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Author Manuscript
Qual Life Res. Author manuscript; available in PMC 2015 February 01.
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Published in final edited form as:
Qual Life Res. 2014 February ; 23(1): 75–88. doi:10.1007/s11136-013-0436-3.
Impact of diagnosis of diabetes on health-related quality of life
among high risk individuals: the Diabetes Prevention Program
outcomes study
D. Marrero,
Indiana University School of Medicine, Indianapolis, IN, USA, DPP Coordinating Center, The
Biostatistics Center, George Washington University, 6110 Executive Blvd., Suite 750, Rockville,
MD 20852, USA
Q. Pan,
DPP Coordinating Center, The Biostatistics Center, George Washington University, 6110
Executive Blvd., Suite 750, Rockville, MD 20852, USA
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E. Barrett-Connor,
Department of Family Medicine, University of California, San Diego, CA, USA
M. de Groot,
Indiana University School of Medicine, Indianapolis, IN, USA
P. Zhang,
Centers for Disease Control and Prevention, Atlanta, GA, USA
C. Percy,
Northern Navajo Medical Center, Shiprock, NM, USA
H. Florez,
University of Miami Miller School of Medicine, Miami VAHS GRECC, FL, USA
R. Ackermann,
Department of Medicine, Northwestern University, Chicago, IL, USA
M. Montez,
University of Texas Health Science Center, San Antonio, TX, USA
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R. R. Rubin, and
Departments of Medicine and Pediatrics, The Johns Hopkins School of Medicine, Baltimore, MD,
USA
the DPPOS Research Group
D. Marrero: dppmail@bsc.gwu.edu
Abstract
Purpose—The purpose of this study is to assess if diagnosis of type 2 diabetes affected healthrelated quality of life (HRQoL) among participants in the Diabetes Prevention Program/Diabetes
Prevention Program Outcome Study and changes with treatment or diabetes duration.
© Springer Science+Business Media Dordrecht 2013
Correspondence to: D. Marrero, dppmail@bsc.gwu.edu.
Clinical trials Registry DPPOS: NCT00038727 and DPP: NCT00004992.
A complete list of the DPPOS Research Group investigators is shown in the Appendix 2.
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Methods—3,210 participants with pre-diabetes were randomized to metformin (MET), intensive
lifestyle intervention (ILS), or placebo (PLB). HRQoL was assessed using the SF-36 including:
(1) 8 SF-36 subscales; (2) the physical component (PCS) and mental component summary (MCS)
scores; and (3) the SF-6D. The sample was categorized by diabetes free versus diagnosed. For
diagnosed subgroup, mean scores in the diabetes-free period, at 6 months, 2, 4 and 6 years postdiagnosis, were compared.
Results—PCS and SF-6D scores declined in all participants in all treatment arms (P <.001).
MCS scores did not change significantly in any treatment arm regardless of diagnosis. ILS
participants reported a greater decrease in PCS scores at 6 months post-diagnosis (P <.001) and a
more rapid decline immediately post-diagnosis in SF-6D scores (P = .003) than the MET or PLB
arms. ILS participants reported a significant decrease in the social functioning subscale at 6
months (P <.001) and two years (P <.001) post-diagnosis.
Conclusions—Participants reported a decline in measures of overall health state (SF-6D) and
overall physical HRQoL, whether or not they were diagnosed with diabetes during the study.
There was no change in overall mental HRQoL. Participants in the ILS arm with diabetes reported
a more significant decline in some HRQoL measures than those in the MET and PLB arms that
developed diabetes.
Keywords
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Diagnosis of diabetes; Health-related quality of life; Pre-diabetes; Type 2 diabetes mellitus;
Prevention
Introduction
Quality of life is a health outcome measured as a combination of physical and social
functioning, and perceived physical and mental well-being [1, 2]. Having a serious chronic
disease such as diabetes can exert a negative impact on health-related quality of life
(HRQoL) [3–9]. Few studies, however, have investigated the impact of the diagnosis of type
2 diabetes on HRQoL. Earlier research reported minimal negative impact following a
diagnosis, with these effects diminishing within a year [6, 10–12]. These minimal effects
may be the result of not understanding the seriousness of the disease, the fact that negative
impact diminishes as the individual adapts to living with the condition, and the limited
impact of early type 2 diabetes on physical well-being [13–15].
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Less is known about how HRQoL changes over time following diagnosis, especially as
diabetes duration increases, but before the onset of complications. Moreover, no study to
date has reported the effect on HRQoL of type 2 diabetes diagnosis in persons who are made
aware of both their personal risk for developing the disease and how serious diabetes is. The
impact of diagnosis on HRQoL in persons who are actively seeking to reduce their personal
risk is unknown.
Participants in the Diabetes Prevention Program Outcome Study (DPPOS) are an ideal
group to study these issues [16]. To participate, they had to have impaired glucose tolerance
(IGT), elevating their risk of developing diabetes, and were made aware of their diabetes
risk. Regular glucose testing allowed relatively precise determination of diabetes onset, and
participants were followed for several years after being informed they had developed
diabetes. The DPPOS cohort is large, varied in age, and race/ethnicity. It also randomized
subjects to one of three study conditions: an intensive lifestyle intervention, use of a
medication, or a placebo control. This study design enables exploration of the effect of
diabetes prevention interventions on HRQoL following diabetes diagnosis.
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This is a unique and heretofore never studied population with regards to how diagnosis of
type 2 diabetes may impact QOL. Previous studies have not been able to accurately ascertain
when the disease actually occurred. In this study, we could do so within six months. Based
on previous research which has investigated how diagnosis impacts QOL ratings over time,
we hypothesize that that our sample would report initially having a negative impact on QOL
to the diagnosis of diabetes followed by a return to values near baseline within the first year.
[6, 10–12] In addition, since our sample is older (mean age at baseline of 50 at baseline) and
was followed for multiple years, and there is a literature documenting that QOL declines
with advancing age, we also hypothesized that decreases in QOL would be faster following
diagnosis than compared to similar aged subjects who remained diabetes free. Finally, we
hypothesized that participation in the different study interventions would influence
perceptions of QOL following diagnosis. Specifically, we hypothesized that subjects in the
intensive lifestyle arm would react more negatively due to the degree of behavior change
required of them in this condition to reduce their risk as compared to subjects in either the
medication or placebo arms.
