Quantitative predictions of impacts on public water supplies are essential for planning climate change adaptations. Monitoring data from five full-scale Scottish drinking water treatment plants (DWTPs) showed that significant correlations exist between conditionally carcinogenic trihalomethanes (THMs) levels, water temperature  and dissolved organic carbon (DOC) , respectively. The strong seasonality of these parameters demonstrated how climate can influence THMs formation. We quantified with laboratory experiments the sensitivity of THMs formation to changes in water temperature and DOC concentration. The laboratory data accurately reproduced real-world THM formation in the DWTPs. We then combined these validated relationships with information from the literature about future trends in mean summer temperatures and surface water DOC in the British Isles, to estimate future global warming impacts on THMs formation in DWTPs that use chlorine for disinfection. An increase in mean summer temperatures will likely increase THM formation, with a 1.8 °C temperature increase and 39% THMs increase by 2050 representing our mid-range scenario. Such an increase has major implications to potable water around the world, either an increased health risk or increased water treatment costs to maintain an equivalent quality potable supply. This becomes particularly evident in regions with extensive peatlands in drinking water catchments where warming effects have been related to increasing dissolved organic carbon (DOC) concentrations, which then impacts water treatability in DWTPs8. DOC is a precursor for the formation of disinfection by-products (DBPs), some of which are regulated due to public health concerns. For example, conditionally carcinogenic trihalomethanes (THMs) are formed as by-products when water is chlorinated to kill pathogens10. The European Union regulates total THMs in drinking water  at 100 μg/L11, whereas the US EPA imposes a maximum annual average of 80 μg/L for these compounds12.Climatic conditions are changinget al.6 DOC concentrations in 22 UK upland waters have increased by an average of 91% over 15 years. Similar concurrent DOC increases have been recorded elsewhere in Europe and North America13. In a detailed analysis of various potential causes of raising DOC in UK lake and streams, Evans et al.14 identified declining anthropogenic sulphur emission and deposition as the main cause. DOC solubility is suppressed by high soil water acidity, which has lessened in the UK as a result of declining sulphur emission and deposition since the 1980s15. However, rising DOC trends were also observed at sites, where non-marine sulphate changes were small, such as Loch Coire nan Arr in Northwest Scotland. Overall, Evans et al.14 concluded that increases in temperature are the second most relevant cause, and could account for a 10–20% DOC increase.According to Evans 17. In this context, water providers need to anticipate the likely magnitude of further climate change effects in order to effectively plan their investment strategy and adaptations18.The dramatic increase in DOC as a THM formation precursor in UK surface waters has implications for drinking water providers, which are challenged to adapt their treatment trains and operations to maintain compliance with drinking water standards into the future17. Furthermore, monitoring and chlorination experiments have established that temperature and DOC are influential in THM formation20. However, to the best of our knowledge no study has yet attempted to reproduce seasonal trends in THMs in treatment works based on independent laboratory experiments.Since water providers regularly adapt and optimize their treatments for economic reasons and in response to regulatory changes and technological developments, it is difficult to relate directly long-term trends in surface water composition and ambient temperature to those observed for THMs in potable water. In this study, we took a different approach by considering seasonal trends in surface water quality and potable water THMs over a calendar year for five DWTPs works, which did not undergo major treatment changes or upgrades during this observation period. It has been previously noted that strong seasonal trends in THMs, with maxima in late summer and minima in early spring, and positive correlations between ambient temperature, raw water DOC, and THMs indicate the susceptibility of drinking water quality to changes in climatic conditionsWe used correlation analysis and extensive monthly monitoring data from five full-scale DWTPs in Scotland to first firmly establish changing DOC and ambient temperature as the main causes for seasonality in THMs formation. We next used controlled laboratory experiments to investigate, for each correlation factor separately, how variations in water temperature and DOC concentration will influence THM formation. We then derived from the laboratory data a quantitative relationship between the changes in water temperature, water DOC and THMs formation, and validated this relationship with the monitoring data from the DWTPs. By combining the insights thus gained with climate change scenarios published in the literature, we were finally in a position to discuss the likely magnitude of future climate change impacts on THMs formation in DWTPs. While this work is particularly relevant for drinking water treatment in regions with extensive peatlands in drinking water catchments, the broader implications apply globally to DWTPs using chlorine for disinfection.21. Further, there were very strong and significant positive correlations between potable and raw water temperature . Equally, there were statistically significant strong and positive correlations between potable and raw water TOC , showing similar seasonality for these parameters in raw and treated water. This is despite substantial TOC reductions during water treatment, by an average 87.0 ± 0.7%. We also analysed seasonal changes in indicators of organic carbon quality in potable water, such as the percentage of organic matter belonging to the hydrophobic organic fraction (HPO), the transphilic organic fraction (TPI), and the hydrophilic organic fraction (HPI). However, no clear seasonal trends in these indicators of organic carbon quality were apparent in potable water  before disinfection with a fixed amount of chlorine at a fixed temperature of either 5, 15, or 25 °C. The treated water from the case study sites sampled before the point of disinfection was diluted to 1 mg/L DOC concentration, if it exceeded this concentration level. By repeating these experiments in the months of March, June, September and December, the potential influence of seasonal changes in DOC composition on THM formation could be investigated.20. On average, 24 ± 2% less THMs were being formed in potable water at 5 °C , as compared to 15 °C , whereas 29 ± 4% more THMs were being formed at 25 °C as compared to 15 °C , rather than changes in NOM reactivity.As expected, a lower content of the THM precursor organic carbon in water led to less THM formation during disinfection with chlorine Fig. . For 5 mn Tables , even thx, DOCx according toThe laboratory data was used to predict the observed seasonality in THMs over the one year monitoring period. Since THM formation was shown to be a function of water temperature and DOC content, THMs (µg/L) can be estimated from those measured a reference point T1. This means that predictions are for a further increase of 0–3.6 °C from current temperatures, or 1.8 °C on average. Evans et al.14 proposed that 10–20% of the 91% increase in UK surface water DOC over 15 years is explained by the 0.66 °C temperature increase, which equates to a 15.2–30.3% increase in surface water DOC per 1 °C temperature increase, or on average 22.7% per 1 °C. Based on these predictions, we developed a matrix of low/medium/high climate change scenarios, consisting of the various combinations of a 0, 1.8 or 3.6 °C increase in surface water temperature by 2050, and 15.2%, 22.7% or 30.3% increases in surface water DOC per 1 °C increase in surface water temperature. Considering the strong correlations observed in our case study we furthermore assumed that these changes in temperature and surface water DOC will correspond to those occurring in potable water at the point of disinfection.The UK’s Adaptation Sub-Committee (ASC) has predicted that regional summer mean temperature will increase in Scotland between 0.9–4.5 °C by the 2050 s, of which 0.9 °C may already have occurredBy combining the climate change predictions with the laboratory data, we estimated the anticipated percent changes in summer THM formation by 2050, if there is no change to the current treatment practice, according to1 ((mg/L)/°C) is the DOC response to the temperature increase. Inserting these values into Eq. We estimated the magnitude of the derivates ∂THMs/∂T and ∂THMs/∂DOC from the laboratory experiments, using the THMFP measured at 1 mg/L DOC and 15 °C as the closest laboratory data reference point for the water quality observed in the summer months in Scotland (i.e. an average temperature of 14.5 °C and 1.3 mg/L DOC at the point of disinfection for June/July/August). The data then suggested a 3.0 ± 0.2% increase in THM formation per 1 °C temperature increase towards 25 °C, and a 63 ± 4% increase in THM formation per 1 mg/L increase in DOC towards 5 mg/L /°C) for the low, medium and high sensitivity scenario derived from Evans et al.14).