We report the case of an HIV-positive female patient with neurofibromatosis type 1 who was treated for recurrent peptic ulcer disease and later developed diabetes mellitus and chronic diarrhoea. A metastasising somatostatinoma was histologically proven and evidence of a concomitant gastrin-producing neuroendocrine tumour was found. Neuroendocrine tumours (NETs) are very rare neoplasms originating from a wide variety of endocrine and nervous system tissue with the ability to produce different hormones. A somatostatin- and gastrin-secreting NET in a patient with HIV has not been reported in the literature, to the best of our knowledge. We discuss oncogenic pathomechanisms related to the underlying conditions and propose stringent monitoring for tumours in HIV-positive patients with phakomatoses as well as initiation of antiretroviral therapy. Neuroendocrine tumours (NETs) are neoplasms originating from a wide variety of endocrine and nervous system tissues with the ability to produce different hormones. Pancreatic hormone-producing NETs are often associated with specific clinical manifestations, resulting from the excessive production and action of the respective hormone.We report on a 30-year-old HIV-positive female patient with neurofibromatosis type 1 (NF1) who presented with Zollinger-Ellison syndrome and later developed diabetes mellitus and chronic diarrhoea. A somatostatinoma was proven histologically and we found laboratory evidence for the concomitant production of gastrin by the tumour. Somatostatinomas are very rare tumours, and a somatostatin- and gastrin-secreting NET in a patient with HIV has not been reported. We discuss oncogenic pathomechanisms related to the underlying conditions and propose stringent monitoring for tumours in HIV-positive patients with phakomatoses.Helicobacter pylori eradication. Her past medical history included visits to the Plastic Surgery Department for removal of a plexiform neurofibroma with enucleation of her right eye.A 30-year-old woman had been seen at the Surgical Department over the past five years for recurrent upper gastro-intestinal bleeds. Peptic ulcer disease was proven gastroscopically on four occasions. She was repeatedly treated with proton-pump inhibitors and on one occasion received empiric triple therapy for 1 She had tested HIV-positive a few months earlier, with a baseline CD4 count of 290 cells/µL. One week prior to admission, diabetes mellitus was diagnosed by the local primary healthcare clinic and she was started on metformin 850 mg bd. She then presented to the Medical Department with a four-day history of diarrhoea without melaena or epigastric pain.Clinically, with numerous neurofibromata, one big plexiform neurofibroma on the right side of her face, several café-au-lait spots and axillary freckling, she fulfilled the criteria for NF1.9 cells/L. The patient received intravenous fluids and antibiotics. Insulin was commenced and antiretroviral therapy (ART) with TDF, 3TC and EFV was started.On admission, she was moderately dehydrated with features of NF1  and 1b. Abdominal ultrasound demonstrated several solid, round lesions in the liver that were confirmed on computed tomography . Liver bA malignant, metastatic neuroendocrine tumour secreting somatostatin and gastrin was diagnosed. Unfortunately, the patient died soon after diagnosis.2 Gender distribution is equal and mean age at presentation is between 51 and 53 years. Occasionally, additional hormones such as glucagon, calcitonin, insulin, gastrin or others are produced. Somatostatinomas often metastasise early and present late, resulting in a poorer prognosis.2 Whilst non-metastatic somatostatinomas with full tumour resection can be cured, metastatic forms often have a fatal course as they are diagnosed late. Appropriate surgery combined with chemotherapy results in an average 5-year survival of about 59.9%.2Somatostatinomas are extremely rare neuroendocrine tumours with an estimated annual incidence of 1 in 40 million per year, arising from the delta cells in the pancreas or, in about 40% of cases, from the duodenum.3 Somatostatinomas in NF1 occur with a higher frequency and are located more often in the duodenum than in patients without NF1, in whom pancreatic tumours dominate. NETs located in the duodenum tend to present less often with a somatostatinoma syndrome but rather with local or non-specific symptoms.3The association between NF1 and somatostatinomas is well documented. Fendrich et al. reported a case of NF1 and a duodenal somatostatinoma, and found 36 other cases in the literature until 2004, of which only 14 involved metastasis.4 It comprises diabetes mellitus (suppression of insulin), steatorrhoea and cholelithiasis (inhibition of cholecystokinin and biliary motility). Additionally, patients often have general symptoms such as nausea and vomiting, abdominal pain and weight loss. Duodenal somatostatinomas might present with abdominal pain, duodenal obstruction, gastro-intestinal bleeding or jaundice, owing to local growth of the tumour.2Most somatostatinomas are symptomatic. The full clinical picture of the somatostatinoma syndrome, characterised as an inhibitory syndrome, was initially reported by Krejs et al. in 1979.The differential diagnosis is wide, depending on the patient's presentation. It includes, amongst others, refractory diabetes mellitus and other endocrine conditions, carcinoids and gastro-intestinal malignancies, pancreatitis, inflammatory bowel disease, coeliac disease, irritable bowel syndrome or even depression.NF1, originally described by Friedrich von Recklinghausen in 1882, is a fairly common hereditary disease that is autosomal-dominantly inherited and occurs in 1:3500 births. It forms part of the neuro-cutaneous syndromes or phakomatoses, a group of genetic conditions predominantly involving tissues of ectodermal origin, mainly the nervous system, skin and eye. NF1 is characterised by the slow evolution of tumour lesions in childhood and adolescence, as well as by a tendency to form hamartomas and a disposition to fatal malignant transformation. The mutated gene, encoding the protein neurofibromin (17q11.2), is a tumour-suppressor gene.5 Patients with this disorder are predisposed to both benign and malignant tumours of neurogenic and non-neurogenic origin. NF1 reduces average life expectancy by 10–15 years, with malignant tumours being the most common cause of death.Involved mechanisms are rat sarcoma viral oncogene homologue (RAS)-mitogen activated protein kinase (MAPK), mammalian target of rapamycin (mTOR) and P21 protein (Cdc42/Rac)-activated kinase (PAK1).6In addition to NF1, our patient was HIV-positive with a low CD4 count. HIV infection is strongly associated with specific malignancies. Kaposi's sarcoma (KS), non-Hodgkin lymphomas (NHLs) and invasive cervical cancer are AIDS-defining illnesses.11Several non-AIDS-defining malignancies appear more common amongst HIV-positive patients, and their incidence is increasing; these include invasive anal carcinoma, Hodgkin lymphomas, skin cancers, leukaemia, lung cancer, multiple myeloma, prostate cancer and others.12 In particular, the associations of Kaposi's sarcoma and primary effusion NHL with human herpes virus type 8 (HHV 8), of primary CNS lymphoma and NHL with Epstein-Barr virus (EBV) and of invasive cervical cancer with human papillomavirus (HPV) are well documented.15Different pathomechanisms, direct and indirect, are involved in the oncogenesis in HIV. Opportunistic co-infections with oncogenic viruses can result in HIV-associated malignancies. Here the role of viral encoded micro-RNA is under investigation.16 Impaired T-cell surveillance in particular may lead to insufficient elimination of transformed cells, resulting in oncogenesis. Furthermore, the HIV TAT protein, a nonstructural protein secreted by infected cells and taken up by uninfected cells, seems to be involved in the pathomechanism of HIV-related malignancies. It has been found to deregulate cellular genes (as pRb2/p130) that work as onco-suppressor proteins.18Immune deficiency itself seems to play a role in HIV malignancies.20 As mentioned above, the mTOR pathway disinhibition is also one of the pathomechanisms involved in the oncogenesis in NF1. Additional mTOR activation in the setting of HIV infection as a compounding contributor may confer a 'second hit’, leading to the question of the potential use of mTOR inhibitors in the treatment of these patients. This point also raises the interesting question of whether HIV patients with an increased risk for malignancies develop these at earlier ages and whether these tumours are more aggressive.Recently, hyperactivation of mTOR has been found to play a role in different aspects of HIV pathology including HIV-associated nephropathy (HIVAN), HIV encephalopathy, and HIV-associated and non-HIV-associated malignancies.People living with HIV and/or AIDS have a significantly increased risk of developing malignancies, as have patients with neurofibromatosis (and other phakomatoses). Different mechanisms are involved in these two independent pro-oncogenic diseases, and there are no data on incidence or prevalence rates for patients affected by both conditions. We assume that these rates might be higher than for HIV or NF1 alone.Therefore, in a setting of high HIV prevalence – such as South Africa – we suggest regular HIV testing in patients with NF1 and other phakomatoses. Frequent follow-up (e.g. 6-monthly) with close monitoring for malignancies and further diagnostic work-up, where a tumour is suspected, is encouraged. Because the risk resulting from the genetic condition is not modifiable, the aim can only be to reduce the tumour risk from HIV infection and immunosuppression. We therefore recommend starting ARVs irrespective of CD4 counts in such patients.


