In 2019, Shams-White and colleagues created the “2018 WCRF/AICR Score” which aimed to standardise how adherence to these recommendations is assessed. The standardised scoring system includes seven of the recommendations concerning weight, physical activity and diet, with an optional eighth recommendation on breastfeeding. To promote transparency and reproducibility, the present paper describes the methodology for operationalisation of the standardised scoring system in the UK Biobank.In 2018, The World Cancer Research Fund (WCRF)/American Institute for Cancer Research (AICR) published ten evidence-based Cancer Prevention Recommendations designed to reduce the risk of cancer UK Biobank recruited >500,000 individuals aged 37–73 years, between 2006 and 2010. In 2021, we held a workshop with experts which aimed to reach consensus on how to operationalise the scoring system using data available within UK Biobank. We used data on anthropometric measurements, physical activity and diet to calculate adherence scores. 24 h dietary assessment data were used to measure adherence to the following recommendations: “Eat a diet rich in wholegrains, vegetables, fruit, and beans”, “Limit consumption of “fast foods” and other processed foods high in fat, starches or sugars” and “Limit consumption of sugar-sweetened drinks”; food frequency questionnaire data were used to assess adherence to “Limit consumption of red and processed meat” and “Limit alcohol consumption”. Participants were allocated points for meeting, partially meeting or not meeting each recommendation, using cut-offs defined in the standardised scoring system.At our workshop, discussions included the use of national guidelines to assess adherence to the recommendation on alcohol consumption, as well as challenges faced including defining the adapted ultra-processed food variables. A total score was calculated for 158,415 participants . We also describe the methodology to derive a partial 5-point adherence score using data from the food frequency questionnaire in 314,616 participants.We describe the methodology used to estimate adherence to the 2018 WCRF/AICR Cancer Prevention Recommendations for participants in the UK Biobank, including some of the challenges faced operationalising the standardised scoring system. The UK Biobank assessed dietary intake using two methods: a touchscreen questionnaire asking 29 diet-related questions (similar to a food-frequency questionnaire (FFQ)) and, in over 200,000 participants, used a web-based 24 h dietary assessment tool “Oxford WebQ” to collect more detailed information . A furthTherefore, the aim of this paper is to describe the methodology used to operationalise the standardised scoring system in the UK Biobank, to promote transparency and reproducibility, as encouraged by Shams-White and colleagues . We also2.2.1.The UK Biobank is a prospective cohort study which recruited 503,317 individuals from the general population aged 37–73 years, 56% female, from 22 recruitment (henceforth “assessment”) centres across the UK  between 2006 and 2010. Full eligibility criteria and recruitment and follow-up methods for UK Biobank are reported on the UK Biobank website . The UK 2.2.Two methods of dietary assessment were used within the UK Biobank during different periods of recruitment. Initially, a short FFQ-based approach, including 29 questions on diet and 18 on alcohol, formed part of the baseline touchscreen questionnaire and was completed by all participants at the assessment centre. The questionnaire captured information on the frequency of consumption of major food groups, including fruits and vegetables, fish, meat and cheese, in the last year.Those participants that were recruited towards the end of the recruitment period (between 2009 and September 2010), also completed a 24 h dietary assessment, the Oxford WebQ , which cIn the present study, we used data from the 24 h dietary assessment (Oxford WebQ) for intakes of food groups for which there is not much variation from day to day, to assess adherence to the recommendations on the intakes of fruits and vegetables, dietary fibre, ultra-processed foods and sugar-sweetened drinks. We used FFQ data to capture the usual intake of foods not consumed daily, for operationalisation of the red meat and alcohol recommendations where the cut-offs are described as intake “per week”, so as not to over or underestimate consumption of these foods.2.3.For operationalisation of the recommendations using 24 h dietary assessment data, intakes were derived by taking the mean of the completed assessments. We excluded any assessments for which the participant answered “no” to the following question “Would you say that what you ate and drank yesterday was fairly typical for you? (UK Biobank data-field 100020). We also excluded any dietary assessments with extreme energy intakes (based on the “Estimated Nutrients” data-field 100002), using the cut-offs described by Perez-Cornago et al. ; < 3,347We used the updated portion sizes assigned by Perez-Cornago et al.  and, whe2.4.The CALIPER UK Study team held a workshop in May 2021 with invited researchers from the WCRF, National Cancer Institute in the United States, Oxford University, Wageningen University, Radboud University and Health Research Institute of the Balearic Islands, including both those who contributed to the creation of the standardised scoring system as well as researchers applying this scoring system in cohorts worldwide. The aim of this workshop was to reach consensus on how to operationalise the scoring system using data available within UK Biobank.3.3.1.A summary of the operationalisation of the standardised scoring system, including the scoring system cut-offs and the UK Biobank data used, can be found in 3.1.1.2. Participants within the “normal” BMI range (18.5–24.9 kg/m2) were classed as fully meeting this sub-recommendation and given a score of 0.5 points. Participants with a BMI classed as “overweight”, who met the sub-recommendation partially, were given 0.25 points, and participants who were underweight (<18.5 kg/m2) or obese (≥30 kg/m2) were given 0 points.Anthropometric data on body mass index  and waist circumference (data-field 48) were used to operationalise this recommendation. These measurements were collected at the assessment centre at the baseline study visit by trained staff using standard protocols. Weight was measured to the nearest 0.1 kg using the Tanita BC-418 MA body composition analyser and height using a Seca 202 height measure. BMI was calculated from weight and height data using the formula BMI = weight (kg)/height (m)Waist circumference was measured at the natural indent  using a Seca 200 tape measure. The creators of the standardised scoring system derived the cut-points for the waist circumference sub-recommendation based on guidelines from the 2018 WCRF/AICR Cancer Prevention Recommendations, the Center for Disease Control and Prevention  and the 3.1.2.The cut-offs for this recommendation are based on the WHO and U.S. Physical Activity Guidelines which advise adults to engage in at least 150 min of moderate-intensity aerobic physical activity or at least 75 min of vigorous-intensity physical activity per week . These gPhysical activity was self-reported and data were collected at the assessment centre study visit using a validated short form of the International Physical Activity Questionnaire (IPAQ) . The que3.1.3.The wholegrains, vegetables, fruit and beans sub-score operationalises two goals pertaining to A. fruit and vegetable and B. fibre intake, described below.3.1.3.1.Data on fruit and vegetable intake in the last 24 h (obtained using 24 h dietary assessment data) were used to assess adherence to this sub-recommendation. Information on the data-fields for the included fruits and vegetables can be found in the We used the frequency data and standard portion sizes for each food item  to calcu3.1.3.2.To operationalise the total dietary fibre intake component of the score, we used the 24 h dietary assessment nutrient data on Englyst fibre intake (data-field 100009). To estimate dietary fibre intake using the Association of Official Analytical Chemists (AOAC) method, we multiplied the dietary fibre variable, derived using the Englyst method, by a conversion factor of 1.33 as described by Lunn and Buttriss . Partici3.1.4.Shams-White and colleagues captured adherence to the recommendation on “fast” and processed foods using an adapted version of the NOVA classification system, which categorises foods according to the extent and purpose of processing . Group 1Firstly, we categorised the food variables available for the 24 h-dietary assessment data according to the NOVA classification system. An adapted UPF (aUPF) variable was created from the foods classified as Group 4 (ultra-processed), excluding food items already accounted for in other score components  to avoid double penalisation as described by Shams-White and colleagues . FurtherSince there are no recommended cut-offs or guidelines for the consumption of UPFs, Shams-White and colleagues applied a subjective approach awarding points according to tertiles , 21. Par3.1.5.At our CALIPER UK workshop, we decided that for the red and processed meat recommendation, data expressed as frequency per week would be better than those obtained using the 24 h dietary assessment to capture usual intake, because red and processed meat may not be eaten on a daily basis. Therefore, data from the touchscreen FFQ-based questionnaire were used to operationalise the recommendation for red and processed meat intake.The meat-related questions in the touchscreen questionnaire asked, “How often do you eat beef (data-field 1369)? (Do not count processed meats),” “How often do you eat lamb/mutton (data-field 1379)? (Do not count processed meats)” and “How often do you eat pork (data-field 1389)? (Do not count processed meats).” Participants were able to answer: “never,” “less than once a week,” “once a week,” “2–4 times a week,” “5–6 times a week,” “once or more daily,” “do not know” or “prefer not to answer.” As described by Bradbury et al. , the folTo assess processed meat intake, the answers