ΔTHMs (%) is the anticipated % change in potable water THMs for a mean summer temperature change of ΔT , and 1.3 * SThe predicted increases in THM formation for the various scenarios ranged from 0 to 100%, with an increase of 39% being the mid-range scenario Table . The eff23, but our monthly monitoring programme was much more comprehensive than earlier studies and comprised 42 parameters of potable and 38 parameters of raw water quality. Nonetheless, amongst the many parameters monitored, only the monthly averages of water temperature, and various indicators of the water organic carbon content had well established statistically significant correlations with the monthly averages of total THMs  would increase cost by about 6%.In response to the increasing THM control challenge, Scottish Water has for example over the past few years, already increased monitoring, by more regularly measuring TOC in raw water, and with the installation of online DOC sensors in water treatment works. Scottish Water has also already adapted and optimized the performance of its operations, for example with enhanced coagulation or the careful evaluation and selection of filtration membranes, and with the addition of treatment steps such as air stripping of THMs, granular activated carbon (GAC) filtration or ion exchange. Such treatment upgrades will inevitably have treatment cost implications. An US EPA reportOur analysis consequently suggests that environmental change affects surface water quality with implications for water treatment and its costs, as illustrated for the control of trihalomethanes formation in drinking water disinfection. Although this study focused on Scottish systems, implications of temperature change and associated changes in source water DOC are global. As such, equivalent studies are urgently elsewhere in the world to fully understand the health and cost implications of climate change on the world’s potable water supply.Case study sites were selected from a total of 93 DWTP sites across Scotland to identify five representative sites. Cluster analysis was performed to achieve this objective using the K-means algorithm by R Studio free software . Preliminary exploratory analysis of data involved the division of DWTP sizes into three categories: small, medium and large. Size was defined by applying a histogram of frequency for size (ML/day) using also R Studio software. Minimum size was allocated at 0.006 ML/day and maximum at 110 ML/day. Frequency plots showed that 47 of the DWTPs from the selected 93 sites (51.6%) were small ranging from 0.006 ML/day to 0.838 ML/day, 26 DWTPs (28.6%) were medium size ranging from 0.838 ML/day to 13.5 ML/day and 18 DWTPs (19.8%) were large ranging from 13.5 ML/day to 110 Ml/day. Twenty centroids were used for K-means with a maximum number of 100 iterations. The K groups were plotted against the following quality variables: total THMs (distribution and potable water), TOC , colour (raw water), chloride (distribution networks) and bromide (raw water) to determine outliers . This approach selected a preliminary set of 21 DWTPs that were grouped per region from which the final selection of five case study sites was narrowed down according to accessibility, type of treatment  and geographical location. The five sites are described in Table 28. All data were extracted and processed for statistical analysis from the Scottish Water Laboratory Information Management System (LIMS). Potable water parameters comprised ammonium, chloride, colour, conductivity, fluoride, pH, nitrate, nitrite, soluble reactive phosphate, sulphate, total organic nitrogen ratio (TON.ratio), total organic carbon (TOC), total organic nitrogen (TON), light transmittance at 220 nm, 254 nm, 270 nm, 350 nm, turbidity, bromide, bromodichloromethane, bromoform, chlorate, chloroform, dibromochloromethane, total trihalomethanes, free chlorine, temperature, total chlorine, aluminium, calcium, copper, iron, lead, magnesium, manganese, phosphorus, potassium, sodium, zinc, the hydrophobic organic fraction (HPO), transphilic organic fraction (TPI), and hydrophilic organic fraction (HPI) of TOC. Raw water parameters comprised alkalinity, ammonium, chloride, colour, conductivity, fluoride, pH, nitrate, nitrite, soluble reactive phosphate, sulphate, TON.ratio, TOC filtered, TOC, TON, light transmittance at 220 nm, 254 nm, 270 nm, 350 nm, UV transmittance, turbidity, bromide, temperature, aluminium, calcium, copper, iron, lead, magnesium, manganese, phosphorus, potassium, sodium, zinc, chlorophyll, the hydrophobic organic fraction (HPO), transphilic organic fraction (TPI), and hydrophilic organic fraction (HPI) of TOC. THMs were measured using a modified in house method based on EPA Method 524.2 for purgeable organic compounds in water by capillary column gas chromatography mass spectrometry. Fractional separation of Natural Organic Matter (NOM) into Hydrophobic (HPO), Transphilic (TPI) and Hydrophilic (HPI) fractions was performed by filtering water samples through a 0.45 µm nylon membrane  before pumping the water through a series of chromatographic columns containing Amberlite® XAD 4 and XAD 7 resins  for the fractional separation. Each fraction was eluted from the resins using an alkali solution (0.1 M NaOH) and rinsed with an acidic solution (0.1 M HCl). A Total Organic Carbon analyser  was used to determine the concentration of the DOC in each fraction29. Measured potable and raw water characteristics for each calendar month were compiled Tables Two sampling strategies were applied during this study to the five case study sites. One strategy collected samples every month from December 2014 to November 2015 and analytical work was performed by Scottish Water Scientific Services (SWSS) for raw water samples and final potable water (i.e. treated) samples. Analysis by SWSS followed a scheduled sampling programme and certified analytical protocols approved by the Drinking Water Quality Regulator (DWQR) for Scotland and the United Kingdom Accreditation Service (UKAS)The second sampling strategy collected samples quarterly in 5 L high density polyethylene containers with caps purchased from VWR, Poole, England. Samples were delivered to the School of Engineering (Newcastle University) where samples were filtered through glass fibre pre filters  to remove larger particulates and then through 0.45 µm polyamide membranes  to be stored at 4 °C until for further use. These samples were used for THMFP measurements.Ammonium chloride, sodium hydroxide, sodium thiosulphate, sodium chloride, potassium bromide, potassium chloride, aluminium oxide, mono and dipotassium phosphate, disodium hydrogen phosphate, calcium carbonate, sodium bicarbonate, iron sulphate heptahydrate, sodium hypochlorite (12%) and manganese sulphate hydrate salts were purchased from BDH  with 99% purity. Sodium sulphate, magnesium carbonate, orthophosphoric acid, pentane  and 1,2-dibromopropane were purchased from Sigma-Aldrich  with 99% purity. A trihalomethanes calibration mix with 2000 µg/mL for each compound in methanol was purchased from Supelco . DOC concentrations were measured in each raw and treated water samples from each case study site in order to be able to dilute them, if necessary, to either 5 mg DOC/L (raw water only) or 1 mg DOC/L, with a salt solution that mimicked typical Scottish raw water composition (Table 4 and 5.85 g NaOH in 250 mL deionised water) and immediately chlorinated with 1.4 mL of a 1000 mg/L sodium hypochlorite solution previously standardised with 0.1 M sodium thiosulphate according to APHA methods and filled to the brim with deionised water to prevent any oxygen from entering the reaction vessel. Each sample was prepared in triplicate and incubated at 5 °C, 15 °C and 25 °C. After seven days (+/−4 hours), THMs were liquid-liquid extracted and analyzed following a previously described method protocol .Water solutions with DOC content of 1 mg/L or 5 mg/L (270 mL) where transferred into amber glass bottles with glass caps and buffered with 2.74 mL phosphate buffer solution  had >50% below detection limit values and were excluded from the correlation analysis. Linear correlation coefficients and corresponding p-values for testing the hypothesis of no correlation against the alternative of a nonzero correlation were then calculated for the remaining parameters using Matlab R2016a ©. To make the seasonal variation of different parameters more readily comparable in a single plot, the monitoring data is displayed in figures as the monthly mean for the five case study sites divided by the mean parameter value for the entire observation period, and the uncertainty is indicated by error bars equal to plus and minus one standard error of the monthly mean divided by the mean parameter value for the entire observation period. THM formation experiments for different conditions were performed in triplicates. Mean values were then calculated for different conditions such as DOC values, temperatures, etc., from the mean value of the triplicate measurements. Experimental outcomes were reported/displayed as the mean plus minus one standard error of the mean. Two-tailed z-tests using Excel 2016 © were used to test the hypothesis of no change in THM formation against the alternative of a nonzero percent change. Two-tailed Welch tests  using Excel 2016 © were used to test the hypothesis of equal means for the two data sets against the alternative of a nonzero difference in the mean.Supplementary information


