Body Mass Index Curves Research Articles

The Geriatric Nutrition Risk Index Is Not a Prognostic Predictor for Postoperative Morbidity in Extremely Elderly Patients Undergoing Surgery for Proximal Femur Fractures

Background/Objectives: The geriatric nutrition risk index (GnRI) has been regarded as a useful predictor of morbidity and mortality in elderly patients. This study aimed to determine the use of the GnRI as a prognostic predictor in extremely elderly patients undergoing proximal femur fracture surgery and the usefulness of evaluation tools related to a patient’s underlying disease and functional capacity in predicting the prognosis of extremely elderly patients. Methods: We analyzed 548 patients who had undergone surgery for proximal femur fracture caused by trauma, with an age of ≥80 years, without other accompanying trauma. Results: Body mass index (BMI) (OR, 1.077; 95% CI, 1.010–1.149; p = 0.023), serum albumin levels (0.389; 0.223–0.678; p = 0.001), and Charlson comorbidity index (CCI) (1.170; 1.014–1.349; p = 0.031) were determined to be predictors of morbidity in a multivariable regression analysis. The area under the curve (AUC) in the receiver operating characteristic curve of BMI was 0.565 (95% CI, 0.493–0.637; p = 0.065), and the optimal cut-off value could not be determined. The AUC of serum albumin was 0.647 (0.576–0.717; p < 0.001), and the optimal cut-off value was 3.65 g/dL (sensitivity, 72.2%; specificity, 52.7%). The AUC of the CCI was 0.648 (0.580–0.715; p < 0.001), and the optimal cut-off value was 6.5 (sensitivity, 63.3%; specificity, 61.4%). Conclusions: The GnRI was not a predictive factor for patient prognosis after proximal femur fractures in extreme elderly patients. Rather, serum albumin level and CCI, which reflect the patient’s underlying comorbid conditions, were more useful in predicting in-hospital morbidity after proximal femur surgery in extremely elderly patients.

Treatment with brivaracetam has no apparent long-term effects on body weight in pediatric patients with epilepsy.

The objective of this study is to evaluate possible long-term effects of treatment with brivaracetam (BRV) on body weight in children with epilepsy. Post hoc analysis of data from patients (aged 1 month to <17 years) with ≥6 months of BRV treatment in a long-term, open-label trial (N01266 [NCT01364597]). Outcomes included body weight and body mass index (BMI) over time (z-score growth curves), and treatment-emergent adverse events (TEAEs). Previous/ongoing medical conditions that may affect body weight (gastrointestinal and metabolic/nutritional disorders); concomitant antiseizure medications (ASMs) were also evaluated. Two hundred nine patients (mean [standard deviation] age 7.9 [4.6] years) were analyzed. Most (154 [73.7%]) had focal-onset seizures. At study initiation, median (range) BRV dose was 1.0 (0.4-7.5) mg/kg/day. Overall, 189 (90.4%) patients had a previous or ongoing medical condition (gastrointestinal disorders: 43 [20.6%]; metabolism/nutritional disorders: 26 [12.4%]). Most patients followed z-score curves for body weight and BMI during BRV treatment, although there were outliers in both directions. Incidences of appetite/weight-change TEAEs were low. Twenty-three (11.0%) patients had a TEAE of decreased appetite and 14 (6.7%) had a TEAE of weight decreased. Long-term adjunctive BRV was well tolerated in growing children with no indication of detrimental effects on body weight. Brivaracetam is an antiseizure medication (ASM) used to treat seizures in people with epilepsy. Some ASMs can lead to changes in people's appetite and weight. Knowing the effect a drug has on appetite and weight is particularly important in children. We looked at 209 children with epilepsy taking brivaracetam and studied changes in their body weight and body mass index over time. The number of reported side effects related to appetite or weight change was low. There was no apparent long-term effect on their body weight, even when taking their medical history and use of other ASMs into account.

Influence of sex and functional status on the value of serum steroid profiling in discriminating adrenocortical carcinoma from adrenocortical adenoma.

