ORIGINAL ARTICLE

Hypoglycemia and glycemic variability areassociated with mortality in non-intensive careunit hospitalized infectious disease patientswith diabetes mellitus
Soichi Takeishi*, Akihiro Mori, Hiroki Hachiya, Takayuki Yumura, Shun Ito, Takashi Shibuya, Shintaro Hayashi,
Nobutoshi Fushimi, Noritsugu Ohashi, Hiromi Kawai
Department of Endocrinology and Diabetes, Ichinomiyanishi Hospital, Ichinomiya, Japan

Keywords
Glycemic variability, Hypoglycemia,
Mortality*Correspondence
Soichi Takeishi
Tel.: +81-586-48-0077
Fax: +81-586-48-0038
E-mail address: souichi19811225@
yahoo.co.jp
J Diabetes Investig 2015
doi: 10.1111/jdi.12436

ABSTRACT
Aims/Introduction: We aimed to identify factors – glycemic control, reactive inflammatory biomarkers or vital signs – associated with mortality in diabetic patients admitted
to hospital for various infections (non-intensive care unit).
Materials and Methods: We retrospectively analyzed the cases of 620 diabetic
patients admitted to hospital for various infections (non-intensive care unit) who underwent glucose monitoring >3 times per day. We extracted data regarding reactive inflammatory biomarkers and vital signs recorded on day 1 of hospital stay, and data onbacteremia and hypoglycemia status, glycemic variability (GV; coefficient of variation andstandard deviation) and mean glucose concentrations during the entire hospital stay. Univariate and stepwise multivariate logistic regression analyses were carried out to determine
the association between these factors and mortality.
Results: The mortality rate was 10.1%. Reactive inflammatory biomarkers, vital signs andbacteremia were not associated with mortality. According to the results of the adjusted
analysis, hypoglycemia showed a significant positive association with mortality, increasing
death risk by 266% (odds ratio [OR] 2.66, 95% confidence interval [95% CI] 1.22–5.83;
P = 0.0006). High coefficient of variation and standard deviation values were significantly
associated with increased mortality, increasing death risk by 18% (OR 1.18, 95% CI
1.01–1.38; P = 0.03) and 9% (OR 1.09, 95% CI 1.01–1.18; P = 0.03), respectively. Mean
glucose concentrations were also significantly associated with mortality, increasing deathrisk by 5% (OR 1.05, 95% CI 1.02–1.08; P = 0.0008).
Conclusions: Glycemic indices (especially hypoglycemia and GV), rather than reactive
inflammatory biomarkers or vital signs, were associated with mortality in non-intensive
care unit diabetes mellitus patients with infections.

INTRODUCTION
Recently, large clinical studies have shown that hypoglycemia isstrongly associated with prognosis in patients with diabetes
mellitus1,2. Furthermore, hypoglycemia is associated with mortality in patients with acute pathological conditions in theintensive care unit (ICU)3–6 and in non-ICU hospitalized dia-

Received 18 July 2015; revised 28 August 2015; accepted 23 September 2015

betes patients7. Glycemic variability (GV) is also thought to be
associated with mortality in ICU patients4,8 and in non-ICU
hospitalized patients with acute pathological conditions9.
Conversely, C-reactive protein (CRP) and arterial oxygen saturation (SpO2) have been reported to be prognostic factors ofpneumonia10,11. Furthermore, reactive inﬂammatory biomarkers
and vital signs are associated with mortality in some infectious
diseases12–14, wherein glycemic control is thought to be compromised15,16 and associated with mortality17,18.

ª 2015 The Authors. Journal of Diabetes Investigation published by Asian Association of the Study of Diabetes (AASD) and Wiley Publishing Asia Pty Ltd
This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution
in any medium, provided the original work is properly cited, the use is non-commercial and no modiﬁcations or adaptations are made.

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Takeishi et al.

Thus, glycemic control, reactive inﬂammatory biomarkers
and vital signs could be associated with prognosis in patientswith infectious diseases. However, which factors – glycemic
control, reactive inﬂammatory biomarkers or vital signs – aremost frequently associated with mortality in diabetic patients
admitted to hospital for various infections (non-ICU) remains
unclear. In the present study, we investigated the association
between all of these factors and mortality in non-ICU diabetes
mellitus patients with infectious diseases who underwent interventions for glycemic control.

