Research Proposal: Nursing Interventions To Decrease Complications Of Diabetes

Research Proposal

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Introduction
Literature & review
Research questions
Objective s: general & specifics
Material & Method
Dependent variables (risk factors of diabetes)
Independent Variables (complications of diabetes)
Inclusion Critics (Diabetic patients)
Exclusion Critics (Non-Diabetic/ Patients who does not want to participate)
Nursing Interventions
Conclusions
Recommendations
Diagrams/ Graphs (Screening tool/ Studies of population knowledge of diabetes)
OPEN

ORIGINAL ARTICLE

Increased cardiovascular risk of treated white coat and masked hypertension in patients with diabetes and chronic kidney disease: the HONEST Study Toshio Kushiro1, Kazuomi Kario2, Ikuo Saito3, Satoshi Teramukai4, Yuki Sato5, Yasuyuki Okuda5 and Kazuyuki Shimada6

The prognostic implications of treated white coat hypertension (WCH) and masked hypertension (MH) in patients with diabetes mellitus (DM) or chronic kidney disease (CKD) are not well documented. Using data from the HONEST study (n=21 591), we investigated the relationships between morning home systolic blood pressure (MHSBP) or clinic systolic blood pressure (CSBP) and cardiovascular (CV) risk in hypertensive patients with and without DM or CKD receiving olmesartan-based antihypertensive therapy. The study included 4426 DM patients and 4346 CKD patients at baseline who had 101 and 87 major CV events, respectively, during the follow-up. Compared with well-controlled non-DM patients (MHSBP o135 mmHg; CSBP o140 mmHg), DM patients with WCH (MHSBP o135 mmHg; CSBP ⩾140 mmHg), MH (MHSBP ⩾135 mmHg; CSBP o140 mmHg) or poorly controlled hypertension (PCH) (MHSBP ⩾135 mmHg; CSBP ⩾140 mm Hg) had significantly higher CV risk (hazard ratio (HR), 2.73, 2.77 and 2.81, respectively). CV risk was also significantly increased in CKD patients with WCH, MH and PCH (HR, 2.14, 1.70 and 2.20, respectively) compared with well-controlled non-CKD patients. Furthermore, DM patients had significantly higher incidence rate than non-DM patients of MHSBP ⩾125 to o135 mmHg (HR, 1.98) and ⩾135 to o145 mm Hg (HR, 2.41). In conclusion, both WCH and MH are associated with increased CV risk, and thus control of both MHSBP and CSBP is important to reduce CV risk in DM or CKD patients. The results also suggest that even lower MHSBP (o125 mm Hg) may be beneficial for DM patients, although this conclusion is limited by the small number of patients. Hypertension Research (2017) 40, 87–95; doi:10.1038/hr.2016.87; published online 11 August 2016

Keywords: cardiovascular diseases; chronic kidney disease; diabetes mellitus; masked hypertension; white coat hypertension

INTRODUCTION Home blood pressure (BP) measurement and ambulatory BP monitoring are widely used in the diagnosis and treatment of hypertension. White coat hypertension (WCH) and masked hypertension (MH) are diagnosed when there is a discrepancy between clinic BP (CBP) and home BP (HBP),1− 3 and ambulatory BP monitoring is useful in diagnosing these types of hypertension.4

The concepts of WCH and MH were originally used to describe untreated hypertensive patients based on epidemiological findings to optimize antihypertensive treatment in these patients. For example, British and Japanese guidelines recommend nonpharmacological treatment for patients with WCH and pharmacological therapy for patients with MH.5,6 For WCH patients who also have a metabolic abnormality or organ disorder, the European Society of Hypertension−European Society of Cardiology guidelines recommend pharmacological therapy.7 However, differences between BP measurements obtained at home or through ambulatory BP

monitoring and CBP may persist despite receiving antihypertensive treatment. In such cases, the patients are described as having ‘treated WCH’ or ‘treated MH’. The Home BP measurement with Olmesartan Naive patients to

Establish Standard Target blood pressure (HONEST) study is a large-scale, prospective, observational study involving more than 20 000 Japanese patients with hypertension; the aim was to investigate the relationship between HBP and CBP and the incidence of cardiovascular (CV) events in patients receiving olmesartan-based therapy.8 In our previous article describing the findings of the HONEST study, we reported a decrease in the proportion of patients with poorly controlled hypertension (PCH), that is, patients whose HBP and CBP were both high, and a consequent increase in the proportion of patients with WCH or MH after treatment with olmesartan.9 After 16 weeks, the numbers of patients in both the WCH group and the MH group were approximately double the numbers at baseline.9,10

1The Life Planning Center Foundation, Tokyo, Japan; 2Division of Cardiovascular Medicine, Department of Medicine, Jichi Medical University School of Medicine, Shimotsuke, Japan; 3Keio University, Yokohama, Japan; 4Department of Biostatistics, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan; 5Daiichi Sankyo, Tokyo, Japan and 6Shin-Oyama City Hospital, Oyama, Japan Correspondence: Professor T Kushiro, The Life Planning Center Foundation, 3-12-12, Mita, Minato-ku, Tokyo 108-0073, Japan. E-mail: kushirot@gmail.com Received 15 February 2016; revised 22 May 2016; accepted 31 May 2016; published online 11 August 2016

Hypertension Research (2017) 40, 87–95 Official journal of the Japanese Society of Hypertension www.nature.com/hr

http://dx.doi.org/10.1038/hr.2016.87
mailto:kushirot@gmail.com
http://www.nature.com/hr
Thus, we have shown that the prevalence of WCH and MH differs between untreated and treated patients. However, little information is available regarding CV risk in treated patients with WCH or MH, and guidelines for the treatment of these patients remain unclear. In patients with complications such as diabetes mellitus (DM) and

chronic kidney disease (CKD), BP control is particularly important to prevent CV events. However, in most previous clinical studies, baseline CBP was used as an indicator of CV risk. Therefore, information is lacking on the relationship between on-treatment BP (especially HBP) and CV risk in patients with DM or CKD. We previously reported the relationship between both morning

HBP (MHBP) and CBP and CV risk during the follow-up period of the HONEST study.8 In the present analysis, we used MHBP and CBP data from the follow-up period to investigate these relationships in WCH and MH patients with and without DM or CKD.

