bmj open...svetlana solovieva, phd 1,2 pertti mutanen, msc 3 harri pihlajamäki, md, phd 4,5 markku...

For peer review only

Role of overweight and obesity in low back disorders among men: a longitudinal study with life course approach

Journal: BMJ Open

Manuscript ID: bmjopen-2015-007805

Article Type: Research

Date Submitted by the Author: 28-Jan-2015

Complete List of Authors: Frilander, Heikki; Finnish Institute of Occupational Health, Centre of Expertise for Health and Work Ability; Finnish Institute of Occupational Health, Disability Prevention Centre Solovieva, Svetlana; Finnish Institute of Occupational Health, Center of Expertice for Health and Work Ability Mutanen, Pertti; Finnish Institute of Occupational Health, Statistics and Health Economics Pihlajamäki, Harri; Seinäjoki Central Hospital, Department of Orthopaedics

and Trauma Surgery; University of Tampere, Heliövaara, Markku; National Institute for Health and Welfare, Department of Health, Functional Capacity and Welfare Viikari-Juntura, Eira; Finnish Institute of Occupational Health,

<b>Primary Subject Heading</b>:

Epidemiology

Secondary Subject Heading: Public health

Keywords: Back pain < ORTHOPAEDIC & TRAUMA SURGERY, EPIDEMIOLOGY, Musculoskeletal disorders < ORTHOPAEDIC & TRAUMA SURGERY

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TITLE PAGE

Role of overweight and obesity in low back disorders among men: a

longitudinal study with life course approach

Heikki Frilander, MD1,2

Svetlana Solovieva, PhD1,2

Pertti Mutanen, MSc3

Harri Pihlajamäki, MD, PhD4,5

Markku Heliövaara, MD, PhD6

Eira Viikari-Juntura, MD, PhD2

Corresponding author: Heikki Frilander, Topeliuksenkatu 41 a A, FI-00250 Helsinki,

Finland, [email protected], telephone +358304741, fax +35892412414

1Centre of Expertise for Health and Work Ability, Finnish Institute of Occupational Health,

Topeliuksenkatu 41 a A, FI-00250 Helsinki, Finland

2Disability Prevention Research Centre, Finnish Institute of Occupational Health,


3Statistics and Health Economics, Finnish Institute of Occupational Health,


4Department of Orthopaedics and Trauma Surgery, Seinäjoki Central Hospital,

Huhtalantie 53, 60220 Seinäjoki, Finland

5University of Tampere, Huhtalantie 53, 60220 Seinäjoki, Finland

6Department of Health, Functional Capacity and Welfare, National Institute for Health

and Welfare, PB 30, 00271 Helsinki, Finland

Keywords: low back pain; radiating low back pain; overweight; obesity; life course

Word count: 2772

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ABSTRACT

Objectives: To assess the association of overweight or obesity with low back pain (LBP)

and clinically defined low back disorders across life course.

Design: A longitudinal study.

Setting: A nation-wide health survey supplemented with data from prior compulsory

military service records.

Participants: Pre-military health records (baseline) were searched for 30-50 year-old

men (n=1385) who participated in a national health examination survey (follow-up).

Methods and outcome measures: Height and weight were measured at baseline and

follow-up, and waist circumference at follow-up. Weight at the ages of 20, 30, 40 and 50

years were inquired, if applicable. Repeated measures of weight were used to calculate

age-standardised mean body mass index (BMI) across life course.

The symptom-based outcome measures at follow-up included prevalence of non-specific

and radiating LBP during the previous 30 days. The clinically defined outcome measures

included chronic low back syndrome and sciatica.

Results: BMI measured at baseline (20 years) predicted radiating LBP in adulthood,

prevalence ratio (PR) being 1.26 (95% CI 1.08-1.46) for one standard deviation (3.0

kg/m2) increase in BMI. Life course BMI was associated with radiating LBP (PR 1.23;

95% CI 1.03-1.48 per one unit increment in Z-score, corresponding to 2.9 kg/m2). The

development of obesity during follow-up was involved with a 1.91-fold increased risk

(95% CI 1.03-3.53) in radiating LBP. Both general and abdominal obesity (defined as

waist-to-height ratio) were associated with radiating LBP (OR 1.64, 95% CI 1.02-2.65

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and 1.44, 95% CI 1.02-2.04). No associations were seen for non-specific LBP.

Conclusions: Our findings imply that being overweight or obese in adolescence as well

as during life course increases the risk of radiating but not non-specific LBP among men.

Taking into account the current global obesity epidemic, emphasis should be put on

preventive measures already in youth, and also at preventing further weight gain during

life course.

Strengths and limitations of this study:

• This study used a longitudinal design with a random population sample combining

data from compulsory military service with a later health examination.

• The low back outcomes included both self-reported symptoms and clinically

defined disorders.

• General and abdominal obesity were defined based on measured weight-related

indicators.

• The availability of multiple measures of weight across life course enabled to

account for the temporal fluctuation of weight over time and allowed to explore

the associations of early indicators of obesity, change in obesity status over time,

as well as life course BMI with the outcomes.

• The military records did not provide measures of abdominal adiposity at baseline,

therefore we were not able to explore the associations of abdominal obesity

across life course.

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INTRODUCTION

Low back disorders are the most prevalent musculoskeletal health concerns in

populations and may cause varying degrees of disability.1 Obesity is another common

public health problem and continues to increase.2 3 The increase has been especially

pronounced in children and adolescents.4 5

Two recent meta-analyses showed that overweight and obesity increase the risk of both

low back pain (LBP) and lumbar radicular pain.6 7 For LBP the associations have been

stronger in women compared with men,8-10 however for lumbar radicular pain no gender

difference has been found.

Nearly all studies have looked at the effects of general obesity defined by body mass

index (BMI). The use of BMI has been criticized for inability to distinguish the difference

between fat and lean mass, especially in men.11 12 Abdominal obesity defined by waist

circumference has been associated with LBP in women, but not in men.8 13 14

Most previous studies on the influence of obesity on low back symptoms have been

cross-sectional and the majority of prospective studies have had a relatively short

follow-up time. Repeated measurements of weight-related factors have been rarely

carried out, especially in young populations.8 15 To our knowledge, there are no studies

that have evaluated the lifetime burden of overweight and obesity on low back

outcomes.

The aim of this study was to assess the association of overweight and obesity with LBP

and clinically defined low back disorders across life course. In a longitudinal approach we

wanted to find out whether high BMI at the age of 20 years or during life course predicts

increased risk of low back disorders later in life. Moreover, we looked whether changes in

BMI during follow-up affect the risk of LBP. Finally, we explored the associations of

general and abdominal obesity with the outcomes.

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MATERIALS AND METHODS

Study population

The current study used a longitudinal design with two waves of data collection (Figure

1). A random sample of the nationally representative Health 2000 Study, comprising of

1866 men aged 30-50 years, formed our study base. The main purposes and methods of

this study have been described in detail elsewhere.16 17 Participants for the first wave of

our data collection (baseline: Musculoskeletal Disorders in Military Service Study,

MSDs@Mil Study) comprised of the 1713 (91.8%) men whose military records were

found from the register of the Finnish Defence Forces. Of the random sample 1586

(85.0%) men participated in the second wave of data collection (the Health 2000 Study,

follow-up). A total of 1536 men (81.4% of the target population) participated in both

waves and formed the eligible sample for the current study.

<Figure 1>

Men who did not participate in the Health 2000 Survey were slightly younger, belonged

more frequently to the Swedish speaking minority, and had more often severe mental

problems than those who participated. Differences in education and socioeconomic

status were minor.16

We excluded subjects with missing data on LBP at follow-up (n=151, 9.8% of the eligible

sample) resulting in a total of 1385 (73.4% of the original population sample) subjects

available for the analyses.

The Section for Epidemiology and Public Health of the Ethics Committee of the Hospital

District of Helsinki and Uusimaa approved the Health 2000 Study and The Ethics

Committee of the Finnish Institute of Occupational Health further approved the

MSDs@Mil study.

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Low back outcomes at follow-up

All men underwent a symptom interview and a comprehensive health examination

(including a physical examination performed by a specially trained physician) or a

condensed health examination at home. The symptom-based outcome measures

included prevalence of LBP (non-specific LBP) and radiating LBP during the previous 30

days. The information on LBP was collected by the following questions: “Have you ever

had low back pain? (yes/no)”. Those who answered yes were asked about recent pain

and about the type of pain: “Have you had low back pain during the preceding 30 days?

