Referat zum Thema Epidemiologie SS2007 Anhaltspunkte

Referat zum Thema Epidemiologie SS2007 Anhaltspunkte

Referat zum Thema Epidemiologie SS2007
Artikel: Klaz al. (2006): Severe acne vulgaris and tobacco smoking in young men. Journal of
Investigative Dermatology 126, 1749-1752
Betreuer:
PD. Dr. med. Michael Weichenthal
(Dermatologie, Tel.: 597-1537, E-Mail: [email protected] )
Dr. rer. nat. Amke Caliebe
(IMIS, Tel.: 597-3199, E-Mail: [email protected])
Anhaltspunkte
Einleitung/Studie.
Motivation, Zielsetzung
Zielgröße, Einflussgröße(n)
Studien-Design
Statistische Verfahren.
Prävalenz
χ2-Test
Odds-Ratio: Bedeutung und Aussage, Verhältnis zum Relativen Risiko
Konfidenzintervalle für Odds-Ratios
Logistische Regression: Interpretation der Ergebnisse, Berechnung der Odds-Ratios,
Adjustierung
Ergebnisse.
Darstellung, Bewertung und Interpretation der Ergebnisse
Diskussion.
Kritische Bewertung der Studie: Repräsentativität, Optimalität des Studiendesigns
Kausalität
Statistische Signifikanz gegenüber klinischer Relevanz
Störgrößen und Maßnahmen dagegen (Confounding, Bias)
Anwendbarkeit und Limitierung der Resultate
Folgestudien
In allen Punkten sollte ein Bezug zur vorliegenden Studie hergestellt werden. Die aufgeführten
Stichpunkte dienen nur der Orientierung. Die Setzung von Schwerpunkten, der Aufbau des Referats
und das eventuelle Einbringen von zusätzlichen Aspekten ist den Referenten überlassen.
ORIGINAL ARTICLE
Severe Acne Vulgaris and Tobacco Smoking
in Young Men
Itay Klaz1,2, Ilan Kochba1, Tzipora Shohat1, Salman Zarka1 and Sarah Brenner2
As the relationship between tobacco smoking and acne remains unclear, we examined the relationship between
cigarette smoking and severe acne in a large cohort of young men. Trained nurses interviewed subjects upon
discharge from compulsory military service, regarding family history, habits, and tobacco smoking habits. Data
was correlated with severe acne status, as diagnosed and coded by board-certified dermatologists. In total,
27,083 male subjects participated in the study from 1983 to 2003, of which 237 (0.88%) had severe acne, 11,718
(43.27%) were active smokers, and 15,365 (56.73%) were nonsmokers at the time of interviews. Active smokers
showed a significantly lower prevalence of severe acne (0.71%) than nonsmokers (1.01%) (P ¼ 0.0078). An inverse
dose-dependent relationship between severe acne prevalence and daily cigarette consumption became
significant from 21 cigarettes a day (w2 and trend test: Po0.0001), odds ratio: 0.2 (95% CI: 0.06–0.63). The study did
not aim to establish a temporal correlation, and passive smoking and acne treatments were not measured.
Previous in vitro and clinical studies strongly support an association with nicotine. We suggest a trial with
topical nicotine treatment for acne to further investigate this association.
Journal of Investigative Dermatology (2006) 126, 1749–1752. doi:10.1038/sj.jid.5700326; published online 27 April 2006
INTRODUCTION
Acne vulgaris affects over 80% of all individuals during
childhood and early adult life, with male subjects more
commonly affected than female subjects. Prevalence of
the severe form, characterized by multiple nodular and
postular-cystic lesions (Kligman and Plewig, 1976; O’Brien
et al., 1998) that can leave permanent physical and
psychological scars (Tan, 2004), ranges from 0.5% to 6%
in males, depending on age and the clinical grading
system used (Cunliffe and Gould, 1979; Rademaker et al.,
1989; Stern, 1992; Lello et al., 1995; Bataille et al., 2002).
