BackgroundLow birth weight (LBW) is at the forefront of 100 core health issues that are used as indicators to assess the global nutrition monitoring framework as reported by the World Health Organization (WHO). Several factors could contribute to LBW, which essentially include intrauterine growth retardation and premature delivery/birth. Moreover, LBW predisposes neonates to several developmental disturbances including both physical and mental disorders. Given that LBW is more common in poor and developing countries, there is not much reliable data that could be used to formulate strategies for controlling this problem. This study, therefore, attempts to assess the prevalence of LBW among newborn babies and its associated maternal risk factors.MethodsThis hospital-based cross-sectional study was carried out between June 2016 and May 2017 (one year) and included 327 LBW babies. A predefined and prevalidated questionnaire was used to obtain data for the study. The data collected included age, religion, parity, birth spacing, pre-pregnancy weight, weight gain during pregnancy, height, mother’s education, occupation, family income, socioeconomic status, obstetric history, previous history of stillbirths and abortions, and history of any LBW baby.ResultsThe prevalence of LBW was noted to be 36.33%. The occurrence of LBW babies was predominant among mothers who were aged <19 years (62.26%) and >35 years (57.14%). Grand multipara women showed the highest rates (53.70%) of LBW babies. Additionally, LBW was predominantly noticed among newborns (46.66%) with a birth spacing of <18 months, those born to mothers with pre-pregnancy weight of <40 Kg (94.04%), mothers with a height of <145 cm (83.46%), mothers who gained <7 kg during the pregnancy (82.20%), illiterate mothers (43.75%), and mothers who were agricultural workers (63.76%). Other maternal factors that could predispose to LBW included lower monthly income (66.25%), low socioeconomic status (52.90%), less number of antenatal visits (59.65%), low blood hemoglobin (100%), history of strenuous physical activities (48.66%), smoking and/or tobacco chewing habit (91.42%), alcoholism (66.66%), lack of iron and folic acid supplementation during pregnancy (64.58%), history of stillbirths (51.51%), and mothers suffering from chronic hypertension, preeclampsia, and eclampsia (47.61%), and tuberculosis (75%). Religion-wise, Muslim mothers revealed the highest prevalence (48.57%) of LBW, followed by Hindus (37.71%) and Christians (20%). The mother’s age, pre-pregnancy weight, weight gain during pregnancy, height of the mother, hemoglobin concentration, weight of the baby, and length of the newborn (p≤0.05) could influence the health of the newborn. However, maternal infections, previous bad obstetrics history, presence of systemic illnesses, and protein and calorie supplementation (p≥0.05) had no significant impact on birth weight.ConclusionsThe results showed that multiple factors are responsible for LBW. Maternal factors such as weight, height, age, parity, weight gained during pregnancy, and anemia during pregnancy could predispose to delivering LBW babies. Additionally, other risk factors for LBW identified in this study were the literacy level of mothers, occupation, family income, socioeconomic status, antenatal care, strenuous physical activity during pregnancy, smoking/tobacco chewing, alcohol/toddy consumption, and iron and folic acid supplementation during pregnancy. Low birth weight (LBW) is one of the most serious challenges in maternal and child health, especially in developing countries such as India. The World Health Organization (WHO) defines LBW as a birth weight smaller than 2,500 grams irrespective of the period of gestation. Based on epidemiological observations, it was noticed that infants weighing less than 2,500 grams are approximately 20 times more likely to die than heavier babies .The WHO has estimated that globally, about 20 million LBW babies are born each year, comprising 15.5% of all live births, and nearly 95.6% of them were born in developing countries. The number of LBW babies is concentrated in two regions of the developing world, Asia (72%) and Africa (22%), and India alone accounts for 40% of LBW births in the developing world . The priAmong the several factors that result in LBW, major factors include preterm birth, intrauterine growth restriction (IUGR), or a combination of both pathological and physiological conditions. Most LBW newborns in Western countries are preterm. However, in India, about two-thirds of LBW babies are born at term . InfantsRecent evidence indicates that obesity, type 2 diabetes mellitus, and cardiovascular disease are more common among adults who were having IUGR at birth. Studies suggest that these may represent an example of “programming,” in which an insult, when applied at a critical or sensitive stage in development, may result in a lifelong effect on the structure or function of the organism . There aThe factors that are considered potential determinants of LBW include low-income level, resource-limited developing countries, maternal factors such as socioeconomic status, inadequate nutrition, bad obstetric history, less frequent antenatal visits, low pre-pregnancy weight, short maternal stature, hypertension, and endemic infections such as malaria, among others . MaternaThis hospital-based cross-sectional study was carried out at Government Medical College and Hospital, Nizamabad, Telangana, India, between June 2016 and May 2017. The study included mothers of 900 newborn babies who gave informed consent to participate, and the study was approved by the institutional ethics committee (IEC/Annexure A-22-06-2016).Inclusion and exclusion criteriaThe inclusion criteria determined were all consecutive live newborn babies born at term and singleton pregnancy, i.e., birth of a single newborn during the delivery. The exclusion criteria included pre-term babies, post-term babies, intrauterine deaths, stillbirths, twins or more pregnancies, and newborns with congenital abnormalities.A predefined and validated questionnaire was used to collect the data. The questionnaire included demographic and clinical characteristics such as age, religion, address, last menstrual period (LMP), expected delivery date (EDD), parity, birth spacing, pre-pregnancy weight, weight gain during pregnancy , height, mother’s education, mother’s occupation, family income, socioeconomic status , antenatal visits and checkups, obstetric history of the mother especially information about previous stillbirths and abortions and past history of any LBW baby, history of any protein and energy supplementation being taken under the Integrated Child Development Services (ICDS) scheme, history of any strenuous physical activity, personal habits such as smoking (both active and passive and tobacco chewing), alcohol/toddy consumption, maternal infections , tuberculosis (TB), and bacterial vaginosis), systemic diseases (hypertension and diabetes mellitus), hemoglobin (HB) concentration (estimated within 15 days prior to delivery), and regular consumption/supplementation of iron and folic acid tablets.Antenatal care was considered adequate if the pregnant woman had a minimum of four antenatal checkups. According to HB concentrations, mild anemia was considered if HB was 10-10.9 g/dL, moderate anemia when HB was 7-9.9 mg/dL, and severe anemia if HB was less than 7 mg/dL. Birth weight was measured within one hour of birth with an electronic weighing machine. Preterm babies were excluded by assessing gestational age with the help of an ultrasound scan, and the gestational maturity of the baby was determined using the new Ballard scoring system.Statistical analysisData were entered and analyzed using Microsoft Office 2007 Excel sheet . The total number of births occurring during the study period was recorded, and the prevalence of LBW babies was calculated. Data were presented in descriptive tables, and inferential statistics were done. Chi-square tests were performed to evaluate the association of various maternal risk factors with LBW. A p-value of less than 0.05 was considered statistically significant. Data analysis was carried out using the Statistical Package for the Social Sciences (SPSS) version 16.0  and EPI Info 3.5.1.Of the 900 newborn babies included in the study, 327 were identified as LBW babies with a prevalence rate of 36.33%. The occurrence of LBW babies was predominant among mothers who were aged <19 years (62.26%) and >35 years (57.14%). The prevalence of LBW based on maternal age is depicted in Table Religion-wise, Muslim mothers revealed the highest prevalence (48.57%) of LBW, followed by Hindus (37.71%) and Christians 20%). Others who either were hesitant to reveal their religion or did not belong to the listed religion gave birth to normal babies (100%). The religion-wise distribution of LBW babies is shown in Table 0%. OtherGrand multipara women showed the highest rates 53.70%) of LBW babies. The number of LBW babies in primiparous women was 102 (33.66%), and in multiparous women, the number of LBW babies was 51 (18.68%). The details of parity among the study participants are detailed in Table .70% of LAdditionally, LBW was predominantly noticed among newborns 46.66%) with a birth spacing of <18 months. In babies with a birth spacing between 18 and 24 months, 90 (30.61%) revealed LBW, and in whom birth spacing was greater than 24 months, the incidence of LBW was 48 (23.88%). The details of the newborn birth spacing are shown in Table 6.66% witThe number of LBW babies for mothers whose pre-pregnancy weight was less than 40 kg was 237 (94.0%), and for those whose pre-pregnancy weight was more than 40 kg, the number of LBW babies was 90 (13.88%). The weight-wise distribution of newborns based on the mother’s pre-pregnancy weight is depicted in Table The relationship between the mother’s weight gain during pregnancy and LBW revealed that when the mother’s weight gain during pregnancy was less than 7 kg, there is an increased probability of LBW babies (82.2%) when compared to the weight gain of more than 7 kg during pregnancy 6.59%). The mother’s weight gain during pregnancy and its relation to LBW babies is shown in Table .59%. TheThe number of LBW babies born to mothers whose height was less than 145 cm was 318 (83.46%), and the number of LBW babies born to mothers whose height was more than 145 cm was nine 1.73%). The incidence of LBW newborns in relation to the mother’s height is depicted in Table 3%. The iA majority of LBW babies were born to mothers who were illiterate , and when the mother’s education was up to primary school, the incidence of LBW was 198 (41.25%). However, when the mother’s education was up to high school  and pre-university , LBW incidences were noted to be least as shown in Table When mothers were agricultural workers, the number of LBW babies born was highest at 132 (63.76%). Among the daily wage labor working mothers, the number of LBW babies born was 150 (51.02%). When mothers were under service type of occupation, the number of LBW babies born was 24 (11.11%), and when mothers were homemakers, the number of LBW babies born was 21 (11.47%) as shown in Table Mothers belonging to the lower socioeconomic class revealed the highest number of LBW babies  when compared to those belonging to the middle class  and the upper middle class  as depicted in Table Other maternal factors that could predispose to LBW included the number of LBW babies in mothers with antenatal visits of less than four times , and in mothers with antenatal visits of more than four times, it stood at 12 (3.22%) as shown in Table In mothers with blood HB concentrations less than 7 mg/dL (severe anemia), 162 (100%) delivered LBW babies. Ninety-nine (22.60%) newborns have LBW and were delivered by mothers with blood HB concentrations between 7 and 9.9 mg/dL (moderate anemia). Interestingly, mothers with blood HB concentrations greater than 9.9 mg/dL delivered 66 (22%) LBW newborns as shown in Table When the history of strenuous physical activities among mothers was assessed, it was noted that strenuous physical activity during pregnancy revealed susceptibility to delivering LBW newborns in 285 (48.46%). However, the incidence of LBW babies was considerably less 13.46%) when the mother had no history of strenuous physical activity during the pregnancy period as shown in Table .46% whenThe number of LBW babies born among mothers who had a history of tobacco smoking and chewing tobacco during pregnancy was higher at 192 (91.42%) compared to those who did not have such habits  as shown in Table The number of LBW babies in mothers who had a history of consumption of alcohol and locally brewed toddy during pregnancy was 192 (66.66%) compared to mothers who did not have such habits  as shown in Table The number of LBW babies born in mothers who had taken protein and calorie supplementation provided under the government scheme was 183 (36.52%). Interestingly, women who did not receive protein and calorie supplementation also revealed a similar incidence of LBW newborns  as shown in Table Conversely, newborns of mothers who were supplemented with iron and folic acid had low rates of LBW babies compared to mothers who did not take any such supplementation as shown in Table In mothers with a past history of abortions, the number of LBW babies was 30 (35.71%). A past history of stillbirths revealed the highest incidence of LBW babies . Additionally, a past history of neonatal deaths (33.33%) and a previous history of birth of LBW babies (30.43%) had no significant relation to LBW babies. The details of the mother’s past obstetric history are detailed in Table The incidence of LBW babies in mothers who had a fever during pregnancy was 21 (24.1%), and those who had malaria was 27 (31.03%), urinary tract infection was 87 (34.93%), and bacterial vaginosis was 39 (35.13%). A high percentage of LBW babies was noticed among mothers who had tuberculosis (75%) as shown in Table The highest percentage of LBW babies were born in mothers with chronic hypertension, preeclampsia, and eclampsia (47.6%), followed by those with diabetes and gestational diabetes (38.46%), bronchial asthma (33.33%), sickle cell anemia (32%), and heart diseases (30.76%) as shown in Table The mother’s age (p=0.002), height (p=0.0001), pre-pregnancy weight (p=0.0001), weight gain during pregnancy (p=0.0001), smoking and alcohol consumption (p=0.0001), education (p=0.0001), occupation (p=0.0001), socioeconomic status (p=0.0001), history of strenuous physical activity (p=0.0001), blood HB concentrations (p=0.0001), number of antenatal visits (p=0.0001), and parity (p=0.0001) are significantly related to LBW. Interestingly, the presence of systemic illness (p=0.763), protein and calorie supplementation (p=0.938), infections during pregnancy (p=0.351), and bad obstetrics history (p=0.337) did not correlate with LBW.Birth weight is the single most important marker for perinatal and neonatal outcomes . LBW is In the present study, the prevalence of LBW was high at 36.3% compared to district-level household and facility survey data, according to which the percentage of LBW was 7.1% in the study area. Moreover, the state-wise data of LBW stood at 8.6% in Telangana state as estimated by the local government . AccordiThe present study showed a higher percentage of LBW babies among mothers who were younger than 19 years of age and those who were aged over 35 years. The study results were consistent with the findings of Agarwal et al.  and KaurIn the present study, religion was found to be significantly associated with LBW babies with a higher percentage of LBW babies noted in Muslim mothers. However, the results of the study by Raghunath et al.  revealedA significant association was found between grand multipara and primipara status with the incidence of LBW. This is consistent with the findings of Kaur et al.  and PrudThe reason for primiparas being increasingly susceptible to delivering LBW babies can be compromised uteroplacental blood flow, which is responsible for delivering oxygen and nutrients to the fetus. Additionally, there may be structural factors that limit uterine capacity in the first pregnancy, after which the uterine size increases. Furthermore, firstborn infants may be exposed to a different maternal immune environment, contributing to relative growth restriction, compared to subsequent pregnancies with consistent paternity. Grand multipara had a higher probability of pregnancy-related complications, and advancing maternal age among them would have also been an additional confounding factor leading to LBW.Birth spacing of fewer than 18 months is significantly associated with LBW. This is consistent with the findings of Kaur et al.  and SumaPre-pregnancy weight of less than 40 kg was significantly associated with LBW in the present study. This was consistent with the study findings of Prudhivi et al. . HoweverWeight gained during pregnancy of fewer than 7 kg was significantly associated with LBW. This is consistent with the study findings of Rajashree et al.  and RaghMother’s height of fewer than 145 cm significantly correlated with LBW. This is consistent with the study findings of Sumana et al. , PrudhivIn the present study, a higher percentage of LBW babies were born to illiterate mothers, followed by those who were educated up to primary school and high school. Similar findings were noted in studies by Kaur et al. , RaghunaLBW was predominantly found among mothers who were agricultural workers. Similar findings were reported by Rajashree et al.  and ShahLow socioeconomic status was noted to predispose women to deliver LBW babies. Identical results were noted in the studies by Agarwal et al. , RaghunaThe number of antenatal visits was also found to influence birth weight as evidenced by the high prevalence of LBW among mothers who reported less than four antenatal visits during pregnancy. Similar findings were noted in a previous study by Kaur et al. . Fewer LSevere anemia appears to be a potential risk factor for LBW, and similar conclusions were drawn in the studies by Gupta et al. , RaghunaThe percentage of LBW babies (36.5%) born to mothers who had taken protein and calorie supplementation under the government scheme was similar (36.5%) to those who had not received such supplementation. However, a previous study from Sudan revealed that maternal undernutrition could be a potential risk factor for LBW .In the present study, a higher percentage of LBW infants were born when the mother had a history of strenuous physical activity. These results were similar to those reported by Kaur et al. . StrenuoTobacco smoking and chewing habits during pregnancy were noted to predispose mothers to deliver LBW babies. Similar findings were reported by Agarwal et al. , BendharAlcohol/toddy consumption during pregnancy was found to predispose mothers to deliver LBW babies. A similar association was noted in a study by Nykjaer et al. . On the In the present study, a high percentage of LBW babies were born when the mother had not taken iron and folic acid supplementation during pregnancy. Similar findings were reported in a study from Nepal, which found that iron and folic supplementation and the number of antenatal visits were associated with LBW .A bad obstetric history was found not to be a significant contributory factor for LBW babies. This is consistent with the study findings of Negi et al.  and BendThe current study results found no significant association between maternal infections and LBW as noticed in a study by Soujanya et al. . ConversA high percentage of LBW babies was noted among mothers who had chronic hypertension, preeclampsia, and eclampsia (47.60%) compared to those with other systemic diseases such as diabetes, bronchial asthma, sickle cell anemia, and heart diseases during pregnancy .An assessment of the results of this study and other studies points to the fact that there is scarce data on the prevalence of LBW and associated modifiable and unmodifiable maternal factors, especially in poor and developing nations such as Ghana and India, respectively .Study limitationsThe present study was carried out at a government hospital, where the facilities are offered free of cost and may not be utilized by all sections of the community. Therefore, study results will not necessarily reflect the population scenario. Another factor that could have caused bias in the study results is imprecise information regarding a few factors attributed to the fact that the majority of mothers were illiterate and belonged to a rural area and did not have appropriate medical records.The results of this study provide empirical support for the potential links that exist between maternal health and infant birth weight. A significant association was found between LBW and maternal factors such as age, weight, height, religion, literacy levels, occupation of mothers, family income, socioeconomic status, antenatal visits/care, strenuous physical activity during pregnancy, smoking/tobacco chewing, alcohol/toddy consumption, and iron and folic acid supplementation, among others. Increased public health awareness programs along with integrated and complementary strategies are required to address the issue of LBW newborns. Interventional programs should be encouraged not only in health sectors but also in all those areas concerned with social development and social welfare. Adolescents should be educated regarding marriage age and nutritious food intake during pregnancy, and the deleterious effects of early teenage marriages. There appear to be multiple maternal factors that affect the newborn’s birth weight. This suggests that a single factor cannot be attributed to the occurrence of LBW babies. Health education, socioeconomic development, maternal nutrition, and increasing availability and accessibility of health services during pregnancy are equally important to minimize the incidences of LBW among newborns.


