/methods , Meals* , Decision Support Techniques*, Humans ; Programming, Linear ; Energy Intake
Περίληψη: Background/Objectives: Translating medical nutrition therapy guidance into practical meal plans requires consistent application of clinical and dietetic rules while accommodating different therapeutic regimens. This study developed and computationally evaluated a transparent Python-based decision-support prototype for diabetic menu planning. Methods: The prototype combines procedural decision rules, food-exchange patterns, nutritional checks, and mixed-integer linear programming (MILP). Two meal-frequency pathways were represented: a conventional multi-meal pathway and a three-main-meal pathway for people treated with modern basal-bolus insulin regimens. For the multi-meal pathway, a structured dataset containing 42 candidate meals (seven alternatives in each of six meal categories) was used for objective-specific optimization and exhaustive evaluation of all 117,649 possible daily combinations under predefined nutritional constraints. Sensitivity to the allowable energy deviation and trade-offs among nutritional objectives were also examined. Results: The prototype produced deterministic pathway selection, exchange allocation, nutritional summaries, and explicit warnings when required information was unavailable. Under the baseline ±6.5% energy tolerance, 5628 of 117,649 six-meal combinations (4.78%) satisfied all nutritional constraints. Objective-specific optimization identified feasible solutions for minimizing energy, carbohydrate, and saturated fat and maximizing dietary fiber. The proportion of feasible combinations ranged from 2.71% at ±5% energy tolerance to 9.47% at ±10%, while comparison of objective-specific solutions demonstrated measurable nutritional trade-offs. The three-meal basal-bolus pathway generated predefined exchange allocation and clinical-review warnings but was not subjected to the same exhaustive combinatorial optimization. Conclusions: The prototype demonstrates the computational feasibility of integrating explicit dietetic rules, regimen-related meal-pattern selection, food-exchange allocation, and mathematical optimization within an inspectable decision-support framework. The findings establish technical feasibility within the evaluated datasets rather than clinical effectiveness. Independent software testing, expert usability assessment, evaluation using larger and version-controlled meal datasets, and prospective clinical validation are required before routine clinical implementation.
References: Diabetes Res Clin Pract. 2026 Jun;236:113263. (PMID: 41997462)
Diabetes Res Clin Pract. 2023 Feb;196:110231. (PMID: 36565723)
Diabetes Res Clin Pract. 2025 Nov;229:112464. (PMID: 40930317)
Clin Nutr. 2026 Apr;59:106595. (PMID: 41734418)
Clin Nutr. 2026 Jun;61:106652. (PMID: 42030900)
Br J Nutr. 1993 Jul;70(1):27-35. (PMID: 8399108)
Healthcare (Basel). 2026 Mar 13;14(6):. (PMID: 41897193)
Diabetes Res Clin Pract. 2026 Apr;234:113171. (PMID: 41730509)
Prim Care Diabetes. 2024 Feb;18(1):65-73. (PMID: 38044201)
Diabetes Res Clin Pract. 2026 Jul;237:113339. (PMID: 42191027)
Diabetes Res Clin Pract. 2026 Jun;236:113268. (PMID: 42009259)
J Adolesc Health. 2024 Oct;75(4S):S47-S61. (PMID: 39293877)
Heliyon. 2024 Sep 11;10(18):e37755. (PMID: 39364243)
Diabetes Res Clin Pract. 2025 Oct;228:112429. (PMID: 40840786)
Diabetes Res Clin Pract. 2026 Aug;238:113326. (PMID: 42191026)
Prim Care Diabetes. 2024 Oct;18(5):471-478. (PMID: 38955658)
Diabetes Care. 2026 Jan 1;49(Supplement_1):S50-S60. (PMID: 41358891)
Prim Care Diabetes. 2024 Jun;18(3):284-290. (PMID: 38423826)
Heliyon. 2023 Dec 18;10(1):e23906. (PMID: 38192845)
Artif Intell Med. 2024 May;151:102859. (PMID: 38564880)
Comput Biol Med. 2024 Sep;180:109001. (PMID: 39126791)
Diabetes Res Clin Pract. 2026 Aug;238:113389. (PMID: 42331228)
Diabetes Res Clin Pract. 2024 Nov;217:111898. (PMID: 39433217)
Diabetes Res Clin Pract. 2026 Feb;232:113112. (PMID: 41577297)
Clin Nutr. 2026 Jun;61:106667. (PMID: 42054965)
Contributed Indexing: Keywords: clinical nutrition; decision support systems; diabetes mellitus; diet optimization; medical nutrition therapy; menu planning; mixed-integer linear programming; rule-based systems
Entry Date(s): Date Created: 20261016 Date Completed: 20261016 Latest Revision: 20261020
Update Code: 20261020
PubMed Central ID: PMC13666910
DOI: 10.3390/nu18193140
PMID: 42884591
Βάση Δεδομένων: MEDLINE
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  Data: A Transparent Rule-Based Decision-Support Prototype for Diabetic Menu Planning and Meal-Combination Optimization: Development and Computational Evaluation.