Polypharmacy is increasingly common in the United States, and contributes to thesubstantial burden of drug-related morbidity. Yet real-world polypharmacypatterns remain poorly characterized. We have counted the incidence ofmulti-drug combinations observed in four billion patient-months of outpatientprescription drug claims from 2007–2014 in the Truven HealthMarketScan® Databases. Prescriptions are grouped into discrete windowsof concomitant drug exposure, which are used to count exposure incidences forcombinations of up to five drug ingredients or ATC drug classes. Among patientstaking any prescription drug, half are exposed to two or more drugs, and 5% areexposed to 8 or more. The most common multi-drug combinations treatmanifestations of metabolic syndrome. Patients are exposed to unique drugcombinations in 10% of all exposure windows. Our analysis of multi-drug exposureincidences provides a detailed summary of polypharmacy in a large US cohort,which can prioritize common drug combinations for future safety and efficacystudies. Similarly highprevalence is reported in other countries , andexacerbated by clinical practices enabling overprescription and insufficientmonitoring11. Drug-related morbidity has becomea substantial healthcare burden: in the United States, adverse drug reactions areprevalent , serious (among top 10 commoncauses of death12), and expensive.Concomitant use of multiple prescription drugs (‘polypharmacy‘) isincreasingly common, with 10% of the population15. But drugscan also interact to increase risk beyond ‘the sum of the parts’,either by canceling an intended drug action, enhancing existing risks, or creatingnew risks. It’s estimated that over 20% of adverse drug reactions are due tounderlying drug interactions17, and that risk of drug interactionincreases with the number of drugs taken18. However, despite increasing awareness of morbidity relatedto polypharmacy, multi-drug exposure patterns remain poorly characterized.Exposure to multiple drugs puts patients at additive risk of each singledrug’s potential adverse outcomes. In a study of an elderly cohort, thestrongest predictor of a potentially harmful medication was the number of drugprescriptions19. The relativestrengths of insurance claims for characterizing population-level drug use are thatdata reflect prescriptions that are actually dispensed to the patient, and captureprescription information across very large cohorts. As for most sources of drug usedata, whether patients actually ingested the drugs remains a limitation.Insurance claims records enable analysis of prescription practices in a large patientcohort, even for drug regimens that would be rare in smaller cohorts. The 21stCentury Cures Act, enacted in December 2016, recognizes the value of, and mandatesthe use of, observational patient-experience data, such as insurance claims, fordrug surveillanceet al. have reported on co-prescription trendsusing self-reported data from a small but nationally-representative cohort of 10,000NHANES participants. The frequency of some drug-pair exposures among elderlyparticipants was also reported2. Toour knowledge, ours is the first study to quantify the incidence of specificcombinations of more than two drugs. Here, we publish a dataset of multi-drug exposure incidence in a large insured cohortin the United States, both in terms of drug ingredients and drug classes. We analyzeoutpatient prescription drug claims from the Truven Health MarketScan®Research Databases, which contain health coverage records for over 100 millionemployees, dependents, and retirees in the United States from 2007–2014,amounting to over 4 billion months of patient observation. This dataset will benefit researchers who study multi-drug safety or efficacy. Themost common multi-drug combinations can be prioritized for subsequent studies ofmulti-drug safety or efficacy. As a side benefit, by mapping drugs to disease basedon indications, the dataset can also provide a summary of comorbidities that drivethe observed prescription trends.This dataset will also benefit practitioners by enabling risk stratification ofpatients based on the multi-drug combinations they are on; for example, the datasetenables analyses identifying associations of specific drug combinations with healthoutcomes (such as emergency department visits), which could enable patient riskstratification at the time of medication reconciliation.Ultimately combined analyses spanning both safety and risk stratification will enablesystematic progress towards safe polypharmacy. With roughly 40 million individualsexperiencing polypharmacy in the US and as many as 10% of all adults worldwide, theexistence of such datasets is crucial for a data-driven quantification of which drugcombinations are risky.http://med.stanford.edu/phs/phs-data-center.html. Thesedatabases represent the health services of approximately 100 million employees,dependents, and retirees in the United States with primary or Medicaresupplemental coverage through privately insured fee-for-service,point-of-service, or capitated health plans. The Commercial Claims andEncounters, and Medicare Supplemental and Coordination of Benefits populationscomprise 90 and 10% of the total cohort respectively, with a mean age of 33 and73 years and gender fraction of 49 and 45% male, creating a combined cohort witha mean age of 37 years and 48% male. Patients are observed for a median of 29months. We analyzed the outpatient prescription drug claims of 150 millionpatients over 4.3 billion months of patient enrollment. We focus on outpatientprescriptions since 90% of all prescriptions are in the outpatientsetting20, andinpatient drug treatment patterns differ substantially.Prescription drug claim data were derived from the Truven Health MarketScan 2007to 2014 Commercial Claims and Encounters and Medicare Supplemental andCoordination of Benefits Databases, which were accessed via the Stanford Centerfor Population Health Sciences Data Core. Further details about the Data Coreand its operating procedures are available at 21), adding drug ingredients occurring commonly in the TruvenHealth MarketScan Databases, and removing vaccines, and vitamins and minerals(which are more often obtained over-the-counter than by prescription).We curated a set of 1429 drugs, defined by RxNORM ingredient level, by beginningwith the 1165 drug ingredients occurring in all of DrugBank, RxNORM, and UMLS. We built a mapping of NDCs to RxNORM-defined drugingredients using the NLM’s RxMix API to match on strings containingdrug names, first with strict matching (which maps the majority of NDCs withvery low error), then with approximate string matching .Combination drugs  count as exposure to each drug ingredient . The approximate drug cost per day was calculatedas the median payment-per-days-supply for all patient orders of that drug. Drugsare also classified by Anatomical Therapeutic Chemical (ATC) class at the secondlevel (‘therapeutic main group’), by mapping RxNORM identifiersto ATC codes. The 864 unique drug ingredients in the dataset map to 79 uniquesecond-level ATC classes.To count concomitant drug exposures from prescription claims, we first scanneddrug prescriptions into discrete exposure windows. We defined exposure periodsas non-overlapping 30-day windows. We selected a 30-day window because it is themost common prescription duration, and thus a natural timescale forprescriptions. A patient is considered exposed to a drug starting from the datethe prescription and for the duration of the days-of-supply. If any of thosedays overlap with a window, the patient is considered exposed in that window. This meAs a known limitation, the method overestimates exposures, and thus concomitantexposures, if a patient does not take the prescription for its full duration. Asshown in Using this method, individual patient prescription claims were converted intodrug exposures in discrete windows , resultiThere are two ways to count multi-drug exposure: exposure to an‘exact’ set of drugs , and exposure to‘at least’ a particular set of N-drugs . Each of these variants captures valuableinformation: ‘exact’ counts quantify the absolute number ofconcomitant drug exposures, and how many patients are exposed to a precise setsof drugs; ‘at least’ counts are important for knowing allpatients exposed to any given drug combination. See example shown in 22.This method counted 220 million unique ‘exact’ drug combinationsexposures, with patients exposed to a median of 2 drugs and 95th-percentile of 8drugs per window . InapprTo count ‘at least’ multi-drug exposures, we created a drug-basedindex to the summarized 220 million ‘exact’ counts.  