Squamous cell lung cancer (SQCLC), which is fatal to humans, is heterogeneous with different genetic and histological features. We used SBMOI, a multi-omics data integration method from previous study, to integrate clinical, gene expression, and somatic mutation data of SQCLC to construct new patient features. Next, random survival forest (RSF) model and SimpleMKL model were constructed to predict the survival of SQCLC patients, and K-means model was constructed to perform molecular subtyping. The results of the RSF model showed that when the dimension of the patient features were 11 x 364 and the hard threshold was 0.2, we obtained the best results, and the AUC value of the 1-year time-dependent ROC curve was 0.706. The SimpleMKL model, constructed using the same patient features, performed exceptionally well, with 1-year, 5-year, and 10-year survival prediction AUC values of 0.944, 0.947 and 0.950, respectively. We used K-means analysis to identify three SQCLC molecular subtypes with significant survival differences. The patient features constructed by SBMOI were used to effectively predict the survival and molecular subtyping of SQCLC patients. In addition, our study further confirmed the effectiveness in multi-omics data integration task and broad applicability of SBMOI.

BACKGROUND AND AIM:In this study, a deep learning algorithm was used to predict the survival rate of colon cancer (CC) patients, and compared its performance with traditional Cox regression. METHODS:In this population-based cohort study, we used the characteristics of patients diagnosed with CC between 2010 and 2015 from the Surveillance, Epidemiology and End Results (SEER) database. The population was randomized into a training set (n = 10 596, 70%) and a test set (n = 4536, 30%). Brier scores, area under the (AUC) receiver operating characteristic curve and calibration curves were used to compare the performance of the three most popular deep learning models, namely, artificial neural networks (ANN), deep neural networks (DNN), and long-short term memory (LSTM) neural networks with Cox proportional hazard (CPH) model. RESULTS:In the independent test set, the Brier values of ANN, DNN, LSTM and CPH were 0.155, 0.149, 0.148, and 0.170, respectively. The AUC values were 0.906 (95% confidence interval [CI] 0.897-0.916), 0.908 (95% CI 0.899-0.918), 0.910 (95% CI 0.901-0.919), and 0.793 (95% CI 0.769-0.816), respectively. Deep learning showed superior promising results than CPH in predicting CC specific survival. CONCLUSIONS:Deep learning showed potential advantages over traditional CPH models in terms of prognostic assessment and treatment recommendations. LSTM exhibited optimal predictive accuracy and has the ability to provide reliable information on individual survival and treatment recommendations for CC patients.

BACKGROUND AND OBJECTIVE:The morbidity of lung adenocarcinoma (LUAD) has been increasing year by year and the prognosis is poor. This has prompted researchers to study the survival of LUAD patients to ensure that patients can be cured in time or survive after appropriate treatment. There is still no fully valid model that can be applied to clinical practice.METHODS:We introduced struc2vec-based multi-omics data integration (SBMOI), which could integrate gene expression, somatic mutations and clinical data to construct mutation gene vectors representing LUAD patient features. Based on the patient features, the random survival forest (RSF) model was used to predict the long- and short-term survival of LUAD patients. To further demonstrate the superiority of SBMOI, we simultaneously replaced scale-free gene co-expression network (FCN) with a protein-protein interaction (PPI) network and a significant co-expression network (SCN) to compare accuracy in predicting LUAD patient survival under the same conditions.RESULTS:Our results suggested that compared with SCN and PPI network, the FCN based SBMOI combined with RSF model had better performance in long- and short-term survival prediction tasks for LUAD patients. The AUC of 1-year, 5-year, and 10-year survival in the validation dataset were 0.791, 0.825, and 0.917, respectively.CONCLUSIONS:This study provided a powerful network-based method to multi-omics data integration. SBMOI combined with RSF successfully predicted long- and short-term survival of LUAD patients, especially with high accuracy on long-term survival. Besides, SBMOI algorithm has the potential to combine with other machine learning models to complete clustering or stratificational tasks, and being applied to other diseases.

