Evolutionary analyses of the major variant surface antigen-encoding genes reveal population structure of Plasmodium falciparum within and between continents.

Malaria remains a major public health problem in many countries. Unlike influenza and HIV, where diversity in immunodominant surface antigens is understood geographically to inform disease surveillance, relatively little is known about the global population structure of PfEMP1, the major variant surface antigen of the malaria parasite Plasmodium falciparum. The complexity of the var multigene family that encodes PfEMP1 and that diversifies by recombination, has so far precluded its use in malaria surveillance. Recent studies have demonstrated that cost-effective deep sequencing of the region of var genes encoding the PfEMP1 DBLα domain and subsequent classification of within host sequences at 96% identity to define unique DBLα types, can reveal structure and strain dynamics within countries. However, to date there has not been a comprehensive comparison of these DBLα types between countries. By leveraging a bioinformatic approach (jumping hidden Markov model) designed specifically for the analysis of recombination within var genes and applying it to a dataset of DBLα types from 10 countries, we are able to describe population structure of DBLα types at the global scale. The sensitivity of the approach allows for the comparison of the global dataset to ape samples of Plasmodium Laverania species. Our analyses show that the evolution of the parasite population emerging out of Africa underlies current patterns of DBLα type diversity. Most importantly, we can distinguish geographic population structure within Africa between Gabon and Ghana in West Africa and Uganda in East Africa. Our evolutionary findings have translational implications in the context of globalization. Firstly, DBLα type diversity can provide a simple diagnostic framework for geographic surveillance of the rapidly evolving transmission dynamics of P. falciparum. It can also inform efforts to understand the presence or absence of global, regional and local population immunity to major surface antigen variants. Additionally, we identify a number of highly conserved DBLα types that are present globally that may be of biological significance and warrant further characterization.

Population Structure Shapes Copy Number Variation in Malaria Parasites.

If copy number variants (CNVs) are predominantly deleterious, we would expect them to be more efficiently purged from populations with a large effective population size (Ne) than from populations with a small Ne. Malaria parasites (Plasmodium falciparum) provide an excellent organism to examine this prediction, because this protozoan shows a broad spectrum of population structures within a single species, with large, stable, outbred populations in Africa, small unstable inbred populations in South America and with intermediate population characteristics in South East Asia. We characterized 122 single-clone parasites, without prior laboratory culture, from malaria-infected patients in seven countries in Africa, South East Asia and South America using a high-density single-nucleotide polymorphism/CNV microarray. We scored 134 high-confidence CNVs across the parasite exome, including 33 deletions and 102 amplifications, which ranged in size from <500 bp to 59 kb, as well as 10,107 flanking, biallelic single-nucleotide polymorphisms. Overall, CNVs were rare, small, and skewed toward low frequency variants, consistent with the deleterious model. Relative to African and South East Asian populations, CNVs were significantly more common in South America, showed significantly less skew in allele frequencies, and were significantly larger. On this background of low frequency CNV, we also identified several high-frequency CNVs under putative positive selection using an FST outlier analysis. These included known adaptive CNVs containing rh2b and pfmdr1, and several other CNVs (e.g., DNA helicase and three conserved proteins) that require further investigation. Our data are consistent with a significant impact of genetic structure on CNV burden in an important human pathogen.

Malaria inhibits surface expression of complement receptor 1 in monocytes/macrophages, causing decreased immune complex internalization.

Complement receptor 1 (CR1) expressed on the surface of phagocytic cells binds complement-bound immune complexes (IC), playing an important role in the clearance of circulating IC. This receptor is critical to prevent accumulation of IC, which can contribute to inflammatory pathology. Accumulation of circulating IC is frequently observed during malaria, although the factors contributing to this accumulation are not clearly understood. We have observed that the surface expression of CR1 on monocytes/macrophages and B cells is strongly reduced in mice infected with Plasmodium yoelii, a rodent malaria model. Monocytes/macrophages from these infected mice present a specific inhibition of complement-mediated internalization of IC caused by the decreased CR1 expression. Accordingly, mice show accumulation of circulating IC and deposition of IC in the kidneys that inversely correlate with the decrease in CR1 surface expression. Our results indicate that malaria induces a significant decrease on surface CR1 expression in the monocyte/macrophage population that results in deficient internalization of IC by monocytes/macrophages. To determine whether this phenomenon is found in human malaria patients, we have analyzed 92 patients infected with either P. falciparum (22 patients) or P. vivax (70 patients) , the most prevalent human malaria parasites. The levels of surface CR1 on peripheral monocytes/macrophages and B cells of these patients show a significant decrease compared with uninfected control individuals in the same area. We propose that this decrease in CR1 plays an essential role in impaired IC clearance during malaria.

