Sex-specific associations between body mass index and death before life expectancy: a comparative study from the USA and Sweden

Abstract

Background: Understanding the impact of obesity on premature mortality is critical, as obesity has become a global health issue. Objective: To contrast the relationship between body mass index (BMI) and premature death (all-cause; circulatory causes) in New York State (USA) and Northern Sweden. Methods: Baseline data were obtained between 1989 and 1999 via questionnaires (USA) and health exams (Sweden), with mortality data from health departments, public sources (USA) and the Swedish Death Register. Premature death was death before life expectancy based on sex and year of birth. Within country and sex, time to premature death was compared across BMI groups (18.5-24.9 kg/m 2 (reference), 25-29.9 kg/m 2 , 30.0-34.9 kg/m 2 , ≥35.0 kg/m 2) using Proportional Hazards regression. Absolute risk (deaths/100,000 person-years) was compared for the same stratifications among nonsmokers. Results: 60,600 Swedish (47.8% male) and 31,198 US subjects (47.7% male) were included. Swedish males with BMI≥30 had increased hazards (HR) of all-cause premature death relative to BMI 18.5-24.9 (BMI 30-34.9, HR = 1.71 (95% CI: 1.44, 2.02); BMI≥35, HR = 2.89 (2.16, 3.88)). BMI≥25 had increased hazards of premature circulatory death (BMI 25-29.9, HR = 1.66 (1.32, 2.08); BMI 30-34.9, HR = 3.02 (2.26, 4.03); BMI≥35, HR = 4.91 (3.05, 7.90)). Among US males, only BMI≥35 had increased hazards of all-cause death (HR = 1.63 (1.25, 2.14)), while BMI 30-34.9 (HR = 1.83 (1.20, 2.79)) and BMI≥35 (HR = 3.18 (1.96, 5.15)) had increased hazards for circulatory death. Swedish females showed elevated hazards with BMI≥30 for all-cause (BMI 30-34.9, HR = 1.42 (1.18, 1.71) and BMI≥35, HR = 1.61 (1.21, 2.15) and with BMI≥35 (HR = 3.11 (1.72, 5.63)) for circulatory death. For US women, increased hazards were observed among BMI≥35 (HR = 2.10 (1.60, 2.76) for all-cause and circulatory HR = 3.04 (1.75, 5.30)). Swedish males with BMI≥35 had the highest absolute risk of premature death (762/100,000 personyears). Conclusions: This study demonstrates a markedly increased risk of premature death associated with increasing BMI among Swedish males, a pattern not duplicated among females.

Quantitative evaluations of variations using the population mean as a baseline for bioinformatics interpretation

“…The MCI also differs from the risk from a factor. Risk is the probability of the occurrence of an event from a causal factor ( Lemans et al, 2019 ; Zhu & Wu, 2019 ; Scribani et al, 2019 ; Hydes et al, 2019 ). Nor is the MCI the same as a diagnostic effect, as a diagnosis considers consistent rates, including both observed consistency and predicted consistency.…”

“…Quantitative variations in a particular event are normally distributed in terms of changes in ratio ( Lemans et al, 2019 ; Zhu & Wu, 2019 ; Zhou, Sun & Liu, 2019 ) and absolute values ( Scribani et al, 2019 ; Hydes et al, 2019 ), although these factors are not comparable ( Hongwei et al, 2020 ; Xiaojun & Hui, 2019 ). When the cardinal number of a ratio is relatively small, an increasing ratio may be very high although the absolute increase may be not high.…”

US Obesity Mortality Trends and Associated Noncommunicable Diseases Contributing Conditions Among White, Black, and Hispanic Individuals by Age from 1999 to 2017

“…As mentioned before, although the use of ICD codes has a positive prediction value of more than 90%, the present study has the limitation that we only used mortality datasets without further information about BMI, obesity class, and other risk factors such as physical activity or smoking, as the existing data in the mortality datasets were either incomplete or unavailable; therefore, their effect and relationship with NCDs cannot be evaluated [ 40 ]. While we could not establish how these risk factors impact obesity mortality, it has been well-documented that smoking, unhealthy eating, and physical inactivity play an important role in higher BMI levels, and as such are contributing factors for adverse health outcome [ 41 ]. In this study, deaths from cardiovascular conditions and cancer were not disaggregated into specific types, and mortality hazard ratios were estimated only for obesity and the selected major chronic conditions.…”

Determination of the effect size of an observed factor based on a multi-variate model for evaluating practical significance of differences between two groups of case-control design

“…Quantitative variations in a particular event are normally distributed in terms of changes in ratios [4][5][6] and absolute values [7][8][9], such as odds ratio (OR) [10] and Youden's index (Y) [11]. When the values of cardinal numbers are relatively small, an increase in a ratio may be very high although the absolute increase may not be.…”