ObjectiveThis study aims to assess the diagnostic utility of circulating tumor cells (CTCs) in conjunction with low-dose computed tomography (LDCT) for differentiating between benign and malignant pulmonary nodules and to substantiate the foundation for their integration into clinical practice.MethodsA systematic literature review was performed independently by two researchers utilizing databases including PubMed, Web of Science, The Cochrane Library, Embase, and Medline, to collate studies up to September 15, 2023, that investigated the application of CTCs in diagnosing pulmonary nodules. A meta-analysis was executed employing Stata 15.0 and Revman 5.4 to calculate the pooled sensitivity, specificity, positive and negative likelihood ratios (PLR and NLR), diagnostic odds ratio (DOR), and the area under the receiver operating characteristic curve (AUC). Additionally, trial sequential analysis was conducted using dedicated TSA software.ResultsThe selection criteria identified 16 studies, encompassing a total of 3409 patients. The meta-analysis revealed that CTCs achieved a pooled sensitivity of 0.84 (95% CI 0.80 to 0.87), specificity of 0.80 (95% CI 0.73 to 0.86), PLR of 4.23 (95% CI 3.12 to 5.72), NLR of 0.20 (95% CI 0.16 to 0.25), DOR of 20.92 (95% CI 13.52 to 32.36), and AUC of 0.89 (95% CI 0.86 to 0.93).ConclusionsCirculating tumor cells demonstrate substantial diagnostic accuracy in distinguishing benign from malignant pulmonary nodules. The incorporation of CTCs into the diagnostic protocol can significantly augment the diagnostic efficacy of LDCT in screening for malignant lung diseases.

BACKGROUND:Breast cancer is the most common malignant tumor among women, and its incidence is increasing annually. At present, the results of the study on whether optical coherence tomography (OCT) can be used as an intraoperative margin assessment method for breast-conserving surgery (BCS) are inconsistent. We herein conducted this systematic review and meta-analysis to assess the diagnostic value of OCT in BCS.METHODS:PubMed, Web of Science, Cochrane Library, and Embase were used to search relevant studies published up to September 15, 2022. We used Review Manager 5.4, Meta-Disc 1.4, and STATA 16.0 for statistical analysis.RESULTS:The results displayed 18 studies with 782 patients included according to the inclusion and exclusion criteria. Meta-analysis showed the pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR) and the area under the curve (AUC) of OCT in the margin assessment of BCS were 0.91 (95% CI 0.88-0.93), 0.88 (95% CI 0.83-0.92), 7.53 (95% CI 5.19-10.93), 0.11(95% CI 0.08-0.14), 70.37 (95% CI 39.78-124.47), and 0.94 (95% CI 0.92-0.96), respectively.CONCLUSIONS:OCT is a promising technique in intraoperative margin assessment of breast cancer patients.

Background Depression is a common mental health problem among veterans, with high mortality. Despite the numerous conducted investigations, the prediction and identification of risk factors for depression are still severely limited. This study used a deep learning algorithm to identify depression in veterans and its factors associated with clinical manifestations.Methods Our data originated from the National Health and Nutrition Examination Survey (2005-2018). A dataset of 2,546 veterans was identified using deep learning and five traditional machine learning algorithms with 10-fold cross-validation. Model performance was assessed by examining the area under the subject operating characteristic curve (AUC), accuracy, recall, specificity, precision, and F1 score.Results Deep learning had the highest AUC (0.891, 95%CI 0.869-0.914) and specificity (0.906) in identifying depression in veterans. Further study on depression among veterans of different ages showed that the AUC values for deep learning were 0.929 (95%CI 0.904-0.955) in the middle-aged group and 0.924(95%CI 0.900-0.948) in the older age group. In addition to general health conditions, sleep difficulties, memory impairment, work incapacity, income, BMI, and chronic diseases, factors such as vitamins E and C, and palmitic acid were also identified as important influencing factors.Conclusions Compared with traditional machine learning methods, deep learning algorithms achieved optimal performance, making it conducive for identifying depression and its risk factors among veterans.

