Lifestyle, social factors, and survival after age 75: population based study

Lifestyle, social factors, and survival after age 75: population based study | BMJ

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Research Lifestyle, social factors, and survival after age 75: population based study BMJ 2012; 345 doi: 10.1136/bmj.e5568 (Published 30 August 2012) Cite this as: BMJ 2012;345:e5568 Health education Health promotion Smoking Smoking and tobacco Sociology Epidemiologic studies More topics

Sports and exercise medicine Fewer topics

Article Related content Read responses (1) Article metrics Debora Rizzuto, PhD student1, Nicola Orsini, associate professor2, Chengxuan Qiu, associate professor1, Hui-Xin Wang, senior researcher1, Laura Fratiglioni, professor13

1Aging Research Center, Department of Neurobiology, Health Care Sciences and Society, Karolinska Institutet and Stockholm University, 113 30 Stockholm, Sweden

2Unit of Nutritional Epidemiology and Unit of Biostatistics, National Institute of Environmental Medicine, Karolinska Institutet

3Stockholm Gerontology Research Center, StockholmCorrespondence to: D Rizzuto debora.rizzuto{at}ki.seAccepted 13 August 2012AbstractObjective To identify modifiable factors associated with longevity among adults aged 75 and older.

Design Population based cohort study.

Setting Kungsholmen, Stockholm, Sweden.

Participants 1810 adults aged 75 or more participating in the Kungsholmen Project, with follow-up for 18 years.

Main outcome measure Median age at death. Vital status from 1987 to 2005.

Results During follow-up 1661 (91.8%) participants died. Half of the participants lived longer than 90 years. Half of the current smokers died 1.0 year (95% confidence interval 0.0 to 1.9 years) earlier than non-smokers. Of the leisure activities, physical activity was most strongly associated with survival; the median age at death of participants who regularly swam, walked, or did gymnastics was 2.0 years (0.7 to 3.3 years) greater than those who did not. The median survival of people with a low risk profile (healthy lifestyle behaviours, participation in at least one leisure activity, and a rich or moderate social network) was 5.4 years longer than those with a high risk profile (unhealthy lifestyle behaviours, no participation in leisure activities, and a limited or poor social network). Even among the oldest old (85 years or older) and people with chronic conditions, the median age at death was four years higher for those with a low risk profile compared with those with a high risk profile.

Conclusion Even after age 75 lifestyle behaviours such as not smoking and physical activity are associated with longer survival. A low risk profile can add five years to women’s lives and six years to men’s. These associations, although attenuated, were also present among the oldest old (=85 years) and in people with chronic conditions.

IntroductionAn increasing proportion of the population in the developed countries lives to very advanced age.1 Although our current knowledge on the determinants of longevity is limited, the general consensus is that longevity is a multifactorial quantitative trait that is influenced by biological, environmental, and psychosocial factors.2 Among all these elements, modifiable risk factors are especially relevant as they are amenable to intervention. Lifestyle, social networks, and leisure activities have been studied individually in relation to longevity in several studies and others have examined the possible association of these factors with longevity while taking into account their coexistence and interactions.3 4 5 6 7 8 9 10 11 12 13 14 Only a few studies, however, have examined the relation between the combinations of various modifiable factors and longevity.15 16 17 Among the previous studies that have included the oldest old population (=85 years),8 9 10 11 12 13 14 15 16 17 only four had an observational period longer than 10 years.8 9 12 16

Briefly, studies have shown that lifestyle factors such as smoking,3 4 10 11 13 14 16 alcohol consumption,3 4 and body weight (both underweight and overweight),8 16 can predict mortality in elderly people. However, it is uncertain whether these associations are applicable to the oldest old. Indeed, studies have indicated that the relation between certain lifestyle factors and mortality may differ among those aged 75 or older compared with younger adults.7 8 9 14 16 Results concerning the association between social network and mortality among the elderly population have been controversial.18 Finally, previous reports have supported the hypothesis that the associations between leisure time activity, especially physical activity,4 5 6 12 13 16 and survival among the elderly population are positive, although these relations have not been confirmed in other studies.11

We examined the associations of independent and combinations of various modifiable factors with median age at death in a cohort aged 75 or more years at entry to the Kungsholmen Project in central Stockholm, Sweden, that was followed for 18 years. Unlike previous studies that reported the associations in terms of relative risks or hazard ratios, we used absolute values, such as differences in survival among different groups.

MethodsThe study was carried out as part of the Kungsholmen Project, a community based longitudinal study on aging and dementia. A detailed description of the study population and the baseline survey has been previously published.19 20 Briefly, the initial cohort included all registered inhabitants in the Kungsholmen district of central Stockholm who were aged 75 years or older at baseline (October 1987). Of the 2368 eligible participants living at home or in institutions, 181 died, 69 moved out of the area before the baseline examination, and 308 refused to participate, leaving 1810 participants (76.4%) who undertook the baseline survey for the current analysis.

Data collectionData on personal characteristics (age, sex, occupation, and education) at baseline was obtained from participants through a face to face interview with trained nurses, following standard protocols.19 20 Educational level was measured as total years of formal schooling. Socioeconomic status was evaluated on the basis of both education and occupation. Education was divided into primary (<8 years) and secondary or above (=8 years). We used a questionnaire developed by an expert in occupational medicine to assess occupation based socioeconomic status. Information collected about lifetime work activities included employer, job title, period of employment, and tasks for all jobs lasting at least six months. We grouped lifetime occupational experiences according to the Swedish socioeconomic classification system.21 The main occupation was defined as the longest job during lifetime, classified as manual work or non-manual work.22

Information on smoking and alcohol consumption was obtained from baseline data or, if information was missing at baseline, from data collected at the first follow-up three years after baseline. Smoking history was assessed by asking participants whether they had ever smoked. Smokers and former smokers were asked how long they had smoked and the number of cigarettes smoked per day. Former smokers were also asked at what age they had stopped smoking. We categorised smoking status as current, former, and never.23

Alcohol consumption was categorised as yes or no. At baseline, only 6% of the participants reported being heavy drinkers (>168 g ethanol per week for men and >112 g ethanol per week for women). We calculated body mass index as weight (kg) divided by the square of the height (m) using direct measures, and we used standard cut-offs to categorise the participants as overweight (body mass index >25), of normal weight (20-25), or underweight (<20).24

Information on leisure activities and social networks was obtained from participants through face to face interviews carried out by trained nurses during the baseline survey.19 20 Participants were asked whether they regularly engaged in any particular activities or belonged to any organisations. If so, they were asked to specify the types of activities or organisations and to report the frequency of participation. We grouped the reported activities into mental, physical, social, and productive according to the classification adopted in previous studies.25 The frequency of participation in any leisure activities was initially recorded as daily, weekly, monthly, or annually. On the basis of the answers, we categorised the frequency as no participation, daily to weekly participation, and monthly participation. Owing to the statistical power of the study, we analysed survival in relation to participation in each type of activity (at least monthly) compared with no participation. Participants were assigned to a particular group if they participated in at least one of that group’s activities. Mental activities included reading books or newspapers, writing, studying, doing crossword puzzles, painting, or drawing. Physical activities encompassed swimming, walking, or gymnastics. Social activities consisted of attending the theatre, concerts, or art exhibitions; traveling; playing cards or games; or participating in social groups or an organisation for older people. Productive activities included gardening, housekeeping, cooking, working for pay after retirement, doing volunteer work, and sewing, knitting, crocheting, or weaving.

