"""AgingCohort_search tool for ToolUniverse.
Provides a searchable registry of major aging and longitudinal cohort studies
worldwide. Contains curated metadata for ~30 studies including study design,
sample size, variable categories, and data access information.
No external API calls -- uses an internal curated database. Cohort metadata
changes rarely (study design, variables, and access methods are stable).
"""
from typing import Dict, Any, List
from .base_tool import BaseTool
from .tool_registry import register_tool
# ---------------------------------------------------------------------------
# Internal cohort database
# ---------------------------------------------------------------------------
_COHORTS: List[Dict[str, Any]] = [
# === Health & Aging (Longitudinal) ===
{
"name": "HRS",
"full_name": "Health and Retirement Study",
"country": "USA",
"design": "longitudinal",
"sample_size": 20000,
"sample_size_note": "20,000+ respondents; biennial interviews",
"age_range": "50+",
"start_year": 1992,
"waves_or_follow_up": "Biennial waves since 1992 (16+ waves)",
"key_variable_categories": [
"demographics",
"nutrition",
"diet",
"grip_strength",
"walking_speed",
"physical_function",
"cognitive",
"biomarkers",
"genetics",
"medications",
"chronic_conditions",
"disability",
"healthcare_utilization",
"employment",
"retirement",
"income",
"wealth",
],
"description": (
"Flagship US longitudinal study of adults aged 50+ covering health, "
"retirement, and aging. Nutrition data available via the 2013 Health "
"Care and Nutrition Study (HCNS) module, including 24-hour dietary "
"recall and supplement use. Physical measures include grip strength "
"and walking speed (since 2006 enhanced face-to-face module). "
"Genetic data (HRS-GWAS) and dried blood spot biomarkers available."
),
"access_method": "Public-use files freely available; restricted data via application",
"access_url": "https://hrs.isr.umich.edu/data-products",
"key_publications": [
"Sonnega et al. (2014) J Gerontol B 69(Suppl 2):S199-S210",
"Juster & Suzman (1995) J Human Resources 30:S7-S56",
],
},
{
"name": "ELSA",
"full_name": "English Longitudinal Study of Ageing",
"country": "UK",
"design": "longitudinal",
"sample_size": 12000,
"sample_size_note": "12,000+ respondents at baseline; refreshment samples added",
"age_range": "50+",
"start_year": 2002,
"waves_or_follow_up": "Biennial waves since 2002 (10+ waves)",
"key_variable_categories": [
"demographics",
"nutrition",
"diet",
"grip_strength",
"walking_speed",
"physical_function",
"cognitive",
"biomarkers",
"genetics",
"medications",
"chronic_conditions",
"disability",
"mental_health",
"social_participation",
"wealth",
],
"description": (
"Core UK aging cohort study harmonized with HRS. Measures grip "
"strength (Smedley dynamometer), walking speed (timed 8-foot walk), "
"and gait speed in even-numbered waves. Nurse visits collect blood "
"biomarkers including inflammatory markers, HbA1c, cholesterol. "
"Dietary data from food frequency questionnaire in selected waves. "
"Genetic data linked to UK Biobank array."
),
"access_method": "UK Data Service; free registration required",
"access_url": "https://www.elsa-project.ac.uk/accessing-elsa-data",
"key_publications": [
"Steptoe et al. (2013) Int J Epidemiol 42(6):1640-1648",
"Marmot et al. (2003) J Epidemiol Community Health 57(10):778-783",
],
},
{
"name": "SHARE",
"full_name": "Survey of Health, Ageing and Retirement in Europe",
"country": "Europe (28 countries)",
"design": "longitudinal",
"sample_size": 140000,
"sample_size_note": "140,000+ respondents across 28 European countries + Israel",
"age_range": "50+",
"start_year": 2004,
"waves_or_follow_up": "Biennial waves since 2004 (9+ waves)",
"key_variable_categories": [
"demographics",
"nutrition",
"diet",
"grip_strength",
"walking_speed",
"physical_function",
"cognitive",
"biomarkers",
"genetics",
"medications",
"chronic_conditions",
"mental_health",
"social_networks",
"employment",
"retirement",
"healthcare_utilization",
],
"description": (
"Pan-European multidisciplinary aging study harmonized with HRS and "
"ELSA. Covers 28 EU countries plus Israel. Measures grip strength "
"(Smedley dynamometer), chair stands, and peak flow. Dried blood "
"spot biomarkers in selected waves. Nutrition questions on fruit/"
"vegetable consumption frequency. SHARE-GWAS and SHARE-omics "
"substudies available."
),
"access_method": "Free registration at SHARE Research Data Center",
"access_url": "https://share-eric.eu/data/data-access",
"key_publications": [
"Borsch-Supan et al. (2013) Int J Epidemiol 42(4):992-1001",
"Borsch-Supan & Jurges (2005) The Survey of Health, Ageing and Retirement in Europe",
],
},
{
"name": "InCHIANTI",
"full_name": "Invecchiare in Chianti (Aging in the Chianti Area)",
"country": "Italy",
"design": "longitudinal",
"sample_size": 1300,
"sample_size_note": "1,300+ participants from Greve in Chianti and Bagno a Ripoli",
"age_range": "20+ (enriched for 65+)",
"start_year": 1998,
"waves_or_follow_up": "3-year follow-up intervals; 6+ follow-ups through 2019",
"key_variable_categories": [
"demographics",
"nutrition",
"diet",
"iron",
"grip_strength",
"walking_speed",
"physical_function",
"cognitive",
"biomarkers",
"genetics",
"medications",
"chronic_conditions",
"body_composition",
"muscle_strength",
"inflammatory_markers",
"anemia",
"micronutrients",
],
"description": (
"Detailed Italian aging study with extensive nutrition and physical "
"function data. Known for pioneering work on iron status, anemia, "
"and physical decline in aging. Detailed dietary assessment via "
"validated food frequency questionnaire (EPIC-FFQ) with nutrient "
"calculations including iron, zinc, folate, B12, and vitamin D. "
"Walking speed measured over 4m and 400m courses. Grip strength "
"via hand dynamometer. Comprehensive blood biomarkers including "
"serum iron, ferritin, transferrin, hepcidin, IL-6, TNF-alpha, "
"CRP. GWAS data available."
