Download Phalangeal BMD measurement as a method for risk evaluation in

PhD-thesis
Teresa Friis-Holmberg
2013
Phalangeal BMD Measurement as
a Method for Risk Evaluation in
Fracture Prevention
Data from the Danish Health Examination Survey 2007–2008
[0]
Risk evaluation in fracture prevention
Phalangeal BMD Measurement as a Method for Risk Evaluation in Fracture Prevention
Data from the Danish Health Examination Survey 2007–2008.
PhD Thesis
Author: Teresa Friis-Holmberg
Copyright® 2013 by Teresa Friis-Holmberg, National Institute of Public Health, University of
Southern Denmark, Copenhagen
Publisher:
National Institute of Public Health
Faculty of Health Sciences
University of Southern Denmark
Øster Farimagsgade 5A
DK-1353 Copenhagen K
Denmark
ISBN: 978-87-7899-251-2
E-ISBN: 978-87-7899-252-9
[I]
Risk evaluation in fracture prevention
Academic advisers and assessment committee:
Academic advisors:
Professor, PhD Mickael Bech
COHERE, Department of Business and Economics
University of Southern Denmark
Odense, Denmark
Professor, PhD Kim Brixen
Institute of Clinical Research
University of Southern Denmark
Odense, Denmark
From December 2009–Marts 2011:
Adjunct Professor, PhD Tine Curtis
National Institute of Public Health
University of Southern Denmark
Copenhagen, Denmark
Assessment Committee:
Dr, PhD Emma Clark
Musculoskeletal Research Unit
University of Bristol
Bristol, United Kingdom
Professor, PhD Lars Bjerrum
Department of Public Health
University of Copenhagen
Copenhagen, Denmark
Clinical associate Professor, PhD Linda Kærlev
Research Unit of Clinical Epidemiology
Institute of Clinical Research
University of Southern Denmark
Odense, Denmark
Defence:
The defence will take place September 3rd 2013 at the National Institute of Public Health, University of
Southern Denmark, Copenhagen, Denmark
[II]
Risk evaluation in fracture prevention
The Thesis is based on the following papers:
Paper I
Teresa Holmberg, Mickael Bech, Tine Curtis, Knud Juel, Morten Grønbæk, Kim Brixen. Association between
passive smoking in adulthood and phalangeal bone mineral density; results from the KRAM study – the
Danish Health Examination Survey 2007–2008. Osteoporosis International 2011;22(12):2989-99
Paper II
Teresa Friis-Holmberg, Kim Brixen, Katrine Hass Rubin, Morten Grønbæk, Mickael Bech. Phalangeal bone
mineral density predicts incident fractures; a prospective cohort study on men and women. Results from the
Danish Health Examination Survey 2007–2008 (DANHES 2007–2008). Archives of Osteoporosis 2012; 7: 29199
Paper III
Teresa Friis-Holmberg, Katrine Hass Rubin, Kim Brixen, Janne Schurmann Tolstrup, Mickael Bech. Fracture risk
prediction using phalangeal bone mineral density or FRAX®? Which is the best method in a Danish cohort of
men and women. Journal of Clinical Densitometry: Assessment of Skeletal Health. Epub ahead of print (DOI
0.1016/j.jocd.2013.03.014)
Paper IV
Teresa Friis-Holmberg, Mickael Bech, Jeppe Gram, Anne Pernille Hermann, Katrine Hass Rubin, Kim Brixen.
Point-of-care phalangeal bone mineral density measurement can reduce the need of dual-energy X-ray
absorptiometry scanning in women at risk of osteoporotic fractures. Submitted.
[III]
Risk evaluation in fracture prevention
Abbreviations
Technical terms and abbreviations
BMC
Bone mineral content (g)
BMD
Bone mineral density (g/cm2)
BMI
Body mass index
DANHES
The Danish Health Examination Survey
DXA
Dual energy x-ray absorptiometry
FRAX
Fracture risk assessments tool (10–year probability of fracture)
HR
Hazard Ratio
LEF
Low energy fracture
RA
Radiographic absorptiometry
SD
Standard deviation
ROSE
The Risk-stratified Osteoporosis Strategy Evaluation study
T-score
The number of standard deviations above or below the mean for a healthy young adult of the
same sex
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Risk evaluation in fracture prevention
[V]
Risk evaluation in fracture prevention
Preface
Preface
The academic work presented in this PhD thesis was carried out between December 2009 and April 2013 at
the research Program for Health Promotion and Prevention, National Institute of Public Health, University of
Southern Denmark in close collaboration with Institute of Clinical Research, University of Southern Denmark.
The thesis was supported by grants from the Region of Southern Denmark and University of Southern
Denmark. Moreover, BMD scans of women in the ROSE-study were supported by InterReg.
First I would like to express my sincere gratitude to my academic supervisors Mickael Bech and Kim Brixen.
Thank you for your wise and constructive comments, for your encouragements and your confidence in me,
and for sharing your huge scientific insight and experience with me. Working with you both has been a
pleasure and a privilege, and you were definitely the perfect supervisor-team. Moreover, a special thank
should also be giving to Tine Curtis, who started it all. You hired me to be a part of DANHES, and saw the
potential in me for this PhD-project. I am looking forward to collaborating with you again.
I would also like to thank all my co-authors for their valuable and wise contributions to the paper included in
this thesis. Special thanks to Morten Grønbæk and Janne Schurmann Tolstrup for discussing epidemiological
and statistical issues with me, to Knud Juel for passing on knowledge related to passive smoking, and to
Katrine Hass Rubin for helping me with FRAX-data and always taking the time to discuss all kind of issues with
me—I sincerely hope that our teamwork will precede in the future. Also I would like to thank Anne Pernille
Hermann, Jeppe Gram and the rest of the ROSE-group for inspiring meetings and discussions, and for
initiating me in the exciting world of osteoporosis and fracture prevention.
Further, I wish to thank the staff at the Osteoporosis Clinics, Hospital of Southwest Jutland and Odense
University Hospital for managing the RA scans of the ROSE-women, especially to Marianne Bøtcher and
Anette Riis Madsen for your help and excellent assistance. Also thank to Claire Gudex for brilliant language
support and comments to the thesis.
My gratitude also goes to every participant in DANHES and the ROSE-study, and to Ola Ekholm at the
National Institute of Public Health for helping me with any matter related to the DANHES dataset as well as
for obtaining register data. Further I would like to thank all my great colleagues at the National institute of
Public Health. I could not have asked for better colleagues, it has been a privilege to be part of such an
inspiring and motivating scientific and social environment.
Finally, my most sincere gratitude goes to my family and friends for their newer ending support, especially to
Tom for bearing with me, for encouraging me and always being there for me. I could never have done this
without you.
Teresa Friis-Holmberg
Copenhagen, Marts 2013
[VI]
Risk evaluation in fracture prevention
[VII]
Risk evaluation in fracture prevention
Table of contents
Table of contents
1. Introduction ................................................................................................................................................... 1
1.1 Osteoporotic fractures, a public health issue .......................................................................................... 1
1.2 Risk factors for osteoporosis and osteoporotic fractures ........................................................................ 2
1.3 Methods for identifying high risk persons ............................................................................................... 5
1.4 Fracture prevention ................................................................................................................................. 8
1.5 Current guidelines and recommendations............................................................................................... 8
1.6 Challenges in fracture prevention .......................................................................................................... 10
2. Aim of the thesis .......................................................................................................................................... 11
3. Methods and data sources........................................................................................................................... 12
3.1 The Danish Health Examination Survey 2007–2008 (DANHES) .............................................................. 13
3.1.1 Phalangeal BMD .............................................................................................................................. 15
3.2.2 Passive smoking in adulthood home ............................................................................................... 15
3.2.3 Clinical risk fractures for low BMD and fractures ............................................................................ 16
3.3 Register data and ascertainment of osteoporotic fractures .................................................................. 17
3.4 Calculation of 10–year probability of fracture by FRAX ......................................................................... 18
3.5 Danish Risk-stratified Osteoporosis Strategy Evaluation study (ROSE) .................................................. 19
3.5.1 Dual-energy X-ray absorptiometry .................................................................................................. 20
3.6 Statistical methods................................................................................................................................. 20
4. Findings ........................................................................................................................................................ 23
4.1 Association between phalangeal BMD and passive smoking in adulthood home.................................. 23
4.2 Fracture risk prediction using phalangeal BMD ..................................................................................... 24
4.3 Phalangeal BMD versus the WHO fracture risk assessment tool (FRAX) and age alone in predicting
osteoporotic fractures ................................................................................................................................. 29
4.4 Correlation between phalangeal BMD and central BMD and application of a triage approach in preselection for DXA ......................................................................................................................................... 30
5. Discussion .................................................................................................................................................... 32
5.1 Passive smoking as a risk factor ............................................................................................................. 32
5.2 Performance of phalangeal BMD measured by RA ............................................................................ 33
5.3 Methodological considerations .............................................................................................................. 37
6. Conclusion and perspectives ........................................................................................................................ 43
6.1 Perspectives ........................................................................................................................................... 43
7. Summary ...................................................................................................................................................... 47
8. Resumé ........................................................................................................................................................ 49
9. References ................................................................................................................................................... 51
10. Papers I–IV ................................................................................................................................................. 63
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[ix]
Risk evaluation in fracture prevention
Introduction
1. Introduction
1.1 Osteoporotic fractures, a public health issue
Osteoporosis is a skeletal disorder characterised by decreased bone strength that results in increased risk of
fractures (1;2), and osteoporotic fractures are the most important clinical complication of osteoporosis.
Osteoporosis is especially prevalent in postmenopausal women and, however, it has historically been
considered as a women’s disease, it is increasingly recognized as a prevalent condition among men as well
(3;4). Thus, about 46% of women and 26% of men will experience at least one osteoporotic fracture after the
age of 50 years (5). Approximately 11,000 hip fractures (6), 7,000 forearm fractures and 2,000 clinical
vertebral fractures occur every year in Denmark (7). It is has been shown that around 85% of all hip fracture
cases also have osteoporosis, which increases the risk of new fractures (8). Although the incidence of hip
fracture is decreasing in Denmark (9), in 2012 Denmark had the highest incidence of hip fracture in an
international comparison (574 per 100,000 person-years in women and 290 per 100,000 person-years in
men) (10). In Western populations until around 1980 it was a general trend that the incidence rates
increased and thereafter the incidence rates stabilised or decreased. A recent review, however, concluded
that in Asia incidence rates continues to increase (11).
Societal costs attributable to osteoporotic fractures are high; this is especially due to the high cost per hip
fracture patient. It is for example the one condition that is associated with the largest number of bed days in
hospitals (12) and the total costs for treatment, care, and rehabilitation per hip fracture case is estimated to
96.000 DKR (approx.16,446 USD) in the first years and 280.000 DKR (approx.47,969 USD) in the first ten
years after a hip fracture (8). Osteoporotic fractures can also have consequences for the individual in the
form of chronic pain, reduced activities of daily living (13) and increased mortality (14-16). The excessive
mortality persists for up to 10 years after hip fracture (15), but also after osteoporotic fractures in general
(14). Health-related quality of life is also reduced after hip and vertebral fractures (17;18), to a level similar
to or worse than that observed in women with other chronic diseases such as diabetes, arthritis and lung
disease (17). Moreover, it have been calculated that the loss of disability-adjusted life years (DALY’s) due to
osteoporotic fractures in Europe is higher than that related to most cancer types, with exception of lung
cancer (19).
Thus, it is evident that osteoporosis through its association with osteoporotic fractures represents a major
public health problem. In order to target public health measures regarding fracture prevention, it is
important to identify individuals with increased risk of osteoporotic fracture.
[1]
Risk evaluation in fracture prevention
Introduction
1.2 Risk factors for osteoporosis and osteoporotic fractures
In most cases, the aetiology of osteoporosis (defined by low bone mineral density, BMD) is multifactorial.
Risk factors for osteoporosis include both non-modifiable, e.g. female gender, and modifiable factors, e.g.
smoking (20-23). Osteoporosis in turn is a significant risk factor for fracture (24;25). Thus, risk factors for low
BMD are also risk factors for fracture. Other risk factors, such as a tendency to falls, however, are related to
fractures but not BMD (26;27). Furthermore, some risk factors, like smoking (28) and prior fracture (29),
affect BMD but in addition have an independent effect on fracture risk. Some of the important risk factors
for osteoporotic fractures and the estimated prevalence in Denmark are seen in Table 1.
Table 1: Risk factors for osteoporotic fractures and estimated prevalence in the Danish population (%)*
Risk factor
Prevalence
(Used definition to estimate prevalence)
Age (5;30)
Female sex (5;21)
Prior fracture (32;33)
(Age ≥ 60 years) (31)
(31)
(Low energy fracture after age 40 years) (34)
(Low energy fracture after age 50 years) (35)
a
Tendency to falls/ history of frequent falls (26;36;37)
(≥ 1 per year)
a
Inheritance / genetics (38-40)
(Hip fracture in first degree relatives)
Low body mass index (41;42)
(BMI <18.5) (43)
a
Premature menopause (44;45)
(<45 years)
Smoking (28;46;47)
(Current daily smoker) (43)
Excessive alcohol intake (48;49)
(>2 units a day) (43)
(>3 units a day) (43)
Physical inactivity (50;51)
(Sedentary leisure time) (43)
(Sedentary work life) (43)
*Based on most reliable numbers available from Statistic Denmark or Danish health surveys
a
Based on unpublished results from the Danish Health Examination Survey 2007–2008
Women
Men
25.5
50.4
11.5
22.4
17.3
8.4
3.8
15.6
19.3
8.0
3.0
16.3
45.5
9.8
11.2
6.8
1.4
22.7
24.5
13.2
15.4
45.4
A large number of medical disorders and medications are also associated with osteoporosis1 and increased
fracture risk. Some of these are endocrine disorders such as type 1-diabetes and thyroid disorders or gastrointestinal diseases such as chronic liver disease, inflammatory bowel disease and coeliac disease (1;29).
Glucocorticoid use is the most common form of drug-related osteoporosis (52;53), but a Danish study
mapping the prescriptiome to fractures in men revealed a large array of other medications also to be
associated with fracture risk (54).
Other risk factors have been suggested. One example is a potential detrimental effect of passive smoking (or
second hand smoke) on BMD and fracture risk. Two animal studies have demonstrated that passive smoking
significantly decreases BMD in rats after only two and four months of exposure, respectively (55;56). In the
few human studies investigating this association (Table 2), most found a negative association between
1
Referred to as ‘secondary osteoporosis’, i.e. resulting from medications or other conditions.
