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First-of-Its-Kind Test Can Predict Dementia up to Nine Years Before Diagnosis

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Tuesday, June 11, 2024

Researchers have developed an innovative method for predicting dementia with over 80% accuracy, up to nine years before diagnosis. Using functional MRI to analyze the default mode network of the brain, the team could identify early signs of dementia by comparing brain connectivity patterns with genetic and health data from UK Biobank volunteers. This method not only improves early detection but also helps in understanding the interaction between genetic factors, social isolation, and Alzheimer’s disease.Queen Mary University researchers have created a method to predict dementia with high accuracy years before diagnosis by analyzing brain network connectivity using fMRI scans.Researchers at Queen Mary University of London have created a new technique that predicts dementia with over 80% accuracy up to nine years prior to diagnosis. This method surpasses traditional approaches like memory tests and measurements of brain shrinkage, two commonly used methods for diagnosing dementia.The team, led by Professor Charles Marshall, developed the predictive test by analyzing functional MRI (fMRI) scans to detect changes in the brain’s ‘default mode network’ (DMN). The DMN connects regions of the brain to perform specific cognitive functions and is the first neural network to be affected by Alzheimer’s disease.The researchers used fMRI scans from over 1,100 volunteers from UK Biobank, a large-scale biomedical database and research resource containing genetic and health information from half a million UK participants, to estimate the effective connectivity between ten regions of the brain that constitute the default mode network. Predictive Accuracy and MethodologyThe researchers assigned each patient with a probability of dementia value based on the extent to which their effective connectivity pattern conforms to a pattern that indicates dementia or a control-like pattern.They compared these predictions to the medical data of each patient, on record with the UK Biobank. The findings showed that the model had accurately predicted the onset of dementia up to nine years before an official diagnosis was made, and with greater than 80% accuracy. In the cases where the volunteers had gone on to develop dementia, it was also found that the model could predict within a two-year margin of error exactly how long it would take that diagnosis to be made.The researchers also examined whether changes to the DMN might be caused by known risk factors for dementia. Their analysis showed that genetic risk for Alzheimer’s disease was strongly associated with connectivity changes in the DMN, supporting the idea that these changes are specific to Alzheimer’s disease. They also found that social isolation was likely to increase the risk of dementia through its effect on connectivity in the DMN.Potential Impact of the ResearchCharles Marshall, Professor and Honorary Consultant Neurologist, led the research team within the Centre for Preventive Neurology at Queen Mary’s Wolfson Institute of Population Health. He said: “Predicting who is going to get dementia in the future will be vital for developing treatments that can prevent the irreversible loss of brain cells that causes the symptoms of dementia. Although we are getting better at detecting the proteins in the brain that can cause Alzheimer’s disease, many people live for decades with these proteins in their brains without developing symptoms of dementia. We hope that the measure of brain function that we have developed will allow us to be much more precise about whether someone is actually going to develop dementia, and how soon, so that we can identify whether they might benefit from future treatments.”Samuel Ereira, lead author and Academic Foundation Programme Doctor at the Centre for Preventive Neurology, Wolfson Institute of Population Health, said: “Using these analysis techniques with large datasets we can identify those at high dementia risk, and also learn which environmental risk factors pushed these people into a high-risk zone. Enormous potential exists to apply these methods to different brain networks and populations, to help us better understand the interplays between environment, neurobiology, and illness, both in dementia and possibly other neurodegenerative diseases. fMRI is a non-invasive medical imaging tool, and it takes about 6 minutes to collect the necessary data on an MRI scanner, so it could be integrated into existing diagnostic pathways, particularly where MRI is already used.”Hojjat Azadbakht, CEO of AINOSTICS (an AI company collaborating with world-leading research teams to develop brain imaging approaches for the early diagnosis of neurological disorders) said: “The approach developed has the potential to fill an enormous clinical gap by providing a non-invasive biomarker for dementia. In the study published by the team at QMUL, they were able to identify individuals who would later develop Alzheimer’s disease up to 9 years before they received a clinical diagnosis. It is during this pre-symptomatic stage that emerging disease-modifying treatments are likely to offer the most benefit for patients.”Reference: “Early detection of dementia with default-mode network effective connectivity” by Sam Ereira, Sheena Waters, Adeel Razi and Charles R. Marshall, 6 June 2024, Nature Mental Health.DOI: 10.1038/s44220-024-00259-5

Queen Mary University researchers have created a method to predict dementia with high accuracy years before diagnosis by analyzing brain network connectivity using fMRI scans....