Design and methods
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The DPP enrolled individuals at high risk for type 2 diabetes at 27 clinic centers. DPP
methods and results are described in detail elsewhere [16–18]. The protocol is available at
http://www.bsc.gwu.edu/dpp. At each DPP center, an institutional review board approved
the protocol and all participants gave written informed consent.
The DPP randomized 3,234 participants. Participants ≥25 years of age had a BMI of ≥24 kg/
m2 (≥22 kg/m2 in Asian Americans), and a plasma glucose concentration of 95–125 mg/dl
(5.3–6.9 mmol/l) in the fasting state (≤125 mg/dl in American Indian centers) and 140–199
mg/dl (7.8–11.0 mmol/l) 2 h after a 75-g oral glucose load. Exclusion criteria included
medication that might contribute to weight loss, conditions that could reduce ability to
participate in the DPP, or inability to complete the 3-week run-in period during which
participants took placebo medicines and recorded their usual eating and physical activity
patterns.
Of this sample, 3,210 had SF-36 forms collected at baseline and are included in this
analysis. This included 1,070 in intensive lifestyle (ILS); 1,066 in metformin (MET); and
1,074 assigned to Placebo (PLB) [19]. The subjects were followed for a median of 8.97
years.
Interventions
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Participants were randomized to one of three interventions: metformin (MET arm), a
placebo pill (PLB arm), or an intensive lifestyle modification program (ILS arm). Goals for
ILS participants were to achieve and maintain a reduction of ≥7 % of initial body weight
through a calorie-controlled, low-fat diet and to engage in physical activity of moderate
intensity, for ≥150 min per week [20].
Measures
SF-36—The short-form health-related quality of life (SF-36) survey was administered as a
measure of overall HRQoL at enrollment in the DPP and annually thereafter. The SF-36
enables measurement of generic health-related quality of life at three levels: 8 subscales that
represent distinct domains of health; component scores for physical and mental health; and a
single utility measure that is frequently used in cost-effectiveness analyses. The 8 domains
of health are as follows: physical functioning (ability to engage in physical activities), rolephysical (impact of physical health on role-based activities), bodily pain (severity and
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impact), general health (global assessment of health), vitality (energy, fatigue), social
functioning (physical or emotional health impact on social activities), role-emotional
(impact of emotional health on role performance), and mental health (depression, anxiety).
Higher scores on all SF-36 scales indicate more favorable levels of functioning. A change of
2 points in these subscales is also considered a clinically significant change [21, 22].
The eight subscales were used to generate two composite scores: the Physical Component
Summary (PCS; physical functioning, role-physical, body pain, and general health) and the
Mental Component Summary (MCS; vitality, social functioning, role-emotional, and mental
health subscales). Both subscale and composite scores were norm-based with a mean of 50
and a standard deviation of 10 in the normative population. The PCS and MCS sub-scales
range from 0 to 100, with higher scores indicating better quality of life relevant to either
mental or physical components. A two-point change in either the MCS or PCS score is
considered clinically significant [21].
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The eight domains of the SF-36 can also be converted into a six-dimensional health state
classification called the SF-6D [22] to derive a single summary preference or utility-based
HRQoL measure. The six dimensions are physical functioning, role limitations, social
functioning, pain, mental health, and vitality. The SF-6D has a range of score from 0.29 to
1.00 with 1.00 indicating “full health.” A difference of 0.04 in SF-6D scores is considered
clinically significant [23].
Diabetes was diagnosed using the 1997 American Diabetes Association criteria [24] based
on an annual oral glucose tolerance test (fasting level ≥7.0 mmol/l and/or a plasma glucose
level ≥11.1 mmol/l) or a semiannual fasting plasma glucose test (level ≥7.0 mmol/l).
Diagnosis required a confirmation test within 6 weeks [16]. After a confirmed diagnosis of
diabetes, participants were informed and advised to see their usual health care provider for
additional treatment considerations. All participants who developed diabetes remained in the
DPP trial to track long-term secondary outcomes and continued to complete annual
questionnaires following their diagnosis.
Demographic and clinical characteristics of the study participants, which might affect the
HRQoL, were also collected in the study [25–27]. This includes measures of obesity (BMI,
weight, and waist-to-hip ratio), comorbid conditions, blood pressure, albumin-to-creatinine
ratios, microalbuminuria, lipids (non-HDL cholesterol) physical activity (average physical
activity hours per week, MAQ Leisure Activity, and LoPar Leisure Activity) [27],
medications that could affect quality of life, and demographic factors (age, sex, race/
ethnicity, marital status, economic status, and smoking history).
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Statistical methods
We studied changes in SF-36 PCS and MCS scores, SF-36 sub-scale scores, and SF-6D
scores. HRQoL changes among those who developed diabetes were compared with changes
among those who had not developed diabetes by data closure and the diabetes-free period of
those who later developed diabetes.
Two time axes were employed in this study. At the time of enrollment of DPP (as discussed
above), all participants were diabetes free and their randomization time was used as time
zero for the diabetes-free period. For those who developed diabetes, we employed a second
time axis where the onset of diabetes was set as time zero. Then, for models using the
second time axis, all diabetes-free person-times was used as the reference set. This diabetesfree person-time included the complete follow-up of participants who had not developed
diabetes by data closure and the prior-diabetes time interval of people who developed
diabetes during the follow-up. It is important to note that the occurrence of diabetes happens
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at different time points in follow-up. Therefore, a participant would be in the diabetes-free
group first and then enters the diagnosed group after he/she was diagnosed with diabetes. A
plot explaining the two time axes is provided in the section “Appendix 1”.
