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Diabetes Mellitus and the Risk of Alzheimer’s Disease: A Nationwide Population-Based Study

  • Chin-Chou Huang ,

    Contributed equally to this work with: Chin-Chou Huang, Chia-Min Chung

    Affiliations Department of Medical Research and Education, Taipei Veterans General Hospital, Taipei, Taiwan, R.O.C, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan, R.O.C, Cardiovascular Research Center, National Yang-Ming University, Taipei, Taiwan, R.O.C, Institute of Pharmacology, National Yang-Ming University, Taipei, Taiwan, R.O.C

  • Chia-Min Chung ,

    Contributed equally to this work with: Chin-Chou Huang, Chia-Min Chung

    Affiliations Environment-Omics-Disease Research Centre, China Medical University Hospital, Taichung, Taiwan, R.O.C, Graduate Institute of Clinical Medical Science, China Medical University, Taichung, Taiwan, R.O.C

  • Hsin-Bang Leu,

    Affiliations Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan, R.O.C, Healthcare and Management Center, Taipei Veterans General Hospital, Taipei, Taiwan, R.O.C, Cardiovascular Research Center, National Yang-Ming University, Taipei, Taiwan, R.O.C, Institute of Clinical Medicine, National Yang-Ming University, Taipei, Taiwan, R.O.C

  • Liang-Yu Lin,

    Affiliations Division of Endocrinology and Metabolism, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan, R.O.C, Faculty of Medicine, National Yang-Ming University, Taipei, Taiwan, R.O.C, Institute of Pharmacology, National Yang-Ming University, Taipei, Taiwan, R.O.C

  • Chun-Chih Chiu,

    Affiliations Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan, R.O.C, Cardiovascular Research Center, National Yang-Ming University, Taipei, Taiwan, R.O.C, Faculty of Medicine, National Yang-Ming University, Taipei, Taiwan, R.O.C

  • Chien-Yi Hsu,

    Affiliations Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan, R.O.C, Cardiovascular Research Center, National Yang-Ming University, Taipei, Taiwan, R.O.C, Faculty of Medicine, National Yang-Ming University, Taipei, Taiwan, R.O.C

  • Chia-Hung Chiang,

    Affiliations Cardiovascular Research Center, National Yang-Ming University, Taipei, Taiwan, R.O.C, Faculty of Medicine, National Yang-Ming University, Taipei, Taiwan, R.O.C, Institute of Clinical Medicine, National Yang-Ming University, Taipei, Taiwan, R.O.C, Division of Cardiology, Department of Medicine, Zhudong Veterans Hospital, HsinChu, Taiwan, R.O.C

  • Po-Hsun Huang,

    Affiliations Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan, R.O.C, Cardiovascular Research Center, National Yang-Ming University, Taipei, Taiwan, R.O.C, Faculty of Medicine, National Yang-Ming University, Taipei, Taiwan, R.O.C, Institute of Clinical Medicine, National Yang-Ming University, Taipei, Taiwan, R.O.C

  • Tzeng-Ji Chen,

    Affiliations Department of Family Medicine, Taipei Veterans General Hospital, Taipei, Taiwan, R.O.C, Institute of Hospital and Health Care Administration, National Yang-Ming University, Taipei, Taiwan, R.O.C

  • Shing-Jong Lin,

    Affiliations Department of Medical Research and Education, Taipei Veterans General Hospital, Taipei, Taiwan, R.O.C, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan, R.O.C, Cardiovascular Research Center, National Yang-Ming University, Taipei, Taiwan, R.O.C, Institute of Clinical Medicine, National Yang-Ming University, Taipei, Taiwan, R.O.C

  • Jaw-Wen Chen ,

    DRWLChan2012@gmail.com (WLC); jwchen@vghtpe.gov.tw (JWC)

    These authors also contributed equally to this work.

    Affiliations Department of Medical Research and Education, Taipei Veterans General Hospital, Taipei, Taiwan, R.O.C, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan, R.O.C, Cardiovascular Research Center, National Yang-Ming University, Taipei, Taiwan, R.O.C, Institute of Pharmacology, National Yang-Ming University, Taipei, Taiwan, R.O.C

  • Wan-Leong Chan

    DRWLChan2012@gmail.com (WLC); jwchen@vghtpe.gov.tw (JWC)

    These authors also contributed equally to this work.

