Risk of Diabetes Mellitus in Adults with Intellectual Disabilities: A Nationwide Cohort Study

Hye Yeon Koo; In Young Cho; Yoo Jin Um; Yong-Moon Mark Park; Kyung Mee Kim; Chung Eun Lee; Kyungdo Han

doi:10.3803/EnM.2024.2126

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Original Article Diabetes, obesity and metabolism Risk of Diabetes Mellitus in Adults with Intellectual Disabilities: A Nationwide Cohort Study Keypoint In this population-based cohort study incorporating approximately four million Korean adults, intellectual disability was associated with an increased risk of incident diabetes mellitus, with a 1.4-fold increase in risk observed in individuals with intellectual disability compared to the general population. Individuals with intellectual disability also showed higher diabetes mellitus risk than those with other disabilities or those with no disability.: Hye Yeon Koo^1,2, In Young Cho³, Yoo Jin Um⁴, Yong-Moon Mark Park^5,6, Kyung Mee Kim⁷, Chung Eun Lee^8,9, Kyungdo Han¹⁰; Endocrinology and Metabolism 2025;40(1):103-111.
DOI: https://doi.org/10.3803/EnM.2024.2126
Published online: November 20, 2024

¹Department of Family Medicine, Seoul National University Bundang Hospital, Seongnam, Korea

²Department of Family Medicine, Seoul National University College of Medicine, Seoul, Korea

³Department of Family Medicine and Supportive Care Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea

⁴Department of Family Medicine, Hallym University Dongtan Sacred Heart Hospital, Hwaseong, Korea

⁵Department of Epidemiology, Fay W. Boozman College of Public Health, University of Arkansas for Medical Sciences, Little Rock, AR, USA

⁶Winthrop P. Rockefeller Cancer Institute, University of Arkansas for Medical Sciences, Little Rock, AR, USA

⁷Department of Social Welfare, Soongsil University, Seoul, Korea

⁸Department of Education, Sungshin Women’s University, Seoul, Korea

⁹Department of Child Psychology and Education, Sungkyunkwan University, Seoul, Korea

¹⁰Department of Statistics and Actuarial Science, Soongsil University, Seoul, Korea

Corresponding authors: Kyungdo Han. Department of Statistics and Actuarial Science, Soongsil University, 369 Sangdo-ro, Dongjak-gu, Seoul 06978, Korea Tel: +82-2-2258-7226, Fax: +82-2-532-6537, E-mail: hkd917@naver.com

In Young Cho. Department of Family Medicine and Supportive Care Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnamgu, Seoul 06351, Korea Tel: +82-2-3410-2447, Fax: +82-2-3414-2832, E-mail: joiy@skku.edu

• Received: July 31, 2024 • Revised: September 30, 2024 • Accepted: October 15, 2024

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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ABSTRACT
GRAPHICAL ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
Supplementary Material
Article information
References

ABSTRACT

Background
Intellectual disability (ID) may be associated with an increased risk of diabetes mellitus (DM). However, evidence from longitudinal studies is scarce, particularly in Asian populations.
Methods
This retrospective cohort study used representative linked data from the Korea National Disability Registration System and the National Health Insurance Service database. Adults (≥20 years) who received a national health examination in 2009 (3,385 individuals with ID and 3,463,604 individuals without ID) were included and followed until 2020. ID was identified using legal registration information. Incident DM was defined by prescription records with relevant diagnostic codes. Multivariable-adjusted Cox proportional hazards regression models were used to estimate the adjusted hazard ratio (aHR) and 95% confidence interval (CI) for DM risks in individuals with ID compared to those without ID.
Results
Over a mean follow-up of 9.8 years, incident DM occurred in 302 (8.9%) individuals with ID and 299,156 (8.4%) individuals without ID. Having ID was associated with increased DM risk (aHR, 1.38; 95% CI, 1.23 to 1.55). Sensitivity analysis confirmed a higher DM risk in individuals with ID (aHR, 1.39; 95% CI, 1.24 to 1.56) than those with other disabilities (aHR, 1.11; 95% CI, 1.10 to 1.13) or no disability (reference). Stratified analysis showed higher DM risk in non-hypertensive subjects (aHR, 1.63; 95% CI, 1.43 to 1.86) compared to hypertensive subjects (aHR, 1.00; 95% CI, 0.80 to 1.26; P for interaction <0.001).
Conclusion
Adults with ID have an increased risk of developing DM, highlighting the need for targeted public health strategies to promote DM prevention in this population.
Keywords: Intellectual disability; Diabetes mellitus; Disability study; Public health

GRAPHICAL ABSTRACT

INTRODUCTION

Intellectual disability (ID), a condition that poses a substantial disease burden and social costs, has shown a continuous increase in prevalence during the last 10 years [1]. In Korea, the number of individuals with registered ID was 221,557 in 2021 [2]. Previous reports have observed that ID is related to increased multimorbidity and mortality rates [3,4], making the healthcare of this population an important public health issue. The most common cause of mortality among people with ID is cardiovascular disease [4], and multiple risk factors of cardiovascular disease are reported to be more common in ID than in the general population [5].

