Four Mass General Articles in Altmetric’s Top Publications of 2019

The Altmetric Top 100 highlights the 100 biggest science stories of the year in 2019 (i.e., the articles that got the most attention online). In addition to scientific journals, they include patents and public policy documents, as well as mainstream media, blogs, Wikipedia and social media platforms. There were four papers co-authored by Massachusetts General Hospital investigators out of 2.7 million research outputs tracked by Altmetric included in this years list.

Below, you can learn more about each paper and find a link to its Altmetric details page, which lists all the blogs, social media posts and news articles where the study has been featured.

*In cases where there are authors from multiple institutions, we identify those from Mass General but not the complete study team.

Large-Scale GWAS Reveals Insights Into the Genetic Architecture of Same-Sex Sexual Behavior

Mass General Investigators: Andrea Ganna, PhD, Robert Maier, PhD, Robbee Wedow, PhD and Benjamin Neale, PhD

Twin and family studies have shown that same-sex sexual behavior is partly genetically influenced, but previous searches for specific genes involved have been underpowered.

We performed a genome-wide association study (GWAS) on 477,522 individuals, revealing five loci significantly associated with same-sex sexual behavior.

In aggregate, all tested genetic variants accounted for 8% to 25% of variation in same-sex sexual behavior, only partially overlapped between males and females, and do not allow meaningful prediction of an individual’s sexual behavior.

Comparing these GWAS results with those for the proportion of same-sex to total number of sexual partners among nonheterosexuals suggests that there is no single continuum from opposite-sex to same-sex sexual behavior.

Overall, our findings provide insights into the genetics underlying same-sex sexual behavior and underscore the complexity of sexuality.

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Coupled Electrophysiological, Hemodynamic and Cerebrospinal Fluid Oscillations in Human Sleep

Nina E. Fultz, Giorgio Bonmassar, PhD, Kawin Setsompop, PhD, Robert A. Stickgold, PhD (BIDMC), Bruce R. Rosen, MD, PhD, Jonathan R. Polimeni, PhD and Laura D. Lewis, PhD

Sleep is essential for both cognition and maintenance of healthy brain function. Slow waves in neural activity contribute to memory consolidation, whereas cerebrospinal fluid (CSF) clears metabolic waste products from the brain.

We discovered a coherent pattern of oscillating electrophysiological, hemodynamic and CSF dynamics that appears during non–rapid eye movement sleep. Neural slow waves are followed by hemodynamic oscillations, which in turn are coupled to CSF flow.

These results demonstrate that the sleeping brain exhibits waves of CSF flow on a macroscopic scale, and these CSF dynamics are interlinked with neural and hemodynamic rhythms.

Whether these two processes are related is not known. We used accelerated neuroimaging to measure physiological and neural dynamics in the human brain.

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Resistance to Autosomal Dominant Alzheimer’s Disease in an APOE3 Christchurch Homozygote: A Case Report

Mass General Contributors: Yakeel T. Quiroz, PhD (Senior Author), Enmanuelle Pardilla-Delgado, PhD, Arabiye Artola, Risa Sperling, MD, Aaron P. Shultz, PhD, Edmarie Guzmán-Vélez, PhD, and Justin Sanchez*

We identified a PSEN1 (presenilin 1) mutation carrier from the world’s largest autosomal dominant Alzheimer’s disease kindred who did not develop mild cognitive impairment until her seventies, three decades after the expected age of clinical onset.

The individual had two copies of the APOE3 Christchurch (R136S) mutation, unusually high brain amyloid levels and limited tau and neurodegenerative measurements.

Our findings have implications for the role of APOE in the pathogenesis, treatment and prevention of Alzheimer’s disease.

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Dissecting Racial Bias in an Algorithm Used to Manage the Health of Populations

Mass General Contributor: Christine Vogeli, PhD

Health systems rely on commercial prediction algorithms to identify and help patients with complex health needs.

We show that a widely used algorithm, typical of this industry-wide approach and affecting millions of patients, exhibits significant racial bias: At a given risk score, black patients are considerably sicker than white patients as evidenced by signs of uncontrolled illnesses.

Remedying this disparity would increase the percentage of Black patients receiving additional help from 17.7% to 46.5%. The bias arises because the algorithm predicts health care costs rather than illness, but unequal access to care means that we spend less money caring for black patients than for white patients.

Thus, despite health care cost appearing to be an effective proxy for health by some measures of predictive accuracy, large racial biases arise.

We suggest that the choice of convenient, seemingly effective proxies for ground truth can be an important source of algorithmic bias in many contexts.

See the initial Mass General Research Institute Blog post announcing the selected publications.