We then performed an intersect operation for each‘at-least’ drug combination of interest. Counting all possibledrug combinations is infeasible, and unnecessary since most combinations arenever observed. The challenge was to create a list of N-drug combinations likelyto have high concomitant exposures. We achieved this with a‘greedy’ approach of constructing N-drug combinations fromN-minus-1 subset drug combinations observed in at least 1000 exposure windows,for each of N=2, 3, 4 and 5 drugs.An additional metric of interest is the extent to which drug combinations areconcomitant beyond what would be expected by chance, given their marginalfrequencies. Drug combinations’ overrepresentation was defined as theratio of the observed-to-expected drug combination incidence in two ways: first(for N>1) based on single-drug frequencies, which gives the overalloverrepresentation; and second (for N>2) based on the minimum of each ofN permutations of (N-1)+1 drug subsets, which is greater than the single-drugoverrepresentation, and gives the overrepresentation of the drug combinationbeyond its subsets. We repeated these computations of ‘exact’ and ‘atleast’ exposure counts, and their overrepresentation, for the 79second-level ATC drug classes. Second-level ATC drug class names were extractedfrom the website of the WHO Collaborating Centre for Drug StatisticsMethodology. Though one drug ingredient can map to multiple ATC classes, wecount only the primary class. Continuing the example in https://github.com/katieq/QuantifyingPolypharmacy). To avoiddisclosing the format of the Truven Health Marketscan Databases, the code beginsat the step of analyzing prescription data extracted into a data-frame withcolumns for a patient identifier, drug identifier, age of prescription, and daysof supply.Code used to generate the dataset is available on a public github repository(The dataset of exposure counts for drug and drug-class combinations is publiclyavailable online at Dryad (Data Citation 1)in 12 tab-delimited data files and a README.txt file. The tab-delimited data filesare outlined below and in N=1-to-5 drugs in five files, with one row per combination.All single drugs (N=1) and drug pairs (N=2) are included; for N=3-to-5, drugcombinations with at least 10,000 exposure counts are included. Exposure countsbelow 100 patient-windows are reported as ‘<100’ toprotect patient privacy. Each row contains N+5 tab-delimitedcolumns comprising: the name for each drug ingredient, the count of windows withconcomitant exposure to this drug combination, potentially concomitant withadditional drugs (atleast_exposure_count), the count of windowswith concomitant exposure to this drug combination and no additional drugs(exact_exposure_count), the ratio of the two previouscolumns (fraction_exact), the ratio of theatleast_exposure_count to the total number of observedwindows with any prescription ,overrepresentation beyond expected based on marginal frequencies of single drugs(observe_per_expect_1s) and (N-1)+1 drug subsets(observe_per_expect_N1), and an estimate of the daily costof the drug combination (estimate_drug_combo_cost_per_day).Data Record 1 contains the exposure incidences for the most common combinationsof N=1-to-5 drug classes, with one rowper combination. As for Data Record 1, all single drug class (N=1) and drugclass pairs (N=2) are included, and drug class combinations with at least 10,000exposure counts are included for N=3-to-5; exact exposure counts of less than100 are reported as ‘>100’. Columns are equivalent toData Record 1’s, with drug class names replacing drug ingredient names.However daily cost can not be calculated at the drug class level.The contents of Data Record 2 are equivalent to Data Record 1, but for level-IIATC drug classes instead of drug ingredients. The record also contains fivefiles for combinations of Data Record 3 contains two tab-delimited files containing the list of 1429 drugingredients and 93 corresponding ATC level-2 drug classes considered in thisstudy. The drug ingredient file contains one drug per row sorted alphabeticallyby drug ingredient name, with five columns for the drug ingredient name, RxNormCUI number, UMLS CUI, Drug Bank ID, ATC code, second-level ATC drug class name,and estimated median cost per day. The drug class mappings file contains one ATClevel-2 drug class per row sorted alpha-numerically by ATC class, with twocolumns for the ATC code and name. .We validated our method in three ways. First, we compared our computationalmethod’s results to manual counting, by reading the dates and days-supplyfor a random sample of ten patients’ 260 drug prescriptions. The counts ofconcomitant drug exposures matched perfectly, indicating that our method does indeedaccurately extract concomitant drug exposures as intended without errors inarithmetic.does affect the DataRecords. Therefore we set the window duration equal to the most common prescriptionduration. Prescriptions in this claims database are most often for 30 days , with about 20% for 10 or fewer days. Thus a 30-day window is anappropriate timescale to capture changes in drug exposures.Second, we conducted a sensitivity analysis on the duration of the discrete exposurewindow, by counting concomitant drug exposures in a random sample of 10,000 patientswith an exposure window of 10, 20, 30, 40, 50, 60, and 90 days. Since allprescriptions are considered ‘exposures’ for the entire duration ofthe window, longer windows slightly increase the mean drug exposure counts , and thus increase the relative incidence  ofprescriptions with short-durations .Thus the choice of exposure window duration ranking of the common drug combinations, but inaccuratelyestimates the incidence, and thus the ranking, of rare drug combinations. Inaddition, smaller cohorts overestimate the patients exposed to unique drugcombinations, never observed elsewhere in the database: In the complete cohort, 10%of drug combinations are observed only once, but in a cohort of 1e5 patients, thatfraction is 20%. Thus while smaller cohorts are sufficient to rank the incidence ofcommon drug combinations, a large patient cohort is required to accurately estimatethe incidence of drug combinations.Finally, we tested the sensitivity to cohort size, by comparing the drug combinationincidence ranking obtained using the entire Truven Health MarketScan cohort(approximately 100 million patients) to a random sample of 100,000 (1e5) patients,and 1,000,000 (1e6) patients. As expected, analysis of smaller cohorts obtains asimilar between drugs, this will introduce bias inthe resulting single drug and drug combination incidences. However, billing datafrom filled prescriptions are more accurate than alternative sources, such asdoctor’s notes or prescription orders that may go unfilled.The accuracy of this dataset as a summary of multi-drug exposure incidences inthe United States is limited to some extent by the underlying data source andour method of computation. The Truven Health MarketScan Research Databasescohort is commercial claims, and not a fully representative sample of the UnitedStates population. We examine drug exposure based on filled prescriptions, butpatients may take none or only a fraction of the dispensed drugs. Since there isbias on adherence 23. Though patient surveys canoffer information about exposure to over-the-counter drugs and supplements, theyrely on patient memory, and lack the cohort size and accuracy of prescriptionrecords.We only observe and analyze prescription drugs, but over-the-counter drugs andsupplements contribute a significant portion of total drug exposuresOur method scans prescription drug claims according to 30-day exposure windows.Shorter or longer exposure windows would increase or decrease apparentmulti-drug exposures respectively. The size of the exposure window affects drugcombinations’ relative incidence , with longer windowsincreasing the apparent incidence of combinations including drugs with shortprescription durations . Howeverthe rankings are agnostic to exposure window duration with our choice of awindow of 30-days, to match the days-of-supply of the majority ofprescriptions.24 . Nonetheless, our multi-drugexposure dataset provides a ranking of common concomitant prescription drugexposures for a large population in the United States.Finally, our analysis uses all data from 2007–2014, ignoring the likelynon-stationarity of prescription patternsThis summary of multi-drug prescription patterns in a large cohort enables furtheranalysis of the trends, safety, or efficacy of multi-drug use.The common multi-drug combinations identified here can now be prioritized foranalysis of association with adverse health outcomes. An example illustratingthis use case is identifying which of the common 3-drug combinations in DataRecord 1 are most overrepresented in the 30-days prior to Emergency Departmentvisits . It is iget_codrugs.R at the code repository (https://github.com/katieq/QuantifyingPolypharmacy). This couldbe repeated for larger drug combinations using the 3-, 4-, or 5-drug tables.This dataset can be used to profile the common co-exposures for any drugingredient or class of interest. This dataset can now be used to calculate the increased risk of undesirablehealth outcomes associated with a particular set of prescriptions. Such a riskestimate can be used to stratify patients according to risk of future adversehealth events, and then to flag prescription changes that place patients in ahigher risk category, or to identify prescription combination changes that lowerpatients’ risk category. Of course, such risk stratification implies nocausality whatsoever; however, such analyses can provide a succinct report onthe risks experienced by a cohort of similarly-treated patients.How to cite this article: Quinn, K. J. & Shah, N. H. A datasetquantifying polypharmacy in the United States. Sci. Data 4:170167doi: 10.1038/sdata.2017.167 (2017).Publisher’s note: Springer Nature remains neutral with regard tojurisdictional claims in published maps and institutional affiliations.


