Materiały źródłowe

• #1 Obesity Prognosis & Life Expectancy | Obesity Health Risks

  https://resources.healthgrades.com/right-care/weight-control-and-obesity/obesity-prognosis-and-life-expectancy

  Obesity is not just a health problem on its own; it can cause many other medical conditions that are detrimental to your health. […] Overall, obesity life expectancy is shorter than the life expectancy of normal-weight people, but there are many factors affecting obesity health risks and prognosis, including coexisting conditions and treatment options. […] Severely obese people, meaning those with a BMI (body mass index) of 40 or greater, have a much greater chance of early death than normal weight people. […] In addition to these specific health problems, people who have obesity also have a higher chance of death from any cause. […] As with life expectancy, obesity prognosis can depend on race.

• #2 Obesity as a predictor for a poor prognosis of COVID-19: A systematic review

  https://pmc.ncbi.nlm.nih.gov/articles/PMC7217103/

  COVID-19 is an emerging pandemic due to droplet infection of 2019-novel coronavirus (2019-nCoV). […] Obesity has been associated with impaired immune system, increasing the susceptibility for 2019-nCoV infection. We aimed to study the impact of obesity to the prognosis and disease severity of COVID-19. […] The findings of all included studies were consistent in stating the contribution of obesity as a risk factor to increase the requirement for advanced medical care. […] This is associated with a higher mortality rate in obese population infected with COVID-19. […] Obesity is an independent risk and prognostic factor for the disease severity and the requirement of advanced medical care in COVID-19. […] The growing prevalence of obesity globally have risen the concern on its additional impact to worsen this pandemic.

• #3 Obesity as a predictor for a poor prognosis of COVID-19: A systematic review

  https://pmc.ncbi.nlm.nih.gov/articles/PMC7217103/

  Hence, this evidence suggests that obesity might act as an independent risk factor for a poor disease progression of COVID-19. […] Taken together, this is the first systematic review to proclaim obesity as an independent risk and prognostic factor for the requirement of advanced medical treatment due to COVID-19.

• #4 Predicting higher child BMI z-score and obesity incidence in Malaysia: a longitudinal analysis of a dynamic cohort study | BMC Public Health | Full Text

  https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-024-18917-9

  To target public health obesity prevention, we need to predict who might become obese i.e. predictors of increasing Body Mass Index (BMI) or obesity incidence. […] Higher baseline BMI z-score predicted higher follow-up BMI z-score both in childhood to late adolescence (0.60; 95% CI: 0.55, 0.65) and early to late adolescence (0.76; 95% CI: 0.70, 0.82). […] Parental obesity consistently predicted five-year obesity incidence in early to late adolescence, but not childhood to early adolescence. […] Having a higher BMI z-score at baseline was a stronger predictor of a higher BMI z-score at follow-up than any parental or sociodemographic factor. […] Five-year obesity incidence was higher between childhood and early adolescence than between early and late adolescence (10.8% and 6.1% respectively), consistent with patterns elsewhere of higher incidence at younger ages.

• #5 Predicting higher child BMI z-score and obesity incidence in Malaysia: a longitudinal analysis of a dynamic cohort study | BMC Public Health | Full Text

  https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-024-18917-9

  Associations between parental overweight/obesity and child obesity incidence were larger in early to late adolescence than in childhood to early adolescence with parental overweight associated with a doubling in odds of five-year obesity incidence and parental obesity associated with a 34 times higher odds.

• #6 Obesity and Cancer Fact Sheet – NCI

  https://www.cancer.gov/about-cancer/causes-prevention/risk/obesity/obesity-fact-sheet

  Several possible mechanisms have been suggested to explain how obesity might increase the risks of some cancers. Fat tissue (also called adipose tissue) produces excess amounts of estrogen, which is known to cause cancer. High levels of estrogen have been associated with increased risks of breast, endometrial, ovarian, and some other cancers. […] A study that used nationally representative data on cancer incidence and mortality and risk factor prevalence estimated that in 2019 among people ages 30 and older in the United States, about 43,720 new cancer cases in men (4.8%) and 92,200 new cancer cases in women (10.6%) were due to excess body weight (overweight and obesity). […] Most of the evidence about obesity in cancer survivors comes from people who were diagnosed with breast, prostate, or colorectal cancer. Research indicates that obesity may worsen several aspects of cancer survivorship, including quality of life, cancer recurrence, cancer progression, prognosis (survival), and risk of certain second primary cancers.