Evaluating a New Digital App-Based Program for Heart Health: Feasibility and Acceptability Pilot Study.

BACKGROUND: Cardiovascular disease (CVD) is the leading cause of death in the United States, affecting a significant proportion of adults. Digital health lifestyle change programs have emerged as a promising method of CVD prevention, offering benefits such as on-demand support, lower cost, and increased scalability. Prior research has shown the effectiveness of digital health interventions in reducing negative CVD outcomes. This pilot study focuses on the Lark Heart Health program, a fully digital artificial intelligence (AI)-powered smartphone app, providing synchronous CVD risk counseling, educational content, and personalized coaching. OBJECTIVE: This pilot study evaluated the feasibility and acceptability of a fully digital AI-powered lifestyle change program called Lark Heart Health. Primary analyses assessed (1) participant satisfaction, (2) engagement with the program, and (3) the submission of health screeners. Secondary analyses were conducted to evaluate weight loss outcomes, given that a major focus of the Heart Health program is weight management. METHODS: This study enrolled 509 participants in the 90-day real-world single-arm pilot study of the Heart Health app. Participants engaged with the app by participating in coaching conversations, logging meals, tracking weight, and completing educational lessons. The study outcomes included participant satisfaction, app engagement, the completion of screeners, and weight loss. RESULTS: On average, Heart Health study participants were aged 60.9 (SD 10.3; range 40-75) years, with average BMI indicating class I obesity. Of the 509 participants, 489 (96.1%) stayed enrolled until the end of the study (dropout rate: 3.9%). Study retention, based on providing a weight measurement during month 3, was 80% (407/509; 95% CI 76.2%-83.4%). Participant satisfaction scores indicated high satisfaction with the overall app experience, with an average score of ≥4 out of 5 for all satisfaction indicators. Participants also showed high engagement with the app, with 83.4% (408/489; 95% CI 80.1%-86.7%) of the sample engaging in ≥5 coaching conversations in month 3. The results indicated that participants were successfully able to submit health screeners within the app, with 90% (440/489; 95% CI 87%-92.5%) submitting all 3 screeners measured in the study. Finally, secondary analyses showed that participants lost weight during the program, with analyses showing an average weight nadir of 3.8% (SD 2.9%; 95% CI 3.5%-4.1%). CONCLUSIONS: The study results indicate that participants in this study were satisfied with their experience using the Heart Health app, highly engaged with the app features, and willing and able to complete health screening surveys in the app. These acceptability and feasibility results provide a key first step in the process of evidence generation for a new AI-powered digital program for heart health. Future work can expand these results to test outcomes with a commercial version of the Heart Health app in a diverse real-world sample.

Weight loss and modeled cost savings in a digital diabetes prevention program.

Background: Participation in the National Diabetes Prevention Program (DPP) can improve individual health through reduced risk of type 2 diabetes and save the healthcare system substantial medical costs associated with a diagnosis of type 2 diabetes and its associated complications. There is less evidence of outcomes and cost savings associated with a fully digital delivery of the DPP. Methods: This study assessed 13,593 members who provided an initial digital weight and subsequently achieved various weight loss and engagement outcomes during their participation in a digital DPP. Analyzed data included both complete observations and missing observations imputed using maximum likelihood estimation. Findings include members' behavioral correlates of weight loss and a literature-based cost-savings estimate associated with achieving three mutually exclusive weight loss or engagement benchmarks: ≥5% weight loss, >2% but <5% weight loss, and completion of ≥4 educational lessons. Results: 11,976 members (88%) provided a weight after 2 months of participation, enabling calculation of their weight nadir. Considering complete data, 97% of members maintained or lost weight. Using the imputed data for these calculations, 32.0% of members achieved ≥5%, 32.4% achieved >2% but <5%, 32.0% maintained ±2%, and 3.6% gained weight. Members who lost the most weight achieved their weight nadir furthest into the program (mean day = 189, SE = 1.4) and had the longest active engagement (mean days = 268, SE = 1.4), particularly compared to members who gained weight (mean nadir day = 119, SE = 3.7; active engagement mean days = 199, SE = 4.9) (both p ≤ 0.0001). Modeled 1-year cost-savings estimates ranged from $11,229,160 to $12,960,875. Conclusions: Members of a fully digital DPP achieved clinical and engagement outcomes during their participation in the program that confer important health benefits and cost savings.