There is scarcity of information about the quality and safety of drinking water in Africa. Without such vital information, sustainable development goal number 6 which promotes availability and sustainable management of water and sanitation remains elusive especially in developing countries. The study aimed at determining concentrations of inorganic compounds, estimated daily intake (EDI), target hazard quotient (THQ), hazard index (HI), incremental lifetime cancer risk (ILCR), and identify safe drinking water source sources in Southwestern Uganda.  This was an observational study in which 40 drinking water samples were collected from georeferenced boreholes, springs, open wells, bottled, and taps within Bushenyi district of Southwestern Uganda. Water samples were analyzed for copper (Cu), iron (Fe), zinc (Zn), lead (Pb), cadmium (Cd), and chromium (Cr) levels using atomic absorption spectrometry (AAS). Water safety measures  were established for each water source and compared with local and international water permissible standards for each analyte. A spatial map was drawn using qGIS®, and analysis of quantitative data was done using MS Excel 2013 at 95% significance. P > 0.05) for water consumed from spring, bottled, and tap sources for Fe and Zn levels. Similarly, no differences were found in the EDI for children and adults (P > 0.05). Furthermore, the HI showed an absence of noncarcinogenic risk associated (HI < 1), although the ILCR was higher in adults than children (P < 0.05) due to high Cd concentrations.  Heavy metals were present in the following order: 11.276 ppm > 4.4623 ppm > 0.81 ppm > 0.612 ppm > 0.161 ppm for Fe, Zn, Pb, Cu, and Cd, respectively, while Cr was not detected. Fe was the primary water heavy metal in the order of open well > borehole > tap > spring > bottled water. This was followed by Zn levels in the order of tap > bottled > spring > borehole > open well. All compounds were within international water safety standards except Pb. Hence, there is need for the government of Uganda to establish water filtration systems, particularly for Pb to improve the quality of water for the general public. The EDI was similar ( The current identified Fe is a major heavy metal in drinking water of Uganda, and boreholes were the major safest sources of drinking water identified in this study. Access to safe drinking water is considered a universal human right by the United Nations convention , 2; howeIn Kampala, the capital city of Uganda, previous studies identified high levels of heavy metals such as lead (Pb), zinc (Zn), iron (Fe), copper (Cu), cadmium (Cd), and chromium (Cr) were detected in drinking water , 11. SimSeveral human-based practices that include pesticide application and industrialization are associated with soil and plant contamination. Subsequently, after the heavy rains, the runoff water carries the pollutants to water reservoirs from which humans and animals consume the contaminated water –39. HeavThis was a cross-sectional study conducted in Bushenyi district of Southwestern Uganda in July 2017. Bushenyi district lies 330.4 km from Kampala Capital city of Uganda by road. Bushenyi is bordered by Rubirizi, Buhweju, Sheema, Mitooma, and Rukungiri districts to the northwest, northeast, east, south, and west, respectively. Ishaka is its largest town which is located 75 km by road from Mbarara district which is the largest city in the region. The district coordinates are 00 32S, 30 11E. In our previous study , milk any = mx; where y = absorbance, m = gradient, and x = concentration for each compound using standard working standard stock solutions for the heavy metals acquired from E. Merck, D-6100, Darmstadt, FR, Germany, as previously described [y = 0.0841x, R2 = 0.8678  For Fe: y = 0.184x, R2 = 0.8748  For Cu: y = 0.299x, R2 = 0.9837  For Zn: y = 0.0296, R2 = 0.8637  For Pb: y = 0.1025x, R2 = 0.9552  For Cd: Usually nitric acid is used for digestion of solid samples and waste water. Since this was drinking water, the use of nitric acid was to serve this particular purpose. It is the reason the statement is brought to the attention of the reader. Distilled water was used to prepare solutions and for dilution purposes. All glassware were washed and dried in the oven at 105°C. Bottles for collecting water samples were cleaned by soaking in dilute nitric acid (10%) and rinsed several times with distilled water prior to sample collection . Linear escribed .  For FeThese equations were used to determine the concentrations of the compounds in each sample as previously described .The data point generated was compared with those from Uganda National Bureau of Standards (UNBS), the United States Environmental Protection Agency (US-EPA), European Union (EU), and the World Health Organization (WHO) using the table drawn from Bamuwamye et al. .C = concentration of the metal (mg/kg), IR = ingestion rate for water, and BW = body weight. In children and adults, IRs were 1 L/day and 2 L/day while the body weight for was 15 kg and 70 kg, respectively [This was modelled using recent Ugandan projections  and was ectively .The target hazard quotient (THQ) was used to generate the hazard index (HI) to determine presence of noncarcinogenic health effects following ingestion of the sampled water. The THQ was determined for Pb, Zn, Cd, Cu, and Fe (US EPA)  using the US EPA , i.e., 0x365 days = 25550 days) [Following chronic exposure to inorganic pollutants in drinking water, the incremental lifetime cancer risk (ILCR) was used to model the cancer risk in the Ugandan population. This was estimated using the following equation:50 days) , 45. In 50 days) , 25.Information acquired from the GPS readings was exported to qGIS® version 3.03 Cirona onto an administrative file for Uganda. A sentinel-2 satellite image number L1C_T3MRV_AO07540_20180816T082305 was acquired from the United States Geographical Surveys (USGS) system to show vegetation cover in the study area.P < 0.05. Information on safety was done using a one-sample t-test, and mean differences were used to define “high” and “low” after subtracting the sample mean from the hypothetical mean. These were used to define safety of drinking water at 95% significance. Furthermore, the EDI for children and adults for each metal was presented as mean ± SEM, and a two-sample t-test was conducted to determine differences in concentrations ingested and significance reported when P < 0.05. The HI was calculated to assess the presence of threat, i.e., HI > 1 as an indicative of a threat [t-test for children and adults was conducted at 95% significance. ILCR greater than 1 × 10−4 was an indicative of a cancer threat and presented with superscripts [Data were entered and analyzed in MS Excel 2013 version after normality testing, after which parametric tests were conducted. Descriptive statistics were conducted and information was presented as mean ± SEM from which a one-way ANOVA was conducted, and significant differences were reported when a threat . Also, srscripts , 25.The study showed that Fe is the major water pollutant in the order of open well > borehole > tap > spring > bottled water. This was followed by high Zn levels in the order of tap > bottled > spring > borehole > open well. Cu and Pb concentrations were relatively comparable where Cu was found to be high in the order of tap > borehole > spring > open well > bottled. Furthermore Pb concentrations were found to be in the order of open well > borehole > tap > bottled > spring. Finally, Cd levels were found to be the lowest in all water samples; however, these were highest in the order of tap > open well, bottled water > spring > borehole. Cr was not detected in all water samples as shown in Concentrations of Fe, Zn, and Cu were permissible by local and international regulatory agencies, while levels of Pb were found to be unacceptable in all water samples except borehole water using UNBS, EU, and WHO cutoff limits. The study also showed that Cd was acceptable using US-EPA and EU cutoffs as shown in P < 0.05). Also, bottled water ingestion of Pb and Cd was different amongst children and adults (P < 0.05). In open well drinking water, daily ingestion of Cu, Pb, and Cd were found to be different amongst children and adults, while significant differences in spring and tap water were only limited to Pb and Cd (P < 0.05) as shown in Ingestion of Zn and Cd from borehole drinking water was found to be significantly different amongst children and adults  higher in children than in adults for Pb > Cd > Zn on borehole water. Open well water had significantly higher THQ in the order of Pb > Cd > Cu > Zn in children than in adults. In addition, bottled, spring, and tap water all had significantly higher THQs in children for Pb > Cd than in adults as shown in The hazard index showed that all water sources were acceptable (HI < 1). The study also showed that the target hazard quotient (THQ) was significantly  in borehole, bottled, and open well drinking water in which adults had higher ILCR than children, i.e., Cd. In addition, the ILCR for spring and tap water was only significantly higher in adults than in children for Cd as shown in The study showed that safe drinking water was associated with boreholes, and a majority of these were located in Kitwe, Nyamiyaga, and Nyabubaare subcounties as well as Ward I of Ishaka-Bushenyi Municipality which are all agricultural areas as shown in P > 0.05) in heavy metal concentrations for Fe, Cu, Zn, Pb, and Cd showing their importance to the general public due to the threat of bioaccumulation following chronic exposure [The study showed that sampled water sources had high Fe concentrations, although Fe concentrations were highest in open water sources . Open waexposure , 49.Concentrations for Fe, Zn, and Cu were acceptable for human ingestion with the exception of Pb Tables  and 6. IP < 0.05) for Zn and Cd amongst children > adults (The estimated daily ingestion (EDI) of heavy metals from borehole drinking water was found to be significantly different ; however, significant differences in borehole water were in Pb > Cd > Zn higher in children > adults . These f; howeverChronic ingestion of heavy metals at very high concentrations in drinking water would subsequently predispose the local population to toxic health effects such as cancer. In this community, the incremental lifetime cancer risk revealed the absence of a threat in children while this was present in adults due to high Pb and Cd levels . This waDrinking water in Southwestern Uganda had high Fe and Zn concentrations. The study identified borehole water as safer source of drinking water, demonstrating a need for increased monitoring by the regulatory authorities for improved water quality. The authors recommend the installation of heavy-metal filters especially against Pb and Cd to reduce on the carcinogenic risk of drinking water in the community. For increased water quality, authorities need to continuously monitor water against major heavy metals in the study since very high levels of metals once ingested can lead to severe toxicological effects in humans.


