Intensive Therapy in Newly Diagnosed Type 2 Diabetes – Results of a 6-Year Randomized Trial

Intensive Therapy in Newly Diagnosed Type 2 Diabetes – Results of a 6-Year Randomized Trial
Beta-Cell Preservation in Type 2 Diabetes
Lindsay B. Harrison, MD, Endocrinology Fellow, Beverley Adams-Huet, MS, Assistant Professor, Xilong Li, PhD, MS,Faculty Associate, Philip Raskin, MD, Professor, and Ildiko Lingvay, MD, MPH, MSCS, Associate Professor
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Abstract
The natural history of type 2 diabetes is hallmarked by progressive loss of beta-cell function coupled with increased insulin resistance. This is thought to be due to the combined toxic effects of hyperglycemia and increased free fatty acids (glucolipotoxicity)1. The shorter and less severe the glucolipotoxicity insulin, the more beta-cell preservation and/or recovery can be expected. A higher beta-cell function has been associated with improved glycemic control, less treatment burden, and fewer microvascular complications2. We hypothesize that early and intensive antihyperglycemic therapy may change the natural course of disease, by preserving beta-cell function.
The Origin Trial explored whether early use of insulin glargine, compared to standard of care treatment, in patients with impaired fasting glucose, impaired glucose tolerance, or newly-diagnosed type 2 diabetes3 can prevent disease progression. In the 1456 patients without diabetes, those who were assigned to insulin glargine were 28% less likely to develop diabetes during the 6.2 years of follow-up, an effect presumed to be due to stabilization/improvement in beta-cell function. While this approach did not show an overall decrease in cardiovascular events during the study follow-up, a proactive treatment approach, very early in the course of the disease, could have longer term impact on the disease course, and thus be superior to the current reactive approach where treatment is only initiated or escalated as the disease worsens.
Short-term (2–3 weeks) intensive insulin treatment has been shown to result in disease remission in some patients, especially those with newly-diagnosed diabetes4–6. Unfortunately this approach alone, without subsequent treatment, is not durable as >50% of patients required additional treatment by one year. Weng et al. compared a short course of oral therapy to insulin therapy in patients with newly diagnosed type 2 diabetes. After normogycemia was attained, treatment was discontinued and patients followed for one year. Fewer patients reached control (83.5% vs 95–97%) or attained remission at one year (26.7% vs 45–50%) in the oral group7, suggesting that initial insulin treatment may have a stronger beta-cell preservation effect, yet still not sufficient to change the course of the disease long-term in the absence of subsequent therapy. Therefore short-term intensive treatment, whether with insulin or oral hypoglycemic agents, in the absence of subsequent maintenance treatment is not sufficient to preserve beta-cell function long term. Chen et al performed a study where both groups received initial insulin therapy followed by randomized to oral therapy or insulin. The insulin group preserved more beta-cell function at 6-months but it is unclear if this was due to the treatment received or the glycemic targets achieved (the oral group had significantly higher HbA1c throughout the study)8.
Long-term studies in patients with newly-diagnosed diabetes evaluated diet therapy, monotherapy with metformin, sulfonyureas, or glitazones, but none of these monotherapy interventions were successful in stabilizing the disease process9,10 beyond the initial 6–18 months. There are no long-term studies to investigate whether insulin is superior to an intensive oral therapy in preserving beta-cell function, especially when similar glycemic control is maintained between groups.
We evaluated whether short term (3-month) insulin therapy followed by either continued insulin regimen versus a multi-drug oral regimen can achieve and maintain long-term (6-year) glycemic control and preserve beta-cell function. Safety and quality of life parameters were also compared.
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Research Design and Methods
We recruited newly-diagnosed treatment-naïve patients with type 2 diabetes from Parkland Memorial Hospital inpatient and outpatient services and accepted self-referrals to the University of Texas Southwestern (UTSW) Clinical Diabetes Research Clinic. The study was approved by the UTSW Institutional Review Board and all patients signed informed consent. Clinical Trials Registration Number: NCT00232583.
Patients were treated with insulin and metformin for three months, and then randomized to treatment with insulin plus metformin (INS) or triple oral therapy (TOT) with metformin, pioglitazone, and glyburide. The results from the first 3-month run-in period, the glycemic control and quality of life at 3 years, and the beta-cell function at 3.5 years were published previously11–13. The current manuscript describes the final study results after 6 years of follow-up, including beta-cell function, glycemic control, safety parameters, quality of life, and analysis of the treatment failure predictors.
Participants/eligibility
Patients were 21 to 70 years old, were diagnosed with type 2 diabetes in the previous 2-months, and were treatment-naive. Exclusion criteria were published previously and are notable for type 1 diabetes–related antibodies and baseline HbA1C level <7%11.
Randomization/Interventions
All participants were initiated on insulin and metformin for a 3-month lead-in period to attain similar glycemic control and reverse any temporary beta-cell stunning due to glucotoxicity by the time of randomization. NovoLog mix 70/30 by Flexpen and metformin were initiated and titrated based on a previously published algorithm12. At the end of 3 months patients were assigned (via blocked randomization stratified by African American race and BMI) to continue the same insulin-based therapy (INS group) or switch to triple oral therapy (TOT group) with metformin (1000 mg twice daily), glyburide, and pioglitazone (45 mg daily) as described previously13. Insulin and glyburide were titrated throughout the study based on home capillary glucose levels, targeting a fasting glucose of <100 mg/dl and pre-dinner value of <140 mg/dl.
Patients were followed at the Clinical Diabetes Research Center at UTSW monthly initially and then quarterly for a total of 72 months. Treatment failure was a predefined study end point defined as Hba1c >8% confirmed by a second reading and occurring after maximization of the glyburide dose or adequate insulin dose titration. A treatment failure in the TOT group would prompt a group switch (from TOT to INS), while a treatment failure in the INS group would lead to intensification of the insulin regimen (3–4 injections/day). Follow-up after treatment failure continued as scheduled and all analysis were performed according to the original assigned group (intention to treat) even after treatment failure.
Measurements
Glycemic control was evaluated by HbA1c (high-performance liquid chromatography at the Clinical Diabetes Laboratory at UTSW) every 3 months. Lipid profile, alanine aminotransferase (ALT), aspartate aminotransferase (AST), creatinine, hemoglobin, hs-CRP (high-sensitivity c-reactive protein), PAI-1 (plasminogen activator inhibitor-1) and fibrinogen were measured twice a year in a commercial laboratory (Quest Diagnostics, Irving, Tx). Evaluation of beta-cell function via mixed meal challenge testing (MMCT) was performed, using high protein Boost concentrate 1gm/kg carbohydrate equivalent, at 0, 6, 12, 18, 30, 42, 54, and 72 months post-randomization. Patients fasted for 12 hrs prior to the test and all anti-diabetic agents were withheld for 24hrs prior to each testing. Glucose and c-peptide were measured at baseline (before the ingestion of the Mixed Meal) and then 7 times over the three-hour test (15, 30, 60, 90, 120, 150, 180 minutes). Glucose was measured using a Yellow Springs Instrument (Yellow Springs, CA) while C-peptide was measured using Radioimmunoassay (Millipore) in the Clinical Diabetes Laboratory at UTSW. Unfortunately the c-peptide specimens from the final (72-month visit) were compromised due to storage failure, therefore these data are missing.
Insulin secretion was estimated using the area under the curve (AUC) for glucose (G) and c-peptide (C) (total, incremental, 0–30 minutes, and 0-maximal production) and then determining ratios (C/G) to estimate insulin production. Insulin sensitivity was calculated using the c-peptide-based Matsuda Index14,15. We used c-peptide instead of insulin levels to eliminate cross contamination with exogenous insulin and insulin antibodies. The calculation was done according to the following formula:

Matsuda Index=500,000v[(C0×G0×333)×(Cmean×Gmean×333)]
The disposition index (DI) was measured by multiplying the insulin secretion (AUCc/AUCG) by the Matsuda Index16. The DI reflects the beta-cell function adjusted for total body insulin sensitivity.
Compliance was estimated by medication inventory at each encounter. Weight was measured on the same scale at every visit. Mild hypoglycemic events (symptoms of low blood glucose accompanied by a documented capillary blood glucose value of <70 mg/dl) and severe hypoglycemic events (symptoms of hypoglycemia that required assistance from another individual for treatment, regardless of capillary blood glucose level) were recorded at each visit. Glucose strips were provided to all patients, and they were asked to check at least 2 times daily throughout the study.
Quality of life was measured using the modified Diabetes Quality of Life Clinical Trial questionnaire at randomization, 6, 18, 42, and 72 months. The details and rational for choosing this assessment tool were described previously13.
Statistical analysis
The original sample size was estimated to detect differences between the INS and TOT groups in the primary outcome of the study, the C-peptide AUC, of 240 ng/ml/min with an estimated standard deviation of 225 ng/ml/min. To detect this effect size, 20 patients in each group was needed for power of 90% at alpha=0.05.
The intention-to-treat analysis is reported which included all subjects according to their randomization treatment assignment, including those who reached the predefined treatment failure endpoint and were switched from TOT to INS.
The AUC from the mixed meal challenge test was computed using the trapezoidal rule. Biochemical measurements, AUC, DI, and insulin sensitivity responses were assessed with mixed linear model repeated measures analysis. Continuous variables that were positively skewed were log transformed prior to analysis. Measurements obtained throughout the 72 months (54 months for c-peptide derived variables) of treatment were included in the analysis. The primary repeated measures models consisted of a treatment group factor, study time (month) factor, and interaction between group and time, with subject modeled as a random effect. Between and within group contrasts were constructed from these models and the difference in response between treatment groups was assessed via an interaction effect. Hypoglycemic event rates were analyzed with Poisson repeated measures models. To assess longitudinal changes in subjects who reached the treatment failure endpoint, the mixed model analysis was further stratified by treatment failure status and interactions between treatment, failure, and time were assessed. Comparison of treatment failure rates was made with the logrank test. Cox proportional hazards regression models were used to compare characteristics by treatment failure status of the two treatment regimens to account for varying failure times and estimate hazard ratios for prediction of treatment failure. Results are presented as mean and standard deviation unless otherwise specified. A two-sided p-value <0.05 was considered statistically significant. Statistical analysis was performed with SAS version 9.3 (SAS Institute, Cary, NC, USA).
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Results
Sixty-three patients were enrolled in the study from November 2003 to June 2005 and 58 completed the 3-month run-in period: 29 were randomized to continue INS and 29 were changed to TOT (Figure 1). Baseline characteristics of the entire population were: 36% female, over 80% were minorities (43% African-American, 38% Hispanic), 44.9±10.1 years old, and were similar between the two groups (Table 1). Completion rates at 6 years in the study were 19/29 (66%) in the INS group and 16/29 (55%) in the TOT group (see Figure 1 for description of drop-outs in each group).

Figure 1
Participant Flowchart. INS- insulin group (treated with insulin+metformin); TOT – triple oral therapy group (treated with metformin+glyburide+pioglitazone); w/o – without.

Table 1
Demographic and biochemical characteristics at randomization and final visit in the study.
Glycemic Control
During the initial 3-month lead-in period HbA1c was substantially improved from 10.8±2.6% to 5.9±0.5% (p<0.0001)12, and 100% of patients had a HbA1c=7%. HbA1c at the final visit in the study was 7.3±1.7% (median 6.5%) in INS and 6.4±1.4% (median 5.9%) in TOT (interaction p=0.42) (Figure 2A and Table 1). At 6 years, 63.2% in INS and 68.8% in TOT (p=0.73) met the ADA target of HbA1c <7% and the average frequency of HbA1c <7% over the entire study was 83.9% for INS and 85.5% for TOT. The total daily dose of insulin increased from 0.63±0.29 units/kg/day at randomization to a final visit dose of 0.92±0.55 units/kg/day in INS (p= 0.008 within INS group).