It is challenging for clinicians to distinguish adrenocortical carcinoma (ACC) from benign adrenocortical adenomas (ACA) in their early stages. This study explored the value of serum steroid profiling as a complementary biomarker for malignancy diagnosis of ACC other than diameter and explored the influence of sex and functional status. In this retrospective study, a matched cohort of patients diagnosed with either ACC or ACA based on histopathology was meticulously paired in a 1:1 ratio according to sex, age, and functional status. Eight serum steroids including 11-deoxycortisol, 11-deoxycorticosterone, progesterone, androstenedione, dehydroepiandrosterone (DHEA), dehydroepiandrosterone sulfate (DHEAS), 17-hydroxyprogesterone, and estradiol, were quantified by liquid chromatography tandem mass spectrometry. We conducted a comparative analysis of the clinical characteristics and serum steroid profiles of patients with ACC and ACA, with further subgroup analysis. The study included 31 patients with ACC and 31 matched patients with ACA. Patients with ACC exhibited significantly larger tumor diameters, lower body mass index (BMI), and higher levels of 11-deoxycortisol, progesterone, and androstenedione than those with ACA. 11-deoxycortisol was the only valuable index for discriminating ACC from ACA, regardless of functional status and sex. Progesterone, DHEA, and DHEAS levels were higher in the functional ACC group than in the non-functional ACC group. Female ACC patients, especially in postmenopausal female exhibited higher levels of androstenedione than male patients. The area under the curve of tumor diameter, 11-deoxycortisol, and BMI was 0.947 (95% CI 0.889-1.000), with a sensitivity of 96.8% and specificity of 90.3%. Serum steroid profiling serves as a helpful discriminative marker for ACC and ACA, with 11-deoxycortisol being the most valuable marker. For other steroid hormones, consideration of sex differences and functional status is crucial.

Development of predictive model for predicting postoperative BMI and optimize bariatric surgery: a single center pilot study

BackgroundThe pilot study addresses the challenge of predicting postoperative outcomes, particularly body mass index (BMI) trajectories, following bariatric surgery. The complexity of this task makes preoperative personalized obesity treatment challenging. ObjectivesTo develop and validate sophisticated machine learning (ML) algorithms capable of accurately forecasting BMI reductions up to 5 years following bariatric surgery aiming to enhance planning and postoperative care. The secondary goal involves the creation of an accessible web-based calculator for healthcare professionals. This is the first article that compares these methods in BMI prediction. SettingThe study was carried out from January 2012 to December 2021 at GZOAdipositas Surgery Center, Switzerland. Preoperatively, data for 1004 patients were available. Six months postoperatively, data for 1098 patients were available. For the time points 12 months, 18 months, 2 years, 3 years, 4 years, and 5 years the following number of follow-ups were available: 971, 898, 829, 693, 589, and 453. MethodsWe conducted a comprehensive retrospective review of adult patients who underwent bariatric surgery (Roux-en-Y gastric bypass or sleeve gastrectomy), focusing on individuals with preoperative and postoperative data. Patients with certain preoperative conditions and those lacking complete data sets were excluded. Additional exclusion criteria were patients with incomplete data or follow-up, pregnancy during the follow-up period, or preoperative BMI ≤30 kg/m2. ResultsThis study analyzed 1104 patients, with 883 used for model training and 221 for final evaluation, the study achieved reliable predictive capabilities, as measured by root mean square error (RMSE). The RMSE values for three tasks were 2.17 (predicting next BMI value), 1.71 (predicting BMI at any future time point), and 3.49 (predicting the 5-year postoperative BMI curve). These results were showcased through a web application, enhancing clinical accessibility and decision-making. ConclusionThis study highlights the potential of ML to significantly improve bariatric surgical outcomes and overall healthcare efficiency through precise BMI predictions and personalized intervention strategies.

Identifying the risk factors for pancreatic fistula after laparoscopic pancreaticoduodenectomy in patients with pancreatic cancer.

Laparoscopic pancreaticoduodenectomy (LPD) is a surgical procedure for treating pancreatic cancer; however, the risk of complications remains high owing to the wide range of organs involved during the surgery and the difficulty of anastomosis. Pancreatic fistula (PF) is a major complication that not only increases the risk of postoperative infection and abdominal hemorrhage but may also cause multi-organ failure, which is a serious threat to the patient's life. This study hypothesized the risk factors for PF after LPD. To identify the risk factors for PF after laparoscopic pancreatoduodenectomy in patients with pancreatic cancer. We retrospectively analyzed the data of 201 patients admitted to the Fudan University Shanghai Cancer Center between August 2022 and August 2023 who underwent LPD for pancreatic cancer. On the basis of the PF's incidence (grades B and C), patients were categorized into the PF (n = 15) and non-PF groups (n = 186). Differences in general data, preoperative laboratory indicators, and surgery-related factors between the two groups were compared and analyzed using multifactorial logistic regression and receiver-operating characteristic (ROC) curve analyses. The proportions of males, combined hypertension, soft pancreatic texture, and pancreatic duct diameter ≤ 3 mm; surgery time; body mass index (BMI); and amylase (Am) level in the drainage fluid on the first postoperative day (Am > 1069 U/L) were greater in the PF group than in the non-PF group (P < 0.05), whereas the preoperative monocyte count in the PF group was lower than that in the non-PF group (all P < 0.05). The logistic regression analysis revealed that BMI > 24.91 kg/m² [odds ratio (OR) =13.978, 95% confidence interval (CI): 1.886-103.581], hypertension (OR = 8.484, 95%CI: 1.22-58.994), soft pancreatic texture (OR = 42.015, 95%CI: 5.698-309.782), and operation time > 414 min (OR = 15.41, 95%CI: 1.63-145.674) were risk factors for the development of PF after LPD for pancreatic cancer (all P < 0.05). The areas under the ROC curve for BMI, hypertension, soft pancreatic texture, and time prediction of PF surgery were 0.655, 0.661, 0.873, and 0.758, respectively. BMI (> 24.91 kg/m²), hypertension, soft pancreatic texture, and operation time (> 414 min) are considered to be the risk factors for postoperative PF.

