Fracture Risk and Risk Factors for Osteoporosis

Results From Two Representative Population-Based Studies in North East Germany (Study of Health in Pomerania: SHIP-2 and SHIP-Trend)

Background: As the population ages, diseases of the elderly are becoming more common, including osteoporosis. Ways to assess the risk of fracture and the distribution and effects of known risk factors for osteoporosis will be important in planning for future healthcare needs, as well as in the development of preventive strategies.

Methods: The study population included 6029 men and women aged 20–90 who underwent examination in the second follow-up wave of the Study of Health in Pomerania (SHIP-2) or in the basal SHIP-Trend Study. The risk of fracture was estimated on the basis of quantitative ultrasonography of the calcaneus. Prior fractures and risk factors for osteoporosis were ascertained in standardized interviews.

Results: 4.6% of the male subjects and 10.6% of the female subjects were judged to have an elevated risk of fracture. The corresponding percentages among subjects over age 65 were 8.8% for men and 28.2% for women. Even among subjects under age 55, risk factors for osteoporosis were associated with lower bone stiffness: the mean stiffness index was 103/98 (men/women) without risk factors, 99/96 with one risk factor, and 93/95 with more than one risk factor. Logistic regression analysis yielded an odds ratio of 1.89 (95% confidence interval: 1.44–2.50; p<0.01) for prevalent fractures among subjects aged 75 and older compared to subjects under age 55.

Conclusion: The data indicate a high prevalence of osteoporosis from age 65 onward. These findings are consistent with those of other studies from Germany and across Europe. Younger men and women should already begin taking steps to counteract modifiable risk factors.

According to current predictions, the proportion of the population older than 65 in the OECD countries will rise to 21% by 2030. In Germany, this proportion is predicted to be as high as 29% (1). Age-related diseases, such as osteoporosis, will therefore gain in importance (2). Because of the enormous relevance of osteoporosis for the individual and the substantial costs to the healthcare system (2), epidemiological osteoporosis research is of essential importance (2–5). To date, however, only few studies have reported data on the prevalence and risk factors for osteoporosis in Germany (3–5). At the same time, such investigations (3–5) are often limited in terms of the selection of study subjects. For example, studies to date have included only information on women from the 45^th year of life (5), men and women from the 50^th year of life (4), or members of an insurance scheme from the 50^th year of life (3). In addition to the prevalence of osteoporosis, risk factors for osteoporotic bone changes are also of interest as knowledge and awareness of these helps to deploy preventive measures in a targeted fashion (6, 7).

In the present study, we analyzed primarily—on the basis of quantitative ultrasound (QUS) measurements—how common a high risk of osteoporotic fractures is in men and women of different ages in the North East of Germany. Secondarily, we investigated how often osteoporotic fractures and risk factors for osteoporosis occur and whether their occurrence is associated with reduced bone stiffness.

Methods

Study population

The study population is based on data from two independent epidemiological cohorts: the Study of Health in Pomerania (SHIP) and SHIP-Trend (8, 9). Both are based on representative samples of the population aged 20–79 in the region of Western Pomerania (8, 9). In SHIP, two-stage cluster sampling was used, whereas SHIP-Trend used a random sample stratified by age and sex. The baseline investigation in SHIP (SHIP-0) took place between 1997 and 2001, the 11-year follow-up investigation (SHIP-2) at the same time as SHIP-Trend in 2008 to 2012. For the analysis we used data from SHIP-2 (30–90 years) and SHIP-Trend (20–79 years) because these studies included quantitative ultrasound (QUS) measurements. 6067 subjects (90%) with valid QUS measurements were eligible for the study; 11 pregnant women and 27 participants with renal failure were excluded. The study population included 6029 participants.

Risk factors for osteoporosis

We selected risk factors for osteoporosis on the basis of the guidelines of the Umbrella Organization for Osteology (“Dachverband Osteologie”) (10) and the World Health Organization’s fracture risk assessment tool (FRAX) (11). We used standardized interviews to inquire about these risk factors and categorized them as modifiable and non-modifiable risk factors.

Modifiable factors included:

• Underweight (body mass index [BMI] <20 kg/m²)
• Nicotine use
• High-risk alcohol use (men ≥30 g/day, women ≥20 g/day)

Non-modifiable risk factors included:

• Liver disease
• Inflammatory joint disorders
• Diabetes mellitus
• Hyperthyroidism
• Previous fractures in subjects from age 55
• Medication with steroids, aromatase inhibitors, androgen antagonists, antiepileptic drugs, sedatives, opiates, neuroleptic drugs, antidepressants, or glitazone in women.

