The Association between Trajectories of Anthropometric Variables and Risk of Diabetes among Prediabetic Chinese

In order to explore the association between trajectories of body mass index (BMI) and mid-upper arm circumference (MUAC) and diabetes and to assess the effectiveness of the models to predict diabetes among Chinese prediabetic people, we conducted this study. Using a national longitudinal study, 1529 cases were involved for analyzing the association between diabetes and BMI trajectories or MUAC trajectories. Growth mixture modeling was conducted among the prediabetic Chinese population to explore the trajectories of BMI and MUAC, and logistic regression was applied to evaluate the association between these trajectories and the risk of diabetes. The receiver operating characteristic curve (ROC) and the area under the curve (AUC) were applied to assess the feasibility of prediction. BMI and MUAC were categorized into 4-class trajectories, respectively. Statistically significant associations were observed between diabetes in certain BMI and MUAC trajectories. The AUC for trajectories of BMI and MUAC to predict diabetes was 0.752 (95% CI: 0.690–0.814). A simple cross-validation using logistic regression indicated an acceptable efficiency of the prediction. Diabetes prevention programs should emphasize the significance of body weight control and maintaining skeletal muscle mass and resistance training should be recommended for prediabetes.

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Trajectories of change after a health-education program in Japan: decay of impact in anxiety, depression, and patient-physician communication

PeerJ ◽

10.7717/peerj.7229 ◽

2019 ◽

Vol 7 ◽

pp. e7229

Author(s):

Min Jeong Park ◽

Joseph Green ◽

Hun Sik Jung ◽

Yoon Soo Park

Keyword(s):

Logistic Regression ◽

Health Education ◽

Growth Mixture Modeling ◽

Mixture Modeling ◽

Mixed Data ◽

Physician Communication ◽

Unexpected Finding ◽

Growth Mixture ◽

Trajectories Of Change ◽

Anxiety Depression

Background Health education can benefit people with chronic diseases. However, in previous research those benefits were small, and reinforcement to maintain them was not effective. A possible explanation is that the benefits appeared to be small and reinforcement appeared to be ineffective because those analyses mixed data from two latent groups: one group of people who needed reinforcement and one group of people who did not. The hypothesis is that mixing the data from those two different groups caused the true effects to be “diluted.” Methods To test that hypothesis we used data from the Chronic Disease Self-Management Program in Japan, focusing on anxiety, depression, and patient-physician communication. To identify latent trajectories of change after the program, we used growth-mixture modeling. Then, to find out which baseline factors were associated with trajectory-group membership, we used logistic regression. Results Growth-mixture modeling revealed two trajectories—two groups that were defined by distinct patterns of change after the program. One of those patterns was improvement followed by backsliding: decay of impact. On anxiety and depression the decay of impact was large enough to be clinically important, and its prevalence was as high as 50%. Next, logistic regression analysis revealed that being in the decay-of-impact group could be predicted from multimorbidity, low self-efficacy, and high scores on anxiety or depression at baseline. In addition, one unexpected finding was an association between multimorbidity and better patient-physician communication. Conclusions These results support the hypothesis that previous findings (i.e., apparently small effect sizes and apparently ineffective reinforcement) actually reflect “dilution” of large effects, which was caused by mixing of data from distinct groups. Specifically, there was one group with decay of impact and one without. Thus, evaluations of health education should include analyses of trajectory-defined groups. These results show how the group of people who are most likely to need reinforcement can be identified even before the educational program begins. Extra attention and reinforcement can then be tailored. They can be focused specifically to benefit the people with the greatest need.

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Combined 18F-FET PET and diffusion kurtosis MRI in post-treatment glioblastoma: differentiation of true progression from treatment related changes

Neuro-Oncology Advances ◽

10.1093/noajnl/vdab044 ◽

2021 ◽

Author(s):

Francesco D’Amore ◽

Farida Grinberg ◽

Jörg Mauler ◽

Norbert Galldiks ◽

Ganna Blazhenets ◽

...