TAZ with key clinical features including cardiomyopathy, neutropenia and skeletal myopathy. In order to gain a better understanding of the range of clinical features, identify targets for monitoring, and increase knowledge of natural history of the disease, we conducted muscle strength testing, functional exercise capacity testing, physical activity assessment, balance assessment and motion reaction time testing in 33 affected individuals and 14 controls. We analyzed data points to provide a cross-sectional quantitative spectrum of disease characteristics. We also compared these data points to the matched data points collected two years prior to provide insight into effects of BTHS over time.Barth syndrome (BTHS) is an X-linked disorder caused by defects in In comparison to controls, pediatric subjects with BTHS present with significantly impaired balance and motion reaction time while adult subjects with BTHS present with significantly impaired motion reaction time. In comparison to controls, subjects with BTHS presented with decreased functional exercise capacity (assessed via 6 MWT), knee extensor strength (both assessed via handheld dynamometry and five times sit-to-stand (5 TSTS)), and self-reported physical activity. Comparison of functional exercise capacity, knee extensor strength and self-reported physical activity from identical cohorts in 2014 and 2016 BTHS showed that the deficits in 6 MWT do not change significantly over the 2 year time span.In this comprehensive assessment of musculoskeletal parameters in a cross-section of individuals with BTHS, we uncovered deficits in motion reaction time and balance, which were previously not known to be abnormal in in BTHS. We also confirmed results of our previous study showing that pediatric and adult subjects with BTHS have decreased functional exercise capacity, knee extensor strength, and physical activity in comparison to controls, r, verifying the importance of including these measures as part of the regular clinical assessment in individuals with BTHS, as well as introducing 5 TSTS as an additional testing parameter. Perhaps most importantly, we demonstrated that 6 MWT results do not significantly vary in pediatric and adult cohorts with BTHS over a 2-year period, supporting this as a reliable quantitative measure of therapeutic outcomes in clinical studies and for clinical monitoring. BTHS  is a rare X-linked disorder with an estimated prevalence of 1/300,000–400,000 live births characterized by cardiomyopathy, neutropenia, growth abnormalities, and skeletal myopathy, among other features . The myoDaily functional activities, such as ambulation, stair climbing, and transfers, require a combination of muscular strength, endurance, and balance. In our prior cross-sectional phenotyping study in with BTHS, we found that affected individuals had significantly impaired functional exercise capacity as measured by 6MWT, knee and hip flexor and extensor strength, and reduced daily activity. In the present study, we expanded upon the musculoskeletal assessments performed in our previous study by including further functional testing in areas known to be affected by hip and knee flexor weakness , 6, and n = 24, Control n = 10), and an adult cohort > 20 years  for the purposes of data analysis, and based on available published normative data.The study group included a total of 33 males with BTHS ranging in age from 6 years to 34 years  and 14 controls ranging in age from 6 years to 30 years . The study population was divided into a pediatric cohort, age 6–19 years (BTHS Average height of the pediatric BTHS participants did not differ significantly from the control population (144.5 cm +/− 24.67 SD (median 143) vs. 148.8 cm +/− 17.18 SD (median 145.5) respectively), and both were within the normal range based on normative population data provided by the CDC . –10. 8–10p = .0013) and post- (p = .04) 6 MWT and dyspnea post- (p = .03) 6 MWT in comparison to controls (Table p = .01) 6 MWT in comparison to controls. Individuals with BTHS present with significantly increased heart rate pre- 6 MWT (p = .006) in comparison to controls (Table Pediatric subjects with BTHS present with significantly increased fatigue pre- p = .003 and posls Table .Table 1Ap = .0017) and adult (p = .005) subjects with BTHS. In pediatric subjects with BTHS, there is a strong correlation between age and knee extensor strength (r = .59). In adult subjects with BTHS, there is a strong negative correlation between age and knee extensor strength (r = −.46). In pediatric and adult controls, there was a very strong positive correlation between age and knee extensor strength . This finding of a strong, positive correlation between age and strength in the pediatric controls is consistent with published reference values [Strength testing was completed in 33 individuals with BTHS  and in 14 controls . Age cohorts were selected because of available published population normal values , 12. Knee values , 14.Fig.n = 33) and entire control cohort , there was a very strong correlation between age and grip strength .Grip strength measurements obtained via HHD for subjects with BTHS were compared to control subjects. Given that values for right and left grip strength were very similar for all subjects, only right grip strength values are provided Fig. , Table 3n = 31) and controls (n = 14) . On average, pediatric controls were able to complete the 5TSTS in 65% of the time of pediatric subjects with BTHS and adult controls were able to complete the 5 TSTS in 58% of the time of adults with BTHS.5 TSTS values (in seconds) were compared between subjects with BTHS n = 1 and conols n = 1 . The average PAQ-C activity score calculated for subjects with BTHS was 2.49 ± 0.67, and the average PAQ-C activity score calculated for controls was 3.41 ± 0.80. This indicates that, on average, subjects with BTHS age 6–14 demonstrate a low to moderate level of physical activity while control subjects demonstrate a moderate to high level of physical activity. Control subjects age 6–14 demonstrate significantly more physical activity in comparison to subjects with BTHS (p = .01). There was a negligible correlation between the average PAQ-C activity score and 6 MWT for subjects with BTHS (r = .18) and positive correlation for controls (r = .33). There was a positive correlation between the average activity reported on the PAQ-C and 6 MWT for both subjects with BTHS (r = .32) and controls (r = .42).Subjects age 15–34. In total, 12 subjects with BTHS between the ages of 15 and 34 completed the International Physical Activity Questionnaire (IPAQ). In total, 5 control subjects between the ages of 15 and 30 completed the IPAQ. Of the 12 questionnaires completed by subjects with BTHS that were utilized in analysis, 3 subjects (25%) indicated a low physical activity level, 1 subject (8.3%) indicated a moderate activity level and 8 subjects (66.7%) indicated a high activity level. Of the 5 questionnaires completed by controls, 5 (100%) indicated a high level of physical activity. In comparison to subjects with BTHS, on average, controls participate in vigorous activity more days per week (p = .005). In subjects with BTHS and controls, there was a moderate correlation between days per week spent walking and 6 MWT distance . In controls, there was a strong, negative correlation between average amount of time spent sitting per day and distance walked on the 6 MWT (r = −.65). In subjects with BTHS there was a negligible correlation between average amount of time spent sitting per day and distance walked on the 6 MWT (r = .14). In subjects with BTHS, there was a negative correlation between days spent participating in moderate activity and 5 TSTS time (r = −.47). In controls, there was a very strong, negative correlation between days spent participating in moderate activity and 5 TSTS time (r = −.86).Of the subjects with BTHS aged 6 to 34, 33 subjects completed the balance and motion reaction assessment using the SWAY Balance application . Of controls aged 6 to 30, 14/14 completed the balance and motion reaction assessment using SWAY Balance. One 6 year old subject with BTHS was only able to complete 1 trial secondary to fatigue. All controls were able to complete 3 trials.p = .02, motion reaction time: p = .03, balance: p = .01, balance left: p = 0.03, balance right: p = 0.02). Subjects with BTHS Age 20+ demonstrate significantly increased motion reaction time in comparison to controls (p = .04).Average baseline scores across all SWAY Balance categories  in subjects with BTHS aged 6–19 are significantly decreased when compared to controls (combined: P = 0.003), Balance (p = 0.004), Feet Together EC (p = 0.03), Single Leg Right EO (p = 0.01).SWAY scores for all subjects with BTHS (aged 6 to 34) were significantly lower compared to control subjects (aged 6 to 30), in the following categories Fig. : Combiner = −.03, r = −.24). Those results were replicated during this study . Based on these results, it appears that if an individual with BTHS reports increased fatigue at baseline, it will negatively impact the distance he is able to complete on the 6 MWT.We compared data from the total BTHS subject cohorts participating in our prior and current studies. During our previous study , we founp = 0.14).During our previous study we found that when normalized for body weight, pediatric subjects with BTHS presented with significantly greater knee extensor strength than adults with BTHS. In