Weight Loss in a Digital Diabetes Prevention Program for People in Health Professional Shortage and Rural Areas.

Individuals with prediabetes living in hard-to-reach and underserved areas experience barriers to accessing traditional in-person preventive health services. The National Diabetes Prevention Program (DPP) is a preventive health care program designed to reduce the risk of developing type 2 diabetes. Although there have been increasing numbers of remote DPPs accessible, there are little data on the clinical outcomes of digital DPPs for members living in hard-to-reach and underserved areas. This study assessed whether living in a designated Health Professional Shortage Area (HPSA) and a rural versus urban area impacted the weight loss of N = 7266 members of a fully digital program called Lark DPP. Secondary analyses included between-group comparisons of program retention and member characteristics, demographics, and socioeconomics. Percent weight loss did not differ by HPSA (P = 0.16) or rural/urban status (P = 0.15), despite greater potential barriers for members residing in HPSAs (eg, highest starting body mass index, lowest income, lowest education). Mean percent weight loss for members residing in an HPSA and rural area was mean (M) = 4.75%, standard error (SE) = 0.09; for members in a non-HPSA, rural area M = 4.96%, SE = 0.16; for members in an HPSA, urban area M = 4.55%, SE = 0.13; and for members in a non-HPSA, urban area M = 4.77%, SE = 0.13. Members of a fully digital DPP achieved weight loss that did not differ by HPSA or urban/rural designation. Fully digital programs offer a solution to reduce the risk of type 2 diabetes in areas where residents may not otherwise have access to diabetes prevention services.

Predictors of program interest in a digital health pilot study for heart health.

Digital health programs can play a key role in supporting lifestyle changes to prevent and reduce cardiovascular disease (CVD) risk. A key concern for new programs is understanding who is interested in participating. Thus, the primary objective of this study was to utilize electronic health records (EHR) to predict interest in a digital health app called Lark Heart Health. Because prior studies indicate that males are less likely to utilize prevention-focused digital health programs, secondary analyses assessed sex differences in recruitment and enrollment. Data were drawn from an ongoing pilot study of the Heart Health program, which provides digital health behavior coaching and surveys for CVD prevention. EHR data were used to predict whether potential program participants who received a study recruitment email showed interest in the program by "clicking through" on the email to learn more. Primary objective analyses used backward elimination regression and eXtreme Gradient Boost modeling. Recruitment emails were sent to 8,649 patients with available EHR data; 1,092 showed interest (i.e., clicked through) and 345 chose to participate in the study. EHR variables that predicted higher odds of showing interest were higher body mass index (BMI), fewer elevated lab values, lower HbA1c, non-smoking status, and identifying as White. Secondary objective analyses showed that, males and females showed similar program interest and were equally represented throughout recruitment and enrollment. In summary, BMI, elevated lab values, HbA1c, smoking status, and race emerged as key predictors of program interest; conversely, sex, age, CVD history, history of chronic health issues, and medication use did not predict program interest. We also found no sex differences in the recruitment and enrollment process for this program. These insights can aid in refining digital health tools to best serve those interested, as well as highlight groups who may benefit from behavioral intervention tools promoted by additional recruitment efforts tailored to their interest.

Pf7: an open dataset of Plasmodium falciparum genome variation in 20,000 worldwide samples.

We describe the MalariaGEN Pf7 data resource, the seventh release of Plasmodium falciparum genome variation data from the MalariaGEN network.  It comprises over 20,000 samples from 82 partner studies in 33 countries, including several malaria endemic regions that were previously underrepresented.  For the first time we include dried blood spot samples that were sequenced after selective whole genome amplification, necessitating new methods to genotype copy number variations.  We identify a large number of newly emerging crt mutations in parts of Southeast Asia, and show examples of heterogeneities in patterns of drug resistance within Africa and within the Indian subcontinent.  We describe the profile of variations in the C-terminal of the csp gene and relate this to the sequence used in the RTS,S and R21 malaria vaccines.  Pf7 provides high-quality data on genotype calls for 6 million SNPs and short indels, analysis of large deletions that cause failure of rapid diagnostic tests, and systematic characterisation of six major drug resistance loci, all of which can be freely downloaded from the MalariaGEN website.

