Glycated Hemoglobin (Hba1c) Values and to Retinopathy, Nephropathy and Cardiovascular Outcomes in the DCCT/EDIC Study

Page 1 of 39 Diabetes

Relationship of Glycated Albumin to Blood Glucose and Glycated Hemoglobin (HbA1C) Values and to Retinopathy, Nephropathy and Cardiovascular Outcomes in the DCCT/EDIC Study

David M. Nathan1, Paula McGee2, Michael W. Steffes3, John M. Lachin2, and the DCCT/EDIC Research Group*

1Diabetes Unit and Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts 2The Biostatistics Center, The George Washington University, Rockville Maryland 3Department of Laboratory Medicine and Pathology, Medical School, University of Minnesota, Minneapolis, Minneapolis

*A complete list of the members of the DCCT/EDIC Research Group is provided in the supplementary appendix published in New England Journal of Medicine, 2011; 365:236676.

Corresponding Author:

Paula McGee, MS

Tel # (301) 8819260

Email address: [email protected]

Mailing address:

6110 Executive Blvd Suite 750 Rockville, MD 20852

Abstract: 195 words Text: 3762 6 tables 2 figures

Diabetes Publish Ahead of Print, published online August 29, 2013 Diabetes Page 2 of 39

Abstract

The association of chronic glycemia, measured by HbA1c, with longterm complications of type

1 diabetes has been well established in the Diabetes Control and Complications Trial (DCCT) and other studies. The role of intermediateterm and acute glycemia and of glucose variability on microvascular and cardiovascular disease is less clear. In order to examine the interrelationships among longterm, intermediateterm and acute measures of glucose and its daily variability, we compared HbA1c, glycated albumin (GA), and 7point glucose profile concentrations measured longitudinally in a casecohort subpopulation of the DCCT. HbA1c and GA were closely correlated with each other and with the mean blood glucose concentrations (MBG) calculated from the 7point profile. The associations of glucose variability and postprandial concentrations with HbA1c and GA were relatively weak and were further attenuated when MBG was included in multivariate models. In the casecohort analyses, HbA1c and GA had similar associations with retinopathy and nephropathy, which were strengthened when both measures were considered together. Only HbA1c was significantly associated with cardiovascular disease. The demonstrated interrelationships among different measures of glycemia will need to be considered in future analyses of their roles in the development of longterm complications of type 1 diabetes.

Page 3 of 39 Diabetes

The Diabetes Control and Complications Trial (DCCT) showed that intensive therapy

aimed at near normal levels of glycated hemoglobin (HbA1c) substantially reduced the risk of

microvascular complications (1), and that mean HbA1c was the principal determinant of risk of

complications (2). Moreover, the difference in risk between the intensive and conventional

treatment groups was virtually completely explained by the difference in levels of HbA1c during

the DCCT (3). The reductions in risk of microvascular complications with intensive therapy

persisted during the Epidemiology of Diabetes Interventions and Complications (EDIC)

observational followup study, despite negligible differences in HbA1c since the end of the

DCCT, a phenomenon named metabolic memory (46). Extended followup of the DCCT cohort

also revealed a major effect of intensive therapy and lower levels of HbA1c on cardiovascular

outcomes, with a 58% reduction in fatal and nonfatal myocardial infarctions and stroke (7). The

DCCT results have been used to set targets for diabetes management based on desired levels of

HbA1c.

HbA1c, the result of the nonenzymatic glycation of hemoglobin, reflects average

glycemia over the preceding 812 weeks (8). Whether average glycemia as reflected by HbA1c is

the major glycemic determinant of complications, or whether other measures of glycemia

contribute, and to what extent, are unknown. Glycemic variability, potentially through an

independent effect on oxidative stress, has been suggested to be an additional mediator of

complications (9,10), but studies have not been consistent in supporting this hypothesis (1112).

Other measures of shorterterm glycemia, such as fructosamine or glycated albumin (GA), have

also been associated with longterm complications (13,14). In addition, GA has been suggested

to be a better index of glucose variability than HbA1c (15). In order to evaluate the contributions

Diabetes Page 4 of 39

of different indices of glycemia to the risk of developing complications, understanding the relationships among them is necessary.

During the DCCT, HbA1c and quarterly 7point daily glucose profiles were obtained from its 1441 subjects, and outcomes were assessed systematically over an average of 6.5 years.

The results herein are based on a substudy of 497 subjects in whom shorterterm glycemia (GA) was measured annually from frozen specimens. We describe the association between HbA1c and

GA levels, and their relationships with mean blood glucose, pre and postprandial glucose concentrations, and measures of withinprofile glucose variation, all derived from the 7point profile. We then evaluate whether and to what extent glycated albumin levels contribute to the risk relationship between HbA1c and retinopathy, nephropathy and CVD events. Analyses relating glucose variation to clinical outcomes will be the focus of a future paper based on all

1441 subjects in the cohort.

Methods

Study Design. The DCCT cohort and study design have been described in detail. (1). In brief, the DCCT enrolled 1441 subjects 1339 years of age who were assigned to receive intensive or conventional therapy. Seven hundred twenty six were members of the primary prevention cohort with no preexisting retinopathy, albumin excretion rate (AER) < 40 mg/24 h and 15 years of diabetes duration, and 715 were members of the secondary intervention cohort with minimal to moderate nonproliferative retinopathy, AER < 200 mg/24 h and 115 years duration. Retinopathy severity was assessed every 6 months by centrally read 7field fundus photographs, and AER was measured centrally from an annual 4hour timed collection. Patients were followed for an average of 6.5 years at DCCT study end in 1993. Thereafter, all

Page 5 of 39 Diabetes

conventional group subjects were instructed in intensive therapy, and all patients referred to their

personal physicians for care. EDIC annual observational followup was initiated in 1994 and

96% of surviving DCCT cohort chose to participate. During the DCCT, mean HbA1c levels were

approximately 2% lower in the intensive than in the conventional treatment group, while during

EDIC, HbA1c levels between the original treatment groups converged and were no longer

different (16).

Case Definitions. For the current casecontrol study, retinopathy was defined as a

sustained 3step progression on the final ETDRS scale (17) during DCCT. Nephropathy was

defined as the onset of microalbuminuria (AER > 40 mg/24 h) during the DCCT among those

free of microalbuminuria at baseline. CVD was defined as the occurrence of any of the following

during the DCCT or during the first 16 years of followup in EDIC: fatal or nonfatal myocardial

infarction or stroke, death judged to be due to cardiovascular disease, subclinical myocardial

infarction present on an annual electrocardiogram, angina confirmed by ischemic changes on

exercise tolerance testing or by clinically significant obstruction on coronary angiography, or

revascularization with angioplasty or coronaryartery bypass (7).