Figure 2
Major study outcomes, by treatment group, as measured over the 6-year study follow-up. A: HbA1c. B: BMI. C: Treatment compliance. D: AUCC/AUCG from mixed meal challenge test. E: Matsuda Index (measure of insulin sensitivity) derived from mixed meal challenge …
Beta-cell Function and Insulin Sensitivity
Beta-cell function remained stable over time in both groups, as measured by AUCC (p=0.13 – primary outcome of the study), AUCC/AUCG (p=0.9), and DI (p=0.8) (Figure 2D and 2F and Table 1), with no between groups differences. Insulin sensitivity (Matsuda index) decreased comparably in both groups over time (p=0.0006 in INS and p=0.02 in TOT) (Figure 2E and Table 1). The baseline-to-30 minutes (p=0.006) and baseline-maximum (p=0.02) C-peptide responses during MMCT increased significantly more over time in the INS compared with TOT group. There was no difference between groups in terms of glucose total AUC, 0–30 minutes, 0-max, or ratio of c-peptide to glucose (total AUC, 0–30, or 0-max) (Table 1). Fasting insulin levels decreased throughout the study in TOT while remaining stable in INS (p=0.0002), yet the relevance of this measurement in the insulin-treated group is very limited.
Treatment Failure
Treatment failure occurred in 8 patients in the INS group (27.6%) and 6 in the TOT group (20.7%) (logrank p=0.93) at an average of 43.1±18.4 months and 29.5±18.8 months, respectively (Figure 3A). Predictors of treatment failure at the time of randomization included a higher systolic blood pressure (p=0.004), fasting c-peptide (p= 0.008), fasting glucose (p=0.008), and a lower insulin sensitivity (p=0.04) (Table 2 and Figure 3B). At the visit prior to failure those who failed had a higher fasting glucose (p=<0.0001) and a lower AUCC/AUCG (p=0.02) and DI (p=0.002) (Figure 3B). At the final visit into the study, the group who failed had significantly higher insulin doses (p=0.007), HbA1c (p=<0.0001), and lower AUCC/AUCG(p=0.0006) and DI (p<0.0001). No other significant predictors were identified at the initial visit, randomization, or visit prior to failure, including treatment assignment, race, age, BMI, weight loss prior to diagnosis, initial insulin dose, medication compliance, inflammatory markers, and other MMCT variables not mentioned previously (Table 2).

Figure 3
Assessment of treatment failure over the 6-year study period in the insulin (INS) and triple oral therapy (TOT) groups. A: Kaplan-Meier curve depicting time of treatment regimen failure by group. The numbers represent the number of patients at risk at …

Table 2
Study participant characteristics by treatment regimen and failure status at A. initial visit, B. randomization visit, C. visit prior to failure; D. last visit in the study.
HbA1c at the time of failure was 9.4±1.8% in INS and 9.5±2.1% in TOT. The average HbA1c in the failure group did not improve with increasing doses of insulin in INS (final visit HbA1c 9.1±1.2%), and improved but did not reach goal after change to insulin treatment in TOT (final visit HbA1c 8.2±1.9%) (Figure 4A and 4B). Insulin sensitivity (Matsuda Index) was not different between those who failed and those who did not in INS (p=0.37), but was lower in the TOT failures (p=0.0004) (Figure 4E and 4F); thus failures in TOT group appear to be the non-responders to the insulin sensitizing effect of this regimen. Absolute insulin production (AUCc/AUCg) and beta-cell function (DI) declined faster over the course of the study in those who failed versus those who did not fail treatment in both INS (p=0.01) and TOT (p<0.0001) (Figures 4C, 4D, 4G, and 4H), despite similar baseline values. Weight did not significantly differ by failure status in either group (Figure 4I and 4J).

Figure 4
Comparison of the changes in HbA1c, AUCc/AUCG, Matsuda index, Disposition Index (DI), and weight over time in the INS group (panels A, C, E, G, I) and TOT group (panels B, D, F, H, J) by failure status,
At the final visit in the study, the total daily dose of insulin in the INS treatment-failures was 1.3±0.3 units/kg/day (n=8) while in the TOT treatment-failures was 1.6±1.0 units/kg/day (n=6), both doses being much higher than the average insulin dose at the same time-point in the entire cohort (0.9 units/kg).
Safety
Most subjects (76%) were obese at randomization with an overall average BMI of 36.1±7.3 kg/m2. Over the 6 years of follow-up, BMI increased significantly and comparably in both groups (INS p=0.04 and TOT p=0.01, p=0.48 between groups) (Figure 2B and Table 1).
ALT increased significantly in INS (p=0.007 between groups). Hemoglobin and creatinine did not differ significantly between study groups (Table 1).
There was an overall low rate of mild hypoglycemia. The rate of hypoglycemia (defined conservatively as a documented capillary blood glucose level <70 mg/dl in the presence of symptoms suggestive of hypoglycemia) decreased rapidly over the first 6 months into the study and remained low thereafter in both groups (from 1.2±2.5 to 0.5±1.0 events/month in INS (p=0.002) and 1.2±1.5 to 0.5±0.9 events/month in TOT (p=0.006), with no difference between groups over time (Table 1). Severe hypoglycemia was rare, with 3 patients in INS having 4 events (3 within the first month post-randomization) and 4 patients in TOT having 7 events (4 within the first two months post-randomization).
Compliance and Quality of Life
Overall compliance with medications throughout the study was 92.8±9.1% in INS and 89.6±10.3% in TOT (p=0.33) (Figure 2C). Compliance was >80% in 85.4% of INS and 74.0% of TOT participants. Compliance rate decreased significantly over the course of 6 years in TOT (p=0.005), while it remained stable in INS (p=0.63).
All 12 domains of the quality of life survey were similar between groups and stable over time, except for current health perceptions which improved more in INS (p=0.002). Satisfaction with insulin treatment and willingness to continue insulin injections were both very high (1.4±0.7 and 1.1±0.4, respectively, on a 1–5 scale where “1=extremely satisfied, extremely willing”) and stable in the INS group throughout the 6 years (Figure 5).