In all our analyses of risk factors and earlier fractures, we excluded participants for whom the relevant data on modifiable or non-modifiable risk factors were lacking.

Quantitative ultrasound measurements

We used the Achilles InSight (GE Medical Systems Ultrasound, GE Healthcare, Chalfont St Giles, UK) system to perform the QUS measurements. The system measures the speed of sound (SOS) and the frequency dependent attenuation (FDA; broadband ultrasound attenuation [BUA]) of a sound wave by penetrating the heel bone and calculates the bone stiffness index on this basis: (0.67×BUA) + (0.28×SOS) −420. The bone stiffness index indicates the risk of an osteoporotic fracture (12). It can also be expressed as a t value—that is, as the individual deviation from the mean for young adults—in standard deviations (SD). A high risk for osteoporotic fractures exists in t values <−2.5 SD, a medium risk between -1 and −2.5 SD, and a low risk > −1 SD. A high risk indicates osteoporosis (12).

Statistical analysis

Categorical variables were reported as numbers and proportions, and continuous variables as medians (1^st–3^rd quartile). The results were visualized as box plots or bar charts. Significance tests were used as follows: group differences in the bone stiffness index, which was distributed approximately normally, were tested by using the t test, group differences in the categorical variables by using the chi-square test. A p-value <0.05 was defined as significant. Logistic regression analysis was used to determine whether fractures were associated with subjects’ sex, age, and study cohort. We reported odds ratios (OR) and 95% confidence intervals (CI). We standardized all data to the age/sex structure of the population of the federal state of Mecklenburg–Western Pomerania as at year-end 2010. Furthermore we used a sensitivity analysis for weighting the drop-out between SHIP-0 and SHIP-2 and for non-responses in SHIP-Trend—based on age, sex, and health-relevant data. We used SAS to conduct all our analyses.

The eBox lists further methodological details.

eBox

Risk factors for osteoporosis

Results

Table 1 shows data on lifestyle, medication use, and risk factors for osteoporosis.

Table 1

Charakteristics of study population

Bone stiffness and osteoporotic fracture risk

For both sexes the QUS measurements fell in all age groups. The highest values were seen in the youngest age group (20–34 years) and the lowest in the oldest age group (≥ 75 years). The median of the bone stiffness index at age 20–34 years versus ≥ 75 years in men was 101 versus 91.3 and in women, 96.8 versus 71.4. In male subjects the measurements fell evenly over the age groups under study, whereas in female subjects, the decrease was minimal between the ages of 20 years and 54 years but deteriorated in the older age groups. The difference by sex of the bone stiffness index therefore widens greatly from age 55 onward, with the values in women dramatically dropping compared with those of men (Figure 1). Altogether 4.6% of men and 10.6% of women were at high risk of fracture. The sensitivity analysis provided comparable results: a high risk of fracture in 5.1% of men and 10.6% of women. In the individual age groups, the following proportions of men and women were found to be at high risk of fracture:

Figure 1

Bone stiffness index by age group and sex in 6029 SHIP-2 and SHIP-Trend subjects

Figure 2

Fracture risk based on the bone stiffness index by age group and sex in 6029 SHIP-2 and SHIP-Trend subjects

• 20–34 years: 1.5% and 0.9%
• ≥ 50 years: 7.2% and 17.9%
• ≥ 65 years: 8.8% and 28.2%
• ≥ 75 years: 12.5% and 46.0%.

Previous fractures

225 (11%) of SHIP-2 subjects and 236 (6%) of SHIP-Trend subjects reported previous fractures (Table 2). The proportions of study participants with previous fractures for all age groups under study were higher in SHIP-2 than in SHIP-Trend, as SHIP-2 recorded all fractures, whereas SHIP-Trend recorded only proximal humeral, hip, femoral, and vertebral fractures.

Table 2

Previous fractures in 6009 SHIP-2 and SHIP-Trend subjects by age group and sex

Men and women ≥ 55 years of age who reported previous fractures had a significantly lower bone stiffness index than subjects without fractures. The median of the bone stiffness index in study participants without fracture was 93.2 (men) and 79.9 (women) and with fracture, 89.8 (men) and 75.3 (women), with the t test result p = 0.02 (men) and p<0.01 (women). Accordingly, people older than 55 with previous fractures were significantly more often at high risk for osteoporotic fractures than people without previous fractures: with fractures, 15 men (17.2%) and 54 women (32.9%); without fracture 71 men (6.8%) and 242 women (19.6%); the chi-square test result for both sexes was p<0.01.