Recent studies have indicated an increased prevalence of diabetes mellitus (DM), an established risk factor for cardiovascular disease, in the population with ID [5-9]. In a meta-analysis that included four studies comparing DM prevalence between people with ID and the general population, the adjusted odds ratio (aOR) of DM was 2.46 in the ID population [7], implying the need for special attention to DM prevention. Factors like poor health habits and obesity are presumed to increase DM risk in the ID population [5,8]. However, most previous studies including those analyzed in the meta-analysis are cross-sectional, and data on incident DM development are rare, especially in Asian populations [5-8]. Additionally, accurate identification of ID at the population level has been difficult due to the scarcity of complete registration data for ID [5,7].

In Korea, the Korea National Disability Registration System (KNDRS) is operated by the government to provide welfare to people with registered disabilities [2]. The database contains information about disability types. This data can be linked to the database of the Korean National Health Insurance Service (NHIS), which is a single insurer providing mandatory health insurance and covers almost the entire population. Hence, we sought to identify ID at a population level using a nationwide dataset from KNDRS and NHIS. The aim of our study was to investigate differences in incident DM risk between people with ID and the general population in Korea.

Data source: We used a dataset from NHIS for this nationwide cohort study. As described above, the NHIS covers almost the entire Korean population, and its database includes information about demographics and claims data (diagnosis codes, prescription records, and medical facility usage) of its beneficiaries. NHIS beneficiaries who are aged ≥40 years or who are employees are also provided with a complimentary national health examination annually or biennially. The results of this health examination, including health questionnaires, anthropometric measurements, and blood tests, are also included in the NHIS database [10].
Study population: Here, 4,234,415 individuals older than 20 years and who received a national health examination in 2009 were initially identified from the NHIS database. Individuals with a missing value in covariates (n=272,803); who had a fasting plasma glucose level equivalent to DM (≥126 mg/dL) at the baseline health examination (n=244,389); who had previous diagnosis of DM (n=128,548); or who were diagnosed with incident DM within 1 year lag period from baseline (n=21,686) were excluded. Finally, 3,566,989 individuals were included as the study population.; This study was approved by the Institutional Review Board of Soongsil University (IRB number SSU-202312-HR-502-1). The requirement for written informed consent was waived as a de-identified database was used for analyses.
Definition of intellectual disability: ID was identified using the KNDRS data linked to the NHIS database. In Korea, people with disabilities need to be registered with KNDRS after completing legal procedures to receive welfare benefits [2]. This system includes 15 types of disabilities including ID. Each disability type was legally defined according to objective criteria based on medical assessment until the revision incorporating social and functional status assessment was implemented in 2019. A medical assessment consists of examination by a qualified specialist, including personal intelligence tests by a psychiatrist, neurologist, or rehabilitation medicine physician affiliated with an eligible institution in the case of ID, issuance of a disability certificate, and documentation of disability for a specified duration. After this, a medical advisory board comprising at least two specialists reviews the documents and determines the eligibility for registration. Therefore, the definition of ID based on this database can be considered precise and reliable.
Covariates: Demographic data, including sex, age, and income level (dichotomized at the lowest 25%), were collected from the eligibility database of NHIS. Lifestyle data, including current smoking, drinking, and regular exercise, were collected based on health questionnaires of the baseline health examination. A person with average alcohol intake >0 g/day was defined as a drinker. Performance of moderate exercise ≥5 days/week or vigorous exercise ≥3 days/week was defined as regular exercise. Anthropometric data, including height, body weight, and blood pressure, were extracted from the data of the health examination. Body mass index (BMI) was calculated as body weight (kg) divided by the square of height (m²). Obesity was defined as BMI of 25 kg/m² or higher [11]. Comorbidities were defined based on the health examination data and International Classification of Diseases, Tenth Revision (ICD-10) diagnostic codes and prescription records from claims data. Hypertension was defined as systolic blood pressure ≥140 mm Hg, diastolic blood pressure ≥90 mm Hg, or prescription records of antihypertensive medications with ICD-10 codes I10–I13 or I15. Dyslipidemia was defined by serum cholesterol ≥240 mg/dL or prescription records of antidyslipidemic medications with ICD-10 code E78. Chronic kidney disease (CKD) was defined as an estimated glomerular filtration rate <60 mL/min/1.73 m² based on Modification of Diet in Renal Disease equation or claims record of dialysis through the special reimbursement codes (V001, V003, V005). Psychiatric disorders, including depression (F32, F33), schizophrenia (F20), and anxiety (F40, F41), and epilepsy (G40, G41, R56, F80.3) were defined by ICD-10 codes.
Study outcome: The main study outcome was incident DM, which was identified based on healthcare usage under ICD-10 codes E11–14 with claims records for antihyperglycemic medication. The study population was followed from 1 year after the baseline health examination date to the date of incident DM, death, or December 31, 2020, whichever came first.
Statistical analysis: Baseline characteristics according to the presence or absence of ID were compared using t tests (for continuous variables) and chi-square tests (for categorical variables). Kaplan-Meier analysis was used to estimate the cumulative incidence probability of DM according to ID. Cox proportional hazards regression models were used to estimate the adjusted hazard ratio (aHR) and 95% confidence interval (CI) for DM risks in individuals with ID compared to those without ID. Schonfeld’s residual was used to assess proportional hazards assumption. Model 1 was unadjusted. Model 2 was adjusted for age and sex. Model 3 was additionally adjusted for income level, smoking, drinking, regular exercise, and BMI. Model 4 was additionally adjusted for comorbidities (hypertension, dyslipidemia, CKD, psychiatric disorders including depression, schizophrenia, and anxiety, and epilepsy) [12]. Sensitivity analysis was performed with adjustment for baseline fasting plasma glucose level. Another sensitivity analysis was performed comparing individuals with ID to individuals with other disabilities, using individuals with no disability as a reference. We assessed the potential effect modification by baseline characteristics, including sex, age, income level, smoking, drinking, regular exercise, obesity, and comorbidities using stratified analyses and tests for interaction based on the likelihood ratio test. The P values provided are two-sided, with the level of significance at 0.05. All analyses were performed using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA).
Data availability statement: The data supporting this study are available from the NHIS. Restrictions apply to the availability of these data, which were used under license for this investigation. Data are available from the authors under the permission of the NHIS.

