Content uploaded by Chun-Hung Tseng
Author content
All content in this area was uploaded by Chun-Hung Tseng on Jul 03, 2014
Content may be subject to copyright.
ARTICLE
The association between chronic osteomyelitis and increased risk
of diabetes mellitus: a population-based cohort study
S.-Y. Lin &C.-L. Lin &C.-H. Tseng &I.-K. Wang &
S.-M. Wang &C.-C. Huang &Y.-J. Chang &C.-H. Kao
Received: 5 February 2014 /Accepted: 11 April 2014
#Springer-Verlag Berlin Heidelberg 2014
Abstract Chronic inflammation is a well-known risk factor
for type 2 diabetes mellitus (T2DM). The influence of chronic
osteomyelitis (COM), an inflammatory disease, on the risk of
developing T2DM remains unknown. This study investigated
the risk of developing T2DM among COM patients. Using a
retrospective cohort study, we identified 20,641 patients with
COM and 82,564 age- and sex-matched controls for compar-
ison from the Taiwan National Health Insurance Database
(NHIRD) from 1997 to 2010. We followed up the COM
cohort and the comparison cohort to compare the incidences
of diabetes (ICD-9-CM code 250) until the end of 2010 or
until the patients were censored because of death or withdraw-
al from the insurance program. The diabetes risk was analyzed
using the Cox proportional hazards regression model. The
incidence of T2DM was 1.6-fold higher in the group of
COM patients than in the comparison group (29.1 vs. 18.2
per 10,000 person-years). The COM patients exhibited a
higher diabetes risk [adjusted hazard ratio (aHR)= 1.64,
95 % confidence interval (CI)=1.44–1.87] after controlling
for the baseline and comorbidities. Younger and higher in-
come patients exhibited a higher COM-to-reference incidence
rate ratio (IRR) for T2DM compared with that of their coun-
terparts. We also observed an increased risk of T2DM in COM
patients with comorbidities (aHR=1.70, 95 % CI=1.47–1.96)
compared with that of their non-COM counterparts. This is the
first study to report the association between COM and an
increased risk of developing T2DM, particularly among youn-
ger and higher income patients.
Introduction
Type 2 diabetes mellitus (T2DM), characterized by hypergly-
cemia and dyslipidemia, is a metabolic disorder caused by
imbalance among β-cell function, physical inactivity, obesity,
chronic fuel surfeit status, and insulin resistance [1]. T2DM
may lead to a substantial health burden on people and direct
and indirect burdens on the healthcare system and society [2].
The prevalence of diabetes has increased substantially in
Asian countries in recent years [3]. This increase has been
attributed to the increase in central obesity, physical inactivity,
the aging population, and the prevalence of Western diets in
Asian populations [3–5]. Although the metabolic manifesta-
tions in both types of diabetes are similar, T2DM is more
heterogeneous than type 1 diabetes, and involves additional
aspects, including genetic factors, epigenetic effects, early-life
environment, lifestyle, nutrition imbalance, socioeconomic
status, a deregulated neurohumoral network, and insulin re-
sistance [6–9]. A previous study documented that adipocyte
dysfunction may increase the concentrations of inflammatory
cytokines and aggravate insulin resistance in muscle [10].
However, the underlying mechanism and complex interaction
between T2DM and inflammation, particularly the chronic
S.<Y. L i n :I.<K. Wang :C.<H. Kao (*)
Graduate Institute of Clinical Medical Science and School of
Medicine, College of Medicine, China Medical University, No. 2,
Yuh-Der Road, Taichung, 404, Taiwan
e-mail: d10040@mail.cmuh.org.tw
S.<Y. L i n :I.<K. Wang :S.<M. Wang :C.<C. Huang
Division of Nephrology and Kidney Institute, China Medical
University Hospital, Taichung, Taiwan
C.<L. Lin :Y. <J. Chang
Management Office for Health Data, China Medical University
Hospital, No. 2, Yu Der Road, Taichung, Taiwan
C.<H. Tseng
Department of Neurology, China Medical University Hospital,
Taichung, Taiwan
C.<H. Kao
Department of Nuclear Medicine and PET Center, China Medical
University Hospital, Taichung, Taiwan
Eur J Clin Microbiol Infect Dis
DOI 10.1007/s10096-014-2126-7
ones (e.g., adipocyte inflammation, periodontitis, and hepatitis
C infection), remain unclear [10–12].