Relationship between body mass indexand lumbar lordosis curve

Background: Body mass index (BMI) is an indicator of health that is often used with the ratio between body weight and height. Health problems related to BMI vary widely, including bone problems such as lordosis. Lordosis is a disorder that causes an abnormally enlarged curvature of the vertebrae or spine. This study aimedto review the correlation between BMI and lumbar lordosis curve. Methods: This study used a literature review design.The articles that discusses the correlation of BMI and increase in the lumbar lordosis curve based on secondary data from published literature on Google Scholar and PubMed using the keywords body mass index, lumbar curve, and lordotic lumbar. Results: From 5 studies discussing the relationship between BMI and lumbar lordosis, it was found that all journals showed that there was a relationship between Body Mass Index and lumbar lordosis. Conclusion: Based on some of the literature that has been discussed, it could be concluded that the BMI correlated with the lumbar lordosis curve.

Waist-to-height ratio is a better predictor of hypertension in women during recovery from anesthesia compared to BMI, waist-to-hip ratio, and waist circumference

This was an observational study of patients with benign breast tumors intended to investigate and compare the predictive value of body mass index (BMI), waist circumference (WC), waist-to-hip ratio (WHR), and waist-to-height ratio (WHtR) for hypertension in the recovery room. Logistic regression analysis was used to determine the association between these body fat anthropometric indices and hypertension. Receiver operating characteristic curve (ROC) analysis was performed to assess the comparative predictive ability. A total of 689 women were evaluated. Patients with BMI ≥28 (kg/m2), WC > 85 cm, WHR ≥0.82, and WHtR ≥0.5 had a significantly higher probability of increased systolic blood pressure (SBP) and diastolic blood pressure (DBP) than patients with less than threshold values (all P < 0.05). The areas under the ROC curve (AUC) of BMI, WC, and WHtR where all modestly significant (all AUC ≥0.65) and nearly identical at 0.6592, 0.65, and 0.6724, respectively. Conclusion: body fat anthropometric indices are useful predicting hypertension during recovery from general anesthesia in patients with benign breast tumors undergoing day surgery; WHtR outperformed the other indices and nearly identical.

Pre-pregnancy body mass index combined with peripheral blood PLGF, DCN, LDH, and UA in a risk prediction model for pre-eclampsia.

This study analyzes the levels of peripheral blood placental growth factor (PLGF), body mass index (BMI), decorin (DCN), lactate dehydrogenase (LDH), uric acid (UA), and clinical indicators of patients with preeclampsia (PE), and establishes a predictive risk model of PE, which can provide a reference for early and effective prediction of PE. 81 cases of pregnant women with PE who had regular prenatal checkups and delivered in Jinshan Branch of Shanghai Sixth People's Hospital from June 2020 to December 2022 were analyzed, and 92 pregnant women with normal pregnancies who had their antenatal checkups and delivered at the hospital during the same period were selected as the control group. Clinical data and peripheral blood levels of PLGF, DCN, LDH, and UA were recorded, and the two groups were subjected to univariate screening and multifactorial logistic regression analysis. Based on the screening results, the diagnostic efficacy of PE was evaluated using the receiver operating characteristic (ROC) curve. Risk prediction nomogram model was constructed using R language. The Bootstrap method (self-sampling method) was used to validate and produce calibration plots; the decision curve analysis (DCA) was used to assess the clinical benefit rate of the model. There were statistically significant differences in age, pre-pregnancy BMI, gestational weight gain, history of PE or family history, family history of hypertension, gestational diabetes mellitus, and history of renal disease between the two groups (P < 0.05). The results of multifactorial binary logistic stepwise regression revealed that peripheral blood levels of PLGF, DCN, LDH, UA, and pre-pregnancy BMI were independent influences on the occurrence of PE (P < 0.05). The area under the curve of PLGF, DCN, LDH, UA levels and pre-pregnancy BMI in the detection of PE was 0.952, with a sensitivity of 0.901 and a specificity of 0.913, which is better than a single clinical diagnostic indicator. The results of multifactor analysis were constructed as a nomogram model, and the mean absolute error of the calibration curve of the modeling set was 0.023, suggesting that the predictive probability of the model was generally compatible with the actual value. DCA showed the predictive model had a high net benefit in the range of 5% to 85%, suggesting that the model has clinical utility value. The occurrence of PE is related to the peripheral blood levels of PLGF, DCN, LDH, UA and pre-pregnancy BMI, and the combination of these indexes has a better clinical diagnostic value than a single index. The nomogram model constructed by using the above indicators can be used for the prediction of PE and has high predictive efficacy.

