50 PART I: 21ST CENTURY HUMAN RESOURCE MANAGEMENT STRATEGIC PLANNING AND LEGAL ISSUES • • • CASE 2-1 STRATEGY-DRIVEN HR MANAGEMENT: NETFLIX. A BEHIND-THE-SCENES LOOK AT DELIVERING ENTERTAINMENT Netflix is a highly successful retailer of movie rental ser­ vices, with a market value of over $25 billion. It offers a subscription service that allows its members to stream shows and movies instantly over the Internet on game con­ soles, Blu-ray players, HDTVs, set-top boxes, home the­ ater systems, phones, and tablets. Netflix also includes a subscription service for those who prefer to receive discs via the US mail (rather than streaming), without the hassle of due dates or late fees. The idea of a home delivery movie service came to CEO Read Hastings when he was forced to pay $40 in overdue fines after returning a video of the movie Apollo 13 to Blockbuster. He realized that he could capitalize on an existing distribution system (the US Post Office) that did not require renters to leave their homes. The Netflix website was launched on August 29, 1997, with only 30 employees and 925 movies available for rent. It used a traditional pay-per-rental model, charging $0.50 per rental plus US postage, and late fees applied. Netflix introduced the monthly subscription concept in September 1999, and then it dropped the single-rental model in early 2000. Since that time, the company has built its reputation on the business model of flat-fee unlimited rentals without due dates, late fees, or ship­ ping and handling fees. In addition, its on line streaming service doesn't have per title rental fees.78 Throughout 2014, Netflix's total sales grew by 21 %, generating a net income of $112 million. 79 Subscribers increased by almost 40% that year, reaching 46 million, and the stock value tripled from 2012 to 2014. But how did they reach that point?80 There are many reasons why Netflix's strategy is suc­ cessful, yet the numbers tell only the results and not the behind-the-scenes story. According to Read Hastings and former chief talent officer (CTO) Patty McCord, this success is not a surprise at all given Netflix's busi­ ness model. But more important, they say, is Netflix's HR strategy, which is to create an environment of fully motivated employees who understand the culture of the company and perform exceptionally well within it. Hastings and McCord had the foresight to document their HR strategy via PowerPoint, and soon these slides went viral, with more than 5 million views on the Web. McCord described Netflix's HR strategy as consisting of the following steps: l. Selecting new employees/recruiting. Hire employees who care about, understand, and then prioritize the company's interests. This will eliminate the need for formal regulations and policies because these employ­ ees will strive to grow the company for their own self-satisfaction. This sets Netflix apart from the many companies that do not hire employee ...

1. 50 PART I: 21ST CENTURY HUMAN RESOURCE
MANAGEMENT STRATEGIC PLANNING AND LEGAL
ISSUES
• • • CASE 2-1 STRATEGY-DRIVEN HR MANAGEMENT:
NETFLIX. A BEHIND-THE-SCENES LOOK AT DELIVERING
ENTERTAINMENT
Netflix is a highly successful retailer of movie rental ser-
vices, with a market value of over $25 billion. It offers a
subscription service that allows its members to stream
shows and movies instantly over the Internet on game con-
soles, Blu-ray players, HDTVs, set-top boxes, home the-
ater systems, phones, and tablets. Netflix also includes a
subscription service for those who prefer to receive discs
via the US mail (rather than streaming), without the hassle
of due dates or late fees.
The idea of a home delivery movie service came to
CEO Read Hastings when he was forced to pay $40 in
overdue fines after returning a video of the movie Apollo
13 to Blockbuster. He realized that he could capitalize
on an existing distribution system (the US Post Office)
that did not require renters to leave their homes. The
Netflix website was launched on August 29, 1997, with
only 30 employees and 925 movies available for rent.
It used a traditional pay-per-rental model, charging
$0.50 per rental plus US postage, and late fees applied.
Netflix introduced the monthly subscription concept in
September 1999, and then it dropped the single-rental
model in early 2000. Since that time, the company has