In view of the controversial association between acne and
smoking (Mills et al., 1993; Schafer et al., 2001; Jemec et al.,
2002; Firooz et al., 2005), we studied the relationship in
a large group of young men being discharged from military
service.
It is crucial to emphasize that any positive effects found
must be traced to specific tobacco components that can be
therapeutically used without smoking (e.g., nicotine patches
or gums), to avoid any ‘‘legitimatizing’’ of smoking based on
its beneficial effects on health.
1
Medical Corps, Israel Defense Forces, Tel Aviv Sourasky Medical Center, Tel
Aviv, Israel and 2Department of Dermatology, Tel Aviv Sourasky Medical
Center, Tel Aviv, Israel
Correspondence: Dr Itay Klaz, Department of Dermatology, Tel Aviv
Sourasky Medical Center, 6 Weizman Street, Tel Aviv 64239, Israel.
E-mail: [email protected]
Abbreviation: IDF, Israel Defense Force
Received 18 September 2005; revised 8 March 2006; accepted 8 March
2006; published online 27 April 2006
& 2006 The Society for Investigative Dermatology
RESULTS
A total of 27,083 males participated in the study during the
years 1983–2003. Approximately 20 military nurses and over
30 board-certified dermatologists participated in the interview process and on the medical profile committees,
respectively. There was severe acne in 237 (0.88%) subjects
(Table 1). At the time of interview, 11,718 (43.27%) were
active smokers and 15,365 (56.73%) were nonsmokers;
0.37% (n ¼ 99) subjects did not report their smoking status.
Categories of the daily cigarette smoking of the 26,984
subjects were 0 cigarettes (n ¼ 15,365), 110 (n ¼ 2,746),
1120 (n ¼ 3,766), 21–30 (n ¼ 1,645), 31–40 (n ¼ 2,086), and
441 (n ¼ 1,475). The prevalence of severe acne was
significantly lower (P ¼ 0.0078) in active smokers (0.71%)
than in nonsmokers (1.01%). There was an inverse, dosedependent relationship between severe acne prevalence and
daily cigarette consumption. The prevalence of acne in the
aforementioned categories was 0.99, 1.27, 1.04, 0.18, 0.24,
and 0.20%, respectively. The inverse relationship became
statistically significant from 21 cigarettes a day (w2 and trend
test: Po0.0001).
When the relationship between daily cigarette smoking
and severe acne prevalence was controlled for father’s origin
and number of siblings, there was still a significant dosedependent association between cigarette consumption and
acne (see Table 2 and Figure 1).
DISCUSSION
The association between acne and smoking has been studied
extensively with varying results. Mills et al. (1993) reported
that 19.7% of 96 male acne vulgaris patients and 12.1% of 60
www.jidonline.org 1749
I Klaz et al.
Acne and smoking in men
Table 1. Prevalence of severe acne and demographic data on subjects
Origin of subjects (% of acne subgroup: no, yes)a
Severe acne
No. of subjects
subgroup
(%) (n=27,073)
7s.d.
West
East
Israel
siblings 7s.d.
No
26,846 (99.12%)
21.8471.52
40.85%
52.65%
6.49%
4.1272.29
21.8571.16
48.93%
43.78%
7.30%
3.5271.70
Yes
237 (0.88%)
Age (years)
Average no. of
a
Descendants of West European and North and South American-born fathers were categorized as ‘‘Western’’, and descendants of fathers born in Eastern
Europe, Africa, and Arab countries were categorized as ‘‘Eastern’’.
