Veterans life insurance

Association of body mass index at the end of life with the risk of Alzheimer’s disease: a population-based 10-year national cohort study

Source of data and study population

The Korean National Health Insurance Service (NHIS), which is a government-run only compulsory social insurer, has created a public database called National Health Information Database (NHID). NHID contains all health care utilization records (including diagnostic and prescription record information), eligibility database (including socio-demographic variables), and national screening database health17. The National Health Screening Program consists of a questionnaire on background, family history, lifestyle, anthropometric measurements and laboratory tests, and is provided every two years to all adults over the age of 40.18. The NHIS-Senior database is composed of a random sample of 10% of the entire elderly population aged ≥60 in NHID in 200219. All individuals in the NHIS-Senior cohort were followed retrospectively from 2002 to 2015, except those who were not eligible for national health insurance.

We collected data from the NHIS-Senior database for all people who participated in the national health screening program from 2002 to 2005. Of the 215,875 participants, we excluded 5,798 people who died before the index date and 6,729 people diagnosed with a type of dementia before the index date. Additionally, we excluded 22,061 people who had been diagnosed with any type of cancer and 1,966 people who had a history of stroke before the index date. We then excluded 20,042 participants aged

Figure 1

Organizational chart of the population studied.

The Institutional Review Board of Veterans Affairs Health Service Medical Center (Seoul, Republic of Korea) approved this study (IRB no. BOHUN 2021-01-059-001) and waived the requirement to obtain written informed consent as NHID provides anonymous information and anonymized data. All research was carried out in accordance with the Declaration of Helsinki of 1964 and its subsequent amendments.

Definitions of outcomes and covariates

Alzheimer’s disease was diagnosed based on the International Classification of Diseases, codes F00 or G30 of the 10th revision (ICD-10). We defined AD in cases where the diagnosis and prescription of anticholinesterases (donepezil, rivastigmine and galantamine) or NOT-methyl-D-aspartate (NMDA) receptor antagonists (memantine) were claimed together on the same day20,21.22. To properly claim anti-dementia medications, physicians must document evidence of cognitive decline according to the following criteria: 1) Mini-Mental State Examination (MMSE) score ≤ 26 and 2) clinical assessment of dementia (CDR) ≥ 1 or Global Deterioration Scale (GDS) Score ≥ 323.

Participants were classified by BMI (kg/m2) as underweight (24. The underweight population was then categorized as having mild (17.0–18.4), moderate (16.0–16.9), or severe (25. Participants answered questionnaires regarding their medical history and health behaviors, such as current smoking, current alcohol consumption, and regular exercise (at least 1 time per week) as part of the national screening program of health. Since health insurance premiums are determined by income level or land ownership in the NHIS, we defined the low-income population as those whose health insurance premiums were below the lowest decile for insured persons or who were recipients of medical aid.

Comorbidities such as hypertension, diabetes, and dyslipidemia were defined by prescribing medications for the disease using the respective ICD-10 codes (I10–13 and I15 for hypertension, E11–14 for diabetes and E78 for dyslipidaemia) at least twice a day. before the index year or if the respective diagnostic criteria were met (systolic blood pressure ≥ 140 mmHg or diastolic blood pressure ≥ 90 mmHg for hypertension; fasting blood glucose ≥ 126 mg/dL for diabetes; and total cholesterol ≥ 240 mg /dL for dyslipidemia) in the results of the national health screening program. Cardiovascular disease (CVD) was identified based on responses to the Self-Reported Questionnaire for Diagnosis of Heart Disease by a Physician as part of the National Health Screening Program.

For each participant, the primary outcome was the occurrence of AD between January 1, 2006 and December 31, 2015, and the number of person-years of follow-up was recorded.

statistical analyzes

Baseline participant characteristics were compared across BMI categories using ANOVA for continuous variables and chi-square test for categorical variables. The data is presented as an average (standard deviation) or a number (%). AD incidence rates were calculated by dividing the number of events per 1000 person-years (PY). Cox proportional hazards regression analyzes were performed to obtain hazard ratios (HR) and 95% confidence intervals (CI) of AD based on baseline BMI categories. The risk of AD was analyzed after adjusting for possible confounding factors. Model 1 was adjusted for age and gender, and Model 2 was further adjusted for lifestyle factors (smoking status, alcohol consumption, and regular exercise) and health status. low income. Model 3 was then adjusted to take into account a history of hypertension, diabetes, dyslipidemia and cardiovascular disease. A stratified analysis was performed by dividing participants into subgroups based on baseline age group (65–74 or ≥75), gender, low-income status, current smoking, alcohol consumption, regular exercise, underlying hypertension, diabetes, dyslipidemia and history. of CVD to test the interactions between the subgroups. In addition, a sensitivity analysis was performed using multiple imputation to additionally handle missing values ​​according to the fully conditional specification method. Statistical analyzes were performed using SAS Enterprise Guide (version 7.1; SAS Institute, Cary, NC, USA) and STATA software (MP, version 17.0; StataCorp, College Station, TX, USA), and significance statistic was defined as two-sided p