Predictors of fractures among the aged: a population-based study with 12-year follow-up in a Finnish municipality

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1 Aging Clinical and Experimental Research among the aged: a population-based study with 12-year follow-up in a Finnish municipality Maarit Piirtola 1,2,3, Tero Vahlberg 4, Raimo Isoaho 1,5,6, Pertti Aarnio 7, and Sirkka-Liisa Kivelä 1,2,8 1Department of Family Medicine, University of Turku, Turku, Finland, 2 Satakunta Central Hospital, Pori, Finland, 3 Härkätie Health Centre, Lieto, Finland, 4 Department of Biostatistics, University of Turku, Turku, Finland, 5 Pori Health Centre, Pori, Finland, 6 Nordic School of Public Health, Gothenburg, Sweden, 7Department of Surgery, Satakunta Central Hospital, Pori, Finland, 8 Unit of Family Medicine, Turku University Hospital, Turku, Finland ABSTRACT. Background and aims: The incidence of fractures is high in older populations. More information is needed about long-term predictors of fractures, for preventive measures. The aim of this study was to analyze gender-specific predictors of fractures among persons aged 65 years or older during a 12- year follow-up. Methods: A true cohort study in the municipality of Lieto, southwestern Finland, started in October Baseline data and information about fractures in 1177 subjects (482 men, 695 women), mean age 73 years (range 65-97), were obtained individually from health care registers during The mean follow-up period was 8.5 years. Subjects having sustained at least one fracture (n=295) were compared with subjects with no fractures during the follow-up. were analyzed using a Poisson regression model, separately by gender. Results: In multivariate Poisson regression analyses, the following predictors of fractures during the 12-year follow-up were identified: reduced handgrip strength (RR 1.6, 95% CI in middle quartiles, RR 2.2, 95% CI in lowest quartile) and body mass index (BMI) (RR 1.9, 95% CI ) or BMI <25 (RR 2.0, 95% CI ) compared with BMI 30 or over among women, and a large number of depressive symptoms (RR 2.1, 95% CI ) among men. A compression fracture in one or more thoracic or upper lumbar vertebrae on chest radiography at baseline was associated with fractures in both women (RR 2.0, 95% CI ) and men (RR 3.5, 95% CI ). Conclusions: The predictors of fractures among aged persons varied by gender, and were associated with both risk factors of falling and bone fragility. (Aging Clin Exp Res 2008; 20: ) INTRODUCTION The incidence of fractures is high in older populations (1-3). Most types of fractures are more common in older women than men (1-5), and the predictors of fractures in women have been the subject of greater interest (6-8). However, the number and incidence of fractures in men have increased during the last few decades (1, 2, 4). A history of falling accidents (8-10), fall-related factors such as neuromuscular and visual impairment (6, 9), advanced age (3, 5, 8, 11), low body mass index (BMI) (12), a previous fracture (8, 11, 13-15), cigarette smoking and excessive alcohol consumption (11), use of systemic corticosteroids, rheumatoid arthritis (11) and low bone mineral density (BMD) (8, 11, 16, 17) have been reported to be associated with new fractures. There is evidence that some clinical risk factors contribute to the fracture risk, independently of BMD (11). Although there are many studies on the risk factors of fractures and several population-based studies with rather long follow-up periods (8, 14, 15, 18-25), there is still inconsistency regarding the interaction of fall-related factors and bone fragility as causative factors in sustaining fractures. Reduced BMD and osteoporosis (BMD T- score lower than 2.5 standard deviations (SD) below the mean for young healthy women) have been used as indicators of bone fragility, and found to be associated with fractures in both women and men (26). Reduced Key words: Aged, epidemiology, follow-up studies, fractures, predictors, risk factors. Correspondence: Maarit Piirtola, University of Turku, Dept. of Family Medicine, Lemminkäisenkatu 1, University of Turku, Finland. Received January 22, 2007; accepted in revised form April 17, Aging Clin Exp Res, Vol. 20, No

2 M. Piirtola, T. Vahlberg, R. Isoaho, et al. Aging Clin Exp Res 19: , 2007 BMD alone predicts only 60-70% of all fractures (17) and less than half of non-vertebral fractures (18). Not all persons with low BMD sustain a fracture after a fall (26). The use of BMD as a predictor of fractures among the oldest old people may be limited (17). Falling accidents and fall-related factors are independent predictors of fractures regardless of BMD (7, 19), although high BMD seems to protect from fractures regardless of falling (17). More information is needed about the predictors of fractures for the development of easy-to-use, sensitive long-term risk assessment and screening tools (26), especially for older men. The aim of this study was to analyze long-term genderspecific predictors of fractures among persons aged 65 years old or older, sustaining at least one fracture during the follow-up of 12 years. METHODS Participants This study is part of a larger, longitudinal, epidemiological population-based study of subjects aged 65 years or older living in the