To determine whether items of the Chinese version of the Montreal Cognitive Assessment Basic (MoCA-BC) could discriminate among cognitively normal controls (NC), and those with mild cognitive impairment (MCI), mild Alzheimer’s disease (AD), and moderate-severe (AD), as well as their sensitivity and specificity.n = 456), mild AD (n = 502) and moderate-severe AD (n = 102) patients were recruited from the memory clinic, Huashan Hospital, Shanghai, China. NC (n = 329) were recruited from health checkup outpatients. Five MoCA-BC item scores were collected in interviews.MCI (The MoCA-BC orientation test had high sensitivity and specificity for discrimination among MCI, mild AD and moderate-severe AD. The delayed recall memory test had high sensitivity and specificity for MCI screening. The verbal fluency test was efficient for detecting MCI and differentiating AD severity.Various items of the MoCA-BC can identify MCI patients early and identify the severity of dementia. Mild cognitive impairment (MCI) is a transitional cognitive state between normal ageing and the early stages of dementia. Alzheimer’s disease (AD) is the most common neurodegenerative disease, and the incidence of AD increases with age. Approximately 10–20% of adults over the age of 65 have MCI, and approximately 10% of these patients may progress to AD [The Montreal Cognitive Assessment (MoCA) had been used as a quick evaluation scale to detect MCI in the group of highly educated elderly adults with an average education level of 13 years . SeveralCompared with the MoCA, the MoCA-BC takes less time , and the change in some non-memory cognitive function tests improves the sensitivity for screening and the acceptance of the participants. There are nine items assessing different cognitive domains in MoCA-BC, including language, attention, calculation, orientation, memory, concentration, executive function, conceptual thinking, and visual perception.There is currently no comparison of the items in the MoCA-BC scale among cognitively normal people, and individuals with MCI, mild AD, and moderate-severe AD. We selects five items in MoCA-BC that scored more than five points in a single item; these tests are the verbal fluency test, visual perception test, immediate memory test, delayed recall memory test and orientation test. Our study may help clinicians to fully understand and distinguish the severity of cognitive impairment.A total of 1389 participants aged between 50 and 90 years old comprised four groups: 329 cognitively NC, 456 MCI, 502 mild AD, and 102 moderate-severe AD participants. From May 2015 to March 2018, participants with MCI or AD were recruited from the Huashan Hospital Memory Clinic. Cognitively NC participants were recruited from their caregivers.The inclusion criteria for all participants were normal or nearly normal eyesight and hearing, no history of alcoholism, drug abuse, head trauma or other serious neuropsychiatric diseases and other disease that would affect their performance to complete all neuropsychological assessments. Participants or their legally authorized caregivers provided informed consent before the commencement of the study. This study had been approved by the ethics committee of Huashan Hospital. All participants underwent complete neurological and neuropsychological assessments, brain imaging and other necessary laboratory tests. The diagnosis decision was made independent of MoCA-BC scores.The diagnosis of MCI was on the basis of the following criteria : complaiAD diagnosis was made according to the National Institute of Aging and Alzheimer’s Association (NAI-AA) diagnostic criteria , which wNormal controls must have no cognitive impairment, CDR score of 0, MMSE score at the cutoff value or above, the Hamilton Depression Rating Scale score at 12 or less and the modified Hachinski Ischaemic Scale score at 4 or less in the past 2 weeks.The following clinical and neuropsychological tests were administered: MMSE, MoCA-BC, CDR, MHIS, and HDRS as mentioned above. Five items from the MoCA-BC were selected for assessment in the present study and included measures of language, orientation, memory , and visual perception: 1. Measure of language: Ask the participants to name as many types of fruit as possible in one minute, and record that number to obtain the corresponding score; 2. Measure of orientation: Ask the participants to describe the time and place, with a total score of 6; 3. Measure of instant recall: The tester lists 5 words, then the participants recalls as many as possible. Repeats the list twice, the number of repeated words is recorded with a total score of 10. This task did not contribute to the standard MoCA-BC score; 4. Measure of delayed recall: following the other tests, the participants is asked to recall the 5 words and scores 3 points per word for free recall, 2 points following semantic classification clues, and 1 point for multiple choice recall; 5. Measure of visual perception: Identify the number of items in overlapping graphics in one minute, with a total score of 10.P < 0.05 considered significant. Receiver operating characteristic (ROC) curve analysis for MCI or AD dementia was performed to obtain cutoff values with sensitivity and specificity measures for the scores of the five items. The area under the ROC curve (AUC) was used to compare the diagnostic performance of the five items. The level of significance was set at α = 0.05.All analyses were carried out in SPSS version 20 for Windows. Pearson correlations were applied for better understanding of the effect of age and education level on performance on the five items. Demographic characteristics for each diagnostic group are described using means and SDs or frequencies where appropriate. Differences in demographic characteristics and cognitive test performance between diagnostic groups were determined by chi-square tests as well as ANOVA with subsequent Bonferroni corrections, with r < 0.2). Verbal fluency, visual perception, and immediate recall were associated with education. Therefore, to reduce the impact of education level on the item scores of MoCA-BC, we divided the population into three levels according to the years of education: low level (≤6 years), middle level (7–12 years), and high level (> 12 years). The study analysed the difference in the five sub-test scores in the MoCA-BC between the NC group, MCI group, mild AD group and moderate-severe AD group based on level of education.Correlation analysis between the five item scores and demographic characteristics were carried out in the control group and are summarized in Table P > 0.05). In the low and middle-level education groups, there was no difference in sex ratio among the NC group, MCI group, mild AD group and moderate-severe AD group. In the middle and high-education level groups, there were differences in age among the NC group, MCI group, mild AD group and moderate-severe AD group.The demographic information according to education in the NC group, MCI group, mild AD group and moderate-severe AD group is shown in Table We show the five item scores  for each subgroup in Table An ROC analysis of each item was performed for discriminating those with MCI from NC, and the delayed recall item with an AUC of 0.808  for screening cognitive impairment and the severity of cognitive impairment. The optimal cutoff of the five items in screening for MCI and AD was analysed, which is helpful for clinicians to more intuitively judge these five domains of cognitive impairment. We found that there were differences in age and gender among the four groups in the high-education level groups, and there was an extremely weak negative correlation between age and cognitive impairment, which is consistent with AD itself being an age-related disease. We used analysis of covariance in the high education level population, using age as a covariate to analyze and control the effect of age on the test results. After that, there were differences between the four groups. In China’s epidemiological survey, women’s cognitive impairment is slightly higher than that of men , and stuIn our study, we found that among the NC, MCI, mild AD, and moderate-severe AD groups, there were differences in delayed recall, verbal fluency, visual perception, immediate recall, and orientation. These relations are similar to F. Clement’s research , 18 . EdOur results showed that the verbal fluency test had validity not only in the MCI group but also in the mild and moderate-severe AD groups in the three education subgroups. Visual perception and immediate recall tests had validity for discrimination among the NC, MCI, mild AD, and moderate-severe AD groups only in the high-level education population, while in low- and middle-level education populations the two items had poor validity for screening those with MCI from NC. This may be due to the need for a high level of education to complete the two items and the lower impairment in visual perception and immediate recall in MCI individuals.Cognitive impairment can be manifested in many aspects, such as memory, orientation, language, executive function, visual perception, calculation, attention, and information processing. The MoCA-BC is a quick, simple, and feasible assessment tool not only for doctors but also for outpatients. Each item in the MoCA-BC is independent, which reflects the characteristics of cognitive impairment of dementia in different stages. Sometimes, elderly people may have vision or hearing impairments, and only part of the test can be completed; therefore, a total score cannot be obtained. The MoCA-BC item score is a good complement for these people. The analysis of the differences between the item scores across groups with different levels of cognitive function is very helpful in distinguishing the types of cognitive dysfunction and determining the field of cognitive impairment. Through each item, we can observe the main areas of cognitive impairment and help identify the types of cognitive impairment, further distinguishing between the recognition of AD, Lewy body dementia, vascular dementia or depression.Of course, the items of the MoCA-BC scale cannot fully satisfy the identification for the stage of AD, so the MoCA-BC needs to be complemented by a combination of more complicated neuropsychological tests.In summary, the MoCA-BC is a comprehensive test that combines sub-items to identify early MCI patients and to identify the severity of dementia. This study observed differences in verbal fluency, visual perception, immediate memory, delayed memory, and orientation among the NC group, MCI group, mild AD group, and moderate-severe AD group. The results of the present study indicate that there is a need for further research on the difference in the item scores between those with AD, Lewy body dementia, vascular dementia and depression.