Chronic osteomyelitis (COM), a well-known chronic in-
fection–inflammation status that is resistant to treatment and
prone to relapse, is a common complication of T2DM [13].
After conducting a literature review regarding the relationship
between T2DM and COM, we observed that most previous
studies have focused on the risk of COM among patients with
T2DM [13–16], whereas few have addressed the possibility
that COM might be a risk factor for T2DM. Using a large
cohort of 23 million patients identified from the Taiwan Na-
tional Health Insurance Database (NHIRD), we assessed the
risk of newly developing T2DM among patients with COM.
Materials and methods
Data sources
We used reimbursement claims data from the Taiwan National
Health Insurance (NHI) program launched in March 1995.
The dataset is managed by the National Health Research
Institutes (NHRI) and the details of the NHI program have
been described elsewhere [17]. We used a subset of the
NHIRD containing healthcare data, including files on inpa-
tient claims and a registry of beneficiaries. The International
Classification of Diseases, 9th Revision, Clinical Modifica-
tion (ICD-9-CM) was used to identify diseases. The high
accuracy and validity of the T2DM and COM diagnosis used
in this study based on the ICD-9 codes applied in the NHIRD
were previously confirmed [18–20]. Taiwan launched the NHI
program in 1995, which is operated by a single buyer, the
government. All insurance claims are scrutinized by medical
reimbursement specialists and through peer review. The diag-
noses of T2DM and COM were based on the ICD-9 codes
determined by clinical physicians. Therefore, the diagnoses
and codes for T2DM and COM should be accurate and
reliable.
Study patients
The COM cohort included patients with newly diagnosed
COM (ICD-9-CM code 730.1) from 1997 to 2010. For each
COM patient, four people without a medical history of COM
and diabetes who were frequency-matched according to sex
and age (each 5-year span) in the same period were randomly
selected as the comparison cohort. The index date for the
COM cohort was defined as the date of diagnosis. The index
date for the comparison cohort was defined as the middle date
of the same index month used for their matched COM pa-
tients. Patients with a medical history of diabetes who were
diagnosed before the index date, younger than 20 years old, or
were missing information on age or sex were excluded.
Outcome measures
We followed up the COM cohort and the comparison cohortto
compare the incidences of diabetes (ICD-9-CM code 250)
until the end of 2010 or until the patient was censored because
of death or withdrawal from the insurance program.
Sociodemographic characteristics including sex, age, and in-
come of the two cohorts were assessed. The baseline comor-
bidities included hypertension (ICD-9-CM codes 401–405),
hyperlipidemia (ICD-9-CM code 272), and chronic kidney
disease (CKD) (ICD-9-CM code 585).
Statistical analysis
Differences in sociodemographic factors and comorbidities
between the COM and the comparison cohorts were examined
using the Chi-square test for category variables and the t-test
for continuous variables. The incidence–density rates accord-
ing to demographic status and comorbidity were calculatedfor
both cohorts. The incidence rate ratio (IRR) with a 95 %
confidence interval (CI) was calculated using the Poisson
regression model. Multivariate Cox proportional hazard
models, adjusted for potential confounding factors, were used
to assess the risk of developing diabetes associated with
COM. To assess the difference in the diabetes-free rates be-
tween the two cohorts, the Kaplan–Meier analysis and log-
rank test were applied. The statistical significance level was
set at a two-tailed probability value of p<0.05. All analyses
were performed using SAS software (SAS System for Win-
dows, version 9.1) and the Kaplan–Meier survival curve was
plotted using R software (R Foundation for Statistical Com-
puting, Vienna, Austria).
Ethics and consent
Personal information was de-identified before the release of
the NHIRD data; therefore, this study was exempt from ap-
proval by the Institutional Review Board.
Results
This study consisted of 20,641 patients in the COM cohort
and 82,564 people in the comparison cohort. Both cohorts
exhibited similar sex and age distributions. Comorbidities
including hypertension, hyperlipidemia, and CKD were more
prevalent in the COM cohort than in the comparison cohort
(p<0.0001) (Table 1).
