Comparison of Body Mass Index (BMI) Categories Based on Asian and Universal Standards and Language Spoken at Home among Asian American University Students

The influence of body mass index (BMI) on pregnancy rates and outcomes among infertile Japanese women

Current recommendations for adequate gestational weight gain (GWG) are based on body mass index (BMI) classes proposed by the WHO. However, for Asian populations alternative BMI classifications have been suggested. This study aims to explore the associations between GWG and pregnancy outcomes using WHO, Asian and Indonesian BMI cutoffs. A prospective longitudinal study was conducted in the Parepare district, Indonesia. Of 1216 pregnant women enrolled, 953 mother-child dyads had complete newborn data. BMI was categorized into: underweight, normal weight, overweight, and obesity according to WHO, Asian and Indonesian BMI cutoffs. IOM criteria were used to determine GWG adequacy. Adverse pregnancy outcomes were: small for gestational age, preterm birth and low birth weight. For all three BMI classifications, logistic regression analyses were performed to examine the association between GWG and pregnancy outcomes. Additionally, we conducted a goodness of fit test to compare the model performance for each BMI classification method. The prevalence of underweight was 11.4% regardless of BMI classification. The prevalence of overweight was 22.2%, 33.3%, and 11.8%, whereas the prevalence of obesity was 6.8%, 14.4%, and 17.3% using the WHO, Asian and Indonesian cutoffs, respectively. Across all BMI classifications, a considerable proportion of women who were classified as underweight (> 70%) or normal weight (> 69%) exhibited inadequate GWG. The Asian BMI classification had the highest proportion of excessive GWG (15.2%). Gestational weight gain classes based on Asian BMI cutoffs corresponded better with adverse pregnancy outcomes than WHO or Indonesian BMI cutoffs. We hypothesize that implementing the Asian BMI classification may better identify risks for adverse pregnancy outcomes, enabling more accurate and timely interventions targeting (pre)pregnancy to improve outcomes for mother and child.

BackgroundThe generalizability of the gestational weight gain (GWG) ranges recommended by the Institute of Medicine (IOM) to Chinese women is disputed.MethodsIn 2016, 16,780 pregnant women who gave birth to live singletons in Changsha, China, were enrolled. First, subjects with optimal pregnancy outcomes were identified for the GWG percentile distribution description and for comparison to the IOM recommendations. Second, all subjects with optimal GWG according to the IOM body mass index (BMI) cutoffs and those with optimal GWG according to the Asian BMI cutoffs were selected. Pregnancy outcomes were compared between those two groups.ResultsA total of 13,717 births with optimal pregnancy outcomes were selected to describe the GWG distribution. The height and central position of the GWG distributions determined by the Asian BMI cutoffs differed from those determined by the IOM BMI cutoffs among the overweight and obese groups. The recommended IOM GWG ranges were narrower than and shifted to the left of the observed distributions. In both BMI classification schemes, however, the IOM-recommended ranges were within the middle 70% (Pc 15th–85th) and 50% (Pc 25th–75th) of the observed distribution. A total of 6438 (38.37%) and 6110 (36.41%) women gained optimal GWG, according to the IOM and Asian BMI classifications, respectively. Compared with those with optimal GWG according to IOM BMI cutoffs, women with optimal GWG according to the Asian BMI cutoffs had lower risks of both macrosomia (adjusted OR = 0.79, 95%CI: 0.67–0.94) and large-for-gestational age (adjusted OR = 0.86, 95%CI: 0.76, 0.98). However, no significantly different risks of preterm, low birthweight, small-for-gestational age, pregnancy-induced hypertension, or gestational diabetes were found between them.ConclusionsThe IOM-recommended GWG ranges are within the middle 70% of the distributions in Chinese women, and pre-pregnancy weight status should be determined by the Asian BMI cut-off points for monitoring and making GWG recommendations to Chinese women.