Introduction: Major symptoms of Gilles de la Tourette syndrome (GTS) are tics, but in 90% of cases, psychiatric comorbidities occur. Self-harm behaviors (SHBs) could result from deliberate action and unintentional injury from tics.Methods: We examined 165 consecutive GTS patients aged 5 to 50 years . The median duration of GTS was 14 years . The patients were evaluated for GTS and comorbid mental disorders according to the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision. Self-harm behavior was diagnosed during the interview. To determine a direct relationship between SHB and clinical variables, we conducted two analyses, at the time of evaluation and lifetime. We also compared the group of children and adults with SHB. We also tried to distinguish between deliberate (non–tic-related SHB) and accidental (tic-related SHB).Results: Lifetime SHB was reported by 65 patients (39.4%), and in 55 of the cases, it was present at the time of evaluation. The age at the onset of SHB was reported in 55 of the cases (84.6%), and the median was 10 years . In 30 of the patients (46.2%), SHB was evaluated as mild; in 26 (40%), as moderate; and in only 9 cases (13.9%), as severe. In the multivariable analysis for the predictor of lifetime SHB, attention-deficit/hyperactivity disorder (p = 0.016) and obsessive-compulsive disorder  were determined as risk factors, while for current SHB, only tic severity (p < 0.0001) was statistically significant. When comparing predictors of SHB for children and adults, tic severity was determined as predictor for lifetime SHB in children (p < 0.0001), while the anxiety disorder was associated with lifetime SHB in adults (p = 0.05). Similarly, tic severity was a predictor of current SHB in the children group (p = 0.001), but this was not confirmed for adults. The group of patients with tic-related and non–tic-related SHB did not differ.Conclusions: Self-harm behavior appears mostly in children and adolescents and rarely begins in adulthood. Self-harm behavior is associated mainly with tic severity, obsessive-compulsive disorder, and attention-deficit/hyperactivity disorder. Clinical correlates of SHB are age related and differ at different points of life. Tic severity is the main factor associated with SHB in children. In the adult group, anxiety disorder and other psychiatric comorbidities may play the most important role. Gilles de la Tourette syndrome (GTS) is a neurodevelopmental disorder affecting children and adults. To diagnose GTS, numerous motor tics as well as at least one vocal tic should be present during a minimum period of 1 year. The onset must be in the childhood. The major symptom is tics, defined as sudden, rapid, recurrent, nonrhythmic motor movements or vocalizations. In 90% of cases of GTS, psychiatric comorbidities occur, most commonly attention-deficit/hyperactivity disorder (ADHD) and obsessive-compulsive disorder (OCD)  , or selfPrevious studies about self-injury in GTS refer to various aspects of this phenomenon. Mathews et al.  focused The term “malignant GTS” was introduced by Cheung et al. . MalignaThere are also a number of case reports describing malignant GTS. Fasano and Galluccio  publisheAs all previous publications have only taken into account deliberate and lifetime self-injury, we wanted to extend current knowledge and investigate both the lifetime and additionally current SHB, as only associations with current clinical characteristics reflect the actual relationship between the two variables. As mentioned earlier, we aimed at assessing the prevalence and clinical correlates of all SHBs (deliberate and inadvertent). We hypothesized that SHBs would be associated with psychiatric comorbidities  and complex motor tics, coprophenomena, and tic severity.In addition, we wanted to investigate and compare predictors of lifetime and current SHB in children and adults with GTS. All previous studies reported only about lifetime SHB as well as never distinguished separately the results for children and adults. Moreover, we attempted to distinguish between tic-related and non–tic-related SHB as we predicted that it could be correlated with different clinical factors. As those phenomena have never been studied from that point of view, the study had an exploratory character.Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision. Obsessive-compulsive symptoms were diagnosed if obsessions and compulsions were egosyntonic, in contrast to egodystonic symptoms that characterize OCD , 9–22 years; 125 males (75.8%)]. The subjects were evaluated from 2013 to 2018. In total, 100 children  and 65 adults  were enrolled. The median age at the onset of tics was 6 years . The median of disease duration was 7 years , 4 years  in children and 18.5 years  in adults; 136 (82.4%) of the patients had at least one psychiatric comorbidity. General characteristics of GTS patients are presented in Types of SHB of the manuscript.During the interview, the patients were asked if they had experienced self-harm and whether they could define its intentionality. Particularly, we asked whether they had this symptom in the past as a consequence of abnormal movements or deliberate action, whether these behaviors were considered entirely involuntary or if they had control over them, and at what age the SHB started. However, many patients could not clearly define the intentionality of the SHB, and often it was difficult to classify these complex behaviors as tics, compulsions, or deliberate action due to symptom overlapping. Nevertheless, we decided to divide SHB in tic-related and non–tic-related according to criteria elucidated in the section The patients were also questioned about the type of injury that had followed the movement, whether or not the SHB left a visible mark on the skin and if it required any medical attention. The SHB in our study manifested as skin picking, self-pinching, wound scratching, head banging, self-hitting, self-biting , injury to the teeth because of grinding, accidental self-mutilation, or burning due to touching hot objects.With regard to intensity, we divided SHB into mild, moderate, and severe. Mild SHB consists of temporary self-harm that does not interfere with everyday functioning, is not reported spontaneously either by the patient or by the family, and does not leave any visual, objectively perceptible signs. Moderate SHB, on the other hand, causes tissue damage, but it is superficial and does not require any additional medical help. Moderate SHB can therefore lead to bruises or wounds, it causes physical pain and potentially may lead to more severe injuries . Finally, severe SHB leads to significant psychological and physical impairment and normally requires additional medical intervention .Despite the difficulties mentioned earlier, we attempted to divide SHB into tic-related  and non–tic-related (deliberate) group. We defined tic-related as self-hitting, while other types of SHB, such as biting, wound scratching, self-injury that leads to wounds, or skin picking or pinching, were considered as non–tic related. Duration of SHB was an important criterion to make this distinction. Tic-related self-hitting was always brief and sudden in contrast to deliberate SHB that was longer and less violent. Sometimes, self-hitting occurred in series, but each movement was very brief, similar to tics. What is more, normally patients described self-hitting as the consequence of tics and therefore accidental movement. Some SHBs that resembled compulsions, as they were more complex, purposeful, prolonged, and directed to reduce inner tension, were included into non–tic-related SHB.Here we summarize all types of SHB that occurred in our group:Tic relatedself-hittingNon–tic relatedskin pickingself-pinchingwound scratchinghead bangingself-biting teeth injury because of grindingdeliberate self-mutilationburning due to touching of hot objectsWe conducted two comparative analyses. The first included the group of patients without any history of SHB (SHB−) and the group of patients with a history of SHB in the past or at the time of evaluation (SHB+). When categorizing patients as having SHB at the time of evaluation, we took into account last 1 week. In the second comparative analysis, we divided all the patients into two groups: SHB current+  and SHB current– (GTS patients without SHB within last week). As a result, SHB current− included both patients without any history of SHB and those with SHB in the past but not at the time of evaluation. Moreover, in this second analysis, we took into account only SHB and other variables present at the time of the examination, as we wanted to determine whether there was a direct relationship between the demographic and clinical parameters and the SHB. Finally, we wanted to compare the two analyses and determine whether the factors that influence the lifetime prevalence of SHB in GTS differ from those that are related to the SHB at the time of examination. For example, a lifetime diagnosis of depression does not necessarily contribute to the occurrence of SHB as both symptoms could have occurred at different moments in the lifetime. The exact relationship could be determined only when both symptoms are present at the same time. As part of lifetime and current comparison, we also have done separate analysis for children and adults as we suspected that those results could differ.t test and the nonparametric data using the Mann-Whitney U test, as appropriate; the categorical data were compared using Fisher exact test (two-sided).The statistical analyses were performed using STATISTICA version 13.1  and SPSS version 25  software. The normality of distribution was assessed using the Shapiro-Wilk test. For the parametric variables, data are presented as arithmetic means and SDs (mean ± SD). For the nonparametric variables, data are presented as median and quartiles . The categorical variables are presented as frequencies (percentages). Parametric data were compared using an independent p < 0.05. All the variables that were significant in the primary analyses were entered into a logistic regression analysis in order to determine the risk factors for the lifetime or current SHB in GTS patients. In addition, gender and age were entered into the multivariate model as control variables.In both the analyses, the comparisons between the groups were considered significantly different when the two-tailed test was When analyzing predictors for lifetime and current SHB in children and adults, we used the same statistical methods as mentioned above. Therefore, all variables that were found significant in univariate analysis were included in the multivariate analysis.p = 0.65).Lifetime SHB was reported by 65 patients (39.4%), whereas in 55 of the cases, it was present currently. The prevalence of SHB did not differ between adults and children: 41.5% (27/65) and 38% (38/100), respectively . In 28 of the cases, SHB had started during childhood (before 11 years of age); in 22 of the cases, during adolescence (12–18 years); and in five of the cases, in adulthood. In 30 of the patients (46.2%), SHB was evaluated as mild; in 26 (40.0%), as moderate; and in 9 cases (13.9%), as severe. The most severe cases included teeth grinding (bruxism), leading to damage to the enamel, and hitting the head that caused damage to the teeth. Another patient was operated on several times due to elbow hitting, whereas another case of self-hitting led to the patient breaking his/her nose. One