Opportunistic Use of Computed Tomography to Determine Muscle-Adipose Ratio Reliably Predicts Wound Complications after Kocher-Langenbeck Surgical Exposure of the Acetabulum.

To determine whether muscle-to-adipose ratio (MAR) along the course of a Kocher-Langenbeck (KL) incision is more accurate at predicting post-operative wound complications following acetabuluar fixation than waist-hip ratio (WHR) or body mass index (BMI). Retrospective case series. Level-1 Trauma Center.Patients/Participants: Patients who sustained an acetabular fracture and had fixation through a KL approach. The calculation of the MAR. Presence of a surgical site infection or a wound healing complication. 193 patients were included in the study and mean follow-up was 17.4 months. Thirty (15.5%) patients developed a wound complication. Seventeen (8.8%) patients developed a superficial infection and thirteen (6.7%) developed a deep infection. Mean BMI for those who developed a wound complication was 35.9. The mean MAR was 0.67 for patients who developed a wound complication versus 0.75 for those who did not. ROC analysis showed an area under curve (AUC) for BMI to be .717 (95% CI, .577-.857, p = .006) and for MAR to be .680 (inverted, 95% CI .507-.854, p = .022). The AUC for WHR was not statistically significant. MAR is a significant predictor of post-operative wound complication in obese patients undergoing treatment of posterior wall acetabular fractures. The higher rate of wound complications in patients with a low MAR should be considered in the treatment of these patients and may be used to guide discussion regarding the risks of surgery, as well as the potential use of adjuncts to reduce wound healing complications. Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.

Body fat ratio as a novel predictor of complications and survival after rectal cancer surgery.

The present study aimed to evaluate the association between body fat ratio (BFR), visceral fat area (VFA), body mass index (BMI) and visceral fat density (VFD) and assess their reliability in assessing risk of postoperative complications and survival status in patients with rectal cancer (RC). The present study retrospectively included 460 patients who underwent surgical treatment for RC at the First Affiliated Hospital of Wannan Medical College (Yijishan Hospital of Wannan Medical College, Wuhu, China) between September 2018 and July 2021. BFR, VFA, BMI, and VFD were measured and basic information, clinical data, complications and survival were recorded. Statistical analysis was performed to determine optimal BFR cut-off and evaluate group differences. BFR demonstrated a significant positive correlation with VFA (R = 0.739) and BMI (R = 0.783) and significant negative correlation with VFD (R = -0.773). The areas under the receiver operating characteristic curve of BFR, VFA, BMI, and VFD in predicting postoperative complications in RC were all >0.7 and the optimal cut-off value of BFR was 24.3. Patients in the BFR-low group had fewer postoperative complications, lower intraoperative indices, shorter hospitalization times and lower costs than those in the BFR-high group. BFR predicted complications with high diagnostic significance and was validated by multiple models. Furthermore, patients in the BFR-high group had a longer overall survival compared with patients in the BFR-low group. BFR was associated with BMI, VFA, and VFD. A BFR threshold of 24.3 was correlated with decreased complications and enhanced long-term survival.

Cumulative burden and trajectories of body mass index and blood pressure from childhood and carotid intima-media thickness in young adulthood

