Scholarlink

<p>Missing and inconsistent nutrient values in food-composition databases hinder comparative nutrition research. We present NutriMatch, a scalable harmonization method that embeds food descriptions with a large-language model, aligns nutritionally equivalent items, and imputes missing nutrients to enrich FCDB coverage. Applied to the Israeli Human Phenotype Project, NutriMatch expanded logged nutrient profiles from 21 to 151 nutrients; in the Australian PREDICT cohort, coverage rose from 43 to the same 151 nutrients using a validated external FCDB. Enriched nutrient sets improved 2-year obesity prediction (AUC 0.63 → 0.67) and increased median <i>R</i>² for body-fat percentage, waist circumference, continuous glucose monitoring, and several blood biomarkers. Models trained in HPP transferred to PREDICT without retraining, achieving Pearson <i>r</i> = 0.32 for visceral fat mass versus 0.24 with baseline nutrients. NutriMatch thus delivers rapid, reproducible harmonization of FCDBs, phenotype-informative nutrient enrichment, and enables robust cross-cohort studies.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

NutriMatch: harmonizing food composition databases with large language models for enhanced nutritional prediction

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月21日

查看全文
  • Adam Jankelow,
  • Anastasia Godneva,
  • Michal Rein,
  • Dorit Samocha-Bonet,
  • Daphna Weissglas-Volkov,
  • Shahar Zohar,
  • Tal Shor,
  • Eran Segal

摘要

Missing and inconsistent nutrient values in food-composition databases hinder comparative nutrition research. We present NutriMatch, a scalable harmonization method that embeds food descriptions with a large-language model, aligns nutritionally equivalent items, and imputes missing nutrients to enrich FCDB coverage. Applied to the Israeli Human Phenotype Project, NutriMatch expanded logged nutrient profiles from 21 to 151 nutrients; in the Australian PREDICT cohort, coverage rose from 43 to the same 151 nutrients using a validated external FCDB. Enriched nutrient sets improved 2-year obesity prediction (AUC 0.63 → 0.67) and increased median R² for body-fat percentage, waist circumference, continuous glucose monitoring, and several blood biomarkers. Models trained in HPP transferred to PREDICT without retraining, achieving Pearson r = 0.32 for visceral fat mass versus 0.24 with baseline nutrients. NutriMatch thus delivers rapid, reproducible harmonization of FCDBs, phenotype-informative nutrient enrichment, and enables robust cross-cohort studies.