MATERIALS AND METHODS
Study design and patient selection

The present study retrospectively analyzed hospital records of
38,367 patients during a 5-year period from 2009 to 2014.
The study was approved by the institutional review board of
Ichinomiyanishi Hospital, Japan. All of the patient data
extracted were anonymized, and informed consent of thepatients was not required.
We selected diabetic patients admitted to hospital for various
infections (non-ICU) and who underwent glucose monitoring>3 times per day. Patients with long durations of hospital stay(>90 days) and those who underwent very few sessions of glucose monitoring (<6 times in total) were excluded. Blood glucose concentrations were measured in capillary blood obtainedby ﬁnger prick using a point-of-care device (ACCU-CHEK
Aviva; Roche Diabetes Care GmbH, Indianapolis, IN, USA).
Mortality was deﬁned as in-hospital death.
Outcomes and statistical analysis

We extracted data regarding the age, sex, body-mass index(BMI), glycosylated hemoglobin concentration, reactive inﬂammatory biomarkers (i.e., white blood cell [WBC] count and
CRP concentrations) and vital signs (i.e., body temperature[BT], systolic blood pressure, diastolic blood pressure, heart rate
[HR] and SpO2) on day 1 of hospital stay. We deﬁned thepresence of bacteremia if it persisted during the entire hospitalstay. We evaluated the presence of underlying etiology of infection, besides diabetes, on day 1 of hospital stay. Hypoglycemia
was deﬁned as a blood glucose level of <70 mg/dL on any test
carried out in the hospital. Hypoglycemia, GV (standard deviation [SD]19,20 and coefﬁcient of variation [CV])21,22 and mean
glucose concentrations were determined from all the glycemic
data collected during the entire hospital stay. We determined
whether the patient had been taking any antidiabetic agents
before hospital admission. We analyzed the association of thesefactors (explanatory variables) with mortality (response variable)
by using a univariate logistic regression analysis. Using a stepwise multivariate logistic regression analysis, we further analyzed the association of the factors (explanatory variables) thatwere signiﬁcantly associated with mortality, as determined using
the univariate logistic regression analysis, with mortality (response variable). A P-value of <0.05 was considered statisticallysigniﬁcant. Data are shown as medians (interquartile range).
2

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RESULTS
Patient characteristics

In total, 620 patients (377 men, 243 women) who underwent
intervention for glycemic control were included in the present
study. The number of patients in each infectious disease category was as follows: pneumonia 341 (55.5%), urinary tract
infections 80 (12.9%), gastrointestinal infections 38 (6.1%), biliary tract infections 77 (12.4%) and other infections, including
surgical infections, 84 (13.5%). A total of 21 patients (3.4%)
showed bacteremia. An underlying etiology of infection (underlying etiology), besides diabetes, was found in 203 patients(32.7%). In total, 54,876 values of glucose concentrations wereanalyzed. The number of glucose readings of the study patientswas 4.4 (3.5–5.4) per day and 71 (50–108) during the hospitalization. Table 1 shows patient characteristics on day 1 of hospital stay and during the entire hospital stay.
Primary outcomes