[2]
Risk evaluation in fracture prevention
Introduction
passive smoking and BMD, bone mineral content (BMC) or self-reported non-spine fractures. Most of these
studies were relatively small, however, and two are only available as preliminary reports (57;58), making it
difficult to compare the methodology and resulting data. Very different approaches to assess exposure to
passive smoking appear to have been used and there is inconsistency between studies regarding the power
of statistical methods. The largest study (n=14,060) using data from the Third National Health and Nutrition
Examination Survey (NHANES-III) investigated the association between serum cotinine (as a marker for
tobacco exposure) and BMC (59). Serum cotinine concentration reflects both passive and active smoking and
as the regression analysis was not stratified or adjusted for smoking status, the results do not show the real
effect of passive smoking. Further epidemiological studies investigating this potential association with more
observations and allowing for confounder control are necessary, which will be one of the focuses in present
thesis.
[3]
Risk evaluation in fracture prevention
Introduction
Table 2: Previous studies of exposure to passive smoking on bone and fracture risk
Author
Year
KIM KH et al.
a
2012 (60)
Country
Korea
Study
design
Crosssectional
Population
(N)
KHANES IV
Postmenopausal
women
55+ years
(925)
Assessment of
Passive Smoking
Self-reported
exposure among
never-smokers:
duration per day
Cigarettes
smoked by family
members per
day.
Outcome
BMD
Femoral neck
Lumbar spine
By DXA
Confounder
control
Age
Height, weight
Alcohol intake
Physical activity
Dietary calcium
intake
Urinary cotinine
concentration
Altunbayrak
O et al.
b
2009 (57)
Turkey
Crosssectional
Postmenopausal
women
40–65 years
Hsu YH et al.
b
2006 (58)
China
Crosssectional
Men and women
25–64 years
(13,376)
Self-reported
exposure to
smoking:
active smoker,
passive smoker,
or neither
Self-reported
exposure:
number of
family members
smoking daily
BMD
Femoral neck
Lumbar spine
By DXA
Not stated
BMD
Hip
By DXA
Age
Height, weight
Occupation
Physical activity
Education
Self-reported
non-spine
fractures
Benson BW,
Shulman JD
2005 (59)
USA
Crosssectional
NHANES III
Men and women
20+ years
(14,060)
Serum cotinine,
as a marker for
tobacco exposure
BMC
Femur
By DXA
Afgahni A
et al.
2003 (61)
Cypress
Crosssectional
Boys and girls
12–years
(466)
Self-reported
exposure: days or
hours around
smokers (in same
room or vehicle)
in last 7 days
BMD & BMC
Forearm
Heel
By DXA
Blum M et al.
2002 (62)
USA
Crosssectional
Premenopausal
women
40–45 years
(151)
Self-reported
history of
exposure to
household
tobacco smoke at
any age (10 years
to present)
BMD
Hip
Lumbar spine
Total body
By DXA
a
b
Published after the publication of paper I in this thesis
Only published as abstracts
[4]
Age
Height, weight
Bone area
Place of birth
Oestrogen
Ethnicity
Activity level
Diabetes
Age
Height
Lean body mass
Fat mass
Sports team
participation
Active smoking
Menarche (girls)
Height, weight,
Pack-years
Education
Daily calcium
intake
Findings
OR=2.26 for
femoral neck
osteoporosis
when exposed at
home
OR=2.02/2.74 for
spine and femoral
neck osteoporosis
If >0 smoked
cigarettes/day by
cohabitants
Association
between urinary
cotinine an selfreported passive
smoking
Lumbar spine and
femoral neck Tscores lower
among active and
passive smokers
Higher OR for
osteoporosis and
non-spine
fracture in men/
premenopausal
women with
exposure to
passive smoking
Inverse
relationship
between serum
cotinine and BMC
(Men: β=-0.62,
Women: β=-0.04)
No association
Subjects exposed
to passive
smoking had
lower mean
adjusted BMD at
total hip and
lumbar spine (0.05 difference)
Risk evaluation in fracture prevention
Introduction
1.3 Methods for identifying high risk persons
Dual-energy X-ray absorptiometry (DXA) is widely used to measure BMD at the hip and lumbar spine and is
considered the “gold standard” for identifying persons at high risk for osteoporotic fracture, diagnosis and to
monitor treatment (25). Since 1994 the diagnosis of osteoporosis has primarily been based on central DXA
(at the hip or spine), by comparing the person’s measured BMD with the mean BMD in healthy young adults
of the same sex and ethnicity (1). Other scanners that measure BMD in the peripheral skeleton are also
available (63) (Table 3).
Table 3: Examples of peripheral BMD systems
System
Single-energy x-ray absorptiometry (SXA)
Computed tomography (CT)
Quantitative ultrasound (QUS)
Digital x-ray radiogrammetry (DXR)
Photodensitometry (PD)
Radiographic absorptiometry (RA)
Measurement site
Forearm
Forearm
Calcaneus (heel)
Phalanges of the hand and metacarpals
Phalanges of the hand and metacarpals
Phalanges of the hand and metacarpals
The RA technique is more than 50 years old (64). Advantages of using the phalanges to measure BMD are the
small amount of surrounding tissue and the easy access. Also, these tissues are relatively insensitive to
ionising radiation. Earlier versions of RA scanners required analysis of the hand radiographs at a central
reading facility and hence a slower response process (65). This is no longer necessary with the newer
versions (66), such as the Alara MetriScan bone densitometer used in the studies presented in this thesis.
This scanner is portable, easy to use, fast and exposes the patients to only low radiation doses (67).
The validity and performance of peripheral BMD systems may be evaluated according to their ability to 1)
predict osteoporotic fractures, 2) identify osteoporosis (as defined by low BMD at hip or lumbar spine), and
3) monitor treatment. Several studies have investigated the association between central and peripheral BMD
and the risk of fracture. Both prospective studies (68-73), case-control studies (74-76) and a meta-analysis
(25) have found that low BMD in finger, hand and forearm is related to increased risk of fracture. Siris et al.
(2001) reported that low peripheral BMD (T-scores ≤-2.5) in postmenopausal women was associated with a
fracture rate around four times higher than normal BMD after 1-year of follow-up (77). None of these
studies used the MetriScan system, however, nor tested the relationship in a population-based cohort that
included men.
Table 4 shows studies that have investigated the correlation between phalangeal BMD as measured with RA
(MetriScan) and hip/lumbar spine BMD as measured with DXA, and assessed the ability of the scanner to
identify osteoporosis.
[5]
Risk evaluation in fracture prevention
Introduction
Table 4: Studies that have evaluated phalangeal densitometry (MetriScan) compared with DXA
Author
year
Dhainaut
et al.
2011 (78)
Country
Population
(N)
Women
LEF* patients
& recruited
from general
population
50–96 years
(355)
Assessment
phalangeal BMD
Arbitrary units
(AU)
Non-dominant
hand
T-score based on
manufacturer’s
database
Outcome
DXA
BMD
Lumbar spine (L2–L4)
Femoral neck
Total hip
T-score based on
manufacturer’s
database
Osteoporosis T-score
≤-2.5 femoral neck
Analyses
Findings
Correlation
coefficient
AUC
(osteoporosis)
Triage thresholds
(90% sensitivity/
90% specificity)
Denmark
Men and
women
LEF* Patients
(74)
AU
Non-dominant
hand
T-score based on
manufacturer’s
database
BMD
Lumbar spine (L2–L4)
Femoral neck
T-score based on
manufacturer’s
database
Osteoporosis T-score
≤-2.5 hip or spine
Correlation
coefficient
AUC
(osteoporosis)
Sensitivity 100%:
number of DXA
scans avoided
Hansen et
al. 2009
(80)
Denmark
Men
Randomly
selected
60–74 years
(218)
AU
Non-dominant
hand
Correlation
coefficient
AUC
(osteoporosis)
Sensitivity/
specificity
Thorpe &
Steel
2008 (66)
/ Blake et
al. 2005
(81)
UK
Women
Attending
routine DXA
55–70 years
(170)
AU
Non-dominant &
dominant hand
T-score based on
manufacturer’s
database
BMD
Lumbar spine (L2–L4)
Total Hip
T-score based on local
reference database
Osteoporosis T-score
≤-2.5 hip or spine
BMD
Lumbar spine (L2–L4)
Femoral neck
T-score based on
manufacturer’s
database for lumbar
spine and NHANES for
total hip
Osteoporosis T-score
≤-2.5 hip or spine
Boonen
et al.
2003 (82)
Belgium
Women
Referred to
Centre for
metabolic
bone diseases
50–75 years
(221)
AU
Non-dominant
hand
T-score based on
manufacturer’s
database
BMD
Lumbar spine (L2–L4)
Femoral neck
T-score based on
manufacturer’s
database
Osteoporosis T-score
≤-2.5 hip or spine
Correlation
coefficient
AUC
(osteoporosis)
Sensitivity/
specificity
28.5% osteoporotic
R=0.65 femoral neck
R=0.65 total hip
R=0.59lumbar spine
AUC=0.84 (all)
AUC=0.83 (general
population)
AUC=0.83 (LEF*
patients)
Cut-off thresholds: <1.5 and <-2.9
34% referred for DXA
39% osteoporotic
R=0.62 femoral neck
R=0.68 lumbar spine
AUC=0.85 (women)
Cut-off:
sensitivity = 100%
Specificity = 30%
19% avoided DXA
scans
7% osteoporotic
R=0.47 total hip
R=0.46 lumbar spine
AUC=0.75
Ex.62 AU cut-off:
sensitivity= 93%
Specificity = 50%
41% osteoporotic
Non-dominant hand:
R=0.54 total hip
R=0.31 lumbar spine
Dominant hand:
R=0.56 total hip
R=0.56 lumbar spine
Cut-off thresholds:
Non-dominant hand
<-0.62 and <-2.4
48% referred for DXA
19% osteoporotic
R total hip not stated
R=0.66 lumbar spine
AUC=0.80
Ex.52 AU cut-off (Tscore <-1.3):
sensitivity= 83%
Specificity = 66%
Buch et
al. 2010
(79)
Norway
Correlation
coefficient
Triage thresholds
(90% sensitivity/
90% specificity)
* Low energy fracture (LEF)
Increasing attention is being paid to the use of risk assessment algorithms (tools) to identify persons at high
risk for osteoporotic fractures. In some studies clinical risk factors have been shown to have similar
discriminatory power as BMD and to enhance prediction of facture risk (83). Several risk assessment tools
have been developed to combine risk factors into a single estimate of fracture risk for an individual. In a
[6]
Risk evaluation in fracture prevention
Introduction
recent systematic review we identified a total of 48 tools (84). The best validated tools and the included risk
factors are shown in Table 5 (seven tools were developed to identify individuals at risk of low BMD (ABONE,
BWC, ORAI, ORISIS, OST, NOF, SCORE) and four to identify individuals with an increased risk of fractures
(FRAX, FRISC GARVAN, Qfracture, SOF)) (84).
Table 5: Risk assessment tools and included clinical risk factors (modified from Rubin KH et al. (84))
Risk factors
Age
Weight
Height
Sex
Ethnicity
BMD
Previous low energy fracture
Parental (hip) fracture, family
history of fracture/osteoporosis
FRAX FRISC GARVAN Qfracture SOF ABONE BWC ORAI OSIRIS OST(A) NOF SCORE
(85) (86)
(87)
(88)
(29) (89)
(90) (91)
(92) (93;94) (95) (96)
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Falls
X
X
X
Smoking
X
X
X
Alcohol
X
X
Menopausal
X
Secondary osteoporosis (as
X
X
X
X
defined in FRAX)*
Rheumatoid arthritis
X
X
Type 2 diabetes
X
Asthma
X
Cardiovascular disease
X
Dementia
X
Glucocorticoid therapy
X
X
Oestrogen therapy
X
X
X
X
Tricyclic antidepressants
X
Benzodiazepine use
X
Anticonvulsant drug use
X
Caffeine
X
Self-reported health
X
Pulse >80 beats/min
X
Physical inactivity
X
Back pain
X
Use arms to stand up from a
X
chair
*These include type I (insulin dependent) diabetes, osteogenesis imperfecta in adults, untreated long-standing hyperthyroidism,
hypogonadism or premature menopause (<45 years), chronic malnutrition, or malabsorption and chronic liver disease
X
X
As seen in table 5, the number of risk factors included in the different tools varies, with BWC being the
simplest tool (includes only weight) and Qfracture the most complex. Although the tools have not yet found
broad acceptance, the WHO Fracture Risk Assessment Tool (FRAX®), which predicts the 10-year probability of
hip and major osteoporotic fractures (85;97), is increasingly used and has been incorporated into guidelines
(98;99). Thus in the UK, FRAX is used for selecting patients to offer treatment (100).
[7]
Risk evaluation in fracture prevention
Introduction
Some researchers have proposed combining the result of a peripheral densitometer and risk assessment tool
in order to select persons to be referred for DXA or identify patients with high risk of osteoporotic fractures
(101-105).
1.4 Fracture prevention
It is a general assumption that efforts to prevent osteoporotic fractures should focus both on achieving a
high peak bone mass, maintaining or increasing bone mass and preventing falls. Prevention could target
modifiable risk factors e.g. low body mass index (BMI), falls, smoking and excessive alcohol intake (previously
mentioned in section 1.2). Studies have shown, for example, that smoking cessation increases BMD (47;106),
and that different types of exercise can improve BMD, and reduce falls and fractures (107-109). Several
drugs can reduce the fracture risk. Randomized, placebo-controlled studies have thus demonstrated that
treatment with vitamin D and calcium (110-113), oestrogen (114), bisphosphonates (115), raloxifene (116),
strontium ranelate (117), parathyroid hormone (118;119) and denosumab (120) reduce the incidence of
fractures by 15–80% depending on the specific drug, skeletal site and study population. Most patients
(approximately 90%) with osteoporosis can be treated with oral bisphosphonates (such as alendronate and
risedronate) that are now available as generics.
1.5 Current guidelines and recommendations
In Denmark (as in other countries), a case-finding strategy has been adopted, where general practitioners
(GPs) are advised to refer persons with one or more risk factors to DXA scan. According the national Danish
guidelines, reimbursement of anti-osteoporotic medication are determined by DXA results unless hip or
spine fracture is present (121;122). In Denmark, drugs are reimbursed in case of 1) X-ray verified hip or spine
fracture; 2) very low BMD (T-score <-4); 3) one or more risk factors for fracture and low BMD (T-score ≤-2.5);
and 4) planned or initiated supra-physiological doses of glucocorticoid (at least 5 mg per day for three or
more months) and osteopenia (T-score <-1) (121;122).
Recommendations issued by international medical societies and authorities differ somewhat. The U.S.
Preventive Services Task Force (USPSTF) recommends screening with DXA in all women aged 65+ years and
in women <65 years with increased risk of fracture (whose 10–year fracture risk is equal to or greater than
that of a 65–year–old white woman without additional risk factors; 9.3% based on FRAX calculation);
diagnosis and treatment is determined from the DXA result (99). These US recommendations conclude that
evidence is lacking about the optimal interval for repeated screening, and that “Men most likely to benefit
[8]
Risk evaluation in fracture prevention
Introduction
from screening have a 10-year risk for osteoporotic fracture equal to or greater than that of a 65-year-old
white woman without risk factors. However, current evidence is insufficient to assess the balance of benefits
and harms of screening for osteoporosis in men” (99).