HIV protease inhibitors kill Thirty-one children from Malawi aged 4–62 months were followed every 3 months and at intercurrent illness visits for ≤47 months (September 2009-December 2011). We compared malaria parasite carriage by blood smear microscopy (BS) and confirmed clinical malaria incidence  in children initiated on HIV antiretroviral therapy (ART) with zidovudine, lamivudine, and either nevirapine (NVP), a non-nucleoside reverse transcriptase inhibitor, or lopinavir-ritonavir (LPV-rtv), a protease inhibitor.We found an association between increased time to recurrent positive BS, but not CCM, when anti-malarial treatment and LPV-rtv based ART were used concurrently and when accounting for a LPV-rtv and antimalarial treatment interaction ; p = 0.03).LPV-rtv in combination with malaria treatment was associated with lower risk of recurrent positive BS, but not CCM, in HIV-infected children. Larger, randomized studies are needed to confirm these findings which may permit ART optimization for malaria-endemic settings.NCT00719602ClinicalTrials.gov  Malaria is highly prevalent in many areas of the world where HIV-infected children live, especially sub-Saharan Africa. Studies have shown that when HIV and malaria are present as co-infections, each disease can enhance the pathogenicity of the other. Moreovein vitro and in vivo data with Plasmodium suggest that HIV PIs kill various life cycle stages of malaria parasites [The World Health Organization (WHO) recommends HIV management with combination antiretroviral therapy (ART), with first line therapy including a non-nucleoside reverse transcriptase inhibitor (NNRTI) and 2 nucleoside reverse transcriptase inhibitors (NRTIs), with few exceptions, and second line therapy including an HIV protease inhibitor (HIV PI) and 2 NRTIs [arasites –7, indicarasites .Because transmission intensity influences the dynamics of malaria infection and intervention efficacy , we sougP1068s was substudy to the International Maternal Pediatric Adolescent AIDS Clinical Trials Network (IMPAACT) study P1060, details of which have been reported elsewhere . BrieflyThe malaria substudy described herein, P1068s, was conducted at three sites with endemic malaria transmission according to published data at the time, which included Kampala, Uganda; Lusaka, Zambia; and Lilongwe, Malawi –14. AnalAll participants from the parent HIV treatment study, P1060 were eligible for inclusion in this malaria substudy. Although not randomized for the primary objectives of the P1068s substudy described herein, it was originally intended the randomization of P1060 would be used for P1068s; however, due to later initiation of this substudy, a number of subjects had switched from the regimen to which they had been originally randomized. Thus, most data collected under P1068s that links treatment to outcome is observational and most analysis was conducted using an “as-treated” approach.Study visits were conducted 2 and 4 weeks after the initiation of treatment, every 4 weeks until week 16, at week 24, and every 12 weeks thereafter, and patients were encouraged to come in for intercurrent illness. Follow up for P1068s paralleled P1060 .History and physical exams were completed at all scheduled and intercurrent illness visits, and children were seen by study physicians at all visits. Heel or finger stick or venous blood were used to prepare thick blood smear using 2% Giemsa for 30 minutes, in addition to dried blood spots  collected on Whatman 903 paper  and handled as previously described  for confIn Malawi, trimethoprim-sulfamethoxazole prophylaxis was administered as per WHO guidelines , 20, andP. falciparum rate (PAPfr2-10) in 2010 of 32%; this encompasses part of the period in which this study was conducted [Malaria transmission in Malawi is perennial and holoendemic, with seasonal increases after the rains from November to April. Malaria mapping analysis in Malawi showed lack of significant changes in transmission between 2000–2010, with a population-adjusted onducted .This substudy was approved by site-specific institutional review boards (IRBs), including the New York University School of Medicine (NYU) IRB  and the Malawian Ministry of Health and Population National Health Sciences Research Committee  and the NIH/NIAID IRB through a reliance agreement with NYU IRB . Each child’s parent or legal guardian provided written informed consent. The study was first opened to accrual on August 19, 2009 with the first patient enrolled September 25, 2009.P. malariae sample from Lilongwe, were reported as P. falciparum. The diagnosis and management of malaria was based readings of blood smears on site, but reported results herein are from reads performed at NYU, with a primary and secondary reads by two microscopists, with discrepant results resolved and a random subset of smears reviewed by a third microscopist.A blood smear was deemed negative if no parasites were seen in > = 200 high-powered fields. All blood smears in the study, with the exception of one http://hydasworldhealth.org/), whose stock smears are read by WHO Level 1-Certified microscopists [Plasmodium falciparum, vivax, ovale or malariae. For the total 60 test slides, the readers’ overall sensitivity was 100%, specificity 92%, and density determination was within +/-25% of the accepted value in 96% of the slides. During the final 2 years of the study, the sensitivity and specificity were both tested at 100%. Separately, DNA was isolated from DBS and parasite genomic DNA was detected using real time from DBS collected for confirmation in parallel to smears.Quality control of blood smear reading was maintained by 30 hours of training which covered both slide preparation, cover slipping, microscope care and maintenance and identification and quantification of malaria parasites, both at NYU and at study sites. Over the time period of the study the NYU microscopists were proficiency tested four times using archived smears provided by Hydas World Health  or confirmed clinical malaria (CCM), defined as a positive BS with diagnosed malaria symptoms, as per WHO guidelines . Only epThirty-one of 288 subjects from the P1060 parent study enrolled in P1068s, and 8 of these 31 children changed regimens based on the primary study before or after enrollment. For this reason we undertook an as-treated analysis, attempting to link observed treatment and observed outcomes. Per-protocol analysis, censoring subjects when they switched regimens, and as-randomized analysis (as randomized for the P1060 parent study) analysis are also provided in Supporting Information  Tables.During this study, another clinical trial suggested that a drug interaction between LPV-rtv and lumefantrine  reduced clinical malaria burden in children on LPV-rtv ART in an area of high transmission intensity . For thiIn addition to evaluation of recurrent malaria events, comparisons of the rate of incident positive BS and CCM per time at risk by HIV treatment type was analyzed using negative binomial models including an offset for time-on-trial and adjusted for potential confounders. HIV treatment was accounted for using the following three time categories of LPV-rtv ART use: LPV-rtv ART assigned at P1060 baseline; LPV-rtv ART duration in months; or majority of time on LPV-rtv ARTReported p-values are not corrected for the number of analyses conducted. Statistical analysis was performed with R software, version 3.1.3.st positive BS and CCM are presented in Supporting Information, From Kamuzu Central Hospital, Lilongwe, Malawi, 31 children were enrolled between September 2009 and December 2011, and demographic information for these patients is summarized in In the as-treated recurrent event analysis, time on LPV-rtv ART prior to positive BS or CCM was not significantly different from NVP ART ; p-value 0.05 . The othIn our P1068s substudy, which was the first of its kind conducted in an area of low to moderate malaria transmission intensity, LPV-rtv in combination with malaria treatment is associated with lower risk of recurrent events of BS, but not CCM, in HIV-infected children.Plasmodium species in the laboratory because of inhibition of these enzymes, of which there are 10 in Plasmodium falciparum, although further investigation is required to clarify the target [HIV protease inhibitors are aspartyl protease inhibitors, and have been hypothesized to exhibit the killing activity for e target , 27, 28.e target , the latA previous study that was conducted in an area of high-intensity transmission has shown similar trends in children: HIV PIs were associated with an overall reduction in malaria for children on LPV-rtv ART . This fiOf note, the as treated recurrent events model also showed that malaria treatment was significantly associated with increased hazard of repeated positive BS and CCM in the presence of NVP ART. This finding was likely due to the strong correlation between receiving a malaria treatment in the current period and having a malaria event in the previous period. We investigated this by including malaria treatment in the model only if there was not a malaria event in the previous period for a subject. This modified version of the model suggested that malaria treatment was associated with reduced hazard of malaria outcomes under all ART regimens, albeit non-significantly due to the small number episodes observed (data not shown). Therefore, one might describe our malaria treatment measure as a mixture of previous illness and malaria treatment.A limitation of our study is our small sample size and the number of children who switched ART regimens. However, because we detected significant findings in a small sample size, and over a long period of time, we believe the effect of LPV-rtv ART may be considerable and as such, a larger study is warranted to investigate and validate this finding.WHO Guidelines now support the use of LPV-rtv ART in children less than 3 years of age because of longer life-expectancy and cost-effectiveness compared to NNRTI ART . WhetherS1 FigA and B: Kaplan Meier Curves depicting time to first (1st) positive blood smear (BS) and time to 1stconfirmed clinical malaria (CCM) case. Overall, we observe a BS positive rate of 0.55 and 0.87 for the 1st year and the 1st two years of follow-up, respectively; for CCM rates are, 0.52 and 0.77 for the 1st year and the 1st two years, respectively. We observe 123 positive BS events, and 109 CCM events over the 97.89 observed person-years. In 1A, shown is the survival proportion and times to 1st positive BS. The median survival times are 408 and 135 days in the randomized to-LPV-rtv ART and randomized to-NVP ART groups, respectively. However, there was an 0.88 infection rate in the LPV-rtv ART group, and an 0.87 rate in the NVP-ART group over follow-up. In 1B, shown is the survival proportion and times to 1st CCM. The median time to 1st CCM, is 417 and 135 days in the randomized to-LPV-rtv ART and randomized to-NVP ART groups, respectively. However, there was an 0.81 CCM rate in the LPV-rtv group, and a 0.73 rate in the NVP-based group over follow-up. Note that for BS, the longest period while on NVP-ART prior to 1st positive BS is shorter compared with LPV-ART, and the figure incorporates subject censoring.(TIFF)Click here for additional data file.S1 File(DOC)Click here for additional data file.S2 File(DOC)Click here for additional data file.S1 TableAs-randomized in P1060 analyses .(DOC)Click here for additional data file.S2 Table(DOC)Click here for additional data file.