Baseline characteristics: Among the study population of 3,566,989 adults, 3,385 (0.1%) had ID. Individuals with ID were more likely to be male, of a younger age, and have a lower income level than individuals without ID. Proportions of smoking, drinking, and regular exercise were lower in individuals with ID, who also had less frequent hypertension, dyslipidemia, CKD, and anxiety and more frequent depression, schizophrenia, and epilepsy than those without ID (all P<0.01). There was no significant difference in BMI between the two groups (Table 1).
Risk of incident DM according to ID: Over an average of 9.8 years of follow-up time, 302 (8.9%) incident DM cases were identified in individuals with ID, compared to 299,156 (8.4%) cases in those without ID. The cumulative incidence probability of DM was higher in individuals with ID (Fig. 1). In Cox proportional hazards regression analysis, individuals with ID had a 38% increased risk of developing DM (aHR, 1.38; 95% CI, 1.23 to 1.55) compared to those without ID after adjustment for potential confounders (sex, age, income level, smoking, drinking, regular exercise, BMI, and comorbidities) (Table 2). The result was similar after adjustment for baseline fasting plasma glucose level (Supplemental Table S1). In sensitivity analysis, individuals with ID showed higher risk of DM (aHR, 1.39; 95% CI, 1.24 to 1.56) than those with other disabilities (aHR, 1.11; 95% CI, 1.10 to 1.13) and those with no disability (reference) (Table 3).
Risk of incident DM stratified by baseline characteristics: In stratified analyses by baseline characteristics, ID was associated with an increased risk of DM across most subgroups; however, differences in the magnitude of association between subgroups did not reach statistical significance, likely due to the small number of events. Interaction analyses showed a significant interaction between hypertension and ID regarding DM risk (P for interaction <0.001). The aHRs for DM risk among individuals with ID were 1.63 (95% CI, 1.43 to 1.86) in the subgroup without hypertension and 1.00 (95% CI, 0.80 to 1.26) in the subgroup with hypertension (Supplemental Table S2).

DISCUSSION

Our population-based cohort study incorporating approximately four million adults demonstrated that ID is associated with an increased risk of incident DM, showing a 1.4-fold increase in risk among individuals with ID compared with the general population, after adjusting for multiple confounders. Individuals with ID also showed higher DM risk than those with other disabilities or those with no disability.

In this study, the prevalence of ID among the study population confirmed by predefined legal criteria was approximately 0.1%, much lower than the 1% reported in a previous meta-analysis [13]. This lower prevalence may be partly attributed to the inclusion of only individuals who participated in health examinations and the study’s setting in a developed country. However, the use of standardized diagnostic criteria likely contributed significantly to the accurate and lower estimate of prevalence [13]. Our ID definition based on objective national registration information is expected to provide more reliable results than those of previous studies based only on psychological assessments.

At baseline, individuals with ID had less frequent hypertension, dyslipidemia, and CKD than individuals without ID. However, this result needs to be interpreted with caution, as we have excluded subjects with a previous diagnosis of DM or a fasting serum glucose level equivalent to DM. As aforementioned comorbidities (hypertension, dyslipidemia, and CKD) are known to share risk factors and commonly coexist with DM [14-16], considerable number of subjects with these comorbidities would also have been excluded. Therefore, the baseline proportion of these diseases among our study population would not exactly reflect the actual prevalence among the entire Korean population. Also, individuals with ID were younger than individuals without ID in our study (mean age: 38.5±12.5 in individuals with ID vs. 46.2±13.8 in individuals without ID, P<0.001). Therefore, the prevalence of comorbidities which usually increase according to age [15-17] could have appeared relatively low in individuals with ID. Indeed, the main outcome of our study (type 2 DM), which is also known to increase with age [18], showed no elevated risk according to ID before adjustment for age and sex, as in model 1. Nevertheless, the increase in incident DM risk according to ID became evident after adjustment for these factors.