This article investigates the prevalence of metabolic syndrome (MS) and the benefits of lowered body mass index (BMI) cutoff point for assessing MS prevalence in a large, nationally representative population sample comprising of 15,365 Thai adults from metropolitan Bangkok who received annual checkup. Prevalence of MS was characterized using the International Diabetes Federation criteria and BMI ≥ 25 kg/m(2) as cutoff revealed that 26.63% of male and 14.90% of female subjects had MS and the prevalence was age dependent. Traditional BMI cutoff of ≥ 30 kg/m(2) underestimated MS prevalence in Thai population while BMI ≥ 25 kg/m(2) was found to be a suitable solution. Common combinations of MS components were identified in order to find common occurrences that may be implicated in the development of diabetes and/or cardiovascular diseases.

For convenience, health practitioners and clinicians are inclined to classify people/patients as overweight or obese based on body mass index (BMI) cutoff points of 25 and 30 kg m−2 respectively, irrespective of age and gender. The purpose of the current study was to identity whether, for the same levels of adiposity, BMI is the same across different age groups and gender. A two-way ANCOVA revealed significant differences in BMI between different age groups and gender (plus an interaction), using body fat (%) as the covariate, data taken from a random sample of the English population (n=2993). Younger people had greater BMI than older people for the same levels of adiposity (differences ranged by 4 BMI units for males, and 3 BMI units for females). In conclusion, if BMI thresholds for overweight (BMI=25 kg m−2) and obese (BMI=30 kg m−2) are to reflect the same levels of adiposity across all gender and age groups within a population, then age- and gender-specific BMI adjustments outlined here are necessary to more accurately/fairly reflect the same critical levels of adiposity.

Health hazards of obesity have been recognized for centuries, appearing, for example, in writings attributed to Hippocrates. From the later decades of the 20th century through the present, there have been numerous epidemiological studies of the relationship between excess weight and the total, or all-cause, mortality rate,1 a critical cumulative measure of the public health impact of any health condition. Using body mass index (BMI), an indicator of relative weight for height (weight [kg]/height [m]2) and a frequently used surrogate for assessment of excess body fat, these studies have found linear, U-shaped, or J-shaped relationships between total mortality and BMI. That is, in some studies, both the thin and the obese were more likely to die than those in between. There is, however, always a point at which increasing BMI is associated with increasing mortality risk, but the BMI at which this occurs varies across studies and populations.2 Currently,3 overweight in adults is defined as a BMI of 25.0 to <30.0 kg/m2 and obesity as a BMI of ≥30.0 kg/m2 (Table 1). A number of studies have found no significant relationship between BMI in the overweight range and mortality rate4 and have shown the nadir of mortality risk to be in the overweight range. In particular, commentaries in both the lay press5–7 and scientific literature2,8,9 subsequent to recent reports from National Health and Nutrition Examination Surveys (NHANES)10,11 have highlighted the confusion and controversy regarding this issue. Some have interpreted the recent data to mean that overweight is not detrimental to health and is not in itself a public health concern and that drawing attention to the need for weight loss in this range will have negative effects on the health and well-being of the general population.8 Others have argued …

Higher BMI is associated with worse asthma control and quality of life but not asthma severity