    Affiliations Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan, R.O.C, Healthcare and Management Center, Taipei Veterans General Hospital, Taipei, Taiwan, R.O.C, Faculty of Medicine, National Yang-Ming University, Taipei, Taiwan, R.O.C

Diabetes Mellitus and the Risk of Alzheimer’s Disease: A Nationwide Population-Based Study

  • Chin-Chou Huang, 
  • Chia-Min Chung, 
  • Hsin-Bang Leu, 
  • Liang-Yu Lin, 
  • Chun-Chih Chiu, 
  • Chien-Yi Hsu, 
  • Chia-Hung Chiang, 
  • Po-Hsun Huang, 
  • Tzeng-Ji Chen, 
  • Shing-Jong Lin
PLOS
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Abstract

Objectives

Possible association between diabetes mellitus (DM) and Alzheimer’s disease (AD) has been controversial. This study used a nationwide population-based dataset to investigate the relationship between DM and subsequent AD incidence.

Methods

Data were collected from Taiwan’s National Health Insurance Research Database, which released a cohort dataset of 1,000,000 randomly sampled people and confirmed it to be representative of the Taiwanese population. We identified 71,433 patients newly diagnosed with diabetes (age 58.74±14.02 years) since January 1997. Using propensity score, we matched them with 71,311 non-diabetic subjects by time of enrollment, age, gender, hypertension, hyperlipidemia, and previous stroke history. All the patients were followed up to December 31, 2007. The endpoint of the study was occurrence of AD.

Results

Over a maximum 11 years of follow-up, diabetic patients experienced a higher incidence of AD than non-diabetic subjects (0.48% vs. 0.37%, p<0.001). After Cox proportional hazard regression model analysis, DM (hazard ratio [HR], 1.76; 95% confidence interval [CI], 1.50–2.07, p<0.001), age (HR, 1.11; 95% CI, 1.10–1.12, p<0.001), female gender (HR, 1.24; 95% CI, 1.06–1.46, p = 0.008), hypertension (HR, 1.30; 95% CI, 1.07–1.59, p = 0.01), previous stroke history (HR, 1.79; 95% CI, 1.28–2.50, p<0.001), and urbanization status (metropolis, HR, 1.32; 95% CI, 1.07–1.63, p = 0.009) were independently associated with the increased risk of AD. Neither monotherapy nor combination therapy with oral antidiabetic medications were associated with the risk of AD after adjusting for underlying risk factors and the duration of DM since diagnosis. However, combination therapy with insulin was found to be associated with greater risk of AD (HR, 2.17; 95% CI, 1.04–4.52, p = 0.039).

Conclusion

Newly diagnosed DM was associated with increased risk of AD. Use of hypoglycemic agents did not ameliorate the risk.

Introduction

Alzheimer’s disease (AD) is the most common neurodegenerative disease worldwide. With the increasing prevalence of AD, more and more people are becoming interested in identifying their risk of developing it [1]. AD causes a huge economic burden in worldwide [2]. There is no cure for the disease, which becomes progressively worse and leads eventually to death [3]. Although other major causes of death have been on the decrease, deaths from AD have been rising dramatically [4], [5]. The median survival from initial diagnosis is only 3.1 years for subjects with probable AD and 3.5 years for subjects with possible AD [5]. In Taiwan, AD is the most common cause of dementia [6]. Therefore, it is important to identify possible risk factors for AD, hoping these will point to effective prevention strategies for patients at risk.

Although some epidemiologic studies have shown that vascular risk factors are related to increased risk of AD, the cause and progression of AD are not well understood [7]. AD is characterized by pathological hallmarks in the brain, i.e., abnormal protein deposits (β-amyloid peptides) and τ-protein fibers (neurofibrillary tangles). To date, numerous studies have attempted to delineate risk factors for development and progression of AD, generating abundant theories on potential risk factors, preventive measures, and therapies. Recent studies have raised the possibility of a connection between diabetes mellitus (DM) and AD. Although some studies have found a higher risk of developing AD in diabetic patients [8][10], the association has been inconsistent [11][13]. Furthermore, the possible impact of hypoglycemic agents on the development of AD has also been unclear. We have therefore conducted a nationwide population-based study using the Taiwan National Health Insurance Research Database (NHIRD) to investigate the relationship between DM and subsequent AD incidence. We have also examined the possible impacts of hypoglycemic agents on the prevention of AD in diabetic patients.

