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Butler, Éadaoin M., José G. B. Derraik, Rachael W. Taylor, and Wayne S. Cutfield. "Prediction Models for Early Childhood Obesity: Applicability and Existing Issues." Hormone Research in Paediatrics 90, no. 6 (2018): 358–67. http://dx.doi.org/10.1159/000496563.
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Statistical models have been developed for the prediction or diagnosis of a wide range of outcomes. However, to our knowledge, only 7 published studies have reported models to specifically predict overweight and/or obesity in early childhood. These models were developed using known risk factors and vary greatly in terms of their discrimination and predictive capacities. There are currently no established guidelines on what constitutes an acceptable level of risk (i.e., risk threshold) for childhood obesity prediction models, but these should be set following consideration of the consequences of false-positive and false-negative predictions, as well as any relevant clinical guidelines. To date, no studies have examined the impact of using early childhood obesity prediction models as intervention tools. While these are potentially valuable to inform targeted interventions, the heterogeneity of the existing models and the lack of consensus on adequate thresholds limit their usefulness in practice.
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Butler, Éadaoin M., José G. B. Derraik, Rachael W. Taylor, and Wayne S. Cutfield. "Childhood obesity: how long should we wait to predict weight?" Journal of Pediatric Endocrinology and Metabolism 31, no. 5 (May 24, 2018): 497–501. http://dx.doi.org/10.1515/jpem-2018-0110.
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AbstractObesity is highly prevalent in children under the age of 5 years, although its identification in infants under 2 years remains difficult. Several clinical prediction models have been developed for obesity risk in early childhood, using a number of different predictors. The predictive capacity (sensitivity and specificity) of these models varies greatly, and there is no agreed risk threshold for the prediction of early childhood obesity. Of the existing models, only two have been practically utilized, but neither have been particularly successful. This commentary suggests how future research may successfully utilize existing early childhood obesity prediction models for intervention. We also consider the need for such models, and how targeted obesity intervention may be more effective than population-based intervention.
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Mukhopadhyay, S., A. Carroll, S. Downs, and T. M. Dugan. "Machine Learning Techniques for Prediction of Early Childhood Obesity." Applied Clinical Informatics 06, no. 03 (2015): 506–20. http://dx.doi.org/10.4338/aci-2015-03-ra-0036.
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Summary Objectives: This paper aims to predict childhood obesity after age two, using only data collected prior to the second birthday by a clinical decision support system called CHICA. Methods: Analyses of six different machine learning methods: RandomTree, RandomForest, J48, ID3, Naïve Bayes, and Bayes trained on CHICA data show that an accurate, sensitive model can be created. Results: Of the methods analyzed, the ID3 model trained on the CHICA dataset proved the best overall performance with accuracy of 85% and sensitivity of 89%. Additionally, the ID3 model had a positive predictive value of 84% and a negative predictive value of 88%. The structure of the tree also gives insight into the strongest predictors of future obesity in children. Many of the strongest predictors seen in the ID3 modeling of the CHICA dataset have been independently validated in the literature as correlated with obesity, thereby supporting the validity of the model. Conclusions: This study demonstrated that data from a production clinical decision support system can be used to build an accurate machine learning model to predict obesity in children after age two. Citation: Dugan TM, Mukhopadhyay S, Carroll AE, Downs SM. Machine learning techniques for prediction of early childhood obesity. Appl Clin Inform 2015; 6: 506–520http://dx.doi.org/10.4338/ACI-2015-03-RA-0036
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Datsenko, Natalya S., Igor O. Marinkin, Tat’yana M. Sokolova, Tat’yana V. Kiseleva, and Anna V. Yakimova. "Early prediction of placental insufficiency in obese women." V.F.Snegirev Archives of Obstetrics and Gynecology 8, no. 1 (March 22, 2021): 40–47. http://dx.doi.org/10.17816/2313-8726-2021-8-40-47.
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Obesity is one of the most important problems in modern health care. The high prevalence of this pathology also affects women of reproductive age, which leads to an increase in the prevalence of obesity in pregnant women.
Purpose of the work ‒ analysis of the effect of adipokine indicators on predicting the development of placental insufficiency in obese women.
Materials and methods. 225 women were examined who were subdivided by such a parameter as obesity into 4 groups: 3 main and 1 control. The control group consisted of 55 pregnant women with an initially normal BMI value (18.5‒24.9 kg/m2). Group 1st included 109 pregnant women with grade I obesity (BMI 31.88 1.4 kg/m2), group 2nd ‒ 34 pregnant women with grade II obesity (BMI 36.6 1.1 kg/m2), group 3rd ‒ 31 pregnant women with grade III obesity (BMI 42.2 1.9 kg/m2).
We studied the data of the anamnesis of pregnant women (somatic and obstetric-gynecological), indicators of adiponectin and omentin, peculiarities of the course of pregnancy and childbirth (data of cardiotocography (CTG), ultrasound markers of disturbances in the formation and functioning of the fetoplacental complex), indicators of labor activity, parameters of newborns (mass-growth, state on the Apgar scale, ponderal index, fetal-placental ratio) and the course of the postpartum period.
When conducting statistical analysis in the case of comparing two dependent (paired) samples of parameters, the paired Students t-test was used.
The results were considered statistically significant if the р was less than 0.05. With this indicator, the value of the probability of difference between the compared categories was more than 95%.
Results. The possibility of predicting the development of placental insufficiency depending on the concentrations of omentin and adiponectin was confirmed. The development of placental insufficiency is most likely with omentin values in the range of 177.6‒191.2 g/ml and adiponectin in the range of 16.0‒22.5 g/ml.
Conclusion. Determination of adipokine levels at 8‒9 weeks gestation may be practically significant in predicting the development of placental insufficiency in obese women.
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Kiseleva, O. I., E. V. Poverennaya, M. A. Pyatnitskiy, E. V. Ilgisonis, V. G. Zgoda, O. A. Plotnikova, K. K. Sharafetdinov, et al. "DOES PROTEOMIC MIRROR REFLECT CLINICAL CHARACTERISTICS OF OBESITY?" http://eng.biomos.ru/conference/articles.htm 1, no. 19 (2021): 129–30. http://dx.doi.org/10.37747/2312-640x-2021-19-129-130.
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Protein patterns were collected, the presence or absence of which allows a fairly good prediction of the patient's weight. Such proteomic patterns with high predictive power should facilitate the transformation of potential biomarker candidates for clinical use for the early stratification of obesity therapy.
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Cheng, Erika R., Rai Steinhardt, and Zina Ben Miled. "Predicting Childhood Obesity Using Machine Learning: Practical Considerations." BioMedInformatics 2, no. 1 (March 8, 2022): 184–203. http://dx.doi.org/10.3390/biomedinformatics2010012.
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Previous studies demonstrate the feasibility of predicting obesity using various machine learning techniques; however, these studies do not address the limitations of these methods in real-life settings where available data for children may vary. We investigated the medical history required for machine learning models to accurately predict body mass index (BMI) during early childhood. Within a longitudinal dataset of children ages 0–4 years, we developed predictive models based on long short-term memory (LSTM), a recurrent neural network architecture, using history EHR data from 2 to 8 clinical encounters to estimate child BMI. We developed separate, sex-stratified models using 80% of the data for training and 20% for external validation. We evaluated model performance using K-fold cross-validation, mean average error (MAE), and Pearson’s correlation coefficient (R2). Two history encounters and a 4-month prediction yielded a high prediction error and low correlation between predicted and actual BMI (MAE of 1.60 for girls and 1.49 for boys). Model performance improved with additional history encounters; improvement was not significant beyond five history encounters. The combined model outperformed the sex-stratified models, with a MAE = 0.98 (SD 0.03) and R2 = 0.72. Our models show that five history encounters are sufficient to predict BMI prior to age 4 for both boys and girls. Moreover, starting from an initial dataset with more than 269 exposure variables, we were able to identify a limited set of 24 variables that can facilitate BMI prediction in early childhood. Nine of these final variables are collected once, and the remaining 15 need to be updated during each visit.
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Ritz, Patrick, Robert Caiazzo, Guillaume Becouarn, Laurent Arnalsteen, Sandrine Andrieu, Philippe Topart, and François Pattou. "Early prediction of failure to lose weight after obesity surgery." Surgery for Obesity and Related Diseases 9, no. 1 (January 2013): 118–21. http://dx.doi.org/10.1016/j.soard.2011.10.022.
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Wahab, Rama J., Vincent W. V. Jaddoe, and Romy Gaillard. "Prediction of Healthy Pregnancy Outcomes in Women with Overweight and Obesity: The Role of Maternal Early-Pregnancy Metabolites." Metabolites 12, no. 1 (December 24, 2021): 13. http://dx.doi.org/10.3390/metabo12010013.
