INTRODUCTION: Preterm birth (PTB) remains a leading cause of perinatal morbidity and mortality worldwide. Understanding Singapore's PTB trends and associated risk factors can inform effective strategies for screening and intervention. This study analyses PTB trends in Singapore from 2017 to 2023, identifies risk factors in this multi-ethnic population and evaluates a predictive model for PTB. METHOD: A retrospective analysis of all PTBs between 22+0 and 36+6 weeks of gestation, from 1 January 2017 to 31 December 2023, was performed by extracting maternal and neonatal data from electronic medical records. These PTBs were taken from the registry of births for Singapore and SingHealth cluster data. Cochran- Armitage trend test and multinomial logistic regression were used. An extreme gradient boosting (XGBoost) model was developed to test and predict the risk of PTB. RESULTS: The PTB rate in Singapore did not show a significant change. However, there was modest downward trend in the SingHealth population from 11.3% to 10.2%, mainly in late spontaneous PTBs (sPTBs). sPTBs accounted for ∼60% of PTBs. Risk factors for very/extreme sPTB included Chinese ethnicity, age ≥35 years, body mass index (BMI) ≥23 kg/m², being unmarried, primiparity, twin pregnancy and maternal blood group AB. The XGBoost model achieved an area under the receiver operating characteristic curve of 0.75, indicating moderate ability to predict PTB. CONCLUSION The overall PTB rate in Singapore has not improved. This study underscores the importance of local factors, particularly advanced maternal age, BMI, primiparity, unmarried, Chinese ethnicity and maternal blood group AB influencing PTB risk. Artificial intelligence methods show promise in improving PTB risk stratification, ultimately supporting personalised care and intervention.
Humans ; Singapore/epidemiology* ; Retrospective Studies ; Female ; Risk Factors ; Premature Birth/ethnology* ; Pregnancy ; Adult ; Infant, Newborn ; Asian People/statistics & numerical data* ; Gestational Age ; Body Mass Index ; Maternal Age ; Logistic Models ; Ethnicity

INTRODUCTION: Endometriosis (EMS) is a common gynaecological disorder linked to metabolic disturbances. However, evidence on the associations between lipid accumulation product (LAP) and visceral adiposity index (VAI) with the risk of EMS remains limited. This study aimed to explore the potential associations between LAP, VAI and EMS. METHOD: Data were obtained from the 1999-2006 National Health and Nutrition Examination Survey (NHANES), including a total of 2046 samples. Weighted multivariable logistic regression models and smoothed curve fitting were used to assess the associations between LAP, VAI and EMS. Additionally, subgroup analyses and interaction tests were conducted to evaluate intergroup differences in the associations between LAP, VAI and EMS. RESULTS: In the fully adjusted model, higher Log2 LAP (odds ratio [OR] 1.256, 95% confidence interval [CI] 1.102-1.431, P=0.0014) and Log2 VAI (OR 1.287, 95% CI 1.105-1.498, P=0.0022) were significantly associated with increased EMS risk. Participants in the highest quartile of Log2 LAP (OR 1.983, P=0.0029) and Log2 VAI (OR 1.690, P=0.0486) had a higher risk of EMS. Subgroup analysis showed stronger associations among women with diabetes (Log2 LAP OR 3.681, P=0.009; Log2 VAI OR 4.849, P=0.041). CONCLUSION Elevated LAP and VAI were independently associated with an increased risk of EMS. LAP and VAI may serve as potential indicators for assessing EMS-related risk, suggesting that visceral obesity and lipid metabolic disturbances might play roles in the pathophysiological process of EMS. These findings underscore the potential of LAP and VAI as non-invasive markers for EMS risk, warranting further validation in clinical settings.
Humans ; Female ; Cross-Sectional Studies ; Endometriosis/metabolism* ; Adult ; Nutrition Surveys ; Intra-Abdominal Fat ; Lipid Accumulation Product ; Middle Aged ; Obesity, Abdominal/complications* ; Adiposity ; Risk Factors ; Logistic Models