Materials and Methods

Database

The National Health Insurance program in Taiwan has operated since 1995 and enrolls nearly all the inhabitants of Taiwan (21,869,478 beneficiaries out of 22,520,776 inhabitants at the end of 2002) [14]. Currently, the NHIRD at the National Health Research Institutes in Miaoli (Taiwan) has charge of the complete National Health Insurance claims database and has published several dozen extracted datasets for researchers. The National Health Research Institutes has released a cohort dataset made of 1,000,000 people who were alive in 2000 and has collected all records on these individuals from 1995 onward. These random samples have been confirmed by the National Health Research Institutes to be representative of the Taiwanese population. In this cohort dataset, each patient’s original identification number has been encrypted to protect privacy. But the encrypting procedure is consistent, so that the linkage of claims belonging to the same patient is feasible within the NHIRD. This study was exempt from full review by the Institutional Review Board, since the dataset used consisted of de-identified secondary data released to the public for research purposes.

Study Patients

This study was conducted with the NHIRD, in which the diagnosis was supposed to be confirmed clinically by the individual physicians in charge for insurance claim purposes. Subjects with previously diagnosed DM and AD before 1997 were excluded from this study. Newly diagnosed diabetic patients were identified from the cohort database (International Classification of Diseases, Ninth Revision, Clinical Modification [ICD-9-CM] code: 250.xx or ICD-9-A code (abridge code): A181) since January 1, 1997. The identification of DM had been proved valid and used in previous studies [15], [16].

We used propensity scoring to match non-diabetic subjects to diabetic patients, a widely used method for avoiding selection bias in databases with large sample sizes [17]. To balance known risk factors across groups, we considered the following variables including the time when subjects were enrolled, age, gender, hypertension, hyperlipidemia, and previous stroke history.

To investigate whether use of DM medication would affect the course of AD, we evaluated patients’ use of DM medications at baseline (including metformin, sulfonylureas, thiazolidinediones, α-glucosidase blockers, non-sulfonylurea insulin secretagouge, and insulin). These medications were identified and classified by the National Drug Code and the Anatomic Therapeutic Chemical Code, a well-accepted international drug classification system coordinated by the WHO Collaborating Center for Drug Statistics Methodology [18].

Alzheimer’s Disease Event Measurement

The endpoint of the study was occurrence of administrative claims with AD (ICD-9-CM code: 331.0) as the main diagnosis, either during hospitalization or subsequent outpatient department visits. All the patients were followed up to December 31, 2007. The diagnoses of AD were based on history, physical examination, laboratory and imaging studies, and the Mini-Mental State Examination [19], internationally accepted criteria for AD (National Institute of Neurological and Communicative Disorders and Stroke–Alzheimer’s Disease and Related Disorders Association) [20], and the Diagnostic and Statistical Manual of Mental Disorders [21]. Similar methods for the identification of AD had been applied in our previous study [22].

Statistical Analysis

Microsoft SQL Server 2005 was used for data management and computing. Statistical analysis was performed utilizing SPSS software (Version 15.0, SPSS Inc., Chicago, IL, USA). All data were expressed as the frequency (percentage) or mean ± standard deviation. The parametric continuous data between the diabetic patients and the non-diabetic subjects were compared by unpaired Student’s t-test. The categorical data between the two groups were compared with Chi-square test and Yates’ correction or Fisher’s exact test as appropriate. Survival analysis was assessed using Kaplan-Meier analysis, with the significance based on the log-rank test. The survival time was calculated from the date of DM diagnosis to the date of AD diagnosis. To assess the independent effects of DM, we conducted Cox proportional hazard regression models in all the patients with age, sex, comorbidities (including hypertension, hyperlipidemia, stroke, coronary artery disease, arrhythmia, heart failure, and depression), geographic area, urbanization status, and medications for DM treatment (including metformin, sulfonylureas, thiazolidinediones, α-glucosidase blockers, non-sulfonylurea insulin secretagouge, and insulin) adjusted simultaneously in the model. To assess the independent effects of medications for DM treatment, we conducted Cox proportional hazard regression models in diabetic patients with age, sex, comorbidities (including hypertension, hyperlipidemia, stroke, coronary artery disease, arrhythmia, heart failure, and depression), geographic area, and urbanization status adjusted simultaneously in the model. Statistical significance was inferred at a two-sided p value of <0.05.