To determine the extent of social networks, we asked participants about marital status, living arrangements, parenthood, and friendships. We also asked about frequency of contact with children and friends or relatives and how satisfied participants were with the frequency of those contacts. On the basis of their answers, we grouped the participants into the three social network categories of rich, moderate, and limited or poor.26 The group with a rich social network included those who were married and lived with someone, had children with whom they were in daily to weekly contact and found this level of contact satisfactory, and had relatives or friends with whom they were in daily to weekly contact and found this level of contact satisfactory. The group with a moderate social network included those who had any two of the three elements. The group with a limited or poor social network included those who had any one or none of the three elements.

If participants were not able to answer the questions (for example, they had cognitive impairment or dementia), then we interviewed an informant, usually someone who was next of kin.

We classified a disease as chronic if it had one or more of the following characteristics: was permanent; was caused by non-physiological changes leading to irreversible damage to a tissue, organ, or system; required rehabilitation; or required a long period of care.27 Multimorbidity was defined as the existence of two or more chronic diseases in one individual; we did not define index diseases.28 We ascertained the participants’ history of chronic diseases using the computerised Stockholm inpatient register system, which covers the period 1969 to 1989 (before baseline). The International Classification of Diseases (eighth revision) was used for all diagnoses in the inpatient register system.

Information about the vital status of the participants in 2005 was derived from death certificates provided by Statistics Sweden.

Statistical analysisAs participants entered in the study at various ages, we analysed baseline age as a confounder and not as a main exposure of interest in the analyses.

The median age at death was the age at which half of the participants had died and the other half were still alive. Survival time was censored for those who were still alive at the end of the study (31 August 2005). We used Laplace regression to model the median age at death as a function of lifestyle factors, leisure activities, and social network.29 Firstly, we estimated differences in median age at death separately by each modifiable factor in age adjusted models. Secondly, we simultaneously adjusted for all the modifiable factors and personal characteristics that were statistically significant in the age adjusted models. Because elderly people often experience chronic illnesses that can affect their lifestyle, we further adjusted for number of chronic conditions.28

Laplace regression is a statistical model that makes inferences on centiles (for example, median) of survival time conditionally on covariates, while taking into account the presence of censored observations. In the absence of covariates, Laplace regression provides estimates of survival centiles similar to the non-parametric Kaplan-Meier method. However, unlike Kaplan-Meier analysis, Laplace regression allows researchers to model the association between continuous exposures, adjusting for confounders, and to assess interactions in predicting survival time.30

The proportion of missing covariate data was 4% for leisure activities, 19% for body mass index, 28% for smoking, and 32% for alcohol consumption. We carried out a complete case analysis based on 60% of the cohort. A sensitivity analysis was done for missing data, with multivariate imputation by chained equations (MICE) to obtain 50 imputed datasets.31 We pooled the estimates using Rubin’s rule to obtain valid statistical inferences.32 All the relevant variables included in the major analyses were used in the multiple imputation models, as was the outcome (age at death).

In the secondary analysis we investigated the relation between various combinations of modifiable factors and median age at death. We defined a reference group as participants with a high risk profile. This group included all participants who had unhealthy lifestyle factors (were overweight or underweight and were current or former smokers), a limited or poor social network, and did not engage in any leisure activities. We estimated median age at death for this group and then compared this with the median age at death for three other groups: those with a moderately high risk profile, those with a moderately low risk profile, and those with a low risk profile. The moderately high risk profile included those participants with at least two of the three risk factors and therefore included those active in at least one leisure activity but who had a poor or limited social network and unhealthy lifestyle factors; or those with a moderate or rich social network but who had unhealthy lifestyle factors and were not engaged in any leisure activities; or those with healthy lifestyle factors (normal weight and never smoked) but who had a limited or poor social network and were not engaged in any leisure activities. The moderately low risk profile included those with only one of the three risk factors and therefore included those active in at least one leisure activity and with a moderate or rich social network but with unhealthy lifestyle factors; or those with a moderate or rich social network and healthy lifestyle factors but who were not engaged in any leisure activities; or those with healthy lifestyle factors who were active in at least one leisure activity but who had a limited or poor social network. The low risk profile included those who had healthy lifestyle factors, had a rich or moderate social network, and engaged in at least one leisure activity.

ResultsTable 1? shows the personal characteristics, lifestyle factors, extent of social networks, leisure activities, and health status of the participants by survival status at 18 years of follow-up. The mean (standard deviation) age at the end of follow-up was 96.1 (3.0) years for survivors and 89.5 (5.4) years for non-survivors. Survivors were more likely than non-survivors to be women, be highly educated, have healthy lifestyle factors, have a better social network, and participate in more leisure activities.

View this table:View PopupView InlineTable 1 Characteristics of study population by survival status at 18 years of follow-up

During the 18 years of follow-up, 149 (8.2%) participants survived and 1661 (91.8%) did not. Overall, 50% of the participants lived to be 90.0 years or older (median age at death).

Table 2? shows the differences in median age at death across the potentially relevant factors. In the age adjusted models, median age at death for participants of normal weight or who had never smoked was about one year longer than those who were underweight (difference in median age at death -1.1, 95% confidence interval -1.7 to -0.4) and current smokers (-1.3, -2.2 to -0.4). Participants who consumed alcohol survived a median of 1.3 years (95% confidence interval 0.7 to 1.8) longer than never drinkers. Half of the participants with a rich social network lived at least 1.6 years (95% confidence interval 0.8 to 2.5) longer than those with a limited or poor social network. Of all the leisure activities, physical activity was associated with the largest difference in median survival; those who were physically active survived more than two years longer than those who were physically inactive (differences in median age at death 2.3 years, 95% confidence interval 1.5 to 3.1).

View this table:View PopupView InlineTable 2 Differences in median age at death (95% confidence intervals) at 18 year follow-up

The multivariable model controlled for all factors that were significantly associated with survival in the age adjusted models. The associations between most factors and survival remained similar in direction and magnitude, except for rich social network and mental activity where the differences in median survival were no longer statistically significant. Further adjustment for multimorbidity attenuated the differences in median survival. Social network did not follow this pattern, as after controlling for multimorbidity people with a rich social network clearly survived longer than people with a limited or poor social network. The magnitude and direction of the differences in median age at death based on the main analysis of complete data and the sensitivity analysis of multiple imputations were similar (table 2).

Table 3? shows the differences in median age at death between the group with the high risk profile (reference group) and the other three groups. The figure? shows the median age at death in all four groups for the entire population and stratified by sex, age at baseline, and number of chronic conditions. Overall, after age 75, lifestyle behaviours such as never smoking, participating in at least one leisure activity, and having frequent contact with children or friends and relatives (and being satisfied with this contact) were associated with survival. Median survival for those in the group with the low risk profile was almost five years longer than that in the group with the high risk profile (table 3). The median age at death was about 83 years for those with a high risk profile and 88 years for those with a low risk profile (figure). Stratified analysis by sex showed that the median age at death was higher for women than for men. The difference in median age at death between people with a low risk profile and those with a high risk profile was six years for men and five years for women (table 3). Stratification by age showed that even in the oldest old participants (=85 years) the median age at death was higher (4.7 years more) if participants belonged to the group with the low risk profile (table 3).