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Short-term (<6 months) and long-term (up to 6 years) changes in HRQoL were estimated
separately within each treatment arm. Repeated HRQoL measurements were collected over
DPP and DPPOS follow-up. Such longitudinal data provide opportunity to study the trend of
HRQoL over time within persons as well as differences between persons, which are usually
modeled by growth curve models [28]. Specifically, we employed linear mixed regressions
because the HRQoL measures are continuous and approximately normal. Besides,
correlations induced by repeated measures from the same participant were modeled by a
compound symmetry correlation structure [29], which assumes a constant positive
correlation coefficient between measurements from the same participant and independence
between measurements from different participants. Four different time intervals postdiagnosis (<6, 6 months–2, 2–4, 4–6 years post-diagnosis) were used as categories of
different durations of diabetes in the analysis to assess differences in the impact of diagnosis
over time. Covariates for adjustment included demographic factors (age, race, sex), baseline
socioeconomic status (marriage, employment, and income), baseline health conditions
(HRQoL, Beck Depression Index score, Beck Anxiety Index score, smoking status, BMI,
fasting plasma glucose level, and 2-hour plasma glucose level) and years since
randomization in diabetes-free participants. All test results are presented without adjustment
for multiplicity. Except the HRQoL values for the diabetes-free group (Fig. 1, left panel),
which are raw average values, all HRQoL measurements post-diabetes occurrences are
predicted from the linear mixed models assuming all covariates taking the average values in
the DPP cohorts.
The sample sizes within all three treatment groups decreased as DPPOS follow-up got
longer due to enrollment of participants over time. Besides, the sample sizes within all
treatments also decreased as the diabetes duration got longer (see Table 1), and there are
fewer diabetic participants in ILS group than the other groups. We assume the HRQoL
patterns among participants who developed diabetes later in the study will not be
systematically different from those who developed diabetes earlier, after adjusting for the
baseline characteristics in the linear mixed model, that is, missing at random.
Results
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At baseline, there were no significant differences among treatment arms on any level of
HRQoL or covariate (Table 1). SF36 component and utility scores by diabetes status and
treatment arm
Figure 1a–c shows SF-6D, MCS, and PCS scores for participants who remained diabetes
free (left) and participants diagnosed with diabetes (right). Notice that the plots on the left
use the DPP follow-up time scale, that is, number of years since DPP randomization on the
x-axis. The HRQoL measures on the right are plotted over number of years since diabetes
diagnosis. All values on the left panel are observed average values within each treatment
group, and all values on the right panel are predicted from the linear mixed models
discussed in the Statistical methods section.
The graph for the SF-6D scores (Fig. 1a) shows that when diabetes-free participants showed
little decline in scores for approximately three years of participation in the study and that
participants in the ILS treatment actually reported a slight increase in scores from baseline to
one year. Beginning at year three, however, participants in all the treatment groups that
remained diabetes free show a progressive decline in SF-6D scores. This is in contrast to
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participants who were diagnosed with diabetes during the study shown on the graph on the
right. The scores post-diabetes diagnoses show a more rapid decline when compared to the
diabetes-free participants. In addition, participants in the lifestyle group who were diagnosed
reported a more severe reduction in the period immediately following diagnosis (P = .003)
compared to the other groups. The difference between the intervention groups, however, was
not statistically different, possibly due to the decreasing sample sizes as the diabetes
duration increased.
Table 2 shows the average decrease in SF-6D scores over the entire follow-up period was
approximately 0.01 per year (P <.001) among both diabetes-free participants and those who
developed diabetes. The decline in SF-6D scores did not reach clinical significance in any
treatment arm for participants who developed diabetes or for those who did not. These data
indicate that our sample did not experience a rapid decline that returned to baseline values as
it was hypothesized. Rather, they show a negative decline that continued over the course of
the study period.
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MCS scores (Fig. 1b) among DM-free participants did not change significantly in any
treatment arm. In addition, MCS scores did not change significantly over the entire followup period in those diagnosed with diabetes, with no significant difference among groups,
though scores in the ILS arm declined immediately following diagnosis, before returning to
levels close to those for the other two treatment arms.
Figure 1c shows PCS scores for participants when they were diabetes free (left panel) and
those who developed diabetes during the trial. During the DM-free period, participants in the
ILS group showed a marked increase (P <.001) in scores from baseline to one year, then a
gradual decline from year one to six. Participants in the MET and PLB groups remained
stable over the course of the study until year five when MET participants reported a decrease
followed by a sharp increase in year six. The fluctuation could result from the small sample
sizes with six years follow-up. PCS scores showed a progressive decline following diabetes
diagnosis among participants in all treatment arms, with no significant differences among
treatment groups in PCS score change over time. Among the DM-free participants, the
slopes (−0.26) are not statistically different between treatments and much smaller than the
slopes post-diabetes (P <.001). In those diagnosed, the slope in ILS is faster (−0.46) than the
other two groups (−0.40 and −0.39). However, a test for the interaction between slope and
treatment in the diagnosed participants does not reach statistical significance (P = .639).
Figure 1 provided evidence for our second hypothesis that decreases in QOL would be faster
following diagnosis than compared to similar aged subjects who remained diabetes free in
the PCS measures, but not in the MCS measures.
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Finally, we speculated that the finding that ILS participants reported more negative scores
post-diabetes than those in the other groups might be due to their greater age (because ILS
delayed the onset of diabetes). We tested for age at diagnosis in each group and saw no
significant difference between the ages at DM onset across the three treatments. Average
age of diabetes onset was 57.53 years in the ILS, 57.37 in the MET, and 56.34 in the PLB
groups.
SF-36 subscale scores by diagnosis status and treatment arm
Research has suggested that relying solely on the MCS–PCS summary scores may be
misleading in understanding how they reflect HRQoL [30, 31]. Thus, to characterize
changes in HRQoL at the level that is closest to participant experience, we analyzed each of
the SF-36 subscale scores. All analyses shown were adjusted for covariates. We report here
only the subscales that showed significant differences between the three treatments for
diagnosed participants.