2. built its reputation on the business model of flat-fee
unlimited rentals without due dates, late fees, or ship-
ping and handling fees. In addition, its on line streaming
service doesn't have per title rental fees.78 Throughout
2014, Netflix's total sales grew by 21 %, generating a
net income of $112 million. 79 Subscribers increased by
almost 40% that year, reaching 46 million, and the stock
value tripled from 2012 to 2014. But how did they reach
that point?80
There are many reasons why Netflix's strategy is suc-
cessful, yet the numbers tell only the results and not the
behind-the-scenes story. According to Read Hastings
and former chief talent officer (CTO) Patty McCord,
this success is not a surprise at all given Netflix's busi-
ness model. But more important, they say, is Netflix's
HR strategy, which is to create an environment of fully
motivated employees who understand the culture of
the company and perform exceptionally well within it.
Hastings and McCord had the foresight to document
their HR strategy via PowerPoint, and soon these slides
went viral, with more than 5 million views on the Web.
McCord described Netflix's HR strategy as consisting of
the following steps:
l. Selecting new employees/recruiting. Hire employees
who care about, understand, and then prioritize the
company's interests. This will eliminate the need for
formal regulations and policies because these employ-
ees will strive to grow the company for their own
self-satisfaction. This sets Netflix apart from the many
companies that do not hire employees who would be
a great fit with the company's culture and that there-
fore still spend great amounts of time and money on
enforcing their HR policies-policies that target only

3. 3 % of their workforce. 81
2. Talent management/matching employees with jobs.
To avoid high employee turnover, a company must
recruit talented people with the right skills, although
mismatches may occur. Layoffs and firings are also
inevitable given changing business cycles. In such
cases, it is HR's duty to place employees in depart-
ments that match the employees' skill sets, as well
as to train employees to meet changing business
needs.82
3. Send the right messages. To boost overall employee
morale, most HR departments throw parties or give
away free items. But when stock prices are decreas-
ing or sales numbers are not as high as predicted,
what use would a company have for an office party?
Netflix executives stated that they have not seen an
HR initiative that truly improved morale. Instead of
cheerleading, employees need to be educated about
how the company earns its revenue and what behav-
iors will drive its success. By receiving clear messages
about how employees should execute and commit
to their duties, employees will be more informed
about the criteria they will need to meet to receive
their bonuses, and they will therefore be more apt to
receive those bonuses. Knowing what to do and how
to do it, employees' motivation will increase, and with
increased motivation, morale and performance will
improve.83
4. Performance evaluation. Netflix implemented
informal 360-degree reviews after realizing that
formal review sessions were not effective. These
informal 360-degree sessions allowed workers to
give honest opinions about themselves and col-

4. leagues-focusing on whether certain policies
should stop, start, continue, or change. Instead of
relying on bureaucratic measures, employees val-
ued these conversations as an organic part of their
work, and those conversations have been demon-
strated to increase employee performance.84 For
example, Netflix found that when its employees
perceived their bosses as less than expert in their
field, employee performance dropped. Employees
indicated that managers who relied on charm or
IQ were not trusted and received low subordinate
appraisals.
Observational Study Medicine®
OPEN
Body mass index and waist circumference are
better predictors of insulin resistance than total
body fat percentage in middle-aged and elderly
Taiwanese
Yiu-Hua Cheng, MDa, Yu-Chung Tsao, MDa,b,c, I-Shiang
Tzeng, PhDd, Hai-Hua Chuang, MDe,
Wen-Cheng Li, MDf,g, Tao-Hsin Tung, PhDh,i, Jau-Yuan Chen,
MDa,c,
∗
Abstract
The incidence of diabetes mellitus is rising worldwide, and
prediabetic screening for insulin resistance (IR) has become
ever more
essential. This study aimed to investigate whether body mass
index (BMI), waist circumference (WC), or body fat percentage