),
"access_method": "Collaborative access via application to InCHIANTI study group",
"access_url": "https://inchiantistudy.net/wp/",
"key_publications": [
"Ferrucci et al. (2000) J Am Geriatr Soc 48(9):1136-1138",
"Guralnik et al. (1994) N Engl J Med 332(9):556-562",
],
},
{
"name": "TILDA",
"full_name": "The Irish Longitudinal Study on Ageing",
"country": "Ireland",
"design": "longitudinal",
"sample_size": 8500,
"sample_size_note": "8,500+ community-dwelling adults aged 50+",
"age_range": "50+",
"start_year": 2009,
"waves_or_follow_up": "Biennial waves since 2009 (6+ waves)",
"key_variable_categories": [
"demographics",
"nutrition",
"diet",
"grip_strength",
"walking_speed",
"physical_function",
"cognitive",
"biomarkers",
"genetics",
"medications",
"chronic_conditions",
"frailty",
"bone_density",
"cardiovascular",
"vision",
],
"description": (
"Irish aging cohort harmonized with HRS/ELSA/SHARE. Notable for "
"comprehensive health assessment centers with objective physical "
"measures: Timed Up and Go (TUG), grip strength, walking speed, "
"dual-energy X-ray absorptiometry (DXA). Nutrition assessed via "
"self-completed food frequency questionnaire. Blood biomarkers "
"include vitamin D, B12, folate, iron studies, HbA1c. GWAS data "
"on Illumina Omni array."
),
"access_method": "ISSDA (Irish Social Science Data Archive); application required",
"access_url": "https://tilda.tcd.ie/data/accessing-data/",
"key_publications": [
"Kearney et al. (2011) J Am Geriatr Soc 59(Suppl 2):S291-S299",
"Whelan & Savva (2013) TILDA Design Report",
],
},
{
"name": "BLSA",
"full_name": "Baltimore Longitudinal Study of Aging",
"country": "USA",
"design": "longitudinal",
"sample_size": 3000,
"sample_size_note": "3,000+ participants; continuous enrollment since 1958",
"age_range": "20-100+",
"start_year": 1958,
"waves_or_follow_up": "Continuous enrollment; visits every 1-4 years depending on age",
"key_variable_categories": [
"demographics",
"nutrition",
"diet",
"grip_strength",
"walking_speed",
"physical_function",
"cognitive",
"biomarkers",
"genetics",
"medications",
"chronic_conditions",
"body_composition",
"imaging",
"personality",
"sensory_function",
],
"description": (
"Oldest continually running scientific study of human aging in the "
"US (since 1958), conducted by NIA/NIH. Comprehensive phenotyping "
"every 1-4 years with detailed physical, cognitive, and metabolic "
"assessments. Nutrition via 7-day food records and dietary recalls. "
"Physical function battery includes grip strength, walking speed, "
"400m walk test, and Short Physical Performance Battery (SPPB). "
"MRI brain imaging, body composition (DXA, CT), and extensive "
"biobanking."
),
"access_method": "NIA BLSA Data Sharing via application to NIA Intramural Research Program",
"access_url": "https://www.nia.nih.gov/research/labs/blsa",
"key_publications": [
"Shock et al. (1984) Normal Human Aging: The Baltimore Longitudinal Study of Aging",
"Ferrucci (2008) Exp Gerontol 43(2):84-89",
],
},
{
"name": "LASA",
"full_name": "Longitudinal Aging Study Amsterdam",
"country": "Netherlands",
"design": "longitudinal",
"sample_size": 3000,
"sample_size_note": "3,000+ at baseline; refreshment cohorts added",
"age_range": "55-85 at baseline",
"start_year": 1992,
"waves_or_follow_up": "3-year follow-up intervals; 10+ waves",
"key_variable_categories": [
"demographics",
"nutrition",
"diet",
"grip_strength",
"walking_speed",
"physical_function",
"cognitive",
"biomarkers",
"medications",
"chronic_conditions",
"mental_health",
"social_functioning",
"disability",
"falls",
"loneliness",
],
"description": (
"Dutch aging study focused on predictors and consequences of changes "
"in autonomy and well-being. Detailed physical function measures "
"including grip strength (Martin Vigorimeter), walking speed (3m "
"and 6m walks), chair stands, and balance. Nutrition assessed by "
"dietary history method and food frequency questionnaire. "
"Biomarkers include 25(OH)D, PTH, cortisol, and inflammatory "
"markers."