Plasmodium falciparum malaria in the Peruvian Amazon, a region of low transmission, is associated with immunologic memory.

The development of clinical immunity to Plasmodium falciparum malaria is thought to require years of parasite exposure, a delay often attributed to difficulties in developing protective antibody levels. In this study, we evaluated several P. falciparum vaccine candidate antigens, including apical membrane antigen 1 (AMA-1), circumsporozoite protein (CSP), erythrocyte binding antigen 175 (EBA-175), and the 19-kDa region of merozoite surface protein 1 (MSP1(19)). After observing a more robust antibody response to MSP1(19), we evaluated the magnitude and longevity of IgG responses specific to this antigen in Peruvian adults and children before, during, and after P. falciparum infection. In this low-transmission region, even one reported prior infection was sufficient to produce a positive anti-MSP1(19) IgG response for >5 months in the absence of reinfection. We also observed an expansion of the total plasmablast (CD19(+) CD27(+) CD38(high)) population in the majority of individuals shortly after infection and detected MSP1-specific memory B cells in a subset of individuals at various postinfection time points. This evidence supports our hypothesis that effective antimalaria humoral immunity can develop in low-transmission regions.

Plasmodium falciparum genetic diversity maintained and amplified over 5 years of a low transmission endemic in the Peruvian Amazon.

Plasmodium falciparum entered into the Peruvian Amazon in 1994, sparking an epidemic between 1995 and 1998. Since 2000, there has been sustained low P. falciparum transmission. The Malaria Immunology and Genetics in the Amazon project has longitudinally followed members of the community of Zungarococha (N = 1,945, 4 villages) with active household and health center-based visits each year since 2003. We examined parasite population structure and traced the parasite genetic diversity temporally and spatially. We genotyped infections over 5 years (2003-2007) using 14 microsatellite (MS) markers scattered across ten different chromosomes. Despite low transmission, there was considerable genetic diversity, which we compared with other geographic regions. We detected 182 different haplotypes from 302 parasites in 217 infections. Structure v2.2 identified five clusters (subpopulations) of phylogenetically related clones. To consider genetic diversity on a more detailed level, we defined haplotype families (hapfams) by grouping haplotypes with three or less loci differences. We identified 34 different hapfams identified. The F(st) statistic and heterozygosity analysis showed the five clusters were maintained in each village throughout this time. A minimum spanning network (MSN), stratified by the year of detection, showed that haplotypes within hapfams had allele differences and haplotypes within a cluster definition were more separated in the later years (2006-2007). We modeled hapfam detection and loss, accounting for sample size and stochastic fluctuations in frequencies overtime. Principle component analysis of genetic variation revealed patterns of genetic structure with time rather than village. The population structure, genetic diversity, appearance/disappearance of the different haplotypes from 2003 to 2007 provides a genome-wide "real-time" perspective of P. falciparum parasites in a low transmission region.

Quantification of Plasmodium falciparum malaria from complex infections in the Peruvian Amazon using quantitative PCR of the merozoite surface protein 1, block 2 (PfMSP1-B2): in vitro dynamics reveal density-dependent interactions.

The majority of Plasmodium falciparum field isolates are defined as complex infections because they contain multiple genetically distinct clones. Studying interactions between clones in complex infections in vivo and in vitro could elucidate important phenomena in malaria infection, transmission and treatment. Using quantitative PCR (qPCR) of the P. falciparum merozoite surface protein 1, block 2 (PfMSP1-B2), we provide a sensitive and efficient genotyping method. This is important for epidemiological studies because it makes it possible to study genotype-specific growth dynamics. We compared 3 PfMSP1-B2 genotyping methods by analysing 79 field isolates from the Peruvian Amazon. In vivo observations from other studies using these techniques led to the hypothesis that clones within complex infections interact. By co-culturing clones with different PfMSP1-B2 genotypes, and measuring parasitaemia using qPCR, we found that suppression of clonal expansion was a factor of the collective density of all clones present in a culture. PfMSP1-B2 qPCR enabled us to find in vitro evidence for parasite-parasite interactions and could facilitate future investigations of growth trends in naturally occurring complex infections.