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Women with obesity receive intensified antenatal care due to their increased risk of pregnancy complications, even though not all of these women develop complications. We developed a model based on maternal characteristics for prediction of healthy pregnancy outcomes in women with obesity or who are overweight. We assessed whether early-pregnancy metabolites improved prediction. In a population-based cohort study among a subsample of 1180 Dutch pregnant women with obesity or who are overweight, we developed a prediction model using 32 maternal socio-demographic, lifestyle, physical and pregnancy-related characteristics. We determined early-pregnancy amino acids, nonesterifed fatty acids, phospholipids and carnitines in blood serum using liquid chromatography-tandem mass spectrometry. A healthy pregnancy outcome was the absence of fetal death, gestational hypertension, preeclampsia, gestational diabetes, caesarian section, preterm birth, large-for-gestational-age at birth, macrosomia, postpartum weight retention and offspring overweight/obesity at 5 years. Maternal age, relationship status, parity, early-pregnancy body mass index, mid-pregnancy gestational weight gain, systolic blood pressure and estimated fetal weight were selected into the model using backward selection (area under the receiver operating characteristic curve: 0.65 (95% confidence interval 0.61 to 0.68)). Early-pregnancy metabolites did not improve model performance. Thus, in women with obesity or who are overweight, maternal characteristics can moderately predict a healthy pregnancy outcome. Maternal early-pregnancy metabolites have no incremental value in the prediction of a healthy pregnancy outcome.
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Gupta, Mehak, Thao-Ly T. Phan, H. Timothy Bunnell, and Rahmatollah Beheshti. "Obesity Prediction with EHR Data: A Deep Learning Approach with Interpretable Elements." ACM Transactions on Computing for Healthcare 3, no. 3 (July 31, 2022): 1–19. http://dx.doi.org/10.1145/3506719.
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Childhood obesity is a major public health challenge. Early prediction and identification of the children at an elevated risk of developing childhood obesity may help in engaging earlier and more effective interventions to prevent and manage obesity. Most existing predictive tools for childhood obesity primarily rely on traditional regression-type methods using only a few hand-picked features and without exploiting longitudinal patterns of children’s data. Deep learning methods allow the use of high-dimensional longitudinal datasets. In this article, we present a deep learning model designed for predicting future obesity patterns from generally available items on children’s medical history. To do this, we use a large unaugmented electronic health records dataset from a large pediatric health system in the United States. We adopt a general LSTM network architecture and train our proposed model using both static and dynamic EHR data. To add interpretability, we have additionally included an attention layer to calculate the attention scores for the timestamps and rank features of each timestamp. Our model is used to predict obesity for ages between 3 and 20 years using the data from 1 to 3 years in advance. We compare the performance of our LSTM model with a series of existing studies in the literature and show it outperforms their performance in most age ranges.
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Musa, Fati, Federick Basaky, and Osaghae E.O. "Obesity prediction using machine learning techniques." Journal of Applied Artificial Intelligence 3, no. 1 (June 30, 2022): 24–33. http://dx.doi.org/10.48185/jaai.v3i1.470.
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Currently, safeguarding the community is vital in terms of finding solution to health related problems which can be achieved through medical research using the advent of technology. Obesity has become worldwide health concern as it is becoming a threat to the future. It is the most common health problems all over the world. Thousands of diseases as well as risks and death are associated to it. An early prediction of a disease will help both doctors and patients to act and minimize if not total eradication of the root cause or work on preventing the disease symptom from further deterioration. Going through patient’s medical history is one of the methods of identifying a disease which most time consuming as processing manually and it comes with an error-prone analyses and expense. Therefore, there is need to scientifically develop a predicting model of the occurrence of the disease or its existence using an automated technique as it is becoming a need of the day. In this research work, we used machine learning techniques on a public clinical available dataset to predict obesity status using different machine learning algorithms. Five machine learning algorithms were applied. Gboost Classifier, Random Forest Classifier, Decision Tree Classifier, K-Nearest Neighbor and Support Vector Machine and the model has shown promising results with as Gboost classifier achieves the highest accuracy of 99.05% as compared to other classifiers. Meanwhile, the K-Nearest Neighbor gave the relatively strong accuracy of 95.74%.
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Butler, Éadaoin M., José G. B. Derraik, Marewa Glover, Susan M. B. Morton, El-Shadan Tautolo, Rachael W. Taylor, and Wayne S. Cutfield. "Acceptability of early childhood obesity prediction models to New Zealand families." PLOS ONE 14, no. 12 (December 2, 2019): e0225212. http://dx.doi.org/10.1371/journal.pone.0225212.
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Logvinova, Oksana V., and Ekaterina A. Troshina. "Prediction of early response to liraglutide therapy in patients with obesity." Obesity and metabolism 17, no. 1 (June 1, 2020): 3–12. http://dx.doi.org/10.14341/omet12274.
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BACKGROUND: The main goal of treating obesity is to reduce the risk of developing its complications and comorbid diseases, which requires a steady decrease in body weight by at least 510%. In Russia in 2016, the list of drugs for the treatment of obesity was supplemented by a glucagon-like peptide 1 receptor agonist (GLP-1) liraglutide . There is evidence that about one third of patients do not achieve a clinically significant decrease in body weight during treatment with liraglutide, while the factors that predict the so-called early response to treatment are currently unknown.
AIM: To identify prognostic factors of an early response to complex therapy of exogenously constitutional obesity, including agonist of GLP-1 receptors liraglutide, and to evaluate the effect of this therapy on the dynamics of levels of endogenous peptide bioregulators of eating behavior (IB).
MATERIALS AND METHODS: The study included 42 patients with exogenously constitutional obesity, which were divided into 2 groups, comparable by sex, age and body mass index (BMI). The first group (n=22) received treatment recommendations for the correction of nutrition and physical activity, as well as liraglutide 3.0 mg for 3 months. The second group (n=20) received only recommendations for the correction of nutrition and physical activity. At the start and after 3 months, anthropometric characteristics and laboratory parameters were evaluated in all patients, including the levels of endogenous peptide bioregulators of IB (leptin, ghrelin, obestatin and GLP-1), their dynamics was compared between groups. Depending on the therapeutic effect, the 1st group was divided into two subgroups: those who achieved (n = 14) and did not achieve (n = 8) a clinically significant decrease in body weight. In both subgroups, baseline characteristics were analyzed as possible prognostic factors for the effectiveness of complex therapy.
RESULTS: To predict an early response to complex therapy, including liraglutide, a mathematical model has been developed that is implemented as a calculator in MS Excel and contains a combination of initial body weight and fasting plasma ghrelin. The dynamics of body weight and BMI in the group of complex therapy was statistically significantly higher than that in the group of isolated lifestyle modifications (ILM).
CONCLUSIONS: The proportion of individuals with an early response to 3.0 mg liraglutide therapy is comparable to that of data from randomized clinical trials. The mathematical model, which includes a combination of initial body weight and plasma ghrelin, allows predicting the likelihood of a clinically significant decrease in body weight after 3 months of using liraglutide 3.0 mg in combination with ILM with a sensitivity of 86% [65%; 97%] and prognostic value of a positive result of 80% [60%; 95%].
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Chang, Chi-Chang, Chun-Chia Chen, Chalong Cheewakriangkrai, Ying Chen Chen, and Shun-Fa Yang. "Risk Prediction of Second Primary Endometrial Cancer in Obese Women: A Hospital-Based Cancer Registry Study." International Journal of Environmental Research and Public Health 18, no. 17 (August 26, 2021): 8997. http://dx.doi.org/10.3390/ijerph18178997.
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Due to the high effectiveness of cancer screening and therapies, the diagnosis of second primary cancers (SPCs) has increased in women with endometrial cancer (EC). However, previous studies providing adequate evidence to support screening for SPCs in endometrial cancer are lacking. This study aimed to develop effective risk prediction models of second primary endometrial cancer (SPEC) in women with obesity (body mass index (BMI) > 25) and included datasets on the incidence of SPEC and the other risks of SPEC in 4480 primary cancer survivors from a hospital-based cancer registry database. We found that obesity plays a key role in SPEC. We used 10 independent variables as predicting variables, which correlated to obesity, and so should be monitored for the early detection of SPEC in endometrial cancer. Our proposed scheme is promising for SPEC prediction and demonstrates the important influence of obesity and clinical data representation in all cases following primary treatments. Our results suggest that obesity is still a crucial risk factor for SPEC in endometrial cancer.
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Kiseleva, Olga I., Viktoriia A. Arzumanian, Ekaterina V. Poverennaya, Mikhail A. Pyatnitskiy, Ekaterina V. Ilgisonis, Victor G. Zgoda, Oksana A. Plotnikova, et al. "Does Proteomic Mirror Reflect Clinical Characteristics of Obesity?" Journal of Personalized Medicine 11, no. 2 (January 21, 2021): 64. http://dx.doi.org/10.3390/jpm11020064.
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Obesity is a frightening chronic disease, which has tripled since 1975. It is not expected to slow down staying one of the leading cases of preventable death and resulting in an increased clinical and economic burden. Poor lifestyle choices and excessive intake of “cheap calories” are major contributors to obesity, triggering type 2 diabetes, cardiovascular diseases, and other comorbidities. Understanding the molecular mechanisms responsible for development of obesity is essential as it might result in the introducing of anti-obesity targets and early-stage obesity biomarkers, allowing the distinction between metabolic syndromes. The complex nature of this disease, coupled with the phenomenon of metabolically healthy obesity, inspired us to perform data-centric, hypothesis-generating pilot research, aimed to find correlations between parameters of classic clinical blood tests and proteomic profiles of 104 lean and obese subjects. As the result, we assembled patterns of proteins, which presence or absence allows predicting the weight of the patient fairly well. We believe that such proteomic patterns with high prediction power should facilitate the translation of potential candidates into biomarkers of clinical use for early-stage stratification of obesity therapy.
