Vertigo is very common in children, but the specific diagnosis and characteristics are not clear. The main objective of this study was to analyze the characteristics of caloric test (CT) and video head impulse test (vHIT) in vestibular migraine of childhood (VMC), probable vestibular migraine of childhood (PVMC), and recurrent vertigo of childhood (RVC), which can provide a reference value for their clinical diagnosis.We selected VMC, PVMC and RVC patients under 18 years of age from the outpatient Department of Otolaryngology–Head and Neck Surgery between May 2021 and August 2022. All patients underwent vestibular function examinations, including eye movement recording CT and vHIT. CT results depended on whether both canal paresis and directional preponderance were under normal limits, and vHIT results depended on the gain values of vestibulo-ocular reflex. The results of both tests were analyzed according to the disease type.P > 0.05). None of the patients showed abnormal vHIT results . The abnormal CT rates were significantly higher than those of abnormal vHIT rates (P < 0.05).Among the 81 pediatric vertigo patients aged 5–17 years, 44 were females and 37 were males. According to the type of vertigo, 29 patients (25.80%) were diagnosed with VMC, 11 (13.58%) with PVMC, and 41 (50.62%) with RVC. The abnormal rates of the CT in VMC, PVMC, and RVC patients were 24.14%, 36.36%, and 17.07%, respectively. There was no significant difference in the abnormal rates among the three groups (VMC, PVMC, and RVC are more likely to be diagnosed by symptoms, as neither CT nor vHIT are specific to any conditions. Due to different clinical presentations of vertigo in pediatric patients, it is critical to further clarify the diagnosis with medical history and clinical characteristics. The prevalence of vertigo and dizziness in pediatric patients ranges from 0.4 to 15% .As most children cannot describe their symptoms precisely, it is difficult to distinguish these types of vestibular disorders accurately. The correct diagnostic evaluation of vertigo in children includes not only a detailed medical history and careful physical examination, but also further vestibular tests based on clinical indications . The calTherefore, it remains unclear whether the results of CT and vHIT are dissociated in pediatric patients with vertigo. Thus, in order to improve the accuracy of clinical diagnosis, this article aimed to compare the characteristics of the CT and vHIT in patients with VMC, PVMC, and RVC, and explored the site and possible pathogenesis of vestibular disorders.The study retrospectively reviewed 29 patients (age 10.90 ± 2.94 years) diagnosed as having VMC, 11 patients (age 11.45 ± 3.01 years) diagnosed as having PVMC, and 41 patients (age 9.51 ± 2.61 years) diagnosed as having RVC. Patients were treated in the Diagnosis and Treatment Center of Hearing Impairment and Vertigo, Xinhua Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China, from May 2021 to August 2022. The vestibular function of all patients was examined by videonystagmography (VNG) examination and vHIT. The specific parameters and response rates of the CT and vHIT results were compared and analyzed among the three groups.Patients diagnosed with VMC, PVMC, and RVC were selected according to the diagnostic criteria of the Bárány Society and International Headache Society .We included patients whose medical history data were complete and who could be clearly diagnosed; who were able to cooperate with all the tests; and who had not taken vestibular inhibitors and other drugs within the past 48 h.We excluded patients with intracranial lesions, hereditary neurological diseases, metabolic diseases, or other systemic diseases; ophthalmic diseases; other peripheral vestibular vertigo diseases, such as Meniere's disease; or external and middle ear diseases.The participants underwent VNG , including the spontaneous nystagmus test, gaze test, random saccade test, smooth pursuit tracking test, optokinetic nystagmus test, positional nystagmus test, and CT.The patients wore Frenzel glasses and sat upright with their eyes in the horizontal plane and looked at the target without moving their head. After calibration, tests were performed in the order listed as follows: (1) Spontaneous nystagmus test: the patient was asked to look forward in a dark room without any stimulation for at least 60 s, and the presence of nystagmus was assessed. (2) Gaze test: the patient was asked to focus on the visual target at the center and four eccentric positions for at least 20 s; the absence of nystagmus and the ability to maintain gaze were regarded as normal outcomes. (3) Random saccade test: the patient was asked to track a target that moved randomly within the range of 5–30° in the horizontal direction, with a time interval of 2 s; delay time, peak velocity, and accuracy were calculated by the computer, and saccadic hypometria, saccadic hypermetria, saccadic disorder, and saccadic slowing were considered as abnormal outcomes. (4) Smooth pursuit tracking test was performed by asking the patient to track the visual target moving in the horizontal plane at 0.1–0.5 Hz; eye movement curves were recorded at the same time, and the smooth sine curve with the eye movement curve basically consistent with the visual target curve was considered a normal result. Another parameter evaluated was gain, which was the ratio of eye movement velocity to visual target velocity; it was calculated by the computer, and the normal value was generally not <0.6. (5) Optokinetic nystagmus test: the patient was required to count a series of targets that continued moving at a constant speed of 20 and 40°/s; the direction of nystagmus was opposite to the direction of target movement, and the amplitude and bidirectional amplitude of nystagmus were basically symmetrical. (6) Positional nystagmus test was recorded in the CT position for 40 s, and the presence of nystagmus was assessed.CT was performed in a dark room using an Air Fix air irrigator  with an airflow of 8 L/min at 50 and 24°C. The patients were placed in the supine position with the head raised at 30° to maintain the horizontal canal in a vertical position. The external auditory canal was irrigated with warm air at 50°C and with cool air at 24°C for caloric testing, respectively. The interval between successive irrigations was 5 min. The sequence used to irrigate the ear canal was right warm (RW), followed by left warm (LW), right cool (RC), and left cool (LC).The peak caloric response for each irrigation was calculated by averaging the slow-phase velocity (SPV) of a few nystagmus beats that had the highest velocities, which were represented by the symbols RW, LW, RC, and LC. The difference between the caloric responses from the right and left ears was quantified by the percentage of canal paresis (CP), and the CP was calculated as [(RW + RC) – (LW + LC)] / (RW + RC + LW + LC) × 100%. Which can be considered as the caloric weakness when the CP-value is over 25%. Another parameter that was evaluated was directional preponderance (DP). DP was calculated as [(RW + LC) – (LW + RC)] / (RW + RC + LW + LC) × 100%, and which can be considered as the caloric weakness when the DP-value is over 30% .vHIT was performed using a video oculography device . The patients were instructed to maintain a sitting position and fixate their gaze on an earth-fixed target in front of both eyes. The target height was flush with the sight line of the patients, and the distance between the patients and target was 1.5 m. The participants were given a video-head impulse tester, which was a special spectacle frame with a built-in camera that tracked pupil movements. The camera of the tester was fixed to the upper-left side of the spectacle frame. For calibration, the patients were asked to keep their heads still and gaze at the alternating laser spot on either side of the target. The patients then provided a slow sinusoidal motion of the head while they kept their gaze fixed on the target.We conducted a formal test after correct calibration according to the software requirements. An experienced laboratory technician delivered at least 20 brief, abrupt, and unpredictable head impulses per side . The patients were required to keep gazing at a fixed target in front of them during the head-shaking process.The reduction in VOR gain was defined as semicircular canals dysfunction. The VOR gain value was automatically calculated as the ratio of eye velocity (°/s) to head velocity (°/s), using the device software. vHIT results were considered abnormal when the VOR value of the HSCs at 60 ms was < 0.8 or the VOR value of the vertical semicircular canals (PSC and ASC) was < 0.7 . The norhttps://www.r-project.org). It is considered statistical significance when P < 0.05.All data were analyzed using SPSS v. 26 software . The chi-squared test was used to analysis the response rate of the VNG and vHIT and the abnormality rate of each parameter. Continuous data are presented as the mean ± standard deviation. In the multiple comparisons of parameters, the analysis of variance was adopted when the variance was homogeneous; and the H test was used when the variance was not homogeneous. Graphs were produced using R version 4.2.1 . According to the type of vertigo, 29 patients  were diagnosed with VMC, 11 patients  were diagnosed with PVMC, and 41 patients  were diagnosed with RVC. The baseline characteristics of patients were generalized in P = 0.222; P = 0.004)  patients with VMC showed abnormal VNG results. No statistically significant differences were obtained among the VMC, PVMC, and RVC groups in the VNG examination results  . Twenty 2 =1.963, P = 0.374)  or DP-values   and the specific parameters CP and DP) of