5. (BF%)
could be a better predictor of IR in a middle-aged and elderly
population. In this cross-sectional, community-based study, 394
individuals (97 with IR and 297 without IR) were enrolled in
the analysis. IR was measured by homeostasis model assessment
(HOMA-IR), and subjects with HOMA-IR value≧75th
percentile were defined as being IR. Associations between IR
and BMI, WC and
BF% were evaluated by t test, chi square, Pearson correlation,
logistic regression, and receiver operating characteristic (ROC)
curves. A total of 394 community-dwelling, middle-aged, and
elderly persons were enrolled; 138 (35%) were male, and 256
were
female (65%). The mean age was 64.41±8.46 years. A
significant association was identified between BMI, WC, BF%,
and IR, with
Pearson correlation coefficients of 0.437 (P< .001), 0.412 (P<
.001), and 0.361 (P< .001), respectively. Multivariate logistic
regression revealed BMI (OR=1.31; 95% CI=1.20–1.42), WC
(OR=1.13; 95% CI=1.08–1.17), and BF% (OR=1.17; 95% CI=
1.11–1.23) to be independent predictors of IR. The area under
curves of BMI andWC, 0.749 and 0.745 respectively, are greater
than
that of BF% 0.687. BMI andWCwere more strongly associated
with IR than was BF%. Excess body weight and body fat
distribution
were more important than total body fat in predicting IR.
Abbreviations: AUC = area under the ROC curve, BF% = body
fat percentage, BMI = body mass index, FPG = fasting plasma
glucose, HDL-C = high-density lipoprotein cholesterol, HOMA-
IR = homeostasis model assessment, IR = insulin resistance,
SBP =
systolic blood pressure, TG = triglyceride, WC = waist
circumference.

6. Keywords: body fat distribution, body mass index, diabetes
mellitus, insulin resistance, obesity, waist circumference
1. Introduction
The incidence of diabetes mellitus (DM) is increasing rapidly
worldwide, threatening to reduce life expectancy around the
globe. The International Diabetes Federation (IDF) has
estimated
that, by 2040, 642 million people will be living with the
disease,
in addition to some 320 million who will have undiagnosed
DM.[1] Thus, pre-DM screening is a critical issue.
Editor: Ediriweera Desapriya.
Authorship: YHC was involved in writing of the manuscript and
analyzed the data. YCT
advice. THT provided statistical advice and analyzed the data.
JYC contributed conce
and revised it critically for important intellectual content and
final approval of the versio
Funding/support: This work was supported by Chang Gung
Memorial Hospital (CORP
The authors have no conflicts of interest to disclose.
a Department of Family Medicine, b Department of
Occupational Medicine, Chang-Gun
University, Taoyuan, d Department of Research, Taipei Tzu Chi
Hospital, Buddhist Tzu
Chang-Gung Memorial Hospital, Taipei Branch, f Department of
Emergency Medicine,
Management, Xiamen Chang-Gung Hospital, Xiamen, China, h
Department of Medical
School of Medicine, Fu-Jen Catholic University, Taipei,
Taiwan.
∗
Correspondence: Jau-Yuan Chen, Department of Family

7. Medicine, Chang-Gung Mem
Taiwan (R.O.C.) (e-mail: [email protected]).
Copyright © 2017 the Author(s). Published by Wolters Kluwer
Health, Inc.
This is an open access article distributed under the Creative
Commons Attribution-No
commercial, as long as it is passed along unchanged and in
whole, with credit to the
Medicine (2017) 96:39(e8126)
Received: 10 November 2016 / Received in final form: 22
August 2017 / Accepted: 1
http://dx.doi.org/10.1097/MD.0000000000008126
1
Insulin resistance (IR) has emerged as a major pathophysio-
logical factor in the development and progression of DM and
metabolic disease.[2] Numerous studies have shown that the
incidence of IR in the elderly ranges from 35% to 50%.[3] Many
of the current methods for quantifying the extension of IR,
the gold standard of these quantification methods is respected as
the hyperinsulinemic normal blood glucose clamp. Although the
, HHC, and WCL conceived and supervised the study. IST
provided statistical
ived, designed and performed the experiments, collected and
analyzed the data,
n to be submitted.
G3C0171-CORPG3C0172, CZRPG3C0053).
g Memorial Hospital, Linkou Branch, cCollege of Medicine,
Chang Gung

8. Chi Medical Foundation, New Taipei city, e Department of
Family Medicine,
Chang-Gung Memorial Hospital, Linkou Branch, Taiwan, g
Department of Health
Research and Education, Cheng-Hsin General Hospital, i
Faculty of Public Health,
orial Hospital, Linkou Branch, No.5, Fuxing St., Guishan Dist.,
Taoyuan City 333,
Derivatives License 4.0, which allows for redistribution,
commercial and non-
author.
September 2017
mailto:[email protected]
http://creativecommons.org/licenses/by-nd/4.0
http://dx.doi.org/10.1097/MD.0000000000008126
Cheng et al. Medicine (2017) 96:39 Medicine
hyperinsulinemic normal blood glucose clamp provides the
benefits of IR for clinical practice (ie, dynamic and accurate
assessment), the drawbacks show that procedures are expensive,
aggressive, and also time-consuming to bring nonconformity for
clinical convenience or large-scale researches.[4] These reasons
also trigger to the development of the homeostasis model
assessment of IR (HOMA-IR) which to provide alternatively a
convenient, trusted, and cost-effective clamp.[5,6]
Although the cause of IR is still unknown, it has a close
correlation with obesity.[7] Obesity can be defined by
measuring
the individual’s body mass index (BMI) by dividing his or her
weight by the square of height (kg/m2). There is increasing