this study, by contrast, adults presented with a slightly higher knee extensor strength on average than pediatric subjects (35.24% ± 10.03 vs. 31.13% ± 10.39) . In 2016, there was a moderate positive correlation between days per week spent walking and 6 MWT (r = .37).n = 11), across the two data collection time points, we saw a statistically significant increase in fatigue reported post 6 MWT (p = .03) and raw left knee extensor strength (p = .04). We did not see a statistically significant difference between any common data points for subjects > 20 years old (n = 7). Perhaps most strikingly, we did not see a statistically significant difference between 6MWT distance across the cohort of 18 subjects  and novel areas of deficiency uncovered in this present study .We found, for the first time, that pediatric subjects with BTHS demonstrate significantly decreased balance and adult subjects with BTHS demonstrate significantly decreased motion reaction time, using a new assessment tool: the SWAY Balance. In evaluating overall trends, we found that motion reaction time testing in both patients with BTHS and controls revealed a trend of an improvement following adolescence, a peak in young adulthood and a slow decline with age. Previous research of motion reaction in other applications has shown a similar phenomenon .There are several considerations to using the SWAY Balance system. We observed significant variability in the 3 serial balance measurements in both patients and controls, which is likely due to postural fatigue. This is not unexpected, as previous studies have demonstrated that muscle fatigue can alter postural control . In the 2peak did not longitudinally change over time (average 5 years), thus arriving at a similar conclusion that exercise tolerance is relatively stable over time. However such studies are not feasible to be performed on a regular basis in most clinics due to equipment availability, complexity of testing, cost, and insurance coverage and therefore have limited practical clinical utility [Deficits in functional exercise capacity via other methodologies have been previously demonstrated in BTHS , and we  utility . Thus, 6We further showed that 6MWT results do not significantly vary in pediatric and adult cohorts with BTHS over a 2-year period. However, several individuals had significant changes in the distance walked on the 6MWT. The majority of these subjects had major health events in the 2-year interim, which likely account for these differences Of the individuals with a significant improvement: Participant 16, who had a 137 m increase in 6 min walk test (shift in Z-score corrected for height from − 6.5 to − 3.9), underwent a cardiac transplant in the interim. While we have previously shown that cardiac function does not directly correlate to 6MWT in this population, multiple mitigating health factors surrounding the transplantation have an important potential role, such as introduction of new medications including steroids, improved overall health condition, and increased daily activities. Participant 8 had a 98 m improvement on his 6-min walk test (shift in Z-score corrected for height from − 4.5 to − 1.3). At the time of the 2014 study, he was recovering from a rib contusion, and was healthy in 2016. Of note, an interim 6MWT obtained in our clinic in 2014 was 512 m, confirming that the 2014 meeting data was likely aberrant due to the injury.Of the individuals with a worsening of distance walked on 6MWT: Participant 4 had an 86-m decrease in 6MWT (shift in Z-score for distance corrected for height from − 2.96 to − 4.5). He was noted to be feeling unusually tired from his busy activities during the test day in 2016 (as noted in his pre-test Borg fatigue score), likely resulting in an aberrant outcome. Further in support of this, 6MWT values obtained in our clinic in 2015 and 2017 were in close agreement with the 2014 value (436 m and 457 m respectively). Participant 2 had a 77-m decrease in 6MWT performance, and unfortunately at this time the participant was suffering from a persistent health decline in multiple domains that preceded the meeting date in 2016 and continued after the meeting.The only individual with a major change in 6MWT distance and no known significant interim health event, is participant 14, with a 70 m improvement. This is not an individual who regularly is seen in our outpatient clinic, so it is not known if one of these values was aberrant due to potentially mitigating circumstances, or this represents a true improvement over time. These findings emphasize the importance of multiple measurements over time in order to identify aberrant values, as well as consideration of major interim health events as contributors to 6MWT performance. In total, however, the two serial studies show that individuals with BTHS never perform within the expected range for height and age, the majority of individuals do not have significant changes in their 6MWT performance over 2 years, and the cohort performance for 6MWT is reliably in the 350–400 m range. Additionally, the 2 serial studies suggest a decline in 6MWT performance with increasing age, however at this time it is not clear if this represents a true age related decline or a phenomenon that appears due to an “adjustment advantage” for height in childhood. Longer-term studies in individual participants will help to bear this out.n = 14) due to availability of control subjects at the study location. We complemented this small control number by also comparing to published population normative values and to previous study data.One drawback of this study is the small number of controls included in this study  to evaluate areas of known deficiency in BTHS , and new tools  to evaluate novel areas of deficiency uncovered in this present study . We further validated the utility of several of these clinical tools with a 2-year follow up study. Importantly, we showed that the 6 MWT does not vary significantly in a cohort of individuals with BTHS over 2 years, and distance walked is very rarely in the normal range in any individual, supporting this as a reliable quantitative measure of therapeutic outcomes in clinical studies and for clinical monitoring.This study was an observational study open to any patient with a biochemically or molecularly confirmed diagnosis of BTHS as well as male controls who attended the Barth Syndrome International Conference in 2016. This study was approved under Johns Hopkins University IRB protocol “IRB00098987 Investigation into clinical, metabolic and molecular factors in Barth Syndrome.” Study enrollment took place in July 2016. Written informed consent was received from participants prior to inclusion in the study.Participating individuals were males aged 6 to 34 who had molecular confirmation of BTHS or unaffected male controls age 6 to 30 years. Age, weight and height were measured and recorded for each participant.The 6 MWT was performed describing methods described in our 2016 paper . All assAll strength assessments were performed in subjects, with BTHS as well as controls, age 6 and up.Knee extensor muscle strength was assessed by a single physical therapist with expertise in muscle strength measurements using protocol identical to that described in our previous paper published in 2016 [Grip strength was measured by a single physical therapist with expertise in muscle strength measurements using published, standardized methodology via a JAMAR Hydraulic Hand Dynamometer. Grip strength was assessed utilizing techniques described in the NIH Toolbox and Jamar Hydraulic Handheld Dynamometer Owner’s Manual . ParticiThe 5 Times Sit to Stand Test (5 TSTS) is a functional assessment of lower extremity strength . This tePhysical activity questionnaires were administered using the same procedures as described in the previous study  to gain Subjects 15 years of age and older completed the International Physical Activity Questionnaire (IPAQ), Short Form, which is designed primarily for population surveillance of physical activity among adults (age 15–69 years). The IPAQ is a validated tool used to evaluate physical activity duration and vigorousness, in the prior 7 days.Balance was assessed in all subjects age 6 and up utilizing the SWAY Balance  on a single iPhone  by 5 assessors trained in SWAY administration . SWAY Balance is an FDA-cleared balance testing system, which uses the built-in tri-axial accelerometers of a mobile electronic device to objectively assess postural movement . The SWAReaction time is a measure of sensory and neuromotor function that encompasses stimulus recognition and processing followed by the initiation of a neuromotor response . SWAY BaPearson r-correlations were determined for 6 MWT distance versus physical activity questionnaire responses and 6 MWT distance versus pre−/post exercise Borg fatigue scoring. R correlations were considered very strong if greater than or equal to 0.7, strong if between 0.4 and 0.69, moderate if between 0.3 and 0.39, weak if between 0.2 and 0.29, and none/negligible if between 0.01 and 0.19. 2-tailed student’s t-tests were used to determine the significant of grip strength, 6 MWT, HHD strength testing, and SWAY balance measurements between patients with BTHS and controls as well as between younger subjects (ages 4–19) and older subjects (ages 20–32).Paired t-tests were performed to determine the relationship between the 2014 and 2016 data points for the 18 participants who participated in both studies including: 6 MWT distance, pre and post fatigue using the Borg scale, quadriceps strength assessed with HHD and physical activity measurements (Table


