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Battlefield 3 Assignments Overview Of Diabetes



The potential of mHealth technologies in the care of patients with diabetes and other chronic conditions has captured the attention of clinicians and researchers. Efforts to date have incorporated a variety of tools and techniques, including Web-based portals, short message service (SMS) text messaging, remote collection of biometric data, electronic coaching, electronic-based health education, secure email communication between visits, and electronic collection of lifestyle and quality-of-life surveys. Each of these tools, used alone or in combination, have demonstrated varying degrees of effectiveness. Some of the more promising results have been demonstrated using regular collection of biometric devices, SMS text messaging, secure email communication with clinical teams, and regular reporting of quality-of-life variables. In this study, we seek to incorporate several of the most promising mHealth capabilities in a patient-centered medical home (PCMH) workflow.


We aim to address underlying technology needs and gaps related to the use of mHealth technology and the activation of patients living with type 2 diabetes. Stated differently, we enable supporting technologies while seeking to influence patient activation and self-care activities.


This is a multisite phased study, conducted within the US Military Health System, that includes a user-centered design phase and a PCMH-based feasibility trial. In phase 1, we will assess both patient and provider preferences regarding the enhancement of the enabling technology capabilities for type 2 diabetes chronic care management. Phase 2 research will be a single-blinded 12-month feasibility study that incorporates randomization principles. Phase 2 research will seek to improve patient activation and self-care activities through the use of the Mobile Health Care Environment with tailored behavioral messaging. The primary outcome measure is the Patient Activation Measure scores. Secondary outcome measures are Summary of Diabetes Self-care Activities Measure scores, clinical measures, comorbid conditions, health services resource consumption, and technology system usage statistics.


We have completed phase 1 data collection. Formal analysis of phase 1 data has not been completed. We have obtained institutional review board approval and began phase 1 research in late fall 2016.


The study hypotheses suggest that patients can, and will, improve their activation in chronic care management. Improved activation should translate into improved diabetes self-care. Expected benefits of this research to the scientific community and health care services include improved understanding of how to leverage mHealth technology to activate patients living with type 2 diabetes in self-management behaviors. The research will shed light on implementation strategies in integrating mHealth into the clinical workflow of the PCMH setting.

Keywords: mHealth, diabetes mellitus, patient activation, patient-centered medical home, patient centered care, eHealth, health information


Diabetes mellitus is a chronic disease with high rates of disability, impaired quality of life, and premature death [1-4]. The prevalence of type 2 diabetes is increasing at an alarming rate in the United States; in 2013, the estimated number of patients was between 20 million and 27 million, or about 7% to 10% of the adult population [2,3]. Research suggests that, if current trends continue, diabetes will be diagnosed in 1 in 3 adults in the United States by 2050 [4,5]. Diabetes is the leading cause of blindness, nontraumatic amputations, and adult renal failure, and reduces life expectancy by 5-10 years [2]. The individual symptom burden (eg, chronic pain, neuropathy, depression, and physical disability) is substantial and significantly increases in the older adult population [1]. In the United States, an average individual with diabetes incurs medical expenditures of about US $13,700 a year, of which about US $7900 is attributable to diabetes [4]. This represents an expenditure about 2.3 times greater than that for a diabetes-free individual [4].

Numerous primary care-based efforts have been aimed at reducing both the disease burden on individuals and the cost of diabetes care. A contemporary strategy is the management of patients with diabetes within the context of the patient-centered medical home (PCMH) setting. A key PCMH principle is the appropriate use of information technology to support optimal patient care, performance measurement, patient education, and enhanced communication [6]. Several case studies from various US health systems show the benefit of the PCMH model to improved diabetes care [7]. There is published evidence on the positive impact of PCMH-based care in psychosocial outcomes of patients with diabetes [8].

The potential of mHealth technologies in the care of patients with diabetes and other chronic conditions has captured the attention of clinicians and researchers. Efforts to date have incorporated a variety of tools and techniques, including Web-based portals [9-11], short message service (SMS) text messaging [9,12-14], remote collection of biometric data [12,15], electronic coaching [14], electronic-based health education [13], secure email communication between visits [16-18], electronic collection of lifestyle and quality-of-life surveys, and personal health records (PHRs). Each of these tools, used alone or in combination, has demonstrated varying degrees of effectiveness. Some of the more promising results have been demonstrated using regular collection of biometric devices (eg, glucometers, activity monitors) [12], SMS text messaging [12-14], secure email communication with clinicians and clinical teams [9,16,17], and regular reporting of quality-of-life variables aligned with decision support. In this study, we seek to incorporate many of the most promising mHealth capabilities in a PCMH workflow led by a clinical advisory team. We aim to address underlying technology needs and gaps related to the use of mHealth technologies and the activation of patients with type 2 diabetes.

The Concept of Patient Activation

Self-management for patients with type 2 diabetes and other chronic conditions includes following complex treatment regimens, monitoring chronic conditions, and making lifestyle changes [19-22]. The chronic care model suggests that activated patients are better able to function in the role of self-manager [21,23]. An activated patient has the motivation, confidence, and skills necessary to enact behavioral changes and make health-related decisions [24-27]. These patients ask questions and collaborate with their health care provider [19,26-28]. Research shows that activated patients have more positive clinical outcomes, are more likely to receive preventive care, and have lower health care-related costs [24,26,29].

A recommended strategy in patient activation is the concept of “preactivating” patients prior to clinical encounters [20,30]. The concept incorporates active targeted communication and follow-up from the health care team [30]. Interventions to include educational programs [31], care coaching [32], and motivational interviewing [33] have been attempted to improve patient activation with varied success [34]. However, these efforts have infrequently been tailored to potential intrinsic differences in how the patients approach their disease. Theoretically, research suggests that patient activation can be increased [19,35-37]. Conceptualizing activation as a dynamic variable allows researchers to target this motivating factor that can potentially influence health behaviors [21,24,38,39].

Previous Research on Patient Portals, Personal Health Records, Patient Activation, and Improved Outcomes

Federal legislation and movement toward patient centeredness in the United States has fueled interest in providing patients with access to their health information, enhanced communication with clinical environments, and greater emphasis on self-care [40-43]. Early research on portal and PHR use and patient activation provided mixed results. Several studies reported a positive significant relationship between use of portals and PHRs and activation of patients [41,43-45], while other studies did not realize a significant finding [40,46,47]. The design of these published studies prevented any in-depth inquiry into why (or not) portal and PHR use influenced patient activation. Their authors posited a variety of possible factors, including the target patient population [44], time since severe diagnosis or symptoms and episodes [46,47], and patient age (activation being higher in adults than in children) [45]. One study suggested that tailoring a portal or PHR intervention to the patient activation level may optimize intervention efficiency [43].

Early research on increased activation and improved clinical outcomes using patient portal and PHR-based interventions has also provided mixed results. Several studies demonstrated a relationship between increased patient activation and improved intermediate clinical outcomes (eg, hypertension, smoking, body mass index, and glycated hemoglobin [HbA1c]) [48], while a major study did not record a significant finding regarding the same outcomes [42]. It is noteworthy that these early studies did not provide substantial detail on design issues related to the portal or PHR, or whether the intervention included behavioral reinforcement.

User-Centered Design

Design science will inform our development and testing [49,50]. User-centered design will guide development, following participatory design methods to understand more specifically how patients experience diabetes on a daily basis, what clinicians need to know from patients, and how to create a shared communication system for better decision making [51]. Consistent with the guidelines set forth by the Science Panel on Interactive Communication and Health [52], our evaluation design will incorporate the 3 elements of formative, process, and outcome evaluation. Methods include (1) clinician focus groups and in-depth patient interviews to define key knowledge variables that are personally and clinically relevant, (2) iterative usability testing with patients, and (3) iterative observations of the system in clinical settings [53].

Military Health System: An Overview

The US Military Health System (MHS) is a large integrated health system that cares for about 9.39 million beneficiaries through its TRICARE insurance product and its substantial direct care system consisting of tertiary facilities, community hospitals, and clinics globally. Nearly 35% of its beneficiary pool are active duty members and their dependents, with a larger population (about 56%) being retirees and their beneficiaries [54]. The MHS direct care system is robust. Facilities are accredited by the Joint Commission (formerly the Joint Commission on Accreditation of Healthcare Organizations), and the MHS operates a dedicated educational infrastructure to support medical and nursing education programs [54]. The MHS has a connecting health information technology infrastructure to support clinical care and clinical operations.


