Identifying the high-benefit population for weight management-based cardiovascular disease prevention in Japan

Abstract

Background: Cardiovascular-disease (CVD) is the leading cause of death, and the association between obesity and CVD is particularly significant among women. Given the evidence highlighting the significance of weight-gain velosity, we aimed to elucidate its influence on cardio-ankle vascular index (CAVI), a reliable surrogate marker of CVD, and identify the high-benefit population where this influence is most pronounced.

Methods: This multicenter retrospective study used electronic data from annual health checkups for workers in Japan. Individuals who voluntarily measured CAVI in 2019 were included, and weight-gain velosity was defined as the mean BMI gain from 2015 to 2019. Our primary outcome was the relationship between weight-gain velosity and CAVI.

Results: Among 459 individuals, 53 had CAVI ≥ 9. Random forest analysis revealed that age was the most important factor, followed by lipid metabolism, weight-gain velosity, and glucose metabolism, with sex being the least important. Non-linear regression analysis of the effect of age on CAVI ≥ 9 showed the effect was pronounced after age 60, and the trend was greater in women. Among individuals aged 60 or younger, the aOR of weight-gain velosity for CAVI ≥ 9 was significantly positive (aOR 11.95, 95 %CI 1.13-126.27), while it was not significant for those older than 60. The relationship between weight-gain velosity and CAVI provides a new perspective on CVD risk factors. The effects of age, especially after 60, and weight-gain velosity in early- to middle-adulthood on arterial stiffness are emphasized.

Conclusions: These findings underscore the importance of weight management under age 60, especially in women.

Keywords: Annual BMI change; Atherosclerosis; Obesity; Prevention.

Fig. 1

Overview of the definitions of terms in Japan, 2015–2019. We defined annual annual BMI change as mean BMI change from 2015 to 2019 (red arrow). All parameters used in the analyses including BMI and CAVI were measured in 2019. CAVI, cardio-ankle vascular index; BMI, body mass index. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 2

Non-linear regression analysis on CAVI in Japan, 2015–2019. A. Heatmap shows the distribution of missing data: blue for valid data, red for missing data. The histogram right to the heatmap shows the incidence of each pattern of data. B. Bar plot representing the variable importance obtained from a random forest model for predicting CAVI ≥ 9. Variables are sorted on the y-axis in order of decreasing importance. The color of the bars represents different groups of variables. C. Predicted CAVI (solid-line) with 95 % CI (shaded-area) calculated by a generalized additive model using age, sex, BMI, annual BMI change, smoking status, hemoglobin, total cholesterol, high density lipoprotein cholesterol, triglyceride, fasting plasma glucose, HbA1c, antidiabetic drug, and antihyperlipidemic drug as covariables. D. Fitted value (solid-line) with 95 % CI (shaded-area) of CAVI ≥ 9 calculated by a generalized additive model using age, sex, BMI, annual BMI change, smoking status, hemoglobin, total cholesterol, high density lipoprotein cholesterol, triglyceride, fasting plasma glucose, HbA1c, antidiabetic drug, and antihyperlipidemic drug as covariables. CAVI, cardio-ankle vascular index; BMI, body mass index. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. S1

Non-linear regression analysis on CAVI classified with overweight status in Japan, 2015–2019. Predicted CAVI (solid-line) with 95% CI (shaded-area) calculated by a generalized additive model using age, sex, BMI, annual BMI change, smoking status, hemoglobin, total cholesterol, high density lipoprotein cholesterol, triglyceride, fasting plasma glucose, HbA1c, antidiabetic drug, and antihyperlipidemic drug as covariables. A for non-overweight participants and B for overweight participants.

Fig. S2

Sex-stratified non-linear regression analysis on CAVI in Japan, 2015–2019. Sex-stratified analyses predicting fitted value (solid-line) with 95% CI (shaded-area) of CAVI ≥9 calculated by a generalized additive model using age, sex, BMI, annual BMI change, smoking status, hemoglobin, total cholesterol, high density lipoprotein cholesterol, triglyceride, fasting plasma glucose, HbA1c, antidiabetic drug, and antihyperlipidemic drug as covariables.

Fig. S3

Graphic abstract.