Chronic inflammation and hypoadiponectinemia are characteristics of obesity‐induced insulin resistance (IR). The effect of an anti‐inflammatory nutrition supplement (AINS) on IR and adiponectin biology in overweight adolescents was investigated. The secondary objective was to examine the extent to which individuals’ biomarker profiles, derived from baseline phenotypes, predicted response or not to the AINS. Additionally, the impact of DNA methylation on intervention efficacy was assessed.Seventy overweight adolescents (13–18 years) were recruited to this randomized controlled crossover trial. Participants received an AINS  and placebo for 8 weeks each. Homeostatic model assessment (HOMA)‐IR, adiponectin, inflammatory profiles, and DNA methylation were assessed. HOMA‐IR was unchanged in the total cohort. High‐molecular‐weight (HMW) adiponectin was maintained following the AINS while it decreased over time following the placebo intervention. HOMA‐IR decreased in 40% of subjects (responders) following the AINS. Responders’ pretreatment phenotype was characterized by higher HOMA‐IR, total and LDL cholesterol, but similar BMI in comparison to nonresponders. HMW adiponectin response to the AINS was associated with bidirectional modulation of adipogenic gene methylation.The AINS modulated adiponectin biology, an early predictor of type 2 diabetes risk, was associated with bidirectional modulation of adipogenic gene methylation in weight‐stable overweight adolescents. HOMA‐IR decreased in a sub‐cohort of adolescents with an adverse metabolic phenotype. Thus, suggesting that more stratified or personalized nutrition approaches may enhance efficacy of dietary interventions. Childhood obesity is a substantial health and economic burden,Responsiveness to nutrition intervention may be partially attributable to baseline phenotype and genetic background.This study addressed the hypothesis that a combination of anti‐inflammatory nutrients modulates IR and related secondary outcomes such as adiponectin, lipid concentrations, and inflammatory markers in weight‐stable overweight and obese adolescents. Furthermore, the impact of baseline phenotype and changes in DNA methylation on intervention efficacy was explored.22.1This randomized double‐blind, placebo‐controlled, crossover trial, named the Teen Nutrition Study, investigated the effect of an anti‐inflammatory nutrition supplement (AINS) versus placebo in weight‐stable overweight and obese adolescents. Participants and their parent(s)/guardian(s) attended one of the three study sites for data collection; 1) the Institute of Food and Health, University College Dublin, 2) the Trinity Centre for Health Sciences, Trinity College Dublin, the University of Dublin, or 3) the National Children's Hospital, Dublin, Ireland. Data were collected by the same research investigators  at all study sites. The study protocol was approved by the Joint Research Ethics Committee of St. James's Hospital and the Adelaide and Meath Hospital, incorporating the National Children's Hospital, Dublin, Ireland. This trial was registered with ClinicalTrials.gov (NCT01665742).2.2Between January 4, 2012 and April 30, 2013, 70 overweight and obese adolescents, aged 13–18 years, were recruited from pediatric weight management outpatient clinics and the general public. Overweight and obesity were defined as a BMI ≥91st and ≥98th percentiles on United Kingdom growth reference charts,2.3Participants were randomized to receive either the AINS or placebo for the initial treatment period. Randomization was conducted in a ratio of 1:1 and was stratified according to age, sex and baseline BMI using the MINIM randomization program.2.4Participants received either the AINS or placebo daily; with healthy eating advice for a period of 8 weeks. Nutrition education was provided at the beginning of each treatment period to encourage weight maintenance. Weight management education was based on the Traffic Light Diet.The AINS comprised LC n‐3 PUFA (1000 mg eicosapentaenoic acid [EPA] and 1000 mg docosahexaenoic acid [DHA]), 567 mg vitamin C, 390 mg α‐tocopherol, 416 mg green tea extract , and 16.5 mg lycopene. These nutrients were delivered via two study products; 1 × 200 mL carton of fruit juice fortified with emulsified fish oil  and 4× film coated tablets containing vitamin C, α‐tocopherol, green tea extract, and lycopene . Matching placebo treatment comprised an iso‐energetic fruit juice fortified with high oleic sunflower oil and 4× film coated tablets containing microcrystalline cellulose. Apart from the fatty acid content, the placebo fruit juice was identical to its active counterpart in nutrient composition. The active and placebo treatments matched for size, shape, color, and flavor, labeled A or B. Participants were instructed to consume supplements daily, with breakfast.2.5The primary outcome was HOMA‐IR.−1) for 24 hours. Cytokine secretion from unstimulated and LPS‐stimulated PBMC was assessed for subacute inflammatory phenotyping indicative of adipose tissue inflammation.Anthropometric measurements were conducted using calibrated equipment. Blood for insulin, glucose, lipid, and inflammatory profiles was collected and processed as previously described.ELISA determined total and HMW adiponectin, soluble CD163 (sCD163), IL‐6, tumor necrosis factor‐α , insulin , complement component C3 , and fetuin‐A . Plasma glucose, total cholesterol, HDL cholesterol, TAG, and apolipoprotein A1 (ApoA1) were measured using RX Daytona, as described previously.n = 55 samples by the McGill University and Génome Québec Innovation Centre  using the Infinium HumanMethylation450 BeadChip assay . Biologically relevant microarray results were technically validated using Sequenom EpiTYPER in a sub‐cohort of participants (n = 22) who demonstrated DNA methylation changes of greatest magnitude in both directions.DNA was extracted from buffy coats using the QIAamp DNA Blood Mini Kit . DNA methylation analysis is described in the online supporting material. Briefly, DNA methylation was analyzed in 2.6Plasma cholesteryl ester fatty acid composition, α‐tocopherol and lycopene were determined pre‐ and post‐intervention arms to assess compliance, as described previously.2.7Adverse events (AEs) were recorded and examined by the pediatric physician (KJ). One AE, a potential intolerance to whey within the fortified juice, occurred. The participant discontinued the trial. No serious adverse events occurred.2.8p < 0.05) to detect a 15% change in HOMA‐IR. Sample size was adjusted for 30% attrition and an anticipated 50% nonresponse rate, to provide sufficient power for the analysis of intervention responsiveness. Statistical analyses were completed using SPSS Statistics . Intention‐to‐treat analysis was used to investigate the effect of the intervention on HOMA‐IR, total and HMW‐adiponectin, and BMI. Participants who discontinued the study were younger and did not differ from adolescents who completed the intervention with respect to gender distribution or age‐ and gender‐normalized weight, height, or body composition . All data were examined for Gaussian distribution, and non‐normally distributed data were transformed. Comparisons between AINS and placebo were made using paired samples t‐tests between delta AINS and delta placebo values. Independent samples t‐tests examined differences in baseline characteristics of participants who completed the trial versus those who discontinued. To explore heterogeneity in responsiveness to the intervention, “responders” were defined as those demonstrating a significant improvement (≥10% reduction in HOMA‐IR) following the AINS while “nonresponders” demonstrated <10% decrease in HOMA‐IR. A 10% response threshold was based on longitudinal research illustrating a mean improvement of approximately 8% in HOMA‐IR in adolescents who transitioned from an overweight BMI to a healthy BMI.t‐tests examined differences in baseline characteristics between responders versus nonresponders. For biomarkers showing significant differences between responders and nonresponders, optimal cutpoints with their percentage accuracy were derived from ROC analysis . Epigenetic data were processed using the Bioconductor Minfi package in R,Sample size calculations were based on the hypothesized decrease in HOMA‐IR. The aim was to enroll 70 participants to give 80% power (Results are presented as mean (SEM) in figures and as mean (SD) in text and tables.3Figure The consort diagram illustrates that 70 adolescents were randomly assigned to receive the AINS or placebo for 8 weeks Figure . Fifty‐eTable Pre‐ and post‐intervention anthropometry, insulin, glucose, lipid and inflammatory marker concentrations in completers are presented in 3.1Figure −2 vs placebo 31.50(6.08) kg m−2, p = 0.951, n = 70) remained stable. Adiponectin receptor ADIPOR1 and ADIPOR2 mRNA levels were significantly higher in PBMC following the AINS, relative to placebo . The reduction in HOMA‐IR following the AINS was significantly greater than placebo in responders , sCD163 , and LDL:HDL cholesterol ratio  all independently predicted HOMA‐IR response. Additionally, changes in sCD163  following the AINS tracked HOMA‐IR changes . By contrast, compliance, baseline BMI and BMI change did not predict HOMA‐IR response . In retrospective ROC