Hypothesis 1: User-centered design will allow developers to create a patient-centered interactive and tailored mobile technology for use in the PCMH setting.

Hypothesis 2: The use of interactive and tailored mobile technology, the Mobile Health Care Environment (MHCE), employed in the PCMH setting will increase the activation of patients with chronic type 2 diabetes.

Hypothesis 3: The use of interactive and tailored mobile technology in a PCMH setting will increase diabetes self-care activities.

Hypothesis 4: Patients who engage at a higher rate with the interactive and tailored mobile technology in a PCMH setting will realize greater improvement in clinical measures.

The primary goal of the research is to enhance patient activation levels and improve self-management of type 2 diabetes through the use of the MHCE in the PCMH setting. While there are published studies aimed at improving the activation and care of patients with diabetes in the United States, to our knowledge, no study has sought to enhance care of patients with diabetes using a fully comprehensive and adaptable MHCE-like system. We seek to demonstrate improvement in patient activation measured by the Patient Activation Measure (PAM) instrument [21,55]. We believe that, in improving their activation, patients will also realize an improvement in diabetes self-care activities measured by their Summary of Diabetes Self-Care Activities (SDSCA) [56] scores.


Trial Design

This is a multisite, phased study conducted within the MHS that includes a user-centered design phase and a PCMH-based feasibility trial. In phase 1, we will assess both patient and provider preferences regarding the enhancement of the MHCE technology capabilities for type 2 diabetes chronic care management. The phase 2 research will be a single-blinded (patients only) 12-month feasibility study that will incorporate randomization principles. We will employ a 1:1 allocation ratio between intervention and control.

Inclusion and Exclusion Criteria

Inclusion criteria for patient participation in phase 1 or 2 research are the following: (1) men and women aged 18 years or older, (2) able to understand and read English, (3) enrolled for primary care to one of the target PCMH sites, and (4) having a diagnosis of type 2 diabetes. Additionally, with respect to phase 2 patients, we will seek to recruit a maximum of 120 (per site), with a distribution of patients with PAM levels 1-4, a sample representative of the patients enrolled in the PCMH. We did not derive the 120 per site recruitment numbers from power calculations, but deemed them to be sufficient. Finally, participants for phase 2 must be available for a 12-month study.

Inclusion criteria for clinician participation in phase 1 or 2 research are the following: (1) being a physician, physician assistant, nurse practitioner, or nurse employed at the target site, and (2) providing care for patients with type 2 diabetes.

Exclusion criteria for patient participation in phase 1 or 2 research are the following: (1) pregnant women, (2) non-English-speaking patients, (3) receiving hospice care, (4) having active cancer and receiving treatment with chemotherapy or radiation therapy, (5) taking warfarin, (6) recipient of gastric bypass or similar procedure, (7) having a diagnosis of uncontrolled hypothyroidism, (8) having known Cushing syndrome, (9) being treated with oral steroids, (10) having known liver disease, (11) having a current diagnosis of cognitive impairments that would interfere with use of technology, (12) having congestive heart failure, in New York Heart Association functional class 3 or 4, and (13) unable to use a mobile device due to cognitive or physical impairments during initial screening. We exclude pregnant women because they require careful monitoring due to potential medical complications for the woman and unborn child. While some mHealth studies seek to include additional exclusions based on age, educational level, or technical literacy, our research team rejected adding any additional exclusion criteria beyond the 13 listed above. We purposely seek the “average” patient with type 2 diabetes in the target population. Feedback from our clinician investigators and research staff at our clinical sites is encouraging that these patients will be capable of using the intervention technology.

Exclusion criteria for clinician participation in phase 1 or 2 research are the following: (1) not affiliated with the target site, and (2) not providing care for patients with type 2 diabetes.

Participant Enrollment

We will recruit patients via review of the PCMH clinic schedule, referrals from providers, distributed posters and fliers, and population health databases. Potential participants will be prescreened through verification of the inclusion and exclusion criteria based on a medical record review. Interested participants will be scheduled for a screening visit with study staff to provide informed consent and be administered the PAM instrument. Patients’ PAM scores will place them in a stratified group, where they will be randomly allocated.

Clinicians practicing in the respective PCMH sites will be invited to participate by word of mouth from the site’s principal investigator; this is a convenience sample. The clinician participants who would like to participate in the study will meet with the senior research associate to review the minimal-risk information sheet to be included in the study. For phase 2, clinicians will sign an informed consent form. The clinician participants will not be blinded in the study, nor allocated to intervention or control groups.

Setting and Site Selection

We seek to purposefully assess the MHCE implementation for diabetes care in 2 distinctly different PCMH environments and geographic locations. The risks of attracting very different populations are mitigated by rather comprehensive inclusion and exclusion criteria, which will ensure similarity regarding patient acuity. The patient base includes those on active duty, retirees, and dependents who have typically spent years in the military and have been stationed at various locations. Both of the selected facilities are federal facilities and operated by the MHS.

Madigan Army Medical Center, the US Army’s second largest military treatment facility located in Tacoma, Washington, is a tertiary facility with a level II trauma center and robust graduate medical education programs. They serve a patient base of approximately 118,000 patients; about 7500 (or >6%) are living with type 2 diabetes. Of the diabetes population, about 15% are active duty members or their dependents, and about 85% are retirees and their dependents. Over half of the patients with diabetes are 57-76 years of age. The study location within the medical center is a PCMH managed by the Department of Internal Medicine. There are approximately 14,300 patients enrolled in this PCMH supported by a staff of 77 (12 staff physicians; 8 residents) responsible for their care.

Mike O’Callaghan Federal Medical Center is a federal facility in the greater Las Vegas, Nevada area, that serves approximately 47,000 patients; about 4500 (or >9%) are living with type 2 diabetes. Of the diabetes population, about 4% are active duty members or their dependents, and about 96% are retirees and their dependents. Over 72% of the patients with diabetes are in their 60s or older. The study location within the medical center is a PCMH managed by the Department of Family Medicine. There are approximately 7500 patients enrolled in this PCMH supported by a staff of 62 (9 staff physicians; 26 residents) responsible for their care.

Description of the Mobile Health Care Environment

The US Department of Defense (DoD) MHCE system is a secure health information system designed to support health services delivery and mHealth. The MHCE meets all physical and information security mandates, as prescribed by federal law and DoD regulation, for the protection of personal health information and personally identifiable information. The MHCE was developed by the DoD Telemedicine and Advanced Technology Research Center as a platform to support mHealth. Its first major application was to support patient engagement for wounded warriors rehabilitating in their communities. In the study, soldiers on average responded to ≥60% of weekly questionnaires related to behavioral health challenges, posttraumatic stress, or traumatic brain injury [57]. Our study is Telemedicine and Advanced Technology Research Center’s second major application. The MHCE is designed to remotely support patients by sending automated reminders, announcements, wellness tips, alerts, and status questionnaires. Figure 1 is a visual example of the graphical user interface that patients will see when accessing the MHCE. In this study, we enhance MHCE capabilities in several ways.

Figure 1

Mobile Health Care Environment home screen (patient view). BP: blood pressure.

Intervention Overview

Our intervention is based on an enhanced MHCE in several ways. First, we add the capability to include collection and visualization of data from Bluetooth-enabled medical devices. This includes mapping data from device output into the MHCE, developing data visualization appropriate for mHealth and clinical care (eg, graphing outcomes, temporal trend patterns), migrating data in an analysis cell, and developing decision-support algorithms that signal safety alerts and need for behavioral reinforcement. Devices used in this study include a scale, glucometer, blood pressure reader, and activity monitor. Second, we expand the capacity of the MHCE analysis cell to manage large amounts of data and to conduct both routine reports and research applications. Third, we add patient activation and associated measurement instruments for capturing baseline and ongoing changes to patient activation. Fourth, we expand the MHCE messaging platform that research associates, and later clinical support staff, can use to send tailored behavioral messaging to patients in an effort to influence greater activation and reinforce positive behavior.

The MHCE can be accessed by mobile phones or tablets that use either an IOS or Android platform. The MHCE requires Internet access for patients to sync data from devices (addressed above) to the MHCE backend portal, to receive tailored behavioral messages, or to use other functions. During the study, patients will additionally receive SMS messages with hyperlinks to a separate secure information system platform used for administration and analysis of PAM and SDSCA instruments. MHCE activity, or lack thereof, will be monitored by senior research associates, who can prompt patients via tailored behavioral messages or direct contact.

