Binary classification of dyslipidemia from the waist-to-hip ratio and body mass index: a comparison of linear, logistic, and CART models.

BACKGROUND: We sought to improve upon previously published statistical modeling strategies for binary classification of dyslipidemia for general population screening purposes based on the waist-to-hip circumference ratio and body mass index anthropometric measurements. METHODS: Study subjects were participants in WHO-MONICA population-based surveys conducted in two Swiss regions. Outcome variables were based on the total serum cholesterol to high density lipoprotein cholesterol ratio. The other potential predictor variables were gender, age, current cigarette smoking, and hypertension. The models investigated were: (i) linear regression; (ii) logistic classification; (iii) regression trees; (iv) classification trees (iii and iv are collectively known as "CART"). Binary classification performance of the region-specific models was externally validated by classifying the subjects from the other region. RESULTS: Waist-to-hip circumference ratio and body mass index remained modest predictors of dyslipidemia. Correct classification rates for all models were 60-80%, with marked gender differences. Gender-specific models provided only small gains in classification. The external validations provided assurance about the stability of the models. CONCLUSIONS: There were no striking differences between either the algebraic (i, ii) vs. non-algebraic (iii, iv), or the regression (i, iii) vs. classification (ii, iv) modeling approaches. Anticipated advantages of the CART vs. simple additive linear and logistic models were less than expected in this particular application with a relatively small set of predictor variables. CART models may be more useful when considering main effects and interactions between larger sets of predictor variables.

Paranasal sinuses in children: size evaluation of maxillary, sphenoid, and frontal sinuses by magnetic resonance imaging and proposal of volume index percentile curves.

Our objective was to establish the age-related 3D size of maxillary, sphenoid, and frontal sinuses. A total of 179 magnetic resonance imaging (MRI) of children under 17 years (76 females, 103 males) were included and sinuses were measured in the three axes. Maxillary sinuses measured at birth (mean+/-standard deviation) 7.3+/-2.7 mm length (or antero-posterior)/4.0+/-0.9 mm height (or cranio-caudal)/2.7+/-0.8 mm width (or transverse). At 16 years old, maxillary sinus measured 38.8+/-3.5 mm/36.3+/-6.2 mm/27.5+/-4.2 mm. Sphenoid sinus pneumatization starts in the third year of life after conversion from red to fatty marrow with mean values of 5.8+/-1.4 mm/8.0+/-2.3 mm/5.8+/-1.0 mm. Pneumatization progresses gradually to reach at 16 years 23.0+/-4.5 mm/22.6+/-5.8 mm/12.8+/-3.1 mm. Frontal sinuses present a wide variation in size and most of the time are not valuable with routine head MRI techniques. They are not aerated before the age of 6 years. Frontal sinuses dimensions at 16 years were 12.8+/-5.0 mm/21.9+/-8.4 mm/24.5+/-13.3 mm. A sinus volume index (SVI) of maxillary and sphenoid sinus was computed using a simplified ellipsoid volume formula, and a table with SVI according to age with percentile variations is proposed for easy clinical application. Percentile curves of maxillary and sphenoid sinuses are presented to provide a basis for objective determination of sinus size and volume during development. These data are applicable to other techniques such as conventional X-ray and CT scan.

Validation of the instability shoulder index score in a multicenter reliability study in 114 consecutive cases.

BACKGROUND: Anterior shoulder stabilization surgery with the arthroscopic Bankart procedure can have a high recurrence rate in certain patients. Identifying these patients to modify outcomes has become a focal point of research. PURPOSE: The Instability Shoulder Index Score (ISIS) was developed to predict the success of arthroscopic Bankart repair. Scores range from 0 to 10, with higher scores predicting a higher risk of recurrence after stabilization. The interobserver reliability of the score is not known. STUDY DESIGN: Cohort study (diagnosis); Level of evidence, 2. METHODS: This is a prospective multicenter (North America and Europe) study of patients suffering from shoulder instability and waiting for stabilization surgery. Five pairs of independent evaluators were asked to score patient instability severity with the ISIS. Patients also completed functional scores (Western Ontario Shoulder Instability Index [WOSI], Disabilities of the Arm, Shoulder and Hand-short version [QuickDASH], and Walch-Duplay test). Data on age, sex, number of dislocations, and type of surgery were collected. The test-retest method and intraclass correlation coefficient (ICC: >0.75 = good, >0.85 = very good, and >0.9 = excellent) were used for analysis. RESULTS: A total of 114 patients with anterior shoulder instability were included, of whom 89 (78%) were men. The mean age was 28 years. The ISIS was very reliable, with an ICC of 0.933. The mean number of dislocations per patient was higher in patients who had an ISIS of ≥6 (25 vs 14; P = .05). Patients who underwent more complex arthroscopic procedures such as Hill-Sachs remplissage or open Latarjet had higher preoperative ISIS outcomes, with a mean score of 4.8 versus 3.4, respectively (P = .002). There was no correlation between the ISIS and the quality-of-life questionnaires, with Pearson correlations all >0.05 (WOSI = 0.39; QuickDASH = 0.97; Walch-Duplay = 0.08). CONCLUSION: Our results show that the ISIS is reliable when used in a multicenter study with anterior traumatic instability populations. There was no correlation between the ISIS and the quality-of-life questionnaires, but surgical decisions reflected its increased use.