Results

A total of 71,433 newly diagnosed diabetic patients (mean age 58.7±14.0 years, female 48.2%) were identified from the 1,000,000 sampling cohort dataset between January 1997 and December 2007. Another 71,311 non-diabetic subjects who were matched using propensity score were enrolled as non-exposure controls. The demographics parameters of study subjects are shown in Table 1. Patients with newly diagnosed DM had more coronary artery disease (7.1% vs. 5.5%, p<0.001), arrhythmia (3.6% vs. 2.8%, p<0.001), heart failure (1.8% vs. 1.0%, p<0.001), and depression (0.3% vs. 0.2%, p = 0.023) than non-diabetic subjects. The medications used for diabetes treatment for patients with DM included metformin (16.5%), sulfonylureas (74.9%), thiazolidinediones (9.9%), α-glucosidase blockers (9.5%), non-sulfonylurea insulin secretagouge (6.5%), and insulin (18.2%).

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Table 1. Demographic data of the patients with and without diabetes mellitus.

https://doi.org/10.1371/journal.pone.0087095.t001

During a maximum 11 years’ follow-up (mean 5.5±3.1 years), 346 (0.48%) of the diabetic patients were diagnosed with AD, and 266 non-diabetic subjects (0.37%) were diagnosed with AD. Figure 1 exhibits the results of a Kaplan-Meier analysis and the log-rank test showed that diabetic patients had significantly higher incidence of AD than non-diabetic subjects (p<0.001). The risk of developing AD increased gradually in association to longer duration of DM since diagnosis (Figure 2). To investigate the independent factors associated with the risk of developing AD, Cox regression analysis was performed, with the finding of DM (hazard ratio [HR], 1.76; 95% confidence interval [CI], 1.50–2.07), p<0.001), age (HR, 1.11; 95% CI, 1.10–1.12, p<0.001), female gender (HR, 1.24; 95% CI, 1.06–1.46, p = 0.008), hypertension (HR, 1.30; 95% CI, 1.07–1.59, p = 0.01), previous stroke history (HR, 1.79; 95% CI, 1.28–2.50, p<0.001), and urbanization status (metropolis, HR, 1.32; 95% CI, 1.07–1.63, p = 0.009) were independently associated with the increased risk of AD (Table 2).

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Figure 1. Kaplan-Meier estimates of survival free of Alzheimer’s disease (AD) events in subjects categorized by diabetes mellitus (DM).

The event-free survival rates were significantly different in two groups (p<0.001 by log rank test).

https://doi.org/10.1371/journal.pone.0087095.g001

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Figure 2. The trend of the incidence of Alzheimer’s disease (AD) according to the duration of diabetes mellitus (DM).

https://doi.org/10.1371/journal.pone.0087095.g002

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Table 2. Independent predictors of Alzheimer’s disease identified by Cox regression analysis.

https://doi.org/10.1371/journal.pone.0087095.t002

Among the 71,433 diabetic patients, there were 2,791 patients with type 1 DM and 68,462 patients with type 2 DM. Both type 1 DM (hazard ratio, 1.89; 95% confident interval, 1.23–2.89, p = 0.004) and type 2 DM (hazard ratio, 1.57; 95% confident interval, 1.34–1.85, p<0.001) increased the risk of AD.