CT were compared. Seven (24.14%) patients with VMC, four (36.36%) with PVMC, and seven (17.07%) with RVC presented with abnormal reactions in the CT. However, there was no statistically significant difference in the abnormal rate of the CT among the three groups (χ= 0.374) . Through= 0.914) ,D.P of CT wP > 0.05).The instantaneous and regression VOR gain values of the three pairs of semicircular canals are compared in P < 0.001) (P = 0.01) and RVC (P = 0.018) were both statistically significant, whereas no statistical difference was observed in patients with PVMC (P = 0.09).We compared and analyzed the results of the CT and vHIT among patients with VMC, PVMC, and RVC. As shown, the abnormality rate in the vHIT among all patients (0.00%) was lower than that in the CT (22.22%) (< 0.001) . The difAs a special group, vertigo in children has challenged clinicians over the years. The diagnosis of vertigo in pediatric patients is difficult, due to the inability of children to describe their symptoms and lack of cooperation with vestibular function examinations. Short-lived manifestations due to rapid compensation make the condition difficult to detect for the parents . The mosThe audio vestibular system in humans is anatomically developed and functionally responsive at birth . HoweverThe occurrence of nystagmus is closely related to vertigo. It refers to the patient's inability to continuously focus on the target; the eye moves slowly to one side and deviates from the target, followed by rapid corrective rebound, which can be physiological or pathological. There are three main types of pathological nystagmus: spontaneous nystagmus, staring nystagmus, and positional nystagmus, which are common in peripheral and central vestibular system lesions. In our study, we found that the three groups of patients had spontaneous nystagmus and positional nystagmus of different frequencies.P < 0.05). However, this may be due to the fact that there were fewer cases in the PVMC group, and the statistical results were not accurate. It was also possible that a patient with PVMC was a possible patient of vestibular migraine, and the clinical symptoms were not obvious, similar to a transitional disease; thus, the extraction rate of nystagmus was not high.Spontaneous nystagmus refers to a continuous, involuntary, rhythmic back and forth movement of the eyeball without inducing factors, which can occur in vestibular peripheral lesions, central lesions, and some eye diseases. It is reported that the frequency of spontaneous nystagmus in children with vertigo is ~17–75% –31, whicPositional nystagmus refers to the nystagmus that occurs when the head is placed in one or several specific positions, but it does not occur in other positions. It is also accompanied by vertigo, which is called positional vertigo. The mechanism of positional nystagmus is unknown. It is generally believed that it is caused by otolith lesions, but there may also be semicircular canal, vestibular nerve, cerebellum, brain stem, brain, and other lesions. Previous studies found positional nystagmus in only 20% of children with vertigo , 33. In The CT belongs to the ultra-low frequency testing method (0.002–0.004 Hz), which mainly detects the function of the supravestibular nerve and horizontal semicircular canals. It can stimulate the horizontal semicircular canal on both sides and is the gold standard for evaluating vestibular function . We founThe vHIT has been shown to be effective for diagnosing VOR deficits resulting from peripheral vestibular impairment, and allows rapid evaluation of the high-frequency (2–5 Hz) function of the three pairs of semicircular canals , 37. OurBefore the widespread application of vHIT to evaluate vestibular function, the CT was considered as the gold standard of unilateral horizontal semicircular canal dysfunction. However, currently, we can detect abnormalities for any semicircular canal and clearly view overt and covert saccades by vHIT. However, these two tests might occasionally produce conflicting results. Many investigations have verified the dissociation between the outcomes of the two tests, and it is common to obtain a normal result in one of the two tests, while an abnormal result in the other, particularly in peripheral vestibular disorders, such as Meniere's disease –16 and vThe article detailedly studied the differences between eye movement recording, CT and vHIT-test results among patients with VMC, PVMC, and RVC. It was preliminarily concluded that CT can detect vestibular function more sensitively than vHIT in children with vertigo. Additionally, through the examination of nystagmus, we obtained a better understanding of vertigo in children. Nonetheless, the sample size of the study is still insufficient. In the future, we can analyze the specific characteristics of different nystagmus, and preliminarily identify that nystagmus is caused by peripheral or central diseases. And, future studies need to expand the sample size for prospective verification and include other vestibular function tests to comprehensively assess the characteristics of vertigo in children.Comprehensive ontological and neurological examinations, including history, physical examination, audiological assessment, vestibular function test, and imaging studies, are required for the proper diagnosis of vertigo in children. The CT and vHIT showed no specific diagnostic value in our patients with VMC, PVMC, and RVC. Therefore, history taking is of the utmost importance, not only in regard to the various symptoms or observations, but also in regard to the sequencing of symptoms and their progression or lessening over time, as RVC, VMC, and PVMC are more likely to be diagnosed based on symptoms.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.Written informed consent was obtained from the minor(s)' legal guardian/next of kin for the publication of any potentially identifiable images or data included in this article.QinZ and QW designed the study, collected patients, statistical analyses, and drafted the manuscript. JC, XW, and YZ assisted in drafting the protocol, conducting data collection and processing, and editing the manuscript. SL, LM, JS, and MS performed data extraction. XT, LM, and XC prepared figures and revised the manuscript. QingZ, YJ, and JY critically evaluated the manuscript. All authors reviewed and approved the final version of the manuscript. All authors contributed to the article and approved the submitted version.This research was funded by the National Natural Science Foundation of China , the Clinical Research Plan of SHDC (SHDC2022CRD013), the Key International Cooperation Project of Shaanxi Province (Grant No. 2020KWZ-019), and the Science and Technology Project of Shanghai Science and Technology Commission (Grant No. 21S31900600).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Obesity as a risk factor of heart disease (HD) is confirmed through observational, laboratory, and intervention studies. However, it cannot explain why HD declines, but obesity increases in the United States in recent decades. This study attempted to understand this paradox.Annual data of national HD mortality (1999–2018) were derived from Wide-Ranging Online Data for Epidemiologic Research, biannual obesity data (1999–2016) from the National Health and Nutrition Examination Survey, and smoking data (1965–1990) were from the National Health Interview Survey. Age-period-cohort method was used to decompose HD mortality into age, period, and cohort effects, and to estimate age-cohort adjusted mortality rates. To explain the paradox, age-cohort adjusted rates were associated with obesity rates to verify the positive obesity-HD relationship, while smoking rates were associated with cohort effects to explain the current declines in HD mortality.r = 0.68 for males, 0.91 for females, 0.89 for White, and 0.69 for Hispanic, p < 0.05), but not significant for Black and Asian. Further, during 1965–1990, the estimated cohort effect showed a decline in HD risk and was positively associated with smoking rates .During 1999–2018, the prevalence of obesity increased while the crude HD mortality rate declined for both sex and all races. After controlling for the curvilinear age effect and consistent declining cohort effect, the age-cohort adjusted HD mortality sustained stable in 1999–2007 and increased thereafter. The age-cohort adjusted rate in 1999–2018  increased from 189.31 to 238.56 for males, 67.23 to 90.28 for females, 115.54 to 157.39 for White, 246.40 to 292.59 for Black, 79.79 to 101.40 for Hispanics, and 49.95 to 62.86 for Asian. The age-cohort adjusted HD mortality rates were positively associated with obesity rates (Study findings reveal potential increase of HD risk and support the positive relationship between obesity and HD risk. Declines in HD mortality in the past two decades are primarily due to tobacco use reduction and this protective effect was entangled in the mortality rates as cohort effect. Heart and other cardiovascular diseases (CVD) are the second leading cause of death in the United States with over 647,000 deaths per year . EffectiThe remarkable paradox of increasing obesity prevalence and declining HD mortality has long been recognized –10; but One likely explanation of the obesity-HD mortality paradox could be the use of the crude mortality rate which is computed by division of the number of deaths by population. Temporal trends measured by crude rates are known to be confounded by age; and the problem is solved by using age-standardized rates to describe temporal trends. However, recent studies demonstrate that age-standardized rates can further be confounded by birth cohort, since people in current year were born in different years in