The US National Osteoporosis Foundation (NOF) recommends DXA testing in women aged 65+ years and in
men aged 70+ years as well as in postmenopausal women and men aged 50–69 years with high risk factor
profile. Moreover, in the newly updated 2013 version, they recommend vertebral imaging in various risk
groups2 to diagnose vertebral fractures (98). Treatment should be initiated in those with 1) hip or vertebral
(clinical or asymptomatic) fractures, 2) low BMD at femoral neck, total hip and lumbar spine (T-score ≤ -2.5),
and 3) in postmenopausal women and men aged 50+ years with T-score between -1.0 and -2.5 at the
femoral neck, total hip or lumbar spine by DXA and a 10-year hip fracture probability ≥3% or a FRAX 10-year
major osteoporotic fracture probability ≥20% (98).
The International Osteoporosis Foundation (IOF) and European Society for Clinical and Economic Aspect of
Osteoporosis and Osteoarthritis (ESCEO) recently updated their European guidance for the diagnosis and
management of osteoporosis in postmenopausal women. It holds three assessment scenarios that depend
on the access to DXA, and based on these specific guidelines can be developed. For example in countries
with limited access to DXA it is recommended that women with fragility fracture should be considered for
treatment. Women without fragility fracture, but with one or more clinical risk factors, the 10–year fracture
probability by FRAX should be used to define intervention thresholds. These are age-specific recommending
either no treatment, measure BMD and consider treatment (100).
Peripheral BMD systems are generally not recommended for diagnosis of osteoporosis (2;98;121). NOF
acknowledges that portable scanners can be used for community-based screening programmes, but “the
results are not equivalent to DXA and abnormal results should therefore be confirmed by physical
examination, risk assessment and central DXA” (98). The UK National Osteoporosis Society (NOS) has
proposed the use of peripheral densitometers in the management of osteoporosis. In this approach,
thresholds are set at 90% sensitivity and 90% specificity for identifying osteoporosis (T-score ≤-2.5) at either
hip or spine (123). If the BMD at a peripheral site is below the lower threshold, treatment is recommended;
if the BMD is between the thresholds, the person should be referred to DXA of hip and spine; and if the BMD
is above the higher threshold, no further action is required (123).
2
1) In women aged 65+ years and men aged 70+ years to diagnose vertebral fracture if T-score is ≤-1.5, 2) In women aged 70+ years
and men aged 80+ years regardless of T-score, 3) In postmenopausal women and men aged 50+ years with a low energy fracture, 4)
In postmenopausal women and men aged 50–69 if there is height loss of 4 cm (1.5 inches) or more, or recent or ongoing long-term
glucocorticoid treatment.
[9]
Risk evaluation in fracture prevention
Introduction
1.6 Challenges in fracture prevention
The identification of individuals with increased risk of osteoporotic fracture is an important challenge in the
field of osteoporosis. Evidence suggests that the current strategy does not perform well. Indeed,
osteoporosis is under-diagnosed and under-treated in Denmark and probably also elsewhere (7). In
Denmark, it have been estimated that it is only around 25% of all persons with a high risk of osteoporotic
fractures (defined as tree or more risk factors) that are referred to DXA examination, and controversy, a
relatively high proportion of examinations are performed in persons with a low risk of osteoporotic fractures
(34). This is supported by similar studies from other countries (124;125). Another challenge is that central
DXA is inaccessible in many countries and regions, and furthermore, it has been found, that longer distances
to DXA-facilities seem to be associated with lower use of DXA (34;35;126). Overall, there is a need of devising
new strategies for identifying persons with high risk of fracture.
[10]
Risk evaluation in fracture prevention
Aim
2. Aim of the thesis
The overall aim of this thesis was to investigate the concept of fracture risk prediction and approaches for
measuring fracture risk. The primary objective was to evaluate a method for measuring BMD at the
phalanges for its ability to predict fracture risk and pre-select individuals at high risk for osteoporotic
fracture. Moreover, it was to investigate passive smoking as a possible new risk factor for osteoporosis that
should be considered when evaluating fracture risk in men and women.
The thesis is based on four studies with different aims:
Paper I
To examine a potential association between phalangeal BMD and passive smoking in the
adulthood home
Paper II
To examine the ability of phalangeal BMD to predict osteoporotic fractures in men and
women
Paper III
To examine the use of phalangeal BMD versus the WHO Fracture Risk Assessment tool (FRAX)
and age alone (and FRAX and BMD in combination in different risk strata) to predict
osteoporotic fractures in men and women
Paper IV
To investigate the correspondence between phalangeal BMD and central DXA BMD and to
define triage thresholds to be used in pre-selection based on NOS recommendations in a
cohort of Danish Women aged 65+ years
[11]
Risk evaluation in fracture prevention
Methods and data sources
3. Methods and data sources
The thesis is primarily based on data from the Danish Health Examination Survey (DANHES) (Papers I, II and
III) and from the Danish national registers (Papers II and III). Paper IV is based on data from the Danish Riskstratified Osteoporosis Strategy Evaluation study (ROSE). An overview of the aims, methods and data used is
presented in Table 6. Further details are provided in the following sections.
Table 6. Overview of aims, methods and data used in the four papers of this thesis
Paper I
To investigate an
association between
phalangeal BMD and
passive smoking
Paper II
To investigate the
ability of phalangeal
BMD to predict
osteoporotic
fractures
Design
Duration of
follow-up
Data source
Cross-sectional
NA
Participants
Men and women
Aged 18+ years
Residing in 12 Danish
municipalities
N= 15,038
Prospective
Mean 3.2 years
[0.03 to 3.82]
DANHES
Danish national
Registers
Men and women
Aged 18+years
Residing in 12 Danish
municipalities
N=15,542
Exposure/
Independent
variable(s)
Passive smoking in
private residence
during adulthood
Phalangeal BMD
Outcome
Phalangeal BMD
Incident fractures
Confounders/
other variables
Sex
Age
Height and weight
Smoking
Physical activity
Education
Alcohol consumption
Body fat percentage
Parental hip fracture
Multiple regression
Sex
Age
Prevalent fractures
Sex
History of osteoporosis
Survival analysis
Survival Analysis
C´statistics
Aim
Statistical
methods
DANHES
[12]
Paper III
To investigate the
ability of phalangeal
BMD, FRAX (calculated
10–year fracture
probability) and age as
well as FRAX and BMD
in combination in
different risk strata to
predict osteoporotic
fractures
Prospective
Mean 4.3 years
[0.03 to 4.94]
DANHES
Danish national
registers
Men and women
Aged 40–90 years
Residing in 12 Danish
municipalities
N=12,758
Phalangeal BMD
10–Year fracture
probability (FRAX)
Age
Incident fractures
Paper IV
To investigate the
correspondence
between phalangeal
BMD measured with
RA and BMD at
lumbar spine and
total hip measured
with DXA
Cross-sectional
NA
ROSE
Women
Aged 65–80 years
Residing in the
Region of Southern
Denmark
N=121
Phalangeal BMD
Lumbar spine and
total hip BMD
Correlation
Bland Altman
Sensitivity, specificity
Risk evaluation in fracture prevention
Methods and data sources
3.1 The Danish Health Examination Survey 2007–2008 (DANHES)
The Danish Health Examination Survey 2007–2008 was the largest combined study on health in Denmark
(127;128). Its aim was to improve knowledge on health and lifestyle in the Danish population, to increase the
general focus on health and lifestyle (diet, smoking, alcohol and exercise) and to establish a research
database for future studies (127;128). All 98 Danish municipalities were invited to apply for participation in
the study and 13 were selected based on their application. During the month when the health examination
survey took place the concerning municipality also had to initiate a range of activities and initiatives for their
citizens aiming at improving health and promoting healthy lifestyles
The health examination survey was conducted by the National Institute of Public Health, University of
Southern Denmark, with input from other research groups, and funded by the Danish Ministry of Health and
the Tryg Foundation. All adult citizens aged 18+ years in the 13 municipalities were invited by letter to
complete a self-report (internet-based) questionnaire comprising 144 items on the following topics: sociodemographics; anthropometry; diet; alcohol consumption; smoking history; physical activity; health
promotion and prevention; quality of life; health, morbidity, symptoms, medication and contact with health
services; pregnancy, childbirths and menopause, and absence from work. People could also answer a
supplementary food frequency questionnaire. If necessary, the person could request a paper version of the
questionnaire. Of the 538,497 people invited to complete the questionnaire, 76,484 responded (Figure 1),
corresponding to an overall response rate of 14%. The response rate ranged between 8% and 20%
depending on municipality.
A representative subsample was invited to participate in a health examination that included measurement of
blood pressure, resting heart rate, height, weight, fat percentage, waist and hip circumference, blood and
hair samples, spirometry, phalangeal BMD, physical performance, muscle strength and aerobic fitness (wattmax or sub watt-max). The invitation included a leaflet with general information about the health
examination. The numbers invited to participate to cover all appointments were based on experience in the
pilot study in Aalborg municipality and on estimates of the geographic and socio-demographic circumstances
in the municipality. Of the 180,103 persons invited to the health examination, 18,065 participated giving a
participation rate of 10.0% (range 7.9–20.3%) (Figure 1). The health examination began with oral information
about the examination and then a screening interview to check for any medical condition that would exclude
the person from the physical tests. Written informed consent was obtained. The examinations were
performed by trained staff from the National Institute of Public Health and approved by the Danish Data
Protection agency (J.nr. 2007-54-0017).
[13]
Risk evaluation in fracture prevention
Methods and data sources
Figure 1. Flow chart showing inclusion of participants in DANHES 2007–2008
Participants invited to
answer questionnaire
All residents in 13
municipalities without
research protection
Representative sample
invited to participate in
health examination
N= 180,103
N=538,497
Drop-out
N=162.038 (90%)
Drop-out
N= 462,013 (85.8%)
Participants participating
in health examination
N= 18,065 (10.0%)
Excluded, no BMDscan*, N=2,521
Participants answered
the questionnaire
N= 76,484 (14.2%)
Participants with BMD
scan
N= 15,544
Participants both answered
the questionnaire and had
a BMD scan
N=15,038
* BMD scan was not a part of the pilot study in Aalborg municipality
[14]
Risk evaluation in fracture prevention
Methods and data sources
3.1.1 Phalangeal BMD
Phalangeal BMD was measured at the middle phalanges of the 2nd, 3rd and 4th fingers on the non-dominant
hand using a compact radiographic absorptiometry (RA) system (Alara MetriScan®, Alara Inc. Fremont, USA).
X-ray exposure was <0.02 µSV per examination. All staffs were trained and capable of operating the scanner
and the results appeared on the integrated screen after about one minute. Pregnant women were excluded.
Participants were asked to remove any jewellery from the non-dominant hand (otherwise the scan was
performed on the dominant hand) and to place the hand on the moulded support plate. BMD was expressed
in arbitrary units (mineral mass/area), g/cm2 and T-scores. The T-score compares measured BMD with the
average BMD for a young healthy subject of the same sex (
) and was derived
from a reference database provided by the manufacturer. This database contained data on 1,500 healthy
females aged 20–85 years; the T-scores were calculated from the group aged 20–39 years. We then used the
WHO T-score cut-offs for osteoporosis and osteopenia (>-1 = normal, between -1 and 2.5 = osteopenia, ≤-2.5
= and osteoporosis) (1). After the health examination, participants were informed on their result and
patients with T-scores below -2.5 and with one or more risk factor were advised to consult their GP. For
papers II and III, we calculated gender-specific T-scores using our own database, including men and women
from DANHES aged 20–39 years as reference.
3.2.2 Passive smoking in adulthood home
The DANHES questionnaire included questions on current exposure to passive smoking (hours/day) and longterm exposure (in household and workplace). In Paper I we only investigated the effect of long-term
exposure.
Prior to the questions on passive smoking, the questionnaire stated: “You are exposed to passive smoking
when you spend time in rooms where people smoke or where smoke from other parts of the building is in
the air. Smokers are also passive smokers when they spend time in rooms where smoke is in the air”.
The question in relation to adulthood passive smoking was: “How many years have you been exposed to
passive smoking daily or almost daily at home as an adult?” Subjects were categorized as “passive smokers”
if they had been exposed to passive smoking for one or more years. Duration of exposure to passive smoking
was classified into four different intervals (unexposed, 1–9 years, 10–19 years and 20+ years).
[15]
Risk evaluation in fracture prevention
Methods and data sources
3.2.3 Clinical risk fractures for low BMD and fractures applied

Smoking
The subjects were also asked about current and past smoking status, whether they had formerly smoked
daily and the average number of cigarettes, cigars and cheroots smoked per day. Smoking status included
the following categories: daily, occasional, former and never smoker. We defined “current” smokers to only
include “daily” smokers (used in paper III). Total pack-years of active smoking were calculated by multiplying
the average number of packs smoked per day by the number of years of daily smoking and were divided into
four intervals: 0, >0–9, 10–19 and 20+ pack-years (paper I).

Alcohol intake
Alcohol intake was measured in units (10 ml or 8 grams of alcohol) per week and in paper I categorized as a
binary variable, i.e. more/less than the recommended maximum intake of alcoholic beverages issued by the
Danish Health and Medicines Authority3 (129) (14 units/week for women; 21 units/week for men). In paper
III, units per week were divided by seven to obtain the average daily consumption (more or less than three
units per day).

Physical activity
The question on physical activity comprised the following categories: a) Heavy exercise and competitive
sports (regularly and several times a week) or exercise or heavy gardening at least four hours a week; these
were defined as vigorous or moderate physical activity in paper I; b) Walking, cycling or other light exercise
at least four hours a week (including Sunday excursions, light gardening and cycling or walking to work);
these were defined as light physical activity in paper I; c) Reading, watching TV or other sedentary activity;
these were defined as sedentary physical activity in paper I.

Body height, weight, BMI and fat percentage
Height and weight were measured as part of the health examination. Height was measured with bare feet to
the nearest cm using a portable stadiometer (Leicester Height Measure, Chasmors Ltd.). Body weight and fat
percentage were measured without shoes, socks and outer garments with a BC 418 MA Segmental Body
Composition Analyser from Tanita (MDD Approved/NAWI Class III). One kilogram was subtracted from the
weight to compensate for clothes. BMI was calculated as weight/ height2 (kg/m2).

Premature menopause
Women were asked if their menstruation had stopped and if so “how old were you, when you last had your
period?” Premature menopause was defined as menopause occurring before the age of 45 years.
3
Lower risk limits have since been defined by the Danish Health and Medicines Authority (7 units/week for women; 14 units/week
for men)
[16]
Risk evaluation in fracture prevention

Methods and data sources
Parental hip fracture
In the questionnaire, subjects were asked if their biological mother, father or siblings had experienced a hip
fracture after the age of 50 years. “Hip fracture in first-degree relative” was registered as a binary variable
(yes/no) in paper I and as “parental hip fracture” (holding only information on mother and father) in paper
III.