Figure 5
Results of modified Diabetes Quality of Life Questionnaire in the insulin treated (INS) and triple oral therapy (TOT) groups. All patients were given the questionnaire to complete at randomization and at 6, 18, 42, and 72 months after randomization. Patients …
Cardiovascular Risk Markers
There was no significant change over time or between groups in total cholesterol, LDL, or triglycerides (Table 1). HDL increased significantly more in INS (p=0.002) though it wasn’t different between groups (p=0.22). Statin use increased over the study period from 34.5% to 79.3% in INS and from 17.2% to 75.9% in TOT.
Systolic blood pressure did not change over time in either group. Diastolic blood pressure decreased over time in TOT (p=0.02) but was not significantly different from INS (p=0.3). The number of blood pressure medications increased from 1.0±1.3 to 1.8±1.5 in INS and from 0.7±0.9 to 1.5±1.3 in TOT (Table 1).
There were no significant changes in hsCRP, fibrinogen, or PAI-1 between groups or over time (Table 1).
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Conclusions
Beta-cell function, as measured by c-peptide secretion (AUCc and AUCC/AUCG) and by DI, as well as good glycemic control were maintained for 6 years in patients with newly-diagnosed type 2 diabetes. These results were observed regardless of randomization to treatment with insulin and metformin or a triple oral hypoglycemic regimen with metformin, glyburide, and pioglitazone, both regimens instituted after an initial 3-month run-in period with insulin and metformin. Insulin sensitivity, measured by the Matsuda Index, decreased in both groups over the course of the study. Overall these findings suggest that early intervention in the course of the disease, with an intensive regimen which has complementary mechanisms of action, can stabilize the course of the disease and preserve the progressive decline in beta-cell function well-known to occur in this patient population.
Despite the overall beta-cell preservation in both groups, nearly 24% of the cohort experienced treatment failure even in the setting of early and intensive treatment. Since the study was designed to compare two intensive treatment interventions and it did not have a conventionally treated control group (using the traditional step-wise treatment algorithm), it is not possible to conclude whether intensive treatment improves failure rate over the traditional step-wise treatment algorithm. Furthermore, we cannot compare failure rate across different studies as failure criteria vary widely, as is the length of follow-up and the study population. The ADOPT study compared three different monotherapy agents in newly-diagnosed patients with type 2 diabetes9. The reported failure rates at 5 years of treatment were 15% with rosiglitazone, 21% with metformin, and 34% with glyburide monotherapy. While these failure rates seem comparable with those seen in our study, the failure definition used in ADOPT was more conservative than ours, defined as fasting plasma glucose of >180 mg/dl on two occasions. Additionally, all treatment groups in this study experienced a gradual decline in beta-cell function, while we observed a stable beta-cell function throughout our follow-up. The TODAY study, which enrolled children with type 2 diabetes (average age 14.0±2.0 years17), defined treatment failure the same as our study (HbA1c>8%). This endpoint was achieved by 51.7% of patients treated with metformin alone and 38.6% with rosiglitazone plus metformin18. While our failure rates were lower than those seen in the TODAY study, we cannot infer whether the improved results are due to the more intensive treatment regimen we employed, or due to a more severe disease state in the young patients with type 2 diabetes enrolled in the TODAY study.
We found several patient characteristics that predicted treatment failure. Interestingly, at the initial visit, when patients were universally hyperglycemic and metabolically decompensated, there were no failure predictors. However, after 3-months of intensive insulin and metformin therapy, when average HbA1c declined from >10% to <6%, there were several predictive variables. The patients who eventually failed had higher systolic blood pressure, fasting glucose (albeit within near-normal range), fasting c-peptide, and lower insulin sensitivity. Therefore these patients, while still well compensated at this time-point, had features of more advanced insulin resistance, which explains the propensity to treatment failure. Patients who failed after randomization to TOT treatment had significantly lower insulin sensitivity compared to those who did not fail (Figure 4F), suggesting that perhaps those who did not respond to the insulin sensitizing effect of pioglitazone were those who failed treatment. This is in contrast to both the TODAY and ADOPT analyses that found lower baseline beta-cell function and higher HbA1c in the patients who failed19,20. The difference in our findings could be explained by the fact that in both ADOPT and TODAY studies baseline beta-cell function was evaluated prior to treatment initiation, while in our study we measured baseline beta-cell function after a 3-months initial treatment with insulin and metformin during which maximal beta-cell recovery occurred. Our patients had much worse metabolic derangements at baseline but any glucolipotoxicity was reversed prior to the randomization, when the first beta-cell function analysis occurred, and perhaps that is why we did not observe lower initial beta-cell function in those who would eventually fail. Furthermore, we believe that the Matsuda index provides a better estimate of insulin sensitivity than the HOMA-IR used in either of these studies, and perhaps a reason why we were able to note insulin resistance as a predictor of treatment failure. At the visit prior (3–6 months) to failure the group who failed had worse fasting glucose, HbA1c, and beta-cell function, these being warnings signs that a failure is impending. The slope of beta-cell function decline in the treatment failure group was much higher compared with the group that did not fail treatment (who had preserved beta-cell throughout the study follow-up) (Figure 4G and H), suggesting that beta-cell decompensation is the event closely preceding the failure event. In summary, our findings suggest that treatment failure occurs in patients who have worse insulin resistance at baseline, which is the first noticeable abnormality occurring months to years prior to failure. Beta-cell decompensation on the other hand occurs in closer proximity to the time of failure and is the final pathophysiologic event leading to the failure.
After failure, despite switch to insulin therapy (in TOT group) or intensification of therapy (in INS group), at the final study visit patients continued to have worse HbA1c, beta-cell function, and insulin sensitivity despite significantly higher doses of insulin. Thus, once patients experience worsening of glycemic control they are unlikely to regain glycemic control and improve beta-cell function. This finding further supports the need to employ all available tools to prevent treatment failure in the first place, as once failure ensures subsequent rescue of glycemic control is unlikely.
Importantly, neither treatment regimen was superior at preserving beta-cell function, preventing decline in insulin sensitivity, or preventing treatment failure. Since the initiation of this study in 2003, the paradigm for diabetes management has shifted with the rise in popularity of glucagon-like peptide-1 (GLP-1) based therapy and the fall from favor of thiazolidinediones (TZDs)21. The TZD component of our TOT regimen likely contributed to its success in maintaining glycemic control through its pleotropic effects on beta-cell function, peripheral insulin sensitivity, and possibly preservation of beta-cell mass22. Though the insulin sensitivity fell in the TOT group over time, this appears to be primarily occurring in participants who failed therapy (treatment non-responders) (Figure 4F). GLP-1 drugs have been shown to primarily improve beta-cell function (with indirect effects on insulin sensitivity) and are more effective in this regard than TZDs23,24. Incorporating these newer drugs early in treatment could potentially have equal or greater effects in stabilizing the disease process25.
It is also important to note the high acceptance rate of insulin therapy, and the fact that all quality of life parameters were similar regardless of the treatment regimen. These findings confirm that insulin treatment is well accepted by patients even in the very early stages of the disease and does not alter quality of life, therefore it can be safely considered as a viable treatment option at any stage of the disease.
Overall, this study shows that beta-cell function and glycemic control can be maintained at a stable level for at least 6 years after diagnosis if an intensive treatment algorithm is initiated at the time of diagnosis of type 2 diabetes. Treatment failures occurred in patients with lower insulin sensitivity at baseline, and those who experienced greater beta-cell decline over the course of the study. Identification of patients at high risk of treatment failure is important, as rescue therapy is unlikely to be successful once treatment failure ensues.
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Acknowledgements
LBH, BAH, and XL have nothing to declare. I.L. serves on Novo Nordisk, Inc scientific advisory board and has received research funding, payable to the University of Texas Southwestern Medical School, from Novo Nordisk and GI Dynamics. P.R. has received research support, payable to the University of Texas Southwestern Medical Center, from Amgen Inc, Amylin Pharmaceuticals, Andromeda Biotech Ltd, Astra Zeneca Pharmaceuticals LP, Boehringer-Ingelheim Pharmaceuticals, Gilead Sciences Inc, Intarcia, Eli Lilly & Company, Novo Nordisk, Pfizer Inc, Reata Pharmaceuticals, and Rhythm Pharmaceuticals Inc. P.R. is an advisor for Amgen and Janssen and is on the Speakers Bureau for Janssen.
The authors thank Cristina Garza and Lourdes Pruneda for skillful assistance with the study volunteers and Laura Golici for database support.
Funding: The study was partly funded by an Investigator Initiated Trial grant from Novo Nordisk, Inc to I.L. Novo Nordisk, Inc played no role in the study design, conduct, or analysis, nor preparation or final approval of the manuscript. I.L. was supported by NIH 1K23RR024470 and B.A.H. by NIH UL1RR024982.
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Footnotes
Disclosures
No other potential conflicts of interest relevant to this article were reported.
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Contributor Information
Lindsay B. Harrison,
Beverley Adams-Huet,
Xilong Li,
Philip Raskin,
Ildiko Lingvay,
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Candidate Number
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Module Leader     Dr. Julia Philippou    Please ensure that you have provided correct information in the boxes.
Module Code     6KNIO319
Module Title     Evidence Based Practice
Title of Assignment    Research Review
Submission Date         Word Count    2,000