Although several studies have explored factors associated with loss to follow-up (LTFU) from HIV care, there remains a gap in understanding how these factors vary by setting, volume of patient and patients’ demographic and clinical characteristics. We determined rates and factors associated with LTFU in HIV care Lilongwe, Malawi.We conducted a retrospective cohort study of HIV-infected individuals aged 15 years or older at the time of registration for HIV care in 12 ART facilities, between April 2012 and March 2013. HIV-positive individuals who had not started ART (pre-ART patients) were clinically assessed to determine ART eligibility at registration and during clinic follow-up visits. ART-eligible patients were initiated on triple antiretroviral combination. Study data were abstracted from patients’ cards, facility ART registers or electronic medical record system from the date of registration for HIV care to a maximum follow-up period of 24 months. Descriptive statistics were undertaken to summarize characteristics of the study patients. Separate univariable and multivariable poisson regression models were used to explore factors associated with LTFU in pre-ART and ART care.A total of 10,812 HIV-infected individuals registered for HIV care. Of these patients, 1,907 (18%) and 8,905 (82%) enrolled in pre-ART and ART care, respectively. Of the 1,907 pre-ART patients, 490 (26%) subsequently initiated ART and were included in both the pre-ART and ART analyses. The LTFU rates among patients in pre-ART and ART care were 48 and 26 per 100 person-years, respectively. Of the 9,105 ART patients with reasons for starting ART, 2,451 (27%) were initiated on ART because of pregnancy or breastfeeding (Option B+) status. Multivariable analysis showed that being ≥35 years and female were associated with decreased risk of LTFU in the pre-ART and ART phases of HIV care. However, being in WHO clinical stage 3 (adjusted risk ratio (aRR) 1.35, 95% confidence interval (CI): 1.20–1.51) and stage 4 , body mass index ≤ 18.4  at ART initiation, poor adherence to clinic appointments  and receiving HIV care in rural facilities  were associated with increased risk of LTFU among ART patients. Being re-initiated on ART once , more than once , and being enrolled at a low-volume facility  were associated with decreased risk of LTFU from ART care.A sizeable proportion of ART LTFU occurred among women enrolled during pregnancy or breast-feeding. Non- compliance to clinic and receiving ART in a rural facility or high-volume facility were associated with increased risk of LTFU from ART care. Developing effective interventions that target high-risk subgroups and contexts may help reduce LTFU from HIV care. Increased access to antiretroviral therapy (ART) in resource-limited settings is an unprecedented success story for HIV programs. As of June 2015, 15.8 million people received ART globally, the majority, 10.7 million in sub-Saharan Africa (SSA).LTFU from HIV care is a major public health concern. LTFU increases the risk of drug resistance and treatment failure in cases where death is not the reason for LTFU. Previous studies showed that patients with treatment interruptions are more likely to develop treatment failure ,5, viralFactors and reasons for LTFU at patient and system levels include advanced HIV disease, older age, male gender, longer distance to clinics in patients not on ART, unregistered transfers out and undocumented deaths in patients on ART –9. FactoEffective early patient tracing may reduce LTFU and improve retention. However, tracing all patients who have missed appointments and those LTFU can be resource and time-intensive. As such, the Malawi guidelines recommend prioritizing tracing patients that miss ART appointment or those eligible for ART . DespiteOptimizing retention by mitigating the varying risk factors for LTFU may greatly impact Malawi’s trajectory towards epidemic control. Retention interventions may be optimised in terms of person, place, and time by identifying individual-level or context specific risk factors that may predispose a patient to be LTFU. This paper describes LTFU and associated factors in pre-ART and ART patients at 12 ART facilities, prior to the implementation of a quality improvement (QI) collaborative aimed at optimizing retention in care in Lilongwe district, Malawi.Data used for this study was obtained from five rural and seven urban ART facilities in Lilongwe District, Malawi. All six high-volume ART facilities, with more than 400 new ART patients per year, used an electronic medical records system (EMRs) for patient management and the remaining low-volume facilities used paper-based systems. Five high-volume facilities were located in urban setting while one high-volume facility was in rural setting. HIV-infected adults were registered for HIV care in the EMRs or paper patient cards. During the observation period, ART eligibility was defined in accordance with the national guidelines: World Health organisation (WHO) clinical stage 3 or 4 or having a CD4 count of ≤ 350 cells/μl. Pregnant and breastfeeding women were also considered eligible irrespective of their CD4 count or WHO clinical stage as Malawi had adopted Option B+ for prevention of mother-to-child transmission (PMTCT) of HIV. Pre-ART patients, HIV-positive individuals who had not started ART (pre-ART patients), were clinically assessed based on WHO clinical criteria every 2 months to determine ART eligibility. For those not eligible based on WHO clinical stage, CD4 was repeated every 6 months when feasible. Therefore, CD4 data were generally available for patients in WHO stage 1 or 2. At each visit, pre-ART and ART patients received co-trimoxazole as prophylaxis to prevent opportunistic infections. ART-eligible patients were initiated on triple antiretroviral (ARV) combination: Tenofovir, Lamivudine, and Efavirenz and routine follow-up visits were scheduled monthly or every two months. Based on the data collection system, subsequent ART clinic appointments were either calculated manually or electronically based on the ARV regimen and prescribed schedule, number of tablets dispensed, and the number of tablets remaining from the previous dispensing visit. ART programme outcomes  were updated on the patient card or in EMRs at each clinic visit or retrospectively. Stopping ART, transferring care or death were also updated retrospectively in facilities with patient tracing systems.This retrospective cohort study used routine clinic data collected from HIV-infected individuals who were 15 years of age or older at the time of registration for HIV care in the ART facilities, between April 2012 and March 2013.before 7 days after a scheduled appointment. Re-initiation was defined as restarting ART treatment after stopping ART for at least 60 days.Pre-ART patients comprised of two categories of patients relative to ART; patients who were not eligible for ART at baseline and those eligible but never started ART for at least three months despite attending clinic consultations. The primary outcome variable was LTFU defined as failure to come to the clinic for at least 60 days from the patient’s appointment date. Patients were classified as (i) “stopped treatment” when they stopped ART during the follow-up period including medical reasons; (ii) “transferred-out” if the patient had a formally recorded transfer to another clinic, (iii) “dead” if the patient died from any cause. Other variables of interest were adherence to clinic appointments and ART re-initiation. Adherence to clinic appointments was defined as having at least 80% of the ART visits a priori. Model results were shown as risk ratios (RR) and adjusted RR (aRR) with 95% confidence intervals (CI).Data were abstracted from individual patient cards, facility ART registers or EMRs from the date a patient registered at an HIV facility up to a maximum follow-up period of 24 months. Patients who initially registered for pre-ART services and later started ART had their data abstracted for pre-ART and for ART follow-up periods. A time-to-event analysis was conducted for pre-ART and ART times, separately. For person-time calculation, the analysis period began when patients enrolled in pre-ART care or initiated ART at the facility. Observation of ‘time at risk’ for LTFU during pre-ART care ended either at the time of an outcome  or at 24 months after pre-ART registration. For ART patients, observation of ‘time at risk’ of LTFU ended either at the time of an outcome  or at 24 months after ART registration. Separate Poisson regression models were used to explore factors associated with LTFU in pre-ART and ART care. A null hypothesis of no within-facility clustering, versus the alternative of some within-facility clustering were tested (p = 0.065). All the analysis did not account for clustering at facility level because within facility variability was not significant. The models were adjusted for age and sex, The study was approved by the Malawi National Health Science Research Committee in Lilongwe, Malawi and by the office of the Associate Director for Science of the U.S. Centers for Disease Control and Prevention in Atlanta, Georgia.A total of 10,812 HIV-positive individuals were enrolled into HIV care at the 12 facilities between April 2012 and March 2013; of these patients, 1,907 (18%) registered for pre-ART care and 8,905 (82%) for ART care. Of the 1,907 pre-ART patients, 490 (26%) subsequently started ART and were included in both the pre-ART and ART analyses, resulting in a total of 9397 ART patients. The majority of patients were enrolled in six high-volume facilities; 1,681 (88%) of pre-ART