Finally, stratified analysis by health conditions revealed that the median age at death for participants with more than one chronic condition who belonged to the group with the low risk profile was 87 years, around five years older than those in the group with the high risk profile (median age at death 82 years, figure)

View larger version:In a new windowDownload as PowerPoint SlideMedian age at death in four risk groups according to combinations of modifiable factors among entire population, men and women separately, older adults (75-84 years) and oldest old adults (=85 years), and by status of chronic conditions. Results were from Laplace regression, adjusted for education

View this table:View PopupView InlineTable 3 Differences in median age at death at 18 years of follow-up for four risk profile groups, in entire population and in strata by sex, age groups, and health status

DiscussionIn this longitudinal study of 1810 older participants followed up for 18 years, several lifestyle behaviours were associated with longevity, even after age 75 and independently of health status. Certain health behaviours remained predictive of survival even among the oldest old (=85 years) and those with multimorbidity. To the best of our knowledge this is the first study that directly provides information about differences in longevity according to several modifiable factors.

Lifestyle factors and survivalSmokers who survived to 75 years had a one year shorter median survival than those who had never smoked. In the Kungsholmen Project population, 83% of the former smokers had quit smoking 15 to 35 years before baseline and 17% had quit five to 14 years before baseline. The pattern of survival in all former smokers in the study population was the same as that of never smokers. In line with our results, previous studies have found an inverse association between smoking and survival among elderly people,11 13 16 whereas other studies have failed to find an association.8 9 12 Our results confirm the negative association between smoking and survival even in old age, and that quitting smoking in middle age reduces the effect on mortality. Because most former smokers in the study had quit smoking 15 to 35 years before baseline, it is not clear if quitting smoking five to 14 years before baseline may still be associated with survival in elderly people, although this seems to be suggested by our results.

Leisure activities and survivalThe positive association between leisure activity, especially regular physical activity, and longevity found in our analysis confirms the results of some previous studies12 13 16 but not others.11 Although the present analysis cannot provide a definite answer about whether the association between lack of physical activity and shorter survival reflects the effect of illness present at baseline, we were able to minimise the confounding effect by adjusting for morbidity and multimorbidity at baseline. After adjustment the association between physical activity and survival was still significant. Moreover, we cannot verify whether physical activity levels reported at baseline were important in themselves or were indicators of an individual’s lifetime history of physical activity.

Combinations of modifiable factors and survivalOur results on the associations between various combinations of modifiable factors and median age at death showed that compared with their respective high risk profile groups, men with a low risk profile gained more years of survival than women with a low risk profile: the women by five years and the men by six years. Even among those aged 85 years or more, the median age at death could be four years higher if the participants had a healthy lifestyle, a rich or moderate social network, and engaged in at least one leisure activity. Finally, the median age at death for people with more than one chronic condition but who belonged to the group with the low risk profile was 87 years, five years later than those with a high risk profile.

Only a few studies have investigated the relation between combinations of modifiable factors and survival. In the Survey in Europe on Nutrition and the Elderly: a Concerted Action (SENECA) study, researchers developed a lifestyle score by combining three lifestyle factors (non-smoking, physical activity, and quality of diet) and found a strong relation between a healthy lifestyle score and survival.15 Another study pooled five healthy behaviours (based on smoking, alcohol consumption, diet, body mass index, and physical activity) and investigated the relation between this group of healthy behaviours and mortality. They found that the hazard ratio in men with a low lifestyle score was statistically higher than in men with a high lifestyle score.17 Researchers working with the Physicians’ Health Study cohort found that the probability of surviving to age 90 was 54% for those with no adverse factors (those who had never smoked, had normal blood pressure and weight, did not have diabetes, and were moderately physically active).16 Our results were similar to the results of those studies in which the probability of survival was significantly higher among those with the healthier lifestyle scores than among those with less healthy lifestyle scores.

Strengths and limitations of the studyThe major strengths of our study were that the study population was from the general population, including people living at home and in institutions; the study design was prospective; the data on extensive modifiable factors were substantial; and follow-up was long-term. Additionally, we accounted for possible reverse causality by considering only the baseline ascertainment of chronic conditions. All previous studies have examined variation in the risk, hazard, or rate ratio of mortality in relation to selected modifiable factors. The interpretation of these commonly used measures of association may not be easy to communicate to patients or to the general public.

The drop-out rate at baseline of the Kungsholmen Project was 23.6% (558/2368), mainly due to refusal (12.4%), death (7.6%), and moving from the area (3.6%). The personal characteristics of those who refused to participate and those who moved did not differ from those of the participants. Only the 181 who dropped out due to death differed from participants, as they were older and more often men. It is likely that those drop-outs led to an overestimation of the median age at death, especially for the oldest old (=85 years) men.

Any interpretation of the results needs to take survival selection into account33 (in this case before age 75). This is especially true for factors that show an inverse association with mortality. The positive associations with mortality are more likely to be simply underestimated. On the other hand, our study population included people who survived to at least 75 years, which enabled us to investigate the associations of independent and combinations of various modifiable factors with survival in a very old population (=75 years). This is particularly relevant given the limited current knowledge about the relations between such modifiable factors and longevity.

In our study only a small proportion of people had a high alcohol consumption. Thus alcohol consumption, which was mostly moderate, may have protected against mortality. However, because of the high rate of missing data (32%) we cannot rule out the possibility that this result may be heavily affected by information bias.34

Although we adjusted for many factors potentially associated with longevity, our analysis did not include all variables that may be associated with longevity (such as quality of diet). Further analyses are also needed to examine the association between incident morbidity and survival. Moreover, we could not assess the relations between changes in modifiable factors over the lifespan and survival because we assessed exposures only at baseline. Furthermore, repeated measurements of exposure would have provided a better understanding about whether accumulation of factors over the lifetime affects the associations between lifestyle or social factors and survival. In addition, whether extra years of life gained through increased longevity are spent in good or bad health is a crucial question, which we did not address in this study.

Ignoring missing data in complete case analysis can potentially lead to biased estimates.35 However, the small differences in the results of complete case and multiple imputation analyses in this study suggest that missing data had little impact on the observed findings.

ConclusionsThe associations between leisure activity, not smoking, and increased survival still existed in those aged 75 years or more, with women’s lives prolonged by five years and men’s by six years. These associations, although attenuated, were still present among people aged 85 or more and in those with chronic conditions. Our results suggest that encouraging favourable lifestyle behaviours even at advanced ages may enhance life expectancy, probably by reducing morbidity.

What is already known on this topicLifestyle factors such as smoking, alcohol consumption, and being underweight or overweight predict mortality among the elderly population

It is uncertain whether these associations are applicable to the oldest old (=85 years) because of mixed results

What this study addsLifestyle behaviours such as smoking and physical activity predict survival even after age 75

The associations of leisure activity and not smoking with increased life expectancy were still present among those aged 85 or more and those with chronic conditions

NotesCite this as: BMJ 2012;345:e5568

FootnotesWe thank the members of the Kungsholmen Project study group for data collection and Kimberly Kane (scientific editor) for useful comments on the text.