In the univariate analysis, among all of the data collected on
day 1 of hospital stay, sex, glycosylated hemoglobin concentration, reactive inﬂammatory biomarkers (WBC count and CRP
concentration), vital signs (BT, systolic blood pressure, diastolicblood pressure, HR and SpO2) and underlying etiology were
not associated with mortality. However, age was signiﬁcantly
associated with increased mortality, and increased the risk ofdeath by 3% (odds ratio [OR] 1.03, 95% conﬁdence interval[95% CI] 1.01–1.06; P = 0.01). A low BMI value was also signiﬁcantly associated with increased mortality, and increased therisk of death by 16% (OR 0.85, 95% CI 0.78–0.91; P < 0.0001).
On the contrary, according to the results of the analysis of thedata collected during the entire hospital stay, hypoglycemia
showed a signiﬁcant positive association with mortality, andincreased the risk of death by 313% (OR 3.13, 95% CI 1.82–
5.41; P < 0.0001). Similarly, a high CV was signiﬁcantly associated with increased mortality, and increased the risk of deathby 5% (OR 1.05, 95% CI 1.02–1.07; P = 0.0001). A high SD
value was also associated with mortality, and increased the riskof death by 3% (OR 1.03, 95% CI 1.02–1.04; P < 0.0001), sohigh mean glucose concentration increased the risk of death by
2% (OR 1.02, 95% CI 1.01–1.02; P < 0.0001). Bacteremia was
not associated with mortality. Use of an antidiabetic agent
before hospital admission was not associated with mortality(Table 2).
After stepwise multivariate adjustment, age was eliminatedfrom the list of explanatory variables. A low BMI value was signiﬁcantly associated with increased mortality, and increased therisk of death by 12% (OR 0.88, 95% CI 0.81–0.95; P = 0.002).
Hypoglycemia showed a signiﬁcant positive association withmortality, and increased the risk of death by 266% (OR 2.66,
95% CI 1.22–5.83; P = 0.0006). Similarly, a high CV was significantly associated with increased mortality, and increased therisk of death by 18% (OR 1.18, 95% CI 1.01–1.38; P = 0.03). A
high SD value was also associated with mortality, and increased
the risk of death by 9% (OR 1.09, 95% CI 1.01–1.18; P = 0.03),ª 2015 The Authors. Journal of Diabetes Investigation published by AASD and Wiley Publishing Asia Pty Ltd

ORIGINAL ARTICLE
Hypoglycemia, GV and mortality in DM

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Table 1 | Characteristics of patientsn (men/women)Type 1 diabetes/type 2 diabetes (n)Pneumonia, n (%)
Urinary tract infection, n (%)
Gastrointestinal infection, n (%)
Biliary tract infection, n (%)
Surgical infection etc., n (%)
Age (years)
BMI (kg/m2)
HbA1c, NGSP (%)
HbA1c, IFCC (mmol/mol)
WBC (mg/dL)
CRP (mg/dL)
BT (°C)SBP (mmHg)
DBP (mmHg)
HR (b.p.m.)
SpO2 (%)
No. blood measurements
Per day
During the hospitalization
Bacteremia, n (%)
Underlying etiology, n (%)
Mean glucose level (mg/dL)
SD (mg/dL)
CV (%)
Hypoglycemia, n (%)
Length of stay (days)
Mortality, n (%)
Prehospital antidiabetic agent
Sulfonylurea agent, n (%)
Metformin, n (%)
Thiazolidinediones, n (%)
a-Glucosidase inhibitor, n (%)
Insulin, n (%)
DPP-4 inhibitors, n (%)
GLP-1 receptor agonists, n (%)
Rapid-acting insulin
secretagogue, n (%)
SGLT-2 inhibitor, n (%)
Insulin regimen in the hospital
Insulin sliding scale, n (%)
Basal–bolus, n (%)
Basal insulin only, n (%)
Other, n (%)
Not receiving any insulin treatment, n (%)

620 (377/243)
4/616
341 (55.0)
80 (12.9)
38 (6.1)
77 (12.4)
84 (13.5)
79.0 (71.0–85.0)
22.1 (19.0–24.9)
6.9 (6.4–7.9)
51.9 (46.5–62.8)
10400 (7,700–14,400)
8.1 (2.8–16.0)
37.0 (36.6–37.5)
124 (107–142)
68 (59–78)
87.5 (77.0–98.0)
96.0 (94.0–98.0)
4.4 (3.5–5.4)
71 (50–108)
21 (3.4)
203 (32.7)
162.6 (135.4–188.8)
48.6 (33.0–66.7)
29.3 (21.7–36.9)
128 (20.6)
17.0 (10.0–30.0)
64 (10.3)
208 (33.5)
129 (20.8)
92 (14.8)
136 (21.9)
90 (14.5)230 (37.1)
12 (1.9)33 (5.3)0 (0)19 (3.0)400 (64.5)
38 (6.1)
13 (2.1)
19 (3.1)
150 (24.2)

Data are shown as median (interquartile range). Hypoglycemia isdefined as any documented in-hospital episode of glucose <70 mg/dL.
BMI, body mass index; BT, body temperature, CRP, C-reactive protein;CV, coefficient of variation; DBP, diastolic blood pressure; DPP, dipeptidyl
peptidase; GLP, glucagon-like peptide; HbA1c, glycosylated hemoglobin;
HR, heart rate; NGSP, National Glycohemoglobin Standardization Program; SBP, systolic blood pressure; SD, standard deviation; SGLT, sodium
glucose co-transporter; SpO2, arterial oxygen saturation; Underlying
etiology, underlying etiology of infection besides diabetes; WBC, whiteblood cell.