• #7 Obesity and Cancer Fact Sheet – NCI

  https://www.cancer.gov/about-cancer/causes-prevention/risk/obesity/obesity-fact-sheet

  Obesity has become more common in the United States in recent yearsso common that it is sometimes referred to as an obesity epidemic. According to the CDC, […] The best evidence linking overweight and obesity to cancer risk comes from large cohort studies, a type of observational study. An International Agency for Research on Cancer (IARC) Working Group concluded, based on a review of more than 1000 such studies, that there is consistent evidence that higher amounts of body fat are associated with an increased risk of a number of cancers. The risk increases associated with obesity are highest for endometrial and esophageal cancers. […] A recent study of more than 2 million people in Spain who were followed for a median of 9 years found evidence that overweight and obesity are linked to 18 cancers, including some not yet considered to be related to obesity. People who have a higher BMI at the time of cancer diagnosis have higher risks of developing a second primary cancer (a cancer not related to the first cancer) in the future.

• #8 Obesity and Cancer Fact Sheet – NCI

  https://www.cancer.gov/about-cancer/causes-prevention/risk/obesity/obesity-fact-sheet

  Most studies of how weight loss affects outcomes in people with cancer have focused on breast cancer. Several randomized clinical trials in breast cancer survivors have reported that weight loss interventions resulted in both weight loss and beneficial changes in biomarkers that have been linked to the association between obesity and prognosis.

• #9 Obesity is a strong predictor of worse clinical outcomes and treatment responses in early rheumatoid arthritis: results from the SWEFOT trial | RMD Open

  https://rmdopen.bmj.com/content/3/2/e000458

  Obesity at baseline was associated with worse continuous-scale clinical outcomes over 24 months (DAS28, HAQ and VAS-pain at last visit: obese vs normal: p0.001; obese vs overweight: p0.05). […] Furthermore, obese patients compared with non-obese patients had significantly greater odds of non-remission at 24 months (adjusted OR (aOR) 5.2; 95% CI 1.8 to 15.2). […] In this early RA trial reflecting todays standard treatment, obesity, in addition to sex, smoking and functional impairment strongly lowered the chance of attaining good clinical outcomes, including remission, todays treatment goal. […] Obesity was found to be the strongest independent predictor of non-remission (adjusted OR (95%CI) 5.2 (1.8 to 15.2)). […] A persistent and dose-dependent association was observed for every clinical outcome measure captured at every follow-up visit; that is to say, a higher BMI category (obeseoverweightnormal weight) was associated with a worse disease activity score, more functional impairment and more painthus validating, but extending previous clinical observations to the randomised setting.

• #10 Obesity is a strong predictor of worse clinical outcomes and treatment responses in early rheumatoid arthritis: results from the SWEFOT trial | RMD Open

  https://rmdopen.bmj.com/content/3/2/e000458

  To conclude, we found that obesitya modifiable lifestyle factorwas associated with worse clinical outcomes over time, measured in disease activity (or its components), functional impairment and pain; but only a marginal inverse association was found for radiographic progression. Obesity at diagnosis was a strong and independent predictor of non-remission after 2 years of treatment according to current guidelines; the other significant predictors being female sex, current smoking and HAQ.

• #11 Can obesity and physical activity predict outcomes of elective knee or hip surgery due to osteoarthritis? A meta-analysis of cohort studies | BMJ Open

  https://bmjopen.bmj.com/content/8/2/e017689

  Objective The aim of this study was to systematically review the literature to identify whether obesity or the regular practice of physical activity are predictors of clinical outcomes in patients undergoing elective hip and knee arthroplasty due to osteoarthritis. […] Our meta-analysis showed that compared to obese participants, non-obese participants report less pain at both short term and long term post-surgery, as well as less disability at long term post-surgery. […] Presurgical obesity is associated with worse clinical outcomes of hip or knee arthroplasty in terms of pain, disability and complications in patients with osteoarthritis. […] Our results suggest that following surgery, non-obese patients experience further reductions in both pain and disability after knee and hip arthroplasty when compared with obese patients, where obesity has been defined as having a BMI of 30kg/m2 or over.

• #12 Can obesity and physical activity predict outcomes of elective knee or hip surgery due to osteoarthritis? A meta-analysis of cohort studies | BMJ Open

  https://bmjopen.bmj.com/content/8/2/e017689

  Our results have a direct impact on clinical practice as the results demonstrate that obese patients have a higher risk of complications and a poorer prognosis in terms of pain and disability postoperatively when compared with non-obese patients. […] Our results have shown that obese patients undergoing hip or knee arthroplasty due to OA have worse outcomes in terms of pain and complications when compared with non-obese patients, with differences more accentuated for patients with knee OA.