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Ziauddeen, Nida, Paul J. Roderick, Gillian Santorelli, John Wright, and Nisreen A. Alwan. "Childhood overweight and obesity at the start of primary school: External validation of pregnancy and early-life prediction models." PLOS Global Public Health 2, no. 6 (June 9, 2022): e0000258. http://dx.doi.org/10.1371/journal.pgph.0000258.
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Tackling the childhood obesity epidemic can potentially be facilitated by risk-stratifying families at an early-stage to receive prevention interventions and extra support. Using data from the Born in Bradford (BiB) cohort, this analysis aimed to externally validate prediction models for childhood overweight and obesity developed as part of the Studying Lifecourse Obesity PrEdictors (SLOPE) study in Hampshire. BiB is a longitudinal multi-ethnic birth cohort study which recruited women at around 28 weeks gestation between 2007 and 2010 in Bradford. The outcome was body mass index (BMI) ≥91st centile for overweight/obesity at 4–5 years. Discrimination was assessed using the area under the receiver operating curve (AUC). Calibration was assessed for each tenth of predicted risk by calculating the ratio of predicted to observed risk and plotting observed proportions versus predicted probabilities. Data were available for 8003 children. The AUC on external validation was comparable to that on development at all stages (early pregnancy, birth, ~1 year and ~2 years). The AUC on external validation ranged between 0.64 (95% confidence interval (CI) 0.62 to 0.66) at early pregnancy and 0.82 (95% CI 0.81 to 0.84) at ~2 years compared to 0.66 (95% CI 0.65 to 0.67) and 0.83 (95% CI 0.82 to 0.84) on model development in SLOPE. Calibration was better in the later model stages (early life ~1 year and ~2 years). The SLOPE models developed for predicting childhood overweight and obesity risk performed well on external validation in a UK birth cohort with a different geographical location and ethnic composition.
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Rai, Neha, Hanjabam Barun Sharma, Renu Kumari, and Jyotsna Kailashiya. "Assessment of obesity indices for prediction of hyperglycemia in adult population of Varanasi (Uttar Pradesh), India." Indian Journal of Physiology and Pharmacology 64 (January 12, 2021): 195–200. http://dx.doi.org/10.25259/ijpp_378_2020.
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Objectives: Obesity is a known risk factor for diabetes mellitus. Multiple obesity indices are available to assess and classify obesity status, including Body Mass Index (BMI), Waist Circumference (WC), and Waist-Height Ratio (WHtR). The present research was conducted to assess these obesity indices for their usefulness in predicting hyperglycemia. Materials and Methods: BMI, WC, WHtR, and Random Capillary Blood Glucose (RCBG) levels were measured in total 188 adult volunteers from Varanasi, Uttar Pradesh, India. Pearson correlation, unpaired Student’s t-test, and Chi-square tests were applied to assess associations and difference of measured parameters among different categories. Receiver operating curve analysis was performed to find best obesity indices to predict hyperglycemia (RCBG ≥140 mg/dl) and to find optimal cut off values for prediction. Results: Age of subjects, WC and WHtR (but not BMI) were found significantly correlated with RCBG levels. WHtR classified highest number of subjects as obese, compared to BMI and WC. WHtR was also found to be the best obesity index to predict hyperglycemia in both male and female subjects. Conclusion: WHtR can be used as cost effective, non-invasive, and convenient obesity index for screening and prediction of hyperglycemia in apparently healthy adult subjects. Thus, identified subjects can further be advised to undergo blood glucose testing for the early detection of diabetes and prediabetes.
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Febriani, Irene. "Undiagnosed Diabetes Prediction With Development of Scoring System Based on Risk Factors." Preventif : Jurnal Kesehatan Masyarakat 11, no. 1 (August 3, 2020): 9–21. http://dx.doi.org/10.22487/preventif.v11i1.54.
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Undiagnosed Diabetes Mellitus (UDDM) is a person condition where has never been diagnosed with diabetes, but when a blood sugar examination survey shows the criteria for diabetes. Late diagnosis is a major problem for diabetes. In some cases, 50% of patients do not know the condition of diabetes mellitus, so the complications of diabetes mellitus become very severe. This study aimed to analyze dominant risk factors and make a risk score for Undiagnosed Diabetes Mellitus (UDDM). Making a risk score was carried out in 2016 based on secondary data from 2013 Basic Health Research (Riskesdas). The study population was adults aged ≥ 18 years, diagnosed early in diabetes during the 2013 Riskesdas, did not suffer from other chronic / contagious diseases. The sample size analyzed amounted to 18,963 people. The value of β coefficient from the results of multiple logistic regression predictive models was used to develop the score. The accuracy of the diabetes predictive score was assessed by ROC (Receiver Operating Characteristic). 2 prediction models developed into risk scores. Model 1 predictions of UDDM with 8 predictors (AUC 73.13%, sensitivity 29.19%, specificity 90.33%, PPV 25.32%, NPV 91.90%, cutoff ≥30), model 2 predictions of UDDM with 5 predictors (AUC 74.22%, sensitivity of 64.91%, spessivity 67.95%, PPV 18.37%, NPV 94.60%, cutoff 21). Undiagnosed diabetes risk factors and predictors in making scores on model 1 were gender, age, hypertension, body mass index, central obesity, HDL and LDL. In model 2 were gender, age, hypertension, body mass index, central obesity.
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Pang, Xueqin, Christopher B. Forrest, Félice Lê-Scherban, and Aaron J. Masino. "Prediction of early childhood obesity with machine learning and electronic health record data." International Journal of Medical Informatics 150 (June 2021): 104454. http://dx.doi.org/10.1016/j.ijmedinf.2021.104454.
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Papasavas, Pavlos. "Comment on: Early prediction of the failure to lose weight after obesity surgery." Surgery for Obesity and Related Diseases 9, no. 1 (January 2013): 121–22. http://dx.doi.org/10.1016/j.soard.2011.11.008.
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Zhang, Aihua, Hui Sun, and Xijun Wang. "Emerging role and recent applications of metabolomics biomarkers in obesity disease research." RSC Advances 7, no. 25 (2017): 14966–73. http://dx.doi.org/10.1039/c6ra28715h.
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Brink, Huguette S., Aart Jan van der Lely, and Joke van der Linden. "The potential role of biomarkers in predicting gestational diabetes." Endocrine Connections 5, no. 5 (September 2016): R26—R34. http://dx.doi.org/10.1530/ec-16-0033.
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Gestational diabetes (GD) is a frequent complication during pregnancy and is associated with maternal and neonatal complications. It is suggested that a disturbing environment for the foetus, such as impaired glucose metabolism during intrauterine life, may result in enduring epigenetic changes leading to increased disease risk in adult life. Hence, early prediction of GD is vital. Current risk prediction models are based on maternal and clinical parameters, lacking a strong predictive value. Adipokines are mainly produced by adipocytes and suggested to be a link between obesity and its cardiovascular complications. Various adipokines, including adiponectin, leptin and TNF&, have shown to be dysregulated in GD. This review aims to outline biomarkers potentially associated with the pathophysiology of GD and discuss the role of integrating predictive biomarkers in current clinical risk prediction models, in order to enhance the identification of those at risk.
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Bello-Chavolla, Omar Yaxmehen, Jessica Paola Bahena-López, Neftali Eduardo Antonio-Villa, Arsenio Vargas-Vázquez, Armando González-Díaz, Alejandro Márquez-Salinas, Carlos A. Fermín-Martínez, J. Jesús Naveja, and Carlos A. Aguilar-Salinas. "Predicting Mortality Due to SARS-CoV-2: A Mechanistic Score Relating Obesity and Diabetes to COVID-19 Outcomes in Mexico." Journal of Clinical Endocrinology & Metabolism 105, no. 8 (May 31, 2020): 2752–61. http://dx.doi.org/10.1210/clinem/dgaa346.
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Abstract Background The SARS-CoV-2 outbreak poses a challenge to health care systems due to its high complication rates in patients with cardiometabolic diseases. Here, we identify risk factors and propose a clinical score to predict COVID-19 lethality, including specific factors for diabetes and obesity, and its role in improving risk prediction. Methods We obtained data of confirmed and negative COVID-19 cases and their demographic and health characteristics from the General Directorate of Epidemiology of the Mexican Ministry of Health. We investigated specific risk factors associated to COVID-19 positivity and mortality and explored the impact of diabetes and obesity on modifying COVID-19-related lethality. Finally, we built a clinical score to predict COVID-19 lethality. Results Among the 177 133 subjects at the time of writing this report (May 18, 2020), we observed 51 633 subjects with SARS-CoV-2 and 5,332 deaths. Risk factors for lethality in COVID-19 include early-onset diabetes, obesity, chronic obstructive pulmonary disease, advanced age, hypertension, immunosuppression, and chronic kidney disease (CKD); we observed that obesity mediates 49.5% of the effect of diabetes on COVID-19 lethality. Early-onset diabetes conferred an increased risk of hospitalization and obesity conferred an increased risk for intensive care unit admission and intubation. Our predictive score for COVID-19 lethality included age ≥ 65 years, diabetes, early-onset diabetes, obesity, age < 40 years, CKD, hypertension, and immunosuppression and significantly discriminates lethal from non-lethal COVID-19 cases (C-statistic = 0.823). Conclusions Here, we propose a mechanistic approach to evaluate the risk for complications and lethality attributable to COVID-19, considering the effect of obesity and diabetes in Mexico. Our score offers a clinical tool for quick determination of high-risk susceptibility patients in a first-contact scenario.