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Physical Component Subscales Among diagnosed participants, analyses of the four
subscales that comprise the PCS (physical functioning, role-physical, general health, and
bodily pain), showed that the role-physical scores decline progressively over time in all
three groups (Fig. 2a). These declines from diagnosis to the period 2–4 year post-diagnosis
are of clinical significance with all changes ≥2 points (P = .007, .008, .081 in the ILS, MET,
and PLA groups, respectively). Lifestyle participants also showed a greater decline in RolePhysical scores in the first six months following diagnosis compared to the other two groups
(P = .026), with further declines after 2 years with diabetes (P = .068). The data for the other
three subscales did not show significant differences.
Mental Component Subscales Analyses of the four subscales that comprise the MCS (i.e.,
Vitality, Role-Emotional, Mental Health, and Social Functioning) showed that participants
in the MET and PLB groups showed little change post-diagnosis, whereas participants in the
ILS intervention reported greater and clinically significant declines in Social Functioning at
6 months (P = .001) and 24 months (P = .001) post-diagnosis (Fig. 2b). The scores from the
ILS participants did rise at 2–4 years and were similar to those of participants in the other
groups, but fell at the 4–6 year time period. A decline in Role-Emotional scores was also
significantly greater in the ILS group at all time points post-diagnosis; however, these
differences did not reach clinical significance until the period 0.5–2 years (P = .190) and
again at 4–6 years (P = .063).
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There were no significant differences between ILS participants and participants in the other
two treatment groups in vitality and mental health score changes. The Role-Emotional score
in the ILS group is approximately one unit lower than the others over time post-diabetes, but
this difference does not reach statistical significance. Collectively, the data regarding
treatment effects support our hypothesis that subjects in the intensive lifestyle arm would
react more negatively than subjects in the other two treatment arms, but in somewhat
selective ways. In particular, subjects in the lifestyle arm did report a more negative effect
initially following diagnosis on quality of life for the PCS scales and some of the mental
component subscales.
Discussion
This is the first study able to determine with relative accuracy when diabetes actually
developed. This allows us to control for exposure effects that result from previous studies
that cannot determine how long persons actually had diabetes before they had HRQoL
assessed. In addition, unlike previous research, which has largely been cross-sectional, this
is the first study that followed a cohort for several years following the diagnosis.
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There are several findings of note. We found a statistically significant, persistent, and
progressive negative decline in measures of overall health state (SF-6D scores) and overall
physical HRQoL (PCS scores) in all treatment arms following diabetes diagnosis, with no
change in measures of overall mental HRQoL (MCS scores). These data were not supportive
of the hypothesis that HRQoL would return to near baseline levels after initial diagnosis,
which is what has been shown in previous research. They suggest that other factors play a
significant role in how persons with prediabetes who participate in a prevention intervention
react to the revelation that they have in fact developed type 2 diabetes.
The pattern of change on all measures of HRQoL following diabetes diagnosis (i.e., a
decline in PCS and SF-6D scores, with no change in MCS scores) mirrored the pattern of
change for the same measures among diabetes-free DPP participants. This suggests that
other factors beyond diabetes diagnosis (e.g., study burden) may have influenced HRQoL
over time. DPP treatment burden was substantial. Participants had to provide many
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biological samples and fill out numerous questionnaires every 6 months for several years.
Aging could also have negatively affected HRQoL. The average age at entry into the trial
was 51; thus, many of the participants may have begun to experience decrements in their
physical capacity that is associated with increasing age. It is known that PCS scores decline
with age and are negatively affected by the onset of complications other than those of
diabetes such as arthritis, heart disease, and emphysema. In this context, the declines
observed here are similar to those in other studies, albeit which did not focus on diabetes
[32, 33]. In this context, the data partially support the study hypothesis that declines would
result from aging, but failed to support that this resulted from the discovery of diabetes per
se.
At baseline, participants reported PCS and MCS scores of 50 and 54, respectively. These are
higher than mean scores for general US population [32]. This may reflect the eligibility
criteria for inclusion in the study. Participants were initially screened to reduce the influence
of such factors as comorbid conditions that would negatively impact HRQoL such as clinical
depression. It is possible that over time participants tended to “regress to the mean” in their
assessments of HRQoL.
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We also observed significant changes among those diagnosed with diabetes in the ILS group
at the level of the PCS component score and Role-Physical subscale compared to the MET
or PLB groups. These finding may reflect the ways in which the diagnosis of diabetes (and
the consequent changes in medical and behavioral management required) affects
participants’ perceived ability to physically perform (e.g., have you had any of the following
problems with your work or other regular daily activities as a result of your physical
health?).
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We found no negative impact of diabetes diagnosis at the level of component scores for
mental HRQoL (MCS scores), but examination of the individual SF-36 subscales that
combine to form this component revealed significant differences in the ILS arm for the
Social Functioning and Role-Emotional subscales following diagnosis. Both of these
subscales ask respondents to report the extent to which physical or mental health status
interferes with their ability to engage in social tasks or perform these tasks at expected levels
(e.g., Did not do work or other activities as carefully as usual?). Consistent with previous
studies [31, 32], this finding suggests that the MCS component scores were not as sensitive
in capturing meaningful changes in mental health HRQoL as the individual subscale scores.
Previous studies have reported a negative mental health impact on HRQoL following
diagnosis, but most studies report this resolving as the patient gains more experience with
treating the disease, usually within one year [6, 10, 11]. We saw, however, a decrease in
HRQoL that persisted for multiple years and in fact continued to decrease. The change
observed is consistent with other measures of the patient experience of diabetes (e.g.,
diabetes distress, hassles), that is, diabetes represents a psychological burden that has the
capacity to adversely affect individuals emotionally and socially over the long-term.
The finding that the greatest declines in HRQoL were observed in the PCS rather than the
MCS scores is not surprising. While it is logical to assume that the impact of a diagnosis of
diabetes would be most strongly expressed through measures of mental and social status,
there is increasing evidence that PCS scores decline as people age [32, 33]. This finding may
also reflect a study effect; in that, all participants received extensive social support from
DPP/DPPOS staff and were provided extensive exposure to counseling and education
services. This may have acted to minimize the negative emotional impact of the diagnosis.