Contributors: DR and LF designed the study. DR and NO did the statistical analyses. DR drafted the manuscript. All authors critically revised the manuscript for important and intellectual content. DR is the guarantor.

Funding: This study was funded by the Swedish Council for Working Life and Social Research, Swedish Research Council for Medicine, Swedish Brain Power, Karolinska Institutet’s Faculty funding for postgraduate students, and the Stiftelsen Ragnhild och Einar Lundströms Minne. The sponsor had no role in study design, data collection, data analysis, data interpretation, the writing of the report, or in the decision to submit the paper for publication. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication.

Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/coi_disclosure.pdf (available on request from the corresponding author) and declare: no support from any organisation for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; and no other relationships or activities that could appear to have influenced the submitted work.

Ethical approval: This study was approved by the ethics committee of Karolinska Institutet, and informed consent was obtained from all participants (87:148; 87:234; 90:251; 94:122, 97:413, and 99:308).

Data sharing: No additional data available.

This is an open-access article distributed under the terms of the Creative Commons Attribution Non-commercial License, which permits use, distribution, and reproduction in any medium, provided the original work is properly cited, the use is non commercial and is otherwise in compliance with the license. See: http://creativecommons.org/licenses/by-nc/2.0/ and http://creativecommons.org/licenses/by-nc/2.0/legalcode.

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Am J Public Health2008;98:1203-8.OpenUrlCrossRefMedlineWeb of Science?Haveman-Nies A, de Groot LP, Burema J, Cruz JA, Osler M, van Staveren WA. Dietary quality and lifestyle factors in relation to 10-year mortality in older Europeans: the SENECA study. Am J Epidemiol2002;156:962-8.OpenUrlFREE Full Text?Yates LB, Djousse L, Kurth T, Buring JE, Gaziano JM. Exceptional longevity in men: modifiable factors associated with survival and function to age 90 years. Arch Intern Med2008;168:284-90.OpenUrlCrossRefMedline?Spencer CA, Jamrozik K, Norman PE, Lawrence-Brown M. A simple lifestyle score predicts survival in healthy elderly men. Prev Med2005;40:712-7.OpenUrlCrossRefMedlineWeb of Science?Holt-Lunstad J, Smith TB, Layton JB. Social relationships and mortality risk: a meta-analytic review. PLoS Med2010;7:e1000316.OpenUrlCrossRefMedline?Fratiglioni L, Viitanen M, Backman L, Sandman PO, Winblad B. Occurrence of dementia in advanced age: the study design of the Kungsholmen Project. Neuroepidemiology1992;11(Suppl 1):29-36.OpenUrlCrossRefMedlineWeb of Science?Fratiglioni L, Viitanen M, von Strauss E, Tontodonati V, Herlitz A, Winblad B. Very old women at highest risk of dementia and Alzheimer’s disease: incidence data from the Kungsholmen Project, Stockholm. Neurology1997;48:132-8.OpenUrlFREE Full Text?Statistics Sweden. Swedish socio-economic classification (SSI). Published in Reports on Statistical Co-ordination, 1982;4:6 (English summary).?Karp A, Kareholt I, Qiu C, Bellander T, Winblad B, Fratiglioni L. Relation of education and occupation-based socioeconomic status to incident Alzheimer’s disease. Am J Epidemiol2004;159:175-83.OpenUrlFREE Full Text?Wang HX, Fratiglioni L, Frisoni GB, Viitanen M, Winblad B. Smoking and the occurrence of Alzheimer’s disease: cross-sectional and longitudinal data in a population-based study. Am J Epidemiol1999;149:640-4.OpenUrlFREE Full Text?Diehr P, Newman AB, Jackson SA, Kuller L, Powe N. Weight-modification trials in older adults: what should the outcome measure be? Curr Control Trials Cardiovasc Med2002;3:1.OpenUrlCrossRefMedline?Wang HX, Karp A, Winblad B, Fratiglioni L. Late-life engagement in social and leisure activities is associated with a decreased risk of dementia: a longitudinal study from the Kungsholmen project. Am J Epidemiol2002;155:1081-7.OpenUrlFREE Full Text?Fratiglioni L, Wang HX, Ericsson K, Maytan M, Winblad B. Influence of social network on occurrence of dementia: a community-based longitudinal study. Lancet2000;355:1315-9.OpenUrlCrossRefMedlineWeb of Science?Timmreck TC, Cole GE, James G, Butterworth DD. Health education and health promotion: a look at the jungle of supportive fields, philosophies and theoretical foundations. Health Educ1987;18:23-8.OpenUrlMedline?Marengoni A, Angleman S, Melis R, Mangialasche F, Karp A, Garmen A, et al. Aging with multimorbidity: a systematic review of the literature. Ageing Res Rev2011;10:430-9.OpenUrlCrossRefMedlineWeb of Science?Bottai M, Zhang J. Laplace regression with censored data. Biom J 52:487-503.?Orsini N, Wolk A, Bottai M. Evaluating percentiles of survival. Epidemiology2012;23:770-1.OpenUrlCrossRefMedline?Van Buuren S, Boshuizen HC, Knook DL. Multiple imputation of missing blood pressure covariates in survival analysis. Stat Med1999;18:681-94.OpenUrlCrossRefMedlineWeb of Science?Rubin DB, Schenker N. Multiple imputation for interval estimation from simple random samples with ignorable nonresponse. J Am Stat Assoc1986;81;366-74.?Glymour MM, Weuve J, Chen JT. Methodological challenges in causal research on racial and ethnic patterns of cognitive trajectories: measurement, selection, and bias. Neuropsychol Rev2008;18:194-213.OpenUrlCrossRefMedlineWeb of Science?Huang W, Qiu C, Winblad B, Fratiglioni L. Alcohol consumption and incidence of dementia in a community sample aged 75 years and older. J Clin Epidemiol2002;55:959-64.OpenUrlCrossRefMedlineWeb of Science?Demissie S, LaValley MP, Horton NJ, Glynn RJ, Cupples LA. Bias due to missing exposure data using complete-case analysis in the proportional hazards regression model. 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Prevalence of abnormalities in knees detected by MRI in adults without knee osteoarthritis: population based observational study (Framingham Osteoarthritis Study)

Prevalence of abnormalities in knees detected by MRI in adults without knee osteoarthritis: population based observational study (Framingham Osteoarthritis Study) | BMJ

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Research Prevalence of abnormalities in knees detected by MRI in adults without knee osteoarthritis: population based observational study (Framingham Osteoarthritis Study) BMJ 2012; 345 doi: 10.1136/bmj.e5339 (Published 29 August 2012) Cite this as: BMJ 2012;345:e5339 Degenerative joint disease Musculoskeletal syndromes Osteoarthritis Pain (neurology) More topics

Immunology (including allergy) Clinical diagnostic tests Radiology Radiology (diagnostics) Calcium and bone Health education Health promotion Obesity (nutrition) Obesity (public health) Fewer topics

Article Related content Article metrics Ali Guermazi, professor of radiology1, Jingbo Niu, research assistant professor of medicine2, Daichi Hayashi, research assistant professor of radiology1, Frank W Roemer, associate professor of radiology13, Martin Englund, associate professor, epidemiologist24, Tuhina Neogi, associate professor of medicine and epidemiology2, Piran Aliabadi, professor of radiology5, Christine E McLennan, project manager6, David T Felson, professor of medicine and epidemiology2

1Department of Radiology, Boston University School of Medicine, FGH Building, 820 Harrison Avenue, Boston, MA 02118, USA

2Clinical Epidemiology Research and Training Unit, Boston University School of Medicine, Boston

3Klinikum Augsburg, Department of Radiology, Augsburg, Germany

4Lund University, Clinical Sciences Lund, Department of Orthopaedics, Lund, Sweden

5Brigham and Women’s Hospital, Harvard Medical School, Department of Radiology, Boston, MA 02115

6OptumInsight Life Sciences, Waltham, MA 02451Correspondence to: A Guermazi guermazi{at}bu.eduAccepted 23 July 2012AbstractObjective To examine use of magnetic resonance imaging (MRI) of knees with no radiographic evidence of osteoarthritis to determine the prevalence of structural lesions associated with osteoarthritis and their relation to age, sex, and obesity.