BACKGROUND: Epidemiological data on chronic diarrhea in the Chinese population are lacking, and the association between obesity and chronic diarrhea in East Asian populations remains inconclusive. This study aimed to investigate the prevalence of chronic diarrhea and its association with obesity in a representative community-dwelling Chinese population. METHODS: This cross-sectional study was based on a multistage, randomized cluster sampling involving 3503 residents aged 20-69 years from representative urban and rural communities in Beijing. Chronic diarrhea was assessed using the Bristol Stool Form Scale (BSFS), and obesity was determined based on body mass index (BMI). Logistic regression analysis and restricted cubic splines were used to evaluate the relationship between obesity and chronic diarrhea. RESULTS: The standardized prevalence of chronic diarrhea in the study population was 12.88%. The average BMI was 24.67 kg/m 2 . Of all the participants, 35.17% (1232/3503) of participants were classified as overweight and 16.13% (565/3503) as obese. After adjustment for potential confounders, individuals with obesity had an increased risk of chronic diarrhea as compared to normal weight individuals (odds ratio = 1.58, 95% confidence interval: 1.20-2.06). A nonlinear association between BMI and the risk of chronic diarrhea was observed in community residents of males and the overall participant group ( P  = 0.026 and 0.017, respectively). CONCLUSIONS This study presents initial findings on the prevalence of chronic diarrhea among residents of Chinese communities while offering substantiated evidence regarding the significant association between obesity and chronic diarrhea. These findings offer a novel perspective on gastrointestinal health management.
Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; Young Adult ; Body Mass Index ; China/epidemiology* ; Chronic Disease/epidemiology* ; Cross-Sectional Studies ; Diarrhea/epidemiology* ; Obesity/complications* ; Prevalence ; East Asian People/statistics & numerical data*

OBJECTIVE

This study aimed to determine the association between Asia-Pacific classification of body mass index, waist circumference and waist hip ratio and clinical outcomes of COVID-19 moderate & severe patients at the height of the COVID-19 pandemic.

A This study involved adult patients diagnosed with COVID-19. 182 patients were analyzed and divided into 167 moderate and 15 severe COVID-19 patients. Primary outcomes (respiratory decompensation, septic shock, and mortality) of patients were compared among Asia Pacific BMI groups.

Among patients with moderate and severe COVID-19, 7 out of 10 patients were obese. Respiratory decompensation and sepsis were more frequently seen in obese patients. Obesity and waist circumference were significantly associated with the odds of respiratory decompensation (95% CI p=0.010 and p=0.002), however this association was not sustained upon adjustment for confounders. On univariate analysis, waist and hip circumferences were significantly associated with the odds of ICU admission (95% CI, p=.013 and p=.034), however after controlling for confounders, only hip ratio retained significant association. Among patients with severe COVID-19, there was insufficient evidence to support significant variations in distributions of outcomes of interest across Asia-Pacific BMI groups.

Our study emphasized that although respiratory decompensation and sepsis were more frequently seen in obese patients. progression of respiratory decompensation and mortality is not significantly associated with obesity as defined by the Asia Pacific BMI classification, warranting the need for larger prospective studies.

INTRODUCTION: Obstructive sleep apnoea (OSA) is a serious but underdiagnosed condition. Demand for the gold standard diagnostic polysomnogram (PSG) far exceeds its availability. More efficient diagnostic methods are needed, even in tertiary settings. Machine learning (ML) models have strengths in disease prediction and early diagnosis. We explored the use of ML with oximetry, demographic and anthropometric data to diagnose OSA. METHODS: A total of 2,996 patients were included for modelling and divided into test and training sets. Seven commonly used supervised learning algorithms were trained with the data. Sensitivity (recall), specificity, positive predictive value (PPV) (precision), negative predictive value, area under the receiver operating characteristic curve (AUC) and F1 measure were reported for each model. RESULTS: In the best performing four-class model (neural network model predicting no, mild, moderate or severe OSA), a prediction of moderate and/or severe disease had a combined PPV of 94%; one out of 335 patients had no OSA and 19 had mild OSA. In the best performing two-class model (logistic regression model predicting no-mild vs. moderate-severe OSA), the PPV for moderate-severe OSA was 92%; two out of 350 patients had no OSA and 26 had mild OSA. CONCLUSION Our study showed that the prediction of moderate-severe OSA in a tertiary setting with an ML approach is a viable option to facilitate early identification of OSA. Prospective studies with home-based oximeters and analysis of other oximetry variables are the next steps towards formal implementation.
Humans ; Oximetry/methods* ; Sleep Apnea, Obstructive/diagnosis* ; Male ; Female ; Middle Aged ; Machine Learning ; Polysomnography ; Adult ; Anthropometry ; ROC Curve ; Aged ; Algorithms ; Predictive Value of Tests ; Sensitivity and Specificity ; Neural Networks, Computer ; Demography