Depression with atypical features and increase in obesity, body mass index, waist circumference, and fat mass: a prospective, population-based study.

IMPORTANCE: Depression and obesity are 2 prevalent disorders that have been repeatedly shown to be associated. However, the mechanisms and temporal sequence underlying this association are poorly understood. OBJECTIVE: To determine whether the subtypes of major depressive disorder (MDD; melancholic, atypical, combined, or unspecified) are predictive of adiposity in terms of the incidence of obesity and changes in body mass index (calculated as weight in kilograms divided by height in meters squared), waist circumference, and fat mass. DESIGN, SETTING, AND PARTICIPANTS: This prospective population-based cohort study, CoLaus (Cohorte Lausannoise)/PsyCoLaus (Psychiatric arm of the CoLaus Study), with 5.5 years of follow-up included 3054 randomly selected residents (mean age, 49.7 years; 53.1% were women) of the city of Lausanne, Switzerland (according to the civil register), aged 35 to 66 years in 2003, who accepted the physical and psychiatric baseline and physical follow-up evaluations. EXPOSURES: Depression subtypes according to the DSM-IV. Diagnostic criteria at baseline and follow-up, as well as sociodemographic characteristics, lifestyle (alcohol and tobacco use and physical activity), and medication, were elicited using the semistructured Diagnostic Interview for Genetic Studies. MAIN OUTCOMES AND MEASURES: Changes in body mass index, waist circumference, and fat mass during the follow-up period, in percentage of the baseline value, and the incidence of obesity during the follow-up period among nonobese participants at baseline. Weight, height, waist circumference, and body fat (bioimpedance) were measured at baseline and follow-up by trained field interviewers. RESULTS: Only participants with the atypical subtype of MDD at baseline revealed a higher increase in adiposity during follow-up than participants without MDD. The associations between this MDD subtype and body mass index (β = 3.19; 95% CI, 1.50-4.88), incidence of obesity (odds ratio, 3.75; 95% CI, 1.24-11.35), waist circumference in both sexes (β = 2.44; 95% CI, 0.21-4.66), and fat mass in men (β = 16.36; 95% CI, 4.81-27.92) remained significant after adjustments for a wide range of possible cofounding. CONCLUSIONS AND RELEVANCE: The atypical subtype of MDD is a strong predictor of obesity. This emphasizes the need to identify individuals with this subtype of MDD in both clinical and research settings. Therapeutic measures to diminish the consequences of increased appetite during depressive episodes with atypical features are advocated.

Skinfold thickness and adiposity index in premature infants.

In order to assess the validity of the weight per square of length ratio as an index of adiposity during the neonatal period, 37 premature infants (gestational age, mean +/- SD, = 31.5 +/- 1.1 weeks, birthweight, mean +/- SD, = 1.448 +/- 147 g) were studied for weight, length and skinfold thickness at 5 sites (biceps, triceps, subscapular, suprailiac and quadriceps) during their stay in the Neonatal Unit of the University Hospital in Lausanne. The results show a significant correlation between the adiposity index and the sum of 5 skinfold thickness sites in premature infants. The adiposity index gives a fair estimate of the body fat mass during the postnatal growth in premature infants.

A meta-analysis of the association of fracture risk and body mass index in women.

Several recent studies suggest that obesity may be a risk factor for fracture. The aim of this study was to investigate the association between body mass index (BMI) and future fracture risk at different skeletal sites. In prospective cohorts from more than 25 countries, baseline data on BMI were available in 398,610 women with an average age of 63 (range, 20-105) years and follow up of 2.2 million person-years during which 30,280 osteoporotic fractures (6457 hip fractures) occurred. Femoral neck BMD was measured in 108,267 of these women. Obesity (BMI ≥ 30 kg/m(2) ) was present in 22%. A majority of osteoporotic fractures (81%) and hip fractures (87%) arose in non-obese women. Compared to a BMI of 25 kg/m(2) , the hazard ratio (HR) for osteoporotic fracture at a BMI of 35 kg/m(2) was 0.87 (95% confidence interval [CI], 0.85-0.90). When adjusted for bone mineral density (BMD), however, the same comparison showed that the HR for osteoporotic fracture was increased (HR, 1.16; 95% CI, 1.09-1.23). Low BMI is a risk factor for hip and all osteoporotic fracture, but is a protective factor for lower leg fracture, whereas high BMI is a risk factor for upper arm (humerus and elbow) fracture. When adjusted for BMD, low BMI remained a risk factor for hip fracture but was protective for osteoporotic fracture, tibia and fibula fracture, distal forearm fracture, and upper arm fracture. When adjusted for BMD, high BMI remained a risk factor for upper arm fracture but was also a risk factor for all osteoporotic fractures. The association between BMI and fracture risk is complex, differs across skeletal sites, and is modified by the interaction between BMI and BMD. At a population level, high BMI remains a protective factor for most sites of fragility fracture. The contribution of increasing population rates of obesity to apparent decreases in fracture rates should be explored. © 2014 American Society for Bone and Mineral Research.