Man With Alzheimer’s Dementia

Researchers have developed an innovative method for predicting dementia with over 80% accuracy, up to nine years before diagnosis. Using functional MRI to analyze the default mode network of the brain, the team could identify early signs of dementia by comparing brain connectivity patterns with genetic and health data from UK Biobank volunteers. This method not only improves early detection but also helps in understanding the interaction between genetic factors, social isolation, and Alzheimer’s disease.

Queen Mary University researchers have created a method to predict dementia with high accuracy years before diagnosis by analyzing brain network connectivity using fMRI scans.

Researchers at Queen Mary University of London have created a new technique that predicts dementia with over 80% accuracy up to nine years prior to diagnosis. This method surpasses traditional approaches like memory tests and measurements of brain shrinkage, two commonly used methods for diagnosing dementia.

The team, led by Professor Charles Marshall, developed the predictive test by analyzing functional MRI (fMRI) scans to detect changes in the brain’s ‘default mode network’ (DMN). The DMN connects regions of the brain to perform specific cognitive functions and is the first neural network to be affected by Alzheimer’s disease.

The researchers used fMRI scans from over 1,100 volunteers from UK Biobank, a large-scale biomedical database and research resource containing genetic and health information from half a million UK participants, to estimate the effective connectivity between ten regions of the brain that constitute the default mode network.

Predictive Accuracy and Methodology

The researchers assigned each patient with a probability of dementia value based on the extent to which their effective connectivity pattern conforms to a pattern that indicates dementia or a control-like pattern.

They compared these predictions to the medical data of each patient, on record with the UK Biobank. The findings showed that the model had accurately predicted the onset of dementia up to nine years before an official diagnosis was made, and with greater than 80% accuracy. In the cases where the volunteers had gone on to develop dementia, it was also found that the model could predict within a two-year margin of error exactly how long it would take that diagnosis to be made.

The researchers also examined whether changes to the DMN might be caused by known risk factors for dementia. Their analysis showed that genetic risk for Alzheimer’s disease was strongly associated with connectivity changes in the DMN, supporting the idea that these changes are specific to Alzheimer’s disease. They also found that social isolation was likely to increase the risk of dementia through its effect on connectivity in the DMN.

Potential Impact of the Research

Charles Marshall, Professor and Honorary Consultant Neurologist, led the research team within the Centre for Preventive Neurology at Queen Mary’s Wolfson Institute of Population Health. He said: “Predicting who is going to get dementia in the future will be vital for developing treatments that can prevent the irreversible loss of brain cells that causes the symptoms of dementia. Although we are getting better at detecting the proteins in the brain that can cause Alzheimer’s disease, many people live for decades with these proteins in their brains without developing symptoms of dementia. We hope that the measure of brain function that we have developed will allow us to be much more precise about whether someone is actually going to develop dementia, and how soon, so that we can identify whether they might benefit from future treatments.”

Samuel Ereira, lead author and Academic Foundation Programme Doctor at the Centre for Preventive Neurology, Wolfson Institute of Population Health, said: “Using these analysis techniques with large datasets we can identify those at high dementia risk, and also learn which environmental risk factors pushed these people into a high-risk zone. Enormous potential exists to apply these methods to different brain networks and populations, to help us better understand the interplays between environment, neurobiology, and illness, both in dementia and possibly other neurodegenerative diseases. fMRI is a non-invasive medical imaging tool, and it takes about 6 minutes to collect the necessary data on an MRI scanner, so it could be integrated into existing diagnostic pathways, particularly where MRI is already used.”

Hojjat Azadbakht, CEO of AINOSTICS (an AI company collaborating with world-leading research teams to develop brain imaging approaches for the early diagnosis of neurological disorders) said: “The approach developed has the potential to fill an enormous clinical gap by providing a non-invasive biomarker for dementia. In the study published by the team at QMUL, they were able to identify individuals who would later develop Alzheimer’s disease up to 9 years before they received a clinical diagnosis. It is during this pre-symptomatic stage that emerging disease-modifying treatments are likely to offer the most benefit for patients.”

Reference: “Early detection of dementia with default-mode network effective connectivity” by Sam Ereira, Sheena Waters, Adeel Razi and Charles R. Marshall, 6 June 2024, Nature Mental Health.
DOI: 10.1038/s44220-024-00259-5

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New method improves the reliability of statistical estimations

The technique can help scientists in economics, public health, and other fields understand whether to trust the results of their experiments.