9. evidence that fat distribution, especially in the abdominal area,
is
correlated with the most severe state of IR.[8–11] Waist
circumference (WC) is defined by the IDF worldwide consensus
as the criteria for abdominal obesity.[12] Additionally, as an
endocrine organ, adipose tissue can secrete free fatty acids and
adipocytokines such as tumor necrosis factor-alpha (TNF-a) and
leptin, which can interfere with the insulin-signaling system and
induce IR.[2] Therefore, the amount of total body fat percentage
(BF%) may also play an important role in pathogenesis of IR.
The aim of this study was to investigate the association
between 3 common obesity indices, BMI, WC, and BF%, to
identify a simple diagnostic indicator for predicting IR among
middle-aged and elderly populations.
2. Methods
2.1. Study design and study subjects
This was a cross-sectional, community-based study. Data for
this
study were collected from a community health promotion
project
of Linkou Chang Gung Memorial Hospital, Taiwan, between
March and August 2014. The 400 participants were 50 to 90
year-olds and enrolled from the residents of Guishan district,
Taoyuan City, Taiwan through a poster promotion or through
notification from the community office. Such enrolled data
through project stored and managed solely to Chang Gung
Memorial Hospital in Linkou. Note that data cannot be publicly
deposited. Each participant completed a questionnaire during a
face-to-face interview. The questionnaire included the
individua-
l’s personal information and medical history. Anthropometric
measurements were taken, and blood sampling was performed
by

10. trained research assistants or nurses, under the supervision of a
medical doctor. The project was approved by the Institutional
Review Board of Linkou Chang Gung Memorial Hospital, and
all participants provided written informed consent before
enrolling in the study. Participants whose data were missing or
incomplete were excluded from the study. The final group
enrolled in the analysis included 394 participants.
2.2. Anthropometric and laboratory measurements
Anthropometric data, such as height, weight, BMI, WC, and
blood pressures (BP), were measured. Height was measured
using
calibrated height meters while the participant stood erect and in
bare feet, with the feet placed together and pointing forward.
The
weight scale was calibrated daily using two 20-kg standard
weights. BMI was calculated as weight divided by the square of
height (kg/m2). WC was measured at a level midway between
the
iliac crest and the lower border of the 12th rib while the
participant stood with his or her feet 25 to 30cm apart. BF%was
measured using an 8-contact electrode bioelectrical impedance
2
analysis (BIA) device (Tanita BC-418 Body Composition
Analyzer, Tanita, Tokyo, Japan). Blood pressure was measured
after a 10-minute rest, with the participant seated, using an
automated sphygmomanometer placed on the participant’s right
arm. The lowest of 3 readings was recorded. Prior to blood
samples being taken, participants were asked to fast for at least
12hours and to avoid consuming high-fat meals or alcohol for at
least 24hours prior to blood samples being taken. Venous blood
samples were obtained between 7 and 10AM, and were stored in
a refrigerator at 4 °C prior to analysis in the hospital laboratory.
The clinical biochemistry workup included measurement of
fasting plasma glucose (FPG), high-density lipoprotein
cholester-