Engagement in Digital Health App-Based Prevention Programs Is Associated With Weight Loss Among Adults Age 65.

Background: Digital health programs have been shown to be feasible and effective for the prevention of chronic diseases such as diabetes. Contrary to expectations, findings also suggest that older adults have higher levels of engagement with digital health programs than younger adults. However, there is a paucity of research examining outcomes among older adults in digital health programs and whether higher engagement is related to better outcomes. Methods: We examined weight loss outcomes for 538 users aged 65 and older participating in one of two app-based prevention programs called the Diabetes Prevention Program and the Prevention Program, respectively. Both programs were available on a single artificial intelligence (AI)-powered digital health platform and shared a common goal of weight loss. We also examined the relationship between key engagement metrics (i.e., conversing with the AI-powered coach, weigh-ins, and initiating educational lessons early in the program) and weight loss outcomes. Results: The average weight loss of all enrollees having a weight measurement after after the 9th week was 4.51%, and the average weight loss of the Diabetes Prevention Program enrollees meeting a minimum engagement level was 8.56%. Greater weight loss was associated with a greater number of days with AI-powered coaching conversations (p = 0.03), more weigh-ins (p = 0.00), and early educational lesson initiation (p = 0.02). Conclusions: Digital health programs powered by AI offer a promising solution for health management among older adults. The results show positive health outcomes using app-based prevention programs, and all three engagement metrics were independently associated with weight loss.

Reach of a Fully Digital Diabetes Prevention Program in Health Professional Shortage Areas.

The National Diabetes Prevention Program (NDPP) offers lifestyle change education to adults at risk for diabetes across the United States, but its reach is curbed due, in part, to limitations of traditional in-person programs. Diabetes Prevention Programs (DPPs) that are fully digital may increase reach by overcoming these barriers. The aim of this research was to examine the reach of Lark's DPP, a fully digital artificial-intelligence-powered DPP. This study assessed geographic features and demographic characteristics of a sample of Lark DPP commercial health plan members with complete data (N = 16,327) and compared several demographic features with a large composite sample of members from DPPs across the nation (NDPP; N = 143,489) and a National Health Interview Survey (NHIS) sample of prediabetic adults in the United States (NHIS; N = 2118). Examination of the Lark DPP sample revealed that 24.4% of members lived in rural areas, 30.8% lived in whole county health professional shortage areas, and only 7.6% of members lived in a zip code with an in-person DPP. When comparing the Lark sample with the NDPP and NHIS samples, Lark DPP enrollees tended to be younger and have a higher body mass index (BMI) (p's < 0.001). Lark provides convenient access to a DPP for individuals living in hard-to-reach areas who may face barriers to participating in in-person or telephonic DPPs or who prefer a digital program. Compared with the NDPP sample, Lark is also reaching younger and higher BMI users, who are traditionally difficult to enroll and have a high need for intervention.

Weight loss in a digital app-based diabetes prevention program powered by artificial intelligence.

Objective: The National Diabetes Prevention Program (DPP) reduces diabetes incidence and associated medical costs but is typically staffing-intensive, limiting scalability. We evaluated an alternative delivery method with 3933 members of a program powered by conversational Artificial Intelligence (AI) called Lark DPP that has full recognition from the Centers for Disease Control and Prevention (CDC). Methods: We compared weight loss maintenance at 12 months between two groups: 1) CDC qualifiers who completed ≥4 educational lessons over 9 months (n = 191) and 2) non-qualifiers who did not complete the required CDC lessons but provided weigh-ins at 12 months (n = 223). For a secondary aim, we removed the requirement for a 12-month weight and used logistic regression to investigate predictors of weight nadir in 3148 members. Results: CDC qualifiers maintained greater weight loss at 12 months than non-qualifiers (M = 5.3%, SE = .8 vs. M = 3.3%, SE = .8; p = .015), with 40% achieving ≥5%. The weight nadir of 3148 members was 4.2% (SE = .1), with 35% achieving ≥5%. Male sex (ß = .11; P = .009), weeks with ≥2 weigh-ins (ß = .68; P < .0001), and days with an AI-powered coaching exchange (ß = .43; P < .0001) were associated with a greater likelihood of achieving ≥5% weight loss. Conclusions: An AI-powered DPP facilitated weight loss and maintenance commensurate with outcomes of other digital and in-person programs not powered by AI. Beyond CDC lesson completion, engaging with AI coaching and frequent weighing increased the likelihood of achieving ≥5% weight loss. An AI-powered program is an effective method to deliver the DPP in a scalable, resource-efficient manner to keep pace with the prediabetes epidemic.

