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To evaluate the associations between adiposity measures (body mass index, waist circumference, and waist-to-height ratio) with decline in glomerular filtration rate (GFR) and with all cause mortality. Individual participant data meta-analysis. Cohorts from 40 countries with data collected between 1970 and 2017. Adults in 39 general population cohorts (n=5 459 014), of which 21 (n=594 496) had data on waist circumference; six cohorts with high cardiovascular risk (n=84 417); and 18 cohorts with chronic kidney disease (n=91 607). GFR decline (estimated GFR decline ≥40%, initiation of kidney replacement therapy or estimated GFR <10 mL/min/1.73 m ) and all cause mortality. Over a mean follow-up of eight years, 246 607 (5.6%) individuals in the general population cohorts had GFR decline (18 118 (0.4%) end stage kidney disease events) and 782 329 (14.7%) died. Adjusting for age, sex, race, and current smoking, the hazard ratios for GFR decline comparing body mass indices 30, 35, and 40 with body mass index 25 were 1.18 (95% confidence interval 1.09 to 1.27), 1.69 (1.51 to 1.89), and 2.02 (1.80 to 2.27), respectively. Results were similar in all subgroups of estimated GFR. Associations weakened after adjustment for additional comorbidities, with respective hazard ratios of 1.03 (0.95 to 1.11), 1.28 (1.14 to 1.44), and 1.46 (1.28 to 1.67). The association between body mass index and death was J shaped, with the lowest risk at body mass index of 25. In the cohorts with high cardiovascular risk and chronic kidney disease (mean follow-up of six and four years, respectively), risk associations between higher body mass index and GFR decline were weaker than in the general population, and the association between body mass index and death was also J shaped, with the lowest risk between body mass index 25 and 30. In all cohort types, associations between higher waist circumference and higher waist-to-height ratio with GFR decline were similar to that of body mass index; however, increased risk of death was not associated with lower waist circumference or waist-to-height ratio, as was seen with body mass index. Elevated body mass index, waist circumference, and waist-to-height ratio are independent risk factors for GFR decline and death in individuals who have normal or reduced levels of estimated GFR.

Background. Body mass index (BMI) may influence the clinical presentation and progression of rheumatoid arthritis (RA). Understanding its impact is crucial to optimizing disease management and tailoring treatment strategies. This multicenter, cross-sectional study aimed to evaluate the impact of BMI on RA management in a real-world setting.   Methods. Patients diagnosed with RA based on the 2010 ACR/EULAR Classification Criteria were enrolled from 18 academic rheumatology centers. Clinical charts were reviewed to collect demographic data, treatment information, and disease activity measures. Data were analyzed using appropriate statistical methods with GraphPad Prism v9.   Results. A total of 1,432 RA patients were included (mean age 61±10 years; 81.4% female; median disease duration 120 months; 70% RF/ACPA positive). The median BMI was 25 (IQR 22.4–28.1), with males presenting significantly higher BMI values (p<0.001). Obesity (BMI >30 kg/m²) was observed in 14.7% of patients, with no significant sex difference. Overweight (BMI 25–30 kg/m²) was more common in males (1.5-fold, p<0.001), while underweight (BMI <20 kg/m²) was 10 times more prevalent in females (p<0.0001). BMI correlated positively with age, disease duration, blood pressure, triglycerides, CRP levels, and disability (assessed by HAQ-DI), but not with disease activity scores (DAS28-ESR or CDAI). A significant inverse correlation was observed with HDL cholesterol (r = -0.16, p < 0.0001). Smoking habits and diabetes prevalence (7.1%) were not significantly affected by BMI; notably, nearly half of diabetic patients were overweight or obese. The 10-year cardiovascular risk (SCORE-2) was similar between obese and non-obese RA patients. Regarding treatment, 43.5% of patients were on corticosteroids (mean prednisone-equivalent dose 4.9±3.4 mg/day), 68.7% on csDMARDs (80% on methotrexate), and 57.3% on b/tsDMARDs, with one-third receiving monotherapy. Obese patients were more likely to be treated with bDMARDs (64.3% vs. 56.1%, p=0.03; OR 1.41) and tsDMARDs (18.2% vs. 9.8%, p=0.007; OR 2.1), while no significant differences emerged for corticosteroid or csDMARD use.   Conclusion. This study highlights the influence of BMI on RA management in a large Italian real-life cohort. While BMI did not significantly affect disease activity, it was associated with increased disability and influenced therapeutic decisions, particularly the use of b/tsDMARDs. Gender differences were evident in BMI distribution, suggesting the need for personalized approaches in RA care based on body composition and sex-specific factors.