Tailored Behavioral Messaging

A primary component of the MHCE system is tailored behavioral messaging. Tailored behavioral messages are more likely than generic messages to facilitate health behavior change when they are aligned with individuals’ beliefs, lifestyle, demographics, social norms, or interests [58-60].

In this study, the research team has developed behavioral messages tailored for each of the 4 PAM score levels; in total we have developed 360 messages. The messages fall within 9 functional areas common to diabetes care: nutrition, home monitoring, physical activity, blood pressure, foot care, medications, smoking, glucose control, and general behavioral reinforcement. The messages are consistent with general concepts and goals of self-management behaviors consistent with the DoD-Veterans Affairs clinical practice guideline for type 2 diabetes and the SDSCA survey instrument.

Since different PAM levels require different strategies, we addressed varying needs through a combination of applied constructs. Specifically, level 1 messages must address the emotional state of feeling overwhelmed and passive with an emphasis on the importance of taking action. To address the needs of PAM level 1 patients, we use constructs from social networks and social support theory [61], specifically that of emotional support that emphasizes expressions of empathy and caring. We encourage a sense of hope by expressing the belief and expectation that the message recipient can change his or her situation and overcome difficulties. Constructs from the transtheoretical model [62] such as visioning, dramatic relief, self-reevaluation, and environmental reevaluation also guided level 1 message development.

PAM level 2 messages build knowledge and self-efficacy to engage in a behavior and focus on ways to take small steps that don’t require much in-depth knowledge. Self-efficacy and the confidence a person feels about performing a particular activity was a primary construct used to develop these messages with a focus on one of the main strategies to build self-efficacy, that of taking small steps that are likely to result in performance accomplishment. Outcome expectations, or the anticipatory outcomes of a behavior, stated in ways that would likely appeal to the expectancies or values a person places on the outcome, was also an important construct [63].

PAM level 3 messages assume some knowledge and focus more on building self-management skills such as goal setting and self-monitoring. For messages in this level, we used transtheoretical model [62] constructs relevant to the preparation and action stages of behavior change.

PAM level 4 messages about staying the course and avoiding relapse when stressed were grounded in the transtheoretical model constructs guiding processes used in the maintenance stage of change. Also used in level 4 message development were strategies developed in a relapse prevention model [64] such as identifying high-risk situations for relapse and the development of specific coping strategies for those situations.

In phase 2 of our study, tailored behavioral messages will be sent to each intervention group participant, via the MHCE accessed through their mobile device, based on both senior research associate-initiated and algorithm-automated schedules and thresholds developed according to PAM level, SDSCA responses, and agreed-upon general rotation. Figure 2 offers examples. The senior research associates will use the MHCE backend portal control panel for manual rotational scheduling of messages to be delivered 3 days per week (typically Monday, Wednesday, and Friday) within the MHCE system. Participant responses to the SDSCA may trigger additional messaging if their clinical readings from biomedical devices exceed established safety thresholds.

Figure 2

Example of tailored health messaging.

Phase 1 User-Centered Design Study Flow

In phase 1 we will evaluate and gain feedback from patients with diabetes regarding MHCE app navigation, use of external devices, ease of use, and satisfaction. We will collect baseline research participant data to include basic demographic data and clinical measures following verification of informed consent. One researcher-facilitator will lead individual participants through usability testing and the additional researcher-observer will document observations. During a facilitator-provided demonstration of the MHCE, the facilitator will ask each participant to concurrently navigate to each component of the MHCE system via a mini tablet device under their control. For each task, we will ask 3 open-ended questions to evaluate task-specific user satisfaction regarding the look and layout of the app, how the app functions, and any specific issues that are confusing. Next, the facilitator will give a brief demonstration of the external devices that will be used in the study: a blood pressure monitor, a glucometer, a digital precision weight scale, and a Fitbit Charge wireless activity and sleep wristband. For each device, we will ask participants to (1) manually upload data, (2) sync each device with the app, and (3) interpret graphs. While it would be preferable to observe the MHCE in the context of where the patient would actually use the system, financial limitations prohibit such expanded usability observation research.

Research staff will evaluate usability by applying definitions and usability evaluation metrics guided by the International Organization for Standardization’s 9241-11 usability framework and mHealth usability research [65]. Specific metrics to evaluate usability are effectiveness, efficiency, and satisfaction. We will evaluate effectiveness via task completion and error coding. We will assess timed task completion as a task being completed with ease, being completed with minor mistakes, or not completed. Errors will be coded using a codebook developed by the phase 1 team. The observer will also note when users commit errors they cannot solve or commit errors that prevent further progress. We will use the Single Ease Question to evaluate informant satisfaction immediately after performing each task [66]. The System Usability Scale (SUS) will evaluate overall informant satisfaction with the MHCE [67].

We will also assess provider preferences in phase 1 using focus groups of clinicians and nurses recruited from the 2 study sites. Two trained qualitative researchers will facilitate the focus groups. We will take field notes during the focus groups and audio record each session to ensure accuracy of the field notes. The facilitators will use a semistructured interview guide to elicit clinician and nurse feedback about the MHCE. After briefly demonstrating the app, facilitators will ask 6 broad questions (with probes), developed by the phase 1 team in conjunction with study coinvestigators. These questions are designed to elicit feedback from participants regarding app design, alerts (general), wording of alerts, perceived usefulness to patients for promoting self-management, clinical usefulness and workflow, and backend portal data summaries. We will probe specific issues related to clinical usefulness of the MHCE in the context of the clinical workflow of the PCMH environment.

A 4-member team will complete a thematically organized data analysis of the clinician and nurse feedback using an inductive narrative approach [68-70]. We will begin with an analysis of field notes from 1 randomly selected provider and 1 nurse to create an initial codebook. We will expand the codebook as we continue to code field notes. The analysis team will divide the coding duties so that each transcript is coded by 2 independent coders [71]. The team will meet during the coding process to address consensus, update the coding structure, and revisit any previously coded field notes that need to be reviewed again based on these updates. Codes will be applied to the transcripts using Atlas.ti software version 7.5.10. Codes drawn from the interview guide will serve as the organizing framework for analysis. As new themes emerge, we will expand the narrative.

Phase 2 Controlled Study: Patient Enrollment and Study Flow

For phase 2 we will recruit 240 patients (120 per site), with half assigned as a control group. Eligible patients will be first assigned to 4 strata according to their PAM score. After all patients are identified and assigned into the strata, simple randomizations will be performed within each stratum to assign patients to either the MHCE or usual care groups. Patients will be randomly allocated to either the control or the intervention group based on their PAM scores.

We will modify the MHCE system between phase 1 and phase 2 research, incorporating phase 1 observations and optimizing system usability at the patient level. We will collect baseline research participant data, including basic demographic data and clinical measures, following verification of informed consent.

MHCE Intervention Versus Usual Care

Patients in both the intervention and usual care (control) groups will receive a device package as outlined in Textbox 1. These devices will collect and record biometric data. All patients will be trained in using biomedical devices and peripheral equipment.

Patient device package (intervention and control groups).

  • Activity monitor (Bluetooth and cloud enabled)

  • Scale (Bluetooth enabled)

  • Blood pressure cuff (Bluetooth enabled)

  • Glucometer (Bluetooth enabled)

For the patient groups allocated to the intervention, their devices will be mapped to the MHCE system accessible from the patients’ mobile phone or an iPad mini tablet device. Data from their biomedical devices will be visually presented in the MHCE with trend and scalable options (Figure 3).

Figure 3

Example of visualization of patient data. BP: blood pressure.

Safety algorithms will be mapped to these clinical data to alert the patient and, depending on the measure, the clinical team when readings exceed established thresholds. The intervention groups will also have full access to and will receive the tailored behavioral messaging outlined above. At time of study enrollment, we will provide a tablet device to patients who are fully eligible to participate, are allocated to the intervention group, but do not have a mobile phone (with iOS or Android operating system).

In both the intervention and control groups, the patients’ clinician and PCMH support team will be notified of the patients’ enrollment in the study. The intervention patients will be encouraged to regularly use the MHCE system as a tool to improve their diabetes self-care.

Initial Outcome Measures for Patient Component

Primary outcome measures are PAM scores. Secondary outcome measures in the study are (1) SDSCA responses, (2) clinical measures (Textbox 2), (3) comorbid conditions (eg, uncontrolled plasma glucose, hypertension, hyperlipidemia, stoke, eye disease, coronary heart disease), (4) SUS survey scores, (5) MHCE usage statistics, and (6) health services utilization measures.