• #13 Does the obesity paradox predict functional outcome in intracerebral hemorrhage? in: Journal of Neurosurgery Volume 129 Issue 5 (2017) Journals

  https://thejns.org/view/journals/j-neurosurg/129/5/article-p1125.xml

  Being overweight or mildly obese has been associated with a decreased risk of death or hospitalization in patients with cardiovascular disease. […] In this study the authors examined whether there was an association between obesity and functional outcome in patients with ICH. […] After adjusting for ICH score, sex, do-not-resuscitate code status, and history of hypertension, being overweight or obese (BMI 25 kg/m2) was associated with twice the odds of having a good outcome compared with patients with BMI 25 kg/m2 (adjusted odds ratio 2.05, 95% confidence interval 1.034.06, p = 0.04). […] In patients with ICH admitted to the NICU, being overweight or obese (BMI 25 kg/m2) was associated with favorable outcome after adjustment for established predictors.

• #14 The association between obesity and quality of life: a retrospective analysis of a large-scale population-based cohort study | BMC Public Health | Full Text

  https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-021-12009-8

  Increasing levels of obesity are associated with reduced HRQoL, although this difference is negligible between those of normal weight and those who are overweight. […] To conclude, in agreement with the established literature we have found a clear inverse relationship between increasing weight status and decreasing HRQoL and confirmed the mediating role of long-term conditions in the reduction of HRQoL in people with obesity. […] The analysis has revealed a clear relationship indicating lower levels of QoL with weight status defined by categories of increasing BMI in individuals with BMIs in the range of 18kg/m2 and above. […] The mediation analysis reveals that the presence of mental or physical health-related LTCs has a limited partial mediating effect on the underlying relationship between weight status and QoL.

• #15 The association between obesity and quality of life: a retrospective analysis of a large-scale population-based cohort study | BMC Public Health | Full Text

  https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-021-12009-8

  This study has demonstrated that further work is needed to establish the existence of, and direction of associations; for example, it seems plausible that not only can factors such as BMI and exercise impact on quality of life (as was assumed in this analysis), but also that variables such as exercise level and BMI are correlated with a plausible association in either direction. […] To conclude, in agreement with the established literature we have found a clear inverse relationship between increasing weight status and decreasing QoL, using a large regional cohort study.

• #16 Estimation of Obesity Levels through the Proposed Predictive Approach Based on Physical Activity and Nutritional Habits

  https://www.mdpi.com/2075-4418/13/18/2949

  The performance metrics used to evaluate the models were accuracy, precision, recall, F1-score, and AUC. According to these results, the LR model obtained the best performance measures for all metrics, similar to those of the model trained using full features. […] Within the scope of this study, the LR model emerged as the frontrunner, highlighting a level of performance better than that of alternative methodologies. The strategic integration of feature-selection methods further amplified the model’s efficiency, reinforcing its applicability in real-world scenarios. These findings serve as a poignant reminder of the interconnectedness between physical activity and nutritional habits in combatting obesity.

• #17

  https://www.linkedin.com/pulse/predicting-obesity-logistic-regression-step-by-step-ravichandran-jlwtc

  Project Overview: Predicting Obesity Using Logistic Regression […] In this project, we utilize logistic regression to predict whether individuals in a synthetic dataset are obese based on their age and weight. The dataset is generated with 500 samples, with each individual assigned a randomly generated UserID, Age (between 18 and 80), and Weight. The Obesity Status is simulated as a binary outcome, where older individuals have a higher probability of being obese. […] The logistic regression model uses weight and age as the key input features to predict obesity (1 = Obese, 0 = Not Obese). […] The model calculates the probability of an individual being obese based on these two features. […] For individuals with higher weights, the model tends to assign a higher probability of obesity, especially as weight increases in combination with age. […] The model’s performance is evaluated using the following metrics: […] The logistic regression model performs well in predicting obesity, showing high accuracy and precision. While the recall for predicting obesity is slightly lower, the overall results suggest the model is effective for binary classification tasks involving health indicators like obesity.

• #18 Applying machine learning approaches for predicting obesity risk using US health administrative claims database – PubMed

  https://pubmed.ncbi.nlm.nih.gov/39327067/

  Body mass index (BMI) is inadequately recorded in US administrative claims databases. […] We aimed to validate the sensitivity and positive predictive value (PPV) of BMI-related diagnosis codes using an electronic medical records (EMR) claims-linked database. Additionally, we applied machine learning (ML) to identify features in US claims databases to predict obesity status. […] The PPV of diagnosis codes from claims alone remained high over the study period (85.4-89.2%); sensitivity was low (16.8-44.8%). […] XGBoost performed the best at predicting obesity with the highest area under the curve (AUC; 79.4%) and the lowest Brier score. […] Obesity prevalence is under-reported in claims databases. […] ML models, with or without explicit obesity, show promise in improving obesity prediction accuracy compared with obesity codes alone. Improved obesity status prediction may assist practitioners and payors to estimate the burden of obesity and investigate the potential unmet needs of current treatments.