There are significant gaps in understanding of the association between levels and rate of change of body mass index (BMI) and blood pressure (BP) at different ages during childhood and carotid intima-media thickness (CIMT) in adulthood. We investigated the association between trajectories of BMI and BP from childhood to adulthood and adult CIMT among Iranian participants in the Tehran Lipid and Glucose Study (TLGS) cohort. A total of 1334 participants (692 men), from the TLGS cohort (1999–2018) with repeated measurements of BMI and BP (2–6 times) from childhood (3–18 years) to young adulthood (20–40 years) were selected. Trajectory parameters included levels and linear slopes of BMI and BP growth curve models, and cumulative burden defined as the area under those curves (AUC). After adjusting for confounders, AUC of BMI and diastolic blood pressure (DBP) were significantly associated with high CIMT in adulthood, with the standardized odds ratios (OR) and 95% confidence interval (95% CI) of 1.35 (1.12–1.62) and 1.27 (1.01–1.60), respectively. Associations between level-independent slopes of BMI and adult CIMT were significantly positive (ORs: 1.27 to 1.26) during childhood ages (3–18 years). Further, levels of BMI (ORs: 1.23 to 1.29) and DBP (ORs: 1.25 to 1.33) during the ages of 13–18 and 11–17 years, respectively, were significantly associated with CIMT in adulthood (all P < 0.05). The cumulative burden of BMI and DBP was associated with CIMT in adulthood. Adolescence is a crucial period for high CIMT, which has implications for early prevention of atherosclerosis.

Low testosterone at age 31 associates with maternal obesity and higher body mass index from childhood until age 46: A birth cohort study.

Low testosterone (T) levels in men associate with increased risks of obesity, type 2 diabetes, metabolic syndrome, and cardiovascular diseases. However, most studies are cross-sectional with follow-up-time<10 years, and data on early growth are limited. To compare prenatal factors and body mass index (BMI) development from birth to age 46 in relation to low T at age 31. Men with low T (T<12.1nmol/L, n=132) and men with normal T at age 31 (n=2561) were derived from the Northern Finland Birth Cohort 1966. Prenatal factors, longitudinal weight and height data from birth to age 14, and cross-sectional weight and height data at ages 31 and 46, and waist-hip-ratio (WHR) and T levels at age 31 were analyzed. Longitudinal modeling and timing of adiposity rebound (AR, second BMI rise at age 5-7 years) were calculated from fitted BMI curves. Results were adjusted for mother's pre-pregnancy BMI and smoking status, birth weight for gestational age, alcohol consumption, education level, smoking status, and WHR at age31. Neither gestational age nor birth weight was associated with low T at age 31; however, maternal obesity during gestation was more prevalent among men with low T (9.8%vs. 3.5%, adjusted aOR: 2.43 [1.19-4.98]). Men with low T had earlier AR (5.28vs. 5.82, aOR: 0.73 [0.56-0.94]) and higher BMI (p<0.001) from AR onward until age 46. Men with both early AR and low T had the highest BMI from AR onward. In men, maternal obesity and early weight gain associate with lower T levels at age 31, independently of adulthood abdominal obesity. Given the well-known health risks related to obesity, and the rising prevalence of maternal obesity, the results of the present study emphasize the importance of preventing obesity that may also affect the later reproductive health of the offspring.

Does the use of color coding facilitate parents’ understanding of body mass index curves?

BackgroundIn France, national guidelines recommend early detection and management of overweight and obesity in children, with multi-year systematic generation of children's body mass index (BMI) curves in primary care. It is important for the parents to understand the BMI curves displayed in the child's health notebook and to become involved in the care with health professionals. ObjectiveThe aim of the current study was to compare parents’ understanding of a BMI curve displayed using color coding versus their understanding of the same curve displayed without color coding. MethodsAn observational, cross-sectional, comparative study was performed between February 1, 2021 and November 15, 2021. Adult parents with at least one child attending primary school were included. Two questionnaires testing parents’ understanding were completed: one showing BMI curves without color coding (five questions) and one showing BMI curve with color coding (five questions). The primary endpoint was the proportion of parents achieving fully correct answers. Comparisons of endpoints between the color-coded and non-color-coded curve were performed using the McNemar test. Factors associated with the primary endpoint were investigated by mixed logistic regression models with the subject as a random effect. ResultsThe 109 participants (45.4% response rate) had an average age of 39.4 ± 6.6 years; 81.7% were women. A total of 214 complete questionnaires were compared: The proportion of participants with fully correct responses was significantly higher using the BMI curve with color coding compared to the curve without color coding (86.0% vs. 54.2%, p<0.0001). In multivariate analysis, the use of color coding was significantly associated with a higher likelihood of achieving fully correct responses (odds ratio: 5.9, 95% CI: 3.0–11.2, p<0.0001). ConclusionThe use of color coding improved parents’ understanding of BMI curves. Further research should explore equally the benefits and risks associated with weight loss and mental health when using a colored BMI curve for the detection and management of overweight and obese children.

Association between excess body mass index trajectory and change in monoclonal protein level in patients diagnosed with monoclonal gammopathy of undetermined significance.