Design Population based observational study.

Setting Community cohort in Framingham, MA, United States (Framingham osteoarthritis study).

Participants 710 people aged >50 who had no radiographic evidence of knee osteoarthritis (Kellgren-Lawrence grade 0) and who underwent MRI of the knee.

Main outcome measures Prevalence of MRI findings that are suggestive of knee osteoarthritis (osteophytes, cartilage damage, bone marrow lesions, subchondral cysts, meniscal lesions, synovitis, attrition, and ligamentous lesions) in all participants and after stratification by age, sex, body mass index (BMI), and the presence or absence of knee pain. Pain was assessed by three different questions and also by WOMAC questionnaire.

Results Of the 710 participants, 393 (55%) were women, 660 (93%) were white, and 206 (29%) had knee pain in the past month. The mean age was 62.3 years and mean BMI was 27.9. Prevalence of “any abnormality” was 89% (631/710) overall. Osteophytes were the most common abnormality among all participants (74%, 524/710), followed by cartilage damage (69%, 492/710) and bone marrow lesions (52%, 371/710). The higher the age, the higher the prevalence of all types of abnormalities detectable by MRI. There were no significant differences in the prevalence of any of the features between BMI groups. The prevalence of at least one type of pathology (“any abnormality”) was high in both painful (90-97%, depending on pain definition) and painless (86-88%) knees.

Conclusions MRI shows lesions in the tibiofemoral joint in most middle aged and elderly people in whom knee radiographs do not show any features of osteoarthritis, regardless of pain.

IntroductionAgeing of the population and increasing obesity contribute to morbidity worldwide. Osteoarthritis is the most prevalent medically treated arthritic condition worldwide (for example, 3532 per 100?000 people in the United States).1 2 Diagnosis of osteoarthritis is made on the basis of clinical examination or radiography. Population based longitudinal studies in the US3 and the United Kingdom4 showed the lifetime risk of knee osteoarthritis increases with age,3 with the risk highest in obese people.3 4 Other prevalence surveys showed that radiographic osteoarthritis of the knee is common in middle aged and older adults.5 6

Although many publications have reported structural changes in people with radiographic knee osteoarthritis, few data are available regarding what structural changes are present in knees without any radiographic features of osteoarthritis. About half of people with knee pain have no radiographic osteoarthritis. In clinical practice, it is unclear how to investigate and manage such people and whether additional imaging with magnetic resonance imaging would be of clinical value. Such data can be collected only in population based studies as people with normal knees are not usually enrolled into clinical studies or undergo further imaging evaluation. Radiography can show osteophytes, bony outgrowths at the joint margin, and narrowing of the joint space, but it cannot visualise soft tissue pathology.7 In contrast, MRI can visualise various tissues that are clinically relevant and have an important role in regard to structural progression not seen on radiography. MRI can also show incidental findings in otherwise asymptomatic people.8 9 In the knee, MRI visualises most components of the joint, including articular cartilage, menisci, intra-articular ligaments, synovium, bone marrow, subchondral cysts, and other periarticular and intra-articular lesions that are not detectable by radiography.10

We used MRI to evaluate the presence of structural changes in knees that were free from radiographic tibiofemoral osteoarthritis. We focused on the tibiofemoral joint, which includes numerous bony and soft tissue structures that can be evaluated by MRI. We evaluated the prevalence of cartilage damage, meniscal lesions, osteophytes, subchondral cysts, bone marrow lesions, ligamentous lesions, attrition, and synovitis on MRI in participants of the Framingham Osteoarthritis Study who had radiographically normal tibiofemoral knee joints. We also assessed whether the prevalence of these features differed according to age, sex, body mass index (BMI), or knee pain.

MethodsStudy design and participantsThe Framingham Community cohort was recruited from the Framingham, MA, census tract data for the year 2000 and random digit telephone dialling. All participants were examined between 2002 and 2005. This study cohort is distinct from the Framingham Heart Study and the Framingham Offspring Study cohorts. Participants were not selected on the basis of having knee or other joint problems, and potential participants were not told that knees were a focus of the study.

Eligible participants were aged at least 50 and ambulatory (the use of assistive devices such as canes and walker was permitted), with no plans to move out of the area for at least five years to accommodate the possibility of longitudinal follow-up. We excluded those with a history of bilateral total knee replacement, rheumatoid arthritis, dementia, or terminal cancer and those who had contraindications to MRI. Of 2582 people aged 50 or older and living in Framingham who were contacted by random digit dialling, 1830 expressed interest in participating in the study.8 Of those, 39 were lost to contact, 194 were ineligible for the study, and 558 declined to participate. Consequently, 1039 were examined, 993 underwent MRI, and 992 had readable scans (one knee per participant, right knee preferred; left knee if right knee not available (fig 1?).

View larger version:In a new windowDownload as PowerPoint SlideFig 1 Selection process of knees included in present study

Knee radiography and gradingParticipants underwent weight bearing posteroanterior knee radiography with the fixed-flexion protocol.11 One musculoskeletal radiologist, who was blinded to the MRI findings and clinical data, graded radiographs using the Kellgren-Lawrence grading system (intraobserver ? 0.83).12 13 Because we wanted to focus on “normal” tibiofemoral knee joints (Kellgren-Lawrence grade 0), we excluded 253 participants with radiographic tibiofemoral osteoarthritis (Kellgren-Lawrence grade 2 or above), doubtful or equivocal findings of radiographic evidence of tibiofemoral osteoarthritis (Kellgren-Lawrence grade 1), or missing radiographs or radiographic readings. Finally, we excluded 30 participants because of unreadable or poor quality MRIs. This resulted in 710 radiographically “normal” tibiofemoral knee joints being included in the final sample for analysis (fig 1?).

MRI grading of osteoarthritis featuresMRI was done with a 1.5 Tesla scanner (Siemens Medical Systems, Erlangen, Germany) with a phased array knee coil. Images from four pulse sequences were used in the assessment of osteoarthritis features: axial, sagittal and coronal fat saturated, proton density weighted, turbo spin echo images (repetition time 3610 msec; echo time 40 msec; slice thickness 3.5 mm; interslice gap 0 mm; echo train length 7; field of view 140 mm × 140mm; matrix 256 × 256) and sagittal T1 weighted spin echo images without fat saturation (repetition time 475 msec; echo time 24 msec; slice thickness 3.5 mm; interslice gap 0 mm; field of view 140 mm×140 mm; matrix 256×256).