female patient ended up blind in one eye and with a severe impairment in the other eye as a result of punching herself in the eyes.The most frequent types of SHB were as follows: self-hitting (n = 44), self-biting (n = 29), and scratching, especially of wounds (n = 20); 26 patients had only one SHB, 22 patients suffered from two types of SHB, and 17 had three or more.p = 0.001, χ2 = 30.436, and R2 Nagelkerke = 0.602).In the comparative analysis of lifetime SHB+ and SHB− groups, there were no differences in age and gender distribution. Nevertheless, patients with SHB had more severe tics and suffered more frequently from psychiatric comorbidities Table 2.p = 0.000, χ2 = 58.282, and R2 Nagelkerke = 0.413). We included only OCD in this analysis as OCD + OCS is also determined by the presence of OCD.In the comparative analysis of the SHB current+ and SHB current− groups, there were also no differences in age and gender distribution. The vast majority of the factors that were significant for lifetime SHB in GTS were also detected for the SHB current+ group; those patients also had higher results on the YGTSS and were more commonly diagnosed with ADHD, OCD, and anxiety disorder and more frequently demonstrated aggressive behavior, as well as social skill problems, than the SHB current− group. Finally, the following variables were significantly associated with current SHB in univariate analyses and were included in logistic regression model: YGTSS, OCD, ADHD, anxiety disorder, depression, aggression, and significant social skill problems. The characteristics of current SHB are shown in General characteristics of both adults and children are presented in p < 0.0001), while the anxiety disorder was associated with lifetime SHB in adults (p = 0.05).In multivariate analyses, tic severity was determined as predictor for lifetime SHB in children : age, YGTSS, OCD, aggression, and social skills problems.p = 0.001); however, none of the variables reached statistical significance in logistic regression for predictors of current SHB in adults. For details, see In multivariate analysis for predictors of current SHB, tic severity was a predictor of current SHB in the children group (Forty-two patients (64.5%) had tic-related SHB, whereas 48 (73.8%) had non–tic-related SHB; in 25 patients (38.5%), both types of SHB occurred. We present only lifetime results, as we do not distinguish between current and lifetime results in this case. When comparing the patients with tic-related and non–tic-related SHB, no differences were found Table 6.Our study further confirms that SHB is an important phenomenon in GTS. To the best of our knowledge, all previous reports have been based on information about the lifetime symptom prevalence. They also did not include the separate analysis of children and adult group or intend to distinguish between deliberate and accidental SHB. For this reason, it was not possible to assess whether deliberate, non–tic-related SHB or inadvertent, tic-related SHB were directly related to the presence of OCD, ADHD, or tics. We investigated both the lifetime and the current prevalence of SHB. Therefore, we believe that our findings better reflect an actual relationship between SHB and other clinical factors. To give an example, when only lifetime SHB is considered in an adolescent, one incident of SHB at the age of 8 years could be, by mistake, interpreted as the result of OCD that started at the age of 10 years.The lifetime prevalence of SHB in our group was 39.4%, which approximates to the values reported by other researchers for this symptom in GTS. Sambrani et al.  actuallyThe age at onset in our group for the majority of cases was in childhood and adolescence and only in a few cases in adulthood. Moreover, recall bias regarding the occurrence of SHB in childhood should be taken into account. For this reason, we can speculate that onset of SHB in adulthood can be even less often. This finding about SHB onset is consistent with reports about SHB in other diseases and in a healthy population –17. InteTaking into account the severity of the SHB, in comparison to the study by Mathews et al. , our groWhen comparing the results for children and adults, it seems that children could have more tic-related SHB as the risk factor for both lifetime and current SHB was tic severity. On the other hand, the only risk factor for adults was anxiety disorder, indicating that in older patients psychiatric comorbidities could play a more important role compared to tic severity in the development of SHB. Surprisingly, neither OCD nor ADHD that correlated with lifetime SHB in the whole group of GTS patients was found to be related to SHB when taken into account children and adults as separate groups in multivariate analyses 5. A smaAlthough our study does not answer the question why patients with GTS engage in SIBs, it provides some clues to understand it. Based on the current literature, NSSI most commonly temporarily alleviates the overwhelming negative emotions . The samSelf-harm behavior, including NSSI and unintentional self-injury (USI), is a frequent and bothersome symptom in GTS.Current SHB is mainly related to tic severity.Lifetime SHB is mainly associated with OCD and ADHD.The clinical correlates of SHB are likely to be age-dependent. Whereas in children with GTS the main risk factor for SHB is tic severity, in group of adults, anxiety disorder and probably other psychiatric comorbidities attribute to development of SHB.We failed to demonstrate any differences between intentional and accidental SHB, and we assume that tic-related and non–tic-related SHBs seem to be overlapping phenomena, extremely difficult to distinguish.Although per definition SIB must be deliberate, some behaviors in GTS are inadvertent, and that could bias the rate of SIB in the previous studies.The biggest limitation of our study is the small study sample. The one-time registration study design may have inﬂuenced the prevalence of SHB. Therefore, we cannot determine whether lifetime SHB had repetitive character. There is also a possible referral bias because the patients were evaluated by a movement disorders specialist, and the cases with a more severe psychopathology were referred to psychiatric clinics. Also, recall bias should be taken into consideration. It is also possible that we did not include patients with extreme SHB as those are usually admitted to emergency units or referred to psychiatrists. As mentioned before, we used diﬀerent methods of data collection from children and adults. Most of the clinical information regarding children was provided by their parents, whereas adults reported it themselves. The severity and intentionality of SHB were evaluated only subjectively, as we do not have any objective measurements to differentiate tic-related and non–tic-related SHB. Moreover, some tics could involve also deliberate self-harm as sometimes there may be an urge to harm before the tic. Therefore, future research should also focus on the development of scales or diagnostic criteria that could help to establish the diagnosis of SHB in GTS and divide those patients into subsequent groups.The datasets generated for this study are available on request to the corresponding author.Collecting of clinical data from patients with GTS has been approved by the Ethics Committee of Medical University of Warsaw (KB/2/2007). All subjects gave written informed consent in accordance with the Declaration of Helsinki.PJ conceived and designed the study and acquired data. PJ and NS set up the electronic database. AJ, PJ, and NS analyzed and interpreted the data, performed visualization, and reviewed and edited the manuscript. NS wrote the original draft of the manuscript.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.


Malnutrition remains a serious health problem in Cambodia with over 10% of children less than five years of age suffering from acute malnutrition. In addition to the presence of nutritional edema, two indicators are recommended by the World Health Organization for the diagnosis of acute malnutrition: weight-for-height Z-scores  and mid-upper arm circumference . Earlier, we showed that WHZ and MUAC identified different subgroups of children with acute malnutrition. To explore factors associated with both indicators of acute malnutrition, we analyzed baseline data from a longitudinal study in three provinces in Cambodia: Phnom Penh , Kratie , and Ratanakiri . Data was available for 4381 children below 30 months of age. Malnutrition rates were higher in the two rural provinces than in the capital. Although both MUAC and WHZ showed gender bias, with MUAC identifying more girls, and WHZ identifying more boys with acute malnutrition, the gender effect was strongest for MUAC. The gender bias of MUAC diminished with older age, but remained significant up to 30 months of age. Only using both MUAC and WHZ as indicators resulted in gender neutral identification of acute malnutrition. WHZ alone always identified more children with acute malnutrition than MUAC alone. In Phnom Penh, MUAC alone identified only 11% with acute malnutrition in addition to WHZ. To conclude, both MUAC and WHZ showed gender bias in this cohort of Cambodian children. In Cambodia, implementation of a MUAC-only or a WHZ-only program for the identification of acute malnutrition would be unethical as it will lead to many children remaining undiagnosed. Acute malnutrition (AM), which includes both marasmus and kwashiorkor, is still a major health threat in children, associated with a remarkably higher risk for mortality and morbidity. Children who are wasted have an almost five times higher risk to die, a risk that increases to over 12 times when stunting is also present .In addition to the presence of nutritional edema, indicating kwashiorkor, the World Health Organization recommends using either weight-for-height Z scores (WHZ) and/or mid-upper arm circumference (MUAC) for diagnosing acute malnutrition in children above six months of age . WhereasHowever, as recently shown by us, MUAC and WHZ identified two different subgroups of Cambodian children suffering from malnutrition , with liBriend and colleagues have argued that genetic differences in fat distribution, for example, the different fat distribution in Asian populations, or differences in length or height could explain the differences between MUAC and WHZ to identify acute malnutrition . To idenThe MyHealth study is a longitudinal cohort study, conducted in three provinces in Cambodia: Phnom Penh , Kratie  and Ratanakiri . For the cohort study over 1000 children and 1000 women were recruited in each province.Lists of pregnant and lactating women, women in reproductive age (WRA) and children under three years were obtained from village health volunteers (VHSGs) and midwives in selected operational districts. In total 5419 households were recruited and teams visited each household to collect data on socio-economic data, dietary intake, water, sanitation and hygiene, and anthropometry of mother and child. In addition, in each province, pregnant women were recruited into the study also, but these are not considered in the present study. Eligible children were children aged between 0 and 30 months of age, and not suffering from a chronic health condition requiring medication . Data foChildren’s heights were measured to the nearest 1 mm. Height measurements (UNICEF measurement boards) were collected in triplicate from each child in the household