Our analysis showed that individuals with ID have an increased risk of incident DM over a mean follow-up of 9.8 years compared with those without ID. Most previous research has examined only DM prevalence [5-7]. The OR and trim- and fill-aOR for prevalent DM were 1.87 (95% CI, 1.40 to 2.48) and 2.46 (95% CI, 1.89 to 3.21) among the ID population compared with age- and gender-matched controls in a meta-analysis of mostly Western studies [7]. Regional differences were observed, with the lowest prevalence in Asia and the highest in Australia. A Korean cross-sectional study showed a lower aOR for the ID population (1.08; 95% CI, 1.02 to 1.14) than that of the previous meta-analysis, implying possible regional or ethnic differences [6]. To the best of our knowledge, only one previous study from Canada has investigated incidence of DM among an ID population compared with the general population [9]; it reported higher annual incidences of DM in individuals with intellectual and developmental disabilities (1.18–1.54/100 population) than in controls (0.73–1.09/100) over a study period of 6 years. However, it did not distinguish between ID and developmental disabilities, did not consider confounders other than sex and age, and was based on a limited population of a specific district. Our nationwide data from the Korean population suggest that ID may increase the risk of incident DM by approximately 40% after adjustment for multiple confounders.

Several factors have been postulated to contribute to increased risk of DM, including high prevalence of obesity or depression, poor health-related behaviors, and low economic status [5-8]. Our analysis showed that DM risk significantly differed according to ID even after adjustment for BMI, comorbidities including depression, lifestyle, and income level. Factors not fully captured in our analysis, including dietary habits, education level, and limited access to healthcare, might contribute to increased DM risk [19,20]. Sensitivity analysis showed that incident DM risk was higher in individuals with ID compared to those with other disabilities and those with no disability as the reference, indicating the possibility of factors characteristic of ID contributing to the increased DM risk. Multiple and severe barriers to healthcare might exist in the ID population, such as discrimination from healthcare providers or communication problems [19,20]. Future research is warranted to investigate the cause of elevated DM risk among individuals with ID.

In stratified analyses, ID was associated with increased DM risk in most subgroups as in the main analysis, but hypertension showed a significant interaction with ID regarding DM risk. The increased risk associated with ID was prominent in those without hypertension but not in those with hypertension. This could be due to shared multiple risk factors between hypertension and DM [15]. It is possible that hypertensive patients, with or without ID, might share strong risk factors for DM that mediate elevated risk in ID, making the differences according to ID not evident. As the common coexistence of hypertension and DM is well-established, physicians treating hypertensive patients can be more proactive in screening and preventing DM than when seeing non-hypertensive patients [21]. However, our study suggests that non-hypertensive individuals with ID are also at high risk for DM, indicating a need for active surveillance for DM in this group. Meanwhile, obesity and dyslipidemia, which also commonly coexist and share risk factors with DM [14], showed no significant interaction with ID. As obesity is not yet generally recognized as a disease and few patients seek treatment specifically for obesity [22], obese individuals may have less opportunities to consult with physicians regarding DM risk management than hypertensive patients. As for dyslipidemia, evidence regarding the effects of modification of shared risk factors like physical inactivity and dietary pattern on lipid profile is relatively insufficient than that on blood pressure, especially in Koreans [23-25]. Therefore, physicians may be less proactive in managing these factors when treating dyslipidemia compared to when treating hypertension, contributing to insignificant interaction between dyslipidemia with ID.

This study has significant public health implications. Evidence has mounted regarding the high prevalence of various chronic diseases including cardiovascular risk factors in the population with ID [5,26]. Alongside this, social burden and medical costs attributable to ID are large and increasing [27]. Our study adds evidence that ID increases DM risk, which is an important risk factor for cardiovascular disease and mortality. Notably, previous reports have shown that the ID population is more likely to suffer multi-comorbidities and early onset of chronic disease than the general population [3,26]. DM can pose a greater risk of cardiovascular events and premature death when it has an early onset or coexists with other cardiovascular risks [21,28]. However, prevention and optimal management of DM can be more difficult for individuals with ID. It can be challenging for them to get sufficient health check-ups, treatment, or information about their disease from healthcare providers [20,29]. Self-care such as dietary management or blood glucose monitoring can also be challenging [29,30]. Continuous education by healthcare providers for individuals with ID and their family or peer group can help promote self-care [29-31]. Physicians should be aware of the elevated health risk and address unmet needs for management in this population. Public health strategies to raise awareness of DM risk and systematic efforts to improve disease management are needed.

This study has several specific strengths. Our population-based national dataset enabled us to examine representative samples of the Korean population, which was not often investigated in previous studies. Unlike most previous studies that investigated DM prevalence, we examined DM incidence, providing stronger evidence for the increased risk. Also, various covariates that were not usually considered in previous research were included in our analyses. The definition of ID was based on national registration information, which ensures objective and nearly complete data. The DM definition was also based on reliable claims data from the NHIS, which contains complete medication prescription records of its beneficiaries.