Table 2. Multiple logistic regression model for acne
prevalence and cigarette consumption
Variable
Odds ratio
95% confidence
interval
Daily cigarette smoking
0 (reference)
1
1–10
1.28
0.88
1.86
11–20
1.06
0.74
1.52
20–30
0.20
0.06
0.63
30–40
0.28
0.11
0.68
440
0.25
0.08
0.78
Western vs Israeli
1.13
0.67
1.89
Western vs Eastern
0.89
0.67
1.19
0.90
0.84
0.98
Ethnic origin
No. of siblings (ordinal variable)
0.6
Severe acne %
0.5
0.4
0.3
0.2
0.1
0
0
1−10
11−20 21−30 31−40 41−50
Cigarettes smoked per day
51−90
Figure 1. Relationship between prevalence of severe acne (n ¼ 237) and
number of cigarettes smoked per day.
female ones were smokers, which was significantly less than
national statistics (Mills et al., 1993). Jemec et al. (2002)
found that smoking was not significantly associated with acne
in a random sample of 186 subjects (odds ratio: 0.54, 95%
CI: 0.17–1.78). Schafer et al. (2001) found in 102 smokers
compared with 184 nonsmokers that acne was significantly
more prevalent in active smokers, but when only 15–40-yearolds were taken into account, there was no association
1750 Journal of Investigative Dermatology (2006), Volume 126
between acne and smoking. Firooz et al. (2005) compared
smoking status of 293 acne patients to 301 patients suffering
from other dermatological conditions. After accounting
for acne’s higher prevalence and greater severity in men,
no significant correlation was found.
Our study in which all subjects were diagnosed by
board-certified dermatologists is the largest sample to date.
Although different dermatologists had participated during the
study years, all used the same diagnosis criteria, thereby
compensating in part for possible inter-observer variability.
Our cohort consisted only of males, which probably skewed
the results in view of the known differences in prevalence and
clinical grading between male and female subjects, most
probably owing to hormonal differences (both androgenic
and oral contraceptive related). In this respect, a study carried
out only on males has the advantage of eliminating certain
confounding gender-specific factors, but any generalizing of
results must be performed with caution.
Although the prevalence of severe acne among smokers
of 10–20 cigarettes per day was higher than the group of
0–10 per day, this finding was not statistically significant.
The inverse relationship became significant from 21 cigarettes a day. A study measuring plasma cortisol levels in
smokers found a similar effect (del Arbol et al., 2000). The
differences between light and heavy smokers may be related
to the effect of nicotine on nicotinic cholinergic receptors. At
low doses, nicotine stimulates acetylcholine receptors,
whereas high doses of nicotine selectively block them (Seyler
et al., 1986). The aspect of passive smoking was not included
in this study owing to the difficulty of accurately estimating
exposure in such a large subject population and long study
duration.
All dermatologists were obliged to refer any patient with
severe acne for official medical coding. Based on internal
auditing, we estimate an under-diagnosis of up to 20% owing
mostly to the large number of participating dermatologists,
some of whom were civilian physicians less familiar with the
military referral directives. The large sample size and the lack
of any selective bias linked with smoking habits on the
diagnosis or medical coding procedure probably compensated for this under-diagnosis. Another limitation of our study
was the inclusion of only patients with severe acne, most of
whom were referred to a dermatologist for retinoid treatment.
However, as the clinico-pathological pathways of both
moderate and severe acne involve inflammatory processes,
I Klaz et al.
Acne and smoking in men
we assumed that the findings can be applied to a broad range
of disease presentations, with possible exclusion of comedonic only grades.
Social factors may also have contributed to the findings of
our study. Individuals with higher self-awareness of health
status, hence less prone to smoking, may have sought
diagnosis and treatment for acne more vigorously than the
smoking population. However, an earlier study of the same
population found similar rates of smoking among subjects
diagnosed with asthma as compared to healthy individuals
(Zimlichman et al., 2004), supporting our assumption that
health awareness was not a major reason for the present
findings. Emotional stress is a known risk factor for smoking
(Simantov et al., 2000). More smokers due to stress are
expected among the severe acne group. Our finding to
the contrary might have significance, but this remains to be
verified.