The study did support the hypothesis that participating in the ILS arm of the trial would
result in a more negative impact on HRQoL. Participants in the ILS arm who were
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diagnosed with diabetes reported a greater decline on the SF-6D than participants in other
two treatment groups, particularly in the immediate period (i.e., 0–6 months from diagnosis)
following diagnosis. In addition, diabetes-free ILS participants reported an increase on the
PCS scale in the period closest to their most active participation in the lifestyle intervention.
This may reflect the significant improvements in the level of physical activity and weight
loss resulting from their efforts at lifestyle modification.
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The general finding that participants in the ILS group showed a more pronounced decline in
HRQoL scores is of note. Participants in lifestyle were asked to adopt new behaviors that
included significant changes in their diet and patterns of physical activity. Thus, they were
adopting new lifestyle behaviors that in almost all cases represented significant departures
from their baseline status. In contrast, participants in both the metformin and placebo control
groups were not asked to adopt new behaviors except taking medication, a behavior that is
both simpler to adopt and probably more familiar to the average participant. Participants in
ILS group may have experienced more intense disappointment when, in spite of actually
losing weight, they still developed diabetes. One way in which their disappointment may
have magnified is from the reactions of others to their “discovery” of diabetes. Significant
others may have expressed a reaction to the diagnosis in terms of the effort expended by the
lifestyle participant: “e.g., too bad, you worked so hard… you lost so much weight.” This
may have acted to intensify the perception of failure and loss in spite of the effort expended
to prevent the disease. It may also have resulted in some degree of experiencing stigma
associated with being labeled as diabetic and being perceived as a failure. This interpretation
is supported in part by the greater changes in the social function scores in the lifestyle group.
As seen in Fig. 2, their initial reaction is quite pronounced but approaches the levels of other
participants after sometime to habituate to the diagnosis that has occurred.
These data suggest that the HRQoL associated with the diagnosis of diabetes and
participation in an intensive lifestyle intervention is a complex phenomenon. This is the first
study to investigate the impact on HRQoL in a population with known risk who were
actively trying to prevent developing the disease. These data are different from the previous
studies that suggest that a diagnosis of diabetes has a minimal impact on HRQoL and tended
to resolve within a year [6, 10, 11]. Prior research, however, has not focused on populations
who were particularly aware of their risk status and exercising considerable efforts to reduce
their risk. In addition, previous studies also assessed HRQoL well after the diagnosis of
diabetes was made.
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Study strengths and limitations warrant consideration. Strengths include the large, racially
and ethnically diverse population; the definitive assessment of glucose tolerance and
diabetes; and that data were collected about health-related quality of life repeatedly from
baseline over several years. In addition, the timing of diagnosis of diabetes could be
pinpointed to a 6 month period, which is a considerable advance over studies that assess
perceived impact of diagnosis retroactively and rely on medical records that may not
accurately capture the time of diagnosis or when diabetes was actually present. It is also the
first study to investigate the impact of diagnosis on a population actively trying to reduce
their risk of developing the disease. Most important, we were able to compare changes in
HRQoL between diabetes-free participants and those who developed diabetes.
Limitations include reliance on generic measures of HRQoL to define the impact of
diagnosis. These measures may not specifically address dimensions of HRQoL that are
linked to the diagnosis of diabetes. Also, the sample was deliberately chosen to be relatively
free of mental health issues, notably depression and anxiety disorders, which are known to
be more prevalent in the general population and particularly in a population of persons with
diabetes. Thus, it is unknown how a sample with a more widely distributed occurrence of
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mental health issues would respond. Extrapolation of these results to other populations that
are not participating in a major clinical trail warrants some caution. Future studies should
examine the perception of expectations for prevention associated with different treatment
modalities. Such studies are feasible with large populations (e.g., HMOs). This study is the
first to quantify how a person’s HRQoL may change as he or she moves from prediabetes to
diabetes. Such information is critical to build a model for diabetes progression that can be
used for evaluating the cost-effectiveness of interventions for preventing diabetes, as well as
to understand how the time horizon for observing changes in quality of life might impact the
findings of these analyses. The negative impact of diabetes diagnosis on HRQoL also
supports the need to develop strategies to help newly diagnosed patients to cope with
diabetes immediately after diagnosis and also emphasize the importance of secondary
prevention to reduce diabetes burden as they age.
Acknowledgments
NIH-PA Author Manuscript
The Research Group gratefully acknowledges the commitment and dedication of the participants of the DPP and
DPPOS. During the DPPOS, the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) of the
National Institutes of Health provided funding to the clinical centers and the Coordinating Center for the design and
conduct of the study, and collection, management, analysis, and interpretation of the data (U01 DK048489). The
Southwestern American Indian Centers were supported directly by the NIDDK, including its Intramural Research
Program, and the Indian Health Service. The General Clinical Research Center Program, National Center for
Research Resources, and the Department of Veterans Affairs supported data collection at many of the clinical
centers. Funding was also provided by the National Institute of Child Health and Human Development, the National
Institute on Aging, the National Eye Institute, the National Heart Lung and Blood Institute, the Office of Research
on Women’s Health, the National Center for Minority Health and Human Disease, the Centers for Disease Control
and Prevention, and the American Diabetes Association. Bristol-Myers Squibb and Parke-Davis provided additional
funding and material support during the DPP, Lipha (Merck-Sante) provided medication, and LifeScan Inc. donated
materials during the DPP and DPPOS. We thank the thousands of volunteers in this program for their devotion to
the goal of diabetes prevention. LifeScan Inc., Health O Meter, Hoechst Marion Roussel, Inc., Merck-Medco
Managed Care, Inc., Merck and Co., Nike Sports Marketing, Slim Fast Foods Co., and Quaker Oats Co. donated
materials, equipment, or medicines for concomitant conditions. McKesson BioServices Corp., Matthews Media
Group, Inc., and the Henry M. Jackson Foundation provided support services under subcontract with the
Coordinating Center. The opinions expressed are those of the investigators and do not necessarily reflect the views
of the funding agencies.
Appendix 1
See Fig. 3.
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Fig. 3.
