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Elevated All-Cause Mortality among Overweight Older People: AI Predicts a High Normal Weight Is Optimal
It has been proposed that being overweight may provide an advantage with respect to mortality in older people, although this has not been investigated fully. Therefore, to confirm that and elucidate the underlying mechanism, we investigated mortality in older people using explainable artificial inte...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9222635/ https://www.ncbi.nlm.nih.gov/pubmed/35735773 http://dx.doi.org/10.3390/geriatrics7030068 |
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author | Nakajima, Kei Yuno, Mariko |
author_facet | Nakajima, Kei Yuno, Mariko |
author_sort | Nakajima, Kei |
collection | PubMed |
description | It has been proposed that being overweight may provide an advantage with respect to mortality in older people, although this has not been investigated fully. Therefore, to confirm that and elucidate the underlying mechanism, we investigated mortality in older people using explainable artificial intelligence (AI) with the gradient-boosting algorithm XGboost. Baseline body mass indexes (BMIs) of 5699 people (79.3 ± 3.9 years) were evaluated to determine the relationship with all-cause mortality over eight years. In the unadjusted model, the first negative (protective) BMI range for mortality was 25.9–28.4 kg/m(2). However, in the adjusted cross-validation model, this range was 22.7–23.6 kg/m(2); the second and third negative BMI ranges were then 25.8–28.2 and 24.6–25.8 kg/m(2), respectively. Conversely, the first advancing BMI range was 12.8–18.7 kg/m(2), which did not vary across conditions with high feature importance. Actual and predicted mortality rates in participants aged <90 years showed a negative-linear or L-shaped relationship with BMI, whereas predicted mortality rates in men aged ≥90 years showed a blunt U-shaped relationship. In conclusion, AI predicted that being overweight may not be an optimal condition with regard to all-cause mortality in older adults. Instead, it may be that a high normal weight is optimal, though this may vary according to the age and sex. |
format | Online Article Text |
id | pubmed-9222635 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-92226352022-06-24 Elevated All-Cause Mortality among Overweight Older People: AI Predicts a High Normal Weight Is Optimal Nakajima, Kei Yuno, Mariko Geriatrics (Basel) Communication It has been proposed that being overweight may provide an advantage with respect to mortality in older people, although this has not been investigated fully. Therefore, to confirm that and elucidate the underlying mechanism, we investigated mortality in older people using explainable artificial intelligence (AI) with the gradient-boosting algorithm XGboost. Baseline body mass indexes (BMIs) of 5699 people (79.3 ± 3.9 years) were evaluated to determine the relationship with all-cause mortality over eight years. In the unadjusted model, the first negative (protective) BMI range for mortality was 25.9–28.4 kg/m(2). However, in the adjusted cross-validation model, this range was 22.7–23.6 kg/m(2); the second and third negative BMI ranges were then 25.8–28.2 and 24.6–25.8 kg/m(2), respectively. Conversely, the first advancing BMI range was 12.8–18.7 kg/m(2), which did not vary across conditions with high feature importance. Actual and predicted mortality rates in participants aged <90 years showed a negative-linear or L-shaped relationship with BMI, whereas predicted mortality rates in men aged ≥90 years showed a blunt U-shaped relationship. In conclusion, AI predicted that being overweight may not be an optimal condition with regard to all-cause mortality in older adults. Instead, it may be that a high normal weight is optimal, though this may vary according to the age and sex. MDPI 2022-06-16 /pmc/articles/PMC9222635/ /pubmed/35735773 http://dx.doi.org/10.3390/geriatrics7030068 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Communication Nakajima, Kei Yuno, Mariko Elevated All-Cause Mortality among Overweight Older People: AI Predicts a High Normal Weight Is Optimal |
title | Elevated All-Cause Mortality among Overweight Older People: AI Predicts a High Normal Weight Is Optimal |
title_full | Elevated All-Cause Mortality among Overweight Older People: AI Predicts a High Normal Weight Is Optimal |
title_fullStr | Elevated All-Cause Mortality among Overweight Older People: AI Predicts a High Normal Weight Is Optimal |
title_full_unstemmed | Elevated All-Cause Mortality among Overweight Older People: AI Predicts a High Normal Weight Is Optimal |
title_short | Elevated All-Cause Mortality among Overweight Older People: AI Predicts a High Normal Weight Is Optimal |
title_sort | elevated all-cause mortality among overweight older people: ai predicts a high normal weight is optimal |
topic | Communication |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9222635/ https://www.ncbi.nlm.nih.gov/pubmed/35735773 http://dx.doi.org/10.3390/geriatrics7030068 |
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