We first perform the same macroscale analyses prevalent in ADHD gut microbiome literature  to observe the degree of correspondence, or any new trends. We then estimate two-way ecological relationships by producing Control and ADHD Microbial Co-occurrence Networks (MCNs), using SparCC correlations (p ≤ 0.01). We perform community detection to find clusters of taxa estimated to mutually cooperate along with their centroids, and centrality calculations to estimate taxa most vital to overall gut ecology. We finally summarize our results, providing conjectures on how they can guide future experiments, some methods for improving our experiments, and general implications for the field.Attention Deficit Hyperactivity Disorder (ADHD) is an increasingly prevalent neuropsychiatric disorder characterized by hyperactivity, inattention, and impulsivity. Symptoms emerge from underlying deficiencies in neurocircuitry, and recent research has suggested a role played by the gut microbiome. The gut microbiome is an ecosystem of interdependent taxa involved in an exponentially complex web of interactions, plus host gene and reaction pathways, some of which involve neurotransmitters with roles in ADHD neurocircuitry. Studies have analyzed the ADHD gut microbiome using macroscale metrics such as diversity and differential abundance, and have proposed several taxa as elevated or reduced in ADHD compared to Control. Few studies have delved into the complex underlying dynamics ultimately responsible for the emergence of such metrics, leaving a largely incomplete, sometimes contradictory, and ultimately inconclusive picture. We aim to help complete this picture by venturing beyond taxa  Attention Deficit Hyperactivity Disorder (ADHD) is a significant mental health problem with a current 3.4% prevalence worldwide . In the microbiome. Studies estimate that the average number of bacterial cells in humans matches or exceeds that of host cells [gut-brain-microbiome axis [The pathogenesis of ADHD is thought to be multifactorial, with heritability estimates at roughly 70–90% . These gst cells , 13. Eacst cells , creatinome axis . This axome axis –20.The fact that individuals with ADHD suffer from gastrointestinal (GI) dysfunction, including childhood digestive difficulties and low-grade inflammation  as well neurotransmitters, including dopamine and serotonin (5-HT) [neuropsychiatric disorders (NPDs) are already well-established. In rodents, anxiety and social behavior have been linked to the gut microbiome that can be attributed to altered neurotransmission in the hippocampus and amygdala [It has also been proposed that gut microbiota may affect our neurobiology by directly or indirectly altering the levels of n (5-HT) , which fn (5-HT) . Connectamygdala . In humaamygdala . It has amygdala , 32. Conamygdala . In humaamygdala . In rodeamygdala , and EF-amygdala . The Autamygdala , which iamygdala , has beeamygdala . In animamygdala –45. Peopamygdala , 47. Conamygdala –50, and amygdala  to help amygdala , and newamygdala .There are limited studies that implicate the gut microbiome on clinically diagnosed ADHD, and recent efforts have been made to survey and summarize their results –57. Two Dialister and reduced genus Phascolarctobacterium [Parabacteroides [One ADHD gut microbiome study  attempteacterium ) and Hypteroides ) propertRuminococcus, Coprococcus, and Lachnoclostridium are all of the Lachnospiraceae family). Taxa in bold and italics had conflicting results over member taxa, in just italics .Bacteroidaceae, Prevotellaceae, and Veillonellaceae . Conflicting results were reported for the following taxa:This order was reported as reduced in ADHD samples compared to Controls in a study with average ages of 19.5 and 27.1 in the respective sample sets , but a lPorphyromonadaceae in ADHD samples compared to Controls, but a second gender-matched study [The same study (with average ages of 19.5 and 27.1 ) reporteed study  with simThe same study  found mBifidobacterium. One Dutch study found a nominal increase in Bifidobacterium in ADHD with average ADHD and Control subject ages of 19.5 and 27.1 years, respectively [Bifidobacterium in ADHD samples during infancy, but not at age 13 [Bifidobacterium  in ADHD children (mean age: 9.3) that actually reversed after micro-nutrient treatment, where elevated Bifidobacterium was observed at high ADHD-Rating Scale IV  scores.Bifidobacterium and cyclohexadienyl dehydratase (CDT) abundances.The current picture of the role played by the gut microbiome in ADHD is therefore still unclear. Most of the effort to connect ADHD to the gut microbiome has involved (1) macroscale population metrics such as diversity, and/or (2) taxa abundances. These properties are in reality emergent from a complex and interdependent interaction web involving taxa, their gene products, and those of the host . Diversiecological relationships between taxa. Microbial taxa have been shown to demonstrate a wide variety of ecological relationships, including cooperation [We have thus far only scratched the surface of this large and exponentially complex interaction web, and every completed piece has value. Multi-omics will continue to be critical, bridging an important gap between taxa, products, and metabolic reactions. We aim to complete another piece, that involves peration , 74 and peration , that ulperation . We estiThis work involves two parts, conducted on a publicly available, gender-matched dataset of 16S gut microbiome sequences. The first involves performing the same macroscale analyses currently prevalent in ADHD gut microbiome literature, to note how this dataset compares, as well as any new and interesting trends. Metrics will include alpha- and beta-diversity, Sparse Partial Least Squares Discriminant Analysis  to estiIn the second part we estimate ecological relationships  within Chttp://biorg.cs.fiu.edu/pluma/pipelines.html), along with processed sequence data. Source code for PluMA is available open source under the MIT software License at http://biorg.cs.fiu.edu/pluma. Supplementary material, including all alpha- and beta-diversity output, taxa plots, differential abundance output, networks, clusters, and central nodes are available in computer-readable TXT and CSV formats at http://biorg.cs.fiu.edu/pluma/ADHD.html.We provide more details on the methods we use for analysis. Our entire downstream analysis pipeline has been built using Plugin-Based Microbiome Analysis  versionOur study starts from a publicly available dataset (Accession Number: PRJNA656791) of raw gut microbiome samples  from an undergraduate student population. Members of this population completed the Adult ADHD Self Report Scale (ASRS) and were classified as “Control” or “ADHD” following the published practice  of scoriThe study used ASRS as its method for case-control classification, and much of our analyses apply protocols which assume discrete classification schemes for samples. It is important to note however, that while the ASRS can be a useful screening tool for ADHD  and has S2 Table shows the average number of sequences retained in each sample after each preprocessing step.Sequences were trimmed using Trimmomatic-0.33  then assAs mentioned, we commenced this study from a publicly available dataset that was released in fully deidentified form and no master key was provided, blocking any possible route for tracing a sample back to an individual. IRB consent was therefore verbally waived for our study, by the FIU Office of Research Integrity.We first perform macroscale analyses on this ADHD dataset that have been performed on other ADHD datasets, compare and contrast our results with those in the literature, and take note of any new and interesting observations.t-test (999 Monte Carlo permutations and Bonferroni [Alpha- and beta-diversity plots were constructed using Qiime (version 1.9.1). Input was provided in the form of raw abundances, sampled at a depth of 15000. For alpha-diversity, we used default metrics: unique taxa count, Chao1 , and PD_nferroni  correctinferroni , confirmnferroni  statistiLinear discriminant analysis Effect Size  with Bonferroni [p ≤ 0.05), ANOVA-like Differential Gene Expression Analysis of Single-Organism and Meta-RNA-Seq , and Analysis of Compositions of Microbiomes with Bias Correction , a spare    (p ≤ 0.nferroni  correctiWe use Qiime 1.9.1  to generp ≤ 0.01) using 100 permutations and 5 iterations, and built Microbial Co-occurrence Networks  using ts  with las  with a Ablatio Triadium  for evant model  that calnt model . Then itat all taxonomic levels starting from phylum. We first observe the upper three