to the question “How often do you eat processed meats ?” (data-field 1349) were used. Intake frequencies were applied as described for red meat above. To assign a portion size for processed meats, we used the portion sizes detailed by Perez-Cornago et al. . For pieParticipants were classed as fully adherent to this recommendation, and allocated 1 point, if their total red meat intake was 500 g or less per week and processed meat intake was less than 21 g per week. Participants who partially adhered to this recommendation, who consumed ≤500 g red meat per week but 21 g – <100 g of processed meat per week were given 0.5 points. Zero points were given to participants who did not adhere to the recommendation and consumed either >500 g red meat per week or ≥100 g processed meat per week.3.1.6.Responses to the question “How much of the following did you drink yesterday?” and the intake of the following drinks were used to assess adherence to the recommendation on sugar-sweetened drinks: carbonated (fizzy) drinks (data-field 100170), fruit drinks, squash or cordial (data-field 100180), dairy/yoghurt-based smoothie (data-field 100230), flavoured milk (data-field 100530), hot chocolate (data-field 100550) and fruit smoothie (data-field 100220). Participants could answer the following: “none”, “1/2”, “1”, “2”, “3”, “4”, “5” or “6+”. Values for participants who answered “none” were coded as “0”, “1/2” were recoded to “0.5” and “6+” were recoded to “6”.Intakes of these drinks were summed to create a mean sugar-sweetened drink intake per week. Assuming that a standard portion (one glass/carton/250 ml) equates to 250 g, participants who drank on average > 1 sugar-sweetened drink per day were allocated 0 points, those who consumed ≤1 scored 0.5 points, and those who did not consume sugar-sweetened drinks scored 1 point.In line with other studies that have operationalised the scoring system , and fol3.1.7.Since Shams-White and colleagues advise use of national guidelines or definitions regarding what constitutes an alcoholic drink  , we usedThe number of units of each alcoholic drink consumed per week were calculated from responses to the touchscreen questionnaire, i.e., for red wine (data-field 1568), white wine or champagne (data-field 1578), beer or cider (data-field 1588), spirits or liqueurs (data-field 1598), fortified wine (data-field 1608) and other alcoholic drinks such as alcopops (data-field 5364). The serving sizes corresponding to the question and units per serving, from the NHS website,Participants who consumed more than 14 units of alcohol per week were given 0 points, those who consumed >0 – ≤14 units of alcohol per week were given 0.5 points and those adhering fully to the recommendation were given 1 point. Further, participants who answered “never” (coded as “6”) or “special occasions only” (coded as “5”) to the question “About how often do you drink alcohol?” (data-field 1558) were allocated 1 point. Participants who answered “one to three times a month” (coded as “4”) to this question were allocated 0.5 points. This is in line with the further guidance on operationalisation of the standardised scoring system by Shams-White and colleagues, which recommends that, given the limited evidence comparing non-drinkers to very rare drinkers, participants who consume up to one drink per month should be classed as non-drinkers, and those consuming more than one drink per month should fall within the 0.5 and 0 point categories, depending on the amount of alcohol consumed .For future sensitivity analyses, we have also calculated a score using the cut-offs described in the standardised scoring system, based on US guidelines (28 g of ethanol (2 drinks) and 14 g of ethanol (1 drink) per day for males and females, respectively) . The mean total score for these 158,415 participants was 3.9 (SD 1.0) points and ranged from 0 to 7 points. The distribution of total scores for female and male participants is illustrated in The methodology (described above) for fully operationalising the standardised scoring system  for assen = 314,616). The mean FFQ-based score based on this 5-point system is 2.64 (SD 0.91) and there was a strong correlation between the full “total score” and the 5-point score . Using the modified 5-point score that is available for a larger subset of participants  will provide greater statistical power for investigations of associations between the adherence score and health-related outcomes.In addition, we have devised a 5-point, FFQ-based score using the baseline touchscreen questionnaire data, which allows assessment of adherence to five of the recommendations concerning (i) body weight, (ii) physical activity, (iii) fruits, vegetables and fibre intake, (iv) red and processed meats intake, and (v) alcohol consumption, in a larger subset of UK Biobank participants  who do not all have 24 h dietary assessment data. We found a strong and statistically significant correlation between the full “total score” and the 5-point score in 127,667 UK Biobank participants. As already described, some previous studies have also reported calculating partial or modified scores . Requests to access the datasets should be directed to john.mathers@newcastle.ac.uk.The datasets presented in this article are not readily available because data are available upon request from UK Biobank (The studies involving human participants were reviewed and approved by The UK Biobank was conducted in accordance with the Declaration of Helsinki and was approved by the North West Multi-Centre Research Ethics Committee (REC reference: 12/NW/03820). The patients/participants provided their written informed consent to participate in this study.FM, SP-S, CC-M, LS, and JM contributed to the conception and design of the study. FM, SP-S, LL, FH, AP-C, MS-W, MZ, EK, RW, GM, MW, DR, CC-M, LS, and JM attended and actively contributed to the CALIPER UK Workshop, which aided the design and execution of the study. FM wrote the first draft of the manuscript. JM, SP-S, LS, FH, CC-M, AP-C, MS-W, MZ, EK, RW, DR, and GM revised the manuscript. All authors contributed to the article and approved the submitted version.This research was funded by grant number IIG_FULL_2020_032 from the Wereld Kanker Onderzoek Fonds (WKOF), as part of the World Cancer Research Fund International grant programme. SPS received financial support from the Chilean Government for their PhD .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.


Although green  and far-red  light play important roles in regulating plant growth and development, they are often considered less useful at stimulating photosynthesis than red  and blue  light. Based on this perception, approaches to modifying the transmission of greenhouse glazing materials include (1) conversion of G photons from sunlight into R photons and (2) exclusion of the near-infrared (>700 nm) fraction of sunlight. We evaluated these approaches using simulated scenarios with light-emitting diodes to determine how partial and complete substitution of G with R light and exclusion of FR light affected the growth of lettuce and tomato grown indoors. The substitution of G with R light had little or no effect on fresh and dry mass of tomato. However, with the presence of FR light, fresh and dry mass of lettuce increased by 22–26% as G light was increasingly substituted with R light. In tomato, excluding FR inhibited plant height, leaf area, and dry mass by 60–71%, 10–37%, and 20–44%, respectively. Similarly, in lettuce, excluding FR inhibited plant diameter, leaf length, and dry mass by 15–23%, 23–33%, or 28–48%, respectively. We conclude that the spectral conversion of G-to-R photons can promote plant growth in at least some crop species, such as lettuce, while the exclusion of FR decreases crop growth and yield. The photon spectrum  refers to the number or portion of photons at each wavelength or waveband, including blue , green , red , and far-red  light . For suna and b absorb B and R light strongly (with the peak absorption at 428 nm and 661 nm for chlorophylls a and 470 nm and 642 nm for chlorophylls b) and G and FR light weakly , and soil and plant analyzer development (SPAD) value, as an index of relative leaf chlorophyll concentration [using a portable chlorophyll meter ]. In addition, plant diameter, length and width of the fifth-most mature leaf, and leaf color [using a colorimeter ] were measured in lettuce, and leaf area [using a leaf-area meter ] was measured in tomato. (Leaf area of lettuce was not measured because their ruffled character prevents accurate measurement.) Leaves with a length of ≥ 5 cm for lettuce or 2 cm for tomato  were counted in leaf number and included in leaf area measurements. Shoot dry mass was measured after the plants were abscised at the growing substrate surface and dried in an oven at ≥66°C for ≥5 d . Shoot moisture content (%) was calculated as the difference between shoot fresh and dry mass per shoot fresh mass following Meng et al.  . We modePartial substitution of G with R light did not influence plant diameter or leaf length of lettuce . HoweverLettuce leaf number decreased by 0.9–1.3 as the percentage of G light in PAR increased from 0 to 33% with FR light, but not without . On averIn lettuce, increasing the proportion of G light in PAR from 0 to 33% decreased shoot fresh mass (by 26%) and dry mass (by 22%) with FR light, but there was little effect in the absence of FR . In tomaThis study used light-emitting diodes (LEDs) to simulate a “best case” scenario with a spectral conversion film, in which G photons were replaced by R photons on a 1:1 basis. G light can induce shade-avoidance responses, including elongation of hypocotyls , 53, steCapsicum annum), soybean (Glycine max), and lettuce at a PPFD of 200 μmol∙m–2∙s–1 and in tomato at a PPFD of 500 μmol∙m–2∙s–1 [Impatiens walleriana), salvia , and petunia (Petunia × hybrida) seedlings, substituting 80 μmol∙m–2∙s–1 of G with R light (while B was constant at 80 μmol∙m–2∙s–1) did not influence leaf area, but it did decrease it in tomato seedlings [–2∙s–1 did not influence leaf area of green-leaf basil (Ocimum basilicum), green mustard (Brassica carinata), green kale (Brassica napus var. pabularia), and red kale (Brassica oleracea) [Brassica juncea) [–2∙s–1 of G with R light did not influence leaf length and width when B light was at 0, 20, or 60 μmol∙m–2∙s–1, but leaf width was 9% smaller when B light was at 100 μmol∙m–2∙s–1 [Substituting G with R light slightly promoted leaf expansion in tomato in the presence of FR light but had little to no effect on leaf size in lettuce . In past∙m–2∙s–1 . In conteedlings . In addileracea) . In cont juncea) . In thre juncea) . HoweverIn this study, when FR light was present, substitution of G with R light had contrasting responses in tomato with respect to extension growth: it slightly increased leaf area yet decreased stem length. Leaf (node) number of tomato was similar among all treatments  and thusFragaria × ananassa) ‘Seolhyang’ increased under G-to-R spectral conversion films, and higher photosynthetic activity under the film was partly attributed to improved electron transport efficiency of PSI and PSII [–2∙s–1 of G light with R light (in a background of 60 μmol∙m–2∙s–1 of B light) increased the YPFD by 8% and shoot dry mass by 33% [–2∙s–1 of G light was substituted with R light, the calculated YPFD increased by 6%  is slightly greater than that of G light (0.87) , 24. A fand PSII , 62. In s by 33% . In thised by 6% . This reed by 6% . Biomassed by 6% , substited by 6% . In toma−2·s−1 (provided by 20%B+80%R LEDs) without FR had 33% lower shoot fresh mass and 28% lower shoot dry mass than plants grown with an additional 87 μmol∙m−2∙s−1 of FR light [−2·s−1 (provided by 5%B+95%R LEDs) without FR (PPE = 0.88) was 25–29% lower than plants grown with FR  [−2∙s−1) and moderate PPFD (432 μmol∙m−2∙s−1), the net photosynthetic rate decreased by 6% and 12%, respectively [The promotion effect of FR light on extension growth of stems and leaves, subsequently increasing crop growth and yield, have been reported in lettuce , 64, tomFR light . In toma3, 0.80) . In addi3, 0.80) , 32. In ectively . Not surectively , 68. Theectively –34.−2·s−1 of G light when B light was constant at 25 μmol·m−2·s−1 [−2·s−1 of FR than the substitution of 60 μmol·m−2·s−1 of G with R light, respectively (–2 s–1 of FR decreased the FR fraction by 0.32–0.49 (or increased iPPE by 0.39–0.50), and substituting G with R caused smaller decreases   . Therefo−2∙s−1 of FR at a peak wavelength of 733 nm. In addition, most of FR photons (94%) in this study were between 700 and 750 nm. The effects of FR light on phytochrome-mediated responses and photosynthesis depend on the wavelengths of FR light. The efficacy of FR photons to inactivate phytochrome is greatest at about 730–740 nm [This study defined FR light 700–800 nm) based on a 100-nm waveband, which is the definition from the American National Standards Institute . We simu0–740 nm . FR ligh0–740 nm , 70. Thu0–800 nm 0–740 nm –33.Different greenhouse covering materials have been developed to block incoming NIR inside greenhouses, including fluid roof covers and NIR-reflecting or NIR-absorbing materials  , 27, 71.G-to-R conversion films for greenhouse coverings or luminescent solar concentrators contain luminescent organic dyes, which absorb G  photons and fluoresce them as R  photons. However, the reported values of PAR transmittance and efficacy of G-to-R conversion films vary depending on the material. For example, the range of transmittances of B, G, R, and PAR of G-to-R converting greenhouse films were 66–93%, 57–95%, 97–98%, and 74–95%, respectively, depending on the concentration of fluorescent dye . CompareA few studies addressed potential decreases in spectral conversion efficacy from photodegradation of luminescent organic dyes. For example, the absorbance of a spectral conversion film decreased by 11% without nano-oxide filters or by 2–8% with nano-oxide filters after one year of exposure to sunlight . In contWe created indoor lighting conditions to simulate “best case” scenarios for greenhouse glazing materials that modify the transmitted solar spectrum: converting G into R photons and almost completely excluding FR light. The simulated conversion of G-to-R photons had relatively small and inconsistent effects on leaf expansion of lettuce and tomato, but it suppressed stem elongation of tomato. In the presence of FR light, substitution of G with R also increased the leaf number in lettuce. Subsequently, the small increases in YPFD and photosynthetic efficiency by substituting G with R light increased lettuce growth in the presence of FR photons, but there was little effect on plant biomass in lettuce without FR photons or in tomato regardless of FR presence. Regardless of modifying the G:R, excluding FR from a light spectrum suppressed leaf expansion and plant growth in both crops. These results suggest that solar spectral conversion from G-to-R light can increase crop productivity in at least some species, including lettuce, while excluding FR photons from sunlight can decrease crop growth and yield. Additional factors, such as decreased PAR transmission, spectral conversion efficiency, photostability, and cost of the greenhouse covering materials should be considered for specific applications.S1 FileThe spectra of nine emulated lighting treatments consisting of blue , green , red , and far-red  light delivered by light-emitting diodes.(XLSX)Click here for additional data file.S2 FilePhoton flux density and spectral properties of nine emulated lighting treatments consisting of blue , green , red , and far-red  light delivered by light-emitting diodes (LEDs).(XLSX)Click here for additional data file.S3 File(XLSX)Click here for additional data file.


In paragraph four of the discussion in the original article , as publIn the fourth paragraph of the “Discussion” part in the original article, all G408L should be corrected to Q408L (including when in G408L/A448T), and L276A/L419F should be corrected to K276N/L419F.T. rubrum isolates with F415S and F415V variants have more retarded growth than WT isolates”.Additionally, “in our study, also” should be inserted between “F415S and” and “F415V” in the second sentence “In agreement with our findings, prior reports have found that T. mentagrophytes has been found in both resistant and susceptible isolates from India”.Finally, a space is missing between the word ”the” and “S443P” in the last sentence “Similarly, theS443P alterations in The corrected paragraph is as below:T. mentagrophytes has been associated with elevated MICs in isolates from Switzerland and India , whereas L335F/A448T and S395P/A448T were associated with discreet MIC elevations in T. mentagrophytes isolates from India [13]. In agreement with our findings, prior reports found that T. rubrum isolates with F415S and in our study, also F415V variants have more retarded growth than WT isolates [9]. This would indicate that isolates with some mutations may be challenging to susceptibility test and additional information can be obtained through SQLE sequencing. In contrast to the alterations discussed above, some alterations are found in both susceptible and resistant isolates or exclusively in susceptible isolates, suggesting they do not affect the terbinafine susceptibility. This was the case for the I479V alteration found in an isolate with an MIC of 0.016 mg/L and thus identical to the modal MIC of the WT population. Similarly, the S443P alteration in T. mentagrophytes has been found in both resistant and susceptible isolates from India [13]; single A448T alternations almost exclusively in susceptible isolates of T. mentagrophytes or T. indotineae from Germany, Iran, and India ; and V444I/A448T and K276N/L419F alterations in T. mentagrophytes isolates from Germany or China were not associated with MIC elevations .Moreover, alteration Q408L or Q408L/A448T in The corrected text appears below. The authors apologize for any inconvenience caused and state that the scientific conclusions are unaffected.


The authors observed that some patients with C9orf72 repeat expansions may present with lower CSF levels of τP-181, while the presence of rare C9orf72 or APP variants may be associated with lower levels of τT or Aβ42, respectively. These possible associations between genotype/phenotype and CSF biomarker levels may prove useful in the FTD-ALS spectrum, but further research is needed.In the original article by Bourbouli et al. , data onGRN mutations, and thus, this biomarker may serve as a screening tool, identifying subjects suitable for appropriate genetic testing in FTD. On the other hand, plasma levels of phospho-tau may prove useful in the discrimination between AD and FTD, since phospho-tau seems to be normal in FTD, but increased in AD.Ntymenou et al.  reviewed42 is a marker of cognitive decline in PD, (c) increased levels of τT, τP-181 and NFL are useful in differentiating PD from other related neurodegenerative disorders, and (d) some inflammation-related markers such as IL-1β, IL-6, and TGF-β are increased in PD. These conclusions are widely accepted among experts in Parkinsonism, and this paper has become “Editor’s choice”.Katayama et al.  reviewedAlthough reduced α-syn may be considered a marker of PD , results42 and τP-181 are abnormal. The reduction of Aβ42 alone may not be sufficient, despite the fact that sometimes it may be indicative of the Alzheimer’s continuum.Biomarkers may lead to a correction of the antemortem diagnosis of neurodegenerative diseases. This is useful in everyday practice for applying the appropriate, currently approved treatments (or avoiding contraindicated ones). Since disease-modifying treatments for AD are currently tested and starting to be approved (including anti-Aβ antibodies), correct diagnosis may be more important than ever, for correct recruitment in clinical trials, for applying the new drugs in early or clinically atypical cases of AD, and for avoiding their use in “amnestic-like” but non-AD cases. In this context, Paraskevas and Kapaki  presenteInterestingly enough, Endres et al.  reportedThe articles published in this Special Issue contribute to the advancement of our understanding of biochemical markers in dementing disorders, including the neurodegenerative proteinopathies.