METHODS Study design The HONEST study is a large-scale, prospective, observational study with a 2-year follow-up period. The study is registered at the University Hospital Medical Information Network (UMIN) Clinical Trials Registry (http://www.umin.ac.jp/ctr/index.htm) with the unique trial number UMIN000002567. The study protocol was approved by the Ethical Committee of Daiichi Sankyo, as well as the review boards of the participating institutions at their discretion. The study conforms to the pharmaceutical affairs laws of Japan and was approved by the Ministry of Health, Labour and Welfare of Japan. The study was carried out at registered medical institutions in compliance with Good Post-marketing Study Practice in Japan and the internal regulations for clinical studies at each institution. The objectives and methods of the HONEST study have been previously reported.11 Participants comprised olmesartan-naive outpatients with essential hypertension who had recorded their MHBP on ⩾ 2 days and CBP on ⩾ 1 day in the 28-day period before starting olmesartan therapy. All patients provided written informed consent. Patients had no specific target BP, and antihypertensive drugs, including olmesartan, were prescribed at the discretion of each patient’s attending physician. The present study was carried out by Daiichi Sankyo Co., Ltd (Tokyo, Japan)

as part of the specific drug use results survey for olmesartan. Medical and statistical advisors provided advice on study protocol and study result interpretation. CV events were adjudicated by the Endpoint Committee, who were blinded to the HBP and CBP.

Measurement of HBP In accordance with the 2009 Japanese Society of Hypertension Guidelines for the Management of Hypertension,12 patients used their own devices, according to the cuff oscillometric method, to measure their BP twice in the morning and twice at bedtime on 2 different days at each of the following measurement points: 1 week, 4 weeks, 16 weeks, 6 months, 12 months, 18 months, and 24 months. For the present analysis, we calculated the mean of the two daily MHBP measurements. Then, for each measurement point, we used the mean MHBP over 2 days. To investigate relationships between HBP and incidence of CV events,

on-treatment BP (mean BP during the follow-up period excluding baseline BP) was used. For patients who had had CV events, the mean BP measurements obtained until the first occurrence of such events was used.

Measurement of CBP CBP was measured at 4 weeks, 16 weeks, 6 months, 12 months, 18 months, and 24 months. For each of these measurement points, one BP measurement was reported. The method used to measure CBP was at the discretion of the attending physicians. Analyses of the relationship between CBP and the incidence of CV events

were performed in the same way as for HBP.

Diagnosis of hypertension type based on patients’ clinic and home blood pressure In the present analysis, we defined the following four categories of hypertension status using clinic systolic BP (CSBP) and morning home systolic BP (MHSBP): MH was defined as CSBP o140 mm Hg and MHSBP ⩾ 135 mm Hg; WCH was defined as CSBP ⩾ 140 mm Hg and MHSBP o135 mm Hg; PCH was defined as CSBP ⩾ 140 mm Hg and MHSBP ⩾ 135 mm Hg; and well-controlled hypertension (CH) was defined as CSBP o140 mm Hg and MHSBP o135 mm Hg (Figure 1). For each BP, the mean BP during the follow-up period excluding baseline BP was used.

End points The primary end point for the present analysis was the first occurrence of a major CV event. This end point was a composite end point of stroke events (cerebral infarction, intracerebral hemorrhage, subarachnoid hemorrhage and unclassified stroke), cardiac events (myocardial infarction and coronary revascularization procedures for angina pectoris) and sudden death.

Statistical analysis The statistical analysis population comprised eligible patients who received olmesartan at least once during the treatment period. Patients included in the present analysis were stratified according to the presence or absence of DM and CKD. The presence of DM was determined by attending physicians who used

clinical findings such as the use of hypoglycemic drugs and abnormal laboratory values. The presence of CKD was determined according to the following criteria: estimated glomerular filtration rate o60 ml min− 1 per 1.73 m2 (estimated glomerular filtration rate= 194× [serum creatinine]− 1.094

× age− 0.287; × 0.739 for female patients), qualitative proteinuria ⩾ 2+ or qualitative proteinuria ⩾ 1+ and renal disease. For each comparison of baseline patient characteristics, categorical data were

analyzed using the χ2 test, and quantitative data were analyzed using the t-test. For each subgroup, the incidence rate for CV events was estimated using the person-years method, and the results were compared by Poisson regression. The association between on-treatment BP and CV risk was analyzed using the multivariate Cox proportional hazards model, including BP, DM, CKD, interaction terms between BP and DM or CKD, sex, age, family history of CV disease, dyslipidemia, history of CV disease and smoking status as a covariate. The association between hypertension type based on on-treatment BP and CV risk was also analyzed using a multivariate Cox proportional hazards model. All statistical tests were two sided using a significance level of 0.05. SAS

version 9.2 software (SAS Institute, Cary, NC, USA) was used for all statistical analyses.

PCHMH

CH WCH

140 mmHg

13 5

m m

H g

Systolic CBP

S ys

to lic

M H

B P

Figure 1 Categories of hypertension status used in the present study. CBP, clinic blood pressure; CH, well-controlled hypertension; MH, masked hypertension; MHBP, morning home blood pressure; PCH, poorly controlled hypertension; WCH, white coat hypertension.

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http://www.umin.ac.jp/ctr/index.htm
RESULTS Patient characteristics and changes in BP Table 1A shows the baseline characteristics of all patients (the full analysis set, n= 21 591) whose data were used in the analysis comparing patients with and without DM. The group of DM patients contained a greater proportion of males, and the DM patients were older than the non-DM patients. The patients with DM had had hypertension longer than those without DM and were more likely to have previously received antihypertensive treatment. Baseline systolic and diastolic BP (both MHBP and CBP) were lower in DM patients than in non-DM patients by ∼ 3 and 5 mmHg, respectively. DM patients were more likely to have had a history of CV disease as well as concomitant dyslipidemia and CKD. Table 1B shows the baseline characteristics of the 21 428 patients

whose data were included in the analysis comparing patients with and without CKD. Data from 163 patients in the full analysis set were excluded because their CKD status could not be determined. The groups of CKD and non-CKD patients had similar proportions of males and females, but CKD patients were ∼ 7 years older than non-CKD patients. The patients with CKD had hypertension longer

than those without CKD and were more likely to have previously received antihypertensive treatment. Baseline systolic and diastolic BP (both MHBP and CBP) were lower in CKD patients than in non-CKD patients by ∼ 2− 3 mmHg and 4− 5 mmHg, respectively. CKD patients were more likely to have had a history of CV disease as well as concomitant dyslipidemia and DM. Figure 2a and b shows changes in BP in DM and non-DM

patients. Compared with non-DM patients, DM patients had significantly lower MHBP (148.9± 16.1/82.9± 11.5 mmHg vs. 151.8± 16.3/88.0± 11.5 mmHg) and CBP (151.2± 18.3/83.1± 13.0 mmHg vs. 154.2± 19.1/88.2± 13.2 mmHg) at baseline. BP in both DM and non-DM patients had reduced significantly by 16 weeks, and this reduction was maintained at 24 months. Figure 2c and d shows changes in BP in CKD and

non-CKD patients. Compared with non-CKD patients, CKD patients had significantly lower MHBP (149.7± 17.2/83.4± 11.8 mmHg vs. 151.6± 16.0/87.9± 11.4 mmHg) and CBP (151.4± 19.8/83.4± 13.5 mmHg vs. 154.2± 18.7/88.1± 13.1 mmHg) at baseline. BP in both CKD and non-CKD patients had reduced significantly by 16 weeks, and this reduction was maintained at 24 months.