Laboratory and clinical analyses. HbA1c was measured quarterly during the DCCT and

annually during EDIC using a high precision, highperformance liquid chromatography (HPLC)

assay with longterm controls to monitor assay stability, as previously described (18). Interassay

coefficients of variation (CVs) have remained less than 2%. Glycated albumin (GA) was

measured using the LUCICA GAL kit (Asahi Kasei Pharma Corporation, Tokyo, Japan) with a

Roche Modular P Chemistry Analyzer (Roche Diagnostics Corporation) on stored, frozen (70 o C)

Diabetes Page 6 of 39

sera collected at each DCCT annual visit between 1983 and 1993. Interassay coefficients of variation (CVs) have remained approximately 2%. The assay has proved to be very stable for samples stored at –70o C for as long as 23 years (19). GA levels are not affected by albumin and hemoglobin levels (20), and the assay is not influenced by the physiologic concentrations of ascorbic acid, bilirubin, and up to 1000mg/dL of glucose (21). The quarterly 7point capillary glucose samples were collected in capillary tubes, placed in diluent and assayed in the central laboratory as previously described (1).

The pooled within subject coefficients of variation computed from all repeated measures over time were 12% for glycated albumin and 9% for HbA1c. Each measures the sum of laboratory variation and within subject variation over time.

Case-Cohort Design. The detailed description of statistical considerations is presented in the online appendix. Briefly, prior Cox proportional hazards analyses of progression of retinopathy (2) showed a 42% risk reduction per 10% lower updated mean HbA1c, with a lower confidence limit of a 36% risk reduction. Using the methods of Lachin (22), a sample size of 145 cases and 145 controls provided 90% power to detect a 36% risk reduction per 10% lower glycated albumin when adjusted for HbA1c or for blood glucose.

A casecohort design (23) was employed for each outcome retinopathy, nephropathy and CVD. A subcohort of 250 subjects was randomly selected from the DCCT cohort of 1441 subjects. This included some cases of each outcome and some noncases (controls) for each outcome type. This provided more than 145 controls for each outcome, specifically 186 for retinopathy, 184 for nephropathy and 225 for CVD. For each outcome, additional cases were drawn at random from the remainder of the cohort (1441 minus 250) to provide a total of 145 retinopathy and 145 nephropathy cases,. All 127 CVD cases (all that were known to exist at that

Page 7 of 39 Diabetes

time) were selected. Note that a given subject could be included as a case or control in more than

one of the three casecohorts. All analyses used the survey weights equal to the inverse of the

sampling probabilities for the cases and controls of each outcome.

Missing Data. The combination of the casecohorts for the 3 outcomes comprised 497

subjects. However, 4.1% of the expected glycated albumin values and 12.4% of the individual

blood glucose values from the 7point profiles were missing, whereas the HbA1c data was nearly

complete. To address the missing data, the statistical technique of multiple imputation was

employed (24) to provide 10 estimates of each missing value yielding 10 complete data sets. A

given analysis was then repeated using each of the 10 complete data sets, and the results

averaged using the methods of Rubin and Schenker (25). The resulting confidence limits and p

values accounted for the overall extent of the original missing data.

Statistical Analysis. From each 7point blood glucose profile we computed the overall

mean, standard deviation, mean amplitude of glycemic excursion (MAGE) (26), and the mean of

the pre and postprandial values separately. The updated mean of each measure at each year was

computed from the annual posttreatment values, with the baseline being the initial value.

Analyses of the association of the updated mean values with the risk of each outcome

were conducted using a modification of the Cox PH for application to a casecohort design (27,

28). The models for retinopathy and nephropathy also were adjusted for diabetes duration; and

models for CVD were adjusted for age, each baseline covariate being among the most strongly

associated with each respective outcome. Analyses used the natural log of each glycemic

measure that had a somewhat stronger association with the outcome (i.e. had better fit) than did

the untransformed values. The effect of each measure (e.g. HbA1c) was described as the percent

change in risk of the outcome per 10% higher value of the measure. (2).

Diabetes Page 8 of 39

Results

The weighted estimates of patient characteristics in the population corresponding to each casecohort sample and the aggregate sample in which the GA was measured showed that the characteristics were similar among the cohorts and similar to those in the original complete DCCT cohort (N = 1441). (Table 1)

Inter-relationships among Measures of Glycemia. The scatterplot of the associations between HbA1c and GA values at all visits combined showed a strong linear relationship with an overall R2 of 0.75. (Figure 1) The estimated mean values of each measure within the intensive and conventional groups over time are shown in Figure 2 and over all 7 years of followup in

Table 2. In this random subsample of the full cohort, the intensive group estimated mean HbA1c tended to track along with that observed in the full cohort except for the final visit at which there was a rise among the small number of subjects (n= 90) followed to that visit. The mean blood glucose and glycated albumin values paralleled those of the HbA1c.

The regression values of the annual HbA1c and glycated albumin values (including baseline) on measures of glycemia and glycemic variation derived from the corresponding annual blood glucose profiles are shown in Table 3. As might be expected from the shorter period of turnover of albumin compared with hemoglobin, resulting in a shorter period of glycemia reflected by GA than with HbA1c, glycated albumin had a slightly stronger association with measures derived from the singleday blood glucose profiles, performed on the same day as the blood sampling, than the associations of HbA1c with glucose concentrations in all models.

The mean concentration of daily blood glucose (model 1) was more strongly associated with the

HbA1c and GA than was the withinprofile standard deviation or the MAGE (models 2 and 3).

Page 9 of 39 Diabetes

When the standard deviation and mean were both employed in the model (model 4), SD was no

longer statistically significant. Similar results applied to models with the mean blood glucose and

the MAGE in which the MAGE is no longer significantly associated with HbA1c or GA (data

not shown).

The associations of both HbA1c and GA with the mean preprandial, mean postprandial

and the value at bedtime glucose concentrations (model 5) were slightly stronger than that with

the overall mean, and both the pre and postprandial means were approximately equally

significant with similar coefficients and SEs. The effect of the bedtime glucose was also

significant but the coefficient was smaller. The final model 6 shows the association of all 7point

blood glucose values in the profile jointly with HbA1c and GA. Both glycated products were

more strongly correlated with the fasting (prebreakfast) and bedtime values than with the other

time points, all but one of which were also nominally significant. Since the mean blood glucose

model R2 was only slightly less than that using all seven values together, only the mean glucose

was employed in analyses in relation to other outcomes.

Retinopathy. The weighted Cox proportional hazards models examining the association

of the measures of glycemia with the risk of sustained 3step retinopathy progression were

conducted for each of the three measures of glycemia (HbA1c, GA, and MBG) separately, then

for two at a time and then using all three (Table 4). The average model chisquare value (over the

10 imputations) was used as a measure of the strength of the association.

Mean blood glucose concentrations derived from the 7point profile had a weaker, albeit

still significant (p < 0.0001), effect than either of the glycated products (shown by the chisquare,

models 13). When employed jointly with either glycation product (models 57), the MBG was

no longer significant. When HbA1c and glycated albumin were considered together, the HbA1c

Diabetes Page 10 of 39

had a slightly smaller pvalue without or with adjustment for the MBG (models 4 and 7). Based on the model chisquare value, adding HbA1c to glycated albumin (model 4 versus 2) or glycated albumin to HbA1c (model 4 versus 1) each resulted in a substantial increase. Adding the MBG to the two glycation products (model 7 versus 4) resulted in a trivial increase.

Nephropathy. The associations of the measures of glycemia with the risk of microalbuminuria (or worse) in weighted Cox proportional hazards models are shown in Table 5.