levels  for an overview of relationships between consistently abundant taxa and an observation of general trends and behaviors. We then move to the lower three levels  which provide a finer level of granularity and enough taxa to perform meaningful community analyses, facilitating a vision of the key players within these dynamics. The lowest possible classification level will attempt to classify at a species level, but because of the lack of variability in the 16S gene at the species level only a limited number of taxa will be classifiable at an appropriate level of confidence. When species-level classification is not possible, the next highest level (genus or family) will be used. To be clear, these networks are based on correlations, which does not imply causation. Results from our analyses should therefore be interpreted as providing guidance and potential targets for future downstream (i.e. pathway or multi-omics) analysis, that ultimately will require lab experimentation for full verification.We analyze these ecological relationships S2 Table shows that this prevalence filter still maintains 91% of sequence counts on average per sample.Once we venture past macroscale community metrics , we apply a prevalence filter to remove scarce taxa and restrict our analysis to core microbiome members of both groups (Control and ADHD) by adopting a prevalence threshold of 50% , 118. ByWith this prevalence filter the final set of taxa will differ slightly for Control and ADHD, as some taxa will meet the prevalence threshold in only one of the two groups. This will create bias in differential abundance calculations, as these taxa will have an artificial zero abundance in one group but not the other. To avoid this bias, we keep taxa that meet the prevalence threshold in one of the two groups in both groups for all differential abundance calculations . For all other methods that do not perform differential abundance calculations between the two groups (taxa plots and co-occurrence networks), we use the prevalence filter as defined above.t-test . Beta-diversity with unweighted and weighted Unifrac [p-values computed from an ADONIS [F = 0.847, 0.581; p-values = 0.707, 0.671 respectively). This lack of alpha- and beta- diversity differences matches several results from other datasets [Qiime  alpha- a Unifrac  distancen ADONIS  statistidatasets , 62, 66.a priori sample category knowledge and have sometimes been shown to differentiate completely random data [One differential abundance technique involves dimensionality reduction, attempting to determine differentiation degree between datasets, by accounting for all variables in each set  but dispdom data .p-values . For more targetted future lab studies, it may also be useful to estimate specific taxa that are elevated in one set of samples  compared to another set of samples . As differential abundance techniques have been shown to vary widely in their results , we use nferroni , Benjaminferroni , and Holnferroni ) though p ≤ 0.05, Bonferroni correction) both as a cladogram  distinguishing ADHD, while Firmicutes  distinguishing Control. The bar graph .The cladogram  shows thar graph  uses Linelevated . Veillonp ≤ 0.05, and ANCOM-BC [p ≤ 0.05. p-values we include the applicable effect size–Linear Discriminant Analysis (LDA) for LEfSe, Log-Fold Change (LFC) for DESeq2, and Effect Size (EFF) for ALDEx2, computed as the ratio between the group difference and the larger of the two internal group variations. Note ANCOM-BC did not report any differentially abundant taxa. Turicibacter and Butyricimonas (in bold) are the only genera reported more than one of these methods, and Turicibacter was actually repoted by three .We also run three other methods: DESeq2  and ALDEANCOM-BC  with a HOdoribacter has also been previously reported as ADHD-elevated [H. parainfluenzae has not been observed as Control-elevated in any ADHD gut microbiome studies, its genus (Haemophilus) has [Veillonellaceae as Control-elevated and member species V. parvula as differentially abundant in ADHD. Our LEfSe analysis found Veillonellaceae members Veillonella and V. dispar as differentially abundant in ADHD samples. Although Turicibacter has never previously reported as a elevated or reduced in any ADHD gut microbiome study, it has been reported in one involving depression in mice [Turicibacter signals the gut to produce serotonin (5-HT) [Genus elevated . Althouglus) has . This salus) has  found fa in mice . Metaboln (5-HT) , which in (5-HT) . Both ADn (5-HT) , for whin (5-HT) , comparen (5-HT) .x-axis are ordered by increasing ASRS score, and the y-axis represents relative abundance.Taxa bar plots visualize taxa relative abundances . We geneBifidobacterium, at high and low ASRS-IV scores [A typical gut microbiome profile  is obserV scores .Bifidobacterium. And indeed, it turns out, Bifidobacterium  has elevated abundances high and low ASRS scores, appearing most responsible for this same behavior in its phylum Actinobacteria  is a highly abundant genus that also mirrors the behavior of its phylum  also appears to follow this trend. These three observations are verified in Class and order levels produced bar charts similar to bacteria . BacteroBifidobacterium, have been used as probiotics and are considered elements of a healthy gut [Bacteroides and its family Bacteroidaceae, as well as several member species, have been reported differentially abundant in ADHD [Bacteroides to be the most important "window" to understanding the human gut [Sutterella stercoricanis was also reported as ADHD elevated [Bacteoridaceae was reported as the most differentially abundant LEfSe Major Depressive Disorder (MDD) taxon in one study [Bacteroides and reduced Bifidobacterium in anxiety [Sutterella is elevated in Autism Spectrum Disorder (ASD) [This is not the first time these taxa have generated interest. Many Actinobacteria, and especially lthy gut –134. As  in ADHD , 65, 68;uman gut . Specieselevated . These sne study . Another anxiety . Suttereer (ASD) , a condier (ASD) .Bifidobacterium, Bacteroides, and Sutterella abundances, no definitive conclusions could be observed. Fundamentally macroscale behaviors emerge from microscale interactions. We attempt to unlock some of these mysteries by now exploring ecological relationships.These analyses produced a few interesting preliminary observations, but once again their birds-eye view limited the depth we could pursue. Our taxa plots showed a perfect example: even though there was a visible trend between ASRS score and Microbial ecological relationships take many forms. They can be positive or negative, mutual  or stroestimate of ecological relationships, that ultimately require further downstream analysis  before establishing official conclusions. With the underlying web of interactions being exponentially complex and large-scale laboratory experiments potentially costly, our results can provide guidance regarding target taxa and avenues to pursue.During our analyses we sometimes use “cooperation” to refer to a positive SparCC correlation and “competition” when referring to a negative. We emphasize, however, that correlations are an #rank, T = Tie). At the phylum level only  A core Proteobacteria-Bacteroidetes positive correlation (est. cooperation) forms. (B) In Control, taxa in (A) have more negative edges with Actinobacteria (est. competition), especially order Bifidobacteriales. The highest magnitude negative edges in both phylum-level MCNs  may be more responsible for this difference than Coriobacteriales (Coriobacteria). While Bifidobacteriales and Coriobacteriales both continue their negative correlations with Bacteroidales (Bacteroidia) in Control, only Coriobacteriales does in ADHD. (C) A shift in Firmicutes-Proteobacteria dynamics. This begins immediately at the phylum level , Bacteroides , and Sutterella , we are now interested in exploring these dynamics at lower taxonomic levels. We will continue to observe Firmicutes-Proteobacteria dynamics, as despite a still unclear picture, a clear distinction is shown between Control and ADHD.Upper-level analysis revealed increased Actinobacteria participation in Control gut ecology, especially order Bifidobacteriales. Much of this involved negative correlations with a core of positively correlated Bacteroidetes  and Proteobacteria . Recalling our taxa plots and anomalous behavior involving genera Bacteroidaceae . Two are dominated by Firmicutes, one family Lachnospiraceae , and the other family Ruminococcaceae . In Control . In ADHD .BB and FR remain at the genus level . A mixed-family Actinobacteria cluster of genera Bifidobacterium and Collinsella forms in both MCNs , and an Actinobacteria, family Coriobacteriaceae-dominated cluster forms in ADHD . A small group of two Clostridiaceae family composes cluster FC in Control . Both MCNs (BM1 (pink), and Control includes a second . At the lowest level we kept cluster names as consistent as possible with genus-level membership  denotes the degree of positive/negative correlation between taxa x and y . Clusters appear as rough squares of positive (green) correlations on the diagonal. We outline each box with the same color as its corresponding Finally, to measure cluster size, tightness, and interactions, we produce a heatmap of taxa correlations  with taxWe first continue to pursue observations (A)-(C) from the upper taxonomic levels. Afterwards, we discuss any new and interesting trends.