Clinical measures in phase 2.

  • Glycated hemoglobin (HbA1c)

  • Low-density lipoprotein

  • High-density lipoprotein

  • Height and weight

  • Abdominal circumference

  • Systolic blood pressure

  • Diastolic blood pressure

Patient Activation Measure Instrument

The self-reported PAM survey is associated with self-management behaviors, medication adherence, patient satisfaction, and quality of life [55,72]. Within a diabetes-specific population, PAM is not related to knowledge regarding HbA1c (the standard measure of average blood glucose level [73]), but is associated with better glycemic control [74]. Interventions, including educational programs [31], care coaching [32], and motivational interviewing [33], have been attempted to improve this activation with varied success. Specifically, patient activation can be increased with targeted, patient-centered, repeated messaging [19]. The PAM is a valid, reliable, unidimensional, probabilistic Guttman-like scale that was validated over a decade ago [21] and is a standard tool to measure patient activation. We will administer the PAM at screening visits in phases 1 and 2, and electronically every 3 months during phase 2 for both the intervention and control groups. Figure 4 outlies the 4 PAM levels.

Figure 4

The 4 levels within the Patient Activation Measure (PAM) survey.

Summary of Diabetes Self-Care Activities Instrument

The SDSCA instrument is a brief self-report instrument for measuring levels of self-management across different components of the diabetes regimen [56]. The SDSCA includes 11 core items associated with diabetes self-care. The SDSCA has been successfully used in numerous diabetes studies both within and outside the United States [56,75-78]. The SDSCA has been validated and is considered a standard instrument in diabetes care for measuring self-care activities, with its validation and reliability published nearly two decades ago [56]. We will administer the SDSCA at the intake visit for phase 2, and electronically every 2 weeks during phase 2 for both the intervention and control groups.

Clinical Measures

We will collect clinical measures (Textbox 2) from patients at intake during phase 1 research. We will collect and compare changes in patient clinical measures for both groups in phase 2 at 3 points: intake, midpoint (month 6), and conclusion (month 12). For patients assigned to the MHCE intervention group, the MHCE system will also record weight, systolic blood pressure, diastolic blood pressure, and blood glucose values to the MHCE module on a regular basis via Wi-Fi or Bluetooth-enabled peripheral equipment.

Clinician Support for Patient Activation Measure

The Clinician Support for Patient Activation Measure (CS-PAM) instrument measures clinician beliefs about patient self-management behavior. The CS-PAM has been a valid and reliable instrument in use since 2010 [25]. The CS-PAM score indicates an individual clinician’s overall level of endorsement or belief about the importance of patient self-management, as well as beliefs about the importance of specific patient competency categories [25].

In phase 2, we will measure clinician support for patient self-management by the CS-PAM. PCMH clinicians (ie, physicians, nurse practitioners, and physician assistants) in this study will take the CS-PAM at 3 points in the study: beginning, midpoint (month 6), and conclusion (month 12).

System Usability Scale Survey

The SUS survey is a 10-item Likert-like scaled survey used to convey a subjective assessment of system usability. The instrument was developed over 15 years ago and is used to measure the usability of websites. The SUS was validated on several occasions, with perhaps the largest validation study (including 10 years’ worth of data) conducted in 2008 [79]. In this study we will substitute the term “MHCE system” for the term “website” in the instrument. We will conduct the SUS survey at the conclusion of the encounter for phase 1 patients, and at midpoint (months 5-6) and study conclusion (months 11-12) for phase 2 patients in the intervention group.

MHCE Usage Statistics

Our technology enablement partners will embed counters (invisible to patients) that track usage of MHCE components. These counters will export usage data to our research analysis database. Summary statistics and trends will be analyzed with comparison.

Comorbid Conditions

We will assess and document comorbid conditions (eg, hypertension, hyperlipidemia) among both the control and intervention groups during prescreening of eligibility, at intake, at study midpoint, and at study conclusion. While not primary outcome measures, any change over time and whether the number and type of comorbid conditions influence patient use of MHCE will be assessed.

Data Analysis Strategy

We will conduct the primary analyses for phase 2 using an intent-to-treat approach. Study participants will be retained in their original assignment groups after the random allocation in the analysis. Achievement of randomization will be evaluated through the comparison of baseline key variables between the MHCE intervention group and the control group. We will also compare baseline key characteristics between eligible patients who participate in the study and those who do not participate to examine the potential for bias.

To test hypotheses 2 and 3, that patients who participate in MHCE will have higher PAM, SDSCA, and SUS scores and improved selected clinical outcomes and comorbid conditions than their counterparts in usual care, we will use multivariate regression models (logistic regression if the outcome is a binary variable and linear regression if the outcome is a continuous variable) with the intervention assignment as the primary independent variable. Stratified analyses will be conducted (eg, sex, race, and initial PAM score).

The primary comparison will be outcomes at 12 months. Additional analyses will use longitudinal analysis models using a generalized estimating equation, which will include outcomes at both 6 and 12 months.

To test hypothesis 4, that patients who engage at a higher rate with the interactive and tailored mobile technology in MHCE will realize greater improvement in clinical measures (eg, HbA1c values; Textbox 2), we will use multivariate linear regression models. Clinical outcomes will be the dependent variables and will be tested separately. The main independent variable will be MHCE usage. We will examine the association between the dependent variable and MHCE usage by using the generalized estimate equation with adjustment of potential confounders (eg, age, sex, race, duration of disease, use or nonuse of insulin).

Trial Status

At the time of publication, we have completed phase 1 data collection. Formal analysis of phase 1 data has not been completed. Institutional review board approval (study and site implementation) has been obtained and phase 1 research commenced in late fall 2016.


The hypotheses of the study suggest that patients can, and will, improve their activation in chronic care self-management. Improved activation should translate into improved diabetes self-care. While not powered in this study, improved self-management activities should lead to fewer emergency situations (and trips to the emergency department), weight loss (in many cases), improved blood pressure, and improved clinical measures. Cumulatively, the gains should translate into improved quality of life if our hypotheses are supported.

This study has been approved by the institutional review boards of Clemson University (protocol #IRB2015-234) and the Madigan Army Medical Center (representing both DoD sites; reference #216073). Study personnel will follow protocol with all informed consent mandates directed by the institutional review boards; informed consent in this study includes both patients and clinicians or key clinical staff. This trial was registered with ClinicalTrials.gov (NCT02949037) on October 31, 2016.


Expected benefits of this research and development effort to the scientific community and health care services include improved understanding of how to advance 3 joint PCMH principles (ie, better coordination of care, improved quality and safety, and enhanced access to care) through the use of mobile technology and improved understanding of how to include mHealth technology in the clinical workflow of the PCMH health services model, as well as improved understanding of how to use mHealth technology to activate patients with a diagnosis of type 2 diabetes in disease self-management behaviors. We also expect to improve understanding of how patient complexity and degree of “sickness” may influence patient use or nonuse of mHealth technologies in self-management of their disease, and to explore how to map patient-entered biomedical data onto clinical documentation and a decision-support platform useful in chronic care management.

Our study design is not immune from potential threats to validity. Patients allocated to the control arm will be issued the same peripheral devices as the intervention group and, while they may not achieve the same degree of activation, they may realize improvement if they use the equipment being issued to them. Though this behavioral mechanism could benefit patients in the control group, a strong activation change in the control arm could conceal the behavioral benefit of our intervention when we compare patient behavior from the 2 arms.

We are aware that we did not conduct a power calculation for sample size, since this project was funded as a feasibility study, not a randomized controlled trial. Thus, sample size estimates are neither required nor appropriate. We additionally recognize that a formal randomized controlled trial would be preferred to our current design. A follow-on randomized controlled trial is our goal once we have collected sufficient data and have a better understanding of how patients will use this chronic care health information technology system. At that point we will legitimately be able to predict the intervention effect and properly power the study.


The authors appreciate and would like to acknowledge the contributions of Terry J Newton, MD (US Army Office of the Surgeon General), who provided feedback on the systemwide implications of the research efforts and offered general guidance. We also acknowledge Ms Holly Pavliscsak (US Army Telemedicine and Advanced Technology Research Center), who helped shape early technology development of our mHealth intervention.

Funding: The study was extramurally funded, via the Broad Agency Announcement, by the US Army Medical Research Acquisition Activity; contract # W81XWH-15-C-0070. The funder did not influence the design of the study or strategies related to its collection, analysis, or interpretation of data.