MRI scans were read by two trained and experienced musculoskeletal radiologists (who did not read the radiographs) using a standardised and validated method called the whole organ magnetic resonance imaging score (WORMS).14 They recorded the presence or absence of the specific features (described below) related to osteoarthritis that were included in our assessment of the tibiofemoral joint (that is, tibial plateaus and the central weight bearing and posterior portions of femoral condyles). In the WORMS system, the tibiofemoral joint is subdivided into 10 different subregions for scoring of each feature. Readings from all subregions were amalgamated within the knee.14 Agreement between observers (? statistic) for the detection of the MRI features was as follows: cartilage damage 0.89; meniscal lesions 0.71; osteophytes 0.73; ligamentous lesions 0.49; bone marrow lesions 0.85; subchondral cysts 0.57; and synovitis 0.63. The relatively low value of ? for ligamentous lesions was because few knees had ligamentous lesions in the reliability sample.

Cartilage damage was considered present if there was a small focal loss less than 1 cm in greatest width or areas of diffuse partial or full thickness loss (WORMS grade =2). In this study we did not consider intrachondral signal alterations (WORMS grade 1), which are thought to occur before cartilage damage develops14 but are of unknown clinical importance, to represent cartilage damage.

Meniscal lesions (WORMS grade =1) included displaced or non-displaced meniscal tears or evidence of previous surgery (including repair and partial or complete resection) and complete maceration or destruction (that is, loss of normal contour and signal homogeneity within the meniscus) within the anterior and posterior horns and the body of the medial and lateral menisci.14

Osteophytes were considered present if there were bony projections that form along different margins of the tibiofemoral joint of the knee (WORMS grade =2). Tiny bony spurs that were equivocal on visual evaluation (that is, “lipping,” WORMS grade =1) were not considered as osteophytes.

Ligamentous abnormalities were defined as the presence of a completely torn anterior or posterior cruciate ligament, or a torn or thickened medial or lateral collateral ligament (WORMS grade =1).

Bone marrow lesions—Subchondral bone marrow lesions, also known as “bone marrow edema-like lesions,”15 were considered present if there are non-cystic subchondral areas of ill defined high signal on proton density weighted MR images with fat signal suppression (WORMS grade =1).

Subchondral cysts were identified as areas of markedly increased signal intensity in the subarticular bone with sharply defined rounded margins and no evidence of internal marrow tissue or trabecular bone on the fat saturated proton density weighted images (WORMS grade =1).

Synovitis was considered present if the synovial cavity was distended and filled with fluid (high signal intensity on fat saturated proton density weighted images), representing synovial thickening and joint effusion (WORMS grade =1).14

Attrition—Flattening or depression of the articular surfaces of the tibia or femur was termed bone attrition, and any degree of deviation from the normal bony contour was considered abnormal (WORMS grade =1).

Additional analysis with a more stringent definition of “abnormality”Currently there is no concrete definition of what is “abnormal” in terms of MRI findings in the knee, and the use of different cut off points for the definition of “abnormality” might produce different results. We also examined a more stringent definition of lesions detected by MRI, which included cartilage damage and osteophytes=WORMS grade =3; all other lesions=grade =2.

Assessment of weight, height, and pain We measured the participants’ weight when they were not wearing shoes with the use of a balance beam scale and measured height with a stadiometer. At the clinic visit all participants were asked about knee symptoms with the following question: “In the past month, have you had any pain, aching, or stiffness in your knee?” (for this study, we focused on pain in the knee with MRI reading). Additionally, we assessed knee pain in three more ways. Participants responded to the questions, “Did you have knee pain lasting at least a month in the past year?” and “Do you have knee pain on most days?” A positive response to these questions was considered to indicate the presence of knee pain. Each participant was also asked to fill out the Western Ontario McMaster University arthritis index (WOMAC) questionnaire, and any score =1 in the pain subscale in the knee was considered to indicate the presence of knee pain. For WOMAC pain, we restricted our analysis to participants who had Kellgren-Lawrence grade 0 knees bilaterally as the WOMAC questionnaire was person based and not knee based.

Statistical analysisWe calculated the prevalence of the aforementioned osteoarthritis features on MRI and stratified the data according to sex, age group (sixth decade, seventh decade, and older), BMI (<25, =25-<30, =30), and the presence of pain. We used ?2 tests to assess the presence of significant differences between men and women, and among different age and BMI groups. For cartilage and bone marrow lesions, results were stratified according to the medial and lateral tibiofemoral compartments of the knee. All statistical analyses were performed with SAS for Windows, version 9.1. Results were considered to be significant when a two tailed P<0.05.

ResultsCharacteristics of study sampleOf the 710 participants, 393 (55%) were women, 660 (93%) were white, and 206 (29%) had painful knees. The mean age was 62.3 (range 51-89), and the mean BMI was 27.9 (range 16.6-50.6) (table 1?).

View this table:View PopupView InlineTable 1 Characteristics of participants without knee abnormalities. Figures are numbers (percentage) unless stated otherwise

Prevalence of bony and soft tissue abnormalities on MRI with standard definitionOverall, 631 (89%) knees had at least one type of abnormality (fig 2?, table 2?). The three most common findings were osteophytes, cartilage damage, and bone marrow lesions. In the location specific analysis, cartilage damage was more prevalent in the medial tibiofemoral compartment (33% (95% confidence interval 30% to 37%), 235/710) than in the lateral tibiofemoral compartment (20% (17% to 23%), 141/710). Likewise, there were more bone marrow lesions in the medial (19% (16% to 22%), 133/710) than in the lateral tibiofemoral compartment (12% (10% to 16%), 87/710).

View larger version:In a new windowDownload as PowerPoint SlideFig 2 Knee with multiple abnormalities on MRI indicating early stage osteoarthritis despite lack of radiographic osteoarthritis. A: coronal fat suppressed proton density weighted image shows several features of early OA detectable only by MRI. White arrowhead shows focal full thickness cartilage defect at central weight bearing part of medial femur. In addition there is adjacent subchondral bone marrow lesion presenting as area of ill defined hyperintensity (arrows). Black arrowheads show meniscal extrusion at medial joint line causing bulging of neighbouring medial collateral ligament (no arrow). B: sagittal proton density weighted image shows isolated degenerative horizontal oblique tear of posterior horn of medial meniscus extending to undersurface of meniscus adjacent to posterior tibial surface (arrows). No associated cartilage damage or subchondral bony alterations are seen

View this table:View PopupView InlineTable 2 Prevalence of MRI features (standard definition*) stratified by sex, pain status, and BMI. Figures are numbers (percentage) of participants

Table 2 summarises the prevalence of each MRI feature overall and in men and women?. The prevalence of meniscal lesions was significantly higher in men than in women (110/317 (35%) v 57/393 (15%); P<0.001). No other features were significantly different between men and women. There were no significant differences in the prevalence of any of the features between BMI groups (table 2). The prevalence of all features was within about 7% among all BMI groups.

