International Classification of Diseases (ICD)-coded obesity predicts risk of incident osteoporotic fracture

Associations between apolipoprotein B and bone mineral density: a population-based study

BACKGROUND

Lipids are critical in bone metabolism, and several studies have highlighted their importance. This study aimed to investigate the relationship between apolipoprotein B (apo B) and bone mineral density (BMD) at different skeletal sites (lumbar spine, femoral neck, and total femur) and to compare the influence of apo B with other traditional lipid markers.

METHODS

The study included participants from the National Health and Nutrition Examination Survey (NHANES) between 2011 and 2016 who had complete data for apo B and BMD at the three skeletal sites. We used weighted multivariate regression analysis, subgroup analysis, and interaction tests to examine associations. Restricted cubic spline (RCS) was used to examine the non-linear relationship.

RESULTS

A total of 4,258 adults were included in the study. Multivariate linear regression analysis showed that the relationship between apo B and BMD varied by skeletal site: a negative association was found with lumbar spine BMD [β = -0.054, 95%CI: (-0.073, -0.035)]. In contrast, a positive association was found with femoral neck BMD [β = 0.031, 95%CI: (0.011, 0.051)] and no significant association between apo B and total femur BMD.

CONCLUSIONS

Our findings suggest that apo B is associated with BMD in a site-specific manner.

Validity of Hospital Codes for Obesity in Ontario, Canada

BACKGROUND

Obesity has a significant impact on population health and health care. Administrative databases may be a useful tool to study obesity at a population level. In this work, we aimed to determine the validity of hospital codes for obesity in Ontario, Canada.

METHODS

Using linked health-care databases (ICES), we conducted a validation study in adults ≥18 years of age who had their height and weight recorded during a hospitalization in southwestern Ontario. We considered a body mass index ≥30 kg/m² as our gold standard definition for obesity. We then examined the validity of 2 International Classification of Diseases---10th revision (ICD-10) coding algorithms for obesity (Algorithm 1, ICD-10 E66.X; and Algorithm 2, ICD-10 E65.X-68.X). As additional analyses, we examined the validity of algorithms in different obesity classes (i.e. obese classes 1, 2 and 3), and in patients with diagnosed diabetes and hypertension.

RESULTS

There were 34,588 patients included in our study (mean age, 62 years; 47% female). Algorithm 1 performed best, with a sensitivity, specificity, positive predictive value and negative predictive value of 8.8%, 99.8%, 95.4% and 65.1%, respectively. The sensitivity of this algorithm was highest in patients with obesity class 3 (27.4%) and in those with diagnosed diabetes.

CONCLUSIONS

Hospital codes for obesity have a high positive predictive value and specificity. These codes can be used to build and study cohorts of patients with obesity in administrative database studies. However, given their limited sensitivity, administrative codes provide inaccurate incidence and prevalence estimates.

Sex differences in the longitudinal associations between body composition and bone stiffness index in European children and adolescents

Fat mass (FM) and fat free mass (FFM) may influence bone health differentially. However, existing evidences on associations between FM, FFM and bone health are inconsistent and vary according to sex and maturity. The present study aims to evaluate longitudinal associations between FM, FFM and bone stiffness index (SI) among European children and adolescents with 6 years follow-up. A sample of 2468 children from the IDEFICS/I.Family was included, with repeated measurements of SI using calcaneal quantitative ultrasound, body composition using skinfold thickness, sedentary behaviors and physical activity using self-administrated questionnaires. Regression coefficients (β) and 99%-confidence intervals (99% CI) were calculated by sex-specified generalized linear mixed effects models to analyze the longitudinal associations between FM and FFM z-scores (zFM and zFFM) and SI percentiles, and to explore the possible interactions between zFM, zFFM and maturity. Baseline zFFM was observed to predict the change in SI percentiles in both boys (β = 4.57, 99% CI: 1.36, 7.78) and girls (β = 3.42, 99% CI: 0.05, 6.79) after 2 years. Moreover, baseline zFFM (β = 8.72, 99% CI: 3.18, 14.27 in boys and β = 5.89, 99% CI: 0.34, 11.44 in girls) and the change in zFFM (β = 6.58, 99% CI: 0.83, 12.34 in boys and β = 4.81, 99% CI: -0.41, 10.02 in girls) were positively associated with the change in SI percentiles after 6 years. In contrast, a negative association was observed between the change in zFM and SI percentiles in boys after 6 years (β = -3.70, 99% CI: -6.99, -0.42). Besides, an interaction was observed between the change in zFM and menarche on the change in SI percentiles in girls at 6 years follow-up (p = .009), suggesting a negative association before menarche while a positive association after menarche. Our findings support the existing evidences for a positive relationship between FFM and SI during growth. Furthermore, long-term FM gain was inversely associated with SI in boys, whereas opposing associations were observed across menarche in girls.