(yes/no)”; “If you had low back pain, did it radiate? (0=no, 1=below knee, 2=above

knee)”. Radiating LBP was defined as pain that radiated either below or above the knee.

The clinically defined outcome measures included chronic low back syndrome and

sciatica diagnosed with predefined criteria by specially trained physicians. 18 The

diagnoses made by the physicians were based on clinical findings and on the previous

diagnoses and chronicity of the symptoms (duration of at least three months). All

diagnoses were categorized as 0=no, 1=probable, 2=definite. In the current study a

clinically defined outcome was present if the subject had a probable or definite diagnosis.

Spinal problems at baseline

Low back symptoms and signs and spinal symptoms and signs (symptoms or signs in the

thoracic or lumbar spine) were recorded at baseline by the examining physician. We

used dichotomized variables (yes/no).

Weight-related factors at baseline and during follow-up

At baseline, as part of the recruit health check-up carried out during the first two weeks

of military service, nurses or trained medics at the garrison health clinics measured the

recruits' height and weight in light clothing. For 114 men (8.2%) data on weight-related

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factors were not found from recruit health examination records, and we used the records

of the pre-military service examination performed approximately 6 months prior to

military service.

At follow-up height and weight as well as waist and hip circumference were measured. In

addition, height at the age of 20 years and weight at the ages of 20, 30, 40 and 50

years were inquired, if applicable. For subjects examined at the ages of 20, 30, 40, or 50

both measured and self-reported weight were available. Comparison of the measured

and self-reported weights at follow-up gave correlation coefficients (Pearson r) 0.80,

0.85, 0.96, and 0.98, for the studied age groups respectively.

BMI was calculated by dividing weight (kg) with the square of height (m2). At follow-up

general overweight and obesity were defined based on BMI using the WHO

recommendation BMI < 25 kg/m2 (normal), 25–29.9 kg/m2 (overweight), and ≥30

kg/m2 (obese). At baseline BMI was dichotomized into BMI < 25 kg/m2 (normal) and

≥25 kg/m2 (overweight/obese).

Abdominal obesity was defined based on waist circumference; <94 cm (normal), 94-

101.9 cm (overweight), and. ≥ 102 cm (obese). Waist-to-height ratio was dichotomized

into <0.5 and >0.5.

Weight-related measures during life course

Baseline and follow-up information of overweight or obesity based on BMI were used to

construct a variable to describe overweight or obesity during life course, consisting of six

categories.

A life course measure for BMI was based on repeated BMI values (at baseline, 30, 40

and 50 years and at follow-up). It was constructed by calculating age-standardized Z-

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scores (mean = 0, standard deviation = 1) for each time-point measure, which were

averaged across time-points.

Potential confounders

Length of education (years) was inquired at follow-up. Data on smoking and physical

activity were collected at baseline and follow-up. At baseline the conscripts were

classified as smokers if they answered “yes” to the question “Do you smoke?”. At follow-

up daily smokers were classified as smokers. At baseline the conscripts were classified as

physically active if they were participating in sports activities at least once a week. At

follow-up subjects who reported that they engage in physical exercise that causes

sweating for at least 30 minutes at least once a week were classified as physically active.

Statistical analyses

Statistical analyses were performed using SAS 9.4. Of 681 (28.7%) missing values on

smoking at baseline, 520 values were imputed using the missing data propensity score

method.19 Still, 161 (6.8%) values remained missing.

The effects of baseline and life course weight-related factors on low back outcomes at

follow-up were studied with Cox regression model with the total length of time of follow-

up being assigned to each individual. The Cox model was used instead of logistic

regression in order to avoid undue inflation of estimates of associations between weight-

related measures and common low back problems.20 21 The prevalence ratios (PR) and

their 95% confidence intervals (95% CI) were adjusted for age, education and smoking

status at follow-up.

The cross-sectional associations of weight-related measures with low back outcomes at

follow-up were studied with binary logistic regression models. The subjects’ age,

education and smoking status at follow-up were included in the multivariable models as

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covariates. We did not adjust for physical activity, since it has been shown to have a U-

shaped association with LBP, and was therefore considered to be an effect modifier and

not a confounder.9 22

Sensitivity analyses were carried out excluding subjects with spinal problems at baseline.

RESULTS

Characteristics of the study subjects and prevalence of outcomes

The characteristics of the study subjects at baseline and follow-up are shown in Table 1.

The average follow-up time was 20.5 years (standard deviation ±6.0 years), ranging

from 6 to 33 years.

<Table 1>

During the follow-up the mean BMI increased by 4.1-4.4 kg/m2 (18-20%) corresponding

to 11-12 kg. The increase was linearly associated with the length of the follow-up time.

Both at baseline and follow-up about one-third of the subjects were regular smokers. In

general the proportion of subjects with regular physical activity was higher at follow-up

than at baseline. The prevalence of spinal symptoms or signs was about 15% at

baseline.

Non-specific LBP occurred commonly (Table 1), whereas radiating type of LBP was less

prevalent. The prevalence of radiating low back pain and clinically defined outcomes

increased with age (p=0.08 for radiating LBP, p=0.002 for chronic low back syndrome

and p=0.01 for sciatica).

Associations of BMI with low back outcomes during life course

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BMI at baseline predicted the one-month prevalence of radiating LBP assessed at follow-

up (Table 2). A one standard deviation increase in baseline BMI, corresponding to 3.0

kg/m2, was associated with a 26% increase in the risk of radiating LBP. A similar trend

was seen in the risk of chronic low back syndrome and sciatica assessed by physician,

although it did not reach statistical significance (Table 2). Sensitivity analyses excluding

subjects with baseline spinal symptoms or signs or those with low back symptoms or

signs showed no change in the observed associations.

Life course BMI (BMI Z-score) was statistically significantly associated with one-month

radiating LBP. A one-unit increase in Z-score (corresponding to 2.92 units of BMI) was

associated with a 23% increase in the risk of radiating LBP (Table 2). Life course BMI

was not associated with the other outcomes.

<Table 2>

Those with normal weight at baseline who substantially gained weight during the follow-

up had an increased risk of one month radiating LBP pain assessed at follow-up (Table

3). Also those who were constantly overweight had a 2-fold increased risk of radiating

LBP.

<Table 3>

Cross-sectional associations of general and abdominal obesity with low back

outcomes

In cross-sectional analyses at follow-up BMI tended to be associated with radiating LBP,

but no associations were seen for the other outcomes. Waist circumference tended to be

associated with radiating LBP as well as with chronic low back syndrome and sciatica

(Table 2). All associations were weaker after mutual adjustment (data not shown).

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Overweight and obesity defined by BMI showed a dose-response relationship with

radiating LBP, with a 64% increase in the risk in obese men. Furthermore, abdominal

obesity defined by waist-to-height ratio was associated with an increased risk of

radiating LBP (1.44 95% CI 1.02-2.04). No associations were found for non-specific LBP,

chronic low back syndrome, or sciatica assessed by physician (Table 2). The exclusion of

subjects with baseline spinal symptoms or signs strengthened the observed associations

between waist circumference and radiating LBP (1.62, 95% CI 1.05-1.78 for abdominal

obesity defined as waist circumference > 102 cm).

DISCUSSION

In our longitudinal population-based study across life course we found that BMI at the

age of 20 predicted radiating LBP later in life. The development of obesity during follow-

up increased radiating LBP. Moreover, BMI during life course was associated with

radiating LBP later in life. Both general and abdominal obesity were associated with

radiating LBP with minor differences in the observed estimates. No associations were

seen for non-specific LBP. Weaker and statistically non-significant associations were seen

for BMI (longitudinally) and waist circumference (cross-sectionally) with the clinically

defined outcomes.

The present study has several strengths. First, we used a unique study population which

was based on the Health 2000 Study and supplemented with military service records of

all men aged 30-50 years. The Health 2000 Study represents well the Finnish adult

population because of the random sample and high participation rate, up to 85%.

Military medical records were obtained for almost all men in the sample. Second, general

and abdominal obesity were defined based on measured weight-related indicators. In

addition we had self-reported information on weight and height for each age decennium.

Third, the richness of weight-related measures allowed us to explore the associations of

early indicators of obesity, change in obesity status over time, as well as life course BMI

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with the outcomes. Fourth, our set of low back outcomes included both symptoms and

clinically defined disorders.

However, the findings of the current study must be interpreted with taking in to

consideration a few limitations. Since only men were included, the population being

highly representative allows to generalize our findings to all men. The health

examination at baseline did not provide measures of abdominal adiposity, therefore we

were not able to look at the associations of abdominal obesity across life course. Due to

the small number of overweight or obese individuals at baseline, we were not able to

detect, whether individuals losing weight during the follow up are less likely to report

back symptoms at the end of follow-up.