Older age groups had more abnormalities of all types. Of the participants in their sixth decade, 86% (271/316) had features of osteoarthritis. The rate increased to 91% (227/249) in the seventh decade and 92% (133/145) in the oldest age group. Specific types of abnormalities (cartilage damage, meniscal lesions, osteophytes, subchondral cysts) also increased with each decade (table 3? and fig 3?). The prevalence of ligamentous lesions, bone marrow lesions, attrition, and synovial thickening and joint effusion was also higher in older age groups, but the differences between groups were not significant.

View this table:View PopupView InlineTable 3 Prevalence of MRI features (standard definition*) stratified by age group. Figures are numbers (percentage) of participants

View larger version:In a new windowDownload as PowerPoint SlideFig 3 Prevalence of osteoarthritis features on MRI in knees without radiographic osteoarthritis stratified by age group with standard and more stringent definitions of MRI abnormalities

The prevalence of attrition (38% v 30%; P=0.04), bone marrow lesions (59% v 50%; P=0.03), and subchondral cysts (31% v 23%; P=0.04) was higher in participants with painful knees than those without pain (table 2). The prevalences for the other features were within about 4% of one another among painful and painless knees with no significant differences (table 2). Indeed, the prevalence of at least one type of MRI detected pathology (“any abnormality”) was high in both painful (91%) and painless (88%) knees (table 2?). Regardless of the definition of pain used, MRI detected abnormalities were highly prevalent in people with (90-97%) and without (86-88%) knee pain. While the prevalence of MRI abnormalities was not significantly different in those with versus those without knee pain for most definitions of pain we tested, the prevalence of “any MRI abnormality” was higher in those with WOMAC pain compared with those without pain (P=0.002). Even so, the prevalence of any MRI abnormality was as high as 86% in those without WOMAC pain.

Prevalence of bony and soft tissue abnormalities on MRI with more stringent definitionWhen we used the more stringent definition of MRI abnormality, overall the prevalence of MRI detected lesions dropped as expected (table 4?), to 14% for osteophytes, 44% for cartilage damage, 16% for bone marrow lesions, 4% for synovitis, 10% for attrition, 5% for subchondral cysts, 8% for meniscal lesions, and 2% for ligamentous lesions. The prevalence of any abnormality, however, remained high (53%, 373/710). Except for bone attrition, painful knees did not differ from those without pain in terms of the prevalence of specific features (table 4?). Regardless of the definition of pain used, any abnormality was present in 57-70% of participants with pain and about half of those without pain (table 5?). There were significant differences between groups with and without pain in three out of four definitions of pain, and the largest difference was seen with WOMAC pain (15%, P<0.001). Prevalence of “any abnormality” in those without WOMAC pain, however, was still high (48%).

View this table:View PopupView InlineTable 4 Prevalence of MRI features (more stringent definition*) stratified by sex, pain status, and BMI. Figures are numbers (percentage) of participants

View this table:View PopupView InlineTable 5 Prevalence of “any abnormality” on MRI stratified by pain status with standard and more stringent definitions of pain. Figures are numbers (percentage) of participants

DiscussionWe found that MRI detected features of osteoarthritis are highly prevalent in the tibiofemoral joint of knees that did not have any radiographic features of osteoarthritis in participants both with and without knee pain. Nearly 90% of our participants had at least one feature of osteoarthritis on MRI. Osteophytes were the most common, followed by cartilage damage and bone marrow lesions. In general, the older the age group, the higher the prevalence of features of osteoarthritis, although differences among age groups were not significant for synovitis and effusion and of borderline significance for ligamentous lesions and bone marrow lesions. Only meniscal lesions were more prevalent in men than women. No significant differences were observed for any type of lesions by BMI.

Strengths and limitationsThis population based study documented the high prevalence of MRI features suggestive of knee osteoarthritis in people without radiographic osteoarthritis. We included only knees that were definitely lacking any radiographic features that could indicate the presence of osteoarthritic changes (Kellgren-Lawrence grade 0) to ensure our analysis is specific. Although Kellgren-Lawrence grade 1 knees also do not qualify for having radiographic osteoarthritis, a “doubtful” bony abnormality is present and one could argue such equivocal findings are difficult to interpret.

Limitations Our sample was primarily (although not exclusively) white, reflecting the population of Framingham, MA. The number of people from other racial or ethnic groups was too small for comparisons. Our prevalence estimates cannot be generalised to adults younger than 50. In particular, meniscal lesions in young active otherwise healthy adults are more likely to be caused by trauma than the degenerative process seen in middle aged and older people. We had no arthroscopic correlation of our MRI findings. Ideally, intra-articular pathology (that is, cartilage, meniscus, and ligaments) should be confirmed by direct visualisation during arthroscopy. Arthroscopy, however, is neither feasible nor ethical in large scale population based studies. Furthermore, arthroscopy cannot visualise some of the MRI findings that are indicative of the osteoarthritis disease process such as subchondral bone marrow lesions. Nearly all the knees in our sample were right knees (with only five left knees). A comparison of 99 people with both right and left knee MRIs in this sample, however, showed no difference in findings, and which knee is studied is therefore unlikely to affect our overall outcome. We did not include the evaluation of radiographic patellofemoral joint pathology in this study because we used the posteroanterior radiograph to classify the tibiofemoral joint of the knee using Kellgren-Lawrence grading. We dealt with this fact by including only subregions of the knee that correspond to the tibiofemoral joint for MRI analysis.

Because we focused on knees with clearly normal radiographic appearance (Kellgren-Lawrence grade 0), we excluded Kellgren-Lawrence grade 1 knees. One might argue that such knees are also without radiographic osteoarthritis and warrant inclusion in our analysis. Inclusion of the 39 Kellgren-Lawrence grade 1 knees in our sample (total 749 knees) did not alter the demographic characteristics of the participants or analytical results for all aspects of the study. There are many ways to define pain, and it is not possible to include all different pain assessment tools available to date in a single study. We selected the WOMAC pain subscale because it has been validated and is widely used.16 17

Our results raise additional questions. More detailed analysis evaluating the factors that could contribute to the differences seen in men and women and the osteoarthritis features in the different age groups would be of interest. Also, comparison of the prevalence of these findings in those with and without radiographic osteoarthritis would tackle the question of whether osteoarthritis is an inevitable consequence of ageing.

Comparison with previous studiesOf our findings, the most notable is that 74% of the knees had osteophytes. As a bony abnormality should be clearly visible on radiograph, we did not expect the prevalence to be this high. Presumably, because the MRI assessment used three imaging planes, it could detect osteophytes that were hidden by the overlapping femur or tibia on posteroanterior view radiographs. This is a substantial problem as the presence of definite osteophytes defines the diagnosis of radiographic osteoarthritis.12 Thus far, epidemiological or clinical studies of knee osteoarthritis depend largely on the radiographic definition of osteoarthritis.18 19 As radiography fails to detect such a large proportion of osteophytes, there could be misclassification of a large number of potentially eligible people in knee osteoarthritis studies and underestimation of the true prevalence of this condition.20

Although cartilage itself is aneural and is unlikely to be a direct cause of knee pain, cartilage damage is associated with change in bone marrow lesions,21 high BMI, meniscal damage, and synovitis or effusion.22 Cartilage thickness has traditionally been assessed by its surrogate marker—the radiographic width of the joint space of the tibiofemoral joint. Narrowing of the joint space, however, can result not only from cartilage damage but also from meniscal lesions.23 It has been shown that radiography is less sensitive than MRI for detection of cartilage loss.7 Thus, it is not surprising to find a high prevalence of cartilage damage on MRI in the knees of middle aged and older people without radiographic joint space narrowing.