As with retinopathy, the mean blood glucose alone had a weaker effect than either of the glycated products (models 13) and was not significant when adjusted for either glycation product (models 57). When HbA1c and glycated albumin were considered together, neither effect was significant, without or with adjustment for the MBG (models 4 and 7).

Cardiovascular Disease. The association of the measures of glycemia with the risk of

developing cardiovascular disease (time to the first event) are shown in Table 6. Unlike

retinopathy and nephropathy where all 145 cases of progression were observed during the DCCT

(maximum 9 years of followup), 113 of the 127 clinical CVD cases were observed during the

EDIC followup study, the mean event time being 14.5 years from randomization. HbA1c alone was significant (p=0.027), although not in any combination with either glycated albumin or blood glucose. Glycated albumin was not significant either alone or in combination with other factors. Mean blood glucose alone was also a significant predictor (p=0.024), but not in combination with other glycemic factors.

Discussion

The availability in DCCT/EDIC of longitudinal measurements of glycemia, including indices of chronic (HbA1c) and intermediate (GA) glycemia, and of acute glucose concentrations and glucose variability from a 7point profile, allowed us to examine the relationships among the

Page 11 of 39 Diabetes

different measures and their association with clinical outcomes. Initial analyses examined the

association of features of the blood glucose profile with HbA1c and GA (Table 3). As expected,

HbA1c and GA were highly correlated. Both were strongly associated with the MBG value, but

with a lesser association with the withinprofile SD or the MAGE, measures of glucose variation.

Furthermore, the SD had no significant association with the level of HbA1c or GA beyond the

association between mean blood glucose and HbA1c or GA. This finding directly contradicts

prior assertions (29). In fact, the mean of the preprandial values, the mean of the postprandial

values and the value at bedtime all had significant effects on both HbA1c and GA when

employed together in a model. When the 7point profile BG values were used jointly, the

bedtime and fasting BG, spanning the longest interval in the day (about 8 hours), had a

somewhat stronger association with both glycation products than the other individual pre or

postprandial values.

Further analyses then assessed the relationship of the MBG, GA and HbA1c with

outcomes. These analyses did not incorporate measures of glucose variation since the principal

objective was to assess whether GA had independent effects above and beyond the MBG and

HbA1c. Analyses of the association of glucose variation with outcomes will be conducted using

data from all 1441 DCCT subjects rather than from the subset of 497 subjects in the current

study who had GA measurements.

Individually, the HbA1c and GA had a strong association with progression of

retinopathy, both in terms of the percent change in risk per percent change in the factor and the

model χ2 value. The strongest association with retinopathy was observed when GA and HbA1c

were included together, both effects being nominally significant at the 0.05 level, supporting a

Diabetes Page 12 of 39

potential independent effect of each measurement as a risk factor. Both HbA1c and GA remained significant when also adjusted for the MBG.

HbA1c and GA each separately had similar associations with nephropathy, but when the

two were employed together, the HbA1c had no independent contribution above the GA. Neither

was significant when adjusted for the MBG. The differential effects of HbA1c and GA on

retinopathy versus nephropathy suggest that the two complications may respond to different

aspects of the history of hyperglycemia. Although the measurement of HbA1c may be problematic and GA preferred in the setting of advanced renal disease (30), virtually none of the

DCCT cohort had such advanced disease during the period of these analyses. The similar correlations of HbA1c and GA with retinopathy and nephropathy provide support to the cross sectional study of the Atherosclerosis Risk in Communities (ARIC) population in which GA was at least as powerful predictor of these complications as HbA1c (13).

For both retinopathy and nephropathy, the relationships with MBG were substantially weaker and were attenuated once GA or HbA1c levels were also considered, indicating that the measurements of long(er)term glycemia prevail as risk factors for microvascular complications.

Intermittent measurements of blood glucose profiles to capture MBG did not provide as strong an association with longterm complications as did HbA1c or glycated albumin. Variability of

HbA1c levels over time, measured as the intraindividual SD of values measured over approximately 6 years, has been suggested to be an independent risk factor for nephropathy and

CVD events (10).

HbA1c had a significant association with CVD outcomes, as did MBG. On the other hand, the association of GA with CVD, although with a qualitatively similar risk profile as

HbA1c, was not significant. No index of chronic glycemia had a significant relationship with

Page 13 of 39 Diabetes

CVD when adjusted for another index of glycemia. Previously, we showed that the DCCT mean

HbA1c as a time dependent covariate was significantly associated with the risk of CVD in the 83

subjects who had developed this outcome at the time of the prior publication (7).

The associations of both glycation products with retinopathy and nephropathy were in

general stronger than that observed with CVD events. Since retinopathy and nephropathy are

more specific to diabetes than CVD, the stronger correlation of chronic hyperglycemia with the

microvascular complications is not surprising. Another reason may be that retinopathy was

assessed every 6 months in relation to the annual HbA1c values during the DCCT that were used

for this study, and nephropathy annually; in contrast, only 14 CVD cases were observed during

the DCCT, when the glycated albumin and HbA1c levels were measured. The majority of the

CVD cases occurred many years after the close of the DCCT, and the analyses herein related the

mean value of the glycation products (and mean BG) up to the end of the DCCT (mean 6.5

years) with CVD events that occurred many years thereafter. Thus, weaker associations should

be expected.

The objective of these analyses was to replicate the major findings from DCCT/EDIC

that included the effects of HbA1c on microvascular complications during the DCCT, and on

CVD during DCCT and EDIC combined, with the addition of glycated albumin results. Thus the

retinopathy and nephropathy cases and controls were selected based on outcomes during the

DCCT only. It would also have been desirable to assess the association of glycated albumin

levels during the DCCT with further progression of retinopathy and nephropathy during EDIC,

the socalled “metabolic memory” phenomenon that was previously shown to be associated with

the DCCT levels of HbA1c. Unfortunately it is not possible to replicate either analysis with the

casecohort design used for the present analysis.

Diabetes Page 14 of 39

However, the associations with CVD do include an assessment of metabolic memory for

CVD since the analysis included cases observed over both the DCCT and EDIC, the

overwhelming majority being observed in EDIC (113/127), As in our prior analyses (7), the

mean HbA1c over the period of the DCCT was strongly associated with the risk of CVD over the

DCCT and EDIC combined. However, the effect of the DCCT mean glycated albumin was

weaker than that of the HbA1c and nonsignificant. This may in part be a result in the lag of up

to 16 years from the end of the DCCT to the last of the EDIC cases observed herein. It is possible that glycated albumin values measured during EDIC, which would have been more proximate to the time of the CVD event, would have had a much stronger association with CVD

risk.

Additional limitations of the current study include our reliance on 7point glucose profiles measured quarterly, but with only the annual measure included in these analyses, to

capture mean blood glucose concentrations and variability over time. Such infrequent

measurements may lead to sampling errors and are not ideal to capture mean glycemia or provide

a true measure of variability. Also, the profiles rely on participant collection, a potentially

significant source of error. Although a modest fraction (~12%) of the profile measurements was

missing, which is another potential limitation, we used sturdy imputation methods to “fill in the blanks”. These provide a more complete picture of the glucose profiles. Even with the potential weakness of the 7point profiles, the blood glucose concentrations were measured in a central laboratory which provides greater accuracy and precision than would be found in selfmonitored or CGM results. The DCCT/EDIC is the only type 1 diabetes study that has the longterm longitudinal measurements of complications and glycemia necessary to examine the relationships we have examined herein.