Early Detection and Prevention. Our results suggest that religiosity is linked to health behaviour; however, this association is not mediated by social network.The present study investigates whether social networks mediate the well-established positive association between religiosity and health behaviour. Most research has focused on traditional public religiosity (e.g. regular church attendance). This study, however, focuses on the Danish population in which non-traditional and private religiosity is common. We utilise data from the Danish population-based project, The online version contains supplementary material available at 10.1007/s10943-022-01650-1. The positive association between religiosity and health is well-established in the literature  private religiosity. The former is defined by the individual’s centralised denominational social identification. This may manifest in terms of their active and open engagement with typical religious social behaviours and group memberships, including regular church/mosque/temple attendance, group prayer , congregation-driven community initiatives , and social activities, etc. This is the type of religiosity most commonly studied in the empirical literature. Private religiosity, on the other hand, indicates a more reserved, moderate, and less-participatory style of religiosity . Here, the individual may still identify as religious or spiritual, but their social interactions may not be dominated by this particular aspect of their self-concept to the same degree as their more traditional and publicly religious counterparts. Rather than limit their social connectedness, this may facilitate a more diverse and less exclusive range of available social interactions and networks.In this paper, we focus on whether the Danish brand of quiet and solitudinous religiosity facilitates social connection and health behaviour to the same extent as more traditional social and participatory religiosity. To explore this question, in the present article, we distinguish between two forms of religiosity: (1) religious social networks (derived through public religiosity), but may also relate to social connectedness that stems from a more general openness to others (characteristic of private religiosity). Specifically, we hypothesise that (1) religious individuals have healthier lifestyles compared to individuals with no religiosity, (2) religiosity is positively associated with social network strength, and (3) the strength of the individual’s social network mediates the association between religiosity and lifestyle.We theorise that in spite of the perceived less social nature of private religiosity, this religious style may actually encourage social connectedness to a similar extent as traditional public religiosity and with similar implications for health behaviour —a pilot study testing the feasibility and acceptability of a targeted intervention to promote healthy lifestyle and reduce the risk of lifestyle-related diseases. The intervention comprised an initial invitation, a subsequent risk stratification based on self-reported lifestyle and electronic patient record data, and finally a targeted intervention specifically for patients with health-risk behaviour or high risk of disease ("An Adjusted Preventive Program Against Lifestyle Related Diseases (TOFpilot2)," n = 6347)  or unhealthy (score below five) categories . Church/mosque attendance was assessed by asking respondents how often they attended religious events, “How often do you go to church, mosque or another religious community event? Please do not include weddings, funerals, and christenings” ”, 4: “Never, almost never” and 5: “Do not know”). The variables were dichotomised such that response categories 1, 2, and 3 represented the category open towards either prayer/meditation practice or church/mosque attendance and response category 4  represented the category closed towards either prayer/meditation practice or church/mosque attendance. If participants responded, “Do not know” (category 5), their entire response was excluded from the statistical analysis as this answer contained no information about religiosity  and quality  of the individual’s social network. The total score for the LSNS-6 ranges from 0 to 30 with a recommended cut point of 12, where respondents with a total score of less than 12 are categorised as having a weak social network   consists of six items. Three concern relatives and three concern friends: “Covariates included sex [“Men” and “Women”], age group , level of education , cohabitation status [“Cohabiting” and “Single”], country of origin [“Denmark” and “Not Denmark”], and employment status [“Employee/selfemployed” and “Not employed”]. The option “Not employed” covered recipients of social security pay outs, retirement benefits, and state education grants. Information on sex, age, level of education, cohabitation status, country of origin, and employment status was retrieved from the national Danish Bureau of Statistics (Statistics Denmark).Independent variables; prayer/meditation practice and church/mosque attendance and can also be found under the descriptive table). Two separate outcomes were used, namely healthy diet and physical activity. All analyses were conducted with both the binary social network variable (referred to as model 1) and the continuous social network sum score variable (referred to as model 2) as a potential mediator. For all associations of interest both unadjusted and adjusted analyses were performed. The overall model is depicted in Fig. R, with the R package medflex. The weighting-based approach was used to estimate the natural direct effect (NDE), natural indirect effect (NIE), and total effect (TE), all presented as odds ratios with a corresponding 95% confidence interval (CI). Likewise, logistic regression models were also applied estimating odds ratios. We applied a statistical significance cut-off level of 5% to all analyses. Logistic regression analyses were performed in Stata version 16.1 (StataCorp LLC).Descriptive statistics were reported according to respondent religiosity category (open or closed towards prayer/meditation practice and/or church/mosque attendance), using the following terms no religiosity (0P0C), public religiosity-church only (0P1C), private religiosity-prayer only (1P0C), and public religiosity-church and prayer (1P1C) (the definition is outlined in section N = 6347), 4633 individuals were invited to participate and 2171 consented to take part in the study. Of these, a total of 2124 filled in the Q1 questionnaire and 1540 filled in the Q2 questionnaire. Ultimately, 1413 responses had no missing data on exposure, outcome, or the potentially mediating variable and were therefore included in the analysis  of the respondents were categorised as ‘no religiosity’ , closely followed by respondents in the category ‘public religiosity-church and prayer’ (n = 446) . Women were more likely than men to be in the categories ‘public religiosity-church and prayer’ , ‘public religiosity-church only’ , and ‘private religiosity-prayer only’ , while men were more likely than women to be in the category ‘no religiosity’ .Of the total patient population  of the study sample had weak social networks. Just under half of these  reported no religiosity (0P0C) with the rest being fairly evenly distributed across the other three categories (public religiosity-church only (0P1C) = 14.7%, n = 21—private religiosity-prayer only (1P0C) = 18.9%, n = 27—public religiosity-church and prayer (1P1C) = 18.9%, n = 27). Thus, among people with weak social networks, the category ‘no religiosity’ (0P0C) was 2.5 to 3 times as large as the categories ‘public religiosity-church only’ (0P1C), ‘private religiosity-prayer only’ (1P0C), and ‘public religiosity-church and prayer’ (1P1C). By contrast, among respondents with strong social networks there was no clear pattern. Most people with strong networks were either in the ‘public religiosity-church and prayer’  or ‘no religiosity’  categories, followed by people in the ‘public religiosity-church only’ (0P1C), 21.6%, n = 274 and ‘private religiosity-prayer only’ (1P0C), 15.1%, n = 192) categories (Supplementary Table 1). However, looking at the continuous social network sum score variable, there were no significant differences between the four categories. People in the ‘public religiosity-church and prayer’ category (1P1C) had the highest mean social network sum score (sum score = 20), closely followed by ‘public religiosity-church only’ (0P1C) with a mean social network sum score of 19. The categories ‘no religiosity’ (0P0C) and ‘private religiosity-prayer only’ (1P0C) both had a mean social network sum score of 18 .Only 10.1% (n = 295) to public religiosity-church and prayer . This increasing trend is nearly identical for people with strong social networks (grey bars), of whom 89.9% had strong social network. Thus, for people with strong social networks, there was a positive association between healthy diet and religiosity. A similar, albeit weaker, association was evident among people with weak social networks. Despite the higher percentage of people who were physically active, there was no obvious pattern of interaction. The religiosity categories were fairly comparable in terms of social network strength and physical activity, barring a relative dip in physical activity for people in the category private religiosity-prayer only (1P0C) who had weak social networks. The highest percentage of individuals who reported being physically active and who had strong social networks were in the category ‘public religiosity-church only’   and model 2  show no significant mediating effect  of social networks in the association between religiosity and healthy diet for any of three categories of religiosity. The indirect effect was either non-significant or very close to one (public religiosity-church only (0P1C), OR: 1.07, 95% CI: 1.02–1.14 and public religiosity-church and prayer (1P1C), OR: 1.09, 95% CI: 1.02–1.16). Therefore, the direct effect  was close to the total effect , and the results on the total effect from the medflex analysis were similar to results from the logistic regression analysis  and ‘public religiosity-church and prayer’  had significantly healthier diet compared to participants categorised as ‘no religiosity’ (0P0C)  and the group categorised as private religiosity-prayer only (1P0C) neither in model 1 or model 2. Conversely, results from model 1 showed that the public religiosity-church only  and public religiosity-church and prayer  categories were significantly more likely to have a healthy diet than the no religiosity (0P0C) category. Like model 1, the analysis for model 2, showed statistically significant As with healthy diet, no significant mediating effect of social networks was detected in the association between religiosity and physical activity in either model 1 or model 2. Mediation results in both models were either close to one (model 2: public religiosity-church only (0P1C), OR: 1.11, 95% CI: 1.03–1.20 and public religiosity-church and prayer (1P1C), OR: 1.14, 95% CI: 1.05–1.23) or non-significant. Therefore, the direct effects were close to the total effects. The results from simple logistic regression models were comparable , private religiosity-prayer only (1P0C), and public religiosity-church and prayer (1P1C)) and physical activity compared to the reference group (no religiosity (0P0C))  of people’s social networks mediates the association between religiosity (public and private) and health behaviour . Our results support our first hypothesis that religious individuals have healthier lifestyles compared to individuals with no religiosity. Specifically, we found that people who were publicly religious (0P1C & 1P1C) were more likely to have a healthy diet than people with no religiosity (0P0C). We did not find support for our second hypothesis that religiosity (public or private) is positively associated with social network strength. Nor did we find any evidence that social network strength mediated the association between religiosity and health behaviour (hypothesis 3). Finally, we found that the relationship between religiosity and diet differed between private and public religious groups. That is, those who were categorised as ‘public religious’ (0P1C and 1P1C) were more likely to have a healthy diet than those categorised as ‘no religiosity’ (0P0C), whereas no such difference was seen between ‘private religiosity-prayer only’ (1P0C) and ‘no religiosity’ individuals. Thus, our results indicate that public religiosity (as opposed to private and no religiosity) is positively linked with a healthy lifestyle—especially in terms of diet. This effect, however, does not seem to be mediated by social network strength.Our results align with past research. A previous study found a positive association between public religiosity (measured by church and/or mosque attendance) and healthy lifestyle. Similar to the present research, the study was based on a sample of Danes .The present study has both strengths and limitations. This study uses a cross-sectional design, preventing any conclusions about causality. Nonetheless, the mediation analyses in this study assume directional causality. These assumptions are regularly applied in logistic regression models, even though we do not know the true nature of the direction and therefore, cannot rule out the possibility of reverse causation. Another limitation worth mentioning is the lack of knowledge about the timewise sequence of religiosity. Depending on why an individual becomes religious might affect health behaviour. For example, is deeply-rooted and longstanding faith (restful religiosity) more likely to be associated with health as opposed to short-term “crisis” religiosity   was not recruited to participate in a project focusing on religiosity and health, also represents a considerable strength. That is, it is unlikely that people chose not to participate in the TOF pilot2 study because they do not wish share information about their religious practice. Finally, to our knowledge, this is the first study to investigate whether social network mediates the association between religiosity and health behaviour among people with a more individualised and private religiosity.In future studies, it would be interesting to include a focus on the stress-buffering effects of social connectedness, which is another potential pathway linking religiosity and health and health behaviour Below is the link to the electronic supplementary material.