Table 1A Comparison of baseline characteristics of patients with and without diabetes mellitus (DM)

All (n=21 591) DM patients (n=4426) Non-DM patients (n=17 165) Pa (non-DM vs. DM)

Male, n (%) 10 670 (49.4) 2474 (55.9) 8196 (47.7) o0.0001 Age, years 64.9±11.9 66.1±10.8 64.5±12.1 o0.0001 Body mass index, kg m−2 24.3±3.7 25.2±4.0 24.0±3.5 o0.0001 Duration of hypertension, years 5.0±4.4 6.2±4.4 4.7±4.4 o0.0001 History of cerebro- or cardiovascular disease, n (%) 2269 (10.5) 655 (14.8) 1614 (9.4) o0.0001 Cerebrovascular disease, n (%) 1432 (6.6) 377 (8.5) 1055 (6.1) o0.0001 Cardiovascular disease, n (%) 981 (4.5) 330 (7.5) 651 (3.8) o0.0001

Complications Dyslipidemia, n (%) 9626 (44.6) 2825 (63.8) 6801 (39.6) o0.0001 Chronic kidney disease, n (%) 4346 (20.1) 1245 (28.1) 3101 (18.1) o0.0001

Current smokers, n (%) 2654 (12.3) 595 (13.4) 2059 (12.0) o0.0001 Regular alcohol drinkers, n (%) 3473 (16.1) 703 (15.9) 2770 (16.1) 0.32 Previous antihypertensive drug use, n (%) 10 872 (50.4) 2805 (63.4) 8067 (47.0) o0.0001 Calcium channel blocker 7783 (36.0) 1995 (45.1) 5788 (33.7) o0.0001 Angiotensin receptor blocker 4581 (21.2) 1401 (31.7) 3180 (18.5) o0.0001 β-Blocker 1380 (6.4) 341 (7.7) 1039 (6.1) o0.0001 Diuretic 1260 (5.8) 381 (8.6) 879 (5.1) o0.0001 Angiotensin-converting enzyme inhibitor 785 (3.6) 273 (6.2) 512 (3.0) o0.0001 α-Blocker 470 (2.2) 160 (3.6) 310 (1.8) o0.0001 Other antihypertensive drugs 97 (0.4) 35 (0.8) 62 (0.4) 0.0001

Clinic measurements Systolic BP, mm Hg 153.6±19.0 151.2±18.3 154.2±19.1 o0.0001 Diastolic BP, mm Hg 87.1±13.3 83.1±13.0 88.2±13.2 o0.0001 Pulse rate, beats per min 74.0±11.2 74.8±11.5 73.8±11.1 o0.0001

Home measurements (morning) Systolic BP, mm Hg 151.2±16.3 148.9±16.1 151.8±16.3 o0.0001 Diastolic BP, mm Hg 86.9±11.7 82.9±11.5 88.0±11.5 o0.0001 Pulse rate, beats per min 70.7±9.8 71.0±10.3 70.7±9.7 0.18

Fasting plasma glucose, mg dl−1 106.0±29.8 136.0±46.6 97.6±14.5 o0.0001 HbA1c, % (NGSP) 6.19±1.09 7.16±1.22 5.64±0.44 o0.0001 eGFR, ml min−1 per 1.73 m2 72.4±20.2 71.6±21.9 72.6±19.7 0.008

Abbreviations: BP, blood pressure; eGFR, estimated glomerular filtration rate; HbA1c, glycated hemoglobin A1c; NGSP, National Glycohemoglobin Standardization Program. aCategorical data analyzed by χ2 test and quantitative data by unpaired t-test.

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CV risk in patients with and without diabetes Table 2A shows the incidence rate for CV events in DM and non-DM patients. CV events were more common in patients with DM than in those without DM (11.34/1000 vs. 5.20/1000 person-years, Po0.0001). Of the major CV events, the incidence rate for cardiac events was particularly high in DM patients compared with that of non-DM patients (6.26/1000 vs. 1.88/1000 person-years, Po0.0001). Figure 3a shows the relationship between the primary end point and

MHSBP in DM and non-DM patients. When the patients with MHSBP o125 mmHg for non-DM patients were defined as a reference, the incidence rate was significantly higher in DM patients with MHSBP ⩾ 135 to o145 mmHg and ⩾ 155 mmHg and in non- DM patients with MHSBP ⩾ 145 too155 mmHg and ⩾ 155 mmHg (hazard ratio (HR), 1.83− 5.22). When we compared the incidence of CV events in DM patients with that in non-DM patients with the same achieved BP range, patients with DM had significantly higher incidence rate than non-DM patients at MHSBP ⩾ 125 to o135 mmHg (HR, 1.98,1.18–3.31, P= 0.0092) and ⩾ 135 to o145 mmHg (HR, 2.41, 1.53–3.82, P= 0.0002). There was a statistically significant interaction in the association of MHSBP with the primary end point between patients with and without DM (interaction P= 0.0376). Figure 3b shows the relationship between

the primary end point and CSBP in DM and non-DM patients. When the patients with CSBP o130 mmHg for non-DM patients were defined as a reference, the incidence rate was significantly higher in DM patients with CSBP o130 mmHg, ⩾ 140 to o150 mmHg, ⩾ 150 to o160 mmHg and ⩾ 160 mmHg, as well as in non-DM patients with CSBP ⩾ 150 to o160 mmHg and ⩾ 160 mmHg (HR, 1.85− 5.84). When we compared the incidence of CV events in DM patients with that of non-DM patients with the same BP range, patients with DM had a significantly higher incidence rate than non-DM patients at CSBP o130 mmHg (HR, 1.91) and ⩾ 130 to o140 mmHg (HR, 1.70). No interaction between CSBP and the primary end point was found between patients with and without DM (interaction P= 0.5824). When the value for non-DM patients with CH was defined as a

reference, the incidence rate was statistically significantly higher in DM patients with WCH, MH and PCH (2.73, 2.77 and 2.81, respectively). In contrast, the incidence rate was statistically significantly higher in only non-DM patients with PCH (HR, 2.23; Figure 4a).

