Lifeboat Foundation

To predict your #longevity, you have two main options. You can rely on the routine tests and measurements your doctor likes to order for you, such as blood pressure, cholesterol levels, weight, and so on. Or you can go down a biohacking rabbit hole the way tech millionaire turned longevity guru Bryan Johnson did to live longer. Johnson’s obsessive self-measurement protocol involves tracking more than a hundred biomarkers, ranging from the telomere length in blood cells to the speed of his urine stream (which, at 25 milliliters per second, he reports, is in the 90th percentile of 40-year-olds).

Scientists crunched the numbers to come up with the single best predictor of how long you’ll live—and arrived at a surprisingly low-tech answer.

New research found that the protein MANF helps cells manage toxic protein clumps, improving cellular health and potentially aiding treatments for age-related diseases like Alzheimer’s and Parkinson’s.

Researchers at McMaster University have uncovered a previously unidentified cell-protective role of a protein, potentially paving the way for new treatments for age-related diseases and promoting healthier aging.

The team has found that a class of protective proteins known as MANF plays a role in the process that keep cells efficient and working well.

Chronological age (CA) does not reflect individual variation in the aging process. However, existing biological age predictors are mostly based on European populations and overlook the widespread nonlinear effects of clinical biomarkers.

Using data from the prospective Dongfeng-Tongji (DFTJ) cohort of elderly Chinese, we propose a physiological aging index (PAI) based on 36 routine clinical biomarkers to measure aging progress. We first determined the optimal level of each biomarker by restricted cubic spline Cox models. For biomarkers with a U-shaped relationship with mortality, we derived new variables to model their distinct effects below and above the optimal levels. We defined PAI as a weighted sum of variables predictive of mortality selected by a LASSO Cox model. To measure aging acceleration, we defined ΔPAI as the residual of PAI after regressing on CA. We evaluated the predictive value of ΔPAI on cardiovascular diseases (CVD) in the DFTJ cohort, as well as nine major chronic diseases in the UK Biobank (UKB).

In the DFTJ training set (n = 12,769, median follow-up: 10.38 years), we identified 25 biomarkers with significant nonlinear associations with mortality, of which 11 showed insignificant linear associations. By incorporating nonlinear effects, we selected CA and 17 clinical biomarkers to calculate PAI. In the DFTJ testing set (n = 15,904, 5.87 years), PAI predict mortality with a concordance index (C-index) of 0.816 (95% confidence interval, [0.796, 0.837]), better than CA (C-index = 0.771 [0.755, 0.788]) and PhenoAge (0.799 [0.784, 0.814]). ΔPAI was predictive of incident CVD and its subtypes, independent of traditional risk factors. In the external validation set of UKB (n = 296,931, 12.80 years), PAI achieved a C-index of 0.749 (0.746, 0.752) to predict mortality, remaining better than CA (0.706 [0.702, 0.709]) and PhenoAge (0.743 [0.739, 0.746]).

Summary: New research suggests that certain nutrients may lower iron buildup in the brain, a factor linked to cognitive decline in aging. Excess non-heme iron, which accumulates over time, contributes to oxidative stress and can impair memory and executive function.

Over three years, participants with higher intake of antioxidants, vitamins, and iron-chelating nutrients showed less brain iron accumulation and better cognitive performance. These findings highlight the potential of diets like the Mediterranean or DASH to support brain health and combat age-related cognitive decline.

Studies using mouse models of lung adenocarcinoma identify an association between age, iron homeostasis and tumour initiation potential that involves NUPR1 and lipocalin-2.

Globally, approximately 139 million people are expected to have Alzheimer’s disease (AD) by 2050. Magnetic resonance imaging (MRI) is an important tool for identifying changes in brain structure that precede cognitive decline and progression with disease; however, its cost limits widespread use.

A new study by investigators from Massachusetts General Hospital (MGH), a founding member of the Mass General Brigham health care system, demonstrates that a simplified, low magnetic field (LF) MRI machine, augmented with machine learning tools, matches conventional MRI in measuring brain characteristics relevant to AD. Findings, published in Nature Communications, highlight the potential of the LF-MRI to help evaluate those with cognitive symptoms.

“To tackle the growing, global health challenge of dementia and cognitive impairment in the aging population, we’re going to need simple, bedside tools that can help determine patients’ underlying causes of cognitive impairment and inform treatment,” said senior author W. Taylor Kimberly, MD, Ph.D., chief of the Division of Neurocritical Care in the Department of Neurology at MGH.

In a recent study published in the journal Nature Aging, researchers identified plasma proteomic biomarkers and dynamic changes associated with brain aging, leveraging a multimodal approach combining brain age gap (BAG) and proteome-wide association analysis.

Background

The global aging population is expected to exceed 1.5 billion individuals aged 65 and above by 2050, highlighting the urgent need to address aging-associated challenges.

A study links healthy plant-based diets to improved physical performance and strength in aging adults, emphasizing diet quality over quantity.

For the first time, scientists have observed a collection of particles, also known as a quasiparticle, that’s massless when moving one direction but has mass in the other direction. The quasiparticle, called a semi-Dirac fermion, was first theorized 16 years ago, but was only recently spotted inside a crystal of semi-metal material called ZrSiS. The observation of the quasiparticle opens the door to future advances in a range of emerging technologies from batteries to sensors, according to the researchers.

The team, led by scientists at Penn State and Columbia University, recently published their discovery in the journal Physical Review X.

“This was totally unexpected,” said Yinming Shao, assistant professor of physics at Penn State and lead author on the paper. “We weren’t even looking for a semi-Dirac fermion when we started working with this material, but we were seeing signatures we didn’t understand—and it turns out we had made the first observation of these wild quasiparticles that sometimes move like they have mass and sometimes move like they have none.”