Keyword(s):

Logistic Regression ◽

Diagnostic Accuracy ◽

Diffusion Mri ◽

Tumour Progression ◽

Characteristic Curve ◽

Area Under The Curve ◽

Regions Of Interest ◽

Fet Pet ◽

Diffusion Kurtosis ◽

And Diffusion

Abstract Background Radiological differentiation of tumour progression (TPR) from treatment-related changes (TRC) in pre-treated glioblastoma is crucial. This study aimed to explore the diagnostic value of diffusion kurtosis MRI combined with information derived from O-(2-[ 18F]-fluoroethyl)-L-tyrosine ( 18F-FET) PET for the differentiation of TPR from TRC in patients with pre-treated glioblastoma. Methods Thirty-two patients with histomolecularly defined and pre-treated glioblastoma suspected of having TPR were included in this retrospective study. Twenty-one patients were included in the TPR group, and 11 patients in the TRC group, as assessed by neuropathology or clinicoradiological follow-up. 3D regions-of-interest were generated based on increased 18F-FET uptake using a brain-to-tumour ratio of 1.6. Furthermore, diffusion MRI kurtosis maps were obtained from the same regions-of-interests using co-registered 18F-FET PET images, and an advanced histogram analysis of diffusion kurtosis map parameters was applied to generated 3D regions-of-interest. Diagnostic accuracy was analysed by receiver-operating characteristic curve analysis and combinations of PET and MRI parameters using multivariate logistic regression. Results Parameters derived from diffusion MRI kurtosis maps show high diagnostic accuracy, up to 88%, for differentiating between TPR and TRC. Logistic regression revealed that the highest diagnostic accuracy of 94% (area under the curve, 0.97; sensitivity, 94%; specificity, 91%) was achieved by combining the maximum tumour-to-brain ratio of 18F-FET uptake and diffusion MRI kurtosis metrics. Conclusions The combined use of 18F-FET PET and MRI diffusion kurtosis maps appears to be a promising approach to improve the differentiation of TPR from TRC in pre-treated glioblastoma and warrants further investigation.

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Trajectories of change after a health-education program in Japan

10.1101/568584 ◽

2019 ◽

Author(s):

MJ Park ◽

Joseph Green ◽

Hun Sik Jung ◽

Yoon Soo Park

Keyword(s):

Logistic Regression ◽

Health Education ◽

Growth Mixture Modeling ◽

Mixture Modeling ◽

Management Program ◽

Mixed Data ◽

Physician Communication ◽

Unexpected Finding ◽

Growth Mixture ◽

Trajectories Of Change

ABSTRACTBackgroundHealth education can benefit people with chronic diseases. However, in previous research those benefits were small, and reinforcement to maintain them was not effective. A possible explanation is that the benefits appeared to be small and reinforcement appeared to be ineffective because those analyses mixed data from two latent groups: one group of people who needed reinforcement and one group of people who did not. The hypothesis is that mixing the data from those two different groups caused the true effects to be “diluted.”MethodsTo test that hypothesis we used data from the Chronic Disease Self-Management Program in Japan, focusing on anxiety, depression, and patient-physician communication. To identify latent trajectories of change after the program, we used growth-mixture modeling. Then, to find out which baseline factors were associated with trajectory-group membership, we used logistic regression.ResultsGrowth-mixture modeling revealed two trajectories – two groups that were defined by distinct patterns of change after the program. One of those patterns was improvement followed by backsliding: decay of impact. On anxiety and depression the decay of impact was large enough to be clinically important, and its prevalence was as high as 50%. Next, logistic regression analysis revealed that being in the decay-of-impact group could be predicted from multimorbidity, low self-efficacy, and high scores on anxiety or depression at baseline. In addition, one unexpected finding was an association between multimorbidity and better patient-physician communication.ConclusionsThese results support the hypothesis that previous findings (i.e. apparently small effect sizes and apparently ineffective reinforcement) actually reflect “dilution” of large effects, which was caused by mixing of data from distinct groups. Specifically, there was one group with decay of impact and one without. Thus, evaluations of health education should include analyses of trajectory-defined groups. These results show how the group of people who are most likely to need reinforcement can be identified even before the educational program begins. Extra attention and reinforcement can then be tailored. They can be focused specifically to benefit the people with the greatest need.