BackgroundEvidence for a relationship between oxidative stress and osteoporotic fractures in humans is limited. Fluorescent oxidation products (FlOPs, excitation/emission wavelengths 320/420nm denoted FlOP_320; 360/420nm [FlOP_360]; and 400/475nm [FlOP_400]) are global biomarkers of oxidative stress, and reflect oxidative damage to proteins, phospholipids, and nucleic acids. We investigated the association between FlOPs and a recent osteoporotic fracture.MethodsWe conducted a case-control study in a Chinese population aged 50 years or older. A recent osteoporotic fracture in the cases was confirmed by x-ray. Cases were matched with community-based non-fracture controls (1:2 ratio) for age (± 4 years) and sex. In addition, we conducted a sensitivity unmatched case-control study which included all fracture cases and all eligible non-fracture controls prior to matching. Plasma FlOPs were measured with a fluorescent microplate reader. We used unconditional logistic regression to analyze the association between FlOPs (per 1-SD increase in logarithmic scale) and fracture; odds ratios (OR) and 95% confidence intervals (95% CI) were reported.ResultsForty-four cases and 88 matched controls (mean age: 68.2 years) were included. After covariate adjustment (i.e., body mass index, physical activity, and smoking), higher FlOP_360 (OR = 1.85; 95% CI = 1.03 – 3.34) and FlOP_400 (OR = 13.29; 95% CI = 3.48 – 50.69) levels, but not FlOP_320 (OR = 0.56; 95% CI = 0.27 – 1.15), were associated with increased fracture risk. Subgroup analyses by fracture site and unmatched case-control study found comparable associations of FlOP_360 and FlOP_400 with hip and non-hip fractures.ConclusionsHigher FlOP_360 and FlOP_400 levels were associated with increased risk of fracture, and this association was comparable for hip and non-hip fractures. Prospective studies are warranted to confirm this finding.

Depression often coexists with many chronic diseases. However, previous studies mainly focused on the association between a single chronic disease or chronic diseases of the elderly and depression. This study included 26,177 adults aged more than 20 years old from the 2007-2018 National Health and Nutrition Examination Survey. Depression was determined by nine questions which were from the Patient Health Questionnaire. We used propensity score matching to reduce the influence of confounders between the depression and non-depression groups. A multivariate logistic regression model was used to analyze the relationship between various chronic diseases and the number of diseases and depression. The prevalence of depression in participants with chronic diseases was higher than that in participants without chronic diseases, 20.8% of participants with chronic bronchitis had depression. After matching and controlling sleep, insurance and smoking, the highest risk of depression (OR = 1.524; 95% CI: 1.162-2.001) was found in people with stroke, followed by arthritis (OR = 1.464; 95% CI: 1.275-1.681). The percentage of participants with two or more chronic diseases with depression and without depression was 68.9% and 51.9%, respectively. Participants with five or more chronic diseases had the highest risk of depression (OR = 3.653; 95% CI: 3.001-4.446). In conclusion, patients with chronic diseases are at higher risk for depression, especially those with multiple chronic diseases. This study suggested that we should pay more attention to the mental health of people with chronic diseases.

OBJECTIVE:The number of heart disease patients is increasing. Establishing a risk assessment model for chronic heart disease (CHD) based on risk factors is beneficial for early diagnosis and timely treatment of high-risk populations. METHODS:Four machine learning models, including logistic regression, support vector machines (SVM), random forests, and extreme gradient boosting (XGBoost), were used to evaluate the CHD among 14 971 participants in the National Health and Nutrition Examination Survey from 2011 to 2018. The area under the receiver-operator curve (AUC) is the indicator that we evaluate the model. RESULTS:In four kinds of models, SVM has the best classification performance (AUC = 0.898), and the AUC value of logistic regression and random forest were 0.895 and 0.894, respectively. Although XGBoost performed the worst with an AUC value of 0.891. There was no significant difference among the four algorithms. In the importance analysis of variables, the three most important variables were taking low-dose aspirin, chest pain or discomfort, and total amount of dietary supplements taken. CONCLUSION:All four machine learning classifiers can identify the occurrence of CHD based on population survey data. We also determined the contribution of variables in the prediction, which can further explore their effectiveness in actual clinical data.