11. ol (HDL-C), low-density lipoprotein cholesterol, total cholester-
ol, and triglyceride (TG) levels. The tests were performed in a
hospital laboratory accredited by the College of American
Pathologists.
2.3. Definition of IR
IR was determined by HOMA and calculated using FPG and
fasting insulin levels for each participant, using the following
formula: HOMA-IR= fasting glucose (mmol/L)� fasting insulin
(mU/mL)/22.5. A HOMA value ≧75th percentile was used as
the
cutoff for defining the main outcome variable of IR. In our
study,
the cutoff value for IR was 2.3.
2.4. Statistical analysis
All continuous variables were expressed as the mean and
standard deviation; categorical variables were expressed as
numbers and percentages. In univariate analysis, the
independent
t test and chi-square test were used to compare variables
between
IR and non-IR groups. Pearson correlation coefficient was used
to
assess correlations between different obesity indices and IR. In
multivariate analysis, binary logistic regression was used to
adjust covariates. Receiver operating characteristic (ROC)
curves
were generated for WC, BMI, and BF% as predictors of IR. The
area under the ROC curve (AUC) and the optimal cut-off points
for IR prediction of BMI, WC, and BF% were determined by the
largest sum of specificity and sensitivity. All tests were 2-sided,
and the level of significance was established at P< .05. Data
were
analyzed using SPSS Statistics Version 22 (IBM, SPSS,
Armonk,

12. NY, IBM Corp).
3. Results
This study recruited 400 participants through poster promotion
or notification from the community office. Four people with
incomplete data and 2 people with extreme data, such asHOMA-
IR: 440.94, 28.99, were excluded; the remaining 394
participants
were enrolled in the study for analysis. The flow diagram is
shown in Fig. 1.
The general characteristics of the study participants are shown
in Table 1. Among the 394 subjects, 97 (24.6%) developed IR.
The final study group included 138 males (35%) and 256
females
(65%), with a mean age of 64.41±8.46 years. The overall
percentage of participants reporting current smoking was
10.6%,
while 19.5%, 50.3%, and 65.7% had DM, hypertension, and
dyslipidemia, respectively. The average BMI,WC, and BF%were
24.55±3.51(kg/m2), 85.04±9.6cm, and 30.02±8.41%, respec-
tively. The mean systolic (SBP) and diastolic BP measurements
were 129.68±16.7 and 77.11±11.27mmHg, respectively.
Overall, the mean FPG, HDL-C, low-density lipoprotein
Figure 1. Flow diagram.
Table 2
Correlations of IR with different obesity indices.
Variable Correlation coefficient (r) P
BMI 0.437 <.001

13. WC 0.412 <.001
BF% 0.361 <.001
BF%=body fat percentage, BMI=body mass index, IR= insulin
resistance, WC=waist
circumference.
Cheng et al. Medicine (2017) 96:39 www.md-journal.com
cholesterol, total cholesterol, and TG levels were 95.61±22.4,
54.37±13.79, 118.65±32.23, 197.34±35.79, and 121.81±
62.95mg/dL, respectively. In those with IR, BMI, WC, and BF%
were significantly higher than those without IR. In addition,
SBP,
FPG, HDL-C, and TG were also significantly different between
the 2 groups.
Table 2 demonstrates the correlations between different
obesity indices and IR. All 3 obesity indices were positively
associated with IR. Pearson correlation coefficients were 0.437,
0.412, and 0.361 for BMI, WC, and BF%, respectively. BMI and
WC showed a stronger correlation with IR compared to BF%.
Figures 2–4 demonstrate the associations of BMI, WC, BF%,
and
IR. There was a trend toward a positive correlation between all
obesity indices and IR.
Table 3 displays the results of the binary logistic regression
analyses, in which IR was the dependent variable, and obesity
indices were the independent variables. Model 1 is a univariate
binary logistic regression model, whereas models 2 and 3 are
multivariate models that are adjusted for different covariates. In
model 2, obesity indices were adjusted for age and sex. In
model
Table 1
General characteristics of participants in the IR and non-IR
groups.

14. Variable Total (n=394) No
Age, y 64.41±8.46
BMI, kg/m2 24.55±3.51
WC, cm 85.04±9.60
BF% 30.02±8.41
SBP, mmHg 129.68±16.70 1
DBP, mmHg 77.11±11.27
FPG, mg/dL 95.61±22.40
HDL-C, mg/dL 54.37±13.79
LDL-C, mg/dL 118.65±32.23 1
TC, mg/dL 197.34±35.79 1
TG, mg/dL 121.81±62.95 1
Male, n, % 138 (35)
Female, n, % 256 (65)
Current smoking, n, % 42 (10.6)
Diabetes mellitus, n, % 77 (19.5)
Hypertension, n, % 198 (50.3)
Dyslipidemia, n, % 259 (65.7)
Data are expressed as mean± standard deviation for continuous
variables and n (%) for categorical variable
≧75%. BF%=body fat percentage, BMI=body mass index,
DBP=diastolic blood pressure, FPG= fa
cholesterol, n=number, IR= insulin resistance, SBP= systolic
blood pressure, TC= total cholesterol, TG
3
3, obesity indices were adjusted for age, sex, current smoking
status, DM, hypertension, and dyslipidemia. In all 3 models,
BMI, WC, and BF% were significantly associated with IR. In
model 3, BMI (odds ratio [OR]: 1.31; 95% confidence interval
[CI]: 1.20–1.43; P< .001), WC (OR: 1.13; 95% CI: 1.08–1.17;
P< .001), and BF% (OR: 1.17; 95% CI: 1.11–1.23; P< .001)
were all significantly associated with IR. A 1-unit increase in