Educational level
Educational level was classified according to the International Standard Classification of Education (ISCED)
combining (on-going or completed) school and vocational education based on information from four
different questions and divided into four categories: <10, 10–12, 13–14, 15+ years.
3.3 Register data and ascertainment of osteoporotic fractures
For papers II and III, participants in the health examination were followed up using the Danish national
registers to identify fracture outcome. This is possible because all persons residing in Denmark are assigned a
unique personal identification number, which is a ten-digit number including the date of birth and four other
digits. This identification number is consistent throughout all national Danish registers and can be used to
link data from all public registers at an individual level (130) and questionnaire data with register data. Data
from DANHES were thus merged with information on fracture and surgical procedures from the National
Patient Register (NPR) and information on death and emigration from the Civil Registration System. NPR
contains data on all patients admitted to any Danish hospital since 1977. The register includes discharge
diagnoses of hospitalized patients indicating the main medical reason for diagnostic procedures or
treatment. Similarly, all outpatient visits and patients seen in emergency rooms are included since 1995.
Discharge diagnoses are coded by physicians according to the tenth version of the International Classification
of Diseases (ICD-10). Surgical procedures are registered by surgical codes according to the Danish version of
the Nordic Classification of Surgical Procedures (131).
We extracted data on prevalent and incident major osteoporotic fractures i.e. vertebral fractures plus
fractures of the humerus, forearm and hip, as these are the most frequently seen fractures caused by
osteoporosis (5). The corresponding ICD-10 codes are shown in Table 7. Incident fractures were defined as
fractures occurring between the date of BMD measurement and the end of follow-up. Fracture events were
calculated as number of persons with a fracture during the follow-up period. Prevalent fractures were
defined as fractures occurring after the 1st January 1994 (when ICD-10 codes were introduced into Denmark
(131)) and before the date of BMD measurement in the health examination. Hip fractures were validated
[17]
Risk evaluation in fracture prevention
Methods and data sources
using surgical codes of primary hip arthroplasty or osteosynthesis (NFB00–92 and NFJ00–92, respectively)
and hip fractures without corresponding surgical procedures were excluded (132).
Table 7. ICD-10 fracture codes used for data extration
Hip fractures
Vertebral fractures
DS720, DS721A–B, DS722
DS120, DS121A–B, DS122A–E, DS220A–L
DS320A–E, DT08A
DS525A–C, DS526
DS422A–C, DS423A
Forearm fractures
Humerus fractures
Individuals were included in the cohort and observed from the date of the health examination with BMD
scan. Follow-up in paper II ended on 1st July 2011 and in paper III on 10th August 2012, date of failure
(fracture), date of death or migration as appropriate.
3.4 Calculation of 10–year probability of fracture by FRAX
In paper III we calculated the 10–year fracture probability of fracture among participants in the DANHES. The
FRAX calculation is based on the following risk factors: age, sex, height (cm), weight (kg), history of fracture,
parental history of hip fracture, current smoking, three or more units of alcohol per day, use of
glucocorticoids within the last 3 months, presence of rheumatoid arthritis, and other types of secondary
osteoporosis (133-135). The algorithm for the FRAX calculation is still not published, but it is possible to
calculate the 10–year fracture probability on an individual level using the FRAX website (97). The FRAX value
was thus calculated by individual risk scoring of the Danish version of FRAX (97) using a programmed call of
the FRAX website (version 3.1) produced by a Danish It-company, Langtved Data, Odense, Denmark (136).
Only persons between 40–90 years can be scored by FRAX. Information on height and weight were obtained
from the health examination. Information on smoking (daily smokers listed as current smokers in FRAX),
alcohol consumption and parental history of hip fracture after the age of 50 years was extracted from the
DANHES questionnaire. Information on fracture history (hip, forearm, vertebral and humerus), presence of
rheumatoid arthritis and other types of secondary osteoporosis was extracted from NPR (the corresponding
ICD-10 codes are listed in Table 8). Information on premature menopause, before the age of 45 years, was
also extracted from the DANHES questionnaire and incorporated in FRAX as “secondary osteoporosis”.
Unfortunately we had no information on glucocorticoid use. FRAX was not calculated if data on height or
weight were missing, and these subjects were excluded from analyses (n=5). We adopted the high-risk
threshold used by the National Osteoporosis Foundation (NOF): 10–year hip fracture probability ≥3% or a
[18]
Risk evaluation in fracture prevention
Methods and data sources
10–year major osteoporosis-related fracture probability ≥20% (98). We also defined an intermediate FRAXrisk category: 10–year hip fracture probability between 1.5% and 3% or a 10–year major osteoporosisrelated fracture probability between 10% and 20%.
Table 8. ICD-10 codes used in the calculation of FRAX
Rheumatoid arthritis
Type 1-diabetes
Osteogenesis imperfecta
Chronic liver disease
Anorexia nervosa
Inflammatory bowel disease e.g. Crohn´s disease
Coeliac disease and malabsorption
Thyroid disorders (hyperthyroidism, thyrotoxicosis)
Premature menopause
DM05
DE10
DQ780
DK72, DK73, DK74
DF50–DF509
DK50, DK51
DK90
DE05
DE283A
3.5 Danish Risk-stratified Osteoporosis Strategy Evaluation study (ROSE)
ROSE is an on-going prospective, randomized population-based study investigating the effect of a two-step
screening programme for osteoporosis. The first step comprises a self-administered questionnaire on risk
factors for osteoporosis based on the Fracture Risk Assessment Tool (FRAX®) issued to both the screening
and control group and used to calculate the absolute risk of fracture. In the second step, subjects in the
screening group with a 10-year probability of major osteoporotic fracture ≥15 % are offered a DXA scan.
Patients with osteoporosis (T-score <-2.5) are advised to see their GP to discuss pharmacological treatment.
A total of 34,000 Danish women aged 65–80 years were selected at random from the Central Person
Register and randomized to the screening or control group (described in Friis-Holmberg T et al.; “The Riskstratified Osteoporosis Strategy Evaluation study (ROSE)”, manuscript in preparation).
In paper IV we selected a subsample of women (n=121) from the ROSE population undergoing DXA scan at
Odense University Hospital, Odense, or Hospital of Southwest Jutland, Esbjerg. These women also
underwent phalangeal BMD measurement as described in section 3.1.1.
The ROSE study is registered in ClinicalTrials.gov (NCT01388244) and performed according to the declaration
of Helsinki II. The ROSE study was processed by the Regional Scientific Ethical Committee for Southern
Denmark (jr.nr S-20090127) and the present substudy was approved as supplement protocol number 2
“Validation of phalangeal densitometer (Alara MetriScan)”. The study was also approved by the Danish Data
Protection Agency.
[19]
Risk evaluation in fracture prevention
Methods and data sources
3.5.1 Dual-energy X-ray absorptiometry
BMD of the lumbar spine (L1-L4) and right hip (total) was measured in the ROSE study with dual-energy X-ray
absorptiometry (DXA) using Hologic Discovery or Hologic Delphi densitometers (Waltham, MA) and
expressed in g/cm2 and T-scores (standard deviation (SD) differing from mean for young adults). The
European Spine Phantom (ESP) was used to check agreement and accuracy between the different scanners.
DXA scans were performed by trained biomedical laboratory technicians. T-scores were calculated using the
NHANES (137) reference database for the total hip and the reference database provided with the Hologic
DXA scanner for the lumbar spine. Osteoporosis was defined and classified according to the WHO definition:
normal (T-score >-1.0), osteopenic (T-score between -1.0 and -2.5) and osteoporotic (T-score <-2.5).
Diagnosis followed current standard guidelines (122) and treatment was carried out in general practice.
3.6 Statistical methods
The studies (papers I–IV) used different statistical analyses, as appropriate for their aims and designs. Further
details on the statistical analyses are provided in the individual papers. For all analyses in all papers
statistically significance was indicated with p-values below 0.05, which is arbitrary but conventionally used
(138). All analyses were performed by the statistical software program STATA 10.0 or 12.1
In paper I, multiple linear regression analysis was used. Analyses were controlled for potential confounders
to affect BMD such as BMI, smoking, physical activity, alcohol consumption and hip fracture in first-degree
relatives. Due to the known biological effect of these variables they all remained in the analyses also when
the results were non-significant; stepwise regressions were not performed. Moreover, educational level and
fat percentage was included in analyses, as it was assumed these parameters could have an independently
effect on phalangeal BMD. Large-scale multiple regression analyses are robust to variations in the prevalence
of risk factors entered as independent variables. Moreover, analyses were stratified by sex due to variations
in BMD and exposure to passive smoking in adulthood at home among men and women. To further
eliminate any confounding effect of smoking subgroup analyses were performed for never-smokers to test if
associations remained statistical significant.
In papers II and III, data were analysed using survival analyses as the aim was to estimate the predictive
performance of phalangeal BMD. Rates of incident fractures per 1,000 person-years were obtained, and Cox
regression was used to obtain hazard ratios (HR), which is robust analysing method taking person-years and
loss to follow-up (death and emigration) into account. HR correspond to the change in risk of fracture per 1
SD change in BMD (paper II), or alternatively corresponding to the WHO-defined T-score values (normal: >-1,
[20]
Risk evaluation in fracture prevention
Methods and data sources
osteopenia: between -1 and 2.5 and osteoporosis: ≤-2.5) (papers II and III). For paper III, a risk category
combining phalangeal BMD and the FRAX score was defined:

Combined risk of major osteoporotic fractures:
1) Low combined risk: FRAX <20% and T-score >-2.5
2) Intermediate combined risk: FRAX ≥20% or T-score ≤-2.5
3) High combined risk: FRAX ≥20% and T-score ≤-2.5.

Combined risk of hip fractures:
1) Low combined risk: FRAXhip <3% and T-score >-2.5
2) Intermediate combined risk: FRAXhip ≥3% or T-score ≤-2.5
3) High combined risk: FRAXhip ≥3% and T-score ≤-2.5.
For paper III, receiver operating characteristic (ROC) curves using C-statistics estimated from Cox regression
were used to assess predictive capability. Each point on the ROC curve marks the sensitivity and
corresponding value of [1-specificity] for a given cut-off point on the risk tool. The area under the curve
(AUC) is an overall estimate of the accuracy of the risk score to identify persons with low BMD. This area
could range from 1 for a perfect test, to 0.5 for a test that performs no better than random chance.
The primary outcome was incident osteoporotic fracture and hip fracture (papers II and III) as well as
forearm, humerus and vertebral fracture (paper II). Participants experiencing two or more types of fracture
during follow-up were included in each subgroup analysis. In paper II, the underlying time axis in the Cox
regression was “age” while in paper III it was “time since health examination”. Analyses were stratified
according to sex in both papers and on age (<50/≥50 years) in paper II and “history of osteoporosis” in paper
III. The analysis for paper II was also adjusted for prevalent fractures.
In paper IV, different measures of agreement were used to investigate the correspondence between total
hip and lumbar spine BMD by DXA and phalangeal BMD by RA and to assess diagnostic performance,
including correlation coefficient (r) displaying all pairwise correlation coefficients, area under the (ROC)
curves and Bland-Altman plots (139;140). Bland-Altman plots were used to quantify the variation in
between-method differences for T-scores by DXA and RA and for the individual participants, which is not
possible to assess using the normal scatter of correlation and correlation coefficients. Furthermore, it was
used to see whether phalangeal T-scores based on the DANHES reference were superior to phalangeal Tscores based on the manufacturer’s reference population. We plotted the differences in DXA T-score and
mean RA T-score against the DXA T-score (the latter being considered the gold standard for measuring BMD
[21]
Risk evaluation in fracture prevention
Methods and data sources
(141)). Finally, sensitivity and specificity for different cut-off points of phalangeal BMD were calculated and
threshold values matching the NOS approach4.
4
Thresholds are set at 90% sensitivity and 90% specificity for identifying osteoporosis (T-score ≤-2.5) at either hip or spine. If the
BMD at a peripheral site is below the lower threshold, then treatment is recommended; if the BMD is between the thresholds, then
the person should be referred to DXA of hip and spine; and if the BMD is above the higher threshold, then no further action is
required
[22]
Risk evaluation in fracture prevention
Findings
4. Findings
A short summary of findings in the four papers including some additional analyses is presented in the
following sections.
4.1 Association between phalangeal BMD and passive smoking in adulthood home
A total of 39.1% (n=5,829) of the participants in DANHES had been exposed to passive smoking in the
adulthood home. Phalangeal BMD was significantly lower in subjects exposed to passive smoking compared
with unexposed subjects, 0.337 vs. 0.339 g/cm2; p<0.05 when adjusted for age, gender, height and weight
and smoking. Figure 2 shows adjusted BMD in different exposure groups.
2
Figure 2. Phalangeal BMD (g/cm ) in subjects unexposed and subjects exposed to
passive smoking for 1–9 years, 10–19 years and 20+ years at home as an adult.
Results are presented as adjusted means ± SEM
0,34
0,335
0 years
1–9 years
BMD (g/cm2) 0,33
10–19 years
0,325
20+ years
0,32
Adjusted* p<0.01
*Adjusted for age, gender, weight, height, smoking (pack-years)
Multiple linear regression analysis showed (after controlling for age, age2, BMI, body fat percentage, smoking
(pack-years), gender, alcohol consumption, leisure time physical activity, level of education (IECED) and
history of hip fracture in first-degree relatives) that exposure to passive smoking for more than 20 years in
the adulthood home was significantly related to phalangeal BMD (men: β=-4.4*10-3; p<0.01 and women: β=2.3*10-3; p<0.05). When T-scores were used in the analysis instead of raw BMD-values, exposure to passive
smoking for more than 20 years in the adulthood home was associated with -0.2 SD in men and -0.1 SD in
women (data not shown). In both men and women, also expected associations between some of the known
risk factors and phalangeal BMD (as shown in table 1) were seen. Were 20+ pack-years, age, body fat
percentage, light and sedentary physical activity, and history of hip fracture in first-degree relatives were
negatively associated with phalangeal BMD, and BMI was positively associated with phalangeal BMD. In men,
[23]
Risk evaluation in fracture prevention
Findings
education longer than 10 years (10–12, 13–14 and 15+ years of education) was negatively associated with
phalangeal BMD. One suggested explanation for this association could be that men with lower education
levels often are employed in job with a higher level of physical activity; e.g. farmers, workmen etc. compared
to men with higher education levels, which potentially may mediate depreciation or increase BMD.
When the analyses were restricted to never-smokers, the same association between phalangeal BMD and
exposure to passive smoking was seen (β=-3.3*10-3; r=-0.03; p=0.01). Gender, age, body fat percentage, light
physical activity, sedentary physical activity, and history of hip fracture in first-degree relatives were again
negatively associated with phalangeal BMD, and BMI was positively associated with phalangeal BMD.