the past –17. ThisAge effect refers to the HD risk associated with chronological ages with higher risk for people of older age, and the risk is largely attributed to the biological aging process. Period effect refers to the HD risk associated with changes in key risk factors over time that may exert effect on all age groups, and the risk represents the “true” temporal trend. Cohort effect likewise refers to the HD risk associated with changes in the key risk factors over the period when the subjects were born, and the risk represents changes in key risk factors in the past uniquely related to the specific birth cohort  analysis, a classic epidemiologic model can disentangle the age, period, and cohort effect intertwined in the data. With the estimated age, period, and cohort effects, age-cohort adjusted rates can be estimated to measure the “true” temporal trends of HD risk. This provides a powerful approach to investigate the obesity-HD risk paradox. However, no reported study in the literature has addressed the HD-obesity paradox using this analytical approach.In this study, we will investigate the paradox as follows with national data. First, we will determine whether HD risk in the United States increased or declined in the past using age-cohort adjusted rates. Second, if the adjusted rate shows an increase in the risk for HD mortality, we will further associate the adjusted rates with prevalence rates of obesity to confirm the relationship between obesity and HD risk. Third, since the reported declines in HD risk were based on age-standardized rates, a declining cohort effect would be the main explanation for an increasing trend described by age-cohort adjusted rates. Finally, we will associate the estimated cohort effects in the past with prevalence rates of tobacco use in the past to confirm the cohort effect.Data regarding the mortality of HDs were derived from the Wide-Ranging Online Data for Epidemiologic Research database, a public domain database sponsored by Center for Disease Prevention and Control . Deaths 2 were classified as obesity.Annual prevalence rates of obesity during 1999–2018 were used for analysis. These rates were derived from National Health and Nutrition Examination Survey from 1999–2000 through 2017–2018  by lineaAnnual prevalence rates of current cigarette smoker among adults during 1965–1990 were used for analysis. The data were derived from the American Lung Association . CurrentThe study was approved by the Institutional Review Board at the University of Florida. This study is secondary data analysis, and no informed consent was required.The outcome variable was the mortality rate of HDs, which was estimated as the number of deaths due to HDs divided by the total population. The mortality rate was computed by single year of age by sex in each year from 1999 to 2018.Three time-related predictors were chronological age, time period, and birth cohort. The chronological age was the registered age when the death occurred, and it ranged from 25 to 84 years in the study. Time period was the year when the death was reported, ranging from 1999 to 2018. Birth cohort was computed by subtracting the age from the time period with a range of 1915 to 1993.The crude mortality rate of HDs was analyzed using APC modeling analysis , 20. Witrijk indicates the HD mortality rate, u was the intercept or grand mean, αi was the effect for age group i , βj was the effect for period j , and γk was the effect for birth cohort k .where The parameters in the model were estimated using the Intrinsic Estimator  in the sThe age-adjusted rate was directly derived from the CDC WONDER. Based on the modeling results, the age-cohort adjusted mortality rate of HD was computed using the following formula :u was the intercept, βj was the period effect , αmedian was the median of age effects, and γmedian was the median of cohort effects. These effects were estimated through Equation 1. To better visualize the differences of crude rate, age-adjusted rate, and age-cohort adjusted rate, the three rates were plotted together by sex and race.where p < 0.05 (two-sided) in statistical inference.Finally, Pearson correlation was used to correlate estimated smoking rates with estimated cohort effects, as well as obesity rates with crude, age-adjusted and age-cohort adjusted HD mortality rates. Type I error was set at Likewise, Crude, age-adjusted, and age-cohort adjusted rate of HD mortality by sex and racial groups are presented in r = 0.68 (p < 0.05) for male and r = 0.91 (p < 0.001) for female, and for White  and Hispanic .Results in p < 0.01) for both sex, and 0.99 (p < 0.01) for White and 0.98 (P < 0.01) for Black.Results in In this study, we successfully extracted information from annual mortality data to investigate the paradox of declining HD mortality along with increasing obesity in United States with national data. According to our analysis, the paradox is primarily due to the incorrect description of a decline trend in HD mortality in the United States using crude or age-standardized rates. These rates are confounded by a strong and sustained declining cohort effect; they are not valid to characterize temporal trends in HD risk. To our knowledge, this is the first empirical study to generate data interpreting the obesity-HD mortality paradox. The paradox was recognized more than a decade ago and reduces our beliefs on the role of weight control for HD prevention . In addiPreviously documented data concluded that the risk of HD mortality has been declining in industrialized countries, including the United States , 9. TherPrevious studies also revealed that the declining trend in HD mortality decelerated, and even showed early signs of reversal in some population subgroups, despite continuous advancements in diagnosis and treatment , 26–31. Findings of this study add new evidence supporting the need for weight-control. Overweight and obese are related to increased HD mortality rates and higher blood pressure in the United States, and individuals with extra bodyweight and hypertension are at increased risk for HD , 3. Of tOne unique and significant contribution of this study is explanation of the obesity-HD mortality paradox. In addition to detecting the increasing trend in HD mortality, there is a positive relation between obesity rate and age-cohort adjusted HD mortality rates. The relationship would be negative if age and cohort are not justified. Findings of this analysis confirm the role of bodyweight as an important predictor of HD, and potentially CVD in general as reported in previous studies , 32, 33.Additionally, an overall decline of cohort effect was observed which was significantly correlated with smoking rates. Previous studies indicated that nearly half of the CVD mortality decline can be attributed to the prevention of risk factors, including cigarette smoking . Our finFinally, we must point out that it is highly misleading to use crude rates or even age-standardized rates to describe time trends of chronic disease mortality. To date, few studies in the literature detected the increasing trend in HD mortality although age-standardized rates were often used, including studies based on the clinical data and population-based survey data. Different from infectious diseases, HD and other CVD alike are chronic in nature; it takes time to complete the process from disease onset to clinical diagnosis, treatment and to the outcome of death. Effect of various influential factors is intertwined with each other. As described in APC modeling, much of which shows as a cohort effect hidden in current data , 34, 35.The study has limitations. First, the analysis was conducted with aggregated data. Effect related to advancement in HD diagnosis and treatment and factors at the individual level cannot be examined. Second, it would be ideal to also use age-cohort adjusted rates of smoking and obesity; but annual data by age for the two measures are not available. The smoking-HD mortality has been well established; regarding the obesity-HD, we believe that using adjusted rates would not change those results qualitatively since obesity has been progressively increasing. However, we recommend using caution while reviewing this part of the results. Last, a full investigation of all factors related to the declining cohort effect and increasing period effect on HD mortality is likely but beyond the scope of the study. We will consider this issue in our future studies.HD risk in the United States increases rather than declines since 1999, and the increased risk is associated with obesity. The obesity-HD paradox is primarily due to confounding effect from birth cohorts that are related with reductions in tobacco use since 1965. Findings of this study not only provide answers to the two-decade long paradox regarding bodyweight and HD risk but also demonstrate increase in HD risk in the Unied States and the significance of bodyweight control in CVD prevention and control. Last, age-cohort adjusted rates provide a measure more valid than crude or age-adjusted rates to describe trend of HD risk. We highly recommend the use of this measure in future studies.https://wonder.cdc.gov/Welcome.html.Publicly available datasets were analyzed in this study. This data can be found here: This study was reviewed and approved by the Institutional Review Board at the University of Florida. This study is second data analysis, and no informed consent was required.BY contributed to the study design, data collection, analysis and interpretation, and writing the first draft. XC contributed to the study design and manuscript review. DL, HY, and PW contributed to the manuscript review. All authors contributed to the article and approved the submitted version.