Despite such strengths, our study also has a few limitations. First, our study population, limited to those who underwent national health examinations, may have been more health-conscious. The national health examination participation rate among disabled people is reported to be lower than that among people without disability (64% for mild and 52% for severe disability and 77% for those without disability in 2018) [10], implying that participants with ID in our study may tend to manage their health more diligently compared to the general population of individuals with ID. This potential selection bias could lead to underestimating the true association between ID and DM. Second, we could not obtain some information that might be relevant to our investigation, such as glycated hemoglobin level and local accessibility to healthcare services (such as nearby medical facilities or transportation options), as we used administrative data. Further investigation might be needed on possible factors associated with ID and DM.

In conclusion, risk of incident DM was increased in individuals with ID compared with those without ID. Public health strategies to promote early prevention of DM among this high-risk population are necessary.

Supplementary Material

Supplemental Table S1.

Risk of Incident Diabetes Mellitus according to Intellectual Disability with Adjustment for Baseline Fasting Plasma Glucose Level

enm-2024-2126-Supplemental-Table-S1.pdf

Supplemental Table S2.

Stratified Analysis of Risk of Diabetes Mellitus by Baseline Characteristics

enm-2024-2126-Supplemental-Table-S2.pdf

Article information

CONFLICTS OF INTEREST

No potential conflict of interest relevant to this article was reported.

ACKNOWLEDGMENTS

This work was funded by Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2023S1A5A2A21083821).

This research was supported in part by the National Center for Advancing Translational Sciences (UL1 TR003107, Yong- Moon Mark Park) of the National Institutes of Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

This study used data from the Korea National Disability Registration System (KNDRS) and Korean National Health Insurance Service (NHIS).

AUTHOR CONTRIBUTIONS

Conception or design: H.Y.K., I.Y.C., Y.J.U., Y.M.M.P., K.M.K., C.E.L., K.H. Acquisition, analysis, or interpretation of data: H.Y.K., I.Y.C., K.H. Drafting the work or revising: H.Y.K., I. Y.C., Y.J.U., Y.M.M.P., K.M.K., C.E.L., K.H. Final approval of the manuscript: I.Y.C., K.H.

Fig. 1.

Cumulative Kaplan-Meier incidence probability of diabetes mellitus according to intellectual disability (ID)^a. ^aAdjusted for sex, age, income level, smoking, drinking, regular exercise, body mass index, hypertension, dyslipidemia, chronic kidney disease, and depression.

Table 1.

Baseline Characteristics

Characteristic	Overall	Intellectual disability
Characteristic	Overall	No	Yes	P value
Number	3,566,989	3,563,604	3,385
Sex				<0.001
Male	1,919,611 (53.8)	1,917,187 (53.8)	2,424 (71.6)
Female	1,647,378 (46.2)	1,646,417 (46.2)	961 (28.4)
Age groups, yr				<0.001
20–39	1,191,147 (33.4)	1,189,346 (33.4)	1,801 (53.2)
40–64	1,972,843 (55.3)	1,971,403 (55.3)	1,440 (42.5)
≥65	402,999 (11.3)	402,855 (11.3)	144 (4.3)
Age, yr	46.2±13.8	46.2±13.8	38.5±12.5	<0.001
Income level, lowest quartile	690,587 (19.4)	688,588 (19.3)	1,999 (59.1)	<0.001
Smoking	928,910 (26.0)	928,363 (26.1)	547 (16.2)	<0.001
Drinking	1,744,127 (48.9)	1,743,334 (48.9)	793 (23.4)	<0.001
Regular exercise	626,150 (17.6)	625,650 (17.6)	500 (14.8)	<0.001
BMI, kg/m²	23.6±3.2	23.6±3.2	23.5±4.0	0.447
Obesity	1,105,731 (31.0)	1,104,632 (31.0)	1,099 (32.5)	0.065
Hypertension	793,423 (22.2)	792,819 (22.3)	604 (17.8)	<0.001
Dyslipidemia	540,103 (15.1)	539,791 (15.2)	312 (9.2)	<0.001
Chronic kidney disease	226,885 (6.4)	226,760 (6.4)	125 (3.7)	<0.001
Depression	99,810 (2.8)	99,671 (2.8)	139 (4.1)	<0.001
Anxiety	210,886 (5.9)	210,736 (5.9)	151 (4.5)	<0.001
Schizophrenia	6,250 (0.2)	6,128 (0.2)	121 (3.6)	<0.001
Epilepsy	25,304 (0.7)	25,022 (0.7)	280 (8.3)	<0.001
Fasting glucose, mg/dL	92.5±11.5	92.5±11.5	90.9±11.6	<0.001
SBP, mm Hg	121.8±14.8	121.8±14.8	120.9±14.9	0.001
DBP, mm Hg	76.0±1.0	76.0±1.0	75.8±10.1	0.154
Total cholesterol, mg/dL	194.8±36.2	194.8±36.2	184.3±37.1	<0.001

Values are expressed as number (%) or mean±standard deviation.

BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure.

Table 2.