4.2 Fracture risk prediction using phalangeal BMD
When DANHES data were merged with register data to obtain information on incident fractures, the mean
follow-up in the total cohort was 3.2 [range: 0.03 to 3.8] years, giving a total of 49,792 person-years. A total
of 307 (2.0%) of the participants had experienced an incident fracture during follow-up. BMD was
significantly lower in subjects with fracture (0.32 vs. 0.34 g/cm2; p<0.001 adjusted for age, gender, prevalent
fractures, height, weight and smoking). Incident fractures were more frequent in women than in men (2.5%
(n=230) vs. 1.2% (n=77), p<0.001). Forearm fractures (n=183) were most frequent, followed by humerus
(n=64), hip (n=39) and vertebral fractures (n=29).
In both sexes, a 1 SD decrease in BMD (T-score units) was associated with an increased risk of fracture when
adjusted for prevalent fractures (and age, as this was used as the underlying time axis in the Cox
regressions). The HR in women was 1.39 (95% CI: 1.24–1.54, p<0.001) and in men 1.47 (95% CI: 1.20–1.79,
p<0.001). In women, a 1 SD decrease in BMD was furthermore significantly associated with an increased risk
of hip, forearm and humerus fracture, and in men with an increased risk of forearm, humerus and vertebral
fracture. A history of fracture (prevalent fracture) did not show an independent statistically significant
association with increased risk of fracture, except for humerus fractures in women.
Figure 3 shows the associations between the categories of T-scores and the risk of major osteoporotic
fractures. In both sexes, a T-score below -2.5 was associated with a three-fold higher risk of fracture
compared with a T-score above -1.0 (when adjusted for prevalent fractures).
[24]
Risk evaluation in fracture prevention
Findings
Figure 3. Hazard ratio (HR) for major osteoporotic fractures using WHO T-score
categories of phalangeal BMD. Adjusted for prevalent fractures.
8
Women
7
Men
6
5
HR 4
3
2
1
0
>-1
-2.49 to 1.0
≤2.5
>-1
-2.49 to 1.0
≤2.5
T-score
After the publication of Paper II, further register data were obtained that provided a longer follow-up period.
Repeated analyses after a mean follow-up of 4.3 years [range: 0.03–4.94] (66,933 person-years) showed that
421 persons (2.7%) had experienced a new major osteoporotic fracture: 58 hip fractures, 250 forearm
fractures, 96 humerus fractures and 41 vertebral fractures. Tables 9 and 10 show the hazard ratios with the
longer follow-up. Similar overall associations were seen as with the shorter follow-up. In men, the overall risk
of fracture per 1 SD decrease in T-score slightly declined from HR=1.47 to 1.41. The risk of forearm and
vertebral fractures declined marginally in men, whereas the risk of hip fractures in women and humerus
fractures in both sexes increased marginally (Table 10).
[25]
Risk evaluation in fracture prevention
Findings
Table 9: Hazard Ratio (HR) per 1 SD decrease in phalangeal BMD (T-score) for fracture at different sites. Mean follow-up of 4.3 years (66,933
person-years). Adjusted for prevalent fracture.
All women
All men
N= 9,297
N=6,245
Women
+50–year old
(N=5,453)
Men
+50–year old
(N=3,922)
HR
95% CI
P-value
HR
95% CI
P-Value
HR
95% CI
P-value
HR
95% CI
P-Value
Fracture overall
Adj. prevalent fracture
1.40
1.19
1.28–1.53
0.83–1.72
<0.001
0.34
1.41
2.41
1.19–1.67
1.14–5.07
<0.001
0.021
1.43
1.18
1.30–1.56
0.81–1.73
<0.001
0.38
1.43
2.48
1.19–1.73
1.06–5.80
<0.001
0.036
Hip
Adj. prevalent fracture
1.64
2.23
1.27–2.13
1.05–4.75
<0.001
0.038
1.28
2.96
0.90–1.82
0.68–13.0
0.17
0.15
1.61
1.98
1.20–2.17
0.56–4.57
0.002
0.11
1.28
2.93
0.90–1.82
0.67–12.9
0.17
0.16
Forearm
Adj. prevalent fracture
1.42
0.95
1.27–1.59
0.57–1.58
<0.001
0.85
1.42
2.50
1.06–1.91
0.72–8.66
0.021
0.15
1.44
0.91
1.28–1.62
0.53–1.56
<0.001
0.73
1.30
1.85
0.89–1.88
0.24–14.1
0.17
0.55
Humerus
Adj. prevalent fracture
1.44
1.52
1.18–1.74
0.73–3.15
<0.001
0.26
1.57
1.96
1.16–2.13
1.47–8.51
0.004
0.372
1.39
1.81
1.14–1.71
0.86–3.82
0.002
0.12
1.61
2.44
1.14–2.29
0.55–10.8
0.007
0.24
Vertebral
Adj. prevalent fracture
0.98
0.45
0.73–1.31
0.06–3.46
0.88
0.45
1.61
1.83
1.04–2.47
0.23–14.4
0.03
0.57
1.07
0.48
0.76–1.50
0.06–3.72
0.95
0.48
1.89
2.07
1.19–2.30
0.26–16.5
0.007
0.49
[26]
Risk evaluation in fracture prevention
Findings
Table 10: Hazard Ratio (HR) for fracture at different sites by phalangeal BMD using WHO T-score
categories. Mean follow-up of 4.3 years (66,933 person-years). Adjusted for prevalent fractures.
Women
Men
N= 9,297
N=6,245
HR
95% CI
P-value
HR
Reference
2.00
3.04
1.23
1.51–2.66
2.20–4.19
0.86–1.77
<0.001
<0.001
0.26
Reference
1.91
2.95
2.49
Reference
1.44
5.66
2.11
0.56–4.53
1.96–16.4
1.06–4.85
0.53
0.001
0.035
Reference
1.07
2.81
3.20
0.39–2.94
0.77–10.3
0.73–14.1
0.89
0.12
0.12
Forearm
>-1.0
-2.49 to -1.0
≤-2.5
Adj. prevalent
fracture
Reference
2.22
2.87
1.01
1.57–3.13
1.90–4.32
0.61–1.67
<0.001
<0.001
0.98
Reference
3.03
1.28
2.71
1.47–6.24
0.16–10.0
0.79–9.34
0.003
0.81
0.11
Humerus
>-1.0
-2.49 to -1.0
≤-2.5
Adj. prevalent
fracture
Reference
1.89
3.21
1.57
1.01–3.56
1.60–6.46
0.76–3.62
0.048
0.001
0.23
Reference
1.62
4.67
2.05
0.71–3.70
1.48–14.7
0.47–8.94
0.26
0.008
0.34
Vertebral
>-1.0
-2.49 to -1.0
≤-2.5
Adj. prevalent
fracture
Reference
1.26
2.12
0.40
0.46–3.48
0.68–6.62
0.05–3.04
0.66
0.20
0.38
Reference
2.32
5.11
1.91
0.77–6.98
1.02–25.5
0.24–15.0
0.13
0.047
0.54
Overall
>-1.0
-2.49 to -1.0
≤-2.5
Adj. prevalent
fracture
95% CI
1.23–2.85
1.42–6.12
1.18–5.24
P-Value
0.004
0.004
0.016
Hip
>-1.0
-2.49 to -1.0
≤-2.5
Adj. prevalent
fracture
To see whether the fracture incidences we observed were representative for the general population, we
compared fracture rates in the DANHES participants who had a RA scan with fracture rates in the general
population i.e. all participants in the participating municipalities (n= 538,082). A total of 2.7% (n=421) in the
DANHES population experienced a new osteoporotic fracture during follow-up compared to 3.2% (n=16,614)
in the background population (p=0.001). When comparing fracture rates per 10,000 person-years, the rate
was higher in the background population for both major osteoporotic fractures overall (Figure 4) and hip
fractures alone (Figure 5), compared to participants in the health examination survey.
[27]
Risk evaluation in fracture prevention
Findings
Figure 4. Major osteoporotic fracture rates per 10.000 person-years for participants in the Health Examination
survey (DANHES) and in the background population.
600
Women
Men
512
500
420
400
300
251
247
193
200
193
142
127
87 97
100
15 12
97
29 16
21 22
28 18
37 36
53 40
57
0
18–39 40–49 50–59 60–69 70–79
years years years years years
≥80
18–39 40–49 50–59 60–69 70–79
years years years years years
Background Population
≥80
DANHES
Figure 5. Hip fracture rates per 10.000 person-years for participants in the Health Examination and in the
background population*
300
281
Women
Men
250
189
200
162
150
88
100
64
44
41
50
23
1 1
7 3
40–49
years
50–59
years
12
3 0
7 2
40–49
years
50–59
years
16 12
22
60–69
years
70–79
years
0
60–69
years
70–79
years
≥80
years
Background Population
*fracture rates <1 among those aged 18–39-year old
[28]
DANHES
≥80
years
Risk evaluation in fracture prevention
Findings
The differences in fracture-rates were particularly marked in the oldest age groups and for hip fracture
(Figures 4 and 5). This may be due in part to a low participation rate in the health examination in the oldest
group (128), and the fact that perhaps more fragile persons are especially less likely to participate. It is a
general assumption that persons participating in health studies tend to be healthier than non-responders
and non–participant.
Although the observed fracture rates in the DANHES population were lower than in the background
population, it is still assumed that the association between phalangeal BMD and fracture risk is generalizable
to the background population—though, perhaps it the exact size of the risk estimates that could be affected
by the skewed participation. To further explore this, other sub-group analyses of fracture-risk were
performed among persons potentially underrepresented in DANHES and those with a potentially higher risk
of fracture—and were associations between BMD and fracture hypothetically could be different. Dailysmokers (n=1,802), who may be underrepresented are analysed separately, and this group may influence the
size of the estimate because smoking, as described in section 1.2 above, is a risk factor as such and influence
BMD negatively. Also, persons above 50 years who had fallen once or more in the last 12 months (as
answered in the questionnaire) (n=1,238) were analysed, because this group may have higher risk of
fractures not only due to low BMD potentially making the effect of BMD insignificant. For daily-smokers, a 1
SD decrease in BMD (T-score units) was still associated with an increased risk of major osteoporotic fracture
when adjusted for prevalent fracture and age (HR= 1.52,95% CI: 1.19–1.94, p<0.001), while among persons
who had fallen a HR of 1.40 (95% CI: 1.45–1.71, p=0.001) was found. These results indicate that the results
are robust despite the lack of completely representativity of the DANHES population.
4.3 Phalangeal BMD versus the WHO fracture risk assessment tool (FRAX) and age alone
in predicting osteoporotic fractures
For paper III, FRAX was calculated on 12,758 DANHES participants, and mean follow-up time was 4.32 years
[0.03–4.94], giving 54,980 person-years (persons <40 years and no height/ weight measurement were
excluded). During follow-up, a total of 395 (3.1%) participants suffered one or more major osteoporotic
fracture, including 54 participants with hip fracture (0.4%). In general, increasing fracture rates and
increasing risk (HR) of major osteoporotic and hip fractures was seen with increasing risk score category—
when examining by the risk strata’s of age, FRAX, phalangeal T-score and the combined risk category (the
result of FRAX and BMD measurement in combination).
[29]
Risk evaluation in fracture prevention
Findings
The highest rate of major osteoporotic fracture was observed in persons with a high combined risk profile
i.e. a high FRAX and a low T-score (FRAX ≥20% and T-score ≤-2.5), followed by persons with low T-score (≤2.5). The highest rate of hip fracture was also observed in persons with a high combined risk.
Examination of the predictive ability of identifying incident fractures based on AUC results of the different
methods gave inconsistent results depending on the approach used (data analysed as continuous variables
or categorical variables based on risk strata). When analysed as continuous variables the combination of
FRAX and T-score performed overall better than FRAX alone, T-score alone and age in the prediction of major
osteoporotic fractures. There was a tendency for T-score to perform less well than the other methods in the
prediction of hip fracture. This was also the case when participants with known osteoporosis at baseline
were excluded from the analyses.
4.4 Correlation between phalangeal BMD and central BMD and application of a triage
approach in pre-selection for DXA
In paper IV the ability of the RA scanner to identify osteoporosis (low BMD) was investigated among 121
women participating in the ROSE-study. Phalangeal T-scores were calculated using either the manufacturer’s
database or DANHES as reference. The number of women with lox BMD (T-score ≤-2.5) ranged from 10
(8.5%, based on DXA at total hip) to 32 (26.5%, based on RA and calculated using the DANHES population as
reference). There was moderate correlation between phalangeal BMD versus total hip BMD (r=0.47) and
lumbar spine BMD (r=0.51), and a fair accuracy to identify women with low BMD at either total hip or lumbar
spine (AUC of 0.80). The mean difference between phalangeal T-score and total hip/lumbar spine T-score
was small and ranged from -0.26 SD to -0.31 SD depending on the site and reference database used for
calculation of T-scores. However, large variation was seen in the agreement of DXA T-scores and RA T-scores
at an individual level, meaning that were quite big differences between the DXA T-score and RA T-score for
some of the women. There tended to be narrower range of agreement (the range of the difference between
DXA T-score and RA T-score) when phalangeal T-score was calculated using the DANHES reference rather
than the manufacturer’s reference.
A triage approach was applied using the NOS recommendations, where thresholds for phalangeal BMD were
set at 90% sensitivity and 90% specificity for identifying osteoporosis (T-score ≤-2.5) at either hip or spine.
This showed that over half of the DXA scans could be avoided. These thresholds would result in a low
proportion of false negatives in the low-risk group (5-6% had osteoporosis), but a high proportion of false
positives in the high-risk group (38-44% women did not have osteoporosis) (Table 11).
[30]
Risk evaluation in fracture prevention
Findings
Table 11: Numbers (and proportion) of participants in each group using thresholds
Manufacturer’s reference
T-scores
Low risk
(above upper) threshold
Osteoporotic
Non-osteoporotic
Medium risk
(between thresholds)
Osteoporotic
Non-osteoporotic
High risk
(below lower threshold)
Osteoporotic
Non-osteoporotic
Total
DANHES reference
T-scores
Total
3 (5.5)
43 (93.5)
46 (38.0)
3 (6.1)
46 (93.9)
49 (40.5)
13 (24.1)
41 (75.9)
54 (44.6)
13 (26.5)
36 (73.4)
49 (40.5)
13 (61.9)
8 (38.1)
21 (17.4)
13 (56.5)
10 (43.5)
23 (19.0)
[31]
Risk evaluation in fracture prevention
Discussion
5. Discussion
To sum up, in present thesis it was found that long-term passive smoking in adulthood home was negatively
associated with phalangeal BMD, also when adjusted for potential confounders. Moreover it was found that
1 SD decrease in phalangeal BMD (T-score) was associated with approximately 40–45 % higher risk of major
osteoporotic fractures. Persons with low phalangeal BMD (T-score ≤-2.5) had a nearly threefold higher risk of
major osteoporotic fractures compared with person with normal phalangeal BMD (T-score >-1). The highest
rate of major osteoporotic fractures was observed in persons with a both a high 10–year fracture probability
(FRAX≥20%) and a low phalangeal BMD (T-score≤-2.5). This group of participants also had the highest rate of
hip fractures. FRAX and T-score in combination analysed as continuous variables performed overall best in
prediction of major osteoporotic fractures. In prediction hip fracture there was a tendency of T-score
performing worse than the other methods. Finally, in woman over 65 years with intermediate or high FRAX
phalangeal BMD showed overall relatively good ability to predict low BMD at either total hip or lumbar spine.