municipality of Lieto, southwestern Finland (27). Baseline data were collected during the first wave of the Lieto Study, between 1 October 1990 and 31 December The original population consisted of all residents in Lieto born in 1926 or earlier and alive at the baseline (n=1283), of whom 1196 (93%), 488 men and 708 women, participated. Fractures and accidents during follow-up Information about radiologically diagnosed fractures was collected individually from the medical records of the Lieto and Turku Health Centers, the Finnish Hospital Discharge Register and the Finnish Cause of Death Statistics from 1 October 1990 to 31 December Data on fractures from these sources were obtained for 482 men and 695 women (n=1177). All subjects with fractures were followed from the beginning of the follow-up to the occurrence of a fracture, and persons with no fractures to the end of the follow-up period (31 December 2002) or to death. Fractures were classified according to ICD-9 codes during and to ICD-10 codes from 1996 onward. All codes were converted into ICD- 10 codes. Fractures were grouped as hip (S72.0, S72.1, S72.2), wrist (S52), tibial and ankle (S82), rib(s) (S22.3, S22.4), proximal humerus (S42.2), and other fractures. Accidents were grouped as moderate (falling from standing height or less), more serious (due to falling from stairs or higher than standing height, but not higher than two meters, or due to traffic accidents) and most serious (due to falling from higher than two meters, impact of a falling object or traffic accident). Fractures with pathological backgrounds (n<5) and those caused by the most serious accidents (n=14) were excluded. For persons who sustained more than one fracture in an accident, the main fracture contributing to the need for treatment was taken into account. The double calculation error due to hospital transfers was eliminated by following the treatment of all fractures individually during the whole 12-year follow-up period (28). Validation of follow-up fracture data Altogether, 616 of the 1177 subjects participating in the first wave (baseline of the present study) of the Lieto Study in also participated in the second wave during We compared the data on fractures collected from the three sources: baseline examination ( ), second wave of the Lieto Study ( ), and follow-up data from the present study until According to the comparison, no diagnosed fractures that had occurred in these 616 participants were missed (28). Fractures before baseline examination Reviews of medical records also covered information on fractures that had occurred before the baseline examination in The quality of background information varied. Only fractures sustained after the age of 45 years, before the baseline examination, were included as a predicting variable of a new fracture during follow-up. The age limit was chosen to coincide with the beginning of the menopause in women and to exclude wartime injuries ( ) in men. Informed consent and ethical approval Informed consent was obtained from all participants or their caregivers. The Joint Ethics Committee of the University of Turku and the Hospital District of Southwest Finland approved the baseline study plan. The Finnish Ministry of Social Affairs and Health, Finnish National Research and Development Centre for Welfare and Health, and Lieto District Health Authority approved the collection of follow-up data (28). Baseline interviews, tests and measurements During the collection of baseline data, participants made two visits to the Lieto Health Centre. The interviews, tests and measurements were carried out by two trained nurses and the clinical examinations by an experienced general practitioner, GP (RI). If a person was not able to come to the Health Centre, the GP and one of the nurses made a visit to the participant s home or to the institution where the person lived. Proxy interviews were made for 76 (6%) subjects with cognitive decline or dementia. The clinical tests conducted at baseline according to the study protocol are described in the original research reports of the Lieto Study (27, 29). Socio-economic factors included information about gender, age, marital status, education, occupation, and ac- 243 Aging Clin Exp Res, Vol. 20, No. 3

3 Aging Clin Exp Res 19: , 2007 commodation. Questions about health and medical status included information about diagnosed diseases, symptoms, and use of drugs. Medical diagnoses and the use of prescription drugs were confirmed from health center records when available. Health behavior was assessed by questions about smoking and physical activity. Engagement in physical exercise was elicited by one dichotomized question: In addition to your ordinary household work, do you do physical exercise as a hobby? The interview also included 14 questions about mobility (e.g., moving outdoors, walking between rooms, negotiating stairs, walking at least 400 metres) and the ability to manage the activities of daily living (ADL: toileting, washing and bathing, dressing and undressing, getting in and out of bed, eating) and instrumental activities of daily living (IADL: doing one s own cooking, doing light housework, doing heavy housework, carrying a 5-kg load at least 100 meters, cutting one s toe nails) (30, 31). The use of