10538 Background: Obesity is a known risk factor for the development of multiple myeloma (MM) and for progression of its asymptomatic premalignant state, monoclonal gammopathy of undetermined significance (MGUS) to MM. Prior studies have demonstrated that monoclonal protein (M-spike) at MGUS diagnosis as well as high M-spike velocity within a year following MGUS diagnosis predict progression of MGUS to MM, making M-spike a useful, readily available, and less invasive (compared to bone marrow biopsy) marker to assess progression risk. However, no studies have evaluated to what extent exposure to high body mass index (BMI) over time influences M-spike trajectory. Methods: Patients diagnosed with MGUS from 1999-2021 in the Veterans Health Administration were identified. We used a natural language processing-based algorithm to confirm MGUS diagnosis and progression to smoldering MM or MM. MGUS patients without IgM or IgD subtype or Diabetes Mellitus and were black or white were included in the analysis. Other exclusion criteria were progression within 5 years of MGUS diagnosis, patients without M-spike level within a year before or after MGUS diagnosis, and patients without BMI data 3 years or more after MGUS diagnosis. The exposure was the trajectory of excess BMI (defined as BMI &gt; 25 kg/m2), defined by an area under the curve (AUC) of excess BMI during the 5 years following MGUS diagnosis. The outcome was continuous M-spike trajectory post-MGUS diagnosis since a prior study demonstrated that this was a reliable marker for progression risk A multivariable mixed model was used to estimate the association of excess BMI trajectory AUC with M-spike trajectory to control for correlation between M-spike values overtime. The covariates included BMI at MGUS diagnosis, M-spike level ( &gt; = 1.5g/dL) at MGUS diagnosis, age at MGUS diagnosis, sex, race, MGUS heavy chain subtype, and light-chain MGUS. Results: After applying the inclusion and exclusion criteria, the analytic cohort consisted of 6757 MGUS patients. Our multivariable analysis demonstrated a positive association between excess BMI trajectory and increasing M-spike trajectory (β = 0.005, p = 0.007). Conclusions: In patients diagnosed with MGUS, increasing BMI over time following MGUS diagnosis was associated with increasing M-spike level. This further supports the relationship between obesity and weight gain in the pathway of MGUS to MM progression.

Dietary diversity and overweight are associated with high intrinsic capacity among Chinese urban older adults (2020−2021)

Background and objectivesThe associations of intrinsic capacity (IC) with dietary diversity and body mass index (BMI) remain unclear in older adults. This study aimed to examine the associations of dietary diversity and BMI on high IC. MethodsThe cross-sectional study used data from the Fujian Prospective Aging Cohort, which included 1972 individuals aged 60–98 from 2020 to 2021. The dietary diversity score (DDS) was constructed with eight food varieties, and consuming ≥five varieties of food daily was considered a high DDS. BMI was grouped into underweight, normal weight, overweight, and obesity according to the Chinese guidelines. High IC was defined as ≥three unimpaired domains of cognition, locomotion, sensory, vitality, and psychology. ResultsMultivariate logistic regression analysis was used to assess the separate association of high DDS and BMI groups with high IC. Compared with low DDS, high DDS had a positive association with high IC (OR = 1.42, 95 % CI = 1.16–1.74). Compared with normal weight, underweight was inversely related to high IC (OR = 0.18, 95 % CI = 0.09–0.36), overweight was positively related to high IC (OR = 1.65, 95 % CI = 1.33–2.06), while no significant association was observed between obesity and high IC. The restricted cubic spline model exhibited an inverted U-shaped nonlinear curve of BMI and high IC and identified an optimal BMI of 25.7 kg/m2 for high IC. ConclusionsHigh DDS is a protective factor of high IC in older adults. Overweight had the most protective association with high IC among the four BMI subgroups. Individuals with overweight and higher dietary diversity had higher IC.

Assessing temporal differences of baseline body mass index, waist circumference, and waist-height ratio in predicting future diabetes.