The COM cohort had a higher incidence rate of diabetes
than the comparison cohort (29.1 vs. 18.2 per 10,000 person-
years; IRR=160, 95 % CI=1.43–1.86), with an adjusted
hazard ratio (aHR) of 1.64 (95 % CI=1.44–1.87) (Table 2).
The COM-to-reference IRR was higher in men (IRR=1.67,
Eur J Clin Microbiol Infect Dis
95 % CI=1.58–1.77) than in women (IRR=1.45, 95 % CI=
1.33–1.58). The age-specific IRR increased with age and was
the highest in the subgroup aged 20–34 years (IRR=5.90,
95 % CI= 5.26–6.62), with an aHR of 4.74 (95 % CI= 2.37–
9.49). The aHR of diabetes was the highest in the subgroup
with a monthly income of over 22,800 New Taiwan dollars
(NT$) (aHR=3.37, 95 % CI=2.04–5.55).
The incidence rate of diabetes was higher in the sub-
groups with hypertension, hyperlipidemia, or CKD com-
pared with that of the non-comorbid counterparts. The
COM-to-reference IRR was 1.58 (95 % CI= 1.06–1.32)
and1.18(95%CI=1.50–1.67) for the groups without and
with hypertension, respectively. The significant COM-to-
reference IRR was also observed in the non-hyperlipidemia
and non-CKD groups. Moreover, the aHRs indicated that
COM was associated with an increased risk of diabetes in
patients without hypertension (aHR= 1.70, 95 % CI= 1.47–
1.96), hyperlipidemia (aHR= 1.66, CI=1.45–1.89), or CKD
(aHR=1.65, 95 % CI=1.45–1.87) (Table 3). The Kaplan–
Meier survival analysis revealed that the diabetes-free rate
was 1.18 % lower in the COM cohort than in the compar-
ison cohort (log-rank p<0.0001) (Fig. 1).
Discussion
This study yielded several intriguing results.First, this study is
the first population-based cohort study to report a significantly
increased T2DM risk associated with COM. Second, the
association between COM and the increased risk of T2DM
was stronger in the wealthier subgroup and the younger
subgroup.
The biological mechanisms of the relationship between
COM and the risk of T2DM remain unclear. Previous studies
have demonstrated that increasing levels of proinflammatory
cytokines that block downstream insulin signaling and inter-
rupt insulin action, such as TNF-α, interleukin-β,and
interleukin-6, contribute to preclinical stages of T2DM [21,
22]. In COM patients, these proinflammatory cytokines were
observed to increase markedly in the bone compartment [23,
24]. Therefore, we suspected that localized proinflammatory
cytokines might exert system effects on whole-body insulin
resistance. In addition to the proposed effect of proinflamma-
tory cytokines, other possible explanations exist for the asso-
ciation between COM and the risk of T2DM. First, patients
with COM might make frequent medical visits and have
routine check-ups, including check-ups on biochemical pro-
files and blood glucose levels, which may increase the likeli-
hood of detecting T2DM. Second, patients with COM are
typically immobile because of pain or difficulty in engaging
in physical activity [25]. Because physical activity prevents
the occurrence of T2DM [26], the immobile status of COM
patients might increase the risks of obesity, metabolic syn-
drome, and T2DM. Furthermore, the genetic predisposition of
these two diseases to COM is linked with IL-1α,IL-4,andIL-
6 polymorphism [27]. A Finnish diabetes prevention study
reported that promoter polymorphisms of TNF-αand IL-6
could predict the risk of T2DM [28]. The association between
COM and the risk of T2DM might also result from the genetic
predisposition to both COM and T2DM of our study cohorts.
Further studies on genetic analysis and the role of physical
activity are recommended in order to elucidate the association.
The present results indicated that, in the subgroup with
higher income, COM patients had a higher risk of developing
T2DM compared with that of the non-COM counterparts,
which is intriguing because T2DM has been reported to be
more prevalent among those with a lower socioeconomic
status [29–31]. Socioeconomic disparities are associated with
insulin resistance and altered glucose metabolism [32,33].