Background: Asthma is a common chronic disease in American adults. The prevalence of asthma has varied over time, but there are few studies on the long-term trend of asthma in American adults. Objective: To describe the prevalence and trend of asthma in American adults from 2005 to 2018 and analyze the risk factors for asthma. Methods: Data collection was performed from National Health and Nutrition Examination Survey 2005 to 2018. The unweighted number and weighted percentages of normal participants and patients with asthma and the trends of asthma were calculated. Weighted univariate logistic regression was used to analyze the risk factors for asthma. Results: A total of 39,601 adults were included in this study. From 2005 to 2018, the overall prevalence of asthma in American adults was 8.41%, whereas that in young, middle-aged, and elderly adults was 8.30%, 8.70%, and 7.92%, respectively. The estimated prevalence of asthma in the overall adults and young adults increased with time (P for trend = .03, difference = 0.023 and P for trend = .007, difference = 0.060, respectively), and the estimated prevalence of middle-aged and elderly adults remained stable with time (P for trend = .33, difference = 0.015 and P for trend = .80, difference = -0.024, respectively). Conclusion: Asthma in American adults was on the rise. Female sex, non-Hispanic Blacks, individuals with low annual household income, active smokers, obese patients, patients with hypertension, patients with diabetes, and individuals with positive asthma family history were associated with a higher risk for developing asthma. ?? 2022 American College of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.

In vitro and vivo studies indicate that oxidative stress contributes to bone loss. Fluorescent oxidation products (FlOPs) are novel biomarkers of oxidative stress; they reflect global oxidative damage of lipids, proteins, carbohydrates, and DNA. However, whether FlOPs are associated with bone mineral density (BMD) is still unclear. In the present study, we examined the association between FlOPs and BMD among male veterans. This cross-sectional study was conducted among participants recruited from the Department of Medical Examination, The Second Hospital of Jilin University in Jilin, China. We identified male veterans who were at least 50 y old between June and October of 2019. Plasma FlOPs were measured with a fluorescent microplate reader (excitation/emission wavelength: 320/420 nm). BMD were measured by dual-energy X-ray absorptiometry (DXA). The association between FlOPs and BMD was tested by multivariable linear regression models. A total of 164 male veterans were enrolled in the study, the average age was 56.6 y. After adjusting for covariates, veterans who had FlOP levels in the highest tertile had a statistically significant lower femoral neck (β = -0.044; p = 0.007) and total hip BMD (β = -0.045; p = 0.020) as compared to those with FlOP levels in the lowest tertile. Similar results were found when FlOPs were treated as a continuous variable (per 1-SD increase, β = -0.014 and p = 0.033 for femoral neck BMD; β = -0.016 and p = 0.047 for total hip BMD). Higher FlOP levels were associated with lower BMD among male veterans.

Two types of progressive muscular dystrophy occur in Tunisian children. The first type is characterized by normal dystrophin assays and affects girls and boys in an autosomal recessive pattern of inheritance. The second type has the features of the typical Duchenne muscular dystrophy (DMD) and has abnormal dystrophin. Between 1974 and 1986, 77 patients with Duchenne muscular dystrophy were examined, 66 were biopsied. Among affected siblings and within family kindreds, we observed both clinical and histopathological variability. However, there was a close correlation between the clinical condition and the biopsy findings in each case, allowing accurate prediction of the patient's course and probable duration of the disease.