The mean difference between phalangeal T-score and total hip T-score as well as lumbar spine T-score was
low, but fairly large variations were seen in the agreement of the two methods at an individual level. When
applying a triage approach based on NOS recommendations over half of DXA scan could be avoided.
The following sections will include discussion of main finding and a section holding methodological
considerations.
5.1 Passive smoking as a risk factor
In present thesis it was found that phalangeal BMD was lower in persons exposed to passive smoking in
adulthood home, and a dose-response relationship was found. In both men and women long-term passive
smoking in adulthood at home (20+ years)as well as pack-years, age, body fat percentage, light and
sedentary physical activity, a history of hip fracture in first degree relatives were negatively associated with
phalangeal BMD, while BMI was positively associated with phalangeal BMD. In addition, longer education
(more than 10 years) was negatively associated with phalangeal BMD in men. This relationship between
long-term passive smoking in adulthood at home was also seen in the group of never-smokers. This
contributes to the current knowledge as shown in table 2, were also other studies have found similar
associations. The most recent study by Kim and colleagues (60) was publish after the publication of paper I.
They evaluated the association between exposure to passive smoking and osteoporosis in never-smoking
Korean postmenopausal women (N=925) participating in the Fourth Korea National Health and Nutrition
Examination Survey. One advantage of this study was that self-reported passive smoking exposure was
validated according to urinary cotinine levels, and participants with high levels were reclassified as current
[32]
Risk evaluation in fracture prevention
Discussion
smoker (60). They found that participants with family members smoking or who had any exposure at home
had higher odds ratios for osteoporosis at the femoral neck, and participant with cohabitants smoking >0
cigarettes per day had higher odds ratios of both osteoporosis at lumbar spine and femoral neck (a doseresponse relationship was seen). They also evaluated the association between exposure at work and low
BMD, but did not see any statistically significant association (60).
In the multiple regression analysis, only exposure for 20 or more years was significantly associated with
phalangeal BMD. It seems like fairly long time, but among participants exposed to passive smoking in private
residence the highest proportion was actually exposed for 20+ years (comprising approx. 20% of all
participants). Furthermore, it is interesting that in men there was an almost equally strong influence on
phalangeal BMD with 20+ years of exposure to passive smoking in adulthood home as with 20+ pack-years
(Coefficient (β):−4.4×10−3 vs. −4.7×10−3). In women, however, the coefficient was a bit lower for exposure to
passive smoking (coefficient (β):−2.3×10−3 vs. −4.2×10−3). In accordance with our observations, Kim et al.
concluded that higher intensities of exposure to passive smoking was associated with increased odds ratios
for osteoporosis that are comparable to those associated with active smoking (60).
5.2 Performance of phalangeal BMD measured by RA
As stated in the introduction the performance of peripheral densitometers may be evaluated according to 1)
the ability in predicting osteoporotic fractures, 2) the ability in identifying osteoporosis (as defined by low
BMD at hip or lumbar spine), and 3) to monitor treatment. In present thesis the RA scanner (MetriScan
densitometer) was evaluated according to the first two points i.e. the ability to predict fractures (paper II)
and the ability to identify osteoporosis (paper IV).
In paper II a 1 SD decrease in phalangeal BMD (T-score) was associated with a higher risk of major
osteoporotic fractures (women HR=1.39 and men HR=1.47). This association was also found when excluding
younger participants (<50 years) and for most types of fractures. In present study the risk of osteoporotic
fractures among men and women was almost identical after the 4.3 years of follow-up (as found in the
additional analyses in the result section), which was also the conclusion in a meta-analysis investigating the
relationship between BMD at femoral neck and the risk of hip and other fractures (142). The results are in
the same range of those from other studies measuring phalangeal BMD or BMD at other sites the peripheral
skeleton (25;68-71;73;75;143). In one of these studies—the often referred meta-analysis by Marshall et al.
(25)—it was shown, that all measuring sites had similar predictive values for “any fracture”, however, BMD
at the spine and hip predicted vertebral fractures (RR= 2.3; 95% CI:1.9-2.8) and hip fractures (RR=2.6; 95%
[33]
Risk evaluation in fracture prevention
Discussion
CI: 2.0-3.5), respectively, more closely (25); supporting a better fracture-risk prediction when using sitespecific measurements.
To our knowledge, this is the first study evaluating the predictive performance of this modern version of a RA
scanner. More importantly these results provides more knowledge on fracture prediction in men, while the
majority of previous studies published have mainly been performed on women (25;68-70;73;143), or have
not reported separate results for men (71). In a Swedish study Nyquist et al. (144) found that forearm BMD
predicted fractures in men aged 50+ years from the general population, with a RR of 1.75 for osteoporotic
fractures when adjusted for age (144), which was a higher risk than found in present thesis. Nyquist et al.
(144) also found a nearly four times higher risk of hip fracture, and in a study of another Swedish group they
found a two times higher risk of hip fracture per SD decrease in BMD units by DXR of hand or wrist
radiographs obtained from men at emergency hospitals (145). As seen in table 9 (result section), we found
the same, however insignificant, association between phalangeal BMD and risk fracture among men.
Furthermore, when evaluating the fracture risk using the WHO values of T-score for osteoporosis, both sexes
had almost the same risk of major osteoporotic fractures. Men and women with low phalangeal BMD (Tscore ≤-2.5) had a nearly threefold higher fracture risk compared with person with normal phalangeal BMD
(T-score >-1). This was higher as found in a major study from 2002 comprising 149,524 postmenopausal
women (RR=2.15; 95% CI: 1.60-2.91)(70).
Even though, the RA scanner used in present study is portable, easy to use and relatively inexpensive
(approx. 14,000 USD) it could be debated if same thing could just as well be done with a fracture risk
assessment tool like FRAX that is available online and free of charge. Thus potentially using clinical risk
factors to triage patients for subsequent spine and hip DXA and treatment (as proposed in the International
Osteoporosis Foundation and European Society for Clinical Economic Aspect of Osteoporosis and
Osteoarthritis European guidance for diagnosis and management of osteoporosis in postmenopausal women
(100)). Based on these considerations, study III was performed. Here it was found that the highest rate of
major osteoporotic fractures was observed in men and women with a both a high 10–year fracture
probability (FRAX≥20%) and a low phalangeal BMD (T-score≤-2.5). This group of participants also had the
highest risk of hip fractures. FRAX and T-score in combination analysed as continuous variables performed
overall best in prediction of major osteoporotic fractures. In prediction hip fracture there was a tendency of
T-score performing less well than the other methods (but only statically significant different to FRAX+T-score
in combination). This is corresponds to the above mentioned observations that BMD measurement at the hip
is superior to other measurement sites with regards to identifying hip fractures, as well as to the fact that
[34]
Risk evaluation in fracture prevention
Discussion
age, included in FRAX, is a highly important risk factor for hip fractures. In concordance to this, AUC of FRAX
and age was almost similar.
One other study has used the same approach of combining the result of the phalangeal BMD scan with the
FRAX algorithm assessing the 10-year probability of fracture (105). They compared the performance of FRAX
with and without the MetriScan densitometer as a screening tool to identify osteoporosis (defined as low hip
or spine BMD by DXA) in women aged 50–90 years in a cross-sectional study (105). They used the same highrisk categories of FRAX (≥20% for major osteoporotic fractures and ≥3% for hip fractures) and T-score (≤-2.5).
When combining FRAX and phalangeal BMD the sensitivity decreased and specificity increased compared to
the two models alone, and they conclude that combination of the two models would significantly improve
the screening of osteoporosis in postmenopausal women (105). AUCs were not statistically significant
different (FRAX hip=0.857; FRAX major=0.854; RA scanner=0.861), but unfortunately they did not report the
combined AUC of FRAX and the RA scanner (105). Also, in contrast to our study, they did not include
osteoporotic fractures as outcome. A study by Durosier et al. (103) evaluated if the detection of women
(aged 70–100 years) at low or high-risk for hip factors would be improved by combining clinical risk factors
and quantitative ultrasound (QUS). They found that the combined risk score (risk factors + QUS) improved
the specificity of detection (103), and that AUCs of the combined risk score were statistically significant
better than clinical risk factors alone and QUS alone (103). Moreover, in a study by Albertson et al. (102) they
found that a combination of high risk defined by a 4-item index (FRAMO5), prior fragility fracture, an low heel
BMD by DXL could identify a small group among elderly women, were most fractures were sustained (71%)
and increase the specificity (102). In general studies comparing and combining peripheral BMD
measurement were performed on women (101-105;146). Our study also included sub-group results showing
that rates and AUCs were lower among men compared to among women.
We chose to use the FRAX algorithm while this is the tool recommended by the WHO and are widely used
both by clinicians and in research. FRAX have already been validated in 18 published studies (84) since its
introduction in 2008 (133). However, also more simple tool exist including fewer risk factors, as shown in
table 6 in the introduction. In a recent systematic review we concluded that no tool performed consistently
better than others and simple tools with fewer risk factors often did as well or better than more complex
tools with more risk factors (like FRAX)(84). For that reason it would have been interesting to combine the
use of the phalangeal densitometer with a simpler tool, which probably would be easier to use in e.g. clinical
practice. In relation to this, Gasser et al. (104) evaluated phalangeal RA with or without clinical risk factors in
5
Fracture and Mortality index based on four binary risk factors (age ≥80 years, weight <60 kg, previous fragility fracture since age 50
years (located at distal radius, proximal humerus, hip or vertebra), and impaired ability to rise)
[35]
Risk evaluation in fracture prevention
Discussion
a general practice setting among postmenopausal women (104). They found that the model including RA in
combination with age, height and weight had the best ability to identify women with osteoporosis (low BMD
by DXA), and predictive performance was not improved by adding additional risk factors to the model.
Furthermore, RA alone was found to perform better than two different models with clinical risk factors alone
(104).
In paper IV a moderate correlation between phalangeal BMD and BMD measured with DXA was found in
Danish woman over 65 years with intermediate or high FRAX. As seen in table 4 (introduction) the
correlation between phalangeal BMD and total hip (r=0.47) as well as lumbar spine BMD (r=0.51) was very
similar to those found in a population-based cohort of Danish men (r= 0.47 for total hip, r=0.46 for lumbar
spine)(80), but slightly weaker than most results reported in the other studies that mostly performed in high
risk populations for example women, who already suffered a low energy fracture (66;78;79;82). In other
studies, the use of the MetriScanner to identify individuals with osteoporosis at either hip or lumbar spine
(expressed by the AUCs) have reported similar results (AUCs ranging between 0.75 and 0.85 (78-80;82)) as
found in our study (AUC=0.80).
To optimise the selection of cut-points we used the NOS triage approach of 90% sensitivity and 90%
specificity. The threshold was higher (T-score: -0.80 and -2.73) when T-scores was calculated from
manufacture database than when T-scores was calculated from DANHES database (T-score: -1.20 and -2.92).
Applying the cut-points 54 women (45%) would be referred to DXA, 21 women (17%) would be treated and
in 46 women (38%) no action should be taken (manufacture based T-scores). When T-scores were calculated
from DANHES reference 49 women (41%) would be referred to DXA, 23 (19%) would be treated and in 49
(41%) no action should be taken. The threshold would give a low proportion of false negative in the low risk
group (6%), but a relatively high proportion of false positives in the high-risk group (38% or 44%, depending
on database used for calculation of T-scores).
In Denmark, however, reimbursement of anti-osteoporotic medication still relies on DXA results unless X-ray
verified hip- or spine fracture is present (122). When applying only the upper-thresholds (T-score ≥-1.2 based
on DANHES reference database) approximately 40% of DXA scan could be avoided in this cohort of women
having an intermediate or high 10–year fracture probability. Furthermore, to see what it would imply if
analyses was restricted to the group of women having a high 10–year fracture probability (FRAX ≥ 20%). In
these women also 40% fell above upper-thresholds, wherefore 40% of DXA scans could be avoided (in 30 of
75 women). Among these only two women (corresponding to 7%) would be false-negative. This is interesting
as it indicates that a relatively high proportion of DXA scans could avoided also among women with a high
fracture risk (FRAX ≥20%) presuming that the objective was to select persons to refer for DXA scan.
[36]
Risk evaluation in fracture prevention
Discussion
Pfister and colleagues (147) proposed screening- and treatment strategies using a portable peripheral
forearm densitometer and the 10–year probability of major osteoporotic fractures calculated by FRAX
among women aged 60–64 years, without assess to central DXA. They concluded that an approach where
treatment is initiated in: 1) women who had a prior fracture or FRAX ≥20%, or based on 2) pDXA results in
women with a FRAX-value between 9.3% and 20% (<9.3% no treatment). This approach would significantly
reduce the number of pDXA examinations and the cost of screening (147). They did not, however, evaluate
the performance of this strategy according to the ability to select women with osteoporosis nor the ability to
predict fracture.
5.3 Methodological considerations
The main data source used in present thesis (DANHES) included 15,544 participants who had a phalangeal
BMD measurement. The population covered both sexes and a broad age range (18–96 years). The large
sample size gave sufficient power for subgroup analysis on the basis of factors such as gender, age, exposure
and fracture type. Although the representativeness of the study population is debatable (this issue is
expanded in the following section), the participants were randomly invited from the general population.
The thesis was based on four observational studies with different study designs, each with its own
advantages and disadvantages: papers I and IV reported cross-sectional studies, while papers III and IV
described prospective general population cohort studies.
In paper I, the cross-sectional design prevented any final conclusions on causality and the direction of the
association between BMD and passive smoking. However, retrospective information on years of smoke
exposure in the adulthood home was used instead of current exposure (hours per day) to capture at least
some of the effect of duration of exposure and to address the time-period problem (148), as it was assumed
beforehand that a long duration of exposure to passive smoking is needed before BMD is affected. A cohort
design following exposed and non-exposed over time would have been preferred. Before these results can
be used in a clinical setting, more studies are needed to determine whether exposure to passive smoking
affects spine and hip BMD. The current analyses can also not address the question of whether passive
smoking increases the incidence of osteoporotic fractures. Kanis et al. (28) concluded in their meta-analysis
on smoking and fracture risk, that low BMD due to tobacco smoking accounted for only 40% of the increased
risk (due to smoking) for osteoporotic fractures overall and 23% of the risk of hip fractures. Similar results
may be found for exposure to passive smoking, and thus prospective studies on fracture risk will be
necessary.
[37]
Risk evaluation in fracture prevention
Discussion
In comparison, the cross-sectional design used in paper IV was the best approach in that context, as the main
focus was on the direct correspondence between results of the RA scanner and the DXA scanner at a specific
time.