By enrolling with the College I have confirmed I have understood and agreed to abide by College regulations pertaining to plagiarism.

Candidate’s initials:                             Date:

Background
Hepatic encephalopathy (HE) is a complex progressive, reversible neuropsychiatric syndrome results from acute or chronic liver disease and it is a major cause of morbidity and mortality (Sargent, 2009). Although the pathogenesis is uncertain, it is considered to be caused by gut derived toxins such as ammonia that reaches the systemic circulation as a result of reduced hepatic clearance and cause cerebral oedema thus HE (O’Grady et al, 2000). As highlighted by Rees et al (2013) this challenging complication of advanced liver disease, occurs in 30-45% patients with liver disease. The aim of the treatment is to reduce the ammonia production and absorption in the gut (Sargent, 2009). According to Prakash and Mullen (2010) current management of acute episodes of HE needs hospital admission and involves uses of antibiotics, lactulose, enema and sometimes L-ornithine L-aspartate (LOLA). It has been observed that there are still some reservations about the usage of LOLA. LOLA is stable salt of amino acid plays an important role in ammonia detoxification (Mittal and Sharma, 2011).

Method
Therefore the back ground question was to find out the efficacy LOLA used for the treatment of HE.  In order to obtain a focused answerable clinical question, the PICO (population, intervention, comparison intervention, and outcome) format developed by Sackett et al (2000) was used. The background question was broken down into key words by using PICO frame work (see table: 1). For example, patients with liver disease (P), L-ornithine L-aspartate (I), None (C), reduction in hepatic encephalopathy (O). Author did not compare the intervention because the background question was to find out the efficacy of the LOLA itself.

Table: 1 PICO analysis
Population    Intervention    Comparison    Outcome
Patients with Liver disease    L-ornithine L-aspartate    None    Reduction in hepatic encephalopathy

The clinical question formulated was: Does L-ornithine L-aspartate (I) reduce incidence of hepatic encephalopathy (O) in patients with liver disease (P)?