),
"access_method": "Data available via LASA data management team; collaboration application",
"access_url": "https://www.lasa-vu.nl/data/availability-data/",
"key_publications": [
"Huisman et al. (2011) Int J Epidemiol 40(4):868-876",
"Deeg et al. (2002) J Gerontol B 57(1):P47-P53",
],
},
{
"name": "CHARLS",
"full_name": "China Health and Retirement Longitudinal Study",
"country": "China",
"design": "longitudinal",
"sample_size": 17000,
"sample_size_note": "17,000+ respondents from 150 counties across China",
"age_range": "45+",
"start_year": 2011,
"waves_or_follow_up": "Biennial waves since 2011 (5+ waves)",
"key_variable_categories": [
"demographics",
"nutrition",
"diet",
"grip_strength",
"walking_speed",
"physical_function",
"cognitive",
"biomarkers",
"genetics",
"medications",
"chronic_conditions",
"disability",
"mental_health",
"healthcare_utilization",
"income",
],
"description": (
"Chinese aging study harmonized with HRS/SHARE/ELSA. Measures grip "
"strength (Yuejian WL-1000 dynamometer), timed walk, chair stands, "
"and balance. Blood-based biomarkers collected via venous blood "
"draw. Nutrition assessed through dietary intake questions. "
"Harmonized cognitive battery including word recall, serial 7s, "
"and drawing tasks. CHARLS-GWAS data available."
),
"access_method": "Publicly available via CHARLS website after registration",
"access_url": "https://charls.pku.edu.cn/en/",
"key_publications": [
"Zhao et al. (2014) Int J Epidemiol 43(1):61-68",
"Chen et al. (2019) J Econ Ageing 13:68-87",
],
},
{
"name": "JSTAR",
"full_name": "Japanese Study of Aging and Retirement",
"country": "Japan",
"design": "longitudinal",
"sample_size": 4200,
"sample_size_note": "4,200+ respondents from 7 municipalities",
"age_range": "50-75 at baseline",
"start_year": 2007,
"waves_or_follow_up": "Biennial waves since 2007 (5+ waves)",
"key_variable_categories": [
"demographics",
"nutrition",
"diet",
"grip_strength",
"walking_speed",
"physical_function",
"cognitive",
"medications",
"chronic_conditions",
"mental_health",
"employment",
"retirement",
"social_participation",
],
"description": (
"Japanese aging panel study harmonized with HRS family. Measures "
"grip strength, usual walking speed (timed walks), and cognitive "
"function (word recall, serial subtraction). Nutrition via brief "
"dietary screener. Covers health, economic status, and social "
"engagement of middle-aged and older Japanese adults."
),
"access_method": "RIETI Data Inquiry; application required",
"access_url": "https://www.rieti.go.jp/en/projects/jstar/",
"key_publications": [
"Ichimura et al. (2009) RIETI Discussion Paper 09-E-047",
"Oshio & Shimizutani (2005) J Japanese Int Economies 19:401-426",
],
},
{
"name": "KLoSA",
"full_name": "Korean Longitudinal Study of Aging",
"country": "South Korea",
"design": "longitudinal",
"sample_size": 10000,
"sample_size_note": "10,000+ nationally representative sample aged 45+",
"age_range": "45+",
"start_year": 2006,
"waves_or_follow_up": "Biennial waves since 2006 (8+ waves)",
"key_variable_categories": [
"demographics",
"nutrition",
"grip_strength",
"physical_function",
"cognitive",
"biomarkers",
"medications",
"chronic_conditions",
"disability",
"mental_health",
"employment",
"retirement",
"income",
],
"description": (
"Korean aging panel harmonized with HRS family. Grip strength "
"measured by dynamometer. Cognitive battery includes K-MMSE, "
"word recall, and serial 7s. Covers demographics, health, "
"employment, income, assets, and subjective well-being of "
"middle-aged and older Korean adults."
),
"access_method": "Korea Employment Information Service (KEIS); free download after registration",
"access_url": "https://survey.keis.or.kr/eng/klosa/klosa01.jsp",
"key_publications": [
"Park et al. (2007) Survey Design of the Korean Longitudinal Study of Ageing",
"Won et al. (2014) Gerontologist 54(Suppl 2):S28-S31",
],
},
# === Nutrition & Health ===
{
"name": "NHANES",
"full_name": "National Health and Nutrition Examination Survey",
"country": "USA",
"design": "cross-sectional",
"sample_size": 10000,
"sample_size_note": "~10,000 per 2-year cycle; continuous since 1999",
"age_range": "All ages (birth to 80+)",
"start_year": 1999,
"waves_or_follow_up": "Continuous 2-year cycles; cross-sectional but linked to NDI mortality follow-up",
"key_variable_categories": [
"demographics",
"nutrition",
"diet",
"iron",
"micronutrients",
"grip_strength",
"physical_function",
"cognitive",
"biomarkers",
"genetics",
"medications",
"chronic_conditions",
"body_composition",
"blood_pressure",
"oral_health",
"environmental_exposures",
],
"description": (
"Gold standard US nutrition and health examination survey. Detailed "
"24-hour dietary recall (2 days) with complete nutrient estimation "
"including iron (heme and non-heme), zinc, folate, all vitamins. "
"Laboratory measures include serum iron, ferritin, TIBC, "
"transferrin saturation, soluble transferrin receptor, CRP, "
"complete blood count. Grip strength (Takei dynamometer, ages 6+) "
"since 2011-2012 cycle. Physical function measures for older "
"adults. GWAS data available in dbGaP."