Chromium (Cr) and cobalt (Co) are the essential elements for producing metal implants, but might have potential health issues. The research on the correlation between metal implants and blood Cr and Co on a large population is still limited.National Health and Nutrition Examination Survey (NHANES) is a program of studies designed to assess the health status of Americans began in the early 1960s. The study was based on the NHANES database from two data collection years (2015–2016 and 2017–2018). The exposure variable of this study was whether the participants had metal objects in the body or not. The outcome variables were blood concentrations of Cr and Co. Age, body mass index, sex, race/ethnicity, income to poverty ratio, tap water behavior, shellfish/fish/tuna/salmon eating habits, level of education, smoking behavior, marital status, blood hemoglobin, and data collection years were included as confounding variables.n = 3150) and the metal objects group (n = 1262). Metal objects was positively correlated to the accumulation of blood Cr  and blood Co . However, the positive correlation of metal objects with blood Cr was only presented in women , but not in men. Meanwhile, the positive relationship between metal objects and blood Cr/Co was not observed in the Asian subgroup.A total of 4412 participants, aged 40 years or older, were included in this analysis, consisting of the without metal objects group (Blood Cr and Co concentrations were statistically higher in people with metal objects, but with race and sex differences.Level IV, cross-sectional studyThe online version contains supplementary material available at 10.1186/s12877-022-03710-3. Metal objects such as artificial joints, pins, plates, or metal suture material are mainly considered treatment options for orthopedic diseases, including osteoarthritis , 2, fracHumans can be exposed to metals in various ways. Drinking water and eating foods such as fish, smoking, geographical environment, and occupational exposures have been suspected as sources of heavy metals –9. ChromFortunately, the extensive data capabilities with thousands of participants in the United States National Health and Nutrition Examination Survey (NHANES) database resolve the data acquisition difficulty. Therefore, this study aimed to reveal the association between metal implants and the blood Co or Cr level (Co/Cr) in the population with different race/ethnicity and sex from NHANES. It was hypothesized that the blood Co/Cr in the participants with metal implants were significantly higher than those without metal implants.n = 13,203), participants with pregnancy (n = 6), participants with missing body mass index (BMI) (n = 76), and participants with missing blood Cr/Co data (n = 1527), a total of 4412 participants were included in this analysis. Approval of this study was obtained from the ethics review board of the National Center for Health Statistics, and written consent was obtained from every participant.The study population was based on the two data collection years of the NHANES database from 2015–2016 and 2017–2018 . The definition of metal objects in the question contained any artificial joints, pins, plates, metal suture material, or other types of metal objects in the body. The metal object should not be visible on the outside of the body or in the mouth. The interviewers aged 40 years and older were eligible to answer yes or no.The exposure variable of this study was the participants with metal objects or not. Newly released in the data collection years of 2015–2016, the NHANES contained the question “Any metal objects inside your body?” in the Medical Conditions Data of the Questionnaire Data section (https://wwwn.cdc.gov/Nchs/Nhanes/2015-2016/CRCO_I.htm).The outcome variable was the whole blood concentrations (ug/L) of Cr and Co, measured using inductively coupled plasma mass spectrometry . The dethttps://wwwn.cdc.gov/nchs/nhanes/. Those confounders were evaluated using prior knowledge [The following categorical variables were included in this analysis: sex , race/ethnicity, income to poverty ratio, tap water behavior, shellfish/fish/tuna/salmon eating habits, level of education, smoking behavior, marital status, and data collection years. In addition, the continuous covariates were also included: age, BMI, and blood hemoglobin. The detailed definitions of the covariates are available at nowledge –19 and dp < 0.05 was deemed statistically significant. The R code of the modified ggplot2 package to draw smooth fitting curves was attached in the Supplement file The weighted χ2 test for categorical variables or linear regression model for continuous variables were applied to calculate the difference between the groups with or without metal objects. A multivariate linear regression model was applied to evaluate the association between metal objects and blood Cr/Co. The subgroup analysis was performed by stratified multivariate regression analysis. Furthermore, to detect trends, smooth curve fittings were used to address the relationship between age/BMI and blood Cr/Co in different groups. All analyses were performed with EmpowerStats software  and the R Project for Statistical Computing , and n = 3150) and with metal objects group (n = 1262), as shown in Table A total of 4412 participants, aged 40 years or older, were included in this analysis, consisting of the without metal objects group (p < 0.001). After adjustment for confounders, this positive association was still present in minimally adjusted model  and fully adjusted model . Meanwhile, metal objects were positively correlated to the blood Co accumulation in all three models. Individuals with metal objects had a 0.079 ug/L greater blood Co than those without metal objects , but not in men. The positive correlation of metal objects with blood Co remained in both men  and women . As reported in Table On subgroup analyses, stratified by sex, reported in Table p = 0.008) but no association with blood Cr (p = 0.474). Among participants without metal objects, no association between age and blood Cr (p = 0.508) or Co (p = 0.269), although U-shaped curves (Fig. Smooth curve fittings are shown in Fig. Co p = 0.69, althoThis article used the NHANES database to explain the relationship between metal implants and concentrations of blood Cr and Co ions in a United States population surveyed between 2015 and 2018 through regression analysis, in an attempt to answer one of the most common questions doctors face with their patients: Doctor, will I have heavy metal poisoning from this implant? It is suggested that the presence of implant does cause the increase of blood ion concentration in the human body, but the concentration is far lower than the toxicological dose (Supplement Fig. Prior to this, many studies focused on implant ion release. The release of ions in total hip arthroplasty mechanically assisted crevice corrosion is always a focus of attention –24. Howe. and Chen et al. found that female adults had higher Co levels than male adults [Meanwhile, analysis of micronutrients in pregnant women revealed the accumulation of Co ions in pregnant women and analyzed its possible proportional relationship with vitamin B12 intake and smoking . Tvermoee adults , 36. HowIn addition, from the results of the stratified analysis, we can infer that there are racial differences in the accumulation of Cr in the body. Whites are the most likely to accumulate Cr, followed by African-American, followed by Hispanics, and Asian people are not easy to accumulate Cr. Similarly, Whites were most likely to accumulate Co, followed by African-American, and Asian and Hispanic people were not easy to accumulate Co. This paper combined the content of metal ions in the body with race analysis, and the mechanism may be related to the differences in genes related to metabolism between races, , 38 whicThis study also noticed a distinct difference in the concentration of Cr and Co ions, which might be related to the distribution pattern of the two ions in tissues. It was found that Co was selectively leached from the alloy and released into the blood during plant corrosion. The free Cr released due to corrosion precipitated in local tissues as Cr phosphate, but did not form organometallic complexes in serum –43. Howe. notes that although Cr and Co concentrations were elevated in patients with metal implants, they were far from the levels that would cause disease [What’s more, Co is part of vitamin B12, an indispensable heavy metal in the body. Studies have shown that Co can increase plasma high-density lipoprotein and decrease low-density lipoprotein, free fatty acids, and triglycerides in mice . By regu disease . However disease , 52.This study has several shortcomings. First, as a cross-sectional study, temporal order and causality may not be clear. It is not possible to determine the sequential relationship between metal implants and blood ion levels in vivo. Second, due to privacy considerations, the NHANES database cannot provide the geographical location of participants, nor can it determine whether they live in an urban or rural environment, close to traffic or industrial areas, which makes it impossible to assess the influence of geographical environment and occupational exposure on metal ion content in the body. Meanwhile, the participants did not report the implantation site and the type of implants in each patient. This is a highly heterogenous group. There is no clinical specificity and no additional determination regarding the type of implant, number of implants, reason for the implants or duration that the implants have been present. Also, the implant might not contain Cr or Co, and the influence of weight-bearing joint and non-weight bearing joint implants might be different. However, this study did notice the elevated blood concentrations of Cr and Co in the participants with metal implants. Patients should be noticed the change after the operation. Although, the concentration is lower than the toxicological dose, it still needs longer follow-up.Additional file 1: Supplement Figure 1. The directed acyclic graphs of confounders.Additional file 2: Supplement Figure 2. The relationship between metal implants and concentrations of blood Cr/Co. It was made by the authors and approved by www.figdraw.com.Additional file 3: Supplement File 1. R package codes.Additional file 4: Supplement Table 1. Some other characteristics of participants