mobility aids was recorded. Weight and height were measured. Diastolic and systolic blood pressures were measured in a sitting position. Handgrip strength was measured with an Elmed Vigorimeter (hand dynamometer) consisting of three different-sized rubber balls, and the measurement was used as an index of muscle strength. The research nurse selected a suitable rubber ball for each person. One technically successful attempt was accepted. If a subject could not use one or the other hand, only one hand was tested. Binocular visual acuity was tested with the Snellen E- Chart. Subjects using glasses were asked to wear them for the test. The ability to read normal print (font 12) was tested in a sitting position from a distance of 40 cm. The ability to hear normal speech with or without a hearing aid at a distance of one to five meters was tested. Cognitive ability was measured with the Mini-Mental State Examination test (MMSE) (32) and depressive symptoms with the Zung Self-rating Depression Scale (ZSDS) (33). Chest roentgenograms were taken from two different angles and checked by the same experienced GP (RI). A compressed fracture was diagnosed if a vertebra was obviously compressed to a wedge shape. In addition to roentgenograms, possible information about kyphotic spine and history of back pain were obtained. Only definite compression fractures were recorded. One or more compression fractures in the thoracic and upper lumbar vertebrae diagnosed in these examinations were used as an indicator of bone fragility. squared (kg/m 2 ) and categorized according to WHO recommendations as <25, and 30. Subjects were coded as having a large number of depressive symptoms if they scored 45 or more points on the ZSDS (34, 35). The ability to manage ADL and IADL functions was categorized into three classes: 1) independent, 2) independent with difficulties, and 3) dependent. Handgrip strength was calculated as the mean of both hands best values. Systolic and diastolic blood pressures and handgrip strength recordings were categorized, based on the lowest quartile, the second and the third quartiles combined, and highest quartile, separately by gender. Methods of analysis A 12-year follow-up period ( ) was used in analyzing the predictors of fractures. Those who had sustained at least one fracture during the followup made up the group of fractured persons and those who had sustained no fractures served as a reference group. The first fracture of each individual was included into the analyses. Univariate and age-adjusted Poisson regression analyses (36) were applied first. The variables showing significant associations with fractures in age-adjusted analyses were further included in multivariate analyses. Age was used as a continuous variable in age-adjusted and multivariate analyses. All analyses were made separately for men and women. Follow-up periods in each analysis were calculated as person-years (PY). Person-years were calculated from the beginning of the follow-up to the occurrence of a fracture or, for persons with no fractures, to the end of the follow-up period (2002) or to death. The results are presented using relative risks (RR) with their 95% confidence intervals (95% CI). In all analyses, p-values of less than 0.05 were considered statistically significant. Statistical analyses were performed with the SAS System for Windows, version 8.2 (SAS Institute Inc, Cary, NC). RESULTS Background At baseline, subjects mean age was 73 years [72.4 (SD 6.6) in men and 73.8 (SD 7.0) in women], range 65 to 97 (Table 1). The mean number of diagnosed diseases was 4.6 (SD 2.7) in men and 5.2 (SD 2.8) in women. At the end of the 12-year follow-up period ( ), 569 persons (205 men, 364 women) were still alive. Statistical methods Categorization The selected baseline variables and their categorization are listed in Table 1. Diagnosed diseases and health behavior variables, with reasonable evidence to serve as predictors of fractures, were included in the analyses. Body mass index (BMI) was calculated as kilograms per height Fractures before baseline Altogether 279 (23%) persons (87 men and 192 women) had sustained at least one fracture after the age of 45 years but before the baseline ( ) (Table 1). The median time-point of having sustained the last fracture was 6 years (0 to 31 years), and the median age of sustaining this fracture was 67 years (48 to 94 years). Aging Clin Exp Res, Vol. 20, No

4 M. Piirtola, T. Vahlberg, R. Isoaho, et al. Aging Clin Exp Res 19: , 2007 Table 1 - Baseline characteristics among men and women who sustained no fractures or sustained at least one fracture during 12-year follow-up. Values are numbers, percentages are shown in brackets. Characteristics Men n=482 Women n=695 No fractures At least one fracture No fractures At least one fracture n=408 (85) n=74 (15) n=474 (68) n=221 (32) Socio-demographic Age group at baseline (69) 38 (51) 285 (60) 121 (55) (27) 33 (45) 140 (30) 86 (39) (4) 3 (4) 49 (10) 14 (6) Marital status - married or co-habiting 305 (75) 56 (76) 195 (41) 92 (42) - single, widowed, divorced or judicial separation 103 (25) 18 (24) 278 (59) 129 (58) Home residence and living partner* - own home or sheltered housing 392 (96) 71 (96) 439 (93) 211 (95) - with another person alone long-term institutional