Shift work may disrupt the sleep and wake cycles and negatively affect physical and mental health. Dementia is a neurodegenerative disorder with progressively declining cognition that is receiving increasing attention. Studies on the association between shift work and dementia are rare. Herein, we conducted a meta-analysis to investigate the association between shift work and dementia.This study was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We searched the PubMed, Embase, and Web of Science databases using a related set of keywords. The inclusion criteria were as follows: (1) adult employees working in a factory, company, or organization; (2) exposure to shift work/non-shift work; and (3) outcome of dementia based on examination or assessment. A meta-analysis using a fixed-effects model was performed. The hazard ratio of dementia was compared between shift workers and non-shift workers.p = 0.04). This association also occurred in night workers for more than 1 year.Five studies were included in the quantitative synthesis, and two were selected for further meta-analysis. A random-effects model showed a modest association between shift work and an increase in dementia cases (pooled hazard ratio = 1.13; 95% confidence interval: 1.04–1.23; Shift work and long-term night work were modestly associated with a higher risk of developing dementia. Avoiding long-term night shifts may be effective in reducing dementia risk. Further studies are required to confirm this hypothesis. Shift work, which is generally defined as a work schedule comprised of irregular or unusual hours compared with a normal daytime work schedule, may disrupt the sleep and wake cycle and have a negative effect on health . A previDementia, which involves a group of symptoms affecting memory, planning, and social abilities severely enough to interfere with one's daily life, is most common caused by Alzheimer's disease. In a previous study, The Alzheimer's Association has reported that cognitive decline and dementia have non-modifiable risk factors, such as age, family history, and apolipoprotein E ε4 allele genetic polymorphisms, and modifiable risk factors, including traumatic brain injury, midlife obesity, midlife hypertension, diabetes, smoking, dyslipidemia, history of depression, and sleep disturbance . Two proA recent review evaluating the association of shift work and dementia, failed to extrapolate definite conclusions, due to the limited number of available studies and heterogenous results . Since sThis study was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The review protocol was registered at PROSPERO  and Kaohsiung Medical University Hospital Institutional Review Broad (KMUHIRB-EXEMPT(I)-20220005).We searched the MEDLINE (PubMed), Embase, and Web of Science databases on January 22, 2022, for related studies. We did not limit the dates of studies published in these databases, and all studies including the target keywords were considered. Two researchers (K–WL and C–CY) performed preliminary searches using different keywords. The researchers separately proposed a set of key search words that were then merged to one final list, as follows: “Schedule, Shift Work” OR “Schedules, Shift Work” OR “Work Schedule, Shift” OR “Night Shift Work” OR “Shift Work, Night” OR “Rotating Shift Work” OR “Shift Work, Rotating” OR “Shift Work Schedule”[Mesh] OR day-time[Title/Abstract] OR night-time[Title/Abstract] OR “Day-time” OR “Night-time” AND “Dementias” OR “Amentia” OR “Amentias” OR “Senile Paranoid Dementia” OR “Dementias, Senile Paranoid” OR “Paranoid Dementia, Senile” OR “Paranoid Dementias, Senile” OR “Senile Paranoid Dementias” OR “Familial Dementia” OR “Dementia, Familial” OR “Dementias, Familial” OR “Familial Dementias” OR “Alzheimer's Disease” OR “Dementia, Senile” OR “Senile Dementia” OR “Dementia, Alzheimer Type” OR “Alzheimer Type Dementia” OR “Alzheimer-Type Dementia (ATD)” OR “Alzheimer Type Dementia (ATD)” OR “Dementia, Alzheimer-Type (ATD)” OR “Alzheimer Type Senile Dementia” OR “Primary Senile Degenerative Dementia” OR “Dementia, Primary Senile Degenerative” OR “Alzheimer Sclerosis” OR “Sclerosis, Alzheimer” OR “Alzheimer Syndrome” OR “Alzheimer Dementia” OR “Alzheimer Dementias” OR “Dementia, Alzheimer” OR “Dementias, Alzheimer” OR “Senile Dementia, Alzheimer Type” OR “Acute Confusional Senile Dementia” OR “Senile Dementia, Acute Confusional” OR “Dementia, Presenile” OR “Presenile Dementia” OR “Alzheimer Disease, Late Onset” OR “Late Onset Alzheimer Disease” OR “Alzheimer's Disease, Focal Onset” OR “Focal Onset Alzheimer's Disease” OR “Familial Alzheimer Disease (FAD)” OR “Alzheimer Disease, Familial (FAD)” OR “Alzheimer Diseases, Familial (FAD)” OR “Familial Alzheimer Diseases (FAD)” OR “Alzheimer Disease, Early Onset” OR “Early Onset Alzheimer Disease” OR “Presenile Alzheimer Dementia” OR “Dementia”[Mesh] OR “Alzheimer Disease”[Mesh]. The search methods for the Embase and Web of Science databases were modified, as appropriate.The inclusion criteria for the study were as follows: (1) included adult employees working in a factory/company/organization; (2) assessed exposure to shift work/non-shift work; and (3) provided outcomes of dementia based on examination or assessment.Two researchers (K–W Lee and C–C Yang) independently evaluated the titles and abstracts of the initially identified studies (first round of screening). After removing the duplicates, the full articles were read for those who met the inclusion criteria and those whose eligibility for the title and abstract screening was unclear. Any disagreements between the two researchers were resolved by three other researchers based on discussion and consensus.From each included study, we extracted information regarding study characteristics, shift work, dementia, and the association between shift work and dementia. We contacted the authors of these for further explanation if the study failed to or imprecisely reported the required data.We obtained the following data regarding study characteristics: publication year, the country where the study was completed, sample size, sampling framework , participant characteristics, and estimated risk of outcome  where appropriate.We defined shift work as “work beyond regular working daytime hours,” including evening shift, night shift, fixed shift, on-call shift, or rotating shift –15.The outcome was as follows: definite diagnosis of dementia according to the diagnostic criteria of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) for major neurocognitive disorders.I2. Funnel plots were used to assess publication bias. We also performed a separate meta-analysis of the subgroups of night shifts over 1 year. All analyses were performed using Review Manager version 5.4 and R version 3.6.2.We computed all pooled HRs from the individual HRs of shift workers and non-shift workers. Standard error (SE) was estimated for the HRs according to the 95% confidence interval (CI). Regarding the main analysis, a random-effects model meta-analysis was used to calculate the pooled HR with 95% CI. We also applied a random-effects model to analyze the possibility of heterogeneity in HRs among these studies, based on their characteristics . Among-sN = 7), systemic review articles (N = 2), animal studies (N = 3), case reports (N = 1), and perspective studies (N = 1). Finally, we recruited five studies in the qualitative review and only two studies reporting the hazard ratio of dementia in shift workers were included for further meta-analysis.Both studies performed additional analyses according to shift work duration. Bokenberger et al.  classifip = 0.04). Low heterogeneity  = 0.64, P = 0.89) was also observed.Variations in the association between shift work and dementia were calculated using a random-effect model . The posI2 = 0%, χ2 (1) = 2.61, p = 0.63).We performed a subgroup analysis of those who had worked night shifts for more than 1 year (2 studies generated 5 HRs); the pooled HR was significant  at 1.13 (95% CI 1.05 to 1.23) . HeterogTo the best of our knowledge, this is the first systematic meta-analysis of the association between dementia and shift work. The estimated pooled HRs of the association between dementia and shift work in the two studies included in the meta-analysis were positive and significant. Both studies had a prospective design and used definite clinical diagnosis of dementia. The samples were from national registrations, and both undertook similar shift work measurements . Some participant characteristics were also similar in these two studies: the baseline age (58.1 ± 10.1 vs. 50.3 ± 4.8 in non-dementia cases and 56.1 ± 5.3 in dementia case), follow-up period (14.1 years vs 12.5 years), and proportion of shift work history (30.1 vs. 37.4%). However, there were some differences between these two studies. One study enrolled people from Denmark, while the other was from Sweden; one enrolled both female and male workers, and the other included only female nurses. Differences in race and sex have been led to different life expectancies ; howevervia increased amyloid β production (Shift work may disturb circadian rhythm, leading to poor sleep quality . Severaloduction . Anotheroduction . Melatonoduction . Howeveroduction , which moduction . Recent oduction . Besidesoduction . And micoduction , 34.Several studies have shown that long-term night shift work may have a negative influence on health and is associated with a higher rate of breast cancer, rectal cancer, atrial fibrillation, coronary heart disease, and ischemic stroke –39. Our The current study had several limitations. First, attrition bias may be a major methodological problem in both longitudinal studies, which may affect our results. For example, jobs with night shift work may require more cognitive load than work conducted during normal day shift, and only individuals who can adapt to shift work might tolerate the working style; thus, the self-selection of workers, the health worker effect, may contribute to attrition bias. Second, there was no information on shift work intensity, such as the number of shifts per week/month or hours of work per shift. Third, the studies used national registers as a source of dementia data, which may contribute to bias in terms of the under-detection of dementia, although data from registries may be the most accessible data form for continuous follow-up of participants. Fourth, we could not consider other potential confounders, such as socioeconomic position, nutrition, smoking, and sleep disturbance which might result in the weak association of dementia and shift work in our study. Fifth, given the exposure to light at night, the situation occurred while individuals on the evening shift, night shift or rotating shift may disrupt the circadian rhythms , we inclWe detected a modest but significant association between shift work and dementia. There was low heterogeneity in the methodologies of the included studies in our meta-analysis. Similarly, subgroup analysis of night work for more than 1 year also revealed a significant association between longer shift work history and risk of dementia. In workplace practice, avoiding long-term night shift work might be effective in reducing the risk of dementia. Further studies are required to confirm this hypothesis.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.C–CY contributed to the conception and design of the study. K–WL and C–CY contributed to acquisition and drafted the article/revised the article. C–HC and H–YC contributed to analysis. C–HH contributed to interpretation of data. All authors read and approved the final manuscript.