In the logistic regression analysis, age was found to be a significant predictor for previous fractures. Older subjects had a greater risk than younger subjects of displaying fractures. Compared with those younger than 55, the OR in those aged 55–64 was 1.19 (95% CI 0.91 to 1.57), in 65–74 year olds 1.47 (95% CI 1.13 to 1.92), and in those aged 75 and older, 1.89 (95% CI 1.44 to 2.50). Sex was not a significant predictor of fractures across all age groups (OR 1.09, 95% CI 0.90 to 1.33). When looking only at subjects aged 55 and older, however, women were at higher risk of fractures than men (OR 1.55, 95% CI 1.17 to 2.05) (Table 3, Table 4).

Table 3

Previous fractures in the entire study population

Table 4

Previous fractures in subjects ≥ 55 years

Risk factors for osteoporosis

Almost half (45.3%) of study subjects did not have any risk factors, but one third (36.1%) had one, and one in six (18.4%) had two or more. In subjects younger than 55, the modifiable risk factors—underweight, nicotine consumption, and high-risk alcohol consumption—had an obvious role (eTable). More than 40% of men and 36% of women in this age range reported at least one of the three modifiable risk factors. From age 55 years onward, the non-modifiable risk factors became crucial for both sexes. The bone stiffness index of subjects younger than 55 without risk factors was significantly higher than in those with one or two risk factors. The median of the bone stiffness index in men and women without risk factors was 101.3 and 96.7; in those with one risk factor, 96.5 and 94.6; and in those with at least two risk factors, 91.4 and 94.0. The result for all t tests was p<0.02. The same was the case for study subjects aged 55 and older, but the differences in women did not reach significance: the medians of the bone stiffness index were 94.5 and 79.8; with one risk factor, 91.5 and 78.3; and with at least two risk factors, 90.4 and 78.9. The t test results were p<0.01 for men and p>0.05 for women.

eTable

Proportions of subjects with modifiable and non-modifiable risk factors

Discussion

Bone stiffness and risk of osteoporotic fractures

In the European Union (EU 27), an estimated 5.5 million men and 22 million women have osteoporosis (13). About 6.6% of men and 22.1% of women aged 50 years and older are affected (13). Three large studies have reported on the prevalence rates of osteoporosis in Germany: BEST (3), BoneEVA (4), and GSTe103 (5) (Table 5). The BEST study (3) provides the most recent estimates. The men in this study and in northern Germany have comparable rates of osteoporosis diagnoses and fracture risk (BEST study: 6%; SHIP: 7.2%). By contrast, women differ, especially in the younger age groups. The proportions of subjects with a high fracture risk in North East Germany (50–64 years: 6.2%; 65–74 years: 12.4%) are notably lower than the osteoporosis prevalence rates in the BEST study (3) (50–64 years: 17%; 65–74 years: 32%). In people aged 75 years and older the values are comparable (BEST: 48% [3]; SHIP: 46%). It is conceivable that women in North East Germany develop osteoporosis to a lesser extent than the national average, for example as a result of the higher BMI (15), which has protective effects (14). On the other hand, it has to be assumed that the present study underestimates the prevalence of osteoporosis. The reasons include the exclusion criteria of the QUS measurements—for example, injuries in the 12 months before the examination. The study population is therefore on average healthier than the average population. Furthermore, the present results are not equal to a diagnosis of osteoporosis. Dual x-ray absorptiometry (DXA) scans are the gold standard in the diagnostic evaluation of osteoporosis. Such an investigation was not possible in SHIP-2 and SHIP-Trend. QUS, however, allows a risk prediction of comparable quality to DXA (10, 16, 17). Several studies have shown that the QUS measurements on the calcaneus predictively indicated the risk of hip fracture (16, 17), and that, like DXA scanning, they predicted prevalent (18) and incident (19) vertebral fractures.

Table 5

Comparison of the prevalence of osteoporosis in the BEST, BoneEVA, and GSTe103 studies with the results of SHIP-2 and SHIP-Trend

Overall, these initial data on bone stiffness in North East Germany underline the vast importance of osteoporosis, since 8.8% of men and 28.2% of women aged 65 and older are at high risk of osteoporotic fractures.