Our results are in agreement with a recent meta-analysis, in which both overweight and

obesity were consistently, though modestly associated with the risk of lumbar radicular

pain.7 Moreover, studies on associations between weight-related factors and non-specific

LBP have reported inconsistent results especially in men.6 9 23 In agreement with previous

literature we found weaker associations for the clinically defined outcomes.7 24 25

To our knowledge this is the first study to report associations of low back disorders with

composite life course information of BMI based on repeated measures with constant

intervals. We found that BMI both at the age of 20 and during life course predicted

radiating LBP later in life. A British birth cohort study found that high BMI at the age of

23 predicted incidence of LBP at age 32 to 33 in both genders.15 Our findings together

with those of the afore-mentioned study suggest that obesity around the age of 20 – and

especially when persistent – is a causal risk factor of radiating LBP.

There are two distinct types of LBP – radiating and non-specific – that have a different

course during life and may have different etiology.6 In line with our findings, in a recent

population-based study among young adults BMI and waist circumference predicted

incidence of radiating but not non-specific LBP.22

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We measured both general obesity and abdominal adiposity. This is important, since BMI

might not be an appropriate measure to capture adiposity in men.12 In agreement with

our findings a Dutch study using a random sample from the general population found

both general and abdominal obesity to be associated with radiating but not non-specific

LBP in men.13 Similarly, a recent study in a representative young adult population

showed both types of adiposity being associated with the incidence of radiating LBP.22

There are several possible explanations for the association between excess weight and

radiating LBP. Obesity could increase the mechanical load on the spine by causing a

higher compression or tear on the lumbar spine structures. Obese people may also be

more prone to injuries.26 In addition, obesity is an important risk factor of

atherosclerosis, which also has been linked with especially sciatic pain.27 Since

abdominal obesity showed an association with radiating LBP, systemic inflammation and

patomechanical pathways involved in the metabolic syndrome may play a role.

In conclusion, our findings indicate that being overweight or obese in adolescence as well

as during life course increases the risk of radiating but not non-specific LBP in men.

Taking into account the current global obesity epidemic, emphasis should be put on

preventive measures already in youth, and also at preventing further weight gain during

life course. Studies on the etiology of LBP would benefit from differentiating between

radiating and non-specific types of pain. Future research should provide life course

studies on the associations of weight-related factors with low back disorders among

women.

CONTRIBUTORS

EV-J, SS and HF conceived and designed the study. MH and HP helped acquire the data.

SS and PM analysed the data. HF wrote the first draft of the manuscript. All authors

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contributed to the interpretation of the data, carefully reviewed the draft and revised it

critically. All authors gave the final approval of the manuscript.

FUNDING

This work was supported by the Academy of Finland (129475) and the Centre for Military

Medicine, Helsinki, Finland.

COMPETING INTERESTS

None declared.

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Health 2000 Health Examination Survey. Publications of the National Public Health Institute.

Helsinki: KTL-National Public Health Institute, Finland, Department of Health and Functional

Capacity, 2004:1-175. http://www.terveys2000.fi/julkaisut/baseline.pdf (accessed

25.1.2015).

18. Heistaro S, Nykyri E, Kaila-Kangas L, et al. Study population and methods. In: Kaila-Kangas L, ed.

Musculoskeletal disorders and diseases in Finland, Results of the Health 2000 Survey.

Helsinki, 2007:8-13. http://www.terveys2000.fi/julkaisut/2007b25.pdf 8 (accessed

25.1.2015).

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19. Rosenbaum PR, Rubin DB. The Central Role of the Propensity Score in Observational Studies for

Causal Effects. Biometrika 1983;70(1):41-55.

20. Barros AJ, Hirakata VN. Alternatives for logistic regression in cross-sectional studies: an empirical

comparison of models that directly estimate the prevalence ratio. BMC medical research

methodology 2003;3:21.

21. Thompson ML, Myers JE, Kriebel D. Prevalence odds ratio or prevalence ratio in the analysis of

cross sectional data: what is to be done? Occup Environ Med 1998;55(4):272-7.

22. Shiri R, Solovieva S, Husgafvel-Pursiainen K, et al. The role of obesity and physical activity in non-

specific and radiating low back pain: the Young Finns study. Semin Arthritis Rheum

2013;42(6):640-50.

23. Kääria S, Leino-Arjas P, Rahkonen O, et al. Risk factors of sciatic pain: a prospective study among

middle-aged employees. Eur J Pain 2011;15(6):584-90.

24. Heliövaara M, Mäkela M, Knekt P, et al. Determinants of sciatica and low-back pain. Spine (Phila

Pa 1976) 1991;16(6):608-14.

25. Jhawar BS, Fuchs CS, Colditz GA, et al. Cardiovascular risk factors for physician-diagnosed lumbar

disc herniation. The spine journal : official journal of the North American Spine Society

2006;6(6):684-91.

26. Hu HY, Chou YJ, Chou P, et al. Association between obesity and injury among Taiwanese adults.

International journal of obesity (2005) 2009;33(8):878-84.

27. Shiri R, Viikari-Juntura E, Leino-Arjas P, et al. The association between carotid intima-media

thickness and sciatica. Semin Arthritis Rheum 2007;37(3):174-81.

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FIGURE LEGEND

Figure 1. Flowchart of the Present Study Sample

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Table 1. Characteristics of the study population at baseline (Military Health Examination), 1967-1994 and follow-up (Health 2000 Survey), 2000-2001.

No. Mean (95% CI) % (95% CI)

Age (years) at baseline 1385 19.7 (19.6 to 19.8)

at follow-up 1385 40.2 (39.9 to 40.5)

BMI (kg/m2) at baseline 1384 22.3 (22.1 to 22.4)

at follow-up 1385 26.5 (26.3 to 26.7)

Regular smokers at baseline 303/880 34.4 (31.3 to 37.7)

at follow-up 454/1380 32.9 (30.4 to 35.5)

Physically active at leisure time at baseline 447/846 52.8 (49.3 to 56.2)

at follow-up 1008/1370 73.6 (71.2 to 75.9)

Prevalence of spinal symptoms and/or signs

at baseline 208/1385 15.0 (13.2 to 17.0)

Prevalence of low back symptoms at baseline 113/1385 8.2 (6.8 to 9.7)

Waist circumference at follow-up 1380 95.3 (94.8 to 95.9)

Hip circumference at follow-up 1380 99.1 (98.7 to 99.5)

Education (years) at follow-up 1366 12.8 (12.6 to 13.0)

LBP, preceding 30 days at follow-up

Non-specific 393/1385 28.4 (26.0 to 30.8)

Radiating 162/1385 11.7 (10.1 to 13.5)

Physician-diagnosed disordersa at follow-up

Chronic low back syndrome 100/1350 7.4 (6.1 to 8.9)

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Sciatica 58/1350 4.3 (3.3-5.5)

LBP, low back pain. a Information is missing for 35 men

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Table 2. Associations of weight-related measures with low back pain (LBP) and physician-diagnosed low back disorders, 1967-2001.

LBP, preceding 30 days

(n=1363)

Chronic LBP syndrome

(n= 1350)

Radiating LBP, preceding 30 days

(n=1363)

Sciatica, assessed by physician

(n=1350)

PR/OR 95% CIa PR/OR 95% CIa PR/OR 95% CIa PR/OR 95% CIa

Longitudinal associations between BMI at baseline and outcomes at follow-up

BMI at baselineb 1.07 0.96 to 1.19 1.18 0.96 to 1.44 1.26 1.08 to 1.46 1.15 0.89 to 1.50

Life course BMI (Z –score)c 0.99 0.86 to 1.14 1.09 0.87 to 1.36 1.23 1.03 to 1.48 1.10 0.83 to 1.46

Cross-sectional associations between weight to related factors and outcomes at follow-up

BMI (continuous)d 0.95 0.84 to 1.07 1.09 0.90 to 1.33 1.16 0.99 to 1.35 1.10 0.86 to 1.41

BMI (categorical)

< 25 kg/m2 1.00 1.00 1.00 1.00

25 to 29.9 kg/m2 1.03 0.80 to 1.34 0.95 0.60 to 1.51 1.42 0.97 to 2.08 0.84 0.47 to 1.51

> 30 kg/m2 0.88 0.61 to 1.25 1.13 0.63 to 2.00 1.64 1.02 to 2.65 1.04 0.50 to 2.15

Waist circumference (cont.)e 1.00 0.88 to 1.12 1.13 0.92 to 1.38 1.15 0.98 to 1.35 1.18 0.92 to 1.52

Waist circumference (cat.)