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  Data: <searchLink fieldCode="AU" term="%22Marić+M%22">Marić M</searchLink>; Nutricionističko Savjetovalište za Prehranu dr. sc. Mirela Marić, Petrova 47, 10000 Zagreb, Croatia.<br /><searchLink fieldCode="AU" term="%22Knights+VA%22">Knights VA</searchLink>; Faculty of Technology and Technical Sciences, University 'St. Kliment Ohridski'-Bitola, Dimitar Vlahov 57, 1400 Veles, North Macedonia.<br /><searchLink fieldCode="AU" term="%22Bytyqi+D%22">Bytyqi D</searchLink>; Faculty of Technology and Technical Sciences, University 'St. Kliment Ohridski'-Bitola, Dimitar Vlahov 57, 1400 Veles, North Macedonia.<br /><searchLink fieldCode="AU" term="%22Jankuloska+V%22">Jankuloska V</searchLink>; Faculty of Technology and Technical Sciences, University 'St. Kliment Ohridski'-Bitola, Dimitar Vlahov 57, 1400 Veles, North Macedonia.<br /><searchLink fieldCode="AU" term="%22Gajdoš+Kljusurić+J%22">Gajdoš Kljusurić J</searchLink>; Faculty of Food Technology and Biotechnology, University of Zagreb, Pierottijeva 6, 10000 Zagreb, Croatia.
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  Data: <searchLink fieldCode="JN" term="%22101521595%22">Nutrients</searchLink> [Nutrients] 2026 Sep 23; Vol. 18 (19). <i>Date of Electronic Publication: </i>2026 Sep 23.
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  Data: <searchLink fieldCode="MM" term="%22Menu+Planning%22">Menu Planning*</searchLink>/<searchLink fieldCode="MM" term="%22Menu+Planning+methods%22">methods</searchLink> <br /><searchLink fieldCode="MM" term="%22Diabetes+Mellitus%22">Diabetes Mellitus*</searchLink>/<searchLink fieldCode="MM" term="%22Diabetes+Mellitus+diet+therapy%22">diet therapy</searchLink> <br /><searchLink fieldCode="MM" term="%22Diet%2C+Diabetic%22">Diet, Diabetic*</searchLink>/<searchLink fieldCode="MM" term="%22Diet%2C+Diabetic+methods%22">methods</searchLink> <br /><searchLink fieldCode="MM" term="%22Meals%22">Meals*</searchLink> <br /><searchLink fieldCode="MM" term="%22Decision+Support+Techniques%22">Decision Support Techniques*</searchLink><br /><searchLink fieldCode="MH" term="%22Humans%22">Humans</searchLink> ; <searchLink fieldCode="MH" term="%22Programming%2C+Linear%22">Programming, Linear</searchLink> ; <searchLink fieldCode="MH" term="%22Energy+Intake%22">Energy Intake</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Background/Objectives: Translating medical nutrition therapy guidance into practical meal plans requires consistent application of clinical and dietetic rules while accommodating different therapeutic regimens. This study developed and computationally evaluated a transparent Python-based decision-support prototype for diabetic menu planning. Methods: The prototype combines procedural decision rules, food-exchange patterns, nutritional checks, and mixed-integer linear programming (MILP). Two meal-frequency pathways were represented: a conventional multi-meal pathway and a three-main-meal pathway for people treated with modern basal-bolus insulin regimens. For the multi-meal pathway, a structured dataset containing 42 candidate meals (seven alternatives in each of six meal categories) was used for objective-specific optimization and exhaustive evaluation of all 117,649 possible daily combinations under predefined nutritional constraints. Sensitivity to the allowable energy deviation and trade-offs among nutritional objectives were also examined. Results: The prototype produced deterministic pathway selection, exchange allocation, nutritional summaries, and explicit warnings when required information was unavailable. Under the baseline ±6.5% energy tolerance, 5628 of 117,649 six-meal combinations (4.78%) satisfied all nutritional constraints. Objective-specific optimization identified feasible solutions for minimizing energy, carbohydrate, and saturated fat and maximizing dietary fiber. The proportion of feasible combinations ranged from 2.71% at ±5% energy tolerance to 9.47% at ±10%, while comparison of objective-specific solutions demonstrated measurable nutritional trade-offs. The three-meal basal-bolus pathway generated predefined exchange allocation and clinical-review warnings but was not subjected to the same exhaustive combinatorial optimization. Conclusions: The prototype demonstrates the computational feasibility of integrating explicit dietetic rules, regimen-related meal-pattern selection, food-exchange allocation, and mathematical optimization within an inspectable decision-support framework. The findings establish technical feasibility within the evaluated datasets rather than clinical effectiveness. Independent software testing, expert usability assessment, evaluation using larger and version-controlled meal datasets, and prospective clinical validation are required before routine clinical implementation.