Previous fractures

Any osteoporotic fracture is associated with an increased risk for further bone fractures (10, 20–22). Prospective studies (20, 23, 24) have reported an association between the occurrence of radius fractures and subsequent hip fracture or neck of femur fractures (20, 23, 24). Accordingly, SHIP-2 and SHIP-Trend participants with previous bone fractures had a lower bone stiffness index and often had a higher risk of fractures than study participants without previous fractures. The risk of osteoporotic fracture is furthermore largely determined by age (10, 25, 26). The increased risk in older subjects compared with younger ones is consistent with the results of a study from the city of Rostock, which is based on emergency surgical reports (27).

Risk factors for osteoporosis

If risk factors for osteoporosis were present the bone stiffness index was reduced. In the age group younger than 55, even only one of the factors under consideration—underweight, nicotine consumption, or high-risk alcohol consumption—contributed to a lower bone stiffness index. The negative effect of these factors on bone density has often been shown (28–30), and the underlying mechanisms are at least partly known (28–30). Malnutrition, nicotine consumption, and alcohol misuse change the trabecular and cortical microarchitecture, lower bone mineral density, and thus increase fracture risk (28–30). In persons with chronic alcoholism, for example, fractures are four times as common as in healthy controls (28). In smokers, the relative risk for bone fractures compared with non-smokers is 1.25 (31). Since the extent of loss of bone mineral density in osteoporosis is crucially dependent on total bone mass (32), which is acquired up to about the 30^th year of life, it is of vital importance to avoid risk factors at this age. In subjects aged 55 or older with or without risk factors, differences in the bone stiffness index are significant only in men. In women, the effect of the studied risk factors on bone mineral density may possibly be overlaid by hormonal changes during the menopause.

Altogether, our study showed that osteoporosis goes hand in hand with the lifestyle factors and comorbidities under study. Especially in younger men and women, the potential exists to affect bone stiffness in a positive way and thereby counteract osteoporosis, by adopting health promoting behaviors and avoid modifiable risk factors or use targeted interventions (33, 34). It is well known that in persons with chronic alcoholism, bone mineral density remains constant after six months’ abstinence, whereas it notably falls if alcohol consumption is continued (35). Furthermore, bone mineral density is lower in smokers than in non-smokers, but it does not differ between non-smokers and former smokers (36). Similarly, the increased hip fracture risk in smokers compared with non-smokers notably decreases after 10 years’ abstinence from tobacco (37).

Strenthgs and limitations of the study

The main strengths of the present study are the number of participants and the population-based study design. Another strength is the standardized and detailed data collection, which allows for exact characterization of subjects. On the other hand, it cannot be ruled out that non-response and dropout introduced selection bias in the study population as people refused to participate, for example, or moved away from the study region. It is possible that subjects with osteoporotic fractures refused to participate in particularly substantial numbers because of resultant impairments to their mobility or because they required care. The sensitivity analysis showed, however, that non-response in SHIP-Trend and dropout between SHIP-0 and SHIP-2 affected the risk of osteoporotic fracture only minimally. Not all subjects were examined by using QUS; reasons included implants or malpositioning. It is therefore probable that especially study participants with low bone stiffness were not considered in the analysis and that bone stiffness in general is therefore overestimated. Additionally, it was not possible to study all potentially relevant risk factors for osteoporotic fractures—for example, no data were collected on participants’ coordination skills or on falls. Information about previous fractures came exclusively from subjects’ own recollections. For this reason, we should assume that the prevalence of fractures was underestimated—for example, because of undetected vertebral fractures.

Conclusion

The present study analyzed for the first time comprehensive data on bone health in North East Germany. The results indicate a high prevalence of osteoporosis in those aged 65 and above, which is consistent with the results from other studies in Germany and the EU. Furthermore, we found that modifiable risk factors for osteoporosis are common, particularly in young men and women, and that previous fractures are associated with an increased risk for future osteoporotic fractures. Altogether the study results underline the importance of the diagnostic evaluation, prevention, and therapy of osteoporosis.

Funding

SHIP is part of the Community Medicine Research net of the University of Greifswald. This study is also a part of the research project Greifswald Approach to Individualized Medicine (GANI_MED). It is supported by the following funding bodies: Germany’s Federal Ministry of Education and Research (01ZZ0403, 01ZZ0103, 01GI0883), the Ministry of Social Affairs and Health of Mecklenburg–Western Pomerania, and the Ministry of Education, Science, and Culture of Mecklenburg–Western Pomerania (03IS2061A).