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Estampador, Angela C., and Paul W. Franks. "Precision Medicine in Obesity and Type 2 Diabetes: The Relevance of Early-Life Exposures." Clinical Chemistry 64, no. 1 (January 1, 2018): 130–41. http://dx.doi.org/10.1373/clinchem.2017.273540.
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Abstract BACKGROUND Type 2 diabetes is highly prevalent and devastating. Obesity is a diabetogenic factor, driving insulin resistance and a compensatory demand for increased insulin secretion from the pancreatic β cells; a failure to address this demand results in diabetes. Accordingly, primary and secondary prevention of obesity are at the core of diabetes prevention programs. The development of obesity and declining β-cell function often span many years or decades before diabetes is clinically manifest. Thus, characterizing the early-life process and risk factors that set disease trajectories may yield novel targets for early intervention and help improve the accuracy of prediction algorithms, factors germane to the emerging field of precision medicine. CONTENT Here, we overview the concepts of precision medicine and fetal programming. We discuss the barriers to preventing obesity and type 2 diabetes in adulthood and present the rationale for considering early-life events in this context. In so doing, we discuss proof-of-concept studies and cutting-edge technological developments that are likely to transform current thinking on the etiology and pathogenesis of obesity and type 2 diabetes. We also review the factors hampering progress, including the success and failures of pregnancy intervention trials. SUMMARY Obesity and type 2 diabetes are among the major health and economic burdens of our time. Defeating these diseases is likely to require life-course approaches, which may include aggressive interventions informed by biomarker profiling undertaken during early life.
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Mkrtchyan, Siranush А., Razmik А. Dunamalyan, Lilit E. Ghukasyan, and Marine А. Mardiyan. "SOCIO-DEMOGRAPHIC AND MEDICO-BIOLOGICAL FACTORS AS PROGNOSTIC INDICATORS OF QUALITY OF LIFE IN EARLY CHILDHOOD." Proceedings of the YSU B: Chemical and Biological Sciences 55, no. 1 (254) (April 28, 2021): 75–84. http://dx.doi.org/10.46991/pysu:b/2021.55.1.075.
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Patient’s quality of life (QL) measures are endowed with independent predictive value and these factors are considered to be more distinct than patient’s general somatic condition for predicting patient’s health condition. However, the number of researches devoted to QL prediction in the field of medical science is low. The aim of research is evaluation of predictive measure of QL of early aged children. Prospective observational study was carried out. The objects of the research were 2362 early age children (3months-3years old) from pediatric polyclinics of Yerevan. QL of children was evaluated with the international questionnaire “QUALIN”. Wald’s analytical method has been applied for predictive evaluation of QL criteria and formation of risk group. For the analysis and evaluation of the statistical material used SPSS Statistics software package. In social-hygienic factors more important were: family type, conflicts in family, disabled child and frequent morbidity families, presence of artificial nutrition since birthday. Among medico-biological factors the presence of two or more diseases in neonatal period, low and high levels of physical development, weight deficit and obesity, child’s health group and respiratory, nervous and digestive system diseases were more significant. In terms of predictive evaluation of QL, it can be stated that a number of medico-biological and socio-hygienic factors affect the overall formation of QL. By means of predictive evaluation of QL one can originally set apart targeted risk groups and if the score of predictive evaluation is +13 and higher, implement health measures, which may provide with improvements of QL criteria.
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Njoku, Kelechi, Amy E. Campbell, Bethany Geary, Michelle L. MacKintosh, Abigail E. Derbyshire, Sarah J. Kitson, Vanitha N. Sivalingam, Andrew Pierce, Anthony D. Whetton, and Emma J. Crosbie. "Metabolomic Biomarkers for the Detection of Obesity-Driven Endometrial Cancer." Cancers 13, no. 4 (February 10, 2021): 718. http://dx.doi.org/10.3390/cancers13040718.
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Endometrial cancer is the most common malignancy of the female genital tract and a major cause of morbidity and mortality in women. Early detection is key to ensuring good outcomes but a lack of minimally invasive screening tools is a significant barrier. Most endometrial cancers are obesity-driven and develop in the context of severe metabolomic dysfunction. Blood-derived metabolites may therefore provide clinically relevant biomarkers for endometrial cancer detection. In this study, we analysed plasma samples of women with body mass index (BMI) ≥ 30 kg/m2 and endometrioid endometrial cancer (cases, n = 67) or histologically normal endometrium (controls, n = 69), using a mass spectrometry-based metabolomics approach. Eighty percent of the samples were randomly selected to serve as a training set and the remaining 20% were used to qualify test performance. Robust predictive models (AUC > 0.9) for endometrial cancer detection based on artificial intelligence algorithms were developed and validated. Phospholipids were of significance as biomarkers of endometrial cancer, with sphingolipids (sphingomyelins) discriminatory in post-menopausal women. An algorithm combining the top ten performing metabolites showed 92.6% prediction accuracy (AUC of 0.95) for endometrial cancer detection. These results suggest that a simple blood test could enable the early detection of endometrial cancer and provide the basis for a minimally invasive screening tool for women with a BMI ≥ 30 kg/m2.
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Oz-Sig, Ozlem, Ozlem Kara, and Hakan Erdogan. "Microalbuminuria and Serum Cystatin C in Prediction of Early-Renal Insufficiency in Children with Obesity." Indian Journal of Pediatrics 87, no. 12 (May 8, 2020): 1009–13. http://dx.doi.org/10.1007/s12098-020-03294-z.
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Abd El–Wahab, Ekram W., Hanan Z. Shatat, and Fahmy Charl. "Adapting a Prediction Rule for Metabolic Syndrome Risk Assessment Suitable for Developing Countries." Journal of Primary Care & Community Health 10 (January 2019): 215013271988276. http://dx.doi.org/10.1177/2150132719882760.
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Background: Metabolic syndrome (MetS) is a cluster of cardiometabolic disturbances that increases the risk of cardiovascular diseases (CVD) and type 2 diabetes mellitus (DM). The early identification of high-risk individuals is the key for halting these conditions. The world is facing a growing epidemic MetS although the magnitude in Egypt is unknown. Objectives: To describe MetS and its determinants among apparently healthy individuals residing in urban and rural communities in Egypt and to establish a model for MetS prediction. Methods: A cross-sectional study was conducted with 270 adults from rural and urban districts in Alexandria, Egypt. Participants were clinically evaluated and interviewed for sociodemographic and lifestyle factors and dietary habits. MetS was defined according to the harmonized criteria set by the AHA/NHLBI. The risk of ischemic heart diseases (IHDs), DM and fatty liver were assessed using validated risk prediction charts. A multiple risk model for predicting MetS was developed, and its performance was compared. Results: In total, 57.8% of the study population met the criteria for MetS and were at high risk for developing IHD, DM, and fatty liver. Silent CVD risk factors were identified in 20.4% of the participants. In our proposed multivariate logistic regression model, the predictors of MetS were obesity [OR (95% CI) = 16.3 (6.03-44.0)], morbid obesity [OR (95% CI) = 21.7 (5.3-88.0)], not working [OR (95% CI) = 2.05 (1.1-3.8)], and having a family history of chronic diseases [OR (95% CI) = 4.38 (2.23-8.61)]. Consumption of caffeine once per week protected against MetS by 27.8-fold. The derived prediction rule was accurate in predicting MetS, fatty liver, high risk of DM, and, to a lesser extent, a 10-year lifetime risk of IHD. Conclusion: Central obesity and sedentary lifestyles are accountable for the rising rates of MetS in our society. Interventions are needed to minimize the potential predisposition of the Egyptian population to cardiometabolic diseases.
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O Abulnaja, Khalid, Kurunthachalam Kannan, Ashgan Mohammed K Al-Manzlawi, Taha A Kumosani, Mohamed Qari, and Said S Moselhy. "Sensitivity, specificity of biochemical markers for early prediction of endothelial dysfunction in atherosclerotic obese subjects." African Health Sciences 22, no. 2 (August 1, 2022): 286–94. http://dx.doi.org/10.4314/ahs.v22i2.32.
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Background: The obesity increased incidence of diabetes, hypertension and atherosclerosis and rate of morbidity and mortality. The main cause of atherosclerosis is endothelial dysfunction and formation of foam cells and macrophage that lead to unfavorable complications. This study evaluated specific biomarkers for endothelial dysfunction as sensitive indices for early predication of atherosclerosis in obese subjects.
Study Design: One hundred fifty male age and sex matching were included in the current study divided into three groups according to body mass index (BMI): Control (BMI ≤ 22), obese (BMI> 28) and obese with atherosclerosis (BMI> 28). Fasting serum was subjected for determination of adhesion molecules, sICAM-1, sVCAM-1, E-selectin, oxo-LDL and 8-iso-PGF2α by ELISA technique.
Results: Data obtained showed that, a significant elevation of serum inflammatory markers CRP, IL-6 and TNF-α and adhesion molecules sICAM-1 (p<0.001) with sensitivity 96%, sVCAM-1 (p <0.01) with sensitivity 92%, E-selectin (p<0.001) with sensitivity 94%, oxo-LDL (p <0.05) and 8-iso-PGF2α (p < 0.001) with sensitivity 97% in obese with atherosclerosis compared with obese and control.