• #19 Childhood overweight and obesity at the start of primary school: External validation of pregnancy and early-life prediction models | PLOS Global Public Health

  https://journals.plos.org/globalpublichealth/article?id=10.1371/journal.pgph.0000258

  Tackling the childhood obesity epidemic can potentially be facilitated by risk-stratifying families at an early-stage to receive prevention interventions and extra support. […] The SLOPE models developed for predicting childhood overweight and obesity risk demonstrated good model performance on external validation in a birth cohort with a different geographical location and ethnic composition. […] Prediction models are used to estimate the probability of developing a particular disease or outcome. Prediction models can provide more accurate risk estimates compared to more subjective predictions and can augment clinical judgement to enable intervention at an early stage before the development of the disease or outcome under consideration. […] The AUC on external validation was comparable to that at model development in SLOPE at all stages (booking, birth, ~1 year and ~2 years). […] Using a 20% risk threshold, 36.9% were identified as high risk at booking and captured 53.3% of overweight and obesity events at 45 years.

• #20 Prediction of childhood overweight and obesity at age 10–11: findings from the Studying Lifecourse Obesity PrEdictors and the Born in Bradford cohorts | International Journal of Obesity

  https://www.nature.com/articles/s41366-023-01356-8

  In England, 41% of children aged 10-11 years live with overweight or obesity. Identifying children at risk of developing overweight or obesity may help target early prevention interventions. We aimed to develop and externally validate prediction models of childhood overweight and obesity at age 10-11 years using routinely collected weight and height measurements at age 4-5 years and maternal and early-life health data. […] This prediction modelling can be applied at 4-5 years to identify the risk for childhood overweight at 10-11 years, with slightly improved prediction with the inclusion of maternal data. These prediction models demonstrate that routinely collected data can be used to target early preventive interventions to reduce the prevalence of childhood obesity. […] We have developed and both internally and externally validated prediction models at 10-11 years using data routinely collected in England at 4-5 years. We then incorporated routinely collected data from earlier time-points starting from early pregnancy. Our analysis shows that it is possible to predict childhood overweight and obesity at age 10-11 (Year 6) at age 4-5 (Year R) with good discrimination (AUC 0.82 on development and 0.83 on external validation). The inclusion of routinely available maternal pregnancy data improves the model discrimination (AUC 0.84 on development and 0.85 on external validation). Both models were well calibrated on development and external validation.

• #21 Prediction of childhood overweight and obesity at age 10–11: findings from the Studying Lifecourse Obesity PrEdictors and the Born in Bradford cohorts | International Journal of Obesity

  https://www.nature.com/articles/s41366-023-01356-8

  These prediction models can be applied at 4-5 years to identify the risk for later childhood overweight at 10-11 years. The inclusion of maternal pregnancy data slightly improves the prediction. These models demonstrate that utilising routinely collected healthcare data can form the basis of a risk identification system to strengthen the long-term preventive element of early years care by quantifying future obesity risk in families.

• #22 Establishing a Prediction Model for Weight Loss Outcomes After LSG in | DMSO

  https://www.dovepress.com/establishing-a-prediction-model-for-weight-loss-outcomes-after-lsg-in–peer-reviewed-fulltext-article-DMSO

  This study developed a preoperative predictive model for weight loss outcomes following LSG by integrating three key indicators: REE/BW, FFMI, and WC. […] In conclusion, the combination of REE/BW, FFMI, and WC provides a relatively accurate preoperative prediction of weight loss outcomes one year post-LSG.

• #23 Establishing a Prediction Model for Weight Loss Outcomes After LSG in | DMSO

  https://www.dovepress.com/establishing-a-prediction-model-for-weight-loss-outcomes-after-lsg-in–peer-reviewed-fulltext-article-DMSO

  In obese Chinese patients with a BMI 32.5 kg/m2, the Inbody-based nomogram integrating REE/BW, FFMI, and WC offers an effective preoperative tool for predicting weight loss outcomes one year after LSG, facilitating surgical planning and postoperative management. […] The preoperative resting energy expenditure to body weight ratio (REE/BW), fat-free mass index (FFMI), and waist circumference (WC) emerged as independent predictive factors for weight loss outcomes at one year post-LSG. […] This study aims to identify key body composition indicators that can predict weight loss outcomes in Chinese patients undergoing LSG and to establish a preoperative predictive model, providing valuable insights for clinical decision-making by bariatric surgeons. […] The five most influential variables were subsequently included in a multivariate logistic regression model, which indicated that REE/BW, FFMI, and WC were independent predictors of weight loss outcomes.