Number, Location, and Time Since Prior Fracture as Predictors of Future Fracture in the Elderly From the General Population

Prognostic tools are available to identify individuals at high risk of osteoporotic fracture and to assist physicians in management decisions. Some authors have suggested improving the predictive ability of these tools by integrating characteristics of prior fractures (number, location, and time since prior fracture). The objectives of this study were: (1) to evaluate the sex- and age-specific associations between characteristics of prior fractures and the occurrence of a future osteoporotic fracture; and (2) to assess whether the characteristics of prior fractures could increase the discriminative ability of fracture risk prediction tools. A retrospective cohort study was conducted using administrative data. Men and women aged ≥66 years were selected and grouped into two cohorts. In cohort #1 (N = 759,500), history of fractures was measured between fiscal years 1997-1998 and 2003-2004, and future fractures were identified between 2004-2005 and 2013-2014. In cohort #2 (N = 807,245), history of fractures was measured between 1997-1998 and 2008-2009, and future fractures were identified between 2009-2010 and 2013-2014. Time until a first hip/femur and major osteoporotic fracture were the outcomes of interest. Adjusted HRs and c-indices were calculated. The association between history of prior fractures and future fracture was stronger in men and younger individuals. The locations of prior fractures associated with the lowest and highest risks were foot/ankle/tibia/fibula (maximal HR = 1.64) and hip/femur (maximal HR = 9.02), respectively. The association was stronger for recent fractures (maximal HR = 4.93), but was still significant for fractures occurring 10 to 12 years prior to the beginning of follow-up (maximal HR = 1.99). Characteristics of prior fractures did not increase model discrimination. Our study confirms that the risk of future fracture increases with the number of prior fractures, varies according to prior fracture location, and decreases with time since prior fracture. However, the integration of these characteristics in current fracture risk prediction tools is not required because it does not improve predictive ability. © 2018 American Society for Bone and Mineral Research.

Data Talks: Obesity-Related Influences on US Mortality Rates

BACKGROUND

In the US, obesity is an epidemiologic challenge and the population fails to comprehend this complex public health issue. To evaluate underlying obesity-impact patterns on mortality rates, we data-mined the 1999-2016 Center for Disease Control WONDER database's vital records.

METHODS

Adopting SAS programming, we scrutinized the mortality and population counts. Using ICD-10 diagnosis codes connected to overweight and obesity, we obtained the obesity-related crude and age-adjusted causes of death. To understand divergent and prevalence trends we compared and contrasted the tabulated obesity-influenced mortality rates with demographic information, gender, and age-related data.

KEY RESULTS

From 1999 to 2016, the obesity-related age-adjusted mortality rates increased by 142%. The ICD-10 overweight and obesity-related death-certificate coding showed clear evidence that obesity factored in the male age-adjusted mortality rate increment to 173% and the corresponding female rate to 117%. It also disproportionately affected the nation-wide minority population death rates. Furthermore, excess weight distributions are coded as contributing features in the crude death rates for all decennial age-groups.

CONCLUSIONS

The 1999-2016 data from ICD-10 death certificate coding for obesity-related conditions indicate that it is affecting all segments of the US population.