One possible explanation for our finding is that the effects of
insulin resistance produced by COM are more easily observed
in wealthier people who are assumed to have fewer alterations
in glucose metabolism. Future studies are warranted for fur-
ther investigation of this discrepancy.
In addition, we observed that, among young people aged
less than 55 years, COM patients had a higher risk of T2DM
compared with that of the non-COM people, which is
Tabl e 1 Demographic characteristics and comorbidities in the chronic
osteomyelitis (COM) cohort and the comparison cohort
Variable Chronic osteomyelitis p-Value
No Yes
N=82,564 N=20,641
n(%) n(%)
Gender
Female 26,772 (32.4) 6,693 (32.4) 0.99
Male 55,792 (67.6) 13,948 (67.6)
Age (years)
Mean ± SD 55.6±18.2 55.9± 18.1 0.63
Age stratification
20–34 years 13,140 (15.9) 3,285 (15.9) 0.99
35–44 years 12,356 (15.0) 3,089 (15.0)
45–54 years 14,320 (17.3) 3,580 (17.3)
55–64 years 13,296 (16.1) 3,324 (16.1)
≥65 years 29,452 (35.7) 7,363 (35.7)
Income (NT$)
a
<15,000 37,270 (45.2) 10,614 (51.4) <0.0001
b
15,000–22,799 26,336 (31.9) 7,194 (34.9)
≥22,800 18,915 (22.9) 2,832 (13.7)
Comorbidity
Hypertension 7,836 (9.49) 5,388 (26.1) <0.0001
b
Hyperlipidemia 1,759 (2.13) 1,137 (5.51) <0.0001
b
Chronic kidney disease 489 (0.59) 710 (3.44) <0.0001
b
a
NT$: New Taiwan dollars per month. One NT$ equals 0.03 US dollar
b
Chi-square test
Eur J Clin Microbiol Infect Dis
Tabl e 2 Overall and specific incidence and hazard ratio of diabetes
Variables Chronic osteomyelitis Compared to non-osteomyelitis Adjusted HR
c
(95 % CI)
No Yes
Event PY Rate
a
Event PY Rate
a
IRR
b
(95 % CI)
All 936 514,178 18.2 328 112,745 29.1 1.60 (1.53, 1.67)*** 1.64 (1.44, 1.87)***
Sex
Female 309 159,973 19.3 99 35,364 28.0 1.45 (1.33, 1.58)*** 1.51 (1.19, 1.90)***
Male 627 354,206 17.7 229 77,381 29.6 1.67 (1.58, 1.77)*** 1.71 (1.46, 1.99)***
Age stratification
20–34 years 14 94,910 1.48 21 24,113 8.71 5.90 (5.26, 6.62)*** 4.74 (2.37, 9.49)***
35–44 years 51 88,908 5.74 55 20,397 27.0 4.70 (4.22, 5.24)*** 3.90 (2.62, 5.80)***
45–54 years 105 93,644 11.2 76 20,477 37.1 3.31 (2.99, 3.66)*** 2.91 (2.15, 3.95)***
55–64 years 232 85,760 27.1 64 18,557 34.5 1.27 (1.13, 1.44)*** 1.23 (0.92, 1.63)
≥65 years 534 150,957 35.4 112 29,201 38.4 1.08 (0.99, 1.18) 1.10 (0.89, 1.36)
Income (NT$)
<15,000 622 225,462 27.6 198 54,665 36.2 1.31 (0.23, 1.41)*** 1.47 (1.25, 1.74)***
15,000–22,799 264 162,596 16.2 106 39,673 26.7 1.65 (1.52, 1.78)*** 1.74 (1.38, 2.19)***
≥22,800 50 125,840 3.97 24 18,406 13.0 3.28 (2.95, 3.65)*** 3.37 (2.04, 5.55)***
Comorbidity
d
No 828 478,889 16.9 246 90,224 26.9
Yes 108 35,289 31.4 82 22,521 36.1 1.58 (1.50, 1.67)*** 1.70 (1.47, 1.96)***
NT$: New Taiwan dollars per month; PY: person-years
a
Rate: incidence rate per 10,000 person-years
b
IRR: incidence rate ratio
c
Adjusted HR: adjusted for age, gender, income, and the presence of any comorbidity; *p<0.05, **p<0.01, ***p<0.0001
d
Comorbidity: patients with any comorbidity were defined as the comorbidity group
Tabl e 3 Incidence and hazard ratio of diabetes by the presence of comorbidities
Variables Chronic osteomyelitis Compared to non-osteomyelitis
No Yes
Event PY Rate
a
Event PY Rate
a
IRR
b
(95 % CI) Adjusted HR
c
(95 % CI)
Comorbidity
Hypertension
No 840 483,021 17.4 256 93,000 27.5 1.58 (1.50, 1.67)*** 1.70 (1.47, 1.96)***
Yes 96 31,157 30.8 72 19,745 36.5 1.18 (1.06, 1.32)** 1.24 (0.90, 1.70)
Hyperlipidemia