The adverse effects of tobacco on the skin are well known
(Misery, 2004). Several studies suggest a possible protective
action of nicotine against the development of inflammatory
skin disorders. The nicotine constituent might even be
beneficial to certain diseases. Positive effects were found in
pemphigus, ulcerative colitis, pyoderma gangrenosum,
aphthous stomatitis, and herpes simplex (Wolf et al., 2004).
Nicotine enhanced keratinocyte adhesion, differentiation,
and apoptosis and inhibited keratinocyte migration (Grando
et al., 1995). Nicotine also inhibited inflammation through
effects on the central and peripheral nervous systems (Sopori
et al., 1998). Nicotine altered immune responses by directly
interacting with T cells. Transdermal application of nicotine
(patches) was followed by a decrease in response to sodium
lauryl sulfate as well as the erythema response to UVB (Mills,
1998). Paradoxically, nicotine worsened buccal inflammation, in contrast to ameliorating small bowel and colonic
inflammation (Eliakim and Karmeli, 2003).
Using current acne status at the time of discharge from
service time excluded subjects previously diagnosed with
severe acne whose diagnosis changed owing to clinical
improvement. However, owing to the cross-sectional nature
of the study, it was not possible to delineate the time
sequence of severe acne development and smoking. Some
subjects may have started smoking after the onset, or even as
a consequence, of acne, or vice versa. The dose-dependent
relationship might indicate that smoking more than 20
cigarettes a day contributed somewhat in improving preexisting acne. Future prospective studies are needed to
establish time sequence and therefore cause and effect.
The following limitations of this large-scale study must be
noted: the inclusion of only males and only severe acne; and
the exclusion from the study protocol of acne therapy, data
on the temporal relationship between smoking and development of acne, and the effect of passive smoking.
The underlying causal mechanisms of the relationship
between severe acne and smoking need further clarification,
but previous in vitro and clinical studies strongly support
an association with nicotine. We suggest a randomized
controlled trial with topical nicotine treatment for acne to
further investigate the significant inverse correlation between
cigarette smoking and severe acne vulgaris observed in
our study.
MATERIALS AND METHODS
The prospectively established database of the Israel Defense Forces
(IDFs) Medical Corps during the years 1983–2003 served as the basis
for this cross-sectional study.
Subjects
The study population came from a large-scale ongoing prospective
survey of health behavior and attitudes, conducted among randomly
selected soldiers of the IDFs. The survey systematically collected
a representative sample of IDFs men at discharge from compulsory
3-year military service, ranging 21–22 years of age, as previously
described (Kark and Laor, 1992). The IDFs Medical Corps Review
Board approved the survey as well as the manner in which informed
consent was obtained from the subjects.
Each IDFs recruit went through medical tests at intake and in
the event of a change in health status during service. Board-certified
dermatologists made the diagnosis of severe acne according to
the military criteria based on the Kligman and Plewig grading (1976)
and the Leeds acne grading system (O’Brien et al., 1998); severe
acne is defined as the presence of nodular and postular-cystic
lesions. The diagnosing dermatologist then referred the soldiers to
a military medical profiling committee for a review of the required
clinical evidence and an official numerical encoding of the
diagnosis. The data referred to the committee did not include the
soldier’s smoking status. We used this code, called the medical
military profile, to classify subjects with and without severe acne
vulgaris.
Data collection
Subjects were asked to participate in the study on the day of
discharge from military service. Trained nurses from the IDF Public
Health Branch interviewed them about smoking status (current
smoker, past smoker, or never smoked), average number of cigarettes
smoked a day (0–10, 10–20, 20–30, 30–40, 440), father’s country of
origin, found to correlate with smoking habits by Zimlichman et al.
(2004), and number of siblings, found to be reversely associated with
adult social class by Blane et al. (1999). Nurses did not know the
current acne coding status of the soldier. Current acne status at
discharge time as reflected by the medical profile was collected
separately for all participants and analyzed against the above
parameters.