under three years to ensure accuracy. Weight were measured to the nearest 0.1 kg . The nutritional status of children was defined length or height-for-age (L/HAZ), weight-for-height (WHZ), and weight-for-age (WAZ) z-scores, calculated according to the Child Growth Standard of the World Health Organization (WHO) using the WHO Anthro software. To ensure the accuracy of the data, extreme values were excluded from the analysis: WAZ < −6 or > 5; L/HAZ < −6 or >5; WHZ < −6 or > 5. Excluded values represented less than 5% of the total values. We used the following cut-offs to define wasting: weight for height < −2 Z-scores (according to the WHO growth charts) or MUAC < 12.5 cm, and to define severe acute malnutrition (SAM): weight for height < −3 Z-scores or MUAC < 11.5 cm. Stunting was defined as a height-for-age < −2 Z-scores.Ethical approval for the MyHealth study was obtained from the Cambodia National Ethical Committee for Health Research, National Institute of Public Health, Ministry of Health, Cambodia. Informed consent was obtained from all participants, with consent obtained from parents or guardians for participating children. All data collected is anonymized and stored on the Kobotoolbox server, with access to the data limited to the core team of researchers only.www.kobotoolbox.org). Anthropometrical indicators  were calculated according to the WHO growth standards [Data was collected on tablets, using the Koboltoolbox software , used as reference category. For the WHZ diagnosis, subjects were categorized in a similar way: children with a WHZ < −2 cm; children with a MUAC < 12.5 cm, but a WHZ ≥ −2; and non-wasted children (WHZ > −2 and MUAC > 12.5 cm). We then performed two multinomial logistic regression models using these categorical variables as a response, adjusting for all the selected covariates, and then plotted the average marginal effect of the gender variable on the indicator category for several values of age. To examine if there was any gender bias when one considers the two diagnoses together , we created a binary variable taking the value 0 if the child is not wasted and 1 if s/he is diagnosed by any of the two indicators. This variable was then used as a response variable in a logistic model including all the covariates, and the average marginal effect of the gender variable was estimated.We used Stata 13 for data management and Stata 13  and R software version 3.4.0 for analysis. The type I error risk was set to 0.05 and the complete cases analysis was used to deal with missing data for all the multivariate analyses.P < 0.05, chi-square). Overall prevalence of AM was lower in Ratanakiri (17.2%) than in Kratie .Data were available for 4381 children  when using both WHZ and MUAC as an indicator. The remaining 42 children with AM in Phnom Penh were below six months of age, and would not have been identified with MUAC, as MUAC is used only in children above six months of age. Hence, in Phnom Penh, only 1/3 of the malnourished children above six months of age was identified with MUAC alone. For Kratie and Ratanakiri, MUAC alone identified 146 and 136 children, respectively . In contrast, in children above six months of age, WHZ alone identified 121 (85.8%), 250 (81.4%), and 161 (73.5%) in Phnom Penh, Kratie and Ratanakiri, respectively.P = 0.021, P < 0.01), being stunted  and being male , being stunted  or living in a rural province (aRRR 2.3 and 2.0 for Kratie and Ratanakiri, respectively, The gender bias of MUAC and WHZ as indicator for acute malnutrition is reflected in the figures A–C belowTo explore the age relationship between acute malnutrition and the two indicators for acute malnutrition, we plotted the probability to be diagnosed with AM by the two indicators alone or combined. As can be seen in In urban Phnom Penh, WHZ alone always diagnosed more children with AM than MUAC alone, whereas in the two rural study sites, MUAC alone diagnosed most of the infants with acute malnutrition between 6 and 12 months of age, before rapidly becoming less important. In urban Phnom Penh and rural Ratanakiri, there was a steady increase in the prevalence of acute malnutrition between 6 and 30 months of age, which was almost exclusively driven by an increase in the number of children having a WHZ < −2. In contrast, in rural Kratie, the prevalence of AM picked around 18 months of age. For all sites, after 18 months of age, WHZ alone became by far the most important indicator to diagnose acute malnutrition with >50% of the cases being diagnosed through WHZ alone.To further explore the relationship between height and indicators of acute malnutrition, we made similar curves for HAZ scores .In general, there was a negative association between the probability of being diagnosed with AM and HAZ, that is, being taller reduces the likelihood for having AM. MUAC as indicator for AM tended to be more affected by length status than WHZ. However, only in Ratanakiri was WHZ as an indicator for AM not related to the HAZ score at all.The present study confirms the earlier study from Cambodia that MUAC and WHZ identify different sub-sets of children  and it hThe present study has some important practical implications. It has been argued that children with a low MUAC are at an especially higher risk of death , and thaAnother important policy implication of the current study is that unfortunately, programs using WHZ alone, are not suitable either. In Ratanakiri, MUAC alone identified 58 additional cases of AM, or 24.9% of the total. Hence, using a WHZ-alone program would have left almost 25% of the children with wasting unidentified. Earlier, we proposed the use of different cut-offs for MUAC for screening for AM, and for initiating treatment for AM [In our current study, almost 18% of the children was identified as being wasted, with around 3% of SAM. In Cambodia, and in most other countries, no program exists for the treatment of MAM, and only cases of SAM are being treated. Indeed, it has been proven difficult to reach most children with SAM, with <10% of the estimated 60,000 SAM cases receiving treatment in 2014. However, an integrated approach, trying to improve the nutritional status of all children with AM, with standard treatment for children with SAM, and supplementary foods for children with MAM, might increase awareness in the Cambodian population for this serious health problem.In the present study, and as reported earlier as well, a low MUAC was also associated with younger age and being stunted ,14. It iA weakness of the current study is that only cross-sectional data was available, with no long-term follow-up on subsequent risk for death or morbidity. As noted above, MUAC has been preferred above WHZ as an indicator for malnutrition, as MUAC was associated with a higher risk for death than a low WHZ . HoweverTo conclude, the present study showed large within country differences in a relative small country as Cambodia, in the ability of MUAC and WHZ to identify children with acute malnutrition. Both indicators showed gender bias, but MUAC did to a far larger extent than WHZ. Overall, WHZ identified more children with acute malnutrition than MUAC. Linear growth status affected MUAC more than WHZ but, even in stunted children, WHZ identified more children overall with acute malnutrition than MUAC. To ensure that no child with acute malnutrition is left without proper treatment and follow-up, both MUAC and WHZ should be used as indicators for nutritional status in children < 5 years of age.


Patient-reported outcomes such as quality of life are gaining importance in the assessment of patients suffering from inflammatory bowel disease (IBD). The association of objectively measured physical activity and quality of life in patients with IBD has not been studied in depth. To investigate the association of disease-specific quality of life and physical activity as well as clinical and biochemical disease activity in patients with IBD.A total of 91 patients with IBD were stratified into 4 groups  and evaluated in terms of disease-specific quality of life (Inflammatory Bowel Disease Questionnaire [IBDQ]), physical activity (accelerometry), body composition , as well as clinical  and biochemical  parameters of disease activity.U test and Kruskal-Wallis test, P < 0.001). The physical activity level was higher in remission than in active disease . The IBDQ was significantly correlated with the duration of strenuous physical activity per day , skeletal muscle mass , and biomarkers of inflammation .In patients with moderate-severe disease activity, the IBDQ was significantly lower as compared to patients in remission . Patients with inflammatory bowel disease (IBD) are at risk of an impaired quality of life ,2. In a Early on, studies on the general population have investigated the impact of physical activity on quality of life. Data from the Health Survey for England show that high levels of activity are associated with better HRQoL and that this link is particularly found using objective measures as compared to surveys . The impHabitual physical activity, however, may represent an aspect which is more meaningful for everyday life in patients with IBD, and furthermore, not all patients are amenable to exercise programs . From thThe present prospective, cross-sectional study was devised to analyze habitual physical activity in adult CD and ulcerative colitis (UC) patients using accelerometry as an objective measure. Taking into account the range of different IBD activity states, patients were stratified into groups including patients with active and quiescent disease for each disease entity. The main objective of this study was to investigate the link between habitual physical activity and disease-specific quality of life in IBD.We performed a prospective, monocentric, cross-sectional, observational study. It complies with the Declaration of Helsinki 2013), was approved by the institutional ethics committee (No. 7275/2016), and was registered at the German Clinical Trials Register (DRKS) as DRKS00011370. Written informed consent was obtained from all patients. The manuscript was drafted in accordance with the STROBE  checklist , education and employment status, medical history, and specific information on actual and former medication, as well as surgical interventions. The International Physical Activity Questionnaire (IPAQ) was used to assess self-administrated physical activity during the preceding week  and the Total body composition was measured using bioelectrical impedance analysis . To minimize interindividual differences, patients were asked to undress except for underwear and to urinate before starting the measurement. The electrodes of the device were accurately cleaned before every use. While the patients stood on feet electrodes and held on to hand electrodes, body composition was analyzed through 8 electrodes and multiple frequencies in 5 body segments.All patients were instructed to wear a biaxial accelerometer  with acceleration sensors for longitudinal and transversal movement, 2 temperature sensors, a heat flow sensor, and a galvanic skin sensor to analyze and measure duration and depth of physical activity. Together with personal information such as weight, height, age, and smoking status, accelerometric data were used to calculate parameters of physical activity with context-specific linear regression. Data analysis was performed using the manufacturer's software package  readout including the duration of