Conclusion: The levels of serum adhesion molecules contributed in the pathogenesis of endothelial dysfunction can be used as sensitive biomarkers for early prediction of atherosclerosis in obese subjects.
Keywords: Obesity; atherosclerosis; endothelial dysfunction.
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Chua, Felix, Rama Vancheeswaran, Adrian Draper, Tejal Vaghela, Matthew Knight, Rahul Mogal, Jaswinder Singh, et al. "Early prognostication of COVID-19 to guide hospitalisation versus outpatient monitoring using a point-of-test risk prediction score." Thorax 76, no. 7 (March 10, 2021): 696–703. http://dx.doi.org/10.1136/thoraxjnl-2020-216425.
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IntroductionRisk factors of adverse outcomes in COVID-19 are defined but stratification of mortality using non-laboratory measured scores, particularly at the time of prehospital SARS-CoV-2 testing, is lacking.MethodsMultivariate regression with bootstrapping was used to identify independent mortality predictors in patients admitted to an acute hospital with a confirmed diagnosis of COVID-19. Predictions were externally validated in a large random sample of the ISARIC cohort (N=14 231) and a smaller cohort from Aintree (N=290).Results983 patients (median age 70, IQR 53–83; in-hospital mortality 29.9%) were recruited over an 11-week study period. Through sequential modelling, a five-predictor score termed SOARS (SpO2, Obesity, Age, Respiratory rate, Stroke history) was developed to correlate COVID-19 severity across low, moderate and high strata of mortality risk. The score discriminated well for in-hospital death, with area under the receiver operating characteristic values of 0.82, 0.80 and 0.74 in the derivation, Aintree and ISARIC validation cohorts, respectively. Its predictive accuracy (calibration) in both external cohorts was consistently higher in patients with milder disease (SOARS 0–1), the same individuals who could be identified for safe outpatient monitoring. Prediction of a non-fatal outcome in this group was accompanied by high score sensitivity (99.2%) and negative predictive value (95.9%).ConclusionThe SOARS score uses constitutive and readily assessed individual characteristics to predict the risk of COVID-19 death. Deployment of the score could potentially inform clinical triage in preadmission settings where expedient and reliable decision-making is key. The resurgence of SARS-CoV-2 transmission provides an opportunity to further validate and update its performance.
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Grummitt, J., H. Gaudreau, M. Steiner, M. Meaney, L. Atkinson, L. Quilty, and R. Levitan. "Prediction of early-onset eating disorders and obesity: the role of neuropsychological performance at age 4." European Neuropsychopharmacology 26 (October 2016): S346—S347. http://dx.doi.org/10.1016/s0924-977x(16)31274-3.
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Pasieshvili, Ludmila, Katerina Ivanova, Alina Andrusha, Tetiana Ivanovna Viun, and Anastasiya Marchenko. "Calcium-phosphorus relationships in the combined course of stable ischemic heart disease in patients with obesity." Inter Collegas 8, no. 2 (July 21, 2021): 106–10. http://dx.doi.org/10.35339/ic.8.2.106-110.
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The purpose of the study was to optimize the diagnosis and prediction of the development of structural and functional disorders of bone tissue in patients with SCHD and obesity. Thus, lipid profile analysis showed a clinically significant increase in total cholesterol and triglycerides in patients with SCHD. Serum bone mineral status did not exceed normal values, but serum total calcium levels were significantly higher in patients with SCHD and obesity compared to other groups. The indicators of calcium-phosphorus metabolism in the daily urine of patients with SCHD were significantly higher. When conducting densitometric studies in patients with SCHD with normal weight, osteopenic conditions were diagnosed more often than in patients with overweight and obesity. That is, the comorbid course of SCHD and obesity is a high risk of osteodeficiency, which is confirmed by early changes in calcium-phosphorus metabolism.
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Aleksandrova, Krasimira, Dariush Mozaffarian, and Tobias Pischon. "Addressing the Perfect Storm: Biomarkers in Obesity and Pathophysiology of Cardiometabolic Risk." Clinical Chemistry 64, no. 1 (January 1, 2018): 142–53. http://dx.doi.org/10.1373/clinchem.2017.275172.
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AbstractBACKGROUNDThe worldwide rise of obesity has provoked intensified research to better understand its pathophysiology as a means for disease prevention. Several biomarkers that may reflect various pathophysiological pathways that link obesity and cardiometabolic diseases have been identified over the past decades.CONTENTWe summarize research evidence regarding the role of established and novel obesity-related biomarkers, focusing on recent epidemiological evidence for detrimental associations with cardiometabolic diseases including obesity-related cancer. The reviewed biomarkers include biomarkers of glucose–insulin homeostasis (insulin, insulin-like growth factors, and C-peptide), adipose tissue biomarkers (adiponectin, omentin, apelin, leptin, resistin, and fatty-acid-binding protein-4), inflammatory biomarkers (C-reactive protein, interleukin 6, tumor necrosis factor α), and omics-based biomarkers (metabolites and microRNAs).SUMMARYAlthough the evidence for many classical obesity biomarkers, including adiponectin and C-reactive protein (CRP), in disease etiology has been initially promising, the evidence for a causal role in humans remains limited. Further, there has been little demonstrated ability to improve disease prediction beyond classical risk factors. In the era of “precision medicine,” there is an increasing interest in novel biomarkers, and the extended list of potentially promising biomarkers, such as adipokines, cytokines, metabolites, and microRNAs, implicated in obesity may bring new promise for improved, personalized prevention. To further evaluate the role of obesity-related biomarkers as etiological and early-disease-prediction targets, well-designed studies are needed to evaluate temporal associations, replicate findings, and test clinical utility of novel biomarkers. In particular, studies to determine the therapeutic implications of novel biomarkers beyond established metabolic risk factors are highly warranted.
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Erzurum Alim, Nural, Aysun Yuksel, Leyla Tevfikoglu Pehlivan, Rahime Evra Karakaya, and Zehra Nur Besler. "Eating Disorder Risk and Factors Associated with Obesity Prejudice Among University Students: A cross-sectional descriptive study." Revista Española de Nutrición Humana y Dietética 26, no. 2 (June 30, 2022): 104–13. http://dx.doi.org/10.14306/renhyd.26.2.1492.
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Introduction: The aim of this study is to evaluate eating disorder risk and factors associated with obesity prejudice among university students. Methodology: This cross-sectional descriptive study was consisted of 1449 university students in Turkey. Anthropometric measurements were self-reported and eating disorder and obesity prejudice symptoms were measured via validated scales. Results: Prevalence of students at low risk for eating disorder was 88.2%, while 60.3% of them were prone to obesity prejudice and 27% of them were obesity prejudiced. The mean eating disorder scores of the underweight and the normal weight group were significantly lower than the overweight group (p = 0.003 and p = 0.019, respectively). The difference between the mean obesity prejudice scores of the normal weight group and the overweight group was found to be significant (p = 0.002). Moreover, in the multiple linear regression analysis, the overweight group had a significant association with obesity prejudice (p<0.001). Conclusion: The risk of eating disorder and obesity prejudice increases among overweight/obese university students. Early prediction of eating disorder and obesity prejudice is crucial to prevent health problems such as obesity and related diseases among university students.
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Mcmillen, I. Caroline, and Jeffrey S. Robinson. "Developmental Origins of the Metabolic Syndrome: Prediction, Plasticity, and Programming." Physiological Reviews 85, no. 2 (April 2005): 571–633. http://dx.doi.org/10.1152/physrev.00053.2003.
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The “fetal” or “early” origins of adult disease hypothesis was originally put forward by David Barker and colleagues and stated that environmental factors, particularly nutrition, act in early life to program the risks for adverse health outcomes in adult life. This hypothesis has been supported by a worldwide series of epidemiological studies that have provided evidence for the association between the perturbation of the early nutritional environment and the major risk factors (hypertension, insulin resistance, and obesity) for cardiovascular disease, diabetes, and the metabolic syndrome in adult life. It is also clear from experimental studies that a range of molecular, cellular, metabolic, neuroendocrine, and physiological adaptations to changes in the early nutritional environment result in a permanent alteration of the developmental pattern of cellular proliferation and differentiation in key tissue and organ systems that result in pathological consequences in adult life. This review focuses on those experimental studies that have investigated the critical windows during which perturbations of the intrauterine environment have major effects, the nature of the epigenetic, structural, and functional adaptive responses which result in a permanent programming of cardiovascular and metabolic function, and the role of the interaction between the pre- and postnatal environment in determining final health outcomes.
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Sghaireen, Mohammed G., Yazan Al-Smadi, Ahmad Al-Qerem, Kumar Chandan Srivastava, Kiran Kumar Ganji, Mohammad Khursheed Alam, Shadi Nashwan, and Yousef Khader. "Machine Learning Approach for Metabolic Syndrome Diagnosis Using Explainable Data-Augmentation-Based Classification." Diagnostics 12, no. 12 (December 10, 2022): 3117. http://dx.doi.org/10.3390/diagnostics12123117.
