FRAX™ and the assessment of fracture probability in men and women from the UK

Summary A fracture risk assessment tool (FRAX™) is developed based on the use of clinical risk factors with or without bone mineral density tests applied to the UK.
Introduction
Several multi-factorial diseases (e.g., diabetes, hypertension) are defined on the basis of important risk factors for the clinical outcome. Hypertension, for example, is defined from the measurement of blood pressure that provides information on the likelihood of stroke. Hypercholesterolaemia and osteoporosis are also examples. In the case of osteoporosis, bone mineral density (BMD) is measured both to provide a diagnosis, and to yield information on fracture risk [1].
Many well-controlled prospective studies with dual energy X-ray absorptiometry (DXA), particularly in elderly women, indicate that the risk of fracture about doubles for each SD reduction in BMD [2, 3]. The measurement of a risk factor for diagnostic use, however, can only capture one aspect of the likelihood of the outcome when the disease is multifactorial, and in osteoporosis, assessment with BMD captures a minority of the fracture risk. For example, the annual incidence of hip fracture increases approximately 30-fold between the ages of 50 and 90 years; but, from the known relationship between BMD and fracture risk and the loss of bone with age, it is expected that hip fracture risk would rise only fourfold [4–6]. Thus, the increase in risk with age is approximately sevenfold greater than can be explained on the basis of BMD alone.
The imperfect capture of risk with BMD alone poses several problems for the clinical assessment of fracture risk. In the context of population screening with BMD alone, the performance characteristics of the test are less than optimal in terms of the trade-off between sensitivity and specificity [1, 7, 8]. Thus, osteoporotic fractures affect a substantial minority of the population, but intervention thresholds based on BMD alone lack sensitivity over most reasonable assumption, i.e., the detection rate is low. For example, at the age of 50 years the proportion of women with osteoporosis is approximately 5% [4]. The proportion of these who will fracture in the next 10 years (i.e., positive predictive value) is about 20%. The detection rate for these fractures (sensitivity) is, however, low, and 96% of fragility fractures would arise in women without osteoporosis given a test like BMD where the fracture risk doubled for each SD decrease [9]. Low sensitivity is one of the reasons why wide-spread population screening is not widely recommended in women at the menopause [1]. Moreover, a normal BMD measurement is no guarantee that a fracture will not occur.
The use of risk factors that add information on fracture risk independently of BMD improves the sensitivity of the assessment for any specificity [8, 9]. Over the past several years, we have undertaken a series of meta-analyses to identify clinical risk factors for fracture that provide independent information on fracture risk [3, 10–16]. The analyses were based on the primary data from prospective population based studies. This permits the inter-dependence of each of the candidate risk factors to be examined so that they can be accurately combined for clinical use. In the case of the clinical risk factors, BMI is used as a continuous variable, so that its distribution is preserved by the addition of dichotomous variables [8]. For hip fracture prediction, the gradient of risk (increase in fracture risk per standard deviation increase in risk score) for the CRFs is comparable to the use of DXA alone, and the gradient of risk is further enhanced by the addition of BMD to the CRFs [17]. These inter-relationships, assessed from multiple populations and validated in independent cohorts, permit the more accurate identification of individuals who will fracture, so that the average risk in any given proportion of the population identified for treatment will be higher [8].
These considerations indicate that assessment can be improved by the integration of clinical risk factors with or without BMD. In other words, treatment should be directed not only on the basis of T-score for BMD, but also on the independent contribution of other validated risk factors. However, the use of different metrics (the T-score, gradients of risk, risk ratios, etc) is confusing for clinicians and patients alike. For this reason, there has been interest in the development of algorithms that express absolute risk, or the probability of fracture within a given time period [18–20], as has been done for cardiovascular and other diseases [21–25]. The aim of the present study was to develop a model for the clinical assessment of fracture probability in men and women based on the epidemiology of the UK.
Methods
Cohorts
In order to identify the relevant risk factors, we used baseline and follow-up data from nine prospective population-based cohorts comprising the Rotterdam Study, The European Vertebral Osteoporosis Study (later the European Prospective Osteoporosis Study (EVOS/EPOS), The Canadian Multicentre Osteoporosis Study (CaMos), Rochester, Sheffield, Dubbo, a cohort from Hiroshima and two cohorts from Gothenburg. Details of each of the cohorts have been recently published elsewhere [10–16].
Baseline and outcome variables
Height and weight were measured using standard techniques in all cohorts. Body mass index (BMI) was calculated as weight divided by height squared (kg/m2) and used as a continuous variable. BMD was assessed at the femoral neck by DXA with the exception of the two Gothenburg cohorts in which BMD was measured elsewhere. Femoral neck BMD was used as a continuous variable (cohort-specific Z-scores excluding the two cohorts from Gothenburg). The clinical risk factors utilised were those identified from the previous meta-analyses [3, 10–16]. These comprised a parental history of hip fracture, exposure to systemic glucocorticoids, a prior history of fragility fracture, current smoking, high intake of alcohol (3 or more units daily on average) and the presence of rheumatoid arthritis as an indicator for secondary osteoporosis.
Fracture ascertainment in the primary cohorts was undertaken by self-report (Sheffield, EVOS/EPOS, Hiroshima) and/or verified from hospital or central data-bases (Gothenburg, CaMos, DOES, Sheffield, EVOS/EPOS, Rochester, Rotterdam).
Models used
Four models were constructed from the risk factor analysis to compute fracture probabilities. These comprised the probability of hip fracture, with and without BMD, and the probability of other major osteoporotic fractures (clinical spine, forearm and proximal humerus), with and without BMD. For each model, fracture and death as continuous hazard functions were computed using a Poisson regression [26, 27] and detailed in the Appendix. In brief, for each risk factor, all significant interactions terms that were identified by meta-analysis were entered (with age, time, sex and the risk factor) with and without BMD [17]. Interactions that were significant for hip fracture risk were also entered into the model for other osteoporotic fractures, and also included in the model for death. Where interactions noted in the “mega-analyses” were no longer significant for both hip fracture and other osteoporotic fractures, these were omitted in a step-wise manner by dropping the interaction with the largest p value. For the death hazard, all significant interactions for fracture risk were included and thereafter omitted if appropriate in a step-wise manner, as described for the fracture hazard.
Epidemiology
The incidence of hip, forearm and proximal humerus fractures in the UK was taken from Singer et al. [28]. Vertebral fractures can be classified as clinically overt fracture, that is, a symptomatic fracture that comes to clinical attention, or a morphometric fracture, which includes both symptomatic and asymptomatic fractures. For the purposes of this study the clinical definition of a vertebral fracture was used. Because data on vertebral fracture risk in the UK are scarce, with a great deal of variation in reported rates [29], the clinical vertebral fracture incidence was calculated by assuming that the ratio of clinical vertebral fracture to hip fracture would be similar in the UK compared to Sweden [30]. The validity of the assumption cannot be directly tested, but in the case of long bone fractures, there is a close concordance of relative incidence at different fracture sites despite marked differences in absolute risk [31]. Swedish fracture risk data were taken from Kanis et al. [32]. The models were then calibrated so that the mean hazard functions of fracture (and death) equalled that of the UK, as detailed in the Appendix.
