Background: India was one of the countries to institute strict measures for Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) control in the early phase. Since, then, the epidemic growth trajectory was slow before registering an explosion of cases due to local cluster transmissions. Methods: We estimated the growth rate and doubling time of SARS-CoV-2 for India and high burden states using crowdsourced time series data. Further, we also estimated the Basic Reproductive Number (R0) and Time-dependent Reproductive number (Rt) using serial intervals from the data. We compared the R0 estimated from five different methods and R0 from SB was further used in the analysis. We modified standard Susceptible-Infectious-Recovered (SIR) models to SIR/Death (SIRD) model to accommodate deaths using R0 with the sequential Bayesian method for simulation in SIRD models. Results: On average, 2.8 individuals were infected by an index case. The mean serial interval was 3.9 days. The R0 estimated from different methods ranged from 1.43 to 1.85. The mean time to recovery was 14 ± 5.3 days. The daily epidemic growth rate of India was 0.16 [95% CI; 0.14, 0.17] with a doubling time of 4.30 days [95% CI; 3.96, 4.70]. From the SIRD model, it can be deduced that the peak of SARS-CoV-2 in India will be around mid-July to early August 2020 with around 12.5% of the population likely to be infected at the peak time. Conclusion: The pattern of spread of SARS-CoV-2 in India is suggestive of community transmission. There is a need to increase funds for infectious disease research and epidemiologic studies. All the current gains may be reversed if air travel and social mixing resume rapidly. For the time being, these must be resumed only in a phased manner and should be back to normal levels only after we are prepared to deal with the disease with efficient tools like vaccines or medicine. Question: What are the estimates of infectious disease parameters of early phase of novel SARS-CoV-2 epidemic in India? Findings: Incidence pattern SARS-CoV-2 shows possible evidence of community transmission. However, the estimated Basic Reproductive Number (R0) is relatively lower than those observed in high burden regions (range 1.43–1.85). Our simulation using susceptible-infectious-recovered/death model shows that peak of SARS-CoV-2 in India is farther than currently projected and is likely to affect around 12.5% of population. Meaning: The lower estimated R0 is indicative of the effectiveness of early social distancing measures and lockdown. Premature relaxation of the current control measures may result in large numbers of cases in India.

Headache is one of the commonest complaints that doctors need to address in clinical settings. The genetic mechanisms of different types of headache are not well understood while it has been suggested that self-reported headache and self-reported migraine were genetically correlated. In this study, we performed a meta-analysis of genome-wide association studies (GWAS) on the self-reported headache phenotype from the UK Biobank and the self-reported migraine phenotype from the 23andMe using the Unified Score-based Association Test (metaUSAT) software for genetically correlated phenotypes (N = 397,385). We identified 38 loci for headaches, of which 34 loci have been reported before and four loci were newly suggested. The LDL receptor related protein 1 (LRP1)—Signal Transducer and Activator of Transcription 6 (STAT6)—Short chainDehydrogenase/Reductase family 9C member 7 (SDR9C7) region in chromosome 12 was the most significantly associated locus with a leading p value of 1.24 × 10^–62 of rs11172113. The One Cut homeobox 2 (ONECUT2) gene locus in chromosome 18 was the strongest signal among the four new loci with a p value of 1.29 × 10^–9 of rs673939. Our study demonstrated that the genetically correlated phenotypes of self-reported headache and self-reported migraine can be meta-analysed together in theory and in practice to boost study power to identify more variants for headaches. This study has paved way for a large GWAS meta-analysis involving cohorts of different while genetically correlated headache phenotypes.

Introduction: Emerging evidence suggests a connection between vulnerability to infections and Alzheimer’s disease (AD). The nectin cell adhesion molecule 2 (NECTIN2) gene coding for a membrane component of adherens junctions is involved in response to infections, and its single nucleotide polymorphism (SNP) rs6859 was significantly associated with AD risk in several human cohorts. It is unclear, however, how exactly rs6859 influences the development of AD pathology. The aggregation of hyperphosphorylated tau protein (pTau) is a key pathological feature of neurodegeneration in AD, which may be induced by infections, among other factors, and potentially influenced by genes involved in both AD and vulnerability to infections, such as NECTIN2.Materials and methods: We conducted a causal mediation analysis (CMA) on a sample of 708 participants in the Alzheimer’s disease Neuroimaging Initiative (ADNI). The relationship between rs6859 and Alzheimer’s disease (AD), with AD (yes/no) as the outcome and pTau-181 levels in the cerebrospinal fluid (CSF) acting as a mediator in this association, was assessed. Adjusted estimates from the probit and linear regression models were used in the CMA model, where an additive model considered an increase in dosage of the rs6859 A allele (AD risk factor).Results: The increase in dose of allele A of the SNP rs6859 resulted in about 0.144 increase per standard deviation (SD) of pTau-181 (95% CI: 0.041, 0.248, p < 0.01). When included together in the probit model, the change in A allele dose and each standard deviation change in pTau-181 predicted 6.84% and 9.79% higher probabilities for AD, respectively. In the CMA, the proportion of the average mediated effect was 17.05% and was higher for the risk allele homozygotes (AA), at 19.40% (95% CI: 6.20%, 43.00%, p < 0.01). The sensitivity analysis confirmed the evidence of a robust mediation effect.Conclusion: This study reported a new potential causal relationship between pTau-181 and AD. We found that the association between rs6859 in the NECTIN2 gene and AD is partly mediated by pTau-181 levels in CSF. The rest of this association may be mediated by other factors. Our finding sheds light on the complex interplay between genetic susceptibility, protein aggregation, and neurodegeneration in AD. Further research, using other biomarkers, is needed to uncover the remaining mechanisms of the association between the NECTIN2 gene and AD.

Background: Impaired brain glucose metabolism is a preclinical feature of neurodegenerative diseases such as Alzheimer’s disease (AD). Infections may promote AD-related pathology. Therefore, we investigated the interplay between infections and APOE4, a strong genetic risk factor for AD. Methods: We analyzed data on 1,509 participants in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database using multivariate linear regression models. The outcomes were rank-normalized hypometabolic convergence index (HCI), statistical regions of interest (SROI) for AD, and mild cognitive impairment (MCI). Marginal mean estimates for infections, stratified by APOE4 carrier status, were then computed. Results: Prior infections were associated with greater HCI [β = 0.15, 95% CI: 0.03, 0.27, p = 0.01]. The combined effects of infections and APOE4 carriers on HCI levels were significantly greater than either variable alone. Among APOE4 carriers, the estimated marginal mean was 0.62, rising to 0.77, with infections (p<0.001), indicating an interaction effect. Carriers with multiple infections showed greater hypometabolism (higher HCI), with an estimate of 0.44 (p = 0.01) compared to 0.11 (p = 0.08) for those with a single infection, revealing a dose-response relationship. The estimates for the association of infections with SROI AD and SROI MCI were β = -0.01 (p = 0.02) and β = -0.01 (p = 0.04), respectively. Conclusion: Our findings suggest that infections and APOE4 jointly contribute to brain glucose hypometabolism and AD pathology, supporting a “multi-hit” mechanism in AD development.