different levels of physical activity: Moderate activity was defined as activity between 3 and 6 metabolic equivalents (METs), vigorous activity between 6 and 9 METs, and very vigorous activity over 9 METs. In addition, the number of steps, average of METs, the physical activity level (PAL) per day, duration of recumbency, and duration of sleep were monitored. Sleep efficiency (duration of sleep/duration of recumbency) and the number of burnt calories  were calculated. To get an exact history of physical activity for each subject per day, the accelerometer initiated 1 measurement per minute.At the beginning, participants were trained in the use of the accelerometer, which was fit to the right upper arm (middle of the humerus closely to the musculus triceps brachii) using an elastic belt. All participants were instructed to wear the accelerometer 24 hours a day, except for the time of showering and swimming because the accelerometer is not waterproof. To get a realistic profile of physical activity, all participants were told to wear the accelerometer continuously for 7 days after their outpatient visit.Blood and fecal samples were collected during the participants' inclusion into the study. Biochemical parameters included hemoglobin (g/dL), leukocyte counts (1.000/μL), ferritin (μg/L), transferrin saturation (TSAT) (%), protein (g/L), C-reactive protein (CRP) (mg/L), and vitamin D3 (ng/mL). Calprotectin (mg/kg) was measured in fresh fecal samples. All laboratory tests were performed by using routine methodology in the laboratory of Hannover Medical School.P > 0.05 shows normality of data. Most variables in our analysis did not show normal distribution, so we chose nonparametric statistics . Correlation analysis was performed using Spearman's rank coefficient.Data were analyzed using SPSS Statistics software version 25.0 . Normal distribution was assessed by the Shapiro-Wilk test, where A total of 94 subjects were screened, and 91 were enrolled into the study. In accordance with the predefined stratification criteria , 39 patients had active disease , and 52 were in remission  filled in the IPAQ. With the information of the questionnaire, we calculated minutes of vigorous, moderate, and sedentary activity per week and duration of activity by feet per week. Three patients (2 in the “active disease” and 1 in the “remission” group) indicated “no activity/I don't know” for the variables vigorous, moderate, and sedentary activity per week. There were 12 patients (7 in the active disease and 5 in the remission group) who indicated “I'm not sure/I don't know” for the variable “sedentary activity.” These data points were omitted from the analysis.As an important aspect which may be affected by disease activity, and may in turn show reciprocal influence on habitual physical activity, parameters of body composition were analyzed. Body mass index (BMI) was not statistically different between the groups Figure a.A primary aim being the investigation of objectively measured habitual physical activity in IBD, accelerometric parameters were compared between the “active disease” and the “remission” group. There was a strong indication for decreased physical activity in the active disease group, shown by a significantly lower PAL Figure b. Total http://links.lww.com/CTG/A116).Next, we focused on quality of life, as previous studies had reported an association with disease activity –4 and alhttp://links.lww.com/CTG/A116]). Importantly, parameters of physical activity were significantly correlated with the IBDQ. Correlation analysis of 1-week accelerometry data showed that the PAL as a measure of energy expenditure was significantly associated with quality of life  were significantly associated with the IBDQ. From the IPAQ, calculated vigorous activity per day was associated with the IBDQ .We then asked whether quality of life is linked to parameters of body composition and habitual physical activity. Indeed, biometrical impedance analysis data significantly correlated with the IBDQ with respect to total body water, protein, fat, and total muscle mass  while measuring their energy expenditures using SenseWear Armband and indirect calorimetry. Yet another example for divergent data on SenseWear Armband measurement precision was reported by Gastin et al. . A positive impact of physical activity on quality of life has been demonstrated for a multitude of chronic conditions, such as type II diabetes . Moreover, we did not see a difference in the impact of disease activity on PAL levels between patients with CD and patients with UC, and in addition, there was no difference in PAL's effect on quality of life between patients with UC and patients with CD. These results underline the important global impact of PAL on IBD patients' quality of life independent of the IBD entity. In our study, both groups—those in active disease and those in remission—did not reach 10.000 steps per day in median as recommended by the WHO  were counted as exclusion criteria for this study, but nevertheless intrinsic habitual activity levels may differ between individuals, and this might deform the results. One possible confounding factor for habitual PALs is working status, for which we did not find a significant difference in active disease vs remission. When comparing PAL levels between the 2 groups, it was taken into account whether the participants were full-time or part-time employed and whether they were unemployed. There was no significant difference in the working status between the groups. Further occupational classifications were not evaluated. Because habitual PALs were objectively measured by accelerometry, the impact of the individual's profession on the activity levels should not bias our results since nonetheless overall daily activity levels were measured on a 24-hour basis for 7 ongoing days.In our study, we could not find a significant difference in BMI between the groups and IBD entities. This might be due to the fact that we included patients with moderate-highly active IBD during an early stage of their IBD flare to rule out that PALs were biased by the initiation of rapid-acting anti-inflammatory therapies . Therefore, the patients were included at an early time point of IBD activity onset, and this might have led to the equal BMI distribution between the groups.Taken together, our study is—to the best of our knowledge—the first to reveal a close association between objectively measured habitual PALs, HRQoL, and disease activity in patients with IBD. Within the framework of patient-reported outcomes, this points to an important function of habitual physical activity as a coeffector, which can be easily assessed using wearables as modulator of patients' quality of life.Guarantor of the article: Miriam Wiestler, MD.Specific author contributions: Miriam Wiestler and Fabian Kockelmann share the first authorship. Masoumeh Attaran-Bandarabadi and Oliver Bachmann share the last authorship. M.W., M.A.B., U.T., and O.B. conceived and designed the study. F.K., M.W., M.K., and M.A.B. acquired the data. F.K., M.W., M.P.M., and O.B. analyzed and interpreted the data. M.W., F.K., and O.B. drafted the article; all authors revised it critically for important intellectual content, approved the version to be published, and agreed to be accountable for all aspects of the work, thereby ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This report includes work performed by F.K. in fulfillment of the requirements for his doctoral thesis.Financial support: O.B. reports grants and personal fees from Takeda Pharma, personal fees from Shield Therapeutics, grants and personal fees from Ferring Pharmaceuticals, grants and personal fees from Pfizer, personal fees from CED Service GmbH, grants and personal fees from Novartis AG, personal fees and other from German Society for Digestive and Metabolic Diseases, grants and personal fees from Janssen Pharmaceutica, grants and personal fees from Merck Sharp & Dohme, grants from the German Center for Infection Research, grants and personal fees from Biogen, grants and personal fees from AbbVie, personal fees from Astellas Pharma, grants and personal fees from Falk Pharma GmbH, grants and personal fees from Bristol-Myers Squibb, and personal fees from Immundiagnostik, outside the submitted work. For the remaining authors, none were declared.Potential competing interests: None to report.✓ Patients with IBD suffer from reduced disease-related quality of life.✓ We objectively measured the association between habitual physical activity, IBD activity, and IBD-related quality of life.✓ Physical activity can be interpreted as a substantial comodulator to improve IBD-related quality of life in patients with IBD.


This study examined the association of hand-grip strength (HGS) and non-alcoholic fatty liver disease (NAFLD) index in older adults.This was a cross-sectional study involving 538 older adults with mean age of 74.3±6.4 years. Body composition parameters including height, percent body fat, body mass index (BMI), waist circumference (WC), was determined using body composition analyzer. HGS was assessed using a dynamometer, and NAFLD was diagnosed by the simple NAFLD score (SNS), hepatic steatosis index (HSI), NAFLD fibrosis score (NFS), and fibrosis 4 calculator (FIB-4). Based on relative HGS, subjects were classified as High HGS, Mid HGS, and Low HGS group. Based on SNS, HSI, NFS and FIB-4 score, subjects were classified as High risk and Low risk group. Logistic regression analyses were used to determine the odds ratio (OR) and 95% confidence interval (CI) of HGS levels for having steatosis and fibrosis.There were linear decreases in NAFLD index such as SNS (P<.001), HSI (P<.001), NFS (P=.001), and FIB-4 (P=.041) across incremental HGS levels. Compared to the High HGS group (reference), the Low HGS group had significantly higher ORs of having SNS , HSI , and NFS .The current findings suggest that a lifestyle intervention consisting of a normal weight and physical fitness should be promoted as a preventive means against NAFLD associated with HGS. The aging phenomenon is not only related to an increased prevalence of various chronic diseases such as arthritis, stroke, angina, lung diseases, and hypertension but also be closely associated with an increased prevalence of non-alcoholic fatty liver disease (NAFLD) in the elderly3. According to a previous overseas study, the prevalence of NAFLD at advanced age was approx. 