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Sinkhole Detection and Characterization Using LiDAR-Derived DEM with Logistic Regression

Remote Sensing ◽

10.3390/rs11131592 ◽

2019 ◽

Vol 11 (13) ◽

pp. 1592 ◽

Cited By ~ 2

Author(s):

Yong Je Kim ◽

Boo Hyun Nam ◽

Heejung Youn

Keyword(s):

Logistic Regression ◽

Characteristic Curve ◽

Area Under The Curve ◽

Susceptibility Map ◽

Cutoff Value ◽

Geometric Characteristics ◽

Probabilistic Detection ◽

Remote Sensing Techniques ◽

Karst Landscapes ◽

Elevation Model

Depressions due to sinkhole formation cause significant structural damages to buildings and civil infrastructure. Traditionally, visual inspection has been used to detect sinkholes, which is a subjective way and time- and labor-consuming. Remote sensing techniques have been introduced for morphometric studies of karst landscapes. This study presents a methodology for the probabilistic detection of sinkholes using LiDAR-derived digital elevation model (DEM) data. The proposed study provides benefits associated with: (1) Detection of unreported sinkholes in rural and/or inaccessible areas, (2) automatic delineation of sinkhole boundaries, and (3) quantification of the geometric characteristics of those identified sinkholes. Among sixteen morphometric parameters, nine parameters were chosen for logistic regression, which was then employed to compute the probability of sinkhole detection; a cutoff value was back-calculated such that the sinkhole susceptibility map well predicted the reported sinkhole boundaries. According to the results of the LR model, the optimal cutoff value was calculated to be 0.13, and the area under the curve (AUC) of the receiver operating characteristic curve (ROC) was 0.90, indicating the model is reliable for the study area. For those identified sinkholes, the geometric characteristics (e.g., depth, length, area, and volume) were computed.

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Is mid-upper arm circumference in Dutch children useful in identifying obesity?

Archives of Disease in Childhood ◽

10.1136/archdischild-2017-313528 ◽

2018 ◽

Vol 104 (2) ◽

pp. 159-165 ◽

Cited By ~ 7

Author(s):

Henk Talma ◽

Paula van Dommelen ◽

Joachim J Schweizer ◽

Boudewijn Bakker ◽

Joana E Kist-van Holthe ◽

...

Keyword(s):

Task Force ◽

Area Under The Curve ◽

Good Alternative ◽

Normal Weight ◽

Overweight And Obesity ◽

Valid Measure ◽

Upper Arm ◽

Arm Circumference ◽

Explained Variance ◽

Mid Upper Arm Circumference

BackgroundMid-upper arm circumference (MUAC) is suggested as being a valid measure in detecting overweight/obesity in children and adolescents, due to the strong relation with weight. We examined this relation and compared MUAC to body mass index (BMI) according to the International Obesity Task Force (IOTF) in children.MethodsAnthropometric data including MUAC were collected in 2009 by trained healthcare professionals in the context of the fifth Dutch Nationwide Growth Study, in a sample of 6167 children (2891 boys and 3276 girls) aged 2–18 years of Dutch origin. We propose MUAC SDS cut-off values for overweight and obesity, and compared MUAC with BMI IOTF in sex-specific and age-specific categories (2–5, 6–11, 12–18 years).ResultsThe area under the curve is used as a measure of diagnostic accuracy; the explained variance (R²) is good to excellent (0.88–0.94). Sensitivity ranges from 51.8% to 95.3% and specificity from 71.4% to 93.8%. Across age and gender groups, 65.1% to 89.0% participants are classified by both MUAC and BMI as normal weight, overweight or obese. We constructed three equations to predict weight using MUAC, with small differences between observed and predicted weight with an explained variance ranging from 0.88 to 0.94.ConclusionsCompared with BMI, MUAC is a valid measure for detecting overweight and obesity and thus a good alternative for BMI. When weight has to be estimated, it can be accurately predicted using MUAC. Based on our observations, we recommend developing diagrams with international (IOTF) cut-offs for MUAC SDS similar to BMI.