care 16 (4) 3 (4) 35 (7) 10 (5) Physical functional abilities Mobility, managing ADL and IADL * - independent with no difficulties in any task 219 (54) 36 (49) 180 (38) 85 (38) - with difficulties or dependence in at least one task 189 (46) 38 (51) 291 (62) 136 (62) Walking between rooms - independent 316 (78) 59 (80) 323 (68) 164 (74) - independent with difficulties 83 (20) 15 (20) 120 (25) 51 (23) - dependent 9 (2) 0 (0) 31 (7) 6 (3) Negotiating stairs - independent 269 (68) 60 (69) 244 (51) 128 (58) - independent with difficulties 93 (23) 15 (20) 151 (32) 66 (30) - dependent 37 (9) 8 (11) 79 (17) 27 (12) Washing and bathing - independent 335 (82) 61 (83) 353 (74) 171 (77) - independent with difficulties 33 (8) 4 (5) 51 (11) 26 (12) - dependent 40 (10) 9 (12) 70 (15) 24 (11) Carrying a 5-kg load at least 100 m - independent 279 (68) 54 (73) 241 (51) 118 (53) - independent with difficulties 52 (13) 9 (12) 66 (14) 33 (15) - dependent 77 (19) 11 (15) 167 (35) 70 (32) Mobility aids* - no aids 347 (85) 63 (85) 371 (79) 175 (79) - walking aids 51 (13) 10 (14) 66 (14) 40 (18) - wheelchair or other 9 (2) 1 (1) 34 (7) 6 (3) Cognitive abilities Cognitive ability (MMSE score)* (82) 58 (80) 377 (80) 176 (80) (12) 14 (19) 53 (11) 31 (14) (6) 1 (1) 40 (9) 14 (6) Health behavior Smoking* - non-smoker 125 (31) 23 (31) 426 (90) 203 (92) - ex-smoker 219 (53) 44 (59) 28 (6) 12 (5) - current smoker 64 (16) 7 (10) 18 (4) 5 (2) Physical exercise (self-reported)* - yes 201 (49) 42 (57) 179 (38) 94 (43) - no 206 (51) 32 (43) 295 (62) 127 (57) Clinical background data Systolic blood pressure (mmhg) M 168+ W (24) 27 (36) 120 (25) 65 (30) M W (51) 30 (41) 252 (53) 98 (44) M <136 W < (25) 17 (23) 102 (22) 58 (26) Diastolic blood pressure (mmhg) (34) 28 (38) 162 (34) 72 (32) (45) 31 (42) 214 (45) 90 (41) (21) 15 (20) 98 (21) 59 (27) (Continued) 245 Aging Clin Exp Res, Vol. 20, No. 3

5 Aging Clin Exp Res 19: , 2007 Table 1 - (Continued) Characteristics Men n=482 Women n=695 No fractures At least one fracture No fractures At least one fracture n=408 (85) n=74 (15) n=474 (68) n=221 (32) Handgrip strength (kpa)* M 86+ W (26) 17 (24) 131 (29) 38 (18) M W (50) 33 (46) 222 (49) 108 (52) M 54 W (24) 22 (30) 97 (22) 61 (30) Body mass index (BMI)* (20) 14 (19) 179 (38) 49 (22) (48) 39 (53) 173 (37) 98 (44) < (32) 21 (28) 121 (26) 74 (34) Binocular sight* (95) 70 (96) 416 (93) 197 (92) < (5) 3 (4) 33 (7) 17 (8) Ability to read print* - yes 382 (96) 71 (96) 432 (95) 204 (94) - no 17 (4) 3 (4) 24 (5) 12 (6) Ability to hear whisper or talk within 1-5 meters (bilateral)* - yes 386 (95) 69 (93) 450 (96) 212 (96) - no 21 (5) 5 (7) 21 (4) 8 (4) Number of prescription drugs (84) 60 (81) 355 (75) 170 (77) 6 65 (16) 14 (19) 119 (25) 51 (23) Parkinson s disease - no 405 (99) 73 (99) 470 (99) 220 (99) - yes 3 (1) 1 (1) 4 (1) 1 (1) Diabetes - no 358 (88) 68 (92) 422 (89) 196 (89) - yes 50 (12) 6 (8) 52 (11) 25 (11) Rheumatoid arthritis - no 398 (98) 73 (99) 464 (98) 216 (98) - yes 10 (2) 1 (1) 10 (2) 5 (2) Cardiovascular disease* - no 285 (72) 50 (69) 349 (78) 167 (79) - yes 111 (28) 22 (31) 98 (22) 44 (21) Depressive symptoms (ZSDS score)* (83) 51 (76) 348 (82) 164 (80) (17) 16 (24) 78 (18) 40 (20) At least one previous fracture after age of 45 years but before baseline - no 339 (83) 56 (76) 355 (75) 148 (67) - yes 69 (17) 18 (24) 119 (25) 73 (33) Compression fracture/s in thoracic or upper lumbar vertebrae visible on chest radiogram at baseline* - no 384 (94) 61 (82) 426 (91) 186 (84) - yes 23 (6) 13 (18) 43 (9) 35 (16) (M=men; W=women). *Number of missing observations of variable by gender (Living partner: Men 19/ Women 45; Mobility, managing ADL and IADL : W 3; Mobility aids: M 1/ W 3; MMSE: M 3/ W 4; Smoking: W 3; Physical exercise: M 1; Handgrip strength: M 14/ W 36; BMI: M 1/ W 1; Binocular sight: M 10/ W 36; Ability to read print: M 9/ W 32; Bilateral hearing: M 1/ W 4; Cardiovascular disease: M 14/ W 37; ZSDS: M 46/ W 65; Compression fractures: M 1/ W 5). Includes 14 questions about mobility (moving outdoors, walking between rooms, negotiating stairs, walking at least 400 meters), ADL (toileting, washing and bathing, dressing and undressing, getting in and out of bed, eating) and IADL (doing one s own cooking, doing light housework, doing heavy housework, carrying a 5-kg load, cutting one s toe nails). Fractures during follow-up During the 12-year follow-up, 295 persons [74 men (15%); 221 (32%) women] sustained at least one fracture. During the follow-up, 19 (26%) of the men s and 42 (19%) of the women s first fractures were of the hip, 7 (9%) and 73 (33%) wrist, 11 (15%) and 22 (10%) tibial and ankle, 16 (22%) and 11 (5%) rib(s), 4 (5%) and 20 (9%) upper part of humerus, and 17 (23%) and 53 (24%) other fractures. Aging Clin Exp Res, Vol. 20, No