ª 2015 The Authors. Journal of Diabetes Investigation published by AASD and Wiley Publishing Asia Pty Ltd

Table 2 | Relationship between the factors (glycemic control, reactive
inflammatory biomarkers and vital signs) and mortality. (n = 620)
Variable

Mortality

Age (years)
Male (n)BMI (kg/m2)
HbA1c, NGSP (%)
WBC (mg/dL)
CRP (mg/dL)
BT (°C)SBP (mmHg)
DBP (mmHg)
HR (b.p.m.)
SpO2 (%)
Bacteremia
Underlying etiology
Mean glucose level (mg/dL)
SD (mg/dL)
CV (%)
Hypoglycemia
Sulfonylurea agent
Metformin
Thiazolidinediones
a-Glucosidase inhibitor
Insulin
DPP-4 inhibitors
GLP-1 receptor agonists
Rapid-acting insulin secretagogue

OR (95% CI)

P-value

1.03 (1.01–1.06)
1.71 (0.96–3.02)
0.85 (0.78–0.91)
0.85 (0.69–1.05)
1.00 (1.00–1.00)
1.01 (0.98–1.04)
0.81 (0.56–1.18)
1.01 (1.00–1.02)
1.02 (1.00–1.04)
1.01 (0.99–1.02)
1.10 (0.98–1.23)
2.03 (0.66–6.20)
1.58 (0.93–2.67)
1.02 (1.01–1.02)
1.03 (1.02–1.04)
1.05 (1.02–1.07)
3.13 (1.82–5.41)
0.89 (0.51–1.55)
0.51 (0.24–1.11)
0.93 (0.44–1.96)
0.63 (0.31–1.28)
1.77 (0.93–3.35)
0.88 (0.51–1.51)
1.00 (1.00–1.00)
0.86 (0.26–0.91)

0.01
0.07
<0.0001
0.13
0.11
0.47
0.28
0.06
0.06
0.46
0.09
0.21
0.09
<0.0001<0.0001
0.0001<0.0001
0.68
0.09
0.85
0.20
0.08
0.63
1.00
0.81

Data were analyzed with univariate logistic regression analysis. BMI,
body mass index; BT, body temperature; CI, confidence interval; CRP,
C-reactive protein; CV, coefficient of variation; DBP, diastolic bloodpressure; DPP, dipeptidyl peptidase; GLP, glucagon-like peptide; HbA1c,
glycosylated hemoglobin; HR, heart rate; NGSP, National Glycohemoglobin Standardization Program; OR, odds ratio; SBP, systolic bloodpressure; SD, standard deviation; SGLT, sodium glucose co-transporter;
SpO2, arterial oxygen saturation; Underlying etiology, underlying
etiology of infection besides diabetes; WBC, white blood cell.

so high mean glucose concentration increased the risk of deathby 5% (OR 1.05, 95% CI 1.02–1.08; P = 0.0008; Table 3).
Relationship of the factors collected during the entire hospital
stay with hypoglycemia

According to the results of the univariate analysis, bacteremia
and mean glucose concentration were not associated withhypoglycemia. A high CV was signiﬁcantly associated withincreased hypoglycemia, and increased the risk of death by
16% (OR 1.16, 95% CI 1.13–1.19; P < 0.0001). A high SD
value was also associated with increased hypoglycemia, andincreased the risk of death by 4% (OR 1.04, 95% CI 1.03–1.05;
P < 0.0001; Table 4).
After stepwise multivariate adjustment, a high CV was significantly associated with increased hypoglycemia, and increased

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Table 3 | Relationship between mortality and the factors that weresignificantly associated with mortality (adjusted). (n = 620)

Table 5 | Relationship between hypoglycemia and the factors thatwere significantly associated with hypoglycemia (adjusted). (n = 620)