analysis, baseline HOMA‐IR level over 1.83 was 45.7% sensitive and 95.6% specific at differentiating responders from nonresponders to the AINS , which was marginally less predictive than baseline insulin .The baseline metabolic phenotype of responders was insulin resistant and dyslipidemic with higher insulin, HOMA‐IR, HOMA‐β, total cholesterol, LDL cholesterol, and lower QUICKI Table . In cont3.4p < 0.05 and absolute correlation co‐efficient >0.4 for all, Table S5, Supporting Information). Reactome pathway analyses demonstrated that positively associated CpG loci were predominantly located on metabolic genes as well as on genes implicated in developmental biology and gene expression , as did cytokine signaling in the immune system (p = 0.001). Figures We investigated whether changes in DNA methylation in response to the AINS were related to HMW adiponectin response. We observed significant associations between the changes in HMW adiponectin and the methylation status of 487 CpG loci following the AINS . However, given the potential biological relevance of these adipogenic gene methylation changes, we sought to validate the findings using an alternate platform. EpiTYPER analysis confirmed statistically significant associations between the changes in HMW adiponectin and the methylation status of CpG loci located on EGR2 and MED4 (Figure 4This study demonstrated that whilst an AINS did not affect HOMA‐IR in the total cohort, it maintained HMW adiponectin concentration, coincident with upregulated adiponectin receptor ADIPOR1 and ADIPOR2 mRNA expression in weight‐stable overweight and obese adolescents. Furthermore, modulation of the CpG methylation status of adipogenic genes was related to the HMW adiponectin response. Interestingly from a personalized nutrition perspective, HOMA‐IR decreased in a sub‐cohort of participants considered to be responders, despite equivalent compliance to nonresponders. Although baseline BMI was comparable between groups, pretreatment responders were more insulin resistant, with higher total and LDL cholesterol concentrations. We provide proof‐of‐principle evidence that intervention response varies by baseline metabolic phenotype, wherein an AINS may have the potential to attenuate IR in high‐risk overweight and obese adolescents.Adiponectin is an insulin‐sensitizing adipocytokine that preserves hepatic insulin sensitivity via a number of mechanisms including increased AMP‐activated protein kinase phosphorylation and reduced expression of gluconeogenic enzymes.Adiponectin mediates its insulin‐sensitizing effects via its receptors ADIPOR1 and ADIPOR2, the expression of which are downregulated in obesity.Although DNA methylation is heritable, the epigenome is flexible and responsive to environmental stimuli.The interrelationship between adiponectin and insulin sensitivity is interesting. Thiazolidinedione treatment is known to increase adiponectin in adults with T2D, but only 50–70% also show improved insulin sensitivity.Within the personalized nutrition paradigm, the objective is to identify biomarkers of risk and response from the baseline metabolic phenotype. To this end, our group and others have demonstrated that baseline dietary, anthropometry, physical activity, biochemistry, and gut microbiota explain up to half of the variability in intervention response.While the strength of this study lies in its robust study design, some limitations exist. Firstly, study participants were predominantly insulin sensitive which may have limited potential efficacy of the AINS intervention. Also, HOMA‐IR is a useful fasting, but nevertheless limited, surrogate measure of IR. Postprandial glycemic control can vary greatly, which is not always apparent in the fasted state.In conclusion, the results of this study are highly relevant within the current prevalence of pediatric obesity. Here we demonstrated that an AINS did not alter IR but did maintain HMW adiponectin and upregulate PBMC adiponectin receptor mRNA expression in overweight and obese adolescents. Furthermore, we showed that the HMW adiponectin response was related to epigenetic modulation of adipogenic genes. From the personalized perspective, the anti‐inflammatory nutrients attenuated obesity‐induced IR in those adolescents with an adverse baseline metabolic phenotype. Importantly, these improvements were observed in the absence of weight loss. Given the challenges associated with weight management, this may represent an attractive preventative measure, which focuses on nutrient inclusion rather than energy restriction.The authors declare no conflict of interest.Supporting MaterialsClick here for additional data file.


Untargeted metabolomics may reveal preventive targets in cognitive aging, including within the food metabolome.A case‐control study nested in the prospective Three‐City study includes participants aged ≥65 years and initially free of dementia. A total of 209 cases of cognitive decline and 209 controls  with slower cognitive decline over up to 12 years are contrasted. Using untargeted metabolomics and bootstrap‐enhanced penalized regression, a baseline serum signature of 22 metabolites associated with subsequent cognitive decline is identified. The signature includes three coffee metabolites, a biomarker of citrus intake, a cocoa metabolite, two metabolites putatively derived from fish and wine, three medium‐chain acylcarnitines, glycodeoxycholic acid, lysoPC(18:3), trimethyllysine, glucose, cortisol, creatinine, and arginine. Adding the 22 metabolites to a reference predictive model for cognitive decline  substantially increases the predictive performance: cross‐validated Area Under the Receiver Operating Curve = 75% [95% CI 70–80%] compared to 62% [95% CI 56–67%].The untargeted metabolomics study supports a protective role of specific foods  and various alterations in the endogenous metabolism responsive to diet in cognitive aging. The Three‐City cohort of older persons with a 12‐year follow‐up for cognition is leveraged to identify, in the serum of initially non‐demented participants, metabolites associated with subsequent cognitive decline. Untargeted metabolomics reveals a signature of 22 metabolites improving cognitive decline prediction. It includes diet‐derived metabolites , as well as endogenous metabolites responsive to diet. Dementia and its main form Alzheimer's disease (AD) are leading causes of accelerated cognitive decline (CD), however are lacking etiological treatment. As pathological processes are thought to evolve over years before CD becomes apparent, early prevention through diet management may be of critical importance.Metabolomics may enable the identification of new pathways and preventive targets in cognitive aging and dementia, particularly within the food metabolome, that is, the part of the human metabolome derived directly from food digestion. Metabolomics provides a global picture of individuals’ biological status, as it simultaneously measures a wide profile of metabolites in biofluids, including diet‐derived metabolites and endogenous metabolites modulated by dietary intake.Epidemiological studies investigating metabolic changes in dementia have only recently emerged, identifying new biomarkers of diagnosis and prognosis.We therefore applied untargeted metabolomics analysis on the serum of participants from a large, well‐established cohort on dementia, who were initially free of dementia at the time of blood draw (baseline) and provided repeated measures of cognition over 12 years, to identify an early serum metabolomics signature of subsequent CD.22.1n = 2104], Dijon [n = 4931], and Montpellier [n = 2259]).The Three‐City (3C) study is a French population‐based cohort on dementia initiated in 1999–2000, including 9294 non‐institutionalized older persons aged ≥65 years selected from the electoral rolls of three cities , diabetes (fasting glucose ≥7.2 mmol L−1 or specific medication) and fasting plasma levels of glucose, cholesterol, and triglycerides (measured by routine enzymatic methods). ApoE‐ε4 genotype was defined as carrying at least one ε4 allele vs absence of ε4 allele. Lifestyle factors included regular physical activity ; smoking status; alcohol consumption; regular consumption of main food/beverage groups. For physical activity, data were missing in 17% of the samples and a specific missing category was created. For all other covariates, missing values were <2% of the samples and the reference category was assigned to missing data  or the median value (for continuous variables).At baseline, regularly consumed medications were recorded and cardio‐metabolic risk factors were assessed, including BMI . Parameters used for peak detection, grouping, retention time correction, quality checks, and signal drift correction are detailed in m/z, and relative intensity was obtained and used for correlation analyses. Ions annotated as adducts, isotopes, or fragments of the same metabolite or with high analytical variability or too low intensity according to predefined thresholds were discarded, leaving a matrix of 301 ions for LASSO regression analysis.LCMS data was processed using Galaxy WorkFlow4Metabolomics, an open access web‐based platform  conditional logistic regression was used to select a set of metabolites associated with the odds of developing CD over 12 years in the case‐control study  of each selected metabolite for greater versus slower CD .n = 1136) of metabolomic profiles were also estimated. Analyses were controlled