No 912 506,574 18.0 313 108,364 28.9 1.60 (1.53, 1.68)*** 1.66 (1.45, 1.89)***
Yes 24 7,605 31.6 15 4,380 34.2 1.09 (0.85, 1.38) 1.06 (0.54, 2.10)
Chronic kidney disease
No 931 512,445 18.2 323 110,939 29.1 1.60 (1.53, 1.68)*** 1.65 (1.45, 1.87)***
Yes 5 1,733 28.9 5 1,806 27.7 0.96 (0.66, 1.39) 1.12 (0.30, 4.13)
PY: person-years
a
Rate: incidence rate, per 10,000 person-years
b
IRR: incidence rate ratio
c
Adjusted HR: adjusted for age, gender, income, and the presence of comorbidities; *p<0.05, **p< 0.01, ***p<0.0001
Eur J Clin Microbiol Infect Dis
remarkable because T2DM is a well-known age-related dis-
ease [34]. Chronic low-grade inflammation is a converging
process linking both normal aging and age-related diseases,
including T2DM [35–37]. However, the present study report-
ed that younger patients had a significantly higher COM-to-
reference hazard ratio of diabetes mellitus than the elderly
subgroup. The underlying mechanism requires further inves-
tigation. We suggest that aging might mask the effects of the
parallel, pathogenic, inflammation-related process of COM in
the development of T2DM.
This study has several strengths. It is the first population-
based study in which the risk of T2DM was compared be-
tween patients with and without COM. Patients with COM
and the age- and sex-matched controls were identified from a
dataset of 23 million enrollees in a national insurance program
comprising over 98 % of the entire population in Taiwan.
Insurance claims of hospitalization and consecutive care of
COM ensure the diagnosis of COM. The Taiwan NHI pro-
gram has a strict auditing system to prevent fraudulent
healthcare claims, thereby ensuring the reliability of COM
diagnosis based on insurance claims. This large population-
based database containing comprehensive electronic medical
records provided complete information on the incidence of
T2DM and associated comorbidities, including hypertension,
hyperlipidemia, and CKD.
However, several limitations should be addressed. We did
not obtain precise information on patients’body weight, body
mass index, and fat distribution. Information on physical
activity was also unavailable. Additionally, we could not rule
out the possibility that the immobilization exhibited by a
proportion of COM patents might predispose COM patients
to a higher risk of T2DM. Consequently, the T2DM risk
assessed in this study might be overestimated. Although our
data indicated that comorbidities were more prevalent among
patients with COM compared with the comparison cohort, the
results suggested a significant association between COM and
T2DM after we controlled for comorbidities and
sociodemographic characteristics. Future studies are required
in order to investigate the underlying mechanisms and con-
firm the causal relationship between COM and T2DM.
In conclusion, the present study reported a significantly
increased risk of developing T2DM in COM patients. We also
observed an increased risk of T2DM in young and wealthy
COM patients. The findings of this study emphasize the
importance of detecting T2DM in COM patients because
medical delays and negligence may reduce the quality of
medical care and increase healthcare costs. Therefore, in ad-
dition to providing adequate antimicrobial treatment and sur-
gical intervention for COM patients, physicians should be
aware of the possibility of developing T2DM in these patients,
particularly in those who are young and economically
advantaged.