Input and output variables
Individual patient details comprise age (50 to 90 years), sex, weight (in kg) and height (in cm). BMI is automatically computed from height and weight. Dichotomised risk variables are then entered:
A prior fragility fracture (yes/no)Parental history of hip fracture (yes/no)Current tobacco smoking (yes/no)Ever long-term use of oral glucocorticoids (yes/no)Rheumatoid arthritis (yes/no)Other causes of secondary osteoporosis (yes/no)Daily alcohol consumption of three or more units daily (yes/no)
A distinction is made between rheumatoid arthritis and other secondary causes of osteoporosis. Rheumatoid arthritis carries a fracture risk over and above that provided by BMD [11]. Whereas this may hold true for other secondary causes of osteoporosis, the evidence base is weak. Of the many secondary causes of osteoporosis, the following have been consistently documented to be associated with a significant increase in fracture risk:
Untreated hypogonadism in men and women, e.g., bilateral oophorectomy or orchidectomy, anorexia nervosa, chemotherapy for breast cancer, hypopituitarism [33–40]Inflammatory bowel disease, e.g., Crohn’s disease and ulcerative colitis [41–43]. It should be noted that the risk is in part dependent on the use of glucocorticoids, but an independent risk remains after adjustment for glucocorticoid exposure [44].Prolonged immobility, e.g., spinal cord injury, Parkinson’s disease, stroke, muscular dystrophy, ankylosing spondylitis [45–50]Organ transplantation [51–54]Type I diabetes [55–58]Thyroid disorders, e.g., untreated hyperthyroidism, over-treated hypothyroidism [59–63]
Whereas there is strong evidence for the association of these disorders and fracture risk, the independence of these risk factors from BMD is uncertain. It was conservatively assumed, therefore, that the fracture risk was mediated via low BMD, but with a risk ratio similar to that noted in rheumatoid arthritis. From an operational view, where the field for rheumatoid arthritis is entered as ‘yes’, a risk is computed with and without BMD. If the field for other secondary osteoporosis is also filled as ‘yes’ this does not contribute to the calculation of fracture probability. Conversely, where the field for rheumatoid arthritis entered as ‘no’, and the field for secondary osteoporosis is ‘yes’, the same β coefficients as used for rheumatoid arthritis contribute to the computation of probability where BMD is not entered. In the presence of BMD, however, no additional risk is assumed in the presence of secondary osteoporosis, since its independence of BMD is uncertain.
If any of the fields for dichotomous variables is not completed, a negative response is assumed. Fractures probability can then be calculated. The output (without BMD) comprises the 10-year probability of hip, clinical spine, shoulder or wrist fracture and the 10-year probability of hip fracture (Fig. 1).
Fig. 1Input and output for the FRAX™ model
Femoral neck BMD can additionally be entered either as a Z-score or a T-score. The transformation of Z- to T-score and vice versa is derived for the NHANES III database for female Caucasians aged 20–29 years [64]. When entered, calculations give the 10-year probabilities as defined above with or without the inclusion of BMD.
Results
Clinical risk factors
The contribution of single clinical risk factors is shown in Table 1 for men and women aged 65 years. In the example, the BMI is set at 25 kg/m2. In the absence of BMD, hip fracture probabilities were higher in women than in men. Each clinical risk factor had a different significance for hip fracture probability, with a family history having the least and a prior fracture the greatest weight in the absence of BMD. The rank order of weighting differed for the major osteoporotic fractures. For example, a parental history of hip fracture was a strong risk factor, close to that provided by a prior fragility fracture. The contribution of age to the assessment of probability is shown in Table 2. In both men and women, there was greater than 100-fold difference in hip fracture probability between the age of 50 years (no risk factors) and 80 years (parental history of hip fracture).
Table 1Ten-year probability (%) of a major osteoporotic fracture or hip fracture in men and women aged 65 years according to the presence of a single clinical risk factor Without BMDT-score −2.5 SDMenWomenMenWomenOsteoporoticaHipOsteoporoticaHipOsteoporoticaHipOsteoporoticaHipNo clinical risk factors4.90.88.61.39.83.612.43.0Parental history of hip fracture9.31.016.01.716.53.722.13.2Current cigarette smoking5.11.19.21.911.05.613.75.1Alcohol intake >2 units daily6.01.210.42.012.55.415.44.6Rheumatoid arthritis6.81.411.72.312.85.016.14.3Oral glucocorticoids7.51.513.72.715.06.119.75.5Previous fragility fracture9.61.916.43.216.05.920.25.0BMI is set at 25 kg/m2. The right-hand panels show probabilities at a T-score of −2.5 SD at the femoral neckaHip, clinical spine, humeral or forearm fractureTable 2Ten-year probability of fracture (%) at the sites shown for men and women with a BMI of 25 kg/m2 according to age and the presence or absence of a single risk factor in the absence of BMD Osteoporotic fractureaHip fracture5060708050607080(a) MenNo clinical risk factors2.83.95.77.20.10.41.33.4Parental history of hip fracture5.57.69.1150.20.62.811Current cigarette smoking2.84.15.97.50.20.71.84.2Alcohol intake >2 units daily3.34.77.19.50.20.72.05.1Rheumatoid arthritis3.75.38.0110.20.82.35.8Oral glucocorticoids4.46.18.5100.30.92.45.5Previous fragility fracture5.87.911120.51.32.75.2(b) WomenNo clinical risk factors3.56.011170.20.72.37.0Parental history of hip fracture6.91217310.30.95.022Current cigarette smoking3.66.512190.31.13.49.5Alcohol intake >2 units daily4.17.314220.31.13.610Rheumatoid arthritis4.78.215250.41.34.112Oral glucocorticoids5.69.818260.51.54.813Previous fragility fracture7.41220280.82.14.911aClinical spine, hip, humeral or forearm fracture
The presence of more than one risk factor increased fracture probability in an incremental manner. For example, in women aged 65 years with a BMI of 25 kg/m2, the 10-year hip fracture probability was 1.3%. With one clinical risk factor, the probability ranged from 1.7% to 3.2%, depending on the risk factor. With two risk factors, the range was 2.5–6.6%, and with 3, 4 and 5 risk factors the ranges were 3.8–11%, 6.7–17% and 13–24%, respectively. In the presence of all six clinical risk factors, the 10-year probability of hip fracture was 30%.
The effect of several clinical risk factors on the 10-year probability of a major osteoporotic fracture is shown in Fig. 2 for a woman aged 65 years and a BMI of 25 kg/m2. In women with rheumatoid arthritis, there was a 33% increase in fracture probability compared with those without rheumatoid (from 9% to 12%). When women additionally took oral glucocorticoids and had a prior fracture there was a fourfold increase in fracture probability.
Fig. 2Effect of combinations of clinical risk factors on the 10-year probability of a major osteoporotic fracture in women aged 65 years and a BMI of 25 kg/m2. [05Ca201]
A higher or lower BMI had a marked effect on fracture probability (Fig. 3). For example, in women at the age of 65 years the 10-year hip fracture probability in the absence of risk factors was 2.3% with a BMI of 20 kg/m2, but was fourfold lower (0.6%) at a BMI of 40 kg/m2. At each level of BMI, fracture probability increased with an increasing number of clinical risk factors.