< 94 cm 1.00 1.00 1.00 1.00

94 to 101.9 cm 1.11 0.83 to 1.47 1.04 0.63 to 1.73 1.03 0.69 to 1.53 0.91 0.47 to 1.75

> 102 cm 1.03 0.77 to 1.39 1.24 0.75 to 2.03 1.31 0.88 to 1.96 1.19 0.64 to 2.23

Waist to height ratio

normal (≤ 0.5) 1.00 1.00 1.00 1.00

critical ( > 0.5) 0.96 0.76 to 1.22 1.33 0.75 to 1.72 1.44 1.02 to 2.04 0.88 0.52 to 1.49

LBP, low back pain; BMI, body mass index; PR, prevalence ratio; OR, odds ratio; CI, confidence interval; cont., continuous; cat., categorical.

a Prevalence ratios (for the longitudinal analyses) and odds ratios (for cross-sectional analyses) and their 95% CIs are adjusted for age, smoking and education.

b Continuous variable, one standard deviation increase, corresponding to 3.0 kg/m2 for baseline BMI. c One-unit increment in Z-score, corresponding to 2.92 units of BMI during follow-up. d Continuous variable, one standard deviation increase, corresponding to 4.0 kg/m2 for follow-up BMI. e Continuous variable, one standard deviation increase, corresponding to 11 cm for waist circumference at follow-up.

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Table 3. Associations of baseline and follow-up overweight and obesity with low back pain, 1967 - 2001.


(n=1363)


(n=1363)

na

ORb 95% CI na ORb 95% CI

BMI at military service BMI during follow-up

< 25 kg/m2 < 25 kg/m2 104 (509) 1.00 48 (509) 1.00

< 25 kg/m2 25 to 29.9 kg/m2 161 (560) 1.00 0.76 to 1.32 70 (560) 1.34 0.89 to 2.00

< 25 kg/m2 > 30 kg/m2 33 (106) 1.18 0.74 to 1.88 17 (106) 1.91 1.03 to 3.53

> 25 kg/m2 < 25 kg/m2 2 (8) 0.84 0.17 to 4.28 0 (8) to to

> 25 kg/m2 25 to 29.9 kg/m2 21 (62) 1.39 0.79 to 2.50 10 (62) 2.10 1.00 to 4.47

> 25 kg/m2 > 30 kg/m2 26 (118) 0.76 0.47 to 1.23 18 (118) 1.65 0.89 to 3.06

BMI, body mass index; LBP, low back pain; CI, confidence interval; OR, odds ratio. a Number with outcome (number of exposed) b OR's and their 95% CIs are adjusted for age, smoking and education.

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Figure1. Flowchart of the Present Study Sample 189x225mm (300 x 300 DPI)

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STROBE 2007 (v4) Statement—Checklist of items that should be included in reports of cohort studies

Section/Topic Item

# Recommendation Reported on page #

Title and abstract 1 (a) Indicate the study’s design with a commonly used term in the title or the abstract Title page

(b) Provide in the abstract an informative and balanced summary of what was done and what was found 2

Introduction

Background/rationale 2 Explain the scientific background and rationale for the investigation being reported 4

Objectives 3 State specific objectives, including any prespecified hypotheses 4

Methods

Study design 4 Present key elements of study design early in the paper 5

Setting 5 Describe the setting, locations, and relevant dates, including periods of recruitment, exposure, follow-up, and data

collection

5

Participants 6 (a) Give the eligibility criteria, and the sources and methods of selection of participants. Describe methods of follow-up 5-7 and Figure 1

(b) For matched studies, give matching criteria and number of exposed and unexposed

Variables 7 Clearly define all outcomes, exposures, predictors, potential confounders, and effect modifiers. Give diagnostic criteria, if

applicable

6-8

Data sources/

measurement

8* For each variable of interest, give sources of data and details of methods of assessment (measurement). Describe

comparability of assessment methods if there is more than one group

5-8

Bias 9 Describe any efforts to address potential sources of bias 8-9

Study size 10 Explain how the study size was arrived at 5 and Figure 1

Quantitative variables 11 Explain how quantitative variables were handled in the analyses. If applicable, describe which groupings were chosen and

why

6-8

Statistical methods 12 (a) Describe all statistical methods, including those used to control for confounding 8-9

(b) Describe any methods used to examine subgroups and interactions 8

(c) Explain how missing data were addressed 6-7 and 8-9

(d) If applicable, explain how loss to follow-up was addressed

(e) Describe any sensitivity analyses 9

Results

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Participants 13* (a) Report numbers of individuals at each stage of study—eg numbers potentially eligible, examined for eligibility, confirmed

eligible, included in the study, completing follow-up, and analysed

5 and Figure 1

(b) Give reasons for non-participation at each stage 5 and Figure 1

(c) Consider use of a flow diagram Figure 1

Descriptive data 14* (a) Give characteristics of study participants (eg demographic, clinical, social) and information on exposures and potential

confounders

Table 1 and page 8

(b) Indicate number of participants with missing data for each variable of interest Table 1

(c) Summarise follow-up time (eg, average and total amount) 9

Outcome data 15* Report numbers of outcome events or summary measures over time Table 1

Main results 16 (a) Give unadjusted estimates and, if applicable, confounder-adjusted estimates and their precision (eg, 95% confidence

interval). Make clear which confounders were adjusted for and why they were included

Tables 2-3

(b) Report category boundaries when continuous variables were categorized Tables 2-3

(c) If relevant, consider translating estimates of relative risk into absolute risk for a meaningful time period Not relevant

Other analyses 17 Report other analyses done—eg analyses of subgroups and interactions, and sensitivity analyses 8-11

Discussion

Key results 18 Summarise key results with reference to study objectives 11

Limitations

Interpretation 20 Give a cautious overall interpretation of results considering objectives, limitations, multiplicity of analyses, results from

similar studies, and other relevant evidence

13

Generalisability 21 Discuss the generalisability (external validity) of the study results 13

Other information

Funding 22 Give the source of funding and the role of the funders for the present study and, if applicable, for the original study on

which the present article is based

1314

*Give information separately for cases and controls in case-control studies and, if applicable, for exposed and unexposed groups in cohort and cross-sectional studies.

Note: An Explanation and Elaboration article discusses each checklist item and gives methodological background and published examples of transparent reporting. The STROBE

checklist is best used in conjunction with this article (freely available on the Web sites of PLoS Medicine at http://www.plosmedicine.org/, Annals of Internal Medicine at

http://www.annals.org/, and Epidemiology at http://www.epidem.com/). Information on the STROBE Initiative is available at www.strobe-statement.org.

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Journal: BMJ Open

Manuscript ID: bmjopen-2015-007805.R1


Date Submitted by the Author: 22-May-2015

Complete List of Authors: Frilander, Heikki; Finnish Institute of Occupational Health, Centre of Expertise for Health and Work Ability; Finnish Institute of Occupational Health, Disability Prevention Centre Solovieva, Svetlana; Finnish Institute of Occupational Health, Center of Expertice for Health and Work Ability Mutanen, Pertti; Finnish Institute of Occupational Health, Statistics and Health Economics Pihlajamäki, Harri; Seinäjoki Central Hospital, Department of Orthopaedics

and Trauma Surgery; University of Tampere, Heliövaara, Markku; National Institute for Health and Welfare, Department of Health, Functional Capacity and Welfare Viikari-Juntura, Eira; Finnish Institute of Occupational Health,


Epidemiology






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TITLE PAGE












Topeliuksenkatu 41 an A, FI-00250 Helsinki, Finland











Word count: 3357

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ABSTRACT



Design: A longitudinal and cross-sectional study.








age-standardised mean body mass index (BMI) across life course. The symptom-based

outcome measures at follow-up included prevalence of non-specific and radiating LBP

during the previous 30 days. The clinically defined outcome measures included chronic

low back syndrome and sciatica.

Results: Baseline BMI (20 years) predicted radiating LBP in adulthood, prevalence ratio

(PR) being 1.26 (95% CI 1.08-1.46) for one standard deviation (3.0 kg/m2) increase in

BMI. Life course BMI was associated with radiating LBP (PR 1.23; 95% CI 1.03-1.48 per

one unit increment in Z-score, corresponding to 2.9 kg/m2). The development of obesity

during follow-up increased the risk of radiating LBP (PR 1.91, 95% CI 1.03-3.53). Both

general and abdominal obesity (defined as waist-to-height ratio) were associated with

radiating LBP (OR 1.64, 95% CI 1.02-2.65 and 1.44, 95% CI 1.02-2.04). No associations

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were seen for non-specific LBP.