Purpose: The transition of Taiwan from an aging to a super-aging society has come with a cost as more elderly now suffer from cognitive impairment. The main purpose of our study was to investigate if early detection can be developed so that timely intervention can be instituted. We analyzed the correlation of cognitive function with ocular physiology and visual functions between senior citizens aged 60 years or older in Taiwan. Methods: Thirty-six healthy subjects were recruited for the study. Addenbrooke’s cognitive examination III (ACE-III), binocular functions , and ocular physiology  were performed, and the data were analyzed via independent t-test, chi-square test, Pearson correlation, linear regression, and ROC (receiver operating characteristic) curve. Results: Data analysis showed that (1) patients with poor eye movement had a strong correlation with the total score and all dimensions of cognitive functions, (2) the thickness of the macula had a strong correlation with attention and memory, and (3) patients with poor eye movement and poor stereopsis in combination with thinner inferior macula appeared to have lower cognitive abilities. Discussion and Conclusions: Cognitive dysfunction is not readily identified during the early stage of cognitive decline. The use of simple and inexpensive ADEM or stereopsis test and comparing the OCT results that are popular in optometry clinics for reference can be diagnostic in identifying patients with mild cognitive impairments. With the combined use of macular pigment density or retinal thickness measurements, it was possible to effectively predict the early degradation of cognition. Alzheimer’s disease is the most common type of dementia ,2,3. StuIn terms of visual physiology, retinal examination can provide a non-invasive method which is similar to the examination of brain pathology ; previouTo summarize, the degradation of cognition may have a causal relationship with the overall binocular visual functions and ocular physiology that is yet to be explicitly explored. The purpose of our study was to demonstrate, for the first time, that the decline in visual function and ocular physiology may be correlated and even interact significantly with MCI.The study was a cross-sectional study that was conducted from 20 November 2020 to 30 March 2021 at Chung Shan Medical University Hospital (CSMUH)-affiliated Dementia Intergraded Care Center. All the procedures were conducted in accordance with the Declaration of Helsinki. Approval was obtained from the Institutional Review Board of CSMUH  . The age and physical, spirit, and compliance situations may have determined the duration for each subject to complete the entire examination. The manuscript is reported according to the STROBE guideline .t-test indicated no significant difference in age and ACE-III cognitive score as far as sex; therefore, the results were analyzed with all 36 subjects combined.All participants had binocular and monocular distance and near visual acuity of 0.8 or better, spherical power ranged from −5.00 D to +2.00 D, and astigmatism was <1.00 D. Twenty-three subjects were already excluded at the recruiting stage owing to eye and/or mental problems. Those with an optic disc ratio not within the normal range could be a high-risk group with high myopia or glaucoma, and were also excluded and referred. A total of 40 people eventually participated and four were later dismissed, one with poor communication because of auditory nerve damage, and three others simply dropped out. Finally, the effective sample was 36 with 6 men and 30 women, and the average age was for men: 68.83 ± 9.07, for women: 73.67 ± 9.44 years, and for all subjects was 72.86 ± 9.43 years. An independent Addenbrooke’s cognitive examination III  was performed for differentiating patients with or without cognitive impairment. The sensitive of ACE-III is reported to be trustworthy for detecting early stages of dementia, and is available in different languages . ACE-IIIBinocular visual function examination included subjective refraction , distance and near visual acuity , phoria (Howell card for distance and near), adult developmental eye movement test (ADEM), Stereo-Fly for stereo acuity test, and the low contrast flip chart for near and distance contrast sensitivity. In order to avoid potential bias, each test was performed by the same optometrist. ADEM was developed by Gené-Sampedro et al.  which waOcular physiology examination included optical coherence tomography  and macular pigment measurement , both of which were performed on patients without mydriasis, in a dim light room. In the examination of macular thickness with OCT within the center area (recorded as a circle with a diameter of 1 mm), the inner area (a circle with a diameter of 1 mm to 3 mm), and the outer area (a circle with a diameter of 3 mm to 6 mm). With OCT, the retinal thickness of the inner and outer area was divided into four quadrants: superior, inferior, nasal, and temporal. Macular pigment is believed to play a beneficial role in visual performance ,30; thert-test for comparing binocular visual functions and ocular physiology between ACE groups; (2) chi-square analysis between the ACE group and ADEM types, distance, and near phoria; (3) linear regression analysis about cognitive function, visual function, and ocular physiology; (4) ROC curve between significant variables; and (5) Pearson correlation analysis between OCT, ADEM, MPS, and stereopsis, a value of p < 0.05 was considered statistically significant.G* Power analysis was used to determine the sample size in this study, in which the condition was set at the effect size d = 0.8, α = 0.05, power (1 − β) = 0.95. Data were analyzed by using SPSS 26.0 statistical software . For the statistics including (1) independent The ACE-III test contains attention, memory, language fluency, language comprehension, and visuospatial categories, and the reliability of the ACE-III cognitive examination was excellent with Cronbach’s alpha coefficient = 0.82. In addition, 15 ACE-normal and 21 ACE-abnormal subjects were found by the ACE score. G* Power analysis showed that there was still enough power after recalculation and adjustment.t-test showed that cognitive function showed significant differences not only in the total score of ACE  but also in all subtests: attention , memory , language fluency , language comprehension , and visuospatial , indicating that the degradation of cognitive function had a wide range of effects  where that in the ACE-abnormal group was lower than that in the ACE-normal group , the inner area of temporal  and nasal  quadrants were not significantly different, but the rest of the macular thickness was significantly different between the two groups: there were obvious differences in the peripheral part of the macula, especially thickness of the outer, the inferior, and the superior areas , and the ADEM_V of the ACE-abnormal group was significantly less than the ACE-normal group; the same results also appeared in ADEM_H , ADEM_Ratio , and stereopsis . Only contrast sensitivity  did not reach a significant difference between the two groups.In term of visual functions, eye movement (ADEM), stereopsis, contrast sensitivity, distance phoria, and near phoria were performed. The difference and the analysis results with the independent sample = 21.96, p = 0.000) in cognitive function, i.e., there was a strong correlation between eye movement and cognitive functions. It is worthwhile to mention that the proportion of ADEM combined disorders in the ACE-abnormal group was much higher than the ACE-normal group, which showed that in the ADEM of patients with cognitive dysfunction, the probability of combined naming and eye movement difficulties was significantly higher in the ACE-abnormal group.Adult saccade eye movement, including vertical time, horizontal time, and ADEM ratio: in the ACE-abnormal group, 3 patients had the normal type, 2 had difficulty in naming, 16 had combined disorder, and none had only eye movement disorder. In the ACE-normal group, 17 had the normal type of ADEM, and 1 had difficulty in naming (as shown in p = 0.416). The near phoria test showed: in the ACE-abnormal group, there were 4 patients with orthophoria, 3 with esotropia, 12 with exophoria, and 2 with monovision; in the ACE-normal group, 3 patients have esophoria and 12 patients with exophoria (as shown in p = 0.235) in cognitive function. The results indicated that there was no correlation between eye position and cognitive function [Distant phoria test showed: in the ACE-abnormal group, there were seven patients with orthophoria, five with esophoria, seven with exophoria, and two with monovision; in the ACE-normal group, six patients were with orthophoria, six with esophoria, and three with exophoria.  had a high ability to predict and explain cognition decline. The three independent variables could be used to explain up to 82.1% (adjusted to 80.4%) of the explained variation generated by the cognitive ability dependent variables .Furthermore, ADEM-V and retinal thickness  also had a high ability to predict and explain ACE attention (71.6%), ADEM-Ratio and OCT_O_I could predict ACE memory for up to 81.0%, ADEM-H and stereopsis could explain 55.5% of ACE language fluency, ADEM-V and stereopsis could explain 68.1% of ACE language comprehension, and ADEM-H could explain 43.8% of ACE visuospatial.The data showed that horizontal eye movement was related to language fluency and visuospatial abilities; vertical eye movement to attention and language comprehension; the overall eye movement ability to memory; the thickness of the retina (OCT_O_I) was related to attention, memory, and overall cognitive ability; and stereopsis to language fluency, language comprehension, and overall cognitive ability.p = 0.000), sensitivity = 0.857, and specificity = 0.867, and clinical identification could set the cut-off point at 38.5 s. Similarly, other eye movement examinations  could also be used as clinically important reference values.Since eye movement, stereopsis, and retinal thickness showed good explanatory capabilities for cognitive ability, the critical criteria (cut-off point) value of various tests in clinical examination was worthy of further discussion. The results of the ROC curve analysis showed that eye movement examination had the most discriminative ability of clinical screening, the AUC (area under the curve) of horizontal eye movement (ADEM_H) was 0.924  and stereopsis also showed good to excellent discriminative abilities, with which OCT_O-I performed at AUC = 0.795, ification.In this study, an ACE score of 83 was used as the cut-off point, and all subjects were divided into either the ACE-normal (>83) or ACE-abnormal (≤83) group. There were significant differences in the performance of the total score and six cognitive dimensions. On the whole, this cognitive screening questionnaire showed great reliability and validity, and it could effectively identify patients with mild cognitive impairments. However, ACE examination was time-consuming, and subjects were required to have sufficient cooperation.Despite these shortcomings, the biggest difference between the ACE-normal cognitive function group and the abnormal group was the proportion of eye movement disorders. With the ADEM test, for patients with abnormal cognitive function, the probability of combined naming and eye movement difficulty was significantly higher than that in the normal group. ADEM has excellent discriminative power in predicting cognitive states under the linear regression and ROC curve, so ADEM can effectively identify the presence of cognitive impairment ; in addiIn terms of visual physiology, the macular pigment density of the ACE-abnormal group was significantly lower than that of the ACE-normal group, and there was a significant difference in the peripheral thickness of the macular  between the normal and the abnormal groups. Previous studies have indicated that the superior and inferior layers are the most obvious  in patieMoreover, linear regression indicated that ADEM, stereopsis, and macular thinness interact to some extent. For example, ADEM-V, ADEM-H, and stereopsis appeared in the cognitive function of language comprehension and language fluency, presumably because the ADEM measurement can be classified as a group of naming disorders or a combined disorder . When itOur present study further pointed to two possibly simpler and more effective screening methods that can be conducted in a clinical setting:We should point out that procedures (1) and (2) described above, for monitoring cognitive impairment through readily available testing of eye movement and stereopsis, and OCT measurement of retinal thickness, must be performed as a whole. In a well-equipped eye clinic, this is not a problem. Otherwise, the correlation between single tests and cognitive function may not be as high as desired (see Mild cognitive impairment (MCI) is a sign and also a necessary process before entering dementia; in the initial stage, it is often considered to be a due phenomenon of aging and ignored. There appears concurrent drastic changes in ocular physiology and visual function. In fact, eye movement, stereo vision, macular thinness are all indicators in the beginning stage of dementia. A reliable testing for MCI should also be a non-investment, more convenient, low-cost, and less time-consuming. Our results showed that testing of visual function and ocular physiology was suitable for the rapid screening of patients with MCI before the onset of overt mobile and cognitive disabilities.