Fig. 3Ten-year probability of hip fracture (%) in women aged 65 years according to the number of clinical risk factors with a BMI of 20 kg/m2 (left-hand panels) and a BMI of 40 kg/m2 (right-hand panels). The intervals reflect the different weights afforded by different risk factors and provide a range of probabilities. [05Ca070]
Bone mineral density
The relationship between fracture probability and BMD is shown in Table 3. In general, fracture risk increased with increasing age and decreasing T-score. At low T-scores, however, fracture probabilities decreased with age, a phenomenon more marked in men than in women. For example, at the age of 50 years, the 10-year probability of hip fracture was 16.0% in men with a T-score of −4 SD and this fell progressively with age, so that at the age of 70 years, the probability was 12.9% and at the age of 90 years was 8.3%.
Table 3Ten-year fracture probability (%) in men and women from the UK with a BMI of 25 kg/m2 and no clinical risk factors according to age and T-score at the femoral neckAgeT-score (SD)(years)+10−1−2−3−4(a) Hip fracture men50<0.10.10.41.34.716.0600.10.20.61.95.615.6700.20.51.12.65.912.9800.61.11.93.46.110.7901.21.82.53.85.68.3(b) Hip fracture women50<0.10.10.20.83.211.7600.10.20.41.44.413.5700.10.40.92.46.316.2800.51.02.14.49.519.7901.22.03.55.89.816.9(c) Osteoporotic fracturea - men502.52.73.55.19.220.9603.03.54.56.811.522.2703.64.35.58.212.820.9803.24.05.27.611.316.9903.13.95.16.99.713.5(d) Osteoporotic fracturea - women503.03.33.85.28.517.7604.24.85.67.912.623.0705.66.88.411.518.230.4805.87.510.113.821.734.3905.17.19.913.820.030.3aHip, clinical spine, humerus or forearm fracture
The differences in probabilities between men and women were much less marked for any given age and T-score than the differences seen with the use of clinical risk factors alone. At a fixed BMD (−2.5 SD in Table 1), the difference in probability between men and women was less evident. For example, the hip fracture probability ratio (women/men) with glucocorticoid use was 1.8 in the absence of BMD but 0.9 at a T-score of −2.5 SD. In the case of all major osteoporotic fractures, probabilities remained higher in women than in men at a T-score of −2.5 SD but the difference in probabilities between men and women was less marked.
Figure 4 compares the probability of hip fracture in men and women with a prior fracture according to age. In the absence of BMD there was approximately a twofold difference in probability between men and women. When the BMD was set at an average for women (Z-score = 0), there was little difference in probabilities up to the age of 75 years. Thereafter, men had a lower probability than women due to the higher death hazard.
Fig. 4Ten-year hip fracture probability (%) in men and women with a prior fracture according to age. The left-hand panel gives probabilities in the absence of BMD. In the right-hand panel probabilities are shown at an average BMD for women at each specific age (i.e., a Z-score = 0). A BMI is set at 24 kg/m2. [05Ca065]
The clinical risk factors added to the information provided by BMD. Isopleths for 10-year fracture probability are shown in Fig. 5 for the combination of several of the clinical risk factors. The clinical risk factors were somewhat less predictive in the presence of BMD in the models. For example, in women aged 65 years and a BMI of 20 kg/m2, the 10-year hip fracture probability in the absence of BMD ranged from 2.3% in the absence of clinical risk factors to 27.9% with four risk factors. When BMD was set constant at a T-score of −2.5 SD, the range was from 2.8% with no clinical risk factor to 19.7% with four risk factors (Fig. 6). The effect of variations in BMI was even more markedly affected by BMD. For example, in women aged 65 years the 10-year hip fracture probability was 2.3% at a BMI of 20 kg/m2 and decreased progressively at higher levels of BMI to 0.6% with a BMI of 40 kg/m2. When BMD was fixed, hip fracture probability remained constant irrespective of BMI (Fig. 7).
Fig. 510-year probability of a major osteoporotic fracture in men and women aged 65 years according to T-score and clinical risk factors. Body mass index is set at 25 kg/m2. [05Ca136]Fig. 6Ten-year probability of hip fracture (%) in women aged 65 years, according to the number of clinical risk factors. The left-hand panel shows the probabilities without BMD at a BMI fixed at 20 kg/m2. The right-hand panel illustrates the effects with BMD fixed at the threshold for osteoporosis. [05Ca073]Fig. 7Effect of variations in BMI on 10-year hip fracture probability (%) in women aged 65 years. Probabilities with BMD are computed at a T-score of −2.8 SD. [05Ca074]
Discussion
The present study provides a model for the assessment of fracture probability in men and women. The model (FRAX™) uses data derived from nine cohorts from around the world, including centres from North America, Europe, Asia and Australia and has been validated in 11 independent cohorts with a similar geographic distribution [17]. The use of primary (but anonymized) data for the model construct permits the interaction of each of the risk factors to be determined to improve the accuracy whereby fracture probability can be computed. The large sample permitted the examination of the general relationship of each risk factor by age, sex, duration of follow up and, for continuous variables (BMD and BMI), the relationship of risk with the variable itself in a manner hitherto not possible. The use of primary data also eliminates the risk of publication bias. The validity of the clinical risk factors identified are supported by the expected relationships between BMD and fracture risk [3].
In the present study, the FRAX™ model has been calibrated to the epidemiology of the UK, but could be calibrated to any country where the epidemiology of fracture and death is known (see Appendix). FRAX™ models for the UK and some other countries are available through the web (http://www.shef.ac.uk/FRAX/index.htm). The approach uses easily obtained clinical risk factors to estimate risk. The estimate can be used alone or with BMD to enhance fracture risk prediction.
Several previous studies have developed models to predict fracture risk from the combination of clinical risk factors and BMD [18, 65–78]. The risk factors used include activities of daily living, impaired cognition, liability to falls, poor overall health, history of stroke, seizure disorder and several different medications. A limitation of many of these studies is that, with the exception of the SOF study [18], and one of the GPRD studies [76], they have not been tested in other cohorts. The model described in this paper has been validated in 11 independent prospectively studied cohorts with in excess of one million patient years [17].
The use of risk factors for case finding presupposes that the risk so identified is responsive to a therapeutic intervention. To test this hypothesis, it would be necessary to recruit patients selected on the basis of the risk factor(s) to a randomised controlled trial (RCT). The risk factor that is best evaluated in this way is BMD, and indeed the vast majority of therapeutic studies have recruited patients on the basis of low BMD as recommended by regulatory agencies in the US and Europe [79, 80]. In recent years, other trials have recruited patients on the basis of age, gender, a prior vertebral fracture and current exposure to glucocorticoids irrespective of BMD, and have shown therapeutic effects similar to those noted in RCT’s based on BMD selection [14, 81–83].
For other risk factors, comparable data are lacking. In the absence of empirical data, an alternative approach is to demonstrate that the presence (or absence) of a risk factor does not adversely influence therapeutic efficacy against fractures. Several studies have shown no significant interaction between response to treatment and the presence or absence of the risk factors used in the present study including age, height, family history of fracture, low body weight or BMI, smoking, alcohol intake or prior non-vertebral fracture [84–88]. In contrast, some risk factors may be associated with less therapeutic efficacy. For example, patients selected on the basis of risk factors for falling may respond less completely to agents that preserve bone mass than patients selected on the basis of low BMD [89]. This concern is greatest in models that omit BMD, because pharmacological agents may not be equally effective across the entire range of BMD [90].