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Use of mid-upper arm circumference to screen for thinness among sub-Saharan African male detainees

Public Health Nutrition ◽

10.1017/s1368980021002913 ◽

2021 ◽

pp. 1-24

Author(s):

David CE Philpott ◽

Valérie Belchior-Bellino ◽

Mija Ververs

Keyword(s):

Characteristic Curve ◽

International Committee ◽

Red Cross ◽

African Countries ◽

Upper Arm ◽

Arm Circumference ◽

Group A ◽

Sub Saharan ◽

Sensitivity Specificity ◽

Mid Upper Arm Circumference

Abstract Objective: Body mass index (BMI) is a time-intensive measurement to assess nutritional status. Mid-upper arm circumference (MUAC) has been studied as a proxy for BMI in adults, but there is no consensus on its optimal use. Design: We calculated sensitivity, specificity, and area under receiver operating characteristic curve (AUROCC) of MUAC for BMI <18.5, <17, and <16 kg/m2. We designed a system using two MUAC cutoffs, with a healthy (non-thin) “green” group, a “yellow” group requiring BMI measurement, and a “red” group who could proceed directly to treatment for thinness. Setting: We retrospectively analyzed monitoring data collected by the International Committee of the Red Cross in places of detention. Participants: 11,917 male detainees in eight African countries. Results: MUAC had excellent discriminatory ability with AUROCC: 0.87, 0.90, and 0.92 for BMI<18.5, BMI<17, and BMI<16 kg/m2, respectively. An upper cutoff of MUAC 25.5 cm to exclude healthy detainees would result in 64% fewer detainees requiring BMI screening and had sensitivity 77% (95%CI 69.4,84.7) and specificity 79.6 (95%CI: 72.6,86.5) for BMI<18.5 kg/m2. A lower cutoff of MUAC<21.0 cm had sensitivity 25.4% (95%CI: 11.7,39.1) and specificity 99.0% (97.9,100.0) for BMI<16 kg/m2. An additional 50kg weight requirement improved specificity to 99.6% (95%CI: 99.0,100.0%) with similar sensitivity. Conclusions: A MUAC cutoff of 25.5 cm, above which detainees are classified as healthy and below receive further screening would result in significant time savings. A cutoff of <21.0 cm and weight <50 kg can identify some detainees with BMI <16 kg/m2 who require immediate treatment.

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The validity of mid-upper arm circumference as an indicator of low BMI in population screening for undernutrition: a study among adult slum dwellers in eastern India

Public Health Nutrition ◽

10.1017/s1368980018001301 ◽

2018 ◽

Vol 21 (14) ◽

pp. 2575-2583 ◽

Cited By ~ 3

Author(s):

Priyanka Das ◽

Argina Khatun ◽

Kaushik Bose ◽

Raja Chakraborty

Keyword(s):