CV risk in patients with and without CKD Table 2B shows the incidence rate for CV events in CKD and non-CKD patients. CKD patients had a significantly higher incidence

Table 1B Comparison of baseline characteristics of patients with and without chronic kidney disease (CKD)

All (n=21 428) CKD patients (n=4346) Non-CKD patients (n=17 082) Pa (non-CKD vs. CKD)

Male, n (%) 10 572 (49.3) 2088 (48.0) 8484 (49.7) 0.06 Age, years 64.9±11.9 70.5±11.0 63.4±11.7 o0.0001 Body mass index, kg m−2 24.3±3.7 24.2±3.7 24.3±3.7 0.25

Duration of hypertension, years 5.0±4.4 6.4±4.4 4.6±4.4 o0.0001 History of cerebro- or cardiovascular disease, n (%) 2237 (10.4) 737 (17.0) 1500 (8.8) o0.0001 Cerebrovascular disease, n (%) 1415 (6.6) 421 (9.7) 994 (5.8) o0.0001 Cardiovascular disease, n (%) 963 (4.5) 376 (8.7) 587 (3.4) o0.0001

Complications Dyslipidemia, n (%) 9531 (44.5) 2299 (52.9) 7232 (42.3) o0.0001 Diabetes mellitus, n (%) 4353 (20.3) 1245 (28.6) 3108 (18.2) o0.0001

Current smokers, n (%) 2633 (12.3) 361 (8.3) 2272 (13.3) o0.0001 Regular alcohol drinkers, n (%) 3458 (16.1) 485 (11.2) 2973 (17.4) o0.0001 Previous antihypertensive drug use 10 779 (50.3) 2748 (63.2) 8031 (47.0) o0.0001 Calcium channel blocker, n (%) 7717 (36.0) 1979 (45.5) 5738 (33.6) o0.0001 Angiotensin receptor blocker 4537 (21.2) 1237 (28.5) 3300 (19.3) o0.0001 β-Blocker 1363 (6.4) 454 (10.4) 909 (5.3) o0.0001 Diuretic 1243 (5.8) 447 (10.3) 796 (4.7) o0.0001 Angiotensin-converting enzyme inhibitor 778 (3.6) 250 (5.8) 528 (3.1) o0.0001 α-Blocker 460 (2.1) 188 (4.3) 272 (1.6) o0.0001 Other antihypertensive drugs 96 (0.4) 49 (1.1) 47 (0.3) o0.0001

Clinic measurements Systolic BP, mm Hg 153.6±18.9 151.4±19.8 154.2±18.7 o0.0001 Diastolic BP, mm Hg 87.2±13.3 83.4±13.5 88.1±13.1 o0.0001 Pulse rate, beats per min 74.0±11.2 73.6±11.7 74.1±11.0 0.02

Home measurements (morning) Systolic BP, mm Hg 151.2±16.2 149.7±17.2 151.6±16.0 o0.0001 Diastolic BP, mm Hg 87.0±11.6 83.4±11.8 87.9±11.4 o0.0001 Pulse rate, beats per min 70.7±9.8 70.2±10.2 70.9±9.7 0.001

Fasting plasma glucose, mg dl−1 105.9±29.7 108.4±30.3 105.2±29.5 0.0002

eGFR, ml min−1 per 1.73 m2 72.3±20.2 50.5±13.5 79.8±16.4 o0.0001

Abbreviations: BP, blood pressure; eGFR, estimated glomerular filtration rate. aCategorical data analyzed by χ2 test and quantitative data by unpaired t-test.

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rate for CV events than non-CKD patients (10.06/1000 vs. 5.44/1000 person-years, Po0.0001). Of the major CV events, both cerebrovas- cular and cardiac events were more common in patients with CKD than in those without CKD (P= 0.0091 and P= 0.0027, respectively). Figure 3c shows the relationship between the primary end point and

MHSBP in CKD and non-CKD patients. When the value of MHSBP o125 mmHg for non-CKD patients was defined as a reference, the incidence rate was significantly higher in CKD patients with MHSBP ⩾145 to o155 mmHg and ⩾155 mmHg, as well as in non-CKD patients with MHSBP ⩾155mmHg (HR, 2.18−5.95). Thus, CV risk tended to increase as MHSBP increased. Figure 3d shows the relationship between the primary end point and CSBP in CKD and non-CKD patients. Similarly, when the value of CSBP o130mmHg for non-CKD patients was defined as a reference, the incidence rate was significantly higher in CKD patients with CSBP 4160mmHg and in non-CKD patients with CSBP ⩾150 to o160mmHg and ⩾160mmHg (HR, 1.83−6.62). Thus, CV risk tended to increase as CSBP increased. When we compared the incidence of CV events in CKD patients with that of non-CKD patients with the same BP range (MHSBP or CSBP), no statistically significant difference in incidence rate was found between CKD and non-CKD patients across any subgroups (HR, 0.64−1.55). In the analysis of patients with and without CKD, no interaction between either MHSBP or CSBP and the primary end point was found (interaction P=0.5168 and 0.8651, respectively), suggesting that the association of MHSBP and CSBP with CV risk is similar in patients with

and without CKD. In a subsequent analysis, patients were classified into four groups according to hypertension type, and when the value for non- CKD patients with CH was defined as a reference, HR was significantly higher in CKD patients with WCH, MH and PCH (2.14, 1.70 and 2.20, respectively). In contrast, the incidence rate was statistically significantly higher only in non-CKD patients with PCH (HR, 2.19) (Figure 4b). In CKD patients with concomitant DM (n= 1245) and those with

concomitant proteinuria (n= 1238), the incidence rates for CV events were 17.50/1000 and 15.87/1000 person-years, respectively. When the value of patients with CKD alone and MHSBP o125 mmHg was used as a reference, the HRs in patients with CKD+DM were 1.168 (P= 0.8583) at o125 mmHg, 2.400 (P= 0.1366) at ⩾ 125 to o135 mmHg, 2.886 (P= 0.0664) at ⩾ 135 to o145 mmHg, 3.123 (P= 0.0711) at ⩾ 145 to o155 mmHg and 3.279 (P= 0.0958) at ⩾ 155 mmHg. With the same reference (MHSBP o125 mmHg in patients with CKD alone), the HRs in patients with CKD+proteinuria were 2.661 (P= 0.2903) at o125 mmHg, 2.494 (P= 0.2455) at ⩾ 125 to o135 mmHg, 2.252 (P= 0.3017) at ⩾ 135 to o145 mmHg, 3.135 (P= 0.1656) at ⩾ 145 to o155 mmHg and 4.742 (P= 0.0664) at ⩾ 155 mmHg.