The present model has several unique features. FRAX™ uses Poisson regression to derive hazard functions of death and fracture. Such hazard functions are continuous as a function of time, unlike Cox’s regression for which the corresponding hazard functions are zero except at the time points of a fracture or death. There are also several advantages of the Poisson model over logistic regression analysis. Logistic regression does not take account of when a fracture occurred, nor whether individuals without a fracture died or when death occurred. Secondly, for the assessment of 10-year probabilities by logistic regression, the observation period should be for 10 years. Moreover, information longer than the 10-year period cannot be used for analysis. The cost of ignoring information when fractures occur and whether and when deaths occur is on the precision of the estimate. In simulation experiments, the Poisson model gives the same precision as logistic regression with fewer numbers of individuals. In our own simulations in the present context (data on file), the Poisson model gave the same precision as logistic regression with half the number of individuals. Finally, the Poisson model allows adjustments to be made for time trends. The ability to use several Poisson models permits the use of data from different sources to integrate fracture and death hazards, and to calibrate to different countries.
A further feature of the FRAX™ model is that it takes account of deaths from all causes. In several recent models of disease probability, this has not been accounted for [23–25]. For example, the probability of stroke has been determined as a function of age, race, smoking, body mass index, atrial fibrillation, HbA1c, systolic blood pressure, ratio of total to HDL cholesterol and duration of diabetes [25], but the risk of dying from other reasons was not taken into account. In the context of osteoporosis, fracture probabilities are markedly underestimated when no account is taken of the competing death hazard [27, 35, 36]. For example, in a study of men followed after orchidectomy, the cumulative incidence of fractures was 19% after 15 years, but the figure was 40% when deaths were considered as a competing event [36].
FRAX™ also takes account of the impact of risk factors on the death hazard. For example, smoking and low BMD are risk factors for fracture but also significant risk factors for death. Thus, at very low T-scores for BMD, hip fracture probabilities decrease with age (see Table 3), in part related to the higher mortality associated with the lower values for BMD.
There are several limitations that should be mentioned. As with nearly all randomly drawn populations, non-response bias may have occurred. The effect is likely to exclude sicker members of society, and may underestimate the absolute fracture risk for example by age. The analyses also have significant limitations that relate to the outcome variables and the characterisation of risk factors. The definition of what was considered to be an osteoporotic fracture was not the same in all cohorts, but the effect of this inconsistency is likely to weaken rather than strengthen the associations that were found. For the hip fracture outcome, the definition was similar in all cohorts, and may explain in part the higher risk ratios associated for this fracture rather than for osteoporotic fracture. Also, the analyses were confined to clinical fractures, and the results might differ from vertebral fractures diagnosed by morphometry or as an incidental radiographic finding.
There are also limitations with the risk factors themselves. In the case of BMI, this was chosen rather than weight as the measure for body composition. This has the advantage that there is less variability across countries and between sexes. A potential drawback is that BMI can be influenced by height loss associated with vertebral deformities. Therefore, in individuals with important loss of height, the risk conferred through BMI could be underestimated [91]. The use of maximal attained height, rather than current height, might be a solution in the future, if it were shown that fracture risk prediction could be improved.
Further problems relate to the construct of the questions to elicit the presence or absence of risk factors, which varied between cohorts. These included questions on family history, prior fracture, smoking and glucocorticoid use. The effect of this heterogeneity is likely to weaken rather than strengthen the associations found. Recall is also subject to errors and was not validated in any of these cohorts. This is particularly problematic in the elderly. In addition, the validity of self-reported alcohol intake is notoriously unreliable [92]. Indeed, alcohol consumption was significantly less in both men and women than that assessed in the UK [93]. Given that these studies were prospective, however, much of this error (with the exception of alcohol intake) should be random, giving rise to non-directional misclassification. Thus, the associations may actually be stronger than reported here. Any underestimate may have limited consequences for case-finding, since the populations to be tested are similar to the populations interrogated. Biases that arise have more significance where causality is inferred.
A further limitation is that several of the clinical risk factors identified take no account of dose-response, but give risk ratios for an average dose or exposure. By contrast, there is good evidence that the risk associated with excess alcohol consumption and the use of glucocorticoids is dose-responsive [14, 94]. In addition, the risk of fracture increases progressively with the number of prior fractures [95]. These limitations are nearly all conservative.
It should be acknowledged that there are many other risk factors that might be considered for incorporation into assessment algorithms. These include BMD at other skeletal sites, ultrasonography, quantitative computed tomography and the biochemical indices of bone turnover. The available information was too sparse to provide a meta-analytic framework, but they should be incorporated into risk assessment algorithms when they are more adequately characterised. Notwithstanding, the present model provides a mechanism to enhance patient assessment by the integration of clinical risk factors alone and/or in combination with BMD.
The application of this methodology to clinical practice will demand a consideration of the fracture probability at which to intervene, both for treatment (an intervention threshold) and for BMD testing (assessment thresholds). These are currently being developed for the UK, based on cost-effectiveness analyses [96]. Intervention thresholds developed for the United Kingdom may not be applicable to other countries. The 10-year probability of fracture varies markedly in different countries [97]. For countries with low hip fracture rates, as found in developing countries, the relative risk at which intervention is cost-effective will be higher, though the absolute risk at which intervention is cost-effective would not change assuming comparable costs. Intervention thresholds would, however, change with differences in costs, particularly fracture costs, which vary markedly world wide. There is also the issue of affordability or willingness to pay for a strategy. The gross domestic product (GDP) per capita provides an index of affordability. The GDP varies markedly in different regions of the world. In the UK, the GDP per capita is estimated at US$ 25,300 in 2002, as compared with US$ 7,000 in Turkey. Thus, for the same fracture risk and the same costs, treatment will be less affordable (at least to health services) in Turkey than in the UK. Nevertheless, individuals in Turkey, rather than society as a whole, may be willing to pay “United Kingdom prices” for health care. There is also a marked heterogeneity in the proportion of GDP devoted to health care, and in the proportion of the population at risk from osteoporotic fracture (i.e., elderly people) [98, 99]. For all these reasons, it is important to define intervention and assessment thresholds on a country by country basis that takes into account the setting for service provision and willingness to pay, as well as considerations of absolute costs.

frax™
fracture probability
clinical risk factors
osteoporotic fracture


Leg blood flow measurements using venous occlusion plethysmography during head-up tilt

We tested whether venous occlusion plethysmography (VOP) is an appropriate method to measure calf blood flow (CBF) during head-up tilt (HUT). CBF measured with VOP was compared with superficial femoral artery blood flow as measured by Doppler ultrasound during incremental tilt angles. Measurements of both methods correlated well (r = 0.86). Reproducibility of VOP was fair in supine position and 30° HUT (CV: 11%–15%). This indicates that VOP is an applicable tool to measure leg blood flow during HUT, especially up to 30° HUT.
Introduction
The main mechanism responsible for maintaining blood pressure during orthostatic stress is arteriolar vasoconstriction [6]. In order to quantify the response in vascular resistance to postural stress, in particular in the leg, it is necessary to measure leg blood flow accurately.
Venous occlusion plethysmography (VOP) is a well-established method to measure calf blood flow, which has been used in a variety of conditions, i.e. exercise [13] and reactive hyperaemia [17]. However, its use for measuring leg blood flow in standing or head-up tilt (HUT) position remains controversial, since an empty venous system has been suggested to be requisite for this method [3, 4, 12]. Since in the upright posture veins are already distended, due to an increase in hydrostatic pressure, further collection of blood may be defined by venous compliance rather than arterial inflow. This may question the validity of measuring blood flow using VOP in dependent limbs or in the upright posture. Although VOP has been used during HUT, accuracy or reproducibility of this method has not been reported [22–24].