Obesity Is Associated With Family History of ESRD in Incident Dialysis Patients

See Article, p 960 Myocardial injury after noncardiac surgery (MINS) has been a focus of attention for perioperative medicine for nearly a decade.1 An international cohort of 15,065 patients aged 45 years or older who underwent noncardiac surgery were used to develop diagnostic criteria for this entity in 2012. MINS was defined as an elevated troponin (peak troponin T [TnT] >0.03 ng/mL), judged to be due to a myocardial ischemia. While 8% of patients experienced this outcome, less than a fifth of those experienced any symptoms at all. However, and rather soberingly, there was a 4-fold increase in the risk of 30-day mortality in patients with MINS.2 While this study used fourth-generation TnT, consistent outcomes have been seen in follow-up investigations using high-sensitivity troponin T (hsTnT) as well.3 With ongoing inconsistent use of fourth-generation and hsTnT, a more universal definition of MINS would entail postoperative troponin levels >99th percentile of the upper reference limit within 30 days after surgery that result from myocardial ischemia without the need of an ischemic feature.4 A substantial amount of asymptomatic myocardial injury occurs during the postoperative period. Much has been published about the well-established and ever-increasing association of perioperative hypotension with MINS.1 This is probably a major contributor to the fact that a third of all postoperative deaths are due to a cardiac complication, and in general death in 30 days after surgery is one of the most common causes of mortality in the United States.5 However, the adjusted risks for MINS and the association of hypotension are much less than the risk associated with a patients’ baseline clinical factors.6 Here, the role of obesity as a risk is of importance. Obesity is a rampant epidemic across the world.7 While the connection of obesity with cardiovascular disease is well established, there is also a possible protective effect or an “obesity paradox” seen both with and without cardiovascular disease.8 In this month’s issue of Anesthesia and Analgesia, Lee9 present a 9-year (2010–2019) retrospective cohort of 35,269 patients with measured postoperative troponins, where they analyzed the association of 3 predefined body mass index (BMI) subgroups (low <18.5 kg/m2, normal 18.5–25 kg/m2, and high >25 kg/m2) and MINS diagnosed with a Troponin I (TnI) above the 99th percentile upper reference as >40 ng/L. The authors concluded that there was a higher incidence of postoperative myocardial infarctions; however, significantly lower 1-year mortality in the high BMI group compared to the normal and low groups, with adjusted hazards of 0.75 and 0.56, respectively, for each of the comparisons. Interestingly, this mortality difference appeared to only differentiate across groups at 1 year, whereas the 30-day mortality outcomes were not different. While this may point to a long-term protective effect of a higher BMI that was not evident in the 30 days postoperative period, this deserves more investigation. In this large, single-center registry study of consecutive patients undergoing noncardiac surgery, they used a TnI immunoassay instead of the hsTnT-based values that have been used by the Vascular events In non-cardiac Surgery patIents cohOrt evaluatioN study (VISION) investigators and are now in use in most parts of the world.3 Diagnostic accuracy of the TnI assay had a sensitivity and specificity of 74% and 84%, respectively, on admission and 94% and 81% at follow-up (6–24 hours after admission), for the detection of myocardial ischemia at the 99th percentile (>40 ng/L). This along with an event rate for a cardiac event or death within 60 days at 24.1%, 55.1%, respectively, for >40–100 ng/L, or >100 ng/L for the TnI immunoassay, puts it in alignment with hsTnT.10 Anywhere from 2% to 40% of patients from 50 to 70 years of age will have elevated preoperative troponins.11 Therefore, it is important to measure baseline troponin when using hsTnT. This is also because the presence of MINS is defined as when there is an increase by at least 5 ng/L from baseline in the postoperative period up to at least 20 ng/L, or irrespective of the preoperative baseline when the postoperative concentration is >65 ng/L.3 Quite often cardiologists are called to evaluate isolated troponin elevations in an asymptomatic patient, and the lack of a baseline value can be a barrier to clinical intervention. Lee9 makes an effort to include baseline troponin values in the data set analyzed, though the numbers of included patients are very low. The question on whom to perform postoperative surveillance monitoring of troponin has been widely debated as well. Most experts agree on this need in all patients >65 years of age, and those >45 years of age with at least 1 cardiovascular risk factor.6 Here, the authors performed routine troponin measurements for moderate- to high-risk surgery or for patients with 1 cardiovascular risk factor. This is reflected by the 35,296 patients with measured troponin out of an initial cohort of 43,019 patients, though an argument could be made for a slightly higher risk selection here compared to recommendations. The authors did include some low-risk patients whose troponins were sampled at the discretion of the attending physician, likely because they were of a higher age or had ongoing recently suspected symptoms of ischemic disease. Despite all this, the troponin surveillance process did not strictly involve serial checks on the first 3 days after surgery, but rather included any value within 30 days after surgery. The definition of obesity as used by Lee9 also needs close examination. The Centers for Disease Control (CDC) define obesity as a BMI >30 kg/m2, while >25 kg/m2 is overweight, whereas the authors used the term high BMI for >25 kg/m2.12 Therefore, one could argue that authors did not have truly obese patients in their cohort. Obesity needs to factor in ethnicity and Asian patients that constituted all of this cohort may actually be reclassified as obese by different threshold standards for this population. Also, an important physiological consideration is that Asians may have a distribution of fat that favors truncal obesity, which per se has a stronger association with cardiovascular complications even at lower BMI numbers. The World Health Organization (WHO) has also proposed a definition of overweight (BMI 23.0–24.9) and obesity (BMI ≥25.0) for this population.13 Interestingly, at this modified Asian obesity cutoff BMI above 25.0 kg/m2, mortality risk was higher among Asians in comparison to the US population.14 The authors did include in their analysis, a BMI of >30 kg/m2 as obese in accordance with the standard CDC definition. However, mortality of these truly obese patients (BMI >30 kg/m2) was only numerically lower though not statistically significant. It also seems difficult to interpret these classifications of BMI as done by Lee9, into 3 specific categories when translated into the clinical context. Does a difference of a single decimal point on the BMI scale suddenly escalate the same patient into a lower risk of MINS-related mortality? An ideal exposure would have used BMI as a continuous variable and allowed the clinician to interpret these results to see the progressively increasing protective effect of BMI on MINS. The authors do provide us smoothened hazard ratio plots of a progressively increasing BMI, but the number of patients drops off sharply at a BMI >30 kg/m2. It is therefore critically important that interested clinician scientists use these results to collaborate on larger patient cohorts in environments where a truly higher BMI patient population is more prevalent. For example, an elegant experiment would be to look at the North American population where there would be a substantial proportion of patients that qualify as truly obese and morbidly obese as well. It would be of great interest to the perioperative medicine community to see the entire spread of MINS and if this obesity paradox is observed along the entire BMI scale up to as high as >40 kg/m2. It is reasonable to assume that at some stage the cardiovascular harm associated with a very high BMI, coupled with the effect modification of a sedentary lifestyle, high underlying lipid subcategories and so forth, would make it difficult to strictly see this so-called protective effect persist at these very high BMI states. There are potential mechanisms that explain the protective effective of a higher BMI in their findings. In general, cytokine is released to mediate protection from inflammation and maintenance of homeostasis, along with specifically for ischemic heart disease, a younger age, higher metabolic reserve, less cachexia, robust blood pressure with allowance for more aggressive cardiac medications are all likely benefit mechanisms for the observed obesity paradox.15 As a single-center cohort, with the possibility of unidentified confounding factors, these results should be interpreted with caution. Troponins were measured in a slightly higher risk population and there is no clear description of intraoperative and postoperative blood pressure management, where it could well be that this higher risk group had more aggressive blood pressure control compared to the nonincluded group. This could have introduced a significant selection bias. Lee9 deserves to be congratulated on the routine use of troponin monitoring with an effort to include baseline monitoring in a very real-world patient cohort that was appropriately adjusted across a wide variety of covariates. The protective effect of a high BMI is easy to label as an obesity paradox; however, this should not tempt the anesthesiologist to reassure the high BMI patient in the preoperative clinic. Until we fully explore the entire range of BMI with an adequate sample size, we are left at best to guess, and a likely speculation would be in fact a U-shaped curve of 1-year mortality and BMI. As of now, being slightly heavier may be better for MINS; however, this relationship may be more complicated and await confirmation from other investigations. DISCLOSURES Name: Ashish K. Khanna, MD, FCCP, FCCM. Contribution: This author helped conceive and write this editorial. Conflicts of Interest: A. K. Khanna consults for Medtronic, Edwards Life Sciences, Philips North America, and Zoll Medical and is also funded with a Clinical and Translational Science Institute (CTSI) NIH/NCTAS KL2 TR001421 award for a trial on continuous postoperative hemodynamic and saturation monitoring. Name: Tong J. Gan, MD, MHS, FRCA, MBA. Contribution: This author helped conceive and write this editorial. Conflicts of Interest: T. J. Gan consults for Acacia, Edwards Life Sciences, Merck, and Medtronic. This manuscript was handled by: Stefan G. De Hert, MD.