The presence and extent of bone marrow lesions and synovial thickening/effusion can be appreciated only on MRI. These lesions have been associated with pain in knees with osteoarthritis.24 25 Furthermore, in people at high risk of developing osteoarthritis, bone marrow lesions in asymptomatic knees with no radiographic osteoarthritis at baseline predict development of pain 15 months later.26 We also found an association of bone marrow lesions with knee pain among people without radiographic osteoarthritis.

A high prevalence of incidental meniscal findings on MRI in participants of the Framingham Osteoarthritis Study has been reported previously.8 One or more meniscal tears was present in 32% (41/127) of knees with symptoms, 23% (146/548) of knees without symptoms, and 24% (187/775) overall when there was no or equivocal radiographic evidence of osteoarthritis. Although the results were similar to the present study, they are not identical because Englund and colleagues included knees with Kellgren-Lawrence grade 0 and 1,8 whereas we focused on Kellgren-Lawrence grade 0.

We saw fewer incidental ligamentous lesions than any other feature. This could be because the semiquantitative scoring system we used only scores a complete tear as a lesion, and partial tears are given a score of zero. Imaging diagnosis of partial ligamentous tears on MRI can be difficult. The role of intra-articular and periarticular ligaments of the knee in predicting structural progression of knee osteoarthritis remains unclear. Disruption of the integrity of these ligaments, however, will probably cause alterations in knee kinematics.

A recent systematic review reported that bone marrow lesions and effusion/synovitis were associated with knee pain.27 In our study, however, these lesions were not significantly more prevalent in participants who had knee pain than in those without, with both definitions of MRI abnormality. This discrepancy is probably because the systematic review included only studies involving mostly people with radiographic knee osteoarthritis. Thus, the conclusion of the systematic review is not applicable to the present study.

Clinical implicationsOur findings indicate that the prevalence of MRI detected osteoarthritis features increases with age in the absence of radiographic features of osteoarthritis. We have shown that MRI is more sensitive than radiographs to changes in bone and soft tissue that are considered features of osteoarthritis,28 29 Our data showed that the prevalence of these MRI detected features is high irrespective of the knee pain status. When we compared the prevalence of MRI abnormalities in knees in people with and without pain, there were two trends. Firstly, and most importantly, the prevalence of MRI findings was extremely high in those without pain, suggesting that using MRI as a diagnostic test for people with normal knee radiographs in this age group would have poor specificity. Secondly, the prevalence of findings was modestly higher in those with pain than in those without, with the difference sometimes reaching significance. These differences, however, were not particularly informative—for example, the highest prevalence of MRI abnormalities was actually in those with mild pain rather than moderate or severe pain.

Thus, MRI features suggestive of osteoarthritis in people without radiographic osteoarthritis are commonly seen in those with or without knee pain, implying that MRI alone is not diagnostically useful to discriminate between people with and without pain in the context of knee osteoarthritis. MRI might still play an important diagnostic role, especially in younger people, in whom other reasons for knee pain should be considered such as inflammatory arthritides, insufficiency fractures, or spontaneous osteonecrosis. Nonetheless, in all likelihood, MRI features of osteoarthritis will be found regardless of the source of the pain. Our study also highlights the limitations of conventional radiography to detect a large number of abnormalities related to osteoarthritis in the knee.20

As high BMI is a known risk factor for both incident knee osteoarthritis and for progression of knee osteoarthritis30 31 we expected to see higher prevalence of MRI features in obese people compared with non-obese people. We did not find high BMI to be associated with higher prevalence of MRI features overall compared with low BMI, but rather that these MRI abnormalities were equally highly prevalent in all BMI groups. We speculate that BMI is important for progression of later stages of osteoarthritis, but potentially age is a much more relevant trigger of early stages of osteoarthritis.

Although there is thought to be only a modest correlation between clinical symptoms and radiographic tibiofemoral osteoarthritis,32 recent work has highlighted an association between structural osteoarthritis pathology and knee pain.33 34 It is important for the clinical community to recognise that findings that would be interpreted as abnormal and suggestive of disease are in fact present in most knees without any pain, even when different definitions of pain are used. That means that the clinical significance of these MRI findings is questionable. The same message has been reported for radiographic findings in patients with low back pain (similar highly prevalent abnormalities were seen in those without low back pain), and this led to discouraging radiographic evaluations in those with low back pain.35

ConclusionsChanges indicative of osteoarthritis are commonly present in the knees of most people aged 50 and over who have no radiographic evidence of tibiofemoral osteoarthritis. Osteophytes, cartilage damage, and bone marrow lesions are especially common among middle aged and older people. These features are common in knees with pain and in those that are painless and can potentially represent pre-radiographic or early stage osteoarthritis. A longitudinal study is needed to determine what proportion of people without radiographic osteoarthritis but with MRI abnormalities subsequently develop radiographic osteoarthritis.

What is already known on this topicMRI can detect features suggestive of knee osteoarthritis that cannot be visualised on conventional radiography, which is insensitive to many findings

In roughly half of people with knee pain, radiography shows no abnormalities

What this study addsChanges indicative of osteoarthritis are commonly present in the knees of most people aged 50 and over who have no radiographic evidence of tibiofemoral osteoarthritis

MRI detected findings of osteoarthritis are common in people with and without knee pain, suggesting that the clinical significance of MRI findings in such knees is not clear

NotesCite this as: BMJ 2012;345:e5339

FootnotesContributors: AG, JN, DH, and DTF conceived and designed the study. AG, JN, FWR, PA, CEM, and DTF collected the data. AG, DH, FWR, ME, TN, and DTF reviewed the literature. AG, JN, DH, FWR, ME, TN, and DTF directed the analyses, which were carried out by JN. All authors participated in the discussion and interpretation of the results. AG and DH organised the writing and wrote the initial drafts. All authors critically revised the manuscript for intellectual content and approved the final versions. AG and DTF are guarantors.

Funding: This study was funded by the National Institutes of Health (AG18393 and AR47785) and the Arthritis Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The researchers work independently of their funders.

Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/coi_disclosure.pdf (available on request from the corresponding author) and declare: AG is the president of Boston Imaging Core Lab (BICL), LLC, and a consultant to Merck Serono, Stryker, Genzyme, AstraZeneca, and Novartis; FWR a vice president and shareholder of BICL and is a consultant to Merck Serono and National Institute of Health; ME is funded by the Swedish Research Council, the Greta and Johan Kock Foundation, King Gustaf V 80-year Birthday Foundation, and the Faculty of Medicine, Lund University, Sweden; TN is supported by NIAMS AR055127 and the Arthritis Foundation Arthritis Investigator Award.

Ethical approval: This study was approved by the institutional review board of Boston University Medical Centre (protocol number H-22674), and written informed consent was obtained from all participants.

Data sharing: No additional data available.

This is an open-access article distributed under the terms of the Creative Commons Attribution Non-commercial License, which permits use, distribution, and reproduction in any medium, provided the original work is properly cited, the use is non commercial and is otherwise in compliance with the license. See: http://creativecommons.org/licenses/by-nc/2.0/ and http://creativecommons.org/licenses/by-nc/2.0/legalcode.

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