patients and 8,390 (89%) of ART patients. On average, low-volume facilities enrolled 170 (range 99−221) patients per year while large volume facilities enrolled 1330 (range 403−4133) patients per year. There were no significant differences in demographic characteristics between patients enrolled at low and high volume facilities.Table 1). Median age at enrolment in pre-ART care was 31 years (interquartile range (IQR): 26−38). Median CD4 count was 409 cells/μl (IQR: 290–555); men had lower median CD4 counts compared to women .Among the pre-ART patients, 1,098 (58%) were female (. Median age at ART initiation was 32 years (IQR: 27−38) and the majority (45%) of patients were aged 25−34 years. Among those with documented WHO clinical stage, 50% had WHO stage 3 or 4 conditions. Among ART patients with documented CD4 counts, women had higher median CD4 counts at ART initiation than men, 241 cells/μl and 186 cells/μl, respectively. Of all patients who initiated ART, 2,451 (26%) were pregnant or breastfeeding and started on ART for PMTCT and 8,380 (89%) were enrolled at high-volume facilities.Of the 9,397 ART patients, 6,213 (66%) were female Table 2). In contrast, among 1,681 pre-ART patients enrolled for HIV care at high-volume facilities, 47% were LTFU after 12 months of follow-up; at 24 months, 54% patients were LTFU. There were no patients reported as transferred-out or dead both at 12 and 24 months of follow-up in pre-ART care at high-volume facilities.Median follow-up time for the 1,907 pre-ART patients was 8.5 (2.4−23) months. Overall rate of LTFU was 48  per 100 person-years. Low-volume facilities did not document any LTFU patients at 12 or 24 months of follow-up  per 100 person-years and the median follow-up was 20.9 (IQR 3–23) months. ART outcomes were not documented in 49 patients: 40 enrolled in low-volume facilities and 9 in high-volume facilities. At 12 months post-ART initiation, more patients were reported to be alive and on ART in low-volume facilities than in high-volume facilities (p<0.001) and the difference was still significant at 24 months .In univariable analysis of pre-ART patients, having a body mass index (BMI) < 18.4, baseline WHO stage 3 and stage 4 were associated with an increased risk of LTFU . Being fIn univariable analysis of ART patients, being female, younger age 15−24 years, BMI <18.5, baseline WHO stages 3 and 4 were associated with increased risk of LTFU . Women wIn the multivariable analysis of ART patients, the association between baseline age, BMI, reason for starting ART, adherence to clinic appointments, ART re-initiation and patient volume facility and LTFU remained significant; the risk of LTFU among patients who were not adherent to clinic appointments was more pronounced . After aThis study describes LTFU from pre-ART and ART care at urban and rural HIV facilities with low and high patient volumes. Overall LTFU was 48% and 26% per year in the pre-ART and ART cohorts, respectively. Among the pre-ART and ART cohorts, being 35 years of age or older and female were associated with reduced risk of LTFU. Advanced WHO clinical stage, low BMI at ART initiation, poor adherence to clinic appointments and receiving HIV care in rural facilities on the other hand were associated with increased risk of LTFU among ART patients. Re-initiation of ART and receiving ART care at low-volume facilities on the other hand were associated with decreased risk of LTFU.Our findings highlight critical LTFU issues in pre-ART and ART cohorts. In the pre-ART cohort, we observed an overall LTFU of 48% per year, which falls within the range of 23–88% reported in a systematic review from SSA. HoweverSince July 2016, Malawi implemented the test-and-treat approach for ART initiation. Given rAmong patients on ART, we observed LTFU of 26% per year, higher in high-volume facilities (27%) compared to low-volume facilities 17%). We acknowledge that some of the ART patients may have continued ART elsewhere without a formal transfer-out note, died or simply stopped ART as previous research in Malawi showed . Previou%. We ackWe also observed that high-volume facilities reported higher LTFU rates compared to low-volume facilities. One possible explanation for this may be that high patient-to-provider ratios limit individualized patient education, patient preparation before ART initiation, and ongoing counselling once individuals are on lifelong therapy. Models of HIV service delivery that reduce congestion at health facilities including community-based ART distribution systems, multi-month ART appointments, utilization of other cadres and/or task shifting for adherence counseling, and providing larger quantities of ARVs per visit for stable patients. Similar to previous studies of pre-ART cohorts, we found that being female, older than 35 years, and having a high CD4 count were associated with a lower risk of LTFU 18]. On . On 18].Among those who started ART, patients were less likely to be LTFU again if they returned to care and re-initiated ART. The decreased LTFU after re-initiation of care may also be attributed to the stable lifestyles of individuals in regards to living with HIV and being in care, to the counseling they received upon return to care, or the experience of negative outcomes such as opportunistic infections and weight loss encountered when care was interrupted. We also noted a high risk of LTFU among younger patients, women, those in urban setting, and those who received care at high-volume facilities. In the multivariable analysis, being female was associated with a lower risk of LTFU after adjusting for confounding factors such as age, reason for starting treatment and compliance to scheduled appointment dates. Younger women, those who started ART due to pregnancy or breastfeeding, and those who had never been reinitiated on treatment were more likely to be LTFU, while those reinitiated on treatment at least once were less likely to be LTFU. These factors may be contributors to LTFU in countries that have adopted a universal Test and Treat strategy for pregnant and breastfeeding women. HIV programs that use Option B and B+ strategies will need to employ specific interventions, such as active tracing of patients that miss their appointments, prioritization of pregnant and breastfeeding women to optimize retention to care. On the other hand, receiving care from low-volume facilities where patient-to-provider ratios may be lower and may allow for more patient-centered care was associated with lower risk of LTFU–thereby contributing to better adherence to scheduled clinic appointments. An additional factor that negatively impacted adherence to clinic visits was receiving care in a rural facility. ART reinitiating also negatively influenced the risk for LTFU among people receiving ART in a rural facility. Non-adherence to clinic visits has been reported to be independently associated with all-cause mortality .In Malawi, some high-volume facilities are piloting Demographic Data Exchange (DDE) systems that generate a unique national identification (ID). The ID enable tracking of unique HIV positive individuals at different points across the HIV care continuum. Such approaches facilitate tracking of ART patients who transfer care without a formal clinical documentation. The DDE technology can be utilized to identify patients who access ARVs at another facility and determine patients who transfer or receive ad-hoc care without a formal documentation. Expanding such systems however will also need to consider how such technology can be mirrored in facilities that use paper-based records. Additionally, such systems will need to be implemented in parallel with the development and implementations of privacy laws and guidelines on protecting the confidentiality and security of personal health information.Our study is subject to some limitations that should be taken into account when interpreting the findings. First, some patients might have been classified as LTFU simply because their true status was not known by the facility where they first sought care. We cannot therefore rule out the possibility that some patients transferred their care elsewhere or may have died. Second, we may have underestimated the true incidence of LTFU due to the incomplete documentation at paper-based facilities. Third, it is possible that some of the patients classified as LTFU may have died resulting in an overestimation of LTFU. Fourth, pre-ART and ART data were not available for some patients in paper-based facilities. Since the models were based on variables with variable data, this might have led to selection bias in our estimates. Lastly, we could not explore the effect of patient volume and location of the facility on pre-ART LTFU because of the small numbers of LTFU patients. Despite these limitations, our study findings shed light on additional factors related to LTFU such as adherence to clinic appointment, patient volume, re-initiation of ART and the effect of Option B+ on the overall ART cohort.In summary, higher rates of LTFU were observed in pre-ART care when compared with ART care. Women who started ART because of Option B+ contributed significantly to LTFU. Non-adherence to clinic appointments, and receiving HIV care in a high-volume facility or rural facility were associated with increased risk of LTFU. Data incompleteness within paper-based data systems.is a challenge that calls for standardized operating systems or EMRs that monitor and easily flag LTFU patients in pre-ART and ART care; this may promote actions that reduce LTFU. Interventions that target high risk sub-groups and contexts may also prevent LTFU.