Variable

Variable

BMI (kg/m2)
Mean glucose level (mg/dL)
SD (mg/dL)
CV (%)
Hypoglycemia, n (%)
R2
SignificanceMortality
OR (95% CI)

P-value

0.88 (0.81–0.95)
1.05 (1.02–1.08)
1.09 (1.01–1.18)
1.18 (1.01–1.38)
2.66 (1.22–5.83)
0.22

0.002
0.0008
0.03
0.03
0.01
<0.0001

Data were analyzed with stepwise multivariate logistic regression analysis. Age was eliminated from the list of explanatory variables. BMI, bodymass index; CI, confidence interval; CV, coefficient of variation; OR, odds
ratio; SD, standard deviation.
Table 4 | Relationship between the factors collected during the entire
hospital stay and hypoglycemia. (n = 620)
Variable

Bacteremia
Mean glucose level (mg/dL)
SD (mg/dL)
CV (%)

Hypoglycemia
OR (95% CI)

P-value

1.14 (0.41–3.14)
1.00 (1.00–1.01)
1.04 (1.03–1.05)
1.16 (1.13–1.19)

0.81
0.48
<0.0001<0.0001

Data were analyzed with univariate logistic regression analysis. CI, confidence interval; CV, coefficient of variation; OR, odds ratio; SD, standard
deviation.

the risk of death by 25% (OR 1.25, 95% CI 1.19–1.32;
P < 0.0001). A high SD value was also associated withincreased hypoglycemia, and increased the risk of death by 4%(OR 1.04, 95% CI 1.02–1.06; P = 0.0004; Table 5).
Determination of SD and CV cut-off values, which have thehighest prediction ability for hypoglycemia, by using receiver
operating characteristic analysis

Regarding SD, when the cut-off value was 58.3 mg/dL, which
has the highest prediction ability, the sensitivity was 70% andthe speciﬁcity was 72%. The area under the curve (AUC) for
hypoglycemia was 0.76 (95% CI 0.72–0.81; P < 0.0001).
Regarding CV, when the cut-off value was 32.2%, which has
the highest prediction ability, the sensitivity was 83% and thespeciﬁcity was 71%. The AUC for hypoglycemia was 0.85 (95%
CI 0.81–0.88; P < 0.0001; Figure 1).
Comparison of study participants stratiﬁed by the risk ofmortality

Table 6 shows stratiﬁcation of study participants according to
the hypoglycemia status and GV. Study participants werestratiﬁed by whether hypoglycemia was present or not. Study
4

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SD (mg/dL)
CV (%)
R2
SignificanceHypoglycemia
OR (95% CI)

P-value

1.04 (1.02–1.06)
1.25 (1.19–1.32)
0.41

0.0004<0.0001<0.0001

Data were analyzed with stepwise multivariate logistic regression analysis. CI, confidence interval; CV, coefficient of variation; OR, odds ratio;
SD, standard deviation.

participants were also stratiﬁed by whether GV was above thecut-off value or not, as this value has the highest predictive
ability of hypoglycemia status using receiver operating characteristic analysis (CV 30%, SD 60 mg/dL).
When study participants were stratiﬁed by hypoglycemia status and CV, the mortality rates of participants in CV categories
3 and 4 were signiﬁcantly higher (414 and 345%, respectively)
than in CV category 1 (OR 4.14, 95% CI 1.06–16.21; P = 0.04;
OR 3.45, 95% CI 1.83–6.52; P = 0.0001, respectively). The mortality rate of participants in CV category 2 was not signiﬁcantly
higher than that of participants in CV category 1.
When study participants were stratiﬁed by hypoglycemia status and SD, the mortality of the participants in SD category 4
was signiﬁcantly higher (462%) than in CV category 1 (OR4.62, 95% CI 2.40–8.90; P < 0.0001). The mortality rates ofparticipants in CV categories 2 and 3 were not signiﬁcantly
higher than that of participants in CV category 1 (Table 7).