Medications for DM treatment were analyzed to investigate the relationship between hypoglycemic agents and risk of developing AD in diabetic patients. In initial crude analysis, monotherapy with sulfonylureas (HR, 0.50; 95% CI, 0.34–0.75) was associated with reduced risk of AD. Combination therapy with non-sulfonylurea insulin secretagouge (HR, 2.58; 95% CI, 1.16–5.75), and either monotherapy (HR, 2.27; 95% CI, 1.47–3.51) or combination therapy with insulin (HR, 3.79; 95% CI, 1.89–7.58) were found to be associated with the risk of AD (Table 3). Neither monotherapy nor combination therapy with oral antidiabetic medications were associated with AD occurrence after adjusting for underlying risk factors and the duration of DM since diagnosis. However, combination therapy with insulin was found to be associated with the greater risk of AD (HR, 2.17; 95% CI, 1.04–4.52, p = 0.039).

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Table 3. Medication for diabetes mellitus and risk of Alzheimer’s disease in diabetic patients.

https://doi.org/10.1371/journal.pone.0087095.t003

Discussion

Our current study revealed that newly diagnosed DM was associated with increased risk of future AD development in this cohort after a maximum of 11 years’ follow-up. Additionally, increasing risk of AD was found to be associated with DM duration, indicating DM maybe an important contribution in the pathogenesis of AD. Furthermore, neither monotherapy nor combination therapy with oral antidiabetic medications were found to be associated with the risk of AD occurrence. However, combination therapy with insulin was found to be associated with the risk of AD occurrence.

The association between DM and AD has been noted in these years. In the Rotterdam Study [8], DM almost doubled the risk of dementia in 6,370 elderly subjects (aged 55 years and older) after average 2.1-year follow up. In the Kungsholmen project [9], DM increases the risk of dementia in 1,301 very old people (aged 75 years and older) in Sweden after 6-year follow-up. In the Canadian Study of Health and Aging [11], however; DM at baseline was associated with incident vascular cognitive impairment but not AD in 5,574 Canadian after 5-year follow-up. Similar results were reported in the Framingham cohort [12] that baseline DM did not increase the risk of incident AD in 2210 participants after 12.7-year follow-up. Different from previous studies [8], [9], [11], [12], our study included patients with newly diagnosed DM from a nationwide cohort dataset in Taiwan. Therefore, the duration of DM was available in our study. This is the largest available database (more than 140,000 subjects) dealing with the relationship between DM and AD risk. Our study results demonstrated diabetic patients carried an increased 1.76 fold risk for AD development, supporting previous studies that DM could be seen as an independent risk factor for incident AD [8], [9]. Furthermore, increasing risk of AD was found to be associated with DM duration in our study, further supporting DM as an important factor influencing in the pathogenesis of AD.

There are some possible mechanisms for the association between DM and AD. First, hyperglycemia may cause increased oxidative stress and accumulation of advanced glycation end-products [23], [24], leading to progressive functional and structural abnormalities in the brain [25]. This hypothesis is further supported by the Hisayama Study [26] conclusion that abnormal response of oral glucose tolerance test after a 75-g oral glucose challenge was closely associated with increased risk of AD, suggesting impaired glucose tolerance contributes to the development of AD. Second, although the cause and progression of AD remains undetermined, β-amyloid peptides deposits are considered as the fundamental cause of the disease. DM is associated with insulin resistance and hyperinsulinaemia, which might interfere with β-amyloid peptides metabolism [27], [28]. Insulin could cross the blood-brain barrier, and the insulin levels in brain are initially higher and then down-regulated in diabetic patients [29]. Since insulin may modulate β-amyloid peptides degradation by regulating expression of the insulin-degrading enzyme [27], the low insulin level in central nervous system may reduce insulin-degrading enzyme levels in brain and thereby impair β-amyloid peptides clearance. The aggregation of β-amyloid peptides is a fundamental neuropathological hallmark of AD.