Federal disclaimer: The views expressed are those of the authors and do not reflect the official policy of the Department of the Army, the Department of the Air Force, the Department of Defense, or the US federal government.


CS-PAMClinician Support Patient Activation Measure
DoDDepartment of Defense
HbA1cglycated hemoglobin
MHCEMobile Health Care Environment
MHSMilitary Health System
PAMPatient Activation Measure
PCMHpatient-centered medical home
PHRpersonal health record
SDSCASummary of Diabetes Self-Care Activities
SMSshort message service
SUSSystem Usability Scale


Contributed by

Authors' Contributions: RG initially conceptualized the study, and both RG and LS wrote the initial draft of the manuscript. PC, EAS, RG, WWS, KWE, MH, and JBM assisted in the setting narrative, enrollment strategies, and institutional review board-related issues. The technical aspects of the intervention and MHCE description were authored by JRL and RG. The biostatistics and data analysis strategy were developed by LC and KT. The patient activation and behavioral messaging component, including examples, was developed by CJD, JEW, SFG, and KOJ. The user-centered design component was researched and authored by JEW and SFG. The outcome measures component was conceptualized and authored by PC, EAS, KT, LS, CJD, JEW, LC, JBM, MH, WWS, and KWE. The clinical components, including inclusion and exclusion criteria, were developed by PC, EAS, CD, WWS, JBM, MH, KWE, and RG. The MHS review and component were authored by JBM, MH, KWE, and RG. All authors read, contributed to, critically reviewed, and approved the final manuscript.

Conflicts of Interest: None declared.


1. Sudore RL, Karter AJ, Huang ES, Moffet HH, Laiteerapong N, Schenker Y, Adams A, Whitmer RA, Liu JY, Miao Y, John PM, Schillinger D. Symptom burden of adults with type 2 diabetes across the disease course: diabetes & aging study. J Gen Intern Med. 2012 Dec;27(12):1674–81. doi: 10.1007/s11606-012-2132-3.http://europepmc.org/abstract/MED/22854982. [PMC free article][PubMed][Cross Ref]

2. Vigersky RA. An overview of management issues in adult patients with type 2 diabetes mellitus. J Diabetes Sci Technol. 2011 Mar 01;5(2):245–50.http://europepmc.org/abstract/MED/21527089. [PMC free article][PubMed]

3. Scollan-Koliopoulos M, Bleich D, Rapp KJ, Wong P, Hofmann CJ, Raghuwanshi M. Health-related quality of life, disease severity, and anticipated trajectory of diabetes. Diabetes Educ. 2013;39(1):83–91. doi: 10.1177/0145721712467697.[PubMed][Cross Ref]

4. American Diabetes Association Economic costs of diabetes in the U.S. in 2012. Diabetes Care. 2013 Apr;36(4):1033–46. doi: 10.2337/dc12-2625.http://europepmc.org/abstract/MED/23468086. [PMC free article][PubMed][Cross Ref]

5. Boyle JP, Thompson TJ, Gregg EW, Barker LE, Williamson DF. Projection of the year 2050 burden of diabetes in the US adult population: dynamic modeling of incidence, mortality, and prediabetes prevalence. Popul Health Metr. 2010 Oct 22;8:29. doi: 10.1186/1478-7954-8-29.https://pophealthmetrics.biomedcentral.com/articles/10.1186/1478-7954-8-29. [PMC free article][PubMed][Cross Ref]

6. Patient-Centered Primary Care Collaborative Joint principles of the patient-centered medical home. 2007. Jan 01, [2017-03-02]. https://www.pcpcc.org/about/medical-homewebcite.

7. Nielsen M, Langner B, Zema C, Hacker T, Grundy P. Benefits of Implementing the Primary Care Patient-Centered Medical Home: A Review of Cost & Quality Results. Washington, DC: Patient-Centered Primary Care Collaborative; 2012. p. 41.

8. Jortberg BT, Miller BF, Gabbay RA, Sparling K, Dickinson WP. Patient-centered medical home: how it affects psychosocial outcomes for diabetes. Curr Diab Rep. 2012 Dec;12(6):721–8. doi: 10.1007/s11892-012-0316-1.[PubMed][Cross Ref]

9. Wade-Vuturo AE, Mayberry LS, Osborn CY. Secure messaging and diabetes management: experiences and perspectives of patient portal users. J Am Med Inform Assoc. 2013 May 1;20(3):519–25. doi: 10.1136/amiajnl-2012-001253.http://jamia.oxfordjournals.org/cgi/pmidlookup?view=long&pmid=23242764. [PMC free article][PubMed][Cross Ref]

10. Yu CH, Bahniwal R, Laupacis A, Leung E, Orr MS, Straus SE. Systematic review and evaluation of web-accessible tools for management of diabetes and related cardiovascular risk factors by patients and healthcare providers. J Am Med Inform Assoc. 2012;19(4):514–22. doi: 10.1136/amiajnl-2011-000307.http://jamia.oxfordjournals.org/cgi/pmidlookup?view=long&pmid=22215057. [PMC free article][PubMed][Cross Ref]

11. Osborn CY, Mayberry LS, Mulvaney SA, Hess R. Patient web portals to improve diabetes outcomes: a systematic review. Curr Diab Rep. 2010 Dec;10(6):422–35. doi: 10.1007/s11892-010-0151-1.http://europepmc.org/abstract/MED/20890688. [PMC free article][PubMed][Cross Ref]

12. Faridi Z, Liberti L, Shuval K, Northrup V, Ali A, Katz DL. Evaluating the impact of mobile telephone technology on type 2 diabetic patients' self-management: the NICHE pilot study. J Eval Clin Pract. 2008 Jun;14(3):465–9. doi: 10.1111/j.1365-2753.2007.00881.x.[PubMed][Cross Ref]

13. Arora S, Peters AL, Agy C, Menchine M. A mobile health intervention for inner city patients with poorly controlled diabetes: proof-of-concept of the TExT-MED program. Diabetes Technol Ther. 2012 Jun;14(6):492–6. doi: 10.1089/dia.2011.0252.[PubMed][Cross Ref]

14. Quinn CC, Shardell MD, Terrin ML, Barr EA, Ballew SH, Gruber-Baldini AL. Cluster-randomized trial of a mobile phone personalized behavioral intervention for blood glucose control. Diabetes Care. 2011 Sep;34(9):1934–42. doi: 10.2337/dc11-0366.http://europepmc.org/abstract/MED/21788632. [PMC free article][PubMed][Cross Ref]

15. Newton KH, Wiltshire EJ, Elley CR. Pedometers and text messaging to increase physical activity: randomized controlled trial of adolescents with type 1 diabetes. Diabetes Care. 2009 May;32(5):813–5. doi: 10.2337/dc08-1974.http://europepmc.org/abstract/MED/19228863. [PMC free article][PubMed][Cross Ref]

The Continuous Erythropoietin Receptor Activator Affects Different Pathways of Diabetic Renal Injury

  1. Jan Menne*,
  2. Joon-Keun Park*,
  3. Nelli Shushakova*,
  4. Michael Mengel,
  5. Matthias Meier* and
  6. Danilo Fliser*
  1. *Departments of Internal Medicine and Pathology, Hannover Medical School, Hannover, Germany
  1. Correspondence:
    Dr. Danilo Fliser, Department of Internal Medicine, Hannover Medical School, Carl-Neuberg-Strasse 1, 30625 Hannover, Germany. Phone: +49-511-532-6319; Fax: +49-511-552366; E-mail: fliser.danilo{at}mh-hannover.de
  • Received for publication July 5, 2006.
  • Accepted for publication April 18, 2007.


This study explored the tissue-protective properties of the continuous erythropoietin receptor activator (CERA) in an experimental model of (nonischemic) diabetic kidney injury (i.e., the db/db mouse). Mice were randomly treated with placebo (n = 25), low-dosage CERA (n = 25), and high-dosage CERA (n = 25). Also studied were 25 nondiabetic db/m mice. Hematocrit was comparable in placebo and low-dosage CERA–treated mice but increased significantly with high-dosage CERA (P < 0.01 versus both). Significantly reduced expression of TGF-β, vascular endothelial growth factor, and collagen IV was found in glomeruli and the tubulointerstitial area with CERA treatment, and these beneficial molecular effects were clearly dosage dependent (both P < 0.05 versus placebo). Similarly, CERA treatment caused a dosage-dependent increase in p-Akt, nephrin, and perlecan tissue expression (all P < 0.05 versus placebo). However, the accelerated mesangial expansion that was observed in placebo-treated db/db mice (versus db/m controls) was significantly reduced only in low-dosage CERA–treated mice (P < 0.01). Moreover, albuminuria was significantly reduced in low- but not high-dosage CERA–treated mice compared with placebo treatment (P < 0.05). In an ancillary study, phlebotomy was performed in high-dosage CERA–treated db/db mice to keep hematocrit within normal (baseline) levels. This procedure resulted in significantly (P < 0.05) less albuminuria as compared with high-dosage CERA–treated mice without phlebotomy, thus preserving the tissue-protective potential of CERA. Long-term CERA treatment has beneficial dosage-dependent effects on molecular pathways of diabetic kidney damage. Low-dosage CERA does not affect hematocrit and therefore may be a feasible method of tissue protection in this setting.