Conclusions: Our findings imply that being overweight or obese in early adulthood as

well as during life course increases the risk of radiating but not non-specific LBP among

men. Taking into account the current global obesity epidemic, emphasis should be put

on preventive measures already in youth, and also at preventing further weight gain

during life course.





defined disorders.

• We used both general and abdominal obesity as weight-related indicators.





• Part of the weight-related measures during life course was self-reported, and

therefore recall bias may have affected the observed associations. The military

records did not provide measures of abdominal adiposity at baseline, therefore

we were not able to explore the associations of abdominal obesity across life

course.

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INTRODUCTION


















outcomes.



wanted to find out, whether high BMI at the age of 20 years or during life course

predicts increased risk of low back disorders later in life. Moreover, we looked whether

changes in BMI during follow-up affect the risk of LBP. Finally, we explored the cross-

sectional associations of general and abdominal obesity with the outcomes.

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Study population


1). A random sample of the nationally representative Health 2000 Study (carried out

during 2000-2001), comprising of 1866 men aged 30-50 years, formed our study base.

The main purposes and methods of this study have been described in detail elsewhere.17

18 Participants for the first wave of our data collection (baseline: Musculoskeletal

Disorders in Military Service Study, MSDs@Mil Study) comprised of the 1713 (91.8%)

men eligible for military service during 1967-1994, whose military records were found

from the register of the Finnish Defence Forces. Of the random sample 1586 (85.0%)

men participated in the second wave of data collection (the Health 2000 Study, follow-

up). A total of 1536 men (81.4% of the initial random sample) participated in both


<Figure 1>

Men who did not participate in the Health 2000 Study were slightly younger, belonged



status were minor.17 Furthermore, there was no difference in baseline BMI between

participants and non-participants of the Health 2000 Study.


sample) resulting in a total of 1385 (74.2% of the initial random sample) subjects





MSDs@Mil study.

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had low back pain? (Yes/no)”. Those who answered yes were asked about recent pain





sciatica diagnosed with predefined criteria by specially trained physicians. 19 20 The

diagnoses made by the physicians were based on previous and current symptoms,

documentation on previous low back diagnoses and clinical findings in the physical

examination. Symptoms with duration of at least three months overall were considered

chronic. All diagnoses were categorized as 0=no, 1=probable, 2=definite. The diagnoses

were classified as probable when the subject did not currently have symptoms, or when

the criteria of the previous diagnoses were vague. In the current study a clinically

defined outcome was present, if the subject had a probable or definite diagnosis


Low back symptoms and signs as well as spinal symptoms and signs (symptoms or signs

in the thoracic or lumbar spine) were recorded at baseline by the examining physician.

We used dichotomized variables (yes/no).


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military service.

At follow-up in addition to measured during health examination height and weight, men were asked

to recall their height at the age of 20 years and weight at the age of 20, 30, 40 and 50 years.






Waist circumference was measured at follow-up. Abdominal obesity was defined based

on waist circumference; <94 cm (normal), 94-101.9 cm (overweight), and. ≥ 102 cm

(obese). Waist-to-height ratio was dichotomized into <0.5 and >0.5.

Accuracy of self-reported height and weight

For all men we had measured data (at baseline) and self-reported data (recalled at

follow-up) for height and weight at the age of 20. The correlation (Pearson correlation

coefficient r) was very high for height (r=0.94, p< 0.0001) and moderate for weight

(r=0.77, p < 0.0005). We also examined the magnitude of recall bias for height at the

age of 20 by the time of recall and found that the older men recalled their height

similarly to the younger men. For men aged 30 (n=124), 40 (n=194) and 50 (n=121) at

the time of Health 2000 study data collection we also had both self-reported and

measured height and weight. All self-reported and measured anthropometric measures

were highly correlated. For men aged 30 the correlation coefficients for height and

weight were r=0.98 and r=0.94, respectively. For men aged 40 the corresponding

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numbers were r=0.97 and r=0.95. For men aged 50 the corresponding numbers were

r=0.95 and r=0.98. These data suggest that recall bias is relatively minor and self-

reported data could be reliably used as a proxy for subjects’ height and weight.

Furthermore, the correlation coefficients (Pearson r) between BMI based on measured

and self-reported weight and height were 0.80, 0.85, 0.96, and 0.98, for the studied age

groups respectively.


Baseline and follow-up information of overweight or obesity based on measured BMI was

used to construct a variable to describe overweight or obesity during life course,

consisting of six categories. The categories were defined as the following: 1= both

baseline and follow-up BMI < 25 kg/m2 (normal weight at both time points), 2= baseline

BMI < 25 kg/m2 and follow-up BMI ranged between 25-29.9 kg/m2 (became overweight

during follow-up), 3= baseline BMI < 25 kg/m2 and follow-up BMI > 30 kg/m2 (became

obese during follow-up),4= baseline BMI > 25 kg/m2 and follow-up BMI < 25 kg/m2

(overweight/obese at baseline, normal weight at follow-up), 5= baseline BMI > 25 kg/m2

and follow-up BMI ranged between 25-29.9 kg/m2 (overweight/obese at baseline and

overweight at follow-up), 6= baseline BMI > 25 kg/m2 and follow-up BMI > 30 kg/m2

(overweight/obese at baseline and obese at follow-up).

A life course measure for BMI was based on repeated BMI values (measured at baseline,

self-reported at the age of 30, 40 and 50 years and measured at follow-up). It was

constructed by calculating age-standardized Z-scores (mean = 0, standard deviation =

1) for each time-point measure, which were averaged across time-points.


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Age at baseline was calculated for the day of the measurements and at follow-up for 1

June 2000. Length of education (years) was inquired at follow-up. Data on smoking and

physical activity were collected at baseline and follow-up. At baseline the conscripts were

classified as smokers if they answered “yes” to the question “Do you smoke?” At follow-









Since our outcomes were based on prevalence, we used prevalence ratios (PR) to

measure the longitudinal and odds ratios (OR) to measure the cross-sectional

associations between weight-related variables and low back outcomes.

The effects of baseline and life course BMI on low back outcomes at follow-up were

studied with Cox regression model with the total length of time of follow-up being

assigned to each individual. The Cox model was used instead of logistic regression in

order to avoid undue inflation of estimates of associations between weight-related

measures and common low back problems.22 23 The prevalence ratios (PR) and their 95%

confidence intervals (95% CI) were adjusted for age and smoking status at baseline and

information of education (years) obtained at follow-up.



education (years) and smoking status at follow-up were included in the multivariable

models as covariates. We did not adjust for physical activity because it was not

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associated with low back outcomes in our study population. Furthermore, physical

activity has been shown to have a U-shaped association with LBP, and was therefore

considered to be an effect modifier and not a confounder.9 24


RESULTS




from 6 to 33 years.

<Table 1>






baseline.


prevalent. The prevalence of radiating LBP and clinically defined outcomes increased with

age (p=0.08 for radiating LBP, p=0.002 for chronic low back syndrome and p=0.01 for

sciatica).


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although it did not reach statistical significance (Table 2). Sensitivity analyses, excluding


signs, showed no change in the observed associations.





<Table 2>


up, had an increased risk of one month radiating LBP pain assessed at follow-up (Table


LBP.

<Table 3>


outcomes




(Table 2). All associations were weaker with both waist circumference and BMI in the

model (mutual adjustment, data not shown).

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radiating LBP, with a 64% increase in the risk in obese men compared with men with

normal weight. Furthermore, abdominal obesity defined by waist-to-height ratio was

associated with an increased risk of radiating LBP (1.44 95% CI 1.02-2.04). No

associations were found for non-specific LBP, chronic low back syndrome, or sciatica

assessed by physician (Table 2). The exclusion of subjects with baseline spinal

symptoms or signs strengthened the observed associations between waist circumference

and radiating LBP (1.62, 95% CI 1.05-1.78 for abdominal obesity defined as waist

circumference > 102 cm).

DISCUSSION








defined outcomes.

The present study has several strengths. First, we used a unique study population, which








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back symptoms at the end of follow-up. Part of the weight-related measures during life

course was self-reported, and therefore recall bias may have affected the observed

associations. However, our assessments confirmed the high accuracy of the self-reported

values used in this study.