Two hypothetical scenarios a DPP participant could experience and the corresponding time
axis for each scenario. First scenario: the participant remained diabetes-free throughout the
follow-up and stayed on the time since DPP randomization axis. Second scenario: the
participant was diagnosed of diabetes two years after DPP randomization and jumped to the
second time axis of time since diabetes diagnosis
Appendix 2: DPPOS research group investigators
Pennington Biomedical Research Center (Baton Rouge, LA)
George A. Bray, MD*
NIH-PA Author Manuscript
Annie Chatellier, RN, CCRC**
Crystal Duncan, LPN
Frank L. Greenway, MD
Erma Levy, RD
Donna H. Ryan, MD
University of Chicago (Chicago, IL)
Kenneth S. Polonsky, MD*
Janet Tobian, MD, PhD*
David Ehrmann, MD*
Margaret J. Matulik, RN, BSN**
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Bart Clark, MD
Kirsten Czech, MS
NIH-PA Author Manuscript
Catherine DeSandre, BA
Ruthanne Hilbrich, RD
Wylie McNabb, EdD
Ann R. Semenske, MS, RD
Jefferson Medical College (Philadelphia, PA)
Barry J. Goldstein, MD, PhD*
Kevin Furlong, DO*
Kellie A. Smith, RN, MSN**
Wendi Wildman, RN**
Constance Pepe, MS, RD
University of Miami (Miami, FL)
Ronald B. Goldberg, MD*
NIH-PA Author Manuscript
Jeanette Calles, MSEd**
Juliet Ojito, RN**
Sumaya Castillo-Florez, MPH
Hermes J. Florez, MD, PhD
Anna Giannella, RD, MS
Olga Lara
Beth Veciana
The University of Texas Health Science Center(San Antonio, TX)
Steven M. Haffner, MD, MPH*
Helen P. Hazuda, PhD*
Maria G. Montez, RN, MSHP, CDE**
NIH-PA Author Manuscript
Carlos Lorenzo, MD, PhD
Arlene Martinez, RN, BSN, CDE
University of Colorado (Denver, CO)
Richard F. Hamman, MD, DrPH*
Lisa Testaverde, MS**
Alexis Bouffard, MA, RN, BSN
Dana Dabelea, MD, PhD
Tonya Jenkins, RD, CDE
Dione Lenz, RN, BSN, CDE
Leigh Perreault, MD
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David W. Price, MD
Sheila C. Steinke, MS
NIH-PA Author Manuscript
Joslin Diabetes Center (Boston, MA)
Edward S. Horton, MD*
Catherine S. Poirier, RN, BSN**
Kati Swift, RN, BSN**
Enrique Caballero, MD
Sharon D. Jackson, MS, RD, CDE
Lori Lambert, MS, RD, LD
Kathleen E. Lawton, RN
Sarah Ledbury, Med, RD
VA Puget Sound Health Care System and University of Washington (Seattle, WA)
Steven E. Kahn, MB, ChB*
Brenda K. Montgomery, RN, BSN, CDE**
NIH-PA Author Manuscript
Wilfred Fujimoto, MD
Robert H. Knopp, MD
Edward W. Lipkin, MD
Michelle Marr, BA
Anne Murillo, BS
Dace Trence, MD
University of Tennessee (Memphis, TN)
Abbas E. Kitabchi, PhD, MD, FACP*
Mary E. Murphy, RN, MS, CDE, MBA**
William B. Applegate, MD, MPH
Michael Bryer-Ash, MD
NIH-PA Author Manuscript
Samuel Dagogo-Jack, MD, MSc, FRCP, FACP
Sandra L. Frieson, RN
Helen Lambeth, RN, BSN
Lynne C. Lichtermann, RN, BSN
Hooman Otkaei, MD
Lily M.K. Rutledge, RN, BSN
Amy R. Sherman, RD, LD
Clara M. Smith, RD, MHP, LDN
Judith E. Soberman, MD
Beverly Williams-Cleaves, MD
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Northwestern University’s Feinberg School of Medicine (Chicago, IL)
Boyd E. Metzger, MD*
NIH-PA Author Manuscript
Mark E. Molitch, MD*
Mariana K. Johnson, MS, RN**
Mimi M. Giles, MS, RD
Diane Larsen, BS
Charlotte Niznik, MS, RN, CDE
Samsam C. Pen, BA
Pamela A. Schinleber, RN, MS
Massachusetts General Hospital (Boston, MA)
David M. Nathan, MD*
Charles McKitrick, BSN**
Heather Turgeon, BSN**
Kathy Abbott
NIH-PA Author Manuscript
Ellen Anderson, MS, RD
Laurie Bissett, MS, RD
Enrico Cagliero, MD
Kali D’Anna
Linda Delahanty, MS, RD
Jose C. Florez, MD, PhD+
Valerie Goldman, MS, RD
Alexandra Poulos
Beverly Tseng
University of California-San Diego (San Diego, CA)
Elizabeth Barrett-Connor, MD*
NIH-PA Author Manuscript
Mary Lou Carrion-Petersen, RN, BSN**
Javiva Horne, RD
Diana Leos, RN, BSN
Sundar Mudaliar, MD
Jean Smith, RN
Karen Vejvoda, RN, BSN, CDE, CCRC
St. Luke’s-Roosevelt Hospital (New York, NY)
F. Xavier Pi-Sunyer, MD*
Jane E. Lee, MS**
Sandra T. Foo, MD
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Susan Hagamen, MS, RN, CDE
Indiana University (Indianapolis, IN)
NIH-PA Author Manuscript
David G. Marrero, PhD*
Susie M. Kelly, RN, CDE**
Ronald T. Ackermann, MD
Edwin S. Fineberg, MD
Angela Hadden
Marcia A. Jackson
Marion S. Kirkman, MD
Kieren J. Mather, MD
Paris J. Roach, MD
Madelyn L. Wheeler, RD
Medstar Research Institute (Washington, DC)
Robert E. Ratner, MD*
NIH-PA Author Manuscript
Vanita Aroda, MD*
Sue Shapiro, RN, BSN, CCRC**
Catherine Bavido-Arrage, MS, RD, LD
Peggy Gibbs
Gabriel Uwaifo, MD
Renee Wiggins, RD
University of Southern California/UCLA Research Center (Alhambra, CA)
Mohammed F. Saad, MD*
Karol Watson, MD*
Medhat Botrous, MD**
Sujata Jinagouda, MD**
NIH-PA Author Manuscript
Maria Budget
Claudia Conzues
Perpetua Magpuri