DISCUSSION
The results of the present study suggest that increased hypoglycemia and high CV value, SD value, and mean glucose concentrations, were signiﬁcantly associated with increased
mortality in the case of non-ICU diabetes mellitus patients with
infectious diseases who underwent interventions for glycemic
control. However, reactive inﬂammatory biomarkers and vital
signs on day 1 of hospital stay were not associated withmortality.
As previously reported, in the case of patients with infectious
diseases, the following clinical measures serve as prognostic factors at the time of admission: CRP concentration and SpO2 inpneumonia10,11; BT, HR and WBC count in urinary tract infections12; BT, blood pressure and WBC count in cholangitis13;
and HR and WBC count in cholecystitis14. In contrast, hyperglycemia in patients hospitalized for community-acquired pneumonia was a predictor of death23. The present study results
suggest that glycemic control intervention, rather than the
aforementioned prognostic factors, is associated with mortality.
Thus, appropriate glycemic control is necessary in the case of
non-ICU patients with infections, as in the case of ICU
patients, and has great clinical importance.
ª 2015 The Authors. Journal of Diabetes Investigation published by AASD and Wiley Publishing Asia Pty Ltd

ORIGINAL ARTICLE
Hypoglycemia, GV and mortality in DM

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1

Figure 1 | Receiver operating characteristic curves
for hypoglycemia in standard deviation (SD) andcoefficient of variation (CV). Regarding SD, when
the cut-off value was 58.3 mg/dL, which has thehighest prediction ability, the sensitivity was 70%
and the specificity was 72%. The area under the
curve (AUC) for hypoglycemia was 0.76 (95%
confidence interval [CI] 0.72–0.81; P < 0.0001).
Regarding CV, when the cut-off value was 32.2%,
which has the highest prediction ability, thesensitivity was 83% and the specificity was 71%.
The AUC for hypoglycemia was 0.85 (95% CI 0.81–
0.88; P < 0.0001).

Sensitivity

0.8

0.6

0.4

0.2

SD

AUC 0.76 (95% CI: 0.72 – 0.81, P < 0.0001)
Cutoﬀ: 58.3 mg/dL, sensitivity: 70 %, speciﬁcity: 72 %CV

AUC 0.85 (95% CI: 0.81 – 0.88, P < 0.0001)
Cutoﬀ: 32.2 %, sensitivity: 83 %, speciﬁcity: 71 %

0
0

0.2

0.4
1 - speciﬁcity

0.6

0.8

1

Table 6 | Stratified study subjects by hypoglycemia status and glycemic variability

CV and hypoglycemia
Hypoglycemia absent (n)Hypoglycemia present (n)Total

SD and hypoglycemia
Hypoglycemia absent (n)Hypoglycemia present (n)Total

CV <30% (n)CV >30% (n)Total

311 (CV category 1)
44 (CV category 3)324

181 (CV category 2)
84 (CV category 4)
296

492
128
620

SD <60 mg/dL (n)SD >60 mg/dL (n)Total

363 (SD category 1)
44 (SD category 3)
407

129 (SD category 2)
84 (SD category 4)
213

492
128
620

CV, coefficient of variation; SD, standard deviation.

As previously reported, hypoglycemia is associated withmortality, and the underlying mechanism for this associationis considered to involve catecholamine that is secreted in surplus quantities in hypoglycemia, causing cardiac load24,25. In
contrast, hypoglycemia is associated with the severity of sepsis: secretion of glucagon from the pancreas and gluconeogenesis in the liver decrease when severe sepsis occurs, causing
multiple organ failure26. In the present study, increased hypoglycemia, but not bacteremia, was signiﬁcantly associated withincreased mortality in non-ICU diabetes mellitus patients with
infectious diseases. Therefore, we elucidated which cause
hypoglycemia – multiple organ failure caused by severe sepsis
or glycemic control interventions. We examined which areassociated with hypoglycemia – bacteremia or glycemic con-ª 2015 The Authors. Journal of Diabetes Investigation published by AASD and Wiley Publishing Asia Pty Ltd

trol interventions. The results showed that bacteremia andmean glucose concentrations were not associated with hypoglycemia. High CV and SD values were associated withincreased hypoglycemia. Therefore, we believe that increased
hypoglycemia was not caused by bacteremia; however, GV
was found to be associated with increased mortality in thepresent study. Next, we investigated the association between
the risk of hypoglycemia and the degree of GV in the present study. The results suggest that the risk of hypoglycemia
increases signiﬁcantly if GV is >60 mg/dL and GV/glucose
concentration is >30%. Thus, treatment should be preferably
adjusted in non-ICU diabetes mellitus patients with infectious
diseases whose GV is <60 mg/dL and GV/glucose concentration is <30%.