Having established the key components of the clinical question, facet analysis was generated by using all possible words for the key elements of the PICO (see table: 2). This was gathered by looking at all the different synonyms, phrases that could be used to describe the key words and also looked at singular, pleural words, abbreviations (Aveyard and Sharp, 2009). For example, the key word ‘patient with liver disease’ was also searched for ‘cirrhosis patients’ and ‘alcoholic liver disease’. Similarly, ‘L-ornithine L-aspartate’ was searched as ‘LOLA’ as well as ‘Ornithine aspartate’.

The search was conducted in three different databases such as CINAHL (Cumulative Index to Nursing and Allied Health Literatuture), Medline and Embase. As described by McKibbon and Marks (1998) CINAHL covers the fields of nursing and allied health literature, while Medline and Embase retrieves the biomedicine, health and drug research and these are the most comprehensive and up-to-date databases.

PICO terms were searched as ‘index term’ (Mesh) followed by each key word in the facet analysis as ‘free text’. Index term search helps to identify controlled vocabulary thesaurus and helps to retrieve all the records in the database on a particular topic, whereas the free text search allows searching key words anywhere in the citation or article (Greenhalagh, 2010). The ‘explode’ command was used with index search to identify the more specific terms related to topic. In order to make the index term more precise by focusing on the narrow aspects of the term ‘subheadings’ were included (Aveyard and Sharp, 2009). The Boolean Operator ‘OR’ was used to link the each key words of the PICO component and ‘AND’ to combine all the concepts together to yield the best results (Craig and Smyth, 2012).

Additional tools such as ‘truncations’ were used to retrieve the records that contain the exact phrases that used in the search (Melynk and Fineout-Overholt, 2005). Truncations (*) are the shortcut devices used to save time, so that all the different variations of the key words can be identified easily. For example, ‘patients with liver disease*’, ‘cirrhosis patient*’. In addition ‘wildcard’ ($) is another tool can be used in some databases to allow identifying different spellings used. However, this was not used as it was not applicable in the search carried out. To narrow the results further, the search was limited to the fields such as ‘English language’, ‘humans’ and ‘randomized controlled trial’ (RCT). As indicated by Polit and Beck (2006) RCT is the most relevant type of study for the efficacy of the treatment. The results retrieved in each databases, the papers relevant to the clinical question and study design are shown in Table: 3. The search history is shown in appendix. After reading the abstracts of the relevant papers, one paper (Abid et al, 2011) was selected for review as it best answers the clinical question and it is the most recent.

Table: 2 Facet Analysis and search plan
Population    Intervention    Comparison    Outcome
Patients with Liver disease    L-ornithine L-aspartate    None    Reduction in hepatic encephalopathy
Facet Analysis
Patients with liver disease*
L-ornithine L-aspartate    None    Reduction in hepatic encephalopathy
Cirrhosis patients*
LOLA
Improvement in hepatic encephalopathy
Alcoholic liver disease
Ornithine aspartate
Patients with hepatic encephalopathy

Table: 3 Findings
Databases searched    Number of hits    Studies relevant to the question    Study designs
CINAHL    1    1    RCT
MEDLINE    9    7    RCT
EMBASE    14    5    RCT
Critical appraisal
The critical appraisal skills programme checklist for RCT (CASP, 2006) was used to critique the paper. It helps to make sense of the study by examining carefully to evaluate the accuracy, relevance and trustworthiness in a systematic way (Holland and Rees, 2010). In this study, the researchers carried out a randomised double blind, placebo controlled prospective study. This research design was appropriate for the study. As indicated by Greenhalgh (2010) RCT is considered to be the most appropriate design to prove the efficacy of the treatment and it is the most solid scientific evidence. The study was clearly focused on the research question to evaluate the effectiveness of LOLA as an additional treatment in all grades of HE in patients with cirrhosis. This was conducted as there was no large controlled clinical trial of proven efficacy of LOLA in the treatment of different grades of HE.

The randomisation, double blinding and concealment of the allocation used in the study prevents the chances of bias, including selection bias, observer bias, and experimental bias, and thus safeguards the robustness of the findings (Craig and Smyth, 2012). The process of the randomization and blinding was well explained. The inclusion and exclusion criteria were clearly stated. Nevertheless, the exclusion criteria were strict and can lead to exclusion bias (Rasinger, 2008).

The study was approved by the institutional ethical review committee and was also registered through clinical trial under government protocol. This is essential in RCT as it involves the human subjects (Ajetunmobi, 2002). Moreover, before the enrolment, an informed consent was acquired from the patients or relatives (where the patients were unable to give consent due to progressive HE).  One limitation is that whether or not the privacy and confidentiality of the participants were maintained is not mentioned. Besides, the study’s participants less represents the local population.

To achieve enough statistical power, a power calculation was carried out at the beginning of the study. Ajetunmobi (2002) asserts that this prevents the chances of type II error. He also suggested that, as a rule of thumb, a power calculation of 80-90% is statistically reliable range. This study showed the power calculation of 80%.
In order to exclude the chances of observer bias, the preparation and administration of treatment in both groups were done uniformly. The researchers assessed multiple outcomes such as grades of HE, serum ammonia level, number connection test (NCT) and duration of hospital stay. Ajetunmobi (2002) cautioned measuring multiple outcome variables can introduce type1 error, which can be a possibility in the study.

West Haven criteria used to assess the grades of HE, is a widely accepted tool. However, it can cause interobserver variations especially in grade 1 HE (Prakash and Mullen, 2010). Besides, the researches acknowledged serum ammonia analysis is not the best method of assessment. Ong et al (2003) argue that LOLA can activate urea cycle in muscles. They also warned this can falsely reduce ammonia in venous sample and leads to potential misinterpretation. In order to exclude the chances of performance bias, the researched carried out the testing and processing of the samples similarly. The NCT was the method widely accepted and used to assess the psychometric analysis of HE.