Recent experimental studies revealed that erythropoietin (EPO) has numerous tissue-protective effects apart from its action on erythropoiesis and that it prevents vascular and tissue damage as a result of ischemia in the heart, the brain, and also the kidney.1,2 EPO maintains normal red blood cell mass by exerting a continuous antiapoptotic activity in erythrocyte precursors via stimulation of crucial surviving intracellular signaling cascades such as JAK2/STAT5 and phosphatidylinositol 3-kinase/Akt pathways.2 This signaling subsequently leads to phosphorylation of the proapoptotic factor Bad, which in turn dissociates from a cell survival factor, Bcl-XL, resulting in protection from programmed cell death. We recently demonstrated that long-term treatment with the recombinant human EPO (rHuEPO) analogue darbepoetin α conferred renal vascular and tissue protection, preserved renal function, and significantly improved survival in the remnant rat kidney model (five-sixths nephrectomy).3 Importantly, we used a hematologically noneffective dosage of darbepoetin to obviate potential adverse effects of rHuEPO therapy. In this experimental setting of chronic renal ischemia, darbepoetin persistently activated the Akt pathway and reduced apoptotic cell death in renal tissue.

In this study, we set out to explore the therapeutic potential of another erythropoiesis-stimulating agent—the continuous erythropoietin receptor activator (CERA)—for tissue protection in an animal model of nonischemic chronic kidney injury (i.e., the leptin receptor knockout db/db mouse). In this animal model for type 2 diabetes, the mice develop characteristic molecular and clinical features of diabetic nephropathy.4 We administered a hematologically noneffective (low) and effective (high) dosage of CERA and studied several molecular pathways of diabetic kidney damage. CERA is chemically synthesized through integration of amide bonds between amino groups of rHuEPO and methoxy polyethylene glycol-succinimidyl butanoic acid. This modification results in a substantial prolongation of duration of action.

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Clinical and Laboratory Data

The db/db mice became obese and developed frank hyperglycemia during the observation period when compared with their nondiabetic db/m littermates (Table 1). Blood glucose levels were already slightly higher in db/db mice in comparison with db/m mice at the start of the observation period, and in the next 2 wk, blood glucose increased significantly and remained consistently higher in db/db mice than in db/m controls. We observed no significant difference in blood glucose levels between untreated and CERA-treated db/db mice. Similarly, body weight was not affected by CERA treatment. Hematocrit increased in all four groups during the study period (Table 1), but this increase was more pronounced in the db/db mice. During the study period, we observed no significant difference in hematocrit levels between placebo- and low-dosage CERA–treated db/db mice. However, hematocrit increased significantly in high-dosage CERA–treated mice in comparison with all other treatment groups. At the end of the treatment period, systolic BP (SBP) was significantly higher in placebo-treated db/db mice (149 ± 3 mmHg) in comparison with db/m control mice (135 ± 3 mmHg). BP was not affected by treatment with low-dosage (146 ± 3 mmHg) and high-dosage (148 ± 4 mmHg) CERA (NS versus placebo-treated db/db mice). Kidney weight increased markedly in db/db mice and was significantly higher than in db/m mice (Table 2). It is interesting that in both CERA-treated groups, we observed a less marked increase in kidney weight as compared with placebo-treated db/db mice. Similarly, the kidney-to-body weight ratio differed significantly between nondiabetic db/m and diabetic db/db mice. In placebo-treated db/db mice, urinary albumin excretion was significantly higher than in db/m controls at baseline, week 6, and week 13. Albumin excretion was significantly reduced compared with placebo-treated db/db mice at both early and late time points with low-dosage CERA treatment. In the high-dosage group, albuminuria was significantly lower after 6 wk of treatment, whereas later, it returned to the level that was observed in placebo-treated db/db mice (Table 1). We observed no significant differences in serum creatinine levels between placebo-treated and low-dosage and high-dosage CERA–treated mice (data not shown).

Table 1.

Clinical and laboratory data in control mice (db/m) and in db/db mice treated with saline (placebo) and low- and high-dosage CERAa

Table 2.

Parameters of kidney function in control mice (db/m) and in db/db mice treated with saline (placebo) and low- and high-dosage CERAa

Renal Histology and Immunohistochemistry

The glomerular appearance in placebo-treated db/db mice showed accelerated mesangial expansion characterized by an increase in periodic acid-Schiff–positive mesangial matrix area relative to that observed in db/m mice at the end of the study period (Figure 1). In mice that were treated with low-dosage CERA, the score was significantly reduced (P < 0.01). In contrast, in mice that were treated with high-dosage CERA, we observed an even higher score than in placebo-treated mice. Furthermore, we analyzed small arteries for evidence of arteriolar hyalinosis as a marker for diabetic vascular damage (Figure 1). We found no arteriolar hyalinosis in db/m control mice, whereas in placebo-treated db/db mice, a mild degree of arteriolar hyalinosis was present. Arteriolar hyalinosis was less frequent in mice that were treated with low-dosage CERA, but similar to the previous finding on mesangial expansion, we observed a higher degree of arteriolar hyalinosis in mice that were treated with high-dosage CERA.

Figure 1.

Mesangial expansion and arteriolar hyalinosis in renal tissue of db/db mice. Diabetic db/db mice were treated with saline (placebo) or continuous erythropoietin receptor activator (CERA) for 14 wk. Db/m nondiabetic mice were used as control. After the perfusion with Ringer solution, kidneys were excised, decapsulated, weighed, immersed in 4% formalin, and embedded in paraffin. Sections were stained with periodic acid-Schiff. (A) Db/m control mice. (B) Db/db placebo-treated mice. (C) Db/db mice that were treated with 0.4 μg CERA/kg per wk (low dosage CERA). (D) Db/db mice that were treated with 1.2 μg CERA/kg per wk (high dosage CERA). A semiquantitative analysis on a scale from 0 to 3 for presence of mesangial expansion and arteriolar hyalinosis (arrows) was performed (E). **P ≤ 0.01 versus db/m control; #P ≤ 0.05 versus db/db placebo treatment; P ≤ 0.05 versus low-dosage CERA.

To examine the underlying molecular mechanism of the clinical and laboratory observation, we first analyzed the expression of TGF-β1 as an important mediator of diabetic kidney damage (Figure 2, A through D). Not unexpected, we found significantly higher TGF-β1 expression in db/db mice as compared with db/m controls. It is interesting that we observed a dosage-dependent and significant reduction of TGF-β1 expression in mice that were treated with CERA. When we analyzed the collagen IV expression in glomeruli and the tubulointerstitial area, we found a similar pattern in the different treatment groups (Figure 2, E through H). Next, we analyzed the expression of nephrin (Figure 3, A through D). We found a significant reduction of the nephrin expression in db/db mice in comparison with db/m mice, whereas in tissue of CERA-treated mice, higher nephrin levels were detectable. The effect was dosage dependent. We found similar results when analyzing glomerular perlecan expression (Figure 3, E through H), a heparan sulphate proteoglycan. It is postulated that such proteoglycans play an important role in holding up the anionic charge of the filtration barrier and thereby prevent loss of albumin. Moreover, we observed an increase in glomerular vascular endothelial growth factor (VEGF) expression in the db/db versus db/m mice. This was reduced in a dosage-dependant manner by treatment with CERA (Figure 3, I through L). Finally, we found a significant reduction of p-Akt expression in renal tissue of placebo-treated db/db mice in comparison with db/m controls (data not shown). This reduction of p-Akt in renal tissue was significantly ameliorated in CERA-treated mice. Again, the effect of CERA was dosage dependent.

Figure 2.