Let’s say an environmental scientist is studying whether exposure to air pollution is associated with lower birth weights in a particular county.They might train a machine-learning model to estimate the magnitude of this association, since machine-learning methods are especially good at learning complex relationships.Standard machine-learning methods excel at making predictions and sometimes provide uncertainties, like confidence intervals, for these predictions. However, they generally don’t provide estimates or confidence intervals when determining whether two variables are related. Other methods have been developed specifically to address this association problem and provide confidence intervals. But, in spatial settings, MIT researchers found these confidence intervals can be completely off the mark.When variables like air pollution levels or precipitation change across different locations, common methods for generating confidence intervals may claim a high level of confidence when, in fact, the estimation completely failed to capture the actual value. These faulty confidence intervals can mislead the user into trusting a model that failed.After identifying this shortfall, the researchers developed a new method designed to generate valid confidence intervals for problems involving data that vary across space. In simulations and experiments with real data, their method was the only technique that consistently generated accurate confidence intervals.This work could help researchers in fields like environmental science, economics, and epidemiology better understand when to trust the results of certain experiments.“There are so many problems where people are interested in understanding phenomena over space, like weather or forest management. We’ve shown that, for this broad class of problems, there are more appropriate methods that can get us better performance, a better understanding of what is going on, and results that are more trustworthy,” says Tamara Broderick, an associate professor in MIT’s Department of Electrical Engineering and Computer Science (EECS), a member of the Laboratory for Information and Decision Systems (LIDS) and the Institute for Data, Systems, and Society, an affiliate of the Computer Science and Artificial Intelligence Laboratory (CSAIL), and senior author of this study.Broderick is joined on the paper by co-lead authors David R. Burt, a postdoc, and Renato Berlinghieri, an EECS graduate student; and Stephen Bates an assistant professor in EECS and member of LIDS. The research was recently presented at the Conference on Neural Information Processing Systems.Invalid assumptionsSpatial association involves studying how a variable and a certain outcome are related over a geographic area. For instance, one might want to study how tree cover in the United States relates to elevation.To solve this type of problem, a scientist could gather observational data from many locations and use it to estimate the association at a different location where they do not have data.The MIT researchers realized that, in this case, existing methods often generate confidence intervals that are completely wrong. A model might say it is 95 percent confident its estimation captures the true relationship between tree cover and elevation, when it didn’t capture that relationship at all.After exploring this problem, the researchers determined that the assumptions these confidence interval methods rely on don’t hold up when data vary spatially.Assumptions are like rules that must be followed to ensure results of a statistical analysis are valid. Common methods for generating confidence intervals operate under various assumptions.First, they assume that the source data, which is the observational data one gathered to train the model, is independent and identically distributed. This assumption implies that the chance of including one location in the data has no bearing on whether another is included. But, for example, U.S. Environmental Protection Agency (EPA) air sensors are placed with other air sensor locations in mind.Second, existing methods often assume that the model is perfectly correct, but this assumption is never true in practice. Finally, they assume the source data are similar to the target data where one wants to estimate.But in spatial settings, the source data can be fundamentally different from the target data because the target data are in a different location than where the source data were gathered.For instance, a scientist might use data from EPA pollution monitors to train a machine-learning model that can predict health outcomes in a rural area where there are no monitors. But the EPA pollution monitors are likely placed in urban areas, where there is more traffic and heavy industry, so the air quality data will be much different than the air quality data in the rural area.In this case, estimates of association using the urban data suffer from bias because the target data are systematically different from the source data.A smooth solutionThe new method for generating confidence intervals explicitly accounts for this potential bias.Instead of assuming the source and target data are similar, the researchers assume the data vary smoothly over space.For instance, with fine particulate air pollution, one wouldn’t expect the pollution level on one city block to be starkly different than the pollution level on the next city block. Instead, pollution levels would smoothly taper off as one moves away from a pollution source.“For these types of problems, this spatial smoothness assumption is more appropriate. It is a better match for what is actually going on in the data,” Broderick says.When they compared their method to other common techniques, they found it was the only one that could consistently produce reliable confidence intervals for spatial analyses. In addition, their method remains reliable even when the observational data are distorted by random errors.In the future, the researchers want to apply this analysis to different types of variables and explore other applications where it could provide more reliable results.This research was funded, in part, by an MIT Social and Ethical Responsibilities of Computing (SERC) seed grant, the Office of Naval Research, Generali, Microsoft, and the National Science Foundation (NSF).