Moreover, a recent large cross-sectional study among 17-year old conscripts (male and

female) showed weaker associations of obesity with clinically defined outcomes than with

symptoms among the men.28





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course during life and may have different etiology.6 In line with our findings, BMI and

waist circumference predicted incidence of radiating but not non-specific LBP in a

population-based study among young adults.24



our findings a Dutch study, using a random sample from the general population, found










patomechanical pathways involved in the metabolic syndrome may play a role, as

discussed in the literature.31 A recent systematic review of twin studies concluded that

individuals being overweight or obese are more likely to have LBP and lumbar disc

generation, but the associations were weaker after controlling for familial factors,

suggesting that obesity and low back pain share common genetic risk factors.32

In conclusion, our findings indicate that being overweight or obese in young adulthood

as well as during life course increases the risk of radiating but not non-specific LBP in

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during life course. Studies on the etiology of LBP would benefit from differentiating

between radiating and non-specific types of pain. Future research should provide life

course studies on the associations of weight-related factors with low back disorders

among women.

CONTRIBUTORS





FUNDING



COMPETING INTERESTS

None declared.

DATA SHARING STATEMENT

No additional data are available.

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REFERENCES


2. Kelly T, Yang W, Chen CS, et al. Global burden of obesity in 2005 and projections to 2030. Int J

Obes (Lond) 2008;32(9):1431-7.









meta-analysis. Am Journal Epidemiol 2010;171(2):135-54.

7. Shiri R, Lallukka T, Karppinen J, et al. Obesity as a risk factor for sciatica: a meta-analysis. Am

Journal Epidemiol 2014;179(8):929-37.



Am Journal Epidemiol 2008;167(9):1110-9.






9.

11. Mikkonen PH, Laitinen J, Remes J, et al. Association between overweight and low back pain: a

population-based prospective cohort study of adolescents. Spine (Phila Pa 1976)

2013;38(12):1026-33.



13. Rothman KJ. BMI-related errors in the measurement of obesity. Int Journal Obes (Lond) 2008;32

Suppl 3:S56-9.


fatness, fat distribution and height. Int J Obes Relat Metab Disord 1997;21(7):600-7.







Functional Capacity, 2008:1-248. http://www.terveys2000.fi/doc/methodologyrep.pdf

(accessed 23.1.2015)




Capacity, 2004:1-175. http://www.terveys2000.fi/julkaisut/baseline.pdf (accessed

25.1.2015)

19. Kaila-Kangas L, Leino-Arjas P, Karppinen J, et al. History of physical work exposures and clinically

diagnosed sciatica among working and nonworking Finns aged 30 to 64. Spine (Phila Pa

1976) 2009;34(9):964-9.

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20. Heliovaara M, Impivaara O, Sievers K, et al. Lumbar disc syndrome in Finland. J Epidemiol

Community Health 1987;41(3):251-8.




comparison of models that directly estimate the prevalence ratio. BMC Med Res Methodol

2003;3:21.





2013;42(6):640-50.




Pa 1976) 1991;16(6):608-14.


disc herniation. Spine J 2006;6(6):684-91.

28. Hershkovich O, Friedlander A, Gordon B, et al. Associations of body mass index and body height

with low back pain in 829,791 adolescents. Am J Epidemiol 2013;178(4):603-9.


Int J Obes 2009;33(8):878-84.



31. Samartzis D, Karppinen J, Cheung JP, et al. Disk degeneration and low back pain: are they fat-

related conditions? Global Spine J 2013;3(3):133-44.

32. Dario AB, Ferreira ML, Refshauge KM, et al. The relationship between obesity, low back pain, and

lumbar disc degeneration when genetics and the environment are considered: a systematic

review of twin studies. Spine J 2015 Feb 7. doi: 10.1016/j.spinee.2015.02.001. [Epub ahead

of print]

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FIGURE LEGEND

Figure 1. Flowchart of the Formation of the Present Study Sample

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No. Mean (95% CI) Prevalence (%, 95% CI)


at follow-up 1385 40.2 (39.9 to 40.5)


at follow-up 1385 26.5 (26.3 to 26.7)


at follow-up 454/1380 32.9 (30.4 to 35.5)


at follow-up 1008/1370 73.6 (71.2 to 75.9)

Spinal symptoms and/or signs at baseline 208/1385 15.0 (13.2 to 17.0)

Low back symptoms at baseline 113/1385 8.2 (6.8 to 9.7)





Non-specific 393/1385 28.4 (26.0 to 30.8)

Radiating 162/1385 11.7 (10.1 to 13.5)



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Sciatica 58/1350 4.3 (3.3-5.5)

LBP, low back pain. An

Information is missing for 35 men

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Table 2. Associations of weight-related measures with low back pain (LBP) and physician-diagnosed low back disorders.


(n=1363)


(n= 1350)


(n=1363)


(n=1350)







BMI (categorical)

< 25 kg/m2 1.00 1.00 1.00 1.00

25 to 29.9 kg/m2 1.03 0.80 to 1.34 0.95 0.60 to 1.51 1.42 0.97 to 2.08 0.84 0.47 to 1.51

> 30 kg/m2 0.88 0.61 to 1.25 1.13 0.63 to 2.00 1.64 1.02 to 2.65 1.04 0.50 to 2.15



< 94 cm 1.00 1.00 1.00 1.00

94 to 101.9 cm 1.11 0.83 to 1.47 1.04 0.63 to 1.73 1.03 0.69 to 1.53 0.91 0.47 to 1.75

> 102 cm 1.03 0.77 to 1.39 1.24 0.75 to 2.03 1.31 0.88 to 1.96 1.19 0.64 to 2.23


normal (≤ 0.5) 1.00 1.00 1.00 1.00



A Prevalence ratios (for the longitudinal analyses) and odds ratios (for cross-sectional analyses) and their 95% CIs are adjusted for age, smoking and education.

B Continuous variable, one standard deviation increase, corresponding to 3.0 kg/m2 for baseline BMI. C One-unit increment in Z-score, corresponding to 2.92 units of BMI during follow-up. D Continuous variable, one standard deviation increase, corresponding to 4.0 kg/m2 for follow-up BMI. E Continuous variable, one standard deviation increase, corresponding to 11 cm for waist circumference at follow-up.

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Table 3. Associations of baseline and follow-up overweight and obesity with low back pain.


(n=1363)


(n=1363)

na

PRb 95% CI na PRb 95% CI

BMI at baseline BMI at follow-up

< 25 kg/m2 < 25 kg/m2 104 (509) 1.00 48 (509) 1.00

< 25 kg/m2 25 to 29.9 kg/m2 161 (560) 1.00 0.76 to 1.32 70 (560) 1.34 0.89 to 2.00

< 25 kg/m2 > 30 kg/m2 33 (106) 1.18 0.74 to 1.88 17 (106) 1.91 1.03 to 3.53

> 25 kg/m2 < 25 kg/m2 2 (8) 0.84 0.17 to 4.28 0 (8) to to

> 25 kg/m2 25 to 29.9 kg/m2 21 (62) 1.39 0.79 to 2.50 10 (62) 2.10 1.00 to 4.47

> 25 kg/m2 > 30 kg/m2 26 (118) 0.76 0.47 to 1.23 18 (118) 1.65 0.89 to 3.06

BMI, body mass index; LBP, low back pain; CI, confidence interval; PR, prevalence ratio. A Number with outcome (number of exposed) b PR's and their 95% CIs are adjusted for age, smoking and education.

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Flowchart of the Formation of the Present Study Sample

169x222mm (300 x 300 DPI)

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Section/Topic Item




Introduction



Methods



collection

5




applicable

6-8

Data sources/

measurement



5-8




why

6-8






Results

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5 and Figure 1




confounders

Table 1 and page 8






Tables 2-3




Discussion


Limitations



13


Other information



1314





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Journal: BMJ Open

Manuscript ID: bmjopen-2015-007805.R2


Date Submitted by the Author: 02-Jul-2015

Complete List of Authors: Frilander, Heikki; Finnish Institute of Occupational Health, Centre of Expertise for Health and Work Ability; Finnish Institute of Occupational Health, Disability Prevention Research Centre Solovieva, Svetlana; Finnish Institute of Occupational Health, Center of Expertice for Health and Work Ability; Finnish Institute of Occupational Health, Disability Prevention Research Centre Mutanen, Pertti; Finnish Institute of Occupational Health, Statistics and Health Economics

Pihlajamäki, Harri; Seinäjoki Central Hospital, Department of Orthopaedics and Trauma Surgery; University of Tampere, Heliövaara, Markku; National Institute for Health and Welfare, Department of Health, Functional Capacity and Welfare Viikari-Juntura, Eira; Finnish Institute of Occupational Health, Disability Prevention Research Centre


Epidemiology






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TITLE PAGE























Word count: 3344

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ABSTRACT



Design: A longitudinal and cross-sectional study.








age-standardised mean body mass index (BMI) across life course. The symptom-based

outcome measures at follow-up included prevalence of non-specific and radiating LBP

during the previous 30 days. The clinically defined outcome measures included chronic

low back syndrome and sciatica.