35%, which is higher than that at middle age and attracted increased attention4.The declining birth rate and prolonged average lifespan have accelerated the global aging phenomenon. In Korea, the proportion of elderly over the age of 65 years among the total population was 13.8% in 2017 and may reach 24.5% by 2030 and 38.1% by 20505. It is also divided according to the progression stage into simple steatosis, steatohepatitis, and fibrosis, and persistent symptoms may progress towards cirrhosis or liver cancer accompanied by inflammatory reactions and necrosis; thus, a close association with early mortality has been reported6. Although the precise mechanism of NAFLD pathogenesis remains unclear, the ‘two-hit’ theory is considered as the most promising mechanism7. This theory includes the ‘1st-hit’, in which excessive accumulation of triglycerides in the healthy liver leads to simple steatosis, and the ‘2nd-hit’, in which oxidative stress and excessive production of inflammatory cytokines drive simple steatosis towards steatohepatitis. The main risk factors of NAFLD are genetic, including age and gender, and clinical, including obesity, type II diabetes, and metabolic syndrome8. Various methods for alleviating these factors such as drug therapy, weight loss, and regular exercise, and physical activities have been reported10. However, in older adults, the side effects of drug therapy and risks of weight loss have highlighted the importance of enhanced physical strength based on regular physical activities and exercise as the most effective alternative for treating NAFLD11.NAFLD occurs when the ratio of lipid accumulation in the liver that has not been caused by alcohol intake or virus infection is greater than 5% of the weight of the liver12. Recently, a close association was reported between the quality of life and sarcopenia in the elderly and NAFLD, which was previously highly emphasized only in children, young adults, and middle-aged13. As a result, physical activity levels and high physical strength in older adults have been reported to play a positive role against NAFLD in the elderly by improving insulin resistance, inflammation, and oxidative stress, regardless of the stage of progression14. Zelber-Sagi et al.15 investigated the association between leisure-time physical activities and NAFLD in 799 adults in Europe and reported that the healthy group exhibited a higher level of weekly physical activities than the NAFLD group, regardless of whether the activity was an aerobic or resistance exercise. Tsunoda et al.16 carried out a 4.4-year follow-up investigation of the association between physical activities and NAFLD in 10,146 adults in Asia and reported that the group performing moderate to intensive physical activities more than three times a week showed an approximately 50% lower risk of NAFLD than the group performing moderate to intensive physical activities less than once a week. Furthermore, Cho et al.17 investigated the risk of NAFLD according to weekly physical activities in 595 adults in Korea and reported that the group performing more than 1500 MET-minutes of weekly physical activities showed an approximately 5-fold decreased risk of NAFLD. Additionally, Kantartzis et al.18 reported that an inversely lower rate of NAFLD was associated with improved physical strength in 50 adults in Europe after the subjects completed a 9-month recommended physical activity program, and Church et al.19 reported that a lower prevalence of NAFLD was correlated with higher cardiorespiratory fitness (CRF) based on associations evaluated in 218 adults in the U.S. Based on these findings in Korea and overseas, an important association appears to exist between NAFLD and physical activities and physical strength. In contrast to studies of adults in middle age, few studies have examined the association between NAFLD and muscular strength in older adults, despite its crucial functional impacts. Moreover, most previous studies focused on determining the association between physical strength and simple steatosis, suggesting that studies are needed to evaluate how physical strength is associated with each stage of NAFLD progression from simple steatosis to steatohepatitis to liver fibrosis.Numerous studies have reported the positive effects of regular physical activities and high physical strength on improving insulin resistance, metabolic syndrome, and obesityThus, the present study was conducted to verify the association between hand-grip strength and indicators of non-alcoholic hepatic steatosis and fibrosis in older adults in Korea.The subjects were 594 older adults over the age of 60 years in Gyeonggi province, without a specific medical condition and who could perform normal daily activities. After excluding 56 subjects with incomplete data because of unmeasured body composition or hand-grip strength or refusing blood collection, 538 subjects  were selected for data analysis. Additionally, detailed explanations of the purpose and methods of this study were given to the subjects in both written and verbal forms and the subjects provided consent to participate in the study. The characteristics of the study participants are presented in 2). To determine the waist circumference, trained investigators used an anthropometric tape to measure at the midpoint between the lower border of the rib cage and iliac crest from the left side of the subject.Height was measured using an automatic measuring device  and body weight and percent body fat were measured with an X-Scan Body Composition Analyzer . Body mass index (BMI) was calculated based on weight (kg)/height  ≥150 mg/dL, total cholesterol (TC) ≥200 mg/dL, high-density lipoprotein cholesterol (HDL-C) ≤40 mg/dL, or low-density lipoprotein cholesterol (LDL-C) ≥130 mg/dL.Dyslipidemia was defined as a case diagnosed by a physician or based on one of the following: guidelines for the management of dyslipidemia25. Diabetes was defined as a case diagnosed by a physician or based on the criteria of fasting blood glucose (FBG) ≥126 mg/dLFor blood collection, 10 mL of cubital venous blood was collected from the subjects using a disposable syringe after over 8 h of fasting. From the collected blood, 7 mL was placed in a serum tube and centrifuged for 10 min in a 4°C  at 3,000 RPM and then stored at –80°C deep until analysis; the remaining 3 mL was placed in a heparinized/EDTA tube for platelet analysis using Sysmex-XN1000  based on the principle of bioimpedance. The stored serum was analyzed for TG, TC, HDL-C, FBG, and albumin using AU680 reagents  and an enzymatic and colorimetric assay. Additionally, aspartate aminotransferase (AST) and alanine aminotransferase (ALT) were analyzed using the modified IFCC UV kinetic method and Cobas 8000 reagents .26, were used. The parameters of SNS were age, BMI, waist circumference, diabetes or dyslipidemia, alcohol intake, regular exercise, and menopause, which were used to categorize subjects with a score of ≥8 as NAFLD high risk. The parameters of HSI were AST, ALT, BMI, gender, and diabetes, which were used to categorize subjects with a score >36 as NAFLD high risk, those with a score <30 as normal, and those with a score of 36–30 as moderate. To screen for hepatic fibrosis, the NAFLD fibrosis score (NFS) and fibrosis 4 calculator (FIB-4), which have proven reliability and validity in Korean subjects28, were used. The parameters of NFS were age, FBG, BMI, platelet, albumin, and AST/ALT ratio, which were used to categorize subjects with a score <-1.455 as normal, those with a score >0.675 as high risk, and those with a score ≤-1.455 and ≤0.675 as moderate risk. The parameters of FIB-4 were age, platelet, AST, and ALT, which were used to categorize subjects with a score >2.67 as high risk, those with a score <1.30 as low risk, and those with a score ≥1.30 and ≤2.67 as moderate.NAFLD was defined as steatosis and fibrosis in the liver. To screen for hepatic steatosis, the simple NAFLD score (SNS) and hepatic steatosis index (HSI), which have proven reliability and validity in Korean subjects29.Hand-grip strength (HGS) was assessed using a dynamometer  while the subject was standing straight, and measurements were conducted twice from the dominant hand of the subject per kg. The resting time between the two measurements was 1 min. The maximum value between the two measured values was used, and the relative HGS was calculated based on HGS (kg)/weight (kg) as describe previously2 test was used to examine the ratios among categorical variables based on the level of relative HGS and gender. Next, the subjects were classified into two groups according to non-alcoholic hepatic steatosis based on SNS and HSI, and into further two groups according to fibrosis based on NFS and FIB-4. Next, the odds ratio (OR) of the level of relative HGS was estimated for non-alcoholic hepatic steatosis and fibrosis. All statistical analyses were conducted using SPSS version 23.0 software ), and the significance level (α) for hypothesis testing was set to 0.05.All continuous data obtained in the present study were expressed as means and standard deviations, and categorical variables were expressed as ratios per group. Based on relative HGS, the subjects were classified as Low HGS (for the lower 33% in both genders); Mid HGS (for the middle 33%); or High HGS (for the upper 33%). The contrasts polynomial of one-way analysis of variance was used to test for linear trends among the measured parameters according to the level of relative HGS. Additionally, the χP < 0.001), BMI (P < 0.001), waist circumference (P < 0.001), body fat (P < 0.001), dyslipidemia (P = 0.011), diabetes (P = 0.029), TG (P = 0.001), and FBG (P = 0.011) with increasing HGS levels, whereas a significant linear increase was observed for HDL-C (P = 0.001) and platelets (P = 0.005). Additionally, with increasing HGS levels, the SNS (P < 0.001) and HSI (P < 0.001) which indicate hepatic steatosis related to NAFLD and the NFS (P = 0.001) and FIB-4 (P = 0.041) which indicate hepatic fibrosis showed significant decreases.P = 0.001) and Low HGS  were significantly higher than the OR of High HGS. Similarly, for the HSI, the ORs of Mid HGS  and Low HGS  were significantly higher than those of High HGS. Even after correcting the values for age and gender, the ORs of Mid HGS  and Low HGS  were significantly higher than those of High HGS for the SNS; the ORs of Mid HGS  and Low HGS  were significantly higher than those of High HGS for the HSI. When the values were corrected for percent body fat and smoking in addition to age and gender, Mid HGS  showed significantly higher ORs than High HGS for the HSI, while no significant differences were observed for the SNS.P = 0.023) and Low HGS  than for High HGS. After correcting the values for age and gender, the OR of Mid HGS  was significantly higher than that of High HGS, and when the values were additionally corrected for percent body fat and smoking, no significant differences were observed. For FIB-4, the OR according to HGS levels did not show significant differences.For NFS, an indicator of hepatic fibrosis, significantly higher ORs were observed for Mid HGS (OR = 1.841, 95% CI = 1.087–3.118, This study aimed to examine the association between HGS levels and indicators of non-alcoholic hepatic steatosis and fibrosis in 538 older adults. Relative HGS was classified into three groups based on the HGS and weight of the subject, and the scores for steatosis and fibrosis were compared. The results showed that the group with a higher HGS displayed a significant decrease in scores for steatosis and fibrosis. When the subjects were categorized into the low-risk and high-risk groups for the steatosis indicators SNS and HSI and fibrosis indicators NFS and FIB-4 based on a previous study, the estimated ORs of HGS levels suggested that when HGS levels were lower, the ORs of having non-alcoholic hepatic steatosis or fibrosis were higher.3. NAFLD includes all aspects of the progression of fatty liver disease not caused by alcohol intake or virus infection. When the symptoms persist, the condition is closely associated with liver cancer and early mortality6. Currently available methods for diagnosing NAFLD include liver biopsy, radiological diagnosis, and hematological tests. However, these methods are invasive and costly. Thus, recent studies have been conducted to develop methods of diagnosing non-alcoholic hepatic steatosis and fibrosis using hematological parameters30. The present study also analyzed the blood