Kathy Ngo
Kathy Xapthalamous
Washington University (St. Louis, MO)
Neil H. White, MD, CDE*
Samia Das, MS, MBA, RD, LD**
Ana Santiago, RD
Angela L. Brown, MD
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Cormarie Wernimont, RD, LD
Johns Hopkins School of Medicine (Baltimore, MD)
NIH-PA Author Manuscript
Christopher D. Saudek, MD* (deceased)
Sherita Hill Golden, MD, MHS, FAHA*
Tracy Whittington, BS**
Jeanne M. Clark, MD
Alicia Greene
Dawn Jiggetts
Henry Mosley
John Reusing
Richard R. Rubin, PhD
Shawne Stephens
Evonne Utsey
University of New Mexico (Albuquerque, NM)
NIH-PA Author Manuscript
David S. Schade, MD*
Karwyn S. Adams, RN, MSN**
Claire Hemphill, RN, BSN**
Penny Hyde, RN, BSN**
Lisa Butler, BUS
Janene L. Canady, RN, CDE
Kathleen Colleran, MD
Ysela Gonzales, RN, MSN
Doris A. Hernandez-McGinnis
Patricia Katz, LPN
Carolyn King
NIH-PA Author Manuscript
Albert Einstein College of Medicine (Bronx, NY)
Jill Crandall, MD*
Janet O. Brown, RN, MPH, MSN**
Elsie Adorno, BS
Helena Duffy, MS, C-ANP
Helen Martinez, RN, MSN, FNP-C
Dorothy Pompi, BA
Harry Shamoon, MD
Elizabeth A. Walker, RN, DNSc, CDE
Judith Wylie-Rosett, EdD, RD
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University of Pittsburgh (Pittsburgh, PA)
Trevor Orchard, MD*
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Susan Jeffries, RN, MSN**
M. Kaye Kramer, BSN, MPH**
Marie Smith, RN, BSN**
Rena R. Wing, PhD
Andrea Kriska, PhD
Jessica Pettigrew, CMA
Linda Semler, MS, RD
Elizabeth Venditti, PhD
Valarie Weinzierl, BS
University of Hawaii (Honolulu, HI)
Richard F. Arakaki, MD*
Narleen K. Baker-Ladao, BS**
NIH-PA Author Manuscript
Mae K. Isonaga, RD, MPH**
Nina E. Bermudez, MS
Marjorie K. Mau, MD
Southwest American Indian Centers (Phoenix, AZ; Ship-rock, NM; Zuni, NM)
William C. Knowler, MD, DrPH*+
Norman Cooeyate**
Mary A. Hoskin, RD, MS**
Camille Natewa**
Carol A. Percy, RN, MS**
Kelly J. Acton, MD, MPH
Vickie L. Andre, RN, FNP
NIH-PA Author Manuscript
Shandiin Begay, MPH
Brian C. Bucca, OD, FAAO
Sherron Cook
Matthew S. Doughty, MD
Justin Glass, MD
Martia Glass, MD
Robert L. Hanson, MD, MPH
Doug Hassenpflug, OD
Louise E. Ingraham, MS, RD, LN
Kathleen M. Kobus, RNC-ANP
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Jonathan Krakoff, MD
Catherine Manus, LPN
NIH-PA Author Manuscript
Cherie McCabe
Sara Michaels, MD
Tina Morgan
Julie A. Nelson, RD
Robert J. Roy
Miranda Smart
Darryl P. Tonemah, PhD
Charlton Wilson, MD
George Washington University Biostatistics Center (DPP Coordinating Center Rockville,
MD)
Sarah Fowler, PhD*
Tina Brenneman**
NIH-PA Author Manuscript
Solome Abebe, MS
Julie Bamdad, MS
Melanie Barkalow
Joel Bethepu
Tsedenia Bezabeh
Jackie Callaghan
Costas Christophi, PhD
Sharon L. Edelstein, ScM
Yuping Gao
Robert Gooding
Adrienne Gottlieb
Nisha Grover
NIH-PA Author Manuscript
Heather Hoffman, PhD
Kathleen Jablonski, PhD
Richard Katz, MD
Preethy Kolinjivadi, MS
John M. Lachin, ScD
Yong Ma, PhD
Susan Reamer
Alla Sapozhnikova
Hanna Sherif, MS
Marinella Temprosa, MS
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Qing Pan, PhD
Mary Foulkes, PhD
NIH-PA Author Manuscript
Nicole Butler
Lifestyle Resource Core
Elizabeth M. Venditti, PhD*
Andrea M. Kriska, PhD
Linda Semler, MS, RD
Valerie Weinzierl, BS
Central Biochemistry Laboratory (Seattle, WA)
Santica Marcovina, PhD, ScD*
Greg Strylewicz, PhD**
John Albers, PhD
Epidemiological Cardiology Research Center-Epicare (Winston-Salem, NC)
Ronald J. Prineas, MD, PhD*
NIH-PA Author Manuscript
Teresa Alexander
Charles Campbell, MS
Sharon Hall
Susan Hensley
Yabing Li, MD
Margaret Mills
Elsayed Soliman, MD
Zhuming Zhang, MD
Fundus Photo Reading Center (Madison, WI)
Ronald Danis, MD*
Matthew Davis, MD*
NIH-PA Author Manuscript
Larry Hubbard*
Ryan Endres**
Deborah Elsas**
Samantha Johnson**
Vonnie Gama
Anne Goulding
Carotid Ultrasound
Gregory Evans
CT Scan Reading Center
Elizabeth Stamm
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Neurocognitive Assessment Group
Jose A. Luchsinger, MD, MPH
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NIH/NIDDK (Bethesda, MD)
Judith Fradkin, MD
Sanford Garfield, PhD
Centers for Disease Control & Prevention (Atlanta, GA)
Edward Gregg, PhD
Ping Zhang, PhD
University of Michigan (Ann Arbor, MI)
William H. Herman, MD, MPH
Morton B. Brown, PhD
Nutrition Coding Center (Columbia, SC)
Elizabeth Mayer-Davis, PhD*
Robert R. Moran, PhD**
NIH-PA Author Manuscript
Quality of Well-Being Center (La Jolla, CA)
Ted Ganiats, MD*
Andrew J. Sarkin, PhD**
Naomi Katzir
Erik Groessl, PhD
Coronary Artery Calcification Reading Center
Matthew Budoff, MD
Chris Dailing
Denotes Principal Investigator*
Denotes Program Coordinator**
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Fig. 1.