Radiation therapy (RT) is a crucial modality for the local control of pelvic cancer (PC), but the effect of pelvic RT on the development of secondary malignancy is still unclear. This study aimed to identify the relationship between radiation therapy received for the treatment of primary PC and subsequent secondary bladder cancer (SBC).The Surveillance, Epidemiology, and End Results (SEER) database (from 1975 to 2015) was queried for PC. Fine-gray competing risk regression and Cox regression analyses were employed to assess the cumulative incidence of SBC. Poisson regression and multiple primary standardized incidence ratios (SIR) were used to evaluate the radiotherapy-associated risk for patients receiving RT. Subgroup analyses of patients stratified by latency time since PC diagnosis, calendar year of PC diagnosis stage, and age at PC diagnosis were also performed. Overall survival (OS) was compared among different treatment groups with SBC by Kaplan–Meier analysis.A total of 318,165 observations showed that the primary cancers were located in pelvic cavity, 256,313 patients did not receive radiation therapy (NRT), 51,347 patients who underwent external beam radiation therapy (EBRT), and 10,505 patients receiving a combination of EBRT and brachytherapy (EBRT–BRT) who developed SBC. Receiving two types of radiotherapy was strongly consistent with a higher risk of developing SBC for PC patients in Fine-Gray competing risk regression . The results of the dynamic SIR and Poisson regression analysis for SBC revealed that a slightly increased risk of SBC was observed after RT in the early latency and was significantly related to the variations of age at PC diagnosis and decreased with time progress. For OS, the SBC after NRT, SBC after EBRT, and SBC after EBRT-BRT of 10-year survival rates were 37.9%, 29.2%, and 22.2%, respectively.Radiotherapy for primary PC was associated with higher risks of developing SBC than patients unexposed to radiotherapy. Different pelvic RT treatment modalities had different effects on the risk of SBC. Radiation therapy (RT) is one of the basic modalities in the treatment of pelvic tumors, including malignancies in the rectum, cervix, ovary, and so on. RT may help decrease the risk of tumor recurrence and significantly improve the prognosis . PatientDuring RT, high doses of ionizing radiation are delivered, which is a form of high-energy electromagnetic radiation reaching deeper internal body structures and eventually causing apoptosis , 9. HoweIn this study, the data obtained from the SEER registries with more than three decades of follow-up were used. We comprehensively analyze the incidence and latency period of subsequent bladder cancer following primary pelvic cancer (PC) irradiation.http://seer.cancer.gov/seerstat/), to download patient data of diagnosed PC from 9 registries in the SEER database between January 1, 1975, and December 31, 2015. Access to and use of the data in the SEER database does not require informed consent from patients because the data and information were anonymized and deidentified before release. The study was approved by the Ethics Committee of the National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, the Chinese Academy of Medical Sciences and Peking Union Medical College .The SEER database is currently the largest publicly available cancer database, covering approximately 34.6% of the U.S. cancer population. In this study, we used SEER*stat software, version 8.3.9 , PCs were chosen at six sites that are routinely treated with radiotherapy . This study collected information on cancer patient demographic profiles and cancer incidence, including age, sex, race, primary tumor site, second primary cancers, stage, grade, limited data on clinicopathological, treatment profiles, and survival data.The SEER program collected information on the first course of treatment. According to the initial treatment modality of PC, patients could be classified into two groups. The RT group was composed of patients with PC who were receiving two types of radiotherapy, including external beam radiation therapy (EBRT) and a combination of external beam radiation therapy with brachytherapy involving implants or isotopes (EBRT–BRT). The no radiotherapy (NRT) group was composed of patients without two types of radiotherapy. Patients receiving brachytherapy, radioisotopes, or combination RT were censored to decrease the bias caused by different types of RT.The primary outcome of this research was to investigate the risk of developing SBC more than one year after treatment for PC. The SEER program has eliminated the involvement of recurrent PC disease according to the ICD-O-3 guidelines. The secondary outcome was to estimate the 10-year overall survival (OS), which was defined as the time from the start of randomization until death due to any cause.2 tests were employed to compare categorical data. Fine-Gray competing risk regression analysis was used to evaluate the cumulative incidence of SBC development. SBC was considered the event, and non-SBC or all-cause death was defended as competing events. Cox proportional hazard regression analysis was performed with SBC to evaluate the hazard ratio (HR) and 95% confidence interval (95% CI) of developing SBC after PC. The multivariable Cox analysis was established by employing a backward selection procedure with variables with 2-sided P<0.05 in univariable studies, which were considered statistically significant and included in multivariable analyses. The radiotherapy-associated risk (RR) was calculated by Poisson regression analysis with the relative risk and 95% CI of SBC development for PC patients receiving radiotherapy compared with those not receiving radiotherapy. These analyses were performed with R software, version 3.5.3. In addition, the standardized incidence ratio (SIR) and 95% CI were also estimated by Poisson regression analysis. The definition of SIR was the ratio of observed developing SBC among PC survivors in the U.S. general population. The SIR were estimated with SEER*Stat 8.3.9. Both SIR and RR were adjusted for age at PC diagnosis, race, sex, and the calendar year of PC diagnosis. They were stratified by latency time since PC diagnosis, age at PC diagnosis, and calendar year of PC diagnosis. The Kaplan–Meier method was used to determine 10-year OS for SBC, and survival differences were calculated by the log-rank test.Fisher’s exact test and χA total of 318,165 patients with PC were identified in the study; 256,313 patients did not receive RT, 61,852 patients received RT, 51,347 of whom were treated with EBRT only, and 10,505 with EBRT–BRT Figure 1After a minimum latency of 1 year from primary PC diagnosis, a total of 2,801 patients developed SBC. Among patients who underwent RT, 717 (1.16%) went on to develop SBC, including 598 (1.16%) patients in the EBRT group and 119 (1.13%) patients in the EBRT–BRT group who developed SPC. In the NRT group, 2084 (0.81%) patients developed SBC. The above data show that, compared with the NRT group, a greater proportion of patients who received EBRT and EBRT–BRT for their primary PC developed an SBC, and no difference was found between the two types of radiotherapy groups  and Cox regression analysis . The cumulative incidence of SBC after NRT, SBC after EBRT and SBC after EBRT-BRT were 1.37%, 2.01%, and 1.64%, respectively Figure 2Subgroup analyses were performed to further evaluate the risk of developing SBC by competing risk regression. We found that the increased risk associated with RT was noted in most subgroups. In analyses of each type of PC, receiving two types of radiotherapy could significantly increase risks in cervix uteri cancer  and corpus uteri cancer  Figure 3. This may be due to the improvement of radiotherapy technology, which makes the treatment process more precise and avoids the occurrence of excessive radiotherapy. In the dynamic age-SIR plot and RR plot, the increasing risk of SBC could be noted at different ages in the PC cancer diagnosis group undergoing RT. Compared with the US general population in the matching age group, PC patients with RT were at a significantly higher risk of developing SBC, especially younger PC patients, and younger patients had a higher incidence of SBC than older patients , year at primary PC diagnosis and age at primary PC diagnosis. From the dynamic SIR plots, the incidence of SBC in PC patients receiving radiotherapy was higher than that in the US general population  Figure 5SPM in cancer survivors account for a significant proportion of the total cancer incidence, ranging from 11% to 25% in adults , 16. TheThis study was a large-scale population-based study that comprehensively assessed the risk of SBC in PC survivors and the survival outcome of SBC. We found that the cumulative incidence of SBC in PC patients who received RT was higher than that in patients without RT. In addition, the incidence of SBC in PC patients receiving RT was higher than that of the entire American population, and the risk of SBC after RT increased with the latency period. Younger patients receiving RT were more likely to develop SBC. In sum, pelvic RT is associated with a potentially increased risk for secondary tumors. In particular, the bladder, which is in close anatomical relation to several pelvic target organs, is likely to be in the radiation range and can consequently receive relatively high doses of radiation.This is the first study using a large population-based cohort to conduct a comprehensive investigation of the prognosis of