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What is considered underweight in Korea?

The present study was performed to investigate the association between underweight severity and the development of fracture in a nationwide general population in Korea. Underweight was subdivided into mild (17.5 ≤ BMI < 18.5), moderate (16.5 ≤ BMI < 17.5), and severe underweight (BMI < 16.5).

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Study design and population

This nationwide population-based study was performed using the Korean National Health Insurance Service (NHIS) database. NHIS is a compulsory national health insurance system that covers approximately 99% of the population of the Republic of Korea for almost all medical processes. NHIS provides regular health check-ups to adults over 40 years old and workers over 20 years old every 1–2 years and obtains information on anthropometric measurements, alcohol consumption, smoking status, and medical history via self-reported questionnaires and laboratory findings. Self-reported data were double-checked through an interview with a doctor, and blood sampling was performed after fasting for at least 8 h. The NHIS claims database contains extensive health information for more than 50,000,000 Koreans. This database includes data regarding diagnoses and comorbidities coded according to the International Classification of Diseases, Tenth Revision (ICD-10), demographic characteristics, prescription medications, health care services such as treatments, and costs for outpatients and inpatients13. Since 2015, the NHIS has provided access to this Korea-representative retrospective cohort database to all researchers whose study protocols have been approved by an official review committee. All data are anonymized, collected regularly, and subjected to careful quality control. This NHIS database has been used in many epidemiological studies in various fields, and its validity has been confirmed in previous studies14,15. From the NHIS database, approximately 4 million participants were randomly selected using simple random sampling. We included individuals over 40 years old who underwent health examinations by the NHIS in 2009. Subjects were excluded if baseline characteristic data were missing, they had a prior diagnosis of fracture before enrollment or a fracture occurring during the 1-year lag period, or they were overweight or obese (BMI ≥ 23.0).

Key variables

Classification of underweight

BMI was calculated by dividing body weight (kilograms) by height squared (m2). The study population was divided into four groups: normal weight (18.5 ≤ BMI < 23.0), mild underweight (17.5 ≤ BMI < 18.5), moderate underweight (16.5 ≤ BMI < 17.5), and severe underweight (BMI < 16.5)12.

Incidence of fracture

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To confirm the causes of all fracture cases, we used ICD-10 codes and hospitalization records from the NHIS system. Fractures were defined using the ICD-10 codes as follows: vertebral fractures (S12.0, S12.1, S12.2, S22.0, S22.1, S32.0, M48.4, and M48.5), hip fractures (S72.0, S72.1, and S72.2), and other fractures, including upper arm fractures (S42.0, S42.2, and S42.3), forearm fractures (S52.5 and S52.6), and lower leg fractures (S82.3, S82.5, and S82.6). A fracture was defined as one hospitalization and/or ≥ two outpatient visits within 1 year with the relevant ICD-10 codes. From January 1, 2010 to December 31, 2018, fracture cases were confirmed by checking the NHIS medical claim records. Participants who died during the follow-up period were censored at the time of death.

Covariates

The NHIS database includes data on demographics, socioeconomic status, comorbidities, and laboratory findings, such as total cholesterol level, blood glucose level, and estimated glomerular filtration rate. Participants were classified according to smoking status as non-smokers, ex-smokers, or current smokers. Participants were classified according to alcohol consumption as non-drinkers, moderate drinkers (< 30 g/day), or heavy drinkers (≥ 30 g/day)16. Regular exercise was defined as at least 20 min of high-intensity physical activity ≥ 3 days per week or at least 30 min of moderate-intensity physical activity ≥ 5 days per week17. Low income was defined as an income < 20th percentile. Diabetes was defined as a fasting blood sugar level > 126 mg/dL or prescription of an antidiabetic agent (ICD-10 codes, E11–E14). Hypertension was defined as an average blood pressure ≥ 140/90 mmHg, or more than one annual prescription of an antihypertensive agent (ICD-10 codes, I10–I13 or I15). Dyslipidemia was defined as a total cholesterol level ≥ 240 mg/dL or more than one annual prescription of an antihyperlipidemic agent (ICD-10 code, E78). Chronic kidney disease (CKD) was defined as an estimated glomerular filtration rate < 60 mL/min/1.73 m2. Previous studies validated the definitions of comorbidities based on the ICD codes15,18.

Statistical analysis

Statistical analyses were performed using the chi-squared test for categorical variables and analysis of variance (ANOVA) for continuous variables. The incidence rate (IR) was calculated by dividing the outcome rate per 1000 person-years (PY) by the total number of fractures. The 95% confidence intervals (CIs) and hazard ratios (HRs) for fractures based on underweight severity were calculated using Cox regression analysis. We constructed a hierarchical model with different levels of demographic, socioeconomic factors, and comorbidities to investigate covariates potentially affecting fracture risk: model 1 was non-adjusted; model 2 was adjusted for age and sex; model 3 was additionally adjusted for other factors, including alcohol consumption, smoking status, low income, and regular exercise; and model 4 was fully adjusted with additional adjustments for comorbidities such as diabetes, hypertension, dyslipidemia, and CKD. We also compared the cumulative incidences of fractures between groups using the Kaplan–Meier method. To examine the effects of clinical conditions on the association between risk of fracture and underweight severity, the HRs for fractures in diverse subgroups were determined by Cox proportional hazards regression analysis and P values for interaction. Stratified subgroup analysis was performed based on age (< 65 and ≥ 65 years old), sex, smoking status, alcohol consumption, household income, regular activity, and comorbidities. All statistical analyses were performed using SAS software (ver. 9.3; SAS Institute, Cary, NC, USA). A two-sided P < 0.05 was taken to indicate statistical significance.

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Ethics statement

The study protocol was approved by the NHIS institutional review board. The informed consent requirement was waived because the data analyses were performed retrospectively using anonymous data derived from the NHIS database in Korea. This study was also approved by the institutional review board of Korea University Ansan Hospital, Republic of Korea (approval no. K2021-2601-001). All research processes were conducted in accordance with the appropriate regulations and guidelines, and this study was performed in accordance with the provisions of the Declaration of Helsinki.

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