Evidence map›Paper›PMID 41698622›Full record

ReviewAdvances in nutrition (Bethesda, Md.)2026

Harnessing Metabolic Insights: A Framework for Dietary Patterns in Chronic Disease Prevention and Management.

Fred K Tabung, Edward L Giovannucci

Abstract readReview
In one paragraph

Review in Advances in nutrition (Bethesda, Md.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Fred K TabungDepartment of Internal Medicine, The Ohio State University College of Medicine, and Comprehensive Cancer Center, Columbus, OH, United States; Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, United States. Electronic address: fred.tabung@osumc.edu.
Edward L GiovannucciDepartment of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, United States; Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Metabolic dysfunction is a major driver of global chronic disease, yet current dietary guidance remains only loosely connected to the biological pathways that underlie these conditions. Historically, nutrition research emphasized individual nutrients or caloric content, overlooking the integrated metabolic effects of whole dietary patterns. Extensive research has linked dietary factors with chronic inflammation, insulin hypersecretion, and insulin resistance, with more recent studies synthesizing these associations into metabolically grounded dietary pattern indices, compared with the conventional nutrient- or calorie-focused approaches. Metabolic dietary patterns, empirically derived food-based indices that predict long-term metabolic biomarkers such as C-peptide and inflammatory cytokines, introduce mechanistic specificity into dietary assessment. This perspective reviews the development and evidence base of these patterns, compares them with conventional dietary pattern approaches, and synthesizes their nutritional characteristics and disease predictive capacity. Although many healthy dietary patterns are associated with improved chronic disease outcomes, metabolic dietary patterns show more consistent and robust associations, suggesting that targeting insulin resistance, a central hub connecting hyperinsulinemia, inflammation, and chronic disease, may better capture metabolically meaningful dietary variation. Because existing evidence is largely observational, we propose a structured translational framework for evaluating metabolic dietary patterns in clinical and community settings. Key tenets include preserving metabolic integrity; clarifying food and beverage intake targets; addressing items with uncertain or counterintuitive metabolic properties; accounting for food combinations and preparation methods; integrating food processing level; and ensuring cultural adaptability. This framework supports the translation of metabolic insights into actionable dietary guidance for precision prevention, clinical care, and public health.

Indexed as

DietDiet, HealthyFeeding BehaviorMetabolic DiseasesBiomarkersChronic DiseaseHumansInflammationInsulin ResistanceBiomarkerschronic inflammationclinical translationinsulin hypersecretioninsulin resistancemajor chronic diseasemetabolic dietary patterns

Identifiers

PMID41698622
PMCPMC12997326

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.