Risk of Incident Diabetes Mellitus according to Intellectual Disability

Intellectual disability	Number	Events	Duration, PY	IR, /1,000 PY	HR (95% CI)
Intellectual disability	Number	Events	Duration, PY	IR, /1,000 PY	Model 1^a	Model 2^b	Model 3^c	Model 4^d
No	3,563,604	299,156	34,942,398.10	8.56	1 (Ref)	1 (Ref)	1 (Ref)	1 (Ref)
Yes	3,385	302	32,864.74	9.19	1.08 (0.96–1.21)	1.42 (1.27–1.59)	1.43 (1.27–1.60)	1.38 (1.23–1.55)

PY, person-years; IR, incidence rate; HR, hazard ratio; CI, confidence interval.

^a Model 1: unadjusted;

^b Model 2: adjusted for sex and age;

^c Model 3: adjusted for sex, age, income level, smoking, drinking, regular exercise, and body mass index;

^d Model 4: adjusted for sex, age, income level, smoking, drinking, regular exercise, body mass index, hypertension, dyslipidemia, chronic kidney disease, psychiatric disorders, and epilepsy.

Table 3.

Risk of Diabetes Mellitus among Individuals with ID or Other Disabilities

Disability	Number	Events	Duration, PY	IR, /1,000 PY	HR (95% CI)
Disability	Number	Events	Duration, PY	IR, /1,000 PY	Model 1^a	Model 2^b	Model 3^c	Model 4^d
No disability	3,392,792	275,129	33,374,750.85	8.24	1 (Ref)	1 (Ref)	1 (Ref)	1 (Ref)
Other disabilities	170,812	24,027	1,567,647.25	15.33	1.88 (1.85–1.90)	1.19 (1.17–1.21)	1.14 (1.12–1.15)	1.11 (1.10–1.13)
ID	3,385	302	32,864.74	9.19	1.12 (1.00–1.25)	1.43 (1.28–1.60)	1.44 (1.28–1.61)	1.39 (1.24–1.56)

ID, intellectual disability; PY, person-years; IR, incidence rate; HR, hazard ratio; CI, confidence interval.

^a Model 1: unadjusted;

^b Model 2: adjusted for sex and age;

^c Model 3: adjusted for sex, age, income level, smoking, drinking, regular exercise, and body mass index;

^d Model 4: adjusted for sex, age, income level, smoking, drinking, regular exercise, body mass index, hypertension, dyslipidemia, chronic kidney disease, psychiatric disorders, and epilepsy.