(A) A core Proteobacteria-Bacteroidetes positive correlation (est. cooperation) forms. Recall the orders involved in this correlation were Burkholderiales (Proteobacteria) and Bacteroidales (Bacteroidetes). BB, with genus Sutterella and multiple Bacteroidales taxa. In ADHD this cluster is larger and includes more Bacteroidales plus some Firmicutes, and nearly all members are positively correlated with its centroid Bacteroides. Additionally it has fewer negative correlations (est. competition) with other clusters.This corresponds to cluster BB is the only cluster with order Burkholderiales and Bacteroidales descendants. Sutterella and Bacteroides. Several others involving Sutterella and its family Alcaligenaceae with cluster BB members are present only in ADHD–support for a larger cluster BB in ADHD. Alcaligenaceae/Sutterella are immediately visible in Cluster BB size, as do the heatmaps  at the lowest level. BB and Control BM1 members, confirms this. Core BB members are shown in bold, while italicized members are unique to Control or ADHD. Taxa of genus-level Control cluster BB members Odoribacter, Adlercruetzia, Parabacteroides (P. distasonis) and Bacteroides (B. ovatus) compose Control cluster BM1 at the lowest level.BB and BM1 taxa in Control, showing higher intra-correlation values (0.61 and 0.62) relative to inter-correlation (0.44).BB members that differ between the MCNs. Cluster BB gains a different Actinobacteria–species B. longum (ADHD) and genus Adlercruetzia . The presence of Firmicutes (yellow) is exclusive to ADHD, including family Streptococcaceae and member genus Streptococcus, plus genus Clostridium. Family Odoribacteriaceae (reported by LEfSe as ADHD-elevated) joins cluster BB only in ADHD, and the sole Clostridium connection to cluster BB is with genus Butyricimonas  and descendant Parabacteroides (genus), until the BB-BM1 “split” where genus Bacteroides becomes centroid of BB and species P. distasonis of BM1. Bacteroidaceae/Bacteroides is a much stronger centroid; in fact over all levels only one cluster BB taxon was not positively correlated . Particularly given the ADHD cluster BB size increase, this could imply a significant role of Bacteroidaceae/Bacteroides in stabilizing a large ADHD Bacteroidetes-dominant community .Bacteroidaceae/Bacteroides and lineages to nearly always have higher importance in Control, supporting a more “global” importance to overall gut ecology as opposed to a more local importance (cluster BB) in ADHD. MCNs agree, as in ADHD Bacteroidaceae/Bacteroides have few connections outside cluster BB .Interestingly ATria  shows BaBB taxa. By contrast in ADHD . Porphyromonadaceae to be the sole cluster BB member negatively correlated with the Firmicutes-dominant portion . In ADHD cluster BB is larger, and more isolated.(B) In Control, taxa in (A) have more negative edges with Actinobacteria (est. competition), especially Bifidobacteriales. We now know taxa from (A) to correspond to cluster BB, which in both MCNs contained one core Proteobacteria  and otherwise primarily Bacteroidetes. We also observed cluster BB taxa to have far more negative correlations (est. competition) with other clusters in Control. We now see if this is also true with order Bifidobacteriales lineages, including genus Bifidobacterium. Our analysis in fact reveals that Bifidobacterium ornegative correlations between anyparent/descendant withanyBacteroidetes or Proteobacteria are exclusive to Control and absent in ADHD.Bifidobacterium and its lineages, grouped and colored as in previous tables. Not only are negative Bacteroidetes correlations exclusive to Control (orange), but these taxa include the most abundant Bacteroidetes family/genus Bacteroidaceae/Bacteroides (ADHD cluster BB centroid), as well as family/genus Porphyromonadaceae/Parabacteroides (Control cluster BB centroid). Another appears at the lowest level between soecues B. adolescentis and B. ovatus. With Proteobacteria, negative Bifidobacterium correlations are also observed with genus Sutterella (core cluster BB member) and family Enterobacteriaceae, also only in Control. Heatmaps confirm family/genus Bifidobacteriaceae/Bifidobacterium to be negatively correlated with cluster BB taxa only in Control (BB (species B. longum with species B. uniformis).Bifidobacterium to even have far more Firmicutes connections (positive and negative) in Control. Collectively 24 correlations were observed in Control, compared to 9 in ADHD, supporting an overall increase in Bifidobacterium participation in Control. ATria .l. ATria  also alm(C) A Shift in Firmicutes-Proteobacteria dynamics. Only two Proteobacteria families/genera were consistently present. One was genus Sutterella , already noted as a core cluster BB member. The other is family Enterobacteriaceae, which our analysis supports being mostly responsible for this shift.Alcaligenaceae/Sutterella, with Firmicutes families Ruminococcaceae (Control) and Clostridiaceae (both). Far more significant are the differences involving family Enterobacteriaceae. One is its negative correlation with genus Oscillospira in ADHD (genus level), that becomes a positive correlation with Oscillospira in Control (lowest level). This is the only time, over all twelve MCNs, where a correlation sign changed between the same two taxa in Control vs. ADHD.Enterobacteriaceae and various Firmicutes occur even at the family level, however. A small mixed-family, Firmicutes-dominant cluster FM forms , ranked #1 for ADHD at the genus and lowest levels (the only taxon to be ranked #1 in two MCNs). Recall Coprobacillus was also reported by DESeq2 as elevated in ADHD compared to Control. Other members of the Erysipelotrichaceae family (including the family itself) were only ranked by ATria in the case of Control. We label it in Dynamics of Enterobacteriaceae was also only ranked in ADHD, across all three lower levels. Its Oscillospira positive correlation  in Control. The sign change takes place at the genus level in ADHD  LEfSe reported taxa: Genera Turicibacter and Odoribacter. Earlier we noted ADHD-elevated family Odoribacteriaceae as an ADHD cluster BB member (Turicibacter (Control) and Odoribacter (ADHD).B member . We now FT . We named this cluster FT because of core member genus Turicibacter. Turicibacter , which joins genus Phascolarctobacterium  in ADHD. Supplementing the earlier trend of less cluster BB negative correlations (est. competition) in ADHD, this also supports the presence of a larger cluster with positive correlations (est. cooperation) as well, with Turicibacter as its centroid  to form in ADHD , where icentroid .Turicibacter provides the sole genus-level , and Adlercruetzia (BB). This continues our observed increases in Actinobacteria and particularly Bifidobacterium involvement in Control gut ecology.In ADHD us-level  FT-BB poctivity, ). At the Control , the FT-FC forms in Control .Cluster  Control , aqua an in ADHD  family CClostridiaceae taxa and ADHD-elevated genus Odoribacter–both at the genus level  is also absent in ADHD.Exclusive to ADHD is a negative correlation (est. competition) between these family us level , and famst level . Genus O(E) Changes in the role of genus Adlercruetzia (Actinobacteria). In contrast to genus Bifidobacterium (Bifidobacteriaceae), Adlercruetzia is a member of the other consistently present Actinobacteria family, Coriobacteriaceae. While the distinguishing feature of family/genus Bifidobacteriaceae/Bifidobacterium was increased Control participation, the distinguishing feature of family Coriobacteriaceae appears to be changes in cluster membership. In fact over all Coriobacteriaceae descendants, only once  connecting clusters FT and BB. Bifidobacterium 1, its positive correlations in Control were entirely with Bacteroidetes taxa . By contrast in ADHD, Adlercruetzia