Obesity is the prominent modifiable risk factor known to influence the occurrence and progression of diabetes other than age, and the objective of this study was to evaluate and compare the predictive value of three simple baseline anthropometric indicators of obesity, body mass index (BMI), waist circumference (WC), and waist-height ratio (WHtR), for the occurrence of diabetes at different time points in the future. The study subjects were 12,823 individuals with normoglycemic at baseline who underwent health screening and had measurements of BMI, WC, and WHtR. The outcome of interest was new-onset diabetes during follow-up. Time-dependent receiver operator characteristics (ROC) curves of baseline BMI, WC, and WHtR for predicting the risk of diabetes in the next 2 to 12 years were constructed and their area under the ROC curves (AUCs) and corresponding optimal threshold values were calculated for each time point, which were used to compare the accuracy and stability of the above three indicators for predicting the occurrence of diabetes in different future periods. During a median follow-up period of 7.02 years, with a maximum follow-up of 13 years, 320 new-onset diabetes were recorded. After adjusting for confounders and comparing standardized hazard ratios (HRs), WC was shown to be the best simple anthropometric indicator of obesity reflecting diabetes risk in all models, followed by WHtR. Time-dependent ROC analysis showed that WC had the highest AUC in predicting the occurrence of diabetes in the short term (2-5 years), and WHtR had the highest AUC in predicting the occurrence of diabetes in the medium to long term (6-12 years), while in any time point, both WC and WHtR had higher AUC than BMI in predicting future diabetes. In addition, we found relatively larger fluctuations in the thresholds of BMI and WC for predicting diabetes over time, while the thresholds of WHtR consistently remained between 0.47-0.50; comparatively speaking, WHtR may have greater application value in predicting future diabetes. Our analysis sustained that central obesity is a more important predictor of diabetes, and in clinical practice, we proposed measuring WHtR as a useful tool for predicting future diabetes.

Accuracy of body mass index compared to whole-body dual energy X-ray absorptiometry in diagnosing obesity in adults in the Eastern Province of Saudi Arabia: A cross-sectional study.

Obesity is a major health concern that requires accurate diagnosis and management. Body mass index (BMI) commonly used to diagnose obesity, has limitations in accurately assessing body fat. Body fat percentage (BF%) from whole-body dual energy X-ray absorptiometry (DEXA) scans is gaining popularity as a more accurate method in diagnosing obesity. This cross-sectional study included 319 adult patients who underwent whole-body DEXA scans in the Eastern Province of Saudi Arabia from May 2016 to December 2021 were recruited from three medical centers, where data for whole-body DEXA were available. Body fat percent was obtained from the whole-body DEXA scan reports and were compared to BMI to evaluate prevalence of obesity. Data was extracted by reviewing patients' records using a structured data collection tool. BMI was defined using WHO criteria, and diagnostic performance was assessed by estimating specificity, sensitivity, likelihood ratios, and predictive values, and by constructing receiver operating characteristic (ROC) curves for BMI to detect obesity by age group. The gender-specific BF% cutoff points revealed a higher prevalence of obesity than BMI cutoff points. BMI misclassified 40.6% of participants, and optimal cutoff points yielding highest area under the curve were 24 kg/m2 and 24.3 kg/m2 for males and females, respectively. The study underscores the importance of using accurate and comprehensive diagnostic tools such as whole-body DEXA scans to assess obesity.

The Association between Body Mass Index Trajectory and the Progression of Monoclonal Gammopathy of Undetermined Significance to Multiple Myeloma

Background: Obesity is a known risk factor for multiple myeloma (MM), as prior studies have shown that body mass index (BMI) greater than 25 kg/m2 (at a fixed time point) increases the risk for MM and its premalignant condition, monoclonal gammopathy of undetermined significance (MGUS). However, to date, no studies have evaluated whether changes in BMI over time affect the progression of MGUS to MM. We aimed to assess the association between changes in BMI following a diagnosis of MGUS and the progression of MGUS. Methods: Patients with MGUS from 1999-2021 in the Veterans Health Administration were identified. We used a natural language processing (NLP) based algorithm to confirm MGUS diagnosis and progression, including smoldering MM and MM. Only patients with IgG, IgA, or light chain MGUS and black and white patients were included in the analyses. Patients with Diabetes Mellitus (DM) were excluded from analysis due to potential confounding bias. Patients who progressed within 5 years of MGUS diagnosis were also excluded. Excess BMI was defined as BMI over 25 kg/m2. To capture the trajectory of excess BMI, the area under the curve (AUC) of excess BMI during the 5 years following MGUS diagnosis was calculated. A multivariable time-to-competing-event analysis was performed to estimate the multivariable-adjusted association of AUC of excess BMI with progression with death as the competing event. The covariates included BMI at MGUS diagnosis, M-protein level (>= 1.5g/dL), age at MGUS diagnosis, sex, race, MGUS heavy chain subtype, light-chain MGUS. Results: After applying our inclusion and exclusion criteria, our analytic cohort included 7,739 MGUS patients. Our multivariable time-to-competing-event analysis revealed a positive association between excess BMI trajectory and MM progression (multivariable-adjusted hazard ratio, aHR, 1.02, 95% confidence interval, CI, 1.01-1.03) - a 2% increase in risk of progression per 1 kg/m2 increase in excess BMI per year following MGUS diagnosis. Conclusion: In patients diagnosed with MGUS, increases in excess BMI by 1 kg/m2 per year were associated with 2% increased risk of progression to MM. This finding suggests that maintaining a healthy weight in patients with MGUS may reduce the risk of progression. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