),
"access_method": "Public-use files freely downloadable; restricted linked data via RDC",
"access_url": "https://www.cdc.gov/nchs/nhanes/index.htm",
"key_publications": [
"Zipf et al. (2013) NCHS Vital Health Stat 1(56)",
"Johnson et al. (2013) Adv Nutr 4(6):368S-373S",
],
},
{
"name": "CHNS",
"full_name": "China Health and Nutrition Survey",
"country": "China",
"design": "longitudinal",
"sample_size": 30000,
"sample_size_note": "30,000+ individuals across 15 provinces",
"age_range": "All ages",
"start_year": 1989,
"waves_or_follow_up": "Waves in 1989, 1991, 1993, 1997, 2000, 2004, 2006, 2009, 2011, 2015, 2018",
"key_variable_categories": [
"demographics",
"nutrition",
"diet",
"iron",
"micronutrients",
"physical_activity",
"physical_function",
"biomarkers",
"medications",
"chronic_conditions",
"body_composition",
"blood_pressure",
"food_security",
"urbanization",
],
"description": (
"Major longitudinal nutrition and health survey in China. Detailed "
"3-day weighed food records with household food inventory. "
"Comprehensive nutrient calculation including iron (from Chinese "
"Food Composition Table). Biomarkers include hemoglobin, ferritin, "
"CRP, HbA1c, lipids, and fasting glucose. Tracks nutrition "
"transition, urbanization, and health changes in China since 1989."
),
"access_method": "Publicly available via UNC Carolina Population Center",
"access_url": "https://www.cpc.unc.edu/projects/china",
"key_publications": [
"Popkin et al. (2010) Int J Epidemiol 39(6):1435-1440",
"Zhang et al. (2014) Eur J Clin Nutr 68(4):466-471",
],
},
{
"name": "FHS",
"full_name": "Framingham Heart Study",
"country": "USA",
"design": "longitudinal",
"sample_size": 5000,
"sample_size_note": "5,209 original cohort; Offspring (5,124), Third Gen (4,095), Omni cohorts",
"age_range": "28-62 (original); multigenerational",
"start_year": 1948,
"waves_or_follow_up": "Continuous follow-up since 1948; biennial exams for original cohort",
"key_variable_categories": [
"demographics",
"nutrition",
"diet",
"physical_function",
"cognitive",
"biomarkers",
"genetics",
"medications",
"chronic_conditions",
"cardiovascular",
"blood_pressure",
"lipids",
"diabetes",
"imaging",
],
"description": (
"Landmark multigenerational cardiovascular cohort study. "
"Nutrition data from food frequency questionnaires (Willett FFQ) "
"in Offspring and Third Generation cohorts. Extensive cardiovascular "
"phenotyping including ECG, echocardiography, CT coronary calcium, "
"carotid IMT. Brain MRI and cognitive testing. Whole-genome "
"sequencing and GWAS data in dbGaP (SHARe/TOPMed). Biomarkers "
"include lipids, glucose, inflammatory markers, BNP, troponin."
),
"access_method": "BioLINCC (NHLBI) for clinical data; dbGaP for genomic data",
"access_url": "https://www.framinghamheartstudy.org/",
"key_publications": [
"Dawber et al. (1951) Am J Public Health 41(3):279-286",
"Kannel et al. (1979) Am Heart J 97(2):159-166",
],
},
{
"name": "Rotterdam Study",
"full_name": "Rotterdam Study (Erasmus Rotterdam Health Study)",
"country": "Netherlands",
"design": "longitudinal",
"sample_size": 15000,
"sample_size_note": "15,000+ participants across 3 sub-cohorts (RS-I, RS-II, RS-III)",
"age_range": "45+ at enrollment",
"start_year": 1990,
"waves_or_follow_up": "Continuous follow-up; examinations every 3-5 years",
"key_variable_categories": [
"demographics",
"nutrition",
"diet",
"iron",
"micronutrients",
"grip_strength",
"walking_speed",
"physical_function",
"cognitive",
"biomarkers",
"genetics",
"medications",
"chronic_conditions",
"cardiovascular",
"bone_density",
"imaging",
"ophthalmology",
],
"description": (
"Large Dutch population-based cohort with detailed phenotyping "
"including brain MRI, cardiac CT, retinal imaging, DXA. Nutrition "
"assessed via validated semiquantitative food frequency "
"questionnaire (389 items) with detailed nutrient calculations "
"including iron, calcium, and micronutrients. Physical function: "
"grip strength, walking speed (timed 15-foot walk), chair stands. "
"GWAS genotyping on Illumina arrays; whole-genome sequencing "
"available. Biomarkers include iron studies (ferritin, transferrin "
"saturation), inflammation, metabolomics."
),
"access_method": "Data management team application; collaboration agreements",
"access_url": "https://www.erasmusmc.nl/en/research/core-facilities/ergo",
"key_publications": [
"Ikram et al. (2020) Eur J Epidemiol 35(8):731-770",
"Hofman et al. (2015) Eur J Epidemiol 30(8):661-708",
],
},
{
"name": "UK Biobank",
"full_name": "UK Biobank",
"country": "UK",
"design": "both",
"sample_size": 500000,
"sample_size_note": "~500,000 participants enrolled 2006-2010; imaging subset ~100K",
"age_range": "40-69 at enrollment",
"start_year": 2006,
"waves_or_follow_up": "Baseline + repeat assessments + ongoing imaging study + linkage to health records",
"key_variable_categories": [
"demographics",
"nutrition",
"diet",
"iron",
"grip_strength",
"walking_speed",
"physical_function",
"cognitive",
"biomarkers",
"genetics",
"medications",
"chronic_conditions",
"body_composition",
"imaging",
"accelerometry",
"proteomics",
"metabolomics",
],
"description": (
"Largest prospective cohort with deep phenotyping and multi-omics. "
"Grip strength (Jamar dynamometer), walking pace (self-report and "
"accelerometer-derived), 6-minute walk distance. Dietary data from "
"Oxford WebQ 24-hour dietary recall (up to 5 completions) and "
"touchscreen food frequency questionnaire. Blood biomarkers include "
"iron, ferritin, transferrin, TIBC, plus 3,000+ protein biomarkers "
"(Olink Explore 3072). Full WGS, WES, genotyping array, "
"metabolomics (Nightingale NMR, Metabolon), and proteomics."