References

1. Kim B, Oh IH, Cheong HK, Hwang JW. Prevalence and medical costs of intellectual disabilities and pervasive developmental disorder in Korea: based on National Health Insurance Service claims data from 2007 to 2019. Psychiatry Investig 2023;20:972–83.Article PubMed PMC PDF
2. Kim M, Jung W, Kim SY, Park JH, Shin DW. The Korea national disability registration system. Epidemiol Health 2023;45:e2023053.Article PubMed PMC PDF
3. Cooper SA, McLean G, Guthrie B, McConnachie A, Mercer S, Sullivan F, et al. Multiple physical and mental health comorbidity in adults with intellectual disabilities: populationbased cross-sectional analysis. BMC Fam Pract 2015;16:110.Article PubMed PMC PDF
4. Glover G, Williams R, Heslop P, Oyinlola J, Grey J. Mortality in people with intellectual disabilities in England. J Intellect Disabil Res 2017;61:62–74.Article PubMed PDF
5. Flygare Wallen E, Ljunggren G, Carlsson AC, Pettersson D, Wandell P. High prevalence of diabetes mellitus, hypertension and obesity among persons with a recorded diagnosis of intellectual disability or autism spectrum disorder. J Intellect Disabil Res 2018;62:269–80.Article PubMed PDF
6. Jung I, Kwon H, Park SE, Han KD, Park YG, Rhee EJ, et al. The prevalence and risk of type 2 diabetes in adults with disabilities in Korea. Endocrinol Metab (Seoul) 2020;35:552–61.Article PubMed PMC PDF
7. Vancampfort D, Schuch F, Van Damme T, Firth J, Suetani S, Stubbs B, et al. Prevalence of diabetes in people with intellectual disabilities and age- and gender-matched controls: a meta-analysis. J Appl Res Intellect Disabil 2022;35:301–11.Article PubMed PDF
8. Cuypers M, Leijssen M, Bakker-van Gijssel EJ, Pouls KP, Mastebroek MM, Naaldenberg J, et al. Patterns in the prevalence of diabetes and incidence of diabetic complications in people with and without an intellectual disability in Dutch primary care: insights from a population-based data-linkage study. Prim Care Diabetes 2021;15:372–7.Article PubMed
9. Balogh RS, Lake JK, Lin E, Wilton A, Lunsky Y. Disparities in diabetes prevalence and preventable hospitalizations in people with intellectual and developmental disability: a population-based study. Diabet Med 2015;32:235–42.Article PubMed
10. Shin DW, Cho J, Park JH, Cho B. National general health screening program in Korea: history, current status, and future direction. Precis Future Med 2022;6:9–31.Article PDF
11. World Health Organization. The Asia-Pacific perspective: redefining obesity and its treatment. Geneva: WHO; 2000.
12. Akrout Brizard B, Limbu B, Baeza-Velasco C, Deb S. Association between epilepsy and psychiatric disorders in adults with intellectual disabilities: systematic review and meta-analysis. BJPsych Open 2021;7:e95.PubMed PMC
13. Maulik PK, Mascarenhas MN, Mathers CD, Dua T, Saxena S. Prevalence of intellectual disability: a meta-analysis of population-based studies. Res Dev Disabil 2011;32:419–36.Article PubMed
14. Bardini G, Rotella CM, Giannini S. Dyslipidemia and diabetes: reciprocal impact of impaired lipid metabolism and Beta-cell dysfunction on micro- and macrovascular complications. Rev Diabet Stud 2012;9:82–93.Article PubMed PMC
15. de Boer IH, Bangalore S, Benetos A, Davis AM, Michos ED, Muntner P, et al. Diabetes and hypertension: a position statement by the American Diabetes Association. Diabetes Care 2017;40:1273–84.Article PubMed PDF
16. Suckling R, Gallagher H. Chronic kidney disease, diabetes mellitus and cardiovascular disease: risks and commonalities. J Ren Care 2012;38 Suppl 1:4–11.Article PubMed
17. Liu HH, Li JJ. Aging and dyslipidemia: a review of potential mechanisms. Ageing Res Rev 2015;19:43–52.Article PubMed
18. Xia M, Liu K, Feng J, Zheng Z, Xie X. Prevalence and risk factors of type 2 diabetes and prediabetes among 53,288 middle-aged and elderly adults in China: a cross-sectional study. Diabetes Metab Syndr Obes 2021;14:1975–85.Article PubMed PMC PDF
19. Lee JE, Kim HR, Shin HI. Accessibility of medical services for persons with disabilities: comparison with the general population in Korea. Disabil Rehabil 2014;36:1728–34.Article PubMed
20. Ali A, Scior K, Ratti V, Strydom A, King M, Hassiotis A. Discrimination and other barriers to accessing health care: perspectives of patients with mild and moderate intellectual disability and their carers. PLoS One 2013;8:e70855.Article PubMed PMC
21. Yildiz M, Esenboga K, Oktay AA. Hypertension and diabetes mellitus: highlights of a complex relationship. Curr Opin Cardiol 2020;35:397–404.Article PubMed
22. Bramlage P, Wittchen HU, Pittrow D, Kirch W, Krause P, Lehnert H, et al. Recognition and management of overweight and obesity in primary care in Germany. Int J Obes Relat Metab Disord 2004;28:1299–308.Article PubMed PDF
23. Rhee EJ, Kim HC, Kim JH, Lee EY, Kim BJ, Kim EM, et al. 2018 Guidelines for the management of dyslipidemia in Korea. J Lipid Atheroscler 2019;8:78–131.Article PubMed PMC PDF
24. Whelton PK, He J, Appel LJ, Cutler JA, Havas S, Kotchen TA, et al. Primary prevention of hypertension: clinical and public health advisory from the National High Blood Pressure Education Program. JAMA 2002;288:1882–8.Article PubMed
25. Lee HY, Shin J, Kim GH, Park S, Ihm SH, Kim HC, et al. 2018 Korean Society of Hypertension guidelines for the management of hypertension: part II-diagnosis and treatment of hypertension. Clin Hypertens 2019;25:20.Article PubMed PMC PDF
26. van den Bemd M, Schalk BW, Bischoff EW, Cuypers M, Leusink GL. Chronic diseases and comorbidities in adults with and without intellectual disabilities: comparative cross-sectional study in Dutch general practice. Fam Pract 2022;39:1056–62.Article PubMed PMC PDF
27. Polder JJ, Meerding WJ, Bonneux L, van der Maas PJ. Healthcare costs of intellectual disability in the Netherlands: a cost-of-illness perspective. J Intellect Disabil Res 2002;46(Pt 2):168–78.Article PubMed
28. Misra S, Ke C, Srinivasan S, Goyal A, Nyriyenda MJ, Florez JC, et al. Current insights and emerging trends in early-onset type 2 diabetes. Lancet Diabetes Endocrinol 2023;11:768–82.Article PubMed
29. Cardol M, Rijken M, van Schrojenstein Lantman-de Valk H. People with mild to moderate intellectual disability talking about their diabetes and how they manage. J Intellect Disabil Res 2012;56:351–60.Article PubMed
30. Hale LA, Trip HT, Whitehead L, Conder J. Self-management abilities of diabetes in people with an intellectual disability living in New Zealand. J Policy Pract Intellect Disabil 2011;8:223–30.Article
31. Sandjojo J, Zedlitz AM, Gebhardt WA, Hoekman J, Dusseldorp E, den Haan JA, et al. Training staff to promote self-management in people with intellectual disabilities. J Appl Res Intellect Disabil 2018;31:840–50.Article PubMed PDF

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Risk of Diabetes Mellitus in Adults with Intellectual Disabilities: A Nationwide Cohort Study

Fig. 1. Cumulative Kaplan-Meier incidence probability of diabetes mellitus according to intellectual disability (ID)a. aAdjusted for sex, age, income level, smoking, drinking, regular exercise, body mass index, hypertension, dyslipidemia, chronic kidney disease, and depression.

Graphical abstract

Fig. 1.