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Gas Stoves Are Poisoning Americans by Releasing Toxic Fumes Associated With Asthma and Lung Cancer

In the United States, gas stoves are the main source of indoor nitrogen dioxide—a toxic gas tied to many health problems—according to a new study

Gas Stoves Are Poisoning Americans by Releasing Toxic Fumes Associated With Asthma and Lung Cancer In the United States, gas stoves are the main source of indoor nitrogen dioxide—a toxic gas tied to many health problems—according to a new study Sarah Kuta - Daily Correspondent December 11, 2025 9:13 a.m. Gas stoves are responsible for more than half of some Americans’ total exposure to toxic nitrogen dioxide, a new study suggests. Pexels A hidden danger may be lurking in your kitchen. Many Americans are breathing in nitrogen dioxide—a harmful pollutant that’s been linked with asthma and lung cancer—from fumes emitted by their gas stoves. A new study, published this month in the journal PNAS Nexus, suggests that gas stoves are the main source of indoor nitrogen dioxide pollution in the United States, responsible for more than half of some Americans’ total exposure to the gas. “We’ve spent billions of dollars cleaning up our air outdoors and nothing to clean up our air indoors,” study co-author Robert Jackson, an environmental scientist at Stanford University, tells SFGATE’s Anna FitzGerald Guth. “As our air outdoors gets cleaner and cleaner, a higher proportion of the pollution we breathe comes from indoor sources.” Scientists and public health experts have long known that nitrogen dioxide is bad for human health. The reddish-brown gas can irritate airways and worsen or even contribute to the development of respiratory diseases like asthma. Children and older individuals are particularly susceptible to its effects. Nitrogen dioxide is a byproduct of burning fuel, so most emissions come from vehicles, power plants and off-road equipment. However, indoors, the primary culprit is the gas stove, the household appliance that burns natural gas or propane to produce controlled flames under individual burners. It’s relatively easy to keep tabs on outdoor nitrogen dioxide concentrations and estimate their corresponding exposure risks, thanks to satellites and ground-level stations located across the country. By contrast, however, indoor sources are “neither systematically monitored nor estimated,” the researchers write in the paper. Did you know? Bans on gas Berkeley, California, became the first city to prohibit gas hookups in most new buildings in 2019, although the ordinance was halted in 2024 after the California Restaurant Association sued. Still, 130 local governments have now implemented zero-emission building ordinances, according to the Building Decarbonization Coalition. For the study, Jackson and his colleagues performed a ZIP-code-level estimate of how much total nitrogen dioxide communities are exposed to. Information came from two databases tracking outdoor nitrogen dioxide concentrations and a building energy use database, which helped the team construct characteristics of 133 million residential dwellings across the country, along with their home appliances. Among individuals who use gas stoves, the appliances are responsible for roughly a quarter of their overall nitrogen dioxide exposure on average, the team found. For those who cook more frequently or for longer durations, gas stoves can be responsible for as much as 57 percent of their total exposure. “Our research shows that if you use a gas stove, you’re often breathing as much nitrogen dioxide pollution indoors from your stove as you are from all outdoor sources combined,” says Jackson in a Stanford statement. Individuals who use gas stoves are exposed to roughly 25 percent more total residential nitrogen dioxide over the long term than those who use electric stoves, which do not emit the gas. Total exposure tends to be highest in big cities, where people often have small living spaces and outdoor levels are also high. Switching from a gas to an electric stove would help roughly 22 million Americans dip below the maximum nitrogen dioxide exposure levels recommended by the World Health Organization, the analyses suggest. The authors recommend replacing gas stoves with electric models whenever possible. “You would never willingly stand over the tailpipe of your car, breathing in pollution,” Jackson tells Women’s Health’s Korin Miller. “Why breathe the same toxins every day in your kitchen?” Dylan Plummer, acting deputy director for building electrification for the Sierra Club, a nonprofit environmental organization, agrees. Plummer, who was not involved with the research, tells Inside Climate News’ Phil McKenna that “years from now, we will look back at the common practice of burning fossil fuels in our homes with horror.” If swapping stoves is not possible, experts have some other tips for reducing nitrogen dioxide exposure. “One thing people could do is to minimize the time the stoves are on,” Jamie Alan, a toxicologist at Michigan State University who was not involved with the research, tells Women’s Health. “Another suggestion would be to increase ventilation,” such as by turning on the range hood and opening a window. Other suggestions by the New York Times’ Rachel Wharton include using a portable induction countertop unit or electric kitchen gadgets like tea kettles, toaster ovens and slow cookers. Get the latest stories in your inbox every weekday.