),
"access_method": "Approved researchers via UK Biobank Access Management System",
"access_url": "https://www.ukbiobank.ac.uk/enable-your-research/apply-for-access",
"key_publications": [
"Sudlow et al. (2015) PLoS Med 12(3):e1001779",
"Bycroft et al. (2018) Nature 562(7726):203-209",
],
},
# === Physical Function Focused ===
{
"name": "SEE",
"full_name": "Salisbury Eye Evaluation Study",
"country": "USA",
"design": "longitudinal",
"sample_size": 2500,
"sample_size_note": "~2,500 participants aged 65+ in Salisbury, Maryland",
"age_range": "65+",
"start_year": 1993,
"waves_or_follow_up": "Follow-up visits at 2, 6, and 8 years",
"key_variable_categories": [
"demographics",
"walking_speed",
"physical_function",
"cognitive",
"vision",
"disability",
"falls",
"medications",
"chronic_conditions",
],
"description": (
"Community-based study linking vision impairment to physical "
"function in older adults. Measured usual walking speed over a "
"timed course, visual acuity, contrast sensitivity, visual field, "
"and disability outcomes. Known for establishing walking speed "
"cut-points predictive of disability."
),
"access_method": "Contact study investigators at Johns Hopkins Wilmer Eye Institute",
"access_url": "https://www.hopkinsmedicine.org/wilmer/research/",
"key_publications": [
"West et al. (1997) Invest Ophthalmol Vis Sci 38(2):557-568",
"Rubin et al. (2001) Arch Ophthalmol 119(2):169-174",
],
},
{
"name": "Health ABC",
"full_name": "Health, Aging, and Body Composition Study",
"country": "USA",
"design": "longitudinal",
"sample_size": 3000,
"sample_size_note": "3,075 well-functioning adults aged 70-79 at baseline",
"age_range": "70-79 at enrollment",
"start_year": 1997,
"waves_or_follow_up": "Annual visits; 15+ years of follow-up",
"key_variable_categories": [
"demographics",
"nutrition",
"diet",
"grip_strength",
"walking_speed",
"physical_function",
"cognitive",
"biomarkers",
"genetics",
"medications",
"chronic_conditions",
"body_composition",
"muscle_strength",
"sarcopenia",
"inflammatory_markers",
"bone_density",
],
"description": (
"NIA-funded study designed to investigate the relationship between "
"body composition and functional decline. Detailed body composition "
"by DXA and CT (thigh muscle, abdominal fat). Grip strength, "
"walking speed (20m), 400m walk, knee extensor strength. "
"Biomarkers include IL-6, TNF-alpha, CRP, insulin, adiponectin. "
"GWAS data in dbGaP. Key resource for sarcopenia and muscle "
"aging research."
),
"access_method": "NIA via application; some data in dbGaP",
"access_url": "https://healthabc.nia.nih.gov/",
"key_publications": [
"Newman et al. (2003) J Gerontol A 58(3):M249-M265",
"Goodpaster et al. (2006) J Gerontol A 61(10):1059-1064",
],
},
{
"name": "EPESE",
"full_name": "Established Populations for Epidemiologic Studies of the Elderly",
"country": "USA",
"design": "longitudinal",
"sample_size": 14000,
"sample_size_note": "~14,000 across 4 US communities (East Boston, Iowa, New Haven, North Carolina)",
"age_range": "65+",
"start_year": 1981,
"waves_or_follow_up": "Annual interviews; physical function assessments at baseline and follow-ups",
"key_variable_categories": [
"demographics",
"walking_speed",
"physical_function",
"cognitive",
"medications",
"chronic_conditions",
"disability",
"mortality",
"hospitalization",
],
"description": (
"NIA-funded multi-site study that pioneered physical performance "
"measures in epidemiologic studies of older adults. Developed the "
"Short Physical Performance Battery (SPPB) including timed 8-foot "
"walk, repeated chair stands, and balance tests. Source of seminal "
"papers linking walking speed to mortality and disability. Hispanic "
"EPESE extension covers Mexican Americans in the Southwest."
),
"access_method": "ICPSR (Inter-university Consortium for Political and Social Research)",
"access_url": "https://www.icpsr.umich.edu/web/NACDA/series/59",
"key_publications": [
"Cornoni-Huntley et al. (1993) NIA Established Populations for Epidemiologic Studies of the Elderly Resource Data Book",
"Guralnik et al. (1994) N Engl J Med 332(9):556-562",
],
},
# === Additional Major Cohorts ===
{
"name": "CHS",
"full_name": "Cardiovascular Health Study",
"country": "USA",
"design": "longitudinal",
"sample_size": 5900,
"sample_size_note": "5,888 adults aged 65+ from 4 US communities",
"age_range": "65+",
"start_year": 1989,
"waves_or_follow_up": "Annual clinic visits through 1999; ongoing follow-up via phone/records",
"key_variable_categories": [
"demographics",
"nutrition",
"diet",
"grip_strength",
"walking_speed",
"physical_function",
"cognitive",
"biomarkers",
"genetics",
"medications",
"chronic_conditions",
"cardiovascular",
"frailty",
"inflammatory_markers",
],
"description": (
"NHLBI-funded study of cardiovascular risk factors in older adults. "
"Known for defining the frailty phenotype (Fried criteria): "
"unintentional weight loss, self-reported exhaustion, low physical "
"activity, slow walking speed, and weak grip strength. Walking "
"speed measured over 15-foot course. Grip strength by Jamar "
"dynamometer. Comprehensive cardiovascular phenotyping with "
"echocardiography, carotid ultrasound, and MRI. GWAS data "
"available."