DISCUSSION In this analysis of data from the large-scale, prospective, observational HONEST study, we investigated the relationship between on-treatment MHBP and CBP and CV risk in patients with and

Morning home blood pressure

60

170 180

90

110 120 130 140 150 160

100

70 80

DM patients Non-DM patients

Diastolic

Systolic

B lo

od p

re ss

ur e

(m m

H g)

0 16 weeks

6 months

12 months

18 month

24 months

Follow-up period

DM

Non-DM

n SBP n DBP n SBP n DBP

4425 148.9±16.1

4425 82.9±11.5

17163 151.8±16.3

17163 88.0±11.5

3980 135.6± 14.1

3978 76.7± 9.7

15530 134.1± 13.1

15525 79.1± 9.7

3547 134.8±13.8

3545 76.3± 10.1

13883 133.3±12.5

13874 78.6±9.4

3644 134.0± 13.2

3643 75.5± 9.5

14213 132.8± 12.1

14207 78.0± 9.1

3207 133.3±12.9

3203 75.1±9.5

12587 131.5±11.6

12574 77.4±8.9

3246 132.6±12.3

3242 74.5±9.2

12819 131.2±11.2

12808 76.8±8.7

Clinic blood pressure

DM patients Non-DM patients

Diastolic

Systolic

B lo

od p

re ss

ur e

(m m

H g)

0 16 weeks

6 months

12 months

18 months

24 months

Follow-up period

DM

Non-DM

n SBP n DBP n SBP n DBP

4425 151.2±18.3

4425 83.1±13.0

17164 154.2±19.1

17164 88.2±13.2

4242 137.1±16.5

4240 76.2±11.5

16214 135.1±15.0

16208 77.9±10.7

3850 136.4±16.1

3849 75.6±11.1

14674 134.4±14.6

14674 77.6±10.5

3991 135.4±15.9

3991 75.1±11.0

15169 133.3±14.2

15167 76.8±10.3

3610 134.8±15.7

3609 74.5±10.9

13674 132.5±14.0

13663 76.4±10.1

3672 134.3±15.3

3671 74.3± 10.9

14063 132.2±13.9

14052 76.0± 10.1

170 180

90

110 120 130 140 150 160

100

70 80

60

60

170 180

90

110 120 130 140 150 160

100

70 80

Diastolic

Systolic

Morning home blood pressure

CKD patients Non-CKD patients

B lo

od p

re ss

ur e

(m m

H g)

0 16 weeks

6 months

12 months

18 months

24 months

Follow-up period

CKD

Non-CKD

n SBP n DBP n SBP n DBP

4346 149.7±17.2

4346 83.4±11.8

17080 151.6±16.0

17080 87.9±11.4

3972 135.4±14.5

3971 76.7±9.8

15401 134.2±13.0

15395 79.1±9.6

3560 134.3±13.4

3558 76.3±9.8

13757 133.4±12.6

13748 78.6±9.4

3645 133.9±13.2

3645 75.7±9.5

14090 132.8±12.1

14083 77.9±9.1

3241 133.0±12.8

3236 75.5±9.2

12451 131.6±11.6

12440 77.3±9.0

3237 132.0±12.1

3235 74.6± 9.0

12723 131.3± 11.2

12710 76.7± 8.7

60

170 180

90

110 120 130 140 150 160

100

70 80

Diastolic

Systolic

Clinic blood pressure

CKD patients Non-CKD patients

B lo

od p

re ss

ur e

(m m

H g)

0 16 weeks

6 months

12 months

18 months

24 months

Follow-up period

CKD

Non-CKD

n SBP n DBP n SBP n DBP

4346 151.4±19.8

4346 83.4±13.5

17080 154.2±18.7

17080 88.1±13.1

4169 136.2±16.3

4166 75.8±11.3

16140 135.4±15.1

16135 78.0±10.7

3790 135.3±15.9

3789 75.5±11.1

14615 134.7±14.6

14615 77.6±10.5

3883 134.1±15.9

3882 74.6±10.9

15146 133.7±14.3

15146 76.9±10.3

3515 133.5±15.5

3514 74.5±10.7

13647 132.8±14.0

13636 76.4±10.1

3548 133.1±15.6

3546 74.1±10.7

14060 132.5±13.8

14050 76.0±10.2

Figure 2 Changes in morning home and clinic blood pressure in patients with and without DM (a, b) and in patients with and without CKD (c, d). *Po0.001 (vs. baseline; Dunnett−Hsu test). CKD, chronic kidney disease; DBP, diastolic blood pressure; DM, diabetes mellitus; SBP, systolic blood pressure.

CV risk of white coat/masked hypertension in DM/CKD T Kushiro et al

91

Hypertension Research

without DM or CKD in a real-world clinical setting. All patients had a similar reduction in BP during the follow-up period, regardless of concomitant DM or CKD. Both MHBP and CBP were independently related to CV risk. WCH and MH were also associated with increased CV risk when complicated with DM or CKD. The findings indicate that control of both MHBP (o135 mmHg) and CBP (o140 mmHg) is important for reducing CV risk in patients with DM or CKD.