15. BMI,
WC, and BF% was, respectively, associated with a 30.6%,
12.5%, and 16.9% increase in risk of IR. Figure 5 shows the
ROC curve of BMI, WC, BF%, and selected covariates as
predictors of IR. In Table 4, the AUC of BMI,WC, and
BF%were
0.749, 0.745, and 0.687, respectively. The AUC of selected
covariates was 0.74487. BMI and WC had a better predictive
performance for IR than BF% and selected covariates. The
optimal cut-off point (for predicting IR) for BMIwas
26.15kg/m2
(sensitivity 0.608, specificity 0.791), for WC was 89.5cm
(sensitivity 0.577, specificity 0.788), and for BF% was
29.15% (sensitivity 0.784, specificity 0.498).
4. Discussion
In this study of middle-aged and elderly Taiwanese subjects, the
cut-off value ofHOMA-IRwas 2.3, which approximates the 2.29
established in an earlier 1156-person Caucasian population
study.[1,13] The results of our study show that 3 obesity indices
–
BMI, WC, and BF% – are all significantly associated with IR in
univariate analysis, while BMI and WC had higher correlation
coefficients compared with BF%. After adjusting for covariates
n-IR (n=297) IR (n=97) P
64.23±8.54 64.98±8.21 .447
23.77±3.10 26.91±3.63 <.001
82.88±8.34 91.66±10.21 <.001
28.66±8.39 34.17±7.07 <.001
27.84±16.28 135.31±16.80 <.001
76.44±11.29 79.18±11.02 .038
90.34±13.56 111.74±33.75 <.001
56.43±13.88 48.05±11.44 <.001

16. 21.35±32.73 110.35±29.30 .003
99.81±36.50 189.80±32.54 .017
10.26±52.56 157.19±77.62 <.001
105 (35.4) 33 (34) .811
192 (64.6) 64 (66) .811
32 (10.8) 10 (10.3) .897
36 (12.1) 41 (42.3) <.001
128 (43.1) 70 (72.2) <.001
185 (62.3) 74 (76.3) .012
s. We divided the participants into 2 groups: IR negative and IR
positive group based on HOMA-IR value
sting plasma glucose, HDL-C=high-density lipoprotein
cholesterol, LDL-C= low-density lipoprotein
= triglyceride, WC=waist circumference.
http://www.md-journal.com
Figure 4. The correlation between BF% and IR. BF%=body fat
percentage,
IR= insulin resistance.
Figure 2. The correlation between BMI and IR. BMI=body mass
index, IR=
insulin resistance.
Cheng et al. Medicine (2017) 96:39 Medicine
such as age, sex, current cigarette smoking status, hypertension,
DM, and dyslipidemia, BMI, WC, and BF% remained
significantly associated with IR. Further, the AUCs of BMI
and WC were larger than that of BF%. In addition, we selected
age, sex, current smoking status, DM, hypertension, and
dyslipidemia as covariates to predict IR (ROC curve plotted in
Fig. 5). The AUCs of BMI and WC were larger than that of
selected covariates.Wemay use BMI andWC to predict IR rather