bladder cancer as the second primary malignancy, revealing a distinct, time‐varying disease course. Our research has the following advantages. First, the findings were based on the SEER database. A total of 318,165 pelvic tumors were included to avoid the selection bias imposed by single-center studies or small-sample studies. Second, the research period was 40 years, which made the conclusions more reliable. Third, competing-risk proportional hazard regression was used to obtain unbiased estimates of the risk factors for SBC. The Kaplan–Meier method and the Cox proportional approach are the main analysis methods of traditional risk prediction models, which can only manage one result and may produce biased results in the presence of competitive risks. However, in our study, the risk of competing is particularly important because a considerable portion of PC survivors usually die for other reasons before the development of SBC.According to the SIR and Poisson regression analysis, we have the following main findings. First, the risk of SBC after radiation therapy decreases with the year of diagnosis, which may be related to the type of radiation therapy they receive. Technical improvements in radiotherapy over the last 30 years have also reduced its side effects on patients. Second, the risk of developing SBC after radiation therapy increases with increasing latency, which may be due to increased screening, improved medical imaging, and the development of treatment strategies that may have significantly increased detection rates and prolonged patient survival, resulting in a higher risk of exposure to subsequent cancers. Third, the risk of SBC after radiotherapy decreases with increasing age at the time of primary cancer diagnosis, which may be because younger patients treated with RT have more prolonged survival and are more likely to develop SBC. Our preliminary findings may help clinicians better understand SPM, and the results suggest that prolonged follow-up is needed for patients treated with pelvic RT, especially younger patients. When tumors occur more than 10 years later, patients are more likely to develop SBC .However, at the same time, our study had some limitations. First, only the initial treatment information of the tumor was recorded in the SEER database, and it was unknown whether delayed RT was performed in the subsequent treatment. Therefore, this may lead to an underestimation of the actual risk of SBC associated with RT. Second, our study cohort was collected retrospectively over a long period of time, during which treatment of the tumors improved considerably. The radiotherapy regimen for each patient was unavailable, which may have influenced the obtainment of more detailed conclusions. Third, family history, smoking status, and weight were the strongest cancer risk factors, but relevant information was unavailable from the SEER database.Radiotherapy for primary PC was associated with higher risks of developing SBC than patients unexposed to radiotherapy. Different pelvic RT treatment modalities had different effects on the risk of SBC. We suggest that patients with pelvic RT, especially young patients, require long-term monitoring of the risk of SBC.The original contributions presented in the study are included in the article/Conception and design: SL, XG, XW, ZJ. Collection and assembly of data: SL, RW, GY, HL, TC. Data analysis and interpretation: SL, RW, XG, XW, ZJ. Manuscript writing, final approval of manuscript, accountable for all aspects of the work: All authors. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Birth weight is a significant determinant of the likelihood of survival of an infant. Babies born at low birth weight are 25 times more likely to die than at normal birth weight. Low birth weight (LBW) affects one out of every seven newborns, accounting for about 14.6 percent of the babies born worldwide. Moreover, the prevalence of LBW varies substantially by region, with 7.2 per cent in the developed regions and 13.7 per cent in Africa, respectively. Ethiopia has a large burden of LBW, around half of Africa. These newborns were more likely to die within the first month of birth or to have long-term implications. These are stunted growth, low IQ, overweight or obesity, developing heart disease, diabetes, and early death. Therefore, the ability to predict the LBW is the better preventive measure and indicator of infant health risks.This study implemented predictive LBW models based on the data obtained from the Ethiopia Demographic and Health Survey 2016. This study was employed to compare and identify the best-suited classifier for predictive classification among Logistic Regression, Decision Tree, Naive Bayes, K-Nearest Neighbor, Random Forest (RF), Support Vector Machine, Gradient Boosting, and Extreme Gradient Boosting.Data preprocessing is conducted, including data cleaning. The Normal and LBW are the binary target category in this study. The study reveals that RF was the best classifier and predicts LBW with 91.60 percent accuracy, 91.60 percent Recall, 96.80 percent ROC-AUC, 91.60 percent F1 Score, 1.05 percent Hamming loss, and 81.86 percent Jaccard score.The RF predicted the occurrence of LBW more accurately and effectively than other classifiers in Ethiopia Demographic Health Survey. Gender of the child, marriage to birth interval, mother’s occupation and mother’s age were Ethiopia’s top four critical predictors of low birth weight in Ethiopia.The online version contains supplementary material available at 10.1186/s12911-022-01981-9. Birth weight has a significant influence on its chances of survival. Low birth weight (LBW) is becoming more an issue, particularly in emerging countries. A major cause of neonatal death is low birth weight, less than 2500 g . Babies Evidence shows that the global prevalence of LBW dropped by 1.2 percent each year between 2000 and 2015, meaning that progress is insufficient to fulfil the World Health Assembly's low birth weight target of 30 percent by 2025 [Low birth weight can be caused by a variety of circumstances, depending on the region , 2. Low Numerous studies have found that the most prevalent causes of low birth weight are prematurity and intrauterine growth restriction. In developed countries, the most common reason is preterm birth, but in underdeveloped countries, the most common cause is intrauterine growth restriction . In addiEven though LBW is a worldwide concern, the condition is most prevalent in the world's poorest areas, Sub-Saharan Africa and Southern Asia , suggestAccording to , EthiopiEthiopia has a birth weight burden of around half of the SSA, at around 11% . As a reThis study ML flows first displays the data source, pre-processing stage, feature selection for the classifier, hyper-parameter tuning, and classifier's functioning technique for the classification.The data of this study was obtained from the fourth wave of the Ethiopia Demographic and Health Survey, which took place in 2016 (EDHS-2016). The EDHS-2016 data collection period ran from January 18 to June 27, 2016. The survey was conducted under Ethiopia's Central Statistical Agency (CSA), with technical assistance supplied by ICF and financial support provided by other international organizations. The goal of the EDHS is to produce up-to-date estimates of the population's significant demographic and health variables . The surSeparate enumeration areas (EAs) were chosen in two phases in each stratum. The EDHS-2016 selected 645 EAs at random from a total of 84 915 EAs in Ethiopia, with a probability proportional to EA size and independent selection in each stratum. The second stage of the sampling procedure involved picking a predetermined number of 28 households from each EA using an equal probability systematic approach . There wLow birth weight (LBW) is the study's target variable. Birth weight data were obtained in the survey for children born five years before the interview. There were 10,641 births in the five years before the survey. However, only 2110 newborns have birth weight data, accounting for around 14% (weighted) of the total number of births in the study.This study employed data pre-processing to eliminate missing values, noisy data, and incompatible data since it is done in its raw form. Pre-processing, cleaning, integrating, transforming, reducing, and discretizing data are helpful . In thisSecond, each sample in the dataset includes over a thousand features; therefore, feature selection is essential. A closer look reveals that the bulk of the features is unsuitable for the implementations. As a result, identifying significant features associated with LBW is an essential initial step. An assessment of the literature , 22 and Relevant features determine the performance of any machine learning model implementation. Feature selection refers to picking critical characteristics for the research work at hand. In addition to lowering data dimensionality, it improves data visualization and understanding. If further study is done, the effects of all 25 qualities must be considered. The topic of categorical data discretization is widely covered in . An imbaSome researchers have used machine learning approaches to anticipate medical problems and other disorders , 23. RanBecause of its ability to predict and ease usage, Extreme Gradient Boosting (XGB) has become one of the most popular machine learning approaches. The regression and classification algorithms are being monitored. The two principles that make up XGB are decision trees and gradient