Statistical analysis
Data were analyzed by the Statistical Analysis System version 9.2.
(SAS Institute Inc., Cary, NC, USA). Proportions of smoking, family
origin, and number of siblings were compared between subjects with
severe acne and those without, using w2 test. Mean number of
cigarettes smoked per day was compared using analysis of variance. Trend tests were performed in 2 N tables when appropriate.
Multiple logistic and linear regression analyses were carried out
taking severe acne prevalence as the dependent variable. All models
included ethnic origin, number of siblings, and quantitative
parameter of smoking status. The LOGISTIC and GLM procedures
were used. Results were expressed as mean7s.d., or n (%); Po0.05
was considered significant.
www.jidonline.org 1751
I Klaz et al.
Acne and smoking in men
CONFLICT OF INTEREST
The authors state no conflict of interest.
Kark JD, Laor A (1992) Cigarette smoking and educational level among young
Israelis upon release from military service in 1988 – a public health
challenge. Isr J Med Sci 28:33–7
ACKNOWLEDGMENTS
Kligman AM, Plewig G (1976) Classification of acne. Cutis 17:520–2
We thank Dr Amir Tirosh and Dr Eyal Zimlichman of the IDFs Medical
Corps for their helpful suggestions. We thank the nurses and medics of the
Army Medical Corps Health Branch for administering the questionnaires.
Funding was provided by the Medical Corps of the IDFs. No external funding
was used.
Lello J, Pearl A, Arroll B, Yallop J, Birchall NM (1995) Prevalence of acne
vulgaris in Auckland senior high school students. N Z Med J 108:287–9
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R. Bender1
Logistische Regression
A. Ziegler2
St. Lange3
Mit Hilfe der linearen Regression lässt sich der Einfluss einer
oder mehrerer erklärender Variablen X1,...,Xm (z.B. X1 = Alter,
X2 = Geschlecht und X3 = Rauchen) auf eine stetige Zielvariable Y
(z.B. Y = systolischer Blutdruck) statistisch untersuchen (3).
Liegt nur eine erklärende Variable X vor, spricht man von der
einfachen linearen Regression (engl.: simple linear regression)
und verwendet die Geradengleichung (5)
Y = α + βX .
Im Fall mehrerer erklärender Variablen X1,...,Xm liegt das Modell
der multiplen linearen Regression (engl.: multiple linear regression) vor, das durch die Gleichung
Y = α + β1X1 + ... + βmXm
beschrieben wird (3). Die Bedeutung der multiplen Regressionsmodelle in der medizinischen Statistik liegt zum einen darin,
den gemeinsamen Einfluss mehrerer Variablen auf eine Zielvariable untersuchen zu können und zum anderen in der Möglichkeit, den interessierenden Effekt einer Variable bezüglich anderer Variablen zu adjustieren, um eine Verzerrung (engl.: bias)
bei der Effektschätzung zu reduzieren (3).
Logistische Regression
Die logistische Regression (engl.: logistic regression) kommt
als Auswertungsmethode in Frage, wenn man den Einfluss erklärender Variablen X1,...,Xm auf eine Zielvariable Y untersuchen
möchte, und Y binäres Messniveau besitzt (z.B. Y = Krankheit
ja/nein). Da Y nur die beiden Werte 1 = ja und 0 = nein annehmen kann, ist die Anwendung der linearen Regression in der Regel nicht sinnvoll. Betrachten wir zur Modellentwicklung zunächst den einfachen Fall von nur einer erklärenden Variable X.
Der Schlüssel zur quantitativen Beschreibung eines Zusammenhangs zwischen Y und X liegt darin, anstelle von Y die Wahrscheinlichkeit für den Eintritt des Zielereignisses p = P(Y = 1) zu
modellieren. In medizinischen Anwendungen ist die Wahrscheinlichkeit p meist ein Risiko für eine bestimmte Krankheit.