Characteristic Curve ◽

Linear Regression Analysis ◽

Urban Slum ◽

Multiple Linear Regression Analysis ◽

Upper Arm ◽

Cross Sectional ◽

Slum Dwellers ◽

Arm Circumference ◽

Low Bmi ◽

Mid Upper Arm Circumference

AbstractObjectiveTo explore the possibility for a statistically appropriate value of mid-upper arm circumference (MUAC) to identify the state of severe undernutrition, based on very low BMI, among adult Indian slum dwellers.DesignCross-sectional study on adults. Height and MUAC were recorded and BMI was computed. Chronic energy deficiency (CED) was determined using the WHO international guidelines as BMI<18·5 kg/m2and normal as BMI≥18·5 kg/m2. Besides calculating mean,sdand 25th, 50th and 75th percentile values, multiple linear regression analysis was undertaken to assess the associations between age, MUAC and BMI. Receiver-operating characteristic curve analysis was performed to determine the best MUAC cut-off to identify CED status. Theχ2test was used to assess significance of the difference in CED prevalence across MUAC categories.SettingAn urban slum in Midnapore town, West Bengal State, India.SubjectsMale (n467) and female (n488) Indian slum dwellers.ResultsMUAC of 22·7 and 21·9 cm, respectively, in males and females were the best cut-off points to differentiate CED from non-CED.ConclusionsResults supported the validity of the WHO-recommended MUAC cut-offs for adults. There is still a need to establish statistically appropriate MUAC cut-offs to predict undernutrition and morbidity.

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Determining a global mid-upper arm circumference cut-off to assess underweight in adults (men and non-pregnant women)

Public Health Nutrition ◽

10.1017/s1368980020000397 ◽

2020 ◽

Vol 23 (17) ◽

pp. 3104-3113 ◽

Cited By ~ 1

Author(s):

Alice M Tang ◽

Mei Chung ◽

Kimberly R Dong ◽

Paluku Bahwere ◽

Kaushik Bose ◽

...

Keyword(s):

Pregnant Women ◽

Operating Characteristic ◽

Meta Analysis ◽

Characteristic Curve ◽

Total Sample ◽

Upper Arm ◽

Arm Circumference ◽

Individual Participant ◽

Low Bmi ◽

Mid Upper Arm Circumference

AbstractObjective:To determine if a global mid-upper arm circumference (MUAC) cut-off can be established to classify underweight in adults (men and non-pregnant women).Design:We conducted an individual participant data meta-analysis (IPDMA) to explore the sensitivity (SENS) and specificity (SPEC) of various MUAC cut-offs for identifying underweight among adults (defined as BMI < 18·5 kg/m2). Measures of diagnostic accuracy were determined every 0·5 cm across MUAC values from 19·0 to 26·5 cm. A bivariate random effects model was used to jointly estimate SENS and SPEC while accounting for heterogeneity between studies. Various subgroup analyses were performed.Setting:Twenty datasets from Africa, South Asia, Southeast Asia, North America and South America were included.Participants:All eligible participants from the original datasets were included.Results:The total sample size was 13 835. Mean age was 32·6 years and 65 % of participants were female. Mean MUAC was 25·7 cm, and 28 % of all participants had low BMI (<18·5 kg/m2). The area under the receiver operating characteristic curve for the pooled dataset was 0·91 (range across studies 0·61–0·98). Results showed that MUAC cut-offs in the range of ≤23·5 to ≤25·0 cm could serve as an appropriate screening indicator for underweight.Conclusions:MUAC is highly discriminatory in its ability to distinguish adults with BMI above and below 18·5 kg/m2. This IPDMA is the first step towards determining a global MUAC cut-off for adults. Validation studies are needed to determine whether the proposed MUAC cut-off of 24 cm is associated with poor functional outcomes.

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Mid-upper arm circumference in detection of weight-for-height Z-score below −3 in children aged 6–59 months

Public Health Nutrition ◽

10.1017/s1368980017004165 ◽

2018 ◽

Vol 21 (10) ◽

pp. 1794-1799 ◽

Cited By ~ 2

Author(s):

Umesh Kapil ◽

RM Pandey ◽

Rahul Bansal ◽

Bhavana Pant ◽

Amit Mohan Varshney ◽

...