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Metabolic syndrome (MetS) is a cluster of risk factors including hypertension, hyperglycemia, dyslipidemia, and abdominal obesity. Metabolism-related risk factors include diabetes and heart disease. MetS is also linked to numerous cancers and chronic kidney disease. All of these variables raise medical costs. Developing a prediction model that can quickly identify persons at high risk of MetS and offer them a treatment plan is crucial. Early prediction of metabolic syndrome will highly impact the quality of life of patients as it gives them a chance for making a change to the bad habit and preventing a serious illness in the future. In this paper, we aimed to assess the performance of various algorithms of machine learning in order to decrease the cost of predictive diagnoses of metabolic syndrome. We employed ten machine learning algorithms along with different metaheuristics for feature selection. Moreover, we examined the effects of data augmentation in the prediction accuracy. The statistics show that the augmentation of data after applying feature selection on the data highly improves the performance of the classifiers.
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O’Rahilly, Stephen, I. Sadaf Farooqi, Giles S. H. Yeo, and Benjamin G. Challis. "Minireview: Human Obesity—Lessons from Monogenic Disorders." Endocrinology 144, no. 9 (September 1, 2003): 3757–64. http://dx.doi.org/10.1210/en.2003-0373.
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Abstract Genetic influences on the determination of human fat mass are profound and powerful, a statement that does not conflict with the obvious influence of environmental factors that drive recent changes in the prevalence of obesity. The assertion of the importance of genetic factors has, until recently, largely been based on twin and adoption studies. However, in the last 6 yr, a number of human genes have been identified in which major missense or nonsense mutations are sufficient in themselves to result in severe early-onset obesity, usually associated with disruption of normal appetite control mechanisms. Progress in the identification of more common, subtler genetic variants that influence fat mass in larger numbers of people has been slower, but discernible. Human genetics will continue to make an invaluable contribution to the study of human obesity by identifying critical molecular components of the human energy balance regulatory systems, pointing the way toward more targeted and effective therapies and assisting the prediction of individual responses to environmental manipulations.
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Onofrei, Viviana Aursulesei, Carmen Lacramioara Zamfir, Ecaterina Anisie, Alexandr Ceasovschih, Mihai Constantin, Florin Mitu, Cristina Andreea Adam, Elena-Daniela Grigorescu, Antoneta Dacia Petroaie, and Daniel Timofte. "Determinants of Arterial Stiffness in Patients with Morbid Obesity. The Role of Echocardiography and Carotid Ultrasound Imaging." Medicina 59, no. 3 (February 22, 2023): 428. http://dx.doi.org/10.3390/medicina59030428.
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Background and objective: Morbid obesity is accompanied by an increased cardiovascular (CV) risk, which justifies a multidisciplinary, integrative approach. Arterial stiffness has a well-defined additional role in refining individual CV risk. Given that echocardiography and carotid ultrasound are usual methods for CV risk characterization, we aimed to identify the imaging parameters with a predictive value for early-onset arterial stiffness. Material and methods: We conducted a study in which 50 patients (divided into two equal groups with morbid obesity and without obesity), age and gender matched, untreated for cardiovascular risk factors, were addressed to bariatric surgery or non-inflammatory benign pathology surgery. Before the surgical procedures, we evaluated demographics, anthropometric data and biochemical parameters including adipokines (chemerin, adiponectin). Arterial stiffness was evaluated using the Medexpert ArteriographTM TL2 device. Transthoracic echocardiography and carotid ultrasound were also performed. We also analyzed adipocyte size and vascular wall thickness in intraoperative biopsies. Results: Left ventricle (LV) mass index (p = 0.2851), LV ejection fraction (LVEF) (p = 0.0073), epicardial adipose tissue thickness (p = 0.0001) as echocardiographic parameters and carotid intima–media thickness (p = 0.0033), relative wall thickness (p = 0.0295), wall to lumen thickness ratio (p = 0.0930) and carotid cross-sectional area (p = 0.0042) as ultrasound parameters were significant measures in our groups and were assessed in relation to adipocyte size, blood vessel wall thickness and adipokines serum levels. Statistical analysis revealed directly proportional relationships between LV mass index (p = 0.008), carotid systolic thickness of the media (p = 0.009), diastolic thickness of the media (p = 0.007), cross-sectional area (p = 0.001) and blood vessel wall thickness. Carotid relative wall thickness positively correlates with adipocyte size (p = 0.023). In patients with morbid obesity, chemerin and adiponectin/chemerin ratio positively correlates with carotid intima–media thickness (p = 0.050), systolic thickness of the media (p = 0.015) and diastolic thickness of the media (p = 0.001). The multiple linear regression models revealed the role of epicardial adipose tissue thickness and carotid cross-sectional area in predicting adipocyte size which in turn is an independent factor for arterial stiffness parameters such as pulse wave velocity, subendocardial viability ratio and aortic augmentation index. Conclusions: Our results suggest that epicardial adipose tissue thickness, carotid intima–media thickness, relative wall thickness and carotid cross-sectional area might be useful imaging parameters for early prediction of arterial stiffness in patients with morbid obesity.
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Stefan, Norbert. "Phenotypes of prediabetes: pathogenesis and consequences for prediction and prevention of type 2 diabetes and cardiovascular diseases." Diabetes mellitus 22, no. 6 (February 28, 2020): 577–81. http://dx.doi.org/10.14341/dm10376.
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The prevalence of type 2 diabetes is increasing world-wide. Thus, it is necessary to better understand its pathogenesis, the risk of diabetes-associated complications and effective treatment strategies. Because type 2 diabetes is a very heterogenous disease, both, related to its pathogenesis and risk of complications, phenotyping strategies in diabetes may help to tailor the preventive strategies based on the individuals risk. As the the hyperglycemic state of prediabetes is already associated with an increased risk of cardiometabolic diseases it is necessary to investigate the impact of phenotypes for predictive and preventive outcomes already in this early state of hyperglycemia. In this review artice I discuss how important phenotypes of prediabetes, such as nonalcoholic fatty liver disease, visceral obesity, insulin secretion defect and insulin resistance can be used to improve the prediction and prevention of type 2 diabetes and cardiovascular disease.
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Rheinwalt, Karl Peter, Uta Drebber, Robert Schierwagen, Sabine Klein, Ulf Peter Neumann, Tom Florian Ulmer, Andreas Plamper, et al. "Baseline Presence of NAFLD Predicts Weight Loss after Gastric Bypass Surgery for Morbid Obesity." Journal of Clinical Medicine 9, no. 11 (October 26, 2020): 3430. http://dx.doi.org/10.3390/jcm9113430.
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Background. Bariatric surgery is a widely used treatment for morbid obesity. Prediction of postoperative weight loss currently relies on prediction models, which mostly overestimate patients’ weight loss. Data about the influence of Non-alcoholic fatty liver disease (NAFLD) on early postoperative weight loss are scarce. Methods. This prospective, single-center cohort study included 143 patients receiving laparoscopic gastric bypass surgery (One Anastomosis-Mini Gastric Bypass (OAGB-MGB) or Roux-en-Y Gastric Bypass (RYGB)). Liver biopsies were acquired at surgery. NAFLD activity score (NAS) assigned patients to “No NAFLD”, “NAFL” or “NASH”. Follow up data were collected at 3, 6 and 12 months. Results. In total, 49.7% of patients had NASH, while 41.3% had NAFL. Compared with the No NAFLD group, NAFL and NASH showed higher body-mass-index (BMI) at follow-up (6 months: 31.0 kg/m2 vs. 36.8 kg/m2 and 36.1 kg/m2, 12 months: 27.0 kg/m2 vs. 34.4 and 32.8 kg/m2) and lower percentage of total body weight loss (%TBWL): (6 months: 27.1% vs. 23.3% and 24.4%; 12 months: 38.5% vs. 30.1 and 32.6%). Linear regression of NAS points significantly predicts percentage of excessive weight loss (%EWL) after 6 months (Cologne-weight-loss-prediction-score). Conclusions. Histopathological presence of NAFLD might lead to inferior postoperative weight reduction after gastric bypass surgery. The mechanisms underlying this observation should be further studied.
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Quotah, Ola F., Lucilla Poston, Angela C. Flynn, and Sara L. White. "Metabolic Profiling of Pregnant Women with Obesity: An Exploratory Study in Women at Greater Risk of Gestational Diabetes." Metabolites 12, no. 10 (September 29, 2022): 922. http://dx.doi.org/10.3390/metabo12100922.
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Gestational diabetes mellitus (GDM) is one of the most prevalent obstetric conditions, particularly among women with obesity. Pathways to hyperglycaemia remain obscure and a better understanding of the pathophysiology would facilitate early detection and targeted intervention. Among obese women from the UK Pregnancies Better Eating and Activity Trial (UPBEAT), we aimed to compare metabolic profiles early and mid-pregnancy in women identified as high-risk of developing GDM, stratified by GDM diagnosis. Using a GDM prediction model combining maternal age, mid-arm circumference, systolic blood pressure, glucose, triglycerides and HbA1c, 231 women were identified as being at higher-risk, of whom 119 women developed GDM. Analyte data (nuclear magnetic resonance and conventional) were compared between higher-risk women who developed GDM and those who did not at timepoint 1 (15+0–18+6 weeks) and at timepoint 2 (23+2–30+0 weeks). The adjusted regression analyses revealed some differences in the early second trimester between those who developed GDM and those who did not, including lower adiponectin and glutamine concentrations, and higher C-peptide concentrations (FDR-adjusted p < 0.005, < 0.05, < 0.05 respectively). More differences were evident at the time of GDM diagnosis (timepoint 2) including greater impairment in β-cell function (as assessed by HOMA2-%B), an increase in the glycolysis-intermediate pyruvate (FDR-adjusted p < 0.001, < 0.05 respectively) and differing lipid profiles. The liver function marker γ-glutamyl transferase was higher at both timepoints (FDR-adjusted p < 0.05). This exploratory study underlines the difficulty in early prediction of GDM development in high-risk women but adds to the evidence that among pregnant women with obesity, insulin secretory dysfunction may be an important discriminator for those who develop GDM.