INTRODUCTION: Visual impairment and obesity remain the major public health issues among school-age students in rural areas of China. Obesity is an underlying risk of vision problems. This study aimed to assess the association between visual impairment and body mass index (BMI) among school-age students in rural northwest China. METHODS: This study included 39,385 students from the 4 th to 9 th grade in rural northwest China. From 2018 to 2020, students underwent an assessment of visual acuity (VA) and completed a questionnaire on family demographics, and height and weight measurements. Multiple logistic regression analyses were used to analyse the data. RESULTS: The association between visual impairment and BMI groups was significant in the study population ( P = 0.002) and in different groups (at the different educational, provincial and national levels) ( P < 0.001, separately). Multiple logistic regression analyses revealed a positive relationship between visual impairment and obesity in the study population, including those attending primary school, Han students and the residents of Ningxia autonomous region. CONCLUSION The association between visual impairment and obesity was significant among school-age students in rural northwest China. There should be implementation of policies to address the problem about visual impairment and obesity among school-age students in rural areas.
Humans ; China/epidemiology* ; Body Mass Index ; Male ; Female ; Rural Population ; Vision Disorders/complications* ; Child ; Adolescent ; Students ; Surveys and Questionnaires ; Logistic Models ; Obesity/complications* ; Visual Acuity ; Cross-Sectional Studies

OBJECTIVE: To observe the efficacy and safety of moxibustion in treating patients with central obesity of phlegm-dampness constitution. METHODS: A total of 66 patients with central obesity of phlegm-dampness constitution were randomly assigned to a moxibustion group (n=33, 3 cases dropped out) and a sham moxibustion group (n=33, 4 cases dropped out). The moxibustion group received mild moxibustion combined with lifestyle intervention; the moxibustion was applied at Shenque (CV8) and bilateral Zusanli (ST36), 30 min per session, maintaining a local skin temperature of (43±1) ℃. The sham moxibustion group received simulated moxibustion combined with lifestyle intervention; the simulated moxibustion was applied at the same acupoints, with the same session length, but with a maintained skin temperature of (37±1) ℃. Both groups were treated once every other day, three times per week for 8 consecutive weeks. Obesity-related physical indicators (waist circumference, hip circumference, body weight, body fat percentage, body mass index [BMI]), constitution evaluation indicators (phlegm-dampness constitution conversion score, symptom score), the impact of weight on quality of life-lite (IWQOL-Lite), the hospital anxiety and depression scale (HADS), and the incidence of adverse events were measured before and after treatment, and after 4 weeks of follow-up. RESULTS: Compared with before treatment, both groups showed significant reductions in waist circumference, hip circumference, body weight, body fat percentage, BMI, phlegm-dampness constitution conversion score and symptom score, IWQOL-Lite, and both anxiety and depression subscale scores of HADS after treatment and at follow-up (P<0.001). These improvements were significantly greater in the moxibustion group than those in the sham moxibustion group (P<0.001, P<0.01, P<0.05). One patient in the moxibustion group experienced a mild burn that resolved with routine care; the incidence of adverse reactions was 3.0% (1/33) in the moxibustion group and 0% (0/33) in the sham moxibustion group, with no statistically significant difference (P>0.05). CONCLUSION On the basis of lifestyle intervention, moxibustion effectively improves obesity-related physical indicators, enhances quality of life, alleviates anxiety and depression, and improves the phlegm-dampness constitution in patients with central obesity. These benefits persist for at least 4 weeks after treatment.
Humans ; Moxibustion ; Male ; Female ; Middle Aged ; Adult ; Obesity, Abdominal/psychology* ; Acupuncture Points ; Treatment Outcome ; Aged ; Quality of Life ; Young Adult ; Body Mass Index

Accurate detection of cephalometric landmarks is crucial for orthodontic diagnosis and treatment planning. Current landmark detection methods are mainly divided into heatmap-based and regression-based approaches. However, these methods often rely on parallel computation of multiple models to improve accuracy, significantly increasing the complexity of training and deployment. This paper presented a novel regression method that can simultaneously detect all cephalometric landmarks in high-resolution X-ray images. By leveraging the encoder module of Transformer, a dual-encoder model was designed to achieve coarse-to-fine localization of cephalometric landmarks. The entire model consisted of three main components: a feature extraction module, a reference encoder module, and a fine-tuning encoder module, responsible for feature extraction and fusion of X-ray images, coarse localization of cephalometric landmarks, and fine localization of landmarks, respectively. The model was fully end-to-end differentiable and could learn the intercorrelation relationships between cephalometric landmarks. Experimental results showed that the successful detection rate (SDR) of our algorithm was superior to other existing methods. It attained the highest 2 mm SDR of 89.51% on test set 1 of the ISBI2015 dataset and 90.68% on the test set of the ISBI2023 dataset. Meanwhile, it reduces memory consumption and enhances the model's popularity and applicability, providing more reliable technical support for orthodontic diagnosis and treatment plan formulation.
Cephalometry/methods* ; Humans ; Algorithms ; Anatomic Landmarks/diagnostic imaging* ; Image Processing, Computer-Assisted/methods* ; X-Rays