The relationship of body mass index (BMI) changes and variability with amyloid-β (Aβ) deposition remained unclear, although there were growing evidence that BMI is associated with the risk of developing cognitive impairment or AD dementia. To determine whether BMI changes and BMI variability affected Aβ positivity, we investigated the association of BMI changes and BMI variability with Aβ positivity, as assessed by PET in a non-demented population.We retrospectively recruited 1,035 non-demented participants ≥50 years of age who underwent Aβ PET and had at least three BMI measurements in the memory clinic at Samsung Medical Center. To investigate the association between BMI change and variability with Aβ deposition, we performed multivariable logistic regression. Further distinctive underlying features of BMI subgroups were examined by employing a cluster analysis model.Decreased  or increased BMI  was associated with a greater risk of Aβ positivity after controlling for age, sex, APOE e4 genotype, years of education, hypertension, diabetes, baseline BMI, and BMI variability. A greater BMI variability  was associated with a greater risk of Aβ positivity after controlling for age, sex, APOE e4 genotype, years of education, hypertension, diabetes, baseline BMI, and BMI change. We also identified BMI subgroups showing a greater risk of Aβ positivity.Our findings suggest that participants with BMI change, especially those with greater BMI variability, are more vulnerable to Aβ deposition regardless of baseline BMI. Furthermore, our results may contribute to the design of strategies to prevent Aβ deposition with respect to weight control. A large amount of evidence suggests that body mass index (BMI) is associated with the risk of developing cognitive impairment. Specifically, mid-life obesity increases the risk of cognitive impairment . PreviouAmyloid-β (Aβ) deposition in the brain is an important pathological hallmark of Alzheimer’s disease (AD), which is the most common cause of dementia. According to the Aβ cascade hypothesis, Aβ deposition in the brain occurs at a very early stage. In fact, Aβ deposition in the brain precedes the development of AD dementia by 10–20 years. Furthermore, non-demented participants with Aβ deposition were more converted to dementia than those without Aβ deposition . TherefoRecently, being underweight or obese in mid-life was found to be associated with an increased risk of Aβ deposition , and indThus, the goal of our study was to investigate the influence of BMI changes and BMI variability on Aβ positivity in a non-demented population. Furthermore, to determine the complex relationships among BMI changes, BMI variability, and Aβ positivity, we evaluated the distinguishable BMI subgroups classified by the cluster analysis model. We hypothesized that decreased BMI and greater BMI variability would have deleterious effects on Aβ deposition.between August 2015 and August 2020. All participants underwent a comprehensive dementia work-up including the standardized cognitive assessment battery ] from the memory clinic in the Department of Neurology at Samsung Medical Center (SMC) in Seoul, Korea. The participants had undergone Aβ PET  battery , APOE geAll participants with normal cognition fulfilled the following conditions: (1) no medical history that is likely to affect cognitive function based on Christensen’s criteria ; (2) no The institutional review board of the SMC approved this study. Written informed consent was obtained from all participants.18F-florbetaben [FBB] PET and 18F-flutemetamol [FMM] PET) scans at SMC using a Discovery STe PET/CT scanner . For FBB or FMM PET, a 20-min emission PET scan in dynamic mode (consisting of 4 min × 5 min frames) was performed 90 min after an injection of a mean dose of 311.5 MBq FBB and 197.7 MBq FMM, respectively. Three-dimensional PET images were reconstructed in a 128 × 128 × 48 matrix with a 2 mm × 2 mm × 3.27 mm voxel size using the ordered-subsets expectation maximization algorithm .All participants underwent Aβ PET  who were blinded to the clinical information, and the images were dichotomized as either Aβ-positive (Aβ+) or Aβ-negative (Aβ−) using visual reads. They discussed any discordant results regarding Aβ positivity to achieve consensus. The FBB PET scan was regarded as positive if the Aβ plaque load was visually rated as 2 or 3 on the brain amyloid plaque load scoring system, and the FMM PET scan was considered positive if one of five brain regions  systematically reviewed using FMM PET was positive in either hemisphere . Represe11C-labeled Pittsburgh compound SUVR.For the sensitivity analyses, Aβ positivity on PET scans was determined using Centiloid (CL) cutoff-based categorization. We used a CL method previously developed by our group  to standThere are three steps to obtain CL values : (1) preTo determine the participants’ CL cutoff-based Aβ positivity, we applied the optimal cutoff value derived using a k-means cluster analysis in 527 independent samples of participants with normal cognition. The cutoff value was set at 27.08, representing the 95th percentile of the lower cluster , and theFor each participant, BMI data were obtained by backtracking their weight and height records in clinical data warehouse of SMC, which were measured at all visits within 3 years after inspecting Aβ PET until March 2000 . We excl2), normal weight (18.5–24.9 kg/m2), obesity (BMI ≥ 25 kg/m2) (Baseline BMIs was defined as the farthest past measurement from the Aβ PET scan . Partici5 kg/m2) .Follow-up BMI was defined as the closest measurement to the Aβ PET scan . We calcb: baseline BMIB, f: follow-up BMIB, Duration: period between BbandBfvia the cluster analysis. In the association analysis, participants were classified into subgroups with non-high variability versus with high variability according to the upper 15-percentile of BMI variability values as a threshold.BMI values at three or more time points were used to obtain BMI variability . Six meat-tests and chi-square tests to compare the demographic and clinical characteristics of Aβ+ and Aβ− groups. To investigate the association between BMI change and variability with Aβ deposition, we performed multivariable logistic regression using BMI change and variability as predictors after controlling for age, sex, APOE e4 genotype, years of education, hypertension, diabetes, and baseline BMI. To further validate the association between BMI change and variability with Aβ deposition, we performed multivariable logistic regression using CL cutoff-based Aβ positivity as an outcome rather than visually rated Aβ positivity.We used independent via the Euclidean distance after each selected feature was scaled through standardization. Finally, we used GMM to identify clusters, each of which consisted of participants with similar BMI patterns, because it can be used when more fine-grained workload characterization and analysis are required , k-means clustering, and self-organizing map . First, required . The optp-values were two-sided, and the significance level was set at 0.05. All analyses were performed using SPSS version 25.0 and R version 4.3.0 .All reported Anonymized data for our analyses presented in the present report are available upon request from the corresponding authors.p < 0.001). Participants who were Aβ+ had a higher frequency of APOE e4 genotype  but a lower frequency of hypertension  and diabetes  than those who were Aβ−.Among the 1,035 participants, 579 individuals were Aβ− and 456 were Aβ+ . ParticiDecreased BMI  was associated with a greater risk of Aβ positivity after controlling for age, sex, APOE e4 genotype, years of education, hypertension, diabetes, baseline BMI, and BMI variability . IncreasA greater BMI variability  was associated with a greater risk of Aβ positivity after controlling for age, sex, APOE e4 genotype, years of education, hypertension, diabetes, baseline BMI, and BMI change .Based on BMI baseline, change, and variability measures, four subgroups were created by GMM and validated with the Silhouette index, AIC and BIC . The BMICompared to the stable subgroup 1, the subgroups showing greater BMI variability had a greater risk of Aβ positivity, especially when BMI was decreasing: subgroup 2 , subgroup 3 , and subgroup 4  .For CL cutoff-based Aβ positivity, decreased BMI  was associated with a greater risk of Aβ positivity after controlling for age, sex, APOE e4 genotype, years of education, hypertension, diabetes, baseline BMI, and BMI variability . IncreasWe systematically investigated the association of Aβ positivity with BMI change and variability in a relatively large group of participants who did not have dementia. In the present study, we found that BMI change (increased or decreased) was associated with a greater risk of Aβ positivity, regardless of baseline BMI status. Furthermore, we noted that greater variability in BMI predicted a greater risk of Aβ positivity despite baseline BMI status and BMI change. Finally, our cluster analysis identified BMI subgroups with specific patterns of BMI change and variability, which eventually showed a greater risk of Aβ positivity. Taken together, our findings suggest that participants with BMI change, especially those with greater BMI variability, are more vulnerable to Aβ deposition regardless of baseline BMI. Furthermore, given the paucity of modifiable risk factors for the development of Aβ, our results may contribute to the design of strategies to prevent Aβ deposition with respect to weight control.Our major finding was that BMI change (increased or decreased) was associated with a greater risk of Aβ positivity, regardless of baseline BMI status. Considering that Aβ deposition is associated with the development of dementia, our findings are supported by previous epidemiologic studies . SpecifiAnother major novel finding was that greater BMI variability was associated with a greater risk of Aβ positivity, regardless of baseline BMI status and BMI changes. For example, even if the BMI measured 5 years ago and the current BMI are at the same level, there is a higher possibility of Aβ deposition if there is a high variation in BMI within the 5 years. We reviewed the literature regarding between BMI and AD related outcome . To the Finally, our cluster analysis identified BMI subgroups with specific patterns of BMI change and variability, which eventually showed a greater risk of Aβ positivity. Specifically, a subgroup with decreased BMI and a greater variability in BMI was predictive of a higher risk of Aβ positivity. This was again replicated in cluster analyses with various BMI measures and Aβ positivity, a prior finding suggestive of the importance of decreased BMI with greater variability in Aβ positivity.The strength of this study is that we investigated the associations between Aβ positivity and BMI changes and BMI variability in a large cohort. However, our study has several limitations that need to be addressed. First, due to the inherent challenges of a retrospective cohort study, we did not provide information about the participants’ amyloid status at baseline. Thus, we were not able to show their causal relationships. However, a retrospective cohort study was considered a realistic alternative given that the change in amyloid appears very slowly and the cost of amyloid PET is very expensive. Second, because we used the Asia-Pacific BMI criteria for participants who are obese and underweight, caution should be taken when generalizing our findings to other races. Third, BMI values were collected from March 2020 until 3 years after the Aβ PET scan, and several BMI values were recorded at 3 years after the Aβ PET scan. This limitation might be mitigated to a certain extent with the existing findings that the annual rate of increasing Aβ deposition is very low . Fourth,We provide a comprehensive understanding of the marked influences of BMI change and variability on the risk of Aβ positivity in non-demented individuals. Furthermore, our findings suggest that rigorous strategies for weight maintenance are required to prevent Aβ deposition in non-demented individuals.