Während Y nur die beiden Ausprägungen 1 und 0 besitzt, kann
das Risiko p jede beliebige Zahl zwischen 0 und 1 annehmen.
Die Chance (engl.: odds) p/(1-p) kann jede beliebige positive Zahl
annehmen (2) und der Logarithmus der Chance log[p/(1-p)], genannt logit, besitzt die ganze reelle Zahlenmenge als Wertebereich. Damit ist es häufig sinnvoll, eine lineare Beziehung zwischen dem logit von p und X anzunehmen, d.h.
T 11
logit (p) = log[p/(1-p)] = α+βX ,
was mathematisch äquivalent ist mit
p=
exp (α + βX)
1 + exp (α + βX)
exp bezeichnet hierbei die Exponentialfunktion. Der rechte
Term obiger Gleichung stellt die so genannte logistische Funktion dar, daher erklärt sich die Bezeichnung »logistische Regression«. Die Erweiterung auf ein multiples Modell mit mehreren erklärenden Variablen erhält man wie bei der linearen Regression,
indem βX ersetzt wird durch die Linearkombination
Institut
AG Epidemiologie und Medizinische Statistik (Leitung: Prof. Dr. M. Blettner), Fakultät für
Gesundheitswissenschaften , Universität Bielefeld
2
Institut für Medizinische Biometrie und Statistik (Direktor: Prof. Dr. A. Ziegler), Universitätsklinikum Lübeck,
Medizinische Universität zu Lübeck
3
Abteilung für Medizinische Informatik, Biometrie u. Epidemiologie (Direktor: Prof. Dr. H.J. Trampisch), RuhrUniversität Bochum
1
Korrespondenz
PD Dr. rer.biol.hum. Ralf Bender · AG Epidemiologie und Medizinische Statistik
Fakultät für Gesundheitswissenschaften
Universität Bielefeld · Postfach 100131 · 33501 Bielefeld · E-Mail: [email protected]
Bibliografie
Dtsch Med Wochenschr 2002; 127: T 11–T 13 · © Georg Thieme Verlag Stuttgart · New York · ISSN 0012-0472
it tsai ttSa t S
Serie | Statistik
Lineare Regression
- Artikel Nr. 14 der Statistik-Serie in der DMW -
Tab.1
Einfache logistische Regressionsanalyse für die Entwicklung einer diabetischen Nephropathie nach 6 Jahren bei 480 Typ 1 Diabetikern.
Risikofaktor
Regressionskoeffizient
Achsenabschnitt
HbA1c
Tab.2
Standardfehler
p-Wert
– 5,089
0,731
0,0001
+0,457
0,089
0,0001
Differenz für Odds Ratio
Odds Ratio
95% Konfidenzintervall
1%
1,58
1,33 – 1,88
Multiple logistische Regressionsanalyse für die Entwicklung einer diabetischen Nephropathie nach 6 Jahren bei 480 Typ 1 Diabetikern.
Statistiken
Risikofaktor
Regressionskoeffizient
Standardfehler
p-Wert
Differenz für Odds Ratio
Achsenabschnitt
– 8,980
1,736
0,0001
HbA1c
+0,464
0,091
0,0001
1%
1,59
1,33 – 1,90
diast. Blutdruck
+0,048
0,019
0,0148
5mm Hg
1,27
1,05 – 1,54
95% Konfidenzintervall
Diabetesdauer
+0,004
0,018
0,8220
5 Jahre
1,02
0,85 – 1,22
Geschlecht
– 0,025
0,249
0,9212
männl. vs. weibl.
0,98
0,60 – 1,59
β1X1+...+βmXm. Zur Schätzung der logistischen Regressionskoeffizienten werden in der Praxis iterative Algorithmen eingesetzt.