Background: Pulmonary function tests are done to access the changes in the respiratory system and its functions. Peak expiratory flow rate (PEFR) is the maximal flow which is achieved during the expiration, which is delivered with maximal force. PEFR is considered as the simplest index of pulmonary function. Such study has not been conducted earlier in this region. Similar other prior studies gave varied results regarding influence of BMI and height, weight on PEFR readings. Aim: To study the variation of PEFR with respect to the anthropometric parameters among males and females subjects of age group of 18-35 years. Methods: This was a cross sectional study, conducted at Silchar Medical College, Department of Physiology, Assam, India. The ethical committee clearance was taken from Institutional Ethical Committee. The subjects with no cardio respiratory disease, non smokers or medicine users that can influence cardio respiratory function were added in the study. PEFR was measured with the subject seated comfortably during the same time of the day for all subjects. Instrument used was Schiller’s Spirovit SP -1 spirometer. Height was recorded using stadiometer and weight was recorded using weighing machine Results: Out of total subjects, 52% were males, 48% were females. PEFR was highly significant in males. For height 145-155 cm, PEFR is highly significant in males, for height 160-165 cm, PEFR is significantly high in males. Also, PEFR is significantly higher in overweight males. Conclusion: PEFR is higher in males than females subjects, in all age groups. The effect of height, age, weight, BMI on PEFR have all been discussed.

Obesity is heritable and predisposes to many diseases. To understand the genetic basis of obesity better, here we conduct a genome-wide association study and Metabochip meta-analysis of body mass index (BMI), a measure commonly used to define obesity and assess adiposity, in up to 339,224 individuals. This analysis identifies 97 BMI-associated loci ( P  < 5 × 10 −8 ), 56 of which are novel. Five loci demonstrate clear evidence of several independent association signals, and many loci have significant effects on other metabolic phenotypes. The 97 loci account for ∼2.7% of BMI variation, and genome-wide estimates suggest that common variation accounts for >20% of BMI variation. Pathway analyses provide strong support for a role of the central nervous system in obesity susceptibility and implicate new genes and pathways, including those related to synaptic function, glutamate signalling, insulin secretion/action, energy metabolism, lipid biology and adipogenesis. A genome-wide association study and Metabochip meta-analysis of body mass index (BMI) detects 97 BMI-associated loci, of which 56 were novel, and many loci have effects on other metabolic phenotypes; pathway analyses implicate the central nervous system in obesity susceptibility and new pathways such as those related to synaptic function, energy metabolism, lipid biology and adipogenesis. Genetic correlates of obesity In the second of two Articles in this issue from the GIANT Consortium, Elizabeth Speliotes and collegues conducted a genome-wide association study and Metabochip meta-analysis of body mass index (BMI), commonly used to define obesity and assess adiposity, to find 97 BMI-associated loci, of which 56 were novel. Many of these loci have significant effects on other metabolic phenotypes. The 97 loci account for about 2.7% of BMI variation, and genome-wide estimates suggest common variation accounts for more than 20% of BMI variation. Pathway analyses implicate the central nervous system in obesity susceptibility including synaptic function, glutamate signaling, insulin secretion/action, energy metabolism, lipid biology and adipogenesis.