Page 15 of 39 Diabetes

Finally, our analyses only used the annual HbA1c and 7point BG values to match the

annual GA values and the current analyses are restricted to a subsample of cases and controls

from the full cohort. Thus, the HbA1c results reported herein differ somewhat from those

previously published, which were based on the quarterly HbA1c measurements in the entire

cohort in relation to each outcome.

In conclusion, we have examined the intercorrelation of three different measures of

glycemia and demonstrated similar relationships among MBG and HbA1c and GA. The

strongest correlations were between MBG and GA and HbA1c, with measures of glucose

variability, including postprandial glycemia, contributing little to the GA and HbA1c values.

Both HbA1c and GA were similarly associated with microvascular complications, but only

HbA1c was associated with the cardiovascular complications. Combining both glycemic

measures appeared to strengthen the relationships with microvascular but not with cardiovascular

complications.

Acknowledgments

A complete list of participants in the DCCT/EDIC Research Group can be found in the New

England Journal of Medicine, 2011;365:23662376.

The DCCT/EDIC has been supported by U01 Cooperative Agreement grants (198293, 2011

2016), and contracts (19822011) with the Division of Diabetes Endocrinology and Metabolic

Diseases of the National Institute of Diabetes and Digestive and Kidney Disease, and through

support by the National Eye Institute, the National Institute of Neurologic Disorders and Stroke,

the Genetic Clinical Research Centers Program (1993 2007), and Clinical Translational Science

Center Program (2006present), Bethesda, Maryland, USA. Industry contributors have had no

Diabetes Page 16 of 39

role in the DCCT/EDIC study but have provided free or discounted supplies or equipment to support participants’ adherence to the study: Abbott Diabetes Care (Alameda, CA), Animas

(Westchester, PA), Bayer Diabetes Care (North America Headquarters, Tarrytown NY) Becton

Dickinson (Franklin Lakes, NJ), CanAm (Atlanta, GA) , Eli Lilly (Indianapolis, IN), Lifescan

(Milpitas, CA) , Medtronic Diabetes (Minneapolis, MI) , Omron (Shelton CT), OmniPod®

Insulin Management System (Bedford, MA) , Roche Diabetes Care (Indianapolis, IN) , and

SanofiAventis (Bridgewater NJ).

The current ancillary project received support from Asahi Kasei Pharma Corporation, Tokyo,

Japan in the form of donated assay kits. Asahi Kasei otherwise had no role in the design, conduct or reporting of this project. DMN was supported in part by the Charlton Foundation for

Innovative Diabetes Research.

None of the authors declared any dualities of interest with regards to the current study.

All authors participated in the design and conduct of the study. DMN, PM and JML wrote the

manuscript with critical review and editing by MWS. JML serves as the guarantor of this

manuscript.

1. The Diabetes Control and Complications Trial Research Group. The effect of intensive treatment of diabetes on the development and progression of longterm complications in insulindependent diabetes mellitus. N Engl J Med 1993;329:977 986 2. DCCT Research Group. The association between glycemic exposure and longterm diabetic complications in the Diabetes Control and Complications Trial. Diabetes 1995; 44:968983. 3. Lachin JM, Genuth S, Nathan DM, Zinman B, Rutledge BN for the DCCT/EDIC Research Group. The effect of glycemic exposure on the risk of microvascular complications in the Diabetes Control and Complications Trial—Revisited. Diabetes 2008; 57:9951001. 4. DCCT/EDIC Research Group Lachin JM, Genuth S, Cleary P, Davis MD, Nathan DM for the EDIC Research Group. Retinopathy and nephropathy in patients with

Page 17 of 39 Diabetes

Type 1 diabetes four years after a trial of intensive therapy. N Engl J Med 2000; 342:3819. 5. Genuth S, Lachin J, Cleary, P, Nathan DM for the DCCT/EDIC Research Group. Effect of intensive therapy on the microvascular complications of type 1 diabetes mellitus. JAMA 2002;287:25632569. 6. Albers JW, Herman WH, PopBusui R, Feldman EL, Martin CL, Cleary PA, Waberski BH, Lachin JM; Diabetes Control and Complications Trial /Epidemiology of Diabetes Interventions and Complications Research Group. Effect of prior intensive insulin treatment during the Diabetes Control and Complications Trial (DCCT) on peripheral neuropathy in type 1 diabetes during the Epidemiology of Diabetes Interventions and Complications (EDIC) Study. Diabetes Care. 2010;33:10906. 7. Nathan DM, Cleary PA, Backlund JYC, Genuth SM, Lachin JM, Orchard TJ, Raskin P, Zinman B. Intensive diabetes treatment and cardiovascular disease in type 1 diabetes mellitus. N Engl J Med 2005; 353:26432653. 8. Nathan DM, Kuenen J, Borg R, Zheng H, Schoenfeld D, Heine RJ for the A1c Derived Average Glucose (ADAG) Study Group. Translating the A1c assay into estimated average glucose values. Diabetes Care 2008; 31: 14738. 9. Hirsch IB, Brownlee M. Beyond hemoglobin A1cneed for additional markers of risk for diabetic microvascular complications. JAMA. 2010;303:22912. 10. Waden J, Forsblom C, Thorn LM, Gordin D, Saraheimo M, Groop PH and the Finnish Diabetic Nephropathy Study group. A1C variability predicts incident cardiovascular events, microalbuminuria, and overt diabetic nephropathy in patients with type 1 diabetes. Diabetes 2009;58:26492655. 11. Service FJ, O'Brien PC. The relation of glycaemia to the risk of development and progression of retinopathy in the Diabetic Control and Complications Trial. Diabetologia. 2001;44:121520. 12. Kilpatrick ES, Rigby AS Atkin SL. The effect of glucose variability on the risk of microvascular complications in type 1 diabetes. Diabetes Care 2006;29: 148690. 13. Selvin E, Francis LMA, Ballantyne CM, Hoogeveen RC, Coresh J, Brancati FL, Steffes MW. Nontraditional markers of glycemia: Associations with microvascular conditions. Diabetes Care 2011; 34:9607 14. Furusyo N, Koga T, Masumi A, Otokozawa S, Kohzuma T, Ikezaki H, Schaefer EJ, Hayashi J. Plasma glycated albumin level and atherosclerosis: results from the Kyushu and Okinawa Population Study. Int J Cardiol 2012; [Epub ahed of print].PMID:22658569 15. Yoshiuchi K, Matsuhisa M, Katakami N, Nakatani Y, Sakamoto K, Matsuoka T, Umayahara Y, Kosugi K, Kaneto H, Yamasaki Y, Hori M: Glycated albumin is a better indicator for glucose excursion than glycated hemoglobin in type 1 and type 2 diabetes. Endocr J 2008;55:503507 16. Nathan DM, Zinman B, Cleary PA, Backlund JY, Genuth S, Miller R, Orchard TJ for DCCT/EDIC Research Group. Modern Day Clinical Course of Type 1 Diabetes Mellitus after 30years Duration. The Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) and Pittsburgh Epidemiology of Diabetes Complications (EDC) Experience 19832005. Arch Int Med 2009; 169:130716.

