To address these gaps, we evaluated 1-year improvement in abnormalities of blood pressure, glucose metabolism, and lipid metabolism detected at annual health check-ups using the Japan Society of Ningen Dock criteria, and identified factors associated with this improvement, irrespective of treatment status.

This retrospective cohort study included adults aged ≥20 years who underwent health check-ups at Jichi Medical University Health Care Centre between April 1, 2008, and March 31, 2023. These were not mandatory occupational health check-ups, but rather voluntarily checks undertaken by individuals. When multiple check-up records were available for the same individual within the same fiscal year, only data from the first check-up were used. Records with missing data on blood pressure, glucose metabolism, or lipid metabolism were excluded.

The study population comprised individuals with blood pressure, glucose metabolism, or lipid metabolism abnormalities during a health check-up and who underwent a subsequent check-up in the following year. Abnormalities were assessed according to the criteria used at our healthcare centre, which were based on the classification criteria of the Japan Society of Ningen Dock and Preventive Medical Care [11]. In this study, findings that required further examination or treatment were considered abnormal. Specifically, blood pressure abnormality was defined any one of the following: (1) Both health check-up and home blood pressure of ≥140/90 mmHg; (2) Health check-up blood pressure of ≥150/95 mmHg with a home blood pressure of ≥125/80 mmHg or missing home blood pressure; or (3) Health check-up blood pressure of ≥160/100 mmHg. Glucose metabolism abnormality was defined any of the following: (1) haemoglobin A1c (HbA1c) of ≥6.5%, according to the National Glycohemoglobin Standardization Program; (2) fasting plasma glucose ≥126 mg/dL; or (3) urine glucose ≥(+). Lipid metabolism abnormality was defined any of the following: (1) low-density lipoprotein cholesterol (LDL-C) of ≥160 mg/dL; (2) triglycerides of ≥300 mg/dL; or (3) high-density lipoprotein cholesterol (HDL-C) of ≤34 mg/dL. Medical consultation was recommended for untreated individuals with these abnormalities.

The primary outcome was the proportion of individuals whose abnormalities of blood pressure, glucose metabolism, or lipid metabolism improved at the subsequent check-up, which was defined as no longer meeting the criteria for abnormality described above. Factors associated with improvement at the subsequent health check-up were also examined.

The following variables were collected: demographic characteristics, anthropometric measurements, responses to self-administered questionnaires on health-related behaviours and medical histories, systolic blood pressure (SBP) and diastolic blood pressure (DBP), and blood and urine test results. Body mass index (BMI) was calculated as weight in kilograms divided by the square of height in meters. Weight loss (%) was calculated as follows: (body weight at the baseline check-up - body weight at the subsequent check-up)/body weight at the baseline check-up × 100, with positive values indicating weight loss and negative values indicating weight gain. Lifestyle-related behaviours included current smoking status, exercise habits, and daily alcohol intake. Exercise habits were defined as engaging in moderate exercise for at least 30 min per session, at least twice a week, for >1 year. Daily alcohol intake was defined as daily consumption of alcohol, regardless of the amount. Medical histories included self-reported use of antihypertensive, antidiabetic, or antihyperlipidaemic medications. In addition, we assessed medical institution visits after the baseline check-up, as confirmed through response letters from consulting physicians, for individuals who were not receiving relevant medication at the baseline check-up.

Analyses were conducted using Stata software version 19.0 (Stata Corp LP, College Station, TX, USA), and a p-value of <0.05 was considered statistically significant. Descriptive statistics were used to summarise baseline characteristics. Categorical variables are described as numbers with percentages, and continuous variables are described as means with standard deviation (SD). Changes in blood pressure and blood test results related to glucose and lipid metabolism between baseline and the subsequent check-up were assessed using paired t-tests. Multivariable logistic regression analysis was performed to examine factors associated with improvement at the subsequent health check-up. Explanatory variables included the following: baseline characteristics (age, sex, obesity status [BMI < 25 or ≥25 kg/m], SBP, HbA1c, LDL-C, HDL-C, triglycerides, use of antihypertensive, antidiabetic, and/or antihyperlipidaemic medication), weight loss, and variables at the subsequent check-up (current smoking status, exercise habit, daily alcohol intake, and use of relevant medication). For each factor, odds ratio (OR) and 95% confidence interval (CI) were calculated. To evaluate potential multicollinearity among covariates, we calculated variance inflation factors (VIFs) using ordinary least squares regression, including all variables entered into multivariable logistic models.

For sensitivity analysis, we repeated the same analyses after excluding individuals who were receiving relevant medication at the baseline check-up.

The study protocol was approved by our institutional review board. In accordance with the Ethical Guidelines for Medical and Health Research Involving Human Subjects in Japan, informed consent was obtained using an opt-out approach, whereby information about the study was disclosed on the institution's website, and participants were given the opportunity to decline participation.