Conflict of interest statement
Dr. Wallaschofski has received honoraria for lectures on the subject “Biomarkers of bone metabolism” from Amgen and Lilly.

The remaining authors declare that no conflict of interest exists.

Manuscript received on 5 December 2014, revised version accepted on 9 March 2015.

Translated from the original German by Birte Twisselmann, PhD.

Corresponding author
Dr. rer. med. Anke Hannemann
Institut für Klinische Chemie und Laboratoriumsmedizin
Universitätsmedizin Greifswald
Ferdinand-Sauerbruch-Straße NK, 17475 Greifswald, Germany

@eTable and eBox available at:
www.aerzteblatt-international.de/15m0365

References

1. OECD.stat: Historical population data and projections (1950–2050). stats.oecd.org/Index.aspx?DataSetCode=POP_PROJ# (last accessed on 9 February 2015).

2. Konnopka A, Jerusel N, Konig HH: The health and economic consequences of osteopenia- and osteoporosis-attributable hip fractures in Germany: estimation for 2002 and projection until 2050. Osteoporos Int 2009; 20: 1117–29

3. Hadji P, Klein S, Gothe H, et al.: The epidemiology of osteoporosis—Bone Evaluation Study (BEST): an analysis of routine health insurance data. Dtsch Arztebl Int 2013; 110: 52–7

4. Haussler B, Gothe H, Gol D, Glaeske G, Pientka L, Felsenberg D: Epidemiology, treatment and costs of osteoporosis in Germany—the BoneEVA Study. Osteoporos Int 2007; 18: 77–84

5. Scheidt-Nave C, Starker A: Osteoporoseprävalenz und assoziierte Versorgungsmuster bei Frauen im Alter ab 45 Jahren in Deutschland. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz 2005; 48: 1338–47

6. Baumeister SE, Alte D, Meyer C, John U: Riskanter Alkoholkonsum und alkoholbezogene Störungen in Vorpommern: Die Studie „Leben und Gesundheit in Vorpommern” (SHIP) und der Bundesgesundheitssurvey 1998 im Vergleich. Gesundheitswesen 2005; 67: 39–47

7. Mons U: Tabakattributable Mortalität in Deutschland und in den deutschen Bundesländern – Berechnungen mit Daten des Mikrozensus und der Todesursachenstatistik. Gesundheitswesen 2010; 73: 238–46

8. John U, Greiner B, Hensel E, et al.: Study of Health In Pomerania (SHIP): a health examination survey in an east German region: objectives and design. Soz Praventivmed 2001; 46: 186–94

9. Volzke H, Alte D, Schmidt CO, et al.: Cohort profile: the study of health in Pomerania. Int J Epidemiol 2010; 40: 294–307

10. Dachverband Osteologie e. V.: Prophylaxe, Diagnostik und Therapie der Osteoporose bei Männern ab dem 60. Lebensjahr und bei postmenopausalen Frauen. S3-Leitlinie des Dachverbands der Deutschsprachigen Wissenschaftlichen Osteologischen Gesellschaften e. V. Kurzfassung und Langfassung. www.dv-osteologie.org/dvo_leitlinien/osteoporose-leitlinie-2014 (last accessed on 9 February 2015).

11. World Health Organization Collaborating Centre for Metabolic Bone Diseases University of Sheffield: FRAX. www.shef.ac.uk/FRAX/tool.aspx?country=14 (last accessed on 9 February 2015).

12. GE Healthcare: Lunar Achilles InSight Lunar Achilles Express. Operator’s Manual. Revision 1; 2009.

13. Hernlund E, Svedbom A, Ivergard M, et al.: Osteoporosis in the European Union: medical management, epidemiology and economic burden. A report prepared in collaboration with the International Osteoporosis Foundation (IOF) and the European Federation of Pharmaceutical Industry Associations (EFPIA). Arch Osteoporos 2013; 8: 136

14. De Laet C, Kanis JA, Oden A, et al.: Body mass index as a predictor of fracture risk: a meta-analysis. Osteoporos Int 2005; 16: 1330–8

15. Benecke A, Vogel H: Übergewicht und Adipositas. Gesundheitsberichterstattung des Bundes 2005; 16: 7–24.

16. Krieg MA, Cornuz J, Ruffieux C, et al.: Prediction of hip fracture risk by quantitative ultrasound in more than 7000 Swiss women > or =70 years of age: comparison of three technologically different bone ultrasound devices in the SEMOF study. J Bone Miner Res 2006; 21: 1457–63