In papers II and III, a prospective cohort design was used to estimate rates and risk of osteoporotic fractures
over time in relation to phalangeal BMD. The closed cohort included the DANHES population, and the linkage
to national registers ensured nearly complete follow-up with precise information on the amount of time
each person had been at risk of fracture. Both inpatient and outpatient visits are registered in the NPR. The
register is considered to be one of the most comprehensive databases internationally (131) and has a high
validity regarding diagnostic codes and, particularly, procedure codes (149;150). One consideration is the
length of follow-up required to obtain sufficient power, i.e. a sufficient number of fractures occurring during
the follow-up period. In Cox’ regression models the number of event observed, rather than the number of
subjects is important. It is generally recognised that for every independent variable included in the model a
minimum of ten and preferable 20 events are needed (138). The initial mean follow-up in the study
presented in paper II was 3.2 years, and when this follow-up time was increased to 4.3 years, the same
general associations were seen between fracture risk and T-score. In the subgroup analyses we still failed to
see a statistically significant association between decrease in BMD and risk of clinical vertebral fractures in
women and hip fractures in men; this may have been different with a longer follow-up period. FRAX has
been criticized because it estimates a 10-year fracture risk, and thus cannot be validated in a time period less
than 10 years (44). However, we used the FRAX score as a predictor and took time to event into account in
our analyses; furthermore, studies with longer follow-up times have shown similar results independent of
the length of follow-up (16).
The data collected did not include information on whether participants received anti-osteoporosis
medication at or after baseline. If they did, then some fractures might have been prevented, thus possibly
underestimating the fracture risk in papers II and III. Likewise, participants in the study were later informed
of their RA result and those with T-scores below -2.5 were advised to seek advice from their GP. Some of the
participants may thus have received anti-osteoporosis medication during follow-up. In paper III, we tried to
take this potential limitation into account by performing subgroup analyses that excluded persons who at
baseline answered that they had osteoporosis. This approach did not change the overall findings.
Selection bias
There may have been selection bias in the DANHES population used in papers I–III. As the focus of DANHES
was diet, smoking, alcohol and physical activity, it is conceivable that persons who decided to participate
[38]
Risk evaluation in fracture prevention
Discussion
were healthier than the general population, thus resulting in self-selection bias. Women in general were
overrepresented and the younger generation, primarily young men, was underrepresented (127;128). The
self-reported education level of the participants also differed from the register-based data on the
background population obtained from Statistics Denmark (151). Individuals with a basic general education
and vocational training as highest completed education were underrepresented in DANHES. Furthermore,
participants had a higher income level than the background population. It is known from the DANHES and
other studies that the proportion of people with unhealthy lifestyles such as smoking (with a negative effect
on bone health) is higher among persons with lower socioeconomic status (127;152). This may have
contributed to the lower rate of incident fractures during follow-up in the DANHES population compared to
the background population in the municipalities. The association between phalangeal BMD and fracture risk
is also believed to exist in the background population, but the size of the risk estimate could be affected (as
debated in the result section).
To further explore whether selection bias affected the association found between long-term passive smoking
and phalangeal BMD in paper I, a weighting was generated to take the skewed non-response into account.
Based on register information on gender, age, education, marital status and income in the background
population from the municipalities, Statistics Denmark was able to calculate a weighting factor (153). When
this was included in the model, the analysis still showed that 20+ years of passive smoking at home as an
adult was significantly related to BMD; this was also the case for multiple regression models including only
never-smokers, men or women. This suggests that the findings may be generalized to other populations with
lower income and educational levels.
The study reported in paper IV used data from the ROSE study that included women aged 65–80 years. One
could discuss if the analyses should have been performed on the DANHES population instead, or on another
cohort including men. However, the use of the ROSE population made it possible to test the reference data
obtained from DANHES when calculating T-scores. It was also a practical decision, as DXA scans were already
being performed in the ROSE study and thus would reduce the cost of this sub-study. Moreover, a similar
study has also been undertaken among Danish men recruited from the general population (80). A limitation
of the study is that, although participant selection to the ROSE study was population-based end
representative of the general population, the RA measurement was only performed in a relatively small
subgroup of women with an intermediate (10–20%) or high (≥20%) 10–year probability of fracture as
defined by FRAX. In retrospect, it would have been more desirable if women with a low fracture probability
on FRAX were included to see if any of these women would have a low phalangeal BMD as well as low hip
and spine BMD.
[39]
Risk evaluation in fracture prevention
Discussion
Measurement error and misclassification
The information on risk factors for fracture and osteoporosis were mostly from the self-report questionnaire
used in DANHES. In paper IV the calculation of FRAX in the ROSE-study was based on self-reports as well. The
answers given to, for example, life-style factors can be difficult to validate, and could be a source of
information bias and thus misclassification. For example, because of the name ‘DANHES’ (‘KRAM’ in Danish6)
it is obvious that the focus of the study was diet, smoking, alcohol and physical activity. This focus may have
made participants more likely to under-report unhealthy lifestyle habits (social disability bias). It is important
to examine, however, whether any misclassification is differential or non-differential, i.e. if the exposure is
related to the outcome (154;155). The information on exposure used in the present thesis was collected
independently of phalangeal BMD (paper I) and fracture outcome (paper II and paper III), and any
misclassification is thus assumed to be non-differential.
In paper I, the assessment of exposure to passive smoking may be biased. Participants were asked
retrospectively about years of exposure in their adulthood home; such information may be prone to
information bias and misclassification. The potential problem with misclassification is perhaps illustrated in
the high proportion of participants who answered ”don’t know” to the question on years of exposure to
passive smoking at home during adulthood (9.0%; n=1,351). The complete-subject approach was used
excluding participants with missing information in the analyses. This is the right approach if the subjects with
complete data are a random sample of the subjects in the study (156). Unfortunately, this was not well
investigated on forehand, but now it is seen that there for example is a tendency of a higher proportion of
missing among the older participants. Preferably a method like imputation could have been used to fill in the
missing values based on the missing-data pattern to see if it would change the results (156). Moreover, only
years of exposure to passive smoking in the adulthood home were included in our analyses. This covered
only one of many potential places of exposure and did not capture the intensity and concentration of
exposure, which is also affected by factors such as the ventilation in the building and the number of smokers
(157). An alternative method of measuring exposure to passive smoking is the use of biomarkers, for
example nicotine and cotinine levels in blood, hair or urine. Serum cotinine was as shown in table 2 in the
introduction found to be associated with decreased BMD in both men and women (59). In a recent study Kim
et al. also found a significant relationship between self-reported second-hand smoke and urinary cotinine
among non-smokers (60). An advantage of using self-reported exposure, however, is that it measures
exposure over a longer time period or life span, whereas biomarkers are only useful in short-term studies
(157).
6
An acronym of Diet, Smoking, Alcohol and Exercise (Kost, Rygning, Alkohol og Motion)
[40]
Risk evaluation in fracture prevention
Discussion
Fracture risk in paper II was evaluated on the basis of T-scores (1 SD decrease in BMD) calculated using
persons of the same gender aged 20–39 years as reference. The use of T-scores and a selected reference
range is always debatable. T-scores were chosen here as they are most often used in the clinical setting
(including at the Danish sites that use RA to screen for osteoporosis). Furthermore, the DANHES population
was chosen as reference instead of the reference ranges provided by the manufacturer while no reference
data exist for the male population. Furthermore, almost identical findings were seen when the analyses were
performed using SD of mean BMD from the DANHES population. In paper IV DXA scans were performed at
two medical centres, and to secure comparable T-score values these were calculated using the same
reference databases for total hip and lumbar spine, respectively. Moreover, DXA scans were performed on
scanners of the same manufacture (Hologic), and the European Spine Phantom was used to check
agreement between the scanners. Moreover, the WHO categories used in papers II and III (normal: >-1,
osteopenia: between -1 and 2.5 and osteoporosis: ≤-2.5) are somewhat arbitrary and the prevalence of
persons with T-scores ≤ -2.5 (defined as osteoporosis) varies across skeletal sites and measurement
techniques (158), as also seen in paper IV. The -2.5 criterion seems to be especially problematic when used
as a diagnostic criteria in men (159). However, it was in papers II and III not used here for diagnostic
purposes but rather for explanatory purposes, as done by others (77). These categories are, however, not
far from the thresholds found in paper IV: T-score: -1.20 and -2.92 when T-scores were calculated from
DANHES database.
In papers II and III, the incidence of major osteoporotic fractures based on information from NPR was used as
outcome, which may have caused some misclassification. Probably only a small fraction of vertebral
fractures are included in NPR. Many vertebral fractures are asymptomatic or associated with only few
uncharacteristic symptoms, thus many vertebral fractures remain undiagnosed (1). To obtain the true
number of vertebral fractures, we should have performed sequential spine X-rays. Hip, vertebral, humerus
and forearm fractures were included in the assessment of prevalent and previous fractures. We choose
fracture types most frequently seen in and caused by osteoporosis (5) and used by FRAX to define major
osteoporotic fractures (97) as it was not possible to validate whether a given fracture was a low-energy
trauma. Moreover, it was only hip fractures that was possible to validate according to corresponding
surgery-codes, and even though NPR is considered comprehensive and of good quality there is, however,
potentially sources of bias for example an incomplete coverage from private hospitals (131). However, it is
again most likely that any misclassification would be non-differential as the NPR data were collected
independently of our research.
[41]
Risk evaluation in fracture prevention
Discussion
In the assessment of prevalent fractures (papers II and III) and secondary causes of osteoporosis (paper III)
ICD-10 codes were used that only include diagnoses after 1994, thus potentially underestimating the real
figures. This may also be the case for conditions included in the FRAX calculation in paper III. The DANHES
database did not include information on glucocorticoid use. In the case of missing information for a risk
factor, it is recommended that FRAX is calculated without this given risk factor (97;134). This could lead to
underestimation of the individual fracture risk by FRAX and thus misclassification of persons in relation to
their level of risk.
Confounding
In paper I, participants exposed to passive smoking in the adulthood home differed significantly from
participants not exposed. Possible confounding was therefore controlled for by including several different
covariates known to affect BMD and to be distributed differently throughout the exposure categories of
passive-smoking (155). These covariates were age, gender, BMI, fat percentage, smoking (pack-years),
alcohol consumption, leisure time physical activity, education level and history of hip fracture in first-degree
relatives. When these covariates were included, a significant association was still found between phalangeal
BMD and passive smoking. To further alleviate this potential source of bias associated with smoking, the
analysis was restricted to never-smokers and daily-smokers—and the association was still seen. The group of
never-smokers was used (instead of non-smokers, including former smokers) to avoid misclassification of
exposure to passive smoking and exposure due to former active smoking. However, the possibility of
unmeasured confounding should also be considered, as information on other factors known to affect BMD
such as e.g. use of glucocorticoids and premature menopause was not obtained. It cannot be ruled out that
these factors could explain some of the association between BMD and passive smoking.
[42]
Risk evaluation in fracture prevention
Conclusion and perspectives
6. Conclusion and perspectives
This thesis contributes to the growing body of evidence demonstrating a negative effect of passive smoking
on bone metabolism. In paper I, we found that exposure to long-term passive smoking in adulthood home
was negatively associated with phalangeal BMD. Moreover, the thesis demonstrates that that phalangeal
BMD measured by radiographic absorptiometry (RA) of the bones in the hand predicts the incidence of
major osteoporotic fractures in both sexes. In paper II, we found that the risk gradient, i.e. the relative risk of
fracture for each SD decrease in BMD when measured with RA, is almost similar to that obtained with DXA.
The thesis also supports that persons with a high combined risk (low phalangeal BMD measured with RA and
a high fracture probability by FRAX) has the highest rate of hip and major osteoporotic fractures. In paper III,
we demonstrated that FRAX and phalangeal T-score in combination analysed as continuous variables
performed overall best in prediction of major osteoporotic fractures, but not in the prediction of hip
fracture. In paper IV we demonstrated that applying a triage approach a high proportion of DXA scan could
be avoided giving a low proportion of false negatives, also among women with a high risk of fracture. The
scanner could potentially be used together with FRAX in order to identify the group of persons where the
highest rates of osteoporotic fractures are sustained. In conclusion, because of its low cost, easy access and
predictive capability, the RA scanner may well be used to pre-select and offer guidance at the point-of-care
to detect and select individuals at high risk of fracture with the need DXA, thus also avoiding unnecessary
DXA scans.
6.1 Perspectives
At present time relatively few studies have examined the association between passive smoking and BMD,
and even fewer have investigated the impact on fracture risk, however, mostly supporting a potential
negative effect. The magnitude of the impact of passive smoking demonstrated in our study indicates that
passive smoking constitutes a challenge in both a clinical and a public health perspective as it feed into the
knowledge on the harmful effect of passive smoking on mortality and morbidity. In Denmark as in many
other countries, the number of daily-smokers decreases and smoking bans has been introduced in most
public places decreasing the exposure to passive smoking. Socioeconomic inequality is closely mirrored by
smoking habits, and in the proportion exposed to passive smoking on a daily basis that is still high especially
among persons with lower socioeconomic status (43). Among persons with a basic general education the
proportion exposed is more than three times higher (28%) than among persons with a higher education (9%)
(43). Thus, from a public health perspective, the harmful effect of passive smoking is still highly relevant, and
potentially open to intervention. Finally, as found in studies of smoking cessation there is evidence that
[43]
Risk evaluation in fracture prevention
Conclusion and perspectives
smoking is a reversible risk factor for bone health (160). This could also be the case for the negative effect of
passive smoking, but more studies are needed to test this idea.
In this thesis, a range of other factors was also found to be associated with phalangeal BMD. Some of these
were well-known factors like age, smoking, body mass index (BMI), physical inactivity and hip-fracture in
first-degree relatives. Interestingly it was found that BMI was positively associated with phalangeal BMD
while fat percentage was negatively associated with phalangeal BMD. This has also been found by others
(161;162), and suggests that overall body composition, and not only BMI, needs to be taken into account
when evaluation the risk of low BMD. Moreover, it was found that higher educational levels among men
were negatively associated with phalangeal BMD. In a review it was found that conflicting evidence exist for
the relationship between osteoporotic fractures and level of income and education (163). This stands in
contrast to what is mostly seen for other health outcomes, where it is a general assumption that lower levels
of education has a negative effect on morbidity and mortality (164). Further studies should investigate, if
there is an inverse relationship between education and fracture risk.
In general, these findings emphasise that evaluation of the risk of osteoporosis and osteoporotic fracture at
an individual level is very complex. This complexity may be lost if the result of a risk factor based algorithm or
of a BMD measurement are applied blindly. Low BMD is only a risk factor for fracture; on the contrary it is a
continuum of fracture risk that increases when the BMD decreases. Thus, any threshold—such as the
definition of osteoporosis as T≤-2.5 - will be arbitrary and a simplification. Finally, BMD or a risk factor based
algorithm test will never be able to predict exactly, who will experience a fracture, since many other factors
will influence the risk of fracture. This must be taken into account when evaluating fracture risk at an
individual level.