Diabetes Page 18 of 39

17. Early Therapy Diabetic Retinopathy Study Research Group. Photocoagulation for diabetic macular edema. Early Therapy Diabetic Retinopathy Report Number 1. Arch Ophthalmol 1985;103:1796806. 18. Steffes M, Cleary P, Goldstein D, Little R, Wiedmeyer HM, Rohlfing C, England J, Bucksa J, Nowicki M: Hemoglobin HbA1c measurements over nearly two decades: sustaining comparable values throughout the Diabetes Control and Complications Trial and the Epidemiology of Diabetes Interventions and Complications study. Clin Chem 2005;51:753758 19. Nathan DM, Steffes MW, Sun W, Rynders GP, Lachin JM. Determining the stability of stored samples retrospectively: the validation of glycated albumin. Clin Chem 2011: 57:28690 20. Kouzuma T, Uemastu Y, Usami T, Imamura S. Study of glycated amino acid elimination reaction for an improved enzymatic glycated albumin measurement method. Clin Chim Acta 2004;346:13543. 21. Nagamine Y, Mitsui K, Nakao T, Matsumoto M, Fujita C, Doi T: Evaluation of the enzymatic method for glycated albumin with liquid type reagent (Lucia GAL) [in Japanese]. Jpn J Med Pharm Sci 51:737745, 2004 22. Horvitz DG, Thompson, DJ (1952) "A generalization of sampling without replacement from a finite universe", Journal of the American Statistical Association 1952;47: 663– 685. 23. Lachin JM. Sample size evaluation for a multiply matched casecontrol study using the score test from a conditional logistic (discrete Cox PH) regression model. Statistics in Medicine 2008;27:25092523 24. Prentice, R. A casecohort design for epidemiologic cohort studies and disease prevention trials. Biometrika 1986 73, 1–11. 25. Raghunathan TE, Lepkowski JM, Hoewyk JV, Solenberger P. A multivariate technique for multiply imputing missing values using a sequence of regression models. Survey Methodology 2001; 27:8595 26. Rubin DB, Schenker N. Multiple imputation for interval estimation from simple random samples with ignorable nonresponse. Journal of the American Statistical Association 1986; 81(394):366374. 27. Molnar G. Quantitative aspects of labile diabetes. Diabetes 1965;14:27988 28. Therneau TM, Li H: Computing the Cox model for casecohort designs. Lifetime Data Analysis 1999;5:99112 29. Barlow WE. Robust variance estimation for the casecohort design. Biometrics 1994;50: 1064–1072 30. McCarter Jr. RJ, Hempe JM, Chalew SA: Mean blood glucose and biological variation have greater influence on HbA1c levels than glucose instability. Diabetes Care 29:352355, 2006 31. Peacock TP, Shihabi ZK, Bleyer AJ, Dolbare EL, Byers JR, Knovich MA, Calles Escandon J, Russell GB, Freedman BI: Comparison of glycated albumin and hemoglobin HbA1c levels in diabetic subjects on hemodialysis. Kidney Int 2008;73:10621068

Page 19 of 39 Diabetes

Table 1. Baseline characteristics* from the weighted glycated albumin casecohort and from the full DCCT cohort.

Weighted Glycated DCCT cohort Albumin sample N=1441 N=497 Age (years) 26.6 ± 7.4 26.8 ± 7.1 Female 50% 47% Adult (age>18 years) 85% 86% Primary prevention cohort 50% 50% Intensive treatment group 49% 49% Duration of diabetes 67.0 ± 48.5 67.6 ± 49.9 (months) BMI (kg/m2) 23.4 ± 2.7 23.5 ± 2.8 AER (mg/24h) 14.3 ± 14.4 15.9 ± 18.8 HbA1c (%) 8.8 ± 1.6 8.9 ± 1.6 HbA1c (mmol/mol) 75.7 ± 18.1 74.0 ± 17.5 Glycated Albumin 29.8 ± 7.4 N/A (mg/ml) Mean blood glucose 238.2 ± 83.1 232.9 ± 81.2 (mg/dl) * Means ± SD or % estimated using the survey sample weights based on the inverse sampling probabilities within strata defined by DCCT Treatment Group, primary versus secondary cohort and whether a case or not for retinopathy, nephropathy or CVD. The sample is only weighted up to N=1439, due to 2 missing outcomes, one each for retinopathy and nephropathy.

Diabetes Page 20 of 39

Table 2. Mean* values and 95% confidence limits for HbA1c, glycated albumin, and mean blood glucose over 17 years of treatment and followup within each treatment group.

Conventional Intensive Difference pvalue

HbA1c (%) 9.4 (9.2, 9.5) 7.4 (7.3, 7.6) 1.9 (1.7, 2.2) <0.0001 HbA1c (mmol/mol) 79.2 (77.5, 80.9) 58.0 (56.2, 21.2 (18.8, <0.0001 59.8) 23.6) Glycated Albumin 30.9 (30.3, 31.4) 23.4 (22.8, 7.47 (6.72, <0.0001 (%) 24.0) 8.24) Mean Blood 240 (233, 247) 169 (164, 174) 71 (63, 79) <0.0001 Glucose (mg/dl) * From a longitudinal mixed model comparing treatment groups adjusted for year of followup as a class variable. Models did not include the baseline value. Observations weighted by the inverse sampling probabilities within strata defined by DCCT Treatment Group, primary versus secondary cohort and whether a case or not for retinopathy, nephropathy or CVD.

Page 21 of 39 Diabetes

Table 3. Association of the annual HbA1c and glycated albumin values with glycemic measures derived from the annual 7point blood glucose profiles in separate weighted longitudinal models*.

HbA1c Glycated Albumin Model Glucose Profile Beta (SE) pvalue Model R2 Beta (SE) pvalue Model R2 # derived Measure 1 Mean BG 0.012501 < 0.0001 0.3754 0.053880 (0.002447) < 0.0001 0.3970 (0.000624)

2 MAGE 0.005088 < 0.0001 0.0958 0.019770 < 0.0001 0.0953 (0.000624) (0.002683)

3 Standard deviation 0.013795 < 0.0001 0.1081 0.053947 < 0.0001 0.1066 (0.001589) (0.006928)

4 Mean BG 0.012497 < 0.0001 0.3757 0.055641 < 0.0001 0.3985 (0.000665) (0.002356) Standard deviation 0.00006929 0.9646 0.007163 0.2860 (0.001555) (0.006622)

5 Mean of 0.005762 (0.000912) < 0.0001 0.3948 0.026763 (0.003398) < 0.0001 0.4138 preprandial concentrations Mean of 0.004602 ( 0.000836) < 0.0001 0.018835 (0.003645) < 0.0001 postprandial concentrations Diabetes Page 22 of 39

Bedtime 0.002598 ( 0.000417) < 0.0001 0.010323 (0.001525) < 0.0001

6 Pre breakfast 0.002668 ( 0.000508) < 0.0001 0.4001 0.011915 (0.002053) < 0.0001 0.4185 Post breakfast 0.001051 ( 0.000474) 0.0282 0.004096 (0.002157) 0.0620 Pre lunch 0.00092 ( 0.000431) 0.0338 0.005303 (0.001891) 0.0063 Post lunch 0.00245 (0.000541) <0.0001 0.010044 (0.002288) 0.0001 Pre dinner 0.001976 (0.000537) 0.0006 0.008821 (0.002041) <0.0001 Post dinner 0.001471 (0.000544) 0.0079 0.00616 (0.002387) 0.0120 Bedtime 0.002557 (0.000459) < 0.0001 0.010131 (0.001782) < 0.0001 * Observations weighted by the inverse sampling probabilities within strata defined by DCCT Treatment Group, primary versus secondary cohort and whether a case or not for retinopathy, nephropathy or CVD.