OBJECTIVE: To analyze the current status and trends of the disease burden of knee osteoarthritis (KOA) in China from 1990 to 2023, and to examine the epidemiological characteristics of age, gender differences, and attribution to high body mass index (BMI), in order to provide a basis for formulating prevention and treatment strategies to reduce the disease burden of KOA in China. METHODS: Based on the 2023 Global Burden of Disease Study (GBD) database, data on the number, rate, and age-standardized rate of incidence, prevalence, disability-adjusted life years (DALYs) for KOA, and DALYs for KOA attributable to high BMI in the Chinese population from 1990 to 2023 were integrated. The Joinpoint 5.4.0.0 software was used to analyze the age and gender differences in KOA and the epidemiological characteristics attributable to high BMI. RESULTS: The standardized incidence, prevalence, and DALYs rates of KOA in China in 2023 increased by 6.46%, 6.43%, and 6.93%, respectively, compared with 1990. In terms of age, the disease burden of KOA in China was lowest in the age group of 30-34 years, with the highest incidence rate in the age group of 50-54 years, whereas the prevalence rate and DALYs rate continued to increase with age, and both were highest in the age group of ≥70 years. In terms of gender, all disease burden standardized rate indicators were higher in females than in males, and the difference widened with age. The rate of BMI-attributable DALYs increased at an annual average rate of 1.57% (95% CI: 1.55, 1.59) from 1990 to 2023, again with significant age and gender differences. CONCLUSION The continued growth of the KOA disease burden and significant population differences characterizing China call for focused attention on the female middle-aged and elderly population, enhanced weight management, and implementation of targeted preventive and control measures.
Humans ; Osteoarthritis, Knee/epidemiology* ; China/epidemiology* ; Male ; Female ; Middle Aged ; Incidence ; Aged ; Adult ; Prevalence ; Body Mass Index ; Disability-Adjusted Life Years ; Cost of Illness ; Aged, 80 and over ; Sex Factors ; Global Burden of Disease ; Age Factors ; Young Adult ; Quality-Adjusted Life Years

OBJECTIVE: To explore the epidemiological characteristics of knee osteoarthritis (KOA) among the elderly in the community, and its correlation with bone mass loss. METHODS: A cross-sectional study was conducted on elderly community population over 50 year old from 12 community health service centers in Zhejiang province. Their gender, age, body mass index (BMI), T value and KOA diagnosis were collected using face to face questionnaire survey. Univariate regression was used to analyze the influence of age, gender, BMI and bone loss on KOA. Logistic multivariate regression model was used to analyze the independent effect of bone mass loss on KOA. RESULTS: Among 4 173 subjects in this study, 1 710 of them were had a KOA. The prevalence rate was 40.9%. The mean age, the proportion of females and the mean BMI in KOA patients were (65.5±3.8) years old, 67.7%(1 158/1 710) and(24.59±1.28) kg·m^-2, respectively, which were significantly higher than (58.5±3.2) years old, 51.3%(1 263/2 463), and (23.48±1.25) kg·m^-2 in non-KOA subjects (P<0.001). In the population aged from 60 to 69 years old, the influence of osteopenia and osteoporosis on the prevalence of KOA was[OR=1.21, 95%CI(1.00, 1.46), P=0.053 2], [OR=1.42, 95%CI(1.14, 1.78), P=0.002 2]. The influence of male and female osteoporosis on the prevalence of KOA was [OR=1.52, 95%CI(1.16, 1.99), P=0.002 7] and [OR=1.87, 95%CI(1.51, 2.32), P<0.000 1], respectively. In the population of 24 kg·m^-2≤BMI<28 kg·m^-2, the influence of osteopenia and osteoporosis on the prevalence of KOA was [OR=1.47, 95%CI(1.21, 1.80), P=0.000 1], [OR=2.69, 95%CI(2.11, 3.42), P<0.000 1], respectively. After controlling the confounding factors of age, gender and BMI, compared with people with normal bone mass, the effect of osteopenia on the prevalence of KOA was [OR=1.34, 95%CI(1.08, 1.67), P=0.009 2], and the effect of osteoporosis on the prevalence of KOA was [OR=1.38, 95%CI(1.06, 1.79), P=0.017 9]. CONCLUSION Elderly overweight women are more likely to develop KOA. Bone mass loss is an independent risk factor for KOA, which will significantly increase the prevalence of KOA in people overweight or aged 60 to 69 years old.
Humans ; Female ; Male ; Aged ; Osteoarthritis, Knee/etiology* ; Middle Aged ; Cross-Sectional Studies ; Bone Density ; Aged, 80 and over ; Incidence ; Body Mass Index ; China/epidemiology* ; Osteoporosis/epidemiology*