Objectives: Data regarding the association between adiponectin levels and body mass index (BMI) and long-term changes in depressive symptoms are limited and inconsistent. Thus, we investigated whether circulating adiponectin levels and BMI were independently and combinedly correlated to longitudinal changes in depressive symptoms.Methods: This prospective cohort study evaluated 269 elderly Japanese individuals aged ≥70 years who participated in the Tsurugaya Project conducted between 2002 and 2012. A short form of the Geriatric Depression Scale (GDS) was used to assess depressive status. Serum adiponectin levels were measured using an enzyme-linked immunosorbent assay or a latex particle-enhanced turbidimetric immunoassay. BMI was calculated as body weight (kg)/height (m2).Results: Multiple linear regression analysis revealed that baseline serum adiponectin levels were positively associated with changes in GDS scores (β = 0.14, P = 0.035). However, no association was observed after adjusting for BMI (β = 0.09, P = 0.185). Low BMI was associated with increased GDS scores at the 10-year follow-up (β = -0.14, P = 0.033). Participants with a combination of high adiponectin levels and low BMI had a 3.3-fold higher risk of worsening depressive symptoms than those with low adiponectin levels and high BMI (odds ratio: 3.35, 95% confidence interval: 1.60–7.00; P = 0.001).Conclusions: This longitudinal study indicated that high serum adiponectin levels and low BMI were both associated with worsening depressive symptoms among older Japanese individuals. Furthermore, the combination of high adiponectin levels and low BMI was associated with worsening depressive symptoms.