Therefore, the purpose of this study was to assess the applicability and reproducibility of VOP for blood flow measurements in the calf (CBF) during HUT at different tilt angles (0°, 30°, 45°, and 70°). In a subgroup CBF measurements by VOP were compared with blood flow measurements using Doppler ultrasound. To assess reproducibility of VOP, measurements were performed twice.
Materials and methods
Subjects
In total eighteen healthy, normotensive subjects aged 21–30 years volunteered to participate in this study. In eight subjects blood flow measurements using VOP were compared with blood flow measurements using Doppler ultrasound (DU). In nine other subjects the VOP measurements were repeated within two weeks to assess reproducibility.
Baseline subject characteristics are illustrated in Table 1. None of the subjects used cardiovascular medication or suffered from cardiovascular disease. All were non-smokers and had no history of syncope. All volunteers refrained from caffeine and alcohol for at least eighteen hours and from food intake for three hours prior to testing. The local medical ethical committee approved the study. All subjects gave their written informed consent.
Table 1Subject characteristicsMean ± SDAge, years25 ± 4Length, cm187 ± 9Body mass, kg80 ± 11Calf circumference, cm38 ± 2Systolic blood pressure, mmHg125 ± 12Diastolic blood pressure, mmHg74 ± 5Heart rate, bpm64 ± 10
Measurements
The subjects lay in supine position on a manually driven tilt table and were supported by a saddle. The venous occlusion cuff was placed around the right thigh and connected to a rapid cuff inflator (Hokanson Stopler E-20, Bellevue, WA 98005, USA). The mercury-in-silastic strain gauge was placed around the thickest part of the calf and was connected to the plethysmograph. Red blood cell velocities and systolic and diastolic vessel diameter of the right superficial femoral artery were measured with a pulsed-colour Doppler device, which is described in detail elsewhere [8]. Reproducibility of DU in the superficial femoral artery was 1.5% for diameter, 14% for blood flow [9].
Protocol
Supine blood flow measurements using DU and VOP were performed after subjects were 30 minutes quietly in supine position. When the subject was 2 minutes into 30° HUT, DU measurements started until 3.5 minutes where after CBF continued for another 3.5 minutes. The venous occlusion pressure was adjusted to the hydrostatic pressure column, which is derived from the vertical distance heart level–thigh level and was calculated as the sinus of the tilt angle * actual distance heart–thigh, and was 75 mmHg during 30° HUT. The same procedure was repeated for 45°, and 70° HUT using a venous occlusion pressure of 87, and 105 mmHg, respectively.
Data analysis
CBF in ml·100 ml−1 · minute−1 was calculated as described previously [25] and values from minute 3.5 until 7 were averaged to calculate CBF for each HUT position. Doppler ultrasound measurements were analyzed as described before [8] to obtain superficial femoral artery blood flow. To compare CBF measurements with superficial femoral artery blood flow, CBF measurements in ml·100 ml−1 minutes−1 were multiplied by lower leg volume (ml) as measured by water displacement.
Statistics
Data are expressed as mean ± standard deviation (SD).
The results of each method were correlated and agreement evaluated according to the method described by Bland and Altman [5]. The limits of agreement are defined as the mean of the relative differences between the two methods ± 2 SD. Student’s t-test was used to test for systemic differences between the two methods.
Reproducibility of the CBF was assessed by calculating the coefficient of variance (CV) from two measurements [25].
To determine whether hemodynamic responses were dependent on the angle of tilt one-way repeated measures ANOVA’s were applied. If significant effects of tilt were observed post-hoc paired t-tests with Bonferroni correction for multiple testing were used. A P-value of <0.05 was considered to indicate significance.
Results
In five volunteers, CBF could not be measured during 70° head-up tilt (HUT), due to a poor plethysmography signal or near fainting of the subject.
Hemodynamic responses to HUT (Figure 1)
CBF and superficial femoral artery blood flow (BF SFA) decreased significantly from supine to 30° with no further decrease with increasing tilt angle (Figure 1). The relative decrease in CBF (46% ± 11%) from supine to 30° was significantly larger than the decrease in BF SFA (40% ± 12%) (P < 0.001).
Fig. 1Absolute values of calf blood flow (CBF) measured with venous occlusion plethysmography and superficial femoral artery blood flow (BF SFA), heart rate (HR) and mean arterial pressure (MAP) during supine position, 30°, 45°, and 70° HUT. One-way repeated measures ANOVA showed a significant effect of tilt for all parameters. *P < 0.05 and indicates significantly different from the position before. ■ = supine;  = 30° HUT;  = 45° HUT; □ = 70° HUT
Agreement VOP and DU
The Pearson correlation between the two methods was 0.86 (P < 0.001) for all data points (Figure 2). The agreement between the two methods was evaluated by plotting the relative difference in each measurement against the mean for all data points and separated for the different tilt angles (Figure 3). The relative mean difference ((VOP–DU)/DU) was −14% ± 22% for all data points in supine position and HUT indicating that overall CBF (VOP) is lower than BF SFA (DU); for supine position the relative mean difference between VOP and DU was 1.5% ± 24%; for 30°: −13% ± 23%; for 45°: −23% ± 19%; for 70°: −23% ± 15%. Limits of agreement for all data points in supine position and during HUT were −58% to 31% and became smaller during HUT. The limits of agreement are reasonable, although CBF during HUT is lower than BF SFA.
Fig. 2Blood flow in the superficial femoral artery (BF SFA) measured by Doppler ultraound during different angles of head-up tilt versus calf blood flow (CBF) measured by venous occlusion plethysmography corrected for lower leg volume. Pearson correlation coefficient is 0.86Fig. 3Relative difference between the blood flow measured by venous occlusion plethysmography (VOP) and the superficial femoral artery blood flow measured by Doppler ultrasound (DU) versus the mean of both flow for each individual subject at different tilt angles
Reproducibility of VOP during head-up tilt
The coefficient of variation (CV) of CBF ranged between 8.7% and 15.0% (Table 2). In 70° HUT, the CV for both parameters was calculated over four subjects only.
Table 2Values of calf blood flow (n = 9)SubjectSupine30° HUT45° HUT70° HUT (n = 4)test 1test 2test 1test 2test 1test 2test 1test 2Mean ± SD2.6 ± 0.62.6 ± 0.81.3 ± 0.31.3 ± 0.3 1.1 ± 0.31.1 ± 0.21.1 ± 0.2 n = 41.2 ± 0.2 n = 4%change ± SD−45 ± 11−49 ± 12−54 ± 11−57 ± 13−44 ± 17−44 ± 14CV15.0%(CI 10.1–29.1)11.0%(CI 7.4–21.3)14.9%(CI 10.0–28.9)8.7%(CI 4.9–33.2)Values of calf blood flow in ml·100 ml−1 · minute−1 and mean absolute and relative data ± SD in supine position, 30°, 45°, and 70° head-up tilt (HUT) for the first and second test. Coefficients of Variation (CV). Missing data in 70° HUT are due to near fainting or a poor plethysmography signal.