parameters and health behavior of the subjects and examined separate groups of subjects with respect to the indicators of steatosis and fibrosis. The results indicated high-risk groups for steatosis as 50.5% SNS and 18.4% HSI and for fibrosis as 27.3% NFS and 11.4% FIB-4, which are similar to the values observed in previous overseas studies31.Because of the rapid increase in the global aging society, problems related to musculoskeletal and chronic disorders in older adults have increased, and the prevalence of NAFLD was shown to be higher in advanced age than in middle age, which has recently attracted increased attention8. As a result, regular physical activities and exercise were reported as the most effective methods for preventing and treating NAFLD, as they lower obesity, enhance insulin sensitivity, and improve blood glucose metabolism32. A high level of physical strength is also known to have positive effects on the prevention of NAFLD11. In line with this, we classified the relative HGS into three groups based on the HGS and body weight of older adults and compared the scores for steatosis and fibrosis. The result showed that the group with a higher HGS level displayed a significant linear decrease in the scores for non-alcoholic hepatic steatosis and fibrosis. The findings agree with those of Krasnoff et al.33 who evaluated 37 adults in the U.S. to determine the association between NAFLD and CRF and reported a lower CRF in the group diagnosed with non-alcoholic hepatic steatosis or steatohepatitis. Additionally, Nagano et al.34 evaluated 84 adults in Japan to determine the association between CRF and NAFLD enzymes and found that a higher CRF led to lower levels of AST and ALT. The findings of our study and previous studies suggest that regular physical activities and high physical strength in older adults have positive effects on improving the main causes of NAFLD: insulin resistance, metabolic syndrome, and obesity14, while sarcopenia may be an independent predictive parameter of NAFLD15.The most well-known risk factors of NAFLD are obesity, insulin resistance, and type II diabetes, and numerous studies have investigated effective ways to improve such metabolic disorders29 who examined the association between relative HGS and NAFLD in 20,957 Chinese adults and reported that a higher HGS led to a significantly lower OR for NAFLD. Additionally, Peng et al.13 examined the association between NAFLD and muscle mass and gait velocity in 2,551 older adults in the U.S. and found that as the severity of NAFLD increased, muscle mass and gait velocity decreased. A high level of physical strength may be a crucial parameter for improving NAFLD, as it prevents sarcopenia and obesity while reducing the risk of chronic diseases in older adults37. Nonetheless, when the values were corrected for percent body fat and smoking, the OR for NAFLD according to HGS levels was shown to be only partially significant, indicating that health behaviors reflected by percent body fat and smoking are important additional factors to consider in the association between NAFLD and physical strength in older adults. Despite this, most previous studies investigated the association between physical strength and simple steatosis. In contrast, the current multilateral study was carried out to determine the association between the HGS and NAFLD including simple steatosis and hepatic fibrosis.Furthermore, when the ORs for non-alcoholic hepatic steatosis or fibrosis were estimated based on the categorized HGS levels, the OR of the Low HGS group was significantly higher than that of the High HGS group with respect to the SNS, HSI, and NFS. However, when the values were corrected for age, gender, percent body fat, and smoking, only the HSI showed a higher OR in the Mid HGS group than in the High HGS group, while the other indicators showed no significant differences. The findings agree with those of Meng et al.In summary, the relative HGS based on the HGS and weight of older adults are inversely related to non-alcoholic hepatic steatosis and fibrosis. Thus, muscular strength in older adults is a potential predictive parameter for NAFLD. The limitations of the study include the difficulty verifying the cause-effect relationships as this was a cross-sectional study; the fact that hematological parameters were used to diagnose NAFLD; and the lack of nutrition survey. Further studies using a prospective design and more precise methods for diagnosing NAFLD are needed.


Airborne light detection and ranging (LiDAR) full waveforms and multibeam echo sounding (MBES) backscatter data contain rich information about seafloor features and are important data sources representing seafloor topography and geomorphology. Currently, to classify seafloor types using MBES, curve features are extracted from backscatter angle responses or grayscale, and texture features are extracted from backscatter images based on gray level co-occurrence matrix (GLCM). To classify seafloor types using LiDAR, waveform features are extracted from bottom returns. This paper comprehensively considers the features of both LiDAR waveforms and MBES backscatter images that include the eight feature factors of the LiDAR full waveforms , skewness, kurtosis, area, distance, and cross-section) and the eight feature factors of MBES backscatter images , entropy, homogeneity, contrast, angular second moment (ASM), correlation, and dissimilarity). Based on a support vector machine (SVM) algorithm with different kernel functions and penalty factors, a new seafloor classification method that merges multiple features is proposed for a beneficial exploration of acousto-optic fusion. The experimental results of the seafloor classification around Yuanzhi Island in the South China Sea indicate that, when LiDAR waveform features are merged (using an Optech Aquarius system) with MBES backscatter image features (using a Sonic 2024) to classify three types of sands, reefs, and rocks, the overall accuracy is improved to 96.71%, and the kappa reaches 0.94. After merging multiple features, the classification accuracies of the SVM, genetic algorithm SVM (GA-SVM) and particle swarm optimization SVM (PSO-SVM) increase by an average of 9.06%, 3.60%, and 2.75%, respectively. Seafloor features reveal important marine environment information for the fields of marine geological surveying, marine engineering construction, and mineral resources on the seabed. To survey seafloor features, the traditional direct sampling method and indirect remote sensing methods that use acoustics, optics, and electromagnetic waves are normally used. Based on the pulse signal obtained by airborne bathymetric light detection and ranging (LiDAR) and the backscatter intensity obtained by multibeam echo sounding (MBES), the return waveforms and mosaic sonar images, respectively, can be generated. The shapes of LiDAR waveforms and the grayscale and texture features of sonar images vary with seafloor type for different surface profiles and reflectivity.A full laser waveform contains both the sea surface and bottom peaks ,2. ThrouThe MBES system can simultaneously acquire bathymetric and backscatter data. These data are usually used to explain seabed geology and sedimentary processes, identify geotechnical disasters and leakage of sea bottom gases, and assess environmental impacts ,7. The mUsing bathymetry data collected by bathymetric LiDAR, Sun et al. proposed a hybrid K-means and SVM algorithm (KSVM) and calculated the gray level co-occurrence matrix (GLCM) to classify K-means as clusters . The resIn summary, there are several methods of seafloor classification, based on return waveform features of laser pulses, based on backscatter image features of MBES, and based on multisource data and multiple features. However, the current classification methods mostly use airborne LiDAR and MBES data separately. For the classification algorithms that merge the multifeatures of both LiDAR and MBES data, overall accuracy is less than 90% when the type of seafloor around islands or reefs contains more than three species. This paper is intended to ascertain more seafloor information through the merging of features that are extracted by both LiDAR and MBES data. Three kinds of SVM classifiers  and particle swarm optimization (PSO)) are used and trained, thus achieving improved classification accuracy for seafloor classification in shallow water areas.bN indicates the background noise of the original waveform, n indicates the number of Gaussian components, iA represents the peak of the ith Gaussian component, ib represents the peak position of the ith Gaussian component, iσ indicates the full width at half maximum (FWHM) of the ith Gaussian component, and y is the amplitude of the waveform at time t .Finally, the mutation operator is executed and the c and the kernel function parameter g for the original SVM give a wide search range. Within this preset range, each possible c and g will be converted to specific chromosomes that can be accepted by the GA to complete the conversion from the feasibility solution space to the chromosome search processing space.When GA is used for SVM parameter optimization, real number-based encoding is used. The penalty factor f(x) is defined by Equation (13). When f(x) is smaller, the adaptability is stronger:PSO is another swarm intelligence-based optimization algorithm, along with the ant colony algorithm (ACA), in the field of computational intelligence. PSO is a type of evolutionary computing technology that is based on swarm intelligence. Compared with GA, PSO has no selection, crossover, or mutation operations. Instead, it conducts research by using particles in the solution space to follow the best examples . A Each n particles are randomly generated in the nR space, and iX =  and the velocity matrix V(t) =  are populated. The best fitness value f(ix) of each particle is compared with the optimal fitness value f of all particles. If f(ix) < f, then the original global best fitness value is replaced with the best fitness value of the particle, and the current state of the particle is saved. Then, according to the improved PSO model:The parameters of the particle swarm include the particle velocity and position. Xt+1) is calculated iteratively to find the optimal SVM parameters c and g.When PSO is used for the optimization of SVM parameters, particles can be represented as There are some accuracy and error metrics listed below (a–d) to reflect the quality of the seafloor classification in the surveying area ,26,27.a. Producer’s accuracy and omission errorProducer’s accuracy (PA) is the map accuracy from the point of view of the map maker (the producer). This is how often real features on the ground are correctly shown on the classified map or the probability that a certain land cover of an area on the ground is classified as such. Producer’s accuracy is a complement of omission error (OE): PA = 100% − OE. It is also the number of reference sites classified accurately divided by the total number of reference sites for that class:b. User’s accuracy and commission errorUser’s accuracy (UA) is accuracy from the point of view of a map user, not the map maker. This is how often a class on the map will actually be present on the ground. User’s accuracy is a complement of commission error (CE): UA = 100% − CE. It is calculated by taking the total number of correct classifications for a particular class and dividing it by the row total:c. Overall