6 M. Piirtola, T. Vahlberg, R. Isoaho, et al. Aging Clin Exp Res 19: , 2007 Table 2 - Predicting factors* and relative risks (RR) with 95% confidence intervals (95% CI) for fractures in univariate, age-adjusted and multivariate Poisson regression analyses among women who sustained at least one fracture during 12-year follow-up. Predicting factor* Univariate analysis Age-adjusted analysis Multivariate analysis# RR (95% CI) p-value RR (95% CI) p-value RR (95% CI) p-value Age group ( ) < ( ) Home residence - own home or sheltered housing long-term institutional care 2.0 ( ) ( ) Mobility, ADL and IADL tasks** - independent with no difficulties in any task with difficulties or dependence in at least one task 1.4 ( ) ( ) Washing and bathing - independent independent with difficulties 1.4 ( ) ( ) dependent 1.8 ( ) ( ) Carrying a 5-kg load at least 100 m - independent independent with difficulties 1.2 ( ) ( ) dependent 1.4 ( ) ( ) Mobility aids - no aids walking aids 1.7 ( ) ( ) wheelchair or other 1.5 ( ) ( ) Cognitive ability (MMSE score) ( ) ( ) ( ) ( ) Diastolic blood pressure (mmhg) ( ) ( ) ( ) ( ) Number of drugs ( ) ( ) Handgrip strength (kpa) ( ) ( ) ( ) ( ) < ( ) < ( ) Body mass index (BMI) ( ) ( ) ( ) < ( ) < ( ) < ( ) Binocular sight (Snellen E-Chart) < ( ) ( ) Ability to read print - yes no 1.9 ( ) ( ) Previous fracture after age of 45 years## - no yes 1.4 ( ) ( ) ( ) Compression fracture/s in thoracic or upper lumbar vertebrae - no yes 2.5 ( ) < ( ) < ( ) *Includes variables showing significant results in univariate Poisson regression analysis. #Age, handgrip strength, BMI, occurrence of a previous fracture after 45 years of age and compression of thoracic or upper lumbar vertebrae were included in multivariate Poisson regression model. Age was used as a continuous variable in age-adjusted and multivariate analyses. **Includes 14 questions about mobility (moving outdoors, walking between rooms, negotiating stairs, walking at least 400 meters), ADL (toileting, washing and bathing, dressing and undressing, getting in and out of bed, eating) and IADL (doing one s own cooking, doing light housework, doing heavy housework, carrying a 5-kg load, cutting one s toe nails). ## At least one registered fracture after age of 45 years but before baseline. 247 Aging Clin Exp Res, Vol. 20, No. 3

7 Aging Clin Exp Res 19: , 2007 Characteristics of accidents causing fractures During the follow-up in , 243 (82%) of fractures included were caused by moderately serious accidents and 16 (6%) by more serious accidents. Information about the type of accident was missing in 36 (12%) of cases. Nineteen percent of the fractures occurred outdoors and 40% indoors; detailed information about the place of accident was missing in 41% of cases. There were no significant differences in the severity (p=0.924) or circumstances (p=0.426) of accidents between genders. According to univariate Poisson regression analysis, being in long-term institutional care, dependence in carrying a 5-kg load at least 100 meters, use of walking aids, low (lowest quartile) diastolic blood pressure (<78 mmhg), BMI scores <30, poor sight (binocular sight <0.3 or inability to read print) and a previous fracture sustained after the age of 45 years but before the baseline were all significantly associated with fractures during the follow-up among women (Table 2). Among men, dependence in negotiating stairs, systolic blood pressure between 136 to 167 mmhg, and a large number of depressive symptoms (ZSDS score 45 or over) were predictors of fractures (Table 3). Advanced age (men 75 to 84 years, women 75+ years), difficulties or dependence in at least one mobility, ADL or IADL task, difficulties or dependence in washing and bathing, impaired cognitive abilities (men MMSE 18 to 23, women MMSE <24), use of 6 or more prescription drugs, reduced handgrip strength (men <55 kpa, women <76 kpa), and a compression fracture in one or more thoracic or upper lumbar vertebrae were all significant predictors for fractures in both women and men. In age-adjusted Poisson regression analysis among women, handgrip strength <76 kpa, BMI scores <30, a previous fracture sustained after the age of 45 years but before the baseline, and a compression fracture in one or more thoracic or upper lumbar vertebrae were significantly related to the risk of fractures (Table 2). Among men, significant predictors were a large number of depressive symptoms (ZSDS score 45 or over) and a compression fracture in one or more thoracic or upper lumbar vertebrae at baseline (Table 3). In multivariate Poisson regression analysis, handgrip strength <76 kpa, BMI scores <30, and a compression fracture in one or more thoracic or upper lumbar vertebrae were independent predictors for fracture among women (Table 2). Among men, only a large number of depressive symptoms (ZSDS score 45 or over) and a compression fracture in one or more thoracic or upper lumbar vertebrae were related to fractures (Table 3). DISCUSSION This 12-year cohort study showed that the predictors of fractures in older Finns differ by gender. In multivariate analyses, low handgrip strength (the lowest quartiles), BMI score lower than 30, and a compression fracture in one or more thoracic or upper lumbar vertebrae were significantly associated with the risk of fractures in women, whereas a large number of depressive symptoms and a compression fracture in one or more thoracic or upper lumbar vertebrae were similarly associated in men. We assume that both risk factors of falling and risk factors of bone fragility are important predictors of fractures. The participation rate during the first wave of the Lieto Study in was 93% (27), one of the highest reported in population studies worldwide. Information about fractures was achieved for nearly all participants (n=1196), but the medical records of all participants were not found in the local health centers, mainly because some subjects had moved from the district during the follow-up. These 19 persons (2%) were excluded from the baseline data of this study. Due to the use of several data collection sources and the organization of the health care system in Finland (37), coverage of fracture data during the follow-up in this study is high. We may have missed only a few vertebral fractures (some minor ones during baseline examination and/or some during follow-up) and fractures sustained by participants who were abroad for several months, but such cases were rare or non-existent. We compared the age distribution of the study subjects (n=1177) with that of the total Finnish population aged 65 years or over in 1991, by gender and by dividing the population into 5-year age groups. No significant differences were found in either men (p=0.081) or women (p=0.092) (28). According to a study by Sund (38), the incidence rates of hip fractures among aged Finns did not differ in the various hospital districts during Our results on the incidence rates of hip fractures in the aged population in Lieto (28) are similar to those obtained in the above register-based study in the whole of Finland (38). There is no published evidence to suggest that the BMD values or incidence rates of other age-related fractures (vertebral, wrist, upper part of humerus, etc.) than hip fractures might vary by hospital district among the aged population in Finland. We assume that our results may be generalized to represent the Finnish aged population. The first wave of the Lieto study in was not originally designed to analyze the risk factors of fractures. Information about previously proven risk factors of fractures, such as bone mineral density, calcium intake, vitamin D intake, family history of fractures, height loss, alcohol consumption, leanness, body balance, and the strength of the lower extremities was not elicited in the baseline study. The number of subjects with BMI <18.5 was small (10 men, 11 women), and the two lowest categories were therefore combined as BMI <25. However, baseline data includes accumulated wide-ranging data Aging Clin Exp Res, Vol. 20, No

8 M. Piirtola, T. Vahlberg, R. Isoaho, et al. Aging Clin Exp Res 19: , 2007 Table 3 - Predicting factors* and relative risks (RR) with 95% confidence intervals (95% CI) for fractures in univariate, age-adjusted and multivariate Poisson regression analyses among men who sustained at least one fracture during 12-year follow-up. Predicting factor* Univariate analysis Age-adjusted analysis Multivariate analysis# RR p-value RR p-value RR p-value Age group ( ) < ( ) Mobility, ADL and IADL tasks** - independent with no difficulties in any task difficulties or dependence in at least one task 1.8 ( ) ( ) Washing and bathing - independent independent with difficulties 1.0 ( ) ( ) dependent 3.3 ( ) ( ) Negotiating stairs - independent independent with difficulties 1.2 ( ) ( ) dependent 2.7 ( ) ( ) Handgrip strength (kpa) ( ) ( ) < ( ) ( ) Cognitive ability (MMSE score) ( ) ( ) < ( ) ( ) Systolic blood pressure (mmhg) ( ) ( ) ( ) ( ) Number of prescription drugs ( ) ( ) ZSDS score ( ) ( ) ( ) Compression fracture/s in thoracic or upper lumbar vertebrae - no yes 3.6 ( ) < ( ) ( ) < *Includes variables showing significant results in univariate Poisson regression analysis. #Age, ZSDS and compression of a thoracic or upper lumbar vertebrae were included in multivariate Poisson regression model. Age was used as a continuous variable in age-adjusted and multivariate analyses. **Includes 14 questions about mobility (moving outdoors, walking between rooms, negotiating stairs, walking at least 400 meters), ADL (toileting, washing and bathing, dressing and undressing, getting in and out of bed, eating) and IADL (doing one s own cooking, doing light housework, doing heavy housework, carrying a 5-kg load, cutting one s toe nails). about participants health and functional status. The thorough collection of data concerning fractures during the 12-year follow-up and the detailed information about the fractures before the actual follow-up, combined with baseline data, yielded unique possibilities of analyzing other clinical risk factors of fractures over a long period. The data were also capable of yielding risk factors of fractures in aged men. The range of yearly fluctuation in the number of fractures was quite wide and the yearly number and incidence of fractures low, which may have had an impact, especially on the results of risk factors in men (28). The small number of fractures did not allow us to analyze risk factors between age groups, severity of accidents, or types of fractures. It has been reported that the incidence rates of all fracture types combined (39, 40) and many subtypes of fractures, such as hip, spine, upper arm and pelvic fractures, increase with age (8, 20, 40), whereas the incidence of wrist and foot or ankle fractures does not (39, 41). Our present results and our previous report on the incidence of fractures support these findings (28). In addition to functional limitations and poor physical performance in older persons, low handgrip strength predicts fractures (8, 42). Low handgrip strength is as- 249 Aging Clin Exp Res, Vol. 20, No. 3