Since DM has been reported to be associated with AD, therapeutic strategies aim at treating DM is a topic of interest for avoiding AD development [30], [31]. In the Rotterdam Study, diabetic patients treated with insulin were at highest risk of dementia [8]. In the Kungsholmen project [9], patients being treated with oral antidiabetic medications had increased risk for dementia and vascular dementia. It has been suggested that metformin, the most widely used insulin sensitizer against peripheral insulin resistance, could sensitize neuronal insulin resistance and significantly improved AD-like changes [32]. Wu et al. [33] reported that antidiabetic medications appear to be useful in alleviating the decline in physical and cognitive functioning among older Mexican Americans with DM, especially for those with a longer duration of the disease. Beeri et al. [34] reported that the combination of insulin with other oral antidiabetic medications is associated with substantially lower neuritic plaque density consistent with the effects of both on the neurobiology of insulin. The association between AD and hypoglycemic agent is inconsistent and still remains controversial [8], [9], [30][34]. In the crude analysis of our study, decreased AD risk was found to be associated with monotherarpy with sulfonylurea; increased risk for AD was associated with combination therapy using non-sulfonylurea insulin secretagouge, and either monotherapy or combination with insulin. Neither monotherapy nor combination therapy with oral antidiabetic medications were found to be associated with the risk of AD after adjustment for underlying risk factors and the duration of DM since diagnosis. This finding suggests DM or underlying comorbidities, not hypoglycemic agent, are more important determinants of future risk of developing AD. Similar observations show that insulin sensitizers may have beneficial effects on AD, and these benefits may be offset later by longer exposure to DM [30], [31], further supporting the idea that duration of diabetes may play an important role in AD pathogenesis. However, combination therapy with insulin was found to be associated with greater risk of AD. This observation is compatible with the Rotterdam Study that found diabetic patients treated with insulin were at the highest risk for dementia. Since combination therapy with insulin may represent greater severity of DM, these patients were at increased risk for AD.

Another important issue is whether antidiabetic medications prolong the life of the patients with AD. In our current study, we failed to find the beneficial effects of insulin or oral antidiabetic medications in prolonging the life of the patients who developed AD (data not shown). However, it was not the goal of our study and the data is limited to the small sample size of the patients with AD. Further studies are still needed to answer this question.

In addition to DM, the incidence of AD was independently associated with age, female gender, hypertension, and previous history of stroke in our study. Our findings were compatible with previous studies that age and female gender were risk factors for AD occurrence [35]. Similar to previous studies [36], we also found that vascular risk factors including hypertension and previous history of stroke were related to an increased risk of AD.

The main strength of our study is the use of a population-based dataset, which enrolls large sample-size subjects and enables us to trace prospectively the differences between the two groups. However, there are still some limitations in our study. First, the diagnosis of DM was identified using the ICD-9 code from the database. This study was conducted with the NHIRD, in which the diagnosis was supposed to be confirmed clinically by the individual physicians in charge. The identification of DM had been proved valid and used in previous studies [15], [16]. Furthermore, the control group was selected from those patients who didn’t develop diabetes over the whole study periods (maximum of up to 11 years). Therefore, the possibility of underestimating of undiagnosed diabetes and prediabetes could be minimized. Second, the diagnosis of AD was identified using the ICD-9 code from both inpatient and outpatient database. Although the database doesn’t contain detailed information, such as dementia rating scale [37], diagnoses of AD are usually made based on history, physical examination, imaging studies, and quantitative functional scale tools such as the Mini-Mental State Examination [19] as well as other well-known criteria for AD diagnosis (National Institute of Neurological and Communicative Disorders and Stroke–Alzheimer’s Disease and Related Disorders Association) [20]. Similar methods for the identification of AD had been applied in previous studies [22]. Third, personal information such as body mass index, education, smoking habit and biochemistry profiles were not available in the database. Fourth, measurements indicating severity of DM, including serum concentration of Hemoglobin A1c, glucose and insulin, were not available. However, information about the medications taken by diabetes patients was clear and confirmed. Therefore, we can still investigate the effect of the medication. Finally the data regarding APOE4 genotype was also not available in the NHIRD dataset.

Conclusions

The present study demonstrates an association between DM and future development of AD, suggesting that DM could play an important role in determining future risk of AD occurrence. However, we found that use of hypoglycemic agents had no beneficial effects for preventing development of AD. Further therapeutic strategies should be investigated for the prevention of AD, such as preventing DM or improving DM treatment.