Body mass index, abdominal adiposity and blood pressure: consistency of their association across developing and developed countries.

BACKGROUND: Obesity is increasing worldwide because developing countries are adopting Western high-fat foods and sedentary lifestyles. In parallel, in many of them, hypertension is rising more rapidly, particularly with age, than in Western countries. OBJECTIVE: To assess the relationship between adiposity and blood pressure (BP) in a developing country with high average BP (The Seychelles, Indian Ocean, population mainly of African origin) in comparison to a developed country with low average BP (Switzerland, population mainly of Caucasian origin). DESIGN: Cross-sectional health examination surveys based on population random samples. SETTING: The main Seychelles island (Mahé) and two Swiss regions (Vaud-Fribourg and Ticino). SUBJECTS: Three thousand one hundred and sixteen adults (age range 35-64) untreated for hypertension. MEASUREMENTS: Body mass index (BMI), waist circumference (WC), waist-to-hip ratio (WHR), systolic and diastolic blood pressure (SBP and DBP, mean of two measures). METHODS: Scatterplot smoothing techniques and gender-specific linear regression models. RESULTS: On average, SBP and DBP were found to increase linearly over the whole variation range of BMI, WHR and WC. A modest, but statistically significant linear association was found between each indicator of adiposity and BP levels in separate regression models controlling for age. The regression coefficients were not significantly different between the Seychelles and the two Swiss regions, but were generally higher in women than in men. For the latter, a gain of 1.7 kg/m(2) in BMI, of 4.5 cm in WC or of 3.4% in WHR corresponded to an elevation of 1 mmHg in SBP. For women, corresponding figures were 1.25 kg/m(2), 2.5 cm and 1.8% respectively. Regression coefficients for age reflected a higher effect of this variable on both SBP and DBP in the Seychelles than in Switzerland. CONCLUSION: These findings suggest a stable linear relation of adiposity with BP, independent of age and body fat distribution, across developed and developing countries. The more rapid increase of BP with age observed in the latter countries are likely to reflect higher genetic susceptibility and/or higher cumulative exposure to another risk factor than adiposity.

Fat-free mass index and fat mass index percentiles in Caucasians aged 18-98 y.

OBJECTIVE: To determine reference values for fat-free mass index (FFMI) and fat mass index (FMI) in a large Caucasian group of apparently healthy subjects, as a function of age and gender and to develop percentile distribution for these two parameters. DESIGN: Cross-sectional study in which bioelectrical impedance analysis (50 kHz) was measured (using tetrapolar electrodes and cross-validated formulae by dual-energy X-ray absorptiometry in order to calculate FFMI (fat-free mass/height squared) and FMI (fat mass/height squared). SUBJECTS: A total of 5635 apparently healthy adults from a mixed non-randomly selected Caucasian population in Switzerland (2986 men and 2649 women), varying in age from 24 to 98 y. RESULTS: The median FFMI (18-34 y) were 18.9 kg/m(2) in young males and 15.4 kg/m(2) in young females. No difference with age in males and a modest increase in females were observed. The median FMI was 4.0 kg/m(2) in males and 5.5 kg/m(2) in females. From young to elderly age categories, FMI progressively rose by an average of 55% in males and 62% in females, compared to an increase in body mass index (BMI) of 9 and 19% respectively. CONCLUSIONS: Reference intervals for FFMI and FMI could be of practical value for the clinical evaluation of a deficit in fat-free mass with or without excess fat mass (sarcopenic obesity) for a given age category, complementing the classical concept of body mass index (BMI) in a more qualitative manner. In contrast to BMI, similar reference ranges seems to be utilizable for FFMI with advancing age, in particular in men.