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Anjum, Muhammad Shoaib, Omer Riaz, and Muhammad Salman Latif. "Diastolic Dysfunction Prediction with Symptoms Using Machine Learning Approach." Vol 4 Issue 3 4, no. 3 (June 30, 2022): 714–26. http://dx.doi.org/10.33411/ijist/2022040312.
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Cardiac disease is the major cause of deaths all over the world, with 17.9 million deaths annually, as per World Health Organization reports. The purpose of this study is to enable a cardiologist to early predict the patient’s condition before performing the echocardiography test. This study aims to find out whether diastolic function or diastolic dysfunction using symptoms through machine learning. We used the unexplored dataset of diastolic dysfunction disease in this study and checked the symptoms with cardiologist to be enough to predict the disease. For this study, the records of 1285 patients were used, out of which 524 patients had diastolic function and the other 761 patients had diastolic dysfunction. The input parameters considered in this detection include patient age, gender, BP systolic, BP diastolic, BSA, BMI, hypertension, obesity, and Shortness of Breath (SOB). Various machine learning algorithms were used for this detection including Random Forest, J.48, Logistic Regression, and Support Vector Machine algorithms. As a result, with an accuracy of 85.45%, Logistic Regression provided promising results and proved efficient for early prediction of cardiac disease. Other algorithms had an accuracy as follow, J.48 (85.21%), Random Forest (84.94%), and SVM (84.94%). Using a machine learning tool and a patient’s dataset of diastolic dysfunction, we can declare either a patient has cardiac disease or not.
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A Ahmed, Alaa, Said S Moselhy, Taha A Kumosani, Etimad A Huwait, Maryam A AL-Ghamdi, Khalid A AL-Madani, Majdi H AlToukhi, and Afnan T Kumosani. "Ultrasonographic and biochemical assessments as early prediction of polycystic ovarian syndrome in obese women." African Health Sciences 20, no. 2 (July 22, 2020): 676–81. http://dx.doi.org/10.4314/ahs.v20i2.18.
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Backgroud: Polycystic ovary syndrome (PCOS) is considered as a common cause of hormonal disturbance and obesity. The diagnosis of PCOS was done by different methods including clinical signs as anovulation, hyperandrogenism, biochem- ical markers and ultrasounographic investigation. This study investigated comparative outcomes of ultrasonographic and biochemical markers for early prediction of PCOS in obese women.
Subjects and methods: Seventy-five patients were clinically diagnosed with obese, PCOS and obese with PCOS and twen- ty-five normal age matched subjects were enrolled as control. Abdominal and transvaginal ultrasonographic for assessment of ovarian properties. In addition, BMI, serum free testosterone, dehydroepiandrosterone (DHEA), insulin, glycosylated hemoglobin (HbA1c) and LDL-c levels were evaluated.
Results: In obese patients with PCOs (20%) ovaries revealed normal appearance in morphology while the rest (80%) showed PCOs in the form of cysts of 2–8 mm in diameter peripherally arranged around stroma. A significant elevation of free testosterone, DHEA and insulin in obese with or without PCOS compared with obese group (p<0.001). A positive correlation with hormonal abnormalities of increased HA1c, LDL-c, free testosterone, DHEA and insulin compared with obese only.
Conclusion: According to our study findings, ovarian morphology combined with biochemical markers is more reliable for early prediction and diagnosis of PCOS for interpretation and management.
Keywords: PCOS; ultrasound; diagnosis; hormones.
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Edington, D. W., and J. Park. "Application of a Prediction Model for Identification of Individuals at Diabetic Risk." Methods of Information in Medicine 43, no. 03 (2004): 273–81. http://dx.doi.org/10.1055/s-0038-1633868.
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SummaryAs a follow-up to our preceding paper, we attempted to extract features of health risk progression for diabetes in Sequential Multi Layered Perceptron (SMLP) via inverse processing of the learned structure. The time-varying risk progress was assessed with risk trajectory and conditional mixture model.Overall risk cut along with the prediction was stable over time and high body mass index (BMI) tops the health behavioral risks predicting the onset of diabetes. For the initial prediction, high BMI (obesity), high blood pressure (BP), high cholesterol, and diet in fatty food were significant. Over time, variations in trajectory were due to changes in BMI, stress, BP, cholesterol, and fatty food intake.We tested the effectiveness of identifying prediabetics by the SMLP by applying the implemented SMLP to a test population of employees from a large manufacturing company, where an early worksite health promotion was initiated (1984). This resulted in a potential sensitivity (71.4%) although there were issues like mapping corresponding risks and large time lags.A secondary test on the similar population as in the previous paper showed a promising sensitivity (86.5%) over 3 years.When combining with targeted screening such as impaired glucose tolerance test only for those predicted to be diabetics, the presented prediction model and extracted features can be used in implementing an effective disease prevention and management program.
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Dimeglio, Chloe, Guillaume Becouarn, Philippe Topart, Rodolphe Bodin, Jean Christophe Buisson, and Patrick Ritz. "Weight Loss Trajectories After Bariatric Surgery for Obesity: Mathematical Model and Proof-of-Concept Study." JMIR Medical Informatics 8, no. 3 (March 9, 2020): e13672. http://dx.doi.org/10.2196/13672.
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Background Obesity surgery has proven its effectiveness in weight loss. However, after a loss phase of about 12 to 18 months, between 20% and 40% of patients regain weight. Prediction of weight evolution is therefore useful for early detection of weight regain. Objective This proof-of-concept study aimed to analyze the postoperative weight trajectories and to identify “curve families” for early prediction of weight regain. Methods This was a monocentric retrospective study with calculation of the weight trajectory of patients having undergone gastric bypass surgery. Data on 795 patients after a 2-year follow-up allowed modeling of weight trajectories according to a hierarchical cluster analysis (HCA) tending to minimize the intragroup distance according to Ward. Clinical judgement was used to finalize the identification of clinically relevant representative trajectories. This modeling was validated on a group of 381 patients for whom the observed weight at 18 months was compared to the predicted weight. Results Two successive HCA produced 14 representative trajectories, distributed among 4 clinically relevant families: Of the 14 weight trajectories, 6 decreased systematically over time or decreased and then stagnated; 4 decreased, increased, and then decreased again; 2 decreased and then increased; and 2 stagnated at first and then began to decrease. A comparison of observed weight and that estimated by modeling made it possible to correctly classify 98% of persons with excess weight loss (EWL) >50% and more than 58% of persons with EWL between 25% and 50%. In the category of persons with EWL >50%, weight data over the first 6 months were adequate to correctly predict the observed result. Conclusions This modeling allowed correct classification of persons with EWL >50% and could identify early after surgery the patients with potentially less that optimal weight loss. Further studies are needed to validate this model in other populations, with other types of surgery, and with other medical-surgical teams.
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Bartáková, Vendula, Beáta Barátová, Lukáš Pácal, Veronika Ťápalová, Silvie Šebestová, Petr Janků, and Kateřina Kaňková. "Development of a New Risk Score for Stratification of Women with Gestational Diabetes Mellitus at High Risk of Persisting Postpartum Glucose Intolerance Using Routinely Assessed Parameters." Life 11, no. 6 (May 23, 2021): 464. http://dx.doi.org/10.3390/life11060464.
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The aims of the study were (i) to find predictive factors for early postpartum conversion of gestational diabetes mellitus (GDM) into persisting glucose intolerance (PGI), (ii) to evaluate potential differences in adverse perinatal outcomes in GDM women with and without early postpartum PGI and, finally, (iii) to establish a risk score to predict postpartum PGI. A cross-sectional study comprised 244 GDM patients with known age, parity, positive family history of diabetes, pre-gestational BMI, comorbidities, smoking history, results of mid-trimester oral glucose tolerance test, HbA1c, obstetric complications, neonatal outcomes and mode of delivery. A risk score was calculated using parameters with highest odds ratios in a statistic scoring model. Significant differences between women with and without PGI postpartum were ascertained for mid-trimester fasting plasma glucose (p < 0.001), HbA1c above 42 mmol/mol (p = 0.035), prevalence of obesity (p = 0.007), hypothyroidism, family history of diabetes and smoking. We also observed higher incidence of prolonged and complicated delivery in PGI group (p = 0.04 and 0.007, respectively). In conclusion, this study identified several parameters with predictive potential for early PGI and also adverse peripartal outcomes. We established a simple risk-stratification score for PGI prediction applicable for GDM affected women prior their leaving maternity ward. Yet, given a relatively small sample size as a main limitation of this study, the proposed score should be validated in the larger cohort.
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Semenov, Yuriy A., Valentina F. Dolgushina, Marina G. Moskvicheva, and Vasiliy S. Chulkov. "Prediction and management model of preterm birth." Annals of the Russian academy of medical sciences 74, no. 4 (October 5, 2019): 221–28. http://dx.doi.org/10.15690/vramn1085.
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Background: It seems relevant to study the contribution of socio-demographic, somatic and obstetric-gynecological factors in the implementation of preterm birth.