Discussion
Calf blood flow (CBF) measured with VOP correlates well with superficial femoral artery blood flow (BF SFA) measured with DU, and can be measured reproducibly during HUT. Since the most profound changes in blood flow with both techniques were already measured in 30° HUT, and the increase in hydrostatic and venous pressure, and concomitant technical difficulties are smallest from supine to 30° HUT we recommend to use VOP for leg blood flow measurements during HUT up to 30°.
The decrease in leg blood flow assessed with VOP and DU, is comparable to tilt-induced blood flow changes in other studies using DU (33%–59%) [2, 7, 10, 11]. The strong relationship between VOP and DU blood flow measurements in the present study is in line with previous studies reporting correlation coefficients varying from 0.57 to 0.99 at rest and during exercise [14, 16, 27]. Head-up tilt affects muscle blood flow more than skin blood flow [21, 28]. Since skin blood flow contributes more to superficial femoral blood flow than to calf blood flow, this may explain the observed discrepancy between the decrease in CBF (∼48%) versus the decrease in superficial femoral artery blood flow (∼40%) in response to HUT.
Reproducibility of baseline CBF (15.0%) is in range with other studies using similar techniques to measure leg blood flow [1, 18, 25]. The coefficient of variation of CBF during HUT was even better (11.0%–17.9%), which indicates that VOP is a reproducible tool to measure tilt-induced vasoconstriction repetitively. The low coefficients of variation of CBF during 70° (8.7%–8.9%) are not representative since these coefficients of variation were calculated over no more than 4 subjects. Not all subjects were able to abstain from moving their legs in 70° HUT position, and some subjects fainted in this position. Moreover, the quality of the plethysmographic tracing became worse at 70° HUT whereas at the lower tilt angles the plethysmography signal is of good quality indicated by the volume pulsations in the plethysmographic tracing for the period of venous occlusion.
Our data and previous studies [15, 19, 20, 26] show that at 30° HUT peripheral vascular responses are accomplished to a large extent. The increase in hydrostatic pressure and concomitant increase in venous pressure is low at 30°. From Figure 4B it can be concluded that at 30° HUT venous compliance is still at the steep portion of the venous compliance curve whereas during 45°, and 70° HUT the venous compliance has shifted to the non-linear part. At 30° HUT increase of the plethysmography signal during venous occlusion is linear while at 45° and 70° HUT, venous distensibility is reduced and consequently results in a non-linear increase in leg volume during inflation of the venous occlusion cuffs, which is illustrated in Figure 5. We therefore recommend using VOP at 30° HUT. For studies focussing on syncope at the endpoint of HUT, which requires larger tilt angles, other techniques to measure leg blood flow should be used.
Fig. 4(A) Pressure-Volume curve and (B) Pressure Compliance curve based on data of a similar group of volunteers measured by venous occlusion plethysmography at different cuff occlusion pressures (for method and protocol see de Groot et al., Journal of applied Physiology, 2005). The increases in calf volume in response to 30°, 45°, and 70° HUT are marked by the dotted lines in the pressure volume curve (A). The different tilting angles correspond with different venous pressures (x-axis). Transferring these venous pressures into the pressure-compliance curve (B) clearly demonstrate that during 30° HUT venous compliance is still on the steep linear part of the curve, whereas during 45°, and 70° HUT the venous compliance is compromisedFig. 5Typical plethysmographic tracing of one individual subject during a complete experiment. Blood flow measurements at 30° HUT start when the plethysmography signal does not change anymore, meaning that venous volume reached a steady state situation. Besides, looking at a typical VOP tracing at 30° HUT, the increase in venous volume is linear during the first 5 seconds of cuff inflation, indicating that blood flow measurements using VOP during 30° HUT are not compromised by a decrease in venous compliance
Limitation
Using VOP, blood flow is defined as limb volume changes over time. During HUT, when the leg is below heart level, volume changes can still be measured using VOP, however, the physiological determinants of these volume changes are complex and it is no longer possible to say with reasonable certainty that a change in volume over time, which most likely reflects flow, is determined by resistance vessel tone. For example, limb blood flow measured using VOP in HUT position can decrease due to an increase in venous pressure, as a result of venous congestion and the associated fall in arterio-venous pressure gradient, without any increase in resistance at the arteriolar level.
In conclusion, this study demonstrates that CBF measured by VOP during HUT is suitable and reproducible. The method is easy applicable and recommended in tilt angles equal to 30° to avoid high hydrostatic and leg venous pressures.

calf blood flow
doppler ultrasound
strain gauge plethysmography
orthostasis
autonomic nervous system


Cell-Assisted Lipotransfer for Cosmetic Breast Augmentation: Supportive Use of Adipose-Derived Stem/Stromal Cells

Background Lipoinjection is a promising treatment but has some problems, such as unpredictability and a low rate of graft survival due to partial necrosis.
Autologous fat transplantation is one promising treatment for facial rejuvenation and soft tissue augmentation because it results in no incisional scar or complications associated with foreign materials. However, certain problems remain, such as unpredictability and a low rate of graft survival due to partial necrosis. Many innovations to overcome these problems have been reported [1, 2, 4–6, 18] and reviewed previously [4, 14]. On the basis of these reports, we tentatively concluded that we could harvest fat with a 2.5-mm cannula or 18-gauge needle at a vacuum lower than 700 mmHg and reinject it using an 18-gauge needle without significant adipocyte damage [14].
Lipoinjection can be used to treat facial changes associated with aging and to correct various types of depressed deformities such as hemifacial microsomia and pectus excavatum. It also has been used in breast augmentation by a limited number of plastic surgeons [3], although the use of autologous fat for breast augmentation has been controversial. Consensus is lacking on whether lipoinjection is safe and appropriate because of microcalcifications that may cause confusion in the evaluation of mammograms. Recently, autologous fat injection has been reevaluated as a potential alternative to artificial implants for breast augmentation [3, 15, 16, 19]. This reevaluation may reflect recent advances in autologous fat transfer and the radiologic detection of breast cancer.
To overcome the problems associated with autologous fat transfer, we use a novel strategy known as cell-assisted lipotransfer (CAL) (Fig. 1). Findings have shown that tissue-specific progenitor cells in the adipose tissue have the capacity to differentiate into various cell lineages [21]. Thus, the progenitors, currently known as adipose-derived stem/stromal cells (ASCs), are expected to become a valuable tool in a wide range of cell-based therapies.
Fig. 1Scheme of cell-assisted lipotransfer. Relatively adipose-derived stem/stromal cell (ASC)-poor aspirated fat is converted to ASC-rich fat by supplementing ASCs isolated from the other half of the aspirated fat. The ASCs are attached to the aspirated fat, which is used as a scaffold in this strategy
The therapeutic concept of CAL was described in our previous report on preclinical studies [9]. We found that aspirated fat has approximately half the number of ASCs found in excised whole fat. There are two main reasons for this relative deficiency. First, a major portion of the ASCs is located around large vessels and left in the donor site after liposuction [9]. Second, a part of the ASCs is released into the fluid portion of liposuction aspirates [20]. The relative deficiency of ASCs may induce postoperative long-term atrophy of injected fat, as partially confirmed in animal studies [8, 9, 11].