9 Aging Clin Exp Res 19: , 2007 sociated with women s menopausal bone loss (43) and future fractures (8, 43). In our data, men s overall handgrip strength was about 10% higher in every quartile compared with women s corresponding values, and reduced handgrip strength was associated with an increased risk of fractures in women, but not in men. We assume that poor handgrip strength reflects poor overall muscle strength, and is one of the risk factors of falling without a direct connection with bone fragility and bone strength. Handgrip strength can easily be measured in outpatient clinics or during home visits, and may be a good tool in screening persons with a high risk of falling and fractures. Low BMI is a well-documented risk factor for future fracture, but the significance of BMI as a risk factor seems to depend on BMD and to vary according to how BMI is categorized (12). In our study, a BMI value <30 at baseline predicted a new fracture in women. There is some evidence, however, that overweight may be a risk factor for sustaining ankle fractures in women (44). Women may benefit from slight overweight at older age, and the lower BMI scores (especially BMI <18.5) in women reflect not only a loss of muscle mass but also of undiagnosed diseases causing bone fragility and osteoporosis, or both. The BMI value, and especially a decrease in BMI, may be an alarm signal of overall poor health and a predictor of fractures, at least in women. Depression is associated with falling accidents in both men and women (45, 46) and with fractures in women (46-48). Depressive symptoms have been reported to decrease BMD, independently of body weight and behavioral factors, such as calcium compliance and exercise, in women (49). A low quality of life as measured by the SF- 36 physical component summary score has been reported as a risk for fractures in menopausal women (21). We found that a large number of depressive symptoms measured with ZSDS independently predicted fractures in men but not in women. This result is opposite that obtained in a Norwegian study (48). A large number of depressive symptoms in addition to cognitive impairment has been shown to be a risk factor for functional decline (50). Depressed persons may be passive in physical exercise and lack appetite, which may lead to a decrease in BMD, muscle strength and body balance. A high risk of fractures may be explained by these factors. We propose that depressive symptoms should be screened in older persons. Smoking is a risk factor of fractures, according to a large meta-analysis including several prospective population-based studies (51). We did not find smoking to be a risk for fractures either in women or men. The proportion of smoking women was very small, and the number of fractures that occurred in men was too small to prove an actual risk. The number of current smokers was quite small even among men, because many Finnish males stop smoking with advancing age. The occurrence of three or more chronic diseases has been reported to be associated with fractures in women (25). In our study, individual diseases showed no association with fractures. We also used the number of prescription drugs to reflect a person s medical condition. There was some evidence of polypharmacy as a predictor of fractures in both genders, but the association was not significant in age-adjusted analyses. We analyzed the interactions between number of depressive symptoms and BMI and between number of prescription drugs and BMI. According to our results, there was some evidence of interaction between BMI and the ZSDS sum score, or number of drugs, in men but not in women. Men who use 6 or more drugs, and men with a high number of depressive symptoms (ZSDS over 45) and a BMI score lower than 25 seem to sustain fewer fractures. These results are based on a small number of events in men, and should be confirmed in studies with larger sample sizes. A previous fracture is a predictor of sustaining a new fracture (8, 20, 23, 25, 44). Our study confirms these findings, even though a fracture before baseline but after the age of 45 years was a significant predictor only in women, and did not predict fractures independently in multivariate analyses. A compression fracture in one or more thoracic or upper lumbar vertebrae seen in chest X- rays at baseline was used as an indicator of bone fragility and osteoporosis, and proved to be the strongest independent predictor for a new fracture in both women and men. Previous vertebral fractures have also been established as risk factors for future fractures in other studies (14, 15, 20, 52). Although bone fragility and low BMD cannot be used as the only predictors of fractures (18, 19), spine X-rays can potentially identify vertebral fractures and find persons at high risk of fracture (20). Previous reports have shown many risk factors of fractures to be common to older men and women (15, 24, 51, 53), but gender differences also seem to exist (48). Many studies analyzing the predictors of fractures have been conducted in women, and information about men is scarce. We found the predictors of fractures to vary between genders. Only a compression fracture in one or more thoracic or upper lumbar vertebrae at baseline was an independent predictor of fractures in both genders. Bone health seems to be an important and independent predictor for future fractures, but some factors related to the risk of falling also proved here to be predictive of fractures. It has been reported that some risk factors of fractures differ by type of fracture (21, 25, 54, 55) or by falling mechanism (56). Risk factors may also vary by age group (11). We suggest that accident types, falling mechanisms, and risk factors of fractures differ not only between type of fracture and gender but also between age group, influencing even the age-specific incidences of fractures. In Aging Clin Exp Res, Vol. 20, No