relationships mostly occur with Firmicutes, including a cluster membership with genus/species Eubacterium/E. dolicum. Several negative correlations are seen between genus Adlercruetzia and different Firmicutes, with no overlap between Control and ADHD. This suggests genus Adlercruetzia may play a significantly different role in Control and ADHD gut ecologies.We earlier noted genus (F) Bacteroidetes-Firmicutes positive correlations (est. cooperation) are entirely exclusive to ADHD, and absent in Control. Clostridium) involve class Bacilli descendants.Clostridium and Butyricimonas, and the ADHD FT-BB connections involving genera Turicibacter and Parabacteroides, and species P.distasonis. We now analyze the remaining top row, between genera Bacteroides (family Bacteroidaceae) and Streptococcus (family Streptococcaceae).We have already seen most of these, including genera BB in ADHD, and we earlier noted family Streptococcaceae and its genus Streptococcus as two of those taxa. Their cluster BB positive correlation was with centroid family/genus Bacteroidaceae/Bacteroides. Additionally cluster BB had almost no negative correlations (est. competition) with FL/FR (collectively 70% of the population) in ADHD, compared to a significant amount in Control.Firmicutes taxa were only ever present in cluster Streptococcus interesting for ADHD is that across all MCNs, it forms the only positive correlation between cluster BB and FL/FR (BB-(FL/FR) competition in ADHD, our MCNs also estimate cooperation only in ADHD, between genera Streptococcus (BB) and Blautia (FL1).What makes genus nd FL/FR . In otheStreptococcus to be negatively correlated with genus Oscillospira in ADHD, a taxon we noted earlier its correlation sign change with family Enterobacteriaceae. ATria  A shift in genera Blautia-Oscillospira dynamics, and their respective clusters. Thus far genus Oscillospira has been noted for two ADHD-exclusive negative correlations, with taxa only ranked in ADHD: family Enterobacteriaceae and genus Streptococcus. Enterobacteriaceae-Oscillospira was the only correlation to ever change sign from Control (positive) to ADHD (negative). Streptococcus was noted for its correlation with genus Blautia, the sole positive correlation between the largest Bacteroidetes-dominant cluster (BB) and Firmicutes-dominant clusters (FL/FR) in any MCN.Oscillospira as a healthy gut taxon [Blautia is actually a taxon that has been associated with obesity [Blautia and Oscillospira are negatively correlated, but not in ADHD  and Streptococcus (Blautia cooperation and Oscillospira competition in ADHD) correlations become interesting, favoring Blautia cooperation and Oscillospira competition. Indeed correlation can never imply causation and further experimental verification is required. But ATria results  with the columns of Blautia’s cluster  that Oscillospira is negatively correlated with Blautia’s entire cluster in Control, and these correlations are completely absent in ADHD.In fact our heatmap  shows byBlautia’s cluster as larger in ADHD, and Oscillospira’s cluster as larger in Control. Blautia and Oscillospira each belong to a cluster dominated by its respective family: Lachnospiraceae (FL), and Ruminococcaceae (FR). Oscillospira is a core FR member and at the lowest level, we see the Control FR cluster (with family Enterobacteriaceae now a member). Blautia is consistently a member of the same cluster as both family Lachnospiraceae taxa in ADHD, comparably larger than its FL3 Control cluster.The lowest level MCNs  also shoOscillospira’s cluster (FR) in Control  in a much more central position in Control  occupies a much more central position  in ADHD, and Oscillospira’s family (Ruminococcaceae) in Control. A couple of noteworthy taxa follow this trend. Species Faecalibacterium prausnitzii (Ruminococcaceae), an anti-inflammatory bacterium [Ruminococcus gnavus (Lachnospiraceae), known to produce an inflammatory polysaccharide [ATria  indicateacterium  touted aacterium , is onlyccharide , is onlyFL, FR, FT) and one is Bacteroidetes-dominant (BB). Four clusters were consistently present in both Control and ADHD MCNs. Three are Firmicutes-dominant (Streptococcus and Blautia: FT is the only Firmicutes-dominant cluster with taxa positively correlated with Bacteroidetes-dominant cluster (BB) members, and all correlations involving FL/FR (largest Firmicute-dominant clusters) and BB taxa are negative. FT is completely disconnected from FL/FR except some ADHD competition. FL-FR competition only happens in Control.Finally, we summarize taxa  and relaTuricibacter . Current literature, as well as our results, suggest this macroscale perspective leaves a largely incomplete picture due to its neglect of underlying complexity. Our goal was to complete more of this picture by venturing deeper, by analyzing two-way ecological relationships (cooperation and competition), plus community detection, and centrality.Traditional analysis methods (i.e. diversity and composition) prevalent in current ADHD gut microbiome literature provide a macroscale representation of a complex ecosystem. Conducting some of these approaches on equal-sized, gender-balanced undergraduate Control and ADHD gut microbiome datasets produced many results that corresponded with this literature, plus a potentially new Control-elevated genus, Bifidobacterium [Bacteroides [Bifidobacterium and reduced genera Bacteroides and Sutterella at ASRS extremes (high and low) imposed significant challenges when interpreting results. Our MCNs estimate that a Bacteroidetes-dominant community (cluster BB) forms in both microbiomes, with Bacteroides and Sutterella both core members, that in ADHD is larger and resides in conditions that favor its cooperation, as opposed to competition in Control. Several cluster BB members have previously been reported as ADHD-elevated, including Butyricimonas , Parabacteroides  taxon for cluster BB, having cooperative with nearly every member. And Bifidobacterium shifted from exclusively competitive relationships with cluster BB members (including its most abundant and centroid) in Control, to exclusively a cooperative relationship in ADHD—with Bacteroides uniformis, one of the taxa previously reported as ADHD-elevated [BB our MCNs estimated Bifidobacterium and its lineages to be much more active in Control gut ecology (24 relationships) compared to ADHD (9 relationships), with ATria ranking Bifidobacterium higher in Control more than 85% of the time. Overall, these results suggest potentially significant differential roles played by both Bifidobacterium and Bacteroides in the Control and ADHD gut ecosystems.Our results provide a deeper meaning to those from the macroscale. Previous differential abundance studies have reported collectively inconclusive results with respect to acterium , 66 and teroides , 65, 70.ctivity, ), Bacterniformis , Rikeneleriaceae , plus twricanis, ), makingelevated . In addiOdoribacter, reported by our analysis and another [Clostridiaceae taxa, one of which (Clostridiaceae 1) was reported by ALDEx2 as ADHD-reduced . Clostridiaceae 1, in turn, was estimated to compete with multiple taxa from cluster BB (much larger in ADHD) in both of our MCNs. Exploring candidate metabolic reactions involved within this triangle may therefore also be important for future experimentation. Butyricimonas, reported by two of our differential analysis methods (LEfSe and ALDEx2) as ADHD-elevated, joined cluster BB in ADHD and provided the sole cooperative relationship with Clostridium, a taxon otherwise completely disconnected from both MCNs. Coprobacillus, reported by DESeq2 as ADHD-elevated, was ranked #1 twice by ATria in the ADHD MCN, which estimated competitive relationships with abundant Lachnospiraceae taxa, reported by another study as Control-elevated [Potential roles played by taxa reported by our differential abundance analysis also became observable. Our MCNs estimated genus  another  as ADHD-elevated .FT (cooperative with cluster BB) was larger in ADHD. Cluster FR  was smaller in ADHD. Ruminococcaceae taxa , which was also reported as ADHD-reduced in two other studies [Ruminococcus gnavus (only ranked by ATria in ADHD), to have ADHD-exclusive competition with F. prausnitzii. Ruminococcaceae member genus Oscillospira was also estimated to have ADHD-exclusive competition, with Proteobacteria family Enterobacteriaceae (cooperative and fellow FR member in Control), and cluster BB member Firmicute Streptococcus. In particular, the shift in relationship between Enterobacteriaceae and Oscillospira from cooperation (Control) to competition (ADHD), as well as the