Real-time quaking-induced conversion (RT-QuIC) is a novel in vitro acellular seed amplification analysis and has been widely used to detect prion diseases. Due to the similar mechanism of abnormal aggregation of α-synuclein, RT-QuIC has great potential for diagnosing Lewy body diseases. This meta-analysis was performed to evaluate the diagnostic accuracy of RT-QuIC for Lewy body diseases. This study followed the PRISMA statement. We searched six databases for relevant studies published until February 20, 2022. Meta-analysis was conducted using RevMan 5.3, Stata 17.0, and Meta-Disc 1.4. Subgroup analyses were performed to explore sources of heterogeneity. A total of 16 studies were included in this study. The pooled sensitivity and specificity were 0.91 (95%CI: 0.85–0.94) and 0.95 (95%CI: 0.90–0.97), respectively. The pooled positive and negative likelihood ratios were 17.16 (95% CI: 9.16–32.14) and 0.10 (95% CI: 0.06–0.17), respectively. The pooled diagnostic odds rate and area under the summary receiver operating characteristic curve were 171.16 (95% CI: 66.64–439.62) and 0.97 (95% CI: 0.96–0.99), respectively. This study was the first meta-analysis on RT-QuIC for Lewy body diseases. RT-QuIC is a reliable and accurate method to diagnose Lewy body diseases.

Background: Among patients with ovarian cancer (OC), the risk of contralateral OC remains controversial and few studies have focused on the occurrence of contralateral OC after conservative surgery. Methods: Basing on the Surveillance, Epidemiology, and End Results (SEER) database registered between 2000 and 2018, Logistic and Cox regressions were established to test the risk factors of contralateral OC. Kaplan-Meier mothed was used to calculate the cumulative risk curve for contralateral OC and compared using log-rank test. Furthermore, the frequency of contralateral OC and standardized incidence ratios (SIRs) were evaluated. Results: 18807 patients were included, 69 patients developed contralateral OC. Logistic and Cox regressions showed patients diagnosed >50 years had lower risk of contralateral OC (Odds ratio [OR]:0.42, 95% confidence interval [CI]: 0.24-0.73; Hazard ratios [HR]:0.44, 95%CI:0.24-0.77). Patients with radical surgery had lower contralateral OC risk (OR:0.20, 95%CI: 0.11-0.36; HR: 0.17, 95%CI: 0.09-0.30). The SIR for contralateral OC was high in all patients (SIR: 2.37, 95%CI: 1.85-3.00) and highest if patients diagnosed <50 years with conservative surgery (SIR: 27.33, 95%CI: 19.86-36.69). However, the SIR for contralateral OC was low in patients diagnosed >= 50 years with radical surgery (SIR: 0.54, 95%CI: 0.26 e1.00). No statistically significant SIRs were observed in patients diagnosed < 50 years with conservative surgery and patients diagnosed <50 years with radical surgery. Conclusions: Our study provided some information for clinicians to assess the risk of contralateral OC and suggested young patients should not undergo hysterectomy to prevent contralateral OC. Moreover, clinical surveillance cannot be relaxed. (c) 2022 Elsevier Ltd, BASO similar to The Association for Cancer Surgery, and the European Society of Surgical Oncology. All rights reserved.

PURPOSE:Advanced machine learning (ML) algorithms can assist rapid medical image recognition and realize automatic, efficient, noninvasive, and convenient diagnosis. We aim to further evaluate the diagnostic performance of ML to distinguish patients with probable Alzheimer's disease (AD) from normal older adults based on structural magnetic resonance imaging (MRI).METHODS:The Medline, Embase, and Cochrane Library databases were searched for relevant literature published up until July 2021. We used the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool and Checklist for Artificial Intelligence in Medical Imaging (CLAIM) to evaluate all included studies' quality and potential bias. Random-effects models were used to calculate pooled sensitivity and specificity, and the Deeks' test was used to assess publication bias.RESULTS:We included 24 models based on different brain features extracted by ML algorithms in 19 papers. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, and area under the summary receiver operating characteristic curve for ML in detecting AD were 0.85 (95%CI 0.81-0.89), 0.88 (95%CI 0.84-0.91), 7.15 (95%CI 5.40-9.47), 0.17 (95%CI 0.12-0.22), 43.34 (95%CI 26.89-69.84), and 0.93 (95%CI 0.91-0.95).CONCLUSION:ML using structural MRI data performed well in diagnosing probable AD patients and normal elderly. However, more high-quality, large-scale prospective studies are needed to further enhance the reliability and generalizability of ML for clinical applications before it can be introduced into clinical practice.