PSAT105 Evaluation of Body Mass Index and Metabolic Parameters in Children with Achondroplasia Participating in the PROPEL Study

Abstract Background Achondroplasia (ACH) is the most common short-limbed skeletal dysplasia, affecting between 1 in 15,000 to 1 in 30,000 live births. Children and adults with ACH have disproportionate short stature and are at risk for several significant co-morbidities, including obstructive sleep apnea, chronic otitis media with conductive hearing loss, and spinal stenosis. Obesity is a health problem in ACH and aggravates breathing difficulties (i.e. sleep apnea), back and joint pain, and reduced mobility. Individuals with ACH are predisposed to abdominal obesity, although the cause is not completely understood. The metabolic effect of visceral obesity does not suggest an association with the development of a diabetic profile. The objective of this study is to evaluate body mass index (BMI) and metabolic parameters in children with ACH participating in the PROPEL study, a prospective, non-interventional study designed to examine baseline growth parameters and health status in children being assessed for potential enrollment into interventional studies with infigratinib, an oral FGFR1–3 inhibitor in development for ACH. Methods Data were analyzed from 86 children (mean age 6.1±2.5 years; female n=52) enrolled in PROPEL. BMI was calculated at enrollment and compared with sex- and age-specific BMI curves for children with ACH in the United States. Cholesterol, triglycerides, and hemoglobin A1c were measured centrally in a subset of children. Results BMI (mean±SD) was 21.2±2.2 in females (range 16.8–26.2) and 20.5±1.6 in males (range 17.9–24.6), with 8/52 girls (15%) and 1/34 boys (2.9%) presenting BMI above the 95% of the sex- and age-specific BMI curves for ACH. The mean±SD for cholesterol and triglycerides measured in a subset of 43 children were 4.2±0.7 mmol/L (normal range [NR] 2.59–4.66) and 0.9±0.5 mmol/L (NR 0.56–1.36), respectively. Cholesterol was elevated in 9/43 children (20.9%), while triglycerides were high in 8/43 (18.7%). Hemoglobin A1c (HbA1c) was measured in 28 children and had a mean±SD of 0.052±0.002 (NR Hb fraction 0.04–0.06). Although all values were within normal ranges, 19/28 (68%) of children had values above the mean for laboratory reference values. Conclusion Results from this work illustrate the importance of using BMI tables developed for children with ACH when providing guidance on weight management. Furthermore, our findings suggest that, in this cohort, average cholesterol and HbA1c levels, although normal, are above the mean for the reference population; this highlights the importance of a healthy diet, weight management and regular physical activity starting at young age. Additional studies are needed to understand the relationship between BMI and body composition in individuals with short stature and to further investigate the clinical relevance of these findings given that no association between increased BMI and metabolic syndrome has been described in adults with ACH. Presentation: Saturday, June 11, 2022 1:00 p.m. - 3:00 p.m.

Long-Term Adiposity and Midlife Carotid Intima-Media Thickness Are Linked Partly Through Intermediate Risk Factors.

This study aims to determine quantitatively the mediation effects of multiple cardiovascular risk factors on the associations of childhood body mass index (BMI) and its cumulative burden with adult carotid intima-media thickness (cIMT). The longitudinal cohort consisted of 1391 adults who had been examined for BMI 4-15 times over 35.0 years on average since childhood and had data on adult cIMT, systolic blood pressure, low-density lipoprotein cholesterol, atherogenic index of plasma, and serum glucose. The area under the curve was used as a measure of cumulative burden of BMI. After adjusting for covariates, the total effects (standardized regression coefficient) of childhood BMI (0.138), adult BMI (0.111), and area under the curve of BMI (0.150) on cIMT were all significant (P<0.001) without mediators included in the model. The mediation effects of adult systolic blood pressure, glucose, atherogenic index of plasma and low-density lipoprotein cholesterol were 8.0%, 4.3%, 3.6%, and 0.0%, respectively, in the model with childhood BMI as the predictor, 23.4%, 15.3%, 12.6%, and 7.2%, respectively, with adult BMI as the predictor, and 14.7%, 8.7%, 6.0%, and 2.0%, respectively, with area under the curve of BMI as the predictor. The direct effects on cIMT were 0.117 (P<0.001) for childhood BMI, 0.046 (P=0.224) for adult BMI, and 0.103 (P<0.001) for area under the curve of BMI after removing the mediation effects. The long-term deleterious impact of adiposity on subclinical changes in vascular structure begins early in life and is accumulated over lifetime. Excess adiposity and higher cIMT are linked partly through other cardiovascular risk factors in later life, especially elevated blood pressure and glucose.