Page 23 of 39 Diabetes

Table 4. Association of measures of glycemia individually and in combination with the risk of sustained 3step progression of retinopathy among 145 cases versus 186 controls in weighted Cox PH models*.

% change in risk for a 10% pvalue Model χ2 (df) higher value Model Model % change (95% CI) covariate(s)

1 HbA1c 67.8 (48.9., 89.0) <.0001 129.2 (2)

2 GA 49.1 (35.8, 63.7) <.0001 128.5 (2)

3 Mean BG 26.3 (18.0, 35.2) <.0001 87.1 (2)

4 GA 23.4 (2.3, 48.7) 0.0276 138.4 (3) HbA1c 33.9 (5.9, 69.4) 0.0149

5 HbA1c 56.6 (31.2, 86.9) <.0001 131.7 (3) Mean BG 5.9 (4.5, 17.4) 0.2747

6 GA 41.8 (24.6, 61.3) <.0001 130.4 (3) Mean BG 5.3 (4.5, 16.1) 0.3034

7 GA 21.6 (0.7, 46.9) 0.0420 138.9 (4) HbA1c 32.0 (2.8, 69.5) 0.0298 Mean BG 2.5 (7.5, 13.6) 0.6378 * Models weighted by the inverse probability of sampling separately for cases and controls within strata defined by primary versus secondary cohort and treatment group. PH models stratified by primary versus secondary cohort and adjusted for baseline diabetes duration.

Diabetes Page 24 of 39

Table 5. Association of measures of glycemia individually and in combination with the risk of microalbuminuria (AER ≥ 40 mg/24 h) or worse among 145 cases versus 184 controls in weighted Cox PH models*.

% change in risk for a 10% pvalue Model χ2 (df) higher value Model Model % change (95% CI) covariate(s)

1 HbA1c 15.7 (4.0, 28.8) 0.0076 17.8 (2)

2 GA 13.5 (4.7, 23.1) 0.0021 20.8 (2)

3 Mean BG 7.6 (1.7, 13.9) 0.0116 15.3 (2)

4 GA 13.3 (5.4, 35.7) 0.1748 20.8 (3) HbA1c 0.3 (21.2, 27.6) 0.9815

5 HbA1c 11.6 (3.9, 29.7) 0.1518 18.6 (3) Mean BG 3.0 (4.9, 11.4) 0.4692

6 GA 12.4 (0.6, 27.1) 0.0628 21.0 (3) Mean BG 1.0 (7.3, 10.1) 0.8184

7 GA 12.5 (7.7, 37.0) 0.2429 21.0 (4) HbA1c 0.1 (21.5, 27.1) 0.9914 Mean BG 1.0 (7.3, 10.1) 0.8172 * Models weighted by the inverse probability of sampling separately for cases and controls within strata defined by primary versus secondary cohort and treatment group. PH models stratified by primary versus secondary cohort and adjusted for baseline diabetes duration.

Page 25 of 39 Diabetes

Table 6. Association of measures of glycemia individually and in combination with the risk of CVD among 127 cases versus 225 controls in weighted Cox PH models.

% change in risk for a 10% pvalue Model χ2 (df) higher value Model Model % change (95% CI) covariate(s)

1 HbA1c 16.0 (1.7, 32.4) 0.0267 54.6 (2)

2 GA 9.2 (0.9, 20.3) 0.0771 50.9 (2)

3 Mean BG 9.1 (1.2, 17.7) 0.0231 53.7 (2)

4 GA 6.6 (25.2, 16.5) 0.5435 55.3 (3) HbA1c 25.9 (6.7, 70.0) 0.1314

5 HbA1c 10.5 (11.6, 38.0) 0.3806 55.4 (3) Mean BG 4.0 (8.5, 18.2) 0.5497

6 GA 0.4 (16.3, 20.5) 0.9665 53.8 (3) Mean BG 8.8 (5.6, 25.5) 0.2432

7 GA 10.3 (29.7, 14.6) 0.3851 57.0 (4) HbA1c 22.0 (10.3, 65.9) 0.2044 Mean BG 6.5 (7.9, 23.1) 0.3962

* Models weighted by the inverse probability of sampling separately for cases and controls within strata defined by primary versus secondary cohort and treatment group. PH models stratified by primary versus secondary cohort and adjusted for baseline age.

Diabetes Page 26 of 39

Figure 1. Scatterplot of all glycated albumin versus HbA1c values. The slope is a 3.37 increase in glycated albumin % per unit increase in HbA1c % (SE = 0.118, p < 0.0001), with R2 = 0.746.

Glycated Albumin (%) Albumin Glycated 0 10 20 30 40 50 60 70

4 6 8 10 12 14 16

HbA1c (%)

Page 27 of 39 Diabetes

Figure 2. Weighted estimates of the mean ± 95% CL of measures of glycemia at each annual visit during the DCCT in the aggregate cohort (N = 497) obtained from a longitudinal model with separate estimates for each group at each visit. A. Glycated Albumin, B. Mean blood glucose, C. HbA1c. Computed as the average of the values at each visit from the 10 imputed data sets with no missing values. Sample sizes at each year of 497, 497, 496, 494, 492, 446, 332, and 214. Observations weighted by the inverse sampling probabilities within strata defined by DCCT Treatment Group, primary versus secondary cohort and whether a case or not for retinopathy, nephropathy or CVD.

A. Glycated Albumin (%)

B. Mean Blood Glucose (mg/dl)

C. HbA1c (%)

Diabetes Page 28 of 39

Page 29 of 39 Diabetes

Supplemental Online Appendix

Relationship of Glycated Albumin to Blood Glucose and Glycated Hemoglobin (HbA1C) Values and to Retinopathy, Nephropathy and Cardiovascular Outcomes in the DCCT/EDIC Study

David M. Nathan1, Paula McGee2, Michael W. Steffes3, John M. Lachin2, and the DCCT/EDIC Research Group*

This online Appendix describes in detail the assessment of sample size, the selection of

the sub-cohort and construction of the case-cohorts for each outcome, the extent of the missing

data in the DCCT, the implementation of the multiple imputations and the case-cohort analyses

using inverse probability weighting.

Sample Size and Power

In prior Cox proportional hazards analyses of progression of retinopathy (1), the log

updated mean HbA1c had a coefficient of 5.13, corresponding to a 42% risk reduction per 10%

lower HbA1c value. The lower two-sided 95% confidence limit was 4.31 corresponding to a

36% risk reduction per 10% lower HbA1c value. Using the methods of Lachin (2), and allowing

for an R2 as high as 0.8 in the regression of HbA1c on glycated albumin and blood glucose, a

sample size of 145 cases and 145 controls provided 90% power to detect a 36% risk reduction

per 10% lower HbA1c when adjusted for glycated albumin, and vice versa when also adjusted

for blood glucose.