CV risk in patients with and without diabetes Comparison of the baseline characteristics of patients with and without DM showed that DM patients were more likely to have CV risk factors such as older age, longer duration of hypertension, a history of CV disease and concomitant dyslipidemia or CKD. The CV risk profile of patients with DM differed from that of those

without DM. CV risk was higher in DM patients than in non-DM

patients at MHSBP ⩾ 125 to o145 mmHg. However, at MHSBP ⩾ 145 mmHg, CV risk was similar in DM and non-DM patients. These findings suggest that DM is a greater risk factor for CV events than BP in the range of MHSBP ⩾ 125 to o145 mmHg. At MHSBP ⩾ 145 mmHg, however, BP makes a greater contribution to CV risk. In addition, although CV risk was significantly higher in DM patients than in non-DM patients at MHSBP ⩾ 125 to o135 mmHg, there was no difference between the two groups at o125 mmHg. Therefore, even lower MHSBP (o125 mmHg) may be beneficial for patients with DM, although the results were limited by the small number of patients with o125 mmHg in the present study. The findings are consistent with those reported in previous studies; the HOMED-BP study13 showed that MHPB ⩾ 125 mmHg was significantly associated with increased CV risk in hypertensive patients with DM. Ushigome et al.14 reported that the optimal home systolic

Table 2A Cardiovascular events during the follow-up period in patients with and without diabetes mellitus (DM)

DM patients Non-DM patients

No. of events

Incidence rate, events/1000

person years No. of events

Incidence rate, events/1000

person years Pa (non-DM vs DM)

Major cardiovascular events 101 11.34 179 5.20 o0.0001 Stroke events 36 4.02 91 2.64 0.03

Atherothrombotic cerebral infarction 12 1.33 31 0.90 0.24

Cardiogenic cerebral infarction 1 0.11 3 0.09 0.83

Lacunar infarction 14 1.56 26 0.75 0.03

Unclassified cerebral infarction 4 0.44 9 0.26 0.37

Cerebral hemorrhage 2 0.22 15 0.43 0.37

Subarachnoid hemorrhage 3 0.33 5 0.14 0.25

Unclassified stroke 1 0.11 2 0.06 0.59

Cardiac events 56 6.26 65 1.88 o0.0001 Myocardial infarction 20 2.23 25 0.72 0.0002

Coronary revascularization procedure for angina

pectoris

37 4.13 40 1.16 o0.0001

Sudden death 11 1.22 24 0.69 0.12

aPoisson regression was used.

Table 2B Cardiovascular events during the follow-up period in patients with and without chronic kidney disease (CKD)

CKD patients Non-CKD patients

No. of events

Incidence rate, events/1000

person-years No. of events

Incidence rate, events/1000

person years Pa (non-CKD vs CKD)

Major cardiovascular events 87 10.06 187 5.44 o0.0001 Stroke events 37 4.26 88 2.55 0.009

Atherothrombotic cerebral infarction 14 1.61 28 0.81 0.04

Cardiogenic cerebral infarction 0 0.00 3 0.09 1.00

Lacunar infarction 9 1.03 31 0.90 0.71

Unclassified cerebral infarction 5 0.57 8 0.23 0.11

Cerebral hemorrhage 5 0.57 12 0.35 0.35

Subarachnoid hemorrhage 2 0.23 6 0.17 0.73

Unclassified stroke 2 0.23 1 0.03 0.09

Cardiac events 37 4.26 81 2.35 0.003

Myocardial infarction 12 1.38 33 0.96 0.28

Coronary revascularization procedure for angina

pectoris

26 2.99 48 1.39 0.002

Sudden death 15 1.72 19 0.55 0.001

aPoisson regression was used.

CV risk of white coat/masked hypertension in DM/CKD T Kushiro et al

92

Hypertension Research

DM

Non-DM

n (%)

Incidence (events /1000 person-years)

n (%)

Incidence (events /1000 person-years)

1567 (36.6)

6.13

7041 (42.4)

3.15

41 1(9.6)

10.77

1280 (7.7)

4.66

1164 (27.2)

13.19

4715 (28.4)

4.57

1135 (26.5)

11.94

3560 (21.5)

7.94

CH 0

4

3

2

1

WCH MH PCH

DM patients Non-DM patients

Reference

1.48 1.28

2.23

1.42

2.73 2.77 2.81

H az

ar d

ra tio

CKD

Non- CKD

n (%)

Incidence (events /1000 person-years)

n (%)

Incidence (events /1000 person-years)

1620 (38.4)

5.11

6917 (41.9)

3.26

362 (8.6)

11.08

1311 (7.9)

4.90

1226 (29.0)

9.36

4614 (28.0)

5.19

1016 (24.1)

11.41

3651 (22.1)

8.15

0

4

3

2

1

CKD patients Non-CKD patients

Reference

1.52 1.41

2.19

1.06

2.14

1.70

2.20

H az

ar d

ra tio

CH WCH MH PCH

Figure 4 Primary end point in well-controlled hypertension (CH), white coat hypertension (WCH), masked hypertension (MH) and poorly controlled hypertension (PCH) in patients with and without diabetes mellitus (DM) adjusted for sex, age, family history of cardiovascular disease, dyslipidemia, chronic kidney disease (CKD), history of cardiovascular disease and smoking status (*Po0.05 vs. non-DM patients with CH) (a) and in patients with and without CKD adjusted for sex, age, family history of cardiovascular disease, dyslipidemia, DM, history of cardiovascular disease and smoking status (*Po0.05 vs. non-CKD patients with CH) (b). CBP, clinic blood pressure; MHBP, morning home blood pressure.

DM

Non-DM

n Incidence rate (events /1000 person- years) n Incidence rate (events /1000 person- years)

Hazard ratio vs non-DM patients

596

4.95

2412

3.86

0.91

1388

8.32

5950

3.17

1.98

1421

11.41

5744

3.76

2.41

637

10.94

1928

8.83

1.00

248

29.33

677

25.40

0.92

0

8 H

az ar

d ra

tio

6

5

4

3

2

7

1

DM patients Non-DM patients

Interaction P = 0.040.77 0.85

1.83

5.22

0.91 1.52

2.06 1.83

4.80

Reference

< 125 125 − < 135 135 − < 145 145 − < 155 ≥ 155 MHSBP (mmHg)

DM

Non-DM

n Incidence rate (events /1000 person- years)

(events /1000 person- years)

n Incidence rate

Hazard ratio vs non-DM patients

1193

11.96

5355

4.29

1.91

1593

7.64

6604

3.45

1.70

1026

10.13

3410

5.13

1.58

395

14.15

1122

8.62

1.49

182

27.51

466

25.01

0.88

0

8

6

5

4

3

2

7

1

DM patients Non-DM patients

Interaction P = 0.580.82

5.84

1.91 1.39

5.15

Reference

1.85

1.17

2.76

H az

ar d

ra tio

< 130 130 − < 140 140 − < 150 150 − < 160 ≥ 160 CSBP (mmHg)

1.85

0

8

6

5

4

3

2

7

1

CKD patients Non-CKD patients

Interaction P = 0.52Reference 0.92 1.12

1.73

5.95

1.00 1.24 1.37 2.18

3.81

CKD

Non- CKD

n

Incidence rate (events /1000 person-years)

