The current Coronavirus disease 2019 or COVID-19 pandemic has infected over two million people and resulted in the death of over one hundred thousand people at the time of writing this review. The disease is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Even though multiple vaccines and treatments are under development so far, the disease is only slowing down under extreme social distancing measures that are difficult to maintain. SARS-COV-2 is an enveloped virus that is surrounded by a lipid bilayer. Lipids are fundamental cell components that play various biological roles ranging from being a structural building block to a signaling molecule as well as a central energy store. The role lipids play in viral infection involves the fusion of the viral membrane to the host cell, viral replication, and viral endocytosis and exocytosis. Since lipids play a crucial function in the viral life cycle, we asked whether drugs targeting lipid metabolism, such as statins, can be utilized against SARS-CoV-2 and other viruses. In this review, we discuss the role of lipid metabolism in viral infection as well as the possibility of targeting lipid metabolism to interfere with the viral life cycle.

Objective We build classification models and risk assessment tools for diabetes, hypertension and comorbidity using machine-learning algorithms on data from Kuwait. We model the increased proneness in diabetic patients to develop hypertension and vice versa. We ascertain the importance of ethnicity (and natives vs expatriate migrants) and of using regional data in risk assessment. Design Retrospective cohort study. Four machine-learning techniques were used: logistic regression, k-nearest neighbours (k-NN), multifactor dimensionality reduction and support vector machines. The study uses fivefold cross validation to obtain generalisation accuracies and errors. Setting Kuwait Health Network (KHN) that integrates data from primary health centres and hospitals in Kuwait. Participants 270 172 hospital visitors (of which, 89 858 are diabetic, 58 745 hypertensive and 30 522 comorbid) comprising Kuwaiti natives, Asian and Arab expatriates. Outcome measures Incident type 2 diabetes, hypertension and comorbidity. Results Classification accuracies of >85% (for diabetes) and >90% (for hypertension) are achieved using only simple non-laboratory-based parameters. Risk assessment tools based on k-NN classification models are able to assign ‘high’ risk to 75% of diabetic patients and to 94% of hypertensive patients. Only 5% of diabetic patients are seen assigned ‘low’ risk. Asian-specific models and assessments perform even better. Pathological conditions of diabetes in the general population or in hypertensive population and those of hypertension are modelled. Two-stage aggregate classification models and risk assessment tools, built combining both the component models on diabetes (or on hypertension), perform better than individual models. Conclusions Data on diabetes, hypertension and comorbidity from the cosmopolitan State of Kuwait are available for the first time. This enabled us to apply four different case–control models to assess risks. These tools aid in the preliminary non-intrusive assessment of the population. Ethnicity is seen significant to the predictive models. Risk assessments need to be developed using regional data as we demonstrate the applicability of the American Diabetes Association online calculator on data from Kuwait.

COVID-19 is challenging healthcare preparedness, world economies, and livelihoods. The infection and death rates associated with this pandemic are strikingly variable in different countries. To elucidate this discrepancy, we analyzed 2431 early spread SARS-CoV-2 sequences from GISAID. We estimated continental-wise admixture proportions, assessed haplotype block estimation, and tested for the presence or absence of strains’ recombination. Herein, we identified 1010 unique missense mutations and seven different SARS-CoV-2 clusters. In samples from Asia, a small haplotype block was identified, whereas samples from Europe and North America harbored large and different haplotype blocks with nonsynonymous variants. Variant frequency and linkage disequilibrium varied among continents, especially in North America. Recombination between different strains was only observed in North American and European sequences. In addition, we structurally modelled the two most common mutations, Spike_D614G and Nsp12_P314L, which suggested that these linked mutations may enhance viral entry and replication, respectively. Overall, we propose that genomic recombination between different strains may contribute to SARS-CoV-2 virulence and COVID-19 severity and may present additional challenges for current treatment regimens and countermeasures. Furthermore, our study provides a possible explanation for the substantial second wave of COVID-19 presented with higher infection and death rates in many countries.

The global epidemic of diabetes and related disorders has not spared the states of Gulf Cooperation Council, particularly Kuwait (1,2). Socioeconomic changes in the post-oil era have promoted a sedentary lifestyle and an intake of high-calorie food. Expatriates, who form the workforce, constitute two-thirds of the Kuwait population; migrants are at greater risk of developing diabetes (3). We undertook a retrospective cohort study using nationwide data from the Kuwait Health Network, which integrates records from primary health clinics and hospitals. The dataset, after quality control, includes 270,172 participants comprising natives and expatriates. Crude prevalence of type 1 diabetes is 2.8% among expatriates and 2.3% among natives (age-group 0–20 years). Asian expatriates (age-group 30–60 years) exhibit higher prevalence of type 2 diabetes (and hypertension) at 33.25% (and 37%) than natives at 25.4% (and 28%). Prevalence of coexistence of diabetes and hypertension is 11%, irrespective of ethnicities. Prevalence increases with advancing …

The State of Kuwait is characterized by settlers from Saudi Arabia, Iran, and other regions of the Arabian Peninsula. The settlements and subsequent admixtures have shaped the genetics of Kuwait. High prevalence of recessive disorders and metabolic syndromes (that increase risk of diabetes) is seen in the peninsula. Understanding the genetic structure of its population will aid studies designed to decipher the underlying causes of these disorders. In this study, we analyzed 572,366 SNP markers from 273 Kuwaiti natives genotyped using the illumina HumanOmniExpress BeadChip. Model-based clustering identified three genetic subgroups with different levels of admixture. A high level of concordance (Mantel test, p=0.0001 for 9999 repeats) was observed between the derived genetic clusters and the surname-based ancestries. Use of Human Genome Diversity Project (HGDP) data to understand admixtures in each group reveals the following: the first group (Kuwait P) is largely of West Asian ancestry, representing Persians with European admixture; the second group (Kuwait S) is predominantly of city-dwelling Saudi Arabian tribe ancestry, and the third group (Kuwait B) includes most of the tent-dwelling Bedouin surnames and is characterized by the presence of 17% African ancestry. Identity by Descent and Homozygosity analyses find Kuwait’s population to be heterogeneous (placed between populations that have large amount of ROH and the ones with low ROH) with Kuwait S as highly endogamous, and Kuwait B as diverse. Population differentiation F_ST estimates place Kuwait P near Asian populations, Kuwait S near Negev Bedouin tribes, and Kuwait B near the Mozabite population. F_ST distances between the groups are in the range of 0.005 to 0.008; distances of this magnitude are known to cause false positives in disease association studies. Results of analysis for genetic features such as linkage disequilibrium decay patterns conform to Kuwait’s geographical location at the nexus of Africa, Europe, and Asia.