Shorter course of LOLA was used in the study, by assuming the treatment for 72 hours would be appropriate than longer duration. An extended follow up process was lacking in this study. More to the point, Ahmad et al (2008) explains the possibility of worsening HE due to rebound ammonia in liver patients. Therefore, a repeat ammonia analysis and a longer follow up could have been beneficial.

Overall LOLA was found safe as an adjuvant therapy in the management of HE. To accomplish the statistical analyses, the researchers used statistical package for social sciences (SPSS), it is the most up-to-date, well known, easy to use software to carry out statistical analyses (Harrington et al, 2009).  Quantitative variables are presented in means differences, whereas qualitative variables in frequencies and percentages. p values were presented wherever confidence intervals are unavailable. p value is the probability it is calculated in the range of 0-1. p value <0.05 indicates the likelihood of result being due to chance is < 1:20, which means the result is statistically significant (Greenhalagh, 2010).

The statistics given are based on the sample size, which is intent-to-treat- analysis. Ajetunmobi (2002) supports, this is the ideal way to carry out the analysis and that was applied in this study. Three patients from control group died hence NCT was not accurate. According to Melynk and Fineout-Overholt (2005), ignoring the drop outs can introduce bias. Even though it is reported that there were no adverse reactions noted, whether or not there will be any side effects of the medicine on patients with different precipitating factors should be rule out.

Notwithstanding the study’s limitation, the researchers concluded that there were significant improvement in the HE and reduction in hospital stay. They also commented that the current study underpinned the former three meta-analyses those proved the benefits of LOLA to manage HE in cirrhotic patients.

Implementation
In order to implement the change in practice, it requires the collective effort of all the multidisciplinary team members, stakeholders, policy makers (Pipe et al, 2005). However, after gathering the information, implementation can be considered using Lewin (1951) force field analytic method. Three stages in the method are unfreezing, moving and refreezing. Greenhalagh (2010) reveals, in the first stage once the driving force is established, identify the resisting force and plan how to overcome that resistance. The driving force in this situation would be the improvement of the patient’s quality of life.

The second stage is developing new behaviours, attitudes and values through organizational structure, policies or techniques. The chances of developing confusion and resistance will be high in this stage (Greenhalgh et al 2004).  However, the resisting force in relation to this factor will be allocation of scarce resources. On the third stage, by finalising and crystallising the ideas and changes are reinforced (Pipe et al, 2005). To overcome the resisting force, the change agent will consult the management, other stakeholders and policy makers then endeavour to convince about the importance of LOLA as recommended therapy for HE.

There are many reasons for recommending LOLA, as already discussed HE is very debilitating condition that seriously impacted on the patient’s quality of life. Besides, the economic burden on HE substantial. It is also estimated that the NHS spends a staggering amount annually to manage patients with HE (British liver trust (BLT), 2010). Additionally, as per the statistics (BLT, 2010) it is estimated that approximately 150,000 in patients with liver disease are treated NHS (National Health Service) hospitals. Therefore, managing patients with LOLA is very beneficial. As already identified by the researchers it will help to improve the patient’s quality of life as well as helps to reduce hospital stay thus contribute some profitable outcome to NHS.

Conclusion
By doing this module, the author learned the concepts of evidence based practice in health care. It also helped to gain the skills to formulate a clinical question and undertake systematic search of databases to find appropriate evidence in relation to the clinical practice. In addition, learned how to read a research paper, critically appraise and find out the limitations and validity of the paper. Furthermore it provided the skills required for implementing the evidence in practice, by using recognised change models.

Appendix

Database(s): Ovid MEDLINE(R) 1946 to November Week 3 2013
Search Strategy:
#    Searches    Results
1    exp Liver Diseases/    439771
2    patients with liver disease*.mp. [mp=title, abstract, original title, name of substance word, subject heading word, keyword heading word, protocol supplementary concept, rare disease supplementary concept, unique identifier]    2706
3    cirrhosis patient*.mp. [mp=title, abstract, original title, name of substance word, subject heading word, keyword heading word, protocol supplementary concept, rare disease supplementary concept, unique identifier]    1305
4    alcoholic liver disease.mp. [mp=title, abstract, original title, name of substance word, subject heading word, keyword heading word, protocol supplementary concept, rare disease supplementary concept, unique identifier]    4000
5    patients with hepatic encephalopathy.mp. [mp=title, abstract, original title, name of substance word, subject heading word, keyword heading word, protocol supplementary concept, rare disease supplementary concept, unique identifier]    321
6    1 or 2 or 3 or 4 or 5    440598
7    L-ornithine L-aspartate.mp.    63
8    LOLA.mp. [mp=title, abstract, original title, name of substance word, subject heading word, keyword heading word, protocol supplementary concept, rare disease supplementary concept, unique identifier]    132
9    ornithine aspartate.mp. [mp=title, abstract, original title, name of substance word, subject heading word, keyword heading word, protocol supplementary concept, rare disease supplementary concept, unique identifier]    34
10    7 or 8 or 9    207
11    exp Hepatic Encephalopathy/    8820
12    reduction in hepatic encephalopathy.mp. [mp=title, abstract, original title, name of substance word, subject heading word, keyword heading word, protocol supplementary concept, rare disease supplementary concept, unique identifier]    1
13    improvement in hepatic encephalopathy.mp. [mp=title, abstract, original title, name of substance word, subject heading word, keyword heading word, protocol supplementary concept, rare disease supplementary concept, unique identifier]    14
14    11 or 12 or 13    8829
15    6 and 10 and 14    55
16    limit 15 to (English language and humans and randomized controlled trial)    9

1.         Efficacy of L-ornithine-L-aspartate as an adjuvant therapy in cirrhotic patients with hepatic encephalopathy.
Abid S. Jafri W. Mumtaz K. Islam M. Abbas Z. Shah HA. Hamid S.
Jcpsp, Journal of the College of Physicians & Surgeons – Pakistan. 21(11):666-71, 2011 Nov.
[Comparative Study. Journal Article. Randomized Controlled Trial. Research Support, Non-U.S. Gov’t]
UI: 22078345
Authors Full Name
Abid, Shahab. Jafri, Wasim. Mumtaz, Khalid. Islam, Muhammad. Abbas, Zaigham. Shah, Hasnain Ali. Hamid, Saeed.