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the Institutional Review Board of the Samsung Medical Center. Written informed consent was obtained from all participants. The patients/participants provided their written informed consent to participate in this study.SHK: writing – original draft, review and editing, formal analysis, and data curation. JHK: writing – review and editing, formal analysis, and data curation. YC: conceptualization. BKC, YSC, HJ, HJK, S-BK, and DLN: data curation. KK: methodology, formal analysis, writing – review and editing, and conceptualization. SWS: methodology, writing – review and editing, and conceptualization. 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. 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Microgravity-induced bone loss is a major concern for space travelers. Ground-based microgravity simulators are crucial to study the effect of microgravity exposure on biological systems and to address the limitations posed by restricted access to real space. In this work, for the first time, we adopt a multidisciplinary approach to characterize the morphological, biochemical, and molecular changes underlying the response of human bone marrow stromal cells to long-term simulated microgravity exposure during osteogenic differentiation. Our results show that osteogenic differentiation is reduced while energy metabolism is promoted. We found novel proteins were dysregulated under simulated microgravity, including CSC1-like protein, involved in the mechanotransduction of pressure signals, and PTPN11, SLC44A1 and MME which are involved in osteoblast differentiation pathways and which may become the focus of future translational projects. The investigation of cell proteome highlighted how simulated microgravity affects a relatively low number of proteins compared to time and/or osteogenic factors and has allowed us to reconstruct a hypothetical pipeline for cell response to simulated microgravity. Further investigation focused on the application of nanomaterials may help to increase understanding of how to treat or minimize the effects of microgravity.The online version contains supplementary material available at 10.1007/s00018-022-04553-2. Since the 1970s space travelers have been known to experience severe bone loss at a rate of 1–1.5% per month, which is only partially responsive to non-pharmacological countermeasures . PharmacThe bone is a metabolically active tissue in which the mineralized matrix is constantly deposited and resorbed by specialized cells. The extracellular matrix (ECM) is constituted by an organic portion, the osteoid, and an inorganic portion formed by different calcium phosphates, among which hydroxyapatite is the most represented. Bone marrow stromal cells (BMSCs) can differentiate into osteoblasts (OBs), which are cells specialized in the synthesis and deposition of the osteoid that is subsequently mineralized . OsteoclPrevious studies showed that exposure to real microgravity can impair OBs differentiation and mitochondrial energy potential, suppressing bone formation , and canDespite developments in space technology, the study of biological specimens in real space conditions is still a constraint. In this regard, ground-based platforms for simulating microgravity on Earth are an effective and invaluable asset. Among others, the random positioning machine (RPM) simulates weightlessness randomly, in a three-dimensional pattern, and represents a bench-top method for gravitational biology and space medicine research .Previous reports have shown that ground-based simulated microgravity (SMG) reduces the osteogenic differentiation potential of BMSCs, although it is acknowledged that many of these studies were performed over short time frames, monitoring the effects of SMG for a maximum of 14 days , 15, 16.The present work aims to provide, for the first time, a comprehensive overview of the morphological, biochemical and molecular changes underlying the response of human BMSCs to long-term microgravity exposure (28 days) during osteogenic differentiation. In addition to expression profiling, analysis probing biomarker genes, bone matrix and cytoskeleton proteins, a study on bone mineralization was conducted along with a bottom-up, label-free, high coverage proteome investigation. Overall, our data pointed to an initial down-regulation of the osteogenic differentiation processes, highlighting proteins there were not identified before in SMG, and indicates that this down-regulation is partially reverted at later time points. This work provides research-based data for developing possible countermeasures against spaceflight-induced osteoporosis.RUNX2), alkaline phosphatase tissue non-specific (ALPL), decorin (DCN), and collagen type I (COL1A1) were analyzed as early markers, and collagen type III (COL3A1), bone morphogenetic protein 2 (BMP2), and bone sialoprotein 2 (IBSP), used as late markers. RUNX2, DCN and COL1A1 were not significantly affected by RPM exposure (data not shown), while COL3A1, ALPL, IBSP and BMP2 expression was downregulated by microgravity exposure at both T8 and T28  or 28 days (T28) Fig. S1). The expression of osteogenic gene markers was assessed by qRT-PCR. Runt-related transcription factor 2  at 28 days , BGLAP, osteonectin (SPARC), and osteopontin   of the sample replicates showed that 59% of variance was due to the first two components Fig. B. The rep = 2.7 × 10–11), while 28 were mapped to the mitochondrial matrix (p = 3.1 × 10–3), 47 to the cell-substrate junction (p = 3.8 × 10–13), and 30 to the actin cytoskeleton (p = 1.5 × 10–3).GO annotation overrepresentation analysis (ORA) of the 481 DAPGs showed that 13 cellular component categories (GO-CC) were significantly enriched . Interestingly, 49 DAPGs were mapped to the GO-CC ECM  and the top 10 PGs most upregulated  in RPM were hierarchically clustered  and helped to narrow down the cellular processes mainly affected by SMG. These 178 GO-BPs were represented in four charts based on their down- and upregulation data, at 8 and 28 days . They presented redundant DAPGs in their list, but this also ensured that the biological significance of the proteins was not underestimated .In general, the overall proteomics coverage was high with respect to previous works (4312 identified PGs). However, the GO-CCs annotation of the DAPGs highlighted a polarized enrichment of non-nuclear proteins. Results from the 481 DAPGs suggest that major changes occurred at early time points (8 days), with the 15.2% of all DAPGs being downregulated by SMG at 8 days.cell fate macro-category included proliferation, differentiation, adhesion, signaling, and death  was followed by a significant upregulation at 28 days of 6 DAPGs    for hydroxyapatite crystal growth  201218_DF_nn_1.raw.PG.NrOfPrecursorsIdentified – sample name” = number of precursor ions used for the identification of each protein group; “T: PG.Pvalue” = the p-value of the Protein Group identification; “T: PG.ProteinGroups” = Uniprot accession numbers of the identified protein groups; “T: PG.Genes” = gene symbols of the identified protein groups. (XLSX 1059 KB)Supplementary file9 Table S2 List of the positive hits from the One-way ANOVA with relative multiple comparison results. Column name specification: “C: ANOVA Significant” = with “+” we indicated a positive significance; “N: -Log ANOVA p value” = the –log(2) of the one-way ANOVA p-value; “T: PG.Names” = extended name of the protein group; “T: Significant pairs” = significant comparisons calculated with Tukey’s Honestly Significant Differences (p<0.05). (XLSX 136 KB)Supplementary file10 Table S3 DAPGs (ANOVA positive hits) were divided in four groups, specified in the first column: T8_up/T28_up = upregulated by SMG at T8/T28; T8_down/T28_down = downregulated by SMG at T8/T28. For each group a separate enrichment was performed using GeneSCF (XLSX 61 KB)Below is the link to the electronic supplementary material.


Perinatal fatigue among women is related to the clinical outcomes of mothers and infants. Perinatal fatigue changes over time, and the trajectory varies according to the predictors and outcomes of the mothers. This, however, has not been documented in any study.This study aimed to identify and characterize the trajectory patterns of perinatal fatigue among women from late pregnancy to 6 months after delivery.n = 1,030). The Mann-Whitney U test and binary logistic regression were used to tie the selected trajectory classes to predictors and outcomes.We used growth mixture modeling to estimate the trajectory of perinatal fatigue at 28 gestational weeks (T0), 37 gestational weeks (T1), 3 days (T2), 1 week (T3), 6 weeks (T4), and 6 months (T5) after delivery with (n = 114) and “persistently low” . Levels of perinatal fatigue among women in the “persistently high” group were higher than those in the “persistently low” group across the six measurements. Complications, fatigue at T0, and employment status in late pregnancy were all significant predictors of trajectories. Additionally, the “persistently high” group had a greater prevalence of difficult baby care and weight retention and a lower prevalence of exclusive breastfeeding.There were two distinct patterns of perinatal fatigue in women: “persistently high” (11.1%, Our study proved the heterogeneity and characteristics of perinatal fatigue among women. Future research should concentrate on developing intervention packages targeted at specific individuals in order to alleviate perinatal fatigue in women. Perinatal fatigue, which includes both pregnancy and postpartum fatigue, is one of the most frequently reported complaints among childbearing women . PerinatHowever, longitudinal data on perinatal fatigue in women are quite scarce. Longitudinal studies have mainly focused on the levels and courses of perinatal fatigue among the population over time, which may obscure individual differences . Only onInvestigating the factors that contribute to prenatal fatigue trajectories is also critical for intervention development. Few aspects of perinatal fatigue trajectories have been reported, though multiple factors are associated with the perinatal fatigue . In thisFrom late pregnancy to 6 months after birth, this prospective, longitudinal study assessed perinatal fatigue in women six times. The particular objectives of this study were to detect and define the trajectory patterns of perinatal fatigue in women from late pregnancy to 6 months after birth. Extending our understanding of the identified perinatal fatigue trajectory types and their related components may help to improve clinical management for perinatal fatigue.0), 37 gestational weeks (T1), 3 days postpartum (T2), 1 week postpartum (T3), 6 weeks postpartum (T4), and 6 months postpartum (T5). This work was approved by the Obstetrics and Gynecology Hospital attributed to Fudan University (No. 2020-191).This study is a prospective, longitudinal study, conducted between December 2020 and September 2021. Participants were asked to fill out questionnaires six times, namely, 28 gestational weeks  weeks was scanned and recruited. The exclusion criteria were women with (a) diagnosed depression before; (b) severe health problems such as heart failure; (c) twins or multiples. All women signed informed consent.0 to T5.Perinatal fatigue was measured by the Postnatal Accumulated Fatigue Scale (PAFS). Professor Emi Mori firstly published the scale in 2016 . Each of0 and T2. At T0, we collected data of maternal age, parity , employment , an education level , monthly family income  and residency . Also, we collected data of planned pregnancy , prenatal training , sleep duration per day , and sleep quality at this time. Of them, sleep quality was evaluated by asking women, “Please score your sleep quality in the past 24 h, ranging from 0 to 10, and 0 score means very dissatisfied, and 10 scores mean very satisfied.” Besides, fetus gender  was obtained by medical records at T2.Self-made questionnaires collected data of situational factors at T0 and T1. Maternal height, prenatal weight, weight at T0, gestation, complication, pain, and fatigue were collected at T0. Of them, the pain was measured by asking one question: “Please score your pain in the past week, ranging from 0 to 10, and 0 score means no pain and 10 scores mean very painful.” Based on these data, we calculated prenatal body mass index (BMI), BMI at T0, weight gain at T0 , and BMI gain at T0 . And “T1 − T0 fatigue” was equal to PAFS fatigue score at T1 minus that at T0, similar calculations were used for (T2 − T1)/(T3 − T2)/(T4 − T3)/(T5 − T4) Fatigue.Self-made questionnaires were also used to collect data of physiologic factors at T0 by asking participants: “How anxious are you in the past week? Choose a number between 0–10 to express the extent of your anxiety, 0 for no anxiety, 1 to 3 scores for mild anxiety, 4 to 6 scores for moderate anxiety, 7 to 9 scores for severe anxiety, and 10 scores for extreme anxiety.”One maternal psychologic factor, anxiety, was measured at T5, including difficult baby care , exclusive breastfeeding , and weight . Also, weight retention was defined as “yes” if weight at T5 was more than prenatal weight, vice versa as “no.”Performance factors among women were regarded as outcomes of perinatal fatigue in this study, which were all collected at TOnly data of participants who completed at least three follow-ups were included in the analysis. Data analysis was carried out in the following two parts based on the two objectives of this study.0 to T5, namely, 0 weeks (T0 − T0), 8 weeks (T1 − T0), 12 weeks (T2 − T0), 13 weeks (T3 − T0), 18 weeks (T4 − T0), 36 weeks (T5 − T0), respectively. Thus, the time score of T0 to T5 was fixed here at 0, 8, 12, 13, 18, 36, representing the equidistant time model. A robust maximum likelihood estimator was used for model estimation. Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and Sample-Size Adjusted BIC (aBIC) were used as the model fit goodness statistics, with smaller values suggesting better model fit. Entropy was used to evaluate the accuracy of classification. The closer the entropy was to 1, the higher the class accuracy was. Likelihood Ratio Test (LRT) compared an n-class model with an n-1-class model, where a significant P value indicated a considerable improvement in the n-class model and thus rejected n-1-model rationally. In this study, the Lo-Mendell-Rubin (LMR) and Bootstrap Likelihood Ratio Test (BLRT) were also used for model comparison. Bayesian posterior probability was applied to export latent trajectory classes after a best-fit model was determined. In this part, GMM was performed with Mplus 8.3. In addition, a rank-sum test was used to examine differences in fatigue scores at six times and during five adjacent times between the trajectories. And P < 0.05 were preliminary remarked as positive.In the first part, we used growth mixture modeling (GMM) to identify the trajectory of perinatal fatigue. GMM is an advanced, person-centered analysis to identify heterogeneous subgroups that comprise distinct courses across time . The timP < 0.05 were preliminary remarked as positive between the trajectory classes. To further describe characteristics of perinatal fatigue trajectory, binary logistic regression (Forward: LR) was applied. The latent trajectory classes as the dependent variable and positive potential predictors as independent variables in the regression model. And variables with P < 0.05 in the regression model were regarded as predictors of perinatal fatigue trajectory. In this part, performance factors were not included in the regression model because they were considered outcomes rather than potential predictors. SPSS version 23.0 was used for Mann-Whitney U and binary logistic regression.In the second part, to characterize the trajectory of perinatal fatigue, the Mann-Whitney U test, and binary logistic regression were implemented. We calculated the Z-score of skewness and kurtosis for all continuous variables . Only ge0), 917 (T1), 970 (T2), 781 (T3), 563 (T4), and 779 (T5), respectively , indicating a relatively higher initial fatigue score in Class 1 than Class 2. The average slope factors of Class 1 and Class 2 were 0.306 (P < 0.001) and −0.110 (P < 0.001), indicating that Class 1’s fatigue score increased significantly over time while Class 2’s decreased significantly over time. In addition, the trajectories of fatigue scores were also visually shown in n = 114) and “persistently low” group , respectively.The mean intercept factor of Class 1 (12.318) was higher than that of Class 2 (10.118), and the variance of the intercept factor between these two classes was 13.586 (0 to T1 (P = 0.001). And no significant differences in fatigue scores were found from T1 to T2, from T2 to T3, and from T3 to T4. Combined 0 to T4, fatigue scores in both classes presented similar upward (from T0 to T3) and downward (from T3 to T4) trends. Oppositely, fatigue scores showed a strong upward trend from T4 to T5 in the “persistently high” group, whereas a solid downward trend in the “persistently low” group (P < 0.001). In addition, for the “persistently high” group, the peak fatigue score showed at T5, followed by that at T3. For the “persistently low” group, the peak of fatigue score showed at T3.P = 0.002) and had worse sleep quality (P < 0.001) at T0 than those in the “persistently low” group. For physiologic factors, women in the “persistently high” group had smaller prenatal weight (P = 0.014), weight at T0 (P = 0.014), BMI at T0 (P = 0.036), more complication (P < 0.001), and severer fatigue at T0 (P < 0.001) than those in “persistently low” group. For psychologic factors, more people in the “persistently high” group reported anxiety status with scores between 7–10 at T0 than the “persistently low” group did (P < 0.001).0 and sleep quality , fatigue at T0 and anxiety , BMI at T0 and prenatal weight , and BMI at T0 and Weight at T0 . Thus, only employment (1 = “employed” and 0 = “unemployed”), complication (1 = “yes” and 0 = “no”), fatigue at T0, and BMI at T0 from 878 cases were introduced into the final model. The χ2 test of the final model was significant (P < 0.001), and the Hosmer and Lemeshow test indicated an acceptable model fit with P = 0.417. As a result, the final model showed three predictors of perinatal fatigue trajectories, i.e., complication , fatigue at T0  and employment .Binary logistic regression was used to analyze the relationships between potential factors and the perinatal fatigue trajectory. The regression model was summarized in vs 16.9%; P < 0.001) and weight retention  were significantly higher in the “persistently high” group than the other group at T5. Also, fewer people reported exclusive breastfeeding  in the “persistently high” group than the other group at T5.In addition, 0, and employment. Additionally, women in the “persistently high” group experienced more adverse clinical outcomes than those in the “persistently low” group, including greater rates of difficult baby care and weight retention and lower rates of exclusive breastfeeding at T5.Our study addressed previously unresolved gaps in the literature by identifying and describing the perinatal fatigue trajectory in a large sample from late pregnancy to 6 months postpartum. The findings of this study are critical for developing therapies to alleviate perinatal fatigue and improve mother and child clinical outcomes. We discovered two unique perinatal fatigue trajectories, namely “persistently high”  and “persistently low” . Additionally, we were the first to identify predicted markers for the perinatal fatigue trajectory, such as complication, fatigue at Tvs 7.4%) of women were at high risk of experiencing extreme fatigue during their perinatal periods. Clinical personnel should identify and monitor these high-risk women more closely.The identification of two distinct trajectories of prenatal fatigue in women confirmed the variability of perinatal fatigue. Though few research have shown this in pregnant women, prior investigations have identified two distinct fatigue trajectories in other populations, such as patients with cancer  and systNotably, levels of perinatal fatigue were significantly higher in women classified as “persistently high” than in women classified as “persistently low” at all six time points from late pregnancy to 6 months after delivery, which was consistent with prior study in other patients . AdditioTwo time points have been identified for decreasing perinatal fatigue in women. One was 1 week postpartum for women classified as “persistently low.” This conclusion is consistent with previous research emphasizing the critical role of 1 week postpartum in preventing perinatal fatigue . The othIn our investigation, a complication was identified as a risk factor for developing an elevated perinatal fatigue pattern. Women who experienced complications during late pregnancy, such as GDM  or hyperAnother intriguing discovery was that exhaustion during late pregnancy may have an effect on how subsequent perinatal fatigue develops over time. Women who had increased fatigue throughout late pregnancy were more likely to fall into the “persistently high” category. Although this discovery has not been replicated in other studies, a similar pattern has been observed in prenatal depression . Thus, hEmployment status was also associated with perinatal fatigue trajectories. This result suggested that women whose employment ratio was “persistently high” had a lower employment ratio during late pregnancy. Similarly to our employment finding, Chien’s study found that unemployed women experienced considerably higher levels of weariness than other women during pregnancy . This diAdditionally, to examine determinants of perinatal fatigue trajectories in women, we evaluated variations in performance parameters such as challenging baby care ratios, EBF, and weight retention at 6 months postpartum across the two trajectories. We discovered that the “persistently high” group had a larger proportion of challenging baby care than the other group, which is consistent with previous reports . Both ofDoes fatigue impact breastfeeding in women during their reproductive years? This issue has dogged health care providers for 15 years . At 6 moIn addition, women need to escape weight retention . At 6 moSeveral limitations should be highlighted regarding this investigation. To begin, our study stopped collecting data on perinatal fatigue 6 months after birth. Fatigue may occur in women who deliver more than a year after delivery . A longeThis study has a few implications for perinatal care research and practice. For research purposes, future empirical investigations could validate and change the perinatal fatigue trajectory classes among women from many cultural backgrounds. Additionally, more predictive models should be developed to investigate and illustrate prenatal fatigue predictors. This will allow for future investigation of the characteristics of prenatal fatigue and the development of more effective therapies targeting sub-populations for lowering individual perinatal fatigue. Additionally, future research should examine the causal links between prenatal fatigue and clinical outcomes such as rates of difficult baby care, exclusive breastfeeding, and weight retention, thereby filling in the gaps in the data. In clinical practice, health care practitioners could undertake timely examinations of women early in their late pregnancy and screen out high-risk women who are at danger of experiencing extreme fatigue throughout the postnatal period.To summarize, this study demonstrates the heterogeneity of perinatal fatigue in women by distinguishing two distinct perinatal fatigue trajectories: “persistently high” and “persistently low.” Additionally, this study examined the characteristics of the two perinatal fatigue trajectories by finding three late-pregnancy predictors: complication, fatigue, and employment. Future research should focus on developing customized intervention packages based on these findings in order to alleviate women’s perinatal fatigue.10.7717/peerj.13387/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj.13387/supp-2Supplemental Information 2Click here for additional data file.10.7717/peerj.13387/supp-3Supplemental Information 3Click here for additional data file.