TGF-β1 and collagen IV expression in renal tissue of db/db mice. Diabetic db/db mice were treated with saline (placebo) or CERA for 14 wk. Db/m nondiabetic mice were used as control. Immunochemistry was performed on paraffin sections. Representative pictures are shown for TGF-β1 (A through D) and collagen IV (E through H) staining. (A and E) Db/m control mice. (B and F) Db/db placebo-treated mice. (C and G) Db/db mice that were treated with 0.4 μg CERA/kg per wk (low-dosage CERA). (D and H) Db/db mice that were treated with 1.2 μg CERA/kg per wk (high-dosage CERA). TGF-β1 expression in glomeruli and the tubulointerstitial collagen IV expression were graded on a scale from 0 to 3. **P ≤ 0.01 versus db/m control; #P ≤ 0.05 versus db/db placebo treatment; ##P ≤ 0.01 versus db/db placebo treatment; ∧∧P ≤ 0.01 versus low-dosage CERA.

Figure 3.

Nephrin, perlecan, and vascular endothelial growth factor (VEGF) expression in renal tissue of db/db mice. Diabetic db/db mice were treated with saline (placebo) or CERA for 14 wk. Db/m nondiabetic mice were used as control. Immunochemistry was performed on cryosections. Representative pictures are shown for nephrin (A through D), perlecan (E through H), and VEGF (I through L). (A, E, and I) Db/m control mice. (B, F, and J) Db/db placebo-treated mice. (C, G, and K) Db/db mice that were treated with 0.4 μg CERA/kg per wk (low-dosage CERA). (D, H, and L) Db/db mice that were treated with 1.2 μg CERA/kg per wk (high-dosage CERA). Nephrin, perlecan, and VEGF expression in glomeruli was graded on a scale from 0 to 3. **P ≤ 0.01 versus db/m control; #P ≤ 0.05 versus db/db placebo treatment; ##P ≤ 0.01 versus db/db placebo treatment; ∧∧P ≤ 0.01 versus low-dosage CERA.

Effect of Phlebotomy in High-Dosage CERA–Treated Mice

Phlebotomy in high-dose CERA–treated mice resulted in hematocrit levels that were comparable to baseline values before the start of CERA treatment (Table 3). SBP at the end of the treatment period in high-dosage CERA–treated mice and in phlebotomized mice was comparable (142 ± 6 versus 146 ± 8 mmHg; NS). In phlebotomized mice, kidney weight as well as the kidney-to-body weight ratio were not significantly different from those in high-dosage CERA–treated db/db mice (data not shown). In contrast, in phlebotomized, high-dosage CERA–treated mice, urinary albumin excretion after 6 and 13 wk of treatment was significantly lower as compared with mice that were treated with high-dose CERA only (Table 3). In general, albumin excretion was lower in both groups of db/db mice as compared with those in the first experimental series because in this experiment, we studied younger animals (4 wk of age). The immunohistochemical analysis of renal tissue revealed similar patterns of TGF-β1, nephrin, and perlecan expression in both groups of db/db mice as compared with the high-dosage CERA–treated mice in the first experiment (Figure 4). Above that, TGF-β1 expression was comparable in mice that were treated with high-dosage CERA and in high-dosage CERA–treated, phlebotomized mice (Figure 4, A and B). This was also true for nephrin (Figure 4, C and D) and perlecan (Figure 4, E and F) tissue expression. In summary, TGF-β1, nephrin, and perlecan expression was not significantly different in mice that were treated with high-dosage CERA (Figure 4, A, C, and E) and high-dosage CERA–treated, phlebotomized db/db mice (Figure 4, B, D, and F). Finally, we found a comparable expression of p-Akt expression in renal tissue of both groups of mice (data not shown).

Figure 4.

TGF-β1, nephrin, and perlecan in renal tissue of phlebotomized db/db mice. Diabetic db/db mice were treated with 1.2 μg CERA/kg per wk (high-dosage CERA) and high-dosage CERA plus phlebotomy for 14 wk. Immunochemistry was performed on paraffin sections. Representative pictures are shown for TGF-β1 (A and B), nephrin (C and D), and perlecan (E and F) staining. (A, C, and E) Db/db mice that were treated with high-dosage CERA. (B, D, and F) Phlebotomized db/db mice that were treated with high-dosage CERA. TGF-β1, nephrin, and perlecan expression in glomeruli was graded on a scale from 0 to 3. *P ≤ 0.05, **P ≤ 0.01 versus high-dosage CERA.

Table 3.

Serum glucose and hematocrit in db/db mice treated with high-dosage CERA and high-dosage CERA accompanied by phlebotomy

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In this study, we documented that long-term treatment with CERA has striking effects on different molecular pathways in an experimental model of diabetic kidney damage: The db/db mouse. Long-term treatment with CERA not only reduced mediators of accumulation of extracellular matrix in diabetic nephropathy, such as TGF-β1, but also prevented the loss of nephrin in glomeruli of treated mice. To our knowledge, this is the first description of a positive effect of an erythropoiesis-stimulating agent in a model of nonischemic renal tissue injury. So far, positive effects of rHuEPO administration have been documented only in experimental models of acute ischemia/reperfusion kidney injury5–8 or chronic vascular injury to the kidney,3 similar to what has been shown in numerous studies in the heart.2 In these experimental settings, the main action of rHuEPO was inhibition of apoptosis in renal and cardiac tissue. However, in most of these studies, very high dosages of rHuEPO have been administered only once or for a short period to obviate potential adverse effects of rHuEPO therapy. Particularly, effects on the number and activation state of thrombocytes and the stimulation of platelet adherence to endothelium could mitigate the beneficial effects of a long-term high-dosage therapy on the kidney.9,10 In addition, changes in blood viscosity because of marked erythrocytosis and tissue generation of endothelin may work against the positive effects of rHuEPO.11 In line with these experimental data are our findings with high-dosage CERA treatment in db/db mice. We could clearly show beneficial dosage-dependent effects of CERA on molecular pathways of diabetic kidney damage, yet mesangial expansion was not reduced after 14 wk of high-dosage CERA treatment. As a consequence, urinary albumin excretion rate, which decreased by week 6 of treatment, increased again with ongoing high-dosage therapy. This result is in contrast with low-dosage CERA, for which the increase in albuminuria during the observation period was significantly reduced compared with placebo treatment, although this renoprotective effect of low-dosage CERA was not complete.

To explore further the confounding effect of increased hematocrit levels in this experimental setting, we performed an additional series of experiments in which high-dosage CERA–treated mice were phlebotomized to achieve hematocrit levels that were comparable to those at baseline (i.e., before the start of high-dosage CERA treatment). We could indeed show that this procedure prevented the adverse effects of increased hematocrit levels (i.e., reduced the increase in microalbuminuria in high-dosage CERA–treated animals, thereby exposing the tissue-protective potential of CERA). Further studies are warranted to elucidate the molecular mechanism(s) by which a rise in hematocrit with hematologically effective CERA dosages (and presumably also with rHuEPO) lessens the beneficial effects on the molecular pathways of diabetic nephropathy. Moreover, establishing a minimal effective dosage may also provide a new strategy to prevent progression in diabetic kidney disease as well as other conditions of chronic kidney injury.

We provide evidence for different mechanisms by which CERA may exert its protective effect on diabetic kidney damage. CERA treatment significantly reduced TGF-β1 expression in renal tissue of db/db mice. Overexpression of TGF-β1 plays an important integral role in the development of diabetic nephropathy, because this fibrogenic cytokine stimulates podocyte expression of collagen IV and VEGF, and the latter action in turn may increase the activity of the VEGF autocrine loop.12,13 Inhibition of TGF-β1 with a specific antibody almost completely prevented the mesangial expansion in db/db mice.14,15 Increased TGF-β1 levels can also cause podocyte detachment and/or apoptosis.16 Thus, inhibition of TGF-β1–induced production of collagen IV by CERA may reduce mesangial matrix expansion. In addition, decreased podocyte loss may be another beneficial effect of CERA-induced downregulation of TGF-β1. CERA treatment could also directly diminish the activity of the VEGF autocrine loop. It has been documented that neutralization of VEGF with a systemically administered anti-VEGF antibody markedly reduced the urinary albumin excretion in db/db mice and the streptozotocin type 1 diabetes model.17,18 Expression of VEGF in the glomerulus is most pronounced in podocytes, where VEGF mRNA and protein expression is stimulated by high glucose concentrations and TGF-β1.12 Increased activity of the VEGF autocrine loop plays an important role in podocyte biology because VEGF by itself stimulates podocytes to produce collagen IV.13,19 Theoretically, treatment with CERA could have mitigated intrarenal VEGF activity indirectly by ameliorating diabetic tissue damage and/or through a direct effect on the VEGF/VEGF receptor pathway. Recently published experimental data provide evidence for such a cross-link between the EPO/EPO receptor and the VEGF/VEGF receptor system, at least in the vascular system.20