Results: Baseline BMI (20 years) predicted radiating LBP in adulthood, prevalence ratio

(PR) being 1.26 (95% CI 1.08-1.46) for one standard deviation (3.0 kg/m2) increase in

BMI. Life course BMI was associated with radiating LBP (PR 1.23; 95% CI 1.03-1.48 per

one unit increment in Z-score, corresponding to 2.9 kg/m2). The development of obesity

during follow-up increased the risk of radiating LBP (PR 1.91, 95% CI 1.03-3.53). Both

general and abdominal obesity (defined as waist-to-height ratio) were associated with

radiating LBP (OR 1.64, 95% CI 1.02-2.65 and 1.44, 95% CI 1.02-2.04). No associations

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were seen for non-specific LBP.

Conclusions: Our findings imply that being overweight or obese in early adulthood as

well as during life course increases the risk of radiating but not non-specific LBP among



during life course.





defined disorders.

• We used both general and abdominal obesity as weight-related indicators.





• Part of the weight-related measures during life course was self-reported, and

therefore recall bias may have affected the observed associations. The military

records did not provide measures of abdominal adiposity at baseline, therefore

we were not able to explore the associations of abdominal obesity across life

course.

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INTRODUCTION


















outcomes.



wanted to find out, whether high BMI at the age of 20 years or during life course

predicts increased risk of low back disorders later in life. Moreover, we looked whether

changes in BMI during follow-up affect the risk of LBP. Finally, we explored the cross-

sectional associations of general and abdominal obesity with the outcomes.

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Study population


1). A random sample of the nationally representative Health 2000 Study (carried out

during 2000-2001), comprising of 1866 men aged 30-50 years, formed our study base.

The main purposes and methods of this study have been described in detail elsewhere.17

18 Participants for the first wave of our data collection (baseline: Musculoskeletal

Disorders in Military Service Study, MSDs@Mil Study) comprised of the 1713 (91.8%)

men eligible for military service during 1967-1994, whose military records were found

from the register of the Finnish Defence Forces. Of the random sample 1586 (85.0%)

men participated in the second wave of data collection (the Health 2000 Study, follow-

up). A total of 1536 men (81.4% of the initial random sample) participated in both


<Figure 1>

Men who did not participate in the Health 2000 Study were slightly younger, belonged



status were minor.17 Furthermore, there was no difference in baseline BMI between

participants and non-participants of the Health 2000 Study.


sample) resulting in a total of 1385 (74.2% of the initial random sample) subjects





MSDs@Mil study.

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had low back pain? (Yes/no)”. Those who answered yes were asked about recent pain





sciatica diagnosed with predefined criteria by specially trained physicians. 19 20 The

diagnoses made by the physicians were based on previous and current symptoms,

documentation on previous low back diagnoses and clinical findings in the physical

examination. Symptoms with duration of at least three months overall were considered

chronic. All diagnoses were categorized as 0=no, 1=probable, 2=definite. The diagnoses

were classified as probable when the subject did not currently have symptoms, or when

the criteria of the previous diagnoses were vague. In the current study a clinically

defined outcome was present, if the subject had a probable or definite diagnosis


Low back symptoms and signs as well as spinal symptoms and signs (symptoms or signs

in the thoracic or lumbar spine) were recorded at baseline by the examining physician.

We used dichotomized variables (yes/no).


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military service.

At follow-up in addition to measured during health examination height and weight, men were asked

to recall their height at the age of 20 years and weight at the age of 20, 30, 40 and 50 years.






Waist circumference was measured at follow-up. Abdominal obesity was defined based

on waist circumference; <94 cm (normal), 94-101.9 cm (overweight), and. ≥ 102 cm

(obese). Waist-to-height ratio was dichotomized into <0.5 and >0.5.

Accuracy of self-reported height and weight

For all men we had measured data (at baseline) and self-reported data (recalled at

follow-up) for height and weight at the age of 20. The correlation (Pearson correlation

coefficient r) was very high for height (r=0.94, p< 0.0001) and moderate for weight

(r=0.77, p < 0.0005). We also examined the magnitude of recall bias for height at the

age of 20 by the time of recall and found that the older men recalled their height

similarly to the younger men. For men aged 30 (n=124), 40 (n=194) and 50 (n=121) at

the time of Health 2000 study data collection we also had both self-reported and

measured height and weight. All self-reported and measured anthropometric measures

were highly correlated. For men aged 30 the correlation coefficients for height and

weight were r=0.98 and r=0.94, respectively. For men aged 40 the corresponding

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numbers were r=0.97 and r=0.95. For men aged 50 the corresponding numbers were

r=0.95 and r=0.98. These data suggest that recall bias is relatively minor and self-

reported data could be reliably used as a proxy for subjects’ height and weight.

Furthermore, the correlation coefficients (Pearson r) between BMI based on measured

and self-reported weight and height were 0.80, 0.85, 0.96, and 0.98, for the studied age

groups respectively.


Baseline and follow-up information of overweight or obesity based on measured BMI was

used to construct a variable to describe overweight or obesity during life course,

consisting of six categories. The categories were defined as the following: 1= both

baseline and follow-up BMI < 25 kg/m2 (normal weight at both time points), 2= baseline

BMI < 25 kg/m2 and follow-up BMI ranged between 25-29.9 kg/m2 (became overweight

during follow-up), 3= baseline BMI < 25 kg/m2 and follow-up BMI > 30 kg/m2 (became

obese during follow-up),4= baseline BMI > 25 kg/m2 and follow-up BMI < 25 kg/m2

(overweight/obese at baseline, normal weight at follow-up), 5= baseline BMI > 25 kg/m2

and follow-up BMI ranged between 25-29.9 kg/m2 (overweight/obese at baseline and

overweight at follow-up), 6= baseline BMI > 25 kg/m2 and follow-up BMI > 30 kg/m2

(overweight/obese at baseline and obese at follow-up).

A life course measure for BMI was based on repeated BMI values (measured at baseline,

self-reported at the age of 30, 40 and 50 years and measured at follow-up). It was

constructed by calculating age-standardized Z-scores (mean = 0, standard deviation =

1) for each time-point measure, which were averaged across time-points.


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Age at baseline was calculated for the day of the measurements and at follow-up for 1

June 2000. Length of education (years) was inquired at follow-up. Data on smoking and

physical activity were collected at baseline and follow-up. At baseline the conscripts were

classified as smokers if they answered “yes” to the question “Do you smoke?” At follow-









Since our outcomes were based on prevalence, we used prevalence ratios (PR) to

measure the longitudinal and odds ratios (OR) to measure the cross-sectional

associations between weight-related variables and low back outcomes.

The effects of baseline and life course BMI on low back outcomes at follow-up were

studied with Cox regression model with the total length of time of follow-up being

assigned to each individual. The Cox model was used instead of logistic regression in

order to avoid undue inflation of estimates of associations between weight-related

measures and common low back problems.22 23 The prevalence ratios (PR) and their 95%

confidence intervals (95% CI) were adjusted for age and smoking status at baseline and

information of education (years) obtained at follow-up.



education (years) and smoking status at follow-up were included in the multivariable

models as covariates. We did not adjust for physical activity because it was not

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associated with low back outcomes in our study population. Furthermore, physical

activity has been shown to have a U-shaped association with LBP, and was therefore

considered to be an effect modifier and not a confounder.9 24


RESULTS




from 6 to 33 years.

<Table 1>






baseline.


prevalent. The prevalence of radiating LBP and clinically defined outcomes increased with

age (p=0.08 for radiating LBP, p=0.002 for chronic low back syndrome and p=0.01 for

sciatica).


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although it did not reach statistical significance (Table 2). Sensitivity analyses, excluding


signs, showed no change in the observed associations.





<Table 2>


up, had an increased risk of one month radiating LBP pain assessed at follow-up (Table


LBP.

<Table 3>


outcomes




(Table 2).