The Sub-Cohort

A random sub-cohort of 250 subjects was selected as the basis for a sub-sample of

controls for each outcome (case type). This included 60 retinopathy cases, 53 nephropathy cases,

and 21 CVD cases. An additional 85 retinopathy cases and 92 nephropathy cases were randomly Diabetes Page 30 of 39

selected from among the remaining cases in the full cohort, yielding 145 cases for each, and all remaining 106 known CVD cases were selected yielding a total of 127 CVD cases. For each outcome, the remainder of the sub-cohort members (i.e. non-cases for that outcome) were used as controls, 186 for retinopathy, 184 for nephropathy and 225 for CVD. Nephropathy cases and controls were only selected from among those patients free of microalbuminuria at baseline in the sub-cohort and the full cohort.

Missing Data

The composite of the case-cohorts for each of the 3 outcomes comprised 497 subjects.

Based on the duration of DCCT follow-up ranging from 4 to 9 years, a total of 3710 quarterly visits were expected. During the DCCT, prior to each quarterly visit, each patient was requested to collect a “profilset” of timed capillary blood collections for measurement of capillary blood glucose before and after each meal (pre-and post-prandial) and at bedtime. A total of 25,970 such blood glucose values were expected. Table A1 presents the number of missing profilset values at each of the seven points in time and the number expected. Patient compliance with the quarterly blood glucose collections was poor, moreso for the bedtime than the pre- and postprandial values. All total 3237 individual profilset values were missing from the 25,970 expected, or 12.5%.

Table A2 shows that a total of about 4% of the expected glycated albumin values were also missing. However, 12.5% of the baseline (year 0) values were missing owing to depletion of stored samples in prior ancillary studies. Only 6 of the expected HbA1c values were missing.

Multiple Imputation

The statistical technique of multiple imputation (3) was employed to address the impact of the missing profilset blood glucose data, and the disproportionate frequency of missing

Page 31 of 39 Diabetes

baseline glycated albumin data. In non-technical terms, multiple imputation uses the associations

among the observed data to impute a value for a missing data point based on regression models

obtained using the other data points observed. The missing data point is imputed by adding a

random error to the conditional expectation based on all the other observed data for that subject.

Herein statistical models regressed the observed GA, HbA1c and profile-derived glucose

values, simultaneously, on each other and other covariates using all available data for each

subject, including neighboring quarterly visits. For example, a missing post-lunch glucose

concentration at year 2 was estimated from the other profile values measured at year 2, those at

21 months and also at 27 months, as well as the HbA1c values at months 21, 24 and 27, and the

GA at month 24. While the case-cohort analyses herein employed only the annual visit blood

glucose values, the quarterly visit blood glucose and HbA1c values immediately before and after

the annual visits were also used in the imputation models because they were strongly correlated

with the annual visit value. The imputation models employed DCCT treatment group (intensive

vs. conventional), age group (adult versus not), sex, five genetic polymorphisms related to

diabetes and outcomes, and C-peptide, all evaluated at baseline, and binary indicator variables to

designate whether the patient was a case for any of the outcomes (retinopathy, nephropathy,

cardiovascular disease, each no/yes). The models also employed the quarterly 7 profilset

capillary blood glucose values, quarterly HbA1c, annual glycated albumin, 6-monthly level of

severity of retinopathy, annual natural log ablumin excretion rate, and the quarterly basal and

meal insulin doses.

To preserve the variation in the data, for each missing value of a measure, an imputed

value was obtained by adding a random error, consistent with the observed variation in the data,

to the conditional expectation that was estimated from the other observed values. This process is

Diabetes Page 32 of 39

repeated for all missing data in the data set, including the missing glycated albumin and HbA1c values.

The multiple imputation was implemented using chained equations (MICE) (4,5) to generate 10 imputed complete data sets. Because the imputation model provides a random imputed value for each missing value, the resulting imputed data sets are different. A given analysis was then conducted separately for the 10 data sets and the summary statistics were then combined into a single overall estimate that takes account of the variation between imputations as well as within (6).

The desired analysis was then conducted separately on each of these ten data sets and a weighted average of the results computed using the methods of Rubin and Schenker (6).

Inverse Probability Weighting

Relative to the full cohort of 1441 subjects, the probability of selection in either the sub- cohort or as an additional case varied for cases and controls within treatment group and cohort.

The Horvitz-Thompson estimation procedure (7) then employed survey weights equal to the inverse of the probability of selection within cases and controls by treatment group and strata.

This provides an unbiased estimate of associations in the general population allowing for the differential sampling probabilities of cases and controls in the case-cohorts. Table A3 presents the strata that were employed in each analysis, the sampling proportions and the inverse probability sampling weight used in the analyses. All analyses used a program that employed survey weighted variance estimation.

Case-cohort Analyses

Longitudinal Quantitative Models

Page 33 of 39 Diabetes

For the analysis of the relationships among the quantitative measures over time, strata

were defined by 32 possible combinations of a retinopathy case versus control, nephropathy case

versus control and CVD case versus control, separately within cohort and treatment group. These

strata and the corresponding weights are shown in Table A3, Section D. The models were

estimated using the SAS SurveyReg procedure with subject as a cluster effect. Estimated means

within each group over all annual visits were obtained from a model with a group by visit

interaction. The model R2 was estimated from the overall model F-test in the longitudinal model

(8).

Proportional Hazards Event-time Models

Barlow (9) described a survey-weighted extension of the Cox Proportional Hazards

model for the analysis of case-cohort data. Therneau and Li (10) describe how this method can

be fit using existing software such as the SAS procedure PHREG. The robust information

sandwich estimate of the covariance matrix of the coefficient estimates (11) was used to obtain

associated inferences (standard errors, confidence intervals). For a given case definition (e.g.

retinopathy), the inverse probability sampling weights were computed separately for cases and

controls within strata defined by DCCT treatment group (intensive/conventional) and cohort

(primary/secondary). These strata and the corresponding weights for the analyses of retinopathy,

nephropathy and CVD are shown in Table A3, Sections A-C, respectively.

In all cases, models were fit for each of the 10 imputed data sets and the results pooled

over sets.