The risk estimates obtained with RA compare well with DXA of hip and lumbar spine in the ability of
predicting osteoporotic fractures. In Denmark the diagnosis of osteoporosis and treatment decision primarily
rely on the result of DXA scans. Therefore, due to the relatively large differences seen between hip/spine
BMD and phalangeal BMD at an individual level, it is not recommend that the RA scanner could be used be
used to diagnose osteoporosis and substitute the DXA scanner. Moreover studies to monitor treatment
based on RA-selection are needed. However, in relation to guidelines recommending that persons with
FRAX-values over a certain threshold are offered treatment, it could be debated if there is sufficient
evidence for these recommendations. We found that the RA scanner could potentially be used together with
FRAX (or perhaps another algorithm) in order to identify the group of persons were the highest rates of
osteoporotic fractures are sustained. FRAX and phalangeal T-score in combination analysed as continuous
variables performed overall best in prediction of major osteoporotic fractures. However, when using the
[44]
Risk evaluation in fracture prevention
Conclusion and perspectives
different risk categories the overall predictive ability decreased for all variables and the combined risk score
of T-score and FRAX was no longer superior. This requires that we are careful when defining risk strata for
categorising participants in low and high risk groups.
A stated in the introduction, the U.S. Preventive Services Task Force (USPSTF) recommends screening with
DXA in all women aged 65+ years. Even though DXA at the hip and spine is the preferred method to evaluate
BMD and fracture risk (25), it is not a suitable method for population screening due to the high cost and low
availability. In principle, the purpose of screening is to improve the chances of survival or to prevent
progression of illness. This includes detection of preliminary stages of diseases and identification of those
who are at increased risk of developing the disease. In 1966 World Health Organization (WHO) presented 10
criteria that have to be met for new screening programmes. These criteria still apply and comprise:
knowledge about the disease, knowledge about suitable screening test, acceptable treatment/consensus on
treatment, and economic considerations (165). Today, there is seemingly sufficient evidence concerning
many of the criteria regarding the disease and potential treatment of osteoporosis. In contrast, too little is
known about effectiveness of screening programs for osteoporosis, cost-effectiveness and subject’s
experience with participating in screening-programs whether these use the RA-scanner or FRAX. The ROSEstudy is expected to provide knowledge of the effectiveness of a screening strategy using a two-step
screening approach by FRAX and DXA that may be implemented in the health care system. The outcome
covers effectiveness in prevention incident clinical fractures, cost-effectiveness, participation rate and
patient preferences.
However, the RA scanner may still well be used to pre-select individuals at high risk for fracture who need a
DXA. One suggestion is that RA scanner could be placed in group medical practices supporting the GP when
making decisions on who actually should be referred for DXA. It is also conceivable, that the scanner could
be placed in health centres, thus referring persons either to the GP or directly to a DXA-scanning facility.
Either way it should be in close cooperation with the primary health care sector. Alternatively it could be
investigated if the RA scanner could be used to enhance the DXA uptake in high-risk group where there
currently is a known problem (34). For example among fracture cases, who have a high risk of suffering a
new fracture—e.g. after five years after a hip fracture 20% will have suffered a new hip fracture and 57% a
another fracture (8). Also, there is a need of improving DXA uptake among persons in systemic treatment
with glucocorticoids. For more than ten year (166) we have been aware on the harmful effect of
glucocorticoids (167), but results from a recent Danish study showed that only approx. 50% in this group
have had a DXA scan (34), and a number action for damages are brought against the Danish Patient
[45]
Risk evaluation in fracture prevention
Conclusion and perspectives
Insurance (166). In all this imply that there are still many problems areas and many unresolved questions on
how we best manage the fracture preventive effort, the RA could well come into use.
[46]
Risk evaluation in fracture prevention
English summary
7. Summary
Fractures associated with osteoporosis are very common in the elderly population. In Denmark and other
countries, a case-finding strategy has been adopted, in which general practitioners are recommended to
refer persons with one or more risk factors for osteoporosis for measurement of bone mineral density (BMD)
by dual-energy X-ray absorptiometry (DXA). A large proportion of patients with high risk of fracture are not
diagnosed or treated, however. Central DXA (i.e. BMD measurement at the hip or spine) is unavailable in
many regions and it has been found that longer distances to DXA-facilities seem to be associated with lower
use of DXA. Other methods for measurement of BMD are available, including portable systems with low Xray exposure that can be used to identify individuals at high risk for fracture who need further examination.
Moreover, a number of risk factors besides BMD are associated with increased risk of osteoporotic fracture,
such as age, gender and body mass index (BMI), and several algorithms using clinical risk factors are available
to assess fracture risk, e.g. the Fracture Risk Assessment Tool (FRAX®) that predicts the 10-year probability of
hip and major osteoporotic fracture.
The overall aim of this thesis was to investigate the concept of fracture risk prediction and approaches for
measuring fracture risk. The primary objective was to evaluate a method for measuring BMD at the phalanx
for its ability to predict fracture and pre-select individuals at high risk for osteoporotic fracture. The thesis is
based on four studies with differing aims. The aim of Paper I was to investigate a possible new risk factor for
osteoporosis that may be considered when evaluating fracture risk, i.e. the association between phalangeal
BMD and passive smoking in the adulthood home, whereas the aim of paper II–IV was to examine the
performance of phalangeal BMD to predict osteoporotic fractures and the ability to identify osteoporosis.
The thesis is primarily based on data from the Danish Health Examination Survey (DANHES) (Papers I, II and
III) and from the Danish national registers (Paper II and III). Paper IV is based on data from the Danish Riskstratified Osteoporosis Strategy Evaluation study (ROSE). The studies used different statistical analyses, as
appropriate for their aims and designs: multiple linear regression analyses, survival analyses, and different
measures of agreement to assess diagnostic performance.
In the DANHES cohort of Danish women and men over 18 years of age phalangeal BMD was lower in persons
exposed to passive smoking in adulthood home, and a dose-response relationship was found. In both men
and women long-term passive smoking in adulthood at home as well as pack-years, age, body fat
percentage, light and sedentary physical activity, a history of hip fracture in first degree relatives were
negatively associated with phalangeal BMD, while BMI was positively associated with phalangeal BMD. In
men, also more than 10 years of education was negatively associated with phalangeal BMD. This relationship
between long-term passive smoking in adulthood at home was also seen in the group of never-smokers.
[47]
Risk evaluation in fracture prevention
English summary
When evaluating the performance of the RA scanner, a 1 SD decrease in phalangeal BMD (T-score) was
associated with approximately 40–45% higher risk of major osteoporotic fractures in both sexes. This
association was also found when excluding younger participants (<50 years) and for most types of fractures.
Persons with low phalangeal BMD (T-score ≤-2.5) had a nearly threefold higher risk of major osteoporotic
fractures compared with person with normal phalangeal BMD (T-score >-1). By combining the use of the RA
scanner and FRAX, the highest rate of major osteoporotic fractures was observed in persons with both a high
10–year fracture probability (FRAX≥20%) and a low phalangeal BMD (T-score≤-2.5). This group of
participants also had the highest rate of hip fractures. FRAX and phalangeal T-score in combination analysed
as continuous variables performed overall best in prediction of major osteoporotic fractures. In prediction of
hip fracture there was a tendency of phalangeal T-score performing worse than the other methods.
In Danish woman over 65 years with intermediate or high FRAX there was a moderate correlation between
phalangeal BMD and total hip as well as lumbar spine BMD (measured with DXA). Phalangeal BMD showed
reasonably good ability to predict low BMD at either total hip or lumbar spine. The mean difference between
phalangeal T-score and total hip T-score as well as lumbar spine T-score was low, but fairly large variations
were seen in the agreement of the two methods at an individual level. When applying a triage approach
based on NOS recommendations, over half of DXA scan could be avoided.
In conclusion, the findings presented in this thesis provide supporting evidence of a potential negative effect
on phalangeal BMD of long-term passive smoking in the adulthood home. It was also shown that phalangeal
BMD measured by radiographic absorptiometry (RA) of the bones in the hand can predict the incidence of
major osteoporotic fractures. Because of its low cost, easy access and predictive capability, the RA scanner is
likely to be useful to identify individuals at high risk for fracture who need further examination and DXA, thus
also avoiding unnecessary DXA scans. Moreover, the method could potentially be used together with FRAX in
order to identify the group of persons in whom the highest rates of osteoporotic fractures are sustained.
[48]
Risk evaluation in fracture prevention
Danish summary
8. Resumé
Osteoporose (knogleskørhed) er en folkesygdom og cirka hver anden kvinde og hver fjerde mand vil i løbet af
livet pådrage sig mindst et osteoporotisk knoglebrud. I Danmark og andre lande anvendes i dag en casefinding strategi til at identificere personer med mulig osteoporose, hvor personer med en eller flere
risikofaktorer for sygdommen anbefales henvist til undersøgelse og knoglescanning (DXA-scanning af ryg og
hofte for måling af knoglemineraltæthed). Knoglemineraltæthed er godt mål for knoglernes styrke, og lav
knoglemineraltæthed er således en vigtig risikofaktor for knoglebrud. Desværre er der en meget stor andel
blandt personer med en høj risiko for knoglebrud, der ikke diagnosticeres og kommer i behandling. Danske
studier har desuden vist, at afstanden til DXA-faciliteter har betydning for, om personer med en høj risiko for
knoglebrud er blevet DXA-scannet. Der findes imidlertid også andre metoder til at måle
knoglemineraltæthed, herunder transportable scannere med lav røntgen stråling, der potentielt kan
anvendes til at identificere de personer, som har en høj risiko for knoglebrud, og som bør have en DXAscanning og eventuelt efterfølgende behandling. Lav knoglemineraltæthed er dog ikke den eneste
risikofaktor for knoglebrud, også risikofaktorer som for eksempel høj alder, at være kvinde og lav body mass
index (BMI) m.fl. øger risikoen for brud. På baggrund af kliniske risikofaktorer er der udviklet flere algoritmer
(værktøjer), der kan bruges til at vurdere en persons risiko for osteoporose eller knoglebrud. En af disse er
”the Fracture Risk Assessment Tool (FRAX)”, som prædikterer en persons 10–års sandsynlighed for at få
hoftebrud eller et osteoporotisk koglebrud.
Det overordnede formål med denne ph.d.-afhandling var derfor først og fremmest at evaluere en fingerscanner, der måler knoglemineraltæthed i fingrenes knogler både i forhold til 1) hvor god den er til at
prædiktere knoglebrud (også sammenholdt med FRAX), og 2) hvor god den er til at udvælge de personer, der
bør have en DXA-scanning. Desuden var et sekundært formål at undersøge om passiv rygning potentielt er
en risikofaktor for lav knoglemineraltæthed. Afhandlingen består af i alt fire studier og er hovedsageligt
baseret på data fra KRAM-undersøgelsen, som var en stor kombineret spørgeskema- og
helbredsundersøgelse, der blev gennemført i 13 kommuner i Danmark i 2007–2008. Her fik i alt 15.544
foretaget en scanning af fingerknoglerne. Disse data blev i nærværende afhandling suppleret med data om
bl.a. knoglebrud fra de danske registre og med DXA-scanningsdata fra et igangværende osteoporose
screeningsstudie (ROSE-studiet). Til at analysere data blev der anvendt forskellige statistiske metoder
passende til de forskellige formål i de enkelte studier: multipel lineær regression (studie 1),
overlevelsesanalyse (studie 1 og 3) og forskellige analyser til at vurdere overensstemmelse (studie 3 og 4) og
diagnostisk præstation (studie 4).
[49]
Risk evaluation in fracture prevention
Danish summary
Afhandlingen viste, at knoglemineraltætheden i fingenes knogler var lavere blandt personer i KRAMundersøgelsen, der havde været udsat for passiv rygning i eget voksenhjem. Blandt både mænd og kvinder
var der en negativ sammenhæng mellem langvarig udsættelse for passiv rygning i eget voksenhjem og
knoglemineraltæthed i fingrenes knogler. Den sammenhæng var der også blandt personer, der aldrig selv
har røget. Desuden viste analyserne en negativ sammenhæng mellem knoglemineraltæthed og en række
andre udfaldsmål: alder, rygning, fedtprocent, fysisk inaktivitet samt oplevet hoftebrud blandt søskende eller
forældre. Modsat var der en positiv sammenhæng mellem BMI og knoglemineraltæthed. Blandt mænd var
der desuden en negativ sammenhæng mellem længerevarende uddannelse og knoglemineraltæthed.
Afhandlingen viste desuden, at der var en 40–45% større risiko for osteoporotisk knoglebrud per 1 SD fald i
knoglemineraltæthed i fingrene (dvs. per standard afvigelsesenhed fra gennemsnittet for raske unge af
samme køn). Den øgede risiko var der også for de fleste typer af knoglebrud analyseret separat (hofte-,
skulder-, ryg- og underarmsbrud). Personer med en knoglemineraltæthed, der kan kategoriseres som lav,
havde en tre gange så stor risiko for knoglebrud sammenlignet med personer, der havde en
knoglemineraltæthed i fingrene, der kan kategoriseres som normal. Når resultatet fra knoglescanningen blev
sammenholdt med deltagernes udregnede FRAX-værdier (deres 10–års sandsynlighed for brud), blev den
højeste knoglebrudsrate observeret blandt de personer, der både havde en lav knoglemineraltæthed og en
høj 10–års sandsynlighed for brud (≥20%). Denne gruppe havde også den højeste rate af hoftebrud.
I gruppen af kvinder fra ROSE-studiet, der både fik lavet en scanning af fingrenes knogler og en DXA-scanning
af hofte og ryg, var der en moderat korrelation mellem de to målinger. Den gennemsnitlige forskel mellem
de to metoder var lav, dog var der store individuelle forskelle. Finger-scanneren havde en forholdsvist god
diagnostisk præstation, og ved at definere grænseværdier for hvornår, der bør tilbydes hhv. behandling,
henvisning til DXA-scanning eller ikke gøres noget, kan over halvdelen af alle DXA-scanninger undgås.
Alt i alt tyder afhandlingens resultater på, at passiv rygning potentielt kan have en negativ effekt på
knoglerne; der var en negativ sammenhæng mellem langvarig udsættelse for passiv rygning i eget
voksenhjem og knoglemineraltæthed i fingrenes knogler. Desuden blev det vist, at finger-scanneren kan
prædiktere knoglebrud. Sammenholdt med den diagnostiske formåen og det faktum, at den er
transportabel, forholdsvist billig og nem at anvende, taler det for, at den kan benyttes til at identificere
personer, der bør tilbydes en DXA-scanning—og på den måde også undgå unødige DXA-scanninger.
Ydermere kan finger-scanneren potentielt anvendes i kombination med FRAX for at finde frem til den gruppe
af personer, hvor den højeste rate af knoglebrud opleves.
[50]
Risk evaluation in fracture prevention
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Papers I–IV
10. Papers I–IV
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Papers I–IV
[64]