Graphical abstract

Risk of Diabetes Mellitus in Adults with Intellectual Disabilities: A Nationwide Cohort Study

Characteristic	Overall	Intellectual disability
Characteristic	Overall	No	Yes	P value
Number	3,566,989	3,563,604	3,385
Sex				<0.001
Male	1,919,611 (53.8)	1,917,187 (53.8)	2,424 (71.6)
Female	1,647,378 (46.2)	1,646,417 (46.2)	961 (28.4)
Age groups, yr				<0.001
20–39	1,191,147 (33.4)	1,189,346 (33.4)	1,801 (53.2)
40–64	1,972,843 (55.3)	1,971,403 (55.3)	1,440 (42.5)
≥65	402,999 (11.3)	402,855 (11.3)	144 (4.3)
Age, yr	46.2±13.8	46.2±13.8	38.5±12.5	<0.001
Income level, lowest quartile	690,587 (19.4)	688,588 (19.3)	1,999 (59.1)	<0.001
Smoking	928,910 (26.0)	928,363 (26.1)	547 (16.2)	<0.001
Drinking	1,744,127 (48.9)	1,743,334 (48.9)	793 (23.4)	<0.001
Regular exercise	626,150 (17.6)	625,650 (17.6)	500 (14.8)	<0.001
BMI, kg/m²	23.6±3.2	23.6±3.2	23.5±4.0	0.447
Obesity	1,105,731 (31.0)	1,104,632 (31.0)	1,099 (32.5)	0.065
Hypertension	793,423 (22.2)	792,819 (22.3)	604 (17.8)	<0.001
Dyslipidemia	540,103 (15.1)	539,791 (15.2)	312 (9.2)	<0.001
Chronic kidney disease	226,885 (6.4)	226,760 (6.4)	125 (3.7)	<0.001
Depression	99,810 (2.8)	99,671 (2.8)	139 (4.1)	<0.001
Anxiety	210,886 (5.9)	210,736 (5.9)	151 (4.5)	<0.001
Schizophrenia	6,250 (0.2)	6,128 (0.2)	121 (3.6)	<0.001
Epilepsy	25,304 (0.7)	25,022 (0.7)	280 (8.3)	<0.001
Fasting glucose, mg/dL	92.5±11.5	92.5±11.5	90.9±11.6	<0.001
SBP, mm Hg	121.8±14.8	121.8±14.8	120.9±14.9	0.001
DBP, mm Hg	76.0±1.0	76.0±1.0	75.8±10.1	0.154
Total cholesterol, mg/dL	194.8±36.2	194.8±36.2	184.3±37.1	<0.001

Intellectual disability	Number	Events	Duration, PY	IR, /1,000 PY	HR (95% CI)
Intellectual disability	Number	Events	Duration, PY	IR, /1,000 PY	Model 1^a	Model 2^b	Model 3^c	Model 4^d
No	3,563,604	299,156	34,942,398.10	8.56	1 (Ref)	1 (Ref)	1 (Ref)	1 (Ref)
Yes	3,385	302	32,864.74	9.19	1.08 (0.96–1.21)	1.42 (1.27–1.59)	1.43 (1.27–1.60)	1.38 (1.23–1.55)

Disability	Number	Events	Duration, PY	IR, /1,000 PY	HR (95% CI)
Disability	Number	Events	Duration, PY	IR, /1,000 PY	Model 1^a	Model 2^b	Model 3^c	Model 4^d
No disability	3,392,792	275,129	33,374,750.85	8.24	1 (Ref)	1 (Ref)	1 (Ref)	1 (Ref)
Other disabilities	170,812	24,027	1,567,647.25	15.33	1.88 (1.85–1.90)	1.19 (1.17–1.21)	1.14 (1.12–1.15)	1.11 (1.10–1.13)
ID	3,385	302	32,864.74	9.19	1.12 (1.00–1.25)	1.43 (1.28–1.60)	1.44 (1.28–1.61)	1.39 (1.24–1.56)

Table 1. Baseline Characteristics

Values are expressed as number (%) or mean±standard deviation.

BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure.

Table 2. Risk of Incident Diabetes Mellitus according to Intellectual Disability

PY, person-years; IR, incidence rate; HR, hazard ratio; CI, confidence interval.

Model 1: unadjusted;

Model 2: adjusted for sex and age;

Model 3: adjusted for sex, age, income level, smoking, drinking, regular exercise, and body mass index;

Model 4: adjusted for sex, age, income level, smoking, drinking, regular exercise, body mass index, hypertension, dyslipidemia, chronic kidney disease, psychiatric disorders, and epilepsy.

Table 3. Risk of Diabetes Mellitus among Individuals with ID or Other Disabilities

ID, intellectual disability; PY, person-years; IR, incidence rate; HR, hazard ratio; CI, confidence interval.

Model 1: unadjusted;

Model 2: adjusted for sex and age;

Model 3: adjusted for sex, age, income level, smoking, drinking, regular exercise, and body mass index;

Model 4: adjusted for sex, age, income level, smoking, drinking, regular exercise, body mass index, hypertension, dyslipidemia, chronic kidney disease, psychiatric disorders, and epilepsy.

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Table 1.

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