17. Hans D, Dargent-Molina P, Schott AM, et al.: Ultrasonographic heel measurements to predict hip fracture in elderly women: the EPIDOS prospective study. Lancet 1996; 348: 511–4

18. Gluer CC, Eastell R, Reid DM, et al.: Association of five quantitative ultrasound devices and bone densitometry with osteoporotic vertebral fractures in a population-based sample: the OPUS Study. J Bone Miner Res 2004; 19: 782–93

19. Hollaender R, Hartl F, Krieg MA, et al.: Prospective evaluation of risk of vertebral fractures using quantitative ultrasound measurements and bone mineral density in a population-based sample of postmenopausal women: results of the Basel Osteoporosis Study. Ann Rheum Dis 2009; 68: 391–6 < /td>

20. van Staa TP, Leufkens HG, Cooper C: Does a fracture at one site predict later fractures at other sites? A British cohort study. Osteoporos Int 2002; 13: 624–9

21. Kanis JA, Johnell O, De Laet C, et al.: A meta-analysis of previous fracture and subsequent fracture risk. Bone 2004; 35: 375–82

22. Johnell O, Kanis JA, Oden A, et al.: Fracture risk following an osteoporotic fracture. Osteoporos Int 2004; 15: 175–9

23. Taylor BC, Schreiner PJ, Stone KL, et al.: Long-term prediction of incident hip fracture risk in elderly white women: study of osteoporotic fractures. J Am Geriatr Soc 2004; 52: 1479–86

24. Schousboe JT, Fink HA, Lui LY, Taylor BC, Ensrud KE: Association between prior non-spine non-hip fractures or prevalent radiographic vertebral deformities known to be at least 10 years old and incident hip fracture. J Bone Miner Res 2006; 21: 1557–64

25. Icks A, Haastert B, Wildner M, Becker C, Meyer G: Trend of hip fracture incidence in Germany 1995–2004: a population-based study. Osteoporos Int 2008; 19: 1139–45

26. Kanis JA, Borgstrom F, De Laet C, et al.: Assessment of fracture risk. Osteoporos Int 2005; 16: 581–9

27. Bassgen K, Westphal T, Haar P, Kundt G, Mittlmeier T, Schober HC: Population-based prospective study on the incidence of osteoporosis-associated fractures in a German population of 200,413 inhabitants. J Public Health (Oxf) 2013; 35: 255–61

28. Maurel DB, Boisseau N, Benhamou CL, Jaffre C: Alcohol and bone: review of dose effects and mechanisms. Osteoporos Int 2012; 23: 1–16

29. Misra M, Klibanski A: Bone health in anorexia nervosa. Curr Opin Endocrinol Diabetes Obes 2011; 18: 376–82

30. Yoon V, Maalouf NM, Sakhaee K: The effects of smoking on bone metabolism. Osteoporos Int 2012; 23: 2081–92

31. Kanis JA, Johnell O, Oden A, et al.: Smoking and fracture risk: a meta-analysis. Osteoporos Int 2005; 16: 155–62

32. Jundt G: Knochen. In: Böcker W, Denk H, Heitz PU, Moch H (eds.): Pathologie. München: Urban & Fischer 2008; 1066–7.

33. Tensil MD, Jonas B, Struber E: Two fully automated web-based interventions for risky alcohol use: randomized controlled trial. J Med Internet Res 2013; 15: e110

34. Wittchen HU, Hoch E, Klotsche J, Muehlig S: Smoking cessation in primary care—a randomized controlled trial of bupropione, nicotine replacements, CBT and a minimal intervention. Int J Methods Psychiatr Res 2011; 20: 28–39

35. Alvisa-Negrin J, Gonzalez-Reimers E, Santolaria-Fernandez F, et al.: Osteopenia in alcoholics: effect of alcohol abstinence. Alcohol Alcohol 2009; 44: 468–75

36. Gerdhem P, Obrant KJ: Effects of cigarette-smoking on bone mass as assessed by dual-energy X-ray absorptiometry and ultrasound. Osteoporos Int 2002; 13: 932–6

37. Cornuz J, Feskanich D, Willett WC, Colditz GA: Smoking, smoking cessation, and risk of hip fracture in women. Am J Med 1999; 106: 311–4