),
"access_method": "BioLINCC (NHLBI) after approval",
"access_url": "https://chs-nhlbi.org/",
"key_publications": [
"Fried et al. (2001) J Gerontol A 56(3):M146-M156",
"Fried et al. (1991) Ann Epidemiol 1(3):263-276",
],
},
{
"name": "WHAS",
"full_name": "Women's Health and Aging Studies (WHAS I & II)",
"country": "USA",
"design": "longitudinal",
"sample_size": 1600,
"sample_size_note": "WHAS I: 1,002 (moderate-to-severe disability); WHAS II: 436 (mild-no disability)",
"age_range": "65+ (WHAS I); 70-79 (WHAS II)",
"start_year": 1992,
"waves_or_follow_up": "Semi-annual to annual visits; up to 10 years follow-up",
"key_variable_categories": [
"demographics",
"nutrition",
"diet",
"iron",
"micronutrients",
"grip_strength",
"walking_speed",
"physical_function",
"cognitive",
"biomarkers",
"medications",
"chronic_conditions",
"disability",
"anemia",
"inflammatory_markers",
"frailty",
],
"description": (
"Landmark studies of the causes and progression of disability in "
"older women. Extensive biomarker panels including iron studies "
"(serum iron, ferritin, transferrin saturation, sTfR), "
"inflammatory markers (IL-6, CRP, TNF-alpha), vitamin D, B12, "
"carotenoids. Grip strength, walking speed (4m), chair stands, "
"balance. Nutritional assessment via Block FFQ. Known for seminal "
"work on anemia, iron deficiency, and physical function in aging."
),
"access_method": "Contact study PI at Johns Hopkins; data sharing via collaboration",
"access_url": "https://www.jhsph.edu/research/centers-and-institutes/johns-hopkins-center-on-aging-and-health/",
"key_publications": [
"Guralnik et al. (1995) J Gerontol A 50(Spec No):M128-M137",
"Fried et al. (1999) J Clin Epidemiol 52(1):27-37",
],
},
{
"name": "HUNT",
"full_name": "Troendelag Health Study (HUNT)",
"country": "Norway",
"design": "longitudinal",
"sample_size": 230000,
"sample_size_note": "~230,000 participants across 4 waves (HUNT1-4) in Troendelag county",
"age_range": "20+ (all adults invited)",
"start_year": 1984,
"waves_or_follow_up": "HUNT1 (1984-86), HUNT2 (1995-97), HUNT3 (2006-08), HUNT4 (2017-19)",
"key_variable_categories": [
"demographics",
"nutrition",
"diet",
"grip_strength",
"physical_function",
"cognitive",
"biomarkers",
"genetics",
"medications",
"chronic_conditions",
"cardiovascular",
"mental_health",
"metabolomics",
"blood_pressure",
],
"description": (
"One of the largest population health surveys in the world, based "
"in Troendelag county, Norway. Grip strength measured in HUNT3 and "
"HUNT4. Extensive biobanking with DNA, serum, plasma. GWAS data "
"on >70,000 participants. Linked to Norwegian health registries "
"for complete ascertainment of hospitalizations, prescriptions, "
"and mortality. Diet assessed by food frequency questionnaire."
),
"access_method": "Application to HUNT Research Centre; ethics approval required",
"access_url": "https://www.ntnu.edu/hunt/data",
"key_publications": [
"Krokstad et al. (2013) Int J Epidemiol 42(4):968-977",
"Holmen et al. (2003) Norsk Epidemiologi 13(1):19-32",
],
},
{
"name": "HAPIEE",
"full_name": "Health, Alcohol and Psychosocial factors In Eastern Europe",
"country": "Czech Republic, Russia, Poland, Lithuania",
"design": "longitudinal",
"sample_size": 36000,
"sample_size_note": "~36,000 participants across 4 Eastern European countries",
"age_range": "45-69 at enrollment",
"start_year": 2002,
"waves_or_follow_up": "Baseline (2002-2005) + follow-up (2006-2008) + ongoing record linkage",
"key_variable_categories": [
"demographics",
"nutrition",
"diet",
"grip_strength",
"walking_speed",
"physical_function",
"cognitive",
"biomarkers",
"medications",
"chronic_conditions",
"cardiovascular",
"alcohol",
"psychosocial",
],
"description": (
"Multi-country study of determinants of cardiovascular disease and "
"other chronic conditions in Eastern Europe. Measures grip strength, "
"walking speed (timed walk), chair stands, and cognitive function. "
"Detailed dietary assessment via food frequency questionnaires "
"adapted for each country. Blood biomarkers include lipids, "
"glucose, HbA1c, CRP, fibrinogen. Focus on alcohol consumption "
"patterns and psychosocial factors."