Keyword(s):

Severe Acute Malnutrition ◽

Characteristic Curve ◽

Predictive Ability ◽

Acute Malnutrition ◽

Test Accuracy ◽

Cross Sectional Survey ◽

Upper Arm ◽

Cross Sectional ◽

Arm Circumference ◽

Mid Upper Arm Circumference

AbstractObjectiveTo evaluate the predictive ability of mid-upper arm circumference (MUAC) for detecting severe wasting (weight-for-height Z-score (WHZ) <−3) among children aged 6–59 months.DesignCross-sectional survey.SettingRural Uttar Pradesh, India.SubjectsChildren (n 18 456) for whom both WHZ (n 18 463) and MUAC were available.ResultsThe diagnostic test accuracy of MUAC for severe wasting was excellent (area under receiver-operating characteristic curve = 0·933). Across the lower range of MUAC cut-offs (110–120 mm), specificity was excellent (99·1–99·9 %) but sensitivity was poor (13·4–37·2 %); with higher cut-offs (140–150 mm), sensitivity increased substantially (94·9–98·8 %) but at the expense of specificity (37·6–71·9 %). The optimal MUAC cut-off to detect severe wasting was 135 mm. Although the prevalence of severe wasting was constant at 2·2 %, the burden of severe acute malnutrition, defined as either severe wasting or low MUAC, increased from 2·46 to 17·26 % with cut-offs of <115 and <135 mm, respectively. An MUAC cut-off <115 mm preferentially selected children aged ≤12 months (OR=11·8; 95 % CI 8·4, 16·6) or ≤24 months (OR=23·4; 95 % CI 12·7, 43·4) and girls (OR=2·2; 95 % CI 1·6, 3·2).ConclusionsBased on important considerations for screening and case detection in the community, modification of the current WHO definition of severe acute malnutrition may not be warranted, especially in the Indian context.

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Mid-upper arm circumference cut-offs for screening thinness and severe thinness in Indian adolescent girls aged 10–19 years in field settings

Public Health Nutrition ◽

10.1017/s1368980019000594 ◽

2019 ◽

Vol 22 (12) ◽

pp. 2189-2199 ◽

Cited By ~ 1

Author(s):

Vani Sethi ◽

Neha Gupta ◽

Sarang Pedgaonkar ◽

Abhishek Saraswat ◽

Konsam Dinachandra Singh ◽

...

Keyword(s):

Diagnostic Accuracy ◽

Adolescent Girls ◽

Gold Standard ◽

Area Under The Curve ◽

Youden Index ◽

Upper Arm ◽

Cross Sectional ◽

Arm Circumference ◽

Context Specific ◽

Mid Upper Arm Circumference

AbstractObjective:(i) To assess diagnostic accuracy of mid-upper arm circumference (MUAC) for screening thinness and severe thinness in Indian adolescent girls aged 10–14 and 15–19 years compared with BMI-for-age Z-score (BAZ) <−2 and <−3 as the gold standard and (ii) to identify appropriate MUAC cut-offs for screening thinness and severe thinness in Indian girls aged 10–14 and 15–19 years.Design:Cross-sectional, conducted October 2016–April 2017.Setting:Four tribal blocks of two eastern India states, Chhattisgarh and Odisha.Participants:Girls (n 4628) aged 10–19 years. Measurements included height, weight and MUAC to calculate BAZ. Standard diagnostic accuracy tests, receiver–operating characteristic curves and Youden index helped arrive at MUAC cut-offs at BAZ < −2 and <−3, as gold standard.Results:Mean MUAC and BMI correlation was positive (0·78, P = 0·001 and r 2 = 0·61). Among 10–14 years, MUAC cut-off corresponding to BAZ < −2 and BAZ < −3 was ≤19·4 and ≤18·9 cm. Among 15–19 years, corresponding values were ≤21·6 and ≤20·7 cm. For both BAZ < −2 and BAZ < −3, specificity was higher in 15–19 v. 10–14 years. State-wise variations existed. MUAC cut-offs ranged from 17·7 cm (10 years) to 22·5 cm (19 years) for BAZ < −2, and from 17·0 cm (10 years) to 21·5 cm (19 years) for BAZ < −3. Single-age area under the curve range was 0·82–0·97.Conclusions:Study provides a case for use of year-wise and sex-wise context-specific MUAC-cut-offs for screening thinness/severe thinness in adolescents, rather than one MUAC cut-off across 10–19 years, depending on purpose and logistic constraints.

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