Incidence rate (events /1000 person-years)

n

Hazard ratio vs non-CKD patients

580

5.21

2398

3.66

1.00

1417

6.87

5862

3.45

1.35

1437

7.91

5681

4.48

1.22

562

13.61

1988

7.97

1.26

255

23.58

663

27.03

0.64

H az

ar d

ra tio

< 125 125 − < 135 135 − < 145 145 − < 155 ≥ 155 MHSBP (mmHg)

CKD

Non- CKD

n

Incidence rate (events /1000 person-years)

Incidence rate (events /1000 person-years)

n

Hazard ratio vs non-CKD patients

1307

9.07

5174

4.53

1.27

1579

5.94

6573

3.82

1.05

902

8.88

3503

5.69

1.01

346

11.93

1160

9.19

0.97

171

41.58

471

19.88

1.55

H az

ar d

ra tio

CKD patients Non-CKD patients

Interaction P = 0.870.85 0.89

< 130 130 − < 140 140 − < 150 150 − 250 mg/dL50,51,59,60, insulin insensitivity, and preferably additional characteristics

such as polyuria and polydipsia49,50. Further information on protocols for characterizing

DM in rodents can be found in the literature61,62. However, as with models of T1DM, no

mouse model perfectly mirrors human T2DM disease. The relative contributions of obesity

to acceleration of OA in DM are particularly important in T2DM and its models. While

isolating the biomechanical effects of obesity from the effects of DM is itself a challenge, in

addition, obesity has well-known metabolic consequences that add further complexity to the

situation. These include increased production of inflammatory cytokines such as IL-1,

TNFα, and adipokines (e.g., adiponectin, leptin, and resistin)63 that promote inflammation and may accelerate OA. Because obesity and T2DM almost always co-exist in human

T2DM, animal studies may be particularly useful in separating the pathological

contributions derived from these different mechanisms.

A popular model of T2DM is the diet-induced obesity model (DIO) which attempts to

simulate human obesity-induced T2DM but often results in only modestly increased glucose

levels51,64–66. The db/db mouse, ob/ob mouse, and fa/fa rat have monogenetic defects that disrupt leptin signaling resulting in hyperphagia and morbid obesity67. This leptin signaling

defect is rare in human T2DM60,64,67, and T2DM models created using polygenic mutations

are better models of human T2DM. The KK.Cg-Ay/J mouse was developed by crossing a spontaneously diabetic strain (KK) with the yellow obese strain (Ay) resulting in a mature-

onset T2DM phenotype68. Both DM and control normal glycemic siblings are obese69. The

NONcNZO10/LtJ mouse (NcZ10) is a polygenic model of T2DM with onset of DM in

adulthood. The NcZ10 model requires a chow content of 10–11% fat for higher penetrance

(90–100%)59. The TALLYHO/JngJ mouse (TH) has polygenic and maturity-onset diabetes

and has high penetrance in males59,70. A short list of commonly available rodent models of

T1DM and T2DM is presented in Table I. Readers are encouraged to review additional

details in the references provided.

Animal studies investigating a link between DM and OA

There is an unfortunate paucity of quality research studies in animal models of DM relevant

to OA. A recent study using STZ-induced T1DM in mice suggested the presence of cartilage

damage after 8 weeks of hyperglycemia, and showed elevated levels of circulating AGEs73.

Both abnormalities were ameliorated by the use of the DM drug pioglitazone. The authors

concluded that this drug response implicated PPARγ in this effect, but it was unclear whether this was related to improvement of hyperglycemia or PPARγ inhibition. One valuable study applied the DIO model to the C57Bl/6 strain and made careful measures of

physiological data and histological OA outcomes74. This study combined a high fat diet

(60% by calorie) with the meniscal ligament injury model to induce OA. Higher OA scores

(increased joint degeneration) were found in the mouse group receiving both high-fat diet

and ligament injury. However, since hyperglycemia was not established until the last month

of the experiment, it is unclear whether the hyperadiposity75 or the rising hyperglycemia was

the driving factor for greater OA progression. None the less, this is a valuable study

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demonstrating acceleration of joint degeneration in association with metabolic changes

typically seen in DM patients.

Effects of DM and hyperglycemia on articular cartilage

Biomechanical properties of cartilage are highly influenced by the composition of the

extracellular matrix (ECM), and there is some evidence that metabolic abnormalities

associated with DM alter cartilage ECM. Early studies in animal models of DM have shown

that decreased collagen production76 and increased proteoglycan catabolism77 occurs in DM

cartilage. Increased proteoglycan catabolism in DM animals has also been observed in non-

articular connective tissues78.

Effects of DM and hyperglycemia on bone

Studies showing delayed fracture healing in animals with DM support the ample clinical

data associating DM with bone abnormalities. The STZ mouse model demonstrates that poor

diabetic fracture healing is related to premature resorption of cartilage at the fracture

callus79, and that this was due to high levels of the inflammatory cytokine TNFα in mice with DM80.

Effects of DM and hyperglycemia on tendons and ligaments

Animal models of DM fairly consistently show histologic and biochemical abnormalities in

tendons and ligaments, as well as less well-characterized biomechanical changes, such as

lower Young’s modulus and increased intra-substance failure81–83. Further, several studies

identify delayed tendon healing after injury in the presence of DM84–87. These types of

tendon and ligament abnormalities are known to promote OA88.

Effects of DM and hyperglycemia on synovium

The synovium of T1DM rats is abnormal and contains fibrotic tissue with higher amounts of

type I collagen and lower quantities of types III and V collagen89. Synovial pathology is a

significant contributing factor to OA; early inflammatory changes in the synovium may

cause damage that then creates long-term production of catabolic mediators90.

Potential mechanisms

Basic science studies have also identified some potential mechanisms linked to DM-

influenced end-organ joint damage. Mediators such as hyperglycemia, AGEs, sorbitol,

adipokines, and cytokines act through oxidative, osmotic, and inflammatory mechanisms to

produce tissue damage (Table II). Further complexity is added by the participation of similar

molecular participants in multiple catabolic pathways and the contributions of similar

metabolic derangements in obesity.

An increasing recognition of a key role for inflammation in both OA98 and DM provides an

important mechanistic link between these two conditions. Significant synovitis occurs in OA

and may be exacerbated by the increased levels of inflammatory cytokines, adipokines, and

prostaglandins seen in DM tissues63. Signaling through pathways of innate immunity, such

as toll-like receptors, also may produce inflammation in both DM92 and OA99.