With the CAL strategy, autologous ASCs are used to enhance angiogenesis, to improve the survival rate of grafts, and to reduce postoperative atrophy. In CAL, half the volume of the aspirated fat is processed for isolation of the stromal vascular fraction (SVF) containing ASCs. During the isolation process, the other half of the aspirated fat is prepared for grafting. Freshly isolated SVF, which we characterized previously [20], is attached to the aspirated fat, with the fat acting as a living scaffold before transplantation. Finally, the SVF-supplemented fat is injected into the target sites. Thus, ASC-poor fat is converted to ASC-rich fat in the preparation process of the injectable material.
In this report, we describe the preliminary results experienced by patients who underwent CAL for cosmetic breast augmentation. This is the first report on the clinical use of ASCs for cosmetic purposes.
Materials and Methods
Patients
From 2003 to 2007, we performed CAL for 70 patients: in the breast for 60 patients (including 8 patients who had breast reconstruction after mastectomy), in the face for 12 patients, and in the hip for 1 patient. For three patients, CAL was performed at two sites. Informed consent was obtained from all the patients. The study protocol conformed to the guidelines of the 1975 Declaration of Helsinki and was approved by individual institutional review boards.
In this study, 40 patients with healthy thoraxes and breasts underwent CAL for purely cosmetic breast augmentation. Patients undergoing breast reconstruction for inborn anomaly or after mastectomy were not included. At this writing, 19 of these 40 patients have been followed for more than 6 months, and the maximum follow-up period has been 42 months. All the patients were Japanese women with a mean body mass index (BMI) of 19.1 ± 1.9. Their ages varied from 20 to 62 years (mean, 35.8 ± 9.1). The mean volume of injected fat was 268.1 ± 47.6 ml on the left side and 277.3 ± 39.1 ml on the right side. The demographic and surgical data for these patients are summarized in Table 1.
Table 1Patient dataNo. of cases40Sex40 F, 0 MAge (years) 35.8 ± 9.1BMI19.7 ± 1.9 Surgical procedure  Group A6  Group B2  Group C32Site of liposuction  Thighs25  Thighs and abdomen13  Thighs and lower legs2Total volume of suctioned fat (ml) 1111.8 ± 164.0Volume of injection (ml)  Left268.1 ± 47.6  Right277.3 ± 39.1Operation time (min)257.1 ± 39.1
Surgical Techniques
Before the procedure began, the liposuction site was infiltrated with combined saline solution and diluted epinephrine (0.001%). With the patient under general anesthesia, adipose tissue was suctioned using a cannula with a 2.5-mm inner diameter and a conventional liposuction machine. Approximately half of the collected liposuction aspirate was used for isolation of the SVF from both the adipose and fluid portions of the liposuction aspirates, as described previously [20]. This cell processing procedure required about 90 min. During the processing period, the other half of the lipoaspirate was harvested as graft material.
The adipose portion of the liposuction aspirates was either washed several times and placed in an upright position to obtain clear separation of fluids and oil (groups A and B) or centrifuged at 700 g for 3 min without washing (group C), then put into a metal jar (500 ml), which was placed in water with crushed ice. In groups A and C, the fresh SVF isolated from both the adipose and fluid portions was added to the graft material. After gentle mixing and a wait of 10 to 15 min for cell adherence to the aspirated fat, the cell-supplemented fat was put into an injection syringe. In Group B, the freshly isolated SVF was resuspended in 60 ml of saline, then diffusely injected into the whole breast mounds separately (30 ml for each breast) immediately after conventional lipoinjection. There were 6 patients in group A, 2 patients in group B, and 32 patients in group C.
For the injection syringe, a 10-ml LeVeen inflator (Boston Scientific Corp., Boston, MA, USA) or our original 20-ml syringe was used because each is a screw-type syringe (with a threaded plunger), and the threaded connections fit both the connecting tube and the needle to allow precise control during injection. To reduce the time of the procedure, two syringes were used. While the one syringe was being used for an injection, the other was being filled with the graft material in preparation for the next injection. An 18-gauge needle (150 mm long) was used for lipoinjection and inserted subcutaneously at one of four points indicated in Figs. 2A and B.
Fig. 2Schematic instruction of the injection method. (A) A small amount of fat tissue is injected as small aliquots or as a thin string with a long needle, using a syringe with a threaded plunger, while the needle is continuously withdrawn. (B) The needle is inserted from either one of two points on the areola margin or one of two points at the inframammary fold in variable directions and planes to achieve a diffuse distribution
The operator took care to insert and place the needle horizontally (parallel to the body) to avoid damaging the pleura and causing a pneumothorax. The needle was inserted in several layers and directions. It was continuously and gradually retracted as the plunger was advanced. This technique was used to obtain a diffuse distribution of the graft material (Figs. 2 and 3). The grafts were injected into the fatty layers on, around, and under the mammary glands, and also into the pectoralis muscles.
Fig. 3A clinical view of injection. The injection needle is rigidly manipulated by an operator while an assistant rotates the plunger according to the operator’s instruction. A high-pressure injection can be performed using a disposable syringe with a threaded plunger. A 150-mm-long, 18-gauge needle is connected to the syringe with a connecting tube threaded at both ends
Results
The transplantation of adipose tissue was successfully performed in all cases, and the time of the injection process ranged from 35 to 60 min for both breasts. Subcutaneous bleeding, occasionally seen in some parts of the breasts, resolved in 1 to 2 weeks.
Transplanted adipose tissue was gradually absorbed during the first 2 postoperative months (especially during the first month), and the breast volume showed a minimal change thereafter, although skin tension sometimes lessened after 2 months. Three representative surgical sites are shown in Figs. 4 to 9. The difference in breast circumference (chest circumference at the nipple minus the chest circumference at the inframammary fold) had increased in all cases by 4 to 8 cm at 6 months, which corresponds to two to three brassiere cup sizes. This circumference increase appeared to correspond to the 100- to 200-ml increase in the volume of each breast mound, which was partially confirmed by our preliminary evaluation using a three-dimensional quantitative measurement system.