17. than selected covariates. It means that BMI andWCmay be more
representative than selected covariates of prediction of IR.
Similar
result was observed in a Japanese employee general health
checkup study, which demonstrated that BMI was more
important in predicting IR than hypertension and hyper-
triglyceridemia.[14] Moreover, based on the findings from a
study of 2746 healthy volunteers, WC was suggested to be used
as the stronger predictor of IR than dyslipidemia and SBP.[15]
The
cutoff values of BMI andWC to predict IR were 26.15kg/m2 and
Figure 3. The correlation between WC and IR. IR= insulin
resistance, WC=
waist circumference.
4
89.5cm, respectively, which nearly meet the obesity criteria
(BMI: 27kg/m2, WC: 90cm in males and 80cm in females) set
by
the Taiwan Ministry of Health and Welfare-Health Promotion
Administration. These results reinforce the relationship between
IR and obesity, andwe suggest that overweight and obese
persons
should be made aware of the risk of IR and standardly screened
for cardiovascular and metabolic disease in advance of
symptoms.
Previous studies have reported the correlation between the
obesity index and IR, but some results have been
inconsistent.[16–19] Samouda et al[16] demonstrated that
adding
the body fat distribution score to the BMI can improve the
prediction of cardiometabolic, inflammatory, and adipokines
profiles. This underscores the importance of BMI and WC for
predicting IR and is in accordance with our study results.
Results

18. of a cross-sectional study led by González-Jiménez et al showed
that subjects with abnormal HOMA-IR values had significantly
higher BMI, body fat content, and WC, and multivariate logistic
regression analysis showed the highest OR for BMI,[19] which
is
consistent with our study results. Results from a study of
Korean
high school students showed that HOMA-IR was significantly
associated with BMI and WC in both sexes. However, this was
true for BF% in male students only,[20] a fact that revealed the
more generalized applicability of BMI and WC in predicting IR.
In contrast, in a Hispanic and African American adolescent
population study, Wedin et al[21] found that instead of BMI,
WC
combined with BF% was the best predictor of IR. Sasaki et al[8]
Table 3
Binary logistic regression of obesity indices and IR.
BMI WC BF%
OR (95% CI) P OR (95% CI) P OR (95% CI) P
Model 1 1.32 (1.22–1.43) <.001 1.11 (1.08–1.15) <.001 1.09
(1.06–1.13) <.001
Model 2 1.33 (1.23–1.44) <.001 1.14 (1.10–1.17) <.001 1.18
(1.12–1.24) <.001
Model 3 1.31 (1.20–1.43) <.001 1.13 (1.08–1.17) <.001 1.17
(1.11–1.23) <.001
Model 1: OR unadjusted. Model 2: OR adjusted by age and sex.
Model 3: OR adjusted by age, sex,
current smoking, hypertension, diabetes mellitus, and
dyslipidemia. BF%=body fat percentage,
BMI=body mass index, CI= confidence interval, IR= insulin
resistance, OR= odds ratio, WC=waist
circumference.

19. Figure 5. ROC curves for WC, BMI, BF%, and selected
covariates as
predictors of IR. BF%=body fat percentage, BMI=body mass
index, IR=
insulin resistance, ROC= receiver operating characteristic
curve, WC=waist
circumference.
Cheng et al. Medicine (2017) 96:39 www.md-journal.com
also disclosed that in a Japanese male population with normal
BMIs, BF(%) was associated with increased IR, while WC was
not. Taken together, the results showed that predictions about
IR
may be influenced by ethnic background, age, and gender-
related
body composition. To the best of our knowledge, our study is
one
of the very few to study the correlation between 3 obesity
indices
and IR in Asian middle-aged and elderly adults.
To summarize, our study results revealed that obesity indices
like BMI and WC are better predictors of IR than BF%, that is,
excess body weight and body fat distribution are more important
than total body fat for predicting IR. In addition, Ganpule-Rao
et al[22] demonstrated that some complex measurements, such
as
magnetic resonance imaging, dual-energy X-ray absorptiometry,
and computed tomography contribute only a small amount to the
prediction of IR. Anthropometric measurements are better
predictors of IR than other advanced tools, which also highlight
the importance of these simple, traditional measures.
Our study had a few limitations. First, this was a cross-