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In a hyperglycemic environment, there is increased production of reactive oxygen species

contributing to tissue damage. Cellular transport of glucose becomes critical and may

contribute to excess oxidative stress. One group found that human OA chondrocytes from

donors aged ≥66 years cultured in high glucose are unable to decrease GLUT1 protein or

reduce glucose transport activity in comparison with normal chondrocytes from young

donors (age 28–35 years)94,100. They also found that high glucose conditions favored

production of oxidants and promoted matrix catabolism which would accelerate OA.

However, age and media osmolarity were not controlled in those experiments101. The effects

of high glucose may be associated with impaired function of ATP-sensitive K+ channels

which couple GLUT to intracellular ATP/ADP levels and membrane potential102,103.

The AGE/RAGE system also plays a role in DM end-organ damage through induction of

inflammation and/or increased oxidative stress. Collagen has an extraordinary low turnover

in many connective tissues and as such is prone to modification by AGEs. The formation of

AGEs is accelerated by high tissue levels of glucose38. AGEs signal through RAGE

(receptor for AGEs) and other receptors to produce various deleterious effects on

chondrocytes including inflammation and cytokine-mediated catabolism104–106 and have

been postulated to play a role in end-organ damage in DM38. Further, AGE mediated cross-

linking of collagen can alter a tissue’s biomechanical properties as shown for cartilage and

tendon35–37. Cross-linking by AGEs may also inhibit ECM turnover by restricting access to

proteolytic sites106. On the other hand, a recent study in dogs suggests that an artificial

increase in AGE levels alone using repeated ribose injections did not accelerate OA in a mild

injury model107, but we know little about the effect of AGEs in the context of the diabetic

milieu. Thus, whether or how AGE’s play a significant role in OA remains unclear.

In the polyol pathway, aldose reductase converts glucose to sorbitol and galactose to

galactitol. This mechanism is activated in DM with excess polyols leading to cellular

osmotic stress108. Although not yet directly linked to OA, there is some evidence that this

pathway is activated in DM in intervertebral disc cartilage and enhances matrix catabolism

via p38 MAPK activation109.

Although not covered in detail in this review, additional DM-relevant pathways have been

proposed. For example, there is considerable evidence that adipokines may induce

inflammation and have adverse effects on cartilage75,110 and tissue healing111. Because

altered adipokine levels are seen in obesity in both the absence and presence of diabetes112,

the contribution of adipokines to OA in obese patients with DM will require further study.

Alteration in angiogenesis, autophagy, and apoptosis are also associated with end-organ

damage in OA58,113–116. Insulin receptors are present on chondrocytes100, and thus, excess

insulin as seen in T2DM patients may also damage cartilage. In one study, PPARγ downregulation was shown to occur in articular chondrocytes exposed to high glucose

media, but methodologic challenges warrant confirmation of this finding73. Whether one or

more pathways are involved, which pathway is most relevant, and how molecular mediators

intersect multiple pathways will require additional study.

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Conclusions

In summary, increasing evidence from the clinic and the laboratory supports an adverse

effect of DM on the development, severity, and therapeutic outcomes for OA. The etiology

and clinical manifestations of OA are complex, and currently we know little about how the

multiple mechanisms altered in DM may affect OA that originates from different causes.

Further, the clinical impact of DM on OA may be underestimated by the high prevalence of

undiagnosed diabetes3. A deeper understanding of OA in the setting of DM could result in

significant improvements in clinical care. For example, reducing OA-related joint pain may

allow DM patients to perform the exercise necessary for cardiovascular health. A full

appreciation of these disease interactions may also reduce the increased medical costs

associated with arthroplasty and other surgeries in patients with comorbid DM20,21,32. To

understand the mechanisms through which DM contributes to OA, further work is clearly

necessary. Future studies of DM-influenced mechanisms may shed light on the general

mechanisms of OA pathogenesis and result in more specific and effective therapies for all

OA patients.

Acknowledgments

We would like to thank the VA Research Service for research space and support (AKR, 101BX000812), an OREF/ Goldberg Arthritis Research Grant (Bucknell and King), and Dr Frank Beier for his thoughtful comments and suggestions on this review.

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As there is no perfect animal model that entirely recapitulates either T1DM or T2DM,

compromises are likely to be required with any one model. Ideally the selected model(s)

should be well characterized in terms of the following:

1. Level and consistency of hyperglycemia. Chemically induced models of T1DM have been known to lose their hyperglycemic state over time due to β- cell regeneration. Hyperglycemia drops among some monogenic T2DM

models.

2. Age at onset of diabetes. T2DM and OA are more common amongst older humans, therefore the use of older animals in these research studies should be

considered and the lifespan of the model should be sufficient to allow

development of OA.

3. Sex differences. There is a gender bias in diabetes severity and age of onset In many rodent models of diabetes.

4. Appropriateness of control group. Different background strains may have greatly different susceptibility to obesity and change in blood glucose.

5. Comorbid conditions. It may be desirable to have present comorbid conditions to answer the specific research questions. Scientists should be

aware that not all DM models have been fully characterized for comorbid

conditions.

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King and Rosenthal Page 18

Table II

Mechanisms present in diabetes that can damage joint tissues

Potential DM mechanisms Contributing pathways Examples of major mediators

Examples of molecular participants

References

Inflammation AGE/RAGE, innate immune pathways

Cytokines, Adipokines, Reactive oxygen species

MMPs, TLR 91,92

Oxidative stress Glucose transporters AGE/RAGE Reactive oxygen species GLUT, ATP, ADP 93–95

Osmotic stress Polyol pathway Sorbitol p38 MAPK 96,97

AGE = advanced glycation end products, RAGE = receptor for AGE, MMP = matrix metalloproteinase, TLR = toll-like receptors, GLUT = glucose transporter family, ATP = adenosine triphosphate, ADP = adenosine diphosphate, MAPK = mitogen-activated protein kinase.

Osteoarthritis Cartilage. Author manuscript; available in PMC 2017 July 27.

SUMMARY
Introduction
Clinical studies
Population-based studies of DM and OA
Other clinical studies of DM and OA
Effect of DM on OA clinical care
Effect of DM on human articular tissues
Animal studies
Animal models of T1DM
Animal models of T2DM
Animal studies investigating a link between DM and OA
Effects of DM and hyperglycemia on articular cartilage
Effects of DM and hyperglycemia on bone
Effects of DM and hyperglycemia on tendons and ligaments
Effects of DM and hyperglycemia on synovium
Potential mechanisms
Conclusions
References
Table I
Table II

 
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