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Parents Might Pass Depression Down To Kids Through One Specific Symptom, Experts Say

By Dennis Thompson HealthDay ReporterTHURSDAY, Dec. 11, 2025 (HealthDay News) — Children of depressed parents are more likely to develop depression...

By Dennis Thompson HealthDay ReporterTHURSDAY, Dec. 11, 2025 (HealthDay News) — Children of depressed parents are more likely to develop depression themselves, and a new study suggests this risk might be tied to one specific symptom of depression.It’s already known that depression in parents can affect how children’s brains respond to positive and negative feedback, researchers said.“If parents are experiencing forms of depression where they’re not enjoying things and aren’t interested in things, that seems to be impacting how their kids are responding to what’s going on around them,” senior researcher Brandon Gibb, director of the Mood Disorders Institute at Binghamton University, said in a news release.“They’re less reactive to positive things and negative things,” he continued. “It seems that parents’ experiences of anhedonia is the key feature of depression impacting how children’s brains are responding, at least in our study, rather than other common symptoms of depression.”For the new study, researchers performed a lab experiment involving more than 200 parents and children ages 7 to 11.The experiment was designed to see how parents’ anhedonic symptoms affect children’s brain responses to positive and negative feedback.“The idea is that if you have this risk factor of being less interested or less engaged or finding things less enjoyable, maybe that’s reflected in how your brain responds to environmental feedback,” said lead researcher Alana Israel, a doctoral student at Binghamton University, a branch of the State University of New York. “Children of parents who have higher levels of anhedonic depressive symptoms should show a reduced response while other depressive symptoms theoretically should not be as related to this specific brain response,” Israel explained in a news release.In the experiment, children were presented with two doors and asked to guess the one with a prize behind it. If they chose the right door, they won money; if they chose wrong, they lost money.Results showed that kids’ response to either winning or losing money was blunted if their parents had higher levels of anhedonic symptoms. “What that tells us is that there is something specific about parents’ anhedonia that may impact children’s neural responses,” Israel said. “It further specifies a group of children who might be at heightened risk for loss of interest or pleasure and lack of engagement, which is a core feature of depression.”Future research should investigate how family dynamics might change if parents with anhedonic symptoms receive treatment or start to feel better, the team said.Researchers said it’s also important to examine whether children’s responses to other sorts of feedback, like social feedback from peers, are also affected by parents’ depression.“There are researchers looking at interventions that are designed to increase positive mood, positive engagement and positive parent-child relationships,” Israel said. “It will be important to see if these findings can identify families who might be most likely to benefit from those types of interventions.”SOURCE: Binghamton University, news release, Dec. 4, 2025Copyright © 2025 HealthDay. All rights reserved.

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We may finally know what a healthy gut microbiome looks like

Our gut microbiome has a huge influence on our overall health, but we haven't been clear on the specific bacteria with good versus bad effects. Now, a study of more than 34,000 people is shedding light on what a healthy gut microbiome actually consists of