O verweight and obesity have become increasingly com- mon; worldwide, at least 1.1 billion adults are overweight and 312 million are obese, when overweight and obesity are defined conventionally as having a body mass index (BMI) of Ͼ25 kg/m 2 and Ͼ30 kg/m 2 , respectively. 1,2In the general population, overweight and obesity are associated with increased risk of developing cardiovascular disease, 3,4 and thus it is not surprising that in cohorts of patients with prevalent ischemic heart disease or acute coronary events, well over 50% are overweight or obese. 5,6Despite the association between obesity and cardiovascular risk in the general population, a multitude of studies have described an inverse correlation between BMI and mortality in patients with coronary artery disease (CAD), including post-coronary revascularization patients and those with acute myocardial infarction (MI); the association between elevated BMI and improved survival has been termed the obesity paradox. 7,8 Article p 482In this issue of Circulation, Zeller et al 9 further investigate the obesity paradox in a cohort of 2229 consecutive patients presenting with acute MI in the Côte d'Or region of France.In assessing the impact of obesity on mortality after MI, the group uses both BMI, a traditional index of obesity, as well as waist circumference, an alternate anthropometric index that is more specific for abdominal obesity.Approximately one-half of the subjects in the study were overweight (BMI 25 to 29.9 kg/m 2 ), one-quarter were obese (BMI Ͼ30 kg/m 2 ) and onehalf had increased waist circumference, which was defined as Ͼ102 cm in men and Ͼ88 cm in women.Left ventricular ejection fraction, type of MI, and acute treatment strategy did not generally differ by BMI or waist circumference values.Of note, BMI was inversely correlated with age and plasma N-terminal pro B-type natriuretic peptide, whereas waist circumference was positively correlated with age and did not correlate with N-terminal pro B-type natriuretic peptide.Consistent with prior studies, survival analysis showed that the risk of death decreased with increasing BMI tertile.In a waist-matched analysis of 832 subjects, BMI was a signifi-

Decision Making in Chronic Hypercapnic Respiratory Failure: A Real Challenge!