Obesity trends and body mass index changes after starting antiretroviral treatment : the Swiss HIV Cohort Study

BACKGROUND: The factors that contribute to increasing obesity rates in human immunodeficiency virus (HIV)-positive persons and to body mass index (BMI) increase that typically occurs after starting antiretroviral therapy (ART) are incompletely characterized. METHODS: We describe BMI trends in the entire Swiss HIV Cohort Study (SHCS) population and investigate the effects of demographics, HIV-related factors, and ART on BMI change in participants with data available before and 4 years after first starting ART. RESULTS: In the SHCS, overweight/obesity prevalence increased from 13% in 1990 (n = 1641) to 38% in 2012 (n = 8150). In the participants starting ART (n = 1601), mean BMI increase was 0.92 kg/m(2) per year (95% confidence interval, .83-1.0) during year 0-1 and 0.31 kg/m(2) per year (0.29-0.34) during years 1-4. In multivariable analyses, annualized BMI change during year 0-1 was associated with older age (0.15 [0.06-0.24] kg/m(2)) and CD4 nadir <199 cells/µL compared to nadir >350 (P < .001). Annualized BMI change during years 1-4 was associated with CD4 nadir <100 cells/µL compared to nadir >350 (P = .001) and black compared to white ethnicity (0.28 [0.16-0.37] kg/m(2)). Individual ART combinations differed little in their contribution to BMI change. CONCLUSIONS: Increasing obesity rates in the SHCS over time occurred at the same time as aging of the SHCS population, demographic changes, earlier ART start, and increasingly widespread ART coverage. Body mass index increase after ART start was typically biphasic, the BMI increase in year 0-1 being as large as the increase in years 1-4 combined. The effect of ART regimen on BMI change was limited.

Differential effect of Charlson index and its components on in-hospital mortality and health costs

Introduction: The Charlson index (Charlson, 1987) is a commonly used comorbidity index in outcome studies. Still, the use of different weights makes its calculation cumbersome, while the sum of its components (comorbidities) is easier to compute. In this study, we assessed the effects of 1) the Charlson index adapted for the Swiss population and 2) the sum of its components (number of comorbidities, maximum 15) on a) in-hospital deaths and b) cost of hospitalization. Methods: Anonymous data was obtained from the administrative database of the department of internal medicine of the Lausanne University Hospital (CHUV). All hospitalizations of adult (>=18 years) patients occurring between 2003 and 2011 were included. For each hospitalization, the Charlson index and the number of comorbidities were calculated. Analyses were conducted using Stata. Results: Data from 32,741 hospitalizations occurring between 2003 and 2011 was analyzed. On bivariate analysis, both the Charlson index and the number of comorbidities were significantly and positively associated with in hospital death. Conversely, multivariate adjustment for age, gender and calendar year using Cox regression showed that the association was no longer significant for the number of comorbidities (table). On bivariate analysis, hospitalization costs increased both with Charlson index and with number of comorbidities, but the increase was much steeper for the number of comorbidities (figure). Robust regression after adjusting for age, gender, calendar year and duration of hospital stay showed that the increase in one comorbidity led to an average increase in hospital costs of 321 CHF (95% CI: 272 to 370), while the increase in one score point of the Charlson index led to a decrease in hospital costs of 49 CHF (95% CI: 31 to 67). Conclusion: Charlson index is better than the number of comorbidities in predicting in-hospital death. Conversely, the number of comorbidities significantly increases hospital costs.

Body composition interpretation. Contributions of the fat-free mass index and the body fat mass index.

OBJECTIVE: Low and high body mass index (BMI) values have been shown to increase health risks and mortality and result in variations in fat-free mass (FFM) and body fat mass (BF). Currently, there are no published ranges for a fat-free mass index (FFMI; kg/m(2)), a body fat mass index (BFMI; kg/m(2)), and percentage of body fat (%BF). The purpose of this population study was to determine predicted FFMI and BFMI values in subjects with low, normal, overweight, and obese BMI. METHODS: FFM and BF were determined in 2986 healthy white men and 2649 white women, age 15 to 98 y, by a previously validated 50-kHz bioelectrical impedance analysis equation. FFMI, BFMI, and %BF were calculated. RESULTS: FFMI values were 16.7 to 19.8 kg/m(2) for men and 14.6 to 16.8 kg/m(2) for women within the normal BMI ranges. BFMI values were 1.8 to 5.2 kg/m(2) for men and 3.9 to 8.2 kg/m(2) for women within the normal BMI ranges. BFMI values were 8.3 and 11.8 kg/m(2) in men and women, respectively, for obese BMI (>30 kg/m(2)). Normal ranges for %BF were 13.4 to 21.7 and 24.6 to 33.2 for men and women, respectively. CONCLUSION: BMI alone cannot provide information about the respective contribution of FFM or fat mass to body weight. This study presents FFMI and BFMI values that correspond to low, normal, overweight, and obese BMIs. FFMI and BFMI provide information about body compartments, regardless of height.