),
"access_method": "Contact study team at University College London; collaboration required",
"access_url": "https://www.ucl.ac.uk/epidemiology-health-care/research/epidemiology-and-public-health/research/hapiee",
"key_publications": [
"Peasey et al. (2006) BMC Public Health 6:255",
"Bobak et al. (2004) Eur Heart J 25(23):2075-2076",
],
},
{
"name": "CLSA",
"full_name": "Canadian Longitudinal Study on Aging",
"country": "Canada",
"design": "longitudinal",
"sample_size": 50000,
"sample_size_note": "~51,000 participants; Tracking (20K telephone) + Comprehensive (31K in-person)",
"age_range": "45-85 at enrollment",
"start_year": 2011,
"waves_or_follow_up": "3-year follow-up intervals planned for 20+ years",
"key_variable_categories": [
"demographics",
"nutrition",
"diet",
"grip_strength",
"walking_speed",
"physical_function",
"cognitive",
"biomarkers",
"genetics",
"medications",
"chronic_conditions",
"body_composition",
"vision",
"hearing",
"oral_health",
"environmental_exposures",
],
"description": (
"Canada's largest study of aging with comprehensive assessments. "
"Physical measures include grip strength (Tracker Freedom "
"dynamometer), 4m walking speed, Timed Up and Go (TUG), and "
"standing balance. Nutrition assessed via Short Diet Questionnaire "
"and food security module. Extensive biobanking (blood, urine). "
"GWAS genotyping available. Linked to Canadian health administrative "
"data. Measures vision, hearing, and respiratory function."
),
"access_method": "CLSA Data Access Application; approved researchers only",
"access_url": "https://www.clsa-elcv.ca/data-access",
"key_publications": [
"Raina et al. (2009) Can J Aging 28(3):221-229",
"Raina et al. (2019) Int J Epidemiol 48(6):1752-1753j",
],
},
{
"name": "NHATS",
"full_name": "National Health and Aging Trends Study",
"country": "USA",
"design": "longitudinal",
"sample_size": 8000,
"sample_size_note": "~8,000 Medicare beneficiaries aged 65+; refreshment sample in 2015",
"age_range": "65+",
"start_year": 2011,
"waves_or_follow_up": "Annual interviews since 2011 (12+ rounds)",
"key_variable_categories": [
"demographics",
"grip_strength",
"walking_speed",
"physical_function",
"cognitive",
"medications",
"chronic_conditions",
"disability",
"assistive_technology",
"caregiving",
"dementia",
"sensory_function",
"pain",
],
"description": (
"Nationally representative study of late-life disability trends and "
"trajectories in the US. Measures grip strength (Jamar), timed "
"usual-pace walk, and Short Physical Performance Battery. "
"Comprehensive disability assessment covering mobility, self-care, "
"household activities, and technology use. Cognitive testing "
"includes clock drawing, delayed word recall, and orientation. "
"Linked to Medicare claims data and National Study of Caregiving "
"(NSOC) for caregiver data."
),
"access_method": "Restricted and public-use files via NHATS website; DUA required for restricted",
"access_url": "https://nhats.org/researcher/data-access",
"key_publications": [
"Kasper & Freedman (2019) NHATS User Guide",
"Freedman et al. (2011) J Gerontol B 66(Suppl 1):i206-i214",
],
},
{
"name": "MIDUS",
"full_name": "Midlife in the United States",
"country": "USA",
"design": "longitudinal",
"sample_size": 7000,
"sample_size_note": "~7,000 adults in MIDUS 1; refreshment in MIDUS 3",
"age_range": "25-74 at MIDUS 1; aging in place",
"start_year": 1995,
"waves_or_follow_up": "MIDUS 1 (1995-96), MIDUS 2 (2004-06), MIDUS 3 (2013-14), MIDUS Refresher (2017-22)",
"key_variable_categories": [
"demographics",
"nutrition",
"grip_strength",
"physical_function",
"cognitive",
"biomarkers",
"medications",
"chronic_conditions",
"mental_health",
"psychosocial",
"stress",
"sleep",
"inflammation",
],
"description": (
"National longitudinal study of health and well-being from midlife "
"through old age. Unique focus on psychosocial factors, stress, "
"and their biological consequences. MIDUS Biomarker Project "
"includes grip strength, waist-hip ratio, salivary cortisol, "
"inflammatory markers (IL-6, CRP, fibrinogen), and comprehensive "
"cardiovascular measures. Cognitive battery in MIDUS 2 and 3. "
"Milwaukee and Japanese (MIDJA) sub-studies for minority and "
"cross-cultural comparisons."
),
"access_method": "ICPSR (public-use) and NACDA; biomarker data via application",
"access_url": "https://midus.wisc.edu/data/",
"key_publications": [
"Brim et al. (2004) How Healthy Are We? A National Study of Well-Being at Midlife",
"Ryff et al. (2017) Dev Psychol 53(6):1067-1078",
],
},
{
"name": "MESA",
"full_name": "Multi-Ethnic Study of Atherosclerosis",
"country": "USA",
"design": "longitudinal",
"sample_size": 6800,
"sample_size_note": "6,814 adults from 6 US communities; 4 racial/ethnic groups",
"age_range": "45-84 at enrollment",
"start_year": 2000,
"waves_or_follow_up": "5 exams over 11 years; ongoing follow-up via records and phone",
"key_variable_categories": [
"demographics",
"nutrition",
"diet",
"grip_strength",
"walking_speed",
"physical_function",
"biomarkers",
"genetics",
"medications",
"chronic_conditions",
"cardiovascular",
"imaging",
"air_pollution",
"proteomics",
"metabolomics",
],
"description": (
"Multi-ethnic cohort study of subclinical cardiovascular disease "
"free of clinical CVD at enrollment. Detailed cardiac imaging "
"(coronary calcium CT, cardiac MRI). Grip strength measured at "
"later exams. Walking speed and physical function added in "
"ancillary studies. Diet assessed via Block FFQ with ethnic-"
"specific modifications. GWAS, WGS, methylation, proteomics, "
"and metabolomics data available. Focus on health disparities "
"across White, Black, Hispanic, and Chinese-American participants."