Author Contributions

Conceived and designed the experiments: CCH CMC HBL LYL CCC CYH CHC PHH SJL. Performed the experiments: CCH CMC HBL LYL TJC. Analyzed the data: CCH CMC TJC. Contributed reagents/materials/analysis tools: HBL CMC TJC SJL. Wrote the paper: CCH CMC HBL LYL JWC WLC.

References

  1. 1. Roberts JS, Tersegno SM (2010) Estimating and disclosing the risk of developing Alzheimer’s disease: challenges, controversies and future directions. Future Neurol 5: 501–517.
  2. 2. Hurd MD, Martorell P, Delavande A, Mullen KJ, Langa KM (2013) Monetary costs of dementia in the United States. N Engl J Med 368: 1326–1334.
  3. 3. Maiorini AF, Gaunt MJ, Jacobsen TM, McKay AE, Waldman LD, et al. (2002) Potential novel targets for Alzheimer pharmacotherapy: I. secretases. J Clin Pharm Ther 27: 169–183.
  4. 4. Thies W, Bleiler L (2011) 2011 Alzheimer’s disease facts and figures. Alzheimers Dement 7: 208–244.
  5. 5. Wolfson C, Wolfson DB, Asgharian M, M’Lan CE, Ostbye T, et al. (2001) Clinical Progression of Dementia Study Group (2001) A reevaluation of the duration of survival after the onset of dementia. N Engl J Med 344: 1111–1116.
  6. 6. Treatment Guideline Subcommittee of the Taiwan Headache Society (2011) Guidelines for the medical treatment of patients with Alzheimer’s disease. Acta Neurol Taiwan 20: 85–100.
  7. 7. Daviglus ML, Plassman BL, Pirzada A, Bell CC, Bowen PE, et al. (2011) Risk Factors and Preventive Interventions for Alzheimer Disease: State of the Science. Arch Neurol 68: 1185–1190.
  8. 8. Ott A, Stolk RP, van Harskamp F, Pols HA, Hofman A, et al. (1999) Diabetes mellitus and the risk of dementia: the Rotterdam Study. Neurology 53: 1937–1942.
  9. 9. Xu WL, Qiu CX, Wahlin A, Winblad B, Fratiglioni L (2004) Diabetes mellitus and risk of dementia in the Kungsholmen project: a 6-year follow-up study. Neurology 63: 1181–1186.
  10. 10. Schrijvers EM, Witteman JC, Sijbrands EJ, Hofman A, Koudstaal PJ, et al. (2010) Insulin metabolism and the risk of Alzheimer disease: the Rotterdam Study. Neurology 75: 1982–1987.
  11. 11. MacKnight C, Rockwood K, Awalt E, McDowell I (2002) Diabetes mellitus and the risk of dementia, Alzheimer’s disease and vascular cognitive impairment in the Canadian Study of Health and Aging. Dement Geriatr Cogn Disord 14: 77–83.
  12. 12. Akomolafe A, Beiser A, Meigs JB, Au R, Green RC, et al. (2006) Diabetes mellitus and risk of developing Alzheimer disease: results from the Framingham Study. Arch Neurol 63: 1551–1555.
  13. 13. Euser SM, Sattar N, Witteman JC, Bollen EL, Sijbrands EJ, et al. (2010) for the PROSPER and Rotterdam Study (2010) A prospective analysis of elevated fasting glucose levels and cognitive function in older people: results from PROSPER and the Rotterdam Study. Diabetes 59: 1601–1607.
  14. 14. Bureau of National Health Insurance (2001) National Health Insurance Annual Statistical Report. Taipei, Taiwan: Bureau of National Health Insurance; 2002.
  15. 15. Chiang CW, Chen CY, Chiu HF, Wu HL, Yang CY (2007) Trends in the use of antihypertensive drugs by outpatients with diabetes in Taiwan, 1997–2003. Pharmacoepidemiol Drug Saf 16: 412–421.
  16. 16. Chen HF, Ho CA, Li CY (2008) Increased risks of hip fracture in diabetic patients of Taiwan: a population-based study. Diabetes Care 31: 75–80.
  17. 17. Sohn MW, Lee TA, Stuck RM, Frykberg RG, Budiman-Mak E (2009) Mortality risk of Charcot arthropathy compared with that of diabetic foot ulcer and diabetes alone. Diabetes Care 32: 816–821.