A high proportion of women start pregnancy overweight or obese. According to the developmental overnutrition hypothesis, this could lead offspring to have metabolic disruption throughout their lives and thus perpetuate the obesity epidemic across generations. Concerns about this hypothesis are influencing antenatal care. However, it is unknown whether maternal pregnancy adiposity is associated with long-term risk of adverse metabolic profiles in offspring, and if so, whether this association is causal, via intrauterine mechanisms, or explained by shared familial  characteristics. We aimed to determine if associations between maternal body mass index (BMI) and offspring systemic cardio-metabolic profile are causal, via intrauterine mechanisms, or due to shared familial factors.N = 5327 to 5377 mother-father-offspring trios) showed that increasing maternal and paternal BMI was associated with an adverse cardio-metabolic profile in offspring. We observed strong positive associations with very-low-density lipoprotein (VLDL)-lipoproteins, VLDL-cholesterol (C), VLDL-triglycerides, VLDL-diameter, branched/aromatic amino acids, glycoprotein acetyls, and triglycerides, and strong negative associations with high-density lipoprotein (HDL), HDL-diameter, HDL-C, HDL2-C, and HDL3-C . Slightly stronger magnitudes of associations were present for maternal compared with paternal BMI across these associations; however, there was no strong statistical evidence for heterogeneity between them . Results were similar in each individual cohort, and in the 2-stage analysis. Offspring BMI showed similar patterns of cross-sectional association with metabolic profile as for parental pre-pregnancy BMI associations but with greater magnitudes. Adjustment of parental BMI–offspring metabolic traits associations for offspring BMI suggested the parental associations were largely due to the association of parental BMI with offspring BMI. Limitations of this study are that inferences cannot be drawn about the role of circulating maternal fetal fuels  on later offspring metabolic profile. In addition, BMI may not reflect potential effects of maternal pregnancy fat distribution.We used 1- and 2-stage individual participant data (IPD) meta-analysis, and a negative-control  to examine the association between maternal pre-pregnancy BMI and offspring serum metabolome from 3 European birth cohorts . Circulating metabolic traits were quantified by high-throughput nuclear magnetic resonance metabolomics. Results from 1-stage IPD meta-analysis  characteristics.Our study was designed to unpick whether maternal pregnancy BMI–offspring metabolic traits associations were due to intrauterine mechanisms (involving overfeeding of the developing fetus) or shared familial characteristics (or both of these) by using paternal BMI–offspring metabolic traits associations as a negative control.If maternal associations represent causal intrauterine effects, as opposed to being due to shared familial factors, they should be stronger than paternal associations with the same metabolic traits.We looked at associations between maternal pre-pregnancy BMI and 153 offspring serum metabolic traits, in adolescence and adulthood, in 3 independent European birth cohorts  and compared these to paternal BMI–offspring metabolic traits as a negative control.Paternal BMI was used as a negative control as it will share most of the confounders of the main exposure  but could not directly influence offspring’s metabolic traits via an intrauterine effect.We found very little evidence of a markedly stronger association of maternal BMI with offspring outcomes.Our findings are more supportive of shared familial factors than intrauterine developmental overnutrition mechanisms for associations of maternal BMI with offspring metabolic traits.Interventions to reduce BMI in all family members may be more beneficial for cardio-metabolic health than focusing on reducing maternal pre-conception or pregnancy BMI. Our hypotheses are that 2 key paths could explain the association of maternal pre-pregnancy BMI with offspring future metabolic traits: (i) intrauterine developmental overnutrition and (ii) confounding of this potential effect via shared familial genetics, socioeconomic, lifestyle, and behavioural characteristics . It is ehttp://www.bristol.ac.uk/alspac/researchers/data-access/ethics/lrec-approvals/#d.en.164120), and ethical approval for the Northern Finish Birth Cohort 1966 and 1986 studies was obtained from the Ethics Committee of Northern Ostrobotnia Hospital District, Finland.All study participants provided written informed consent, and study protocols were approved by the relevant local ethics committees. Specifically, ethical approval for the Avon Longitudinal Study of Parents and Children was obtained from the ALSPAC Ethics and Law Committee and the Local Research Ethics Committees (full details at The parent-specific BMI associations with offspring metabolic profiles were examined in 1 British and 2 Finnish birth cohorts with nuclear magnetic resonance (NMR)–based serum metabolomics: Avon Longitudinal Study of Parents and Children ,13 . Offspring sex was obtained from obstetric records, and parental and offspring ages were calculated from their dates of birth and dates of questionnaires or clinic assessments. Parental occupation was classified into social class groups from I  to IV . Highest educational qualification for both parents was collapsed into 1 of 5 categories from none/Certificate of Secondary Education  to university degree.In NFBC86, parental height, weight, occupation, and smoking status; offspring sex; and maternal parity were collected using questionnaires given to all mothers at their first antenatal clinic visit. As in ALSPAC, paternal weight and height were reported by their partner (the mother). Level of education was obtained from questionnaires in 2001–2002. Parental and offspring ages were derived from their dates of birth and dates of assessments. Parental education was categorised into 8 categories from no occupational education to university degree, and occupation was categorised into 6 categories from entrepreneur to no occupation.In NFBC66, maternal height, weight, occupation, smoking status, parity, and offspring sex were reported by mothers at the first antenatal clinic visit (16th week of gestation), or in questionnaires administered between the 24th and 28th week of gestation. Offspring age at serum collection was derived from their date of birth and date of attendance at the 1997–1998 follow-up clinic. Maternal age in pregnancy was derived from year of birth and the date of pregnancy questionnaire completion. Education was categorised into 9 categories from none or circulating school to beyond matriculation exam, and occupation was categorised into 5 categories ranging from I  to V (no occupation). Information on paternal BMI was not collected in NFBC66; therefore, this cohort’s data were used to test for replication of the maternal-offspring association results only.In all cohorts, head of household social class was defined as the highest occupation of either parent. For the 1-stage individual participant data (IPD) –18 meta-A comprehensive profiling of offspring circulating lipids, lipoproteins, and metabolites was done by a high-throughput NMR metabolomics platform, providing a snapshot of offspring serum metabolome at follow-up ,21. In ACollectively, the 153 metabolic traits measured by the platform represent a broad molecular signature of systemic metabolism ,21. The A draft analysis plan was written by DLSF and DAL in March 2015. This was shared with other cohort analysts and investigators, and a final analysis plan was agreed upon in September 2015 see , with anData cleaning and checking were performed separately for each cohort. One-stage and 2-stage IPD –18 meta-We conducted 3 sets of IPD meta-analysis that included maternal versus paternal comparisons:A 1-stage IPD meta-analysis restricted to the 2 cohorts with data on both maternal and paternal BMI (ALSPAC and NFBC86) which were our main analyses. In these analyses, offspring metabolic traits were standardised (z-scored) across both cohorts and then regressed on maternal and paternal z-scored BMI  with adjustment for the harmonised covariables and a binary variable reflecting whether the participant was from ALSPAC or NFBC86. These analyses were conducted on between 5,327 and 5,377 trios (numbers with complete data varied slightly for different metabolic traits). This approach has greater statistical efficiency than a 2-stage IPD meta-analysis, and restricting to the 2 cohorts with both maternal and paternal BMI ensures that exposure-outcome comparisons are possible within all parental-offspring trio data available. It assumes that the 2 cohorts are from the same population to which inferences are being made.N = 5,327 to 5,277). This has greater statistical power for the maternal associations and for determining differences between mothers and fathers. It assumes that all 3 cohorts are from the same population to which inference is being made. This includes the assumption that paternal BMI would be similarly associated with offspring metabolic traits in NFBC66 as in the other 2 cohorts if we would have been able to model data on this unmeasured trait.A 1-stage IPD meta-analysis, undertaken as described above, but including the maternal BMI–offspring metabolite associations from NFBC66. This sensitivity analysis assessed associations of maternal BMI with offspring metabolic traits in up to 4,874 additional mother-offspring pairs  from ALSPAC, NFBC86, and NFBC66 and compared these with the same associations of paternal BMI with offspring metabolic traits in ALSPAC and NFBC86 only  was performed, and then pooled results from each cohort were meta-analysed together using the random effects inverse-variance-weighted method (stage 2). The standardisation of offspring metabolic traits and parental BMI was undertaken within each cohort in this method. We used this method to (a) explore whether our standardisation across cohorts of offspring metabolic traits and parental BMI, and harmonisation of potential confounders in the 1-stage IPD meta-analyses, had notably influenced results; (b) test for heterogeneity in association results between the 3 cohorts  using the Iatistics , which pP values were calculated using bootstrap means and standard errors and compared to a z-distribution.Magnitudes of maternal and paternal associations were compared by presenting the results one on top of the other so that the extent to which point estimates and their respective confidence intervals differ (or not) can be clearly seen. In addition, the magnitudes of maternal and paternal associations were compared to each other using linear fit and a random intercept and slope multilevel model. The latter was used to overcome the fact that as metabolic traits are correlated, the independence assumption of the linear fit analyses are likely to be violated, which could bias results. We defined clusters according to metabolic trait-classes, which resulted in 27 classes with between 1 and 9 metabolic traits in each class see . This monear fit  were comP < 0.05 becomes P < 0.003  when multiple testing is considered for assessing associations with the 153 metabolic traits.To establish a threshold that takes into account multiple testing and the correlation structure of the metabolic traits data, principal components analysis (PCA) was carried out on the z-scored metabolic traits data . The ratN = 5,266 to 5,316 trios from ALSPAC and NFBC86 cohorts.We performed 2 additional analyses, using the 1-stage IPD meta-analysis only, that aimed to explore whether any maternal  pre-pregnancy BMI associations with offspring metabolic traits were mediated by the relationship of the parental BMI with offspring BMI. First, we examined the associations of offspring BMI (assessed at the same time as blood collection) with their metabolic traits. Second, we repeated the meta-analysis described above, in point 1, with additional adjustment for offspring BMI. Both analyses were undertaken in Statistical analyses were conducted using R version 3.0.1  and Stata version 14.1 .Characteristics of the 3 study populations are shown in N = 5,327 to 5,377 trios). P < 0.003). The associations were mostly in the same direction for both parents and were in the direction of more adverse cardio-metabolic risk in offspring with higher parental BMI in pre-pregnancy.Figs 2, and HDL3 cholesterol sub-fractions showed inverse associations, with magnitudes similar to the very large and large HDL lipoprotein subclasses. Associations of parental BMI with apolipoprotein A-I and B were in opposite directions, in agreement with the association pattern for lipoprotein subclasses.Amongst lipoprotein lipids, the most marked positive associations were observed in 5 very-low-density lipoprotein (VLDL) subclasses . There were no robust associations of parental BMI with offspring intermediate-density lipoprotein (IDL) and low-density lipoprotein (LDL) subclasses. Strong inverse associations were observed for the very large and large high-density lipoprotein (HDL) subclasses with the exception of structural triglycerides that exhibited no clear associations. Lipoprotein particle sizes followed the same pattern of associations as those observed with lipoprotein subclasses. Parental BMI was strongly positively associated with offspring VLDL cholesterol, VLDL triglycerides, triglycerides, and remnant cholesterol, with the same direction and comparable magnitudes to the first 5 VLDL lipoprotein subclasses. Conversely, HDL, HDLParental BMI was strongly positively associated with offspring branched-chain amino acids and maternal BMI with aromatic amino acids.Mono-unsaturated fatty acids (MUFA) was the only FA clearly associated with parental BMI when assessed alone or as a proportion of total FAs . Several of the other FAs showed associations with parental BMI, but only as proportions of total FAs. Poly-unsaturated (PUFA) to total FAs , ratio of linoleic acid (LA) to total FAs , and omega 6 to total FA ratio were all inversely associated with parental BMI.Parental BMI was not clearly associated with any of the glycolysis-related metabolites, except citrate where there was an inverse association. Parental BMI was strongly positively associated with offspring glycoprotein acetyls, an inflammatory marker.2 = 0.89 and slope = 0.78 ± 0.02; Pboot > 0.003). Docosahexaenoic acid (DHA)-to-total FA ratio was the only metabolic trait that showed some suggestion of difference in association between maternal and paternal BMI  to paternal BMI–offspring metabolic trait associations in the 2 cohorts with paternal BMI , the maternal associations were essentially the same as those described above; no statistical evidence for stronger maternal associations emerged  to those seen for parental BMI but were considerably stronger in magnitude . When adWe report, to the best of our knowledge, the first study that investigates the potential influence of maternal pre-pregnancy BMI on adult offspring serum metabolome to determine whether intrauterine mechanisms related to developmental overnutrition result in metabolic disruption in adults when they are in their reproductive years. We found similar associations of both maternal and paternal BMI with offspring systemic metabolism 16–17 years later and 31 years later , suggesting that shared familial genetic, socioeconomic, and lifestyle characteristics rather than an intrauterine programming effect explain the associations of maternal pre-pregnancy BMI with offspring metabolic measures Figs –4.2-C and HDL3-C. Associations were also seen with FA ratios and some amino acids. In further analyses, we demonstrated that in addition to similar associations between paternal BMI with offspring metabolic traits , the offspring BMI was associated in a similar pattern (but with stronger magnitudes) to their metabolic traits, further supporting the notion that shared familial characteristics explain these associations. Lastly, we found that parental associations with metabolic traits attenuated markedly with adjustment for offspring BMI, suggesting that shared familial characteristics drive associations of maternal pre-pregnancy BMI and paternal BMI with their offspring BMI, which in turn results in metabolic disruption , VLDL-triglycerides, VLDL-diameter, branched/aromatic amino acids, glycoprotein acetyls, and triglycerides, and inverse associations with HDL-lipoprotein, HDL-diameter, HDL-C, HDLsruption .N = 70 to 4,871; offspring age range: 4–8 years). Our results also contrast reports from some of the same studies that found no association of maternal BMI with offspring triglycerides [N = 4,871) to our main analysis approach reported similar associations of maternal BMI with total cholesterol, LDL-C, and HDL-C to those seen for paternal BMI [Previous studies have compared associations of maternal and/or paternal BMI, measured pre-pregnancy, with offspring adiposity  to explore causal inference. Using paternal BMI as a negative control makes it possible to disentangle the extent to which maternal BMI–offspring metabolite associations are due to casual intrauterine effects or confounding by familial factors  ,35. As wLimitations of our study include the use of parental BMI as a measure of adiposity; different body composition characteristics, such as fat distribution or fat-to-lean body mass ratio, may show differential associations with offspring serum metabolome. Moreover, maternal BMI was self-reported, and paternal BMI was reported by their partners , which might have led to misclassification of parental BMI. Another limitation is that inferences cannot be drawn about the role of circulating maternal fetal fuels  on later offspring metabolic profile, as these were not measured during pregnancy in our cohorts. It is possible that non-paternity for some of the fathers has affected our results (for the NFBCs we had no information on this possibility). However, any impact of non-paternity would be likely to selectively reduce paternal BMI associations, since there would be no shared genetic effects on the association of paternal BMI with offspring metabolic traits, and family lifestyle and socioeconomic associations may be weaker between non-biological parents and offspring. This would thus tend to enhance maternal-paternal differences, whereas we see similar associations between parents. Despite adjusting for several potential confounders, residual confounding may still explain the associations that we have observed between parental BMI and offspring. For example, we were unable to adjust for parental physical activity or dietary intake. These confounders are likely to be similar for maternal and paternal BMI and so would be unlikely to bias our inference about specific maternal effects on offspring metabolism . Whilst we have a large sample size, we may lack power to detect small differences between maternal and paternal BMI associations with offspring metabolic traits, especially as the human circulating metabolome is a tightly controlled homeostatic system, and even small differences between some metabolic traits might have important clinical impact. However, we think this is unlikely for many of the traits we have examined. First, Figs In conclusion, the similarity of association between pre-pregnancy maternal BMI and paternal BMI with offspring metabolic profiles suggests that maternal BMI associations are likely to be largely due to shared genetic or familial lifestyle confounding rather than intrauterine mechanisms. Further replication of these findings in other larger studies, including with measured fat distribution in mothers and fathers at the time of the mother’s pregnancy, would be valuable, though we are not aware of any studies with relevant data currently to be able to do that.S1 FigIPD = individual participant data; analysis 1 is our main analysis.(PDF)Click here for additional data file.S2 Fig(PDF)Click here for additional data file.S3 Fig(PDF)Click here for additional data file.S4 Fig(PDF)Click here for additional data file.S5 Fig(PDF)Click here for additional data file.S6 Fig(PDF)Click here for additional data file.S7 Fig(PDF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 Table(PDF)Click here for additional data file.S4 Table(PDF)Click here for additional data file.S5 Table(PDF)Click here for additional data file.S6 Table(PDF)Click here for additional data file.S1 Text(PDF)Click here for additional data file.S2 Text(PDF)Click here for additional data file.S3 Text(PDF)Click here for additional data file.