IntroductionThis study evaluates the impact of implementing body mass index (BMI) cutoff points on 6-month postoperative outcomes following total ankle replacement (TAR).MethodsThe Nationwide Readmissions Database (NRD) was queried from 2015 to 2020 to identify 5865 patients undergoing primary elective TAR, stratified into groups by 5 BMI point intervals. Preoperative demographics, comorbidities, postoperative outcomes, and total length of stay (LOS) were analyzed between cohorts, with additional multivariate regression analyses conducted to control for predictors other than BMI.ResultsMultivariate regression analysis of 180-day postoperative outcomes found that preoperative BMI of 40 to 44.9 and ≥45 kg/m2 was significantly predictive of increased risk of overall complication, adverse discharge, and LOS greater than 4 days.ConclusionA BMI above 40 kg/m2 is associated with a significantly increased risk of complications (odds ratio [OR] = 1.960; P < .001), adverse discharge (OR = 2.030; P < .001), and extended LOS (OR = 2.171; P < .001).Levels of Evidence:Level III, Retrospective Cohort Study.

Body mass index (BMI) can influence image quality in low dose computed tomography (LDCT) through higher image noise levels. We evaluated whether BMI affects lung nodule detection by artificial intelligence (AI) software and a human reader. The study utilized chest LDCT scans from the Lifelines cohort. We included 1.5% participants at highest BMI (mean=39.8, sd=3.0), and 1.5% at lowest BMI (mean=18.7, sd=0.9). Nodule detection was performed by AI software and by a trained human reader (HR). Two chest radiologists reviewed detection discrepancies, with disagreements resolved by an expert radiologist. Sensitivity and false positives per scan (FP/scan) were compared between BMI groups, for AI versus HR. There were 176 participants in both groups, with 131 nodules in high BMI, and 136 in low BMI. AI detected 356 nodular findings and HR 251, including 154 nodules found by both. AI's sensitivity was 0.75 (95% confidence interval 0.66-0.82) in high BMI, and 0.80 (0.72-0.86) in low BMI groups (p=0.37). FP/scan was 0.30 and 0.55 in high and low BMI, respectively (p=0.005). HR's sensitivity was 0.76 (0.68-0.83) in high BMI, and 0.84 (0.76-0.89) in low BMI groups (p=0.17), with FP/scan of 0.05 and 0.16, respectively (p=0.09). In both BMI groups, AI had more FP/scan than the human reader (p<0.001). Sensitivity for lung nodule detection in LDCT was not significantly different for high versus low BMI, either for AI or human reader. Compared to the human reader, AI had higher FP/scan in both BMI groups.

To evaluate the significance of both low and high body mass index (BMI) as a biomarker in first-line tyrosine kinase inhibitors (TKIs) for metastatic renal cell carcinoma (mRCC). The oncological outcome of 235 patients with mRCC treated with TKI from 2007 to 2018 was reviewed retrospectively. All patients received first-line TKI as therapy. We analyzed the relationship between BMI (low and high) and disease control rate. The primary outcome was progression free survival and overall survival, and the association between BMI and survival prognosis was evaluated. The median BMI was 22.5 kg/m2 , and 25 patients (10.7%) had a low BMI (<18.5 kg/m2 ), 158 patients (67.2%) had a normal BMI (18.5-25 kg/m2 ), and 52 patients (22.1%) had a high BMI (≥ 25 kg/m2 ). Patients in the low BMI group had a significantly lower disease control rate, whereas patients in the high BMI group had a significantly higher disease control rate (p=0.002 and p=0.030, respectively). A log-rank test showed prognosis to be significantly poorer in the low BMI group and to be significantly better in the high BMI group than that in the normal BMI group. Multivariable Cox regression analysis showed that low BMI was an independent indicator of poor prognosis, whereas high BMI was an independent indicator of favorable prognosis. We showed the impact of both low and high BMI on predicting therapeutic efficacy and prognosis in mRCC patients treated with TKI.