Acknowledgments This work was supported by study projects in
China Medical University (CMU102-BC-2) the Taiwan Ministry of
Health and Welfare Clinical Trial and Research Center of Excellence
(DOH102-TD-B-111-004), the Taiwan Ministry of Health and Welfare
Cancer Research Center for Excellence (MOHW103-TD-B-111-03), and
the International Research-Intensive Centers of Excellence in Taiwan
(NSC101-2911-I-002-303). The funders had no role in the study design,
data collection and analysis, decision to publish, or manuscript prepara-
tion. No additional external funding was received for this study. All
authors state that they have no conflicts of interest.
Author contributions All the authors have contributed substantially
and are in agreement on the manuscript content. Conception/design:
Shih-Yi Lin, Yen-Jung Chang, Chia-Hung Kao. Provision of study ma-
terials: Chia-Hung Kao. Collection and/or assembly of data: all authors.
Data analysis and interpretation: Shih-Yi Lin, Cheng-Li Lin, Yen-Jung
Chang, Chia-Hung Kao. Manuscript writing: all authors. Final approval
of manuscript: all authors.
Conflict of interest All authors report no conflicts of interest.
References
1. Nolan CJ, Damm P, Prentki M (2011) Type 2 diabetes across gener-
ations: from pathophysiology to prevention and management. Lancet
378:169–181
2. Crandall JP, Knowler WC, Kahn SE et al (2008) The prevention of
type 2 diabetes. Nat Clin Pract Endocrinol Metab 4:382–393
3. Chan JC, Malik V, Jia Wet al (2009) Diabetes in Asia: epidemiology,
risk factors, and pathophysiology. JAMA 301:2129–2140
4. Misra A, Khurana L (2008) Obesity and the metabolic syndrome in
developing countries. J Clin Endocrinol Metab 93(11 Suppl 1):S9–
S30
5. Liu KH, Chan YL, Chan WB et al (2006) Mesenteric fat thickness is
an independent determinant of metabolic syndrome and identifies
subjects with increased carotid intima-media thickness. Diabetes
Care 29:379–384
Fig. 1 The Kaplan–Meier curves of the diabetes mellitus-free rates in
patients with and without chronic osteomyelitis
Eur J Clin Microbiol Infect Dis
6. Diabetes Genetics Initiative of Broad Institute of Harvard and MIT,
Lund University, and Novartis Institutes of BioMedical Research et al
(2007) Genome-wide association analysis identifies loci for type 2
diabetes and triglyceride levels. Science 316:1331–1336
7. Pinney SE, Simmons RA (2010) Epigenetic mechanisms in the devel-
opment of type 2 diabetes. Trends Endocrinol Metab 21:223–229
8. Malik VS, Popkin BM, Bray GA et al (2010) Sugar-sweetened
beverages and risk of metabolic syndrome and type 2 diabetes: a
meta-analysis. Diabetes Care 33:2477–2483
9. Popkin BM, Horton S, Kim S et al (2001) Trends in diet, nutritional
status, and diet-related noncommunicable diseases in China and
India: the economic costs of the nutrition transition. Nutr Rev 59:
379–390
10. Guilherme A, Virbasius JV, Puri V et al (2008) Adipocyte dysfunc-
tions linking obesity to insulin resistance and type 2 diabetes. Nat
Rev Mol Cell Biol 9:367–377
11. Ide R, Hoshuyama T, Wilson D et al (2011) Periodontal disease and
incident diabetes: a seven-year study. J Dent Res 90:41–46
12. Basaranoglu M, Basaranoglu G (2011) Pathophysiology of insulin
resistance and steatosis in patients with chronic viral hepatitis. World
J Gastroenterol 17:4055–4062
13. Hatzenbuehler J, Pulling TJ (2011) Diagnosis and management of
osteomyelitis. Am Fam Physician 84:1027–1033
14. Game F (2010) Management of osteomyelitis of the foot in diabetes
mellitus. Nat Rev Endocrinol 6:43–47
15. Berendt AR, Peters EJ, Bakker K et al (2008) Diabetic foot osteo-
myelitis: a progress report on diagnosis and a systematic review of
treatment. Diabetes Metab Res Rev 24(Suppl 1):S145–S161
16. Senneville E, Lombart A, Beltrand E et al (2008) Outcome of diabetic
foot osteomyelitis treated nonsurgically: a retrospective cohort study.