20. sectional study; thus, the causal relationship between obesity
indices (like BMI, WC, and BF%) and IR could not be evaluated
and determined. Second, the number of participants in this study
was relatively small, and they were recruited from a single
community, so selection bias should be considered.
Table 4
The AUC, sensitivity, and specificity by the optimized cut-off
point
of different obesity indices in predicting IR.
AUC (95% CI) Sensitivity Specificity
Cut-off
point
BMI 0.749 (0.693–0.804) 0.608 0.791 26.15
WC 0.745 (0.689–0.801) 0.577 0.788 89.5
BF% 0.687 (0.630–0.745) 0.784 0.498 29.15
Selected covariates 0.745 (0.687–0.802) 0.804 0.599 0.19
Selected covariates: age, sex, current smoking status, DM,
hypertension, and dyslipidemia. AUC of
WC=0.74522, AUC of selected covariates=0.74487. AUC= area
under the ROC curve, BF%=
body fat percentage, BMI=body mass index, CI= confidence
interval, DM=diabetes mellitus, IR=
insulin resistance, ROC= receiver operating characteristic
curve, WC=waist circumference.
5
5. Conclusion
The results of this study demonstrate that obesity indices like
BMI
andWC are stronger surrogate markers than BF% for predicting

21. IR. Individuals with high BMI or WC require more aggressive
lifestyle modifications and primary prevention of diabetes,
cardiovascular disease, and metabolic disease. BMI and WC
are 2 obesity indices that are effective, inexpensive, and
noninvasive. They are also easily measurable, which can help
the primary care physician in primary prevention and earlier
intervention against diabetes and metabolic diseases among
middle-aged and elderly populations.
Acknowledgments
The authors thank Chang Gung Memorial Hospital
(CORPG3C0171-CORPG3C0172, CZRPG3C0053) for the
support.
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Body mass index and waist circumference are better predictors
of insulin resistance than total body fat percentage in middle-
aged and elderly TaiwaneseOutline placeholder1
Introduction2.4 Statistical analysis3 Results4
DiscussionAcknowledgmentsReferences
Description of in-class activity/written assignment
MDLS
These exercises will help you work through the process of
critically reading and analyzing scientific journal articles. For
each article, Answer the general questions listed below along
with any specific questions added by the instructor or members
of your group. For the last article, students will answer the
general questions and design additional questions specific to the
article. Keep in mind the focus of the exercise is interpretation

26. of statistical methods, not necessarily the research findings.
Also consider that being unclear about was published in a
journal article does not necessarily indicate a failure on your
part to understand. A good article should make concepts clear to
a reader who has some understanding of basic statistical
concepts. Include your personal impression in your critique.
The general questions are:
Using these questions as the basis for your presentation or
paper, I have filled in some information pertaining to the format
you should use.
Begin with a brief synopsis of the paper (one to two paragraphs,
IN YOUR OWN WORDS) that describes the work performed,
the reason for doing the study, the research question, and the
main finding(s).
Now answer the following questions. You may simply list them
and answer or format as you see fit, as long as everything is
addressed, if possible.
1. What are the authors affiliations and who funded the study?
This information may provide insight into the level of expertise
of the authors and the potential for bias.
2. What basic research question are the authors trying to
answer? Do the data come from one study or are they from
various sources (aggregated data). What makes that research
question significant? (Why does it matter?)
3. What data did the authors collect? Is missing data accounted
for? Is the data available for other researchers to evaluate?
4. What statistic tests were utilized? Was the methodology
clear? If correlation or regression was used, did the authors
include confidence intervals or make the dataset publicly
available?
This will be the most important part of your paper. Be sure to

27. describe the test and how it was used in this particular study.
Also note other tests that could have been used, if appropriate.
( be sure to describe the statically tests deeply)
5. What is the authors' interpretation of their data? Were the
interpretations clearly stated? In some articles, note if a p-value
was used and if you can tell how the p-value was derived (what
type of testing) and if confidence intervals were also reported.
Many journals require confidence intervals in addition to, or
instead of, p-values
6. Do you agree with the authors interpretation and use of a
particular test? Can you suggest a better method of interpreting,
analyzing or presenting data? Did the authors attempt to extend
their results to what is already known on the topic?
7. Do you think that the data they collected supports their
conclusions? Why or why not?
Finish with a brief wrap up of the findings and possible future
research.
Make sure all the content is IN YOUR OWN WORDS.
Plagiarism from the article itself or other sources will result in
a reduction of your score.
Grading rubrics:
For the final paper:
Possible Points
Earned Points
1. Was each general question answered?
10
2. Was each statistical method used in the article discussed?
15
3. Was a moderate level of understanding demonstrated?

28. 35
4. Was the proper terminology used and were terms not
commonly used by lay people defined?
15
5. Was the paper well-organized and clearly written by the
student? (not copied and pasted)
25