  18. 18. WHO Collaborating Center for Drug Statistics Methodology (2003) ATC Index with DDDs 2003. WHO: Oslo.
  19. 19. Folstein MF, Folstein SE, McHugh PR (1975) “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 12: 189–198.
  20. 20. McKhann G, Drachman D, Folstein M, Katzman R, Price D, et al. (1984) Clinical diagnosis of Alzheimer’s disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s disease. Neurology 34: 939–944.
  21. 21. American Psychiatric Association (2000). Diagnostic and statistical manual of mental disorders: DSM-IV-TR, 4th Edition Text Revision ed. Washington, DC: American Psychiatric Association.
  22. 22. Hsu CY, Huang CC, Chan WL, Huang PH, Chiang CH, et al. (2013) Angiotensin-receptor blockers and risk of Alzheimer’s disease in hypertension population–a nationwide cohort study. Circ J 77: 405–410.
  23. 23. Biessels GJ, van der Heide LP, Kamal A, Bleys RL, Gispen WH (2002) Ageing and diabetes: implications for brain function. Eur J Pharmacol 441: 1–14.
  24. 24. Smith MA, Sayre LM, Monnier VM, Perry G (1995) Radical AGEing in Alzheimer’s disease. Trends Neurosci 18: 172–176.
  25. 25. Gispen WH, Biessels GJ (2000) Cognition and synaptic plasticity in diabetes mellitus. Trends Neurosci 23: 542–549.
  26. 26. Ohara T, Doi Y, Ninomiya T, Hirakawa Y, Hata J, et al. (2011) Glucose tolerance status and risk of dementia in the community: the Hisayama study. Neurology 77: 1126–1134.
  27. 27. Zhao L, Teter B, Morihara T, Lim GP, Ambegaokar SS, et al. (2004) Insulin-degrading enzyme as a downstream target of insulin receptor signaling cascade: implications for Alzheimer’s disease intervention. J Neurosci 24: 11120–11126.
  28. 28. Craft S, Watson GS (2004) Insulin and neurodegenerative disease: shared and specific mechanisms. Lancet Neurol 3: 169–178.
  29. 29. Banks WA, Jaspan JB, Kastin AJ (1997) Selective, physiological transport of insulin across the blood-brain barrier: novel demonstration by species-specific radioimmunoassays. Peptides 18: 1257–1262.
  30. 30. Akter K, Lanza EA, Martin SA, Myronyuk N, Rua M, et al. (2011) Diabetes mellitus and Alzheimer’s disease: shared pathology and treatment? Br J Clin Pharmacol 71: 365–376.
  31. 31. Cholerton B, Baker LD, Craft S (2011) Insulin resistance and pathological brain ageing. Diabet Med 28: 1463–1475.
  32. 32. Gupta A, Bisht B, Dey CS (2011) Peripheral insulin-sensitizer drug metformin ameliorates neuronal insulin resistance and Alzheimer’s-like changes. Neuropharmacology 60: 910–920.
  33. 33. Wu JH, Haan MN, Liang J, Ghosh D, Gonzalez HM, et al. (2003) Impact of antidiabetic medications on physical and cognitive functioning of older Mexican Americans with diabetes mellitus: a population-based cohort study. Ann Epidemiol 13: 369–376.
  34. 34. Beeri MS, Schmeidler J, Silverman JM, Gandy S, Wysocki M, et al. (2008) Insulin in combination with other diabetes medication is associated with less Alzheimer neuropathology. Neurology 71: 750–757.
  35. 35. de la Torre JC (2004) Is Alzheimer’s disease a neurodegenerative or a vascular disorder? Data, dogma, and dialectics. Lancet Neurol 3: 184–190.
  36. 36. Morris JC (1993) The clinical dementia rating (CDR): current version and scoring rules. Neurology 43: 2412–2414.
  37. 37. Bachman DL, Wolf PA, Linn R, Knoefel JE, Cobb J, et al. (1992) Prevalence of dementia and probable senile dementia of the Alzheimer type in the Framingham study. Neurology 42: 115–119.