Relationships Between Blood Pressure Reduction, Weight Loss, and Engagement in a Digital App-Based Hypertension Care Program: Observational Study.

BACKGROUND: Home blood pressure (BP) monitoring is recommended for people with hypertension; however, meta-analyses have demonstrated that BP improvements are related to additional coaching support in combination with self-monitoring, with little or no effect of self-monitoring alone. High-contact coaching requires substantial resources and may be difficult to deliver via human coaching models. OBJECTIVE: This observational study assessed changes in BP and body weight following participation in a fully digital program called Lark Hypertension Care with coaching powered by artificial intelligence (AI). METHODS: Participants (N=864) had a baseline systolic BP (SBP) ≥120 mm Hg, provided their baseline body weight, and had reached at least their third month in the program. The primary outcome was the change in SBP at 3 and 6 months, with secondary outcomes of change in body weight and associations of changes in SBP and body weight with participant demographics, characteristics, and program engagement. RESULTS: By month 3, there was a significant drop of -5.4 mm Hg (95% CI -6.5 to -4.3; P<.001) in mean SBP from baseline. BP did not change significantly (ie, the SBP drop maintained) from 3 to 6 months for participants who provided readings at both time points (P=.49). Half of the participants achieved a clinically meaningful drop of ≥5 mm Hg by month 3 (178/349, 51.0%) and month 6 (98/199, 49.2%). The magnitude of the drop depended on starting SBP. Participants classified as hypertension stage 2 had the largest mean drop in SBP of -12.4 mm Hg (SE 1.2 mm Hg) by month 3 and -13.0 mm Hg (SE 1.6 mm Hg) by month 6; participants classified as hypertension stage 1 lowered by -5.2 mm Hg (SE 0.8) mm Hg by month 3 and -7.3 mm Hg (SE 1.3 mm Hg) by month 6; participants classified as elevated lowered by -1.1 mm Hg (SE 0.7 mm Hg) by month 3 but did not drop by month 6. Starting SBP (ß=.11; P<.001), percent weight change (ß=-.36; P=.02), and initial BMI (ß=-.56; P<.001) were significantly associated with the likelihood of lowering SBP ≥5 mm Hg by month 3. Percent weight change acted as a mediator of the relationship between program engagement and drop in SBP. The bootstrapped unstandardized indirect effect was -0.0024 (95% CI -0.0052 to 0; P=.002). CONCLUSIONS: A hypertension care program with coaching powered by AI was associated with a clinically meaningful reduction in SBP following 3 and 6 months of program participation. Percent weight change was significantly associated with the likelihood of achieving a ≥5 mm Hg drop in SBP. An AI-powered solution may offer a scalable approach to helping individuals with hypertension achieve clinically meaningful reductions in their BP and associated risk of cardiovascular disease and other serious adverse outcomes via healthy lifestyle changes such as weight loss.

Discovering Engagement Personas in a Digital Diabetes Prevention Program.

Digital health technologies are shaping the future of preventive health care. We present a quantitative approach for discovering and characterizing engagement personas: longitudinal engagement patterns in a fully digital diabetes prevention program. We used a two-step approach to discovering engagement personas among n = 1613 users: (1) A univariate clustering method using two unsupervised k-means clustering algorithms on app- and program-feature use separately and (2) A bivariate clustering method that involved comparing cluster labels for each member across app- and program-feature univariate clusters. The univariate analyses revealed five app-feature clusters and four program-feature clusters. The bivariate analysis revealed five unique combinations of these clusters, called engagement personas, which represented 76% of users. These engagement personas differed in both member demographics and weight loss. Exploring engagement personas is beneficial to inform strategies for personalizing the program experience and optimizing engagement in a variety of digital health interventions.

Hematological profiles of malaria-infected patients in an endemic area of Peru. / Perfiles hematológicos en pacientes infectados con malaria en un área endémica del Perú.