Average treatment-specific HRQoL scores over DPP follow-up among diabetes-free
participants * time (left); Predicted average treatment-specific HRQoL scores post-diabetes
(right)
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Fig. 2.
Average treatment-specific HRQoL subscale scores over DPP follow-up among diabetesfree participants * time (left); Predicted average treatment-specific HRQoL subscale scores
post-diabetes (right)
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Table 1
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Sample characteristics at baseline (no significant differences between groups) and sample sizes with different
diabetes durations
Variable
Lifestyle
Metformin
Placebo
Total/Avg
Sample size
Baseline
1,070
1,066
1,074
3,210
0–0.5 year post-DM
262
327
389
978
0.5–2 year post-DM
223
318
379
920
2–4 year post-DM
182
231
302
715
4–6 year post-DM
116
141
157
414
Age
50.6 (11.3)
50.9 (10.3)
50.3 (10.4)
50.6 (10.7)
Female
729 (68 %)
705 (66 %)
742 (69 %)
2,176 (68 %)
Demographic characteristics
White
576 (54 %)
599 (56 %)
579 (54 %)
1,754 (55 %)
African-American
203 (19 %)
218 (20 %)
220 (20 %)
641 (20 %)
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Hispanic
178 (17 %)
161 (15 %)
168 (16 %)
507 (16 %)
American Indian
58 (5 %)
52 (5 %)
59 (5 %)
169 (5 %)
Asian
55 (5 %)
36 (3 %)
48 (4 %)
139 (4 %)
Married
654 (61 %)
658 (62 %)
671 (62 %)
1,983 (62 %)
Employed
779 (73 %)
821 (77 %)
785 (73 %)
2,385 (74 %)
Annual household income ($10 K)
5.3 (1.7)
5.4 (1.7)
5.3 (1.8)
5.30 (1.7)
70 (7 %)
71 (7 %)
84 (8 %)
225 (7 %)
Clinical characteristics
Smoke
(kg/m2)
33.9 (6.8)
33.9 (6.6)
34.1 (6.7)
34.0 (6.7)
Beck depression score
4. 6 (4.5)
4.5 (4.4)
4.6 (4.7)
4.6 (4.6)
Beck anxiety inventory score
4.1 (5.1)
3.9 (4.9)
4.0 (5.0)
4.0 (5.0)
Fasting glucose (mg/dL)
106.3 (8.1)
106.5 (8.5)
106.8 (8.4)
106.5 (8.3)
2-hour glucose (mg/dL)
164.4 (16.9)
165.2 (17.2)
164.5 (17.1)
164.7 (17.1)
Body mass index
Outcome variables
Physical functioning
85.8 (16.6)
84.7 (17.5)
85.6 (17.0)
85.4 (17.0)
Role-physical
88.5 (25.4)
87.7 (26.1)
87.9 (26.5)
88.0 (26.0)
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Body pain
78.7 (20.1)
78.2 (19.4)
77.6 (19.8)
78.2 (19.8)
General health
76.1 (16.3)
75.2 (16.5)
76.2 (16.7)
75.8 (16.5)
Vitality
65.0 (18.8)
65.2 (18.2)
65.5 (18.8)
65.2 (18.6)
Social functioning
90.5 (17.1)
90.6 (17.1)
90.7 (16.4)
90.6 (16.9)
Role-emotional
89.9 (24.5)
89.7 (25.0)
89.5 (24.8)
89.7 (24.8)
Mental health
81.9 (13.8)
82.3 (13.5)
82.4 (13.7)
82.2 (13.7)
Physical component score (PCS)
50.6 (6.9)
50.1 (7.3)
50.3 (7.2)
50.3 (7.1)
Mental component score (MCS)
53.8 (7.6)
54.1 (7.7)
54.0 (7.4)
54.0 (7.5)
SF-6D utility score
0.80 (0.10)
0.80 (0.10)
0.80 (0.10)
0.80 (0.10)
*
Categorical characteristics are described as number (%); continuous characteristics are expressed as mean (standard deviation)
Qual Life Res. Author manuscript; available in PMC 2015 February 01.
NIH-PA Author Manuscript
NIH-PA Author Manuscript
NIH-PA Author Manuscript
Table 2
DM-free
Change rate
Marrero et al.
Linear trend of HRQoL over time before and after diabetes diagnosis
Post-DM
P value
Lifestyle
Change rate
Metformin
P value
Change rate
Placebo
P value
Change rate
P value
Qual Life Res. Author manuscript; available in PMC 2015 February 01.
SF6D
−0.0107
<.001
−0.0097
<.001
−0.0108
<.001
−0.0104
<.001
MCS
−0.05
.007
0.13
.063
0.08
0.162
0.04
.415
PCS
−0.26
<.001
−0.46
<.001
−0.39
<.001
−0.40
<.001
The P values are for the hypothesis that the corresponding slope equals to 0. The interaction between treatment and the slope over time post-DM is not significantly different from zero (P value = .608, .639,
and .584 for SF6D, PCS, and MCS, respectively). However, the difference between the slopes before and after diabetes diagnosis (DM-free vs. post-DM) is significantly different from zero in PCS and
MCS (P value = .577, <.001 and .002 for SF6D, PCS, and MCS, respectively)
*
All change rates are in the unit of points per year
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