),
"access_method": "BioLINCC (NHLBI) and MESA Coordinating Center; dbGaP for genomics",
"access_url": "https://www.mesa-nhlbi.org/",
"key_publications": [
"Bild et al. (2002) Am J Epidemiol 156(9):871-881",
"Burke et al. (2016) J Am Heart Assoc 5(3):e002640",
],
},
{
"name": "ALSA",
"full_name": "Australian Longitudinal Study of Ageing",
"country": "Australia",
"design": "longitudinal",
"sample_size": 2000,
"sample_size_note": "~2,087 participants aged 70+ from Adelaide, South Australia",
"age_range": "70+",
"start_year": 1992,
"waves_or_follow_up": "13 waves over 18 years (1992-2010)",
"key_variable_categories": [
"demographics",
"nutrition",
"diet",
"grip_strength",
"walking_speed",
"physical_function",
"cognitive",
"biomarkers",
"medications",
"chronic_conditions",
"disability",
"social_networks",
"depression",
"sensory_function",
],
"description": (
"Australian aging cohort with comprehensive assessment of health, "
"social, and economic aspects of aging. Physical performance "
"measures include grip strength, walking speed (timed 3m walk), "
"and balance. Dietary intake assessed by food frequency "
"questionnaire. Blood biomarkers collected at clinical assessments. "
"Cognitive testing includes MMSE and detailed neuropsychological "
"battery."
),
"access_method": "Flinders University Centre for Ageing Studies; application required",
"access_url": "https://www.flinders.edu.au/college-education-psychology-social-work/alsa",
"key_publications": [
"Andrews et al. (2002) Australasian J Ageing 21(4):169-176",
"Luszcz et al. (2014) Int J Epidemiol 43(6):1829-1830",
],
},
{
"name": "HELIAD",
"full_name": "Hellenic Longitudinal Investigation of Ageing and Diet",
"country": "Greece",
"design": "longitudinal",
"sample_size": 2000,
"sample_size_note": "~2,000 participants from Larissa and Marousi, Greece",
"age_range": "65+",
"start_year": 2009,
"waves_or_follow_up": "Follow-up visits every 3 years",
"key_variable_categories": [
"demographics",
"nutrition",
"diet",
"iron",
"micronutrients",
"grip_strength",
"physical_function",
"cognitive",
"biomarkers",
"medications",
"chronic_conditions",
"mediterranean_diet",
"dementia",
],
"description": (
"Greek aging cohort with special focus on the Mediterranean diet "
"and cognitive decline. Detailed dietary assessment via "
"semiquantitative food frequency questionnaire (EPIC-Greece) with "
"comprehensive nutrient calculations including iron, B-vitamins, "
"antioxidants. Mediterranean Diet Score (MedDietScore) calculated. "
"Physical function measures include grip strength. Comprehensive "
"neuropsychological assessment for dementia and mild cognitive "
"impairment."
),
"access_method": "Collaboration with HELIAD investigators at National and Kapodistrian University of Athens",
"access_url": "https://www.heliad.org/",
"key_publications": [
"Dardiotis et al. (2014) Neuroepidemiology 43(3-4):263-275",
"Scarmeas et al. (2018) Alzheimers Dement 14(12):1553-1562",
],
},
{
"name": "ATTICA",
"full_name": "ATTICA Study",
"country": "Greece",
"design": "longitudinal",
"sample_size": 3000,
"sample_size_note": "3,042 adults from the greater Athens area",
"age_range": "18+ at enrollment",
"start_year": 2001,
"waves_or_follow_up": "Baseline (2001-02) + 5-year + 10-year + 20-year follow-ups",
"key_variable_categories": [
"demographics",
"nutrition",
"diet",
"iron",
"micronutrients",
"biomarkers",
"medications",
"chronic_conditions",
"cardiovascular",
"inflammatory_markers",
"mediterranean_diet",
"oxidative_stress",
],
"description": (
"Greek cardiovascular cohort study with extensive nutritional and "
"inflammatory biomarker data. Dietary assessment via validated "
"semiquantitative FFQ with detailed iron and micronutrient "
"calculations. Mediterranean diet adherence scored. Blood "
"biomarkers include iron, ferritin, hsCRP, IL-6, TNF-alpha, "
"fibrinogen, homocysteine, oxidized LDL, adiponectin, and lipids. "
"Focus on cardiovascular risk, metabolic syndrome, and "
"inflammation."
),
"access_method": "Collaboration with ATTICA investigators at Harokopio University",
"access_url": "https://www.hua.gr/en/",
"key_publications": [
"Pitsavos et al. (2003) BMC Public Health 3:32",
"Panagiotakos et al. (2007) J Am Coll Cardiol 49(22):2226-2233",
],
},
]
def _token_match_count(text: str, tokens: list[str]) -> int:
"""Return how many query tokens appear in text."""
lower = text.lower()
return sum(1 for tok in tokens if tok in lower)
def _cohort_searchable_text(c: Dict[str, Any]) -> str:
"""Build a single searchable string from all cohort fields."""
parts = [
c.get("name", ""),
c.get("full_name", ""),
c.get("country", ""),
c.get("design", ""),
c.get("description", ""),
c.get("age_range", ""),
c.get("waves_or_follow_up", ""),
c.get("sample_size_note", ""),
]
parts.extend(c.get("key_variable_categories", []))
parts.extend(c.get("key_publications", []))
return " ".join(parts).lower()