accuracyOverall accuracy (OA) essentially tells us, out of all reference sites, what proportion were mapped correctly. Overall accuracy is usually expressed as a percent, with 100% accuracy being a perfect classification where all reference sites were classified correctly. Overall accuracy is the easiest to calculate and understand, but ultimately only provides the map user and producer with basic accuracy information:d. Kappa coefficientijN represents the element of column i and row j in the error matrix, OP represents observed agreement, and CP represents chance agreement.The Kappa coefficient is generated from a statistical test to evaluate the accuracy of a classification. Kappa essentially evaluates how well the classification performed compared to just randomly assigning values, i.e., whether the classification did better than random. The kappa coefficient can range from –1 to 1. A value of 0 indicates that the classification is no better than a random classification. A negative number indicates that the classification is significantly worse than random. A value close to 1 indicates that the classification is significantly better than random:2. This area has a typical tropical marine climate, where corals and plankton breed vigorously, forming a large number of coral reefs at the high platforms near the coast. At the same time, there are dense aquatic plants such as seaweed and kelp in the vicinity of the reef. Therefore, there are also many types of seafloor, including rocks, coral reefs, and sands, which makes this area suitable for detecting the effectiveness of different seafloor classification methods.The experimental area of this paper is located in Yuanzhi Island, Xisha Archipelago, South China Sea. The scope of the survey area is shown in 2. The MBES in this paper uses the SONIC 2024 system . The underwater surveying is carried out in an airborne LiDAR survey area with a maximum range of 500 m, a span resolution of 1.25 cm, a coverage width of 10°–160°, 256 beams, and a measuring point density of 10–20 points/m2. There are no available MBES data in the nearshore area of Yuanzhi Island because the water is too shallow, and the boat cannot safely approach the survey area.The airborne LiDAR in this paper uses the Optech Aquarius system . The system features a circular scanning mode, whose maximum bathymetric ability is 1.5 times the disk transparency . The depth measuring accuracy is 0.25 m root mean square error (RMSE), which uses only a blue-green laser with a wavelength of 532 nm to achieve integrated over- and underwater surveying. The water in the survey area is clear and transparent, with an SDD of 9 m, an east–west width of 1.5 km, a north–south distance of 2 km, and a water depth of approximately 20 m. During data acquisition, the flying height is 300 m, the laser scanning nadir angle is 20°, the sounding frequency is 550 kHz, and the measuring point density is 5–10 points/mThe experimental data in this paper include four primary components: LiDAR point clouds, LiDAR waveforms, MBES backscatter images and high-resolution images. According to an overview of the survey area and real high-resolution images, the seafloor can be divided into three types: sands, reefs, and rocks. In this paper, we selected 1560 sand samples, of which 1092 were used for training. We also selected 518 reef samples, of which 362 were used for training. Finally, we selected 504 rock samples, of which 352 were used for training. The extracted sample feature information was input into SVM, GA-SVM, and PSO-SVM classifiers. A five-fold cross validation strategy was adopted and the classification results, including mean errors and accuracy metrics, are given in The classification results based on LiDAR waveform features are shown in The classification results based on MBES backscatter features are shown in The classification results based on multifeatures are presented in The experimental results indicate that the classification accuracy significantly improved after merging the LiDAR and MBES multifeatures. After optimizing the parameters of the SVM classifier by using the GA and PSO algorithms, the classification accuracy also improved. Among the methods examined, the PSO-SVM classifier based on multifeatures had the highest overall accuracy of 96.71%, and its kappa coefficient was 0.94. The classification result using the SVM classifier based on MBES backscatter features had the lowest overall accuracy of 78.82% and a kappa coefficient of 0.61. The statistics of classification accuracy are illustrated in When classifying seafloor types based on LiDAR waveform features, the PSO-SVM classifier had the highest classification accuracy (up to 94.54%), and its kappa coefficient was 0.90. The SVM classifier had the lowest classification accuracy of 88.73% and a kappa coefficient of 0.80. Among the three types of seafloor, the classification accuracy of sands was relatively high. For the three classifiers, SVM, GA-SVM, and PSO-SVM, PA reached 96.86%, 97.31%, 97.82%, respectively. The classification accuracy of rocks was low. For SVM, GA-SVM, and PSO-SVM, PA was only 57.14%, 72.82%, and 83.33%, respectively. Through the above analysis, it was found that the performance of PSO-SVM was better than that of both SVM and GA-SVM.When classifying seafloor types based on MBES backscatter features, the PSO-SVM classifier had the highest classification accuracy (up to 93.38%), and its kappa coefficient was 0.88. The SVM classifier had the lowest classification accuracy of 78.82% and a kappa coefficient of 0.61. Among the three types of seafloor, the classification accuracy of reefs was relatively high. For GA-SVM and PSO-SVM, PA reached 94.98% and 98.46%, respectively. However, the classification accuracy of rocks is relatively low. For the two classifiers, SVM and GA-SVM, PA reached 64.88% and 88.89%, respectively. Through the above analysis, it is found that the classification accuracy of PSO-SVM increased with the increase of particle swarm size. It can be seen from As shown in This study focused on providing an effective classification method for mapping the benthic substrate based on multifeatures extracted from LiDAR and MBES. According to the experimental results in (1) Data fusionRegistration is the process of transforming different data sets into one coordinate system. Data may come from different sensors, times, depths, or viewpoints. Although LiDAR and MBES measurements were collected individually, they were all transformed from a sensor coordinate system to the WGS-84 geodetic coordinate system and projected to the UTM plane coordinate system. During this process, measurement errors caused by mounting misalignment and attitude change were also considered. Thus, LiDAR and MBES data sets were accurately registered through coordinate transformation, map projection, and point matching.(2) Sample size2, and that of MBES is 10–20 points/m2. Afterwards, a gridded digital elevation model (DEM) with a resolution of 1 m was generated by linearly interpolating the point cloud. According to the actual type of substrate in the survey area, three sample species were selected as sands, reefs, and rocks. The number of samples was set according to the area percentage of different substrates in the study area to make the spatial distribution of training samples more reasonable.Spatial resolution refers to the size or dimension of the smallest unit that can be distinguished in detail, which is used to characterize the product to distinguish the details of ground targets. In this paper, we tend to use point density to represent spatial resolution. For all products, the measuring point density of LiDAR is 5–10 points/m(3) Feature selectionFeature selection is helpful to avoid correlated features and improve classification accuracy. It has been proven in our previous research that, with fewer features mentioned in this paper, the classification accuracy would decline obviously, thus 16 features were chosen for experimental verification, eight LiDAR features and eight MBES features.(4) Model performanceIn our experiment, a five-fold cross-validation strategy was adopted and mean errors and accuracy metrics are presented to understand more about model performance. Among the nine classification combinations, the PSO-SVM classifier based on multifeatures had the highest overall accuracy of 96.71%, and its kappa coefficient was 0.94. Over-fitting and underfitting can occur in machine learning, in particular. The potential for overfitting depends not only on the number of parameters and data, but also on the conformability of the model structure with the data shape and the magnitude of model error compared to the expected level of noise or error in the data. In order to avoid over-fitting, the following solutions are given: (1) select appropriate stopping criteria to make the training of the machine to a suitable extent; (2) keep verification data sets and verify training results; and (3) get additional data for cross-validation.(5) Practical importanceLiDAR bathymetry is limited by laser penetration and water turbidity. Its maximum bathymetric range is generally less than 50 m, so it is suitable for clearer shallow water areas. The attenuation of sound in water is much lower than that of laser, so MBES bathymetry is suitable for deeper areas. MBES transducers are usually mounted on ships, and it is difficult to approach shallow coastal areas due to the safety factors of sailing. LiDAR scanners are usually mounted on airplanes or unmanned aerial vehicles, and are almost unaffected by ground conditions. These two sources of data sets can be comprehensively utilized to achieve full coverage measurement of islands and reefs. To sum up, it was necessary to combine two data sources for our study, and we tend to believe that the method has a certain universality in similar areas.This paper comprehensively considers the features of both airborne LiDAR waveforms and ship-borne MBES backscatter images and proposes a seafloor classification method using extracted multifeatures. Three kinds of classifiers, SVM, GA-SVM, and PSO-SVM, were used to classify the seafloor types around Yuanzhi Island in the South China Sea. In this survey area, three types of seafloor  were detected. The influence of different classifiers and different features on classification was obtained through quantitative analysis. In general, the classification results of the three classifiers achieved good performance. The overall accuracy and kappa coefficient of the PSO-SVM were higher  than those of GA-SVM and SVM. The experimental results indicate that the fusion of LiDAR and MEBS data can reveal more features of seafloor, which is beneficial in improving the accuracy and reliability of seafloor classification. After merging the multifeatures, the classification accuracy of SVM, GA-SVM, and PSO-SVM increased by an average of 9.06%, 3.60%, and 2.75%, respectively.In addition, although the multifeatures can be obtained through feature extraction, excessive features influence the efficiency of target detection and classification. If the correlation between features and seafloor types is small, then overfitting of the target detection and classification may occur, thereby decreasing the classification accuracy. In the future, feature selection criteria that are based on principal component analysis (PCA) or factor analysis (FA) will be used to select the most important feature parameters from multifeatures.