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Table 7 | Comparison between categories on risk of mortality

CV and hypoglycemia
CV category 1
CV category 2*
CV category 3*
CV category 4*
SD and hypoglycemia
SD category 1
SD category 2*
SD category 3*
SD category 4*

OR (95% CI)

P-value

Reference
1.34 (0.68–2.64)
4.14 (1.06–16.21)
3.45 (1.83–6.52)

Reference
0.4
0.04
0.0001

Reference
1.80 (0.90–3.61)
2.33 (0.89–6.09)
4.62 (2.40–8.90)

Reference
0.10
0.08<0.0001*In comparison with category 1. CI, confidence interval; CV, coefficientof variation; OR, odds ratio; SD, standard deviation.

GV has also been reported to be associated with mortality27.
GV increases oxidative stress, and thereby causes vascularendothelial dysfunction28. Therefore, oxidative stress is considered to be associated with increased mortality. In fact, the concentration of protein kinase C-b, which is an index of oxidative
stress, increases when glucose concentration decreases; that is,from hyperglycemia status to normoglycemia status29. In addition, the mean amplitude of glycemic excursion was reported
to be signiﬁcantly associated with 8-isoprostaglandin F2a concentration in the urine, which is an index of oxidative stress inpatients with type 2 diabetes30.
We suggested that increased hypoglycemia and GV were signiﬁcantly associated with increased mortality in the present
study. Therefore, we stratiﬁed study participants by hypoglycemia status and GV to investigate the combined effect ofhypoglycemia and high GV on the risk of mortality. These
results suggest that the combined effect of hypoglycemia andhigh GV increase the risk of mortality more strongly thaneither individually.
Hyperglycemia has also been shown to increase oxidative
stress and mortality rate17,18. Increased oxidative stress causes
insulin resistance and promotes a repetitive cycle, thereby
increasing the severity of hyperglycemia31. In any case, glycemic
instability is likely to be associated with mortality, and thepresent results support this observation.
Our data suggest that glycemic control interventions after
hospital admission, rather than reactive inﬂammatory biomarkers or vital signs on admission, which were previously reported
as prognostic factors, was associated with prognosis in nonICU diabetes mellitus patients with infectious diseases. Thus,
glycemic control interventions after hospital admission, rather
than the severity of infectious diseases on admission, dictates
prognosis in non-ICU diabetes mellitus patients with infectious
diseases. In this study, we did not investigate changes in reactive inﬂammatory biomarkers or vital signs after admission,and therefore, we could not determine associations between glycemic control and these parameters in infectious diseases.
Impaired glycemic control has been reported to be associated6

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with severe infectious diseases15, sand therefore, poor glycemic
control after admission would lead to poor prognosis of infectious diseases. Not only intervention for glycemic control, butalso highly intensive care is necessary in the case of patientswith infectious disease who show poor glycemic control.
The results of the present study suggest that increased hypoglycemia and high CV value, SD value, and mean glucose concentrations during the entire hospital stay, rather than reactiveinﬂammatory biomarkers or vital signs on day 1 of hospital stay,were associated with increased mortality in non-ICU diabetes
mellitus patients with infectious diseases. As a result of thisstudy, we believe that glycemic control should be carried out
with the goal of avoiding clinical glycemic instability. However,the present study was limited by certain factors. First, this study
was carried out in a single center; second, interventions for glycemic control were not carried out according to a uniﬁed protocol; third, reactive inﬂammatory biomarkers and vital signs were
evaluated only according to data collected on day 1 of hospital
stay; and ﬁnally, underlying etiology and cause of death variedamong patients. Prospective studies with continuous glucose
monitoring to further investigate the relationship between glycemic control and clinical outcomes are necessary.

ACKNOWLEDGMENT
This work was supported in part by the Ichinomiyanishi
Hospital. Parts of this study have been presented in poster presentation at the 75th Scientiﬁc Sessions of the American Diabetes Association, Boston, Massachusetts, from 5 to 9 June 5
2015.
DISCLOSURE
The authors declare no conﬂict of interest.
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