The trillions of microscopic bacteria that reside in our gut have an outsized role in our healthTHOM LEACH/SCIENCE PHOTO LIBRARY We often hear talk of things being good for our microbiome, and in turn, good for our health. But it wasn’t entirely clear what a healthy gut microbiome consisted of. Now, a study of more than 34,000 people has edged us closer towards understanding the mixes of microbes that reliably signal we have low inflammation, good immunity and healthy cholesterol levels. Your gut microbiome can influence your immune system, rate of ageing and your risk of poor mental health. Despite a profusion of home tests promising to reveal the make-up of your gut community, their usefulness has been debated, because it is hard to pin down what defines a “good” microbial mix. Previous measures mainly looked at species diversity, with a greater array of bacteria being better. But it is difficult to identify particular communities of interacting organisms that are implicated in a specific aspect of our health, because microbiomes vary so much from person to person. “There is a very intricate relationship between the food we eat, the composition of our gut microbiome, and the effects the gut microbiome has on our health. The only way to try to map these connections is having large enough sample sizes,” says Nicola Segata at the University of Trento in Italy. To create such a map, Segata and his colleagues have assessed a dataset from more than 34,500 people who took part in the PREDICT programme in the UK and US, run by microbiome testing firm Zoe, and validated the results against data from 25 other cohorts from Western countries. Of the thousands of species that reside in the human gut, the researchers focused on 661 bacterial species that were found in more than 20 per cent of the Zoe participants. They used this to determine the 50 bacteria most associated with markers of good health – assessed via markers such as body mass index and blood glucose levels – and the 50 most linked to bad health. The 50 “good bug” species – 22 of which are new to science – seem to influence four key areas: heart and blood cholesterol levels; inflammation and immune health; body fat distribution; and blood sugar control. The participants who were deemed healthy, because they had no known medical conditions, had about 3.6 more of these species than people with a condition, while people at a healthy weight hosted about 5.2 more of them than those with obesity. The researchers suggest that good or bad health outcomes may come about due to the vital role the gut microbiome plays in releasing chemicals involved in cholesterol transport, inflammation reduction, fat metabolism and insulin sensitivity. As to the specific species that were present, most microbes in both the “good” and “bad” rankings belong to the Clostridia class. Within this class, species in the Lachnospiraceae family featured 40 times, with 13 seemingly having favourable effects and 27 unfavourable. “The study highlights bacterial groups that could be further investigated regarding their potential positive or negative impact [on] health conditions, such as high blood glucose levels or obesity,” says Ines Moura at the University of Leeds, UK. The link between these microbes and diet was assessed via food questionnaires and data logged on the Zoe app, where users are advised to aim for at least 30 different plants a week and at least three portions a day of fermented foods, with an emphasis on fibre and not too many ultra processed options. The researchers found that most of the microbes either aligned with a generally healthy diet and better health, or with a worse diet and poorer health. But 65 of the 661 microbes didn’t fit in. “These 65 bacteria are a testament to the fact that the picture is still more complex than what we saw,” says Segata, who also works as a consultant for Zoe. “The effects may depend on the other microbes that are there, or the specific strain of the bacterium or the specific diet.” This sorting of “good” versus “bad” bacteria has enabled the researchers to create a 0 to 1000 ranking scale for the overall health of someone’s gut microbiota, which is already used as part of Zoe’s gut health tests. “Think of a healthy gut microbiome as a community of chemical factories. We want large numbers of species, we want the good ones outnumbering the bad ones, and when you get that, then you’re producing really healthy chemicals, which have impacts across the body,” says team member Tim Spector at King’s College London and co-founder of Zoe. 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Vanishing Springs

The Florida you know is a lie. In contrast to what Americans have been told, Florida’s magic is not found in the giant mouse, the rolling green golf courses, or in the beachside palaces. The beauty and uniqueness of Florida is under our feet - the aquifer. This pure, crystal clear water is the life blood of our state and without it, life as we know it in the sunshine state would not exist. This life sustaining force is only seen where it bubbles up to the surface through Florida’s collection of 1,000 springs. These springs were originally what drew society to this land. Wealthy tourists in the 1900’s flooded the Florida springs seeking medicinal cures from its pristine waters. As springs became a popular tourist destination, spring houses were built around these pools of water. When this development began, the springs began flowing less and some eventually stopped altogether. People assumed this was the natural order of this wonder and moved on. Theme parks, golf courses and resorts were erected to entice tourists to visit Florida, the natural beauty of the state soon faded out of our memory. The springs of Florida now silently suffer from the effects of continued development in Florida. The current strain we are placing on the fragile ecosystem is choking the life out of our state. Overdevelopment is one of the leading forces that is damaging the springs. The once pure sources of water no longer boil up like a fountain the way they have for centuries. The water that the springs do produce is polluted by nitrates. This pollution fuels the growth of toxic algae blooms, which are taking over springs and the rivers they feed, thus putting our health at risk. Over 90% of our drinking water gushes out of these sapphire pools. These glorious reservoirs have begun to shrink. If something is not done soon, the springs will simply become part of Florida history. In southern Florida they already have. Springs once bubbled up all across the state. They were wiped out in South Florida decades ago by the ditching and draining of the landscape as well as over-pumping of the aquifer. This water was then sprayed on suburban lawns and farmers' fields, run through showers and flushed down toilets, turned into steam to crank turbines for electricity, or siphoned into plastic bottles for sale around the country. Because of poor use and neglect of our greatest resource, we will soon be without this supply of fresh water. Floridians regard their water supply as abundant and cheap, when the fact is it's neither. Until this attitude changes, the springs will not be rescued.

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