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  Label: References
  Group: RefInfo
  Data: Diabetes Res Clin Pract. 2026 Jun;236:113263. (PMID: <searchLink fieldCode="PM" term="%2241997462%22">41997462)</searchLink><br />Diabetes Res Clin Pract. 2023 Feb;196:110231. (PMID: <searchLink fieldCode="PM" term="%2236565723%22">36565723)</searchLink><br />Diabetes Res Clin Pract. 2025 Nov;229:112464. (PMID: <searchLink fieldCode="PM" term="%2240930317%22">40930317)</searchLink><br />Clin Nutr. 2026 Apr;59:106595. (PMID: <searchLink fieldCode="PM" term="%2241734418%22">41734418)</searchLink><br />Clin Nutr. 2026 Jun;61:106652. (PMID: <searchLink fieldCode="PM" term="%2242030900%22">42030900)</searchLink><br />Br J Nutr. 1993 Jul;70(1):27-35. (PMID: <searchLink fieldCode="PM" term="%228399108%22">8399108)</searchLink><br />Healthcare (Basel). 2026 Mar 13;14(6):. (PMID: <searchLink fieldCode="PM" term="%2241897193%22">41897193)</searchLink><br />Diabetes Res Clin Pract. 2026 Apr;234:113171. (PMID: <searchLink fieldCode="PM" term="%2241730509%22">41730509)</searchLink><br />Prim Care Diabetes. 2024 Feb;18(1):65-73. (PMID: <searchLink fieldCode="PM" term="%2238044201%22">38044201)</searchLink><br />Diabetes Res Clin Pract. 2026 Jul;237:113339. (PMID: <searchLink fieldCode="PM" term="%2242191027%22">42191027)</searchLink><br />Diabetes Res Clin Pract. 2026 Jun;236:113268. (PMID: <searchLink fieldCode="PM" term="%2242009259%22">42009259)</searchLink><br />J Adolesc Health. 2024 Oct;75(4S):S47-S61. (PMID: <searchLink fieldCode="PM" term="%2239293877%22">39293877)</searchLink><br />Heliyon. 2024 Sep 11;10(18):e37755. (PMID: <searchLink fieldCode="PM" term="%2239364243%22">39364243)</searchLink><br />Diabetes Res Clin Pract. 2025 Oct;228:112429. (PMID: <searchLink fieldCode="PM" term="%2240840786%22">40840786)</searchLink><br />Diabetes Res Clin Pract. 2026 Aug;238:113326. (PMID: <searchLink fieldCode="PM" term="%2242191026%22">42191026)</searchLink><br />Prim Care Diabetes. 2024 Oct;18(5):471-478. (PMID: <searchLink fieldCode="PM" term="%2238955658%22">38955658)</searchLink><br />Diabetes Care. 2026 Jan 1;49(Supplement_1):S50-S60. (PMID: <searchLink fieldCode="PM" term="%2241358891%22">41358891)</searchLink><br />Prim Care Diabetes. 2024 Jun;18(3):284-290. (PMID: <searchLink fieldCode="PM" term="%2238423826%22">38423826)</searchLink><br />Heliyon. 2023 Dec 18;10(1):e23906. (PMID: <searchLink fieldCode="PM" term="%2238192845%22">38192845)</searchLink><br />Artif Intell Med. 2024 May;151:102859. (PMID: <searchLink fieldCode="PM" term="%2238564880%22">38564880)</searchLink><br />Comput Biol Med. 2024 Sep;180:109001. (PMID: <searchLink fieldCode="PM" term="%2239126791%22">39126791)</searchLink><br />Diabetes Res Clin Pract. 2026 Aug;238:113389. (PMID: <searchLink fieldCode="PM" term="%2242331228%22">42331228)</searchLink><br />Diabetes Res Clin Pract. 2024 Nov;217:111898. (PMID: <searchLink fieldCode="PM" term="%2239433217%22">39433217)</searchLink><br />Diabetes Res Clin Pract. 2026 Feb;232:113112. (PMID: <searchLink fieldCode="PM" term="%2241577297%22">41577297)</searchLink><br />Clin Nutr. 2026 Jun;61:106667. (PMID: <searchLink fieldCode="PM" term="%2242054965%22">42054965)</searchLink>
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      – SubjectFull: Programming, Linear
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      – SubjectFull: Meals
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