dynamic involved in the triangle composed of genera Streptococcus, Blautia, and Oscillospira  were al studies , 68. OurThis exploration can add deeper meaning to these results through pursuit of additional studies targeting some of these taxa and relationships, including multi-omics  and/or pp-value for correlations in our MCNs, future work could incorporate multiple hypothesis corrections and sensitivity analysis for ATria for more thorough statistical relevance validation. More meaning to relationships in our MCNs can also be uncovered, through causality studies. Causality would give direction to edges, enabling detection of both two- and one-way , it is of course possible (and likely) that changes within attributes of the target population can impact ecological relationships. We are merely laying the groundwork, and with very few studies even attempting this level of analysis , an enorensalism , amensalensalism ) relatioensalism , which densalism   can be Also given that ASRS does not offer an official diagnosis and this metadata did provide ASRS scores, an immediate future goal should be a migration from case-control to continuous data analysis with respect to ASRS. Our taxa plots, which observed abundances of core taxa with respect to ASRS score, have already started this process. Some of our other analysis protocols  will trivially propagate. While differential abundance analysis will ultimately be difficult without explicit sample classifications, networks have potential given some of the strategies mentioned above. For example, multi-omic network techniques such as marginal correlation analysis have already proven useful for correlating heterogeneous datasets , making A more complete understanding of ADHD and the gut microbiome will best equip the community to make the right decisions when administering treatment(s). Our results, coupled with those in the literature, suggest that the gut microbiota cannot afford to be ignored when it comes to ADHD, and treatments directly targeting the gut microbiome have potential. Encouraging results have been uncovered for gluten and casein-free diets , MicrobiS1 Figx-axis by increasing ASRS score.Bar plot of taxa relative abundance, at the class level. Samples are ordered on the (PNG)Click here for additional data file.S2 Figx-axis by increasing ASRS score.Bar plot of taxa relative abundance, at the order level. Samples are ordered on the (PNG)Click here for additional data file.S3 Figx-axis by increasing ASRS score.Bar plot of taxa relative abundance, at the family level. Samples are ordered on the (PNG)Click here for additional data file.S4 Figx-axis by increasing ASRS score.Bar plot of taxa relative abundance, using the lowest possible classification level. Samples are ordered on the (PNG)Click here for additional data file.S5 FigBifidobacterium. Samples are ordered on the x-axis by increasing ASRS score.Relative abundance plot of (PNG)Click here for additional data file.S6 FigBacteroides. Samples are ordered on the x-axis by increasing ASRS score.Relative abundance plot of (PNG)Click here for additional data file.S7 FigSutterella. Samples are ordered on the x-axis by increasing ASRS score.Relative abundance plot of (PNG)Click here for additional data file.S1 TableData obtained from the openly available BioProject (PRJNA656791), along with cited protocols –92.(DOCX)Click here for additional data file.S2 TableAverage number of sequences retained after every preprocessing step.(DOCX)Click here for additional data file.S3 TableEvery correlation in all MCNs, grouped by taxonomy. Orange = only found in Control, purple = only found in ADHD, grey = found in both. +(green) = positive correlation, -(red) = negative correlation.(DOCX)Click here for additional data file.S4 TableBold taxa are ranked #1.ATria rankings of all taxa over all MCNs, grouped by taxonomy. Dark orange = only ranked in Control, dark purple = only ranked in ADHD, light orange = higher ranked in Control, light purple = higher ranked in ADHD, grey = evenly ranked. (DOCX)Click here for additional data file.S5 TableCommunities within Control and ADHD family-level MCNs. Phylum: purple = Bacteroidetes, yellow = Firmicutes, brown = Actinobacteria, blue = Proteobacteria.(DOCX)Click here for additional data file.S6 TableCommunities within Control and ADHD genus-level MCNs. Phylum: purple = Bacteroidetes, yellow = Firmicutes, brown = Actinobacteria, blue = Proteobacteria.(DOCX)Click here for additional data file.S7 TableCommunities within Control and ADHD MCNs using the lowest possible level of taxonomic classification. Phylum: purple = Bacteroidetes, yellow = Firmicutes, brown-Actinobacteria, blue = Proteobacteria.(DOCX)Click here for additional data file.


This cross-sectional study estimates prevalence and trends of ADHD diagnosis among US children and adolescents from 2017 to 2022. An analysis of the National Health Interview Survey (NHIS)2 reported that the prevalence of ADHD among children increased from 6.1% in 1997 to 1998 to 10.2% in 2015 to 2016. Similarly, the National Survey of Children’s Health showed a 42.0% increase from 2003 to 2011.3 However, the latest prevalence has not been updated in the past 6 years. The aim of this study was to estimate the prevalence and trends of ADHD among children and adolescents in the US in 2017 to 2022.Attention-deficit hyperactivity disorder (ADHD) is the most common childhood-onset neurodevelopmental disorder, characterized by persistent and impairing inattention, hyperactivity, and impulsivity, with a high prevalence among US children and considerable implications for individuals and families.4 Information about ADHD diagnosed by a physician or other health professional was reported by a parent or guardian. The final sample child and adolescent response rate was 45.8% to 60.6% from 2017 to 2022. The Guangdong Pharmaceutical University academic review board deemed the study exempt from review because of the use of deidentified data. The NHIS has been approved by the National Center for Health Statistics ethics review board, and all respondents provided verbal informed consent before participation. The study followed the STROBE reporting guideline.Data for this cross-sectional study were obtained from the NHIS, 2017 to 2022, a nationally representative cross-sectional survey using multistage, stratified sampling.5P values for overall differences across strata were calculated using χ2 tests. Trends in prevalence over time were tested using a weighted logistic regression model, which adjusted for age, sex, race and ethnicity, family educational level, and family income-to-poverty ratio. Race and ethnicity were self-reported in the survey and were assessed to gather demographic information for health care research and investigate health disparities. Statistical analyses were conducted using survey procedures in SAS statistical software version 9.4 (SAS Institute). A 2-sided P < .05 was considered statistically significant.The weighted prevalence of ADHD among individuals in the US aged 4 to 17 years from 2017 to 2022 was calculated based on the complex sampling design following NHIS statistical guidelines.A total of 37 609 individuals aged 4 to 17 years  were included. Among them, 4098 children and adolescents (10.90%) were reported to have ever been diagnosed with ADHD. The weighted prevalence of ADHD was 10.20%  in 2017 to 2018, 10.08%  in 2019 to 2020, and 10.47%  in 2021 to 2022. There were significant differences in prevalence by age, sex, race and ethnicity, and family income-to-poverty ratio .P for trend = .31). All subgroups evaluated also showed no significant change in prevalence from 2017 to 2022  in earlier years (1978-2005).6 The prevalence of ADHD differed significantly by age, sex, race and ethnicity, and family income-to-poverty ratio, consistent with previous study findings.3Based on US national representative data, the estimated ADHD prevalence was 10.08% to 10.47% among children and adolescents aged 4 to 17 years from 2017 to 2022, which was similar to the prevalence from the NHIS in 2015 to 2016 (10.20%).This study has some limitations. First, information on ADHD provided by parents may lead to misreporting and recall bias. Second, the NHIS underwent a major redesign in 2019, which may affect comparability with prior years, and the COVID-19 pandemic affected data collection in 2020, which may also affect survey estimates. Given that the estimated ADHD prevalence was still high, further investigation is warranted to assess potentially modifiable risk factors and provide adequate resources for treatment of individuals with ADHD in the future.