boosting. Decision trees are likely the most easily interpretable machine learning algorithms known, and they may be pretty powerful when used in conjunction with the correct techniques. The nodes and leaves of a decision tree resemble the nodes and leaves of a tree with a base and numerous branches .XGB is a boosting method that is part of an ensemble learning algorithm. The goal of XGB is to keep adding trees and performing feature splitting to expand a tree. When a tree is added, it learns a new function to suit the residuals of the previous prediction. The score of a sample is predicted when k trees are created after training. In reality, each tree will have a matching leaf node based on the features of this sample, and each leaf node correlates to a score. The sum of the scores for each tree equals the sample's predicted value. XGB adds a regular term to the loss function's second-order Taylor expansion to balance the model's complexity and the loss function's decline. It looks for the optimal solution globally and prevents model overfitting to a large extent.K-Nearest Neighbor (K-NN) method selects the K samples with the most significant sample similarity to be graded, the voting approach determines which group the samples belong to, and the instance is divided into this class. The K-NN classification algorithm performs poorly in an adversarial environment. It is simple to use and maybe put to good use in some circumstances .The Random Forest (RF) classifier has addressed classification and regression problems. Each split decision employs the notion of multiple random tree creation, with training bootstrap, sample bagging, voting system, and randomly picked characteristics. It takes substantially less input preparation, uses many types of features without normalization, and is quick to train and optimize according to hyperparameters .The LBW outcomes prediction model can be created and Logistic Regression (LR), Decision Tree (DT), Naive Bayes (NB), K-Nearest Neighbor (K-NN), Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting (GB), and Extreme Gradient Boosting (XGB) , 23, 24aHyperparameters describe the depiction architecture, and hyperparameter tuning is the process of optimizing model design. These approaches demonstrate how to use the space of potential hyperparameter values to describe likely model structures. This study employed Randomized Search cross-validation to improve the parameters of Logistic Regression (LR), Decision Tree (DT), Naive Bayes (NB), K-Nearest Neighbor (K-NN), Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting (GB), and Extreme Gradient Boosting (XGB) , 23, 24.Evaluation metrics are used to assess the performance and effectiveness of the implemented predictive model . Their sAccuracy measures how many of the cases are correctly identified/predicted by the model , i.e. coThe total count of the sample is 2110. As shown in Table Through data processing, the number of samples in the data set is 2110 instance and 25 features. The LBW and Normal weight samples ratio is 1:9, which belongs to the category imbalanced data set. The study dataset resampled to improve the imbalance ratio. This study employed the SMOTE method [Once the preprocessing is done, the implementation of predictive models can commence. Numerous combinations of parametric values and settings are carried out. The model is fine-tuned to get optimal performance. These settings are divided into four categories general, booster, learning task, and command line. Tuning can be done in a grid or by searching randomly. The grid search is used in this study. With many factors, grid search for the best solution might be challenging. It can be readily addressed by focusing on a smaller number of parameters with suitable parameter ranges. K-fold cross-validation is used during the model selection step to assess model performance. Packages and libraries of Python utilized. The grid search is done in the following manner.First, to check for over-fitting, the “n estimators” that define the model's epoch are set to 100 and early stopping rounds to 5. Second, find the best learning rate and gamma at the same time, as they have a direct impact on the model's performance. 0.01, 0.02, 0.03, 0.06, 0.1, 0.2, and 0.3 are the grid values for the learning rate, whereas 0, 0.1, 0.2, 0.5, and 1 are the grid values for the gamma. For model tuning, all potential combinations of these two parameter values are tested, and the ones that perform best are kept as the optimal values. Third, make a grid search across the max depth and min child weight in the ranges of 1–10 using the optimal learning rate values and gamma. Fourth, make a grid search across the regularization parameter reg lambda and subsample in the 0.1–1 range simultaneously. Fifth, re-examine the model using a simultaneous grid search over gamma, reg lambda, and subsample to check for discrepancies between the optimal values. This study divided the data into a training set and test set account 80% and 20%, respectively. The model performance, such as prediction evaluation metrics, can be compared against different ML algorithms and parameter settings. This study used the popular fivefold cross-validation to avoid overfitting.This study compared the eight different classification methods to verify the superiority of one of the proposed methods. The LBW outcome prediction of Logistic Regression (LR) Fig. , DecisoiIn this study, the RF model is the best classifier with all performance metrics and predicts LBW. 96.01% of average Precision implies the tradeoff between Precision and recall and computes the Area Under the Precision-Recall Curve. 89.72% of accuracy indicates the amount of correctly predicted cases in the XGB of LBW. 96.13% of Sensitivity or recall implies actual LBW cases predicted as LBW by the classifier. F1-Score implied result of the Precision and recall of the model. Area Under Curve (AUC) was 90.01%, which provides a comprehensive assessment of the accuracy of a model by screening the range of threshold values for decision making.As Table Furthermore, the Jaccard score is the ratio of the size of the intersection to the size of the union of predicted and ground truth label classes.When combining predicted and actual classes, it is called a similarity coefficient. The best classification is one, and the poorest rating is 0. This study’s Jaccard score is 0.819, implying that the actual labels are best predicted.Overall, the RF classifier outperforms the others. It is not proof that RF is always better than other algorithms, as the nature of the data might significantly impact which algorithms are employed. It could be argued that, among the most common tools, RF is the best tool for implementing a predictive model employing the EDHS. The confusion matrix is shown in Fig. Finally, RF classification of LBW, the results of an attribute in all the promotion trees are weighted and summed and then averaged to obtain the importance score. The  RF model provides an order of important feature for enhancing the accuracy of the prediction model while performing low birth weight prediction using the tree model method. As It is shown in Fig. According to previous research , 15, 16,As the feature importance rank identified, Gender of the child, marriage to birth interval, mother’s occupation and mother's age were the top four critical predictors of low birth weight in Ethiopia, according to the XGB model. This study is roughly in line with the findings research , 16, 22.According to a 2011 EDHS study, there is a link between maternal education and proxy LBW . MultiplThe mother's age was one of the most critical indicators of low birth weight in Ethiopia in this study. In Ethiopia, a mother's age increases the likelihood of a low birth weight baby. The similarities between these studies might be due to mothers' physiological, psychological, and behavioral immaturity in this age range, which affects their children's ability to provide enough nourishment and care. Maternal education was linked to proxy LBW . This stNeonates born in rural areas have a higher risk of proxy LBW than those born in urban areas . HoweverInfant mortality and its implications have been linked to low birth weight. Predicting the LBW is thus a valuable preventative tool and predictor of newborn health hazards. This study used the Ethiopia Demographic and Health Survey 2016 to develop predictive LBW models. This study used Logistic Regression (LR), Decision Tree (DT), Naive Bayes (NB), K-Nearest Neighbor (K-NN), Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting (GB), and Extreme Gradient Boosting (XGB) to compare and find the best classifier for predictive classification. Before applying the predictive models, data preprocessing is carried out, including data cleaning. In this study, the classifier categories are normal and LBW. RF was the best classifier, predicting LBW with 91.60 percent accuracy, 91.60 percent Recall, 96.80 percent ROC-AUC, 91.60 percent F1 Score, 1.05 percent Hamming loss, and 81.86 percent Jaccard score, according to the research. As a result, the RF predicts the occurrence of LBW correctly and more effectively than other classifiers. Gender of the child, marriage to birth interval, mother’s occupation and mother’s age were Ethiopia's top four critical predictors of low birth weight. This study has a direct importance on Ethiopian health and preventative policymaking related with low birth weight.Additional file 1. Explanatory Data Analysis of Low birth Weight in Ethiopia based on EDHS 2016.