References:

1. DCCT Research Group. The association between glycemic exposure and long-term diabetic complications in the Diabetes Control and Complications Trial. Diabetes

Diabetes Page 34 of 39

1995; 44:968-983. 2. Lachin JM. Sample size evaluation for a multiply matched case-control study using the score test from a conditional logistic (discrete Cox PH) regression model. Statistics in Medicine 2008;27:2509-2523 3. Rubin, D.B. Multiple Imputation for Nonresponse in Surveys. New York: John Wiley & Sons, 1987. 4. Raghunathan TE, Lepkowski JM, Hoewyk JV, Solenberger P. A multivariate technique for multiply imputing missing values using a sequence of regression models. Survey Methodology 2001; 27:85-95 5. Buuren S and Groothuis-Oudshoorn K. MICE: Multivariate imputation by chained equations in R. Journal of statistical software 2011; 45.3, 1-67. 6. Rubin DB, Schenker N. Multiple imputation for interval estimation from simple random samples with ignorable nonresponse. Journal of the American Statistical Association 1986; 81(394):366-374. 7. Horvitz DG, Thompson, DJ (1952) "A generalization of sampling without replacement from a finite universe", Journal of the American Statistical Association 1952;47: 663– 685. 8. Edwards LJ, Muller KE, Wolfinger RD, Qaqish BF, Schabenberger O. An R2 statistic for fixed effects in the linear mixed model. Statistics in Medicine 2008;27:6127-57 9. Barlow WE. Robust variance estimation for the case-cohort design. Biometrics 1994;50: 1064–1072 10. Therneau TM, Li H: Computing the Cox model for case-cohort designs. Lifetime Data Analysis 1999;5:99-112 11. Lin DY, Wei LJ. The robust inference for the Cox proportional hazards model. Journal of the American Statistical Association 1989; 84:1074–1078

Page 35 of 39 Diabetes

Table A1. Number (n) and percent (%) missing an individual blood glucose profilset value at each annual visit among the 497 subjects in the case-cohort sample of 497 subjects, and the expected total number of subjects for whom a profilset was expected at that annual visit.

Bedtime Pre-prandial Blood glucose Post-prandial Blood glucose Blood glucose Breakfast Lunch Dinner Breakfast Lunch Dinner Year n % n n % n n % n n % n n % EXPECTED 0 16 3.2 25 5 27 5.4 27 5.4 20 4 21 4.2 33 6.6 497 1 36 7.2 33 6.6 33 6.6 35 7 36 7.2 43 8.7 46 9.3 497 2 33 6.7 39 7.9 43 8.7 38 7.7 38 7.7 41 8.3 54 10.9 496 3 42 8.5 46 9.3 47 9.5 54 10.9 52 10.5 53 10.7 61 12.3 494 4 67 13.6 75 15.2 76 15.4 69 14 66 13.4 75 15.2 93 18.9 492 5 93 20.9 94 21.1 96 21.5 93 20.9 94 21.1 97 21.7 111 24.9 446 6 90 27.1 89 26.8 90 27.1 92 27.7 87 26.2 92 27.7 103 31 332 7 73 34.1 74 34.6 71 33.2 74 34.6 74 34.6 74 34.6 79 36.9 214 8 44 32.8 42 31.3 41 30.6 44 32.8 40 29.9 42 31.3 46 34.3 134 9 21 19.4 24 22.2 20 18.5 24 22.2 19 17.6 23 21.3 24 22.2 108 515 13.9% 541 14.6% 544 14.7% 550 14.8% 526 14.2% 561 15.1% 650 17.5% 3710

Diabetes Page 36 of 39

Table A2. Number (n) and percent (%) missing a glycated albumin value or a HbA1c value at each annual visit among the 497 subjects in the case-cohort sample, and the expected total number of subjects for whom a value was expected at that annual visit.

Missing Glycated Albumin Missing HbA1c year n % n % EXPECTED 0 62 12.5 0 0 497 1 16 3.2 0 0 497 2 6 1.2 0 0 496 3 17 3.4 1 0.2 494 4 12 2.4 1 0.2 492 5 20 4.5 1 0.2 446 6 7 2.1 2 0.6 332 7 7 3.3 1 0.5 214 8 5 3.7 0 0 134 9 0 0 0 0 108 TOTAL 152 4.1% 6 0.16 3710

Page 37 of 39 Diabetes

Table A3. Sampling fractions and inverse probability weights for analyses of each outcome stratified by treatment group (Intensive versus conventional), primary/secondary cohort, and case (1) versus control (0).

A Retinopathy

Group Cohort Case/ Case-Cohort Full Cohort Sample Weight Control Sample Size Size Intensive Primary 0 43 328 328/43 Intensive Primary 1 12 20 20/12 Intensive Secondary 0 52 315 315/52 Intensive Secondary 1 24 48 48/24 Conventional Primary 0 40 291 291/40 Conventional Primary 1 54 87 87/54 Conventional Secondary 0 51 235 235/51 Conventional Secondary 1 55 116 116/55

B Nephropathy:

Group Cohort Case/ Case-Cohort Full Cohort Sample Weight Control Sample Size Size Intensive Primary 0 42 305 305/42 Intensive Primary 1 22 41 41/22 Intensive Secondary 0 42 256 256/42 Intensive Secondary 1 39 69 69/39 Conventional Primary 0 46 311 311/46 Conventional Primary 1 42 67 67/42 Conventional Secondary 0 42 99 99/42 Conventional Secondary 1 54 217 217/54

C. CVD:

Group Cohort Case/ Case-Cohort Full Cohort Sample Weight Control Sample Size Size Intensive Primary 0 46 328 328/46

Diabetes Page 38 of 39

Intensive Primary 1 20 20 1.0 Intensive Secondary 0 59 331 331/59 Intensive Secondary 1 32 32 1.0 Conventional Primary 0 49 345 345/49 Conventional Primary 1 33 33 1.0 Conventional Secondary 0 310 71 310/71 Conventional Secondary 1 42 42 1.0

D Overall and Longitudinal

Case(1) / Control(0) Group Cohort CVD Nephro- Retino- Case-Cohort Full Cohort Weight pathy pathy Sample Size Sample Size Intensive Primary 0 0 0 37 278 278/37 Intensive Primary 0 0 1 7 11 11/7 Intensive Primary 0 1 0 17 31 31/17 Intensive Primary 0 1 1 4 7 7/4 Intensive Primary 1 0 0 14 14 1.0 Intensive Primary 1 0 1 2 2 1.0 Intensive Primary 1 1 0 4 4 1.0 Intensive Primary 1 1 1 0 0 N/A Intensive Secondary 0 0 0 30 224 224/30 Intensive Secondary 0 0 1 13 19 19/13 Intensive Secondary 0 1 0 32 68 68/32 Intensive Secondary 0 1 1 7 20 20/7 Intensive Secondary 1 0 0 18 18 1.0 Intensive Secondary 1 0 1 4 4 1.0 Intensive Secondary 1 1 0 5 5 1.0 Intensive Secondary 1 1 1 5 5 1.0 Conventional Primary 0 0 0 31 235 235/31

Page 39 of 39 Diabetes

Conventional Primary 0 0 1 34 49 49/34 Conventional Primary 0 1 0 22 34 34/22 Conventional Primary 0 1 1 20 27 27/20 Conventional Primary 1 0 0 19 19 1.0 Conventional Primary 1 0 1 8 8 1.0 Conventional Primary 1 1 0 3 3 1.0 Conventional Primary 1 1 1 3 3 1.0 Conventional Secondary 0 0 0 40 147 147/40 Conventional Secondary 0 0 1 27 55 55/27 Conventional Secondary 0 1 0 17 53 53/17 Conventional Secondary 0 1 1 32 54 54/32 Conventional Secondary 1 0 0 21 21 1.0 Conventional Secondary 1 0 1 2 2 1.0 Conventional Secondary 1 1 0 14 14 1.0 Conventional Secondary 1 1 1 5 5 1.0