To examine the association of baseline body mass index (BMI) and BMI change with cognitive impairment among older adults in China. The study included data from the Chinese Longitudinal Healthy Longevity Study, a national community-based prospective cohort study from 2002 to 2018. Baseline BMI and BMI change were available for 12,027 adults aged older than 65 years. Cognitive impairment was defined as Chinese version of the Mini Mental State Examination score lower than 18. Multivariable Cox proportional hazard model was used. Among 12,027 participants (mean age was 81.23 years old and 47.48% were male), the proportion of underweight, normal, overweight and obese at baseline was 33.87%, 51.39%, 11.39% and 3.34%, respectively. During an average of 5.9 years’ follow-up, 3086 participants (4.35 per 100 person-years) with incident cognitive impairment were identified. Compared with normal weight group, adjusted hazard ratio (AHR) for cognitive impairment was 0.86 (95% CI 0.75–0.99) among overweight group, whereas corresponding AHR was 1.02 (95% CI 0.94–1.10) in underweight and 1.01 (95% CI 0.80–1.28) in obese participants. Large weight loss (<-10%) was significantly associated with an increased risk of cognitive impairment (AHR, 1.42, 95% CI 1.29–1.56), compared to stable weight status group (-5% ~ 5%). In the restricted cubic spline models, BMI change showed a reverse J-shaped association with cognitive impairment. BMI-defined overweight, but not obesity, was associated with a lower risk of cognitive impairment among elderly Chinese adults, while large weight loss was associated with an increased risk. These findings are consistent with weight loss in the prodromal phase of dementia.

Background A substantial proportion of maternal pregnancy complications, adverse birth outcomes and neurodevelopmental delay in children may be attributable to high maternal pre-pregnancy Body Mass Index (BMI). However, BMI alone is insufficient for the identification of all at-risk mothers and children as many women with non-obesity(< 30 kg/m 2 ) or normal weight(18.5–24.99 kg/m 2 ) and their children may suffer from adversities. Evidence suggests that BMI-related metabolic changes during pregnancy may predict adverse mother–child outcomes better than maternal anthropometric BMI. Methods In a cohort of 425 mother–child dyads, we identified maternal BMI-defined metabolome based on associations of 95 metabolic measures measured three times during pregnancy with maternal pre-pregnancy BMI. We then examined whether maternal BMI-defined metabolome performed better than anthropometric BMI in predicting gestational diabetes, hypertensive disorders, gestational weight gain (GWG), Caesarian section delivery, child gestational age and weight at birth, preterm birth, admission to neonatal intensive care unit (NICU), and childhood neurodevelopment. Based on metabolic measures with the highest contributions to BMI-defined metabolome, including inflammatory and glycolysis-related measures, fatty acids, fluid balance, ketone bodies, lipids and amino acids, we created a set of maternal high BMI-related polymetabolic risk scores (PMRSs), and in an independent replication cohort of 489 mother–child dyads tested their performance in predicting the same set of mother–child outcomes in comparison to anthropometric BMI. Results BMI-defined metabolome predicted all of the studied mother–child outcomes and improved their prediction over anthropometric BMI, except for gestational hypertension and GWG. BMI-related PMRSs predicted gestational diabetes, preeclampsia, Caesarian section delivery, admission to NICU, lower gestational age at birth, lower cognitive development score of the child, and improved their prediction over anthropometric BMI. BMI-related PMRSs predicted gestational diabetes, preeclampsia, Caesarean section delivery, NICU admission and child’s lower gestational age at birth even at the levels of maternal non-obesity and normal weight. Conclusions Maternal BMI-defined metabolome improves the prediction of pregnancy complications, birth outcomes, and neurodevelopment in children over anthropometric BMI. The novel, BMI-related PMRSs generated based on the BMI-defined metabolome have the potential to become biomarkers identifying at-risk mothers and their children for timely targeted interventions even at the level of maternal non-obesity and normal weight.

Abstract Introduction We conducted a meta-analysis of the conflicting epidemiologic evidence on the association between midlife body mass index (BMI) and dementia. Methods We searched standard databases to identify prospective, population-based studies of dementia risk by midlife underweight, overweight, and obesity. We performed random-effects meta-analyses and meta-regressions of adjusted relative risk (RR) estimates and formally explored between-study heterogeneity. Results We included 19 studies on 589,649 participants (2040 incident dementia cases) followed up for up to 42 years. Midlife (age 35 to 65 years) obesity (BMI ≥ 30) (RR, 1.33; 95% confidence interval [CI], 1.08–1.63), but not overweight (25 < BMI < 30) (RR, 1.07; 95% CI, 0.96–1.20), was associated with dementia in late life. The association with midlife underweight (RR, 1.39; 95% CI, 1.13–1.70) was potentially driven by residual confounding ( P from meta-regression = .004), selection ( P  = .046), and information bias ( P  = .007). Discussion Obesity in midlife increases the risk of dementia. The association between underweight and dementia remains controversial.