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radiating LBP, with a 42% and 64% increase in the risk in overweight and obese men,

respectively, compared with men with normal weight. Furthermore, abdominal obesity

defined by waist-to-height ratio was associated with an increased risk of radiating LBP

(1.44 95% CI 1.02-2.04). No associations were found for non-specific LBP, chronic low

back syndrome, or sciatica assessed by physician (Table 2). The exclusion of subjects

with baseline spinal symptoms or signs strengthened the observed associations between

waist circumference and radiating LBP (1.62, 95% CI 1.05-1.78 for abdominal obesity

defined as waist circumference > 102 cm).

DISCUSSION








defined outcomes.

The present study has several strengths. First, we used a unique study population, which








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back symptoms at the end of follow-up. Part of the weight-related measures during life

course was self-reported, and therefore recall bias may have affected the observed

associations. However, our assessments confirmed the high accuracy of the self-reported

values used in this study.






Moreover, a recent large cross-sectional study among 17-year old conscripts (male and

female) showed weaker associations of obesity with clinically defined outcomes than with

symptoms among the men.28





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course during life and may have different etiology.6 In line with our findings, BMI and

waist circumference predicted incidence of radiating but not non-specific LBP in a

population-based study among young adults.24



our findings a Dutch study, using a random sample from the general population, found










patomechanical pathways involved in the metabolic syndrome may play a role, as

discussed in the literature.31 A recent systematic review of twin studies concluded that

individuals being overweight or obese are more likely to have LBP and lumbar disc

generation, but the associations were weaker after controlling for familial factors,

suggesting that obesity and low back pain share common genetic risk factors.32

In conclusion, our findings indicate that being overweight or obese in young adulthood

as well as during life course increases the risk of radiating but not non-specific LBP in

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during life course. Studies on the etiology of LBP would benefit from differentiating

between radiating and non-specific types of pain. Future research should provide life

course studies on the associations of weight-related factors with low back disorders

among women.

CONTRIBUTORS





FUNDING



COMPETING INTERESTS

None declared.

DATA SHARING STATEMENT

No additional data are available.

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REFERENCES


2. Kelly T, Yang W, Chen CS, et al. Global burden of obesity in 2005 and projections to 2030. Int J

Obes (Lond) 2008;32(9):1431-7.









meta-analysis. Am Journal Epidemiol 2010;171(2):135-54.

7. Shiri R, Lallukka T, Karppinen J, et al. Obesity as a risk factor for sciatica: a meta-analysis. Am

Journal Epidemiol 2014;179(8):929-37.









9.

11. Mikkonen PH, Laitinen J, Remes J, et al. Association between overweight and low back pain: a

population-based prospective cohort study of adolescents. Spine (Phila Pa 1976)

2013;38(12):1026-33.



13. Rothman KJ. BMI-related errors in the measurement of obesity. Int Journal Obes (Lond) 2008;32

Suppl 3:S56-9.


fatness, fat distribution and height. Int J Obes Relat Metab Disord 1997;21(7):600-7.







Functional Capacity, 2008:1-248. Available at http://urn.fi/URN:NBN:fi-fe201204193320

(accessed 2.7.2015).




Capacity, 2004:1-175. Available at http://urn.fi/URN:NBN:fi-fe201204193452 (accessed

2.7.2015).

19. Kaila-Kangas L, Leino-Arjas P, Karppinen J, et al. History of physical work exposures and clinically

diagnosed sciatica among working and nonworking Finns aged 30 to 64. Spine (Phila Pa

1976) 2009;34(9):964-9.

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20. Heliovaara M, Impivaara O, Sievers K, et al. Lumbar disc syndrome in Finland. J Epidemiol

Community Health 1987;41(3):251-8.




comparison of models that directly estimate the prevalence ratio. BMC Med Res Methodol

2003;3:21.





2013;42(6):640-50.




Pa 1976) 1991;16(6):608-14.


disc herniation. Spine J 2006;6(6):684-91.

28. Hershkovich O, Friedlander A, Gordon B, et al. Associations of body mass index and body height

with low back pain in 829,791 adolescents. Am J Epidemiol 2013;178(4):603-9.


Int J Obes 2009;33(8):878-84.



31. Samartzis D, Karppinen J, Cheung JP, et al. Disk degeneration and low back pain: are they fat-

related conditions? Global Spine J 2013;3(3):133-44.

32. Dario AB, Ferreira ML, Refshauge KM, et al. The relationship between obesity, low back pain, and

lumbar disc degeneration when genetics and the environment are considered: a systematic

review of twin studies. Spine J 2015;15(5):1106-17.

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FIGURE LEGEND

Figure 1. Flowchart of the Formation of the Present Study Sample

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No. Mean (95% CI) Prevalence (%, 95% CI)


at follow-up 1385 40.2 (39.9 to 40.5)


at follow-up 1385 26.5 (26.3 to 26.7)


at follow-up 454/1380 32.9 (30.4 to 35.5)


at follow-up 1008/1370 73.6 (71.2 to 75.9)

Spinal symptoms and/or signs at baseline 208/1385 15.0 (13.2 to 17.0)

Low back symptoms at baseline 113/1385 8.2 (6.8 to 9.7)





Non-specific 393/1385 28.4 (26.0 to 30.8)

Radiating 162/1385 11.7 (10.1 to 13.5)



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Sciatica 58/1350 4.3 (3.3-5.5)

LBP, low back pain. An

Information is missing for 35 men

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Table 2. Associations of weight-related measures with low back pain (LBP) and physician-diagnosed low back disorders.


(n=1363)


(n= 1350)


(n=1363)


(n=1350)







BMI (categorical)

< 25 kg/m2 1.00 1.00 1.00 1.00

25 to 29.9 kg/m2 1.03 0.80 to 1.34 0.95 0.60 to 1.51 1.42 0.97 to 2.08 0.84 0.47 to 1.51

> 30 kg/m2 0.88 0.61 to 1.25 1.13 0.63 to 2.00 1.64 1.02 to 2.65 1.04 0.50 to 2.15



< 94 cm 1.00 1.00 1.00 1.00

94 to 101.9 cm 1.11 0.83 to 1.47 1.04 0.63 to 1.73 1.03 0.69 to 1.53 0.91 0.47 to 1.75

> 102 cm 1.03 0.77 to 1.39 1.24 0.75 to 2.03 1.31 0.88 to 1.96 1.19 0.64 to 2.23


normal (≤ 0.5) 1.00 1.00 1.00 1.00



A Prevalence ratios (for the longitudinal analyses) and odds ratios (for cross-sectional analyses) and their 95% CIs are adjusted for age, smoking and education.

B Continuous variable, one standard deviation increase, corresponding to 3.0 kg/m2 for baseline BMI. C One-unit increment in Z-score, corresponding to 2.92 units of BMI during follow-up. D Continuous variable, one standard deviation increase, corresponding to 4.0 kg/m2 for follow-up BMI. E Continuous variable, one standard deviation increase, corresponding to 11 cm for waist circumference at follow-up.

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Table 3. Associations of baseline and follow-up overweight and obesity with low back pain.


(n=1363)


(n=1363)

na

PRb 95% CI na PRb 95% CI

BMI at baseline BMI at follow-up

< 25 kg/m2 < 25 kg/m2 104 (509) 1.00 48 (509) 1.00

< 25 kg/m2 25 to 29.9 kg/m2 161 (560) 1.00 0.76 to 1.32 70 (560) 1.34 0.89 to 2.00

< 25 kg/m2 > 30 kg/m2 33 (106) 1.18 0.74 to 1.88 17 (106) 1.91 1.03 to 3.53

> 25 kg/m2 < 25 kg/m2 2 (8) 0.84 0.17 to 4.28 0 (8) - -

> 25 kg/m2 25 to 29.9 kg/m2 21 (62) 1.39 0.79 to 2.50 10 (62) 2.10 1.00 to 4.47

> 25 kg/m2 > 30 kg/m2 26 (118) 0.76 0.47 to 1.23 18 (118) 1.65 0.89 to 3.06

BMI, body mass index; LBP, low back pain; CI, confidence interval; PR, prevalence ratio. A Number with outcome (number of exposed) b PR's and their 95% CIs are adjusted for age, smoking and education.

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Flowchart of the Formation of the Present Study Sample

169x222mm (300 x 300 DPI)

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Section/Topic Item




Introduction



Methods



collection

5




applicable

6-8

Data sources/

measurement



5-8




why

6-8






Results

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5 and Figure 1




confounders

Table 1 and page 8






Tables 2-3




Discussion


Limitations



13


Other information



1314





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bmj open...svetlana solovieva, phd 1,2 pertti mutanen, msc 3 harri pihlajamäki, md, phd 4,5 markku...

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