Neurocognitive impairments are associated with child and adult ADHD in clinical settings. However, it is unknown whether adult ADHD symptoms in the general population are associated with the same pattern of cognitive impairment. We examined this using a prospective, population-based cohort spanning birth to age 25 years.We examined associations between self-reported adult ADHD symptoms and cognitive task performance (attention and response inhibition) in adulthood and childhood.B = −0.03, 95% CI , p = .005; response inhibition B = −0.03, 95% CI , p = .002).Self-rated ADHD symptoms at age 25 were associated with poorer performance in age 25 cognitive tasks capturing ADHD-related functioning (attention Neurocognitive impairments linked to adult ADHD symptoms in the general population, are similar to those found in people with childhood ADHD symptoms and are consistent with findings in adult ADHD clinical samples. Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental condition that typically onsets in childhood and is characterized by impairing levels of inattention and/or excessive motor activity and impulsivity. ADHD also affects adults with a prevalence of approximately 2.5% .Research suggests that there are differences in ADHD in adulthood compared to ADHD in childhood. For example, adults with ADHD show fewer hyperactivity symptoms than children . ResearcADHD has long been associated with neurocognitive deficits and several theories and pathways have been proposed to explain the neurocognitive mechanisms underlying ADHD . ExtensiStudies of adults with a clinical diagnosis of ADHD generally report that the pattern of neurocognitive deficits is similar to that found in children with ADHD; small to moderate effect sizes across multiple cognitive domains, with notable impairments relative to controls in attention, response inhibition and working memory . SimilarAttentional processes have long been considered as an important neurocognitive domain associated with ADHD . In partPrevious research shows that ADHD symptom scores behave as a continually distributed trait in terms of association with risk factors and adverse outcomes in population samples . TherefoEven though neurocognitive deficits are not a defining criterion for ADHD, research has shown links between neurocognitive deficits, daily life functioning and quality of life . Those wConsidering this lack of population based research into adult ADHD and neurocognitive profiles , the aim(i) ADHD symptoms at age 25 were associated with ADHD cognitive task performance at age 25.(ii) ADHD symptoms at age 25 were associated with ADHD cognitive task performance in childhood.This was conducted both using total ADHD symptom scores and ADHD symptom domains of inattention and hyperactivity-impulsivity separately.http://www.bristol.ac.uk/alspac/researchers/our-data/. Further details regarding establishment of the cohort and data collection can be seen in the Supplementary Materials and can be found elsewhere  were used for this analysis. ALSPAC is a well-established longitudinal birth cohort study. Pregnant women resident in Avon, UK with expected dates of delivery between 1st April 1991 and 31st December 1992 were invited to take part in the study. The initial number of pregnancies enrolled was 14,541 . Of these initial pregnancies, “there was a total of 14,676 fetuses, resulting in 14,062 live births and 13,988 children who were alive at 1 year of age.” Part of this data was collected using REDCap , 2019. Plsewhere .Where individuals from the same family were included as participants, for example for multiple births, only data from the oldest sibling was included. Ethical approval for the study was obtained from the ALSPAC Ethics and Law Committee and the Local Research Ethics Committees. Informed consent for the use of data collected via questionnaires and clinics was obtained from participants following the recommendations of the ALSPAC Ethics and Law Committee at the time.ADHD symptoms were measured at age 25 years using the self-rated Barkley Adult ADHD Rating Scale (BAARS-IV) . A parenADHD symptoms in childhood were measured using the Development and Well-Being Assessment (DAWBA) . The DAWAttention was assessed at age 8 using the Sky Search task from the Test of Everyday Attention for Children (TEA-Ch) . ParticiAttention in adulthood (age 25 years) was assessed online using the Sustained Attention to Response Task (SART) . The SAR10, multiplied by minus one so that higher scores reflect better cognitive performance and subsequently standardized to mean = 0, Standard Deviation (SD) = 1 to aid interpretation.Child and adult attention scores were transformed using logResponse inhibition in childhood (age 8) was assessed using the TEA-Ch Opposite Worlds task , which iResponse inhibition in adulthood (age 25) was assessed online using the Double Trouble task  . This taSD = 1 to aid interpretationChild response inhibition scores were multiplied by minus one so that both child and adult higher scores reflect better cognitive performance; all scores were standardized to mean = 0, https://www.cambridgebrainsciences.com). Details are in Supplementary Material.Child cognitive measures were assessed at in-person clinic visits, whilst the adult assessments were conducted online. Data cleaning and removal of outliers for both attention (Sustained Attention to Response Task- SART) and response inhibition tasks (Double Trouble) was undertaken in accordance with guidance and instructions from Cambridge Brain Sciences , with multiple imputation (MI) used to impute missing cognitive and ADHD symptom data. Full details of the multiple imputation strategy are given in the Associations between ADHD symptoms and neurocognitive tasks across adulthood and childhood were examined using separate regressions with performance on each cognitive task as the outcome. Analysis was conducted first using total ADHD symptoms with subsequent analysis for the inattention and hyperactive/impulsive symptoms separately. At age 25, the device type used to complete the task  was included as a covariate. Sex was included as a covariate in all analyses.Sensitivity analyses were conducted by (i) using a categorical definition of ADHD in adulthood  , (ii) usn = 4,145), (ii) including anyone with available age 25 cognitive or (self-rated) ADHD data using multiple imputation combined with inverse probability weighting to the “full” ALSPAC sample , and (iii) including only those with complete age 25 and childhood ADHD and cognitive task data (n = 792). Details of the multiple imputation strategy are given in the Supplementary Material.Analyses for addressing missing data were examined by (i) including anyone with available age 25 cognitive or (self-rated) ADHD data (n = 1543) were 33.3% male. The mean self-rated adult ADHD symptom score using the Barkley scale was 1.42,  and the mean childhood parent-rated ADHD symptom score using the DAWBA was 0.44,   . Within-B = −0.03, 95% CI , p = .005; response inhibition B = −0.03, 95% CI , p = .002). When examining inattention and hyperactive-impulsivity scores separately, we observed similar patterns of associations.As shown in B = −0.02, 95% CI  p = .125; response inhibition B = −0.02, 95% CI , p = .128).Also shown in Overall the results show that despite the differences in measurements and developmental period of childhood and adulthood, effect estimates were similar and small in magnitude.B = −0.05 , p = .592; response inhibition B = −0.17 , p = .049). We also observed this pattern when using parent-rated adult ADHD total scores, which showed moderate association with self-rated adult ADHD scores , p = 2x10-10) , p = .270; response inhibition B = −0.07, 95% CI , p = .002). Each analysis using different approaches to missing data also found a similar pattern of results (Supplementary Table 7).Sensitivity analyses between dichotomized adult ADHD scores and cognitive tasks at age 25 showed a similar pattern of associations for the attention and response inhibition tasks but with wider confidence intervals (attention Supplementary Table 5).Sensitivity analyses between childhood ADHD and cognitive task performance at age 25 showed an association between higher ADHD scores in childhood and poorer performance on the response inhibition task at age 25, with weaker evidence for the attention task: a similar pattern as was observed for childhood cognitive task performance. Separate ADHD symptom domain analyses found associations of childhood inattention with both cognitive tasks at age 25, whereas for childhood hyperactivity-impulsivity there was only a weak association with attention at age 25  in adulthood. These associations were similar for both inattention and hyperactivity-impulsivity ADHD symptom domains.Looking across time between ADHD symptoms at age 25 and cognitive tasks measured during childhood, the results showed weak evidence of association but a consistent pattern of elevated adult ADHD symptoms being associated with lower scores on the cognitive tasks across timepoints (childhood and age 25). Sensitivity analyses using an ADHD symptom cut-point and parent-rated adult ADHD symptoms also showed similar results which implied that neither the measurement scoring, nor rater, had an impact on the interpretation of our results.Thus, our investigations indicate that associations between adult ADHD symptoms and neurocognitive impairments in a population sample are similar to those observed in studies of clinical samples. The estimates found overall were small in this population cohort. Childhood and adult clinical studies  have tenThis study provides insight into associations between adult ADHD symptoms and neurocognitive impairment in a large population sample. Previous work has shown differences in adult ADHD symptom reporting, with adults in clinical studies tending to under-report their ADHD behaviors compared to other informants such as parents , whereasn = 4145). Limiting the sample to those with just adult and cognitive task data likely resulted in this subsample not being representative of the wider population nor adequately capturing those at the extreme end of the ADHD symptom distribution—instead it is more akin to an opportunistic sample of adults at age 25, which may have resulted in less variance. Our findings were robust when using different approaches to missing data, including multiple imputation combined with IPW to the “full” ALSPAC sample, although these methods may also be subject to error and all assume data are missing at random (MAR). Secondly, the study focused on two cognitive measures associated with ADHD (attention and response inhibition). There are many different cognitive domains  related to ADHD which have not yet been investigated in a population sample and future research could include these different neurocognitive domains. In addition, the cognitive tasks differed across time. This was necessary due to the developmental differences of the individuals when completing the measures, whilst the tasks were chosen to map onto the same domains. We also know that the presentation of ADHD differs over development. The magnitude of the associations across time and the direct comparison of within time associations should therefore be considered in light of these differences.Findings should be considered in light of several limitations. Firstly, ALSPAC, like most longitudinal studies, suffers from non-random attrition, where individuals who are at higher risk of psychopathology are more likely to drop out . There wIn summary, in a large, prospective longitudinal population sample, this study assessed the pattern of associations between ADHD symptoms at age 25 and neurocognitive impairments in both childhood and adulthood. Self-reported ADHD symptoms at age 25 were associated with neurocognitive impairments at a similar magnitude to ADHD symptoms reported in childhood. This indicates that, in the general population, ADHD symptoms reported at age 25 are reflected in cognitive impairments that are typical of ADHD. A similar pattern of association was also found for inattention and hyperactivity-impulsivity. The study suggests that neurocognitive impairments are similar for adult ADHD symptoms in the general population as those reported for studies in childhood, which is consistent with findings in adult ADHD clinical samples. Understanding more about neurocognitive impairment in adulthood can help shape the understanding of ADHD as a disorder across the lifespan. Further, identifying impairments in adults with ADHD through the assessment of neurocognitive deficits may also aid in the tailoring of suitable employment, training and personalized treatment options.Click here for additional data file.Supplemental material, sj-docx-1-jad-10.1177_10870547231201870 for Young Adult ADHD Symptoms in the General Population and Neurocognitive Impairment by Sharifah Shameem Agha, Lucy Riglin, Rhian Carbury, Rachel Blakey, Amy Shakeshaft, Ajay K. Thapar, Kate Tilling, Stephan Collishaw, Evie Stergiakouli, Anita Thapar and Kate Langley in Journal of Attention Disorders