The meta-analysis was prepared to evaluate the diagnostic value of surface-enhanced Raman spectroscopy for patients with breast cancer and to provide some statistically significant reference for the detection process. PubMed, Web of Science, Cochrane library, and Embase databases were retrieved for randomized clinical trials by two independent reviewers. A comprehensive search was performed on July 15, 2022. Meta-Disc 1.4 was used to calculate Spearman correlation coefficient. STATA 14.1 software was used to evaluate the diagnostic effect and the performance of subgroup analysis, which could be conducted to explore the potential sources of heterogeneity. Review manager 5.3 software was performed for quality assessment in our meta-analysis. The results displayed 19 studies with 1299 patients were included according to the inclusion and exclusion criteria. Meta-analysis displayed the pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio and the area under the curve of using surface enhanced Raman spectroscopy for diagnosing the patients with breast cancer were 0.91 (95% CI 0.85-0.94), 0.94 (95% CI 0.91-0.96), 14.06 (95% CI 9.25-21.35), 0.10 (95% CI 0.06-0.17), 140.64 (95% CI 59.63-331.70), and 0.97 (95% CI 0.95-0.98), respectively. The source of specificity heterogeneity was from different sample volume mixed with nanoparticle. Surface-enhanced Raman spectroscopy had high sensitivity and specificity in the diagnosis of breast cancer. It was an accurate and effective technique for patients with breast cancer.

OBJECTIVE:To explore the risk factors and prognostic factors of invasive ductal carcinoma (IDC) and to predict the survival of IDC patients with metastasis.METHOD:We used multivariate logistic regression to identify independent risk factors affecting metastasis in IDC patients and used Cox regression to identify independent prognostic factors affecting the overall survival of patients with metastasis. Nomogram was used to predict survival, while C-index and calibration curves were used to measure the performance of nomogram. Kaplan-Meier method was used to calculate the survival curves of patients with different independent prognostics factors and different metastatic sites, and the differences were compared by log-rank test. The data of our study were obtained from the Surveillance, Epidemiology and End Results cancer registry.RESULT:Our study included 226,094 patients with IDC. In multivariate analysis, independent risk factors of metastasis included age, race, marital status, income, geographic region, grade, T stage, N stage, subtype, surgery and radiotherapy. Independent prognostic factors included age, race, marital status, income, geographic region, grade, T stage, N stage, subtype, surgery and chemotherapy. We established a nomogram, of which the C-index was 0.701 (0.693, 0.709), with the calibration curves showing that the disease-specific survival between actual observation and prediction had a good consistency. The survival curves of different metastatic patterns were significantly different (log-rank test: χ2  = 18784, p < 0.001; χ2  = 47.1, p < 0.001; χ2  = 20, p < 0.001).CONCLUSION:The nomogram we established may provide risk assessment and survival prediction for IDC patients with metastasis, which can be used for clinical decision-making and reference.