Finally, CERA may also directly act on podocytes, possibly by activating Akt, similar to what has been shown for rHuEPO in endothelial glomerular and tubular kidney cells.21 In addition, the CERA-induced increase in nephrin and perlecan cell content may result in reduced glomerular basement membrane permeability and albuminuria. Perlecan is a negatively charged heparan-sulfate proteoglycan that is responsible for permselectivity of the negatively charged glomerular basement membrane, whereas nephrin, a transmembrane protein with a large extracellular portion, forms the molecular substrate of the slit diaphragm.22 It is interesting that an important target of nephrin-induced signaling is Bad, the proapoptotic protein of the Bcl-2 family that is also involved in erythrocyte proliferation.23 Thus, CERA-induced signaling and maintenance of adequate nephrin content could prevent podocyte apoptosis, a truly beneficial effect given that terminally differentiated podocytes do not proliferate.23 In diabetes, nephrin protein production is downregulated, and the decrease in nephrin correlates with the broadening of the foot process widths.24,25 In addition, the podocyte number and their density have been reported to be markedly reduced in glomeruli of patients with type 1 and type 2 diabetes.26,27 In this regard, protection of podocytes seems to be of paramount importance in preventing the development and progression of human diabetic nephropathy.22,28,29

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Long-term treatment with a long-acting erythropoiesis-stimulating agent has striking dosage-dependent effects on molecular pathways of diabetic kidney damage. However, in contrast to high-dosage CERA treatment, which resulted in a significant increase in hematocrit that abrogated these beneficial effects, low-dosage therapy (as well as phlebotomy- in high-dosage–treated animals) fully exposed the tissue-protective potential of CERA. Therefore, treatment with low-dosage CERA may be a feasible method of long-term tissue protection, and further studies are needed to establish minimal effective therapeutic dosages that are not associated with potentially harmful consequences of an increase in hematocrit. This proposal is corroborated by the results of recently published large trials in patients with chronic kidney disease, in which (almost) complete correction of anemia with an erythropoiesis-stimulating agent did not retard progression.30,31

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Animal Model and Study Protocol

The Animal Care Committee of Lower Saxony approved the study. We studied 75 male diabetic db/db (BKS.Cg-m+/+Leprdb) and 25 male nondiabetic db/m control mice (Charles River Laboratories, Sulz-feld, Germany). All mice were 6 wk of age at the beginning of the study. They were housed with 12:12-h light-dark cycles and had free access to food and water. Using a parallel-group study design, we randomly allocated the db/db mice to receive a weekly injection of (1) 0.9% NaCl (placebo; n = 25), (2) 0.4 μg CERA/kg body wt (low dosage; n = 25), and (3) 1.2 μg CERA/kg body wt (high dosage; n = 25). CERA was provided by Roche Diagnostics (Penzberg, Germany). It is chemically synthesized through integration of amide bonds between amino groups of rHuEPO and methoxy polyethylene glycol-succinimidyl butanoic acid. The resulting molecular weight of approximately 60,000 Da causes a considerably prolonged elimination half-life, thereby prolonging the action of CERA on its target tissues. Recently published reports have specified that the half-life of action of CERA in humans is approximately 130 h.32 We determined hematologically noneffective (low) and effective (high) dosages of CERA in an ancillary dosage-finding experiment.

The main study duration was 14 wk, and we assessed body weight of mice at baseline and every 2 wk after the start of treatment. In addition, SBP was measured in conscious mice with an occlusive tail-cuff plethysmograph attached to a pneumatic pulse transducer (TSE BP system, Bad Homburg, Germany). For these measurements, we kept mice at 37°C. Furthermore, we assessed blood glucose with an Ascensia glucometer (Bayer Diagnostics, Leverkusen, Germany) and hematocrit with a vet scil abc (SCIL, Vierheim, Germany) at baseline and at regular intervals throughout the study. Finally, for the assessment of 24-h albuminuria, we placed mice in separate mouse diuresis cages (Tecniplast, Hohenpeissenberg, Germany) with access to water (but not food) for 24 h. We measured urinary albumin concentration at baseline and after 6 and 13 wk with an ELISA specific for mouse albumin (Albuwell M; Exocell, Philadelphia, PA). SBP was measured by a noninvasive tail-cuff method with the BP-2000 BP analysis system (Visitech Systems, Apex, NC). Ten mice from each group underwent repeated measurements on two different days 2 wk before the study end.

Renal Histology and Immunohistochemistry

After 14 wk of follow-up, we obtained renal tissue for morphologic and immunohistochemical analyses. For this purpose, 10 mice from each group were narcotized and the kidneys were perfused with Ringer lactate via the left ventricle. After the mice were killed, sagittal sections of the kidney were fixed in 4% neutral buffered formalin, embedded in paraffin, sectioned at 2 μm, and stained with the periodic acid-Schiff reagent. Coded tissue sections were analyzed by an investigator who was blinded with respect to the allocation of mice to the treatment groups. Forty glomeruli from each mouse were examined, and the amount of mesangial expansion was graded as follows: 0, no changes; 1, minor, mesangial expansion up to the diameter of two nuclei; 2, moderate, mesangial expansion with a diameter of 3 to 4 nuclei; 3, severe, mesangial expansion with a diameter of more than 4 nuclei. Similarly, arteriolar hyalinosis in the tissue sections was graded as follows: 0, no changes; 1, minor, few arterioles involved; 2, moderate, <50% of arterioles involved; 3, severe, >50% of arterioles involved.

Immunohistochemistry was performed on cryostat sections of the frozen kidneys or on paraffin sections as described previously.33 The following primary antibodies were used: Anti–TGF-β1 (Santa Cruz Biotechnology, Santa Cruz, CA; cat. no. sc-146), anti-VEGF (Santa Cruz Biotechnology; cat. no. sc-152), anti–type IV collagen (Southern Biotechnology, Birmingham, AL; cat. no. 1340-01), polyclonal rabbit anti-rat phospho-Akt (Ser473), polyclonal guinea pig anti-nephrin (Research Diagnostics, Concord, MA), and monoclonal rat anti-perlecan (Research Diagnostics). For indirect immunofluorescence, nonspecific binding sites were blocked with 10% normal donkey serum (Jackson ImmunoResearch, West Grove, PA) for 30 min. Sections were then incubated with the primary antibody for 1 h. For fluorescence visualization of bound primary antibodies, sections were further incubated with Cy3-conjugated secondary antibodies (Jackson ImmunoResearch) for 1 h. Specimens were analyzed using a Zeiss Axioplan-2 imaging microscope with the computer program AxioVision 3.0 (Zeiss, Jena, Germany). Semiquantitative analysis of nephrin, perlecan, TGF-β1, and VEGF expression in 40 glomeruli per animal; of collagen IV and p-AKT expression in the kidney; and TGF-β1, collagen I, and p-AKT expression in the heart was done by using the following scoring system: 0, no; 1, weak; 2, moderate; and 3, strong expression. The investigator performing these immunohistochemical analyses had no knowledge of the treatment group assignment.

Effect of Phlebotomy in High-Dosage CERA–Treated Mice

In an additional experiment, we investigated the effect of phlebotomy in high-dosage CERA–treated db/db mice. Using a parallel-group study design, we randomly allocated 4-wk-old db/db mice to receive a weekly injection of (1) 1.2 μg CERA/kg body wt (high dosage; n = 20) and (2) 1.2 μg CERA/kg body wt (n = 20) plus phlebotomy. Phlebotomy was performed in narcotized mice at regular intervals throughout to keep hematocrit within normal (i.e., baseline) levels. The assessment of clinical and laboratory data and the immuno-histochemical evaluation of renal tissue were identical as described previously. In these additional experiments, we analyzed only the expression of key molecules of diabetic injury in renal tissue (TGF-β1, nephrin, and perlecan).

Statistical Analyses

We used the InStat statistical program (InStat, San Jose, CA). The statistical significance was set at P < 0.05, and data are presented as means ± SEM. We compared baseline and end point characteristics with ANOVA and appropriately corrected t test for random data.

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This study was supported by an unrestricted research grant from Roche Diagnostics (Penzberg, Germany).

  • © 2007 American Society of Nephrology


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