T 12
Odds Ratio
Wie bei der linearen Regression muss auch bei der logistischen
Regression die Modellgüte (engl.: goodness-of-fit) untersucht
werden. Auf die entsprechenden Methoden können wir hier
nicht eingehen. Der interessierte Leser sei auf die Literatur verwiesen (5). Außer der logistischen Regression für binäre Zielvariablen gibt es Modellerweiterungen für nominale und ordinale
Daten. Das bekannteste Modell ist hierbei das proportionale
Odds Modell für ordinale Zielvariablen (1).
Beispiel
Mit Hilfe der logistischen Regression wurde der Einfluss von Risikofaktoren auf die Entwicklung der diabetischen Nephropathie bei Typ 1 Diabetikern untersucht (7). Betrachten wir zunächst nur das glykierte Hämoglobin (HbA1c) als Risikofaktor. In
Dtsch Med Wochenschr 2002; 127: T11–T13 · R. Bender, Logistische Regression
Risiko für Nephropathie
Da in der medizinische Forschung oftmals binäre Zielvariablen
auftreten, wird die logistische Regression in der Praxis sehr häufig angewendet. Eine besondere Stellung erhält das logistische
Regressionsmodell dadurch, dass man sowohl für prospektive
Kohortenstudien als auch für retrospektive Fall-Kontroll Studien sinnvoll interpretierbare Effektschätzer erhält. Das gebräuchliche Effektmaß in der Epidemiologie ist das Odds Ratio
(OR), das als Verhältnis der Chancen zwischen exponierten und
nicht exponierten Personen definiert ist (2). Aus dem Regressionskoeffizient β einer logistischen Regression kann direkt das
Odds Ratio berechnet werden durch OR = exp(β). In einem multiplen Modell kann für die Beziehung zwischen Y und einer erklärenden Variablen Xj das aus βj berechnete ORj =exp(βj) als das
nach allen anderen erklärenden Variablen adjustierte Odds Ratio betrachtet werden. Bei stetigen erklärenden Variablen bezieht sich der Wert des Odds Ratios auf die Erhöhung der erklärenden Variablen um jeweils 1 Einheit bzw. auf den Anstieg einer vorher definierten klinisch relevanten Differenz (siehe Beispiel).
1,0
0,9
0,8
0,7
0,6
0,5
0,4
0,3
0,2
0,1
0,0
4
5
6
7
8
9
10 11 12 13 14 15 16
HbA1c (%)
17
Abb.1 Risiko für die Entwicklung einer diabetische Nephropathie
nach 6 Jahren in Abhängigkeit vom HbA1c bei Typ 1 Diabetes, berechnet mit Hilfe einfacher logistischer Regressionsanalyse (n = 480).
der einfachen logistischen Regressionsanalyse ist das HbA1c ein
signifikanter Risikofaktor (Tab.1). Die Stärke des Effekts lässt
sich mit Hilfe des Odds Ratios angeben. Pro Einheit des HbA1c
(1%) steigt die Chance nach 6 Jahren eine diabetische Nephropathie zu entwickeln um den Faktor von OR = 1.6 (95% Konfidenzintervall 1,3–1,9).
Dieser Zusammenhang lässt sich auch grafisch veranschaulichen, indem das Risiko als Funktion des Risikofaktors dargestellt wird (Abb.1). Für HbA1c-Werte im Normalbereich (4,3–
6,1%) liegt das Risiko, eine diabetische Nephropathie zu entwickeln, unter 10%, während es bei extrem hohen HbA1c-Werten
von 16% und höher auf über 90% ansteigt.
Diese Ergebnisse verdeutlichen die starke Assoziation zwischen
der Stoffwechseleinstellung und dem Risiko diabetischer Spätschäden bei Typ 1 Diabetes. Um zu zeigen, dass eine Reduktion
des HbA1c auch zu einer Reduktion des Risikos für diabetische
Spätschäden führt, benötigt man allerdings entsprechende Er-
Literatur
Tab.3
Übersetzung (deutsch – englisch).