Hypertension is associated with body mass index (BMI) and cardiovascular and cerebrovascular diseases (CCDs). Whether hypertension modifies the relationship between BMI and CCDs is still unclear. We examined the association between BMI and CCDs and tested whether effect measure modification was present by hypertension. We identified a population-based sample of 3,942 participants in Shuncheng, Fushun, Liaoning, China. Hypertension was defined as any past use of antihypertensive medication or having a measured systolic/diastolic blood pressure ≥130/80 mm Hg. BMI was calculated from measured body weight and body height. Data on diagnosed CCDs were self-reported and validated in the medical records. We used logistic regression models to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for associations between BMI and CCDs. Higher BMI was associated with increased odds of having CCDs (OR = 1.19, 95% CI: 1.07–1.31). This association was significantly modified by hypertension (P for interaction <0.001), with positive associations observed among hypertensive individuals (OR = 1.28, 95% CI: 1.14–1.42). Age, sex, and diabetic status did not modify the relationship between BMI and CCDs (all P for interaction >0.10). Although higher BMI was associated with increased odds of CCDs, the relationship was mainly limited to hypertensive patients.

Higher intake of β-carotene and β-cryptoxanthin were associated with lower risk of osteoporosis. A very high intake of lutein + zeaxanthin was also associated with lower risk of osteoporosis. These results support the beneficial role of carotenoids on bone health. To examine the associations of α-carotene, β-carotene, β-cryptoxanthin, lycopene, and lutein + zeaxanthin intake with the risk of osteoporosis based on the cross-sectional data from the National Health and Nutrition Examination Survey (NHANES), 2005–2018. This study identified individuals ≥ 50 years old with valid and complete data on carotenoid intake and bone mineral density (BMD). Intake of α-carotene, β-carotene, β-cryptoxanthin, lycopene, and lutein + zeaxanthin was averaged from two 24-h recall interviews. BMD was measured by dual-energy X-ray absorptiometry (DXA) and converted to T-scores; osteoporosis was defined as a T-score ≤  − 2.5. We used logistic regression models to test the associations between carotenoids and osteoporosis, adjusting for factors such as age, sex, race, and education. Participants were on average 61.9 years of age, with 57.5% identifying as females. Higher quintiles of β-carotene (odds ratio [OR] for quintile 5 vs. 1:0.33; 95% CI: 0.19–0.59; P for trend = 0.010) and β-cryptoxanthin intake (OR for quintile 5 vs. 1:0.61; 95% CI: 0.39–0.97; P for trend = 0.037) were associated with reduced risk of osteoporosis. Similar and marginally significant results for lutein + zeaxanthin intake was found (OR for quintile 5 vs. 1:0.53; 95% CI: 0.30–0.94; P for trend = 0.076). There was no association of α-carotene and lycopene intake with osteoporosis. These associations did not differ by sex (all P_interaction > 0.05). Higher β-carotene and β-cryptoxanthin intake was associated with decreased osteoporosis risk. A very high intake of lutein + zeaxanthin was also associated with lower risk of osteoporosis.

Context Bone mineral density (BMD) T-score references may be updated when the peak BMD of the population is unclear and warrants reevaluation. Objective To update BMD T-score references using the peak BMD from the most recent National Health and Nutrition Examination Survey (NHANES) data. Methods This cross-sectional study used NHANES data from 2005 to 2014. Non-Hispanic White females between the ages of 10 and 40 years (N = 1549) were our target population to estimate peak BMD (SD). Individuals aged ≥ 50 years (N = 5523) were used to compare the percentages of osteoporosis and low bone mass based on existing and updated BMD T-score references. BMD data within the age at attainment of peak BMD ± 5 years were used to calculate updated BMD T-score references. Results The updated average of BMD (SD) for diagnosing osteoporosis at the femoral neck and lumbar spine were 0.888 g/cm2 (0.121 g/cm2) and 1.065 g/cm2 (0.122 g/cm2), respectively. The percentages of individuals with osteoporosis at the femoral neck and low bone mass at the femoral neck and lumbar spine based on the updated BMD T-score references were higher than the percentages of people designated with these outcomes under the existing guidelines (P < 0.001). However, we observed the opposite pattern for lumbar spine osteoporosis (P < 0.001). Conclusions We calculated new BMD T-score references at the femoral neck and lumbar spine. We found significant differences in the percentages of individuals classified as having osteoporosis and low bone mass between the updated and existing BMD T-score references.