Brain signals measured over a series of experiments have inherent variability because of different physical and mental conditions among multiple subjects and sessions. Such variability complicates the analysis of data from multiple subjects and sessions in a consistent way, and degrades the performance of subject-transfer decoding in a brain–machine interface (BMI). To accommodate the variability in brain signals, we propose 1) a method for extracting spatial bases (or a dictionary) shared by multiple subjects, by employing a signal-processing technique of dictionary learning modified to compensate for variations between subjects and sessions, and 2) an approach to subject-transfer decoding that uses the resting-state activity of a previously unseen target subject as calibration data for compensating for variations, eliminating the need for a standard calibration based on task sessions. Applying our methodology to a dataset of electroencephalography (EEG) recordings during a selective visual–spatial attention task from multiple subjects and sessions, where the variability compensation was essential for reducing the redundancy of the dictionary, we found that the extracted common brain activities were reasonable in the light of neuroscience knowledge. The applicability to subject-transfer decoding was confirmed by improved performance over existing decoding methods. These results suggest that analyzing multisubject brain activities on common bases by the proposed method enables information sharing across subjects with low-burden resting calibration, and is effective for practical use of BMI in variable environments. •Novel method for extracting spatial bases of brain signals shared by multisubjects.•Subject-transfer decoding using activities on the common spatial bases.•Calibration of the decoders for target subjects using resting-state recordings.•Robust EEG analysis results based on a dataset of more than forty subjects.•Better subject-transfer decoding performance than existing methods.

Background Although obesity is a well-recognised risk factor for cardiovascular disease (CVD), the impact of long-term body mass index (BMI) changes in overweight or obese adults, on the risk of heart failure, CVD and mortality has not been quantified. Methods This population-based cohort study used routine UK primary care electronic health data linked to secondary care and death-registry records. We identified adults who were overweight or obese, free from CVD and who had repeated BMI measures. Using group-based trajectory modelling, we examined the BMI trajectories of these individuals and then determined incidence rates of CVD, heart failure and mortality associated with the different trajectories. Cox-proportional hazards regression determined hazards ratios for incident outcomes. Results 264,230 individuals (mean age 49.5 years (SD 12.7) and mean BMI 33.8 kg/m 2 (SD 6.1)) were followed-up for a median duration of 10.9 years. Four BMI trajectories were identified, corresponding at baseline, with World Health Organisation BMI classifications for overweight, class-1, class-2 and class-3 obesity respectively. In all four groups, there was a small, stable upwards trajectory in BMI (mean BMI increase of 1.06 kg/m 2 (± 3.8)). Compared with overweight individuals, class-3 obese individuals had hazards ratios (HR) of 3.26 (95% CI 2.98–3.57) for heart failure, HR of 2.72 (2.58–2.87) for all-cause mortality and HR of 3.31 (2.84–3.86) for CVD-related mortality, after adjusting for baseline demographic and cardiovascular risk factors. Conclusion The majority of adults who are overweight or obese retain their degree of overweight or obesity over the long term. Individuals with stable severe obesity experience the worst heart failure, CVD and mortality outcomes. These findings highlight the high cardiovascular toll exacted by continuing failure to tackle obesity.

Childhood and adolescent obesity have reached epidemic levels in the United States. Currently, about 17% of US children are presenting with obesity. Obesity can affect all aspects of the children including their psychological as well as cardiovascular health; also, their overall physical health is affected. The association between obesity and other conditions makes it a public health concern for children and adolescents. Due to the increase in the prevalence of obesity among children, a variety of research studies have been conducted to discover what associations and risk factors increase the probability that a child will present with obesity. While a complete picture of all the risk factors associated with obesity remains elusive, the combination of diet, exercise, physiological factors, and psychological factors is important in the control and prevention of childhood obesity; thus, all researchers agree that prevention is the key strategy for controlling the current problem. Primary prevention methods are aimed at educating the child and family, as well as encouraging appropriate diet and exercise from a young age through adulthood, while secondary prevention is targeted at lessening the effect of childhood obesity to prevent the child from continuing the unhealthy habits and obesity into adulthood. A combination of both primary and secondary prevention is necessary to achieve the best results. This review article highlights the health implications including physiological and psychological factors comorbidities, as well as the epidemiology, risk factors, prevention, and control of childhood and adolescent obesity in the United States.