1
Englisch
erklärende Variable
explanatory variable
Zielvariable
response variable
einfache lineare Regression
simple linear regression
multiple lineare Regression
multiple linear regression
adjustieren
adjust
Verzerrung
bias
logistische Regression
logistic regression
binär
binary
Chance
odds
Kohortenstudie
cohort study
Fall-Kontroll Studie
case-control study
Regressionskoeffizient
regression coefficient
adjustiertes Odds Ratio
adjusted odds ratio
Modellgüte
goodness-of-fit
proportionales Odds Modell
proportional odds model
2
3
4
5
6
7
Bender R, Grouven U. Ordinal logistic regression in medical research.
J R Coll Physic London 1997; 31: 546–551
Bender R, Lange S. Die Vierfeldertafel. Dtsch Med Wochenschr 2001;
126: T36–T38
Bender R, Ziegler A, Lange S. Multiple Regression. Dtsch Med Wochenschr 2002; 127: T8–T10
The Diabetes Control and Complications Trial Research Group. The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes
mellitus. N Engl J Med 1993; 329: 977–986
Hosmer DW, Lemeshow S. Applied Logistic Regression. Wiley, New
York, 1989
Lange S, Bender R. (Lineare) Regression/Korrelation. Dtsch Med Wochenschr 2001; 126: T33–T35
Mühlhauser I, Bender R, Bott U, Jörgens V, Grüsser M, Wagener W,
Overmann H, Berger M. Cigarette smoking and progression of retinopathy and nephropathy in type 1 diabetes. Diabet Med 1996; 13:
536–543
Statistiken
Deutsch
gebnisse einer randomisierten klinischen Therapiestudie, wie
z.B. den Diabetes Control and Complications Trial (DCCT, 4).
Neben dem glykierten Hämoglobin gibt es noch weitere Risikofaktoren, die hier in Betracht gezogen werden müssen, vor allem Blutdruck, Diabetesdauer und möglicherweise das Geschlecht. Die Ergebnisse einer multiplen logistischen Regressionsanalyse zeigen, dass das HbA1c und der diastolische Blutdruck signifikante Risikofaktoren darstellen, während ein Effekt der Diabetesdauer und des Geschlechts nicht nachweisbar
ist (Tab.2).
T 13
Zur Darstellung des Odds Ratios wurde für den diastolischen
Blutdruck eine Differenz von 5mm Hg und für die Diabetesdauer von 5 Jahren gewählt, da eine Erhöhung dieser Risikofaktoren
um jeweils eine Einheit (1mm Hg bzw. 1 Jahr) nicht als klinisch
relevante Änderung angesehen wird. Es lässt sich somit darstellen, dass bei einem Anstieg des diastolischen Blutdrucks um
5mm Hg die Chance, nach 6 Jahren eine diabetische Nephropathie zu entwickeln, um den Faktor von OR = 1,3 (95% Konfidenzintervall 1,1–1,5) erhöht ist. Für das HbA1c erhält man ähnliche
Resultate wie im einfachen Modell, d.h. in diesem Fall gibt es
kaum Unterschiede zwischen den rohen und den adjustierten
Resultaten bezüglich des Zusammenhangs zwischen der Stoffwechseleinstellung und dem Risiko einer diabetischen Nephropathie. Die englischen Bezeichnungen der hier diskutierten Begriffe zeigt Tab.3.
kurzgefasst: Mit Hilfe der multiplen logistischen Regression lässt sich der Einfluss erklärender Variablen (Risikofaktoren) auf eine binäre Zielvariable (z.B. Krankheit ja/
nein) untersuchen. Aus den Regressionskoeffizienten
lassen sich adjustierte Odds Ratios als Maß für die Stärke
des Zusammenhangs berechnen.
Dtsch Med Wochenschr 2002; 127: T11–T13 · R. Bender, Logistische Regression