10 M. Piirtola, T. Vahlberg, R. Isoaho, et al. Aging Clin Exp Res 19: , 2007 future studies, the predictors of fractures should be analyzed by type of fracture, severity of accident, and gender and age. These subgroup analyses require meta-analyses or large data sets gathered in multicenter studies. CONCLUSIONS This study showed that the predictors of fractures during the 12-year follow-up differ between genders. Reduced handgrip muscle strength, a BMI score <30, and a compression fracture in one or more thoracic or upper lumbar vertebrae on chest radiography at baseline in women and a large number of depressive symptoms (ZS- DS score 45 or over) and a compression fracture in one or more thoracic or upper lumbar vertebrae in men, were independent predictors of fractures. The results show that both risk factors of falling and of bone fragility are important predictors of fractures. As the number of aged people is increasing, it is important to screen them for predictors of fractures and to implement preventive action. Our results on long-term risk factors provide information for screening older persons at high risk of fractures. ACKNOWLEDGEMENTS The baseline study was supported by the Academy of Finland, Yrjö Jahnsson Foundation, and Finnish Anti-Tuberculosis Association. Data collection of fractures and analysis of predictors of fractures were supported by the La Carita Foundation, Finnish Cultural Foundation, Juho Vainio Foundation, grants from Satakunta Hospital District, and Lieto Health Centre, and the scholarship fund of the Ageing, Wellbeing and Technology graduate school. We are indebted to all those persons who attended baseline data collection , and especially to Jukka Saukkoriipi for technical assistance in biostatistics. We also thank the Lieto Health Centre and Turku Health Centre personnel for their positive attitude, as well as the assistance of Teemu Kemppainen and Eila Räisänen during collection of fracture data. We are also grateful to Prof. Harri Suominen, Department of Health Sciences, University of Jyväskylä for his comments during data analysis and in writing the manuscript. 8. Albrand G, Munoz F, Sornay-Rendu E, DuBoeuf F, Delmas PD. Independent predictors of all osteoporosis-related fractures in healthy postmenopausal women: the OFELY study. Bone 2003; 32: Kannus P, Uusi-Rasi K, Palvanen M, Parkkari J. Non-pharmacological means to prevent fractures among older adults. Ann Med 2005; 37: Lee SH, Dargent-Molina P, Breart G. Risk factors for fractures of the proximal humerus: results from the EPIDOS prospective study. J Bone Miner Res 2002; 17: Kanis JA, Borgstrom F, De Laet C, et al. Assessment of fracture risk. Osteoporos Int 2005; 16: 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: Kanis JA, Johnell O, De Laet C, et al. A meta-analysis of previous fracture and subsequent fracture risk. Bone 2004; 35: van der Klift M, de Laet CE, McCloskey EV, et al. Risk factors for incident vertebral fractures in men and women: the Rotterdam Study. J Bone Miner Res 2004; 19: Fujiwara S, Kasagi F, Masunari N, Naito K, Suzuki G, Fukunaga M. Fracture prediction from bone mineral density in Japanese men and women. J Bone Miner Res 2003; 18: Cummings SR, Cawthon PM, Ensrud KE, Cauley JA, Fink HA, Orwoll ES. BMD and risk of hip and nonvertebral fractures in older men: A prospective study and comparison with older women. J Bone Miner Res 2006; 21: Cheng S, Suominen H, Sakari-Rantala R, Laukkanen P, Avikainen V, Heikkinen E. Calcaneal bone mineral density predicts fracture occurrence: a five-year follow-up study in elderly people. J Bone Miner Res 1997; 12: Schuit SC, van der Klift M, Weel AE, et al. Fracture incidence and association with bone mineral density in elderly men and women: the Rotterdam Study. Bone 2004; 34: Kaptoge S, Benevolenskaya LI, Bhalla AK, et al. Low BMD is less predictive than reported falls for future limb fractures in women across Europe: results from the European Prospective Osteoporosis Study. Bone 2005; 36: Kaptoge S, Armbrecht G, Felsenberg D, et al. Whom to treat? The contribution of vertebral X-rays to risk-based algorithms for fracture prediction. Results from the European Prospective Osteoporosis Study. Osteoporos Int 2006; 17: Papaioannou A, Joseph L, Ioannidis G, et al. Risk factors associated with incident clinical vertebral and nonvertebral fractures in postmenopausal women: the Canadian Multicentre Osteoporosis Study (CaMos). Osteoporos Int 2005; 16: Melton LJ 3rd, Atkinson EJ, O Connor MK, O Fallon WM, Riggs BL. Bone density and fracture risk in men. 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