• #24 Next-Generation Sequencing of a Large Gene Panel for Outcome Prediction of Bariatric Surgery in Patients with Severe Obesity

  https://www.mdpi.com/2077-0383/11/24/7531

  Obesity is a chronic disease in which abnormal deposition of fat threatens health, leading to diabetes, cardiovascular diseases, cancer, and other chronic illnesses. […] To this purpose, we developed an NGS gene panel, comprising 72 diagnostic genes and 244 candidate genes, and we sequenced 247 adult obese Italian patients. […] The NGS-based panel, including diagnostic and candidate genes used in this study, could play a role in evaluating, diagnosing, and managing obese individuals, and may help in predicting the outcome of bariatric surgery. […] A patient’s genetic risk score can be used to determine individual predisposition to obesity. […] Moreover, in the preoperative assessment phase of bariatric surgery, genetic tests may be useful in identifying patients that will be responsive to bariatric surgery, as well as in choosing the most suitable bariatric procedure.

• #25 Next-Generation Sequencing of a Large Gene Panel for Outcome Prediction of Bariatric Surgery in Patients with Severe Obesity

  https://www.mdpi.com/2077-0383/11/24/7531

  Here, we report the first Italian study and one of the first in Europe to evaluate the use of NGS in predicting the outcome of bariatric surgery. […] Target sequencing of a panel of genes including diagnostic and candidate genes may be useful in evaluating, diagnosing, and managing obese individuals, and may predict the outcome of bariatric surgery. […] Our results support already published scientific articles, and indicate that genetic testing could be useful to predict the outcome of bariatric surgery, and that patients with predicted deleterious variants to genes correlated to obesity could respond less to bariatric surgery. […] We propose that testing the genetics of severe obesity patients before performing bariatric surgery could be useful in the clinical management of patients with severe obesity.

• #26 Subclassification of obesity for precision prediction of cardiometabolic diseases | Nature Medicine

  https://www.nature.com/articles/s41591-024-03299-7

  Obesity and cardiometabolic disease often, but not always, coincide. Distinguishing subpopulations within which cardiometabolic risk diverges from the risk expected for a given body mass index (BMI) may facilitate precision prevention of cardiometabolic diseases. […] We detected five discordant profiles consisting of individuals with cardiometabolic biomarkers higher or lower than expected given their BMI, which generally increases disease risk, in total representing ~20% of the total population. […] Subtle BMI-discordances in biomarkers affected disease risk. For instance, a 10% higher probability of having a discordant lipid profile was associated with a 5% higher risk of MACE (hazard ratio in women 1.05, 95% confidence interval 1.03, 1.06, P=4.191010; hazard ratio in men 1.05, 95% confidence interval 1.04, 1.06, P=9.331014).

• #27 Subclassification of obesity for precision prediction of cardiometabolic diseases | Nature Medicine

  https://www.nature.com/articles/s41591-024-03299-7

  Multivariate prediction models for MACE and type 2 diabetes performed better when incorporating discordant profile information (likelihood ratio test P0.001). […] We found that discordant and concordant profiles also differed in their BMIbiomarker relationships (that is, how biomarkers change when BMI increases, using profile allocation probabilities as regression weights to obtain profile-specific estimates). […] We estimated profile-specific prevalence of various cardiometabolic comorbidities associated with the biomarkers selected using allocation probabilities as weights. […] The subset of individuals who were free of cardiometabolic conditions (CHD, stroke, type 1 diabetes (T1D), T2D, hypertension, liver failure, rheumatoid arthritis), had similar profile allocations, with the exception of lower DHG profile probabilities.

• #28 Subclassification of obesity for precision prediction of cardiometabolic diseases | Nature Medicine

  https://www.nature.com/articles/s41591-024-03299-7

  We found that adding profile information only marginally increased the variance explained in diabetes progression in UKB. However, in RS, where the median glucose values were higher, the fraction increased to 812% (likelihood ratio P0.001). […] Our analyses show that accuracy can be improved when discordancy variables are included in prediction models. […] Collectively, these discordant clusters characterize ~20% of the general population and improve the precision and accuracy of CVD and T2D risk prediction to a similar degree as contemporary clinical risk markers such as LDL.

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