Aims: Assessment of the prognostic significance of socio-demographic, obstetric-gynecological and somatic factors in the prediction of preterm birth and associated adverse pregnancy outcomes with subsequent validation of the prognostic model.
Materials and methods: Cohort study with a mixed cohort. A retrospective assessment of socio-demographic factors, harmful habits, obstetric and gynecological pathology, somatic diseases, course and outcomes of pregnancy was carried out with the assessment of the status of newborns in 1246 women with subsequent construction of a predictive model of preterm birth and adverse outcomes of pregnancy using Regression with Optimal Scaling and its prospective validation in 100 women. Results: The most significant predictors, that increase the chance of preterm birth and adverse pregnancy outcomes, were history of premature birth, irregular monitoring during pregnancy, history of pelvic inflammatory disease, smoking, obesity, the onset of sexual activity up to 16 years, cardiovascular and endocrine diseases. Intellectual job reduced the chance of preterm birth and adverse pregnancy outcomes This multivariate predictive model has a diagnostic value. The score of risk factors 25 points had a sensitivity of 73%, a specificity of 71%, the area under the ROC curve (AUC) 0.76 (good quality), p0.001. After stratification of high-risk groups by maternal and perinatal pathology the following list of diagnostic and therapeutic measures is introduced and actively implemented in antenatal clinics. To stratificate this model, we prospectively analyze the course and pregnancy outcomes of 100 women divided into 2 groups: group 1 ― 50 women with preterm delivery, group 2 ― 50 women with term delivery. A total score of 25 and above had 44% of women in group 1 and only 10% of women in group 2 (sensitivity 81.4%, specificity 61.6%, positive predictive value 44%, negative predictive value 90%, positive likelihood ratio 2.2 [1.53.0], negative likelihood ratio 0.3 [0.130.68]).
Conclusions: We have proposed a model for predicting preterm birth and delivery and perinatal losses using the available characteristics of pregnant women from early pregnancy with moderate indicators of diagnostic value. Further validation of the model in the general population of pregnant women is required.
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Aubron, Cecile, Carmela E. Corallo, Maya O. Nunn, Michael J. Dooley, and Allen C. Cheng. "Evaluation of the Accuracy of a Pharmacokinetic Dosing Program in Predicting Serum Vancomycin Concentrations in Critically III Patients." Annals of Pharmacotherapy 45, no. 10 (October 2011): 1193–98. http://dx.doi.org/10.1177/106002801104501001.
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Background Optimization of the timing of appropriate antibiotics is crucial to improve the management of patients in severe sepsis and septic shock. Vancomycin is commonly used empirically in cases of nosocomial infections in critically ill patients. Therefore, early optimization of vancomycin pharmacokinetics is likely to improve outcomes. Objective TO evaluate a pharmacokinetic program to predict serum vancomycin concentrations in accordance with administered dose, weight, height, and creatinine clearance in a critically ill population. Methods We conducted a prospective observational single-center study in a 45-bed intensive care unit (ICU). All patients hospitalized in the ICU requiring intravenous treatment with vancomycin for a suspected Infection were enrolled. The modalities of vancomycin therapy and the monitoring of serum concentrations were left to the discretion of the treating clinician. We compared the measured serum vancomycin concentrations with those predicted by the MM-USCPACK program and analyzed the factors influencing the prediction. Results Fifty-four intravenous vancomycin courses were administered in 48 critically ill patients over the 3-month study. The precision was considered acceptable, based on a relative precision equal to 8.9% (interquartile range 3.5–18.9%) and the relative bias for all predictions was equal to -1.3%. Overall, 77.3% of predictions were within 20% of observed concentrations; factors correlating with a poorer prediction were a change in renal function, obesity, and the magnitude of organ dysfunction on initiation of vancomycin (expressed by a Systemic Organ Failure Assessment score >11). Conclusions The MM-USCPACK program is a useful and reliable tool for prediction of serum vancomycin concentrations in patients hospitalized in ICU and likely reflects the close monitoring of renal function in this setting. For some patients (more severely ill, obese, or significant change in renal function during vancomycin therapy), predictions were less precise.
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Smouter, Leandro, André de Camargo Smolarek, William Cordeiro de Souza, Valderi de Abreu de Lima, and Luis Paulo Gomes Mascarenhas. "CARDIORESPIRATORY FITNESS ASSOCIATED TO TEENAGERS’ FAT: VO2MAX CUTOFF POINT." Revista Paulista de Pediatria 37, no. 1 (January 2019): 73–81. http://dx.doi.org/10.1590/1984-0462/;2019;37;1;00017.
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ABSTRACT Objective: To associate the Maximal Oxygen Uptake (VO2max) with body fat percentage (%BF), and to establish the best VO2max cutoff point for predicting risk %BF in teenagers. Methods: This study was carried out with 979 subjects aged 10 to 18.8 years, 556 (56.8%) girls. The 20 m shuttle run protocol determined the VO2max, which was analyzed in quintiles and in a numeric scale. Cutaneous fold equations determined the %BF, later classified as risk to health/obesity when >25 in girls and >20 in boys. Regression method was used - Odds Ratio (OR) and Receiver Operating Characteristics Curve (ROC curve) with α <5%. Results: From the total number of valid cases, 341 (65.6%) girls and 202 (53.2%) boys presented %BF of risk, and a larger proportion of %BF of risk was observed in the 1st quintile of the VO2max for both genders. There was inverse association between VO2max and %BF of risk from the 4th quintile (OR 1.84, 95%CI 1.05-3.24) until the 1st quintile (OR 4.74, 95%CI 2.44-9.19) for girls, and from the 2nd quintile (OR 2.99, 95%CI 1.48-6.00) until the 1st quintile (OR 5.60, 95%CI 2.64-11.87) for boys. As analytic highlights, VO2max Cutoff points for prediction of %BF of risk were ≤40.9 mL/kg-1/min-1 (AUC: 0.65; p<0.001) for girls and ≤44.8 mL/kg-1/min-1 (AUC: 0.66; p<0.001)for boys.. Conclusions: VO2max was inversely associated to the %BF, and VO2max cutoff points for prediction of %BF of risk are important results to generate action to fight early obesity.
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Li, Xu, Xiaolin Guo, Huifan Ji, Junqi Niu, and Pujun Gao. "Relationships between Metabolic Comorbidities and Occurrence, Severity, and Outcomes in Patients with Acute Pancreatitis: A Narrative Review." BioMed Research International 2019 (October 7, 2019): 1–8. http://dx.doi.org/10.1155/2019/2645926.
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Background. The population of patients with acute pancreatitis treated by the staff at our department of gastroenterology includes those with mild and self-limited disease ranging to those with severe and fatal disease. Early diagnosis and accurate prediction of the severity and outcome of this disease, which is commonly seen by our department, is important for a successful outcome. Metabolic comorbidities (e.g., diabetes mellitus, fatty liver, obesity, and metabolic syndrome) are relevant to the severity and progression of many diseases. The objective of this review was to examine clinical relationships between metabolic comorbidities and occurrence, severity, and outcome of acute pancreatitis.
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Hussan, Hisham, Jing Zhao, Abraham K. Badu-Tawiah, Peter Stanich, Fred Tabung, Darrell Gray, Qin Ma, Matthew Kalady, and Steven K. Clinton. "Utility of machine learning in developing a predictive model for early-age-onset colorectal neoplasia using electronic health records." PLOS ONE 17, no. 3 (March 10, 2022): e0265209. http://dx.doi.org/10.1371/journal.pone.0265209.
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Background and aims The incidence of colorectal cancer (CRC) is increasing in adults younger than 50, and early screening remains challenging due to cost and under-utilization. To identify individuals aged 35–50 years who may benefit from early screening, we developed a prediction model using machine learning and electronic health record (EHR)-derived factors. Methods We enrolled 3,116 adults aged 35–50 at average-risk for CRC and underwent colonoscopy between 2017–2020 at a single center. Prediction outcomes were (1) CRC and (2) CRC or high-risk polyps. We derived our predictors from EHRs (e.g., demographics, obesity, laboratory values, medications, and zip code-derived factors). We constructed four machine learning-based models using a training set (random sample of 70% of participants): regularized discriminant analysis, random forest, neural network, and gradient boosting decision tree. In the testing set (remaining 30% of participants), we measured predictive performance by comparing C-statistics to a reference model (logistic regression). Results The study sample was 55.1% female, 32.8% non-white, and included 16 (0.05%) CRC cases and 478 (15.3%) cases of CRC or high-risk polyps. All machine learning models predicted CRC with higher discriminative ability compared to the reference model [e.g., C-statistics (95%CI); neural network: 0.75 (0.48–1.00) vs. reference: 0.43 (0.18–0.67); P = 0.07] Furthermore, all machine learning approaches, except for gradient boosting, predicted CRC or high-risk polyps significantly better than the reference model [e.g., C-statistics (95%CI); regularized discriminant analysis: 0.64 (0.59–0.69) vs. reference: 0.55 (0.50–0.59); P<0.0015]. The most important predictive variables in the regularized discriminant analysis model for CRC or high-risk polyps were income per zip code, the colonoscopy indication, and body mass index quartiles. Discussion Machine learning can predict CRC risk in adults aged 35–50 using EHR with improved discrimination. Further development of our model is needed, followed by validation in a primary-care setting, before clinical application.