OBJECTIVES.: To evaluate the variation of hematological profiles of patients infected with uncomplicated Plasmodium vivax (Pv) and P. falciparum (Pf) malaria before, during and after treatment in a population of the Loreto region. MATERIALS AND METHODS.: This study was conducted between 2010 and 2012, in Zungarococha (Iquitos). The 425 participants had three visits (visit 1-day 0-before treatment, visit 2-day 7-during treatment, visit 3-day 28-after treatment), complete blood count, microscopic and molecular diagnosis (PCR). RESULTS.: At the first visit, 93 (21.9%) participants were found positive for Pv and 34 (8.0%) for Pf. All positives showed a reduction in hematocrit, white blood cell count (WBC), ablated and segmented neutrophils, eosinophils and platelets (p<0.001) compared to the negative group. A higher percentage of ablated neutrophils was found in Pf and segmented neutrophils in Pv compared to the negative group. Variations in hematological profiles were observed after treatment for both species; ablated neutrophils decreased, platelets increased, eosinophils increased at day 7 and declined at day 28, hematocrit and segmented neutrophils decreased at day 7 and normalized at day 28. Interspecies differences over time showed a bigger daily decrease in ablated neutrophils in Pv-infected when compared to Pf. CONCLUSIONS.: The hematological profile in uncomplicated malaria-positive patients varies over time during and after treatment. These are indicators of disease progression and help in the therapeutic surveillance of Plasmodium-infected patients.

OBJETIVOS.: Evaluar la variación de los perfiles hematológicos antes, durante y después del tratamiento de pacientes infectados con malaria no complicada por Plasmodium vivax (Pv) y P. falciparum (Pf) en una población de la región Loreto. MATERIALES Y MÉTODOS.: El estudio se realizó entre 2010 y 2012, en Zungarococha (Iquitos). Los 425 participantes tuvieron tres visitas (visita 1-día 0-antes del tratamiento, visita 2-día 7-durante tratamiento, visita 3-día 28-después del tratamiento), hemograma completo, diagnóstico microscópico y molecular (PCR). RESULTADOS.: En la primera visita, se encontraron 93 (21,9%) positivos a Pv y 34 (8,0%) a Pf. Todos los positivos mostraron una reducción en los indicadores hematológicos de hematocrito, recuento de glóbulos blancos (RGB), neutrófilos abastonados y segmentados, eosinófilos y plaquetas (p<0.001) en comparación con el grupo negativo. Se encontró un porcentaje mayor de neutrófilos abastonados en Pf y de neutrófilos segmentados en Pv comparado al grupo negativo. Se observó variaciones en los perfiles hematológicos después del tratamiento para ambas especies, los neutrófilos abastonados disminuyeron, las plaquetas aumentaron, los eosinófilos se incrementaron al día 7 y decaen el día 28, el hematocrito y los neutrófilos segmentados disminuyeron al día 7 y se normalizaron el día 28. Las diferencias entre especies en el tiempo mostraron una disminución diaria de neutrófilos abastonados en infectados con Pv que en Pf. CONCLUSIONES.: El perfil hematológico en pacientes positivos a malaria no complicada varía en el tiempo durante y después del tratamiento. Estos son indicadores de la progresión de la enfermedad y ayudan en la vigilancia terapéutica de pacientes infectados con Plasmodium.

Malaria immunoepidemiology in low transmission: correlation of infecting genotype and immune response to domains of Plasmodium falciparum merozoite surface protein 3.

Malaria caused by Plasmodium falciparum is a major cause of global infant mortality, and no effective vaccine currently exists. Multiple potential vaccine targets have been identified, and immunoepidemiology studies have played a major part in assessing those candidates. When such studies are carried out in high-transmission settings, individuals are often superinfected with complex mixtures of genetically distinct P. falciparum types, making it impossible to directly correlate the genotype of the infecting antigen with the antibody response. In contrast, in regions of low transmission P. falciparum infections are often genetically simple, and direct comparison of infecting genotype and antigen-specific immune responses is possible. As a test of the utility of this approach, responses against several domains and allelic variants of the vaccine candidate P. falciparum merozoite surface protein 3 (PfMSP3) were tested in serum samples collected near Iquitos, Peru. Antibodies recognizing both the conserved C-terminal and the more variable N-terminal domain were identified, but anti-N-terminal responses were more prevalent, of higher titers, and primarily of cytophilic subclasses. Comparing antibody responses to different PfMSP3 variants with the PfMSP3 genotype present at the time of infection showed that anti-N-terminal responses were largely allele class specific, but there was some evidence for responses that cross-reacted across allele classes. Evidence for cross-reactive responses was much stronger when variants within one allele class were tested, which has implications for the rational development of genotype-transcending PfMSP3-based vaccines.