2.         A randomized controlled trial comparing lactulose, probiotics, and L-ornithine L-aspartate in treatment of minimal hepatic encephalopathy.
Mittal VV. Sharma BC. Sharma P. Sarin SK.
European Journal of Gastroenterology & Hepatology. 23(8):725-32, 2011 Aug.
[Comparative Study. Journal Article. Randomized Controlled Trial]
UI: 21646910
Authors Full Name
Mittal, Vibhu Vibhas. Sharma, Barjesh Chander. Sharma, Praveen. Sarin, Shiv Kumar.

3.         The effect of L-ornithine L-aspartate and branch chain amino acids on encephalopathy and nutritional status in liver cirrhosis with malnutrition.
Ndraha S. Hasan I. Simadibrata M.
Acta Medica Indonesiana. 43(1):18-22, 2011 Jan.
[Journal Article. Randomized Controlled Trial]
UI: 21339541
Authors Full Name
Ndraha, Suzanna. Hasan, Irsan. Simadibrata, Marcellus.

4.         A double-blind, randomized, placebo-controlled trial of intravenous L-ornithine-L-aspartate on postural control in patients with cirrhosis.
Schmid M. Peck-Radosavljevic M. Konig F. Mittermaier C. Gangl A. Ferenci P.
Liver International. 30(4):574-82, 2010 Apr.
[Comparative Study. Journal Article. Randomized Controlled Trial]
UI: 20456040
Authors Full Name
Schmid, Monika. Peck-Radosavljevic, Markus. Konig, Franz. Mittermaier, Christian. Gangl, Alfred. Ferenci, Peter.

5.         Efficacy of oral L-ornithine-L-aspartate in cirrhotic patients with hyperammonemic hepatic encephalopathy. Results of a randomized, lactulose-controlled study.
Poo JL. Gongora J. Sanchez-Avila F. Aguilar-Castillo S. Garcia-Ramos G. Fernandez-Zertuche M. Rodriguez-Fragoso L. Uribe M.
Annals of Hepatology. 5(4):281-8, 2006 Oct-Dec.
[Journal Article. Randomized Controlled Trial]
UI: 17151582
Authors Full Name
Poo, Jorge Luis. Gongora, Julieta. Sanchez-Avila, Francisco. Aguilar-Castillo, Sergio. Garcia-Ramos, Guillermo. Fernandez-Zertuche, Mario. Rodriguez-Fragoso, Lourdes. Uribe, Misael.

6.         L-ornithine-L-aspartate infusion efficacy in hepatic encephalopathy.
Ahmad I. Khan AA. Alam A. Dilshad A. Butt AK. Shafqat F. Malik K. Sarwar S.
Jcpsp, Journal of the College of Physicians & Surgeons – Pakistan. 18(11):684-7, 2008 Nov.
[Clinical Trial. Journal Article. Randomized Controlled Trial]
UI: 18983791
Authors Full Name
Ahmad, Irfan. Khan, Anwaar A. Alam, Altaf. Dilshad, Akif. Butt, Arshad Kamal. Shafqat, Farzana. Malik, Kashif. Sarwar, Shahid.

7.         Efficacy of oral L-ornithine-L-aspartate in cirrhotic patients with hyperammonemic hepatic encephalopathy. Results of a randomized, lactulose-controlled study.
Poo JL. Gongora J. Sanchez-Avila F. Aguilar-Castillo S. Garcia-Ramos G. Fernandez-Zertuche M. Rodriguez-Fragoso L. Uribe M.
Annals of Hepatology. 5(4):281-8, 2006 Oct-Dec.
[Journal Article. Randomized Controlled Trial]
UI: 17151582
Authors Full Name
Poo, Jorge Luis. Gongora, Julieta. Sanchez-Avila, Francisco. Aguilar-Castillo, Sergio. Garcia-Ramos, Guillermo. Fernandez-Zertuche, Mario. Rodriguez-Fragoso, Lourdes. Uribe, Misael.

8.         Oral L-ornithine-L-aspartate therapy of chronic hepatic encephalopathy: results of a placebo-controlled double-blind study.
Stauch S. Kircheis G. Adler G. Beckh K. Ditschuneit H. Gortelmeyer R. Hendricks R. Heuser A. Karoff C. Malfertheiner P. Mayer D. Rosch W. Steffens J.
Journal of Hepatology. 28(5):856-64, 1998 May.
[Clinical Trial. Journal Article. Randomized Controlled Trial]
UI: 9625322
Authors Full Name
Stauch, S. Kircheis, G. Adler, G. Beckh, K. Ditschuneit, H. Gortelmeyer, R. Hendricks, R. Heuser, A. Karoff, C. Malfertheiner, P. Mayer, D. Rosch, W. Steffens, J.

9.         Therapeutic efficacy of L-ornithine-L-aspartate infusions in patients with cirrhosis and hepatic encephalopathy: results of a placebo-controlled, double-blind study.
Kircheis G. Nilius R. Held C. Berndt H. Buchner M. Gortelmeyer R. Hendricks R. Kruger B. Kuklinski B. Meister H. Otto HJ. Rink C. Rosch W. Stauch S.
Hepatology. 25(6):1351-60, 1997 Jun.
[Clinical Trial. Journal Article. Multicenter Study. Randomized Controlled Trial]
UI: 9185752
Authors Full Name
Kircheis, G. Nilius, R. Held, C. Berndt, H. Buchner, M. Gortelmeyer, R. Hendricks, R. Kruger, B. Kuklinski, B. Meister, H. Otto, H J. Rink, C. Rosch, W. Stauch, S.