Possible pitfalls in the prediction of weight gain in middle-aged normal-weight individuals: Results from the NDB-K7Ps-study-2

The prevalence of obesity has not decreased worldwide and obesity-related morbidities have been increasing steadily. However, few studies have investigated factors contributing to weight gain in normal-weight individuals. Thus, in this community-based cohort study, we aimed to investigate factors co...

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Published in:Obesity research & clinical practice 2024-07, Vol.18 (4), p.255-262
Main Authors: Nakajima, Kei, Sekine, Airi, Higuchi, Ryoko, Enokido, Mai, Matsui, Sadako
Format: Article
Language:English
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Glycated hemoglobin
Machine learning
Smoking
Triglyceride
Weight gain
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creator Nakajima, Kei
Sekine, Airi
Higuchi, Ryoko
Enokido, Mai
Matsui, Sadako
description The prevalence of obesity has not decreased worldwide and obesity-related morbidities have been increasing steadily. However, few studies have investigated factors contributing to weight gain in normal-weight individuals. Thus, in this community-based cohort study, we aimed to investigate factors contributing to weight gain in normal-weight participants. Clinical variables and 10 % increase in weight over 10 years (10 %IBW10Y) were retrospectively investigated in apparently healthy 615,077 normal-weight (body mass index (BMI) 21.0–24.9 kg/m2) participants aged 40–64 years who had regularly undergone health checkup. Machine learning and logistic regression analysis (nested case-control study) were used to predict 10 %IBW10Y. In total, 6.8 % of men and 8.9 % of women had 10 %IBW10Y (P 
doi_str_mv 10.1016/j.orcp.2024.07.004
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However, few studies have investigated factors contributing to weight gain in normal-weight individuals. Thus, in this community-based cohort study, we aimed to investigate factors contributing to weight gain in normal-weight participants. Clinical variables and 10 % increase in weight over 10 years (10 %IBW10Y) were retrospectively investigated in apparently healthy 615,077 normal-weight (body mass index (BMI) 21.0–24.9 kg/m2) participants aged 40–64 years who had regularly undergone health checkup. Machine learning and logistic regression analysis (nested case-control study) were used to predict 10 %IBW10Y. In total, 6.8 % of men and 8.9 % of women had 10 %IBW10Y (P &lt; 0.0001). The prevalence of obesity (BMI ≥25.0 kg/m2) after 10 years and weight gain were higher in participants with 10 %IBW10Y (72.3 %, 8.9 kg) (case-group) versus those without 10 %IBW10Y (11.5 %, −0.4 kg) (control-group), respectively. Machine learning showed positive contributing factors to 10 %IBW10Y were, in descending order, age early 40 s, current smoking, female sex, low serum triglyceride (≤59 mg/dL), and low glycated hemoglobin (HbA1c) (≤4.9 %). Age early 60 s, non-smoking, male sex, high triglyceride (≥200 mg/dL), and HbA1c 6.0 %−6.9 % were negative contributing factors. Logistic regression analysis showed similar results except for high HbA1c (≥7.5 %) as a positive contributing factor. 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subjects Glycated hemoglobin
Machine learning
Smoking
Triglyceride
Weight gain
title Possible pitfalls in the prediction of weight gain in middle-aged normal-weight individuals: Results from the NDB-K7Ps-study-2
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