Sexual recombination is a signature of a persisting malaria epidemic in Peru.

BACKGROUND: The aim of this study was to consider the impact that multi-clone, complex infections have on a parasite population structure in a low transmission setting. In general, complexity of infection (minimum number of clones within an infection) and the overall population level diversity is expected to be minimal in low transmission settings. Additionally, the parasite population structure is predicted to be clonal, rather than sexual due to infrequent parasite inoculation and lack of recombination between genetically distinct clones. However, in this low transmission of the Peruvian Amazon, complex infections are becoming more frequent, in spite of decreasing infection prevalence. In this study, it was hypothesized that sexual recombination between distinct clonal lineages of Plasmodium falciparum parasites were altering the subpopulation structure and effectively maintaining the population-level diversity. METHODS: Fourteen microsatellite markers were chosen to describe the genetic diversity in 313 naturally occurring P. falciparum infections from Peruvian Amazon. The population and subpopulation structure was characterized by measuring: clusteredness, expected heterozygosity (He), allelic richness, private allelic richness, and linkage disequilibrium. Next, microsatellite haplotypes and alleles were correlated with P. falciparum merozoite surface protein 1 Block 2 (Pfmsp1-B2) to examine the presence of recombinant microsatellite haplotypes. RESULTS: The parasite population structure consists of six genetically diverse subpopulations of clones, called "clusters". Clusters 1, 3, 4, and 6 have unique haplotypes that exceed 70% of the total number of clones within each cluster, while Clusters 2 and 5 have a lower proportion of unique haplotypes, but still exceed 46%. By measuring the He, allelic richness, and private allelic richness within each of the six subpopulations, relatively low levels of genetic diversity within each subpopulation (except Cluster 4) are observed. This indicated that the number of alleles, and not the combination of alleles, are limited. Next, the standard index of association (IAS) was measured, which revealed a significant decay in linkage disequilibrium (LD) associated with Cluster 6, which is indicative of independent assortment of alleles. This decay in LD is a signature of this subpopulation approaching linkage equilibrium by undergoing sexual recombination. To trace possible recombination events, the two most frequent microsatellite haplotypes observed over time (defined by either a K1 or Mad20) were selected as the progenitors and then potential recombinants were identified in within the natural population. CONCLUSIONS: Contrary to conventional low transmission models, this study provides evidence of a parasite population structure that is superficially defined by a clonal backbone. Sexual recombination does occur and even arguably is responsible for maintaining the substructure of this population.

Older Adults Engage With Personalized Digital Coaching Programs at Rates That Exceed Those of Younger Adults.

Background: The US population is aging and has an expanding set of healthcare needs for the prevention and management of chronic conditions. Older adults contribute disproportionately to US healthcare costs, accounting for 34% of total healthcare expenditures in 2014 but only 15% of the population. Fully automated, digital health programs offer a scalable and cost-effective option to help manage chronic conditions. However, the literature on technology use suggests that older adults face barriers to the use of digital technologies that could limit their engagement with digital health programs. The objective of this study was to characterize the engagement of adults 65 years and older with a fully automated digital health platform called Lark Health and compare their engagement to that of adults aged 35-64 years. Methods: We analyzed data from 2,169 Lark platform users across four different coaching programs (diabetes prevention, diabetes care, hypertension care, and prevention) over a 12-month period. We characterized user engagement as participation in digital coaching conversations, meals logged, and device measurements. We compared engagement metrics between older and younger adults using nonparametric bivariate analyses. Main Results: Aggregate engagement across all users during the 12-month period included 1,623,178 coaching conversations, 588,436 meals logged, and 203,693 device measurements. We found that older adults were significantly more engaged with the digital platform than younger adults, evidenced by older adults participating in a larger median number of coaching conversations (514 vs. 428) and logging more meals (174 vs. 89) and device measurements (39 vs. 28) all p ≤ 0.01. Conclusions: Older adult users of a commercially available, fully digital health platform exhibited greater engagement than younger adults. These findings suggest that despite potential barriers, older adults readily adopted digital health technologies. Fully digital health programs may present a widely scalable and cost-effective alternative to traditional telehealth models that still require costly touchpoints with human care providers.