Diabetes Care 31:637–642
17. Kreng VB, Yang CT (2011) The equality of resource allocation in
health care under the National Health Insurance System in Taiwan.
Health Policy 100:203–210
18. Chung WS, Peng CL, Lin CL et al (2013) Rheumatoid arthritis
increases the risk of deep vein thrombosis and pulmonary thrombo-
embolism: a nationwide cohort study. Ann Rheum Dis. doi:10.1136/
annrheumdis-2013-203380
19. Chung WS, Lin CL, Ho FM et al (2014) Asthma increases pulmonary
thromboembolism risk: a nationwide population cohort study. Eur
Respir J 43:801–807
20. Cheng CL, Kao YH, Lin SJ et al (2011) Validation of the National
Health Insurance Research Database with ischemic stroke cases in
Taiwan. Pharmacoepidemiol Drug Saf 20:236–242
21. Hotamisligil GS (2006) Inflammation and metabolic disorders.
Nature 444:860–867
22. Medzhitov R, Janeway C Jr (2000) Innate immunity. N Engl J Med
343:338–344
23. Fullilove S, Jellis J, Hughes SP et al (2000) Local and systemic
concentrations of tumour necrosis factor-alpha, interleukin-6 and
interleukin-8 in bacterial osteomyelitis. Trans R Soc Trop Med Hyg
94:221–224
24. Biffl WL, Moore EE, Moore FA et al (1996) Interleukin-6 delays
neutrophil apoptosis via a mechanism involving platelet-activating
factor. J Trauma 40:575–578
25. Gonzalez BE, Teruya J, Mahoney DH Jr et al (2006) Venous throm-
bosis associated with staphylococcal osteomyelitis in children.
Pediatrics 117:1673–1679
26. Helmrich SP, Ragland DR, Leung RW et al (1991) Physical activity
and reduced occurrence of non-insulin-dependent diabetes mellitus.
N Engl J Med 325:147–152
27. Tsezou A, Poultsides L, Kostopoulou F et al (2008) Influence of
interleukin 1alpha (IL-1alpha), IL-4, and IL-6 polymorphisms on
genetic susceptibility to chronic osteomyelitis. Clin Vaccine
Immunol 15:1888–1890
28. Kubaszek A, Pihlajamäki J, Komarovski V et al (2003) Promoter
polymorphisms of the TNF-alpha (G-308A) and IL-6 (C-174G)
genes predict the conversion from impaired glucose tolerance to type
2 diabetes: the Finnish Diabetes Prevention Study. Diabetes 52:
1872–1876
29. Rabi DM, Edwards AL, Southern DA et al (2006) Association of
socio-economic status with diabetes prevalence and utilization of
diabetes care services. BMC Health Serv Res 6:124
30. Robbins JM, Vaccarino V, Zhang H et al (2001) Socioeconomic
status and type 2 diabetes in African American and non-Hispanic
white women and men: evidence from the Third National Health and
Nutrition Examination Survey. Am J Public Health 91:76–83
31. Agardh E, Allebeck P, Hallqvist J et al (2011) Type 2 diabetes
incidence and socio-economic position: a systematic review and
meta-analysis. Int J Epidemiol 40:804–818
32. McEwen BS, Gianaros PJ (2010) Central role of the brain in stress
and adaptation: links to socioeconomic status, health, and disease.
Ann N Y Acad Sci 1186:190–222
33. Adler NE, Boyce T, Chesney MA et al (1994) Socioeconomic status
and health. The challenge of the gradient. Am Psychol 49:15–24
34. Chung HY, Cesari M, Anton S et al (2009) Molecular inflammation:
underpinnings of aging and age-related diseases. Ageing Res Rev 8:
18–30
35. Chung HY, Sung B, Jung KJ et al (2006) The molecular inflamma-
tory process in aging. Antioxid Redox Signal 8:572–581
36. Vasto S, Candore G, Balistreri CR et al (2007) Inflammatory net-
works in ageing, age-related diseases and longevity. Mech Ageing
Dev 128:83–91
37. Lorenzo C, Okoloise M, Williams K et al (2003) The metabolic
syndrome as predictor of type 2 diabetes: the San Antonio heart
study. Diabetes Care 26:3153–3159
Eur J Clin Microbiol Infect Dis