Fig. 4Clinical views of a patient in group A (patient 1). Preoperative (left) and postoperative (right) views at 24 months. A 22-year-old woman underwent breast augmentation using cell-assisted lipotransfer (CAL) (290 ml in each breast), with satisfactory results at 24 months. Her breast circumference increased by 5 cm, and her augmented breast mounds remained soft and natural appearing without injection scars or subcutaneous indurationsFig. 5Radiologic views showing the chest of patient 1. (A) A preoperative computed tomography (CT) image in the horizontal plane of the nipples. (B) A horizontal image 12 months after surgery. Note that the adipose tissue is augmented both subcutaneously and under the mammary glands. (C) Mammograms at 12 months show no calcification or other abnormal signsFig. 6Clinical views of a patient in group C (patient 2). Preoperative (top) and postoperative (bottom) views at 12 months. A 32-year-old woman underwent breast augmentation with cell-assisted lipotransfer (CAL) (280 ml in each breast). Her breast circumference difference increased from 9 cm (baseline) to 14.5 cm (at 12 months). The breast mounds are soft and natural appearing with no visible injection scarsFig. 7Radiologic views showing the chest of patient 2. (A) A preoperative computed tomography (CT) image in the horizontal plane at the level of the nipples. (B and C) Horizontal images by magnetic resonance imaging (MRI) 12 months after surgery: (B) T1-image; (C) T2-image. The adipose tissue is augmented around and under the mammary glands. A small cyst (<10 mm) appears in the fatty layer under the right mammary gland. (D) Mammograms at 12 months show no abnormal signs such as calcificationsFig. 8Clinical views of a patient in group C (patient 3). Preoperative (top) and postoperative (bottom) views at 24 months. A 30-year-old woman underwent breast augmentation with cell-assisted lipotransfer (CAL) (310 ml in each breast). Her breasts were dramatically augmented with an increase in breast circumference difference by 8 cm at 24 months. The breast mounds were soft with no subcutaneous indurations. An original inframammary fold on the left breast is slightly visible, but injection scars are not visibleFig. 9Radiologic views of patient 3. (A) A preoperative computed tomography (CT) image in the horizontal plane at the level of the nipples. Only a very thin fatty layer is observed around the mammary glands. (B) A horizontal magnetic resonance image (MRI) (T1 weighted) 24 months after surgery. Transplanted adipose tissues survived and formed thick layers around and under the mammary glands. (C) Mammograms 24 months after surgery show no abnormal signs
Compared with breast augmentation using implants of the same size, augmentation with CAL showed a lower height but a more natural contour of the breasts. All cases but one (see later) showed natural softness of the breasts without any palpable nodules at 6 months, and all the patients were satisfied with the resulting texture, softness, contour, and absence of foreign materials despite the limited size increase possible with autologous tissue—Cyst formation (<12 mm) was detected by magnetic resonance imaging (MRI) in two patients, and microcalcification was detected by mammogram in two patients at 24 months. In one of two patients in group B, fibrous breast tissue and fibrosis on the sternum were observed by computed tomography (CT) scan at 6 months, and the breasts were found to be harder than in other cases.
Discussion
Modifications of lipoinjection techniques to improve the survival rate for injected fat have been attempted. From these, it is well accepted that adipose tissue should be placed as small aliquots [3], preferably within an area 3 mm in diameter [1]. Because it takes a long time to perform ideally diffuse distribution of suctioned fat [3], we have used a disposable syringe with a threaded plunger and connections, a very long needle (150 mm), and an assistant to rotate the plunger. As a result, only 35 to 60 min are required for injection in both breasts. These devices are critical to performing large-volume lipoinjection safely and precisely in a short time.
In addition, the harvesting, preserving, and refining of graft materials also are important, as repeatedly indicated in the literature. We used a relatively large suction cannula, centrifuged the aspirated fat in some cases, and kept it cooled until transplantation. In this study, the clinical results (increase in breast circumference) appeared to be better in group C (using centrifuged fat) than in group A (using noncentrifuged fat), although quantitative measurement and statistical comparison were not done. In a previous study, we found that centrifugation of aspirated fat is substantially influential because centrifugation at 1,200 g decreases the fat volume by 30%, damaging 12% of the adipocytes and 0% of the ASCs. This leads to the concentration of cell numbers per volume of adipocytes and ASCs by 25% and 43%, respectively [7].
In addition, centrifugation may be especially beneficial in our treatment because water content in the graft material may disturb the adherence of ASCs to the adipose tissue and interfere with differentiation into expected lineages. Any ASCs floating in a solution, which is a nonphysiologic environment, may migrate over distances, penetrate into the lymphatic flow, and differentiate unexpectedly. We believe that such migration and altered cell differentiation caused the development of fibrotic tissue on the sternum of one patient in group B. Thus, we conclude that centrifuged fat combined with ASCs as cell pellets (group C procedure) was best among the three methods used in this study.
Although small cystic formation and microcalcification were detected in some cases, the microcalcification was easily distinguished from that associated with breast cancer, and the overall cosmetic results were generally satisfactory and encouraging. Almost all the patients were satisfied with their enlarged and soft breasts with a natural contour. Both CT scans and MRI showed that transplanted fat tissue survived and formed a significant thickness of the fatty layer not only subcutaneously on and around the mammary glands, but also between the mammary glands and the pectoralis muscles. Breast volume stabilized 2 to 3 months after transplantation.
Maximum breast augmentation using the described technique varied among the patients and appeared to be 100 to 200 ml. Although these volumes may be smaller than those achieved with large artificial implants, a definite advantage is that patients need not be concerned about postoperative complications induced by artificial implants such as rupture, infection, capsular contracture, unnatural contour, hardness, neurologic symptoms, and immune response. Compared with our dozens of patients who underwent conventional autologous lipoinjection to the breasts, augmentation effects were apparently higher with CAL. A 2- to 3-cm increase in breast circumference was common with the conventional procedure, compared with the 4- to 8-cm increase seen in this trial of CAL, although the augmentation effect varied among patients. The measurement system we recently devised may help to quantify the difference in augmented volume in the future.
Adipose tissue contains not only adipogenic progenitor cells, but also multipotent stem cells, which can differentiate into fat, bone, cartilage, and types of tissue [21, 22]. Suctioned fat appears to lose a significant number of these precursors during liposuction and the preparation processes as compared with nonsuctioned adipose tissue [9]. This relative deficiency of precursors may contribute to the low survival rate and long-term atrophy of transplanted lipoaspirates. In CAL, the deficit of ASCs was compensated by supplementing ASCs. To maximize the biologic function and avoid unexpected behavior of ASCs, it seems important to ensure adherence of supplemented ASCs to adipocytes or connective tissue.
With this novel treatment, ASCs have four possible roles, which were partly confirmed in preclinical studies [8, 9, 11]. First, ASCs can differentiate into adipocytes and contribute to the regeneration of adipose tissue. Second, ASCs can differentiate into endothelial cells and also probably into vascular mural cells [8, 10, 12], resulting in the promotion of angiogenesis and graft survival. Third, ASCs are known to release angiogenic growth factors in response to hypoxia and other conditions [13], and these factors influence surrounding host tissue. In their final role, which may be the most influential, ASCs survive as original ASCs [9]. In the adipose tissue, ASCs reside between adipocytes or in the extracellular matrix, especially around vessels, and contribute to the turnover of adipose tissue, which is known to be very slow (2 years or more) [17]. However, adipose grafts probably turn over during the first 2 to 3 months after transplantation because they experience temporary ischemia followed by reperfusion injury. This turnover, the replacement process of the adipose tissue, is conducted by tissue-specific progenitor cells, which are ASCs. The relative deficiency of ASCs in aspirated fat may affect the replacement process and lead to postoperative atrophy of grafted fat, which is known to occur commonly during the first 6 months after lipoinjection.
The freshly isolated SVF used in CAL contains not only ASCs but also vascular endothelial cells, pericytes, blood cells (both white and red), and other cells, as previously described [20]. After transplantation, ASCs may interact with other cells such as vascular endothelial cells, and supplementation with the SVF may be superior to supplementation with ASCs alone in this treatment. However, further studies are needed to elucidate the synergistic effects of ASCs with other cells contained in the graft.
In this preliminary study, satisfactory clinical results were generally achieved without any major complications. Thus, we can conclude that CAL is sufficiently safe for continuation of the study, though controlled investigations and accumulated long-term results are needed to elucidate the overall safety and efficacy of the treatment. A variety of new innovations, including stem cell technology, may be developed and may contribute to the improvement of autologous tissue transplantation and regeneration. Further improvements of the technique may cause autologous tissue transfer to become the first choice for breast augmentation in the future.

breast augmentation
adipose-derived stem/stromal cells
lipoinjection
cell therapy
tissue engineering