for the False Discovery Rate (FDR) using Benjamini–Hochberg procedure. Pearson correlations between ions of interest were also examined. Statistical analyses were performed using the R software version 3.3.2. Penalized and un‐penalized conditional logistic regressions were fitted as stratified discrete‐time Cox proportional hazards models using the penalized and survival R packages, respectively.Moreover, several supplementary analyses were conducted to assist in metabolite identification and interpretation of findings. Pearson correlations of the 22 metabolites were estimated from the signature with food/nutrient intakes assessed by the brief baseline FFQ or the comprehensive FFQ and 24‐h dietary recall collected two years later . In contrast, compared to controls, cases with greater CD consumed a higher number of medications, had higher blood glucose and were more often diabetics, carried more often the ApoE‐ε4 allele and practiced less regularly exercise . The frequency of consumption of main food groups were similar between groups except for chocolate that was more frequently consumed among controls (p < 0.01). Differences in intakes of more detailed food groups and nutrient/food compounds is provided in The participants were 76 years old on average at baseline; 66% were female and 29% had reached secondary school or over Table . Partici3.1Figure Table m/z values, retention times, elementary composition and MS/MS fragments) and associated information supporting identification of the 22 metabolites are given in sn‐glycero‐3‐phosphocholine (lysoPC(18:3)), cortisol, undecanoylcarnitine/4,8 dimethylnonanoylcarnitine, arginine, lauroylcarnitine) and six unidentified metabolites  increased the predictive performance for cognitive decline from a cross‐validated AUC of 62% (95% CI 56–67%) to 75% (95% CI 70–80%) (3.1.1m/z 497.2383), a diterpenoid metabolite previously reported as a robust biomarker of coffee intake.m/z 211.1441, 15th‐ranked in bootstrap selection), another known biomarker of coffee intake,m/z 195.0876, third‐ranked) was unexpectedly associated with higher odds of CD (OR = 1.75).The metabolites with the highest rate of selection , a biomarker of citrus intakem/z 251.1278, fourth‐ranked: OR = 0.74), a structural derivative of a reported fish biomarker, 3‐carboxy‐4‐methyl‐5‐propyl‐2‐furanopropionic acid (CMPF),m/z 271.2056, seventh‐ranked; OR = 0.69) possibly linked to red wine intake (see below correlations with dietary intake), and iii) cyclo , a diketopiperazine reported in chocolate, and other fermented and heated foods.The second‐ranked metabolite was identified as proline betaine , cortisol  and a medium‐chain acylcarnitine . In contrast, six of the endogenous metabolites were associated with greater odds of CD, including GDCA , glucose , creatinine , trimethyllysine , L‐arginine , and lauroylcarnitine . Myristoylcarnitine  were less clearly associated with CD.Three endogenous metabolites were associated with lower odds of CD, including lysoPC(18:3)  when further adjusting for ApoE‐ε4, diabetes and physical activity. Only the association of glucose to greater CD was attenuated when adjusting for diabetes, as expected; and the association of myristoylcarnitine with lower CD arose when further adjusting for ApoE‐ε4 status and physical activity (OR = 0.81).m/z 129.0658, 160.1331, 271.2056, 159.0276, 245.0768, and 256.6796) remain unidentified. Their spectral data are provided in Despite our efforts, six ions (3.2r = 0.39 and r = 0.37 respectively) and hydroxycinnamates , while unexpectedly, caffeine was not . Moreover, caffeine was not significantly correlated with other dietary sources of caffeine, including tea and chocolate (p > 0.13), while soda drinks, a known source of caffeine, were not examined as poorly consumed in this older population. To provide additional insight on the relationship between coffee and CD in our cohort, we analyzed the correlations between all ions of the untargeted metabolomics profile (irrespective of the signature) and the intakes of coffee and its major polyphenol class, hydroxycinnamates. Five ions/metabolites were significantly correlated with coffee intake, including atractyligenin glucuronide and cyclo(leucyl‐prolyl) that had the strongest correlations  showed high correlations with coffee intake (r = 0.39), hydroxycinnamates (r = 0.26), atractyligenin glucuronide (r = 0.55), and cyclo(leucyl‐prolyl) (r = 0.38). This unidentified ion , the major metabolite of caffeine, and trigonelline , previously proposed as a biomarker of coffee intake,r = 0.26 and r = 0.18) and showed no association with CD . In conclusion, atractyligenin glucuronide, cyclo(leucyl‐prolyl), and m/z 96.0444 were the best markers of coffee intake and were all associated with a reduced risk of CD while caffeine, paraxanthine, and trigonelline reflected coffee intake much less accurately and were inconsistently associated with CD.In supplementary analyses, we examined the correlations between the 22 serum metabolites from the signature and food/nutrient intakes recorded in the 3C cohort , total citrus , citrus‐specific flavanones , and vitamin C intake  but not with citrus fruit intake . CMPFP was not correlated with any food/nutrient (r = 0.37). Both compounds are gut microbial metabolites of furan fatty acids that are found in high amounts in marine fish.r = 0.27, p < 0.001, and r = −0.02, p = 0.93, respectively).Proline betaine was correlated with citrus juice intake (m/z 271.2056) correlated with wine intake (r = 0.34), red wine intake (r = 0.25), caloric intake (r = 0.19), polysaccharides (r = 0.21), alcohol (r = 0.27), and major wine polyphenols including dihydroflavonols (r = 0.25), proanthocyanidins , stilbenes (r = 0.26), and lignans (r = 0.24) . Cyclo correlated with chocolate intake , PACs , the major polyphenols of cocoa, and serum levels of theobromine , a purine alkaloid primarily found in chocolate in Western diets.r = 0.20) and although not selected in the 22 metabolite‐signature, was found negatively associated to CD (univariate OR for 1SD‐increase = 0.85).One unidentified compound m/z 1.2056 co4Using untargeted metabolomics, we identified in the serum of older persons free of dementia, 22 metabolites associated with subsequent CD over 12 years. Several metabolites were related to coffee intake including atractyligenin glucuronide and cyclo(leucyl‐prolyl), two biomarkers of coffee intake, that clearly suggest a protective association of coffee consumption with CD in this older population. There is biological and epidemiological evidence supporting a protective role of coffee on neurological function. Observational studies, including the 3C study,Proline betaine, a well‐validated biomarker of citrus intake,Cyclo and theobromine, derivatives of cocoa products, showed lower odds of CD, which is in accordance with previous observational results on chocolate intake and reduced CD,r = 0.44) between TML and the gut microbial metabolite of carnitine trimethylamine‐N‐oxide (TMAO), which has been linked to cardiovascular diseases.The signature included several endogenous metabolites previously reported to be dysregulated in metabolic syndrome or early in the course of AD, for example, the three acylcarnitines . Changes in plasma acylcarnitines are indicators of incomplete fatty acid beta‐oxidation in mitochondria and have been associated with overfeeding, high‐fat diets and metabolic syndrome.The findings regarding glucose and creatinine are in accordance with previous literature,Our study has major strengths, including a population‐based prospective design with repeated cognitive assessment over up to 12 years, a rigorous case‐control sampling inspired by incidence density sampling (as recommended for nested case control studies), and the use of state‐of‐the art methods for both untargeted metabolomics and statistical modeling. However, some limitations should be stressed. New findings from this discovery study will require external validation. Only the accumulation of high‐quality metabolomics studies performed on independent prospective cohorts with complementary analytical platforms will allow developing a consolidated metabolic signature that can be used universally to predict later CD. Although we controlled for major possible confounders, we cannot rule out that other factors might have influenced the metabolic profiles, and residual confounding is still possible as in any epidemiological study. Moreover, data collected by dietary surveys is prone to measurement error, thus secondary analyses of correlations with food and nutrient intakes should be interpreted with caution.In conclusion, we discovered in non‐demented participants from a prospective cohort, a serum signature of subsequent CD over 12 years, which increased the predictive ability beyond that of standard predictors by 13%. The top metabolites were derived from food/beverages and suggested a protective association of coffee, cocoa, and fish with CD, while possible negative effects of citrus juice and caffeine deserves further investigation in focused studies. The signature also revealed endogenous metabolites related to cardiometabolic health and known to be negatively affected by an unbalanced diet. Whether replicated in independent cohorts, our results will provide new targets for preventive/therapeutic nutritional strategies against CD in older persons.The authors declare no conflict of interest.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.